Apply black and test with flake8

Author: AntSimi

doc/conf.py

@@ -12,8 +12,8 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
-import os
+# import sys
+# import os
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -39,7 +39,7 @@
 sphinx_gallery_conf = {
      'examples_dirs': '../examples',   # path to your example scripts
      'gallery_dirs': 'python_module',
-     'capture_repr': ('_repr_html_', '__repr__'),
+     'capture_repr': ('_repr_html_'),
      'line_numbers': False,
      'filename_pattern': '/pet',
 }

examples/pet_display_id.py

@@ -12,9 +12,9 @@
 c = EddiesObservations.load_file(data.get_path("Cyclonic_20190223.nc"))
 
 # Plot
-fig = plt.figure(figsize=(15,8))
+fig = plt.figure(figsize=(15, 8))
 ax = fig.add_subplot(111)
-ax.set_aspect('equal')
+ax.set_aspect("equal")
 ax.set_xlim(0, 360)
 ax.set_ylim(-80, 80)
 a.display(ax, label="Anticyclonic", color="r", lw=1)

examples/pet_one_track.py

@@ -13,13 +13,12 @@
 
 eddy = a.extract_ids([9672])
 eddy_f = a.extract_ids([9672])
-eddy_f.median_filter(1, 'time', 'lon').loess_filter(5, 'time', 'lon')
-eddy_f.median_filter(1, 'time', 'lat').loess_filter(5, 'time', 'lat')
+eddy_f.median_filter(1, "time", "lon").loess_filter(5, "time", "lon")
+eddy_f.median_filter(1, "time", "lat").loess_filter(5, "time", "lat")
 fig = plt.figure(figsize=(20, 8))
 ax = fig.add_subplot(111)
 ax.set_xlim(17, 22.5)
 ax.set_ylim(35, 36.5)
 ax.grid()
-eddy.plot(ax, color='r', lw=.5)
-eddy_f.plot(ax, color='g', lw=1)
-
+eddy.plot(ax, color="r", lw=0.5)
+eddy_f.plot(ax, color="g", lw=1)

examples/tracking_diagnostics/pet_center_count.py

@@ -9,20 +9,23 @@
 import py_eddy_tracker_sample
 
 
-a = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr"))
-c = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr"))
+a = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
+)
+c = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
+)
 a = a.merge(c)
 # Plot
 fig = plt.figure(figsize=(20, 8))
 ax = fig.add_subplot(111)
 ax.set_xlim(-5, 37)
 ax.set_ylim(30, 46)
-step = .1
+step = 0.1
 t0, t1 = a.period
 g = a.grid_count(((-5, 37, step), (30, 46, step)), center=True)
-g.vars['count'] = g.vars['count'] / (step ** 2 * (t1 - t0))
-m = g.display(ax, name='count', vmin=0, vmax=2)
+g.vars["count"] = g.vars["count"] / (step ** 2 * (t1 - t0))
+m = g.display(ax, name="count", vmin=0, vmax=2)
 ax.grid()
 cb = plt.colorbar(m, cax=fig.add_axes([0.95, 0.05, 0.01, 0.9]))
-cb.set_label('Eddies by 1°^2 by day')
-
+cb.set_label("Eddies by 1°^2 by day")

examples/tracking_diagnostics/pet_lifetime.py

@@ -8,38 +8,46 @@
 import py_eddy_tracker_sample
 from numpy import arange
 
-a = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr"))
-c = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr"))
+a = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
+)
+c = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
+)
 
 # Plot
 fig = plt.figure()
-ax_lifetime = fig.add_axes([0.05, 0.55, .4, .4])
-ax_cum_lifetime = fig.add_axes([0.55, 0.55, .4, .4])
-ax_ratio_lifetime = fig.add_axes([0.05, 0.05, .4, .4])
-ax_ratio_cum_lifetime = fig.add_axes([0.55, 0.05, .4, .4])
+ax_lifetime = fig.add_axes([0.05, 0.55, 0.4, 0.4])
+ax_cum_lifetime = fig.add_axes([0.55, 0.55, 0.4, 0.4])
+ax_ratio_lifetime = fig.add_axes([0.05, 0.05, 0.4, 0.4])
+ax_ratio_cum_lifetime = fig.add_axes([0.55, 0.05, 0.4, 0.4])
 
 
-cum_a, bins, _ = ax_cum_lifetime.hist(a['n'], histtype='step', bins=arange(0,800,1), label='Anticyclonic', color='r')
-cum_c, bins, _ = ax_cum_lifetime.hist(c['n'], histtype='step', bins=arange(0,800,1), label='Cyclonic', color='b')
+cum_a, bins, _ = ax_cum_lifetime.hist(
+    a["n"], histtype="step", bins=arange(0, 800, 1), label="Anticyclonic", color="r"
+)
+cum_c, bins, _ = ax_cum_lifetime.hist(
+    c["n"], histtype="step", bins=arange(0, 800, 1), label="Cyclonic", color="b"
+)
 
-x = (bins[1:] + bins[:-1]) / 2.
-ax_ratio_cum_lifetime.plot(x, cum_c/cum_a)
+x = (bins[1:] + bins[:-1]) / 2.0
+ax_ratio_cum_lifetime.plot(x, cum_c / cum_a)
 
 nb_a, nb_c = cum_a[:-1] - cum_a[1:], cum_c[:-1] - cum_c[1:]
-ax_lifetime.plot(x[1:], nb_a, label='Anticyclonic', color='r')
-ax_lifetime.plot(x[1:], nb_c, label='Cyclonic', color='b')
+ax_lifetime.plot(x[1:], nb_a, label="Anticyclonic", color="r")
+ax_lifetime.plot(x[1:], nb_c, label="Cyclonic", color="b")
 
 ax_ratio_lifetime.plot(x[1:], nb_c / nb_a)
 
 
 for ax in (ax_lifetime, ax_cum_lifetime, ax_ratio_cum_lifetime, ax_ratio_lifetime):
-    ax.set_xlim(0,365)
+    ax.set_xlim(0, 365)
     if ax in (ax_lifetime, ax_cum_lifetime):
-        ax.set_ylim(1,None)
-        ax.set_yscale('log')
+        ax.set_ylim(1, None)
+        ax.set_yscale("log")
         ax.legend()
     else:
-        ax.set_ylim(0,2)
-        ax.set_ylabel('Ratio Cyclonic/Anticyclonic')
-    ax.set_xlabel('Lifetime (days)')
+        ax.set_ylim(0, 2)
+        ax.set_ylabel("Ratio Cyclonic/Anticyclonic")
+    ax.set_xlabel("Lifetime (days)")
     ax.grid()

examples/tracking_diagnostics/pet_pixel_used.py

@@ -9,20 +9,24 @@
 import py_eddy_tracker_sample
 
 
-a = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr"))
-c = TrackEddiesObservations.load_file(py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr"))
+a = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
+)
+c = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
+)
 
 # Plot
 fig = plt.figure(figsize=(15, 20))
 ax_a = fig.add_subplot(311)
-ax_a.set_title('Anticyclonic frequency')
+ax_a.set_title("Anticyclonic frequency")
 ax_c = fig.add_subplot(312)
-ax_c.set_title('Cyclonic frequency')
+ax_c.set_title("Cyclonic frequency")
 ax_all = fig.add_subplot(313)
-ax_all.set_title('All eddies frequency')
+ax_all.set_title("All eddies frequency")
 
 step = 0.1
-kwargs_pcolormesh = dict(cmap='terrain_r', vmin=0, vmax=.75)
+kwargs_pcolormesh = dict(cmap="terrain_r", vmin=0, vmax=0.75)
 g_a = a.grid_count(((-5, 37, step), (30, 46, step)), intern=True)
 t0, t1 = a.period
 g_a.vars["count"] = g_a.vars["count"] / (t1 - t0)

examples/tracking_diagnostics/pet_propagation.py

@@ -26,22 +26,23 @@ def cum_distance_by_track(distance, track):
     new_distance[i + 1] = d_cum
     return new_distance
 
+
 a = TrackEddiesObservations.load_file(
     py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
 )
 c = TrackEddiesObservations.load_file(
     py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
 )
 
-a.median_filter(1, 'time', 'lon').loess_filter(5, 'time', 'lon')
-a.median_filter(1, 'time', 'lat').loess_filter(5, 'time', 'lat')
-c.median_filter(1, 'time', 'lon').loess_filter(5, 'time', 'lon')
-c.median_filter(1, 'time', 'lat').loess_filter(5, 'time', 'lat')
+a.median_filter(1, "time", "lon").loess_filter(5, "time", "lon")
+a.median_filter(1, "time", "lat").loess_filter(5, "time", "lat")
+c.median_filter(1, "time", "lon").loess_filter(5, "time", "lon")
+c.median_filter(1, "time", "lat").loess_filter(5, "time", "lat")
 
 d_a = distance(a.longitude[:-1], a.latitude[:-1], a.longitude[1:], a.latitude[1:])
 d_c = distance(c.longitude[:-1], c.latitude[:-1], c.longitude[1:], c.latitude[1:])
-d_a = cum_distance_by_track(d_a, a["track"]) / 1000.
-d_c = cum_distance_by_track(d_c, c["track"]) / 1000.
+d_a = cum_distance_by_track(d_a, a["track"]) / 1000.0
+d_c = cum_distance_by_track(d_c, c["track"]) / 1000.0
 
 # Plot
 fig = plt.figure()
@@ -69,7 +70,12 @@ def cum_distance_by_track(distance, track):
 ax_ratio_propagation.plot(x[1:], nb_c / nb_a)
 
 
-for ax in (ax_propagation, ax_cum_propagation, ax_ratio_cum_propagation, ax_ratio_propagation):
+for ax in (
+    ax_propagation,
+    ax_cum_propagation,
+    ax_ratio_cum_propagation,
+    ax_ratio_propagation,
+):
     ax.set_xlim(0, 1000)
     if ax in (ax_propagation, ax_cum_propagation):
         ax.set_ylim(1, None)

setup.py

@@ -27,7 +27,7 @@
     ],
     zip_safe=False,
     entry_points=dict(
-        console_scripts=["MergeEddies = py_eddy_tracker.appli:merge_eddies",]
+        console_scripts=["MergeEddies = py_eddy_tracker.appli:merge_eddies"]
     ),
     package_data={
         "py_eddy_tracker.featured_tracking": ["*.nc"],

share/fig.py

@@ -1,53 +1,63 @@
 from matplotlib import pyplot as plt
 from py_eddy_tracker.dataset.grid import RegularGridDataset
-grid_name, lon_name, lat_name = 'nrt_global_allsat_phy_l4_20190223_20190226.nc', 'longitude', 'latitude'
+
+grid_name, lon_name, lat_name = (
+    "nrt_global_allsat_phy_l4_20190223_20190226.nc",
+    "longitude",
+    "latitude",
+)
 if False:
 
     h = RegularGridDataset(grid_name, lon_name, lat_name)
-    
+
     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]))
     h = RegularGridDataset(grid_name, lon_name, lat_name)
-    h.bessel_high_filter('adt', 500, order=3)
-    ax = fig.add_axes([.02, .02, .9, .45])
-    ax.set_title('ADT Filtered (m)')
-    ax.set_aspect('equal')
+    h.bessel_high_filter("adt", 500, order=3)
+    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('png/filter.png')
+    plt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.45]))
+    fig.savefig("png/filter.png")
 
 if True:
     import logging
-    logging.getLogger().setLevel('DEBUG') # Values: ERROR, WARNING, INFO, DEBUG
+
+    logging.getLogger().setLevel("DEBUG")  # Values: ERROR, WARNING, INFO, DEBUG
     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)
     # h.bessel_high_filter('adt', 300, order=1)
     date = datetime(2019, 2, 23)
     a, c = h.eddy_identification(
-        'adt', 'ugos', 'vgos', # Variable to use for identification
-        date, # Date of identification
-        0.002, # step between two isolines of detection (m)
+        "adt",
+        "ugos",
+        "vgos",  # Variable to use for identification
+        date,  # Date of identification
+        0.002,  # step between two isolines of detection (m)
         # 0.02, # step between two isolines of detection (m)
-        pixel_limit=(5, 2000), # Min and max of pixel can be include in contour
-        shape_error=55, # Error maximal of circle fitting over contour to be accepted
-        bbox_surface_min_degree=.125 ** 2, # degrees surface minimal to take in account contour
-        )
-    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)
+        pixel_limit=(5, 2000),  # Min and max of pixel can be include in contour
+        shape_error=55,  # Error maximal of circle fitting over contour to be accepted
+        bbox_surface_min_degree=0.125
+        ** 2,  # degrees surface minimal to take in account contour
+    )
+    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('png/eddies.png')
+    fig.savefig("png/eddies.png")