def norm_img(data, mode='max'):
out = np.nan_to_num(data)
out -= np.amin(out)
if mode == 'max':
out /= np.amax(out)
elif mode == 'mean':
out /= np.amean(out)
return out
def set_concn_rate_results(self, concn_array, rate_array):
# This doesn't take account of detail_level!
# See update_concn_rate_results()
calculated_flux = \
numpy.sum(numpy.absolute(rate_array))/2
rel_diff = abs(numpy.sum(rate_array))/calculated_flux
max_concn = numpy.max(concn_array)
amean_concn = amean(concn_array)
min_concn = numpy.min(concn_array)
concn_rate = self.find('./simulation/concn_rate')
if concn_rate == None:
sim = self.find('./simulation')
concn_rate = xmlTree.SubElement(sim, 'concn_rate')
concn_rate.set('calculated_flux', str(calculated_flux))
concn_rate.set('rel_diff', str(rel_diff))
concn_rate.set('max_concn', str(max_concn))
concn_rate.set('amean_concn', str(amean_concn))
concn_rate.set('min_concn', str(min_concn))
def obj_func(point, args):
ls_radius = np.array(
[indx_rad_phi(point, i)[0] for i in range(df.shape[0])])
return np.log(amean(ls_radius)) - np.log(gmean(ls_radius))
def _centermesh(self, vert_sequence):
"""Translates mesh centroid to XYZ coordinate origin."""
centroid = amean(vert_sequence, axis=0)
return array([subtract(vert,centroid) for vert in vert_sequence])
