
import numpy as np
import xsar as sr
import logging
import rasterio
import pickle
import xarray as xr

import matplotlib.pyplot as plt

import bokeh.io
bokeh.io.output_notebook()
import holoviews as hv
hv.extension('bokeh')
import datashader as ds
from holoviews.operation.datashader import datashade,rasterize
from IPython.display import display, HTML

logging.basicConfig()
logger = logging.getLogger('xsar')
logger.setLevel(logging.DEBUG)


filename = '/home/alevieux/Documents/Data/S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_992F.SAFE'
chunks = {'xtrack': 500, 'atrack': 200}

x0 = 0
y0 = 0

tilesize = 6400
xend = tilesize-1 # -1 car les slice inclus la valeur superieur (pas sarwing 6400x6400)
yend = tilesize-1 # -1 car les slice inclus la valeur superieur (pas sarwing 6400x6400)

polarization = 'VV'


def cmod5n_forward(v,phi,theta):
    '''! ---------
! cmod5n_forward(v, phi, theta)
! inputs:
! v in [m/s] wind velocity (always >= 0)
! phi in [deg] angle between azimuth and wind direction
! (= D - AZM)
! theta in [deg] incidence angle
! output:
! CMOD5_N NORMALIZED BACKSCATTER (LINEAR)
!
! All inputs must be Numpy arrays of equal sizes
!
! A. STOFFELEN MAY 1991 ECMWF CMOD4
! A. STOFFELEN, S. DE HAAN DEC 2001 KNMI CMOD5 PROTOTYPE
! H. HERSBACH JUNE 2002 ECMWF COMPLETE REVISION
! J. de Kloe JULI 2003 KNMI, rewritten in fortan90
! A. Verhoef JAN 2008 KNMI, CMOD5 for neutral winds
! K.F.Dagestad OCT 2011 NERSC, Vectorized Python version
!---------------------------------------------------------------------
'''
        
    from numpy import cos, exp, tanh, array
    
    DTOR = 57.29577951
    THETM = 40.
    THETHR = 25.
    ZPOW = 1.6
    
    # NB: 0 added as first element below, to avoid switching from 1-indexing to 0-indexing
    C = [0, -0.6878, -0.7957, 0.3380, -0.1728, 0.0000, 0.0040, 0.1103, 0.0159,
          6.7329, 2.7713, -2.2885, 0.4971, -0.7250, 0.0450,
          0.0066, 0.3222, 0.0120, 22.7000, 2.0813, 3.0000, 8.3659,
          -3.3428, 1.3236, 6.2437, 2.3893, 0.3249, 4.1590, 1.6930]
    Y0 = C[19]
    PN = C[20]
    A = C[19]-(C[19]-1)/C[20]

    B = 1./(C[20]*(C[19]-1.)**(3-1))

# ! ANGLES
    FI=phi/DTOR
    CSFI = cos(FI)
    CS2FI= 2.00 * CSFI * CSFI - 1.00

    X = (theta - THETM) / THETHR
    XX = X*X

    # ! B0: FUNCTION OF WIND SPEED AND INCIDENCE ANGLE
    A0 =C[ 1]+C[ 2]*X+C[ 3]*XX+C[ 4]*X*XX
    A1 =C[ 5]+C[ 6]*X
    A2 =C[ 7]+C[ 8]*X

    GAM=C[ 9]+C[10]*X+C[11]*XX
    S0 =C[12]+C[13]*X
    
    # V is missing! Using V=v as substitute, this is apparently correct
    V=v
    S = A2*V
    S_vec = S.copy()
    SlS0 = [S_vec<S0]
    
    #print(SlS0)
    #print(S_vec)
    #print(S0)
    
    S_vec[SlS0]=S0[SlS0]
    A3=1./(1.+exp(-S_vec))
    SlS0 = (S<S0)
    A3[SlS0]=A3[SlS0]*(S[SlS0]/S0[SlS0])**( S0[SlS0]*(1.- A3[SlS0]))
    #A3=A3*(S/S0)**( S0*(1.- A3))
    B0=(A3**GAM)*10.**(A0+A1*V)
        
    # ! B1: FUNCTION OF WIND SPEED AND INCIDENCE ANGLE
    B1 = C[15]*V*(0.5+X-tanh(4.*(X+C[16]+C[17]*V)))
    B1 = C[14]*(1.+X)- B1
    B1 = B1/(exp( 0.34*(V-C[18]) )+1.)

    # ! B2: FUNCTION OF WIND SPEED AND INCIDENCE ANGLE
    V0 = C[21] + C[22]*X + C[23]*XX
    D1 = C[24] + C[25]*X + C[26]*XX
    D2 = C[27] + C[28]*X

    V2 = (V/V0+1.)
    V2ltY0 = V2<Y0
    V2[V2ltY0] = A+B*(V2[V2ltY0]-1.)**PN
    B2 = (-D1+D2*V2)*exp(-V2)

    # ! CMOD5_N: COMBINE THE THREE FOURIER TERMS
    CMOD5_N = B0*(1.0+B1*CSFI+B2*CS2FI)**ZPOW
    return CMOD5_N
    

def cmod5n_inverse(sigma0_obs, phi, incidence, iterations=10):
    '''! ---------
! cmod5n_inverse(sigma0_obs, phi, incidence, iterations)
! inputs:
! sigma0_obs Normalized Radar Cross Section [linear units]
! phi in [deg] angle between azimuth and wind direction
! (= D - AZM)
! incidence in [deg] incidence angle
! iterations: number of iterations to run
! output:
! Wind speed, 10 m, neutral stratification
!
! All inputs must be Numpy arrays of equal sizes
!
! This function iterates the forward CMOD5N function
! until agreement with input (observed) sigma0 values
!---------------------------------------------------------------------
'''
    from numpy import ones, array
    
    # First guess wind speed
    V = array([10.])*ones(sigma0_obs.shape);
    step=10.
    
    # Iterating until error is smaller than threshold
    for iterno in range(1, iterations):
        #print iterno
        sigma0_calc = cmod5n_forward(V, phi, incidence)
        ind = sigma0_calc-sigma0_obs>0
        V = V + step
        V[ind] = V[ind] - 2*step
        step = step/2

    #mdict={'s0obs':sigma0_obs,'s0calc':sigma0_calc}
    #from scipy.io import savemat
    #savemat('s0test',mdict)
        
    return V


def model_ifr2(wind_speed, wind_dir, inc_angle):
    """
    ; Renvoie une NRCS prédite par modèle IFR2
    ;
    ; INPUTS:
    ;  inc_angle:
    ;  wind_speed:
    ;  wind_dir:
    ;
    ; OPTIONAL INPUTS:
    ;  wind_speed:
    ;
    """

    C = np.zeros(26)

    # init CMOD-IFR2 coef
    C[0]  =  0.0
    C[1]  = -2.437597
    C[2]  = -1.5670307
    C[3]  =  0.3708242
    C[4]  = -0.040590
    C[5]  =  0.404678
    C[6]  =  0.188397
    C[7]  = -0.027262
    C[8]  =  0.064650
    C[9]  =  0.054500
    C[10] =  0.086350
    C[11] =  0.055100
    C[12] =  -0.058450
    C[13] = -0.096100
    C[14] = 0.412754
    C[15] =  0.121785
    C[16] =  -0.024333
    C[17] = 0.072163
    C[18] =  -0.062954
    C[19] =  0.015958
    C[20] = -0.069514
    C[21] = -0.062945
    C[22] =  0.035538
    C[23] = 0.023049
    C[24] =  0.074654
    C[25] =  -0.014713
    
    
    T=inc_angle
    wind=wind_speed

    tetai = (T - 36.)/19.
    xSQ = tetai*tetai
    P0 = 1.
    P1 = tetai
    P2 = (3.*xSQ-1.)/2.
    P3 = (5.*xSQ-3.)*tetai/2.
    ALPH = C[1] + C[2] * P1 + C[3] * P2 + C[4]*P3
    BETA = C[5] + C[6] * P1 + C[7] * P2
    ang = wind_dir
    cosi  = np.cos(np.deg2rad(ang))
    cos2i = 2.*cosi*cosi - 1.
    #b1   = self.funcB1(T, wind)
    ############## fonction funcB1 ###################
    tetamin = 18.
    tetamax = 58.
    tetanor = (2.*T - (tetamin+tetamax))/(tetamax-tetamin)
    vmin = 3.
    vmax = 25.
    vitnor = (2.*wind - (vmax+vmin))/(vmax-vmin)
    pv0 = 1.
    pv1 = vitnor
    pv2 = 2*vitnor*pv1 - pv0
    pv3 = 2*vitnor*pv2 - pv1
    pt0 = 1.
    pt1 = tetanor
    pt2 = 2*tetanor*pt1 - pt0
    pt3 = 2*tetanor*pt2 - pt1
    b1 = C[8] + C[9]*pv1 + (C[10]+C[11]*pv1)*pt1 + (C[12]+C[13]*pv1)*pt2
    ##################################################
    #b2   = self.funcB2(T, wind)
    ############## fonction funcB2 ###################
    tetamin = 18.
    tetamax = 58.
    tetanor = (2.*T - (tetamin+tetamax))/(tetamax-tetamin)
    vmin = 3.
    vmax = 25.
    vitnor = (2.*wind - (vmax+vmin))/(vmax-vmin)
    pv0 = 1.
    pv1 = vitnor
    pv2 = 2*vitnor*pv1 - pv0
    pv3 = 2*vitnor*pv2 - pv1
    pt0 = 1.
    pt1 = tetanor
    pt2 = 2*tetanor*pt1 - pt0
    pt3 = 2*tetanor*pt2 - pt1
    result = C[14] + C[15]*pt1 + C[16]*pt2 + \
             (C[17]+C[18]*pt1+C[19]*pt2)*pv1 + (C[20]+C[21]*pt1+C[22]*pt2)*pv2 + \
             (C[23]+C[24]*pt1+C[25]*pt2)*pv3
    b2=result    
    ##################################################
    
    b0 = np.power(10., (ALPH+BETA*np.sqrt(wind)))
    sig  = b0 * ( 1. + b1*cosi + np.tanh(b2)*cos2i )
    return sig


image_full_resolution = sr.open_dataset(filename, chunks=chunks, pol_dim=True)[0]
image_full_resolution

DEBUG:xsar.utils:timing load_digital_number : 0.0s. mem: +5.3Mb
DEBUG:xsar.utils:timing load_geometry : 0.1s. mem: +1.9Mb
DEBUG:xsar.utils:timing luts_from_xml_calibration : 0.2s. mem: +18.7Mb
DEBUG:xsar.utils:timing luts_from_xml_calibration : 0.1s. mem: +19.6Mb
DEBUG:xsar.utils:timing luts_from_xml_noise : 0.3s. mem: +18.7Mb
DEBUG:xsar.utils:timing luts_from_xml_noise : 0.4s. mem: +10.5Mb
DEBUG:xsar.utils:timing load_luts : 1.0s. mem: +75.4Mb
DEBUG:xsar.utils:timing luts_from_xml_anotation : 0.2s. mem: +9.5Mb
DEBUG:xsar.utils:timing luts_from_xml_anotation : 0.1s. mem: +13.9Mb
DEBUG:xsar.utils:timing load_luts : 0.3s. mem: +14.3Mb
DEBUG:xsar.utils:timing load_lon_lat : 0.1s. mem: +9.2Mb
DEBUG:xsar.utils:timing _get_subdatasets : 1.8s. mem: +132.6Mb
DEBUG:xsar.utils:timing apply_cf_convention : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing open_dataset : 1.8s. mem: +133.7Mb

<xarray.Dataset>
Dimensions:         (atrack: 16778, pol: 2, xtrack: 25187)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) int64 0 1 2 3 4 5 ... 16773 16774 16775 16776 16777
  * xtrack          (xtrack) int64 0 1 2 3 4 5 ... 25182 25183 25184 25185 25186
Data variables:
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(2, 200, 500), meta=np.ndarray>
    time            (atrack) datetime64[ns] dask.array<chunksize=(200,), meta=np.ndarray>
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    sigma0_lut      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0_lut      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    noise_lut       (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
Attributes:
    footprint:       POLYGON ((-67.84221143971432 20.72564283093837, -70.2216...
    coverage:        251km * 170km (xtrack * atrack )
    pixel_xtrack_m:  10.0
    pixel_atrack_m:  10.2
    ipf_version:     2.84
    swath_type:      IW
    polarizations:   VV VH
    product_type:    GRD
    mission:         SENTINEL-1
    satellite:       A
    start_date:      2017-09-07 10:30:20.936409
    stop_date:       2017-09-07 10:30:45.935264
    subdataset:      IW
    geometry:                                                              ge...
    Conventions:     CF-1.7

array(['VV', 'VH'], dtype=object)

array([    0,     1,     2, ..., 16775, 16776, 16777])

array([    0,     1,     2, ..., 25184, 25185, 25186])


tile_6400x6400_full_resolution = image_full_resolution.sel(xtrack=range(x0, tilesize), atrack=range(y0, tilesize))
tile_6400x6400_full_resolution

<xarray.Dataset>
Dimensions:         (atrack: 6400, pol: 2, xtrack: 6400)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) int64 0 1 2 3 4 5 ... 6394 6395 6396 6397 6398 6399
  * xtrack          (xtrack) int64 0 1 2 3 4 5 ... 6394 6395 6396 6397 6398 6399
Data variables:
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(2, 200, 500), meta=np.ndarray>
    time            (atrack) datetime64[ns] dask.array<chunksize=(200,), meta=np.ndarray>
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    sigma0_lut      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0_lut      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    noise_lut       (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
Attributes:
    footprint:       POLYGON ((-67.84221143971432 20.72564283093837, -70.2216...
    coverage:        251km * 170km (xtrack * atrack )
    pixel_xtrack_m:  10.0
    pixel_atrack_m:  10.2
    ipf_version:     2.84
    swath_type:      IW
    polarizations:   VV VH
    product_type:    GRD
    mission:         SENTINEL-1
    satellite:       A
    start_date:      2017-09-07 10:30:20.936409
    stop_date:       2017-09-07 10:30:45.935264
    subdataset:      IW
    geometry:                                                              ge...
    Conventions:     CF-1.7

array(['VV', 'VH'], dtype=object)

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


sigma0_tile_6400x6400_full_resolution_vv = tile_6400x6400_full_resolution.sigma0.sel(pol=polarization)
nesz_tile_6400x6400_full_resolution_vv = tile_6400x6400_full_resolution.nesz.sel(pol=polarization)
sigma0_denoised_tile_6400x6400_full_resolution_vv = np.maximum(sigma0_tile_6400x6400_full_resolution_vv - nesz_tile_6400x6400_full_resolution_vv, 0)
sigma0_denoised_tile_6400x6400_full_resolution_vv

<xarray.DataArray (atrack: 6400, xtrack: 6400)>
dask.array<maximum, shape=(6400, 6400), dtype=float64, chunksize=(200, 500), chunktype=numpy.ndarray>
Coordinates:
    pol      <U2 'VV'
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array('VV', dtype='<U2')

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv.pkl', 'rb'), encoding='bytes')
sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv_dataset = xr.DataArray(sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv, dims=( "xtrack", "atrack"), coords={"xtrack":range(0,6400,1), "atrack":range(0,6400,1)})
sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv_dataset

<xarray.DataArray (xtrack: 6400, atrack: 6400)>
array([[0.        , 0.        , 0.        , ..., 0.23613784, 0.21474715,
        0.21177629],
       [0.        , 0.        , 0.        , ..., 0.31893268, 0.37767802,
        0.32808411],
       [0.        , 0.        , 0.        , ..., 0.18040625, 0.20881903,
        0.23771222],
       ...,
       [0.        , 0.        , 0.        , ..., 0.42235672, 0.56886138,
        0.42028585],
       [0.        , 0.        , 0.        , ..., 0.49402371, 0.37178744,
        0.31713959],
       [0.        , 0.        , 0.        , ..., 0.58101842, 0.28710154,
        0.22380615]])
Coordinates:
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array([[0.        , 0.        , 0.        , ..., 0.23613784, 0.21474715,
        0.21177629],
       [0.        , 0.        , 0.        , ..., 0.31893268, 0.37767802,
        0.32808411],
       [0.        , 0.        , 0.        , ..., 0.18040625, 0.20881903,
        0.23771222],
       ...,
       [0.        , 0.        , 0.        , ..., 0.42235672, 0.56886138,
        0.42028585],
       [0.        , 0.        , 0.        , ..., 0.49402371, 0.37178744,
        0.31713959],
       [0.        , 0.        , 0.        , ..., 0.58101842, 0.28710154,
        0.22380615]])

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


# slice car plantage de jupyter car trop de donnée (timeout) mais pour ça valide 
ds_xsar = sigma0_denoised_tile_6400x6400_full_resolution_vv.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))
ds_sarwing = sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv_dataset.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwingsigma0 denoised %s" % polarization)
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_sigma0_denoised_tile_6400x6400_full_resolution_vv_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        sigma0_denoised_tile_6400x6400_full_resolution_vv
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation sigma0 denoised %s xsar/sarwing" % polarization)


incidence_full_resolution_xtrack_line1 = image_full_resolution.incidence.sel(atrack=[0])
incidence_full_resolution_xtrack_line1

<xarray.DataArray 'incidence' (atrack: 1, xtrack: 25187)>
dask.array<getitem, shape=(1, 25187), dtype=float32, chunksize=(1, 500), chunktype=numpy.ndarray>
Coordinates:
  * atrack   (atrack) int64 0
  * xtrack   (xtrack) int64 0 1 2 3 4 5 ... 25181 25182 25183 25184 25185 25186

array([0])

array([    0,     1,     2, ..., 25184, 25185, 25186])


sarwing_incidence_line1_image_full_resolution = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_incidence_line1_image_full_resolution.pkl', 'rb'), encoding='bytes')
sarwing_incidence_line1_image_full_resolution_dataset = xr.DataArray(sarwing_incidence_line1_image_full_resolution, dims=( "xtrack", "atrack"), coords={"xtrack":range(0,1,1), "atrack":range(0,25187,1)})
sarwing_incidence_line1_image_full_resolution_dataset

<xarray.DataArray (xtrack: 1, atrack: 25187)>
array([[30.8156529 , 30.81635922, 30.81706554, ..., 45.98284466,
        45.98334994, 45.9838552 ]])
Coordinates:
  * xtrack   (xtrack) int64 0
  * atrack   (atrack) int64 0 1 2 3 4 5 ... 25181 25182 25183 25184 25185 25186

array([[30.8156529 , 30.81635922, 30.81706554, ..., 45.98284466,
        45.98334994, 45.9838552 ]])

array([0])

array([    0,     1,     2, ..., 25184, 25185, 25186])


hv.Scatter(
        (incidence_full_resolution_xtrack_line1,sarwing_incidence_line1_image_full_resolution),
        kdims='xsar', vdims='sarwing').opts(title='Incidence xtrack line1 full resolution xsar/sarwing') * hv.Path([(30,30),(50,50)]).opts(color='black', line_width=2, alpha=0.2)


nrcs_gmf_nice_display_full_resolution_incidence_line1 = model_ifr2(10., 45, incidence_full_resolution_xtrack_line1.values)
nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset = xr.DataArray(nrcs_gmf_nice_display_full_resolution_incidence_line1, dims=( "atrack", "xtrack"), coords={"atrack":range(0,1,1), "xtrack":range(0,25187,1)})
nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset

<xarray.DataArray (atrack: 1, xtrack: 25187)>
array([[0.09855015, 0.09854061, 0.09853113, ..., 0.02241875, 0.02241801,
        0.02241727]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0
  * xtrack   (xtrack) int64 0 1 2 3 4 5 ... 25181 25182 25183 25184 25185 25186

array([[0.09855015, 0.09854061, 0.09853113, ..., 0.02241875, 0.02241801,
        0.02241727]], dtype=float32)

array([0])

array([    0,     1,     2, ..., 25184, 25185, 25186])


sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1 = [pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1.pkl', 'rb'), encoding='bytes')]
sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset = xr.DataArray(sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1, dims=( "atrack", "xtrack"), coords={"atrack":range(0,1,1), "xtrack":range(0,25187,1)})
sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset

<xarray.DataArray (atrack: 1, xtrack: 25187)>
array([[0.09856078, 0.09855118, 0.09854159, ..., 0.02241968, 0.02241894,
        0.0224182 ]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0
  * xtrack   (xtrack) int64 0 1 2 3 4 5 ... 25181 25182 25183 25184 25185 25186

array([[0.09856078, 0.09855118, 0.09854159, ..., 0.02241968, 0.02241894,
        0.0224182 ]], dtype=float32)

array([0])

array([    0,     1,     2, ..., 25184, 25185, 25186])


hv.Scatter(
        (nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset,sarwing_nrcs_gmf_nice_display_full_resolution_incidence_line1_dataset),
        kdims='xsar', vdims='sarwing').opts(title='Nrcs gmf with incidence xtrack line1 full resolution xsar/sarwing') * hv.Path([(0.,0.),(0.1,0.1)]).opts(color='black', line_width=2, alpha=0.2)


nrcs_gmf_tile_6400x6400_full_resolution_slice = nrcs_gmf_nice_display_full_resolution_incidence_line1[0][x0:tilesize:1]
nrcs_gmf_tile_6400x6400_full_resolution = np.outer(np.ones((6400//1,), dtype=np.float32), nrcs_gmf_tile_6400x6400_full_resolution_slice)
nrcs_gmf_tile_6400x6400_full_resolution_dataset = xr.DataArray(nrcs_gmf_tile_6400x6400_full_resolution, dims=( "xtrack", "atrack"), coords={"atrack":range(0,6400,1), "xtrack":range(0,6400,1)})
nrcs_gmf_tile_6400x6400_full_resolution_dataset
#nrcs_slice = self._tab_nrcs[polarization, swath][x0:x0+xsize:xfactor]
#nrcs = np.outer(np.ones((ysize//yfactor,), dtype=np.float32), nrcs_slice)

<xarray.DataArray (xtrack: 6400, atrack: 6400)>
array([[0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       ...,
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array([[0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       ...,
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434],
       [0.09855015, 0.09854061, 0.09853113, ..., 0.05735269, 0.05734854,
        0.05734434]], dtype=float32)

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


sarwing_nrcs_gmf_tile_6400x6400_full_resolution = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_nrcs_gmf_tile_6400x6400_full_resolution.pkl', 'rb'), encoding='bytes')
sarwing_nrcs_gmf_tile_6400x6400_full_resolution_dataset = xr.DataArray(sarwing_nrcs_gmf_tile_6400x6400_full_resolution, dims=( "xtrack", "atrack"), coords={"atrack":range(0,6400,1), "xtrack":range(0,6400,1)})
sarwing_nrcs_gmf_tile_6400x6400_full_resolution_dataset

<xarray.DataArray (xtrack: 6400, atrack: 6400)>
array([[0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       ...,
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array([[0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       ...,
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961],
       [0.09856078, 0.09855118, 0.09854159, ..., 0.057348  , 0.0573438 ,
        0.05733961]], dtype=float32)

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


ds_xsar = nrcs_gmf_tile_6400x6400_full_resolution_dataset.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))
ds_sarwing = sarwing_nrcs_gmf_tile_6400x6400_full_resolution_dataset.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwing nrcs gmf %s")
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_nrcs_gmf_tile_6400x6400_full_resolution_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        nrcs_gmf_tile_6400x6400_full_resolution_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation nrcs gmf full resolution %s xsar/sarwing" % polarization)


image_size_atrack = image_full_resolution.atrack.size
image_size_xtrack = image_full_resolution.xtrack.size

nx = 40
ny = int(np.round((float(image_size_atrack)/float(image_size_xtrack))*float(nx)))

spacing_x = int(image_size_xtrack/nx)
spacing_y = int(image_size_atrack/ny)

image_reduite_nx_ny = image_full_resolution.sel(xtrack=slice(0, image_size_xtrack, spacing_x), atrack=slice(0, image_size_atrack, spacing_y))
image_reduite_nx_ny_incidence = image_reduite_nx_ny.incidence
image_reduite_nx_ny_incidence

<xarray.DataArray 'incidence' (atrack: 28, xtrack: 41)>
dask.array<getitem, shape=(28, 41), dtype=float32, chunksize=(1, 1), chunktype=numpy.ndarray>
Coordinates:
  * atrack   (atrack) int64 0 621 1242 1863 2484 ... 14904 15525 16146 16767
  * xtrack   (xtrack) int64 0 629 1258 1887 2516 ... 23273 23902 24531 25160

array([    0,   621,  1242,  1863,  2484,  3105,  3726,  4347,  4968,  5589,
        6210,  6831,  7452,  8073,  8694,  9315,  9936, 10557, 11178, 11799,
       12420, 13041, 13662, 14283, 14904, 15525, 16146, 16767])

array([    0,   629,  1258,  1887,  2516,  3145,  3774,  4403,  5032,  5661,
        6290,  6919,  7548,  8177,  8806,  9435, 10064, 10693, 11322, 11951,
       12580, 13209, 13838, 14467, 15096, 15725, 16354, 16983, 17612, 18241,
       18870, 19499, 20128, 20757, 21386, 22015, 22644, 23273, 23902, 24531,
       25160])


sarwing_nrcs_model_inc_reduce_nx_ny_28_41 = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_nrcs_model_inc_reduce_nx_ny_28_41.pkl', 'rb'), encoding='bytes')
sarwing_nrcs_model_inc_reduce_nx_ny_28_41_dataset = xr.DataArray(sarwing_nrcs_model_inc_reduce_nx_ny_28_41, dims=("atrack", "xtrack"), coords={"atrack":range(0, 16777, 621), "xtrack":range(0,25186,629)})
sarwing_nrcs_model_inc_reduce_nx_ny_28_41_dataset

<xarray.DataArray (atrack: 28, xtrack: 41)>
array([[30.8156529 , 31.25819079, 31.69794762, ..., 45.34153468,
        45.6641198 , 45.9838552 ],
       [30.81627416, 31.25880969, 31.69856409, ..., 45.34204994,
        45.66463226, 45.98436487],
       [30.816891  , 31.25942416, 31.69917615, ..., 45.34256166,
        45.6651412 , 45.98487105],
       ...,
       [30.82984159, 31.2723235 , 31.71202358, ..., 45.35332675,
        45.67584725, 45.99551827],
       [30.83034905, 31.27282909, 31.71252727, ..., 45.35374911,
        45.67626717, 45.99593574],
       [30.83085263, 31.27333084, 31.71302716, ..., 45.35416824,
        45.67668385, 45.99634997]])
Coordinates:
  * atrack   (atrack) int64 0 621 1242 1863 2484 ... 14904 15525 16146 16767
  * xtrack   (xtrack) int64 0 629 1258 1887 2516 ... 23273 23902 24531 25160

array([[30.8156529 , 31.25819079, 31.69794762, ..., 45.34153468,
        45.6641198 , 45.9838552 ],
       [30.81627416, 31.25880969, 31.69856409, ..., 45.34204994,
        45.66463226, 45.98436487],
       [30.816891  , 31.25942416, 31.69917615, ..., 45.34256166,
        45.6651412 , 45.98487105],
       ...,
       [30.82984159, 31.2723235 , 31.71202358, ..., 45.35332675,
        45.67584725, 45.99551827],
       [30.83034905, 31.27282909, 31.71252727, ..., 45.35374911,
        45.67626717, 45.99593574],
       [30.83085263, 31.27333084, 31.71302716, ..., 45.35416824,
        45.67668385, 45.99634997]])

array([    0,   621,  1242,  1863,  2484,  3105,  3726,  4347,  4968,  5589,
        6210,  6831,  7452,  8073,  8694,  9315,  9936, 10557, 11178, 11799,
       12420, 13041, 13662, 14283, 14904, 15525, 16146, 16767])

array([    0,   629,  1258,  1887,  2516,  3145,  3774,  4403,  5032,  5661,
        6290,  6919,  7548,  8177,  8806,  9435, 10064, 10693, 11322, 11951,
       12580, 13209, 13838, 14467, 15096, 15725, 16354, 16983, 17612, 18241,
       18870, 19499, 20128, 20757, 21386, 22015, 22644, 23273, 23902, 24531,
       25160])


hv.Scatter(
        (image_reduite_nx_ny_incidence,sarwing_nrcs_model_inc_reduce_nx_ny_28_41_dataset),
        kdims='xsar', vdims='sarwing').opts(title='Incidence nrcs model image reduce nx ny xsar/sarwing') * hv.Path([(30,30),(50,50)]).opts(color='black', line_width=2, alpha=0.2)


nrcs_model_image_reduite_nx_ny = model_ifr2(10., 45, image_reduite_nx_ny_incidence.values)
nrcs_model_image_reduite_nx_ny_mean = nrcs_model_image_reduite_nx_ny.mean()
nrcs_model_image_reduite_nx_ny_mean

0.045541365


nrcs_model_image_reduite_nx_ny_dataset = xr.DataArray(nrcs_model_image_reduite_nx_ny, dims=("atrack", "xtrack"), coords={"atrack":range(0, 16777, 621), "xtrack":range(0,25186,629)}) 
nrcs_model_image_reduite_nx_ny_dataset

<xarray.DataArray (atrack: 28, xtrack: 41)>
array([[0.09855015, 0.09280175, 0.08749451, ..., 0.0234123 , 0.02291511,
        0.02243665],
       [0.09854177, 0.09279399, 0.08748737, ..., 0.0234115 , 0.02291433,
        0.02243593],
       [0.09853341, 0.09278632, 0.08748034, ..., 0.02341067, 0.02291358,
        0.02243518],
       ...,
       [0.09835786, 0.09262494, 0.08733183, ..., 0.02339372, 0.02289733,
        0.02241961],
       [0.09835084, 0.09261851, 0.08732589, ..., 0.02339306, 0.02289668,
        0.02241901],
       [0.09834402, 0.09261229, 0.08732022, ..., 0.02339239, 0.02289606,
        0.0224184 ]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0 621 1242 1863 2484 ... 14904 15525 16146 16767
  * xtrack   (xtrack) int64 0 629 1258 1887 2516 ... 23273 23902 24531 25160

array([[0.09855015, 0.09280175, 0.08749451, ..., 0.0234123 , 0.02291511,
        0.02243665],
       [0.09854177, 0.09279399, 0.08748737, ..., 0.0234115 , 0.02291433,
        0.02243593],
       [0.09853341, 0.09278632, 0.08748034, ..., 0.02341067, 0.02291358,
        0.02243518],
       ...,
       [0.09835786, 0.09262494, 0.08733183, ..., 0.02339372, 0.02289733,
        0.02241961],
       [0.09835084, 0.09261851, 0.08732589, ..., 0.02339306, 0.02289668,
        0.02241901],
       [0.09834402, 0.09261229, 0.08732022, ..., 0.02339239, 0.02289606,
        0.0224184 ]], dtype=float32)

array([    0,   621,  1242,  1863,  2484,  3105,  3726,  4347,  4968,  5589,
        6210,  6831,  7452,  8073,  8694,  9315,  9936, 10557, 11178, 11799,
       12420, 13041, 13662, 14283, 14904, 15525, 16146, 16767])

array([    0,   629,  1258,  1887,  2516,  3145,  3774,  4403,  5032,  5661,
        6290,  6919,  7548,  8177,  8806,  9435, 10064, 10693, 11322, 11951,
       12580, 13209, 13838, 14467, 15096, 15725, 16354, 16983, 17612, 18241,
       18870, 19499, 20128, 20757, 21386, 22015, 22644, 23273, 23902, 24531,
       25160])


sarwing_nrcs_model_image_reduite_nx_ny = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_nrcs_model_image_reduite_nx_ny.pkl', 'rb'), encoding='bytes')
sarwing_nrcs_model_image_reduite_nx_ny_dataset = xr.DataArray(sarwing_nrcs_model_image_reduite_nx_ny, dims=("atrack", "xtrack"), coords={"atrack":range(0, 16777, 621), "xtrack":range(0,25186,629)})
sarwing_nrcs_model_image_reduite_nx_ny_dataset

<xarray.DataArray (atrack: 28, xtrack: 41)>
array([[0.09856078, 0.09278457, 0.08748592, ..., 0.02339262, 0.02289416,
        0.0224182 ],
       [0.09855234, 0.09277681, 0.08747878, ..., 0.02339181, 0.02289338,
        0.02241746],
       [0.09854396, 0.09276911, 0.08747169, ..., 0.023391  , 0.02289261,
        0.02241672],
       ...,
       [0.09836823, 0.09260766, 0.08732313, ..., 0.02337407, 0.02287638,
        0.02240117],
       [0.09836136, 0.09260134, 0.08731731, ..., 0.0233734 , 0.02287575,
        0.02240056],
       [0.09835453, 0.09259507, 0.08731154, ..., 0.02337274, 0.02287512,
        0.02239995]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 0 621 1242 1863 2484 ... 14904 15525 16146 16767
  * xtrack   (xtrack) int64 0 629 1258 1887 2516 ... 23273 23902 24531 25160

array([[0.09856078, 0.09278457, 0.08748592, ..., 0.02339262, 0.02289416,
        0.0224182 ],
       [0.09855234, 0.09277681, 0.08747878, ..., 0.02339181, 0.02289338,
        0.02241746],
       [0.09854396, 0.09276911, 0.08747169, ..., 0.023391  , 0.02289261,
        0.02241672],
       ...,
       [0.09836823, 0.09260766, 0.08732313, ..., 0.02337407, 0.02287638,
        0.02240117],
       [0.09836136, 0.09260134, 0.08731731, ..., 0.0233734 , 0.02287575,
        0.02240056],
       [0.09835453, 0.09259507, 0.08731154, ..., 0.02337274, 0.02287512,
        0.02239995]], dtype=float32)

array([    0,   621,  1242,  1863,  2484,  3105,  3726,  4347,  4968,  5589,
        6210,  6831,  7452,  8073,  8694,  9315,  9936, 10557, 11178, 11799,
       12420, 13041, 13662, 14283, 14904, 15525, 16146, 16767])

array([    0,   629,  1258,  1887,  2516,  3145,  3774,  4403,  5032,  5661,
        6290,  6919,  7548,  8177,  8806,  9435, 10064, 10693, 11322, 11951,
       12580, 13209, 13838, 14467, 15096, 15725, 16354, 16983, 17612, 18241,
       18870, 19499, 20128, 20757, 21386, 22015, 22644, 23273, 23902, 24531,
       25160])


hv.Scatter(
        (nrcs_model_image_reduite_nx_ny_dataset,sarwing_nrcs_model_image_reduite_nx_ny_dataset),
        kdims='xsar', vdims='sarwing').opts(title='Nrcs model image reduite nx ny') * hv.Path([(0.,0.),(0.12,0.12)]).opts(color='black', line_width=2, alpha=0.2)


sarwing_nrcs_model_image_reduite_nx_ny_mean = sarwing_nrcs_model_image_reduite_nx_ny_dataset.values.mean()
print("Sarwing nrcs model mean : %s" %sarwing_nrcs_model_image_reduite_nx_ny_mean)
nrcs_model_image_reduite_nx_ny_mean = nrcs_model_image_reduite_nx_ny_dataset.values.mean()
print("Xsar nrcs model mean : %s" %nrcs_model_image_reduite_nx_ny_mean)
diff_nrcs_model_mean = sarwing_nrcs_model_image_reduite_nx_ny_mean - nrcs_model_image_reduite_nx_ny_mean
print("Diff : %s" % diff_nrcs_model_mean)

Sarwing nrcs model mean : 0.045515634
Xsar nrcs model mean : 0.045541365
Diff : -2.573058e-05


icontrast_tile_6400x6400_full_resolution_vv = np.sqrt(sigma0_denoised_tile_6400x6400_full_resolution_vv / (nrcs_gmf_tile_6400x6400_full_resolution_dataset / nrcs_model_image_reduite_nx_ny_mean))
icontrast_tile_6400x6400_full_resolution_vv

<xarray.DataArray (atrack: 6400, xtrack: 6400)>
dask.array<sqrt, shape=(6400, 6400), dtype=float64, chunksize=(200, 500), chunktype=numpy.ndarray>
Coordinates:
    pol      <U2 'VV'
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array('VV', dtype='<U2')

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


sarwing_icontrast_tile_6400x6400_full_resolution_vv = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_full_res/sarwing_icontrast_tile_6400x6400_full_resolution_vv.pkl', 'rb'), encoding='bytes')
sarwing_icontrast_tile_6400x6400_full_resolution_vv_dataset = xr.DataArray(sarwing_icontrast_tile_6400x6400_full_resolution_vv, dims=("atrack", "xtrack"), coords={"atrack":range(0, 6400, 1), "xtrack":range(0,6400,1)})
sarwing_icontrast_tile_6400x6400_full_resolution_vv_dataset

<xarray.DataArray (atrack: 6400, xtrack: 6400)>
array([[0.        , 0.        , 0.        , ..., 0.43291631, 0.41285804,
        0.41000731],
       [0.        , 0.        , 0.        , ..., 0.50311895, 0.54751736,
        0.51032345],
       [0.        , 0.        , 0.        , ..., 0.37839636, 0.40711966,
        0.43438886],
       ...,
       [0.        , 0.        , 0.        , ..., 0.57897641, 0.67195512,
        0.57759751],
       [0.        , 0.        , 0.        , ..., 0.62617404, 0.54323082,
        0.50173934],
       [0.        , 0.        , 0.        , ..., 0.67907245, 0.47736965,
        0.42149161]])
Coordinates:
  * atrack   (atrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399
  * xtrack   (xtrack) int64 0 1 2 3 4 5 6 ... 6393 6394 6395 6396 6397 6398 6399

array([[0.        , 0.        , 0.        , ..., 0.43291631, 0.41285804,
        0.41000731],
       [0.        , 0.        , 0.        , ..., 0.50311895, 0.54751736,
        0.51032345],
       [0.        , 0.        , 0.        , ..., 0.37839636, 0.40711966,
        0.43438886],
       ...,
       [0.        , 0.        , 0.        , ..., 0.57897641, 0.67195512,
        0.57759751],
       [0.        , 0.        , 0.        , ..., 0.62617404, 0.54323082,
        0.50173934],
       [0.        , 0.        , 0.        , ..., 0.67907245, 0.47736965,
        0.42149161]])

array([   0,    1,    2, ..., 6397, 6398, 6399])

array([   0,    1,    2, ..., 6397, 6398, 6399])


ds_xsar = icontrast_tile_6400x6400_full_resolution_vv.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))
ds_sarwing = sarwing_icontrast_tile_6400x6400_full_resolution_vv_dataset.sel(xtrack=slice(0,6400,40), atrack=slice(0,6400,40))


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwing nrcs gmf %s")
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_icontrast_tile_6400x6400_full_resolution_vv_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        icontrast_tile_6400x6400_full_resolution_vv
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation Icontrast %s xsar/sarwing" % polarization)


x_pixel_size = image_full_resolution.pixel_xtrack_m
y_pixel_size = image_full_resolution.pixel_atrack_m

# calcul coef de reduction image
if round(x_pixel_size ==25) and round(y_pixel_size) ==25 :
    coef_reducx = 4  
    coef_reducy = 4                
elif round(x_pixel_size) ==40 and round(y_pixel_size) ==40 :
    coef_reducx = 4
    coef_reducy = 4
    resolution = [1,2,3,4]                
    resolution_values = [160,320,640,1280]                
elif round(x_pixel_size) ==4 and round(y_pixel_size) ==4 :
    coef_reducx = 24
    coef_reducy = 24
elif round(x_pixel_size) == 75 and round(y_pixel_size) == 75:
    coef_reducx = 2
    coef_reducy = 2
else:
    coef_reducx = int(round(100/x_pixel_size))
    coef_reducy = int(round(100/y_pixel_size))

if (coef_reducx <= 2) or (coef_reducy <= 2):
    coef_xyfac = 2
else:
    coef_xyfac = 1


image_entiere_resolution_200m = sr.open_dataset(filename, chunks=chunks, pol_dim=True, resolution={'atrack': coef_reducx*2, 'xtrack': coef_reducy*2}, resampling='rms')[0]
image_entiere_resolution_200m

DEBUG:xsar.utils:timing gdal_rms : 13.0s. mem: +1939.2Mb
DEBUG:xsar.utils:timing load_digital_number : 13.0s. mem: +1939.2Mb
DEBUG:xsar.utils:timing load_geometry : 0.1s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_calibration : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_calibration : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_noise : 0.2s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_noise : 0.2s. mem: +0.0Mb
DEBUG:xsar.utils:timing load_luts : 0.5s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_anotation : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing luts_from_xml_anotation : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing load_luts : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing load_lon_lat : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing _get_subdatasets : 13.8s. mem: +1939.2Mb
DEBUG:xsar.utils:timing apply_cf_convention : 0.0s. mem: +0.0Mb
DEBUG:xsar.utils:timing open_dataset : 13.8s. mem: +1939.2Mb

<xarray.Dataset>
Dimensions:         (atrack: 838, pol: 2, xtrack: 1259)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) int64 10 30 50 70 90 ... 16690 16710 16730 16750
  * xtrack          (xtrack) int64 10 30 50 70 90 ... 25110 25130 25150 25170
Data variables:
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(2, 200, 500), meta=np.ndarray>
    time            (atrack) datetime64[ns] dask.array<chunksize=(200,), meta=np.ndarray>
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(200, 500), meta=np.ndarray>
    sigma0_lut      (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0_lut      (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    noise_lut       (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 500), meta=np.ndarray>
Attributes:
    footprint:       POLYGON ((-67.84221143971432 20.72564283093837, -70.2216...
    coverage:        251km * 170km (xtrack * atrack )
    pixel_xtrack_m:  10.0
    pixel_atrack_m:  10.2
    ipf_version:     2.84
    swath_type:      IW
    polarizations:   VV VH
    product_type:    GRD
    mission:         SENTINEL-1
    satellite:       A
    start_date:      2017-09-07 10:30:20.936409
    stop_date:       2017-09-07 10:30:45.935264
    subdataset:      IW
    geometry:                                                              ge...
    Conventions:     CF-1.7

array(['VV', 'VH'], dtype=object)

array([   10,    30,    50, ..., 16710, 16730, 16750])

array([   10,    30,    50, ..., 25130, 25150, 25170])


image_tile_6400x6400_resolution_200m = image_entiere_resolution_200m.sel(atrack=slice(0,tilesize,1), xtrack=slice(0, tilesize, 1))
image_tile_6400x6400_resolution_200m

<xarray.Dataset>
Dimensions:         (atrack: 320, pol: 2, xtrack: 320)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) int64 10 30 50 70 90 ... 6310 6330 6350 6370 6390
  * xtrack          (xtrack) int64 10 30 50 70 90 ... 6310 6330 6350 6370 6390
Data variables:
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(2, 200, 320), meta=np.ndarray>
    time            (atrack) datetime64[ns] dask.array<chunksize=(200,), meta=np.ndarray>
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(200, 320), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(200, 320), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(200, 320), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(200, 320), meta=np.ndarray>
    sigma0_lut      (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    gamma0_lut      (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    noise_lut       (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 200, 320), meta=np.ndarray>
Attributes:
    footprint:       POLYGON ((-67.84221143971432 20.72564283093837, -70.2216...
    coverage:        251km * 170km (xtrack * atrack )
    pixel_xtrack_m:  10.0
    pixel_atrack_m:  10.2
    ipf_version:     2.84
    swath_type:      IW
    polarizations:   VV VH
    product_type:    GRD
    mission:         SENTINEL-1
    satellite:       A
    start_date:      2017-09-07 10:30:20.936409
    stop_date:       2017-09-07 10:30:45.935264
    subdataset:      IW
    geometry:                                                              ge...
    Conventions:     CF-1.7

array(['VV', 'VH'], dtype=object)

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


sigma0_tile_6400x6400_resolution_200m = image_tile_6400x6400_resolution_200m.sigma0.sel(pol="VV")
nesz_tile_6400x6400_resolution_200m = image_tile_6400x6400_resolution_200m.nesz.sel(pol="VV")
sigma0_denoised_tile_6400x6400_resolution_200m = np.maximum(sigma0_tile_6400x6400_resolution_200m - nesz_tile_6400x6400_resolution_200m, 0)
sigma0_denoised_tile_6400x6400_resolution_200m

<xarray.DataArray (atrack: 320, xtrack: 320)>
dask.array<maximum, shape=(320, 320), dtype=float64, chunksize=(200, 320), chunktype=numpy.ndarray>
Coordinates:
    pol      <U2 'VV'
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array('VV', dtype='<U2')

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_200m_res/sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv.pkl', 'rb'), encoding='bytes')
sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv_dataset = xr.DataArray(sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv, dims=("atrack", "xtrack"), coords={"atrack":range(10, 6410, 20), "xtrack":range(10,6410,20)})
sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv_dataset

<xarray.DataArray (atrack: 320, xtrack: 320)>
array([[0.0075167 , 0.30455014, 0.31355365, ..., 0.1680926 , 0.15415108,
        0.23695434],
       [0.00849136, 0.28968571, 0.34740543, ..., 0.29469358, 0.15658292,
        0.2068327 ],
       [0.01302386, 0.3759196 , 0.33058255, ..., 0.14776455, 0.16849741,
        0.17930682],
       ...,
       [0.06471831, 0.26140021, 0.27778305, ..., 0.13325178, 0.27991836,
        0.11522134],
       [0.07320797, 0.18854888, 0.28034545, ..., 0.12300987, 0.19803064,
        0.16787129],
       [0.07193727, 0.2583752 , 0.2937353 , ..., 0.23234468, 0.14732436,
        0.30766247]])
Coordinates:
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array([[0.0075167 , 0.30455014, 0.31355365, ..., 0.1680926 , 0.15415108,
        0.23695434],
       [0.00849136, 0.28968571, 0.34740543, ..., 0.29469358, 0.15658292,
        0.2068327 ],
       [0.01302386, 0.3759196 , 0.33058255, ..., 0.14776455, 0.16849741,
        0.17930682],
       ...,
       [0.06471831, 0.26140021, 0.27778305, ..., 0.13325178, 0.27991836,
        0.11522134],
       [0.07320797, 0.18854888, 0.28034545, ..., 0.12300987, 0.19803064,
        0.16787129],
       [0.07193727, 0.2583752 , 0.2937353 , ..., 0.23234468, 0.14732436,
        0.30766247]])

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


# slice car plantage de jupyter car trop de donnée (timeout) mais pour ça valide 
ds_xsar = sigma0_denoised_tile_6400x6400_resolution_200m
ds_sarwing = sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv_dataset


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwingsigma0 denoised %s" % polarization)
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_sigma0_denoised_tile_6400x6400_200m_resolution_vv_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        sigma0_denoised_tile_6400x6400_resolution_200m
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation sigma0 denoised resolution 200m %s xsar/sarwing" % polarization)


nrcs_gmf_tile_6400x6400_200m_resolution_slice = nrcs_gmf_nice_display_full_resolution_incidence_line1[0][x0:tilesize:20]
nrcs_gmf_tile_6400x6400_200m_resolution = np.outer(np.ones((tilesize//(coef_reducy*2),), dtype=np.float32), nrcs_gmf_tile_6400x6400_200m_resolution_slice)
nrcs_gmf_tile_6400x6400_200m_resolution_dataset = xr.DataArray(nrcs_gmf_tile_6400x6400_200m_resolution, dims=( "xtrack", "atrack"), coords={"atrack":range(10,6400,coef_reducx*2), "xtrack":range(10,6400,coef_reducy*2)})
nrcs_gmf_tile_6400x6400_200m_resolution_dataset

<xarray.DataArray (xtrack: 320, atrack: 320)>
array([[0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       ...,
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array([[0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       ...,
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361],
       [0.09855015, 0.09836017, 0.09817068, ..., 0.05759089, 0.05750714,
        0.05742361]], dtype=float32)

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


sarwing_nrcs_gmf_tile_6400x6400_200m_resolution = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_200m_res/sarwing_nrcs_gmf_tile_6400x6400_200m_resolution.pkl', 'rb'), encoding='bytes')
sarwing_nrcs_gmf_tile_6400x6400_200m_resolution_dataset = xr.DataArray(sarwing_nrcs_gmf_tile_6400x6400_200m_resolution, dims=("xtrack", "atrack"), coords={"atrack":range(10, 6400, 20), "xtrack":range(10,6400,20)})
sarwing_nrcs_gmf_tile_6400x6400_200m_resolution_dataset

<xarray.DataArray (xtrack: 320, atrack: 320)>
array([[0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       ...,
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936]], dtype=float32)
Coordinates:
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array([[0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       ...,
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936],
       [0.09856078, 0.0983691 , 0.09817797, ..., 0.05758784, 0.0575035 ,
        0.05741936]], dtype=float32)

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


# slice car plantage de jupyter car trop de donnée (timeout) mais pour ça valide 
ds_xsar = nrcs_gmf_tile_6400x6400_200m_resolution_dataset
ds_sarwing = sarwing_nrcs_gmf_tile_6400x6400_200m_resolution_dataset


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwingsigma0 denoised %s" % polarization)
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_nrcs_gmf_tile_6400x6400_200m_resolution_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        nrcs_gmf_tile_6400x6400_200m_resolution_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation nrcs gmf resolution 200m %s xsar/sarwing" % polarization)


icontrast_tile_6400x6400_200m_resolution_vv = np.sqrt(sigma0_denoised_tile_6400x6400_resolution_200m / (nrcs_gmf_tile_6400x6400_200m_resolution_dataset / nrcs_model_image_reduite_nx_ny_mean))
icontrast_tile_6400x6400_200m_resolution_vv

<xarray.DataArray (atrack: 320, xtrack: 320)>
dask.array<sqrt, shape=(320, 320), dtype=float64, chunksize=(200, 320), chunktype=numpy.ndarray>
Coordinates:
    pol      <U2 'VV'
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array('VV', dtype='<U2')

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


sarwing_icontrast_tile_6400x6400_200m_resolution = pickle.load(open('/home/alevieux/Documents/Debug/validation_image_contrast_200m_res/sarwing_icontrast_tile_6400x6400_200m_resolution.pkl', 'rb'), encoding='bytes')
sarwing_icontrast_tile_6400x6400_200m_resolution_dataset = xr.DataArray(sarwing_icontrast_tile_6400x6400_200m_resolution, dims=("atrack", "xtrack"), coords={"atrack":range(10, 6400, 20), "xtrack":range(10,6400,20)})
sarwing_icontrast_tile_6400x6400_200m_resolution_dataset

sarwing_icontrast_tile_6400x6400_200m_resolution_dataset

<xarray.DataArray (atrack: 320, xtrack: 320)>
array([[0.05891715, 0.37538798, 0.38126698, ..., 0.36449302, 0.34930632,
        0.43339456],
       [0.06262055, 0.36611245, 0.4013207 , ..., 0.48261438, 0.35205081,
        0.40491207],
       [0.07755291, 0.41705991, 0.39148329, ..., 0.34174333, 0.36519916,
        0.37700709],
       ...,
       [0.17287883, 0.34777946, 0.35886089, ..., 0.32452745, 0.47070504,
        0.30221608],
       [0.18386853, 0.29536766, 0.36051224, ..., 0.31180631, 0.39591263,
        0.36478698],
       [0.18226581, 0.34576129, 0.36902122, ..., 0.42853006, 0.34148406,
        0.49384241]])
Coordinates:
  * atrack   (atrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390
  * xtrack   (xtrack) int64 10 30 50 70 90 110 ... 6290 6310 6330 6350 6370 6390

array([[0.05891715, 0.37538798, 0.38126698, ..., 0.36449302, 0.34930632,
        0.43339456],
       [0.06262055, 0.36611245, 0.4013207 , ..., 0.48261438, 0.35205081,
        0.40491207],
       [0.07755291, 0.41705991, 0.39148329, ..., 0.34174333, 0.36519916,
        0.37700709],
       ...,
       [0.17287883, 0.34777946, 0.35886089, ..., 0.32452745, 0.47070504,
        0.30221608],
       [0.18386853, 0.29536766, 0.36051224, ..., 0.31180631, 0.39591263,
        0.36478698],
       [0.18226581, 0.34576129, 0.36902122, ..., 0.42853006, 0.34148406,
        0.49384241]])

array([  10,   30,   50, ..., 6350, 6370, 6390])

array([  10,   30,   50, ..., 6350, 6370, 6390])


ds_xsar = icontrast_tile_6400x6400_200m_resolution_vv
ds_sarwing = sarwing_icontrast_tile_6400x6400_200m_resolution_dataset


diff = (ds_xsar - ds_sarwing).rename("Diff xsar - sarwing nrcs gmf %s")
std = np.nanstd(diff)
mean = np.nanmean(diff)
plots=[]
r = np.nanpercentile(ds_sarwing,98)
#r = 0.71
plots.append(
    rasterize(hv.Image(
        sarwing_icontrast_tile_6400x6400_200m_resolution_dataset
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="sarwing"))
    )
plots.append(
    rasterize(hv.Image(
        icontrast_tile_6400x6400_200m_resolution_vv
        ).opts(cmap='gray',colorbar=True,tools=['hover'],title="xsar"))
    )
plots.append(
    hv.Scatter(
        (ds_xsar,ds_sarwing),
        kdims='xsar', vdims='sarwing'
        ).opts(alpha=0.01,xlim=(0,r),ylim=(0,r),title='std=%.3f bias=%.3f' % (std,mean)) * hv.Path([(0.,0.),(r,r)]).opts(color='black', line_width=2, alpha=0.2)
    )
hv.Layout(plots).cols(2).opts(title="Validation Icontrast reolution 200m %s xsar/sarwing" % polarization)

Constantes¶

Code model Cmod5f¶

Model IFR2 (utilisé dans sarwing)¶

Image Entiere Haute Resolution¶

Calcul d'une nice display haute resolution de la premiére tuile 6400x6400 de notre image en polarization VV¶

Selection de la tuile¶

Recuperation du sigma0 denoisé en polarization VV de la tuile haute resolution¶

Code¶

Validation avec Sarwing¶

Recuperation nrcs (sarwing) -> nrcs_gmf(xsar)¶

Recuperation des incidence de la premiére ligne de mon image à haute resolution¶

Code¶

Validation¶

Calucl nrcs_gmf avec la premiére ligne d'incidence¶

Code¶

Validation¶

Calucl nrcs_gmf full pour la tuile 6400x6400 haute resolution¶

Code¶

Validation¶

Calucl nrcs_model, spacing dans l'incidence et calucl de mean avec le model cmod5f¶

Code (incidence nx_ny)¶

Validation¶

Code (calucl du mean avec le model cmod5f)¶

Validation¶

Calucl du I (Sarwing) -> IContrast (Xsar)¶

Code¶

Validation¶

Calucl IContrast1 (Sarwing) -> IContrast_resolution_200m tuile 6400x6400¶

Sigma0 denoised tile 6400x6400 resolution 200m¶

Code¶

Calcul coeff reduction¶

Ouvertue avec resampling rms à 200m¶

Selection de la 1er tuile 6400x6400¶

Recupération et calcul du sigma0 denoised vv¶

Validation¶

Nrcs gmf avec la premiére full resolution¶

Code¶

Validaton¶

Calcul IContrast_resolution_200m sur la tuile 6400x6400¶

Code¶

Validation¶