return sio.loadmat(filename)
# The pymks takes a different format of data than our .mat data
def shuffle_ndarray(data):
n, m, k = data.shape
new_data = np.zeros((k, n, m))
for i in xrange(k):
new_data[i, :, :] = data[:, :, i]
return new_data
def analyze_data_slice(al_data_slice):
# prim basis
prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2])
disc_basis = prim_basis.discretize(al_data_slice)
correlations = correlate(disc_basis)
return correlations
if __name__ == '__main__':
al_data_blob = load_data(
'/Users/Astraeus/Dropbox/Project 8883/800_1_pp1.mat')
al_data = shuffle_ndarray(al_data_blob['phase_field_solid'])
# draw_microstructures(al_data[10:11, :,:])
# Compute correlations for a single time
corrs = analyze_data_slice(al_data[10:11, :, :])
draw_correlations(corrs[0].real)
# Just loads the data from the .mat file in dropbox
def load_data(filename):
return sio.loadmat(filename)
# The pymks takes a different format of data than our .mat data
def shuffle_ndarray(data):
n,m,k = data.shape
new_data = np.zeros((k,n,m))
for i in xrange(k):
new_data[i, :, :] = data[:, :, i]
return new_data
def analyze_data_slice(al_data_slice):
# prim basis
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
disc_basis = prim_basis.discretize(al_data_slice)
correlations = correlate(disc_basis)
return correlations
if __name__ == '__main__':
al_data_blob = load_data('/Users/Astraeus/Dropbox/Project 8883/800_1_pp1.mat')
al_data = shuffle_ndarray(al_data_blob['phase_field_solid'])
# draw_microstructures(al_data[10:11, :,:])
# Compute correlations for a single time
corrs = analyze_data_slice(al_data[10:11, :, :])
draw_correlations(corrs[0].real)
# Get a representative slice from the block (or ave or whatever we decide on)
best_slice = get_best_slice(metadatum['data'])
# Get 2-pt Stats for the best slice
print "--->Getting 2pt stats"
metadatum['stats'] = get_correlations_for_slice(best_slice)
print metadata[0]['stats'].shape
# Construct X and Y for PCA and linkage
print "-->Creating X and Y"
i = 0
for metadatum in metadata:
x[i,0:6*metadatum['x']**2] = metadatum['stats'].flatten()
prim_basis = PrimitiveBasis(n_states=3, domain=[0,2])
x_ = prim_basis.discretize(metadata[0]['data'])
x_corr = correlate(x_)
draw_correlations(x_corr.real)
quit()
# Reduce all 2-pt Stats via PCA
# Try linear reg on inputs and outputs
reducer = PCA(n_components=3)
linker = LinearRegression()
model = MKSHomogenizationModel(dimension_reducer=reducer,
property_linker=linker,
compute_correlations=False)
model.n_components = 40
model.fit(metadatum['stats'], y, periodic_axes=[0, 1])
print model.reduced_fit_data
