Up various

Plots/Hists/hist.py

 
-from datetime import datetime, timedelta
 import matplotlib.pyplot as plt
+import tol_colors as tc
 
 import lib
 import lib.data.hists
 
 fixes = dict(
-    Y=2007,
+    # Y=2007,
     number=2,
     coef=0,
     scale=30,
@@ -24,27 +24,14 @@ ds = ds.squeeze()
 # For SOM variables
 # ds['hist'] = ds['hist'].where(ds.nbins != 0, 0.)
 
+ds['hist'] = ds['hist'].sum('time')
+ds.load()
+lib.data.hists.normalize_hist(ds)
+
 fig, ax = plt.subplots(figsize=(5, 5))
 fig.subplots_adjust(left=0.05, bottom=0.15, right=0.98, top=0.98)
+cset = tc.tol_cset('vibrant')
 
-s = datetime(2007, 4, 1)
-H = ds.sel(time=slice(s, s+timedelta(days=8))).sum('time')
-# H = lib.data.hists.normalize_hist(ds.sum('time'))
-
-H = H.coarsen(nbins=12, boundary='pad').sum()
-H = H.coarsen(nbins_p=12, boundary='pad').min()
-# H['hist'] = H.hist.where(H.nbins < 250/3., 0)
-H = lib.data.hists.normalize_hist(H)
-
-cs = H.hist.cumsum('nbins')
-ax.plot(H.bins[:-1], H.hist.sel(mask='bkg'), color='b', ls='--')
-ax.plot(H.bins[:-1], H.hist.sel(mask='frt'), color='r', ls='-')
-ax = ax.twinx()
-ax.plot(H.bins[:-1], cs.sel(mask='bkg'), color='b', ls='--')
-ax.plot(H.bins[:-1], cs.sel(mask='frt'), color='r', ls='-')
-ax.set_xlabel(variable)
-
-med = lib.data.hists.get_percentile(H, .5)
-
-ax.axvline(med.sel(mask='bkg').values, color='b')
-ax.axvline(med.sel(mask='frt').values, color='r')
+bins = ds.bins[:-1]
+ax.plot(bins, ds.hist.sel(mask='bkg'), color=cset.teal)
+ax.plot(bins, ds.hist.sel(mask='frt'), color=cset.orange)

Plots/Hists/hists_total.py

+"""Plots histograms for CHL and SST over both zones."""
+
+from os import path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tol_colors as tc
+
+import lib
+import lib.data.hists
+
+
+args = lib.get_args(['region', 'days'])
+
+fixes = dict(
+    scale=30.,
+    number=2,
+    coef=0
+)
+
+cset = tc.tol_cset('vibrant')
+
+month = 11
+
+
+def plot(ax, variable, zone, threshold):
+    fixes.update(dict(zone=zone, threshold=threshold))
+
+    ds = lib.data.hists.get_data(args, fixes=fixes)
+    ds = ds.sel(variable=variable)
+    ds = ds.squeeze()
+    mth_idx = ds.groupby('time.month').groups[month]
+    ds = ds.isel(time=mth_idx)
+
+    ds['hist'] = ds['hist'].sum('time')
+    ds.load()
+
+    if variable == 'analysed_sst':
+        n_coarsen = 20
+        ds = ds.coarsen(nbins=n_coarsen, boundary='pad').mean()
+        ds = ds.coarsen(nbins_p=n_coarsen, boundary='pad').mean()
+
+    lib.data.hists.normalize_hist(ds)
+    ds['hist'] /= ds.hist.max()
+
+    bins = ds.bins[:-1]
+    ax.plot(bins, ds.hist.sel(mask='bkg'), color=cset.teal)
+    ax.plot(bins, ds.hist.sel(mask='frt'), color=cset.orange)
+
+
+fig, axes = plt.subplots(2, 2, figsize=(10, 8))
+fig.subplots_adjust(left=0.10, bottom=0.05, right=0.97, top=0.95,
+                    wspace=0.3, hspace=0.2)
+
+for i, var in enumerate(['CHL', 'analysed_sst']):
+    axes[i, 0].set_ylabel(var)
+    for j, (zone, threshold) in enumerate([('GS_S', 6.0), ('GS_N', 15.0)]):
+        ax = axes[i, j]
+        plot(ax, var, zone, threshold)
+
+axes[0, 0].set_title('South')
+axes[0, 1].set_title('North')
+
+
+fig.canvas.draw()
+fig.savefig(path.join(lib.root_plot, 'Hists', 'total_month',
+                      'month_{:02d}.png'.format(month)),
+            dpi=150)

Plots/front_probability.py

+"""Plot front presence probability on a month."""
+
+from cmocean import cm
+import matplotlib.pyplot as plt
+import numpy as np
+
+import lib
+import lib.data.ostia
+import lib.data.front_probability
+import lib.data.SN_separation
+
+
+args = lib.get_args(['period', 'fixes'])
+
+thr = lib.data.SN_separation.get_data(args)
+thr = lib.data.SN_separation.smooth(thr, time_step=8)
+thr = thr.isel(time=0)
+# thr = thr.mean('time')
+
+sst = lib.data.ostia.get_data(args)['analysed_sst']
+sst = sst.mean('time')
+
+p_frt = lib.data.front_probability.get_data(args)['p_frt']
+p_frt = p_frt.mean('time')
+
+fig, axes = plt.subplots(1, 2, figsize=(10, 5))
+fig.subplots_adjust(left=0.05, bottom=0.05, right=0.98, top=0.98)
+ax1, ax2 = axes
+
+sst.plot.imshow(ax=ax1, cmap=cm.thermal, center=False)
+
+cmap = plt.cm.get_cmap('gray_r').copy()
+# cmap.set_bad('w')
+cmap.set_under('w')
+cmap.set_over('w')
+p_frt.plot.imshow(ax=ax2, cmap=cmap,
+                  norm=plt.Normalize(clip=False, vmin=1e-3))
+
+for ax in axes:
+    sst.plot.contour(ax=ax, colors='k', add_colorbar=False,
+                     levels=[thr.values])
+    np.isfinite(sst).plot.contour(ax=ax, add_colorbar=False,
+                                  colors='k', linewidths=0.5)
+    ax.set_aspect('equal')

Plots/front_probability_total.py

+"""Plot frontal presence probability over all data."""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import tol_colors as tc
+
+import lib
+import lib.data.front_probability
+import Plots.util as plot_util
+
+args = dict(period='total')
+
+ds = lib.data.front_probability.get_data(args)
+
+fig, ax = plt.subplots(figsize=(6, 5))
+fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.98)
+
+cax = plot_util.add_cax(ax)
+
+ds.p_frt.plot.imshow(ax=ax, cmap="gray_r", cbar_ax=cax,
+                     norm=plt.cm.colors.LogNorm(vmin=1e-2, vmax=1))
+np.isfinite(ds.p_frt).plot.contour(
+    ax=ax, colors='k', linewidths=0.5,
+    levels=[0.5],
+    add_labels=False, add_colorbar=False)
+
+cset = tc.tol_cset('vibrant')
+levels = [5e-3, 1e-2, 2e-2, 5e-2]
+ds.p_frt.plot.contour(ax=ax, linewidths=0.7, add_labels=False, levels=levels,
+                      colors=cset)
+
+for i, lvl in enumerate(levels):
+    cax.axhline(lvl, color=cset[i])
+
+ax.set_aspect('equal')