update example

Author: AntSimi

examples/10_tracking_diagnostics/pet_center_count.py

@@ -3,8 +3,8 @@
 ======================
 
 """
-
 from matplotlib import pyplot as plt
+from matplotlib.colors import LogNorm
 from py_eddy_tracker.observations.tracking import TrackEddiesObservations
 import py_eddy_tracker_sample
 
@@ -15,16 +15,48 @@
 c = TrackEddiesObservations.load_file(
     py_eddy_tracker_sample.get_path("eddies_med_adt_allsat_dt2018/Cyclonic.zarr")
 )
-a = a.merge(c)
+
+t0, t1 = a.period
+
 # Plot
-fig = plt.figure(figsize=(13.5, 5))
-ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])
-ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
-ax.set_aspect("equal")
+fig = plt.figure(figsize=(12, 18.5))
+ax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])
+ax_a.set_title("Anticyclonic frequency")
+ax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])
+ax_c.set_title("Cyclonic frequency")
+ax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])
+ax_all.set_title("All eddies frequency")
+ax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])
+ax_ratio.set_title("Ratio cyclonic / Anticyclonic")
+
 step = 0.1
-t0, t1 = a.period
-g = a.grid_count(((-7, 37, step), (30, 46, step)), center=True)
-m = g.display(ax, name="count", vmin=0, vmax=2, factor=1 / (step ** 2 * (t1 - t0)))
-ax.grid()
-cb = plt.colorbar(m, cax=fig.add_axes([0.95, 0.05, 0.01, 0.9]))
+bins = ((-10, 37, step), (30, 46, step))
+kwargs_pcolormesh = dict(
+    cmap="terrain_r", vmin=0, vmax=2, factor=1 / (step ** 2 * (t1 - t0)), name="count"
+)
+g_a = a.grid_count(bins, intern=True, center=True)
+m = g_a.display(ax_a, **kwargs_pcolormesh)
+
+g_c = c.grid_count(bins, intern=True, center=True)
+m = g_c.display(ax_c, **kwargs_pcolormesh)
+
+ratio = g_c.vars["count"] / g_a.vars["count"]
+
+m_c = g_c.vars["count"].mask
+m = m_c & g_a.vars["count"].mask
+g_c.vars["count"][m_c] = 0
+g_c.vars["count"] += g_a.vars["count"]
+g_c.vars["count"].mask = m
+
+m = g_c.display(ax_all, **kwargs_pcolormesh)
+cb = plt.colorbar(m, cax=fig.add_axes([0.94, 0.27, 0.01, 0.7]))
 cb.set_label("Eddies by 1°^2 by day")
+
+g_c.vars["count"] = ratio
+m = g_c.display(ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm())
+plt.colorbar(m, cax=fig.add_axes([0.94, 0.02, 0.01, 0.2]))
+
+for ax in (ax_a, ax_c, ax_all, ax_ratio):
+    ax.set_aspect("equal")
+    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
+    ax.grid()

examples/10_tracking_diagnostics/pet_histo.py

@@ -14,36 +14,45 @@
 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")
+kwargs_a = dict(label="Anticyclonic", color="r", histtype="step", density=True)
+kwargs_c = dict(label="Cyclonic", color="b", histtype="step", density=True)
 
 # Plot
-fig = plt.figure()
-ax = fig.add_subplot(111)
-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(figsize=(12, 7))
 
-fig = plt.figure()
-ax = fig.add_subplot(111)
-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()
+for x0, name, title, xmax, factor, bins in zip(
+    (0.4, 0.72, 0.08),
+    ("radius_s", "speed_average", "amplitude"),
+    ("Speed radius (km)", "Speed average (cm/s)", "Amplitude (cm)"),
+    (100, 50, 20),
+    (0.001, 100, 100),
+    (arange(0, 2000, 1), arange(0, 1000, 0.5), arange(0.0005, 1000, 0.2)),
+):
+    ax_hist = fig.add_axes((x0, 0.24, 0.27, 0.35))
+    nb_a, _, _ = ax_hist.hist(a[name] * factor, bins=bins, **kwargs_a)
+    nb_c, _, _ = ax_hist.hist(c[name] * factor, bins=bins, **kwargs_c)
+    ax_hist.set_xticklabels([])
+    ax_hist.set_xlim(0, xmax)
+    ax_hist.grid()
 
-fig = plt.figure()
-ax = fig.add_subplot(111)
-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_cum = fig.add_axes((x0, 0.62, 0.27, 0.35))
+    ax_cum.hist(a[name] * factor, bins=bins, cumulative=-1, **kwargs_a)
+    ax_cum.hist(c[name] * factor, bins=bins, cumulative=-1, **kwargs_c)
+    ax_cum.set_xticklabels([])
+    ax_cum.set_title(title)
+    ax_cum.set_xlim(0, xmax)
+    ax_cum.set_ylim(0, 1)
+    ax_cum.grid()
 
-ax.set_xlim(0, 50)
-ax.legend()
-ax.grid()
+    ax_ratio = fig.add_axes((x0, 0.06, 0.27, 0.15))
+    ax_ratio.set_xlim(0, xmax)
+    ax_ratio.set_ylim(0, 2)
+    ax_ratio.plot((bins[1:] + bins[:-1]) / 2, nb_c / nb_a)
+    ax_ratio.axhline(1, color="k")
+    ax_ratio.grid()
+    ax_ratio.set_xlabel(title)
+
+ax_cum.set_ylabel("Cumulative\npercent distribution")
+ax_hist.set_ylabel("Percent of observations")
+ax_ratio.set_ylabel("Ratio percent\nCyc/Acyc")
+ax_cum.legend()

examples/10_tracking_diagnostics/pet_pixel_used.py

@@ -3,8 +3,8 @@
 ======================
 
 """
-
 from matplotlib import pyplot as plt
+from matplotlib.colors import LogNorm
 from py_eddy_tracker.observations.tracking import TrackEddiesObservations
 import py_eddy_tracker_sample
 
@@ -18,34 +18,43 @@
 t0, t1 = a.period
 
 # Plot
-fig = plt.figure(figsize=(12, 14))
-ax_a = fig.add_axes([0.03, 0.66, 0.90, 0.33])
+fig = plt.figure(figsize=(12, 18.5))
+ax_a = fig.add_axes([0.03, 0.75, 0.90, 0.25])
 ax_a.set_title("Anticyclonic frequency")
-ax_c = fig.add_axes([0.03, 0.33, 0.90, 0.33])
+ax_c = fig.add_axes([0.03, 0.5, 0.90, 0.25])
 ax_c.set_title("Cyclonic frequency")
-ax_all = fig.add_axes([0.03, 0, 0.90, 0.33])
+ax_all = fig.add_axes([0.03, 0.25, 0.90, 0.25])
 ax_all.set_title("All eddies frequency")
+ax_ratio = fig.add_axes([0.03, 0.0, 0.90, 0.25])
+ax_ratio.set_title("Ratio cyclonic / Anticyclonic")
 
 step = 0.1
 bins = ((-10, 37, step), (30, 46, step))
-kwargs_pcolormesh = dict(cmap="terrain_r", vmin=0, vmax=0.75)
+kwargs_pcolormesh = dict(
+    cmap="terrain_r", vmin=0, vmax=0.75, factor=1 / (t1 - t0), name="count"
+)
 g_a = a.grid_count(bins, intern=True)
-m = g_a.display(ax_a, name="count", factor=1 / (t1 - t0), **kwargs_pcolormesh)
+m = g_a.display(ax_a, **kwargs_pcolormesh)
 
 g_c = c.grid_count(bins, intern=True)
-m = g_c.display(ax_c, name="count", factor=1 / (t1 - t0), **kwargs_pcolormesh)
+m = g_c.display(ax_c, **kwargs_pcolormesh)
+
+ratio = g_c.vars["count"] / g_a.vars["count"]
 
 m_c = g_c.vars["count"].mask
 m = m_c & g_a.vars["count"].mask
 g_c.vars["count"][m_c] = 0
 g_c.vars["count"] += g_a.vars["count"]
 g_c.vars["count"].mask = m
 
-m = g_c.display(ax_all, name="count", factor=1 / (t1 - t0), **kwargs_pcolormesh)
+m = g_c.display(ax_all, **kwargs_pcolormesh)
+plt.colorbar(m, cax=fig.add_axes([0.95, 0.27, 0.01, 0.7]))
 
-for ax in (ax_a, ax_c, ax_all):
+g_c.vars["count"] = ratio
+m = g_c.display(ax_ratio, name="count", vmin=0.1, vmax=10, norm=LogNorm())
+plt.colorbar(m, cax=fig.add_axes([0.95, 0.02, 0.01, 0.2]))
+
+for ax in (ax_a, ax_c, ax_all, ax_ratio):
     ax.set_aspect("equal")
     ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
     ax.grid()
-
-plt.colorbar(m, cax=fig.add_axes([0.95, 0.05, 0.01, 0.9]))