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metrics.py
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metrics.py
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import logging
from matplotlib.ticker import ScalarFormatter
import datetime
import einops
import numpy as np
import xarray as xr
import matplotlib
import os
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm, colors
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import pandas as pd
import shapely
from shapely import wkt
#import geopandas as gpd
import cartopy
from os.path import expanduser
#cartopy.config['pre_existing_data_dir'] = expanduser('/gpfswork/rech/yrf/uba22to/4dvarnet-core/shapefiles/natural_earth/physical')
#cartopy.config['data_dir'] = '/gpfswork/rech/yrf/uba22to/4dvarnet-core/shapefiles/natural_earth/physical'
from cartopy import crs as ccrs
import cartopy.feature as cfeature
from cartopy.io.shapereader import Reader
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cv2
import matplotlib.animation as animation
import matplotlib.gridspec as gridspec
import xrft
from spectral import *
def plot_snr(gt,oi,pred,resfile):
'''
gt: 3d numpy array (Ground Truth)
oi: 3d numpy array (OI)
pred: 3d numpy array (4DVarNet-based predictions)
resfile: string
'''
dt = pred.shape[1]
# Compute Signal-to-Noise ratio
f, pf = avg_err_rapsd2dv1(oi,gt,4.,True)
wf = 1./f
snr_oi = [wf, pf]
f, pf = avg_err_rapsd2dv1(pred,gt,4.,True)
wf = 1./f
snr_pred = [wf, pf]
# plot Signal-to-Noise ratio
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(snr_oi[0],snr_oi[1],color='red',linewidth=2,label='OI')
ax.plot(snr_pred[0],snr_pred[1],color='blue',linewidth=2,label='4DVarNet')
ax.set_xlabel("Wavenumber", fontweight='bold')
ax.set_ylabel("Signal-to-noise ratio", fontweight='bold')
ax.set_xscale('log') ; ax.set_yscale('log')
plt.legend(loc='best',prop=dict(size='small'),frameon=False)
plt.xticks([50, 100, 200, 500, 1000], ["50km", "100km", "200km", "500km", "1000km"])
ax.invert_xaxis()
plt.grid(which='both', linestyle='--')
plt.savefig(resfile) # save the figure
fig = plt.gcf()
plt.close() # close the figure
return fig
def plot_nrmse(gt, oi, pred, resfile, time):
'''
gt: 3d numpy array (Ground Truth)
oi: 3d numpy array (OI)
pred: 3d numpy array (4DVarNet-based predictions)
resfile: string
time: 1d array-like of time corresponding to the experiment
'''
# Compute daily nRMSE scores
nrmse_oi = []
nrmse_pred = []
for i in range(len(oi)):
nrmse_oi.append(nrmse(gt[i], oi[i]))
nrmse_pred.append(nrmse(gt[i], pred[i]))
# plot nRMSE time series
plt.plot(range(len(oi)),nrmse_oi,color='red',
linewidth=2,label='OI')
plt.plot(range(len(pred)),nrmse_pred,color='blue',
linewidth=2,label='4DVarNet')
# graphical options
plt.ylabel('nRMSE')
plt.xlabel('Time (days)')
plt.xticks(range(0,len(gt)),time,rotation=45, ha='right')
plt.margins(x=0)
plt.grid(True,alpha=.3)
plt.legend(loc='upper left',prop=dict(size='small'),frameon=False,bbox_to_anchor=(0,1.02,1,0.2),ncol=2,mode="expand")
plt.savefig(resfile,bbox_inches="tight") # save the figure
fig = plt.gcf()
plt.close() # close the figure
return fig
def plot_mse(gt, oi, pred, resfile, time):
'''
gt: 3d numpy array (Ground Truth)
oi: 3d numpy array (OI)
pred: 3d numpy array (4DVarNet-based predictions)
resfile: string
time: 1d array-like of time corresponding to the experiment
'''
# Compute daily nRMSE scores
mse_oi = []
mse_pred = []
grad_mse_oi = []
grad_mse_pred = []
for i in range(len(oi)):
mse_oi.append(mse(gt[i], oi[i]))
mse_pred.append(mse(gt[i], pred[i]))
grad_mse_oi.append(mse(gradient(gt[i],2), gradient(oi[i],2)))
grad_mse_pred.append(mse(gradient(gt[i],2), gradient(pred[i],2)))
print("mse_oi = ", np.nanmean(mse_oi))
print("mse_pred = ", np.nanmean(mse_pred))
print("grad_mse_oi = ", np.nanmean(grad_mse_oi))
print("grad_mse_pred = ", np.nanmean(grad_mse_pred))
print("percentage_ssh = ", np.abs(np.nanmean(mse_oi)-np.nanmean(mse_pred))/np.nanmean(mse_oi))
print("percentage_ssh_grad = ", np.abs(np.nanmean(grad_mse_oi)-np.nanmean(grad_mse_pred))/np.nanmean(grad_mse_oi))
# plot nRMSE time series
plt.plot(range(len(oi)),mse_oi,color='red',
linewidth=2,label='OI')
plt.plot(range(len(pred)),mse_pred,color='blue',
linewidth=2,label='4DVarNet')
# graphical options
plt.ylabel('MSE')
plt.xlabel('Time (days)')
plt.xticks(range(0,len(gt)),time,rotation=45, ha='right')
plt.margins(x=0)
plt.grid(True,alpha=.3)
plt.legend(loc='upper left',prop=dict(size='small'),frameon=False,bbox_to_anchor=(0,1.02,1,0.2),ncol=2,mode="expand")
plt.savefig(resfile,bbox_inches="tight") # save the figure
fig = plt.gcf()
plt.close() # close the figure
return fig
def plot(ax, lon, lat, data, title, cmap, norm, extent=[-65, -55, 30, 40], gridded=True, colorbar=True, orientation="horizontal"):
ax.set_extent(list(extent))
if gridded:
im=ax.pcolormesh(lon, lat, data, cmap=cmap, \
norm=norm, edgecolors='face', alpha=1, \
transform=ccrs.PlateCarree(central_longitude=0.0))
else:
im=ax.scatter(lon, lat, c=data, cmap=cmap, s=1, \
norm=norm, edgecolors='face', alpha=1, \
transform=ccrs.PlateCarree(central_longitude=0.0))
# im.set_clim(vmin,vmax)
if colorbar==True:
clb = plt.colorbar(im, orientation=orientation, extend='both', pad=0.1, ax=ax)
ax.set_title(title, pad=10, fontsize = 15)
ax.add_feature(cfeature.LAND.with_scale('10m'), zorder=100,
edgecolor='k', facecolor='white')
try:
gl = ax.gridlines(alpha=0.5, zorder=200)#,draw_labels=True)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.bottom_labels = False
gl.right_labels = False
gl.xlabel_style = {'fontsize': 10, 'rotation' : 45}
gl.ylabel_style = {'fontsize': 10}
except Exception as e:
import traceback
print(traceback.format_exc())
def gradient(img, order):
""" calculate x, y gradient and magnitude """
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = sobelx/8.0
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = sobely/8.0
sobel_norm = np.sqrt(sobelx*sobelx+sobely*sobely)
if (order==0):
return sobelx
elif (order==1):
return sobely
else:
return sobel_norm
def plot_maps(gt,obs,oi,pred,lon,lat,resfile,grad=False,
crop=None, orthographic=True,supervised=True):
if crop is not None:
ilon = np.where((lon>=crop[0]) & (lon<=crop[1]))[0]
ilat = np.where((lat>=crop[2]) & (lat<=crop[3]))[0]
gt = (gt[:,ilat,:])[:,:,ilon]
obs = (obs[:,ilat,:])[:,:,ilon]
oi = (oi[:,ilat,:])[:,:,ilon]
pred = (pred[:,ilat,:])[:,:,ilon]
lon = lon[ilon]
lat = lat[ilat]
extent = [np.min(lon)-1,np.max(lon)+1,np.min(lat)-1,np.max(lat)+1]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])
if orthographic:
crs = ccrs.Orthographic(central_lon,central_lat)
#crs = ccrs.Orthographic(-30,45)
else:
crs = ccrs.PlateCarree(central_longitude=central_lon)
if grad:
vmax = np.nanmax(np.abs(gradient(oi, 2)))
vmin = 0
cm = plt.cm.viridis
norm = colors.PowerNorm(gamma=0.7, vmin=vmin, vmax=vmax)
else:
vmax = np.nanmax(np.abs(oi))
vmin = -1.*vmax
cm = plt.cm.coolwarm
norm = colors.Normalize(vmin=vmin, vmax=vmax)
extent = [np.min(lon),np.max(lon),np.min(lat),np.max(lat)]
fig = plt.figure(figsize=(15,9))
gs = gridspec.GridSpec(2, 4)
gs.update(wspace=0.5)
if supervised:
ax1 = fig.add_subplot(gs[0, :2], projection=crs)
ax2 = fig.add_subplot(gs[0, 2:], projection=crs)
ax3 = fig.add_subplot(gs[1, :2], projection=crs)
ax4 = fig.add_subplot(gs[1, 2:], projection=crs)
if grad:
plot(ax1, lon, lat, gradient(gt, 2), r"$\nabla_{GT}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, np.where(np.isnan(obs), np.nan, 0.), "OBS (mask)", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, gradient(oi, 2), r"$\nabla_{OI}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax4, lon, lat, gradient(pred, 2), r"$\nabla_{4DVarNet}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
plot(ax1, lon, lat, gt, 'GT', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, obs, 'OBS', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, oi, 'OI', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax4, lon, lat, pred, '4DVarNet', extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
ax1 = fig.add_subplot(gs[0, 1:3], projection=crs)
ax2 = fig.add_subplot(gs[1, :2], projection=crs)
ax3 = fig.add_subplot(gs[1, 2:], projection=crs)
if grad:
plot(ax1, lon, lat, np.where(np.isnan(obs), np.nan, 0.), "OBS (mask)", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, gradient(oi, 2), r"$\nabla_{OI}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, gradient(pred, 2), r"$\nabla_{4DVarNet}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
plot(ax1, lon, lat, obs, 'OBS', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, oi, 'OI', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, pred, '4DVarNet', extent=extent, cmap=cm, norm=norm, colorbar=False)
# Colorbar
cbar_ax = fig.add_axes([0.1, 0.05, 0.8, 0.01])
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm._A = []
cbar = fig.colorbar(sm, cax=cbar_ax, orientation='horizontal', pad=3.0)
plt.savefig(resfile) # save the figure
fig = plt.gcf()
plt.close() # close the figure
return fig
def plot_maps_oi(gt,obs,pred,lon,lat,resfile,grad=False,
crop=None, orthographic=False,supervised=True):
if crop is not None:
ilon = np.where((lon>=crop[0]) & (lon<=crop[1]))[0]
ilat = np.where((lat>=crop[2]) & (lat<=crop[3]))[0]
gt = (gt[:,ilat,:])[:,:,ilon]
obs = (obs[:,ilat,:])[:,:,ilon]
pred = (pred[:,ilat,:])[:,:,ilon]
lon = lon[ilon]
lat = lat[ilat]
extent = [np.min(lon)-1,np.max(lon)+1,np.min(lat)-1,np.max(lat)+1]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])
if orthographic:
#crs = ccrs.Orthographic(central_lon,central_lat)
crs = ccrs.Orthographic(-30,45)
else:
crs = ccrs.PlateCarree(central_longitude=0.0)
if grad:
vmax = np.nanmax(np.abs(gradient(gt, 2)))
vmin = 0
cm = plt.cm.viridis
norm = colors.PowerNorm(gamma=0.7, vmin=vmin, vmax=vmax)
else:
vmax = np.nanmax(np.abs(gt))
vmin = -1.*vmax
cm = plt.cm.coolwarm
norm = colors.Normalize(vmin=vmin, vmax=vmax)
extent = [np.min(lon),np.max(lon),np.min(lat),np.max(lat)]
fig = plt.figure(figsize=(15,9))
gs = gridspec.GridSpec(2, 4)
gs.update(wspace=0.5)
if supervised:
ax1 = fig.add_subplot(gs[0, :2], projection=crs)
ax2 = fig.add_subplot(gs[0, 2:], projection=crs)
ax3 = fig.add_subplot(gs[1, 1:3], projection=crs)
if grad:
plot(ax1, lon, lat, gradient(gt, 2), r"$\nabla_{GT}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, np.where(np.isnan(obs), np.nan, 0.), "OBS (mask)", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, gradient(pred, 2), r"$\nabla_{4DVarNet}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
plot(ax1, lon, lat, gt, 'GT', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, obs, 'OBS', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, pred, '4DVarNet', extent=extent, cmap=cm, norm=norm, colorbar=False)
# Colorbar
cbar_ax = fig.add_axes([0.1, 0.05, 0.8, 0.01])
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm._A = []
cbar = fig.colorbar(sm, cax=cbar_ax, orientation='horizontal', pad=3.0)
plt.savefig(resfile) # save the figure
fig = plt.gcf()
plt.close() # close the figure
return fig
def animate_maps(gt, obs, oi, pred, lon, lat, resfile,
crop=None, orthographic=True, dw=4, grad=False, supervised=True):
if dw>1:
# decrease the resolution
Nlon = len(lon)
Nlat = len(lat)
ilon = np.arange(0,Nlon,dw)
ilat = np.arange(0,Nlat,dw)
gt = (gt[:,ilat,:])[:,:,ilon]
obs = (obs[:,ilat,:])[:,:,ilon]
oi = (oi[:,ilat,:])[:,:,ilon]
pred = (pred[:,ilat,:])[:,:,ilon]
lon = lon[ilon]
lat = lat[ilat]
if crop is not None:
ilon = np.where((lon>=crop[0]) & (lon<=crop[1]))[0]
ilat = np.where((lat>=crop[2]) & (lat<=crop[3]))[0]
gt = (gt[:,ilat,:])[:,:,ilon]
obs = (obs[:,ilat,:])[:,:,ilon]
oi = (oi[:,ilat,:])[:,:,ilon]
pred = (pred[:,ilat,:])[:,:,ilon]
lon = lon[ilon]
lat = lat[ilat]
extent = [np.min(lon)-1,np.max(lon)+1,np.min(lat)-1,np.max(lat)+1]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])
if grad:
vmax = np.nanmax([np.nanmax(np.abs(gradient(oi[i], 2))) for i in range(len(oi))])
vmin = 0
cm = plt.cm.viridis
norm = colors.PowerNorm(gamma=0.7, vmin=vmin, vmax=vmax)
else:
vmax = np.nanmax(np.abs(oi))
vmin = -1.*vmax
cm = plt.cm.coolwarm
norm = colors.Normalize(vmin=vmin, vmax=vmax)
def animate(i):
print(i)
if supervised:
ax1.clear()
ax2.clear()
ax3.clear()
ax4.clear()
if grad==False:
plot(ax1, lon, lat, gt[i], 'GT', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, obs[i], 'OBS', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, oi[i], 'OI', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax4, lon, lat, pred[i], '4DVarNet', extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
plot(ax1, lon, lat, gradient(gt[i], 2), r"$\nabla_{GT}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, np.where(np.isnan(obs[i]), np.nan, 0.), "OBS (mask)", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, gradient(oi[i], 2), r"$\nabla_{OI}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax4, lon, lat, gradient(pred[i], 2), r"$\nabla_{4DVarNet}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
ax1.clear()
ax2.clear()
ax3.clear()
if grad==False:
plot(ax1, lon, lat, obs[i], 'OBS', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, oi[i], 'OI', extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, pred[i], '4DVarNet', extent=extent, cmap=cm, norm=norm, colorbar=False)
else:
#plot(ax1, lon, lat, gradient(obs[i], 2), r"$\nabla_{OBS}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax1, lon, lat, np.where(np.isnan(obs[i]), np.nan, 0.), "OBS (mask)", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax2, lon, lat, gradient(oi[i], 2), r"$\nabla_{OI}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
plot(ax3, lon, lat, gradient(pred[i], 2), r"$\nabla_{4DVarNet}$", extent=extent, cmap=cm, norm=norm, colorbar=False)
fig = plt.figure(figsize=(15,9))
gs = gridspec.GridSpec(2, 4)
gs.update(wspace=0.1)
if orthographic:
crs = ccrs.Orthographic(central_lon,central_lat)
#crs = ccrs.Orthographic(-30,45)
else:
crs = ccrs.PlateCarree(central_longitude=central_lon)
if supervised:
ax1 = fig.add_subplot(gs[0, :2], projection=crs)
ax2 = fig.add_subplot(gs[0, 2:], projection=crs)
ax3 = fig.add_subplot(gs[1, :2], projection=crs)
ax4 = fig.add_subplot(gs[1, 2:], projection=crs)
else:
ax1 = fig.add_subplot(gs[0, 1:3], projection=crs)
ax1.set_extent(extent)
ax2 = fig.add_subplot(gs[1, :2], projection=crs)
ax2.set_extent(extent)
ax3 = fig.add_subplot(gs[1, 2:], projection=crs)
ax3.set_extent(extent)
plt.subplots_adjust(hspace=0.05)
# Colorbar
cbar_ax = fig.add_axes([0.1, 0.05, 0.8, 0.02])
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm._A = []
cbar = fig.colorbar(sm, cax=cbar_ax, orientation='horizontal', pad=3.0)
ani = animation.FuncAnimation(fig, animate, frames=len(gt), interval=200, repeat=False)
writergif = animation.PillowWriter(fps=3)
writer = animation.FFMpegWriter(fps=3)
ani.save(resfile, writer = writer)
plt.close()
def plot_ensemble(pred,lon,lat,resfile,crop=None,
orthographic=True):
vmax = np.nanmax(np.abs(pred))
vmin = -1.*vmax
cm = plt.cm.coolwarm
norm = colors.Normalize(vmin=vmin, vmax=vmax)
grad_vmax = np.nanmax(np.abs(gradient(pred,2)))
grad_vmin = 0
cm_grad = plt.cm.viridis
norm_grad = colors.PowerNorm(gamma=0.7, vmin=grad_vmin, vmax=grad_vmax)
if crop is not None:
ilon = np.where((lon>=crop[0]) & (lon<=crop[1]))[0]
ilat = np.where((lat>=crop[2]) & (lat<=crop[3]))[0]
pred = (pred[ilat,:,:])[:,ilon,:]
lon = lon[ilon]
lat = lat[ilat]
extent = [np.min(lon)-1,np.max(lon)+1,np.min(lat)-1,np.max(lat)+1]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])
if orthographic:
crs = ccrs.Orthographic(central_lon,central_lat)
#crs = ccrs.Orthographic(-30,45)
else:
crs = ccrs.PlateCarree(central_longitude=central_lon)
n_members = pred.shape[-1]
fig, ax = plt.subplots(2,n_members,figsize=(5*n_members,15),squeeze=False,
subplot_kw=dict(projection=crs))
for i in range(n_members):
plot(ax[0,i],lon,lat,pred[:,:,i],'M'+str(i),extent=extent,cmap=cm,norm=norm,colorbar=False)
plot(ax[1,i],lon,lat,gradient(pred[:,:,i],2),r"$\nabla_{M"+str(i)+"}$",extent=extent,
cmap=cm_grad,norm=norm_grad, colorbar=False)
plt.savefig(resfile) # save the figure
plt.close() # close the figure
def maps_score(resfile, ds, lon, lat):
mesh_lat, mesh_lon = np.meshgrid(lat, lon)
mesh_lat = mesh_lat.T
mesh_lon = mesh_lon.T
cor_map_oi = xr.corr(ds['GT'],ds['OI'], dim='Time')
rmse_map_oi = (((ds['GT'] - ds['OI'])**2).mean(dim=('Time')))**0.5
cor_map_pred = xr.corr(ds['GT'],ds['4DVarNet'], dim='Time')
rmse_map_pred = (((ds['GT'] - ds['4DVarNet'])**2).mean(dim=('Time')))**0.5
lat = ds.latitude.values
lon = ds.longitude.values
extent = [np.min(lon),np.max(lon),np.min(lat),np.max(lat)]
central_lon = np.mean(extent[:2])
central_lat = np.mean(extent[2:])
fig = plt.figure(figsize=(20,20))
ax1 = fig.add_subplot(221,projection=ccrs.PlateCarree(central_longitude=central_lon))
ax2 = fig.add_subplot(222,projection=ccrs.PlateCarree(central_longitude=central_lon))
ax3 = fig.add_subplot(223,projection=ccrs.PlateCarree(central_longitude=central_lon))
ax4 = fig.add_subplot(224,projection=ccrs.PlateCarree(central_longitude=central_lon))
vmax = cor_map_oi.max()
vmin = cor_map_oi.min()
cmap_cor = plt.cm.coolwarm
norm_cor = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
plot(ax1,lon,lat,cor_map_oi.values,'Correlation OI',extent=extent,cmap=cmap_cor,norm=norm_cor)
plot(ax2,lon,lat,cor_map_pred.values,'Correlation 4DVarNet',extent=extent,cmap=cmap_cor,norm=norm_cor)
vmax = rmse_map_oi.max()
vmin = 0.
cmap_rmse = plt.cm.viridis
norm_rmse = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
plot(ax3,lon,lat,rmse_map_oi.values,'RMSE OI',extent=extent,cmap=cmap_rmse,norm=norm_rmse)
plot(ax4,lon,lat,rmse_map_pred.values,'RMSE 4DVarNet',extent=extent,cmap=cmap_rmse,norm=norm_rmse)
plt.savefig(resfile)
fig = plt.gcf()
plt.close()
return fig
def save_netcdf(saved_path1, gt, oi, pred, lon, lat, time,
time_units='days since 2012-10-01 00:00:00'):
'''
saved_path1: string
pred: 3d numpy array (4DVarNet-based predictions)
lon: 1d numpy array
lat: 1d numpy array
time: 1d array-like of time corresponding to the experiment
'''
mesh_lat, mesh_lon = np.meshgrid(lat, lon)
mesh_lat = mesh_lat.T
mesh_lon = mesh_lon.T
dt = pred.shape[1]
xrdata = xr.Dataset( \
data_vars={'longitude': (('lat', 'lon'), mesh_lon), \
'latitude': (('lat', 'lon'), mesh_lat), \
'Time': (('time'), time), \
'GT': (('time', 'lat', 'lon'), gt),
'OI': (('time', 'lat', 'lon'), oi),
'4DVarNet': (('time', 'lat', 'lon'), pred)}, \
coords={'lon': lon, 'lat': lat, 'time': np.arange(len(pred))})
xrdata.time.attrs['units'] = time_units
xrdata.to_netcdf(path=saved_path1, mode='w')
def nrmse(ref, pred):
'''
ref: Ground Truth fields
pred: interpolated fields
'''
return np.sqrt(np.nanmean(((ref - np.nanmean(ref)) - (pred - np.nanmean(pred))) ** 2)) / np.nanstd(ref)
def nrmse_scores(gt, oi, pred, resfile):
'''
gt: 3d numpy array (Ground Truth)
oi: 3d numpy array (OI)
pred: 3d numpy array (4DVarNet-based predictions)
resfile: string
'''
# Compute daily nRMSE scores
nrmse_oi = []
nrmse_pred = []
for i in range(len(oi)):
nrmse_oi.append(nrmse(gt[i], oi[i]))
nrmse_pred.append(nrmse(gt[i], pred[i]))
tab_scores = np.zeros((2, 3))
tab_scores[0, 0] = np.nanmean(nrmse_oi)
tab_scores[0, 1] = np.percentile(nrmse_oi, 5)
tab_scores[0, 2] = np.percentile(nrmse_oi, 95)
tab_scores[1, 0] = np.nanmean(nrmse_pred)
tab_scores[1, 1] = np.percentile(nrmse_pred, 5)
tab_scores[1, 2] = np.percentile(nrmse_pred, 95)
np.savetxt(fname=resfile, X=tab_scores, fmt='%2.2f')
return tab_scores
def mse(ref, pred):
'''
ref: Ground Truth fields
pred: interpolated fields
'''
return np.nanmean(((ref-np.nanmean(ref))-(pred-np.nanmean(pred)))**2)
def mse_scores(gt, oi, pred, resfile):
'''
gt: 3d numpy array (Ground Truth)
oi: 3d numpy array (OI)
pred: 3d numpy array (4DVarNet-based predictions)
resfile: string
'''
# Compute daily nRMSE scores
mse_oi = []
mse_pred = []
for i in range(len(oi)):
mse_oi.append(mse(gt[i], oi[i]))
mse_pred.append(mse(gt[i], pred[i]))
tab_scores = np.zeros((2, 3))
tab_scores[0, 0] = np.nanmean(mse_oi)
tab_scores[0, 1] = np.percentile(mse_oi, 5)
tab_scores[0, 2] = np.percentile(mse_oi, 95)
tab_scores[1, 0] = np.nanmean(mse_pred)
tab_scores[1, 1] = np.percentile(mse_pred, 5)
tab_scores[1, 2] = np.percentile(mse_pred, 95)
np.savetxt(fname=resfile, X=tab_scores, fmt='%2.2f')
def compute_metrics(x_test, x_rec):
# MSE
mse = np.mean((x_test - x_rec) ** 2)
# MSE for gradient
gx_rec = np.gradient(x_rec, axis=[1, 2])
gx_rec = np.sqrt(gx_rec[0] ** 2 + gx_rec[1] ** 2)
gx_test = np.gradient(x_test, axis=[1, 2])
gx_test = np.sqrt(gx_test[0] ** 2 + gx_test[1] ** 2)
gmse = np.mean((gx_test - gx_rec) ** 2)
ng = np.mean((gx_rec) ** 2)
return {'mse': mse, 'mseGrad': gmse, 'meanGrad': ng}
def get_psd_score(x_t, x, ref, with_fig=False):
def psd_score(da: xr.DataArray) -> xr.DataArray:
err = x_t - da
psd_x_t = (
x_t.copy()
.pipe(
lambda _da: xrft.isotropic_power_spectrum(_da, dim=['lat', 'lon'], window='hann', detrend='linear'))
.mean(['time'])
).compute()
psd_err = (
err.copy()
.pipe(
lambda _da: xrft.isotropic_power_spectrum(_da, dim=['lat', 'lon'], window='hann', detrend='linear'))
.mean(['time'])
).compute()
psd_score = 1 - psd_err / psd_x_t
return psd_score
ref_score = psd_score(ref)
model_score = psd_score(x)
ref_score = ref_score.where(model_score > 0, drop=True).compute()
model_score = model_score.where(model_score > 0, drop=True).compute()
psd_plot_data: xr.DataArray = xr.DataArray(
einops.rearrange([model_score.data, ref_score.data], 'var wl -> var wl'),
name='PSD score',
dims=('var', 'wl'),
coords={
'wl': ('wl', 20 * 5 * 1 / model_score.freq_r.data, {'long_name': 'Wavelength', 'units': 'km'}),
'var': ('var', ['model', 'OI'], {}),
},
)
try:
print('here')
print(psd_plot_data.wl.data)
spatial_resolution_model = (
xr.DataArray(
psd_plot_data.wl.data,
dims=['psd'],
coords={'psd': psd_plot_data.sel(var='model').data}
).interp(psd=0.5)
).data
except (KeyError, ValueError) as e:
spatial_resolution_model = -1
try:
spatial_resolution_ref = (
xr.DataArray(
psd_plot_data.wl.data,
dims=['psd'],
coords={'psd': psd_plot_data.sel(var='OI').data}
).interp(psd=0.5)
).data
except (KeyError, ValueError) as e:
spatial_resolution_ref = -1
if not with_fig:
return spatial_resolution_model, spatial_resolution_ref
fig, ax = plt.subplots()
if spatial_resolution_model == -1:
plt.close()
return fig, spatial_resolution_model, spatial_resolution_ref
psd_plot_data.plot.line(x='wl', ax=ax)
# Plot vertical line there
for i, (sr, var) in enumerate([(spatial_resolution_ref, 'OI'), (spatial_resolution_model, 'model')]):
plt.axvline(sr, ymin=0, color='0.5', ls=':')
plt.annotate(f"resolution {var}: {float(sr):.2f} km", (sr * 1.1, 0.1 * i))
plt.axhline(0.5, xmin=0, color='k', ls='--')
plt.ylim([0, 1])
plt.close()
return fig, spatial_resolution_model, spatial_resolution_ref
def rmse_based_scores(da_rec, da_ref):
# boost swot rmse score
logging.info(' Compute RMSE-based scores...')
# RMSE(t) based score
rmse_t = 1.0 - (((da_rec - da_ref)**2).mean(dim=('lon', 'lat')))**0.5/(((da_ref)**2).mean(dim=('lon', 'lat')))**0.5
# RMSE(x, y) based score
# rmse_xy = 1.0 - (((da_rec - da_ref)**2).mean(dim=('time')))**0.5/(((da_ref)**2).mean(dim=('time')))**0.5
rmse_xy = (((da_rec - da_ref)**2).mean(dim=('time')))**0.5
rmse_t = rmse_t.rename('rmse_t')
rmse_xy = rmse_xy.rename('rmse_xy')
# Temporal stability of the error
reconstruction_error_stability_metric = rmse_t.std().values
# Show leaderboard SSH-RMSE metric (spatially and time averaged normalized RMSE)
leaderboard_rmse = 1.0 - (((da_rec - da_ref) ** 2).mean()) ** 0.5 / (
((da_ref) ** 2).mean()) ** 0.5
logging.info(' => Leaderboard SSH RMSE score = %s', np.round(leaderboard_rmse.values, 2))
logging.info(' Error variability = %s (temporal stability of the mapping error)', np.round(reconstruction_error_stability_metric, 2))
return rmse_t, rmse_xy, np.round(leaderboard_rmse.values, 5), np.round(reconstruction_error_stability_metric, 5)
def psd_based_scores(da_rec, da_ref):
# boost-swot-psd-score
logging.info(' Compute PSD-based scores...')
# Compute error = SSH_reconstruction - SSH_true
err = (da_rec - da_ref)
err = err.chunk({"lat":1, 'time': err['time'].size, 'lon': err['lon'].size})
# make time vector in days units
err['time'] = (err.time - err.time[0]) / np.timedelta64(1, 'D')
# Rechunk SSH_true
signal = da_ref.chunk({"lat":1, 'time': da_ref['time'].size, 'lon': da_ref['lon'].size})
# make time vector in days units
signal['time'] = (signal.time - signal.time[0]) / np.timedelta64(1, 'D')
# Compute PSD_err and PSD_signal
psd_err = xrft.power_spectrum(err, dim=['time', 'lon'], detrend='constant', window=True).compute()
psd_signal = xrft.power_spectrum(signal, dim=['time', 'lon'], detrend='constant', window=True).compute()
# Averaged over latitude
mean_psd_signal = psd_signal.mean(dim='lat').where((psd_signal.freq_lon > 0.) & (psd_signal.freq_time > 0), drop=True)
mean_psd_err = psd_err.mean(dim='lat').where((psd_err.freq_lon > 0.) & (psd_err.freq_time > 0), drop=True)
# return PSD-based score
psd_based_score = (1.0 - mean_psd_err/mean_psd_signal)
# Find the key metrics: shortest temporal & spatial scales resolved based on the 0.5 contour criterion of the PSD_score
level = [0.5]
cs = plt.contour(1./psd_based_score.freq_lon.values,1./psd_based_score.freq_time.values, psd_based_score, level)
x05, y05 = cs.collections[0].get_paths()[0].vertices.T
plt.close()
shortest_spatial_wavelength_resolved = np.min(x05)
shortest_temporal_wavelength_resolved = np.min(y05)
logging.info(' => Leaderboard Spectral score = %s (degree lon)',
np.round(shortest_spatial_wavelength_resolved, 2))
logging.info(' => shortest temporal wavelength resolved = %s (days)',
np.round(shortest_temporal_wavelength_resolved, 2))
psd_da = (1.0 - mean_psd_err/mean_psd_signal)
psd_da.name = 'psd_score'
return psd_da.to_dataset(), np.round(shortest_spatial_wavelength_resolved, 3), np.round(shortest_temporal_wavelength_resolved, 3)
def plot_psd_score(ds):
fig, ax = plt.subplots()
#ax.invert_yaxis()
#ax.invert_xaxis()
c1 = plt.contourf(1./(ds['freq_lon']), 1./ds['freq_time'], ds['psd_score'],
levels=np.arange(0,1.1, 0.1), cmap='RdYlGn', extend='both')
cbar = plt.colorbar(pad=0.01)
plt.xlabel('spatial wavelenght (degree_lon)', fontweight='bold', fontsize=20)
plt.ylabel('temporal wavelenght (days)', fontweight='bold', fontsize=20)
#plt.xscale('log')
#plt.yscale('log')
plt.grid(linestyle='--', lw=1, color='w')
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.title('PSD-based score', fontweight='bold', fontsize=20)
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter())
c2 = plt.contour(1./(ds['freq_lon']), 1./ds['freq_time'], ds['psd_score'], levels=[0.5], linewidths=2, colors='k')
cbar.add_lines(c2)
bbox_props = dict(boxstyle="round,pad=0.5", fc="w", ec="k", lw=2)
ax.annotate('Resolved scales',
xy=(1.15, 0.8),
xycoords='axes fraction',
xytext=(1.15, 0.55),
bbox=bbox_props,
arrowprops=
dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='center')
ax.annotate('UN-resolved scales',
xy=(1.15, 0.2),
xycoords='axes fraction',
xytext=(1.15, 0.45),
bbox=bbox_props,
arrowprops=
dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='center')
plt.close()
return fig