import numpy as np
import h5py
import xarray as xr
from util.util import get_grid_values_all
def acspo_validate(oper_file, cspp_file, rel_tol=0.001, outfile_nc=None):
h5f_oper = h5py.File(oper_file, 'r')
h5f_cspp = h5py.File(cspp_file, 'r')
lon_cspp = get_grid_values_all(h5f_cspp, 'lon')
lat_cspp = get_grid_values_all(h5f_cspp, 'lat')
print('cspp shape: ', lat_cspp.shape)
c_idx = lat_cspp.shape[1] // 2
cntr_lon_cspp = lon_cspp[:, c_idx]
cntr_lat_cspp = lat_cspp[:, c_idx]
sst_cspp = get_grid_values_all(h5f_cspp, 'sea_surface_temperature')[0, ]
l2p_flags_cspp = get_grid_values_all(h5f_cspp, 'l2p_flags')[0, ]
lon_oper = get_grid_values_all(h5f_oper, 'lon')
lat_oper = get_grid_values_all(h5f_oper, 'lat')
cntr_lon_oper = lon_oper[:, c_idx]
cntr_lat_oper = lat_oper[:, c_idx]
print('oper shape: ', lat_oper.shape)
sst_oper = get_grid_values_all(h5f_oper, 'sea_surface_temperature')[0, ]
l2p_flags_oper = get_grid_values_all(h5f_oper, 'l2p_flags')[0, ]
# generate a ndarray of boolean, True: clear or probably clear
cspp_clear = (l2p_flags_cspp & (1 << 15)) == 0
oper_clear = (l2p_flags_oper & (1 << 15)) == 0
start_idx_oper, stop_idx_oper = -1, -1
start_idx_cspp, stop_idx_cspp = -1, -1
for k in range(len(cntr_lat_oper)):
c_a = np.logical_and(np.isclose(cntr_lat_oper[k], cntr_lat_cspp), np.isclose(cntr_lon_oper[k], cntr_lon_cspp))
if np.size(np.nonzero(c_a)[0]) == 1:
if start_idx_oper == -1:
start_idx_oper = k
start_idx_cspp = np.nonzero(c_a)[0][0]
else:
stop_idx_oper = k
stop_idx_cspp = np.nonzero(c_a)[0][0]
print('oper start, stop ', start_idx_oper, stop_idx_oper)
print('cspp start, stop ', start_idx_cspp, stop_idx_cspp)
lon_cspp = lon_cspp[start_idx_cspp:stop_idx_cspp, :]
lat_cspp = lat_cspp[start_idx_cspp:stop_idx_cspp, :]
lon_oper = lon_oper[start_idx_oper:stop_idx_oper, :]
lat_oper = lat_oper[start_idx_oper:stop_idx_oper, :]
cspp_clear = cspp_clear[start_idx_cspp:stop_idx_cspp, :]
oper_clear = oper_clear[start_idx_oper:stop_idx_oper, :]
overlap_shape = lon_cspp.shape
print('overlap shape, size: ', overlap_shape, np.size(lon_cspp))
# check lon/lat arrays ------------------------------------
print(np.sum(np.isclose(lon_cspp, lon_oper, rtol=rel_tol)))
print(np.sum(np.isclose(lat_cspp, lat_oper, rtol=rel_tol)))
sst_cspp_2d = sst_cspp[start_idx_cspp:stop_idx_cspp, :]
sst_oper_2d = sst_oper[start_idx_oper:stop_idx_oper, :]
sst_cspp_2d = np.where(cspp_clear, sst_cspp_2d, np.nan)
sst_oper_2d = np.where(oper_clear, sst_oper_2d, np.nan)
sst_cspp = sst_cspp_2d.flatten()
sst_oper = sst_oper_2d.flatten()
oper_clear = oper_clear.flatten()
cspp_clear = cspp_clear.flatten()
lon_cspp = lon_cspp.flatten()
lat_cspp = lat_cspp.flatten()
lon_oper = lon_oper.flatten()
lat_oper = lat_oper.flatten()
both_clear = oper_clear & cspp_clear
sst_cspp = sst_cspp[both_clear]
sst_oper = sst_oper[both_clear]
lon_cspp = lon_cspp[both_clear]
lat_cspp = lat_cspp[both_clear]
lon_oper = lon_oper[both_clear]
lat_oper = lat_oper[both_clear]
both_valid = np.invert(np.isnan(sst_cspp)) & np.invert(np.isnan(sst_oper))
print('number of clear and valid SSTs in both: ', np.sum(both_valid))
valid_sst_cspp = sst_cspp[both_valid]
valid_sst_oper = sst_oper[both_valid]
valid_lon_cspp = lon_cspp[both_valid]
valid_lat_cspp = lat_cspp[both_valid]
valid_lon_oper = lon_oper[both_valid]
valid_lat_oper = lat_oper[both_valid]
xarray_data = xr.Dataset({
'sst_cspp': xr.DataArray(valid_sst_cspp, coords=None, dims=None, name='sst_cspp'),
'sst_oper': xr.DataArray(valid_sst_oper, coords=None, dims=None, name='sst_oper'),
'cspp_lat': xr.DataArray(valid_lat_cspp, coords=None, dims=None, name='cspp_lat'),
'cspp_lon': xr.DataArray(valid_lon_cspp, coords=None, dims=None, name='cspp_lon'),
'oper_lat': xr.DataArray(valid_lat_oper, coords=None, dims=None, name='oper_lat'),
'oper_lon': xr.DataArray(valid_lon_oper, coords=None, dims=None, name='oper_lon'),
})
if outfile_nc is not None:
xarray_data.to_netcdf(outfile_nc)
print('fraction approx equal: ',
np.sum(np.isclose(valid_sst_cspp, valid_sst_oper, rtol=rel_tol))/np.sum(both_valid))
# print(np.histogram((sst_cspp[both_valid] - sst_oper[both_valid]), bins=10))
h5f_oper.close()
h5f_cspp.close()
return sst_cspp_2d, sst_oper_2d
def analyze_plot():
import h5py
h5f_a = h5py.File('/Users/tomrink/20241107055000.nc', 'r')
h5f_b = h5py.File('/Users/tomrink/20241107072000.nc', 'r')
h5f_c = h5py.File('/Users/tomrink/20241107073000.nc', 'r')
h5f_d = h5py.File('/Users/tomrink/20241107170000.nc', 'r')
h5f_e = h5py.File('/Users/tomrink/20241107171000.nc', 'r')
h5f_f = h5py.File('/Users/tomrink/20241107202000.nc', 'r')
h5f_g = h5py.File('/Users/tomrink/20241107203000.nc', 'r')
import numpy as np
sst_cspp_all = np.concatenate([h5f_a['sst_cspp'][0, :], h5f_b['sst_cspp'][0, :], h5f_c['sst_cspp'][0, :], h5f_d['sst_cspp'][0, :], h5f_e['sst_cspp'][0, :], h5f_f['sst_cspp'][0, :], h5f_g['sst_cspp'][0, :]])
sst_cspp_all.shape
sst_oper_all = np.concatenate([h5f_a['sst_oper'][0, :], h5f_b['sst_oper'][0, :], h5f_c['sst_oper'][0, :], h5f_d['sst_oper'][0, :], h5f_e['sst_oper'][0, :], h5f_f['sst_oper'][0, :], h5f_g['sst_oper'][0, :]])
sst_oper_all.shape
diff = sst_cspp_all - sst_oper_all
import matplotlib.pyplot as plt
plt.hist(diff, bins=40)
plt.yscale('log')
plt.title('ACSPO SST (CSPP - OPER)')
import h5py
h5f_a = h5py.File('/Users/tomrink/20241107055000.nc', 'r')
h5f_b = h5py.File('/Users/tomrink/20241107072000.nc', 'r')
h5f_c = h5py.File('/Users/tomrink/20241107073000.nc', 'r')
h5f_d = h5py.File('/Users/tomrink/20241107170000.nc', 'r')
h5f_e = h5py.File('/Users/tomrink/20241107171000.nc', 'r')
h5f_f = h5py.File('/Users/tomrink/20241107202000.nc', 'r')
h5f_g = h5py.File('/Users/tomrink/20241107203000.nc', 'r')
import numpy as np
sst_cspp_all = np.concatenate([h5f_a['sst_cspp'][0, :], h5f_b['sst_cspp'][0, :], h5f_c['sst_cspp'][0, :], h5f_d['sst_cspp'][0, :], h5f_e['sst_cspp'][0, :], h5f_f['sst_cspp'][0, :], h5f_g['sst_cspp'][0, :]])
sst_oper_all = np.concatenate([h5f_a['sst_oper'][0, :], h5f_b['sst_oper'][0, :], h5f_c['sst_oper'][0, :], h5f_d['sst_oper'][0, :], h5f_e['sst_oper'][0, :], h5f_f['sst_oper'][0, :], h5f_g['sst_oper'][0, :]])
diff = sst_cspp_all - sst_oper_all
import matplotlib.pyplot as plt
plt.hist(diff, bins=40)
plt.yscale('log')
plt.xlabel('SST (\u00b0K)')
plt.title('ACSPO SST (CSPP - OPER), Clear Sky, 2024-11_07')
import numpy as np
c_a = np.isclose(sst_cspp_all, sst_oper_all, rtol=0.001)
c_a.shape
sst_cspp_all.shape
c_a_i = np.invert(c_a)
plt.scatter(sst_cspp_all[c_a_i], diff[c_a_i])
plt.close()
plt.scatter(sst_cspp_all[c_a_i], diff[c_a_i], s=1)
plt.title('ACSPO SST (CSPP-OPER) > rel_tol=0.001 vs SST')
plt.xlabel('SST (\u00b0K)')
plt.ylabel('SST (\u00b0K)')
plt.show()
import h5py
h5f_a = h5py.File('/Users/tomrink/20241107055000.nc', 'r')
h5f_b = h5py.File('/Users/tomrink/20241107072000.nc', 'r')
h5f_c = h5py.File('/Users/tomrink/20241107073000.nc', 'r')
h5f_d = h5py.File('/Users/tomrink/20241107170000.nc', 'r')
h5f_e = h5py.File('/Users/tomrink/20241107171000.nc', 'r')
h5f_f = h5py.File('/Users/tomrink/20241107202000.nc', 'r')
h5f_g = h5py.File('/Users/tomrink/20241107203000.nc', 'r')
import numpy as np
sst_cspp_all = np.concatenate(
[h5f_a['sst_cspp'][0, :], h5f_b['sst_cspp'][0, :], h5f_c['sst_cspp'][0, :], h5f_d['sst_cspp'][0, :],
h5f_e['sst_cspp'][0, :], h5f_f['sst_cspp'][0, :], h5f_g['sst_cspp'][0, :]])
sst_cspp_all.shape
sst_oper_all = np.concatenate(
[h5f_a['sst_oper'][0, :], h5f_b['sst_oper'][0, :], h5f_c['sst_oper'][0, :], h5f_d['sst_oper'][0, :],
h5f_e['sst_oper'][0, :], h5f_f['sst_oper'][0, :], h5f_g['sst_oper'][0, :]])
sst_oper_all.shape
diff = sst_cspp_all - sst_oper_all
import matplotlib.pyplot as plt
plt.hist(diff, bins=40)
plt.yscale('log')
plt.title('ACSPO SST (CSPP - OPER)')
import h5py
h5f_a = h5py.File('/Users/tomrink/20241107055000.nc', 'r')
h5f_b = h5py.File('/Users/tomrink/20241107072000.nc', 'r')
h5f_c = h5py.File('/Users/tomrink/20241107073000.nc', 'r')
h5f_d = h5py.File('/Users/tomrink/20241107170000.nc', 'r')
h5f_e = h5py.File('/Users/tomrink/20241107171000.nc', 'r')
h5f_f = h5py.File('/Users/tomrink/20241107202000.nc', 'r')
h5f_g = h5py.File('/Users/tomrink/20241107203000.nc', 'r')
import numpy as np
sst_cspp_all = np.concatenate(
[h5f_a['sst_cspp'][0, :], h5f_b['sst_cspp'][0, :], h5f_c['sst_cspp'][0, :], h5f_d['sst_cspp'][0, :],
h5f_e['sst_cspp'][0, :], h5f_f['sst_cspp'][0, :], h5f_g['sst_cspp'][0, :]])
sst_oper_all = np.concatenate(
[h5f_a['sst_oper'][0, :], h5f_b['sst_oper'][0, :], h5f_c['sst_oper'][0, :], h5f_d['sst_oper'][0, :],
h5f_e['sst_oper'][0, :], h5f_f['sst_oper'][0, :], h5f_g['sst_oper'][0, :]])
diff = sst_cspp_all - sst_oper_all
import matplotlib.pyplot as plt
plt.hist(diff, bins=40)
plt.yscale('log')
plt.xlabel('SST (\u00b0K)')
plt.title('ACSPO SST (CSPP - OPER), Clear Sky, 2024-11_07')
import numpy as np
c_a = np.isclose(sst_cspp_all, sst_oper_all, rtol=0.001)
c_a.shape
sst_cspp_all.shape
c_a_i = np.invert(c_a)
plt.scatter(sst_cspp_all[c_a_i], diff[c_a_i])
plt.close()
plt.scatter(sst_cspp_all[c_a_i], diff[c_a_i], s=1)
plt.title('ACSPO SST (CSPP-OPER) > rel_tol=0.001 vs SST')
plt.xlabel('SST (\u00b0K)')
plt.ylabel('SST (\u00b0K)')
plt.show()