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Adjust the benchmark for acollinearity sampling #111

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Adjust the benchmark for acollinearity sampling #111

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49 changes: 35 additions & 14 deletions t19_acollinearity/runAnalysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,20 +58,29 @@ def analyse_one_folder(output_folder):
ax1 = f.add_subplot(221)
f.tight_layout(pad = 5.0)
ax1.set_title('BackToBack acollinearity')
a, b, c = plot_fit_histogram(180-BTB_aco_angle, ax1, with_text = True)
# a, b, c = plot_fit_histogram(180-BTB_aco_angle, ax1, with_text = True)
sigma = plot_fit_histogram(180-BTB_aco_angle, ax1, with_text = True)
fwhm = 2 * np.sqrt(2 * np.log(2)) * sigma
plt.savefig("acoDistComparison.png")
plt.close()
print("folder: " + output_folder)
print(' amplitude: ' + str(round(a, 2)) + ' < 4.00 ')
print(' abs(mean): ' + str(abs(round(b, 2))) + ' < 0.02 ')
print(' sigma: ' + str(round(c, 2)) + ' < 0.25 ')
# print(' amplitude: ' + str(round(a, 2)) + ' < 4.00 ')
# print(' abs(mean): ' + str(abs(round(b, 2))) + ' < 0.02 ')
# print(' sigma: ' + str(round(c, 2)) + ' < 0.25 ')
print(' FWHM: ' + str(round(fwhm, 2)) + ' ~= 0.5 ')

returnBool = True
if (abs(a) >= 4.00):
returnBool = False
if (abs(b) >= 0.02):
returnBool = False
if (abs(c) >= 0.25):

# if (abs(a) >= 4.00):
# returnBool = False
# if (abs(b) >= 0.02):
# returnBool = False
# if (abs(c) >= 0.25):
# returnBool = False

if (abs((fwhm - 0.5) / 0.5) >= 0.1): # allow 10% relative absolute error
returnBool = False

print(' ' + str(returnBool))
return(returnBool)

Expand All @@ -87,22 +96,34 @@ def process_binary(filename):
def gaus(x,a,m,sigma):
return a*np.exp(-(x-m)**2/(2*sigma**2))

# rayleigh distribution
def rayleigh(x, sigma):
return x / sigma ** 2 * np.exp(-x ** 2 / (2 * sigma ** 2))

def plot_fit_histogram(data,ax, with_text=True):
counts, _, axes = ax.hist(data,density=True, bins=100)
x = np.linspace(min(data), max(data), 100)
p0 = [3, 0, 0.5]
popt,pcov = curve_fit(gaus,x,counts,p0=p0)
ax.plot(x, gaus(x,popt[0],popt[1],popt[2]))
# p0 = [3, 0, 0.5]
# popt,pcov = curve_fit(gaus,x,counts,p0=p0)
# ax.plot(x, gaus(x,popt[0],popt[1],popt[2]))
# ax.set_xlabel('acollinearity [°]')
# ax.set_ylabel('frequency [AU]')
# textstr = '\n'.join((r'$a=%.2f$' % (popt[0], ), r'$\mu=%.2f$' % (popt[1], ), r'$\sigma=%.2f$' % (popt[2], )))

# Fit a Rayleigh distribution instead
popt, pcov = curve_fit(rayleigh,x,counts, p0=1, bounds=(0, np.inf))
ax.plot(x, rayleigh(x,*popt))
ax.set_xlabel('acollinearity [°]')
ax.set_ylabel('frequency [AU]')
textstr = '\n'.join((r'$a=%.2f$' % (popt[0], ), r'$\mu=%.2f$' % (popt[1], ), r'$\sigma=%.2f$' % (popt[2], )))
textstr = r'$\sigma=%.2f$' % (popt[0], )

if with_text:
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.7, 0.95, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='top', bbox=props)

return popt[0], popt[1], popt[2]
# return popt[0], popt[1], popt[2]
return popt[0]

# -----------------------------------------------------------------------------
if __name__ == '__main__':
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