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plot_muons_results_pass4.py
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plot_muons_results_pass4.py
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import photon_stream as ps
import numpy as np
import matplotlib.pyplot as plt
import os
from scipy.stats import binned_statistic_2d
from datetime import datetime as dt
dpi = 150
figw = 10
figh = 10
m = np.fromfile('muon.info', dtype=ps.muons.extraction.header_dtype)
print('Total number of muons', m.shape[0]/1e6, 'M muon events')
# suspicious time
# end = (57200-40587)*86400
# start = (57100-40587)*86400
# past_start = m['UnixTime_s'] > start
# before_end = m['UnixTime_s'] < end
# fine = past_start*before_end
# XY ring centers all time
#-------------------------
fnm = 'xy_ring_centers_all_time.png'
if not os.path.exists(fnm):
bincounts, xbinedges, ybinedges = np.histogram2d(
x=m['ring_center_x'],
y=m['ring_center_y'],
bins=[xbinedges, ybinedges])
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.imshow(
bincounts,
extent=[
xbinedges[0],
xbinedges[-1],
ybinedges[0],
xbinedges[-1]
],
cmap='viridis'
)
fov_circle = plt.Circle((0, 0), 2.25, linewidth=0.2, edgecolor='r', fill=False, zorder=10)
ax.add_artist(fov_circle)
plt.title('muon ring center distribution')
ax.xaxis.set_ticks_position('bottom')
plt.xlabel('direction x/deg')
plt.ylabel('direction y/deg')
plt.colorbar()
plt.savefig(fnm,dpi=dpi)
plt.close()
# XY number of photons on full ring all time
#-------------------------------------------
fnm = 'xy_photon_count_on_full_ring_all_time.png'
if not os.path.exists(fnm):
h1,h2,h3,h4 = binned_statistic_2d(
m['ring_center_x'],
m['ring_center_y'],
values=m['number_muon_photons']/m['ring_overlap_with_fov'],
statistic='mean',
bins=[xbinedges,ybinedges])
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.imshow(
h1,
extent=[
xbinedges[0],
xbinedges[-1],
ybinedges[0],
xbinedges[-1]
],
cmap='viridis'
)
fov_circle = plt.Circle((0, 0), 2.25, linewidth=0.2, edgecolor='r', fill=False, zorder=10)
ax.add_artist(fov_circle)
plt.title('reconstructed number of photons on full muon ring')
ax.xaxis.set_ticks_position('bottom')
plt.xlabel('direction x/deg')
plt.ylabel('direction y/deg')
plt.colorbar()
plt.savefig(fnm,dpi=dpi)
plt.close()
# XY number of photons all time
#------------------------------
fnm = 'xy_photon_count_all_time.png'
if not os.path.exists(fnm):
h1,h2,h3,h4 = binned_statistic_2d(
m['ring_center_x'],
m['ring_center_y'],
values=m['number_muon_photons'],
statistic='mean',
bins=[xbinedges,ybinedges])
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.imshow(
h1,
extent=[
xbinedges[0],
xbinedges[-1],
ybinedges[0],
xbinedges[-1]
],
cmap='viridis'
)
fov_circle = plt.Circle((0, 0), 2.25, linewidth=0.2, edgecolor='r', fill=False, zorder=10)
ax.add_artist(fov_circle)
plt.title('number of photons on visible muon ring')
ax.xaxis.set_ticks_position('bottom')
plt.xlabel('direction x/deg')
plt.ylabel('direction y/deg')
plt.colorbar()
ax.add_artist(fov_circle)
plt.savefig(fnm,dpi=dpi)
plt.close()
# XY arrival_time all time
#------------------------------
fnm = 'xy_arrival_time_all_time.png'
if not os.path.exists(fnm):
cut_max_arr_time = 3e-8
valid = m['arrival_time'] < cut_max_arr_time
h1,h2,h3,h4 = binned_statistic_2d(
m['ring_center_x'][valid],
m['ring_center_y'][valid],
values=m['arrival_time'][valid],
statistic='mean',
bins=[xbinedges,ybinedges])
#notnan = np.logical_not(np.isnan(h1))
h5 = h1 - np.nanmin(h1)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.imshow(
h5,
extent=[
xbinedges[0],
xbinedges[-1],
ybinedges[0],
xbinedges[-1]
],
cmap='viridis'
)
plt.colorbar()
fov_circle = plt.Circle((0, 0), 2.25, linewidth=0.2, edgecolor='r', fill=False, zorder=10)
ax.add_artist(fov_circle)
plt.title('(mean relative arrival time of muon ring)/s')
ax.xaxis.set_ticks_position('bottom')
plt.xlabel('direction x/deg')
plt.ylabel('direction y/deg')
plt.savefig(fnm,dpi=dpi)
plt.close()
# Ring Radius histogram
#------------------------------
fnm = 'ring_radius_histogram_all_time.png'
if not os.path.exists(fnm):
min_radius = np.min(m['ring_radius'])
max_radius = np.max(m['ring_radius'])
nbins = int(0.1*np.sqrt(m.shape[0]))
step = (max_radius - min_radius)/nbins
binedges = np.linspace(min_radius, max_radius, nbins)
bincounts, binedges = np.histogram(m['ring_radius'], bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts)
plt.title('muon ring radius distribution, bin width '+str(round(1e3*step,3))+'mdeg')
plt.xlabel('muon ring radius/deg')
plt.ylabel('intensity/1')
plt.savefig(fnm,dpi=dpi)
plt.close()
# Ring overlap histogram
#------------------------------
fnm = 'ring_overlap_histogram_all_time.png'
if not os.path.exists(fnm):
nbins = int(0.1*np.sqrt(m.shape[0]))
step = 0.99/nbins
binedges = np.linspace(0, 0.99, nbins)
bincounts, binedges = np.histogram(m['ring_overlap_with_fov'], bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts)
plt.title('full overlap is excluded here')
plt.xlabel('ring_overlap_with_fov/1')
plt.ylabel('intensity/1')
plt.savefig(fnm,dpi=dpi)
plt.close()
# arrival time histogram
#------------------------------
fnm = 'arrival_time_histogram_all_time.png'
if not os.path.exists(fnm):
min_t = np.min(m['arrival_time'])
max_t = np.max(m['arrival_time'])
nbins = int(0.1*np.sqrt(m.shape[0]))
step = (max_t - min_t)/nbins
binedges = np.linspace(min_t, max_t, nbins)
bincounts, binedges = np.histogram(m['arrival_time'], bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts)
plt.title('arrival time distribution, bin width '+str(round(1e12*step,3))+'ps')
plt.xlabel('arrival time/s')
plt.ylabel('intensity/1')
plt.savefig(fnm,dpi=dpi)
plt.semilogy()
plt.savefig('log_'+fnm,dpi=dpi)
plt.close()
# event_number histogram
#------------------------------
fnm = 'event_number_histogram_all_time.png'
if not os.path.exists(fnm):
min_t = np.min(m['Event'])
max_t = np.max(m['Event'])
nbins = int(0.1*np.sqrt(m.shape[0]))
step = (max_t - min_t)/nbins
binedges = np.linspace(min_t, max_t, nbins)
bincounts, binedges = np.histogram(m['Event'], bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts)
plt.xlabel('Event number/1')
plt.ylabel('intensity/1')
plt.savefig(fnm,dpi=dpi)
plt.semilogy()
plt.savefig('log_'+fnm,dpi=dpi)
plt.close()
# run_number histogram
#------------------------------
fnm = 'run_number_histogram_all_time.png'
if not os.path.exists(fnm):
min_t = np.min(m['Run'])
max_t = np.max(m['Run'])
nbins = int(0.01*np.sqrt(m.shape[0]))
step = (max_t - min_t)/nbins
binedges = np.linspace(min_t, max_t, nbins)
bincounts, binedges = np.histogram(m['Run'], bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts)
plt.xlabel('Run number/1')
plt.ylabel('intensity/1')
plt.savefig(fnm,dpi=dpi)
plt.semilogy()
plt.savefig('log_'+fnm,dpi=dpi)
plt.close()
# Ring Radius histogram vs time
#------------------------------
fnm = 'ring_radius_histogram_vs_time'
if not os.path.exists(fnm+'0.png'):
min_radius = np.min(m['ring_radius'])
max_radius = np.max(m['ring_radius'])
nbins1 = int(np.sqrt(m.shape[0]))
ntimebins = 64
nbins = int(nbins1/ntimebins)
step = (max_radius - min_radius)/nbins
binedges = np.linspace(min_radius, max_radius, nbins)
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
time_bin_edges = np.linspace(start, end, ntimebins)
time_bin_idx = np.digitize(m['UnixTime_s'], bins=time_bin_edges)
for t in range(ntimebins-1):
radii_in_time_range = m['ring_radius'][time_bin_idx==t]
bincounts, binedges = np.histogram(radii_in_time_range, bins=binedges)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.step(binedges[0:-1], bincounts/radii_in_time_range.shape[0])
plt.title('muon ring radius distribution, bin width '+str(round(1e3*step,3))+'mdeg\n from '+dt.fromtimestamp(time_bin_edges[t]).strftime('%Y-%m-%d')+' to '+dt.fromtimestamp(time_bin_edges[t+1]).strftime('%Y-%m-%d'))
plt.ylim([0.0, 0.06])
plt.xlabel('muon ring radius/deg')
plt.ylabel('intensity/normalized')
plt.savefig(fnm+str(t)+'.png',dpi=dpi)
plt.close()
#------------------------------
fnm = 'number_photons_vs_arrival_time.png'
if not os.path.exists(fnm):
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['number_muon_photons'],
m['arrival_time'],
bins=[
np.linspace(25, 400, 375),
np.linspace(1e-8, 3e-8, 1000),
],
cmap='viridis')
plt.colorbar()
plt.xlabel('number_muon_photons/1')
plt.ylabel('arrival_time/s')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'number_photons_vs_ring_radius.png'
if not os.path.exists(fnm):
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['number_muon_photons'],
m['ring_radius'],
bins=[
np.linspace(25, 400, 375),
np.linspace(.5, 1.5, 1000),
],
cmap='viridis')
plt.colorbar()
plt.xlabel('number_muon_photons/1')
plt.ylabel('ring_radius/deg')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'number_photons_vs_ring_radius.png'
if not os.path.exists(fnm):
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['number_muon_photons'],
m['ring_radius'],
bins=[
np.linspace(25, 400, 375),
np.linspace(.5, 1.5, 1000),
],
cmap='viridis')
plt.colorbar()
plt.xlabel('number_muon_photons/1')
plt.ylabel('ring_radius/deg')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'arrival_time_vs_ring_radius.png'
if not os.path.exists(fnm):
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['arrival_time'],
m['ring_radius'],
bins=[
np.linspace(1e-8, 3e-8, 1000),
np.linspace(0.5, 1.5, 1000)
],
cmap='viridis')
plt.colorbar()
#plt.title('muon ring radius distribution, bin width '+str(round(1e3*step,3))+'mdeg')
plt.xlabel('arrival_time/s')
plt.ylabel('ring_radius/deg')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'date_vs_number_photons.png'
if not os.path.exists(fnm):
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
ntimebins = 1000
time_bin_edges = np.linspace(start, end, ntimebins)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['UnixTime_s'],
m['number_muon_photons'],
bins=[
time_bin_edges,
np.linspace(25, 400, 375)
],
cmap='viridis')
plt.colorbar()
plt.xlabel('UnixTime/s')
plt.ylabel('number_muon_photons/1')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'date_vs_ring_radius.png'
if not os.path.exists(fnm):
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
ntimebins = 1000
time_bin_edges = np.linspace(start, end, ntimebins)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['UnixTime_s'],
m['ring_radius'],
bins=[
time_bin_edges,
np.linspace(0.5, 1.5, 1000)
],
cmap='viridis')
plt.colorbar()
plt.xlabel('UnixTime/s')
plt.ylabel('ring_radius/deg')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'date_vs_arrival_time.png'
if not os.path.exists(fnm):
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
ntimebins = 1000
time_bin_edges = np.linspace(start, end, ntimebins)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['UnixTime_s'],
m['arrival_time'],
bins=[
time_bin_edges,
np.linspace(1.5e-8, 3e-8, 1000),
],
cmap='viridis')
plt.colorbar()
plt.title('main bulk of arrival times')
plt.xlabel('UnixTime/s')
plt.ylabel('arrival_time/s')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'date_vs_late_arrival_time.png'
if not os.path.exists(fnm):
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
ntimebins = 100
time_bin_edges = np.linspace(start, end, ntimebins)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['UnixTime_s'],
m['arrival_time'],
bins=[
time_bin_edges,
np.linspace(3e-8, 7e-8, 100),
],
cmap='viridis')
plt.colorbar()
plt.title('late arrival times past 3e-8s')
plt.xlabel('UnixTime/s')
plt.ylabel('arrival_time/s')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'date_vs_ring_overlap_with_fov.png'
if not os.path.exists(fnm):
start = m['UnixTime_s'].min()
end = m['UnixTime_s'].max()
ntimebins = 1000
time_bin_edges = np.linspace(start, end, ntimebins)
fig, ax = plt.subplots(figsize=(figw,figh), dpi=dpi)
plt.hist2d(
m['UnixTime_s'],
m['ring_overlap_with_fov'],
bins=[
time_bin_edges,
np.linspace(0, 0.99, 1000),
],
cmap='viridis')
plt.colorbar()
plt.title('Ring overlap with fov vs date\nexcluding full overlap')
plt.xlabel('UnixTime/s')
plt.ylabel('ring overlap/1')
plt.savefig(fnm,dpi=dpi)
plt.close()
#------------------------------
fnm = 'rates.png'
if not os.path.exists(fnm):
night = -1
run = -1
new_run = False
events_in_run = 0
start = 0
for event in m:
if event['Night'] > night:
night = event['Night']
run = event['Run']
new_run = True
if event['Run'] > run:
run = event['Run']
new_run = True
if new_run:
print(night, run, events_in_run)
start = event['UnixTime']
new_run = False
events_in_run = 0
else:
events_in_run += 1