# Theano errors when using complex python functions inside PyMC3 models

**URL:** <https://discourse.pymc.io/t/theano-errors-when-using-complex-python-functions-inside-pymc3-models/5059>\
**Category:** Questions\
**Tags:** theano\
**Created:** [May 10, 2020, 8:13pm UTC](https://discourse.pymc.io/t/theano-errors-when-using-complex-python-functions-inside-pymc3-models/5059 "2020-05-10T20:13:38Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![oscarbranson](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/oscarbranson/32/3759_2.png) [@oscarbranson](https://discourse.pymc.io/u/oscarbranson)\
**Post date:** [May 10, 2020, 8:13pm UTC](https://discourse.pymc.io/t/theano-errors-when-using-complex-python-functions-inside-pymc3-models/5059/1 "2020-05-10T20:13:38Z")

</div>

I have an MCMC problem that requires calls to a python function that employs a zero-finder internally. It’s throwing errors that I’m not sure how to diagnose (I’m new to Theano)… any pointers greatly appreciated!

```python
import pymc3 as pm
import cbsyst as cb # the package I'm calling - for calculating seawater carbon chemistry.

# example problem: what DIC is consistent with an observed pCO2 and TA
obs_pCO2 = 433.5
TA = 2400

with pm.Model() as m:
    DIC = pm.Normal('DIC', 2000, 200)
    
    mu = cb.Csys(DIC=DIC, TA=TA).pCO2
    sigma = pm.Exponential('sigma', 1)
    
    pCO2 = pm.Normal('pCO2', mu, sigma, observed=obs_pCO2)
    
    pm.sample()

```

```auto
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-18-49e7ddbb65c4> in <module>
      3 DIC = pm.Normal('DIC', 2000, 200)
      4 
----> 5 mu = cb.Csys(DIC=DIC, TA=TA).pCO2
      6 sigma = pm.Exponential('sigma', 1)
      7 

~/GitHub/cbsyst/cbsyst/cbsyst.py in Csys(pHtot, DIC, CO2, HCO3, CO3, TA, fCO2, pCO2, BT, Ca, Mg, T_in, S_in, P_in, T_out, S_out, P_out, TP, TSi, pHsws, pHfree, pHNBS, Ks, pdict, unit)
    224 fCO2=ps.fCO2, pCO2=ps.pCO2,
    225 T_in=ps.T_in, BT=ps.BT, TP=ps.TP, TSi=ps.TSi,
--> 226 TS=ps.TS, TF=ps.TF, Ks=ps.Ks))
    227 
    228 # calculate pHs on all scales, if not done before.

~/GitHub/cbsyst/cbsyst/carbon_fns.py in calc_C_species(pHtot, DIC, CO2, HCO3, CO3, TA, fCO2, pCO2, T_in, BT, TP, TSi, TS, TF, Ks)
    607 TS=TS,
    608 TF=TF,
--> 609 Ks=Ks)
    610 H = ch(pHtot)
    611 

~/GitHub/cbsyst/cbsyst/carbon_fns.py in TA_DIC(TA, DIC, BT, TP, TSi, TS, TF, Ks)
    342 
    343 while any(abs(deltapH) > 1):
--> 344 deltapH[abs(deltapH) > 1] /= 2
    345 
    346 pHx += deltapH

~/miniconda3/envs/py3/lib/python3.7/site-packages/theano/tensor/var.py in __getitem__ (self, args)
    556 
    557 if advanced_boolean:
--> 558 return theano.tensor.subtensor.advanced_boolean_subtensor(self, *args)
    559 elif advanced:
    560 if (axis is not None and

~/miniconda3/envs/py3/lib/python3.7/site-packages/theano/gof/op.py in __call__ (self, *inputs, **kwargs)
    613 """
    614 return_list = kwargs.pop('return_list', False)
--> 615 node = self.make_node(*inputs, **kwargs)
    616 
    617 if config.compute_test_value != 'off':

~/miniconda3/envs/py3/lib/python3.7/site-packages/theano/tensor/subtensor.py in make_node(self, x, *index)
   2177 
   2178 index = tuple(map(as_index_variable, index))
-> 2179 bcast = adv_index_broadcastable_pattern(x, index)
   2180 return gof.Apply(self,
   2181 (x,) + index,

~/miniconda3/envs/py3/lib/python3.7/site-packages/theano/tensor/subtensor.py in adv_index_broadcastable_pattern(a, idx)
   2114 # 2 - True = 1; 2 - False = 2
   2115 fakeshape = [2 - bc for bc in a.broadcastable]
-> 2116 retshape = np.empty(fakeshape)[newidx].shape
   2117 return tuple([dim == 1 for dim in retshape])
   2118 

IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 2

```

The error is definitely coming from inside the zero-finder part of the function. I’m open to modifying the zero-finder to work with Theano… but is this even possible? At this stage I’m just looking for some friendly pointers before spending a whole lot of time trying going down blind alleys!