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ProcessBursts.py
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ProcessBursts.py
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#!/usr/bin/python3
from __future__ import division
import math
import os
import sys
import time
import numpy as np
import scipy.stats
from scipy.optimize import curve_fit
from math import log10
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from scipy import pi as nombrepi
from scipy import signal, ndimage
from tqdm import tqdm
from matplotlib import colors as mcolors
import functools
print = functools.partial(print, flush=True) # print doesn't happen til script ends so force it to flush... windows thing?
import pandas as pd
import corrfns
import itertools
import csv
bursts = pd.read_csv('bursts.csv')
# Gaussian 2d Fit Stuff
# Source: https://gist.github.com/andrewgiessel/6122739
# Source: https://stackoverflow.com/questions/21566379/fitting-a-2d-gaussian-function-using-scipy-optimize-curve-fit-valueerror-and-m
def gaussian(height, center_x, center_y, width_x, width_y, rotation):
"""Returns a gaussian function with the given parameters"""
width_x = float(width_x)
width_y = float(width_y)
rotation = np.deg2rad(rotation)
center_x_old = center_x
center_x = center_x * np.cos(rotation) - center_y * np.sin(rotation)
center_y = center_x_old * np.sin(rotation) + center_y * np.cos(rotation)
def rotgauss(x,y):
xp = x * np.cos(rotation) - y * np.sin(rotation)
yp = x * np.sin(rotation) + y * np.cos(rotation)
g = height*np.exp( -(((center_x-xp)/width_x)**2 +((center_y-yp)/width_y)**2)/2. )
return g
return rotgauss
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments """
total = data.sum()
X, Y = np.indices(data.shape)
x = (X*data).sum()/total
y = (Y*data).sum()/total
col = data[:, int(y)]
width_x = np.sqrt(abs((np.arange(col.size)-y)**2*col).sum()/col.sum())
row = data[int(x), :]
width_y = np.sqrt(abs((np.arange(row.size)-x)**2*row).sum()/row.sum())
height = data.max()
return height, x, y, width_x, width_y, 2.0
def twoD_Gaussian(point, amplitude, xo, yo, sigma_x, sigma_y, theta):
x, y = point
xo = float(xo)
yo = float(yo)
a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2)
b = (np.sin(2*theta))/(2*sigma_x**2) - (np.sin(2*theta))/(2*sigma_y**2)
c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_y**2)
g = amplitude*np.exp( - a*((x-xo)**2) - b*(x-xo)*(y-yo) - c*((y-yo)**2))
return g.ravel()
def fitgaussian(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution found by a fit"""
params = moments(data)
errorfunction = lambda p: np.ravel(gaussian(*p)(*np.indices(data.shape)) - data)
p, success = scipy.optimize.leastsq(errorfunction, params)
return p, success
def fitgaussiannlsq(data, sigma=0):
# use curve-fit (non-linear leastsq)
x = range(0, 1023); y = range(0, 1023)
x, y = np.meshgrid(x, y)
params = moments(data)#+ (0.,)
sigma = np.zeros(len(data.ravel())) + sigma
popt, pcov = scipy.optimize.curve_fit(twoD_Gaussian, (x, y), data.ravel(), p0=params, sigma=sigma, absolute_sigma=True)
return popt, pcov
def dedisperse(intensity, DM, nu_high, chan_width, timestep):
"""
DM: pc/cm^3
nu_high: MHz
chan_width: MHz
timestep: ms
"""
dedispersed = np.copy(intensity)
shifts = [0 for i in range(0, len(intensity))]
for i, row in enumerate(dedispersed):
nu_low = nu_high - i*chan_width
deltat = 4.14937759336e6 * (nu_low**-2 - nu_high**-2) * DM
channelshift = -int(round(deltat/timestep))
dedispersed[i] = np.roll(dedispersed[i], channelshift)
return dedispersed
folder = 'data'
outfolder = 'figures'
parameterfile = 'frb121102params.csv'
sigmax_error = []
sigmay_error = []
angle_error = []
drift_errors = []
drifts = []
autocorrs = []
auto_sigmas = []
red_chisqs = []
if os.path.exists(parameterfile) and False:
popts = pd.read_csv(parameterfile, index_col=0)[['amplitude', 'xo', 'yo', 'sigmax', 'sigmay', 'theta']]
perrs = pd.read_csv(parameterfile, index_col=0)[['amp_error', 'xo_error', 'yo_error', 'sigmax_error', 'sigmay_error', 'angle_error']]
else:
popts, perrs = [], []
corredges = [(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 250),
(10, 300),
(10, 250),
(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 300),
(10, 300)]
# Stuff for figure 4
burst2data = {}
burst3data = {}
### Stuff for DM/drift covariance
centerDM = 559.7 # pc/cm^3
leftDM = centerDM*0.99, centerDM*1.01 # "any bona fide dispersion measure variations are at the <= 1% level" - Michilli et al. 2018
trialDMs = np.linspace(centerDM*0.99, centerDM*1.01, num=10)
DMcovariancedata = []
for trialDM in tqdm([centerDM]):
ploti = itertools.count(start=1, step=1)
plt.figure(figsize=(24, 36))
for burst, filename, edge, burstname in tqdm( zip(range(1, len(bursts['filename'])+1), bursts['filename'], bursts['edge'], bursts['name']), total=len(bursts['filename']) ):
if 'gajjar' in filename:
continue
if burst in [9, 10]:
continue
if burst > 16:
continue
#print('processing {}'.format(filename))
#junk, nchan, nbin, I, Q, U, V = np.loadtxt('{}/{}'.format(folder, filename), delimiter=' ', unpack=True)
#Q, U, V = None, None, None
junk, nchan, nbin, I = [], [], [], []
with open('{}/{}'.format(folder, filename)) as infile:
for line in infile:
junki, nchani, nbini, Ii, _, _, _ = line.split(' ')
junk.append(junki)
nchan.append(nchani)
nbin.append(nbini)
I.append(Ii)
junk = np.array(junk, dtype=float)
nchan = np.array(nchan, dtype=float)
nbin = np.array(nbin, dtype=float)
I = np.array(I, dtype=float)
n = len(junk)
edge = int(edge)
# print("Data loaded")
binmax = int(nbin[n-1])+1
frequencymax = (int(nchan[n-1])+1)
intensity = np.zeros((frequencymax, binmax))
X = np.zeros(binmax)
Y = np.zeros(frequencymax)
# what are these?
tmin = 500
tmax = 1500
#### 1. remove noise
intensitynoise1 = np.zeros(tmin-1)
intensitynoise2 = np.zeros(binmax-tmax)
for i in tqdm(range(frequencymax-50,51,-1), desc='noise removal', disable=True):
Y[i-1] = 4.15 + (i-1) * 1.5625 # ?
for j in range(1,tmin) :
intensitynoise1[j-1] = (I[j-1 + binmax*(frequencymax-i)])/(tmin-1)
for j in range(tmax+1,binmax+1) :
intensitynoise2[j-1-tmax] = (I[j-1 + binmax*(frequencymax-i)])/(binmax-tmax)
a = sum(intensitynoise1)
b = sum(intensitynoise2)
for j in range(1,binmax+1) :
X[j-1] = j-1
intensity[i-1,j-1] = I[j-1 + binmax*(frequencymax-i)] - (a+b)/2
#### 2. find autocorrelation
burstwindow = intensity[:,edge:edge+frequencymax]
ddm = trialDM - centerDM
burstwindow = dedisperse(burstwindow, ddm, (4100.78125 + 1.5625*burstwindow.shape[0]), 1.5625, 0.01024)
# print("finding auto-correlation...")
#corr = signal.correlate2d(burstwindow, burstwindow, mode='full')
#print(corr.shape)
corr = corrfns.auto_corr2D_viafft(burstwindow)
#### 2.5 Autocorr noise
autocorr_sigma = np.std( corr[:, corredges[burst-1][0]:corredges[burst-1][1]] )
# print('autocorr_sigma', autocorr_sigma)
auto_sigmas.append(autocorr_sigma)
#### 3. Fit Gaussian to autocorrelation. Load from parameter file if already found.
if os.path.exists(parameterfile) and ddm == 0:
#print('loading fit...')
# popt is [amplitude, xo, yo, sigma_x, sigma_y, theta]
popt, perr = popts.iloc[burst-1], perrs.iloc[burst-1]
else:
# print("finding fit...")
try:
popt, pcov = fitgaussiannlsq(corr, sigma=autocorr_sigma)
perr = np.sqrt(np.diag(pcov))
except (RuntimeError, ValueError):
print('no fit found')
popt, perr = [-1,-1,-1,-1,-1,-1], [-1,-1,-1,-1,-1,-1]
popts.append(popt)
perrs.append(perr)
#### 3. Fit Gaussian to autocorrelation
x = range(0, 1023); y = range(0, 1023)
x, y = np.meshgrid(x, y)
fitmap2 = twoD_Gaussian((x, y), *popt).reshape(1023, 1023)
# print('solution nlsq:', popt)
# print('parameter 1sigma:', perr)
# print('pcov diag:', np.diag(pcov))
# with np.printoptions(precision=3, suppress=True):
# print('pcov:')
# print(pcov)
# calculate reduced chisquared
residuals = corr - fitmap2
chisq = np.sum((residuals / autocorr_sigma) ** 2)
red_chisq = chisq / (corr.shape[0]*corr.shape[1] - len(popt)) # this is chisq/(M-N)
red_chisqs.append(red_chisq)
# print('reduced chisq:', red_chisq)
### 3.5 Compute drift and error
theta = popt[-1] if popt[3] > popt[4] else popt[-1] - np.pi/2
# print('solver angle:', popt[-1], 'pos angle:', theta)
slope = np.tan(theta)
drift = 1.5625/0.01024 * slope # MHz/ms
drift_error = (1.5625/0.01024) * (perr[-1] * (1/np.cos(theta))**2)
sigmax_error.append(perr[3])
sigmay_error.append(perr[4])
angle_error.append(perr[5])
drift_errors.append(drift_error)
drifts.append(drift)
#print('drift:', drift, "pm", drift_error)
## DM covariance data: each row is [burst, trialdm, drift, drift_error, popt(6), perr(6)]
DMcovariancedata.append(np.concatenate(([burst, trialDM, drift, drift_error], popt, perr)))
with open('frb121102_dmcovariance.csv', 'a') as covariance_file:
datawriter = csv.writer(covariance_file, delimiter=',')
datawriter.writerow(DMcovariancedata[-1])
#### 4. Plot
cmap = plt.get_cmap('gray')
cmap.set_bad(color = 'k', alpha = 1.) # potentially hides important errors in the data!
#plt.figure(figsize=(17,8))
#plt.subplot(121)
plt.subplot(7, 4, next(ploti))
plt.title("Burst #{}".format(burst))
burstextents = [0,
10.24e-6 * burstwindow.shape[-1] * 1e3,
4100.78125 * 1e-3,
(4100.78125 + 1.5625*burstwindow.shape[-2]) * 1e-3]
plt.imshow(burstwindow, cmap=cmap, interpolation='bicubic',aspect='auto', origin="lower", extent=burstextents)
plt.xlabel("Time (ms)", size=15)
plt.ylabel("Frequency (GHz)", size=15)
plt.colorbar()
#plt.subplot(122)
plt.subplot(7, 4, next(ploti))
#plt.title("Corr #{}. $\\nu_D$ = {:.2f} MHz/ms $\pm$ {:.2E}, peak = {:.0f}, $\sigma_x$ = {:.0f} $\sigma_y$ = {:.0f}".format(burst, drift, drift_error, np.max(corr), popt[3], popt[4]))
plt.title("Corr #{}. $\\nu_D$ = {:.2f} MHz/ms $\pm$ {:.2E}, $\sigma_x$ = {:.0f} $\sigma_y$ = {:.0f}, tdm = {:.2f}".format(burst, drift, drift_error, popt[3], popt[4], trialDM))
corrextents = [-10.24e-6 * (corr.shape[1]/2) * 1e3,
10.24e-6 * (corr.shape[1]/2) * 1e3 ,
(4100.78125 - (2*4100.78125 + 1.5625*burstwindow.shape[0])/2 )*2,
((4100.78125 + 1.5625*burstwindow.shape[-2]) - (2*4100.78125 + 1.5625*burstwindow.shape[0])/2 )*2]
#plt.imshow(corr, cmap=cmap, interpolation='bicubic',aspect='auto', origin="lower") # linear scale
#plt.clim(0, np.max(corr)/10)
plt.imshow(corr, cmap=cmap, interpolation='bicubic', aspect='auto', origin="lower", norm=mcolors.LogNorm(vmin=1), extent=corrextents)
plt.xlabel("Time Shift (ms)", size=15)
plt.ylabel("Frequency Shift (MHz)", size=15)
plt.colorbar()
if popt[0] != -1:
plt.contour(fitmap2, [popt[0]/4, popt[0]*0.9], colors='w', alpha=0.5, origin='lower', extent=corrextents)
if burst == 2:
burst2data['corr'] = corr
burst2data['burstwindow'] = burstwindow
burst2data['fitmap'] = fitmap2
burst2data['popt'] = popt
if burst == 3:
burst3data['corr'] = corr
burst3data['burstwindow'] = burstwindow
burst3data['fitmap'] = fitmap2
burst3data['popt'] = popt
# plt.tight_layout()
# print('saved {}/burst_{}_figure.png'.format(outfolder, burst))
plt.savefig('{}/burst_{}_dm_{}.png'.format(outfolder, burst, trialDM))
plt.close()
# DMcovariancedata
# datadump = pd.DataFrame(DMcovariancedata, columns=['name', 'trialDM', 'drift', 'drift_error', 'amplitude', 'xo', 'yo', 'sigmax', 'sigmay', 'theta', 'amp_error', 'xo_error', 'yo_error', 'sigmax_error', 'sigmay_error', 'angle_error'])
# datadump.to_csv('frb121102_dmcovariance.csv')