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generate_table.py
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Gregory Ashton authoredGregory Ashton authored
plot_data.py 2.03 KiB
import pyfstat
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from oct2py import octave
import glob
from scipy.stats import rv_continuous, chi2
filenames = glob.glob("CollectedOutput/*.txt")
plt.style.use('paper')
Tspan = 100 * 86400
class maxtwoFinNoise_gen(rv_continuous):
def _pdf(self, twoF, Ntrials):
F = twoF/2.0
alpha = (1 + F)*np.exp(-F)
a = Ntrials/2.0*F*np.exp(-F)
b = (1 - alpha)**(Ntrials-1)
return a*b
df_list = []
for fn in filenames:
df = pd.read_csv(
fn, sep=' ', names=['dF0', 'dF1', 'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df)
df = pd.concat(df_list)
print 'Number of samples = ', len(df)
fig, ax = plt.subplots()
ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1,
label='Monte-Carlo histogram')
maxtwoFinNoise = maxtwoFinNoise_gen(a=0)
Ntrials_effective, loc, scale = maxtwoFinNoise.fit(df.twoF.values, floc=0, fscale=1)
print 'Ntrials effective = {:1.2e}'.format(Ntrials_effective)
twoFsmooth = np.linspace(0, df.twoF.max(), 1000)
best_fit_pdf = maxtwoFinNoise.pdf(twoFsmooth, Ntrials_effective)
ax.plot(twoFsmooth, best_fit_pdf, '-r',
label=r'$p(2\mathcal{{F}}_{{\rm max}})$ for {} $N_{{\rm trials}}$'
.format(pyfstat.texify_float(Ntrials_effective, d=2)))
pval = 1e-6
twoFsmooth_HD = np.linspace(
twoFsmooth[np.argmax(best_fit_pdf)], df.twoF.max(), 100000)
best_fit_pdf_HD = maxtwoFinNoise.pdf(twoFsmooth_HD, Ntrials_effective)
spacing = twoFsmooth_HD[1]-twoFsmooth_HD[0]
print twoFsmooth_HD[np.argmin(np.abs(best_fit_pdf_HD - pval))], spacing
ax.set_xlabel('$\widetilde{2\mathcal{F}}$')
ax.set_xlim(0, 60)
ax.legend(frameon=False, fontsize=6, loc=2)
fig.tight_layout()
fig.savefig('allsky_noise_twoF_histogram.png')
from latex_macro_generator import write_to_macro
write_to_macro('AllSkyMCNoiseOnlyMaximum', '{:1.1f}'.format(np.max(df.twoF)),
'../macros.tex')
write_to_macro('AllSkyMCNoiseN', len(df), '../macros.tex')