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plot_fn.py
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#!/usr/bin/python
from scipy.interpolate import interp1d
import esutil as eu
import configparser
import matplotlib as mpl
import sys
from scipy import optimize
import matplotlib.font_manager as fm
from matplotlib.backends.backend_pdf import PdfPages
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
import numpy as np
import math
import matplotlib
matplotlib.use('Pdf')
# Input: reads five ascii files: central pixel and four neighbors
# Each file has 4 columns: linear flux spots | % residual spots | linear flux flats | % residual flats
# DATA
pp = PdfPages("fn.pdf")
print("Output PDF: fn.pdf ")
# PLOTS
# Do the plotting here
# plt.minorticks_on()
# plt.tight_layout()
# We do not have matplotlib 1.1, with the 'style' package. Modify the matplotlibrc file parameters instead
mpl.rc('lines', linewidth=1, color='black', linestyle='-')
mpl.rc('font', family='serif', weight='normal', size=9.0)
mpl.rc('text', color='black', usetex=False)
mpl.rc('axes', edgecolor='black', linewidth=1, grid=False, titlesize=10,
labelsize=10, labelweight='normal', labelcolor='black')
mpl.rc('legend', numpoints=1, fontsize='8', shadow=False, frameon=False)
mpl.rc('lines', linewidth=1, color='black', linestyle='-')
mpl.rc('font', family='serif', weight='normal', size=9.0)
mpl.rc('text', color='black', usetex=False)
mpl.rc('axes', edgecolor='black', linewidth=1, grid=False, titlesize=10,
labelsize=10, labelweight='normal', labelcolor='black')
#mpl.rc('axes.formatter', limits=[-4,4])
#mpl.rc('axes.formatter', limits=[-4,4])
mpl.rcParams['xtick.major.size'] = 5
mpl.rcParams['xtick.minor.size'] = 4
mpl.rcParams['xtick.major.pad'] = 8
mpl.rcParams['xtick.minor.pad'] = 8
mpl.rcParams['xtick.labelsize'] = '7.0'
mpl.rcParams['xtick.minor.width'] = 1.0
mpl.rcParams['xtick.major.width'] = 1.0
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['ytick.minor.size'] = 4
mpl.rcParams['ytick.major.pad'] = 8
mpl.rcParams['ytick.minor.pad'] = 8
mpl.rcParams['ytick.labelsize'] = '7.0'
mpl.rcParams['ytick.minor.width'] = 1.0
mpl.rcParams['ytick.major.width'] = 1.0
mpl.rc('legend', numpoints=1, fontsize='8', shadow=False, frameon=False)
# Plot parameters
#plt.subplots_adjust(hspace=0.01, wspace=0.01)
prop = fm.FontProperties(size=6.0)
marker_size = 6.0
alpha = 0.7
loc_label = "upper right"
visible_x, visible_y = True, True
inset = False
factor = 1.0
#root = "/Users/amalagon/NL_plots/ASCII_FILES_TO_PLOT/"
# In lucius (Jan 2021):
root = "/home/aplazas/github/bf_hxrg_ppl/ASCII_FILES_TO_PLOT/"
# directory_with_files="H_FILTER_CENTER_PPL/"
def read_3x3_into_dict(file="", flux=False, nframes=4):
f = open(file)
if flux:
dict = {"1": [[], [], []], "2": [[], [], []], "3": [[], [], []], "4": [[], [], []],
"5": [[], [], []], "6": [[], [], []], "7": [[], [], []], "8": [[], [], []], "9": [[], [], []]} # y (f_N), yerr, x (mean signal in electrons)
else:
dict = {"1": [[], []], "2": [[], []], "3": [[], []], "4": [[], []],
"5": [[], []], "6": [[], []], "7": [[], []], "8": [[], []], "9": [[], []]}
for line in f:
s = line.split(' ')
#import ipdb; ipdb.set_trace()
for i in range(1, nframes+1):
dict[s[0]][0].append(float(s[i]))
dict[s[0]][1].append(float(s[i+nframes]))
if flux:
dict[s[0]][2].append(float(s[i+2*nframes]))
f.close()
for key in dict:
dict[key][0] = np.array(dict[key][0])
dict[key][1] = np.array(dict[key][1])
if flux:
dict[key][2] = np.array(dict[key][2])
return dict
def read_surrounding_into_dict(file="", nframes=4):
f = open(file)
dict = {"1": [[], []], "2": [[], []]}
for line in f:
s = line.split(' ')
# print s[0]
for i in range(1, nframes+1):
dict[s[0]][0].append(float(s[i]))
dict[s[0]][1].append(float(s[i+nframes]))
f.close()
for key in dict:
dict[key][0] = np.array(dict[key][0])
dict[key][1] = np.array(dict[key][1])
return dict
def fitfunc(x, m):
x = np.array(x)
return m*x
pinit = [0.1]
def linear_fit(x, y, y_err):
pfinal, covar = optimize.curve_fit(
fitfunc, x, y, p0=pinit, sigma=y_err, maxfev=100000)
return pfinal[0], np.sqrt(covar[0])
# Read files
Config = configparser.ConfigParser()
Config.read(sys.argv[1])
sc_temp = Config.get('params', 'DoSigmaClipping') # sigma clipping?
if sc_temp == "True":
sc = True
elif sc_temp == "False":
sc = False
else:
print("Enter a valid parameter for 'DoSigmaClipping'")
sys.exit()
sigma_cut = float(Config.get('params', 'SigmaClippingCut'))
delta_t = float(Config.get('params', 'FramTimePPL')) # 0.837632
ppl_dir_center = Config.get('params', 'PPLDataDirCenter')
# root+"CENTER_PPL/"
nframes = int(Config.get('params', 'NFramesPPL'))
nframes_sim = int(Config.get('params', 'NFramesSim'))
nframes_corner = int(Config.get('params', 'NFramesCornerPPL'))
sim_dir_center_NL_corr = Config.get('params', 'SimDirCenterNLCorrected')
# root+"CENTER_SIM_NL_CORRECTED/
sim_dir_center_not_NL_corr = Config.get('params', 'SimDirCenterNLNotCorrected')
# root+"CENTER_SIM_NL_NOT_CORRECTED
sim_dir_center_nothing = Config.get('params', 'SimDirCenterNothing')
# root+"SIMS_NOTHING
DirSimB = Config.get('params', 'SimDirCenterBHistogram')
# root+"CENTER_SIM_BF_90RAMPS_V7
ppl_dir_corner_r1 = Config.get('params', 'PPLDataDirCornerR1')
# root+"H_FILTER_CENTER_PPL/FEB15_FILMSTRIP_V2_DARK_SUB_YES_ZERO_FRAME_YES_REGION1/"
ppl_dir_corner_r2 = Config.get('params', 'PPLDataDirCornerR2')
ppl_dir_corner_r3 = Config.get('params', 'PPLDataDirCornerR3')
ppl_dir_corner_r4 = Config.get('params', 'PPLDataDirCornerR4')
# PARAMS
# root="/Users/amalagon/NL_plots/ASCII_FILES_TO_PLOT/"
# temp_dir="MAR21_H_BAND_F11_CUBIC/" #"FEB26_QUAD_DARK_SUBTRACTED_ZERO_FRAME_YES/"
# nframes=5
# nframes_corner=6
ppl_center_dict = read_3x3_into_dict(
ppl_dir_center + "/jay_metric.dat", flux=True, nframes=nframes)
#
#ppl_center_dict=read_3x3_into_dict (root+"CENTER_PPL/" + "jay_metric.dat", flux=True, nframes=nframes)
#ppl_corner_dict=read_3x3_into_dict (root+"CORNER_PPL/jay_metric_corner.dat" )
sim_center_dict = read_3x3_into_dict(
sim_dir_center_NL_corr + "/jay_metric.dat", nframes=nframes_sim)
sim_center_nc_dict = read_3x3_into_dict(
sim_dir_center_not_NL_corr + "/jay_metric_sim_NL_not_corr.dat", nframes=nframes_sim)
sim_nothing = read_3x3_into_dict(
sim_dir_center_nothing + "/jay_metric.dat", nframes=nframes_sim)
#flats_120v100=read_3x3_into_dict (root+"FLATS_120_VS_FLATS_100/jay_metric_oct21.dat")
# sim_center_bf=read_3x3_into_dict(root+"CENTER_SIM_BF_90RAMPS_V7/jay_metric.dat")
# print ppl_center_dict
# print ppl_corner_dict
#samples=range(1, len(ppl_center_dict['1'][0]) + 1)
# THIS IS ACTUALLY MEAN SIGNAL PER FRAME ( (sample + 1 + sample) / 2) in ELECTRONS
samples = list(range(1, len(ppl_center_dict['2'][0]) + 1))
# print ppl_center_dict['2'][2], ppl_center_dict['5'][2]
# sys.exit()
simulations_flux = {'1': [402.957, 677.249, 955.99, 1240.43], '2': [1825.97, 3078.59, 4363.04, 5672.92], '3': [402.915, 677.282, 957.607, 1241.39],
'4': [1827.36, 3079.58, 4361.75, 5672.8], '5': [28285.2, 46977, 65505.4, 83830], '6': [1832.58, 3088.91, 4374.23, 5687.13],
'7': [402.496, 676.444, 954.201, 1238.62], '8': [1826.36, 3079.46, 4365.73, 5677.67], '9': [403.374, 677.778, 956.593, 1240.58]}
prop = fm.FontProperties(size=4.5)
# SIMULATIONS
size_fn = 7.5
# PAGE 1
fig = plt.figure()
for key in ppl_center_dict:
ax = fig.add_subplot(3, 3, int(key))
plt.errorbar(simulations_flux[key], sim_center_nc_dict[key][0]/factor,
yerr=sim_center_nc_dict[key][1], fmt='b-o', markersize=5, label='sim+noise+NL')
plt.errorbar(simulations_flux[key], sim_center_dict[key][0]/factor,
yerr=sim_center_dict[key][1], fmt='g--o', markersize=5, label='sim+noise+NL, NL corr.')
plt.errorbar(simulations_flux[key], sim_nothing[key][0]/factor,
yerr=sim_nothing[key][1], fmt='r-o', markersize=5, label='sim+no noise')
if key == '5':
m, m_err = linear_fit(
ppl_center_dict[key][2], ppl_center_dict[key][0], None)
print("m, m_err", m, m_err)
plt.ylim([-0.1, 0.1])
plt.yticks([-0.1, -0.05, 0.0, 0.05, 0.1], fontsize=size_fn)
plt.xticks([20000, 55000, 90000], fontsize=size_fn)
if inset == True:
a = plt.axes([0.52, 0.52, 0.08, 0.08])
plt.errorbar(
samples, sim_center_nc_dict[key][0], yerr=sim_center_nc_dict[key][1], fmt='b-o', markersize=3, label='')
#plt.errorbar (samples,flats_120v100[key][0], yerr=flats_120v100[key][1], fmt='m-o', markersize=3, label='flat1 vs flat2')
plt.errorbar(
samples, sim_center_dict[key][0], yerr=sim_center_dict[key][1], fmt='g--o', markersize=3, label='')
plt.errorbar(
samples, sim_nothing[key][0], yerr=sim_nothing[key][1], fmt='r-o', markersize=3, label='')
#plt.errorbar (samples,ppl_center_dict[key][0], yerr=ppl_center_dict[key][1], fmt='k-o', markersize=3, label='PPL data')
plt.ylim([-0.1, 0.1])
# plt.xlim([0,5])
# plt.xticks([])
plt.yticks([-0.1, -0.05, 0.0, 0.05, 0.1], fontsize=size_fn)
plt.xticks([20000, 60000, 90000], fontsize=size_fn)
# plt.ylim([-0.1,0.1])
elif key in ['2', '4', '6', '8']:
print(" ")
plt.ylim([-0.0018, 0.0018])
plt.yticks([-0.002, -0.001, 0.0, 0.001, 0.002], fontsize=size_fn)
plt.xticks([1500, 4000, 6000], fontsize=size_fn)
else:
plt.ylim([-0.0004, 0.0004])
plt.yticks([-0.0004, -0.0002, 0.0, 0.0002, 0.0004], fontsize=size_fn)
plt.xticks([350, 850, 1350], fontsize=size_fn)
if key in ['1', '4', '7']:
ax.set_ylabel(r"$f_{N}$", size=14)
else:
ax.set_ylabel("", size=14)
if key in ['7', '8', '9']:
ax.set_xlabel("Signal (e$^-$)", size=9.5)
# plt.xlim([0,5])
else:
ax.set_xlabel("", size=14)
# plt.xlim([0,5])
if key == '2':
plt.legend(loc='upper right', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
#plt.xlim([20000, 90000])
#plt.xticks(range(20000,90000,5), fontsize=10)
# if key in ['2','4','6','8']:
#plt.xlim([1000, 6000])
plt.tight_layout()
pp.savefig()
factor = 1.0
# PLOT ONLY THE PPL DATA
# PAGE 2
fig = plt.figure()
for key in ppl_center_dict:
ax = fig.add_subplot(3, 3, int(key))
plt.errorbar(ppl_center_dict[key][2], ppl_center_dict[key][0],
yerr=ppl_center_dict[key][1], fmt='k-o', markersize=5, label='PPL data')
if key == '5':
m, m_err = linear_fit(samples, ppl_center_dict[key][0], None)
print(m, m_err)
#plt.ylim([-0.023, 0.023])
# plt.xlim([5e3,8e4])
#plt.yticks([-0.025, -0.0125, 0.0, 0.0125, 0.025], fontsize=size_fn)
#plt.xticks([2e4,5e4,7.6e4], fontsize=size_fn)
#plt.yticks([-0.005, -0.0025, 0.0, 0.0025, 0.005], fontsize=size_fn)
#plt.xticks([2e4,4e4,6e4], fontsize=size_fn)
# plt.ylim([-0.1,0.1])
elif key in ['2', '4', '6', '8']:
print(" ")
# plt.ylim([-0.006,0.006])
# plt.xlim([500,2300])
#plt.yticks([-0.005, -0.0025, 0.0, 0.0025, 0.005], fontsize=size_fn)
#plt.xticks([2000,5000,8000], fontsize=size_fn)
#plt.yticks([-0.005, -0.0025, 0.0, 0.0025, 0.005], fontsize=size_fn)
#plt.xticks([10000,17500,25000], fontsize=size_fn)
else:
print(" ")
#plt.yticks([-0.0005, -0.00025, 0.0, 0.00025, 0.0005], fontsize=size_fn)
#plt.xticks([500,1500,2400], fontsize=size_fn)
#plt.yticks([-0.0025, -0.00125, 0.0, 0.00125, 0.0025], fontsize=size_fn)
#plt.xticks([5000,10000,15000], fontsize=size_fn)
if key in ['1', '4', '7']:
ax.set_ylabel(r"$f_{N}$", size=14)
else:
ax.set_ylabel("", size=14)
if key in ['7', '8', '9']:
ax.set_xlabel("Signal (e$^-$)", size=9.5)
# plt.xlim([0,5])
else:
ax.set_xlabel("", size=14)
# plt.xlim([0,5])
# if key in ['1', '2', '3', '4', '5', '6']:
# ax.set_xticklabels([float(x) for x in ax.get_xticks()], visible=True)
# if key in ['2', '3', '6', '8', '9']:
# ax.set_yticklabels([float(y) for y in ax.get_yticks()], visible=True)
# if key in ['7', '8', '9']:
# ax.set_xticklabels([ x for x in ax.get_xticks()], visible=True)
if key == '1':
plt.legend(loc='upper left', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
# if key == '5':
# plt.xlim([15000, 80000])
# if key in ['2','4','6','8']:
# plt.xlim([1000, 8000])
# for axi in ax.flat:
# ax.xaxis.set_major_locator(plt.MaxNLocator(5))
# ax.yaxis.set_major_locator(plt.MaxNLocator(5))
# ax.grid(b=True)
plt.tight_layout()
pp.savefig()
# pp.close()
# sys.exit()
# Plot relative sizes
# PAGE 3
# data_ppl_center=np.genfromtxt(root+"CENTER_PPL/jay_relative_size_oct25.dat")
# data_ppl_center=np.genfromtxt(root+"H_FILTER_CENTER_PPL/"+temp_dir+"jay_relative_size.dat")
data_ppl_center = np.genfromtxt(ppl_dir_center+"/jay_relative_size.dat")
#data_sim_nl= np.genfromtxt(root+"CENTER_SIM_NL_CORRECTED/relative_size_oct21.dat")
#data_sim_nl_nc= np.genfromtxt(root+"CENTER_SIM_NL_NOT_CORRECTED/relative_size_sim_NL_not_corr.dat")
# data_sim_nothing=np.genfromtxt(root+"SIMS_NOTHING/relative_size.dat")
# data_flats_120v100=np.genfromtxt(root+"FLATS_120_VS_FLATS_100/relative_size_oct21.dat")
# data_sim_bf=np.genfromtxt(root+"CENTER_SIM_BF_90RAMPS_V7/jay_relative_size.dat")
# ,data_sim_nl_nc, data_sim_nl, data_sim_nothing] #, data_sim_bf]
data_vec = [data_ppl_center]
# ,'m','c' ] #ppl, nl corrected, nl non corrected, noiseless sims
c = ['k', 'b', 'g', 'r']
# , 'sim+noise+NL', 'sim+noise+NL, NL corr.', 'sim+no noise'] #, 'flat fields', 'sim+BF']
label_vec = ['PPL data']
prop = fm.FontProperties(size=10)
fig = plt.figure()
for i, data in enumerate(data_vec):
x = data[:, 2]
y = data[:, 0]
yerr = data[:, 1]
print(c[i], y)
size_final_mean = 100*(y - y[0])/y[0]
ax = fig.add_subplot(111)
plt.errorbar(x, size_final_mean, yerr=yerr, markersize=8,
fmt=c[i]+'-o', label=label_vec[i])
plt.legend(loc='upper left', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
# plt.xlim([0,5])
ax.set_xlabel("Average of mean signal in central pixel (e$^-$)", size=12)
ax.set_ylabel("Relative change in size (%)", size=12)
# ax.grid(b=True)
ax.set_xticklabels([float(x) for x in ax.get_xticks()], visible=True, size=9)
ax.set_yticklabels([float(y) for y in ax.get_yticks()], visible=True, size=9)
ax.xaxis.set_major_locator(plt.MaxNLocator(6))
pp.savefig()
# Plot B histogram
# PAGE 4
#from matplotlib.ticker import NullFormatter
#b = 2*(c1)*(c2)/fc
# new_B = (m/fc)*(NORM/(val0*delta_t/1000)) # delta_s in miliseconds
# fc,b, c1, c2, m, m_err, m/fc, new_B
#data_B=np.genfromtxt (root+"CENTER_PPL/jay_B.dat")
data_B = np.genfromtxt(ppl_dir_center+"/jay_B.dat")
# data_B=np.genfromtxt(root+"jay_B_FINAL_DEC4.dat")
fc = data_B[:, 0]
b1 = data_B[:, 1]
c1 = data_B[:, 2]
c2 = data_B[:, 3]
m = data_B[:, 4]
me = data_B[:, 5]
b2 = data_B[:, 7] # CHANGED to new B, not
#c2_flats=np.genfromtxt (root+"H_FILTER_CENTER_PPL/jay_c2_flats.dat")
#c2_spots=np.genfromtxt (root+"H_FILTER_CENTER_PPL/jay_c2_spots.dat")
c2_flats = np.genfromtxt(ppl_dir_center + "/jay_c2_flats.dat")
c2_spots = np.genfromtxt(ppl_dir_center + "/jay_c2_spots.dat")
vec = [(fc, 'Fc', 'green'), (b1, "B, method 1", 'blue'), (c1, "c1", 'yellow'), (c2, "c2", 'magenta'), (m, "m",
'cyan'), (b2, "B (1/e$^-$)", 'red'), (c2_flats, "$C_2$ flats", 'red'), (c2_spots, "$C_2$ spots", 'blue')]
# print "b: ", np.min(b), np.max(b), np.median(b)
# print "fc: ", np.min(fc), np.max(fc), np.median(fc)
b = b2
if sc:
mean_b, sigma_b, indices = eu.stat.sigma_clip(
b, niter=10, nsig=sigma_cut, get_indices=True, verbose=True)
b = b[indices]
fc = fc[indices]
print("mean b, std b, std err of mean, min, max after 3-sigma clipping: ",
mean_b, sigma_b, sigma_b/np.sqrt(len(b)), np.min(b), np.max(b))
mean_fc, sigma_fc, indices = eu.stat.sigma_clip(
fc, niter=10, nsig=sigma_cut, get_indices=True, verbose=True)
b = b[indices]
fc = fc[indices]
print("mean fc, std fc, std err of mean after 3-sigma clipping: ",
mean_fc, sigma_fc, sigma_fc/np.sqrt(len(fc)))
print("new b: ", np.mean(b), np.std(b), np.std(b)/np.sqrt(len(b)))
print("Min and max b: ", np.min(b), np.max(b))
label_string = "Mean: %g \n Std. Error of Mean: %g" % (
np.mean(b), np.std(b)/np.sqrt(len(b)))
# Bin
bin = eu.stat.Binner(fc, b)
# b.dohist(nperbin=10)
bin.dohist(nbin=8)
bin.calc_stats()
x = np.array(bin['xmean'])
y = np.array(bin['ymean'])
y_err = np.array(bin['yerr'])
prop = fm.FontProperties(size=9)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(fc, b, 'r.', alpha=0.7)
ax.errorbar(x, y, yerr=y_err, ecolor='b', fmt='b.-',
markersize=9, label=label_string)
plt.legend(loc='upper right', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
#plt.ylim([-6e-6, 6e-6])
# plt.xlim([1000,7000])
ax.set_xlabel('F_C')
ax.set_ylabel('B, method 2')
pp.savefig()
# pp.close()
print("hello")
"""
fig=plt.figure()
for data in data_vec:
if data[1] == 'center': frame=5
if data[1] == 'n1': frame=2
if data[1] == 'n2': frame=4
if data[1] == 'n3': frame=6
if data[1] == 'n4': frame=8
ax=fig.add_subplot(3,3,frame)
x1,y1,x2,y2 = data[0][:,0], data[0][:,1], data[0][:,2],data[0][:,3]
print np.nanmedian(y1), np.nanmean(y1)
print np.nanmedian(y2), np.nanmean(y2)
print " "
plt.scatter (x1,y1, c='r', alpha=0.2)
plt.scatter (x2,y2, c='b', alpha=0.2)
plt.ylabel("NL residual", size=7.5)
plt.xlabel("Signal (e)", size=7.5)
plt.tight_layout()
#plt.ylim([-5,5])
pp.savefig()
pp.close()
"""
"""
for v in vec:
mean_v, sigma_v, indices = eu.stat.sigma_clip(v[0],niter=10, nsig=sigma_cut, get_indices=True, verbose=True)
#mean_v, sigma_v= np.mean (v[0]), np.std(v[0])
x=v[0][indices]
#x=v[0]
fig=fig=plt.figure()
ax=fig.add_subplot(111)
n, bins, patches_out = ax.hist(x, 50, normed=False, facecolor=v[2], alpha=0.9, label="Number of spots: %.4g \n Mean: %.4g \n Std. Dev.: %.4g \n Std. Err.: %.4g" %(len(x),mean_v, sigma_v, sigma_v/np.sqrt(len(x))))
ax.set_title('%s'%v[1], size=13)
ax.legend(loc='upper left' , fancybox=True, ncol=1, numpoints=1, prop = prop)
ax.tick_params(axis='both', which='major', labelsize=9)
#if v[2] == 'red':
# ax.set_title('Only read and shot noise')
pp.savefig()
"""
# Plot B2 histogram from data and simulations together
# PAGE 5
mpl.rc('axes.formatter', limits=[-4, 4])
mpl.rc('axes.formatter', limits=[-4, 4])
mpl.rcParams['xtick.labelsize'] = '10.0'
# root+"CENTER_SIM_BF_90RAMPS_V7/
data_B_sim = np.genfromtxt(DirSimB+"/jay_B.dat")
b2_sim = data_B_sim[:, 7] # new B, 7, not 6
if sc:
mean_b2_sim, sigma_b2_sim, indices = eu.stat.sigma_clip(
b2_sim, niter=10, nsig=sigma_cut, get_indices=True, verbose=True)
b2_sim = b2_sim[indices]
mean_b2, sigma_b2, indices = eu.stat.sigma_clip(
b2, niter=10, nsig=sigma_cut, get_indices=True, verbose=True)
b2 = b2[indices]
else:
mean_b2_sim, sigma_b2_sim = np.mean(b2_sim), np.std(b2_sim)
mean_b2, sigma_b2 = np.mean(b2), np.std(b2)
fig = fig = plt.figure()
ax = fig.add_subplot(111)
n, bins, patches_out = ax.hist(b2, 50, density=False, facecolor='red', alpha=0.9, label="PPL data: \n Number of spots: %.4g \n Mean: %.4g \n Std. Dev.: %.4g \n Std. Err.: %.4g" % (
len(b2), mean_b2, sigma_b2, sigma_b2/np.sqrt(len(b2))))
ax.legend(loc='upper left', fancybox=True, ncol=1, numpoints=1, prop=prop)
n, bins, patches_out = ax.hist(b2_sim, 50, density=False, facecolor='green', alpha=0.9, label="Simulations: \n Number of spots: %.4g \n Mean: %.4g \n Std. Dev.: %.4g \n Std. Err.: %.4g" % (
len(b2_sim), mean_b2_sim, sigma_b2_sim, sigma_b2_sim/np.sqrt(len(b2_sim))))
ax.legend(loc='upper left', fancybox=True, ncol=1, numpoints=1, prop=prop)
ax.tick_params(axis='both', which='major', labelsize=9)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
#ax.set_title("B (1/e$^{-}$)")
ax.set_xlabel("B (1/e$^{-}$)")
# pp.savefig(bbox_inches="tight")
pp.savefig()
mpl.rc('axes.formatter', limits=[-6, 6])
mpl.rc('axes.formatter', limits=[-6, 6])
mpl.rcParams['xtick.labelsize'] = '10.0'
# PLOT Residuals of model for each central spot in flats, for central pixel
# median_flux_flats=np.genfromtxt(root+"H_FILTER_CENTER_PPL/"+"jay_median_flux_flats_center_pixel.dat")
# median_flux_spots=np.genfromtxt(root+"CENTER_PPL/"+"jay_median_flux_spots_center_pixel.dat")
# if not len(median_flux_flats) == len(median_flux_spots):
# print "Median flux vectors do not have same length"
# sys.exit()
# median_flux_flats/=1e4
# data_residuals_flats=np.genfromtxt (root+"H_FILTER_CENTER_PPL/"+"jay_residual_center_pixel_flat.dat") #_oct25.dat")
#data_residuals=np.genfromtxt (root+"CENTER_PPL/"+"jay_residual_center_pixel_flat.dat")
# data_residuals_spots=np.genfromtxt (root+"CENTER_PPL/"+"jay_residual_center_pixel_spots.dat") #_oct25.dat")
#frame_number=range(1, len(data_residuals_flats[0,:])+1)
# print frame_number
#mean_y = np.median(data_residuals_flats, axis=0)
# fig=fig=plt.figure()
# ax=fig.add_subplot(211)
# for y in data_residuals_flats:
# ax.errorbar( median_flux_flats, y, yerr=None, ecolor = 'b', fmt='b.', markersize=6, label='', alpha=0.3)
#ax.errorbar( frame_number, mean_y, yerr=None, ecolor = 'r', fmt='r.-', markersize=9, label='', alpha=1.0)
#ax.set_xlabel('Frame Number (time)')
#ax.set_ylabel('Residual (%): (data - model) / model')
# ax.grid(b=True)
# plt.xlim([0,6])
# ax=fig.add_subplot(111)
# for y in data_residuals_flats:
# print median_flux_flats, y, len(median_flux_flats), len(y)
# ax.errorbar( median_flux_flats, y, yerr=None, ecolor = 'g', fmt='g.', markersize=7, label='', alpha=0.05)
#ax.errorbar( median_flux_flats, mean_y, yerr=None, ecolor = 'r', fmt='r.-', markersize=11, label='', alpha=1.0)
# print median_flux_flats, mean_y
#ax.set_xlabel('Average signal (e$^-$)')
#ax.set_ylabel(r"Residual (%)")
# ax.grid(b=True)
#plt.ylim([-0.25, 0.25])
# plt.xlim([0,6])
#ax.set_yticklabels([float(y) for y in ax.get_yticks()], visible=True, size=9)
# pp.savefig()
print("Before Page 6 ")
# Plot NL relative residuals for all the pixels
# PAGE 6
median_flux_flats_dict = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
mean_y_dict = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
fig = fig = plt.figure()
for i in range(1, 10):
median_flux_flats = np.genfromtxt(
ppl_dir_center+"/jay_median_flux_flats_pixel_%g.dat" % i)
data_residuals_flats = np.genfromtxt(
ppl_dir_center+"/jay_residual_pixel_%g_flat.dat" % i)
# median_flux_flats=np.genfromtxt(root+"CENTER_PPL/"+"jay_median_flux_flats_pixel_%g.dat"%i)
#data_residuals_flats=np.genfromtxt (root+"CENTER_PPL/"+"jay_residual_pixel_%g_flat.dat" %i)
mean_y = np.median(data_residuals_flats, axis=0)
# Save vectors to use below
median_flux_flats_dict["%s" % i] = median_flux_flats
mean_y_dict["%s" % i] = mean_y
ax = fig.add_subplot(3, 3, i)
for y in data_residuals_flats:
ax.errorbar(median_flux_flats, y, yerr=None, ecolor='g',
fmt='g.', markersize=7, label='', alpha=0.1)
ax.errorbar(median_flux_flats, mean_y, yerr=None, ecolor='r',
fmt='r.-', markersize=11, label='', alpha=1.0)
if i in [7, 8, 9]:
ax.set_xlabel('Average signal \n (e$^-$)', size=9.5)
if i in [1, 4, 7]:
ax.set_ylabel(r"Residual (%)", size=9)
#plt.ylim([-0.25, 0.25])
#ax.set_yticklabels([float(y) for y in ax.get_yticks()], visible=True, size=7)
plt.yticks([-0.25, 0.0, 0.25], fontsize=size_fn)
#plt.xticks([1.0e4, 4.0e4, 7.0e4, 1.0e5], fontsize=size_fn)
plt.tight_layout()
pp.savefig()
print("Before page 6, part 2")
# PAGE 6, part 2: Absolute residuals (not relative). Note that I use (almost) the same variables as above.
median_flux_flats_dict_absolute = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
mean_y_dict_absolute = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
fig = fig = plt.figure()
for i in range(1, 10):
median_flux_flats_absolute = np.genfromtxt(
ppl_dir_center+"/jay_median_flux_flats_pixel_%g.dat" % i)
data_residuals_flats_absolute = np.genfromtxt(
ppl_dir_center+"/jay_residual_absolute_pixel_%g_flat.dat" % i)
# median_flux_flats=np.genfromtxt(root+"CENTER_PPL/"+"jay_median_flux_flats_pixel_%g.dat"%i)
#data_residuals_flats=np.genfromtxt (root+"CENTER_PPL/"+"jay_residual_pixel_%g_flat.dat" %i)
mean_y_absolute = np.median(data_residuals_flats_absolute, axis=0)
# Save vectors to use below
median_flux_flats_dict_absolute["%s" % i] = median_flux_flats_absolute
mean_y_dict_absolute["%s" % i] = mean_y_absolute
ax = fig.add_subplot(3, 3, i)
for y in data_residuals_flats_absolute:
ax.errorbar(median_flux_flats_absolute, y, yerr=None,
ecolor='b', fmt='b.', markersize=7, label='', alpha=0.1)
ax.errorbar(median_flux_flats_absolute, mean_y_absolute, yerr=None,
ecolor='r', fmt='r.-', markersize=11, label='', alpha=1.0)
if i in [7, 8, 9]:
ax.set_xlabel('Average signal \n (e$^-$)', size=9.5)
if i in [1, 4, 7]:
ax.set_ylabel(r"Absolute Residual", size=9)
#plt.ylim([-20, 20])
#ax.set_yticklabels([float(y) for y in ax.get_yticks()], visible=True, size=7)
#plt.yticks([-0.25, 0.0, 0.25], fontsize=size_fn)
#plt.xticks([1.0e4, 4.0e4, 7.0e4, 1.0e5], fontsize=size_fn)
plt.tight_layout()
pp.savefig()
# APPLES TO APPLES COMPARISON OF ERROR INDUCED BY NL ON SIGNAL
# PAGE 8
# delta_t=3.0 #seconds
# simulated_mean_spot_fluxes={'1': 139.028, '2': 521.738, '3':163.384, \
# '4':531.722, '5':5047.73, '6':520.802, \
# '7': 147.19, '8': 520.826, '9': 151.987}
# H filter
# delta_t=3.350
# simulated_mean_spot_fluxes={'1': 845.621, '2': 1105.43, '3':847.195, \
# '4':1207.35, '5':2772.44, '6':1192.33, \
# '7': 844.803, '8': 1109.37, '9': 845.586}
simulated_mean_spot_fluxes_data = np.genfromtxt(
ppl_dir_center+"/jay_median_spot_fluxes.dat")
simulated_mean_spot_fluxes = {'1': simulated_mean_spot_fluxes_data[0], '2': simulated_mean_spot_fluxes_data[1], '3': simulated_mean_spot_fluxes_data[2],
'4': simulated_mean_spot_fluxes_data[3], '5': simulated_mean_spot_fluxes_data[4], '6': simulated_mean_spot_fluxes_data[5],
'7': simulated_mean_spot_fluxes_data[6], '8': simulated_mean_spot_fluxes_data[7], '9': simulated_mean_spot_fluxes_data[8]}
# delta_t=3.350528
# delta_t=3.0
# Dictionary of fluxes for average ramps ine ach pixel of spot:
prop = fm.FontProperties(size=7)
#ppl_center_dict_NO_NORM=read_3x3_into_dict (root+"PPL_DATA_NO_NORM/jay_metric.dat", flux=True)
ppl_center_dict_NO_NORM = read_3x3_into_dict(ppl_dir_center+"/jay_metric_no_norm.dat", flux=True, nframes=nframes)
error_NO_NORM = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
res = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
residual_functions_dict = {'1': [], '2': [], '3': [],
'4': [], '5': [], '6': [],
'7': [], '8': [], '9': []}
# 1. Interpolate residual funcion
print(" ")
print(mean_y_dict, "hola")
# sys.exit(1)
# mean_y=np.array([0.08,0.09,0.005,0.05,0.01])/100
# mean_y=np.array([0.08,0.07,0.06,0.05,0.04])
# Generate the residual function objects by interpolating the mean residual curves per pixel (read data from Fig. 6)
for key in residual_functions_dict:
median_flux_flats = median_flux_flats_dict[key]
mean_y = mean_y_dict[key]
median_flux_flats[0] = 0.0 # Define zero residual at zero signal
mean_y[0] = 0.0
# residual=interp1d(median_flux_flats, np.abs(mean_y)/100) #,bounds_error=False, fill_value=np.mean(np.abs(mean_y))/100) ## Divide by 100 because mean_y is in %
### TEMP, Jan 2021: extrapolate on
residual = interp1d(median_flux_flats, np.abs(mean_y)/100, fill_value= "extrapolate")
# print median_flux_flats, np.mean(np.abs(mean_y))
residual_functions_dict[key] = residual
print("key, median_flux_flats, np.abs(mean_y)/100: ",
key, median_flux_flats, np.abs(mean_y)/100)
def get_error_no_norm_ramp(pixel_number_string, end_sample=5):
simulated_ramp = np.array(list(range(1, end_sample+1))) * \
simulated_mean_spot_fluxes[pixel_number_string]*delta_t
print("key, simulated ramp: ", key, simulated_ramp)
error = simulated_ramp * \
residual_functions_dict[pixel_number_string](simulated_ramp)
print("error: ", error)
vec = []
for i in range(len(error)-1):
print("pixel_number_string: ", pixel_number_string,
i, (error[i+1]-error[i]) / delta_t)
vec.append((error[i+1]-error[i]) / delta_t)
vec2 = []
first = vec[0]
for x in vec:
vec2.append(x-first)
return np.array(vec2)
def get_residual_func(pixel_number_string, end_sample=5):
simulated_ramp = np.array(list(range(1, end_sample+1))) * \
simulated_mean_spot_fluxes[pixel_number_string]*delta_t
return residual_functions_dict[pixel_number_string](simulated_ramp)
fig = plt.figure()
for key in ppl_center_dict:
ax = fig.add_subplot(3, 3, int(key))
#plt.errorbar (ppl_center_dict[key][2] , np.abs(1000*ppl_center_dict_NO_NORM[key][0]), yerr=ppl_center_dict[key][1], fmt='k-o', markersize=5, label='PPL data')
plt.errorbar(ppl_center_dict[key][2], (1000*ppl_center_dict_NO_NORM[key][0]),
yerr=ppl_center_dict[key][1], fmt='k-o', markersize=5, label='PPL data')
error_NO_NORM[key] = get_error_no_norm_ramp(key, end_sample=nframes+1)
print("error_NO_NORM[key: ", error_NO_NORM[key])
#plt.errorbar (ppl_center_dict[key][2] , np.abs(error_NO_NORM[key]), yerr=None, fmt='r-o', markersize=5, label='NL error')
plt.errorbar(ppl_center_dict[key][2], (error_NO_NORM[key]),
yerr=None, fmt='r-o', markersize=5, label='NL error')
# if key == '5':
# m, m_err=linear_fit (samples,1000*ppl_center_dict_NO_NORM[key][0]/factor, None)
# print m, m_err
# else:
# print " "
# plt.xlim([500,2300])
if key in ['1', '4', '7']:
ax.set_ylabel(r"|$F_{k}-F_{1}|$ (e$^-$/s)", size=9)
else:
ax.set_ylabel("", size=14)
if key in ['7', '8', '9']:
ax.set_xlabel("Signal (e$^-$)", size=9.5)
else:
ax.set_xlabel("", size=14)
if key == '5':
plt.legend(loc='upper left', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
if key == '5':
#plt.xlim([15000, 80000])
#plt.yticks([0.0, 50, 100, 150], fontsize=size_fn)
#plt.xticks([1.5e4,4.5e4,7.5e4], fontsize=size_fn)
print(" ")
elif key in ['1', '3', '7', '9']:
#plt.yticks([0.0, 1.0, 2.0, 3.0,4.0], fontsize=size_fn)
#plt.xticks([500,1500,2500], fontsize=size_fn)
print(" ")
else:
print(" ")
#plt.xticks([2e3,5e3,8e3], fontsize=size_fn)
#plt.yticks([0.0, 10.0, 20.0, 30.0], fontsize=size_fn)
plt.tight_layout()
pp.savefig()
# Plot only the residual function
# PAGE 8
fig = plt.figure()
for key in ppl_center_dict:
ax = fig.add_subplot(3, 3, int(key))
res[key] = get_residual_func(key, end_sample=nframes+1)
print("res[key]: ", res[key])
plt.plot((res[key]), 'b-o', markersize=5, label='Residual function')
if key == '5':
m, m_err = linear_fit(
samples, 1000*ppl_center_dict_NO_NORM[key][0]/factor, None)
print(m, m_err)
else:
print(" ")
# plt.xlim([500,2300])
if key in ['1', '4', '7']:
ax.set_ylabel(r"Interpolated NL residual", size=8)
else:
ax.set_ylabel("", size=14)
if key in ['7', '8', '9']:
ax.set_xlabel("Frame number", size=9)
else:
ax.set_xlabel("", size=14)
if key == '5':
plt.legend(loc='upper left', fancybox=True, ncol=1,
numpoints=1, prop=prop, handlelength=3)
if key == '5':
print(" ")
#plt.xlim([15000, 80000])
if key in ['2', '4', '6', '8']:
print(" ")
#plt.xlim([1000, 8000])
ax.set_yticklabels([float(y)
for y in ax.get_yticks()], visible=True, size=7)
plt.tight_layout()
pp.savefig()
# Plot charge conservation
# PAGE 9
# surr=read_surrounding_into_dict(root+"CENTER_PPL/"+"jay_metric_surrounding.dat")
surr = read_surrounding_into_dict(
ppl_dir_center+"/jay_metric_surrounding.dat", nframes=nframes)
fig = plt.figure()
samples = list(range(1, nframes+1))
ax = fig.add_subplot(1, 1, 1)
ax.errorbar(samples, surr['1'][0]/factor, yerr=surr['1'][1],