def fill(self, cpf):
"""
Fill the histogram using a list of numbers and compare
with the Poisson.
@param [in] A list of the clusters per frame.
"""
lg.info(" *")
lg.info(" * Starting the fill routine for RateHistogram.")
lg.info(" *")
## A list of the clusters per frame.
self.__cpf = cpf
## The number of frames.
self.__n_f = len(self.__cpf)
## The total number of clusters.
self.__n_k = sum(self.__cpf)
## The estimated overall rate.
self.__Lambda_est = float(self.__n_k) / float(self.__n_f)
## The standard error on the estimated overall rate.
self.__Lambda_est_err = np.sqrt(float(self.__n_k)) / float(self.__n_f)
lg.info(" * Number of frames supplied (N) = %d" % (self.__n_f))
lg.info(" * The total number of clusters (M) = %d" % (self.__n_k))
lg.info(" *")
lg.info(" * => \Lambda = (%f +- %f) [particles per unit time]" % (self.__Lambda_est, self.__Lambda_est_err))
lg.info(" *")
# Work out the bin details.
self.max_x = max(self.__cpf)
# Round this up to the nearest 10.
self.max_x_r = np.floor(float(self.max_x)/10.) * 10. + 10.
# Create the bin edges array for the data:
# * Bin width one, rounded-up max x value.
self.bins = np.arange(0, self.max_x_r + self.bin_w, self.bin_w)
# Plot the histogram.
## A list of the histogram bin contents (n).
self.__n = None
## A list of the histogram bins.
self.__bins = None
#self.n, self.bins, self.patches = plt.hist(self.cpf, bins=self.bins, histtype='stepfilled')
self.__n, self.__bins = np.histogram(self.__cpf, bins=self.bins)
lg.info(" * The histogram bin contents:")
lg.info(self.__n)
lg.info(" *--> Total number of frames (check): %d" % (sum(self.__n)))
lg.info(" *")
lg.info(" * The histogram bin values:")
lg.info(self.__bins)
lg.info(" *")
# Set the colour of the histogram patches (make configurable?).
#plt.setp(self.patches, 'facecolor', '#44BB11', 'alpha', 0.75, 'linewidth', 0.0)
#print("* DEBUG: Number of hit pixels in each frame:", self.__cpf)
#print("* DEBUG: Max number of clusters per frame : %6d" % (self.max_x))
#print("* DEBUG: x max : %6d" % (self.max_x_r))
#print("* DEBUG: x bin width : %6d" % (self.bin_w))
#print("* DEBUG: bins:", self.bins)
#print("* DEBUG:")
# Fit to Poisson?
if self.__n[0] != self.__n_f: # If not completely empty...
## The Poisson function to fit to!
fitfunc = lambda p, x: p[0]*pow(p[1],x)*pow(np.e,-p[1])/factorial(x)
## A list of the initial guess for the parameters.
p0 = [float(self.__n_f), float(self.__Lambda_est)]
## The error function to perform the fitting.
errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function
## The fitted parameters.
p1 = None
## Did the fit succeed?
success = None
# Use the least square method from optimize to perform a check.
# We won't actually use this...
p1, success = optimize.leastsq(errfunc, p0[:], args=(self.__bins[:-1], self.__n))
if success == 1:
lg.info(" * FIT SUCCEEDED!")
lg.info(" * Fitted values [Scale, \\lambda]:")
lg.info(p1)
lg.info(" *")
# Use the estimated parameters from the Bayesian method.
p_est = [float(self.__n_f), float(self.__Lambda_est)]
# Plot the Poisson distribution from the estimated values.
plt.bar(self.__bins, fitfunc(p_est, self.__bins), color='#CCCCCC')
# Plot the Poisson distribution with the fitted values.
#plt.bar(self.__bins, fitfunc(p1, self.__bins), color='#CCCCCC')
# The expected y values from the estimated parameters.
y_est = fitfunc(p_est, self.__bins)
# The expected y values from the fitted parameters.
y_fit = fitfunc(p1, self.__bins)
# The distances from the predicted values - Bayesian estimates.
ds_est = errfunc(p_est, self.__bins[:-1], self.__n)
# The distances from the predicted values - fitted estimates.
ds_fit = errfunc(p1, self.__bins[:-1], self.__n)
for i, binval in enumerate(self.__bins[:-1]):
lg.info(" * | % 3d | % 3d | % 3d | % 6.3f | % 10.8f | % 6.3f | % 10.8f |" %
(i, binval, self.__n[i], y_est[i], ds_est[i], y_fit[i], ds_fit[i]))
# Firstly, we'll need an array of the bin centres to put the points
# at (rather than the bin edges). Note the slicing of the bin edges
# array to get the N bins (as otherwise we'd have N+1 bin edges).
self.bins_centres = 0.5*(self.__bins[1:]+self.__bins[:-1])
#lg.info(" * The bin centres:")
#lg.info(self.bins_centres)
#lg.info(" *")
# Some bins will be empty. To avoid plotting these, we can replace
# the contents of that bin with "nan" (not a number).
# matplotlib then cleverly skips over these, leaving the x axis
# clean and shiny.
self.__n = [np.nan if x==0 else float(x) for x in self.__n]
# Calculate the errors on the number of clusters counted (Poisson).
self.__err = np.sqrt(self.__n)
# Calculate the Chi^2 values for the estimated distribution.
chi2_est, n_deg_est, chi2_div_est = chi2(self.__n, y_est, 1)
lg.info(" * Estimated distribution (\hat{\Lambda}):")
lg.info(" *--> \Chi^2 = % 7.5f" % (chi2_est))
lg.info(" *--> N_freedom = % d" % (n_deg_est))
lg.info(" *")
# Calculate the Chi^2 values for the fitted distribution.
chi2_fit, n_deg_fit, chi2_div_fit = chi2(self.__n, y_fit, 1)
lg.info(" * Fitted distribution:")
lg.info(" *--> \Chi^2 = % 7.5f" % (chi2_fit))
lg.info(" *--> N_freedom = % d" % (n_deg_fit))
lg.info(" *")
# Plot the real data points as an "errorbar" plot
plt.errorbar(self.bins_centres, \
self.__n, \
fmt='d', \
color='black', \
yerr=self.__err, \
ecolor='black', \
# capthick=2, \
elinewidth=1)
# Save the figure.
# Custom plot limits if required.
#plt.ylim([0, 16])
#plt.xlim([0, 20])
self.plot.savefig(self.__output_path + "/%s.png" % (self.__name))
self.plot.savefig(self.__output_path + "/%s.ps" % (self.__name))
return self.__Lambda_est, self.__Lambda_est_err, chi2_est, n_deg_est
def __init__(self, data_points):
## A list of DataPoints.
self.__dps = data_points
# Calculate all of the properties of the data points.
# Get the estimated attenuation coefficient and B_0 by fitting
# ln(B_i) vs. d_i to a straight line.
## The estimate of the attenuation coefficient.
self.__mu_est = None
## The estimated error on the attenuation coefficient (MLL).
self.__mu_est_err_mll = None
## The estimate of the mean free path.
self.__mfp_est = None
## The estimated error on the mean free path (MLL).
self.__mfp_est_err_mll = None
## The estimated initial attempts (fit).
self.__B_0_est = None
# First, let's use the curve_fit function to fit the points
# to a straight line.
## An array of the x values (thicknesses).
self.__xs = np.array(self.get_thicknesses())
## An array of the y values (thicknesses).
self.__ys = np.array(self.get_log_of_successes())
## A list of the estimated parameters, m and c.
self.__parameter_estimates = None
## The covariance matrix of the parameter estimates.
self.__covariance_matrix = None
# Perform the fitting.
self.__parameter_estimates, self.__covariance_matrix = curve_fit(straight_line, self.__xs, self.__ys)
# Assign the estimate values and errors.
self.__mu_est = -1.0 * self.__parameter_estimates[0]
#
self.__mfp_est = 1.0 / self.__mu_est
#
self.__B_0_est = np.exp(self.__parameter_estimates[1])
# Now use the Maximum Log Likelihood method to estimate the error.
# Loop over the data points
sum_of_terms = 0.0
lg.info(" *")
lg.info(" * Looping over the data points:")
lg.info(" *")
for dp in sorted(self.__dps):
lg.info(" * | d_i = % 5.2f [mm] | B_i = % 8d |" % (dp.get_thickness(), dp.get_count()))
B_i_times_B_0 = dp.get_count() * self.__B_0_est
B_0_minus_B_i = self.__B_0_est - dp.get_count()
count_frac = float(B_i_times_B_0) / float(B_0_minus_B_i)
d_i_squared = dp.get_thickness() * dp.get_thickness()
d_i_squared_times_count_frac = d_i_squared * count_frac
sum_of_terms += d_i_squared_times_count_frac
lg.info(" * |-----------------------------------|")
lg.info(" * | d_i^{2} = %f" % (d_i_squared))
lg.info(" * | |")
lg.info(" * | | B_i * B_0 = %d" % (B_i_times_B_0))
lg.info(" * | | B_0 - B_i = %d" % (B_0_minus_B_i))
lg.info(" * | |-->Y/Z = %f" % (count_frac))
lg.info(" * | |")
lg.info(" * | *-->X*Y/Z = %f" % (d_i_squared_times_count_frac))
lg.info(" * |")
lg.info(" *")
lg.info(" * Sum of terms = %f [mm^{2}] " % (sum_of_terms))
lg.info(" *")
#
self.__mu_est_err_mll = 1.0/(np.sqrt(sum_of_terms))
#
self.__mu_est_err_mll_pc = 100.0 * (self.__mu_est_err_mll/self.__mu_est)
#
self.__mfp_est_err_mll = (1.0/(self.__mu_est * self.__mu_est)) * self.__mu_est_err_mll
#
self.__mfp_est_err_mll_pc = 100.0 * (self.__mfp_est_err_mll / self.__mfp_est)
#
lg.info(" * 1/sqrt(sum) = %f [mm^{-1}]" % (self.__mu_est_err_mll))
lg.info(" *")
lg.info(" *")
lg.info(" * from curve_fit:")
lg.info(" *")
lg.info(" *--> \mu (MLL) = (% 10.5f \pm % 10.5f) [mm^{-1}] % 6.2f %%" % \
(self.__mu_est, self.__mu_est_err_mll, self.__mu_est_err_mll_pc))
lg.info(" *")
lg.info(" *--> <x> = 1 / \mu = (% 10.5f \pm % 10.5f) [mm] % 6.2f %%" % \
(self.__mfp_est, self.__mfp_est_err_mll, self.__mfp_est_err_mll_pc))
lg.info(" *")
lg.info(" *--> B_0 = % 10.2f particles" % (self.__B_0_est))
lg.info(" *")
# Calculate the Chi^2 values for the estimated distribution.
#chi2_est, n_deg_est, chi2_div_est = chi2(self.get_successes(), self.get_predicted_successes(), 2)
self.__chi_squared_value, self.__chi_squared_dof, chi2_div_est = \
chi2(self.get_log_of_successes(), self.get_predicted_log_of_successes(), 2)
lg.info(" * Estimated distribution (\hat{\mu}, \hat{B_{0}}):")
lg.info(" *--> \Chi^2 = % 7.5f" % (self.__chi_squared_value))
lg.info(" *--> N_freedom = % d" % (self.__chi_squared_dof))
lg.info(" *")
