#%%

import numpy as np

import scipy.stats as stats

import seaborn as sns

import matplotlib.pyplot as plt

import cmdstanpy

import os

cmdstanpy.utils.cxx_toolchain_path()

#%% [markdown]

## Generate Claims

# Simple generator of lognormal claims

#%%

# parameters

mu = 0.5

sigma = 1

dist = stats.lognorm(np.exp(mu), sigma)

# generate claims

claims = dist.rvs(size=1000)

# graph

sns.distplot(claims)

#%% [markdown]

# \begin{aligned}

# X_i & \sim LN(mu, sigma) \\

# mu & \sim N( \mu_{mu}, 1 ) \\

# sigma & \sim exp( \lambda_{sigma} ) \\

# \mu_{mu} & = 1 \\

# \lambda_{sigma} & = 1 \\

# \end{aligned}

# model

#%%

print(os.getcwd())

#%%

# priors

# prior predictive checks

mu_mu = 0.5

lambda_sigma = 1

samples = 100

prior_scale = stats.norm(mu_mu, 1).rvs(size=samples) # note stats.expon using scale notation so scale = 1 / lambda

prior_shape = stats.expon(1 / lambda_sigma).rvs(size=samples)

#%%

fig, ax = plt.subplots(1,1)

x = np.linspace(0, 200)

for sample in range(samples):

prior_dist = stats.lognorm(prior_scale[sample], prior_shape[sample])

ax.plot(x, prior_dist.pdf(x), 'r-', alpha=0.05)

#%%

# Build Model

file_path = os.path.join(os.getcwd(), 'model_1.stan')

model = cmdstanpy.CmdStanModel(stan_file=file_path)

model.name

model.stan_file

model.exe_file

print(model.code())

#%%

# Sample

stan_data = {

'N': claims.shape[0],

'claims': claims.tolist()

}

fit = model.sample(data=stan_data) #, output_dir='./model_output')

print(fit)

print(fit.sample.shape)

print(fit.summary())

print(fit.diagnose())

# diagnostics

# validations

# single dist

# add cat

# add variate

# add time

# %%