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Commit cf621e6f authored by BRAUX Emmanuel's avatar BRAUX Emmanuel
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UP fix notebook message

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%% Cell type:markdown id:a6c07fb9-260d-49a3-9156-9d27a3ccf1ac tags:
We perform the inference of a pretrained 4dvarnet model and perform the comparison
with the DInEOF and eDInEOF models.
We then choose a range of days specified by a start_date and end_date over which we perform inference.
%% Cell type:markdown id:f98bbe84 tags:
Download Datas :
- `data/Obs_SPM_log10_aNam.nc`,
- `data/Obs_SPM_log10_aNam_removed_50percent_patch_again.nc`,
- `data/land_mask_OSE.nc`
- `data/checkpoints/val_mse=24.5515-epoch=197.ckpt`
%% Cell type:code id:e464928f tags:
``` python
!mkdir -p data
# GT
!wget -q --show-progress 'https://s3.eu-west-2.wasabisys.com/4dvarnet/german-wadden-sea/Obs_SPM_log10_aNam.nc' -O 'data/Obs_SPM_log10_aNam.n'c
# patch
!wget -q --show-progress 'https://s3.eu-west-2.wasabisys.com/4dvarnet/german-wadden-sea/Obs_SPM_log10_aNam_removed_50percent_patch_again.nc' -O 'data/Obs_SPM_log10_aNam_removed_50percent_patch_again.nc'
# Land mask
!wget -q --show-progress 'https://s3.eu-west-2.wasabisys.com/4dvarnet/german-wadden-sea/land_mask_OSE.nc' -O 'data/land_mask_OSE.nc'
# checkpoint
!mkdir -p data/checkpoints
!wget -q --show-progress 'https://s3.eu-west-2.wasabisys.com/4dvarnet/german-wadden-sea/val_mse=24.5515-epoch=197.ckpt' -O 'data/checkpoints/val_mse=24.5515-epoch=197.ckpt'
```
%% Cell type:markdown id:30a87915 tags:
Process Datas (It could take minutes, be patient ...)
Process Datas :
%% Cell type:code id:9430a338 tags:
``` python
!rm -rf outputs/main
!python main.py xp=base hydra.run.dir=./outputs/main
```
%% Cell type:code id:9a15d307 tags:
``` python
... It could take minutes, be patient ...
```
%% Cell type:code id:de503e67 tags:
``` python
# We import the libraries needed for the pipeline
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import scipy.sparse.linalg as sp
import matplotlib.animation as animation
import warnings
plt.rcParams["animation.html"] = "jshtml"
plt.rcParams['figure.dpi'] = 150
plt.ioff()
```
%% Cell type:code id:4fa7b0dd tags:
``` python
# The two performance metrics we use for cross-validation in (e)DInEOF algorithms)
def rmse(x,y):
return np.sqrt(np.mean((x-y)**2))
def RE(gt,pred):
ep=0.0001
return np.mean(np.abs((10**gt+ep)-(10**pred+ep))/np.abs(10**gt+ep))*100
```
%% Cell type:code id:b132951b tags:
``` python
# DInEOF algorithm (from https://doi.org/10.1016/j.ocemod.2004.08.001)
def DInEOF(X, mask_miss, mask_test=None, k=1, iter_=1000):
X_rec_temp = np.zeros(X.shape)
score = np.zeros([3,iter_])
for i in range(iter_):
X_rec_out = X_rec_temp.copy()
X_rec_temp[~mask_miss] = X[~mask_miss].copy()
#u, s, vh = sp.svds(X_rec_temp,k=k)
#X_rec_temp = u@np.diag(s)@vh
A = X_rec_temp.T@X_rec_temp
p2, v = sp.eigs(A,k)
if np.sum(np.imag(p2))!=0:
warning.warn("Caution non-zero imaginary part has been discard")
p = np.sqrt(np.real(p2))
v = np.real(v)
u = X_rec_temp@(v@np.diag(1./p))
X_rec_temp = u@np.diag(p)@v.T
score[0,i] = i
score[1,i] = k
score[2,i] = rmse(X[mask_test], X_rec_temp[mask_test])
if i>0:
if score[2,i]>score[2,i-1]:
score = score[:,0:i+1]
return X_rec_out, score
elif np.abs(score[2,i]-score[2,i-1])<1.e-4:
k+=1
X_rec_out = X_rec_temp
return X_rec_out, score
```
%% Cell type:code id:2857040f tags:
``` python
# eDInEOF algorithm (from https://doi.org/10.5194/os-5-475-2009)
def eDInEOF(X, mask_miss, mask_test, dt=1, alpha=1.e-2 , s_=5, k=1, iter_=1000):
N = X.shape[1]
F = -2*np.eye(N,N)+np.eye(N,N,-1)+np.eye(N,N,1)
F[1,0] = 2
F[-2,-1] = 2
F = (alpha/dt**2)*F
X_rec_temp = np.zeros(X.shape)
score = np.zeros([3,iter_])
for i in range(iter_):
X_rec_out = X_rec_temp.copy()
X_rec_temp[~mask_miss] = X[~mask_miss].copy()
for s in range(s_):
X_rec_temp = X_rec_temp+X_rec_temp@F
#u, sigma, vh = sp.svds(X_rec_temp, k=k)
#X_rec_temp = u@np.diag(sigma)@vh
A = X_rec_temp.T@X_rec_temp
p2, v = sp.eigs(A,k)
if np.sum(np.imag(p2))!=0:
warning.warn("Caution non-zero imaginary part has been discard")
p = np.sqrt(np.real(p2))
v = np.real(v)
u = X_rec_temp@(v@np.diag(1./p))
X_rec_temp = u@np.diag(p)@v.T
score[0,i] = i
score[1,i] = k
score[2,i] = rmse(X[mask_test], X_rec_temp[mask_test])
if i>0:
if score[2,i]>score[2,i-1]:
score = score[:,0:i+1]
return X_rec_out, score
elif np.abs(score[2,i]-score[2,i-1])<1.e-4:
k+=1
X_rec_out = X_rec_temp
return X_rec_out, score
```
%% Cell type:code id:4cb86eae tags:
``` python
#This cell is to prepare for computing DInEOF and eDInEOF
var = 'SPM'
start_date='2020-06-01'
end_date='2020-06-26'
#OSE
data = xr.open_dataset('data/Obs_SPM_log10_aNam_removed_50percent_patch_again.nc').sel(time=slice(start_date,end_date)) #Gappy observations
land_mask = xr.open_dataset('data/land_mask_OSE.nc')
nlat = data.lat.size
nlon = data.lon.size
nb_sea_pix = np.sum(np.isnan(land_mask.mask.values)) # Nb of sea pixels to be filled
idx_sea = np.isnan(land_mask.mask.values.flatten()) # Sea pixels locations
dt = len(data.time) # Length of the time-serie
X = np.full([nb_sea_pix,dt],np.nan) # Init. of the matrix to store vectorized sea-pixels
# Loop to build the data matrix to be used in completion algorithms
for t in range(dt):
data_t = data[var][t].values.flatten()
X[:,t] = data_t[idx_sea]
mask_mv = np.isnan(X) # Missing data mask matrix
mask_data = 1-mask_mv # Data mask matrix
data_index = np.where(mask_data) # Data index
mask_crossval = np.full([nb_sea_pix,dt], False, dtype=bool) # Init. mask matrix for cross-validation data
rand_pick = np.random.permutation(data_index[0].size)[0:int(.01*data_index[0].size)]
data_pick = (data_index[0][rand_pick],data_index[1][rand_pick]) # Random selection of data for cros-validation (1%)
mask_crossval[data_pick] = True
mask_mv[mask_crossval] = True
# Data normalization
mu = np.nanmean(X)
std = np.nanstd(X)**2
X = (X-mu)/std
X = np.where(np.isnan(X),0,X)
```
%% Cell type:code id:ae7da4dd tags:
``` python
# Apply DInEOF to the selected data
X_rec, score = DInEOF(X, mask_mv, mask_crossval)
X_rec = X_rec*std+mu
# Reshaping
rec_DInEOF = np.zeros([dt,nlat,nlon])
for t in range(dt):
data_t = np.full([nlat*nlon], np.nan)
data_t[idx_sea] = X_rec[:,t]
rec_DInEOF[t] = data_t.reshape(nlat,nlon)
#saving reconstruction
rec_DInEOF = xr.DataArray(rec_DInEOF,name=var,coords=[data.time[0:dt],data.lat,data.lon],dims=['time','lat','lon'])
#OSE
rec_DInEOF.to_netcdf('data/DInEOF_log10.nc')
```
%% Cell type:code id:f52da7d4-0622-41ec-a00b-51551518fa95 tags:
``` python
# Apply eDInEOF to the selected data
X_rec, score_e = eDInEOF(X, mask_mv, mask_crossval)
X_rec = X_rec*std+mu
# Reshaping
rec_eDInEOF = np.zeros([dt,nlat,nlon])
for t in range(dt):
data_t = np.full([nlat*nlon], np.nan)
data_t[idx_sea] = X_rec[:,t]
rec_eDInEOF[t] = data_t.reshape(nlat,nlon)
# saving reconstruction
rec_eDInEOF = xr.DataArray(rec_eDInEOF,name=var,coords=[data.time[0:dt],data.lat,data.lon],dims=['time','lat','lon'])
#OSE
rec_eDInEOF.to_netcdf('data/eDInEOF_log10.nc')
```
%% Cell type:code id:63c77a1b tags:
``` python
# Compute the RMSE, RE of the three algorithms
# #load data OSE
rec_DInEOF = xr.open_dataset('data/DInEOF_log10.nc')#.sel(time="2018")
rec_eDInEOF = xr.open_dataset('data/eDInEOF_log10.nc')#.sel(time="2018")
data4DVarnet = xr.open_dataset("outputs/main/base/TestonOSE_DutchWaddenSea/test_data.nc")
GT = xr.open_dataset('data/Obs_SPM_log10_aNam.nc').sel(time=slice(start_date,end_date))
Obs = xr.open_dataset('data/Obs_SPM_log10_aNam_removed_50percent_patch_again.nc').sel(time=slice(start_date,end_date))
#declare the pixels in which we process RMSE and RE
mask_obs_minus_GT = ~np.isnan(GT.SPM.values) & np.isnan(Obs.SPM)#mask_obs_minus_GT = True if Obs is nan and GT is not nan (and False otherwise)
#score_DInEOF
score_DInEOF = rmse(GT.SPM.values[mask_obs_minus_GT],rec_DInEOF.SPM.values[mask_obs_minus_GT])
print("RMSE DinEOF: ",score_DInEOF)
#RE_DInEOF
RE_DInEOF = RE(GT.SPM.values[mask_obs_minus_GT],rec_DInEOF.SPM.values[mask_obs_minus_GT])
print("RE DinEOF: ", RE_DInEOF)
#score_eDInEOF
score_eDInEOF = rmse(GT.SPM.values[mask_obs_minus_GT],rec_eDInEOF.SPM.values[mask_obs_minus_GT])
print("RMSE eDinEOF: ",score_eDInEOF)
#RE_eDInEOF
RE_eDInEOF = RE(GT.SPM.values[mask_obs_minus_GT],rec_eDInEOF.SPM.values[mask_obs_minus_GT])
print("RE eDinEOF: ",RE_eDInEOF)
#score_4DVarNetwith
score_4DVarNet = rmse(GT.SPM.values[mask_obs_minus_GT],data4DVarnet.out.values[mask_obs_minus_GT])
print("RMSE 4DVarNEt: ",score_4DVarNet)
#RE_4DVarNet
RE_4DVarNet = RE(data4DVarnet.tgt.values[mask_obs_minus_GT],data4DVarnet.out.values[mask_obs_minus_GT])
print("RE 4DVarNEt: ",RE_4DVarNet)
```
%% Cell type:code id:402d80d2 tags:
``` python
#We load the land mask and the satelite observations
land_mask=xr.open_dataset('data/land_mask_OSE.nc')
GT = xr.open_dataset('data/Obs_SPM_log10_aNam.nc').sel(time=slice(start_date,end_date))
```
%% Cell type:code id:e982290b-5322-49f3-a586-db3c47034893 tags:
``` python
#plot all algorithms for some specific days
rang_min=5#220,130
rang_max=rang_min+1
step=1
lon = GT.lon
lat = GT.lat
nlat=lat.size
nlon=lon.size
# transpose land_mask
land_mask_transposed = land_mask.transpose('lat', 'lon')
v_min=0.0#np.min(data.tgt[rang_min:rang_max:10])
v_max=2.0#np.max(data.tgt[rang_min:rang_max:10])
plots_path='plots'
import os
if not os.path.exists(plots_path):
os.makedirs(plots_path)
for t in range(rang_min, rang_max, step):
fig, axes = plt.subplots(2, 3, figsize=(4 * 4, 2 * 4 * (nlat / nlon))) # Adjust size as needed for 2 rows, 3 columns
# First subplot (Row 1, Column 1)
mappable0 = axes[0, 0].pcolormesh(lon, lat, data4DVarnet.tgt[t], cmap='jet', vmin=v_min, vmax=v_max)
axes[0, 0].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
axes[0, 0].set_title(GT.time[t].dt.strftime("%B %d, %Y").values + " - Gappy Target")
# Second subplot (Row 1, Column 2)
mappable1 = axes[0, 1].pcolormesh(lon, lat, data4DVarnet.inp[t], cmap='jet', vmin=v_min, vmax=v_max)
axes[0, 1].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
axes[0, 1].set_title("Simulated Observation")
# Third subplot (Row 1, Column 3)
# If you have a third type of data for this row, use it here
# Example:
mappable2 = axes[0, 2].pcolormesh(lon, lat, rec_DInEOF.SPM.values[t], cmap='jet', vmin=v_min, vmax=v_max)
axes[0, 1].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
axes[0, 2].set_title("DInEOF")
# Fourth subplot (Row 2, Column 1)
mappable3 = axes[1, 0].pcolormesh(lon, lat, rec_eDInEOF.SPM.values[t], cmap='jet', vmin=v_min, vmax=v_max)
axes[1, 0].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
axes[1, 0].set_title("eDInEOF")
# Fifth subplot (Row 2, Column 2)
mappable4 = axes[1, 1].pcolormesh(lon, lat, data4DVarnet.out[t], cmap='jet', vmin=v_min, vmax=v_max)
axes[1, 1].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
axes[1, 1].set_title("4DVarNEt")
# Sixth subplot (Row 2, Column 3)
#mappable5 = axes[1, 2].pcolormesh(lon, lat, data4DVarnet_Conv3D_OSSE2OSE.out[t], cmap='jet', vmin=v_min, vmax=v_max)
#axes[1, 2].pcolormesh(lon, lat, land_mask.mask, cmap='Greys', vmin=0, vmax=2)
#axes[1, 2].set_title("4DVarNEt_Conv3D_OSSE2OSE")
# Colorbar setup
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.02, 0.7]) # Position for the colorbar
fig.colorbar(mappable0, cax=cbar_ax, extend='min')
# Save the figure
fig.savefig(f"plots/plot_4DVarNet_vs_Unet_vs_DinEOF_SCHISM_time{t}.png", dpi=300)
plt.show()
```
......
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