{ "metadata": { "name": "", "signature": "sha256:fa65139e4f23575644c0e1d153e1d275e8ff8a50165f61a219a40d093f57b5f1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FastDM Python wrapper demo\n", "\n", "A short demo of how easy you can fit DDM models using fastdm and Python, on multiple cores" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import fastdm\n", "import pandas" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "load in data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = pandas.load('/home/gdholla1/data/tdcs/data/leiden/leiden.pandas')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/pandas/core/common.py:2769: FutureWarning: load is deprecated, use read_pickle\n", " warnings.warn(\"load is deprecated, use read_pickle\", FutureWarning)\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up the model. We feed in:\n", " * A Pandas DataFrame with columns RT and response\n", " * A dictionary indicating which parameters depend on which conditions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = fastdm.FastDM(data, \n", " depends_on={'a':['acc_spd', 'stimulation'],\n", " 't0':['acc_spd', 'stimulation'],},\n", " method='ml')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fit using 10 CPU cores at the same time" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model.fit(nproc=10)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Running fast-dm on experiment_2.ctl\n", "Running fast-dm on experiment_1.ctl\n", "Running fast-dm on experiment_4.ctl\n", "Running fast-dm on experiment_7.ctl\n", "Running fast-dm on experiment_5.ctl\n", "Running fast-dm on experiment_3.ctl\n", "Running fast-dm on experiment_6.ctl\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can extract a FastDMResult-object" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pars = model.get_parameters()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py:615: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n", " ParserWarning)\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That has a dataframe _parameters_ as an attribute" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pars.parameters" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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a_acc_anodala_acc_shama_spd_anodala_spd_shamdfitmethodpenaltyst0svszrt0_acc_anodalt0_acc_shamt0_spd_anodalt0_spd_shamtimevsubj_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 27.44 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 6.93 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 22.65 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 10.56 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 17.28 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 6.58 3.0034 6
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " a_acc_anodal a_acc_sham a_spd_anodal a_spd_sham d fit \\\n", "subj_idx \n", "2 NaN 1.2304 NaN 0.8493 -0.0036 0.3139 \n", "1 2.0622 NaN 1.0335 NaN 0.0385 0.1232 \n", "4 NaN 0.8207 NaN 0.7250 -0.0145 0.4445 \n", "7 1.1676 NaN 1.1529 NaN -0.0145 0.1342 \n", "5 1.5793 NaN 0.8046 NaN 0.0079 0.4626 \n", "3 1.2111 NaN 0.7283 NaN -0.0202 0.8502 \n", "6 NaN 0.7969 NaN 0.6579 -0.0094 0.0999 \n", "\n", " method penalty st0 sv szr t0_acc_anodal t0_acc_sham \\\n", "subj_idx \n", "2 KS 0 0.1019 0.4961 0.4870 NaN 0.4718 \n", "1 KS 0 0.0786 1.0405 0.3692 0.4299 NaN \n", "4 KS 0 0.1906 0.6474 0.3718 NaN 0.3533 \n", "7 KS 0 0.1796 0.8161 0.3894 0.2723 NaN \n", "5 KS 0 0.1140 0.5652 0.4924 0.5314 NaN \n", "3 KS 0 0.2516 1.0173 0.1926 0.5259 NaN \n", "6 KS 0 0.1201 1.2397 0.3093 NaN 0.3737 \n", "\n", " t0_spd_anodal t0_spd_sham time v subj_idx \n", "subj_idx \n", "2 NaN 0.4259 11.58 1.4959 2 \n", "1 0.3931 NaN 27.44 2.6697 1 \n", "4 NaN 0.3469 6.93 0.1914 4 \n", "7 0.2697 NaN 22.65 0.4535 7 \n", "5 0.4500 NaN 10.56 2.0744 5 \n", "3 0.3912 NaN 17.28 0.3991 3 \n", "6 NaN 0.3776 6.58 3.0034 6 " ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also make a 'melted' dataframe for a single parameter" ] }, { "cell_type": "code", "collapsed": false, "input": [ "p = pars.melted_parameters('t0')\n", "p" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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subj_idxt0acc_spdstimulation
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
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " subj_idx t0 acc_spd stimulation\n", "1 1 0.4299 acc anodal\n", "3 7 0.2723 acc anodal\n", "4 5 0.5314 acc anodal\n", "5 3 0.5259 acc anodal\n", "7 2 0.4718 acc sham\n", "9 4 0.3533 acc sham\n", "13 6 0.3737 acc sham\n", "15 1 0.3931 spd anodal\n", "17 7 0.2697 spd anodal\n", "18 5 0.4500 spd anodal\n", "19 3 0.3912 spd anodal\n", "21 2 0.4259 spd sham\n", "23 4 0.3469 spd sham\n", "27 6 0.3776 spd sham" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make some nice plots using [seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import seaborn as sns" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "p = pars.melted_parameters('t0')\n", "sns.factorplot('acc_spd', 't0', hue='stimulation', data=p, units='subj_idx',)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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39LLB69ng9eTGs+hNJOiOdVCQVKP10DPbmFpQwrkLj12Sk4lHoSMiJyUWi7Fg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\nYj8AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "p = pars.melted_parameters('a')\n", "sns.factorplot('stimulation', 'a', hue='acc_spd', data=p, units='subj_idx',)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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