spellllinnnnggggg

README.md

@@ -49,7 +49,7 @@ command line. They are listed below with a brief description:
 - `flint_potato`: Attempt to peel out known sources from a measurement set using
   [potatopeel](https://gitlab.com/Sunmish/potato/-/tree/main). Criteria used to
   assess which sources to peel is fairly minimumal, and at the time of writing
-  only the reference set of sources paackaged within `flint` are
+  only the reference set of sources packaged within `flint` are
   considered. -`flint_archive`: Operations around archiving and copying final
   data products into place. -`flint_catalogue`: Download reference catalogues
   that are expected by `flint`
@@ -152,15 +152,15 @@ corresponding options defined in the `defaults` scope.
 
 `flint_config` can be used to generate a template file, which can then be
 tweaked. The template file uses YAML to define scope and settings. So, use the
-YAML standard when modifying this file. There are primative verification
+YAML standard when modifying this file. There are primitive verification
 functions to ensure the modified template file is correctly form.
 
 ## CLI Configuration file
 
-To help manage (and avoid) long CLI calls to conffigure `flint`, most command
+To help manage (and avoid) long CLI calls to configure `flint`, most command
 line options may be dumped into a new-line delimited text file which can then be
 set as the `--cli-config` option of some workflows. See the `configargparse`
-python utility to read up on more on how options may be overridden if speficied
+python utility to read up on more on how options may be overridden if specified
 in both the text file and CLI call.
 
 ## Validation Plots

flint/bptools/preflagger.py

@@ -43,7 +43,7 @@ class PhaseOutlierResults(NamedTuple):
     unwrapped_residual_std: float
     """The std. of the residual unwrapped phases in radians"""
     flag_cut: float
-    """The adopted signifance level that a outlier should be before outlier_mask is set to True"""
+    """The adopted significance level that a outlier should be before outlier_mask is set to True"""
 
 
 # TODO: Pass in parameters directly so we don't have to have an instance of PhaseOutlierResults
@@ -63,7 +63,7 @@ def plot_phase_outlier(
     Returns:
         Path: Path of the output image file
     """
-    logger.debug(f"Creating phase outlier plot, writting {str(output_path)}.")
+    logger.debug(f"Creating phase outlier plot, writing {str(output_path)}.")
 
     complex_gains = phase_outlier_results.complex_gains
     init_model_gains = phase_outlier_results.init_model_gains
@@ -315,7 +315,7 @@ def flags_over_threshold(
     ), f"The provided {thresh=} should be a fraction between 0 to 1. "
 
     number_flagged = np.sum(flags)
-    # Use the shape incase multi-dimensional array passed in
+    # Use the shape in case multi-dimensional array passed in
     total_flagged = np.prod(flags.shape)
 
     frac_flagged = number_flagged / total_flagged
@@ -397,7 +397,7 @@ def flag_mean_residual_amplitude(
     Args:
         complex_gains (np.ndarray): The set of complex gains to be considered
         use_robust (bool, optional): Whether to use robust statistics (median, MAD)  or mean/std to calculate the statistic against. Defaults to True.
-        polynomical_order (int, optional): The order of the polynomical (numpy.polyfit) to use to compute the baseline. Defaults to 5.
+        polynomical_order (int, optional): The order of the polynomial (numpy.polyfit) to use to compute the baseline. Defaults to 5.
         plot_path (Path): The location to save the output file to
         plot_title (Optional[str], optional): Title to add to the figure. Defaults to None.
 
@@ -416,7 +416,7 @@ def flag_mean_residual_amplitude(
     poly_vals = np.polyval(poly_coeffs, idxs)
 
     residual = amplitudes - poly_vals
-    # Although the mask above should be sufficent, trust nothing
+    # Although the mask above should be sufficient, trust nothing
     mask = np.isfinite(residual)
 
     # TODO: Consider use of an iterative clipping method
@@ -514,7 +514,7 @@ def construct_mesh_ant_flags(mask: np.ndarray) -> np.ndarray:
 
     assert (
         len(mask.shape) == 3
-    ), f"Expect array of shape (ant, chnnel, pol), received {mask.shape=}"
+    ), f"Expect array of shape (ant, channel, pol), received {mask.shape=}"
     accumulate_mask = np.zeros_like(mask[0], dtype=bool)
 
     nant = mask.shape[0]

flint/bptools/smoother.py

@@ -10,19 +10,19 @@
 def divide_bandpass_by_ref_ant_preserve_phase(
     complex_gains: np.ndarray, ref_ant: int
 ) -> np.ndarray:
-    """Divide the bandpass compelx gains (solved for initially by something like
+    """Divide the bandpass complex gains (solved for initially by something like
     calibrate) by a nominated reference antenna. In the case of ``calibrate``
     there is no implicit reference antenna. This is valid for cases where the
     xy-phase is set to 0 (true via the ASKAP on-dish calibrator).
 
     This particular function is most appropriate for the `calibrate` style
     solutions, which solve for the Jones in one step. In HMS notation this
-    are normally split into two separate 2x2 matricies, one for the gains
+    are normally split into two separate 2x2 matrices, one for the gains
     with zero off-diagonal elements and a leakage matrix with ones on
     the diagonal.
 
     This is the preferred function to use whena attempting to set a
-    phase reference antenna to precomuted Jones bandpass solutions.
+    phase reference antenna to precomputed Jones bandpass solutions.
 
     The input complex gains should be in the form:
     >> (ant, channel, pol)
@@ -51,7 +51,7 @@ def divide_bandpass_by_ref_ant_preserve_phase(
         f"Dividing bandpass gain solutions using reference antenna={ref_ant}, using correct phasor"
     )
 
-    # Unpack the valuse for short hand use
+    # Unpack the values for short hand use
     g_x = complex_gains[:, :, 0]
     g_xy = complex_gains[:, :, 1]
     g_yx = complex_gains[:, :, 2]
@@ -89,7 +89,7 @@ def divide_bandpass_by_ref_ant_preserve_phase(
 
 
 def divide_bandpass_by_ref_ant(complex_gains: np.ndarray, ref_ant: int) -> np.ndarray:
-    """Divide the bandpass compelx gains (solved for initially by something like
+    """Divide the bandpass complex gains (solved for initially by something like
     calibrate) by a nominated reference antenna. In the case of ``calibrate``
     there is no implicit reference antenna.
 
@@ -121,7 +121,7 @@ def divide_bandpass_by_ref_ant(complex_gains: np.ndarray, ref_ant: int) -> np.nd
         f"Dividing bandpass gain solutions using reference antenna={ref_ant} with shifted phasor"
     )
 
-    # Make a copy of the data to avoid editing it whereever else it might be.
+    # Make a copy of the data to avoid editing it wherever else it might be.
     # Trust nothing you pirate.
     complex_gains = complex_gains.copy()
 
@@ -191,9 +191,9 @@ def smooth_data(
     # This is the filling in of the blanks
     data[mask] = d_interp
 
-    # Now we smooth. This savgol filter fits a polynomial to succesive subsets of data
+    # Now we smooth. This savgol filter fits a polynomial to successive subsets of data
     # in a manner similar to a box car. The final positional argument here denoted the
-    # behavour of the edge where the window (second positional argument) does not have
+    # behaviour of the edge where the window (second positional argument) does not have
     # enough data. This process is similar to the original implemented in bptools, except
     # here we are using a polynomial, not a set of harmonic basis functions.
     smoothed_data = savgol_filter(data, window_size, polynomial_order)

flint/coadd/linmos.py

@@ -20,7 +20,7 @@ class LinmosCommand(NamedTuple):
     cmd: str
     """The yandasoft linmos task that will be executed"""
     parset: Path
-    """The output location that the generated linmos parset has been writen to"""
+    """The output location that the generated linmos parset has been written to"""
     image_fits: Path
     """Path to the output linmos image created (or will be). """
 
@@ -262,7 +262,7 @@ def generate_linmos_parameter_set(
         weight_list (str, optional): If not None, this string will be embedded into the yandasoft linmos parset as-is. It should represent the formatted string pointing to weight files, and should be equal length of the input images. If None it is internally generated. Defaults to None.
         holofile (Optional[Path], optional): Path to a FITS cube produced by the holography processing pipeline. Used by linmos to appropriate primary-beam correct the images. Defaults to None.
         cutoff (float, optional): Pixels whose primary beam attenuation is below this cutoff value are blanked. Defaults to 0.001.
-        pol_axis (Optional[float], optional): The physical orientation of the ASKAP third-axis. This is provided (with some assumptions about the orientation of the holography) to correctly rotate the attentuation of the beams when coadding. If None we hope for the best. Defaults to None.
+        pol_axis (Optional[float], optional): The physical orientation of the ASKAP third-axis. This is provided (with some assumptions about the orientation of the holography) to correctly rotate the attenuation of the beams when coadding. If None we hope for the best. Defaults to None.
         overwrite (bool, optional): If True and the parset file already exists, overwrite it. Otherwise a FileExistsError is raised should the parset exist. Defaults to True.
 
     Returns:
@@ -317,7 +317,7 @@ def generate_linmos_parameter_set(
         # f"linmos.beamangle        = {beam_angle_list}\n"
         f"linmos.imagetype        = fits\n"
         f"linmos.outname          = {str(parent_dir / linmos_names.image_fits.stem)}\n"
-        f"linmos.outweight        = {str(parent_dir / linmos_names.weight_fits.stem)}\n"
+        f"linmos.outweigh        = {str(parent_dir / linmos_names.weight_fits.stem)}\n"
         f"# For ASKAPsoft>1.3.0\n"
         f"linmos.useweightslog    = true\n"
         f"linmos.weighttype       = Combined\n"

flint/flagging.py

@@ -24,7 +24,7 @@ class AOFlaggerCommand(NamedTuple):
     ms: MS
     """The MS object that was flagged"""
     strategy_file: Optional[Path] = None
-    """The path to the aoflagging stategy file to use"""
+    """The path to the aoflagging strategy file to use"""
 
 
 def flag_ms_zero_uvws(ms: MS, chunk_size: int = 10000) -> MS:
@@ -82,7 +82,7 @@ def nan_zero_extreme_flag_ms(
 ) -> MS:
     """Will flag a MS based on NaNs or zeros in the nominated data column of a measurement set.
     These NaNs might be introduced into a column via the application of a applysolutions task.
-    Zeros might be introduced by the correlator dropping cycles and not appropriately settting the
+    Zeros might be introduced by the correlator dropping cycles and not appropriately setting the
     corresponding FLAG column (although this might have been fixed).
 
     There is also an optional component to flag based on extreme Stokes-V values.
@@ -181,7 +181,7 @@ def create_aoflagger_cmd(ms: MS) -> AOFlaggerCommand:
     flagging_strategy = get_packaged_resource_path(
         package="flint.data.aoflagger", filename="ASKAP.lua"
     )
-    logger.info(f"Flagging using the stategy file {flagging_strategy}")
+    logger.info(f"Flagging using the strategy file {flagging_strategy}")
 
     cmd = f"aoflagger -column {ms.column} -strategy {flagging_strategy} -v {str(ms.path.absolute())}"
 
@@ -316,7 +316,7 @@ def get_parser() -> ArgumentParser:
     )
 
     nan_zero_parser = subparser.add_parser(
-        "nanflag", help="Flag visibilities tthat are either NaN or zeros. "
+        "nanflag", help="Flag visibilities that are either NaN or zeros. "
     )
     nan_zero_parser.add_argument(
         "ms", type=Path, help="The measurement set that will be flagged. "

flint/options.py

@@ -92,7 +92,7 @@ class FieldOptions(NamedTuple):
     to contain properties of the data that are being processed,
     rather how those data will be processed.
 
-    These settins are not meant to be adjustable throughout
+    These settings are not meant to be adjustable throughout
     rounds of self-calibration.
     """
 
@@ -123,15 +123,15 @@ class FieldOptions(NamedTuple):
     no_imaging: bool = False
     """Whether to skip the imaging process (including self-calibration)"""
     reference_catalogue_directory: Optional[Path] = None
-    """Path to the directory container the refernce catalogues, used to generate valiation plots"""
+    """Path to the directory container the reference catalogues, used to generate validation plots"""
     linmos_residuals: bool = False
     """Linmos the cleaning residuals together into a field image"""
     beam_cutoff: float = 150
     """Cutoff in arcseconds to use when calculating the common beam to convol to"""
     fixed_beam_shape: Optional[List[float]] = None
     """Specify the final beamsize of linmos field images in (arcsec, arcsec, deg)"""
     pb_cutoff: float = 0.1
-    """Primary beam attentuation cutoff to use during linmos"""
+    """Primary beam attenuation cutoff to use during linmos"""
     use_preflagger: bool = True
     """Whether to apply (or search for solutions with) bandpass solutions that have gone through the preflagging operations"""
     use_smoothed: bool = False
@@ -147,9 +147,9 @@ class FieldOptions(NamedTuple):
     sbid_copy_path: Optional[Path] = None
     """Path that final processed products will be copied into. If None no copying of file products is performed. See ArchiveOptions. """
     rename_ms: bool = False
-    """Rename MSs throught rounds of imaging and self-cal instead of creating copies. This will delete data-columns throughout. """
+    """Rename MSs throughout rounds of imaging and self-cal instead of creating copies. This will delete data-columns throughout. """
     stokes_v_imaging: bool = False
-    """Specifies whether Stokes-V imaging will be carried out after the final round of imagin (whether or not self-calibration is enabled). """
+    """Specifies whether Stokes-V imaging will be carried out after the final round of imagine (whether or not self-calibration is enabled). """
 
     def with_options(self, **kwargs) -> FieldOptions:
         _dict = self._asdict()

flint/selfcal/casa.py

@@ -39,10 +39,10 @@ class GainCalOptions(NamedTuple):
     gaintype: str = "G"
     """The gain type that would be solved for. """
     nspw: int = 1
-    """The number of spectral windows to use during the self-calibration rountine. If 1, no changes
+    """The number of spectral windows to use during the self-calibration routine. If 1, no changes
     are made to the measurement set. If more than 1, then the measurement will be reformed to form
     a new measurement set conforming to the number of spws set. This process can be fragile as the
-    casa tasks sometimes do not like the configurate, so ye warned."""
+    casa tasks sometimes do not like the configure, so ye warned."""
 
     def with_options(self, **kwargs) -> GainCalOptions:
         _dict = self._asdict()
@@ -194,7 +194,7 @@ def merge_spws_in_ms(ms_path: Path) -> Path:
     set using the `cvel` casa task. This can be a little fragile.
 
     The `cvel` task creates a new measurement set, so there would
-    temporially be a secondary measurment set.
+    temporially be a secondary measurement set.
 
     Args:
         ms_path (Path): The measurement set that should have its SPWs merged together.
@@ -261,7 +261,7 @@ def gaincal_applycal_ms(
     # Pirates like easy things though.
     cal_ms = copy_and_clean_ms_casagain(ms=ms, round=round, rename_ms=rename_ms)
 
-    # Archive straight after copying incase we skip the gaincal and return
+    # Archive straight after copying in case we skip the gaincal and return
     if archive_input_ms and not rename_ms:
         zip_folder(in_path=ms.path)
 
@@ -317,7 +317,7 @@ def gaincal_applycal_ms(
 
     # This is used for when a frequency dependent self-calibration solution is requested
     # It is often useful (mandatory!) to have a single spw for some tasks - both of the casa
-    # and everyone else varity.
+    # and everyone else variety.
     if gain_cal_options.nspw > 1:
         # putting it all back to a single spw
         cal_ms_path = merge_spws_in_ms(ms_path=cal_ms.path)

tests/test_aocalibrate.py

@@ -127,7 +127,7 @@ def test_known_bad_sols(ao_sols_known_bad):
 
 def test_sols_same_with_plots(ao_sols_known_bad):
     # Had a thought at one point the plktting was updating th mutable numpy array before
-    # it was writen back to file. Wrote the test, and it passed. Test stays
+    # it was written back to file. Wrote the test, and it passed. Test stays
     a = flag_aosolutions(
         solutions_path=ao_sols_known_bad, plot_solutions_throughout=False
     )

tests/test_configuration.py

@@ -46,9 +46,9 @@ def strategy(tmpdir):
         output_yaml=Path(tmpdir) / "example.yaml", selfcal_rounds=3
     )
 
-    strat = load_strategy_yaml(input_yaml=output, verify=False)
+    strategy = load_strategy_yaml(input_yaml=output, verify=False)
 
-    return strat
+    return strategy
 
 
 @pytest.fixture
@@ -88,7 +88,7 @@ def test_verify_options_with_class_operations(package_strategy_operations):
 
 
 def test_verify_getoptions_with_class_operations(package_strategy_operations):
-    """Check whether the get options inteface works with the operations
+    """Check whether the get options interface works with the operations
     section of the strategy file"""
     strategy = package_strategy_operations
     verify_configuration(input_strategy=strategy)
@@ -132,7 +132,7 @@ def test_verify_options_with_class(package_strategy):
 
 
 def test_create_yaml_file(tmpdir):
-    # ensure a default yaml stategy can be created
+    # ensure a default yaml strategy can be created
     output = create_default_yaml(
         output_yaml=Path(tmpdir) / "example.yaml", selfcal_rounds=3
     )
@@ -148,11 +148,11 @@ def test_create_and_load(tmpdir):
 
     assert output.exists()
 
-    strat = load_strategy_yaml(input_yaml=output)
-    assert isinstance(strat, Strategy)
+    strategy = load_strategy_yaml(input_yaml=output)
+    assert isinstance(strategy, Strategy)
 
-    strat = load_strategy_yaml(input_yaml=output, verify=False)
-    assert isinstance(strat, Strategy)
+    strategy = load_strategy_yaml(input_yaml=output, verify=False)
+    assert isinstance(strategy, Strategy)
 
 
 def test_verify(tmpdir):
@@ -163,22 +163,22 @@ def test_verify(tmpdir):
     )
 
     assert output.exists()
-    strat = load_strategy_yaml(input_yaml=output, verify=False)
-    assert isinstance(strat, Strategy)
+    strategy = load_strategy_yaml(input_yaml=output, verify=False)
+    assert isinstance(strategy, Strategy)
 
-    _ = verify_configuration(input_strategy=strat)
+    _ = verify_configuration(input_strategy=strategy)
 
-    strat["ddd"] = 123
+    strategy["ddd"] = 123
     with pytest.raises(ValueError):
-        verify_configuration(input_strategy=strat)
+        verify_configuration(input_strategy=strategy)
 
 
 def test_load_yaml_none():
     # make sure an error is raise if None is passed in. This
     # should be checked as the default value of FieldOptions.imaging_strategy
     # is None.
     with pytest.raises(TypeError):
-        _ = load_strategy_yaml(input_yaml=None)
+        _ = load_strategy_yaml(input_yaml=None)  # type: ignore
 
 
 def test_mode_options_mapping_creation():

tests/test_linmos_coadd.py

@@ -1,6 +1,6 @@
 """Tests related to components in the yandasoft linmos coadd.
 At the moment this is not testing the actual application. Just
-some of trhe helper functions around it.
+some of the helper functions around it.
 """
 
 from pathlib import Path

tests/test_potato.py

@@ -56,7 +56,7 @@ def ms_example(tmpdir):
 
 
 def test_source_in_image_fov():
-    """Test to see if souce is within an image FoV"""
+    """Test to see if source is within an image FoV"""
     # This comes from a test wsclean produced image. Not sure
     # why or how a minus crval1 has come out.
     wcs_dict = dict(
@@ -204,7 +204,7 @@ def test_load_peel_sources():
 
 
 def test_pb_models():
-    """See if the respones can be loaded and executed"""
+    """See if the responses can be loaded and executed"""
     freq = 1.0 * u.GHz
     aperture = 12 * u.m
     offset = np.arange(0, 200) * u.arcmin