shape,  loc,  scale  = sm.lognorm.fit(dataToLearn, floc = 0)

for b in bounds:
    toPlot.append((b, currCount+sm.lognorm.ppf(b, s = shape, loc = loc, scale = scale)))

for i, d in enumerate(dataToLearn):
    dataToLearn2 += int(w[i] * 100) * [d]

import numpy as np

dataToLearn = np.array([1,2,3,4,5])
weights = np.array([1,2,1,1,3])

print(np.repeat(dataToLearn, weights))
# Output: array([1, 2, 2, 3, 4, 5, 5, 5])

import timeit

code_before = """
weights = np.array([1,2,1,1,3] * 1000)
dataToLearn = np.array([1,2,3,4,5] * 1000)
dataToLearn2 = []
for i, d in enumerate(dataToLearn):
    dataToLearn2 += int(weights[i]) * [d]
"""

code_after = """
weights = np.array([1,2,1,1,3] * 1000)
dataToLearn = np.array([1,2,3,4,5] * 1000)
np.repeat(dataToLearn, weights)
"""

print(timeit.timeit(code_before, setup="import numpy as np", number=1000))
print(timeit.timeit(code_after, setup="import numpy as np", number=1000))

import numpy as np
from scipy.stats import lognorm


# Sample data and weights.  To enable an exact comparison with
# the method of generating an array with the values repeated
# according to their weight, I use an array of weights that is
# all integers.
x = np.array([2.5, 8.4, 9.3, 10.8, 6.8, 1.9, 2.0])
w = np.array([  1,   1,   2,    1,   3,   3,   1])


#-----------------------------------------------------------------------------
# Fit the log-normal distribution by creating an array containing the values
# repeated according to their weight.
xx = np.repeat(x, w)

# Use the explicit formulas for the MLE of the log-normal distribution.
lnxx = np.log(xx)
muhat = np.mean(lnxx)
varhat = np.var(lnxx)

shape = np.sqrt(varhat)
scale = np.exp(muhat)

print("MLE using repeated array: shape=%7.5f   scale=%7.5f" % (shape, scale))


#-----------------------------------------------------------------------------
# Use the explicit formulas for the weighted MLE of the log-normal
# distribution.

lnx = np.log(x)
muhat = np.average(lnx, weights=w)
# varhat is the weighted variance of ln(x).  There isn't a function in
# numpy for the weighted variance, so we compute it using np.average.
varhat = np.average((lnx - muhat)**2, weights=w)

shape = np.sqrt(varhat)
scale = np.exp(muhat)

print("MLE using weights:        shape=%7.5f   scale=%7.5f" % (shape, scale))


#-----------------------------------------------------------------------------
# Might as well check that we get the same result from lognorm.fit() using the
# repeated array

shape, loc, scale = lognorm.fit(xx, floc=0)

print("MLE using lognorm.fit:    shape=%7.5f   scale=%7.5f" % (shape, scale))

MLE using repeated array:  shape=0.70423   scale=4.57740
MLE using weights:         shape=0.70423   scale=4.57740
MLE using lognorm.fit:     shape=0.70423   scale=4.57740