def find_percentile(my_dir, file_dir, filter1, filter2, filter3, flux_filter1,
flux_filter2, flux_filter3, flux_filter1_err,
flux_filter2_err, flux_filter3_err, z):
all_percentile = []
data_point = (flux_filter1, flux_filter2, flux_filter3)
data_point = np.asarray(data_point)
mc_file_name = file_name(my_dir, file_dir, z)
mc_dict = load(mc_file_name)
mc_dict = add_error(mc_dict, filter1, flux_filter1_err)
mc_dict = add_error(mc_dict, filter2, flux_filter2_err)
mc_dict = add_error(mc_dict, filter3, flux_filter3_err)
# print 'redshift: ', z
Ia_percentile = kde3d(mc_dict['type_Ia']['flux'][filter1],
mc_dict['type_Ia']['flux'][filter2],
mc_dict['type_Ia']['flux'][filter3], data_point)
Ibc_percentile = kde3d(mc_dict['type_Ibc']['flux'][filter1],
mc_dict['type_Ibc']['flux'][filter2],
mc_dict['type_Ibc']['flux'][filter3], data_point)
II_percentile = kde3d(mc_dict['type_II']['flux'][filter1],
mc_dict['type_II']['flux'][filter2],
mc_dict['type_II']['flux'][filter3], data_point)
my_percentile = (Ia_percentile, Ibc_percentile, II_percentile)
my_percentile = np.asarray(my_percentile)
all_percentile.append(my_percentile)
all_percentile = np.squeeze(all_percentile)
return all_percentile
def get_data(my_dir, file_dir, filter1, filter2, filter3, flux_filter1_err,
flux_filter2_err, flux_filter3_err, z):
indices = [i for i, x in enumerate(file_dir) if x == '_']
n = int(file_dir[1:indices[0]])
n_Ibc_start = int(n)
n_II_start = 2 * n_Ibc_start
mc_file_name = file_name(my_dir, file_dir, z)
mc_dict = load(mc_file_name)
mc_dict = add_error(mc_dict, filter1, flux_filter1_err)
mc_dict = add_error(mc_dict, filter2, flux_filter2_err)
mc_dict = add_error(mc_dict, filter3, flux_filter3_err)
all_filter1 = np.concatenate(
(mc_dict['type_Ia']['flux'][filter1],
mc_dict['type_Ibc']['flux'][filter1],
mc_dict['type_II']['flux'][filter1]))
all_filter2 = np.concatenate(
(mc_dict['type_Ia']['flux'][filter2],
mc_dict['type_Ibc']['flux'][filter2],
mc_dict['type_II']['flux'][filter2]))
all_filter3 = np.concatenate(
(mc_dict['type_Ia']['flux'][filter3],
mc_dict['type_Ibc']['flux'][filter3],
mc_dict['type_II']['flux'][filter3]))
data = [all_filter1, all_filter2, all_filter3]
data = zip(*data)
all_sources = pd.DataFrame(data, columns=[filter1, filter2, filter3],
index=range(3*n))
all_sources['Type'] = 'Type Ia'
all_sources.ix[n_Ibc_start:n_II_start, 'Type'] = 'Type Ibc'
all_sources.ix[n_II_start:, 'Type'] = 'Type II'
all_features = copy.copy(all_sources)
all_label = all_sources["Type"]
del all_features["Type"]
X = copy.copy(all_features.values)
Y = copy.copy(all_label.values)
return X, Y
def flux_fluxDiff_arrays(my_dir, file_dir, filter1, filter2, z):
mc_file_name = file_name(my_dir, file_dir, z)
mc_dict = load(mc_file_name)
type_Ia_flux_diff = (mc_dict['type_Ia']['flux'][filter2] -
mc_dict['type_Ia']['flux'][filter1])
type_Ibc_flux_diff = (mc_dict['type_Ibc']['flux'][filter2] -
mc_dict['type_Ibc']['flux'][filter1])
type_II_flux_diff = (mc_dict['type_II']['flux'][filter2] -
mc_dict['type_II']['flux'][filter1])
flux = [mc_dict['type_Ia']['flux'][filter1],
mc_dict['type_Ibc']['flux'][filter1],
mc_dict['type_II']['flux'][filter1]]
diff = [type_Ia_flux_diff, type_Ibc_flux_diff, type_II_flux_diff]
for i, item in enumerate(diff):
flux[i] = mask(flux[i], 98)
diff[i] = mask(diff[i], 98)
return flux, diff
def get_data(my_dir, file_dir, filter1, filter2, filter3, flux_filter1_err,
flux_filter2_err, flux_filter3_err, z):
indices = [i for i, x in enumerate(file_dir) if x == '_']
n = int(file_dir[1:indices[0]])
n_Ibc_start = int(n)
n_II_start = 2 * n_Ibc_start
mc_file_name = file_name(my_dir, file_dir, z)
mc_dict = load(mc_file_name)
mc_dict = add_error(mc_dict, filter1, flux_filter1_err)
mc_dict = add_error(mc_dict, filter2, flux_filter2_err)
mc_dict = add_error(mc_dict, filter3, flux_filter3_err)
all_filter1 = np.concatenate((mc_dict['type_Ia']['flux'][filter1],
mc_dict['type_Ibc']['flux'][filter1],
mc_dict['type_II']['flux'][filter1]))
all_filter2 = np.concatenate((mc_dict['type_Ia']['flux'][filter2],
mc_dict['type_Ibc']['flux'][filter2],
mc_dict['type_II']['flux'][filter2]))
all_filter3 = np.concatenate((mc_dict['type_Ia']['flux'][filter3],
mc_dict['type_Ibc']['flux'][filter3],
mc_dict['type_II']['flux'][filter3]))
data = [all_filter1, all_filter2, all_filter3]
data = zip(*data)
all_sources = pd.DataFrame(data,
columns=[filter1, filter2, filter3],
index=range(3 * n))
all_sources['Type'] = 'Type Ia'
all_sources.ix[n_Ibc_start:n_II_start, 'Type'] = 'Type Ibc'
all_sources.ix[n_II_start:, 'Type'] = 'Type II'
all_features = copy.copy(all_sources)
all_label = all_sources["Type"]
del all_features["Type"]
X = copy.copy(all_features.values)
Y = copy.copy(all_label.values)
return X, Y
def flux_fluxDiff_arrays(my_dir, file_dir, filter1, filter2, z):
mc_file_name = file_name(my_dir, file_dir, z)
mc_dict = load(mc_file_name)
type_Ia_flux_diff = (mc_dict['type_Ia']['flux'][filter2] -
mc_dict['type_Ia']['flux'][filter1])
type_Ibc_flux_diff = (mc_dict['type_Ibc']['flux'][filter2] -
mc_dict['type_Ibc']['flux'][filter1])
type_II_flux_diff = (mc_dict['type_II']['flux'][filter2] -
mc_dict['type_II']['flux'][filter1])
flux = [
mc_dict['type_Ia']['flux'][filter1],
mc_dict['type_Ibc']['flux'][filter1],
mc_dict['type_II']['flux'][filter1]
]
diff = [type_Ia_flux_diff, type_Ibc_flux_diff, type_II_flux_diff]
for i, item in enumerate(diff):
flux[i] = mask(flux[i], 98)
diff[i] = mask(diff[i], 98)
return flux, diff