{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Trip Distribution\n\nIn this example, we calibrate a Synthetic Gravity Model that same model plus IPF (Fratar/Furness).\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Imports\nfrom uuid import uuid4\nfrom tempfile import gettempdir\nfrom os.path import join\nfrom aequilibrae.utils.create_example import create_example\nimport pandas as pd\nimport numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the example project inside our temp folder\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fldr = join(gettempdir(), uuid4().hex)\n\nproject = create_example(fldr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We get the demand matrix directly from the project record\n# so let's inspect what we have in the project\nproj_matrices = project.matrices\nprint(proj_matrices.list())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We get the demand matrix\ndemand = proj_matrices.get_matrix(\"demand_omx\")\ndemand.computational_view([\"matrix\"])\n\n# And the impedance\nimpedance = proj_matrices.get_matrix(\"skims\")\nimpedance.computational_view([\"time_final\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a function to plot the Trip Length Frequency Distribution\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from math import log10, floor\nimport matplotlib.pyplot as plt\n\n\ndef plot_tlfd(demand, skim, name):\n plt.clf()\n b = floor(log10(skim.shape[0]) * 10)\n n, bins, patches = plt.hist(\n np.nan_to_num(skim.flatten(), 0),\n bins=b,\n weights=np.nan_to_num(demand.flatten()),\n density=False,\n facecolor=\"g\",\n alpha=0.75,\n )\n\n plt.xlabel(\"Trip length\")\n plt.ylabel(\"Probability\")\n plt.title(\"Trip-length frequency distribution\")\n plt.savefig(name, format=\"png\")\n return plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from aequilibrae.distribution import GravityCalibration" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for function in [\"power\", \"expo\"]:\n gc = GravityCalibration(matrix=demand, impedance=impedance, function=function, nan_as_zero=True)\n gc.calibrate()\n model = gc.model\n # We save the model\n model.save(join(fldr, f\"{function}_model.mod\"))\n\n # We can save an image for the resulting model\n _ = plot_tlfd(gc.result_matrix.matrix_view, impedance.matrix_view, join(fldr, f\"{function}_tfld.png\"))\n\n # We can save the result of applying the model as well\n # We can also save the calibration report\n with open(join(fldr, f\"{function}_convergence.log\"), \"w\") as otp:\n for r in gc.report:\n otp.write(r + \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We save a trip length frequency distribution for the demand itself\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt = plot_tlfd(demand.matrix_view, impedance.matrix_view, join(fldr, \"demand_tfld.png\"))\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Forecast\nWe create a set of *'future'* vectors by applying some models\nand apply the model for both deterrence functions\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel\nfrom aequilibrae.matrix import AequilibraeData\nimport numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zonal_data = pd.read_sql(\"Select zone_id, population, employment from zones order by zone_id\", project.conn)\n# We compute the vectors from our matrix\n\nargs = {\n \"file_path\": join(fldr, \"synthetic_future_vector.aed\"),\n \"entries\": demand.zones,\n \"field_names\": [\"origins\", \"destinations\"],\n \"data_types\": [np.float64, np.float64],\n \"memory_mode\": True,\n}\n\nvectors = AequilibraeData()\nvectors.create_empty(**args)\n\nvectors.index[:] = zonal_data.zone_id[:]\n\n# We apply a trivial regression-based model and balance the vectors\nvectors.origins[:] = zonal_data.population[:] * 2.32\nvectors.destinations[:] = zonal_data.employment[:] * 1.87\nvectors.destinations *= vectors.origins.sum() / vectors.destinations.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We simply apply the models to the same impedance matrix now\nfor function in [\"power\", \"expo\"]:\n model = SyntheticGravityModel()\n model.load(join(fldr, f\"{function}_model.mod\"))\n\n outmatrix = join(proj_matrices.fldr, f\"demand_{function}_model.aem\")\n args = {\n \"impedance\": impedance,\n \"rows\": vectors,\n \"row_field\": \"origins\",\n \"model\": model,\n \"columns\": vectors,\n \"column_field\": \"destinations\",\n \"nan_as_zero\": True,\n }\n\n gravity = GravityApplication(**args)\n gravity.apply()\n\n # We get the output matrix and save it to OMX too,\n gravity.save_to_project(name=f\"demand_{function}_model_omx\", file_name=f\"demand_{function}_model.omx\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We update the matrices table/records and verify that the new matrices are indeed there\nproj_matrices.update_database()\nprint(proj_matrices.list())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now run IPF for the future vectors\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "args = {\n \"matrix\": demand,\n \"rows\": vectors,\n \"columns\": vectors,\n \"column_field\": \"destinations\",\n \"row_field\": \"origins\",\n \"nan_as_zero\": True,\n}\n\nipf = Ipf(**args)\nipf.fit()\n\nipf.save_to_project(name=\"demand_ipf\", file_name=\"demand_ipf.aem\")\nipf.save_to_project(name=\"demand_ipf_omx\", file_name=\"demand_ipf.omx\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(proj_matrices.list())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "project.close()" ] } ], "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 0 }