def GetData():
MagRange = (8.25, 13, 0.25)
mags = np.arange(*MagRange)
brighttimes, darktimes = [], []
pb = progressbarClass(2. * mags.size)
counter = 1
for mag in mags:
result = get_bright_contrib(mag)
brighttimes.append(result)
pb.progress(counter)
counter += 1
for mag in mags:
result = get_dark_contrib(mag)
darktimes.append(result)
pb.progress(counter)
counter += 1
return mags, np.log10(brighttimes), np.log10(darktimes)
def run(self):
# The total integral of a 2d Gaussian to infinity in each direction
total = self.Integrate((-np.Inf, np.Inf, -np.Inf, np.Inf), (0., 0.))[0]
fractions = []
pb = progressbarClass(self.N)
counter = 0
# Main loop
while counter < self.N:
# Pick two random coordinates
x = (self.xRange[1] - self.xRange[0]) * np.random.ranf() + self.xRange[0]
y = (self.yRange[1] - self.yRange[0]) * np.random.ranf() + self.yRange[0]
# Integrate the new offset Gaussian in the centre pixel and take the fraction
Fraction = self.Integrate((-0.5, 0.5, -0.5, 0.5), (x, y))[0] / total
# Add the result to the list
fractions.append(Fraction)
pb.progress(counter+1)
counter += 1
med_val = np.median(fractions)
med_val_err = np.std(fractions)
# Log the fractions
# Makes the plot easier to read
fractions = np.log10(fractions)
# Create the histogram
vals, edges = np.histogram(fractions, bins=50, range=(-1, 0))
# Counting errors
errs = np.sqrt(vals)
# Width of a bin
binWidth = np.diff(edges)[0]
# Normalise the histogram
# I realise that the numpy.histogram function inclues a density parameter which
# achieves the same thing but I need the errors from the raw values
Integral = float(np.sum(vals))
# Divide the values by the integral of the system
normalisedVals = vals / Integral
normalisedErrs = errs / Integral
# Get the bin centres
centres = edges[:-1] + binWidth / 2.
# Print a line at the most probable value
MostProbable = 10 ** centres[normalisedVals==normalisedVals.max()][0]
fig = plt.figure()
ax = fig.add_subplot(111)
# Plot the histogram
ax.plot(10 ** centres, normalisedVals, 'k-', drawstyle='steps-mid')
ax.set_xlabel(r'Fraction')
ax.set_ylabel(r'Probability')
ax.set_xscale('log')
ticks = [0.1, 0.2, 0.5, 1]
ax.set_xticks(ticks)
ax.set_xticklabels(ticks)
ax.axvline(MostProbable, color='k', ls=':', zorder=-10)
ax.axvline(med_val, color='g')
ax.axvline(med_val - med_val_err / 2., color='b')
ax.axvline(med_val + med_val_err / 2., color='b')
with tables.openFile('out.h5', 'w') as outfile:
outfile.createArray('/', 'x', 10 ** centres)
outfile.createArray('/', 'y', normalisedVals)
outfile.root._v_attrs.most_probable = med_val
outfile.root._v_attrs.sd = med_val_err
plt.show()