isort/black

Author: AntSimi

check.sh

@@ -1,7 +1,5 @@
-isort src tests examples
-black src tests examples
-blackdoc src tests examples
-flake8 tests examples src --count --select=E9,F63,F7,F82 --show-source --statistics
-# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-flake8 tests examples src --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+isort .
+black .
+blackdoc .
+flake8 .
 python -m pytest -vv --cov py_eddy_tracker --cov-report html

doc/grid_identification.rst

@@ -47,38 +47,42 @@ Activate verbose
 .. code-block:: python
 
     from py_eddy_tracker import start_logger
-    start_logger().setLevel('DEBUG') # Available options: ERROR, WARNING, INFO, DEBUG
+
+    start_logger().setLevel("DEBUG")  # Available options: ERROR, WARNING, INFO, DEBUG
 
 Run identification
 
 .. code-block:: python
 
     from datetime import datetime
+
     h = RegularGridDataset(grid_name, lon_name, lat_name)
-    h.bessel_high_filter('adt', 500, order=3)
+    h.bessel_high_filter("adt", 500, order=3)
     date = datetime(2019, 2, 23)
     a, c = h.eddy_identification(
-        'adt', 'ugos', 'vgos', # Variables used for identification
-        date, # Date of identification
-        0.002, # step between two isolines of detection (m)
-        pixel_limit=(5, 2000), # Min and max pixel count for valid contour
-        shape_error=55, # Error max (%) between ratio of circle fit and contour
-        )
+        "adt",
+        "ugos",
+        "vgos",  # Variables used for identification
+        date,  # Date of identification
+        0.002,  # step between two isolines of detection (m)
+        pixel_limit=(5, 2000),  # Min and max pixel count for valid contour
+        shape_error=55,  # Error max (%) between ratio of circle fit and contour
+    )
 
 Plot the resulting identification
 
 .. code-block:: python
 
-    fig = plt.figure(figsize=(15,7))
-    ax = fig.add_axes([.03,.03,.94,.94])
-    ax.set_title('Eddies detected -- Cyclonic(red) and Anticyclonic(blue)')
-    ax.set_ylim(-75,75)
-    ax.set_xlim(0,360)
-    ax.set_aspect('equal')
-    a.display(ax, color='b', linewidth=.5)
-    c.display(ax, color='r', linewidth=.5)
+    fig = plt.figure(figsize=(15, 7))
+    ax = fig.add_axes([0.03, 0.03, 0.94, 0.94])
+    ax.set_title("Eddies detected -- Cyclonic(red) and Anticyclonic(blue)")
+    ax.set_ylim(-75, 75)
+    ax.set_xlim(0, 360)
+    ax.set_aspect("equal")
+    a.display(ax, color="b", linewidth=0.5)
+    c.display(ax, color="r", linewidth=0.5)
     ax.grid()
-    fig.savefig('share/png/eddies.png')
+    fig.savefig("share/png/eddies.png")
 
 .. image:: ../share/png/eddies.png
 
@@ -87,7 +91,8 @@ Save identification data
 .. code-block:: python
 
     from netCDF import Dataset
-    with Dataset(date.strftime('share/Anticyclonic_%Y%m%d.nc'), 'w') as h:
+
+    with Dataset(date.strftime("share/Anticyclonic_%Y%m%d.nc"), "w") as h:
         a.to_netcdf(h)
-    with Dataset(date.strftime('share/Cyclonic_%Y%m%d.nc'), 'w') as h:
+    with Dataset(date.strftime("share/Cyclonic_%Y%m%d.nc"), "w") as h:
         c.to_netcdf(h)

doc/grid_load_display.rst

@@ -7,50 +7,56 @@ Loading grid
 .. code-block:: python
 
     from py_eddy_tracker.dataset.grid import RegularGridDataset
-    grid_name, lon_name, lat_name = 'share/nrt_global_allsat_phy_l4_20190223_20190226.nc', 'longitude', 'latitude'
+
+    grid_name, lon_name, lat_name = (
+        "share/nrt_global_allsat_phy_l4_20190223_20190226.nc",
+        "longitude",
+        "latitude",
+    )
     h = RegularGridDataset(grid_name, lon_name, lat_name)
 
 Plotting grid
 
 .. code-block:: python
 
     from matplotlib import pyplot as plt
+
     fig = plt.figure(figsize=(14, 12))
-    ax = fig.add_axes([.02, .51, .9, .45])
-    ax.set_title('ADT (m)')
+    ax = fig.add_axes([0.02, 0.51, 0.9, 0.45])
+    ax.set_title("ADT (m)")
     ax.set_ylim(-75, 75)
-    ax.set_aspect('equal')
-    m = h.display(ax, name='adt', vmin=-1, vmax=1)
+    ax.set_aspect("equal")
+    m = h.display(ax, name="adt", vmin=-1, vmax=1)
     ax.grid(True)
-    plt.colorbar(m, cax=fig.add_axes([.94, .51, .01, .45]))
+    plt.colorbar(m, cax=fig.add_axes([0.94, 0.51, 0.01, 0.45]))
 
 
 Filtering
 
 .. code-block:: python
 
     h = RegularGridDataset(grid_name, lon_name, lat_name)
-    h.bessel_high_filter('adt', 500, order=3)
+    h.bessel_high_filter("adt", 500, order=3)
 
 
 Save grid
 
 .. code-block:: python
 
-    h.write('/tmp/grid.nc')
+    h.write("/tmp/grid.nc")
 
 
 Add second plot
 
 .. code-block:: python
 
-    ax = fig.add_axes([.02, .02, .9, .45])
-    ax.set_title('ADT Filtered (m)')
-    ax.set_aspect('equal')
+    ax = fig.add_axes([0.02, 0.02, 0.9, 0.45])
+    ax.set_title("ADT Filtered (m)")
+    ax.set_aspect("equal")
     ax.set_ylim(-75, 75)
-    m = h.display(ax, name='adt', vmin=-.1, vmax=.1)
+    m = h.display(ax, name="adt", vmin=-0.1, vmax=0.1)
     ax.grid(True)
-    plt.colorbar(m, cax=fig.add_axes([.94, .02, .01, .45]))
-    fig.savefig('share/png/filter.png')
+    plt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.45]))
+    fig.savefig("share/png/filter.png")
 
 .. image:: ../share/png/filter.png

doc/spectrum.rst

@@ -11,7 +11,7 @@ Load data
 
     raw = RegularGridDataset(grid_name, lon_name, lat_name)
     filtered = RegularGridDataset(grid_name, lon_name, lat_name)
-    filtered.bessel_low_filter('adt', 150, order=3)
+    filtered.bessel_low_filter("adt", 150, order=3)
 
     areas = dict(
         sud_pacific=dict(llcrnrlon=188, urcrnrlon=280, llcrnrlat=-64, urcrnrlat=-7),
@@ -23,24 +23,34 @@ Compute and display spectrum
 
 .. code-block:: python
 
-    fig = plt.figure(figsize=(10,6))
+    fig = plt.figure(figsize=(10, 6))
     ax = fig.add_subplot(111)
-    ax.set_title('Spectrum')
-    ax.set_xlabel('km')
+    ax.set_title("Spectrum")
+    ax.set_xlabel("km")
     for name_area, area in areas.items():
 
-        lon_spec, lat_spec = raw.spectrum_lonlat('adt', area=area)
-        mappable = ax.loglog(*lat_spec, label='lat %s raw' % name_area)[0]
-        ax.loglog(*lon_spec, label='lon %s raw' % name_area, color=mappable.get_color(), linestyle='--')
-
-        lon_spec, lat_spec = filtered.spectrum_lonlat('adt', area=area)
-        mappable = ax.loglog(*lat_spec, label='lat %s high' % name_area)[0]
-        ax.loglog(*lon_spec, label='lon %s high' % name_area, color=mappable.get_color(), linestyle='--')
-
-    ax.set_xscale('log')
+        lon_spec, lat_spec = raw.spectrum_lonlat("adt", area=area)
+        mappable = ax.loglog(*lat_spec, label="lat %s raw" % name_area)[0]
+        ax.loglog(
+            *lon_spec,
+            label="lon %s raw" % name_area,
+            color=mappable.get_color(),
+            linestyle="--"
+        )
+
+        lon_spec, lat_spec = filtered.spectrum_lonlat("adt", area=area)
+        mappable = ax.loglog(*lat_spec, label="lat %s high" % name_area)[0]
+        ax.loglog(
+            *lon_spec,
+            label="lon %s high" % name_area,
+            color=mappable.get_color(),
+            linestyle="--"
+        )
+
+    ax.set_xscale("log")
     ax.legend()
     ax.grid()
-    fig.savefig('share/png/spectrum.png')
+    fig.savefig("share/png/spectrum.png")
 
 
 .. image:: ../share/png/spectrum.png
@@ -49,18 +59,23 @@ Compute and display spectrum ratio
 
 .. code-block:: python
 
-    fig = plt.figure(figsize=(10,6))
+    fig = plt.figure(figsize=(10, 6))
     ax = fig.add_subplot(111)
-    ax.set_title('Spectrum ratio')
-    ax.set_xlabel('km')
+    ax.set_title("Spectrum ratio")
+    ax.set_xlabel("km")
     for name_area, area in areas.items():
-        lon_spec, lat_spec = filtered.spectrum_lonlat('adt', area=area, ref=raw)
-        mappable = ax.plot(*lat_spec, label='lat %s high' % name_area)[0]
-        ax.plot(*lon_spec, label='lon %s high' % name_area, color=mappable.get_color(), linestyle='--')
-
-    ax.set_xscale('log')
+        lon_spec, lat_spec = filtered.spectrum_lonlat("adt", area=area, ref=raw)
+        mappable = ax.plot(*lat_spec, label="lat %s high" % name_area)[0]
+        ax.plot(
+            *lon_spec,
+            label="lon %s high" % name_area,
+            color=mappable.get_color(),
+            linestyle="--"
+        )
+
+    ax.set_xscale("log")
     ax.legend()
     ax.grid()
-    fig.savefig('share/png/spectrum_ratio.png')
+    fig.savefig("share/png/spectrum_ratio.png")
 
 .. image:: ../share/png/spectrum_ratio.png

examples/01_general_things/pet_storage.py

@@ -15,9 +15,9 @@
   manage eddies associated in networks, the ```track``` and ```segment``` fields allow to separate observations
 """
 
-import py_eddy_tracker_sample
 from matplotlib import pyplot as plt
 from numpy import arange, outer
+import py_eddy_tracker_sample
 
 from py_eddy_tracker.data import get_demo_path
 from py_eddy_tracker.observations.network import NetworkObservations

examples/02_eddy_identification/pet_eddy_detection_ACC.py

@@ -9,8 +9,7 @@
 """
 from datetime import datetime
 
-from matplotlib import pyplot as plt
-from matplotlib import style
+from matplotlib import pyplot as plt, style
 
 from py_eddy_tracker import data
 from py_eddy_tracker.dataset.grid import RegularGridDataset
@@ -65,7 +64,8 @@ def set_fancy_labels(fig, ticklabelsize=14, labelsize=14, labelweight="semibold"
     y_name="latitude",
     # Manual area subset
     indexs=dict(
-        latitude=slice(100 - margin, 220 + margin), longitude=slice(0, 230 + margin),
+        latitude=slice(100 - margin, 220 + margin),
+        longitude=slice(0, 230 + margin),
     ),
 )
 g_raw = RegularGridDataset(**kw_data)
@@ -187,10 +187,16 @@ def set_fancy_labels(fig, ticklabelsize=14, labelsize=14, labelweight="semibold"
     ax.set_ylabel("With filter")
 
     ax.plot(
-        a_[field][i_a] * factor, a[field][j_a] * factor, "r.", label="Anticyclonic",
+        a_[field][i_a] * factor,
+        a[field][j_a] * factor,
+        "r.",
+        label="Anticyclonic",
     )
     ax.plot(
-        c_[field][i_c] * factor, c[field][j_c] * factor, "b.", label="Cyclonic",
+        c_[field][i_c] * factor,
+        c[field][j_c] * factor,
+        "b.",
+        label="Cyclonic",
     )
     ax.set_aspect("equal"), ax.grid()
     ax.plot((0, 1000), (0, 1000), "g")

examples/02_eddy_identification/pet_interp_grid_on_dataset.py

@@ -43,7 +43,7 @@ def update_axes(ax, mappable=None):
 # %%
 # Compute and store eke in cm²/s²
 aviso_map.add_grid(
-    "eke", (aviso_map.grid("u") ** 2 + aviso_map.grid("v") ** 2) * 0.5 * (100 ** 2)
+    "eke", (aviso_map.grid("u") ** 2 + aviso_map.grid("v") ** 2) * 0.5 * (100**2)
 )
 
 eke_kwargs = dict(vmin=1, vmax=1000, cmap="magma_r")

examples/02_eddy_identification/pet_statistics_on_identification.py

@@ -4,9 +4,9 @@
 
 Some statistics on raw identification without any tracking
 """
-import numpy as np
 from matplotlib import pyplot as plt
 from matplotlib.dates import date2num
+import numpy as np
 
 from py_eddy_tracker import start_logger
 from py_eddy_tracker.data import get_remote_demo_sample

examples/06_grid_manipulation/pet_lavd.py

@@ -158,9 +158,7 @@ def update(i_frame):
 # %%
 # Format LAVD data
 lavd = RegularGridDataset.with_array(
-    coordinates=("lon", "lat"),
-    datas=dict(lavd=lavd.T, lon=x_g, lat=y_g,),
-    centered=True,
+    coordinates=("lon", "lat"), datas=dict(lavd=lavd.T, lon=x_g, lat=y_g), centered=True
 )
 
 # %%

examples/07_cube_manipulation/pet_cube.py

@@ -4,9 +4,10 @@
 
 Example which use CMEMS surface current with a Runge-Kutta 4 algorithm to advect particles.
 """
+from datetime import datetime, timedelta
+
 # sphinx_gallery_thumbnail_number = 2
 import re
-from datetime import datetime, timedelta
 
 from matplotlib import pyplot as plt
 from matplotlib.animation import FuncAnimation