From be28e2ff29443fa35992428e09af1ecc39616ea2 Mon Sep 17 00:00:00 2001 From: JSKenyon Date: Tue, 30 Jan 2024 08:46:28 +0200 Subject: [PATCH 1/2] Merge v0.2.1 development branch (#324) * Use nearest-neighbour interpolation in regions where extrapolation is required. (#285) * Fix version drift. * Bump to 0.2.0 * Use nearest-neighbour interpolation for points requiring extrapolation. * Utilise environment variable when dask.address is unset. (#288) * Fix version drift. * Bump to 0.2.0 * Inspect envvar for scheduler address when one isn't specified. * Encode environment varraible as ascii. * Simplify. * Add plotting functionality (#290) * Fix version drift. * Bump to 0.2.0 * Initial commit of basic plotting functionality. * Change naming convention. * Improve transform argument. * Simplify transform selection. * Add rudimentary time and frequency selection. * Checkpoint ploter changes. Can now handle scans and spws, but is very slow. * More work on plotter - can now plot datasets in parallel. * Some tidying. * Slightly improve plot speed. Dominant cost is still saving the figures. * Commit some minor changes which speed up figure saving. * Lots of tiny fixes. * Tiny cosmetic changes. * Add custom tick formatter so that plots are the same size regardless. * Add matplotlib dependency. * Rework construction of plotting dictionary. Add a few utility functions which will likely be useful in other places in QC. * Rename variable to avoid confusion. * Fix bug affecting recursive grouping. * Avoid copies in grouping code. * Checkpoint work on extending functionality. * Make plotter more powerful. Add colourization option. Begin simplifying interface. * Allow user specification of colourmap. * Add plotsize parameter. * Fix #293 - OOB access caused by `output.subtract_directions` (#294) * Fix version drift. * Bump to 0.2.0 * Fix #293. * Namedbackups (#296) * Fix version drift. * Bump to 0.2.0 * Add optional label and single field selection to backup app * remove item instead of pop@index * do not .remove() from xds_list * Simplify using some existing functionality. --------- Co-authored-by: JSKenyon Co-authored-by: landmanbester * Selectively disable MAD flagging criteria (#298) * Fix version drift. * Bump to 0.2.0 * Setting MAD threshold to zero will disable flagging on a given statistic. * Disable mad flagging on off-diagonals by default (#300) * Fix version drift. * Bump to 0.2.0 * Disable flagging based on off-diagonal correlations in the mad flagger by default. This should make the mad flagger less agressive on data with unmodelled polarised emission. * Fix bug affecting non-standard columns in `input_ms.data_column` (#301) * Fix version drift. * Bump to 0.2.0 * Fix a bug afecting the use of non-standard columns in data column input. * Don't allow restore app to overwrite metadata (#307) * assign to ms to avoid over-writing metadata in restore app * zip datasets in enumerate * add comment to document failure case * use backup_column_name in restore app * Apply OCD. --------- Co-authored-by: landmanbester Co-authored-by: JSKenyon * Fix for summary reporting SOURCE_ID as FIELD_ID (#309) * Fix version drift. * Bump to 0.2.0 * Make summary correctly report FIELD_ID and SOURCE_ID. * Fix receptor summary (#310) * Fix version drift. * Bump to 0.2.0 * Fix incorrect assumption that FEED substable will always have 2 receptors. * Fix similar problem affecting parallactic angle construction. * Update missing column selection for compatibility with upsteam changes. * Fix xarray dims (#318) * Fix version drift. * Bump to 0.2.0 * Move all usage of xds.dims[dim] to xds.sizes[dim] in preparation for change of return type in xds.dims. * Fixes for changes relating to Numba error types. (#319) * Move now-deprecated graph metrics function into the scheduler plugin code. (#320) * Make small changes to enable 3.11 compatibilty. Requires changes in stimela + a release. (#321) * Restringify keys in scheduler plugin. (#322) * Update pyproject.toml. Add poetry.lock. Update docs. (#323) * Drop 3.8. Commit poetry lock file. * Update stimela requirement. * Update docs. * Set min and max versions in pyproject.toml. * Remove python3.8 from test matrix. --------- Co-authored-by: Landman Bester Co-authored-by: landmanbester --- .github/workflows/ci.yaml | 2 +- docs/source/installation.rst | 38 +- poetry.lock | 2887 ++++++++++++++++++ pyproject.toml | 34 +- quartical/apps/backup.py | 54 +- quartical/apps/plotter.py | 335 ++ quartical/apps/summary.py | 35 +- quartical/calibration/calibrate.py | 16 +- quartical/calibration/constructor.py | 8 +- quartical/calibration/mapping.py | 2 +- quartical/config/argument_schema.yaml | 20 +- quartical/config/external.py | 4 +- quartical/config/helper.py | 3 +- quartical/data_handling/angles.py | 2 +- quartical/data_handling/bda.py | 14 +- quartical/data_handling/model_handler.py | 8 +- quartical/data_handling/ms_handler.py | 40 +- quartical/data_handling/selection.py | 12 +- quartical/executor.py | 12 +- quartical/flagging/flagging.py | 12 +- quartical/flagging/flagging_kernels.py | 9 +- quartical/gains/amplitude/kernel.py | 6 + quartical/gains/baseline.py | 4 +- quartical/gains/complex/diag_kernel.py | 6 + quartical/gains/complex/kernel.py | 6 + quartical/gains/crosshand_phase/kernel.py | 6 + quartical/gains/datasets.py | 8 +- quartical/gains/delay/kernel.py | 6 + quartical/gains/delay_and_offset/kernel.py | 6 + quartical/gains/leakage/kernel.py | 2 + quartical/gains/phase/kernel.py | 6 + quartical/gains/rotation/kernel.py | 6 + quartical/gains/rotation_measure/kernel.py | 6 + quartical/gains/tec_and_offset/kernel.py | 6 + quartical/interpolation/interpolants.py | 35 +- quartical/interpolation/interpolate.py | 8 +- quartical/scheduling/__init__.py | 76 +- quartical/statistics/logging.py | 4 +- quartical/utils/collections.py | 15 + quartical/utils/datasets.py | 36 + testing/tests/calibration/test_calibrate.py | 16 +- testing/tests/gains/test_amplitude.py | 10 +- testing/tests/gains/test_complex.py | 10 +- testing/tests/gains/test_crosshand_phase.py | 10 +- testing/tests/gains/test_delay.py | 10 +- testing/tests/gains/test_delay_and_offset.py | 10 +- testing/tests/gains/test_diag_complex.py | 10 +- testing/tests/gains/test_phase.py | 10 +- testing/tests/gains/test_rotation.py | 10 +- testing/tests/gains/test_rotation_measure.py | 10 +- testing/tests/gains/test_tec_and_offset.py | 10 +- 51 files changed, 3735 insertions(+), 176 deletions(-) create mode 100644 poetry.lock create mode 100644 quartical/apps/plotter.py create mode 100644 quartical/utils/datasets.py diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 6590d5c3..fe72c40c 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -18,7 +18,7 @@ jobs: strategy: matrix: os: [ubuntu-20.04, ubuntu-22.04] - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.9", "3.10"] steps: - name: Set up Python ${{ matrix.python-version }} diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 6cdaef14..155984e1 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -3,8 +3,11 @@ Installation This page details QuartiCal's recommended installation procedure. -Ubuntu 18.04+ -~~~~~~~~~~~~~ +Ubuntu 18.04+ via pip +~~~~~~~~~~~~~~~~~~~~~ + +This is the preferred method of installation. It is simple but may be +vulnerable to upstream changes. If you wish to install QuartiCal in a virtual environment (recommended), see `Using a virtual environment`_. @@ -25,6 +28,37 @@ QuartiCal can be installed by running the following: pip3 install -e path/to/repo/ +Ubuntu 18.04+ via poetry +~~~~~~~~~~~~~~~~~~~~~~~~ + +Installing via poetry is less simple but should always work. + +Firstly, install `poetry `_ + +Assuming you have cloned the repository from git and checked out the relevant +tag, run the following from inside the QuartiCal folder: + +.. code:: bash + + poetry install + +.. note:: + + This will automatically install QuartiCal into a new virtual environment + matching your system Python. The Python version can be changed prior to + installation using: + + .. code:: bash + + poetry env use python3.10 + +Users can enter the QuartiCal virtual environment using: + + .. code:: bash + + poetry -C path/to/repo shell + + Using a virtual environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..f9cb2208 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2887 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiobotocore" +version = "2.11.1" +description = "Async client for aws services using botocore and aiohttp" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiobotocore-2.11.1-py3-none-any.whl", hash = "sha256:904a7ad7cc8671d662cfd596906dafe839118ea2a66332c37908e3dcfdee1e45"}, + {file = "aiobotocore-2.11.1.tar.gz", hash = "sha256:0b095af50da2d6f94e93ca959e2a4876f0f0d84d534b61b21d8e050832d04ab6"}, +] + +[package.dependencies] +aiohttp = 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"Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering :: Astronomy" @@ -25,21 +24,22 @@ include = [ ] [tool.poetry.dependencies] -python = "^3.8" -tbump = "^6.10.0" -columnar = "^1.4.1" -"ruamel.yaml" = "^0.17.26" -dask = {extras = ["diagnostics"], version = "^2023.1.0"} -distributed = "^2023.1.0" -dask-ms = {extras = ["s3", "xarray", "zarr"], version = "^0.2.16"} -codex-africanus = {extras = ["dask", "scipy", "astropy", "python-casacore"], version = "^0.3.4"} -astro-tigger-lsm = "^1.7.2" -loguru = "^0.7.0" -requests = "^2.31.0" -pytest = "^7.3.1" -omegaconf = "^2.3.0" -colorama = "^0.4.6" -stimela = "2.0rc4" +python = "^3.9" +astro-tigger-lsm = ">=1.7.2, <=1.7.3" +codex-africanus = {extras = ["dask", "scipy", "astropy", "python-casacore"], version = ">=0.3.4, <=0.3.4"} +colorama = ">=0.4.6, <=0.4.6" +columnar = ">=1.4.1, <=1.4.1" +dask = {extras = ["diagnostics"], version = ">=2023.5.0, <=2023.12.1"} +dask-ms = {extras = ["s3", "xarray", "zarr"], version = ">=0.2.16, <=0.2.18"} +distributed = ">=2023.5.0, <=2023.12.1" +loguru = ">=0.7.0, <=0.7.2" +matplotlib = ">=3.5.1, <=3.8.2" +omegaconf = ">=2.3.0, <=2.3.0" +pytest = ">=7.3.1, <=7.4.4" +requests = ">=2.31.0, <=2.31.0" +"ruamel.yaml" = ">=0.17.26, <=0.17.40" +stimela = "2.0rc8" +tbump = ">=6.10.0, <=6.11.0" [tool.poetry.scripts] goquartical = 'quartical.executor:execute' @@ -47,7 +47,7 @@ goquartical-config = 'quartical.config.parser:create_user_config' goquartical-backup = 'quartical.apps.backup:backup' goquartical-restore = 'quartical.apps.backup:restore' goquartical-summary = 'quartical.apps.summary:summary' - +goquartical-plot = 'quartical.apps.plotter:plot' [build-system] requires = ["poetry-core"] diff --git a/quartical/apps/backup.py b/quartical/apps/backup.py index fcb44a69..a5325259 100644 --- a/quartical/apps/backup.py +++ b/quartical/apps/backup.py @@ -1,5 +1,6 @@ import argparse from math import prod, ceil +from quartical.data_handling.selection import filter_xds_list from daskms import xds_from_storage_ms, xds_to_storage_table from daskms.experimental.zarr import xds_to_zarr, xds_from_zarr from daskms.fsspec_store import DaskMSStore @@ -10,8 +11,9 @@ def backup(): parser = argparse.ArgumentParser( description='Backup any Measurement Set column to zarr. Backups will ' - 'be labelled automatically using the current datetime, ' - 'the Measurement Set name and the column name.' + 'be labelled using a combination of the passed in label ' + '(defaults to datetime), the Measurement Set name and ' + 'the column name.' ) parser.add_argument( @@ -33,19 +35,34 @@ def backup(): type=str, help='Name of column to be backed up.' ) + parser.add_argument( + '--label', + type=str, + help='An explicit label to include in the backup name. Defaults to ' + 'datetime at which the backup was created. Full name will be ' + 'given by [label]-[msname]-[column].bkp.qc.' + ) parser.add_argument( '--nthread', type=int, default=1, help='Number of threads to use.' ) + parser.add_argument( + '--field-id', + type=int, + help='Field ID to back up.' + ) args = parser.parse_args() ms_name = args.ms_path.full_path.rsplit("/", 1)[1] column_name = args.column_name - timestamp = time.strftime("%Y%m%d-%H%M%S") + if args.label: + label = args.label + else: + label = time.strftime("%Y%m%d-%H%M%S") # This call exists purely to get the relevant shape and dtype info. data_xds_list = xds_from_storage_ms( @@ -55,8 +72,11 @@ def backup(): group_cols=("FIELD_ID", "DATA_DESC_ID", "SCAN_NUMBER"), ) + # Use existing functionality. TODO: Improve and expose DDID selection. + xdso = filter_xds_list(data_xds_list, args.field_id) + # Compute appropriate chunks (256MB by default) to keep zarr happy. - chunks = [chunk_by_size(xds[column_name]) for xds in data_xds_list] + chunks = [chunk_by_size(xds[column_name]) for xds in xdso] # Repeat of above call but now with correct chunking information. data_xds_list = xds_from_storage_ms( @@ -67,9 +87,12 @@ def backup(): chunks=chunks ) + # Use existing functionality. TODO: Improve and expose DDID selection. + xdso = filter_xds_list(data_xds_list, args.field_id) + bkp_xds_list = xds_to_zarr( - data_xds_list, - f"{args.zarr_dir.url}::{timestamp}-{ms_name}-{column_name}.bkp.qc", + xdso, + f"{args.zarr_dir.url}::{label}-{ms_name}-{column_name}.bkp.qc", ) dask.compute(bkp_xds_list, num_workers=args.nthread) @@ -112,9 +135,26 @@ def restore(): zarr_root, zarr_name = args.zarr_path.url.rsplit("/", 1) zarr_xds_list = xds_from_zarr(f"{zarr_root}::{zarr_name}") + backup_column_name = list(zarr_xds_list[0].data_vars.keys()).pop() + + # This will fail if the column does not exist but if we allow all columns + # we need to select out the relevant dims for rechunking below + ms_xds_list = xds_from_storage_ms( + args.ms_path, + columns=(args.column_name,), + index_cols=("TIME",), + group_cols=("FIELD_ID", "DATA_DESC_ID", "SCAN_NUMBER"), + ) + + for i, (ds, dsr) in enumerate(zip(ms_xds_list, zarr_xds_list)): + dsr = dsr.chunk(ds.chunks) + data_array = getattr(dsr, backup_column_name) + ms_xds_list[i] = ds.assign( + {args.column_name: (data_array.dims, data_array.data)} + ) restored_xds_list = xds_to_storage_table( - zarr_xds_list, + ms_xds_list, args.ms_path, columns=(args.column_name,), rechunk=True diff --git a/quartical/apps/plotter.py b/quartical/apps/plotter.py new file mode 100644 index 00000000..35f18977 --- /dev/null +++ b/quartical/apps/plotter.py @@ -0,0 +1,335 @@ +import argparse +import math +import xarray +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import ticker +import matplotlib.cm as cm +from itertools import product, chain +from daskms.experimental.zarr import xds_from_zarr +from daskms.fsspec_store import DaskMSStore +from concurrent.futures import ProcessPoolExecutor, as_completed +from quartical.utils.collections import flatten +from quartical.utils.datasets import recursive_group_by_attr + + +TRANSFORMS = { + "raw": np.array, + "amplitude": np.abs, + "phase": np.angle, + "real": np.real, + "imag": np.imag +} + + +class CustomFormatter(ticker.ScalarFormatter): + + def __init__(self, *args, precision=None, **kwargs): + + super().__init__(*args, *kwargs) + + self.precision = precision + + def _set_format(self): + # set the format string to format all the ticklabels + if len(self.locs) < 2: + # Temporarily augment the locations with the axis end points. + _locs = [*self.locs, *self.axis.get_view_interval()] + else: + _locs = self.locs + locs = (np.asarray(_locs) - self.offset) / 10. ** self.orderOfMagnitude + loc_range = np.ptp(locs) + # Curvilinear coordinates can yield two identical points. + if loc_range == 0: + loc_range = np.max(np.abs(locs)) + # Both points might be zero. + if loc_range == 0: + loc_range = 1 + if len(self.locs) < 2: + # We needed the end points only for the loc_range calculation. + locs = locs[:-2] + loc_range_oom = int(math.floor(math.log10(loc_range))) + # first estimate: + sigfigs = max(0, 3 - loc_range_oom) + # refined estimate: + thresh = 1e-3 * 10 ** loc_range_oom + while sigfigs >= 0: + if np.abs(locs - np.round(locs, decimals=sigfigs)).max() < thresh: + sigfigs -= 1 + else: + break + sigfigs += 1 + sigfigs = self.precision or sigfigs + self.format = f'%{sigfigs + 3}.{sigfigs}f' + if self._usetex or self._useMathText: + self.format = r'$\mathdefault{%s}$' % self.format + + +def cli(): + + parser = argparse.ArgumentParser( + description="Rudimentary plotter for QuartiCal gain solutions." + ) + + parser.add_argument( + "input_path", + type=DaskMSStore, + help="Path to input gains, e.g. path/to/dir/G. Accepts valid s3 urls." + ) + parser.add_argument( + "output_path", + type=DaskMSStore, + help="Path to desired output location." + ) + parser.add_argument( + "--plot-var", + type=str, + default="gains", + help="Name of data variable to plot." + ) + parser.add_argument( + "--flag-var", + type=str, + default="gain_flags", + help="Name of data variable to use as flags." + ) + parser.add_argument( + "--xaxis", + type=str, + default="gain_time", + choices=("gain_time", "gain_freq", "param_time", "param_freq"), + help="Name of coordinate to use for x-axis." + ) + parser.add_argument( + "--transform", + type=str, + default="raw", + choices=list(TRANSFORMS.keys()), + help="Transform to apply to data before plotting." + ) + parser.add_argument( + "--iter-attrs", + type=str, + nargs="+", + default=["FIELD_ID", "DATA_DESC_ID", "SCAN_NUMBER"], + help=( + "Attributes (datasets) over which to iterate. Omission will " + "result in concatenation in the omitted axis i.e. omit " + "SCAN_NUMBER to to include all scans in a single plot." + ) + ) + parser.add_argument( + "--iter-axes", + type=str, + nargs="+", + default=["antenna", "direction", "correlation"], + help=( + "Axes over which to iterate when generating plots i.e. produce a " + "plot per unique combination of the specified axes." + ) + ) + parser.add_argument( + "--mean-axis", + type=str, + default=None, + help=( + "If set, will plot a heavier line to indicate the mean of the " + "plotted quantity along this axis." + ) + ) + parser.add_argument( + "--colourize-axis", + type=str, + default=None, + help="Axis to colour by." + ) + parser.add_argument( + "--time-range", + type=float, + nargs=2, + default=[None], + help="Time range to plot." + ) + parser.add_argument( + "--freq-range", + type=float, + nargs=2, + default=[None], + help="Frequency range to plot." + ) + parser.add_argument( + "--nworker", + type=int, + default=1, + help="Number of processes to use while plotting." + ) + parser.add_argument( + "--colourmap", + type=str, + default="plasma", + help=( + "Colourmap to use with --colourize-axis. Supports all matplotlib " + "colourmaps." + ) + ) + parser.add_argument( + "--fig-size", + type=float, + nargs=2, + default=[5, 5], + help="Figure size in inches. Expects two values, width and height." + ) + return parser.parse_args() + + +def to_plot_dict(xdsl, iter_attrs): + + grouped = recursive_group_by_attr(xdsl, iter_attrs) + + return { + k: xarray.combine_by_coords(v, combine_attrs="drop_conflicts") + for k, v in flatten(grouped).items() + } + + +def _plot(group, xds, args): + + xds = xds.compute(scheduler="single-threaded") + + if args.freq_range or args.time_range: + time_ax, freq_ax = xds[args.plot_var].dims[:2] + xds = xds.sel( + { + time_ax: slice(*args.time_range), + freq_ax: slice(*args.freq_range) + } + ) + + dims = xds[args.plot_var].dims # Dimensions of plot quantity. + assert all(map(lambda x: x in dims, args.iter_axes)), ( + f"Some or all of {args.iter_axes} are not present on " + f"{args.plot_var}." + ) + + # Grab the required transform from the dict. + transform = TRANSFORMS[args.transform] + + # NOTE: This mututates the data variables in place. + data = xds[args.plot_var].values + flags = xds[args.flag_var].values + data[np.where(flags)] = np.nan # Set flagged values to nan (not plotted). + xds = xds.drop_vars(args.flag_var) # No more use for flags. + + # Construct list of lists containing axes over which we iterate i.e. + # produce a plot per combination of these values. + iter_axes_itr = [xds[x].values.tolist() for x in args.iter_axes] + + # Figure out axes included in a single plot. + excluded_dims = {*args.iter_axes, args.xaxis} + agg_axes = [d for d in dims if d not in excluded_dims] + agg_axes_itr = [range(xds.sizes[x]) for x in agg_axes] + + # Figure out axes included in a single plot after taking the mean. + excluded_dims = {*args.iter_axes, args.xaxis, args.mean_axis} + mean_agg_axes = [d for d in dims if d not in excluded_dims] + mean_agg_axes_itr = [range(xds.sizes[x]) for x in mean_agg_axes] + + if args.colourize_axis: + n_colour = xds.sizes[args.colourize_axis] + colourmap = cm.get_cmap(args.colourmap) + colours = [colourmap(i / n_colour) for i in range(n_colour)] + else: + n_colour = 2 + colours = ["k", "r"] + + fig, ax = plt.subplots(figsize=args.fig_size) + + for ia in product(*iter_axes_itr): + + sel = {ax: val for ax, val in zip(args.iter_axes, ia)} + + xda = xds.sel(sel)[args.plot_var] + + ax.clear() + + for aa in product(*agg_axes_itr): + + subsel = {ax: val for ax, val in zip(agg_axes, aa)} + pxda = xda.isel(subsel) + + ax.plot( + pxda[args.xaxis].values, + transform(pxda.values), + color=colours[subsel.get(args.colourize_axis, 0)], + linewidth=0.1 + ) + + if args.mean_axis: + + mxda = xda.mean(args.mean_axis) + + for ma in product(*mean_agg_axes_itr): + + subsel = {ax: val for ax, val in zip(mean_agg_axes, ma)} + pxda = mxda.isel(subsel) + + ax.plot( + pxda[args.xaxis].values, + transform(pxda.values), + color=colours[subsel.get(args.colourize_axis, 1)] + ) + + ax.title.set_text("\n".join([f"{k}: {v}" for k, v in sel.items()])) + ax.title.set_fontsize("medium") + ax.set_xlabel(f"{args.xaxis}") + ax.set_ylabel(f"{args.transform}({ia[-1]})") + + formatter = CustomFormatter(precision=2) + formatter.set_scientific(True) + formatter.set_powerlimits((0, 0)) + ax.yaxis.set_major_formatter(formatter) + ax.yaxis.set_major_locator(ticker.LinearLocator(numticks=5)) + + fig_name = "-".join(map(str, chain.from_iterable(sel.items()))) + + root_subdir = f"{xds.NAME}-{args.plot_var}-{args.transform}" + leaf_subdir = "-".join(group) + subdir_path = f"{root_subdir}/{leaf_subdir}" + + args.output_path.makedirs(subdir_path, exist_ok=True) + + fig.savefig( + f"{args.output_path.full_path}/{subdir_path}/{fig_name}.png", + bbox_inches="tight" # SLOW, but slightly unavoidable. + ) + + plt.close() + + +def plot(): + + args = cli() + + non_colourizable_axes = {*args.iter_axes, args.mean_axis, args.xaxis} + if args.colourize_axis and args.colourize_axis in non_colourizable_axes: + raise ValueError(f"Cannot colourize using axis {args.colourize_axis}.") + + # Path to gain location. + gain_path = DaskMSStore("::".join(args.input_path.url.rsplit("/", 1))) + + xdsl = xds_from_zarr(gain_path) + + # Select only the necessary fields for plotting on each dataset. + xdsl = [xds[[args.plot_var, args.flag_var]] for xds in xdsl] + + # Partitioned dictionary of xarray.Datasets. + xdsd = to_plot_dict(xdsl, args.iter_attrs) + + with ProcessPoolExecutor(max_workers=args.nworker) as ppe: + futures = [ppe.submit(_plot, k, xds, args) for k, xds in xdsd.items()] + + for future in as_completed(futures): + try: + future.result() + except Exception as exc: + print(f"Exception raised in process pool: {exc}") diff --git a/quartical/apps/summary.py b/quartical/apps/summary.py index c6e5d915..0ac071c6 100644 --- a/quartical/apps/summary.py +++ b/quartical/apps/summary.py @@ -99,7 +99,7 @@ def feed_info(path): for i, arrs in enumerate(zipper): for vals in zip(*arrs): msg += f" {i:<4} {vals[0]:<8} {' '.join(vals[1]):<8} " \ - f"{'{:.4f} {:.4f}'.format(*vals[2]):<16}\n" + f"{' '.join([f'{x:.4f}' for x in vals[2]]):<16}\n" logger.info(msg) @@ -116,25 +116,32 @@ def field_info(path): field_xds = xds_from_storage_table(path + "::FIELD")[0] - ids = [i for i in field_xds.SOURCE_ID.values] + field_ids = list(range(field_xds.sizes['row'])) + source_ids = [i for i in field_xds.SOURCE_ID.values] names = [n for n in field_xds.NAME.values] phase_dirs = [pd for pd in field_xds.PHASE_DIR.values] ref_dirs = [rd for rd in field_xds.REFERENCE_DIR.values] delay_dirs = [dd for dd in field_xds.REFERENCE_DIR.values] msg = "Field summary:\n" - msg += " {:<4} {:<16} {:<16} {:<16} {:<16}\n".format("ID", "NAME", - "PHASE_DIR", - "REF_DIR", - "DELAY_DIR") + msg += " {:<10} {:<10} {:<16} {:<16} {:<16} {:<16}\n".format( + "FIELD_ID", + "SOURCE_ID", + "NAME", + "PHASE_DIR", + "REF_DIR", + "DELAY_DIR" + ) - zipper = zip(ids, names, phase_dirs, ref_dirs, delay_dirs) + zipper = zip( + field_ids, source_ids, names, phase_dirs, ref_dirs, delay_dirs + ) for vals in zipper: - msg += f" {vals[0]:<4} {vals[1]:<16} " \ - f"{'{:.4f} {:.4f}'.format(*vals[2][0]):<16} " \ + msg += f" {vals[0]:<10} {vals[1]:<10} {vals[2]:<16} " \ f"{'{:.4f} {:.4f}'.format(*vals[3][0]):<16} " \ - f"{'{:.4f} {:.4f}'.format(*vals[4][0]):<16}\n" + f"{'{:.4f} {:.4f}'.format(*vals[4][0]):<16} " \ + f"{'{:.4f} {:.4f}'.format(*vals[5][0]):<16}\n" logger.info(msg) @@ -189,7 +196,7 @@ def spw_info(path): chunks={"row": 1, "chan": -1} ) - n_chan_per_spw = [xds.dims["chan"] for xds in spw_xds_list] + n_chan_per_spw = [xds.sizes["chan"] for xds in spw_xds_list] bw_per_spw = [xds.TOTAL_BANDWIDTH.values.item() for xds in spw_xds_list] @@ -234,9 +241,9 @@ def pointing_info(path): def dimension_summary(xds_list): - rows_per_xds = [xds.dims["row"] for xds in xds_list] - chan_per_xds = [xds.dims["chan"] for xds in xds_list] - corr_per_xds = [xds.dims["corr"] for xds in xds_list] + rows_per_xds = [xds.sizes["row"] for xds in xds_list] + chan_per_xds = [xds.sizes["chan"] for xds in xds_list] + corr_per_xds = [xds.sizes["corr"] for xds in xds_list] utime_per_xds = [np.unique(xds.TIME.values).size for xds in xds_list] diff --git a/quartical/calibration/calibrate.py b/quartical/calibration/calibrate.py index 83ed022b..b8439c02 100644 --- a/quartical/calibration/calibrate.py +++ b/quartical/calibration/calibrate.py @@ -134,7 +134,7 @@ def add_calibration_graph( """ # TODO: Does this check belong here or elsewhere? - have_dd_model = any(xds.dims['dir'] > 1 for xds in data_xds_list) + have_dd_model = any(xds.sizes['dir'] > 1 for xds in data_xds_list) have_dd_chain = any(term.direction_dependent for term in chain) if have_dd_model and not have_dd_chain: @@ -253,6 +253,18 @@ def make_visibility_output( itr = enumerate(zip(data_xds_list, mapping_xds_list)) + if output_opts.subtract_directions: + n_dir = data_xds_list[0].sizes['dir'] # Should be the same over xdss. + requested = set(output_opts.subtract_directions) + valid = set(range(n_dir)) + invalid = requested - valid + if invalid: + raise ValueError( + f"User has specified output.subtract_directions as " + f"{requested} but the following directions are not present " + f"in the model: {invalid}." + ) + for xds_ind, (data_xds, mapping_xds) in itr: data_col = data_xds.DATA.data model_col = data_xds.MODEL_DATA.data @@ -271,7 +283,7 @@ def make_visibility_output( [mapping_xds.get(f"{k}_dir_map").data for k in gain_terms.keys()] ) - corr_mode = data_xds.dims["corr"] + corr_mode = data_xds.sizes["corr"] is_bda = hasattr(data_xds, "ROW_MAP") # We are dealing with BDA. row_map = data_xds.ROW_MAP.data if is_bda else None diff --git a/quartical/calibration/constructor.py b/quartical/calibration/constructor.py index 42e8ac3d..ec6b6412 100644 --- a/quartical/calibration/constructor.py +++ b/quartical/calibration/constructor.py @@ -51,7 +51,7 @@ def construct_solver( weight_col = data_xds.WEIGHT.data flag_col = data_xds.FLAG.data gain_terms = gain_xds_lod[xds_ind] - corr_mode = data_xds.dims["corr"] + corr_mode = data_xds.sizes["corr"] block_id_arr = get_block_id_arr(data_col) aux_block_info = { @@ -77,7 +77,7 @@ def construct_solver( blocker.add_input( v.name, v.data, - ("row",) if v.dims == ("time",) else v.dims + ("row",) if set(v.dims) == {"time"} else v.dims ) blocker.add_input( @@ -272,8 +272,8 @@ def expand_specs(gain_terms): # represents frequency chunks and the inner-most list contains the # specs per term. Might be possible to do this with arrays instead. - n_t_chunks = set(xds.dims["time_chunk"] for xds in gain_terms.values()) - n_f_chunks = set(xds.dims["freq_chunk"] for xds in gain_terms.values()) + n_t_chunks = set(xds.sizes["time_chunk"] for xds in gain_terms.values()) + n_f_chunks = set(xds.sizes["freq_chunk"] for xds in gain_terms.values()) assert len(n_t_chunks) == 1, "Chunking in time is inconsistent." assert len(n_f_chunks) == 1, "Chunking in freq is inconsistent." diff --git a/quartical/calibration/mapping.py b/quartical/calibration/mapping.py index be5b4056..4607977b 100644 --- a/quartical/calibration/mapping.py +++ b/quartical/calibration/mapping.py @@ -63,7 +63,7 @@ def make_mapping_datasets(data_xds_list, chain): freq_interval ) - n_dir = data_xds.dims["dir"] + n_dir = data_xds.sizes["dir"] dir_map = gain_obj.make_dir_map( n_dir, diff --git a/quartical/config/argument_schema.yaml b/quartical/config/argument_schema.yaml index 5e5ad044..52c78af9 100644 --- a/quartical/config/argument_schema.yaml +++ b/quartical/config/argument_schema.yaml @@ -288,26 +288,36 @@ mad_flags: Multiplicative factor which determines whether or not a chi-squared value is considered to deviate significantly from the median of a given baseline. Values greater than MAD_bl*threshold_bl will be - flagged. + flagged. Set to zero to disable flagging on this statistic. threshold_global: dtype: float - default: 5 + default: 10 info: Multiplicative factor which determines whether or not a chi-squared value is considered to deviate significantly from the median of a given data chunk. Values greater than MAD*threshold_global will be - flagged. + flagged. Set to zero to disable flagging on this statistic. max_deviation: dtype: float - default: 5 + default: 0 info: Multiplicative factor which determines whether or not the MAD estimate on a given baseline is considered to deviate too much from the global MAD estimate. If the MAD estimate over all baselines is X, and the MAD estimate on a specific baseline is X_bl, baselines for which - X_bl > max_deviation*X will be flagged. + X_bl > max_deviation*X will be flagged. Set to zero to disable flagging + on this statistic. + + use_off_diagonals: + dtype: bool + default: False + info: + Controls whether or not the mad flagger will be run on the off-diagonal + elements of the residual. This is disabled by default as the residual + will tend to contain structure in the absence of a polarised model and + adequate leakage calibration. solver: terms: diff --git a/quartical/config/external.py b/quartical/config/external.py index ce426e05..de7dbd39 100644 --- a/quartical/config/external.py +++ b/quartical/config/external.py @@ -1,4 +1,4 @@ -from dataclasses import make_dataclass +from dataclasses import make_dataclass, field from omegaconf import OmegaConf as oc from typing import Dict, Any from scabha.cargo import Parameter @@ -20,7 +20,7 @@ def finalize_structure(additional_config): FinalConfig = make_dataclass( "FinalConfig", - [(t, Gain, Gain()) for t in terms], + [(t, Gain, field(default_factory=Gain)) for t in terms], bases=(BaseConfig,) ) diff --git a/quartical/config/helper.py b/quartical/config/helper.py index f4885da6..d81db760 100644 --- a/quartical/config/helper.py +++ b/quartical/config/helper.py @@ -81,7 +81,8 @@ def help(): # Include a generic gain term in the help config. additional_config = [oc.from_dotlist(["solver.terms=['gain']"])] - HelpConfig = finalize_structure(additional_config) + help_class = finalize_structure(additional_config) + HelpConfig = help_class() if len(sys.argv) == 1 or help_arg == "help": print_help(HelpConfig) diff --git a/quartical/data_handling/angles.py b/quartical/data_handling/angles.py index 343ab466..62ca7c34 100644 --- a/quartical/data_handling/angles.py +++ b/quartical/data_handling/angles.py @@ -75,7 +75,7 @@ def make_parangle_xds_list(ms_path, data_xds_list): coords={ "utime": np.arange(sum(xds.UTIME_CHUNKS)), "ant": xds.ant, - "receptor": np.arange(2) + "receptor": np.arange(feedtab.sizes['receptors']) }, attrs={ "FEED_TYPE": feed_type, diff --git a/quartical/data_handling/bda.py b/quartical/data_handling/bda.py index 83b0d023..466d65f3 100644 --- a/quartical/data_handling/bda.py +++ b/quartical/data_handling/bda.py @@ -64,7 +64,7 @@ def process_bda_input(data_xds_list, spw_xds_list, weight_column): "WEIGHT_SPECTRUM for BDA data.") # Figure out the highest frequency resolution and its DDID. - spw_dims = {i: xds.dims["chan"] for i, xds in enumerate(spw_xds_list)} + spw_dims = {i: xds.sizes["chan"] for i, xds in enumerate(spw_xds_list)} max_nchan_ddid = max(spw_dims, key=spw_dims.get) max_nchan = spw_dims[max_nchan_ddid] @@ -72,7 +72,7 @@ def process_bda_input(data_xds_list, spw_xds_list, weight_column): for xds in data_xds_list: - upsample_factor = max_nchan//xds.dims["chan"] + upsample_factor = max_nchan//xds.sizes["chan"] weight_col = xds.WEIGHT_SPECTRUM.data @@ -83,7 +83,7 @@ def process_bda_input(data_xds_list, spw_xds_list, weight_column): weight_col/upsample_factor)}) # Create a selection which will upsample the frequency axis. - selection = np.repeat(np.arange(xds.dims["chan"]), upsample_factor) + selection = np.repeat(np.arange(xds.sizes["chan"]), upsample_factor) bda_xds = bda_xds.sel({"chan": selection}) @@ -101,7 +101,7 @@ def process_bda_input(data_xds_list, spw_xds_list, weight_column): bda_xds_list = [xarray.concat(xdss, dim="row") for xdss in xds_merge_list] - bda_xds_list = [xds.chunk({"row": xds.dims["row"]}) + bda_xds_list = [xds.chunk({"row": xds.sizes["row"]}) for xds in bda_xds_list] # This should guarantee monotonicity in time (not baseline). @@ -223,10 +223,10 @@ def process_bda_output(xds_list, ref_xds_list, output_cols): chan_ind = dims.index('chan') - nchan = xds.dims['chan'] - ref_nchan = ref_xds.dims['chan'] + nchan = xds.sizes['chan'] + ref_nchan = ref_xds.sizes['chan'] - shape[chan_ind: chan_ind + 1] = [ref_xds.dims['chan'], -1] + shape[chan_ind: chan_ind + 1] = [ref_xds.sizes['chan'], -1] data = data.reshape(shape) diff --git a/quartical/data_handling/model_handler.py b/quartical/data_handling/model_handler.py index 9871c5ad..07f2d3a7 100644 --- a/quartical/data_handling/model_handler.py +++ b/quartical/data_handling/model_handler.py @@ -56,7 +56,7 @@ def add_model_graph( # which require them. P Jones is applied to predicted components # internally, so we only need to consider model columns for now. - n_corr = {xds.dims["corr"] for xds in data_xds_list}.pop() + n_corr = {xds.sizes["corr"] for xds in data_xds_list}.pop() if model_opts.apply_p_jones: # NOTE: Applying parallactic angle when there are fewer than four @@ -183,9 +183,9 @@ def assign_identity_model(data_xds_list): model_dims = [ ( - xds.dims['row'], - xds.dims['chan'], - xds.dims['corr'] + xds.sizes['row'], + xds.sizes['chan'], + xds.sizes['corr'] ) for xds in data_xds_list ] diff --git a/quartical/data_handling/ms_handler.py b/quartical/data_handling/ms_handler.py index 3b923432..3566deff 100644 --- a/quartical/data_handling/ms_handler.py +++ b/quartical/data_handling/ms_handler.py @@ -30,7 +30,7 @@ def read_xds_list(model_columns, ms_opts): antenna_xds = xds_from_storage_table(ms_opts.path + "::ANTENNA")[0] - n_ant = antenna_xds.dims["row"] + n_ant = antenna_xds.sizes["row"] logger.info("Antenna table indicates {} antennas were present for this " "observation.", n_ant) @@ -89,19 +89,27 @@ def read_xds_list(model_columns, ms_opts): if ms_opts.weight_column not in known_weight_cols: schema[ms_opts.weight_column] = {'dims': ('chan', 'corr')} - try: - data_xds_list = xds_from_storage_ms( - ms_opts.path, - columns=columns, - index_cols=("TIME",), - group_cols=ms_opts.group_by, - chunks=chunking_per_data_xds, - table_schema=["MS", {**schema}] + known_data_cols = ("DATA", "CORRECTED_DATA") + if ms_opts.data_column not in known_data_cols: + schema[ms_opts.data_column] = {'dims': ('chan', 'corr')} + + data_xds_list = xds_from_storage_ms( + ms_opts.path, + columns=columns, + index_cols=("TIME",), + group_cols=ms_opts.group_by, + chunks=chunking_per_data_xds, + table_schema=["MS", {**schema}] + ) + + missing_columns = set().union( + *[set(columns) - set(xds.data_vars.keys()) for xds in data_xds_list] + ) + + if missing_columns: + raise ValueError( + f"Invalid/missing column specified as input: {missing_columns}." ) - except RuntimeError as e: - raise RuntimeError( - f"Invalid/missing column specified. Underlying error: {e}." - ) from e spw_xds_list = xds_from_storage_table( ms_opts.path + "::SPECTRAL_WINDOW", @@ -131,7 +139,7 @@ def read_xds_list(model_columns, ms_opts): for xds_ind, xds in enumerate(data_xds_list): # Add coordinates to the xarray datasets. _xds = xds.assign_coords({"corr": corr_types, - "chan": np.arange(xds.dims["chan"]), + "chan": np.arange(xds.sizes["chan"]), "ant": ant_names}) # Add the actual channel frequecies to the xds - this is in preparation @@ -228,7 +236,7 @@ def write_xds_list(xds_list, ref_xds_list, ms_path, output_opts): xds = xds.isel(corr=u_corr_ind) # If the xds has fewer correlations than the measurement set, reindex. - if xds.dims["corr"] < ms_n_corr: + if xds.sizes["corr"] < ms_n_corr: xds = xds.reindex(corr=corr_types, fill_value=0) # Do some special handling on the flag column if we reindexed - @@ -401,7 +409,7 @@ def postprocess_xds_list(data_xds_list, parangle_xds_list, output_opts): data with postprocessing operations applied. """ - n_corr = {xds.dims["corr"] for xds in data_xds_list}.pop() + n_corr = {xds.sizes["corr"] for xds in data_xds_list}.pop() if output_opts.apply_p_jones_inv: # NOTE: Applying parallactic angle when there are fewer than four diff --git a/quartical/data_handling/selection.py b/quartical/data_handling/selection.py index 4f6f9301..bd0f66c3 100644 --- a/quartical/data_handling/selection.py +++ b/quartical/data_handling/selection.py @@ -1,7 +1,13 @@ -def filter_xds_list(xds_list, fields, ddids): +def filter_xds_list(xds_list, fields=[], ddids=[]): - filter_fields = {"FIELD_ID": fields, - "DATA_DESC_ID": ddids} + # If we specify an int, make it a list. Might be worth improving. + fields = [fields] if isinstance(fields, int) else fields + ddids = [ddids] if isinstance(ddids, int) else ddids + + filter_fields = { + "FIELD_ID": fields, + "DATA_DESC_ID": ddids + } for k, v in filter_fields.items(): fil = filter(lambda xds: getattr(xds, k) in v, xds_list) diff --git a/quartical/executor.py b/quartical/executor.py index ed0bcec7..06fc85ae 100644 --- a/quartical/executor.py +++ b/quartical/executor.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- from contextlib import ExitStack +import os import sys from loguru import logger import dask @@ -68,9 +69,14 @@ def _execute(exitstack): # distributed enviroment. This *may* be dangerous. Monitor. dask.config.set({"distributed.worker.daemon": False}) - if dask_opts.address: - logger.info("Initializing distributed client.") - client = exitstack.enter_context(Client(dask_opts.address)) + address = dask_opts.address or os.environ.get("DASK_SCHEDULER_ADDRESS") + + if address: + logger.info( + f"Initializing distributed client using scheduler address: " + f"{address}" + ) + client = exitstack.enter_context(Client(address)) else: logger.info("Initializing distributed client using LocalCluster.") cluster = LocalCluster( diff --git a/quartical/flagging/flagging.py b/quartical/flagging/flagging.py index 9755c40b..f5100ce2 100644 --- a/quartical/flagging/flagging.py +++ b/quartical/flagging/flagging.py @@ -123,6 +123,8 @@ def valid_median(arr): def add_mad_graph(data_xds_list, mad_opts): + diag_corrs = ['RR', 'LL', 'XX', 'YY'] + bl_thresh = mad_opts.threshold_bl gbl_thresh = mad_opts.threshold_global max_deviation = mad_opts.max_deviation @@ -143,10 +145,17 @@ def add_mad_graph(data_xds_list, mad_opts): flag_col = xds.FLAG.data ant1_col = xds.ANTENNA1.data ant2_col = xds.ANTENNA2.data - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] n_bl_w_autos = (n_ant * (n_ant - 1))/2 + n_ant n_t_chunk, n_f_chunk, _ = residuals.numblocks + if mad_opts.use_off_diagonals: + corr_sel = tuple(np.arange(residuals.shape[-1])) + else: + corr_sel = tuple( + [i for i, c in enumerate(xds.corr.values) if c in diag_corrs] + ) + wres = da.blockwise( compute_whitened_residual, ("rowlike", "chan", "corr"), residuals, ("rowlike", "chan", "corr"), @@ -228,6 +237,7 @@ def add_mad_graph(data_xds_list, mad_opts): gbl_thresh, None, bl_thresh, None, max_deviation, None, + corr_sel, None, n_ant, None, dtype=np.int8, align_arrays=False, diff --git a/quartical/flagging/flagging_kernels.py b/quartical/flagging/flagging_kernels.py index c6a24a8b..21d7eb8a 100644 --- a/quartical/flagging/flagging_kernels.py +++ b/quartical/flagging/flagging_kernels.py @@ -99,6 +99,7 @@ def compute_mad_flags( gbl_threshold, bl_threshold, max_deviation, + corr_sel, n_ant ): @@ -110,11 +111,11 @@ def compute_mad_flags( scale_factor = 1.4826 - bl_threshold2 = bl_threshold ** 2 - gbl_threshold2 = gbl_threshold ** 2 - max_deviation2 = max_deviation ** 2 + bl_threshold2 = bl_threshold ** 2 or np.inf + gbl_threshold2 = gbl_threshold ** 2 or np.inf + max_deviation2 = max_deviation ** 2 or np.inf - for corr in range(n_corr): + for corr in corr_sel: gbl_mad_real = gbl_mad_and_med_real[0, 0, corr, 0] * scale_factor gbl_med_real = gbl_mad_and_med_real[0, 0, corr, 1] diff --git a/quartical/gains/amplitude/kernel.py b/quartical/gains/amplitude/kernel.py index 2f37b752..b197d601 100644 --- a/quartical/gains/amplitude/kernel.py +++ b/quartical/gains/amplitude/kernel.py @@ -227,6 +227,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -449,6 +451,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -505,6 +509,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/gains/baseline.py b/quartical/gains/baseline.py index 133bc27e..54352ca1 100644 --- a/quartical/gains/baseline.py +++ b/quartical/gains/baseline.py @@ -37,7 +37,7 @@ def compute_baseline_corrections( weight_col = data_xds._WEIGHT.data # The weights exiting the solver. ant1_col = data_xds.ANTENNA1.data ant2_col = data_xds.ANTENNA2.data - corr_mode = data_xds.dims["corr"] + corr_mode = data_xds.sizes["corr"] time_maps = tuple( [mapping_xds.get(f"{k}_time_map").data for k in gain_dict.keys()] @@ -63,7 +63,7 @@ def compute_baseline_corrections( req_args.extend([fm, ("gain_freq",)]) req_args.extend([dm, ("direction",)]) - n_ant = data_xds.dims["ant"] + n_ant = data_xds.sizes["ant"] n_bla = int((n_ant*(n_ant - 1))/2 + n_ant) bl_corr = da.blockwise( diff --git a/quartical/gains/complex/diag_kernel.py b/quartical/gains/complex/diag_kernel.py index dc45c38e..8bd6ba59 100644 --- a/quartical/gains/complex/diag_kernel.py +++ b/quartical/gains/complex/diag_kernel.py @@ -206,6 +206,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -428,6 +430,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -484,6 +488,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) def impl( diff --git a/quartical/gains/complex/kernel.py b/quartical/gains/complex/kernel.py index 446a946a..f8ca97dc 100644 --- a/quartical/gains/complex/kernel.py +++ b/quartical/gains/complex/kernel.py @@ -207,6 +207,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -429,6 +431,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -485,6 +489,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) def impl( diff --git a/quartical/gains/crosshand_phase/kernel.py b/quartical/gains/crosshand_phase/kernel.py index 74fb5678..53adfff4 100644 --- a/quartical/gains/crosshand_phase/kernel.py +++ b/quartical/gains/crosshand_phase/kernel.py @@ -225,6 +225,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -453,6 +455,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -509,6 +513,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/gains/datasets.py b/quartical/gains/datasets.py index 5d686196..f35c5384 100644 --- a/quartical/gains/datasets.py +++ b/quartical/gains/datasets.py @@ -192,7 +192,7 @@ def populate_net_xds_list( net_xds = net_gains[net_name] - net_shape = tuple(net_xds.dims[d] for d in net_xds.GAIN_AXES) + net_shape = tuple(net_xds.sizes[d] for d in net_xds.GAIN_AXES) corr_mode = net_shape[-1] @@ -401,7 +401,7 @@ def scaffold_from_data_xds(data_xds, gain_obj): freq_chunks = gain_obj.make_freq_chunks(freq_map) - n_dir = data_xds.dims["dir"] + n_dir = data_xds.sizes["dir"] dir_map = gain_obj.make_dir_map( n_dir, @@ -418,8 +418,8 @@ def scaffold_from_data_xds(data_xds, gain_obj): id_attrs = {f: data_xds.attrs[f] for f, _ in partition_schema} # TODO: Move this the the gain object? - n_corr = data_xds.dims["corr"] - n_ant = data_xds.dims["ant"] + n_corr = data_xds.sizes["corr"] + n_ant = data_xds.sizes["ant"] chunk_spec = gain_spec_tup( time_chunks, freq_chunks, diff --git a/quartical/gains/delay/kernel.py b/quartical/gains/delay/kernel.py index d95efa02..209da746 100644 --- a/quartical/gains/delay/kernel.py +++ b/quartical/gains/delay/kernel.py @@ -254,6 +254,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -493,6 +495,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -553,6 +557,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/gains/delay_and_offset/kernel.py b/quartical/gains/delay_and_offset/kernel.py index eebc09e1..70e41d95 100644 --- a/quartical/gains/delay_and_offset/kernel.py +++ b/quartical/gains/delay_and_offset/kernel.py @@ -248,6 +248,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -487,6 +489,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -547,6 +551,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/gains/leakage/kernel.py b/quartical/gains/leakage/kernel.py index b9cbc6e6..8fa6921a 100644 --- a/quartical/gains/leakage/kernel.py +++ b/quartical/gains/leakage/kernel.py @@ -200,6 +200,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) def impl( diff --git a/quartical/gains/phase/kernel.py b/quartical/gains/phase/kernel.py index 50a03cc5..7fa76da0 100644 --- a/quartical/gains/phase/kernel.py +++ b/quartical/gains/phase/kernel.py @@ -232,6 +232,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -460,6 +462,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -516,6 +520,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/gains/rotation/kernel.py b/quartical/gains/rotation/kernel.py index f18fca70..66cc58c2 100644 --- a/quartical/gains/rotation/kernel.py +++ b/quartical/gains/rotation/kernel.py @@ -228,6 +228,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -463,6 +465,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -521,6 +525,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) if corr_mode.literal_value == 4: diff --git a/quartical/gains/rotation_measure/kernel.py b/quartical/gains/rotation_measure/kernel.py index 8dc2dc5f..3c935f23 100644 --- a/quartical/gains/rotation_measure/kernel.py +++ b/quartical/gains/rotation_measure/kernel.py @@ -234,6 +234,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -474,6 +476,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -534,6 +538,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) if corr_mode.literal_value == 4: diff --git a/quartical/gains/tec_and_offset/kernel.py b/quartical/gains/tec_and_offset/kernel.py index 155ac67e..dd3d95ac 100644 --- a/quartical/gains/tec_and_offset/kernel.py +++ b/quartical/gains/tec_and_offset/kernel.py @@ -250,6 +250,8 @@ def nb_compute_jhj_jhr( corr_mode ): + coerce_literal(nb_compute_jhj_jhr, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. row_weights_idx = ms_inputs.fields.index('ROW_WEIGHTS') row_weights_type = ms_inputs[row_weights_idx] @@ -490,6 +492,8 @@ def compute_update(native_imdry, corr_mode): @overload(compute_update, jit_options=PARALLEL_JIT_OPTIONS) def nb_compute_update(native_imdry, corr_mode): + coerce_literal(nb_compute_update, ["corr_mode"]) + # We want to dispatch based on this field so we need its type. jhj = native_imdry[native_imdry.fields.index('jhj')] @@ -550,6 +554,8 @@ def nb_finalize_update( corr_mode ): + coerce_literal(nb_finalize_update, ["corr_mode"]) + set_identity = factories.set_identity_factory(corr_mode) param_to_gain = param_to_gain_factory(corr_mode) diff --git a/quartical/interpolation/interpolants.py b/quartical/interpolation/interpolants.py index 0c384645..6e70733e 100644 --- a/quartical/interpolation/interpolants.py +++ b/quartical/interpolation/interpolants.py @@ -25,8 +25,8 @@ def linear2d_interpolate_gains(source_xds, target_xds): i_t_axis, i_f_axis = source_xds.GAIN_AXES[:2] t_t_axis, t_f_axis = target_xds.GAIN_AXES[:2] - i_t_dim = source_xds.dims[i_t_axis] - i_f_dim = source_xds.dims[i_f_axis] + i_t_dim = source_xds.sizes[i_t_axis] + i_f_dim = source_xds.sizes[i_f_axis] interp_axes = {} @@ -35,11 +35,32 @@ def linear2d_interpolate_gains(source_xds, target_xds): if i_f_dim > 1: interp_axes[i_f_axis] = target_xds[t_f_axis].data - target_xda = source_xds.params.interp( + # NOTE: The below is the path of least resistance but may not be the most + # efficient method for this mixed-mode interpolation - it may be possible + # to do better using multiple RegularGridInterpoator objects. + + # Interpolate using linear interpolation, filling points outside the + # domain with NaNs. + in_domain_xda = source_xds.params.interp( + interp_axes, + kwargs={"fill_value": np.nan} + ) + + # Interpolate using nearest-neighbour interpolation, extrapolating points + # outside the domain. + out_domain_xda = source_xds.params.interp( interp_axes, + method="nearest", kwargs={"fill_value": "extrapolate"} ) + # Combine the linear and nearest neighbour interpolation done above i.e. + # use use linear interpolation inside the domain and nearest-neighbour + # interpolation anywhere extrapolation was required. + target_xda = in_domain_xda.where( + da.isfinite(in_domain_xda), out_domain_xda + ) + if i_t_dim == 1: target_xda = target_xda.reindex( {i_t_axis: target_xds[t_t_axis].data}, @@ -103,12 +124,12 @@ def spline2d_interpolate_gains(source_xds, target_xds): i_t_axis, i_f_axis = source_xds.GAIN_AXES[:2] t_t_axis, t_f_axis = target_xds.GAIN_AXES[:2] - if source_xds.dims[i_t_axis] < 4 or source_xds.dims[i_f_axis] < 4: + if source_xds.sizes[i_t_axis] < 4 or source_xds.sizes[i_f_axis] < 4: raise ValueError( f"Cubic spline interpolation requires at least four " f"values along an axis. After concatenation, the " f"(time, freq) dimensions of the interpolating dataset were " - f"{(source_xds.dims[i_t_axis], source_xds.dims[i_f_axis])}." + f"{(source_xds.sizes[i_t_axis], source_xds.sizes[i_f_axis])}." ) interp_arr = da.blockwise( @@ -120,8 +141,8 @@ def spline2d_interpolate_gains(source_xds, target_xds): target_xds[t_f_axis].values, None, dtype=np.float64, adjust_chunks={ - "t": target_xds.dims[t_t_axis], - "f": target_xds.dims[t_f_axis] + "t": target_xds.sizes[t_t_axis], + "f": target_xds.sizes[t_f_axis] } ) diff --git a/quartical/interpolation/interpolate.py b/quartical/interpolation/interpolate.py index b37b39e1..417c3747 100644 --- a/quartical/interpolation/interpolate.py +++ b/quartical/interpolation/interpolate.py @@ -90,7 +90,7 @@ def load_and_interpolate_gains(gain_xds_lod, chain, output_directory): ) # Remove time/chan chunking and rechunk by antenna. - merged_xds = merged_xds.chunk({**merged_xds.dims, "antenna": 1}) + merged_xds = merged_xds.chunk({**merged_xds.sizes, "antenna": 1}) # Create a converter object to handle moving between native and # interpolation representations. @@ -221,11 +221,11 @@ def reindex_and_rechunk(interpolated_xds, reference_xds): # If we are loading a term with a differing number of correlations, # this should handle selecting them out/padding them in. - if interpolated_xds.dims[axis] < reference_xds.dims[axis]: + if interpolated_xds.sizes[axis] < reference_xds.sizes[axis]: interpolated_xds = interpolated_xds.reindex( {"correlation": reference_xds[axis]}, fill_value=0 ) - elif interpolated_xds.dims[axis] > reference_xds.dims[axis]: + elif interpolated_xds.sizes[axis] > reference_xds.sizes[axis]: interpolated_xds = interpolated_xds.sel( {"correlation": reference_xds[axis]} ) @@ -240,7 +240,7 @@ def reindex_and_rechunk(interpolated_xds, reference_xds): { t_t_axis: t_chunks, t_f_axis: f_chunks, - "antenna": interpolated_xds.dims["antenna"] + "antenna": interpolated_xds.sizes["antenna"] } ) diff --git a/quartical/scheduling/__init__.py b/quartical/scheduling/__init__.py index 5a6b7932..de66bf8a 100644 --- a/quartical/scheduling/__init__.py +++ b/quartical/scheduling/__init__.py @@ -3,7 +3,7 @@ from distributed import SchedulerPlugin from dask.core import reverse_dict from dask.base import tokenize -from dask.order import graph_metrics, ndependencies +from dask.order import ndependencies def install_plugin(dask_scheduler=None, **kwargs): @@ -96,8 +96,11 @@ def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, # Create a unique token for each set of partition roots. TODO: This is # very strict. What about nodes with very similar roots? Tokenization # may be overkill too. - root_tokens = {tokenize(*sorted(v)): v for v in part_roots.values()} - + root_tokens = { + tokenize( + *sorted(v, key=lambda x: str(x)) + ): v for v in part_roots.values() + } hash_map = defaultdict(set) group_offset = 0 @@ -129,7 +132,9 @@ def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, pdn = part_dependents[pn] if pdp: - groups = hash_map[tokenize(*sorted(part_roots[pn]))] + groups = hash_map[ + tokenize(*sorted(part_roots[pn], key=lambda x: str(x))) + ] else: # Special case - no dependencies. groups = {group_offset} group_offset += 1 @@ -153,3 +158,66 @@ def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, task.worker_restrictions = set() task.worker_restrictions |= {assignee} task.loose_restrictions = False + + +# NOTE: The graph metrics function has been copied directly from +# dask/order.py. This function was removed upstream but is currently +# critical for the scheduler plugin. This is a temporary work around +# while we consider revised strategies. Original version appears at: +# https://github.com/dask/dask/blob/9f7d55705f0eb74a172f5fdc3690aa65fbe21762/dask/order.py +def graph_metrics(dependencies, dependents, total_dependencies): + """Useful measures of a graph used by ``dask.order.order``""" + + result = {} + num_needed = {k: len(v) for k, v in dependents.items() if v} + current = [] + current_pop = current.pop + current_append = current.append + for key, deps in dependents.items(): + if not deps: + val = total_dependencies[key] + result[key] = (1, val, val, 0, 0) + for child in dependencies[key]: + num_needed[child] -= 1 + if not num_needed[child]: + current_append(child) + + while current: + key = current_pop() + parents = dependents[key] + if len(parents) == 1: + (parent,) = parents + ( + total_dependents, + min_dependencies, + max_dependencies, + min_heights, + max_heights, + ) = result[parent] + result[key] = ( + 1 + total_dependents, + min_dependencies, + max_dependencies, + 1 + min_heights, + 1 + max_heights, + ) + else: + ( + total_dependents_, + min_dependencies_, + max_dependencies_, + min_heights_, + max_heights_, + ) = zip(*(result[parent] for parent in dependents[key])) + result[key] = ( + 1 + sum(total_dependents_), + min(min_dependencies_), + max(max_dependencies_), + 1 + min(min_heights_), + 1 + max(max_heights_), + ) + for child in dependencies[key]: + num_needed[child] -= 1 + if not num_needed[child]: + current_append(child) + return result diff --git a/quartical/statistics/logging.py b/quartical/statistics/logging.py index 03592228..f0d6021c 100644 --- a/quartical/statistics/logging.py +++ b/quartical/statistics/logging.py @@ -78,8 +78,8 @@ def log_summary_stats(stats_xds_list): for sxds_group in sxds_groups: - max_nt_chunk = max([sxds.dims["t_chunk"] for sxds in sxds_group]) - max_nf_chunk = max([sxds.dims["f_chunk"] for sxds in sxds_group]) + max_nt_chunk = max([sxds.sizes["t_chunk"] for sxds in sxds_group]) + max_nf_chunk = max([sxds.sizes["f_chunk"] for sxds in sxds_group]) ids = [f"T{t}F{f}" for t in range(max_nt_chunk) for f in range(max_nf_chunk)] # noqa diff --git a/quartical/utils/collections.py b/quartical/utils/collections.py index 31da7d34..42199925 100644 --- a/quartical/utils/collections.py +++ b/quartical/utils/collections.py @@ -1,3 +1,4 @@ +from collections.abc import MutableMapping from collections import defaultdict @@ -9,3 +10,17 @@ def freeze_default_dict(ddict): ddict[k] = freeze_default_dict(v) return dict(ddict) + + +def flatten(dictionary, parent_key=()): + """Flatten dictionary. Adapted from https://stackoverflow.com/a/6027615.""" + items = [] + + for key, value in dictionary.items(): + new_key = parent_key + (key,) if parent_key else (key,) + if isinstance(value, MutableMapping): + items.extend(flatten(value, new_key).items()) + else: + items.append((new_key, value)) + + return dict(items) diff --git a/quartical/utils/datasets.py b/quartical/utils/datasets.py new file mode 100644 index 00000000..537bc3a9 --- /dev/null +++ b/quartical/utils/datasets.py @@ -0,0 +1,36 @@ +from collections.abc import Iterable + + +def group_by_attr(xdsl, attr, default="?"): + """Group list of xarray datasets based on value of attribute.""" + + attr_vals = {xds.attrs.get(attr, default) for xds in xdsl} + + return { + f"{attr}_{attr_val}": [ + xds for xds in xdsl if xds.attrs.get(attr, default) == attr_val + ] + for attr_val in attr_vals + } + + +def _recursive_group_by_attr(partition_dict, attrs): + + for k, v in partition_dict.items(): + partition_dict[k] = group_by_attr(v, attrs[0]) + + if len(attrs[1:]): + _recursive_group_by_attr(partition_dict[k], attrs[1:]) + + +def recursive_group_by_attr(xdsl, keys): + + if not isinstance(keys, Iterable): + keys = [keys] + + group_dict = group_by_attr(xdsl, keys[0]) + + if len(keys[1:]): + _recursive_group_by_attr(group_dict, keys[1:]) + + return group_dict diff --git a/testing/tests/calibration/test_calibrate.py b/testing/tests/calibration/test_calibrate.py index 7f1cb100..f3d95390 100644 --- a/testing/tests/calibration/test_calibrate.py +++ b/testing/tests/calibration/test_calibrate.py @@ -24,7 +24,7 @@ def opts(base_opts, time_chunk, freq_chunk, time_int, freq_int): @pytest.fixture(scope="module") def expected_t_ints(single_xds, chain): - n_row = single_xds.dims["row"] + n_row = single_xds.sizes["row"] t_ints = [term.time_interval or n_row for term in chain] expected_t_ints = [] @@ -62,7 +62,7 @@ def expected_t_ints(single_xds, chain): @pytest.fixture(scope="module") def expected_f_ints(single_xds, chain): - n_chan = single_xds.dims["chan"] + n_chan = single_xds.sizes["chan"] f_ints = [term.freq_interval or n_chan for term in chain] expected_f_ints = [] @@ -109,7 +109,7 @@ def test_data_coords(single_xds, term_xds_dict): data_coords = ["ant", "dir", "corr"] gain_coords = ["antenna", "direction", "correlation"] - assert all(single_xds.dims[dc] == gxds.dims[gc] + assert all(single_xds.sizes[dc] == gxds.sizes[gc] for gxds in term_xds_dict.values() for dc, gc in zip(data_coords, gain_coords)) @@ -118,7 +118,7 @@ def test_data_coords(single_xds, term_xds_dict): def test_t_chunking(single_xds, term_xds_dict): """Check that time chunking of the gain xds list is correct.""" - assert all(len(single_xds.UTIME_CHUNKS) == gxds.dims["time_chunk"] + assert all(len(single_xds.UTIME_CHUNKS) == gxds.sizes["time_chunk"] for gxds in term_xds_dict.values()) @@ -126,7 +126,7 @@ def test_t_chunking(single_xds, term_xds_dict): def test_f_chunking(single_xds, term_xds_dict): """Check that frequency chunking of the gain xds list is correct.""" - assert all(len(single_xds.chunks["chan"]) == gxds.dims["freq_chunk"] + assert all(len(single_xds.chunks["chan"]) == gxds.sizes["freq_chunk"] for gxds in term_xds_dict.values()) @@ -134,7 +134,7 @@ def test_f_chunking(single_xds, term_xds_dict): def test_t_ints(term_xds_dict, expected_t_ints): """Check that the time intervals are correct.""" - assert all(int(sum(eti)) == gxds.dims["gain_time"] + assert all(int(sum(eti)) == gxds.sizes["gain_time"] for eti, gxds in zip(expected_t_ints, term_xds_dict.values())) @@ -142,7 +142,7 @@ def test_t_ints(term_xds_dict, expected_t_ints): def test_f_ints(term_xds_dict, expected_f_ints): """Check that the frequency intervals are correct.""" - assert all(int(sum(efi)) == gxds.dims["gain_freq"] + assert all(int(sum(efi)) == gxds.sizes["gain_freq"] for efi, gxds in zip(expected_f_ints, term_xds_dict.values())) @@ -163,7 +163,7 @@ def test_chunk_spec(single_xds, term_xds_dict, expected_t_ints, """Check that the chunking specs are correct.""" n_row, n_chan, n_ant, n_dir, n_corr = \ - [single_xds.dims[d] for d in ["row", "chan", "ant", "dir", "corr"]] + [single_xds.sizes[d] for d in ["row", "chan", "ant", "dir", "corr"]] specs = [tuple([tuple(tic), tuple(fic), (n_ant,), (n_dir,), (n_corr,)]) for tic, fic in zip(expected_t_ints, expected_f_ints)] diff --git a/testing/tests/gains/test_amplitude.py b/testing/tests/gains/test_amplitude.py index 11ac6605..25bfd0e4 100644 --- a/testing/tests/gains/test_amplitude.py +++ b/testing/tests/gains/test_amplitude.py @@ -39,13 +39,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr) @@ -81,7 +81,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_complex.py b/testing/tests/gains/test_complex.py index c369e4c7..6b011702 100644 --- a/testing/tests/gains/test_complex.py +++ b/testing/tests/gains/test_complex.py @@ -39,13 +39,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr) @@ -79,7 +79,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_crosshand_phase.py b/testing/tests/gains/test_crosshand_phase.py index 9afc8838..23ae5c46 100644 --- a/testing/tests/gains/test_crosshand_phase.py +++ b/testing/tests/gains/test_crosshand_phase.py @@ -40,13 +40,13 @@ def true_gain_list(predicted_xds_list): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (len(utime_chunks), chan_chunks, n_ant, n_dir, n_corr) @@ -78,7 +78,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_delay.py b/testing/tests/gains/test_delay.py index 53063a25..51cb7706 100644 --- a/testing/tests/gains/test_delay.py +++ b/testing/tests/gains/test_delay.py @@ -40,13 +40,13 @@ def true_gain_list(predicted_xds_list): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chan_freq = xds.CHAN_FREQ.data chan_width = chan_freq[1] - chan_freq[0] @@ -86,7 +86,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_delay_and_offset.py b/testing/tests/gains/test_delay_and_offset.py index 7d4215f2..ed58db58 100644 --- a/testing/tests/gains/test_delay_and_offset.py +++ b/testing/tests/gains/test_delay_and_offset.py @@ -40,13 +40,13 @@ def true_gain_list(predicted_xds_list): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chan_freq = xds.CHAN_FREQ.data chan_width = chan_freq[1] - chan_freq[0] @@ -94,7 +94,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_diag_complex.py b/testing/tests/gains/test_diag_complex.py index 957d64e3..4f193c5d 100644 --- a/testing/tests/gains/test_diag_complex.py +++ b/testing/tests/gains/test_diag_complex.py @@ -44,13 +44,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr) @@ -84,7 +84,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_phase.py b/testing/tests/gains/test_phase.py index 63c04276..12e38192 100644 --- a/testing/tests/gains/test_phase.py +++ b/testing/tests/gains/test_phase.py @@ -39,13 +39,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr) @@ -79,7 +79,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_rotation.py b/testing/tests/gains/test_rotation.py index 3afb0da6..f009f606 100644 --- a/testing/tests/gains/test_rotation.py +++ b/testing/tests/gains/test_rotation.py @@ -38,13 +38,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chunking = (utime_chunks, chan_chunks, n_ant, n_dir, n_corr) @@ -75,7 +75,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_rotation_measure.py b/testing/tests/gains/test_rotation_measure.py index 55d8e894..40f92ba2 100644 --- a/testing/tests/gains/test_rotation_measure.py +++ b/testing/tests/gains/test_rotation_measure.py @@ -40,13 +40,13 @@ def true_gain_list(predicted_xds_list, solve_per): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] lambda_sq = (299792458/xds.CHAN_FREQ.data)**2 @@ -82,7 +82,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data diff --git a/testing/tests/gains/test_tec_and_offset.py b/testing/tests/gains/test_tec_and_offset.py index 05087090..6f68279d 100644 --- a/testing/tests/gains/test_tec_and_offset.py +++ b/testing/tests/gains/test_tec_and_offset.py @@ -39,13 +39,13 @@ def true_gain_list(predicted_xds_list): for xds in predicted_xds_list: - n_ant = xds.dims["ant"] + n_ant = xds.sizes["ant"] utime_chunks = xds.UTIME_CHUNKS n_time = sum(utime_chunks) chan_chunks = xds.chunks["chan"] - n_chan = xds.dims["chan"] - n_dir = xds.dims["dir"] - n_corr = xds.dims["corr"] + n_chan = xds.sizes["chan"] + n_dir = xds.sizes["dir"] + n_corr = xds.sizes["corr"] chan_freq = xds.CHAN_FREQ.data max_tec = 1/(1/chan_freq[-1] - 1/chan_freq[-2]) @@ -96,7 +96,7 @@ def corrupted_data_xds_list(predicted_xds_list, true_gain_list): for xds, gains in zip(predicted_xds_list, true_gain_list): - n_corr = xds.dims["corr"] + n_corr = xds.sizes["corr"] ant1 = xds.ANTENNA1.data ant2 = xds.ANTENNA2.data From 57e14131a5b110a0f3a81f80cfeb0769da45500b Mon Sep 17 00:00:00 2001 From: Jonathan Kenyon Date: Tue, 30 Jan 2024 08:49:51 +0200 Subject: [PATCH 2/2] Bump to 0.2.1 --- pyproject.toml | 2 +- tbump.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 85d84bd3..bee95174 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "quartical" -version = "0.2.0" +version = "0.2.1" description = "Fast and flexible calibration suite for radio interferometer data." repository = "https://github.com/ratt-ru/QuartiCal" documentation = "https://quartical.readthedocs.io" diff --git a/tbump.toml b/tbump.toml index 78ae3488..bdc5b3cc 100644 --- a/tbump.toml +++ b/tbump.toml @@ -2,7 +2,7 @@ github_url = "https://github.com/ratt-ru/QuartiCal/" [version] -current = "0.2.0" +current = "0.2.1" # Example of a semver regexp. # Make sure this matches current_version before