Up separation plot

Plots/jet_separation.py

 
 from os import path
 
+from cmocean import cm
 import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+import tol_colors as tc
 
 import lib
 import lib.data.ostia
+import lib.data.globcolour
 import lib.data.SN_separation
+import lib.data.hi
+import lib.data.p_frt_mask
 
 
 plt.switch_backend('agg')
 
 fixes = dict(
-    Y=2007
+    Y=2007,
+    # m=1,
+    # d=1,
+    number=2,
+    scale=30.,
+    coef=0,
 )
 
+threshold_S = 6.0
+threshold_N = 15.0
+SS_lat = 32.
+
 odir = path.join(lib.root_plot, 'SN_separation')
 
 st = lib.data.ostia.get_data(fixes=fixes)
 thr = lib.data.SN_separation.get_data(fixes=fixes)
 st['thr'] = lib.data.SN_separation.smooth(thr, 8)
-st = st.isel(time=slice(None, None, 10))
-st = st.sel(lat=slice(30, None))
-st['GS_N'] = st.analysed_sst > st.thr
-
-im_kw = dict(
-    add_labels=False,
-    vmin=0,
-    cmap='inferno'
-)
-hatch_kw = dict(
-    add_labels=False,
-    add_colorbar=False,
-    alpha=0.,
-    levels=[0, 0.5],
-    hatches=['', '. .']
-)
+
+oc = lib.data.globcolour.get_data(fixes=fixes, days=8)
+hi = lib.data.hi.get_data(fixes=fixes)
+
+st, hi = lib.fix_time_daily(st, hi)
+
+st, oc, hi = xr.align(st, oc, hi, exclude=['lon', 'lat'])
+
+mask = lib.data.p_frt_mask.get_data(fixes=dict(threshold=1e-2)).mask
+
+cset = tc.tol_cset('bright')
 
 for i, d in enumerate(st.time.dt.strftime('%F').values):
-    fig, ax = plt.subplots(figsize=(5, 5))
-    fig.subplots_adjust(left=0.05, bottom=0.05, right=0.98, top=0.98)
-    im = st.analysed_sst.isel(time=i).plot.imshow(ax=ax, **im_kw)
-    st.GS_N.isel(time=i).plot.contourf(ax=ax, **hatch_kw)
-    ax.set_aspect('equal')
+    fig, axes = plt.subplots(1, 3, figsize=(10.7, 3.1), sharex=True, sharey=True)
+    fig.subplots_adjust(left=0.03, bottom=0.07, right=0.99, top=0.90,
+                        wspace=0.05)
+    ax1, ax2, ax3 = axes
+
+    sst = st.isel(time=i)
+    chl = oc.isel(time=i)
+    hit = hi.isel(time=i)
+
+    ax2.set_title(sst.time.dt.strftime('%F').values)
+
+    HI = lib.data.hi.apply_coef(hit, lib.data.hi.get_coef(fixes))
+    frt = HI > threshold_N
+    frt = frt.where(sst.analysed_sst < sst.thr, HI > threshold_S)
+    frt = frt.where(np.isfinite(sst.analysed_sst))
+    frt = frt.where(mask)
+
+    sst.analysed_sst.plot.imshow(ax=ax1, cmap=cm.thermal, center=False,
+                                 add_labels=False)
+    chl.CHL.plot.imshow(ax=ax2, cmap=cm.algae,
+                        norm=plt.cm.colors.LogNorm(1e-2, 10),
+                        add_labels=False)
+    frt.plot.imshow(ax=ax3, cmap='Greens',
+                    add_labels=False, add_colorbar=False)
+    np.isfinite(sst.analysed_sst).plot.contour(
+        ax=ax3, add_colorbar=False, add_labels=False,
+        colors='k', linewidths=0.5)
+
+    for ax in axes:
+        sst.analysed_sst.plot.contour(ax=ax, levels=[sst.thr.values],
+                                      add_colorbar=False, add_labels=False,
+                                      linewidths=0.5, colors=cset.purple)
+        ax.axhline(SS_lat, color=cset.purple, lw=0.5)
+        ax.set_aspect('equal')
+
     fig.canvas.draw()
     fig.savefig(path.join(odir, '{}.png'.format(d)), dpi=250)
     plt.close(fig)