{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example illustrates data plotting for summary statistics of observed vs. simulated data. This can be used for easy assessment of how good we can fit the data.\n", "\n", "Let’s start to import the necessary classes. We also set up matplotlib and we’re going to use numpy and pandas as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# install if not done yet\n", "!pip install pyabc --quiet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pyabc.visualization import plot_data_default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define summary statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we will define some arbitrary summary statistics to be used in this example. We will define different summary statistics with different data types. Data types of the value of the summary statistic can be 1d numpy array, 2d numpy array, pandas data frame." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "observed = {\n", " 'data 1': np.array([1, 1, 2, 2, 3, 3, 4, 4]),\n", " 'data 2': pd.DataFrame({'measurement': [1, 2, 3, 4, 1, 2, 3, 4]}),\n", " 'data 3': pd.DataFrame(\n", " {\n", " 'measurement 1': [1, 2, 3, 4, 1, 2, 3, 4],\n", " 'measurement 2': [1, 5, 4, 6, 7, 2, 6, 2],\n", " }\n", " ),\n", " 'data 4': np.array([[4, 4, 5, 3, 2, 2, 1, 2], [4, 3, 4, 3, 1, 1, 2, 1]]),\n", " 'data 5': np.array([1, 1, 2, 2, 3, 3, 4, 4]),\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We do the same for the simulated data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "simulated = {\n", " 'data 1': np.array([1, 2, 4, 6, 8, 10, 12, 14]),\n", " 'data 2': pd.DataFrame({'measurement': [1, 2, 4, 6, 1, 2, 4, 6]}),\n", " 'data 3': pd.DataFrame(\n", " {\n", " 'measurement 1': [2, 3, 4, 5, 3, 4, 1, 2],\n", " 'measurement 2': [1, 6, 5, 4, 6, 2, 5, 2],\n", " }\n", " ),\n", " 'data 4': np.array(\n", " [[13, 13, 9, 7, 8, 3, 2, 1], [14, 12, 10, 9, 6, 4, 3, 2]]\n", " ),\n", " 'data 5': np.array([1, 2, 4, 6, 8, 10, 12, 14]),\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have defined two dictionaries for both the observed and simulated summary statistics, we can call the plotting function from pyABC.visualization" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_data_default(observed, simulated)\n", "plt.gcf().set_size_inches(9, 6)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is also a function ``pyabc.visualization.plot_data_callback`` operating via callback functions and thus allowing more flexibility. This function is illustrated in the conversion reaction notebook." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }