Upgrade python minimum to 3.8 and tidy up packaging

.ci_helpers/docker/setup-services.py

@@ -17,9 +17,7 @@
 
 def parse_args():
     parser = argparse.ArgumentParser(description="Setup services for testing")
-    parser.add_argument(
-        "--deploy", action="store_true", help="Flag to setup docker services"
-    )
+    parser.add_argument("--deploy", action="store_true", help="Flag to setup docker services")
     parser.add_argument(
         "--tear-down",
         action="store_true",
@@ -36,9 +34,7 @@ def parse_args():
         sys.exit(1)
 
     if not any([args.deploy, args.tear_down]):
-        print(
-            "Please provide either --deploy or --tear-down flags. For more help use --help flag."
-        )
+        print("Please provide either --deploy or --tear-down flags. For more help use --help flag.")
         sys.exit(0)
 
     if args.deploy:
@@ -48,9 +44,7 @@ def parse_args():
         os.system(f"docker-compose -f {COMPOSE_FILE} pull")
 
         print("3) Bringing up services.")
-        os.system(
-            f"docker-compose -f {COMPOSE_FILE} up -d --remove-orphans --force-recreate"
-        )
+        os.system(f"docker-compose -f {COMPOSE_FILE} up -d --remove-orphans --force-recreate")
 
         print(f"4) Deleting old test folder at {TEST_DATA_PATH}")
         if TEST_DATA_PATH.exists():

.ci_helpers/py3.10.yaml

@@ -4,16 +4,14 @@ channels:
 dependencies:
   - python=3.10
   - dask
-  - matplotlib
   - netCDF4
-  - numpy
   - pynmea2
   - pytz
   - scipy
   - xarray
   - zarr
   - fsspec
-  - requests
-  - aiohttp
   - s3fs
+  - matplotlib-base
+  - cmocean
   - mamba=0.20.0

.ci_helpers/py3.8.yaml

@@ -4,16 +4,14 @@ channels:
 dependencies:
   - python=3.8
   - dask
-  - matplotlib
   - netCDF4
-  - numpy
   - pynmea2
   - pytz
   - scipy
   - xarray
   - zarr
   - fsspec
-  - requests
-  - aiohttp
   - s3fs
+  - matplotlib-base
+  - cmocean
   - mamba=0.20.0

.ci_helpers/py3.9.yaml

@@ -4,16 +4,14 @@ channels:
 dependencies:
   - python=3.9
   - dask
-  - matplotlib
   - netCDF4
-  - numpy
   - pynmea2
   - pytz
   - scipy
   - xarray
   - zarr
   - fsspec
-  - requests
-  - aiohttp
   - s3fs
+  - matplotlib-base
+  - cmocean
   - mamba=0.20.0

.ci_helpers/run-test.py

@@ -45,9 +45,7 @@
         default="",
         help="Comma separated list of changed files.",
     )
-    parser.add_argument(
-        "--pytest-args", type=str, help="Optional pytest args", default=""
-    )
+    parser.add_argument("--pytest-args", type=str, help="Optional pytest args", default="")
     parser.add_argument(
         "--local",
         action="store_true",

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+max-line-length = 100
+output-file = flake8_log.txt
+tee = True
+ignore = E722,E203,W503,T001

.github/workflows/ci.yaml

@@ -21,12 +21,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-        - python-version: 3.7
-          experimental: false
         - python-version: 3.8
           experimental: false
         - python-version: 3.9
-          experimental: true
+          experimental: false
         - python-version: "3.10"
           experimental: true
     services:
@@ -91,7 +89,7 @@ jobs:
       - name: Install echopype
         shell: bash -l {0}
         run: |
-          python -m pip install -e .
+          python -m pip install -e .[plot]
       - name: Running All Tests
         shell: bash -l {0}
         run: |

.github/workflows/pr.yaml

@@ -17,12 +17,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-        - python-version: 3.7
-          experimental: false
         - python-version: 3.8
           experimental: false
         - python-version: 3.9
-          experimental: true
+          experimental: false
         - python-version: "3.10"
           experimental: true
     services:
@@ -87,7 +85,7 @@ jobs:
       - name: Install echopype
         shell: bash -l {0}
         run: |
-          python -m pip install -e .
+          python -m pip install -e .[plot]
       - name: Finding changed files
         id: files
         uses: lsetiawan/get-changed-files@pr_target

.readthedocs.yml

@@ -18,9 +18,8 @@ formats: []
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-   version: 3.7
+   version: "3.8"
    install:
-      - requirements: requirements.txt
       - requirements: docs/requirements.txt
       - method: pip
         path: .

docs/source/contributing.rst

@@ -76,12 +76,14 @@ Create a `conda <https://docs.conda.io>`_ environment for echopype development
 
 .. code-block:: bash
 
-    conda create -c conda-forge -n echopype --yes python=3.9 --file requirements.txt --file requirements-dev.txt
+    conda create -c conda-forge -n echopype --yes python=3.9 --file requirements-dev.txt
     conda activate echopype
     # ipykernel is recommended, in order to use with JupyterLab and IPython
     # to aid with development. We recommend you install JupyterLab separately
     conda install -c conda-forge ipykernel
-    pip install -e .
+    # plot is an extra set of requirements that can be used for plotting.
+    # the command will install all the dependencies along with plotting dependencies.
+    pip install -e .[plot]
 
 See the :doc:`installation` page to simply install the latest echopype release from conda or PyPI.
 

echopype/calibrate/api.py

@@ -20,9 +20,7 @@ def _compute_cal(
     # Check on waveform_mode and encode_mode inputs
     if echodata.sonar_model == "EK80":
         if waveform_mode is None or encode_mode is None:
-            raise ValueError(
-                "waveform_mode and encode_mode must be specified for EK80 calibration"
-            )
+            raise ValueError("waveform_mode and encode_mode must be specified for EK80 calibration")
         elif waveform_mode not in ("BB", "CW"):
             raise ValueError("Input waveform_mode not recognized!")
         elif encode_mode not in ("complex", "power"):
@@ -54,9 +52,7 @@ def _compute_cal(
     # Perform calibration
     if cal_type == "Sv":
 
-        sv_dataset = cal_obj.compute_Sv(
-            waveform_mode=waveform_mode, encode_mode=encode_mode
-        )
+        sv_dataset = cal_obj.compute_Sv(waveform_mode=waveform_mode, encode_mode=encode_mode)
 
         if "water_level" in echodata.platform.data_vars.keys():
             # add water_level to the created xr.Dataset

echopype/calibrate/calibrate_azfp.py

@@ -31,9 +31,7 @@ def get_cal_params(self, cal_params):
         # Params from the Beam group
         for p in ["EL", "DS", "TVR", "VTX", "Sv_offset", "equivalent_beam_angle"]:
             # substitute if None in user input
-            self.cal_params[p] = (
-                cal_params[p] if p in cal_params else self.echodata.beam[p]
-            )
+            self.cal_params[p] = cal_params[p] if p in cal_params else self.echodata.beam[p]
 
     def get_env_params(self):
         """Get env params using user inputs or values from data file.
@@ -51,9 +49,7 @@ def get_env_params(self):
 
         # Salinity and pressure always come from user input
         if ("salinity" not in self.env_params) or ("pressure" not in self.env_params):
-            raise ReferenceError(
-                "Please supply both salinity and pressure in env_params."
-            )
+            raise ReferenceError("Please supply both salinity and pressure in env_params.")
         else:
             self.env_params["salinity"] = self.env_params["salinity"]
             self.env_params["pressure"] = self.env_params["pressure"]
@@ -84,9 +80,7 @@ def compute_range_meter(self, cal_type):
             'Sv' for calculating volume backscattering strength, or
             'Sp' for calculating point backscattering strength
         """
-        self.range_meter = self.echodata.compute_range(
-            self.env_params, azfp_cal_type=cal_type
-        )
+        self.range_meter = self.echodata.compute_range(self.env_params, azfp_cal_type=cal_type)
 
     def _cal_power(self, cal_type, **kwargs):
         """Calibrate to get volume backscattering strength (Sv) from AZFP power data.
@@ -112,9 +106,7 @@ def _cal_power(self, cal_type, **kwargs):
         # scaling factor (slope) in Fig.G-1, units Volts/dB], see p.84
         a = self.cal_params["DS"]
         EL = (
-            self.cal_params["EL"]
-            - 2.5 / a
-            + self.echodata.beam.backscatter_r / (26214 * a)
+            self.cal_params["EL"] - 2.5 / a + self.echodata.beam.backscatter_r / (26214 * a)
         )  # eq.(5)
 
         if cal_type == "Sv":

echopype/calibrate/calibrate_base.py

@@ -83,9 +83,7 @@ def __init__(
         >>> echopype.calibrate.compute_Sv(echodata, env_params=env_params)
         """  # noqa
         if interp_method not in VALID_INTERP_METHODS[data_kind]:
-            raise ValueError(
-                f"invalid interp_method {interp_method} for data_kind {data_kind}"
-            )
+            raise ValueError(f"invalid interp_method {interp_method} for data_kind {data_kind}")
 
         self.env_params = env_params
         self.data_kind = data_kind
@@ -112,13 +110,9 @@ def _apply(self, echodata) -> Dict[str, xr.DataArray]:
 
         if self.data_kind == "mobile":
             if np.isnan(echodata.platform["location_time"]).all():
-                raise ValueError(
-                    "cannot perform mobile interpolation without location_time"
-                )
+                raise ValueError("cannot perform mobile interpolation without location_time")
             # compute_range needs indexing by ping_time
-            interp_plat = echodata.platform.interp(
-                {"location_time": echodata.beam["ping_time"]}
-            )
+            interp_plat = echodata.platform.interp({"location_time": echodata.beam["ping_time"]})
 
             result = {}
             for var, values in env_params.data_vars.items():
@@ -132,18 +126,15 @@ def _apply(self, echodata) -> Dict[str, xr.DataArray]:
                         interp_plat["longitude"].data,
                     )
                 )
-                interp = scipy.interpolate.griddata(
-                    points, values, xi, method=self.interp_method
-                )
+                interp = scipy.interpolate.griddata(points, values, xi, method=self.interp_method)
                 result[var] = ("ping_time", interp)
             env_params = xr.Dataset(
                 data_vars=result, coords={"ping_time": interp_plat["ping_time"]}
             )
         else:
             # TODO: organized case
             min_max = {
-                dim: {"min": env_params[dim].min(), "max": env_params[dim].max()}
-                for dim in dims
+                dim: {"min": env_params[dim].min(), "max": env_params[dim].max()} for dim in dims
             }
 
             extrap = env_params.interp(
@@ -153,25 +144,18 @@ def _apply(self, echodata) -> Dict[str, xr.DataArray]:
                 kwargs={"fill_value": "extrapolate" if len(dims) == 1 else None},
             )
             # only keep unique indexes; xarray requires that indexes be unique
-            extrap_unique_idx = {
-                dim: np.unique(extrap[dim], return_index=True)[1] for dim in dims
-            }
+            extrap_unique_idx = {dim: np.unique(extrap[dim], return_index=True)[1] for dim in dims}
             extrap = extrap.isel(**extrap_unique_idx)
             interp = env_params.interp(
                 {dim: echodata.platform[dim].data for dim in dims},
                 method=self.interp_method,
             )
-            interp_unique_idx = {
-                dim: np.unique(interp[dim], return_index=True)[1] for dim in dims
-            }
+            interp_unique_idx = {dim: np.unique(interp[dim], return_index=True)[1] for dim in dims}
             interp = interp.isel(**interp_unique_idx)
 
             if self.extrap_method is not None:
                 less = extrap.sel(
-                    {
-                        dim: extrap[dim][extrap[dim] < min_max[dim]["min"]]
-                        for dim in dims
-                    }
+                    {dim: extrap[dim][extrap[dim] < min_max[dim]["min"]] for dim in dims}
                 )
                 middle = interp.sel(
                     {
@@ -185,10 +169,7 @@ def _apply(self, echodata) -> Dict[str, xr.DataArray]:
                     }
                 )
                 greater = extrap.sel(
-                    {
-                        dim: extrap[dim][extrap[dim] > min_max[dim]["max"]]
-                        for dim in dims
-                    }
+                    {dim: extrap[dim][extrap[dim] > min_max[dim]["max"]] for dim in dims}
                 )
 
                 # remove empty datasets (xarray does not allow any dims from any datasets