# Live sample plots¶

This notebook illustrates how we can have live sample plots when calling the sample function with live_plot=True. It is based on the “Coal mining disasters” case study in the Getting started notebook.

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import numpy as np
from pymc3 import Model, Exponential, DiscreteUniform, Poisson, sample
from pymc3.math import switch

%matplotlib notebook

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disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,
1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)
year = np.arange(1851, 1962)

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with Model() as disaster_model:

switchpoint = DiscreteUniform('switchpoint', lower=year.min(), upper=year.max(), testval=1900)

# Priors for pre- and post-switch rates number of disasters
early_rate = Exponential('early_rate', 1)
late_rate = Exponential('late_rate', 1)

# Allocate appropriate Poisson rates to years before and after current
rate = switch(switchpoint >= year, early_rate, late_rate)

disasters = Poisson('disasters', rate, observed=disaster_data)

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with disaster_model:
trace = sample(10000, live_plot=True, skip_first=100, refresh_every=300, roll_over=1000)