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analysis.tle.parodi.jm.steplim.py
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analysis.tle.parodi.jm.steplim.py
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#!/usr/bin/env python
import os,glob2 as glob,sys,matplotlib as mpl,matplotlib.pyplot as plt,numpy as np,pickle,tle,plot
from scipy.stats import norm
'''
## Wat moet er gebeuren?
'''
sources = glob.glob("./**/*.pylist")#,recursive=True
if len(sources) < 2:
print "None or one file found, exiting..."
sys.exit()
nr = len(sources)
print nr, "files found:", sources[:3], '...', sources[-3:]
data_singleton = {}
# Load up
for fp in sources:
row = {}
row['data'] = [pickle.load(open(fp))] #list of lists
dn,fn=os.path.split(fp)
#parodi tle
if 'run.UzFE' in fp: #10um
row['nprim'] = int(1e4)
row['type'] = 'tle10'
#'steplim'
# continue
if 'run.ASZg' in fp: #10um, 100jobs, skip
row['nprim'] = int(1e4)
row['type'] = 'tle10-2'
#'steplim'
# continue
if 'run.Yr1h' in fp: #100um
row['nprim'] = int(1e4)
row['type'] = 'tle100'
#'steplim'
# continue
if 'run.ERzl' in fp: #1um
row['nprim'] = int(1e4)
row['type'] = 'tle1'
#'steplim'
# continue
if 'run.R75J' in fp:
row['nprim'] = int(1e4)
row['type'] = 'analog' #FAKE ANALOG, USE AS REFERENCE
#axes
if 'Z.' in fn:
row['axis'] = 'Z'
if 'Y.' in fn:
row['axis'] = 'Y'
if 'E.' in fn:
row['axis'] = 'E'
if 'X.' in fn:
row['axis'] = 'X'
#masks
if 'sum' in fn:
row['mask'] = 'sum'
row['axis'] = 'sum'
row['data'] = [row['data']]
if 'nofilter' in fn:
row['mask'] = 'nofilter'
if 'beam' in fn:
row['mask'] = 'beam'
if 'std' in fn:
row['mask'] = 'std'
if 'box2' in fn:
row['mask'] = 'box2'
if 'box1' in fn:
row['mask'] = 'box1'
if 'box5' in fn:
row['mask'] = 'box5'
if 'box7' in fn:
row['mask'] = 'box7'
if 'box8' in fn:
row['mask'] = 'box8'
# if 'run.UzFE' in fp:
# row['mask'] = 'steplim'
# if 'run.ASZg' in fp:
# row['mask'] = 'steplim'
row['plotgroup'] = row['type']+row['axis']+row['mask']+str(row['nprim'])
if row['plotgroup'] not in data_singleton.keys():
data_singleton[row['plotgroup']] = row
else:
data_singleton[row['plotgroup']]['data'].extend(row['data'])
# convert batch data into mu,variance
for key, value in data_singleton.iteritems():
#key is plotgroup, value is row
data = np.array(value['data'])
data = np.rollaxis(data,1)
value['yield'] = []
value['var'] = []
for binn in data:
# N=float(len(binn))
# mu=sum(binn)/N
# sqbinn=[(i-mu)**2 for i in binn]
# var= ( sum(sqbinn) ) / (N*(N-1)) # Walters 2002/Chetty 2006
# var= ( sum(sqbinn) ) / (N-1) # WE MUST REMOVE 1 N FACTOR, BECAUSE WE LOOK AT YIELD, NOT COUNT
# # var = sum-of-squares/# - sq(sum/#)
# # var = ( sum([i**2 for i in binn]) / N ) - ( (mu)**2 ) #JM!!!
# value['yield'].append(mu)
# value['var'].append(var)
(mu, sigma) = norm.fit(binn) #returns actually sigma
value['yield'].append(mu)
value['var'].append(sigma**2)
# 4 options to compute var tested:
# - JM and Python: same result
# - Walter and Chetty: same result
# if we remove a factor N from Walter/Chetty, all results are the same
# WE MUST REMOVE 1 N FACTOR, BECAUSE WE LOOK AT YIELD, NOT COUNT
value['data'] = None #save some space when printing
# convert to object usable with tle.plt
uit = {}
for key, value in data_singleton.iteritems():
# we moeten toe naar data_singleton['prims']['Z']['yield']
# extra tussenstapje: data_singleton['analog']['mask']['prims']['Z']['yield']
if value['type'] not in uit.keys():
uit[value['type']] = {}
typ = uit[value['type']]
if value['mask'] not in typ.keys():
typ[value['mask']] = {}
mskset = typ[value['mask']]
if value['nprim'] not in mskset.keys():
mskset[value['nprim']] = {}
nprimset = mskset[value['nprim']]
if value['axis'] not in nprimset.keys():
nprimset[value['axis']] = {}
axset = nprimset[value['axis']]
axset['yield'] = value['yield']
axset['var'] = value['var']
### Relunc of two boxes.
tle_1_2 = uit['tle1']['box2']
tle_1_8 = uit['tle1']['box8']
tle_10_2 = uit['tle10']['box2']
tle_10_8 = uit['tle10']['box8']
tle_102_2 = uit['tle10-2']['box2']
tle_102_8 = uit['tle10-2']['box8']
tle_100_2 = uit['tle100']['box2']
tle_100_8 = uit['tle100']['box8']
an_2 = uit['analog']['box2']
an_8 = uit['analog']['box8']
tle.setprojectiondata(tle_1_2,an_2[max(an_2.keys())],'batchparodi')
tle.setprojectiondata(tle_1_8,an_8[max(an_8.keys())],'batchparodi')
tle.setprojectiondata(tle_10_2,an_2[max(an_2.keys())],'batchparodi')
tle.setprojectiondata(tle_10_8,an_8[max(an_8.keys())],'batchparodi')
tle.setprojectiondata(tle_102_2,an_2[max(an_2.keys())],'batchparodi')
tle.setprojectiondata(tle_102_8,an_8[max(an_8.keys())],'batchparodi')
tle.setprojectiondata(tle_100_2,an_2[max(an_2.keys())],'batchparodi')
tle.setprojectiondata(tle_100_8,an_8[max(an_8.keys())],'batchparodi')
f, (ll,lr) = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
tl = ll.twinx()
tr = lr.twinx()
tle_2_dict={}
tle_2_dict['tle 1um'] = tle_1_2[1e4]
tle_2_dict['tle 10um'] = tle_10_2[1e4]
tle_2_dict['tle 10um 2'] = tle_102_2[1e4]
tle_2_dict['tle 100um'] = tle_100_2[1e4]
tle_8_dict={}
tle_8_dict['tle 1um'] = tle_1_8[1e4]
tle_8_dict['tle 10um'] = tle_10_8[1e4]
tle_8_dict['tle 10um 2'] = tle_102_8[1e4]
tle_8_dict['tle 100um'] = tle_100_8[1e4]
tle.get_relunc(tl,tle_2_dict,an_2[max(an_2.keys())],'E')
tle.get_relunc(tr,tle_8_dict,an_8[max(an_8.keys())],'E')
# tle.get_yield(ll,tle_1_2,an_2[max(an_2.keys())],'E')
# tle.get_yield(lr,tle_1_8,an_8[max(an_8.keys())],'E')
tl.set_ylim([1e-2,5])
tr.set_ylim([1e-2,5])
ll.yaxis.set_label_position("right")
lr.yaxis.set_label_position("right")
tl.yaxis.set_label_position("left")
tr.yaxis.set_label_position("left")
tl.set_title("Medium 2 (Bone)")
tr.set_title("Medium 8 (Muscle)")
# tl.set_ylim([1e-1,1.1e2])
# tr.set_ylim([1e-1,1.1e2])
ll.yaxis.set_visible(False)
lr.yaxis.set_visible(False)
# tr.yaxis.set_visible(False)
tr.plot([0],[0],color='#cccccc',linewidth=0,label='Yield (not to scale)')
for child in ll.get_children() + lr.get_children():
if isinstance(child, mpl.lines.Line2D) or isinstance(child, mpl.collections.PolyCollection):
child.set_color('#cccccc')
tl.set_ylabel('Relative Uncertainty [\%]')
ll.set_xlabel('PG energy [MeV]')
ll.xaxis.set_label_coords(1.1,-0.1)
lgd = tr.legend(loc='upper right', bbox_to_anchor=(1.7, 1.),frameon=False)
[label.set_linewidth(1) for label in lgd.get_lines()]
f.savefig('boxes-relunc-jm.pdf', bbox_inches='tight')
plt.close('all')