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lc.py
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lc.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 30 18:48:42 2020
@author: asif
"""
import numpy as np
import matplotlib.pyplot as plt
import linecache
plt.style.use('seaborn-whitegrid')
def Read_t_file(file_name):
"""Reads time from files for swift website spectra time slicing"""
t=[]
with open(file_name,'r') as reader:
temp=reader.readline().strip().split()[-1].split('-')
t.append(temp[0])
t.append(temp[1])
for line in reader.readlines():
t.append(line.strip().split()[-1].split('-')[-1])
return np.array(t,dtype=np.float32)
def read_corr(no_of_specs,base_name="spec"):
"""Reads File count rate and correction factor"""
count_r=[]
corr_fact=[]
for i in range(no_of_specs):
path="./"+base_name+str(i)+"/"+base_name+str(i)+"pc_fit.fit"
# path="./spec0/spec0pc_fit.fit"
temp_count_r=linecache.getline(path,9).strip().replace('File count rate: ','')
count_r.append(temp_count_r)
temp_corr_fact=linecache.getline(path,10).strip().replace('Corr factor:\t','')
corr_fact.append(temp_corr_fact)
count_r=np.array(count_r,dtype=np.float32)
corr_fact=np.array(corr_fact,dtype=np.float32)
return [count_r,corr_fact]
def Read_lc_qdp(qdp_file_name):
with open(qdp_file_name,'r') as reader:
lines=reader.readlines()
modes=[]
data=[]
b=iter(lines[9:])
data=[]
block=[]
for line in b:
if line[0].isalpha():
data.append(block)
block=[]
line=next(b)
modes.append(line.strip().split(' ')[1])
line=next(b)
line=next(b)
block.append(line.strip().split('\t'))
# print(line)
data.append(block)
data.pop(0)
return [modes,data]
def Log_reader(base_name,no_of_specs):
"""Takes in base name and number of spectra as argument. Returns Photon index and normalisation
The data is of the format [lower lim, upper lim, -ve error, +ve error] """
ph_idx=[]
norm=[]
for i in range(no_of_specs):
log_name=base_name+str(i)+".log"
with open(log_name,'r') as reader:
log=reader.readlines()
line=log[-11]
temp=line.strip().split(' ')
temp=[i for i in temp if i !='']
temp1=temp[-1][1:-1].split(',')
ph_idx.append(temp[2:-1]+temp1)
line=log[-7]
temp=line.strip().split(' ')
temp=[i for i in temp if i !='']
temp1=temp[-1][1:-1].split(',')
norm.append(temp[2:-1]+temp1)
return [np.array(ph_idx,dtype=np.float64),np.array(norm,dtype=np.float64)]
def create_blocks(data,edges):
"""Breaks (sorted)data into blocks according to edges and returns indices for np.split"""
idx=[]
for e in edges[1:-1]:
temp=np.where(data<e)
idx.append(temp[0][-1]+1)
return idx
# def calc_CF()
lc_filename="curve_mod.qdp"
modes,data=Read_lc_qdp(lc_filename)
PC_data=np.array(data[-1],dtype=np.float32)
time_file_name="tbin_bb.txt"
GRB="080207"
text_file="ph_idx_"+"GRB "+ GRB+".txt"
t_bin=Read_t_file(time_file_name)
no_of_specs=16
base_name="spec"
ph,nm=Log_reader(base_name,no_of_specs) #Data directly from log
ph_idx=ph[:,0] - ph[:,2]
norm=nm[:,0] - nm[:,2]
# data=np.genfromtxt(text_file,skip_header=1) #Data from plotter
# t_ph=data[:,:3]
# ph_data=data[:,3:]
[file_count_r,corr_fact]=read_corr(no_of_specs,base_name)
t_data=PC_data[:,:3]
time=t_data[:,0]
cnt_data=PC_data[:,3:]
block_idx=create_blocks(time,t_bin)#Gets indices for splitting into blocks
cnt_block=np.split(cnt_data,block_idx)#Splits the initial data into blocks
cf=(norm*662.5)/(file_count_r*corr_fact)
ff=[]
for i in range(no_of_specs):
conversion=cf[i]*np.power(10,-(ph_idx[i]-1))
temp=conversion*cnt_block[i]
ff.append(temp)
flux=np.vstack(ff)
flux=flux*1e-6#Converting from uJansky to Jansky
plt.errorbar(time,flux[:,0],fmt='.k',xerr=np.abs(t_data[:,1:].T),yerr=np.abs(flux[:,1:]).T,ecolor='lightgray', elinewidth=3, capsize=0,label="data")
# plt.scatter(time,flux[:,0])
plt.xlabel("Times(s)")
plt.ylabel("Flux(J)")
plt.title("GRB "+GRB)
# plt.scatter(time,cnt_data[:,0])
plt.xscale("log")
plt.yscale("log")
plt.savefig(GRB+"_flux.png",dpi=800
final_data=np.hstack((t_data,flux))
np.savetxt("flux_"+"GRB "+ GRB+".txt",final_data,header="Time(s) Time_+error Time_-ve_error Flux(J) Flux_+ve_error Flux__ve_error")