Merge pull request #1250 from OSOceanAcoustics/dev

Release/v0.8.3

Author: leewujung

docs/source/whats-new.md

@@ -4,6 +4,26 @@ What's new
 See [GitHub releases page](https://github.com/OSOceanAcoustics/echopype/releases) for the complete history.
 
 
+# v0.8.3 (2024 December 24)
+
+## Overview
+
+This release includes a bug fix for changes from the previous release and a few functionality enhancements.
+
+## Enhancements
+- Add parser support for EK80 MRU1 datagram (#1242)
+- Add support for `consolidate` subpackage functions to accept both in-memory or stored datasets (#1216)
+- Add test for ES60 spare field decoding issue (#1233)
+- Add test for EK80 missing `receiver_sampling_freq` error (#1234)
+
+## Bug fixes
+- Fixed reshape bug in `pad_shorter_ping` that was remnant from `use_swap` full refactoring (#1234)
+
+
+
+
+
+
 # v0.8.2 (2023 November 20)
 
 ## Overview

echopype/consolidate/api.py

@@ -1,5 +1,6 @@
 import datetime
 import pathlib
+from pathlib import Path
 from typing import Optional, Union
 
 import numpy as np
@@ -8,14 +9,14 @@
 from ..calibrate.ek80_complex import get_filter_coeff
 from ..echodata import EchoData
 from ..echodata.simrad import retrieve_correct_beam_group
-from ..utils.io import validate_source_ds_da
+from ..utils.io import get_file_format, open_source
 from ..utils.prov import add_processing_level
-from .split_beam_angle import add_angle_to_ds, get_angle_complex_samples, get_angle_power_samples
+from .split_beam_angle import get_angle_complex_samples, get_angle_power_samples
 
 POSITION_VARIABLES = ["latitude", "longitude"]
 
 
-def swap_dims_channel_frequency(ds: xr.Dataset) -> xr.Dataset:
+def swap_dims_channel_frequency(ds: Union[xr.Dataset, str, pathlib.Path]) -> xr.Dataset:
     """
     Use frequency_nominal in place of channel to be dataset dimension and coorindate.
 
@@ -24,8 +25,9 @@ def swap_dims_channel_frequency(ds: xr.Dataset) -> xr.Dataset:
 
     Parameters
     ----------
-    ds : xr.Dataset
-        Dataset for which the dimension will be swapped
+    ds : xr.Dataset or str or pathlib.Path
+        Dataset or path to a file containing the Dataset
+        for which the dimension will be swapped
 
     Returns
     -------
@@ -35,6 +37,7 @@ def swap_dims_channel_frequency(ds: xr.Dataset) -> xr.Dataset:
     -----
     This operation is only possible when there are no duplicated frequencies present in the file.
     """
+    ds = open_source(ds, "dataset", {})
     # Only possible if no duplicated frequencies
     if np.unique(ds["frequency_nominal"]).size == ds["frequency_nominal"].size:
         return (
@@ -50,7 +53,7 @@ def swap_dims_channel_frequency(ds: xr.Dataset) -> xr.Dataset:
 
 
 def add_depth(
-    ds: xr.Dataset,
+    ds: Union[xr.Dataset, str, pathlib.Path],
     depth_offset: float = 0,
     tilt: float = 0,
     downward: bool = True,
@@ -64,8 +67,9 @@ def add_depth(
 
     Parameters
     ----------
-    ds : xr.Dataset
-        Source Sv dataset to which a depth variable will be added.
+    ds : xr.Dataset or str or pathlib.Path
+        Source Sv dataset or path to a file containing the Source Sv dataset
+        to which a depth variable will be added.
         Must contain `echo_range`.
     depth_offset : float
         Offset along the vertical (depth) dimension to account for actual transducer
@@ -114,6 +118,7 @@ def add_depth(
     # else:
     #     tilt = 0
 
+    ds = open_source(ds, "dataset", {})
     # Multiplication factor depending on if transducers are pointing downward
     mult = 1 if downward else -1
 
@@ -132,7 +137,11 @@ def add_depth(
 
 
 @add_processing_level("L2A")
-def add_location(ds: xr.Dataset, echodata: EchoData = None, nmea_sentence: Optional[str] = None):
+def add_location(
+    ds: Union[xr.Dataset, str, pathlib.Path],
+    echodata: Optional[Union[EchoData, str, pathlib.Path]],
+    nmea_sentence: Optional[str] = None,
+):
     """
     Add geographical location (latitude/longitude) to the Sv dataset.
 
@@ -142,10 +151,12 @@ def add_location(ds: xr.Dataset, echodata: EchoData = None, nmea_sentence: Optio
 
     Parameters
     ----------
-    ds : xr.Dataset
-        An Sv or MVBS dataset for which the geographical locations will be added to
-    echodata
-        An `EchoData` object holding the raw data
+    ds : xr.Dataset or str or pathlib.Path
+        An Sv or MVBS dataset or path to a file containing the Sv or MVBS
+        dataset for which the geographical locations will be added to
+    echodata : EchoData or str or pathlib.Path
+        An ``EchoData`` object or path to a file containing the ``EchoData``
+        object holding the raw data
     nmea_sentence
         NMEA sentence to select a subset of location data (optional)
 
@@ -174,6 +185,9 @@ def sel_interp(var, time_dim_name):
             # Values may be nan if there are ping_time values outside the time_dim_name range
             return position_var.interp(**{time_dim_name: ds["ping_time"]})
 
+    ds = open_source(ds, "dataset", {})
+    echodata = open_source(echodata, "echodata", {})
+
     if "longitude" not in echodata["Platform"] or echodata["Platform"]["longitude"].isnull().all():
         raise ValueError("Coordinate variables not present or all nan")
 
@@ -198,12 +212,12 @@ def sel_interp(var, time_dim_name):
 
 def add_splitbeam_angle(
     source_Sv: Union[xr.Dataset, str, pathlib.Path],
-    echodata: EchoData,
+    echodata: Union[EchoData, str, pathlib.Path],
     waveform_mode: str,
     encode_mode: str,
     pulse_compression: bool = False,
     storage_options: dict = {},
-    return_dataset: bool = True,
+    to_disk: bool = True,
 ) -> xr.Dataset:
     """
     Add split-beam (alongship/athwartship) angles into the Sv dataset.
@@ -218,8 +232,9 @@ def add_splitbeam_angle(
     source_Sv: xr.Dataset or str or pathlib.Path
         The Sv Dataset or path to a file containing the Sv Dataset,
         to which the split-beam angles will be added
-    echodata: EchoData
-        An ``EchoData`` object holding the raw data
+    echodata: EchoData or str or pathlib.Path
+        An ``EchoData`` object or path to a file containing the ``EchoData``
+        object holding the raw data
     waveform_mode : {"CW", "BB"}
         Type of transmit waveform
 
@@ -240,19 +255,20 @@ def add_splitbeam_angle(
     storage_options: dict, default={}
         Any additional parameters for the storage backend, corresponding to the
         path provided for ``source_Sv``
-    return_dataset: bool, default=True
-        If ``True``, ``source_Sv`` with split-beam angles added will be returned.
-        ``return_dataset=False`` is useful when ``source_Sv`` is a path and
+    to_disk: bool, default=True
+        If ``False``, ``to_disk`` with split-beam angles added will be returned.
+        ``to_disk=True`` is useful when ``source_Sv`` is a path and
         users only want to write the split-beam angle data to this path.
 
     Returns
     -------
     xr.Dataset or None
-        If ``return_dataset=False``, nothing will be returned.
-        If ``return_dataset=True``, either the input dataset ``source_Sv``
+        If ``to_disk=False``, nothing will be returned.
+        If ``to_disk=True``, either the input dataset ``source_Sv``
         or a lazy-loaded Dataset (from the path ``source_Sv``)
         with split-beam angles added will be returned.
 
+
     Raises
     ------
     ValueError
@@ -279,6 +295,19 @@ def add_splitbeam_angle(
     `echodata`` will be identical. If this is not the case, only angle data corresponding
     to channels existing in ``source_Sv`` will be added.
     """
+    # ensure that when source_Sv is a Dataset then to_disk should be False
+    if not isinstance(source_Sv, (str, Path)) and to_disk:
+        raise ValueError(
+            "The input source_Sv must be a path when to_disk=True, "
+            "so that the split-beam angles can be written to disk!"
+        )
+
+    # obtain the file format of source_Sv if it is a path
+    if isinstance(source_Sv, (str, Path)):
+        source_Sv_type = get_file_format(source_Sv)
+
+    source_Sv = open_source(source_Sv, "dataset", storage_options)
+    echodata = open_source(echodata, "echodata", storage_options)
 
     # ensure that echodata was produced by EK60 or EK80-like sensors
     if echodata.sonar_model not in ["EK60", "ES70", "EK80", "ES80", "EA640"]:
@@ -287,22 +316,6 @@ def add_splitbeam_angle(
             "transducers, split-beam angles cannot be added to source_Sv!"
         )
 
-    # validate the source_Sv type or path (if it is provided)
-    source_Sv, file_type = validate_source_ds_da(source_Sv, storage_options)
-
-    # initialize source_Sv_path
-    source_Sv_path = None
-
-    if isinstance(source_Sv, str):
-        # store source_Sv path so we can use it to write to later
-        source_Sv_path = source_Sv
-
-        # TODO: In the future we can improve this by obtaining the variable names, channels,
-        #  and dimension lengths directly from source_Sv using zarr or netcdf4. This would
-        #  prevent the unnecessary loading in of the coordinates, which the below statement does.
-        # open up Dataset using source_Sv path
-        source_Sv = xr.open_dataset(source_Sv, engine=file_type, chunks={}, **storage_options)
-
     # raise not implemented error if source_Sv corresponds to MVBS
     if source_Sv.attrs["processing_function"] == "commongrid.compute_MVBS":
         raise NotImplementedError("Adding split-beam data to MVBS has not been implemented!")
@@ -364,9 +377,18 @@ def add_splitbeam_angle(
             theta, phi = get_angle_complex_samples(ds_beam, angle_params)
 
     # add theta and phi to source_Sv input
-    source_Sv = add_angle_to_ds(
-        theta, phi, source_Sv, return_dataset, source_Sv_path, file_type, storage_options
-    )
+    theta.attrs["long_name"] = "split-beam alongship angle"
+    phi.attrs["long_name"] = "split-beam athwartship angle"
+
+    # add the split-beam angles to the provided Dataset
+    source_Sv["angle_alongship"] = theta
+    source_Sv["angle_athwartship"] = phi
+    if to_disk:
+        if source_Sv_type == "netcdf4":
+            source_Sv.to_netcdf(mode="a", **storage_options)
+        else:
+            source_Sv.to_zarr(mode="a", **storage_options)
+        source_Sv = open_source(source_Sv, "dataset", storage_options)
 
     # Add history attribute
     history_attr = (

echopype/consolidate/split_beam_angle.py

@@ -2,7 +2,7 @@
 Contains functions necessary to compute the split-beam (alongship/athwartship)
 angles and add them to a Dataset.
 """
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 import numpy as np
 import xarray as xr
@@ -245,79 +245,3 @@ def get_angle_complex_samples(
         )
 
     return theta, phi
-
-
-def add_angle_to_ds(
-    theta: xr.Dataset,
-    phi: xr.Dataset,
-    ds: xr.Dataset,
-    return_dataset: bool,
-    source_ds_path: Optional[str] = None,
-    file_type: Optional[str] = None,
-    storage_options: dict = {},
-) -> Optional[xr.Dataset]:
-    """
-    Adds the split-beam angle data to the provided input ``ds``.
-
-    Parameters
-    ----------
-    theta: xr.Dataset
-        The calculated split-beam alongship angle
-    phi: xr.Dataset
-        The calculated split-beam athwartship angle
-    ds: xr.Dataset
-        The Dataset that ``theta`` and ``phi`` will be added to
-    return_dataset: bool
-        Whether a dataset will be returned or not
-    source_ds_path: str, optional
-        The path to the file corresponding to ``ds``, if it exists
-    file_type: {"netcdf4", "zarr"}, optional
-        The file type corresponding to ``source_ds_path``
-    storage_options: dict, default={}
-        Any additional parameters for the storage backend, corresponding to the
-        path ``source_ds_path``
-
-    Returns
-    -------
-    xr.Dataset or None
-        If ``return_dataset=False``, nothing will be returned. If ``return_dataset=True``
-        either the input dataset ``ds`` or a lazy-loaded Dataset (obtained from
-        the path provided by ``source_ds_path``) with the split-beam angle data added
-        will be returned.
-    """
-
-    # TODO: do we want to add anymore attributes to these variables?
-    # add appropriate attributes to theta and phi
-    theta.attrs["long_name"] = "split-beam alongship angle"
-    phi.attrs["long_name"] = "split-beam athwartship angle"
-
-    if source_ds_path is not None:
-        # put the variables into a Dataset, so they can be written at the same time
-        # add ds attributes to splitb_ds since they will be overwritten by to_netcdf/zarr
-        splitb_ds = xr.Dataset(
-            data_vars={"angle_alongship": theta, "angle_athwartship": phi},
-            coords=theta.coords,
-            attrs=ds.attrs,
-        )
-
-        # release any resources linked to ds (necessary for to_netcdf)
-        ds.close()
-
-        # write the split-beam angle data to the provided path
-        if file_type == "netcdf4":
-            splitb_ds.to_netcdf(path=source_ds_path, mode="a", **storage_options)
-        else:
-            splitb_ds.to_zarr(store=source_ds_path, mode="a", **storage_options)
-
-        if return_dataset:
-            # open up and return Dataset in source_ds_path
-            return xr.open_dataset(source_ds_path, engine=file_type, chunks={}, **storage_options)
-
-    else:
-        # add the split-beam angles to the provided Dataset
-        ds["angle_alongship"] = theta
-        ds["angle_athwartship"] = phi
-
-        if return_dataset:
-            # return input dataset with split-beam angle data
-            return ds

echopype/convert/parse_base.py

@@ -600,8 +600,14 @@ def pad_shorter_ping(data_list) -> np.ndarray:
         lens = np.array([len(item) for item in data_list])
         if np.unique(lens).size != 1:  # if some pings have different lengths along range
             if data_list[0].ndim == 2:
-                # Angle data have an extra dimension for alongship and athwartship samples
-                mask = lens[:, None, None] > np.array([np.arange(lens.max())] * 2).T
+                # Data may have an extra dimension:
+                #  - Angle data have an extra dimension for alongship and athwartship samples
+                #  - Complex data have an extra dimension for different transducer sectors
+                mask = (
+                    lens[:, None, None]
+                    > np.array([np.arange(lens.max())] * data_list[0].shape[1]).T
+                )
+
             else:
                 mask = lens[:, None] > np.arange(lens.max())
 

echopype/convert/set_groups_ek80.py

@@ -406,6 +406,17 @@ def set_platform(self) -> xr.Dataset:
                         "standard_name": "sound_frequency",
                     },
                 ),
+                "heading": (
+                    ["time2"],
+                    np.array(self.parser_obj.mru.get("heading", [np.nan])),
+                    {
+                        "long_name": "Platform heading (true)",
+                        "standard_name": "platform_orientation",
+                        "units": "degrees_north",
+                        "valid_min": 0.0,
+                        "valid_max": 360.0,
+                    },
+                ),
             },
             coords={
                 "channel": (