Commit 1ef6d597 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Adds labels to the noise MC plots

parent 35692a4c
import pyfstat
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
......@@ -34,12 +35,17 @@ 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')
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(
......@@ -48,11 +54,9 @@ 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.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1)
twoFsmooth = np.linspace(0, df.twoF.max(), 100)
# ax.plot(twoFsmooth, maxtwoFinNoise(twoFsmooth, 8e5), '-r')
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')
......
import pyfstat
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
......@@ -33,14 +34,17 @@ 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)
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')
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(
......@@ -51,6 +55,7 @@ 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)
fig.tight_layout()
fig.savefig('directed_noise_twoF_histogram.png')
......
Paper/allsky_noise_twoF_histogram.png

37.9 KB | W: | H:

Paper/allsky_noise_twoF_histogram.png

46.5 KB | W: | H:

Paper/allsky_noise_twoF_histogram.png
Paper/allsky_noise_twoF_histogram.png
Paper/allsky_noise_twoF_histogram.png
Paper/allsky_noise_twoF_histogram.png
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Paper/directed_noise_twoF_histogram.png

37.5 KB | W: | H:

Paper/directed_noise_twoF_histogram.png

45.8 KB | W: | H:

Paper/directed_noise_twoF_histogram.png
Paper/directed_noise_twoF_histogram.png
Paper/directed_noise_twoF_histogram.png
Paper/directed_noise_twoF_histogram.png
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