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Some changes in operators.py. 1) Line 186: Do prior covariance matrix… #10

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Some changes in operators.py. 1) Line 186: Do prior covariance matrix… #10

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20 changes: 17 additions & 3 deletions eoldas_ng/operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,6 +183,14 @@ def der_cost ( self, x_dict, state_config ):
# Single parameter for all sites/locations etc
# This should really be in the __init__ method!
sigma = self.inv_cov[param]
# Max Chernetskiy change
# Do covariance matrix in the case if we have prior as a vector
# climatology for example...
# It's stupid but I don't want to change something in the code
else:
sigma = self.inv_cov[param]
self.inv_cov[param] = sp.dia_matrix ( ( np.ones(n_elems)*sigma, 0 ), shape=(n_elems, n_elems))
# End of Max change

self.inv_cov[param] = sp.dia_matrix ( ( np.ones(n_elems)*sigma, 0 ), shape=(n_elems, n_elems))

Expand Down Expand Up @@ -646,7 +654,10 @@ def __init__ ( self, state_grid, state, observations, mask, emulators, bu, \
self.state = state
self.observations = observations
try:
self.n_bands, self.n_obs = self.observations.shape
# Max Chernetskiy Edit!!!
# Change (n_bands, n_obs) to (n_obs, n_bands)
# otherwise 'assertion error'
self.n_obs, self.n_bands = self.observations.shape
except:
raise ValueError, "Typically, obs should be (n_obs, n_bands)"
self.mask = mask
Expand Down Expand Up @@ -756,6 +767,7 @@ def time_step ( self, this_loc ):
"""Returns relevant information on the observations for a particular time step.
"""
tag = np.round( self.mask[ this_loc, 2:].astype (np.int)/5.)*5

tag = tuple ( (tag[:2].astype(np.int)).tolist() )
this_obs = self.observations[ this_loc, :]
return self.emulators[tag], this_obs, [ self.band_pass, self.bw ]
Expand Down Expand Up @@ -850,7 +862,8 @@ def der_der_cost ( self, x_dict, state_config, state, epsilon=1.0e-5 ):
this_obsop, this_obs, this_extra = self.time_step ( \
this_obs_loc )
xs = x_params[:, itime]*1
dummy, df_0, dummy_fwd = self.calc_mismatch ( this_obsop, \
#!!!Max Edit: gradient was missed
dummy, df_0, dummy_fwd, gradient = self.calc_mismatch ( this_obsop, \
xs, this_obs, self.bu, *this_extra )
iloc = 0
iiloc = 0
Expand All @@ -859,7 +872,8 @@ def der_der_cost ( self, x_dict, state_config, state, epsilon=1.0e-5 ):
continue
xxs = xs[i]*1
xs[i] += epsilon
dummy, df_1, dummy_fwd = self.calc_mismatch ( this_obsop, \
#!!!Max Edit: gradient was missed
dummy, df_1, dummy_fwd, gradient = self.calc_mismatch ( this_obsop, \
xs, this_obs, self.bu, *this_extra ) # Calculate d2f/d2x
hs = (df_1 - df_0)/epsilon
if fin_diff == 2: # CONSTANT
Expand Down