3d vis test

commit_changes_visualization_NOA.py

@@ -0,0 +1,287 @@
+"""Visualization utilities for plotting time series and spatiotemporal data."""
+
+import jax.numpy as jnp
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_time_series(U_lst,
+                     t=None,
+                     time_series_labels=None,
+                     line_formats = None,
+                     state_var_names=None,
+                     t_lim = None,
+                     figsize = (20,8),
+                     x_label = r'$t$',
+                     title = None,
+                     **plot_kwargs):
+    """Plot time series data with separate panels for each state variable.
+
+    Parameters
+    ----------
+    U_lst : 2D array or list of 2D arrays
+        If a 2D array, shape should be (Nt, Nu) where Nu is the number of state
+        variables and Nt is the number of time points.If a list of 2D arrays,
+        each array should have shape (Nt, Nu) and represent different time series.
+    t : 1D array, optional
+        1D array of time points. If None, the time points will be assumed to be
+        evenly spaced from 0 to Nt-1.
+    time_series_labels : list of strings, optional
+        List of strings containing the labels for each time series to be shown in
+        a legend. If None, no labels will be shown.
+    line_formats : list of strings, optional
+        List of strings containing the line formats for each time series. If None,
+        default line format will be used.
+    state_var_names : list of strings, optional
+        List of strings containing the names of the state variables. If None,
+        no y-axis labels will be shown.
+    t_lim : tuple, optional
+        Limit for the x-axis. If None, the x-axis will be set to the full
+        range of time points.
+    figsize : tuple, optional
+        Size of the figure to be created. Default is (20, 8).
+    x_label : string, optional
+        Label for the x-axis. Default is r'$t$'.
+    title : string, optional
+        Title of the plot. If None, no title is shown.
+    plot_kwargs : dict, optional
+        Additional arguments to pass to the plot function.
+    """
+    # Input validation
+    if not isinstance(U_lst, list):
+        if not isinstance(U_lst, jnp.ndarray | np.ndarray) or U_lst.ndim != 2:
+            raise TypeError("U_lst must be a 2D JAX or NumPy array or a list of \
+                            2D JAX/NumPy arrays.")
+        U_lst = [U_lst]
+    else:
+        if not all(
+            isinstance(U, jnp.ndarray | np.ndarray) and U.ndim == 2 for U in U_lst):
+            raise TypeError("All elements in U_lst must be 2D JAX or NumPy arrays.")
+        if not all(U.shape == U_lst[0].shape for U in U_lst):
+            raise ValueError("All arrays in U_lst must have the same shape.")
+
+
+    Nu = U_lst[0].shape[1]
+    Nt = U_lst[0].shape[0]
+
+    if t is not None:
+        if not isinstance(t, jnp.ndarray | np.ndarray) or t.ndim != 1:
+            raise TypeError("t must be a 1D JAX or NumPy array.")
+        if len(t) != Nt:
+            raise ValueError(f"Length of t ({len(t)}) must match the number of time\
+                             points in U_lst ({Nt}).")
+
+    if time_series_labels is not None:
+        if not isinstance(time_series_labels, list):
+            raise TypeError("time_series_labels must be a list of strings.")
+        if len(time_series_labels) != len(U_lst):
+            raise ValueError(f"Length of time_series_labels ({len(time_series_labels)})\
+                             must match the number of time series ({len(U_lst)}).")
+
+    if line_formats is not None:
+        if not isinstance(line_formats, list):
+            raise TypeError("line_formats must be a list of strings.")
+        if len(line_formats) != len(U_lst):
+            raise ValueError(f"Length of line_formats ({len(line_formats)}) must \
+                             match the number of time series ({len(U_lst)}).")
+
+    if state_var_names is not None:
+        if not isinstance(state_var_names, list):
+            raise TypeError("state_var_names must be a list of strings.")
+        if len(state_var_names) != Nu:
+            raise ValueError(f"Length of state_var_names ({len(state_var_names)}) \
+                             must match the number of state variables ({Nu}).")
+
+    if t_lim is not None and not isinstance(t_lim, int | float):
+         raise TypeError("t_lim must be a number (int or float).")
+
+    # defaults
+    plot_kwargs.setdefault('linewidth', 2)
+
+    # setup time vectors
+    if t is None:
+        t = jnp.arange(Nt)
+    if t_lim is None:
+        t_lim = t[-1]
+
+    # handle optional inputs
+    if time_series_labels is None:
+        time_series_labels = [None for _ in range(len(U_lst))]
+    if line_formats is None:
+        line_formats = ['-' for _ in range(len(U_lst))]
+
+    # plot
+    fig, axs = plt.subplots(Nu, figsize = figsize)
+    # Ensure axs is always iterable, even if Nu=1
+    if Nu == 1:
+        axs = [axs]
+    for i in range(Nu):
+        for j, Y in enumerate(U_lst):
+            axs[i].plot(t, Y[:, i], line_formats[j], label=time_series_labels[j],
+                         **plot_kwargs)
+            axs[i].set_xlim([0, t_lim])
+        if state_var_names is not None:
+            axs[i].set(ylabel=state_var_names[i])
+    if time_series_labels[0] is not None:
+        axs[0].legend(loc='upper right')
+    axs[-1].set(xlabel=x_label)
+    if title is not None:
+        axs[0].set_title(title, fontsize=14)
+    plt.show()
+
+def imshow_1D_spatiotemp(U,
+                         tN,
+                         domain=(0,1),
+                         figsize=(20, 6),
+                         title = None,
+                         x_label = r'$t$',
+                         **imshow_kwargs):
+    """
+    Plot 1D spatiotemporal data using imshow.
+
+    Parameters
+    ----------
+    U: 2D array
+        Data to be plotted, shape should be (Nt, Nx) where Nt is the number of time
+        points and Nx is the number of spatial points
+    tN: float
+        Final time of the simulation
+    domain: tuple of length 2
+        Bounds of the spatial domain, default is (0, 1)
+    figsize: tuple
+        Size of the figure to be created, default is (20, 6)
+    title: string, optional
+        Title of the plot, if None no title is shown
+    x_label: string, optional
+        Label for the x-axis, default is r'$t$'
+    **imshow_kwargs: additional arguments to pass to imshow
+    """
+    # Input validation
+    if not isinstance(U, jnp.ndarray | np.ndarray) or U.ndim != 2:
+        raise TypeError("U must be a 2D JAX or NumPy array.")
+    if not isinstance(domain, tuple) or len(domain) != 2:
+        raise TypeError("domain must be a tuple of length 2.")
+    if not all(isinstance(x, int | float) for x in domain):
+        raise TypeError("Both elements of domain must be numbers (int or float).")
+
+    #set defaults for imshow
+    imshow_kwargs.setdefault('aspect', 'auto')
+    imshow_kwargs.setdefault('origin', 'lower')
+    imshow_kwargs.setdefault('cmap', 'RdGy')
+    imshow_kwargs.setdefault('extent', [0, tN, domain[0], domain[1]])
+
+    plt.figure(figsize=figsize, dpi=200)
+    plt.imshow(U.T, **imshow_kwargs)
+    plt.ylabel('x')
+    plt.xlabel(x_label)
+    if title is not None:
+        plt.title(title)
+    plt.colorbar(pad = 0.01, label = r'$u$')
+    plt.show()
+
+# TODO: add plot_attrator function to visualize 2D/3D attractors in state space
+
+
+
+def plot_in_3D_state_space(U_lst,
+                           time_series_labels=None,
+                           line_formats=["-", "r--"],
+                           state_var_names=None,
+                           figsize = (20,8),
+                           title = None,
+                           **plot_kwargs):
+    """ **STEP 1 - clear, one-line summary:** Plot time series data to visualize 3D attractors in state space.
+
+    Parameters
+    ----------
+    **STEP 2 - 
+    <param_name> : <type and optional?>
+        <description and shape>**
+    U_lst : 2D array or list of 2D arrays
+        If a 2D array, shape should be (Nt, 3) with 3 state
+        variables, where Nt is the number of time points. If a list of 2D arrays,
+        each array should have shape (Nt, 3) and represent different time series.
+    time_series_labels : list of strings, optional
+        List of strings containing the labels for each time series to be shown in
+        a legend. If None, no labels will be shown.
+    line_formats : list of strings, optional
+        List of strings containing the line formats for each time series. If None,
+        default line format will be used.
+    state_var_names : list of strings, optional
+        List of strings containing the names of the state variables. If None,
+        no axis labels will be shown.
+    figsize : tuple, optional
+        Size of the figure to be created. Default is (20, 8).
+    title : string, optional
+        Title of the plot. If None, no title is shown.
+    plot_kwargs : dict, optional
+        Additional arguments to pass to the plot function.
+    """
+
+    # **STEP 3** 
+    # Input validation
+    if not isinstance(U_lst, list):
+        if not isinstance(U_lst, jnp.ndarray | np.ndarray) or U_lst.ndim != 2:
+            raise TypeError("U_lst must be a 2D JAX or NumPy array or a list of \
+                            2D JAX/NumPy arrays.")
+        U_lst = [U_lst]
+    else:
+        if not all(
+            isinstance(U, jnp.ndarray | np.ndarray) and U.ndim == 2 for U in U_lst):
+            raise TypeError("All elements in U_lst must be 2D JAX or NumPy arrays.")
+        if not all(U.shape == U_lst[0].shape for U in U_lst):
+            raise ValueError("All arrays in U_lst must have the same shape.")
+
+    Nu = U_lst[0].shape[1]
+
+    if time_series_labels is not None:
+        if not isinstance(time_series_labels, list):
+            raise TypeError("time_series_labels must be a list of strings.")
+        if len(time_series_labels) != len(U_lst):
+            raise ValueError(f"Length of time_series_labels ({len(time_series_labels)})\
+                             must match the number of time series ({len(U_lst)}).")
+
+    if line_formats is not None:
+        if not isinstance(line_formats, list):
+            raise TypeError("line_formats must be a list of strings.")
+        if len(line_formats) != len(U_lst):
+            raise ValueError(f"Length of line_formats ({len(line_formats)}) must \
+                             match the number of time series ({len(U_lst)}).")
+
+    if state_var_names is not None:
+        if not isinstance(state_var_names, list):
+            raise TypeError("state_var_names must be a list of strings.")
+        if len(state_var_names) != Nu:
+            raise ValueError(f"Length of state_var_names ({len(state_var_names)}) \
+                             must match the number of state variables ({Nu}).")
+
+    # **STEP 4** 
+    # defaults
+    plot_kwargs.setdefault('linewidth', 2)
+
+    # **STEP 5** 
+    # handle optional inputs
+    if time_series_labels is None:
+        time_series_labels = [None for _ in range(len(U_lst))]
+    if line_formats is None:
+        line_formats = ['-' for _ in range(len(U_lst))]
+
+    # **STEP 5 - CODE:** 
+    # plot
+    fig, axs = plt.subplots(subplot_kw={"projection": "3d"}, figsize = figsize)
+    # Ensure axs is always iterable, even if Nu=1
+    if Nu == 1:
+        axs = [axs]
+    for j, Y in enumerate(U_lst):
+        axs.plot(Y[:, 0], Y[:, 1], Y[:, 2], line_formats[j], 
+                        label=time_series_labels[j],
+                        **plot_kwargs)
+    if state_var_names is not None:
+        axs.set(xlabel=state_var_names[0])
+        axs.set(ylabel=state_var_names[1])
+        axs.set(zlabel=state_var_names[2])
+    if time_series_labels[0] is not None:
+        axs.legend(loc='upper right')
+    if title is not None:
+        axs.set_title(title, fontsize=14)
+    plt.show()

how to commit changes.txt

@@ -0,0 +1,58 @@
+0. activate miniconda environment ("myenv"):
+conda activate myenv
+
+
+1. go to directory:
+cd C:\Users\nk675\Documents\GitHub\OpenReservoirComputing
+
+
+2. run tests locally - in cmd:
+pytest
+ruff check <relative_path_to_file_in_OpenReservoirComputing>.py
+
+2. run tests locally - in vscode (with miniconda virtual environment):
+....
+
+
+3. repeat tests until all tests pass
+
+
+4. go to path:
+cd C:\Users\nk675\Documents\GitHub\OpenReservoirComputing
+
+
+5. switch to branch - if new branch:
+git checkout main
+git pull
+git checkout -b <new_branch_name>
+
+5. switch to branch - if existing branch:
+git checkout <existing_branch_name>
+
+
+6. stage changes - in vscode:
+ -->> Source control (Ctrl+Shift+G)
+ -->> press "+" by files with changes to commit
+ -->> in "Message" write a short summary of changes 
+ -->> press "Commit"
+
+6. stage changes - in cmd:
+git add <changed_file_name>.py
+
+
+7. push - if new branch:
+git push origin <new_branch_name>
+
+7. push - if existing branch:
+git push
+
+
+8. pull - in GitHub browser:
+ -->> go to "Pull request" tab
+ -->> press "New Pull request"
+ -->> choose the base branch to commit to (left): usually "main". choose the compare branch to commit (right): <branch_name>
+ -->> title + description of change
+ -->> press "Create pull request"
+
+
+

installs_NOA.txt

@@ -0,0 +1,24 @@
+##install packages for a specific Python 3.11:
+
+python3.11 -m pip install <package_name>
+
+
+##works even if <command> isn’t on your PATH:
+
+python -m <command>
+
+
+
+##create new miniconda environment:
+
+conda create -n <env_name> python=3.11
+
+
+##activate miniconda environment:
+
+conda activate <env_name>
+
+
+##install packages in conda (after activating the environment):
+conda install <package_name>
+