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import os
os.environ['THEANO_FLAGS'] = 'device=cuda,floatX=float64'
import pymc3 as pm

with pm.Model():
  x = pm.Normal('x', shape=1000)
  trace = pm.sample(chains=1, cores=1)

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import theano
from theano import function, config, shared, tensor as tt
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))

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import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import arviz as az

from pymc3.step_methods.arraystep import BlockedStep

b = [2,1.5]
sigma = 2

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import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm

from pymc3.step_methods.arraystep import BlockedStep


# ### Create simulated data under linear regression model

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Y = xr.DataArray(Y_raw, dims=('observed_columns','rows'))
Y = Y.assign_coords({'observed_columns':np.arange(d),'rows':np.arange(n)})

def post_hoc_fun(W, psi, Y, rng=None):
    '''
    Recovers latent variables given observation Y and single draw of W, psi
    from the posterior distribution.
    '''
    k, d = W.shape[-2:]
    _, n = Y.shape

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import pandas as pd

# Run this as a notebook so that the bash cell magic ! works
import pandas as pd

! wget https://aqs.epa.gov/aqsweb/airdata/daily_44201_2020.zip
    
filepath = './daily_44201_2020.csv'
df = pd.read_csv(filepath)

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import enum
import numpy as np

from mesa import Agent, Model
from mesa.time import RandomActivation
from mesa.space import MultiGrid
from mesa.datacollection import DataCollector
from tqdm import tqdm

class InfectionModel(Model):

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colors = [
    'tab:blue',
'tab:orange',
'tab:green',
'tab:red',
'tab:purple',
'tab:brown',
'tab:pink',
'tab:gray',
'tab:olive',

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---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-6e14f40a5c7d> in <module>
      5     p = pm.Uniform("p", 0, 1)
      6     pm.Binomial("w", p=p, n=2, observed=1)
----> 7     inference_data = pm.sample(500, chains=2, return_inferencedata=True)
      8 
      9     assert inference_data

~/anaconda3/envs/pymc3-dev-py39/lib/python3.9/site-packages/pymc3-3.11.1-py3.9.egg/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)

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## Combined model
c_comb = np.asarray([[16,29,4],
                [16,29,6],
                [14,30,4],
                [16,29,3],
                [16,31,5],
                [13,29,5],
                [15,32,5],
                [15,29,6],
                [17,31,6],