Add numpydoc Pre-Commit For Codice & Glows Pt.7 Last One (IMAP-Scienc…

…e-Operations-Center#652) * numpydoc precommit change * numpydoc precommit change * numpydoc update for glows * numpydoc update codice * Apply suggestions from code review Co-authored-by: Maxine Hartnett <117409426+maxinelasp@users.noreply.github.com> * final --------- Co-authored-by: Maxine Hartnett <117409426+maxinelasp@users.noreply.github.com>
bourque · Jun 21, 2024 · 2437799 · 2437799
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -36,5 +36,5 @@ repos:
     rev: 'v1.7.0'
     hooks:
       - id: numpydoc-validation
-        exclude: '^imap_processing/tests/|.*test.*|^imap_processing/glows|^imap_processing/codice'
-        # Will need to remove exclude section once all numpydoc changes are made.
+        exclude: '^imap_processing/tests/|.*test.*'
+
diff --git a/imap_processing/codice/codice_l0.py b/imap_processing/codice/codice_l0.py
@@ -1,14 +1,14 @@
-"""Perform CoDICE L0 processing.
+"""
+Perform CoDICE L0 processing.
 
 This module contains a function to decommutate CoDICE CCSDS packets using
 XTCE packet definitions.
 
 For more information on this process and the latest versions of the packet
 definitions, see https://lasp.colorado.edu/galaxy/display/IMAP/CoDICE.
 
-Use
----
-
+Notes
+-----
     from imap_processing.codice.codice_l0 import decom_packets
     packet_file = '/path/to/raw_ccsds_20230822_122700Z_idle.bin'
     packet_list = decom_packets(packet_file)
@@ -20,7 +20,8 @@
 
 
 def decom_packets(packet_file: Path) -> list:
-    """Decom CoDICE data packets using CoDICE packet definition.
+    """
+    Decom CoDICE data packets using CoDICE packet definition.
 
     Parameters
     ----------
@@ -30,7 +31,7 @@ def decom_packets(packet_file: Path) -> list:
     Returns
     -------
     list : list
-        all the unpacked data.
+        All the unpacked data.
     """
     packet_to_xtce_mapping = {
         "imap_codice_l0_hi-counters-aggregated_20240429_v001.pkts": "P_COD_HI_INST_COUNTS_AGGREGATED.xml",  # noqa

diff --git a/imap_processing/codice/codice_l1a.py b/imap_processing/codice/codice_l1a.py
@@ -1,10 +1,10 @@
-"""Perform CoDICE l1a processing.
+"""
+Perform CoDICE l1a processing.
 
 This module processes decommutated CoDICE packets and creates L1a data products.
 
-Use
----
-
+Notes
+-----
     from imap_processing.codice.codice_l0 import decom_packets
     from imap_processing.codice.codice_l1a import process_codice_l1a
     packets = decom_packets(packet_file)
@@ -38,23 +38,24 @@
 
 
 class CoDICEL1aPipeline:
-    """Contains methods for processing L0 data and creating L1a data products.
+    """
+    Contains methods for processing L0 data and creating L1a data products.
 
-    Attributes
+    Parameters
     ----------
     table_id : int
         A unique ID assigned to a specific table configuration. This field is
         used to link the overall acquisition and processing settings to a
-        specific table configuration
+        specific table configuration.
     plan_id : int
         The plan table that was in use.  In conjunction with ``plan_step``,
-        describes which counters are included in the data packet
+        describes which counters are included in the data packet.
     plan_step : int
         Plan step that was active when the data was acquired and processed. In
         conjunction with ``plan_id``, describes which counters are included
-        in the data packet
+        in the data packet.
     view_id : int
-        Provides information about how data was collapsed and/or compressed
+        Provides information about how data was collapsed and/or compressed.
 
     Methods
     -------
@@ -80,22 +81,22 @@ def __init__(self, table_id: int, plan_id: int, plan_step: int, view_id: int):
     def create_science_dataset(
         self, start_time: np.datetime64, data_version: str
     ) -> xr.Dataset:
-        """Create an ``xarray`` dataset for the unpacked science data.
+        """
+        Create an ``xarray`` dataset for the unpacked science data.
 
         The dataset can then be written to a CDF file.
 
         Parameters
         ----------
         start_time : numpy.datetime64
-            The start time of the packet, used to determine epoch data variable
+            The start time of the packet, used to determine epoch data variable.
         data_version : str
-        Version of the data product being created
-
+            Version of the data product being created.
 
         Returns
         -------
-        xarray.Dataset
-            ``xarray`` dataset containing the science data and supporting metadata
+        dataset : xarray.Dataset
+            The ``xarray`` dataset containing the science data and supporting metadata.
         """
         # Set the CDF attrs
         cdf_attrs = ImapCdfAttributes()
@@ -173,7 +174,8 @@ def create_science_dataset(
         return dataset
 
     def get_acquisition_times(self):
-        """Retrieve the acquisition times via the Lo stepping table.
+        """
+        Retrieve the acquisition times via the Lo stepping table.
 
         Get the acquisition times from the data file based on the values of
         ``plan_id`` and ``plan_step``
@@ -216,7 +218,8 @@ def get_acquisition_times(self):
             self.acquisition_times.append(lo_stepping_values.acq_time[row_number])
 
     def get_data_products(self, apid: int):
-        """Retrieve various settings for defining the data products.
+        """
+        Retrieve various settings for defining the data products.
 
         Parameters
         ----------
@@ -230,7 +233,8 @@ def get_data_products(self, apid: int):
         self.dataset_name = config["dataset_name"]
 
     def get_esa_sweep_values(self):
-        """Retrieve the ESA sweep values.
+        """
+        Retrieve the ESA sweep values.
 
         Get the ElectroStatic Analyzer (ESA) sweep values from the data file
         based on the values of ``plan_id`` and ``plan_step``
@@ -260,7 +264,8 @@ def get_esa_sweep_values(self):
         self.esa_sweep_values = sweep_table["esa_v"].values
 
     def unpack_science_data(self, science_values: str):
-        """Unpack the science data from the packet.
+        """
+        Unpack the science data from the packet.
 
         For LO SW Species Counts data, the science data within the packet is a
         blob of compressed values of length 2048 bits (16 species * 128 energy
@@ -270,7 +275,7 @@ def unpack_science_data(self, science_values: str):
         Parameters
         ----------
         science_values : str
-            A string of binary data representing the science values of the data
+            A string of binary data representing the science values of the data.
         """
         self.compression_algorithm = constants.LO_COMPRESSION_ID_LOOKUP[self.view_id]
         self.collapse_table_id = constants.LO_COLLAPSE_TABLE_ID_LOOKUP[self.view_id]
@@ -288,7 +293,8 @@ def unpack_science_data(self, science_values: str):
 
 
 def get_params(packet) -> tuple[int, int, int, int]:
-    """Return the four 'main' parameters used for l1a processing.
+    """
+    Return the four 'main' parameters used for l1a processing.
 
     The combination of these parameters largely determines what steps/values
     are used to create CoDICE L1a data products and what steps are needed in
@@ -297,23 +303,23 @@ def get_params(packet) -> tuple[int, int, int, int]:
     Parameters
     ----------
     packet : space_packet_parser.parser.Packet
-        A packet for the APID of interest
+        A packet for the APID of interest.
 
     Returns
     -------
     table_id : int
         A unique ID assigned to a specific table configuration. This field is
         used to link the overall acquisition and processing settings to a
-        specific table configuration
+        specific table configuration.
     plan_id : int
         The plan table that was in use.  In conjunction with ``plan_step``,
-        describes which counters are included in the data packet
+        describes which counters are included in the data packet.
     plan_step : int
         Plan step that was active when the data was acquired and processed. In
         conjunction with ``plan_id``, describes which counters are included
-        in the data packet
+        in the data packet.
     view_id : int
-        Provides information about how data was collapsed and/or compressed
+        Provides information about how data was collapsed and/or compressed.
     """
     table_id = packet.data["TABLE_ID"].raw_value
     plan_id = packet.data["PLAN_ID"].raw_value
@@ -324,19 +330,20 @@ def get_params(packet) -> tuple[int, int, int, int]:
 
 
 def process_codice_l1a(file_path: Path | str, data_version: str) -> xr.Dataset:
-    """Process CoDICE l0 data to create l1a data products.
+    """
+    Will process CoDICE l0 data to create l1a data products.
 
     Parameters
     ----------
     file_path : pathlib.Path | str
-        Path to the CoDICE L0 file to process
+        Path to the CoDICE L0 file to process.
     data_version : str
-        Version of the data product being created
+        Version of the data product being created.
 
     Returns
     -------
     dataset : xarray.Dataset
-        ``xarray`` dataset containing the science data and supporting metadata
+        The ``xarray`` dataset containing the science data and supporting metadata.
     """
     # TODO: Once simulated data for codice-hi is acquired, there shouldn't be a
     # need to split the processing based on the file_path, so this function can

diff --git a/imap_processing/codice/codice_l1b.py b/imap_processing/codice/codice_l1b.py
@@ -1,13 +1,13 @@
-"""Perform CoDICE l1b processing.
+"""
+Perform CoDICE l1b processing.
 
 This module processes CoDICE l1a files and creates L1a data products.
 
-Use
----
-
-    from imap_processing.codice.codice_l0 import decom_packets
-    from imap_processing.codice.codice_l1b import process_codice_l1b
-    dataset = process_codice_l1b(l1a_file)
+Notes
+-----
+from imap_processing.codice.codice_l0 import decom_packets
+from imap_processing.codice.codice_l1b import process_codice_l1b
+dataset = process_codice_l1b(l1a_file)
 """
 
 import logging
@@ -25,21 +25,22 @@
 
 
 def create_hskp_dataset(l1a_dataset, cdf_attrs) -> xr.Dataset:
-    """Create an ``xarray`` dataset for the housekeeping data.
+    """
+    Create an ``xarray`` dataset for the housekeeping data.
 
     The dataset can then be written to a CDF file.
 
     Parameters
     ----------
     l1a_dataset : xr.Dataset
-        The L1a dataset that is being processed
+        The L1a dataset that is being processed.
     cdf_attrs : ImapCdfAttributes
-        The CDF attributes for the dataset
+        The CDF attributes for the dataset.
 
     Returns
     -------
-    xarray.Dataset
-        ``xarray`` dataset containing the science data and supporting metadata
+    l1b_dataset : xarray.Dataset
+        The ``xarray`` dataset containing the science data and supporting metadata.
     """
     epoch = l1a_dataset.coords["epoch"]
     l1b_dataset = xr.Dataset(
@@ -82,24 +83,25 @@ def create_hskp_dataset(l1a_dataset, cdf_attrs) -> xr.Dataset:
 def create_science_dataset(
     l1a_dataset: xr.Dataset, cdf_attrs, dataset_name
 ) -> xr.Dataset:
-    """Create an ``xarray`` dataset for the science data.
+    """
+    Create an ``xarray`` dataset for the science data.
 
     The dataset can then be written to a CDF file.
 
     Parameters
     ----------
     l1a_dataset : xr.Dataset
-        The L1a dataset that is being processed
+        The L1a dataset that is being processed.
     cdf_attrs : ImapCdfAttributes
-        The CDF attributes for the dataset
+        The CDF attributes for the dataset.
     dataset_name : str
         The name that is used to construct the data variable name and reference
-        the CDF attributes (e.g. ``imap_codice_l1b_hi_omni``)
+        the CDF attributes (e.g. ``imap_codice_l1b_hi_omni``).
 
     Returns
     -------
-    xarray.Dataset
-        ``xarray`` dataset containing the science data and supporting metadata
+    l1b_dataset : xarray.Dataset
+        The ``xarray`` dataset containing the science data and supporting metadata.
     """
     # Retrieve the coordinates from the l1a dataset
     epoch = l1a_dataset.coords["epoch"]
@@ -153,19 +155,20 @@ def create_science_dataset(
 
 
 def process_codice_l1b(file_path: Path, data_version: str) -> xr.Dataset:
-    """Process CoDICE l1a data to create l1b data products.
+    """
+    Will process CoDICE l1a data to create l1b data products.
 
     Parameters
     ----------
     file_path : pathlib.Path | str
-        Path to the CoDICE L1a file to process
+        Path to the CoDICE L1a file to process.
     data_version : str
-        Version of the data product being created
+        Version of the data product being created.
 
     Returns
     -------
-    dataset : xarray.Dataset
-        ``xarray`` dataset containing the science data and supporting metadata
+    l1b_dataset : xarray.Dataset
+        The``xarray`` dataset containing the science data and supporting metadata.
     """
     logger.info(f"\nProcessing {file_path.name} file.")
 

diff --git a/imap_processing/codice/constants.py b/imap_processing/codice/constants.py
@@ -1,10 +1,11 @@
-"""Contains constants variables to support CoDICE processing.
+"""
+Contains constants variables to support CoDICE processing.
 
 The ``plan_id``, ``plan_step``, and ``view_id`` mentioned in this module are
 derived from the packet data.
 
-Acronyms
---------
+Notes
+-----
 SW = SunWard
 NSW = Non-SunWard
 PUI = PickUp Ion