diff --git a/.github/workflows/python-poetry-package.yml b/.github/workflows/python-poetry-package.yml index 8362155..bd5bc85 100644 --- a/.github/workflows/python-poetry-package.yml +++ b/.github/workflows/python-poetry-package.yml @@ -28,15 +28,15 @@ jobs: version: 1.4.0 virtualenvs-create: true installer-parallel: false - - name: Install contsub + - name: Install package run: | poetry install --with tests --no-interaction - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - poetry run flake8 contsub tests --count --select=E9,F63,F7,F82 --show-source --statistics + poetry run flake8 --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - poetry run flake8 contsub tests --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + poetry run flake8 --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: CI tests run: | poetry run pytest diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..ae0f8ec --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,9 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.12.9 + hooks: + # Run the linter. + - id: ruff-check + # Run the formatter. + - id: ruff-format diff --git a/README.rst b/README.rst index 48fab6f..8f70c9b 100644 --- a/README.rst +++ b/README.rst @@ -1,4 +1,4 @@ -Contsub +Mowjsub ======= .. image:: https://img.shields.io/pypi/v/contsub.svg diff --git a/docs/source/conf.py b/docs/source/conf.py index 6cee449..75990d0 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -7,31 +7,32 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -import contsub from datetime import date -project = 'contsub' -author = 'Amir Kazemi-Moridani, Sphesihle Makhathini, Mika Naidoo' -release = '1.0.2' -copyright = f'{date.today().year}, {author}' +import mowjsub -release = contsub.__version__ -version = contsub.__version__ +project = "contsub" +author = "Amir Kazemi-Moridani, Sphesihle Makhathini, Mika Naidoo" +release = "1.0.2" +copyright = f"{date.today().year}, {author}" + +release = mowjsub.__version__ +version = mowjsub.__version__ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -templates_path = ['_templates'] +templates_path = ["_templates"] exclude_patterns = [] -language = 'en' +language = "en" # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = 'furo' -#html_static_path = ['build/html/_static'] +html_theme = "furo" +# html_static_path = ['build/html/_static'] needs_sphinx = "8.1" diff --git a/docs/source/how-to/installation.rst b/docs/source/how-to/installation.rst index f9bb772..2114b4d 100644 --- a/docs/source/how-to/installation.rst +++ b/docs/source/how-to/installation.rst @@ -3,7 +3,7 @@ Installation ============= -The stable version Simms 3.0 is available on `PyPI `_ and can be installed using: +The stable version Mowj-sub is available on `PyPI `_ and can be installed using: .. code-block:: bash diff --git a/contsub/__init__.py b/mowjsub/__init__.py similarity index 50% rename from contsub/__init__.py rename to mowjsub/__init__.py index c0cd9b4..184b2ee 100644 --- a/contsub/__init__.py +++ b/mowjsub/__init__.py @@ -1,9 +1,12 @@ from importlib import metadata + from omegaconf import OmegaConf __version__ = metadata.version(__package__) -BIN = OmegaConf.create({ - "im_plane": "imcontsub", - "vis_plane": "viscontsub", -}) \ No newline at end of file +BIN = OmegaConf.create( + { + "im_plane": "im-mowjsub", + "vis_plane": "vis-mowjsub", + } +) diff --git a/contsub/exceptions.py b/mowjsub/exceptions.py similarity index 94% rename from contsub/exceptions.py rename to mowjsub/exceptions.py index f7102d8..a17493d 100644 --- a/contsub/exceptions.py +++ b/mowjsub/exceptions.py @@ -1,20 +1,26 @@ class alreadyOpen(Exception): pass + class alreadyClosed(Exception): pass + class noBeamTable(Exception): pass + class tableExists(Exception): pass + class tableDimMismatch(Exception): pass + class CubeDimIsSmall(Exception): pass + class BadFitError(Exception): - pass \ No newline at end of file + pass diff --git a/contsub/fitfuncs.py b/mowjsub/fitfuncs.py similarity index 79% rename from contsub/fitfuncs.py rename to mowjsub/fitfuncs.py index b287150..19bdb4b 100644 --- a/contsub/fitfuncs.py +++ b/mowjsub/fitfuncs.py @@ -1,20 +1,31 @@ -from scipy.interpolate import splev, splrep, make_smoothing_spline -from scipy.ndimage import median_filter -from scabha import init_logger from abc import abstractmethod -from . import BIN -from .exceptions import BadFitError + import numpy as np +from scabha import init_logger from scipy import fftpack -from contsub import utils +from scipy.interpolate import make_smoothing_spline, splev, splrep +from scipy.ndimage import median_filter + +from mowjsub import utils + +from . import BIN +from .exceptions import BadFitError log = init_logger(BIN.im_plane) + class FitFunc: - def __init__(self, freqs, order:int=None, velwidth:float=None, - chanwidth:int=None, fit_lam:int=None, fit_tol:float=0): + def __init__( + self, + freqs, + order: int = None, + velwidth: float = None, + chanwidth: int = None, + fit_lam: int = None, + fit_tol: float = 0, + ): """ - + Args: order (_type_): _description_ velwidth (_type_): _description_ @@ -28,17 +39,17 @@ def __init__(self, freqs, order:int=None, velwidth:float=None, self.chanwidth = chanwidth self.fit_lam = fit_lam - def invalid_point_count(self, data:np.ndarray, mask:np.ndarray): - """ Calculates the number of invalid data points in a spectrum. + def invalid_point_count(self, data: np.ndarray, mask: np.ndarray): + """Calculates the number of invalid data points in a spectrum. Args: data (np.ndarray): 1D spectrum mask (np.ndarray): Binary mask """ mask[np.isnan(data)] = True - + return mask, mask.sum() - + def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=True): """_summary_ @@ -56,33 +67,35 @@ def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=Tr return False else: return mask, invalid - + @abstractmethod def fit(self, x, data, mask, weight): pass def default_prepare(self): if self.velwidth: - self.chanwidth = utils.chans_in_velwidth(self.freqs*1e6, self.velwidth*1000) + self.chanwidth = utils.chans_in_velwidth(self.freqs * 1e6, self.velwidth * 1000) log.info(f"Velocity of {self.velwidth} km/s corresponds to {self.chanwidth} channels") elif self.chanwidth is None: raise RuntimeError("Neither chanwidth or velwitdth are set. Cannot proceed.") - + if self.chanwidth % 2 == 0: self.chanwidth += 1 - + self.preped = True - + + class FitBSpline(FitFunc): """ - BSpline fitting function based on `splev`, `splrep` in `scipy.interpolate` + BSpline fitting function based on `splev`, `splrep` in `scipy.interpolate` """ + def prepare(self): self.default_prepare() - + self.max_spline_order = int(self.nchan / self.chanwidth) + 1 log.info(f"max spline order: {self.max_spline_order}") - + rs = np.random.SeedSequence() self.rng = np.random.default_rng(rs) self.preped = True @@ -90,53 +103,58 @@ def prepare(self): def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the spline fit and the residuals from the fit - + x : x values for the fit data : values to be fit by spline - weight : weights for fitting the Spline. + weight : weights for fitting the Spline. To mask values, set the corresponding weight to zero. """ if not self.preped: self.prepare() - + mask, invalid = self.is_fit_possible(data, mask, raise_exception=True) nvalid = self.nchan - invalid - + if nvalid < (self.order + 1): raise BadFitError("Not enough valid points for spline fit, returning original data.") - + # Mask invalid or zero-weight points mask[np.where(np.isnan(data))] = True - x_masked = self.freqs[~mask] + x_masked = self.freqs[~mask] data_masked = data[~mask] - + knotind = np.linspace(0, x_masked.size, self.max_spline_order, dtype=int)[1:-1] chwid = max(1, (self.nchan // self.max_spline_order) // 8) knots_idx = self.rng.integers(-chwid, chwid, size=knotind.shape) + knotind - knots_idx = np.unique(np.clip(knots_idx, 1, x_masked.size-1)) # avoid edges - + knots_idx = np.unique(np.clip(knots_idx, 1, x_masked.size - 1)) # avoid edges + knot_positions = x_masked[knots_idx] - - + if isinstance(weights, np.ndarray): - splCfs = splrep(x_masked, data_masked, task=-1, - w=weights[~mask], t = knot_positions, k=self.order) + splCfs = splrep( + x_masked, + data_masked, + task=-1, + w=weights[~mask], + t=knot_positions, + k=self.order, + ) else: - splCfs = splrep(x_masked, data_masked, task=-1, - t=knot_positions, k=self.order) + splCfs = splrep(x_masked, data_masked, task=-1, t=knot_positions, k=self.order) + + return splev(self.freqs, splCfs) - return splev(self.freqs, splCfs) class FitGCVSpline(FitFunc): """ Polynomial fitting function using scipy.interpolate.make_smoothing_spline """ - + def prepare(self): self.preped = True - - def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): + + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """_summary_ Args: @@ -148,14 +166,14 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): Returns: _type_: _description_ """ - + if not self.preped: self.prepare() mask, _ = self.is_fit_possible(data, mask, raise_exception=True) - + x_masked = self.freqs[~mask] data_masked = data[~mask] - + try: if isinstance(weights, np.ndarray): smooth_func = make_smoothing_spline(x_masked, data_masked, lam=self.fit_lam, w=weights[~mask]) @@ -165,61 +183,67 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): raise BadFitError(f"Polynomial fitting failed: {e}") return smooth_func(self.freqs) + + class FitMedFilter(FitFunc): """ - Median filtering class for continuum subtraction + Median filtering class for continuum subtraction """ - + def prepare(self): self.default_prepare() - + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the median filtered data as line emission - + data (np.ndarray) : values to be fit mask (np.ndarray) : a mask (not implemented really) weight (np.ndarray) : weights """ - + if not self.preped: self.prepare() - + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) if isinstance(weights, np.ndarray): - #TODO(Sphe) + # TODO(Sphe) pass - + if isinstance(mask, np.ndarray): data[mask] = np.nan - - pad_size = int(self.chanwidth/2) + + pad_size = int(self.chanwidth / 2) padded_data = np.pad(data, pad_size, mode="linear_ramp") - filtered = np.nanmedian(np.lib.stride_tricks.sliding_window_view(padded_data, self.chanwidth), axis = 1) - + filtered = np.nanmedian( + np.lib.stride_tricks.sliding_window_view(padded_data, self.chanwidth), + axis=1, + ) + return filtered - + + class FitMedFilterFast(FitFunc): """ - Median filtering class for continuum subtraction + Median filtering class for continuum subtraction """ - + def prepare(self): self.default_prepare() - + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the median filtered data as line emission - + data (np.ndarray) : values to be fit mask (np.ndarray) : a mask (not implemented really) weight (np.ndarray) : weights """ - + if not self.preped: self.prepare() - + mask = self.is_fit_possible(data, mask, raise_exception=True)[0] if isinstance(mask, np.ndarray): @@ -234,19 +258,20 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): data_filled = data # Use scipy.ndimage.median_filter for speed - filtered = median_filter(data_filled, size=self.chanwidth, mode='reflect') + filtered = median_filter(data_filled, size=self.chanwidth, mode="reflect") return filtered + class FitPolynomial(FitFunc): """ Polynomial fitting function using numpy.polyfit """ - + def prepare(self): pass - - def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): + + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """_summary_ Args: @@ -258,48 +283,49 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): Returns: _type_: _description_ """ - + if not self.preped: self.prepare() - + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) - + x_masked = self.freqs[~mask] data_masked = data[~mask] - + try: if isinstance(weights, np.ndarray): coeffs = np.polyfit(x_masked, data_masked, self.order, w=weights[~mask]) else: coeffs = np.polyfit(x_masked, data_masked, self.order) return np.polyval(coeffs, self.freqs) - + except Exception as e: raise BadFitError(f"Polynomial fitting failed: {e}") - + + class FitDCT(FitFunc): """ - Median filtering class for continuum subtraction + Median filtering class for continuum subtraction """ + def prepare(self, dct_type=1): - self.default_prepare() - + fnorm_dict = { - 1: 1 / np.sqrt( 2 * self.nchan), - 2: np.sqrt(2 / (self.nchan - 1)) / 2, - 3: 1 / np.sqrt( 2 * self.nchan), - 4: 1 / np.sqrt( 2 * self.nchan), + 1: 1 / np.sqrt(2 * self.nchan), + 2: np.sqrt(2 / (self.nchan - 1)) / 2, + 3: 1 / np.sqrt(2 * self.nchan), + 4: 1 / np.sqrt(2 * self.nchan), } - + self.fnorm = fnorm_dict[dct_type] self.dct_type = dct_type self.preped = True - + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the median filtered data as line emission - + x : x values for the fit y : values to be fit mask : a mask (not implemented really) @@ -308,19 +334,19 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): if not self.preped: self.prepare() - + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) - + if isinstance(weights, np.ndarray): - #TODO(Sphe) + # TODO(Sphe) pass - + baseline = FitMedFilterFast(self.freqs, velwidth=self.velwidth, chanwidth=self.chanwidth) baseline_median = baseline.fit(data, mask=mask, weights=None) - + dct_data = fftpack.dct(baseline_median, type=self.dct_type) - sort_idx = np.argsort(np.absolute(dct_data))[:-self.order] + sort_idx = np.argsort(np.absolute(dct_data))[: -self.order] dct_data[sort_idx] = 0 dct_fit = fftpack.idct(dct_data, type=self.dct_type) * self.fnorm**2 - + return dct_fit diff --git a/contsub/image_plane.py b/mowjsub/image_plane.py similarity index 88% rename from contsub/image_plane.py rename to mowjsub/image_plane.py index 9b0aa1f..5596875 100644 --- a/contsub/image_plane.py +++ b/mowjsub/image_plane.py @@ -1,28 +1,30 @@ import numpy as np from scabha import init_logger + from . import BIN from .exceptions import BadFitError - log = init_logger(BIN.im_plane) -class ContSub(): + +class ContSub: """ a class for performing continuum subtraction on data """ - def __init__(self, fit_func): + + def __init__(self, fit_func, nomask=False): """ each object can be initiliazed by passing a data cube, a fitting function, and a mask Args: fit_func (Callable) : a fitting function should be built on FitFunc class """ self.fit_func = fit_func - - + self.nomask = nomask + def fitContinuum(self, cube, mask): """ fits the data with the desired function and returns the continuum and the line - + Args: cube (Array): Data cube to subtract continuum from mask (Array): Binary data weights. True -> will be used in fit, False will not be used in fit. @@ -30,39 +32,38 @@ def fitContinuum(self, cube, mask): Returns: Array: Continuum fit """ - + dimx, dimy, _ = cube.shape - + cont_model = np.zeros_like(cube) + if isinstance(mask, np.ndarray): nomask = False mask = np.asarray(mask, dtype=bool) else: nomask = True - + fitfunc = self.fit_func - skipped_lines = 0 + skipped_lines = 0 for ra in range(dimx): for dec in range(dimy): - # slice the data cube for the current pixel - slc = ra,dec,slice(None) + slc = ra, dec, slice(None) cube_ij = cube[slc] if nomask: mask_ij = np.full_like(cube_ij, False, dtype=bool) else: mask_ij = mask[slc] - + try: cont_model[slc] = fitfunc.fit(cube_ij, mask_ij, weights=None) except BadFitError: # Flag LOS and continue if too many pixels are flagged cont_model[slc] = np.full_like(cube_ij, np.nan) skipped_lines += 1 - + if skipped_lines > 0: log.info(f"This worker set {skipped_lines} spectra to NaN because of --cont-fit-tol.") - + return cont_model - \ No newline at end of file diff --git a/contsub/masking.py b/mowjsub/masking.py similarity index 68% rename from contsub/masking.py rename to mowjsub/masking.py index 6e1f3ce..3e6bf72 100644 --- a/contsub/masking.py +++ b/mowjsub/masking.py @@ -1,50 +1,57 @@ +import warnings +from abc import ABC, abstractmethod + import numpy as np -from scipy.signal import convolve -from scipy import ndimage from scabha import init_logger -from abc import ABC, abstractmethod -from contsub import BIN -import warnings +from scipy import ndimage +from scipy.signal import convolve + +from mowjsub import BIN log = init_logger(BIN.im_plane) -class Mask(): + +class Mask: """ mask class creates a mask using a specific masking method """ + def __init__(self, method): """ method should be defined when creating a Mask object Method should be built on the ClipMethod class """ self.method = method - + def getMask(self, data): """ calculates the mask given the data """ return self.method.createMask(data) - + class ClipMethod(ABC): """ Abstract class for different methods of making masks """ + def __init__(self): pass - + @abstractmethod def createMask(self, data): pass - + + class PixSigmaClip(ClipMethod): """ simple sigma clipping class """ - def __init__(self, n, sm_kernel = None, dilation = 0, method = 'rms'): + + def __init__(self, n, sm_kernel=None, dilation=0, method="rms"): """ has to define the multiple of sigma for clipping and the method for calculating the sigma - + n : multiple of sigma for clipping method : 'rms' or 'mad' for calculating the rms """ @@ -58,73 +65,73 @@ def __init__(self, n, sm_kernel = None, dilation = 0, method = 'rms'): self.sm = sm_kernel[:, None, None] else: self.sm = sm_kernel - if method == 'rms': + if method == "rms": self.function = self.__rms() - elif method == 'mad': + elif method == "mad": self.function = self.__mad() - + def createMask(self, data): """ - calculate a mask from the given data + calculate a mask from the given data """ sm_data = self.__smooth(data) with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=RuntimeWarning) - sigma = self.function(sm_data)[...,np.newaxis] - mask = np.abs(sm_data) < self.n*sigma - + warnings.simplefilter("ignore", category=RuntimeWarning) + sigma = self.function(sm_data)[..., np.newaxis] + mask = np.abs(sm_data) < self.n * sigma + struct_dil = ndimage.generate_binary_structure(len(data.shape), 1) struct_erd = ndimage.generate_binary_structure(len(data.shape), 2) - + for i in range(self.dilate): - mask = ndimage.binary_dilation(mask, structure=struct_dil, - border_value=1).astype(mask.dtype) - - for i in range(self.dilate+2): - mask = ndimage.binary_erosion(mask, structure=struct_erd, - border_value=1).astype(mask.dtype) - + mask = ndimage.binary_dilation(mask, structure=struct_dil, border_value=1).astype(mask.dtype) + + for i in range(self.dilate + 2): + mask = ndimage.binary_erosion(mask, structure=struct_erd, border_value=1).astype(mask.dtype) + return mask - + def __smooth(self, data): if self.sm is None: return data else: - sm_data = convolve(data, self.sm, mode = 'same') + sm_data = convolve(data, self.sm, mode="same") return sm_data - + def __rms(self): - return lambda x: np.sqrt(np.nanmean(np.square(x), axis = 2)) - + return lambda x: np.sqrt(np.nanmean(np.square(x), axis=2)) + def __mad(self): - return lambda x: np.nanmedian(np.abs(np.nanmean(x)-x), axis = 2) - + return lambda x: np.nanmedian(np.abs(np.nanmean(x) - x), axis=2) + + class ChanSigmaClip(ClipMethod): """ simple sigma clipping class """ - def __init__(self, n, method = 'rms'): + + def __init__(self, n, method="rms"): """ has to define the multiple of sigma for clipping and the method for calculating the sigma - + n : multiple of sigma for clipping method : 'rms' or 'mad' for calculating the rms """ self.n = n - if method == 'rms': + if method == "rms": self.function = self.__rms() - elif method == 'mad': + elif method == "mad": self.function = self.__mad() - + def createMask(self, data): """ - calculate a mask from the given data + calculate a mask from the given data """ - sigma = self.function(data)[:,None,None] - return np.abs(data) < self.n*sigma - + sigma = self.function(data)[:, None, None] + return np.abs(data) < self.n * sigma + def __rms(self): - return lambda x: np.sqrt(np.nanmean(np.square(x), axis = (0,1))) - + return lambda x: np.sqrt(np.nanmean(np.square(x), axis=(0, 1))) + def __mad(self): - return lambda x: np.nanmedian(np.abs(np.nanmean(x)-x), axis = (0,1)) + return lambda x: np.nanmedian(np.abs(np.nanmean(x) - x), axis=(0, 1)) diff --git a/contsub/parser/__init__.py b/mowjsub/parser/__init__.py similarity index 100% rename from contsub/parser/__init__.py rename to mowjsub/parser/__init__.py diff --git a/contsub/parser/imcontsub.py b/mowjsub/parser/im_mowjsub.py similarity index 76% rename from contsub/parser/imcontsub.py rename to mowjsub/parser/im_mowjsub.py index bdf9a37..956c11e 100644 --- a/contsub/parser/imcontsub.py +++ b/mowjsub/parser/im_mowjsub.py @@ -1,103 +1,109 @@ -import contsub -from scabha.schema_utils import clickify_parameters, paramfile_loader -import click -from scabha.basetypes import File -from omegaconf import OmegaConf import glob import os -from contsub import BIN -from scabha import init_logger -from contsub.image_plane import ContSub -from contsub.fitfuncs import ( - FitBSpline, - FitPolynomial, - FitMedFilter, - FitGCVSpline, -) +import time + import astropy.io.fits as fitsio -from contsub.utils import zds_from_fits, get_automask, subtract_fits +import click import dask.array as da -import time -import numpy as np import dask.multiprocessing +import numpy as np +from omegaconf import OmegaConf +from scabha import init_logger +from scabha.basetypes import File +from scabha.schema_utils import clickify_parameters, paramfile_loader +import mowjsub +from mowjsub import BIN +from mowjsub.fitfuncs import ( + FitBSpline, + FitGCVSpline, + FitMedFilter, + FitPolynomial, +) +from mowjsub.image_plane import ContSub +from mowjsub.utils import get_automask, subtract_fits, zds_from_fits command = BIN.im_plane -thisdir = os.path.dirname(__file__) +thisdir = os.path.dirname(__file__) source_files = glob.glob(f"{thisdir}/library/*.yaml") sources = [File(item) for item in source_files] -parserfile = File(f"{thisdir}/{command}.yaml") -config = paramfile_loader(parserfile, sources)[command] +parserfile = File(f"{thisdir}/im_mowjsub.yaml") +config = paramfile_loader(parserfile, sources)["im_mowjsub"] log = init_logger(BIN.im_plane) -@click.command("imcontsub") -@click.version_option(str(contsub.__version__)) + +@click.command("im-mowjsub") +@click.version_option(str(mowjsub.__version__)) @clickify_parameters(config) -def runit(**kwargs): +def runit(**kwargs): start_time = time.time() - + opts = OmegaConf.create(kwargs) - + if opts.cont_fit_tol > 100: log.warning("Requested --cont-fit-tol is larger than 100 percent. Assuming it is 100.") opts.cont_fit_tol = 100 - + infits = File(opts.input_image) - + if opts.output_prefix: prefix = opts.output_prefix else: prefix = f"{infits.BASEPATH}-contsub" - + outcont = File(f"{prefix}-cont.fits") outline = File(f"{prefix}-line.fits") - + if opts.overwrite is False and (outcont.EXISTS or outline.EXISTS): raise RuntimeError("At least one output file exists, but --no-overwrite has been set. Unset it to proceed.") if opts.fit_model in "spline polynomial dct".split() and not getattr(opts, "order", False): raise RuntimeError("The parameter 'order' is required for fit-model={opts.fit_model}.") - + if opts.fit_model in "spline median-filter dct".split() and not getattr(opts, "vel_width", False): raise RuntimeError("The parameter 'vel-width' is required for fit-model={opts.fit_model}.") - + velwidth = opts.vel_width or opts.segments - chunks = dict(ra = opts.ra_chunks or 64, dec=None, spectral=None) - + chunks = dict(ra=opts.ra_chunks or 64, dec=None, spectral=None) + rest_freq = opts.rest_freq - zds = zds_from_fits(infits.PATH, chunks=chunks, rest_freq=rest_freq, hdu_idx=opts.hdu_index, add_freqs=True) + zds = zds_from_fits( + infits.PATH, + chunks=chunks, + rest_freq=rest_freq, + hdu_idx=opts.hdu_index, + add_freqs=True, + ) base_dims = ["ra", "dec", "spectral", "stokes"] if not hasattr(zds, "stokes"): base_dims.remove("stokes") - + dims_string = "ra,dec,spectral" has_stokes = "stokes" in base_dims stokes_idx = opts.stokes_index - + log.info(f"Input data dimensions: {zds.DATA.dims}") log.info(f"Input data shape: {zds.DATA.shape}") - + if has_stokes: - cube = zds.DATA[...,stokes_idx] + cube = zds.DATA[..., stokes_idx] else: cube = zds.DATA - - + nomask = True if getattr(opts, "mask_image", None): mask = zds_from_fits(opts.mask_image, chunks=chunks, rest_freq=rest_freq).DATA nomask = False - signature = f"({dims_string}),({dims_string}) -> ({dims_string})" meta = (np.ndarray((), cube.dtype),) xspec = zds.FREQS.data.compute() - - dask.config.set(scheduler='threads', num_workers = opts.nworkers) + + dask.config.set(scheduler="threads", num_workers=opts.nworkers) dblocks = cube.data.blocks futures = [] - + if opts.fit_model in ["spline", "b-spline"]: fitfunc = FitBSpline(xspec, order=opts.order, velwidth=velwidth, fit_tol=opts.cont_fit_tol) fitfunc.prepare() @@ -110,53 +116,60 @@ def runit(**kwargs): elif opts.fit_model == "gcv-spline": fitfunc = FitGCVSpline(xspec, fit_tol=opts.cont_fit_tol) fitfunc.prepare(lam=opts.gcv_lambda) - + get_mask = da.gufunc( lambda _data: get_automask(_data, fitfunc, opts.sigma_clip), signature=f"({dims_string}) -> ({dims_string})", meta=(np.ndarray((), cube.dtype),), allow_rechunk=True, ) - - for biter,dblock in enumerate(dblocks): + + for biter, dblock in enumerate(dblocks): if opts.sigma_clip: mask_future = get_mask(dblock) elif nomask is False: mask_future = mask.data.blocks[biter] else: mask_future = da.zeros_like(dblock, dtype=bool) - + contfit = ContSub(fitfunc, nomask=False) - + getfit = da.gufunc( contfit.fitContinuum, signature=signature, meta=meta, allow_rechunk=True, ) - - futures.append(getfit( - dblock, - mask_future, - )) - - continuum = da.concatenate(futures).transpose((2,1,0)) + + futures.append( + getfit( + dblock, + mask_future, + ) + ) + + continuum = da.concatenate(futures).transpose((2, 1, 0)) if has_stokes: - continuum = continuum[np.newaxis,...] - + continuum = continuum[np.newaxis, ...] + header = zds.attrs["header"] out_ds_cont = fitsio.PrimaryHDU(continuum, header=header) - + out_ds_cont.writeto(outcont.PATH, overwrite=opts.overwrite) log.info(f"Continuum model cube written to: {outcont}") - - out_ds_line = subtract_fits(infits.PATH, outcont.PATH, chunks={0: opts.ra_chunks, 1:None, 2:None}) + + out_ds_line = subtract_fits( + infits.PATH, + outcont.PATH, + hdu_idx=opts.hdu_index, + chunks={0: opts.ra_chunks, 1: None, 2: None}, + ) log.info(f"Writing residual data (line cube) to: {outline}") out_ds_line.writeto(outline.PATH, overwrite=opts.overwrite) # DONE dtime = time.time() - start_time - hours = int(dtime/3600) - mins = dtime/60 - hours*60 - secs = (mins%1) * 60 + hours = int(dtime / 3600) + mins = dtime / 60 - hours * 60 + secs = (mins % 1) * 60 log.info(f"Finished. Runtime {hours}:{int(mins)}:{secs:.1f}") diff --git a/contsub/parser/imcontsub.yaml b/mowjsub/parser/im_mowjsub.yaml similarity index 98% rename from contsub/parser/imcontsub.yaml rename to mowjsub/parser/im_mowjsub.yaml index 3bfe753..9a48fda 100644 --- a/contsub/parser/imcontsub.yaml +++ b/mowjsub/parser/im_mowjsub.yaml @@ -1,4 +1,3 @@ - inputs: input-image: info: Input image @@ -47,7 +46,7 @@ inputs: info: GCV spline penalty. Zero is equivalent to an interpolating spline, high values lead to a flatter curve. If unset the parameter will be estimated using the GCV criterion; this can be very slow. Experience suggests that values chanwidth/nchan work best. dtype: float segments: - info: "## This has been replaced by --vel-width. It will be removed in future releases ## Width of spline segments or median filter window in km/s. If given as a list, then it must have same sixe as --order." + info: "## This has been replaced by --vel-width. It will be removed in future releases ## Width of spline segments or median filter window in km/s. If given as a list, then it must have same size as --order." dtype: float cont-fit-tol: info: "Minimum perentage of valid spectrum data points required to do a fit. @@ -63,7 +62,6 @@ inputs: info: "Index of stokes channel (zero-based) to use." dtype: int default: 0 - choices: [0,1,2,3] rest-freq: info: Cube rest frequency in MHz. Will ignore the one in the FITS header if it exists. dtype: float @@ -95,4 +93,4 @@ inputs: - error - critical outputs: - {} + {} \ No newline at end of file diff --git a/contsub/parser/viscontsub.py b/mowjsub/parser/vis_mowjsub.py similarity index 60% rename from contsub/parser/viscontsub.py rename to mowjsub/parser/vis_mowjsub.py index 351d228..2dc80fd 100644 --- a/contsub/parser/viscontsub.py +++ b/mowjsub/parser/vis_mowjsub.py @@ -1,45 +1,47 @@ -import contsub -from scabha.schema_utils import clickify_parameters, paramfile_loader -import click -from scabha.basetypes import File -from omegaconf import OmegaConf import glob import os -from contsub import BIN -from scabha import init_logger -import dask.array as da import time + +import click +import dask.array as da +import dask.multiprocessing import numpy as np import xarray as xr -import dask.multiprocessing -from contsub.utils import ms_to_xarray_dataset, get_ds_from_msdsl -from contsub.visibility_plane import VisContSub -from contsub.fitfuncs import FitBSpline, FitPolynomial -from dask.diagnostics import ProgressBar -from tqdm.dask import TqdmCallback from daskms import xds_from_ms, xds_to_table +from omegaconf import OmegaConf +from scabha import init_logger +from scabha.basetypes import File +from scabha.schema_utils import clickify_parameters, paramfile_loader +from tqdm.dask import TqdmCallback + +import mowjsub +from mowjsub import BIN +from mowjsub.fitfuncs import FitBSpline, FitPolynomial +from mowjsub.utils import get_ds_from_msdsl, ms_to_xarray_dataset +from mowjsub.visibility_plane import VisContSub log = init_logger(BIN.vis_plane) command = BIN.vis_plane -thisdir = os.path.dirname(__file__) +thisdir = os.path.dirname(__file__) source_files = glob.glob(f"{thisdir}/library/*.yaml") sources = [File(item) for item in source_files] -parserfile = File(f"{thisdir}/{command}.yaml") -config = paramfile_loader(parserfile, sources)[command] +parserfile = File(f"{thisdir}/vis_mowjsub.yaml") +config = paramfile_loader(parserfile, sources)["vis_mowjsub"] -@click.command("viscontsub") -@click.version_option(str(contsub.__version__)) + +@click.command("vis-mowjsub") +@click.version_option(str(mowjsub.__version__)) @clickify_parameters(config) -def runit(**kwargs): +def runit(**kwargs): start_time = time.time() - + opts = OmegaConf.create(kwargs) ms = opts.ms spwid = opts.spwid fieldid = opts.field_id chunksize = opts.row_chunks - segments = opts.segments[0] + velwidth = opts.vel_width or opts.segments method = opts.fit_model order = opts.order[0] nworkers = opts.nworkers @@ -49,99 +51,94 @@ def runit(**kwargs): zarr_name = opts.load_from_cache cont_tol = opts.cont_fit_tol - if opts.load_from_cache: temp_zarr = zarr_name else: - temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) - temp_zarr = 'tmp.zarr' + temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) + temp_zarr = "tmp.zarr" ds = xr.open_zarr(temp_zarr, chunks=outchunks) - - xspec = ds.coords['FREQ'] + + xspec = ds.coords["FREQ"] futures = [] - - if method == 'spline': - fitfunc = FitBSpline(order, segments, randomState=None, seq=None) - elif method == 'polynomial': + + if method == "spline": + fitfunc = FitBSpline(order, velwidth, randomState=None, seq=None) + elif method == "polynomial": fitfunc = FitPolynomial(order, cont_tol) else: raise ValueError(f"Unknown fitting method: {method}. Supported methods: 'spline', 'polynomial'.") - + base_dims = "TIME, BASELINE, FREQ, CORR" signature = f"(FREQ),({base_dims}),({base_dims}),({base_dims}) -> ({base_dims})" meta = (np.ndarray((), ds.VIS.dtype),) - dask.config.set(scheduler='threads', num_workers = nworkers) + dask.config.set(scheduler="threads", num_workers=nworkers) for biter, dblock in enumerate(ds.VIS.data.blocks): - #if biter > 0: - #continue + # if biter > 0: + # continue flags = ds.FLAG.data.blocks[biter] weights = ds.WEIGHT.data.blocks[biter] contfit = VisContSub(fitfunc, fit_tol=opts.cont_fit_tol) get_cont = da.gufunc( - contfit.vis_cont_sub, - signature=signature, - meta=meta, - allow_rechunk=True, - ) - futures.append(get_cont( - xspec, - dblock, - flags, - weights, + contfit.vis_cont_sub, + signature=signature, + meta=meta, + allow_rechunk=True, + ) + futures.append( + get_cont( + xspec, + dblock, + flags, + weights, ), ) - - + continuum_dask = da.concatenate(futures) - - continuum_xarray = xr.DataArray( - data=continuum_dask, - dims=ds.VIS.dims, - coords=ds.VIS.coords - ) - continuum = continuum_xarray.stack(row = ('time','baseline')) - continuum = continuum.transpose("row",...).chunk({"row": chunksize}) + continuum_xarray = xr.DataArray(data=continuum_dask, dims=ds.VIS.dims, coords=ds.VIS.coords) + + continuum = continuum_xarray.stack(row=("time", "baseline")) + continuum = continuum.transpose("row", ...).chunk({"row": chunksize}) - ms_dsl = xds_from_ms( - ms, + ms, index_cols=["TIME", "ANTENNA1", "ANTENNA2"], group_cols=["FIELD_ID", "DATA_DESC_ID"], - chunks={"row": chunksize } + chunks={"row": chunksize}, ) - + msds = get_ds_from_msdsl(ms_dsl, spwid, fieldid) - ms_ds = msds.assign(**{ - output_column: ( - ("row", "chan", "corr"), - getattr(msds, input_column).data - continuum.data, + ms_ds = msds.assign( + **{ + output_column: ( + ("row", "chan", "corr"), + getattr(msds, input_column).data - continuum.data, ), - }) + } + ) if opts.output_ms: ms_name = opts.output_ms writes = [xds_to_table(ms_ds, ms_name, columns=["FLAG", "WEIGHT", output_column])] print(f"Writing new MS with FLAG, WEIGHT, and {output_column}") - + else: writes = [xds_to_table(ms_ds, ms, [output_column])] print(f"Writing line data to column '{output_column}' in {ms}...") - with TqdmCallback(desc="Writing line data to MS"): da.compute(writes) print(f"UV plane continuum subtraction completed. Data written to column '{output_column}' in {ms}.") # DONE dtime = time.time() - start_time - hours = int(dtime/3600) - mins = dtime/60 - hours*60 - secs = (mins%1) * 60 + hours = int(dtime / 3600) + mins = dtime / 60 - hours * 60 + secs = (mins % 1) * 60 log.info(f"Runtime {hours}:{int(mins)}:{secs:.1f}") diff --git a/contsub/parser/viscontsub.yaml b/mowjsub/parser/vis_mowjsub.yaml similarity index 56% rename from contsub/parser/viscontsub.yaml rename to mowjsub/parser/vis_mowjsub.yaml index c9fa537..a41f7a0 100644 --- a/contsub/parser/viscontsub.yaml +++ b/mowjsub/parser/vis_mowjsub.yaml @@ -1,8 +1,3 @@ -libs: - spline: - _include: (contsub.parser.library)spline.yaml - polyn: - _include: (contsub.parser.library)polyn.yaml inputs: ms: @@ -21,12 +16,31 @@ inputs: dtype: str default: LINE_DATA fit-model: - info: Fit function to model the continuum. ** Only the spline and polynomial available in this version **. + info: "Fit function to model the continuum. WARNING: A median-filter continuum model may subsume low SNR line emission, use it with great care." dtype: str choices: - - spline - - polynomial - default: spline + - b-spline + - spline + - polynomial + - median-filter + - scipy-median-filter + - gcv-spline + default: b-spline + order: + info: Order of spline/polynomial or number of top coefficeints to use for DCT reconstruction + dtype: int + vel-width: + info: Width of spline segments or median filter window in km/s. + dtype: float + chan-width: + info: Width of spline segments or median filter window in number of channels. + dtype: int + gcv-lambda: + info: GCV spline penalty. Zero is equivalent to an interpolating spline, high values lead to a flatter curve. If unset the parameter will be estimated using the GCV criterion; this can be very slow. Experience suggests that values chanwidth/nchan work best. + dtype: float + segments: + info: "## This has been replaced by --vel-width. It will be removed in future releases ## Width of spline segments or median filter window in km/s. If given as a list, then it must have same size as --order." + dtype: float spwid: info: Spectral Window ID dtype: int @@ -64,8 +78,6 @@ inputs: info: Load the MS from a cache (give Zarr file name) if available, otherwise create it. dtype: File required: False - _use: - - libs.spline.inputs outputs: diff --git a/mowjsub/stimelating/im_mowjsub_param.yaml b/mowjsub/stimelating/im_mowjsub_param.yaml new file mode 100644 index 0000000..a7146dc --- /dev/null +++ b/mowjsub/stimelating/im_mowjsub_param.yaml @@ -0,0 +1,93 @@ +input-image: + info: Input image + dtype: File + must_exist: yes + required: yes + policies: + positional: yes +output-prefix: + info: Name of ouput image + dtype: str +mask-image: + info: Mask image + dtype: File + must_exist: true +sigma-clip: + info: Sigma clip for each iteration. Only required if mask-image is not given. + dtype: List[float] + policies: + repeat: "[]" +automask-per-iter: + info: Generate a new mask per iteration. + dtype: bool + default: False +fit-model: + info: "Fit function to model the continuum. WARNING: A median-filter continuum model may subsume low SNR line emission, use it with great care." + dtype: str + choices: + - b-spline + - spline + - polynomial + - median-filter + - scipy-median-filter + - gcv-spline + default: b-spline +order: + info: Order of spline/polynomial or number of top coefficeints to use for DCT reconstruction + dtype: int +vel-width: + info: Width of spline segments or median filter window in km/s. + dtype: float +chan-width: + info: Width of spline segments or median filter window in number of channels. + dtype: int +gcv-lambda: + info: GCV spline penalty. Zero is equivalent to an interpolating spline, high values lead to a flatter curve. If unset the parameter will be estimated using the GCV criterion; this can be very slow. Experience suggests that values chanwidth/nchan work best. + dtype: float +segments: + info: "## This has been replaced by --vel-width. It will be removed in future releases ## Width of spline segments or median filter window in km/s. If given as a list, then it must have same size as --order." + dtype: float +cont-fit-tol: + info: "Minimum perentage of valid spectrum data points required to do a fit. + Spectra below this tolerance will be set to NaN. + Leaving this unset may result in poor or NaN spectra in the output cubes" + dtype: float + default: 0 +overwrite: + info: Overwrite output image if it already exists + dtype: bool + default: yes +stokes-index: + info: "Index of stokes channel (zero-based) to use." + dtype: int + default: 0 +rest-freq: + info: Cube rest frequency in MHz. Will ignore the one in the FITS header if it exists. + dtype: float +stokes-axis: + info: "### DEPRECATED #### Set this flag if the input image has a stokes dimension. (Default is True)." + default: yes + dtype: bool +hdu-index: + info: FITS primary HDU index + dtype: int + default: 0 + abbreviation: hi +ra-chunks: + info: Chunking along RA-axis. If set to zero, no Chunking is perfomed. + dtype: int + default: 64 +nworkers: + info: Number of workers (one per CPU) + dtype: int + default: 4 +loglevel: + info: Log level for the output + dtype: str + default: info + choices: + - info + - debug + - trace + - error + - critical diff --git a/mowjsub/stimelating/im_mowjsub_recipe.yaml b/mowjsub/stimelating/im_mowjsub_recipe.yaml new file mode 100644 index 0000000..6a3d24d --- /dev/null +++ b/mowjsub/stimelating/im_mowjsub_recipe.yaml @@ -0,0 +1,39 @@ +_include: + - (mowjsub.stimelating)mowjsub_cabs.yaml + +opts: + backend: + select: native + +im-mowjsub: + name: im-mowjsub + info: Image plane continuum subtraction + inputs: + indir: + info: Input Directory + dtype: Directory + default: input # Insert your input directory here + must_exist: yes + outdir: + info: Output Directory + dtype: Directory + default: output # Insert your output directory here + must_exist: yes + + steps: + im-mowjsub: + info: Image plane cont sub + cab: im-mowjsub + params: #adjust the parameters below as needed, see mowjsub.stimelating.im_mowjsub_param.yaml for all input parameters + input-image: '{recipe.indir}/.fits' # Insert the name of your input FITS cube here + output-prefix: '{recipe.outdir}/im_mowjsub' # Insert the prefix for you cubes (or leave unchanged) + fit-model: b-spline + order: 3 + vel-width: 400 + + + + + + + diff --git a/mowjsub/stimelating/mowjsub_cabs.yaml b/mowjsub/stimelating/mowjsub_cabs.yaml new file mode 100644 index 0000000..b39091b --- /dev/null +++ b/mowjsub/stimelating/mowjsub_cabs.yaml @@ -0,0 +1,12 @@ +cabs: + im-mowjsub: + info: Perform image plane continuum subtraction on an input FITs file + command: im-mowjsub + inputs: + _include: (mowjsub.stimelating)im_mowjsub_param.yaml + + vis-mowjsub: + info: Perform visibility plane continuum subtraction on an input FITs file + command: vis-mowjsub + inputs: + _include: (mowjsub.stimelating)vis_mowjsub_param.yaml diff --git a/mowjsub/stimelating/vis_mowjsub_param.yaml b/mowjsub/stimelating/vis_mowjsub_param.yaml new file mode 100644 index 0000000..651e553 --- /dev/null +++ b/mowjsub/stimelating/vis_mowjsub_param.yaml @@ -0,0 +1,78 @@ +ms: + info: Input MS file + dtype: File + must_exist: yes + required: yes + policies: + positional: true +input-column: + info: Column which contains the data to be continuum subtracted. + dtype: str + default: DATA +output-column: + info: Column name to write the continuum subtracted data to + dtype: str + default: LINE_DATA +fit-model: + info: "Fit function to model the continuum. WARNING: A median-filter continuum model may subsume low SNR line emission, use it with great care." + dtype: str + choices: + - b-spline + - spline + - polynomial + - median-filter + - scipy-median-filter + - gcv-spline + default: b-spline +order: + info: Order of spline/polynomial or number of top coefficeints to use for DCT reconstruction + dtype: int +vel-width: + info: Width of spline segments or median filter window in km/s. + dtype: float +chan-width: + info: Width of spline segments or median filter window in number of channels. + dtype: int +gcv-lambda: + info: GCV spline penalty. Zero is equivalent to an interpolating spline, high values lead to a flatter curve. If unset the parameter will be estimated using the GCV criterion; this can be very slow. Experience suggests that values chanwidth/nchan work best. + dtype: float +segments: + info: "## This has been replaced by --vel-width. It will be removed in future releases ## Width of spline segments or median filter window in km/s. If given as a list, then it must have same size as --order." + dtype: float +spwid: + info: Spectral Window ID + dtype: int + default: 0 +field-id: + info: Field ID + dtype: int + default: 0 +row-chunks: + info: Chunking strategy (Done along the time axis) + dtype: int + default: 10000 +time-chunks: + info: Chunk size for time axis + dtype: int + default: 64 +bl-chunks: + info: Chunk size for baseline axis + dtype: int + default: 10 +cont-fit-tol: + info: "Minimum perentage of valid spectrum data points required to do a fit. + If the percentage of data points is below this percentage, original data will be returned." + dtype: float + default: 0 +nworkers: + info: Number of workers (one per CPU) + dtype: int + default: 4 +output-ms: + info: If provided, write the output to a new MS with this name. Otherwise, add new column to the input MS. + dtype: File + required: False +load-from-cache: + info: Load the MS from a cache (give Zarr file name) if available, otherwise create it. + dtype: File + required: False \ No newline at end of file diff --git a/contsub/utils.py b/mowjsub/utils.py similarity index 51% rename from contsub/utils.py rename to mowjsub/utils.py index 1e7c119..5c2781c 100644 --- a/contsub/utils.py +++ b/mowjsub/utils.py @@ -1,20 +1,22 @@ -import xarray as xr -from astropy.wcs import WCS -from contsub.masking import PixSigmaClip, Mask -from contsub.image_plane import ContSub -from contsub import BIN +import datetime +import warnings from typing import Dict -from scabha import init_logger -from astropy import units + import astropy.io.fits as fitsio -from scabha.basetypes import File +import dask.array as da import numpy as np -import datetime +import xarray as xr +from astropy import units +from astropy.wcs import WCS from daskms import xds_from_ms, xds_from_table -from xarrayfits import xds_from_fits -import dask.array as da +from scabha import init_logger +from scabha.basetypes import File from tqdm.dask import TqdmCallback -import warnings +from xarrayfits import xds_from_fits + +from mowjsub import BIN +from mowjsub.image_plane import ContSub +from mowjsub.masking import Mask, PixSigmaClip warnings.filterwarnings("ignore", message=".*does not have a Zarr V3 specification.*") warnings.filterwarnings("ignore", message=".*Consolidated metadata is currently not part.*") @@ -38,15 +40,16 @@ def get_automask(cube, fitfunc, sigma_clip): log.info("Creating binary mask as requested") contsub = ContSub(fitfunc) cont_model = contsub.fitContinuum(cube, mask=None) - + clip = PixSigmaClip(sigma_clip) - + mask = Mask(clip).getMask(cube - cont_model) log.info("Mask created sucessfully") - + return ~mask -def chans_in_velwidth(freqs:np.ndarray, velwidth:float): + +def chans_in_velwidth(freqs: np.ndarray, velwidth: float): """ Calculates the number of channels in given velocity width @@ -57,16 +60,16 @@ def chans_in_velwidth(freqs:np.ndarray, velwidth:float): speed_c = 2.998e8 df_low = np.partition(freqs, 2)[:2] df_high = np.partition(freqs, 2)[-2:] - + dv_high = np.abs(np.diff(df_low) / np.mean(df_low)) * speed_c dv_low = np.abs(np.diff(df_high) / np.mean(df_high)) * speed_c dv = np.mean([dv_low, dv_high]) - + return int(velwidth / dv) def zds_from_fits(fname, chunks=None, rest_freq=None, hdu_idx=0, add_freqs=False): - """ Creates Zarr store from a FITS file. The resulting array has + """Creates Zarr store from a FITS file. The resulting array has dimensions = RA, DEC, SPECTRAL[, STOKES] Args: @@ -80,14 +83,13 @@ def zds_from_fits(fname, chunks=None, rest_freq=None, hdu_idx=0, add_freqs=False Returns: Zarr: Zarr array (persistant store, mode=w) """ - chunks = chunks or dict(ra=64,dec=None, spectral=None) + chunks = chunks or dict(ra=64, dec=None, spectral=None) fds = xds_from_fits(fname, hdus=hdu_idx)[0] - + header = fds.hdu.header if rest_freq: - header["RESTFREQ"] = rest_freq * 1e6 # set it to Hz + header["RESTFREQ"] = rest_freq * 1e6 # set it to Hz wcs = WCS(header, naxis="spectral stokes".split()) - axis_names = [header["CTYPE1"], header["CTYPE2"]] + wcs.axis_type_names if not wcs.has_spectral: @@ -99,29 +101,30 @@ def zds_from_fits(fname, chunks=None, rest_freq=None, hdu_idx=0, add_freqs=False if len(axis_names) == 4: new_names.append("stokes") - coords = dict([(a,fds.hdu[b].values) for a,b in zip(new_names,axis_names)]) + coords = dict([(a, fds.hdu[b].values) for a, b in zip(new_names, axis_names)]) if add_freqs: data_vars = { "DATA": (new_names, fds_xyz.data), - "FREQS": (("spectral",), FitsHeader(header).retFreq()), + "FREQS": (("spectral",), FitsHeader(header).retFreq()), } else: data_vars = { - "DATA" : (new_names, fds_xyz.data), + "DATA": (new_names, fds_xyz.data), } ds = xr.Dataset( data_vars, - coords = coords, - attrs = dict( - info =f"Temporary copy of data from FITS file: {fname}", - header = fitsio.Header(header), - ), + coords=coords, + attrs=dict( + info=f"Temporary copy of data from FITS file: {fname}", + header=fitsio.Header(header), + ), ) return ds.chunk(chunks) -def subtract_fits(data_file: File, model_file: File, chunks: Dict): - """ Returns the residual of two FITS files as a FitsPrimaryHDU object + +def subtract_fits(data_file: File, model_file: File, hdu_idx: int, chunks: Dict): + """Returns the residual of two FITS files as a FitsPrimaryHDU object Args: data_file (File): FITS file of the data @@ -131,24 +134,22 @@ def subtract_fits(data_file: File, model_file: File, chunks: Dict): Returns: FitsPrimaryHDU """ - - data_ds = xds_from_fits(data_file, chunks=chunks)[0] - model_ds = xds_from_fits(model_file, chunks=chunks)[0] + + data_ds = xds_from_fits(data_file, hdus=hdu_idx, chunks=chunks)[0] + model_ds = xds_from_fits(model_file, hdus=hdu_idx, chunks=chunks)[0] residual_ds = data_ds.hdu.data - model_ds.hdu.data - - out_ds = data_ds.assign(hdu=( - data_ds.hdu.dims, - residual_ds, - data_ds.hdu.attrs), + + out_ds = data_ds.assign( + hdu=(data_ds.hdu.dims, residual_ds, data_ds.hdu.attrs), ) header = fitsio.Header(data_ds.hdu.header) return fitsio.PrimaryHDU(out_ds.hdu.data, header=header) -class FitsHeader(): +class FitsHeader: def __init__(self, header: Dict): self._header = header.copy() - + def retFreq(self): """ Extract the part of the cube name that will be used in the name of @@ -162,88 +163,92 @@ def retFreq(self): Returns ------- frequency - a 1D numpy array of channel frequencies in MHz + a 1D numpy array of channel frequencies in MHz """ - - if not ('TIMESYS' in self._header): - self._header['TIMESYS'] = 'utc' - elif self._header['TIMESYS'] != 'utc': - self._header['TIMESYS'] = 'utc' - freqDim = self._header['NAXIS3'] - wcs3d=WCS(self._header) + + if "TIMESYS" not in self._header: + self._header["TIMESYS"] = "utc" + elif self._header["TIMESYS"] != "utc": + self._header["TIMESYS"] = "utc" + freqDim = self._header["NAXIS3"] + wcs3d = WCS(self._header) try: wcsfreq = wcs3d.spectral - except: - wcsfreq = wcs3d.sub(['spectral']) - return np.around(wcsfreq.pixel_to_world(np.arange(0,freqDim)).to(units.MHz).value, decimals = 7) - + except: # noqa: E722 TODO(Mika) fix this + wcsfreq = wcs3d.sub(["spectral"]) + return np.around( + wcsfreq.pixel_to_world(np.arange(0, freqDim)).to(units.MHz).value, + decimals=7, + ) + def getAppendHeader(self, nchan): - return self.spectralSplitHeader(nchan, orig = 'append_fits') - + return self.spectralSplitHeader(nchan, orig="append_fits") + def getTableHeader(self, nchan): - self._header['NAXIS2'] = nchan - self._header['NCHAN'] = nchan - if 'OBSERVER' in list(self._header.keys()): - self._header.remove('OBSERVER') - self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') - self._header['ORIGIN'] = 'A. Kazemi-Moridani (table_header)' + self._header["NAXIS2"] = nchan + self._header["NCHAN"] = nchan + if "OBSERVER" in list(self._header.keys()): + self._header.remove("OBSERVER") + self._header["DATE"] = str(datetime.datetime.now()).replace(" ", "T") + self._header["ORIGIN"] = "A. Kazemi-Moridani (table_header)" return self._header - + def getCombineHeader(self, dimx, dimy): - self._header['NAXIS1'] = int(dimx) - self._header['NAXIS2'] = int(dimy) - # self._header['CRPIX1'] = xcen + self._header["NAXIS1"] = int(dimx) + self._header["NAXIS2"] = int(dimy) + # self._header['CRPIX1'] = xcen # self._header['CRPIX2'] = ycen - if 'OBSERVER' in list(self._header.keys()): - self._header.remove('OBSERVER') - self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') - self._header['ORIGIN'] = 'A. Kazemi-Moridani (combine_spatial)' + if "OBSERVER" in list(self._header.keys()): + self._header.remove("OBSERVER") + self._header["DATE"] = str(datetime.datetime.now()).replace(" ", "T") + self._header["ORIGIN"] = "A. Kazemi-Moridani (combine_spatial)" return self._header - - def getPrimeHeader(self, nchan, ydim, xdim, mask = False, orig = 'prime_header'): - self._header['NAXIS1'] = int(xdim) - self._header['NAXIS2'] = int(ydim) - self._header['NAXIS3'] = int(nchan) + + def getPrimeHeader(self, nchan, ydim, xdim, mask=False, orig="prime_header"): + self._header["NAXIS1"] = int(xdim) + self._header["NAXIS2"] = int(ydim) + self._header["NAXIS3"] = int(nchan) if mask: - self._header['BITPIX'] = 8 - if orig == 'prime_header': - orig = 'mask_header' - if 'OBSERVER' in list(self._header.keys()): - self._header.remove('OBSERVER') - self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') - self._header['ORIGIN'] = f'A. Kazemi-Moridani ({orig})' + self._header["BITPIX"] = 8 + if orig == "prime_header": + orig = "mask_header" + if "OBSERVER" in list(self._header.keys()): + self._header.remove("OBSERVER") + self._header["DATE"] = str(datetime.datetime.now()).replace(" ", "T") + self._header["ORIGIN"] = f"A. Kazemi-Moridani ({orig})" return self._header - - def spectralSplitHeader(self, nchan, sfreq = None, orig = 'spectral_split'): - self._header['NAXIS3'] = nchan - if sfreq != None: - self._header['CRVAL3'] = sfreq - if 'OBSERVER' in list(self._header.keys()): - self._header.remove('OBSERVER') - self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') - self._header['ORIGIN'] = f'A. Kazemi-Moridani ({orig})' + + def spectralSplitHeader(self, nchan, sfreq=None, orig="spectral_split"): + self._header["NAXIS3"] = nchan + if sfreq is not None: + self._header["CRVAL3"] = sfreq + if "OBSERVER" in list(self._header.keys()): + self._header.remove("OBSERVER") + self._header["DATE"] = str(datetime.datetime.now()).replace(" ", "T") + self._header["ORIGIN"] = f"A. Kazemi-Moridani ({orig})" return self._header - - def spatialSplitHeader(self, xlims, ylims, chans = None): + + def spatialSplitHeader(self, xlims, ylims, chans=None): xdn, xup = xlims ydn, yup = ylims freq = self.retFreq() - if chans != None: + if chans is not None: chdn, chup = chans - self._header['NAXIS3'] = chup - chdn #make sure this is correct it was 'chup - chdn + 1' before - self._header['CRVAL3'] = freq[chdn]*1e6 - xor = self._header['CRPIX1'] - yor = self._header['CRPIX2'] - self._header['CRPIX1'] = xor - xdn - self._header['CRPIX2'] = yor - ydn - self._header['NAXIS1'] = xup-xdn - self._header['NAXIS2'] = yup-ydn - if 'OBSERVER' in list(self._header.keys()): - self._header.remove('OBSERVER') - self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') - self._header['ORIGIN'] = 'A. Kazemi-Moridani (spatial_split)' + self._header["NAXIS3"] = chup - chdn # make sure this is correct it was 'chup - chdn + 1' before + self._header["CRVAL3"] = freq[chdn] * 1e6 + xor = self._header["CRPIX1"] + yor = self._header["CRPIX2"] + self._header["CRPIX1"] = xor - xdn + self._header["CRPIX2"] = yor - ydn + self._header["NAXIS1"] = xup - xdn + self._header["NAXIS2"] = yup - ydn + if "OBSERVER" in list(self._header.keys()): + self._header.remove("OBSERVER") + self._header["DATE"] = str(datetime.datetime.now()).replace(" ", "T") + self._header["ORIGIN"] = "A. Kazemi-Moridani (spatial_split)" return self._header - + + def get_ds_from_msdsl(ms_dsl, field_id=0, data_desc_id=0): found_ds = False for ds in ms_dsl: @@ -254,11 +259,17 @@ def get_ds_from_msdsl(ms_dsl, field_id=0, data_desc_id=0): return ds else: raise ValueError("Dataset with FIELD_ID=1 and DATA_DESC_ID=1 not found in the MS.") - -def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, - outchunks = {'time': 64, 'baseline': 64}, save_to_zarr=False): - - """ Creates Zarr store from a input MS. The resulting array has + + +def ms_to_xarray_dataset( + ms_path, + spw_id: int, + field_id: int, + chunks: int, + outchunks={"time": 64, "baseline": 64}, + save_to_zarr=False, +): + """Creates Zarr store from a input MS. The resulting array has dimensions = time, basline, SPECTRAL, corr Args: @@ -267,38 +278,38 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, field_id (int): Field ID chunks (int): How to chunk the data outchunks (dict, optional): xarray chunk object. Defaults to {'time': 64, 'baseline': 64}. - save_to_zarr (bool, optional): Save the output to Zarr. Defaults to False. + save_to_zarr (bool, optional): Save the output to Zarr. Defaults to False. Returns: Zarr: Zarr array (persistant store, mode=w) """ ms_dsl = xds_from_ms( - ms_path, + ms_path, index_cols=["TIME", "ANTENNA1", "ANTENNA2"], group_cols=["FIELD_ID", "DATA_DESC_ID"], - chunks={"row": chunks } + chunks={"row": chunks}, ) - + spw_table = xds_from_table(f"{ms_path}::SPECTRAL_WINDOW")[0] field_table = xds_from_table(f"{ms_path}::FIELD")[0] antenna_table = xds_from_table(f"{ms_path}::ANTENNA")[0] - frequencies = spw_table.CHAN_FREQ.data[spw_id] - channel_width = spw_table.CHAN_WIDTH.data[spw_id][0] + frequencies = spw_table.CHAN_FREQ.data[spw_id] + channel_width = spw_table.CHAN_WIDTH.data[spw_id][0] ref_frequency = spw_table.REF_FREQUENCY.data[spw_id] - phase_center = field_table.PHASE_DIR.data[field_id].compute()[0] + phase_center = field_table.PHASE_DIR.data[field_id].compute()[0] antenna_names = [name.strip() for name in antenna_table.NAME.data.compute()] ds = get_ds_from_msdsl(ms_dsl, field_id=field_id, data_desc_id=spw_id) - - times = ds.TIME.data + + times = ds.TIME.data visibilities = ds.DATA.data flags = ds.FLAG.data - weights = ds.WEIGHT_SPECTRUM.data + weights = ds.WEIGHT_SPECTRUM.data uvw = ds.UVW.data - + nant = antenna_table.NAME.size - nbl = nant*(nant-1) // 2 + nbl = nant * (nant - 1) // 2 nrow, nchan, ncorr = ds.DATA.shape ntimes = nrow // nbl @@ -307,46 +318,49 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, reshaped_vis = da.reshape(visibilities, (ntimes, nbl, nchan, ncorr)) reshaped_flags = da.reshape(flags, (ntimes, nbl, nchan, ncorr)) reshaped_weights = da.reshape(weights, (ntimes, nbl, nchan, ncorr)) - - if ncorr == 2: - corr_labels = ['XX', 'YY'][:ncorr] + + if ncorr == 2: + corr_labels = ["XX", "YY"][:ncorr] else: - corr_labels = ['XX', 'XY', 'YX', 'YY'][:ncorr] + corr_labels = ["XX", "XY", "YX", "YY"][:ncorr] dataset = xr.Dataset( - { #TO-DO: reshape the data for all - 'VIS': ([ 'time', 'baseline' , 'spectral', 'corr'], reshaped_vis), #time here will be time indicies - 'FLAG': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_flags), - 'WEIGHT': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_weights), - 'UVW': (('time', 'baseline', 'uvw'), uvw.reshape(ntimes, nbl, 3)), + { # TO-DO: reshape the data for all + "VIS": ( + ["time", "baseline", "spectral", "corr"], + reshaped_vis, + ), # time here will be time indicies + "FLAG": (["time", "baseline", "spectral", "corr"], reshaped_flags), + "WEIGHT": (["time", "baseline", "spectral", "corr"], reshaped_weights), + "UVW": (("time", "baseline", "uvw"), uvw.reshape(ntimes, nbl, 3)), }, coords={ - 'FREQ': frequencies, - 'CORR':corr_labels, - 'TIME': unique_times, - 'BASELINE': np.arange(nbl), + "FREQ": frequencies, + "CORR": corr_labels, + "TIME": unique_times, + "BASELINE": np.arange(nbl), }, attrs={ - - 'ref_freq': float(ref_frequency), - 'channel_width': float(channel_width), - 'phase_center_ra': float(phase_center[0]), # radians - 'phase_center_dec': float(phase_center[1]), # radians - 'phase_center_ra_deg': float(np.degrees(phase_center[0])), # degrees - 'phase_center_dec_deg': float(np.degrees(phase_center[1])), # degrees - 'antenna_names': antenna_names, - 'nant': nant, - 'DATA_DESC_ID': spw_id, - 'FIELD_ID': field_id - }) + "ref_freq": float(ref_frequency), + "channel_width": float(channel_width), + "phase_center_ra": float(phase_center[0]), # radians + "phase_center_dec": float(phase_center[1]), # radians + "phase_center_ra_deg": float(np.degrees(phase_center[0])), # degrees + "phase_center_dec_deg": float(np.degrees(phase_center[1])), # degrees + "antenna_names": antenna_names, + "nant": nant, + "DATA_DESC_ID": spw_id, + "FIELD_ID": field_id, + }, + ) dataset = dataset.chunk(outchunks) if not save_to_zarr: return dataset else: - outpath = f'tmp.zarr' - write_to_zarr = dataset.to_zarr(outpath, mode='w', compute=False) + outpath = "tmp.zarr" + write_to_zarr = dataset.to_zarr(outpath, mode="w", compute=False) with TqdmCallback(desc="Writing to Zarr"): da.compute(write_to_zarr) - return outpath \ No newline at end of file + return outpath diff --git a/contsub/visibility_plane.py b/mowjsub/visibility_plane.py similarity index 82% rename from contsub/visibility_plane.py rename to mowjsub/visibility_plane.py index bdb4725..c89119c 100644 --- a/contsub/visibility_plane.py +++ b/mowjsub/visibility_plane.py @@ -1,13 +1,15 @@ -import numpy as np -import numpy as np from typing import Callable -class VisContSub(): +import numpy as np + + +class VisContSub: """ a class for performing continuum subtraction on visibility data """ + def __init__(self, fit_func: Callable): - """ Fit contiuum basline to visibility data + """Fit contiuum basline to visibility data Args: fit_func (Callable): Function to model contiuum baseline @@ -15,13 +17,12 @@ def __init__(self, fit_func: Callable): self.fit_func = fit_func - def vis_cont_sub(self, visdata: np.ndarray, - flags: np.ndarray, weights: np.ndarray): + def vis_cont_sub(self, visdata: np.ndarray, flags: np.ndarray, weights: np.ndarray): """ Fits the visibility data with the desired function and returns the continuum subtracted data """ ntime, nbaseline, _, ncorr = visdata.shape - + for tme in range(ntime): for bl in range(nbaseline): for corr in range(ncorr): @@ -29,7 +30,7 @@ def vis_cont_sub(self, visdata: np.ndarray, vis_slice = visdata[slc] flag_slice = flags[slc] weights_slice = weights[slc] - + real_model = self.fit_func.fit(vis_slice.real, flag_slice, weights_slice) imag_model = self.fit_func.fit(vis_slice.imag, flag_slice, weights_slice) diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 91a1eab..0000000 --- a/poetry.lock +++ /dev/null @@ -1,4975 +0,0 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. - 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-[[package]] -name = "zipp" -version = "3.23.0" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.9" -groups = ["main"] -markers = "python_version < \"3.12\"" -files = [ - {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}, - {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}, -] - -[package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] -cover = ["pytest-cov"] -doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] -type = ["pytest-mypy"] - -[metadata] -lock-version = "2.1" -python-versions = ">=3.10,<3.14" -content-hash = "30e778572e87760c05d9ee2f8afb6402b4e6560f94e34eab5fd88a5f8b8bea09" diff --git a/pyproject.toml b/pyproject.toml index be8f5da..6341a65 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,49 +1,63 @@ -[tool.poetry] -name = "contsub" +[project] +name = "mowjsub" version = "2.0rc1" description = "Radio astronomy data continuum subtraction tools" -authors = ["Sphesihle Makhathini, Mika Naidoo", "Amir Kazemi-Moridani"] -license = "MIT" +authors = [ + { name = "Sphesihle Makhathini" }, + { name = "Mika Naidoo" }, + { name = "Amir Kazemi-Moridani" }, +] +requires-python = ">=3.10,<3.14" readme = "README.rst" +license = "MIT" +license-files = ["LICENSE"] +dependencies = [ + "numpy", + "astropy", + "scipy", + "xarray-fits", + "xarray", + "stimela>=2.1.2,<3", + "zarr", + "tdqm", + "dask-ms", + "dask", +] + +[project.scripts] +im-mowjsub = "mowjsub.parser.im_mowjsub:runit" +vis-mowjsub = "mowjsub.parser.vis_mowjsub:runit" + +[dependency-groups] +dev = [ + "jupyter", + "matplotlib", +] + +ruff = [ + "pre-commit>=4.3.0", + "ruff>=0.12.9", +] + +tests = [ + "pytest==8.4.1", + {include-group = "ruff"} + ] + +docs = [ + "Sphinx>=8.1,<9", + "sphinx-copybutton", + "furo", + "sphinx-rtd-theme", + "sphinx_click==6.0.0", +] + +[tool.hatch.build.targets.sdist] +include = ["mowjsub"] + +[tool.hatch.build.targets.wheel] +include = ["mowjsub"] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry.dependencies] -python = ">=3.10,<3.14" -numpy = "*" -astropy = "*" -scipy = "*" -xarray-fits = "*" -xarray = "*" -stimela = "^2.1.2" -zarr = "*" -tdqm = "*" -dask-ms = "*" -dask = "*" - - -[tool.poetry.scripts] -imcontsub = "contsub.parser.imcontsub:runit" -viscontsub = "contsub.parser.viscontsub:runit" - -[tool.poetry.group.dev.dependencies] -jupyter = "*" -matplotlib = "*" - -[tool.poetry.group.tests] -optional = true -[tool.poetry.group.tests.dependencies] -pytest = "8.4.1" -flake8 = "*" - - -[tool.poetry.group.docs] -optional = true -[tool.poetry.group.docs.dependencies] -Sphinx = "^8.1" -sphinx-copybutton = "*" -furo = "*" -sphinx-rtd-theme = "*" -sphinx_click = "6.0.0" +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000..3f7b2d7 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,9 @@ +line-length = 180 + +target-version = "py310" + +[lint] +extend-select = [ + "E501", # Line length + "I" # isort - sort imports +] diff --git a/tests/test_main.py b/tests/test_main.py index f4f9600..d4c0a6d 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -1,102 +1,103 @@ import unittest + import numpy as np from scabha import init_logger -from contsub.fitfuncs import ( + +from mowjsub import utils +from mowjsub.fitfuncs import ( FitBSpline, FitGCVSpline, FitMedFilter, FitMedFilterFast, FitPolynomial, - #FitDCT, + # FitDCT, ) -from contsub import utils log = init_logger("contsub") + class TestFitsFunc(unittest.TestCase): def setUp(self): self.nchan = nchan = 1000 - xvals = np.linspace(0,2*np.pi, nchan) + xvals = np.linspace(0, 2 * np.pi, nchan) noise = np.random.randn(nchan) * 1.5 # --- simulate line profile --- nterm = 3 amps = np.random.uniform(0.1, 0.3, nterm) - shifts = np.random.uniform(0, np.pi/4, nterm) - omegas = 2*np.pi * np.random.uniform(0.2, 1, nterm) + shifts = np.random.uniform(0, np.pi / 4, nterm) + omegas = 2 * np.pi * np.random.uniform(0.2, 1, nterm) - line = np.sum([amp*np.sin(omega*xvals+shift) for amp,omega,shift in zip(amps, omegas, shifts)], axis=0) + line = np.sum( + [amp * np.sin(omega * xvals + shift) for amp, omega, shift in zip(amps, omegas, shifts)], + axis=0, + ) line /= amps.sum() self.data = data = line + noise dfreq = 6500 * 1e-6 freq0 = 1361 - self.freqs = freqs = freq0 + np.linspace(0, nchan*dfreq, nchan) + self.freqs = freqs = freq0 + np.linspace(0, nchan * dfreq, nchan) - nans = tuple(set(np.random.randint(20,80,10))) + nans = tuple(set(np.random.randint(20, 80, 10))) data[(nans,)] = np.nan # --- self.mask = mask = np.zeros(nchan, dtype=bool) - #---- and flags - mask_start = np.random.randint(10, nchan-10) + # ---- and flags + mask_start = np.random.randint(10, nchan - 10) mask_end = mask_start + 20 mask[mask_start:mask_end] = True - mask_start = np.random.randint(10, nchan-10) + mask_start = np.random.randint(10, nchan - 10) mask_end = mask_start + 40 mask[mask_start:mask_end] = True - #---- + # ---- self.velwidth = 300 - self.chanwidth = utils.chans_in_velwidth(freqs*1e6, self.velwidth*1e3) - + self.chanwidth = utils.chans_in_velwidth(freqs * 1e6, self.velwidth * 1e3) + def test_median_filter(self): - baseline_func = FitMedFilter(self.freqs, velwidth=self.velwidth) baseline = baseline_func.fit(self.data, mask=self.mask, weights=None) - + assert baseline.shape == self.data.shape - + def test_median_filter_fast(self): - baseline_func = FitMedFilterFast(self.freqs, velwidth=self.velwidth) baseline_vel = baseline_func.fit(self.data, mask=self.mask, weights=None) assert baseline_vel.shape == self.data.shape - + # test if chanwidth gives same result as velwidth baseline_func = FitMedFilterFast(self.freqs, chanwidth=self.chanwidth) baseline_chan = baseline_func.fit(self.data, mask=self.mask, weights=None) - + assert np.allclose(baseline_vel, baseline_chan, atol=1e-6) def test_polynomial(self): - baseline_func = FitPolynomial(self.freqs, order=3) baseline = baseline_func.fit(self.data, mask=self.mask, weights=None) - + assert baseline.shape == self.data.shape def test_b_spline(self): - baseline_func = FitBSpline(self.freqs, order=3, velwidth=self.velwidth) baseline_vel = baseline_func.fit(self.data, mask=self.mask, weights=None) - + # test if chanwidth gives same result as velwidth baseline_func = FitBSpline(self.freqs, order=3, chanwidth=self.chanwidth) baseline_chan = baseline_func.fit(self.data, mask=self.mask, weights=None) - + assert baseline_chan.shape == self.data.shape baseline_chan_std = baseline_chan.std() - + perr = np.abs(baseline_vel.std() - baseline_chan_std) / baseline_chan_std # tolerate a 5% error because the knots are chosen using a random generator - assert perr < 5/100 - + assert perr < 5 / 100 + def test_gcv_spline(self): - baseline_func = FitGCVSpline(self.freqs) baseline = baseline_func.fit(self.data, mask=self.mask, weights=None) - + assert baseline.shape == self.data.shape