{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Health care facilities - study steps\n", "------------------------------------\n", "This notebook runs a simulation with only a few people, just to show the study steps." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pysimpactcyan\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Setting data directory to /usr/local/share/simpact-cyan/\n" ] } ], "source": [ "# First, we'll need to tell the Simpact simulation engine to use the MaxART-specific simulation\n", "simpact = pysimpactcyan.PySimpactCyan()\n", "simpact.setSimulationPrefix(\"maxart\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using identifier 'maxart-2016-10-16-18-27-46_9172_L0IMzk26-'\n", "Results will be stored in directory '/tmp/simptest'\n", "Running simpact executable...\n", "Done.\n", "\n", "# read seed from /dev/urandom\n", "# Rng engine mt19937\n", "# Using seed 269339523\n", "# WARNING! Ignoring negative values when reading /usr/local/share/simpact-cyan/SWZ10adjv4.tif\n", "# Performing extra check on read configuration parameters\n", "# WARNING: ignoring consistency check for config key facilities.geo.coords (config value is '/usr/local/share/simpact-cyan/maxart-facilities.csv')\n", "# WARNING: ignoring consistency check for config key facilities.randomization (config value is '/usr/local/share/simpact-cyan//maxart-randomization-fake_1.csv')\n", "# WARNING: ignoring consistency check for config key population.agedistfile (config value is '/usr/local/share/simpact-cyan/sa_2003.csv')\n", "# mNRM: using advanced algorithm\n", "# Release version\n", "# Simpact version is: 0.20.0rc1\n", "# Current simulation time is 15.0083\n", "# Number of events executed is 2877\n", "# Started with 200 people, ending with 190 (difference is -10)\n" ] } ], "source": [ "# We configure the simulation to use one of the files with fake randomization data\n", "\n", "cfg = { \"facilities.randomization\": \"${SIMPACT_DATA_DIR}/maxart-randomization-fake_1.csv\" }\n", "r = simpact.run(cfg, \"/tmp/simptest\")\n", "\n", "# We then read the steps as they have been written to a CSV log file\n", "steps = pd.read_csv(r[\"logsteps\"])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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timeBulandzeni ClinicHerefords ClinicHhukwini ClinicHoro ClinicMaguga ClinicMalandzela Nazarene ClinicMshingishingini Nazarene ClinicNdvwabangeni Nazarene ClinicNdzingeni Nazarene ClinicNtfonjeni ClinicPeak Nazarene ClinicPigg's Peak Government HospitalSigangeni ClinicSiphocosini Clinic
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98.00000PostPostPostPostPostPostPostPostPostPostPostPostPostPost
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" ], "text/plain": [ " time Bulandzeni Clinic Herefords Clinic Hhukwini Clinic Horo Clinic \\\n", "0 5.00000 C C C C \n", "1 5.33333 C T C C \n", "2 5.66667 C I C C \n", "3 6.00000 C I C T \n", "4 6.33333 T I C I \n", "5 6.66667 I I T I \n", "6 7.00000 I I I I \n", "7 7.33333 I I I I \n", "8 7.66667 I I I I \n", "9 8.00000 Post Post Post Post \n", "\n", " Maguga Clinic Malandzela Nazarene Clinic Mshingishingini Nazarene Clinic \\\n", "0 C C C \n", "1 C C C \n", "2 T C C \n", "3 I C C \n", "4 I C C \n", "5 I T C \n", "6 I I C \n", "7 I I T \n", "8 I I I \n", "9 Post Post Post \n", "\n", " Ndvwabangeni Nazarene Clinic Ndzingeni Nazarene Clinic Ntfonjeni Clinic \\\n", "0 C C C \n", "1 T C C \n", "2 I C T \n", "3 I C I \n", "4 I C I \n", "5 I C I \n", "6 I T I \n", "7 I I I \n", "8 I I I \n", "9 Post Post Post \n", "\n", " Peak Nazarene Clinic Pigg's Peak Government Hospital Sigangeni Clinic \\\n", "0 C C C \n", "1 C C C \n", "2 C C C \n", "3 T C C \n", "4 I C C \n", "5 I C C \n", "6 I T C \n", "7 I I T \n", "8 I I I \n", "9 Post Post Post \n", "\n", " Siphocosini Clinic \n", "0 C \n", "1 C \n", "2 C \n", "3 C \n", "4 T \n", "5 I \n", "6 I \n", "7 I \n", "8 I \n", "9 Post " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's show this step data. The first line is written when the study is started (by default\n", "# 5 years into the simulation). At that point, all facilities are in the control stage ('C').\n", "# Then, the stage each facility is in is written at regular intervals, when new facilities enter\n", "# the transitioning stage ('T'), or when they advance to the intervention stage ('I'). A final\n", "# line is written when the study has ended ('Post' study).\n", "steps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }