Tutorial (easy)
=====================

Once you have downloaded the patch clamp data as previously described, you may run scripts to estimate models from this data.
The following script will show you the easy way to use :mod:`fit_neuron` to fit a model 
to the data and then make voltage trace predictions.  The script loads data, fits the model,
makes predictions, and then plots the predictions against the recorded data.  

For more information on the model object used, see: :class:`fit_neuron.models.gLIF.gLIF_model`.
For more information on loading data, see :func:`fit_neuron.data.data_loader.load_neuron_data`. 

.. literalinclude:: test_model.py
    :language: python


Running this script will generate the following figure.  

.. plot:: 

	import numpy as np
	import pylab 
	from fit_neuron.data import load_neuron_data
	from fit_neuron.models import gLIF_model

	def easy_test():
		
		# instanciate model
		model = gLIF_model()
		
		# load data 
		(file_id_list,X_list,Y_list,dt) = load_neuron_data(1,input_type="noise_only",max_file_ct=4) 
		
		# fit model
		model.fit(X_list, Y_list, dt)
		
		# predict data via model
		Y_pred_list = model.predict(X_list)
		
		# plot predicted data vs actual data for the first voltage trace in X_list
		t_arr = dt * np.arange(len(Y_pred_list[0]))
				
		pylab.plot(t_arr,Y_list[0],color='blue',label='True voltage')
		pylab.plot(t_arr,Y_pred_list[0],color='green',label='Predicted voltage')
		pylab.legend()
		
		pylab.xlabel("Time (s)")
		pylab.ylabel("Voltage (mV)")
		pylab.title("Response to Input Current")
		pylab.show() 
		
	if __name__ == '__main__':
		easy_test()


.. note:: 
	The methods here do not simulate the upward rise in voltage when the neuron spikes.   
	The convention used is that the model returns a :mod:`numpy` typed value of :math:`V = \text{NaN}`
	whenever the neuron is spiking.  For this reason, the holes in the predicted voltage traces seen 
	above correspond to predictions that the neuron is spiking at those times.