I implemented a little wrapper for fast-dm which makes it easier to use fast-dm in a python environment. You can fide the code on github. There is also an IPython notebook. that shows how to use it.

FastDM Python wrapper demo

A short demo of how easy you can fit DDM models using fastdm and Python, on multiple cores

In [5]:

import fastdm
import pandas

load in data

In [9]:

data = pandas.load('/home/gdholla1/data/tdcs/data/leiden/leiden.pandas')

Set up the model. We feed in: * A Pandas DataFrame with columns RT and response * A dictionary indicating which parameters depend on which conditions

In [10]:

model = fastdm.FastDM(data, 
                      depends_on={'a':['acc_spd', 'stimulation'],
                                  't0':['acc_spd', 'stimulation'],})

We fit using 10 CPU cores at the same time

In [11]:

model.fit(nproc=10)

Running fast-dm on experiment_2.ctl
Running fast-dm on experiment_7.ctl
Running fast-dm on experiment_4.ctl
Running fast-dm on experiment_5.ctl
Running fast-dm on experiment_1.ctl
Running fast-dm on experiment_3.ctl
Running fast-dm on experiment_6.ctl

Out[11]:

<multiprocessing.pool.Pool at 0x4ba1490>

Now we can extract a FastDMResult-object

In [31]:

pars = model.get_parameters()

That has a dataframe parameters as an attribute

In [41]:

pars.parameters

Out[41]:

	a_acc_anodal	a_acc_sham	a_spd_anodal	a_spd_sham	d	fit	method	penalty	st0	sv	szr	t0_acc_anodal	t0_acc_sham	t0_spd_anodal	t0_spd_sham	time	v	subj_idx
subj_idx
2	NaN	1.2304	NaN	0.8493	-0.0036	0.3139	KS	0	0.1019	0.4961	0.4870	NaN	0.4718	NaN	0.4259	11.58	1.4959	2
1	2.0622	NaN	1.0335	NaN	0.0385	0.1232	KS	0	0.0786	1.0405	0.3692	0.4299	NaN	0.3931	NaN	27.93	2.6697	1
4	NaN	0.8207	NaN	0.7250	-0.0145	0.4445	KS	0	0.1906	0.6474	0.3718	NaN	0.3533	NaN	0.3469	6.59	0.1914	4
7	1.1676	NaN	1.1529	NaN	-0.0145	0.1342	KS	0	0.1796	0.8161	0.3894	0.2723	NaN	0.2697	NaN	22.60	0.4535	7
5	1.5793	NaN	0.8046	NaN	0.0079	0.4626	KS	0	0.1140	0.5652	0.4924	0.5314	NaN	0.4500	NaN	10.38	2.0744	5
3	1.2111	NaN	0.7283	NaN	-0.0202	0.8502	KS	0	0.2516	1.0173	0.1926	0.5259	NaN	0.3912	NaN	17.62	0.3991	3
6	NaN	0.7969	NaN	0.6579	-0.0094	0.0999	KS	0	0.1201	1.2397	0.3093	NaN	0.3737	NaN	0.3776	6.57	3.0034	6

You can also make a ‘melted’ dataframe for a single parameter

In [43]:

p = pars.melted_parameters('t0')
p

Out[43]:

	subj_idx	t0	acc_spd	stimulation
1	1	0.4299	acc	anodal
3	7	0.2723	acc	anodal
4	5	0.5314	acc	anodal
5	3	0.5259	acc	anodal
7	2	0.4718	acc	sham
9	4	0.3533	acc	sham
13	6	0.3737	acc	sham
15	1	0.3931	spd	anodal
17	7	0.2697	spd	anodal
18	5	0.4500	spd	anodal
19	3	0.3912	spd	anodal
21	2	0.4259	spd	sham
23	4	0.3469	spd	sham
27	6	0.3776	spd	sham

Let’s make some nice plots using seaborn

In [44]:

import seaborn as sns

In [45]:

p = pars.melted_parameters('t0')
sns.factorplot('acc_spd', 't0', hue='stimulation', data=p, units='subj_idx',)

Out[45]:

<seaborn.axisgrid.FacetGrid at 0x62cd290>

In [47]:

p = pars.melted_parameters('a')
sns.factorplot('stimulation', 'a', hue='acc_spd', data=p, units='subj_idx',)

Out[47]:

<seaborn.axisgrid.FacetGrid at 0x62a47d0>

Fast-dm Python wrapper

FastDM Python wrapper demo

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