def test_chain_list(self):
param = ['a', 'b']
X2_list = [1, 1, 2]
pos_list = [[1, 0], [2, 0], [3, 0]]
vel_list = [[-1, 0], [0, 0], [1, 0]]
chain = X2_list, pos_list, vel_list, None
samples_mcmc = np.random.random((10, 1000))
dist_mcmc = np.random.random(1000)
chain_list = [['PSO', chain, param],
['COSMOHAMMER', samples_mcmc, param, dist_mcmc],
['EMCEE', samples_mcmc, param],
['MULTINEST', samples_mcmc, param, dist_mcmc]]
output_plots.plot_chain_list(chain_list, index=0)
plt.close()
output_plots.plot_chain_list(chain_list, index=1, num_average=10)
plt.close()
output_plots.plot_chain_list(chain_list, index=2, num_average=10)
plt.close()
output_plots.plot_chain_list(chain_list, index=3, num_average=10)
plt.close()
mcmc_new_list = np.asarray(mcmc_new_list)
idx = 1 #The translated flux for the host
v_l = np.percentile(mcmc_new_list[:, idx], 16, axis=0)
v_m = np.percentile(mcmc_new_list[:, idx], 50, axis=0)
v_h = np.percentile(mcmc_new_list[:, idx], 84, axis=0)
#print labels_new[idx], ":", v_l, v_m, v_h
print "The inferred", labels_new[idx], "mag:"
print "lower limit:", -2.5 * np.log10(v_h) + zp
print "The mid fit:", -2.5 * np.log10(v_m) + zp
print "upper limit", -2.5 * np.log10(v_l) + zp
#%%
print "Check the convergency of the PSO chains:"
import lenstronomy.Plots.output_plots as out_plot
for i in range(len(chain_list)):
f, axes = out_plot.plot_chain_list(chain_list, 0)
plt.show()
#%%test the MCMC chain convergency
#
#import lenstronomy.Plots.output_plots as plot_mcmc_behaviour
fig = plt.figure(figsize=(20, 15))
ax = fig.add_subplot(111)
out_plot.plot_mcmc_behaviour(ax, samples_mcmc, param_mcmc, dist_mcmc)
plt.show()
#%% Plot the image again:
band_seq = ['G', 'R', 'I', 'Z', 'Y']
filename = galaxy_ID + '_HSC-{0}.fits'.format(band)
sub_bkg = True
from flux_profile import galaxy_total_compare
def test_raise(self):
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
sigma_bkg=1)
#kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma_x': 1,
'sigma_y': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(
multi_band_list=[[kwargs_data, {
'psf_type': 'NONE'
}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
arrow_size=0.02,
cmap_string="gist_heat")
f, ax = plt.subplots(1, 1, figsize=(4, 4))
ax = lensPlot.source_plot(ax=ax,
numPix=10,
deltaPix_source=0.1,
v_min=None,
v_max=None,
with_caustics=False,
caustic_color='yellow',
fsize=15,
plot_scale='bad')
plt.close()
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
sigma_bkg=1)
# kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma_x': 1,
'sigma_y': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(
multi_band_list=[[kwargs_data, {
'psf_type': 'NONE'
}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
bands_compute=[False],
arrow_size=0.02,
cmap_string="gist_heat")
lensPlot._select_band(band_index=0)
with self.assertRaises(ValueError):
output_plots.plot_chain_list(chain_list=[['WRONG']], index=0)