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fold_beta.py
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fold_beta.py
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# coding: utf-8
"""Very quick hack of a time series folding script
.. moduleauthor:: Benjamin Mort <[email protected]>
"""
import logging
import math
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
# Require python3
if sys.version_info[0] != 3 or sys.version_info[1] < 5:
print('This script requires Python version >= 3.5')
sys.exit(1)
def cos_fit(x, *p):
"""Fitting function"""
A, B, phi = p
return A + B * np.cos(2 * math.pi * x + phi * math.pi)
def main():
"""Main function."""
# Create logger.
log = logging.getLogger()
log.addHandler(logging.StreamHandler(sys.stdout))
log.setLevel('DEBUG')
data = np.loadtxt(os.path.join('data', 'angleAndBeta_Bluejet_Redjet.txt'))
data = data[data[:, 0].argsort()]
date = data[:, 0]
beta = data[:, 1]
# fold_period = 13.08
fold_period = 11.24
num_bins = 10
time_limit = [900, 1600]
remove_large_values = False
select_range = True
remove_range = False
corrected_values = np.copy(beta)
corrected_date = np.copy(date)
if remove_large_values:
values = corrected_values - np.mean(corrected_values)
limit = 1 * np.std(values)
print('* Removing values > |%f| from mean' % np.abs(limit))
corrected_date = corrected_date[np.abs(values) < limit]
corrected_values = corrected_values[np.abs(values) < limit]
if select_range:
print('* Removing values outside range %f - %f' %
(time_limit[0], time_limit[1]))
times = corrected_date - corrected_date[0]
idx_min = np.argmax(times >= time_limit[0])
idx_max = np.argmax(times >= time_limit[1])
if idx_max == 0:
idx_max = times.size
corrected_date = corrected_date[idx_min:idx_max]
corrected_values = corrected_values[idx_min:idx_max]
if remove_range:
delete_range = [900, 1300]
print('* Removing values inside range %f - %f' %
(delete_range[0], delete_range[1]))
times = corrected_date - corrected_date[0]
idx_min = np.argmax(times >= delete_range[0])
idx_max = np.argmax(times >= delete_range[1])
if idx_max == 0:
idx_max = times.size()
corrected_date = np.delete(corrected_date, range(idx_min, idx_max + 1))
corrected_values = np.delete(corrected_values,
range(idx_min, idx_max + 1))
phase = corrected_date / fold_period # Convert to phase
phase = phase % 1 # Only keep fractional phase values.
# Sort by phase value
sort_idx = phase.argsort()
phase = phase[sort_idx]
phase_value = corrected_values[sort_idx]
bin_sum = np.zeros(num_bins)
bin_count = np.zeros(num_bins)
bin_width = 1 / num_bins
for i in range(phase_value.size):
n = int(phase[i] / bin_width)
bin_sum[n] += phase_value[i]
bin_count[n] += 1
print('bin_width:', bin_width)
bin_mean = bin_sum / bin_count
bin_std = np.zeros(num_bins)
for i in range(phase_value.size):
n = int(phase[i] / bin_width)
bin_std[n] += (phase_value[i] - bin_mean[n])**2
bin_std = np.sqrt(bin_std / bin_count)
bin_edges = np.arange(num_bins) * bin_width
bin_centres = bin_edges + bin_width / 2
# Fit to raw phase
p0 = [0.2, 0.4, 0]
coeff, _ = curve_fit(cos_fit, phase, phase_value, p0=p0)
x_fit = np.linspace(0, 1, 200)
y_fit = cos_fit(x_fit, *coeff)
# Fit to binned phase
p0 = [0.2, 0.4, 0]
coeff_bin, _ = curve_fit(cos_fit, bin_centres, bin_mean, p0=p0)
x_fit_bin = np.linspace(0, 1, 200)
y_fit_bin = cos_fit(x_fit, *coeff)
fig, ax = plt.subplots(nrows=3, figsize=(10, 8))
fig.subplots_adjust(left=0.08, right=0.97, bottom=0.08, top=0.95,
hspace=0.4, wspace=0.0)
if corrected_date.size < date.size:
ax[0].plot(date, beta, 'r.', ms=3, alpha=0.8, label='removed')
ax[0].plot(corrected_date, corrected_values, '.', ms=3)
ax[0].set_xlabel('Julian date')
ax[0].set_ylabel('Signal amplitude')
ax[0].set_title('Beta')
if corrected_date.size < date.size:
ax[0].legend(loc='best', fontsize='x-small')
ax[0].grid(True)
ax[1].plot(phase, phase_value, '.', ms=3)
ax[1].plot(x_fit, y_fit, 'r--',
label=r'$%.3f %+.2f cos(2 \pi x %+.3f \pi)$' %
(coeff[0], coeff[1], coeff[2]))
ax[1].legend(loc='best', fontsize='x-small')
ax[1].set_xlabel(r'Phase / $2\pi$')
ax[1].set_ylabel('Signal amplitude')
ax[1].set_title('Folded period = %.4f' % fold_period)
ax[1].grid(True)
# ax[2].plot(bin_edges, bins, '.', ms=3)
ax[2].errorbar(bin_centres, bin_mean,
yerr=bin_std, linestyle='none',
marker='.')
ax[2].plot(x_fit_bin, y_fit_bin, 'r--',
label=r'$%.3f %+.2f cos(2 \pi x %+.3f \pi)$' %
(coeff_bin[0], coeff_bin[1], coeff_bin[2]))
range_ = np.abs(np.max(bin_mean) - np.min(bin_mean))
ax[2].set_ylim(np.min(bin_mean) - range_ / 2, np.max(bin_mean) + range_ / 2)
ax[2].legend(loc='best', fontsize='x-small')
ax[2].set_xlabel(r'Phase / $2\pi$')
ax[2].set_ylabel('Signal amplitude')
ax[2].set_title('Binned folded period = %.4f' % fold_period)
ax[2].grid(True)
plt.savefig('beta_folded_%02ibins_times_%04.0f-%04.0f_%03.5f.png' %
(num_bins, time_limit[0], time_limit[1], fold_period))
plt.show()
if __name__ == '__main__':
main()