Change color of cyclone and anticyclone

Author: AntSimi

examples/pet_display_track.py

@@ -17,13 +17,18 @@
 
 a = a.extract_with_length((7 * 20, -1))
 c = c.extract_with_length((7 * 20, -1))
+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')
+
 
 # Plot
 fig = plt.figure(figsize=(15, 8))
 ax = fig.add_subplot(111)
 ax.set_aspect("equal")
 ax.set_xlim(-5, 37)
 ax.set_ylim(30, 46)
-a.plot(ax, ref=-10, label="Anticyclonic", color="b", lw=0.1)
-c.plot(ax, ref=-10, label="Cyclonic", color="r", lw=0.1)
+a.plot(ax, ref=-10, label="Anticyclonic", color="r", lw=0.1)
+c.plot(ax, ref=-10, label="Cyclonic", color="b", lw=0.1)
 ax.legend()

examples/pet_histo.py

@@ -8,35 +8,42 @@
 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")
+)
+kwargs_a = dict(label="Anticyclonic", color="r")
+kwargs_c = dict(label="Cyclonic", color="b")
+
 # Plot
 fig = plt.figure()
 ax = fig.add_subplot(111)
-ax.hist(a['amplitude'], histtype='step', bins=arange(0.0005,1,0.002), label='Anticyclonic')
-ax.hist(c['amplitude'], histtype='step', bins=arange(0.0005,1,0.002), label='Cyclonic')
-ax.set_xlabel('Amplitude (m)')
-ax.set_xlim(0,.5)
+ax.hist(a["amplitude"], histtype="step", bins=arange(0.0005, 1, 0.002), **kwargs_a)
+ax.hist(c["amplitude"], histtype="step", bins=arange(0.0005, 1, 0.002), **kwargs_c)
+ax.set_xlabel("Amplitude (m)")
+ax.set_xlim(0, 0.5)
 ax.legend()
 ax.grid()
 
 fig = plt.figure()
 ax = fig.add_subplot(111)
-bins = arange(0,200,1)
-ax.hist(a['radius_s'] / 1000., histtype='step', bins=bins, label='Anticyclonic')
-ax.hist(c['radius_s'] / 1000., histtype='step', bins=bins, label='Cyclonic')
-ax.set_xlabel('Speed_radius (km)')
-ax.set_xlim(0,150)
+bins = arange(0, 200, 1)
+ax.hist(a["radius_s"] / 1000.0, histtype="step", bins=bins, **kwargs_a)
+ax.hist(c["radius_s"] / 1000.0, histtype="step", bins=bins, **kwargs_c)
+ax.set_xlabel("Speed_radius (km)")
+ax.set_xlim(0, 150)
 ax.legend()
 ax.grid()
 
 fig = plt.figure()
 ax = fig.add_subplot(111)
-bins = arange(0,100,1)
-ax.hist(a['speed_average'] * 100., histtype='step', bins=bins, label='Anticyclonic')
-ax.hist(c['speed_average'] * 100., histtype='step', bins=bins, label='Cyclonic')
-ax.set_xlabel('speed_average (cm/s)')
+bins = arange(0, 100, 1)
+ax.hist(a["speed_average"] * 100.0, histtype="step", bins=bins, **kwargs_a)
+ax.hist(c["speed_average"] * 100.0, histtype="step", bins=bins, **kwargs_c)
+ax.set_xlabel("speed_average (cm/s)")
 
-ax.set_xlim(0,50)
+ax.set_xlim(0, 50)
 ax.legend()
-ax.grid()
+ax.grid()

examples/pet_lifetime.py

@@ -19,15 +19,15 @@
 ax_ratio_cum_lifetime = fig.add_axes([0.55, 0.05, .4, .4])
 
 
-cum_a, bins, _ = ax_cum_lifetime.hist(a['n'], histtype='step', bins=arange(0,800,1), label='Anticyclonic', color='b')
-cum_c, bins, _ = ax_cum_lifetime.hist(c['n'], histtype='step', bins=arange(0,800,1), label='Cyclonic', color='r')
+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)
 
 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='b')
-ax_lifetime.plot(x[1:], nb_c, label='Cyclonic', color='r')
+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)
 

examples/pet_one_track.py

@@ -0,0 +1,25 @@
+"""
+One Track
+===================
+
+"""
+from matplotlib import pyplot as plt
+from py_eddy_tracker.observations.tracking import TrackEddiesObservations
+import py_eddy_tracker_sample
+
+a = TrackEddiesObservations.load_file(
+    py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Anticyclonic.zarr")
+)
+
+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')
+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)
+

examples/pet_propagation.py

@@ -32,6 +32,12 @@ def cum_distance_by_track(distance, track):
 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')
+
 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.
@@ -45,20 +51,20 @@ def cum_distance_by_track(distance, track):
 ax_ratio_cum_propagation = fig.add_axes([0.55, 0.05, 0.4, 0.4])
 
 
-bins = arange(0, 1500, 25)
+bins = arange(0, 1500, 10)
 cum_a, bins, _ = ax_cum_propagation.hist(
-    d_a, histtype="step", bins=bins, label="Anticyclonic", color="b"
+    d_a, histtype="step", bins=bins, label="Anticyclonic", color="r"
 )
 cum_c, bins, _ = ax_cum_propagation.hist(
-    d_c, histtype="step", bins=bins, label="Cyclonic", color="r"
+    d_c, histtype="step", bins=bins, label="Cyclonic", color="b"
 )
 
 x = (bins[1:] + bins[:-1]) / 2.0
 ax_ratio_cum_propagation.plot(x, cum_c / cum_a)
 
 nb_a, nb_c = cum_a[:-1] - cum_a[1:], cum_c[:-1] - cum_c[1:]
-ax_propagation.plot(x[1:], nb_a, label="Anticyclonic", color="b")
-ax_propagation.plot(x[1:], nb_c, label="Cyclonic", color="r")
+ax_propagation.plot(x[1:], nb_a, label="Anticyclonic", color="r")
+ax_propagation.plot(x[1:], nb_c, label="Cyclonic", color="b")
 
 ax_ratio_propagation.plot(x[1:], nb_c / nb_a)