updated linting

binder/Live_Demo.ipynb

@@ -548,9 +548,7 @@
     "# Note: The vertical mean is automatically weighted.\n",
     "fig = plt.figure(figsize=mapsize)\n",
     "xr_kwargs = dict(robust=True, center=False, cmap=\"inferno\")\n",
-    "anim = od100m.animate.horizontal_section(\n",
-    "    varName=\"Ertel_PV\", meanAxes=\"Z\", **xr_kwargs\n",
-    ")\n",
+    "anim = od100m.animate.horizontal_section(varName=\"Ertel_PV\", meanAxes=\"Z\", **xr_kwargs)\n",
     "plt.close()"
    ]
   },

ci/environment-py36.yml

@@ -23,12 +23,12 @@ dependencies:
   - codecov
   - flake8
   - ffmpeg
-  - black<20.8b1
+  - black
   - aiohttp
   - pandas
   - matplotlib
+  - xmitgcm
   - pip
   - pip:
-    - git+https://github.com/MITgcm/xmitgcm.git
     - black-nb
     - pre-commit

ci/environment-py37.yml

@@ -23,12 +23,12 @@ dependencies:
   - codecov
   - flake8
   - ffmpeg
-  - black<20.8b1
+  - black
   - aiohttp
   - pandas
   - matplotlib
+  - xmitgcm
   - pip
   - pip:
-    - git+https://github.com/MITgcm/xmitgcm.git
     - black-nb
     - pre-commit

ci/environment-py38.yml

@@ -23,12 +23,12 @@ dependencies:
   - codecov
   - flake8
   - ffmpeg
-  - black<20.8b1
+  - black
   - aiohttp
   - pandas
   - matplotlib
+  - xmitgcm
   - pip
   - pip:
-    - git+https://github.com/MITgcm/xmitgcm.git
     - black-nb
     - pre-commit

docs/Kogur.ipynb

@@ -665,9 +665,7 @@
     "    ms=ms,\n",
     "    label=\"path1\",\n",
     ")\n",
-    "_ = ax.plot(\n",
-    "    ds[\"XC\"].squeeze(), ds[\"YC\"].squeeze(), \"k.\", ms=ms, label=\"mooring\"\n",
-    ")\n",
+    "_ = ax.plot(ds[\"XC\"].squeeze(), ds[\"YC\"].squeeze(), \"k.\", ms=ms, label=\"mooring\")\n",
     "_ = ax.scatter(\n",
     "    ds[\"X_Vtransport\"].where(ds[\"dir_Vtransport\"] == 1),\n",
     "    ds[\"Y_Vtransport\"].where(ds[\"dir_Vtransport\"] == 1),\n",
@@ -805,8 +803,7 @@
     "lines = cum_oflow_moor.squeeze().plot.line(hue=\"path\", linewidth=3)\n",
     "tot_mean_oflow_moor = cum_oflow_moor.isel(mooring=-1).mean(\"path\")\n",
     "title = ax.set_title(\n",
-    "    \"TOTAL MEAN OVERFLOW TRANSPORT: {0:.1f} Sv\"\n",
-    "    \"\".format(tot_mean_oflow_moor.values)\n",
+    "    \"TOTAL MEAN OVERFLOW TRANSPORT: {0:.1f} Sv\" \"\".format(tot_mean_oflow_moor.values)\n",
     ")"
    ]
   },

docs/Particles.ipynb

@@ -502,16 +502,12 @@
     "mask_DSO = xr.where(od_lag.dataset[\"Sigma0\"] >= DSO_threshold, 1, 0)\n",
     "\n",
     "# Mask particles always in the overflow\n",
-    "mask_dense = xr.where(\n",
-    "    mask_DSO.sum(\"time\") == len(od_lag.dataset[\"time\"]), 1, 0\n",
-    ")\n",
+    "mask_dense = xr.where(mask_DSO.sum(\"time\") == len(od_lag.dataset[\"time\"]), 1, 0)\n",
     "\n",
     "# Mask mixed particles\n",
     "mask_mixed = xr.where(\n",
     "    np.logical_and(\n",
-    "        np.logical_and(\n",
-    "            mask_DSO.isel(time=0) == 1, mask_DSO.isel(time=-1) == 0\n",
-    "        ),\n",
+    "        np.logical_and(mask_DSO.isel(time=0) == 1, mask_DSO.isel(time=-1) == 0),\n",
     "        mask_DSO.sum(\"time\") <= len(od_lag.dataset[\"time\"]) / 2,\n",
     "    ),\n",
     "    1,\n",
@@ -700,8 +696,7 @@
     "        contour_kwargs=contour_kwargs,\n",
     "    )\n",
     "tit = \"\\n\".join(\n",
-    "    [\"c) \"]\n",
-    "    + [f\"{col}: {tit}\" for _, (col, tit) in enumerate(zip(colors, titles))]\n",
+    "    [\"c) \"] + [f\"{col}: {tit}\" for _, (col, tit) in enumerate(zip(colors, titles))]\n",
     ")\n",
     "ax.set_title(tit)\n",
     "\n",

docs/Statistics.ipynb

@@ -392,10 +392,7 @@
     "print(\"\\nVariables added:\")\n",
     "for var in od_snapshot.dataset.data_vars:\n",
     "    if any(\n",
-    "        [\n",
-    "            (var == v or var == f\"weight_{v}\" or var == f\"w_mean_{v}\")\n",
-    "            for v in var_list\n",
-    "        ]\n",
+    "        [(var == v or var == f\"weight_{v}\" or var == f\"w_mean_{v}\") for v in var_list]\n",
     "    ):\n",
     "        print(\n",
     "            \"{:>15}: {} [{}]\".format(\n",
@@ -639,25 +636,17 @@
     "    print(\"%s\" % (units))\n",
     "\n",
     "    # Plot the PDF\n",
-    "    p0 = ax.plot(\n",
-    "        bin_mid, var_hist, \"-\", color=hist_color, label=\"PDF\", linewidth=4\n",
-    "    )\n",
+    "    p0 = ax.plot(bin_mid, var_hist, \"-\", color=hist_color, label=\"PDF\", linewidth=4)\n",
     "    ax.set_xlabel(var_name + \" [\" + units + \"]\", fontsize=15)\n",
-    "    ax.set_ylabel(\n",
-    "        \"Probability distribution function\", fontsize=15, color=hist_color\n",
-    "    )\n",
+    "    ax.set_ylabel(\"Probability distribution function\", fontsize=15, color=hist_color)\n",
     "    ax.tick_params(axis=\"y\", labelcolor=hist_color)\n",
     "    ax.grid(True)\n",
     "    ax.set_xlim(var_limits)\n",
     "\n",
     "    # Compute and plot the CDF\n",
     "    ax2 = ax.twinx()\n",
-    "    p1 = ax2.plot(\n",
-    "        var_qiles2, qiles2, \"-\", color=qiles_color, label=\"CDF\", linewidth=4\n",
-    "    )\n",
-    "    ax2.set_ylabel(\n",
-    "        \"Cumulative distribution function\", fontsize=15, color=qiles_color\n",
-    "    )\n",
+    "    p1 = ax2.plot(var_qiles2, qiles2, \"-\", color=qiles_color, label=\"CDF\", linewidth=4)\n",
+    "    ax2.set_ylabel(\"Cumulative distribution function\", fontsize=15, color=qiles_color)\n",
     "    ax2.tick_params(axis=\"y\", labelcolor=qiles_color)\n",
     "    ax2.set_xlim(var_limits)\n",
     "\n",
@@ -666,9 +655,7 @@
     "    l1 = ax.axvline(var_mean, linestyle=\"-\", color=\"w\", linewidth=2)\n",
     "    l2 = ax.axvline(var_median, linestyle=\"--\", color=stat_color3, linewidth=2)\n",
     "    l3 = ax.axvline(var_mode, linestyle=\"--\", color=stat_color4, linewidth=2)\n",
-    "    ax.legend(\n",
-    "        (l2, l3, p2), (\"median\", \"mode\", \"mean +/- std\"), loc=\"upper left\"\n",
-    "    )"
+    "    ax.legend((l2, l3, p2), (\"median\", \"mode\", \"mean +/- std\"), loc=\"upper left\")"
    ]
   },
   {

docs/Tutorial.ipynb

@@ -894,9 +894,7 @@
     }
    ],
    "source": [
-    "ds = ospy.compute.gradient(\n",
-    "    od, varNameList=[\"Temp\", \"S\", \"Sigma0\"], axesList=[\"time\"]\n",
-    ")\n",
+    "ds = ospy.compute.gradient(od, varNameList=[\"Temp\", \"S\", \"Sigma0\"], axesList=[\"time\"])\n",
     "print(ds.data_vars)"
    ]
   },
@@ -1010,15 +1008,9 @@
     }
    ],
    "source": [
-    "ds = ospy.compute.weighted_mean(\n",
-    "    od, varNameList=[\"Temp\", \"S\"], storeWeights=False\n",
-    ")\n",
+    "ds = ospy.compute.weighted_mean(od, varNameList=[\"Temp\", \"S\"], storeWeights=False)\n",
     "for var in ds.data_vars:\n",
-    "    print(\n",
-    "        \"{} = {} {}\".format(\n",
-    "            var, ds[var].values, ds[var].attrs.pop(\"units\", \"\")\n",
-    "        )\n",
-    "    )"
+    "    print(\"{} = {} {}\".format(var, ds[var].values, ds[var].attrs.pop(\"units\", \"\")))"
    ]
   },
   {

oceanspy/llc_rearrange.py

@@ -7,7 +7,13 @@ class LLCtransformation:
     """ A class containing the transformation of LLCgrids"""
 
     def __init__(
-        self, ds, varlist, transformation, centered="Atlantic", faces="all", drop=False,
+        self,
+        ds,
+        varlist,
+        transformation,
+        centered="Atlantic",
+        faces="all",
+        drop=False,
     ):
         self._ds = ds  # xarray.DataSet
         self._varlist = varlist  # variables names to be transformed
@@ -17,9 +23,14 @@ def __init__(
 
     @classmethod
     def arctic_centered(
-        self, ds, varlist, centered="Arctic", faces="all", drop=False,
+        self,
+        ds,
+        varlist,
+        centered="Arctic",
+        faces="all",
+        drop=False,
     ):
-        """ Transforms the dataset by removing faces as a dimension, into a
+        """Transforms the dataset by removing faces as a dimension, into a
         new dataset centered at the arctic, while preserving the grid.
         """
         Nx = len(ds["X"])
@@ -149,9 +160,14 @@ def arctic_centered(
 
     @classmethod
     def arctic_crown(
-        self, ds, varlist, centered, faces="all", drop=False,
+        self,
+        ds,
+        varlist,
+        centered,
+        faces="all",
+        drop=False,
     ):
-        """ Transforms the dataset in which faces appears as a dimension into
+        """Transforms the dataset in which faces appears as a dimension into
         one without faces, with grids and variables sharing a common grid
         orientation.
         """
@@ -370,7 +386,7 @@ def init_vars(ds, DSNEW, varlist):
 
 
 def drop_size(ds):
-    """ Drops a row and/or column from interior (scalar) points, creating
+    """Drops a row and/or column from interior (scalar) points, creating
     len(X)<len(Xp1) of staggered grids
     """
     coords = {}

oceanspy/subsample.py

@@ -1345,7 +1345,7 @@ def particle_properties(od, times, Ypart, Xpart, Zpart, **kwargs):
 
 
 def get_maskH(ds, add_Hbdr, add_Vbdr, XRange, YRange):
-    """ Define this function to avoid repeated code. First time this runs,
+    """Define this function to avoid repeated code. First time this runs,
     the objective is to figure out which faces survive the cutout. This info
     is then passed, when transforming llc-grids, to llc_rearrange. Second
     time this code runs, it gets applied on a dataset without faces as a

oceanspy/tests/test_llc_rearrange.py

@@ -33,8 +33,7 @@
     ],
 )
 def test_original_dims(od, var, expected):
-    """ test original dimensions
-    """
+    """test original dimensions"""
     dims = Dims([dim for dim in od._ds[var].dims][::-1])
     assert dims == expected
 
@@ -383,7 +382,7 @@ def test_init_vars(od, tNX, tNY, X0, varlist):
 
 
 def _is_connect(faces, rotated=False):
-    """ do faces in a facet connect? Not applicable to arc cap, and only
+    """do faces in a facet connect? Not applicable to arc cap, and only
     applicable to either rotated or not rotated facets"""
     if rotated is False:
         A_fac = _np.array([0, 1, 2])

oceanspy/tests/test_plot.py

@@ -210,7 +210,10 @@ def test_hor_sec_warn(od_in):
     with pytest.warns(UserWarning):
         plt.close()
         ax = horizontal_section(
-            od_in, varName="Eta", contourName="Depth", subplot_kws={"projection": None},
+            od_in,
+            varName="Eta",
+            contourName="Depth",
+            subplot_kws={"projection": None},
         )
         assert isinstance(ax, xr.plot.FacetGrid)