for i in range(len(overhead)): print i, overhead[i] name = pf.read_column(filename, 0, dtype=str, stripper='|', splitter = '|') # T90 T90 = pf.read_column(filename, 1, stripper='|', splitter = '|') T90_mask = np.isfinite(T90) # pflx peak_flux_1024 = pf.read_column(filename, 34, stripper='|', splitter = '|' ) peak_flux_1024_err = pf.read_column(filename, 35, stripper='|', splitter = '|' ) peak_flux_1024_mask = np.isfinite(peak_flux_1024) # Band spectrum at pflx nd_pflx_band_ampl = pf.read_data(filename, 79, stripper='|', splitter = '|') nd_pflx_band_epeak = pf.read_data(filename, 82, stripper='|', splitter = '|') nd_pflx_band_alpha = pf.read_data(filename, 85, stripper='|', splitter = '|') nd_pflx_band_beta = pf.read_data(filename, 88, stripper='|', splitter = '|') pflx_band_redchisq = pf.read_column(filename, 99, stripper='|', splitter = '|') pflx_band_dof = pf.read_column(filename, 101, stripper='|', splitter = '|') pflx_best_fitting_model = pf.read_column(filename, 21, dtype=str, stripper='|', splitter = '|') # Band spectrum time integrated nd_flnc_band_ampl = pf.read_data(filename, 9, stripper='|', splitter = '|') nd_flnc_band_epeak = pf.read_data(filename, 12, stripper='|', splitter = '|') nd_flnc_band_alpha = pf.read_data(filename, 15, stripper='|', splitter = '|') nd_flnc_band_beta = pf.read_data(filename, 18, stripper='|', splitter = '|') flnc_best_fitting_model = pf.read_column(filename, 23, dtype=str, stripper='|', splitter = '|')
###############################################################################
# Settings: what to plot
beamf_type = 'dics' # can be lcmv or dics
# plot_type can be "corr" for correlation, "foc" for focality or "ori" for
# orientation error
plot_type = 'foc'
###############################################################################
# Read in the data and plot settings
data = read_data(beamf_type,
plot_type,
exclude_deep_vertices=False,
radius=0.055,
plot_deep_vertices=True)
title, kwargs = get_plotting_specs(beamf_type, plot_type)
###############################################################################
# Plot the different NORMALIZATIONS contrasted with each other
options = [
'weight_norm=="unit-noise-gain" and normalize_fwd==False',
'weight_norm=="none" and normalize_fwd==True',
'weight_norm=="none" and normalize_fwd==False'
]
labels = [
'Weight normalization', 'Lead field normalization', 'No normalization'
]
# Create original BAT6
filename_og = root_dir + 'catalogs/BAT6_cat/BAT6_2012.txt'
name_og = pf.read_column(filename_og, 0, dtype=str, splitter='\t|')
redshift_og = pf.read_column(filename_og, 1, dtype=float, splitter='\t|')
redshift_og_mask = np.isfinite(redshift_og)
obs_redshift_og_masked = np.zeros(len(redshift_og))
# Extended BAT6 : eBAT6
file_eBAT6_obs = root_dir + 'catalogs/BAT6_cat/eBAT6_cat.txt'
obs_name = pf.read_column(file_eBAT6_obs,
0,
stripper='|',
splitter='|',
dtype=str)
obs_redshift = pf.read_column(file_eBAT6_obs, 1, stripper='|', splitter='|')
obs_redshift2 = pf.read_data(file_eBAT6_obs, 1, stripper='|', splitter='|')
obs_redshift_mask = np.isfinite(obs_redshift)
obs_redshift_masked = np.zeros(len(obs_redshift))
obs_redshift_masked = obs_redshift[obs_redshift_mask]
file_Swift_obs = root_dir + 'catalogs/Swift_cat/Swift_pflx_cat.txt'
obs_name_S2 = pf.read_column(file_Swift_obs, 0, dtype=str)
obs_name_S = []
for i in range(len(obs_name_S2)):
if int(obs_name_S2[i][:2]) <= 14:
obs_name_S.append(obs_name_S2[i])
obs_name_S = np.asarray(obs_name_S).astype(str)
file_Swift_obsb = root_dir + 'catalogs/Swift_cat/Swift_cat.txt'
obs_name_S2b = pf.read_column(file_Swift_obsb, 0, dtype=str)
obs_name_Sb = []
import config
from plotting_functions import get_plotting_specs, scatter_plot, read_data
###############################################################################
# Settings: what to plot
beamf_type = 'dics' # can be lcmv or dics
# plot_type can be "corr" for correlation, "foc" for focality or "ori" for
# orientation error
plot_type = 'foc'
###############################################################################
# Read in the data and plotting settings
data = read_data(beamf_type, plot_type)
title, kwargs = get_plotting_specs(beamf_type, plot_type)
###############################################################################
# WEIGHT NORMALIZATION
base = 'weight_norm=="unit-noise-gain" and normalize_fwd==False and %s'
if beamf_type == 'lcmv':
options = [
base % 'inversion=="matrix" and reduce_rank==False',
base % 'inversion=="matrix" and reduce_rank==True',
base % 'inversion=="single"',
base % 'inversion=="matrix" and reduce_rank==False and \
pick_ori=="none"'
]
import config
from plotting_functions import get_plotting_specs, scatter_plot, read_data
###############################################################################
# Settings: what to plot
beamf_type = 'dics' # can be lcmv or dics
# plot_type can be "corr" for correlation, "foc" for focality or "ori" for
# orientation error
plot_type = 'foc'
###############################################################################
# Read in the data and plotting settings
select_vertices = 'shallow' # 'deep', 'shallow', or None
data = read_data(beamf_type, plot_type, select_vertices)
title, kwargs = get_plotting_specs(beamf_type, plot_type, select_vertices)
###############################################################################
# WEIGHT vs. LEAD FIELD vs. NO NORMALIZATION
options = [
'weight_norm=="unit-noise-gain" and normalize_fwd==False',
'weight_norm=="none" and normalize_fwd==True',
'weight_norm=="none" and normalize_fwd==False'
]
labels = [
'Weight normalization', 'Lead field normalization', 'No normalization'
]
from plotting_functions import (get_plotting_specs, scatter_plot, read_data,
scatter_plot_hover)
###############################################################################
# Settings: what to plot
beamf_type = 'dics' # can be lcmv or dics
# plot_type can be "corr" for correlation, "foc" for focality or "ori" for
# orientation error
plot_type = 'foc'
###############################################################################
# Read in the data and plot settings
data = read_data(beamf_type, plot_type, select_vertices='shallow')
title, kwargs = get_plotting_specs(beamf_type, plot_type)
###############################################################################
# Plot the different NORMALIZATIONS contrasted with each other
options = [
'weight_norm=="unit-noise-gain" and normalize_fwd==False',
'weight_norm=="none" and normalize_fwd==True',
'weight_norm=="none" and normalize_fwd==False'
]
labels = [
'Weight normalization', 'Lead field normalization', 'No normalization'
]
colors = [config.cols['orchid'], config.cols['sky'], config.cols['spring']]
full_title = (title % 'Normalization')
import plotting_functions as pf
#from astroML.plotting import hist
from scipy.stats import chi2
from scipy import interpolate
# plt.style.use('ggplot')
#plt.style.use('presentation')
matplotlib.rc('font', **{'family': 'serif', 'serif': ['Palatino']})
matplotlib.rc('text', usetex=False)
filename = 'GBM_cat_complete.txt'
verbose = False
peak_flux_1024_nd = pf.read_data(filename,
34,
stripper='|',
splitter='|',
single_err=True) # Batse flux (50-300 keV)
pflx_band_alpha_nd = pf.read_data(filename, 85, stripper='|', splitter='|')
pflx_band_beta_nd = pf.read_data(filename, 88, stripper='|', splitter='|')
fig = plt.figure(figsize=(12, 10), tight_layout=True)
axa = fig.add_subplot(211)
axb = fig.add_subplot(212, sharex=axa)
pflx_band_alpha_nd[0] = -pflx_band_alpha_nd[0]
pflx_band_beta_nd[0] = -pflx_band_beta_nd[0]
pf.scatter_incomplete_ndarray(axa,
peak_flux_1024_nd,
pflx_band_alpha_nd,
color='k',
alpha=0.9,
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import plotting_functions as pf
from matplotlib.transforms import blended_transform_factory
plt.style.use('ggplot')
fig = plt.figure()
ax = fig.add_subplot(111)
root_dir = '/nethome/palmerio/1ere_annee/Frederic/GRB_population_code/Model_outputs/'
filename = root_dir +'run_LIA/EpGBM_constraint.dat'
Ep_bins = pf.read_data(filename, 0)
Ep_hist_mod = pf.read_data(filename, 1)
Ep_hist_obs = pf.read_data(filename, 2)
x=np.linspace(1.,4., 500)
y = max(Ep_hist_obs) * pf.gaussian(x, 2.25, 0.35)
y2 = max(Ep_hist_obs) * pf.gaussian(x, 2.25, 0.375)
ep = np.linspace(1,4, 100)
ep_gauss = pf.gaussian(ep, 2.2, 0.4)*max(Ep_hist_obs)
ax.plot(Ep_bins, Ep_hist_obs, label = 'Observations')
#ax.plot(Ep_bins, Ep_hist_mod, label = 'MC simulation')
#ax.plot(ep, ep_gauss, ls=':', lw=2)
ax.plot(x,y, label='gaussian')
ax.plot(x,y2, label='gaussian2')
ax.legend(loc='best')