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plots.py
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import os
import xarray as xr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LogNorm
from pyldas.grids import EASE2
from pyldas.interface import LDAS_io
from myprojects.timeseries import calc_anomaly
def plot_increments():
cal = LDAS_io('incr','US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('incr','US_M36_SMOS_DA_nocal_scaled_pentadal')
incr_var_cal = (cal.timeseries['srfexc'] + cal.timeseries['rzexc'] - cal.timeseries['catdef']).var(dim='time').values
incr_var_uncal = (uncal.timeseries['srfexc'] + uncal.timeseries['rzexc'] - uncal.timeseries['catdef']).var(dim='time').values
print np.nanmean(incr_var_cal), np.nanmean(incr_var_uncal)
tc = LDAS_io().tilecoord
tg = LDAS_io().tilegrids
tc.i_indg -= tg.loc['domain', 'i_offg'] # col / lon
tc.j_indg -= tg.loc['domain', 'j_offg'] # row / lat
lons = np.unique(tc.com_lon.values)
lats = np.unique(tc.com_lat.values)[::-1]
lons, lats = np.meshgrid(lons, lats)
llcrnrlat = 24
urcrnrlat = 51
llcrnrlon = -128
urcrnrlon = -64
figsize = (10, 10)
cmap = 'jet'
fontsize = 18
cbrange = (0, 25)
plt.figure(figsize=figsize)
plt.subplot(211)
plt_img = np.ma.masked_invalid(incr_var_cal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon,urcrnrlon=urcrnrlon, resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Increment variance (calibrated RTM)', fontsize=fontsize)
plt.subplot(212)
plt_img = np.ma.masked_invalid(incr_var_uncal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Increment variance (uncalibrated RTM)', fontsize=fontsize)
plt.tight_layout()
plt.show()
def increment_density_plot():
cal = LDAS_io('incr','US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('incr','US_M36_SMOS_DA_nocal_scaled_pentadal')
incr_var_cal = (cal.timeseries['srfexc'] + cal.timeseries['rzexc'] - cal.timeseries['catdef']).values.flatten()
incr_var_cal = incr_var_cal[~np.isnan(incr_var_cal)]
incr_var_uncal = (uncal.timeseries['srfexc'] + uncal.timeseries['rzexc'] - uncal.timeseries['catdef']).values.flatten()
incr_var_uncal = incr_var_uncal[~np.isnan(incr_var_uncal)]
ind = (incr_var_cal != 0) & (incr_var_uncal != 0)
incr_var_cal = incr_var_cal[ind]
incr_var_uncal = incr_var_uncal[ind]
figsize = (10, 8)
fontsize = 16
plt.figure(figsize=figsize)
plt.hist2d(incr_var_cal, incr_var_uncal, bins=[100, 100], range=[[-60, 60], [-60, 60]], cmap='jet', norm=LogNorm(), normed=True)
plt.xlabel('Increment variance (calibrated RTM)',fontsize=fontsize)
plt.ylabel('Increment variance (uncalibrated RTM)',fontsize=fontsize)
plt.xticks(fontsize=fontsize)
plt.yticks(fontsize=fontsize)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=fontsize-2)
plt.tight_layout()
plt.show()
def plot_innovation_variance():
ds = xr.open_dataset(r"D:\work\LDAS\2018-02_scaling\_new\diagnostics\filter_diagnostics.nc")
innov_var_cal = ds['innov_var'][:, :, 1, :].mean(dim='species').values
innov_var_uncal = ds['innov_var'][:, :, 3, :].mean(dim='species').values
print np.nanmean(innov_var_cal), np.nanmean(innov_var_uncal)
lons = ds.lon.values
lats = ds.lat.values
lons, lats = np.meshgrid(lons, lats)
llcrnrlat = 24
urcrnrlat = 51
llcrnrlon = -128
urcrnrlon = -64
figsize = (10, 10)
cmap = 'jet'
fontsize = 18
cbrange = (0, 120)
plt.figure(figsize=figsize)
plt.subplot(211)
plt_img = np.ma.masked_invalid(innov_var_cal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon,urcrnrlon=urcrnrlon, resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Innovation variance (calibrated RTM)', fontsize=fontsize)
plt.subplot(212)
plt_img = np.ma.masked_invalid(innov_var_uncal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Innovation variance (uncalibrated RTM)', fontsize=fontsize)
plt.tight_layout()
plt.show()
def plot_normalized_innovation_variance():
ds = xr.open_dataset(r"D:\work\LDAS\2018-02_scaling\_new\diagnostics\filter_diagnostics.nc")
innov_var_cal = ds['norm_innov_var'][:, :, 1, :].mean(dim='species').values
innov_var_uncal = ds['norm_innov_var'][:, :, 3, :].mean(dim='species').values
print np.nanmean(innov_var_cal), np.nanmean(innov_var_uncal)
lons = ds.lon.values
lats = ds.lat.values
lons, lats = np.meshgrid(lons, lats)
llcrnrlat = 24
urcrnrlat = 51
llcrnrlon = -128
urcrnrlon = -64
figsize = (10, 10)
cmap = 'jet'
fontsize = 18
cbrange = (0, 3)
plt.figure(figsize=figsize)
plt.subplot(211)
plt_img = np.ma.masked_invalid(innov_var_cal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon,urcrnrlon=urcrnrlon, resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Normalized innovation variance (calibrated RTM)', fontsize=fontsize)
plt.subplot(212)
plt_img = np.ma.masked_invalid(innov_var_uncal)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, plt_img, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('Normalized innovation variance (uncalibrated RTM)', fontsize=fontsize)
plt.tight_layout()
plt.show()
def plot_ismn_statistics():
fname = r"D:\work\LDAS\2018-02_scaling\ismn_eval\no_da_cal_uncal_ma_harm\validation_masked.csv"
res = pd.read_csv(fname, index_col=0)
variables = ['sm_surface','sm_rootzone','sm_profile']
runs = ['OL', 'DA_ref', 'DA_pent']
legend = ['open loop', 'calibrated', 'uncalibrated']
# networks = ['SCAN','USCRN']
# res.index = res.network
# res = res.loc[networks,:]
r_title = 'Pearson R '
ubrmsd_title = 'ubRMSD '
plt.figure(figsize=(7, 8))
offsets = [-0.2, 0, 0.2]
cols = ['lightblue', 'lightgreen', 'coral']
# offsets = [-0.3,-0.1,0.1,0.3]
# cols = ['lightblue', 'lightgreen', 'coral', 'brown']
fontsize = 12
ax = plt.subplot(211)
plt.grid(color='k', linestyle='--', linewidth=0.25)
data = list()
ticks = list()
pos = list()
colors = list()
for i, var in enumerate(variables):
ticks.append(var)
for col, offs, run in zip(cols, offsets, runs):
tmp_data = res['corr_' + run + '_ma_' + var].values
tmp_data = tmp_data[~np.isnan(tmp_data)]
data.append(tmp_data)
pos.append(i + 1 + offs)
colors.append(col)
box = ax.boxplot(data, whis=[5, 95], showfliers=False, positions=pos, widths=0.1, patch_artist=True)
for patch, color in zip(box['boxes'], colors):
patch.set(color='black', linewidth=2)
patch.set_facecolor(color)
for patch in box['medians']:
patch.set(color='black', linewidth=2)
for patch in box['whiskers']:
patch.set(color='black', linewidth=1)
plt.figlegend((box['boxes'][0:4]), legend, 'upper left', fontsize=fontsize)
plt.xticks(np.arange(len(variables)) + 1, ticks, fontsize=fontsize)
plt.yticks(fontsize=fontsize)
plt.xlim(0.5, len(ticks) + 0.5)
plt.ylim(0.0, 1.0)
for i in np.arange(len(variables)):
plt.axvline(i + 0.5, linewidth=1, color='k')
ax.set_title(r_title, fontsize=fontsize)
# ---------------------------------------------------------------------------------------------------------
ax = plt.subplot(212)
plt.grid(color='k', linestyle='--', linewidth=0.25)
data = list()
ticks = list()
pos = list()
colors = list()
for i, var in enumerate(variables):
ticks.append(var)
for col, offs, run in zip(cols, offsets, runs):
tmp_data = res['ubrmsd_' + run + '_ma_' + var].values
tmp_data = tmp_data[~np.isnan(tmp_data)]
data.append(tmp_data)
pos.append(i + 1 + offs)
colors.append(col)
box = ax.boxplot(data, whis=[5, 95], showfliers=False, positions=pos, widths=0.1, patch_artist=True)
for patch, color in zip(box['boxes'], colors):
patch.set(color='black', linewidth=2)
patch.set_facecolor(color)
for patch in box['medians']:
patch.set(color='black', linewidth=2)
for patch in box['whiskers']:
patch.set(color='black', linewidth=1)
# plt.figlegend((box['boxes'][0:4]),runs,'upper left',fontsize=fontsize)
plt.xticks(np.arange(len(variables)) + 1, ticks, fontsize=fontsize)
plt.yticks(fontsize=fontsize)
plt.xlim(0.5, len(ticks) + 0.5)
plt.ylim(0, 0.1)
for i in np.arange(len(variables)):
plt.axvline(i + 0.5, linewidth=1, color='k')
ax.set_title(ubrmsd_title, fontsize=fontsize)
plt.show()
def plot_timeseries():
# Colorado
# lat = 39.095962936
# lon = -106.918945312
# Nebraska
# lat = 41.203456192
# lon = -102.249755859
# New Mexico
# lat = 31.522361470
# lon = -108.528442383
# Oklahoma
lat = 35.205233348
lon = -97.910156250
cal = LDAS_io('incr','US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('incr','US_M36_SMOS_DA_nocal_scaled_pentadal')
incr_var_cal = (cal.timeseries['srfexc'] + cal.timeseries['rzexc'] - cal.timeseries['catdef']).var(dim='time').values
incr_var_uncal = (uncal.timeseries['srfexc'] + uncal.timeseries['rzexc'] - uncal.timeseries['catdef']).var(dim='time').values
col, row = LDAS_io().grid.lonlat2colrow(lon, lat, domain=True)
title = 'increment variance (calibrated): %.2f increment variance (uncalibrated): %.2f' % (incr_var_cal[row,col], incr_var_uncal[row,col])
# title = ''
fontsize = 12
cal = LDAS_io('ObsFcstAna', 'US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('ObsFcstAna', 'US_M36_SMOS_DA_nocal_scaled_pentadal')
orig = LDAS_io('ObsFcstAna', 'US_M36_SMOS_noDA_unscaled')
ts_obs_cal = cal.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_cal.name = 'Tb obs (calibrated)'
ts_obs_uncal = uncal.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_uncal.name = 'Tb obs (uncalibrated)'
ts_obs_orig = orig.read_ts('obs_obs', lon, lat, species=3, lonlat=True)
ts_obs_orig.name = 'Tb obs (uncalibrated, unscaled)'
ts_fcst_cal = cal.read_ts('obs_fcst', lon, lat, species=3, lonlat=True)
ts_fcst_cal.name = 'Tb fcst (calibrated)'
ts_fcst_uncal = uncal.read_ts('obs_fcst', lon, lat, species=3, lonlat=True)
ts_fcst_uncal.name = 'Tb fcst (uncalibrated)'
df = pd.concat((ts_obs_cal, ts_obs_uncal, ts_obs_orig, ts_fcst_cal, ts_fcst_uncal),axis=1).dropna()
plt.figure(figsize=(19,8))
ax1 = plt.subplot(211)
df.plot(ax=ax1, ylim=[140,300], xlim=['2010-01-01','2017-01-01'], fontsize=fontsize, style=['-','--',':','-','--'], linewidth=2)
plt.xlabel('')
plt.title(title, fontsize=fontsize+2)
cols = df.columns.values
for i,col in enumerate(df):
df[col] = calc_anomaly(df[col], method='ma', longterm=True).values
if i < 3:
cols[i] = col[0:7] + 'anomaly ' + col[7::]
else:
cols[i] = col[0:7] + ' anomaly' + col[7::]
df.columns = cols
df.dropna(inplace=True)
ax2 = plt.subplot(212, sharex=ax1)
df.plot(ax=ax2, ylim=[-60,60], xlim=['2010-01-01','2017-01-01'], fontsize=fontsize, style=['-','--',':','-','--'], linewidth=2)
plt.xlabel('')
plt.tight_layout()
plt.show()
def plot_ismn_locations():
fname = r"D:\work\LDAS\2018-02_scaling\ismn_eval\no_da_cal_uncal_ma_harm\validation_masked.csv"
res = pd.read_csv(fname, index_col=0)
lats = res['lat'].values
lons = res['lon'].values
llcrnrlat = 24
urcrnrlat = 51
llcrnrlon = -128
urcrnrlon = -64
figsize = (10, 5)
plt.figure(num=None, figsize=figsize, dpi=90, facecolor='w', edgecolor='k')
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
x, y = m(lons, lats)
plt.plot(x, y, 'o', markersize=7, markeredgecolor='k', markerfacecolor='coral')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
plt.title('ISMN station locations', fontsize=14)
plt.tight_layout()
plt.show()
def plot_model_parameters():
cal = LDAS_io(exp='US_M36_SMOS_DA_calibrated_scaled').read_params('RTMparam')
uncal = LDAS_io(exp='US_M36_SMOS_DA_nocal_scaled_pentadal').read_params('RTMparam')
tc = LDAS_io().tilecoord
tg = LDAS_io().tilegrids
tc.i_indg -= tg.loc['domain', 'i_offg'] # col / lon
tc.j_indg -= tg.loc['domain', 'j_offg'] # row / lat
lons = np.unique(tc.com_lon.values)
lats = np.unique(tc.com_lat.values)[::-1]
lons, lats = np.meshgrid(lons, lats)
llcrnrlat = 24
urcrnrlat = 51
llcrnrlon = -128
urcrnrlon = -64
figsize = (14, 8)
cbrange = (0, 1)
cmap = 'jet'
fontsize = 16
plt.figure(figsize=figsize)
plt.subplot(221)
img = np.full(lons.shape, np.nan)
img[tc.j_indg.values, tc.i_indg.values] = cal['bh'].values
img_masked = np.ma.masked_invalid(img)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, img_masked, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('bh (calibrated)', fontsize=fontsize)
plt.subplot(222)
img = np.full(lons.shape, np.nan)
img[tc.j_indg.values, tc.i_indg.values] = cal['bv'].values
img_masked = np.ma.masked_invalid(img)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, img_masked, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('bv (calibrated)', fontsize=fontsize)
plt.subplot(223)
img = np.full(lons.shape, np.nan)
img[tc.j_indg.values, tc.i_indg.values] = uncal['bh'].values
img_masked = np.ma.masked_invalid(img)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, img_masked, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('bh (uncalibrated)', fontsize=fontsize)
plt.subplot(224)
img = np.full(lons.shape, np.nan)
img[tc.j_indg.values, tc.i_indg.values] = uncal['bv'].values
img_masked = np.ma.masked_invalid(img)
m = Basemap(projection='mill', llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
im = m.pcolormesh(lons, lats, img_masked, cmap=cmap, latlon=True)
im.set_clim(vmin=cbrange[0], vmax=cbrange[1])
cb = m.colorbar(im, "bottom", size="7%", pad="4%")
for t in cb.ax.get_xticklabels():
t.set_fontsize(fontsize)
for t in cb.ax.get_yticklabels():
t.set_fontsize(fontsize)
plt.title('bv (uncalibrated)', fontsize=fontsize)
plt.tight_layout()
plt.show()
if __name__=='__main__':
plot_ismn_statistics()