# I used to code this below for my monte carlo simulation and it worked well. (Beta distribution)

import numpy as np
from scipy import stats
from scipy.stats import beta
from distfit import distfit
import matplotlib.pyplot as plt
import seaborn as sns

#Set the random seed for reproducibility
np.random.seed(1)
a = 0.141753
b = 269.581

#Perform the Monte Carlo simulation
nr_simulations = 10000
data = np.empty(1)
for i in range(nr_simulations-1):
sam=np.random.beta(a, b,1)
data=np.append(data,sam)

if sam < 0:
nr_simulations+= 1
print(np.size(data))
print(*data)

# ============================================================== But I encounter memory error when my data is lognormal distribution and the code is below.

import numpy as np
from scipy import stats
from scipy.stats import beta
from distfit import distfit
import matplotlib.pyplot as plt
import seaborn as sns

#Set the random seed for reproducibility
np.random.seed(0)

#Define the parameters of the lognormal distribution
s = 6.0195
loc = -2.27181e-17
scale = 0.373877

#Perform the Monte Carlo simulation
nr_simulations = 10000
data = np.empty(1)
for i in range(nr_simulations-1):
sam=np.random.lognormal(mean=loc, sigma=s, size=1)
data=np.append(data,sam)
if sam < 0:
nr_simulations+= 1
dist = distfit()
result=dist.fit_transform(data)
dist.plot_summary()
dist.plot()
plt.show()
print(result)

# fig, ax = dist.plot(chart=‘pdf’) fig, ax = dist.plot(chart=‘cdf’)

I think the “transform” coding is making a problem.

How can I solve this trouble?

I hope to hear any kinds of advice. Thank you!

Please edit you post to surround each code block with triple backtick lines

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so that indents are preserved.