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test.single.2
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Heinz-Bernd Eggenstein authoredexperimental: -added alternative method for twiddle factor calc, using a smaller LUT (256 * float2 ) via Taylor series to 3rd order, seems to be almost as accurate as method with 2 bigger LUTs, but faster. -improved method w/ 2 bigger LUTs to use LUTs of float2 -improved method using slow sin/cos functions (now uses sincos combined function), still slow - preparaed plan struct to have method switchable at plan creation time. TODO: load smaller LUT for Taylor series approx into shared mem.Heinz-Bernd Eggenstein authoredexperimental: -added alternative method for twiddle factor calc, using a smaller LUT (256 * float2 ) via Taylor series to 3rd order, seems to be almost as accurate as method with 2 bigger LUTs, but faster. -improved method w/ 2 bigger LUTs to use LUTs of float2 -improved method using slow sin/cos functions (now uses sincos combined function), still slow - preparaed plan struct to have method switchable at plan creation time. TODO: load smaller LUT for Taylor series approx into shared mem.
plot_data.py 1.18 KiB
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
filenames = glob.glob("CollectedOutput/*.txt")
plt.style.use('paper')
Tspan = 100 * 86400
def maxtwoFinNoise(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)
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)
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')