def cosmicCorrection(file, binsize=10, removalRange=10):
'''
function finds and deletes cosmic ray suspects. Assigns each arrival time to a time bin,
then returns number of counts for each time bin. Calculates average and threshold
for cosmic ray suspects. Assigns time bins with high counts to a dict (weirdones) with time as key and count
as value. Deletes time bins before and time bins after the suspected high count.
Parameters
----------
file: string or int
H5 File name
binsize: int
size of time bins. Default is 10 microseconds.
removalRange: int
time bins to be deleted around cosmic ray occurrence. Default is 10 time bins.
Returns
-------
Dictionary with keys:
'returnDict': dictionary with keys as counts and values as # of bins with that count. Used in histogram.
'peaks': list of times where peak of cosmic ray occurred
'cutOut': list of all deleted times, including peaks and surrounding areas
'goodones': list of times with no cosmic ray suspects
'''
file = pt.Photontable(str(file)) # grab data
start_time = time.time() # def bin size in microseconds
photons = file.photonTable.read(
) # grabs list for all photon arrivals (read)
hist, bins = np.histogram(
photons['Time'], bins=int(
(photons['Time'].max() + 1) /
binsize)) # returns tuple. hist -> counts/bin and bins -> bin
unique, counts = np.unique(hist, return_counts=True)
avgcounts = np.average(list(unique), weights=list(counts))
threshold = np.ceil(6 * poisson.std(avgcounts, loc=0) + avgcounts)
localmax = scipy.signal.find_peaks(
hist, height=threshold, threshold=10, distance=30)[0] * 10
cutOut = []
for i in localmax:
for k in range(i - removalRange, i + removalRange + 1):
cutOut.append(k)
print("--- %s seconds ---" % (time.time() - start_time))
unique, counts = np.unique(hist, return_counts=True)
goodOnes = np.delete(bins, cutOut)
returnDict = dict(zip(unique, counts))
return {
'returnDict': returnDict,
'peaks': localmax,
'cutOut': cutOut,
'goodOnes': goodOnes,
'threshold': threshold
}
def findCosmicRayTimeStamps(self, pList, binSize=10, threshold=None):
s = time.time()
photons = pList
# hist, bins = np.histogram(photons['Time'], bins=int((photons['Time'].max()+1)/binSize))
hist = np.bincount((photons['Time'] / 10).astype(int))
if threshold is None:
unique, counts = np.unique(hist, return_counts=True)
avgCounts = np.average(list(unique), weights=list(counts))
threshold = np.ceil(6 * poisson.std(avgCounts, loc=0) + avgCounts)
peaks = scipy.signal.find_peaks(
hist, height=threshold, threshold=0, distance=20)[0] * 10
cutOut = np.unique(
np.array([np.arange(peak - 50, peak + 101, 1) for peak in peaks]))
e = time.time()
print(
f"---- It took {int(e-s)} seconds to find the comic ray peaks ----"
)
return peaks, cutOut
def std(self, dist):
return poisson.std(*self._get_params(dist))
def std(self, n, p):
std = poisson.std(self, n, p)
return std
from scipy.stats import poisson
import numpy as np
# Assume the number of typo errors on a single page of a book follows
# Poisson distribution with parameter 1/3. Calculate the probability
# that on one page there are
# no typos (pmf(0,1/3) = 0.7165313105737893)
# exactly two typos = (pmf(0,1/3) = 0.03980729503187717)
k = 1
mu = 2.5
expect = poisson.expect(args=(mu,), loc=0)
mean = poisson.mean(mu)
var = poisson.var(mu)
sigma = poisson.std(mu)
pmf = poisson.pmf(k, mu, loc=0)
cdf = poisson.cdf(k, mu, loc=0)
e_x_squared = mu**2 + mu
print('expected = ', expect)
print('mean = ', mean)
print('variance = ', var)
print('std. dev. = ',sigma)
print('pmf = ', pmf)
print('cdf = ', cdf)
print('E[X]^2 = ', e_x_squared)