From 4b46cf14fb2a5c7a5fad704f9941d476ffa64034 Mon Sep 17 00:00:00 2001 From: MaxBille Date: Thu, 4 Sep 2025 18:21:51 +0200 Subject: [PATCH 01/21] started translating the MP2_release cylinderFlow to current lettuce master. including: obstacle_cylinder.py, example-Simulation-script, HWBB and IBB boundary conditions, MEA for Bounce back boundaries, drag and lift observables... --- lettuce/ext/_flows/obstacle_cylinder.py | 277 ++++++++++++++++++++++++ 1 file changed, 277 insertions(+) create mode 100644 lettuce/ext/_flows/obstacle_cylinder.py diff --git a/lettuce/ext/_flows/obstacle_cylinder.py b/lettuce/ext/_flows/obstacle_cylinder.py new file mode 100644 index 00000000..80c0eb48 --- /dev/null +++ b/lettuce/ext/_flows/obstacle_cylinder.py @@ -0,0 +1,277 @@ +import warnings +from typing import Union, List, Optional + +import numpy as np +import torch + +from . import ExtFlow +from ... import UnitConversion, Context, Stencil, Equilibrium +from ...util import append_axes +from .. import (EquilibriumBoundaryPU, BounceBackBoundary, + EquilibriumOutletP, AntiBounceBackOutlet) + +__all__ = ['ObstacleCylinder'] + + +class ObstacleCylinder: + """ + unified version of 2D and 3D cylinder flow + refined version of flow/obstacle.py, for cylinder-flow in 2D or 3D. The dimensions will be assumed from + len(resolution) + + Flow: + - inflow (EquilibriumBoundaryPU) at x=0, outflow (EquilibriumOutletP) at x=xmax + - further boundaries depend on parameters: + - lateral (y direction): periodic, no-slip wall, slip wall + - lateral (z direction): periodic (only if lattice.D==3) + - obstacle: cylinder obstacle centered at (y_lu/2+x_offset, y_LU/2+y_offset), + with radius=char_length, uniform symmetry in z-direction + - boundary condition for obstacle can be chosen: hwbb, fwbb, ibb1 + AND storage-formats: fwbb, fwbbc (compact), hwbb, hwbbc1, hwbbc2, hwbbc3, ibb1, ibb1c1, ibb1c2 + - c: compact implementation (= DIY-sparse, faster and less memory) + - c2 better than c1 + - c3 is hwbb-only for use with older simulation-classes that don't contain a store_f_collided call! + recommendation: use fwbbc, hwbbc2, ibb1c2 for best runtime- and memory-efficiency + - initial pertubation (trigger Von Kármán vortex street for Re>46) can be initialized in y and z direction + - initial velocity can be 0, u_char or a parabolic profile (parabolic if lateral_walls = "bounceback") + - inlet/inflow velocity can be uniform u_char or parabolic + + Parameters: + ---------- + + ---------- + """ + def __init__(self, context: Context, resolution: Union[int, List[int]], + reynolds_number, mach_number, + char_length_pu, char_length_lu, char_velocity_pu=1, + lateral_walls='periodic', bc_type='fwbb', perturb_init=True, u_init=0, + x_offset=0, y_offset=0): + + self.char_length_pu = char_length_pu # characteristic length + + # self.units = UnitConversion( + # reynolds_number=reynolds_number, + # mach_number=mach_number, + # characteristic_length_lu=char_length_lu, + # characteristic_length_pu=char_length_pu, + # characteristic_velocity_pu=char_velocity_pu # reminder: u_char_lu = Ma * cs_lu = Ma * 1/sqrt(3) + # ) + + # flow and boundary settings + self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street + self.u_init = u_init # toggle: initial solution velocity profile type + self.lateral_walls = lateral_walls # toggle: lateral walls to be bounce back (bounceback), slip wall (slip) or periodic (periodic) + self.bc_type = bc_type # toggle: bounce back algorithm: halfway (hwbb), fullway (fwbb), linearly interpolated (ibb1) + + # initialize masks (init with zeros) + self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) + self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes + self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls + self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calc.) + + # cylinder geometry in LU (1-based indexing!) + self.x_offset = x_offset + self.y_offset = y_offset + self.radius = char_length_lu / 2 + self.y_pos = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing + self.x_pos = self.y_pos + self.x_offset # keep symmetry of cylinder in x and y direction + + xyz = tuple(np.linspace(1, n, n) for n in self.shape) # Tupel of index-lists (1-n (one-based!)) + if self.units.lattice.D == 2: + x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y-index + elif self.units.lattice.D == 3: + x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- and z-index + else: + print("WARNING: something went wrong in LU-gird-index generation, lattice.D must be 2 or 3!") + + condition = np.sqrt((x_lu - self.x_pos) ** 2 + (y_lu - self.y_pos) ** 2) < self.radius + self.obstacle_mask[np.where(condition)] = 1 + self.solid_mask[np.where(condition)] = 1 + + # indexing doesn't need z-Index for 3D, everything is broadcasted along z! + if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': # if top and bottom are link-based BC + self.wall_mask[:, [0, -1]] = True # don't mark wall nodes as inlet + self.solid_mask[np.where(self.wall_mask)] = 1 # mark solid walls + self.in_mask[0, 1:-1] = True # inlet on the left, except for top and bottom wall (y=0, y=y_max) + else: # if lateral_wals == 'periodic', no walls + self.in_mask[0, :] = True # inlet on the left (x=0) + + # generate parabolic velocity profile for inlet BC if lateral_walls (top and bottom) are bounce back walls (== channel-flow) + self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() # u = [ux,uy,uz] = [1,0,0] in PU // uniform cahracteristic velocity in x-direction + if self.lateral_walls == 'bounceback': + ## parabolic velocity profile, zeroing on the edges + ## How to parabola: + ## 1.parabola in factoriezed form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) + ## 2.parabola with a maximum and zero at x1=0 und x2=x0: y=-x*(x-x0) + ## 3.scale parabola, to make y_s(x_s)=1 the maximum: y=-x*(x-x0)*(1/(x0/2)²) + ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, also making the integral of a homogeneous velocity profile with u=1 and the parabolic profile being equal + (nx, ny, nz) = self.shape # number of gridpoints in y direction + parabola_y = np.zeros((1, ny)) + y_coordinates = np.linspace(0, ny, + ny) # linspace() creates n points between 0 and ny, including 0 and ny: + # top and bottom velocity values will be zero to agree with wall-boundary-condition + parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( + y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile + # scale with 1.5 to achieve a mean velocity of u_char! -> DIFFERENT FROM cylinder2D and cylinder3D (!) + if self.units.lattice.D == 2: + # in 2D u1 needs Dimension 1 x ny (!) + velocity_y = np.zeros_like(parabola_y) # y-velocities = 0 + self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) # stack/pack u-field + elif self.units.lattice.D == 3: + ones_z = np.ones(nz) + parabola_yz = parabola_y[:, :, np.newaxis] * ones_z + parabola_yz_zeros = np.zeros_like(parabola_yz) + # create u_xyz inlet yz-plane: + self.u_inlet = np.stack([parabola_yz, parabola_yz_zeros, parabola_yz_zeros], axis=0) # stack/pack u-field + + def make_units(self, reynolds_number, mach_number, resolution: List[int] + ) -> 'UnitConversion': + return UnitConversion( + reynolds_number=reynolds_number, mach_number=mach_number, + characteristic_length_lu=self.char_length_lu, + characteristic_length_pu=self.char_length, + characteristic_velocity_pu=self.char_velocity + ) + + @property + def obstacle_mask(self): + return self._obstacle_mask + + @obstacle_mask.setter + def obstacle_mask(self, m): + assert isinstance(m, np.ndarray) and m.shape == self.shape + self._obstacle_mask = m.astype(bool) + # self.solid_mask[np.where(self._obstacle_mask)] = 1 # (!) this line is not doing what it should! solid_mask is now defined in the initial solution (see below)! + + def initial_solution(self, x): + p = np.zeros_like(x[0], dtype=float)[None, ...] + u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() + u_max_pu = append_axes(u_max_pu, self.units.lattice.D) + self.solid_mask[np.where(self.obstacle_mask)] = 1 # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD + ### initial velocity field: "u_init"-parameter + # 0: uniform u=0 + # 1: uniform u=1 or parabolic (depends on lateral_walls -> bounceback => parabolic; slip, periodic => uniform) + u = (1 - self.solid_mask) * u_max_pu + if self.u_init == 0: + u = u * 0 # uniform u=0 + else: + if self.lateral_walls == 'bounceback': # parabolic along y, uniform along x and z (similar to poiseuille-flow) + ny = self.shape[1] # number of gridpoints in y direction + ux_factor = np.zeros(ny) # vector for one column (u(x=0)) + # multiply parabolic profile with every column of the velocity field: + y_coordinates = np.linspace(0, ny, ny) + ux_factor[1:-1] = - y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 + if self.units.lattice.D == 2: + u = np.einsum('k,ijk->ijk', ux_factor, u) + elif self.units.lattice.D == 3: + u = np.einsum('k,ijkl->ijkl', ux_factor, u) + else: # lateral_walls == periodic or slip + # initiale velocity u_PU=1 on every fluid node + u = (1 - self.solid_mask) * u_max_pu + + ### perturb initial velocity field-symmetry (in y and z) to trigger 'von Karman' vortex street + if self.perturb_init: # perturb initial solution in y + # overlays a sine-wave on the second column of nodes x_lu=1 (index 1) + ny = x[1].shape[1] + if u.max() < 0.5 * self.units.characteristic_velocity_pu: + # add perturbation for small velocities + #OLD 2D: u[0][1] += np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 1.0 + amplitude_y = np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 + if self.units.lattice.D == 2: + u[0][1] += amplitude_y + elif self.units.lattice.D == 3: + nz = x[2].shape[2] + plane_yz = np.ones_like(u[0, 1]) # plane of ones + u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # plane of amplitude in y + amplitude_z = np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 # amplitude in z + u[0][1] += np.einsum('z,yz->yz', amplitude_z, plane_yz) + else: + # multiply scaled down perturbation if velocity field is already near u_char + #OLD 2D: u[0][1] *= 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.3 + factor = 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.1 + if self.units.lattice.D == 2: + u[0][1] *= factor + elif self.units.lattice.D == 3: + nz = x[2].shape[1] + plane_yz = np.ones_like(u[0, 1, :, :]) + u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) + factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # pertubation in z-direction + u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) + return p, u + + @property + def grid(self): + # THIS IS NOT USED AT THE MOMENT. QUESTION: SHOULD THIS BE ONE- OR ZERO-BASED? Indexing or "node-number"? + xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.shape) # tuple of lists of x,y,(z)-values/indices + return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices + + @property + def boundaries(self): + # inlet ("left side", x[0],y[1:-1], z[:]) + inlet_boundary = EquilibriumBoundaryPU( + self.in_mask, + self.units.lattice, self.units, + # self.units.characteristic_velocity_pu * self._unit_vector()) + self.u_inlet) # works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU + + # lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) + lateral_boundary = None # stays None if lateral_walls == 'periodic' + if self.lateral_walls == 'bounceback': + if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back + lateral_boundary = HalfwayBounceBackBoundary(self.wall_mask, self.units.lattice) + else: # else use fullway bounce back + lateral_boundary = FullwayBounceBackBoundary(self.wall_mask, self.units.lattice) + elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-walöl (symmetry boundary) + lateral_boundary = SlipBoundary(self.wall_mask, self.units.lattice, 1) # slip on x(z)-plane + + # outlet ("right side", x[-1],y[:], (z[:])) + if self.units.lattice.D == 2: + outlet_boundary = EquilibriumOutletP(self.units.lattice, [1, 0]) # outlet in positive x-direction + else: # self.units.lattice.D == 3: + outlet_boundary = EquilibriumOutletP(self.units.lattice, [1, 0, 0]) # outlet in positive x-direction + + # obstacle (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) -> to be set via obstacle_mask.setter + obstacle_boundary = None + # (!) the obstacle_boundary should alway be the last boundary in the list of boundaries to correctly calculate forces on the obstacle + if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': + obstacle_boundary = HalfwayBounceBackBoundary(self.obstacle_mask, self.units.lattice) + elif self.bc_type == 'ibb1' or self.bc_type == 'IBB1': + obstacle_boundary = InterpolatedBounceBackBoundary(self.obstacle_mask, self.units.lattice, + x_center=(self.shape[1] / 2 - 0.5), + y_center=(self.shape[1] / 2 - 0.5), radius=self.radius) + elif self.bc_type == 'ibb1c1': + obstacle_boundary = InterpolatedBounceBackBoundary_compact_v1(self.obstacle_mask, self.units.lattice, + x_center=(self.shape[1] / 2 - 0.5), + y_center=(self.shape[1] / 2 - 0.5), radius=self.radius) + elif self.bc_type == 'ibb1c2': + obstacle_boundary = InterpolatedBounceBackBoundary_compact_v2(self.obstacle_mask, self.units.lattice, + x_center=(self.shape[1] / 2 - 0.5), + y_center=(self.shape[1] / 2 - 0.5), + radius=self.radius) + elif self.bc_type == 'fwbbc': + obstacle_boundary = FullwayBounceBackBoundary_compact(self.obstacle_mask, self.units.lattice) + elif self.bc_type == 'hwbbc1': + obstacle_boundary = HalfwayBounceBackBoundary_compact_v1(self.obstacle_mask, self.units.lattice) + elif self.bc_type == 'hwbbc2': + obstacle_boundary = HalfwayBounceBackBoundary_compact_v2(self.obstacle_mask, self.units.lattice) + elif self.bc_type == 'hwbbc3': + obstacle_boundary = HalfwayBounceBackBoundary_compact_v3(self.obstacle_mask, self.units.lattice) + else: # use Fullway Bounce Back + obstacle_boundary = FullwayBounceBackBoundary(self.obstacle_mask, self.units.lattice) + + if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list + return [ + inlet_boundary, + outlet_boundary, + obstacle_boundary + ] + else: + return [ + inlet_boundary, + outlet_boundary, + lateral_boundary, + obstacle_boundary + ] + + def _unit_vector(self, i=0): + return np.eye(self.units.lattice.D)[i] \ No newline at end of file From 4ab333965e51f2dda6f34a0b14a7941cefbe58e1 Mon Sep 17 00:00:00 2001 From: MaxBille Date: Wed, 22 Oct 2025 16:27:35 +0200 Subject: [PATCH 02/21] started translating the MP2_release cylinderFlow to current lettuce master. including: obstacle_cylinder.py, example-Simulation-script, HWBB and IBB boundary conditions, MEA for Bounce back boundaries, drag and lift observables... --- lettuce/ext/_flows/obstacle_cylinder.py | 109 +++++++++++++++--------- 1 file changed, 68 insertions(+), 41 deletions(-) diff --git a/lettuce/ext/_flows/obstacle_cylinder.py b/lettuce/ext/_flows/obstacle_cylinder.py index 80c0eb48..ab479914 100644 --- a/lettuce/ext/_flows/obstacle_cylinder.py +++ b/lettuce/ext/_flows/obstacle_cylinder.py @@ -13,7 +13,7 @@ __all__ = ['ObstacleCylinder'] -class ObstacleCylinder: +class ObstacleCylinder(ExtFlow): """ unified version of 2D and 3D cylinder flow refined version of flow/obstacle.py, for cylinder-flow in 2D or 3D. The dimensions will be assumed from @@ -44,18 +44,22 @@ class ObstacleCylinder: def __init__(self, context: Context, resolution: Union[int, List[int]], reynolds_number, mach_number, char_length_pu, char_length_lu, char_velocity_pu=1, - lateral_walls='periodic', bc_type='fwbb', perturb_init=True, u_init=0, - x_offset=0, y_offset=0): + lateral_walls='periodic', bc_type='fwbb', + perturb_init=True, u_init=0, + x_offset=0, y_offset=0, + stencil: Optional[Stencil] = None, + equilibrium: Optional[Equilibrium] = None): self.char_length_pu = char_length_pu # characteristic length + self.char_length_lu = char_length_lu # characteristic length in PU + # TODO: praktische Dopplung wenn char_length_lu UND resolution zusammen mit char_length_pu angegeben werden... + self.resolution = self.make_resolution(resolution, stencil) # shape in LU, if only INT, a cube shaped domain is assumed + self.char_velocity_pu = char_velocity_pu - # self.units = UnitConversion( - # reynolds_number=reynolds_number, - # mach_number=mach_number, - # characteristic_length_lu=char_length_lu, - # characteristic_length_pu=char_length_pu, - # characteristic_velocity_pu=char_velocity_pu # reminder: u_char_lu = Ma * cs_lu = Ma * 1/sqrt(3) - # ) + # initialize super class with unit conversion, equilibrium, context etc. + ExtFlow.__init__(self, context, resolution, reynolds_number, + mach_number, stencil, equilibrium) + # UnitConversion: defined below unter make_units(), executed by ExtFlow; flow object gets units-attibute! # flow and boundary settings self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street @@ -76,19 +80,24 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.y_pos = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing self.x_pos = self.y_pos + self.x_offset # keep symmetry of cylinder in x and y direction - xyz = tuple(np.linspace(1, n, n) for n in self.shape) # Tupel of index-lists (1-n (one-based!)) + # MESHGRID of x, y, (z) index (LU) + xyz = tuple(np.linspace(1, n, n) for n in self.resolution) # Tupel of index-lists (1-n (one-based!)) if self.units.lattice.D == 2: x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y-index elif self.units.lattice.D == 3: x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- and z-index else: + x_lu, y_lu, z_lu = 1,1,1 print("WARNING: something went wrong in LU-gird-index generation, lattice.D must be 2 or 3!") + # BASIC mask-Version of circular cylinder. + #TODO: Options for obstacle def: 1) basic mask (no interpolation), 2) IBB-index and mask, 3) OCC-enabled condition = np.sqrt((x_lu - self.x_pos) ** 2 + (y_lu - self.y_pos) ** 2) < self.radius self.obstacle_mask[np.where(condition)] = 1 self.solid_mask[np.where(condition)] = 1 - # indexing doesn't need z-Index for 3D, everything is broadcasted along z! + # MASKS for solid boundaries (lateral walls, obstacle, solid (= obstacle and walls), inlet) + # (INFO): indexing doesn't need z-Index for 3D, everything is broadcasted along z! if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': # if top and bottom are link-based BC self.wall_mask[:, [0, -1]] = True # don't mark wall nodes as inlet self.solid_mask[np.where(self.wall_mask)] = 1 # mark solid walls @@ -97,18 +106,17 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.in_mask[0, :] = True # inlet on the left (x=0) # generate parabolic velocity profile for inlet BC if lateral_walls (top and bottom) are bounce back walls (== channel-flow) - self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() # u = [ux,uy,uz] = [1,0,0] in PU // uniform cahracteristic velocity in x-direction + self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() # u = [ux,uy,uz] = [1,0,0] in PU // uniform characteristic velocity in x-direction if self.lateral_walls == 'bounceback': ## parabolic velocity profile, zeroing on the edges ## How to parabola: - ## 1.parabola in factoriezed form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) + ## 1.parabola in factorized form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) ## 2.parabola with a maximum and zero at x1=0 und x2=x0: y=-x*(x-x0) ## 3.scale parabola, to make y_s(x_s)=1 the maximum: y=-x*(x-x0)*(1/(x0/2)²) ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, also making the integral of a homogeneous velocity profile with u=1 and the parabolic profile being equal - (nx, ny, nz) = self.shape # number of gridpoints in y direction + (nx, ny, nz) = self.resolution # number of gridpoints in y direction parabola_y = np.zeros((1, ny)) - y_coordinates = np.linspace(0, ny, - ny) # linspace() creates n points between 0 and ny, including 0 and ny: + y_coordinates = np.linspace(0, ny, ny) # linspace() creates n points between 0 and ny, including 0 and ny: # top and bottom velocity values will be zero to agree with wall-boundary-condition parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile @@ -129,22 +137,30 @@ def make_units(self, reynolds_number, mach_number, resolution: List[int] return UnitConversion( reynolds_number=reynolds_number, mach_number=mach_number, characteristic_length_lu=self.char_length_lu, - characteristic_length_pu=self.char_length, - characteristic_velocity_pu=self.char_velocity + characteristic_length_pu=self.char_length_pu, + characteristic_velocity_pu=self.char_velocity_pu ) + # gibt immer N-dimensional zurück! (flow.resolution darf auch n int sein, dann wird kubisch angenommen) + def make_resolution(self, resolution: Union[int, List[int]], + stencil: Optional['Stencil'] = None) -> List[int]: + if isinstance(resolution, int): + return [resolution] * (stencil.d or self.stencil.d) + else: + return resolution + @property def obstacle_mask(self): return self._obstacle_mask @obstacle_mask.setter def obstacle_mask(self, m): - assert isinstance(m, np.ndarray) and m.shape == self.shape + assert isinstance(m, np.ndarray) and m.shape == self.resolution self._obstacle_mask = m.astype(bool) # self.solid_mask[np.where(self._obstacle_mask)] = 1 # (!) this line is not doing what it should! solid_mask is now defined in the initial solution (see below)! - def initial_solution(self, x): - p = np.zeros_like(x[0], dtype=float)[None, ...] + def initial_pu(self): + p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() u_max_pu = append_axes(u_max_pu, self.units.lattice.D) self.solid_mask[np.where(self.obstacle_mask)] = 1 # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD @@ -156,7 +172,7 @@ def initial_solution(self, x): u = u * 0 # uniform u=0 else: if self.lateral_walls == 'bounceback': # parabolic along y, uniform along x and z (similar to poiseuille-flow) - ny = self.shape[1] # number of gridpoints in y direction + ny = self.resolution[1] # number of gridpoints in y direction ux_factor = np.zeros(ny) # vector for one column (u(x=0)) # multiply parabolic profile with every column of the velocity field: y_coordinates = np.linspace(0, ny, ny) @@ -166,13 +182,13 @@ def initial_solution(self, x): elif self.units.lattice.D == 3: u = np.einsum('k,ijkl->ijkl', ux_factor, u) else: # lateral_walls == periodic or slip - # initiale velocity u_PU=1 on every fluid node + # initial velocity u_PU=1 on every fluid node u = (1 - self.solid_mask) * u_max_pu ### perturb initial velocity field-symmetry (in y and z) to trigger 'von Karman' vortex street if self.perturb_init: # perturb initial solution in y # overlays a sine-wave on the second column of nodes x_lu=1 (index 1) - ny = x[1].shape[1] + ny = self.grid[0][1].shape[1] if u.max() < 0.5 * self.units.characteristic_velocity_pu: # add perturbation for small velocities #OLD 2D: u[0][1] += np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 1.0 @@ -180,7 +196,7 @@ def initial_solution(self, x): if self.units.lattice.D == 2: u[0][1] += amplitude_y elif self.units.lattice.D == 3: - nz = x[2].shape[2] + nz = self.grid[0][2].shape[2] plane_yz = np.ones_like(u[0, 1]) # plane of ones u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # plane of amplitude in y amplitude_z = np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 # amplitude in z @@ -192,27 +208,33 @@ def initial_solution(self, x): if self.units.lattice.D == 2: u[0][1] *= factor elif self.units.lattice.D == 3: - nz = x[2].shape[1] + nz = self.grid[0][2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # pertubation in z-direction u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) return p, u + def make_solid_boundary_data(self): + # TODO: wo sollte das SolidBoundaryData Objekt definiert werden bzw. die Klasse definiert werden? + obstacle_solid_boudnary_data = + + # OPTION 1: calculate directly from analytic function (old MP2 compact2) + # OPTION 2: calculate via OCC (Philipp) -> see house/MA + # OPTION 3 (FWBB, HWBB only): calculate from mask or only give mask... + @property def grid(self): # THIS IS NOT USED AT THE MOMENT. QUESTION: SHOULD THIS BE ONE- OR ZERO-BASED? Indexing or "node-number"? - xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.shape) # tuple of lists of x,y,(z)-values/indices + xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.resolution) # tuple of lists of x,y,(z)-values/indices return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices @property def boundaries(self): # inlet ("left side", x[0],y[1:-1], z[:]) - inlet_boundary = EquilibriumBoundaryPU( - self.in_mask, - self.units.lattice, self.units, - # self.units.characteristic_velocity_pu * self._unit_vector()) - self.u_inlet) # works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU + inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, + mask=self.in_mask, + velocity=self.u_inlet) # (is this still true??): works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU # lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) lateral_boundary = None # stays None if lateral_walls == 'periodic' @@ -226,27 +248,32 @@ def boundaries(self): # outlet ("right side", x[-1],y[:], (z[:])) if self.units.lattice.D == 2: - outlet_boundary = EquilibriumOutletP(self.units.lattice, [1, 0]) # outlet in positive x-direction + outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) # outlet in positive x-direction else: # self.units.lattice.D == 3: - outlet_boundary = EquilibriumOutletP(self.units.lattice, [1, 0, 0]) # outlet in positive x-direction + outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # outlet in positive x-direction # obstacle (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) -> to be set via obstacle_mask.setter obstacle_boundary = None # (!) the obstacle_boundary should alway be the last boundary in the list of boundaries to correctly calculate forces on the obstacle + + #TODO: Wo sollte die "condition" bzw. die Maske bzw. der f_index bzw. die OCC-Berechnung beheimatet sein? Flow, Boundary, was eigenes? + # IDEE: SolidBoundaryData Objekt wird vom Flow berechnet und an die BBBC übergeben. + # ...Für FWBB oder HWBB würde aber auch eine Maske reichen... + # SOLID-Boundaries nach: object = Classname(Flow?, mask?, SolidBoundaryData?) if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': obstacle_boundary = HalfwayBounceBackBoundary(self.obstacle_mask, self.units.lattice) elif self.bc_type == 'ibb1' or self.bc_type == 'IBB1': obstacle_boundary = InterpolatedBounceBackBoundary(self.obstacle_mask, self.units.lattice, - x_center=(self.shape[1] / 2 - 0.5), - y_center=(self.shape[1] / 2 - 0.5), radius=self.radius) + x_center=(self.resolution[1] / 2 - 0.5), + y_center=(self.resolution[1] / 2 - 0.5), radius=self.radius) elif self.bc_type == 'ibb1c1': obstacle_boundary = InterpolatedBounceBackBoundary_compact_v1(self.obstacle_mask, self.units.lattice, - x_center=(self.shape[1] / 2 - 0.5), - y_center=(self.shape[1] / 2 - 0.5), radius=self.radius) + x_center=(self.resolution[1] / 2 - 0.5), + y_center=(self.resolution[1] / 2 - 0.5), radius=self.radius) elif self.bc_type == 'ibb1c2': obstacle_boundary = InterpolatedBounceBackBoundary_compact_v2(self.obstacle_mask, self.units.lattice, - x_center=(self.shape[1] / 2 - 0.5), - y_center=(self.shape[1] / 2 - 0.5), + x_center=(self.resolution[1] / 2 - 0.5), + y_center=(self.resolution[1] / 2 - 0.5), radius=self.radius) elif self.bc_type == 'fwbbc': obstacle_boundary = FullwayBounceBackBoundary_compact(self.obstacle_mask, self.units.lattice) From 6c1eda173f273ab8c4376f2a137ca297480e06ad Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 29 Oct 2025 18:05:48 +0100 Subject: [PATCH 03/21] progress on cylinder flow with efficient bounce back boundaries (ported from MP2 and MA). obstacle_cylinder.py implements the flow, the boundary classes are implemented in the respective files under /_boundary WorkInProgress, see ToDOs --- lettuce/ext/_boundary/__init__.py | 10 +- .../_boundary/fullway_bounce_back_boundary.py | 152 ++++++++++ .../_boundary/halfway_bounce_back_boundary.py | 198 +++++++++++++ ...inear_interpolated_bounce_back_boundary.py | 215 +++++++++++++++ lettuce/ext/_boundary/solid_boundary_data.py | 15 + lettuce/ext/_flows/obstacle_cylinder.py | 259 +++++++++++++++--- 6 files changed, 806 insertions(+), 43 deletions(-) create mode 100644 lettuce/ext/_boundary/fullway_bounce_back_boundary.py create mode 100644 lettuce/ext/_boundary/halfway_bounce_back_boundary.py create mode 100644 lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py create mode 100644 lettuce/ext/_boundary/solid_boundary_data.py diff --git a/lettuce/ext/_boundary/__init__.py b/lettuce/ext/_boundary/__init__.py index 53fd9531..cf91dba3 100644 --- a/lettuce/ext/_boundary/__init__.py +++ b/lettuce/ext/_boundary/__init__.py @@ -3,11 +3,19 @@ from .equilibrium_boundary_pu import EquilibriumBoundaryPU from .equilibrium_outlet_p import EquilibriumOutletP from .partially_saturated_boundary import PartiallySaturatedBC +from .solid_boundary_data import SolidBoundaryData +from .fullway_bounce_back_boundary import FullwayBounceBackBoundary +from .halfway_bounce_back_boundary import HalfwayBounceBackBoundary +from .linear_interpolated_bounce_back_boundary import LinearInterpolatedBounceBackBoundary __all__ = [ 'AntiBounceBackOutlet', 'BounceBackBoundary', + 'FullwayBounceBackBoundary', + 'HalfwayBounceBackBoundary', + 'LinearInterpolatedBounceBackBoundary', 'EquilibriumBoundaryPU', 'EquilibriumOutletP', - 'PartiallySaturatedBC' + 'PartiallySaturatedBC', + 'SolidBoundaryData' ] diff --git a/lettuce/ext/_boundary/fullway_bounce_back_boundary.py b/lettuce/ext/_boundary/fullway_bounce_back_boundary.py new file mode 100644 index 00000000..34afae00 --- /dev/null +++ b/lettuce/ext/_boundary/fullway_bounce_back_boundary.py @@ -0,0 +1,152 @@ +import torch +import numpy as np + +from typing import List, Optional +from ... import Boundary, Flow, Context + +__all__ = ["FullwayBounceBackBoundary"] + +class FullwayBounceBackBoundary(Boundary): + + def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): + self.context = context + self.flow = flow + + self.mask = mask + self.solid_mask = mask + + if periodicity is None: + periodicity = (False, False, False if self.flow.stencil.d == 3 else None) + + # TODO: correct periodicity and solid-to-solid contact: periodicity attribute and global solid mask! in neighbor search + # global_solid_mask to filter out all "fake" fluid neighbors, which are outside the FWBB but not in the fluid region + if global_solid_mask is None: + global_solid_mask = np.zeros_like(self.mask, dtype=bool) + other_solid_bc_mask = np.where(~self.mask, global_solid_mask, False) # exclude self.mask from global_solid_mask + + if calc_force is not None: + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.context.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.calc_force = True + else: + self.calc_force = False + + ### create f_mask_fwbb, needed for force-calculation + # ...(marks all fs which streamed into the boundary in prior streaming step) + # ... in other words: marks all fs that need to be bounced + self.f_index_fwbb = [] + + if self.flow.stencil.d == 2: + nx, ny = mask.shape # domain size in x and y + + # f_mask: [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid + ix_sp, iy_sp = np.where(mask) + # np.arrays: list of (ix_sp) x-indizes and (iy_sp) y-indizes in the boundary.mask + # ...to enable iteration over all boundary/wall/object-nodes + for sp_index in range(0, len(ix_sp)): # for all TRUE-nodes in boundary.mask + for q_index in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.flow.stencil.d, dtype=int) + if ix_sp[sp_index] == 0 and self.context.stencil.e[q_index, 0] == -1 and periodicity[0]: # searching border on left + border[0] = -1 + elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index, 0] == 1 and periodicity[0]: # searching border on right + border[0] = 1 + if iy_sp[sp_index] == 0 and self.context.stencil.e[q_index, 1] == -1 and periodicity[1]: # searching border on left + border[1] = -1 + elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index, 1] == 1 and periodicity[1]: # searching border on right + border[1] = 1 + try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + if (not mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0]*nx, + iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1]*ny] + and not other_solid_bc_mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0]*nx, + iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1]*ny]): + # if the neighbour of sp_index is False in the boundary.mask, sp_index is ix_sp solid node, neighbouring ix_sp fluid node: + # ...the direction pointing from the fluid neighbour to solid sp_index is marked on the solid sp_index + + self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index]]) # list of [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid + except IndexError: + pass # just ignore this iteration since there is no neighbor there + if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = mask.shape + + ix_sp, iy_sp, iz_sp = np.where(mask) + for sp_index in range(0, len(ix_sp)): + for q_index in range(0, self.flow.stencil.q): + border = np.zeros(self.flow.stencil.d, dtype=int) + if ix_sp[sp_index] == 0 and self.context.stencil.e[q_index, 0] == -1 and periodicity[0]: # searching border on left + border[0] = -1 + elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index, 0] == 1 and periodicity[0]: # searching border on right + border[0] = 1 + if iy_sp[sp_index] == 0 and self.context.stencil.e[q_index, 1] == -1 and periodicity[1]: # searching border on left + border[1] = -1 + elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index, 1] == 1 and periodicity[1]: # searching border on right + border[1] = 1 + if iz_sp[sp_index] == 0 and self.context.stencil.e[q_index, 2] == -1 and periodicity[2]: # searching border on left + border[2] = -1 + elif iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index, 2] == 1 and periodicity[2]: # searching border on right + border[2] = 1 + try: # try in case the neighboring cell does not exist (and f pointing out of simulation domain) + if (not mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0] * nx, + iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1] * ny, + iz_sp[sp_index] + self.context.stencil.e[q_index, 2] - border[2] * nz] + and not other_solid_bc_mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0] * nx, + iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1] * ny, + iz_sp[sp_index] + self.context.stencil.e[q_index, 2] - border[2] * nz]): + self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index], iz_sp[sp_index]]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), device=self.flow.stencil.device, dtype=torch.int64) # the batch-index has to be integer + #PHILIPP_occ_angepasst? self.f_index = torch.tensor(self.f_index, device=self.flow.stencil.device, dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.flow.stencil.device, + dtype=torch.int64) # batch-index has to be ix_sp tensor + + + def __call__(self, flow): + # FULLWAY-BBBC: inverts populations on all boundary nodes + + # calc force on boundary:# + if self.calc_force: + self.calc_force_on_boundary(flow.f) + # bounce (invert populations on boundary nodes) + # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) + + if self.flow.stencil.d == 2: + flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]] + if self.flow.stencil.d == 3: + flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]] = flow.f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]] + + def calc_force_on_boundary(self, f): + if self.flow.stencil.d == 2: + self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2]], self.flow.stencil.e[self.f_index_fwbb[:, 0]]) + if self.flow.stencil.d == 3: + self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], + self.f_index_fwbb[:, 1], + self.f_index_fwbb[:, 2], + self.f_index_fwbb[:, 3]], self.flow.stencil.e[self.f_index_fwbb[:, 0]]) + + def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Optional[torch.Tensor]: + assert self.mask.shape == f_shape[1:] + return self.context.convert_to_tensor(self.mask) + + def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: + pass + + def native_available(self) -> bool: + return False + + def native_generator(self, index: int): + pass \ No newline at end of file diff --git a/lettuce/ext/_boundary/halfway_bounce_back_boundary.py b/lettuce/ext/_boundary/halfway_bounce_back_boundary.py new file mode 100644 index 00000000..c86bc211 --- /dev/null +++ b/lettuce/ext/_boundary/halfway_bounce_back_boundary.py @@ -0,0 +1,198 @@ +import torch +import numpy as np + +from typing import List, Optional +from ... import Boundary, Flow, Context +from lettuce.lettuce.ext import SolidBoundaryData + +__all__ = ["HalfwayBounceBackBoundary"] + +class HalfwayBounceBackBoundary(Boundary): + + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): + + self.context = context + self.flow = flow + + self.mask = solid_boundary_data.solid_mask + self.solid_mask = solid_boundary_data.solid_mask + + # global_solid_mask to filter out all "fake" fluid neighbors, which are outside this HWBB but not in the fluid region + if global_solid_mask is None: + global_solid_mask = self.mask + + if periodicity is None: + periodicity = (False, False, False if self.flow.context.d == 3 else None) + + if calc_force is not None: + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.calc_force = True + else: + self.calc_force = False + + self.f_index = [] + self.f_collided = None + + # combine f_index_lt and f_index_gt to self.f_index + if (hasattr(solid_boundary_data, "f_index_gt") or hasattr(solid_boundary_data, "f_index_lt")) and len(solid_boundary_data.f_index_lt.shape) == len(solid_boundary_data.f_index_gt.shape): # if solid_boundary_data contains batch_indices, use them + self.f_index = np.concatenate((solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt), axis=0) + elif hasattr(solid_boundary_data, "f_index_gt") and solid_boundary_data.f_index_lt.shape[0] == 0: + self.f_index = solid_boundary_data.f_index_gt + elif hasattr(solid_boundary_data, "f_index_lt") and solid_boundary_data.f_index_gt.shape[0] == 0: + self.f_index = solid_boundary_data.f_index_lt + else: #else do ghetto-neighbour_search below + print("(INFO) HWBB didn't find solid_boundary_data, doing legacy neighbour_search on mask...") + # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d + if self.flow.context.d == 2: + nx, ny = self.mask.shape # domain size in x and y + a, b = np.where(self.mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area + + for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask + for i in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.flow.context.d, dtype=int) + + if a[p] == 0 and self.flow.stencil.e[i, 0] == -1 and periodicity[0]: # searching border on left [x] + border[0] = -1 + elif a[p] == nx - 1 and self.flow.stencil.e[i, 0] == 1 and periodicity[0]: # searching border on right [x] + border[0] = 1 + + if b[p] == 0 and self.flow.stencil.e[i, 1] == -1 and periodicity[1]: # searching border on left [y] + border[1] = -1 + elif b[p] == ny - 1 and self.flow.stencil.e[i, 1] == 1 and periodicity[1]: # searching border on right [y] + border[1] = 1 + + try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + if (not self.mask[a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny] + and not global_solid_mask[ + a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny]): + # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: + # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour + + self.f_index.append([self.flow.stencil.opposite[i], + a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + if self.flow.context.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = self.mask.shape + a, b, c = np.where(self.mask) + + for p in range(0, len(a)): + for i in range(0, self.flow.stencil.q): + border = np.zeros(self.flow.context.d, dtype=int) + # x - direction + if a[p] == 0 and self.flow.stencil.e[i, 0] == -1 and periodicity[0]: # searching border on left + border[0] = -1 + elif a[p] == nx - 1 and self.flow.stencil.e[i, 0] == 1 and periodicity[0]: # searching border on right + border[0] = 1 + # y - direction + if b[p] == 0 and self.flow.stencil.e[i, 1] == -1 and periodicity[1]: # searching border on left + border[1] = -1 + elif b[p] == ny - 1 and self.flow.stencil.e[i, 1] == 1 and periodicity[1]: # searching border on right + border[1] = 1 + # z - direction + if c[p] == 0 and self.flow.stencil.e[i, 2] == -1 and periodicity[2]: # searching border on left + border[2] = -1 + elif c[p] == nz - 1 and self.flow.stencil.e[i, 2] == 1 and periodicity[2]: # searching border on right + border[2] = 1 + + try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) + if (not self.mask[a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i, 2] - border[2] * nz] + and not global_solid_mask[ + a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i, 2] - border[2] * nz]): + + self.f_index.append([self.flow.stencil.opposite[i], + a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i, 2] - border[2] * nz]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + # convert relevant tensors: + self.f_index = torch.tensor(np.array(self.f_index), device=self.flow.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.flow.context.device, + dtype=torch.int64) # batch-index has to be a tensor + # f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + # f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + # self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) + + def __call__(self, flow): + # calc force on boundary: + if self.calc_force: + self.calc_force_on_boundary() + # bounce (invert populations on fluid nodes neighboring solid nodes) + # f = torch.where(self.f_mask[self.flow.stencil.opposite], f_collided[self.flow.stencil.opposite], f) + + if self.flow.context.d == 2: + flow.f[self.opposite_tensor[self.f_index[:, 0]], + self.f_index[:, 1], + self.f_index[:, 2]] = self.f_collided[:, 0] + if self.flow.context.d == 3: + flow.f[self.opposite_tensor[self.f_index[:, 0]], + self.f_index[:, 1], + self.f_index[:, 2], + self.f_index[:, 3]] = self.f_collided[:, 0] + + def make_no_collision_mask(self, shape: List[int], context: 'Context' + ) -> Optional[torch.Tensor]: + # INFO: for the halfway bounce back boundary, a no_collision_mask ist not necessary, because the no_stream_mask + # ...prevents interaction between nodes inside and outside the boundary region. + # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, + # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values + # ...in the whole domain (including the boundary region)! + assert self.mask.shape == shape[1:] + return self.context.convert_to_tensor(self.mask) + + def make_no_streaming_mask(self, shape: List[int], context: 'Context' + ) -> Optional[torch.Tensor]: + assert self.mask.shape == shape[1:] # all dimensions of f except the 0th (q) + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). + # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally + return self.context.convert_to_tensor(self.mask) + + def calc_force_on_boundary(self): + # calculate force on boundary by momentum exchange method (MEA, MEM) according to Kruger et al., 2017, pp.215-217: + # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) is summed for all... + # ...populations pointing at the surface of the boundary + self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], self.flow.stencil.e[self.f_index[:, 0]]) + + #TODO: find a way to use pre- and post-Streaming Populations for bounce... + def store_f_collided(self, f_collided): + if self.flow.context.d == 2: + self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y + self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y + if self.flow.context.d == 3: + self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z + self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z + + def initialize_f_collided(self): + f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) + self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) + + def native_available(self) -> bool: + return True + + def native_generator(self, index: int): + pass \ No newline at end of file diff --git a/lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py b/lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py new file mode 100644 index 00000000..ce38f584 --- /dev/null +++ b/lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py @@ -0,0 +1,215 @@ +import torch +import numpy as np + +from typing import List, Optional +from ... import Boundary, Flow, Context +from lettuce.lettuce.ext import SolidBoundaryData + +__all__ = ["LinearInterpolatedBounceBackBoundary"] + + + + +# THIS IS THE IBB in compact implementation WITH OCC-support +class LinearInterpolatedBounceBackBoundary(Boundary): + """Interpolated Bounce Back Boundary Condition first introduced by Bouzidi et al. (2001), as described in Kruger et al. + (2017) + - linear or quadratic interpolation of populations to retain the true boundary location between fluid- and + solid-node + * version 2.0: using given indices and distances between fluid- and solid-node + of boundary link and boundary surface for interpolation! + """ + + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_force=None): + self.context = context + self.flow = flow + + self.mask = solid_boundary_data.solid_mask + self.solid_mask = solid_boundary_data.solid_mask + if calc_force is not None: + self.force_sum = torch.zeros_like(self.context.convert_to_tensor( + self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.calc_force = True + else: + self.calc_force = False + + # convert relevant tensors: + ### TODO: fix batch-index-datatype...? + self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer + self.f_index_gt = torch.tensor(solid_boundary_data.f_index_gt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer + + self.d_lt = self.context.convert_to_tensor(solid_boundary_data.d_lt) + self.d_gt = self.context.convert_to_tensor(solid_boundary_data.d_gt) + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.context.device, dtype=torch.int64) # batch-index has to be a tensor + + self.f_collided_lt = None + self.f_collided_gt = None + # will be populated in initialize_f_collided() method + + # DEPRECATED: got moved to method initialize f_collided + # f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values + # f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values + # f_collided_lt_opposite = torch.zeros_like(self.d_lt) + # f_collided_gt_opposite = torch.zeros_like(self.d_gt) + # self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) + # self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) + #TODO: does the f_collided etc. have to be reworked due to new lettuce master pre/post collision boundary stuff? + + def __call__(self, flow): + ## f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout + + if self.flow.stencil.d == 2: + # BOUNCE + # if d <= 0.5 + if len(self.f_index_lt) != 0: + flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]] + # if d > 0.5 + if len(self.f_index_gt) != 0: + flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + ( + 1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + + if self.flow.stencil.d == 3: + # BOUNCE + # if d <= 0.5 + if len(self.f_index_lt) != 0: + flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]] + # if d > 0.5 + if len(self.f_index_gt) != 0: + flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + ( + 1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + + # CALC. FORCE on boundary (MEM, MEA) + if self.calc_force: + self.calc_force_on_boundary(flow.f) + + def make_no_streaming_mask(self, shape, context: Context): + assert self.mask.shape == shape[1:] # all dimensions of f except the 0th (q) + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). + # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally + # return torch.tensor(self.mask, dtype=torch.bool) + return self.context.convert_to_tensor(self.mask) + + def make_no_collision_mask(self, shape, context: Context): + # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, + # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values + # ...in the whole domain (including the boundary region)! + assert self.mask.shape == shape[1:] + # return torch.tensor(self.mask, dtype=torch.bool) # self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask) + + def calc_force_on_boundary(self, f_bounced): + ### force = e * (f_collided + f_bounced[opp.]) + if self.flow.stencil.d == 2: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]], + self.flow.stencil.e[self.f_index_lt[:, 0]].float()) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]], + self.flow.stencil.e[self.f_index_gt[:, 0]].float()) + if self.flow.stencil.d == 3: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]], + self.flow.stencil.e[self.f_index_lt[:, 0]].float()) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]], + self.flow.stencil.e[self.f_index_gt[:, 0]].float()) + + # TODO: find a way to use pre- and post-Streaming Populations for bounce... + def store_f_collided(self, f_collided): + for f_index_lgt, f_collided_lgt in zip([self.f_index_lt, self.f_index_gt], + [self.f_collided_lt, self.f_collided_gt]): + if len(f_index_lgt) != 0: + for d in range(self.flow.stencil.d): + indices = [f_index_lgt[:, 0], # q + f_index_lgt[:, 1], # x + f_index_lgt[:, 2]] # y + if self.flow.stencil.d == 3: + indices.append(f_index_lgt[:, 3]) + f_collided_lgt[:, 0] = torch.clone(f_collided[indices]) + indices[0] = self.opposite_tensor[f_index_lgt[:, 0]] + f_collided_lgt[:, 1] = torch.clone(f_collided[indices]) + # TODO: compare performance of THIS to original hardcoded "store_f_collided()" of IBB1, see below + + # >>> OLD version "semi hardcoded" + # def store_f_collided(self, f_collided): + # if self.flow.stencil.d == 2: + # if len(self.f_collided_lt) != 0: + # self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + # self.f_index_lt[:, 1], # x + # self.f_index_lt[:, 2]]) # y + # self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + # self.f_index_lt[:, 1], # x + # self.f_index_lt[:, 2]]) # y + # if len(self.f_collided_gt) != 0: + # self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + # self.f_index_gt[:, 1], # x + # self.f_index_gt[:, 2]]) # y + # self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + # self.f_index_gt[:, 1], # x + # self.f_index_gt[:, 2]]) # y + # if self.flow.stencil.d == 3: + # if len(self.f_collided_lt) != 0: + # self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + # self.f_index_lt[:, 1], # x + # self.f_index_lt[:, 2], # y + # self.f_index_lt[:, 3]]) # z + # self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + # self.f_index_lt[:, 1], # x + # self.f_index_lt[:, 2], # y + # self.f_index_lt[:, 3]]) # z + # if len(self.f_collided_gt) != 0: + # self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + # self.f_index_gt[:, 1], # x + # self.f_index_gt[:, 2], # y + # self.f_index_gt[:, 3]]) # z + # self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q + # self.f_index_gt[:, 1], # x + # self.f_index_gt[:, 2], # y + # self.f_index_gt[:, 3]]) # z + # <<< OLD version "semi hardcoded" + + # TODO: find a way to use pre- and post-Streaming Populations for bounce... + def initialize_f_collided(self): + f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values + f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values + f_collided_lt_opposite = torch.zeros_like(self.d_lt) + f_collided_gt_opposite = torch.zeros_like(self.d_gt) + self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) + self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) + + def native_available(self) -> bool: + return False + + def native_generator(self, index: int): + return None diff --git a/lettuce/ext/_boundary/solid_boundary_data.py b/lettuce/ext/_boundary/solid_boundary_data.py new file mode 100644 index 00000000..594ba98a --- /dev/null +++ b/lettuce/ext/_boundary/solid_boundary_data.py @@ -0,0 +1,15 @@ + +import numpy as np + +__all__ = ['SolidBoundaryData'] + +# index lists for Bounce Back Boundaries: populations for FWBB, HWBB; IBB. +# - d and the differentiation of lt (less than 0.5) and gt (greater than 0.5) is only for IBB +class SolidBoundaryData(dict): + f_index_lt: np.ndarray + f_index_gt: np.ndarray + d_lt: np.ndarray + d_gt: np.ndarray + points_inside: np.ndarray + solid_mask: np.ndarray + not_intersected: np.ndarray = np.ndarray([]) \ No newline at end of file diff --git a/lettuce/ext/_flows/obstacle_cylinder.py b/lettuce/ext/_flows/obstacle_cylinder.py index ab479914..e4c6a05f 100644 --- a/lettuce/ext/_flows/obstacle_cylinder.py +++ b/lettuce/ext/_flows/obstacle_cylinder.py @@ -8,7 +8,7 @@ from ... import UnitConversion, Context, Stencil, Equilibrium from ...util import append_axes from .. import (EquilibriumBoundaryPU, BounceBackBoundary, - EquilibriumOutletP, AntiBounceBackOutlet) + EquilibriumOutletP, AntiBounceBackOutlet, SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary) __all__ = ['ObstacleCylinder'] @@ -215,13 +215,196 @@ def initial_pu(self): u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) return p, u - def make_solid_boundary_data(self): + def make_solid_boundary_data(self, x_center, y_center, radius): + pass + # NOT YET IMPLEMENTED; OCC-version for index lists # TODO: wo sollte das SolidBoundaryData Objekt definiert werden bzw. die Klasse definiert werden? - obstacle_solid_boudnary_data = - # OPTION 1: calculate directly from analytic function (old MP2 compact2) - # OPTION 2: calculate via OCC (Philipp) -> see house/MA - # OPTION 3 (FWBB, HWBB only): calculate from mask or only give mask... + # cylinder definitions: + # OPTION 1: calculate directly from analytic function -> see make_ibb_index_lists below + # OPTION 2: calculate via OCC (Philipp) -> see external house/MA + # OPTION 3 (FWBB, HWBB only): calculate from mask or only give mask... + + def make_ibb_index_lists(self, x_center, y_center, radius): + + # this method relies on self.obstacle_mask too! + f_index_lt = [] # indices of relevant populations (for bounce back and force-calculation) with d<=0.5 + f_index_gt = [] # indices of relevant populations (for bounce back and force-calculation) with d>0.5 + d_lt = [] # distances between node and boundary for d<0.5 + d_gt = [] # distances between node and boundary for d>0.5 + + # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d + if self.stencil.d == 2: + # TODO: the 2D and 3D options could be condensed/unified + nx, ny = self.resolution # domain size in x and y + a, b = np.where(self.obstacle_mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area + + for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask + for i in range(0, self.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.stencil.d, dtype=int) + + if a[p] == 0 and self.stencil.e[i, 0] == -1: # searching border on left [x] + border[0] = -1 + elif a[p] == nx - 1 and self.stencil.e[i, 0] == 1: # searching border on right [x] + border[0] = 1 + + if b[p] == 0 and self.stencil.e[i, 1] == -1: # searching border on left [y] + border[1] = -1 + elif b[p] == ny - 1 and self.stencil.e[i, 1] == 1: # searching border on right [y] + border[1] = 1 + + try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + if not self.obstacle_mask[a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny]: + # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: + # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate + cx = self.stencil.e[ + self.stencil.opposite[i], 0] # link-direction x to solid node + cy = self.stencil.e[ + self.stencil.opposite[i], 1] # link-direction y to solid node + + # pq-formula + h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + d_lt.append(d1) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny]) + else: # d>0.5 + d_gt.append(d1) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + d_lt.append(d2) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny]) + else: # d>0.5 + d_gt.append(d2) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny]) + else: # neither d1 or d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], + b[p], self.stencil.e[i, 0], self.stencil.e[i, 1], + self.stencil.e[i, 2]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + if self.stencil.d == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = self.obstacle_mask.shape + a, b, c = np.where(self.obstacle_mask) + + for p in range(0, len(a)): + for i in range(0, self.stencil.q): + border = np.zeros(self.stencil.d, dtype=int) + # x - direction + if a[p] == 0 and self.stencil.e[i, 0] == -1: # searching border on left + border[0] = -1 + elif a[p] == nx - 1 and self.stencil.e[i, 0] == 1: # searching border on right + border[0] = 1 + # y - direction + if b[p] == 0 and self.stencil.e[i, 1] == -1: # searching border on left + border[1] = -1 + elif b[p] == ny - 1 and self.stencil.e[i, 1] == 1: # searching border on right + border[1] = 1 + # z - direction + if c[p] == 0 and self.stencil.e[i, 2] == -1: # searching border on left + border[2] = -1 + elif c[p] == nz - 1 and self.stencil.e[i, 2] == 1: # searching border on right + border[2] = 1 + + try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) + if not self.obstacle_mask[a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny, + c[p] + self.stencil.e[i, 2] - border[2] * nz]: + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate + # Z-coordinate not needed for cylinder ! + + cx = self.stencil.e[ + self.stencil.opposite[i], 0] # link-direction x to solid node + cy = self.stencil.e[ + self.stencil.opposite[i], 1] # link-direction y to solid node + # Z-coordinate not needed for cylinder ! + + # pq-formula + h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # print("xb,yb,i,d1,d2 xf, yf, cx, cy:", a[p], b[p], i, d1, d2, px, py, cx, cy) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + d_lt.append(d1) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny, + c[p] + self.stencil.e[i, 2] - border[2] * nz]) + else: # d>0.5 + d_gt.append(d1) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny, + c[p] + self.stencil.e[i, 2] - border[2] * nz]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + d_lt.append(d2) + f_index_lt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny, + c[p] + self.stencil.e[i, 2] - border[2] * nz]) + else: # d>0.5 + d_gt.append(d2) + f_index_gt.append([self.stencil.opposite[i], + a[p] + self.stencil.e[i, 0] - border[0] * nx, + b[p] + self.stencil.e[i, 1] - border[1] * ny, + c[p] + self.stencil.e[i, 2] - border[2] * nz]) + else: # neither d1 nor d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], + b[p], c[p], self.stencil.e[i, 0], self.stencil.e[i, 1], + self.stencil.e[i, 2]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + # output as np.array: f_index_lt, f_index_gt, d_lt, d_gt + return [f_index_lt, f_index_gt, d_lt, d_gt] + @property def grid(self): @@ -231,20 +414,26 @@ def grid(self): @property def boundaries(self): + + + # inlet ("left side", x[0],y[1:-1], z[:]) inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, mask=self.in_mask, velocity=self.u_inlet) # (is this still true??): works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU - # lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) + # >>> lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) + #TODO: implement lateral walls (top, bottom) for cylinder in channel (with or without wall friction) + # - implementation depends on positional definition: mask or index-lists or solid_boundary_data object! lateral_boundary = None # stays None if lateral_walls == 'periodic' - if self.lateral_walls == 'bounceback': - if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back - lateral_boundary = HalfwayBounceBackBoundary(self.wall_mask, self.units.lattice) - else: # else use fullway bounce back - lateral_boundary = FullwayBounceBackBoundary(self.wall_mask, self.units.lattice) - elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-walöl (symmetry boundary) - lateral_boundary = SlipBoundary(self.wall_mask, self.units.lattice, 1) # slip on x(z)-plane + # if self.lateral_walls == 'bounceback': + # if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back + # lateral_boundary = HalfwayBounceBackBoundary(self.wall_mask, self.units.lattice) + # else: # else use fullway bounce back + # lateral_boundary = FullwayBounceBackBoundary(self.wall_mask, self.units.lattice) + # elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-walöl (symmetry boundary) + # lateral_boundary = SlipBoundary(self.wall_mask, self.units.lattice, 1) # slip on x(z)-plane + # <<< # outlet ("right side", x[-1],y[:], (z[:])) if self.units.lattice.D == 2: @@ -256,35 +445,21 @@ def boundaries(self): obstacle_boundary = None # (!) the obstacle_boundary should alway be the last boundary in the list of boundaries to correctly calculate forces on the obstacle - #TODO: Wo sollte die "condition" bzw. die Maske bzw. der f_index bzw. die OCC-Berechnung beheimatet sein? Flow, Boundary, was eigenes? - # IDEE: SolidBoundaryData Objekt wird vom Flow berechnet und an die BBBC übergeben. - # ...Für FWBB oder HWBB würde aber auch eine Maske reichen... - # SOLID-Boundaries nach: object = Classname(Flow?, mask?, SolidBoundaryData?) - if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': - obstacle_boundary = HalfwayBounceBackBoundary(self.obstacle_mask, self.units.lattice) + solid_boundary_data = SolidBoundaryData() + solid_boundary_data.solid_mask = self.obstacle_mask + + # index-lists for circular cylinder [f_index_lt, f_index_gt, d_lt, d_gt] + solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.dgt = self.make_ibb_index_lists( + x_center=self.x_pos, y_center=self.y_pos, radius=self.radius) + + if self.bc_type == 'fwbb' or self.bc_type == 'FWBB': + obstacle_boundary = FullwayBounceBackBoundary(self.context, self, self.obstacle_mask) #TODO: add periodicity, global_solid_mask and calc_force + elif self.bc_type == 'hwbb' or self.bc_type == 'HWBB': + obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, solid_boundary_data) #TODO: add periodicity, global_solid_mask and calc_force elif self.bc_type == 'ibb1' or self.bc_type == 'IBB1': - obstacle_boundary = InterpolatedBounceBackBoundary(self.obstacle_mask, self.units.lattice, - x_center=(self.resolution[1] / 2 - 0.5), - y_center=(self.resolution[1] / 2 - 0.5), radius=self.radius) - elif self.bc_type == 'ibb1c1': - obstacle_boundary = InterpolatedBounceBackBoundary_compact_v1(self.obstacle_mask, self.units.lattice, - x_center=(self.resolution[1] / 2 - 0.5), - y_center=(self.resolution[1] / 2 - 0.5), radius=self.radius) - elif self.bc_type == 'ibb1c2': - obstacle_boundary = InterpolatedBounceBackBoundary_compact_v2(self.obstacle_mask, self.units.lattice, - x_center=(self.resolution[1] / 2 - 0.5), - y_center=(self.resolution[1] / 2 - 0.5), - radius=self.radius) - elif self.bc_type == 'fwbbc': - obstacle_boundary = FullwayBounceBackBoundary_compact(self.obstacle_mask, self.units.lattice) - elif self.bc_type == 'hwbbc1': - obstacle_boundary = HalfwayBounceBackBoundary_compact_v1(self.obstacle_mask, self.units.lattice) - elif self.bc_type == 'hwbbc2': - obstacle_boundary = HalfwayBounceBackBoundary_compact_v2(self.obstacle_mask, self.units.lattice) - elif self.bc_type == 'hwbbc3': - obstacle_boundary = HalfwayBounceBackBoundary_compact_v3(self.obstacle_mask, self.units.lattice) - else: # use Fullway Bounce Back - obstacle_boundary = FullwayBounceBackBoundary(self.obstacle_mask, self.units.lattice) + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=True) + else: # use basic mask Bounce Back + obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list return [ From 703594094f10e294747b56e3bc74b3ead8fe3712 Mon Sep 17 00:00:00 2001 From: mbille Date: Fri, 14 Nov 2025 16:52:19 +0100 Subject: [PATCH 04/21] Moved all files for advanced_cylinder-example with efficient bounce back boundaries to folder examples/advanced_projects/efficient_bounce_back_obstacle. Includes boundary conditions, obstacle_cylinder.py and placeholders for simulation-scripts for Re40-300 and Re3900, and a jupyter-notebook to run the scripts interactively _simulation.py: added comments for clarification --- .../00_run_parameterized_project.ipynb | 39 + .../01a_script_cylinder_lowRe.py | 2 + .../01b_script_cylinder_highRe.py | 2 + .../__init__.py | 11 + .../ebb_simulation.py | 71 + .../fullway_bounce_back_boundary.py | 2 +- .../halfway_bounce_back_boundary.py | 2 +- ...inear_interpolated_bounce_back_boundary.py | 2 +- .../obstacle_cylinder.py | 58 +- .../solid_boundary_data.py | 0 lettuce/_simulation.py | 28 +- lettuce/ext/_boundary/__init__.py | 8 - pyproject.toml | 1 + uv.lock | 1385 +++++++++++++++-- 14 files changed, 1478 insertions(+), 133 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py rename {lettuce/ext/_boundary => examples/advanced_projects/efficient_bounce_back_obstacle}/fullway_bounce_back_boundary.py (99%) rename {lettuce/ext/_boundary => examples/advanced_projects/efficient_bounce_back_obstacle}/halfway_bounce_back_boundary.py (99%) rename {lettuce/ext/_boundary => examples/advanced_projects/efficient_bounce_back_obstacle}/linear_interpolated_bounce_back_boundary.py (99%) rename {lettuce/ext/_flows => examples/advanced_projects/efficient_bounce_back_obstacle}/obstacle_cylinder.py (95%) rename {lettuce/ext/_boundary => examples/advanced_projects/efficient_bounce_back_obstacle}/solid_boundary_data.py (100%) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb new file mode 100644 index 00000000..caf9201d --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-11-14T15:40:54.515141Z", + "start_time": "2025-11-14T15:40:54.512769Z" + } + }, + "source": "# this script should contain the description, documentation and code-Blocks to parameterize and rund the cylinder simulations interactively", + "outputs": [], + "execution_count": 1 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py new file mode 100644 index 00000000..ec1df5a6 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -0,0 +1,2 @@ + +# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py new file mode 100644 index 00000000..c26c4604 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py @@ -0,0 +1,2 @@ + +# this file should contain all the MP2 stuff with Re3900 and velocity profiles... \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py b/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py new file mode 100644 index 00000000..fa823920 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/__init__.py @@ -0,0 +1,11 @@ +from .solid_boundary_data import SolidBoundaryData +from .fullway_bounce_back_boundary import FullwayBounceBackBoundary +from .halfway_bounce_back_boundary import HalfwayBounceBackBoundary +from .linear_interpolated_bounce_back_boundary import LinearInterpolatedBounceBackBoundary + +__all__ = [ + 'FullwayBounceBackBoundary', + 'HalfwayBounceBackBoundary', + 'LinearInterpolatedBounceBackBoundary', + 'SolidBoundaryData' +] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py new file mode 100644 index 00000000..dba1b110 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -0,0 +1,71 @@ +import warnings + +import torch +import numpy as np + +from timeit import default_timer as timer +from typing import List, Optional +from abc import ABC, abstractmethod + +from . import * +from lettuce.lettuce.cuda_native import NativeCollision, Generator, StreamingStrategy + +from lettuce.lettuce._simulation import * +from lettuce.lettuce import Flow + +__all__ = ['EbbSimulation'] + +class EbbSimulation(Simulation): + def __init__(self, flow: 'Flow', collision: 'Collision', + reporter: List['Reporter']): + flow.context.use_native = False + super().__init__(flow, collision, reporter) + + if hasattr(flow, 'post_streaming_boundaries'): + # list of boundaries that are applied AFTER the streaming step + self.post_streaming_boundaries = flow.post_streaming_boundaries + else: + self.post_streaming_boundaries = [] + + # get indices of post_streaming_boundaries, that need f_collided to be stored + self.store_f_collided_post_streaming_boundaries_index = [] + + for i, boundary in enumerate(self.post_streaming_boundaries): + if hasattr(boundary, "store_f_collided"): + self.store_f_collided_post_streaming_boundaries_index.append(i) + + # redefine collide_and_stream() to include the storage of f_collided for use in post_streaming_boundaries + def collide_and_stream(*_, **__): + self._collide() + self._pass_f_collided() # pass f to post_streaming_boundaries between collision and streaming substep + self._stream() + + self._collide_and_stream = collide_and_stream + + + def _post_streaming_boundaries(self): + # runs all the post_streaming_boundaries; required for efficient BBBC + + for boundary in self.post_streaming_boundaries: + boundary(self.flow) + + def _pass_f_collided(self): + # passes f to post_streaming_boundaries, for later use + + for idx in self.store_f_collided_post_streaming_boundaries_index: + self.post_streaming_boundaries[idx].store_f_collided(self.flow.f) + + def __call__(self, num_steps): + beg = timer() + + if self.flow.i == 0: + self._report() + + for _ in range(num_steps): + self._collide_and_stream(self) + self._post_streaming_boundaries() + self.flow.i += 1 + self._report() + + end = timer() + return num_steps * self.flow.rho().numel() / 1e6 / (end - beg) diff --git a/lettuce/ext/_boundary/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py similarity index 99% rename from lettuce/ext/_boundary/fullway_bounce_back_boundary.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 34afae00..56aca1de 100644 --- a/lettuce/ext/_boundary/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -2,7 +2,7 @@ import numpy as np from typing import List, Optional -from ... import Boundary, Flow, Context +from lettuce.lettuce import Boundary, Flow, Context __all__ = ["FullwayBounceBackBoundary"] diff --git a/lettuce/ext/_boundary/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py similarity index 99% rename from lettuce/ext/_boundary/halfway_bounce_back_boundary.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index c86bc211..132ddb7a 100644 --- a/lettuce/ext/_boundary/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -2,7 +2,7 @@ import numpy as np from typing import List, Optional -from ... import Boundary, Flow, Context +from lettuce.lettuce import Boundary, Flow, Context from lettuce.lettuce.ext import SolidBoundaryData __all__ = ["HalfwayBounceBackBoundary"] diff --git a/lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py similarity index 99% rename from lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index ce38f584..9954be3c 100644 --- a/lettuce/ext/_boundary/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -2,7 +2,7 @@ import numpy as np from typing import List, Optional -from ... import Boundary, Flow, Context +from lettuce.lettuce import Boundary, Flow, Context from lettuce.lettuce.ext import SolidBoundaryData __all__ = ["LinearInterpolatedBounceBackBoundary"] diff --git a/lettuce/ext/_flows/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py similarity index 95% rename from lettuce/ext/_flows/obstacle_cylinder.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index e4c6a05f..e1874390 100644 --- a/lettuce/ext/_flows/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -4,11 +4,12 @@ import numpy as np import torch -from . import ExtFlow -from ... import UnitConversion, Context, Stencil, Equilibrium -from ...util import append_axes -from .. import (EquilibriumBoundaryPU, BounceBackBoundary, - EquilibriumOutletP, AntiBounceBackOutlet, SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary) +from lettuce.lettuce.ext._flows import ExtFlow +from lettuce.lettuce import UnitConversion, Context, Stencil, Equilibrium +from lettuce.lettuce.util import append_axes +from lettuce.lettuce.ext import (EquilibriumBoundaryPU, BounceBackBoundary, + EquilibriumOutletP, AntiBounceBackOutlet) +from lettuce.examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary __all__ = ['ObstacleCylinder'] @@ -413,19 +414,18 @@ def grid(self): return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices @property - def boundaries(self): - - + def post_boundaries(self): # inlet ("left side", x[0],y[1:-1], z[:]) inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, mask=self.in_mask, velocity=self.u_inlet) # (is this still true??): works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU + lateral_boundary = None # stays None if lateral walls are not specified... (NOT IMPLEMENTED YET, see below) # >>> lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) #TODO: implement lateral walls (top, bottom) for cylinder in channel (with or without wall friction) # - implementation depends on positional definition: mask or index-lists or solid_boundary_data object! - lateral_boundary = None # stays None if lateral_walls == 'periodic' + # # if self.lateral_walls == 'bounceback': # if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back # lateral_boundary = HalfwayBounceBackBoundary(self.wall_mask, self.units.lattice) @@ -441,9 +441,24 @@ def boundaries(self): else: # self.units.lattice.D == 3: outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # outlet in positive x-direction + # create and return boundary-list + if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list + return [ + inlet_boundary, + outlet_boundary, + ] + else: + return [ + inlet_boundary, + outlet_boundary, + lateral_boundary, + ] + + @property + def post_streaming_boundaries(self): # obstacle (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) -> to be set via obstacle_mask.setter obstacle_boundary = None - # (!) the obstacle_boundary should alway be the last boundary in the list of boundaries to correctly calculate forces on the obstacle + # (!) the obstacle_boundary should always be the last boundary in the list of boundaries to correctly calculate forces on the obstacle solid_boundary_data = SolidBoundaryData() solid_boundary_data.solid_mask = self.obstacle_mask @@ -453,27 +468,18 @@ def boundaries(self): x_center=self.x_pos, y_center=self.y_pos, radius=self.radius) if self.bc_type == 'fwbb' or self.bc_type == 'FWBB': - obstacle_boundary = FullwayBounceBackBoundary(self.context, self, self.obstacle_mask) #TODO: add periodicity, global_solid_mask and calc_force + obstacle_boundary = FullwayBounceBackBoundary(self.context, self, + self.obstacle_mask) # TODO: add periodicity, global_solid_mask and calc_force elif self.bc_type == 'hwbb' or self.bc_type == 'HWBB': - obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, solid_boundary_data) #TODO: add periodicity, global_solid_mask and calc_force + obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, + solid_boundary_data) # TODO: add periodicity, global_solid_mask and calc_force elif self.bc_type == 'ibb1' or self.bc_type == 'IBB1': - obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=True) + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, + calc_force=True) else: # use basic mask Bounce Back obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) - if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list - return [ - inlet_boundary, - outlet_boundary, - obstacle_boundary - ] - else: - return [ - inlet_boundary, - outlet_boundary, - lateral_boundary, - obstacle_boundary - ] + return [obstacle_boundary] def _unit_vector(self, i=0): return np.eye(self.units.lattice.D)[i] \ No newline at end of file diff --git a/lettuce/ext/_boundary/solid_boundary_data.py b/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py similarity index 100% rename from lettuce/ext/_boundary/solid_boundary_data.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py diff --git a/lettuce/_simulation.py b/lettuce/_simulation.py index 1ea64fcc..0a3251b3 100644 --- a/lettuce/_simulation.py +++ b/lettuce/_simulation.py @@ -77,21 +77,31 @@ def __init__(self, flow: 'Flow', collision: 'Collision', if len(self.pre_boundaries) + len(self.post_boundaries) > 0: + # fill masks with value of self.collision_index (= number of pre-boundaries) self.no_collision_mask = self.context.full_tensor( flow.resolution, self.collision_index, dtype=torch.uint8) self.no_streaming_mask = self.context.full_tensor( [flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) + # iterate over pre-boundaries for i, boundary in enumerate(self.pre_boundaries): + # if the i-th boundary returns a non-None no_collision_mask, + # ...write its index (i) to every entry in the global no_collision_mask + # ...that is True in the boundaries no_collision_mask ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: self.no_collision_mask[ncm] = i + + # if the i-th boundary returns a non-None no_streaming_mask, + # ...add its True-entries to the global no_streaming_mask nsm = boundary.make_no_streaming_mask( [it for it in self.flow.f.shape], context=self.context) if nsm is not None: self.no_streaming_mask |= nsm + # iterate over post-boundaries + # ...(similar to pre-b., but start with index collision_index+1) for i, boundary in enumerate(self.post_boundaries,start=self.collision_index+1): ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) @@ -212,19 +222,33 @@ def _stream(self): return self.flow.f def _collide(self): - if self.no_collision_mask is None: + # runs collision and all pre- and post-boundaries (which are pre- or post-collision, but pre-streaming) + if self.no_collision_mask is None: # COLLISION EVERYWHERE + # run pre-boundaries for boundary in self.pre_boundaries: self.flow.f = boundary(self.flow) + # run collision self.flow.f = self.collision(self.flow) + # run post-boundaries for boundary in self.post_boundaries: self.flow.f = boundary(self.flow) - else: + else: # SELECTIVE COLLISION (no_collision_mask (which is not a boolean mask!)) + # boundary-indices in no_collision_mask: + # pre_boundaries have indices: 0 to len(pre_boundaries)-1 + # collision_index: len(pre_boundaries) + # post_boundaries have indices: len(pre_boundaries)+1 to len(pre_boundaries)+len(post_boundaries) + # run pre-boundaries for i, boundary in enumerate(self.pre_boundaries): + # where the collision-int equals the boundary-index, apply the boundary, everywhere else, do nothing torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) + # run collision + # ... where the collision-int equals the number of pre-boundaries, + # ...apply collision, everywhere else, do nothing torch.where(torch.eq(self.no_collision_mask, self.collision_index), self.collision(self.flow), self.flow.f, out=self.flow.f) + # run post-boundaries for i, boundary in enumerate(self.post_boundaries, start=self.collision_index+1): torch.where(torch.eq(self.no_collision_mask, i), boundary(self.flow), self.flow.f, out=self.flow.f) diff --git a/lettuce/ext/_boundary/__init__.py b/lettuce/ext/_boundary/__init__.py index cf91dba3..2baf9b02 100644 --- a/lettuce/ext/_boundary/__init__.py +++ b/lettuce/ext/_boundary/__init__.py @@ -3,19 +3,11 @@ from .equilibrium_boundary_pu import EquilibriumBoundaryPU from .equilibrium_outlet_p import EquilibriumOutletP from .partially_saturated_boundary import PartiallySaturatedBC -from .solid_boundary_data import SolidBoundaryData -from .fullway_bounce_back_boundary import FullwayBounceBackBoundary -from .halfway_bounce_back_boundary import HalfwayBounceBackBoundary -from .linear_interpolated_bounce_back_boundary import LinearInterpolatedBounceBackBoundary __all__ = [ 'AntiBounceBackOutlet', 'BounceBackBoundary', - 'FullwayBounceBackBoundary', - 'HalfwayBounceBackBoundary', - 'LinearInterpolatedBounceBackBoundary', 'EquilibriumBoundaryPU', 'EquilibriumOutletP', 'PartiallySaturatedBC', - 'SolidBoundaryData' ] diff --git a/pyproject.toml b/pyproject.toml index 727f7d90..5a62935d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,7 @@ dependencies = [ "h5py==3.11.0", "matplotlib>=3.9.2", "mmh3>=4.1.0", + "notebook>=7.4.7", "numpy>=2.1.0", "packaging>=24.1", "pyevtk>=1.6.0", diff --git a/uv.lock b/uv.lock index 697ed9d2..0efdf8fb 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.12.*" resolution-markers = [ "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')", @@ -23,6 +23,198 @@ conflicts = [[ { package = "lettuce", extra = "cu130" }, ]] +[[package]] +name = "anyio" +version = "4.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "idna" }, + { name = "sniffio" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" }, +] + +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, +] + +[[package]] +name = "argon2-cffi" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "argon2-cffi-bindings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" }, +] + +[[package]] +name = "argon2-cffi-bindings" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121, upload-time = "2025-07-30T10:01:50.815Z" }, + { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177, upload-time = "2025-07-30T10:01:51.681Z" }, + { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090, upload-time = "2025-07-30T10:01:53.184Z" }, + { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246, upload-time = "2025-07-30T10:01:54.145Z" }, + { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126, upload-time = "2025-07-30T10:01:55.074Z" }, + { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343, upload-time = "2025-07-30T10:01:56.007Z" }, + { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" }, + { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180, upload-time = "2025-07-30T10:01:57.759Z" }, + { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715, upload-time = "2025-07-30T10:01:58.56Z" }, + { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149, upload-time = "2025-07-30T10:01:59.329Z" }, +] + +[[package]] +name = "arrow" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "python-dateutil" }, + { name = "tzdata" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931, upload-time = "2025-10-18T17:46:46.761Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797, upload-time = "2025-10-18T17:46:45.663Z" }, +] + +[[package]] +name = "asttokens" +version = "3.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918, upload-time = "2024-11-30T04:30:10.946Z" }, +] + +[[package]] +name = "async-lru" +version = "2.0.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b2/4d/71ec4d3939dc755264f680f6c2b4906423a304c3d18e96853f0a595dfe97/async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb", size = 10380, upload-time = "2025-03-16T17:25:36.919Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/03/49/d10027df9fce941cb8184e78a02857af36360d33e1721df81c5ed2179a1a/async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943", size = 6069, upload-time = "2025-03-16T17:25:35.422Z" }, +] + +[[package]] +name = "attrs" +version = "25.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251, upload-time = "2025-10-06T13:54:44.725Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" }, +] + +[[package]] +name = "babel" +version = "2.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852, upload-time = "2025-02-01T15:17:41.026Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" }, +] + +[[package]] +name = "beautifulsoup4" +version = "4.14.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "soupsieve" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/77/e9/df2358efd7659577435e2177bfa69cba6c33216681af51a707193dec162a/beautifulsoup4-4.14.2.tar.gz", hash = "sha256:2a98ab9f944a11acee9cc848508ec28d9228abfd522ef0fad6a02a72e0ded69e", size = 625822, upload-time = "2025-09-29T10:05:42.613Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/fe/3aed5d0be4d404d12d36ab97e2f1791424d9ca39c2f754a6285d59a3b01d/beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515", size = 106392, upload-time = "2025-09-29T10:05:43.771Z" }, +] + +[[package]] +name = "bleach" +version = "6.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533, upload-time = "2025-10-27T17:57:39.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437, upload-time = "2025-10-27T17:57:37.538Z" }, +] + +[package.optional-dependencies] +css = [ + { name = "tinycss2" }, +] + +[[package]] +name = "certifi" +version = "2025.11.12" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" }, +] + +[[package]] +name = "cffi" +version = "2.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pycparser", marker = "implementation_name != 'PyPy' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" }, + { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" }, + { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529, upload-time = "2025-09-08T23:22:47.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" }, + { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" }, + { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519, upload-time = "2025-09-08T23:22:51.364Z" }, + { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572, upload-time = "2025-09-08T23:22:52.902Z" }, + { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963, upload-time = "2025-09-08T23:22:54.518Z" }, + { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361, upload-time = "2025-09-08T23:22:55.867Z" }, + { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" }, + { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" }, + { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762, upload-time = "2025-09-08T23:22:59.668Z" }, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425, upload-time = "2025-10-14T04:40:53.353Z" }, + { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162, upload-time = "2025-10-14T04:40:54.558Z" }, + { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558, upload-time = "2025-10-14T04:40:55.677Z" }, + { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497, upload-time = "2025-10-14T04:40:57.217Z" }, + { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240, upload-time = "2025-10-14T04:40:58.358Z" }, + { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471, upload-time = "2025-10-14T04:40:59.468Z" }, + { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864, upload-time = "2025-10-14T04:41:00.623Z" }, + { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647, upload-time = "2025-10-14T04:41:01.754Z" }, + { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110, upload-time = "2025-10-14T04:41:03.231Z" }, + { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839, upload-time = "2025-10-14T04:41:04.715Z" }, + { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667, upload-time = "2025-10-14T04:41:05.827Z" }, + { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535, upload-time = "2025-10-14T04:41:06.938Z" }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816, upload-time = "2025-10-14T04:41:08.101Z" }, + { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694, upload-time = "2025-10-14T04:41:09.23Z" }, + { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131, upload-time = "2025-10-14T04:41:10.467Z" }, + { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390, upload-time = "2025-10-14T04:41:11.915Z" }, + { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, +] + [[package]] name = "click" version = "8.1.7" @@ -44,6 +236,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "comm" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" }, +] + [[package]] name = "contourpy" version = "1.3.2" @@ -74,6 +275,55 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, ] +[[package]] +name = "debugpy" +version = "1.8.17" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/15/ad/71e708ff4ca377c4230530d6a7aa7992592648c122a2cd2b321cf8b35a76/debugpy-1.8.17.tar.gz", hash = "sha256:fd723b47a8c08892b1a16b2c6239a8b96637c62a59b94bb5dab4bac592a58a8e", size = 1644129, upload-time = "2025-09-17T16:33:20.633Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/2b/9d8e65beb2751876c82e1aceb32f328c43ec872711fa80257c7674f45650/debugpy-1.8.17-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:f14467edef672195c6f6b8e27ce5005313cb5d03c9239059bc7182b60c176e2d", size = 2549522, upload-time = "2025-09-17T16:33:38.466Z" }, + { url = "https://files.pythonhosted.org/packages/b4/78/eb0d77f02971c05fca0eb7465b18058ba84bd957062f5eec82f941ac792a/debugpy-1.8.17-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:24693179ef9dfa20dca8605905a42b392be56d410c333af82f1c5dff807a64cc", size = 4309417, upload-time = "2025-09-17T16:33:41.299Z" }, + { url = "https://files.pythonhosted.org/packages/37/42/c40f1d8cc1fed1e75ea54298a382395b8b937d923fcf41ab0797a554f555/debugpy-1.8.17-cp312-cp312-win32.whl", hash = "sha256:6a4e9dacf2cbb60d2514ff7b04b4534b0139facbf2abdffe0639ddb6088e59cf", size = 5277130, upload-time = "2025-09-17T16:33:43.554Z" }, + { url = "https://files.pythonhosted.org/packages/72/22/84263b205baad32b81b36eac076de0cdbe09fe2d0637f5b32243dc7c925b/debugpy-1.8.17-cp312-cp312-win_amd64.whl", hash = "sha256:e8f8f61c518952fb15f74a302e068b48d9c4691768ade433e4adeea961993464", size = 5319053, upload-time = "2025-09-17T16:33:53.033Z" }, + { url = "https://files.pythonhosted.org/packages/b0/d0/89247ec250369fc76db477720a26b2fce7ba079ff1380e4ab4529d2fe233/debugpy-1.8.17-py2.py3-none-any.whl", hash = "sha256:60c7dca6571efe660ccb7a9508d73ca14b8796c4ed484c2002abba714226cfef", size = 5283210, upload-time = "2025-09-17T16:34:25.835Z" }, +] + +[[package]] +name = "decorator" +version = "5.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" }, +] + +[[package]] +name = "defusedxml" +version = "0.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" }, +] + +[[package]] +name = "executing" +version = "2.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488, upload-time = "2025-09-01T09:48:10.866Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" }, +] + +[[package]] +name = "fastjsonschema" +version = "2.21.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" }, +] + [[package]] name = "filelock" version = "3.18.0" @@ -100,6 +350,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d0/9c/df0ef2c51845a13043e5088f7bb988ca6cd5bb82d5d4203d6a158aa58cf2/fonttools-4.59.0-py3-none-any.whl", hash = "sha256:241313683afd3baacb32a6bd124d0bce7404bc5280e12e291bae1b9bba28711d", size = 1128050, upload-time = "2025-07-16T12:04:52.687Z" }, ] +[[package]] +name = "fqdn" +version = "1.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015, upload-time = "2021-03-11T07:16:29.08Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121, upload-time = "2021-03-11T07:16:28.351Z" }, +] + [[package]] name = "fsspec" version = "2025.7.0" @@ -109,6 +368,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2f/e0/014d5d9d7a4564cf1c40b5039bc882db69fd881111e03ab3657ac0b218e2/fsspec-2025.7.0-py3-none-any.whl", hash = "sha256:8b012e39f63c7d5f10474de957f3ab793b47b45ae7d39f2fb735f8bbe25c0e21", size = 199597, upload-time = "2025-07-15T16:05:19.529Z" }, ] +[[package]] +name = "h11" +version = "0.16.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, +] + [[package]] name = "h5py" version = "3.11.0" @@ -124,6 +392,43 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2b/b2/0ee327933ffa37af1fc7915df7fc067e6009adcd8445d55ad07a9bec11b5/h5py-3.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:21dbdc5343f53b2e25404673c4f00a3335aef25521bd5fa8c707ec3833934892", size = 2970991, upload-time = "2024-04-10T10:51:01.555Z" }, ] +[[package]] +name = "httpcore" +version = "1.0.9" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "h11" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" }, +] + +[[package]] +name = "httpx" +version = "0.28.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "certifi" }, + { name = "httpcore" }, + { name = "idna" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" }, +] + +[[package]] +name = "idna" +version = "3.11" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582, upload-time = "2025-10-12T14:55:20.501Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" }, +] + [[package]] name = "iniconfig" version = "2.1.0" @@ -133,6 +438,87 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" }, ] +[[package]] +name = "ipykernel" +version = "7.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "appnope", marker = "sys_platform == 'darwin' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "comm" }, + { name = "debugpy" }, + { name = "ipython" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "matplotlib-inline" }, + { name = "nest-asyncio" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b9/a4/4948be6eb88628505b83a1f2f40d90254cab66abf2043b3c40fa07dfce0f/ipykernel-7.1.0.tar.gz", hash = "sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db", size = 174579, upload-time = "2025-10-27T09:46:39.471Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a3/17/20c2552266728ceba271967b87919664ecc0e33efca29c3efc6baf88c5f9/ipykernel-7.1.0-py3-none-any.whl", hash = "sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c", size = 117968, upload-time = "2025-10-27T09:46:37.805Z" }, +] + +[[package]] +name = "ipython" +version = "9.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "decorator" }, + { name = "ipython-pygments-lexers" }, + { name = "jedi" }, + { name = "matplotlib-inline" }, + { name = "pexpect", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'emscripten' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "prompt-toolkit" }, + { name = "pygments" }, + { name = "stack-data" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/29/e6/48c74d54039241a456add616464ea28c6ebf782e4110d419411b83dae06f/ipython-9.7.0.tar.gz", hash = "sha256:5f6de88c905a566c6a9d6c400a8fed54a638e1f7543d17aae2551133216b1e4e", size = 4422115, upload-time = "2025-11-05T12:18:54.646Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/aa/62893d6a591d337aa59dcc4c6f6c842f1fe20cd72c8c5c1f980255243252/ipython-9.7.0-py3-none-any.whl", hash = "sha256:bce8ac85eb9521adc94e1845b4c03d88365fd6ac2f4908ec4ed1eb1b0a065f9f", size = 618911, upload-time = "2025-11-05T12:18:52.484Z" }, +] + +[[package]] +name = "ipython-pygments-lexers" +version = "1.1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, +] + +[[package]] +name = "isoduration" +version = "20.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "arrow" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649, upload-time = "2020-11-01T11:00:00.312Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321, upload-time = "2020-11-01T10:59:58.02Z" }, +] + +[[package]] +name = "jedi" +version = "0.19.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "parso" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" }, +] + [[package]] name = "jinja2" version = "3.1.6" @@ -145,6 +531,217 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, ] +[[package]] +name = "json5" +version = "0.12.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/12/ae/929aee9619e9eba9015207a9d2c1c54db18311da7eb4dcf6d41ad6f0eb67/json5-0.12.1.tar.gz", hash = "sha256:b2743e77b3242f8d03c143dd975a6ec7c52e2f2afe76ed934e53503dd4ad4990", size = 52191, upload-time = "2025-08-12T19:47:42.583Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/85/e2/05328bd2621be49a6fed9e3030b1e51a2d04537d3f816d211b9cc53c5262/json5-0.12.1-py3-none-any.whl", hash = "sha256:d9c9b3bc34a5f54d43c35e11ef7cb87d8bdd098c6ace87117a7b7e83e705c1d5", size = 36119, upload-time = "2025-08-12T19:47:41.131Z" }, +] + +[[package]] +name = "jsonpointer" +version = "3.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6a/0a/eebeb1fa92507ea94016a2a790b93c2ae41a7e18778f85471dc54475ed25/jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef", size = 9114, upload-time = "2024-06-10T19:24:42.462Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595, upload-time = "2024-06-10T19:24:40.698Z" }, +] + +[[package]] +name = "jsonschema" +version = "4.25.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "attrs" }, + { name = "jsonschema-specifications" }, + { name = "referencing" }, + { name = "rpds-py" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/74/69/f7185de793a29082a9f3c7728268ffb31cb5095131a9c139a74078e27336/jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85", size = 357342, upload-time = "2025-08-18T17:03:50.038Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bf/9c/8c95d856233c1f82500c2450b8c68576b4cf1c871db3afac5c34ff84e6fd/jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63", size = 90040, upload-time = "2025-08-18T17:03:48.373Z" }, +] + +[package.optional-dependencies] +format-nongpl = [ + { name = "fqdn" }, + { name = "idna" }, + { name = "isoduration" }, + { name = "jsonpointer" }, + { name = "rfc3339-validator" }, + { name = "rfc3986-validator" }, + { name = "rfc3987-syntax" }, + { name = "uri-template" }, + { name = "webcolors" }, +] + +[[package]] +name = "jsonschema-specifications" +version = "2025.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "referencing" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" }, +] + +[[package]] +name = "jupyter-client" +version = "8.6.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-core" }, + { name = "python-dateutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019, upload-time = "2024-09-17T10:44:17.613Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105, upload-time = "2024-09-17T10:44:15.218Z" }, +] + +[[package]] +name = "jupyter-core" +version = "5.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "platformdirs" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" }, +] + +[[package]] +name = "jupyter-events" +version = "0.12.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jsonschema", extra = ["format-nongpl"] }, + { name = "packaging" }, + { name = "python-json-logger" }, + { name = "pyyaml" }, + { name = "referencing" }, + { name = "rfc3339-validator" }, + { name = "rfc3986-validator" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/9d/c3/306d090461e4cf3cd91eceaff84bede12a8e52cd821c2d20c9a4fd728385/jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b", size = 62196, upload-time = "2025-02-03T17:23:41.485Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb", size = 19430, upload-time = "2025-02-03T17:23:38.643Z" }, +] + +[[package]] +name = "jupyter-lsp" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/5a/9066c9f8e94ee517133cd98dba393459a16cd48bba71a82f16a65415206c/jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245", size = 54823, upload-time = "2025-08-27T17:47:34.671Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/60/1f6cee0c46263de1173894f0fafcb3475ded276c472c14d25e0280c18d6d/jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f", size = 76687, upload-time = "2025-08-27T17:47:33.15Z" }, +] + +[[package]] +name = "jupyter-server" +version = "2.17.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "argon2-cffi" }, + { name = "jinja2" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "jupyter-events" }, + { name = "jupyter-server-terminals" }, + { name = "nbconvert" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "prometheus-client" }, + { name = "pywinpty", marker = "(os_name == 'nt' and sys_platform != 'darwin') or (os_name == 'nt' and extra != 'extra-7-lettuce-cpu') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130')" }, + { name = "pyzmq" }, + { name = "send2trash" }, + { name = "terminado" }, + { name = "tornado" }, + { name = "traitlets" }, + { name = "websocket-client" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5b/ac/e040ec363d7b6b1f11304cc9f209dac4517ece5d5e01821366b924a64a50/jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5", size = 731949, upload-time = "2025-08-21T14:42:54.042Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/80/a24767e6ca280f5a49525d987bf3e4d7552bf67c8be07e8ccf20271f8568/jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f", size = 388221, upload-time = "2025-08-21T14:42:52.034Z" }, +] + +[[package]] +name = "jupyter-server-terminals" +version = "0.5.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pywinpty", marker = "(os_name == 'nt' and sys_platform != 'darwin') or (os_name == 'nt' and extra != 'extra-7-lettuce-cpu') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130')" }, + { name = "terminado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fc/d5/562469734f476159e99a55426d697cbf8e7eb5efe89fb0e0b4f83a3d3459/jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269", size = 31430, upload-time = "2024-03-12T14:37:03.049Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa", size = 13656, upload-time = "2024-03-12T14:37:00.708Z" }, +] + +[[package]] +name = "jupyterlab" +version = "4.4.10" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "async-lru" }, + { name = "httpx" }, + { name = "ipykernel" }, + { name = "jinja2" }, + { name = "jupyter-core" }, + { name = "jupyter-lsp" }, + { name = "jupyter-server" }, + { name = "jupyterlab-server" }, + { name = "notebook-shim" }, + { name = "packaging" }, + { name = "setuptools" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6a/5d/75c42a48ff5fc826a7dff3fe4004cda47c54f9d981c351efacfbc9139d3c/jupyterlab-4.4.10.tar.gz", hash = "sha256:521c017508af4e1d6d9d8a9d90f47a11c61197ad63b2178342489de42540a615", size = 22969303, upload-time = "2025-10-22T14:50:58.768Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f7/46/1eaa5db8d54a594bdade67afbcae42e9a2da676628be3eb39f36dcff6390/jupyterlab-4.4.10-py3-none-any.whl", hash = "sha256:65939ab4c8dcd0c42185c2d0d1a9d60b254dc8c46fc4fdb286b63c51e9358e07", size = 12293385, upload-time = "2025-10-22T14:50:54.075Z" }, +] + +[[package]] +name = "jupyterlab-pygments" +version = "0.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900, upload-time = "2023-11-23T09:26:37.44Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884, upload-time = "2023-11-23T09:26:34.325Z" }, +] + +[[package]] +name = "jupyterlab-server" +version = "2.28.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "babel" }, + { name = "jinja2" }, + { name = "json5" }, + { name = "jsonschema" }, + { name = "jupyter-server" }, + { name = "packaging" }, + { name = "requests" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830, upload-time = "2025-10-22T13:59:16.767Z" }, +] + [[package]] name = "kiwisolver" version = "1.4.8" @@ -168,6 +765,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/dc/85/220d13d914485c0948a00f0b9eb419efaf6da81b7d72e88ce2391f7aed8d/kiwisolver-1.4.8-cp312-cp312-win_arm64.whl", hash = "sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476", size = 65424, upload-time = "2024-12-24T18:29:44.38Z" }, ] +[[package]] +name = "lark" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732, upload-time = "2025-10-27T18:25:56.653Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151, upload-time = "2025-10-27T18:25:54.882Z" }, +] + [[package]] name = "lettuce" version = "0.2.3" @@ -177,37 +783,43 @@ dependencies = [ { name = "h5py" }, { name = "matplotlib" }, { name = "mmh3" }, + { name = "notebook" }, { name = "numpy" }, { name = "packaging" }, { name = "pyevtk" }, { name = "pytest" }, { name = "setuptools" }, - { name = "torch", version = "2.6.0+cu124", source = { registry = "https://download.pytorch.org/whl/cu124" }, marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.6.0+cu124", source = { registry = "https://download.pytorch.org/whl/cu124" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.8.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.9.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.8.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, { name = "vtk" }, ] [package.optional-dependencies] cpu = [ { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] cu124 = [ - { name = "torch", version = "2.6.0+cu124", source = { registry = "https://download.pytorch.org/whl/cu124" } }, + { name = "torch", version = "2.6.0+cu124", source = { registry = "https://download.pytorch.org/whl/cu124" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] cu126 = [ - { name = "torch", version = "2.8.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" } }, + { name = "torch", version = "2.8.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] cu128 = [ - { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } }, + { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, ] cu130 = [ - { name = "torch", version = "2.9.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" } }, + { name = "torch", version = "2.9.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, ] [package.metadata] @@ -216,6 +828,7 @@ requires-dist = [ { name = "h5py", specifier = "==3.11.0" }, { name = "matplotlib", specifier = ">=3.9.2" }, { name = "mmh3", specifier = ">=4.1.0" }, + { name = "notebook", specifier = ">=7.4.7" }, { name = "numpy", specifier = ">=2.1.0" }, { name = "packaging", specifier = ">=24.1" }, { name = "pyevtk", specifier = ">=1.6.0" }, @@ -279,6 +892,27 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d2/92/c2b9464a0562feb6ae780bdc152364810862e07ef5e6affa2b7686028db2/matplotlib-3.9.2-cp312-cp312-win_amd64.whl", hash = "sha256:5413401594cfaff0052f9d8b1aafc6d305b4bd7c4331dccd18f561ff7e1d3bd3", size = 7832805, upload-time = "2024-08-13T01:44:55.947Z" }, ] +[[package]] +name = "matplotlib-inline" +version = "0.2.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110, upload-time = "2025-10-23T09:00:22.126Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516, upload-time = "2025-10-23T09:00:20.675Z" }, +] + +[[package]] +name = "mistune" +version = "3.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d7/02/a7fb8b21d4d55ac93cdcde9d3638da5dd0ebdd3a4fed76c7725e10b81cbe/mistune-3.1.4.tar.gz", hash = "sha256:b5a7f801d389f724ec702840c11d8fc48f2b33519102fc7ee739e8177b672164", size = 94588, upload-time = "2025-08-29T07:20:43.594Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7a/f0/8282d9641415e9e33df173516226b404d367a0fc55e1a60424a152913abc/mistune-3.1.4-py3-none-any.whl", hash = "sha256:93691da911e5d9d2e23bc54472892aff676df27a75274962ff9edc210364266d", size = 53481, upload-time = "2025-08-29T07:20:42.218Z" }, +] + [[package]] name = "mmh3" version = "4.1.0" @@ -312,6 +946,70 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, ] +[[package]] +name = "nbclient" +version = "0.10.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "nbformat" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/87/66/7ffd18d58eae90d5721f9f39212327695b749e23ad44b3881744eaf4d9e8/nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193", size = 62424, upload-time = "2024-12-19T10:32:27.164Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d", size = 25434, upload-time = "2024-12-19T10:32:24.139Z" }, +] + +[[package]] +name = "nbconvert" +version = "7.16.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "beautifulsoup4" }, + { name = "bleach", extra = ["css"] }, + { name = "defusedxml" }, + { name = "jinja2" }, + { name = "jupyter-core" }, + { name = "jupyterlab-pygments" }, + { name = "markupsafe" }, + { name = "mistune" }, + { name = "nbclient" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "pandocfilters" }, + { name = "pygments" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715, upload-time = "2025-01-28T09:29:14.724Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525, upload-time = "2025-01-28T09:29:12.551Z" }, +] + +[[package]] +name = "nbformat" +version = "5.10.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "fastjsonschema" }, + { name = "jsonschema" }, + { name = "jupyter-core" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" }, +] + +[[package]] +name = "nest-asyncio" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" }, +] + [[package]] name = "networkx" version = "3.5" @@ -321,6 +1019,34 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406, upload-time = "2025-05-29T11:35:04.961Z" }, ] +[[package]] +name = "notebook" +version = "7.4.7" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, + { name = "jupyterlab" }, + { name = "jupyterlab-server" }, + { name = "notebook-shim" }, + { name = "tornado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/04/09/f6f64ba156842ef68d3ea763fa171a2f7e7224f200a15dd4af5b83c34756/notebook-7.4.7.tar.gz", hash = "sha256:3f0a04027dfcee8a876de48fba13ab77ec8c12f72f848a222ed7f5081b9e342a", size = 13937702, upload-time = "2025-09-27T08:00:22.536Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6c/d7/06d13087e20388926e7423d2489e728d2e59f2453039cdb0574a7c070e76/notebook-7.4.7-py3-none-any.whl", hash = "sha256:362b7c95527f7dd3c4c84d410b782872fd9c734fb2524c11dd92758527b6eda6", size = 14342894, upload-time = "2025-09-27T08:00:18.496Z" }, +] + +[[package]] +name = "notebook-shim" +version = "0.2.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167, upload-time = "2024-02-14T23:35:18.353Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307, upload-time = "2024-02-14T23:35:16.286Z" }, +] + [[package]] name = "numpy" version = "2.1.0" @@ -380,7 +1106,8 @@ name = "nvidia-cublas-cu12" version = "12.8.4.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/29/99/db44d685f0e257ff0e213ade1964fc459b4a690a73293220e98feb3307cf/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0", size = 590537124, upload-time = "2025-03-07T01:43:53.556Z" }, @@ -431,7 +1158,8 @@ name = "nvidia-cuda-cupti-cu12" version = "12.8.90" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/d5/1f/b3bd73445e5cb342727fd24fe1f7b748f690b460acadc27ea22f904502c8/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed", size = 9533318, upload-time = "2025-03-07T01:40:10.421Z" }, @@ -480,7 +1208,8 @@ name = "nvidia-cuda-nvrtc-cu12" version = "12.8.93" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029, upload-time = "2025-03-07T01:42:13.562Z" }, @@ -531,7 +1260,8 @@ name = "nvidia-cuda-runtime-cu12" version = "12.8.90" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/7c/75/f865a3b236e4647605ea34cc450900854ba123834a5f1598e160b9530c3a/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d", size = 965265, upload-time = "2025-03-07T01:39:43.533Z" }, @@ -559,11 +1289,13 @@ name = "nvidia-cudnn-cu12" version = "9.10.2.21" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] dependencies = [ { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/fa/41/e79269ce215c857c935fd86bcfe91a451a584dfc27f1e068f568b9ad1ab7/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8", size = 705026878, upload-time = "2025-06-06T21:52:51.348Z" }, @@ -636,10 +1368,11 @@ name = "nvidia-cufft-cu12" version = "11.3.3.83" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] dependencies = [ - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/60/bc/7771846d3a0272026c416fbb7e5f4c1f146d6d80704534d0b187dd6f4800/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a", size = 193109211, upload-time = "2025-03-07T01:44:56.873Z" }, @@ -673,7 +1406,8 @@ name = "nvidia-cufile-cu12" version = "1.13.1.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834, upload-time = "2025-03-07T01:45:50.723Z" }, @@ -723,7 +1457,8 @@ name = "nvidia-curand-cu12" version = "10.3.9.90" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/45/5e/92aa15eca622a388b80fbf8375d4760738df6285b1e92c43d37390a33a9a/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd", size = 63625754, upload-time = "2025-03-07T01:46:10.735Z" }, @@ -789,12 +1524,13 @@ name = "nvidia-cusolver-cu12" version = "11.7.3.90" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] dependencies = [ - { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/c8/32/f7cd6ce8a7690544d084ea21c26e910a97e077c9b7f07bf5de623ee19981/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0", size = 267229841, upload-time = "2025-03-07T01:46:54.356Z" }, @@ -854,10 +1590,11 @@ name = "nvidia-cusparse-cu12" version = "12.5.8.93" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] dependencies = [ - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/bc/f7/cd777c4109681367721b00a106f491e0d0d15cfa1fd59672ce580ce42a97/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc", size = 288117129, upload-time = "2025-03-07T01:47:40.407Z" }, @@ -883,7 +1620,9 @@ name = "nvidia-cusparselt-cu12" version = "0.7.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/73/b9/598f6ff36faaece4b3c50d26f50e38661499ff34346f00e057760b35cc9d/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8878dce784d0fac90131b6817b607e803c36e629ba34dc5b433471382196b6a5", size = 283835557, upload-time = "2025-02-26T00:16:54.265Z" }, @@ -924,6 +1663,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5c/5b/4e4fff7bad39adf89f735f2bc87248c81db71205b62bcc0d5ca5b606b3c3/nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adf27ccf4238253e0b826bce3ff5fa532d65fc42322c8bfdfaf28024c0fbe039", size = 322364134, upload-time = "2025-06-03T21:58:04.013Z" }, ] +[[package]] +name = "nvidia-nccl-cu12" +version = "2.27.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bb/1c/857979db0ef194ca5e21478a0612bcdbbe59458d7694361882279947b349/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:31432ad4d1fb1004eb0c56203dc9bc2178a1ba69d1d9e02d64a6938ab5e40e7a", size = 322400625, upload-time = "2025-06-26T04:11:04.496Z" }, + { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229, upload-time = "2025-06-26T04:11:28.385Z" }, +] + [[package]] name = "nvidia-nccl-cu13" version = "2.27.7" @@ -974,7 +1722,8 @@ name = "nvidia-nvjitlink-cu12" version = "12.8.93" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836, upload-time = "2025-03-07T01:49:55.661Z" }, @@ -982,6 +1731,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ed/d7/34f02dad2e30c31b10a51f6b04e025e5dd60e5f936af9045a9b858a05383/nvidia_nvjitlink_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:bd93fbeeee850917903583587f4fc3a4eafa022e34572251368238ab5e6bd67f", size = 268553710, upload-time = "2025-03-07T01:56:24.13Z" }, ] +[[package]] +name = "nvidia-nvshmem-cu12" +version = "3.3.20" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/9d/3dd98852568fb845ec1f7902c90a22b240fe1cbabda411ccedf2fd737b7b/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b0b960da3842212758e4fa4696b94f129090b30e5122fea3c5345916545cff0", size = 124484616, upload-time = "2025-08-04T20:24:59.172Z" }, + { url = "https://files.pythonhosted.org/packages/3b/6c/99acb2f9eb85c29fc6f3a7ac4dccfd992e22666dd08a642b303311326a97/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5", size = 124657145, upload-time = "2025-08-04T20:25:19.995Z" }, +] + [[package]] name = "nvidia-nvshmem-cu13" version = "3.3.24" @@ -1034,7 +1792,8 @@ name = "nvidia-nvtx-cu12" version = "12.8.90" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "sys_platform == 'linux' or sys_platform == 'win32'", + "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] wheels = [ { url = "https://files.pythonhosted.org/packages/10/c0/1b303feea90d296f6176f32a2a70b5ef230f9bdeb3a72bddb0dc922dc137/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615", size = 91161, upload-time = "2025-03-07T01:42:23.922Z" }, @@ -1051,6 +1810,36 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124", size = 53985, upload-time = "2024-06-09T23:19:21.909Z" }, ] +[[package]] +name = "pandocfilters" +version = "1.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454, upload-time = "2024-01-18T20:08:13.726Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663, upload-time = "2024-01-18T20:08:11.28Z" }, +] + +[[package]] +name = "parso" +version = "0.8.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d4/de/53e0bcf53d13e005bd8c92e7855142494f41171b34c2536b86187474184d/parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a", size = 401205, upload-time = "2025-08-23T15:15:28.028Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/16/32/f8e3c85d1d5250232a5d3477a2a28cc291968ff175caeadaf3cc19ce0e4a/parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887", size = 106668, upload-time = "2025-08-23T15:15:25.663Z" }, +] + +[[package]] +name = "pexpect" +version = "4.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ptyprocess" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, +] + [[package]] name = "pillow" version = "11.3.0" @@ -1070,6 +1859,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/16/8f/b13447d1bf0b1f7467ce7d86f6e6edf66c0ad7cf44cf5c87a37f9bed9936/pillow-11.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:2aceea54f957dd4448264f9bf40875da0415c83eb85f55069d89c0ed436e3542", size = 2423067, upload-time = "2025-07-01T09:14:33.709Z" }, ] +[[package]] +name = "platformdirs" +version = "4.5.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/61/33/9611380c2bdb1225fdef633e2a9610622310fed35ab11dac9620972ee088/platformdirs-4.5.0.tar.gz", hash = "sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312", size = 21632, upload-time = "2025-10-08T17:44:48.791Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/73/cb/ac7874b3e5d58441674fb70742e6c374b28b0c7cb988d37d991cde47166c/platformdirs-4.5.0-py3-none-any.whl", hash = "sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3", size = 18651, upload-time = "2025-10-08T17:44:47.223Z" }, +] + [[package]] name = "pluggy" version = "1.6.0" @@ -1079,6 +1877,68 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, ] +[[package]] +name = "prometheus-client" +version = "0.23.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/23/53/3edb5d68ecf6b38fcbcc1ad28391117d2a322d9a1a3eff04bfdb184d8c3b/prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce", size = 80481, upload-time = "2025-09-18T20:47:25.043Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b8/db/14bafcb4af2139e046d03fd00dea7873e48eafe18b7d2797e73d6681f210/prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99", size = 61145, upload-time = "2025-09-18T20:47:23.875Z" }, +] + +[[package]] +name = "prompt-toolkit" +version = "3.0.52" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "wcwidth" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" }, +] + +[[package]] +name = "psutil" +version = "7.1.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e1/88/bdd0a41e5857d5d703287598cbf08dad90aed56774ea52ae071bae9071b6/psutil-7.1.3.tar.gz", hash = "sha256:6c86281738d77335af7aec228328e944b30930899ea760ecf33a4dba66be5e74", size = 489059, upload-time = "2025-11-02T12:25:54.619Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ef/94/46b9154a800253e7ecff5aaacdf8ebf43db99de4a2dfa18575b02548654e/psutil-7.1.3-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2bdbcd0e58ca14996a42adf3621a6244f1bb2e2e528886959c72cf1e326677ab", size = 238359, upload-time = "2025-11-02T12:26:25.284Z" }, + { url = "https://files.pythonhosted.org/packages/68/3a/9f93cff5c025029a36d9a92fef47220ab4692ee7f2be0fba9f92813d0cb8/psutil-7.1.3-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:bc31fa00f1fbc3c3802141eede66f3a2d51d89716a194bf2cd6fc68310a19880", size = 239171, upload-time = "2025-11-02T12:26:27.23Z" }, + { url = "https://files.pythonhosted.org/packages/ce/b1/5f49af514f76431ba4eea935b8ad3725cdeb397e9245ab919dbc1d1dc20f/psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb428f9f05c1225a558f53e30ccbad9930b11c3fc206836242de1091d3e7dd3", size = 263261, upload-time = "2025-11-02T12:26:29.48Z" }, + { url = "https://files.pythonhosted.org/packages/e0/95/992c8816a74016eb095e73585d747e0a8ea21a061ed3689474fabb29a395/psutil-7.1.3-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56d974e02ca2c8eb4812c3f76c30e28836fffc311d55d979f1465c1feeb2b68b", size = 264635, upload-time = "2025-11-02T12:26:31.74Z" }, + { url = "https://files.pythonhosted.org/packages/55/4c/c3ed1a622b6ae2fd3c945a366e64eb35247a31e4db16cf5095e269e8eb3c/psutil-7.1.3-cp37-abi3-win_amd64.whl", hash = "sha256:f39c2c19fe824b47484b96f9692932248a54c43799a84282cfe58d05a6449efd", size = 247633, upload-time = "2025-11-02T12:26:33.887Z" }, + { url = "https://files.pythonhosted.org/packages/c9/ad/33b2ccec09bf96c2b2ef3f9a6f66baac8253d7565d8839e024a6b905d45d/psutil-7.1.3-cp37-abi3-win_arm64.whl", hash = "sha256:bd0d69cee829226a761e92f28140bec9a5ee9d5b4fb4b0cc589068dbfff559b1", size = 244608, upload-time = "2025-11-02T12:26:36.136Z" }, +] + +[[package]] +name = "ptyprocess" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, +] + +[[package]] +name = "pure-eval" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, +] + +[[package]] +name = "pycparser" +version = "2.23" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/fe/cf/d2d3b9f5699fb1e4615c8e32ff220203e43b248e1dfcc6736ad9057731ca/pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2", size = 173734, upload-time = "2025-09-09T13:23:47.91Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/e3/59cd50310fc9b59512193629e1984c1f95e5c8ae6e5d8c69532ccc65a7fe/pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934", size = 118140, upload-time = "2025-09-09T13:23:46.651Z" }, +] + [[package]] name = "pyevtk" version = "1.6.0" @@ -1091,6 +1951,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/06/da/d57b07c565cc5f8705da436cfcc44754343416c1187d83f04d5443d5cc1a/pyevtk-1.6.0-py3-none-any.whl", hash = "sha256:057415512ab902785c72251df8f8450e4bbc9eab16677f091a4b8ee53b9cc775", size = 20521, upload-time = "2023-06-05T15:25:25.625Z" }, ] +[[package]] +name = "pygments" +version = "2.19.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, +] + [[package]] name = "pyparsing" version = "3.2.3" @@ -1127,6 +1996,157 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] +[[package]] +name = "python-json-logger" +version = "4.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/29/bf/eca6a3d43db1dae7070f70e160ab20b807627ba953663ba07928cdd3dc58/python_json_logger-4.0.0.tar.gz", hash = "sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f", size = 17683, upload-time = "2025-10-06T04:15:18.984Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/e5/fecf13f06e5e5f67e8837d777d1bc43fac0ed2b77a676804df5c34744727/python_json_logger-4.0.0-py3-none-any.whl", hash = "sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2", size = 15548, upload-time = "2025-10-06T04:15:17.553Z" }, +] + +[[package]] +name = "pywinpty" +version = "3.0.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f3/bb/a7cc2967c5c4eceb6cc49cfe39447d4bfc56e6c865e7c2249b6eb978935f/pywinpty-3.0.2.tar.gz", hash = "sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004", size = 30669, upload-time = "2025-10-03T21:16:29.205Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/02/4e/1098484e042c9485f56f16eb2b69b43b874bd526044ee401512234cf9e04/pywinpty-3.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:99fdd9b455f0ad6419aba6731a7a0d2f88ced83c3c94a80ff9533d95fa8d8a9e", size = 2050391, upload-time = "2025-10-03T21:19:01.642Z" }, +] + +[[package]] +name = "pyyaml" +version = "6.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, +] + +[[package]] +name = "pyzmq" +version = "27.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi", marker = "implementation_name == 'pypy' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" }, + { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" }, + { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" }, + { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" }, + { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" }, + { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" }, + { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" }, + { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184, upload-time = "2025-09-08T23:08:15.163Z" }, + { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480, upload-time = "2025-09-08T23:08:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993, upload-time = "2025-09-08T23:08:18.926Z" }, +] + +[[package]] +name = "referencing" +version = "0.37.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "attrs" }, + { name = "rpds-py" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036, upload-time = "2025-10-13T15:30:48.871Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766, upload-time = "2025-10-13T15:30:47.625Z" }, +] + +[[package]] +name = "requests" +version = "2.32.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "charset-normalizer" }, + { name = "idna" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" }, +] + +[[package]] +name = "rfc3339-validator" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513, upload-time = "2021-05-12T16:37:54.178Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490, upload-time = "2021-05-12T16:37:52.536Z" }, +] + +[[package]] +name = "rfc3986-validator" +version = "0.1.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760, upload-time = "2019-10-28T16:00:19.144Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242, upload-time = "2019-10-28T16:00:13.976Z" }, +] + +[[package]] +name = "rfc3987-syntax" +version = "1.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "lark" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239, upload-time = "2025-07-18T01:05:05.015Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046, upload-time = "2025-07-18T01:05:03.843Z" }, +] + +[[package]] +name = "rpds-py" +version = "0.28.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/48/dc/95f074d43452b3ef5d06276696ece4b3b5d696e7c9ad7173c54b1390cd70/rpds_py-0.28.0.tar.gz", hash = "sha256:abd4df20485a0983e2ca334a216249b6186d6e3c1627e106651943dbdb791aea", size = 27419, upload-time = "2025-10-22T22:24:29.327Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b8/5c/6c3936495003875fe7b14f90ea812841a08fca50ab26bd840e924097d9c8/rpds_py-0.28.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:6b4f28583a4f247ff60cd7bdda83db8c3f5b05a7a82ff20dd4b078571747708f", size = 366439, upload-time = "2025-10-22T22:22:04.525Z" }, + { url = "https://files.pythonhosted.org/packages/56/f9/a0f1ca194c50aa29895b442771f036a25b6c41a35e4f35b1a0ea713bedae/rpds_py-0.28.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d678e91b610c29c4b3d52a2c148b641df2b4676ffe47c59f6388d58b99cdc424", size = 348170, upload-time = "2025-10-22T22:22:06.397Z" }, + { url = "https://files.pythonhosted.org/packages/18/ea/42d243d3a586beb72c77fa5def0487daf827210069a95f36328e869599ea/rpds_py-0.28.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e819e0e37a44a78e1383bf1970076e2ccc4dc8c2bbaa2f9bd1dc987e9afff628", size = 378838, upload-time = "2025-10-22T22:22:07.932Z" }, + { url = "https://files.pythonhosted.org/packages/e7/78/3de32e18a94791af8f33601402d9d4f39613136398658412a4e0b3047327/rpds_py-0.28.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5ee514e0f0523db5d3fb171f397c54875dbbd69760a414dccf9d4d7ad628b5bd", size = 393299, upload-time = "2025-10-22T22:22:09.435Z" }, + { url = "https://files.pythonhosted.org/packages/13/7e/4bdb435afb18acea2eb8a25ad56b956f28de7c59f8a1d32827effa0d4514/rpds_py-0.28.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5f3fa06d27fdcee47f07a39e02862da0100cb4982508f5ead53ec533cd5fe55e", size = 518000, upload-time = "2025-10-22T22:22:11.326Z" }, + { url = "https://files.pythonhosted.org/packages/31/d0/5f52a656875cdc60498ab035a7a0ac8f399890cc1ee73ebd567bac4e39ae/rpds_py-0.28.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:46959ef2e64f9e4a41fc89aa20dbca2b85531f9a72c21099a3360f35d10b0d5a", size = 408746, upload-time = "2025-10-22T22:22:13.143Z" }, + { url = "https://files.pythonhosted.org/packages/3e/cd/49ce51767b879cde77e7ad9fae164ea15dce3616fe591d9ea1df51152706/rpds_py-0.28.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8455933b4bcd6e83fde3fefc987a023389c4b13f9a58c8d23e4b3f6d13f78c84", size = 386379, upload-time = "2025-10-22T22:22:14.602Z" }, + { url = "https://files.pythonhosted.org/packages/6a/99/e4e1e1ee93a98f72fc450e36c0e4d99c35370220e815288e3ecd2ec36a2a/rpds_py-0.28.0-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:ad50614a02c8c2962feebe6012b52f9802deec4263946cddea37aaf28dd25a66", size = 401280, upload-time = "2025-10-22T22:22:16.063Z" }, + { url = "https://files.pythonhosted.org/packages/61/35/e0c6a57488392a8b319d2200d03dad2b29c0db9996f5662c3b02d0b86c02/rpds_py-0.28.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e5deca01b271492553fdb6c7fd974659dce736a15bae5dad7ab8b93555bceb28", size = 412365, upload-time = "2025-10-22T22:22:17.504Z" }, + { url = "https://files.pythonhosted.org/packages/ff/6a/841337980ea253ec797eb084665436007a1aad0faac1ba097fb906c5f69c/rpds_py-0.28.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:735f8495a13159ce6a0d533f01e8674cec0c57038c920495f87dcb20b3ddb48a", size = 559573, upload-time = "2025-10-22T22:22:19.108Z" }, + { url = "https://files.pythonhosted.org/packages/e7/5e/64826ec58afd4c489731f8b00729c5f6afdb86f1df1df60bfede55d650bb/rpds_py-0.28.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:961ca621ff10d198bbe6ba4957decca61aa2a0c56695384c1d6b79bf61436df5", size = 583973, upload-time = "2025-10-22T22:22:20.768Z" }, + { url = "https://files.pythonhosted.org/packages/b6/ee/44d024b4843f8386a4eeaa4c171b3d31d55f7177c415545fd1a24c249b5d/rpds_py-0.28.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2374e16cc9131022e7d9a8f8d65d261d9ba55048c78f3b6e017971a4f5e6353c", size = 553800, upload-time = "2025-10-22T22:22:22.25Z" }, + { url = "https://files.pythonhosted.org/packages/7d/89/33e675dccff11a06d4d85dbb4d1865f878d5020cbb69b2c1e7b2d3f82562/rpds_py-0.28.0-cp312-cp312-win32.whl", hash = "sha256:d15431e334fba488b081d47f30f091e5d03c18527c325386091f31718952fe08", size = 216954, upload-time = "2025-10-22T22:22:24.105Z" }, + { url = "https://files.pythonhosted.org/packages/af/36/45f6ebb3210887e8ee6dbf1bc710ae8400bb417ce165aaf3024b8360d999/rpds_py-0.28.0-cp312-cp312-win_amd64.whl", hash = "sha256:a410542d61fc54710f750d3764380b53bf09e8c4edbf2f9141a82aa774a04f7c", size = 227844, upload-time = "2025-10-22T22:22:25.551Z" }, + { url = "https://files.pythonhosted.org/packages/57/91/f3fb250d7e73de71080f9a221d19bd6a1c1eb0d12a1ea26513f6c1052ad6/rpds_py-0.28.0-cp312-cp312-win_arm64.whl", hash = "sha256:1f0cfd1c69e2d14f8c892b893997fa9a60d890a0c8a603e88dca4955f26d1edd", size = 217624, upload-time = "2025-10-22T22:22:26.914Z" }, +] + +[[package]] +name = "send2trash" +version = "1.8.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/fd/3a/aec9b02217bb79b87bbc1a21bc6abc51e3d5dcf65c30487ac96c0908c722/Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf", size = 17394, upload-time = "2024-04-07T00:01:09.267Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9", size = 18072, upload-time = "2024-04-07T00:01:07.438Z" }, +] + [[package]] name = "setuptools" version = "80.9.0" @@ -1145,16 +2165,47 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, ] +[[package]] +name = "sniffio" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372, upload-time = "2024-02-25T23:20:04.057Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" }, +] + +[[package]] +name = "soupsieve" +version = "2.8" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6d/e6/21ccce3262dd4889aa3332e5a119a3491a95e8f60939870a3a035aabac0d/soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f", size = 103472, upload-time = "2025-08-27T15:39:51.78Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/14/a0/bb38d3b76b8cae341dad93a2dd83ab7462e6dbcdd84d43f54ee60a8dc167/soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c", size = 36679, upload-time = "2025-08-27T15:39:50.179Z" }, +] + +[[package]] +name = "stack-data" +version = "0.6.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "asttokens" }, + { name = "executing" }, + { name = "pure-eval" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, +] + [[package]] name = "sympy" version = "1.13.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "mpmath", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "mpmath", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/ca/99/5a5b6f19ff9f083671ddf7b9632028436167cd3d33e11015754e41b249a4/sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f", size = 7533040, upload-time = "2024-07-19T09:26:51.238Z" } wheels = [ @@ -1172,32 +2223,58 @@ resolution-markers = [ "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", "(sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", + "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')", "sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", "sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", ] dependencies = [ - { name = "mpmath" }, + { name = "mpmath", marker = "(sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, ] +[[package]] +name = "terminado" +version = "0.18.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ptyprocess", marker = "os_name != 'nt' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "pywinpty", marker = "(os_name == 'nt' and sys_platform != 'darwin') or (os_name == 'nt' and extra != 'extra-7-lettuce-cpu') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (os_name != 'nt' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130')" }, + { name = "tornado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154, upload-time = "2024-03-12T14:34:36.569Z" }, +] + +[[package]] +name = "tinycss2" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" }, +] + [[package]] name = "torch" version = "2.6.0+cu124" source = { registry = "https://download.pytorch.org/whl/cu124" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "filelock", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cublas-cu12", version = "12.4.5.8", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-cupti-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-nvrtc-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, @@ -1211,10 +2288,10 @@ dependencies = [ { name = "nvidia-nccl-cu12", version = "2.21.5", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvjitlink-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvtx-cu12", version = "12.4.127", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.13.1", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.13.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "triton", version = "3.2.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "extra == 'extra-7-lettuce-cu124' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-linux_x86_64.whl", hash = "sha256:a393b506844035c0dac2f30ea8478c343b8e95a429f06f3b3cadfc7f53adb597" }, @@ -1241,42 +2318,21 @@ wheels = [ { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:a47b7986bee3f61ad217d8a8ce24605809ab425baf349f97de758815edd2ef54" }, ] -[[package]] -name = "torch" -version = "2.8.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "filelock", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/49/0c/2fd4df0d83a495bb5e54dca4474c4ec5f9c62db185421563deeb5dabf609/torch-2.8.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:e2fab4153768d433f8ed9279c8133a114a034a61e77a3a104dcdf54388838705", size = 101906089, upload-time = "2025-08-06T14:53:52.631Z" }, - { url = "https://files.pythonhosted.org/packages/99/a8/6acf48d48838fb8fe480597d98a0668c2beb02ee4755cc136de92a0a956f/torch-2.8.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b2aca0939fb7e4d842561febbd4ffda67a8e958ff725c1c27e244e85e982173c", size = 887913624, upload-time = "2025-08-06T14:56:44.33Z" }, - { url = "https://files.pythonhosted.org/packages/af/8a/5c87f08e3abd825c7dfecef5a0f1d9aa5df5dd0e3fd1fa2f490a8e512402/torch-2.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:2f4ac52f0130275d7517b03a33d2493bab3693c83dcfadf4f81688ea82147d2e", size = 241326087, upload-time = "2025-08-06T14:53:46.503Z" }, - { url = "https://files.pythonhosted.org/packages/be/66/5c9a321b325aaecb92d4d1855421e3a055abd77903b7dab6575ca07796db/torch-2.8.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:619c2869db3ada2c0105487ba21b5008defcc472d23f8b80ed91ac4a380283b0", size = 73630478, upload-time = "2025-08-06T14:53:57.144Z" }, -] - [[package]] name = "torch" version = "2.8.0+cpu" source = { registry = "https://download.pytorch.org/whl/cpu" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "filelock", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "(sys_platform != 'darwin' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-linux_s390x.whl", hash = "sha256:0e34e276722ab7dd0dffa9e12fe2135a9b34a0e300c456ed7ad6430229404eb5" }, @@ -1292,13 +2348,12 @@ version = "2.8.0+cu126" source = { registry = "https://download.pytorch.org/whl/cu126" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "filelock", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-cupti-cu12", version = "12.6.80", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-nvrtc-cu12", version = "12.6.77", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, @@ -1313,10 +2368,10 @@ dependencies = [ { name = "nvidia-nccl-cu12", version = "2.27.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvtx-cu12", version = "12.6.77", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, { name = "triton", version = "3.4.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "extra == 'extra-7-lettuce-cu126' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu126/torch-2.8.0%2Bcu126-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ce6e6a1f4803ad62d1fe51cec3fe5ca14bcd8bc7cace7b09d5590f8147fa16ad" }, @@ -1329,13 +2384,12 @@ version = "2.8.0+cu128" source = { registry = "https://download.pytorch.org/whl/cu128" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "filelock", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-cupti-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-nvrtc-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, @@ -1350,10 +2404,10 @@ dependencies = [ { name = "nvidia-nccl-cu12", version = "2.27.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvtx-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "triton", version = "3.4.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (platform_machine != 'x86_64' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform != 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "extra == 'extra-7-lettuce-cu128' or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu128/torch-2.8.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4354fc05bb79b208d6995a04ca1ceef6a9547b1c4334435574353d381c55087c" }, @@ -1366,13 +2420,12 @@ version = "2.9.0+cu130" source = { registry = "https://download.pytorch.org/whl/cu130" } resolution-markers = [ "sys_platform == 'linux' or sys_platform == 'win32'", - "sys_platform != 'linux' and sys_platform != 'win32'", ] dependencies = [ - { name = "filelock", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "fsspec", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "jinja2", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "networkx", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cublas", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-cupti", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-cuda-nvrtc", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, @@ -1388,10 +2441,10 @@ dependencies = [ { name = "nvidia-nvjitlink", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvshmem-cu13", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "nvidia-nvtx", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "setuptools", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, { name = "triton", version = "3.5.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, - { name = "typing-extensions", marker = "(extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform == 'linux' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu130/torch-2.9.0%2Bcu130-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:3aef05b6247261f4a7c440be9a052c4be36c673c6721920181a4ac9a66d6c2a2" }, @@ -1399,6 +2452,78 @@ wheels = [ { url = "https://download.pytorch.org/whl/cu130/torch-2.9.0%2Bcu130-cp312-cp312-win_amd64.whl", hash = "sha256:b9979a7c0a1c9544a857fc2390ebc89938f116eaaf6a359a0d46597402ca51da" }, ] +[[package]] +name = "torch" +version = "2.9.1" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130'", + "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", + "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", + "sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", + "sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", + "extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130'", +] +dependencies = [ + { name = "filelock", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "fsspec", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "jinja2", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "networkx", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cuda-cupti-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cuda-nvrtc-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cuda-runtime-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cudnn-cu12", version = "9.10.2.21", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cufft-cu12", version = "11.3.3.83", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cufile-cu12", version = "1.13.1.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-curand-cu12", version = "10.3.9.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cusolver-cu12", version = "11.7.3.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-cusparselt-cu12", version = "0.7.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nccl-cu12", version = "2.27.5", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvshmem-cu12", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "nvidia-nvtx-cu12", version = "12.8.90", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "setuptools", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "sympy", version = "1.14.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, + { name = "triton", version = "3.5.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130')" }, + { name = "typing-extensions", marker = "(sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'darwin' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'darwin' and extra != 'extra-7-lettuce-cpu') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu124') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cpu' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu126') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu124' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu128') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu126' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra == 'extra-7-lettuce-cu128' and extra == 'extra-7-lettuce-cu130') or (sys_platform == 'linux' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130') or (sys_platform == 'win32' and extra != 'extra-7-lettuce-cpu' and extra != 'extra-7-lettuce-cu124' and extra != 'extra-7-lettuce-cu126' and extra != 'extra-7-lettuce-cu128' and extra != 'extra-7-lettuce-cu130')" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/27/07c645c7673e73e53ded71705045d6cb5bae94c4b021b03aa8d03eee90ab/torch-2.9.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:da5f6f4d7f4940a173e5572791af238cb0b9e21b1aab592bd8b26da4c99f1cd6", size = 104126592, upload-time = "2025-11-12T15:20:41.62Z" }, + { url = "https://files.pythonhosted.org/packages/19/17/e377a460603132b00760511299fceba4102bd95db1a0ee788da21298ccff/torch-2.9.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:27331cd902fb4322252657f3902adf1c4f6acad9dcad81d8df3ae14c7c4f07c4", size = 899742281, upload-time = "2025-11-12T15:22:17.602Z" }, + { url = "https://files.pythonhosted.org/packages/b1/1a/64f5769025db846a82567fa5b7d21dba4558a7234ee631712ee4771c436c/torch-2.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:81a285002d7b8cfd3fdf1b98aa8df138d41f1a8334fd9ea37511517cedf43083", size = 110940568, upload-time = "2025-11-12T15:21:18.689Z" }, + { url = "https://files.pythonhosted.org/packages/6e/ab/07739fd776618e5882661d04c43f5b5586323e2f6a2d7d84aac20d8f20bd/torch-2.9.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:c0d25d1d8e531b8343bea0ed811d5d528958f1dcbd37e7245bc686273177ad7e", size = 74479191, upload-time = "2025-11-12T15:21:25.816Z" }, +] + +[[package]] +name = "tornado" +version = "6.5.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/09/ce/1eb500eae19f4648281bb2186927bb062d2438c2e5093d1360391afd2f90/tornado-6.5.2.tar.gz", hash = "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0", size = 510821, upload-time = "2025-08-08T18:27:00.78Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f6/48/6a7529df2c9cc12efd2e8f5dd219516184d703b34c06786809670df5b3bd/tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6", size = 442563, upload-time = "2025-08-08T18:26:42.945Z" }, + { url = "https://files.pythonhosted.org/packages/f2/b5/9b575a0ed3e50b00c40b08cbce82eb618229091d09f6d14bce80fc01cb0b/tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef", size = 440729, upload-time = "2025-08-08T18:26:44.473Z" }, + { url = "https://files.pythonhosted.org/packages/1b/4e/619174f52b120efcf23633c817fd3fed867c30bff785e2cd5a53a70e483c/tornado-6.5.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e", size = 444295, upload-time = "2025-08-08T18:26:46.021Z" }, + { url = "https://files.pythonhosted.org/packages/95/fa/87b41709552bbd393c85dd18e4e3499dcd8983f66e7972926db8d96aa065/tornado-6.5.2-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882", size = 443644, upload-time = "2025-08-08T18:26:47.625Z" }, + { url = "https://files.pythonhosted.org/packages/f9/41/fb15f06e33d7430ca89420283a8762a4e6b8025b800ea51796ab5e6d9559/tornado-6.5.2-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108", size = 443878, upload-time = "2025-08-08T18:26:50.599Z" }, + { url = "https://files.pythonhosted.org/packages/11/92/fe6d57da897776ad2e01e279170ea8ae726755b045fe5ac73b75357a5a3f/tornado-6.5.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c", size = 444549, upload-time = "2025-08-08T18:26:51.864Z" }, + { url = "https://files.pythonhosted.org/packages/9b/02/c8f4f6c9204526daf3d760f4aa555a7a33ad0e60843eac025ccfd6ff4a93/tornado-6.5.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4", size = 443973, upload-time = "2025-08-08T18:26:53.625Z" }, + { url = "https://files.pythonhosted.org/packages/ae/2d/f5f5707b655ce2317190183868cd0f6822a1121b4baeae509ceb9590d0bd/tornado-6.5.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04", size = 443954, upload-time = "2025-08-08T18:26:55.072Z" }, + { url = "https://files.pythonhosted.org/packages/e8/59/593bd0f40f7355806bf6573b47b8c22f8e1374c9b6fd03114bd6b7a3dcfd/tornado-6.5.2-cp39-abi3-win32.whl", hash = "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0", size = 445023, upload-time = "2025-08-08T18:26:56.677Z" }, + { url = "https://files.pythonhosted.org/packages/c7/2a/f609b420c2f564a748a2d80ebfb2ee02a73ca80223af712fca591386cafb/tornado-6.5.2-cp39-abi3-win_amd64.whl", hash = "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f", size = 445427, upload-time = "2025-08-08T18:26:57.91Z" }, + { url = "https://files.pythonhosted.org/packages/5e/4f/e1f65e8f8c76d73658b33d33b81eed4322fb5085350e4328d5c956f0c8f9/tornado-6.5.2-cp39-abi3-win_arm64.whl", hash = "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af", size = 444456, upload-time = "2025-08-08T18:26:59.207Z" }, +] + +[[package]] +name = "traitlets" +version = "5.14.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" }, +] + [[package]] name = "triton" version = "3.2.0" @@ -1436,6 +2561,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f5/3a/e991574f3102147b642e49637e0281e9bb7c4ba254edb2bab78247c85e01/triton-3.5.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c9e71db82261c4ffa3921cd050cd5faa18322d2d405c30eb56084afaff3b0833", size = 170476535, upload-time = "2025-10-13T16:38:05.18Z" }, ] +[[package]] +name = "triton" +version = "3.5.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/db/53/2bcc46879910991f09c063eea07627baef2bc62fe725302ba8f46a2c1ae5/triton-3.5.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:275a045b6ed670dd1bd005c3e6c2d61846c74c66f4512d6f33cc027b11de8fd4", size = 159940689, upload-time = "2025-11-11T17:51:55.938Z" }, + { url = "https://files.pythonhosted.org/packages/f2/50/9a8358d3ef58162c0a415d173cfb45b67de60176e1024f71fbc4d24c0b6d/triton-3.5.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d2c6b915a03888ab931a9fd3e55ba36785e1fe70cbea0b40c6ef93b20fc85232", size = 170470207, upload-time = "2025-11-11T17:41:00.253Z" }, +] + [[package]] name = "typing-extensions" version = "4.14.1" @@ -1445,6 +2579,33 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b5/00/d631e67a838026495268c2f6884f3711a15a9a2a96cd244fdaea53b823fb/typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76", size = 43906, upload-time = "2025-07-04T13:28:32.743Z" }, ] +[[package]] +name = "tzdata" +version = "2025.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" }, +] + +[[package]] +name = "uri-template" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678, upload-time = "2023-06-21T01:49:05.374Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140, upload-time = "2023-06-21T01:49:03.467Z" }, +] + +[[package]] +name = "urllib3" +version = "2.5.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185, upload-time = "2025-06-18T14:07:41.644Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" }, +] + [[package]] name = "vtk" version = "9.5.0" @@ -1459,3 +2620,39 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/80/05/4ebe00ecec0dc3504928fe2004fa14c1a338a63e7d5e60019b077ee0b20e/vtk-9.5.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:879256ccbb65d12904ca3f9159cfca1c3812d605ee3062c9c04acf5c6f812164", size = 103685048, upload-time = "2025-06-24T09:41:46.062Z" }, { url = "https://files.pythonhosted.org/packages/e3/7d/4f95e47d0f44119a07b39bfaa8f2e07b6b77534f3fd3ac7be2b2066a2afc/vtk-9.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:ea4fc23ddf7f12021dbbf116f5f764240a2774ad1df2e7655191ca599093080d", size = 63924841, upload-time = "2025-06-24T09:41:51.1Z" }, ] + +[[package]] +name = "wcwidth" +version = "0.2.14" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293, upload-time = "2025-09-22T16:29:53.023Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" }, +] + +[[package]] +name = "webcolors" +version = "25.10.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491, upload-time = "2025-10-31T07:51:03.977Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905, upload-time = "2025-10-31T07:51:01.778Z" }, +] + +[[package]] +name = "webencodings" +version = "0.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" }, +] + +[[package]] +name = "websocket-client" +version = "1.9.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576, upload-time = "2025-10-07T21:16:36.495Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616, upload-time = "2025-10-07T21:16:34.951Z" }, +] From 555182a8d8cf4b8efc2bba94db38389c05ab1dd3 Mon Sep 17 00:00:00 2001 From: mbille Date: Tue, 18 Nov 2025 17:06:15 +0100 Subject: [PATCH 05/21] temp_comit: progress of today --- .../01a_script_cylinder_lowRe.py | 291 +++++++++++++++++- .../obstacle_cylinder.py | 2 + 2 files changed, 292 insertions(+), 1 deletion(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index ec1df5a6..f1ebe8ef 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -1,2 +1,291 @@ -# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) \ No newline at end of file +# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) + +# IMPORT + +import lettuce as lt +from .obstacle_cylinder import ObstacleCylinder +from .ebb_simulation import EbbSimulation + +import matplotlib.pyplot as plt +from scipy.signal import find_peaks + +import sys +import warnings + +import numpy as np + +## OLD from lettuce import (LettuceException) +## OLD from lettuce.unit import UnitConversion +## OLD from lettuce.util import append_axes +## OLD from lettuce.boundary import EquilibriumBoundaryPU, EquilibriumOutletP, AntiBounceBackOutlet +## OLD from lettuce.flows.obstacleCylinder import ObstacleCylinder + +## OLD from lettuce.equilibrium import QuadraticEquilibrium_LessMemory + +## OLD from lettuce.max import draw_circular_mask + +import torch +import time +import datetime +import os +import psutil +import shutil +from pyevtk.hl import imageToVTK +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter + +import pickle +from copy import deepcopy +from timeit import default_timer as timer +from collections import Counter + +warnings.simplefilter("ignore") # todo: is this needed? + +# ARGUMENT PARSING: this scipt is supposed to be called with arguments, detailling all simulation- and system-parameters + +parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) + +# context and I/O +parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") +parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") +parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") +parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. walltime [s] to run the simulationn(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole scipt may take longer to execute, depending on I/O etc.") + +parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") ##FORMER: --cluster +parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") +parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") +parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") + +# flow physics and geometry +parser.add_argument("--re", default=200, type=float, help="Reynolds number") +parser.add_argument("--ma", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") +parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") + +parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") +parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") +parser.add_argument("--domain_length_x_in_d", default=None, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") +parser.add_argument("--domain_height_y_in_d", default=None, help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") +parser.add_argument("--domain_width_z_in_d", default=None, help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") + +parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") +parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") + +# solver settings +parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0, end of sim will be step_start+n_steps") +parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, t_start will be calculated by PU/LU-conversion of step_start") +parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") +parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions infered from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") +#TODO: check dimension and stencil match, OR: issue warning, choose stencil matching domain width +parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") +parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") + +# reporter settings +#TODO: add vtk reporter +#TODO: add drag reporter +#TODO: add lift reporter +#TODO: add NAN reporter +#TODO: add watchdog reporter +#TODO: add highMa reporter + +# put arguments in dictionary +args = vars(parser.parse_args()) + +########################################################### + +# CREATE timestamp, sim-ID, outdir and outdir_data +name = args["name"] +outdir = args["outdir"] +outdir_data = args["outdir_data"] + +default_device = args["default_device"] +float_dtype = args["float_dtype"] +t_sim_max = args["t_sim_max"] + +text_output_only = args["text_output_only"] +no_data_flag = args["no_data_flag"] + +timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") +sim_id = str(timestamp) + "-" + name + +os.makedirs(outdir+"/"+sim_id) # create output dir +print(f"outdir/simID = {outdir}/{sim_id}") +if outdir_data is None: # save data to regular outdir, if data-dir is not specified + outdir_data = outdir +outdir = outdir+"/"+sim_id # adding individal sim-ID to outdir path to get individual DIR per simulation +outdir_data = outdir_data+"/"+sim_id +if not os.path.exists(outdir_data): + os.makedirs(outdir_data) # create output dir for large/many files, if specified + print(f"Outdir_DATA/simID = {outdir}/{sim_id}") + +# print all arguments +print(f"Input arguments: {args}") + +# save input arguments/parameters to file in outdir: +output_file = open(outdir+"/input_parameters.txt", "a") +for key in args: + output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) +output_file.close() + +### SAVE SCRIPT: save this script to outdir +print(f"\nSaving simulation script to outdir...") +temp_script_name = sim_id + "_" + os.path.basename(__file__) +shutil.copy(__file__, outdir+"/"+temp_script_name) +print(f"Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") + +# START LOGGER -> get all terminal output into file +old_stdout = sys.stdout +sys.stdout = Logger(outdir) + +##################################### + +# PROCESS AND SET PARAMETERS + +# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients +if args["re"] > 1000: + periodic_start = 0.4 +else: + periodic_start = 0.9 + +# calculate domain and obstacle geometry +if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: + domain_height_y_in_d = 3 +else: + domain_height_y_in_d = args["domain_height_y_in_d"] + +if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : + domain_length_x_in_d = 2 * domain_height_y_in_d # D/X = domain length in X- / flow-direction +else: + domain_length_x_in_d = args["domain_length_x_in_d"] + +if args["domain_width_in_d"] is None: # will be 2D + dims = 2 +else: # will be 3D + dims = 3 + + if args["domain_width_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node + domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node + print("(!) setting domain_width_in_d to 1 lattice node") + +# if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain +# ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder +# ...use odd DpY to use odd GPD +gpd_correction = False +if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: + gpd_correction = True # gpd will be corrected + gpd_setup = args["char_length_lu"] # store old gpd for output + char_length_lu = int(gpd_setup / 2) * 2 # make gpd even + print("(!) domain_height_y_ind_d (DpY) is even, gridpoints per diameter (GPD, char_length_lu) will be set to" + str( + char_length_lu) + ". Use odd domain_height_in_D (DpY) to enable use of odd GPD (char_length_lu)!") +else: + char_length_lu = args["char_length_lu"] + +char_length_pu = args["char_length_pu"] +char_velocity_pu = args["char_velocity_pu"] +reynolds_number = args["reynolds_number"] +mach_number = args["mach_number"] + +perturb_init = args["perturb_init"] +u_init_condition = args["u_init_condition"] + +# calculate lu-domain-resolution, total number of gridpoints and check correct stencil +if dims == 2: + resolution = [domain_length_x_in_d * char_length_lu, domain_height_y_in_d * char_length_lu] + number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d + if args["stencil"] == "D2Q9": + stencil = lt.D2Q9() + else: + print("WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") + stencil= lt.D2Q9() +else: + resolution = [domain_length_x_in_d * char_length_lu, domain_height_y_in_d * char_length_lu, domain_width_z_in_d * char_length_lu] + number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d + if args["stencil"] == "D3Q15": + stencil = lt.D3Q15() + elif args["stencil"] == "D3Q19": + stencil = lt.D3Q19() + elif args["stencil"] == "D3Q27": + stencil = lt.D3Q27() + else: + print("WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") + stencil = lt.D3Q27() + +# read dtype +if float_dtype == "float32" or float_dtype == "single": + float_dtype = torch.float32 +elif float_dtype == "double" or float_dtype == "float64": + float_dtype = torch.float64 +elif float_dtype == "half" or float_dtype == "float16": + float_dtype = torch.float16 + +# OVERWRITE n_steps, if t_target is given +T_target = 0 +if args["t_target"] > 0: + T_target = args["t_target"] + n_steps = int(T_target * ((char_length_lu) / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) + +# check EQLM parameter +if args["eqlm"]: + # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? + pass + +########################################### +#todo parameter (args) vtk_fps, +# lateral_walls = args["lateral_walls"] +# #vtk_fps = 10 # FramesPerSecond (PU) for vtk-output +# cuda_device = args["default_device"] +# #nan_reporter = args["nan_reporter"] + +### +context = lt.Context(default_device) + +flow = ObstacleCylinder(context=context, resolution=resolution, + reynolds_number=re, mach_number=ma, + char_length_pu=, char_length_lu=, char_velocity_pu=, + bc_type=, stencil=stencil, equilibrium=) + +relaxation_parameter_tau = flow.units.relaxation_parameter_lu +collision_operator = lt.BGKCollision(relaxation_parameter_tau) + +#TODO reporter + +simulation = EbbSimulation(flow, collision_operator,reporter=[]) + + +simulation(num_steps=n_steps) + +# Process arguments and set parameters +# - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) +# - flow physics: char_velocity, re, ma, density, presure, length, domain (resolution) +# - solver settings + +# save input parameters to file + +# save this script to outdir for later reference + +# start logger + +# DEFINE AUXILIARY methods (or load from other helper-file) + + +# SETUP SIMULATOR +# - stencil (infer dim from stencil), dtype, equilibrium, +# - calculate obstacle geometry and domain constraints + +# save geometry input to file + +# flow, collision,...classes + +# (opt.) plot stuff (velocity, geometry etc.) + +# initialize REPORTERS + +# RUN SIMULATION + +# print stats + +# export stats + +# plotting and post processing +# - save data + +# reset lOGGER (!) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index e1874390..b755b45d 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -42,6 +42,8 @@ class ObstacleCylinder(ExtFlow): ---------- """ + # TODO: rewrite description above + def __init__(self, context: Context, resolution: Union[int, List[int]], reynolds_number, mach_number, char_length_pu, char_length_lu, char_velocity_pu=1, From 69a2e8aa4cf5bca9605f68caa16da7360a722964 Mon Sep 17 00:00:00 2001 From: mbille Date: Tue, 18 Nov 2025 18:04:44 +0100 Subject: [PATCH 06/21] debugging before tests of first cylinder flow --- .../00_run_parameterized_project.ipynb | 45 ++++++++++++++++ .../01a_script_cylinder_lowRe.py | 53 ++++++++++++------- .../ebb_simulation.py | 7 ++- .../fullway_bounce_back_boundary.py | 2 +- .../halfway_bounce_back_boundary.py | 4 +- .../helperCode.py | 19 +++++++ ...inear_interpolated_bounce_back_boundary.py | 4 +- .../obstacle_cylinder.py | 52 +++++++++--------- 8 files changed, 135 insertions(+), 51 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index caf9201d..1b81005a 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -13,6 +13,51 @@ "source": "# this script should contain the description, documentation and code-Blocks to parameterize and rund the cylinder simulations interactively", "outputs": [], "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-18T17:02:19.035502Z", + "start_time": "2025-11-18T17:02:17.234776Z" + } + }, + "cell_type": "code", + "source": "!python 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 3", + "id": "cdd448d543dd26be", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outdir/simID = ./datafolder/251118_180218-cylinder_lowRe\r\n", + "Input arguments: {'name': 'cylinder_lowRe', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 3, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': None, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'n_steps': 100000, 't_target': 0, 'collision': 'bgk', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'fwbb'}\r\n", + "\r\n", + "Saving simulation script to outdir...\r\n", + "Saved simulation script to './datafolder/251118_180218-cylinder_lowRe/251118_180218-cylinder_lowRe_01a_script_cylinder_lowRe.py'\r\n", + "WARNING: wrong stencil choice for 2D simulation, D2Q9 is used\r\n", + "initializing context\r\n", + "initializing flow\r\n", + "Traceback (most recent call last):\r\n", + " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py\", line 246, in \r\n", + " flow = ObstacleCylinder(context=context, resolution=resolution,\r\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n", + " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py\", line 76, in __init__\r\n", + " ExtFlow.__init__(self, context, resolution, reynolds_number,\r\n", + " File \"/home/mbille/lettuce_25/lettuce/lettuce/ext/_flows/_ext_flow.py\", line 31, in __init__\r\n", + " Flow.__init__(self, context, resolution, self.make_units(\r\n", + " File \"/home/mbille/lettuce_25/lettuce/lettuce/_flow.py\", line 95, in __init__\r\n", + " self.initialize()\r\n", + " File \"/home/mbille/lettuce_25/lettuce/lettuce/_flow.py\", line 129, in initialize\r\n", + " initial_p, initial_u = self.initial_pu()\r\n", + " ^^^^^^^^^^^^^^^^^\r\n", + " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py\", line 198, in initial_pu\r\n", + " ny = self.grid[0][1].shape[1]\r\n", + " ~~~~~~~~~~~~~~~~~~~~~^^^\r\n", + "IndexError: tuple index out of range\r\n" + ] + } + ], + "execution_count": 16 } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index f1ebe8ef..e343e4bc 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -4,11 +4,11 @@ # IMPORT import lettuce as lt -from .obstacle_cylinder import ObstacleCylinder -from .ebb_simulation import EbbSimulation +from obstacle_cylinder import ObstacleCylinder +from ebb_simulation import EbbSimulation import matplotlib.pyplot as plt -from scipy.signal import find_peaks +#from scipy.signal import find_peaks import sys import warnings @@ -34,6 +34,8 @@ from pyevtk.hl import imageToVTK from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter +from helperCode import Logger + import pickle from copy import deepcopy from timeit import default_timer as timer @@ -57,8 +59,8 @@ parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") # flow physics and geometry -parser.add_argument("--re", default=200, type=float, help="Reynolds number") -parser.add_argument("--ma", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") +parser.add_argument("--reynolds_number", default=200, type=float, help="Reynolds number") +parser.add_argument("--mach_number", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") @@ -102,7 +104,7 @@ t_sim_max = args["t_sim_max"] text_output_only = args["text_output_only"] -no_data_flag = args["no_data_flag"] +no_data_flag = args["no_data"] timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") sim_id = str(timestamp) + "-" + name @@ -141,7 +143,7 @@ # PROCESS AND SET PARAMETERS # calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients -if args["re"] > 1000: +if args["reynolds_number"] > 1000: periodic_start = 0.4 else: periodic_start = 0.9 @@ -157,7 +159,7 @@ else: domain_length_x_in_d = args["domain_length_x_in_d"] -if args["domain_width_in_d"] is None: # will be 2D +if args["domain_width_z_in_d"] is None: # will be 2D dims = 2 else: # will be 3D dims = 3 @@ -218,6 +220,7 @@ float_dtype = torch.float16 # OVERWRITE n_steps, if t_target is given +n_steps = args["n_steps"] T_target = 0 if args["t_target"] > 0: T_target = args["t_target"] @@ -236,15 +239,25 @@ # #nan_reporter = args["nan_reporter"] ### -context = lt.Context(default_device) +print("initializing context") +context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) +print("initializing flow") flow = ObstacleCylinder(context=context, resolution=resolution, - reynolds_number=re, mach_number=ma, - char_length_pu=, char_length_lu=, char_velocity_pu=, - bc_type=, stencil=stencil, equilibrium=) - -relaxation_parameter_tau = flow.units.relaxation_parameter_lu -collision_operator = lt.BGKCollision(relaxation_parameter_tau) + reynolds_number=reynolds_number, mach_number=mach_number, + char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, + bc_type=args["bbbc_type"], stencil=stencil) + +collision_operator = None +if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": + collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) +elif args["collision"].casefold() == "kbc": + if dims == 2: + collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) + else: + collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) +else: # default to bgk + collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) #TODO reporter @@ -255,7 +268,7 @@ # Process arguments and set parameters # - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) -# - flow physics: char_velocity, re, ma, density, presure, length, domain (resolution) +# - flow physics: char_velocity, re, ma, density, pressure, length, domain (resolution) # - solver settings # save input parameters to file @@ -285,7 +298,11 @@ # export stats -# plotting and post processing +# plotting and post-processing # - save data -# reset lOGGER (!) \ No newline at end of file +# reset lLOGGER (!) + +## END OF SCRIPT +print(f"\n♬ THE END ♬") +sys.stdout = old_stdout \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py index dba1b110..23340180 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -7,11 +7,10 @@ from typing import List, Optional from abc import ABC, abstractmethod -from . import * -from lettuce.lettuce.cuda_native import NativeCollision, Generator, StreamingStrategy +from lettuce.cuda_native import NativeCollision, Generator, StreamingStrategy -from lettuce.lettuce._simulation import * -from lettuce.lettuce import Flow +from lettuce._simulation import * +from lettuce import Flow __all__ = ['EbbSimulation'] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 56aca1de..3486b0fb 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -2,7 +2,7 @@ import numpy as np from typing import List, Optional -from lettuce.lettuce import Boundary, Flow, Context +from lettuce import Boundary, Flow, Context __all__ = ["FullwayBounceBackBoundary"] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index 132ddb7a..3915d4e2 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -2,8 +2,8 @@ import numpy as np from typing import List, Optional -from lettuce.lettuce import Boundary, Flow, Context -from lettuce.lettuce.ext import SolidBoundaryData +from lettuce import Boundary, Flow, Context +from solid_boundary_data import SolidBoundaryData __all__ = ["HalfwayBounceBackBoundary"] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py new file mode 100644 index 00000000..b0a1b60f --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py @@ -0,0 +1,19 @@ +import os +import sys + + +class Logger(object): + def __init__(self, outdir): + self.terminal = sys.stdout + self.filename = os.path.join(outdir, "log") + + def write(self, message): + self.terminal.write(message) + f = open(self.filename, "a") + f.write(message) + f.close() + + def flush(self): + # this flush method is needed for python 3 compatibility. This handles the flush command by doing nothing. + # you might want to specify some extra behavior here. + pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index 9954be3c..2b9320c2 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -2,8 +2,8 @@ import numpy as np from typing import List, Optional -from lettuce.lettuce import Boundary, Flow, Context -from lettuce.lettuce.ext import SolidBoundaryData +from lettuce import Boundary, Flow, Context +from solid_boundary_data import SolidBoundaryData __all__ = ["LinearInterpolatedBounceBackBoundary"] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index b755b45d..e956a782 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -4,12 +4,12 @@ import numpy as np import torch -from lettuce.lettuce.ext._flows import ExtFlow -from lettuce.lettuce import UnitConversion, Context, Stencil, Equilibrium -from lettuce.lettuce.util import append_axes -from lettuce.lettuce.ext import (EquilibriumBoundaryPU, BounceBackBoundary, +from lettuce.ext._flows import ExtFlow +from lettuce import UnitConversion, Context, Stencil, Equilibrium +from lettuce.util import append_axes +from lettuce.ext import (EquilibriumBoundaryPU, BounceBackBoundary, EquilibriumOutletP, AntiBounceBackOutlet) -from lettuce.examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary +from examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary __all__ = ['ObstacleCylinder'] @@ -59,10 +59,7 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.resolution = self.make_resolution(resolution, stencil) # shape in LU, if only INT, a cube shaped domain is assumed self.char_velocity_pu = char_velocity_pu - # initialize super class with unit conversion, equilibrium, context etc. - ExtFlow.__init__(self, context, resolution, reynolds_number, - mach_number, stencil, equilibrium) - # UnitConversion: defined below unter make_units(), executed by ExtFlow; flow object gets units-attibute! + # flow and boundary settings self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street @@ -72,10 +69,17 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], # initialize masks (init with zeros) self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) - self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calc.) + # initialize super class with unit conversion, equilibrium, context etc. + ExtFlow.__init__(self, context, resolution, reynolds_number, + mach_number, stencil, equilibrium) + # UnitConversion: defined below unter make_units(), executed by ExtFlow; flow object gets units-attibute! + + self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes + + # cylinder geometry in LU (1-based indexing!) self.x_offset = x_offset self.y_offset = y_offset @@ -85,9 +89,9 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], # MESHGRID of x, y, (z) index (LU) xyz = tuple(np.linspace(1, n, n) for n in self.resolution) # Tupel of index-lists (1-n (one-based!)) - if self.units.lattice.D == 2: + if self.stencil.d == 2: x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y-index - elif self.units.lattice.D == 3: + elif self.stencil.d == 3: x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- and z-index else: x_lu, y_lu, z_lu = 1,1,1 @@ -124,11 +128,11 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile # scale with 1.5 to achieve a mean velocity of u_char! -> DIFFERENT FROM cylinder2D and cylinder3D (!) - if self.units.lattice.D == 2: + if self.stencil.d == 2: # in 2D u1 needs Dimension 1 x ny (!) velocity_y = np.zeros_like(parabola_y) # y-velocities = 0 self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) # stack/pack u-field - elif self.units.lattice.D == 3: + elif self.stencil.d == 3: ones_z = np.ones(nz) parabola_yz = parabola_y[:, :, np.newaxis] * ones_z parabola_yz_zeros = np.zeros_like(parabola_yz) @@ -165,7 +169,7 @@ def obstacle_mask(self, m): def initial_pu(self): p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() - u_max_pu = append_axes(u_max_pu, self.units.lattice.D) + u_max_pu = append_axes(u_max_pu, self.stencil.d) self.solid_mask[np.where(self.obstacle_mask)] = 1 # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD ### initial velocity field: "u_init"-parameter # 0: uniform u=0 @@ -180,9 +184,9 @@ def initial_pu(self): # multiply parabolic profile with every column of the velocity field: y_coordinates = np.linspace(0, ny, ny) ux_factor[1:-1] = - y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 - if self.units.lattice.D == 2: + if self.stencil.d == 2: u = np.einsum('k,ijk->ijk', ux_factor, u) - elif self.units.lattice.D == 3: + elif self.stencil.d == 3: u = np.einsum('k,ijkl->ijkl', ux_factor, u) else: # lateral_walls == periodic or slip # initial velocity u_PU=1 on every fluid node @@ -196,9 +200,9 @@ def initial_pu(self): # add perturbation for small velocities #OLD 2D: u[0][1] += np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 1.0 amplitude_y = np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 - if self.units.lattice.D == 2: + if self.stencil.d == 2: u[0][1] += amplitude_y - elif self.units.lattice.D == 3: + elif self.stencil.d == 3: nz = self.grid[0][2].shape[2] plane_yz = np.ones_like(u[0, 1]) # plane of ones u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # plane of amplitude in y @@ -208,9 +212,9 @@ def initial_pu(self): # multiply scaled down perturbation if velocity field is already near u_char #OLD 2D: u[0][1] *= 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.3 factor = 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.1 - if self.units.lattice.D == 2: + if self.stencil.d == 2: u[0][1] *= factor - elif self.units.lattice.D == 3: + elif self.stencil.d == 3: nz = self.grid[0][2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) @@ -438,9 +442,9 @@ def post_boundaries(self): # <<< # outlet ("right side", x[-1],y[:], (z[:])) - if self.units.lattice.D == 2: + if self.stencil.d == 2: outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) # outlet in positive x-direction - else: # self.units.lattice.D == 3: + else: # self.stencil.d == 3: outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # outlet in positive x-direction # create and return boundary-list @@ -484,4 +488,4 @@ def post_streaming_boundaries(self): return [obstacle_boundary] def _unit_vector(self, i=0): - return np.eye(self.units.lattice.D)[i] \ No newline at end of file + return np.eye(self.stencil.d)[i] \ No newline at end of file From d1e7638c977670bb80677e7c9399ff5c0e2d5ac3 Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 19 Nov 2025 15:03:06 +0100 Subject: [PATCH 07/21] commit before trying to find error in master regarding solid boundaries --- examples/advanced_flows/PorousMedium.ipynb | 136 +++++++++++------ .../00_run_parameterized_project.ipynb | 125 ++++++++++++---- .../01a_script_cylinder_lowRe.py | 80 ++++++---- .../fullway_bounce_back_boundary.py | 52 +++---- .../obstacle_cylinder.py | 103 +++++++------ examples/simple_flows/Obstacle.ipynb | 141 ++++++++++++------ 6 files changed, 417 insertions(+), 220 deletions(-) diff --git a/examples/advanced_flows/PorousMedium.ipynb b/examples/advanced_flows/PorousMedium.ipynb index bd0cacfa..e0820c7a 100644 --- a/examples/advanced_flows/PorousMedium.ipynb +++ b/examples/advanced_flows/PorousMedium.ipynb @@ -17,8 +17,8 @@ "id": "cc4dc7ad", "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:17:35.756583Z", - "start_time": "2025-08-06T10:17:34.563226Z" + "end_time": "2025-11-19T13:57:17.637116Z", + "start_time": "2025-11-19T13:57:16.579485Z" } }, "source": [ @@ -46,8 +46,8 @@ "id": "8d15f368", "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:17:35.760246Z", - "start_time": "2025-08-06T10:17:35.757289Z" + "end_time": "2025-11-19T13:57:17.644045Z", + "start_time": "2025-11-19T13:57:17.641276Z" } }, "source": [ @@ -84,8 +84,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:17:35.772949Z", - "start_time": "2025-08-06T10:17:35.760759Z" + "end_time": "2025-11-19T13:57:17.690084Z", + "start_time": "2025-11-19T13:57:17.686937Z" } }, "cell_type": "code", @@ -125,8 +125,8 @@ "id": "worst-deputy", "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:17:35.784139Z", - "start_time": "2025-08-06T10:17:35.773676Z" + "end_time": "2025-11-19T13:57:17.738071Z", + "start_time": "2025-11-19T13:57:17.735412Z" } }, "source": [ @@ -153,8 +153,8 @@ "id": "8b4de7ef", "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:17:36.085609Z", - "start_time": "2025-08-06T10:17:35.786027Z" + "end_time": "2025-11-19T13:57:17.999051Z", + "start_time": "2025-11-19T13:57:17.784042Z" } }, "source": [ @@ -234,8 +234,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-08-06T10:17:36.237426Z", - "start_time": "2025-08-06T10:17:36.086138Z" + "end_time": "2025-11-19T13:57:18.274040Z", + "start_time": "2025-11-19T13:57:18.003148Z" } }, "id": "788ba01aaac63b34", @@ -256,7 +256,10 @@ "image/png": "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" }, "metadata": {}, - "output_type": "display_data" + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], "execution_count": 6 @@ -274,8 +277,8 @@ "id": "d80f0401", "metadata": { "ExecuteTime": { - "end_time": "2025-08-06T10:18:09.438991Z", - "start_time": "2025-08-06T10:17:36.238034Z" + "end_time": "2025-11-19T13:58:10.577911Z", + "start_time": "2025-11-19T13:57:18.278374Z" } }, "source": [ @@ -299,27 +302,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "it 500 u_avg[-1] = 0.009691138791684406 the relative change in mean vel is inf %\n", - "it 1000 u_avg[-1] = 0.009739418591561515 the relative change in mean vel is 0.4957154210307779 %\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", - "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[7]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[32m 4\u001B[39m u_avg_new = \u001B[32m0\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m j \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(it_floating_avg):\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m \u001B[43msimulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mavgs_per_check\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# print(flow.u().mean())\u001B[39;00m\n\u001B[32m 8\u001B[39m u_avg_new += flow.u().mean()\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:204\u001B[39m, in \u001B[36mSimulation.__call__\u001B[39m\u001B[34m(self, num_steps)\u001B[39m\n\u001B[32m 202\u001B[39m \u001B[38;5;28mself\u001B[39m._collide_and_stream(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m 203\u001B[39m \u001B[38;5;28mself\u001B[39m.flow.i += \u001B[32m1\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m204\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 206\u001B[39m end = timer()\n\u001B[32m 207\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m num_steps * \u001B[38;5;28mself\u001B[39m.flow.rho().numel() / \u001B[32m1e6\u001B[39m / (end - beg)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:193\u001B[39m, in \u001B[36mSimulation._report\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 191\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_report\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 192\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m reporter \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.reporter:\n\u001B[32m--> \u001B[39m\u001B[32m193\u001B[39m \u001B[43mreporter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:47\u001B[39m, in \u001B[36mVTKReporter.__call__\u001B[39m\u001B[34m(self, simulation)\u001B[39m\n\u001B[32m 44\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m d \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(simulation.flow.stencil.d):\n\u001B[32m 45\u001B[39m \u001B[38;5;28mself\u001B[39m.point_dict[\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mu\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33mxyz\u001B[39m\u001B[33m'\u001B[39m[d]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m] = (\n\u001B[32m 46\u001B[39m simulation.flow.context.convert_to_ndarray(u[d, ...]))\n\u001B[32m---> \u001B[39m\u001B[32m47\u001B[39m \u001B[43mwrite_vtk\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msimulation\u001B[49m\u001B[43m.\u001B[49m\u001B[43mflow\u001B[49m\u001B[43m.\u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mfilename_base\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:11\u001B[39m, in \u001B[36mwrite_vtk\u001B[39m\u001B[34m(point_dict, id, filename_base)\u001B[39m\n\u001B[32m 10\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mwrite_vtk\u001B[39m(point_dict, \u001B[38;5;28mid\u001B[39m=\u001B[32m0\u001B[39m, filename_base=\u001B[33m\"\u001B[39m\u001B[33m./data/output\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m---> \u001B[39m\u001B[32m11\u001B[39m \u001B[43mvtk\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgridToVTK\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43mf\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[43mfilename_base\u001B[49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m_\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[38;5;28;43mid\u001B[39;49m\u001B[38;5;132;43;01m:\u001B[39;49;00m\u001B[33;43m08d\u001B[39;49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 12\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 13\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 14\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 15\u001B[39m \u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:339\u001B[39m, in \u001B[36mgridToVTK\u001B[39m\u001B[34m(path, x, y, z, cellData, pointData, fieldData, start)\u001B[39m\n\u001B[32m 337\u001B[39m w.appendData((x, y, z))\n\u001B[32m 338\u001B[39m \u001B[38;5;66;03m# Write data\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m339\u001B[39m \u001B[43m_appendDataToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[43mw\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcellData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfieldData\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 341\u001B[39m \u001B[38;5;66;03m# Close file\u001B[39;00m\n\u001B[32m 342\u001B[39m w.save()\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:122\u001B[39m, in \u001B[36m_appendDataToFile\u001B[39m\u001B[34m(vtkFile, cellData, pointData, fieldData)\u001B[39m\n\u001B[32m 120\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m keys:\n\u001B[32m 121\u001B[39m data = pointData[key]\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m \u001B[43mvtkFile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mappendData\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 124\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m cellData \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 125\u001B[39m keys = \u001B[38;5;28mlist\u001B[39m(cellData.keys())\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/vtk.py:641\u001B[39m, in \u001B[36mVtkFile.appendData\u001B[39m\u001B[34m(self, data)\u001B[39m\n\u001B[32m 638\u001B[39m writeBlockSize(\u001B[38;5;28mself\u001B[39m.xml.stream, block_size)\n\u001B[32m 639\u001B[39m \u001B[38;5;66;03m# else:\u001B[39;00m\n\u001B[32m 640\u001B[39m \u001B[38;5;66;03m# writeBlockSize64Bit(self.xml.stream, block_size)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m641\u001B[39m \u001B[43mwriteArrayToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mxml\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 643\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 644\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/evtk.py:99\u001B[39m, in \u001B[36mwriteArrayToFile\u001B[39m\u001B[34m(stream, data)\u001B[39m\n\u001B[32m 95\u001B[39m \u001B[38;5;66;03m# NOTE: VTK expects data in FORTRAN order\u001B[39;00m\n\u001B[32m 96\u001B[39m \u001B[38;5;66;03m# This is only needed when a multidimensional array has C-layout\u001B[39;00m\n\u001B[32m 97\u001B[39m dd = np.ravel(data, order=\u001B[33m\"\u001B[39m\u001B[33mF\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m99\u001B[39m bin_pack = \u001B[43mstruct\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpack\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43mdd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 100\u001B[39m stream.write(bin_pack)\n", - "\u001B[31mKeyboardInterrupt\u001B[39m: " + "it 500 u_avg[-1] = 0.010375465103949419 the relative change in mean vel is inf %\n", + "it 1000 u_avg[-1] = 0.014891512711759439 the relative change in mean vel is 30.32631872411333 %\n", + "it 1500 u_avg[-1] = nan the relative change in mean vel is nan %\n" ] } ], @@ -328,20 +313,54 @@ { "cell_type": "code", "id": "natural-portrait", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:58:10.694939Z", + "start_time": "2025-11-19T13:58:10.635548Z" + } + }, "source": [ "u_plot = [context.convert_to_ndarray(_) for _ in u_avg[1:]]\n", "plt.plot(np.log10(np.abs(u_plot)))\n", "plt.xlabel(f'{it_check} iterations')\n", "plt.ylabel(f'mean log10(|velocity|) [lus]')" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'mean log10(|velocity|) [lus]')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 8 }, { "cell_type": "code", "id": "63df80e6", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:58:11.152299Z", + "start_time": "2025-11-19T13:58:10.699248Z" + } + }, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "k = nu*u.mean()/(delta_rho_lu/nx)**2\n", @@ -363,18 +382,45 @@ "axes[3].set_title(\"Mean fluid velocity along x\")\n", "axes[3].plot(u[0].mean(axis=-1));\n" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 9 }, { "cell_type": "code", "id": "needed-courage", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:58:11.160437Z", + "start_time": "2025-11-19T13:58:11.158617Z" + } + }, "source": [ "print(f'Porosity = {phi*100} % and Permeability = {k} [m^2]')" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Porosity = 69.74983215332031 % and Permeability = nan [m^2]\n" + ] + } + ], + "execution_count": 10 } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index 1b81005a..ec6213c5 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -17,47 +17,114 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-11-18T17:02:19.035502Z", - "start_time": "2025-11-18T17:02:17.234776Z" + "end_time": "2025-11-19T13:05:37.005589Z", + "start_time": "2025-11-19T13:05:35.542760Z" } }, "cell_type": "code", - "source": "!python 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 3", + "source": [ + "%matplotlib inline\n", + "%run 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 10 --n_steps 1000\n", + "\n", + "pass" + ], "id": "cdd448d543dd26be", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "outdir/simID = ./datafolder/251118_180218-cylinder_lowRe\r\n", - "Input arguments: {'name': 'cylinder_lowRe', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 3, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': None, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'n_steps': 100000, 't_target': 0, 'collision': 'bgk', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'fwbb'}\r\n", - "\r\n", - "Saving simulation script to outdir...\r\n", - "Saved simulation script to './datafolder/251118_180218-cylinder_lowRe/251118_180218-cylinder_lowRe_01a_script_cylinder_lowRe.py'\r\n", - "WARNING: wrong stencil choice for 2D simulation, D2Q9 is used\r\n", - "initializing context\r\n", - "initializing flow\r\n", - "Traceback (most recent call last):\r\n", - " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py\", line 246, in \r\n", - " flow = ObstacleCylinder(context=context, resolution=resolution,\r\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n", - " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py\", line 76, in __init__\r\n", - " ExtFlow.__init__(self, context, resolution, reynolds_number,\r\n", - " File \"/home/mbille/lettuce_25/lettuce/lettuce/ext/_flows/_ext_flow.py\", line 31, in __init__\r\n", - " Flow.__init__(self, context, resolution, self.make_units(\r\n", - " File \"/home/mbille/lettuce_25/lettuce/lettuce/_flow.py\", line 95, in __init__\r\n", - " self.initialize()\r\n", - " File \"/home/mbille/lettuce_25/lettuce/lettuce/_flow.py\", line 129, in initialize\r\n", - " initial_p, initial_u = self.initial_pu()\r\n", - " ^^^^^^^^^^^^^^^^^\r\n", - " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py\", line 198, in initial_pu\r\n", - " ny = self.grid[0][1].shape[1]\r\n", - " ~~~~~~~~~~~~~~~~~~~~~^^^\r\n", - "IndexError: tuple index out of range\r\n" + "SCRIPT: Writing arguments to dictionary...\n", + "SCRIPT: Input arguments are: \n", + "{'name': 'cylinder_lowRe', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 10, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': None, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'n_steps': 1000, 't_target': 0, 'collision': 'bgk', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'fwbb'}\n", + "\n", + "SCRIPT: Creating timestamt, simulation ID and creating output directory...\n", + "outdir/simID = ./datafolder/251119_140535-cylinder_lowRe\n", + "outdir_DATA/simID = ./datafolder/251119_140535-cylinder_lowRe/251119_140535-cylinder_lowRe\n", + "SCRIPT: Writing input parameters to file: ./datafolder/251119_140535-cylinder_lowRe/input_parameters.txt\n", + "SCRIPT: Saving simulation script to outdir...\n", + "-> Saved simulation script to './datafolder/251119_140535-cylinder_lowRe/251119_140535-cylinder_lowRe_01a_script_cylinder_lowRe.py'\n", + "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", + "SCRIPT: Processing parameters...\n", + "(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used\n", + "\n", + "(INFO) parameters set for simulation of 1000 steps, representing 0.000 seconds [PU]!\n", + "\n", + "SCRIPT: initializing solver components...\n", + "-> initializing context...\n", + "-> initializing flow...\n", + "-> initializing collision operator...\n", + "-> initializing reporters...\n", + "\n", + "SCRIPT: initializing simulation object...\n", + "\n", + "SCRIPT: running simulation for 1000 steps...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:84: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", + " no_collision_mask[self.index] = 1\n", + "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:78: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", + " no_streaming_mask[[np.setdiff1d(np.arange(shape[0]), self.velocities)]\n", + "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py:284: RuntimeWarning: invalid value encountered in sqrt\n", + " d1 = - h1 + np.sqrt(h1 * h1 - h2)\n", + "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py:285: RuntimeWarning: invalid value encountered in sqrt\n", + " d2 = - h1 - np.sqrt(h1 * h1 - h2)\n", + "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:68: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", + " rho_w = self.rho_outlet * torch.ones_like(rho[here])\n", + "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:69: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", + " u_w = u[other]\n", + "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:71: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", + " feq[here] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max Velocity: 1.00162220850023\n", + "\n", + "♬ THE END ♬\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py:288: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAEYCAYAAAAeSdikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABTFUlEQVR4nO3df3QU1d0/8PfsJrsbIBuMQEIg4WflR5VEgsT4q2K3RODQptJTRB+JCFhsoMJ+FYkCQfwRaitiayT+glQqBbVCq/EJjVHw8SGABHIebIWCxCZiNkALCQSyu9mZ7x8hC+vewZ0kk52N79c59xxy987svbNhs5+9935GUhRFAREREREREX3nmcLdASIiIiIiIjIGBohEREREREQEgAEiERERERERXcAAkYiIiIiIiAAwQCQiIiIiIqILGCASERERERERAAaIREREREREdAEDRCIiIiIiIgLAAJGIiIiIiIguYIBIREREREREANoRIH788ceYOnUqkpKSIEkStm7d+q3HbN++HWPHjoXVasXw4cNRXFzcjq4SERERERGRnjQHiE1NTUhNTUVhYWFI7aurqzFlyhRMmDABVVVVWLhwIebMmYNt27Zp7iwRERERERHpR1IURWn3wZKELVu2IDs7W7XNI488gpKSEnz22Wf+ujvvvBOnT59GaWlpe5+aiIiIiIiIOlmU3k9QUVEBh8MRUJeVlYWFCxeqHuN2u+F2u/0/y7KM//znP7jyyishSZJeXSUiikiKouDMmTNISkqCycSt5URERNR+ugeILpcLCQkJAXUJCQlobGzE+fPnERMTE3RMQUEBHn/8cb27RkTUrdTW1mLgwIHh7gYRERFFMN0DxPbIy8uD0+n0/9zQ0ICUlBTU1tbCbreHsWdERMbT2NiI5ORkxMbGhrsrREREFOF0DxATExNRX18fUFdfXw+73S6cPQQAq9UKq9UaVG+32xkgEhGp4BJ8IiIi6ijdN6tkZmaivLw8oK6srAyZmZl6PzURERERERFpoDlAPHv2LKqqqlBVVQWg9TYWVVVVqKmpAdC6PHTmzJn+9vPmzcPRo0exePFiHDx4EC+++CLefPNNLFq0qHNGQERERERERJ1Cc4C4d+9eXHvttbj22msBAE6nE9deey2WL18OAKirq/MHiwAwZMgQlJSUoKysDKmpqXj22Wfx6quvIisrq5OGQERERERERJ2hQ/dB7CqNjY2Ii4tDQ0MD9yASEX0D3yOJiIios/CGWURERERERATAoLe5ICIiIiKi7qO5uRkejyfc3VBlsVhgs9nC3Q1DYIBIRERERES6aW5uxpBBveA67gt3V1QlJiaiurqaQSIYIBIRERERkY48Hg9cx32orhwEe6zxdrg1npExJP1f8Hg8DBDBAJGIiIiIiLpAz16txWh8hk/Z2bUYIBIRERERke5kKJBhvGjMiH0KJwaIRERERESkO6/ig9eAd9jzKnK4u2AoxlsETERERERE3U7bDKIRixYff/wxpk6diqSkJEiShK1bt37rMdu3b8fYsWNhtVoxfPhwFBcXB7UpLCzE4MGDYbPZkJGRgT179gQ8/vLLL+PWW2+F3W6HJEk4ffp00DkGDx4MSZICyqpVqzSNjwEiERERERHpToYCnwGL1gCxqakJqampKCwsDKl9dXU1pkyZggkTJqCqqgoLFy7EnDlzsG3bNn+bzZs3w+l0Ij8/H/v27UNqaiqysrJw/Phxf5tz587h9ttvx6OPPnrZ51u5ciXq6ur8ZcGCBZrGxyWmRERERESku+6yB3HSpEmYNGlSyO2LioowZMgQPPvsswCAUaNG4ZNPPsFzzz2HrKwsAMDq1asxd+5czJo1y39MSUkJ1q1bhyVLlgAAFi5cCKB1NvJyYmNjkZiYqGlMl+IMIhERERER6c6rKIYtANDY2BhQ3G53p4y7oqICDocjoC4rKwsVFRUAWm8DUllZGdDGZDLB4XD422ixatUqXHnllbj22mvxm9/8Bi0tLZqO5wwiERERERHprm1Jp9G09Sk5OTmgPj8/HytWrOjw+V0uFxISEgLqEhIS0NjYiPPnz+PUqVPw+XzCNgcPHtT0XL/61a8wduxYxMfHY+fOncjLy0NdXR1Wr14d8jkYIBIRERERke58ijHvOdjWp9raWtjtdn+91WoNU4/az+l0+v89ZswYWCwW/OIXv0BBQUHI42GASEREREREupMvFKNp65Pdbg8IEDtLYmIi6uvrA+rq6+tht9sRExMDs9kMs9ksbNORvYQAkJGRgZaWFnz55ZcYMWJESMdwDyIREREREelOhgSfAYsMSddxZ2Zmory8PKCurKwMmZmZAACLxYL09PSANrIso7y83N+mvaqqqmAymdCvX7+Qj+EMIhERERER6c6rSPAq+gZj7aG1T2fPnsWRI0f8P1dXV6Oqqgrx8fFISUlBXl4ejh07htdffx0AMG/ePLzwwgtYvHgx7rvvPnz44Yd48803UVJS4j+H0+lETk4Oxo0bh/Hjx2PNmjVoamryZzUFWvcyulwu/3MfOHAAsbGxSElJQXx8PCoqKrB7925MmDABsbGxqKiowKJFi/Bf//VfuOKKK0IeHwNEIiIiIiLSXduMndFo7dPevXsxYcIE/89t+/5ycnJQXFyMuro61NTU+B8fMmQISkpKsGjRIjz//PMYOHAgXn31Vf8tLgBg+vTpOHHiBJYvXw6Xy4W0tDSUlpYGJK4pKirC448/7v/5lltuAQCsX78e9957L6xWKzZt2oQVK1bA7XZjyJAhWLRoUcC+xFBIiqIYcKtooMbGRsTFxaGhoUGXdcFERJGM75FERGRkbX+nPvksCb1ijbfD7ewZGTdd/TX/jl7AGUQiIiIiItJdd5lB7O4YIBIRERERke58MMFnwByZvnB3wGAYIBIRERERke5aFBO8ivECxBbDb7jrWgwQiYiIiIhIdz7FBJ8BA0QfA8QADBCJiIiIiEh3MiTIBlxiKoMR4qUYIBIRERERke6YpCYyMEAkIiIiIiLdeRUzvIo53N0I4uUEYgAGiERE1C3Isoyvv/4asbGxkCR+G0xE9E2KouDMmTNISkqCydT1Sz1lg2Yx5RLTQAwQiYioW/j666+RnJwc7m4QERlebW0tBg4c2OXPa9wkNQwQL8UAkYiIuoXY2FgAwA3jFyMqyuqvN+38v9BPomHmUTKrLJOSgj/8SNHitlKU4M9wdLS4rc0aVKfE2IRt5Z7B9b5Y8Xk9gnqPXfwBzh0XXO+xC5vCGxv8gaslVny3MalnS1BdtM0rbBtjDW5rixa3tZiDn89skoVto6TgekkSf2g0CerVfnVEbU0Gnq0wCa4DaSerBEGyYK+brIh/eXxycL2i0jbU52o558HO6a/63y+7mgwTk9REAAaIRETULbQtK42KsiIq6mKAZJLEgZHKSTQ01RAgSuI/t8J6k0qAaBIEiObgOgCQBfVSlEXcVhCQ+iziD3BmQb1KF+CzCQKjGJUAMSY46DPHiK+v2RocDIr6BQBRXRggiupU2xr4wygDxM7RGQGiJAefo6MBov/cYVqG71Mk+DSMoasYsU/hxACRiIiIiIh051WiDJqkhgHipRggEhERERGR7nwGTVLjM/CsfjgwQCQiIiIiIt3JMOZyTi6sDsQAkYiIuhWTuwUmX/Cets6myOJvnIVbE2WdPn4YIfOeShdEHwEltQ+Ggnq1vVai/Vpa2qotbhPt1zJ3wqyCcH+ZWkIbA8xiiPbOcV9iKy37ClsE+wcBwO0L/ujt9Yl/Kz2Cep/KeUVvR6L/F75z4V3eadwkNcbrUzgxQCQiIiIiIt15FTOiDLkHMfxfzhgJA0QiIiIiItKdce+DaLw+hVO7rkZhYSEGDx4Mm82GjIwM7Nmz57Lt16xZgxEjRiAmJgbJyclYtGgRmpub29VhIiIiIiKKPG1JaoxY6CLNM4ibN2+G0+lEUVERMjIysGbNGmRlZeHQoUPo169fUPuNGzdiyZIlWLduHW644Qb885//xL333gtJkrB69epOGQQRERERERmbrEiq930MJyP2KZw0B4irV6/G3LlzMWvWLABAUVERSkpKsG7dOixZsiSo/c6dO3HjjTfirrvuAgAMHjwYM2bMwO7duzvYdSIiomAmjy8gSY2R02sogn0vnfExRRLtp1G7EKKtN2rbcbRs09FwXlF31bYEiarVPtyJ6tXaim5or5r8Rty1kM+r5nI3Ng86rwES2nzXqL0+ooQ0omQ0AHDOGx1U1+wJrgMAT0vwXr0WQR2g8v9F9Pt/Tnh4l5ENOlvHJDWBNF0Nj8eDyspKOByOiycwmeBwOFBRUSE85oYbbkBlZaV/GerRo0fx/vvvY/LkyarP43a70djYGFCIiIiIiChyeRWzYQtdpGkG8eTJk/D5fEhISAioT0hIwMGDB4XH3HXXXTh58iRuuukmKIqClpYWzJs3D48++qjq8xQUFODxxx/X0jUiIiIiIjIwWTGp3i4knIzYp3DS/Wps374dTz/9NF588UXs27cP77zzDkpKSvDEE0+oHpOXl4eGhgZ/qa2t1bubRERERESkIx8AHyQDFrqUphnEPn36wGw2o76+PqC+vr4eiYmJwmOWLVuGe+65B3PmzAEAXHPNNWhqasL999+Pxx57DCZTcIxqtVphtVq1dI2IiIiIiAyMM4iRQdPVsFgsSE9PR3l5ub9OlmWUl5cjMzNTeMy5c+eCgkCzuXWdr2hzPhERERERdT8tBthrKCot3IMYQHO47HQ68corr+APf/gDPv/8czzwwANoamryZzWdOXMm8vLy/O2nTp2KtWvXYtOmTaiurkZZWRmWLVuGqVOn+gNFIiKiziI1ewNKxFGU0EsnkBRFUKBSgttqG5tK6SBFkYRFS1tZUDqD6LxqRdN5IYVcugOTpAhLR9uqaZvpCizi180rm4NKc0uUsJxzW4JLs7g0nwsuniZx8QpLdHA5J86Y2lV8ismwRYuPP/4YU6dORVJSEiRJwtatW7/1mO3bt2Ps2LGwWq0YPnw4iouLg9p8273mX375Zdx6662w2+2QJAmnT58OOsd//vMf3H333bDb7ejduzdmz56Ns2fPahqf5ttcTJ8+HSdOnMDy5cvhcrmQlpaG0tJSf+KampqagBnDpUuXQpIkLF26FMeOHUPfvn0xdepUPPXUU1qfmoiIiIiIIlR3uQ9iU1MTUlNTcd999+GOO+741vbV1dWYMmUK5s2bhzfeeAPl5eWYM2cO+vfvj6ysLACh3Wv+3LlzuP3223H77bcHTMhd6u6770ZdXR3Kysrg9Xoxa9Ys3H///di4cWPI45OUCFjn2djYiLi4ODQ0NMBut4e7O0REhsL3yFZt1+GH31uEKPPFfey+Q0dCP4mk4UOCJP7GWRKsjpHMKt9ORwd/my9ZxN/wSzZbUJ3SI7gOAJSewfUtseK9/V578HfF7jjxCh93XPD1cfcWXzOvPfjjRUsv8R0ElV4tQXXRMeLZX5stuN4aJU4xYY0KPm+USdwHs6A+ShK3lQSzUVpmqC4386WHjt4z0aRyHbqS2rURfbDX0laNaE9ai8osk+ieh6L7HQLA2ebg/4fNbnHbFk/w/0PZq7L6LsT7jsrnm/HV/BVd/vei7f154f/+GNZe4Z3FFHGf9WLNjX9t13WRJAlbtmxBdna2aptHHnkEJSUl+Oyzz/x1d955J06fPo3S0lIAQEZGBq677jq88MILAFq38SUnJ2PBggVB95rfvn07JkyYgFOnTqF3797++s8//xyjR4/Gp59+inHjxgEASktLMXnyZHz11VdISkoKaUzckUlERERERLrTsuS6qwuAoPuwu93uThl3RUVFwH3kASArK8t/H/n23Gte7Xl69+7tDw4BwOFwwGQyYffu3SGfR/MSUyIiIiIiIq28ihkmAyaE8Sqts+TJyckB9fn5+VixYkWHz+9yuYT3kW9sbMT58+dx6tQpzfeaV3uetuWobaKiohAfHw+XyxXyeRggEhFRtyJ5vZB835EFMmq7RET1Ou0oUV0VqeXpREv/LpNkJuTTauiCiFqCF5No6Z7KObQsG9WyXFIL0Ti0LDtVuwWAkZeehtpW27JTleXUvuCAp9kr/ojt9gTXe5vFbZVmwXL1FpX3thCXmErnwxucGX0PYm1tbcAS0+/qbfcYIBIRERERke4Ug94HUbnQJ7vdrsvezMTEROF95O12O2JiYmA2mzXfa17teY4fPx5Q19LSgv/85z+azmO8V4iIiIiIiLodHyTDFj1lZmYG3EceAMrKyvz3kW/PvebVnuf06dOorKz013344YeQZRkZGRkhn4cziEREREREpLsW2QSTbLw9iC2yOBOymrNnz+LIkYsZsqurq1FVVYX4+HikpKQgLy8Px44dw+uvvw4AmDdvHl544QUsXrwY9913Hz788EO8+eabKCkp8Z/D6XQiJycH48aNw/jx47FmzZqAe80DrXsMXS6X/7kPHDiA2NhYpKSkID4+HqNGjcLtt9+OuXPnoqioCF6vF/Pnz8edd94ZcgZTgAEiERERERF1ARmS6t7ecNLap71792LChAn+n51OJwAgJycHxcXFqKurQ01Njf/xIUOGoKSkBIsWLcLzzz+PgQMH4tVXX/XfAxH49nvNA0BRUREef/xx/8+33HILAGD9+vW49957AQBvvPEG5s+fjx/+8IcwmUyYNm0afve732kaH++DSEQU4fge2artOjgGz0eU6WJigZbqf4V+kki7D2KMOIGCpvsgxgU/n5b7IHoEdQDgEd0HMVblPog9g7+9V7sPolVwH0RbdPD9DgHAIrgPYrSG+yCq3q9QkP1DdG9EtXN0xj0TO6qj90YEujZJjV7XQS1himifnEdl5uuc1xJUd8YdXAcATeeD/x96zov/z+uRpEY+34zah5eF7T6Id314Fyy9xNcmnDxnPdh428bv/N/RNpxBJCKi7sXjBUzcYq+Ljn5GN8BX0moBgaRTBlEtfdCSZVOvvhmBUcem9lq0yMHvN6LMpgDQ0hJcr7jFbSV38HlNHpUvsQR9E11GqTm8742yQZPUGLFP4cQAkYiIiIiIdCfDoLe5MOCy13BigEhERERERLrzKSa0GHC2zmfAPoUTA0QiIiIiItIdl5hGBgaIRERERESkO1kx6BJTA/YpnBggEhFRt6K0+KCYxFktSSDE7IdaacozImirqHxgE9XLKs8l+tBn1tCxzkgQIzqHUZOw0EWiPWmqv5Oi4wWJawBAFmUhbVH5PRMkpDF51ZLUBFcJf83EyYG7THe5zUV3xwCRiIiIiIh0xxnEyMAAkYiIiIiIdNcimyCpzK6Gk+hWJd9lDBCJiIiIiEh3nEGMDAwQiYiIiIhIdwqMud+Pu4IDMUAkIiIiIiLdcQYxMjBAJCKi7kVWwO+DSU+iGRCTyq+cpFPG0o5mR1WbxTF9x/7vqF4zDZdB9FooasfLwW0lQR0ASD5BW5/KeUPNYqp2fBdpkU2AAff7cQ9iIAaIRERERESkO84gRgYGiEREREREpDtFkVTvJxlORuxTODFAJCIiIiIi3cmQDJmkxoh9CicGiEREREREpDsuMY0MDBCJiOi7SeIHAgAQfnHeCZemo5+39EruQp1HVoITe5gkOQw96aa0/B8StBX+Dwrz255PNkEyYEIYnwH7FE4MEImIiIiISHfcgxgZGCASEREREZHuFIMuMWWAGIgBIhERERER6U7BZe4RGUYG7FJYccEtERHprqCgANdddx1iY2PRr18/ZGdn49ChQwFtmpubkZubiyuvvBK9evXCtGnTUF9fH6YeExFRZ/MpJsMWuohXg4iIdLdjxw7k5uZi165dKCsrg9frxcSJE9HU1ORvs2jRIrz77rt46623sGPHDnz99de44447wthrIiLqTG1ZTI1Y6CIuMSUiIt2VlpYG/FxcXIx+/fqhsrISt9xyCxoaGvDaa69h48aNuO222wAA69evx6hRo7Br1y5cf/31IT+XZDZBMvH7T1109DOU2vEazivKbmpSOd6kUyZUk2BBGrOudi9aXmPR75lqkmRTcFvFLD6vqF6JUjlxiL9+Spg/+SuKQZeYGrBP4cS/oERE1OUaGhoAAPHx8QCAyspKeL1eOBwOf5uRI0ciJSUFFRUVYekjERF1rrYspkYsdBFnEImIqEvJsoyFCxfixhtvxNVXXw0AcLlcsFgs6N27d0DbhIQEuFwu4Xncbjfcbrf/58bGRt36TEREHWfUYMyIfQonziASEVGXys3NxWeffYZNmzZ16DwFBQWIi4vzl+Tk5E7qIRER6cEnS4YtWnz88ceYOnUqkpKSIEkStm7d+q3HbN++HWPHjoXVasXw4cNRXFwc1KawsBCDBw+GzWZDRkYG9uzZE/B4KMncJEkKKlr/3jJAJCKiLjN//ny89957+OijjzBw4EB/fWJiIjweD06fPh3Qvr6+HomJicJz5eXloaGhwV9qa2v17DoREXVQ6x7E8C8nDS7axtHU1ITU1FQUFhaG1L66uhpTpkzBhAkTUFVVhYULF2LOnDnYtm2bv83mzZvhdDqRn5+Pffv2ITU1FVlZWTh+/Li/TajJ3NavX4+6ujp/yc7O1jS+dgWI3xbdftPp06eRm5uL/v37w2q14qqrrsL777/fnqcmIqIIpCgK5s+fjy1btuDDDz/EkCFDAh5PT09HdHQ0ysvL/XWHDh1CTU0NMjMzhee0Wq2w2+0BBQAQHQVER18s6p0KLt2FJIVevmNMkiIsHW2r5fno8iItw6RZUoKLSRaXqOCCKEVYZEtw8akUUVs5WlzCKfyBYOfsQZw0aRKefPJJ/PSnPw2pfVFREYYMGYJnn30Wo0aNwvz58/Gzn/0Mzz33nL/N6tWrMXfuXMyaNQujR49GUVERevTogXXr1gGAP5nb6tWrcdtttyE9PR3r16/Hzp07sWvXroDn6927NxITE/3FZrNpGp/mADGU6PZSHo8HP/rRj/Dll1/i7bffxqFDh/DKK69gwIABWp+aiIgiVG5uLv74xz9i48aNiI2Nhcvlgsvlwvnz5wEAcXFxmD17NpxOJz766CNUVlZi1qxZyMzM1JTBlIiIjEsxcAFa97JfWi7d594RFRUVAUnYACArK8ufhM3j8aCysjKgjclkgsPh8LfRkswtNzcXffr0wfjx47Fu3TooGr8A1Zyk5tLoFmiNiEtKSrBu3TosWbIkqP26devwn//8Bzt37kT0hW9yBw8erPVpiYgogq1duxYAcOuttwbUr1+/Hvfeey8A4LnnnoPJZMK0adPgdruRlZWFF198sYt7SkREelFkCYrG/X5doa1P39zLnp+fjxUrVnT4/C6XCwkJCQF1CQkJaGxsxPnz53Hq1Cn4fD5hm4MHD/rPEUoyt5UrV+K2225Djx498Le//Q2//OUvcfbsWfzqV78Kub+aAsS26DYvL89f983o9pv++te/IjMzE7m5ufjLX/6Cvn374q677sIjjzwCs9ms5emJiChChfLtpc1mQ2FhYch7OoiIKMIYNIspLvSptrb24nYFtG5liDTLli3z//vaa69FU1MTfvOb32gKEDUtMT158qRqdKuWhvzo0aN4++234fP58P7772PZsmV49tln8eSTT6o+j9vtDpriJSIiIiKiyCXa+m2UAiBoX3tnBYiJiYlB2Ubr6+tht9sRExODPn36wGw2C9u0JWprTzI3AMjIyMBXX32labms7llMZVlGv3798PLLLyM9PR3Tp0/HY489hqKiItVjmLqciIiIiKh7CXcims5KUqNVZmZmQBI2ACgrK/MnYbNYLEhPTw9oI8syysvL/W3ak8wNAKqqqnDFFVdoCnY1LTENJbr9pv79+yM6OjpgOemoUaPgcrng8XhgsViCjsnLy4PT6fT/3NjYyCCRiIhCEx0FmDRvsTcOLZlFOyELqSI4h9pnJS1tIapXbStYgqyS7VPSkAVUy9XpyuyinfFcHT2HCV03XiPTkiFV7ZqbTXJQnSXKJ2zriQ6ul23iPsiCj+lKtNp/ztDqZCm4r11KkS7zphFGGvt09uxZHDlyxP9zdXU1qqqqEB8fj5SUFOTl5eHYsWN4/fXXAQDz5s3DCy+8gMWLF+O+++7Dhx9+iDfffBMlJSX+czidTuTk5GDcuHEYP3481qxZg6amJn/el0uTucXHx8Nut2PBggUBydzeffdd1NfX4/rrr4fNZkNZWRmefvppPPTQQ5rGp+kv6KXRbdv9NNqi2/nz5wuPufHGG7Fx40bIsgyTqXXC8p///Cf69+8vDA6B1vW+kbjml4iIiIiIxBS5tRiN1j7t3bsXEyZM8P/cNrGVk5OD4uJi1NXVoaamxv/4kCFDUFJSgkWLFuH555/HwIED8eqrryIrK8vfZvr06Thx4gSWL18Ol8uFtLQ0lJaWBmzt+7ZkbtHR0SgsLMSiRYugKAqGDx/uTzCqhaRozHu6efNm5OTk4KWXXvJHt2+++SYOHjyIhIQEzJw5EwMGDEBBQQGA1s2e3//+95GTk4MFCxbg8OHDuO+++/CrX/0Kjz32WEjP2djYiLi4ODQ0NARsHCUiIr5Htmm7Do4hCxBluvglY8vRL0M/iabZO/EuDUmQgE0yq+zoENynUbKKvzyVBF+cKj3E97ZSegbXt8SKv3j1xAX3wR0n7q+o3hMnbAqvPfjjRUus+FOY0rMlqC46xitsa7MF11tVZmusUcHnjRLM9gDiWSDV+yMKpmbUZjZF5+jOM4gmnWao9JrhVZtBlBXB77osTq54viX4/9BZt/j/29nzwfWeZvF8jSyq93VwBvF8M75yLu/yvxdt788pLy+HSeV9K5zkc82ouX/ld/7vaBvNa3C+LbqtqanxzxQCrelit23bhkWLFmHMmDEYMGAAHnzwQTzyyCOdNwoiIiIiIjI+rm42vHZt0pg/f77qktLt27cH1WVmZmLXrl3teSoiIiIiIuoGuiIhTHsYsU/hFMG7+ImIiIIpNisUcyfvYxcsJ5VMHf9AIXVxQpruSlPiGi1LQVWmOkTnUF2OqqFvXZkopzPotZxUpDOSyXSU2nlFy5YtguXNAGCzhH4DAa85+PkUlSWmwgBHlPvJJO5Xl1FgzBlEI/YpjBggEhERERGR/rpJFtPujgEiERERERHpjzOIEYEBIhERERER6Y8ziBGBASIREREREelOUVqL0RixT+HEAJGIiIiIiPQnS63FaIzYpzBigEhERN2KHBMF2Rx84+qwMoWeuVATlcymiqherQuiU6h9VtLyGUrDeUXdVUvaKqrWkkFUS4ZLLRlPteiMLJt63ei+O9CS8VSNKEOr2odm2eQLquuh8hYk6lmUWZwNtkWQ8bRFDv29RJTZ1Gf2hHy8HiSltRiNEfsUTgwQiYiIiIhIf0xSExEYIBIRERERkf6YpCYiMEAkIiIiIiL9cQYxIjBAJCIiIiIi/ckXitEYsU9hxACRiIi6FZ81ClLUxT9vOqWH0Y9k4B5rSDwj+kJeUckEIUoGoyVBTGckk+lo0hdNiXIibLpClLDlu0jtOghyySDKJG4bLUhoI1vE/4l8SvCJO5qAp8Xsxj87dIYO4hLTiMAAkYiIiIiIdMcsppGBASIREREREemPexAjAgNEIiIiIiLSnQRjztZxgWkgBohERERERKQ/7kGMCAwQiYiIiIhIf1xiGhEYIBIRUbfi66FDFlNFlJHQ3BlnDp2k4RtuwaAVleMFiRJV2wqPV2uqIeOpqF6tC6KMpWoZRPVqK8xMqtZWwydPZgs1NrXXOMrUEnpbwWus9rqbdViL6YGn08+pCQPEiMAAkYiIiIiIdMcsppGBASIREREREelPhjFvSm/EPoURA0QiIiIiItIdZxAjAwNEIiIiIiLSH7OYRgQGiERE1K202ExA9MXMK9Fh7IvutCSu0fL5R6WtXp+hRAliRHWXqw+VWtIYTYlnRG2ZjEYzWZQhySBEr5GWxDMxZq+wbc8od8htbabg+mjJJ2wbqmZZ/FxdhklqIoJx/2cSEREREVG3IcnGLVp8/PHHmDp1KpKSkiBJErZu3fqtx2zfvh1jx46F1WrF8OHDUVxcHNSmsLAQgwcPhs1mQ0ZGBvbs2RPweHNzM3Jzc3HllVeiV69emDZtGurr6wPa1NTUYMqUKejRowf69euHhx9+GC0twZl2L4cBIhERERER6U+5uA/RSEXrDGJTUxNSU1NRWFgYUvvq6mpMmTIFEyZMQFVVFRYuXIg5c+Zg27Zt/jabN2+G0+lEfn4+9u3bh9TUVGRlZeH48eP+NosWLcK7776Lt956Czt27MDXX3+NO+64w/+4z+fDlClT4PF4sHPnTvzhD39AcXExli9frml8XGJKRERERET66yZLTCdNmoRJkyaF3L6oqAhDhgzBs88+CwAYNWoUPvnkEzz33HPIysoCAKxevRpz587FrFmz/MeUlJRg3bp1WLJkCRoaGvDaa69h48aNuO222wAA69evx6hRo7Br1y5cf/31+Nvf/oZ//OMf+OCDD5CQkIC0tDQ88cQTeOSRR7BixQpYLJaQ+ssZRCIiIiIi0p9i4AKgsbExoLjdwXtG26OiogIOhyOgLisrCxUVFQAAj8eDysrKgDYmkwkOh8PfprKyEl6vN6DNyJEjkZKS4m9TUVGBa665BgkJCQHP09jYiL///e8h95cBIhERERER6S7cS0kvu8wUQHJyMuLi4vyloKCgU8btcrkCgjYASEhIQGNjI86fP4+TJ0/C5/MJ27hcLv85LBYLevfufdk2onO0PRYqLjElIqJuxWc1QYoO4/efJg2pPrW01aIrs5uqHa+lbQepnVaUdVItC6qmLKaC9WhaMpOqnbcryV2c1t/IGUs7ymoOTgAiylYKAFdEnwuui2oSto01NQef1xT6jJbod/Kct2NZUDvM4EtMa2trYbfb/dVWqzVMHQovBohERERERKS7S2frjKStT3a7PSBA7CyJiYlB2Ubr6+tht9sRExMDs9kMs9ksbJOYmOg/h8fjwenTpwNmEb/Z5puZT9vO2dYmFN336xwiIiIiIjKWcO81VNl/qKfMzEyUl5cH1JWVlSEzMxMAYLFYkJ6eHtBGlmWUl5f726SnpyM6OjqgzaFDh1BTU+Nvk5mZiQMHDgRkPi0rK4Pdbsfo0aND7i9nEImIiIiISH8GX2IaqrNnz+LIkSP+n6urq1FVVYX4+HikpKQgLy8Px44dw+uvvw4AmDdvHl544QUsXrwY9913Hz788EO8+eabKCkp8Z/D6XQiJycH48aNw/jx47FmzRo0NTX5s5rGxcVh9uzZcDqdiI+Ph91ux4IFC5CZmYnrr78eADBx4kSMHj0a99xzD5555hm4XC4sXboUubm5mpbLMkAkIiIiIiLdGX2Jaaj27t2LCRMm+H92Op0AgJycHBQXF6Ourg41NTX+x4cMGYKSkhIsWrQIzz//PAYOHIhXX33Vf4sLAJg+fTpOnDiB5cuXw+VyIS0tDaWlpQFJZ5577jmYTCZMmzYNbrcbWVlZePHFF/2Pm81mvPfee3jggQeQmZmJnj17IicnBytXrtR4PRTFgC9ToMbGRsTFxaGhoUGXdcFERJGM75Gt2q7D2OlPwmyx+evj/rir4yc3mYOqJLUEM2ZBW0EdAEiW6OBKlW95JVtwvdLDJmgJyD2D23rt4vN64oK/K3bbxTtQPHHBY/bECZvCYw/+eOGLFSfIMPXyBtXZYjzCtj2swW1jooPrAMAaFZw8JNqk0gedktQYISGNFnolr4m0JDWi19NiFv/uxAoS0sRbxIlnEqIbg+qSok8J28abzwbV2QWJa9SIxtB0Robjmtou/3vR9v581UNPw2wVv2+Fk8/djH/+9tHv/N/RNpxBJCIiIiIi/XWTJabdHQNEIiIiIiLSHwPEiNCu+f7CwkIMHjwYNpsNGRkZQelU1WzatAmSJCE7O7s9T0tERERERBGqK298r7XQRZoDxM2bN8PpdCI/Px/79u1DamoqsrKyAtKpinz55Zd46KGHcPPNN7e7s0REFJk+/vhjTJ06FUlJSZAkCVu3bg14XFEULF++HP3790dMTAwcDgcOHz4cns4SEZE+ZAMX8tMcIK5evRpz587FrFmzMHr0aBQVFaFHjx5Yt26d6jE+nw933303Hn/8cQwdOrRDHSYiosjT1NSE1NRUFBYWCh9/5pln8Lvf/Q5FRUXYvXs3evbsiaysLDQ3h56QgYiIjC3cs4ScQQyNpj2IHo8HlZWVyMvL89eZTCY4HA5UVFSoHrdy5Ur069cPs2fPxv/8z/986/O43W643RczQjU2Bmd8IiKiyDFp0iRMmjRJ+JiiKFizZg2WLl2Kn/zkJwCA119/HQkJCdi6dSvuvPNOTc/li5aAaH0yMUYMSZ/x65TgUkhtCJJOn+SEWUxVNiaJskN2RrZSLefQK9todyAj9Guj9hpraSv6fbCZxJl1e5mDv/QSZSsFgMSoM0F1sVJwZl4AMAuGLJoFOmMO81QZ9yBGBE0ziCdPnoTP5wu4HwcAJCQkwOVyCY/55JNP8Nprr+GVV14J+XkKCgoQFxfnL8nJyVq6SUREEaS6uhoulwsOh8NfFxcXh4yMjMt++eh2u9HY2BhQiIjIuMI9S8gZxNDoelOaM2fO4J577sErr7yCPn36hHxcXl4eGhoa/KW2tlbHXhIRUTi1fcGo5ctHgF8mEhFFHMXAhfw0LTHt06cPzGYz6uvrA+rr6+uRmJgY1P6LL77Al19+ialTp/rrZLl1ajsqKgqHDh3CsGHDgo6zWq2wqtwomIiICGj9MtHpdPp/bmxsZJBIRGRgRp2tM2KfwknTDKLFYkF6ejrKy8v9dbIso7y8HJmZmUHtR44ciQMHDqCqqspffvzjH2PChAmoqqriH3IiIvJ/wRjql49trFYr7HZ7QCEiIgML9ywhZxBDomkGEQCcTidycnIwbtw4jB8/HmvWrEFTUxNmzZoFAJg5cyYGDBiAgoIC2Gw2XH311QHH9+7dGwCC6omI6LtpyJAhSExMRHl5OdLS0gC0zgbu3r0bDzzwgObzKVGtxVB0ShqjGw3dVc2VIvpKXuVr+o5eHrXkLloSkHQlvRLadEbiGr3OqxctCWn0YhZcs2jJJ2zb0+QOqrObxNmae5uCE9LEmcziPgiug0kwDxRlCm+SGs4gRgbNf0KnT5+OEydOYPny5XC5XEhLS0Npaal/70hNTQ1MJl23NhIRUYQ5e/Ysjhw54v+5uroaVVVViI+PR0pKChYuXIgnn3wS3/ve9zBkyBAsW7YMSUlJyM7ODl+niYiocxl1ts6IfQqjdn3HOn/+fMyfP1/42Pbt2y97bHFxcXuekoiIItjevXsxYcIE/89tewdzcnJQXFyMxYsXo6mpCffffz9Onz6Nm266CaWlpbDZbOHqMhERdTJJAQR3BQk7ziAGMtoiHCIi6oZuvfVWKIr6X2BJkrBy5UqsXLmyC3tFRERdijOIEYEBIhERERER6Y57ECMDA0QiIiIiItIfZxAjAgNEIiLqVhRTa+l2OprqUzXbaMdOG2k6I4ModR9qWVC1vIWIsuWqZdCNloIzk6plPLUJ/s/3kCwqfQhua5aCR9HCLKZCRuxTODFAJCIiIiIi/ckXitEYsU9hxACRiIiIiIh0xxnEyMAAkYiIiIiI9Mc9iBGBASIREREREelOUhRIl7nlUbgYsU/hxACRiIgoXARJJLQd/x3LMEO6kxX+TkUiUUIaI5Lk1mI0RuxTODFAJCIiIiIi/XGJaURggEhERERERLpjkprIwACRiIiIiIj0xxnEiBAZC5aJiIiIiCiitc0gGrFoVVhYiMGDB8NmsyEjIwN79uxRbev1erFy5UoMGzYMNpsNqampKC0tDWhz5swZLFy4EIMGDUJMTAxuuOEGfPrppwFt6uvrce+99yIpKQk9evTA7bffjsOHDwe0ufXWWyFJUkCZN2+eprExQCQiIiIiIv0pFxPVGKlonUHcvHkznE4n8vPzsW/fPqSmpiIrKwvHjx8Xtl+6dCleeukl/P73v8c//vEPzJs3Dz/96U+xf/9+f5s5c+agrKwMGzZswIEDBzBx4kQ4HA4cO3as9dIpCrKzs3H06FH85S9/wf79+zFo0CA4HA40NTUFPN/cuXNRV1fnL88884ym8TFAJCKi7kX5RtFCksSFyGBkRQoqRiArJmExKhMUYekoGZKweJUoQTELi0dRgopb8YZcvIpPWMJKUYxbNFi9ejXmzp2LWbNmYfTo0SgqKkKPHj2wbt06YfsNGzbg0UcfxeTJkzF06FA88MADmDx5Mp599lkAwPnz5/HnP/8ZzzzzDG655RYMHz4cK1aswPDhw7F27VoAwOHDh7Fr1y6sXbsW1113HUaMGIG1a9fi/Pnz+NOf/hTwfD169EBiYqK/2O12TeMz7v9YIiIiIiLqNsK9jPTblpg2NjYGFLfbHTQGj8eDyspKOBwOf53JZILD4UBFRYVw3G63GzabLaAuJiYGn3zyCQCgpaUFPp/vsm3a+nJpG5PJBKvV6m/T5o033kCfPn1w9dVXIy8vD+fOnQvl5bl4Xk2tiYiIiIiI2uObKzyMVAAkJycjLi7OXwoKCoKGcPLkSfh8PiQkJATUJyQkwOVyCYedlZWF1atX4/Dhw5BlGWVlZXjnnXdQV1cHAIiNjUVmZiaeeOIJfP311/D5fPjjH/+IiooKf5uRI0ciJSUFeXl5OHXqFDweD37961/jq6++8rcBgLvuugt//OMf8dFHHyEvLw8bNmzAf/3Xf13+dfkGZjElIiIiIiLdST5AMuD0lHRh5W1tbW3Ackyr1dop53/++ecxd+5cjBw5EpIkYdiwYZg1a1bAktQNGzbgvvvuw4ABA2A2mzF27FjMmDEDlZWVAIDo6Gi88847mD17NuLj42E2m+FwODBp0iQolyyRvf/++/3/vuaaa9C/f3/88Ic/xBdffIFhw4aF1F8DvkRERERERNTdhHsZ6bctMbXb7QFFFCD26dMHZrMZ9fX1AfX19fVITEwUjrtv377YunUrmpqa8K9//QsHDx5Er169MHToUH+bYcOGYceOHTh79ixqa2uxZ88eeL3egDbp6emoqqrC6dOnUVdXh9LSUvz73/8OaPNNGRkZAIAjR46E/DpxBpGIiLoVyXfx22DDU+QOHq8tsQJBNZmLqQvvlG2EhDJG6ENnECWUkdG1Y/MKkvB4FbOwbbMcHVTXKNsELYEG2SOobRG2NQvqzYIEW2fkDr7ndFQ7EsJ0CQ19slgsSE9PR3l5ObKzswEAsiyjvLwc8+fPv+yxNpsNAwYMgNfrxZ///Gf8/Oc/D2rTs2dP9OzZE6dOncK2bduEGUjj4uIAtCau2bt3L5544gnV56yqqgIA9O/fP8QRMkAkIiIiIqIu0N57DupNa5+cTidycnIwbtw4jB8/HmvWrEFTUxNmzZoFAJg5cyYGDBjg38O4e/duHDt2DGlpaTh27BhWrFgBWZaxePFi/zm3bdsGRVEwYsQIHDlyBA8//DBGjhzpPycAvPXWW+jbty9SUlJw4MABPPjgg8jOzsbEiRMBAF988QU2btyIyZMn48orr8T//d//YdGiRbjlllswZsyYkMfHAJGIiIiIiPR3SUIYQ9HYp+nTp+PEiRNYvnw5XC4X0tLSUFpa6k9cU1NTA5Pp4sxyc3Mzli5diqNHj6JXr16YPHkyNmzYgN69e/vbNDQ0IC8vD1999RXi4+Mxbdo0PPXUU4iOvjjrXFdXB6fTifr6evTv3x8zZ87EsmXL/I9bLBZ88MEH/oA1OTkZ06ZNw9KlSzWNT1IUI87zBmpsbERcXBwaGho038eDiKi743tkq7brMGbmUzBbLi7ZuvI1cdpxIbV7HgqyKkgmlbbm4KVlUpT4+1gpWlCvkhRBiglehqbEiNvKvYLbeu0WYVtPXHAf3HZxigJ3XPCYPXHCpvDag5ey+WLFa3/NvYKXx9liRMvrgB7W4Poe0V5hW6tZsOzOJF5iFyWoV7sfnkkStDXitMhl6LXE1Aj3PNSyxFTLa2wT/D4BQGx0c1BdorVR2DbF8u+guuTo4DoAGBAVfI44k8r/IVGdaInpGRlXjarv8r8Xbe/P109eiaho8ZLacGrxNmPX+8u/839H23AGkYiIiIiIdNddlph2dwwQiYiIiIhIf91kiWl3xwCRiIi6FbMXMHePBI2BOrojRO3wDp5W9Zt30RJG3ZY1is8rWmoozi2pXx+MsPS0u2QsDZXastGOUlu6KlpWK8pWCgBnBBlLT8s9hG0tvuDlpM1K8HJWAIgWLIk1C67D2TBneOYMYmRggEhERERERPqTldZiNEbsUxgxQCQiIiIiIt1JCiCY7Aw7ziAGYoBIRERERET6U5SOL5fXgxH7FEYMEImIiIiISHfcgxgZGCASEVG3YmpRDJEYJKx0Smij12UVdVdtCEoHE66oJrQR1as9lSApiei+eWrn1ev387uWjKarqV1ftxyc+ui8T5yk5lRLz6C6aEmcOcarBH9MjzWdF7Y1h5ik5lyLD0Cd8BxdgllMIwIDRCIiIiIi0p3kUyAZ8As8yWe8PoUTA0QiIiIiItKdpCiQDLjfz4h9CicGiEREREREpD8uMY0IDBCJiIiIiEh/zGIaEYJ3WYegsLAQgwcPhs1mQ0ZGBvbs2aPa9pVXXsHNN9+MK664AldccQUcDsdl2xMRERERUffTlsXUiIUu0jyDuHnzZjidThQVFSEjIwNr1qxBVlYWDh06hH79+gW13759O2bMmIEbbrgBNpsNv/71rzFx4kT8/e9/x4ABAzplEERERG1MHgVmo30brFd/5NDPq7bHpsN7bzoj46mGtqIha8lMalZJ9Kkl26gsSm8qyGzaeo7g7JLMNmocatlnRdRetxZhFlOLyvOF/st+Tg4+h03qpXJeURbT4Lrz3paQn18PTFITGTTPIK5evRpz587FrFmzMHr0aBQVFaFHjx5Yt26dsP0bb7yBX/7yl0hLS8PIkSPx6quvQpZllJeXd7jzREREREQUIdqWmBqxkJ+mANHj8aCyshIOh+PiCUwmOBwOVFRUhHSOc+fOwev1Ij4+XrWN2+1GY2NjQCEiIiIiogimGLiQn6YA8eTJk/D5fEhISAioT0hIgMvlCukcjzzyCJKSkgKCzG8qKChAXFycvyQnJ2vpJhERERERGUzbbS6MWOiidiWpaa9Vq1Zh06ZN2LJlC2w2m2q7vLw8NDQ0+EttbW0X9pKIiIiIiDqdrAA+AxYN+7m/CzQlqenTpw/MZjPq6+sD6uvr65GYmHjZY3/7299i1apV+OCDDzBmzJjLtrVarbBarVq6RkREBAAwuxWYI/mPvVrftYypg9+Gq+aQENV3RltRUw2JXNROK0oqonpewaDVkpJoSTQiC5LXaEmMEmnUxia6Dl1Nr+veIhjbeV+0sK3od0qU5AYAzpqCPwtHmcRjECWkEXG7vSG104tRZ+uM2Kdw0vS/1WKxID09PSDBTFvCmczMTNXjnnnmGTzxxBMoLS3FuHHj2t9bIiIiIiKKTArCn4xGWMJ9YYxF820unE4ncnJyMG7cOIwfPx5r1qxBU1MTZs2aBQCYOXMmBgwYgIKCAgDAr3/9ayxfvhwbN27E4MGD/XsVe/XqhV69xKl6iYiIiIiomzFqxlAj9imMNAeI06dPx4kTJ7B8+XK4XC6kpaWhtLTUn7impqYGJtPFicm1a9fC4/HgZz/7WcB58vPzsWLFio71noiIiIiIIoMMiG4jGnbdd9V3u2gOEAFg/vz5mD9/vvCx7du3B/z85ZdftucpiIjoO6qwsBC/+c1v4HK5kJqait///vcYP358uLtFREQdJMkyJAPuwZVk4/UpnMK/Y5iIiOiCzZs3w+l0Ij8/H/v27UNqaiqysrJw/PjxcHeNiIg6Kux7DS9TyK9dM4hERER6WL16NebOnevf115UVISSkhKsW7cOS5YsCekcZq8MsxLGb4NF2UbFSQo7nlpdy4eaTsg2qiF5pyaizKJq2Ua1ZDcVkVXWt0kdPK/asjmT4GJqyejZXTKedodxqGW1FfGoZCbVkvFUS7bcUK+v1+0J+Zy6MGowZsQ+hRFnEImIyBA8Hg8qKyvhcDj8dSaTCQ6HAxUVFUHt3W43GhsbAwoRERmYbOCiUWFhIQYPHgybzYaMjAzs2bNHta3X68XKlSsxbNgw2Gw2pKamorS0NKDNmTNnsHDhQgwaNAgxMTG44YYb8Omnnwa0qa+vx7333oukpCT06NEDt99+Ow4fPhzQprm5Gbm5ubjyyivRq1cvTJs2LegWhd+GASIRERnCyZMn4fP5/EnP2iQkJPgzYF+qoKAAcXFx/pKcnNxVXSUionaQZNmwRQut2yGWLl2Kl156Cb///e/xj3/8A/PmzcNPf/pT7N+/399mzpw5KCsrw4YNG3DgwAFMnDgRDocDx44dAwAoioLs7GwcPXoUf/nLX7B//34MGjQIDocDTU1N/vMsWrQI7777Lt566y3s2LEDX3/9Ne644w5N42OASEREESkvLw8NDQ3+UltbG+4uERHR5ciKcYsGl26HGD16NIqKitCjRw+sW7dO2H7Dhg149NFHMXnyZAwdOhQPPPAAJk+ejGeffRYAcP78efz5z3/GM888g1tuuQXDhw/HihUrMHz4cKxduxYAcPjwYezatQtr167FddddhxEjRmDt2rU4f/48/vSnPwEAGhoa8Nprr2H16tW47bbbkJ6ejvXr12Pnzp3YtWtXyOPjHkQiIjKEPn36wGw2By2Fqa+vR2JiYlB7q9UKq9Xq/1m5sIekpaU5oJ2keDX0Qm2PUfD3qep71kRtxd9OC+tllT1ygqaKT9wD2Rd8jpYW8QegFm/wRwGfR7x/yucJPq/PrdKHZsHeu2hxhyVTS/B5VV43ny+4vsUrbmsyC57PJH4tFMEFVtsDJgleIrW2oj2IWnSHvXvdnei1V/19ELVV+R3RZQ9iU+seRCVce+4Mvgfxm1sVvvl3Bri4HSIvL89fd7ntEEDrlgibzRZQFxMTg08++QQA0NLSAp/Pd9k2bnfrm+2lbUwmE6xWKz755BPMmTMHlZWV8Hq9AVs1Ro4ciZSUFFRUVOD666//9msBBohERGQQFosF6enpKC8vR3Z2NgBAlmWUl5er3lrpUmfOnAEA7N6xqv2d0JLIRcvndi0xKhGRzs6cOYO4uLgwPLNBA8QLb/Lf3Kogum/75bZDHDx4UHj2rKwsrF69GrfccguGDRuG8vJyvPPOO/D5Wr/Eio2NRWZmJp544gmMGjUKCQkJ+NOf/oSKigoMHz4cwMVALy8vDy+99BJ69uyJ5557Dl999RXq6uoAAC6XCxaLBb179w7qm2irhhoGiEREZBhOpxM5OTkYN24cxo8fjzVr1qCpqcmf1fRykpKSUFtbi9jYWJw5cwbJycmora2F3W7vgp53ncbGRo4tAnFskam7jU1RFJw5cwZJSUnh6oAxA8QLffrm6/zN2cP2ev755zF37lyMHDkSkiRh2LBhmDVrVsCS1A0bNuC+++7DgAEDYDabMXbsWMyYMQOVlZUAgOjoaLzzzjuYPXs24uPjYTab4XA4MGnSpE6fEWaASEREhjF9+nScOHECy5cvh8vlQlpaGkpLS4O+qRUxmUwYOHAgAEC6sAbQbrd3iw91IhxbZOLYIlN3Glt4Zg4v8PnU18aHk9zap1BeZ63bIQCgb9++2Lp1K5qbm/Hvf/8bSUlJWLJkCYYOHepvM2zYMOzYsQNNTU1obGxE//79MX369IA26enpqKqqQkNDAzweD/r27YuMjAyMGzcOAJCYmAiPx4PTp08HzCJerm8iTFJDRESGMn/+fPzrX/+C2+3G7t27kZGREe4uERFRZ+jKG99rLSG6dDtEm7btEJmZmZc91mazYcCAAWhpacGf//xn/OQnPwlq07NnT/Tv3x+nTp3Ctm3bhG3i4uLQt29fHD58GHv37vW3SU9PR3R0dEDfDh06hJqamm/t26U4g0hERERERPqTFahv9g4jjVlMv207xMyZMzFgwAAUFBQAAHbv3o1jx44hLS0Nx44dw4oVKyDLMhYvXuw/57Zt26AoCkaMGIEjR47g4YcfxsiRIwO2WLz11lvo27cvUlJScODAATz44IPIzs7GxIkTAbQGjrNnz4bT6UR8fDzsdjsWLFiAzMzMkBPUAAwQiYioG7JarcjPz++0/SNGwrFFJo4tMnXnsYWFwfcghurbtkPU1NTAZLq4ULO5uRlLly7F0aNH0atXL0yePBkbNmwIWAba0NCAvLw8fPXVV4iPj8e0adPw1FNPITo62t+mrq4OTqcT9fX16N+/P2bOnIlly5YF9O25556DyWTCtGnT4Ha7kZWVhRdffFHT+CQlbHluQ9fY2Ii4uDg0NDR0m/XfRESdhe+RRERkZG1/pxz9f4EokyXc3QnSInvwQd1L/Dt6AWcQiYiIiIhIfwZPUkOtGCASEREREZH+uskS0+6OASIREREREemPAWJEYIBIRERERET66yZZTLs73geRiIi6lcLCQgwePBg2mw0ZGRnYs2dPuLvULh9//DGmTp2KpKQkSJKErVu3BjyuKAqWL1+O/v37IyYmBg6HA4cPHw5PZzUoKCjAddddh9jYWPTr1w/Z2dk4dOhQQJvm5mbk5ubiyiuvRK9evTBt2rSgm1Ib0dq1azFmzBj/zbYzMzPx3//93/7HI3VcIqtWrYIkSVi4cKG/LlLHt2LFCkiSFFBGjhzpfzxSx2VEiuyD4jNg4R7EAAwQiYio29i8eTOcTify8/Oxb98+pKamIisrC8ePHw931zRrampCamoqCgsLhY8/88wz+N3vfoeioiLs3r0bPXv2RFZWFpqbm7u4p9rs2LEDubm52LVrF8rKyuD1ejFx4kQ0NTX52yxatAjvvvsu3nrrLezYsQNff/017rjjjjD2OjQDBw7EqlWrUFlZib179+K2227DT37yE/z9738HELnj+qZPP/0UL730EsaMGRNQH8nj+/73v4+6ujp/+eSTT/yPRfK4DKcrb3yvtZAfb3NBRBTh+B55UUZGBq677jq88MILAABZlpGcnIwFCxZgyZIlYe5d+0mShC1btiA7OxtA6+xhUlIS/t//+3946KGHALTeQyshIQHFxcW48847w9hbbU6cOIF+/fphx44duOWWW9DQ0IC+ffti48aN+NnPfgYAOHjwIEaNGoWKigpNN3s2gvj4ePzmN7/Bz372s24xrrNnz2Ls2LF48cUX8eSTTyItLQ1r1qyJ6NdtxYoV2Lp1K6qqqoIei+RxGUnb36kfxt6NKMmAt7lQPCg/8wb/jl7AGUQiIuoWPB4PKisr4XA4/HUmkwkOhwMVFRVh7Fnnq66uhsvlChhrXFwcMjIyIm6sDQ0NAFoDKQCorKyE1+sNGNvIkSORkpISUWPz+XzYtGkTmpqakJmZ2W3GlZubiylTpgSMA4j81+3w4cNISkrC0KFDcffdd6OmpgZA5I/LcMI9S8gZxJAwSQ0REXULJ0+ehM/nQ0JCQkB9QkICDh48GKZe6cPlcgGAcKxtj0UCWZaxcOFC3Hjjjbj66qsBtI7NYrGgd+/eAW0jZWwHDhxAZmYmmpub0atXL2zZsgWjR49GVVVVRI8LADZt2oR9+/bh008/DXoskl+3jIwMFBcXY8SIEairq8Pjjz+Om2++GZ999llEj8uIFFmGIsnh7kYQRTFen8KJASIRERGFRW5uLj777LOA/V6RbsSIEaiqqkJDQwPefvtt5OTkYMeOHeHuVofV1tbiwQcfRFlZGWw2W7i706kmTZrk//eYMWOQkZGBQYMG4c0330RMTEwYe9YN+WTAgAEiGCAG4BJTIiLqFvr06QOz2RyUXbC+vh6JiYlh6pU+2sYTyWOdP38+3nvvPXz00UcYOHCgvz4xMREejwenT58OaB8pY7NYLBg+fDjS09NRUFCA1NRUPP/88xE/rsrKShw/fhxjx45FVFQUoqKisGPHDvzud79DVFQUEhISInp8l+rduzeuuuoqHDlyJOJfN8NRlNZgzHCFS0wvxQCRiIi6BYvFgvT0dJSXl/vrZFlGeXk5MjMzw9izzjdkyBAkJiYGjLWxsRG7d+82/FgVRcH8+fOxZcsWfPjhhxgyZEjA4+np6YiOjg4Y26FDh1BTU2P4sYnIsgy32x3x4/rhD3+IAwcOoKqqyl/GjRuHu+++2//vSB7fpc6ePYsvvvgC/fv3j/jXzWgUWTFsoYu4xJSIiLoNp9OJnJwcjBs3DuPHj8eaNWvQ1NSEWbNmhbtrmp09exZHjhzx/1xdXY2qqirEx8cjJSUFCxcuxJNPPonvfe97GDJkCJYtW4akpCR/plOjys3NxcaNG/GXv/wFsbGx/n1ccXFxiImJQVxcHGbPng2n04n4+HjY7XYsWLAAmZmZhs8YmZeXh0mTJiElJQVnzpzBxo0bsX37dmzbti2ixwUAsbGx/n2ibXr27Ikrr7zSXx+p43vooYcwdepUDBo0CF9//TXy8/NhNpsxY8aMiH/dDEeRARhwOSeXmAZggEhERN3G9OnTceLECSxfvhwulwtpaWkoLS0NSuYSCfbu3YsJEyb4f3Y6nQCAnJwcFBcXY/HixWhqasL999+P06dP46abbkJpaanh94etXbsWAHDrrbcG1K9fvx733nsvAOC5556DyWTCtGnT4Ha7kZWVhRdffLGLe6rd8ePHMXPmTNTV1SEuLg5jxozBtm3b8KMf/QhA5I4rVJE6vq+++gozZszAv//9b/Tt2xc33XQTdu3ahb59+wKI3HEZkdfXDAXGuyl9C7zh7oKh8D6IREQRju+RRERkZM3NzRgyZIihM78mJiaiurra8F+ydQXOIBIRERERkW5sNhuqq6vh8XjC3RVVFouFweEFDBCJiIiIiEhXNpuNAViEYBZTIiIiIiIiAsAAkYiIiIiIiC5ggEhEREREREQAGCASERERERHRBQwQiYiIiIiICEA7A8TCwkIMHjwYNpsNGRkZ2LNnz2Xbv/XWWxg5ciRsNhuuueYavP/+++3qLBEREREREelHc4C4efNmOJ1O5OfnY9++fUhNTUVWVhaOHz8ubL9z507MmDEDs2fPxv79+5GdnY3s7Gx89tlnHe48ERERERERdR5JURRFywEZGRm47rrr8MILLwAAZFlGcnIyFixYgCVLlgS1nz59OpqamvDee+/5666//nqkpaWhqKgopOdsbGxEXFwcGhoaYLfbtXSXiKjb43skERERdZYoLY09Hg8qKyuRl5fnrzOZTHA4HKioqBAeU1FRAafTGVCXlZWFrVu3qj6P2+2G2+32/9zQ0ACg9UMQEREFantv1Ph9HxEREVEQTQHiyZMn4fP5kJCQEFCfkJCAgwcPCo9xuVzC9i6XS/V5CgoK8PjjjwfVJycna+kuEdF3yr///W/ExcWFuxtEREQUwTQFiF0lLy8vYNbx9OnTGDRoEGpqar7TH34aGxuRnJyM2tra7/QyMl4HXoM2vA6tGhoakJKSgvj4+HB3hYiIiCKcpgCxT58+MJvNqK+vD6ivr69HYmKi8JjExERN7QHAarXCarUG1cfFxX2nPwS2sdvtvA7gdQB4DdrwOrQymXjnIiIiIuoYTZ8mLBYL0tPTUV5e7q+TZRnl5eXIzMwUHpOZmRnQHgDKyspU2xMREREREVF4aF5i6nQ6kZOTg3HjxmH8+PFYs2YNmpqaMGvWLADAzJkzMWDAABQUFAAAHnzwQfzgBz/As88+iylTpmDTpk3Yu3cvXn755c4dCREREREREXWI5gBx+vTpOHHiBJYvXw6Xy4W0tDSUlpb6E9HU1NQELHO64YYbsHHjRixduhSPPvoovve972Hr1q24+uqrQ35Oq9WK/Px84bLT7xJeh1a8DrwGbXgdWvE6EBERUWfRfB9EIiIiIiIi6p6Y0YCIiIiIiIgAMEAkIiIiIiKiCxggEhEREREREQAGiERERERERHSBYQLEwsJCDB48GDabDRkZGdizZ89l27/11lsYOXIkbDYbrrnmGrz//vtd1FN9abkOr7zyCm6++WZcccUVuOKKK+BwOL71ukUCrb8LbTZt2gRJkpCdna1vB7uI1utw+vRp5Obmon///rBarbjqqqu6xf8LrddhzZo1GDFiBGJiYpCcnIxFixahubm5i3qrj48//hhTp05FUlISJEnC1q1bv/WY7du3Y+zYsbBarRg+fDiKi4t17ycRERFFPkMEiJs3b4bT6UR+fj727duH1NRUZGVl4fjx48L2O3fuxIwZMzB79mzs378f2dnZyM7OxmeffdbFPe9cWq/D9u3bMWPGDHz00UeoqKhAcnIyJk6ciGPHjnVxzzuP1mvQ5ssvv8RDDz2Em2++uYt6qi+t18Hj8eBHP/oRvvzyS7z99ts4dOgQXnnlFQwYMKCLe965tF6HjRs3YsmSJcjPz8fnn3+O1157DZs3b8ajjz7axT3vXE1NTUhNTUVhYWFI7aurqzFlyhRMmDABVVVVWLhwIebMmYNt27bp3FMiIiKKeIoBjB8/XsnNzfX/7PP5lKSkJKWgoEDY/uc//7kyZcqUgLqMjAzlF7/4ha791JvW6/BNLS0tSmxsrPKHP/xBry7qrj3XoKWlRbnhhhuUV199VcnJyVF+8pOfdEFP9aX1Oqxdu1YZOnSo4vF4uqqLXULrdcjNzVVuu+22gDqn06nceOONuvazKwFQtmzZctk2ixcvVr7//e8H1E2fPl3JysrSsWdERETUHYR9BtHj8aCyshIOh8NfZzKZ4HA4UFFRITymoqIioD0AZGVlqbaPBO25Dt907tw5eL1exMfH69VNXbX3GqxcuRL9+vXD7Nmzu6KbumvPdfjrX/+KzMxM5ObmIiEhAVdffTWefvpp+Hy+rup2p2vPdbjhhhtQWVnpX4Z69OhRvP/++5g8eXKX9NkouuN7JBEREXWNqHB34OTJk/D5fEhISAioT0hIwMGDB4XHuFwuYXuXy6VbP/XWnuvwTY888giSkpKCPhhGivZcg08++QSvvfYaqqqquqCHXaM91+Ho0aP48MMPcffdd+P999/HkSNH8Mtf/hJerxf5+fld0e1O157rcNddd+HkyZO46aaboCgKWlpaMG/evIhfYqqV2ntkY2Mjzp8/j5iYmDD1jIiIiIwu7DOI1DlWrVqFTZs2YcuWLbDZbOHuTpc4c+YM7rnnHrzyyivo06dPuLsTVrIso1+/fnj55ZeRnp6O6dOn47HHHkNRUVG4u9altm/fjqeffhovvvgi9u3bh3feeQclJSV44oknwt01IiIioogQ9hnEPn36wGw2o76+PqC+vr4eiYmJwmMSExM1tY8E7bkObX77299i1apV+OCDDzBmzBg9u6krrdfgiy++wJdffompU6f662RZBgBERUXh0KFDGDZsmL6d1kF7fhf69++P6OhomM1mf92oUaPgcrng8XhgsVh07bMe2nMdli1bhnvuuQdz5swBAFxzzTVoamrC/fffj8ceewwm03fjOzG190i73c7ZQyIiIrqssH9aslgsSE9PR3l5ub9OlmWUl5cjMzNTeExmZmZAewAoKytTbR8J2nMdAOCZZ57BE088gdLSUowbN64ruqobrddg5MiROHDgAKqqqvzlxz/+sT9zY3Jycld2v9O053fhxhtvxJEjR/wBMgD885//RP/+/SMyOATadx3OnTsXFAS2Bc2KoujXWYPpju+RRERE1EXCnSVHURRl06ZNitVqVYqLi5V//OMfyv3336/07t1bcblciqIoyj333KMsWbLE3/5///d/laioKOW3v/2t8vnnnyv5+flKdHS0cuDAgXANoVNovQ6rVq1SLBaL8vbbbyt1dXX+cubMmXANocO0XoNv6i5ZTLVeh5qaGiU2NlaZP3++cujQIeW9995T+vXrpzz55JPhGkKn0Hod8vPzldjYWOVPf/qTcvToUeVvf/ubMmzYMOXnP/95uIbQKc6cOaPs379f2b9/vwJAWb16tbJ//37lX//6l6IoirJkyRLlnnvu8bc/evSo0qNHD+Xhhx9WPv/8c6WwsFAxm81KaWlpuIZAREREEcIQAaKiKMrvf/97JSUlRbFYLMr48eOVXbt2+R/7wQ9+oOTk5AS0f/PNN5WrrrpKsVgsyve//32lpKSki3usDy3XYdCgQQqAoJKfn9/1He9EWn8XLtVdAkRF0X4ddu7cqWRkZChWq1UZOnSo8tRTTyktLS1d3OvOp+U6eL1eZcWKFcqwYcMUm82mJCcnK7/85S+VU6dOdX3HO9FHH30k/L/eNvacnBzlBz/4QdAxaWlpisViUYYOHaqsX7++y/tNREREkUdSlO/QuisiIiIiIiJSFfY9iERERERERGQMDBCJiIiIiIgIAANEIiIiIiIiuoABIhEREREREQFggEhEREREREQXMEAkIiIiIiIiAAwQiYiIiIiI6AIGiERERERERASAASIRERERERFdwACRiIiIiIiIADBAJCIiIiIiogsYIBIREREREREA4P8DuTzuZk9elAYAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "execution_count": 16 + "execution_count": 15 } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index e343e4bc..13321a2e 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -41,7 +41,7 @@ from timeit import default_timer as timer from collections import Counter -warnings.simplefilter("ignore") # todo: is this needed? +#warnings.simplefilter("ignore") # todo: is this needed? # ARGUMENT PARSING: this scipt is supposed to be called with arguments, detailling all simulation- and system-parameters @@ -77,7 +77,6 @@ parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, t_start will be calculated by PU/LU-conversion of step_start") parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions infered from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") -#TODO: check dimension and stencil match, OR: issue warning, choose stencil matching domain width parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") @@ -90,11 +89,16 @@ #TODO: add highMa reporter # put arguments in dictionary +print("SCRIPT: Writing arguments to dictionary...") args = vars(parser.parse_args()) +# print all arguments +print(f"SCRIPT: Input arguments are: \n{args}\n") + ########################################################### # CREATE timestamp, sim-ID, outdir and outdir_data +print("SCRIPT: Creating timestamt, simulation ID and creating output directory...") name = args["name"] outdir = args["outdir"] outdir_data = args["outdir_data"] @@ -117,31 +121,30 @@ outdir_data = outdir_data+"/"+sim_id if not os.path.exists(outdir_data): os.makedirs(outdir_data) # create output dir for large/many files, if specified - print(f"Outdir_DATA/simID = {outdir}/{sim_id}") - -# print all arguments -print(f"Input arguments: {args}") +print(f"outdir_DATA/simID = {outdir}/{sim_id}") # save input arguments/parameters to file in outdir: +print(f"SCRIPT: Writing input parameters to file: {outdir}/input_parameters.txt") output_file = open(outdir+"/input_parameters.txt", "a") for key in args: output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) output_file.close() ### SAVE SCRIPT: save this script to outdir -print(f"\nSaving simulation script to outdir...") +print(f"SCRIPT: Saving simulation script to outdir...") temp_script_name = sim_id + "_" + os.path.basename(__file__) shutil.copy(__file__, outdir+"/"+temp_script_name) -print(f"Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") +print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") # START LOGGER -> get all terminal output into file -old_stdout = sys.stdout -sys.stdout = Logger(outdir) +# print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") +# old_stdout = sys.stdout +# sys.stdout = Logger(outdir) ##################################### # PROCESS AND SET PARAMETERS - +print(f"SCRIPT: Processing parameters...") # calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients if args["reynolds_number"] > 1000: periodic_start = 0.4 @@ -163,10 +166,9 @@ dims = 2 else: # will be 3D dims = 3 - - if args["domain_width_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node + if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node - print("(!) setting domain_width_in_d to 1 lattice node") + print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_in_d to 1 lattice node") # if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain # ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder @@ -196,7 +198,7 @@ if args["stencil"] == "D2Q9": stencil = lt.D2Q9() else: - print("WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") + print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") stencil= lt.D2Q9() else: resolution = [domain_length_x_in_d * char_length_lu, domain_height_y_in_d * char_length_lu, domain_width_z_in_d * char_length_lu] @@ -208,7 +210,7 @@ elif args["stencil"] == "D3Q27": stencil = lt.D3Q27() else: - print("WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") + print("(!) WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") stencil = lt.D3Q27() # read dtype @@ -221,10 +223,14 @@ # OVERWRITE n_steps, if t_target is given n_steps = args["n_steps"] -T_target = 0 +t_target = args["t_target"] if args["t_target"] > 0: - T_target = args["t_target"] - n_steps = int(T_target * ((char_length_lu) / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) + n_steps = int(t_target * ((char_length_lu) / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) +else: + t_target = t_target / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) + +print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") + # check EQLM parameter if args["eqlm"]: @@ -238,16 +244,19 @@ # cuda_device = args["default_device"] # #nan_reporter = args["nan_reporter"] +print("SCRIPT: initializing solver components...") + ### -print("initializing context") +print("-> initializing context...") context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) -print("initializing flow") +print("-> initializing flow...") flow = ObstacleCylinder(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, bc_type=args["bbbc_type"], stencil=stencil) +print("-> initializing collision operator...") collision_operator = None if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) @@ -259,13 +268,34 @@ else: # default to bgk collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) +print("-> initializing reporters...") #TODO reporter +print("\nSCRIPT: initializing simulation object...") simulation = EbbSimulation(flow, collision_operator,reporter=[]) - +print(f"\nSCRIPT: running simulation for {n_steps} steps...") simulation(num_steps=n_steps) + +fig, axes = plt.subplots(1, 2, figsize=(10, 3)) +fig.subplots_adjust(right=0.85) +u = flow.u_pu.cpu().numpy() +print("Max Velocity:", u.max()) +im2 = axes[1].imshow(u[0, ...].T, origin="lower") +cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7]) +fig.colorbar(im2, cax=cbar_ax) +fig.show() + + +## END OF SCRIPT +print(f"\n♬ THE END ♬") +# sys.stdout = old_stdout + + +################################################################ +# script components below... + # Process arguments and set parameters # - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) # - flow physics: char_velocity, re, ma, density, pressure, length, domain (resolution) @@ -301,8 +331,4 @@ # plotting and post-processing # - save data -# reset lLOGGER (!) - -## END OF SCRIPT -print(f"\n♬ THE END ♬") -sys.stdout = old_stdout \ No newline at end of file +# reset LOGGER (!) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 3486b0fb..393be170 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -26,7 +26,7 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup if calc_force is not None: self.force_sum = torch.zeros_like(self.context.convert_to_tensor( - self.context.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.calc_force = True else: self.calc_force = False @@ -48,19 +48,19 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.flow.stencil.d, dtype=int) - if ix_sp[sp_index] == 0 and self.context.stencil.e[q_index, 0] == -1 and periodicity[0]: # searching border on left + if ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]: # searching border on left border[0] = -1 - elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index, 0] == 1 and periodicity[0]: # searching border on right + elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]: # searching border on right border[0] = 1 - if iy_sp[sp_index] == 0 and self.context.stencil.e[q_index, 1] == -1 and periodicity[1]: # searching border on left + if iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]: # searching border on left border[1] = -1 - elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index, 1] == 1 and periodicity[1]: # searching border on right + elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]: # searching border on right border[1] = 1 try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) - if (not mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0]*nx, - iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1]*ny] - and not other_solid_bc_mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0]*nx, - iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1]*ny]): + if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny] + and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny]): # if the neighbour of sp_index is False in the boundary.mask, sp_index is ix_sp solid node, neighbouring ix_sp fluid node: # ...the direction pointing from the fluid neighbour to solid sp_index is marked on the solid sp_index @@ -74,38 +74,38 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup for sp_index in range(0, len(ix_sp)): for q_index in range(0, self.flow.stencil.q): border = np.zeros(self.flow.stencil.d, dtype=int) - if ix_sp[sp_index] == 0 and self.context.stencil.e[q_index, 0] == -1 and periodicity[0]: # searching border on left + if ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]: # searching border on left border[0] = -1 - elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index, 0] == 1 and periodicity[0]: # searching border on right + elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]: # searching border on right border[0] = 1 - if iy_sp[sp_index] == 0 and self.context.stencil.e[q_index, 1] == -1 and periodicity[1]: # searching border on left + if iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]: # searching border on left border[1] = -1 - elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index, 1] == 1 and periodicity[1]: # searching border on right + elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]: # searching border on right border[1] = 1 - if iz_sp[sp_index] == 0 and self.context.stencil.e[q_index, 2] == -1 and periodicity[2]: # searching border on left + if iz_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 2] == -1 and periodicity[2]: # searching border on left border[2] = -1 - elif iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index, 2] == 1 and periodicity[2]: # searching border on right + elif iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index][ 2] == 1 and periodicity[2]: # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist (and f pointing out of simulation domain) - if (not mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0] * nx, - iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1] * ny, - iz_sp[sp_index] + self.context.stencil.e[q_index, 2] - border[2] * nz] - and not other_solid_bc_mask[ix_sp[sp_index] + self.context.stencil.e[q_index, 0] - border[0] * nx, - iy_sp[sp_index] + self.context.stencil.e[q_index, 1] - border[1] * ny, - iz_sp[sp_index] + self.context.stencil.e[q_index, 2] - border[2] * nz]): + if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, + iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz] + and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, + iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, + iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz]): self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index], iz_sp[sp_index]]) except IndexError: pass # just ignore this iteration since there is no neighbor there - self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), device=self.flow.stencil.device, dtype=torch.int64) # the batch-index has to be integer - #PHILIPP_occ_angepasst? self.f_index = torch.tensor(self.f_index, device=self.flow.stencil.device, dtype=torch.int64) # the batch-index has to be integer - self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.flow.stencil.device, + self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), device=flow.context.device, dtype=torch.int64) # the batch-index has to be integer + #PHILIPP_occ_angepasst? self.f_index = torch.tensor(self.f_index, device=flow.context.device, dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=flow.context.device, dtype=torch.int64) # batch-index has to be ix_sp tensor def __call__(self, flow): # FULLWAY-BBBC: inverts populations on all boundary nodes - + print("FWBB call") # calc force on boundary:# if self.calc_force: self.calc_force_on_boundary(flow.f) @@ -113,6 +113,7 @@ def __call__(self, flow): # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) if self.flow.stencil.d == 2: + print("FWBB call, dim 2") flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], @@ -126,6 +127,7 @@ def __call__(self, flow): self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], self.f_index_fwbb[:, 3]] + print("FWBB call, DONE") def calc_force_on_boundary(self, f): if self.flow.stencil.d == 2: diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index e956a782..26044618 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -195,7 +195,7 @@ def initial_pu(self): ### perturb initial velocity field-symmetry (in y and z) to trigger 'von Karman' vortex street if self.perturb_init: # perturb initial solution in y # overlays a sine-wave on the second column of nodes x_lu=1 (index 1) - ny = self.grid[0][1].shape[1] + ny = self.grid[1].shape[1] if u.max() < 0.5 * self.units.characteristic_velocity_pu: # add perturbation for small velocities #OLD 2D: u[0][1] += np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 1.0 @@ -203,7 +203,7 @@ def initial_pu(self): if self.stencil.d == 2: u[0][1] += amplitude_y elif self.stencil.d == 3: - nz = self.grid[0][2].shape[2] + nz = self.grid[2].shape[2] plane_yz = np.ones_like(u[0, 1]) # plane of ones u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # plane of amplitude in y amplitude_z = np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 # amplitude in z @@ -215,7 +215,7 @@ def initial_pu(self): if self.stencil.d == 2: u[0][1] *= factor elif self.stencil.d == 3: - nz = self.grid[0][2].shape[1] + nz = self.grid[2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # pertubation in z-direction @@ -252,30 +252,28 @@ def make_ibb_index_lists(self, x_center, y_center, radius): # ...they have to take the periodicity into account... border = np.zeros(self.stencil.d, dtype=int) - if a[p] == 0 and self.stencil.e[i, 0] == -1: # searching border on left [x] + if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left [x] border[0] = -1 - elif a[p] == nx - 1 and self.stencil.e[i, 0] == 1: # searching border on right [x] + elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right [x] border[0] = 1 - if b[p] == 0 and self.stencil.e[i, 1] == -1: # searching border on left [y] + if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left [y] border[1] = -1 - elif b[p] == ny - 1 and self.stencil.e[i, 1] == 1: # searching border on right [y] + elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right [y] border[1] = 1 try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) - if not self.obstacle_mask[a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny]: + if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]: # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate - cx = self.stencil.e[ - self.stencil.opposite[i], 0] # link-direction x to solid node - cy = self.stencil.e[ - self.stencil.opposite[i], 1] # link-direction y to solid node + px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate + cx = self.stencil.e[self.stencil.opposite[i]][ 0] # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][ 1] # link-direction y to solid node # pq-formula h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 @@ -293,30 +291,30 @@ def make_ibb_index_lists(self, x_center, y_center, radius): if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # neither d1 or d2 is real and between 0 and 1 print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], - b[p], self.stencil.e[i, 0], self.stencil.e[i, 1], - self.stencil.e[i, 2]) + b[p], self.stencil.e[i][0], self.stencil.e[i][1], + self.stencil.e[i][2]) except IndexError: pass # just ignore this iteration since there is no neighbor there @@ -328,36 +326,36 @@ def make_ibb_index_lists(self, x_center, y_center, radius): for i in range(0, self.stencil.q): border = np.zeros(self.stencil.d, dtype=int) # x - direction - if a[p] == 0 and self.stencil.e[i, 0] == -1: # searching border on left + if a[p] == 0 and self.stencil.e[i][0] == -1: # searching border on left border[0] = -1 - elif a[p] == nx - 1 and self.stencil.e[i, 0] == 1: # searching border on right + elif a[p] == nx - 1 and self.stencil.e[i][0] == 1: # searching border on right border[0] = 1 # y - direction - if b[p] == 0 and self.stencil.e[i, 1] == -1: # searching border on left + if b[p] == 0 and self.stencil.e[i][1] == -1: # searching border on left border[1] = -1 - elif b[p] == ny - 1 and self.stencil.e[i, 1] == 1: # searching border on right + elif b[p] == ny - 1 and self.stencil.e[i][1] == 1: # searching border on right border[1] = 1 # z - direction - if c[p] == 0 and self.stencil.e[i, 2] == -1: # searching border on left + if c[p] == 0 and self.stencil.e[i][2] == -1: # searching border on left border[2] = -1 - elif c[p] == nz - 1 and self.stencil.e[i, 2] == 1: # searching border on right + elif c[p] == nz - 1 and self.stencil.e[i][2] == 1: # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) - if not self.obstacle_mask[a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny, - c[p] + self.stencil.e[i, 2] - border[2] * nz]: + if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]: # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate + px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate # Z-coordinate not needed for cylinder ! cx = self.stencil.e[ - self.stencil.opposite[i], 0] # link-direction x to solid node + self.stencil.opposite[i]][ 0] # link-direction x to solid node cy = self.stencil.e[ - self.stencil.opposite[i], 1] # link-direction y to solid node + self.stencil.opposite[i]][ 1] # link-direction y to solid node # Z-coordinate not needed for cylinder ! # pq-formula @@ -378,34 +376,34 @@ def make_ibb_index_lists(self, x_center, y_center, radius): if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny, - c[p] + self.stencil.e[i, 2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny, - c[p] + self.stencil.e[i, 2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny, - c[p] + self.stencil.e[i, 2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i, 0] - border[0] * nx, - b[p] + self.stencil.e[i, 1] - border[1] * ny, - c[p] + self.stencil.e[i, 2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # neither d1 nor d2 is real and between 0 and 1 print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], - b[p], c[p], self.stencil.e[i, 0], self.stencil.e[i, 1], - self.stencil.e[i, 2]) + b[p], c[p], self.stencil.e[i][0], self.stencil.e[i][1], + self.stencil.e[i][2]) except IndexError: pass # just ignore this iteration since there is no neighbor there @@ -452,6 +450,7 @@ def post_boundaries(self): return [ inlet_boundary, outlet_boundary, + BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) ] else: return [ diff --git a/examples/simple_flows/Obstacle.ipynb b/examples/simple_flows/Obstacle.ipynb index 3811510a..c3ea3a97 100644 --- a/examples/simple_flows/Obstacle.ipynb +++ b/examples/simple_flows/Obstacle.ipynb @@ -10,24 +10,32 @@ }, { "cell_type": "code", - "execution_count": 1, "id": "95c5e8f8", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:16.825448Z", + "start_time": "2025-11-19T13:55:16.822985Z" + } + }, "source": [ "import lettuce as lt\n", "from matplotlib import pyplot as plt" - ] + ], + "outputs": [], + "execution_count": 6 }, { "cell_type": "code", - "execution_count": 2, "id": "3a1158ed", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:16.841456Z", + "start_time": "2025-11-19T13:55:16.830873Z" + } + }, "source": [ "def run(ny=64, *axes):\n", - " context = lt.Context()\n", + " context = lt.Context(use_native=False)\n", " flow = lt.Obstacle(\n", " context=context, resolution=[2 * ny, ny], reynolds_number=50.0,\n", " mach_number=0.05, domain_length_x=10.1, stencil=lt.D2Q9()\n", @@ -42,16 +50,20 @@ " u = flow.u_pu.cpu().numpy()\n", " print(\"Max Velocity:\", u.max())\n", " return axes[1].imshow(u[0, ...].T, origin=\"lower\")" - ] + ], + "outputs": [], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 3, "id": "5ab90e9e", "metadata": { - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-11-19T13:55:16.886296Z", + "start_time": "2025-11-19T13:55:16.883594Z" + } }, - "outputs": [], "source": [ "def run_and_plot(n):\n", " fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", @@ -59,79 +71,124 @@ " im2 = run(n, *axes)\n", " cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7])\n", " fig.colorbar(im2, cax=cbar_ax)" - ] + ], + "outputs": [], + "execution_count": 8 }, { "cell_type": "code", - "execution_count": 4, "id": "c49cbef2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:21.857446Z", + "start_time": "2025-11-19T13:55:16.937645Z" + } + }, + "source": "run_and_plot(32)", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Max Velocity: 1.5470362\n" + "Max Velocity: nan\n" ] }, { "data": { - "image/png": "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\n", "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" + "
" + ], + "image/png": "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" }, - "output_type": "display_data" + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "source": "run_and_plot(32)" + "execution_count": 9 }, { "cell_type": "code", - "execution_count": 5, "id": "466ba599", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:31.350123Z", + "start_time": "2025-11-19T13:55:21.907009Z" + } + }, + "source": "run_and_plot(64)", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Max Velocity: 1.5507787\n" + "Max Velocity: nan\n" ] }, { "data": { - "image/png": "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\n", "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3IAAADuCAYAAAB8mzVEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAsr0lEQVR4nO3de3SU1b3/8c8kkIuEBEFICAQIqAvwAgoSI7bVmmMKLo8orcqPHilS/OlJrJDTongUqMea6imIlwhqK6hLD+qy0uIFD40CP47hlkCPFrlJKjGQgCgJieTCPPv3h2baKQEy4zPMnifv11p7LWbPnme+mwV8+WbvZz8+Y4wRAAAAACBmxEU7AAAAAABAaCjkAAAAACDGUMgBAAAAQIyhkAMAAACAGEMhBwAAAAAxhkIOAAAAAGIMhRwAAAAAxBgKOQAAAACIMV2iHQAAAAAA72hqalJLS0u0wzihhIQEJSUlRTuMb41CDgAAAIArmpqalD0wRTUH/NEO5YQyMjJUWVkZ88UchRwAAAAAV7S0tKjmgF+7N2cptbt9d3HVH3F09ugqtbS0UMgBAAAAwN9L6e5TSndftMM4jiP7YgoXhRwAAAAAVzly5EQ7iHbYGVV4KOQAAAAAuKrVOGo10Y7ieK2GQg4AAAAA2uXIyC/7KjnHwpjCRSEHAAAAwFWOjJVFk40xhYtCDgAAAICr/MbIb+wrmmyMKVwUcgAAAABc5XzTbGNjTOGikAMAAADgqhZj1GLh6peNMYWLQg4AAACAq1iRizwKOQAAAACucuST38KHb/NAcAAAAAA4Acd83WxjY0zhopADAAAA4KoWxalFcdEO4zgt0Q7ARRRyAAAAAFzlGJ8cY982RhtjCheFHAAAAABX+S29R87GmMJFIQcAAADAVX7FyW/h1kp/tANwEYUcAAAAAFcZS7dWGgtjCheFHAAAAABXtZh4dTX2rci1UMgBAAAAQPsc+eRYuLXSkXeeP0AhBwAAAMBVHHYSeRRyAAAAAFzlN3HyW7i10m9YkQMAAACAdh1TvFoVH+0wjnMs2gG4yLpCznEc7du3T927d5fP552lTwBwizFGR44cUWZmpuLi7Ptp5+lCvgCAE4t2rmBFLvKsK+T27dunrKysaIcBANarqqpS//79ox1G1JAvAODUopUrHMVx2EmEWVfIde/eXZJ0ucari7pGORoAsM8xtWqd3g78e9lZtc2/qqpKqampUY4GAOxSX1+vrKysqOUKv/HJb+FR/zbGFC7rCrm27TFd1FVdfBRyAHCcb36Y2Nm3E7bNPzU1lUIOAE4gWrnCrzj5LVyR87MiBwAAAADtazVd1GrsO+yklRU5AAAAAGifIzu3MTrRDsBFIa93VldX68c//rF69eql5ORkXXDBBdq8eXPgfWOM5syZo759+yo5OVl5eXnatWuXq0EDAOxGrgCAzq3tsBMbm1eENJMvv/xSY8eOVdeuXfXOO+9o27Ztmj9/vs4888zAmEceeUSPP/64Fi9erA0bNqhbt27Kz89XU1OT68EDAOxDrgAAtD1+wMbmFSFtrXz44YeVlZWlJUuWBPqys7MDvzbGaOHChbrvvvt03XXXSZJeeOEFpaena/ny5br55ptdChsAYCtyBQCg1cSri5X3yHnnsJOQStI//vGPGj16tH70ox+pT58+uuiii/Tss88G3q+srFRNTY3y8vICfWlpacrJyVFZWVm712xublZ9fX1QAwDErkjkCol8AQCxpO3UShubV4Q0kz179mjRokU655xz9O677+qOO+7Qz372Mz3//POSpJqaGklSenp60OfS09MD7/2j4uJipaWlBRoPdwWA2BaJXCGRLwAgljjGZ23zipC2VjqOo9GjR+uhhx6SJF100UX66KOPtHjxYk2ZMiWsAGbPnq2ioqLA67aHFwIAYlMkcoVEvgCAWOJYuvrVaQ876du3r4YPHx7UN2zYMO3du1eSlJGRIUmqra0NGlNbWxt47x8lJiYGHubKQ10BIPZFIldI5AsAiCWOibO2eUVIMxk7dqx27NgR1Ldz504NHDhQ0tc3s2dkZKi0tDTwfn19vTZs2KDc3FwXwgUA2I5cAQBoNfHWNq8IaWvlzJkzddlll+mhhx7SjTfeqI0bN+qZZ57RM888I0ny+XyaMWOGHnzwQZ1zzjnKzs7W/fffr8zMTE2YMCES8QMALEOuAAD4Jfll3/1o/mgH4KKQCrlLLrlEb7zxhmbPnq0HHnhA2dnZWrhwoSZPnhwYM2vWLDU2Nuq2227T4cOHdfnll2vlypVKSkpyPXgAgH3IFQAAW7cx2hhTuHzG2PUwhfr6eqWlpekKXacuvq7RDgcArHPMtGq1/qC6urpOfZ9YW77o7L8PANCeaP0b2fa9s8t+oKQU+/4v39TQquLclZ7IHSGtyAEAAADAqRj55Fi4tdJYGFO4KOQAAAAAuKrViVecY9/BIq2OE+0QXEMhBwAAAMBVfkufI2djTOGikAMAAADgKsf45Bj7tjHaGFO4KOQAAAAAuMpRnBwLV79sjClcFHIAAAAAXNXqxCnOsa9oarUwpnBRyAEAAABwlbH0OXLGwpjCRSEHAAAAwFV++eS38Kh/G2MKl3dKUgAAAABWcMzfDjyxq4U+l7Vr1+raa69VZmamfD6fli9ffsrPrF69WhdffLESExN19tlna+nSpUHvz5s3Tz6fL6gNHTo0pLgo5AAAAAC4yvlma6WNLVSNjY0aMWKESkpKOjS+srJS11xzja688kpt3bpVM2bM0E9/+lO9++67QePOO+887d+/P9DWrVsXUlxsrQQAAADgqlYTJ5+F96O1hhHTuHHjNG7cuA6PX7x4sbKzszV//nxJ0rBhw7Ru3To9+uijys/PD4zr0qWLMjIyQo6njX2/uwAAAABiWrRX3U61IldfXx/UmpubXZt7WVmZ8vLygvry8/NVVlYW1Ldr1y5lZmZq8ODBmjx5svbu3RvS91DIAQAAAHCVo2jfC3eC9s1hJ1lZWUpLSwu04uJi1+ZeU1Oj9PT0oL709HTV19fr6NGjkqScnBwtXbpUK1eu1KJFi1RZWanvfOc7OnLkSIe/h62VAAAAAFxl9LeiySbmm5iqqqqUmpoa6E9MTDytcfz9Vs0LL7xQOTk5GjhwoF599VVNmzatQ9egkAMAAADgqmNOvHxOfLTDOM6xb2JKTU0NKuTclJGRodra2qC+2tpapaamKjk5ud3P9OjRQ+eee652797d4e9hayUAAAAAV0V9C+VJWqTl5uaqtLQ0qG/VqlXKzc094WcaGhr0ySefqG/fvh3+Hgo5AAAAAK5yvtlaaWMLVUNDg7Zu3aqtW7dK+vrxAlu3bg0cTjJ79mzdcsstgfG333679uzZo1mzZmn79u166qmn9Oqrr2rmzJmBMT//+c+1Zs0a/fWvf9UHH3yg66+/XvHx8Zo0aVKH42JrJQAAAABXna7Vr1CFE9PmzZt15ZVXBl4XFRVJkqZMmaKlS5dq//79QSdOZmdn66233tLMmTP12GOPqX///vrtb38b9OiBzz77TJMmTdKhQ4fUu3dvXX755Vq/fr169+7d4bgo5AAAAAC4ykuF3BVXXCFjzAnfX7p0abuf2bJlywk/s2zZspDj+EcUcgAAAABcdcyJk8+x7y6uYxbGFC4KOQAAAACuMpKljx/wDgo5AAAAAK7y0tZKW1HIAQAAAHAVhVzkUcgBAAAAcNUxJ06y8H407pEDAAAAgBMwxidj4eqXjTGFi0IOAAAAgKvCffh2pNkYU7go5AAAAAC4invkIo9CDgAAAICr2FoZeRRyAAAAAFzlt/SB4H4LYwoXhRwAAAAAVxlLt1ayIgcAAAAAJ2AkGRPtKI5nYUhho5ADAAAA4CpHPvksPCGSUysBAAAA4AQ47CTyKOQAAAAAuMrv+CTHvqLJb2FM4aKQAwAAAOAqVuQij0IOAAAAgKso5CKPQg4AAACAqxzjk8/CosnGRyKEi0IOAAAAgKscR/JZeD+a40Q7AvdQyAEAAABwFVsrI49CDgAAAICrjOx8+LaNMYUr7tt8+Ne//rV8Pp9mzJgR6GtqalJBQYF69eqllJQUTZw4UbW1td82TgBAjCJXAEDn07YiZ2PzirALuU2bNunpp5/WhRdeGNQ/c+ZMrVixQq+99prWrFmjffv26YYbbvjWgQIAYg+5AgA6KWNx84iwCrmGhgZNnjxZzz77rM4888xAf11dnX73u99pwYIF+v73v69Ro0ZpyZIl+uCDD7R+/XrXggYA2I9cAQCdl3F8cixsxsIDWMIVViFXUFCga665Rnl5eUH95eXlam1tDeofOnSoBgwYoLKysnav1dzcrPr6+qAGAIh9buYKiXwBALEk2tsnO8PWypAPO1m2bJkqKiq0adOm496rqalRQkKCevToEdSfnp6umpqadq9XXFysX/7yl6GGAQCwmNu5QiJfAEBMMb6vm21sjClMIa3IVVVV6a677tJLL72kpKQkVwKYPXu26urqAq2qqsqV6wIAoiMSuUIiXwBALDHG3uYVIa3IlZeX68CBA7r44osDfX6/X2vXrtWTTz6pd999Vy0tLTp8+HDQT1pra2uVkZHR7jUTExOVmJgYXvRe5/MpLjlZio+PahimpUWmuTmqMQCIHZHIFRL5AgBiibH0fjQbYwpXSIXcVVddpQ8//DCob+rUqRo6dKjuvvtuZWVlqWvXriotLdXEiRMlSTt27NDevXuVm5vrXtSdRPzZ2fr0hxk62t8fvSD8Usb/+JS6fAvFHIAOIVcAACR56oRIG4VUyHXv3l3nn39+UF+3bt3Uq1evQP+0adNUVFSknj17KjU1VXfeeadyc3N16aWXuhd1J9HSv4cum/BnPdqvNGoxHHaO6bv6N6W9nUAhB6BDyBUAAFsPFrExpnCFfNjJqTz66KOKi4vTxIkT1dzcrPz8fD311FNuf02nYHw+devSrJQ49+4x6ajPjjXo7cZztetoupIOxEv+KK4KAvAccgUAeJytz2yzMaYwfetCbvXq1UGvk5KSVFJSopKSkm97aUTRS3UX6aUl/6Se21s16K+fy9/EahyA8JErAKCz8X3TbGNjTOFxfUUOsc1vHDky2tmYrvTyo4pbs0WsxQFA7MleON+V61TO+DdXrgOgk3G+abaxMaYwUcghoM45qqLP/knv7zhXiXuSlL2/liIOAAAAoeM5chFHIYeAz/1+/b/3LtCwJyplmprlHDkS7ZAAAAAQg2x9ZpuNMYWLQg7af6xBf27ppQ+bhij5gE/Hag9KDmtxANBZsZ0SwLfGYScRRyEH/ebgd/Xua5eqW7VR5p+/kEMRBwAAgG/B5/jks/Dh2zbGFC4KOWjT5wM1YMUh+f+yw0v3fwIAQsRKHADXsCIXcRRyndQBf6MerL1C62sHqa7iLHWvr4p2SAAAAPAKDjuJOAq5TmpbS3eVvn6JBv7+gHo3fCp/7YFohwQAAACvYEUu4ijkOpkv/V+pxi9VHD1f3fYb+XfsjnZIAIAoYjslgIigkIu4uGgHgNPHbxz9vPpqXf/Cv+mFp3+gXhVfRjskAAAAeJHjs7eFaO3atbr22muVmZkpn8+n5cuXn/Izq1ev1sUXX6zExESdffbZWrp06XFjSkpKNGjQICUlJSknJ0cbN24MKS4KuU5m9e5zNOTpT5X+ZJmc/90e7XAAAADgQT5jbwtVY2OjRowYoZKSkg6Nr6ys1DXXXKMrr7xSW7du1YwZM/TTn/5U7777bmDMK6+8oqKiIs2dO1cVFRUaMWKE8vPzdeBAx293YmtlJ/DZsQa9cHiUtjemK2F3skxTk7eehggACBlbKgFElIe2Vo4bN07jxo3r8PjFixcrOztb8+fPlyQNGzZM69at06OPPqr8/HxJ0oIFCzR9+nRNnTo18Jm33npLzz33nO65554OfQ8rcp3Au41na9nzV2n/LwYr+6UaOXX10Q4JAAAAiJr6+vqg1tzc7Nq1y8rKlJeXF9SXn5+vsrIySVJLS4vKy8uDxsTFxSkvLy8wpiNYkfOwZtOqr5xWfdLUR2fuPKa4dVvFo74BoPNiFQ7A6eJTeNsYI63tDrmsrKyg/rlz52revHmufEdNTY3S09OD+tLT01VfX6+jR4/qyy+/lN/vb3fM9u0dv/WJQs6jvnJaVPDZVVqzebiSq+M1cM8hijgAAACcHmEeLBJx38RUVVWl1NTUQHdiYmK0IgobhZxHfWVatWbDeRr64CcyjY3yN7m3XAwAAACclOX3yKWmpgYVcm7KyMhQbW1tUF9tba1SU1OVnJys+Ph4xcfHtzsmIyOjw99DIecxnx1r0HtfDdLOpguUXBMnc+SInKamaIcFAIgStlMCiIZwT4iMtNMRU25urt5+++2gvlWrVik3N1eSlJCQoFGjRqm0tFQTJkyQJDmOo9LSUhUWFnb4eyjkPOZ3X47R6y9cobQ9fg3ceUh+F2/cBAAAADrE8hW5UDQ0NGj37t2B15WVldq6dat69uypAQMGaPbs2aqurtYLL7wgSbr99tv15JNPatasWbr11lv13nvv6dVXX9Vbb70VuEZRUZGmTJmi0aNHa8yYMVq4cKEaGxsDp1h2BIWch/iNo48bMtT3fxqk9f/LPXEA0ImxEgcgmnzO18024cS0efNmXXnllYHXRUVFkqQpU6Zo6dKl2r9/v/bu3Rt4Pzs7W2+99ZZmzpypxx57TP3799dvf/vbwKMHJOmmm27SwYMHNWfOHNXU1GjkyJFauXLlcQegnAyFnAd86f9Ks/b9k9ZUDlHczm4a/HkNRRwAAACix/i+brYJI6YrrrhC5iTPYF66dGm7n9myZctJr1tYWBjSVsp/RCHnAfv8Pq17Z4TO+e2nMk3Vcg7XRTskAAAAdGYe2lppKwq5GHbA36jtrd20+avzlHRQOla9TzrJTwsAAACA06EzH3ZyulDIxbAHar6v998YpeRao4zNX8qhiAMAAIANWJGLOAq5GPbB/kEa+FqN/Lv2yMJ7SQEAANBZWXrYiZf+00whF2P2H2vQgs+/o82HBqjxzz3la6iMdkgAAABAMFbkIo5CLsZUtJyld17JVdY7X+jsus/kP/h5tEMCAAAAgnCPXORRyMWIOueovvD79dHRc5TymSPnf7d7aWUYAAAAQAgo5GKA3zia8dnV+p/V5yvpgE/9PvyCIg4AAADW8tIDwW1FIRcDjsmv1R+fq2GPfiLn0Bdy/DzuGwAAAJbz0DZGG1HIWazrkRa9vfM8tThdlPTXROlok8yxY9EOCwAAADg5DjuJOAo5i8Xt+FSDFwzUju7nKbv6gPwNjdEOCQAAADglDjuJPAo5i/nr66VNH6qLJDZTAgAAIGawIhdxFHIAAAAAXMVhJ5FHIQcAAADAXazIRRyFHAAAAABXcY9c5FHIAQAAAHAXK3IRRyEHAAAAwFXcIxd5FHIAAAAA3MWKXMRRyAEAAABwFffIRR6FHAAAAAB3sSIXcRRyAAAAANxFIRdxcaEMLi4u1iWXXKLu3burT58+mjBhgnbs2BE0pqmpSQUFBerVq5dSUlI0ceJE1dbWuho0AMBu5AsA6Nzatlba2LwipEJuzZo1Kigo0Pr167Vq1Sq1trbq6quvVmNjY2DMzJkztWLFCr322mtas2aN9u3bpxtuuMH1wAEA9iJfAEDnFu1irTMUciFtrVy5cmXQ66VLl6pPnz4qLy/Xd7/7XdXV1el3v/udXn75ZX3/+9+XJC1ZskTDhg3T+vXrdemll7oXOQDAWuQLAOjk2FoZcSGtyP2juro6SVLPnj0lSeXl5WptbVVeXl5gzNChQzVgwACVlZW1e43m5mbV19cHNQCAt5AvAKATMhY2Dwm7kHMcRzNmzNDYsWN1/vnnS5JqamqUkJCgHj16BI1NT09XTU1Nu9cpLi5WWlpaoGVlZYUbEgDAQuQLAOh82h4IbmPzirALuYKCAn300UdatmzZtwpg9uzZqqurC7SqqqpvdT0AgF3IFwDQ+UT7PjjukTuBwsJCvfnmm1q7dq369+8f6M/IyFBLS4sOHz4c9FPW2tpaZWRktHutxMREJSYmhhMGAMBy5AsA6KRs3cpoY0xhCmlFzhijwsJCvfHGG3rvvfeUnZ0d9P6oUaPUtWtXlZaWBvp27NihvXv3Kjc3152IAQDWI18AQOcW7VU3VuT+QUFBgV5++WX94Q9/UPfu3QP3MaSlpSk5OVlpaWmaNm2aioqK1LNnT6WmpurOO+9Ubm4uJ5ABQCdCvgCATo4VuYgLqZBbtGiRJOmKK64I6l+yZIl+8pOfSJIeffRRxcXFaeLEiWpublZ+fr6eeuopV4IFAMQG8gUAdG62HixiY0zhCqmQM+bUJWxSUpJKSkpUUlISdlAAgNhGvgCATo4VuYgL67ATAAAAADgRnzHydeCHeqebjTGFi0IOAAAAgLtYkYs4CjkAAAAArrL1hEgbYwoXhRwAAAAAV3HYSeRRyAEAAABwF1srI45CDgAAAICr2FoZeRRyAAAAANzFilzEUcgBAAAAcJcx8jkWVk08fgAAAAAA2sfWysijkAMAAADgLrZWRlxctAMAAAAA4C1tjx+wsYWjpKREgwYNUlJSknJycrRx48YTjm1tbdUDDzygIUOGKCkpSSNGjNDKlSuDxsybN08+ny+oDR06NKSYKOQAAAAAuMtY3EL0yiuvqKioSHPnzlVFRYVGjBih/Px8HThwoN3x9913n55++mk98cQT2rZtm26//XZdf/312rJlS9C48847T/v37w+0devWhRQXhRwAAAAAV/kcY20L1YIFCzR9+nRNnTpVw4cP1+LFi3XGGWfoueeea3f8iy++qHvvvVfjx4/X4MGDdccdd2j8+PGaP39+0LguXbooIyMj0M4666yQ4qKQAwAAAOCqtsNObGySVF9fH9Sam5vbnUdLS4vKy8uVl5cX6IuLi1NeXp7Kysra/Uxzc7OSkpKC+pKTk49bcdu1a5cyMzM1ePBgTZ48WXv37g3p95hCDgAAAIC7or198hRbK7OyspSWlhZoxcXF7U7j888/l9/vV3p6elB/enq6ampq2v1Mfn6+FixYoF27dslxHK1atUq///3vtX///sCYnJwcLV26VCtXrtSiRYtUWVmp73znOzpy5MipfmcDOLUSAAAAgKtsf/xAVVWVUlNTA/2JiYmufcdjjz2m6dOna+jQofL5fBoyZIimTp0atBVz3LhxgV9feOGFysnJ0cCBA/Xqq69q2rRpHfoeCjkAAAAArgr3frRIa4spNTU1qJA7kbPOOkvx8fGqra0N6q+trVVGRka7n+ndu7eWL1+upqYmHTp0SJmZmbrnnns0ePDgE35Pjx49dO6552r37t0dngtbKwEAAAC4K9rbJ106tTIhIUGjRo1SaWlpoM9xHJWWlio3N/ekn01KSlK/fv107Ngxvf7667ruuutOOLahoUGffPKJ+vbt2+HYWJEDAAAA4Crbt1aGoqioSFOmTNHo0aM1ZswYLVy4UI2NjZo6daok6ZZbblG/fv0C99lt2LBB1dXVGjlypKqrqzVv3jw5jqNZs2YFrvnzn/9c1157rQYOHKh9+/Zp7ty5io+P16RJkzocF4UcAAAAAHc55utmmzBiuummm3Tw4EHNmTNHNTU1GjlypFauXBk4AGXv3r2Ki/vbRsempibdd9992rNnj1JSUjR+/Hi9+OKL6tGjR2DMZ599pkmTJunQoUPq3bu3Lr/8cq1fv169e/fucFwUcgAAAADcFcY2xtMizJgKCwtVWFjY7nurV68Oev29731P27ZtO+n1li1bFl4gf4dCDgAAAICrfMbSw06MfTGFi0IOAAAAgKu8dI+crSjkAAAAALjLY1srbUQhBwAAAMBVPmOs3MZoY0zhopADAAAA4Cqf38hn4T5Gn9++mMJFIQcAAADAXWytjDgKOQAAAADuMubrZhsbYwoThRwAAAAAV3FqZeRRyAEAAABwFytyEUchBwAAAMBVHHYSeRRyAAAAANzFYScRRyEHAAAAwFU8Ry7yKOQAAAAAuIt75CKOQg4AAACAu4wkJ9pBtMM7dRyFHAAAAAB3+Rwjn8++Ss7neKeSo5ADAAAA4C62VkYchRwAAAAAdzmSfNEOoh32LRKGjUIOAAAAgKs4tTLyKOQAAAAAuMtxJAvvkZNjYUxhiovUhUtKSjRo0CAlJSUpJydHGzdujNRXAQBiFLkCADyq7R45G5tHRKSQe+WVV1RUVKS5c+eqoqJCI0aMUH5+vg4cOBCJrwMAxCByBQB4mGNx84iIFHILFizQ9OnTNXXqVA0fPlyLFy/WGWecoeeeey4SXwcAiEHkCgDwrrZ75GxsXuF6IdfS0qLy8nLl5eX97Uvi4pSXl6eysrLjxjc3N6u+vj6oAQC8LdRcIZEvACCmRHv7ZCfYWun6YSeff/65/H6/0tPTg/rT09O1ffv248YXFxfrl7/85XH9x9TqqSevA4BbjqlVkmRiOBmFmiukE+cLCjoAOF7bv41RyxV+S/cx+i2MKUxRP7Vy9uzZKioqCryurq7W8OHDtU5vRzEqALDfkSNHlJaWFu0wTpsT5YusrKwoRgUAdoterrB19cvGmMLjeiF31llnKT4+XrW1tUH9tbW1ysjIOG58YmKiEhMTA69TUlJUVVUlY4wGDBigqqoqpaamuh3maVVfX6+srCxPzEXy1ny8NBfJW/Px0lwkd+djjNGRI0eUmZnpUnSnX6i5Qmo/X2zbtk3Dhw/nz4mFvDQXyVvz8dJcJG/Nx1O5wtZtjDbGFCbXC7mEhASNGjVKpaWlmjBhgiTJcRyVlpaqsLDwlJ+Pi4tT//79A8vBqampMf+Xso2X5iJ5az5emovkrfl4aS6Se/OJ9ZW4b5srpK/zRb9+/STx58RmXpqL5K35eGkukrfm44lc4RhZufrlWBhTmCKytbKoqEhTpkzR6NGjNWbMGC1cuFCNjY2aOnVqJL4OABCDyBUA4GGOX5I/2lEcz7EwpjBFpJC76aabdPDgQc2ZM0c1NTUaOXKkVq5cedxN7QCAzotcAQAexopcxEXssJPCwsIOb49pT2JioubOnRt0P0Ss8tJcJG/Nx0tzkbw1Hy/NRfLefNxCrgjmpfl4aS6St+bjpblI3pqPl+bCPXKR5zOxfH41AAAAAGvU19crLS1NeX3/r7rEJUQ7nOMcc1r0p/1Pq66uLubvqYz64wcAAAAAeAwrchFHIQcAAADAXX6/ZCw8WITDTgAAAADgBFiRizgKOQAAAADu4tTKiKOQAwAAAOAqYxwZ40Q7jOPYGFO44qIdQHtKSko0aNAgJSUlKScnRxs3box2SKdUXFysSy65RN27d1efPn00YcIE7dixI2hMU1OTCgoK1KtXL6WkpGjixImqra2NUsSh+fWvfy2fz6cZM2YE+mJpPtXV1frxj3+sXr16KTk5WRdccIE2b94ceN8Yozlz5qhv375KTk5WXl6edu3aFcWIT8zv9+v+++9Xdna2kpOTNWTIEP3Hf/yH/v4AWlvns3btWl177bXKzMyUz+fT8uXLg97vSNxffPGFJk+erNTUVPXo0UPTpk1TQ0PDaZzF35xsPq2trbr77rt1wQUXqFu3bsrMzNQtt9yiffv2BV3DpvnEIvKFXWI9V0jeyRexnCskb+WLTpsrHEfyW9gcCrmIeeWVV1RUVKS5c+eqoqJCI0aMUH5+vg4cOBDt0E5qzZo1Kigo0Pr167Vq1Sq1trbq6quvVmNjY2DMzJkztWLFCr322mtas2aN9u3bpxtuuCGKUXfMpk2b9PTTT+vCCy8M6o+V+Xz55ZcaO3asunbtqnfeeUfbtm3T/PnzdeaZZwbGPPLII3r88ce1ePFibdiwQd26dVN+fr6ampqiGHn7Hn74YS1atEhPPvmkPv74Yz388MN65JFH9MQTTwTG2DqfxsZGjRgxQiUlJe2+35G4J0+erL/85S9atWqV3nzzTa1du1a33Xbb6ZpCkJPN56uvvlJFRYXuv/9+VVRU6Pe//7127Nihf/7nfw4aZ9N8Yg35wi6xniskb+WLWM4VkrfyRafNFY5jb/MKY5kxY8aYgoKCwGu/328yMzNNcXFxFKMK3YEDB4wks2bNGmOMMYcPHzZdu3Y1r732WmDMxx9/bCSZsrKyaIV5SkeOHDHnnHOOWbVqlfne975n7rrrLmNMbM3n7rvvNpdffvkJ33ccx2RkZJj//M//DPQdPnzYJCYmmv/6r/86HSGG5JprrjG33nprUN8NN9xgJk+ebIyJnflIMm+88UbgdUfi3rZtm5FkNm3aFBjzzjvvGJ/PZ6qrq09b7O35x/m0Z+PGjUaS+fTTT40xds8nFpAv7OGFXGGMt/KFV3KFMd7KF50hV9TV1RlJ5qqU/2Pyu//EunZVyv8xkkxdXV20f6u+NatW5FpaWlReXq68vLxAX1xcnPLy8lRWVhbFyEJXV1cnSerZs6ckqby8XK2trUFzGzp0qAYMGGD13AoKCnTNNdcExS3F1nz++Mc/avTo0frRj36kPn366KKLLtKzzz4beL+yslI1NTVBc0lLS1NOTo51c5Gkyy67TKWlpdq5c6ck6c9//rPWrVuncePGSYq9+bTpSNxlZWXq0aOHRo8eHRiTl5enuLg4bdiw4bTHHKq6ujr5fD716NFDUuzPJ5rIF3bxQq6QvJUvvJorJO/nC6/kCuM41javsOqwk88//1x+v1/p6elB/enp6dq+fXuUogqd4ziaMWOGxo4dq/PPP1+SVFNTo4SEhMBfyjbp6emqqamJQpSntmzZMlVUVGjTpk3HvRdL89mzZ48WLVqkoqIi3Xvvvdq0aZN+9rOfKSEhQVOmTAnE296fO9vmIkn33HOP6uvrNXToUMXHx8vv9+tXv/qVJk+eLEkxN582HYm7pqZGffr0CXq/S5cu6tmzp9Vzk76+T+juu+/WpEmTlJqaKim25xNt5At7eCVXSN7KF17NFZK384WncoWx9NRKDz1+wKoVOa8oKCjQRx99pGXLlkU7lLBVVVXprrvu0ksvvaSkpKRoh/OtOI6jiy++WA899JAuuugi3XbbbZo+fboWL14c7dDC8uqrr+qll17Syy+/rIqKCj3//PP6zW9+o+effz7aoeEEWltbdeONN8oYo0WLFkU7HFgk1vOFl3KF5K18Qa6IPZ7LFX7n64eCW9fCW5EL5XCt1tZWPfDAAxoyZIiSkpI0YsQIrVy58ltdsz1WFXJnnXWW4uPjjzvNqra2VhkZGVGKKjSFhYV688039f7776t///6B/oyMDLW0tOjw4cNB422dW3l5uQ4cOKCLL75YXbp0UZcuXbRmzRo9/vjj6tKli9LT02NmPn379tXw4cOD+oYNG6a9e/dKUiDeWPlz94tf/EL33HOPbr75Zl1wwQX6l3/5F82cOVPFxcWSYm8+bToSd0ZGxnEHWRw7dkxffPGFtXNrS8yffvqpVq1aFfgJqxSb87EF+cIOXsoVkrfyhVdzheTNfOHFXGEcY20LVaiHa9133316+umn9cQTT2jbtm26/fbbdf3112vLli1hX7M9VhVyCQkJGjVqlEpLSwN9juOotLRUubm5UYzs1IwxKiws1BtvvKH33ntP2dnZQe+PGjVKXbt2DZrbjh07tHfvXivndtVVV+nDDz/U1q1bA2306NGaPHly4NexMp+xY8ced7T3zp07NXDgQElSdna2MjIyguZSX1+vDRs2WDcX6esTruLigv/qxsfHy/lmz3eszadNR+LOzc3V4cOHVV5eHhjz3nvvyXEc5eTknPaYT6UtMe/atUt/+tOf1KtXr6D3Y20+NiFf2MFLuULyVr7waq6QvJcvPJsrjGNvC9GCBQs0ffp0TZ06VcOHD9fixYt1xhln6Lnnnmt3/Isvvqh7771X48eP1+DBg3XHHXdo/Pjxmj9/ftjXbI9V98hJUlFRkaZMmaLRo0drzJgxWrhwoRobGzV16tRoh3ZSBQUFevnll/WHP/xB3bt3D+xZTktLU3JystLS0jRt2jQVFRWpZ8+eSk1N1Z133qnc3FxdeumlUY7+eN27dw/cr9GmW7du6tWrV6A/VuYzc+ZMXXbZZXrooYd04403auPGjXrmmWf0zDPPSFLgmUcPPvigzjnnHGVnZ+v+++9XZmamJkyYEN3g23HttdfqV7/6lQYMGKDzzjtPW7Zs0YIFC3TrrbdKsns+DQ0N2r17d+B1ZWWltm7dqp49e2rAgAGnjHvYsGH6wQ9+ENjq1NraqsLCQt18883KzMy0aj59+/bVD3/4Q1VUVOjNN9+U3+8P/LvQs2dPJSQkWDefWEO+iD4v5QrJW/kilnOF5K180VlzhXGMjM+++9FMiPfItR2uNXv27EDfqQ7Xam5uPm67eXJystatWxf2NdsV1TMzT+CJJ54wAwYMMAkJCWbMmDFm/fr10Q7plPT13ZzHtSVLlgTGHD161Pzrv/6rOfPMM80ZZ5xhrr/+erN///7oBR2ivz9S2pjYms+KFSvM+eefbxITE83QoUPNM888E/S+4zjm/vvvN+np6SYxMdFcddVVZseOHVGK9uTq6+vNXXfdZQYMGGCSkpLM4MGDzb//+7+b5ubmwBhb5/P++++3+/dkypQpxpiOxX3o0CEzadIkk5KSYlJTU83UqVPNkSNHojCbk8+nsrLyhP8uvP/++1bOJxaRL+wTy7nCGO/ki1jOFcZ4K190tlzR9viByzXeXKHrrGuXa7yRZKqqqkxdXV2gNTU1tTuf6upqI8l88MEHQf2/+MUvzJgxY9r9zKRJk8zw4cPNzp07jd/vN//93/9tkpOTTUJCQtjXbI/PGA8d3QIAAAAgapqampSdnW31iZopKSlqaGgI6ps7d67mzZt33Nh9+/apX79++uCDD4K2Hc+aNUtr1qxp9xEQBw8e1PTp07VixQr5fD4NGTJEeXl5eu6553T06NGwrtke67ZWAgAAAIhNSUlJqqysVEtLS7RDOSFjjHw+X1BfYmJiu2PDOVyrd+/eWr58uZqamnTo0CFlZmbqnnvu0eDBg8O+Znso5AAAAAC4JikpyROPJJGCD9dquwez7XCtwsLCk342KSlJ/fr1U2trq15//XXdeOON3/qaf49CDgAAAABO4FSHa91yyy3q169f4PEeGzZsUHV1tUaOHKnq6mrNmzdPjuNo1qxZHb5mR1DIAQAAAMAJ3HTTTTp48KDmzJmjmpoajRw5UitXrlR6erokae/evUGP+2hqatJ9992nPXv2KCUlRePHj9eLL76oHj16dPiaHcFhJwAAAAAQY6x6IDgAAAAA4NQo5AAAAAAgxlDIAQAAAECMoZADAAAAgBhDIQcAAAAAMYZCDgAAAABiDIUcAAAAAMQYCjkAAAAAiDEUcgAAAAAQYyjkAAAAACDGUMgBAAAAQIyhkAMAAACAGPP/AT7fRw6HVWyRAAAAAElFTkSuQmCC" }, - "output_type": "display_data" + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } ], - "source": "run_and_plot(64)" + "execution_count": 10 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:51.538933Z", + "start_time": "2025-11-19T13:55:31.442594Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": "run_and_plot(128)", - "id": "dc896c757ce5a2ed" + "id": "dc896c757ce5a2ed", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max Velocity: nan\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 11 }, { "cell_type": "code", - "execution_count": null, "id": "f21453ce", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-19T13:55:51.591439Z", + "start_time": "2025-11-19T13:55:51.589880Z" + } + }, + "source": [], "outputs": [], - "source": [] + "execution_count": null } ], "metadata": { From 251abe6ff99797a5bf6a1a8e8fdd941432cd7ccb Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 19 Nov 2025 16:32:11 +0100 Subject: [PATCH 08/21] fwbb and hwbb seem to be running now... Ibb1 gives NaN, but code runs... --- .../01a_script_cylinder_lowRe.py | 3 +- .../ebb_simulation.py | 17 ++- .../fullway_bounce_back_boundary.py | 3 +- .../halfway_bounce_back_boundary.py | 75 ++++++----- ...inear_interpolated_bounce_back_boundary.py | 116 +++++++++--------- .../obstacle_cylinder.py | 17 +-- 6 files changed, 124 insertions(+), 107 deletions(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 13321a2e..9b46c808 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -254,7 +254,7 @@ flow = ObstacleCylinder(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=args["bbbc_type"], stencil=stencil) + bc_type=str(args["bbbc_type"]), stencil=stencil) print("-> initializing collision operator...") collision_operator = None @@ -282,6 +282,7 @@ fig.subplots_adjust(right=0.85) u = flow.u_pu.cpu().numpy() print("Max Velocity:", u.max()) +im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7]) fig.colorbar(im2, cax=cbar_ax) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py index 23340180..2654e7e0 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -26,12 +26,27 @@ def __init__(self, flow: 'Flow', collision: 'Collision', else: self.post_streaming_boundaries = [] + # adjust No_collision_ and no_streaming_mask for use of post_streaming_boundaries (!) + if len(self.post_streaming_boundaries) > 0: + for i, boundary in enumerate(self.post_streaming_boundaries, start=self.collision_index + 1 + len(self.post_boundaries)): + print("nCM making: i, boundary = ", i, boundary) + print("collision index is:", self.collision_index) + ncm = boundary.make_no_collision_mask( + [it for it in self.flow.f.shape[1:]], context=self.context) + if ncm is not None: + self.no_collision_mask[ncm] = i + nsm = boundary.make_no_streaming_mask( + [it for it in self.flow.f.shape], context=self.context) + if nsm is not None: + self.no_streaming_mask |= nsm + # get indices of post_streaming_boundaries, that need f_collided to be stored self.store_f_collided_post_streaming_boundaries_index = [] - for i, boundary in enumerate(self.post_streaming_boundaries): if hasattr(boundary, "store_f_collided"): self.store_f_collided_post_streaming_boundaries_index.append(i) + if hasattr(boundary, "initialize_f_collided"): + boundary.initialize_f_collided() # redefine collide_and_stream() to include the storage of f_collided for use in post_streaming_boundaries def collide_and_stream(*_, **__): diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 393be170..3f64f97c 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -141,8 +141,7 @@ def calc_force_on_boundary(self, f): self.f_index_fwbb[:, 3]], self.flow.stencil.e[self.f_index_fwbb[:, 0]]) def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Optional[torch.Tensor]: - assert self.mask.shape == f_shape[1:] - return self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: pass diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index 3915d4e2..308f0c1f 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -22,7 +22,7 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, global_solid_mask = self.mask if periodicity is None: - periodicity = (False, False, False if self.flow.context.d == 3 else None) + periodicity = (False, False, False if self.flow.stencil.d == 3 else None) if calc_force is not None: self.force_sum = torch.zeros_like(self.context.convert_to_tensor( @@ -44,7 +44,7 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, else: #else do ghetto-neighbour_search below print("(INFO) HWBB didn't find solid_boundary_data, doing legacy neighbour_search on mask...") # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d - if self.flow.context.d == 2: + if self.flow.stencil.d == 2: nx, ny = self.mask.shape # domain size in x and y a, b = np.where(self.mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area @@ -54,32 +54,32 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, # ...they have to take the periodicity into account... border = np.zeros(self.flow.context.d, dtype=int) - if a[p] == 0 and self.flow.stencil.e[i, 0] == -1 and periodicity[0]: # searching border on left [x] + if a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]: # searching border on left [x] border[0] = -1 - elif a[p] == nx - 1 and self.flow.stencil.e[i, 0] == 1 and periodicity[0]: # searching border on right [x] + elif a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]: # searching border on right [x] border[0] = 1 - if b[p] == 0 and self.flow.stencil.e[i, 1] == -1 and periodicity[1]: # searching border on left [y] + if b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]: # searching border on left [y] border[1] = -1 - elif b[p] == ny - 1 and self.flow.stencil.e[i, 1] == 1 and periodicity[1]: # searching border on right [y] + elif b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]: # searching border on right [y] border[1] = 1 try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) - if (not self.mask[a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny] + if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny] and not global_solid_mask[ - a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny]): + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]): # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour self.f_index.append([self.flow.stencil.opposite[i], - a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny]) + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]) except IndexError: pass # just ignore this iteration since there is no neighbor there - if self.flow.context.d == 3: # like 2D, but in 3D...guess what... + if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... nx, ny, nz = self.mask.shape a, b, c = np.where(self.mask) @@ -87,34 +87,34 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, for i in range(0, self.flow.stencil.q): border = np.zeros(self.flow.context.d, dtype=int) # x - direction - if a[p] == 0 and self.flow.stencil.e[i, 0] == -1 and periodicity[0]: # searching border on left + if a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]: # searching border on left border[0] = -1 - elif a[p] == nx - 1 and self.flow.stencil.e[i, 0] == 1 and periodicity[0]: # searching border on right + elif a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]: # searching border on right border[0] = 1 # y - direction - if b[p] == 0 and self.flow.stencil.e[i, 1] == -1 and periodicity[1]: # searching border on left + if b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]: # searching border on left border[1] = -1 - elif b[p] == ny - 1 and self.flow.stencil.e[i, 1] == 1 and periodicity[1]: # searching border on right + elif b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]: # searching border on right border[1] = 1 # z - direction - if c[p] == 0 and self.flow.stencil.e[i, 2] == -1 and periodicity[2]: # searching border on left + if c[p] == 0 and self.flow.stencil.e[i][ 2] == -1 and periodicity[2]: # searching border on left border[2] = -1 - elif c[p] == nz - 1 and self.flow.stencil.e[i, 2] == 1 and periodicity[2]: # searching border on right + elif c[p] == nz - 1 and self.flow.stencil.e[i][ 2] == 1 and periodicity[2]: # searching border on right border[2] = 1 try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) - if (not self.mask[a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, - c[p] + self.flow.stencil.e[i, 2] - border[2] * nz] + if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz] and not global_solid_mask[ - a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, - c[p] + self.flow.stencil.e[i, 2] - border[2] * nz]): + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]): self.f_index.append([self.flow.stencil.opposite[i], - a[p] + self.flow.stencil.e[i, 0] - border[0] * nx, - b[p] + self.flow.stencil.e[i, 1] - border[1] * ny, - c[p] + self.flow.stencil.e[i, 2] - border[2] * nz]) + a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, + b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, + c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz]) except IndexError: pass # just ignore this iteration since there is no neighbor there @@ -129,37 +129,36 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, def __call__(self, flow): # calc force on boundary: + print("calling HWBB") if self.calc_force: self.calc_force_on_boundary() # bounce (invert populations on fluid nodes neighboring solid nodes) # f = torch.where(self.f_mask[self.flow.stencil.opposite], f_collided[self.flow.stencil.opposite], f) - if self.flow.context.d == 2: + if self.flow.stencil.d == 2: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2]] = self.f_collided[:, 0] - if self.flow.context.d == 3: + if self.flow.stencil.d == 3: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2], self.f_index[:, 3]] = self.f_collided[:, 0] - def make_no_collision_mask(self, shape: List[int], context: 'Context' + def make_no_collision_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: # INFO: for the halfway bounce back boundary, a no_collision_mask ist not necessary, because the no_stream_mask # ...prevents interaction between nodes inside and outside the boundary region. # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values # ...in the whole domain (including the boundary region)! - assert self.mask.shape == shape[1:] - return self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask, dtype=bool) - def make_no_streaming_mask(self, shape: List[int], context: 'Context' + def make_no_streaming_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: - assert self.mask.shape == shape[1:] # all dimensions of f except the 0th (q) # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally - return self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask, dtype= bool) def calc_force_on_boundary(self): # calculate force on boundary by momentum exchange method (MEA, MEM) according to Kruger et al., 2017, pp.215-217: @@ -169,14 +168,14 @@ def calc_force_on_boundary(self): #TODO: find a way to use pre- and post-Streaming Populations for bounce... def store_f_collided(self, f_collided): - if self.flow.context.d == 2: + if self.flow.stencil.d == 2: self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y - if self.flow.context.d == 3: + if self.flow.stencil.d == 3: self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2], # y diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index 2b9320c2..51d71b50 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -32,7 +32,9 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_f self.calc_force = True else: self.calc_force = False + #TODO: this if-statement with NONE and TRUE is wrong. When calc_force=False, it triggers calc_force=TRUE (!) + print("IBB: calc force is:", self.calc_force) # convert relevant tensors: ### TODO: fix batch-index-datatype...? self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer @@ -99,22 +101,22 @@ def __call__(self, flow): if self.calc_force: self.calc_force_on_boundary(flow.f) - def make_no_streaming_mask(self, shape, context: Context): - assert self.mask.shape == shape[1:] # all dimensions of f except the 0th (q) + def make_no_streaming_mask(self, f_shape, context: Context): # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally # return torch.tensor(self.mask, dtype=torch.bool) - return self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask, dtype=bool) - def make_no_collision_mask(self, shape, context: Context): + def make_no_collision_mask(self, f_shape, context: Context): # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values # ...in the whole domain (including the boundary region)! - assert self.mask.shape == shape[1:] + # return torch.tensor(self.mask, dtype=torch.bool) # self.context.convert_to_tensor(self.mask) - return self.context.convert_to_tensor(self.mask) + return self.context.convert_to_tensor(self.mask, dtype=bool) def calc_force_on_boundary(self, f_bounced): + #TODO: is not working in NEW lettuce ### force = e * (f_collided + f_bounced[opp.]) if self.flow.stencil.d == 2: self.force_sum = torch.einsum('i..., id -> d', @@ -146,58 +148,58 @@ def calc_force_on_boundary(self, f_bounced): self.flow.stencil.e[self.f_index_gt[:, 0]].float()) # TODO: find a way to use pre- and post-Streaming Populations for bounce... - def store_f_collided(self, f_collided): - for f_index_lgt, f_collided_lgt in zip([self.f_index_lt, self.f_index_gt], - [self.f_collided_lt, self.f_collided_gt]): - if len(f_index_lgt) != 0: - for d in range(self.flow.stencil.d): - indices = [f_index_lgt[:, 0], # q - f_index_lgt[:, 1], # x - f_index_lgt[:, 2]] # y - if self.flow.stencil.d == 3: - indices.append(f_index_lgt[:, 3]) - f_collided_lgt[:, 0] = torch.clone(f_collided[indices]) - indices[0] = self.opposite_tensor[f_index_lgt[:, 0]] - f_collided_lgt[:, 1] = torch.clone(f_collided[indices]) - # TODO: compare performance of THIS to original hardcoded "store_f_collided()" of IBB1, see below - - # >>> OLD version "semi hardcoded" # def store_f_collided(self, f_collided): - # if self.flow.stencil.d == 2: - # if len(self.f_collided_lt) != 0: - # self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q - # self.f_index_lt[:, 1], # x - # self.f_index_lt[:, 2]]) # y - # self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q - # self.f_index_lt[:, 1], # x - # self.f_index_lt[:, 2]]) # y - # if len(self.f_collided_gt) != 0: - # self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q - # self.f_index_gt[:, 1], # x - # self.f_index_gt[:, 2]]) # y - # self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q - # self.f_index_gt[:, 1], # x - # self.f_index_gt[:, 2]]) # y - # if self.flow.stencil.d == 3: - # if len(self.f_collided_lt) != 0: - # self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q - # self.f_index_lt[:, 1], # x - # self.f_index_lt[:, 2], # y - # self.f_index_lt[:, 3]]) # z - # self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q - # self.f_index_lt[:, 1], # x - # self.f_index_lt[:, 2], # y - # self.f_index_lt[:, 3]]) # z - # if len(self.f_collided_gt) != 0: - # self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q - # self.f_index_gt[:, 1], # x - # self.f_index_gt[:, 2], # y - # self.f_index_gt[:, 3]]) # z - # self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q - # self.f_index_gt[:, 1], # x - # self.f_index_gt[:, 2], # y - # self.f_index_gt[:, 3]]) # z - # <<< OLD version "semi hardcoded" + # for f_index_lgt, f_collided_lgt in zip([self.f_index_lt, self.f_index_gt], + # [self.f_collided_lt, self.f_collided_gt]): + # if len(f_index_lgt) != 0: + # for d in range(self.flow.stencil.d): + # indices = [f_index_lgt[:, 0], # q + # f_index_lgt[:, 1], # x + # f_index_lgt[:, 2]] # y + # if self.flow.stencil.d == 3: + # indices.append(f_index_lgt[:, 3]) + # f_collided_lgt[:, 0] = torch.clone(f_collided[indices]) + # indices[0] = self.opposite_tensor[f_index_lgt[:, 0]] + # f_collided_lgt[:, 1] = torch.clone(f_collided[indices]) + # # TODO: compare performance of THIS to original hardcoded "store_f_collided()" of IBB1, see below + + #>>> OLD version "semi hardcoded" + def store_f_collided(self, f_collided): + if self.flow.stencil.d == 2: + if len(self.f_collided_lt) != 0: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]]) # y + self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]]) # y + if len(self.f_collided_gt) != 0: + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]]) # y + self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]]) # y + if self.flow.stencil.d == 3: + if len(self.f_collided_lt) != 0: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]]) # z + self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]]) # z + if len(self.f_collided_gt) != 0: + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]]) # z + self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]]) # z + #<<< OLD version "semi hardcoded" # TODO: find a way to use pre- and post-Streaming Populations for bounce... def initialize_f_collided(self): diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 26044618..92cb9d39 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -408,7 +408,7 @@ def make_ibb_index_lists(self, x_center, y_center, radius): pass # just ignore this iteration since there is no neighbor there # output as np.array: f_index_lt, f_index_gt, d_lt, d_gt - return [f_index_lt, f_index_gt, d_lt, d_gt] + return [np.array(f_index_lt), np.array(f_index_gt), np.array(d_lt), np.array(d_gt)] @property @@ -450,7 +450,7 @@ def post_boundaries(self): return [ inlet_boundary, outlet_boundary, - BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) + #BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask,dtype=bool)) ] else: return [ @@ -469,19 +469,20 @@ def post_streaming_boundaries(self): solid_boundary_data.solid_mask = self.obstacle_mask # index-lists for circular cylinder [f_index_lt, f_index_gt, d_lt, d_gt] - solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.dgt = self.make_ibb_index_lists( + solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.d_gt = self.make_ibb_index_lists( x_center=self.x_pos, y_center=self.y_pos, radius=self.radius) - if self.bc_type == 'fwbb' or self.bc_type == 'FWBB': + print("CYLINDER: the bc_type was given as:", self.bc_type) + if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': obstacle_boundary = FullwayBounceBackBoundary(self.context, self, self.obstacle_mask) # TODO: add periodicity, global_solid_mask and calc_force - elif self.bc_type == 'hwbb' or self.bc_type == 'HWBB': + elif self.bc_type.casefold() == 'hwbb' or self.bc_type == 'HWBB': obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, solid_boundary_data) # TODO: add periodicity, global_solid_mask and calc_force - elif self.bc_type == 'ibb1' or self.bc_type == 'IBB1': - obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, - calc_force=True) + elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data) else: # use basic mask Bounce Back + print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted...") obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) return [obstacle_boundary] From ea1705f8bce28146723efd2359d8f9ba8b81b185 Mon Sep 17 00:00:00 2001 From: mbille Date: Fri, 21 Nov 2025 14:57:55 +0100 Subject: [PATCH 09/21] ibb1 seems to work, now has to be tested with observables etc. to compare to MP2 and cylinder2025 results! --- .../01a_script_cylinder_lowRe.py | 56 ++- .../Interpolated_compact2_forComparison.py | 319 ++++++++++++++++++ .../ebb_simulation.py | 6 +- .../halfway_bounce_back_boundary.py | 5 +- .../obstacle_cylinder.py | 42 +-- lettuce/ext/_boundary/equilibrium_outlet_p.py | 11 +- 6 files changed, 392 insertions(+), 47 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 9b46c808..c779ea20 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -26,6 +26,8 @@ ## OLD from lettuce.max import draw_circular_mask import torch +torch.autograd.set_detect_anomaly(True) + import time import datetime import os @@ -65,9 +67,9 @@ parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") -parser.add_argument("--domain_length_x_in_d", default=None, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") -parser.add_argument("--domain_height_y_in_d", default=None, help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") -parser.add_argument("--domain_width_z_in_d", default=None, help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") +parser.add_argument("--domain_length_x_in_d", default=None, type=float, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") +parser.add_argument("--domain_height_y_in_d", default=None, type=float,help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") +parser.add_argument("--domain_width_z_in_d", default=None, type=float,help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") @@ -98,7 +100,7 @@ ########################################################### # CREATE timestamp, sim-ID, outdir and outdir_data -print("SCRIPT: Creating timestamt, simulation ID and creating output directory...") +print("SCRIPT: Creating timestamp, simulation ID and creating output directory...") name = args["name"] outdir = args["outdir"] outdir_data = args["outdir_data"] @@ -169,6 +171,8 @@ if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_in_d to 1 lattice node") + else: + domain_width_z_in_d = args["domain_width_z_in_d"] # if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain # ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder @@ -193,7 +197,7 @@ # calculate lu-domain-resolution, total number of gridpoints and check correct stencil if dims == 2: - resolution = [domain_length_x_in_d * char_length_lu, domain_height_y_in_d * char_length_lu] + resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d if args["stencil"] == "D2Q9": stencil = lt.D2Q9() @@ -201,7 +205,7 @@ print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") stencil= lt.D2Q9() else: - resolution = [domain_length_x_in_d * char_length_lu, domain_height_y_in_d * char_length_lu, domain_width_z_in_d * char_length_lu] + resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d if args["stencil"] == "D3Q15": stencil = lt.D3Q15() @@ -271,22 +275,40 @@ print("-> initializing reporters...") #TODO reporter +# VTK Reporter -> visualization +if True: + vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") + # export obstacle + mask_dict = dict() + if dims ==2: + mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) + else: + mask_dict["mask"] = flow.obstacle_mask.astype(int) + imageToVTK( + path=outdir_data+"/vtk/obstacle_point", + pointData=mask_dict, + ) + imageToVTK( + path=outdir_data+"/vtk/obstacle_cell", + cellData=mask_dict, + ) + print("\nSCRIPT: initializing simulation object...") -simulation = EbbSimulation(flow, collision_operator,reporter=[]) +simulation = EbbSimulation(flow, collision_operator,reporter=[vtk_reporter]) print(f"\nSCRIPT: running simulation for {n_steps} steps...") simulation(num_steps=n_steps) - -fig, axes = plt.subplots(1, 2, figsize=(10, 3)) -fig.subplots_adjust(right=0.85) -u = flow.u_pu.cpu().numpy() -print("Max Velocity:", u.max()) -im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") -im2 = axes[1].imshow(u[0, ...].T, origin="lower") -cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7]) -fig.colorbar(im2, cax=cbar_ax) -fig.show() +if dims == 2: + fig, axes = plt.subplots(1, 2, figsize=(10, 3)) + fig.subplots_adjust(right=0.85) + u = flow.u_pu.cpu().numpy() + print("Max Velocity:", u.max()) + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") + im2 = axes[1].imshow(u[0, ...].T, origin="lower") + cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7]) + fig.colorbar(im2, cax=cbar_ax) + fig.show() ## END OF SCRIPT diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py b/examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py new file mode 100644 index 00000000..6bd7bd08 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py @@ -0,0 +1,319 @@ +import numpy as np +import torch + +class InterpolatedBounceBackBoundary_compact_v2: + + def __init__(self, mask, lattice, x_center, y_center, radius, interpolation_order=1): + t_init_start = time.time() + self.interpolation_order = interpolation_order + self.mask = mask # location of solid-nodes + self.lattice = lattice + self.force_sum = torch.zeros_like(self.lattice.convert_to_tensor( + self.lattice.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + + self.f_index_lt = [] # indices of relevant populations (for bounce back and force-calculation) with d<=0.5 + self.f_index_gt = [] # indices of relevant populations (for bounce back and force-calculation) with d>0.5 + self.d_lt = [] # distances between node and boundary for d<0.5 + self.d_gt = [] # distances between node and boundary for d>0.5 + + # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d + if self.lattice.D == 2: + nx, ny = mask.shape # domain size in x and y + a, b = np.where(mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area + + for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask + for i in range(0, self.lattice.Q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. + # ...they have to take the periodicity into account... + border = np.zeros(self.lattice.D, dtype=int) + + if a[p] == 0 and self.lattice.stencil.e[i, 0] == -1: # searching border on left [x] + border[0] = -1 + elif a[p] == nx - 1 and self.lattice.e[i, 0] == 1: # searching border on right [x] + border[0] = 1 + + if b[p] == 0 and self.lattice.stencil.e[i, 1] == -1: # searching border on left [y] + border[1] = -1 + elif b[p] == ny - 1 and self.lattice.e[i, 1] == 1: # searching border on right [y] + border[1] = 1 + + try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + if not mask[a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]: + # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: + # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate + cx = self.lattice.stencil.e[ + self.lattice.stencil.opposite[i], 0] # link-direction x to solid node + cy = self.lattice.stencil.e[ + self.lattice.stencil.opposite[i], 1] # link-direction y to solid node + + # pq-formula + h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + self.d_lt.append(d1) + self.f_index_lt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) + else: # d>0.5 + self.d_gt.append(d1) + self.f_index_gt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + self.d_lt.append(d2) + self.f_index_lt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) + else: # d>0.5 + self.d_gt.append(d2) + self.f_index_gt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) + else: # neither d1 or d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], + b[p], self.lattice.stencil.e[i, 0], self.lattice.stencil.e[i, 1], + self.lattice.stencil.e[i, 2]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + if self.lattice.D == 3: # like 2D, but in 3D...guess what... + nx, ny, nz = mask.shape + a, b, c = np.where(mask) + + for p in range(0, len(a)): + for i in range(0, self.lattice.Q): + border = np.zeros(self.lattice.D, dtype=int) + # x - direction + if a[p] == 0 and self.lattice.stencil.e[i, 0] == -1: # searching border on left + border[0] = -1 + elif a[p] == nx - 1 and self.lattice.e[i, 0] == 1: # searching border on right + border[0] = 1 + # y - direction + if b[p] == 0 and self.lattice.stencil.e[i, 1] == -1: # searching border on left + border[1] = -1 + elif b[p] == ny - 1 and self.lattice.e[i, 1] == 1: # searching border on right + border[1] = 1 + # z - direction + if c[p] == 0 and self.lattice.stencil.e[i, 2] == -1: # searching border on left + border[2] = -1 + elif c[p] == nz - 1 and self.lattice.e[i, 2] == 1: # searching border on right + border[2] = 1 + + try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) + if not mask[a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, + c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]: + + # calculate intersection point of boundary surface and link -> + # ...calculate distance between fluid node and boundary surface on the link + px = a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate + py = b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate + # Z-coodinate not needed for cylinder ! + + cx = self.lattice.stencil.e[ + self.lattice.stencil.opposite[i], 0] # link-direction x to solid node + cy = self.lattice.stencil.e[ + self.lattice.stencil.opposite[i], 1] # link-direction y to solid node + # Z-coodinate not needed for cylinder ! + + # pq-formula + h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h2 = (px * px + py * py + x_center * x_center + y_center * y_center + - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( + cx * cx + cy * cy) # q + + d1 = - h1 + np.sqrt(h1 * h1 - h2) + d2 = - h1 - np.sqrt(h1 * h1 - h2) + + # print("xb,yb,i,d1,d2 xf, yf, cx, cy:", a[p], b[p], i, d1, d2, px, py, cx, cy) + + # distance from fluid node to the "true" boundary location + # choose correct d and assign d and f_index + if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 + + if d1 <= 0.5: + self.d_lt.append(d1) + self.f_index_lt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, + c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) + else: # d>0.5 + self.d_gt.append(d1) + self.f_index_gt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, + c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) + + elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 + + if d2 <= 0.5: + self.d_lt.append(d2) + self.f_index_lt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, + c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) + else: # d>0.5 + self.d_gt.append(d2) + self.f_index_gt.append([self.lattice.stencil.opposite[i], + a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, + b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, + c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) + else: # neither d1 or d2 is real and between 0 and 1 + print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], + b[p], c[p], self.lattice.stencil.e[i, 0], self.lattice.stencil.e[i, 1], + self.lattice.stencil.e[i, 2]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + + # convert relevant tensors: + self.f_index_lt = torch.tensor(np.array(self.f_index_lt), device=self.lattice.device, + dtype=torch.int64) # the batch-index has to be integer + self.f_index_gt = torch.tensor(np.array(self.f_index_gt), device=self.lattice.device, + dtype=torch.int64) # the batch-index has to be integer + self.d_lt = self.lattice.convert_to_tensor(np.array(self.d_lt)) + self.d_gt = self.lattice.convert_to_tensor(np.array(self.d_gt)) + self.opposite_tensor = torch.tensor(self.lattice.stencil.opposite, device=self.lattice.device, + dtype=torch.int64) # batch-index has to be a tensor + + f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values + f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values + f_collided_lt_opposite = torch.zeros_like(self.d_lt) + f_collided_gt_opposite = torch.zeros_like(self.d_gt) + self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) + self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) + + print("IBB initialization took " + str(time.time() - t_init_start) + "seconds") + + def __call__(self, f): + ## f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout + + if self.lattice.D == 2: + # BOUNCE + # if d <= 0.5 + f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * f[self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]] + # if d > 0.5 + f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + + if self.lattice.D == 3: + # BOUNCE + # if d <= 0.5 + f[self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * f[self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]] + # if d > 0.5 + f[self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + + + # CALC. FORCE on boundary (MEM, MEA) + self.calc_force_on_boundary(f) + return f + + def make_no_stream_mask(self, f_shape): + assert self.mask.shape == f_shape[1:] # all dimensions of f except the 0th (q) + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). + # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally + return self.lattice.convert_to_tensor(self.mask) + + def make_no_collision_mask(self, f_shape): + # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, + # ...in the initial solution of your flow, especially if visualization or post processing uses the field-values + # ...in the whole domain (including the boundary region)! + assert self.mask.shape == f_shape[1:] + return self.lattice.convert_to_tensor(self.mask) + + def calc_force_on_boundary(self, f_bounced): + ### force = e * (f_collided + f_bounced[opp.]) + if self.lattice.D == 2: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]], + self.lattice.e[self.f_index_lt[:, 0]]) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2]], + self.lattice.e[self.f_index_gt[:, 0]]) + if self.lattice.D == 3: + self.force_sum = torch.einsum('i..., id -> d', + self.f_collided_lt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_lt[:, 0]], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]], + self.lattice.e[self.f_index_lt[:, 0]]) \ + + torch.einsum('i..., id -> d', + self.f_collided_gt[:, 0] + f_bounced[ + self.opposite_tensor[self.f_index_gt[:, 0]], + self.f_index_gt[:, 1], + self.f_index_gt[:, 2], + self.f_index_gt[:, 3]], + self.lattice.e[self.f_index_gt[:, 0]]) + + def store_f_collided(self, f_collided): + if self.lattice.D == 2: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]]) # y + self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2]]) # y + + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]]) # y + self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2]]) # y + if self.lattice.D == 3: + self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]]) # z + self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_index_lt[:, 1], # x + self.f_index_lt[:, 2], # y + self.f_index_lt[:, 3]]) # z + + self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]]) # z + self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + self.f_index_gt[:, 1], # x + self.f_index_gt[:, 2], # y + self.f_index_gt[:, 3]]) # z \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py index 2654e7e0..cef2d48f 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -20,7 +20,7 @@ def __init__(self, flow: 'Flow', collision: 'Collision', flow.context.use_native = False super().__init__(flow, collision, reporter) - if hasattr(flow, 'post_streaming_boundaries'): + if hasattr(flow.__class__, 'post_streaming_boundaries'): # list of boundaries that are applied AFTER the streaming step self.post_streaming_boundaries = flow.post_streaming_boundaries else: @@ -43,9 +43,9 @@ def __init__(self, flow: 'Flow', collision: 'Collision', # get indices of post_streaming_boundaries, that need f_collided to be stored self.store_f_collided_post_streaming_boundaries_index = [] for i, boundary in enumerate(self.post_streaming_boundaries): - if hasattr(boundary, "store_f_collided"): + if hasattr(boundary.__class__, "store_f_collided"): self.store_f_collided_post_streaming_boundaries_index.append(i) - if hasattr(boundary, "initialize_f_collided"): + if hasattr(boundary.__class__, "initialize_f_collided"): boundary.initialize_f_collided() # redefine collide_and_stream() to include the storage of f_collided for use in post_streaming_boundaries diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index 308f0c1f..59ad7aa4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -9,7 +9,7 @@ class HalfwayBounceBackBoundary(Boundary): - def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): self.context = context self.flow = flow @@ -36,10 +36,13 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData = None, # combine f_index_lt and f_index_gt to self.f_index if (hasattr(solid_boundary_data, "f_index_gt") or hasattr(solid_boundary_data, "f_index_lt")) and len(solid_boundary_data.f_index_lt.shape) == len(solid_boundary_data.f_index_gt.shape): # if solid_boundary_data contains batch_indices, use them + print("HWBB: SBD has got attributs f_index_gt and f_index_lt!") self.f_index = np.concatenate((solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt), axis=0) elif hasattr(solid_boundary_data, "f_index_gt") and solid_boundary_data.f_index_lt.shape[0] == 0: + print("HWBB: SBD has got attributf_index_gt (NO f_index_lt)!") self.f_index = solid_boundary_data.f_index_gt elif hasattr(solid_boundary_data, "f_index_lt") and solid_boundary_data.f_index_gt.shape[0] == 0: + print("HWBB: SBD has got attributf_index_lt (NO f_index_gt)!") self.f_index = solid_boundary_data.f_index_lt else: #else do ghetto-neighbour_search below print("(INFO) HWBB didn't find solid_boundary_data, doing legacy neighbour_search on mask...") diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 92cb9d39..62a4189d 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -59,33 +59,35 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.resolution = self.make_resolution(resolution, stencil) # shape in LU, if only INT, a cube shaped domain is assumed self.char_velocity_pu = char_velocity_pu + print("OBST_Cylinder: self.resolution=", self.resolution) # flow and boundary settings - self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street - self.u_init = u_init # toggle: initial solution velocity profile type + self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street + self.u_init = u_init # toggle: initial solution velocity profile type self.lateral_walls = lateral_walls # toggle: lateral walls to be bounce back (bounceback), slip wall (slip) or periodic (periodic) - self.bc_type = bc_type # toggle: bounce back algorithm: halfway (hwbb), fullway (fwbb), linearly interpolated (ibb1) + self.bc_type = bc_type # toggle: bounce back algorithm: halfway (hwbb), fullway (fwbb), linearly interpolated (ibb1) # initialize masks (init with zeros) self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) - self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls - self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calc.) + self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls + self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calc.) # initialize super class with unit conversion, equilibrium, context etc. ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, stencil, equilibrium) # UnitConversion: defined below unter make_units(), executed by ExtFlow; flow object gets units-attibute! - self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes + self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes - # cylinder geometry in LU (1-based indexing!) + # cylinder geometry in LU self.x_offset = x_offset self.y_offset = y_offset - self.radius = char_length_lu / 2 - self.y_pos = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing - self.x_pos = self.y_pos + self.x_offset # keep symmetry of cylinder in x and y direction + self.radius_lu = char_length_lu / 2 + #todo: das PLUS hier ist ein Problem! + self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing + self.x_pos_lu = self.y_pos_lu + self.x_offset # keep symmetry of cylinder in x and y direction # MESHGRID of x, y, (z) index (LU) xyz = tuple(np.linspace(1, n, n) for n in self.resolution) # Tupel of index-lists (1-n (one-based!)) @@ -99,10 +101,12 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], # BASIC mask-Version of circular cylinder. #TODO: Options for obstacle def: 1) basic mask (no interpolation), 2) IBB-index and mask, 3) OCC-enabled - condition = np.sqrt((x_lu - self.x_pos) ** 2 + (y_lu - self.y_pos) ** 2) < self.radius + condition = np.sqrt((x_lu - self.x_pos_lu) ** 2 + (y_lu - self.y_pos_lu) ** 2) < self.radius_lu self.obstacle_mask[np.where(condition)] = 1 self.solid_mask[np.where(condition)] = 1 + print("OBST_Cylinder: x_pos, y_pos, radius:", self.x_pos_lu, self.y_pos_lu, self.radius_lu) + # MASKS for solid boundaries (lateral walls, obstacle, solid (= obstacle and walls), inlet) # (INFO): indexing doesn't need z-Index for 3D, everything is broadcasted along z! if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': # if top and bottom are link-based BC @@ -148,7 +152,7 @@ def make_units(self, reynolds_number, mach_number, resolution: List[int] characteristic_velocity_pu=self.char_velocity_pu ) - # gibt immer N-dimensional zurück! (flow.resolution darf auch n int sein, dann wird kubisch angenommen) + # gibt immer N-dimensional zurück! (flow.resolution darf auch ein int sein, dann wird quadratisch/kubisch angenommen) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): @@ -243,7 +247,7 @@ def make_ibb_index_lists(self, x_center, y_center, radius): # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d if self.stencil.d == 2: # TODO: the 2D and 3D options could be condensed/unified - nx, ny = self.resolution # domain size in x and y + nx, ny = self.obstacle_mask.shape # domain size in x and y a, b = np.where(self.obstacle_mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask @@ -313,8 +317,7 @@ def make_ibb_index_lists(self, x_center, y_center, radius): b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # neither d1 or d2 is real and between 0 and 1 print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], - b[p], self.stencil.e[i][0], self.stencil.e[i][1], - self.stencil.e[i][2]) + b[p], self.stencil.e[i][0], self.stencil.e[i][1]) except IndexError: pass # just ignore this iteration since there is no neighbor there @@ -352,10 +355,8 @@ def make_ibb_index_lists(self, x_center, y_center, radius): py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate # Z-coordinate not needed for cylinder ! - cx = self.stencil.e[ - self.stencil.opposite[i]][ 0] # link-direction x to solid node - cy = self.stencil.e[ - self.stencil.opposite[i]][ 1] # link-direction y to solid node + cx = self.stencil.e[self.stencil.opposite[i]][0] # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][1] # link-direction y to solid node # Z-coordinate not needed for cylinder ! # pq-formula @@ -469,8 +470,9 @@ def post_streaming_boundaries(self): solid_boundary_data.solid_mask = self.obstacle_mask # index-lists for circular cylinder [f_index_lt, f_index_gt, d_lt, d_gt] + # [np.array(f_index_lt), np.array(f_index_gt), np.array(d_lt), np.array(d_gt)] solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.d_gt = self.make_ibb_index_lists( - x_center=self.x_pos, y_center=self.y_pos, radius=self.radius) + x_center=self.x_pos_lu-1, y_center=self.y_pos_lu-1, radius=self.radius_lu) print("CYLINDER: the bc_type was given as:", self.bc_type) if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': diff --git a/lettuce/ext/_boundary/equilibrium_outlet_p.py b/lettuce/ext/_boundary/equilibrium_outlet_p.py index e2fbf458..489330e2 100644 --- a/lettuce/ext/_boundary/equilibrium_outlet_p.py +++ b/lettuce/ext/_boundary/equilibrium_outlet_p.py @@ -65,23 +65,22 @@ def __call__(self, flow: 'Flow'): other = [slice(None)] + self.neighbor rho = flow.rho() u = flow.u() - rho_w = self.rho_outlet * torch.ones_like(rho[here]) - u_w = u[other] + rho_w = self.rho_outlet * torch.ones_like(rho[tuple(here)]) + u_w = u[tuple(other)] feq = flow.f - feq[here] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[ + feq[tuple(here)] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[ ..., 0] return torch.einsum("q...,q...->q...", [feq, torch.ones_like(flow.f)]) def make_no_streaming_mask(self, shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: no_streaming_mask = context.zero_tensor(shape, dtype=bool) - no_streaming_mask[[np.setdiff1d(np.arange(shape[0]), self.velocities)] - + self.index] = 1 + no_streaming_mask[tuple([np.setdiff1d(np.arange(shape[0]), self.velocities)] + self.index)] = 1 return no_streaming_mask def make_no_collision_mask(self, shape: List[int], context: 'Context'): no_collision_mask = context.zero_tensor(shape, dtype=torch.bool) - no_collision_mask[self.index] = 1 + no_collision_mask[tuple(self.index)] = 1 return no_collision_mask def native_available(self) -> bool: From a04b42f83836590a9e7fef9489af93859dc91d0b Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 26 Nov 2025 17:14:05 +0100 Subject: [PATCH 10/21] implemented Drag, Lift and more print-statements for cylinder-flow example to validate against MP2 and cylinder-paper data; to be tested... --- .../01a_script_cylinder_lowRe.py | 445 ++++++++++++++++-- .../fullway_bounce_back_boundary.py | 18 +- .../halfway_bounce_back_boundary.py | 16 +- ...inear_interpolated_bounce_back_boundary.py | 11 +- .../observables_force_coefficients.py | 59 +++ .../obstacle_cylinder.py | 12 +- .../reporter_NaNreporter.py | 98 ++++ lettuce/ext/_reporter/observable_reporter.py | 3 +- 8 files changed, 605 insertions(+), 57 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index c779ea20..689a6614 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -6,9 +6,10 @@ import lettuce as lt from obstacle_cylinder import ObstacleCylinder from ebb_simulation import EbbSimulation +from observables_force_coefficients import DragCoefficient, LiftCoefficient import matplotlib.pyplot as plt -#from scipy.signal import find_peaks +from scipy.signal import find_peaks import sys import warnings @@ -74,6 +75,11 @@ parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") +# additional: +# - char_density (PU house) +# - char_pressure +# - vicsosity PU (house, + # solver settings parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0, end of sim will be step_start+n_steps") parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, t_start will be calculated by PU/LU-conversion of step_start") @@ -82,13 +88,35 @@ parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") -# reporter settings -#TODO: add vtk reporter -#TODO: add drag reporter -#TODO: add lift reporter -#TODO: add NAN reporter -#TODO: add watchdog reporter -#TODO: add highMa reporter +# reporter and observable settings +#TODO: add vtk reporter (fps_pu, interval_lu, +# - 3D full +# - 2D slice normal (x,y,z) +# - obstacle point, obstacle cell speichern und abh. von 2D/3D korrekt formatieren +#TODO: add drag/lift reporter +# - periodic start relative (relative start of interval to do statistics over +# - plot lift, drag, strouhal number (try except...) +#TODO: add U-profile-reporter (output True, store/calculate true) +# -> condense in own functions +# -> make script to plot from data (see PLOTTING scripts in CYLINDER-paper for layout) +#TODO: add NAN reporter (on/off, interval) +#TODO: add watchdog reporter (on/off, interval) +#TODO: add highMa reporter (on/off, interval) +#TODO: add 2D-mp4-reporter... (fps_video, number of frames ODER fps_pu) + +# Checkpointing +# TODO: add checkpointing-utilities (read, write) +# - checkpoint IN path +# - checkpoint OUT path (if only one is given, take this one for both) +# - checkpoint i_start, t_start?, + +# OUTPUTS: +# - parameters INPUT +# - print 2D-slice with mask(s) +# - parameters SIMULATED (before sim()) +# - 2D slice last frame +# - output condensed observables (drag, lift, strouhal) to file... @MP2 +# - stats and performance (end) # put arguments in dictionary print("SCRIPT: Writing arguments to dictionary...") @@ -139,9 +167,9 @@ print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") # START LOGGER -> get all terminal output into file -# print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") -# old_stdout = sys.stdout -# sys.stdout = Logger(outdir) +print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") +old_stdout = sys.stdout +sys.stdout = Logger(outdir) ##################################### @@ -170,7 +198,7 @@ dims = 3 if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node - print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_in_d to 1 lattice node") + print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_z_in_d to 1 lattice node") else: domain_width_z_in_d = args["domain_width_z_in_d"] @@ -242,11 +270,6 @@ pass ########################################### -#todo parameter (args) vtk_fps, -# lateral_walls = args["lateral_walls"] -# #vtk_fps = 10 # FramesPerSecond (PU) for vtk-output -# cuda_device = args["default_device"] -# #nan_reporter = args["nan_reporter"] print("SCRIPT: initializing solver components...") @@ -258,7 +281,7 @@ flow = ObstacleCylinder(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=str(args["bbbc_type"]), stencil=stencil) + bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True) print("-> initializing collision operator...") collision_operator = None @@ -272,8 +295,28 @@ else: # default to bgk collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) +print("\nSCRIPT: initializing simulation object...") +simulation = EbbSimulation(flow, collision_operator,reporter=[]) + + +# REPORTERS: initialize and append to simulation.reporter print("-> initializing reporters...") -#TODO reporter + +# DRAG and LIFT Force Cefficients and respective reporters: +cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d +DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[0], solid_mask=simulation.post_streaming_boundaries[0].mask, area_pu=cylinder_cross_sectional_area) +DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) +simulation.reporter.append(DragReporter) +LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[0], solid_mask=simulation.post_streaming_boundaries[0].mask, area_pu=cylinder_cross_sectional_area) +LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) +simulation.reporter.append(LiftReporter) + +# NAN REPORTER (if nan detected -> stop simulation) +# TODO: add NaN reporter +# - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... + +# Watchdog/Progress-Reporter +# TODO: Progress-Reporter # VTK Reporter -> visualization if True: @@ -293,27 +336,367 @@ cellData=mask_dict, ) -print("\nSCRIPT: initializing simulation object...") -simulation = EbbSimulation(flow, collision_operator,reporter=[vtk_reporter]) + simulation.reporter.append(vtk_reporter) -print(f"\nSCRIPT: running simulation for {n_steps} steps...") -simulation(num_steps=n_steps) +# DRAW CYLINDER-MASK in 2D (xy-plane) +# TODO: port draw_circular_mask function from MP2/Paper to helperCode.py + +################################################## +# PRINT PARAMETERS prior to simulation: +print("shape_LU:", flow.resolution) +print("T with", n_steps, "steps:", + round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 2), + "seconds") +print("n_steps to simulate 1 second:", + round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") +print("n_steps to simulate", t_target, "seconds:", + t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), + "steps") + +# RUN SIMULATION: +print(f"\nSCRIPT: running simulation for {n_steps} steps...") +t_start = time.time() +mlups = simulation(num_steps=n_steps) +t_end = time.time() +runtime = t_end - t_start + +# output stats +print("MLUPS:", mlups) +print("PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") +print("number of steps:", n_steps) +print("runtime: ", runtime, "seconds (", round(runtime / 60, 2), "minutes )") + +print("current VRAM (MB): ", torch.cuda.memory_allocated(context.device) / 1024 / 1024) +print("max. VRAM (MB): ", torch.cuda.max_memory_allocated(context.device) / 1024 / 1024) + +[cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] +print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) + +ram = psutil.virtual_memory() +print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( + round(ram.total / (1024 * 1024), 2)) + " MB") + +### export stats +if not no_data_flag: + output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-STATS ###") + output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\n") + output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.close() + +# PLOTTING 2D IMAGE + +fig, axes = plt.subplots(1, 2, figsize=(10, 3)) +fig.subplots_adjust(right=0.85) +u = flow.u_pu.cpu().numpy() +print("Max Velocity:", u.max()) if dims == 2: - fig, axes = plt.subplots(1, 2, figsize=(10, 3)) - fig.subplots_adjust(right=0.85) - u = flow.u_pu.cpu().numpy() - print("Max Velocity:", u.max()) im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") - cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7]) - fig.colorbar(im2, cax=cbar_ax) - fig.show() +elif dims == 3: + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") + im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") +cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) +fig.colorbar(im2, cax=cbar_ax) +fig.show() + + +# PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: + +# COEFF. OF DRAG +try: + try: + drag_coefficient = np.array(DragReporter.out) + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag Cd") + ax.set_ylim([0.5, 1.6]) # change y-limits + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timestep (simulation time / LU)") + except: + print("(!) drag_plotting didn't work...'") + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/drag_coefficient.png") + except: + print("(!) saving 'drag_coefficient.png' failed!") + try: + np.savetxt(outdir_data + "/drag_coefficient.txt", drag_coefficient, + header="stepLU | timePU | Cd FROM str(timestamp)") + except: + print("(!) saving drag_timeseries failed!") + ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, + drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) + if not no_data_flag: + try: + plt.savefig(outdir_data + "/drag_coefficient_adjusted.png") + except: + print("(!) saving drag_coefficient_adjusted.png failed!") + plt.close() +except: + print("(!) analysing drag_coefficient didn't work'") + +# peak finder: try calculating the mean drag coefficient from an integer number of periods, if a clear periodic signal is found +try: + values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] + + peaks_max = find_peaks(values, prominence=((values.max() - values.min()) / 2)) + peaks_min = find_peaks(-values, prominence=((values.max() - values.min()) / 2)) + if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: + peak_number = peaks_max[0].shape[0] + else: + peak_number = peaks_min[0].shape[0] + + if peaks_min[0][0] < peaks_max[0][0]: + first_peak = peaks_min[0][0] + last_peak = peaks_max[0][peak_number - 1] + else: + first_peak = peaks_max[0][0] + last_peak = peaks_min[0][peak_number - 1] + + drag_mean = values[first_peak:last_peak].mean() + drag_mean_simple = values.mean() + + print("Cd, simple mean: ", drag_mean_simple) + print("Cd, peak_finder mean:", drag_mean) + + drag_stepsLU = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 0] + peak_max_y = values[peaks_max[0]] + peak_max_x = drag_stepsLU[peaks_max[0]] + peak_min_y = values[peaks_min[0]] + peak_min_x = drag_stepsLU[peaks_min[0]] + + plt.plot(drag_stepsLU, values) + plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) + plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) + plt.scatter(drag_stepsLU[first_peak], values[first_peak]) + plt.scatter(drag_stepsLU[last_peak], values[last_peak]) + plt.savefig(outdir_data + "/drag_coefficient_peakfinder.png") + peakfinder = True +except: # if signal is not sinusoidal enough, calculate only simple mean value + print( + "peak-finding didn't work... probably no significant peaks visible (Re<46?), or periodic region not reached (T too small)") + values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] + drag_mean_simple = values.mean() + peakfinder = False + print("Cd, simple mean:", drag_mean_simple) +try: + plt.close() +except: + pass + +# LIFT COEFFICIENT +try: + lift_coefficient = np.array(LiftReporter.out) + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(lift_coefficient[:, 1], lift_coefficient[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Lift Cl") + ax.set_ylim((-1.1, 1.1)) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + if not no_data_flag: + try: + plt.savefig(outdir_data + "/lift_coefficient.png") + except: + print("(!) saving lift_coefficient.png didn't work!") + try: + np.savetxt(outdir_data + "/lift_coefficient.txt", lift_coefficient, + header="stepLU | timePU | Cl FROM str(timestamp)") + except: + print("(!) saving lift_timeline didn't work!") + Cl_min = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].min() + Cl_max = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].max() + print("Cl_peaks: \nmin", Cl_min, "\nmax", Cl_max) + plt.close() +except: + print("(!) analysing lift_coefficient didn't work!") + +# plot DRAG and LIFT together: +try: + fig, ax = plt.subplots(layout="constrained") + drag_ax = ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2], color="tab:blue", label="Drag") + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag Cd") + ax.set_ylim((0.5, 1.6)) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + + ax2 = ax.twinx() + lift_ax = ax2.plot(lift_coefficient[:, 1], lift_coefficient[:, 2], color="tab:orange", label="Lift") + ax2.set_ylabel("Coefficient of Lift Cl") + ax2.set_ylim((-1.1, 1.1)) + + fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/dragAndLift_coefficient.png") + except: + print("(!) saving dragAndLift_coefficient.png didn't work!") + plt.close() +except: + print("(!) plotting drag and lift together didn't work!") + +# STROUHAL number: (only makes sense for Re>46 and if periodic state is reached) +try: + ### prototyped fft for frequency detection and calculation of strouhal-number + # ! Drag_frequency is 2* Strouhal-Freq. Lift-freq. is Strouhal-Freq. + + X = np.fft.fft(lift_coefficient[:, 2]) # fft result (amplitudes) + N = len(X) # number of freqs + n = np.arange(N) # freq index + T = N * flow.units.convert_time_to_pu(1) # total time measured (T_PU) + freq = n / T # frequencies (x-axis of spectrum) + + plt.figure + plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) + plt.xlabel("Freq (Hz)") + plt.ylabel("FFT Amplitude |X(freq)|") + plt.xlim(0, 1) + # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging + plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum + + if not no_data_flag: + plt.savefig(outdir_data + "/fft_Cl.png") + + freq_res = freq[1] - freq[0] # frequency-resolution + X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum + freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude + print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") + # f = Strouhal for St=f*D/U and D=U=1 in PU +except: + print("fft for Strouhal didn't work") + freq_res = 0 + freq_peak = 0 +plt.close() + + +# EXPORT OBSERVABLES: +if not no_data_flag: + ### CUDA-VRAM-summary: + output_file = open(outdir_data + "/" + timestamp + "_GPU_memory_summary.txt", "a") + output_file.write("DATA for " + timestamp + "\n\n") + output_file.write(torch.cuda.memory_summary(context.device)) + output_file.close() + + try: + ### list present torch tensors: + output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") + total_bytes = 0 + import gc + + for obj in gc.get_objects(): + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): + output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) + total_bytes = total_bytes + obj.nelement() * obj.element_size() + except: + pass + # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) + output_file.close() + + ### count occurence of tensors in list of tensors: + from collections import Counter + + my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") + data = my_file.read() + my_file.close() + data_into_list = data.split("\n") + c = Counter(data_into_list) + output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") + for k, v in c.items(): + output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) + output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) + output_file.close() + except: + print("(!) counting tensors didn't work!") + +# output parameters, stats and observables +if not no_data_flag: + output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-Parameters ###") + output_file.write("\nRe = " + str(reynolds_number)) + output_file.write("\nn_steps = " + str(n_steps)) + output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") + output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) + if gpd_correction: + output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( + flow.char_length_lu) + " because D/Y is even") + output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) + output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) + if flow.stencil.d == 3: + output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) + output_file.write("\nshape_LU: " + str(flow.resolution)) + output_file.write(("\ntotal_number_of_gridpoints: " + str(flow.rho(flow.f).numel()))) + output_file.write("\nbc_type = " + str()) +# output_file.write("\nlateral_walls = " + str(lateral_walls)) +# output_file.write("\nstencil = " + str(stencil_choice)) +# output_file.write("\ncollision = " + str(collision)) + output_file.write("\n") + output_file.write("\nMa = " + str(mach_number)) + output_file.write("\ntau = " + str(flow.units.relaxation_parameter_lu)) +# output_file.write("\ngrid_reynolds_number (Re_g) = " + str(re_g)) + output_file.write("\n") + output_file.write("\nsetup_diameter_PU = " + str(flow.char_length_lu)) + output_file.write("\nflow_velocity_PU = " + str(flow.char_length_lu)) +# output_file.write("\nu_init = " + str(u_init)) + output_file.write("\nperturb_init = " + str(perturb_init)) + output_file.write("\n") +# output_file.write("\noutput_vtk = " + str(output_vtk)) +# output_file.write("\nvtk_fps = " + str(vtk_fps)) + + output_file.write("\n\n### SIM-STATS ###") + output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\n") + + output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + + output_file.write("\n\n### OBSERVABLES ###") + output_file.write("\nCoefficient of drag between " + str(round(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_coefficient[int(drag_coefficient.shape[0] - 1), 1], 2)) + " s:") + output_file.write("\nCd_mean, simple = " + str(drag_mean_simple)) + if peakfinder: + output_file.write("\nCd_mean, peak_finder = " + str(drag_mean)) + else: + output_file.write("\nnoPeaksFound") + output_file.write( + "\nCd_min = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min())) + output_file.write( + "\nCd_max = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max())) + output_file.write("\n") + output_file.write("\nCoefficient of lift:") + output_file.write("\nCl_min = " + str(Cl_min)) + output_file.write("\nCl_max = " + str(Cl_max)) + output_file.write("\n") + output_file.write("\nStrouhal number:") + output_file.write("\nSt +- df = " + str(freq_peak) + " +- " + str(freq_res) + " Hz") + output_file.write("\n") + output_file.close() ## END OF SCRIPT print(f"\n♬ THE END ♬") -# sys.stdout = old_stdout + +# reset stdout (from LOGGER, see above) +sys.stdout = old_stdout ################################################################ diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 3f64f97c..f61478b4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -8,12 +8,12 @@ class FullwayBounceBackBoundary(Boundary): - def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): + def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force=None): self.context = context self.flow = flow self.mask = mask - self.solid_mask = mask + # self.solid_mask = mask if periodicity is None: periodicity = (False, False, False if self.flow.stencil.d == 3 else None) @@ -26,7 +26,7 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup if calc_force is not None: self.force_sum = torch.zeros_like(self.context.convert_to_tensor( - self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.flow.torch_stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.calc_force = True else: self.calc_force = False @@ -112,14 +112,14 @@ def __call__(self, flow): # bounce (invert populations on boundary nodes) # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) - if self.flow.stencil.d == 2: + if self.flow.torch_stencil.d == 2: print("FWBB call, dim 2") flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] - if self.flow.stencil.d == 3: + if self.flow.torch_stencil.d == 3: flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], @@ -130,15 +130,15 @@ def __call__(self, flow): print("FWBB call, DONE") def calc_force_on_boundary(self, f): - if self.flow.stencil.d == 2: + if self.flow.torch_stencil.d == 2: self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], - self.f_index_fwbb[:, 2]], self.flow.stencil.e[self.f_index_fwbb[:, 0]]) - if self.flow.stencil.d == 3: + self.f_index_fwbb[:, 2]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) + if self.flow.torch_stencil.d == 3: self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], - self.f_index_fwbb[:, 3]], self.flow.stencil.e[self.f_index_fwbb[:, 0]]) + self.f_index_fwbb[:, 3]], self.flow.torch_stencil.e[self.f_index_fwbb[:, 0]]) def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Optional[torch.Tensor]: return self.context.convert_to_tensor(self.mask, dtype=bool) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index 59ad7aa4..ff90c371 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -9,13 +9,13 @@ class HalfwayBounceBackBoundary(Boundary): - def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global_solid_mask=None, periodicity: tuple[bool,...] = None, calc_force=None): + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force=None): self.context = context self.flow = flow self.mask = solid_boundary_data.solid_mask - self.solid_mask = solid_boundary_data.solid_mask + # self.solid_mask = solid_boundary_data.solid_mask # global_solid_mask to filter out all "fake" fluid neighbors, which are outside this HWBB but not in the fluid region if global_solid_mask is None: @@ -138,11 +138,11 @@ def __call__(self, flow): # bounce (invert populations on fluid nodes neighboring solid nodes) # f = torch.where(self.f_mask[self.flow.stencil.opposite], f_collided[self.flow.stencil.opposite], f) - if self.flow.stencil.d == 2: + if self.flow.torch_stencil.d == 2: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2]] = self.f_collided[:, 0] - if self.flow.stencil.d == 3: + if self.flow.torch_stencil.d == 3: flow.f[self.opposite_tensor[self.f_index[:, 0]], self.f_index[:, 1], self.f_index[:, 2], @@ -167,18 +167,20 @@ def calc_force_on_boundary(self): # calculate force on boundary by momentum exchange method (MEA, MEM) according to Kruger et al., 2017, pp.215-217: # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) is summed for all... # ...populations pointing at the surface of the boundary - self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], self.flow.stencil.e[self.f_index[:, 0]]) + self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], self.flow.torch_stencil.e[self.f_index[:, 0]]) + #TODO: muss stencil.e noch als torch-tensor gespeihert werden, damit das hier schnell läuft? + #TODO: kann man den einfach konstant rausschreiben? dann muss das nicht ausgewertet werden... @(FWBB, @HWBB, @IBB) #TODO: find a way to use pre- and post-Streaming Populations for bounce... def store_f_collided(self, f_collided): - if self.flow.stencil.d == 2: + if self.flow.torch_stencil.d == 2: self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q self.f_index[:, 1], # x self.f_index[:, 2]]) # y - if self.flow.stencil.d == 3: + if self.flow.torch_stencil.d == 3: self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q self.f_index[:, 1], # x self.f_index[:, 2], # y diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index 51d71b50..331a8767 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -25,7 +25,7 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_f self.flow = flow self.mask = solid_boundary_data.solid_mask - self.solid_mask = solid_boundary_data.solid_mask + # self.solid_mask = solid_boundary_data.solid_mask if calc_force is not None: self.force_sum = torch.zeros_like(self.context.convert_to_tensor( self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) @@ -43,6 +43,7 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_f self.d_lt = self.context.convert_to_tensor(solid_boundary_data.d_lt) self.d_gt = self.context.convert_to_tensor(solid_boundary_data.d_gt) self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.context.device, dtype=torch.int64) # batch-index has to be a tensor + # TODO: exchange for flow.torch_tencil.opposite self.f_collided_lt = None self.f_collided_gt = None @@ -124,13 +125,13 @@ def calc_force_on_boundary(self, f_bounced): self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2]], - self.flow.stencil.e[self.f_index_lt[:, 0]].float()) \ + self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + torch.einsum('i..., id -> d', self.f_collided_gt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2]], - self.flow.stencil.e[self.f_index_gt[:, 0]].float()) + self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) if self.flow.stencil.d == 3: self.force_sum = torch.einsum('i..., id -> d', self.f_collided_lt[:, 0] + f_bounced[ @@ -138,14 +139,14 @@ def calc_force_on_boundary(self, f_bounced): self.f_index_lt[:, 1], self.f_index_lt[:, 2], self.f_index_lt[:, 3]], - self.flow.stencil.e[self.f_index_lt[:, 0]].float()) \ + self.flow.torch_stencil.e[self.f_index_lt[:, 0]]) \ + torch.einsum('i..., id -> d', self.f_collided_gt[:, 0] + f_bounced[ self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2], self.f_index_gt[:, 3]], - self.flow.stencil.e[self.f_index_gt[:, 0]].float()) + self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) # TODO: find a way to use pre- and post-Streaming Populations for bounce... # def store_f_collided(self, f_collided): diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py new file mode 100644 index 00000000..32aa214c --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py @@ -0,0 +1,59 @@ +from typing import Optional + +import torch +import numpy as np + +from examples.advanced_projects.efficient_bounce_back_obstacle.obstacle_cylinder import ObstacleCylinder +from lettuce import Reporter, Flow, Observable + +__all__ = ['DragCoefficient', 'LiftCoefficient'] + +#TODO: write this as "force"-coefficient and make X, Y, Z asl 0,1,2 direction choosable (parameter) +class DragCoefficient(Observable): + """The drag coefficient of an obstacle, calculated using momentum exchange method (MEM, MEA) + + calculates the density, gets the force in x direction on the obstacle boundary, + calculates the coefficient of drag + """ + + def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): + super().__init__(flow) + self.obstacle_boundary = obstacle_boundary + self.solid_mask = solid_mask + self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) + self.nan_tensor = self.context.convert_to_tensor(torch.nan) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask) + + def __call__(self, f: torch.Tensor = None): + #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region + # rho_mean (excluding (solid) boundary region, where values are non-physical): + rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) + rho_mean = torch.nanmean(rho_tmp) + force_x_lu = self.obstacle_boundary.force_sum[0] # get current force on obstacle in x direction + drag_coefficient = force_x_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) # calculate drag_coefficient in LU + return drag_coefficient + + +class LiftCoefficient(Observable): + """The drag coefficient of an obstacle, calculated using momentum exchange method (MEM, MEA) + + calculates the density, gets the force in x direction on the obstacle boundary, + calculates the coefficient of drag + """ + + def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): + super().__init__(flow) + self.obstacle_boundary = obstacle_boundary + self.solid_mask = solid_mask + self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) + self.nan_tensor = self.context.convert_to_tensor(torch.nan) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask) + + def __call__(self, f: torch.Tensor = None): + #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region + # rho_mean (excluding (solid) boundary region, where values are non-physical): + rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) + rho_mean = torch.nanmean(rho_tmp) + force_y_lu = self.obstacle_boundary.force_sum[1] # get current force on obstacle in x direction + lift_coefficient = force_y_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) # calculate drag_coefficient in LU + return lift_coefficient \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 62a4189d..e993cbb4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -50,6 +50,7 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], lateral_walls='periodic', bc_type='fwbb', perturb_init=True, u_init=0, x_offset=0, y_offset=0, + calc_force_coefficients=False, stencil: Optional[Stencil] = None, equilibrium: Optional[Equilibrium] = None): @@ -68,6 +69,9 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.lateral_walls = lateral_walls # toggle: lateral walls to be bounce back (bounceback), slip wall (slip) or periodic (periodic) self.bc_type = bc_type # toggle: bounce back algorithm: halfway (hwbb), fullway (fwbb), linearly interpolated (ibb1) + self.calc_force_coefficients = calc_force_coefficients + self.periodicity = (False, False, True if len(self.resolution) == 3 else None) + # initialize masks (init with zeros) self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls @@ -477,14 +481,14 @@ def post_streaming_boundaries(self): print("CYLINDER: the bc_type was given as:", self.bc_type) if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': obstacle_boundary = FullwayBounceBackBoundary(self.context, self, - self.obstacle_mask) # TODO: add periodicity, global_solid_mask and calc_force + self.obstacle_mask, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'hwbb' or self.bc_type == 'HWBB': obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, - solid_boundary_data) # TODO: add periodicity, global_solid_mask and calc_force + solid_boundary_data, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': - obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data) + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=self.calc_force_coefficients) else: # use basic mask Bounce Back - print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted...") + print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... - will fall back to using BounceBackBoundary") obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) return [obstacle_boundary] diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py new file mode 100644 index 00000000..d616cee3 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py @@ -0,0 +1,98 @@ +#TODO: WORK IN PROGRESS... -> this was copied from lettuce issue #248 and branch MaxBille:174-reporter-to-interrupt-simulation + +import torch +import numpy as np + +from lettuce import Reporter, Simulation +from ebb_simulation import EbbSimulation + +class NaNReporter(Reporter): + """reports any NaN and aborts the simulation""" + # WARNING: too many NaNs in very large simulations can confuse torch and + # trigger an error, when trying to create and store the nan_location + # tensor. + # ...to avoid this, leave outdir=None to omit creation and file-output of + # nan_location. This will not impact the abortion of sim. by NaN_Reporter + + def __init__(self, interval=100, outdir=None, vtk=False): + self.outdir = outdir + self.vtk = vtk + self.name = 'NaN' # what is the name for? + self.failed_iteration = None + super().__init__(interval) + + def __call__(self, simulation: 'Simulation'): + if simulation.flow.i % self.interval == 0: + if self.is_failed(simulation): + self.failed_iteration = simulation.flow.i + if self.outdir is not None: + self.outputs(simulation) + + print( + f'(!) ABORT MESSAGE: FailReporter detected {self.name}' + f' in f (interval = {self.interval}) at iteration ' + f'{simulation.flow.i}. See log in {self.outdir} for ' + f'details!') + # telling simulation to abort simulation by setting i too high + simulation.flow.i = int(simulation.flow.i + 1e10) + # the 1e10 is "a lot", but in a very unlikely case of a very long simulation, + # where num_steps >=1e10, this would not work... + + def outputs(self, simulation: 'Simulation'): + if not os.path.exists(self.outdir): + os.mkdir(self.outdir) + + my_file = open(f"{self.outdir}/{self.name}_reporter.txt", + "w") + self.show_max(my_file, simulation) + my_file.write(f"(!) {self.name} detected at \n") + + for location in self.locations_string(simulation): + my_file.write(f"{location:6} ") + my_file.write("\n") + for fail in self.failed_locations_list(simulation): + for fail_dim in fail: + my_file.write(f"{fail_dim:6} ") + my_file.write("\n") + if len(self.failed_locations_list(simulation)) >= 100: + my_file.write(f"(!) {self.name} detected for more " + f"than 100 values. Showing only first " + f"100 values\n") + + my_file.close() + + # write vtk output with u and p fields + if self.vtk: + vtkreporter = VTKReporter( + 1, filename_base=self.outdir + f"/{self.name}_fail") + vtkreporter(simulation) + + def is_failed(self, simulation: 'Simulation') -> bool: + # checks if any item of self.fails(simulation) is true + return self.fails(simulation).any().item() + + def fails(self, simulation: 'Simulation') -> torch.Tensor: + # returns a tensor with ([q],x,[y,z]) dimensions indicating whether + # fail condition applies + return torch.isnan(simulation.flow.f) + + def failed_locations_list(self, simulation: 'Simulation') -> np.ndarray: + # getting fail locations (and possibly values) + failed_locations_list = [] + for fail_dim in torch.where(self.fails(simulation)): + failed_locations_list.append( + simulation.context.convert_to_ndarray(fail_dim)) + if len(failed_locations_list[0]) > 100: + return np.stack(failed_locations_list, axis=-1)[:100] + return np.stack(failed_locations_list, axis=-1) + + def locations_string(self, simulation: 'Simulation') -> List[str]: + locations = ["q", "x"] + if simulation.flow.stencil.d > 1: + locations.append("y") + if simulation.flow.stencil.d > 2: + locations.append("z") + return locations + + def show_max(self, my_file, simulation: 'Simulation'): + pass \ No newline at end of file diff --git a/lettuce/ext/_reporter/observable_reporter.py b/lettuce/ext/_reporter/observable_reporter.py index 59805b14..9d6fd4ac 100644 --- a/lettuce/ext/_reporter/observable_reporter.py +++ b/lettuce/ext/_reporter/observable_reporter.py @@ -179,7 +179,8 @@ def __init__(self, observable, interval=1, out=sys.stdout): self.observable = observable self.out = [] if out is None else out self._parameter_name = observable.__class__.__name__ - print('steps ', 'time ', self._parameter_name) + if out is not None: + print('steps ', 'time ', self._parameter_name) def __call__(self, simulation: 'Simulation'): if simulation.flow.i % self.interval == 0: From 237c5f9863d9dd71c2528bb94a8ab3ee3ace5df0 Mon Sep 17 00:00:00 2001 From: mbille Date: Thu, 27 Nov 2025 14:46:02 +0100 Subject: [PATCH 11/21] corrected some stuff for better compatibility with master branch: - comments - print-statements - ToDos - revert changes in .ipynbs (less clutter in later PR) --- examples/advanced_flows/PorousMedium.ipynb | 136 ++++++----------- .../fullway_bounce_back_boundary.py | 3 - ...inear_interpolated_bounce_back_boundary.py | 6 +- .../obstacle_cylinder.py | 10 +- examples/simple_flows/Obstacle.ipynb | 141 ++++++------------ lettuce/ext/_boundary/__init__.py | 2 +- 6 files changed, 95 insertions(+), 203 deletions(-) diff --git a/examples/advanced_flows/PorousMedium.ipynb b/examples/advanced_flows/PorousMedium.ipynb index e0820c7a..bd0cacfa 100644 --- a/examples/advanced_flows/PorousMedium.ipynb +++ b/examples/advanced_flows/PorousMedium.ipynb @@ -17,8 +17,8 @@ "id": "cc4dc7ad", "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:57:17.637116Z", - "start_time": "2025-11-19T13:57:16.579485Z" + "end_time": "2025-08-06T10:17:35.756583Z", + "start_time": "2025-08-06T10:17:34.563226Z" } }, "source": [ @@ -46,8 +46,8 @@ "id": "8d15f368", "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:57:17.644045Z", - "start_time": "2025-11-19T13:57:17.641276Z" + "end_time": "2025-08-06T10:17:35.760246Z", + "start_time": "2025-08-06T10:17:35.757289Z" } }, "source": [ @@ -84,8 +84,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:57:17.690084Z", - "start_time": "2025-11-19T13:57:17.686937Z" + "end_time": "2025-08-06T10:17:35.772949Z", + "start_time": "2025-08-06T10:17:35.760759Z" } }, "cell_type": "code", @@ -125,8 +125,8 @@ "id": "worst-deputy", "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:57:17.738071Z", - "start_time": "2025-11-19T13:57:17.735412Z" + "end_time": "2025-08-06T10:17:35.784139Z", + "start_time": "2025-08-06T10:17:35.773676Z" } }, "source": [ @@ -153,8 +153,8 @@ "id": "8b4de7ef", "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:57:17.999051Z", - "start_time": "2025-11-19T13:57:17.784042Z" + "end_time": "2025-08-06T10:17:36.085609Z", + "start_time": "2025-08-06T10:17:35.786027Z" } }, "source": [ @@ -234,8 +234,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2025-11-19T13:57:18.274040Z", - "start_time": "2025-11-19T13:57:18.003148Z" + "end_time": "2025-08-06T10:17:36.237426Z", + "start_time": "2025-08-06T10:17:36.086138Z" } }, "id": "788ba01aaac63b34", @@ -256,10 +256,7 @@ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAGzCAYAAAB3vfPfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC+2ElEQVR4nOydd1gUydPHv7uyBMlIUkAwgZ45wykqnoooYsYMZsWMWU+9OzlzzjkimAOCEbN3YjrF4xQDooABMZBhSVvvH/7YVyRtmNlZcD7P088DOz1V1T2zW9M93VUCIiLw8PDw8PCUEYRcG8DDw8PDwyMPvOPi4eHh4SlT8I6Lh4eHh6dMwTsuHh4eHp4yBe+4eHh4eHjKFLzj4uHh4eEpU/COi4eHh4enTME7Lh4eHh6eMgXvuHh4eHh4yhS841IB165dg0AgwLFjx7g2RS15/fo1BAIB9u7dy7UpBVixYgWqV6+OChUqoFGjRnKfz/V1z9d/7do1TvTLAhvXvl27dmjXrh1j8njUD95xKYhAIJCpqPOPhjpz9uxZ/P7775zpv3jxImbOnIlWrVphz549WLx4cbF1AwMDsXbtWtUZx6NSPnz4gDFjxsDKygra2tqws7PDiBEjCtU7dOgQmjRpAm1tbZiZmWHEiBH49OlTqfLznXdxZdSoUSWen5WVhYkTJ8LMzAzW1tb4888/C9V58+YN9PT08Pfff8vecDVGg2sDyir+/v4F/t+/fz9CQ0MLfV6nTh1ERkaq0rQyh62tLTIzMyESiaSfnT17Fps2beLMeV25cgVCoRC7du2CpqZmiXUDAwPx33//YcqUKaoxrhxR1LVXlosXLzImKy4uDq1atQIAjB07FlZWVnj37h3u3r1boN6WLVswbtw4/PLLL1i9ejXevHmDdevW4f79+7hz5w60tbWL1WFmZlbodwMAzp8/j4CAAHTq1KlEG1esWIH9+/fj119/RWpqKhYuXIgaNWpgwIAB0jozZsyAh4eHtC1lHd5xKcjgwYML/H/79m2EhoYW+hxAuXNc6enp0NXVZUyeQCAo8YvNBQkJCdDR0SnVafEoBxvXnslrNmbMGGhoaODevXuoVKlSkXWys7Mxd+5ctGnTBqGhoRAIBACAn3/+Gd26dcOOHTswceLEYnXo6uoW+buxd+9eGBgYoFu3biXaGBISgmnTpmHmzJkAvjrb06dPSx3XX3/9heDgYDx9+lSmNpcF+KlCFSKRSLBo0SJYW1tDW1sbv/zyC6KiogrVu3PnDjp37gxDQ0NUrFgRbdu2lWmIn/9O4/Dhw5g7dy4sLS2hq6sLDw8PxMXFFap/9OhRNG3aFDo6OjA1NcXgwYPx9u3bAnWGDh0KPT09vHz5El26dIG+vj4GDRoE4KsDmzZtGmxsbKClpQUHBwesXLkS3yccCA0NRevWrWFkZAQ9PT04ODhg7ty50uPfv+cYOnQoNm3aBKDglCwRwc7ODt27dy/UFrFYDENDQ4wZM6bEPsrNzYWfnx9q1KgBLS0t2NnZYe7cucjKypLWEQgE2LNnD9LT06W6i3sH065dO5w5cwYxMTHSunZ2dgXqsH3dga9TQT169ICuri7Mzc3h6+tboE3fIs91j42Nhbu7O/T09GBlZSW9LhEREWjfvj10dXVha2uLwMDAAud/+fIF06dPR/369aGnpwcDAwO4ubnh0aNHBeoV9Y4rX/fbt2/Ro0cP6OnpwczMDNOnT0deXl6pfVHUO64NGzagbt26qFixIoyNjdGsWbNCNn/P06dPce7cOcyYMQOVKlWCWCxGTk5OoXr//fcfkpKS0K9fP6nTAiDtt0OHDpVq8/e8f/8eV69eRa9evUp17JmZmTA2Npb+b2JigoyMDABf773Jkydj5syZsLa2ltsOtYV4GGH8+PFUXHdevXqVAFDjxo2padOmtGbNGvr999+pYsWK1KJFiwJ1L1++TJqamuTk5ESrVq2iNWvWUIMGDUhTU5Pu3LlTog35eurXr08NGjSg1atX0+zZs0lbW5vs7e0pIyNDWnfPnj0EgJo3b05r1qyh2bNnk46ODtnZ2VFiYqK0nre3N2lpaVGNGjXI29ubtm7dSvv37yeJRELt27cngUBAI0eOpI0bN1K3bt0IAE2ZMkV6/n///UeamprUrFkzWrduHW3dupWmT59Obdq0kdZ59eoVAaA9e/YQEdGtW7eoY8eOBID8/f2lhYjo119/JZFIRJ8/fy7Q9iNHjhAAunHjRol95O3tTQCoT58+tGnTJvLy8iIA1KNHD2kdf39/cnZ2Ji0tLanuly9fFinv4sWL1KhRIzI1NZXWPXnyZIHrwfZ1z8jIIHt7e9LW1qaZM2fS2rVrqWnTptSgQQMCQFevXpXWlee6a2tr008//URjx46lTZs20c8//yy9TlWqVKEZM2bQhg0bqG7dulShQgWKjo6Wnn/v3j2qUaMGzZ49m7Zt20YLFy4kKysrMjQ0pLdv30rrfX/tv9Vdt25dGj58OG3ZsoV69+5NAGjz5s0l9gURUdu2balt27bS/7dv3y695tu2baN169bRiBEjaNKkSSXK2bBhAwGg48ePU/v27QkAVahQgTp37kyvXr2S1rt16xYBoN27dxeSYWZmRjo6OpSXl1eq3d+yevVqAkChoaGl1h0xYgTVq1eP/v33X7p16xZZWlrSn3/+KW171apVC3z3ywO842IIWRxXnTp1KCsrS/r5unXrCABFREQQEZFEIqFatWqRq6srSSQSab2MjAyqVq0adezYsUQb8vVYWVlRSkqK9PP8H/V169YREVF2djaZm5tTvXr1KDMzU1ovJCSEANCCBQukn+X/0M+ePbuArlOnThEA6Rcknz59+pBAIKCoqCgiIlqzZg0BoI8fPxZrd1E/XsX157NnzwgAbdmypcDnHh4eZGdnV6Dfvic8PJwA0MiRIwt8Pn36dAJAV65cKdBuXV3dYmV9S9euXcnW1rbQ56q67mvXriUAdOTIEeln6enpVLNmzQKOS5HrvnjxYulniYmJpKOjQwKBgA4dOiT9/OnTpwSAfvvtN+lnYrG40I/1q1evSEtLixYuXFjgs6IcF4AC9YhI+gBQGt87ru7du1PdunVLPe97Jk2aRACoUqVK1LlzZzp8+DCtWLGC9PT0qEaNGpSenk5ERB8/fiSBQEAjRowocH5+vwCgT58+yaW7adOmVLlyZZkcXlxcHNWtW1eqy9nZmVJTUykpKYnMzMwKXKvyAj9VqEKGDRtWYP7d2dkZABAdHQ0ACA8Px4sXLzBw4EB8/vwZnz59wqdPn5Ceno5ffvkFN27cgEQiKVWPl5cX9PX1pf/36dMHlStXxtmzZwEA9+/fR0JCAsaNG1dgGqJr166oXbs2zpw5U0imj49Pgf/Pnj2LChUqYNKkSQU+nzZtGogI586dAwAYGRkBAIKCgmSyvTTs7e3RsmVLBAQESD/78uULzp07h0GDBhWYqvme/PZPnTq1kM0Aimw3E7B93c+ePYvKlSujT58+0s8qVqyI0aNHF6inyHUfOXKk9G8jIyM4ODhAV1cXnp6e0s8dHBxgZGQkbQ8AaGlpQSj8+vOSl5eHz58/S6eJHzx4UHKH/Y+xY8cW+N/Z2bmADlkxMjLCmzdvcO/ePbnOS0tLAwBYWlrizJkz8PT0xPTp07Fjxw68fPlSOtVoamoKT09P7Nu3D6tWrUJ0dDRu3ryJfv36SRedZGZmyqz3+fPn+Oeff9C/f39pH5aEtbU1Hj58iIcPH+Lx48e4du0a9PT08Mcff8DBwQH9+vXDX3/9hZYtW8LGxgaTJk1Cdna2XH2hbvCOS4VUrVq1wP/589KJiYkAgBcvXgAAvL29YWZmVqDs3LkTWVlZSE5OLlVPrVq1CvwvEAhQs2ZNvH79GgAQExMD4OsPzvfUrl1bejwfDQ2NQvPjMTExqFKlSgEHCXxdRfmtjn79+qFVq1YYOXIkLCws0L9/fxw5ckQpJ+bl5YW///5bquPo0aPIycnBkCFDSjwvJiYGQqEQNWvWLPC5paUljIyMCrWbKdi+7jExMahZs2Yhp/399ZX3uucv6/4WQ0NDWFtbF9JlaGgobQ/w9d3KmjVrUKtWLWhpacHU1BRmZmb4999/ZbqHi9JtbGxcQIeszJo1C3p6emjRogVq1aqF8ePHy/TuUEdHBwDg6elZwIH07dsXGhoauHXrlvSzbdu2oUuXLpg+fTpq1KiBNm3aoH79+tKFFXp6ejLbm/9Qlv8uWRZEIhEaNWqEn376CUKhEE+fPsXmzZuxbt06fPnyBV27dkWPHj1w9OhRhIaGYtGiRTLLVkf4VYUqpEKFCkV+Tv9bzJD/Y75ixYpiN7zK8wVgim+fnuVFR0cHN27cwNWrV3HmzBmcP38ehw8fRvv27XHx4sVi+6Qk+vfvD19fXwQEBGDu3Lk4cOAAmjVrVuQPclGUNCpjg7J63Yuzu7T2AMDixYsxf/58DB8+HH5+fjAxMYFQKMSUKVNkemhR5L4ojjp16uDZs2cICQnB+fPncfz4cWzevBkLFizAH3/8Uex5VapUAQBYWFgUsq1SpUoFnKihoSGCgoIQGxuL169fw9bWFra2tvj5559hZmYmnXmQhcDAQDg4OKBp06byNfQbfH19MXjwYDRp0gT+/v4wMTHBnDlzAAAzZ87EokWLSmy7usM7LjWiRo0aAAADAwN06NBBYTn5T/D5EBGioqLQoEEDAF/3zgDAs2fP0L59+wJ1nz17Jj1eEra2trh06RJSU1MLjLryl9x+K0MoFOKXX36R7nFZvHgxfv31V1y9erXYdpbkXExMTNC1a1cEBARg0KBB+Pvvv2XaAGxrawuJRIIXL15IR4bA1w2mSUlJMrVbXltlQdnrbmtri//++w9EVMCWZ8+eFaqX/7mi111Wjh07BhcXF+zatavA50lJSTA1NWVMj6zo6uqiX79+6NevH7Kzs9GrVy8sWrQIc+bMKXbVXr7j+H7FZXZ2Nj59+lRoRAh8HV3nj7CTkpLwzz//oHfv3jLbeefOHURFRWHhwoUyn/M9ISEhuHXrlvR34N27d6hcubL0eJUqVQq1qazBTxWqEU2bNkWNGjWwcuVK6fz6t3z8+FEmOfv370dqaqr0/2PHjuH9+/dwc3MDADRr1gzm5ubYunVrgSXT586dQ2RkJLp27Vqqji5duiAvLw8bN24s8PmaNWsgEAikur58+VLo3PxRRXHLtQFI94klJSUVeXzIkCF48uQJZsyYgQoVKqB///4y2QygkJNbvXo1AMjU7uJslWX6qziUve5dunTBu3fvCoSWysjIwPbt2wvUY+K6y0qFChUKbYs4evQoJz+Ynz9/LvC/pqYmfvrpJxBRkcvb82nXrh3Mzc0REBAAsVgs/Xzv3r3Iy8tDx44dS9Q7Z84c5ObmwtfXt8DnT58+RWxsbJHn5L83GzhwYImyiyM7OxtTp07FvHnzYG5uDuDriDEqKgq5ubkAvu4rtbS0VEi+usCPuNQIoVCInTt3ws3NDXXr1sWwYcNgZWWFt2/f4urVqzAwMEBwcHCpckxMTNC6dWsMGzYMHz58wNq1a1GzZk1p6BiRSIRly5Zh2LBhaNu2LQYMGIAPHz5g3bp1sLOzK/RFK4pu3brBxcUFv/76K16/fo2GDRvi4sWLCAoKwpQpU6SjiIULF+LGjRvo2rUrbG1tkZCQgM2bN8Pa2hqtW7cuVn7+0+6kSZPg6upayDl17doVlSpVwtGjR+Hm5ib9kpZEw4YN4e3tje3btyMpKQlt27bF3bt3sW/fPvTo0QMuLi6lyijO1sOHD2Pq1Klo3rw59PT0St00+i3KXvdRo0Zh48aN8PLywj///IPKlSvD398fFStWLFCPiesuK+7u7li4cCGGDRuGn3/+GREREQgICED16tUZ0yErnTp1gqWlJVq1agULCwtERkZi48aN6Nq1a6F3tN+ipaWFFStWwNvbG23atMGQIUMQGxuLdevWwdnZGb169ZLWXbp0Kf777z+0bNkSGhoaOHXqFC5evIg///wTzZs3LyC3Tp06aNu2baFwcHl5eTh8+DAcHR2l3x95WbduHQBg8uTJ0s+6dOmC8ePHY+DAgfj555/h5+dXYNFNmYS7BY3lC1mWwx89erTA50UtBSYievjwIfXq1YsqVapEWlpaZGtrS56ennT58uUSbcjXc/DgQZozZw6Zm5uTjo4Ode3alWJiYgrVP3z4MDVu3Ji0tLTIxMSEBg0aRG/evClQp6Rl4ampqeTr60tVqlQhkUhEtWrVohUrVhRY0n358mXq3r07ValShTQ1NalKlSo0YMAAev78eYn9kJubSxMnTiQzMzMSCARF9u24ceMIAAUGBpbYL9+Sk5NDf/zxB1WrVo1EIhHZ2NjQnDlzSCwWy9zu70lLS6OBAweSkZERAZAujVfVdSciiomJIQ8PD6pYsSKZmprS5MmT6fz584X2cREpd93btm1b5NJyW1tb6tq1q/R/sVhM06ZNo8qVK5OOjg61atWKwsLCCi1VL245fFG6f/vtt2K/Y9/b+K2Obdu2UZs2baT9WqNGDZoxYwYlJyeXKouI6ODBg9SwYUPS0tIiCwsLmjBhQoHtJkRftxS0aNGC9PX1qWLFiuTo6Fhge8K3AChgXz7512v9+vUy2fU98fHxpK+vT6dPny507Ny5c1S7dm0yMjIiLy8v6VL+soqA6LvxPE+Z5dq1a3BxccHRo0cLLI0ur/j6+mLXrl2Ij48vNLrg4eEpv/DvuHjKJGKxGAcOHEDv3r15p8XD84PBv+PiKVMkJCTg0qVLOHbsGD5//lxgLp+Hh+fHgHdcPGWKJ0+eYNCgQTA3N8f69esVSvDIw8NTtuHfcfHw8PDwlCn4d1w8PDw8PGUK3nHx8PDw8JQpyuQ7LolEgnfv3kFfX1/lced4eHh4eJSHiJCamooqVarIHQu1TDqud+/ewcbGhmszeHh4eHiUJC4uTu7szGXSceWHaYmLi4OBgYFSsv7++28MGTKkUDwzZdDW1saCBQswZswYaGiUyS7m+Y6oqCi4urri06dPjMqtVKkS9u/fX2L4Kx6e8khKSgpsbGxKDLtVHGVyVWFKSgoMDQ2RnJyslOO6cuUKPDw8kJ6ezqB1/09ERATq1avHimwe1bJixQrMnDmTFdnGxsb49OmTwqljeHjKIsr8jpfbb4pEIimyfOunt2zZwprTAgA/Pz9Gsv7yFE/+dV2wYIE0bcWHDx8Y7XeJRIKVK1cyJu97iIi/T3h45EGewIb5QS6/LQ4ODtLjmZmZNG7cODIxMSFdXV3q1asXxcfHF5ARExNDXbp0IR0dHTIzM6Pp06dTTk6OXAEWk5OTCUCxQTLv3btHpqamZGxsXKjMnj2b7t+/T35+fqSpqVmoPUyXsWPHytU2RXjy5Andv3+/2PJ9ANnywKdPn+ju3btkZ2dHxsbGpKGhIe1zIyMjMjY2pk2bNtHTp0+V1uXj48P6fTJs2DAGeoWHp+xQ2u94Scj9AqZu3bq4dOmS9P9v3+H4+vrizJkzOHr0KAwNDTFhwgT06tVLmiY7Ly8PXbt2haWlJW7duoX379/Dy8sLIpEIixcvlteUYhk/fnyx7yKWLl2KpUuXMqarNNga0eWPArKysrB582bEx8cXW3fy5Mno1KmTNB9VWSc9PR2jRo3CyZMnizyen8Nr/PjxsLOzg7+/v1LvkNgclatShyw8evQIp0+fhqWlpTQNDg+P2iGPl/vtt9+oYcOGRR5LSkoikUhUIIVDZGQkAaCwsDAiIjp79iwJhcICo7AtW7aQgYEBZWVlyWxHaZ66RYsWrD8hy1pMTU3pwoULMretNCQSCQUEBFDLli2l6T5kKRYWFtS6dWt69+5dmR6BZWZmUq9eveS6BlWrVqVHjx4prNPLy4v1+8TT05PBXpKd9PR0un79Ojk6OpKjoyNVr16dAJCurq70M19fX0pKSiqQroaHR1mUGXHJ7bgqVqxIlStXpmrVqtHAgQOleZ4uX75MACgxMbHAOVWrVqXVq1cTEdH8+fMLOb7o6GgCQA8ePChWr1gspuTkZGmJi4srM44LAPn7+8vTzSXi7+8vl8P6vgiFQhowYECh61QW+PjxI/Xs2VOhdhsbG9O9e/cU0lseHVdaWhqdOHGCmjRpItP9pKGhQdu2bVPqAYCH51uUcVxyLc5o2bIl9u7di/Pnz2PLli149eoVnJ2dkZqaivj4eGhqasLIyKjAORYWFtJprPj4eFhYWBQ6nn+sOJYsWQJDQ0NpKWt7uHbv3l1kSnZ5OXjwIMaNG1coJbo8SCQSHDx4EJMmTVJKDhdEREQUOz1YGomJiVi/fj3DFpVdZsyYgV69euHBgwcy3Qe5ubkYM2YMBg4ciIiICBVYyMNTPHI5Ljc3N/Tt2xcNGjSAq6srzp49i6SkJBw5coQt+wAAc+bMQXJysrTExcWxqo9prl69CrFYrPD5RIQDBw5g9OjRSE1NZcSmAwcOYOjQoUhJSWFEHttkZ2dj2LBhSsk4duwYTp06xYxBZZS0tDSMGzcO27ZtU+j8x48fo1OnToiKimLYMh4e2VFqObyRkRHs7e0RFRUFS0tLZGdnS1+M5/PhwwdYWloCACwtLfHhw4dCx/OPFYeWlhYMDAwKlB+JQ4cOwdvbm5FRWz5EhP379+PEiROMyWQTIkJCQoJSMjIzMxntw7LIxYsXsWXLFqWW38fHx2PkyJEMWsXDIx9KOa60tDS8fPkSlStXRtOmTSESiXD58mXp8WfPniE2NhZOTk4AACcnJ0RERBT4AQoNDYWBgQF++uknZUxRe+bOnavQeXl5eUr/0JTE6tWr8eXLF1ZkqyOrVq1CcnKyXOdMmzat0BQ4k+jp6WH27Nmsyc8nPT0dixYtYkTWo0ePcOjQoTI33cxTTpDnhdi0adPo2rVr9OrVK/r777+pQ4cOZGpqSgkJCURENHbsWKpatSpduXKF7t+/T05OTuTk5CQ9Pzc3l+rVq0edOnWi8PBwOn/+PJmZmdGcOXPkejFXllYV5peaNWvSy5cv5WonEdHKlSuVWowhSzlz5ozcdqkasVhMOjo6jLT3w4cPcumWSCRkbm7OWv8bGRlRXl4eSz33/1y/fp3Re0kkEtHjx49Zt5unfKKyxRlv3rzBgAED4ODgAE9PT1SqVAm3b9+GmZkZAGDNmjVwd3dH79690aZNG1haWhaYiqpQoQJCQkJQoUIFODk5YfDgwfDy8sLChQvlMaNMEhUVhZ07d8p1zvv373H69GnWn2qnTZvGqvyyjkAgwOjRo1mTP3r0aJVkOZg5cyaj91JOTg4f8YOHGxh3oyqgLI64ANCkSZMoOztb5nY+evRIJXZpaWnRypUrmbo8rMDUiGvp0qWUm5srt/4nT56Qvr4+432vp6dH4eHhLPRYYVq2bMm4/S1btlSJ7TzlD5WNuHiUY/369bhz5w7XZhQiKyurxO0I6kCFChXQoUMHpeVYWVmhQoUKcp9Xp04dBAYGFtrOoQxmZmY4cOAAGjZsyJjM4vj333/x8eNHxuW+fv2af8/Fo3J4x6Vi+C+5YmhoaGDq1KlKyWjdujWcnZ0VPt/d3R27d++GSCRSyg4AEAqF2LlzJ7p37660LFkICgpCdHS0SnTx8LAN77jUmBcvXnBtglrRqlUrTJgwQaFzNTQ00K5dO9ja2iplQ5cuXRAUFCR9r6sIJiYmOHnyJLp166aULTw8Pyq841Jj/vrrL65NUCtEIhHc3d0VchoTJ06En58fI3a4ublh9+7dCjlRHx8f7N69Gx4eHipZkJGPs7MzKleurDJ9PDxswjsuFaKpqSlXRmRlI0WUR1xdXXH48GEYGBjI1JdCoRCTJk3Cn3/+yagd7u7uWLlyJW7evAlXV1fo6+tDU1OzUD1NTU3o6+tDX18fM2bMwOrVq1U2Pfgt7dq1Q9WqVRmXu3btWpU6YB4eAODzyquQqVOnwtHRkWszyjwuLi74/Pkz1qxZg3nz5iE7O7vIeo6OjnBxccGff/7JSnZhLS0ttG7dGmfOnAER4eDBgzh+/HiBOt27d8eQIUMAfHWiXGY5dnJyYnxxEJ/h+8fj+vXrhSIkAV/jzqrq9413XCpEIBDwT6cMoaGhgRkzZsDMzKzYOJBdunRhZZTxPfmrFIcMGSJ1UurI77//jvXr1zO296pnz56oUqUKI7J41BuJRIJp06ZBLBbj2LFjReY7tLGxQdeuXWFsbMxofsUiYXptvipgch+XlpYW/fbbbxQREUGVK1dmba9UnTp15E4lEhkZScbGxqzv46pZsyZ9+fKFgSvDUxoxMTEUFRVVZElJSWFVt0Qioa1btzJyz2hra9O2bdtYtZeHe2JiYmjNmjVka2sr870hFArJ1taW9u3bV2JEGJXl41IXmHRcCxculJ4XHh5OEyZMID09Pcadw2+//aZQW5csWcK645I35BaP/MTExNCGDRvIzMys2OswaNAgCggIYNWO8PBwqlu3rlL3i0AgUPsN6zzKc/36dTI1NVXqPtm/f3+x8nnH9R2tWrUqsUNFIhG5ubnRmTNnioxk4e/vT0KhkDHHIBKJ6OPHjwq1NSoqiurVq8ea09LQ0KB3794pZBuPbKSlpZGTk5NM10NPT4/c3NwUTnopC//++y9ZWloq/GPk5+enUPQRnrJDWFiYXKOs4oqjo2OxOnjH9R0fP34kFxcXatu2bZHlyZMnJaavl0gkFBAQQEZGRkpfOAsLCwoODlaqvePHj2fNcW3dupVPyc4isbGxMjutb4uBgQHdv3+fNbtevHhB1atXl8smbW1tWrlyJe+0yjm3b99mbNaJd1zfoEyD5SEgIIBEIpHCF01PT09pp0VElJ2dTcOGDWPcaTVo0IAiIiIY6CmeooiPj6d27dopfH3s7Ozo5s2brNl379498vPzk+ke79GjB/9O6wehbdu2jP3GsOW4+FWFJTBgwADpappPnz7JvBpLJBLB2NgYe/bsQZcuXZS2QyQSYd26ddDQ0MCuXbsYWRVmZ2eH06dPKx1JQh7y8vLw+fPnQp9ra2uXy+SgPXv2RFhYmMLnv379GlevXoWTk5NC8RVLo1mzZmjatCl69OgBIsLIkSPx+vXrAnXWrVuHunXrwsrKCiYmJozbwKM+5ObmYvPmzbh//z7XppSOEo6ZM1Q14spHIpHQ7NmzZZrycXFxoUWLFrE2/TZmzBiln4IaNmxI0dHRrNhXFDExMbR//35avnx5kfY4OzvT/v37y0ReMFm5efMmGRoaMvLU+vTpU5XZLZFIChSeH4d///2X8VkdfsTFIQKBAEuWLEF0dDRu3bol/dzPzw8GBgaYPHmy9LP27duzurdl1apVyM7Oxp49e+Q+VygUYtu2bXB0dES1atVYsK4gRITff/8dV69exc2bN4utd/PmTdy8eRPGxsbo0qULFixYgFq1apXpPW87d+6UO9NycSxevBj79u1jRFZplOU+51EOKksBwBX3z9yh6hFXcSQmJnJiQ2ZmJr17944cHR2pYcOGpK2tXeJTT+3atalhw4Z06tQplT1FHzt2jMzMzEhDQ0PupzQjIyPq0aMHffr0SSW2Mk1iYiJ169aNsadWKysrrpvE8wNgb2/Pj7h+BIyMjDjRq62tjcqVK0vfn2zcuBHv3r0rtv60adNQqVIlVZmHEydOYPDgwcVGtCiNpKQknDp1CgCwb9++Mvf+68KFCwgODubaDB4euSgqjJO6wjuuckBxUcrz8vKQmZkJAEhLSwMA6OnpsWYHEeHEiRMYO3aswk7rW06dOgVvb28cOnQIWlpaDFjIw8NTHuCjw5dTwsLC8Oeff8LExERazMzMcPjwYdbyfL179w6enp5FxjFTlFOnTqFfv36sZO/lYYdbt24hODgYMTExXJvCIyNUlt5vgR9xlTvevXuHP//8E6dOncL79+8LHMvJyUH//v3RokULtG3bFsuWLWP0ZTyTAVy/JSgoCGPHjkXnzp0Zl82jPHfv3sXevXul/588eRLx8fFo06YN6tatC+BrHrL69etzZCFPaZS5RTmKv8rjDnVZnKGONGrUSKaXpgKBgIYNG0apqamM6JV1I6uipWrVqpSRkcGIrWyTlpZGPXv2LNeLMzIzMyk6OppcXFxKjL+YXywsLMjOzo4WLVpEMTExXJtfpoiJiSE/Pz+ys7OTllatWlF0dDRj3wmJRELm5ub84gwe1XP58mW8fPlSprpEhD179qBNmzYYOnSo0rq/fPmCnJwcpeUUx7t37xASEoK+ffuWWjclJQUHDhwo9LmDgwN++eUXNswrgK6uLqMLSjw8PBiTxQQhISG4fPky1q5dK/M5Hz58AAD8+uuvWLJkCU6cOIGOHTuyZGH54NmzZwgNDcXs2bORnp5e4Njr169RvXp1+Pj4oFOnTujRo4dSuvgRlwrgR1yFuX79OllbW8v9RNSoUSP6/PmzUrrDw8OpWrVqrI228ouLi0uxNuTk5NDNmzepW7du5OLiUuT51tbW1K1bN5owYQJlZ2cXGWCZKR48eMBIrEuhUKjSDcilcerUKUaezK2srOjSpUtcN0dt+fjxo8yzJyYmJnTo0CGldbKR1omPVfgNvOMqTGBgoMI319u3b5XSHRoayrrTKs5xpaWl0Y0bN6hFixal7mfLLwKBgPT09MjMzIwePXqkVNtLomPHjkq3eenSpWoTweLUqVOkpaXF2PXU1tamK1eucN0stUSe1EwASFNTU+m4o7dv3y4zjotfVchTZoiOji4QgYOIMHv2bLRp0wZ3796VeQk+ESEtLQ0fP35E7969MXfuXCQmJjJub2BgoFILSmrVqoX27durzTTO5MmTkZWVxZg8sViM/v3748KFC4zJLA+cP38er169kuuc7Oxs7Ny5Uym9FStWVOp8VcI7Lp4yQ0xMDG7fvg0iQmBgIBo3bozNmzcrJTMqKgpLlixB7969pXvdmMLU1BT79++X+12OtrY2rKyscPr0aTRv3pxRmxQhOzsb8+fPL3GTu6IkJCTg7NmzrL4fLUvk5OQgJCREoe0fBw8eVGpZe61atQqEr1NneMfFU+ZISEjAoEGD8OjRI8aW31+9ehW9evXC27dvGZGXj5mZGYKDg+Ht7Q0HB4dS6xsYGGDHjh2Ii4tD7dq1GbVFUc6fP48///yTNeeyfv16LF68mJWtFGWNv//+G5s2bVLoXLFYrFQ2Am1tbXTo0AHm5uYKy1AV/KpCnjLH0qVLWZEbGhqKBw8ewMrKilG5Wlpa2Lt3L+7fv4/IyEhcvXq1UJDkli1bYvz48TAyMkK3bt0Y1a8MmZmZWLduHet6VqxYgblz50IoLF/P0kSEOXPmFBqtzp8/H7Vq1SqyvqKkpKRg69at+PnnnxWW4e7ujp07d6J3797qPQpW7DUet/CLMwpz9uxZhVax2djYUEJCglK6VbU4o1WrVjRr1iyFAvfKWkxMTFhPrpmRkUEfPnwoUJKSkljVqSjJycms9nd+0dbWphcvXnDdXMZ49eoV7d27l8zNzUkoFBZqr5GREfXs2ZP+/fffAqtbr1y5olQ/DhkyRGnbJRIJBQcHk4mJidouzuAdVzni8OHDpKurK/NNVbt2bUbSw6vKcVWrVo3atGnDup7WrVszcDXKBzt27CCBQKCS6+vu7s51cxnh3r17ckVaX7ZsmfRcZRyXjo4OI8vi8wkKClJ6S8fq1auLlc87Lh4p1apVI01NTZluKm9vb0Z0JiYmUt++fVXy46aKYmBgQP7+/oz0TVnH0dFRZf1eHhzX8+fPqXr16nK1W1tbm1auXEm5ubmUmJhI/fv3V6j/zM3NGd86cefOHZo5c2aRo8aSiq6uLi1dupRyc3OLlc1HzuCR8uzZM5w8eRJjx44tdom3vb09mjRpgh07djCi08jICNbW1ozIUgdSUlIQFxfHtRk8ZZBOnTrh9evXcp0jFosxY8YM1KtXD66urujUqRPOnDmD1NRUueS0bNmS8a0TLVq0QLNmzaCrq4vbt2/jxYsXiIqKKrKumZkZmjVrBnNzc2zfvh0ikYi1rRy84ypniEQieHp6QiQS4e3btzh27BiuX78OABg1ahQaNGiAli1bMr7MulevXggMDJSG9mGaUaNGYd++fcjOzmZFPg+Pshw8eFDhzAhEhG3btsHFxQXDhg3D6tWr8d9//8l8voeHB3bv3q2Q7tIQCoVYsGABAOD27du4f/9+kfVsbW1Vt7BIiVEkZ/BThbLz8eNHioqKoqioKEpLS2NVV9u2bVmZQtLX1yd/f3+ZI2MwUYyNjenJkyes9ldZgJ8qlJ2RI0cq1X4tLS1KT08nIqJHjx6RqampTOd169aNEhMTuW28AvCRM3iKxdTUFDVq1ECNGjWgq6vLqq5Tp06hXbt2jMo0NDTEzp07ERAQwEhySllJTEzkR3c8nNGgQQMEBwdjzJgx0NbWLrZet27dcOzYMc6ysXMF77h4GMPIyAj79u1DmzZtGJEnEAiwdetWeHp6MiKPR35EIhHXJpQJbt++jdDQUKVkZGdn488//5T+7+joiK1bt+LEiRM4deoU6tWrBw0NDWhoaGD79u04deoUduzYAU1NTWXNL3Pw77h4GKVq1ao4e/YsOnbsqNQufmNjY2zZsoV3Whxz5MgR2NjYIDc3l1U9AoFAbSKFKEJWVpbSMwJEhKSkpEKfu7m5AQA6duyIvLw8AICenp7axLDkAn7ExcM4urq6OH78OPz8/BRabailpYWtW7eiX79+P/SXUx1QVeDVihUrYvHixSrRxQZt27ZVemGClpYWVq5cWezxihUrQl9fH/r6+j/894J3XDysULlyZcybNw+XLl3C+vXrYWJiUuKXTV9fHyYmJpg8eTLu3r1baKS1evXqEuf6mUZHRwcVKlRQmT51RU9PDytWrGBdj76+Pus6eMoP/FQhD6s4ODjAwcEB48ePx6RJk/D58+ci602aNEm6D6UoB1e9enWVPmXOmzcP9erVU5k+dUUoFMLZ2Rk1a9Ysdv8OExw8eLDMv09r0aIFDhw4oPCUobOzMzQ0+J9kWeB7iUclCIVCbNy4UWkZquJHn4r5lqZNm6JDhw6sOa6uXbuiTp06rMhWJaNGjYKfn5/Cm9enTZv2Qy60UAR+qpCnTKClpYX9+/erRFeLFi0wadIklegqKyxbtgytW7dmRfa1a9dw9epVVmSrmlOnTsHS0lKuczQ1NTFv3jy0b9+eJavKH7zj4ikzNGzYUCWJFTU1NVnf81bWMDAwwKlTp1j5cU1PT4e3tzeOHj3KuGxV06RJExw9ehR2dnYynzNjxgz4+fnxoy054B0XT5mhRo0a6NSpE6s6dHV1sWTJElZ1lFUqVaqE/fv3o2XLlozLzs7OxtixY3Hq1CnGZauCvLw8ZGRkICMjA3FxcUhOTi71HB0dHcycORPz589XgYXlC/4dF0+Z4o8//sDbt2+xd+9eVuT7+/uzNiVWHsjNzWUtU/GXL19w8eJFuLq6QkdHhxUdbBAWFobz589j2bJlAICcnJxS+8jFxQVnzpyBlpZWuUueqQp4x1XOmDdvXqGo0sbGxvj999+5MYhhKlSogE2bNiEnJwcBAQGMym7dujUaNWrEqMzyxKdPn+Dt7Y179+6xpmPLli2YPn06qlevzpoOpoiNjcWqVatw+PBhuYNLm5qaIjAwECNGjGDJunIO86ET2ae04Izx8fH08eNHFVvFDUlJSRQREUH29vZUq1atIvPmaGhoUK1atah27doUGRlZLoITp6SkkLe3NyNJDo2MjKhFixZKZ4Iu7/z9998qCbZbFhJ5fvjwgerUqaNUO3V1dWnz5s0l5qwqz/CJJL+jRYsW1LhxY9q2bRvrEdG5Ii8vj3bv3k1ubm5yf2E8PDzKhWOXSCTk5eWl1I9H1apV6erVq1w3pUxga2urEsdlYmJCN2/e5Lq5xfL06VNq0qQJY+3dvHkz103iBN5xfUeLFi2kN8WwYcMoLy9PxRayi0QiIT8/P9LQ0FD4y+Li4kIrVqxQKmOqRCKh3Nxcys3NpV27dlHPnj2pZ8+e1KtXL0pKSpIeY5OkpCQ6ceIE1a1bV67Rl4aGBi1atIjCwsJYta88oSrHBYDGjh3LdXOLJCEhgRo3bsxoW3V1dWn79u2MZy9Wd3jH9R3fOi6BQEDe3t6UlJSkYivZQSwW07x585RyWvlFJBLRwoULKTs7Wy4bXr58SWFhYTR9+nQyNDQkQ0ND0tTULCDbwMCADA0NycrKim7evElhYWGUlZXFUq8QpaWl0blz58jR0ZEcHR3Jzs6uUHtNTU3J0dGRBgwYIHWsPLIRHR1NlpaWP7Tjev36tdLTg8UVTU1NCg0N5bqJKkUZx1XuF2cQEfbt2wcA2LlzZ5kPqbJkyZICqQ+UIScnBwsWLMCIESNQpUoVmc7577//MGTIEISHh5dYLyUlBQCQnJwMZ2dnAICfnx/mzZunlM3Foauri86dO6Nz584AgHv37uH8+fMF6jRo0ADdu3dnRX95Z/ny5YiPj+faDE5Zt24dIiMjWZGdnZ2N/fv3o23btmU+9JVKYN6Pso88Iy6o+VOcPKSnpxc5klC2uLu7SzOvFkdGRgYNGDCAqlWrprAeLS0tatKkCV24cEFFPcbDFGPHjlXZaAsAmZmZ0fXr17lutpQ7d+6Qubk56+0eNWoU101VGXwGZBn5+++/8ezZM67NUJhZs2bh9evXjMsNCQkpcbn8ly9f4OXlhYMHD+LVq1cK68nKysKDBw/QrVs3XLx4UWE5POWfjx8/Fpmbigvy8vJw5swZJCQksK6LzUDG5YkfynFFREQgJCSEazMU4tGjR7h06RJr8s+dO1fkNIhEIoGPjw+OHTvGmK7s7Gx4e3vjwoULjMnkKV+0a9cOjRs35toMAF+n1JcvX64SXZGRkYWmuHkK80M5LgD48OEDsrOzGZOXlpaGyMjIAiUmJoYx+cDX93RhYWF4+vQpo3K/5b///sP79+8LfR4XF4dz584xri8+Ph6enp548eIF47J5mGfp0qWwsrJSmb7atWvDxsZGZfrUhfj4eERERHBthtpTtlcqKMCKFSswbNgwpdIoJCQkwN/fH8DXH/zvww9Vq1YN48ePh66uLsaOHauMuQCAjIwMTqKVR0REYNCgQYUicTBFSkoKDh48iAULFrAin4c5DA0Nf9jEmidPnkRubi7XZvB8ww/nuJQhMzMTK1aswJkzZ3D37t1i67169QrTp0+HpqYmjh49CqFQiICAAJibm6vQWvn59ddfcePGDYhEIhARrl+/zvrT35YtWzB79mw+MnYZQEtLS2W61Ol+2L17N++41IwfbqpQUUJCQmBiYoLff/+9RKf1LdnZ2bhy5QouXbqEtm3b4uLFi2rzwrkowsPDpcFB3759i6lTp7KuMz4+HhMmTGBdD4/yqCpyu62tLVatWqUSXTxlE95xyUBwcDC8vLwgFotBRArJePr0KVxdXTFx4kTk5eUxbCHzEBFycnJUoktVeniUw8rKCj179mRdz/jx48v8fkseduEdVymEhITAy8sLiYmJjMg7cOAARowYgfT0dEbksYW3t7fKdJ04cYKVBSA8zGJoaIhu3bpBW1ubNR1GRkbo168fa/IVwdTUlGsTeL7jh3NcLVu2hJGRkUx1Q0JC0Lt3b8an9/bt26eyNPTy0LlzZ+kL+Ldv36pMb0pKCmsLQHiYZdiwYViyZAkrIyIbGxucOHECVatWZVy2MuzevZtVZ80jP0o5rqVLl0IgEGDKlCnSz8RiMcaPH49KlSpBT08PvXv3LpSrJjY2Fl27dkXFihVhbm6OGTNmqOzlZ9++fVG5cuVS64WEhGDkyJGMLp3/lu3bt8sVQkcVK7omTpzIT9HwlMq4ceMYT/SopaWFAwcOwMXFhVG5TCAQCFSqj08sWToK99C9e/ewbds2NGjQoMDnvr6+CA4OxtGjR3H9+nW8e/cOvXr1kh7Py8tD165dkZ2djVu3bmHfvn3Yu3evSpZEGxgYyOS0srKycOXKFbmTw8lDeHg4XF1dZaqrq6vLWsZfHh550dTUxJkzZ2BpacmIPEtLSzg6OuLnn39mRB7TCAQCODg4qESXs7Mzv1hJFhSJMZWamkq1atWi0NBQatu2LU2ePJmIvqaYEIlEdPToUWndyMhIAiBNH3H27FkSCoUUHx8vrbNlyxYyMDCQOXq4orEKO3ToIJP858+fqyQem5GREQUHB8tkU3BwMKu2ODk5UXR0tFSfvb29SmPTHT58WKZ+4FEfQkNDafr06UpddwsLC7p06RLXTSmV0NBQ1r8DQqGQduzYwXVTVYbKYxWOHz8eXbt2RYcOHQp8/s8//yAnJ6fA57Vr10bVqlURFhYGAAgLC0P9+vVhYWEhrePq6oqUlBQ8fvy4SH1ZWVlISUkpUOTF2NgY8+fPR2JiIhITE3Ho0CE4OjoiNjYWiYmJBd5jjR8/Xm75ipCUlISzZ8/KVNfZ2RleXl6sTFsIBAJ06NAB1apVY1y2rKSlpUmvTVlYdfkjIpFIpNcoMTERTZs2xYwZMzBv3jyZ3xvnY2RkhCVLluDSpUv45Zdf2DGYQVq0aIEBAwawqsPQ0BCDBg1iVUd5Qe4XGocOHcKDBw9w7969Qsfi4+OhqalZ6Ca2sLCQvs+Jj48v4LTyj+cfK4olS5bgjz/+kNfUAowcORKurq7Sd1b5+5Xs7OwgEAigq6uLHTt2oEWLFioNxBsdHY3379+XOoVpaGiIvXv34ubNm0oFui2K0aNHK92/yjJy5EiMGjUKwNf0Jy4uLnBycuLUJp7/58GDB7h16xYmT55c6JizszO2bNmCkJAQZGVlITc3t8g9X6ampmjXrh0MDAywbds2VKhQQeXvjxTFwMAArq6uCAkJYW0h0YEDBxh/d1hukWd4FhsbS+bm5vTo0SPpZ99OFQYEBJCmpmah85o3b04zZ84kIqJRo0ZRp06dChxPT08nAHT27Nki9YrFYkpOTpaWuLg4haYKZSmtWrUiXV1dlU6TnT59WuZrsHnzZsb1v3v3rpCebdu2qbQPvi+2trY0fPhw+vz5s8x9w8MODx8+pNq1a5d6zebMmUNERDk5ObR7927auXNngSLrtLg6s2PHDlbu9/bt21NcXBzXzVMpKsuAfPLkSQJAFSpUkBbga5bhChUq0KVLlwgAJSYmFjivatWqtHr1aiIimj9/PjVs2LDA8ejoaAJADx48kMkOWd5x6evrk6GhIac/vmw4rtzcXNqyZQvp6ekprVdfX5927dpFeXl5hfTExsZy3i8AqEmTJvThwweZ+4eHWZ48eSJzHiqRSEQzZsygjIwMrs1mjby8PNqxYwfp6+szdo+3adOm3GRolweVOa6UlBSKiIgoUJo1a0aDBw+miIgI6eKMY8eOSc95+vQpAYUXZ3z7Y7Rt2zYyMDAgsVgskx2lNXjt2rX066+/UsuWLTn/4WXaceWzfft2EolECuscMGAA7d+/v1j5iYmJ1KFDB877BgBNnDhR7v7hUZ7bt28rlLjU19e3yIeh8sSePXtIS0tL6Xu7Q4cOhR70fxRU5riK4tupQqKvmVKrVq1KV65cofv375OTkxM5OTlJj+fm5lK9evWoU6dOFB4eTufPnyczMzPpNIMslNbg1NRUatSoEec/uGw6LolEQleuXKEhQ4bI9QXS1tam4cOHU0pKSqk6du3aRQKBgPP+sbGxobt378rdRzyK8+LFC6pevbpC10tPT4/S0tK4bgLrbNu2jbS1tRXqIyMjIzp37hy9efOG62Zwhlo5rszMTBo3bhwZGxtTxYoVqWfPnvT+/fsC57x+/Zrc3NxIR0eHTE1Nadq0aZSTkyOzztIafOXKFbX4wWXTceWTk5NDHz58oI4dO1KHDh1IU1OzSGfVoUMHcnV1pU+fPsnc17m5udS9e3fO+wcAnTt3TuE+4pGfBw8eKHW9evXqxXUTWEcikVBmZib5+PhQhw4dqHLlyqX2S9u2balDhw708OFDrs3nHGUcl4BIwaixHJKSkgJDQ0MkJyfDwMCg0PGWLVvKHMFdHTh9+jS6devGiKxt27YVioOor68vXbEnL0FBQejfvz/EYjET5ilM3759ERAQAJFIVOTxjRs3Ijo6ushj7u7uaN++PZvmlTsePnyIJk2aKHx+t27dcPr0aQYtUn9CQ0OlaYByc3Mxb948dO7cGe3atQPwdduJj48PHz7qf5T2O14SfHyfcsaYMWMYlde9e3ccPnwYgwcP5jSe4OnTp5GTkyN1XJmZmfj8+TP69OmD5ORkvH79uljneuDAAVSqVAkdO3bEsmXL1H7JcXp6OpKTkwt8VrFiRbn3SikKEWH48OEq0VWe6NixIzp27Ajgax92794dZmZmMDEx4diy8gfvuDgmf4O2OuPh4QF/f3+MHDkSnz594tocZGZmYsKECdi9e7dM9T9+/IiPHz/i6dOnEIvF6NKlC3r06MGukQrw119/4fnz57h48SIOHz5c4JizszOGDh0KMzMzxkbnxUFEiI2NZVVHeUeVYaJ+SJidtVQNioZ8UscyYcIEFfee4oSGhpKvr6/CbVXmveO8efMoJyeH8vLyaPz48Ur1uaGhIQUEBHDdnVJSUlKoX79+Mq3gMzY2pr59+9KzZ89YtWnNmjVK9XG3bt1YtY+n7MPp4gwuKC+Oy87OjtLT01Xce8qRnZ1N8+bNo2bNmpGNjU2pbRQKhdSsWTNq1qwZXb58WaZziirnzp2jmzdvkpGREQmFQqX7Xltbm/bt20cSiYTT/nz79q1C96ulpSU9f/6cNbuUXZzBOy6e0lDGcfFThRzi7e2NihUrcm2GXIhEIvj5+cHPzw9hYWE4f/48AODixYu4ffu2tN7MmTOhra0NbW1tzJ49WxraZ+rUqfD19ZVLZ4sWLWBnZ4fVq1czlhtNLBZj1KhREAqFGDx4MCMy5eXNmzcYPHiwQguJ4uPj0b17dwQEBKBx48aM22ZpaYl27drh2rVrcp+rqamJvn37Mm4TD48UFhwp65SHEZe2tjbFxMSouOfY4/Xr13T79m1pyc3NLbLe+/fvqX379jL3U+3atenZs2fk4+PDyhYHIyOjAtkMVEVubi4jG+SrVavGWmSRFStWKDS6tbS0pOzsbFZs4ik/8FOF36HujsvS0rLYuIw/AmKxWCbn1ahRI4qPj6cNGzawej1Gjhyp8inbv//+m3R0dBixPzAwkBUbJRKJ3GlLHBwcKCIighV7eMoX/D6u7zh48CAGDhzIgWWlo6WlhRMnTqBLly6YO3eudJVerVq1MGPGDI6tUx3v3r3DmTNnsGDBgkJZAbS1tbF27Vq0bdsWtWvXxoYNGzBp0iRW7YmOjmYkrcuff/5ZaEXe1KlTUbt27QKf9erVCydPnlRaHwA4ODjg6dOnjMj6npycHEydOhUbN24sta6NjQ1OnDiBZs2asWILT/lCmX1c5XLElZycTNbW1ko/yWpqatKzZ89o3rx5VK1aNcae8K2trcnGxqbANIyWlhbZ2NiQjY0NDR06lJ4/f17sdFt54v379xQbG1ugfBsG59WrV2RkZMT6KLhly5YK2Z+UlERPnjwhBwcHsrGxKTJ+pJmZGdnY2NDSpUspOjqaAgMDFQ4VVFSxs7OjhIQEpi5JITIyMmjq1Klkb29PZmZmBXQLhUKqVasWOTg4sL7Skad8wY+4iuDRo0cYNGhQsckpZaFPnz4IDAyESCRCSkoK+vTpg9DQUIXlyYufnx/atWuH1q1bq0xncdy+fRt37tyR/m9iYoIhQ4awrvfFixewt7dnXY+dnZ3cec5SU1MxYsQIHD16VOZzjIyM4OrqWmiflrJMmTIFa9asYVRmUdy8eRPHjx+X/q+vrw8/Pz/W9fKUP/gRVzFERETIFD+sqNKnTx/6+PFjAXlv376ls2fPkpmZGSORoWUpdnZ2dP/+fZW/7M7KyqLTp0+Tq6srubq6Fgq4qqurKz02a9YsEovFrCwt79Wrl8r6WR42bdpEbdq0UYltspQpU6Yw3vc8PGzCj7hKICoqCqNHj0Z4eDgSExNLle3g4IDmzZtj9+7dxcbFy8zMBADMmDEDT548KbLOy5cvGYs+oKWlBUdHRwQEBMDKyooRmcXx5csXhIeHY8yYMYiNjZVmjC4JgUAALS0tbNu2DS1atCj0PkcZbG1tVRLFwdLSEtevX5dpdPf582eMHDmyyCy/XKGqERcPD1PwIy4ZOHr0KI0ePbrYJ1ZtbW1atmxZgezOivLy5Utq3rw540/VGzZsUNq2ksjOzqZ+/fopZWP9+vUZ6cN8qlatqrJRi4+PT6n2ZGVlkaenJ+cjrO8LP+LiKWvwG5BloE+fPnBzc8PkyZOLPC4UChkbKTx69Aj37t1jRNa3LFq0CM7OzmjYsCGjcnNzc7F582Zs2bJF6dVpERER6NatG65fvw47OztmDFQBQqFQpiC2I0eOxJEjR9g3iIeHp1h+GMcFALq6uvjpp59Y1XHhwgUMGDCAFdnx8fHo0aOH3IsISmPjxo1yR7MoidjYWLRv3x4nTpxAo0aNGJPLJlWrVsXixYtLraeOwWcNDAzQokULrs3g4VEZQq4NKG+sW7cOWVlZrMn/+PEjdu3aVeAzIoJEIim2lIREIsGyZcsYt/PVq1cYPHgwXrx4oZScefPmMWSR8pw+fbrYd5pcUqVKFdYelnh41BHecTHIjh07cOnSJVZ1pKen4+zZs0hOTkZ6ejoePHgAJycnmJmZFVuCg4OLHCkkJiZi8ODBhTYAM8Xjx4/RoUMH5OXlKSwjPwmfOhAZGYmPHz9ybUYhvl2erk7ExcXJtLiHh0defqipQrYRi8XIyclhXc+JEyego6MDHR0d7Ny5s9T6Hh4eaN68OXr06IFp06ZBS0sLwNfAuAcPHmTV1oSEBBw/fhyenp4KnW9iYgJXV1dcuHCBYcsKooo9aWzg7u4Oa2trrs2Q8tdff0kD8wYHB6NVq1bSRIpVq1aFl5cXh9bxlBuYXyvCPsqsRmGLvLw8Wr58Oeery0orQ4cOJbFYTMnJyeTg4KASnd26dVNqH9r69etZtU9bW5tev35dog2PHj1S6QpHWUrXrl1ZC7ArLxkZGfTgwYMS+8jAwIB27NhBeXl5XJvLowbwQXbVgKioKFail7NRBg0apHJ7d+3apXDfBgcHk7m5OWu2rVu3rlQbFN3IzlZxc3OjnJwchfuUSWJjY8nFxUWmSPICgYC2bNnCtck8agC/HF4NoK8PAVybIRMBAQFISkpSqb2lLRIpCXd3d+zYsQN9+/Zl/J1JgwYN0L59+xLr7N27V6bN66rAzs4Os2fPRs+ePaGhoR5f32vXruHq1asy1SUi+Pr6QlNTE8OHD2fZMvVFLBZj6tSpBb4XGhoaWL16NTQ1NTm0rIzAsBNVCeo44nrx4gXnT+HqXExMTOjdu3cK929WVhbVrFmTUZtq1apVqk2pqanUo0cPTvqsevXqVLNmTWnx9/en9+/fK9yHbPDx40cyNTWVu226urp08uRJrs1XGSkpKfTs2TOqV68e2draFpsJ3MbGhmxtbenixYtqMw1cFKmpqfTixYsCRd7vNz/i4lF7vnz5otTqQk1NTZw9exb9+vXDw4cPlbanSZMmOHr0KCpXrlxivVOnTnES2qlv374IDAxUm1FVcRw/flyamkce0tPTkZaWxoJF6kd6ejp8fHwQEBBQat24uDgAQKdOndCiRQsMGTIEI0eOhLa2Nttmlsr79+9x7NgxAMCDBw+wd+/eAsdr166NcePGQV9fH0OHDmXXGLldnRrAj7jKZomLi1O6n58+fUoBAQGkoaGhsB329vYyp+Dw9/dXaR+JRCLq27cvffr0Sem+UgX16tVTuK2NGjWipKQkrpvAGhKJhLZt20YuLi5K3ROjR4/mNMVRVlYW/f777+To6CiTvdra2uTm5kbu7u4lXl9+xKUG6OjooGbNmoiKiuLalHKNg4MD7O3toaenh9WrV+PWrVsyb0GwsLDATz/9hMDAQFhaWrJsaekYGBigSZMmBT7bv38/LCwsfoj3HOHh4axu1ucSIsLGjRsxdepU5ObmKiVrx44dyMnJwdq1a+UPRqskwcHBGDBgADIzM2V+Ty0Wi3Hu3DkAQI8ePWR+/ykPvOOSg3/++adAnLpx48bB1tYWAGBlZYVJkyaxnqmX52s0eg8PD3h4eGDDhg3IzMzE5s2bERMTU2T9zp07w8XFBT/99BPc3d1VbG3x/PTTT6x8qXm4Z8eOHYw4LeCrE9yzZw8AYOfOnRAKVRM3IigoCEOHDkV6errCMsRiMYMW/T+84yqF7OxsJCYmYuTIkXj48CHevn0rPXb06FHo6upi8uTJ6NGjBypWrAhNTU0+WgBL5F+LxMREeHp6gohQq1YtbNmyBW3atIGuri4EAkGh8ywsLGBmZsaBxcUjFAqxdetWrs3gFCMjI5X9CKuSjIwMHDp0iBGn9S179+6FSCTC2rVroaOjw6jsbyEihISEYNiwYUhKSmJNj1IoOu/JJap6x5WXl0fz5s2TaV7XwsKC7ty5Q25ubpy/S1LH4ujoSF++fFH4Wly6dIlmz55d4rz6pUuXGLz6X2HrHZeGhka5eL/j6+urcB/4+/tzbT7jJCcn06BBg1j9LrG9Dy4oKEipd8jff+9L6iuA34DMOIsWLZJpU2V+sbe3p+XLl6ssO3JZKjt27FD4Opw7d44sLS1L1WFlZUWhoaEM3gFEAQEBrPSHr6+vyrNas0F8fLzCG9n379/PStZsLomMjGT9u9S8eXNKSEhgxf7Tp08zutmfd1zfwLbjEovFNH/+fBKJRHJfKAMDA86cg7oWQ0NDOnHihELX4uXLl6SrqyuzLj09Pbp8+TKj9wIbiSMV7Q91QyKR0MGDB8nIyEjuPtDX16cWLVrQw4cPKTU1leumMELdunVV8p169eoV47ZnZmbSpEmTGLWTLcfFv+MqgtjYWPj5+Sl0bkpKCqpWrYoqVarg9u3bDFtWNmnfvj169uyp0Ln+/v5yvRxOS0vDyJEjER0dLf3s2rVruHXrVqG6mpqamDp1aonvWbS0tNC3b18EBwcjMzNTPuOLoVGjRnBwcGBEFtcIBAL0798fQqEQ3t7ecr2MT01Nxd27d9G4cWP4+Phg48aNZf6dV3Jyskr0HDhwgPGUPy9evMD69esZlckaynhormB7xKXse6pWrVpJd8krI6c8FD09PYqIiFDoOmRmZpK1tbXcOrW1tWnNmjWUkpJCHTp0KFaGQCAgJycnCggIKHXKys7OjpH+sLCwoBcvXijUH+pKTk4OzZs3T6n3IgKBgKZMmVKmp08zMzNVFtOyWrVqjNt/+/Ztxu3kpwq/gW3HxUQU8BUrVtDHjx+pcePGSstycnKiFi1aqOQLwXTp3LmzwtHAfXx8FNbbtWtXatmypUx1BQIBBQQElGhLVFQU1a5dW+n+6Nevn0J9oa6IxWKaOnUqY/fL5s2buW6SwowZM0Zl3ys2HBcbTpctx1W2x+VqjEQigampKQ4dOoRNmzZBT09PbhlWVlbYvHkzjh07hvnz57NgJbu4ublh3759Ck//KBMi6syZM7hz545MdYkIY8eOxeHDh4utU6NGDQQGBqJWrVoK29S3b19s27ZN4fPVkaSkJKxevZoxeQcPHlQohJQ6oMz9yjVEVKbs5x0Xy9jb22PcuHF4/Pgx/Pz8YGdnV2J9a2trVKtWDTt27MDdu3fh4+ODKlWq4JdffilTyQ7t7Ozg6uoKc3PzQseICK9evcLMmTNRrVq1AmXBggWIjo7Ghw8fVGpvamoqRo0ahcuXLxdbp3Hjxrhw4QIcHBzkimBgaWmJ/v37Y+fOnTA0NGTCXLVh8ODBjMq7efMmunXrxqhMntL5+PEj4/vO2IRfnKEiqlatinnz5mHGjBmYOXNmsU83c+bMgZWVVaHPdXR04O7ujqCgIKSkpLBtrlK4ubnh+PHjRW6SfPToEa5du4YZM2YUGarJz88Pfn5+aNiwoUKjVGVITU1FRkZGiXWqVauGp0+f4ujRowgJCcH+/fuLrWtsbIyBAwdi0qRJsLe3Z9pctSA+Pr5MyOQpmRkzZuDLly9cmyEzvOMqggoVKrAmQ0tLC+vWrVNIpqenJyQSCby8vGSOz6dKNDQ00LFjR+zatatIpxUTE4MBAwYgMjKyVFmPHj1iw8RSWbx4MTp06FBqZIK+ffvC3d0dffv2lX4WGBgIJycnVKtWDQCgr6+Ptm3bsmovl+zdu7fYMFvKEB8fj82bN2PcuHGMy2YTJn43eGRE4Td5HCLvS703b97QzZs3ZV4k8OjRI6VeSLZu3Zq11VESiYRSU1Np+vTpcm2OZrMYGRlRmzZtKDIyksRicZF2P3/+nJFFL2wXkUhEKSkpCl2brKysHyot/cyZM1m7DmPHjuW6eXIjFovJyspKJfcp04szFi1apNC+1dIKv49LTv777z9p/pu7d+/i2rVrmDp1KjQ0NGBgYIA5c+YUe66NjQ3c3d0REhIit16hUIgRI0ZAJBIpbHtJCAQC6OnpYdmyZahcuTKCg4Nx7do1VnSVhJmZGaZOnQrg6/us/v37l1h/+fLliI2NVYVpnPEjRHTnKR4tLa0iY2WywahRoxiVN3fuXKxbtw4JCQmMymWLcum4Bg8ejL/++qvQNMbKlSsBfJ3SSkpKwvTp02FqalroZjM2NoaHhwcuXrwod8DcSpUqwdPTU7kGyIBQKMTUqVMxcOBAJCQk4OzZsyU6YyYwMDDAtWvXUKFCBWhpacm8ifby5cs4fvw4q7YBXx2HUChkLSI1D09pbN++HV26dGFdz4ABA1jXoc6Uy1WFL168KHHuPTc3F8uXL4elpaU0o+f3jBo1Cr///rtcS7kdHBxw8eJFVKxYUW6bFcXS0hINGjSQpldhE6FQiIYNG6JBgwZyRX5ITk5GYmKiUroFAgHatWtX7HGRSITFixfD29tbKT3Ozs6sjZZ5yj/Vq1dnXUeLFi0YX7hERIzKY5ty6bhkRSKRYOnSpcUenzVrlsxhVSwtLXHgwAE0atSIIevKBxkZGdiwYYPScjQ1NbFr1y74+/sXkGdnZwd/f3/4+/tj6tSpGD58OExNTRXWM2XKFLVIk85TNqlcuTLjWwS+pXHjxjhw4IBS93hRCAQCzJ49m1GZrKLE+zzOKO2lnjxRJjQ0NMjX15fS09OLlJWdnU0fPnwgDw8PatCgQZFl165d9PnzZzabXCpsBYP9thgZGcm8+CAiIoIePXpEN2/eZGwRSY8ePYjoa7qZDx8+0IcPH4pMldK+fXuF5JuYmNC1a9cYvS7lmTVr1pC2tjbj95lIJCI/Pz+um6cwGRkZNGzYMMb7RSAQ0KRJk1iz+/nz5zJlYZCn8CGfvoFJx5Vfbt++reJWMI+XlxerjsvHx6fUmH6PHj2ixYsXs/KD5uLiIlM/fP78mTp27CiXbENDQzp16hQTl+GHgo14nHZ2dlw3S2nEYjHj34GRI0eyvmp16dKlZcJx/dBTheUNLS0tVuUPHz682FVT2dnZGDhwIPr27Yu5c+cyvkBCJBKVOK37LSYmJti7dy9cXFxkqq+lpQV/f390795dGRN/SNjIxFsepmo1NTWxYsUKRt5FCQQCDB8+HKtWrWI9ev7AgQPh6OjIqg5GUMY7cwUbI65169aV+aR2OTk5ZGFhwcpoy97enp4/f16k3o8fP1KvXr1Y0ZtftLS0ip3OLY7MzEzq1asXVa9evUiZQqGQ3NzcKCgoiInu/yF58+YNo9e5Xr16FBcXx3WzGEEikVB2djYNHTpU4f5o1KgRTZw4kXJzc2XW++DBAwoODqbg4GA6d+6c3L9rqamp5OTkpNYjrnK5HF4RFixYgPHjx3O++z0tLQ1z5swpcpWPp6cn2rRpU+y5GhoamDBhAuMBeW1tbXHgwIEiA8zm5ORgzJgxOHHiBKM6mUBbWxvHjx/H7du3cf/+fdy7dw/79++HlZUVZs+eDU1NTYwcObLM54DiEhMTE3h5eZUY+koexowZA2tra0ZkcY1AIIBIJMKmTZvQtGlT7N69Gw8fPpT5/JYtW8Lf31+mwM6nT5/GxYsXAQChoaF4/vw5gK8zFfn3eKVKlfDHH3+UKktPTw8HDx6El5cXbty4IbO9KkVuV6cGsDHiOnnypGob8R1isZh+/fVXsrW1LdZGU1NTcnJyok+fPhUrJysri/F3TB4eHkXqSk5OZn2klV/27Nmj9Ig4NTWVXr58WW6e6NWF1NRUGj58OAkEAqWucf/+/VlLVaQOfPjwgc6dO0d2dnYllsWLF9PLly8pISGhRHkpKSk0YMAAqlatmkwZqDU0NKhatWoUGBhI7969K9XeT58+KZ1OiV+c8Q3lbXFGXl6eXOFznJ2dS03dvXz5cqV/SACQm5sbZWRkFKlj1apVKnFa9evXp8ePH7PQ8zxMosxKOk9PT7mmw8o7GRkZtHnz5gJl586d0oc3f39/pVYRV69enTZv3lziQzDR14VOPj4+CidSLWl1KO+4vkNexyUQCOjOnTsqbsX/s3DhQrmXjDs6OhbrUIiIcnNz6Y8//lDYeWloaJCbm1uxT2ZxcXFUt25d1p2WjY2NwhmUeVRLcnIyBQUFUYMGDWS6n4VCIdWsWZOCgoJK/QH9EcjNzaUHDx6Qh4cHderUqcjvpLu7O3l4eJCOjg4j3y8PDw/Kyckp1ba7d+/SsmXLSCQSlZjpWiAQkEgkIjc3NwoODi5RNu+4vkNexzVz5kzOgqO+f/+eWrVqJfcNJxQKKSwsrETZubm5tHz5cmrWrJlcsu3s7CgyMpIyMzOLlX3kyBHWnVbNmjVLHVnyqB/p6ekUGhpKrVu3piZNmhS6ro0aNaLWrVvTqVOn5F5wU1559+4dubi4sLKNRBbn9eHDh1JtzM3NpZSUFHrz5g05OztT69atCxV/f39KSUkp8bcjH2Ucl4CojMX6AJCSkgJDQ0MkJycXmdCvZcuWuHv3rkyyrKysEBgYWOKiBzbZvHkzxo8fr9C5rVu3xs2bN0utl5KSgo0bN8Lf3x9Pnz4ttt6wYcNQs2ZNdOzYEc2bNy9RZpUqVfD+/Xu5bZaHbdu2YfTo0azq4JGNoKCgAhmlq1evjpEjR5Z6XlJSEjZv3lzgs5EjRxaZYPRH5fPnzxg4cKB0cQUX7Nu3D15eXirVWdrveEn80I7L0NAQly5dQrNmzdgws1QkEglWrlyJWbNmKXS+rI4rn1evXiE5ObnY4zVr1pR534kqHJeNjQ3u3r0LS0tLVvXwFObz5884cOAA9u7dCwCIjY0tkGhQT08PNWvWBAB06NABs2bNQqVKlVQWHb28kJaWBnd3d1y/fp1TO2xtbXH16lVpLjlVoIzjKpfL4V1cXEp0XNra2ujZsycmTZpUotO6du1aoR/nn3/+mbGAtq9fv1bYaSmCojfl58+fcfHiRWhqaqJ37964c+cOMjMzGbauMHFxcXJH5+dRnr/++gtdu3YtMdN2WloawsPDAQDh4eFYtWoVDh06pJLMCOWFN2/ewNvbm3OnBXxN8tqzZ0/pNVV3yqXjWrhwIerWrQsiwpQpUwpEJh8wYAB69eqFPn36FHt+fHw8Zs2ahdDQ0EKOy9nZGe3atcPChQtZs58JlixZUmhacNKkSWjatKnMMq5du4Y9e/bg48ePOHfuHDQ1NdGvXz+EhYUhKSmJYYt5uCYpKQmTJ0/GjRs3SnRaRUFEGDNmDM6cOYO5c+fKlT3gR+Xu3bu4cuUK12ZIiYmJwZEjR8rGw4fcb8XUAHle6iUkJFB8fLy0lPQyODk5mYYNG0ampqYlvswsLTCvrOTm5tKSJUsUfqnaunXrAvLi4uLo9OnTZGFhUWQ2UyMjI7KwsKBZs2aV+PL03bt31KRJEzIwMFD5i+LvS0xMjFJ9zCMb79+/l3sRT3HFysqKoqKiuG6SWpOSkkImJiacf7++L7NmzVJZH/CrChni+PHjcl1kX19fpVcjbtq0SWnHlZGRQcuXL6fGjRvLfO6kSZPo9OnTheyJjo6m1q1bc/4F4h2X6oiJiaG2bdsyet1q165NDx8+5LppaktycjJjWROYLO3ataPY2FiV9QHvuJTk06dP1KBBA7kuslAopDlz5iild/v27QrdwEKhkG7evElEpHAsNHNzcwoKCpJuakxKSipy+TLvuMo3Z8+eZeXarV27tszH/2SLsWPHcv7dKq6oKrUP77gYQNFRhqOjo1I/rnl5eTR//ny5nVeLFi0oIyODnj17VmwQWVmKhoaGdJOxq6sr51+ab0u9evVKDXvDoxxisZgMDQ1ZuX5CoZBev37NdRPVEkdHR86/X8WVsuC4+Oii/yMnJ0eh827fvo2zZ88qrFcoFOL333/H1KlTZT6nVatWOHToEN6/f49BgwYhOjpaYf25ubnYsGEDrly5gsePHysshw0mTpwIMzMzrs0o1+zYsQPp6emsyJZIJFi/fj0rsnl+bHjHBSA5ORlZWVmc6RcKhfDz88OsWbMgEolKrNu4cWOcPHkS1apVQ3h4OO7fv6+0/qNHj+LSpUt48+aN0rKYQltbm5FcRjzFk5qaimPHjiE3N5c1HceOHSuw/4uHhwnK5XJ4eVm/fj3n+xe0tbWxdOlS6Onp4c2bN7hy5QpevHgB4Gu6kmHDhkEoFOLPP/+EqakpcnJyMHToUEZ0R0VFYcmSJYzIYgo3NzcMHDiQazPKLcnJyRg9ejTre4hiY2Mxb968QtEzeHiUQa4R15YtW9CgQQMYGBjAwMAATk5OOHfunPS4WCzG+PHjUalSJejp6aF379748OFDARmxsbHo2rUrKlasCHNzc8yYMYPVJz5Z8PT0hL29Pac25DNv3jxs3boVBw8exMmTJ6Vl27Zt2Lp1K0xNTbk2kXX09fUxY8YMrs0o1/j4+ODIkSNcm/HDUtrMCk/JyDXisra2xtKlS1GrVi0QEfbt24fu3bvj4cOHqFu3Lnx9fXHmzBkcPXoUhoaGmDBhAnr16oW///4bAJCXl4euXbvC0tISt27dwvv37+Hl5QWRSITFixez0kBZcHBwgLGxMWf6i6Jp06ZybRYuT5w4cQJOTk5cm1FuiY6OVquNrz8ihw8fhrW1NSQSCdemFMDKygqGhoZcm1E6yq4MMTY2pp07d1JSUhKJRCI6evSo9FhkZCQBkEYxP3v2LAmFQoqPj5fW2bJlCxkYGFBWVlaxOsRiMSUnJ0tLXFycwqtRiqNly5YKrcCpXbs2/fPPP4zZISvZ2dmkr6/P+Qokpku7du34RI8s8vjxY2rUqJFKr6mPjw/XzVY7xGIxDRo0iPPv2/elrGxAVnhxRl5eHg4dOoT09HQ4OTnhn3/+QU5ODjp06CCtU7t2bVStWhVhYWEAgLCwMNSvXx8WFhbSOq6urkhJSSlxRduSJUtgaGgoLTY2NoqaXSybNm1S6EmjWbNmaNKkCeP2qBptbW1oaHD3ylNPTw+Ojo4IDAwsN6nb1ZGnT59y/j6XB9DS0kL//v2hpaXFtSlShEIhKlasyLUZMiG344qIiICenh60tLQwduxYnDx5Ej/99BPi4+OhqakJIyOjAvUtLCwQHx8P4GsMwG+dVv7x/GPFMWfOHCQnJ0tLXFycvGaXStOmTbFz5065zunWrRv27NnDuC1cMG7cODg7Oyt8fqNGjdCvX79S06EUhZ2dHS5cuIBbt26hcuXKCtvAUzI5OTnSaXse7nF3d1d5KpGSqFu3LubPn8+1GTIht+NycHBAeHg47ty5Ax8fH3h7e+PJkyds2CZFS0tLuiAkv7BB8+bN0a5dO5nqenh4YNeuXZyOUphEKBTC19dX4ZfGXl5eOHToEAICArBr1y7o6urKdN6iRYvg7++Pn3/+mU+JwTIZGRlYu3Yt12bwfMOwYcNgYmLCtRlSysx3UNl5yl9++YVGjx5Nly9fJgCUmJhY4HjVqlVp9erVREQ0f/58atiwYYHj0dHRBIAePHggs062YhUSfQ17dO/ePapTpw7p6ekVmgO2srKi7t27U2pqKuO65SUoKIiRee1atWpRSkoKERGdPn2ajI2N5Tp/0KBBlJaWVsC2+Ph4evfuHfXq1Yvq1KlToDRq1IhevXpF7969kyltOA8zPHr0SOXx8UQiEfn5+XHddLXmwYMHZGlpyfn7LXt7e5kyITMFpyGfXFxcyNvbW7o449ixY9JjT58+JaDw4oxvO2fbtm1kYGBAYrFYZp1sOq5v2bFjB02fPr1AUafAoa9evVJ4Ucm35bfffisgNygoiIyMjGQ6d8CAAbzzKSMwca/IW6pVq8Z1s8sEN2/eJF9fX9LQ0ODUeZWVxRlyOa7Zs2fT9evX6dWrV/Tvv//S7NmzSSAQ0MWLF4noa+DIqlWr0pUrV+j+/fvk5ORETk5O0vNzc3OpXr161KlTJwoPD6fz58+TmZmZ3IFqVeW4ygK//fabUjeqpqamNFbht4SFhdHMmTNJW1ubtLS0pPWFQiFpa2uTvb09Xb58mb58+cJBq3kUgQvHVbt2ba6bXWaQSCR07do1Gjx4MGlra5OOjg5pa2ur9HoxkfFCVlTmuIYPH062trakqalJZmZm9Msvv0idFhFRZmYmjRs3joyNjalixYrUs2dPev/+fQEZr1+/Jjc3N9LR0SFTU1OaNm2a3E/svOP6f3Jzc2nkyJEK3aSWlpZ09uzZYmXn5eWRWCymf//9l1xdXcnV1ZUWLlxIYrG4xO0LPOoJF47r2bNnXDe7zJGTk0NisZjEYjFlZmaqNPi1UCikGzduqKSdyvyOC4iIUMZISUmBoaEhkpOTWVuoUZYQi8XYvn07Dhw4gHv37sl0jo6ODo4dO4YuXbqwbB2PuuDo6Ig7d+6oVOerV69gZ2enUp1c8O+//2L37t2FPu/evTtcXFyUkv3w4UOVbrm5du0a2rZty7oeZX7Hy8eSuB8cbW1tTJo0CZ6ennB1dUViYiISEhIKBQ42NTWFjo4Ohg4dCm9vb9SoUYMji3l4yj7Z2dn48OEDJkyYgLt37xa5pScgIAAmJiZwdXXFsmXLoKOjw4Gl5Q/ecZUjLC0t8ejRIwDAqlWr8OzZswLHx40bh0aNGnFgGc+PSKtWraCvr8+1GTLx8eNHnDp1Cj179pQpHigRYcmSJfj9999LrPfp0yd8+vQJz58/R3Z2NtatW6dWm47LKrzjKqdMmzaNaxN41Ix58+bBw8MDqno7MGjQIFSqVEkluuSBiCCRSDB58mS8e/cOAJCYmIhr167h4MGD0iAK1tbWWLNmDSpUqFBIxqpVq7Bo0SK59G7btg1aWlpYu3Zt2dkvpabwjouH5wfB2dkZAoFAJY7LyMioUJQcdSAyMhKXL1/GvHnzkJKSUqgvrl69Kv1bIBBg//79WLJkCVxcXFC7dm0AX0dn586dUyj57MaNG5GTk4Ply5fz+eaUgHdcDBASElLioojevXujQYMGKrSIh6cw2tra6NevHw4ePMi6LmdnZ/Tq1Yt1PfJw69YtDBo0CK9fv5apPhEhOTkZ48aNg52dHQIDA1GvXj2MGDFC4ej6EokEW7ZswZAhQ/gMCMrA4OpGlVHaMsrk5ORCkRyYJDc3l758+UJeXl7UrFkzMjMzK3GJabVq1ahZs2a0atWqQpFFeHhUSXBwcIF9eWwUHR0devLkCddNLcCjR4/I2tpaqXbZ2NjQuXPnGOmjCxcuyGx7eno6+fj4qGQ5vJeXlzSKDttwGjmDC0prsKOjI3l6erKmf+3atSQQCBS6MXR1denatWus2cbDUxpbt24lHR0d1n783NzcKDc3l+tmSrl79y6ZmJgw0jZFv/ffFyMjI7n6aNGiRSpxXGUlcobCaU3UGYlEgrt37+LmzZuMy962bRtmzZql8HuC9PR0DBkypMBcOg+PKhkzZgzMzMxYkz9nzpwiFzRwxebNm/HlyxdGZCn6vf8eeRNIdu3aFTVr1mREd3FYW1ujT58+rOpginLpuADg9evX8PDwwPv37xmRl5ubi3Xr1mHKlCmF9kfJS1xcHHr27Im//vqLEdt4eOTlxIkTjC+e0NLSgp+fH1q2bMmoXEXJy8vDli1bcPjwYa5NUZqGDRuiWbNmZV4HYzA+/lMBpQ0xW7RoIR36rlmzhhGd69evZ3xYbmJiQpcvX2bEPh4eeblx4wbZ2dkxdj+rWxT4T58+MTa1x3QxMDCQezpVLBZTz549WbGnc+fOKs94wb/j+o5vHZeDg4PS+rZv3066urqs3DDW1tbS6Pk8PzbZ2dmUmZlZZGGa/Dh4165dU3qxhpubG4WGhlJ2djbjdirDu3fv1NZxmZmZKfQeMCEhgXHn1blz5yIDbbMN/46rBNLT0wtFkJCHxMREhIaGIj09nUGr/p83b97g0qVLyMvLY0U+j/qTm5uLS5cuoVevXjA2Ni5UatasidDQUCQkJCitKyEhAaGhoahVqxaMjY3RuXNnhae+a9WqBU9PT5w6dQodOnRQOAkpW/Tq1Utlm63l5ciRIwq9BzQzM8Phw4fRs2dPRuxwdXVFUFBQ2cs8zrwfZR95RlwAaMqUKQrrunLlCutPXyKRSC0SU/Jww+LFi2W6Tzw8PCgjI0NhPZmZmeTh4aH0/Tpy5EhavXo13bt3j8FeYJ7GjRtzPrIqrty6dUuptiUlJSk98nJzc6OEhASGelt++KnC72DKcWVlZZG9vb1KbuQBAwYo0yU8akpqaiq9efNGWr5N4ZOVlUV+fn5y5VyqVasWbdiwQS4bsrOzaenSpVSrVi2F78/JkydTREQERUZGsrpHkknU0XGJRCKaNWuWUg8g+SQmJlJERATVrFmT9PX1ZdKvq6tLtra29ODBA/r8+TMDvaw4yjguPnJGCRAR4uLiVKLr7du3KtHDoxpu3LiBFy9e4Pz58zh27Jj080WLFsHCwgLt27fHvn378Mcff8gl98WLF7h48SL69esn85L2f//9F7Nnz5ZLz/esW7cOtWvXxtixY5WS86Mzc+ZM/Pnnn4zIMjIygpGREV68eIHjx4/j7NmzePnyJa5fv16orqOjI3766Se4uLhg8ODBjOjnkh/CcfEBLXlUhUQiwa1bt+Dl5YWYmJhCx3/99VcAX39I/vnnH4V0BAcHY8SIETh16hSEwpJfU+fl5cHPz08hPd8zY8YMaGpqYvjw4YzIKw8IhUKZ92RNnToVCxYsYMWO3r17o3fv3oiNjcXdu3cLHW/cuHH5SmPE/ACQfeSZKqxZs6bCw/KIiAiVpc5u1KgRJyt7eJjjzZs31Lx5c9ZWoH5bZI28MHToUEb1enp6UlJSkgp6U3nYnips3LgxffnyhYYMGUI1a9Ystl79+vXp119/VbtVl1zDTxWWQIUKFRRO3jZ9+nSIxWKGLSqa8PBw7Nu3T+kpHR7u2LJli8wZqJVFLBbj+PHj8PT0LLZOeHg44/YcOXIE3t7eZSJztpeXFx4+fMiafA0NDRgbG2P//v2IjIwsdqPz+PHjWY1U8kPCgiNlHXlGXKGhoQrrcXV1VcloCwBpaGgwtlmaR/X8/fffVLlyZZXdLwCoT58+Jdq0a9cuVvTa29tTixYtqEWLFvTw4UNKT09XUS/LB5sbkAUCAb//Ukn4fVzF0KRJEzRu3Fjh83/++edS3yEwhZOTEyZPnqwSXTzMkpubi65duzIWXowJxGIx7t+/z4rs58+f4+7du7h79y6aNGmCrl274vjx48jOzmZFn6JoaWmxFn6qZcuWqF+/PiuyeUqn3DquJk2a4ODBg0plYJ01a5bK0mwLBAJ+EUkZRt02kH/58gVbtmxhXQ8R4dq1a+jTp0+paexVjZ6eHqZMmcKK7KlTp0JXV5cV2TylUy7fcdWtWxfLli3j55V5VMK7d+/kjvatLAKBAFWqVFGpztJYuXIljIyMMHPmTJXrfvXqVaERn52dHTw8PDBx4kRs2rSJkWukoaGBCRMmwN3dXWlZZYXk5GTEx8cDAPbs2YOAgAAAX+/Bw4cPw8TEBEKhEDVr1lTdwzfzM5fso8zcqDzk5OTQgAEDWH9XIRAIaNWqVay2hYc9evToodJ3W0Dpqwrfvn2rcpsAUIcOHejt27cq6fecnBxav349rVmzhkxNTQvZMnXqVFqzZg09fvyYJkyYwEj7Jk2apJK2qQt79uyhPn36lNovIpGIVqxYQQ8fPpRZtjK/4wIiNQ3mVQIpKSkwNDREcnIyDAwMWNV1/fp1tG/fntUnah0dHXz8+JGfeiij/PPPP3B2dkZmZqbKdJqbm+P9+/fFvoN99+4drKysVGbPt1y+fBnt27dnTX5WVhZOnDiBnTt34sqVK6XWr1evHurVqwdtbW3s3btXIZ0CgQBjx47FihUryt33NCsrC0QEb29vJCUlFTh248YNuVZW165dG+fOnYOdnV2pdZX6HZfb1akBqhpxERHl5eWRr68vq0+px44dY70dPOyRk5Mjc8gdJoqdnR2Fh4eXaNOnT5+odu3anIy6TExMWNuzFBERQZUrVyaRSCS3XYqcA3ydEfH19S0Qrqs8EBUVRcePHycdHR1G96va2NhQZGRkqfr5VYUsIhQK0aNHD8aT7uXj5OSk1MpHnh8Pb29vNGzYsMQ6lSpVwowZM1RkUUHYWl348OFDeHp64v3798jJyZH7/PxzHBwcsHLlSpiampZ6Tps2bbBmzRosX74cGhrlZ0nA69evMXDgQPTu3RuZmZmM7leNi4uDp6cn/vvvP8ZkFkIRT801qhxx5XP79m3Gn0ytra3p/fv3KmsDD3scPHhQJSOZO3fuyBzR++DBgyqL/PJtEQgE5OPjw2j/JiYmkrW1NSP26erq0r59+ygqKoqePHlCnp6eVLlyZWnp378/PXnyhJ48eULx8fGMtoNr0tPTadiwYYwmEC2u2Nra0uvXr4u1hY8OrwKSkpLI2dmZ0QvL9JebhztevHhBDRo0UOg+6NatGw0bNqzYsEE6Ojo0bNgwhdKI9O3bV+WOK79NTBIQEMCoff3791fp74c6kJqaSoMHD1bpfeDo6FisPfziDBXx+vVr3L59G2PGjEFKSorCcpo1a4YZM2aga9eu5e5FL1MUtxhGnfe7/ffff/Dw8MCrV6/kOu/evXto1qwZ7t27V+S5Ojo66Natm0I2/fXXX+jatatS96sieHh4ICgoiBFZRIQmTZogPDycEXn5REZGonbt2ozKVGeGDx+OPXv2qFSno6MjwsLCijzGL85QMXfv3qUmTZrIHUzV0tKSnJ2dKTExkRO71Z309HS6f/8+Xb9+nczMzMjY2LhQmTlzJt2/f59evXrFtblFEhsbS9WrV5fpfmjUqBGtX79eoRTu8qCKZKjfFl1dXfry5Qtj9vv5+ZFQKGTczjp16jBmo7oTExOj8pBkYHHExTsuJdixYwd5enrKdAGnTp2qVNzE8s7x48dp7NixMn8hGjVqRH5+fowk5GOaiIgImj9/frEPNo0aNaLffvuNxGKxSuy5f/++Sn+s9PT0GFtVGBsbS23atGHFThsbG0ZsLAuMHz9e5U6Ld1zfoS6Oi4joy5cvFBYWRmFhYTR79mwyNDSUlsDAQOmx8raUlilev35Njo6OZGJiotAXo3///pSUlMT6qEUR7t+/T2FhYXT16lXq3Lmz9F54+fKlyu1Q5Y/V9u3bSSKRMGL7zZs3WbNTU1OTlixZwoid6szNmzfJwsKiXDmu8rO+kyOMjY3h6OgI4GvgzW+zm1aoUIErs8oE//33H9zd3YtMuCgrhw4dwrFjx7BkyRJMmzZNrd5/NW3aVPp3mzZtVBawmUvs7e3RunVrtboOxZGdnY3o6GiuzWCdxMREfPjwgWszGKX8f5NUiEAgQIUKFaSFp3iePn2KQYMGKeW08snNzcXcuXOxadMmBixjhx/BadnY2ODAgQOoU6cO16bw/I+srCyVBFtWNfyIi0flvH79Gp06dUJcXBxjMnNycjBjxgwYGRlh8ODBjMnlkQ1LS0uEhobCwcGBa1N4viEnJweXLl3i2gzGKf+PgTxqx9ixYxl1WvmIxWIEBwerfOn3j07t2rUREhLCOy0elcE7Lh6VQUQ4duwY/vnnH9Z0HDlyBGPGjGFNflmkVq1aGDJkCKMyNTU1MWrUKJw7dw6HDx8u8D6PSYRCIWvTrKamppg0aRIrsnnYhXdcPCojKioKAwcOxKdPn1jVc+fOHVbllzUMDAzQsWNHxjbrN27cGO/fv8fmzZvRuXNnNGjQgBG5ReHo6Ihp06axIltHRwf16tVjRTYPu/COi0dlSCQShYKjysuXL18Yi9pQXhgyZAhjiyb+/PNPmJiYqCTorFAoZE3P6NGjWZHLwz684+JRGSNHjlSJnuTkZMZeSIvFYsTHxxdZ8vLyGNGhKnbs2AETExOFzxcKhRg7dizatGnDoFWlM2XKFLRq1YpRmfr6+hg4cCCjMtURgUAAIyMjTnSbmJhg+/btrMjmHRePynj9+jXXJshFcHAwpkyZgsqVKxdZVq9ejdu3b3NtpszUr18fFy5cgL29vULnjxkzBlu2bIGenh7DlpWMubk5Tp06xajMhQsXonr16ozKVEd0dXWxa9cuTnTv3LkT9evXZ0e4EhuyOUOdImfwyA5TaSlkKbVq1ZIrjfi3REZGUv/+/cnIyKhUPTVr1qS7d+8y21Esc/fuXTIzM5OrP4cPH07p6emc2ZyVlUUTJ05k5N5o1KgRPX36lLO2qJrg4GCVR8xo3749xcXFlWgXH/KJp0ygSscFgM6cOSO3jc+ePSNLS0u59BgZGSnsJLniy5cvtH37dmrUqBHVqlWrUJu0tbWpUaNG1Lp1a4qPj6fMzEyuTSaxWEwzZ84kDQ0Nhe+JOnXqUGhoKD148KBAKc+OLCsri4YPH66S75y1tTW5urpSampqqXbxIZ94eBhi+vTpiI+Pl+ucpKQkdO/eHQEBAWjdujVLljGLsbExRo0ahVGjRuHVq1eFppOqVKmCcePGcWRd0WhpaWHZsmUwNjbGxYsXcfXqVbllNG3aFK6uroXS5lhbW2PMmDGoWLEifH19y0TIKlnR1NREz549ceLECSQlJbGiQ0NDA9OnT4eHhwecnJxY0VEARTw41/AjrrKJKkdcIpGILly4IJd9J06ckGl6sLgyf/58ysvLY6n3eL7lzZs31KpVK9LX1yeRSMTYfaOhoUFOTk505MgRSktL47qZjBIcHMx4Ruy6devSzZs3KSwsTO7AynwiSZ4ygY2NDd68eaMSXT4+Pti0aZPMT84ZGRmYOnUqtm3bprBOgUCABw8eoFGjRgrL4JGdvLw8EBF27dqFc+fOFTp+7do1JCcnKyRbIBDA1tYWwcHB5WqvV0hICEaOHMlI0N3GjRvj4sWLMDU1Veh8PpEkT5lAlSOuCRMmyGVbdHQ0I3ofPHjAUu/xyMPp06fJ1NRU6evZoEEDioyM5Lo5jBIcHExaWloK94muri5t3ryZnj17ppQdyvyO88vheVRGQECASvRoaGjA1NQU79+/x8uXL1mb1y+KIUOGgMreJEa5IiQkBEOGDGEkQsu///6LTp06lbmtHCXh7u6OEydOoHr16tDU1JT5PG1tbVSvXh2nT5+Gj4+PwtsqmIB3XDwqw8LCQiV6DAwMUKlSJXTs2BE1a9aEt7c3Nm7ciNDQUNZ1v3//nndcHBISEoK+ffsqPEVYFHFxcejcuTPCw8MZk8k1Xbp0wcuXL7Fw4UJ07Nix1Ppubm5YvHgxXr58ifbt26vAwpLh33HxqIz09HRMnz4dW7du5US/lZUVGjdujD179hSal3/16hUjG1ItLS3x9u3bHyL/lrpx7tw5DBs2jLWkiStWrMD06dNZkc0l7969K9UpN23alPEHT2V+x/nl8DwqQ1dXF+vXr0dUVBQnOYLevn2Lt2/fol27dggODka1atUY13H8+HHeaXFAVlYWQkNDWc30++uvv8LDw4PTKTI2qFKlCqpUqcK1GXLBOy6eEjlw4AD++++/Ap/17t0bzZs3V0ieSCTCsGHDcP36dZUE3C2Kx48fY+DAgTh48CDs7OwAfI2r1r17d6WD8+ro6DBgIY+8fPjwAWvWrGFVR3Z2NrZt24ZVq1YVW2fJkiXSacqaNWti5MiRSE1NxaJFiwrUGzduHKpWrcqqveUapZaFcAS/qpBdUlNTyc3NjerXr08GBgaFVhVZW1tT165d6cOHD5SVlSW3fIlEQvv37ycdHR2VrTIsqtSvX59ycnKkdu3Zs4c0NTWVWsmoDhEmfkTCw8NVcs9YWVlJdebk5NCHDx+ob9++VL9+fapfv36BPWV6enpUv359ql27diE51atXl54TERFBKSkpHPYeN/Ahn3gY4+3bt9S5c2eZv8hz5syh3NxchXQFBgaSoaEhZ45LT0+PsrOzC9jk7u6ukCwzMzMKCgpi4hLwKIC9vb1K7hkjIyO6desWSSQSWrJkCWNyO3XqRAEBAQp/l8oivOPiYQSxWEydOnWS6wsnFArJz89PYZ3+/v4kEAg4cVwaGhq0fPnyAvZcunRJ7ugCQqGQd1ocU1S8RbZK9erVacCAAUqNzosqAoGA/vjjD667UmXwjotHaRISEqhjx44KfeFEIhHNnz+fxGKx3Hrz8vIoISGBJk+eLNdTs5GRETVs2JCEQqFSPxbdunUrZNPVq1epYcOGMjkwExMTOnXqlNzhbniYRZWOi80iEolozpw5P8SUM++4eJRmxYoVSn/plI0w8ObNG5ozZw75+voWKd/W1pbmzJlDc+bMoaNHj1JeXh6ZmJgw7rjyWb9+PXl4eBR77tixY+nUqVNKtZmHGcqL48ovc+bM4bpLWYePVahGZGZmIjw8HLNmzSry+PLly9GoUSNoa2ur2LLiiYmJQbt27ZSODtC5c+ciY8bJS15eHsLCwgpt5DUyMiqQmE4ikcDMzAxfvnxRWFe3bt1w+vTpYo9//PgRT58+LfJY8+bN1eo6/sjY29vjxYsXXJvBGCKRCL///jvmzp3LtSmswe/jUgMyMjJw+fJlLF68GPfv30dubm6R9ZydndGsWTPMnTsXHTp0UIvl0xkZGYyEtImIiFDeGAAVKlRQSXoQoVCIZs2alVjHzMwMZmZmrNvCw/MtOTk5uHTpEoYOHVrm9lipAn6nJAMcOnQIo0ePhoeHB27fvl2s0wKA3Nxc3L59Gx4eHhg9ejQOHz6sQkuLZv369VyboBBCoRCTJk1S+PyKFStizpw5DFrEw8McV69exY0bN7g2Qz1het5SFajLOy6JREL+/v6kr6+v8Fy2vr4+BQYGcvpyv3LlyozMy3+7x0VVREREKJxDq2bNmgX2cfGUXe7cucP5eyk2ioWFBX358oXr7mUFlUWHX7JkCZo3bw59fX2Ym5ujR48eePbsWYE6YrEY48ePR6VKlaCnp4fevXsXCsMSGxuLrl27omLFijA3N8eMGTNKHKWoKwcPHoSXlxdSU1MVlpGamorBgwfjyJEjDFr241CvXj0cOnQIlpaWcp3XuHFjnD9/Hhoa/Gx5eUDe619WSEhIKJStmUfOqcLr169j/PjxuH37NkJDQ5GTk4NOnTohPT1dWsfX1xfBwcE4evQorl+/jnfv3qFXr17S43l5eejatSuys7Nx69Yt7Nu3D3v37sWCBQuYa5UKkEgk2LBhAyORwCUSCTZu3MjZDfrnn39yopcpXF1d4e/vL3OKhpo1ayIwMBA1atRg2TIeVWFmZgYfHx+uzeBRFcoM9RISEggAXb9+nYiIkpKSSCQS0dGjR6V1IiMjCQCFhYUREdHZs2dJKBRSfHy8tM6WLVvIwMBA5vBBXE8VpqWlkY+PD6MbZ4VCIU2cOJGTdOFPnjwps1OF3xIaGkrOzs7FJskzNzenNm3a0Nu3bzm1k4cdgoKClN4eoY7l6tWrXHctKyjzO67UPEl+MEkTExMAwD///IOcnBx06NBBWqd27dqoWrUqwsLC4OjoiLCwMNSvX79AiHxXV1f4+Pjg8ePHaNy4cSE9WVlZyMrKkv6fkpJSol179uzB0KFDZU7bLi/Pnz/Hli1bGJWZP4IbOXIkGjRowKjs0jA3N4ebm5vSS9lHjBjBkEWK0aFDB3To0AFbtmwpMnlkvXr10K1bN9UbxqMSPDw8sHXrVgwZMqTA70VZx9fXFw8fPuTaDLVCYcclkUgwZcoUtGrVCvXq1QMAxMfHQ1NTE0ZGRgXqWlhYID4+Xlrn+7wu+f/n1/meJUuW4I8//pDZtu3bt+PBgwdYsWIFK/ts2Myoq8psvflUqlQJ7u7uuHTpksIR23V1dTFkyBCGLVMMfsrox6VXr16YNm0a4uLiGJOppaWF5cuXY/LkyYzJlAdjY2NO9KozCi+HHz9+PP777z8cOnSISXuKZM6cOUhOTpaW0m7K/HdGgYGBjNtCROjXrx/jcvPx9PTkJIPuuHHjMG/ePIVGqZaWljhy5Ahq1qzJgmU8PLJToUIFXLp0SfowzQQSiQQxMTGMyZMXddgyo24o5LgmTJiAkJAQXL16FdbW1tLPLS0tkZ2dXWjU8OHDB+mqH0tLy0KrDPP/L25lkJaWFgwMDAoUWdixY4dSURWKgoiQl5fHqMzv5XPFjBkzoKWlJdc5mpqa2L17N7p06cKSVTw88mFvbw9/f39prjVlycnJwerVqxmRJS8CgYBPTFoEcvUIEWHChAk4efIkrly5UiiDbNOmTSESiXD58mXpZ8+ePUNsbCycnJwAAE5OToiIiEBCQoK0TmhoKAwMDPDTTz8p05ZC3L59GxkZGYzKLM/o6OggJiYGv//+e6kr9LS0tFC/fn0EBQXBzc1NRRby8MjGxYsX8ebNG67N4GEJud5xjR8/HoGBgQgKCoK+vr70nZShoSF0dHRgaGiIESNGYOrUqTAxMYGBgQEmTpwIJycnODo6AgA6deqEn376CUOGDMHy5csRHx+PefPmYfz48XI/7fMwj7m5OX777Tfo6uri06dPOHnyJJ4/f16gjpubGzp06ICpU6dyZCUPT/G8ffsW58+fL5N7Q7/Hw8NDLcLCqRtyOa78lXTt2rUr8Hn+Kj4AWLNmDYRCIXr37o2srCy4urpi8+bN0roVKlRASEgIfHx84OTkBF1dXXh7e2PhwoXKtYSHUaZPnw4A8PLywsePHwscc3BwKLcbPnnKPs+ePcPVq1e5NkNpNDQ0MHjwYFSsWJFrU9SOchkdvmXLlrh79y4AIC4ursB7OGVhIiJ5SZibmyM+Pp61pfw8POWZ3NxcWFhYsPb9BAAbGxvUq1dPqVW4sjB48GDs27ev3L7jUiY6fPnsER4enh8WtqcIO3bsiDNnzmDmzJms6dDX14e3t3e5dVrKUq57ZfHixahcuTKjMoVCIfbt28eozG/Zt28fP9ri4VGQiRMnKhU7VFYEAgHmz5+PqKgoNGzYkFHZRkZGCAoKKhDIgacg5dZxVatWDW3btkWFChUYl92wYUM0b96ccbktW7YskCiRh4dHPuLj41nfUhIZGYno6GikpKQgNDRUGkGICSpXrowjR47AxcWFMZnlkXIZGtvQ0BDr169Hy5YtWZFvY2ODHj164N69e4zK7dWrF6ysrBiVycPDwyxhYWHo168fKlasyFi+LIFAAC0tLRw4cADt27dnRGZ5plw6rlOnTrG+Emf69OmIiorCnj17GJE3cuRITJkyhRFZyvLq1Su8ffsWAFCnTh1UqlSJY4t4eNSL+/fvMyarWbNm6Nu3L3x8fKCvr8+Y3PJMuXRcqlg+qqmpiS1btiA3Nxf+/v5KyfL29sbmzZshEokYsk4xdu/ejejoaISGhkpXZfbq1Qt16tTBL7/8wk9f8Kg9AwYMwLlz59Q+yO7o0aNhY2MDAJg0aZLcq+p+dMrlcnhVkpycjMmTJ2P//v1yz60LBAIMHToUa9eu5awdaWlpeP78OUaMGIGoqCikpaUVWc/S0hL29vY4duwYDAwM+M3iPGpJbm4uKlWqVGoGCS4QCAS4cuUKDA0NYW9vD11dXa5N4hR+Ofx3XLt2TWUx/wwNDbFnzx6MHz++yJQsxdGsWTNMmjQJu3bt4sxpxcbGolu3bmjatCnCw8OLdVrA15feN27cgLm5OSZPnozMzEwVWsrDU/YhInz+/BmNGzf+4Z2W0jCTEky1lJaAzNnZmby8vKQJLlVFZGQk7dy5k7S1tYtNCqetrU27du2ip0+fqtS2ojh48KDCye3evHnDtfk8PIWQSCT0xx9/cJ78sbjSpEkTrrtIbVAmkWS5nCrMj5xhaGiIyMhIxvdylcaHDx+KHfEJBIJC+ci44OHDh2jfvr3C+b+aNGmCM2fOSEM/xcfH49OnT4Xq1ahRQy1ireXl5SEyMhIA0Ldv3wLtnjhxIjw8PAAAtra2/AvyMk54eDg6depUKFSZOtCkSRP8888/XJuhFigzVViuHRcArFq1ig8GWwRNmjRROquqr68vli9fjg0bNuDkyZO4efNmoToTJkxAhw4d0L17d6V0KcONGzdw+fJlmeJhDh06FJ06dcKAAQNUYBkPWyxYsAB+fn5cm1GI8ui47t27h2vXrhX6XCgUYuLEicVmmlBqrQKzgz/VUNoQs0WLFgWm5jZt2qRiC9Wb7OxsatSokdLTHjY2NtS2bVsSCAQl1jMzMyMXFxeKi4tTaTvT0tLIzc2NqlatKle7DA0Nae/evSSRSFRqLw9zREdHU/369Rmb4hMIBKShocFPFf6P9PR08vPzIxcXF6pevXqx7R0zZkyxMpSZKiz3jgsA9ejRgz59+qRiK9UXrt4B2NnZUWRkpEraGBMTQ05OTgrbKhQKaefOnSqxlYcdPn/+zMgDmkAgoBkzZtDmzZtJV1f3h3Zcz58/p8OHD5O2tnapD6wAyNHRsVhZyjiucrmq8HtOnTqFBw8ecG2G2sBVnqLXr19jwIABePr0Kat6Pn/+DC8vL4SFhSksQyKRYMKECdi/fz+DlvGoEhMTExw5cgQtWrRQWIaLiws2btyIJUuWwMfHR7r3SlHGjx+v1Plc8urVKwwaNAj9+vWDWCzmNFv7D+G4AGDMmDFqvynxRyA8PBz3799n9abv0aMHrl+/rrQcsViMCRMm4NSpU8obxcMJtWrVwunTpxEaGoqqVavKvP/Q2NgYrVu3xpEjRzBu3DhpzFNzc3OFbTE3N0evXr0UPp9LPnz4gM6dOzMe5k5RfhjH9f79e06fEHj+n5EjR0IsFrMi+6+//sKTJ08Yk5eamorTp0/z+9bKMBYWFujQoQNiYmKwZMkSjB49uthg1j179sTo0aNx+vRp3Lx5E6ampgWOBwUFoU2bNnLbYG9vjzNnzsDIyEiRJnBKZGQkOnfuXCgTOpeUy5BPPCWjDjl+8vLyGJdJRPD392c8ieCePXuwaNEimZf1f9s2IoJAIIBAIFCLfv/R8fX1BQBEREQgKiqq0HEXF5cSnYuRkRH279+PIUOGFLmKtihMTU0RGBiIpk2bKmQzl8THx2PgwIEIDw/n2pSCKPiOjlPkXZwBgDQ1Nenhw4eqNVRNEYvF1LBhQ04WaOQXDw8Pxtt15swZ0tTUZMXekl4yExGlpKRQWFgYnT9/noyMjMjQ0LBAad++PYWFhVFYWBi9fPmS8bbzqJbU1FTq2LEjWVtbl3jf2NraUkREBNfmKkyDBg1Y+94oszjjhxlxZWdnY8aMGQgNDeXaFM7R0tLi/Om/pPBSipKTk4Ps7GzG5QJAenp6sccOHjyIixcvYu/evcXWuXLlCpycnAAAjRo1Qvfu3TFnzhw+5iNLXLlypci9Rd8yefJkhTMf6Onp4eLFi7hx4wbOnz+PJUuWFDg+ZMgQ1KpVCx07dkS9evUU0sE1ISEhiI2N5dqMIvlhHJempiZWr17NtRlqw5YtW9CxY0eVZIstD7x48QIbN27EhAkTpJ89e/YMAwcORHR0tFwRSMLDwxEeHo7nz59j48aNMDQ0ZCXh6Y+ERCJBUlIS/vjjD/z111949+4d4uPjSzzn5MmT6NmzJ6ZMmQJjY2OFMo+3adMGrVq1Qu/evQt8XqtWLc4DgCtDTk4OgoKCFI6swzrKDCO5QpGpQm1tbcrMzFSxperNzZs3yc7OjpOpwvbt2zPenlOnTrFq86xZs6S6/vnnH7K0tFRapkAgoJUrVzLeFz8a+/fvl2lfUVFFQ0ODdu/eTY8fP+a6GWrD2bNnWZ9i5/dx8ShE69atsX//fpWvdNLQ0FCbpJmK8O+//2LQoEGlPtHLAhFh7ty52Lx5MwOW/ZgcOHAAEyZMUHjVcG5uLoYPH45Bgwbh1atXDFvHwwY/jOOysbFRaCqgvOPs7IwLFy6gdu3aJa6aMzMzQ8uWLeHv76+0o7Ozs0OnTp2UksEVT58+RadOnRjdRJ2dnY1p06bh8OHDjMn8ESAi7Nu3D2PHjmUk/1Z4eDjat2+PuLg4BqwruxCR2vfBD/OOa9OmTfyL8GJo0aIFIiMjsXXrVrx48QLA12zINjY26NixIwCgc+fO0r+vXr2K3bt3K6xv48aNZe5amJqaok2bNhg7diw+fPjAuHyxWIyTJ0/C3d2dz9UkIwEBARg+fDgkEgljMl+/fo1u3brhwIEDZXZRhbJkZGRg4sSJXJtRIj+E4+rZs2eZ3EOhasaOHSv9u0+fPjA2Nkbt2rUL1Zs+fTrOnTuH9+/fy62jf//+aN68uVJ2FkeFChUgFAoZ/SHLp3LlykhNTcWjR48Yl53P4cOHsXTpUt5xycCRI0cwceJEVq71o0ePcPPmzR/WcZUFyv1UoY6ODlxdXWFiYsK1KWUKJyenIp0WANSpUwc3b96Eg4ODXDIHDBgAf39/1q5Fly5dMGLECFZkOzg44MKFC6yvsurRower8ssDaWlpCA0NZfVaTJ48WaEHMx7VUO4d15IlSzBmzBiuzSh31KhRA4GBgbC3t5f5HB0dHcyYMQO+vr7w9fUtdZ+NvAiFQtaWlY8ZMwZ79uxhRfa3xMbGIigoiHU9ZZnXr19j586drOrIycnBpk2bWNXBowSKLpfkElmWw2tra9OaNWsoJydHxdb9WMTFxVFgYCBZW1tLi7a2tkxLZc3MzMjBwYEmTpxIGRkZjNgTHx/PSCqLb8uyZcvowYMHKtsq4Ovry0hflFeaNWumkutQs2ZNSkpK4rq5Kic9PZ0qV67ML4dXNVpaWli9ejWmTJkCDY0f4jUeZ1hbW2PAgAGIi4tDXFwcLl26JPO7gY8fP+LZs2fYsGEDfH19GYneb2FhgePHjystJ5/q1avD2dkZFy9eZEwmj+KQCle8RUVF4Y8//lCJLnWiYsWKrI9olaVcOq7FixfDx8eHazN+OD5+/IiBAwfi/v37cp+7bds2zJo1S+kI/hKJBL///rtSMvIxMjLCwYMH4eTkpNKoKyEhIfj3339Vpq8swcZiDJ7CcB0SrjTU2zoFad26Ndcm/JB06dJFqYSdGzZswMSJExWOY5iUlAQvLy8cOHBAYRvysba2xpUrV5RKQqgoL168QEJCgsr1lgUmTJjAynYEnoJ06NABo0aN4tqMYimXjotH9Vy4cAHR0dFKyZBIJNi0aRMiIiIUOv/69esICAhQatQmEAgwZ84cHDx4EI0bNwYAPo+bGpGRkcG1CT8EGhoaar3XkndcPEqTm5uLoKAgxvJgTZ48WaF8XSVFcJeVP/74A35+fgVG7aqOuDJgwABORno8PN8yffr0YrfEcA3vuHiU5vr169iyZQtj8iIiIhAWFibXOampqRg2bJjSuhctWlTkU70iWW8VxdLSskxHFucpH9ja2uLMmTNcm1EkvOPiURqmp9LEYrFCiyGYeHFfnIwFCxYoLZuHp6xhbm6OwYMHc21GIXjHxaMUYrGYkZGOuqOlpQUzMzPW9WhqasLCwoJ1PWWVNWvWqOQ68AAfPnzAP//8g5CQEK5NKQS/yYlHaT59+sS1Caxjb2+PadOmYfbs2azqqV69OmbNmsWqjrKMiYmJypZqGxsbo127dirRpQ58+vQJ/v7+0v9PnDiBv/76i0OLiod3XDzlgooVK2L+/Pn47bfflJKzYMECVKxYschj/fr1Q2BgIKt7rJYvX86a7PLCsmXLMHToUNb1VK1aFR4eHqzr4QIiQlZWFiZPnizNQZaamorbt29zbJls8I6LRymEQiEaNWrE+Q1foUIFNGzYUGk5DRs2LDbeoZ2dHVxcXFhzXHXq1MHPP//MiuzyRNu2bVGrVi1pCh62OHnyJKvyueLRo0e4fv06Zs2aBbFYzLU5CsG/4+JRCk1NTfj5+XFtBgCgXr16cHR0VPh8Jycn1K1bt8Q6K1asYCVoc8OGDXHkyBFUqlSJcdnlDTs7O9Y3x3p6esLc3JxVHVxw69Yt9OzZE5MnTy6zTgvgHRcPA2hpaTGaQ0pPTw9r1qyR+7waNWrg5MmTsLKykvtca2trnDx5EtWrVy+xnkgkgqenJ6PtrVatGkJCQvj8T3IwYsQIdO7cmRXZ2tra6NmzZ7nLixYeHo5evXpJpwbLMrzj4lEaZ2dnjB49mjF5bm5usLGxUehcS0tLhd5LdOvWTebVfO3bt8fWrVvh6ekpt57vady4Ma5cuQJra2ulZf1ImJiY4NixY9DU1GRUrqamJlatWoX+/fszKpdrwsLC0KlTp/ITLkuxwPfcokw4fB52CA8PZywVwq1bt5SyJS0tjY4cOUJNmjQhgUBQrB6BQEDNmzenI0eOUFpamtx60tPTadSoUSXqKKnUrl2bHj9+rFRbS0IikVBeXp60lDdyc3Pp2LFj5Obmxlgqk1WrVnHdLMa5e/cuVa9eXSWpYL4vbKU1ERCVvUBsKSkpMDQ0RHJyMicRBsLDw5Gbm1vkMYFAgMaNG6t9dGU26N69O06fPq3w+To6Oli8eDEmTJjASDqatLQ0/PPPP5g+fXqRx1etWoUmTZpAT09PYR1ZWVlITU1F79698fz5c8THx5dYX1NTEw0aNMBvv/2Gtm3bQl9fX2HdRZGSkoLnz58DAM6fP49Vq1ZJj61fvx516tQB8HV6t379+ozq5oqMjAz06dMH586dU1hGo0aNMHz4cIwbN461ZKRckJ6eDmtra9YzdxeHo6NjsVFwlPkd5x2XjKSlpWH16tUgIixbtgyZmZlF1hMKhZgzZw40NTUxduzYcvmCtzhSU1MxaNAgBAcHK3T+mjVrMGXKFGaNUiGXL1/GjRs3pP8nJCTg0aNH6Nixo/QzQ0NDTJ06lRX9Bw4cQGhoKPbv319qXWNjY0yZMgWDBg1CjRo1WLFHlSQnJ2P16tU4fPgwnj17Jte5/fr1w969e6Gtrc2Sddyxfft2jB07lrNA0Ww5Ln6qsBRycnJo5cqV1LhxY7mHyQ0aNKA5c+ZQdnY263aqCwkJCdStWzfS09OTuZ9EIhEtX7683GWrzsjIoJiYGNb1PH78mFq2bEmGhoYKTVeOHj2axGIx63aqgmfPntHu3bvJ0NCQDA0NSUdHp0B7dXV1pceOHTtGt2/fps+fP3NtNiuIxWJydHTkZIowv7A1Vcg7rhLIy8ujRYsWKX3xfH19KSsrS2a9sbGxdPz4cWn5+PEji61knry8PHr69Cn17NmTjIyMiu2X+vXrU8+ePWn9+vUkkUi4NrvM8ebNGzp+/DiZmZkpfY96e3tTSkoK101ijNzcXMrNzaUbN25Qz549pSU8PFx6rLyzdOlSTp0Wm46L34BcAmvXrlU6EgPwdQpMW1sbixcvLrbOixcvsGLFCgBfU4ZfvXpVeszd3R2VK1cG8DW7s6mpqdI2sYlQKISDgwNOnDiBoKCgYpMiOjo6lpv3LKomIyMDw4YNQ2hoKCPy9u3bhwoVKmDXrl2MyOOa/PdUzs7OcHZ25tgabijX2aKV8ehcwfaIKzs7m5YsWULa2tqMPXmIRCKaNWsWZWRkFNCVlJREnp6eZG5uLpMcS0tL8vHxUWgVHE/5wd3dnfGnY4FAQCNHjixXI68flaysLPL19eVHXD8Sq1evxpw5cxiVmZOTg2XLliEnJwcrVqyAUChEcnIyRo4ciWPHjsksJz4+Hlu2bEFeXh42btwIkUjEqJ086s+dO3dYCTtFRNi5cydycnKwdevWcrlYoayQkZGBHTt2FPjM09NTOvNSGk+fPlVoE3+ZQQmnzhlsjrhycnLIxsaGtScQPT09Sk9Pp5ycHOrTp49Ssjp06EAHDx5kvA941JenT59SzZo1WX9SfvfuHddN/SG5d+8eubq6kouLS6Fr0rJlS3J1daVTp06VKufRo0ecj7bAj7hUh6+vL+Li4liTn5aWhi5duqBSpUo4ceKEUrIuXboEGxsbuLu7K7UXiafscP36dURFRbGup1evXnJnoeZRjvv37+OXX35BSkpKkcfv3LkDALhy5Qr27duHpk2bwt7eXpUmqg0/3i7ZEvjvv/8K7MNhi+vXryvttPLZs2cPZsyYwYgsHvUmLy9PZbm6oqOjceHCBZXo4gH++usv9O3bt1in9S05OTkYOHAgNmzYoALL1BPecX3D8+fPWc21xBaHDh1CYmIi12bwsExiYqLKVoolJCSobRLB8sbDhw/Rt29fvH79Wq7zAgICEBQUVOTmYgcHB0yePJkhC9UP3nH9j5ycHFy7do1rMxQiKSkJ48eP59oMHpbx8fGR6YmcKR49eoSPHz+qTN+PSo8ePUoNFVYUiYmJ8PT0REZGRqFjWlpaMgeNLovwjut/ZGRkYNOmTVybwcNTLKrelxMcHKyS92k/Mjt37lTq4SB/tXJRCAQCheWqO7zjKiccP34chw8f5toMHh4eObh3716xcU9lgYiKfS8/efJkpRKrqjO84yonZGdnIysri2szeHh41AQdHR0MGTKkXI68eMfFw8PDU07x8vLiJPWTgYEBVqxYge3bt7Min9/HxcPDw1NO0dPTw4ULF9C/f3+5Vy3Kg1AohLu7O4RCIUxNTbFlyxZUqFCBtdEe77h4eHh4yjEtW7aEv78/evbsiU+fPjEm19vbG05OTgAADQ0NDBs2THUJdOUNtXH9+nVyd3eXpmk/efJkgeMSiYTmz59PlpaWpK2tTb/88gs9f/68QJ3Pnz/TwIEDSV9fnwwNDWn48OGUmpoqsw1shHySSCS0detWzkOkKFr09PQKXQue8sX48eNJIBCo7J6aOHFiucnTpa58/vxZ+luqSBGJRBQdHS2Trvv37zOSAsfGxoZGjRqldKBvZX7H5XaP6enpaNiwYbFLx5cvX47169dj69atuHPnDnR1deHq6gqxWCytM2jQIDx+/BihoaEICQnBjRs3MHr0aHlNYRSBQAAzMzNObVCGbt26oUePHlybwcMi69atg6Ghocr0GRoaQktLS2X6fkRMTEzQt29fhc/v2rUrrKysZKrbtGlTnDhxAuPHj1c4OLejoyNu3bqF7du3Q1dXVyEZjKCMx8R3Iy6JREKWlpa0YsUK6WdJSUmkpaUlDQb75MkTAkD37t2T1jl37hwJBAJ6+/atTHrZCrIbHx9PnTt35nz0pEjx8vJitC+4JDs7m7KysgoUVSWazMnJoaysLNq1axe5ubmRm5sbnT9/nrKysjhPPpibm8vIE7MspXr16oVmSnjY4cWLFyQUCuW+RgKBgM6ePSu3PolEQhcuXKABAwaQpqZmqXo0NTVJW1ubjh07RlFRUYy1m7MMyN87rpcvXxIAevjwYYF6bdq0oUmTJhER0a5du8jIyKjA8ZycHKpQoQKdOHGiSD1isZiSk5OlJS4ujhXHRUS0cuVK0tLS4twRyVNMTEwoMzOT8b5QNTdv3qSrV69SkyZNSFdXt0BZuXIlXb16lWJjY1nRnZmZSVevXiUXFxfS1dUlkUhU4Iurq6tL3t7elJSUxIp+Wbl+/bpK7qmhQ/+vvTOPq6pa///nHDhwUGQSQREFysSUJL+pxL2ZoiiSKXIrkas4lZmZM5rU/YZdNeWbl1JuSn4NNSeoropNqCHOikOg4JSaAxmTI+PhwNnP7w9/nK9HDnCGPZwD6/16PS9lD2s969n77GfvZ631rEmStrM1wXEcbd++nVxdXQ2+PkqlklauXGnWy5Rarabbt29TSEgIDRo0SK+MGTOGKioqqLKykscWP8JiHNfRo0cJaLgkwhtvvEFjxowhIqJly5ZR9+7dG5TVoUMHWrNmjd564uPj9V48oRaSXLJkieTOyBh5//33BbGDWJw+fZoWLFhg0MKdgwYNoiVLlvCuw6JFiwyydXR0NKWkpPBev6H8+eefNHToUEHvJ5lMRnfu3JGsja2V7du367wwNSXLly+XWl2zafGOS8wvLiKi33//ndq1aye5Q2pOFAoFxcXFWdXXVmVlJRUVFWnl/Pnz5O3tbVS7bW1tKSAggLZv3252CFGlUtGHH35o8AMDAA0ePFiQN1BDKS4upueee06Qe0qpVNLq1aslD4u2RjiOo/Pnz9PMmTPJ09NTr0RGRlJeXh6p1Wqp1TUbi3FcQoUKn0TIhSTrycjIMGu0jxgSHx8vWPuF4LvvvqNx48bxagNzFtKsra2lefPmmVRvTEyMpEvcx8XFCeK01q1bJ1mbGP9H/QvZ4/+K1c8rFhbjuOoHZ6xcuVJHOX2DM06fPq09Zs+ePRYxOONJ9u7da1D4SgpZsGAB1dbWCtp+Plm3bh05ODjwbgcXFxdKTU01Safy8nKjvrSelCtXruiUV/9waUz4RK1W05w5c3i1ZVJSEq86MhhNIarjKi8vp5ycHMrJySEAlJiYSDk5OXTz5k0iIlqxYgW5uLhQeno6nTt3jiIiIsjPz08nnDV8+HDq06cPZWdn05EjR+iZZ56h6Ohog3VorsGXL1+m69evG9s0vdy5c4c++eQTi3Bg3t7eNHz4cCotLaWamhpe2ic0dXV1lJSUJKj9lEolZWdnG61bbm4u2dramlxvYGAg3blzh3Jycmjz5s3k7u7eqISHh1NOTg7l5eXxZtuamhqKi4ujHj16mGU/R0dH+uKLL1h4kCEqojqurKwsvTf/xIkTiej/JiB7enqSvb09DRkyhC5fvqxTxt27dyk6OpocHR3JycmJJk+ezOsE5P79+1NAQADl5uYa27xG+fzzz+nVV1+VxGGNHz+e4uLi6Pjx47y1Ryzu379v0lBfY2XmzJmk0WiM0m3AgAFm1eni4kIRERFGnePs7EzLli1r8Jswh9LSUoqLiyNfX1+j2zB27Fhav349b7owGIZijuOSEelZPtPCKSsrg7OzMx4+fKg3gWRQUBBOnjyJbt26oV+/fti2bZvZdRYUFGDIkCG4cuWK2WUZS//+/XH06FHY2kqToYvjOFRWVmr/ViqVBk9gnDJlCjZs2CCUalrkcjmCgoJgY2MD4NGE7OnTp8PGxgZt2rTRe87LL7+Mw4cPC66bPnr16oWsrCxeJ73n5+fjwIED+OCDDwA8Wqvp8Yn/ANCuXTsAgJeXF9avX4+AgAC4uLjwpoM1UVlZCY7jsHTpUhw7dkxn3+zZsxEWFgY7O7tWPwm7/j66cOECYmNjGz2ub9+++Oc//wlHR0eDchQ29xxvEt7dqAgY8sWF//9G2blzZ7PDM+fPnzfpbZYvCQkJkaQ/6+bNm5Senk4rV64khUKhlVmzZlF6erpBdn38WogpMpmMFAoFBQYGNuiLqsfcLy5zpVevXrx+eRE9inio1WpSq9WUkZFBo0aN0srYsWNJpVKRWq22qv5RvikpKaH09HTq2rVro32ccrmcFAoFRUZG0vfff2/013xL4ZdffqEZM2aQQqEwKHKiUCgoOTnZoGiXZIMzpMIYxwWAevfuTRcvXjSprsuXL9Pzzz8v6QPuwIED5pjLJOLj42ngwIFN6hUYGEizZs1qcmiuVI7rcQkKCqKFCxc20E1qxwWA+vXrRzdu3BDyUjIe4/PPP6cRI0YYdY3kcjlNnTqVMjMzpVZfVNLS0sjJycmk+7pHjx40Y8aMJqfqMMf1BPoell9//bXRI7s4jqNt27ZJ/nAT03FVVVXRvHnzjBq0EBkZ2WhGCUtwXMCjL7C5c+fq6HblyhWzBmfwJf7+/lRYWCjG5W213Lhxg5YvX27WICFXV1fKysqikpISqZsjKH/++Sdt27aNl7mso0ePbrQe5rieQN/DUqlUGj1RV6VSCTKE21gRy3HV1tbSrFmzTNIxMjKS7t+/36BMS3FcAKh///46oTmVSkWjRo2SXC8A2gn6DP755ZdfyNnZmbdr9eKLL/Kas8/SaC7SYqytGkPU7PDWTG1trVHH19XVCaSJ4bz++ut47rnnRKmrqqoKX375pUnn7ty5E2+++WaD7aZmoRaCkydPYuzYsXj48CEAwN7eHlOnTrWIpc0PHTqEX375RWo1Whwcx+Hrr7/WXnM+OHHiBE6ePMlbeWJTV1eH2tpa1NbWgh4bm0dESEtLw7lz5yTUzjBajeNSqVQYN26cUeeMHz8e1dXVAmnUPG3btsXQoUPh5uYmSn1jx45FTU2NyecfOnQIv/32m862b7/9VrzF5QwgJycHp0+f1v4dHh6OhIQEKJVKCbUCioqKkJmZCbVaLakeLYm6ujp88sknvIwqfpI333xTshGppnLy5EkcOnQIw4YNg5ubG9zc3LBp0yYcOnQIxcXFSE1NxYQJE3D//n2pVW0e0z8opcOUUCEACg0NNaqesLAwScNHq1at4sNcBnHo0CHy8fExW+cpU6bolKtSqSgmJkbyUNzj0rt37wbtX7ZsmeR6ATA4ewyjee7cuSPowpv9+vWTuokGcfHiRVq0aFGT4dIRI0YI0i3CQoWtCHt7eyQmJuLdd98Vrc7s7GzcvHnT7HKqq6t1vtrs7e2RlJSEiRMnWkRIrjFiY2Nx9uxZjBo1Ch4eHloxen6JmTx48EDU+loaDx8+xIEDBxAYGIiBAwfqhML4Ji8vD2vXrhWsfD74888/MXLkSKxYsaLJcOmPP/4oaXTJWJjjsjDqndbcuXMlm3BsDtu3b8fu3bt1tjk7O2Pjxo2YOHGiRFo1j52dHXr37o309HQUFRVppbGVvoXi9ddfF7W+lkJVVRW2bNmC0NBQhISE4Ny5czh//rygdapUKuzbtw93794VtB5Tyc/Px5AhQ3D16lWpVeGdVuO4FAoF5s2b1+Qx9GiUpVbEZsyYMdiyZYuoX1pismrVKmzevFm0wSamIpPJtCI2Utx3LYH58+cjJiZGp/9SDHbu3Ilr166JWqchFBQUICYmBpcuXZJaFUGwvld6E7GxsUFISEiD7dXV1bhy5QoqKirw2muvgeM47T6xwzZ9+vSx+jduV1dXODs7693n5OSE8ePH4+jRo8jLyxNZM0ZLpLy8HLGxsVi/fr3UqlgUZ86cQW5urtRqCEarcVyvv/56g9Dbrl27kJmZiX//+98SadXyiIyMxLBhw5o85t///jcqKyuxefNmkbQyjaqqKvz4449Sq8FogiNHjmDdunVSq2FREBGmTZsmtRqC0iocl0KhwOTJk7WOq6qqCnv27MHbb7+NO3fuSKzd/6FWq8FxnN7h4xqNBtXV1ZDL5Y0mjTUHOzs7yOVynS9OUzAkIamNjQ1Wr14NmUyGr7/+2qz6TMHBwaHJ/RzHYc2aNdi6dStOnDghklYMY1GpVNqEwoxWhknjKyXGmOHwbdu2peTkZG26pwsXLpCfn58oS20YK3K5XGcF2r1799L3339P33//PS1evJgUCgV5e3trt33//feNJpA1Fo1GQ4MGDTJL/06dOhm1plNdXR1NmjRJdDs3NeSc4zj67LPPBB1G3ZRMnjyZj8vZKqioqCA7OzvJf7emrAUnJBzHkYeHh+R2AYQbDt/iHZe/v792+6VLlyRPmNuceHh4UHZ2Nk2fPt2gvGovvvgivf/++7yssPvNN9+YlbtvxYoVRtdZWVlJSUlJ1KdPH1HsO2nSJKqqqmpUny+++MKsVZHNFTaPy3BWrlwp2QvG48IcV+PCHNdjGOK4HBwcKCkpiW7dukVEjy7m5s2bJb+QzYlMJjP6ppPJZPTWW2/R9evXzVrFluM4kzNCu7q60rVr10yuu7i4mH7++Wfy9fUlX19fHRs4OTlpt5uTc65t27a0a9cuvfXX1tbS559/LnluSua4DMfcCAFfwhxX48Ic12M01+CEhAT63//9X51tO3bskPRNWix5st2m8O233xrlILy9vXlf8iE3N5emT59O06dPp7S0NO32HTt2kKurq9F2USqVtGHDhkbr+/333yW/diEhIY1m2Wc0hDku/TDHZaEY22CNRkOhoaGSX0QxpF27dvTVV1+ZHTpMS0sjhULRZOhQoVDQkiVL6NixY2bVZSw7d+406iVEoVDQl19+2WSZ48aNk/zabdy4USQLtgwGDx4s+TUbO3as3lURpCY5OVly2wDCOa4WP6qwuroa//jHP7B//36pVRGF8vJyTJ8+Hd27d8dLL71kcjlvvPEGwsPDwXEcoqKiUFlZqbO/S5cu+PLLL9GmTRvY2NiYq7ZRRERE4O7du0hISMCPP/7Y6HyVZ599FoMGDUJCQgLatm3baHm///47MjMzBdK2eWxtbbFgwQL8/e9/l0wHayQtLQ2dO3eWNDGxv78/XFxcJKu/nps3b6KgoED7d/v27eHj48NLGjdLpMU7rsWLFyMxMVFqNURFrVYjJSUFwcHBJjsVmUyGdu3aAQAyMjL4VM9s6nVbunQpZs6cia+++krvcVFRUXj66aebLS8xMRFFRUV8q2kwjo6OWLp0qUVl0bcGmpvWIDTe3t4YMmSIpDpoNBosXboUGRkZOlM3PDw80LNnzxbruFp0qPDjjz9uFf1a+sTBwYFKS0tFuiLWS01NDU2ePFnS6/Tdd99JbQarRKPRUFJSkiTXTSaT0alTpyRt+/r16ykwMJC3kZV8LrZZLyw7vJEUFBQgKyvL6MUjzaF79+6Ijo5GdHQ0evToIVq9+qiursakSZMk1cEa2Lt3LzZs2CBJ3e7u7tiyZQtee+01Seq3duRyOQYMGIDu3buLXvdLL72EZ599VvR660lJScFbb72Fs2fPmp3f0sfHB9HR0Th8+DACAwN50lBYWmSosKqqChMmTMCBAwdErTcsLAyrV68GAPz666+IiIjAH3/8IaoOj2NuFozWgLk/elOZNWsWhg8fjvDwcEnqbykEBgZiy5YtGDVqlGjh3oEDB2LTpk1N9psKyaZNmzB37lyjz+vQoQNWrlzZYPtTTz2l7Q/fsmUL3njjDctPzmv6x6p0NPeJef/+fUkyY3Tp0kVnaOzIkSMlCWPUi52dHa1fv16sy2KV7N69W/TrMn78eFKpVFI3vUVx/vx5CggIMGkOojESHBxsUmiLL8rKymjMmDEm6d65c2eD5gnevn2bunXrxou9oqOjG62HhQothNGjR6N///7av7dt24ZXX31VMn3UajWqqqokq5+hHwcHB4NyOjIMp2fPnsjLy0NSUhIWLlyI4OBgnf1ubm5YuHAh5s6da9IgGB8fHyxcuBDp6emiLy76OPv378c333xj0rm3b9/G0qVLmz3Oy8sLP/zwAxYuXGjWiMnIyEikpKSYfH5TtMhQoaXg6OiI9evX48KFC/j73/+O8vJyVFVVsTWXGAyBmDBhAgDgxo0buHHjhnZ727Zt0a9fP3Ach9GjRyM5ORm7d+9GbW1to8PplUolbGxssHTpUoSEhIjW/1NbW4uSkhKMHz++wT5zk4Lv2rULMTExDRz7k/j7+yMhIQEvvfQSoqOjwXGcQSsk29nZwcvLCxs2bEBAQACUSqVZ+jYGc1wC4+npCU9PT+2w1Li4OFy8eBEAkJWVBZVKJaV6DEaLxNfXF76+vg22y+VyvPzyy/jrX/8KjUaDvXv3Ys2aNXrL+Oqrr9C+fXsoFApRFhXlOA6//PILUlJSsHPnTkHmpxUWFhrl/EaOHIl79+5Bo9Fg/PjxqK6uxtGjR1FWVqY9xtfXVztQ5a233sLIkSOhUCh41/1xmOMSCTs7OwDAv/71L+02Pz8/nbdChjh88MEHqKioAABcv35dYm0YUmBjYwMbGxu8+uqrkobzH2fNmjWYO3cu6urqpFZFh/pn13/+8x8AQGpqKkpLS7X7+/TpY1ayA1NgjosnfHx88NFHHzV7XHFxMVQqFVatWiX4iEOlUqmdRNzaKSwsxE8//YQVK1bg999/l2zEpVwuR/v27SWpm2GZ1NXV4YsvvkBcXJzgTsvR0dHs9fzGjh3LkzZmYPRwDgugudEoVVVVFBYWJupIsTlz5jSp8507dyg5OZm30TqGSHh4uBDmtzqys7Opc+fOot4PjUnXrl2lNgfDwkhMTBTt/ps+fbrUzdXCRhU+gYODA1JSUhAaGipKfTKZrMl5FRqNBlOmTME777yDq1eviqITANFzCFoi586dw/jx43H79m2pVQEAltaJoQPHcfjss89Eq69+FXhrp2W0Qg9eXl4YPHgwDh06JGgSThcXF6xZswZdunTRu7+0tBRTp07F7t27BdNBHw4ODti8ebOodVoaeXl5CAkJwb1796RWRcuuXbukVqFF8eDBA+1gp8fp0qULvL29JdDIOObNm6eTHFdIfHx88D//8z+i1CU0LdZxAY9G8FVXV2PJkiWClG9vb4+1a9c2GvOtqKjA1KlTkZ6eLkj9TTFhwoRW3b+VnZ2N8ePHW5TTCg8Pb/QFh2EcR44cwZ49e3D16lWkpqY22D9w4ECEhYVh0aJFoowINIWLFy/i4MGDotX35ptvCjY8XXQECF0KjjGxUZVKRWfOnKGhQ4eSm5sbKZVKXmLFSqWStm7d2mi9lZWVNGrUKEn6USZMmEDl5eV8mtyq0Gg0tGLFCsn7sx6XsLAwKikpkdo0Vk95eTmdPn3aoD5LuVxOffr0oW3btpm9Pp0Q7NixQ5R7z9XVleLj46mmpkbqJuvAFpI0AI7jSKPR0Ndff01RUVEUFRVFXbp0MelGcHFxodTU1Cbre//99yV5QDo7OzfpUFsD165dk9xRPS5KpZKqq6ulNovVc/PmTRo4cKBJ18ASfxNCO64ePXrQpEmTSK1WS91UvTDHZSL79+8nR0dHo26Gd955h3bv3t1kuZcuXaKAgABJHpKbNm0yyyYtgWnTpknurOolPDycNm/eTBqNRmqzWD1bt241+To4Ozs3+7IpNkI6rlWrVtGZM2ekbmKTsBWQTSQkJARXr14Fx3F47733tJ28tbW1uHv3Ljp27Kg91t3dHWlpaXB1dW0yTkxEOHnyJPLz8wXXvx4/Pz+MGDECH3zwATw9PUWr11LZs2eP1CoAAIYNG4a0tLRW3dfIFzk5OZgxY4bJ5z98+BAZGRl49dVXJcvqLiZjx46Fh4eH1GoIB/9+VHj4+uJqjNLSUkpPTzfp3JqaGmrbtq0ob/OdO3em2NhY+uOPP3i2gHXj6+sr6VeWv78/xcbGUllZmdSmaDE8//zzvFybzMxMqZuiRcgvruLiYqmb1yzsi4tn3N3dMWrUKJPOramp4VmbxvHz88Onn34qWn2MRyNJGxulNmHCBERFRcHDwwMBAQEia9Zyqa2t5S0x9YcffohDhw4BgN4sFfb29myunRXAHBfPTJgwAZWVlVKrwRCIH374AQMGDNC7z9bWlk36FoClS5fi7NmzvJSVk5ODjIwMbN68We/cypUrV2LAgAGiZIL39PSEt7c376nfAgMDW/yyOcxx8YyYX1wM8VEoFC3+oWBp1NbW8lZWTU1Nk9GUmTNn4plnnsErr7yCjz/+GM7OzrzV/SR/+ctf8Morr2DdunW8lhsbGyuo3pYA+yZmMBiMx7hy5QpWrVqF0aNH48GDB4LW5ebmxmtoMiIiAhEREQYfz3EcCgoKtPLpp5/C398fMTExOtsNWYtLTNgXF4PBYOjhwIEDGDNmDDZs2IDOnTsLUsfy5cvx4MEDJCcnm12WUqnEqFGjDB7FeurUKRw+fBjz589vsO+3337Dli1btH9Pnz4dw4cPN7nvn2/YFxfPtJQkltbM4sWLBSl39OjR6NWrlyBlMyyTffv2ISYmRtB8p/Pnz+clLdW//vUvTJkypdnjOI5Dfn4+YmJi9DotfaxduxaTJ0/G3/72N1EThTcGc1w8s2nTJjg4OEitRqtm6NChele/NQcHBwcMHToU7u7uvJbLaJ6uXbtK+ps6duyY3kS+fPHUU09h48aNJvdLtWvXDkFBQQgLC2v22OLiYrzyyisIDg7G5cuXjarn3r172LlzJwYNGoTffvvNJF15g//R+cIj9DwucxBrHpdMJqPk5GSpm2uxJCUl8Wrv5557TuomtWr4msdlqoSGhgrexq1bt9KkSZOM0ksul9O6desMKv/evXsUHh7Oiz169epFOTk5ZrWXpXyyINRqNXXs2FHwH5JCoWATXJuguLiYt8VE27ZtS4cPH5a6Sa2ao0ePSuq4Bg4cSCqVSvB2lpWV0alTp6hPnz5NvgA7OzvTpEmT6PTp0wanExs+fDivNvH19aU7d+6Y3FZznuMyIp5m9olIWVkZnJ2d8fDhQzg5OUmtTgOysrIwePBgQet4+eWXsWfPnpazTIEAqNVqREREICMjw+Qyunbtik2bNmHQoEH8KcYwmvLycgwbNgwnTpyQTIfPPvsMc+bMEaUujuNw8OBBrF27Vu/+1atXw9PT0+C+sZycHAwfPhwlJSV8qonU1FRERUWZdK5Zz3GT3aWEWPIXFxFRYWEh7283T4qpKalaG4WFhSaHR9q1a0dZWVlSN4Hx/7l27RoFBwdL9tWVmJgotQlMZsaMGYLYpFevXibrxEKFFsjKlSvJ3t6e9xtFoVDQggUL2DIZRvDw4UPKy8ujHj16kJOTU7M27t69O3366adUWFgoteqMJ7h37x4FBweTh4eH6I7LycmJ9u/fL7UJjGbv3r2C9bvb2trSkiVLTNKLOS4LZfHixbzfKAsWLJC6WVbNuHHjGrVtYGAgLViwQJS+DIZ5ZGVl0cyZM0kmkzV6Pf/2t7/x/vvr3Lmz1E03CrVaTVOnThXUoQ8aNIhu3bpltG6sj8tCUavVWLx4MRISEsBxnFllyWQyzJ49G5988gkbbm8EHMdBrVZj5syZuHHjBvLz81FUVKT3WG9vb/To0QM9e/ZEQkJCkwl1GdLDcRwOHDjQ6G+rW7dueOqpp3hL0As8SsL7z3/+E/Pnz7eKvJQVFRVwc3PjNW2WPjIzM43u12d9XBZMXV0dLVmyhBQKhclvNHK5nBYtWkR1dXVSN8eq4DiOVq1aRUql0mibK5VKSklJkboJDDMoKyujwMBA3r8w5HI5/ec//5G6eQZRXl5u1rPHUDFluRhznuNsArLA2NjY4B//+AdWr16N8PBwo88fPHgwVq1ahWXLllnFG54lsW7dOsTGxkKlUhl9rkqlwrvvvou5c+fi9OnTAmjHEBobGxt4e3vzXi7HcdBoNLyXyzAcFioUkbt37+LWrVuIjIxEXV0dCgsLG4Q5ZDIZvLy8IJPJ8O233+Lpp59Ghw4dJNLYOikoKMCwYcNQUFDAyxIznTp1QlZWFvz9/XnQjiEWd+/eRYcOHXgNFdbj7e2N8+fPW9zzp6qqCvfv38e1a9cwbdo0cBwnSpYLsUOFLLGeiLRv3x7t27fHjRs3AACLFi1CaWmpzjFOTk5ITExkfStmkJ6ejkuXLvFWXmFhIUJDQ7Fjxw7069ePt3IZ1svt27ct7qtLpVJh9uzZWL9+vdSqCA5zXBKyYsUKqVVocaxbtw4LFy7kvdw//vgD3333HXNcDIuEiBAbG9sqnBbAkuwyWhA///wzZs2aJdjaQatXr8axY8cEKZvBMJXy8nK8++67jWbZEJqOHTuKHjJljovRIlCr1di6daugK1CrVCps2rTJ4kJEjNaLRqNBbGwskpOTzZ5yYyoxMTHo27evqHUyx8VoEdTU1CAtLU3wejZv3oyysjLB62EwDEGj0WDr1q2S1S+TySSZV8ocF6NFcOTIEVHeOKurqzFhwgTB62EwDOH48eOoq6uTrP5p06YJtnBrU0jmuL744gv4+vpCqVQiKCgIJ0+elEoVRgtg2bJlooVKWKjQOnB0dMTbb78tSNnTpk1D27ZtBSnbGBISEgQNjzdHfHy8JCOgJXFcaWlpmDdvHuLj4/Hrr78iMDAQYWFhvKfcZzAYrRd7e3uEhITwXq6zszMiIiJgZ2fHe9nWQrt27fDVV1/Bw8NDkvolcVyJiYmYOnUqJk+ejJ49eyI5ORlt2rRBSkqKFOowGEZx/fp1nD17Vmo1GAbQvXt3PPvss7yW+dprr2H48OG8lmlNKJVKJCUlYcqUKZDLpQnaiT6PS61W48yZM4iLi9Nuk8vlCA0NxfHjx/WeU1NTo/M5/PDhQwBgneQMLWLG+S9duoS9e/fCz89PtDoZpvH0008jJSUF4eHhePDggdnlubq6YurUqRbz7BE6ea4+EhISEBkZabYN6s83KbOJ0dkNzeT27dsEgI4dO6azfcGCBdS/f3+958THxwueJJIJEyZMmIgvBQUFRvsRq8icERcXh3nz5mn/fvDgAXx8fHDr1i04OztLqJnlUlZWhi5duqCgoMDi8qlZAsw+TcPs0zTMPk1jiH2ICOXl5fDy8jK6fNEdl7u7O2xsbFBcXKyzvbi4GB07dtR7jr29Pezt7Rtsd3Z2ZjdNMzg5OTEbNQGzT9Mw+zQNs0/TNGcfUz88RO9Zs7OzwwsvvIDMzEztNo7jkJmZieDgYLHVYTAYDIaVIUmocN68eZg4cSL69u2L/v374/PPP0dlZSUmT54shToMBoPBsCIkcVxRUVEoLS3FRx99hKKiIjz//PPIyMiAp6enQefb29sjPj5eb/iQ8Qhmo6Zh9mkaZp+mYfZpGqHtY5ULSTIYDAaj9cJyFTIYDAbDqmCOi8FgMBhWBXNcDAaDwbAqmONiMBgMhlXBHBeDwWAwrAqrdFytdS2vQ4cOYeTIkfDy8oJMJsOuXbt09hMRPvroI3Tq1AkODg4IDQ3FlStXdI65d+8exo0bBycnJ7i4uODNN99ERUWFiK0QjuXLl6Nfv35o164dPDw8MHr0aFy+fFnnGJVKhRkzZqB9+/ZwdHTEa6+91iCLy61btzBixAi0adMGHh4eWLBggaSL9fHF2rVr0bt3b202g+DgYPz888/a/a3ZNvpYsWIFZDIZ5syZo93Wmm20ePFiyGQyHenRo4d2v6i2MTq7ocSkpqaSnZ0dpaSk0Pnz52nq1Knk4uJCxcXFUqsmOD/99BN9+OGHtGPHDgJAO3fu1Nm/YsUKcnZ2pl27dtHZs2dp1KhR5OfnR9XV1dpjhg8fToGBgXTixAk6fPgwdevWjaKjo0VuiTCEhYXRhg0bKD8/n3Jzc+mVV16hrl27UkVFhfaYd955h7p06UKZmZl0+vRpevHFF+kvf/mLdn9dXR0FBARQaGgo5eTk0E8//UTu7u4UFxcnRZN4Zffu3fTjjz/Sb7/9RpcvX6YPPviAFAoF5efnE1Hrts2TnDx5knx9fal37940e/Zs7fbWbKP4+Hjq1asXFRYWaqW0tFS7X0zbWJ3j6t+/P82YMUP7t0ajIS8vL1q+fLmEWonPk46L4zjq2LEjffrpp9ptDx48IHt7e9q+fTsREV24cIEA0KlTp7TH/PzzzySTyej27dui6S4WJSUlBIAOHjxIRI/soVAo6Ntvv9Uec/HiRQJAx48fJ6JHLwdyuZyKioq0x6xdu5acnJyopqZG3AaIgKurK61fv57Z5jHKy8vpmWeeoX379tHAgQO1jqu12yg+Pp4CAwP17hPbNlYVKqxfyys0NFS7rbm1vFoL169fR1FRkY5tnJ2dERQUpLXN8ePH4eLigr59+2qPCQ0NhVwuR3Z2tug6C039um1ubm4AgDNnzqC2tlbHRj169EDXrl11bPTcc8/pZHEJCwtDWVkZzp8/L6L2wqLRaJCamorKykoEBwcz2zzGjBkzMGLECB1bAOz+AYArV67Ay8sLTz31FMaNG4dbt24BEN82VrGsST137tyBRqNpkBrK09MTly5dkkgry6CoqAgA9Nqmfl9RUVGDpbZtbW3h5uamPaalwHEc5syZg7/+9a8ICAgA8Kj9dnZ2cHFx0Tn2SRvps2H9PmsnLy8PwcHBUKlUcHR0xM6dO9GzZ0/k5ua2etsAQGpqKn799VecOnWqwb7Wfv8EBQVh48aN8Pf3R2FhIT7++GMMGDAA+fn5otvGqhwXg2EoM2bMQH5+Po4cOSK1KhaFv78/cnNz8fDhQ3z33XeYOHEiDh48KLVaFkFBQQFmz56Nffv2QalUSq2OxREeHq79f+/evREUFAQfHx988803cHBwEFUXqwoVmrKWV2uhvv1N2aZjx44oKSnR2V9XV4d79+61KPu99957+OGHH5CVlQVvb2/t9o4dO0KtVjdYwv1JG+mzYf0+a8fOzg7dunXDCy+8gOXLlyMwMBCrVq1itsGjcFdJSQn+67/+C7a2trC1tcXBgwexevVq2NrawtPTs9Xb6HFcXFzQvXt3XL16VfT7x6ocF1vLq3H8/PzQsWNHHduUlZUhOztba5vg4GA8ePAAZ86c0R6zf/9+cByHoKAg0XXmGyLCe++9h507d2L//v3w8/PT2f/CCy9AoVDo2Ojy5cu4deuWjo3y8vJ0HPy+ffvg5OSEnj17itMQEeE4DjU1Ncw2AIYMGYK8vDzk5uZqpW/fvhg3bpz2/63dRo9TUVGBa9euoVOnTuLfP0YPLZGY1NRUsre3p40bN9KFCxfo7bffJhcXF52RKi2V8vJyysnJoZycHAJAiYmJlJOTQzdv3iSiR8PhXVxcKD09nc6dO0cRERF6h8P36dOHsrOz6ciRI/TMM8+0mOHw06dPJ2dnZzpw4IDOkN2qqirtMe+88w517dqV9u/fT6dPn6bg4GAKDg7W7q8fsjts2DDKzc2ljIwM6tChQ4sYzrxo0SI6ePAgXb9+nc6dO0eLFi0imUxGe/fuJaLWbZvGeHxUIVHrttH8+fPpwIEDdP36dTp69CiFhoaSu7s7lZSUEJG4trE6x0VElJSURF27diU7Ozvq378/nThxQmqVRCErK4sANJCJEycS0aMh8f/93/9Nnp6eZG9vT0OGDKHLly/rlHH37l2Kjo4mR0dHcnJyosmTJ1N5ebkEreEffbYBQBs2bNAeU11dTe+++y65urpSmzZtKDIykgoLC3XKuXHjBoWHh5ODgwO5u7vT/Pnzqba2VuTW8M+UKVPIx8eH7OzsqEOHDjRkyBCt0yJq3bZpjCcdV2u2UVRUFHXq1Ins7Oyoc+fOFBUVRVevXtXuF9M2bD0uBoPBYFgVVtXHxWAwGAwGc1wMBoPBsCqY42IwGAyGVcEcF4PBYDCsCua4GAwGg2FVMMfFYDAYDKuCOS4Gg8FgWBXMcTEYDAbDqmCOi8FgMBhWBXNcDAaDwbAqmONiMBgMhlXx/wA1UgLFwCzNNwAAAABJRU5ErkJggg==" }, "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "output_type": "display_data" } ], "execution_count": 6 @@ -277,8 +274,8 @@ "id": "d80f0401", "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:58:10.577911Z", - "start_time": "2025-11-19T13:57:18.278374Z" + "end_time": "2025-08-06T10:18:09.438991Z", + "start_time": "2025-08-06T10:17:36.238034Z" } }, "source": [ @@ -302,9 +299,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "it 500 u_avg[-1] = 0.010375465103949419 the relative change in mean vel is inf %\n", - "it 1000 u_avg[-1] = 0.014891512711759439 the relative change in mean vel is 30.32631872411333 %\n", - "it 1500 u_avg[-1] = nan the relative change in mean vel is nan %\n" + "it 500 u_avg[-1] = 0.009691138791684406 the relative change in mean vel is inf %\n", + "it 1000 u_avg[-1] = 0.009739418591561515 the relative change in mean vel is 0.4957154210307779 %\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[7]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[32m 4\u001B[39m u_avg_new = \u001B[32m0\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m j \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(it_floating_avg):\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m \u001B[43msimulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mavgs_per_check\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# print(flow.u().mean())\u001B[39;00m\n\u001B[32m 8\u001B[39m u_avg_new += flow.u().mean()\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:204\u001B[39m, in \u001B[36mSimulation.__call__\u001B[39m\u001B[34m(self, num_steps)\u001B[39m\n\u001B[32m 202\u001B[39m \u001B[38;5;28mself\u001B[39m._collide_and_stream(\u001B[38;5;28mself\u001B[39m)\n\u001B[32m 203\u001B[39m \u001B[38;5;28mself\u001B[39m.flow.i += \u001B[32m1\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m204\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 206\u001B[39m end = timer()\n\u001B[32m 207\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m num_steps * \u001B[38;5;28mself\u001B[39m.flow.rho().numel() / \u001B[32m1e6\u001B[39m / (end - beg)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/_simulation.py:193\u001B[39m, in \u001B[36mSimulation._report\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 191\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_report\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 192\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m reporter \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.reporter:\n\u001B[32m--> \u001B[39m\u001B[32m193\u001B[39m \u001B[43mreporter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:47\u001B[39m, in \u001B[36mVTKReporter.__call__\u001B[39m\u001B[34m(self, simulation)\u001B[39m\n\u001B[32m 44\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m d \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(simulation.flow.stencil.d):\n\u001B[32m 45\u001B[39m \u001B[38;5;28mself\u001B[39m.point_dict[\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mu\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[33m'\u001B[39m\u001B[33mxyz\u001B[39m\u001B[33m'\u001B[39m[d]\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m] = (\n\u001B[32m 46\u001B[39m simulation.flow.context.convert_to_ndarray(u[d, ...]))\n\u001B[32m---> \u001B[39m\u001B[32m47\u001B[39m \u001B[43mwrite_vtk\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msimulation\u001B[49m\u001B[43m.\u001B[49m\u001B[43mflow\u001B[49m\u001B[43m.\u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mfilename_base\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/lettuce/ext/_reporter/vtk_reporter.py:11\u001B[39m, in \u001B[36mwrite_vtk\u001B[39m\u001B[34m(point_dict, id, filename_base)\u001B[39m\n\u001B[32m 10\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mwrite_vtk\u001B[39m(point_dict, \u001B[38;5;28mid\u001B[39m=\u001B[32m0\u001B[39m, filename_base=\u001B[33m\"\u001B[39m\u001B[33m./data/output\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m---> \u001B[39m\u001B[32m11\u001B[39m \u001B[43mvtk\u001B[49m\u001B[43m.\u001B[49m\u001B[43mgridToVTK\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43mf\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[43mfilename_base\u001B[49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m_\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[38;5;28;43mid\u001B[39;49m\u001B[38;5;132;43;01m:\u001B[39;49;00m\u001B[33;43m08d\u001B[39;49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 12\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 13\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 14\u001B[39m \u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marange\u001B[49m\u001B[43m(\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mp\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 15\u001B[39m \u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpoint_dict\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:339\u001B[39m, in \u001B[36mgridToVTK\u001B[39m\u001B[34m(path, x, y, z, cellData, pointData, fieldData, start)\u001B[39m\n\u001B[32m 337\u001B[39m w.appendData((x, y, z))\n\u001B[32m 338\u001B[39m \u001B[38;5;66;03m# Write data\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m339\u001B[39m \u001B[43m_appendDataToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[43mw\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcellData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpointData\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfieldData\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 341\u001B[39m \u001B[38;5;66;03m# Close file\u001B[39;00m\n\u001B[32m 342\u001B[39m w.save()\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/hl.py:122\u001B[39m, in \u001B[36m_appendDataToFile\u001B[39m\u001B[34m(vtkFile, cellData, pointData, fieldData)\u001B[39m\n\u001B[32m 120\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m keys:\n\u001B[32m 121\u001B[39m data = pointData[key]\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m \u001B[43mvtkFile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mappendData\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 124\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m cellData \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 125\u001B[39m keys = \u001B[38;5;28mlist\u001B[39m(cellData.keys())\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/vtk.py:641\u001B[39m, in \u001B[36mVtkFile.appendData\u001B[39m\u001B[34m(self, data)\u001B[39m\n\u001B[32m 638\u001B[39m writeBlockSize(\u001B[38;5;28mself\u001B[39m.xml.stream, block_size)\n\u001B[32m 639\u001B[39m \u001B[38;5;66;03m# else:\u001B[39;00m\n\u001B[32m 640\u001B[39m \u001B[38;5;66;03m# writeBlockSize64Bit(self.xml.stream, block_size)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m641\u001B[39m \u001B[43mwriteArrayToFile\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mxml\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 643\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 644\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/Dokumente/frameworks/lettuce/.venv/lib/python3.12/site-packages/pyevtk/evtk.py:99\u001B[39m, in \u001B[36mwriteArrayToFile\u001B[39m\u001B[34m(stream, data)\u001B[39m\n\u001B[32m 95\u001B[39m \u001B[38;5;66;03m# NOTE: VTK expects data in FORTRAN order\u001B[39;00m\n\u001B[32m 96\u001B[39m \u001B[38;5;66;03m# This is only needed when a multidimensional array has C-layout\u001B[39;00m\n\u001B[32m 97\u001B[39m dd = np.ravel(data, order=\u001B[33m\"\u001B[39m\u001B[33mF\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m99\u001B[39m bin_pack = \u001B[43mstruct\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpack\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43mdd\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 100\u001B[39m stream.write(bin_pack)\n", + "\u001B[31mKeyboardInterrupt\u001B[39m: " ] } ], @@ -313,54 +328,20 @@ { "cell_type": "code", "id": "natural-portrait", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:58:10.694939Z", - "start_time": "2025-11-19T13:58:10.635548Z" - } - }, + "metadata": {}, "source": [ "u_plot = [context.convert_to_ndarray(_) for _ in u_avg[1:]]\n", "plt.plot(np.log10(np.abs(u_plot)))\n", "plt.xlabel(f'{it_check} iterations')\n", "plt.ylabel(f'mean log10(|velocity|) [lus]')" ], - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'mean log10(|velocity|) [lus]')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 8 + "outputs": [], + "execution_count": null }, { "cell_type": "code", "id": "63df80e6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:58:11.152299Z", - "start_time": "2025-11-19T13:58:10.699248Z" - } - }, + "metadata": {}, "source": [ "u = context.convert_to_ndarray(flow.u_pu)\n", "k = nu*u.mean()/(delta_rho_lu/nx)**2\n", @@ -382,45 +363,18 @@ "axes[3].set_title(\"Mean fluid velocity along x\")\n", "axes[3].plot(u[0].mean(axis=-1));\n" ], - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 9 + "outputs": [], + "execution_count": null }, { "cell_type": "code", "id": "needed-courage", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:58:11.160437Z", - "start_time": "2025-11-19T13:58:11.158617Z" - } - }, + "metadata": {}, "source": [ "print(f'Porosity = {phi*100} % and Permeability = {k} [m^2]')" ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Porosity = 69.74983215332031 % and Permeability = nan [m^2]\n" - ] - } - ], - "execution_count": 10 + "outputs": [], + "execution_count": null } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index f61478b4..17c309b9 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -105,7 +105,6 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup def __call__(self, flow): # FULLWAY-BBBC: inverts populations on all boundary nodes - print("FWBB call") # calc force on boundary:# if self.calc_force: self.calc_force_on_boundary(flow.f) @@ -113,7 +112,6 @@ def __call__(self, flow): # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) if self.flow.torch_stencil.d == 2: - print("FWBB call, dim 2") flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2]] = flow.f[self.f_index_fwbb[:, 0], @@ -127,7 +125,6 @@ def __call__(self, flow): self.f_index_fwbb[:, 1], self.f_index_fwbb[:, 2], self.f_index_fwbb[:, 3]] - print("FWBB call, DONE") def calc_force_on_boundary(self, f): if self.flow.torch_stencil.d == 2: diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index 331a8767..61fae6a5 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -49,6 +49,8 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_f self.f_collided_gt = None # will be populated in initialize_f_collided() method + #TODO: remove old/deprecated Code/comments below + # DEPRECATED: got moved to method initialize f_collided # f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values # f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values @@ -117,7 +119,7 @@ def make_no_collision_mask(self, f_shape, context: Context): return self.context.convert_to_tensor(self.mask, dtype=bool) def calc_force_on_boundary(self, f_bounced): - #TODO: is not working in NEW lettuce + # (!) THIS version should be more efficient (because vectorized) than one with a for-loop ### force = e * (f_collided + f_bounced[opp.]) if self.flow.stencil.d == 2: self.force_sum = torch.einsum('i..., id -> d', @@ -202,7 +204,7 @@ def store_f_collided(self, f_collided): self.f_index_gt[:, 3]]) # z #<<< OLD version "semi hardcoded" - # TODO: find a way to use pre- and post-Streaming Populations for bounce... + # TODO: is there an efficient way to run ibb as a pre-collision or post-collision boundary in the new lettuce timestep? (regarding new lettuce 2025+) def initialize_f_collided(self): f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index e993cbb4..14f3dfd2 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -172,13 +172,12 @@ def obstacle_mask(self): def obstacle_mask(self, m): assert isinstance(m, np.ndarray) and m.shape == self.resolution self._obstacle_mask = m.astype(bool) - # self.solid_mask[np.where(self._obstacle_mask)] = 1 # (!) this line is not doing what it should! solid_mask is now defined in the initial solution (see below)! def initial_pu(self): p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() u_max_pu = append_axes(u_max_pu, self.stencil.d) - self.solid_mask[np.where(self.obstacle_mask)] = 1 # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD + self.solid_mask[np.where(self.obstacle_mask)] = 1 # TODO: is this line needed? OLD: # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD ### initial velocity field: "u_init"-parameter # 0: uniform u=0 # 1: uniform u=1 or parabolic (depends on lateral_walls -> bounceback => parabolic; slip, periodic => uniform) @@ -206,7 +205,6 @@ def initial_pu(self): ny = self.grid[1].shape[1] if u.max() < 0.5 * self.units.characteristic_velocity_pu: # add perturbation for small velocities - #OLD 2D: u[0][1] += np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 1.0 amplitude_y = np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 if self.stencil.d == 2: u[0][1] += amplitude_y @@ -218,7 +216,6 @@ def initial_pu(self): u[0][1] += np.einsum('z,yz->yz', amplitude_z, plane_yz) else: # multiply scaled down perturbation if velocity field is already near u_char - #OLD 2D: u[0][1] *= 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.3 factor = 1 + np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * 0.1 if self.stencil.d == 2: u[0][1] *= factor @@ -226,7 +223,7 @@ def initial_pu(self): nz = self.grid[2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) - factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # pertubation in z-direction + factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # perturbation in z-direction u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) return p, u @@ -473,8 +470,7 @@ def post_streaming_boundaries(self): solid_boundary_data = SolidBoundaryData() solid_boundary_data.solid_mask = self.obstacle_mask - # index-lists for circular cylinder [f_index_lt, f_index_gt, d_lt, d_gt] - # [np.array(f_index_lt), np.array(f_index_gt), np.array(d_lt), np.array(d_gt)] + # load index-lists for circular cylinder into SBD object [f_index_lt, f_index_gt, d_lt, d_gt]: solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.d_gt = self.make_ibb_index_lists( x_center=self.x_pos_lu-1, y_center=self.y_pos_lu-1, radius=self.radius_lu) diff --git a/examples/simple_flows/Obstacle.ipynb b/examples/simple_flows/Obstacle.ipynb index c3ea3a97..3811510a 100644 --- a/examples/simple_flows/Obstacle.ipynb +++ b/examples/simple_flows/Obstacle.ipynb @@ -10,32 +10,24 @@ }, { "cell_type": "code", + "execution_count": 1, "id": "95c5e8f8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:16.825448Z", - "start_time": "2025-11-19T13:55:16.822985Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "import lettuce as lt\n", "from matplotlib import pyplot as plt" - ], - "outputs": [], - "execution_count": 6 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "3a1158ed", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:16.841456Z", - "start_time": "2025-11-19T13:55:16.830873Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "def run(ny=64, *axes):\n", - " context = lt.Context(use_native=False)\n", + " context = lt.Context()\n", " flow = lt.Obstacle(\n", " context=context, resolution=[2 * ny, ny], reynolds_number=50.0,\n", " mach_number=0.05, domain_length_x=10.1, stencil=lt.D2Q9()\n", @@ -50,20 +42,16 @@ " u = flow.u_pu.cpu().numpy()\n", " print(\"Max Velocity:\", u.max())\n", " return axes[1].imshow(u[0, ...].T, origin=\"lower\")" - ], - "outputs": [], - "execution_count": 7 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "5ab90e9e", "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-11-19T13:55:16.886296Z", - "start_time": "2025-11-19T13:55:16.883594Z" - } + "scrolled": true }, + "outputs": [], "source": [ "def run_and_plot(n):\n", " fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", @@ -71,124 +59,79 @@ " im2 = run(n, *axes)\n", " cbar_ax = fig.add_axes([0.88, 0.15, 0.04, 0.7])\n", " fig.colorbar(im2, cax=cbar_ax)" - ], - "outputs": [], - "execution_count": 8 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c49cbef2", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:21.857446Z", - "start_time": "2025-11-19T13:55:16.937645Z" - } - }, - "source": "run_and_plot(32)", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Max Velocity: nan\n" + "Max Velocity: 1.5470362\n" ] }, { "data": { + "image/png": "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\n", "text/plain": [ - "
" - ], - "image/png": "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" + "
" + ] + }, + "metadata": { + "needs_background": "light" }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "output_type": "display_data" } ], - "execution_count": 9 + "source": "run_and_plot(32)" }, { "cell_type": "code", + "execution_count": 5, "id": "466ba599", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:31.350123Z", - "start_time": "2025-11-19T13:55:21.907009Z" - } - }, - "source": "run_and_plot(64)", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Max Velocity: nan\n" + "Max Velocity: 1.5507787\n" ] }, { "data": { + "image/png": "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\n", "text/plain": [ - "
" - ], - "image/png": "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" + "
" + ] + }, + "metadata": { + "needs_background": "light" }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "output_type": "display_data" } ], - "execution_count": 10 + "source": "run_and_plot(64)" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:51.538933Z", - "start_time": "2025-11-19T13:55:31.442594Z" - } - }, + "metadata": {}, "cell_type": "code", + "outputs": [], + "execution_count": null, "source": "run_and_plot(128)", - "id": "dc896c757ce5a2ed", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max Velocity: nan\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 11 + "id": "dc896c757ce5a2ed" }, { "cell_type": "code", + "execution_count": null, "id": "f21453ce", - "metadata": { - "ExecuteTime": { - "end_time": "2025-11-19T13:55:51.591439Z", - "start_time": "2025-11-19T13:55:51.589880Z" - } - }, - "source": [], + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { diff --git a/lettuce/ext/_boundary/__init__.py b/lettuce/ext/_boundary/__init__.py index 2baf9b02..53fd9531 100644 --- a/lettuce/ext/_boundary/__init__.py +++ b/lettuce/ext/_boundary/__init__.py @@ -9,5 +9,5 @@ 'BounceBackBoundary', 'EquilibriumBoundaryPU', 'EquilibriumOutletP', - 'PartiallySaturatedBC', + 'PartiallySaturatedBC' ] From 5d51b3a6c540238ae30924b002114abcc0edd0b1 Mon Sep 17 00:00:00 2001 From: mbille Date: Thu, 27 Nov 2025 17:53:05 +0100 Subject: [PATCH 12/21] tested FWBB, HWBB, IBB against MP2 code. Visually identical results, numerically there are diffs. of ~1% in mean Drag... this might be due to the new pre/ost-boundary strategy of lettuce 2025. Needs further investigation (issue already open). Further improvements and corrections: - implemented lateral walls option (for FWBB) - removed some comments and old code sections. NEXT will be output/data/vtk cleaning and example .ipynb script for execution --- .../01a_script_cylinder_lowRe.py | 12 ++-- .../ebb_simulation.py | 15 +++- .../halfway_bounce_back_boundary.py | 1 - .../observables_force_coefficients.py | 4 +- .../obstacle_cylinder.py | 71 ++++++++++--------- 5 files changed, 62 insertions(+), 41 deletions(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 689a6614..1eac1485 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -74,6 +74,7 @@ parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") +parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") # additional: # - char_density (PU house) @@ -89,6 +90,7 @@ parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") # reporter and observable settings +parser.add_argument("--vtk3D", action='store_true', help="output 3D vtk files") #TODO: add vtk reporter (fps_pu, interval_lu, # - 3D full # - 2D slice normal (x,y,z) @@ -259,7 +261,7 @@ if args["t_target"] > 0: n_steps = int(t_target * ((char_length_lu) / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) else: - t_target = t_target / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) + t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") @@ -281,7 +283,7 @@ flow = ObstacleCylinder(context=context, resolution=resolution, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True) + bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) print("-> initializing collision operator...") collision_operator = None @@ -304,10 +306,10 @@ # DRAG and LIFT Force Cefficients and respective reporters: cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d -DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[0], solid_mask=simulation.post_streaming_boundaries[0].mask, area_pu=cylinder_cross_sectional_area) +DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) simulation.reporter.append(DragReporter) -LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[0], solid_mask=simulation.post_streaming_boundaries[0].mask, area_pu=cylinder_cross_sectional_area) +LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) simulation.reporter.append(LiftReporter) @@ -319,7 +321,7 @@ # TODO: Progress-Reporter # VTK Reporter -> visualization -if True: +if args["vtk3D"]: vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") # export obstacle mask_dict = dict() diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py index cef2d48f..8bd3c87b 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -28,8 +28,19 @@ def __init__(self, flow: 'Flow', collision: 'Collision', # adjust No_collision_ and no_streaming_mask for use of post_streaming_boundaries (!) if len(self.post_streaming_boundaries) > 0: + + # create NCM and NSM, if there where no pre_ or post_boundaries that triggered their creation in super-class + if self.no_collision_mask is None: + # fill masks with value of self.collision_index (= number of pre-boundaries) + self.no_collision_mask = self.context.full_tensor( + flow.resolution, self.collision_index, dtype=torch.uint8) + if self.no_streaming_mask is None: + self.no_streaming_mask = self.context.full_tensor( + [flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) + + for i, boundary in enumerate(self.post_streaming_boundaries, start=self.collision_index + 1 + len(self.post_boundaries)): - print("nCM making: i, boundary = ", i, boundary) + print("SIMULATION: creating no_collision_mask entries for: i, boundary = ", i, boundary) print("collision index is:", self.collision_index) ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) @@ -44,8 +55,10 @@ def __init__(self, flow: 'Flow', collision: 'Collision', self.store_f_collided_post_streaming_boundaries_index = [] for i, boundary in enumerate(self.post_streaming_boundaries): if hasattr(boundary.__class__, "store_f_collided"): + # append boundary to lis of boundaries that need f_collided state between collision and streaming substep self.store_f_collided_post_streaming_boundaries_index.append(i) if hasattr(boundary.__class__, "initialize_f_collided"): + # initialize the f_collided storage in each of those boundaries boundary.initialize_f_collided() # redefine collide_and_stream() to include the storage of f_collided for use in post_streaming_boundaries diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index ff90c371..fa843676 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -132,7 +132,6 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global def __call__(self, flow): # calc force on boundary: - print("calling HWBB") if self.calc_force: self.calc_force_on_boundary() # bounce (invert populations on fluid nodes neighboring solid nodes) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py index 32aa214c..e15b2d54 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py @@ -22,7 +22,7 @@ def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): self.solid_mask = solid_mask self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) self.nan_tensor = self.context.convert_to_tensor(torch.nan) - self.solid_mask = self.context.convert_to_tensor(self.solid_mask) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region @@ -47,7 +47,7 @@ def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): self.solid_mask = solid_mask self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) self.nan_tensor = self.context.convert_to_tensor(torch.nan) - self.solid_mask = self.context.convert_to_tensor(self.solid_mask) + self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 14f3dfd2..3e0e2dc5 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -89,7 +89,6 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], self.x_offset = x_offset self.y_offset = y_offset self.radius_lu = char_length_lu / 2 - #todo: das PLUS hier ist ein Problem! self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing self.x_pos_lu = self.y_pos_lu + self.x_offset # keep symmetry of cylinder in x and y direction @@ -177,7 +176,8 @@ def initial_pu(self): p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() u_max_pu = append_axes(u_max_pu, self.stencil.d) - self.solid_mask[np.where(self.obstacle_mask)] = 1 # TODO: is this line needed? OLD: # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD + #OLD? self.solid_mask[np.where(self.obstacle_mask)] = 1 # TODO: is this line needed? OLD: # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD + ### initial velocity field: "u_init"-parameter # 0: uniform u=0 # 1: uniform u=1 or parabolic (depends on lateral_walls -> bounceback => parabolic; slip, periodic => uniform) @@ -229,7 +229,7 @@ def initial_pu(self): def make_solid_boundary_data(self, x_center, y_center, radius): pass - # NOT YET IMPLEMENTED; OCC-version for index lists + # NOT YET IMPLEMENTED; OCC-version for index lists; CURRENTLY only make_ibb_index_lists is used with analytic function (see below) # TODO: wo sollte das SolidBoundaryData Objekt definiert werden bzw. die Klasse definiert werden? # cylinder definitions: @@ -415,7 +415,6 @@ def make_ibb_index_lists(self, x_center, y_center, radius): @property def grid(self): - # THIS IS NOT USED AT THE MOMENT. QUESTION: SHOULD THIS BE ONE- OR ZERO-BASED? Indexing or "node-number"? xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.resolution) # tuple of lists of x,y,(z)-values/indices return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices @@ -425,21 +424,7 @@ def post_boundaries(self): # inlet ("left side", x[0],y[1:-1], z[:]) inlet_boundary = EquilibriumBoundaryPU(flow=self, context=self.context, mask=self.in_mask, - velocity=self.u_inlet) # (is this still true??): works with a 1 x D vector or an ny x D vector thanks to einsum-magic in EquilibriumBoundaryPU - - lateral_boundary = None # stays None if lateral walls are not specified... (NOT IMPLEMENTED YET, see below) - # >>> lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) - #TODO: implement lateral walls (top, bottom) for cylinder in channel (with or without wall friction) - # - implementation depends on positional definition: mask or index-lists or solid_boundary_data object! - # - # if self.lateral_walls == 'bounceback': - # if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back - # lateral_boundary = HalfwayBounceBackBoundary(self.wall_mask, self.units.lattice) - # else: # else use fullway bounce back - # lateral_boundary = FullwayBounceBackBoundary(self.wall_mask, self.units.lattice) - # elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-walöl (symmetry boundary) - # lateral_boundary = SlipBoundary(self.wall_mask, self.units.lattice, 1) # slip on x(z)-plane - # <<< + velocity=self.u_inlet) # outlet ("right side", x[-1],y[:], (z[:])) if self.stencil.d == 2: @@ -448,21 +433,33 @@ def post_boundaries(self): outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # outlet in positive x-direction # create and return boundary-list - if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list - return [ - inlet_boundary, - outlet_boundary, - #BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask,dtype=bool)) - ] - else: - return [ - inlet_boundary, - outlet_boundary, - lateral_boundary, - ] + return [ + inlet_boundary, + outlet_boundary, + ] @property def post_streaming_boundaries(self): + # LATERAL WALLS (optional) + lateral_boundary = None # stays None if lateral walls are not specified... (NOT IMPLEMENTED YET, see below) + # >>> lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) + + if self.lateral_walls == 'bounceback': + if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back + print( + "(!) OBST. CYLINDER: lateral walls can currenlty only be FWBB, HWBB requirest Solid Boundary Data which is not implemented yet for lateral walls!") + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + # TODO (low prio): implement ibb- and hwbb-option for lateral walls + else: # else use fullway bounce back + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-wall (symmetry boundary) + print("(!) OBST. CYLINDER: lateral walls can currenlty only be FWBB, slip boundary is not implemented yet!") + lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, + periodicity=self.periodicity) + # TODO (low prio): implement slip-option for lateral walls + # obstacle (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) -> to be set via obstacle_mask.setter obstacle_boundary = None # (!) the obstacle_boundary should always be the last boundary in the list of boundaries to correctly calculate forces on the obstacle @@ -487,7 +484,17 @@ def post_streaming_boundaries(self): print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... - will fall back to using BounceBackBoundary") obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) - return [obstacle_boundary] + # create and return boundary-list + if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list + return [ + obstacle_boundary + ] + else: + return [ + lateral_boundary, + obstacle_boundary + ] + #OLD: return [obstacle_boundary] def _unit_vector(self, i=0): return np.eye(self.stencil.d)[i] \ No newline at end of file From 4ee0fd66069befb54390c609c3bb5fe9fd0f4c73 Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 3 Dec 2025 18:42:21 +0100 Subject: [PATCH 13/21] tidying of 01a_script_cylinder_lowRe.py - methods for force coefficient plotting and analysis were outsourced to data_processing_and_plotting.py - files for NaN-, HighMa-, and Progress-Reporter created (but work in progress) --- .../01a_script_cylinder_lowRe.py | 416 ++++------ .../251203_01a_script_cylinder_lowRe.py | 764 ++++++++++++++++++ .../data_processing_and_plotting.py | 552 +++++++++++++ .../reporter_NaNreporter.py | 7 +- .../reporter_high_ma_reporter.py | 1 + .../reporter_progress_reporter.py | 2 + 6 files changed, 1473 insertions(+), 269 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 1eac1485..15d6c0e4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -1,52 +1,49 @@ # this file should contain all the MP2 stuff for low RE (basically a reworked MP1) +#################### # IMPORT -import lettuce as lt -from obstacle_cylinder import ObstacleCylinder -from ebb_simulation import EbbSimulation -from observables_force_coefficients import DragCoefficient, LiftCoefficient - -import matplotlib.pyplot as plt -from scipy.signal import find_peaks - -import sys -import warnings - import numpy as np - -## OLD from lettuce import (LettuceException) -## OLD from lettuce.unit import UnitConversion -## OLD from lettuce.util import append_axes -## OLD from lettuce.boundary import EquilibriumBoundaryPU, EquilibriumOutletP, AntiBounceBackOutlet -## OLD from lettuce.flows.obstacleCylinder import ObstacleCylinder - -## OLD from lettuce.equilibrium import QuadraticEquilibrium_LessMemory - -## OLD from lettuce.max import draw_circular_mask - import torch torch.autograd.set_detect_anomaly(True) -import time -import datetime +import sys +import warnings import os import psutil import shutil +import traceback + +import matplotlib.pyplot as plt +from scipy.signal import find_peaks + +import time +import datetime + from pyevtk.hl import imageToVTK + from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter +# LETTUCE RELATED +import lettuce as lt +from obstacle_cylinder import ObstacleCylinder +from ebb_simulation import EbbSimulation +from observables_force_coefficients import DragCoefficient, LiftCoefficient + +# AUX. CODE from helperCode import Logger +from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries -import pickle -from copy import deepcopy -from timeit import default_timer as timer -from collections import Counter +# import pickle +# from copy import deepcopy +# from timeit import default_timer as timer +# from collections import Counter -#warnings.simplefilter("ignore") # todo: is this needed? +#? warnings.simplefilter("ignore") # todo: is this needed? -# ARGUMENT PARSING: this scipt is supposed to be called with arguments, detailling all simulation- and system-parameters +#################### +# ARGUMENT PARSING: this script is supposed to be called with arguments, detailing all simulation- and system-parameters parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) @@ -76,17 +73,18 @@ parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") -# additional: +#TODO additional physics and flow-parameters: # - char_density (PU house) # - char_pressure # - vicsosity PU (house, -# solver settings -parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0, end of sim will be step_start+n_steps") -parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, t_start will be calculated by PU/LU-conversion of step_start") +# LBM solver settings +parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") +parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") -parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions infered from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") +parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") +#TODO: how to use EQLM in lettuce 2025? parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") # reporter and observable settings @@ -112,6 +110,8 @@ # - checkpoint OUT path (if only one is given, take this one for both) # - checkpoint i_start, t_start?, + +########################################################### # OUTPUTS: # - parameters INPUT # - print 2D-slice with mask(s) @@ -120,6 +120,8 @@ # - output condensed observables (drag, lift, strouhal) to file... @MP2 # - stats and performance (end) + +########################################################### # put arguments in dictionary print("SCRIPT: Writing arguments to dictionary...") args = vars(parser.parse_args()) @@ -173,17 +175,20 @@ old_stdout = sys.stdout sys.stdout = Logger(outdir) -##################################### +########################################################### # PROCESS AND SET PARAMETERS print(f"SCRIPT: Processing parameters...") # calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients +#TODO: take absolute PU-time values here: +# - periodic_start_Re100_PU ~= 75-100 seconds +# - periodic_start hängt von Einschwingzeit ab! Diese hängt von der Domänengröße ab! if args["reynolds_number"] > 1000: periodic_start = 0.4 else: periodic_start = 0.9 -# calculate domain and obstacle geometry +# calculate domain and obstacle geometry and infer dimensions (2D, 3D) if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: domain_height_y_in_d = 3 else: @@ -204,16 +209,17 @@ else: domain_width_z_in_d = args["domain_width_z_in_d"] +# CORRECT GPT for symmetry: # if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain # ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder -# ...use odd DpY to use odd GPD +# ...(!) use odd DpY to use odd GPD! gpd_correction = False if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: gpd_correction = True # gpd will be corrected gpd_setup = args["char_length_lu"] # store old gpd for output char_length_lu = int(gpd_setup / 2) * 2 # make gpd even print("(!) domain_height_y_ind_d (DpY) is even, gridpoints per diameter (GPD, char_length_lu) will be set to" + str( - char_length_lu) + ". Use odd domain_height_in_D (DpY) to enable use of odd GPD (char_length_lu)!") + char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) to enable use of odd GPD (char_length_lu)!") else: char_length_lu = args["char_length_lu"] @@ -259,18 +265,17 @@ n_steps = args["n_steps"] t_target = args["t_target"] if args["t_target"] > 0: - n_steps = int(t_target * ((char_length_lu) / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) + n_steps = int(t_target * (char_length_lu / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) else: t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) -print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") - - # check EQLM parameter if args["eqlm"]: # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? pass +print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") + ########################################### print("SCRIPT: initializing solver components...") @@ -304,7 +309,7 @@ # REPORTERS: initialize and append to simulation.reporter print("-> initializing reporters...") -# DRAG and LIFT Force Cefficients and respective reporters: +# DRAG and LIFT Force Coefficients and respective reporters: cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) @@ -317,12 +322,17 @@ # TODO: add NaN reporter # - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... +# HighMa Reporter (if Ma>0.3 retected -> report and/or stop simulation) +# TODO: HighMa Reporter + # Watchdog/Progress-Reporter # TODO: Progress-Reporter # VTK Reporter -> visualization if args["vtk3D"]: + #TODO: make stuff parameterized: interval, outdir, ... vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") + # export obstacle mask_dict = dict() if dims ==2: @@ -346,29 +356,35 @@ ################################################## # PRINT PARAMETERS prior to simulation: -print("shape_LU:", flow.resolution) -print("T with", n_steps, "steps:", +print(f"\nSCRIPT: spacial and temporal dimensions:") +print("domain shape (LU):", flow.resolution) +print("t_target (PU) with", n_steps, "steps (LU):", round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 2), "seconds") -print("n_steps to simulate 1 second:", +print("steps to simulate 1 second PU:", round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") -print("n_steps to simulate", t_target, "seconds:", +print("steps to simulate", t_target, " (t_target, PU) seconds:", t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") +################################################## # RUN SIMULATION: -print(f"\nSCRIPT: running simulation for {n_steps} steps...") +print(f"\n#################################################") +print(f"\nSCRIPT: running simulation for {n_steps} steps...\n") +print(f"#################################################\n") t_start = time.time() mlups = simulation(num_steps=n_steps) t_end = time.time() runtime = t_end - t_start -# output stats +################################################## +# OUTPUT STATS: +print(f"### STATS ###") print("MLUPS:", mlups) -print("PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") -print("number of steps:", n_steps) -print("runtime: ", runtime, "seconds (", round(runtime / 60, 2), "minutes )") - +print("simulated PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") +print("simulated LU-steps: ", n_steps) +print("runtime (WALLTIME) of simulation(num_steps): ", runtime, "seconds (= ", round(runtime / 60, 2), "minutes )") +print("\n") print("current VRAM (MB): ", torch.cuda.memory_allocated(context.device) / 1024 / 1024) print("max. VRAM (MB): ", torch.cuda.max_memory_allocated(context.device) / 1024 / 1024) @@ -394,12 +410,12 @@ output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.close() +################################################## # PLOTTING 2D IMAGE - fig, axes = plt.subplots(1, 2, figsize=(10, 3)) fig.subplots_adjust(right=0.85) u = flow.u_pu.cpu().numpy() -print("Max Velocity:", u.max()) +print("\nMax Velocity:", u.max()) if dims == 2: im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") @@ -410,180 +426,42 @@ fig.colorbar(im2, cax=cbar_ax) fig.show() - +################################################## # PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: +print("\nSCRIPT: processing, plotting and saving data...\n") -# COEFF. OF DRAG -try: - try: - drag_coefficient = np.array(DragReporter.out) - fig, ax = plt.subplots(constrained_layout=True) - ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2]) - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Drag Cd") - ax.set_ylim([0.5, 1.6]) # change y-limits - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timestep (simulation time / LU)") - except: - print("(!) drag_plotting didn't work...'") - - if not no_data_flag: - try: - plt.savefig(outdir_data + "/drag_coefficient.png") - except: - print("(!) saving 'drag_coefficient.png' failed!") - try: - np.savetxt(outdir_data + "/drag_coefficient.txt", drag_coefficient, - header="stepLU | timePU | Cd FROM str(timestamp)") - except: - print("(!) saving drag_timeseries failed!") - ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, - drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) - if not no_data_flag: - try: - plt.savefig(outdir_data + "/drag_coefficient_adjusted.png") - except: - print("(!) saving drag_coefficient_adjusted.png failed!") - plt.close() -except: - print("(!) analysing drag_coefficient didn't work'") - -# peak finder: try calculating the mean drag coefficient from an integer number of periods, if a clear periodic signal is found -try: - values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] - - peaks_max = find_peaks(values, prominence=((values.max() - values.min()) / 2)) - peaks_min = find_peaks(-values, prominence=((values.max() - values.min()) / 2)) - if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: - peak_number = peaks_max[0].shape[0] - else: - peak_number = peaks_min[0].shape[0] +# DRAG +drag_timeseries = np.array(np.array(DragReporter.out)) +plot_force_coefficient(drag_timeseries, ylabel="Coefficient of Drag $C_{D}$", ylim=(0.5, 1.6), + secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/drag") +drag_prominence = ((abs(drag_timeseries.max()) - abs(drag_timeseries.min())) / 2) +drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, prominence=drag_prominence,name="drag", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) +#TODO: check if prominence with (asb(max) - abs(min))/2 works for both lift and drag +print(f"DRAG STATS:") #\n{drag_stats}") +for key, value in drag_stats.items(): + print(f"{key:<20} = {str(value)}") + +# STATS ARE: {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, +# "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, +# "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} + +print("\n") + +# LIFT +lift_timeseries = np.array(np.array(LiftReporter.out)) +plot_force_coefficient(lift_timeseries, ylabel="Coefficient of Lift$C_{L}$", ylim=(-1.1, 1.1), + secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/lift") +lift_prominence = ((abs(lift_timeseries.max()) - abs(lift_timeseries.min())) / 2) +lift_stats = analyze_periodic_timeseries(lift_timeseries, periodic_start_rel=0.5, prominence=lift_prominence,name="lift", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) +print(f"LIFT STATS:") #\n {lift_stats}") +for key, value in lift_stats.items(): + print(f"{key:<20} = {str(value)}") + +# STROUHAL number +# f = Strouhal for St=f*D/U and D=U=1 in PU +print("Strouhal number is: ", lift_stats["frequency_fit"] * flow.char_length_pu/flow.char_velocity_pu) - if peaks_min[0][0] < peaks_max[0][0]: - first_peak = peaks_min[0][0] - last_peak = peaks_max[0][peak_number - 1] - else: - first_peak = peaks_max[0][0] - last_peak = peaks_min[0][peak_number - 1] - - drag_mean = values[first_peak:last_peak].mean() - drag_mean_simple = values.mean() - - print("Cd, simple mean: ", drag_mean_simple) - print("Cd, peak_finder mean:", drag_mean) - - drag_stepsLU = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 0] - peak_max_y = values[peaks_max[0]] - peak_max_x = drag_stepsLU[peaks_max[0]] - peak_min_y = values[peaks_min[0]] - peak_min_x = drag_stepsLU[peaks_min[0]] - - plt.plot(drag_stepsLU, values) - plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) - plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) - plt.scatter(drag_stepsLU[first_peak], values[first_peak]) - plt.scatter(drag_stepsLU[last_peak], values[last_peak]) - plt.savefig(outdir_data + "/drag_coefficient_peakfinder.png") - peakfinder = True -except: # if signal is not sinusoidal enough, calculate only simple mean value - print( - "peak-finding didn't work... probably no significant peaks visible (Re<46?), or periodic region not reached (T too small)") - values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] - drag_mean_simple = values.mean() - peakfinder = False - print("Cd, simple mean:", drag_mean_simple) -try: - plt.close() -except: - pass -# LIFT COEFFICIENT -try: - lift_coefficient = np.array(LiftReporter.out) - fig, ax = plt.subplots(constrained_layout=True) - ax.plot(lift_coefficient[:, 1], lift_coefficient[:, 2]) - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Lift Cl") - ax.set_ylim((-1.1, 1.1)) - - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timesteps (simulation time / LU)") - if not no_data_flag: - try: - plt.savefig(outdir_data + "/lift_coefficient.png") - except: - print("(!) saving lift_coefficient.png didn't work!") - try: - np.savetxt(outdir_data + "/lift_coefficient.txt", lift_coefficient, - header="stepLU | timePU | Cl FROM str(timestamp)") - except: - print("(!) saving lift_timeline didn't work!") - Cl_min = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].min() - Cl_max = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].max() - print("Cl_peaks: \nmin", Cl_min, "\nmax", Cl_max) - plt.close() -except: - print("(!) analysing lift_coefficient didn't work!") - -# plot DRAG and LIFT together: -try: - fig, ax = plt.subplots(layout="constrained") - drag_ax = ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2], color="tab:blue", label="Drag") - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Drag Cd") - ax.set_ylim((0.5, 1.6)) - - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timesteps (simulation time / LU)") - - ax2 = ax.twinx() - lift_ax = ax2.plot(lift_coefficient[:, 1], lift_coefficient[:, 2], color="tab:orange", label="Lift") - ax2.set_ylabel("Coefficient of Lift Cl") - ax2.set_ylim((-1.1, 1.1)) - - fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) - - if not no_data_flag: - try: - plt.savefig(outdir_data + "/dragAndLift_coefficient.png") - except: - print("(!) saving dragAndLift_coefficient.png didn't work!") - plt.close() -except: - print("(!) plotting drag and lift together didn't work!") - -# STROUHAL number: (only makes sense for Re>46 and if periodic state is reached) -try: - ### prototyped fft for frequency detection and calculation of strouhal-number - # ! Drag_frequency is 2* Strouhal-Freq. Lift-freq. is Strouhal-Freq. - - X = np.fft.fft(lift_coefficient[:, 2]) # fft result (amplitudes) - N = len(X) # number of freqs - n = np.arange(N) # freq index - T = N * flow.units.convert_time_to_pu(1) # total time measured (T_PU) - freq = n / T # frequencies (x-axis of spectrum) - - plt.figure - plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) - plt.xlabel("Freq (Hz)") - plt.ylabel("FFT Amplitude |X(freq)|") - plt.xlim(0, 1) - # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging - plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum - - if not no_data_flag: - plt.savefig(outdir_data + "/fft_Cl.png") - - freq_res = freq[1] - freq[0] # frequency-resolution - X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum - freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude - print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") - # f = Strouhal for St=f*D/U and D=U=1 in PU -except: - print("fft for Strouhal didn't work") - freq_res = 0 - freq_peak = 0 -plt.close() # EXPORT OBSERVABLES: @@ -594,38 +472,41 @@ output_file.write(torch.cuda.memory_summary(context.device)) output_file.close() - try: - ### list present torch tensors: - output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") - total_bytes = 0 - import gc - - for obj in gc.get_objects(): - try: - if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): - output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) - total_bytes = total_bytes + obj.nelement() * obj.element_size() - except: - pass - # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) - output_file.close() - - ### count occurence of tensors in list of tensors: - from collections import Counter - - my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") - data = my_file.read() - my_file.close() - data_into_list = data.split("\n") - c = Counter(data_into_list) - output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") - for k, v in c.items(): - output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) - output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) - output_file.close() - except: - print("(!) counting tensors didn't work!") + # TODO: make this optional... + if False: + try: + ### list present torch tensors: + output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") + total_bytes = 0 + import gc + + for obj in gc.get_objects(): + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): + output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) + total_bytes = total_bytes + obj.nelement() * obj.element_size() + except: + pass + # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) + output_file.close() + + ### count occurence of tensors in list of tensors: + from collections import Counter + + my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") + data = my_file.read() + my_file.close() + data_into_list = data.split("\n") + c = Counter(data_into_list) + output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") + for k, v in c.items(): + output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) + output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) + output_file.close() + except: + print("(!) counting tensors didn't work!") +# TODO: cleanup of parms, stats, obs... # output parameters, stats and observables if not no_data_flag: output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") @@ -673,23 +554,22 @@ output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\n\n### OBSERVABLES ###") - output_file.write("\nCoefficient of drag between " + str(round(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_coefficient[int(drag_coefficient.shape[0] - 1), 1], 2)) + " s:") - output_file.write("\nCd_mean, simple = " + str(drag_mean_simple)) - if peakfinder: - output_file.write("\nCd_mean, peak_finder = " + str(drag_mean)) - else: - output_file.write("\nnoPeaksFound") + output_file.write("\nCoefficient of drag between " + str(round(drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + " s:") + output_file.write("\nCd_mean, simple = " + str(drag_stats["mean_simple"])) + output_file.write("\nCd_mean, peak_finder = " + str(drag_stats["mean_periodcorrected"])) output_file.write( - "\nCd_min = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min())) + "\nCd_min = " + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) output_file.write( - "\nCd_max = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max())) + "\nCd_max = " + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) output_file.write("\n") output_file.write("\nCoefficient of lift:") - output_file.write("\nCl_min = " + str(Cl_min)) - output_file.write("\nCl_max = " + str(Cl_max)) + output_file.write("\nCl_min = " + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) + output_file.write("\nCl_max = " + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) output_file.write("\n") output_file.write("\nStrouhal number:") - output_file.write("\nSt +- df = " + str(freq_peak) + " +- " + str(freq_res) + " Hz") + output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + str(lift_stats["fft_resolution"]) + " Hz") + output_file.write( + "\nSINE-FIT: St = " + str(lift_stats["frequency_fit"])) output_file.write("\n") output_file.close() diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py new file mode 100644 index 00000000..f065c50f --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py @@ -0,0 +1,764 @@ + +# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) + +#################### +# IMPORT + +import numpy as np +import torch +torch.autograd.set_detect_anomaly(True) + +import sys +import warnings +import os +import psutil +import shutil +import traceback + +import matplotlib.pyplot as plt +from scipy.signal import find_peaks + +import time +import datetime + +from pyevtk.hl import imageToVTK + +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter + +# LETTUCE RELATED +import lettuce as lt +from obstacle_cylinder import ObstacleCylinder +from ebb_simulation import EbbSimulation +from observables_force_coefficients import DragCoefficient, LiftCoefficient + +# AUX. CODE +from helperCode import Logger + +# import pickle +# from copy import deepcopy +# from timeit import default_timer as timer +# from collections import Counter + +#? warnings.simplefilter("ignore") # todo: is this needed? + +#################### +# ARGUMENT PARSING: this script is supposed to be called with arguments, detailing all simulation- and system-parameters + +parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) + +# context and I/O +parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") +parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") +parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") +parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. walltime [s] to run the simulationn(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole scipt may take longer to execute, depending on I/O etc.") + +parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") ##FORMER: --cluster +parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") +parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") +parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") + +# flow physics and geometry +parser.add_argument("--reynolds_number", default=200, type=float, help="Reynolds number") +parser.add_argument("--mach_number", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") +parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") + +parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") +parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") +parser.add_argument("--domain_length_x_in_d", default=None, type=float, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") +parser.add_argument("--domain_height_y_in_d", default=None, type=float,help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") +parser.add_argument("--domain_width_z_in_d", default=None, type=float,help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") + +parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") +parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") +parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") + +#TODO additional physics and flow-parameters: +# - char_density (PU house) +# - char_pressure +# - vicsosity PU (house, + +# LBM solver settings +parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") +parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") +parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") +parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") +parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") +#TODO: how to use EQLM in lettuce 2025? +parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") + +# reporter and observable settings +parser.add_argument("--vtk3D", action='store_true', help="output 3D vtk files") +#TODO: add vtk reporter (fps_pu, interval_lu, +# - 3D full +# - 2D slice normal (x,y,z) +# - obstacle point, obstacle cell speichern und abh. von 2D/3D korrekt formatieren +#TODO: add drag/lift reporter +# - periodic start relative (relative start of interval to do statistics over +# - plot lift, drag, strouhal number (try except...) +#TODO: add U-profile-reporter (output True, store/calculate true) +# -> condense in own functions +# -> make script to plot from data (see PLOTTING scripts in CYLINDER-paper for layout) +#TODO: add NAN reporter (on/off, interval) +#TODO: add watchdog reporter (on/off, interval) +#TODO: add highMa reporter (on/off, interval) +#TODO: add 2D-mp4-reporter... (fps_video, number of frames ODER fps_pu) + +# Checkpointing +# TODO: add checkpointing-utilities (read, write) +# - checkpoint IN path +# - checkpoint OUT path (if only one is given, take this one for both) +# - checkpoint i_start, t_start?, + + +########################################################### +# OUTPUTS: +# - parameters INPUT +# - print 2D-slice with mask(s) +# - parameters SIMULATED (before sim()) +# - 2D slice last frame +# - output condensed observables (drag, lift, strouhal) to file... @MP2 +# - stats and performance (end) + + +########################################################### +# put arguments in dictionary +print("SCRIPT: Writing arguments to dictionary...") +args = vars(parser.parse_args()) + +# print all arguments +print(f"SCRIPT: Input arguments are: \n{args}\n") + +########################################################### + +# CREATE timestamp, sim-ID, outdir and outdir_data +print("SCRIPT: Creating timestamp, simulation ID and creating output directory...") +name = args["name"] +outdir = args["outdir"] +outdir_data = args["outdir_data"] + +default_device = args["default_device"] +float_dtype = args["float_dtype"] +t_sim_max = args["t_sim_max"] + +text_output_only = args["text_output_only"] +no_data_flag = args["no_data"] + +timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") +sim_id = str(timestamp) + "-" + name + +os.makedirs(outdir+"/"+sim_id) # create output dir +print(f"outdir/simID = {outdir}/{sim_id}") +if outdir_data is None: # save data to regular outdir, if data-dir is not specified + outdir_data = outdir +outdir = outdir+"/"+sim_id # adding individal sim-ID to outdir path to get individual DIR per simulation +outdir_data = outdir_data+"/"+sim_id +if not os.path.exists(outdir_data): + os.makedirs(outdir_data) # create output dir for large/many files, if specified +print(f"outdir_DATA/simID = {outdir}/{sim_id}") + +# save input arguments/parameters to file in outdir: +print(f"SCRIPT: Writing input parameters to file: {outdir}/input_parameters.txt") +output_file = open(outdir+"/input_parameters.txt", "a") +for key in args: + output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) +output_file.close() + +### SAVE SCRIPT: save this script to outdir +print(f"SCRIPT: Saving simulation script to outdir...") +temp_script_name = sim_id + "_" + os.path.basename(__file__) +shutil.copy(__file__, outdir+"/"+temp_script_name) +print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") + +# START LOGGER -> get all terminal output into file +print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") +old_stdout = sys.stdout +sys.stdout = Logger(outdir) + +########################################################### + +# PROCESS AND SET PARAMETERS +print(f"SCRIPT: Processing parameters...") +# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients +#TODO: take absolute PU-time values here: +# - periodic_start_Re100_PU ~= 75-100 seconds +# - periodic_start hängt von Einschwingzeit ab! Diese hängt von der Domänengröße ab! +if args["reynolds_number"] > 1000: + periodic_start = 0.4 +else: + periodic_start = 0.9 + +# calculate domain and obstacle geometry and infer dimensions (2D, 3D) +if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: + domain_height_y_in_d = 3 +else: + domain_height_y_in_d = args["domain_height_y_in_d"] + +if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : + domain_length_x_in_d = 2 * domain_height_y_in_d # D/X = domain length in X- / flow-direction +else: + domain_length_x_in_d = args["domain_length_x_in_d"] + +if args["domain_width_z_in_d"] is None: # will be 2D + dims = 2 +else: # will be 3D + dims = 3 + if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node + domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node + print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_z_in_d to 1 lattice node") + else: + domain_width_z_in_d = args["domain_width_z_in_d"] + +# CORRECT GPT for symmetry: +# if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain +# ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder +# ...(!) use odd DpY to use odd GPD! +gpd_correction = False +if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: + gpd_correction = True # gpd will be corrected + gpd_setup = args["char_length_lu"] # store old gpd for output + char_length_lu = int(gpd_setup / 2) * 2 # make gpd even + print("(!) domain_height_y_ind_d (DpY) is even, gridpoints per diameter (GPD, char_length_lu) will be set to" + str( + char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) to enable use of odd GPD (char_length_lu)!") +else: + char_length_lu = args["char_length_lu"] + +char_length_pu = args["char_length_pu"] +char_velocity_pu = args["char_velocity_pu"] +reynolds_number = args["reynolds_number"] +mach_number = args["mach_number"] + +perturb_init = args["perturb_init"] +u_init_condition = args["u_init_condition"] + +# calculate lu-domain-resolution, total number of gridpoints and check correct stencil +if dims == 2: + resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] + number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d + if args["stencil"] == "D2Q9": + stencil = lt.D2Q9() + else: + print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") + stencil= lt.D2Q9() +else: + resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] + number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d + if args["stencil"] == "D3Q15": + stencil = lt.D3Q15() + elif args["stencil"] == "D3Q19": + stencil = lt.D3Q19() + elif args["stencil"] == "D3Q27": + stencil = lt.D3Q27() + else: + print("(!) WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") + stencil = lt.D3Q27() + +# read dtype +if float_dtype == "float32" or float_dtype == "single": + float_dtype = torch.float32 +elif float_dtype == "double" or float_dtype == "float64": + float_dtype = torch.float64 +elif float_dtype == "half" or float_dtype == "float16": + float_dtype = torch.float16 + +# OVERWRITE n_steps, if t_target is given +n_steps = args["n_steps"] +t_target = args["t_target"] +if args["t_target"] > 0: + n_steps = int(t_target * (char_length_lu / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) +else: + t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) + +# check EQLM parameter +if args["eqlm"]: + # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? + pass + +print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") + +########################################### + +print("SCRIPT: initializing solver components...") + +### +print("-> initializing context...") +context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) + +print("-> initializing flow...") +flow = ObstacleCylinder(context=context, resolution=resolution, + reynolds_number=reynolds_number, mach_number=mach_number, + char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, + bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) + +print("-> initializing collision operator...") +collision_operator = None +if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": + collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) +elif args["collision"].casefold() == "kbc": + if dims == 2: + collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) + else: + collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) +else: # default to bgk + collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) + +print("\nSCRIPT: initializing simulation object...") +simulation = EbbSimulation(flow, collision_operator,reporter=[]) + + +# REPORTERS: initialize and append to simulation.reporter +print("-> initializing reporters...") + +# DRAG and LIFT Force Coefficients and respective reporters: +cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d +DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) +DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) +simulation.reporter.append(DragReporter) +LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) +LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) +simulation.reporter.append(LiftReporter) + +# NAN REPORTER (if nan detected -> stop simulation) +# TODO: add NaN reporter +# - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... + +# HighMa Reporter (if Ma>0.3 retected -> report and/or stop simulation) +# TODO: HighMa Reporter + +# Watchdog/Progress-Reporter +# TODO: Progress-Reporter + +# VTK Reporter -> visualization +if args["vtk3D"]: + #TODO: make stuff parameterized: interval, outdir, ... + vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") + + # export obstacle + mask_dict = dict() + if dims ==2: + mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) + else: + mask_dict["mask"] = flow.obstacle_mask.astype(int) + imageToVTK( + path=outdir_data+"/vtk/obstacle_point", + pointData=mask_dict, + ) + imageToVTK( + path=outdir_data+"/vtk/obstacle_cell", + cellData=mask_dict, + ) + + simulation.reporter.append(vtk_reporter) + + +# DRAW CYLINDER-MASK in 2D (xy-plane) +# TODO: port draw_circular_mask function from MP2/Paper to helperCode.py + +################################################## +# PRINT PARAMETERS prior to simulation: +print(f"\nSCRIPT: spacial and temporal dimensions:") +print("domain shape (LU):", flow.resolution) +print("t_target (PU) with", n_steps, "steps (LU):", + round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 2), + "seconds") +print("steps to simulate 1 second PU:", + round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") +print("steps to simulate", t_target, " (t_target, PU) seconds:", + t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), + "steps") + +################################################## +# RUN SIMULATION: +print(f"\n#################################################") +print(f"\nSCRIPT: running simulation for {n_steps} steps...") +print(f"#################################################\n") +t_start = time.time() +mlups = simulation(num_steps=n_steps) +t_end = time.time() +runtime = t_end - t_start + +################################################## +# OUTPUT STATS: +print(f"### STATS ###") +print("MLUPS:", mlups) +print("simulated PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") +print("simulated LU-steps: ", n_steps) +print("runtime (WALLTIME) of simulation(num_steps): ", runtime, "seconds (= ", round(runtime / 60, 2), "minutes )") +print("\n") +print("current VRAM (MB): ", torch.cuda.memory_allocated(context.device) / 1024 / 1024) +print("max. VRAM (MB): ", torch.cuda.max_memory_allocated(context.device) / 1024 / 1024) + +[cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] +print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) + +ram = psutil.virtual_memory() +print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( + round(ram.total / (1024 * 1024), 2)) + " MB") + +### export stats +if not no_data_flag: + output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-STATS ###") + output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\n") + output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.close() + +################################################## +# PLOTTING 2D IMAGE +fig, axes = plt.subplots(1, 2, figsize=(10, 3)) +fig.subplots_adjust(right=0.85) +u = flow.u_pu.cpu().numpy() +print("Max Velocity:", u.max()) +if dims == 2: + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") + im2 = axes[1].imshow(u[0, ...].T, origin="lower") +elif dims == 3: + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") + im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") +cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) +fig.colorbar(im2, cax=cbar_ax) +fig.show() + +################################################## +# PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: + +# COEFF. OF DRAG +try: + try: + drag_coefficient = np.array(DragReporter.out) + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag Cd") + ax.set_ylim((0.5, 1.6)) # change y-limits + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timestep (simulation time / LU)") + except: + print("(!) drag_plotting didn't work...'") + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/drag_coefficient.png") + except: + print("(!) saving 'drag_coefficient.png' failed!") + try: + np.savetxt(outdir_data + "/drag_coefficient.txt", drag_coefficient, + header="stepLU | timePU | Cd FROM str(timestamp)") + except: + print("(!) saving drag_timeseries failed!") + ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, + drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) + if not no_data_flag: + try: + plt.savefig(outdir_data + "/drag_coefficient_adjusted.png") + except: + print("(!) saving drag_coefficient_adjusted.png failed!") + plt.close() +except: + print("(!) analysing drag_coefficient didn't work'") + +# peak finder: try calculating the mean drag coefficient from an integer number of periods, if a clear periodic signal is found +try: + values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] + + peaks_max = find_peaks(values, prominence=((values.max() - values.min()) / 2)) + peaks_min = find_peaks(-values, prominence=((values.max() - values.min()) / 2)) + if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: + peak_number = peaks_max[0].shape[0] + else: + peak_number = peaks_min[0].shape[0] + + if peaks_min[0][0] < peaks_max[0][0]: + first_peak = peaks_min[0][0] + last_peak = peaks_max[0][peak_number - 1] + else: + first_peak = peaks_max[0][0] + last_peak = peaks_min[0][peak_number - 1] + + drag_mean = values[first_peak:last_peak].mean() + drag_mean_simple = values.mean() + + print("Cd, simple mean: ", drag_mean_simple) + print("Cd, peak_finder mean:", drag_mean) + + # PLOT PEAK-FINDING: + drag_stepsLU = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 0] + peak_max_y = values[peaks_max[0]] + peak_max_x = drag_stepsLU[peaks_max[0]] + peak_min_y = values[peaks_min[0]] + peak_min_x = drag_stepsLU[peaks_min[0]] + + plt.plot(drag_stepsLU, values) + plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) + plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) + plt.scatter(drag_stepsLU[first_peak], values[first_peak]) + plt.scatter(drag_stepsLU[last_peak], values[last_peak]) + plt.savefig(outdir_data + "/drag_coefficient_peakfinder.png") + peakfinder = True +except: # if signal is not sinusoidal enough, calculate only simple mean value + print( + "peak-finding didn't work... probably no significant peaks visible (Re<46?), or periodic region not reached (T too small)") + values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] + drag_mean_simple = values.mean() + peakfinder = False + print("Cd, simple mean:", drag_mean_simple) +try: + plt.close() +except: + pass + +# LIFT COEFFICIENT +try: + lift_coefficient = np.array(LiftReporter.out) + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(lift_coefficient[:, 1], lift_coefficient[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Lift Cl") + ax.set_ylim((-1.1, 1.1)) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + if not no_data_flag: + try: + plt.savefig(outdir_data + "/lift_coefficient.png") + except: + print("(!) saving lift_coefficient.png didn't work!") + try: + np.savetxt(outdir_data + "/lift_coefficient.txt", lift_coefficient, + header="stepLU | timePU | Cl FROM str(timestamp)") + except: + print("(!) saving lift_timeline didn't work!") + Cl_min = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].min() + Cl_max = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].max() + print("Cl_peaks: \nmin", Cl_min, "\nmax", Cl_max) + plt.close() +except: + print("(!) analysing lift_coefficient didn't work!") + +############################################# +# plot DRAG and LIFT together: +try: + fig, ax = plt.subplots(layout="constrained") + drag_ax = ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2], color="tab:blue", label="Drag") + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag Cd") + ax.set_ylim((0.5, 1.6)) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + + ax2 = ax.twinx() + lift_ax = ax2.plot(lift_coefficient[:, 1], lift_coefficient[:, 2], color="tab:orange", label="Lift") + ax2.set_ylabel("Coefficient of Lift Cl") + ax2.set_ylim((-1.1, 1.1)) + + fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/dragAndLift_coefficient.png") + except: + print("(!) saving dragAndLift_coefficient.png didn't work!") + plt.close() +except: + print("(!) plotting drag and lift together didn't work!") + +##################################################### +# STROUHAL number: (only makes sense for Re>46 and if periodic state is reached) +try: + ### prototyped fft for frequency detection and calculation of strouhal-number + # ! Drag_frequency is 2* Strouhal-Freq. Lift-freq. is Strouhal-Freq. + + X = np.fft.fft(lift_coefficient[:, 2]) # fft result (amplitudes) + N = len(X) # number of freqs + n = np.arange(N) # freq index + T = N * flow.units.convert_time_to_pu(1) # total time measured (T_PU) + freq = n / T # frequencies (x-axis of spectrum) + + plt.figure() + plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) + plt.xlabel("Freq (Hz)") + plt.ylabel("FFT Amplitude |X(freq)|") + plt.xlim(0, 1) + # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging + plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum + + if not no_data_flag: + plt.savefig(outdir_data + "/fft_Cl.png") + + freq_res = freq[1] - freq[0] # frequency-resolution + X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum + freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude + print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") + # f = Strouhal for St=f*D/U and D=U=1 in PU +except: + print("fft for Strouhal didn't work") + freq_res = 0 + freq_peak = 0 +plt.close() + +# TODO: calc St from f when: f = Strouhal for St=f*D/U and D=U=1 in PU + + +# EXPORT OBSERVABLES: +if not no_data_flag: + ### CUDA-VRAM-summary: + output_file = open(outdir_data + "/" + timestamp + "_GPU_memory_summary.txt", "a") + output_file.write("DATA for " + timestamp + "\n\n") + output_file.write(torch.cuda.memory_summary(context.device)) + output_file.close() + + # TODO: make this optional... + try: + ### list present torch tensors: + output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") + total_bytes = 0 + import gc + + for obj in gc.get_objects(): + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): + output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) + total_bytes = total_bytes + obj.nelement() * obj.element_size() + except: + pass + # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) + output_file.close() + + ### count occurence of tensors in list of tensors: + from collections import Counter + + my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") + data = my_file.read() + my_file.close() + data_into_list = data.split("\n") + c = Counter(data_into_list) + output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") + for k, v in c.items(): + output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) + output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) + output_file.close() + except: + print("(!) counting tensors didn't work!") + +# TODO: cleanup of parms, stats, obs... +# output parameters, stats and observables +if not no_data_flag: + output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") + output_file.write("DATA for " + timestamp) + output_file.write("\n\n### SIM-Parameters ###") + output_file.write("\nRe = " + str(reynolds_number)) + output_file.write("\nn_steps = " + str(n_steps)) + output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") + output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) + if gpd_correction: + output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( + flow.char_length_lu) + " because D/Y is even") + output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) + output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) + if flow.stencil.d == 3: + output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) + output_file.write("\nshape_LU: " + str(flow.resolution)) + output_file.write(("\ntotal_number_of_gridpoints: " + str(flow.rho(flow.f).numel()))) + output_file.write("\nbc_type = " + str()) +# output_file.write("\nlateral_walls = " + str(lateral_walls)) +# output_file.write("\nstencil = " + str(stencil_choice)) +# output_file.write("\ncollision = " + str(collision)) + output_file.write("\n") + output_file.write("\nMa = " + str(mach_number)) + output_file.write("\ntau = " + str(flow.units.relaxation_parameter_lu)) +# output_file.write("\ngrid_reynolds_number (Re_g) = " + str(re_g)) + output_file.write("\n") + output_file.write("\nsetup_diameter_PU = " + str(flow.char_length_lu)) + output_file.write("\nflow_velocity_PU = " + str(flow.char_length_lu)) +# output_file.write("\nu_init = " + str(u_init)) + output_file.write("\nperturb_init = " + str(perturb_init)) + output_file.write("\n") +# output_file.write("\noutput_vtk = " + str(output_vtk)) +# output_file.write("\nvtk_fps = " + str(vtk_fps)) + + output_file.write("\n\n### SIM-STATS ###") + output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\n") + + output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\n") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + + output_file.write("\n\n### OBSERVABLES ###") + output_file.write("\nCoefficient of drag between " + str(round(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_coefficient[int(drag_coefficient.shape[0] - 1), 1], 2)) + " s:") + output_file.write("\nCd_mean, simple = " + str(drag_mean_simple)) + if peakfinder: + output_file.write("\nCd_mean, peak_finder = " + str(drag_mean)) + else: + output_file.write("\nnoPeaksFound") + output_file.write( + "\nCd_min = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min())) + output_file.write( + "\nCd_max = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max())) + output_file.write("\n") + output_file.write("\nCoefficient of lift:") + output_file.write("\nCl_min = " + str(Cl_min)) + output_file.write("\nCl_max = " + str(Cl_max)) + output_file.write("\n") + output_file.write("\nStrouhal number:") + output_file.write("\nSt +- df = " + str(freq_peak) + " +- " + str(freq_res) + " Hz") + output_file.write("\n") + output_file.close() + + +## END OF SCRIPT +print(f"\n♬ THE END ♬") + +# reset stdout (from LOGGER, see above) +sys.stdout = old_stdout + + +################################################################ +# script components below... + +# Process arguments and set parameters +# - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) +# - flow physics: char_velocity, re, ma, density, pressure, length, domain (resolution) +# - solver settings + +# save input parameters to file + +# save this script to outdir for later reference + +# start logger + +# DEFINE AUXILIARY methods (or load from other helper-file) + + +# SETUP SIMULATOR +# - stencil (infer dim from stencil), dtype, equilibrium, +# - calculate obstacle geometry and domain constraints + +# save geometry input to file + +# flow, collision,...classes + +# (opt.) plot stuff (velocity, geometry etc.) + +# initialize REPORTERS + +# RUN SIMULATION + +# print stats + +# export stats + +# plotting and post-processing +# - save data + +# reset LOGGER (!) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py new file mode 100644 index 00000000..616c8951 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -0,0 +1,552 @@ +# THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM THE CYLINDER-SIMULATION + +import numpy as np +import matplotlib.pyplot as plt +from typing import Any, Callable +import traceback +from scipy.signal import find_peaks +from scipy.optimize import curve_fit + +from jedi.inference.gradual.typing import Callable +from sympy.physics.units import frequency + + +# DRAG COEFFICIENT +# LIFT COEFFICIENT +# DRAG+LIFT PNG +# STROUHAL +# U-profile + +####################################################################### + +# PLOT FORCE COEFFICIENTs (data-array [i, t, value], ylabel-string, ylim tupel[float,float], outdir (for png and .txt), save_timeseries +# - plot data with ylim, and ylabel +# - save png to outdir +# - save timeseries txt to outdir +# - plot data with adjusted ylim +# - save png to outdir + +def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], secax_functions_tuple: tuple[Any,Any], filenamebase, save_timeseries = False): + # PLOT + try: + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(data_array[:, 1], data_array[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel(str(ylabel)) + ax.set_ylim(ylim) # change y-limits + secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) + secax.set_xlabel("timestep (simulation time / LU)") + except Exception as e: + print(f"(WARNING!) plotting of {ylabel} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # SAVE PNG + try: + plt.savefig(filenamebase + ".png") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} PLOT to {filenamebase}.png didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # PLOT with ylim ADJUSTED + #TODO: not implemented yet... + + # previous version was: + # ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, + # drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) + # ... not applicable for lift-coefficient + + # SAVE .txt timeseries + #TODO: make this a seperate function... + if save_timeseries: + try: + np.savetxt(filenamebase + ".txt", data_array, + header=f"stepLU | timePU | {ylabel}") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} TIMESERIES to {filenamebase}.txt didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + +# ANALYZE PERIODIC SIGNAL (verbose (plot peak-finding), +# - min, max, mean (simple + window-corrected) (über ganzzahlige Periodenzahl gemittelt) +# - RETURN: dictionary mit: Name, min, max, mean_simple, mean_n_periodic, mean-min, mean-max (v.a. für Cl, nach neuen Skripten) +# - print error if peak-finding didn't work +# - (opt. FFT, periodicity, frequency) +# - FFT +# - print FFT spectrum +# - (!) NEW FREQUENCY detection from MP2/Paper + +def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float,prominence: float, name: str = "periodic timeseries", outdir=None, pu_per_step = None, verbose=False): + values_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 2] + steps_LU_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 0] + mean_periodcorrected = None + max_mean = None + min_mean = None + + try: + peaks_max = find_peaks(values_periodic, prominence=prominence) # drag-prominence: ((values.max() - values.min()) / 2); lift-prominence: (lift1100_1500[:,2].max()+lift1100_1500[:,2].min())/2); oder lift: lift_converged[:,2].max()*0.5 + peaks_min = find_peaks(-values_periodic, prominence=prominence) + if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: + peak_number = peaks_max[0].shape[0] + else: + peak_number = peaks_min[0].shape[0] + if peaks_min[0] < peaks_max[0]: + first_peak = peaks_min[0] + last_peak = peaks_max[peak_number - 1] + else: + first_peak = peaks_max[0] + last_peak = peaks_min[peak_number - 1] + + if verbose: + peak_max_y = values_periodic[peaks_max[0]] + peak_max_x = steps_LU_periodic[peaks_max[0]] + peak_min_y = values_periodic[peaks_min[0]] + peak_min_x = steps_LU_periodic[peaks_min[0]] + + plt.plot(steps_LU_periodic, values_periodic) + plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) + plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) + plt.scatter(steps_LU_periodic[first_peak], values_periodic[first_peak]) + plt.scatter(steps_LU_periodic[last_peak], values_periodic[last_peak]) + + max_mean = values_periodic[peaks_max[0]].mean() + plt.axhline(y=max_mean, color="r", ls="--", lw=0.5) + min_mean = values_periodic[peaks_min[0]].mean() + plt.axhline(y=min_mean, color="r", ls="--", lw=0.5) + if outdir is not None: + plt.savefig(outdir + f"/{name}_peakfinder.png") + + mean_periodcorrected = values_periodic[first_peak:last_peak].mean() + except Exception as e: + print(f"(WARNING!) peak finding for {name} didn't work... See Python Stack Trace below:") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + mean_simple = values_periodic.mean() + min_simple = values_periodic.min() + max_simple = values_periodic.max() + + # sine-fit + def sine_func(xx, a, b, c, d): + return a * np.sin(2 * np.pi * b * xx + c) + d + + frequency_fit = None + try: + coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, p0=(0.7, 0.2, 0.5, 0)) + fig, ax = plt.subplots(constrained_layout=True) + if verbose: + plt.plot(steps_LU_periodic, steps_LU_periodic, steps_LU_periodic, + sine_func(steps_LU_periodic, *coefficients)) + plt.legend(["timeseries", "sine-fit"]) + ax.set_xlabel("physical time / s") + ax.set_ylabel("timeseries") + # ax.set_ylim([-1, 1]) + if outdir is not None: + plt.savefig(outdir + f"/{name}_sine-fit.png") + frequency_fit = coefficients[1] + except Exception as e: + print(f"(WARNING!) sine-fitting for {name} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + # FFT + freq_peak = None + freq_res = None + + if pu_per_step is not None: + try: + X = np.fft.fft(values_periodic) # fft result (amplitudes) + N = len(X) # number of freqs + n = np.arange(N) # freq index + T = N * pu_per_step # total time measured (T_PU) + freq = n / T # frequencies (x-axis of spectrum) + + if verbose: + plt.figure() + plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) + plt.xlabel("Freq (Hz)") + plt.ylabel("FFT Amplitude |X(freq)|") + plt.xlim(0, 1) + # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging + plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum + + if outdir is not None: + plt.savefig(outdir + f"/{name}_fft.png") + + freq_res = freq[1] - freq[0] # frequency-resolution + X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum + freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude + # print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") + # f = Strouhal for St=f*D/U and D=U=1 in PU + except Exception as e: + print(f"(WARNING!) fft for {name} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") + + + return {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, + "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, + "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} + + +# SNIP: MP2 fit sinewave to Cl for better frequency-measurement) +### FIT SINEWAVE to converged Cl to get better St-measurement +# 1. get converged lift-curve +# 2. fit sinewave with starting-freq 0.2 +# 3. store freq + +# PLOT FORCE COEFFICIENTs together! -> im Skript + +# u-PROFILES + +####################################### + +### load reference data from diIlio_path: +# avg_u_start = 0.5 +# diIlio_path = "../literature/DiIlio_2018/" +# +# # import reference data: (data is: first collumn Y/D, second column u_d/u_char) +# # ux +# p1_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_LS1993.csv', delimiter=';') +# p2_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_LS1993.csv', delimiter=';') +# p3_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_LS1993.csv', delimiter=';') +# +# p1_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_KM2000.csv', delimiter=';') +# p2_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_KM2000.csv', delimiter=';') +# p3_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_KM2000.csv', delimiter=';') +# +# p1_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_WR2008.csv', delimiter=';') +# p2_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_WR2008.csv', delimiter=';') +# p3_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_WR2008.csv', delimiter=';') +# +# p1_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_DI2018.csv', delimiter=';') +# p2_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_DI2018.csv', delimiter=';') +# p3_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_DI2018.csv', delimiter=';') +# +# # uy +# p1_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_LS1993.csv', delimiter=';') +# p2_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_LS1993.csv', delimiter=';') +# p3_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_LS1993.csv', delimiter=';') +# +# p1_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_KM2000.csv', delimiter=';') +# p2_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_KM2000.csv', delimiter=';') +# p3_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_KM2000.csv', delimiter=';') +# +# p1_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_WR2008.csv', delimiter=';') +# p2_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_WR2008.csv', delimiter=';') +# p3_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_WR2008.csv', delimiter=';') +# +# p1_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_DI2018.csv', delimiter=';') +# p2_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_DI2018.csv', delimiter=';') +# p3_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_DI2018.csv', delimiter=';') +# +# # uxux +# p1_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_DI2018.csv', delimiter=';') +# p1_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_KM2000.csv', delimiter=';') +# p1_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_R2016.csv', delimiter=';') +# p2_BM1994_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_BM1994.csv', delimiter=';') +# p2_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_DI2018.csv', delimiter=';') +# p2_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_KM2000.csv', delimiter=';') +# p2_LS1993_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_LS1993.csv', delimiter=';') +# p2_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_R2016.csv', delimiter=';') +# p3_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_DI2018.csv', delimiter=';') +# p3_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_KM2000.csv', delimiter=';') +# p3_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_R2016.csv', delimiter=';') +# +# # uyuy +# p1_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos1_DI2018.csv', delimiter=';') +# p1_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos1_R2016.csv', delimiter=';') +# p2_BM1994_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_BM1994.csv', delimiter=';') +# p2_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_DI2018.csv', delimiter=';') +# p2_LS1993_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_LS1993.csv', delimiter=';') +# p2_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_R2016.csv', delimiter=';') +# p3_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos3_DI2018.csv', delimiter=';') +# p3_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos3_R2016.csv', delimiter=';') +# +# # uxuy +# p1_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_BM1994.csv', delimiter=';') +# p1_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_DI2018.csv', delimiter=';') +# p1_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_R2016.csv', delimiter=';') +# p2_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_BM1994.csv', delimiter=';') +# p2_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_DI2018.csv', delimiter=';') +# p2_LS1993_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_LS1993.csv', delimiter=';') +# p2_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_R2016.csv', delimiter=';') +# p3_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_BM1994.csv', delimiter=';') +# p3_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_DI2018.csv', delimiter=';') +# p3_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') +# +# # plot 2x (CO) FIGURES: 5 AvgProfiles (for 10 GPD each) + 1 Legend +# +# gpds = np.arange(24,44,2) +# bc = "ibb1" +# +# color_list = list(matplotlib.colors.TABLEAU_COLORS.keys()) +# colormap = plt.cm.coolwarm#viridis # cmaps: viridis, plasma, inferno, magma, cividis, (tab10) +# color_index_list = np.linspace(0,1,10) +# x_tick_list = np.arange(-3,3+1,1) +# alpha_value = 0.7 +# +# #paths_dict[bc+"_"+co+"_GPD"+str(gpd)] # contains full path +# with matplotlib.rc_context({'lines.linewidth': 0.5,'font.size': 5}): +# for co in cos: +# fig, axs = plt.subplots(3,2, sharex=True, figsize=(3.4876, 3.4876)) # 5 AvgProfiles + 1 Legend in last +# +# if co == "reg": +# co_label = "REG" +# elif co == "kbc": +# co_label = "KBC" +# legend_elements=[] +# color_index = 0 +# for gpd in gpds: +# # LOAD DATA FROM SIM +# avg_u1 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_t-avg.npy") +# avg_u2 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_t-avg.npy") +# avg_u3 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_t-avg.npy") +# +# avg_u1_x = avg_u1[0] # u_x component over y at pos 1 +# avg_u2_x = avg_u2[0] # u_x component over y at pos 2 +# avg_u3_x = avg_u3[0] # u_x component over y at pos 3 +# +# avg_u1_y = avg_u1[1] # u_y component over y at pos 1 +# avg_u2_y = avg_u2[1] # u_y component over y at pos 2 +# avg_u3_y = avg_u3[1] # u_y component over y at pos 3 +# +# y_in_D = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_YinD.npy") +# +# u1_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_x.npy") # contains y_in_D (index 0) and data (index 1) +# u2_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_x.npy") +# u3_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_x.npy") +# u1_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_y.npy") +# u2_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_y.npy") +# u3_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_y.npy") +# +# u1_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReShearStress.npy") +# u2_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReShearStress.npy") +# u3_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReShearStress.npy") +# +# # PLOT DATA IN FIGURE +# axs[0,0].plot(y_in_D, avg_u1_x, y_in_D, avg_u2_x - 1, y_in_D, avg_u3_x - 2, color=colormap(color_index_list[color_index])) +# axs[0,1].plot(y_in_D, avg_u1_y, y_in_D, avg_u2_y - 1, y_in_D, avg_u3_y - 2, color=colormap(color_index_list[color_index])) +# axs[1,0].plot(u1_diff_sq_mean_x[0],u1_diff_sq_mean_x[1], u2_diff_sq_mean_x[0], u2_diff_sq_mean_x[1]-0.5, u3_diff_sq_mean_x[0],u3_diff_sq_mean_x[1]-1, color=colormap(color_index_list[color_index])) +# axs[1,1].plot(u1_diff_sq_mean_y[0],u1_diff_sq_mean_y[1], u2_diff_sq_mean_y[0], u2_diff_sq_mean_y[1]-0.5, u3_diff_sq_mean_y[0],u3_diff_sq_mean_y[1]-1, color=colormap(color_index_list[color_index])) +# axs[2,0].plot(u1_diff_xy_mean[0], u1_diff_xy_mean[1], u2_diff_xy_mean[0], u2_diff_xy_mean[1]-0.5, u3_diff_xy_mean[0], u3_diff_xy_mean[1]-1, color=colormap(color_index_list[color_index])) +# +# # set invisible point for legend entry: +# axs[2,1].plot(-1000,-1000, color=colormap(color_index_list[color_index]), +# #label="GPD "+str(gpd)) +# label=str(gpd)) +# legend_elements.append(matplotlib.lines.Line2D([0],[0], color=colormap(color_index_list[color_index]), lw=1, label=str(gpd))) +# color_index = color_index+1 +# +# for ax in axs.flat: +# ax.set_xticks(ticks=x_tick_list) +# +# axs[0,0].set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") +# axs[0,0].set_ylim([-2.5, +2]) +# axs[0,0].set_xlim([-3, 3]) +# axs[0,0].set_yticks(ticks=[-2,-1,0,1,2]) +# axs[0,0].set_yticks(ticks=[-2.5,-1.5,-0.5,0.5,1.5,2.5], minor=True) +# # axs[0,0].text(-2.8, 1.3, "X/D = 1.06", fontsize=4.5) +# # axs[0,0].text(-2.8, 0.3, "X/D = 1.54", fontsize=4.5) +# # axs[0,0].text(-2.8, -0.7, "X/D = 1.54", fontsize=4.5) +# +# axs[0,1].set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") +# axs[0,1].set_ylim([-2.5, +0.5]) +# axs[0,1].set_xlim([-3, 3]) +# axs[0,1].set_yticks(ticks=[-2,-1,0]) +# axs[0,1].set_yticks(ticks=[-2.5,-1.5,-0.5, 0.5], minor=True) +# +# axs[1,0].set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") +# axs[1,0].set_ylim([-1.2, 0.3]) +# axs[1,0].set_xlim([-3, 3]) +# axs[1,0].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[1,1].set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") +# axs[1,1].set_ylim([-1.2, 0.3]) +# axs[1,1].set_xlim([-3, 3]) +# axs[1,1].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[2,0].set_xlabel("y/D") +# axs[2,0].set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") +# axs[2,0].set_ylim([-1.2, 0.2]) +# axs[2,0].set_xlim([-3, 3]) +# axs[2,0].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[2,1].set_xlabel("y/D") +# axs[2,1].set_ylim([0, 1]) +# axs[2,1].tick_params(left=False, labelleft=False) +# axs[2,1].legend(handles=legend_elements,edgecolor="white",ncol=2, loc="lower left") +# axs[2,1].text(-2.7,0.85,"Collision operator: "+co_label) +# axs[2,1].text(-2.7,0.63,"Resolution in GPD:") +# +# plt.savefig("../plots/3D_Re3900_AvgProfile_GPD_"+co+".png") +# +# # ax.set_xlabel("y/D") +# # ax.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") +# # ax.set_ylim([-2.5, +2]) +# # ax.set_xlim([-3, 3]) +# +# # ax.set_xlabel("y/D") +# # ax.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") +# # ax.set_ylim([-2.5, +1.5]) +# # ax.set_xlim([-3, 3]) +# +# # ax.set_xlabel("y/D") +# # ax.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") +# # ax.set_ylim([-1.2, 0.8]) +# # ax.set_xlim([-3, 3]) +# +# # ax.set_xlabel("y/D") +# # ax.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") +# # ax.set_ylim([-1.2, 0.8]) +# # ax.set_xlim([-3, 3]) +# +# # ax.set_xlabel("y/D") +# # ax.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") +# # ax.set_ylim([-1.2, 0.8]) +# # ax.set_xlim([-3, 3]) +# +# +# +# ### highest Res for both CO against literature +# gpd=42 +# with matplotlib.rc_context({'lines.linewidth': 0.5,'font.size': 5, 'lines.markersize': 1.2, 'lines.markeredgewidth': 0.3}): +# for co in cos: +# fig, axs = plt.subplots(3,2, sharex=True, figsize=(3.4876, 3.4876)) # 5 AvgProfiles + 1 Legend in last +# +# if co == "reg": +# co_label = "REG" +# elif co == "kbc": +# co_label = "KBC" +# +# # LOAD DATA FROM SIM +# avg_u1 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_t-avg.npy") +# avg_u2 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_t-avg.npy") +# avg_u3 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_t-avg.npy") +# +# avg_u1_x = avg_u1[0] # u_x component over y at pos 1 +# avg_u2_x = avg_u2[0] # u_x component over y at pos 2 +# avg_u3_x = avg_u3[0] # u_x component over y at pos 3 +# +# avg_u1_y = avg_u1[1] # u_y component over y at pos 1 +# avg_u2_y = avg_u2[1] # u_y component over y at pos 2 +# avg_u3_y = avg_u3[1] # u_y component over y at pos 3 +# +# y_in_D = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_YinD.npy") +# +# u1_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_x.npy") # contains y_in_D (index 0) and data (index 1) +# u2_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_x.npy") +# u3_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_x.npy") +# u1_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_y.npy") +# u2_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_y.npy") +# u3_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_y.npy") +# +# u1_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReShearStress.npy") +# u2_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReShearStress.npy") +# u3_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReShearStress.npy") +# +# # PLOT DATA IN FIGURE +# my_data = axs[0,0].plot(y_in_D, avg_u1_x, y_in_D, avg_u2_x - 1, y_in_D, avg_u3_x - 2, color="red", label="present") +# ref_LS = axs[0,0].plot(p1_LS1993_ux[:, 0], p1_LS1993_ux[:, 1], p2_LS1993_ux[:, 0], p2_LS1993_ux[:, 1], p3_LS1993_ux[:, 0], +# p3_LS1993_ux[:, 1], marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") +# ref_KM = axs[0,0].plot(p1_KM2000_ux[:, 0], p1_KM2000_ux[:, 1], p2_KM2000_ux[:, 0], p2_KM2000_ux[:, 1], p3_KM2000_ux[:, 0], +# p3_KM2000_ux[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") +# ref_WR = axs[0,0].plot(p1_WR2008_ux[:, 0], p1_WR2008_ux[:, 1], p2_WR2008_ux[:, 0], p2_WR2008_ux[:, 1], p3_WR2008_ux[:, 0], +# p3_WR2008_ux[:, 1], ls="dashdot", marker="", color="k", label="Wissink & Rodi (2008)") +# ref_DI = axs[0,0].plot(p1_DI2018_ux[:, 0], p1_DI2018_ux[:, 1], p2_DI2018_ux[:, 0], p2_DI2018_ux[:, 1], p3_DI2018_ux[:, 0], +# p3_DI2018_ux[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") +# +# axs[0,1].plot(y_in_D, avg_u1_y, y_in_D, avg_u2_y - 1, y_in_D, avg_u3_y - 2, color="red", label="present") +# ref_LS = axs[0,1].plot(p1_LS1993_uy[:, 0], p1_LS1993_uy[:, 1], p2_LS1993_uy[:, 0], p2_LS1993_uy[:, 1], p3_LS1993_uy[:, 0], +# p3_LS1993_uy[:, 1], ls="",marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") +# ref_KM = axs[0,1].plot(p1_KM2000_uy[:, 0], p1_KM2000_uy[:, 1], p2_KM2000_uy[:, 0], p2_KM2000_uy[:, 1], p3_KM2000_uy[:, 0], +# p3_KM2000_uy[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") +# ref_WR = axs[0,1].plot(p1_WR2008_uy[:, 0], p1_WR2008_uy[:, 1], p2_WR2008_uy[:, 0], p2_WR2008_uy[:, 1], p3_WR2008_uy[:, 0], +# p3_WR2008_uy[:, 1], ls="dashdot", marker="", color="k", label="Wissink & Rodi (2008)") +# ref_DI = axs[0,1].plot(p1_DI2018_uy[:, 0], p1_DI2018_uy[:, 1], p2_DI2018_uy[:, 0], p2_DI2018_uy[:, 1], p3_DI2018_uy[:, 0], +# p3_DI2018_uy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") +# +# axs[1,0].plot(u1_diff_sq_mean_x[0],u1_diff_sq_mean_x[1], u2_diff_sq_mean_x[0], u2_diff_sq_mean_x[1]-0.5, u3_diff_sq_mean_x[0],u3_diff_sq_mean_x[1]-1, color="red", label="present") +# ref_LS = axs[1,0].plot(p2_LS1993_uxux[:, 0], p2_LS1993_uxux[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") +# ref_R = axs[1,0].plot(p1_R2016_uxux[:, 0], p1_R2016_uxux[:, 1], p3_R2016_uxux[:, 0], p3_R2016_uxux[:, 1], +# p3_R2016_uxux[:, 0], p3_R2016_uxux[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") +# ref_KM = axs[1,0].plot(p1_KM2000_uxux[:, 0], p1_KM2000_uxux[:, 1], p2_KM2000_uxux[:, 0], p2_KM2000_uxux[:, 1], +# p3_KM2000_uxux[:, 0], p3_KM2000_uxux[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") +# ref_BM = axs[1,0].plot(p2_BM1994_uxux[:, 0], p2_BM1994_uxux[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") +# ref_DI = axs[1,0].plot(p1_DI2018_uxux[:, 0], p1_DI2018_uxux[:, 1], p2_DI2018_uxux[:, 0], p2_DI2018_uxux[:, 1], +# p3_DI2018_uxux[:, 0], p3_DI2018_uxux[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") +# +# axs[1,1].plot(u1_diff_sq_mean_y[0],u1_diff_sq_mean_y[1], u2_diff_sq_mean_y[0], u2_diff_sq_mean_y[1]-0.5, u3_diff_sq_mean_y[0],u3_diff_sq_mean_y[1]-1, color="red", label="present") +# ref_BM = axs[1,1].plot(p2_BM1994_uyuy[:, 0], p2_BM1994_uyuy[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") +# ref_LS = axs[1,1].plot(p2_LS1993_uyuy[:, 0], p2_LS1993_uyuy[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") +# ref_R = axs[1,1].plot(p1_R2016_uyuy[:, 0], p1_R2016_uyuy[:, 1], p3_R2016_uyuy[:, 0], p3_R2016_uyuy[:, 1], +# p3_R2016_uyuy[:, 0], p3_R2016_uyuy[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") +# ref_DI = axs[1,1].plot(p1_DI2018_uyuy[:, 0], p1_DI2018_uyuy[:, 1], p2_DI2018_uyuy[:, 0], p2_DI2018_uyuy[:, 1], +# p3_DI2018_uyuy[:, 0], p3_DI2018_uyuy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") +# +# axs[2,0].plot(u1_diff_xy_mean[0], u1_diff_xy_mean[1], u2_diff_xy_mean[0], u2_diff_xy_mean[1]-0.5, u3_diff_xy_mean[0], u3_diff_xy_mean[1]-1, color="red", label="present") +# ref_BM = axs[2,0].plot(p2_BM1994_uxuy[:, 0], p2_BM1994_uxuy[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") +# ref_LS = axs[2,0].plot(p2_LS1993_uxuy[:, 0], p2_LS1993_uxuy[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") +# ref_R = axs[2,0].plot(p1_R2016_uxuy[:, 0], p1_R2016_uxuy[:, 1], p3_R2016_uxuy[:, 0], p3_R2016_uxuy[:, 1], +# p3_R2016_uxuy[:, 0], p3_R2016_uxuy[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") +# ref_DI = axs[2,0].plot(p1_DI2018_uxuy[:, 0], p1_DI2018_uxuy[:, 1], p2_DI2018_uxuy[:, 0], p2_DI2018_uxuy[:, 1], +# p3_DI2018_uxuy[:, 0], p3_DI2018_uxuy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") +# +# # set invisible point for legend entry: +# axs[2,1].legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0], ref_R[0], ref_BM[0]],edgecolor="white", loc="lower center", fontsize=4.5) +# color_index = color_index+1 +# +# for ax in axs.flat: +# ax.set_xticks(ticks=x_tick_list) +# +# axs[0,0].set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") +# axs[0,0].set_ylim([-2.5, +2]) +# axs[0,0].set_xlim([-3, 3]) +# axs[0,0].set_yticks(ticks=[-2,-1,0,1,2]) +# axs[0,0].set_yticks(ticks=[-2.5,-1.5,-0.5,0.5,1.5,2.5], minor=True) +# # axs[0,0].text(-2.8, 1.3, "X/D = 1.06", fontsize=4.5) +# # axs[0,0].text(-2.8, 0.3, "X/D = 1.54", fontsize=4.5) +# # axs[0,0].text(-2.8, -0.7, "X/D = 1.54", fontsize=4.5) +# +# axs[0,1].set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") +# axs[0,1].set_ylim([-2.5, +0.5]) +# axs[0,1].set_xlim([-3, 3]) +# axs[0,1].set_yticks(ticks=[-2,-1,0]) +# axs[0,1].set_yticks(ticks=[-2.5,-1.5,-0.5, 0.5], minor=True) +# +# axs[1,0].set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") +# axs[1,0].set_ylim([-1.2, 0.3]) +# axs[1,0].set_xlim([-3, 3]) +# axs[1,0].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[1,1].set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") +# axs[1,1].set_ylim([-1.2, 0.3]) +# axs[1,1].set_xlim([-3, 3]) +# axs[1,1].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[2,0].set_xlabel("y/D") +# axs[2,0].set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") +# axs[2,0].set_ylim([-1.2, 0.2]) +# axs[2,0].set_xlim([-3, 3]) +# axs[2,0].set_yticks(ticks=[-1,-0.5,0]) +# +# axs[2,1].set_xlabel("y/D") +# axs[2,1].tick_params(left=False, labelleft=False) +# axs[2,1].set_ylim([0, 1]) +# #axs[2,1].text(-0.8,0.8,co_label, fontsize=11) +# axs[2,1].text(-2.7,0.85,"Collision operator: "+co_label) +# +# plt.savefig("../plots/3D_Re3900_AvgProfile_vsLit_"+co+".png") \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py index d616cee3..29ee1570 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py @@ -2,8 +2,12 @@ import torch import numpy as np +import os -from lettuce import Reporter, Simulation +from typing import List + +from lettuce._simulation import Reporter, Simulation +from lettuce.ext._reporter import VTKReporter from ebb_simulation import EbbSimulation class NaNReporter(Reporter): @@ -34,6 +38,7 @@ def __call__(self, simulation: 'Simulation'): f'{simulation.flow.i}. See log in {self.outdir} for ' f'details!') # telling simulation to abort simulation by setting i too high + #TODO: works only with BREAKABLE SIMULATION with a while-loop... simulation.flow.i = int(simulation.flow.i + 1e10) # the 1e10 is "a lot", but in a very unlikely case of a very long simulation, # where num_steps >=1e10, this would not work... diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py new file mode 100644 index 00000000..6430b0cd --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py @@ -0,0 +1 @@ +#TODO: this is work in progress in PR #248 (NaNReporter and HighMa reporter) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py new file mode 100644 index 00000000..efb0f25f --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py @@ -0,0 +1,2 @@ +# TODO: this is work in progress in PR #283 with branch 275-progress-reporter + From 666ea19968acf68485795b25961a709bfaef1ba7 Mon Sep 17 00:00:00 2001 From: mbille Date: Tue, 9 Dec 2025 17:28:44 +0100 Subject: [PATCH 14/21] corrected drag- and lift-plotting, peak-finding and plotting of peak-finding --- .../00_run_parameterized_project.ipynb | 4 +- .../01a_script_cylinder_lowRe.py | 105 +++++---- .../data_processing_and_plotting.py | 217 ++++++++++++++++-- 3 files changed, 264 insertions(+), 62 deletions(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index ec6213c5..6b751532 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -24,9 +24,7 @@ "cell_type": "code", "source": [ "%matplotlib inline\n", - "%run 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 10 --n_steps 1000\n", - "\n", - "pass" + "%run 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 20 --n_steps 10000 --bbbc_type fwbb --reynolds_number 200 --domain_height_y_in_d 3 --domain_width_z_in_d 3 --collision bgk --name 3Dsim_10000 --mach_number 0.1\n" ], "id": "cdd448d543dd26be", "outputs": [ diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 15d6c0e4..3e5b338b 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -13,10 +13,8 @@ import os import psutil import shutil -import traceback import matplotlib.pyplot as plt -from scipy.signal import find_peaks import time import datetime @@ -33,14 +31,8 @@ # AUX. CODE from helperCode import Logger -from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries +from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask -# import pickle -# from copy import deepcopy -# from timeit import default_timer as timer -# from collections import Counter - -#? warnings.simplefilter("ignore") # todo: is this needed? #################### # ARGUMENT PARSING: this script is supposed to be called with arguments, detailing all simulation- and system-parameters @@ -73,11 +65,6 @@ parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") -#TODO additional physics and flow-parameters: -# - char_density (PU house) -# - char_pressure -# - vicsosity PU (house, - # LBM solver settings parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") @@ -117,7 +104,7 @@ # - print 2D-slice with mask(s) # - parameters SIMULATED (before sim()) # - 2D slice last frame -# - output condensed observables (drag, lift, strouhal) to file... @MP2 +# - output condensed observables (drag, lift, Strouhal) to file... # - stats and performance (end) @@ -149,12 +136,17 @@ os.makedirs(outdir+"/"+sim_id) # create output dir print(f"outdir/simID = {outdir}/{sim_id}") -if outdir_data is None: # save data to regular outdir, if data-dir is not specified + +# save data to regular outdir, if data-dir is not specified +if outdir_data is None: outdir_data = outdir -outdir = outdir+"/"+sim_id # adding individal sim-ID to outdir path to get individual DIR per simulation + +# adding individal sim-ID to outdir path to get individual DIR per simulation +outdir = outdir+"/"+sim_id outdir_data = outdir_data+"/"+sim_id if not os.path.exists(outdir_data): - os.makedirs(outdir_data) # create output dir for large/many files, if specified + # create output dir for large/many files, if specified + os.makedirs(outdir_data) print(f"outdir_DATA/simID = {outdir}/{sim_id}") # save input arguments/parameters to file in outdir: @@ -182,7 +174,8 @@ # calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients #TODO: take absolute PU-time values here: # - periodic_start_Re100_PU ~= 75-100 seconds -# - periodic_start hängt von Einschwingzeit ab! Diese hängt von der Domänengröße ab! +# - the start of the periodic region depends on the time the flow needs +# to sattle.. This time is also dependent on the domain size! if args["reynolds_number"] > 1000: periodic_start = 0.4 else: @@ -195,7 +188,8 @@ domain_height_y_in_d = args["domain_height_y_in_d"] if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : - domain_length_x_in_d = 2 * domain_height_y_in_d # D/X = domain length in X- / flow-direction + # D/X = domain length in X- / flow-direction + domain_length_x_in_d = 2 * domain_height_y_in_d else: domain_length_x_in_d = args["domain_length_x_in_d"] @@ -203,9 +197,11 @@ dims = 2 else: # will be 3D dims = 3 - if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node - domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node - print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_z_in_d to 1 lattice node") + if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : + # if less than 1 lattice node... ->set to 1 lattice node + domain_width_z_in_d = 1/args["char_length_lu"] + print("(!) domain_width_z_in_d is less than 1 lattice node: " + "setting domain_width_z_in_d to 1 lattice node") else: domain_width_z_in_d = args["domain_width_z_in_d"] @@ -218,8 +214,10 @@ gpd_correction = True # gpd will be corrected gpd_setup = args["char_length_lu"] # store old gpd for output char_length_lu = int(gpd_setup / 2) * 2 # make gpd even - print("(!) domain_height_y_ind_d (DpY) is even, gridpoints per diameter (GPD, char_length_lu) will be set to" + str( - char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) to enable use of odd GPD (char_length_lu)!") + print("(!) domain_height_y_ind_d (DpY) is even, " + "gridpoints per diameter (GPD, char_length_lu) will be set to" + + str(char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) " + "to enable use of odd GPD (char_length_lu)!") else: char_length_lu = args["char_length_lu"] @@ -233,7 +231,8 @@ # calculate lu-domain-resolution, total number of gridpoints and check correct stencil if dims == 2: - resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] + resolution = [int(domain_length_x_in_d * char_length_lu), + int(domain_height_y_in_d * char_length_lu)] number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d if args["stencil"] == "D2Q9": stencil = lt.D2Q9() @@ -241,7 +240,9 @@ print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") stencil= lt.D2Q9() else: - resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] + resolution = [int(domain_length_x_in_d * char_length_lu), + int(domain_height_y_in_d * char_length_lu), + int(domain_width_z_in_d * char_length_lu)] number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d if args["stencil"] == "D3Q15": stencil = lt.D3Q15() @@ -285,10 +286,10 @@ context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) print("-> initializing flow...") -flow = ObstacleCylinder(context=context, resolution=resolution, +flow = ObstacleCylinder(context=context, resolution=resolution, stencil=stencil, reynolds_number=reynolds_number, mach_number=mach_number, char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) + bc_type=str(args["bbbc_type"]), calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) print("-> initializing collision operator...") collision_operator = None @@ -311,9 +312,11 @@ # DRAG and LIFT Force Coefficients and respective reporters: cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d + DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) simulation.reporter.append(DragReporter) + LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) simulation.reporter.append(LiftReporter) @@ -352,7 +355,7 @@ # DRAW CYLINDER-MASK in 2D (xy-plane) -# TODO: port draw_circular_mask function from MP2/Paper to helperCode.py +draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) ################################################## # PRINT PARAMETERS prior to simulation: @@ -432,28 +435,45 @@ # DRAG drag_timeseries = np.array(np.array(DragReporter.out)) -plot_force_coefficient(drag_timeseries, ylabel="Coefficient of Drag $C_{D}$", ylim=(0.5, 1.6), - secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/drag") -drag_prominence = ((abs(drag_timeseries.max()) - abs(drag_timeseries.min())) / 2) -drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, prominence=drag_prominence,name="drag", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) -#TODO: check if prominence with (asb(max) - abs(min))/2 works for both lift and drag +plot_force_coefficient(drag_timeseries, ylabel="Coefficient of Drag $C_{D}$", + ylim=(0.5, 1.6), + secax_functions_tuple=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu), + filenamebase=outdir_data+"/drag", periodic_start=periodic_start, adjust_ylim=True) +#OLD: drag_prominence = ((abs(drag_timeseries[2].max()) - abs(drag_timeseries[2].min())) * 0.5) +drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, + name="drag", verbose=True, + pu_per_step=flow.units.convert_time_to_pu(1), + outdir=outdir_data) + print(f"DRAG STATS:") #\n{drag_stats}") for key, value in drag_stats.items(): print(f"{key:<20} = {str(value)}") -# STATS ARE: {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, -# "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, -# "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} +# STATS ARE: {"mean_simple": mean_simple, +# "mean_periodcorrected": mean_periodcorrected, +# "min_simple": min_simple, +# "max_simple": max_simple, +# "max_mean": max_mean, +# "min_mean": min_mean, +# "frequency_fit": frequency_fit, +# "frequency_fft": freq_peak, +# "fft_resolution": freq_res} print("\n") # LIFT lift_timeseries = np.array(np.array(LiftReporter.out)) plot_force_coefficient(lift_timeseries, ylabel="Coefficient of Lift$C_{L}$", ylim=(-1.1, 1.1), - secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), filenamebase=outdir_data+"/lift") -lift_prominence = ((abs(lift_timeseries.max()) - abs(lift_timeseries.min())) / 2) -lift_stats = analyze_periodic_timeseries(lift_timeseries, periodic_start_rel=0.5, prominence=lift_prominence,name="lift", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), outdir=outdir_data) -print(f"LIFT STATS:") #\n {lift_stats}") + secax_functions_tuple=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu), + filenamebase=outdir_data+"/lift", periodic_start=periodic_start) +#OLD: lift_prominence = ((abs(lift_timeseries[2].max()) - abs(lift_timeseries[2].min())) * 0.5) +lift_stats = analyze_periodic_timeseries(lift_timeseries, periodic_start_rel=0.5, + name="lift", + verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), + outdir=outdir_data) +print(f"LIFT STATS:") for key, value in lift_stats.items(): print(f"{key:<20} = {str(value)}") @@ -597,7 +617,6 @@ # DEFINE AUXILIARY methods (or load from other helper-file) - # SETUP SIMULATOR # - stencil (infer dim from stencil), dtype, equilibrium, # - calculate obstacle geometry and domain constraints diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py index 616c8951..c890f7c9 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -2,13 +2,12 @@ import numpy as np import matplotlib.pyplot as plt -from typing import Any, Callable +from typing import Any import traceback from scipy.signal import find_peaks from scipy.optimize import curve_fit -from jedi.inference.gradual.typing import Callable -from sympy.physics.units import frequency +from collections import Counter # DRAG COEFFICIENT @@ -26,7 +25,7 @@ # - plot data with adjusted ylim # - save png to outdir -def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], secax_functions_tuple: tuple[Any,Any], filenamebase, save_timeseries = False): +def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], secax_functions_tuple: tuple[Any,Any], filenamebase, save_timeseries = False, periodic_start=0, adjust_ylim=False): # PLOT try: fig, ax = plt.subplots(constrained_layout=True) @@ -54,12 +53,33 @@ def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[floa print("--------------------------\n") # PLOT with ylim ADJUSTED - #TODO: not implemented yet... + if adjust_ylim: + try: + fig, ax = plt.subplots(constrained_layout=True) + ax.plot(data_array[:, 1], data_array[:, 2]) + ax.set_xlabel("physical time / s") + ax.set_ylabel(str(ylabel)) + ylim_2 = ( data_array[int(data_array.shape[0] * periodic_start - 1):,2].min() * 0.5, + data_array[int(data_array.shape[0] * periodic_start - 1):,2].max() * 1.2) + ax.set_ylim(ylim_2) # change y-limits + secax = ax.secondary_xaxis('top', functions=secax_functions_tuple) + secax.set_xlabel("timestep (simulation time / LU)") + except Exception as e: + print(f"(WARNING!) plotting of {ylabel} didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") - # previous version was: - # ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, - # drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) - # ... not applicable for lift-coefficient + # SAVE PNG + try: + plt.savefig(filenamebase + "_ylimadjusted.png") + except Exception as e: + print(f"(WARNING!) saving of {ylabel} PLOT to {filenamebase}_ylimadjusted.png didn't work...") + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") # SAVE .txt timeseries #TODO: make this a seperate function... @@ -83,26 +103,34 @@ def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[floa # - print FFT spectrum # - (!) NEW FREQUENCY detection from MP2/Paper -def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float,prominence: float, name: str = "periodic timeseries", outdir=None, pu_per_step = None, verbose=False): +def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float,prominence: float = None, name: str = "periodic timeseries", outdir=None, pu_per_step = None, verbose=False): values_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 2] steps_LU_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 0] mean_periodcorrected = None max_mean = None min_mean = None + if prominence is None: + prominence = ((values_periodic.max() - values_periodic.min()) * 0.5) + #The prominence value is up for debate; + # The value given here only reliably catches all peaks, + # if the signal is simple and periodically converged + try: peaks_max = find_peaks(values_periodic, prominence=prominence) # drag-prominence: ((values.max() - values.min()) / 2); lift-prominence: (lift1100_1500[:,2].max()+lift1100_1500[:,2].min())/2); oder lift: lift_converged[:,2].max()*0.5 peaks_min = find_peaks(-values_periodic, prominence=prominence) + if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: peak_number = peaks_max[0].shape[0] else: peak_number = peaks_min[0].shape[0] - if peaks_min[0] < peaks_max[0]: - first_peak = peaks_min[0] - last_peak = peaks_max[peak_number - 1] + + if peaks_min[0][0] < peaks_max[0][0]: + first_peak = peaks_min[0][0] + last_peak = peaks_max[0][peak_number - 1] else: - first_peak = peaks_max[0] - last_peak = peaks_min[peak_number - 1] + first_peak = peaks_max[0][0] + last_peak = peaks_min[0][peak_number - 1] if verbose: peak_max_y = values_periodic[peaks_max[0]] @@ -110,6 +138,7 @@ def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: floa peak_min_y = values_periodic[peaks_min[0]] peak_min_x = steps_LU_periodic[peaks_min[0]] + plt.subplots(constrained_layout=True) plt.plot(steps_LU_periodic, values_periodic) plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) @@ -144,7 +173,7 @@ def sine_func(xx, a, b, c, d): coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, p0=(0.7, 0.2, 0.5, 0)) fig, ax = plt.subplots(constrained_layout=True) if verbose: - plt.plot(steps_LU_periodic, steps_LU_periodic, steps_LU_periodic, + plt.plot(steps_LU_periodic, values_periodic, steps_LU_periodic, sine_func(steps_LU_periodic, *coefficients)) plt.legend(["timeseries", "sine-fit"]) ax.set_xlabel("physical time / s") @@ -202,6 +231,162 @@ def sine_func(xx, a, b, c, d): "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} +def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, + filebase=".", print_data=False): + ### calculate and export 2D obstacle_mask as .png + grid_x = gridpoints_per_diameter + 2 + if output_data: + output_file = open(filebase + "/obstacle_mask_info.txt", "a") + output_file.write("GPD = " + str(gridpoints_per_diameter) + "\n") + if print_data: + print("GPD = " + str(gridpoints_per_diameter)) + # define radius and position for a symmetrical circular Cylinder-Obstacle + radius_LU = 0.5 * gridpoints_per_diameter + y_pos_LU = 0.5 * grid_x + 0.5 + x_pos_LU = y_pos_LU + + # get x,y,z meshgrid of the domain (LU) + xyz = tuple(np.linspace(1, n, n) for n in (grid_x, + grid_x)) # tupel of list indizes (1-n (non zero-based!)) + xLU, yLU = np.meshgrid(*xyz, + indexing='ij') # meshgrid of x- and y- indizes -> * unpacks the tuple to be two values and now a tuple + + # define cylinder (LU) (circle) + obstacle_mask_for_visualization = np.sqrt( + (xLU - x_pos_LU) ** 2 + (yLU - y_pos_LU) ** 2) < radius_LU + + nx, ny = obstacle_mask_for_visualization.shape # number of x- and y-nodes (Skalar) + + rand_mask = np.zeros((nx, ny), + dtype=bool) # for all the solid nodes, neighboring fluid nodes + rand_mask_f = np.zeros((flow.stencil.q, nx, ny), + dtype=bool) # same, but including q-dimension + rand_xq = [] # list of all x-values (incl. q-multiplicity) + rand_yq = [] # list of all y-values (incl. q-multiplicity) + + a, b = np.where( + obstacle_mask_for_visualization) # np.array: list of (a) x-coordinates und (b) y-coordinates of the obstacle_mask_for_visualization + # ...to iterate over all boudnary/object/wall nodes + for p in range(0, + len(a)): # for all True-ndoes in obstacle_mask_for_visualization + for i in range(0, + flow.stencil.q): # for all stencil directions c_i (lattice.stencil.e) + try: # try in case the neighboring cell does not exist (an f pointing out of the simulation domain) + if not obstacle_mask_for_visualization[ + a[p] + flow.stencil.e[i][0], b[p] + flow.stencil.e[ + i][1]]: + # if neighbor in +(e_x, e_y; e is c_i) is False, we are on the object-surface (self True with neighbor False) + rand_mask[a[p], b[p]] = 1 + rand_mask_f[flow.stencil.opposite[i], a[p], b[p]] = 1 + rand_xq.append(a[p]) + rand_yq.append(b[p]) + except IndexError: + pass # just ignore this iteration since there is no neighbor there + rand_x, rand_y = np.where(rand_mask) # list of all surface coordinates + x_pos = sum(rand_x) / len(rand_x) # x-coordinate of circle center + y_pos = sum(rand_y) / len(rand_y) # y-coordinate of circle center + + # calculate all radii and r_max and r_min + r_max = 0 + r_min = gridpoints_per_diameter + radii = np.zeros_like(rand_x, + dtype=float) # list of all redii (without q-dimension) in LU + for p in range(0, len(rand_x)): # for all nodes + radii[p] = np.sqrt( + (rand_x[p] - x_pos) ** 2 + (rand_y[ + p] - y_pos) ** 2) # calculate distance to circle center + if radii[p] > r_max: + r_max = radii[p] + if radii[p] < r_min: + r_min = radii[p] + + # calculate all radii (with q-multiplicity) + radii_q = np.zeros_like(rand_xq, dtype=float) + for p in range(0, len(rand_xq)): + radii_q[p] = np.sqrt( + (rand_xq[p] - x_pos) ** 2 + (rand_yq[p] - y_pos) ** 2) + + ### all relative radii in relation to gpd/2 + radii_relative = radii / ( + radius_LU - 0.5) # (substract 0.5 because "true" boundary location is 0.5LU further out than node-coordinates) + radii_q_relative = radii_q / (radius_LU - 0.5) + + # calc. mean rel_radius + r_rel_mean = sum(radii_relative) / len(radii_relative) + rq_rel_mean = sum(radii_q_relative) / len(radii_q_relative) + + ## AREA calculation + area_theory = np.pi * ( + gridpoints_per_diameter / 2) ** 2 # area = pi*r² in LU² + area = len( + a) # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it + + if output_data: + output_file.write( + "\nr_rel_mean: " + str(sum(radii_relative) / len(radii_relative))) + output_file.write("\nrq_rel_mean: " + str( + sum(radii_q_relative) / len(radii_q_relative))) + output_file.write("\nr_rel_min: " + str(r_max / (radius_LU - 0.5))) + output_file.write("\nr_rel_max: " + str(r_min / (radius_LU - 0.5))) + output_file.write("\n\narea_rel: " + str(area / area_theory)) + + output_file.write("\n\nradii: " + str(Counter(radii))) + output_file.write("\nradii_q: " + str(Counter(radii_q)) + "\n\n") + output_file.close() + if print_data: + print("area_rel: " + str(area / area_theory)) + + ### PLOT Mask + plt.figure() + plt.imshow(obstacle_mask_for_visualization) + # plt.xticks(np.arange(gridpoints_per_diameter + 2), minor=True) + # plt.yticks(np.arange(gridpoints_per_diameter + 2), minor=True) + ax = plt.gca() + xmin, xmax = ax.get_xlim() + ymax, ymin = ax.get_ylim() + if gridpoints_per_diameter >= 10: + plt.xticks(np.arange(0, xmax, int(xmax / 10))) + plt.yticks(np.arange(0, ymax, int(ymax / 10))) + else: + plt.xticks(np.arange(0, xmax, 1)) + plt.yticks(np.arange(0, ymax, 1)) + plt.title("GPD = " + str(gridpoints_per_diameter)) + ax.set_xticks(np.arange(-.5, xmax, 1), minor=True) + ax.set_yticks(np.arange(-.5, ymax, 1), minor=True) + + # grid thickness, cicrle, node marker + x, y = np.meshgrid(np.linspace(0, int(xmax), int(xmax + 1)), + np.linspace(0, int(ymax), int(ymax + 1))) + if gridpoints_per_diameter < 30: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=2) + circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), + gridpoints_per_diameter / 2, color='r', fill=False, + linewidth=1) + ax.add_patch(circle) + plt.plot(x, y, marker='.', linestyle='', color="b", markersize=1) + elif gridpoints_per_diameter < 70: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=1) + circle = plt.Circle((xmax / 2 - 0.25, ymax / 2 - 0.25), + gridpoints_per_diameter / 2, color='r', fill=False, + linewidth=0.5) + ax.add_patch(circle) + elif gridpoints_per_diameter < 100: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=0.5) + elif gridpoints_per_diameter < 150: + ax.grid(which="minor", color="k", axis='both', linestyle='-', + linewidth=0.25) + + if output_data: + plt.savefig(filebase + "/obstacle_mask_GPD" + str( + gridpoints_per_diameter) + ".png") + if print_data: + plt.show() + else: + plt.close() + # SNIP: MP2 fit sinewave to Cl for better frequency-measurement) ### FIT SINEWAVE to converged Cl to get better St-measurement # 1. get converged lift-curve From 381b6b4b3ccb642c8af28db8078ccf42e6f077f3 Mon Sep 17 00:00:00 2001 From: mbille Date: Thu, 11 Dec 2025 17:19:48 +0100 Subject: [PATCH 15/21] implemented profile-reporter, added profile-reference-data, implemented profile-data-post-processing und -plotting. implemented advanced vtk reporter (3D with step start and end and 2D for XY-slices) --- .../01a_script_cylinder_lowRe.py | 403 ++++++-- .../data_processing_and_plotting.py | 868 +++++++++++------- .../Fig09_ux_profile_pos1_DI2018.csv | 67 ++ .../Fig09_ux_profile_pos1_KM2000.csv | 125 +++ .../Fig09_ux_profile_pos1_LS1993.csv | 41 + .../Fig09_ux_profile_pos1_WR2008.csv | 100 ++ .../Fig09_ux_profile_pos2_DI2018.csv | 50 + .../Fig09_ux_profile_pos2_KM2000.csv | 119 +++ .../Fig09_ux_profile_pos2_LS1993.csv | 41 + .../Fig09_ux_profile_pos2_WR2008.csv | 91 ++ .../Fig09_ux_profile_pos3_DI2018.csv | 48 + .../Fig09_ux_profile_pos3_KM2000.csv | 103 +++ .../Fig09_ux_profile_pos3_LS1993.csv | 41 + .../Fig09_ux_profile_pos3_WR2008.csv | 80 ++ .../Fig10_uy_profile_pos1_DI2018.csv | 51 + .../Fig10_uy_profile_pos1_KM2000.csv | 96 ++ .../Fig10_uy_profile_pos1_LS1993.csv | 41 + .../Fig10_uy_profile_pos1_WR2008.csv | 81 ++ .../Fig10_uy_profile_pos2_DI2018.csv | 43 + .../Fig10_uy_profile_pos2_KM2000.csv | 95 ++ .../Fig10_uy_profile_pos2_LS1993.csv | 41 + .../Fig10_uy_profile_pos2_WR2008.csv | 78 ++ .../Fig10_uy_profile_pos3_DI2018.csv | 35 + .../Fig10_uy_profile_pos3_KM2000.csv | 94 ++ .../Fig10_uy_profile_pos3_LS1993.csv | 41 + .../Fig10_uy_profile_pos3_WR2008.csv | 76 ++ .../Fig11_uxux_profile_pos1_DI2018.csv | 45 + .../Fig11_uxux_profile_pos1_KM2000.csv | 98 ++ .../Fig11_uxux_profile_pos1_R2016.csv | 57 ++ .../Fig11_uxux_profile_pos2_BM1994.csv | 63 ++ .../Fig11_uxux_profile_pos2_DI2018.csv | 40 + .../Fig11_uxux_profile_pos2_KM2000.csv | 98 ++ .../Fig11_uxux_profile_pos2_LS1993.csv | 41 + .../Fig11_uxux_profile_pos2_R2016.csv | 56 ++ .../Fig11_uxux_profile_pos3_DI2018.csv | 40 + .../Fig11_uxux_profile_pos3_KM2000.csv | 94 ++ .../Fig11_uxux_profile_pos3_R2016.csv | 56 ++ .../Fig12_uyuy_profile_pos1_DI2018.csv | 27 + .../Fig12_uyuy_profile_pos1_R2016.csv | 68 ++ .../Fig12_uyuy_profile_pos2_BM1994.csv | 54 ++ .../Fig12_uyuy_profile_pos2_DI2018.csv | 36 + .../Fig12_uyuy_profile_pos2_LS1993.csv | 41 + .../Fig12_uyuy_profile_pos2_R2016.csv | 56 ++ .../Fig12_uyuy_profile_pos3_DI2018.csv | 36 + .../Fig12_uyuy_profile_pos3_R2016.csv | 61 ++ .../Fig13_uxuy_profile_pos1_BM1994.csv | 26 + .../Fig13_uxuy_profile_pos1_DI2018.csv | 31 + .../Fig13_uxuy_profile_pos1_R2016.csv | 32 + .../Fig13_uxuy_profile_pos2_BM1994.csv | 35 + .../Fig13_uxuy_profile_pos2_DI2018.csv | 25 + .../Fig13_uxuy_profile_pos2_LS1993.csv | 41 + .../Fig13_uxuy_profile_pos2_R2016.csv | 31 + .../Fig13_uxuy_profile_pos3_BM1994.csv | 40 + .../Fig13_uxuy_profile_pos3_DI2018.csv | 29 + .../Fig13_uxuy_profile_pos3_R2016.csv | 38 + .../reporter_ProfileReporter.py | 51 + .../reporter_advanced_vtk_reporter.py | 251 +++++ 57 files changed, 4212 insertions(+), 434 deletions(-) create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_WR2008.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_BM1994.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_KM2000.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_BM1994.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_BM1994.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_BM1994.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_LS1993.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_BM1994.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_DI2018.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_R2016.csv create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py create mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 3e5b338b..3ccf8123 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -13,6 +13,7 @@ import os import psutil import shutil +import resource import matplotlib.pyplot as plt @@ -27,11 +28,13 @@ import lettuce as lt from obstacle_cylinder import ObstacleCylinder from ebb_simulation import EbbSimulation +from reporter_ProfileReporter import ProfileReporter +from reporter_advanced_vtk_reporter import VTKReporterAdvanced, VTKsliceReporter from observables_force_coefficients import DragCoefficient, LiftCoefficient # AUX. CODE from helperCode import Logger -from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask +from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask, ProfilePlotter #################### @@ -75,28 +78,50 @@ parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") # reporter and observable settings -parser.add_argument("--vtk3D", action='store_true', help="output 3D vtk files") -#TODO: add vtk reporter (fps_pu, interval_lu, -# - 3D full -# - 2D slice normal (x,y,z) -# - obstacle point, obstacle cell speichern und abh. von 2D/3D korrekt formatieren -#TODO: add drag/lift reporter -# - periodic start relative (relative start of interval to do statistics over -# - plot lift, drag, strouhal number (try except...) -#TODO: add U-profile-reporter (output True, store/calculate true) -# -> condense in own functions -# -> make script to plot from data (see PLOTTING scripts in CYLINDER-paper for layout) -#TODO: add NAN reporter (on/off, interval) -#TODO: add watchdog reporter (on/off, interval) -#TODO: add highMa reporter (on/off, interval) -#TODO: add 2D-mp4-reporter... (fps_video, number of frames ODER fps_pu) +parser.add_argument("--periodic_region_start_relative", default=None, type=float, help="RELATIVE (0.0-1.0) assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") +parser.add_argument("--periodic_region_start_pu", default=None, type=float, help="ABSOLUTE PU-time; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") +parser.add_argument("--periodic_region_start_lu", default=None, type=int, help="ABSOLUTE LU-steps; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") + +parser.add_argument("--calc_u_profiles", action='store_true', help="calculate average velocity profiles similar to [Di Ilio et al. 2018] and output plots and time-averages data for plots") +parser.add_argument("--output_u_profiles_timeseries", default=False, help="output average velocity profiles over time (full timeseries)") +parser.add_argument("--profile_reference_path", default="../profile_reference_data/", type=str, help="path to reference profiles from [Di Ilio et al. 2018]") + +parser.add_argument("--vtk_full_basic", action='store_true', help="output vtk files of full domain each interval steps") +parser.add_argument("--vtk_full_basic_interval", type=int, help="step interval for output of basic full vtk files") + +parser.add_argument("--vtk_3D", action='store_true', help="output vtk files of full domain each interval steps between start and end (if start and end are defined!)") +parser.add_argument("--vtk_3D_fps", type=float) +parser.add_argument("--vtk_3D_step_interval", type=float) +parser.add_argument("--vtk_3D_t_interval", type=float) +parser.add_argument("--vtk_3D_step_start", type=int) +parser.add_argument("--vtk_3D_step_end", type=int) +parser.add_argument("--vtk_3D_t_start", type=float) +parser.add_argument("--vtk_3D_t_end", type=float) + +parser.add_argument("--vtk_slice2D", action='store_true', help="toggle vtk-output of 2D slice of WHOLE DOMAIN (!) to outdir_data, if set True (1)") +parser.add_argument("--vtk_slice2D_fps", type=float) +parser.add_argument("--vtk_slice2D_step_interval", type=float) +parser.add_argument("--vtk_slice2D_t_interval", type=float) +parser.add_argument("--vtk_slice2D_step_start", type=int) +parser.add_argument("--vtk_slice2D_step_end", type=int) +parser.add_argument("--vtk_slice2D_t_start", type=float) +parser.add_argument("--vtk_slice2D_t_end", type=float) + +#TODO: add NAN reporter (on/off, interval,...) +#TODO: add watchdog reporter (on/off, interval,...) +#TODO: add highMa reporter (on/off, interval,...) + +#TODO: add 2D-mp4-reporter... (fps_video, number of frames OR fps_pu) # Checkpointing # TODO: add checkpointing-utilities (read, write) +# - is NOT implemented in lettuce 2025 atm. # - checkpoint IN path # - checkpoint OUT path (if only one is given, take this one for both) # - checkpoint i_start, t_start?, +parser.add_argument("--count_tensors", action="store_true", help="(for debugging: count all tensors, their sizes and memory consumption on the GPU, to find memory leaks") + ########################################################### # OUTPUTS: @@ -171,15 +196,6 @@ # PROCESS AND SET PARAMETERS print(f"SCRIPT: Processing parameters...") -# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients -#TODO: take absolute PU-time values here: -# - periodic_start_Re100_PU ~= 75-100 seconds -# - the start of the periodic region depends on the time the flow needs -# to sattle.. This time is also dependent on the domain size! -if args["reynolds_number"] > 1000: - periodic_start = 0.4 -else: - periodic_start = 0.9 # calculate domain and obstacle geometry and infer dimensions (2D, 3D) if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: @@ -195,6 +211,7 @@ if args["domain_width_z_in_d"] is None: # will be 2D dims = 2 + domain_width_z_in_d = None else: # will be 3D dims = 3 if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : @@ -270,6 +287,25 @@ else: t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) +# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients +# - periodic_start_Re100_PU ~= 75-100 seconds +# - the start of the periodic region depends on the time the flow needs +# to sattle.. This time is also dependent on the domain size! +#TODO (optional): change periodic_start parameter to abs. step-number, instead of relative start: +# - ease of use for reporters and processing +# - no round-off error in preocessing LU-parameter, if set +if args["periodic_region_start_lu"] is not None and args["periodic_region_start_lu"] >= 0: + periodic_start = args["periodic_region_start_lu"] / n_steps +elif args["periodic_region_start_pu"] is not None and args["periodic_region_start_pu"] >= 0: + periodic_start = args["periodic_region_start_pu"] / t_target +elif args["periodic_region_start_relative"] is not None and args["periodic_region_start_relative"] >= 0 and args["periodic_region_start_relative"] < 1.0: + periodic_start = args["periodic_region_start_relative"] +else: + if args["reynolds_number"] > 1000: + periodic_start = 0.4 + else: + periodic_start = 0.9 + # check EQLM parameter if args["eqlm"]: # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? @@ -321,6 +357,23 @@ LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) simulation.reporter.append(LiftReporter) +# VELOCITY- and REYNOLDS-STRESS profiles over space and time: +if args["calc_u_profiles"]: + # define positions + position_1 = flow.x_pos_lu - 0.5 + 1.06 * flow.radius_lu * 2 # int(round(flow.x_pos + 1.06 * flow.radius * 2 , 0)) + position_2 = flow.x_pos_lu - 0.5 + 1.54 * flow.radius_lu * 2 # int(round(flow.x_pos + 1.54 * flow.radius * 2 , 0)) + position_3 = flow.x_pos_lu - 0.5 + 2.02 * flow.radius_lu * 2 # int(round(flow.x_pos + 2.02 * flow.radius * 2 , 0)) + print("ProfileReporters at x-positions:" + " p1: " + str(position_1) + " p2: " + str( + position_2) + " p3: " + str(position_3)) + + # create and append profileReporters + ProfileReporter1 = ProfileReporter(flow=flow, interval=1, position_lu=position_1, i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter1) + ProfileReporter2 = ProfileReporter(flow=flow, interval=1, position_lu=position_2, i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter2) + ProfileReporter3 = ProfileReporter(flow=flow, interval=1, position_lu=position_3, i_start= int(n_steps * periodic_start)) + simulation.reporter.append(ProfileReporter3) + # NAN REPORTER (if nan detected -> stop simulation) # TODO: add NaN reporter # - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... @@ -332,9 +385,10 @@ # TODO: Progress-Reporter # VTK Reporter -> visualization -if args["vtk3D"]: - #TODO: make stuff parameterized: interval, outdir, ... - vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") + +# BASIC lettuce vtk output +if args["vtk_full_basic"]: + vtk_reporter = lt.VTKReporter(interval=args["vtk_full_basic_interval"], filename_base=outdir_data+"/vtk/out") # export obstacle mask_dict = dict() @@ -354,6 +408,85 @@ simulation.reporter.append(vtk_reporter) +# ADVANCED vtk output +# 3D +if args["vtk_3D"] and dims == 3: + if args["vtk_3D_t_start"] is not None: + #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") + vtk_3d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_3D_t_start"]))) + elif args["vtk_3D_step_start"] is not None: + vtk_3d_i_start = int(args["vtk_3D_step_start"]) + else: + vtk_3d_i_start = 0 + + if args["vtk_3D_t_end"] is not None: + #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") + vtk_3d_i_end = int(flow.units.convert_time_to_lu(args["vtk_3D_t_end"])) + elif args["vtk_3D_step_end"] is not None: + vtk_3d_i_end = args["vtk_3D_step_end"] + else: + vtk_3d_i_end = n_steps + + if args["vtk_3D_t_interval"] is not None and args["vtk_3D_t_interval"] > 0: + vtk_3d_interval = int(flow.units.convert_time_to_lu(args["vtk_3D_t_interval"])) + elif args["vtk_3D_step_interval"] is not None and args["vtk_3D_step_interval"] > 0: + vtk_3d_interval = args["vtk_3D_step_interval"] + elif args["vtk_3D_fps"] is not None and args["vtk_3D_fps"] > 0: + vtk_3d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_3D_fps"])) + else: + vtk_3d_interval = 1 + + if vtk_3d_interval < 1: + vtk_3d_interval = 1 + + vtk_3d_reporter = VTKReporterAdvanced(flow, + interval=int(vtk_3d_interval), + filename_base=outdir_data + "/vtk/out", + imin=vtk_3d_i_start, imax=vtk_3d_i_end) + simulation.reporter.append(vtk_3d_reporter) + vtk_3d_reporter.output_mask(flow.solid_mask, outdir_data + "/vtk", "solid_mask", point=True) + + +# slice2D (2D slice at Z/2) +if args["vtk_slice2D"]: + if args["vtk_slice2D_t_start"] is not None and args["vtk_slice2D_t_start"] > 0: + #print("(vtk) overwriting vtk_step_start with {}, because vtk_t_start = {}") + vtk_slice2d_i_start = int(round(flow.units.convert_time_to_lu(args["vtk_slice2D_t_start"]))) + elif args["vtk_slice2D_step_start"] is not None and args["vtk_slice2D_step_start"] > 0: + vtk_slice2d_i_start = int(args["vtk_slice2D_step_start"]) + else: + vtk_slice2d_i_start = 0 + + if args["vtk_slice2D_t_end"] is not None and args["vtk_slice2D_t_end"] > 0: + #print("(vtk) overwriting vtk_step_end with {}, because vtk_t_end = {}") + vtk_slice2d_i_end = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_end"])) + elif args["vtk_slice2D_step_end"] is not None and args["vtk_slice2D_step_end"] > 0: + vtk_slice2d_i_end = int(args["vtk_slice2D_step_end"]) + else: + vtk_slice2d_i_end = n_steps # Q: must this be target? + + if args["vtk_slice2D_t_interval"] is not None and args["vtk_slice2D_t_interval"] > 0: + vtk_slice2d_interval = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_interval"])) + elif args["vtk_slice2D_step_interval"] is not None and args["vtk_slice2D_step_interval"] > 0: + vtk_slice2d_interval = int(args["vtk_slice2D_step_interval"]) + elif args["vtk_slice2D_fps"] is not None and args["vtk_slice2D_fps"] > 0: + vtk_slice2d_interval = int(flow.units.convert_time_to_lu(1 / args["vtk_slice2D_fps"])) + else: + vtk_slice2d_interval = 1 + + if vtk_slice2d_interval < 1: + vtk_slice2d_interval = 1 + + if args["vtk_slice2D"]: + vtk_domainSlice_reporter = VTKsliceReporter( flow, + interval=int(vtk_slice2d_interval), + filename_base=outdir_data + "/vtk/slice_domain/slice_domain", + sliceXY=([0,resolution[0]-1],[0,resolution[1]-1]), + sliceZ=int(resolution[2]/2), + imin=vtk_slice2d_i_start, imax=vtk_slice2d_i_end) + simulation.reporter.append(vtk_domainSlice_reporter) + + # DRAW CYLINDER-MASK in 2D (xy-plane) draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) @@ -362,34 +495,34 @@ print(f"\nSCRIPT: spacial and temporal dimensions:") print("domain shape (LU):", flow.resolution) print("t_target (PU) with", n_steps, "steps (LU):", - round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 2), + round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 3), "seconds") print("steps to simulate 1 second PU:", - round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") -print("steps to simulate", t_target, " (t_target, PU) seconds:", - t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), - "steps") + round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3), "steps") +print(f"steps to simulate {t_target:.3f} (t_target, PU) seconds: {t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3):.3f} steps") ################################################## # RUN SIMULATION: print(f"\n#################################################") -print(f"\nSCRIPT: running simulation for {n_steps} steps...\n") +print(f"\nSCRIPT ({datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}): running simulation for {n_steps} steps...\n") print(f"#################################################\n") t_start = time.time() mlups = simulation(num_steps=n_steps) t_end = time.time() runtime = t_end - t_start +print(f"***** SIMULATION FINISHED AT {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} *****\n") ################################################## # OUTPUT STATS: print(f"### STATS ###") -print("MLUPS:", mlups) -print("simulated PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") +print(f"MLUPS: {mlups:.3f}") +print(f"simulated PU-Time: {flow.units.convert_time_to_pu(n_steps)} seconds") print("simulated LU-steps: ", n_steps) -print("runtime (WALLTIME) of simulation(num_steps): ", runtime, "seconds (= ", round(runtime / 60, 2), "minutes )") -print("\n") -print("current VRAM (MB): ", torch.cuda.memory_allocated(context.device) / 1024 / 1024) -print("max. VRAM (MB): ", torch.cuda.max_memory_allocated(context.device) / 1024 / 1024) +print(f"runtime (WALLTIME) of simulation(num_steps): {runtime:.3f} seconds (= ", round(runtime / 60, 2), "minutes )") +print("") +print("### HARDWARE UTILIZATION ###") +print(f"current GPU VRAM (MB) usage: {torch.cuda.memory_allocated(device=context.device)/1024/1024:.3f}") +print(f"max. GPU VRAM (MB) usage: {torch.cuda.max_memory_allocated(device=context.device)/1024/1024:.3f}") [cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) @@ -400,34 +533,57 @@ ### export stats if not no_data_flag: - output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") + output_file = open(outdir + "/stats.txt", "a") output_file.write("DATA for " + timestamp) output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write( + f"\nruntime: {runtime:.3f} seconds (= {round(runtime / 60, 2)} min = {round(runtime / 3600, 2)} h)") output_file.write("\nMLUPS = " + str(mlups)) output_file.write("\n") - output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_current [MB] = " + str( + torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = " + str( + torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str( + round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str( + round(cpuLoad15, 2)) + " %") + output_file.write("\nCurrent total RAM usage [MB]: " + str( + round(ram.used / (1024 * 1024), 2)) + " of " + str( + round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nmaximum total RAM usage ('MaxRSS') [MB]: " + str( + round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, + 2)) + " MB") output_file.close() + # output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") + # output_file.write("DATA for " + timestamp) + # output_file.write("\n\n### SIM-STATS ###") + # output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + # output_file.write("\nMLUPS = " + str(mlups)) + # output_file.write("\n") + # output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + # output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + # output_file.write("\n") + # output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + # output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + # output_file.close() ################################################## -# PLOTTING 2D IMAGE +# PLOTTING 2D IMAGE of VELOCITY fig, axes = plt.subplots(1, 2, figsize=(10, 3)) fig.subplots_adjust(right=0.85) u = flow.u_pu.cpu().numpy() -print("\nMax Velocity:", u.max()) +#print("\nMax Velocity:", u.max()) if dims == 2: im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") -elif dims == 3: +else: im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) fig.colorbar(im2, cax=cbar_ax) fig.show() +#TODO: plot image of pressure ################################################## # PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: @@ -460,7 +616,7 @@ # "frequency_fft": freq_peak, # "fft_resolution": freq_res} -print("\n") +print("") # LIFT lift_timeseries = np.array(np.array(LiftReporter.out)) @@ -477,11 +633,52 @@ for key, value in lift_stats.items(): print(f"{key:<20} = {str(value)}") +# plot DRAG and LIFT together: +try: + fig, ax = plt.subplots(layout="constrained") + drag_ax = ax.plot(drag_timeseries[:, 1], drag_timeseries[:, 2], color="tab:blue", label="Drag") + ax.set_xlabel("physical time / s") + ax.set_ylabel("Coefficient of Drag $C_{D}$") + ylim_adjusted = (drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].min() * 0.5, + drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].max() * 1.2) + #ax.set_ylim((0.5, 1.6)) + ax.set_ylim(ylim_adjusted) + + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax.set_xlabel("timesteps (simulation time / LU)") + + ax2 = ax.twinx() + lift_ax = ax2.plot(lift_timeseries[:, 1], lift_timeseries[:, 2], color="tab:orange", label="Lift") + ax2.set_ylabel("Coefficient of Lift $C_{L}$") + ax2.set_ylim((-1.1, 1.1)) + + fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) + + if not no_data_flag: + try: + plt.savefig(outdir_data + "/dragAndLift_coefficient.png") + except: + print("(!) saving dragAndLift_coefficient.png didn't work!") + plt.show() +except: + print("(!) plotting drag and lift together didn't work!") + # STROUHAL number # f = Strouhal for St=f*D/U and D=U=1 in PU print("Strouhal number is: ", lift_stats["frequency_fit"] * flow.char_length_pu/flow.char_velocity_pu) +# AVERAGE VELOCITY and REYNOLDS STRESS PROFILES +if args["calc_u_profiles"] and args["profile_reference_path"] is not None: + profile_plotter = ProfilePlotter(flow, output_path=outdir_data, + reference_data_path=args["profile_reference_path"], + i_timeseries=ProfileReporter1.i_out, + u_timeseries1=ProfileReporter1.out, + u_timeseries2=ProfileReporter2.out, + u_timeseries3=ProfileReporter3.out) + profile_plotter.process_data() + profile_plotter.plot_velocity_profiles(show_reference=True, save=True) + profile_plotter.plot_reynolds_stress_profiles(show_reference=True, save=True) # EXPORT OBSERVABLES: @@ -492,8 +689,7 @@ output_file.write(torch.cuda.memory_summary(context.device)) output_file.close() - # TODO: make this optional... - if False: + if args["count_tensors"]: try: ### list present torch tensors: output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") @@ -532,59 +728,73 @@ output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") output_file.write("DATA for " + timestamp) output_file.write("\n\n### SIM-Parameters ###") - output_file.write("\nRe = " + str(reynolds_number)) - output_file.write("\nn_steps = " + str(n_steps)) - output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") - output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) + output_file.write("\n{:30s} {:30s}".format("Re", str(reynolds_number))) + output_file.write("\n{:30s} {:30s}".format("Ma", str(mach_number))) + output_file.write("\n{:30s} {:30s}".format("n_steps", str(n_steps))) + output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) + output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", str(flow.char_length_lu))) + output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) if gpd_correction: - output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( + output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + str( flow.char_length_lu) + " because D/Y is even") - output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) - output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) + output_file.write("\nDpX (D/X) = ".ljust(30) + str(domain_length_x_in_d)) + output_file.write("\nDpY (D/Y) = ".ljust(30) + str(domain_height_y_in_d)) if flow.stencil.d == 3: - output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) - output_file.write("\nshape_LU: " + str(flow.resolution)) - output_file.write(("\ntotal_number_of_gridpoints: " + str(flow.rho(flow.f).numel()))) - output_file.write("\nbc_type = " + str()) -# output_file.write("\nlateral_walls = " + str(lateral_walls)) -# output_file.write("\nstencil = " + str(stencil_choice)) -# output_file.write("\ncollision = " + str(collision)) + output_file.write("\nDpZ (D/Z) = ".ljust(30) + str(domain_width_z_in_d)) + + #output_file.write("\nn_steps = " + str(n_steps)) + # output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") + # output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) + # if gpd_correction: + # output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( + # flow.char_length_lu) + " because D/Y is even") + # output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) + # output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) + # if flow.stencil.d == 3: + # output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) + + output_file.write("\nshape_LU: ".ljust(30) + str(flow.resolution)) + output_file.write(("\ntotal_number_of_gridpoints: ".ljust(30) + str(flow.rho(flow.f).numel()))) + output_file.write("\nbc_type = ".ljust(30) + str()) + output_file.write("\nlateral_walls = ".ljust(30) + str(flow.lateral_walls)) + output_file.write("\nstencil = ".ljust(30) + str(flow.stencil)) + output_file.write("\ncollision = ".ljust(30) + str(collision_operator)) output_file.write("\n") - output_file.write("\nMa = " + str(mach_number)) - output_file.write("\ntau = " + str(flow.units.relaxation_parameter_lu)) -# output_file.write("\ngrid_reynolds_number (Re_g) = " + str(re_g)) + # output_file.write("\nMa = " + str(mach_number)) + output_file.write("\nrelaxation parameter tau [LU]".ljust(30) + str(flow.units.relaxation_parameter_lu)) + output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(30) + str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) output_file.write("\n") - output_file.write("\nsetup_diameter_PU = " + str(flow.char_length_lu)) - output_file.write("\nflow_velocity_PU = " + str(flow.char_length_lu)) + output_file.write("\ncylinder diameter PU = ".ljust(30) + str(flow.units.characteristic_length_pu)) + output_file.write("\ncharacteristic velocity PU = ".ljust(30) + str(flow.units.characteristic_velocity_pu)) # output_file.write("\nu_init = " + str(u_init)) - output_file.write("\nperturb_init = " + str(perturb_init)) + output_file.write("\nperturb_init = ".ljust(30) + str(perturb_init)) output_file.write("\n") # output_file.write("\noutput_vtk = " + str(output_vtk)) # output_file.write("\nvtk_fps = " + str(vtk_fps)) output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") - output_file.write("\nMLUPS = " + str(mlups)) + output_file.write("\nruntime = ".ljust(30) + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = ".ljust(30) + str(mlups)) output_file.write("\n") - output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_current [MB] = ".ljust(30) + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = ".ljust(30) + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(30) + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: ".ljust(30) + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\n\n### OBSERVABLES ###") output_file.write("\nCoefficient of drag between " + str(round(drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + " s:") - output_file.write("\nCd_mean, simple = " + str(drag_stats["mean_simple"])) - output_file.write("\nCd_mean, peak_finder = " + str(drag_stats["mean_periodcorrected"])) + output_file.write("\nCd_mean, simple = ".ljust(30) + str(drag_stats["mean_simple"])) + output_file.write("\nCd_mean, peak_finder = ".ljust(30) + str(drag_stats["mean_periodcorrected"])) output_file.write( - "\nCd_min = " + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) + "\nCd_min = ".ljust(30) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) output_file.write( - "\nCd_max = " + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) + "\nCd_max = ".ljust(30) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) output_file.write("\n") output_file.write("\nCoefficient of lift:") - output_file.write("\nCl_min = " + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) - output_file.write("\nCl_max = " + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) + output_file.write("\nCl_min = ".ljust(30) + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) + output_file.write("\nCl_max = ".ljust(30) + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) output_file.write("\n") output_file.write("\nStrouhal number:") output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + str(lift_stats["fft_resolution"]) + " Hz") @@ -593,6 +803,31 @@ output_file.write("\n") output_file.close() +# export flow physics to file: +output_file = open(outdir+"/flow_physics_parameters.txt", "a") +output_file.write('\n{:30s}'.format("FLOW PHYSICS and units:")) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("Ma", str(reynolds_number))) +output_file.write('\n{:30s} {:30s}'.format("Re", str(mach_number))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("Relaxation Parameter LU", str(flow.units.relaxation_parameter_lu))) +output_file.write('\n{:30s} {:30s}'.format("l_char_LU", str(flow.units.characteristic_length_lu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_LU", str(flow.units.characteristic_velocity_lu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_LU", str(flow.units.viscosity_lu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_LU", str(flow.units.characteristic_pressure_lu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_LU", str(flow.units.characteristic_density_lu))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("l_char_PU", str(flow.units.characteristic_length_pu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_PU", str(flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_PU", str(flow.units.viscosity_pu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_PU", str(flow.units.characteristic_pressure_pu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_PU", str(flow.units.characteristic_density_pu))) +output_file.write('\n') +output_file.write('\n{:30s} {:30s}'.format("grid reynolds number Re_g", str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5))))) +output_file.write('\n{:30s} {:30s}'.format("flow through time PU [s]", str(domain_length_x_in_d*flow.units.characteristic_length_pu/flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("flow through time LU", str(domain_length_x_in_d*flow.units.characteristic_length_lu/flow.units.characteristic_velocity_lu))) +output_file.write('\n') +output_file.close() ## END OF SCRIPT print(f"\n♬ THE END ♬") diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py index c890f7c9..5d2ec3b3 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -1,5 +1,6 @@ # THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM THE CYLINDER-SIMULATION +import os import numpy as np import matplotlib.pyplot as plt from typing import Any @@ -154,11 +155,14 @@ def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: floa mean_periodcorrected = values_periodic[first_peak:last_peak].mean() except Exception as e: - print(f"(WARNING!) peak finding for {name} didn't work... See Python Stack Trace below:") - print("\n--- Python Stack Trace ---") - full_trace = traceback.format_exc() - print(full_trace) - print("--------------------------\n") + print(f"(WARNING!) peak finding for {name} didn't work... This might just be because there is no converged periodic region!") + if verbose: + print("Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:") + + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") mean_simple = values_periodic.mean() min_simple = values_periodic.min() @@ -235,9 +239,7 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, filebase=".", print_data=False): ### calculate and export 2D obstacle_mask as .png grid_x = gridpoints_per_diameter + 2 - if output_data: - output_file = open(filebase + "/obstacle_mask_info.txt", "a") - output_file.write("GPD = " + str(gridpoints_per_diameter) + "\n") + if print_data: print("GPD = " + str(gridpoints_per_diameter)) # define radius and position for a symmetrical circular Cylinder-Obstacle @@ -322,6 +324,8 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, a) # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it if output_data: + output_file = open(filebase + "/obstacle_mask_info.txt", "a") + output_file.write("GPD = " + str(gridpoints_per_diameter) + "\n") output_file.write( "\nr_rel_mean: " + str(sum(radii_relative) / len(radii_relative))) output_file.write("\nrq_rel_mean: " + str( @@ -387,351 +391,515 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, else: plt.close() +class ProfilePlotter: + + def __init__(self, flow, output_path, reference_data_path, i_timeseries, u_timeseries1, u_timeseries2, u_timeseries3): + self.flow = flow + self.output_path = output_path + self.i_timeseries = i_timeseries + self.u_timeseries1 = u_timeseries1 + self.u_timeseries2 = u_timeseries2 + self.u_timeseries3 = u_timeseries3 + + os.makedirs(self.output_path + "/ProfileReporter_Data/") + + self.import_profile_reference_data(reference_data_path) + + def import_profile_reference_data(self, data_path): + # import reference data: (data is: first column Y/D, second column u_d/u_char) + + # ux + self.p1_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_LS1993.csv', delimiter=';') + self.p2_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_LS1993.csv', delimiter=';') + self.p3_LS1993_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_LS1993.csv', delimiter=';') + + self.p1_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_KM2000.csv', delimiter=';') + self.p2_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_KM2000.csv', delimiter=';') + self.p3_KM2000_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_KM2000.csv', delimiter=';') + + self.p1_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_WR2008.csv', delimiter=';') + self.p2_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_WR2008.csv', delimiter=';') + self.p3_WR2008_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_WR2008.csv', delimiter=';') + + self.p1_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos1_DI2018.csv', delimiter=';') + self.p2_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos2_DI2018.csv', delimiter=';') + self.p3_DI2018_ux = np.genfromtxt( + data_path + 'Fig09_ux_profile_pos3_DI2018.csv', delimiter=';') + + # uy + self.p1_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_LS1993.csv', delimiter=';') + self.p2_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_LS1993.csv', delimiter=';') + self.p3_LS1993_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_LS1993.csv', delimiter=';') + + self.p1_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_KM2000.csv', delimiter=';') + self.p2_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_KM2000.csv', delimiter=';') + self.p3_KM2000_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_KM2000.csv', delimiter=';') + + self.p1_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_WR2008.csv', delimiter=';') + self.p2_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_WR2008.csv', delimiter=';') + self.p3_WR2008_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_WR2008.csv', delimiter=';') + + self.p1_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos1_DI2018.csv', delimiter=';') + self.p2_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos2_DI2018.csv', delimiter=';') + self.p3_DI2018_uy = np.genfromtxt( + data_path + 'Fig10_uy_profile_pos3_DI2018.csv', delimiter=';') + + # uxux + self.p1_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_DI2018.csv', delimiter=';') + self.p1_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_KM2000.csv', delimiter=';') + self.p1_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_DI2018.csv', delimiter=';') + self.p2_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_KM2000.csv', delimiter=';') + self.p2_LS1993_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos2_R2016.csv', delimiter=';') + self.p3_DI2018_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_DI2018.csv', delimiter=';') + self.p3_KM2000_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_KM2000.csv', delimiter=';') + self.p3_R2016_uxux = np.genfromtxt( + data_path + 'Fig11_uxux_profile_pos3_R2016.csv', delimiter=';') + + # uyuy + self.p1_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos1_DI2018.csv', delimiter=';') + self.p1_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_DI2018.csv', delimiter=';') + self.p2_LS1993_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos2_R2016.csv', delimiter=';') + self.p3_DI2018_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos3_DI2018.csv', delimiter=';') + self.p3_R2016_uyuy = np.genfromtxt( + data_path + 'Fig12_uyuy_profile_pos3_R2016.csv', delimiter=';') + + # uxuy + self.p1_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_BM1994.csv', delimiter=';') + self.p1_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_DI2018.csv', delimiter=';') + self.p1_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos1_R2016.csv', delimiter=';') + self.p2_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_BM1994.csv', delimiter=';') + self.p2_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_DI2018.csv', delimiter=';') + self.p2_LS1993_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_LS1993.csv', delimiter=';') + self.p2_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos2_R2016.csv', delimiter=';') + self.p3_BM1994_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_BM1994.csv', delimiter=';') + self.p3_DI2018_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_DI2018.csv', delimiter=';') + self.p3_R2016_uxuy = np.genfromtxt( + data_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') + + def process_data(self, save=False): + # CALCULATE remporal velocity averages + avg_u1_temporal = np.mean(np.array(self.u_timeseries1), axis=0) + avg_u2_temporal = np.mean(np.array(self.u_timeseries2), axis=0) + avg_u3_temporal = np.mean(np.array(self.u_timeseries3), axis=0) + + self.avg_u1_x = avg_u1_temporal[0] + self.avg_u1_y = avg_u1_temporal[1] + + self.avg_u2_x = avg_u2_temporal[0] + self.avg_u2_y = avg_u2_temporal[1] + + self.avg_u3_x = avg_u3_temporal[0] + self.avg_u3_y = avg_u3_temporal[1] + + if save: + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos1" + "_t-avg.npy", avg_u1_temporal) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos2" + "_t-avg.npy", avg_u2_temporal) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos3" + "_t-avg.npy", avg_u3_temporal) + + # Y_inD for y-axis and plotting + self.y_in_D = (np.arange(self.avg_u1_x.shape[ + 0]) + 1 - self.flow.y_pos_lu) / self.flow.char_length_lu # y/D for figure + if save: + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_YinD.npy", + self.y_in_D) + + # CALCULATE turbulent reynolds stresses + # diff between timeseries and time_average -> u' + u1_diff = self.u_timeseries1 - avg_u1_temporal + u2_diff = self.u_timeseries2 - avg_u2_temporal + u3_diff = self.u_timeseries3 - avg_u3_temporal + + # square of diff -> u'^2 + u1_diff_sq = u1_diff ** 2 + u2_diff_sq = u2_diff ** 2 + u3_diff_sq = u3_diff ** 2 + + # ux'*uy' + u1_diff_xy = u1_diff[:, 0, :] * u1_diff[:, 1, :] + u2_diff_xy = u2_diff[:, 0, :] * u2_diff[:, 1, :] + u3_diff_xy = u3_diff[:, 0, :] * u3_diff[:, 1, :] + + # time_average of u'² and ux'uy' + self.u1_diff_sq_mean = np.mean(u1_diff_sq, axis=0) # time average + self.u2_diff_sq_mean = np.mean(u2_diff_sq, axis=0) # time average + self.u3_diff_sq_mean = np.mean(u3_diff_sq, axis=0) # time average + self.u1_diff_xy_mean = np.mean(u1_diff_xy, axis=0) # time average + self.u2_diff_xy_mean = np.mean(u2_diff_xy, axis=0) # time average + self.u3_diff_xy_mean = np.mean(u3_diff_xy, axis=0) # time average + + if save: # save reynolds stresses + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_x.npy", + np.array([self.y_in_D, self.u1_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_x.npy", + np.array([self.y_in_D, self.u2_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_x.npy", + np.array([self.y_in_D, self.u3_diff_sq_mean[0]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReStress_y.npy", + np.array([self.y_in_D, self.u1_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReStress_y.npy", + np.array([self.y_in_D, self.u2_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReStress_y.npy", + np.array([self.y_in_D, self.u3_diff_sq_mean[1]])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_1_ReShearStress.npy", + np.array([self.y_in_D, self.u1_diff_xy_mean])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_2_ReShearStress.npy", + np.array([self.y_in_D, self.u2_diff_xy_mean])) + np.save( + self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_3_ReShearStress.npy", + np.array([self.y_in_D, self.u3_diff_xy_mean])) + + def save_timeseries_to_files(self, basepath): + # timeseries (i) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "_timeseries_steps.npy", np.array(self.i_timeseries)) + #timeseries (u) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos1" + "_timeseries_data.npy", np.array(self.u_timeseries1)) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos2" + "_timeseries_data.npy", np.array(self.u_timeseries2)) + np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + + "pos3" + "_timeseries_data.npy", np.array(self.u_timeseries3)) + + def plot_velocity_profiles(self, show_reference = False, save = False): + cm = 1 / 2.54 + + if not show_reference: + # PLOT ux + fig, (ax_ux, ax_uy) = plt.subplots(1, 2, constrained_layout=True, + figsize=(30 * cm, 10 * cm)) + ax_ux.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x, self.y_in_D, self.avg_u3_x) + ax_ux.set_xlabel("y/D") + ax_ux.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") + ax_ux.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + # PLOT uy + ax_uy.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y, self.y_in_D, self.avg_u3_y) + ax_uy.set_xlabel("y/D") + ax_uy.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") + ax_uy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_velocity_noReference.png") + plt.close() + else: + plt.show() + else: + # PLOT ux against references + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x - 1, self.y_in_D, self.avg_u3_x - 2) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") + ref_LS = ax.plot(self.p1_LS1993_ux[:, 0], self.p1_LS1993_ux[:, 1], + self.p2_LS1993_ux[:, 0], self.p2_LS1993_ux[:, 1], + self.p3_LS1993_ux[:, 0], + self.p3_LS1993_ux[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_KM = ax.plot(self.p1_KM2000_ux[:, 0], self.p1_KM2000_ux[:, 1], + self.p2_KM2000_ux[:, 0], self.p2_KM2000_ux[:, 1], + self.p3_KM2000_ux[:, 0], + self.p3_KM2000_ux[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_WR = ax.plot(self.p1_WR2008_ux[:, 0], self.p1_WR2008_ux[:, 1], + self.p2_WR2008_ux[:, 0], self.p2_WR2008_ux[:, 1], + self.p3_WR2008_ux[:, 0], + self.p3_WR2008_ux[:, 1]) + plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", + label="Wissink & Rodi (2008)") + ref_DI = ax.plot(self.p1_DI2018_ux[:, 0], self.p1_DI2018_ux[:, 1], + self.p2_DI2018_ux[:, 0], self.p2_DI2018_ux[:, 1], + self.p3_DI2018_ux[:, 0], + self.p3_DI2018_ux[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") + ax.set_ylim((-2.5, +2)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], + ref_DI[0]], loc='best') + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_ux_withReference.png") + plt.close() + else: + plt.show() + + # PLOT uy against references + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y - 1, self.y_in_D, + self.avg_u3_y - 2) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_LS = ax.plot(self.p1_LS1993_uy[:, 0], self.p1_LS1993_uy[:, 1], + self.p2_LS1993_uy[:, 0], self.p2_LS1993_uy[:, 1], + self.p3_LS1993_uy[:, 0], + self.p3_LS1993_uy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_KM = ax.plot(self.p1_KM2000_uy[:, 0], self.p1_KM2000_uy[:, 1], + self.p2_KM2000_uy[:, 0], self.p2_KM2000_uy[:, 1], + self.p3_KM2000_uy[:, 0], + self.p3_KM2000_uy[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_WR = ax.plot(self.p1_WR2008_uy[:, 0], self.p1_WR2008_uy[:, 1], + self.p2_WR2008_uy[:, 0], self.p2_WR2008_uy[:, 1], + self.p3_WR2008_uy[:, 0], + self.p3_WR2008_uy[:, 1]) + plt.setp(ref_WR, ls="dashdot", lw=1.5, marker="", color="k", + label="Wissink & Rodi (2008)") + ref_DI = ax.plot(self.p1_DI2018_uy[:, 0], self.p1_DI2018_uy[:, 1], + self.p2_DI2018_uy[:, 0], self.p2_DI2018_uy[:, 1], + self.p3_DI2018_uy[:, 0], + self.p3_DI2018_uy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") + ax.set_ylim((-2.5, +1.5)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], + ref_DI[0]], loc='best') + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uy_withReference.png") + plt.close() + else: + plt.show() + + def plot_reynolds_stress_profiles(self, show_reference=False, save=False): + cm = 1 / 2.54 + if not show_reference: + fig, (ax_xx, ax_yy, ax_xy) = plt.subplots(1, 3, + figsize=(40 * cm, 10 * cm), + constrained_layout=True) + ax_xx.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, self.u2_diff_sq_mean[0], + self.y_in_D, self.u3_diff_sq_mean[0]) + ax_xx.set_xlabel("y/D") + ax_xx.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") + ax_xx.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + ax_yy.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, self.u2_diff_sq_mean[1], + self.y_in_D, self.u3_diff_sq_mean[1]) + ax_yy.set_xlabel("y/D") + ax_yy.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") + ax_yy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + ax_xy.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, self.u2_diff_xy_mean, self.y_in_D, + self.u3_diff_xy_mean) + ax_xy.set_xlabel("y/D") + ax_xy.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") + ax_xy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_reynoldsStresses_noReference.png") + plt.close() + else: + plt.show() + else: + # plot reynolds stresses against reference + # uxux - streamwise + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, + self.u2_diff_sq_mean[0] - 0.5, self.y_in_D, + self.u3_diff_sq_mean[0] - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_LS = ax.plot(self.p2_LS1993_uxux[:, 0], self.p2_LS1993_uxux[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uxux[:, 0], self.p1_R2016_uxux[:, 1], + self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1], + self.p3_R2016_uxux[:, 0], self.p3_R2016_uxux[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_KM = ax.plot(self.p1_KM2000_uxux[:, 0], self.p1_KM2000_uxux[:, 1], + self.p2_KM2000_uxux[:, 0], self.p2_KM2000_uxux[:, 1], + self.p3_KM2000_uxux[:, 0], self.p3_KM2000_uxux[:, 1]) + plt.setp(ref_KM, ls="dotted", lw=1.5, marker="", color="k", + label="Kravchenko & Moin (2000)") + ref_BM = ax.plot(self.p2_BM1994_uxux[:, 0], self.p2_BM1994_uxux[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_DI = ax.plot(self.p1_DI2018_uxux[:, 0], self.p1_DI2018_uxux[:, 1], + self.p2_DI2018_uxux[:, 0], self.p2_DI2018_uxux[:, 1], + self.p3_DI2018_uxux[:, 0], self.p3_DI2018_uxux[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend( + handles=[my_data[0], ref_LS[0], ref_R[0], ref_KM[0], ref_BM[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxux_withReference.png") + plt.close() + else: + plt.show() + + # uyuy - cross-stream + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, + self.u2_diff_sq_mean[1] - 0.5, self.y_in_D, + self.u3_diff_sq_mean[1] - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_BM = ax.plot(self.p2_BM1994_uyuy[:, 0], self.p2_BM1994_uyuy[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_LS = ax.plot(self.p2_LS1993_uyuy[:, 0], self.p2_LS1993_uyuy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uyuy[:, 0], self.p1_R2016_uyuy[:, 1], + self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1], + self.p3_R2016_uyuy[:, 0], self.p3_R2016_uyuy[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_DI = ax.plot(self.p1_DI2018_uyuy[:, 0], self.p1_DI2018_uyuy[:, 1], + self.p2_DI2018_uyuy[:, 0], self.p2_DI2018_uyuy[:, 1], + self.p3_DI2018_uyuy[:, 0], self.p3_DI2018_uyuy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uyuy_withReference.png") + plt.close() + else: + plt.show() + + # uxuy - Reynolds shear stress + fig, ax = plt.subplots(constrained_layout=True) + my_data = ax.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, + self.u2_diff_xy_mean - 0.5, self.y_in_D, + self.u3_diff_xy_mean - 1) + plt.setp(my_data, ls="-", lw=1, marker="", color="red", + label="lettuce") + ref_BM = ax.plot(self.p2_BM1994_uxuy[:, 0], self.p2_BM1994_uxuy[:, 1]) + plt.setp(ref_BM, ls="dashdot", lw=1.5, marker="", color="k", + label="Beaudan & Moin (1994)") + ref_LS = ax.plot(self.p2_LS1993_uxuy[:, 0], self.p2_LS1993_uxuy[:, 1]) + plt.setp(ref_LS, ls="", lw=1, marker="s", fillstyle='none', + color="k", label="Lorenco & Shih (1993)") + ref_R = ax.plot(self.p1_R2016_uxuy[:, 0], self.p1_R2016_uxuy[:, 1], + self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1], + self.p3_R2016_uxuy[:, 0], self.p3_R2016_uxuy[:, 1]) + plt.setp(ref_R, ls="--", lw=1.5, marker="", color="k", + label="Rajani et al. (2016)") + ref_DI = ax.plot(self.p1_DI2018_uxuy[:, 0], self.p1_DI2018_uxuy[:, 1], + self.p2_DI2018_uxuy[:, 0], self.p2_DI2018_uxuy[:, 1], + self.p3_DI2018_uxuy[:, 0], self.p3_DI2018_uxuy[:, 1]) + plt.setp(ref_DI, ls="--", lw=1.5, marker="", color="tab:blue", + label="Di Ilio et al. (2018)") + ax.set_xlabel("y/D") + ax.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") + ax.set_ylim((-1.2, 0.8)) + ax.set_xlim((-3, 3)) + ax.legend(handles=[my_data[0], ref_BM[0], ref_LS[0], ref_R[0], + ref_DI[0]], loc='best') + + if save: + plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxuy_withReference.png") + plt.close() + else: + plt.show() + + +# PLOTTING + # speichere u-profile + # speichere y_in_D-Achse + # ux ohne REF + # uy ohne ref + # ux MIT REF + # uy mit REF + # BERECHNE turbuklente REynolds spannungen + # SPEICHERE REyynolds-spannungen + # plotte xx, yy, xy spannungen + # speichere plot + # plotte reynoldssüannungen gegen reference + +################################################################ + # SNIP: MP2 fit sinewave to Cl for better frequency-measurement) ### FIT SINEWAVE to converged Cl to get better St-measurement # 1. get converged lift-curve # 2. fit sinewave with starting-freq 0.2 # 3. store freq -# PLOT FORCE COEFFICIENTs together! -> im Skript - -# u-PROFILES - -####################################### - -### load reference data from diIlio_path: -# avg_u_start = 0.5 -# diIlio_path = "../literature/DiIlio_2018/" -# -# # import reference data: (data is: first collumn Y/D, second column u_d/u_char) -# # ux -# p1_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_LS1993.csv', delimiter=';') -# p2_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_LS1993.csv', delimiter=';') -# p3_LS1993_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_LS1993.csv', delimiter=';') -# -# p1_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_KM2000.csv', delimiter=';') -# p2_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_KM2000.csv', delimiter=';') -# p3_KM2000_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_KM2000.csv', delimiter=';') -# -# p1_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_WR2008.csv', delimiter=';') -# p2_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_WR2008.csv', delimiter=';') -# p3_WR2008_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_WR2008.csv', delimiter=';') -# -# p1_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos1_DI2018.csv', delimiter=';') -# p2_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos2_DI2018.csv', delimiter=';') -# p3_DI2018_ux = np.genfromtxt(diIlio_path + 'Fig09_ux_profile_pos3_DI2018.csv', delimiter=';') -# -# # uy -# p1_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_LS1993.csv', delimiter=';') -# p2_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_LS1993.csv', delimiter=';') -# p3_LS1993_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_LS1993.csv', delimiter=';') -# -# p1_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_KM2000.csv', delimiter=';') -# p2_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_KM2000.csv', delimiter=';') -# p3_KM2000_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_KM2000.csv', delimiter=';') -# -# p1_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_WR2008.csv', delimiter=';') -# p2_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_WR2008.csv', delimiter=';') -# p3_WR2008_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_WR2008.csv', delimiter=';') -# -# p1_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos1_DI2018.csv', delimiter=';') -# p2_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos2_DI2018.csv', delimiter=';') -# p3_DI2018_uy = np.genfromtxt(diIlio_path + 'Fig10_uy_profile_pos3_DI2018.csv', delimiter=';') -# -# # uxux -# p1_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_DI2018.csv', delimiter=';') -# p1_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_KM2000.csv', delimiter=';') -# p1_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos1_R2016.csv', delimiter=';') -# p2_BM1994_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_BM1994.csv', delimiter=';') -# p2_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_DI2018.csv', delimiter=';') -# p2_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_KM2000.csv', delimiter=';') -# p2_LS1993_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_LS1993.csv', delimiter=';') -# p2_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos2_R2016.csv', delimiter=';') -# p3_DI2018_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_DI2018.csv', delimiter=';') -# p3_KM2000_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_KM2000.csv', delimiter=';') -# p3_R2016_uxux = np.genfromtxt(diIlio_path + 'Fig11_uxux_profile_pos3_R2016.csv', delimiter=';') -# -# # uyuy -# p1_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos1_DI2018.csv', delimiter=';') -# p1_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos1_R2016.csv', delimiter=';') -# p2_BM1994_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_BM1994.csv', delimiter=';') -# p2_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_DI2018.csv', delimiter=';') -# p2_LS1993_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_LS1993.csv', delimiter=';') -# p2_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos2_R2016.csv', delimiter=';') -# p3_DI2018_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos3_DI2018.csv', delimiter=';') -# p3_R2016_uyuy = np.genfromtxt(diIlio_path + 'Fig12_uyuy_profile_pos3_R2016.csv', delimiter=';') -# -# # uxuy -# p1_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_BM1994.csv', delimiter=';') -# p1_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_DI2018.csv', delimiter=';') -# p1_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos1_R2016.csv', delimiter=';') -# p2_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_BM1994.csv', delimiter=';') -# p2_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_DI2018.csv', delimiter=';') -# p2_LS1993_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_LS1993.csv', delimiter=';') -# p2_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos2_R2016.csv', delimiter=';') -# p3_BM1994_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_BM1994.csv', delimiter=';') -# p3_DI2018_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_DI2018.csv', delimiter=';') -# p3_R2016_uxuy = np.genfromtxt(diIlio_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') -# -# # plot 2x (CO) FIGURES: 5 AvgProfiles (for 10 GPD each) + 1 Legend -# -# gpds = np.arange(24,44,2) -# bc = "ibb1" -# -# color_list = list(matplotlib.colors.TABLEAU_COLORS.keys()) -# colormap = plt.cm.coolwarm#viridis # cmaps: viridis, plasma, inferno, magma, cividis, (tab10) -# color_index_list = np.linspace(0,1,10) -# x_tick_list = np.arange(-3,3+1,1) -# alpha_value = 0.7 -# -# #paths_dict[bc+"_"+co+"_GPD"+str(gpd)] # contains full path -# with matplotlib.rc_context({'lines.linewidth': 0.5,'font.size': 5}): -# for co in cos: -# fig, axs = plt.subplots(3,2, sharex=True, figsize=(3.4876, 3.4876)) # 5 AvgProfiles + 1 Legend in last -# -# if co == "reg": -# co_label = "REG" -# elif co == "kbc": -# co_label = "KBC" -# legend_elements=[] -# color_index = 0 -# for gpd in gpds: -# # LOAD DATA FROM SIM -# avg_u1 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_t-avg.npy") -# avg_u2 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_t-avg.npy") -# avg_u3 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_t-avg.npy") -# -# avg_u1_x = avg_u1[0] # u_x component over y at pos 1 -# avg_u2_x = avg_u2[0] # u_x component over y at pos 2 -# avg_u3_x = avg_u3[0] # u_x component over y at pos 3 -# -# avg_u1_y = avg_u1[1] # u_y component over y at pos 1 -# avg_u2_y = avg_u2[1] # u_y component over y at pos 2 -# avg_u3_y = avg_u3[1] # u_y component over y at pos 3 -# -# y_in_D = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_YinD.npy") -# -# u1_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_x.npy") # contains y_in_D (index 0) and data (index 1) -# u2_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_x.npy") -# u3_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_x.npy") -# u1_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_y.npy") -# u2_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_y.npy") -# u3_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_y.npy") -# -# u1_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReShearStress.npy") -# u2_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReShearStress.npy") -# u3_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReShearStress.npy") -# -# # PLOT DATA IN FIGURE -# axs[0,0].plot(y_in_D, avg_u1_x, y_in_D, avg_u2_x - 1, y_in_D, avg_u3_x - 2, color=colormap(color_index_list[color_index])) -# axs[0,1].plot(y_in_D, avg_u1_y, y_in_D, avg_u2_y - 1, y_in_D, avg_u3_y - 2, color=colormap(color_index_list[color_index])) -# axs[1,0].plot(u1_diff_sq_mean_x[0],u1_diff_sq_mean_x[1], u2_diff_sq_mean_x[0], u2_diff_sq_mean_x[1]-0.5, u3_diff_sq_mean_x[0],u3_diff_sq_mean_x[1]-1, color=colormap(color_index_list[color_index])) -# axs[1,1].plot(u1_diff_sq_mean_y[0],u1_diff_sq_mean_y[1], u2_diff_sq_mean_y[0], u2_diff_sq_mean_y[1]-0.5, u3_diff_sq_mean_y[0],u3_diff_sq_mean_y[1]-1, color=colormap(color_index_list[color_index])) -# axs[2,0].plot(u1_diff_xy_mean[0], u1_diff_xy_mean[1], u2_diff_xy_mean[0], u2_diff_xy_mean[1]-0.5, u3_diff_xy_mean[0], u3_diff_xy_mean[1]-1, color=colormap(color_index_list[color_index])) -# -# # set invisible point for legend entry: -# axs[2,1].plot(-1000,-1000, color=colormap(color_index_list[color_index]), -# #label="GPD "+str(gpd)) -# label=str(gpd)) -# legend_elements.append(matplotlib.lines.Line2D([0],[0], color=colormap(color_index_list[color_index]), lw=1, label=str(gpd))) -# color_index = color_index+1 -# -# for ax in axs.flat: -# ax.set_xticks(ticks=x_tick_list) -# -# axs[0,0].set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") -# axs[0,0].set_ylim([-2.5, +2]) -# axs[0,0].set_xlim([-3, 3]) -# axs[0,0].set_yticks(ticks=[-2,-1,0,1,2]) -# axs[0,0].set_yticks(ticks=[-2.5,-1.5,-0.5,0.5,1.5,2.5], minor=True) -# # axs[0,0].text(-2.8, 1.3, "X/D = 1.06", fontsize=4.5) -# # axs[0,0].text(-2.8, 0.3, "X/D = 1.54", fontsize=4.5) -# # axs[0,0].text(-2.8, -0.7, "X/D = 1.54", fontsize=4.5) -# -# axs[0,1].set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") -# axs[0,1].set_ylim([-2.5, +0.5]) -# axs[0,1].set_xlim([-3, 3]) -# axs[0,1].set_yticks(ticks=[-2,-1,0]) -# axs[0,1].set_yticks(ticks=[-2.5,-1.5,-0.5, 0.5], minor=True) -# -# axs[1,0].set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") -# axs[1,0].set_ylim([-1.2, 0.3]) -# axs[1,0].set_xlim([-3, 3]) -# axs[1,0].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[1,1].set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") -# axs[1,1].set_ylim([-1.2, 0.3]) -# axs[1,1].set_xlim([-3, 3]) -# axs[1,1].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[2,0].set_xlabel("y/D") -# axs[2,0].set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") -# axs[2,0].set_ylim([-1.2, 0.2]) -# axs[2,0].set_xlim([-3, 3]) -# axs[2,0].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[2,1].set_xlabel("y/D") -# axs[2,1].set_ylim([0, 1]) -# axs[2,1].tick_params(left=False, labelleft=False) -# axs[2,1].legend(handles=legend_elements,edgecolor="white",ncol=2, loc="lower left") -# axs[2,1].text(-2.7,0.85,"Collision operator: "+co_label) -# axs[2,1].text(-2.7,0.63,"Resolution in GPD:") -# -# plt.savefig("../plots/3D_Re3900_AvgProfile_GPD_"+co+".png") -# -# # ax.set_xlabel("y/D") -# # ax.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") -# # ax.set_ylim([-2.5, +2]) -# # ax.set_xlim([-3, 3]) -# -# # ax.set_xlabel("y/D") -# # ax.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") -# # ax.set_ylim([-2.5, +1.5]) -# # ax.set_xlim([-3, 3]) -# -# # ax.set_xlabel("y/D") -# # ax.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") -# # ax.set_ylim([-1.2, 0.8]) -# # ax.set_xlim([-3, 3]) -# -# # ax.set_xlabel("y/D") -# # ax.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") -# # ax.set_ylim([-1.2, 0.8]) -# # ax.set_xlim([-3, 3]) -# -# # ax.set_xlabel("y/D") -# # ax.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") -# # ax.set_ylim([-1.2, 0.8]) -# # ax.set_xlim([-3, 3]) -# -# -# -# ### highest Res for both CO against literature -# gpd=42 -# with matplotlib.rc_context({'lines.linewidth': 0.5,'font.size': 5, 'lines.markersize': 1.2, 'lines.markeredgewidth': 0.3}): -# for co in cos: -# fig, axs = plt.subplots(3,2, sharex=True, figsize=(3.4876, 3.4876)) # 5 AvgProfiles + 1 Legend in last -# -# if co == "reg": -# co_label = "REG" -# elif co == "kbc": -# co_label = "KBC" -# -# # LOAD DATA FROM SIM -# avg_u1 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_t-avg.npy") -# avg_u2 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_t-avg.npy") -# avg_u3 = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_t-avg.npy") -# -# avg_u1_x = avg_u1[0] # u_x component over y at pos 1 -# avg_u2_x = avg_u2[0] # u_x component over y at pos 2 -# avg_u3_x = avg_u3[0] # u_x component over y at pos 3 -# -# avg_u1_y = avg_u1[1] # u_y component over y at pos 1 -# avg_u2_y = avg_u2[1] # u_y component over y at pos 2 -# avg_u3_y = avg_u3[1] # u_y component over y at pos 3 -# -# y_in_D = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_YinD.npy") -# -# u1_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_x.npy") # contains y_in_D (index 0) and data (index 1) -# u2_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_x.npy") -# u3_diff_sq_mean_x = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_x.npy") -# u1_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReStress_y.npy") -# u2_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReStress_y.npy") -# u3_diff_sq_mean_y = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReStress_y.npy") -# -# u1_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_1_ReShearStress.npy") -# u2_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_2_ReShearStress.npy") -# u3_diff_xy_mean = np.load(paths_dict[bc+"_"+co+"_GPD"+str(gpd)][0]+"/AvgVelocity_Data" + "/AvgVelocity_3_ReShearStress.npy") -# -# # PLOT DATA IN FIGURE -# my_data = axs[0,0].plot(y_in_D, avg_u1_x, y_in_D, avg_u2_x - 1, y_in_D, avg_u3_x - 2, color="red", label="present") -# ref_LS = axs[0,0].plot(p1_LS1993_ux[:, 0], p1_LS1993_ux[:, 1], p2_LS1993_ux[:, 0], p2_LS1993_ux[:, 1], p3_LS1993_ux[:, 0], -# p3_LS1993_ux[:, 1], marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") -# ref_KM = axs[0,0].plot(p1_KM2000_ux[:, 0], p1_KM2000_ux[:, 1], p2_KM2000_ux[:, 0], p2_KM2000_ux[:, 1], p3_KM2000_ux[:, 0], -# p3_KM2000_ux[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") -# ref_WR = axs[0,0].plot(p1_WR2008_ux[:, 0], p1_WR2008_ux[:, 1], p2_WR2008_ux[:, 0], p2_WR2008_ux[:, 1], p3_WR2008_ux[:, 0], -# p3_WR2008_ux[:, 1], ls="dashdot", marker="", color="k", label="Wissink & Rodi (2008)") -# ref_DI = axs[0,0].plot(p1_DI2018_ux[:, 0], p1_DI2018_ux[:, 1], p2_DI2018_ux[:, 0], p2_DI2018_ux[:, 1], p3_DI2018_ux[:, 0], -# p3_DI2018_ux[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") -# -# axs[0,1].plot(y_in_D, avg_u1_y, y_in_D, avg_u2_y - 1, y_in_D, avg_u3_y - 2, color="red", label="present") -# ref_LS = axs[0,1].plot(p1_LS1993_uy[:, 0], p1_LS1993_uy[:, 1], p2_LS1993_uy[:, 0], p2_LS1993_uy[:, 1], p3_LS1993_uy[:, 0], -# p3_LS1993_uy[:, 1], ls="",marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") -# ref_KM = axs[0,1].plot(p1_KM2000_uy[:, 0], p1_KM2000_uy[:, 1], p2_KM2000_uy[:, 0], p2_KM2000_uy[:, 1], p3_KM2000_uy[:, 0], -# p3_KM2000_uy[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") -# ref_WR = axs[0,1].plot(p1_WR2008_uy[:, 0], p1_WR2008_uy[:, 1], p2_WR2008_uy[:, 0], p2_WR2008_uy[:, 1], p3_WR2008_uy[:, 0], -# p3_WR2008_uy[:, 1], ls="dashdot", marker="", color="k", label="Wissink & Rodi (2008)") -# ref_DI = axs[0,1].plot(p1_DI2018_uy[:, 0], p1_DI2018_uy[:, 1], p2_DI2018_uy[:, 0], p2_DI2018_uy[:, 1], p3_DI2018_uy[:, 0], -# p3_DI2018_uy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") -# -# axs[1,0].plot(u1_diff_sq_mean_x[0],u1_diff_sq_mean_x[1], u2_diff_sq_mean_x[0], u2_diff_sq_mean_x[1]-0.5, u3_diff_sq_mean_x[0],u3_diff_sq_mean_x[1]-1, color="red", label="present") -# ref_LS = axs[1,0].plot(p2_LS1993_uxux[:, 0], p2_LS1993_uxux[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") -# ref_R = axs[1,0].plot(p1_R2016_uxux[:, 0], p1_R2016_uxux[:, 1], p3_R2016_uxux[:, 0], p3_R2016_uxux[:, 1], -# p3_R2016_uxux[:, 0], p3_R2016_uxux[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") -# ref_KM = axs[1,0].plot(p1_KM2000_uxux[:, 0], p1_KM2000_uxux[:, 1], p2_KM2000_uxux[:, 0], p2_KM2000_uxux[:, 1], -# p3_KM2000_uxux[:, 0], p3_KM2000_uxux[:, 1], ls="dotted", marker="", color="k", label="Kravchenko & Moin (2000)") -# ref_BM = axs[1,0].plot(p2_BM1994_uxux[:, 0], p2_BM1994_uxux[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") -# ref_DI = axs[1,0].plot(p1_DI2018_uxux[:, 0], p1_DI2018_uxux[:, 1], p2_DI2018_uxux[:, 0], p2_DI2018_uxux[:, 1], -# p3_DI2018_uxux[:, 0], p3_DI2018_uxux[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") -# -# axs[1,1].plot(u1_diff_sq_mean_y[0],u1_diff_sq_mean_y[1], u2_diff_sq_mean_y[0], u2_diff_sq_mean_y[1]-0.5, u3_diff_sq_mean_y[0],u3_diff_sq_mean_y[1]-1, color="red", label="present") -# ref_BM = axs[1,1].plot(p2_BM1994_uyuy[:, 0], p2_BM1994_uyuy[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") -# ref_LS = axs[1,1].plot(p2_LS1993_uyuy[:, 0], p2_LS1993_uyuy[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") -# ref_R = axs[1,1].plot(p1_R2016_uyuy[:, 0], p1_R2016_uyuy[:, 1], p3_R2016_uyuy[:, 0], p3_R2016_uyuy[:, 1], -# p3_R2016_uyuy[:, 0], p3_R2016_uyuy[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") -# ref_DI = axs[1,1].plot(p1_DI2018_uyuy[:, 0], p1_DI2018_uyuy[:, 1], p2_DI2018_uyuy[:, 0], p2_DI2018_uyuy[:, 1], -# p3_DI2018_uyuy[:, 0], p3_DI2018_uyuy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") -# -# axs[2,0].plot(u1_diff_xy_mean[0], u1_diff_xy_mean[1], u2_diff_xy_mean[0], u2_diff_xy_mean[1]-0.5, u3_diff_xy_mean[0], u3_diff_xy_mean[1]-1, color="red", label="present") -# ref_BM = axs[2,0].plot(p2_BM1994_uxuy[:, 0], p2_BM1994_uxuy[:, 1], ls="--", marker="", color="g", label="Beaudan & Moin (1994)") -# ref_LS = axs[2,0].plot(p2_LS1993_uxuy[:, 0], p2_LS1993_uxuy[:, 1], ls="", marker="s", fillstyle='none', color="k", label="Lorenco & Shih (1993)") -# ref_R = axs[2,0].plot(p1_R2016_uxuy[:, 0], p1_R2016_uxuy[:, 1], p3_R2016_uxuy[:, 0], p3_R2016_uxuy[:, 1], -# p3_R2016_uxuy[:, 0], p3_R2016_uxuy[:, 1], ls="--", marker="", color="k", label="Rajani et al. (2016)") -# ref_DI = axs[2,0].plot(p1_DI2018_uxuy[:, 0], p1_DI2018_uxuy[:, 1], p2_DI2018_uxuy[:, 0], p2_DI2018_uxuy[:, 1], -# p3_DI2018_uxuy[:, 0], p3_DI2018_uxuy[:, 1], ls="--", marker="", color="tab:blue", label="Di Ilio et al. (2018)") -# -# # set invisible point for legend entry: -# axs[2,1].legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0], ref_R[0], ref_BM[0]],edgecolor="white", loc="lower center", fontsize=4.5) -# color_index = color_index+1 -# -# for ax in axs.flat: -# ax.set_xticks(ticks=x_tick_list) -# -# axs[0,0].set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") -# axs[0,0].set_ylim([-2.5, +2]) -# axs[0,0].set_xlim([-3, 3]) -# axs[0,0].set_yticks(ticks=[-2,-1,0,1,2]) -# axs[0,0].set_yticks(ticks=[-2.5,-1.5,-0.5,0.5,1.5,2.5], minor=True) -# # axs[0,0].text(-2.8, 1.3, "X/D = 1.06", fontsize=4.5) -# # axs[0,0].text(-2.8, 0.3, "X/D = 1.54", fontsize=4.5) -# # axs[0,0].text(-2.8, -0.7, "X/D = 1.54", fontsize=4.5) -# -# axs[0,1].set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") -# axs[0,1].set_ylim([-2.5, +0.5]) -# axs[0,1].set_xlim([-3, 3]) -# axs[0,1].set_yticks(ticks=[-2,-1,0]) -# axs[0,1].set_yticks(ticks=[-2.5,-1.5,-0.5, 0.5], minor=True) -# -# axs[1,0].set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") -# axs[1,0].set_ylim([-1.2, 0.3]) -# axs[1,0].set_xlim([-3, 3]) -# axs[1,0].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[1,1].set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") -# axs[1,1].set_ylim([-1.2, 0.3]) -# axs[1,1].set_xlim([-3, 3]) -# axs[1,1].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[2,0].set_xlabel("y/D") -# axs[2,0].set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") -# axs[2,0].set_ylim([-1.2, 0.2]) -# axs[2,0].set_xlim([-3, 3]) -# axs[2,0].set_yticks(ticks=[-1,-0.5,0]) -# -# axs[2,1].set_xlabel("y/D") -# axs[2,1].tick_params(left=False, labelleft=False) -# axs[2,1].set_ylim([0, 1]) -# #axs[2,1].text(-0.8,0.8,co_label, fontsize=11) -# axs[2,1].text(-2.7,0.85,"Collision operator: "+co_label) -# -# plt.savefig("../plots/3D_Re3900_AvgProfile_vsLit_"+co+".png") \ No newline at end of file +# OPTIONAL: PLOT FORCE COEFFICIENTs together! -> im Skript diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv new file mode 100644 index 00000000..0cc4ba74 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_DI2018.csv @@ -0,0 +1,67 @@ +-2.984375000000001; 1.0574712643678161 +-2.890625000000001; 1.0651340996168583 +-2.718750000000001; 1.0689655172413794 +-2.515625000000001; 1.0766283524904217 +-2.304687500000001; 1.091954022988506 +-2.054687500000001; 1.10727969348659 +-1.7578125000000009; 1.1379310344827587 +-1.5156250000000004; 1.1570881226053642 +-1.2187500000000004; 1.2107279693486592 +-0.9687500000000004; 1.2605363984674332 +-0.8125000000000004; 1.3065134099616862 +-0.7734375000000004; 1.318007662835249 +-0.734375; 1.3256704980842913 +-0.7031250000000004; 1.318007662835249 +-0.6796875000000004; 1.2796934865900385 +-0.6562500000000009; 1.2260536398467434 +-0.6406250000000004; 1.149425287356322 +-0.6132812500000004; 1.0191570881226055 +-0.58984375; 0.8812260536398469 +-0.5625000000000004; 0.6973180076628351 +-0.5312500000000004; 0.5057471264367814 +-0.48828125; 0.30651340996168597 +-0.44140625000000044; 0.1724137931034484 +-0.40234375000000044; 0.09578544061302674 +-0.33984375; 0.019157088122605526 +-0.25390625000000044; -0.06513409961685834 +-0.18359375000000044; -0.13409961685823735 +-0.109375; -0.2030651340996168 +-0.03125; -0.2452107279693485 +0; -0.2567049808429118 +0.03125; -0.26053639846743293 +0.062499999999999556; -0.2528735632183907 +0.1015625; -0.22222222222222188 +0.15624999999999956; -0.1685823754789273 +0.22656250000000044; -0.09195402298850563 +0.30468749999999956; -0.02298850574712663 +0.37499999999999956; 0.06130268199233724 +0.42187499999999956; 0.13026819923371624 +0.46484375000000044; 0.24904214559387006 +0.49218749999999956; 0.360153256704981 +0.5234374999999996; 0.5134099616858239 +0.5468749999999996; 0.6436781609195406 +0.5703125000000004; 0.8122605363984676 +0.5976562499999996; 0.9501915708812263 +0.6289062499999996; 1.1187739463601534 +0.6718750000000004; 1.264367816091954 +0.6914062499999996; 1.302681992337165 +0.7265624999999996; 1.3256704980842913 +0.7656249999999996; 1.3256704980842913 +0.8125000000000004; 1.310344827586207 +0.8671874999999996; 1.2950191570881229 +0.9296875000000004; 1.2758620689655176 +1.0078124999999996; 1.252873563218391 +1.1015625000000004; 1.2298850574712645 +1.2109375000000004; 1.2068965517241381 +1.3671875000000004; 1.1800766283524906 +1.4999999999999996; 1.1609195402298853 +1.6328125000000004; 1.1455938697318009 +1.7968750000000004; 1.1302681992337167 +1.9531250000000004; 1.1149425287356323 +2.0937500000000004; 1.1034482758620692 +2.2500000000000004; 1.0957854406130272 +2.4218750000000013; 1.0842911877394639 +2.5937500000000004; 1.0766283524904217 +2.7968750000000004; 1.0651340996168583 +2.9687500000000004; 1.0613026819923372 +2.9960937500000004; 1.0613026819923372 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv new file mode 100644 index 00000000..5e0bc661 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_KM2000.csv @@ -0,0 +1,125 @@ +-2.984375000000001; 1.0613026819923372 +-2.9179687500000013; 1.0613026819923372 +-2.8515625000000013; 1.0613026819923372 +-2.789062500000001; 1.0651340996168583 +-2.718750000000001; 1.0689655172413797 +-2.652343750000001; 1.0689655172413794 +-2.582031250000001; 1.0766283524904217 +-2.5195312500000013; 1.0766283524904217 +-2.453125000000001; 1.0804597701149428 +-2.386718750000001; 1.0842911877394636 +-2.324218750000001; 1.0842911877394636 +-2.253906250000001; 1.0919540229885059 +-2.187500000000001; 1.0957854406130272 +-2.121093750000001; 1.099616858237548 +-2.054687500000001; 1.1034482758620692 +-1.9882812500000009; 1.1149425287356323 +-1.9218750000000007; 1.1149425287356323 +-1.8554687500000009; 1.1149425287356323 +-1.7890625000000009; 1.1226053639846745 +-1.7226562500000009; 1.1302681992337165 +-1.6562500000000004; 1.1340996168582376 +-1.5898437500000004; 1.14176245210728 +-1.5234375000000004; 1.145593869731801 +-1.4570312500000004; 1.153256704980843 +-1.3945312500000004; 1.1609195402298853 +-1.3203125000000009; 1.1724137931034484 +-1.2578125000000004; 1.1839080459770117 +-1.1953125000000009; 1.2068965517241381 +-1.1289062500000004; 1.2183908045977014 +-1.0625000000000004; 1.2298850574712645 +-1.0000000000000004; 1.245210727969349 +-0.9375000000000004; 1.2605363984674332 +-0.8710937500000004; 1.2758620689655176 +-0.8085937500000004; 1.3026819923371653 +-0.7500000000000004; 1.3218390804597706 +-0.6914062500000009; 1.3065134099616857 +-0.6562500000000009; 1.2490421455938698 +-0.6406250000000004; 1.1839080459770117 +-0.625; 1.1187739463601534 +-0.6210937500000009; 1.0574712643678161 +-0.6132812500000004; 0.9923371647509578 +-0.6054687500000004; 0.9233716475095788 +-0.5976562500000004; 0.8620689655172413 +-0.5898437500000009; 0.7931034482758623 +-0.5781250000000004; 0.735632183908046 +-0.5742187500000004; 0.6628352490421456 +-0.5664062500000004; 0.5977011494252873 +-0.55859375; 0.5325670498084292 +-0.5468750000000004; 0.47126436781609193 +-0.5390625000000004; 0.4022988505747125 +-0.5273437500000004; 0.33716475095785436 +-0.5156250000000009; 0.27203065134099624 +-0.4921875; 0.210727969348659 +-0.47656250000000044; 0.14942528735632177 +-0.43359375000000044; 0.10344827586206895 +-0.39453125000000044; 0.04980842911877392 +-0.3515625; -0.003831417624521105 +-0.30078125000000044; -0.04214559386973171 +-0.25000000000000044; -0.08812260536398453 +-0.19140625000000044; -0.12643678160919558 +-0.13671875; -0.16091954022988508 +-0.08203125000000044; -0.19540229885057459 +-0.015625000000000444; -0.210727969348659 +0.046874999999999556; -0.2145593869731801 +0.10937499999999956; -0.18773946360153237 +0.16015624999999956; -0.14176245210727956 +0.21875000000000044; -0.10727969348659006 +0.26953124999999956; -0.05363984674329503 +0.31640624999999956; -0.026819923371647736 +0.36718749999999956; 0.01532567049808442 +0.41406249999999956; 0.06513409961685834 +0.44921874999999956; 0.11877394636015337 +0.48437499999999956; 0.1724137931034484 +0.5039062500000004; 0.23754789272030674 +0.5234374999999996; 0.298850574712644 +0.5390625000000004; 0.3639846743295021 +0.5546874999999996; 0.4291187739463602 +0.5585937499999996; 0.49425287356321834 +0.5664062500000004; 0.5593869731800767 +0.5781250000000004; 0.6245210727969348 +0.5898437499999996; 0.685823754789272 +0.5976562499999996; 0.7509578544061302 +0.6054687500000004; 0.8199233716475098 +0.6132812499999996; 0.8812260536398469 +0.6210937499999996; 0.9501915708812263 +0.6249999999999996; 1.0114942528735633 +0.6406250000000004; 1.0804597701149428 +0.6445312500000004; 1.1417624521072798 +0.6562499999999996; 1.2068965517241381 +0.6718750000000004; 1.2720306513409965 +0.7148437500000004; 1.318007662835249 +0.7812500000000004; 1.3218390804597702 +0.8437500000000004; 1.2988505747126438 +0.9062499999999996; 1.2796934865900385 +0.9687499999999996; 1.256704980842912 +1.0312500000000004; 1.2375478927203065 +1.0976562500000004; 1.2183908045977014 +1.1601562500000004; 1.2068965517241381 +1.2265625000000004; 1.1954022988505748 +1.2929687499999996; 1.1877394636015326 +1.3593749999999996; 1.1724137931034484 +1.4257812500000004; 1.1724137931034484 +1.4882812500000004; 1.153256704980843 +1.5546875000000004; 1.149425287356322 +1.6250000000000004; 1.1417624521072798 +1.6914062500000004; 1.1340996168582376 +1.7539062500000004; 1.1302681992337165 +1.8203125000000004; 1.1187739463601534 +1.8906250000000004; 1.1149425287356323 +1.9570312500000004; 1.1072796934865903 +2.0273437500000004; 1.1072796934865905 +2.0898437500000004; 1.1034482758620692 +2.1562500000000004; 1.1034482758620692 +2.2226562500000004; 1.099616858237548 +2.2851562500000004; 1.0919540229885059 +2.3593750000000013; 1.088122605363985 +2.4218750000000013; 1.0842911877394636 +2.4843750000000004; 1.0766283524904217 +2.5546875000000004; 1.0804597701149428 +2.6210937500000004; 1.0804597701149428 +2.6875000000000004; 1.0804597701149428 +2.7500000000000004; 1.0651340996168583 +2.8203124999999996; 1.0613026819923372 +2.8906250000000004; 1.0613026819923372 +2.9531250000000004; 1.0613026819923372 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_LS1993.csv new file mode 100644 index 00000000..e7753bc6 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_LS1993.csv @@ -0,0 +1,41 @@ +-1.5976562500000009; 1.1226053639846745 +-1.5156250000000009; 1.1302681992337165 +-1.4375000000000004; 1.1379310344827587 +-1.3554687500000009; 1.153256704980843 +-1.2773437500000004; 1.160919540229885 +-1.1953125000000004; 1.1800766283524908 +-1.1132812500000004; 1.1839080459770117 +-1.0390625000000009; 1.1954022988505746 +-0.9609375000000004; 1.2145593869731803 +-0.8828125000000004; 1.2375478927203067 +-0.80078125; 1.2375478927203065 +-0.7148437500000004; 1.1992337164750957 +-0.6367187500000004; 1.0076628352490422 +-0.55859375; 0.7088122605363982 +-0.4804687500000009; 0.4406130268199233 +-0.40234375000000044; 0.2490421455938696 +-0.31640625; 0.1034482758620694 +-0.2421875; -0.05363984674329547 +-0.16406250000000044; -0.1340996168582378 +-0.08593750000000044; -0.18390804597701216 +0; -0.22222222222222188 +0.08203124999999956; -0.2030651340996168 +0.16015624999999956; -0.14176245210727956 +0.23046874999999956; -0.03831417624521105 +0.31640624999999956; 0.11494252873563227 +0.39843750000000044; 0.27586206896551735 +0.47656250000000044; 0.4444444444444444 +0.5624999999999996; 0.7126436781609198 +0.6328125000000004; 1.0000000000000002 +0.7148437500000004; 1.1839080459770117 +0.7968749999999996; 1.2490421455938698 +0.8789062500000004; 1.2452107279693487 +0.9609375000000004; 1.2298850574712645 +1.0390625000000004; 1.210727969348659 +1.1171874999999996; 1.199233716475096 +1.1992187500000004; 1.195402298850575 +1.2773437500000004; 1.191570881226054 +1.3554687500000004; 1.1685823754789273 +1.4335937500000004; 1.1685823754789275 +1.5195312500000004; 1.1455938697318009 +1.5976562500000004; 1.1570881226053642 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_WR2008.csv new file mode 100644 index 00000000..9f8c10d4 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos1_WR2008.csv @@ -0,0 +1,100 @@ +-2.960937500000001; 1.0727969348659006 +-2.882812500000001; 1.0727969348659006 +-2.800781250000001; 1.0766283524904217 +-2.7148437500000027; 1.0804597701149425 +-2.636718750000001; 1.080459770114943 +-2.554687500000001; 1.0842911877394636 +-2.468750000000001; 1.091954022988506 +-2.382812500000001; 1.095785440613027 +-2.3046875000000013; 1.099616858237548 +-2.226562500000001; 1.10727969348659 +-2.140625000000001; 1.1111111111111112 +-2.058593750000001; 1.1111111111111112 +-1.9804687500000007; 1.1226053639846745 +-1.894531250000001; 1.1226053639846745 +-1.8125000000000009; 1.1302681992337167 +-1.7343750000000009; 1.1379310344827587 +-1.6484375000000009; 1.1455938697318009 +-1.5625000000000009; 1.153256704980843 +-1.4765625000000009; 1.153256704980843 +-1.3906250000000009; 1.1647509578544064 +-1.3203125000000009; 1.1724137931034482 +-1.23828125; 1.1800766283524906 +-1.1562500000000004; 1.203065134099617 +-1.0781250000000004; 1.21455938697318 +-1.0039062500000004; 1.2222222222222223 +-0.9218750000000013; 1.2413793103448272 +-0.83984375; 1.252873563218391 +-0.7578125; 1.264367816091954 +-0.6875000000000004; 1.2298850574712643 +-0.6523437500000004; 1.153256704980843 +-0.6406250000000004; 1.0804597701149428 +-0.62109375; 0.9961685823754789 +-0.6054687500000004; 0.9195402298850577 +-0.59375; 0.8390804597701149 +-0.5820312500000004; 0.7586206896551726 +-0.5703125000000004; 0.6781609195402301 +-0.5507812500000009; 0.6015325670498086 +-0.5468750000000004; 0.5210727969348661 +-0.5312500000000004; 0.4406130268199233 +-0.5078125000000004; 0.360153256704981 +-0.47656250000000044; 0.29118773946360177 +-0.42968750000000044; 0.22222222222222232 +-0.3789062500000009; 0.15708812260536398 +-0.34765625; 0.08045977011494232 +-0.30078125000000044; 0.011494252873563315 +-0.26171875000000044; -0.05747126436781613 +-0.21875000000000044; -0.12260536398467403 +-0.16796875; -0.18773946360153282 +-0.109375; -0.2452107279693485 +-0.046875000000000444; -0.29885057471264354 +0.03515625; -0.29501915708812243 +0.10546875; -0.2567049808429118 +0.16796874999999956; -0.2030651340996168 +0.20703124999999956; -0.13409961685823735 +0.26171874999999956; -0.07662835249042166 +0.28515625000000044; -0.003831417624521105 +0.34374999999999956; 0.06130268199233724 +0.37890624999999956; 0.1340996168582378 +0.42187499999999956; 0.2030651340996168 +0.46484375000000044; 0.27586206896551735 +0.49609375000000044; 0.34865900383141746 +0.5273437499999996; 0.4252873563218391 +0.5429687500000004; 0.5057471264367814 +0.5585937499999996; 0.5823754789272031 +0.5742187500000004; 0.6666666666666667 +0.5820312499999996; 0.7394636015325671 +0.5898437499999996; 0.8237547892720309 +0.5976562499999996; 0.9042145593869733 +0.6132812500000004; 1.0000000000000002 +0.6328125000000004; 1.0804597701149428 +0.6562499999999996; 1.160919540229885 +0.6757812500000004; 1.2375478927203065 +0.7382812500000004; 1.2873563218390804 +0.8203125000000004; 1.2720306513409965 +0.8945312499999996; 1.245210727969349 +0.9765624999999996; 1.2222222222222225 +1.0546875000000004; 1.203065134099617 +1.1367187500000004; 1.1954022988505748 +1.2187499999999996; 1.1839080459770117 +1.2968750000000004; 1.1839080459770117 +1.3750000000000004; 1.1647509578544064 +1.4609375000000004; 1.1647509578544064 +1.5429687500000004; 1.153256704980843 +1.6250000000000004; 1.14176245210728 +1.7031250000000004; 1.1340996168582376 +1.7851562500000004; 1.1226053639846745 +1.8671875000000004; 1.1226053639846745 +1.9492187500000004; 1.1072796934865903 +2.0351562500000004; 1.10727969348659 +2.1132812500000004; 1.095785440613027 +2.1953125000000004; 1.091954022988506 +2.2812500000000013; 1.088122605363985 +2.3632812500000004; 1.0842911877394636 +2.4453125000000004; 1.0804597701149428 +2.5312500000000004; 1.0804597701149428 +2.6093750000000004; 1.0804597701149428 +2.6875000000000004; 1.0804597701149428 +2.7734375000000013; 1.0727969348659006 +2.8593749999999996; 1.0727969348659006 +2.9335937499999996; 1.0613026819923372 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_DI2018.csv new file mode 100644 index 00000000..72da7543 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_DI2018.csv @@ -0,0 +1,50 @@ +-3.000000000000001; 0.05363984674329503 +-2.828125000000001; 0.06130268199233724 +-2.625000000000001; 0.06896551724137945 +-2.351562500000001; 0.07662835249042121 +-2.101562500000001; 0.08812260536398497 +-1.8125000000000009; 0.10344827586206895 +-1.5078125000000009; 0.11877394636015337 +-1.1484375000000004; 0.14176245210728 +-0.8828125000000004; 0.15325670498084287 +-0.8046875; 0.14559386973180066 +-0.7500000000000004; 0.12260536398467448 +-0.6914062500000009; 0.08812260536398453 +-0.6406250000000004; 0.026819923371647292 +-0.59375; -0.05747126436781658 +-0.5468750000000004; -0.1647509578544062 +-0.48828125; -0.31417624521072796 +-0.42968750000000044; -0.47509578544061304 +-0.36328125000000044; -0.6398467432950192 +-0.29687500000000044; -0.7854406130268199 +-0.21484375000000044; -0.9540229885057472 +-0.12890625000000044; -1.0957854406130267 +-0.08593749999999956; -1.157088122605364 +-0.02734375; -1.1954022988505746 +0.0078125; -1.2030651340996172 +0.046875; -1.1877394636015324 +0.08593749999999956; -1.1532567049808429 +0.12499999999999956; -1.1034482758620694 +0.17968749999999956; -0.9961685823754789 +0.22656249999999956; -0.8965517241379315 +0.30468749999999956; -0.7241379310344822 +0.39062500000000044; -0.5440613026819925 +0.47656249999999956; -0.35249042145593856 +0.5546874999999996; -0.1762452107279695 +0.6171874999999996; -0.04214559386973171 +0.6562499999999996; 0.022988505747126187 +0.7070312500000004; 0.07279693486590011 +0.7929687499999996; 0.11877394636015337 +0.8828125000000004; 0.1340996168582378 +0.9843750000000004; 0.1340996168582378 +1.0781249999999996; 0.13026819923371624 +1.1874999999999996; 0.12643678160919558 +1.3125000000000004; 0.12260536398467403 +1.4375000000000004; 0.11494252873563227 +1.6953125000000004; 0.10344827586206895 +1.9921875000000004; 0.08812260536398497 +2.2187500000000004; 0.08045977011494232 +2.4687500000000004; 0.07279693486590011 +2.6953125000000004; 0.0651340996168579 +2.9062500000000013; 0.05747126436781613 +3.0000000000000004; 0.05747126436781613 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_KM2000.csv new file mode 100644 index 00000000..e3ad61a9 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_KM2000.csv @@ -0,0 +1,119 @@ +-2.984375000000001; 0.05363984674329503 +-2.917968750000001; 0.05747126436781613 +-2.855468750000001; 0.06130268199233724 +-2.781250000000001; 0.06513409961685834 +-2.718750000000001; 0.06130268199233724 +-2.652343750000001; 0.06513409961685834 +-2.585937500000001; 0.07279693486590055 +-2.5195312500000013; 0.07279693486590055 +-2.449218750000001; 0.07279693486590011 +-2.390625000000001; 0.08045977011494232 +-2.312500000000001; 0.08045977011494232 +-2.250000000000001; 0.08045977011494232 +-2.187500000000001; 0.08429118773946387 +-2.121093750000001; 0.08812260536398497 +-2.050781250000001; 0.09195402298850563 +-1.9882812500000009; 0.09578544061302674 +-1.9179687500000004; 0.10344827586206895 +-1.8554687500000009; 0.10344827586206895 +-1.7812500000000004; 0.10344827586206895 +-1.7187500000000009; 0.10727969348659006 +-1.6523437500000009; 0.11111111111111116 +-1.5859375000000004; 0.10727969348659006 +-1.5156250000000004; 0.11494252873563227 +-1.4531250000000004; 0.12260536398467448 +-1.3828125000000004; 0.12260536398467448 +-1.3164062500000009; 0.1340996168582378 +-1.2500000000000004; 0.1340996168582378 +-1.1835937500000004; 0.14176245210728 +-1.1210937500000004; 0.14559386973180066 +-1.0507812500000004; 0.14942528735632177 +-0.9843750000000004; 0.15708812260536398 +-0.9218750000000004; 0.16091954022988508 +-0.8515625000000004; 0.16091954022988508 +-0.7851562500000004; 0.1685823754789273 +-0.7265625000000009; 0.14559386973180066 +-0.6796875000000004; 0.09961685823754785 +-0.6367187500000004; 0.04597701149425282 +-0.6132812500000004; -0.015325670498083976 +-0.5859375000000009; -0.07662835249042166 +-0.5664062500000004; -0.13409961685823735 +-0.5468750000000004; -0.1992337164750957 +-0.5273437500000004; -0.26053639846743337 +-0.5078125000000004; -0.32183908045977017 +-0.4843750000000009; -0.3831417624521074 +-0.46875000000000044; -0.44827586206896575 +-0.4492187500000009; -0.509578544061303 +-0.42578125000000044; -0.5708812260536402 +-0.40234375000000044; -0.632183908045977 +-0.375; -0.6896551724137931 +-0.34375; -0.7509578544061304 +-0.3125; -0.8084291187739461 +-0.28906250000000044; -0.8697318007662833 +-0.26562500000000044; -0.9272030651340999 +-0.234375; -0.9846743295019156 +-0.2109375; -1.0459770114942533 +-0.17968750000000044; -1.1034482758620694 +-0.15625000000000044; -1.1685823754789273 +-0.11718750000000044; -1.2222222222222223 +-0.08203125000000044; -1.2758620689655173 +-0.03125; -1.3141762452107284 +0.03515625; -1.3141762452107284 +0.08203125; -1.2681992337164751 +0.11718749999999956; -1.2145593869731806 +0.15234374999999956; -1.157088122605364 +0.18359375000000044; -1.0996168582375483 +0.21484374999999956; -1.0421455938697322 +0.24218749999999956; -0.9808429118773945 +0.26171875000000044; -0.9195402298850577 +0.29687500000000044; -0.8620689655172415 +0.32031250000000044; -0.804597701149425 +0.34765624999999956; -0.7432950191570882 +0.37890624999999956; -0.685823754789272 +0.40234374999999956; -0.6245210727969348 +0.42187499999999956; -0.5593869731800765 +0.44921874999999956; -0.5019157088122608 +0.48046874999999956; -0.44444444444444464 +0.49218749999999956; -0.3793103448275863 +0.5195312499999996; -0.31800766283524906 +0.5468749999999996; -0.1915708812260526 +0.5742187500000004; -0.13409961685823735 +0.5937499999999996; -0.06896551724137945 +0.6132812499999996; -0.003831417624521105 +0.6484374999999996; 0.04980842911877392 +0.6796874999999996; 0.10727969348659006 +0.7343749999999996; 0.14942528735632177 +0.7929687499999996; 0.1685823754789273 +0.8632812499999996; 0.1685823754789273 +0.9257812500000004; 0.1647509578544062 +0.9960937500000004; 0.1647509578544062 +1.0585937500000004; 0.16091954022988508 +1.1250000000000004; 0.14942528735632177 +1.1953125000000004; 0.14559386973180066 +1.2617187500000004; 0.14559386973180066 +1.3281250000000004; 0.1379310344827589 +1.3945312499999996; 0.14176245210728 +1.4570312500000004; 0.1340996168582378 +1.5234375000000004; 0.12260536398467403 +1.5898437500000004; 0.11494252873563227 +1.6562500000000004; 0.11111111111111116 +1.7265625000000004; 0.10727969348659006 +1.7890625000000004; 0.0996168582375474 +1.8593750000000004; 0.10344827586206895 +1.9257812500000004; 0.09578544061302674 +1.9921875000000004; 0.09195402298850563 +2.0546875000000004; 0.08429118773946387 +2.1250000000000004; 0.08429118773946387 +2.1914062500000004; 0.08429118773946387 +2.2578125000000004; 0.08045977011494232 +2.324218750000004; 0.07662835249042121 +2.3906250000000004; 0.08045977011494232 +2.457031250000002; 0.068965517241379 +2.5234375000000004; 0.07279693486590011 +2.5937500000000004; 0.07279693486590055 +2.6562500000000004; 0.06513409961685834 +2.7226562499999996; 0.06130268199233724 +2.7890624999999996; 0.05363984674329503 +2.859374999999989; 0.05363984674329503 +2.9218750000000013; 0.05363984674329503 +2.9960937500000004; 0.05363984674329503 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_LS1993.csv new file mode 100644 index 00000000..7f5d0685 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_LS1993.csv @@ -0,0 +1,41 @@ +-1.5937500000000004; 0.09961685823754785 +-1.5156250000000004; 0.11111111111111116 +-1.4375000000000004; 0.11111111111111116 +-1.3632812500000004; 0.11494252873563227 +-1.2851562500000004; 0.12260536398467448 +-1.1992187500000004; 0.12260536398467448 +-1.1171875000000004; 0.11877394636015337 +-1.0390625000000004; 0.12260536398467448 +-0.9609375000000004; 0.12643678160919514 +-0.8828125000000004; 0.11877394636015337 +-0.796875; 0.06513409961685834 +-0.7148437500000004; -0.003831417624521105 +-0.6406250000000004; -0.13409961685823735 +-0.5664062500000004; -0.27203065134099624 +-0.48046875; -0.421455938697318 +-0.40234375000000044; -0.5900383141762453 +-0.32421875000000044; -0.720306513409962 +-0.23828125; -0.8582375478927204 +-0.16406250000000044; -0.9578544061302683 +-0.08203125000000044; -1.03448275862069 +-0.00390625; -1.0766283524904217 +0.078125; -1.0459770114942528 +0.16015624999999956; -0.9846743295019156 +0.22656250000000044; -0.8429118773946365 +0.31640624999999956; -0.6973180076628354 +0.39843750000000044; -0.5670498084291187 +0.47656249999999956; -0.43678160919540243 +0.5546874999999996; -0.27969348659003845 +0.6328125000000004; -0.08812260536398453 +0.7109375000000004; 0.0344827586206895 +0.7929687499999996; 0.09578544061302674 +0.8710937499999996; 0.14942528735632177 +0.9492187500000004; 0.1379310344827589 +1.0351562500000004; 0.1379310344827589 +1.1171874999999996; 0.13793103448275845 +1.1953125000000004; 0.14176245210727956 +1.2734375000000004; 0.1340996168582378 +1.3476562500000004; 0.1340996168582378 +1.4257812500000004; 0.1340996168582378 +1.5117187500000004; 0.1340996168582378 +1.6054687500000004; 0.11111111111111116 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_WR2008.csv new file mode 100644 index 00000000..97b944a3 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos2_WR2008.csv @@ -0,0 +1,91 @@ +-2.964843750000001; 0.06513409961685834 +-2.886718750000001; 0.06896551724137945 +-2.808593750000001; 0.06513409961685834 +-2.722656250000001; 0.06896551724137945 +-2.640625000000001; 0.07662835249042121 +-2.558593750000001; 0.08045977011494232 +-2.496093750000001; 0.07279693486590011 +-2.394531250000001; 0.08045977011494232 +-2.316406250000001; 0.08045977011494232 +-2.230468750000001; 0.08045977011494232 +-2.148437500000001; 0.08045977011494232 +-2.066406250000001; 0.08045977011494232 +-1.984375000000001; 0.08045977011494232 +-1.9023437500000009; 0.07662835249042121 +-1.8164062500000009; 0.08429118773946387 +-1.7343750000000009; 0.08812260536398497 +-1.6562500000000004; 0.09195402298850563 +-1.5703125000000004; 0.09961685823754785 +-1.4921875000000009; 0.10727969348659006 +-1.4101562500000009; 0.10344827586206895 +-1.3281250000000004; 0.09961685823754785 +-1.2460937500000004; 0.11877394636015337 +-1.1640625000000004; 0.12260536398467448 +-1.0781250000000004; 0.11111111111111116 +-1.0000000000000004; 0.11111111111111116 +-0.9179687500000009; 0.10344827586206895 +-0.8320312500000009; 0.09961685823754785 +-0.7578125000000004; 0.07662835249042121 +-0.69140625; 0.026819923371647292 +-0.6523437500000004; -0.04214559386973171 +-0.6132812500000004; -0.11111111111111116 +-0.5703125000000004; -0.1800766283524906 +-0.5351562500000004; -0.25670498084291227 +-0.5039062500000004; -0.32567049808429127 +-0.46093750000000044; -0.4022988505747125 +-0.43359375000000044; -0.47509578544061304 +-0.39843750000000044; -0.5478927203065136 +-0.36718750000000044; -0.6206896551724141 +-0.32421875000000044; -0.6934865900383147 +-0.28906250000000044; -0.7662835249042148 +-0.25390625000000044; -0.8390804597701154 +-0.2265625; -0.911877394636015 +-0.17578125; -0.9770114942528738 +-0.1328125000000009; -1.0421455938697322 +-0.07812500000000044; -1.0996168582375483 +-0.007812500000000444; -1.14176245210728 +0.062499999999999556; -1.1111111111111116 +0.12499999999999956; -1.0574712643678161 +0.17187499999999956; -0.9923371647509578 +0.21093749999999956; -0.9195402298850577 +0.24218749999999956; -0.842911877394636 +0.28906250000000044; -0.7777777777777777 +0.32031250000000044; -0.7011494252873565 +0.35546875000000044; -0.6245210727969348 +0.38671874999999956; -0.5555555555555554 +0.41796874999999956; -0.4789272030651337 +0.46875000000000044; -0.4099616858237547 +0.49609375000000044; -0.3371647509578546 +0.5351562500000004; -0.26436781609195403 +0.5624999999999996; -0.19157088122605348 +0.5898437499999996; -0.11494252873563227 +0.6289062499999996; -0.04980842911877392 +0.6874999999999996; 0.01532567049808442 +0.7460937499999996; 0.07279693486590011 +0.8125000000000004; 0.10727969348659006 +0.9023437499999996; 0.11111111111111116 +0.9843750000000004; 0.11877394636015337 +1.0664062500000004; 0.12260536398467448 +1.1445312500000004; 0.11877394636015337 +1.2343750000000004; 0.11111111111111116 +1.3125000000000004; 0.10344827586206895 +1.3984375000000004; 0.09961685823754785 +1.4726562500000004; 0.09578544061302674 +1.5585937500000004; 0.09961685823754785 +1.6406250000000004; 0.09195402298850563 +1.7226562500000004; 0.08812260536398497 +1.8046875000000004; 0.09195402298850563 +1.8867187500000004; 0.08045977011494232 +1.9726562500000004; 0.08045977011494232 +2.0546874999999996; 0.07662835249042121 +2.1328125000000004; 0.07279693486590055 +2.2148437500000004; 0.07662835249042121 +2.2890625000000013; 0.07279693486590055 +2.3789062500000004; 0.07279693486590055 +2.4648437499999996; 0.07279693486590055 +2.5468749999999996; 0.06896551724137945 +2.6210937500000013; 0.07279693486590055 +2.7109374999999996; 0.06896551724137945 +2.7890624999999996; 0.07279693486590055 +2.8750000000000004; 0.06130268199233724 +2.9531250000000004; 0.06130268199233768 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_DI2018.csv new file mode 100644 index 00000000..a11bddf9 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_DI2018.csv @@ -0,0 +1,48 @@ +-2.992187500000001; -0.9540229885057472 +-2.742187500000001; -0.946360153256705 +-2.445312500000001; -0.9425287356321839 +-2.210937500000001; -0.9348659003831421 +-2.031250000000001; -0.9348659003831421 +-1.6796875000000004; -0.9272030651340999 +-1.3750000000000004; -0.9272030651340999 +-1.1718750000000004; -0.9348659003831421 +-1.1015625000000004; -0.9386973180076628 +-0.9531250000000004; -0.9578544061302683 +-0.8828125000000004; -0.9731800766283527 +-0.8242187500000004; -0.9923371647509578 +-0.7500000000000004; -1.0268199233716477 +-0.6640625; -1.0804597701149432 +-0.6093750000000004; -1.1226053639846745 +-0.5312500000000004; -1.2145593869731806 +-0.46484375000000044; -1.3026819923371646 +-0.37109375; -1.4252873563218396 +-0.28125; -1.5325670498084296 +-0.18750000000000044; -1.6475095785440614 +-0.12500000000000044; -1.7126436781609207 +-0.0625; -1.7509578544061308 +0.0078125; -1.7624521072796937 +0.08593749999999956; -1.739463601532567 +0.14062499999999956; -1.701149425287357 +0.18750000000000044; -1.6628352490421459 +0.24218749999999956; -1.6015325670498082 +0.30468749999999956; -1.5249042145593874 +0.40624999999999956; -1.4022988505747125 +0.5156249999999996; -1.287356321839081 +0.5937499999999996; -1.2030651340996172 +0.6953124999999996; -1.1149425287356318 +0.7812500000000004; -1.0574712643678161 +0.8749999999999996; -1.0114942528735629 +1.0156250000000004; -0.9693486590038316 +1.1328125000000004; -0.9540229885057472 +1.2656250000000004; -0.946360153256705 +1.3984375000000004; -0.9386973180076632 +1.5468750000000004; -0.9348659003831421 +1.6796875000000004; -0.9348659003831421 +1.8281250000000004; -0.9348659003831421 +2.0000000000000004; -0.9386973180076632 +2.1875000000000004; -0.9386973180076632 +2.3593750000000013; -0.9425287356321839 +2.6015625000000004; -0.946360153256705 +2.8671875000000004; -0.9501915708812261 +2.9960937500000004; -0.9501915708812261 +3.0000000000000004; -0.9540229885057472 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_KM2000.csv new file mode 100644 index 00000000..cdd48c5d --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_KM2000.csv @@ -0,0 +1,103 @@ +-2.976562500000001; -0.9501915708812261 +-2.910156250000001; -0.946360153256705 +-2.839843750000001; -0.946360153256705 +-2.777343750000001; -0.9501915708812261 +-2.707031250000001; -0.946360153256705 +-2.644531250000001; -0.9425287356321839 +-2.578125000000001; -0.9425287356321839 +-2.507812500000001; -0.9425287356321839 +-2.441406250000001; -0.9425287356321839 +-2.375000000000001; -0.9386973180076632 +-2.308593750000001; -0.9425287356321839 +-2.242187500000001; -0.9386973180076628 +-2.175781250000001; -0.9348659003831421 +-2.105468750000001; -0.9348659003831421 +-2.042968750000001; -0.9348659003831421 +-1.9765625000000009; -0.9348659003831421 +-1.9062500000000007; -0.931034482758621 +-1.8398437500000009; -0.9348659003831421 +-1.7734375000000009; -0.931034482758621 +-1.7070312500000009; -0.9233716475095788 +-1.6406250000000004; -0.9272030651340999 +-1.5742187500000004; -0.9272030651340999 +-1.5078125000000004; -0.9272030651340999 +-1.4414062500000009; -0.9233716475095788 +-1.3710937500000009; -0.9272030651340999 +-1.3085937500000009; -0.9348659003831421 +-1.2382812500000004; -0.9348659003831421 +-1.1718750000000004; -0.9348659003831421 +-1.1015625000000004; -0.9386973180076632 +-1.0390625000000004; -0.9425287356321843 +-0.9726562500000004; -0.9540229885057472 +-0.9062500000000004; -0.9616858237547894 +-0.8437500000000004; -0.9770114942528738 +-0.7812500000000004; -1.0076628352490427 +-0.7265625000000009; -1.0459770114942528 +-0.6757812500000004; -1.0842911877394634 +-0.62890625; -1.1302681992337167 +-0.5820312500000004; -1.1800766283524906 +-0.55078125; -1.2375478927203067 +-0.5156250000000009; -1.2911877394636013 +-0.47265625000000044; -1.3371647509578546 +-0.43750000000000044; -1.3984674329501914 +-0.40234375000000044; -1.4521072796934869 +-0.35937500000000044; -1.5057471264367814 +-0.32421875000000044; -1.563218390804598 +-0.29687500000000044; -1.6206896551724141 +-0.26171875000000044; -1.6743295019157087 +-0.22265625000000044; -1.7279693486590042 +-0.17968750000000044; -1.7816091954022988 +-0.12890625000000044; -1.8237547892720305 +-0.07812500000000044; -1.8582375478927209 +-0.011718750000000444; -1.877394636015326 +0.050781249999999556; -1.8735632183908044 +0.10937499999999956; -1.8429118773946365 +0.16015624999999956; -1.8007662835249048 +0.19921874999999956; -1.7509578544061308 +0.23046875000000044; -1.6934865900383147 +0.27734374999999956; -1.6436781609195403 +0.30468749999999956; -1.5862068965517242 +0.35156249999999956; -1.5363984674329503 +0.39062500000000044; -1.4865900383141768 +0.42187499999999956; -1.4291187739463602 +0.44921874999999956; -1.371647509578544 +0.49609375000000044; -1.3180076628352495 +0.5273437499999996; -1.264367816091954 +0.5624999999999996; -1.2107279693486594 +0.6093750000000004; -1.160919540229885 +0.6523437499999996; -1.1149425287356318 +0.6992187499999996; -1.0689655172413794 +0.7539062500000004; -1.0268199233716477 +0.8125000000000004; -1.0000000000000004 +0.8749999999999996; -0.9770114942528738 +0.9374999999999996; -0.9578544061302683 +1.0039062499999996; -0.946360153256705 +1.0703124999999996; -0.9425287356321839 +1.1367187499999996; -0.9348659003831421 +1.2031250000000004; -0.9348659003831412 +1.2695312500000004; -0.9272030651340999 +1.3359375000000004; -0.9272030651340999 +1.4062500000000004; -0.9195402298850577 +1.4687500000000004; -0.9195402298850577 +1.5390625000000004; -0.9272030651340999 +1.6054687500000004; -0.9272030651340999 +1.6718750000000004; -0.9272030651340999 +1.7382812500000004; -0.9272030651340999 +1.8046875000000004; -0.9233716475095788 +1.8710937500000004; -0.9233716475095788 +1.9375000000000004; -0.9272030651340999 +2.0078125000000004; -0.931034482758621 +2.0703125000000004; -0.9348659003831421 +2.1406250000000013; -0.9348659003831421 +2.2070312500000004; -0.9348659003831421 +2.2734375000000004; -0.9425287356321839 +2.339843749999997; -0.9425287356321839 +2.4062500000000004; -0.9425287356321839 +2.4726562500000013; -0.9425287356321839 +2.5429687500000013; -0.9425287356321839 +2.6093750000000004; -0.9425287356321839 +2.6757812500000013; -0.9425287356321839 +2.7421875000000004; -0.9501915708812261 +2.8085937500000013; -0.9501915708812261 +2.8750000000000004; -0.9501915708812261 +2.9414062499999996; -0.9501915708812261 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_LS1993.csv new file mode 100644 index 00000000..91ab164d --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_LS1993.csv @@ -0,0 +1,41 @@ +-1.5937500000000004; -0.9501915708812261 +-1.5234375000000004; -0.9348659003831421 +-1.4414062500000009; -0.9348659003831421 +-1.3632812500000004; -0.9348659003831421 +-1.2773437500000004; -0.9425287356321839 +-1.1953125000000009; -0.9425287356321839 +-1.1210937500000004; -0.9578544061302683 +-1.0390625000000004; -0.9731800766283523 +-0.95703125; -0.9885057471264367 +-0.8828125000000009; -1.0574712643678161 +-0.8046875; -1.0919540229885056 +-0.7187500000000004; -1.1264367816091956 +-0.6445312500000004; -1.2068965517241383 +-0.55859375; -1.3065134099616862 +-0.4804687500000009; -1.3984674329501914 +-0.40234375000000044; -1.513409961685824 +-0.32812500000000044; -1.5708812260536398 +-0.25000000000000044; -1.6590038314176248 +-0.1640625; -1.7164750957854404 +-0.08203125000000044; -1.7509578544061308 +0; -1.7739463601532575 +0.07421875; -1.7662835249042153 +0.15624999999999956; -1.7318007662835257 +0.23828124999999956; -1.685823754789272 +0.31640624999999956; -1.6015325670498082 +0.39453125000000044; -1.494252873563219 +0.47656249999999956; -1.3831417624521074 +0.5546874999999996; -1.2911877394636013 +0.6328125000000004; -1.1992337164750957 +0.7070312500000004; -1.1264367816091956 +0.7929687499999996; -1.0574712643678161 +0.8749999999999996; -1.0191570881226055 +0.9453124999999996; -0.9731800766283527 +1.0390624999999996; -0.9386973180076632 +1.1171874999999996; -0.9348659003831421 +1.1953125000000004; -0.9348659003831421 +1.2812500000000004; -0.9272030651340999 +1.3593749999999996; -0.9233716475095788 +1.4375000000000004; -0.9195402298850577 +1.5039062500000004; -0.911877394636015 +1.5898437500000004; -0.9233716475095788 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_WR2008.csv new file mode 100644 index 00000000..731e02c4 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig09_ux_profile_pos3_WR2008.csv @@ -0,0 +1,80 @@ +-2.960937500000001; -0.946360153256705 +-2.875000000000001; -0.9501915708812261 +-2.792968750000001; -0.9501915708812261 +-2.707031250000001; -0.946360153256705 +-2.625000000000001; -0.931034482758621 +-2.546875000000001; -0.9348659003831421 +-2.464843750000001; -0.9348659003831421 +-2.382812500000001; -0.9386973180076632 +-2.300781250000001; -0.9386973180076632 +-2.218750000000001; -0.9425287356321839 +-2.144531250000001; -0.931034482758621 +-2.050781250000001; -0.931034482758621 +-1.9726562500000009; -0.931034482758621 +-1.8867187500000009; -0.931034482758621 +-1.8125000000000009; -0.9386973180076628 +-1.7226562500000009; -0.9386973180076628 +-1.6406250000000004; -0.9386973180076632 +-1.5625000000000009; -0.931034482758621 +-1.4765625000000009; -0.9272030651340999 +-1.3984375000000004; -0.9272030651340999 +-1.3125000000000004; -0.9348659003831421 +-1.2343750000000004; -0.9348659003831421 +-1.1484375000000004; -0.9501915708812261 +-1.0703125000000004; -0.9616858237547894 +-0.9843750000000004; -0.9731800766283527 +-0.9062500000000004; -0.9923371647509578 +-0.8281250000000004; -1.0229885057471266 +-0.7578125000000004; -1.0613026819923372 +-0.6875000000000004; -1.1034482758620694 +-0.6328125000000004; -1.1647509578544062 +-0.5664062500000004; -1.2222222222222228 +-0.5117187500000004; -1.2758620689655173 +-0.46484375000000044; -1.3409961685823757 +-0.41015625000000044; -1.3984674329501914 +-0.36718750000000044; -1.471264367816092 +-0.3125; -1.5325670498084292 +-0.26171875000000044; -1.5900383141762457 +-0.2031250000000009; -1.6513409961685825 +-0.12890625000000044; -1.685823754789272 +-0.05859375; -1.7164750957854404 +0.023437499999999556; -1.7164750957854404 +0.1015625; -1.7088122605363982 +0.17187499999999956; -1.6666666666666665 +0.23046874999999956; -1.613026819923372 +0.28906250000000044; -1.5517241379310347 +0.33984374999999956; -1.494252873563219 +0.39062500000000044; -1.4329501915708813 +0.45312499999999956; -1.371647509578544 +0.49609375000000044; -1.3026819923371646 +0.5546874999999996; -1.245210727969349 +0.6054687500000004; -1.1839080459770113 +0.6601562499999996; -1.1264367816091956 +0.7304687499999996; -1.0766283524904217 +0.7968749999999996; -1.0306513409961688 +0.8710937499999996; -0.9961685823754789 +0.9492187500000004; -0.9846743295019156 +1.0312500000000004; -0.9655172413793105 +1.1132812499999996; -0.9616858237547894 +1.1874999999999996; -0.9540229885057472 +1.2734375000000004; -0.9425287356321843 +1.3515625000000004; -0.9386973180076632 +1.4375000000000004; -0.9386973180076632 +1.5195312500000004; -0.9386973180076632 +1.6054687499999996; -0.9386973180076632 +1.6875000000000004; -0.9386973180076632 +1.7656250000000004; -0.9425287356321839 +1.8515625000000004; -0.9386973180076628 +1.9335937500000004; -0.9386973180076632 +2.0156250000000004; -0.9386973180076632 +2.0976562500000004; -0.9386973180076628 +2.1796875000000004; -0.9386973180076632 +2.2734374999999996; -0.9386973180076628 +2.3476562500000004; -0.9386973180076628 +2.4296875000000013; -0.9386973180076632 +2.5078124999999996; -0.9386973180076632 +2.5937500000000004; -0.9425287356321839 +2.6757812500000013; -0.9386973180076632 +2.757812500000002; -0.9386973180076632 +2.8437500000000013; -0.9386973180076628 +2.9257812500000004; -0.9425287356321839 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_DI2018.csv new file mode 100644 index 00000000..3da107bf --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_DI2018.csv @@ -0,0 +1,51 @@ +-2.9924242424242395; -3.3306690738754696e-16 +-2.6666666666666643; 0 +-2.3636363636363615; 0.006016847172081841 +-1.9772727272727257; 0.01504211793020449 +-1.6136363636363624; 0.02707581227436806 +-1.3106060606060597; 0.0481347773766545 +-1.045454545454545; 0.0782190132370636 +-0.8181818181818179; 0.11432009626955464 +-0.689393939393939; 0.14741275571600454 +-0.6590909090909092; 0.1534296028880865 +-0.6363636363636362; 0.1564380264741273 +-0.6136363636363624; 0.15042117930204557 +-0.583333333333333; 0.138387484957882 +-0.5606060606060606; 0.1233453670276774 +-0.5227272727272729; 0.08423586040914577 +-0.4924242424242422; 0.048134777376654614 +-0.4469696969696968; 0.00601684717208173 +-0.4015151515151514; -0.024067388688327584 +-0.34090909090909083; -0.048134777376654614 +-0.2803030303030307; -0.06618531889290014 +-0.21212121212121238; -0.07220216606498198 +-0.15909090909090917; -0.06618531889290014 +-0.09848484848484862; -0.051143200962695534 +-0.060606060606060996; -0.036101083032491155 +-8.881784197001252e-16; -0.006016847172081952 +0.06818181818181746; 0.02707581227436806 +0.1515151515151505; 0.06919374247894094 +0.19696969696969635; 0.08122743682310452 +0.24242424242424132; 0.08423586040914544 +0.29545454545454497; 0.07521058965102267 +0.34848484848484773; 0.06016847172081807 +0.3939393939393927; 0.03910950661853185 +0.43939393939393856; 0.0030084235860409203 +0.4772727272727266; -0.039109506618532075 +0.5303030303030294; -0.09927797833935015 +0.5681818181818166; -0.14139590854392325 +0.5984848484848477; -0.16245487364620936 +0.6439393939393927; -0.1684717208182912 +0.6893939393939386; -0.16245487364620936 +0.7499999999999982; -0.14440433212996406 +0.8636363636363624; -0.11432009626955486 +1.0227272727272716; -0.08423586040914588 +1.1666666666666652; -0.06317689530685933 +1.3560606060606037; -0.042117930204572884 +1.5681818181818166; -0.027075812274368283 +1.8106060606060588; -0.012033694344163792 +2.106060606060603; -0.0030084235860410313 +2.4090909090909065; 0.0030084235860408093 +2.6590909090909056; 0.0030084235860408093 +2.8712121212121176; 0.00601684717208173 +2.9962121212121176; 0.00601684717208173 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_KM2000.csv new file mode 100644 index 00000000..25a38bdd --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_KM2000.csv @@ -0,0 +1,96 @@ +-2.9772727272727244; 0.0030084235860406983 +-2.9128787878787854; 0.009025270758122761 +-2.848484848484846; 0.01203369434416357 +-2.7803030303030276; 0.009025270758122539 +-2.7121212121212097; 0.01203369434416357 +-2.6477272727272703; 0.01504211793020449 +-2.5795454545454524; 0.009025270758122761 +-2.515151515151513; 0.01504211793020449 +-2.4507575757575735; 0.0180505415162453 +-2.3825757575757556; 0.01504211793020449 +-2.3143939393939377; 0.0180505415162453 +-2.2499999999999982; 0.0180505415162453 +-2.18181818181818; 0.01203369434416357 +-2.1136363636363615; 0.01203369434416357 +-2.0492424242424225; 0.0180505415162453 +-1.984848484848483; 0.01504211793020449 +-1.916666666666665; 0.02105896510228633 +-1.8522727272727257; 0.03008423586040898 +-1.7840909090909074; 0.02105896510228633 +-1.7159090909090895; 0.02105896510228633 +-1.6515151515151505; 0.03008423586040898 +-1.587121212121211; 0.03610108303249082 +-1.5189393939393927; 0.03910950661853185 +-1.4507575757575748; 0.04512635379061358 +-1.3863636363636354; 0.04512635379061358 +-1.3181818181818175; 0.04512635379061358 +-1.253787878787878; 0.05415162454873634 +-1.1893939393939386; 0.06919374247894094 +-1.1249999999999991; 0.06919374247894083 +-1.0568181818181812; 0.0782190132370636 +-0.9886363636363629; 0.08122743682310452 +-0.9242424242424243; 0.09025270758122739 +-0.8598484848484844; 0.09626955475330912 +-0.795454545454545; 0.10228640192539096 +-0.7272727272727275; 0.1083032490974728 +-0.6628787878787872; 0.12033694344163648 +-0.6022727272727271; 0.09927797833935015 +-0.5568181818181817; 0.0631768953068591 +-0.5265151515151518; 0.01504211793020449 +-0.4962121212121211; -0.030084235860409203 +-0.4469696969696977; -0.06618531889290014 +-0.39015151515151514; -0.09626955475330934 +-0.33333333333333304; -0.11732851985559567 +-0.2651515151515156; -0.12635379061371854 +-0.2007575757575757; -0.11131167268351383 +-0.14393939393939403; -0.08724428399518647 +-0.09090909090909083; -0.057160048134777375 +-0.04166666666666741; -0.027075812274368283 +0.01515151515151425; 0.0030084235860408093 +0.06439393939393945; 0.03309265944644979 +0.10984848484848397; 0.07220216606498175 +0.16666666666666607; 0.10529482551143188 +0.21969696969696884; 0.12936221419975913 +0.2878787878787872; 0.138387484957882 +0.35227272727272396; 0.1383874849578821 +0.4090909090909083; 0.11732851985559556 +0.46590909090908994; 0.08724428399518636 +0.5037878787878771; 0.048134777376654614 +0.549242424242423; 0.0030084235860408093 +0.5833333333333321; -0.04211793020457277 +0.6325757575757569; -0.07220216606498198 +0.6969696969696964; -0.08423586040914566 +0.7651515151515138; -0.0722021660649822 +0.8295454545454533; -0.06919374247894094 +0.8977272727272707; -0.060168471720818406 +0.9621212121212102; -0.051143200962695534 +1.0265151515151505; -0.045126353790613805 +1.094696969696968; -0.045126353790613916 +1.1628787878787863; -0.039109506618532075 +1.2272727272727249; -0.030084235860409314 +1.2916666666666643; -0.027075812274368394 +1.3598484848484826; -0.021058965102286664 +1.4242424242424212; -0.021058965102286553 +1.4886363636363615; -0.015042117930204824 +1.5606060606060588; -0.015042117930204824 +1.6212121212121184; -0.006016847172082063 +1.6893939393939368; -0.009025270758122872 +1.7575757575757551; -0.009025270758122872 +1.8257575757575735; -0.006016847172081952 +1.890151515151513; -0.006016847172082063 +1.9545454545454515; -2.220446049250313e-16 +2.0303030303030276; -0.006016847172082063 +2.090909090909088; -0.009025270758122872 +2.1590909090909065; -0.009025270758122761 +2.223484848484846; -0.009025270758122872 +2.2916666666666643; -0.009025270758122872 +2.3598484848484818; -0.006016847172081952 +2.4280303030303; -0.0030084235860410313 +2.4924242424242395; -0.009025270758122872 +2.556818181818179; -0.006016847172081952 +2.6212121212121193; -0.009025270758122872 +2.685606060606057; -0.0030084235860410313 +2.7537878787878745; -0.0030084235860410313 +2.8219696969696937; -0.0030084235860409203 +2.890151515151513; -0.0030084235860410313 +2.9583333333333304; 0.0030084235860408093 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_LS1993.csv new file mode 100644 index 00000000..f0a1fd4a --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_LS1993.csv @@ -0,0 +1,41 @@ +-1.5946969696969684; -0.11432009626955486 +-1.5189393939393936; -0.1203369434416367 +-1.4393939393939386; -0.10830324909747302 +-1.3560606060606046; -0.10830324909747302 +-1.2840909090909083; -0.09626955475330934 +-1.1969696969696964; -0.07521058965102301 +-1.1174242424242413; -0.060168471720818406 +-1.0303030303030298; -0.048134777376654836 +-0.9545454545454533; -0.027075812274368283 +-0.8787878787878785; -0.006016847172082063 +-0.7954545454545454; 0.0481347773766545 +-0.7159090909090908; 0.09326113116726809 +-0.6401515151515147; 0.15042117930204557 +-0.5606060606060606; 0.17749699157641385 +-0.4772727272727275; 0.15042117930204568 +-0.4015151515151514; 0.08423586040914532 +-0.32196969696969724; 0.07220216606498187 +-0.23863636363636465; 0.07220216606498175 +-0.15151515151515138; 0.03910950661853185 +-0.08333333333333437; 0.06016847172081807 +0; 0.11131167268351383 +0.07954545454545414; 0.09927797833935015 +0.15909090909090828; 0.00601684717208173 +0.2386363636363633; -0.039109506618531964 +0.31818181818181657; -0.08724428399518669 +0.3977272727272725; -0.171480144404332 +0.47348484848484773; -0.23164861612515042 +0.5492424242424239; -0.3309265944645009 +0.6363636363636349; -0.3820697954271962 +0.712121212121211; -0.33694344163658296 +0.7916666666666652; -0.30986762936221457 +0.8749999999999991; -0.2978339350180509 +0.9621212121212102; -0.2827918170878462 +1.049242424242423; -0.2557160048134778 +1.1136363636363624; -0.2587244283995186 +1.1931818181818157; -0.23164861612515064 +1.2727272727272698; -0.23766546329723248 +1.3636363636363615; -0.2135980746089049 +1.4318181818181799; -0.2105896510228641 +1.5151515151515138; -0.19554753309265938 +1.590909090909089; -0.18953068592057776 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_WR2008.csv new file mode 100644 index 00000000..8817f100 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos1_WR2008.csv @@ -0,0 +1,81 @@ +-2.950757575757573; 0.01504211793020449 +-2.86742424242424; 0.0180505415162453 +-2.7840909090909065; 0.02105896510228622 +-2.7045454545454524; 0.02105896510228622 +-2.621212121212119; 0.02406738868832725 +-2.5378787878787854; 0.0180505415162453 +-2.4545454545454524; 0.02707581227436806 +-2.3749999999999982; 0.02707581227436806 +-2.2916666666666647; 0.02707581227436806 +-2.21212121212121; 0.02707581227436806 +-2.128787878787877; 0.03610108303249071 +-2.045454545454544; 0.03610108303249071 +-1.9621212121212104; 0.04512635379061358 +-1.8749999999999987; 0.04512635379061358 +-1.8030303030303019; 0.0481347773766545 +-1.712121212121211; 0.05415162454873634 +-1.636363636363635; 0.0631768953068591 +-1.5530303030303019; 0.06919374247894083 +-1.4696969696969684; 0.0782190132370636 +-1.3901515151515138; 0.08724428399518647 +-1.3068181818181808; 0.09927797833935015 +-1.2272727272727262; 0.10529482551143188 +-1.1439393939393931; 0.11732851985559556 +-1.0643939393939386; 0.12936221419975924 +-0.9810606060606055; 0.1413959085439228 +-0.9090909090909083; 0.15944645006016822 +-0.8295454545454541; 0.1835138387484957 +-0.7499999999999996; 0.20457280385078203 +-0.6780303030303032; 0.2316486161251502 +-0.606060606060606; 0.2316486161251502 +-0.5568181818181817; 0.18050541516245477 +-0.5075757575757573; 0.12936221419975913 +-0.4621212121212124; 0.0782190132370636 +-0.4015151515151514; 0.03309265944644979 +-0.33712121212121193; -0.009025270758122761 +-0.2613636363636367; -0.03309265944645012 +-0.18181818181818166; -0.04211793020457277 +-0.10227272727272751; -0.021058965102286553 +-0.03030303030303072; 0.01203369434416346 +0.045454545454544526; 0.03309265944644979 +0.12121212121212022; 0.06618531889289991 +0.20075757575757525; 0.0782190132370636 +0.2803030303030294; 0.07220216606498187 +0.3560606060606055; 0.04211793020457266 +0.4128787878787863; -0.0030084235860410313 +0.46969696969696884; -0.051143200962695645 +0.5189393939393927; -0.10529482551143199 +0.5643939393939377; -0.15944645006016855 +0.6060606060606046; -0.20457280385078225 +0.632575757575756; -0.21359807460890512 +0.6893939393939386; -0.20156438026474144 +0.7613636363636349; -0.17148014440433224 +0.8409090909090899; -0.14440433212996406 +0.8901515151515129; -0.12635379061371854 +0.9204545454545441; -0.12635379061371854 +0.9356060606060597; -0.1203369434416367 +0.9886363636363615; -0.11131167268351383 +1.0681818181818166; -0.09326113116726853 +1.1477272727272707; -0.07821901323706404 +1.2272727272727249; -0.06919374247894094 +1.3106060606060588; -0.0571600481347776 +1.390151515151513; -0.051143200962695645 +1.473484848484846; -0.045126353790613916 +1.553030303030301; -0.03309265944645012 +1.6325757575757551; -0.024067388688327473 +1.7159090909090882; -0.024067388688327473 +1.799242424242422; -0.021058965102286553 +1.8787878787878762; -0.006016847172081952 +1.9621212121212093; -0.009025270758122761 +2.045454545454543; -0.006016847172081952 +2.1287878787878762; -0.006016847172081952 +2.2083333333333304; -0.009025270758122872 +2.2916666666666643; -3.3306690738754696e-16 +2.3712121212121184; 0.00601684717208173 +2.4545454545454506; 0.009025270758122539 +2.5340909090909056; 0.009025270758122539 +2.6212121212121184; 0.00601684717208173 +2.7007575757575717; 0.009025270758122539 +2.7840909090909047; 0.01203369434416357 +2.8674242424242395; 0.009025270758122761 +2.9583333333333304; 0.009025270758122539 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_DI2018.csv new file mode 100644 index 00000000..456ef135 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_DI2018.csv @@ -0,0 +1,43 @@ +-2.9999999999999973; -0.9867629362214198 +-2.7348484848484826; -0.9837545126353789 +-2.340909090909089; -0.9747292418772563 +-1.916666666666665; -0.95367027677497 +-1.6818181818181808; -0.9416365824308063 +-1.5530303030303019; -0.9265944645006016 +-1.356060606060605; -0.908543922984356 +-1.1969696969696964; -0.8814681107099877 +-1.0681818181818175; -0.8604091456077015 +-0.9507575757575752; -0.8303249097472923 +-0.8106060606060601; -0.7882069795427195 +-0.7045454545454541; -0.740072202166065 +-0.6363636363636362; -0.7129963898916968 +-0.606060606060606; -0.7039711191335739 +-0.5606060606060606; -0.6979542719614923 +-0.5075757575757573; -0.7009626955475333 +-0.4621212121212124; -0.7190132370637788 +-0.4015151515151514; -0.7490974729241878 +-0.34090909090909083; -0.7912154031287605 +-0.21212121212121238; -0.8784596871239471 +-0.09848484848484862; -0.9747292418772561 +0.06060606060606055; -1.0980746089049336 +0.23484848484848442; -1.230445246690734 +0.31060606060605966; -1.2755716004813475 +0.3712121212121202; -1.3116726835138381 +0.44696969696969635; -1.3387484957882068 +0.507575757575756; -1.3477737665463292 +0.5454545454545441; -1.3477737665463296 +0.6060606060606055; -1.3327316486161251 +0.6818181818181808; -1.290613718411552 +0.7954545454545441; -1.230445246690734 +0.9015151515151496; -1.1913357400722018 +1.045454545454544; -1.1492178098676291 +1.1893939393939377; -1.1191335740072201 +1.3636363636363615; -1.0890493381468112 +1.5378787878787854; -1.0679903730445246 +1.6590909090909074; -1.055956678700361 +1.9090909090909065; -1.0409145607701564 +2.1515151515151487; -1.0288808664259927 +2.477272727272724; -1.0168471720818295 +2.765151515151512; -1.010830324909747 +2.939393939393936; -1.0048134777376654 +2.9962121212121167; -1.0048134777376654 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_KM2000.csv new file mode 100644 index 00000000..695c7c89 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_KM2000.csv @@ -0,0 +1,95 @@ +-2.9469696969696946; -0.9807460890493381 +-2.848484848484846; -0.9807460890493381 +-2.780303030303028; -0.9807460890493381 +-2.7121212121212097; -0.9807460890493384 +-2.6477272727272703; -0.9807460890493381 +-2.5757575757575735; -0.9747292418772563 +-2.511363636363634; -0.9717208182912153 +-2.4469696969696946; -0.9626955475330926 +-2.3787878787878767; -0.9657039711191334 +-2.2916666666666647; -0.9626955475330926 +-2.2462121212121193; -0.9657039711191334 +-2.1780303030303014; -0.9657039711191334 +-2.1136363636363615; -0.9657039711191334 +-2.041666666666665; -0.9596871239470518 +-1.9772727272727257; -0.9566787003610108 +-1.912878787878786; -0.9506618531889287 +-1.8446969696969684; -0.9476534296028881 +-1.780303030303029; -0.9386281588447652 +-1.712121212121211; -0.9356197352587242 +-1.6439393939393927; -0.9265944645006016 +-1.5833333333333321; -0.9326113116726836 +-1.5189393939393927; -0.9175691937424787 +-1.4507575757575748; -0.9175691937424791 +-1.3863636363636354; -0.905535499398315 +-1.3219696969696964; -0.8935018050541513 +-1.253787878787878; -0.8874849578820697 +-1.1893939393939386; -0.8724428399518652 +-1.128787878787878; -0.8634175691937424 +-1.0681818181818175; -0.8483754512635379 +-0.9999999999999973; -0.8303249097472919 +-0.9356060606060606; -0.812274368231047 +-0.8712121212121207; -0.7972322503008423 +-0.75; -0.7551143200962694 +-0.6893939393939399; -0.7340553549939833 +-0.643939393939394; -0.6949458483754513 +-0.583333333333333; -0.6708784596871239 +-0.518939393939394; -0.6618531889290012 +-0.4621212121212124; -0.6829121540312876 +-0.40909090909090917; -0.7129963898916968 +-0.3522727272727275; -0.743080625752106 +-0.3030303030303032; -0.776173285198556 +-0.25; -0.806257521058965 +-0.19696969696969768; -0.8393501805054152 +-0.14015151515151514; -0.8694344163658242 +-0.09090909090909083; -0.905535499398315 +-0.04166666666666741; -0.9386281588447652 +0.007575757575756903; -0.9747292418772561 +0.06439393939393856; -1.0048134777376654 +0.11742424242424176; -1.0348977135980744 +0.16666666666666607; -1.0709987966305654 +0.21590909090908994; -1.1070998796630565 +0.2727272727272716; -1.1311672683513838 +0.31818181818181746; -1.1702767749699152 +0.3749999999999991; -1.2003610108303246 +0.43181818181818077; -1.2274368231046928 +0.4848484848484844; -1.2575210589651018 +0.5340909090909083; -1.296630565583634 +0.5984848484848477; -1.290613718411552 +0.6666666666666652; -1.260529482551143 +0.712121212121211; -1.2364620938628157 +0.7727272727272716; -1.209386281588447 +0.8257575757575744; -1.1823104693140793 +0.8901515151515138; -1.1642599277978336 +0.9469696969696955; -1.1401925391095062 +1.011363636363635; -1.1251504211793018 +1.0757575757575744; -1.1070998796630565 +1.1401515151515138; -1.1010830324909744 +1.2045454545454524; -1.083032490974729 +1.2651515151515138; -1.0649819494584833 +1.3333333333333313; -1.055956678700361 +1.3977272727272707; -1.0439229843561972 +1.4621212121212102; -1.0318892900120336 +1.5265151515151496; -1.0288808664259927 +1.5909090909090882; -1.016847172081829 +1.6590909090909074; -1.0138387484957883 +1.723484848484846; -1.0078219013237062 +1.7878787878787854; -1.0018050541516241 +1.8598484848484818; -0.9987966305655839 +1.924242424242422; -0.9897713598074608 +1.9886363636363606; -0.9897713598074606 +2.05681818181818; -0.9867629362214198 +2.1212121212121184; -0.9807460890493381 +2.189393939393936; -0.9927797833935011 +2.2537878787878762; -0.9777376654632973 +2.3219696969696955; -0.9717208182912155 +2.386363636363633; -0.9687123947051745 +2.4545454545454515; -0.9657039711191334 +2.51893939393939; -0.9566787003610108 +2.58712121212121; -0.9596871239470516 +2.655303030303026; -0.9596871239470518 +2.719696969696967; -0.956678700361011 +2.7878787878787845; -0.9566787003610108 +2.856060606060603; -0.9566787003610108 +2.9242424242424203; -0.9566787003610108 +2.9886363636363598; -0.9566787003610108 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_LS1993.csv new file mode 100644 index 00000000..0e3cc0e5 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_LS1993.csv @@ -0,0 +1,41 @@ +-1.5946969696969684; -1.0469314079422385 +-1.5189393939393927; -1.0288808664259927 +-1.4431818181818175; -1.0138387484957883 +-1.3598484848484835; -1.0018050541516241 +-1.280303030303029; -1.0018050541516246 +-1.2007575757575752; -0.9807460890493381 +-1.1249999999999991; -0.95367027677497 +-1.0492424242424239; -0.9356197352587242 +-0.9621212121212119; -0.9025270758122744 +-0.8749999999999996; -0.8814681107099877 +-0.8030303030303028; -0.8303249097472923 +-0.7196969696969697; -0.7882069795427195 +-0.6439393939393936; -0.8212996389891692 +-0.5606060606060606; -0.812274368231047 +-0.4810606060606064; -0.8212996389891695 +-0.4015151515151518; -0.8303249097472926 +-0.31818181818181834; -0.8363417569193742 +-0.2424242424242431; -0.8543922984356197 +-0.16666666666666696; -0.9145607701564374 +-0.08333333333333393; -0.9987966305655834 +-0.0037878787878788955; -1.0499398315282793 +0.07196969696969635; -1.0800240673886878 +0.14772727272727249; -1.2093862815884475 +0.23484848484848442; -1.3598074608904933 +0.31060606060606055; -1.462093862815884 +0.3939393939393927; -1.4891696750902526 +0.46590909090908994; -1.5884476534296024 +0.5530303030303019; -1.6185318892900113 +0.6249999999999991; -1.5974729241877257 +0.712121212121211; -1.5433212996389885 +0.7916666666666652; -1.4560770156438023 +0.8749999999999991; -1.395908543922984 +0.9507575757575744; -1.356799037304452 +1.0303030303030294; -1.3357400722021655 +1.1136363636363624; -1.3116726835138381 +1.1893939393939377; -1.28459687123947 +1.257575757575756; -1.254512635379061 +1.3560606060606037; -1.230445246690734 +1.4318181818181799; -1.2214199759326108 +1.511363636363634; -1.2123947051744888 +1.590909090909089; -1.2063778580024067 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_WR2008.csv new file mode 100644 index 00000000..bb8a7ad9 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos2_WR2008.csv @@ -0,0 +1,78 @@ +-2.9431818181818157; -0.9807460890493381 +-2.848484848484846; -0.9807460890493381 +-2.765151515151513; -0.9747292418772563 +-2.689393939393937; -0.9717208182912155 +-2.6060606060606037; -0.9687123947051745 +-2.518939393939392; -0.9657039711191334 +-2.4393939393939372; -0.9657039711191334 +-2.356060606060604; -0.9657039711191334 +-2.2840909090909074; -0.9657039711191334 +-2.2499999999999982; -0.9566787003610108 +-2.1931818181818166; -0.9506618531889289 +-2.113636363636362; -0.9506618531889289 +-2.0303030303030285; -0.9596871239470518 +-1.954545454545453; -0.9446450060168471 +-1.8674242424242409; -0.9326113116726836 +-1.784090909090907; -0.9326113116726836 +-1.7045454545454533; -0.9356197352587244 +-1.6249999999999987; -0.92057761732852 +-1.5416666666666652; -0.9115523465703971 +-1.462121212121211; -0.8995186522262332 +-1.382575757575756; -0.8935018050541518 +-1.3030303030303019; -0.875451263537906 +-1.2196969696969693; -0.8664259927797832 +-1.1363636363636354; -0.8513838748495787 +-1.0568181818181812; -0.8393501805054152 +-0.9848484848484844; -0.8182912154031291 +-0.8977272727272725; -0.8002406738868834 +-0.8257575757575752; -0.7821901323706378 +-0.7462121212121211; -0.7521058965102291 +-0.6780303030303032; -0.7160048134777378 +-0.6022727272727271; -0.6949458483754513 +-0.5265151515151518; -0.6919374247894102 +-0.4393939393939399; -0.6949458483754513 +-0.37500000000000044; -0.7220216606498194 +-0.2954545454545454; -0.7490974729241874 +-0.22727272727272796; -0.7851985559566784 +-0.16666666666666696; -0.8333333333333331 +-0.10606060606060641; -0.8814681107099877 +-0.06439393939393989; -0.9296028880866426 +-0.0037878787878788955; -0.9777376654632973 +0.045454545454544526; -1.0288808664259927 +0.10227272727272707; -1.0740072202166062 +0.15909090909090828; -1.1251504211793018 +0.22348484848484773; -1.1642599277978336 +0.2916666666666661; -1.2003610108303246 +0.3598484848484844; -1.2394705174488565 +0.4280303030303019; -1.2665463297232242 +0.507575757575756; -1.2876052948255108 +0.587121212121211; -1.2815884476534296 +0.6666666666666652; -1.2575210589651018 +0.7424242424242404; -1.2364620938628152 +0.8181818181818166; -1.2063778580024067 +0.8901515151515138; -1.1762936221419973 +0.969696969696968; -1.1612515042117928 +1.0454545454545432; -1.1432009626955475 +1.1287878787878771; -1.1251504211793018 +1.2083333333333313; -1.1101083032490973 +1.2916666666666643; -1.1010830324909748 +1.3712121212121193; -1.0950661853188923 +1.4545454545454524; -1.08604091456077 +1.5340909090909065; -1.0740072202166062 +1.6136363636363615; -1.055956678700361 +1.6969696969696946; -1.055956678700361 +1.7765151515151487; -1.0499398315282789 +1.8560606060606037; -1.0379061371841156 +1.9393939393939368; -1.0379061371841156 +2.02272727272727; -1.031889290012034 +2.102272727272724; -1.0228640192539107 +2.185606060606058; -1.025872442839952 +2.26893939393939; -1.0138387484957883 +2.348484848484847; -1.010830324909747 +2.43181818181818; -1.0048134777376654 +2.511363636363633; -1.0078219013237062 +2.594696969696966; -1.0078219013237062 +2.681818181818179; -1.0078219013237062 +2.761363636363634; -1.0048134777376654 +2.8409090909090873; -1.0078219013237062 +2.9242424242424203; -1.0048134777376654 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_DI2018.csv new file mode 100644 index 00000000..2a44c390 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_DI2018.csv @@ -0,0 +1,35 @@ +-2.9999999999999973; -1.9825511432009622 +-2.651515151515149; -1.9735258724428397 +-2.21212121212121; -1.9584837545126357 +-1.6439393939393927; -1.925391095066185 +-1.2045454545454537; -1.8832731648616123 +-0.8560606060606055; -1.823104693140794 +-0.6553030303030303; -1.783995186522262 +-0.583333333333333; -1.7749699157641392 +-0.5; -1.7719614921780984 +-0.4318181818181821; -1.7749699157641392 +-0.34090909090909083; -1.7990373044524666 +-0.2651515151515156; -1.823104693140794 +-0.15909090909090917; -1.8772563176895307 +-0.09090909090909083; -1.925391095066185 +-8.881784197001252e-16; -1.997593261131167 +0.11363636363636287; -2.0878459687123945 +0.15909090909090828; -2.117930204572803 +0.23484848484848442; -2.1570397111913358 +0.34848484848484773; -2.196149217809867 +0.43181818181818077; -2.2141997593261125 +0.4772727272727266; -2.2172081829121537 +0.5606060606060597; -2.2141997593261125 +0.7272727272727257; -2.1871239470517447 +0.8939393939393927; -2.1510228640192537 +1.0303030303030285; -2.126955475330926 +1.2121212121212102; -2.099879663056558 +1.3712121212121193; -2.084837545126353 +1.507575757575756; -2.07280385078219 +1.6818181818181799; -2.060770156438026 +1.9393939393939368; -2.0457280385078214 +2.174242424242421; -2.0367027677496985 +2.3787878787878753; -2.027677496991576 +2.598484848484845; -2.021660649819494 +2.810606060606058; -2.0156438026474124 +2.9962121212121176; -2.0126353790613716 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_KM2000.csv new file mode 100644 index 00000000..c2eab632 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_KM2000.csv @@ -0,0 +1,94 @@ +-2.969696969696967; -1.9885679903730442 +-2.9015151515151487; -1.985559566787003 +-2.8371212121212093; -1.9825511432009626 +-2.7689393939393914; -1.9825511432009622 +-2.7045454545454524; -1.9825511432009622 +-2.636363636363634; -1.9825511432009626 +-2.5681818181818157; -1.9825511432009626 +-2.5037878787878762; -1.9795427196149213 +-2.4356060606060588; -1.9735258724428397 +-2.3712121212121193; -1.9825511432009626 +-2.30681818181818; -1.9705174488567985 +-2.2386363636363615; -1.9675090252707577 +-2.174242424242423; -1.9675090252707577 +-2.1060606060606046; -1.9675090252707577 +-2.0378787878787863; -1.9645006016847169 +-1.9734848484848468; -1.9645006016847169 +-1.9053030303030285; -1.9584837545126352 +-1.8409090909090895; -1.955475330926594 +-1.7727272727272712; -1.9494584837545124 +-1.7045454545454533; -1.9464500601684716 +-1.6439393939393927; -1.9404332129963895 +-1.5757575757575744; -1.9314079422382666 +-1.5113636363636354; -1.925391095066185 +-1.4393939393939386; -1.9163658243080626 +-1.3749999999999991; -1.9103489771359805 +-1.3106060606060597; -1.8983152827918168 +-1.2462121212121207; -1.8922984356197343 +-1.1742424242424234; -1.883273164861612 +-1.1136363636363629; -1.8742478941034895 +-1.0492424242424239; -1.865222623345367 +-0.9810606060606055; -1.850180505415162 +-0.9166666666666661; -1.8351383874849576 +-0.8522727272727266; -1.823104693140794 +-0.7878787878787872; -1.8050541516245486 +-0.7234848484848486; -1.7900120336943441 +-0.6628787878787881; -1.7689530685920571 +-0.6022727272727275; -1.7509025270758118 +-0.5378787878787876; -1.7418772563176894 +-0.4734848484848486; -1.747894103489771 +-0.4053030303030303; -1.7569193742478935 +-0.3484848484848486; -1.7809867629362208 +-0.2916666666666665; -1.8110709987966298 +-0.2310606060606064; -1.8321299638989164 +-0.17803030303030365; -1.8622141997593253 +-0.12500000000000044; -1.8983152827918168 +-0.07954545454545503; -1.9344163658243074 +-0.034090909090909616; -1.9765342960288805 +0.011363636363635798; -2.0126353790613716 +0.05681818181818077; -2.057761732851985 +0.09090909090909038; -2.093862815884476 +0.1401515151515147; -2.132972322503008 +0.18939393939393856; -2.166064981949458 +0.24242424242424132; -2.196149217809867 +0.29924242424242387; -2.229241877256317 +0.3636363636363633; -2.2442839951865214 +0.424242424242423; -2.2533092659446448 +0.4886363636363624; -2.262334536702767 +0.5568181818181799; -2.2563176895306856 +0.6212121212121202; -2.2503008423586035 +0.6856060606060597; -2.2382671480144403 +0.7462121212121193; -2.2202166064981945 +0.8106060606060588; -2.20517448856799 +0.8749999999999991; -2.1901323706377855 +0.9356060606060597; -2.1750902527075806 +0.9999999999999982; -2.160048134777376 +1.0643939393939377; -2.1419975932611304 +1.1287878787878771; -2.132972322503008 +1.1969696969696955; -2.1209386281588447 +1.261363636363635; -2.1089049338146806 +1.3257575757575744; -2.1028880866425985 +1.3939393939393918; -2.1028880866425985 +1.4621212121212093; -2.099879663056558 +1.5265151515151496; -2.087845968712394 +1.590909090909089; -2.0848375451263537 +1.6590909090909074; -2.0848375451263537 +1.723484848484846; -2.07280385078219 +1.7916666666666643; -2.0697954271961487 +1.8522727272727249; -2.066787003610108 +1.9204545454545432; -2.063778580024067 +1.9924242424242395; -2.0547533092659442 +2.053030303030301; -2.0487364620938626 +2.1212121212121184; -2.0427196149217806 +2.185606060606057; -2.045728038507821 +2.26893939393939; -2.0427196149217806 +2.3181818181818157; -2.0367027677496985 +2.386363636363633; -2.036702767749699 +2.4583333333333286; -2.0336943441636577 +2.52272727272727; -2.030685920577617 +2.590909090909088; -2.030685920577617 +2.651515151515148; -2.027677496991576 +2.719696969696967; -2.024669073405535 +2.7954545454545423; -2.021660649819494 +2.856060606060603; -2.0216606498194944 +2.9204545454545423; -2.021660649819494 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_LS1993.csv new file mode 100644 index 00000000..7cbf1424 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_LS1993.csv @@ -0,0 +1,41 @@ +-1.5984848484848473; -2.0758122743682312 +-1.5189393939393927; -2.0758122743682303 +-1.4356060606060597; -2.069795427196149 +-1.3560606060606055; -2.0547533092659447 +-1.2765151515151505; -2.0487364620938626 +-1.2007575757575752; -2.027677496991576 +-1.1174242424242418; -2.021660649819494 +-1.0416666666666665; -2.0096269554753303 +-0.9621212121212119; -1.9945848375451258 +-0.8825757575757569; -1.9825511432009622 +-0.8030303030303028; -1.9283995186522263 +-0.7234848484848486; -1.8772563176895307 +-0.6439393939393936; -1.8351383874849576 +-0.5606060606060606; -1.808062575210589 +-0.4848484848484853; -1.8110709987966298 +-0.4015151515151514; -1.7960288808664253 +-0.32954545454545414; -1.84416365824308 +-0.2386363636363642; -1.9223826714801442 +-0.15909090909090917; -2.0066185318892895 +-0.08333333333333393; -2.063778580024067 +-8.881784197001252e-16; -2.0998796630565577 +0.07196969696969635; -2.1931407942238264 +0.1553030303030294; -2.2262334536702766 +0.23484848484848442; -2.241275571600481 +0.31060606060605966; -2.2924187725631766 +0.3863636363636358; -2.316486161251504 +0.46590909090908994; -2.3646209386281587 +0.5530303030303019; -2.406738868832732 +0.6363636363636349; -2.4247894103489767 +0.712121212121211; -2.3916967509025264 +0.7916666666666661; -2.3646209386281583 +0.8749999999999991; -2.3164861612515035 +0.9621212121212102; -2.30445246690734 +1.0303030303030285; -2.2984356197352582 +1.1249999999999982; -2.30445246690734 +1.1931818181818166; -2.2924187725631766 +1.2727272727272707; -2.2864019253910945 +1.3522727272727249; -2.2653429602888084 +1.424242424242422; -2.2533092659446448 +1.507575757575756; -2.2442839951865214 +1.5909090909090882; -2.232250300842358 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_WR2008.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_WR2008.csv new file mode 100644 index 00000000..8867ce69 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig10_uy_profile_pos3_WR2008.csv @@ -0,0 +1,76 @@ +-2.9431818181818157; -1.9825511432009626 +-2.859848484848482; -1.9735258724428397 +-2.776515151515149; -1.9735258724428397 +-2.6931818181818157; -1.9735258724428397 +-2.6136363636363615; -1.9675090252707577 +-2.530303030303028; -1.9675090252707577 +-2.4469696969696946; -1.9645006016847164 +-2.3674242424242404; -1.9675090252707577 +-2.2803030303030285; -1.9584837545126352 +-2.2007575757575735; -1.9584837545126352 +-2.1174242424242404; -1.9524669073405532 +-2.0378787878787863; -1.9434416365824307 +-1.954545454545453; -1.9404332129963895 +-1.8712121212121198; -1.9404332129963895 +-1.795454545454544; -1.9374247894103487 +-1.7083333333333321; -1.9283995186522263 +-1.6249999999999987; -1.9223826714801442 +-1.549242424242423; -1.9163658243080621 +-1.4621212121212106; -1.9133574007220209 +-1.3787878787878776; -1.9073405535499397 +-1.2954545454545445; -1.8922984356197352 +-1.2196969696969688; -1.8862815884476531 +-1.1401515151515147; -1.8742478941034895 +-1.0530303030303023; -1.8682310469314074 +-0.9734848484848477; -1.8592057761732845 +-0.8939393939393936; -1.8471720818291208 +-0.8106060606060601; -1.8321299638989172 +-0.7348484848484844; -1.8170878459687119 +-0.6553030303030303; -1.8020457280385074 +-0.5719696969696972; -1.783995186522262 +-0.4886363636363642; -1.7870036101083024 +-0.41287878787878807; -1.7990373044524666 +-0.33712121212121193; -1.8200962695547531 +-0.2613636363636367; -1.853188929001203 +-0.1931818181818188; -1.8862815884476531 +-0.12878787878787934; -1.9283995186522254 +-0.07196969696969768; -1.9735258724428397 +-0.011363636363636687; -2.0186522262334536 +0.05681818181818121; -2.0577617328519855 +0.11742424242424176; -2.096871239470517 +0.20075757575757525; -2.120938628158844 +0.2689393939393927; -2.1540312876052945 +0.34090909090908994; -2.1811070998796627 +0.4204545454545441; -2.2021660649819492 +0.5037878787878771; -2.2141997593261125 +0.5833333333333321; -2.211191335740072 +0.6628787878787863; -2.196149217809867 +0.7386363636363624; -2.1811070998796627 +0.8257575757575744; -2.163056558363417 +0.9015151515151496; -2.1450060168471716 +0.9848484848484826; -2.135980746089049 +1.0643939393939377; -2.129963898916967 +1.1439393939393918; -2.1179302045728035 +1.2272727272727249; -2.1089049338146806 +1.3068181818181799; -2.1028880866425985 +1.386363636363634; -2.096871239470517 +1.473484848484846; -2.084837545126353 +1.553030303030301; -2.0758122743682303 +1.6325757575757551; -2.063778580024067 +1.7121212121212093; -2.0697954271961487 +1.7954545454545432; -2.060770156438026 +1.8749999999999973; -2.0547533092659447 +1.9583333333333304; -2.0457280385078214 +2.0416666666666643; -2.0457280385078214 +2.1212121212121184; -2.0397111913357393 +2.2083333333333304; -2.0457280385078214 +2.2878787878787845; -2.03971119133574 +2.3712121212121184; -2.030685920577617 +2.4507575757575735; -2.030685920577617 +2.5340909090909065; -2.030685920577617 +2.6212121212121184; -2.021660649819494 +2.696969696969692; -2.021660649819494 +2.7840909090909047; -2.021660649819494 +2.8636363636363598; -2.015643802647412 +2.928030303030299; -2.0156438026474124 +2.9848484848484818; -2.0156438026474124 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_DI2018.csv new file mode 100644 index 00000000..6c85f7cc --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_DI2018.csv @@ -0,0 +1,45 @@ +-2.996129032258067; 0 +-1.0374193548387107; 0.001626016260162566 +-0.8361290322580657; 0.004878048780487809 +-0.7509677419354848; 0.011382113821138073 +-0.7045161290322586; 0.021138211382113803 +-0.6812903225806459; 0.0373983739837398 +-0.6425806451612908; 0.0796747967479674 +-0.596129032258065; 0.1284552845528455 +-0.5729032258064524; 0.1382113821138211 +-0.5496774193548397; 0.1382113821138211 +-0.5264516129032266; 0.13170731707317074 +-0.49548387096774293; 0.12520325203252025 +-0.4567741935483878; 0.12195121951219512 +-0.42580645161290365; 0.12520325203252025 +-0.3870967741935485; 0.1284552845528455 +-0.3406451612903232; 0.13495934959349587 +-0.32516129032258156; 0.13495934959349587 +-0.29419354838709744; 0.13170731707317074 +-0.1393548387096777; 0.10406504065040645 +-0.08516129032258091; 0.0959349593495934 +-0.04645161290322575; 0.09105691056910559 +-0.01935483870967758; 0.08943089430894302 +0.007741935483871032; 0.09105691056910559 +0.05806451612903185; 0.09430894308943072 +0.185806451612903; 0.11056910569105693 +0.2632258064516133; 0.12195121951219512 +0.3329032258064517; 0.13008130081300817 +0.3522580645161284; 0.13170731707317074 +0.38709677419354804; 0.1284552845528455 +0.41806451612903217; 0.12520325203252025 +0.44129032258064527; 0.12357723577235769 +0.4800000000000004; 0.12520325203252025 +0.5070967741935481; 0.13170731707317074 +0.5303225806451612; 0.1382113821138211 +0.5535483870967743; 0.1430894308943088 +0.5883870967741931; 0.13495934959349587 +0.6154838709677417; 0.10894308943089426 +0.6464516129032258; 0.06829268292682922 +0.6696774193548389; 0.03902439024390236 +0.7122580645161296; 0.01788617886178856 +0.7664516129032259; 0.008130081300812941 +0.8322580645161297; 0.004878048780487809 +0.9058064516129036; 0.001626016260162566 +1.0141935483870972; 0.001626016260162566 +2.9883870967741952; -1.1102230246251565e-16 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_KM2000.csv new file mode 100644 index 00000000..504112e8 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_KM2000.csv @@ -0,0 +1,98 @@ +-2.9767741935483896; -1.1102230246251565e-16 +-2.9148387096774218; 0 +-2.845161290322583; -1.1102230246251565e-16 +-2.77935483870968; 0 +-2.7135483870967763; 0 +-2.6477419354838734; -0.001626016260162566 +-2.5780645161290345; -1.1102230246251565e-16 +-2.512258064516131; 0 +-2.442580645161293; 0 +-2.3767741935483895; -1.1102230246251565e-16 +-2.3109677419354857; -0.001626016260162566 +-2.245161290322583; -1.1102230246251565e-16 +-2.1793548387096795; 0 +-2.1096774193548407; 0 +-2.0438709677419373; 0 +-1.974193548387099; -1.1102230246251565e-16 +-1.9122580645161307; -0.001626016260162788 +-1.8425806451612923; 0 +-1.7729032258064534; 0 +-1.7109677419354856; 0 +-1.6412903225806468; -1.1102230246251565e-16 +-1.5754838709677434; 0 +-1.5096774193548397; 0.001626016260162566 +-1.4400000000000008; 0.001626016260162677 +-1.374193548387098; 0.001626016260162566 +-1.3045161290322596; 0.003252032520325132 +-1.2425806451612917; 0.003252032520325132 +-1.1729032258064525; 0.003252032520325132 +-1.1070967741935496; 0.004878048780487809 +-1.0412903225806462; 0.004878048780487809 +-0.9716129032258074; 0.004878048780487809 +-0.901935483870969; 0.004878048780487698 +-0.8361290322580657; 0.004878048780487698 +-0.7741935483870979; 0.008130081300812941 +-0.7316129032258076; 0.027642276422764067 +-0.700645161290324; 0.05365853658536579 +-0.6812903225806459; 0.07967479674796762 +-0.6580645161290333; 0.10731707317073169 +-0.6270967741935491; 0.13008130081300817 +-0.588387096774194; 0.1430894308943088 +-0.5419354838709682; 0.12195121951219512 +-0.48774193548387146; 0.10894308943089426 +-0.42193548387096813; 0.1121951219512195 +-0.3561290322580648; 0.11544715447154463 +-0.29419354838709744; 0.10894308943089426 +-0.2283870967741941; 0.1008130081300812 +-0.16645161290322585; 0.08943089430894302 +-0.1045161290322576; 0.07967479674796729 +-0.04258064516129023; 0.07317073170731703 +0.027096774193547724; 0.07479674796747948 +0.08903225806451598; 0.08292682926829253 +0.15096774193548335; 0.09430894308943072 +0.2129032258064516; 0.10243902439024377 +0.2787096774193545; 0.11056910569105693 +0.34451612903225737; 0.11544715447154463 +0.4064516129032265; 0.11056910569105693 +0.4683870967741939; 0.1121951219512195 +0.5264516129032257; 0.12682926829268282 +0.572903225806451; 0.1447154471544715 +0.6077419354838707; 0.12845528455284538 +0.6193548387096772; 0.10243902439024377 +0.6387096774193557; 0.0747967479674797 +0.6658064516129034; 0.04878048780487809 +0.7006451612903231; 0.024390243902438935 +0.7509677419354848; 0.009756097560975507 +0.8206451612903232; 0.004878048780487698 +0.886451612903226; 0.003252032520325132 +0.9522580645161298; 0.001626016260162677 +1.0219354838709682; 0.001626016260162566 +1.0838709677419365; 0.001626016260162566 +1.1535483870967749; 0.001626016260162566 +1.2193548387096778; 0.003252032520325132 +1.2890322580645162; 0.003252032520325132 +1.35483870967742; 0.003252032520325354 +1.4206451612903237; 0.003252032520325132 +1.4864516129032266; 0.003252032520325132 +1.5522580645161295; 0.003252032520325132 +1.6219354838709688; 0.003252032520325132 +1.6877419354838725; 0.003252032520325132 +1.7535483870967754; 0.003252032520325354 +1.8193548387096783; 0.003252032520325132 +1.8890322580645167; 0.003252032520325021 +1.9548387096774205; 0.001626016260162677 +2.0245161290322597; 0.003252032520325132 +2.0903225806451626; 0.001626016260162677 +2.156129032258062; 0.003252032520325132 +2.225806451612904; 0.003252032520325132 +2.2916129032258086; 0.003252032520325132 +2.3574193548387115; 0.003252032520325132 +2.427096774193549; 0.003252032520325354 +2.4929032258064545; 0.004878048780487698 +2.5587096774193574; 0.003252032520325132 +2.628387096774196; 0.003252032520325132 +2.6941935483870987; 0.003252032520325132 +2.7600000000000016; 0.003252032520325132 +2.8258064516129036; 0.003252032520325132 +2.8916129032258073; 0.003252032520325132 +2.9612903225806493; 0.003252032520325021 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_R2016.csv new file mode 100644 index 00000000..ad0645a0 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos1_R2016.csv @@ -0,0 +1,57 @@ +-2.9109677419354862; 0.001626016260162566 +-2.7832258064516155; -1.1102230246251565e-16 +-2.6477419354838734; -1.1102230246251565e-16 +-2.5083870967741957; -0.001626016260162566 +-2.376774193548389; 0 +-2.2451612903225824; -1.1102230246251565e-16 +-2.105806451612905; 0 +-1.9741935483870985; -1.1102230246251565e-16 +-1.8425806451612923; 0 +-1.70709677419355; -1.1102230246251565e-16 +-1.579354838709679; 0 +-1.4400000000000013; -1.1102230246251565e-16 +-1.3083870967741946; 0.001626016260162677 +-1.172903225806453; 0.003252032520325132 +-1.0374193548387103; 0.004878048780487809 +-0.9058064516129045; 0.00975609756097573 +-0.7741935483870979; 0.014634146341463428 +-0.700645161290324; 0.06178861788617884 +-0.6425806451612908; 0.1170731707317072 +-0.6038709677419365; 0.15609756097560967 +-0.5574193548387099; 0.16422764227642273 +-0.4722580645161294; 0.17398373983739834 +-0.3406451612903232; 0.15121951219512186 +-0.21677419354838712; 0.1284552845528455 +-0.12387096774193607; 0.08617886178861789 +-0.011612903225806548; 0.04552845528455285 +0.0696774193548384; 0.07317073170731703 +0.17806451612903196; 0.11544715447154463 +0.2864516129032255; 0.14634146341463417 +0.38709677419354804; 0.16097560975609748 +0.43354838709677423; 0.16422764227642273 +0.5187096774193556; 0.16097560975609748 +0.5574193548387099; 0.14796747967479673 +0.6270967741935483; 0.1284552845528455 +0.6503225806451614; 0.10894308943089426 +0.6812903225806455; 0.06666666666666654 +0.6967741935483867; 0.05365853658536579 +0.7587096774193549; 0.034146341463414664 +0.8051612903225811; 0.026016260162601612 +0.8825806451612905; 0.01788617886178867 +0.9329032258064522; 0.01300813008130075 +1.0180645161290327; 0.008130081300812941 +1.0683870967741935; 0.004878048780487809 +1.176774193548388; 0.003252032520325132 +1.3200000000000003; 0.003252032520325354 +1.4477419354838714; 0.001626016260162566 +1.5754838709677426; 0 +1.7109677419354847; -1.1102230246251565e-16 +1.8503225806451624; 0 +1.9858064516129046; 0 +2.1135483870967757; 0 +2.2529032258064525; 0 +2.384516129032259; -1.1102230246251565e-16 +2.520000000000003; -1.1102230246251565e-16 +2.65935483870968; 0 +2.7832258064516138; 0 +2.910967741935486; -1.1102230246251565e-16 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_BM1994.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_BM1994.csv new file mode 100644 index 00000000..6069b07e --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_BM1994.csv @@ -0,0 +1,63 @@ +-1.9509677419354858; -0.49918699186991866 +-1.858064516129034; -0.49918699186991866 +-1.7767741935483885; -0.4975609756097562 +-1.6025806451612916; -0.49593495934959353 +-1.5251612903225822; -0.49593495934959353 +-1.4477419354838723; -0.4975609756097562 +-1.3625806451612914; -0.49593495934959353 +-1.277419354838711; -0.49430894308943085 +-1.200000000000001; -0.4926829268292684 +-1.1148387096774206; -0.4926829268292684 +-1.0296774193548397; -0.4926829268292684 +-0.9561290322580653; -0.4878048780487806 +-0.8709677419354844; -0.4813008130081301 +-0.793548387096775; -0.46829268292682935 +-0.7277419354838721; -0.45040650406504057 +-0.6735483870967749; -0.42113821138211394 +-0.6348387096774202; -0.3951219512195122 +-0.596129032258065; -0.36585365853658536 +-0.5574193548387107; -0.33333333333333337 +-0.5187096774193556; -0.31382113821138213 +-0.4800000000000004; -0.30731707317073165 +-0.42580645161290365; -0.3008130081300814 +-0.3406451612903232; -0.29756097560975603 +-0.3135483870967746; -0.2991869918699187 +-0.2632258064516133; -0.30731707317073165 +-0.2245161290322586; -0.3203252032520326 +-0.18580645161290388; -0.33333333333333337 +-0.1587096774193557; -0.35121951219512193 +-0.11225806451612952; -0.3788617886178861 +-0.06580645161290377; -0.40325203252032515 +-0.023225806451613096; -0.41463414634146345 +0.003870967741935516; -0.41951219512195126 +0.023225806451612208; -0.4178861788617888 +0.0696774193548384; -0.40487804878048783 +0.09677419354838612; -0.39349593495934954 +0.13548387096774128; -0.37073170731707317 +0.18193548387096747; -0.35121951219512193 +0.21677419354838712; -0.3268292682926829 +0.29032258064516103; -0.3056910569105692 +0.34064516129032274; -0.29756097560975625 +0.38709677419354804; -0.3008130081300814 +0.4490322580645163; -0.3040650406504065 +0.5264516129032257; -0.31382113821138213 +0.572903225806451; -0.3365853658536585 +0.5999999999999996; -0.35609756097560974 +0.6425806451612903; -0.38861788617886195 +0.6812903225806455; -0.41463414634146345 +0.7200000000000006; -0.4406504065040652 +0.781935483870968; -0.4617886178861791 +0.8516129032258064; -0.47804878048780475 +0.9406451612903224; -0.4861788617886179 +1.0141935483870972; -0.48943089430894304 +1.087741935483872; -0.4926829268292684 +1.176774193548388; -0.49593495934959353 +1.258064516129033; -0.49430894308943085 +1.3470967741935498; -0.49593495934959353 +1.4245161290322592; -0.49593495934959353 +1.5058064516129042; -0.49430894308943085 +1.5948387096774201; -0.49593495934959353 +1.676129032258065; -0.4975609756097562 +1.7535483870967754; -0.4975609756097562 +1.8425806451612914; -0.49918699186991866 +1.9200000000000008; -0.49918699186991866 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_DI2018.csv new file mode 100644 index 00000000..f3b8820e --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_DI2018.csv @@ -0,0 +1,40 @@ +-3.0000000000000027; -0.5008130081300813 +-1.6412903225806468; -0.49918699186991866 +-1.3238709677419367; -0.4975609756097562 +-1.2309677419354852; -0.49593495934959353 +-1.0838709677419365; -0.4926829268292684 +-0.8903225806451625; -0.4813008130081301 +-0.7664516129032264; -0.4601626016260162 +-0.7122580645161296; -0.4390243902439025 +-0.6425806451612908; -0.4 +-0.5264516129032266; -0.343089430894309 +-0.4645161290322588; -0.3219512195121951 +-0.39483870967741996; -0.3105691056910568 +-0.3174193548387101; -0.3089430894308943 +-0.2632258064516133; -0.3154471544715448 +-0.2012903225806455; -0.330081300813008 +-0.12387096774193607; -0.37073170731707317 +-0.03870967741935516; -0.4065040650406503 +0.007741935483871032; -0.41463414634146345 +0.054193548387096335; -0.40813008130081296 +0.08516129032258046; -0.39837398373983735 +0.14709677419354872; -0.3691056910569107 +0.22451612903225815; -0.3317073170731707 +0.2632258064516133; -0.310569105691057 +0.31741935483870964; -0.29593495934959335 +0.3522580645161284; -0.2943089430894311 +0.4025806451612901; -0.29756097560975625 +0.46451612903225836; -0.3170731707317075 +0.5496774193548388; -0.35609756097560974 +0.6503225806451614; -0.413008130081301 +0.7200000000000006; -0.4487804878048781 +0.774193548387097; -0.46178861788617886 +0.8593548387096783; -0.47804878048780497 +0.9290322580645167; -0.4861788617886179 +1.0374193548387103; -0.4910569105691057 +1.169032258064517; -0.49593495934959353 +1.3006451612903227; -0.4975609756097562 +1.5096774193548397; -0.497560975609756 +1.9974193548387111; -0.4991869918699189 +2.8180645161290334; -0.49918699186991866 +2.9961290322580645; -0.5008130081300813 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_KM2000.csv new file mode 100644 index 00000000..80e136d4 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_KM2000.csv @@ -0,0 +1,98 @@ +-2.9767741935483896; -0.5008130081300813 +-2.9109677419354862; -0.5008130081300813 +-2.845161290322583; -0.5008130081300813 +-2.7754838709677445; -0.5008130081300813 +-2.7096774193548407; -0.5008130081300813 +-2.643870967741938; -0.5008130081300813 +-2.5780645161290345; -0.5024390243902438 +-2.5083870967741957; -0.5024390243902438 +-2.442580645161293; -0.5008130081300813 +-2.376774193548389; -0.5024390243902438 +-2.3070967741935506; -0.5008130081300813 +-2.241290322580647; -0.5024390243902438 +-2.1754838709677435; -0.5024390243902438 +-2.1096774193548407; -0.49918699186991866 +-2.040000000000002; -0.49918699186991866 +-1.9741935483870985; -0.4991869918699189 +-1.9045161290322596; -0.4991869918699189 +-1.8387096774193568; -0.49918699186991866 +-1.7729032258064525; -0.4975609756097562 +-1.7032258064516146; -0.49593495934959353 +-1.594838709677421; -0.4975609756097562 +-1.505806451612905; -0.4943089430894311 +-1.4361290322580662; -0.4943089430894311 +-1.3703225806451629; -0.49430894308943085 +-1.277419354838711; -0.4926829268292684 +-1.169032258064517; -0.4910569105691057 +-1.095483870967743; -0.4910569105691057 +-1.0412903225806458; -0.4910569105691057 +-0.9677419354838719; -0.48943089430894327 +-0.901935483870969; -0.48455284552845546 +-0.8400000000000012; -0.47804878048780497 +-0.7819354838709689; -0.4666666666666667 +-0.7316129032258076; -0.445528455284553 +-0.700645161290324; -0.4227642276422764 +-0.6696774193548398; -0.3967479674796749 +-0.6387096774193552; -0.3707317073170734 +-0.6038709677419365; -0.34634146341463434 +-0.5690322580645168; -0.3219512195121953 +-0.5225806451612911; -0.3056910569105692 +-0.4567741935483878; -0.2943089430894309 +-0.3870967741935494; -0.29756097560975625 +-0.32516129032258156; -0.3040650406504065 +-0.2748387096774194; -0.3219512195121953 +-0.2245161290322586; -0.3414634146341463 +-0.18580645161290388; -0.3626016260162602 +-0.13548387096774217; -0.38373983739837414 +-0.08903225806451642; -0.40487804878048783 +-0.030967741935484128; -0.4081300813008132 +0.03870967741935516; -0.4081300813008132 +0.08903225806451598; -0.3918699186991871 +0.13548387096774128; -0.3707317073170734 +0.18193548387096747; -0.35121951219512193 +0.22838709677419367; -0.3317073170731707 +0.27483870967741897; -0.3105691056910572 +0.3367741935483872; -0.29756097560975625 +0.4025806451612901; -0.2943089430894309 +0.468387096774193; -0.2959349593495936 +0.5187096774193556; -0.3105691056910568 +0.5535483870967743; -0.33495934959349605 +0.5922580645161295; -0.3577235772357724 +0.6193548387096772; -0.38211382113821146 +0.6503225806451614; -0.40813008130081296 +0.6812903225806455; -0.432520325203252 +0.7277419354838708; -0.45365853658536615 +0.781935483870968; -0.4699186991869918 +0.8400000000000007; -0.48292682926829256 +0.9058064516129036; -0.48943089430894327 +0.9716129032258074; -0.48943089430894327 +1.0374193548387103; -0.4926829268292684 +1.103225806451614; -0.4943089430894311 +1.1729032258064525; -0.49593495934959353 +1.2425806451612909; -0.49593495934959353 +1.304516129032259; -0.4975609756097562 +1.3741935483870975; -0.4975609756097562 +1.443870967741936; -0.49918699186991866 +1.5058064516129042; -0.49918699186991866 +1.5754838709677426; -0.4975609756097562 +1.6412903225806463; -0.4975609756097562 +1.7032258064516137; -0.49918699186991866 +1.772903225806453; -0.49918699186991866 +1.8387096774193559; -0.49918699186991866 +1.9045161290322596; -0.49918699186991866 +1.974193548387098; -0.4991869918699189 +2.040000000000002; -0.4975609756097562 +2.1096774193548393; -0.49918699186991866 +2.175483870967744; -0.49918699186991866 +2.241290322580647; -0.4975609756097562 +2.307096774193549; -0.49918699186991866 +2.3729032258064517; -0.49918699186991866 +2.4464516129032257; -0.4991869918699189 +2.5083870967741957; -0.4975609756097562 +2.578064516129034; -0.49918699186991866 +2.6438709677419387; -0.49918699186991866 +2.709677419354839; -0.5008130081300813 +2.7754838709677454; -0.5008130081300813 +2.845161290322583; -0.5008130081300813 +2.910967741935486; -0.5008130081300813 +2.9767741935483887; -0.502439024390244 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_LS1993.csv new file mode 100644 index 00000000..0db64975 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_LS1993.csv @@ -0,0 +1,41 @@ +-1.6064516129032271; -0.4829268292682928 +-1.5212903225806467; -0.48943089430894327 +-1.4400000000000013; -0.4878048780487806 +-1.3509677419354853; -0.48943089430894327 +-1.277419354838711; -0.4861788617886179 +-1.1961290322580655; -0.48455284552845523 +-1.1148387096774206; -0.47967479674796765 +-1.0335483870967752; -0.4764227642276423 +-0.9522580645161298; -0.46341463414634154 +-0.8709677419354844; -0.4487804878048781 +-0.7974193548387105; -0.41951219512195126 +-0.7083870967741941; -0.38699186991869927 +-0.6348387096774202; -0.35121951219512193 +-0.5496774193548397; -0.330081300813008 +-0.4722580645161294; -0.310569105691057 +-0.3870967741935485; -0.2991869918699187 +-0.3135483870967746; -0.29105691056910576 +-0.24000000000000066; -0.33333333333333337 +-0.1587096774193557; -0.36747967479674803 +-0.08129032258064539; -0.4 +0.007741935483871032; -0.41463414634146345 +0.08129032258064495; -0.4 +0.1625806451612899; -0.36585365853658536 +0.23999999999999932; -0.33333333333333337 +0.3290322580645162; -0.29105691056910576 +0.4064516129032265; -0.3008130081300814 +0.48387096774193594; -0.310569105691057 +0.5729032258064519; -0.32845528455284556 +0.6464516129032258; -0.3528455284552846 +0.7316129032258063; -0.38861788617886195 +0.8129032258064521; -0.4178861788617886 +0.8903225806451616; -0.4487804878048781 +0.9754838709677429; -0.46341463414634154 +1.0490322580645168; -0.4764227642276423 +1.1303225806451618; -0.47804878048780475 +1.2077419354838712; -0.48455284552845523 +1.2929032258064517; -0.4861788617886179 +1.370322580645162; -0.4878048780487806 +1.451612903225807; -0.48943089430894327 +1.5329032258064528; -0.48943089430894327 +1.610322580645163; -0.48455284552845546 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_R2016.csv new file mode 100644 index 00000000..1a603ad1 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos2_R2016.csv @@ -0,0 +1,56 @@ +-2.945806451612906; -0.5008130081300813 +-2.8103225806451637; -0.49918699186991866 +-2.678709677419361; -0.5008130081300816 +-2.53935483870968; -0.49918699186991866 +-2.4116129032258087; -0.4991869918699189 +-2.2761290322580665; -0.49918699186991866 +-2.136774193548389; -0.49918699186991866 +-2.005161290322582; -0.49918699186991866 +-1.9432258064516148; -0.49918699186991866 +-1.788387096774195; -0.49918699186991866 +-1.6064516129032271; -0.49593495934959353 +-1.4477419354838723; -0.49593495934959353 +-1.26967741935484; -0.49430894308943085 +-1.200000000000001; -0.48943089430894327 +-1.080000000000001; -0.4861788617886179 +-0.9367741935483882; -0.46991869918699203 +-0.8206451612903232; -0.45040650406504057 +-0.7200000000000011; -0.41463414634146345 +-0.6270967741935491; -0.3691056910569107 +-0.5341935483870972; -0.32845528455284556 +-0.4180645161290326; -0.3089430894308943 +-0.30967741935483906; -0.30731707317073165 +-0.27870967741935493; -0.31382113821138213 +-0.2012903225806455; -0.34634146341463434 +-0.11612903225806503; -0.3902439024390246 +-0.030967741935484128; -0.4113821138211383 +0; -0.41463414634146345 +0.030967741935484128; -0.4113821138211383 +0.12387096774193562; -0.3788617886178861 +0.20129032258064505; -0.34471544715447167 +0.2787096774193545; -0.310569105691057 +0.34064516129032274; -0.29756097560975625 +0.4025806451612901; -0.3040650406504065 +0.44129032258064527; -0.310569105691057 +0.5264516129032257; -0.330081300813008 +0.6232258064516127; -0.3691056910569107 +0.7122580645161296; -0.4113821138211383 +0.7316129032258063; -0.4227642276422764 +0.789677419354839; -0.4439024390243903 +0.8554838709677428; -0.4552845528455284 +0.9212903225806457; -0.4666666666666667 +1.0529032258064523; -0.4861788617886179 +1.1806451612903235; -0.49430894308943085 +1.3316129032258068; -0.49918699186991866 +1.4632258064516135; -0.49918699186991866 +1.6103225806451622; -0.49918699186991866 +1.7341935483870978; -0.49918699186991866 +1.8735483870967755; -0.49918699186991866 +2.0051612903225813; -0.49918699186991866 +2.136774193548388; -0.49918699186991866 +2.2761290322580656; -0.49918699186991866 +2.407741935483873; -0.49918699186991866 +2.539354838709679; -0.5008130081300813 +2.6864516129032276; -0.49918699186991866 +2.8180645161290334; -0.49918699186991866 +2.934193548387098; -0.49918699186991866 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_DI2018.csv new file mode 100644 index 00000000..5012ab50 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_DI2018.csv @@ -0,0 +1,40 @@ +-3.0000000000000027; -1 +-1.7032258064516141; -1 +-1.5329032258064528; -0.9967479674796748 +-1.3238709677419367; -0.9934959349593498 +-1.1458064516129043; -0.9869918699186991 +-0.998709677419356; -0.9756097560975611 +-0.8670967741935498; -0.9528455284552845 +-0.7316129032258076; -0.9186991869918698 +-0.6812903225806459; -0.9040650406504066 +-0.607741935483872; -0.8845528455284554 +-0.5419354838709682; -0.8731707317073168 +-0.4567741935483878; -0.8634146341463417 +-0.37161290322580687; -0.861788617886179 +-0.30967741935483906; -0.8634146341463417 +-0.24000000000000066; -0.8715447154471546 +-0.18580645161290388; -0.8796747967479676 +-0.11612903225806503; -0.8959349593495937 +-0.04645161290322575; -0.9089430894308944 +0; -0.9154471544715447 +0.03870967741935427; -0.9154471544715447 +0.07741935483870943; -0.9121951219512193 +0.12387096774193562; -0.9024390243902439 +0.19354838709677402; -0.8861788617886178 +0.2632258064516133; -0.8747967479674795 +0.3096774193548386; -0.8682926829268295 +0.3561290322580639; -0.8634146341463417 +0.3987096774193546; -0.861788617886179 +0.45677419354838733; -0.8634146341463417 +0.5109677419354837; -0.8731707317073173 +0.6425806451612903; -0.8991869918699188 +0.7122580645161296; -0.9154471544715447 +0.8206451612903232; -0.9430894308943091 +0.96; -0.9723577235772359 +1.0529032258064523; -0.9821138211382116 +1.1845161290322581; -0.9902439024390245 +1.3393548387096779; -0.9934959349593498 +1.5019354838709686; -0.9951219512195123 +1.7032258064516137; -0.9983739837398374 +2.1135483870967757; -1 +3.0000000000000027; -1 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_KM2000.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_KM2000.csv new file mode 100644 index 00000000..d7828064 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_KM2000.csv @@ -0,0 +1,94 @@ +-2.972903225806454; -1.0016260162601625 +-2.9070967741935507; -1.0032520325203254 +-2.841290322580648; -1.0032520325203254 +-2.7754838709677445; -1.0016260162601625 +-2.7058064516129052; -1.0032520325203254 +-2.6400000000000023; -1.0016260162601625 +-2.574193548387099; -1.0016260162601625 +-2.50451612903226; -1.0016260162601625 +-2.4387096774193573; -1.0032520325203254 +-2.369032258064518; -1 +-2.303225806451615; -1 +-2.233548387096776; -1.0016260162601625 +-2.1716129032258085; -1.0032520325203254 +-2.10193548387097; -1.0016260162601625 +-2.0361290322580663; -1.0016260162601625 +-1.970322580645163; -1 +-1.9045161290322596; -1 +-1.8348387096774208; -0.9983739837398374 +-1.769032258064518; -0.9983739837398374 +-1.7032258064516141; -0.9983739837398374 +-1.6374193548387113; -0.9983739837398374 +-1.571612903225808; -0.9983739837398374 +-1.501935483870969; -0.9983739837398374 +-1.4361290322580658; -0.9951219512195123 +-1.3703225806451629; -0.9934959349593498 +-1.300645161290324; -0.9918699186991872 +-1.2348387096774207; -0.9902439024390245 +-1.1690322580645174; -0.9902439024390245 +-1.0993548387096785; -0.9869918699186991 +-1.0374193548387107; -0.9821138211382113 +-0.9716129032258061; -0.9739837398373995 +-0.9058064516129041; -0.9642276422764228 +-0.8554838709677428; -0.9495934959349593 +-0.801290322580646; -0.9317073170731708 +-0.7470967741935493; -0.913821138211382 +-0.696774193548388; -0.8959349593495937 +-0.6464516129032263; -0.8764227642276422 +-0.5922580645161295; -0.8634146341463417 +-0.5303225806451617; -0.8504065040650407 +-0.4645161290322588; -0.8439024390243902 +-0.402580645161291; -0.8455284552845529 +-0.3406451612903232; -0.8569105691056912 +-0.2825806451612909; -0.8715447154471546 +-0.23225806451612963; -0.8910569105691056 +-0.18193548387096836; -0.9089430894308944 +-0.12774193548387158; -0.9235772357723578 +-0.06580645161290377; -0.9365853658536584 +-0.003870967741935516; -0.9414634146341464 +0.06193548387096737; -0.9349593495934961 +0.11612903225806459; -0.9219512195121952 +0.1664516129032254; -0.9024390243902439 +0.21677419354838712; -0.8845528455284554 +0.27096774193548345; -0.8650406504065044 +0.3251612903225807; -0.8520325203252034 +0.39096774193548356; -0.8439024390243902 +0.45677419354838733; -0.8455284552845529 +0.5187096774193556; -0.8569105691056912 +0.5767741935483865; -0.8715447154471546 +0.6270967741935483; -0.886178861788618 +0.685161290322581; -0.9008130081300815 +0.7354838709677418; -0.91869918699187 +0.789677419354839; -0.9382113821138215 +0.8438709677419363; -0.9528455284552845 +0.9058064516129036; -0.9642276422764228 +0.9638709677419355; -0.9788617886178862 +1.0296774193548393; -0.9853658536585369 +1.0916129032258075; -0.9886178861788618 +1.1651612903225814; -0.9902439024390245 +1.2270967741935488; -0.9934959349593498 +1.2967741935483872; -0.9934959349593498 +1.362580645161291; -0.9934959349593498 +1.4283870967741947; -0.9934959349593498 +1.4941935483870976; -0.9934959349593498 +1.563870967741936; -0.9934959349593494 +1.629677419354839; -0.9951219512195121 +1.6993548387096782; -0.996747967479675 +1.765161290322582; -0.9967479674796748 +1.8309677419354848; -0.9983739837398374 +1.8967741935483877; -1 +1.966451612903227; -1 +2.0283870967741953; -1.0016260162601625 +2.0980645161290337; -1 +2.167741935483872; -1 +2.2296774193548403; -1 +2.3032258064516142; -1 +2.369032258064517; -1 +2.43483870967742; -1 +2.5006451612903238; -0.9983739837398377 +2.5664516129032267; -1 +2.632258064516132; -1 +2.701935483870969; -1 +2.7677419354838726; -0.9983739837398374 +2.8335483870967773; -1 +2.9032258064516148; -1 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_R2016.csv new file mode 100644 index 00000000..040f3f52 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig11_uxux_profile_pos3_R2016.csv @@ -0,0 +1,56 @@ +-2.9419354838709704; -1 +-2.810322580645164; -1 +-2.674838709677421; -1 +-2.53935483870968; -1 +-2.407741935483873; -1 +-2.2683870967741955; -1 +-2.1212903225806468; -0.9967479674796748 +-2.005161290322582; -0.9967479674796748 +-1.8696774193548404; -0.9951219512195123 +-1.7419354838709693; -0.9951219512195123 +-1.6025806451612916; -0.9951219512195123 +-1.4709677419354854; -0.9934959349593498 +-1.3354838709677437; -0.9902439024390245 +-1.1961290322580655; -0.9821138211382116 +-1.0683870967741944; -0.9674796747967481 +-0.9445161290322592; -0.9495934959349593 +-0.8206451612903232; -0.9252032520325203 +-0.7083870967741941; -0.894308943089431 +-0.596129032258065; -0.8699186991869919 +-0.5690322580645168; -0.8650406504065044 +-0.48774193548387146; -0.8601626016260163 +-0.4335483870967751; -0.861788617886179 +-0.35612903225806525; -0.8682926829268293 +-0.301935483870968; -0.8764227642276422 +-0.23225806451612963; -0.889430894308943 +-0.18580645161290388; -0.8959349593495937 +-0.10064516129032341; -0.8991869918699188 +-0.05032258064516126; -0.9040650406504066 +0.030967741935484128; -0.9056910569105691 +0.08516129032258046; -0.9040650406504066 +0.18193548387096747; -0.8975609756097563 +0.2129032258064516; -0.8910569105691059 +0.31741935483870964; -0.8764227642276422 +0.41032258064516114; -0.8552845528455285 +0.45677419354838733; -0.8504065040650407 +0.5303225806451612; -0.8552845528455285 +0.5806451612903221; -0.8634146341463417 +0.6619354838709679; -0.8829268292682927 +0.7045161290322586; -0.8926829268292683 +0.789677419354839; -0.9121951219512193 +0.9212903225806457; -0.9479674796747967 +1.0451612903225813; -0.9674796747967481 +1.1535483870967749; -0.9804878048780489 +1.2929032258064517; -0.9902439024390245 +1.4245161290322592; -0.9934959349593494 +1.563870967741936; -0.9951219512195121 +1.7032258064516137; -0.9951219512195123 +1.8270967741935493; -0.996747967479675 +1.966451612903227; -0.9967479674796748 +2.101935483870969; -0.9983739837398374 +2.2374193548387113; -0.9967479674796748 +2.37677419354839; -0.9967479674796748 +2.5083870967741966; -0.9967479674796748 +2.6322580645161313; -0.996747967479675 +2.7677419354838726; -0.9967479674796748 +2.903225806451614; -0.996747967479675 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_DI2018.csv new file mode 100644 index 00000000..7a708ac3 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_DI2018.csv @@ -0,0 +1,27 @@ +-2.9990238388820396; -0.00014687100894006377 +-1.1558775174681464; 0.0014495530012770264 +-0.9546513220989175; 0.0038441890166026615 +-0.7962392108508012; 0.007835249042145387 +-0.7277366762570217; 0.015019157088122459 +-0.6506713248390188; 0.030983397190293638 +-0.5650431565967939; 0.059719029374201704 +-0.5136662556514588; 0.07328863346104714 +-0.42803808740923355; 0.0820689655172413 +-0.3124400602822299; 0.08925287356321832 +-0.1968420331552263; 0.09803320561941242 +-0.10693245650089; 0.1044189016602809 +-0.017022879846553707; 0.10601532567049793 +0.12426359775311724; 0.10282247765006375 +0.158514865050007; 0.10042784163473811 +0.29551993423756784; 0.08526181353767548 +0.38114810247979225; 0.07568326947637277 +0.4924647211946853; 0.06929757343550436 +0.5352788053157971; 0.06371008939974437 +0.5823742978490216; 0.05333333333333323 +0.6380326072064673; 0.035772669220944964 +0.7193793670365816; 0.018212005108556695 +0.7878819016303611; 0.010229885057471133 +0.92060556240581; 0.005440613026819752 +1.2588368269625985; 0.0022477650063855714 +2.1236813262090717; -0.00014687100894006377 +2.997088642279765; -0.00014687100894006377 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_R2016.csv new file mode 100644 index 00000000..7f7e2f13 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos1_R2016.csv @@ -0,0 +1,68 @@ +-2.9540690505548715; 0.0014495530012769153 +-2.819204685573367; 0.0006513409961684813 +-2.6843403205918626; 0.0014495530012769153 +-2.551616659816414; 0.0014495530012769153 +-2.418892999040965; 0.0006513409961683703 +-2.2818879298534047; 0.0006513409961684813 +-2.1534456774900677; -0.00014687100894006377 +-2.022862720920674; 0.0006513409961683703 +-1.8837169475270588; 0.0006513409961684813 +-1.7467118783394988; 0.0006513409961684813 +-1.6139882175640499; -0.00014687100894006377 +-1.47698314837649; -0.00014687100894006377 +-1.3485408960131529; -0.00014687100894006377 +-1.213676531031648; 0.0022477650063855714 +-1.0873749828743664; 0.0038441890166026615 +-0.9674955473352513; 0.005440613026819752 +-0.9161186463899162; 0.006238825031928408 +-0.8411939991779693; 0.009431673052362588 +-0.8090834360871351; 0.010229885057471133 +-0.7876763940265792; 0.013422733077905369 +-0.7106110426085763; 0.02300127713920802 +-0.6699376626935192; 0.030185185185185093 +-0.5864501986573503; 0.04694763729246482 +-0.5521989313604596; 0.05413154533844178 +-0.47513357994245764; 0.07169220945082999 +-0.4408823126455679; 0.0812707535121327 +-0.3531134401972871; 0.09723499361430382 +-0.3167214686943409; 0.10282247765006375 +-0.22895259624606057; 0.11719029374201778 +-0.19256062474311486; 0.12118135376756056 +-0.08980682285244468; 0.13475095785440605 +-0.0641183723797778; 0.1379438058748403 +0.0257912042745585; 0.14113665389527447 +0.06860528839567115; 0.13954022988505738 +0.15209275243184006; 0.13155810983397181 +0.19704754075900865; 0.1251724137931033 +0.27411289217701107; 0.11399744572158355 +0.3212083847102347; 0.1044189016602809 +0.398273736128238; 0.0900510855683268 +0.4410878202493498; 0.0812707535121327 +0.5095903548431302; 0.06530651340996152 +0.5524044389642428; 0.05572796934865887 +0.6273290861761893; 0.039763729246487745 +0.665861761885191; 0.03337803320561927 +0.7514899301274154; 0.01741379310344815 +0.796444718454584; 0.011826309067688223 +0.8735100698725864; 0.0022477650063855714 +0.924886970817921; 0.0022477650063855714 +1.0105151390601463; -0.00014687100894006377 +1.0618920400054819; -0.0009450830140487199 +1.1432387998355953; -0.00254150702426581 +1.186052883956708; -0.00254150702426581 +1.2759624606110442; -0.003339719029374355 +1.3294800657624348; -0.003339719029374355 +1.4108268255925482; -0.004936143039591445 +1.455781613919716; -0.003339719029374355 +1.5414097821619412; -0.003339719029374355 +1.586364570489109; -0.003339719029374355 +1.6848369639676681; -0.003339719029374355 +1.836826962597618; -0.0041379310344829 +1.973832031785177; -0.003339719029374355 +2.115118509384848; -0.00254150702426581 +2.2478421701602977; -0.00254150702426581 +2.376284422523635; -0.00254150702426581 +2.504726674886972; -0.00254150702426581 +2.6460131524866437; -0.00254150702426581 +2.7787368132620927; -0.003339719029374355 +2.8943348403890967; -0.00254150702426581 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_BM1994.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_BM1994.csv new file mode 100644 index 00000000..77231834 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_BM1994.csv @@ -0,0 +1,54 @@ +-1.8708727222907249; -0.5006257982120053 +-1.802370187696945; -0.5006257982120053 +-1.7146013152486645; -0.49902937420178817 +-1.6375359638306626; -0.49902937420178817 +-1.5540484997944923; -0.4990293742017884 +-1.470561035758323; -0.49823116219667973 +-1.391354980134265; -0.4990293742017884 +-1.3164303329223181; -0.4990293742017884 +-1.220098643649815; -0.49663473818646264 +-1.1430332922318125; -0.4958365261813542 +-1.061686532401699; -0.49344189016602846 +-0.9781990683655293; -0.49184546615581115 +-0.8968523085354154; -0.4854597701149428 +-0.8133648444992461; -0.4782758620689658 +-0.7405809014933551; -0.4663026819923375 +-0.6720783668995747; -0.4471455938697322 +-0.62070146595424; -0.42399744572158393 +-0.5778873818331274; -0.39845466155811016 +-0.5393547061241262; -0.3713154533844192 +-0.5008220304151245; -0.3457726692209453 +-0.4622893547061242; -0.31863346104725443 +-0.4194752705850111; -0.29149425287356345 +-0.37666118646389846; -0.2683461047254153 +-0.3210028771064528; -0.242005108556833 +-0.2567817509247843; -0.22045338441890183 +-0.19256062474311442; -0.20927841634738142 +-0.10693245650088867; -0.20927841634738042 +-0.021304288258664705; -0.21326947637292482 +0.060042471571448264; -0.21167305236270773 +0.14138923140156212; -0.20848020434227355 +0.22701739964378742; -0.21566411238825045 +0.28695711741334495; -0.23242656449553023 +0.34475613097684743; -0.2547765006385698 +0.3918516235100702; -0.28111749680715215 +0.4368064118372388; -0.3058620689655175 +0.47962049595835143; -0.3314048531289914 +0.5138717632552412; -0.35694763729246515 +0.54384162214002; -0.3824904214559389 +0.5866557062611326; -0.4096296296296299 +0.6294697903822453; -0.4351724137931037 +0.6894095081518028; -0.4543295019157091 +0.7600527469516383; -0.47109195402298876 +0.8392588025756966; -0.48146871008939995 +0.9184648581997541; -0.48705619412515966 +0.9933895054117015; -0.4926436781609198 +1.0790176736539259; -0.49663473818646264 +1.1646458418961512; -0.495836526181354 +1.2459926017262655; -0.4974329501915713 +1.3359021783806009; -0.4974329501915713 +1.4172489382107152; -0.4974329501915713 +1.4985956980408286; -0.49823116219667973 +1.6612892177010563; -0.49902937420178817 +1.819701328949173; -0.49902937420178817 +1.9117516098095653; -0.5006257982120053 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_DI2018.csv new file mode 100644 index 00000000..473f41ad --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_DI2018.csv @@ -0,0 +1,36 @@ +-2.9990238388820396; -0.4990293742017884 +-1.9479380737087275; -0.4990293742017884 +-1.2800383614193724; -0.4974329501915713 +-1.0359980819290313; -0.4910472541507027 +-0.9289628716262497; -0.4870561941251599 +-0.8048020276750236; -0.4734865900383144 +-0.7534251267296885; -0.4647062579821204 +-0.7063296341964653; -0.4527330779054919 +-0.6463899164269078; -0.4287867177522353 +-0.5693245650089049; -0.38488505747126467 +-0.48797780517879197; -0.32661558109834 +-0.3959275243183993; -0.26914431673052386 +-0.3424099191670087; -0.24360153256704997 +-0.301736539251952; -0.22763729246487885 +-0.23109330045211607; -0.21566411238825056 +-0.17543499109467042; -0.21167305236270773 +-0.0641183723797778; -0.21167305236270773 +0.030072612686670386; -0.21326947637292493 +0.10285655569256136; -0.21167305236270773 +0.17992190711056377; -0.2076819923371649 +0.2398616248801213; -0.20848020434227343 +0.28695711741334495; -0.2164623243933591 +0.3554596520071245; -0.242005108556833 +0.43894711604329384; -0.29309067688378054 +0.5524044389642428; -0.3784993614303961 +0.6209069735580224; -0.42240102171136673 +0.6894095081518028; -0.4511366538952748 +0.7429271133031925; -0.46550446998722883 +0.8092889436909179; -0.47747765006385723 +0.9077613371694762; -0.48625798212005134 +1.0790176736539259; -0.49344189016602824 +1.2331483764899307; -0.4974329501915713 +1.4686258391560498; -0.4990293742017884 +1.9010480887792864; -0.4990293742017884 +2.4405055487053033; -0.4990293742017884 +2.992807233867654; -0.4990293742017884 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_LS1993.csv new file mode 100644 index 00000000..446c31ad --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_LS1993.csv @@ -0,0 +1,41 @@ +-1.6118475133579944; -0.49344189016602824 +-1.5326414577339365; -0.49184546615581115 +-1.4491539936977675; -0.49024904214559406 +-1.3613851212494863; -0.4910472541507027 +-1.275756953007261; -0.4878544061302683 +-1.1986916015892586; -0.4854597701149429 +-1.1173448417591452; -0.48306513409961715 +-1.0552644197835317; -0.4790740740740743 +-0.9674955473352513; -0.4734865900383144 +-0.8925709001233044; -0.45911877394636047 +-0.8005206192629126; -0.43996168582375506 +-0.7213145636388543; -0.4271902937420182 +-0.6421085080147959; -0.3888761174968074 +-0.562902452390738; -0.3481673052362708 +-0.48155569256062414; -0.308256704980843 +-0.4109124537607882; -0.26515325670498124 +-0.3188621729003973; -0.23801404853129016 +-0.23751541307028257; -0.20528735632183925 +-0.15616865324016915; -0.18293742017879966 +-0.0726811892039998; -0.17016602809706294 +0.00866557062611406; -0.16537675606641133 +0.08359021783806053; -0.17096424010217137 +0.1670776818742299; -0.18772669220945082 +0.2441430332922323; -0.2164623243933591 +0.3212083847102347; -0.23801404853128993 +0.4004144403342931; -0.2651532567049809 +0.47962049595835143; -0.308256704980843 +0.5588265515824089; -0.3489655172413796 +0.6465954240306901; -0.38807790549169896 +0.7236607754486926; -0.42639208173690957 +0.8092889436909179; -0.43836526181353797 +0.8863542951089194; -0.45911877394636047 +0.9655603507329777; -0.4702937420178803 +1.042625702150981; -0.4782758620689658 +1.1175503493629275; -0.48306513409961693 +1.192474996574874; -0.48466155810983425 +1.2845252774352662; -0.48785440613026854 +1.3765755582956585; -0.4894508301404856 +1.451500205507605; -0.4910472541507027 +1.5349876695437743; -0.49184546615581115 +1.6206158377859996; -0.4942401021711369 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_R2016.csv new file mode 100644 index 00000000..28a61495 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos2_R2016.csv @@ -0,0 +1,56 @@ +-2.932662008494315; -0.4974329501915713 +-2.799938347718866; -0.4974329501915713 +-2.6672146869434172; -0.49663473818646264 +-2.5409131387861357; -0.49663473818646264 +-2.3974859569804083; -0.49663473818646264 +-2.26476229620496; -0.49663473818646264 +-2.1341793396355673; -0.49663473818646264 +-2.0035963830661734; -0.4974329501915713 +-1.8580284970543914; -0.49663473818646264 +-1.7253048362789425; -0.49663473818646264 +-1.596862583915605; -0.49663473818646264 +-1.459857514728045; -0.4926436781609198 +-1.3207117413344291; -0.4918454661558114 +-1.209395122619537; -0.48865261813537697 +-1.1730031511165913; -0.4894508301404856 +-1.0638272366077541; -0.4838633461047257 +-0.9375256884504726; -0.4734865900383144 +-0.8090834360871351; -0.4543295019157091 +-0.7063296341964653; -0.43277777777777804 +-0.6849225921359086; -0.42080459770114975 +-0.62070146595424; -0.39286717752235034 +-0.5350732977120152; -0.3505619412515967 +-0.4580079462940123; -0.31544061302682014 +-0.368098369639676; -0.26595146871008957 +-0.27818879298534016; -0.2356194125159644 +-0.17757569530072592; -0.20129629629629664 +-0.09622893547061206; -0.18213920817369122 +-0.05983696396766591; -0.17734993614303984 +-0.025585696670776148; -0.1765517241379313 +0.04719824633511438; -0.17734993614303984 +0.08144951363200503; -0.18293742017879977 +0.158514865050007; -0.20209450830140496 +0.20132894917111965; -0.21007662835249064 +0.29123852582545595; -0.2356194125159644 +0.37258528565557025; -0.26914431673052386 +0.4410878202493498; -0.3042656449553003 +0.5352788053157971; -0.35694763729246515 +0.6080627483216885; -0.3904725415070245 +0.7022537333881358; -0.4303831417624524 +0.796444718454584; -0.4575223499361434 +0.8991985203452542; -0.479872286079183 +1.05761063159337; -0.4910472541507027 +1.1860528839567062; -0.4918454661558116 +1.305932319495823; -0.4926436781609198 +1.43437457185916; -0.49184546615581115 +1.5970680915193878; -0.4974329501915713 +1.7212289354706138; -0.495836526181354 +1.8539525962460628; -0.4974329501915713 +1.973832031785177; -0.4974329501915713 +2.1236813262090717; -0.4974329501915713 +2.2521235785724087; -0.4974329501915713 +2.3848472393478577; -0.4974329501915713 +2.5389779421838625; -0.4974329501915713 +2.6609980819290326; -0.49663473818646287 +2.8044252637347595; -0.4974329501915713 +2.9285861076859856; -0.49663473818646264 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_DI2018.csv new file mode 100644 index 00000000..f44dcefb --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_DI2018.csv @@ -0,0 +1,36 @@ +-2.9968831346759837; -0.9987100893997449 +-2.037847650363064; -0.9979118773946365 +-1.5026715988491577; -0.9955172413793107 +-1.3100082203041512; -0.9923243933588763 +-1.1730031511165913; -0.9875351213282251 +-0.9974654062200301; -0.9747637292464884 +-0.8561789286203587; -0.954010217113666 +-0.7619879435539114; -0.9292656449553005 +-0.6592341416632412; -0.8853639846743298 +-0.5393547061241262; -0.8239016602809712 +-0.436600904233456; -0.762439335887612 +-0.32956569393067525; -0.7041698595146872 +-0.2482189341005614; -0.6698467432950195 +-0.15402794903411365; -0.6339272030651346 +-0.07696259761611124; -0.6123754789272036 +-0.008460063022331266; -0.6027969348659006 +0.03863542951089238; -0.6012005108556837 +0.09001233045622747; -0.6075862068965519 +0.1627962734621189; -0.6227522349936145 +0.21417317440745443; -0.6411111111111112 +0.30408275106178984; -0.6810217113665393 +0.415399369776682; -0.7432822477650067 +0.5309973969036861; -0.8183141762452111 +0.6209069735580224; -0.8709961685823759 +0.7022537333881358; -0.9117049808429123 +0.7664748595698052; -0.9348531289910607 +0.8285552815454187; -0.9524137931034484 +0.9099020413755321; -0.9667816091954025 +1.0019523222359243; -0.9779565772669223 +1.1389573914234834; -0.988333333333334 +1.2930880942594882; -0.9931226053639852 +1.3744348540896025; -0.9939208173690937 +1.6184751335799437; -0.9979118773946365 +1.8625154130702848; -0.9971136653895281 +2.4362241402931923; -0.9979118773946365 +2.999229346485822; -0.9995083014048536 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_R2016.csv new file mode 100644 index 00000000..43532ca0 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig12_uyuy_profile_pos3_R2016.csv @@ -0,0 +1,61 @@ +-2.9283806000822037; -1.000306513409962 +-2.795656939306755; -1.000306513409962 +-2.6672146869434172; -1.000306513409962 +-2.5366317303740242; -1.000306513409962 +-2.3932045485682973; -1.000306513409962 +-2.26476229620496; -1.000306513409962 +-2.1277572270174; -1.000306513409962 +-1.9950335662419512; -1.000306513409962 +-1.8558877928483355; -0.9979118773946365 +-1.7317269488971094; -0.9963154533844192 +-1.592581175503494; -0.9955172413793107 +-1.459857514728045; -0.9963154533844192 +-1.3314152623647075; -0.9939208173690937 +-1.1901287847650361; -0.9875351213282255 +-1.0574051239895876; -0.9763601532567054 +-0.9589327305110285; -0.9651851851851856 +-0.8390532949719138; -0.9388441890166033 +-0.7384401972872991; -0.9101085568326952 +-0.6442492122208514; -0.8701979565772675 +-0.6249828743663515; -0.8550319284802046 +-0.5778873818331274; -0.831085568326948 +-0.5436361145362376; -0.816717752234994 +-0.49654062200301397; -0.7911749680715203 +-0.4151938621729001; -0.7448786717752238 +-0.3509727359912316; -0.7081609195402303 +-0.27818879298534016; -0.665057471264368 +-0.2011234415673373; -0.6267432950191574 +-0.17543499109467042; -0.6123754789272036 +-0.10693245650089; -0.591621966794381 +-0.0683997807918888; -0.5852362707535124 +0.017228387450336058; -0.5812452107279698 +0.06432387998356015; -0.5852362707535124 +0.14138923140156212; -0.5996040868454664 +0.1713590902863409; -0.6155683269476375 +0.2120324702013976; -0.6411111111111114 +0.24628373749828825; -0.6554789272030654 +0.29551993423756784; -0.6810217113665393 +0.3661631730374033; -0.7217305236270757 +0.4453692286614608; -0.762439335887612 +0.5202938758734081; -0.8047445721583657 +0.5887964104671886; -0.8494444444444449 +0.659439649267024; -0.8869604086845471 +0.6894095081518028; -0.9021264367816095 +0.7450678175092493; -0.9236781609195406 +0.7878819016303611; -0.931660280970626 +0.8606658446362525; -0.9540102171136657 +0.9933895054117015; -0.9715708812260542 +1.1303945745992614; -0.9843422733077909 +1.2545554185504875; -0.9907279693486595 +1.4044047129743813; -0.9939208173690937 +1.5414097821619412; -0.9963154533844194 +1.6741334429373902; -0.9987100893997449 +1.8047163995067832; -0.9995083014048536 +1.939580764488288; -0.9995083014048536 +2.074445129469791; -1.000306513409962 +2.205028086039185; -1.0034993614303964 +2.342033155226746; -1.0034993614303962 +2.474756816002194; -1.0034993614303964 +2.603199068365531; -1.0034993614303962 +2.740204137553091; -1.0034993614303962 +2.872927798328539; -1.004297573435505 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_BM1994.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_BM1994.csv new file mode 100644 index 00000000..c527ddf1 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_BM1994.csv @@ -0,0 +1,26 @@ +-1.9596469104665823; 1.1102230246251565e-16 +-0.8171500630517023; 0 +-0.7225725094577546; 0.0031796502384738856 +-0.6658259773013873; 0.02702702702702714 +-0.6279949558638083; 0.04292527821939601 +-0.5674653215636831; 0.03974562798092224 +-0.4880201765447669; 0.014308426073132041 +-0.43883984867591463; 0.004769475357710773 +-0.3329129886506941; -0.007949125596184325 +-0.2459016393442628; -0.007949125596184325 +-0.16645649432534704; -0.004769475357710551 +-0.07566204287515754; -0.0015898251192367763 +0.08701134930643128; 0.0031796502384738856 +0.15510718789407285; 0.004769475357710773 +0.2572509457755352; 0.007949125596184492 +0.32534678436317677; 0.007949125596184492 +0.40100882723833475; 0.0015898251192369983 +0.47667087011349274; -0.0111287758346581 +0.5296343001261024; -0.027027027027026973 +0.5598991172761654; -0.03974562798092196 +0.6204287515762923; -0.04133545310015885 +0.6506935687263553; -0.03338632750397452 +0.669609079445145; -0.020667726550079424 +0.7150063051702391; -0.006359300476947438 +0.7755359394703651; 1.1102230246251565e-16 +1.9445145018915495; 1.1102230246251565e-16 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_DI2018.csv new file mode 100644 index 00000000..41b71912 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_DI2018.csv @@ -0,0 +1,31 @@ +-2.992433795712484; 1.1102230246251565e-16 +-0.8776796973518284; 0 +-0.7641866330390923; 0.0015898251192369983 +-0.7112232030264818; 0.004769475357710773 +-0.6431273644388402; 0.022257551669316478 +-0.6052963430012608; 0.03338632750397463 +-0.5825977301387146; 0.034976152623211576 +-0.5523329129886507; 0.02861685214626397 +-0.49180327868852514; 0.00635930047694766 +-0.44640605296343017; -0.004769475357710551 +-0.39344262295081966; -0.015898251192368762 +-0.3404791929382096; -0.019077901430842537 +-0.2723833543505676; -0.014308426073131875 +-0.1815889029003781; -0.0111287758346581 +-0.12105926860025251; -0.0111287758346581 +-0.06809583858764201; -0.007949125596184325 +0.022698612862547485; -0.0015898251192367763 +0.12105926860025207; 0.007949125596184492 +0.21185372005044112; 0.014308426073132041 +0.2534678436317783; 0.014308426073132097 +0.28751576292559644; 0.012718600953895154 +0.45397225725094525; -0.006359300476947438 +0.4993694829760402; -0.014308426073131875 +0.548549810844893; -0.031796502384737524 +0.5825977301387137; -0.038155802861685184 +0.6204287515762923; -0.03338632750397441 +0.6733921815889028; -0.014308426073131875 +0.7074401008827227; -0.0031796502384736636 +0.7490542244640608; 0 +0.8625472887767964; 1.1102230246251565e-16 +2.9962168978562413; 1.1102230246251565e-16 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_R2016.csv new file mode 100644 index 00000000..56bb51f9 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos1_R2016.csv @@ -0,0 +1,32 @@ +-2.822194199243379; 1.1102230246251565e-16 +-0.9382093316519544; -0.0015898251192367763 +-0.8020176544766708; 0.0015898251192369983 +-0.7112232030264818; 0.012718600953895154 +-0.6696090794451455; 0.02702702702702714 +-0.6015132408575026; 0.044515103338632844 +-0.5485498108448934; 0.04610492845786973 +-0.4842370744010087; 0.031796502384737746 +-0.43505674653215687; 0.022257551669316478 +-0.38209331651954637; -0.006359300476947438 +-0.34804539722572514; -0.020667726550079424 +-0.2723833543505676; -0.030206677265500748 +-0.21563682219419933; -0.03338632750397441 +-0.13997477931904134; -0.031796502384737524 +-0.08701134930643084; -0.0238473767885532 +-0.022698612862548373; -0.004769475357710551 +0.026481715006304807; 0.00635930047694766 +0.09079445145018905; 0.023847376788553365 +0.13619167717528313; 0.03338632750397469 +0.22698612862547307; 0.03338632750397463 +0.27238335435056715; 0.031796502384737746 +0.3518284993694829; 0.01589825119236893 +0.3858764186633037; 0.004769475357710773 +0.45397225725094525; -0.031796502384737524 +0.5561160151324085; -0.03974562798092196 +0.6052963430012603; -0.04133545310015885 +0.6809583858764183; -0.02225755166931631 +0.7225725094577555; -0.012718600953894987 +0.7906683480453971; 0.0015898251192369983 +0.9419924337957131; 0.0015898251192369983 +1.0895334174022695; 0.0015898251192369983 +2.8411097099621685; -0.0015898251192367763 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_BM1994.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_BM1994.csv new file mode 100644 index 00000000..17ffc479 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_BM1994.csv @@ -0,0 +1,35 @@ +-1.9445145018915508; -0.5007949125596184 +-0.9533417402269864; -0.5023847376788554 +-0.8701134930643128; -0.5023847376788554 +-0.798234552332914; -0.4992050874403816 +-0.7604035308953345; -0.4976152623211446 +-0.7112232030264818; -0.4880763116057233 +-0.6506935687263558; -0.46740858505564375 +-0.593947036569987; -0.44197138314785367 +-0.5447667087011352; -0.4244833068362479 +-0.5069356872635566; -0.41812400635930047 +-0.45018915510718793; -0.422893481717011 +-0.39344262295081966; -0.4387917329093799 +-0.31021437578814615; -0.45945945945945943 +-0.25346784363177877; -0.46740858505564375 +-0.2080706179066838; -0.4737678855325914 +-0.15510718789407374; -0.48330683624801263 +-0.09079445145018905; -0.4912559618441972 +0; -0.5023847376788554 +0.08322824716267263; -0.5119236883942767 +0.24211853720050414; -0.5325914149443561 +0.31399747793190436; -0.5453100158982512 +0.36696090794451486; -0.5580286168521463 +0.4047919293820934; -0.5691573926868044 +0.45397225725094525; -0.5802861685214628 +0.5258511979823455; -0.5818759936406995 +0.5523329129886507; -0.575516693163752 +0.593947036569987; -0.5596184419713831 +0.6355611601513242; -0.5421303656597775 +0.6658259773013873; -0.5294117647058824 +0.7112232030264813; -0.5151033386327505 +0.7641866330390927; -0.5039745627980922 +0.8133669609079437; -0.5023847376788554 +0.8701134930643128; -0.5007949125596184 +0.9609079445145019; -0.5007949125596184 +1.9255989911727607; -0.5007949125596184 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_DI2018.csv new file mode 100644 index 00000000..62a3c5ad --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_DI2018.csv @@ -0,0 +1,25 @@ +-2.992433795712484; -0.4992050874403816 +-0.9079445145018914; -0.5007949125596184 +-0.8095838587641864; -0.5007949125596184 +-0.7566204287515763; -0.4976152623211446 +-0.6960907944514503; -0.48330683624801285 +-0.6052963430012608; -0.44833068362480133 +-0.5447667087011356; -0.4260731319554849 +-0.5107187894073135; -0.41812400635930047 +-0.46910466582597676; -0.4165341812400636 +-0.41992433795712447; -0.42130365659777413 +-0.22320302648171486; -0.465818759936407 +-0.13997477931904356; -0.48489666136724974 +0.31399747793190436; -0.5564387917329093 +0.3631778058007562; -0.5659777424483307 +0.4161412358133667; -0.575516693163752 +0.4501891551071884; -0.5802861685214628 +0.48423707440100827; -0.5802861685214628 +0.5296343001261032; -0.577106518282989 +0.5674653215636818; -0.5643879173290939 +0.6279949558638087; -0.5389507154213037 +0.6809583858764183; -0.5182829888712243 +0.7187894073139978; -0.5071542130365659 +0.7793190416141229; -0.4992050874403816 +0.8549810844892809; -0.4976152623211446 +2.9962168978562413; -0.5007949125596184 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_LS1993.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_LS1993.csv new file mode 100644 index 00000000..68c69066 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_LS1993.csv @@ -0,0 +1,41 @@ +-1.600252206809584; -0.5007949125596184 +-1.5208070617906682; -0.5007949125596184 +-1.4451450189155106; -0.5007949125596184 +-1.3543505674653216; -0.5007949125596184 +-1.2786885245901636; -0.5007949125596184 +-1.1992433795712483; -0.5007949125596184 +-1.119798234552333; -0.5007949125596184 +-1.0365699873896594; -0.5007949125596184 +-0.9609079445145019; -0.4976152623211446 +-0.8852459016393448; -0.4912559618441972 +-0.8020176544766713; -0.47217806041335453 +-0.7225725094577546; -0.45468998410174877 +-0.6431273644388393; -0.43402225755166934 +-0.5674653215636831; -0.40699523052464226 +-0.4880201765447669; -0.3910969793322734 +-0.4010088272383361; -0.39586645468998405 +-0.32156368221941944; -0.4149443561208267 +-0.24211853720050458; -0.4260731319554849 +-0.16267339218158883; -0.44833068362480133 +-0.08322824716267263; -0.4753577106518283 +0; -0.4992050874403816 +0.07566204287515665; -0.5262321144674086 +0.15132408575031508; -0.5516693163751988 +0.24211853720050414; -0.575516693163752 +0.32534678436317677; -0.5866454689984101 +0.4123581336696098; -0.604133545310016 +0.4804539722572505; -0.6089030206677265 +0.5561160151324094; -0.5945945945945946 +0.6431273644388389; -0.5691573926868044 +0.7263556116015133; -0.546899841017488 +0.805800756620429; -0.5310015898251194 +0.8890290037831017; -0.5119236883942767 +0.9609079445145019; -0.5039745627980922 +1.0441361916771745; -0.5039745627980922 +1.127364438839848; -0.5039745627980922 +1.203026481715007; -0.5023847376788554 +1.2938209331651924; -0.5023847376788554 +1.3656998738965953; -0.5007949125596184 +1.445145018915511; -0.5023847376788554 +1.5283732660781846; -0.4992050874403816 +1.6078184110970994; -0.5023847376788554 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_R2016.csv new file mode 100644 index 00000000..4407168d --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos2_R2016.csv @@ -0,0 +1,31 @@ +-2.9432534678436317; -0.5007949125596184 +-1.059268600252207; -0.5007949125596184 +-0.9306431273644384; -0.4992050874403816 +-0.8247162673392188; -0.49443561208267084 +-0.7793190416141238; -0.4848966613672495 +-0.707440100882724; -0.467408585055644 +-0.6658259773013873; -0.45945945945945943 +-0.6052963430012608; -0.4356120826709061 +-0.5674653215636831; -0.41971383147853725 +-0.49936948297604156; -0.3990461049284578 +-0.46532156368221944; -0.39427662957074716 +-0.36696090794451486; -0.39427662957074716 +-0.3177805800756617; -0.39904610492845793 +-0.2459016393442628; -0.4133545310015898 +-0.11349306431273609; -0.4515103338632751 +-0.015132408575031953; -0.49443561208267084 +0.060529634300126034; -0.5325914149443561 +0.17023959646910392; -0.5691573926868044 +0.2686002522068094; -0.5961844197138314 +0.30264817150063106; -0.6009538950715422 +0.3934426229508192; -0.6025437201907792 +0.4388398486759142; -0.6025437201907792 +0.5145018915510722; -0.5866454689984101 +0.6431273644388398; -0.548489666136725 +0.7339218158890288; -0.5230524642289348 +0.7717528373266074; -0.5166931637519873 +0.8776796973518284; -0.5023847376788554 +0.9987389659520804; -0.4992050874403816 +1.1500630517023955; -0.4976152623211446 +1.4148802017654472; -0.5007949125596184 +2.8713745271122324; -0.4976152623211446 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_BM1994.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_BM1994.csv new file mode 100644 index 00000000..28f3196f --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_BM1994.csv @@ -0,0 +1,40 @@ +-1.9445145018915508; -0.9999999999999999 +-1.2938209331651955; -0.9999999999999999 +-1.119798234552333; -0.9984101748807629 +-1.0403530895334177; -0.9968203497615261 +-0.9722572509457761; -0.9936406995230526 +-0.8776796973518284; -0.985691573926868 +-0.8171500630517023; -0.9761526232114469 +-0.7301387137452711; -0.9554848966613675 +-0.6506935687263558; -0.9332273449920511 +-0.5939470365699875; -0.918918918918919 +-0.5258511979823455; -0.9014308426073129 +-0.44262295081967284; -0.891891891891892 +-0.37831021437578816; -0.891891891891892 +-0.3404791929382096; -0.896661367249603 +-0.27994955863808313; -0.9030206677265499 +-0.22320302648171575; -0.9157392686804452 +-0.14754098360655776; -0.9443561208267092 +-0.08701134930643173; -0.9666136724960258 +0.022698612862546597; -1.015898251192369 +0.08322824716267263; -1.0413354531001593 +0.1361916771752849; -1.0635930047694755 +0.20807061790668335; -1.0858505564387912 +0.2648171500630516; -1.0985691573926868 +0.2988650693568724; -1.1033386327503978 +0.3631778058007562; -1.1096979332273453 +0.4350567465321564; -1.108108108108108 +0.461538461538459; -1.1081081081081083 +0.5182849936948299; -1.0969793322734502 +0.5750315258511973; -1.0842607313195547 +0.6128625472887759; -1.0747217806041336 +0.6620428751576286; -1.0620031796502385 +0.7414880201765444; -1.0413354531001588 +0.8058007566204273; -1.0286168521462642 +0.8663303909205551; -1.017488076311606 +0.9041614123581327; -1.012718600953895 +0.960907944514501; -1.0079491255961845 +1.0365699873896599; -1.004769475357711 +1.127364438839849; -1.003179650238474 +1.2105926860025216; -1.003179650238474 +1.952080706179066; -0.9999999999999999 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_DI2018.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_DI2018.csv new file mode 100644 index 00000000..5fc1ee79 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_DI2018.csv @@ -0,0 +1,29 @@ +-2.992433795712484; -0.9999999999999999 +-1.2786885245901636; -0.9999999999999999 +-1.1954602774274905; -0.9999999999999999 +-1.0441361916771754; -0.9984101748807633 +-0.9609079445145019; -0.9968203497615266 +-0.9079445145018914; -0.9920508744038156 +-0.8247162673392179; -0.9809220985691575 +-0.7263556116015129; -0.9586645468998413 +-0.6469104665825975; -0.9364069952305251 +-0.5145018915510717; -0.9014308426073133 +-0.4615384615384617; -0.8934817170111283 +-0.4161412358133667; -0.8903020667726552 +-0.35182849936948424; -0.8903020667726552 +-0.2875157629255991; -0.8950715421303658 +-0.23076923076923128; -0.9030206677265497 +-0.17023959646910525; -0.9205087440381562 +-0.09079445145018905; -0.9507154213036567 +0.10214375788145968; -1.0413354531001584 +0.17780580075661945; -1.0667726550079495 +0.24211853720050414; -1.0842607313195551 +0.32534678436317765; -1.0969793322734498 +0.3858764186633019; -1.1001589825119238 +0.48423707440100827; -1.0922098569157397 +0.578814627994956; -1.0731319554848966 +0.7679697351828496; -1.0254372019077898 +0.8776796973518284; -1.0111287758346585 +0.9911727616645649; -1.003179650238474 +1.218158890290037; -1.001589825119237 +2.9962168978562413; -0.9999999999999999 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_R2016.csv b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_R2016.csv new file mode 100644 index 00000000..6454d695 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/profile_reference_data/Fig13_uxuy_profile_pos3_R2016.csv @@ -0,0 +1,38 @@ +-2.9432534678436317; -0.9999999999999999 +-1.592686002522068; -0.9999999999999999 +-1.4754098360655736; -0.9999999999999999 +-1.331651954602774; -0.9984101748807629 +-1.2143757881462798; -0.9952305246422896 +-1.08953341740227; -0.9920508744038158 +-1.0365699873896594; -0.9888712241653418 +-0.9609079445145019; -0.9825119236883942 +-0.9155107187894074; -0.9777424483306835 +-0.8360655737704912; -0.9682034976152626 +-0.7112232030264818; -0.9427662957074722 +-0.5750315258511982; -0.9141494435612082 +-0.45397225725094614; -0.8966613672496028 +-0.4161412358133667; -0.893481717011129 +-0.36317780580075665; -0.891891891891892 +-0.31778058007566257; -0.893481717011129 +-0.2875157629255991; -0.8982511923688395 +-0.21563682219419933; -0.9125596184419712 +-0.17780580075662034; -0.9252782193958667 +-0.10970996216897788; -0.9475357710651827 +-0.011349306431273742; -0.9984101748807633 +0.06809583858764201; -1.0317965023847377 +0.09836065573770547; -1.0429252782193958 +0.13619167717528402; -1.062003179650239 +0.18915510718789363; -1.0842607313195547 +0.2799495586380827; -1.1017488076311608 +0.3102143757881466; -1.1049284578696343 +0.397225725094577; -1.1065182829888713 +0.4464060529634297; -1.1017488076311608 +0.5258511979823455; -1.0906200317965027 +0.6733921815889028; -1.0635930047694755 +0.7868852459016393; -1.0333863275039743 +0.8965952080706181; -1.0206677265500792 +0.9344262295081966; -1.014308426073132 +1.0290037831021435; -1.0047694753577106 +1.172761664564943; -1.001589825119237 +1.3089533417402262; -0.9999999999999999 +2.9356872635561153; -0.9999999999999999 diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py new file mode 100644 index 00000000..369ab3ca --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py @@ -0,0 +1,51 @@ + + +import torch +import numpy as np +import os + +from typing import List + +from lettuce._simulation import Reporter, Simulation + +class ProfileReporter(Reporter): + + def __init__(self, interval: int, flow, position_lu, i_start): + super().__init__(interval) + self.flow = flow + self.i_start = i_start + self.x_position = int(round(position_lu, 0)) + + # linear interpolation of u if x_pos is off grid + # ... positions x_pos# and weights w# + self.interpol = False + if position_lu % 1 != 0: + self.interpol = True + self.x_pos1 = int(np.floor(position_lu)) + self.x_pos2 = int(np.ceil(position_lu)) + self.w2 = position_lu - self.x_pos1 + self.w1 = 1 - self.w2 + print("ProfileReporter: requested position is off grid! " + "Interpolating u(" + str(position_lu) + ") = " + + str(self.w1) + " * u(" + str(self.x_pos1) + ") + " + + str(self.w2) + " * u(" + str(self.x_pos2) + ")") + + self.i_out = [] + self.out = [] + + def __call__(self, simulation: 'Simulation'): + if simulation.flow.i % self.interval == 0 and simulation.flow.i >= self.i_start: + # calculate or interpolate velocity profile in y-direction at position + if self.interpol: + u = self.flow.u(self.flow.f)[:, self.x_pos1] * self.w1 + \ + self.flow.u(self.flow.f)[:, self.x_pos2] * self.w2 + else: + u = self.flow.u(self.flow.f)[:, self.x_position] + u = self.flow.units.convert_velocity_to_pu(u).cpu().numpy() + self.i_out.append(self.flow.i) + if self.flow.stencil.d == 2: + self.out.append(u) + elif self.flow.stencil.d == 3: + # average over z-axis (axial cross stream for cylinder obstacle) + self.out.append(np.mean(u, axis=2)) + diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py new file mode 100644 index 00000000..5a5cec16 --- /dev/null +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py @@ -0,0 +1,251 @@ + + +import os +import numpy as np +import pyevtk.hl as vtk + +from lettuce._simulation import Reporter, Simulation + +__all__ = [ + "write_vtk", "VTKReporterAdvanced", "VTKsliceReporter" +] + +def write_vtk(point_dict, id=0, filename_base="./data/output", origin: tuple[float, float, float] = (0.0, 0.0, 0.0)): + vtk.imageToVTK( + path=f"{filename_base}_{id:08d}", + origin=origin, + spacing=(1.0, 1.0, 1.0), + cellData=None, + pointData=point_dict, + fieldData=None, + ) + +class VTKReporterAdvanced(Reporter): + """General VTK Reporter for velocity and pressure""" + "EDIT (M.Bille: can insert solid-mask to pin observables to zero, inside solid obstacle, " \ + "...useful for boundaries that store populations inside the boundary-region (FWBB), making obs(f) deviate from 0" + + def __init__(self, flow, interval=50, filename_base="./data/output", + solid_mask=None, imin=0, imax=None): + super().__init__(interval) + self.flow = flow + if interval < 0: + self.interval = 1 + else: + self.interval = interval + self.filename_base = filename_base + self.imin=imin + if imax is None: + self.imax = 1e15 + elif imax <= 0: + self.imax = 1 + else: + self.imax = imax + if solid_mask is not None and flow.stencil.d == 2: + self.solid_mask = solid_mask[..., None] + else: + self.solid_mask = solid_mask + directory = os.path.dirname(filename_base) + if not os.path.isdir(directory): + os.makedirs(directory) + self.point_dict = dict() + + def __call__(self, simulation): + if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: + u = self.flow.units.convert_velocity_to_pu(self.flow.u(self.flow.f)) + p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(self.flow.f)) + #rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) + if self.flow.stencil.d == 2: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) +#ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ..., None])) # for boundaries that store populations "inside" the boundary + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ..., None]) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ..., None])) +#ALTERNATIVE: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, u[d, ..., None])) + #self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + else: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) + #ORIGINAL: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) +#ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ...])) + for d in range(self.flow.stencil.d): + #ORIGINAL: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ...])) + + #self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) + write_vtk(self.point_dict, self.flow.i, self.filename_base) + + def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): + """ + Outputs the no_collision_mask of the simulation object as VTK-file with range [0,1] + Usage: vtk_reporter.output_mask(simulation.no_collision_mask) + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! + + USE: in Paraview use Filter:Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... + """ + + if outdir is None: + filename_base = self.filename_base + else: + filename_base = outdir+"/"+str(name) + + mask_dict = dict() + + mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 else mask[..., None].astype(int) # extension to pseudo-3D is needed for vtk-export to work + + if point: + vtk.imageToVTK( + path=filename_base +"_point", + pointData=mask_dict + ) + if no_offset: + vtk.imageToVTK( + path=filename_base +"_cell_noOffset", + cellData=mask_dict + ) + vtk.imageToVTK( + path=filename_base + "_cell", + cellData=mask_dict, + origin=(-0.5, -0.5, -0.5), + spacing=(1.0, 1.0, 1.0) + ) + +class VTKsliceReporter(Reporter): + '''reports a certain specified area portion of the domain as vtk-file + ''' + def __init__(self, flow, interval=50, filename_base="./data/output", + solid_mask=None, sliceXY=None, sliceZ=None, imin=0, + imax=None): + super().__init__(interval) + self.flow = flow + self.interval = interval + self.filename_base = filename_base + self.imin = imin + if imax is None: + self.imax = 1e15 + elif imax <= 0: + self.imax = 1 + else: + self.imax = imax + + if solid_mask is not None and self.flow.stencil.d == 2: + self.solid_mask = solid_mask[..., None] + else: + self.solid_mask = solid_mask + + directory = os.path.dirname(filename_base) + if not os.path.isdir(directory): + os.makedirs(directory) + + self.point_dict = dict() + + + if sliceXY is not None: + if sliceZ is None: + sliceZ = 0 + self.xmin = sliceXY[0][0] + self.ymin = sliceXY[1][0] + self.xmax = sliceXY[0][1] + self.ymax = sliceXY[1][1] + self.z_index = sliceZ + else: + self.z_index = None + # OPTIONAL: Abfrage, welche schaut ob xmin == xmax (und y... und z...) und falls das der Fall ist, dann dort entsprechend eine "None" Dimension anhängt und nur einen Wert nimmt, also ein "slice" + # Wahrscheinlich am sinnvollsten direkt unten im Call dann abzufragen aus einem ([xmin, xmax],[ymin, ymax],[zmin, zmax]) tupel, in dem dann Werte gleich sind, wenn in dieser Ebne geslicet werden soll. + + + + def __call__(self, simulation): + if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: + if self.z_index is not None: + f = self.flow.f[:,self.xmin:self.xmax+1, self.ymin:self.ymax+1, self.z_index, None] + # (!) None is needed because single-slice omits the last dimension and will result in bad dimension in conversion of u and rho/p below! + #print(f.shape) + else: + f = self.flow.f + u = self.flow.units.convert_velocity_to_pu(self.flow.u(f)) + p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(f)) + # rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) + if self.flow.stencil.d == 2: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) + # ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ..., None])) # for boundaries that store populations "inside" the boundary + for d in range(self.flow.stencil.d): + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ..., None]) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ..., None])) + # ALTERNATIVE: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, u[d, ..., None])) + # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + else: + if self.solid_mask is None: + self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) + else: + self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) + # ORIGINAL: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) + # ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ...])) + for d in range(self.flow.stencil.d): + # ORIGINAL: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + if self.solid_mask is None: + self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + else: + self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ...])) + + # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) + write_vtk(self.point_dict, self.flow.i, self.filename_base, origin=(self.xmin, self.ymin, self.z_index)) # origin to show slice at correct position when superimposing on 3D vtk-data! + + def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): + """ + Outputs the no_collision_mask of the simulation object as VTK-file with range [0,1] + Usage: vtk_reporter.output_mask(simulation.no_collision_mask) + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! + + USE: in Paraview use Filter:Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... + """ + + if outdir is None: + filename_base = self.filename_base + else: + filename_base = outdir + "/" + str(name) + + mask_dict = dict() + + mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 else mask[..., None].astype( + int) # extension to pseudo-3D is needed for vtk-export to work + + if point: + vtk.imageToVTK( + path=filename_base + "_point", + pointData=mask_dict + ) + if no_offset: + vtk.imageToVTK( + path=filename_base + "_cell_noOffset", + cellData=mask_dict + ) + vtk.imageToVTK( + path=filename_base + "_cell", + cellData=mask_dict, + origin=(-0.5, -0.5, -0.5), + spacing=(1.0, 1.0, 1.0) + ) \ No newline at end of file From e5c1ba5f7827e379063b492c88e6238a72bf7e1b Mon Sep 17 00:00:00 2001 From: mbille Date: Thu, 11 Dec 2025 17:22:34 +0100 Subject: [PATCH 16/21] minor changes --- .../01a_script_cylinder_lowRe.py | 53 +++++++++---------- 1 file changed, 26 insertions(+), 27 deletions(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py index 3ccf8123..8022d6c2 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py @@ -733,14 +733,13 @@ output_file.write("\n{:30s} {:30s}".format("n_steps", str(n_steps))) output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", str(flow.char_length_lu))) - output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) if gpd_correction: output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + str( flow.char_length_lu) + " because D/Y is even") - output_file.write("\nDpX (D/X) = ".ljust(30) + str(domain_length_x_in_d)) - output_file.write("\nDpY (D/Y) = ".ljust(30) + str(domain_height_y_in_d)) + output_file.write("\nDpX (D/X) = ".ljust(31) + str(domain_length_x_in_d)) + output_file.write("\nDpY (D/Y) = ".ljust(31) + str(domain_height_y_in_d)) if flow.stencil.d == 3: - output_file.write("\nDpZ (D/Z) = ".ljust(30) + str(domain_width_z_in_d)) + output_file.write("\nDpZ (D/Z) = ".ljust(31) + str(domain_width_z_in_d)) #output_file.write("\nn_steps = " + str(n_steps)) # output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") @@ -753,48 +752,48 @@ # if flow.stencil.d == 3: # output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) - output_file.write("\nshape_LU: ".ljust(30) + str(flow.resolution)) - output_file.write(("\ntotal_number_of_gridpoints: ".ljust(30) + str(flow.rho(flow.f).numel()))) - output_file.write("\nbc_type = ".ljust(30) + str()) - output_file.write("\nlateral_walls = ".ljust(30) + str(flow.lateral_walls)) - output_file.write("\nstencil = ".ljust(30) + str(flow.stencil)) - output_file.write("\ncollision = ".ljust(30) + str(collision_operator)) + output_file.write("\nshape_LU: ".ljust(31) + str(flow.resolution)) + output_file.write(("\ntotal_number_of_gridpoints: ".ljust(31) + str(flow.rho(flow.f).numel()))) + output_file.write("\nbc_type = ".ljust(31) + str()) + output_file.write("\nlateral_walls = ".ljust(31) + str(flow.lateral_walls)) + output_file.write("\nstencil = ".ljust(31) + str(flow.stencil)) + output_file.write("\ncollision = ".ljust(31) + str(collision_operator)) output_file.write("\n") # output_file.write("\nMa = " + str(mach_number)) - output_file.write("\nrelaxation parameter tau [LU]".ljust(30) + str(flow.units.relaxation_parameter_lu)) - output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(30) + str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) + output_file.write("\nrelaxation parameter tau [LU]".ljust(31) + str(flow.units.relaxation_parameter_lu)) + output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(31) + str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) output_file.write("\n") - output_file.write("\ncylinder diameter PU = ".ljust(30) + str(flow.units.characteristic_length_pu)) - output_file.write("\ncharacteristic velocity PU = ".ljust(30) + str(flow.units.characteristic_velocity_pu)) + output_file.write("\ncylinder diameter PU = ".ljust(31) + str(flow.units.characteristic_length_pu)) + output_file.write("\ncharacteristic velocity PU = ".ljust(31) + str(flow.units.characteristic_velocity_pu)) # output_file.write("\nu_init = " + str(u_init)) - output_file.write("\nperturb_init = ".ljust(30) + str(perturb_init)) + output_file.write("\nperturb_init = ".ljust(31) + str(perturb_init)) output_file.write("\n") # output_file.write("\noutput_vtk = " + str(output_vtk)) # output_file.write("\nvtk_fps = " + str(vtk_fps)) output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = ".ljust(30) + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") - output_file.write("\nMLUPS = ".ljust(30) + str(mlups)) + output_file.write("\nruntime = ".ljust(31) + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nMLUPS = ".ljust(31) + str(mlups)) output_file.write("\n") - output_file.write("\nVRAM_current [MB] = ".ljust(30) + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = ".ljust(30) + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_current [MB] = ".ljust(31) + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = ".ljust(31) + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(30) + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: ".ljust(30) + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(31) + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: ".ljust(31) + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\n\n### OBSERVABLES ###") output_file.write("\nCoefficient of drag between " + str(round(drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + " s:") - output_file.write("\nCd_mean, simple = ".ljust(30) + str(drag_stats["mean_simple"])) - output_file.write("\nCd_mean, peak_finder = ".ljust(30) + str(drag_stats["mean_periodcorrected"])) + output_file.write("\nCd_mean, simple = ".ljust(31) + str(drag_stats["mean_simple"])) + output_file.write("\nCd_mean, peak_finder = ".ljust(31) + str(drag_stats["mean_periodcorrected"])) output_file.write( - "\nCd_min = ".ljust(30) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) + "\nCd_min = ".ljust(31) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) output_file.write( - "\nCd_max = ".ljust(30) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) + "\nCd_max = ".ljust(31) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) output_file.write("\n") output_file.write("\nCoefficient of lift:") - output_file.write("\nCl_min = ".ljust(30) + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) - output_file.write("\nCl_max = ".ljust(30) + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) + output_file.write("\nCl_min = ".ljust(31) + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) + output_file.write("\nCl_max = ".ljust(31) + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) output_file.write("\n") output_file.write("\nStrouhal number:") output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + str(lift_stats["fft_resolution"]) + " Hz") From b24cb63571f644686c0314ea850fbaa5ac5d66a5 Mon Sep 17 00:00:00 2001 From: mbille Date: Mon, 15 Dec 2025 15:16:49 +0100 Subject: [PATCH 17/21] unified cylinder simulation script; minor corrections in files, removal of un-implemented reporter-files. --- .../00_run_parameterized_project.ipynb | 298 +++++++++--- ...Re.py => 01_script_cylinder_simulation.py} | 458 ++++++++++-------- .../01b_script_cylinder_highRe.py | 2 - .../obstacle_cylinder.py | 5 +- .../reporter_NaNreporter.py | 103 ---- .../reporter_high_ma_reporter.py | 1 - .../reporter_progress_reporter.py | 2 - ...LD_Interpolated_compact2_forComparison.py} | 0 8 files changed, 478 insertions(+), 391 deletions(-) rename examples/advanced_projects/efficient_bounce_back_obstacle/{01a_script_cylinder_lowRe.py => 01_script_cylinder_simulation.py} (72%) delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py rename examples/advanced_projects/efficient_bounce_back_obstacle/{Interpolated_compact2_forComparison.py => zOLD_Interpolated_compact2_forComparison.py} (100%) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index 6b751532..3bc65d16 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -1,8 +1,40 @@ { "cells": [ { - "cell_type": "code", - "id": "initial_id", + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## ADVANCED PROJECT: CYLINDER FLOW\n", + "This .ipynb contains examples for invocation of the 01_script_cylinder_simulation.py script, that conducts a simulation of the flow around a circular cylinder.\n", + "The code is ported from projects MP1, MP2 and Master’s Thesis of M.Bille. Some of the most prominent features implemented are:\n", + "- efficient bounce back boundary conditions (Fullway, Halfway, linear Interpolated) for solid bodies, that allow calculation of force on the solid body by Momentum exchange algorithm (MEA)\n", + "- simulation-class that allows:\n", + " - boundaries can be called after the streaming step\n", + " - boundaries can use post-collision populations for their bounce back algorithm (needed for HWBB and IBB algorithm)\n", + "- observables: coefficient of lift, coefficient of drag\n", + "- reporter: profile reporter for average velocity and reynolds stress profiles\n", + "- data-processing and -plotting utilities for the measured observables\n", + "\n", + "### Examples\n", + "1. 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", + "2. 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", + "\n", + "### Usage\n", + "The example command line calls and parameters below serve as examples for the use of 01_script_cylinder_simulation.py. There are lots of parameters available through arguments (see script itself). The \"%run\" command is used here, to simulate a bash command line of \"python scriptname\"\n", + "\n", + "The script will conduct the simulation and write data to an individually named folder in the directory specified for the \"--outdir\" parameter. This contains plots, .txt files etc.\n", + "The output can be controlled through parameters (see script content (argument parser) itself). Plots etc. are named accordingly in the output data directory.\n", + "\n", + "You might want to output 2D or 3D vtk-data for further analysis and visualization. But beware the amount of storage you might need and set your parameters accordingly (see script parameters and code).\n", + "\n", + "### Note\n", + "- Some of the functionalities might not work flawlessly through this .ipynb-implementation (warnings etc.): just use a regular python console or similar\n", + "- Some functionalities throw a warning, when the flow data con not be processed, because it doesn't meet certain physical characteristics (for example: peak finding in a periodic force coefficient timeseries). This is known and ok.\n", + "- The command line output of the script (including useful messages) might not appear in the notebook. This might be because of the \"%run\" command, see the .txt-output in the output directory for the full log and output, or execute the script in a python console." + ], + "id": "62eec9273a6c4271" + }, + { "metadata": { "collapsed": true, "ExecuteTime": { @@ -10,98 +42,161 @@ "start_time": "2025-11-14T15:40:54.512769Z" } }, - "source": "# this script should contain the description, documentation and code-Blocks to parameterize and rund the cylinder simulations interactively", - "outputs": [], - "execution_count": 1 + "cell_type": "markdown", + "source": [ + "### Example 1:\n", + "#### 2D Simulation at Re = 200: periodic vortex shedding behind a circular cylinder, calculation of force coefficients (lift, drag) and Strouhal number\n", + "NOTE: these default parameters take approx. 2 min to run on an NVIDIA 2060super\n", + "\n", + "Defaults: `01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9`" + ], + "id": "d918fef7d168e9ab" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-11-19T13:05:37.005589Z", - "start_time": "2025-11-19T13:05:35.542760Z" + "end_time": "2025-12-15T12:42:25.774523Z", + "start_time": "2025-12-15T12:40:16.188252Z" } }, "cell_type": "code", "source": [ "%matplotlib inline\n", - "%run 01a_script_cylinder_lowRe.py --outdir \"./datafolder\" --char_length_lu 20 --n_steps 10000 --bbbc_type fwbb --reynolds_number 200 --domain_height_y_in_d 3 --domain_width_z_in_d 3 --collision bgk --name 3Dsim_10000 --mach_number 0.1\n" + "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 100 --bbbc_type ibb1 --reynolds_number 200 --domain_height_y_in_d 5 --collision bgk --name 2D_simulation_cylinder_Re200 --mach_number 0.1 --stencil D2Q9" ], "id": "cdd448d543dd26be", "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "SCRIPT: Writing arguments to dictionary...\n", - "SCRIPT: Input arguments are: \n", - "{'name': 'cylinder_lowRe', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 10, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': None, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'n_steps': 1000, 't_target': 0, 'collision': 'bgk', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'fwbb'}\n", - "\n", - "SCRIPT: Creating timestamt, simulation ID and creating output directory...\n", - "outdir/simID = ./datafolder/251119_140535-cylinder_lowRe\n", - "outdir_DATA/simID = ./datafolder/251119_140535-cylinder_lowRe/251119_140535-cylinder_lowRe\n", - "SCRIPT: Writing input parameters to file: ./datafolder/251119_140535-cylinder_lowRe/input_parameters.txt\n", - "SCRIPT: Saving simulation script to outdir...\n", - "-> Saved simulation script to './datafolder/251119_140535-cylinder_lowRe/251119_140535-cylinder_lowRe_01a_script_cylinder_lowRe.py'\n", - "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", - "SCRIPT: Processing parameters...\n", - "(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used\n", - "\n", - "(INFO) parameters set for simulation of 1000 steps, representing 0.000 seconds [PU]!\n", - "\n", - "SCRIPT: initializing solver components...\n", - "-> initializing context...\n", - "-> initializing flow...\n", - "-> initializing collision operator...\n", - "-> initializing reporters...\n", - "\n", - "SCRIPT: initializing simulation object...\n", - "\n", - "SCRIPT: running simulation for 1000 steps...\n" + "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py:619: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:84: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", - " no_collision_mask[self.index] = 1\n", - "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:78: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", - " no_streaming_mask[[np.setdiff1d(np.arange(shape[0]), self.velocities)]\n", - "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py:284: RuntimeWarning: invalid value encountered in sqrt\n", - " d1 = - h1 + np.sqrt(h1 * h1 - h2)\n", - "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py:285: RuntimeWarning: invalid value encountered in sqrt\n", - " d2 = - h1 - np.sqrt(h1 * h1 - h2)\n", - "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:68: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", - " rho_w = self.rho_outlet * torch.ones_like(rho[here])\n", - "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:69: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", - " u_w = u[other]\n", - "/home/mbille/lettuce_25/lettuce/lettuce/ext/_boundary/equilibrium_outlet_p.py:71: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:345.)\n", - " feq[here] = flow.equilibrium(flow, rho_w[..., None], u_w[..., None])[\n" - ] + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max Velocity: 1.00162220850023\n", - "\n", - "♬ THE END ♬\n" - ] + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py:288: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " fig.show()\n" - ] + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } }, { "data": { "text/plain": [ - "
" + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAGwCAYAAAB1mRuuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABDbElEQVR4nO3de3QUZbr2/ysJdCccOgFDTmMABQWJ4Swh41myiRB5RZkloJsBBBENjhBfwIwH1O02bNyOoCI6noJLkcOMOrMBQXYgMEIUCYQzOEA0OtABhXRjgIQkz+8P39SPloDdWCF0+H7WqjVU1d1P35VaTl+ruurpEGOMEQAAAGwT2tANAAAANDYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABs1qShG2hMampqtH//frVs2VIhISEN3Q4AAPCDMUZHjx5VQkKCQkPtufZEwLLR/v37lZiY2NBtAACAc/Dtt9/q0ksvtWUsApaNWrZsKUnavftbxcW5GrgbAADgD6/Xq8TEROtz3A4ELBvVfi3YsqVLLhcBCwCAYGLn7T3c5A4AAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADY7IIKWNOnT1dISIgmTpxobTtx4oQyMzN1ySWXqEWLFhoyZIhKS0t9XldSUqKMjAw1a9ZMMTExmjx5sqqqqnxq8vPz1bNnTzmdTnXs2FG5ubmnvf/s2bPVvn17hYeHKyUlRevXr6+PwwQAAI3cBROwvvzyS73++uvq2rWrz/ZJkybpf/7nf7Ro0SKtXr1a+/fv15133mntr66uVkZGhiorK7Vu3TrNnTtXubm5evLJJ62a4uJiZWRk6Oabb1ZRUZEmTpyosWPHavny5VbNggULlJWVpWnTpmnjxo3q1q2b0tPTdfDgwfo/eAAA0LiYC8DRo0fNFVdcYVasWGFuvPFG8/DDDxtjjCkrKzNNmzY1ixYtsmp37txpJJmCggJjjDFLly41oaGhxu12WzVz5swxLpfLVFRUGGOMmTJliklKSvJ5z6FDh5r09HRrvU+fPiYzM9Nar66uNgkJCSYnJ+eMfZ84ccJ4PB5r+fbbb40ks3+/59z/GAAA4LzyeDxGkvF47Pv8viCuYGVmZiojI0NpaWk+2wsLC3Xy5Emf7Z07d1bbtm1VUFAgSSooKFBycrJiY2OtmvT0dHm9Xm3fvt2q+fnY6enp1hiVlZUqLCz0qQkNDVVaWppVU5ecnBxFRkZaS2Ji4jn+BQAAQGPS4AFr/vz52rhxo3Jyck7b53a75XA4FBUV5bM9NjZWbrfbqjk1XNXur913thqv16vjx4/r+++/V3V1dZ01tWPUJTs7Wx6Px1q+/fZb/w4aAAA0ak0a8s2//fZbPfzww1qxYoXCw8MbspVz4nQ65XQ6G7oNAABwgWnQK1iFhYU6ePCgevbsqSZNmqhJkyZavXq1XnrpJTVp0kSxsbGqrKxUWVmZz+tKS0sVFxcnSYqLizvtqcLa9V+qcblcioiIUHR0tMLCwuqsqR0DAADAXw0asPr166etW7eqqKjIWnr37q177rnH+nfTpk2Vl5dnvWb37t0qKSlRamqqJCk1NVVbt271edpvxYoVcrlc6tKli1Vz6hi1NbVjOBwO9erVy6empqZGeXl5Vg0AAIC/GvQrwpYtW+rqq6/22da8eXNdcskl1vYxY8YoKytLrVu3lsvl0kMPPaTU1FT17dtXktS/f3916dJFI0aM0IwZM+R2u/X4448rMzPT+vpu/PjxeuWVVzRlyhTde++9WrlypRYuXKglS5ZY75uVlaWRI0eqd+/e6tOnj2bOnKny8nKNHj36PP01AABAY9GgAcsfL774okJDQzVkyBBVVFQoPT1dr776qrU/LCxMixcv1gMPPKDU1FQ1b95cI0eO1DPPPGPVXHbZZVqyZIkmTZqkWbNm6dJLL9Wbb76p9PR0q2bo0KE6dOiQnnzySbndbnXv3l3Lli077cZ3AACAXxJijDEN3URj4fV6FRkZqf37PYqPdzV0OwAAwA+1n98ej0culz2f3w0+TQMAAEBjQ8ACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYNGrDmzJmjrl27yuVyyeVyKTU1VZ988om1/6abblJISIjPMn78eJ8xSkpKlJGRoWbNmikmJkaTJ09WVVWVT01+fr569uwpp9Opjh07Kjc397ReZs+erfbt2ys8PFwpKSlav359vRwzAABo/Bo0YF166aWaPn26CgsLtWHDBt1yyy26/fbbtX37dqvmvvvu04EDB6xlxowZ1r7q6mplZGSosrJS69at09y5c5Wbm6snn3zSqikuLlZGRoZuvvlmFRUVaeLEiRo7dqyWL19u1SxYsEBZWVmaNm2aNm7cqG7duik9PV0HDx48P38IAADQqIQYY0xDN3Gq1q1b6/nnn9eYMWN00003qXv37po5c2adtZ988oluu+027d+/X7GxsZKk1157TVOnTtWhQ4fkcDg0depULVmyRNu2bbNeN2zYMJWVlWnZsmWSpJSUFF1zzTV65ZVXJEk1NTVKTEzUQw89pEcfffSMvVZUVKiiosJa93q9SkxM1P79HsXHu37tnwIAAJwHXq9XkZGR8ng8crns+fy+YO7Bqq6u1vz581VeXq7U1FRr+/vvv6/o6GhdffXVys7O1rFjx6x9BQUFSk5OtsKVJKWnp8vr9VpXwQoKCpSWlubzXunp6SooKJAkVVZWqrCw0KcmNDRUaWlpVs2Z5OTkKDIy0loSExPP/Q8AAAAajSYN3cDWrVuVmpqqEydOqEWLFvroo4/UpUsXSdLdd9+tdu3aKSEhQVu2bNHUqVO1e/duffjhh5Ikt9vtE64kWetut/usNV6vV8ePH9eRI0dUXV1dZ82uXbvO2nt2draysrKs9dorWAAA4OLW4AGrU6dOKioqksfj0V/+8heNHDlSq1evVpcuXTRu3DirLjk5WfHx8erXr5/27t2rDh06NGDXP3E6nXI6nQ3dBgAAuMA0+FeEDodDHTt2VK9evZSTk6Nu3bpp1qxZddampKRIkvbs2SNJiouLU2lpqU9N7XpcXNxZa1wulyIiIhQdHa2wsLA6a2rHAAAACESDB6yfq6mp8blx/FRFRUWSpPj4eElSamqqtm7d6vO034oVK+RyuayvGVNTU5WXl+czzooVK6z7vBwOh3r16uVTU1NTo7y8PJ97wQAAAPzVoF8RZmdna8CAAWrbtq2OHj2qefPmKT8/X8uXL9fevXs1b948DRw4UJdccom2bNmiSZMm6YYbblDXrl0lSf3791eXLl00YsQIzZgxQ263W48//rgyMzOtr+7Gjx+vV155RVOmTNG9996rlStXauHChVqyZInVR1ZWlkaOHKnevXurT58+mjlzpsrLyzV69OgG+bsAAIAgZxrQvffea9q1a2ccDodp06aN6devn/n000+NMcaUlJSYG264wbRu3do4nU7TsWNHM3nyZOPxeHzG+Prrr82AAQNMRESEiY6ONo888og5efKkT82qVatM9+7djcPhMJdffrl55513Tuvl5ZdfNm3btjUOh8P06dPHfP755wEfj8fjMZLM/v2eXy4GAAAXhNrP759njF/jgpsHK5jVzqPBPFgAAASPRj0PFgAAQGNBwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJv5FbBCQ0MVFhYW8PLMM8/Ud/8AAAAXnCb+FBUXF5/T4FFRUef0OgAAgGDmV8Bq165dffcBAADQaHAPFgAAgM38uoJVa+fOnZo/f77+8Y9/6JtvvtGxY8fUpk0b9ejRQ/3799fvfvc7OZ3O+uoVAAAgKIQYY8wvFW3cuFFTpkzRZ599pmuvvVZ9+vRRQkKCIiIidPjwYW3btk3/+Mc/5PV6NWXKFE2cOPGiDFper1eRkZHav9+j+HhXQ7cDAAD8UPv57fF45HLZ8/nt11eEQ4YM0Z133im32628vDzl5OTooYce0tixYzVlyhS9++67Ki4u1uLFi7Vp0ya98MILfr35nDlz1LVrV7lcLrlcLqWmpuqTTz6x9p84cUKZmZm65JJL1KJFCw0ZMkSlpaU+Y5SUlCgjI0PNmjVTTEyMJk+erKqqKp+a/Px89ezZU06nUx07dlRubu5pvcyePVvt27dXeHi4UlJStH79er+OAQAA4Of8+orwq6++UtOmTX+xLjU1VampqTp58qRfb37ppZdq+vTpuuKKK2SM0dy5c3X77bdr06ZNSkpK0qRJk7RkyRItWrRIkZGRmjBhgu68806tXbtWklRdXa2MjAzFxcVp3bp1OnDggH7/+9+radOmeu655yT99ARkRkaGxo8fr/fff195eXkaO3as4uPjlZ6eLklasGCBsrKy9NprryklJUUzZ85Uenq6du/erZiYGL+OBQAAwGIuMK1atTJvvvmmKSsrM02bNjWLFi2y9u3cudNIMgUFBcYYY5YuXWpCQ0ON2+22aubMmWNcLpepqKgwxhgzZcoUk5SU5PMeQ4cONenp6dZ6nz59TGZmprVeXV1tEhISTE5OTkC9ezweI8ns3+8J6HUAAKDh1H5+ezz2fX4HdJO7JL300kt+1/7hD3/wu7a6ulqLFi1SeXm5UlNTVVhYqJMnTyotLc2q6dy5s9q2bauCggL17dtXBQUFSk5OVmxsrFWTnp6uBx54QNu3b1ePHj1UUFDgM0ZtzcSJEyVJlZWVKiwsVHZ2trU/NDRUaWlpKigoOGvPFRUVqqiosNa9Xq/fxwsAABqvgAPWiy++qEOHDunYsWPWRKJlZWVq1qyZ2rRpY9WFhIT4FbC2bt2q1NRUnThxQi1atNBHH32kLl26qKioSA6H47TJSmNjY+V2uyVJbrfbJ1zV7q/dd7Yar9er48eP68iRI6qurq6zZteuXWftPScnR08//fQvHiMAALi4BDwP1n/+53+qe/fu2rlzpw4fPqzDhw9r586d6tmzp5599lkVFxeruLhY+/bt82u8Tp06qaioSF988YUeeOABjRw5Ujt27Aj4QBpCdna2PB6PtXz77bcN3RIAALgABHwF64knntBf/vIXderUydrWqVMnvfjii/rd736ne+65J6DxHA6HOnbsKEnq1auXvvzyS82aNUtDhw5VZWWlysrKfK5ilZaWKi4uTpIUFxd32tN+tU8Znlrz8ycPS0tL5XK5FBERYf1uYl01tWOcidPpvCinowAAAGcX8BWsAwcOnDYNgvTTPVQ/DynnoqamRhUVFerVq5eaNm2qvLw8a9/u3btVUlKi1NRUST89tbh161YdPHjQqlmxYoVcLpe6dOli1Zw6Rm1N7RgOh0O9evXyqampqVFeXp5VAwAAEJBA74q/7bbbTI8ePUxhYaG1bcOGDaZnz55m0KBBAY316KOPmtWrV5vi4mKzZcsW8+ijj5qQkBDz6aefGmOMGT9+vGnbtq1ZuXKl2bBhg0lNTTWpqanW66uqqszVV19t+vfvb4qKisyyZctMmzZtTHZ2tlWzb98+06xZMzN58mSzc+dOM3v2bBMWFmaWLVtm1cyfP984nU6Tm5trduzYYcaNG2eioqJ8nk70B08RAgAQfOrjKcKAA9bBgwfNgAEDTEhIiHE4HMbhcJjQ0FAzYMAAU1paGtBY9957r2nXrp1xOBymTZs2pl+/fla4MsaY48ePmwcffNC0atXKNGvWzNxxxx3mwIEDPmN8/fXXZsCAASYiIsJER0ebRx55xJw8edKnZtWqVaZ79+7G4XCYyy+/3Lzzzjun9fLyyy+btm3bGofDYfr06WM+//zzgI7FGAIWAADBqD4Cll8/lVOXr776ynrKrnPnzrryyittu6oWrPipHAAAgk99/FROwDe512rfvr2MMerQoYOaNDnnYQAAABqdgG9yP3bsmMaMGaNmzZopKSlJJSUlkqSHHnpI06dPt71BAACAYBNwwMrOztbmzZuVn5+v8PBwa3taWpoWLFhga3MAAADBKODv9j7++GMtWLBAffv2VUhIiLU9KSlJe/futbU5AACAYBTwFaxDhw4pJibmtO3l5eU+gQsAAOBiFXDA6t27t5YsWWKt14aqN998k4k5AQAAdA5fET733HMaMGCAduzYoaqqKs2aNUs7duzQunXrtHr16vroEQAAIKgEfAXruuuu0+bNm1VVVaXk5GR9+umniomJUUFBgXr16lUfPQIAAASVgK5gnTx5Uvfff7+eeOIJvfHGG/XVEwAAQFAL6ApW06ZN9de//rW+egEAAGgUAv6KcPDgwfr444/roRUAAIDGIeCb3K+44go988wzWrt2rXr16qXmzZv77P/DH/5gW3MAAADBKOAfe77sssvOPFhIiPbt2/ermwpW/NgzAADBp8F+7Nnr9VpvWFxcbMsbAwAANFZ+3YPVqlUrHTx4UJJ0yy23qKysrD57AgAACGp+BawWLVrohx9+kCTl5+fr5MmT9doUAABAMPPrK8K0tDTdfPPNuuqqqyRJd9xxhxwOR521K1eutK87AACAIORXwHrvvfc0d+5c7d27V6tXr1ZSUpKaNWtW370BAAAEpYCfIrz55pv10UcfKSoqqp5aCl48RQgAQPBpsKcIT7Vq1Spb3hgAAKCx8usm9+nTp+vYsWN+DfjFF19oyZIlv6opAACAYOZXwNqxY4fatWunBx98UJ988okOHTpk7auqqtKWLVv06quv6re//a2GDh2qli1b1lvDAAAAFzq/viJ89913tXnzZr3yyiu6++675fV6FRYWJqfTaV3Z6tGjh8aOHatRo0YpPDy8XpsGAAC4kAV8k3tNTY22bNmib775RsePH1d0dLS6d++u6Ojo+uoxaHCTOwAAweeCuMk9NDRU3bt3V/fu3W1pAAAAoLHx6x4sSXrrrbfOuv/o0aMaO3bsr24IAAAg2PkdsLKysnTbbbfJ7Xaftm/58uVKSkrSl19+aWtzAAAAwcjvgLV582aVl5crKSlJH3zwgaSfrlqNGTNGgwYN0r//+79rw4YN9dYoAABAsPD7Hqz27dtr1apVmjlzpu677z69//772rp1q1q0aKG1a9fqmmuuqc8+AQAAgkbAN7nff//9WrNmjT7++GM1b95cixcvVnJycn30BgAAEJT8/opQktauXatu3bpp165dWrZsmQYMGKDU1FTNmjWrvvoDAAAIOn4HrEceeUS33HKLBg0apI0bN6p///5auHCh3nrrLT377LO66aabVFxcHNCb5+Tk6JprrlHLli0VExOjwYMHa/fu3T41N910k0JCQnyW8ePH+9SUlJQoIyNDzZo1U0xMjCZPnqyqqiqfmvz8fPXs2VNOp1MdO3ZUbm7uaf3Mnj1b7du3V3h4uFJSUrR+/fqAjgcAAEAKIGD97W9/0//+7//qhRde8JmpfejQodq2bZsiIyPVtWvXgN589erVyszM1Oeff64VK1bo5MmT6t+/v8rLy33q7rvvPh04cMBaZsyYYe2rrq5WRkaGKisrtW7dOs2dO1e5ubl68sknrZri4mJlZGTo5ptvVlFRkSZOnKixY8dq+fLlVs2CBQuUlZWladOmaePGjerWrZvS09N18ODBgI4JAABAxk/l5eW/WPPuu+/6O1ydDh48aCSZ1atXW9tuvPFG8/DDD5/xNUuXLjWhoaHG7XZb2+bMmWNcLpepqKgwxhgzZcoUk5SU5PO6oUOHmvT0dGu9T58+JjMz01qvrq42CQkJJicnx+/+PR6PkWT27/f4/RoAANCwaj+/PR77Pr/9voLVrFmzX6wZMWLEuSc9SR6PR5LUunVrn+3vv/++oqOjdfXVVys7O9v6/UNJKigoUHJysmJjY61t6enp8nq92r59u1WTlpbmM2Z6eroKCgokSZWVlSosLPSpCQ0NVVpamlVTl4qKCnm9Xp8FAADAr6cIS0pKzmnwqKgov3/Tp6amRhMnTtS1116rq6++2tp+9913q127dkpISNCWLVs0depU7d69Wx9++KEkye12+4QrSdZ67aSoZ6rxer06fvy4jhw5ourq6jprdu3adcaec3Jy9PTTT/t1fAAA4OLhV8Bq3769QkJCZAL4XeiQkBBNmzbN516os8nMzNS2bdv02Wef+WwfN26c9e/k5GTFx8erX79+2rt3rzp06OB3P/UhOztbWVlZ1rrX61ViYmIDdgQAAC4EfgWsmpqaem1iwoQJWrx4sdasWaNLL730rLUpKSmSpD179qhDhw6Ki4s77Wm/0tJSSVJcXJz1v7XbTq1xuVyKiIhQWFiYwsLC6qypHaMuTqdTTqfTv4MEAAAXjYDmwbKbMUYTJkzQRx99pJUrV+qyyy77xdcUFRVJkuLj4yVJqamp2rp1q8/TfitWrJDL5VKXLl2smry8PJ9xVqxYodTUVEmSw+FQr169fGpqamqUl5dn1QAAAPgr4Jnc7ZSZmal58+bpb3/7m1q2bGndMxUZGamIiAjt3btX8+bN08CBA3XJJZdoy5YtmjRpkm644QZrSoj+/furS5cuGjFihGbMmCG3263HH39cmZmZ1tWl8ePH65VXXtGUKVN07733auXKlVq4cKGWLFli9ZKVlaWRI0eqd+/e6tOnj2bOnKny8nKNHj36/P9hAABAcLPtecRzIKnO5Z133jHGGFNSUmJuuOEG07p1a+N0Ok3Hjh3N5MmTT3uM8uuvvzYDBgwwERERJjo62jzyyCPm5MmTPjWrVq0y3bt3Nw6Hw1x++eXWe5zq5ZdfNm3btjUOh8P06dPHfP755wEdD9M0AAAQfOpjmoYQYwK4cx1n5fV6FRkZqf37PYqP9+/pSQAA0LBqP789Ho/fsx/8kga9BwsAAKAxOueAVVlZqd27d5/2m38AAAAXu4AD1rFjxzRmzBg1a9ZMSUlJ1iSkDz30kKZPn257gwAAAMEm4ICVnZ2tzZs3Kz8/3+dHn9PS0rRgwQJbmwMAAAhGAU/T8PHHH2vBggXq27evQkJCrO1JSUnau3evrc0BAAAEo4CvYB06dEgxMTGnbS8vL/cJXAAAABergANW7969fSborA1Vb775JrOeAwAA6By+Inzuuec0YMAA7dixQ1VVVZo1a5Z27NihdevWafXq1fXRIwAAQFAJ+ArWddddp6KiIlVVVSk5OVmffvqpYmJiVFBQoF69etVHjwAAAEGFmdxtxEzuAAAEn/qYyd2vrwi9Xq/fA9rVGAAAQLDyK2BFRUX5/YRgdXX1r2oIAAAg2PkVsFatWmX9++uvv9ajjz6qUaNGWU8NFhQUaO7cucrJyamfLgEAAIJIwPdg9evXT2PHjtXw4cN9ts+bN09//vOflZ+fb2d/QYV7sAAACD71cQ9WwE8RFhQUqHfv3qdt7927t9avX29LUwAAAMEs4ICVmJioN95447Ttb775phITE21pCgAAIJgFPNHoiy++qCFDhuiTTz5RSkqKJGn9+vX65z//qb/+9a+2NwgAABBsAr6CNXDgQH311VcaNGiQDh8+rMOHD2vQoEH66quvNHDgwProEQAAIKgw0aiNuMkdAIDg02ATjZ5qzZo1Z91/ww03nHMzAAAAjUHAAeumm246bdupk5Ay0SgAALjYBXwP1pEjR3yWgwcPatmyZbrmmmv06aef1kePAAAAQSXgK1iRkZGnbfu3f/s3ORwOZWVlqbCw0JbGAAAAglXAV7DOJDY2Vrt377ZrOAAAgKAV8BWsLVu2+KwbY3TgwAFNnz5d3bt3t6svAACAoBVwwOrevbtCQkL089kd+vbtq7ffftu2xgAAAIJVwAGruLjYZz00NFRt2rRReHi4bU0BAAAEs4DvwVq9erXi4uLUrl07tWvXTomJiQoPD1dlZaXefffd+ugRAAAgqAQcsEaPHi2Px3Pa9qNHj2r06NG2NAUAABDMAg5YxhifiUVrfffdd3VO4QAAAHCx8fserB49eigkJEQhISHq16+fmjT5/19aXV2t4uJi3XrrrfXSJAAAQDDx+wrW4MGDdfvtt8sYo/T0dN1+++3WMmzYML3++ut67733AnrznJwcXXPNNWrZsqViYmI0ePDg0+bSOnHihDIzM3XJJZeoRYsWGjJkiEpLS31qSkpKlJGRoWbNmikmJkaTJ09WVVWVT01+fr569uwpp9Opjh07Kjc397R+Zs+erfbt2ys8PFwpKSlav359QMcDAAAgSTIBys3NNcePHw/0ZXVKT08377zzjtm2bZspKioyAwcONG3btjU//vijVTN+/HiTmJho8vLyzIYNG0zfvn3Nb3/7W2t/VVWVufrqq01aWprZtGmTWbp0qYmOjjbZ2dlWzb59+0yzZs1MVlaW2bFjh3n55ZdNWFiYWbZsmVUzf/5843A4zNtvv222b99u7rvvPhMVFWVKS0v9Ph6Px2Mkmf37Pb/yLwMAAM6X2s9vj8e+z++AA1Z9OnjwoJFkVq9ebYwxpqyszDRt2tQsWrTIqtm5c6eRZAoKCowxxixdutSEhoYat9tt1cyZM8e4XC5TUVFhjDFmypQpJikpyee9hg4datLT0631Pn36mMzMTGu9urraJCQkmJycHL/7J2ABABB86iNg+fUVYevWrfX9999Lklq1aqXWrVufcfk1ap9OrB2nsLBQJ0+eVFpamlXTuXNntW3bVgUFBZKkgoICJScnKzY21qpJT0+X1+vV9u3brZpTx6itqR2jsrJShYWFPjWhoaFKS0uzaupSUVEhr9frswAAAPh1k/uLL76oli1bWv+u6ynCX6umpkYTJ07Utddeq6uvvlqS5Ha75XA4FBUV5VMbGxsrt9tt1Zwarmr31+47W43X69Xx48d15MgRVVdX11mza9euM/ack5Ojp59+OvCDBQAAjZpfAWvkyJHWv0eNGlUvjWRmZmrbtm367LPP6mX8+pCdna2srCxr3ev1KjExsQE7AgAAFwK/AlYgX325XK6Am5gwYYIWL16sNWvW6NJLL7W2x8XFqbKyUmVlZT5XsUpLSxUXF2fV/Pxpv9qnDE+t+fmTh6WlpXK5XIqIiFBYWJjCwsLqrKkdoy5Op1NOpzPg4wUAAI2bX/dgRUVFqVWrVmddamsCYYzRhAkT9NFHH2nlypW67LLLfPb36tVLTZs2VV5enrVt9+7dKikpUWpqqiQpNTVVW7du1cGDB62aFStWyOVyqUuXLlbNqWPU1tSO4XA41KtXL5+ampoa5eXlWTUAAAD+8usK1qpVq+rlzTMzMzVv3jz97W9/U8uWLa17piIjIxUREaHIyEiNGTNGWVlZat26tVwulx566CGlpqaqb9++kqT+/furS5cuGjFihGbMmCG3263HH39cmZmZ1tWl8ePH65VXXtGUKVN07733auXKlVq4cKGWLFli9ZKVlaWRI0eqd+/e6tOnj2bOnKny8nJ+/gcAAATOtucRz4GkOpd33nnHqjl+/Lh58MEHTatWrUyzZs3MHXfcYQ4cOOAzztdff20GDBhgIiIiTHR0tHnkkUfMyZMnfWpWrVplunfvbhwOh7n88st93qPWyy+/bNq2bWscDofp06eP+fzzzwM6HqZpAAAg+NTHNA0hxhgTaCg7cuSI3nrrLe3cuVOS1KVLF40ePfpXT9MQ7LxeryIjI7V/v0fx8YHfiwYAAM6/2s9vj8dzTveS1yXgH3tes2aN2rdvr5deeklHjhzRkSNH9NJLL+myyy7TmjVrbGkKAAAgmAV8BSs5OVmpqamaM2eOwsLCJP30Y88PPvig1q1bp61bt9ZLo8GAK1gAAASfC+IK1p49e/TII49Y4UqSwsLClJWVpT179tjSFAAAQDALOGD17NnTuvfqVDt37lS3bt1saQoAACCY+TVNw6n+8Ic/6OGHH9aePXusqRI+//xzzZ49W9OnT9eWLVus2q5du9rXKQAAQJAI+B6s0NCzX/QKCQmRMUYhISGqrq7+Vc0FG+7BAgAg+NTHPVgBX8EqLi625Y0BAAAaq4ADVrt27eqjDwAAgEYj4IAlSfv379dnn32mgwcPqqamxmffH/7wB1saAwAACFYBB6zc3Fzdf//9cjgcuuSSSxQSEmLtCwkJIWABAICLXsAB64knntCTTz6p7OzsX7zhHQAA4GIUcEI6duyYhg0bRrgCAAA4g4BT0pgxY7Ro0aL66AUAAKBRCHgerOrqat122206fvy4kpOT1bRpU5/9f/rTn2xtMJgwDxYAAMHngpgHKycnR8uXL1enTp0k6bSb3AEAAC52AQesF154QW+//bZGjRpVD+0AAAAEv4DvwXI6nbr22mvroxcAAIBGIeCA9fDDD+vll1+uj14AAAAahYC/Ily/fr1WrlypxYsXKykp6bSb3D/88EPbmgMAAAhGAQesqKgo3XnnnfXRCwAAQKMQcMB655136qMPAACARsOW6di9Xq/mzJmj3r172zEcAABAUAv4CtapVq1apbffflsffvihIiMjdccdd9jVFwAAQNAKOGD961//Um5urt555x2VlZXpyJEjmjdvnu666y4mGgUAAFAAXxH+9a9/1cCBA9WpUycVFRXphRde0P79+xUaGqrk5GTCFQAAwP/j9xWsoUOHaurUqVqwYIFatmxZnz0BAAAENb+vYI0ZM0azZ8/Wrbfeqtdee01Hjhypz74AAACClt8B6/XXX9eBAwc0btw4ffDBB4qPj9ftt98uY4xqamrqs0cAAICgEtA0DRERERo5cqRWr16trVu3KikpSbGxsbr22mt19913M4s7AACAfsU8WFdccYWee+45ffvtt3rvvfd07NgxDR8+3M7eAAAAglKIMcbYNdjBgwcVExNj13BBx+v1KjIyUvv3exQf72rodgAAgB9qP789Ho9cLns+v22Zyb3WuYSrNWvWaNCgQUpISFBISIg+/vhjn/2jRo1SSEiIz3Lrrbf61Bw+fFj33HOPXC6XoqKiNGbMGP34448+NVu2bNH111+v8PBwJSYmasaMGaf1smjRInXu3Fnh4eFKTk7W0qVLAz4eAAAAWwPWuSgvL1e3bt00e/bsM9bceuutOnDggLV88MEHPvvvuecebd++XStWrNDixYu1Zs0ajRs3ztrv9XrVv39/tWvXToWFhXr++ef11FNP6c9//rNVs27dOg0fPlxjxozRpk2bNHjwYA0ePFjbtm2z/6ABAECjZutXhL9WSEiIPvroIw0ePNjaNmrUKJWVlZ12ZavWzp071aVLF3355ZfWbyEuW7ZMAwcO1HfffaeEhATNmTNHjz32mNxutxwOhyTp0Ucf1ccff6xdu3ZJ+mmer/Lyci1evNgau2/fvurevbtee+01v/rnK0IAAIJPg35FuGbNGlVVVdnypoHKz89XTEyMOnXqpAceeEA//PCDta+goEBRUVE+PzSdlpam0NBQffHFF1bNDTfcYIUrSUpPT9fu3but+bwKCgqUlpbm877p6ekqKCg4Y18VFRXyer0+CwAAgN8B6+abb9bhw4frs5c63XrrrXr33XeVl5en//qv/9Lq1as1YMAAVVdXS5Lcbvdp9341adJErVu3ltvttmpiY2N9amrXf6mmdn9dcnJyFBkZaS2JiYm/7mABAECj4PdP5TTUN4nDhg2z/p2cnKyuXbuqQ4cOys/PV79+/Rqkp1rZ2dnKysqy1r1eLyELAAAEdpP7hfCDzpdffrmio6O1Z88eSVJcXJwOHjzoU1NVVaXDhw8rLi7OqiktLfWpqV3/pZra/XVxOp1yuVw+CwAAgN9XsKSfbjh3Op1nranv2dy/++47/fDDD4qPj5ckpaamqqysTIWFherVq5ckaeXKlaqpqVFKSopV89hjj+nkyZNq2rSpJGnFihXq1KmTWrVqZdXk5eVp4sSJ1nutWLFCqamp9Xo8AACg8QkoYLVs2VIRERG2NvDjjz9aV6Mkqbi4WEVFRWrdurVat26tp59+WkOGDFFcXJz27t2rKVOmqGPHjkpPT5ckXXXVVbr11lt133336bXXXtPJkyc1YcIEDRs2TAkJCZKku+++W08//bTGjBmjqVOnatu2bZo1a5ZefPFF630ffvhh3XjjjXrhhReUkZGh+fPna8OGDT5TOQAAAPjF+CkkJMSUlpb6W+63VatWGUmnLSNHjjTHjh0z/fv3N23atDFNmzY17dq1M/fdd59xu90+Y/zwww9m+PDhpkWLFsblcpnRo0ebo0eP+tRs3rzZXHfddcbpdJrf/OY3Zvr06af1snDhQnPllVcah8NhkpKSzJIlSwI6Fo/HYySZ/fs9gf8hAABAg6j9/PZ47Pv89nserLCwMB04cOCi/imcX8I8WAAABJ8GnQfLzxwGAABw0fM7YL3xxhvWDeEAAAA4M78D1rhx46xZz6Wfflrm59MaAAAA4Fd8Rbh06VKVl5fb3hAAAECwC2iiUQAAAPwyvwNWSEjIaTO5XwgzuwMAAFxoAvotwlNncj9x4oTGjx+v5s2b+9TV90zuAAAAFzq/A9bvf/97nytW//7v/14vDQEAAAQ7vwNWbm5uPbYBAADQePh9D9a+ffuYbBQAAMAPfgesK664QocOHbLWmQcLAACgbsyDBQAAYDPmwQIAALAZ82ABAADYjHmwAAAAbOZ3wBo5cqTPOvNgAQAA1M3vgPXOO+/UZx8AAACNBje5AwAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYLMGD1hr1qzRoEGDlJCQoJCQEH388cc++40xevLJJxUfH6+IiAilpaXpn//8p0/N4cOHdc8998jlcikqKkpjxozRjz/+6FOzZcsWXX/99QoPD1diYqJmzJhxWi+LFi1S586dFR4eruTkZC1dutT24wUAAI1fgwes8vJydevWTbNnz65z/4wZM/TSSy/ptdde0xdffKHmzZsrPT1dJ06csGruuecebd++XStWrNDixYu1Zs0ajRs3ztrv9XrVv39/tWvXToWFhXr++ef11FNP6c9//rNVs27dOg0fPlxjxozRpk2bNHjwYA0ePFjbtm2rv4MHAACNk7mASDIfffSRtV5TU2Pi4uLM888/b20rKyszTqfTfPDBB8YYY3bs2GEkmS+//NKq+eSTT0xISIj517/+ZYwx5tVXXzWtWrUyFRUVVs3UqVNNp06drPW77rrLZGRk+PSTkpJi7r//fr/793g8RpLZv9/j92sAAEDDqv389njs+/xu8CtYZ1NcXCy32620tDRrW2RkpFJSUlRQUCBJKigoUFRUlHr37m3VpKWlKTQ0VF988YVVc8MNN8jhcFg16enp2r17t44cOWLVnPo+tTW171OXiooKeb1enwUAAOCCDlhut1uSFBsb67M9NjbW2ud2uxUTE+Ozv0mTJmrdurVPTV1jnPoeZ6qp3V+XnJwcRUZGWktiYmKghwgAABqhCzpgXeiys7Pl8Xis5dtvv23olgAAwAXggg5YcXFxkqTS0lKf7aWlpda+uLg4HTx40Gd/VVWVDh8+7FNT1xinvseZamr318XpdMrlcvksAAAAF3TAuuyyyxQXF6e8vDxrm9fr1RdffKHU1FRJUmpqqsrKylRYWGjVrFy5UjU1NUpJSbFq1qxZo5MnT1o1K1asUKdOndSqVSur5tT3qa2pfR8AAAB/NXjA+vHHH1VUVKSioiJJP93YXlRUpJKSEoWEhGjixIl69tln9fe//11bt27V73//eyUkJGjw4MGSpKuuukq33nqr7rvvPq1fv15r167VhAkTNGzYMCUkJEiS7r77bjkcDo0ZM0bbt2/XggULNGvWLGVlZVl9PPzww1q2bJleeOEF7dq1S0899ZQ2bNigCRMmnO8/CQAACHa2PY94jlatWmUknbaMHDnSGPPTVA1PPPGEiY2NNU6n0/Tr18/s3r3bZ4wffvjBDB8+3LRo0cK4XC4zevRoc/ToUZ+azZs3m+uuu844nU7zm9/8xkyfPv20XhYuXGiuvPJK43A4TFJSklmyZElAx8I0DQAABJ/6mKYhxBhjGjDfNSper1eRkZHav9+j+HjuxwIAIBjUfn57PB7b7qdu8K8IAQAAGhsCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2u+AD1lNPPaWQkBCfpXPnztb+EydOKDMzU5dccolatGihIUOGqLS01GeMkpISZWRkqFmzZoqJidHkyZNVVVXlU5Ofn6+ePXvK6XSqY8eOys3NPR+HBwAAGqELPmBJUlJSkg4cOGAtn332mbVv0qRJ+p//+R8tWrRIq1ev1v79+3XnnXda+6urq5WRkaHKykqtW7dOc+fOVW5urp588kmrpri4WBkZGbr55ptVVFSkiRMnauzYsVq+fPl5PU4AANA4hBhjTEM3cTZPPfWUPv74YxUVFZ22z+PxqE2bNpo3b55+97vfSZJ27dqlq666SgUFBerbt68++eQT3Xbbbdq/f79iY2MlSa+99pqmTp2qQ4cOyeFwaOrUqVqyZIm2bdtmjT1s2DCVlZVp2bJlfvfq9XoVGRmp/fs9io93/boDBwAA50Xt57fH45HLZc/nd1BcwfrnP/+phIQEXX755brnnntUUlIiSSosLNTJkyeVlpZm1Xbu3Flt27ZVQUGBJKmgoEDJyclWuJKk9PR0eb1ebd++3ao5dYzamtoxzqSiokJer9dnAQAAuOADVkpKinJzc7Vs2TLNmTNHxcXFuv7663X06FG53W45HA5FRUX5vCY2NlZut1uS5Ha7fcJV7f7afWer8Xq9On78+Bl7y8nJUWRkpLUkJib+2sMFAACNQJOGbuCXDBgwwPp3165dlZKSonbt2mnhwoWKiIhowM6k7OxsZWVlWeter5eQBQAALvwrWD8XFRWlK6+8Unv27FFcXJwqKytVVlbmU1NaWqq4uDhJUlxc3GlPFdau/1KNy+U6a4hzOp1yuVw+CwAAQNAFrB9//FF79+5VfHy8evXqpaZNmyovL8/av3v3bpWUlCg1NVWSlJqaqq1bt+rgwYNWzYoVK+RyudSlSxer5tQxamtqxwAAAAjEBR+w/u///b9avXq1vv76a61bt0533HGHwsLCNHz4cEVGRmrMmDHKysrSqlWrVFhYqNGjRys1NVV9+/aVJPXv319dunTRiBEjtHnzZi1fvlyPP/64MjMz5XQ6JUnjx4/Xvn37NGXKFO3atUuvvvqqFi5cqEmTJjXkoQMAgCB1wd+D9d1332n48OH64Ycf1KZNG1133XX6/PPP1aZNG0nSiy++qNDQUA0ZMkQVFRVKT0/Xq6++ar0+LCxMixcv1gMPPKDU1FQ1b95cI0eO1DPPPGPVXHbZZVqyZIkmTZqkWbNm6dJLL9Wbb76p9PT08368AAAg+F3w82AFE+bBAgAg+Fy082ABAAAEEwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAIJeebkUEvLTUl7e0N0AAAELAADAdgQsAAAAmxGwAAAAbEbAAgAAsBkB62dmz56t9u3bKzw8XCkpKVq/fn1DtwQAAIIMAesUCxYsUFZWlqZNm6aNGzeqW7duSk9P18GDBxu6NQAAEERCjDGmoZu4UKSkpOiaa67RK6+8IkmqqalRYmKiHnroIT366KO/+Hqv16vIyEjt2eNRXJyrvtsFLjrl5VJs7E//Li2Vmjc/+3YA8IfX61VCQqQ8Ho9cLns+v5vYMkojUFlZqcLCQmVnZ1vbQkNDlZaWpoKCgjpfU1FRoYqKCmvd4/FIkjp29NZvswCsQOXvdgA4s58+t+285kTA+n++//57VVdXK/Zn/+8cGxurXbt21fmanJwcPf3003XsSayHDgEAQH364YcfFBkZactYBKxfITs7W1lZWdZ6WVmZ2rVrp5KSEttOEM6N1+tVYmKivv32W9su9+LccC4uHJyLCwfn4sLi8XjUtm1btW7d2rYxCVj/T3R0tMLCwlRaWuqzvbS0VHFxcXW+xul0yul0nrY9MjKS/2AuEC6Xi3NxgeBcXDg4FxcOzsWFJTTUvmf/eIrw/3E4HOrVq5fy8vKsbTU1NcrLy1NqamoDdgYAAIINV7BOkZWVpZEjR6p3797q06ePZs6cqfLyco0ePbqhWwMAAEGEgHWKoUOH6tChQ3ryySfldrvVvXt3LVu27LQb38/E6XRq2rRpdX5tiPOLc3Hh4FxcODgXFw7OxYWlPs4H82ABAADYjHuwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAI0e/ZstW/fXuHh4UpJSdH69evPWr9o0SJ17txZ4eHhSk5O1tKlS89Tp41fIOfijTfe0PXXX69WrVqpVatWSktL+8VzB/8F+t9Frfnz5yskJESDBw+u3wYvIoGei7KyMmVmZio+Pl5Op1NXXnkl/z9lk0DPxcyZM9WpUydFREQoMTFRkyZN0okTJ85Tt43XmjVrNGjQICUkJCgkJEQff/zxL74mPz9fPXv2lNPpVMeOHZWbmxv4Gxv4bf78+cbhcJi3337bbN++3dx3330mKirKlJaW1lm/du1aExYWZmbMmGF27NhhHn/8cdO0aVOzdevW89x54xPoubj77rvN7NmzzaZNm8zOnTvNqFGjTGRkpPnuu+/Oc+eNT6DnolZxcbH5zW9+Y66//npz++23n59mG7lAz0VFRYXp3bu3GThwoPnss89McXGxyc/PN0VFRee588Yn0HPx/vvvG6fTad5//31TXFxsli9fbuLj482kSZPOc+eNz9KlS81jjz1mPvzwQyPJfPTRR2et37dvn2nWrJnJysoyO3bsMC+//LIJCwszy5YtC+h9CVgB6NOnj8nMzLTWq6urTUJCgsnJyamz/q677jIZGRk+21JSUsz9999fr31eDAI9Fz9XVVVlWrZsaebOnVtfLV40zuVcVFVVmd/+9rfmzTffNCNHjiRg2STQczFnzhxz+eWXm8rKyvPV4kUj0HORmZlpbrnlFp9tWVlZ5tprr63XPi82/gSsKVOmmKSkJJ9tQ4cONenp6QG9F18R+qmyslKFhYVKS0uztoWGhiotLU0FBQV1vqagoMCnXpLS09PPWA//nMu5+Lljx47p5MmTtv6w58XoXM/FM888o5iYGI0ZM+Z8tHlROJdz8fe//12pqanKzMxUbGysrr76aj333HOqrq4+X203SudyLn7729+qsLDQ+hpx3759Wrp0qQYOHHheesb/z67PbmZy99P333+v6urq02Z1j42N1a5du+p8jdvtrrPe7XbXW58Xg3M5Fz83depUJSQknPYfEQJzLufis88+01tvvaWioqLz0OHF41zOxb59+7Ry5Urdc889Wrp0qfbs2aMHH3xQJ0+e1LRp085H243SuZyLu+++W99//72uu+46GWNUVVWl8ePH649//OP5aBmnONNnt9fr1fHjxxUREeHXOFzBwkVn+vTpmj9/vj766COFh4c3dDsXlaNHj2rEiBF64403FB0d3dDtXPRqamoUExOjP//5z+rVq5eGDh2qxx57TK+99lpDt3bRyc/P13PPPadXX31VGzdu1IcffqglS5boP/7jPxq6NZwjrmD5KTo6WmFhYSotLfXZXlpaqri4uDpfExcXF1A9/HMu56LWf//3f2v69On63//9X3Xt2rU+27woBHou9u7dq6+//lqDBg2yttXU1EiSmjRpot27d6tDhw7123QjdS7/XcTHx6tp06YKCwuztl111VVyu92qrKyUw+Go154bq3M5F0888YRGjBihsWPHSpKSk5NVXl6ucePG6bHHHlNoKNdDzpczfXa7XC6/r15JXMHym8PhUK9evZSXl2dtq6mpUV5enlJTU+t8TWpqqk+9JK1YseKM9fDPuZwLSZoxY4b+4z/+Q8uWLVPv3r3PR6uNXqDnonPnztq6dauKioqs5f/8n/+jm2++WUVFRUpMTDyf7Tcq5/LfxbXXXqs9e/ZYIVeSvvrqK8XHxxOufoVzORfHjh07LUTVBl/DTwafV7Z9dgd2//3Fbf78+cbpdJrc3FyzY8cOM27cOBMVFWXcbrcxxpgRI0aYRx991Kpfu3atadKkifnv//5vs3PnTjNt2jSmabBJoOdi+vTpxuFwmL/85S/mwIED1nL06NGGOoRGI9Bz8XM8RWifQM9FSUmJadmypZkwYYLZvXu3Wbx4sYmJiTHPPvtsQx1CoxHouZg2bZpp2bKl+eCDD8y+ffvMp59+ajp06GDuuuuuhjqERuPo0aNm06ZNZtOmTUaS+dOf/mQ2bdpkvvnmG2OMMY8++qgZMWKEVV87TcPkyZPNzp07zezZs5mm4Xx4+eWXTdu2bY3D4TB9+vQxn3/+ubXvxhtvNCNHjvSpX7hwobnyyiuNw+EwSUlJZsmSJee548YrkHPRrl07I+m0Zdq0aee/8UYo0P8uTkXAsleg52LdunUmJSXFOJ1Oc/nll5v//M//NFVVVee568YpkHNx8uRJ89RTT5kOHTqY8PBwk5iYaB588EFz5MiR8994I7Nq1ao6//+/9u8/cuRIc+ONN572mu7duxuHw2Euv/xy88477wT8viHGcO0RAADATtyDBQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAL/CDTfcoHnz5tk65o4dO3TppZeqvLzc1nEBnD8ELABBZdSoUQoJCTlt2bNnz3nv5e9//7tKS0s1bNgwa1v79u01c+bM02qfeuopde/e3a9xu3Tpor59++pPf/qTTZ0CON8IWACCzq233qoDBw74LJdddtlpdZWVlfXax0svvaTRo0crNNT+/ysdPXq05syZo6qqKtvHBlD/CFgAgo7T6VRcXJzPEhYWpptuukkTJkzQxIkTFR0drfT0dEnStm3bNGDAALVo0UKxsbEaMWKEvv/+e2u88vJy/f73v1eLFi0UHx+vF154QTfddJMmTpx4xh4OHTqklStXatCgQed0DHVdhWvfvr21/9/+7d90+PBhrV69+pzGB9CwCFgAGpW5c+fK4XBo7dq1eu2111RWVqZbbrlFPXr00IYNG7Rs2TKVlpbqrrvusl4zefJkrV69Wn/729/06aefKj8/Xxs3bjzr+3z22Wdq1qyZrrrqqnPq89Srb3v27FHHjh11ww03WPsdDoe6d++uf/zjH+c0PoCG1aShGwCAQC1evFgtWrSw1gcMGKBFixZJkq644grNmDHD2vfss8+qR48eeu6556xtb7/9thITE/XVV18pISFBb731lt577z3169dP0k8h7dJLLz1rD998841iY2Pr/Hpw6tSpevzxx322VVZWqkuXLtZ6XFycJMkYoyFDhigyMlKvv/66z2sSEhL0zTffnLUPABcmAhaAoHPzzTdrzpw51nrz5s2tf/fq1cundvPmzVq1apVPIKu1d+9eHT9+XJWVlUpJSbG2t27dWp06dTprD8ePH1d4eHid+yZPnqxRo0b5bHvppZe0Zs2a02r/+Mc/qqCgQBs2bFBERITPvoiICB07duysfQC4MBGwAASd5s2bq2PHjmfcd6off/xRgwYN0n/913+dVhsfH3/OTx9GR0fryJEjZ9z38/5at259Wt17772nF198Ufn5+frNb35z2v7Dhw+rQ4cO59QfgIbFPVgAGrWePXtq+/btat++vTp27OizNG/eXB06dFDTpk31xRdfWK85cuSIvvrqq7OO26NHD7nd7jOGrF9SUFCgsWPH6vXXX1ffvn3rrNm2bZt69OhxTuMDaFgELACNWmZmpg4fPqzhw4fryy+/1N69e7V8+XKNHj1a1dXVatGihcaMGaPJkydr5cqV2rZtm0aNGvWLUy/06NFD0dHRWrt2bcA9ud1u3XHHHRo2bJjS09Pldrvldrt16NAhq+brr7/Wv/71L6WlpQU8PoCGR8AC0KglJCRo7dq1qq6uVv/+/ZWcnKyJEycqKirKClHPP/+8rr/+eg0aNEhpaWm67rrrTruX6+fCwsI0evRovf/++wH3tGvXLpWWlmru3LmKj4+3lmuuucaq+eCDD9S/f3+1a9cu4PEBNLwQY4xp6CYA4EJz0003qXv37nXOyl7L7XYrKSlJGzdutDUIVVZW6oorrtC8efN07bXX2jYugPOHK1gAcI7i4uL01ltvqaSkxNZxS0pK9Mc//pFwBQQxniIEgF9h8ODBto9ZexM+gODFV4QAAAA24ytCAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBm/x/G3+S7xE9wiAAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -109,6 +204,73 @@ "display_id": null } }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Example 2:\n", + "#### 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", + "Note: This simulation may take very long (hours) to finish (reasons: 3D stencil, high Reynolds)" + ], + "id": "fc77a746f992e219" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-15T14:15:42.183638Z", + "start_time": "2025-12-15T13:36:46.912037Z" + } + }, + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 30 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles" + ], + "id": "a25110bac2f3c90a", + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", + "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py:549\u001B[39m\n\u001B[32m 546\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m#################################################\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m 548\u001B[39m t_start = time.time()\n\u001B[32m--> \u001B[39m\u001B[32m549\u001B[39m mlups = \u001B[43msimulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnum_steps\u001B[49m\u001B[43m=\u001B[49m\u001B[43mn_steps\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 550\u001B[39m t_end = time.time()\n\u001B[32m 551\u001B[39m runtime = t_end - t_start\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py:95\u001B[39m, in \u001B[36mEbbSimulation.__call__\u001B[39m\u001B[34m(self, num_steps)\u001B[39m\n\u001B[32m 93\u001B[39m \u001B[38;5;28mself\u001B[39m._post_streaming_boundaries()\n\u001B[32m 94\u001B[39m \u001B[38;5;28mself\u001B[39m.flow.i += \u001B[32m1\u001B[39m\n\u001B[32m---> \u001B[39m\u001B[32m95\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 97\u001B[39m end = timer()\n\u001B[32m 98\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m num_steps * \u001B[38;5;28mself\u001B[39m.flow.rho().numel() / \u001B[32m1e6\u001B[39m / (end - beg)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/_simulation.py:259\u001B[39m, in \u001B[36mSimulation._report\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 257\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_report\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 258\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m reporter \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.reporter:\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m \u001B[43mreporter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/ext/_reporter/observable_reporter.py:187\u001B[39m, in \u001B[36mObservableReporter.__call__\u001B[39m\u001B[34m(self, simulation)\u001B[39m\n\u001B[32m 185\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, simulation: \u001B[33m'\u001B[39m\u001B[33mSimulation\u001B[39m\u001B[33m'\u001B[39m):\n\u001B[32m 186\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m simulation.flow.i % \u001B[38;5;28mself\u001B[39m.interval == \u001B[32m0\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m187\u001B[39m observed = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mobservable\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconvert_to_ndarray\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 188\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mobservable\u001B[49m\u001B[43m(\u001B[49m\u001B[43msimulation\u001B[49m\u001B[43m.\u001B[49m\u001B[43mflow\u001B[49m\u001B[43m.\u001B[49m\u001B[43mf\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 189\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(observed.shape) < \u001B[32m2\u001B[39m\n\u001B[32m 190\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(observed.shape) == \u001B[32m0\u001B[39m:\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/_context.py:120\u001B[39m, in \u001B[36mContext.convert_to_ndarray\u001B[39m\u001B[34m(tensor)\u001B[39m\n\u001B[32m 117\u001B[39m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[32m 118\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_to_ndarray\u001B[39m(tensor: Union[torch.Tensor, List]) -> np.ndarray:\n\u001B[32m 119\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(tensor, torch.Tensor):\n\u001B[32m--> \u001B[39m\u001B[32m120\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtensor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdetach\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcpu\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m.numpy()\n\u001B[32m 121\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 122\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m np.array(tensor)\n", + "\u001B[31mKeyboardInterrupt\u001B[39m: " + ] + }, { "data": { "text/plain": [ @@ -122,7 +284,7 @@ } } ], - "execution_count": 15 + "execution_count": 5 } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py similarity index 72% rename from examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py index 8022d6c2..a78ecfc3 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01a_script_cylinder_lowRe.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py @@ -75,7 +75,7 @@ parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") #TODO: how to use EQLM in lettuce 2025? -parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") +parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1) for the solid obstacle") # reporter and observable settings parser.add_argument("--periodic_region_start_relative", default=None, type=float, help="RELATIVE (0.0-1.0) assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") @@ -107,32 +107,29 @@ parser.add_argument("--vtk_slice2D_t_start", type=float) parser.add_argument("--vtk_slice2D_t_end", type=float) -#TODO: add NAN reporter (on/off, interval,...) -#TODO: add watchdog reporter (on/off, interval,...) -#TODO: add highMa reporter (on/off, interval,...) +#TODO (optional): add NAN reporter (on/off, interval,...) +#TODO (optional): add watchdog reporter (on/off, interval,...) +#TODO (optional): add highMa reporter (on/off, interval,...) -#TODO: add 2D-mp4-reporter... (fps_video, number of frames OR fps_pu) +#TODO (optional): add 2D-mp4-reporter... (fps_video, number of frames OR fps_pu) # Checkpointing -# TODO: add checkpointing-utilities (read, write) -# - is NOT implemented in lettuce 2025 atm. -# - checkpoint IN path -# - checkpoint OUT path (if only one is given, take this one for both) -# - checkpoint i_start, t_start?, - +# TODO (optional): add checkpointing-utilities (read, write): +''' Checkpointing is NOT implemented in lettuce 2025 at the moment. +It was present in lettuce 0.2.3 +Relevant parameters and functionalities would/could include: + - checkpoint IN path (where to READ a checkpoint from) + - checkpoint OUT path (where to WRITE a checkpoint to) + - checkpoint i_start, or t_start (which timestep (in LU or PU) + does the checkpoint correspond to. + - should stuff continue on, or start from 0? (observables, time (step number), + statistics (mean, max, min of observables etc.) +''' + +# for debugging purposes: parser.add_argument("--count_tensors", action="store_true", help="(for debugging: count all tensors, their sizes and memory consumption on the GPU, to find memory leaks") -########################################################### -# OUTPUTS: -# - parameters INPUT -# - print 2D-slice with mask(s) -# - parameters SIMULATED (before sim()) -# - 2D slice last frame -# - output condensed observables (drag, lift, Strouhal) to file... -# - stats and performance (end) - - ########################################################### # put arguments in dictionary print("SCRIPT: Writing arguments to dictionary...") @@ -223,9 +220,13 @@ domain_width_z_in_d = args["domain_width_z_in_d"] # CORRECT GPT for symmetry: -# if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain -# ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder -# ...(!) use odd DpY to use odd GPD! +''' +if D/Y (height of the domain in number of cylinder diameters) is even, +the resulting GPD (gridpoints per diemeter) can't be odd for a symmetrical +cylinder and symmetrical domain! + - if D/Y is even, GPD will be corrected to be even for symmetrical cylinder + => use odd D/Y value to use an odd GPD value! +''' gpd_correction = False if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: gpd_correction = True # gpd will be corrected @@ -246,7 +247,8 @@ perturb_init = args["perturb_init"] u_init_condition = args["u_init_condition"] -# calculate lu-domain-resolution, total number of gridpoints and check correct stencil +# calculate lu-domain-resolution, +# ...total number of gridpoints and check correct stencil if dims == 2: resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] @@ -260,7 +262,8 @@ resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] - number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d + number_of_gridpoints = (char_length_lu ** 3 * domain_length_x_in_d + * domain_height_y_in_d * domain_width_z_in_d) if args["stencil"] == "D3Q15": stencil = lt.D3Q15() elif args["stencil"] == "D3Q19": @@ -283,14 +286,21 @@ n_steps = args["n_steps"] t_target = args["t_target"] if args["t_target"] > 0: - n_steps = int(t_target * (char_length_lu / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) + n_steps = int(t_target * (char_length_lu / char_length_pu) + * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) else: - t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) - -# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients -# - periodic_start_Re100_PU ~= 75-100 seconds -# - the start of the periodic region depends on the time the flow needs -# to sattle.. This time is also dependent on the domain size! + t_target = n_steps / (char_length_lu/char_length_pu + * char_velocity_pu/(mach_number*1/np.sqrt(3))) + +# calculate relative starting point for observables that are calculated over a +# ...temporally converged or periodic region: +''' +- the temporal beginning of the periodic region depends on the time the flow +needs to converge into - for example - a von Karman vortex street. + => This time is also dependent on the domain size! +EXAMPLE: For an Re=100 in a reasonable domain size, the flow needs + t_PU = 75-100 seconds to reach it's periodic state +''' #TODO (optional): change periodic_start parameter to abs. step-number, instead of relative start: # - ease of use for reporters and processing # - no round-off error in preocessing LU-parameter, if set @@ -298,7 +308,8 @@ periodic_start = args["periodic_region_start_lu"] / n_steps elif args["periodic_region_start_pu"] is not None and args["periodic_region_start_pu"] >= 0: periodic_start = args["periodic_region_start_pu"] / t_target -elif args["periodic_region_start_relative"] is not None and args["periodic_region_start_relative"] >= 0 and args["periodic_region_start_relative"] < 1.0: +elif (args["periodic_region_start_relative"] is not None and + 0 <= args["periodic_region_start_relative"] < 1.0): periodic_start = args["periodic_region_start_relative"] else: if args["reynolds_number"] > 1000: @@ -311,34 +322,43 @@ # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? pass -print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") +# print temporal parameters: steps, T_PU +print(f"\n(INFO) parameters set for simulation of {n_steps} steps, " + f"representing {t_target:.3f} seconds [PU]!\n") ########################################### +# INITIALIZE SOLVER COMPONENTS print("SCRIPT: initializing solver components...") -### +# CONTEXT print("-> initializing context...") context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) +# FLOW print("-> initializing flow...") flow = ObstacleCylinder(context=context, resolution=resolution, stencil=stencil, reynolds_number=reynolds_number, mach_number=mach_number, - char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=str(args["bbbc_type"]), calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) + char_length_pu=char_length_pu, char_length_lu=char_length_lu, + char_velocity_pu=char_velocity_pu, + bc_type=str(args["bbbc_type"]), calc_force_coefficients=True, + lateral_walls=args["lateral_walls"]) +# COLLISION OPERATOR print("-> initializing collision operator...") collision_operator = None if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) elif args["collision"].casefold() == "kbc": - if dims == 2: - collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) - else: - collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) + # if dims == 2: + # collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) + # else: + # collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) + collision_operator = lt.KBCCollision(tau=flow.units.relaxation_parameter_lu) else: # default to bgk collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) +# SIMULATION print("\nSCRIPT: initializing simulation object...") simulation = EbbSimulation(flow, collision_operator,reporter=[]) @@ -346,51 +366,60 @@ # REPORTERS: initialize and append to simulation.reporter print("-> initializing reporters...") -# DRAG and LIFT Force Coefficients and respective reporters: -cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d +# DRAG and LIFT (Force Coefficients) and respective reporters: +cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else ( + flow.char_length_pu*domain_width_z_in_d) -DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) +DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], + solid_mask=simulation.post_streaming_boundaries[-1].mask, + area_pu=cylinder_cross_sectional_area) DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) simulation.reporter.append(DragReporter) -LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) +LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], + solid_mask=simulation.post_streaming_boundaries[-1].mask, + area_pu=cylinder_cross_sectional_area) LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) simulation.reporter.append(LiftReporter) # VELOCITY- and REYNOLDS-STRESS profiles over space and time: if args["calc_u_profiles"]: # define positions - position_1 = flow.x_pos_lu - 0.5 + 1.06 * flow.radius_lu * 2 # int(round(flow.x_pos + 1.06 * flow.radius * 2 , 0)) - position_2 = flow.x_pos_lu - 0.5 + 1.54 * flow.radius_lu * 2 # int(round(flow.x_pos + 1.54 * flow.radius * 2 , 0)) - position_3 = flow.x_pos_lu - 0.5 + 2.02 * flow.radius_lu * 2 # int(round(flow.x_pos + 2.02 * flow.radius * 2 , 0)) + position_1 = flow.x_pos_lu - 0.5 + 1.06 * flow.radius_lu * 2 + position_2 = flow.x_pos_lu - 0.5 + 1.54 * flow.radius_lu * 2 + position_3 = flow.x_pos_lu - 0.5 + 2.02 * flow.radius_lu * 2 print("ProfileReporters at x-positions:" + " p1: " + str(position_1) + " p2: " + str( position_2) + " p3: " + str(position_3)) # create and append profileReporters - ProfileReporter1 = ProfileReporter(flow=flow, interval=1, position_lu=position_1, i_start= int(n_steps * periodic_start)) + ProfileReporter1 = ProfileReporter(flow=flow, interval=1, position_lu=position_1, + i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter1) - ProfileReporter2 = ProfileReporter(flow=flow, interval=1, position_lu=position_2, i_start= int(n_steps * periodic_start)) + ProfileReporter2 = ProfileReporter(flow=flow, interval=1, position_lu=position_2, + i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter2) - ProfileReporter3 = ProfileReporter(flow=flow, interval=1, position_lu=position_3, i_start= int(n_steps * periodic_start)) + ProfileReporter3 = ProfileReporter(flow=flow, interval=1, position_lu=position_3, + i_start= int(n_steps * periodic_start)) simulation.reporter.append(ProfileReporter3) # NAN REPORTER (if nan detected -> stop simulation) -# TODO: add NaN reporter +# TODO (optional): add NaN reporter (see PR for that topic) # - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... -# HighMa Reporter (if Ma>0.3 retected -> report and/or stop simulation) -# TODO: HighMa Reporter +# HighMa Reporter (if Ma>0.3 detected -> report and/or stop simulation) +# TODO (optional): HighMa Reporter # Watchdog/Progress-Reporter -# TODO: Progress-Reporter +# TODO (optional): Progress-Reporter -# VTK Reporter -> visualization +# VTK Reporter: for visualization etc. -# BASIC lettuce vtk output +# BASIC lettuce vtk output (see also: advanced vtk reporter below) if args["vtk_full_basic"]: - vtk_reporter = lt.VTKReporter(interval=args["vtk_full_basic_interval"], filename_base=outdir_data+"/vtk/out") + vtk_reporter = lt.VTKReporter(interval=args["vtk_full_basic_interval"], + filename_base=outdir_data+"/vtk/out") - # export obstacle + # export obstacle mask for visualization mask_dict = dict() if dims ==2: mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) @@ -444,7 +473,8 @@ filename_base=outdir_data + "/vtk/out", imin=vtk_3d_i_start, imax=vtk_3d_i_end) simulation.reporter.append(vtk_3d_reporter) - vtk_3d_reporter.output_mask(flow.solid_mask, outdir_data + "/vtk", "solid_mask", point=True) + vtk_3d_reporter.output_mask(flow.solid_mask, outdir_data + "/vtk", "solid_mask", + point=True) # slice2D (2D slice at Z/2) @@ -463,7 +493,7 @@ elif args["vtk_slice2D_step_end"] is not None and args["vtk_slice2D_step_end"] > 0: vtk_slice2d_i_end = int(args["vtk_slice2D_step_end"]) else: - vtk_slice2d_i_end = n_steps # Q: must this be target? + vtk_slice2d_i_end = n_steps if args["vtk_slice2D_t_interval"] is not None and args["vtk_slice2D_t_interval"] > 0: vtk_slice2d_interval = int(flow.units.convert_time_to_lu(args["vtk_slice2D_t_interval"])) @@ -478,39 +508,50 @@ vtk_slice2d_interval = 1 if args["vtk_slice2D"]: - vtk_domainSlice_reporter = VTKsliceReporter( flow, - interval=int(vtk_slice2d_interval), - filename_base=outdir_data + "/vtk/slice_domain/slice_domain", - sliceXY=([0,resolution[0]-1],[0,resolution[1]-1]), - sliceZ=int(resolution[2]/2), - imin=vtk_slice2d_i_start, imax=vtk_slice2d_i_end) + vtk_domainSlice_reporter = VTKsliceReporter(flow, + interval=int(vtk_slice2d_interval), + filename_base=outdir_data + "/vtk/slice_domain/slice_domain", + sliceXY=([0,resolution[0]-1],[0,resolution[1]-1]), + sliceZ=int(resolution[2]/2), + imin=vtk_slice2d_i_start, imax=vtk_slice2d_i_end) simulation.reporter.append(vtk_domainSlice_reporter) # DRAW CYLINDER-MASK in 2D (xy-plane) -draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) +try: + draw_circular_mask(flow, flow.char_length_lu, filebase=outdir_data, output_data=True) +except: + print("(!) Drawing of circular cylinder mask did not work...") ################################################## -# PRINT PARAMETERS prior to simulation: +# PRINT relevant PARAMETERS prior to simulation: print(f"\nSCRIPT: spacial and temporal dimensions:") print("domain shape (LU):", flow.resolution) print("t_target (PU) with", n_steps, "steps (LU):", - round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 3), + round(n_steps * (flow.char_length_pu / flow.char_length_lu) + * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 3), "seconds") print("steps to simulate 1 second PU:", - round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3), "steps") -print(f"steps to simulate {t_target:.3f} (t_target, PU) seconds: {t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3):.3f} steps") + round((flow.char_length_lu / flow.char_length_pu) + * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3), "steps") +print(f"steps to simulate {t_target:.3f} (t_target, PU) seconds: {t_target + * round((flow.char_length_lu / flow.char_length_pu) + * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 3):.3f} steps") ################################################## # RUN SIMULATION: print(f"\n#################################################") -print(f"\nSCRIPT ({datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}): running simulation for {n_steps} steps...\n") +print(f"\nSCRIPT ({datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}): " + f"running simulation for {n_steps} steps...\n") print(f"#################################################\n") + t_start = time.time() mlups = simulation(num_steps=n_steps) t_end = time.time() runtime = t_end - t_start -print(f"***** SIMULATION FINISHED AT {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} *****\n") + +print(f"***** SIMULATION FINISHED AT " + f"{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} *****\n") ################################################## # OUTPUT STATS: @@ -518,15 +559,20 @@ print(f"MLUPS: {mlups:.3f}") print(f"simulated PU-Time: {flow.units.convert_time_to_pu(n_steps)} seconds") print("simulated LU-steps: ", n_steps) -print(f"runtime (WALLTIME) of simulation(num_steps): {runtime:.3f} seconds (= ", round(runtime / 60, 2), "minutes )") +print(f"runtime (WALLTIME) of simulation(num_steps): " + f"{runtime:.3f} seconds (= ", round(runtime / 60, 2), "minutes )") print("") +# GPU VRAM print("### HARDWARE UTILIZATION ###") -print(f"current GPU VRAM (MB) usage: {torch.cuda.memory_allocated(device=context.device)/1024/1024:.3f}") -print(f"max. GPU VRAM (MB) usage: {torch.cuda.max_memory_allocated(device=context.device)/1024/1024:.3f}") - +print(f"current GPU VRAM (MB) usage: " + f"{torch.cuda.memory_allocated(device=context.device)/1024/1024:.3f}") +print(f"max. GPU VRAM (MB) usage: " + f"{torch.cuda.max_memory_allocated(device=context.device)/1024/1024:.3f}") +# CPU [cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] -print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) - +print("CPU % avg. over last 1 min, 5 min, 15 min; ", + round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) +# RAM ram = psutil.virtual_memory() print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( round(ram.total / (1024 * 1024), 2)) + " MB") @@ -537,7 +583,8 @@ output_file.write("DATA for " + timestamp) output_file.write("\n\n### SIM-STATS ###") output_file.write( - f"\nruntime: {runtime:.3f} seconds (= {round(runtime / 60, 2)} min = {round(runtime / 3600, 2)} h)") + f"\nruntime: {runtime:.3f} seconds (= {round(runtime / 60, 2)} " + f"min = {round(runtime / 3600, 2)} h)") output_file.write("\nMLUPS = " + str(mlups)) output_file.write("\n") output_file.write("\nVRAM_current [MB] = " + str( @@ -555,35 +602,23 @@ round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, 2)) + " MB") output_file.close() - # output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") - # output_file.write("DATA for " + timestamp) - # output_file.write("\n\n### SIM-STATS ###") - # output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") - # output_file.write("\nMLUPS = " + str(mlups)) - # output_file.write("\n") - # output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - # output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) - # output_file.write("\n") - # output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - # output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") - # output_file.close() ################################################## # PLOTTING 2D IMAGE of VELOCITY fig, axes = plt.subplots(1, 2, figsize=(10, 3)) fig.subplots_adjust(right=0.85) u = flow.u_pu.cpu().numpy() -#print("\nMax Velocity:", u.max()) -if dims == 2: +if dims == 2: # 2D im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") im2 = axes[1].imshow(u[0, ...].T, origin="lower") -else: - im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") +else: # 3D + im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, + int(flow.solid_mask.shape[2] / 2)].T), origin="lower") im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) fig.colorbar(im2, cax=cbar_ax) -fig.show() -#TODO: plot image of pressure +plt.show() +#TODO (optional): plot image of pressure ################################################## # PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: @@ -595,8 +630,8 @@ ylim=(0.5, 1.6), secax_functions_tuple=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu), - filenamebase=outdir_data+"/drag", periodic_start=periodic_start, adjust_ylim=True) -#OLD: drag_prominence = ((abs(drag_timeseries[2].max()) - abs(drag_timeseries[2].min())) * 0.5) + filenamebase=outdir_data+"/drag", periodic_start=periodic_start, + adjust_ylim=True) drag_stats = analyze_periodic_timeseries(drag_timeseries, periodic_start_rel=0.5, name="drag", verbose=True, pu_per_step=flow.units.convert_time_to_pu(1), @@ -605,18 +640,19 @@ print(f"DRAG STATS:") #\n{drag_stats}") for key, value in drag_stats.items(): print(f"{key:<20} = {str(value)}") - -# STATS ARE: {"mean_simple": mean_simple, -# "mean_periodcorrected": mean_periodcorrected, -# "min_simple": min_simple, -# "max_simple": max_simple, -# "max_mean": max_mean, -# "min_mean": min_mean, -# "frequency_fit": frequency_fit, -# "frequency_fft": freq_peak, -# "fft_resolution": freq_res} - print("") +''' +reminder: + STATS ARE: {"mean_simple": mean_simple, + "mean_periodcorrected": mean_periodcorrected, + "min_simple": min_simple, + "max_simple": max_simple, + "max_mean": max_mean, + "min_mean": min_mean, + "frequency_fit": frequency_fit, + "frequency_fft": freq_peak, + "fft_resolution": freq_res} +''' # LIFT lift_timeseries = np.array(np.array(LiftReporter.out)) @@ -636,19 +672,22 @@ # plot DRAG and LIFT together: try: fig, ax = plt.subplots(layout="constrained") - drag_ax = ax.plot(drag_timeseries[:, 1], drag_timeseries[:, 2], color="tab:blue", label="Drag") + drag_ax = ax.plot(drag_timeseries[:, 1], drag_timeseries[:, 2], color="tab:blue", + label="Drag") ax.set_xlabel("physical time / s") ax.set_ylabel("Coefficient of Drag $C_{D}$") - ylim_adjusted = (drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].min() * 0.5, - drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].max() * 1.2) - #ax.set_ylim((0.5, 1.6)) + ylim_adjusted = (drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, 2].min() + * 0.5, drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1):, + 2].max() * 1.2) ax.set_ylim(ylim_adjusted) - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) + secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, + flow.units.convert_time_to_pu)) secax.set_xlabel("timesteps (simulation time / LU)") ax2 = ax.twinx() - lift_ax = ax2.plot(lift_timeseries[:, 1], lift_timeseries[:, 2], color="tab:orange", label="Lift") + lift_ax = ax2.plot(lift_timeseries[:, 1], lift_timeseries[:, 2], color="tab:orange", + label="Lift") ax2.set_ylabel("Coefficient of Lift $C_{L}$") ax2.set_ylim((-1.1, 1.1)) @@ -665,7 +704,8 @@ # STROUHAL number # f = Strouhal for St=f*D/U and D=U=1 in PU -print("Strouhal number is: ", lift_stats["frequency_fit"] * flow.char_length_pu/flow.char_velocity_pu) +print("Strouhal number is: ", lift_stats["frequency_fit"] + * flow.char_length_pu/flow.char_velocity_pu) # AVERAGE VELOCITY and REYNOLDS STRESS PROFILES if args["calc_u_profiles"] and args["profile_reference_path"] is not None: @@ -699,11 +739,11 @@ for obj in gc.get_objects(): try: if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): - output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) + output_file.write("\n" + str(obj.size()) + ", " + + str(obj.nelement() * obj.element_size())) total_bytes = total_bytes + obj.nelement() * obj.element_size() except: pass - # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) output_file.close() ### count occurence of tensors in list of tensors: @@ -722,8 +762,8 @@ except: print("(!) counting tensors didn't work!") -# TODO: cleanup of parms, stats, obs... -# output parameters, stats and observables +############################################ +# OUTPUT parameters, stats and observables if not no_data_flag: output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") output_file.write("DATA for " + timestamp) @@ -732,26 +772,15 @@ output_file.write("\n{:30s} {:30s}".format("Ma", str(mach_number))) output_file.write("\n{:30s} {:30s}".format("n_steps", str(n_steps))) output_file.write("\n{:30s} {:30s}".format("t_target [s]", str(t_target))) - output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", str(flow.char_length_lu))) + output_file.write("\n{:30s} {:30s}".format("gridpoints_per_diameter (GPD)", + str(flow.char_length_lu))) if gpd_correction: - output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + str( - flow.char_length_lu) + " because D/Y is even") + output_file.write("\n(!) gpd was corrected from: " + str(gpd_setup) + " to " + + str(flow.char_length_lu) + " because D/Y is even") output_file.write("\nDpX (D/X) = ".ljust(31) + str(domain_length_x_in_d)) output_file.write("\nDpY (D/Y) = ".ljust(31) + str(domain_height_y_in_d)) if flow.stencil.d == 3: output_file.write("\nDpZ (D/Z) = ".ljust(31) + str(domain_width_z_in_d)) - - #output_file.write("\nn_steps = " + str(n_steps)) - # output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") - # output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) - # if gpd_correction: - # output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( - # flow.char_length_lu) + " because D/Y is even") - # output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) - # output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) - # if flow.stencil.d == 3: - # output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) - output_file.write("\nshape_LU: ".ljust(31) + str(flow.resolution)) output_file.write(("\ntotal_number_of_gridpoints: ".ljust(31) + str(flow.rho(flow.f).numel()))) output_file.write("\nbc_type = ".ljust(31) + str()) @@ -760,43 +789,65 @@ output_file.write("\ncollision = ".ljust(31) + str(collision_operator)) output_file.write("\n") # output_file.write("\nMa = " + str(mach_number)) - output_file.write("\nrelaxation parameter tau [LU]".ljust(31) + str(flow.units.relaxation_parameter_lu)) - output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(31) + str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) + output_file.write("\nrelaxation parameter tau [LU]".ljust(31) + + str(flow.units.relaxation_parameter_lu)) + output_file.write("\ngrid_reynolds_number (Re_g) = ".ljust(31) + + str(flow.units.characteristic_velocity_lu/ + ((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5)))) output_file.write("\n") - output_file.write("\ncylinder diameter PU = ".ljust(31) + str(flow.units.characteristic_length_pu)) - output_file.write("\ncharacteristic velocity PU = ".ljust(31) + str(flow.units.characteristic_velocity_pu)) -# output_file.write("\nu_init = " + str(u_init)) + output_file.write("\ncylinder diameter PU = ".ljust(31) + + str(flow.units.characteristic_length_pu)) + output_file.write("\ncharacteristic velocity PU = ".ljust(31) + + str(flow.units.characteristic_velocity_pu)) output_file.write("\nperturb_init = ".ljust(31) + str(perturb_init)) output_file.write("\n") -# output_file.write("\noutput_vtk = " + str(output_vtk)) -# output_file.write("\nvtk_fps = " + str(vtk_fps)) output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = ".ljust(31) + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") + output_file.write("\nruntime = ".ljust(31) + str(runtime) + + " seconds (=" + str(runtime / 60) + " minutes)") output_file.write("\nMLUPS = ".ljust(31) + str(mlups)) output_file.write("\n") - output_file.write("\nVRAM_current [MB] = ".ljust(31) + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = ".ljust(31) + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_current [MB] = ".ljust(31) + + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) + output_file.write("\nVRAM_peak [MB] = ".ljust(31) + + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(31) + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: ".ljust(31) + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") + output_file.write("\nCPU load % avg. over last 1, 5, 15 min: ".ljust(31) + + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + + " %, " + str(round(cpuLoad15, 2)) + " %") + output_file.write("\ntotal current RAM usage [MB]: ".ljust(31) + + str(round(ram.used / (1024 * 1024), 2)) + " of " + + str(round(ram.total / (1024 * 1024), 2)) + " MB") output_file.write("\n\n### OBSERVABLES ###") - output_file.write("\nCoefficient of drag between " + str(round(drag_timeseries[int(drag_timeseries.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + " s:") - output_file.write("\nCd_mean, simple = ".ljust(31) + str(drag_stats["mean_simple"])) - output_file.write("\nCd_mean, peak_finder = ".ljust(31) + str(drag_stats["mean_periodcorrected"])) + output_file.write("\nCoefficient of drag between " + + str(round(drag_timeseries[int(drag_timeseries.shape[0] + * periodic_start - 1), 1], 2)) + " s and " + + str(round(drag_timeseries[int(drag_timeseries.shape[0] - 1), 1], 2)) + + " s:") + output_file.write("\nCd_mean, simple = ".ljust(31) + + str(drag_stats["mean_simple"])) + output_file.write("\nCd_mean, peak_finder = ".ljust(31) + + str(drag_stats["mean_periodcorrected"])) output_file.write( - "\nCd_min = ".ljust(31) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None else drag_stats["min_simple"])) + "\nCd_min = ".ljust(31) + str(drag_stats["min_mean"] if drag_stats["min_mean"] is not None + else drag_stats["min_simple"])) output_file.write( - "\nCd_max = ".ljust(31) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None else drag_stats["max_simple"])) + "\nCd_max = ".ljust(31) + str(drag_stats["max_mean"] if drag_stats["max_mean"] is not None + else drag_stats["max_simple"])) output_file.write("\n") output_file.write("\nCoefficient of lift:") - output_file.write("\nCl_min = ".ljust(31) + str(lift_stats["min_mean"] if lift_stats["min_mean"] is not None else lift_stats["min_simple"])) - output_file.write("\nCl_max = ".ljust(31) + str(lift_stats["max_mean"] if lift_stats["max_mean"] is not None else lift_stats["max_simple"])) + output_file.write("\nCl_min = ".ljust(31) + str(lift_stats["min_mean"] + if lift_stats["min_mean"] is not None + else lift_stats["min_simple"])) + output_file.write("\nCl_max = ".ljust(31) + str(lift_stats["max_mean"] + if lift_stats["max_mean"] is not None + else lift_stats["max_simple"])) output_file.write("\n") output_file.write("\nStrouhal number:") - output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + str(lift_stats["fft_resolution"]) + " Hz") + output_file.write("\nFFT: St +- df = " + str(lift_stats["frequency_fft"]) + " +- " + + str(lift_stats["fft_resolution"]) + " Hz") output_file.write( "\nSINE-FIT: St = " + str(lift_stats["frequency_fit"])) output_file.write("\n") @@ -809,67 +860,48 @@ output_file.write('\n{:30s} {:30s}'.format("Ma", str(reynolds_number))) output_file.write('\n{:30s} {:30s}'.format("Re", str(mach_number))) output_file.write('\n') -output_file.write('\n{:30s} {:30s}'.format("Relaxation Parameter LU", str(flow.units.relaxation_parameter_lu))) -output_file.write('\n{:30s} {:30s}'.format("l_char_LU", str(flow.units.characteristic_length_lu))) -output_file.write('\n{:30s} {:30s}'.format("u_char_LU", str(flow.units.characteristic_velocity_lu))) -output_file.write('\n{:30s} {:30s}'.format("viscosity_LU", str(flow.units.viscosity_lu))) -output_file.write('\n{:30s} {:30s}'.format("p_char_LU", str(flow.units.characteristic_pressure_lu))) -output_file.write('\n{:30s} {:30s}'.format("rho_char_LU", str(flow.units.characteristic_density_lu))) +output_file.write('\n{:30s} {:30s}'.format("Relaxation Parameter LU", + str(flow.units.relaxation_parameter_lu))) +output_file.write('\n{:30s} {:30s}'.format("l_char_LU", + str(flow.units.characteristic_length_lu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_LU", + str(flow.units.characteristic_velocity_lu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_LU", + str(flow.units.viscosity_lu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_LU", + str(flow.units.characteristic_pressure_lu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_LU", + str(flow.units.characteristic_density_lu))) output_file.write('\n') -output_file.write('\n{:30s} {:30s}'.format("l_char_PU", str(flow.units.characteristic_length_pu))) -output_file.write('\n{:30s} {:30s}'.format("u_char_PU", str(flow.units.characteristic_velocity_pu))) -output_file.write('\n{:30s} {:30s}'.format("viscosity_PU", str(flow.units.viscosity_pu))) -output_file.write('\n{:30s} {:30s}'.format("p_char_PU", str(flow.units.characteristic_pressure_pu))) -output_file.write('\n{:30s} {:30s}'.format("rho_char_PU", str(flow.units.characteristic_density_pu))) +output_file.write('\n{:30s} {:30s}'.format("l_char_PU", + str(flow.units.characteristic_length_pu))) +output_file.write('\n{:30s} {:30s}'.format("u_char_PU", + str(flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("viscosity_PU", + str(flow.units.viscosity_pu))) +output_file.write('\n{:30s} {:30s}'.format("p_char_PU", + str(flow.units.characteristic_pressure_pu))) +output_file.write('\n{:30s} {:30s}'.format("rho_char_PU", + str(flow.units.characteristic_density_pu))) output_file.write('\n') -output_file.write('\n{:30s} {:30s}'.format("grid reynolds number Re_g", str(flow.units.characteristic_velocity_lu/((1 / np.sqrt(3.0))**2 * (flow.units.relaxation_parameter_lu - 0.5))))) -output_file.write('\n{:30s} {:30s}'.format("flow through time PU [s]", str(domain_length_x_in_d*flow.units.characteristic_length_pu/flow.units.characteristic_velocity_pu))) -output_file.write('\n{:30s} {:30s}'.format("flow through time LU", str(domain_length_x_in_d*flow.units.characteristic_length_lu/flow.units.characteristic_velocity_lu))) +output_file.write('\n{:30s} {:30s}'.format("grid reynolds number Re_g", + str(flow.units.characteristic_velocity_lu/ + ((1 / np.sqrt(3.0))**2 + * (flow.units.relaxation_parameter_lu - 0.5))))) +output_file.write('\n{:30s} {:30s}'.format("flow through time PU [s]", + str(domain_length_x_in_d + *flow.units.characteristic_length_pu/ + flow.units.characteristic_velocity_pu))) +output_file.write('\n{:30s} {:30s}'.format("flow through time LU", + str(domain_length_x_in_d + *flow.units.characteristic_length_lu/ + flow.units.characteristic_velocity_lu))) output_file.write('\n') output_file.close() ## END OF SCRIPT print(f"\n♬ THE END ♬") -# reset stdout (from LOGGER, see above) +# reset stdout (from LOGGER, see above (beginning)) sys.stdout = old_stdout - -################################################################ -# script components below... - -# Process arguments and set parameters -# - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) -# - flow physics: char_velocity, re, ma, density, pressure, length, domain (resolution) -# - solver settings - -# save input parameters to file - -# save this script to outdir for later reference - -# start logger - -# DEFINE AUXILIARY methods (or load from other helper-file) - -# SETUP SIMULATOR -# - stencil (infer dim from stencil), dtype, equilibrium, -# - calculate obstacle geometry and domain constraints - -# save geometry input to file - -# flow, collision,...classes - -# (opt.) plot stuff (velocity, geometry etc.) - -# initialize REPORTERS - -# RUN SIMULATION - -# print stats - -# export stats - -# plotting and post-processing -# - save data - -# reset LOGGER (!) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py deleted file mode 100644 index c26c4604..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01b_script_cylinder_highRe.py +++ /dev/null @@ -1,2 +0,0 @@ - -# this file should contain all the MP2 stuff with Re3900 and velocity profiles... \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 3e0e2dc5..4776e45c 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -481,8 +481,9 @@ def post_streaming_boundaries(self): elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=self.calc_force_coefficients) else: # use basic mask Bounce Back - print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... - will fall back to using BounceBackBoundary") - obstacle_boundary = BounceBackBoundary(self.context.convert_to_tensor(self.obstacle_mask)) + print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... - will fall back to using FullwayBounceBackBoundary") + obstacle_boundary = FullwayBounceBackBoundary(self.context, self, + self.obstacle_mask, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) # create and return boundary-list if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py deleted file mode 100644 index 29ee1570..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_NaNreporter.py +++ /dev/null @@ -1,103 +0,0 @@ -#TODO: WORK IN PROGRESS... -> this was copied from lettuce issue #248 and branch MaxBille:174-reporter-to-interrupt-simulation - -import torch -import numpy as np -import os - -from typing import List - -from lettuce._simulation import Reporter, Simulation -from lettuce.ext._reporter import VTKReporter -from ebb_simulation import EbbSimulation - -class NaNReporter(Reporter): - """reports any NaN and aborts the simulation""" - # WARNING: too many NaNs in very large simulations can confuse torch and - # trigger an error, when trying to create and store the nan_location - # tensor. - # ...to avoid this, leave outdir=None to omit creation and file-output of - # nan_location. This will not impact the abortion of sim. by NaN_Reporter - - def __init__(self, interval=100, outdir=None, vtk=False): - self.outdir = outdir - self.vtk = vtk - self.name = 'NaN' # what is the name for? - self.failed_iteration = None - super().__init__(interval) - - def __call__(self, simulation: 'Simulation'): - if simulation.flow.i % self.interval == 0: - if self.is_failed(simulation): - self.failed_iteration = simulation.flow.i - if self.outdir is not None: - self.outputs(simulation) - - print( - f'(!) ABORT MESSAGE: FailReporter detected {self.name}' - f' in f (interval = {self.interval}) at iteration ' - f'{simulation.flow.i}. See log in {self.outdir} for ' - f'details!') - # telling simulation to abort simulation by setting i too high - #TODO: works only with BREAKABLE SIMULATION with a while-loop... - simulation.flow.i = int(simulation.flow.i + 1e10) - # the 1e10 is "a lot", but in a very unlikely case of a very long simulation, - # where num_steps >=1e10, this would not work... - - def outputs(self, simulation: 'Simulation'): - if not os.path.exists(self.outdir): - os.mkdir(self.outdir) - - my_file = open(f"{self.outdir}/{self.name}_reporter.txt", - "w") - self.show_max(my_file, simulation) - my_file.write(f"(!) {self.name} detected at \n") - - for location in self.locations_string(simulation): - my_file.write(f"{location:6} ") - my_file.write("\n") - for fail in self.failed_locations_list(simulation): - for fail_dim in fail: - my_file.write(f"{fail_dim:6} ") - my_file.write("\n") - if len(self.failed_locations_list(simulation)) >= 100: - my_file.write(f"(!) {self.name} detected for more " - f"than 100 values. Showing only first " - f"100 values\n") - - my_file.close() - - # write vtk output with u and p fields - if self.vtk: - vtkreporter = VTKReporter( - 1, filename_base=self.outdir + f"/{self.name}_fail") - vtkreporter(simulation) - - def is_failed(self, simulation: 'Simulation') -> bool: - # checks if any item of self.fails(simulation) is true - return self.fails(simulation).any().item() - - def fails(self, simulation: 'Simulation') -> torch.Tensor: - # returns a tensor with ([q],x,[y,z]) dimensions indicating whether - # fail condition applies - return torch.isnan(simulation.flow.f) - - def failed_locations_list(self, simulation: 'Simulation') -> np.ndarray: - # getting fail locations (and possibly values) - failed_locations_list = [] - for fail_dim in torch.where(self.fails(simulation)): - failed_locations_list.append( - simulation.context.convert_to_ndarray(fail_dim)) - if len(failed_locations_list[0]) > 100: - return np.stack(failed_locations_list, axis=-1)[:100] - return np.stack(failed_locations_list, axis=-1) - - def locations_string(self, simulation: 'Simulation') -> List[str]: - locations = ["q", "x"] - if simulation.flow.stencil.d > 1: - locations.append("y") - if simulation.flow.stencil.d > 2: - locations.append("z") - return locations - - def show_max(self, my_file, simulation: 'Simulation'): - pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py deleted file mode 100644 index 6430b0cd..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_high_ma_reporter.py +++ /dev/null @@ -1 +0,0 @@ -#TODO: this is work in progress in PR #248 (NaNReporter and HighMa reporter) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py deleted file mode 100644 index efb0f25f..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_progress_reporter.py +++ /dev/null @@ -1,2 +0,0 @@ -# TODO: this is work in progress in PR #283 with branch 275-progress-reporter - diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py b/examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py similarity index 100% rename from examples/advanced_projects/efficient_bounce_back_obstacle/Interpolated_compact2_forComparison.py rename to examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py From c3c40622a534cc102bf3fecc4ef3fac0eba6bc5d Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 17 Dec 2025 18:07:57 +0100 Subject: [PATCH 18/21] final touches and prlimilary test for Re200 and Re3900 run of cylinder flow with 01_script_cylinder_simulation.py through ipynb: 00_run_parameterized_project.ipynb added "show" parameter to plotting-reporters methods, to not only plot and save but also show the plotted data --- .../00_run_parameterized_project.ipynb | 491 ++++++++++++++++-- .../01_script_cylinder_simulation.py | 15 +- .../data_processing_and_plotting.py | 40 +- 3 files changed, 486 insertions(+), 60 deletions(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index 3bc65d16..ac92a73f 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "source": [ "## ADVANCED PROJECT: CYLINDER FLOW\n", - "This .ipynb contains examples for invocation of the 01_script_cylinder_simulation.py script, that conducts a simulation of the flow around a circular cylinder.\n", + "This .ipynb contains examples for invocation of the `01_script_cylinder_simulation.py` script, that conducts a simulation of the flow around a circular cylinder.\n", "The code is ported from projects MP1, MP2 and Master’s Thesis of M.Bille. Some of the most prominent features implemented are:\n", "- efficient bounce back boundary conditions (Fullway, Halfway, linear Interpolated) for solid bodies, that allow calculation of force on the solid body by Momentum exchange algorithm (MEA)\n", "- simulation-class that allows:\n", @@ -20,17 +20,17 @@ "2. 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", "\n", "### Usage\n", - "The example command line calls and parameters below serve as examples for the use of 01_script_cylinder_simulation.py. There are lots of parameters available through arguments (see script itself). The \"%run\" command is used here, to simulate a bash command line of \"python scriptname\"\n", + "The example command line calls and parameters below serve as examples for the use of `01_script_cylinder_simulation.py`. There are lots of parameters available through arguments (see script itself). The \"%run\" command is used here, to simulate a bash command line call of `python 01_script_cylinder_simulation.py `\n", "\n", - "The script will conduct the simulation and write data to an individually named folder in the directory specified for the \"--outdir\" parameter. This contains plots, .txt files etc.\n", + "The script will conduct the simulation and write data to an individually named folder in the directory specified for the `--outdir` parameter. This contains plots, .txt files etc.\n", "The output can be controlled through parameters (see script content (argument parser) itself). Plots etc. are named accordingly in the output data directory.\n", "\n", "You might want to output 2D or 3D vtk-data for further analysis and visualization. But beware the amount of storage you might need and set your parameters accordingly (see script parameters and code).\n", "\n", "### Note\n", - "- Some of the functionalities might not work flawlessly through this .ipynb-implementation (warnings etc.): just use a regular python console or similar\n", - "- Some functionalities throw a warning, when the flow data con not be processed, because it doesn't meet certain physical characteristics (for example: peak finding in a periodic force coefficient timeseries). This is known and ok.\n", - "- The command line output of the script (including useful messages) might not appear in the notebook. This might be because of the \"%run\" command, see the .txt-output in the output directory for the full log and output, or execute the script in a python console." + "- Some of the functionalities might not work flawlessly through this .ipynb-implementation (warnings etc.): just use a regular python console or call the script with parameters from a bash script.\n", + "- Some functionalities throw a warning, when the flow data con not be processed, because it doesn't meet certain physical characteristics (for example: peak finding in a periodic force coefficient timeseries). This is intended. The errors are captured and forwarded to the output, without crashing the script.\n", + "- The command line output of the script (including useful messages) might not appear in the notebook, depending on your configuration of Jupyter. See the .txt-output in the output directory for the full log and output, or execute the script in a python console." ], "id": "62eec9273a6c4271" }, @@ -55,8 +55,21 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-15T12:42:25.774523Z", - "start_time": "2025-12-15T12:40:16.188252Z" + "end_time": "2025-12-17T16:41:19.649550Z", + "start_time": "2025-12-17T16:41:19.646809Z" + } + }, + "cell_type": "code", + "source": "import os", + "id": "febb287720d9f09e", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-17T16:04:29.456923Z", + "start_time": "2025-12-17T16:03:05.111157Z" } }, "cell_type": "code", @@ -67,11 +80,50 @@ "id": "cdd448d543dd26be", "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py:619: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " fig.show()\n" + "SCRIPT: Writing arguments to dictionary...\n", + "SCRIPT: Input arguments are: \n", + "{'name': '2D_simulation_cylinder_Re200', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 200.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': None, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 100.0, 'collision': 'bgk', 'stencil': 'D2Q9', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': False, 'output_u_profiles_timeseries': False, 'profile_reference_path': '../profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", + "\n", + "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", + "outdir/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200\n", + "outdir_DATA/simID = ./datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200\n", + "SCRIPT: Writing input parameters to file: ./datafolder/251217_170307-2D_simulation_cylinder_Re200/input_parameters.txt\n", + "SCRIPT: Saving simulation script to outdir...\n", + "-> Saved simulation script to './datafolder/251217_170307-2D_simulation_cylinder_Re200/251217_170307-2D_simulation_cylinder_Re200_01_script_cylinder_simulation.py'\n", + "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", + "SCRIPT: Processing parameters...\n", + "\n", + "(INFO) parameters set for simulation of 34641 steps, representing 100.000 seconds [PU]!\n", + "\n", + "SCRIPT: initializing solver components...\n", + "-> initializing context...\n", + "-> initializing flow...\n", + "OBST_Cylinder: self.resolution= [200, 100]\n", + "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", + "-> initializing collision operator...\n", + "\n", + "SCRIPT: initializing simulation object...\n", + "CYLINDER: the bc_type was given as: ibb1\n", + "IBB: calc force is: True\n", + "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", + "collision index is: 0\n", + "-> initializing reporters...\n", + "\n", + "SCRIPT: spacial and temporal dimensions:\n", + "domain shape (LU): [200, 100]\n", + "t_target (PU) with 34641 steps (LU): 100.0 seconds\n", + "steps to simulate 1 second PU: 346.41 steps\n", + "steps to simulate 100.000 (t_target, PU) seconds: 34641.000 steps\n", + "\n", + "#################################################\n", + "\n", + "SCRIPT (2025-12-17 17:03:07): running simulation for 34641 steps...\n", + "\n", + "#################################################\n", + "\n" ] }, { @@ -79,7 +131,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -87,12 +139,55 @@ "display_id": null } }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***** SIMULATION FINISHED AT 2025-12-17 17:04:26 *****\n", + "\n", + "### STATS ###\n", + "MLUPS: 8.869\n", + "simulated PU-Time: 99.99995337498915 seconds\n", + "simulated LU-steps: 34641\n", + "runtime (WALLTIME) of simulation(num_steps): 78.118 seconds (= 1.3 minutes )\n", + "\n", + "### HARDWARE UTILIZATION ###\n", + "current GPU VRAM (MB) usage: 9.747\n", + "max. GPU VRAM (MB) usage: 15.871\n", + "CPU % avg. over last 1 min, 5 min, 15 min; 2.66 1.05 0.58\n", + "current total RAM usage [MB]: 10744.05 of 64219.7 MB\n", + "\n", + "SCRIPT: processing, plotting and saving data...\n", + "\n", + "DRAG STATS:\n", + "mean_simple = 1.693653928340924\n", + "mean_periodcorrected = 1.6928978755967055\n", + "min_simple = 1.5952873956700555\n", + "max_simple = 1.7953284200403388\n", + "max_mean = 1.7873442402797335\n", + "min_mean = 1.617481548579985\n", + "frequency_fit = 0.1999999999241945\n", + "frequency_fft = 0.0\n", + "fft_resolution = 0.01999827742257103\n", + "\n", + "LIFT STATS:\n", + "mean_simple = -0.013755539770956112\n", + "mean_periodcorrected = -0.0005787575118849577\n", + "min_simple = -0.7790682792632936\n", + "max_simple = 0.7791679323475318\n", + "max_mean = 0.7675340372781098\n", + "min_mean = -0.7712608122909177\n", + "frequency_fit = 0.20000000029835424\n", + "frequency_fft = 0.21998105164828133\n", + "fft_resolution = 0.01999827742257103\n" + ] + }, { "data": { "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -105,7 +200,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -118,7 +213,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -131,7 +226,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -144,7 +239,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -157,7 +252,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -170,7 +265,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -183,7 +278,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -196,7 +291,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -209,7 +304,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -217,6 +312,15 @@ "display_id": null } }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strouhal number is: 0.20000000029835424\n", + "\n", + "♬ THE END ♬\n" + ] + }, { "data": { "text/plain": [ @@ -230,7 +334,7 @@ } } ], - "execution_count": 3 + "execution_count": 1 }, { "metadata": {}, @@ -238,37 +342,344 @@ "source": [ "### Example 2:\n", "#### 3D Simulation at Re = 3900: turbulent vortex shedding behind a circular cylinder, calculation of average velocity and reynolds stress profiles and comparison to literature\n", - "Note: This simulation may take very long (hours) to finish (reasons: 3D stencil, high Reynolds)" + "Note: This simulation may take very long (hours) to finish (reasons: 3D stencil, high Reynolds number,...), depending on your settings\n", + "\n", + "The following settings take 1:30 minutes to run on an RTX 2060super:\n", + "\n", + "`01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles`\n", + "\n", + "as a showcase to see, if the code works. For physically meaningful data, you need to increase `--t_target` to >10²-10³ seconds..." ], "id": "fc77a746f992e219" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-12-15T14:15:42.183638Z", - "start_time": "2025-12-15T13:36:46.912037Z" + "end_time": "2025-12-17T16:45:15.690110Z", + "start_time": "2025-12-17T16:43:45.533416Z" } }, "cell_type": "code", "source": [ "%matplotlib inline\n", - "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 30 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles" + "%run 01_script_cylinder_simulation.py --outdir \"./datafolder\" --char_length_lu 20 --t_target 1 --bbbc_type ibb1 --reynolds_number 3900 --domain_height_y_in_d 5 --domain_width_z_in_d 3 --collision kbc --name 3D_simulation_cylinder_Re3900 --mach_number 0.1 --stencil D3Q27 --calc_u_profiles --profile_reference_path \"./profile_reference_data/\" --show_u_profiles" ], "id": "a25110bac2f3c90a", "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[31m---------------------------------------------------------------------------\u001B[39m", - "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", - "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py:549\u001B[39m\n\u001B[32m 546\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33m#################################################\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m 548\u001B[39m t_start = time.time()\n\u001B[32m--> \u001B[39m\u001B[32m549\u001B[39m mlups = \u001B[43msimulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnum_steps\u001B[49m\u001B[43m=\u001B[49m\u001B[43mn_steps\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 550\u001B[39m t_end = time.time()\n\u001B[32m 551\u001B[39m runtime = t_end - t_start\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py:95\u001B[39m, in \u001B[36mEbbSimulation.__call__\u001B[39m\u001B[34m(self, num_steps)\u001B[39m\n\u001B[32m 93\u001B[39m \u001B[38;5;28mself\u001B[39m._post_streaming_boundaries()\n\u001B[32m 94\u001B[39m \u001B[38;5;28mself\u001B[39m.flow.i += \u001B[32m1\u001B[39m\n\u001B[32m---> \u001B[39m\u001B[32m95\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_report\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 97\u001B[39m end = timer()\n\u001B[32m 98\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m num_steps * \u001B[38;5;28mself\u001B[39m.flow.rho().numel() / \u001B[32m1e6\u001B[39m / (end - beg)\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/_simulation.py:259\u001B[39m, in \u001B[36mSimulation._report\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 257\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_report\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[32m 258\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m reporter \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m.reporter:\n\u001B[32m--> \u001B[39m\u001B[32m259\u001B[39m \u001B[43mreporter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/ext/_reporter/observable_reporter.py:187\u001B[39m, in \u001B[36mObservableReporter.__call__\u001B[39m\u001B[34m(self, simulation)\u001B[39m\n\u001B[32m 185\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, simulation: \u001B[33m'\u001B[39m\u001B[33mSimulation\u001B[39m\u001B[33m'\u001B[39m):\n\u001B[32m 186\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m simulation.flow.i % \u001B[38;5;28mself\u001B[39m.interval == \u001B[32m0\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m187\u001B[39m observed = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mobservable\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconvert_to_ndarray\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 188\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mobservable\u001B[49m\u001B[43m(\u001B[49m\u001B[43msimulation\u001B[49m\u001B[43m.\u001B[49m\u001B[43mflow\u001B[49m\u001B[43m.\u001B[49m\u001B[43mf\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 189\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(observed.shape) < \u001B[32m2\u001B[39m\n\u001B[32m 190\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(observed.shape) == \u001B[32m0\u001B[39m:\n", - "\u001B[36mFile \u001B[39m\u001B[32m~/lettuce_25/lettuce/lettuce/_context.py:120\u001B[39m, in \u001B[36mContext.convert_to_ndarray\u001B[39m\u001B[34m(tensor)\u001B[39m\n\u001B[32m 117\u001B[39m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[32m 118\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mconvert_to_ndarray\u001B[39m(tensor: Union[torch.Tensor, List]) -> np.ndarray:\n\u001B[32m 119\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(tensor, torch.Tensor):\n\u001B[32m--> \u001B[39m\u001B[32m120\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtensor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdetach\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcpu\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m.numpy()\n\u001B[32m 121\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 122\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m np.array(tensor)\n", - "\u001B[31mKeyboardInterrupt\u001B[39m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "SCRIPT: Writing arguments to dictionary...\n", + "SCRIPT: Input arguments are: \n", + "{'name': '3D_simulation_cylinder_Re3900', 'default_device': 'cuda', 'float_dtype': 'float64', 't_sim_max': 259200, 'text_output_only': False, 'no_data': False, 'outdir': './datafolder', 'outdir_data': None, 'reynolds_number': 3900.0, 'mach_number': 0.1, 'char_velocity_pu': 1, 'char_length_lu': 20, 'char_length_pu': 1, 'domain_length_x_in_d': None, 'domain_height_y_in_d': 5.0, 'domain_width_z_in_d': 3.0, 'perturb_init': False, 'u_init_condition': 0, 'lateral_walls': 'periodic', 'n_steps': 100000, 't_target': 1.0, 'collision': 'kbc', 'stencil': 'D3Q27', 'eqlm': False, 'bbbc_type': 'ibb1', 'periodic_region_start_relative': None, 'periodic_region_start_pu': None, 'periodic_region_start_lu': None, 'calc_u_profiles': True, 'show_u_profiles': True, 'output_u_profiles_timeseries': False, 'profile_reference_path': './profile_reference_data/', 'vtk_full_basic': False, 'vtk_full_basic_interval': None, 'vtk_3D': False, 'vtk_3D_fps': None, 'vtk_3D_step_interval': None, 'vtk_3D_t_interval': None, 'vtk_3D_step_start': None, 'vtk_3D_step_end': None, 'vtk_3D_t_start': None, 'vtk_3D_t_end': None, 'vtk_slice2D': False, 'vtk_slice2D_fps': None, 'vtk_slice2D_step_interval': None, 'vtk_slice2D_t_interval': None, 'vtk_slice2D_step_start': None, 'vtk_slice2D_step_end': None, 'vtk_slice2D_t_start': None, 'vtk_slice2D_t_end': None, 'count_tensors': False}\n", + "\n", + "SCRIPT: Creating timestamp, simulation ID and creating output directory...\n", + "outdir/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900\n", + "outdir_DATA/simID = ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900\n", + "SCRIPT: Writing input parameters to file: ./datafolder/251217_174345-3D_simulation_cylinder_Re3900/input_parameters.txt\n", + "SCRIPT: Saving simulation script to outdir...\n", + "-> Saved simulation script to './datafolder/251217_174345-3D_simulation_cylinder_Re3900/251217_174345-3D_simulation_cylinder_Re3900_01_script_cylinder_simulation.py'\n", + "SCRIPT: Starting stdout-LOGGER (see outdir for log file)\n", + "SCRIPT: Processing parameters...\n", + "\n", + "(INFO) parameters set for simulation of 346 steps, representing 1.000 seconds [PU]!\n", + "\n", + "SCRIPT: initializing solver components...\n", + "-> initializing context...\n", + "-> initializing flow...\n", + "OBST_Cylinder: self.resolution= [200, 100, 60]\n", + "OBST_Cylinder: x_pos, y_pos, radius: 50.5 50.5 10.0\n", + "-> initializing collision operator...\n", + "\n", + "SCRIPT: initializing simulation object...\n", + "CYLINDER: the bc_type was given as: ibb1\n", + "IBB: calc force is: True\n", + "SIMULATION: creating no_collision_mask entries for: i, boundary = 3 \n", + "collision index is: 0\n", + "-> initializing reporters...\n", + "ProfileReporters at x-positions: p1: 71.2 p2: 80.8 p3: 90.4\n", + "ProfileReporter: requested position is off grid! Interpolating u(71.2) = 0.7999999999999972 * u(71) + 0.20000000000000284 * u(72)\n", + "ProfileReporter: requested position is off grid! Interpolating u(80.8) = 0.20000000000000284 * u(80) + 0.7999999999999972 * u(81)\n", + "ProfileReporter: requested position is off grid! Interpolating u(90.4) = 0.5999999999999943 * u(90) + 0.4000000000000057 * u(91)\n", + "\n", + "SCRIPT: spacial and temporal dimensions:\n", + "domain shape (LU): [200, 100, 60]\n", + "t_target (PU) with 346 steps (LU): 0.999 seconds\n", + "steps to simulate 1 second PU: 346.41 steps\n", + "steps to simulate 1.000 (t_target, PU) seconds: 346.410 steps\n", + "\n", + "#################################################\n", + "\n", + "SCRIPT (2025-12-17 17:43:46): running simulation for 346 steps...\n", + "\n", + "#################################################\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "***** SIMULATION FINISHED AT 2025-12-17 17:45:12 *****\n", + "\n", + "### STATS ###\n", + "MLUPS: 4.857\n", + "simulated PU-Time: 0.9988159656980528 seconds\n", + "simulated LU-steps: 346\n", + "runtime (WALLTIME) of simulation(num_steps): 85.493 seconds (= 1.42 minutes )\n", + "\n", + "### HARDWARE UTILIZATION ###\n", + "current GPU VRAM (MB) usage: 577.019\n", + "max. GPU VRAM (MB) usage: 1845.631\n", + "CPU % avg. over last 1 min, 5 min, 15 min; 4.18 5.31 5.19\n", + "current total RAM usage [MB]: 11127.89 of 64219.7 MB\n", + "\n", + "SCRIPT: processing, plotting and saving data...\n", + "\n", + "(WARNING!) peak finding for drag didn't work... This might just be because there is no converged periodic region!\n", + "Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:\n", + "\n", + "--- Python Stack Trace ---\n", + "Traceback (most recent call last):\n", + " File \"/home/mbille/lettuce_25/lettuce/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py\", line 129, in analyze_periodic_timeseries\n", + " if peaks_min[0][0] < peaks_max[0][0]:\n", + " ~~~~~~~~~~~~^^^\n", + "IndexError: index 0 is out of bounds for axis 0 with size 0\n", + "\n", + "--------------------------\n", + "\n", + "DRAG STATS:\n", + "mean_simple = 0.1732521376257104\n", + "mean_periodcorrected = None\n", + "min_simple = -0.724629230527121\n", + "max_simple = 1.1230686001217727\n", + "max_mean = None\n", + "min_mean = None\n", + "frequency_fit = 0.19893275083624243\n", + "frequency_fft = 1.9794866372215736\n", + "fft_resolution = 1.9794866372215736\n", + "\n", + "LIFT STATS:\n", + "mean_simple = 0.00042755743250013793\n", + "mean_periodcorrected = -0.002531614794975356\n", + "min_simple = -0.02564869868377675\n", + "max_simple = 0.025483797803982536\n", + "max_mean = 0.01726717693382942\n", + "min_mean = -0.02071004755938259\n", + "frequency_fit = 0.202221662229376\n", + "frequency_fft = 1.9794866372215736\n", + "fft_resolution = 1.9794866372215736\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strouhal number is: 0.202221662229376\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "♬ THE END ♬\n" ] }, { @@ -284,7 +695,7 @@ } } ], - "execution_count": 5 + "execution_count": 3 } ], "metadata": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py index a78ecfc3..b5e073c4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py @@ -6,6 +6,8 @@ import numpy as np import torch +from sympy import false + torch.autograd.set_detect_anomaly(True) import sys @@ -82,8 +84,9 @@ parser.add_argument("--periodic_region_start_pu", default=None, type=float, help="ABSOLUTE PU-time; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") parser.add_argument("--periodic_region_start_lu", default=None, type=int, help="ABSOLUTE LU-steps; assumed start of the periodic region for measurement of temporal and spacial averaging of observables (drag, lift , velocity profiles...)") -parser.add_argument("--calc_u_profiles", action='store_true', help="calculate average velocity profiles similar to [Di Ilio et al. 2018] and output plots and time-averages data for plots") -parser.add_argument("--output_u_profiles_timeseries", default=False, help="output average velocity profiles over time (full timeseries)") +parser.add_argument("--calc_u_profiles", action='store_true', help="calculate average velocity and reynolds stress profiles similar to [Di Ilio et al. 2018] and output plots and time-averages data for plots") +parser.add_argument("--show_u_profiles", action='store_true', help="plot average velocity and reynolds stress profiles") +parser.add_argument("--output_u_profiles_timeseries", default=False, help="output average velocity and reynolds stress profiles over time (full timeseries)") parser.add_argument("--profile_reference_path", default="../profile_reference_data/", type=str, help="path to reference profiles from [Di Ilio et al. 2018]") parser.add_argument("--vtk_full_basic", action='store_true', help="output vtk files of full domain each interval steps") @@ -717,8 +720,12 @@ u_timeseries3=ProfileReporter3.out) profile_plotter.process_data() - profile_plotter.plot_velocity_profiles(show_reference=True, save=True) - profile_plotter.plot_reynolds_stress_profiles(show_reference=True, save=True) + profile_plotter.plot_velocity_profiles(show_reference=True, save=True, + show=args["show_u_profiles"] + if args["show_u_profiles"] is not None else False) + profile_plotter.plot_reynolds_stress_profiles(show_reference=True, + save=True, show=args["show_u_profiles"] + if args["show_u_profiles"] is not None else False) # EXPORT OBSERVABLES: diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py index 5d2ec3b3..4f4c106e 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -629,7 +629,7 @@ def save_timeseries_to_files(self, basepath): np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "pos3" + "_timeseries_data.npy", np.array(self.u_timeseries3)) - def plot_velocity_profiles(self, show_reference = False, save = False): + def plot_velocity_profiles(self, show_reference = False, save = False, show = False): cm = 1 / 2.54 if not show_reference: @@ -649,9 +649,11 @@ def plot_velocity_profiles(self, show_reference = False, save = False): if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_velocity_noReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() + else: # PLOT ux against references fig, ax = plt.subplots(constrained_layout=True) @@ -689,9 +691,10 @@ def plot_velocity_profiles(self, show_reference = False, save = False): ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_ux_withReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() # PLOT uy against references fig, ax = plt.subplots(constrained_layout=True) @@ -731,11 +734,12 @@ def plot_velocity_profiles(self, show_reference = False, save = False): ref_DI[0]], loc='best') if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uy_withReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() - def plot_reynolds_stress_profiles(self, show_reference=False, save=False): + def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = False): cm = 1 / 2.54 if not show_reference: fig, (ax_xx, ax_yy, ax_xy) = plt.subplots(1, 3, @@ -761,9 +765,10 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False): if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_reynoldsStresses_noReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() else: # plot reynolds stresses against reference # uxux - streamwise @@ -804,9 +809,10 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False): if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxux_withReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() # uyuy - cross-stream fig, ax = plt.subplots(constrained_layout=True) @@ -840,9 +846,10 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False): if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uyuy_withReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() # uxuy - Reynolds shear stress fig, ax = plt.subplots(constrained_layout=True) @@ -876,9 +883,10 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False): if save: plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxuy_withReference.png") - plt.close() - else: + if show: plt.show() + else: + plt.close() # PLOTTING From 60e978a1910a3088af9e4f914469de37981f76c7 Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 17 Dec 2025 18:29:06 +0100 Subject: [PATCH 19/21] minor changes: spelling --- .../01_script_cylinder_simulation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py index b5e073c4..41dde333 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py @@ -222,7 +222,7 @@ else: domain_width_z_in_d = args["domain_width_z_in_d"] -# CORRECT GPT for symmetry: +# CORRECT GPD for symmetry: ''' if D/Y (height of the domain in number of cylinder diameters) is even, the resulting GPD (gridpoints per diemeter) can't be odd for a symmetrical From 60bc03293a939899724cde5350bb2ffc6f5bbf52 Mon Sep 17 00:00:00 2001 From: mbille Date: Wed, 17 Dec 2025 18:55:24 +0100 Subject: [PATCH 20/21] minor changes: removed Reporter from import list of observables_force_coefficients.py --- .../observables_force_coefficients.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py index e15b2d54..69a79b8a 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py @@ -4,7 +4,7 @@ import numpy as np from examples.advanced_projects.efficient_bounce_back_obstacle.obstacle_cylinder import ObstacleCylinder -from lettuce import Reporter, Flow, Observable +from lettuce import Flow, Observable __all__ = ['DragCoefficient', 'LiftCoefficient'] From 8f12b6a325bd2caffc63bee3d94cc2b3de747823 Mon Sep 17 00:00:00 2001 From: mbille Date: Thu, 18 Dec 2025 15:40:16 +0100 Subject: [PATCH 21/21] FOR REVIEW! code formatting, minor repairs, comments, desctiptions, cleanup --- .../00_run_parameterized_project.ipynb | 13 - .../01_script_cylinder_simulation.py | 49 +- .../251203_01a_script_cylinder_lowRe.py | 764 ------------------ .../data_processing_and_plotting.py | 331 ++++---- .../ebb_simulation.py | 40 +- .../fullway_bounce_back_boundary.py | 99 ++- .../halfway_bounce_back_boundary.py | 181 +++-- .../helperCode.py | 4 +- ...inear_interpolated_bounce_back_boundary.py | 163 ++-- .../observables_force_coefficients.py | 61 +- .../obstacle_cylinder.py | 373 +++++---- .../reporter_ProfileReporter.py | 9 +- .../reporter_advanced_vtk_reporter.py | 125 +-- .../solid_boundary_data.py | 4 +- ...OLD_Interpolated_compact2_forComparison.py | 319 -------- 15 files changed, 847 insertions(+), 1688 deletions(-) delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py delete mode 100644 examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb index ac92a73f..9bfe9647 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/00_run_parameterized_project.ipynb @@ -52,19 +52,6 @@ ], "id": "d918fef7d168e9ab" }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-17T16:41:19.649550Z", - "start_time": "2025-12-17T16:41:19.646809Z" - } - }, - "cell_type": "code", - "source": "import os", - "id": "febb287720d9f09e", - "outputs": [], - "execution_count": 1 - }, { "metadata": { "ExecuteTime": { diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py index 41dde333..eff600b4 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/01_script_cylinder_simulation.py @@ -1,17 +1,30 @@ -# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) +""" + This script contains the code to run the efficient bounce back project with + the circular cylinder obstacle. + It is supposed to be invoked with parameters (arguments)! Examples are + given in the respective .ipynb file. + + Content: + - ARGUMENT PARSING: read all command line arguments or apply defaults + - PARAMETER PROCESSING: all relevant parameters are processed, missing + stuff ist calculated + - INITIALIZATION AND ASSEMBLING OF SOLVER COMPONENTS: stencil, context etc. + - SIMULATION CALL: running the simulation + - DATA POST-PROCESSING AND PLOTTING: does what it's name suggests... + + Work-In-Progress NOTE: + In some places there are TODOs and placeholders for future functionality! + This script still works, they are just future improvements! +""" #################### # IMPORT import numpy as np import torch -from sympy import false - -torch.autograd.set_detect_anomaly(True) import sys -import warnings import os import psutil import shutil @@ -38,9 +51,11 @@ from helperCode import Logger from data_processing_and_plotting import plot_force_coefficient, analyze_periodic_timeseries, draw_circular_mask, ProfilePlotter +torch.autograd.set_detect_anomaly(True) #################### -# ARGUMENT PARSING: this script is supposed to be called with arguments, detailing all simulation- and system-parameters +# ARGUMENT PARSING: this script is supposed to be called with arguments, +# detailing all simulation- and system-parameters parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) @@ -48,9 +63,9 @@ parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") -parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. walltime [s] to run the simulationn(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole scipt may take longer to execute, depending on I/O etc.") +parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. wall time [s] to run the simulation(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole script may take longer to execute, depending on I/O etc.") -parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") ##FORMER: --cluster +parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") @@ -75,7 +90,7 @@ parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") -parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") +parser.add_argument("--eqlm", action="store_true", help="use Equilibrium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") #TODO: how to use EQLM in lettuce 2025? parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1) for the solid obstacle") @@ -110,11 +125,12 @@ parser.add_argument("--vtk_slice2D_t_start", type=float) parser.add_argument("--vtk_slice2D_t_end", type=float) +## FUTURE IMPROVEMENTS (which need arguments/parameters) #TODO (optional): add NAN reporter (on/off, interval,...) #TODO (optional): add watchdog reporter (on/off, interval,...) #TODO (optional): add highMa reporter (on/off, interval,...) -#TODO (optional): add 2D-mp4-reporter... (fps_video, number of frames OR fps_pu) +#TODO (optional): add 2D-mp4-animation-reporter... (fps_video, number of frames OR fps_pu) # Checkpointing # TODO (optional): add checkpointing-utilities (read, write): @@ -166,7 +182,7 @@ if outdir_data is None: outdir_data = outdir -# adding individal sim-ID to outdir path to get individual DIR per simulation +# adding individual sim-ID to outdir path to get individual DIR per simulation outdir = outdir+"/"+sim_id outdir_data = outdir_data+"/"+sim_id if not os.path.exists(outdir_data): @@ -225,7 +241,7 @@ # CORRECT GPD for symmetry: ''' if D/Y (height of the domain in number of cylinder diameters) is even, -the resulting GPD (gridpoints per diemeter) can't be odd for a symmetrical +the resulting GPD (gridpoints per diameter) can't be odd for a symmetrical cylinder and symmetrical domain! - if D/Y is even, GPD will be corrected to be even for symmetrical cylinder => use odd D/Y value to use an odd GPD value! @@ -306,7 +322,7 @@ ''' #TODO (optional): change periodic_start parameter to abs. step-number, instead of relative start: # - ease of use for reporters and processing -# - no round-off error in preocessing LU-parameter, if set +# - no round-off error in processing LU-parameter, if set if args["periodic_region_start_lu"] is not None and args["periodic_region_start_lu"] >= 0: periodic_start = args["periodic_region_start_lu"] / n_steps elif args["periodic_region_start_pu"] is not None and args["periodic_region_start_pu"] >= 0: @@ -323,7 +339,8 @@ # check EQLM parameter if args["eqlm"]: # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? - pass + print("SCRIPT: EQLM-parameter was set, but usage of EQLM is not implemented yet!" + "...using default equilibrium") # print temporal parameters: steps, T_PU print(f"\n(INFO) parameters set for simulation of {n_steps} steps, " @@ -407,7 +424,7 @@ # NAN REPORTER (if nan detected -> stop simulation) # TODO (optional): add NaN reporter (see PR for that topic) -# - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... +# - NEEDS breakable simulation (!) # HighMa Reporter (if Ma>0.3 detected -> report and/or stop simulation) # TODO (optional): HighMa Reporter @@ -753,7 +770,7 @@ pass output_file.close() - ### count occurence of tensors in list of tensors: + ### count occurrence of tensors in list of tensors: from collections import Counter my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py b/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py deleted file mode 100644 index f065c50f..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/251203_01a_script_cylinder_lowRe.py +++ /dev/null @@ -1,764 +0,0 @@ - -# this file should contain all the MP2 stuff for low RE (basically a reworked MP1) - -#################### -# IMPORT - -import numpy as np -import torch -torch.autograd.set_detect_anomaly(True) - -import sys -import warnings -import os -import psutil -import shutil -import traceback - -import matplotlib.pyplot as plt -from scipy.signal import find_peaks - -import time -import datetime - -from pyevtk.hl import imageToVTK - -from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter - -# LETTUCE RELATED -import lettuce as lt -from obstacle_cylinder import ObstacleCylinder -from ebb_simulation import EbbSimulation -from observables_force_coefficients import DragCoefficient, LiftCoefficient - -# AUX. CODE -from helperCode import Logger - -# import pickle -# from copy import deepcopy -# from timeit import default_timer as timer -# from collections import Counter - -#? warnings.simplefilter("ignore") # todo: is this needed? - -#################### -# ARGUMENT PARSING: this script is supposed to be called with arguments, detailing all simulation- and system-parameters - -parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) - -# context and I/O -parser.add_argument("--name", default="cylinder_lowRe", help="name of the simulation, appears in output directory name") -parser.add_argument("--default_device", default="cuda", type=str, help="run on cuda or cpu") -parser.add_argument("--float_dtype", default="float64", choices=["float32", "float64", "single", "double", "half"], help="data type for floating point calculations in torch") -parser.add_argument("--t_sim_max", default=(72*60*60), type=float, help="max. walltime [s] to run the simulationn(); default is 72 h. simulation will stops at 0.99*t_max_sim; IMPORTANT: this whole scipt may take longer to execute, depending on I/O etc.") - -parser.add_argument("--text_output_only", action='store_true', help="if you don't want pngs etc. to open, please use this flag; data is still saved to files") ##FORMER: --cluster -parser.add_argument("--no_data", action='store_true', help="set, if you want no directories created and no date saved. Only direct output") -parser.add_argument("--outdir", default=os.getcwd(), type=str, help="directory to save output files to; default is CWD") -parser.add_argument("--outdir_data", default=None, type=str, help="directory to save large/many files to; if not set, everything os saved to outdir") - -# flow physics and geometry -parser.add_argument("--reynolds_number", default=200, type=float, help="Reynolds number") -parser.add_argument("--mach_number", default=0.1, type=float, help="Mach number (should stay < 0.3, and < 0.1 for highest accuracy. low Ma can lead to instability because of round of errors ") -parser.add_argument("--char_velocity_pu", default=1, type=float, help="characteristic velocity of the flow in physical units (PU)") - -parser.add_argument("--char_length_lu", default=1, type=int, help="characteristic length of the flow in lattice units. Number of gridpoints per diameter for a circular cylinder") -parser.add_argument("--char_length_pu", default=1, type=float, help="characteristic length of the flow in physical units. Diameter of the cylinder in PU") -parser.add_argument("--domain_length_x_in_d", default=None, type=float, help="domain length in x-direction (direction of flow) in number of cylinder-diameters") -parser.add_argument("--domain_height_y_in_d", default=None, type=float,help="domain height in y-direction (orthogonal to flow and cylinder axis) in number of cylinder-diameters") -parser.add_argument("--domain_width_z_in_d", default=None, type=float,help="domain width in z-direction (orthogonal to flow, parallel to cylinder axis) in number of cylinder-diameters; IMPORTANT: if not set, 2D-Simulation is performed") - -parser.add_argument("--perturb_init", action='store_true', help="perturb initial velocity profile to trigger vortex shedding") -parser.add_argument("--u_init_condition", default=0, type=int, help="initial velocity field: # 0: uniform u=0, # 1: uniform u=1, # 2: parabolic, amplitude u_char_lu (similar to poiseuille-flow)") -parser.add_argument("--lateral_walls", default='periodic', help="OPTIONS: 'periodic' or 'bounceback'; add lateral walls, converting the flow to a cylinder in a channel. The velocity profile will be adjusted to be parabolic!") - -#TODO additional physics and flow-parameters: -# - char_density (PU house) -# - char_pressure -# - vicsosity PU (house, - -# LBM solver settings -parser.add_argument("--n_steps", default=100000, type=int, help="number of steps to simulate, overwritten by t_target, if t_target is >0") -parser.add_argument("--t_target", default=0, type=float, help="time in PU to simulate, overwrites n_steps if t_target > 0") -parser.add_argument("--collision", default="bgk", type=str, choices=["kbc", "bgk", "reg", 'reg', "bgk_reg", 'kbc', 'bgk', 'bgk_reg'], help="collision operator (bgk, kbc, reg)") -parser.add_argument("--stencil", default="D3Q27", choices=['D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'], help="stencil (D2Q9, D3Q27, D3Q19, D3Q15), IMPORTANT: should match number of dimensions inferred from domain_width! Otherwise default D2Q9 or D3Q27 will be chosen for 2D and 3D respectively") -parser.add_argument("--eqlm", action="store_true", help="use Equilibium LessMemory to save ~20% on GPU VRAM, sacrificing ~2% performance") -#TODO: how to use EQLM in lettuce 2025? -parser.add_argument("--bbbc_type", default='fwbb', help="bounce back algorithm (fwbb, hwbb, ibb1, fwbbc, hwbbc2, ibb1c2) for the solid obstacle") - -# reporter and observable settings -parser.add_argument("--vtk3D", action='store_true', help="output 3D vtk files") -#TODO: add vtk reporter (fps_pu, interval_lu, -# - 3D full -# - 2D slice normal (x,y,z) -# - obstacle point, obstacle cell speichern und abh. von 2D/3D korrekt formatieren -#TODO: add drag/lift reporter -# - periodic start relative (relative start of interval to do statistics over -# - plot lift, drag, strouhal number (try except...) -#TODO: add U-profile-reporter (output True, store/calculate true) -# -> condense in own functions -# -> make script to plot from data (see PLOTTING scripts in CYLINDER-paper for layout) -#TODO: add NAN reporter (on/off, interval) -#TODO: add watchdog reporter (on/off, interval) -#TODO: add highMa reporter (on/off, interval) -#TODO: add 2D-mp4-reporter... (fps_video, number of frames ODER fps_pu) - -# Checkpointing -# TODO: add checkpointing-utilities (read, write) -# - checkpoint IN path -# - checkpoint OUT path (if only one is given, take this one for both) -# - checkpoint i_start, t_start?, - - -########################################################### -# OUTPUTS: -# - parameters INPUT -# - print 2D-slice with mask(s) -# - parameters SIMULATED (before sim()) -# - 2D slice last frame -# - output condensed observables (drag, lift, strouhal) to file... @MP2 -# - stats and performance (end) - - -########################################################### -# put arguments in dictionary -print("SCRIPT: Writing arguments to dictionary...") -args = vars(parser.parse_args()) - -# print all arguments -print(f"SCRIPT: Input arguments are: \n{args}\n") - -########################################################### - -# CREATE timestamp, sim-ID, outdir and outdir_data -print("SCRIPT: Creating timestamp, simulation ID and creating output directory...") -name = args["name"] -outdir = args["outdir"] -outdir_data = args["outdir_data"] - -default_device = args["default_device"] -float_dtype = args["float_dtype"] -t_sim_max = args["t_sim_max"] - -text_output_only = args["text_output_only"] -no_data_flag = args["no_data"] - -timestamp = datetime.datetime.now().strftime("%y%m%d_%H%M%S") -sim_id = str(timestamp) + "-" + name - -os.makedirs(outdir+"/"+sim_id) # create output dir -print(f"outdir/simID = {outdir}/{sim_id}") -if outdir_data is None: # save data to regular outdir, if data-dir is not specified - outdir_data = outdir -outdir = outdir+"/"+sim_id # adding individal sim-ID to outdir path to get individual DIR per simulation -outdir_data = outdir_data+"/"+sim_id -if not os.path.exists(outdir_data): - os.makedirs(outdir_data) # create output dir for large/many files, if specified -print(f"outdir_DATA/simID = {outdir}/{sim_id}") - -# save input arguments/parameters to file in outdir: -print(f"SCRIPT: Writing input parameters to file: {outdir}/input_parameters.txt") -output_file = open(outdir+"/input_parameters.txt", "a") -for key in args: - output_file.write('{:30s} {:30s}\n'.format(str(key), str(args[key]))) -output_file.close() - -### SAVE SCRIPT: save this script to outdir -print(f"SCRIPT: Saving simulation script to outdir...") -temp_script_name = sim_id + "_" + os.path.basename(__file__) -shutil.copy(__file__, outdir+"/"+temp_script_name) -print(f"-> Saved simulation script to '{str(outdir+'/'+temp_script_name)}'") - -# START LOGGER -> get all terminal output into file -print(f"SCRIPT: Starting stdout-LOGGER (see outdir for log file)") -old_stdout = sys.stdout -sys.stdout = Logger(outdir) - -########################################################### - -# PROCESS AND SET PARAMETERS -print(f"SCRIPT: Processing parameters...") -# calc. relative starting point of peak_finding for Cd_mean Measurement to cut of any transients -#TODO: take absolute PU-time values here: -# - periodic_start_Re100_PU ~= 75-100 seconds -# - periodic_start hängt von Einschwingzeit ab! Diese hängt von der Domänengröße ab! -if args["reynolds_number"] > 1000: - periodic_start = 0.4 -else: - periodic_start = 0.9 - -# calculate domain and obstacle geometry and infer dimensions (2D, 3D) -if args["domain_height_y_in_d"] is None or args["domain_height_y_in_d"] <= 1: - domain_height_y_in_d = 3 -else: - domain_height_y_in_d = args["domain_height_y_in_d"] - -if args["domain_length_x_in_d"] is None or args["domain_length_x_in_d"] <=1 : - domain_length_x_in_d = 2 * domain_height_y_in_d # D/X = domain length in X- / flow-direction -else: - domain_length_x_in_d = args["domain_length_x_in_d"] - -if args["domain_width_z_in_d"] is None: # will be 2D - dims = 2 -else: # will be 3D - dims = 3 - if args["domain_width_z_in_d"] <= 1/args["char_length_lu"] : # if less than 1 lattice node - domain_width_z_in_d = 1/args["char_length_lu"] # set to 1 lattice node - print("(!) domain_width_z_in_d is less than 1 lattice node: setting domain_width_z_in_d to 1 lattice node") - else: - domain_width_z_in_d = args["domain_width_z_in_d"] - -# CORRECT GPT for symmetry: -# if DpY is even, resulting GPD can't be odd for symmetrical cylinder and domain -# ...if DpY is even, GPD will be corrected to be even for symmetrical cylinder -# ...(!) use odd DpY to use odd GPD! -gpd_correction = False -if domain_height_y_in_d % 2 == 0 and args["char_length_lu"] % 2 != 0: - gpd_correction = True # gpd will be corrected - gpd_setup = args["char_length_lu"] # store old gpd for output - char_length_lu = int(gpd_setup / 2) * 2 # make gpd even - print("(!) domain_height_y_ind_d (DpY) is even, gridpoints per diameter (GPD, char_length_lu) will be set to" + str( - char_length_lu) + ". Use odd domain_height_Y_in_D (DpY) to enable use of odd GPD (char_length_lu)!") -else: - char_length_lu = args["char_length_lu"] - -char_length_pu = args["char_length_pu"] -char_velocity_pu = args["char_velocity_pu"] -reynolds_number = args["reynolds_number"] -mach_number = args["mach_number"] - -perturb_init = args["perturb_init"] -u_init_condition = args["u_init_condition"] - -# calculate lu-domain-resolution, total number of gridpoints and check correct stencil -if dims == 2: - resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu)] - number_of_gridpoints = char_length_lu ** 2 * domain_length_x_in_d * domain_height_y_in_d - if args["stencil"] == "D2Q9": - stencil = lt.D2Q9() - else: - print("(!) WARNING: wrong stencil choice for 2D simulation, D2Q9 is used") - stencil= lt.D2Q9() -else: - resolution = [int(domain_length_x_in_d * char_length_lu), int(domain_height_y_in_d * char_length_lu), int(domain_width_z_in_d * char_length_lu)] - number_of_gridpoints = char_length_lu ** 3 * domain_length_x_in_d * domain_height_y_in_d * domain_width_z_in_d - if args["stencil"] == "D3Q15": - stencil = lt.D3Q15() - elif args["stencil"] == "D3Q19": - stencil = lt.D3Q19() - elif args["stencil"] == "D3Q27": - stencil = lt.D3Q27() - else: - print("(!) WARNING: wrong stencil choice for 3D simulation, D3Q27 is used") - stencil = lt.D3Q27() - -# read dtype -if float_dtype == "float32" or float_dtype == "single": - float_dtype = torch.float32 -elif float_dtype == "double" or float_dtype == "float64": - float_dtype = torch.float64 -elif float_dtype == "half" or float_dtype == "float16": - float_dtype = torch.float16 - -# OVERWRITE n_steps, if t_target is given -n_steps = args["n_steps"] -t_target = args["t_target"] -if args["t_target"] > 0: - n_steps = int(t_target * (char_length_lu / char_length_pu) * (char_velocity_pu / (mach_number * 1 / np.sqrt(3)))) -else: - t_target = n_steps / (char_length_lu/char_length_pu * char_velocity_pu/(mach_number*1/np.sqrt(3))) - -# check EQLM parameter -if args["eqlm"]: - # TODO: use EQLM ( QuadraticEquilibriumLessMemory() ) how is this used in new lettuce? - pass - -print(f"\n(INFO) parameters set for simulation of {n_steps} steps, representing {t_target:.3f} seconds [PU]!\n") - -########################################### - -print("SCRIPT: initializing solver components...") - -### -print("-> initializing context...") -context = lt.Context(device=default_device, dtype=float_dtype,use_native=False) - -print("-> initializing flow...") -flow = ObstacleCylinder(context=context, resolution=resolution, - reynolds_number=reynolds_number, mach_number=mach_number, - char_length_pu=char_length_pu, char_length_lu=char_length_lu, char_velocity_pu=char_velocity_pu, - bc_type=str(args["bbbc_type"]), stencil=stencil, calc_force_coefficients=True, lateral_walls=args["lateral_walls"]) - -print("-> initializing collision operator...") -collision_operator = None -if args["collision"].casefold() == "reg" or args["collision"].casefold() == "bgk_reg": - collision_operator = lt.RegularizedCollision(tau=flow.units.relaxation_parameter_lu) -elif args["collision"].casefold() == "kbc": - if dims == 2: - collision_operator = lt.KBCCollision2D(tau=flow.units.relaxation_parameter_lu) - else: - collision_operator = lt.KBCCollision3D(tau=flow.units.relaxation_parameter_lu) -else: # default to bgk - collision_operator = lt.BGKCollision(tau=flow.units.relaxation_parameter_lu) - -print("\nSCRIPT: initializing simulation object...") -simulation = EbbSimulation(flow, collision_operator,reporter=[]) - - -# REPORTERS: initialize and append to simulation.reporter -print("-> initializing reporters...") - -# DRAG and LIFT Force Coefficients and respective reporters: -cylinder_cross_sectional_area = flow.char_length_pu if dims==2 else flow.char_length_pu*domain_width_z_in_d -DragObservable = DragCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) -DragReporter = lt.ObservableReporter(DragObservable, interval=1, out=None) -simulation.reporter.append(DragReporter) -LiftObservable = LiftCoefficient(flow, simulation.post_streaming_boundaries[-1], solid_mask=simulation.post_streaming_boundaries[-1].mask, area_pu=cylinder_cross_sectional_area) -LiftReporter = lt.ObservableReporter(LiftObservable, interval=1, out=None) -simulation.reporter.append(LiftReporter) - -# NAN REPORTER (if nan detected -> stop simulation) -# TODO: add NaN reporter -# - NEEDS breakable simulation (!) -> while loop. see ISSUE/Pull-request... - -# HighMa Reporter (if Ma>0.3 retected -> report and/or stop simulation) -# TODO: HighMa Reporter - -# Watchdog/Progress-Reporter -# TODO: Progress-Reporter - -# VTK Reporter -> visualization -if args["vtk3D"]: - #TODO: make stuff parameterized: interval, outdir, ... - vtk_reporter = lt.VTKReporter(interval=1, filename_base=outdir_data+"/vtk/out") - - # export obstacle - mask_dict = dict() - if dims ==2: - mask_dict["mask"] = flow.obstacle_mask[...,None].astype(int) - else: - mask_dict["mask"] = flow.obstacle_mask.astype(int) - imageToVTK( - path=outdir_data+"/vtk/obstacle_point", - pointData=mask_dict, - ) - imageToVTK( - path=outdir_data+"/vtk/obstacle_cell", - cellData=mask_dict, - ) - - simulation.reporter.append(vtk_reporter) - - -# DRAW CYLINDER-MASK in 2D (xy-plane) -# TODO: port draw_circular_mask function from MP2/Paper to helperCode.py - -################################################## -# PRINT PARAMETERS prior to simulation: -print(f"\nSCRIPT: spacial and temporal dimensions:") -print("domain shape (LU):", flow.resolution) -print("t_target (PU) with", n_steps, "steps (LU):", - round(n_steps * (flow.char_length_pu / flow.char_length_lu) * (mach_number * 1 / np.sqrt(3) / flow.char_velocity_pu), 2), - "seconds") -print("steps to simulate 1 second PU:", - round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), "steps") -print("steps to simulate", t_target, " (t_target, PU) seconds:", - t_target * round((flow.char_length_lu / flow.char_length_pu) * (flow.char_velocity_pu / (mach_number * 1 / np.sqrt(3))), 2), - "steps") - -################################################## -# RUN SIMULATION: -print(f"\n#################################################") -print(f"\nSCRIPT: running simulation for {n_steps} steps...") -print(f"#################################################\n") -t_start = time.time() -mlups = simulation(num_steps=n_steps) -t_end = time.time() -runtime = t_end - t_start - -################################################## -# OUTPUT STATS: -print(f"### STATS ###") -print("MLUPS:", mlups) -print("simulated PU-Time: ", flow.units.convert_time_to_pu(n_steps), " seconds") -print("simulated LU-steps: ", n_steps) -print("runtime (WALLTIME) of simulation(num_steps): ", runtime, "seconds (= ", round(runtime / 60, 2), "minutes )") -print("\n") -print("current VRAM (MB): ", torch.cuda.memory_allocated(context.device) / 1024 / 1024) -print("max. VRAM (MB): ", torch.cuda.max_memory_allocated(context.device) / 1024 / 1024) - -[cpuLoad1, cpuLoad5, cpuLoad15] = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()] -print("CPU % avg. over last 1 min, 5 min, 15 min; ", round(cpuLoad1, 2), round(cpuLoad5, 2), round(cpuLoad15, 2)) - -ram = psutil.virtual_memory() -print("current total RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str( - round(ram.total / (1024 * 1024), 2)) + " MB") - -### export stats -if not no_data_flag: - output_file = open(outdir_data + "/" + timestamp + "_stats.txt", "a") - output_file.write("DATA for " + timestamp) - output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") - output_file.write("\nMLUPS = " + str(mlups)) - output_file.write("\n") - output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") - output_file.close() - -################################################## -# PLOTTING 2D IMAGE -fig, axes = plt.subplots(1, 2, figsize=(10, 3)) -fig.subplots_adjust(right=0.85) -u = flow.u_pu.cpu().numpy() -print("Max Velocity:", u.max()) -if dims == 2: - im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask.T), origin="lower") - im2 = axes[1].imshow(u[0, ...].T, origin="lower") -elif dims == 3: - im1 = axes[0].imshow(context.convert_to_ndarray(flow.solid_mask[:, :, int(flow.solid_mask.shape[2] / 2)].T), origin="lower") - im2 = axes[1].imshow(u[0, ...][:, :, int(flow.solid_mask.shape[2] / 2)].T, origin="lower") -cbar_ax = fig.add_axes((0.88, 0.15, 0.04, 0.7)) -fig.colorbar(im2, cax=cbar_ax) -fig.show() - -################################################## -# PROCESS DATA: calculate and SAVE OBSERVABLES AND PLOTS: - -# COEFF. OF DRAG -try: - try: - drag_coefficient = np.array(DragReporter.out) - fig, ax = plt.subplots(constrained_layout=True) - ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2]) - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Drag Cd") - ax.set_ylim((0.5, 1.6)) # change y-limits - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timestep (simulation time / LU)") - except: - print("(!) drag_plotting didn't work...'") - - if not no_data_flag: - try: - plt.savefig(outdir_data + "/drag_coefficient.png") - except: - print("(!) saving 'drag_coefficient.png' failed!") - try: - np.savetxt(outdir_data + "/drag_coefficient.txt", drag_coefficient, - header="stepLU | timePU | Cd FROM str(timestamp)") - except: - print("(!) saving drag_timeseries failed!") - ax.set_ylim((drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min() * 0.5, - drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max() * 1.2)) - if not no_data_flag: - try: - plt.savefig(outdir_data + "/drag_coefficient_adjusted.png") - except: - print("(!) saving drag_coefficient_adjusted.png failed!") - plt.close() -except: - print("(!) analysing drag_coefficient didn't work'") - -# peak finder: try calculating the mean drag coefficient from an integer number of periods, if a clear periodic signal is found -try: - values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] - - peaks_max = find_peaks(values, prominence=((values.max() - values.min()) / 2)) - peaks_min = find_peaks(-values, prominence=((values.max() - values.min()) / 2)) - if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: - peak_number = peaks_max[0].shape[0] - else: - peak_number = peaks_min[0].shape[0] - - if peaks_min[0][0] < peaks_max[0][0]: - first_peak = peaks_min[0][0] - last_peak = peaks_max[0][peak_number - 1] - else: - first_peak = peaks_max[0][0] - last_peak = peaks_min[0][peak_number - 1] - - drag_mean = values[first_peak:last_peak].mean() - drag_mean_simple = values.mean() - - print("Cd, simple mean: ", drag_mean_simple) - print("Cd, peak_finder mean:", drag_mean) - - # PLOT PEAK-FINDING: - drag_stepsLU = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 0] - peak_max_y = values[peaks_max[0]] - peak_max_x = drag_stepsLU[peaks_max[0]] - peak_min_y = values[peaks_min[0]] - peak_min_x = drag_stepsLU[peaks_min[0]] - - plt.plot(drag_stepsLU, values) - plt.scatter(peak_max_x[:peak_number], peak_max_y[:peak_number]) - plt.scatter(peak_min_x[:peak_number], peak_min_y[:peak_number]) - plt.scatter(drag_stepsLU[first_peak], values[first_peak]) - plt.scatter(drag_stepsLU[last_peak], values[last_peak]) - plt.savefig(outdir_data + "/drag_coefficient_peakfinder.png") - peakfinder = True -except: # if signal is not sinusoidal enough, calculate only simple mean value - print( - "peak-finding didn't work... probably no significant peaks visible (Re<46?), or periodic region not reached (T too small)") - values = drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2] - drag_mean_simple = values.mean() - peakfinder = False - print("Cd, simple mean:", drag_mean_simple) -try: - plt.close() -except: - pass - -# LIFT COEFFICIENT -try: - lift_coefficient = np.array(LiftReporter.out) - fig, ax = plt.subplots(constrained_layout=True) - ax.plot(lift_coefficient[:, 1], lift_coefficient[:, 2]) - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Lift Cl") - ax.set_ylim((-1.1, 1.1)) - - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timesteps (simulation time / LU)") - if not no_data_flag: - try: - plt.savefig(outdir_data + "/lift_coefficient.png") - except: - print("(!) saving lift_coefficient.png didn't work!") - try: - np.savetxt(outdir_data + "/lift_coefficient.txt", lift_coefficient, - header="stepLU | timePU | Cl FROM str(timestamp)") - except: - print("(!) saving lift_timeline didn't work!") - Cl_min = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].min() - Cl_max = lift_coefficient[int(lift_coefficient[:, 2].shape[0] * periodic_start):, 2].max() - print("Cl_peaks: \nmin", Cl_min, "\nmax", Cl_max) - plt.close() -except: - print("(!) analysing lift_coefficient didn't work!") - -############################################# -# plot DRAG and LIFT together: -try: - fig, ax = plt.subplots(layout="constrained") - drag_ax = ax.plot(drag_coefficient[:, 1], drag_coefficient[:, 2], color="tab:blue", label="Drag") - ax.set_xlabel("physical time / s") - ax.set_ylabel("Coefficient of Drag Cd") - ax.set_ylim((0.5, 1.6)) - - secax = ax.secondary_xaxis('top', functions=(flow.units.convert_time_to_lu, flow.units.convert_time_to_pu)) - secax.set_xlabel("timesteps (simulation time / LU)") - - ax2 = ax.twinx() - lift_ax = ax2.plot(lift_coefficient[:, 1], lift_coefficient[:, 2], color="tab:orange", label="Lift") - ax2.set_ylabel("Coefficient of Lift Cl") - ax2.set_ylim((-1.1, 1.1)) - - fig.legend(loc="upper left", bbox_to_anchor=(0, 1), bbox_transform=ax.transAxes) - - if not no_data_flag: - try: - plt.savefig(outdir_data + "/dragAndLift_coefficient.png") - except: - print("(!) saving dragAndLift_coefficient.png didn't work!") - plt.close() -except: - print("(!) plotting drag and lift together didn't work!") - -##################################################### -# STROUHAL number: (only makes sense for Re>46 and if periodic state is reached) -try: - ### prototyped fft for frequency detection and calculation of strouhal-number - # ! Drag_frequency is 2* Strouhal-Freq. Lift-freq. is Strouhal-Freq. - - X = np.fft.fft(lift_coefficient[:, 2]) # fft result (amplitudes) - N = len(X) # number of freqs - n = np.arange(N) # freq index - T = N * flow.units.convert_time_to_pu(1) # total time measured (T_PU) - freq = n / T # frequencies (x-axis of spectrum) - - plt.figure() - plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) - plt.xlabel("Freq (Hz)") - plt.ylabel("FFT Amplitude |X(freq)|") - plt.xlim(0, 1) - # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging - plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum - - if not no_data_flag: - plt.savefig(outdir_data + "/fft_Cl.png") - - freq_res = freq[1] - freq[0] # frequency-resolution - X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum - freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude - print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") - # f = Strouhal for St=f*D/U and D=U=1 in PU -except: - print("fft for Strouhal didn't work") - freq_res = 0 - freq_peak = 0 -plt.close() - -# TODO: calc St from f when: f = Strouhal for St=f*D/U and D=U=1 in PU - - -# EXPORT OBSERVABLES: -if not no_data_flag: - ### CUDA-VRAM-summary: - output_file = open(outdir_data + "/" + timestamp + "_GPU_memory_summary.txt", "a") - output_file.write("DATA for " + timestamp + "\n\n") - output_file.write(torch.cuda.memory_summary(context.device)) - output_file.close() - - # TODO: make this optional... - try: - ### list present torch tensors: - output_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "a") - total_bytes = 0 - import gc - - for obj in gc.get_objects(): - try: - if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): - output_file.write("\n" + str(obj.size()) + ", " + str(obj.nelement() * obj.element_size())) - total_bytes = total_bytes + obj.nelement() * obj.element_size() - except: - pass - # output_file.write("\n\ntotal bytes for tensors:"+str(total_bytes)) - output_file.close() - - ### count occurence of tensors in list of tensors: - from collections import Counter - - my_file = open(outdir_data + "/" + timestamp + "_GPU_list_of_tensors.txt", "r") - data = my_file.read() - my_file.close() - data_into_list = data.split("\n") - c = Counter(data_into_list) - output_file = open(outdir_data + "/" + timestamp + "_GPU_counted_tensors.txt", "a") - for k, v in c.items(): - output_file.write("type,size,bytes: {}, number: {}\n".format(k, v)) - output_file.write("\ntotal bytes for tensors:" + str(total_bytes)) - output_file.close() - except: - print("(!) counting tensors didn't work!") - -# TODO: cleanup of parms, stats, obs... -# output parameters, stats and observables -if not no_data_flag: - output_file = open(outdir_data + "/" + timestamp + "_parms_stats_obs.txt", "a") - output_file.write("DATA for " + timestamp) - output_file.write("\n\n### SIM-Parameters ###") - output_file.write("\nRe = " + str(reynolds_number)) - output_file.write("\nn_steps = " + str(n_steps)) - output_file.write("\nT_target = " + str(flow.units.convert_time_to_pu(n_steps)) + " seconds") - output_file.write("\ngridpoints_per_diameter (gpd) = " + str(flow.char_length_lu)) - if gpd_correction: - output_file.write("\ngpd was corrected from: " + str(gpd_setup) + " to " + str( - flow.char_length_lu) + " because D/Y is even") - output_file.write("\nDpX (D/X) = " + str(domain_length_x_in_d)) - output_file.write("\nDpY (D/Y) = " + str(domain_height_y_in_d)) - if flow.stencil.d == 3: - output_file.write("\nDpZ (D/Z) = " + str(domain_width_z_in_d)) - output_file.write("\nshape_LU: " + str(flow.resolution)) - output_file.write(("\ntotal_number_of_gridpoints: " + str(flow.rho(flow.f).numel()))) - output_file.write("\nbc_type = " + str()) -# output_file.write("\nlateral_walls = " + str(lateral_walls)) -# output_file.write("\nstencil = " + str(stencil_choice)) -# output_file.write("\ncollision = " + str(collision)) - output_file.write("\n") - output_file.write("\nMa = " + str(mach_number)) - output_file.write("\ntau = " + str(flow.units.relaxation_parameter_lu)) -# output_file.write("\ngrid_reynolds_number (Re_g) = " + str(re_g)) - output_file.write("\n") - output_file.write("\nsetup_diameter_PU = " + str(flow.char_length_lu)) - output_file.write("\nflow_velocity_PU = " + str(flow.char_length_lu)) -# output_file.write("\nu_init = " + str(u_init)) - output_file.write("\nperturb_init = " + str(perturb_init)) - output_file.write("\n") -# output_file.write("\noutput_vtk = " + str(output_vtk)) -# output_file.write("\nvtk_fps = " + str(vtk_fps)) - - output_file.write("\n\n### SIM-STATS ###") - output_file.write("\nruntime = " + str(runtime) + " seconds (=" + str(runtime / 60) + " minutes)") - output_file.write("\nMLUPS = " + str(mlups)) - output_file.write("\n") - - output_file.write("\nVRAM_current [MB] = " + str(torch.cuda.memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\nVRAM_peak [MB] = " + str(torch.cuda.max_memory_allocated(context.device) / 1024 / 1024)) - output_file.write("\n") - output_file.write("\nCPU load % avg. over last 1, 5, 15 min: " + str(round(cpuLoad1, 2)) + " %, " + str(round(cpuLoad5, 2)) + " %, " + str(round(cpuLoad15, 2)) + " %") - output_file.write("\ntotal current RAM usage [MB]: " + str(round(ram.used / (1024 * 1024), 2)) + " of " + str(round(ram.total / (1024 * 1024), 2)) + " MB") - - output_file.write("\n\n### OBSERVABLES ###") - output_file.write("\nCoefficient of drag between " + str(round(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1), 1], 2)) + " s and " + str(round(drag_coefficient[int(drag_coefficient.shape[0] - 1), 1], 2)) + " s:") - output_file.write("\nCd_mean, simple = " + str(drag_mean_simple)) - if peakfinder: - output_file.write("\nCd_mean, peak_finder = " + str(drag_mean)) - else: - output_file.write("\nnoPeaksFound") - output_file.write( - "\nCd_min = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].min())) - output_file.write( - "\nCd_max = " + str(drag_coefficient[int(drag_coefficient.shape[0] * periodic_start - 1):, 2].max())) - output_file.write("\n") - output_file.write("\nCoefficient of lift:") - output_file.write("\nCl_min = " + str(Cl_min)) - output_file.write("\nCl_max = " + str(Cl_max)) - output_file.write("\n") - output_file.write("\nStrouhal number:") - output_file.write("\nSt +- df = " + str(freq_peak) + " +- " + str(freq_res) + " Hz") - output_file.write("\n") - output_file.close() - - -## END OF SCRIPT -print(f"\n♬ THE END ♬") - -# reset stdout (from LOGGER, see above) -sys.stdout = old_stdout - - -################################################################ -# script components below... - -# Process arguments and set parameters -# - I/O: create timestamp, sim-ID, outdir (path) and outdir_data (path) -# - flow physics: char_velocity, re, ma, density, pressure, length, domain (resolution) -# - solver settings - -# save input parameters to file - -# save this script to outdir for later reference - -# start logger - -# DEFINE AUXILIARY methods (or load from other helper-file) - - -# SETUP SIMULATOR -# - stencil (infer dim from stencil), dtype, equilibrium, -# - calculate obstacle geometry and domain constraints - -# save geometry input to file - -# flow, collision,...classes - -# (opt.) plot stuff (velocity, geometry etc.) - -# initialize REPORTERS - -# RUN SIMULATION - -# print stats - -# export stats - -# plotting and post-processing -# - save data - -# reset LOGGER (!) \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py index 4f4c106e..9781d62e 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/data_processing_and_plotting.py @@ -1,4 +1,12 @@ -# THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM THE CYLINDER-SIMULATION +""" + THIS FILE CONTAINS THE UTILITIES TO PROCESS AND PLOT THE REPORTED DATA FROM + THE CYLINDER OBSTACLE SIMULATION SCRIPT + - plotting of force coefficients + - analysis of periodic (force coefficient) signals + - drawing of circular cylinder mask in 2D + - plotting- and analysis utility for average velocity and reynolds + stress profiles +""" import os import numpy as np @@ -7,26 +15,19 @@ import traceback from scipy.signal import find_peaks from scipy.optimize import curve_fit - from collections import Counter -# DRAG COEFFICIENT -# LIFT COEFFICIENT -# DRAG+LIFT PNG -# STROUHAL -# U-profile - -####################################################################### +def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], + secax_functions_tuple: tuple[Any,Any], filenamebase, + save_timeseries = False, periodic_start=0, adjust_ylim=False): + """ + - plot force coefficient timeseries + - (opt.) save png to outdir + - contains exception-capture functionality if data is not plot-abel, + save-able etc. + """ -# PLOT FORCE COEFFICIENTs (data-array [i, t, value], ylabel-string, ylim tupel[float,float], outdir (for png and .txt), save_timeseries -# - plot data with ylim, and ylabel -# - save png to outdir -# - save timeseries txt to outdir -# - plot data with adjusted ylim -# - save png to outdir - -def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[float, float], secax_functions_tuple: tuple[Any,Any], filenamebase, save_timeseries = False, periodic_start=0, adjust_ylim=False): # PLOT try: fig, ax = plt.subplots(constrained_layout=True) @@ -53,7 +54,7 @@ def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[floa print(full_trace) print("--------------------------\n") - # PLOT with ylim ADJUSTED + # PLOT with ylim automatically ADJUSTED to fit data (additionally) if adjust_ylim: try: fig, ax = plt.subplots(constrained_layout=True) @@ -76,14 +77,15 @@ def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[floa try: plt.savefig(filenamebase + "_ylimadjusted.png") except Exception as e: - print(f"(WARNING!) saving of {ylabel} PLOT to {filenamebase}_ylimadjusted.png didn't work...") + print(f"(WARNING!) saving of {ylabel} PLOT " + f"to {filenamebase}_ylimadjusted.png didn't work...") print("\n--- Python Stack Trace ---") full_trace = traceback.format_exc() print(full_trace) print("--------------------------\n") # SAVE .txt timeseries - #TODO: make this a seperate function... + #TODO (optional): make this a seperate function... if save_timeseries: try: np.savetxt(filenamebase + ".txt", data_array, @@ -95,30 +97,33 @@ def plot_force_coefficient(data_array: np.ndarray, ylabel: str, ylim: tuple[floa print(full_trace) print("--------------------------\n") -# ANALYZE PERIODIC SIGNAL (verbose (plot peak-finding), -# - min, max, mean (simple + window-corrected) (über ganzzahlige Periodenzahl gemittelt) -# - RETURN: dictionary mit: Name, min, max, mean_simple, mean_n_periodic, mean-min, mean-max (v.a. für Cl, nach neuen Skripten) -# - print error if peak-finding didn't work -# - (opt. FFT, periodicity, frequency) -# - FFT -# - print FFT spectrum -# - (!) NEW FREQUENCY detection from MP2/Paper + def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: float,prominence: float = None, name: str = "periodic timeseries", outdir=None, pu_per_step = None, verbose=False): + """ + ANALYZE PERIODIC SIGNAL (verbose = plot peak-finding), + - outputs: min, max, mean (simple + corrected for integer number of periods) + - RETURN: dictionary mit: Name, min, max, mean_simple, mean_n_periodic, + mean-min, mean-max (v.a. für Cl, nach neuen Skripten) + - print error if peak-finding didn't work + - do FFT, print FFT spectrum + - detect frequency of signal by sine-functino fitting + (can be more accurate than FFT for strictly sinusoidal signals!) + """ values_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 2] steps_LU_periodic = data_array[int(data_array.shape[0] * periodic_start_rel - 1):, 0] mean_periodcorrected = None - max_mean = None - min_mean = None + max_mean = None # mean maximum value from all high peaks + min_mean = None # mean minimum value from all low peaks if prominence is None: prominence = ((values_periodic.max() - values_periodic.min()) * 0.5) - #The prominence value is up for debate; - # The value given here only reliably catches all peaks, - # if the signal is simple and periodically converged + # The prominence value is up for debate; + # The value given here only reliably catches all peaks, + # if the signal is simple and periodically converged try: - peaks_max = find_peaks(values_periodic, prominence=prominence) # drag-prominence: ((values.max() - values.min()) / 2); lift-prominence: (lift1100_1500[:,2].max()+lift1100_1500[:,2].min())/2); oder lift: lift_converged[:,2].max()*0.5 + peaks_max = find_peaks(values_periodic, prominence=prominence) peaks_min = find_peaks(-values_periodic, prominence=prominence) if peaks_min[0].shape[0] - peaks_max[0].shape[0] > 0: @@ -155,7 +160,9 @@ def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: floa mean_periodcorrected = values_periodic[first_peak:last_peak].mean() except Exception as e: - print(f"(WARNING!) peak finding for {name} didn't work... This might just be because there is no converged periodic region!") + print(f"(WARNING!) peak finding for {name} didn't work... " + f"This might just be because there is no converged periodic region," + f"so don't worry if you expected this!") if verbose: print("Analyze Periodic Timeseries (verbose):-> see Python Stack Trace below:") @@ -164,17 +171,20 @@ def analyze_periodic_timeseries(data_array: np.ndarray, periodic_start_rel: floa print(full_trace) print("--------------------------\n") + # simple mean, max, min calculation mean_simple = values_periodic.mean() min_simple = values_periodic.min() max_simple = values_periodic.max() # sine-fit + # (for better statistics and frequency analysis of purely sinusoidal signals) def sine_func(xx, a, b, c, d): return a * np.sin(2 * np.pi * b * xx + c) + d frequency_fit = None try: - coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, p0=(0.7, 0.2, 0.5, 0)) + coefficients, values = curve_fit(sine_func, steps_LU_periodic, values_periodic, + p0=(0.7, 0.2, 0.5, 0)) fig, ax = plt.subplots(constrained_layout=True) if verbose: plt.plot(steps_LU_periodic, values_periodic, steps_LU_periodic, @@ -188,12 +198,14 @@ def sine_func(xx, a, b, c, d): frequency_fit = coefficients[1] except Exception as e: print(f"(WARNING!) sine-fitting for {name} didn't work...") - print("\n--- Python Stack Trace ---") - full_trace = traceback.format_exc() - print(full_trace) - print("--------------------------\n") + if verbose: + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") # FFT + # run FFT on signal and get dominant frequency freq_peak = None freq_res = None @@ -207,77 +219,94 @@ def sine_func(xx, a, b, c, d): if verbose: plt.figure() - plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") # plot spectrum |X|(f) + # plot spectrum |X|(f) + plt.stem(freq, np.abs(X), 'b', markerfmt=" ", basefmt="-b") plt.xlabel("Freq (Hz)") plt.ylabel("FFT Amplitude |X(freq)|") plt.xlim(0, 1) - # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # for debugging - plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) # ylim, where highes peak is on left half of full spectrum + # print("max. Amplitude np.abx(X).max():", np.abs(X).max()) # uncomment for debugging + + # ylim, where highes peak is on left half of full spectrum: + plt.ylim(0, np.abs(X[:int(X.shape[0] * 0.5)]).max()) if outdir is not None: plt.savefig(outdir + f"/{name}_fft.png") freq_res = freq[1] - freq[0] # frequency-resolution - X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) # get |X| Amplitude for left half of full spectrum + + # get |X| Amplitude for left half of full spectrum + X_abs = np.abs(X[:int(X.shape[0] * 0.4)]) freq_peak = freq[np.argmax(X_abs)] # find frequency with the highest amplitude - # print("Frequency Peak:", freq_peak, "+-", freq_res, "Hz") - # f = Strouhal for St=f*D/U and D=U=1 in PU except Exception as e: print(f"(WARNING!) fft for {name} didn't work...") - print("\n--- Python Stack Trace ---") - full_trace = traceback.format_exc() - print(full_trace) - print("--------------------------\n") + if verbose: + print("\n--- Python Stack Trace ---") + full_trace = traceback.format_exc() + print(full_trace) + print("--------------------------\n") - return {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, "min_simple": min_simple, - "max_simple": max_simple, "max_mean": max_mean, "min_mean": min_mean, - "frequency_fit": frequency_fit, "frequency_fft": freq_peak, "fft_resolution": freq_res} + return {"mean_simple": mean_simple, "mean_periodcorrected": mean_periodcorrected, + "min_simple": min_simple, "max_simple": max_simple, "max_mean": max_mean, + "min_mean": min_mean, "frequency_fit": frequency_fit, "frequency_fft": freq_peak, + "fft_resolution": freq_res} def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, filebase=".", print_data=False): - ### calculate and export 2D obstacle_mask as .png + """ + calculate and draw a 2D representation of: + - the circular cylinder + - the basic solid mask + """ + grid_x = gridpoints_per_diameter + 2 if print_data: print("GPD = " + str(gridpoints_per_diameter)) # define radius and position for a symmetrical circular Cylinder-Obstacle - radius_LU = 0.5 * gridpoints_per_diameter - y_pos_LU = 0.5 * grid_x + 0.5 - x_pos_LU = y_pos_LU + radius_lu = 0.5 * gridpoints_per_diameter + y_pos_lu = 0.5 * grid_x + 0.5 + x_pos_lu = y_pos_lu # get x,y,z meshgrid of the domain (LU) - xyz = tuple(np.linspace(1, n, n) for n in (grid_x, - grid_x)) # tupel of list indizes (1-n (non zero-based!)) - xLU, yLU = np.meshgrid(*xyz, - indexing='ij') # meshgrid of x- and y- indizes -> * unpacks the tuple to be two values and now a tuple + + # tupel of list indizes (1-n (non-zero-based!)) + xyz = tuple(np.linspace(1, n, n) for n in (grid_x, grid_x)) + # meshgrid of x- and y- indizes -> * unpacks the tuple to be two values and now a tuple + x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # define cylinder (LU) (circle) obstacle_mask_for_visualization = np.sqrt( - (xLU - x_pos_LU) ** 2 + (yLU - y_pos_LU) ** 2) < radius_LU + (x_lu - x_pos_lu) ** 2 + (y_lu - y_pos_lu) ** 2) < radius_lu nx, ny = obstacle_mask_for_visualization.shape # number of x- and y-nodes (Skalar) - rand_mask = np.zeros((nx, ny), - dtype=bool) # for all the solid nodes, neighboring fluid nodes - rand_mask_f = np.zeros((flow.stencil.q, nx, ny), - dtype=bool) # same, but including q-dimension + # for all the solid nodes, neighboring fluid nodes + rand_mask = np.zeros((nx, ny), dtype=bool) + + # same, but including q-dimension + rand_mask_f = np.zeros((flow.stencil.q, nx, ny), dtype=bool) + rand_xq = [] # list of all x-values (incl. q-multiplicity) rand_yq = [] # list of all y-values (incl. q-multiplicity) - a, b = np.where( - obstacle_mask_for_visualization) # np.array: list of (a) x-coordinates und (b) y-coordinates of the obstacle_mask_for_visualization - # ...to iterate over all boudnary/object/wall nodes - for p in range(0, - len(a)): # for all True-ndoes in obstacle_mask_for_visualization - for i in range(0, - flow.stencil.q): # for all stencil directions c_i (lattice.stencil.e) - try: # try in case the neighboring cell does not exist (an f pointing out of the simulation domain) + # np.array: list of + # (a) x-coordinates and + # (b) y-coordinates of the obstacle_mask_for_visualization + a, b = np.where(obstacle_mask_for_visualization) + # ...to iterate over all boundary/object/wall nodes + for p in range(0,len(a)): + # for all True-nodes in obstacle_mask_for_visualization + for i in range(0,flow.stencil.q): + # for all stencil directions c_i (lattice.stencil.e) + try: + # try in case the neighboring cell does not exist + # (an f pointing out of the simulation domain) if not obstacle_mask_for_visualization[ - a[p] + flow.stencil.e[i][0], b[p] + flow.stencil.e[ - i][1]]: - # if neighbor in +(e_x, e_y; e is c_i) is False, we are on the object-surface (self True with neighbor False) + a[p] + flow.stencil.e[i][0], b[p] + flow.stencil.e[i][1]]: + # if neighbor in +(e_x, e_y; e is c_i) is False, + # we are on the object-surface (self True with neighbor False) rand_mask[a[p], b[p]] = 1 rand_mask_f[flow.stencil.opposite[i], a[p], b[p]] = 1 rand_xq.append(a[p]) @@ -291,12 +320,12 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, # calculate all radii and r_max and r_min r_max = 0 r_min = gridpoints_per_diameter - radii = np.zeros_like(rand_x, - dtype=float) # list of all redii (without q-dimension) in LU + + # list of all radii (without q-dimension) in LU + radii = np.zeros_like(rand_x, dtype=float) for p in range(0, len(rand_x)): # for all nodes - radii[p] = np.sqrt( - (rand_x[p] - x_pos) ** 2 + (rand_y[ - p] - y_pos) ** 2) # calculate distance to circle center + # calculate distance to circle center + radii[p] = np.sqrt((rand_x[p] - x_pos) ** 2 + (rand_y[p] - y_pos) ** 2) if radii[p] > r_max: r_max = radii[p] if radii[p] < r_min: @@ -305,23 +334,22 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, # calculate all radii (with q-multiplicity) radii_q = np.zeros_like(rand_xq, dtype=float) for p in range(0, len(rand_xq)): - radii_q[p] = np.sqrt( - (rand_xq[p] - x_pos) ** 2 + (rand_yq[p] - y_pos) ** 2) + radii_q[p] = np.sqrt((rand_xq[p] - x_pos) ** 2 + (rand_yq[p] - y_pos) ** 2) ### all relative radii in relation to gpd/2 - radii_relative = radii / ( - radius_LU - 0.5) # (substract 0.5 because "true" boundary location is 0.5LU further out than node-coordinates) - radii_q_relative = radii_q / (radius_LU - 0.5) + radii_relative = radii / (radius_lu - 0.5) + # (subtract 0.5 because "true" boundary location is 0.5LU + # further out than node-coordinates) + radii_q_relative = radii_q / (radius_lu - 0.5) # calc. mean rel_radius r_rel_mean = sum(radii_relative) / len(radii_relative) rq_rel_mean = sum(radii_q_relative) / len(radii_q_relative) ## AREA calculation - area_theory = np.pi * ( - gridpoints_per_diameter / 2) ** 2 # area = pi*r² in LU² - area = len( - a) # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it + area_theory = np.pi * (gridpoints_per_diameter / 2) ** 2 # area = pi*r² in LU² + # area in LU = number of nodes, because every node has a cell of 1LU x 1LU around it + area = len(a) if output_data: output_file = open(filebase + "/obstacle_mask_info.txt", "a") @@ -330,8 +358,8 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, "\nr_rel_mean: " + str(sum(radii_relative) / len(radii_relative))) output_file.write("\nrq_rel_mean: " + str( sum(radii_q_relative) / len(radii_q_relative))) - output_file.write("\nr_rel_min: " + str(r_max / (radius_LU - 0.5))) - output_file.write("\nr_rel_max: " + str(r_min / (radius_LU - 0.5))) + output_file.write("\nr_rel_min: " + str(r_max / (radius_lu - 0.5))) + output_file.write("\nr_rel_max: " + str(r_min / (radius_lu - 0.5))) output_file.write("\n\narea_rel: " + str(area / area_theory)) output_file.write("\n\nradii: " + str(Counter(radii))) @@ -358,7 +386,7 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, ax.set_xticks(np.arange(-.5, xmax, 1), minor=True) ax.set_yticks(np.arange(-.5, ymax, 1), minor=True) - # grid thickness, cicrle, node marker + # grid thickness, circle, node marker x, y = np.meshgrid(np.linspace(0, int(xmax), int(xmax + 1)), np.linspace(0, int(ymax), int(ymax + 1))) if gridpoints_per_diameter < 30: @@ -391,9 +419,18 @@ def draw_circular_mask(flow, gridpoints_per_diameter, output_data=False, else: plt.close() + class ProfilePlotter: - - def __init__(self, flow, output_path, reference_data_path, i_timeseries, u_timeseries1, u_timeseries2, u_timeseries3): + """ + utility to handle profile reporter data + - load reference values + - process profile reporter data + - plot and save data with or without references + """ + + + def __init__(self, flow, output_path, reference_data_path, i_timeseries, + u_timeseries1, u_timeseries2, u_timeseries3): self.flow = flow self.output_path = output_path self.i_timeseries = i_timeseries @@ -406,7 +443,8 @@ def __init__(self, flow, output_path, reference_data_path, i_timeseries, u_times self.import_profile_reference_data(reference_data_path) def import_profile_reference_data(self, data_path): - # import reference data: (data is: first column Y/D, second column u_d/u_char) + # import reference data: + # (data is: first column Y/D, second column u_d/u_char) # ux self.p1_LS1993_ux = np.genfromtxt( @@ -531,7 +569,7 @@ def import_profile_reference_data(self, data_path): data_path + 'Fig13_uxuy_profile_pos3_R2016.csv', delimiter=';') def process_data(self, save=False): - # CALCULATE remporal velocity averages + """CALCULATE temporal velocity averages""" avg_u1_temporal = np.mean(np.array(self.u_timeseries1), axis=0) avg_u2_temporal = np.mean(np.array(self.u_timeseries2), axis=0) avg_u3_temporal = np.mean(np.array(self.u_timeseries3), axis=0) @@ -557,8 +595,8 @@ def process_data(self, save=False): + "pos3" + "_t-avg.npy", avg_u3_temporal) # Y_inD for y-axis and plotting - self.y_in_D = (np.arange(self.avg_u1_x.shape[ - 0]) + 1 - self.flow.y_pos_lu) / self.flow.char_length_lu # y/D for figure + self.y_in_D = ((np.arange(self.avg_u1_x.shape[0]) + 1 - self.flow.y_pos_lu) + / self.flow.char_length_lu) if save: np.save( self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_YinD.npy", @@ -618,6 +656,7 @@ def process_data(self, save=False): np.array([self.y_in_D, self.u3_diff_xy_mean])) def save_timeseries_to_files(self, basepath): + """save the FULL timeseries to files""" # timeseries (i) np.save(basepath + "/ProfileReporter_Data" + "/ProfileReporter_" + "_timeseries_steps.npy", np.array(self.i_timeseries)) @@ -630,25 +669,31 @@ def save_timeseries_to_files(self, basepath): + "pos3" + "_timeseries_data.npy", np.array(self.u_timeseries3)) def plot_velocity_profiles(self, show_reference = False, save = False, show = False): + """plot tht average velocity profiles""" cm = 1 / 2.54 if not show_reference: # PLOT ux fig, (ax_ux, ax_uy) = plt.subplots(1, 2, constrained_layout=True, figsize=(30 * cm, 10 * cm)) - ax_ux.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x, self.y_in_D, self.avg_u3_x) + ax_ux.plot(self.y_in_D, self.avg_u1_x, + self.y_in_D, self.avg_u2_x, + self.y_in_D, self.avg_u3_x) ax_ux.set_xlabel("y/D") ax_ux.set_ylabel(r"$\bar{u}_{x}$/$u_{char}$") ax_ux.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) # PLOT uy - ax_uy.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y, self.y_in_D, self.avg_u3_y) + ax_uy.plot(self.y_in_D, self.avg_u1_y, + self.y_in_D, self.avg_u2_y, + self.y_in_D, self.avg_u3_y) ax_uy.set_xlabel("y/D") ax_uy.set_ylabel(r"$\bar{u}_{y}$/$u_{char}$") ax_uy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_velocity_noReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_velocity_noReference.png") if show: plt.show() else: @@ -657,7 +702,9 @@ def plot_velocity_profiles(self, show_reference = False, save = False, show = Fa else: # PLOT ux against references fig, ax = plt.subplots(constrained_layout=True) - my_data = ax.plot(self.y_in_D, self.avg_u1_x, self.y_in_D, self.avg_u2_x - 1, self.y_in_D, self.avg_u3_x - 2) + my_data = ax.plot(self.y_in_D, self.avg_u1_x, + self.y_in_D, self.avg_u2_x - 1, + self.y_in_D, self.avg_u3_x - 2) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p1_LS1993_ux[:, 0], self.p1_LS1993_ux[:, 1], self.p2_LS1993_ux[:, 0], self.p2_LS1993_ux[:, 1], @@ -690,7 +737,8 @@ def plot_velocity_profiles(self, show_reference = False, save = False, show = Fa ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0]], loc='best') if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_ux_withReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_ux_withReference.png") if show: plt.show() else: @@ -698,8 +746,9 @@ def plot_velocity_profiles(self, show_reference = False, save = False, show = Fa # PLOT uy against references fig, ax = plt.subplots(constrained_layout=True) - my_data = ax.plot(self.y_in_D, self.avg_u1_y, self.y_in_D, self.avg_u2_y - 1, self.y_in_D, - self.avg_u3_y - 2) + my_data = ax.plot(self.y_in_D, self.avg_u1_y, + self.y_in_D, self.avg_u2_y - 1, + self.y_in_D, self.avg_u3_y - 2) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p1_LS1993_uy[:, 0], self.p1_LS1993_uy[:, 1], @@ -733,38 +782,44 @@ def plot_velocity_profiles(self, show_reference = False, save = False, show = Fa ax.legend(handles=[my_data[0], ref_LS[0], ref_KM[0], ref_WR[0], ref_DI[0]], loc='best') if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uy_withReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uy_withReference.png") if show: plt.show() else: plt.close() def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = False): + """plot average reynolds stress profiles""" cm = 1 / 2.54 if not show_reference: fig, (ax_xx, ax_yy, ax_xy) = plt.subplots(1, 3, figsize=(40 * cm, 10 * cm), constrained_layout=True) - ax_xx.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, self.u2_diff_sq_mean[0], + ax_xx.plot(self.y_in_D, self.u1_diff_sq_mean[0], + self.y_in_D, self.u2_diff_sq_mean[0], self.y_in_D, self.u3_diff_sq_mean[0]) ax_xx.set_xlabel("y/D") ax_xx.set_ylabel(r"$\overline{u_{x}'u_{x}'}$/$u_{char}^2$") ax_xx.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) - ax_yy.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, self.u2_diff_sq_mean[1], + ax_yy.plot(self.y_in_D, self.u1_diff_sq_mean[1], + self.y_in_D, self.u2_diff_sq_mean[1], self.y_in_D, self.u3_diff_sq_mean[1]) ax_yy.set_xlabel("y/D") ax_yy.set_ylabel(r"$\overline{u_{y}'u_{y}'}$/$u_{char}^2$") ax_yy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) - ax_xy.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, self.u2_diff_xy_mean, self.y_in_D, - self.u3_diff_xy_mean) + ax_xy.plot(self.y_in_D, self.u1_diff_xy_mean, + self.y_in_D, self.u2_diff_xy_mean, + self.y_in_D, self.u3_diff_xy_mean) ax_xy.set_xlabel("y/D") ax_xy.set_ylabel(r"$\overline{u_{x}'u_{y}'}$/$u_{char}^2$") ax_xy.legend(["x/D = 1.06", "x/D = 1.54", "x/D = 2.02"]) if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_reynoldsStresses_noReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_reynoldsStresses_noReference.png") if show: plt.show() else: @@ -773,9 +828,9 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = # plot reynolds stresses against reference # uxux - streamwise fig, ax = plt.subplots(constrained_layout=True) - my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[0], self.y_in_D, - self.u2_diff_sq_mean[0] - 0.5, self.y_in_D, - self.u3_diff_sq_mean[0] - 1) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[0], + self.y_in_D, self.u2_diff_sq_mean[0] - 0.5, + self.y_in_D, self.u3_diff_sq_mean[0] - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_LS = ax.plot(self.p2_LS1993_uxux[:, 0], self.p2_LS1993_uxux[:, 1]) @@ -808,7 +863,8 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = ref_DI[0]], loc='best') if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxux_withReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uxux_withReference.png") if show: plt.show() else: @@ -816,9 +872,9 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = # uyuy - cross-stream fig, ax = plt.subplots(constrained_layout=True) - my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[1], self.y_in_D, - self.u2_diff_sq_mean[1] - 0.5, self.y_in_D, - self.u3_diff_sq_mean[1] - 1) + my_data = ax.plot(self.y_in_D, self.u1_diff_sq_mean[1], + self.y_in_D, self.u2_diff_sq_mean[1] - 0.5, + self.y_in_D, self.u3_diff_sq_mean[1] - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_BM = ax.plot(self.p2_BM1994_uyuy[:, 0], self.p2_BM1994_uyuy[:, 1]) @@ -845,7 +901,8 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = ref_DI[0]], loc='best') if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uyuy_withReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uyuy_withReference.png") if show: plt.show() else: @@ -853,9 +910,9 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = # uxuy - Reynolds shear stress fig, ax = plt.subplots(constrained_layout=True) - my_data = ax.plot(self.y_in_D, self.u1_diff_xy_mean, self.y_in_D, - self.u2_diff_xy_mean - 0.5, self.y_in_D, - self.u3_diff_xy_mean - 1) + my_data = ax.plot(self.y_in_D, self.u1_diff_xy_mean, + self.y_in_D, self.u2_diff_xy_mean - 0.5, + self.y_in_D, self.u3_diff_xy_mean - 1) plt.setp(my_data, ls="-", lw=1, marker="", color="red", label="lettuce") ref_BM = ax.plot(self.p2_BM1994_uxuy[:, 0], self.p2_BM1994_uxuy[:, 1]) @@ -882,32 +939,10 @@ def plot_reynolds_stress_profiles(self, show_reference=False, save=False, show = ref_DI[0]], loc='best') if save: - plt.savefig(self.output_path + "/ProfileReporter_Data" + "/ProfileReporter_uxuy_withReference.png") + plt.savefig(self.output_path + "/ProfileReporter_Data" + + "/ProfileReporter_uxuy_withReference.png") if show: plt.show() else: plt.close() - -# PLOTTING - # speichere u-profile - # speichere y_in_D-Achse - # ux ohne REF - # uy ohne ref - # ux MIT REF - # uy mit REF - # BERECHNE turbuklente REynolds spannungen - # SPEICHERE REyynolds-spannungen - # plotte xx, yy, xy spannungen - # speichere plot - # plotte reynoldssüannungen gegen reference - -################################################################ - -# SNIP: MP2 fit sinewave to Cl for better frequency-measurement) -### FIT SINEWAVE to converged Cl to get better St-measurement -# 1. get converged lift-curve -# 2. fit sinewave with starting-freq 0.2 -# 3. store freq - -# OPTIONAL: PLOT FORCE COEFFICIENTs together! -> im Skript diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py index 8bd3c87b..02536b0e 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/ebb_simulation.py @@ -1,13 +1,7 @@ -import warnings - import torch -import numpy as np from timeit import default_timer as timer -from typing import List, Optional -from abc import ABC, abstractmethod - -from lettuce.cuda_native import NativeCollision, Generator, StreamingStrategy +from typing import List from lettuce._simulation import * from lettuce import Flow @@ -15,6 +9,11 @@ __all__ = ['EbbSimulation'] class EbbSimulation(Simulation): + """ + advanced simulation class, with + - (additional) post_streaming_boundaries (substeps: C -> S -> B -> R) + - post_collision population information for post_streaming_boundaries + """ def __init__(self, flow: 'Flow', collision: 'Collision', reporter: List['Reporter']): flow.context.use_native = False @@ -29,19 +28,25 @@ def __init__(self, flow: 'Flow', collision: 'Collision', # adjust No_collision_ and no_streaming_mask for use of post_streaming_boundaries (!) if len(self.post_streaming_boundaries) > 0: - # create NCM and NSM, if there where no pre_ or post_boundaries that triggered their creation in super-class + # create NCM and NSM, + # ...if there were no pre_ or post_boundaries that triggered + # ...their creation in super-class if self.no_collision_mask is None: # fill masks with value of self.collision_index (= number of pre-boundaries) self.no_collision_mask = self.context.full_tensor( flow.resolution, self.collision_index, dtype=torch.uint8) if self.no_streaming_mask is None: - self.no_streaming_mask = self.context.full_tensor( - [flow.stencil.q, *flow.resolution], self.collision_index, dtype=torch.uint8) + self.no_streaming_mask = self.context.full_tensor([flow.stencil.q, + *flow.resolution], + self.collision_index, + dtype=torch.uint8) - for i, boundary in enumerate(self.post_streaming_boundaries, start=self.collision_index + 1 + len(self.post_boundaries)): - print("SIMULATION: creating no_collision_mask entries for: i, boundary = ", i, boundary) - print("collision index is:", self.collision_index) + for i, boundary in enumerate(self.post_streaming_boundaries, + start=self.collision_index + 1 + + len(self.post_boundaries)): +# FOR DEBUGGING: print("SIMULATION: creating no_collision_mask entries for: i, boundary = ", i, boundary) +# FOR DEBUGGING: print("collision index is:", self.collision_index) ncm = boundary.make_no_collision_mask( [it for it in self.flow.f.shape[1:]], context=self.context) if ncm is not None: @@ -55,16 +60,19 @@ def __init__(self, flow: 'Flow', collision: 'Collision', self.store_f_collided_post_streaming_boundaries_index = [] for i, boundary in enumerate(self.post_streaming_boundaries): if hasattr(boundary.__class__, "store_f_collided"): - # append boundary to lis of boundaries that need f_collided state between collision and streaming substep + # append boundary to lis of boundaries that need + # ...f_collided state between collision and streaming substep self.store_f_collided_post_streaming_boundaries_index.append(i) if hasattr(boundary.__class__, "initialize_f_collided"): # initialize the f_collided storage in each of those boundaries boundary.initialize_f_collided() - # redefine collide_and_stream() to include the storage of f_collided for use in post_streaming_boundaries + # redefine collide_and_stream() to include the storage of f_collided + # ...for use in post_streaming_boundaries def collide_and_stream(*_, **__): self._collide() - self._pass_f_collided() # pass f to post_streaming_boundaries between collision and streaming substep + # pass f to post_streaming_boundaries between collision and streaming substep + self._pass_f_collided() self._stream() self._collide_and_stream = collide_and_stream diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py index 17c309b9..b14b2cd8 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/fullway_bounce_back_boundary.py @@ -7,86 +7,112 @@ __all__ = ["FullwayBounceBackBoundary"] class FullwayBounceBackBoundary(Boundary): - - def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force=None): + """ + FULLWAY BOUNCE BACK BOUNDARY CONDITION (FWBB): + - inverts populations on solid boundary nodes, + instead of colliding them + - (!) mind observable-artifacts on solid regions. They do not affect + the flow in the fluid region, but can mess with visualization and + non-local statistics (mean velocity etc.) + - able to calculate fluid force on boundary by momentum exchange + """ + + def __init__(self, context, flow, mask, global_solid_mask=None, + periodicity: tuple[bool,bool,bool|None] = None, calc_force=False): self.context = context self.flow = flow self.mask = mask - # self.solid_mask = mask if periodicity is None: periodicity = (False, False, False if self.flow.stencil.d == 3 else None) - # TODO: correct periodicity and solid-to-solid contact: periodicity attribute and global solid mask! in neighbor search - # global_solid_mask to filter out all "fake" fluid neighbors, which are outside the FWBB but not in the fluid region + # global_solid_mask (optional) to filter out all "fake" fluid neighbors, + # ...which are outside the FWBB but not in the fluid region if global_solid_mask is None: global_solid_mask = np.zeros_like(self.mask, dtype=bool) - other_solid_bc_mask = np.where(~self.mask, global_solid_mask, False) # exclude self.mask from global_solid_mask - if calc_force is not None: + # exclude self.mask from global_solid_mask for "other" solid mask + other_solid_bc_mask = np.where(~self.mask, global_solid_mask, False) + + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( - self.flow.torch_stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.flow.torch_stencil.e[0])) self.calc_force = True else: self.calc_force = False - ### create f_mask_fwbb, needed for force-calculation + ### create f_index_fwbb, needed for force-calculation # ...(marks all fs which streamed into the boundary in prior streaming step) # ... in other words: marks all fs that need to be bounced self.f_index_fwbb = [] if self.flow.stencil.d == 2: nx, ny = mask.shape # domain size in x and y - - # f_mask: [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid ix_sp, iy_sp = np.where(mask) # np.arrays: list of (ix_sp) x-indizes and (iy_sp) y-indizes in the boundary.mask # ...to enable iteration over all boundary/wall/object-nodes for sp_index in range(0, len(ix_sp)): # for all TRUE-nodes in boundary.mask - for q_index in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + for q_index in range(0, self.flow.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) + # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.flow.stencil.d, dtype=int) - if ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]: # searching border on left + if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 + and periodicity[0]): # searching border on left border[0] = -1 - elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]: # searching border on right + elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 + and periodicity[0]): # searching border on right border[0] = 1 - if iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]: # searching border on left + if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 + and periodicity[1]): # searching border on left border[1] = -1 - elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]: # searching border on right + elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 + and periodicity[1]): # searching border on right border[1] = 1 - try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + try: # try in case the neighboring cell does not exist + # (= an f pointing out of the simulation domain) if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny] and not other_solid_bc_mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0]*nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1]*ny]): - # if the neighbour of sp_index is False in the boundary.mask, sp_index is ix_sp solid node, neighbouring ix_sp fluid node: - # ...the direction pointing from the fluid neighbour to solid sp_index is marked on the solid sp_index + # if the neighbour of sp_index is False in the boundary.mask, + # ...sp_index is ix_sp solid node, neighbouring ix_sp fluid node: + # the direction pointing from the fluid neighbour to solid + # ...sp_index is marked on the solid sp_index - self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index]]) # list of [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid + # list of [q, nx, ny], marks all fs on the boundary-border, which point into the boundary/solid + self.f_index_fwbb.append([self.flow.stencil.opposite[q_index], ix_sp[sp_index], iy_sp[sp_index]]) except IndexError: pass # just ignore this iteration since there is no neighbor there if self.flow.stencil.d == 3: # like 2D, but in 3D...guess what... nx, ny, nz = mask.shape - ix_sp, iy_sp, iz_sp = np.where(mask) for sp_index in range(0, len(ix_sp)): for q_index in range(0, self.flow.stencil.q): border = np.zeros(self.flow.stencil.d, dtype=int) - if ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 and periodicity[0]: # searching border on left + if (ix_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 0] == -1 + and periodicity[0]): # searching border on left border[0] = -1 - elif ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 and periodicity[0]: # searching border on right + elif (ix_sp[sp_index] == nx - 1 and self.flow.stencil.e[q_index][ 0] == 1 + and periodicity[0]): # searching border on right border[0] = 1 - if iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 and periodicity[1]: # searching border on left + if (iy_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 1] == -1 + and periodicity[1]): # searching border on left border[1] = -1 - elif iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 and periodicity[1]: # searching border on right + elif (iy_sp[sp_index] == ny - 1 and self.flow.stencil.e[q_index][ 1] == 1 + and periodicity[1]): # searching border on right border[1] = 1 - if iz_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 2] == -1 and periodicity[2]: # searching border on left + if (iz_sp[sp_index] == 0 and self.flow.stencil.e[q_index][ 2] == -1 + and periodicity[2]): # searching border on left border[2] = -1 - elif iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index][ 2] == 1 and periodicity[2]: # searching border on right + elif (iz_sp[sp_index] == nz - 1 and self.flow.stencil.e[q_index][ 2] == 1 + and periodicity[2]): # searching border on right border[2] = 1 - try: # try in case the neighboring cell does not exist (and f pointing out of simulation domain) + try: # try in case the neighboring cell does not exist + # (and f pointing out of simulation domain) if (not mask[ix_sp[sp_index] + self.flow.stencil.e[q_index][ 0] - border[0] * nx, iy_sp[sp_index] + self.flow.stencil.e[q_index][ 1] - border[1] * ny, iz_sp[sp_index] + self.flow.stencil.e[q_index][ 2] - border[2] * nz] @@ -97,19 +123,24 @@ def __init__(self, context, flow, mask, global_solid_mask=None, periodicity: tup except IndexError: pass # just ignore this iteration since there is no neighbor there - self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), device=flow.context.device, dtype=torch.int64) # the batch-index has to be integer - #PHILIPP_occ_angepasst? self.f_index = torch.tensor(self.f_index, device=flow.context.device, dtype=torch.int64) # the batch-index has to be integer - self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=flow.context.device, + self.f_index_fwbb = torch.tensor(np.array(self.f_index_fwbb), + device=flow.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=flow.context.device, dtype=torch.int64) # batch-index has to be ix_sp tensor def __call__(self, flow): # FULLWAY-BBBC: inverts populations on all boundary nodes + # calc force on boundary:# if self.calc_force: self.calc_force_on_boundary(flow.f) + # bounce (invert populations on boundary nodes) - # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) + # -> basically an efficient version of: + # f = torch.where(self.mask, f[self.flow.stencil.opposite], f) if self.flow.torch_stencil.d == 2: flow.f[self.opposite_tensor[self.f_index_fwbb[:, 0]], @@ -127,6 +158,8 @@ def __call__(self, flow): self.f_index_fwbb[:, 3]] def calc_force_on_boundary(self, f): + """calculate fluid force on all relevant boundary nodes by + momentum exchange""" if self.flow.torch_stencil.d == 2: self.force_sum = 2 * torch.einsum('i..., id -> d', f[self.f_index_fwbb[:, 0], self.f_index_fwbb[:, 1], @@ -141,10 +174,12 @@ def make_no_collision_mask(self, f_shape: List[int], context: 'Context') -> Opti return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_streaming_mask(self, shape: List[int], context: 'Context') -> Optional[torch.Tensor]: + """FWBB needs streaming to invert the populations on the solid itself!""" pass def native_available(self) -> bool: return False def native_generator(self, index: int): + # not implemented yet pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py index fa843676..9e1d7b76 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/halfway_bounce_back_boundary.py @@ -9,24 +9,34 @@ class HalfwayBounceBackBoundary(Boundary): - def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, calc_force=None): + """ + HALFWAY BOUNCE BACK BOUNDARY CONDITION (HWBB): + - inverts populations on fluid nodex, neighboring boundary + - improved temporal accuracy in comparison to FWBB + - able to calculate fluid force on boundary by momentum exchange + """ + + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, + global_solid_mask=None, periodicity: tuple[bool,bool,bool|None] = None, + calc_force: bool = False): self.context = context self.flow = flow self.mask = solid_boundary_data.solid_mask - # self.solid_mask = solid_boundary_data.solid_mask - # global_solid_mask to filter out all "fake" fluid neighbors, which are outside this HWBB but not in the fluid region + # global_solid_mask to filter out all "fake" fluid neighbors, + # which are outside this HWBB but not in the fluid region if global_solid_mask is None: global_solid_mask = self.mask if periodicity is None: periodicity = (False, False, False if self.flow.stencil.d == 3 else None) - if calc_force is not None: + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( - self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.flow.stencil.e[0])) self.calc_force = True else: self.calc_force = False @@ -34,47 +44,67 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global self.f_index = [] self.f_collided = None - # combine f_index_lt and f_index_gt to self.f_index - if (hasattr(solid_boundary_data, "f_index_gt") or hasattr(solid_boundary_data, "f_index_lt")) and len(solid_boundary_data.f_index_lt.shape) == len(solid_boundary_data.f_index_gt.shape): # if solid_boundary_data contains batch_indices, use them + # COMPATIBILITY to unified Solid Boundary Data object! + # -> combine f_index_lt and f_index_gt (needed for IBB1) to self.f_index + + # if solid_boundary_data contains batch_indices, use them + if ((hasattr(solid_boundary_data, "f_index_gt") + or hasattr(solid_boundary_data, "f_index_lt")) + and len(solid_boundary_data.f_index_lt.shape) + == len(solid_boundary_data.f_index_gt.shape)): print("HWBB: SBD has got attributs f_index_gt and f_index_lt!") - self.f_index = np.concatenate((solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt), axis=0) - elif hasattr(solid_boundary_data, "f_index_gt") and solid_boundary_data.f_index_lt.shape[0] == 0: + self.f_index = np.concatenate((solid_boundary_data.f_index_lt, + solid_boundary_data.f_index_gt), axis=0) + elif (hasattr(solid_boundary_data, "f_index_gt") + and solid_boundary_data.f_index_lt.shape[0] == 0): print("HWBB: SBD has got attributf_index_gt (NO f_index_lt)!") self.f_index = solid_boundary_data.f_index_gt - elif hasattr(solid_boundary_data, "f_index_lt") and solid_boundary_data.f_index_gt.shape[0] == 0: + elif (hasattr(solid_boundary_data, "f_index_lt") + and solid_boundary_data.f_index_gt.shape[0] == 0): print("HWBB: SBD has got attributf_index_lt (NO f_index_gt)!") self.f_index = solid_boundary_data.f_index_lt - else: #else do ghetto-neighbour_search below - print("(INFO) HWBB didn't find solid_boundary_data, doing legacy neighbour_search on mask...") - # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d + else: #else do extra neighbour_search below + print("(INFO) HWBB didn't find solid_boundary_data, " + "doing legacy neighbour_search on mask...") + # searching boundary-fluid-interface and append indices to f_index if self.flow.stencil.d == 2: nx, ny = self.mask.shape # domain size in x and y - a, b = np.where(self.mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area + + # x- and y-index of boundaryTRUE nodes for iteration over boundary area + a, b = np.where(self.mask) for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask - for i in range(0, self.flow.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + for i in range(0, self.flow.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.flow.context.d, dtype=int) - if a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]: # searching border on left [x] + if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 + and periodicity[0]): # searching border on left [x] border[0] = -1 - elif a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]: # searching border on right [x] + elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 + and periodicity[0]): # searching border on right [x] border[0] = 1 - if b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]: # searching border on left [y] + if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 + and periodicity[1]): # searching border on left [y] border[1] = -1 - elif b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]: # searching border on right [y] + elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 + and periodicity[1]): # searching border on right [y] border[1] = 1 - try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) + try: # try in case the neighboring cell does not exist + # (= an f pointing out of the simulation domain) if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny] and not global_solid_mask[ a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny]): - # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: - # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour + # if the neighbour of p is False in the boundary.mask, + # p is a solid node, neighbouring a fluid node: + # ...the direction pointing from the fluid neighbour + # to solid p is marked on the neighbour self.f_index.append([self.flow.stencil.opposite[i], a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, @@ -90,22 +120,29 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global for i in range(0, self.flow.stencil.q): border = np.zeros(self.flow.context.d, dtype=int) # x - direction - if a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 and periodicity[0]: # searching border on left + if (a[p] == 0 and self.flow.stencil.e[i][ 0] == -1 + and periodicity[0]): # searching border on left border[0] = -1 - elif a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 and periodicity[0]: # searching border on right + elif (a[p] == nx - 1 and self.flow.stencil.e[i][ 0] == 1 + and periodicity[0]): # searching border on right border[0] = 1 # y - direction - if b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 and periodicity[1]: # searching border on left + if (b[p] == 0 and self.flow.stencil.e[i][ 1] == -1 + and periodicity[1]): # searching border on left border[1] = -1 - elif b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 and periodicity[1]: # searching border on right + elif (b[p] == ny - 1 and self.flow.stencil.e[i][ 1] == 1 + and periodicity[1]): # searching border on right border[1] = 1 # z - direction - if c[p] == 0 and self.flow.stencil.e[i][ 2] == -1 and periodicity[2]: # searching border on left + if (c[p] == 0 and self.flow.stencil.e[i][ 2] == -1 + and periodicity[2]): # searching border on left border[2] = -1 - elif c[p] == nz - 1 and self.flow.stencil.e[i][ 2] == 1 and periodicity[2]: # searching border on right + elif (c[p] == nz - 1 and self.flow.stencil.e[i][ 2] == 1 + and periodicity[2]): # searching border on right border[2] = 1 - try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) + try: # try in case the neighboring cell does not exist + # (an f pointing out of simulation domain) if (not self.mask[a[p] + self.flow.stencil.e[i][ 0] - border[0] * nx, b[p] + self.flow.stencil.e[i][ 1] - border[1] * ny, c[p] + self.flow.stencil.e[i][ 2] - border[2] * nz] @@ -122,20 +159,24 @@ def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, global pass # just ignore this iteration since there is no neighbor there # convert relevant tensors: - self.f_index = torch.tensor(np.array(self.f_index), device=self.flow.context.device, + self.f_index = torch.tensor(np.array(self.f_index), + device=self.flow.context.device, dtype=torch.int64) # the batch-index has to be integer - self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.flow.context.device, + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=self.flow.context.device, dtype=torch.int64) # batch-index has to be a tensor - # f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) - # f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) - # self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) def __call__(self, flow): + # HWBB: bounce populations on neighboring fluid nodes + # calc force on boundary: if self.calc_force: self.calc_force_on_boundary() + # bounce (invert populations on fluid nodes neighboring solid nodes) - # f = torch.where(self.f_mask[self.flow.stencil.opposite], f_collided[self.flow.stencil.opposite], f) + # efficient version of: + # f = torch.where(self.f_mask[self.flow.stencil.opposite], + # f_collided[self.flow.stencil.opposite], f) if self.flow.torch_stencil.d == 2: flow.f[self.opposite_tensor[self.f_index[:, 0]], @@ -149,47 +190,58 @@ def __call__(self, flow): def make_no_collision_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: - # INFO: for the halfway bounce back boundary, a no_collision_mask ist not necessary, because the no_stream_mask - # ...prevents interaction between nodes inside and outside the boundary region. - # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, - # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values - # ...in the whole domain (including the boundary region)! + # INFO: for the halfway bounce back boundary, + # a no_collision_mask ist not necessary, because the no_stream_mask + # prevents interaction between nodes inside and outside the boundary region. + # INFO: pay attention to the initialization of observable/moment-fields + # (u, rho,...) on the boundary nodes, in the initial solution of your flow, + # especially if visualization or post-processing uses the field-values + # in the whole domain (including the boundary region)! return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_streaming_mask(self, f_shape: List[int], context: 'Context' ) -> Optional[torch.Tensor]: - # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). - # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), + # ...but CAN be (x,y,z) (z optional). + # ...in the latter case, torch.where() broadcasts the mask to (q,x,y,z), + # ...so ALL q populations of a lattice-node are marked equally return self.context.convert_to_tensor(self.mask, dtype= bool) def calc_force_on_boundary(self): - # calculate force on boundary by momentum exchange method (MEA, MEM) according to Kruger et al., 2017, pp.215-217: - # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) is summed for all... - # ...populations pointing at the surface of the boundary - self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], self.flow.torch_stencil.e[self.f_index[:, 0]]) - #TODO: muss stencil.e noch als torch-tensor gespeihert werden, damit das hier schnell läuft? - #TODO: kann man den einfach konstant rausschreiben? dann muss das nicht ausgewertet werden... @(FWBB, @HWBB, @IBB) - - #TODO: find a way to use pre- and post-Streaming Populations for bounce... + """calculate the fluid force on boundary by momentum exchange""" + # calculate force on boundary by momentum exchange method (MEA, MEM) + # according to Kruger et al., 2017, pp.215-217: + # momentum (f_i*c_i - f_i_opposite*c_i_opposite = 2*f_i*c_i for a resting boundary) + # is summed for all populations pointing at the surface of the boundary + self.force_sum = 2 * torch.einsum('i..., id -> d', self.f_collided[:, 0], + self.flow.torch_stencil.e[self.f_index[:, 0]]) + def store_f_collided(self, f_collided): + """store populations between collision and streaming, because they are + needed for calculation of bounce and force!""" if self.flow.torch_stencil.d == 2: - self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q - self.f_index[:, 1], # x - self.f_index[:, 2]]) # y - self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q - self.f_index[:, 1], # x - self.f_index[:, 2]]) # y + self.f_collided[:, 0] = torch.clone( + f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y + self.f_collided[:, 1] = torch.clone( + f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2]]) # y if self.flow.torch_stencil.d == 3: - self.f_collided[:, 0] = torch.clone(f_collided[self.f_index[:, 0], # q - self.f_index[:, 1], # x - self.f_index[:, 2], # y - self.f_index[:, 3]]) # z - self.f_collided[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index[:, 0]], # q - self.f_index[:, 1], # x - self.f_index[:, 2], # y - self.f_index[:, 3]]) # z + self.f_collided[:, 0] = torch.clone( + f_collided[self.f_index[:, 0], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z + self.f_collided[:, 1] = torch.clone( + f_collided[self.opposite_tensor[self.f_index[:, 0]], # q + self.f_index[:, 1], # x + self.f_index[:, 2], # y + self.f_index[:, 3]]) # z def initialize_f_collided(self): + """initialize the tensors to store post-collision populations""" f_collided = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) f_collided_opposite = torch.zeros_like(self.f_index[:, 0], dtype=self.flow.context.dtype) self.f_collided = torch.stack((f_collided, f_collided_opposite), dim=1) @@ -198,4 +250,5 @@ def native_available(self) -> bool: return True def native_generator(self, index: int): + # not implemented yet pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py index b0a1b60f..9be701c7 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/helperCode.py @@ -3,6 +3,7 @@ class Logger(object): + """logging functionality proposed by P.Spelten to log stdout to file""" def __init__(self, outdir): self.terminal = sys.stdout self.filename = os.path.join(outdir, "log") @@ -14,6 +15,7 @@ def write(self, message): f.close() def flush(self): - # this flush method is needed for python 3 compatibility. This handles the flush command by doing nothing. + # this flush method is needed for python 3 compatibility. + # This handles the flush command by doing nothing. # you might want to specify some extra behavior here. pass \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py index 61fae6a5..4954504b 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/linear_interpolated_bounce_back_boundary.py @@ -1,67 +1,59 @@ import torch -import numpy as np -from typing import List, Optional from lettuce import Boundary, Flow, Context from solid_boundary_data import SolidBoundaryData __all__ = ["LinearInterpolatedBounceBackBoundary"] - - - -# THIS IS THE IBB in compact implementation WITH OCC-support class LinearInterpolatedBounceBackBoundary(Boundary): - """Interpolated Bounce Back Boundary Condition first introduced by Bouzidi et al. (2001), as described in Kruger et al. - (2017) - - linear or quadratic interpolation of populations to retain the true boundary location between fluid- and - solid-node - * version 2.0: using given indices and distances between fluid- and solid-node - of boundary link and boundary surface for interpolation! + """Interpolated Bounce Back Boundary Condition (IBB or IBB1) + first introduced by Bouzidi et al. (2001), as described in Kruger et al. (2017) + - linear interpolation of populations to retain the true boundary location between fluid- + and solid-node + - improved accuracy in comparison to FWBB and HWBB + - able to calculate fluid force on boundary by momentum exchange + - (!) RELIES on the SolidBoundaryData object to be provided, + which supplies the f_index and interpolation constants. """ - def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_force=None): + def __init__(self, context, flow, solid_boundary_data: SolidBoundaryData, calc_force: bool =False): self.context = context self.flow = flow self.mask = solid_boundary_data.solid_mask - # self.solid_mask = solid_boundary_data.solid_mask - if calc_force is not None: + + if calc_force: + # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) self.force_sum = torch.zeros_like(self.context.convert_to_tensor( - self.flow.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) + self.flow.stencil.e[0])) self.calc_force = True else: self.calc_force = False - #TODO: this if-statement with NONE and TRUE is wrong. When calc_force=False, it triggers calc_force=TRUE (!) - print("IBB: calc force is:", self.calc_force) - # convert relevant tensors: - ### TODO: fix batch-index-datatype...? - self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer - self.f_index_gt = torch.tensor(solid_boundary_data.f_index_gt, device=self.context.device, dtype=torch.int64) # the batch-index has to be integer + # convert relevant data to tensors: + ### TODO (QUESTION): fix batch-index-datatype (integer)? + self.f_index_lt = torch.tensor(solid_boundary_data.f_index_lt, + device=self.context.device, + dtype=torch.int64) # the batch-index has to be integer + self.f_index_gt = torch.tensor(solid_boundary_data.f_index_gt, + device=self.context.device, + dtype=torch.int64) # the batch-index has to be integer self.d_lt = self.context.convert_to_tensor(solid_boundary_data.d_lt) self.d_gt = self.context.convert_to_tensor(solid_boundary_data.d_gt) - self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, device=self.context.device, dtype=torch.int64) # batch-index has to be a tensor - # TODO: exchange for flow.torch_tencil.opposite + self.opposite_tensor = torch.tensor(self.flow.stencil.opposite, + device=self.context.device, + dtype=torch.int64) # batch-index has to be a tensor + # TODO (optional): replace self.opposite_tensor with flow.torch_stencil.opposite self.f_collided_lt = None self.f_collided_gt = None # will be populated in initialize_f_collided() method - #TODO: remove old/deprecated Code/comments below - - # DEPRECATED: got moved to method initialize f_collided - # f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values - # f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values - # f_collided_lt_opposite = torch.zeros_like(self.d_lt) - # f_collided_gt_opposite = torch.zeros_like(self.d_gt) - # self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) - # self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) - #TODO: does the f_collided etc. have to be reworked due to new lettuce master pre/post collision boundary stuff? def __call__(self, flow): - ## f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout + """ IBB1: interpolate bounced populations from two fluid nodes""" + ## reminder: f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout if self.flow.stencil.d == 2: # BOUNCE @@ -69,16 +61,17 @@ def __call__(self, flow): if len(self.f_index_lt) != 0: flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], - self.f_index_lt[:, 2]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ - self.f_index_lt[:, 0], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2]] + self.f_index_lt[:, 2]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2]]) # if d > 0.5 if len(self.f_index_gt) != 0: flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], - self.f_index_gt[:, 2]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + ( - 1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + self.f_index_gt[:, 2]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) if self.flow.stencil.d == 3: # BOUNCE @@ -87,40 +80,41 @@ def __call__(self, flow): flow.f[self.opposite_tensor[self.f_index_lt[:, 0]], self.f_index_lt[:, 1], self.f_index_lt[:, 2], - self.f_index_lt[:, 3]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * flow.f[ - self.f_index_lt[:, 0], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2], - self.f_index_lt[:, 3]] + self.f_index_lt[:, 3]] = (2 * self.d_lt * self.f_collided_lt[:, 0] + + (1 - 2 * self.d_lt) * flow.f[ + self.f_index_lt[:, 0], + self.f_index_lt[:, 1], + self.f_index_lt[:, 2], + self.f_index_lt[:, 3]]) # if d > 0.5 if len(self.f_index_gt) != 0: flow.f[self.opposite_tensor[self.f_index_gt[:, 0]], self.f_index_gt[:, 1], self.f_index_gt[:, 2], - self.f_index_gt[:, 3]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + ( - 1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] + self.f_index_gt[:, 3]] = ((1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1]) # CALC. FORCE on boundary (MEM, MEA) if self.calc_force: self.calc_force_on_boundary(flow.f) def make_no_streaming_mask(self, f_shape, context: Context): - # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). - # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally - # return torch.tensor(self.mask, dtype=torch.bool) + # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), + # but CAN be (x,y,z) (z optional). + # in the latter case, torch.where broadcasts the mask to (q,x,y,z), + # so ALL q populations of a lattice-node are marked equally return self.context.convert_to_tensor(self.mask, dtype=bool) def make_no_collision_mask(self, f_shape, context: Context): - # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, - # ...in the initial solution of your flow, especially if visualization or post-processing uses the field-values - # ...in the whole domain (including the boundary region)! - - # return torch.tensor(self.mask, dtype=torch.bool) # self.context.convert_to_tensor(self.mask) + # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) + # on the boundary nodes, in the initial solution of your flow, + # especially if visualization or post-processing uses the field-values + # in the whole domain (including the boundary region)! return self.context.convert_to_tensor(self.mask, dtype=bool) def calc_force_on_boundary(self, f_bounced): - # (!) THIS version should be more efficient (because vectorized) than one with a for-loop - ### force = e * (f_collided + f_bounced[opp.]) + """calulate the fluid force on the boundary by Momentum Exchange""" + ### basically: force = e * (f_collided + f_bounced[opp.]) if self.flow.stencil.d == 2: self.force_sum = torch.einsum('i..., id -> d', self.f_collided_lt[:, 0] + f_bounced[ @@ -150,64 +144,58 @@ def calc_force_on_boundary(self, f_bounced): self.f_index_gt[:, 3]], self.flow.torch_stencil.e[self.f_index_gt[:, 0]]) - # TODO: find a way to use pre- and post-Streaming Populations for bounce... - # def store_f_collided(self, f_collided): - # for f_index_lgt, f_collided_lgt in zip([self.f_index_lt, self.f_index_gt], - # [self.f_collided_lt, self.f_collided_gt]): - # if len(f_index_lgt) != 0: - # for d in range(self.flow.stencil.d): - # indices = [f_index_lgt[:, 0], # q - # f_index_lgt[:, 1], # x - # f_index_lgt[:, 2]] # y - # if self.flow.stencil.d == 3: - # indices.append(f_index_lgt[:, 3]) - # f_collided_lgt[:, 0] = torch.clone(f_collided[indices]) - # indices[0] = self.opposite_tensor[f_index_lgt[:, 0]] - # f_collided_lgt[:, 1] = torch.clone(f_collided[indices]) - # # TODO: compare performance of THIS to original hardcoded "store_f_collided()" of IBB1, see below - - #>>> OLD version "semi hardcoded" + def store_f_collided(self, f_collided): + """store populations between collision and streaming, because they are + needed for calculation of bounce and force!""" if self.flow.stencil.d == 2: if len(self.f_collided_lt) != 0: self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2]]) # y - self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_collided_lt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q self.f_index_lt[:, 1], # x - self.f_index_lt[:, 2]]) # y + self.f_index_lt[:, 2]])) # y if len(self.f_collided_gt) != 0: self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2]]) # y - self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q + self.f_collided_gt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q self.f_index_gt[:, 1], # x - self.f_index_gt[:, 2]]) # y + self.f_index_gt[:, 2]])) # y if self.flow.stencil.d == 3: if len(self.f_collided_lt) != 0: self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2], # y self.f_index_lt[:, 3]]) # z - self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q + self.f_collided_lt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q self.f_index_lt[:, 1], # x self.f_index_lt[:, 2], # y - self.f_index_lt[:, 3]]) # z + self.f_index_lt[:, 3]])) # z if len(self.f_collided_gt) != 0: self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2], # y self.f_index_gt[:, 3]]) # z - self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q + self.f_collided_gt[:, 1] = ( + torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:, 0]], # q self.f_index_gt[:, 1], # x self.f_index_gt[:, 2], # y - self.f_index_gt[:, 3]]) # z - #<<< OLD version "semi hardcoded" + self.f_index_gt[:, 3]])) # z - # TODO: is there an efficient way to run ibb as a pre-collision or post-collision boundary in the new lettuce timestep? (regarding new lettuce 2025+) def initialize_f_collided(self): - f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values - f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values + """initialize the tensors to store post-collision populations""" + + # float-tensor with number of (x_b nodes with d<=0.5) values + f_collided_lt = torch.zeros_like(self.d_lt) + + # float-tensor with number of (x_b nodes with d>0.5) values + f_collided_gt = torch.zeros_like(self.d_gt) + f_collided_lt_opposite = torch.zeros_like(self.d_lt) f_collided_gt_opposite = torch.zeros_like(self.d_gt) self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) @@ -217,4 +205,5 @@ def native_available(self) -> bool: return False def native_generator(self, index: int): + # not implemented yet return None diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py index 69a79b8a..2d886acf 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/observables_force_coefficients.py @@ -1,59 +1,74 @@ -from typing import Optional +"""Observables: force coefficients calculated from the force on an obstacle + - coefficient of drag + - coefficient of lift +""" import torch -import numpy as np - -from examples.advanced_projects.efficient_bounce_back_obstacle.obstacle_cylinder import ObstacleCylinder from lettuce import Flow, Observable __all__ = ['DragCoefficient', 'LiftCoefficient'] -#TODO: write this as "force"-coefficient and make X, Y, Z asl 0,1,2 direction choosable (parameter) +# TODO (optional): write this as "force"-coefficient and make +# X, Y, Z as 0,1,2 direction choosable (parameter) to unify drag and lift class DragCoefficient(Observable): - """The drag coefficient of an obstacle, calculated using momentum exchange method (MEM, MEA) - - calculates the density, gets the force in x direction on the obstacle boundary, - calculates the coefficient of drag + """The coefficient of drag of an obstacle, calculated using momentum exchange method (MEM, MEA) + - calculates the density, + - gets the force in x direction on the obstacle boundary, + - calculates the coefficient of drag """ def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): super().__init__(flow) self.obstacle_boundary = obstacle_boundary self.solid_mask = solid_mask - self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) + + # cross-sectional area of obstacle in LU + # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) + self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ + self.flow.units.characteristic_length_pu) + ** (self.flow.stencil.d-1)) self.nan_tensor = self.context.convert_to_tensor(torch.nan) self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): - #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region - # rho_mean (excluding (solid) boundary region, where values are non-physical): rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) rho_mean = torch.nanmean(rho_tmp) - force_x_lu = self.obstacle_boundary.force_sum[0] # get current force on obstacle in x direction - drag_coefficient = force_x_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) # calculate drag_coefficient in LU + + # get current force on obstacle in x direction + force_x_lu = self.obstacle_boundary.force_sum[0] + + # calculate drag_coefficient in LU + drag_coefficient = force_x_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) return drag_coefficient class LiftCoefficient(Observable): - """The drag coefficient of an obstacle, calculated using momentum exchange method (MEM, MEA) - - calculates the density, gets the force in x direction on the obstacle boundary, - calculates the coefficient of drag + """The coefficient of lift of an obstacle, calculated using momentum exchange method (MEM, MEA) + - calculates the density, + - gets the force in y direction on the obstacle boundary, + - calculates the coefficient of lift """ def __init__(self, flow, obstacle_boundary, solid_mask, area_pu): super().__init__(flow) self.obstacle_boundary = obstacle_boundary self.solid_mask = solid_mask - self.area_lu = area_pu * (self.flow.units.characteristic_length_lu/self.flow.units.characteristic_length_pu) ** (self.flow.stencil.d-1) # cross-sectional area of obstacle in LU (! lengthdimension in 2D -> area-dimension = self.lattice.D-1) + + # cross-sectional area of obstacle in LU + # (! mind length-dimension in 2D -> area-dimension = self.lattice.D-1) + self.area_lu = (area_pu * (self.flow.units.characteristic_length_lu/ + self.flow.units.characteristic_length_pu) + ** (self.flow.stencil.d-1)) self.nan_tensor = self.context.convert_to_tensor(torch.nan) self.solid_mask = self.context.convert_to_tensor(self.solid_mask, dtype=bool) def __call__(self, f: torch.Tensor = None): - #OLD rho = torch.mean(self.lattice.rho(f[:, 0, ...])) # simple rho_mean, including the boundary region - # rho_mean (excluding (solid) boundary region, where values are non-physical): rho_tmp = torch.where(self.solid_mask, self.nan_tensor, self.flow.rho(f)) rho_mean = torch.nanmean(rho_tmp) - force_y_lu = self.obstacle_boundary.force_sum[1] # get current force on obstacle in x direction - lift_coefficient = force_y_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) # calculate drag_coefficient in LU + + # get current force on obstacle in y direction + force_y_lu = self.obstacle_boundary.force_sum[1] + + # calculate lift_coefficient in LU + lift_coefficient = force_y_lu / (0.5 * rho_mean * self.flow.units.characteristic_velocity_lu ** 2 * self.area_lu) return lift_coefficient \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py index 4776e45c..de63c8f6 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/obstacle_cylinder.py @@ -1,4 +1,3 @@ -import warnings from typing import Union, List, Optional import numpy as np @@ -7,8 +6,7 @@ from lettuce.ext._flows import ExtFlow from lettuce import UnitConversion, Context, Stencil, Equilibrium from lettuce.util import append_axes -from lettuce.ext import (EquilibriumBoundaryPU, BounceBackBoundary, - EquilibriumOutletP, AntiBounceBackOutlet) +from lettuce.ext import (EquilibriumBoundaryPU, EquilibriumOutletP) from examples.advanced_projects.efficient_bounce_back_obstacle import SolidBoundaryData, FullwayBounceBackBoundary, HalfwayBounceBackBoundary, LinearInterpolatedBounceBackBoundary __all__ = ['ObstacleCylinder'] @@ -16,33 +14,24 @@ class ObstacleCylinder(ExtFlow): """ - unified version of 2D and 3D cylinder flow - refined version of flow/obstacle.py, for cylinder-flow in 2D or 3D. The dimensions will be assumed from - len(resolution) + FLOW AROUND A CIRCULAR CYLINDER in 2D or 3D Flow: - inflow (EquilibriumBoundaryPU) at x=0, outflow (EquilibriumOutletP) at x=xmax - further boundaries depend on parameters: - lateral (y direction): periodic, no-slip wall, slip wall - - lateral (z direction): periodic (only if lattice.D==3) + - lateral (z direction): periodic (only if 3D) - obstacle: cylinder obstacle centered at (y_lu/2+x_offset, y_LU/2+y_offset), with radius=char_length, uniform symmetry in z-direction - boundary condition for obstacle can be chosen: hwbb, fwbb, ibb1 - AND storage-formats: fwbb, fwbbc (compact), hwbb, hwbbc1, hwbbc2, hwbbc3, ibb1, ibb1c1, ibb1c2 - - c: compact implementation (= DIY-sparse, faster and less memory) - - c2 better than c1 - - c3 is hwbb-only for use with older simulation-classes that don't contain a store_f_collided call! - recommendation: use fwbbc, hwbbc2, ibb1c2 for best runtime- and memory-efficiency - - initial pertubation (trigger Von Kármán vortex street for Re>46) can be initialized in y and z direction - - initial velocity can be 0, u_char or a parabolic profile (parabolic if lateral_walls = "bounceback") + - FWBB: fullway bounce back + - HWBB: halfway bounce back + - IBB1: linear interpolated bounce back + - initial perturbation (trigger Von Kármán vortex street for Re>46) can be initialized in y and z direction + - initial velocity can be 0, u_char or a parabolic profile (parabolic if lateral_walls are True) - inlet/inflow velocity can be uniform u_char or parabolic - - Parameters: - ---------- - - ---------- """ - # TODO: rewrite description above + def __init__(self, context: Context, resolution: Union[int, List[int]], reynolds_number, mach_number, @@ -54,97 +43,140 @@ def __init__(self, context: Context, resolution: Union[int, List[int]], stencil: Optional[Stencil] = None, equilibrium: Optional[Equilibrium] = None): - self.char_length_pu = char_length_pu # characteristic length - self.char_length_lu = char_length_lu # characteristic length in PU - # TODO: praktische Dopplung wenn char_length_lu UND resolution zusammen mit char_length_pu angegeben werden... - self.resolution = self.make_resolution(resolution, stencil) # shape in LU, if only INT, a cube shaped domain is assumed + self.char_length_pu = char_length_pu + self.char_length_lu = char_length_lu self.char_velocity_pu = char_velocity_pu - print("OBST_Cylinder: self.resolution=", self.resolution) - + # domain "shape" in LU. + # If only one INT, a square/cube-shaped domain is assumed + self.resolution = self.make_resolution(resolution, stencil) # flow and boundary settings - self.perturb_init = perturb_init # toggle: introduce asymmetry in initial solution to trigger v'Karman Vortex Street - self.u_init = u_init # toggle: initial solution velocity profile type - self.lateral_walls = lateral_walls # toggle: lateral walls to be bounce back (bounceback), slip wall (slip) or periodic (periodic) - self.bc_type = bc_type # toggle: bounce back algorithm: halfway (hwbb), fullway (fwbb), linearly interpolated (ibb1) + self.perturb_init = perturb_init + # toggle: introduce asymmetry in initial solution to trigger + # v'Karman Vortex Street + + self.u_init = u_init + # toggle: initial solution velocity profile type + + self.lateral_walls = lateral_walls + # toggle: lateral walls to be bounce back (bounceback), + # slip wall (slip) or periodic (periodic) + + self.bc_type = bc_type + # toggle: bounce back algorithm: + # - halfway (hwbb), + # - fullway (fwbb), + # - linearly interpolated (ibb1) self.calc_force_coefficients = calc_force_coefficients self.periodicity = (False, False, True if len(self.resolution) == 3 else None) + # initialize masks (init with zeros) - self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) # marks all solid nodes (obstacle, walls, ...) - self.wall_mask = np.zeros_like(self.solid_mask) # marks lateral (top+bottom) walls - self._obstacle_mask = np.zeros_like(self.solid_mask) # marks all obstacle nodes (for fluid-solid-force_calc.) + self.solid_mask = np.zeros(shape=self.resolution, dtype=bool) + # marks all solid nodes (obstacle, walls, ...) + + self.wall_mask = np.zeros_like(self.solid_mask) + # marks lateral (top+bottom) walls + + self._obstacle_mask = np.zeros_like(self.solid_mask) + # marks all obstacle nodes (for fluid-solid-force_calculation) # initialize super class with unit conversion, equilibrium, context etc. ExtFlow.__init__(self, context, resolution, reynolds_number, mach_number, stencil, equilibrium) - # UnitConversion: defined below unter make_units(), executed by ExtFlow; flow object gets units-attibute! - self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) # marks all inlet nodes + self.in_mask = np.zeros(self.grid[0].shape, dtype=bool) + # marks all inlet nodes # cylinder geometry in LU self.x_offset = x_offset self.y_offset = y_offset self.radius_lu = char_length_lu / 2 - self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset # y_position of cylinder-center in 1-based indexing - self.x_pos_lu = self.y_pos_lu + self.x_offset # keep symmetry of cylinder in x and y direction + self.y_pos_lu = self.resolution[1] / 2 + 0.5 + self.y_offset + # y_position of cylinder-center in 1-based indexing + self.x_pos_lu = self.y_pos_lu + self.x_offset + # keep symmetry of cylinder in x and y direction # MESHGRID of x, y, (z) index (LU) - xyz = tuple(np.linspace(1, n, n) for n in self.resolution) # Tupel of index-lists (1-n (one-based!)) + xyz = tuple(np.linspace(1, n, n) for n in self.resolution) + # Tupel of index-lists (1-n (one-based!)) if self.stencil.d == 2: - x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y-index + # meshgrid of x-, y-index + x_lu, y_lu = np.meshgrid(*xyz, indexing='ij') elif self.stencil.d == 3: - x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- and z-index + # meshgrid of x-, y- and z-index + x_lu, y_lu, z_lu = np.meshgrid(*xyz, indexing='ij') else: x_lu, y_lu, z_lu = 1,1,1 - print("WARNING: something went wrong in LU-gird-index generation, lattice.D must be 2 or 3!") + print("CYLINDER OBSTACLE -> WARNING: something went wrong in LU-gird-index generation," + " lattice.D must be 2 or 3!") + + # Options for obstacle definition: + # 1) basic mask (no interpolation) -> see right below + # 2) IBB-index and mask -> see method make_ibb_index_lists below + # TODO: 3) OCC-enabled -> read externally supplied SolidBoundaryData - # BASIC mask-Version of circular cylinder. - #TODO: Options for obstacle def: 1) basic mask (no interpolation), 2) IBB-index and mask, 3) OCC-enabled - condition = np.sqrt((x_lu - self.x_pos_lu) ** 2 + (y_lu - self.y_pos_lu) ** 2) < self.radius_lu + # Opt1: basic mask (no interpolation) + condition = np.sqrt((x_lu - self.x_pos_lu) ** 2 + + (y_lu - self.y_pos_lu) ** 2) < self.radius_lu self.obstacle_mask[np.where(condition)] = 1 self.solid_mask[np.where(condition)] = 1 - print("OBST_Cylinder: x_pos, y_pos, radius:", self.x_pos_lu, self.y_pos_lu, self.radius_lu) + print("CYLINDER OBSTACLE: x_pos, y_pos, radius:", self.x_pos_lu, self.y_pos_lu, self.radius_lu) # MASKS for solid boundaries (lateral walls, obstacle, solid (= obstacle and walls), inlet) # (INFO): indexing doesn't need z-Index for 3D, everything is broadcasted along z! - if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': # if top and bottom are link-based BC + if self.lateral_walls == 'bounceback' or self.lateral_walls == 'slip': + # if top and bottom are link-based BC self.wall_mask[:, [0, -1]] = True # don't mark wall nodes as inlet self.solid_mask[np.where(self.wall_mask)] = 1 # mark solid walls - self.in_mask[0, 1:-1] = True # inlet on the left, except for top and bottom wall (y=0, y=y_max) + self.in_mask[0, 1:-1] = True + # inlet on the left, except for top and bottom wall (y=0, y=y_max) else: # if lateral_wals == 'periodic', no walls self.in_mask[0, :] = True # inlet on the left (x=0) - # generate parabolic velocity profile for inlet BC if lateral_walls (top and bottom) are bounce back walls (== channel-flow) - self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() # u = [ux,uy,uz] = [1,0,0] in PU // uniform characteristic velocity in x-direction + # generate parabolic velocity profile for inlet BC if lateral_walls + # (top and bottom) are bounce back walls (== channel-flow) + self.u_inlet = self.units.characteristic_velocity_pu * self._unit_vector() + # u = [ux,uy,uz] = [1,0,0] in PU // uniform characteristic velocity in x-direction + if self.lateral_walls == 'bounceback': - ## parabolic velocity profile, zeroing on the edges + """ + parabolic velocity profile, zeroing on the edges ## How to parabola: ## 1.parabola in factorized form (GER: "Nullstellenform"): y = (x-x1)*(x-x2) ## 2.parabola with a maximum and zero at x1=0 und x2=x0: y=-x*(x-x0) ## 3.scale parabola, to make y_s(x_s)=1 the maximum: y=-x*(x-x0)*(1/(x0/2)²) - ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, also making the integral of a homogeneous velocity profile with u=1 and the parabolic profile being equal + ## (4. optional) scale amplitude with 1.5 to have a mean velocity of 1, + also making the integral of a homogeneous velocity profile with u=1 + and the parabolic profile being equal + """ + (nx, ny, nz) = self.resolution # number of gridpoints in y direction parabola_y = np.zeros((1, ny)) - y_coordinates = np.linspace(0, ny, ny) # linspace() creates n points between 0 and ny, including 0 and ny: - # top and bottom velocity values will be zero to agree with wall-boundary-condition + y_coordinates = np.linspace(0, ny, ny) + # linspace() creates n points between 0 and ny, including 0 and ny: + # top and bottom velocity values will be zero to agree with wall-boundary-condition + parabola_y[:, 1:-1] = - 1.5 * np.array(self.u_inlet).max() * y_coordinates[1:-1] * ( y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 # parabolic velocity profile - # scale with 1.5 to achieve a mean velocity of u_char! -> DIFFERENT FROM cylinder2D and cylinder3D (!) + # scale with 1.5 to achieve a mean velocity of u_char! + if self.stencil.d == 2: # in 2D u1 needs Dimension 1 x ny (!) velocity_y = np.zeros_like(parabola_y) # y-velocities = 0 - self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) # stack/pack u-field + + # stack/pack u-field + self.u_inlet = np.stack([parabola_y, velocity_y], axis=0) elif self.stencil.d == 3: ones_z = np.ones(nz) parabola_yz = parabola_y[:, :, np.newaxis] * ones_z parabola_yz_zeros = np.zeros_like(parabola_yz) - # create u_xyz inlet yz-plane: - self.u_inlet = np.stack([parabola_yz, parabola_yz_zeros, parabola_yz_zeros], axis=0) # stack/pack u-field + # create u_xyz inlet yz-plane and stack/pack u-field + self.u_inlet = np.stack([parabola_yz, parabola_yz_zeros, parabola_yz_zeros], axis=0) def make_units(self, reynolds_number, mach_number, resolution: List[int] ) -> 'UnitConversion': @@ -155,7 +187,6 @@ def make_units(self, reynolds_number, mach_number, resolution: List[int] characteristic_velocity_pu=self.char_velocity_pu ) - # gibt immer N-dimensional zurück! (flow.resolution darf auch ein int sein, dann wird quadratisch/kubisch angenommen) def make_resolution(self, resolution: Union[int, List[int]], stencil: Optional['Stencil'] = None) -> List[int]: if isinstance(resolution, int): @@ -173,24 +204,35 @@ def obstacle_mask(self, m): self._obstacle_mask = m.astype(bool) def initial_pu(self): + """initial flow field in physical units: + - considers perturbation and dimensionality + """ p = np.zeros_like(self.grid[0], dtype=float)[None, ...] u_max_pu = self.units.characteristic_velocity_pu * self._unit_vector() u_max_pu = append_axes(u_max_pu, self.stencil.d) - #OLD? self.solid_mask[np.where(self.obstacle_mask)] = 1 # TODO: is this line needed? OLD: # This line is needed, because the obstacle_mask.setter does not define the solid_mask properly (see above) #OLD - ### initial velocity field: "u_init"-parameter + """ + initial velocity field: "u_init"-parameter # 0: uniform u=0 - # 1: uniform u=1 or parabolic (depends on lateral_walls -> bounceback => parabolic; slip, periodic => uniform) + # 1: uniform u=1 or parabolic + # depends on lateral_walls: + # - bounceback => parabolic; + # - slip, periodic => uniform + """ u = (1 - self.solid_mask) * u_max_pu if self.u_init == 0: u = u * 0 # uniform u=0 else: - if self.lateral_walls == 'bounceback': # parabolic along y, uniform along x and z (similar to poiseuille-flow) + if self.lateral_walls == 'bounceback': + # parabolic along y, uniform along x and z (similar to poiseuille-flow) + ny = self.resolution[1] # number of gridpoints in y direction ux_factor = np.zeros(ny) # vector for one column (u(x=0)) + # multiply parabolic profile with every column of the velocity field: y_coordinates = np.linspace(0, ny, ny) - ux_factor[1:-1] = - y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) * 1 / (ny / 2) ** 2 + ux_factor[1:-1] = (- y_coordinates[1:-1] * (y_coordinates[1:-1] - ny) + * 1 / (ny / 2) ** 2) if self.stencil.d == 2: u = np.einsum('k,ijk->ijk', ux_factor, u) elif self.stencil.d == 3: @@ -205,14 +247,22 @@ def initial_pu(self): ny = self.grid[1].shape[1] if u.max() < 0.5 * self.units.characteristic_velocity_pu: # add perturbation for small velocities - amplitude_y = np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 + amplitude_y = (np.sin(np.linspace(0, ny, ny) / ny * 2 * np.pi) + * self.units.characteristic_velocity_pu * 0.1) if self.stencil.d == 2: u[0][1] += amplitude_y elif self.stencil.d == 3: nz = self.grid[2].shape[2] - plane_yz = np.ones_like(u[0, 1]) # plane of ones - u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) # plane of amplitude in y - amplitude_z = np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * self.units.characteristic_velocity_pu * 0.1 # amplitude in z + + # plane of ones + plane_yz = np.ones_like(u[0, 1]) + + # plane of amplitude in y + u[0][1] = np.einsum('y,yz->yz', amplitude_y, plane_yz) + + # amplitude in z + amplitude_z = (np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) + * self.units.characteristic_velocity_pu * 0.1) u[0][1] += np.einsum('z,yz->yz', amplitude_z, plane_yz) else: # multiply scaled down perturbation if velocity field is already near u_char @@ -223,36 +273,38 @@ def initial_pu(self): nz = self.grid[2].shape[1] plane_yz = np.ones_like(u[0, 1, :, :]) u[0][1] = np.einsum('y,yz->yz', factor, u[0][1]) - factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 # perturbation in z-direction + + # perturbation in z-direction + factor = 1 + np.sin(np.linspace(0, nz, nz) / nz * 2 * np.pi) * 0.1 u[0][1] = np.einsum('z,yz->yz', factor, u[0][1]) return p, u def make_solid_boundary_data(self, x_center, y_center, radius): - pass - # NOT YET IMPLEMENTED; OCC-version for index lists; CURRENTLY only make_ibb_index_lists is used with analytic function (see below) - # TODO: wo sollte das SolidBoundaryData Objekt definiert werden bzw. die Klasse definiert werden? - - # cylinder definitions: - # OPTION 1: calculate directly from analytic function -> see make_ibb_index_lists below - # OPTION 2: calculate via OCC (Philipp) -> see external house/MA - # OPTION 3 (FWBB, HWBB only): calculate from mask or only give mask... + print("(!) CYLINDER OBSTACLE WARNING: make_solid_boundary_data() is not implemented yet!") + # NOT YET IMPLEMENTED; OCC-version for index lists; + # CURRENTLY only make_ibb_index_lists is used with analytic function (see below) + # TODO (future): make reading of externally supplied SolidBoundaryData object a thing def make_ibb_index_lists(self, x_center, y_center, radius): - - # this method relies on self.obstacle_mask too! - f_index_lt = [] # indices of relevant populations (for bounce back and force-calculation) with d<=0.5 - f_index_gt = [] # indices of relevant populations (for bounce back and force-calculation) with d>0.5 + """calculate interpolation constants and population indices for + HWBB and IBB1 BBBC + """ + # INFO: this method relies on self.obstacle_mask too! + f_index_lt = [] # indices of relevant populations with d<=0.5 + f_index_gt = [] # indices of relevant populations with d>0.5 d_lt = [] # distances between node and boundary for d<0.5 d_gt = [] # distances between node and boundary for d>0.5 # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d if self.stencil.d == 2: - # TODO: the 2D and 3D options could be condensed/unified + # TODO (optional): the 2D and 3D options could be condensed/unified nx, ny = self.obstacle_mask.shape # domain size in x and y - a, b = np.where(self.obstacle_mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area + a, b = np.where(self.obstacle_mask) + # x- and y-index of boundaryTRUE nodes for iteration over boundary area for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask - for i in range(0, self.stencil.q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) + for i in range(0, self.stencil.q): + # for all stencil-directions c_i (lattice.stencil.e in lettuce) # check for boundary-nodes neighboring the domain-border. # ...they have to take the periodicity into account... border = np.zeros(self.stencil.d, dtype=int) @@ -270,18 +322,25 @@ def make_ibb_index_lists(self, x_center, y_center, radius): try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny]: - # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: - # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour + # if the neighbour of p is False in the boundary.mask, + # p is a solid node, neighbouring a fluid node: + # the direction pointing from the fluid neighbour + # to solid p is marked on the neighbour # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate - cx = self.stencil.e[self.stencil.opposite[i]][ 0] # link-direction x to solid node - cy = self.stencil.e[self.stencil.opposite[i]][ 1] # link-direction y to solid node + px = a[p] + self.stencil.e[i][0] - border[0] * nx + # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny + # fluid node y-coordinate + cx = self.stencil.e[self.stencil.opposite[i]][ 0] + # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][ 1] + # link-direction y to solid node # pq-formula - h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) + / (cx * cx + cy * cy)) # p/2 h2 = (px * px + py * py + x_center * x_center + y_center * y_center - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( cx * cx + cy * cy) # q @@ -296,28 +355,29 @@ def make_ibb_index_lists(self, x_center, y_center, radius): if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny]) else: # neither d1 or d2 is real and between 0 and 1 - print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], + print("IBB WARNING: d1 is", d1, "; d2 is", d2, + "for boundaryPoint x,y,ci", a[p], b[p], self.stencil.e[i][0], self.stencil.e[i][1]) except IndexError: pass # just ignore this iteration since there is no neighbor there @@ -345,23 +405,29 @@ def make_ibb_index_lists(self, x_center, y_center, radius): elif c[p] == nz - 1 and self.stencil.e[i][2] == 1: # searching border on right border[2] = 1 - try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) + try: # try in case the neighboring cell does not exist + # (an f pointing out of simulation domain) if not self.obstacle_mask[a[p] + self.stencil.e[i][0] - border[0] * nx, b[p] + self.stencil.e[i][1] - border[1] * ny, c[p] + self.stencil.e[i][2] - border[2] * nz]: # calculate intersection point of boundary surface and link -> # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.stencil.e[i][0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.stencil.e[i][1] - border[1] * ny # fluid node y-coordinate + px = a[p] + self.stencil.e[i][0] - border[0] * nx + # fluid node x-coordinate + py = b[p] + self.stencil.e[i][1] - border[1] * ny + # fluid node y-coordinate # Z-coordinate not needed for cylinder ! - cx = self.stencil.e[self.stencil.opposite[i]][0] # link-direction x to solid node - cy = self.stencil.e[self.stencil.opposite[i]][1] # link-direction y to solid node + cx = self.stencil.e[self.stencil.opposite[i]][0] + # link-direction x to solid node + cy = self.stencil.e[self.stencil.opposite[i]][1] + # link-direction y to solid node # Z-coordinate not needed for cylinder ! # pq-formula - h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 + h1 = ((px * cx + py * cy - cx * x_center - cy * y_center) + / (cx * cx + cy * cy)) # p/2 h2 = (px * px + py * py + x_center * x_center + y_center * y_center - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( cx * cx + cy * cy) # q @@ -369,8 +435,6 @@ def make_ibb_index_lists(self, x_center, y_center, radius): d1 = - h1 + np.sqrt(h1 * h1 - h2) d2 = - h1 - np.sqrt(h1 * h1 - h2) - # print("xb,yb,i,d1,d2 xf, yf, cx, cy:", a[p], b[p], i, d1, d2, px, py, cx, cy) - # distance from fluid node to the "true" boundary location # choose correct d and assign d and f_index if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 @@ -378,32 +442,33 @@ def make_ibb_index_lists(self, x_center, y_center, radius): if d1 <= 0.5: d_lt.append(d1) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny, - c[p] + self.stencil.e[i][2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d1) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny, - c[p] + self.stencil.e[i][2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 if d2 <= 0.5: d_lt.append(d2) f_index_lt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny, - c[p] + self.stencil.e[i][2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # d>0.5 d_gt.append(d2) f_index_gt.append([self.stencil.opposite[i], - a[p] + self.stencil.e[i][0] - border[0] * nx, - b[p] + self.stencil.e[i][1] - border[1] * ny, - c[p] + self.stencil.e[i][2] - border[2] * nz]) + a[p] + self.stencil.e[i][0] - border[0] * nx, + b[p] + self.stencil.e[i][1] - border[1] * ny, + c[p] + self.stencil.e[i][2] - border[2] * nz]) else: # neither d1 nor d2 is real and between 0 and 1 - print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], + print("IBB WARNING: d1 is", d1, "; d2 is", d2, + "for boundaryPoint x,y,z,ci", a[p], b[p], c[p], self.stencil.e[i][0], self.stencil.e[i][1], self.stencil.e[i][2]) except IndexError: @@ -415,7 +480,8 @@ def make_ibb_index_lists(self, x_center, y_center, radius): @property def grid(self): - xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) for n in self.resolution) # tuple of lists of x,y,(z)-values/indices + xyz = tuple(self.units.convert_length_to_pu(np.linspace(0, n, n)) + for n in self.resolution) # tuple of lists of x,y,(z)-values/indices return np.meshgrid(*xyz, indexing='ij') # meshgrid of x-, y- (und z-)values/indices @property @@ -426,11 +492,11 @@ def post_boundaries(self): mask=self.in_mask, velocity=self.u_inlet) - # outlet ("right side", x[-1],y[:], (z[:])) + # outlet ("right side" (positive x-direction), x[-1],y[:], (z[:])) if self.stencil.d == 2: - outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) # outlet in positive x-direction + outlet_boundary = EquilibriumOutletP(direction=[1, 0], flow=self) else: # self.stencil.d == 3: - outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # outlet in positive x-direction + outlet_boundary = EquilibriumOutletP(direction=[1, 0, 0], flow=self) # create and return boundary-list return [ @@ -440,53 +506,71 @@ def post_boundaries(self): @property def post_streaming_boundaries(self): - # LATERAL WALLS (optional) - lateral_boundary = None # stays None if lateral walls are not specified... (NOT IMPLEMENTED YET, see below) - # >>> lateral walls ("top and bottom walls", x[:], y[0,-1], z[:]) + """walls that should use the efficient bounce back boundaries""" + # LATERAL WALLS (optional) ("top and bottom walls", x[:], y[0,-1], z[:]) + lateral_boundary = None + # stays None if lateral walls are not specified... if self.lateral_walls == 'bounceback': if self.bc_type == 'hwbb' or self.bc_type == 'HWBB': # use halfway bounce back - print( - "(!) OBST. CYLINDER: lateral walls can currenlty only be FWBB, HWBB requirest Solid Boundary Data which is not implemented yet for lateral walls!") + print("(!) OBST. CYLINDER: lateral walls can currenlty only be " + "FWBB, HWBB requirest Solid Boundary Data which is not " + "implemented yet for lateral walls!") lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) - # TODO (low prio): implement ibb- and hwbb-option for lateral walls + # TODO (optional, low prio): implement ibb- and hwbb-option for lateral walls else: # else use fullway bounce back lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) - elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': # use slip-wall (symmetry boundary) - print("(!) OBST. CYLINDER: lateral walls can currenlty only be FWBB, slip boundary is not implemented yet!") + elif self.lateral_walls == 'slip' or self.bc_type == 'SLIP': + # use slip-wall (symmetry boundary) + print("(!) OBST. CYLINDER: lateral walls can currenlty only be " + "FWBB, slip boundary is not implemented yet!") lateral_boundary = FullwayBounceBackBoundary(self.context, self, self.wall_mask, periodicity=self.periodicity) - # TODO (low prio): implement slip-option for lateral walls + # TODO (optional, low prio): implement slip-option for lateral walls - # obstacle (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) -> to be set via obstacle_mask.setter - obstacle_boundary = None - # (!) the obstacle_boundary should always be the last boundary in the list of boundaries to correctly calculate forces on the obstacle + # obstacle + # (for example: obstacle "cylinder" with radius centered at position x_pos, y_pos) + # (!) the obstacle_boundary should always be the last boundary + # in the list of boundaries to correctly calculate forces on the obstacle solid_boundary_data = SolidBoundaryData() solid_boundary_data.solid_mask = self.obstacle_mask - # load index-lists for circular cylinder into SBD object [f_index_lt, f_index_gt, d_lt, d_gt]: - solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, solid_boundary_data.d_lt, solid_boundary_data.d_gt = self.make_ibb_index_lists( + # load index-lists for circular cylinder into SBD object + # [f_index_lt, f_index_gt, d_lt, d_gt]: + (solid_boundary_data.f_index_lt, solid_boundary_data.f_index_gt, + solid_boundary_data.d_lt, solid_boundary_data.d_gt) = self.make_ibb_index_lists( x_center=self.x_pos_lu-1, y_center=self.y_pos_lu-1, radius=self.radius_lu) print("CYLINDER: the bc_type was given as:", self.bc_type) if self.bc_type.casefold() == 'fwbb' or self.bc_type == 'FWBB': obstacle_boundary = FullwayBounceBackBoundary(self.context, self, - self.obstacle_mask, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) + self.obstacle_mask, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'hwbb' or self.bc_type == 'HWBB': obstacle_boundary = HalfwayBounceBackBoundary(self.context, self, - solid_boundary_data, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) + solid_boundary_data, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) elif self.bc_type.casefold() == 'ibb1' or self.bc_type == 'IBB1': - obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, solid_boundary_data, calc_force=self.calc_force_coefficients) + obstacle_boundary = LinearInterpolatedBounceBackBoundary(self.context, self, + solid_boundary_data, + calc_force=self.calc_force_coefficients) else: # use basic mask Bounce Back - print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... - will fall back to using FullwayBounceBackBoundary") + print("OBSTACLE CYLINDER - WARNING: bc_type can't be interpreted... " + "- will fall back to using FullwayBounceBackBoundary") obstacle_boundary = FullwayBounceBackBoundary(self.context, self, - self.obstacle_mask, periodicity = self.periodicity, calc_force=self.calc_force_coefficients) + self.obstacle_mask, + periodicity = self.periodicity, + calc_force=self.calc_force_coefficients) # create and return boundary-list - if lateral_boundary is None: # if lateral boundary is periodic...don't include the lateral_boundary object in the boundaries-list + if lateral_boundary is None: + # if lateral boundary is periodic... + # don't include the lateral_boundary object in the boundaries-list return [ obstacle_boundary ] @@ -495,7 +579,6 @@ def post_streaming_boundaries(self): lateral_boundary, obstacle_boundary ] - #OLD: return [obstacle_boundary] def _unit_vector(self, i=0): return np.eye(self.stencil.d)[i] \ No newline at end of file diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py index 369ab3ca..3d11c209 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_ProfileReporter.py @@ -1,5 +1,3 @@ - - import torch import numpy as np import os @@ -9,7 +7,12 @@ from lettuce._simulation import Reporter, Simulation class ProfileReporter(Reporter): - + """ reporter to save average velocity profiles + -> use the ProfilePlotter in post-processing to further process + and plot data! + """ + + def __init__(self, interval: int, flow, position_lu, i_start): super().__init__(interval) self.flow = flow diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py index 5a5cec16..4f0166d2 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/reporter_advanced_vtk_reporter.py @@ -1,5 +1,3 @@ - - import os import numpy as np import pyevtk.hl as vtk @@ -10,7 +8,8 @@ "write_vtk", "VTKReporterAdvanced", "VTKsliceReporter" ] -def write_vtk(point_dict, id=0, filename_base="./data/output", origin: tuple[float, float, float] = (0.0, 0.0, 0.0)): +def write_vtk(point_dict, id=0, filename_base="./data/output", + origin: tuple[float, float, float] = (0.0, 0.0, 0.0)): vtk.imageToVTK( path=f"{filename_base}_{id:08d}", origin=origin, @@ -21,9 +20,11 @@ def write_vtk(point_dict, id=0, filename_base="./data/output", origin: tuple[flo ) class VTKReporterAdvanced(Reporter): - """General VTK Reporter for velocity and pressure""" - "EDIT (M.Bille: can insert solid-mask to pin observables to zero, inside solid obstacle, " \ - "...useful for boundaries that store populations inside the boundary-region (FWBB), making obs(f) deviate from 0" + """ + Advanced VTK reporter. Adds functionality: + - take solid_mask and pin observable values inside the solid to 0 + -> useful for suboptimal initialization or use of FWBB boundary + """ def __init__(self, flow, interval=50, filename_base="./data/output", solid_mask=None, imin=0, imax=None): @@ -54,47 +55,52 @@ def __call__(self, simulation): if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: u = self.flow.units.convert_velocity_to_pu(self.flow.u(self.flow.f)) p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(self.flow.f)) - #rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) +#if you want density as well: rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) if self.flow.stencil.d == 2: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) else: - self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) -#ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ..., None])) # for boundaries that store populations "inside" the boundary + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ..., None])) for d in range(self.flow.stencil.d): if self.solid_mask is None: - self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ..., None]) + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ..., None])) else: - self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ..., None])) -#ALTERNATIVE: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, u[d, ..., None])) - #self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + self.point_dict[f"u{'xyz'[d]}"] = ( + np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ..., None]))) +#if you want density as well: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) else: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) else: - self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) - #ORIGINAL: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) -#ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ...])) + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ...])) for d in range(self.flow.stencil.d): - #ORIGINAL: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) if self.solid_mask is None: - self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ...])) else: - self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(u[d, ...])) + self.point_dict[f"u{'xyz'[d]}"] = ( + np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(u[d, ...]))) - #self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) +#if you want density as well: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) write_vtk(self.point_dict, self.flow.i, self.filename_base) def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): """ - Outputs the no_collision_mask of the simulation object as VTK-file with range [0,1] - Usage: vtk_reporter.output_mask(simulation.no_collision_mask) - UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. - location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, - if translated by (-0.5,-0.5,-0.5) LU. - Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! + output the mask as a vtk-file for visualizatione etc. - USE: in Paraview use Filter:Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or + point-clouds! + USE: in Paraview use Filter: + Threshold -> Above Upper Threshold (Upper Threshold 0.9) + -> Solid Color -> Volume/Wireframe,... """ if outdir is None: @@ -104,7 +110,9 @@ def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=Fal mask_dict = dict() - mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 else mask[..., None].astype(int) # extension to pseudo-3D is needed for vtk-export to work + mask_dict["mask"] = mask.astype(int) if len(mask.shape) == 3 \ + else mask[..., None].astype(int) + # extension to pseudo-3D is needed for Paraview if point: vtk.imageToVTK( @@ -124,7 +132,8 @@ def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=Fal ) class VTKsliceReporter(Reporter): - '''reports a certain specified area portion of the domain as vtk-file + ''' + reports a certain specified area portion of the domain as vtk-file ''' def __init__(self, flow, interval=50, filename_base="./data/output", solid_mask=None, sliceXY=None, sliceZ=None, imin=0, @@ -163,8 +172,11 @@ def __init__(self, flow, interval=50, filename_base="./data/output", self.z_index = sliceZ else: self.z_index = None - # OPTIONAL: Abfrage, welche schaut ob xmin == xmax (und y... und z...) und falls das der Fall ist, dann dort entsprechend eine "None" Dimension anhängt und nur einen Wert nimmt, also ein "slice" - # Wahrscheinlich am sinnvollsten direkt unten im Call dann abzufragen aus einem ([xmin, xmax],[ymin, ymax],[zmin, zmax]) tupel, in dem dann Werte gleich sind, wenn in dieser Ebne geslicet werden soll. + # TODO (OPTIONAL): + # check if xmin == xmax (and y... and z...) and if so, add a "None" + # dimension and take only ONE value, so this becomes a slice. + # -> Probably best to do this in call() from a tuple: + # ([xmin, xmax],[ymin, ymax],[zmin, zmax]) @@ -172,55 +184,58 @@ def __call__(self, simulation): if self.flow.i % self.interval == 0 and self.imin <= self.flow.i <= self.imax: if self.z_index is not None: f = self.flow.f[:,self.xmin:self.xmax+1, self.ymin:self.ymax+1, self.z_index, None] - # (!) None is needed because single-slice omits the last dimension and will result in bad dimension in conversion of u and rho/p below! - #print(f.shape) + # (!) None is needed because single-slice omits the last dimension and will result + # in bad dimension in conversion of u and rho/p below! else: f = self.flow.f u = self.flow.units.convert_velocity_to_pu(self.flow.u(f)) p = self.flow.units.convert_density_lu_to_pressure_pu(self.flow.rho(f)) - # rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) + # if you want the density: rho = self.flow.units.convert_density_to_pu(self.flow.rho(f)) if self.flow.stencil.d == 2: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ..., None]) else: - self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ..., None])) - # ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ..., None])) # for boundaries that store populations "inside" the boundary + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ..., None])) for d in range(self.flow.stencil.d): if self.solid_mask is None: - self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ..., None]) + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ..., None])) else: self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, - self.flow.context.convert_to_ndarray(u[d, ..., None])) - # ALTERNATIVE: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, u[d, ..., None])) - # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) + self.flow.context.convert_to_ndarray(u[d, ..., None])) + # if you want the density: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ..., None]) else: if self.solid_mask is None: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) else: - self.point_dict["p"] = np.where(self.solid_mask, 0, self.flow.context.convert_to_ndarray(p[0, ...])) - # ORIGINAL: self.point_dict["p"] = self.flow.context.convert_to_ndarray(p[0, ...]) - # ALTERNATIVE: self.point_dict["p"] = self.flow.context.convert_to_ndarray(torch.where(self.solid_mask, 0, p[0, ...])) + self.point_dict["p"] = np.where(self.solid_mask, 0, + self.flow.context.convert_to_ndarray(p[0, ...])) for d in range(self.flow.stencil.d): - # ORIGINAL: self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) if self.solid_mask is None: - self.point_dict[f"u{'xyz'[d]}"] = self.flow.context.convert_to_ndarray(u[d, ...]) + self.point_dict[f"u{'xyz'[d]}"] = ( + self.flow.context.convert_to_ndarray(u[d, ...])) else: self.point_dict[f"u{'xyz'[d]}"] = np.where(self.solid_mask, 0, - self.flow.context.convert_to_ndarray(u[d, ...])) + self.flow.context.convert_to_ndarray(u[d, ...])) - # self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) - write_vtk(self.point_dict, self.flow.i, self.filename_base, origin=(self.xmin, self.ymin, self.z_index)) # origin to show slice at correct position when superimposing on 3D vtk-data! + # # if you want the density: self.point_dict["rho"] = self.flow.context.convert_to_ndarray(rho[0, ...]) + write_vtk(self.point_dict, self.flow.i, self.filename_base, + origin=(self.xmin, self.ymin, self.z_index)) + # origin added to show slice at correct position when superimposing on 3D vtk-data! def output_mask(self, mask, outdir=None, name="mask", point=False, no_offset=False): """ - Outputs the no_collision_mask of the simulation object as VTK-file with range [0,1] - Usage: vtk_reporter.output_mask(simulation.no_collision_mask) - UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. - location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, - if translated by (-0.5,-0.5,-0.5) LU. - Attention: point data is misleading, looking at masks rendered as solid objects or point-clouds! + output the mask as a vtk-file for visualizatione etc. - USE: in Paraview use Filter:Threshold -> Above Upper Threshold (Upper Threshold 0.9) -> Solid Color -> Volume/Wireframe,... + UPDATE 28.08.2024 (MBille: outputs mask as cell data. cell data represents the approx. + location of solid boundaries, assuming Fullway or Halfway Bounce Back implementation, + if translated by (-0.5,-0.5,-0.5) LU. + Attention: point data is misleading, looking at masks rendered as solid objects or + point-clouds! + USE: in Paraview use Filter: + Threshold -> Above Upper Threshold (Upper Threshold 0.9) + -> Solid Color -> Volume/Wireframe,... """ if outdir is None: diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py b/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py index 594ba98a..021cd40a 100644 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py +++ b/examples/advanced_projects/efficient_bounce_back_obstacle/solid_boundary_data.py @@ -1,10 +1,10 @@ - import numpy as np __all__ = ['SolidBoundaryData'] # index lists for Bounce Back Boundaries: populations for FWBB, HWBB; IBB. -# - d and the differentiation of lt (less than 0.5) and gt (greater than 0.5) is only for IBB +# - d and the differentiation of lt (less than 0.5) +# and gt (greater than 0.5) is only for IBB class SolidBoundaryData(dict): f_index_lt: np.ndarray f_index_gt: np.ndarray diff --git a/examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py b/examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py deleted file mode 100644 index 6bd7bd08..00000000 --- a/examples/advanced_projects/efficient_bounce_back_obstacle/zOLD_Interpolated_compact2_forComparison.py +++ /dev/null @@ -1,319 +0,0 @@ -import numpy as np -import torch - -class InterpolatedBounceBackBoundary_compact_v2: - - def __init__(self, mask, lattice, x_center, y_center, radius, interpolation_order=1): - t_init_start = time.time() - self.interpolation_order = interpolation_order - self.mask = mask # location of solid-nodes - self.lattice = lattice - self.force_sum = torch.zeros_like(self.lattice.convert_to_tensor( - self.lattice.stencil.e[0])) # summed force vector on all boundary nodes, in D dimensions (x,y,(z)) - - self.f_index_lt = [] # indices of relevant populations (for bounce back and force-calculation) with d<=0.5 - self.f_index_gt = [] # indices of relevant populations (for bounce back and force-calculation) with d>0.5 - self.d_lt = [] # distances between node and boundary for d<0.5 - self.d_gt = [] # distances between node and boundary for d>0.5 - - # searching boundary-fluid-interface and append indices to f_index, distance to boundary to d - if self.lattice.D == 2: - nx, ny = mask.shape # domain size in x and y - a, b = np.where(mask) # x- and y-index of boundaryTRUE nodes for iteration over boundary area - - for p in range(0, len(a)): # for all TRUE-nodes in boundary.mask - for i in range(0, self.lattice.Q): # for all stencil-directions c_i (lattice.stencil.e in lettuce) - # check for boundary-nodes neighboring the domain-border. - # ...they have to take the periodicity into account... - border = np.zeros(self.lattice.D, dtype=int) - - if a[p] == 0 and self.lattice.stencil.e[i, 0] == -1: # searching border on left [x] - border[0] = -1 - elif a[p] == nx - 1 and self.lattice.e[i, 0] == 1: # searching border on right [x] - border[0] = 1 - - if b[p] == 0 and self.lattice.stencil.e[i, 1] == -1: # searching border on left [y] - border[1] = -1 - elif b[p] == ny - 1 and self.lattice.e[i, 1] == 1: # searching border on right [y] - border[1] = 1 - - try: # try in case the neighboring cell does not exist (= an f pointing out of the simulation domain) - if not mask[a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]: - # if the neighbour of p is False in the boundary.mask, p is a solid node, neighbouring a fluid node: - # ...the direction pointing from the fluid neighbour to solid p is marked on the neighbour - - # calculate intersection point of boundary surface and link -> - # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate - cx = self.lattice.stencil.e[ - self.lattice.stencil.opposite[i], 0] # link-direction x to solid node - cy = self.lattice.stencil.e[ - self.lattice.stencil.opposite[i], 1] # link-direction y to solid node - - # pq-formula - h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 - h2 = (px * px + py * py + x_center * x_center + y_center * y_center - - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( - cx * cx + cy * cy) # q - - d1 = - h1 + np.sqrt(h1 * h1 - h2) - d2 = - h1 - np.sqrt(h1 * h1 - h2) - - # distance from fluid node to the "true" boundary location - # choose correct d and assign d and f_index - if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 - - if d1 <= 0.5: - self.d_lt.append(d1) - self.f_index_lt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) - else: # d>0.5 - self.d_gt.append(d1) - self.f_index_gt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) - - elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 - - if d2 <= 0.5: - self.d_lt.append(d2) - self.f_index_lt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) - else: # d>0.5 - self.d_gt.append(d2) - self.f_index_gt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny]) - else: # neither d1 or d2 is real and between 0 and 1 - print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,ci", a[p], - b[p], self.lattice.stencil.e[i, 0], self.lattice.stencil.e[i, 1], - self.lattice.stencil.e[i, 2]) - except IndexError: - pass # just ignore this iteration since there is no neighbor there - - if self.lattice.D == 3: # like 2D, but in 3D...guess what... - nx, ny, nz = mask.shape - a, b, c = np.where(mask) - - for p in range(0, len(a)): - for i in range(0, self.lattice.Q): - border = np.zeros(self.lattice.D, dtype=int) - # x - direction - if a[p] == 0 and self.lattice.stencil.e[i, 0] == -1: # searching border on left - border[0] = -1 - elif a[p] == nx - 1 and self.lattice.e[i, 0] == 1: # searching border on right - border[0] = 1 - # y - direction - if b[p] == 0 and self.lattice.stencil.e[i, 1] == -1: # searching border on left - border[1] = -1 - elif b[p] == ny - 1 and self.lattice.e[i, 1] == 1: # searching border on right - border[1] = 1 - # z - direction - if c[p] == 0 and self.lattice.stencil.e[i, 2] == -1: # searching border on left - border[2] = -1 - elif c[p] == nz - 1 and self.lattice.e[i, 2] == 1: # searching border on right - border[2] = 1 - - try: # try in case the neighboring cell does not exist (an f pointing out of simulation domain) - if not mask[a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, - c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]: - - # calculate intersection point of boundary surface and link -> - # ...calculate distance between fluid node and boundary surface on the link - px = a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx # fluid node x-coordinate - py = b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny # fluid node y-coordinate - # Z-coodinate not needed for cylinder ! - - cx = self.lattice.stencil.e[ - self.lattice.stencil.opposite[i], 0] # link-direction x to solid node - cy = self.lattice.stencil.e[ - self.lattice.stencil.opposite[i], 1] # link-direction y to solid node - # Z-coodinate not needed for cylinder ! - - # pq-formula - h1 = (px * cx + py * cy - cx * x_center - cy * y_center) / (cx * cx + cy * cy) # p/2 - h2 = (px * px + py * py + x_center * x_center + y_center * y_center - - 2 * px * x_center - 2 * py * y_center - radius * radius) / ( - cx * cx + cy * cy) # q - - d1 = - h1 + np.sqrt(h1 * h1 - h2) - d2 = - h1 - np.sqrt(h1 * h1 - h2) - - # print("xb,yb,i,d1,d2 xf, yf, cx, cy:", a[p], b[p], i, d1, d2, px, py, cx, cy) - - # distance from fluid node to the "true" boundary location - # choose correct d and assign d and f_index - if d1 <= 1 and np.isreal(d1): # d should be between 0 and 1 - - if d1 <= 0.5: - self.d_lt.append(d1) - self.f_index_lt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, - c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) - else: # d>0.5 - self.d_gt.append(d1) - self.f_index_gt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, - c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) - - elif d2 <= 1 and np.isreal(d2): # d should be between 0 and 1 - - if d2 <= 0.5: - self.d_lt.append(d2) - self.f_index_lt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, - c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) - else: # d>0.5 - self.d_gt.append(d2) - self.f_index_gt.append([self.lattice.stencil.opposite[i], - a[p] + self.lattice.stencil.e[i, 0] - border[0] * nx, - b[p] + self.lattice.stencil.e[i, 1] - border[1] * ny, - c[p] + self.lattice.stencil.e[i, 2] - border[2] * nz]) - else: # neither d1 or d2 is real and between 0 and 1 - print("IBB WARNING: d1 is", d1, "; d2 is", d2, "for boundaryPoint x,y,z,ci", a[p], - b[p], c[p], self.lattice.stencil.e[i, 0], self.lattice.stencil.e[i, 1], - self.lattice.stencil.e[i, 2]) - except IndexError: - pass # just ignore this iteration since there is no neighbor there - - # convert relevant tensors: - self.f_index_lt = torch.tensor(np.array(self.f_index_lt), device=self.lattice.device, - dtype=torch.int64) # the batch-index has to be integer - self.f_index_gt = torch.tensor(np.array(self.f_index_gt), device=self.lattice.device, - dtype=torch.int64) # the batch-index has to be integer - self.d_lt = self.lattice.convert_to_tensor(np.array(self.d_lt)) - self.d_gt = self.lattice.convert_to_tensor(np.array(self.d_gt)) - self.opposite_tensor = torch.tensor(self.lattice.stencil.opposite, device=self.lattice.device, - dtype=torch.int64) # batch-index has to be a tensor - - f_collided_lt = torch.zeros_like(self.d_lt) # float-tensor with number of (x_b nodes with d<=0.5) values - f_collided_gt = torch.zeros_like(self.d_gt) # float-tensor with number of (x_b nodes with d>0.5) values - f_collided_lt_opposite = torch.zeros_like(self.d_lt) - f_collided_gt_opposite = torch.zeros_like(self.d_gt) - self.f_collided_lt = torch.stack((f_collided_lt, f_collided_lt_opposite), dim=1) - self.f_collided_gt = torch.stack((f_collided_gt, f_collided_gt_opposite), dim=1) - - print("IBB initialization took " + str(time.time() - t_init_start) + "seconds") - - def __call__(self, f): - ## f_collided_lt = [f_collided_lt, f_collided_lt.opposite] (!) in compact storage-layout - - if self.lattice.D == 2: - # BOUNCE - # if d <= 0.5 - f[self.opposite_tensor[self.f_index_lt[:, 0]], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * f[self.f_index_lt[:, 0], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2]] - # if d > 0.5 - f[self.opposite_tensor[self.f_index_gt[:, 0]], - self.f_index_gt[:, 1], - self.f_index_gt[:, 2]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] - - if self.lattice.D == 3: - # BOUNCE - # if d <= 0.5 - f[self.opposite_tensor[self.f_index_lt[:, 0]], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2], - self.f_index_lt[:, 3]] = 2 * self.d_lt * self.f_collided_lt[:, 0] + (1 - 2 * self.d_lt) * f[self.f_index_lt[:, 0], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2], - self.f_index_lt[:, 3]] - # if d > 0.5 - f[self.opposite_tensor[self.f_index_gt[:, 0]], - self.f_index_gt[:, 1], - self.f_index_gt[:, 2], - self.f_index_gt[:, 3]] = (1 / (2 * self.d_gt)) * self.f_collided_gt[:, 0] + (1 - 1 / (2 * self.d_gt)) * self.f_collided_gt[:, 1] - - - # CALC. FORCE on boundary (MEM, MEA) - self.calc_force_on_boundary(f) - return f - - def make_no_stream_mask(self, f_shape): - assert self.mask.shape == f_shape[1:] # all dimensions of f except the 0th (q) - # no_stream_mask has to be dimensions: (q,x,y,z) (z optional), but CAN be (x,y,z) (z optional). - # ...in the latter case, torch.where broadcasts the mask to (q,x,y,z), so ALL q populations of a lattice-node are marked equally - return self.lattice.convert_to_tensor(self.mask) - - def make_no_collision_mask(self, f_shape): - # INFO: pay attention to the initialization of observable/moment-fields (u, rho,...) on the boundary nodes, - # ...in the initial solution of your flow, especially if visualization or post processing uses the field-values - # ...in the whole domain (including the boundary region)! - assert self.mask.shape == f_shape[1:] - return self.lattice.convert_to_tensor(self.mask) - - def calc_force_on_boundary(self, f_bounced): - ### force = e * (f_collided + f_bounced[opp.]) - if self.lattice.D == 2: - self.force_sum = torch.einsum('i..., id -> d', - self.f_collided_lt[:, 0] + f_bounced[ - self.opposite_tensor[self.f_index_lt[:, 0]], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2]], - self.lattice.e[self.f_index_lt[:, 0]]) \ - + torch.einsum('i..., id -> d', - self.f_collided_gt[:, 0] + f_bounced[ - self.opposite_tensor[self.f_index_gt[:, 0]], - self.f_index_gt[:, 1], - self.f_index_gt[:, 2]], - self.lattice.e[self.f_index_gt[:, 0]]) - if self.lattice.D == 3: - self.force_sum = torch.einsum('i..., id -> d', - self.f_collided_lt[:, 0] + f_bounced[ - self.opposite_tensor[self.f_index_lt[:, 0]], - self.f_index_lt[:, 1], - self.f_index_lt[:, 2], - self.f_index_lt[:, 3]], - self.lattice.e[self.f_index_lt[:, 0]]) \ - + torch.einsum('i..., id -> d', - self.f_collided_gt[:, 0] + f_bounced[ - self.opposite_tensor[self.f_index_gt[:, 0]], - self.f_index_gt[:, 1], - self.f_index_gt[:, 2], - self.f_index_gt[:, 3]], - self.lattice.e[self.f_index_gt[:, 0]]) - - def store_f_collided(self, f_collided): - if self.lattice.D == 2: - self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q - self.f_index_lt[:, 1], # x - self.f_index_lt[:, 2]]) # y - self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q - self.f_index_lt[:, 1], # x - self.f_index_lt[:, 2]]) # y - - self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q - self.f_index_gt[:, 1], # x - self.f_index_gt[:, 2]]) # y - self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q - self.f_index_gt[:, 1], # x - self.f_index_gt[:, 2]]) # y - if self.lattice.D == 3: - self.f_collided_lt[:, 0] = torch.clone(f_collided[self.f_index_lt[:, 0], # q - self.f_index_lt[:, 1], # x - self.f_index_lt[:, 2], # y - self.f_index_lt[:, 3]]) # z - self.f_collided_lt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_lt[:,0]], # q - self.f_index_lt[:, 1], # x - self.f_index_lt[:, 2], # y - self.f_index_lt[:, 3]]) # z - - self.f_collided_gt[:, 0] = torch.clone(f_collided[self.f_index_gt[:, 0], # q - self.f_index_gt[:, 1], # x - self.f_index_gt[:, 2], # y - self.f_index_gt[:, 3]]) # z - self.f_collided_gt[:, 1] = torch.clone(f_collided[self.opposite_tensor[self.f_index_gt[:,0]], # q - self.f_index_gt[:, 1], # x - self.f_index_gt[:, 2], # y - self.f_index_gt[:, 3]]) # z \ No newline at end of file