From 50e96a9f5779d7d8b5090dbfeecb54084bad7a10 Mon Sep 17 00:00:00 2001 From: Mika Naidoo Date: Fri, 18 Jul 2025 10:35:49 +0000 Subject: [PATCH 1/9] added functionality for doing BSpline continuum subtraction in the uv plane --- contsub/__init__.py | 2 +- contsub/fitfuncs.py | 6 +- contsub/parser/viscontsub.py | 147 ++ contsub/parser/viscontsub.yaml | 61 + contsub/utils.py | 104 + contsub/visibility_plane.py | 48 + poetry.lock | 3532 ++++++++++++++++++++++++++++++++ pyproject.toml | 4 + 8 files changed, 3902 insertions(+), 2 deletions(-) create mode 100644 contsub/parser/viscontsub.py create mode 100644 contsub/parser/viscontsub.yaml create mode 100644 contsub/visibility_plane.py create mode 100644 poetry.lock diff --git a/contsub/__init__.py b/contsub/__init__.py index ae0c87a..c0cd9b4 100644 --- a/contsub/__init__.py +++ b/contsub/__init__.py @@ -5,5 +5,5 @@ BIN = OmegaConf.create({ "im_plane": "imcontsub", - "vis_plane": "imcontsub", + "vis_plane": "viscontsub", }) \ No newline at end of file diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 4605de8..1d4f456 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -57,7 +57,7 @@ def __init__(self, order, velWidth, randomState=None, seq=None): """ self.order = order self.velwidth = velWidth - self.preped = False + self.preped = False if randomState and seq: rs = np.random.SeedSequence(entropy = randomState, spawn_key = (seq,)) else: @@ -74,6 +74,10 @@ def fit(self, x, data, weights): weight : weights for fitting the Spline. To mask values, set the corresponding weight to zero. """ + + if not self.preped: + self.prepare(x) + nchan = len(x) knotind = np.linspace(0, nchan, self.imax, dtype = int)[1:-1] chwid = (nchan // self.imax) // 8 diff --git a/contsub/parser/viscontsub.py b/contsub/parser/viscontsub.py new file mode 100644 index 0000000..b4d6a43 --- /dev/null +++ b/contsub/parser/viscontsub.py @@ -0,0 +1,147 @@ +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 numpy as np +import xarray as xr +import dask.multiprocessing +from contsub.utils import ms_to_xarray_dataset +from contsub.visibility_plane import VisContSub +from contsub.fitfuncs import FitBSpline +from dask.diagnostics import ProgressBar +from tqdm.dask import TqdmCallback +from daskms import xds_from_ms, xds_to_table + +log = init_logger(BIN.vis_plane) + +command = BIN.vis_plane +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] + +@click.command("viscontsub") +@click.version_option(str(contsub.__version__)) +@clickify_parameters(config) +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] + method = opts.fit_model + order = opts.order[0] + output_prefix = opts.output + nworkers = opts.nworkers + outchunks = dict(time=opts.time_chunks, bl_chunks=opts.bl_chunks) + output_column = opts.output_column + + temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) + ds = xr.open_zarr(temp_zarr, chunks=outchunks) + + xspec = ds.coords['spectral'] + + futures = [] + + if method == 'spline': + fitfunc = FitBSpline(order, segments, randomState=None, seq=None) + else: + raise ValueError(f"Unknown fitting method: {method}. Supported methods: 'spline'.") + base_dims = "time,baseline,spectral,corr" + signature = f"(spectral),({base_dims}),({base_dims}),({base_dims}) -> ({base_dims})" + meta = (np.ndarray((), ds.vis.dtype),) + + dask.config.set(scheduler='threads', num_workers = nworkers) + + for biter, dblock in enumerate(ds.vis.data.blocks): + #if biter > 0: + #continue + flags = ds.flags.data.blocks[biter] + weights = ds.weights.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, + ), + ) + + + 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",...) + + visdata = ds.vis.stack(row=('time', 'baseline')).transpose("row", ...) + + line = visdata - continuum + + ms_dsl = xds_from_ms( + ms, + index_cols=["TIME", "ANTENNA1", "ANTENNA2"], + chunks={"row": chunksize } + ) + + #debugging_prints + #print(f"Total MS rows: {sum(ds_chunk.sizes['row'] for ds_chunk in ms_dsl)}") + #print(f"Line data shape: {line.shape}") + #print(f"Line data dims: {line.dims}") + #print(f"Line data chunks: {line.chunks}") + + #for i, ds_chunk in enumerate(ms_dsl): + #print(f"Chunk {i}: {ds_chunk.sizes['row']} rows") + + writes = [] + start_row = 0 + + for i, ds_chunk in enumerate(ms_dsl): + num_rows_in_chunk = ds_chunk.sizes['row'] + end_row = start_row + num_rows_in_chunk + + line_slice = line[start_row:end_row, :, :] + target_row_chunks = ds_chunk.chunks['row'] + line_slice_rechunked = line_slice.chunk({'row': target_row_chunks}) + + ms_dsl[i] = ds_chunk.assign(**{ + output_column: (("row", "chan", "corr"), line_slice_rechunked.data) + }) + + writes.append(xds_to_table(ms_dsl, 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 + log.info(f"Runtime {hours}:{int(mins)}:{secs:.1f}") diff --git a/contsub/parser/viscontsub.yaml b/contsub/parser/viscontsub.yaml new file mode 100644 index 0000000..adb9b9a --- /dev/null +++ b/contsub/parser/viscontsub.yaml @@ -0,0 +1,61 @@ +libs: + spline: + _include: (contsub.parser.library)spline.yaml + polyn: + _include: (contsub.parser.library)polyn.yaml + +inputs: + ms: + info: Input MS file + dtype: File + must_exist: yes + required: yes + spwid: + info: Spectral Window ID + dtype: int + default: 0 + field-id: + info: Field ID + dtype: int + default: 0 + row-chunks: #TO-DO: check + 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 + output-column: + info: Column name to write the continuum subtracted data to + dtype: str + default: corrected + fit-model: + info: Fit function to model the continuum. ** Only the spline is available in this version **. + dtype: str + choices: + - spline + default: spline + 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." + dtype: float + default: 0 + output: + info: Output file prefix + dtype: File + default: contsub_output + nworkers: + info: Number of workers (one per CPU) + dtype: int + default: 4 + _use: + - libs.spline.inputs + + +outputs: + {} \ No newline at end of file diff --git a/contsub/utils.py b/contsub/utils.py index 6a8e49a..3c86b79 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -12,6 +12,11 @@ from scabha.basetypes import File import numpy as np import datetime +from astropy.time import Time +from daskms import xds_from_ms, xds_from_table +import dask.array as da +from dask.diagnostics import ProgressBar + log = init_logger(BIN.im_plane) @@ -214,3 +219,102 @@ def spatialSplitHeader(self, xlims, ylims, chans = None): self._header['DATE'] = str(datetime.datetime.now()).replace(' ','T') self._header['ORIGIN'] = 'A. Kazemi-Moridani (spatial_split)' return self._header + +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: + ms path (str|path): MS file_ + spw_id (int): Spectral window ID + 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. + Returns: + Zarr: Zarr array (persistant store, mode=w) + """ + ms_dsl = xds_from_ms( + ms_path, + index_cols=["TIME", "ANTENNA1", "ANTENNA2"], + 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] + ref_frequency = spw_table.REF_FREQUENCY.data[spw_id] + + phase_center = field_table.PHASE_DIR.data[field_id].compute()[0] + + antenna_names = [name.strip() for name in antenna_table.NAME.data.compute()] + ds = ms_dsl[0] + + times = ds.TIME.data + antenna1 = ds.ANTENNA1.data + antenna2 = ds.ANTENNA2.data + visibilities = ds.DATA.data + flags = ds.FLAG.data + weights = ds.WEIGHT_SPECTRUM.data + uvw = ds.UVW.data + + nant = antenna_table.NAME.size + nbl = nant*(nant-1) // 2 + nrow, nchan, ncorr = ds.DATA.shape + ntimes = nrow // nbl + + unique_times = np.unique(times) + + + 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] + else: + 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 + 'flags': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_flags), + 'weights': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_weights), + 'uvw': (('time', 'baseline', 'coord'), uvw.reshape(ntimes, nbl, 3)), + }, + coords={ + 'spectral': 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, + 'n_antennas': nant, + 'spw_id': spw_id, + 'field_id': field_id + }) + dataset = dataset.chunk(outchunks) + if not save_to_zarr: + return dataset + else: + outpath = f'{ms_path}-spw{spw_id}-field{field_id}-tmp.zarr' + write_to_zarr = dataset.to_zarr(outpath, mode='w', compute=False) + + with ProgressBar(): + da.compute(write_to_zarr) + + return outpath \ No newline at end of file diff --git a/contsub/visibility_plane.py b/contsub/visibility_plane.py new file mode 100644 index 0000000..df1e0a1 --- /dev/null +++ b/contsub/visibility_plane.py @@ -0,0 +1,48 @@ +from contsub.fitfuncs import FitBSpline +import numpy as np +import xarray as xr +import dask.array as da +import numpy as np +from typing import Callable + +class VisContSub(): + """ + a class for performing continuum subtraction on visibility data + """ + def __init__(self, fit_func: Callable, fit_tol: float=0): + """ + Args: + method (str): Fitting method to use, e.g., 'spline' + order (int): Order of the spline fit + vel_width (float): Velocity width for the fit + """ + + self.fit_func = fit_func + self.fit_tol = fit_tol + + + + def vis_cont_sub(self, xspec: np.ndarray, 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, nchan, ncorr = visdata.shape + + for tme in range(ntime): + for bl in range(nbaseline): + for corr in range(ncorr): + vis_slice = visdata[tme, bl, :, corr] + flag_slice = flags[tme, bl, :, corr] + weights_slice = weights[tme, bl, :, corr] + + weights_slice[flag_slice] = 0 + real_model = self.fit_func.fit(xspec, vis_slice.real, weights_slice) + imag_model = self.fit_func.fit(xspec, 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+numpy = ">=1.24" +packaging = ">=23.2" +pandas = ">=2.1" + +[package.extras] +accel = ["bottleneck", "flox", "numba (>=0.54)", "numbagg", "opt_einsum", "scipy"] +complete = ["xarray[accel,etc,io,parallel,viz]"] +dev = ["hypothesis", "jinja2", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "sphinx", "sphinx_autosummary_accessors", "xarray[complete]"] +etc = ["sparse"] +io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap ; python_version < \"3.10\"", "scipy", "zarr"] +parallel = ["dask[complete]"] +viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"] + +[[package]] +name = "xarray-fits" +version = "0.2.5" +description = "xarray Datasets for FITS-like data" +optional = false +python-versions = "<4.0,>=3.9" +groups = ["main"] +files = [ + {file = "xarray_fits-0.2.5-py3-none-any.whl", hash = "sha256:bfd46fabc51bd4200dc4dcdee45d951c55313fdc6d4b418e9e3eae89ea49771a"}, + {file = "xarray_fits-0.2.5.tar.gz", hash = "sha256:811ec6ee0b45f2915df1f4ee45381206571783b558f51ced07805b3aed16e73a"}, +] + +[package.dependencies] +astropy = ">=6.0.0,<7.0.0" +dask = {version = ">=2024.1.0,<2025", extras = ["array"]} +fsspec = ">=2024,<2025" +xarray = ">=2024,<2025" + +[package.extras] +testing = ["distributed[testing] (>=2024.1.0,<2025)", "pytest[testing] (>=8.1.1,<9.0.0)"] + +[[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,<3.13" +content-hash = "911f176f05e79d0eb37a0e144b79172f0d29b7d45e9ebb4bbeb51767d69aa21f" diff --git a/pyproject.toml b/pyproject.toml index 4858810..34bf135 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,11 +16,15 @@ numpy = "^1.22.4" astropy = "^6.0.0" scipy = "^1.12.0" xarray-fits = "*" +xarray = "^2025.7.1" stimela = "^2.1.2" +zarr = "*" +tdqm = "*" [tool.poetry.scripts] imcontsub = "contsub.parser.imcontsub:runit" +viscontsub = "contsub.parser.viscontsub:runit" [tool.poetry.group.dev.dependencies] jupyter = "*" From 54dc019bfc1aca207bd36908159bcf2fa91440d1 Mon Sep 17 00:00:00 2001 From: Mika Naidoo Date: Mon, 28 Jul 2025 12:23:28 +0000 Subject: [PATCH 2/9] added functionality for polynomial fitting --- contsub/fitfuncs.py | 44 +++++++++++++++++++ contsub/parser/viscontsub.py | 80 +++++++++++++++++----------------- contsub/parser/viscontsub.yaml | 15 ++++++- contsub/utils.py | 41 +++++++++++------ 4 files changed, 125 insertions(+), 55 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 1d4f456..0f3c476 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -132,3 +132,47 @@ def fit(self, x, data, mask, weight): # resMed = nanMed[~np.isnan(nanMed)] resMed = nanMed return resMed, cp_data-resMed + +class FitPolynomial(FitFunc): + """ + Polynomial fitting function using numpy.polyfit + """ + def __init__(self, order): + """ + order (int): Order/degree of the polynomial + """ + self.order = order + self.preped = False + + def prepare(self, x): + """ + Prepare for polynomial fitting + """ + nchan = len(x) + log.info(f"Polynomial fitting: nchan = {nchan}, order = {self.order}") + self.preped = True + + def fit(self, x, data, weights=None): + """ + Fit polynomial to data + + Args: + x: x values for the fit + data: values to be fit by polynomial + mask: boolean mask for valid data points (optional) + weights: weights for fitting (optional) + + Returns: + poly_fit: polynomial evaluated at x points + """ + if not self.preped: + self.prepare(x) + + if weights is not None: + poly_coeffs = np.polyfit(x, data, self.order, w=weights) + else: + poly_coeffs = np.polyfit(x, data, self.order) + + poly_fit = np.polyval(poly_coeffs, x) + + return poly_fit diff --git a/contsub/parser/viscontsub.py b/contsub/parser/viscontsub.py index b4d6a43..0c43bdf 100644 --- a/contsub/parser/viscontsub.py +++ b/contsub/parser/viscontsub.py @@ -12,9 +12,9 @@ import numpy as np import xarray as xr import dask.multiprocessing -from contsub.utils import ms_to_xarray_dataset +from contsub.utils import ms_to_xarray_dataset, get_ds_from_msdsl from contsub.visibility_plane import VisContSub -from contsub.fitfuncs import FitBSpline +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 @@ -45,30 +45,37 @@ def runit(**kwargs): output_prefix = opts.output nworkers = opts.nworkers outchunks = dict(time=opts.time_chunks, bl_chunks=opts.bl_chunks) + input_column = opts.input_column output_column = opts.output_column - temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) + #if load_from_cache := opts.load_from_cache: + temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) + #TODO(mika): consider caching the MS to a Zarr store + temp_zarr = 'tmp.zarr' ds = xr.open_zarr(temp_zarr, chunks=outchunks) - xspec = ds.coords['spectral'] + xspec = ds.coords['FREQ'] futures = [] if method == 'spline': fitfunc = FitBSpline(order, segments, randomState=None, seq=None) + elif method == 'polynomial': + fitfunc = FitPolynomial(order) else: - raise ValueError(f"Unknown fitting method: {method}. Supported methods: 'spline'.") - base_dims = "time,baseline,spectral,corr" - signature = f"(spectral),({base_dims}),({base_dims}),({base_dims}) -> ({base_dims})" - meta = (np.ndarray((), ds.vis.dtype),) + 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) - for biter, dblock in enumerate(ds.vis.data.blocks): + for biter, dblock in enumerate(ds.VIS.data.blocks): #if biter > 0: #continue - flags = ds.flags.data.blocks[biter] - weights = ds.weights.data.blocks[biter] + 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( @@ -90,53 +97,46 @@ def runit(**kwargs): continuum_xarray = xr.DataArray( data=continuum_dask, - dims=ds.vis.dims, - coords=ds.vis.coords + dims=ds.VIS.dims, + coords=ds.VIS.coords ) continuum = continuum_xarray.stack(row = ('time','baseline')) - continuum = continuum.transpose("row",...) - - visdata = ds.vis.stack(row=('time', 'baseline')).transpose("row", ...) + continuum = continuum.transpose("row",...).chunk({"row": chunksize}) - line = visdata - continuum ms_dsl = xds_from_ms( ms, index_cols=["TIME", "ANTENNA1", "ANTENNA2"], + group_cols=["FIELD_ID", "DATA_DESC_ID"], chunks={"row": chunksize } ) - #debugging_prints - #print(f"Total MS rows: {sum(ds_chunk.sizes['row'] for ds_chunk in ms_dsl)}") - #print(f"Line data shape: {line.shape}") - #print(f"Line data dims: {line.dims}") - #print(f"Line data chunks: {line.chunks}") - - #for i, ds_chunk in enumerate(ms_dsl): - #print(f"Chunk {i}: {ds_chunk.sizes['row']} rows") + #continuum= da.from_array(continuum, chunks={"row": chunksize }) + + + msds = get_ds_from_msdsl(ms_dsl, spwid, fieldid) - writes = [] - start_row = 0 - - for i, ds_chunk in enumerate(ms_dsl): - num_rows_in_chunk = ds_chunk.sizes['row'] - end_row = start_row + num_rows_in_chunk - - line_slice = line[start_row:end_row, :, :] - target_row_chunks = ds_chunk.chunks['row'] - line_slice_rechunked = line_slice.chunk({'row': target_row_chunks}) - ms_dsl[i] = ds_chunk.assign(**{ - output_column: (("row", "chan", "corr"), line_slice_rechunked.data) + + #import pdb; pdb.set_trace() + ms_ds = msds.assign(**{ + output_column: ( + ("row", "chan", "corr"), + getattr(msds, input_column).data - continuum.data, + ), }) - - writes.append(xds_to_table(ms_dsl, ms, [output_column])) + + + #writes = [xds_to_table(msds, ms+".new", [output_column])] + #TODO(mika): add functionality to write to a new MS + + 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) + da.compute(writes) print(f"UV plane continuum subtraction completed. Data written to column '{output_column}' in {ms}.") # DONE diff --git a/contsub/parser/viscontsub.yaml b/contsub/parser/viscontsub.yaml index adb9b9a..4e6b1c7 100644 --- a/contsub/parser/viscontsub.yaml +++ b/contsub/parser/viscontsub.yaml @@ -10,6 +10,10 @@ inputs: dtype: File must_exist: yes required: yes + input-column: + info: Column which contains the data to be continuum subtracted. + dtype: str + default: DATA spwid: info: Spectral Window ID dtype: int @@ -33,12 +37,13 @@ inputs: output-column: info: Column name to write the continuum subtracted data to dtype: str - default: corrected + default: CONTSUB_DATA_LINE fit-model: info: Fit function to model the continuum. ** Only the spline is available in this version **. dtype: str choices: - spline + - polynomial default: spline cont-fit-tol: info: "Minimum perentage of valid spectrum data points required to do a fit. @@ -53,6 +58,14 @@ inputs: info: Number of workers (one per CPU) dtype: int default: 4 + load-from-cache: + info: Load the MS from a cache (Zarr store) if available, otherwise create it. + dtype: bool + default: False + cache-dir: + info: Directory to store the cache (Zarr store) in. + dtype: Directory + default: contsub_cache_dir _use: - libs.spline.inputs diff --git a/contsub/utils.py b/contsub/utils.py index 3c86b79..717bb90 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -219,7 +219,18 @@ def spatialSplitHeader(self, xlims, ylims, chans = None): 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: + if ds.FIELD_ID == field_id and ds.DATA_DESC_ID == data_desc_id: + found_ds = True + break + if found_ds: + 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): @@ -239,6 +250,7 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, ms_dsl = xds_from_ms( ms_path, index_cols=["TIME", "ANTENNA1", "ANTENNA2"], + group_cols=["FIELD_ID", "DATA_DESC_ID"], chunks={"row": chunks } ) @@ -254,7 +266,7 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, phase_center = field_table.PHASE_DIR.data[field_id].compute()[0] antenna_names = [name.strip() for name in antenna_table.NAME.data.compute()] - ds = ms_dsl[0] + ds = get_ds_from_msdsl(ms_dsl, field_id=field_id, data_desc_id=spw_id) times = ds.TIME.data antenna1 = ds.ANTENNA1.data @@ -270,6 +282,7 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, ntimes = nrow // nbl unique_times = np.unique(times) + reshaped_vis = da.reshape(visibilities, (ntimes, nbl, nchan, ncorr)) @@ -283,16 +296,16 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, dataset = xr.Dataset( { #TO-DO: reshape the data for all - 'vis': ([ 'time', 'baseline' , 'spectral', 'corr'], reshaped_vis), #time here will be time indicies - 'flags': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_flags), - 'weights': ([ 'time', 'baseline', 'spectral', 'corr'], reshaped_weights), - 'uvw': (('time', 'baseline', 'coord'), uvw.reshape(ntimes, nbl, 3)), + '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={ - 'spectral': frequencies, - 'corr': corr_labels, - 'time': unique_times, - 'baseline': np.arange(nbl), + 'FREQ': frequencies, + 'CORR':corr_labels, + 'TIME': unique_times, + 'BASELINE': np.arange(nbl), }, attrs={ @@ -303,15 +316,15 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, '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, - 'n_antennas': nant, - 'spw_id': spw_id, - 'field_id': field_id + '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'{ms_path}-spw{spw_id}-field{field_id}-tmp.zarr' + outpath = f'tmp.zarr' write_to_zarr = dataset.to_zarr(outpath, mode='w', compute=False) with ProgressBar(): From e128f8121c9b1afa9b5261f0b2d34a7ecfe15206 Mon Sep 17 00:00:00 2001 From: Mika <1678178@students.wits.ac.za> Date: Wed, 10 Sep 2025 14:59:39 +0200 Subject: [PATCH 3/9] updated polynomial function, added functionality for writing to new ms, added functionality for loading from cache. --- contsub/fitfuncs.py | 78 ++- contsub/parser/viscontsub.py | 40 +- contsub/parser/viscontsub.yaml | 82 ++- contsub/utils.py | 13 +- poetry.lock | 1076 +++++++++++++++++++++++++++++++- pyproject.toml | 7 +- 6 files changed, 1174 insertions(+), 122 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 0f3c476..d3b4a9c 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -12,7 +12,7 @@ class FitFunc: """ abstract class for writing fitting functions """ - def __init__(self, order, velwidth): + def __init__(self, order, velwidth, cont_tol): """ Args: @@ -22,6 +22,7 @@ def __init__(self, order, velwidth): self.order = order self.velwidth = velwidth self.preped = False + self.cont_tol = cont_tol def prepare(self, x): nchan = len(x) @@ -78,14 +79,27 @@ def fit(self, x, data, weights): if not self.preped: self.prepare(x) - nchan = len(x) - knotind = np.linspace(0, nchan, self.imax, dtype = int)[1:-1] - chwid = (nchan // self.imax) // 8 - knots = lambda: self.rng.integers(-chwid, chwid, size = knotind.shape)+knotind - - - splCfs = splrep(x, data, task = -1, w = weights, t = x[knots()], k = self.order) - spl = splev(x, splCfs) + # Mask invalid or zero-weight points + mask = (weights > 0) & np.isfinite(x) & np.isfinite(data) & np.isfinite(weights) + x_masked = x[mask] + data_masked = data[mask] + weights_masked = weights[mask] + + + if len(x_masked) < (self.order + 1): + print("Not enough valid points for spline fit, returning original data.") + return np.copy(data) # fallback: just return input + + nchan = len(x_masked) + knotind = np.linspace(0, nchan, self.imax, dtype=int)[1:-1] + chwid = max(1, (nchan // self.imax) // 8) + knots_idx = self.rng.integers(-chwid, chwid, size=knotind.shape) + knotind + knots_idx = np.clip(knots_idx, 1, nchan - 20) # avoid edges + knot_positions = np.unique(x_masked[knots_idx]) + + splCfs = splrep(x_masked, data_masked, task=-1, + w=weights_masked, t = knot_positions, k=self.order) + spl = splev(x, splCfs) return spl class FitMedFilter(FitFunc): @@ -137,12 +151,13 @@ class FitPolynomial(FitFunc): """ Polynomial fitting function using numpy.polyfit """ - def __init__(self, order): + def __init__(self, order, cont_tol = 0): """ order (int): Order/degree of the polynomial """ self.order = order self.preped = False + self.cont_tol = cont_tol def prepare(self, x): """ @@ -153,26 +168,31 @@ def prepare(self, x): self.preped = True def fit(self, x, data, weights=None): - """ - Fit polynomial to data - - Args: - x: x values for the fit - data: values to be fit by polynomial - mask: boolean mask for valid data points (optional) - weights: weights for fitting (optional) - - Returns: - poly_fit: polynomial evaluated at x points - """ if not self.preped: self.prepare(x) - + if weights is not None: - poly_coeffs = np.polyfit(x, data, self.order, w=weights) - else: - poly_coeffs = np.polyfit(x, data, self.order) - - poly_fit = np.polyval(poly_coeffs, x) + mask = (weights > 0) & np.isfinite(x) & np.isfinite(data) & np.isfinite(weights) + x_masked = x[mask] + data_masked = data[mask] + weights_masked = weights[mask] + - return poly_fit + else: + mask = np.isfinite(x) & np.isfinite(data) + x_masked = x[mask] + data_masked = data[mask] + weights_masked = None + + if (len(x_masked)/len(x))*100 > self.cont_tol: + return np.zeros_like(data) + + try: + if weights_masked is not None: + coeffs = np.polyfit(x_masked, data_masked, self.order, w=weights_masked) + else: + coeffs = np.polyfit(x_masked, data_masked, self.order) + return np.polyval(coeffs, x) + except Exception as e: + print(f"Polynomial fitting failed: {e}") + return np.copy(data) \ No newline at end of file diff --git a/contsub/parser/viscontsub.py b/contsub/parser/viscontsub.py index 0c43bdf..351d228 100644 --- a/contsub/parser/viscontsub.py +++ b/contsub/parser/viscontsub.py @@ -42,16 +42,20 @@ def runit(**kwargs): segments = opts.segments[0] method = opts.fit_model order = opts.order[0] - output_prefix = opts.output nworkers = opts.nworkers outchunks = dict(time=opts.time_chunks, bl_chunks=opts.bl_chunks) input_column = opts.input_column output_column = opts.output_column + 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' - #if load_from_cache := opts.load_from_cache: - temp_zarr = ms_to_xarray_dataset(ms, spwid, fieldid, chunksize, save_to_zarr=True) - #TODO(mika): consider caching the MS to a Zarr store - temp_zarr = 'tmp.zarr' ds = xr.open_zarr(temp_zarr, chunks=outchunks) xspec = ds.coords['FREQ'] @@ -61,7 +65,7 @@ def runit(**kwargs): if method == 'spline': fitfunc = FitBSpline(order, segments, randomState=None, seq=None) elif method == 'polynomial': - fitfunc = FitPolynomial(order) + fitfunc = FitPolynomial(order, cont_tol) else: raise ValueError(f"Unknown fitting method: {method}. Supported methods: 'spline', 'polynomial'.") @@ -111,28 +115,24 @@ def runit(**kwargs): group_cols=["FIELD_ID", "DATA_DESC_ID"], chunks={"row": chunksize } ) - - #continuum= da.from_array(continuum, chunks={"row": chunksize }) - - + msds = get_ds_from_msdsl(ms_dsl, spwid, fieldid) - - - #import pdb; pdb.set_trace() ms_ds = msds.assign(**{ output_column: ( ("row", "chan", "corr"), getattr(msds, input_column).data - continuum.data, ), }) - - - #writes = [xds_to_table(msds, ms+".new", [output_column])] - #TODO(mika): add functionality to write to a new MS - - writes = [xds_to_table(ms_ds, ms, [output_column])] - print(f"Writing line data to column '{output_column}' in {ms}...") + + 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"): diff --git a/contsub/parser/viscontsub.yaml b/contsub/parser/viscontsub.yaml index 4e6b1c7..3cf8ad7 100644 --- a/contsub/parser/viscontsub.yaml +++ b/contsub/parser/viscontsub.yaml @@ -6,14 +6,27 @@ libs: inputs: ms: - info: Input MS file - dtype: File - must_exist: yes - required: yes + 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 + 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. ** Only the spline and polynomial available in this version **. + dtype: str + choices: + - spline + - polynomial + default: spline spwid: info: Spectral Window ID dtype: int @@ -27,45 +40,30 @@ inputs: dtype: int default: 10000 time-chunks: - info: Chunk size for time axis - dtype: int - default: 64 + info: Chunk size for time axis + dtype: int + default: 64 bl-chunks: - info: Chunk size for baseline axis - dtype: int - default: 10 - output-column: - info: Column name to write the continuum subtracted data to - dtype: str - default: CONTSUB_DATA_LINE - fit-model: - info: Fit function to model the continuum. ** Only the spline is available in this version **. - dtype: str - choices: - - spline - - polynomial - default: spline + 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. - Spectra below this tolerance will be set to NaN." - dtype: float - default: 0 - output: - info: Output file prefix - dtype: File - default: contsub_output + 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 + 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 (Zarr store) if available, otherwise create it. - dtype: bool - default: False - cache-dir: - info: Directory to store the cache (Zarr store) in. - dtype: Directory - default: contsub_cache_dir + info: Load the MS from a cache (give Zarr file name) if available, otherwise create it. + dtype: File + required: False _use: - libs.spline.inputs diff --git a/contsub/utils.py b/contsub/utils.py index 717bb90..e133c1e 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -1,4 +1,3 @@ -from xarrayfits import xds_from_fits import xarray as xr from astropy.wcs import WCS from contsub.image_plane import ContSub @@ -15,7 +14,11 @@ from astropy.time import Time from daskms import xds_from_ms, xds_from_table import dask.array as da -from dask.diagnostics import ProgressBar +from tqdm.dask import TqdmCallback +import warnings + +warnings.filterwarnings("ignore", message=".*does not have a Zarr V3 specification.*") +warnings.filterwarnings("ignore", message=".*Consolidated metadata is currently not part.*") log = init_logger(BIN.im_plane) @@ -327,7 +330,7 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, outpath = f'tmp.zarr' write_to_zarr = dataset.to_zarr(outpath, mode='w', compute=False) - with ProgressBar(): - da.compute(write_to_zarr) + with TqdmCallback(desc="Writing to Zarr"): + da.compute(write_to_zarr) - return outpath \ No newline at end of file + return outpath \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 5ed6ad5..bed4248 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. 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"moto[s3,server]", "obstore (>=0.5.1)", "requests", "s3fs (>=2023.10.0)"] +test = ["coverage (>=7.10)", "hypothesis", "mypy", "packaging", "pytest (<8.4)", "pytest-accept", "pytest-asyncio", "pytest-cov", "pytest-xdist", "rich", "tomlkit", "uv"] [[package]] name = "zipp" @@ -3528,5 +4556,5 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" -python-versions = ">=3.10,<3.13" -content-hash = "911f176f05e79d0eb37a0e144b79172f0d29b7d45e9ebb4bbeb51767d69aa21f" +python-versions = ">=3.10,<3.14" +content-hash = "4e1bf0cb3e10ccfb444c1c3d90a46f20e9abd91bc11ef984df20d6f4bb4de13a" diff --git a/pyproject.toml b/pyproject.toml index 34bf135..464e8fc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,15 +11,17 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.poetry.dependencies] -python = ">=3.10,<3.13" +python = ">=3.10,<3.14" numpy = "^1.22.4" astropy = "^6.0.0" scipy = "^1.12.0" xarray-fits = "*" -xarray = "^2025.7.1" +xarray = "*" stimela = "^2.1.2" zarr = "*" tdqm = "*" +dask-ms = "*" +dask = "<2024.11.0" [tool.poetry.scripts] @@ -28,6 +30,7 @@ viscontsub = "contsub.parser.viscontsub:runit" [tool.poetry.group.dev.dependencies] jupyter = "*" +matplotlib = "*" [tool.poetry.group.tests] optional = true From e970e40e1f707d7acacd2235c350497a493e10dc Mon Sep 17 00:00:00 2001 From: Mika <1678178@students.wits.ac.za> Date: Wed, 10 Sep 2025 16:24:06 +0200 Subject: [PATCH 4/9] corrected polynomial fit sign error --- contsub/fitfuncs.py | 2 +- contsub/parser/viscontsub.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index d3b4a9c..3987aa7 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -184,7 +184,7 @@ def fit(self, x, data, weights=None): data_masked = data[mask] weights_masked = None - if (len(x_masked)/len(x))*100 > self.cont_tol: + if (len(x_masked)/len(x))*100 <= self.cont_tol: return np.zeros_like(data) try: diff --git a/contsub/parser/viscontsub.yaml b/contsub/parser/viscontsub.yaml index 3cf8ad7..c9fa537 100644 --- a/contsub/parser/viscontsub.yaml +++ b/contsub/parser/viscontsub.yaml @@ -35,7 +35,7 @@ inputs: info: Field ID dtype: int default: 0 - row-chunks: #TO-DO: check + row-chunks: info: Chunking strategy (Done along the time axis) dtype: int default: 10000 From 402e68e02f4e0937baad65f81fd4ee0562221d57 Mon Sep 17 00:00:00 2001 From: sphemakh Date: Sun, 21 Sep 2025 19:26:47 +0200 Subject: [PATCH 5/9] remove redundant fitting models parameter files --- contsub/fitfuncs.py | 12 +- contsub/parser/imcontsub.yaml | 20 +- contsub/parser/library/__init__.py | 0 contsub/parser/library/dct.yaml | 4 - contsub/parser/library/polyn.yaml | 4 - contsub/parser/library/spline.yaml | 14 - poetry.lock | 2114 ++++++++++++++++------------ 7 files changed, 1218 insertions(+), 950 deletions(-) delete mode 100644 contsub/parser/library/__init__.py delete mode 100644 contsub/parser/library/dct.yaml delete mode 100644 contsub/parser/library/polyn.yaml delete mode 100644 contsub/parser/library/spline.yaml diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 120c3d6..6ce9fef 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -88,7 +88,7 @@ def fit(self, x, data, weights): if len(x_masked) < (self.order + 1): #TODO(Sphe) maybe return raise an exception, and let the caller of this function decide what to do. - print("Not enough valid points for spline fit, returning original data.") + log.debug("Not enough valid points for spline fit, returning original data.") return np.copy(data) # fallback: just return input nchan = len(x_masked) @@ -142,7 +142,7 @@ def fit(self, x, data, mask, weight): if not (mask is None): data[np.logical_not(mask)] = np.nan - if self._imax%2 ==0: + if self._imax%2 == 0: window = self._imax + 1 else: window = self._imax @@ -181,7 +181,6 @@ def fit(self, x, data, weights=None): x_masked = x[mask] data_masked = data[mask] weights_masked = weights[mask] - else: mask = np.isfinite(x) & np.isfinite(data) @@ -190,6 +189,7 @@ def fit(self, x, data, weights=None): weights_masked = None if (len(x_masked)/len(x))*100 <= self.cont_tol: + #TODO(Sphe) Not sure if this is the right thing to do return np.zeros_like(data) try: @@ -199,5 +199,7 @@ def fit(self, x, data, weights=None): coeffs = np.polyfit(x_masked, data_masked, self.order) return np.polyval(coeffs, x) except Exception as e: - print(f"Polynomial fitting failed: {e}") - return np.copy(data) \ No newline at end of file + log.debug(f"Polynomial fitting failed: {e}") + + # TODO(Sphe) Rather raise an exception and let caller of function decide what to do. + return data diff --git a/contsub/parser/imcontsub.yaml b/contsub/parser/imcontsub.yaml index 53e55cc..ef55c7c 100644 --- a/contsub/parser/imcontsub.yaml +++ b/contsub/parser/imcontsub.yaml @@ -1,8 +1,3 @@ -libs: - spline: - _include: (contsub.parser.library)spline.yaml - polyn: - _include: (contsub.parser.library)polyn.yaml inputs: input-image: @@ -33,7 +28,20 @@ inputs: dtype: str choices: - spline + - polynomial + - median-filter default: spline + order: + info: Order of spline/polynomial. If given as a list of size N, then N iterations will be perfomed. + dtype: List[int] + policies: + repeat: "[]" + default: 3 + segments: + info: Width of spline segments or median filter window in km/s. If given as a list, then it must have same sixe as --order. + dtype: List[float] + policies: + repeat: "[]" 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. @@ -79,7 +87,5 @@ inputs: - trace - error - critical - _use: - - libs.spline.inputs outputs: {} diff --git a/contsub/parser/library/__init__.py b/contsub/parser/library/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/contsub/parser/library/dct.yaml b/contsub/parser/library/dct.yaml deleted file mode 100644 index a91399e..0000000 --- a/contsub/parser/library/dct.yaml +++ /dev/null @@ -1,4 +0,0 @@ -inputs: - {} -outputs: - {} \ No newline at end of file diff --git a/contsub/parser/library/polyn.yaml b/contsub/parser/library/polyn.yaml deleted file mode 100644 index a91399e..0000000 --- a/contsub/parser/library/polyn.yaml +++ /dev/null @@ -1,4 +0,0 @@ -inputs: - {} -outputs: - {} \ No newline at end of file diff --git a/contsub/parser/library/spline.yaml b/contsub/parser/library/spline.yaml deleted file mode 100644 index eada64a..0000000 --- a/contsub/parser/library/spline.yaml +++ /dev/null @@ -1,14 +0,0 @@ -inputs: - order: - info: Order of spline. If given as a list of size N, then N iterations will be perfomed. - dtype: List[int] - policies: - repeat: "[]" - default: 3 - segments: - info: Width of spline segments in km/s. If given as a list, then it must have same sixe as --order. - dtype: List[float] - policies: - repeat: "[]" -outputs: - {} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index bed4248..bc6b491 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,34 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. + +[[package]] +name = "accessible-pygments" +version = "0.0.5" +description = "A collection of accessible pygments styles" +optional = false +python-versions = ">=3.9" +groups = ["docs"] +files = [ + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872"}, +] + +[package.dependencies] +pygments = ">=1.5" + +[package.extras] +dev = 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"sha256:17db72f37f2489452d2137ac891c4133b8f976f9189d8efd3e75f3b3add84e8c"}, + {file = "zarr-3.1.3-py3-none-any.whl", hash = "sha256:45f67f87f65f14fa453f99dd8110a5936b7ac69f3a21981d33e90407c80c302a"}, + {file = "zarr-3.1.3.tar.gz", hash = "sha256:01342f3e26a02ed5670db608a5576fbdb8d76acb5c280bd2d0082454b1ba6f79"}, ] [package.dependencies] @@ -4526,12 +4807,13 @@ packaging = ">=22.0" typing-extensions = ">=4.9" [package.extras] +cli = ["typer"] docs = ["astroid (<4)", "numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "pytest", "rich", "s3fs (>=2023.10.0)", "sphinx (==8.1.3)", "sphinx-autoapi (==3.4.0)", "sphinx-autobuild (>=2021.3.14)", "sphinx-copybutton", "sphinx-design", "sphinx-issues", "sphinx-reredirects", "towncrier"] gpu = ["cupy-cuda12x"] optional = ["rich", "universal-pathlib"] remote = ["fsspec (>=2023.10.0)", "obstore (>=0.5.1)"] remote-tests = ["botocore", "fsspec (>=2023.10.0)", "moto[s3,server]", "obstore (>=0.5.1)", "requests", "s3fs (>=2023.10.0)"] -test = ["coverage (>=7.10)", "hypothesis", "mypy", "packaging", "pytest (<8.4)", "pytest-accept", "pytest-asyncio", "pytest-cov", "pytest-xdist", "rich", "tomlkit", "uv"] +test = ["coverage (>=7.10)", "hypothesis", "mypy", "numpydoc", "packaging", "pytest (<8.4)", "pytest-accept", "pytest-asyncio", "pytest-cov", "pytest-xdist", "rich", "tomlkit", "uv"] [[package]] name = "zipp" @@ -4557,4 +4839,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.1" python-versions = ">=3.10,<3.14" -content-hash = "4e1bf0cb3e10ccfb444c1c3d90a46f20e9abd91bc11ef984df20d6f4bb4de13a" +content-hash = "ca0b6714b5601777b7f54673493dc9713f3f2c183b8381aa3b6b4bf497115c28" From 8afe1b173d1c057ced804f2c18b78b9fefdccafd Mon Sep 17 00:00:00 2001 From: sphemakh Date: Thu, 25 Sep 2025 09:52:38 +0200 Subject: [PATCH 6/9] add median-filter and dct baseline fits --- contsub/fitfuncs.py | 285 +++++++++++++++++++++------------- contsub/image_plane.py | 59 +++---- contsub/parser/imcontsub.py | 114 +++++++------- contsub/parser/imcontsub.yaml | 19 ++- 4 files changed, 263 insertions(+), 214 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 6ce9fef..4a0d801 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -1,48 +1,77 @@ from scipy.interpolate import splev, splrep import sys from scabha import init_logger -from abc import ABC, abstractmethod +from abc import abstractmethod from . import BIN +from .exceptions import BadFitError import numpy as np +from scipy import fftpack log = init_logger(BIN.im_plane) - class FitFunc: """ abstract class for writing fitting functions """ - def __init__(self, order, velwidth, cont_tol): + def __init__(self, freqs, velwidth:float, fit_tol:float=0): """ Args: order (_type_): _description_ velwidth (_type_): _description_ """ - self.order = order self.velwidth = velwidth - self.preped = False - self.cont_tol = cont_tol - - def prepare(self, x): - nchan = len(x) - msort = np.argpartition(x, -2) + self.fit_tol = fit_tol + self.freqs = freqs + self.nchan = freqs.size + + def prepare(self): + freqs = self.freqs + msort = np.argpartition(freqs, -2) m1l, m2l = msort[-2:] m1h, m2h = msort[:2] if np.abs(m1l - m2l) == 1 and np.abs(m1h - m2h) == 1: - dvl = np.abs(x[m1l]-x[m2l])/np.mean([x[m1l],x[m2l]])*3e5 - dvh = np.abs(x[m1h]-x[m2h])/np.mean([x[m1h],x[m2h]])*3e5 - self.dv = (dvl+dvh)/2 - self.imax = int(nchan / (self.velwidth//self.dv))+1 + dvl = np.abs(freqs[m1l]-freqs[m2l])/np.mean([freqs[m1l],freqs[m2l]])*3e5 + dvh = np.abs(freqs[m1h]-freqs[m2h])/np.mean([freqs[m1h],freqs[m2h]])*2.998e5 + dv = (dvl+dvh)/2 + self.imax = int(self.velwidth/dv) + if self.imax %2 == 0: + self.imax += 1 + log.info(f"nchan = {self.nchan}, dv = {dv}, {self.velwidth}km/s in chans: {self.imax}") else: - log.error('The frequency values are not changing monotonically, aborting') + log.error('probably x values are not changing monotonically, aborting') sys.exit(1) - - - log.info(f"nchan = {nchan}, dv = {self.dv}, {self.velwidth}km/s in chans:" - f" {self.velwidth//self.dv}, max order spline = {self.imax}") self.preped = True + 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 + """ + data_count = np.isnan(data).sum() + mask_count = mask.sum() + + return data_count + mask_count + + def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=True): + """_summary_ + + Args: + data (np.ndarray): _description_ + mask (np.ndarray): _description_ + """ + invalid = self.invalid_point_count(data, mask) + nchan = data.size + valid_fraction = (1 - invalid / nchan) * 100 + if valid_fraction < self.fit_tol: + if raise_exception: + raise BadFitError(f"Fraction of valid data points {valid_fraction:.2f} is less than the required tolerance (--cont-fit-tol {self.fit_tol})") + else: + return False + else: + return True @abstractmethod def fit(self, x, data, mask, weight): @@ -52,13 +81,19 @@ class FitBSpline(FitFunc): """ BSpline fitting function based on `splev`, `splrep` in `scipy.interpolate` """ - def __init__(self, order, velWidth, randomState=None, seq=None): + def __init__(self, freqs, order, velwidth, fit_tol=0, randomState=None, seq=None): """ needs to know the order of the spline and the number of knots """ self.order = order - self.velwidth = velWidth - self.preped = False + self.velwidth = velwidth + self.fit_tol = fit_tol + self.freqs = freqs + self.nchan = freqs.size + self.prepare() + self.max_spline_order = int(self.nchan / self.imax) + 1 + log.info(f"max spline order: {self.max_spline_order}") + if randomState and seq: rs = np.random.SeedSequence(entropy = randomState, spawn_key = (seq,)) else: @@ -66,7 +101,7 @@ def __init__(self, order, velWidth, randomState=None, seq=None): self.rng = np.random.default_rng(rs) - def fit(self, x, data, weights): + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the spline fit and the residuals from the fit @@ -76,79 +111,64 @@ def fit(self, x, data, weights): To mask values, set the corresponding weight to zero. """ - if not self.preped: - self.prepare(x) + self.is_fit_possible(data, mask, raise_exception=True) + nvalid = self.nchan - self.invalid_point_count(data, mask) + + if nvalid < (self.order + 1): + raise BadFitError("Not enough valid points for spline fit, returning original data.") # Mask invalid or zero-weight points - mask = (weights > 0) & np.isfinite(x) & np.isfinite(data) & np.isfinite(weights) - x_masked = x[mask] - data_masked = data[mask] - weights_masked = weights[mask] + mask[np.where(np.isnan(data))] = True + x_masked = self.freqs[~mask] + data_masked = data[~mask] - - if len(x_masked) < (self.order + 1): - #TODO(Sphe) maybe return raise an exception, and let the caller of this function decide what to do. - log.debug("Not enough valid points for spline fit, returning original data.") - return np.copy(data) # fallback: just return input - - nchan = len(x_masked) - knotind = np.linspace(0, nchan, self.imax, dtype=int)[1:-1] - chwid = max(1, (nchan // self.imax) // 8) + 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.clip(knots_idx, 1, nchan - 20) # avoid edges - knot_positions = np.unique(x_masked[knots_idx]) - - splCfs = splrep(x_masked, data_masked, task=-1, - w=weights_masked, t = knot_positions, k=self.order) - spl = splev(x, splCfs) - return spl + 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) + else: + splCfs = splrep(x_masked, data_masked, task=-1, + t=knot_positions, k=self.order) + + return splev(self.freqs, splCfs) + class FitMedFilter(FitFunc): """ Median filtering class for continuum subtraction """ - def __init__(self, velWidth): + def __init__(self, freqs, velwidth, fit_tol=0): """ needs to know the order of the spline and the number of knots """ - self._velwid = velWidth - - def prepare(self, x): - msort = np.argpartition(x, -2) - m1l, m2l = msort[-2:] - m1h, m2h = msort[:2] - if np.abs(m1l - m2l) == 1 and np.abs(m1h - m2h) == 1: - dvl = np.abs(x[m1l]-x[m2l])/np.mean([x[m1l],x[m2l]])*3e5 - dvh = np.abs(x[m1h]-x[m2h])/np.mean([x[m1h],x[m2h]])*3e5 - dv = (dvl+dvh)/2 - self._imax = int(self._velwid//dv) - if self._imax %2 == 0: - self._imax += 1 - log.info('len(x) = {}, dv = {}, {}km/s in chans: {}'.format(len(x), dv, self._velwid, self._velwid//dv)) - else: - log.debug('probably x values are not changing monotonically, aborting') - sys.exit(1) - + self.velwidth = velwidth + self.fit_tol = fit_tol + self.freqs = freqs + self.nchan = freqs.size + self.prepare() - def fit(self, x, data, mask, weight): + + 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) - weight : weights + data (np.ndarray) : values to be fit + mask (np.ndarray) : a mask (not implemented really) + weight (np.ndarray) : weights """ - if not (mask is None): - data[np.logical_not(mask)] = np.nan + self.is_fit_possible(data, mask, raise_exception=True) - if self._imax%2 == 0: - window = self._imax + 1 - else: - window = self._imax + if isinstance(mask, np.ndarray): + data[mask] = np.nan - padded_data = np.pad(data, window//2, mode="linear_ramp") - filtered = np.nanmedian(np.lib.stride_tricks.sliding_window_view(padded_data, window), axis = 1) + padded_data = np.pad(data, self.imax//2, mode="linear_ramp") + filtered = np.nanmedian(np.lib.stride_tricks.sliding_window_view(padded_data, self.imax), axis = 1) return filtered @@ -156,50 +176,101 @@ class FitPolynomial(FitFunc): """ Polynomial fitting function using numpy.polyfit """ - def __init__(self, order, cont_tol = 0): + def __init__(self, freqs, order, fit_tol=0): """ order (int): Order/degree of the polynomial """ self.order = order - self.preped = False - self.cont_tol = cont_tol + self.fit_tol = fit_tol + self.freqs = freqs + self.nchan = freqs.size + self.prepare() - def prepare(self, x): + def prepare(self): """ Prepare for polynomial fitting """ - nchan = len(x) - log.info(f"Polynomial fitting: nchan = {nchan}, order = {self.order}") - self.preped = True + + log.info(f"Polynomial fitting: nchan = {self.nchan}, order = {self.order}") + self.preped = True - def fit(self, x, data, weights=None): - if not self.preped: - self.prepare(x) + def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): + """_summary_ - if weights is not None: - mask = (weights > 0) & np.isfinite(x) & np.isfinite(data) & np.isfinite(weights) - x_masked = x[mask] - data_masked = data[mask] - weights_masked = weights[mask] + Args: + x (np.ndarray): _description_ + data (np.ndarray): _description_ + weights (np.ndarray): _description_ + mask (np.ndarray): _description_ + + Returns: + _type_: _description_ + """ - else: - mask = np.isfinite(x) & np.isfinite(data) - x_masked = x[mask] - data_masked = data[mask] - weights_masked = None + self.is_fit_possible(data, mask, raise_exception=True) + + + x_masked = self.freqs[~mask] + data_masked = data[~mask] - if (len(x_masked)/len(x))*100 <= self.cont_tol: - #TODO(Sphe) Not sure if this is the right thing to do - return np.zeros_like(data) - try: - if weights_masked is not None: - coeffs = np.polyfit(x_masked, data_masked, self.order, w=weights_masked) + 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, x) + return np.polyval(coeffs, self.freqs) + except Exception as e: - log.debug(f"Polynomial fitting failed: {e}") + log.error(f"Polynomial fitting failed: {e}") + sys.exit(1) - # TODO(Sphe) Rather raise an exception and let caller of function decide what to do. - return data +class FitDCT(FitFunc): + """ + Median filtering class for continuum subtraction + """ + def __init__(self, freqs, velwidth, ncoef, dct_type=1, fit_tol=0): + """ + needs to know the order of the spline and the number of knots + """ + self.velwidth = velwidth + self.fit_tol = fit_tol + self.preped = False + self.dct_type = dct_type + self.ncoef = ncoef + self.freqs = freqs + self.nchan = freqs.size + + 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), + } + + self.fnorm = fnorm_dict[dct_type] + print(self.fnorm) + + self.prepare() + + 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) + weight : weights + """ + baseline = FitMedFilter(self.freqs, self.velwidth) + + 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.ncoef] + 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/contsub/image_plane.py index 1d70539..4a5ac7f 100644 --- a/contsub/image_plane.py +++ b/contsub/image_plane.py @@ -1,6 +1,7 @@ import numpy as np from scabha import init_logger from . import BIN +from .exceptions import BadFitError log = init_logger(BIN.im_plane) @@ -9,77 +10,65 @@ class ContSub(): """ a class for performing continuum subtraction on data """ - def __init__(self, fit_func, nomask, fit_tol=0): + def __init__(self, fit_func, nomask): """ 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 nomask (bool): Ignore mask if set fit_tol (float): If set, will skip lines with more than this percentage of masked pixels. - Default is 0, which means no skipping. - If set to 100, will skip all lines with any masked pixels. - If set to 0, will not skip any lines. + Default is 0, which means no skipping. + f set to 100, will skip all lines with any masked pixels. + If set to 0, will not skip any lines. Returns: None """ self.nomask = nomask self.fit_func = fit_func - self.fit_tol = fit_tol - def fitContinuum(self, xspec, cube, mask): + def fitContinuum(self, cube, mask): """ fits the data with the desired function and returns the continuum and the line Args: - xspec (Array): Spectrum coordinates cube (Array): Data cube to subtract continuum from - mask (Array): Binary data weights. True -> will be used in fir, False will not be used in fit. + mask (Array): Binary data weights. True -> will be used in fit, False will not be used in fit. Returns: Array: Continuum fit """ - dimx, dimy, nchan = cube.shape + dimx, dimy, _ = cube.shape cont_model = np.zeros_like(cube) - nomask = self.nomask - if nomask: - mask = None + if isinstance(mask, np.ndarray): + nomask = False + mask = np.asarray(mask, dtype=bool) + else: + nomask = True fitfunc = self.fit_func - if not fitfunc.preped: - fitfunc.prepare(xspec) 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) - mask_ij = mask[slc] if nomask == False else None cube_ij = cube[slc] + if nomask: + mask_ij = np.full_like(cube_ij, False, dtype=bool) + else: + mask_ij = mask[slc] - # Find and mask NaNs in the data - nanvals_idx = np.where(np.isnan(cube_ij)) - nansize = len(nanvals_idx[0]) - if nansize == nchan: + 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) - continue - elif nansize > 0: - if nomask: - mask_ij = np.ones_like(cube_ij) - mask_ij[nanvals_idx] = 0 - - # Flag LOS and continue if too many pixels are flagged - if self.fit_tol > 0: - if isinstance(mask_ij, np.ndarray) and \ - (nchan - mask_ij.sum()) / nchan > self.fit_tol/100: - skipped_lines += 1 - cont_model[slc] = np.full_like(cube_ij, np.nan) - continue - - cont_model[slc] = fitfunc.fit(xspec, cube_ij, - weights = mask_ij) + skipped_lines += 1 if skipped_lines > 0: log.info(f"This worker set {skipped_lines} spectra to NaN because of --cont-fit-tol.") diff --git a/contsub/parser/imcontsub.py b/contsub/parser/imcontsub.py index 71091bc..fc95367 100644 --- a/contsub/parser/imcontsub.py +++ b/contsub/parser/imcontsub.py @@ -8,7 +8,12 @@ from contsub import BIN from scabha import init_logger from contsub.image_plane import ContSub -from contsub.fitfuncs import FitBSpline +from contsub.fitfuncs import ( + FitBSpline, + FitPolynomial, + FitMedFilter, + FitDCT, +) import astropy.io.fits as fitsio from contsub.utils import zds_from_fits, get_automask, subtract_fits import dask.array as da @@ -51,16 +56,13 @@ def runit(**kwargs): 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.segments is None: - raise RuntimeError("Required option 'segments' is not set. Please run 'imcontsub --help' for more information.") - - if len(opts.order) != len(opts.segments): - raise ValueError("If setting multiple --order and --segments, they must be of equal length. " - f"Got {len(opts.order)} orders and {len(opts.segments)} segments.") - niter = len(opts.order) - - + 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) rest_freq = opts.rest_freq @@ -83,69 +85,57 @@ def runit(**kwargs): nomask = True - automask = False if getattr(opts, "mask_image", None): mask = zds_from_fits(opts.mask_image, chunks=chunks, rest_freq=rest_freq).DATA nomask = False - if getattr(opts, "sigma_clip", None): - automask = True - sigma_clip = list(opts.sigma_clip) - else: - sigma_clip = [] - - if len(sigma_clip) < niter and automask: - log.warning(f"Only {len(sigma_clip)} sigma-clips provided, but {niter} iterations requested." - " Using last value for unspecified iterations.") - sigma_clip.extend([sigma_clip[-1]] * (niter - len(sigma_clip))) - get_mask = da.gufunc( get_automask, - signature=f"(spectral),({dims_string}),(),(),() -> ({dims_string})", + signature=f"(spectral),({dims_string}) -> ({dims_string})", meta=(np.ndarray((), cube.dtype),), allow_rechunk=True, ) - signature = f"(spectral),({dims_string}),({dims_string}) -> ({dims_string})" + signature = f"({dims_string}),({dims_string}) -> ({dims_string})" meta = (np.ndarray((), cube.dtype),) - xspec = zds.FREQS.data + xspec = zds.FREQS.data.compute() dask.config.set(scheduler='threads', num_workers = opts.nworkers) dblocks = cube.data.blocks - for iter_i in range(niter): - log.info(f"Loading delayed compute for iteration {iter_i+1}/{niter} of continuum modelling.") - futures = [] - fitfunc = FitBSpline(opts.order[iter_i], opts.segments[iter_i]) - for biter,dblock in enumerate(dblocks): - if (nomask and automask) or (iter_i > 0 and automask): - mask_future = get_mask(xspec, - dblock, - sigma_clip[iter_i], - opts.order[iter_i], - opts.segments[iter_i], - ) - elif nomask is False: - mask_future = mask.data.blocks[biter] == False - else: - mask_future = da.ones_like(dblock, dtype=bool) - - contfit = ContSub(fitfunc, nomask=False, - fit_tol=opts.cont_fit_tol) - - getfit = da.gufunc( - contfit.fitContinuum, - signature=signature, - meta=meta, - allow_rechunk=True, - ) - - futures.append(getfit( - xspec, - dblock, - mask_future, - )) - - dblocks = list(futures) + futures = [] + + if opts.fit_model == "spline": + fitfunc = FitBSpline(xspec, opts.order, velwidth, fit_tol=opts.cont_fit_tol) + elif opts.fit_model == "polynomial": + fitfunc = FitPolynomial(xspec, opts.order, fit_tol=opts.cont_fit_tol) + elif opts.fit_model == "median-filter": + fitfunc = FitMedFilter(xspec, velwidth, fit_tol=opts.cont_fit_tol) + elif opts.fit_model == "dct": + fitfunc = FitDCT(xspec, velwidth, opts.order, fit_tol=opts.cont_fit_tol) + + for biter,dblock in enumerate(dblocks): + if opts.sigma_clip: + mask_future = get_mask(dblock, fitfunc, sigma_clip=opts.sigma_clip) + 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, + )) + + dblocks = list(futures) continuum = da.concatenate(futures).transpose((2,1,0)) if has_stokes: @@ -154,12 +144,12 @@ def runit(**kwargs): header = zds.attrs["header"] out_ds_cont = fitsio.PrimaryHDU(continuum, header=header) - out_ds_cont.writeto(outcont, overwrite=opts.overwrite) + out_ds_cont.writeto(outcont.PATH, overwrite=opts.overwrite) log.info(f"Continuum model cube written to: {outcont}") - out_ds_line = subtract_fits(infits, outcont, chunks={0: opts.ra_chunks, 1:None, 2:None}) + out_ds_line = subtract_fits(infits.PATH, outcont.PATH, chunks={0: opts.ra_chunks, 1:None, 2:None}) log.info(f"Writing residual data (line cube) to: {outline}") - out_ds_line.writeto(outline, overwrite=opts.overwrite) + out_ds_line.writeto(outline.PATH, overwrite=opts.overwrite) # DONE dtime = time.time() - start_time diff --git a/contsub/parser/imcontsub.yaml b/contsub/parser/imcontsub.yaml index ef55c7c..9b2673b 100644 --- a/contsub/parser/imcontsub.yaml +++ b/contsub/parser/imcontsub.yaml @@ -24,24 +24,23 @@ inputs: dtype: bool default: False fit-model: - info: "### NOT IMPLEMENTED ### Fit function to model the continuum." + 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 - median-filter + - dct default: spline order: - info: Order of spline/polynomial. If given as a list of size N, then N iterations will be perfomed. - dtype: List[int] - policies: - repeat: "[]" - default: 3 - segments: + 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. If given as a list, then it must have same sixe as --order. - dtype: List[float] - policies: - repeat: "[]" + 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." + 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. From 5c9c736dd52acf11439f19ecd8820f51ee36a5dc Mon Sep 17 00:00:00 2001 From: sphemakh Date: Thu, 25 Sep 2025 09:53:42 +0200 Subject: [PATCH 7/9] accomodate new fit-models --- contsub/exceptions.py | 3 ++ contsub/parser/utils.py | 74 ----------------------------------------- contsub/utils.py | 12 +++---- 3 files changed, 7 insertions(+), 82 deletions(-) delete mode 100644 contsub/parser/utils.py diff --git a/contsub/exceptions.py b/contsub/exceptions.py index e575e06..f7102d8 100644 --- a/contsub/exceptions.py +++ b/contsub/exceptions.py @@ -15,3 +15,6 @@ class tableDimMismatch(Exception): class CubeDimIsSmall(Exception): pass + +class BadFitError(Exception): + pass \ No newline at end of file diff --git a/contsub/parser/utils.py b/contsub/parser/utils.py deleted file mode 100644 index 932cc84..0000000 --- a/contsub/parser/utils.py +++ /dev/null @@ -1,74 +0,0 @@ -import os -from scabha import configuratt -from scabha.cargo import Parameter, EmptyDictDefault -from omegaconf import OmegaConf -from typing import Dict, Any, List -from dataclasses import dataclass - - -thisdir = os.path.dirname(__file__) - - -class File(str): - def __init__(self, path, check=True): - self.path = os.path.abspath(path) - self.name = os.path.basename(path) - self.exists = os.path.exists(path) - self.dirname = os.path.dirname(path) - self.basename = os.path.splitext(self.name)[0] - - if check: - if not self.exists: - raise FileNotFoundError(f"File {self.path} does not exist.") - self.isfile = os.path.isfile(self.path) - - -class Directory(File): - @property - def isdir(self): - if self.check and not os.path.isdir(self.path): - raise FileNotFoundError(f"File {self.path} is not a directory. (does it exist?)") - else: - return True - - -@dataclass -class SchemaSpec: - inputs: Dict[str,Parameter] - outputs: Dict[str,Parameter] - libs: Dict[str, Any] = EmptyDictDefault() - -def load(parser: File, use_sources: List = []) -> Dict: - """Load a scabha-style parameter defintion using. - - Args: - name (str): Name of parameter definition to load - use_sources (List, optional): Parameter definition dependencies - (a.k.a files specified via_include) - - Returns: - Dict: Schema object - """ - - args_defn = OmegaConf.structured(SchemaSpec) - struct_args, _ = configuratt.load_nested([parser.path], structured=args_defn, - use_sources=use_sources, use_cache=False) - schema = OmegaConf.create(struct_args) - - return schema[parser.basename] - -def load_sources(sources: List[str|File]): - __sources = [None]*len(sources) - for i, src in enumerate(sources): - if isinstance(src, str): - if src.endswith((".yaml", ".yml")): - sources[i] = File(src) - else: - try: - sources[i] = File(os.path.join(thisdir,f"{src}.yaml")) - except FileNotFoundError: - raise FileNotFoundError(f"Name {src} does not match a known parameter file.") - - __sources[i], _ = configuratt.load(sources[i], use_cache=False) - return __sources[i] - diff --git a/contsub/utils.py b/contsub/utils.py index 79d009d..64f7358 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -11,8 +11,8 @@ from scabha.basetypes import File import numpy as np import datetime -from astropy.time import Time from daskms import xds_from_ms, xds_from_table +from xarrayfits import xds_from_fits import dask.array as da from tqdm.dask import TqdmCallback import warnings @@ -23,25 +23,21 @@ log = init_logger(BIN.im_plane) -def get_automask(xspec, cube, sigma_clip=5, order=3, segments=400): +def get_automask(xspec, cube, fitfunc, sigma_clip): """ Generate a binary mask by sigma-thresholding the input cube Args: xspec (Array): Spectral coordinates cube (Array): Data cdube - sigma_clip (int, optional): _description_. Defaults to 5. - order (int, optional): _description_. Defaults to 3. - segments (int, optional): Length of spline segment in km/s. Defaults to 400. + sigma_clip(float): Sigma clip level Returns: Array : Binary mask (False is masked, True is not) """ log.info("Creating binary mask as requested") - fitfunc = FitBSpline(order, segments) - contsub = ContSub(fitfunc, nomask=True, fit_tol=60) - cont_model = contsub.fitContinuum(xspec, cube, mask=None) + cont_model = fitfunc.fitContinuum(xspec, cube, mask=None) clip = PixSigmaClip(sigma_clip) From 7077ab6c9f1d6742216325eab35f677b906db32a Mon Sep 17 00:00:00 2001 From: sphemakh Date: Fri, 26 Sep 2025 08:26:24 +0200 Subject: [PATCH 8/9] modify FitFunc.prepare --- contsub/fitfuncs.py | 214 ++++++++++++++++++---------------- contsub/image_plane.py | 3 +- contsub/parser/imcontsub.py | 18 +-- contsub/parser/imcontsub.yaml | 8 +- contsub/utils.py | 34 +++++- pyproject.toml | 12 +- 6 files changed, 166 insertions(+), 123 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index 4a0d801..a7f7c93 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -1,21 +1,20 @@ from scipy.interpolate import splev, splrep -import sys from scabha import init_logger from abc import abstractmethod from . import BIN from .exceptions import BadFitError import numpy as np from scipy import fftpack +from contsub import utils +from scipy.interpolate import make_smoothing_spline, make_splrep log = init_logger(BIN.im_plane) class FitFunc: - """ - abstract class for writing fitting functions - """ - def __init__(self, freqs, velwidth:float, fit_tol:float=0): + def __init__(self, freqs, order:int=None, velwidth:float=None, + chanwidth:int=None, fit_tol:float=0): """ - + Args: order (_type_): _description_ velwidth (_type_): _description_ @@ -24,24 +23,9 @@ def __init__(self, freqs, velwidth:float, fit_tol:float=0): self.fit_tol = fit_tol self.freqs = freqs self.nchan = freqs.size - - def prepare(self): - freqs = self.freqs - msort = np.argpartition(freqs, -2) - m1l, m2l = msort[-2:] - m1h, m2h = msort[:2] - if np.abs(m1l - m2l) == 1 and np.abs(m1h - m2h) == 1: - dvl = np.abs(freqs[m1l]-freqs[m2l])/np.mean([freqs[m1l],freqs[m2l]])*3e5 - dvh = np.abs(freqs[m1h]-freqs[m2h])/np.mean([freqs[m1h],freqs[m2h]])*2.998e5 - dv = (dvl+dvh)/2 - self.imax = int(self.velwidth/dv) - if self.imax %2 == 0: - self.imax += 1 - log.info(f"nchan = {self.nchan}, dv = {dv}, {self.velwidth}km/s in chans: {self.imax}") - else: - log.error('probably x values are not changing monotonically, aborting') - sys.exit(1) - self.preped = True + self.order = order + self.preped = False + self.chanwidth = chanwidth def invalid_point_count(self, data:np.ndarray, mask:np.ndarray): """ Calculates the number of invalid data points in a spectrum. @@ -50,10 +34,9 @@ def invalid_point_count(self, data:np.ndarray, mask:np.ndarray): data (np.ndarray): 1D spectrum mask (np.ndarray): Binary mask """ - data_count = np.isnan(data).sum() - mask_count = mask.sum() + mask[np.isnan(data)] = True - return data_count + mask_count + return mask, mask.sum() def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=True): """_summary_ @@ -62,7 +45,7 @@ def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=Tr data (np.ndarray): _description_ mask (np.ndarray): _description_ """ - invalid = self.invalid_point_count(data, mask) + mask, invalid = self.invalid_point_count(data, mask) nchan = data.size valid_fraction = (1 - invalid / nchan) * 100 if valid_fraction < self.fit_tol: @@ -71,36 +54,37 @@ def is_fit_possible(self, data: np.ndarray, mask: np.ndarray, raise_exception=Tr else: return False else: - return True + 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) + 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` """ - def __init__(self, freqs, order, velwidth, fit_tol=0, randomState=None, seq=None): - """ - needs to know the order of the spline and the number of knots - """ - self.order = order - self.velwidth = velwidth - self.fit_tol = fit_tol - self.freqs = freqs - self.nchan = freqs.size - self.prepare() - self.max_spline_order = int(self.nchan / self.imax) + 1 - log.info(f"max spline order: {self.max_spline_order}") - - if randomState and seq: - rs = np.random.SeedSequence(entropy = randomState, spawn_key = (seq,)) - else: - rs = np.random.SeedSequence() - self.rng = np.random.default_rng(rs) + 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 + def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ returns the spline fit and the residuals from the fit @@ -111,8 +95,11 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): To mask values, set the corresponding weight to zero. """ - self.is_fit_possible(data, mask, raise_exception=True) - nvalid = self.nchan - self.invalid_point_count(data, mask) + 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.") @@ -138,22 +125,53 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): return splev(self.freqs, splCfs) +class FitGCVSpline(FitFunc): + """ + Polynomial fitting function using scipy.interpolate.make_smoothing_spline + """ + + def prepare(self, lam=None): + self.lam = lam + self.preped = True + + def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): + """_summary_ + + Args: + x (np.ndarray): _description_ + data (np.ndarray): _description_ + weights (np.ndarray): _description_ + mask (np.ndarray): _description_ + Returns: + _type_: _description_ + """ + + if not self.preped: + self.prepare() + mask, invalid = self.is_fit_possible(data, mask, raise_exception=True) + nvalid = self.nchan - invalid + + 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.lam, w=weights[~mask]) + else: + smooth_func = make_splrep(x_masked, data_masked, s=nvalid) #, lam=self.lam) + except Exception as e: + raise BadFitError(f"Polynomial fitting failed: {e}") + + return smooth_func(self.freqs) class FitMedFilter(FitFunc): """ Median filtering class for continuum subtraction """ - def __init__(self, freqs, velwidth, fit_tol=0): - """ - needs to know the order of the spline and the number of knots - """ - self.velwidth = velwidth - self.fit_tol = fit_tol - self.freqs = freqs - self.nchan = freqs.size - self.prepare() - + 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 @@ -162,13 +180,22 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): mask (np.ndarray) : a mask (not implemented really) weight (np.ndarray) : weights """ - self.is_fit_possible(data, mask, raise_exception=True) + + if not self.preped: + self.prepare() + + mask = self.is_fit_possible(data, mask, raise_exception=True)[0] + + if isinstance(weights, np.ndarray): + #TODO(Sphe) + pass if isinstance(mask, np.ndarray): data[mask] = np.nan - - padded_data = np.pad(data, self.imax//2, mode="linear_ramp") - filtered = np.nanmedian(np.lib.stride_tricks.sliding_window_view(padded_data, self.imax), axis = 1) + + 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) return filtered @@ -176,23 +203,9 @@ class FitPolynomial(FitFunc): """ Polynomial fitting function using numpy.polyfit """ - def __init__(self, freqs, order, fit_tol=0): - """ - order (int): Order/degree of the polynomial - """ - self.order = order - self.fit_tol = fit_tol - self.freqs = freqs - self.nchan = freqs.size - self.prepare() - + def prepare(self): - """ - Prepare for polynomial fitting - """ - - log.info(f"Polynomial fitting: nchan = {self.nchan}, order = {self.order}") - self.preped = True + pass def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): """_summary_ @@ -207,8 +220,10 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): _type_: _description_ """ - self.is_fit_possible(data, mask, raise_exception=True) - + if not self.preped: + self.prepare() + + mask = self.is_fit_possible(data, mask, raise_exception=True)[0] x_masked = self.freqs[~mask] data_masked = data[~mask] @@ -221,24 +236,15 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): return np.polyval(coeffs, self.freqs) except Exception as e: - log.error(f"Polynomial fitting failed: {e}") - sys.exit(1) + raise BadFitError(f"Polynomial fitting failed: {e}") class FitDCT(FitFunc): """ Median filtering class for continuum subtraction """ - def __init__(self, freqs, velwidth, ncoef, dct_type=1, fit_tol=0): - """ - needs to know the order of the spline and the number of knots - """ - self.velwidth = velwidth - self.fit_tol = fit_tol - self.preped = False - self.dct_type = dct_type - self.ncoef = ncoef - self.freqs = freqs - self.nchan = freqs.size + def prepare(self, dct_type=1): + + self.default_prepare() fnorm_dict = { 1: 1 / np.sqrt( 2 * self.nchan), @@ -248,9 +254,7 @@ def __init__(self, freqs, velwidth, ncoef, dct_type=1, fit_tol=0): } self.fnorm = fnorm_dict[dct_type] - print(self.fnorm) - - self.prepare() + self.preped = True def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): """ @@ -261,16 +265,22 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): mask : a mask (not implemented really) weight : weights """ - baseline = FitMedFilter(self.freqs, self.velwidth) + + if not self.preped: + self.prepare() + + mask = self.is_fit_possible(data, mask, raise_exception=True)[0] - baseline_median = baseline.fit(data, mask=mask, weights=None) + if isinstance(weights, np.ndarray): + #TODO(Sphe) + pass + baseline = FitMedFilter(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.ncoef] + 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/contsub/image_plane.py index 4a5ac7f..be429c7 100644 --- a/contsub/image_plane.py +++ b/contsub/image_plane.py @@ -63,8 +63,7 @@ def fitContinuum(self, cube, mask): mask_ij = mask[slc] try: - cont_model[slc] = fitfunc.fit(cube_ij, - mask_ij, weights=None) + 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) diff --git a/contsub/parser/imcontsub.py b/contsub/parser/imcontsub.py index fc95367..96f5cd3 100644 --- a/contsub/parser/imcontsub.py +++ b/contsub/parser/imcontsub.py @@ -12,7 +12,7 @@ FitBSpline, FitPolynomial, FitMedFilter, - FitDCT, + FitGCVSpline, ) import astropy.io.fits as fitsio from contsub.utils import zds_from_fits, get_automask, subtract_fits @@ -104,14 +104,18 @@ def runit(**kwargs): dblocks = cube.data.blocks futures = [] - if opts.fit_model == "spline": - fitfunc = FitBSpline(xspec, opts.order, velwidth, fit_tol=opts.cont_fit_tol) + if opts.fit_model in ["spline", "b-spline"]: + fitfunc = FitBSpline(xspec, order=opts.order, velwidth=velwidth, fit_tol=opts.cont_fit_tol) + fitfunc.prepare() elif opts.fit_model == "polynomial": - fitfunc = FitPolynomial(xspec, opts.order, fit_tol=opts.cont_fit_tol) + fitfunc = FitPolynomial(xspec, order=opts.order, fit_tol=opts.cont_fit_tol) + fitfunc.prepare() elif opts.fit_model == "median-filter": - fitfunc = FitMedFilter(xspec, velwidth, fit_tol=opts.cont_fit_tol) - elif opts.fit_model == "dct": - fitfunc = FitDCT(xspec, velwidth, opts.order, fit_tol=opts.cont_fit_tol) + fitfunc = FitMedFilter(xspec, velwidth=velwidth, fit_tol=opts.cont_fit_tol) + fitfunc.prepare() + elif opts.fit_model == "gcv-spline": + fitfunc = FitGCVSpline(xspec, fit_tol=opts.cont_fit_tol) + fitfunc.prepare(lam=opts.gcv_lambda) for biter,dblock in enumerate(dblocks): if opts.sigma_clip: diff --git a/contsub/parser/imcontsub.yaml b/contsub/parser/imcontsub.yaml index 9b2673b..c564d32 100644 --- a/contsub/parser/imcontsub.yaml +++ b/contsub/parser/imcontsub.yaml @@ -27,17 +27,21 @@ inputs: 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 - - dct - default: spline + - 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. If given as a list, then it must have same sixe as --order. dtype: float + gcv-lambda: + info: GCV spline penalty. Zero is equivalent to an interpolating spline, high values lead to a flatter curve. If unset (recommended) the parameter will be estimated using the GCV criterion. + 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." dtype: float diff --git a/contsub/utils.py b/contsub/utils.py index 64f7358..682f776 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -1,12 +1,10 @@ import xarray as xr from astropy.wcs import WCS -from contsub.image_plane import ContSub from contsub.masking import PixSigmaClip, Mask -from contsub.fitfuncs import FitBSpline from contsub import BIN from typing import Dict from scabha import init_logger -import astropy.units as u +from astropy import units import astropy.io.fits as fitsio from scabha.basetypes import File import numpy as np @@ -46,6 +44,34 @@ def get_automask(xspec, cube, fitfunc, sigma_clip): return mask +def chans_in_velwidth(freqs:np.ndarray, velwidth:float): + """ + Calculates the number of channels in given velocity width + + Args: + freqs (np.ndarray): Frequency grid in Hz. + velwidth (float): Velocity width in m/s + """ + speed_c = 2.998e8 + df_low = np.partition(freqs, 2)[:2] + df_high = np.partition(freqs, 2)[-2:] + + dv_high = np.diff(df_low) / np.mean(df_low) * speed_c + dv_low = 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 @@ -157,7 +183,7 @@ def retFreq(self): wcsfreq = wcs3d.spectral except: wcsfreq = wcs3d.sub(['spectral']) - return np.around(wcsfreq.pixel_to_world(np.arange(0,freqDim)).to(u.MHz).value, decimals = 7) + 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') diff --git a/pyproject.toml b/pyproject.toml index 6e49356..ec829fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,8 +1,8 @@ [tool.poetry] name = "contsub" -version = "1.0.5" +version = "2.0.0rb" description = "Radio astronomy data continuum subtraction tools" -authors = ["Amir Kazemi-Moridani, Sphesihle Makhathini, Mika Naidoo"] +authors = ["Sphesihle Makhathini, Mika Naidoo", "Amir Kazemi-Moridani"] license = "MIT" readme = "README.rst" @@ -12,16 +12,16 @@ build-backend = "poetry.core.masonry.api" [tool.poetry.dependencies] python = ">=3.10,<3.14" -numpy = "^1.22.4" -astropy = "^6.0.0" -scipy = "^1.12.0" +numpy = "^*" +astropy = "*" +scipy = "*" xarray-fits = "*" xarray = "*" stimela = "^2.1.2" zarr = "*" tdqm = "*" dask-ms = "*" -dask = "<2024.11.0" +dask = "*" [tool.poetry.scripts] From 970ee891660bced7b85adaf8cf78de60809fcfcf Mon Sep 17 00:00:00 2001 From: sphemakh Date: Mon, 29 Sep 2025 15:29:32 +0200 Subject: [PATCH 9/9] add unit tests --- contsub/fitfuncs.py | 66 +++- contsub/image_plane.py | 12 +- contsub/parser/imcontsub.py | 17 +- contsub/parser/imcontsub.yaml | 8 +- contsub/utils.py | 32 +- contsub/visibility_plane.py | 38 +- poetry.lock | 675 ++++++++++++++++++++-------------- pyproject.toml | 4 +- tests/test_main.py | 142 ++++--- 9 files changed, 592 insertions(+), 402 deletions(-) diff --git a/contsub/fitfuncs.py b/contsub/fitfuncs.py index a7f7c93..b287150 100644 --- a/contsub/fitfuncs.py +++ b/contsub/fitfuncs.py @@ -1,4 +1,5 @@ -from scipy.interpolate import splev, splrep +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 @@ -6,13 +7,12 @@ import numpy as np from scipy import fftpack from contsub import utils -from scipy.interpolate import make_smoothing_spline, make_splrep log = init_logger(BIN.im_plane) class FitFunc: def __init__(self, freqs, order:int=None, velwidth:float=None, - chanwidth:int=None, fit_tol:float=0): + chanwidth:int=None, fit_lam:int=None, fit_tol:float=0): """ Args: @@ -26,6 +26,7 @@ def __init__(self, freqs, order:int=None, velwidth:float=None, self.order = order self.preped = False 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. @@ -63,6 +64,7 @@ def fit(self, x, data, mask, weight): def default_prepare(self): if self.velwidth: 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.") @@ -116,6 +118,7 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): 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) @@ -130,8 +133,7 @@ class FitGCVSpline(FitFunc): Polynomial fitting function using scipy.interpolate.make_smoothing_spline """ - def prepare(self, lam=None): - self.lam = lam + def prepare(self): self.preped = True def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): @@ -149,17 +151,16 @@ def fit(self, data: np.ndarray, mask:np.ndarray, weights: np.ndarray): if not self.preped: self.prepare() - mask, invalid = self.is_fit_possible(data, mask, raise_exception=True) - nvalid = self.nchan - invalid + 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.lam, w=weights[~mask]) + smooth_func = make_smoothing_spline(x_masked, data_masked, lam=self.fit_lam, w=weights[~mask]) else: - smooth_func = make_splrep(x_masked, data_masked, s=nvalid) #, lam=self.lam) + smooth_func = make_smoothing_spline(x_masked, data_masked, lam=self.fit_lam) except Exception as e: raise BadFitError(f"Polynomial fitting failed: {e}") @@ -184,7 +185,7 @@ 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)[0] + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) if isinstance(weights, np.ndarray): #TODO(Sphe) @@ -198,6 +199,44 @@ def fit(self, data: np.ndarray, mask: np.ndarray, weights: np.ndarray): 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 + """ + + 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): + data[mask] = np.nan + + # Fill NaNs with nearest value for filtering + nan_mask = np.isnan(data) + if np.any(nan_mask): + data_filled = np.copy(data) + data_filled[nan_mask] = np.interp(np.flatnonzero(nan_mask), np.flatnonzero(~nan_mask), data[~nan_mask]) + else: + data_filled = data + + # Use scipy.ndimage.median_filter for speed + filtered = median_filter(data_filled, size=self.chanwidth, mode='reflect') + + return filtered class FitPolynomial(FitFunc): """ @@ -223,7 +262,7 @@ 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)[0] + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) x_masked = self.freqs[~mask] data_masked = data[~mask] @@ -254,6 +293,7 @@ def prepare(self, dct_type=1): } 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): @@ -269,13 +309,13 @@ 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)[0] + mask, _ = self.is_fit_possible(data, mask, raise_exception=True) if isinstance(weights, np.ndarray): #TODO(Sphe) pass - baseline = FitMedFilter(self.freqs, velwidth=self.velwidth, chanwidth=self.chanwidth) + 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) diff --git a/contsub/image_plane.py b/contsub/image_plane.py index be429c7..9b0aa1f 100644 --- a/contsub/image_plane.py +++ b/contsub/image_plane.py @@ -10,20 +10,12 @@ class ContSub(): """ a class for performing continuum subtraction on data """ - def __init__(self, fit_func, nomask): + def __init__(self, fit_func): """ 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 - nomask (bool): Ignore mask if set - fit_tol (float): If set, will skip lines with more than this percentage of masked pixels. - Default is 0, which means no skipping. - f set to 100, will skip all lines with any masked pixels. - If set to 0, will not skip any lines. - Returns: - None """ - self.nomask = nomask self.fit_func = fit_func @@ -71,6 +63,6 @@ def fitContinuum(self, cube, mask): 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/parser/imcontsub.py b/contsub/parser/imcontsub.py index 96f5cd3..bdf9a37 100644 --- a/contsub/parser/imcontsub.py +++ b/contsub/parser/imcontsub.py @@ -89,12 +89,6 @@ def runit(**kwargs): mask = zds_from_fits(opts.mask_image, chunks=chunks, rest_freq=rest_freq).DATA nomask = False - get_mask = da.gufunc( - get_automask, - signature=f"(spectral),({dims_string}) -> ({dims_string})", - meta=(np.ndarray((), cube.dtype),), - allow_rechunk=True, - ) signature = f"({dims_string}),({dims_string}) -> ({dims_string})" meta = (np.ndarray((), cube.dtype),) @@ -117,9 +111,16 @@ def runit(**kwargs): 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): if opts.sigma_clip: - mask_future = get_mask(dblock, fitfunc, sigma_clip=opts.sigma_clip) + mask_future = get_mask(dblock) elif nomask is False: mask_future = mask.data.blocks[biter] else: @@ -139,8 +140,6 @@ def runit(**kwargs): mask_future, )) - dblocks = list(futures) - continuum = da.concatenate(futures).transpose((2,1,0)) if has_stokes: continuum = continuum[np.newaxis,...] diff --git a/contsub/parser/imcontsub.yaml b/contsub/parser/imcontsub.yaml index c564d32..3bfe753 100644 --- a/contsub/parser/imcontsub.yaml +++ b/contsub/parser/imcontsub.yaml @@ -31,16 +31,20 @@ inputs: - 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. If given as a list, then it must have same sixe as --order. + 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 (recommended) the parameter will be estimated using the GCV criterion. + 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." diff --git a/contsub/utils.py b/contsub/utils.py index 682f776..1e7c119 100644 --- a/contsub/utils.py +++ b/contsub/utils.py @@ -1,6 +1,7 @@ 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 from typing import Dict from scabha import init_logger @@ -21,7 +22,7 @@ log = init_logger(BIN.im_plane) -def get_automask(xspec, cube, fitfunc, sigma_clip): +def get_automask(cube, fitfunc, sigma_clip): """ Generate a binary mask by sigma-thresholding the input cube @@ -35,14 +36,15 @@ def get_automask(xspec, cube, fitfunc, sigma_clip): """ log.info("Creating binary mask as requested") - cont_model = fitfunc.fitContinuum(xspec, cube, mask=None) + 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 + return ~mask def chans_in_velwidth(freqs:np.ndarray, velwidth:float): """ @@ -56,22 +58,12 @@ def chans_in_velwidth(freqs:np.ndarray, velwidth:float): df_low = np.partition(freqs, 2)[:2] df_high = np.partition(freqs, 2)[-2:] - dv_high = np.diff(df_low) / np.mean(df_low) * speed_c - dv_low = np.diff(df_high) / np.mean(df_high) * speed_c + 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 @@ -208,7 +200,6 @@ def getCombineHeader(self, dimx, dimy): 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) @@ -265,7 +256,7 @@ def get_ds_from_msdsl(ms_dsl, field_id=0, data_desc_id=0): 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): + 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 @@ -287,7 +278,6 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, 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] @@ -302,8 +292,6 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, ds = get_ds_from_msdsl(ms_dsl, field_id=field_id, data_desc_id=spw_id) times = ds.TIME.data - antenna1 = ds.ANTENNA1.data - antenna2 = ds.ANTENNA2.data visibilities = ds.DATA.data flags = ds.FLAG.data weights = ds.WEIGHT_SPECTRUM.data @@ -315,8 +303,6 @@ def ms_to_xarray_dataset(ms_path, spw_id:int, field_id:int, chunks:int, ntimes = nrow // nbl unique_times = np.unique(times) - - reshaped_vis = da.reshape(visibilities, (ntimes, nbl, nchan, ncorr)) reshaped_flags = da.reshape(flags, (ntimes, nbl, nchan, ncorr)) diff --git a/contsub/visibility_plane.py b/contsub/visibility_plane.py index df1e0a1..bdb4725 100644 --- a/contsub/visibility_plane.py +++ b/contsub/visibility_plane.py @@ -1,7 +1,4 @@ -from contsub.fitfuncs import FitBSpline import numpy as np -import xarray as xr -import dask.array as da import numpy as np from typing import Callable @@ -9,40 +6,33 @@ class VisContSub(): """ a class for performing continuum subtraction on visibility data """ - def __init__(self, fit_func: Callable, fit_tol: float=0): - """ + def __init__(self, fit_func: Callable): + """ Fit contiuum basline to visibility data + Args: - method (str): Fitting method to use, e.g., 'spline' - order (int): Order of the spline fit - vel_width (float): Velocity width for the fit + fit_func (Callable): Function to model contiuum baseline """ self.fit_func = fit_func - self.fit_tol = fit_tol - - - def vis_cont_sub(self, xspec: np.ndarray, 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, nchan, ncorr = visdata.shape + ntime, nbaseline, _, ncorr = visdata.shape for tme in range(ntime): for bl in range(nbaseline): for corr in range(ncorr): - vis_slice = visdata[tme, bl, :, corr] - flag_slice = flags[tme, bl, :, corr] - weights_slice = weights[tme, bl, :, corr] + slc = tme, bl, slice(None), corr + vis_slice = visdata[slc] + flag_slice = flags[slc] + weights_slice = weights[slc] - weights_slice[flag_slice] = 0 - real_model = self.fit_func.fit(xspec, vis_slice.real, weights_slice) - imag_model = self.fit_func.fit(xspec, vis_slice.imag, weights_slice) + 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) - visdata[tme,bl,:,corr] = real_model + 1j * imag_model + visdata[slc] = real_model + 1j * imag_model 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FitMedFilter, + FitMedFilterFast, + FitPolynomial, + #FitDCT, +) +from contsub import utils +log = init_logger("contsub") -# Test the ContSub class -def test_contsub_initialization(): - # Create a ContSub instance with a mock FitFunc - fit_func = FitBSpline(order=3, velWidth=1000, randomState=None, seq=None) - contsub = ContSub(fit_func=fit_func, nomask=True, fit_tol=0) +class TestFitsFunc(unittest.TestCase): + def setUp(self): + self.nchan = nchan = 1000 + xvals = np.linspace(0,2*np.pi, nchan) + noise = np.random.randn(nchan) * 1.5 - # Check if the instance is created correctly - assert isinstance(contsub, ContSub) - assert contsub.fit_func == fit_func - - -def test_contsub_fitContinuum(): - # Create a ContSub instance with a mock FitFunc - fit_func = FitBSpline(order=3, velWidth=200, randomState=None, seq=None) - contsub = ContSub(fit_func=fit_func, nomask=True, fit_tol=0) - - # Mock data for xspec and cube - nchan = 128 - npix = 10 - xspec = np.linspace(1411, 1412, nchan) # Mock spectral coordinates - cube = np.random.randn(npix, npix, nchan) * 1e-3 # Mock data cube - mask = None # No mask - - # Call the fitContinuum method - cont_model = contsub.fitContinuum(xspec, cube, mask) - - # Check if the output is as expected (mocked for simplicity) - assert cont_model is not None - assert cont_model.shape == cube.shape - - -def test_contsub_fitContinuum_with_mask(): - # Create a ContSub instance with a mock FitFunc - fit_func = FitBSpline(order=3, velWidth=200, randomState=None, seq=None) - contsub = ContSub(fit_func=fit_func, nomask=False, fit_tol=0) + # --- 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) + + 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 - # Mock data for xspec and cube - nchan = 128 - npix = 10 - xspec = np.linspace(1411, 1412, nchan) # Mock spectral coordinates - cube = np.random.randn(npix, npix, nchan) * 1e-3 # Mock data cube - mask = np.random.choice([True, False], size=(npix, npix, nchan)) # Random mask + dfreq = 6500 * 1e-6 + freq0 = 1361 + self.freqs = freqs = freq0 + np.linspace(0, nchan*dfreq, nchan) - # Call the fitContinuum method - cont_model = contsub.fitContinuum(xspec, cube, mask) + 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) + mask_end = mask_start + 20 + mask[mask_start:mask_end] = True + + 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) + + 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) - # Check if the output is as expected (mocked for simplicity) - assert cont_model is not None - assert cont_model.shape == cube.shape + 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 + + 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