From 7142176eddd98254335baf676abb47c4d2de578a Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 10:29:43 -0400 Subject: [PATCH 01/34] test capacity implementation --- notebooks/lscp_capacity.ipynb | 795 ++++++++++++++++++++++++++++++++++ 1 file changed, 795 insertions(+) create mode 100644 notebooks/lscp_capacity.ipynb diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb new file mode 100644 index 00000000..299c1279 --- /dev/null +++ b/notebooks/lscp_capacity.ipynb @@ -0,0 +1,795 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "source": [ + "# Location Set Covering Problem (LSCP)\n", + "\n", + "*Authors:* [Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", + "\n", + "Location Set Covering is a problem realized by Toregas, et al. (1971). He figured out that emergency services must have placed according to a response time, since, there is a allowable maximum service time when it's discussed how handle an emergency activity. Therefore he proprosed a model named LSCP that:\n", + "\n", + "_Minimize the number of facilities needed and locate them so that every demand area is covered within a predefined maximal service distance or time._ Church L., Murray, A. (2018)\n", + "\n", + "**LSCP can be written as:**\n", + "\n", + "$\\begin{array} \\displaystyle \\textbf{Minimize} & \\sum_{j=1}^{n}{x_j} && (1) \\\\\n", + "\\displaystyle \\textbf{Subject to:} & \\sum_{j\\in N_i}{x_j} \\geq 1 & \\forall i & (2) \\\\\n", + " & x_j \\in {0,1} & \\forall j & (3) \\\\ \\end{array}$\n", + " \n", + "$\\begin{array} \\displaystyle \\textbf{Where:}\\\\ & & \\displaystyle i & \\small = & \\textrm{index referencing nodes of the network as demand} \\\\\n", + "& & j & \\small = & \\textrm{index referencing nodes of the network as potential facility sites} \\\\\n", + "& & S & \\small = & \\textrm{maximal acceptable service distance or time standard} \\\\\n", + "& & d_{ij} & \\small = & \\textrm{shortest distance or travel time between nodes} \\quad i \\quad \\textrm{and} \\quad j \\\\\n", + "& & N_i & \\small = & \\{j | d_{ij} < S\\} \\\\\n", + "& & x_j & \\small = & \\begin{cases} \n", + " 1, \\text{if a facility is located at node} \\quad j\\\\\n", + " 0, \\text{otherwise} \\\\\n", + " \\end{cases} \\end{array}$\n", + " \n", + "_This excerpt above was quoted from Church L., Murray, A. (2018)_\n", + "\n", + "This tutorial solves LSCP using `spopt.locate.coverage.LSCP` instance that depends on a array 2D representing the costs between facilities candidate sites and demand points. For that it uses a lattice 10x10 with simulated points to calculate the costs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/spaghetti/network.py:36: FutureWarning: The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.\n", + " warnings.warn(f\"{dep_msg}\", FutureWarning)\n" + ] + } + ], + "source": [ + "from spopt.locate.coverage import LSCP\n", + "from spopt.locate.util import simulated_geo_points\n", + "\n", + "import numpy\n", + "import geopandas\n", + "import pulp\n", + "import spaghetti\n", + "from shapely.geometry import Point\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the model needs a distance cost matrix we should define some variables. In the comments, it's defined what these variables are for but solver. The solver, assigned below as `pulp.PULP_CBC_CMD`, is an interface to optimization solver developed by [COIN-OR](https://github.com/coin-or/Cbc). If you want to use another optimization interface as Gurobi or CPLEX see this [guide](https://coin-or.github.io/pulp/guides/how_to_configure_solvers.html) that explains how to achieve this." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "CLIENT_COUNT = 10 # quantity demand points\n", + "FACILITY_COUNT = 5 # quantity supply points\n", + "\n", + "SERVICE_RADIUS = 8 # maximum service radius in meters\n", + "\n", + "# Random seeds for reproducibility\n", + "CLIENT_SEED = 5 \n", + "FACILITY_SEED = 6 \n", + "\n", + "solver = pulp.PULP_CBC_CMD(msg=False, warmStart=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lattice 10x10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create lattice 10x10 with 9 vertical lines in interior." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "lattice = spaghetti.regular_lattice((0, 0, 10, 10), 9, exterior=True)\n", + "ntw = spaghetti.Network(in_data=lattice)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transform spaghetti instance into geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "street = spaghetti.element_as_gdf(ntw, arcs=True)\n", + "\n", + "street_buffered = geopandas.GeoDataFrame(\n", + " geopandas.GeoSeries(street[\"geometry\"].buffer(0.2).unary_union),\n", + " crs=street.crs,\n", + " columns=[\"geometry\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the network created by spaghetti we can verify that it seems a district with quarters and streets." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "street.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate points in a network\n", + "\n", + "The function `simulated_geo_points` simulates points inside a network. In this case, it uses a lattice network 10x10 created by using spaghetti package. \n", + "Below we use the function defined above and simulate the points inside lattice bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "client_points = simulated_geo_points(street_buffered, needed=CLIENT_COUNT, seed=CLIENT_SEED)\n", + "facility_points = simulated_geo_points(\n", + " street_buffered, needed=FACILITY_COUNT, seed=FACILITY_SEED\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the 100 client and 5 facility points we can see that the function generates dummy points to an area of 10x10 which is the area created by our lattice created on previous cells." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "street.plot(ax=ax, alpha=0.8, zorder=1, label='streets')\n", + "facility_points.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=5)')\n", + "client_points.plot(ax=ax, color='black', label='clients points ($n$=100)')\n", + "plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transform simulated points to real points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use cost matrix or geodataframes we have to pay attention in some details. The client and facility points simulated don't belong to network, so if we calculate the distances now we are supposed to receive a wrong result. Before calculating distances we snap points to the networok and then calculate the distances." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we snap points that is not spatially belong to network and create new real points geodataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ntw.snapobservations(client_points, \"clients\", attribute=True)\n", + "clients_snapped = spaghetti.element_as_gdf(\n", + " ntw, pp_name=\"clients\", snapped=True\n", + ")\n", + "\n", + "ntw.snapobservations(facility_points, \"facilities\", attribute=True)\n", + "facilities_snapped = spaghetti.element_as_gdf(\n", + " ntw, pp_name=\"facilities\", snapped=True\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the plot seems more organized as the points belong to network. \n", + "The network created is plotted below with facility points and clients points:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "street.plot(ax=ax, alpha=0.8, zorder=1, label='streets')\n", + "facilities_snapped.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=5)')\n", + "clients_snapped.plot(ax=ax, color='black', label='clients points ($n$=100)')\n", + "plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating the cost matrix " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate distance between clients and facilities." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "cost_matrix = ntw.allneighbordistances(\n", + " sourcepattern=ntw.pointpatterns[\"clients\"],\n", + " destpattern=ntw.pointpatterns[\"facilities\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The expected result here is a Dijkstra distance between clients and facilities points, so we our case an array 2D 100x5." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12.60302601, 3.93598651, 8.16571655, 6.04319467, 5.65607701],\n", + " [13.10096347, 4.43392397, 8.66365401, 6.54113213, 5.15813955],\n", + " [ 6.9095462 , 4.2425067 , 2.47223674, 0.34971486, 5.34955682],\n", + " [ 2.98196832, 7.84581224, 3.45534114, 3.57786302, 6.25374871],\n", + " [ 7.5002892 , 6.32806975, 4.55779979, 6.43527791, 11.75939222],\n", + " [ 0.60209077, 11.42987132, 5.03940023, 7.16192211, 9.8378078 ],\n", + " [ 5.37335867, 6.20113923, 2.43086927, 4.30834738, 9.6324617 ],\n", + " [ 5.40801577, 5.41976478, 3.02929369, 1.15181557, 4.85108725],\n", + " [ 3.68807115, 8.51585171, 2.12538061, 4.24790249, 7.94717417],\n", + " [14.22503627, 4.60274429, 9.78772681, 7.66520493, 4.98931924]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 10, 10, 10, 10])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "facility_capacity = numpy.array([10, 10, 10, 10, 10])\n", + "facility_capacity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With ``LSCP.from_cost_matrix`` we model LSC problem to cover all demand points with $p$ facility points within `max_coverage` meters as service radius using cost matrix calculated previously." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_from_cost_matrix = LSCP.from_cost_matrix(cost_matrix, SERVICE_RADIUS, facility_constraints=facility_capacity )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LSCP:\n", + "MINIMIZE\n", + "1*x_0_ + 1*x_1_ + 1*x_2_ + 1*x_3_ + 1*x_4_ + 0\n", + "SUBJECT TO\n", + "_C1: x_1_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C2: x_1_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C3: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C4: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C5: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", + "\n", + "_C6: x_0_ + x_2_ + x_3_ >= 1\n", + "\n", + "_C7: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", + "\n", + "_C8: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C9: x_0_ + x_2_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C10: x_1_ + x_3_ + x_4_ >= 1\n", + "\n", + "_C11: - 10 x_0_ >= -4.9\n", + "\n", + "_C12: - 10 x_1_ >= -6.4\n", + "\n", + "_C13: - 10 x_2_ >= -4.9\n", + "\n", + "_C14: - 10 x_3_ >= -10\n", + "\n", + "_C15: - 10 x_4_ >= -4.9\n", + "\n", + "VARIABLES\n", + "0 <= x_0_ <= 1 Integer\n", + "0 <= x_1_ <= 1 Integer\n", + "0 <= x_2_ <= 1 Integer\n", + "0 <= x_3_ <= 1 Integer\n", + "0 <= x_4_ <= 1 Integer" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lscp_from_cost_matrix.problem" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_from_cost_matrix = lscp_from_cost_matrix.solve(solver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected result is an instance of LSCP." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lscp_from_cost_matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using GeoDataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assigning predefined location using a geodataframe column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "facilities_snapped['predefined_loc'] = numpy.array([0, 0, 0, 0, 1])\n", + "facilities_snapped" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With ``LSCP.from_geodataframe`` we model the LSC problem to cover all demand points with $p$ facility points within `max_coverage` meters as service radius using geodataframes without calculating the cost matrix previously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_from_geodataframe = LSCP.from_geodataframe(\n", + " clients_snapped, facilities_snapped, \"geometry\", \"geometry\", SERVICE_RADIUS, distance_metric=\"euclidean\"\n", + ")\n", + "lscp_from_geodataframe = lscp_from_geodataframe.solve(solver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected result is an instance of LSCP." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_from_geodataframe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modelling LSCP with preselected facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_preselected_from_geodataframe = LSCP.from_geodataframe(\n", + " clients_snapped, facilities_snapped, \"geometry\", \"geometry\", SERVICE_RADIUS, predefined_facility_col=\"predefined_loc\", distance_metric=\"euclidean\"\n", + ")\n", + "lscp_preselected_from_geodataframe = lscp_preselected_from_geodataframe.solve(solver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cell below describe the plotting of the results. For each method from LSCP class (from_cost_matrix, from_geodataframe) there is a plot displaying the facility site that was selected with a star colored and the points covered with the same color. Sometimes the demand points will be colored with not expected colors, it represents the coverage overlapping." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.patches import Patch\n", + "import matplotlib.lines as mlines\n", + "\n", + "dv_colors_arr = [\n", + " \"darkcyan\",\n", + " \"mediumseagreen\",\n", + " \"cyan\",\n", + " \"darkslategray\",\n", + " \"lightskyblue\",\n", + " \"limegreen\",\n", + " \"darkgoldenrod\",\n", + " \"peachpuff\",\n", + " \"coral\",\n", + " \"mediumvioletred\",\n", + " \"blueviolet\",\n", + " \"fuchsia\",\n", + " \"thistle\",\n", + " \"lavender\",\n", + " \"saddlebrown\",\n", + "] \n", + "\n", + "dv_colors = { f\"y{i}\":dv_colors_arr[i] for i in range(len(dv_colors_arr))}\n", + "\n", + "def plot_results(model, facility_points):\n", + " arr_points = []\n", + " fac_sites = []\n", + " \n", + " for i in range(FACILITY_COUNT):\n", + " if model.fac2cli[i]:\n", + "\n", + " geom = client_points.iloc[model.fac2cli[i]]['geometry']\n", + " arr_points.append(geom)\n", + " fac_sites.append(i)\n", + "\n", + " fig, ax = plt.subplots(figsize=(6, 6))\n", + " legend_elements = []\n", + "\n", + " street.plot(ax=ax, alpha=1, color='black', zorder=1)\n", + " legend_elements.append(mlines.Line2D(\n", + " [],\n", + " [],\n", + " color='black',\n", + " label='streets',\n", + " ))\n", + "\n", + " facility_points.plot(ax=ax, color='brown', marker=\"*\", markersize=80, zorder=2)\n", + " legend_elements.append(mlines.Line2D(\n", + " [],\n", + " [],\n", + " color='brown',\n", + " marker=\"*\",\n", + " linewidth=0,\n", + " label=f'facility sites ($n$={FACILITY_COUNT})'\n", + " ))\n", + "\n", + " for i in range(len(arr_points)):\n", + " gdf = geopandas.GeoDataFrame(arr_points[i])\n", + "\n", + " l = f\"y{fac_sites[i]}\"\n", + "\n", + " label = f\"coverage_points by y{fac_sites[i]}\"\n", + " legend_elements.append(Patch(facecolor=dv_colors[l], edgecolor=\"k\", label=label))\n", + "\n", + " gdf.plot(ax=ax, zorder=3, alpha=0.7, edgecolor=\"k\", color=dv_colors[l], label=label)\n", + " facility_points.iloc[[fac_sites[i]]].plot(ax=ax,\n", + " marker=\"*\",\n", + " markersize=200 * 3.0,\n", + " alpha=0.8,\n", + " zorder=4,\n", + " edgecolor=\"k\",\n", + " facecolor=dv_colors[l])\n", + " \n", + " legend_elements.append(mlines.Line2D(\n", + " [],\n", + " [],\n", + " color=dv_colors[l],\n", + " marker=\"*\",\n", + " ms=20 / 2,\n", + " markeredgecolor=\"k\",\n", + " linewidth=0,\n", + " alpha=0.8,\n", + " label=f\"y{fac_sites[i]} facility selected\",\n", + " ))\n", + "\n", + " plt.title(\"LSCP\", fontweight=\"bold\")\n", + " plt.legend(handles = legend_elements, loc='upper left', bbox_to_anchor=(1.05, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSCP built from cost matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_results(lscp_from_cost_matrix, facility_points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSCP built from geodataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_results(lscp_from_geodataframe, facility_points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may notice that the models are different. This result is expected as the distance between facility and demand points is calculated with different metrics. The cost matrix is calculated with dijkstra distance while the distance using geodataframe is calculated with euclidean distance. \n", + "\n", + "But why it needs just one facility point to cover all of those demand points? It can be explained by the nature of the problem. The problem was configured in a synthetic manner, the street is created with 10x10 lattice and the max_coverage parameter is 8 meters, so this result is not weird at all. You can change the max_coverage parameter to 2 meters and you will obtain a different result but be aware with how many points will be covered." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSCP with preselected facilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_results(lscp_preselected_from_geodataframe, facility_points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [Church, R. L., & Murray, A. T. (2018). Location covering models: History, applications and advancements (1st edition 2018). Springer](https://www.springer.com/gb/book/9783319998459)\n", + "- [Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations Research, 19(6), 1363–1373.](https://pubsonline.informs.org/doi/abs/10.1287/opre.19.6.1363)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "56b72aab97c5d88c22a6bf5872989e2e65e9296dc12395fbfb8350007c775deb" + }, + "kernelspec": { + "display_name": "Python 3.8.13 ('geo_env')", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From da114100480589396050bbbf6c11ee1c60081262 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 10:29:58 -0400 Subject: [PATCH 02/34] add capacity constraint --- spopt/locate/base.py | 47 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 014502d6..735f8291 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -411,6 +411,53 @@ def add_predefined_facility_constraint( "before predefined facility must set facility variable" ) + @staticmethod + def add_facility_capacity_constraint( + obj: T_FacModel, model, ni, cl_ni, range_facility, range_client + ) -> None: + """ + set facility capacity constraint: + In plain-ish English : + Demand at (i) multiplied by the fraction of demand (i) assigned to facility (j) must be <= to facility (j)'s capacity. a_i(Z_i_j) <= C_j(X_j) + n1_1 * fac_var1 + n1_2 * fac_var1 + ... + nij * fac_varj >= dem_var[i] + + Parameters + ---------- + obj: T_FacModel + bounded type of LocateSolver class + model: pulp.LpProblem + optimization model problem + ni: np.array + two-dimensional array that defines candidate sites between facility points within a distance to supply {i} demand point + cl_ni: np.array + one-dimensional array that defines capacity limits of facility points + range_facility: range + range of facility points quantity + range_client: range + range of demand points quantity + + Returns + ------- + None + """ + if hasattr(obj, "fac_vars"): #and hasattr(obj, "cli_vars"): + fac_vars = getattr(obj, "fac_vars") + #dem_vars = getattr(obj, "cli_vars") + + ni_t = ni.transpose() #shift array so facilities represented by a row and each column a demand node value + dem = ni_t.shape[1] # total demand pts + + for j in range_facility: + zij = sum(ni_t[j]) # sum of demand pts assigned to a facility. + model += ( + pulp.lpSum([ ni_t[j][i] * zij/dem for i in range_client ]) + >= cl_ni[j] * fac_vars[j] + ) + else: + raise AttributeError( + "before setting constraints must set facility variable" + ) + @staticmethod def add_maximal_coverage_constraint( obj: T_FacModel, model, ni, range_facility, range_client From 5dcd9c45dc307c6aed2c494bbb23046523f09af8 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 10:30:11 -0400 Subject: [PATCH 03/34] implement capacity constraint --- spopt/locate/coverage.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 7b242a9d..2934d55a 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -60,6 +60,7 @@ def from_cost_matrix( cost_matrix: np.array, service_radius: float, predefined_facilities_arr: np.array = None, + facility_constraints: np.array = None, name: str = "LSCP", ): """ @@ -145,6 +146,11 @@ def from_cost_matrix( lscp, lscp.problem, lscp.aij, r_fac, r_cli ) + if facility_constraints is not None: + FacilityModelBuilder.add_facility_capacity_constraint( + lscp, lscp.problem, lscp.aij, facility_constraints, r_fac, r_cli + ) + return lscp @classmethod From 6f832cbc6d5a47a963459083e5070f6f9ac62c55 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 14:44:14 -0400 Subject: [PATCH 04/34] add dev notes --- spopt/locate/coverage.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 2934d55a..832b1ae7 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -131,6 +131,8 @@ def from_cost_matrix( model = pulp.LpProblem(name, pulp.LpMinimize) lscp = LSCP(name, model) + #if capacities exist, create later + #will also need to add demand variables FacilityModelBuilder.add_facility_integer_variable(lscp, r_fac, "x[{i}]") lscp.aij = np.zeros(cost_matrix.shape) From bc01c58d19d4072de3dc262b63d290d037c94534 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 14:44:45 -0400 Subject: [PATCH 05/34] update LP problem --- spopt/locate/base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 735f8291..e6541f1d 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -450,8 +450,8 @@ def add_facility_capacity_constraint( for j in range_facility: zij = sum(ni_t[j]) # sum of demand pts assigned to a facility. model += ( - pulp.lpSum([ ni_t[j][i] * zij/dem for i in range_client ]) - >= cl_ni[j] * fac_vars[j] + pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) + <= cl_ni[j] * fac_vars[j] ) else: raise AttributeError( From b33d51cffbf786b5822758e51230f523c10f7b81 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 14:45:22 -0400 Subject: [PATCH 06/34] rerun notebook --- notebooks/lscp_capacity.ipynb | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 299c1279..6a42205e 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -608,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -709,9 +709,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plot_results(lscp_from_cost_matrix, facility_points)" ] From 5b428c2dc0ceb0f86690b0833c3e2931199a772e Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 16:42:18 -0400 Subject: [PATCH 07/34] update client_assign_integer_variable to set cat --- spopt/locate/base.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index e6541f1d..f60e5745 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -208,7 +208,7 @@ def add_client_integer_variable(obj: T_FacModel, range_client, var_name) -> None @staticmethod def add_client_assign_integer_variable( - obj: T_FacModel, range_client, range_facility, var_name + obj: T_FacModel, range_client, range_facility, var_name, lp_category ) -> None: """ @@ -223,22 +223,31 @@ def add_client_assign_integer_variable( var_name: str formatted string client assigning variable name + lp_category: pulp.LpVariable parameter + The category this variable is in, Integer or Continuous Returns ------- None """ - cli_assgn_vars = [ - [ - pulp.LpVariable( - var_name.format(i=i, j=j), lowBound=0, upBound=1, cat=pulp.LpInteger - ) - for j in range_facility + #lp_category should be either pulp.LpContinuous OR pulp.LpInteger + if lp_category != pulp.LpBinary: + cli_assgn_vars = [ + [ + pulp.LpVariable( + var_name.format(i=i, j=j), lowBound=0, upBound=1, cat=lp_category + ) + for j in range_facility + ] + for i in range_client ] - for i in range_client - ] - setattr(obj, "cli_assgn_vars", cli_assgn_vars) + setattr(obj, "cli_assgn_vars", cli_assgn_vars) + + else: + #error message indicating improper pulp variable parameter + #what kind of error should we create here? + pass @staticmethod def add_weight_continuous_variable(obj: T_FacModel) -> None: From 1ab0cfe71bda4f6f9c6721610a17c3e03cd07e34 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 16:43:47 -0400 Subject: [PATCH 08/34] update function parameter definition --- spopt/locate/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index f60e5745..f86b8a77 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -224,7 +224,7 @@ def add_client_assign_integer_variable( formatted string client assigning variable name lp_category: pulp.LpVariable parameter - The category this variable is in, Integer or Continuous + The category this variable is in, pulp.LpInteger or pulp.LpContinuous Returns ------- From 3016189b1fa517954475a399d4a5b2f6d82866b1 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Wed, 31 Aug 2022 16:55:38 -0400 Subject: [PATCH 09/34] add facility capacity and demand quantity params --- spopt/locate/coverage.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 832b1ae7..73b2b05d 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -60,7 +60,8 @@ def from_cost_matrix( cost_matrix: np.array, service_radius: float, predefined_facilities_arr: np.array = None, - facility_constraints: np.array = None, + facility_capacity: np.array = None, + demand_quantity: np.array = None, name: str = "LSCP", ): """ @@ -133,6 +134,7 @@ def from_cost_matrix( #if capacities exist, create later #will also need to add demand variables + #warn users if they pass demand_quantity array, but no facility capacities FacilityModelBuilder.add_facility_integer_variable(lscp, r_fac, "x[{i}]") lscp.aij = np.zeros(cost_matrix.shape) @@ -148,9 +150,9 @@ def from_cost_matrix( lscp, lscp.problem, lscp.aij, r_fac, r_cli ) - if facility_constraints is not None: + if facility_capacity is not None: FacilityModelBuilder.add_facility_capacity_constraint( - lscp, lscp.problem, lscp.aij, facility_constraints, r_fac, r_cli + lscp, lscp.problem, lscp.aij, facility_capacity, r_fac, r_cli ) return lscp From c36e68a28ed9b03af2aca2c4b942deb026dd27c8 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 1 Sep 2022 11:40:47 -0400 Subject: [PATCH 10/34] add warning: input demand quant w/ no fac capacity --- spopt/locate/coverage.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 73b2b05d..a3bfe41e 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -60,8 +60,8 @@ def from_cost_matrix( cost_matrix: np.array, service_radius: float, predefined_facilities_arr: np.array = None, - facility_capacity: np.array = None, - demand_quantity: np.array = None, + facility_capacity_arr: np.array = None, #one-dimensional + demand_quantity_arr: np.array = None, #one-dimensional name: str = "LSCP", ): """ @@ -131,6 +131,12 @@ def from_cost_matrix( model = pulp.LpProblem(name, pulp.LpMinimize) lscp = LSCP(name, model) + + if demand_quantity_arr is not None and facility_capacity_arr is None: + warnings.warn( + "Demand quantities supplied with no facility capacities. Model cannot solve for capacities without facility capacity values.", + Warning, + ) #if capacities exist, create later #will also need to add demand variables @@ -150,9 +156,9 @@ def from_cost_matrix( lscp, lscp.problem, lscp.aij, r_fac, r_cli ) - if facility_capacity is not None: + if facility_capacity_arr is not None: FacilityModelBuilder.add_facility_capacity_constraint( - lscp, lscp.problem, lscp.aij, facility_capacity, r_fac, r_cli + lscp, lscp.problem, lscp.aij, facility_capacity_arr, r_fac, r_cli ) return lscp From 2a25602720994a91501b1eeb0dfebc9a515c643a Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 2 Sep 2022 09:28:08 -0400 Subject: [PATCH 11/34] update models for capacity constraint --- spopt/locate/base.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index f86b8a77..ac4e1d4f 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -230,7 +230,6 @@ def add_client_assign_integer_variable( ------- None """ - #lp_category should be either pulp.LpContinuous OR pulp.LpInteger if lp_category != pulp.LpBinary: cli_assgn_vars = [ [ @@ -246,7 +245,7 @@ def add_client_assign_integer_variable( else: #error message indicating improper pulp variable parameter - #what kind of error should we create here? + #what kind of error should we create here? This is a message for developers, not users. pass @staticmethod @@ -422,7 +421,7 @@ def add_predefined_facility_constraint( @staticmethod def add_facility_capacity_constraint( - obj: T_FacModel, model, ni, cl_ni, range_facility, range_client + obj: T_FacModel, model, ni, cl_ni, dq_ni, range_facility, range_client ) -> None: """ set facility capacity constraint: @@ -440,6 +439,8 @@ def add_facility_capacity_constraint( two-dimensional array that defines candidate sites between facility points within a distance to supply {i} demand point cl_ni: np.array one-dimensional array that defines capacity limits of facility points + dq_ni: np.array + one-dimensional array that defines demand quantities for demand points range_facility: range range of facility points quantity range_client: range @@ -449,22 +450,23 @@ def add_facility_capacity_constraint( ------- None """ - if hasattr(obj, "fac_vars"): #and hasattr(obj, "cli_vars"): + if hasattr(obj, "fac_vars") and hasattr(obj, "cli_assgn_vars"): fac_vars = getattr(obj, "fac_vars") - #dem_vars = getattr(obj, "cli_vars") + cli_assn_vars = getattr(obj, "cli_assgn_vars") - ni_t = ni.transpose() #shift array so facilities represented by a row and each column a demand node value - dem = ni_t.shape[1] # total demand pts + ni_t = ni.transpose() #may not even need this any more for j in range_facility: - zij = sum(ni_t[j]) # sum of demand pts assigned to a facility. + #Demand at (i) multiplied by the fraction of demand (i) assigned to facility (j) must be <= to facility (j)'s capacity. a_i(Z_i_j) <= C_j(X_j) + #zij = sum(ni_t[j]) # sum of demand pts assigned to a facility. model += ( - pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) + #pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) + pulp.lpSum([ dq_ni[i] * cli_assn_vars[i][j] for i in range_client ]) <= cl_ni[j] * fac_vars[j] ) else: raise AttributeError( - "before setting constraints must set facility variable" + "before setting constraints must set facility variable and demand quantity variable" #might want to update this message later ) @staticmethod From 87cd5ce160a1d7445e1dce8ebee08592dfdf4165 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 2 Sep 2022 09:29:26 -0400 Subject: [PATCH 12/34] add case statements for capacity parameter inputs --- spopt/locate/coverage.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index a3bfe41e..1c63fabc 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -138,29 +138,34 @@ def from_cost_matrix( Warning, ) - #if capacities exist, create later - #will also need to add demand variables - #warn users if they pass demand_quantity array, but no facility capacities - FacilityModelBuilder.add_facility_integer_variable(lscp, r_fac, "x[{i}]") - lscp.aij = np.zeros(cost_matrix.shape) lscp.aij[cost_matrix <= service_radius] = 1 + if demand_quantity_arr is None and facility_capacity_arr is not None: + demand_quantity_arr = np.ones(cost_matrix.shape[0]) + + #if capacities exist, create later? + FacilityModelBuilder.add_facility_integer_variable(lscp, r_fac, "x[{i}]") + if predefined_facilities_arr is not None: FacilityModelBuilder.add_predefined_facility_constraint( lscp, lscp.problem, predefined_facilities_arr ) - lscp.__add_obj() - FacilityModelBuilder.add_set_covering_constraint( - lscp, lscp.problem, lscp.aij, r_fac, r_cli - ) + if demand_quantity_arr is not None: + FacilityModelBuilder.add_client_assign_integer_variable( + lscp, r_cli, r_fac, "z[{i}_{j}]", pulp.LpContinuous) if facility_capacity_arr is not None: FacilityModelBuilder.add_facility_capacity_constraint( - lscp, lscp.problem, lscp.aij, facility_capacity_arr, r_fac, r_cli + lscp, lscp.problem, lscp.aij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli ) + lscp.__add_obj() + FacilityModelBuilder.add_set_covering_constraint( + lscp, lscp.problem, lscp.aij, r_fac, r_cli + ) + return lscp @classmethod From 69dc93f5df037b6df41c80e233fbe4ecc7ccd4a5 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 2 Sep 2022 10:35:10 -0400 Subject: [PATCH 13/34] add dev notes --- spopt/locate/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index ac4e1d4f..99b5dae8 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -462,7 +462,7 @@ def add_facility_capacity_constraint( model += ( #pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) pulp.lpSum([ dq_ni[i] * cli_assn_vars[i][j] for i in range_client ]) - <= cl_ni[j] * fac_vars[j] + <= cl_ni[j] * fac_vars[j] #my concern is that fac_vars[j] isn't correct. Shows up as zero in the LP problem formulation, but I thought this would just be a decision variable that is assigned 0 or 1 depending on if a facility is selected? ) else: raise AttributeError( From 171264a3914d72f559e9664e2fa65dfc05d013f9 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 2 Sep 2022 10:35:20 -0400 Subject: [PATCH 14/34] rerun notebook --- notebooks/lscp_capacity.ipynb | 242 +++++++++++++++++++++++++++++----- 1 file changed, 209 insertions(+), 33 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 6a42205e..583388af 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -149,7 +149,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -210,7 +210,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -292,7 +292,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -403,6 +403,27 @@ "facility_capacity" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demand_quantity = numpy.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "demand_quantity" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -412,16 +433,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "lscp_from_cost_matrix = LSCP.from_cost_matrix(cost_matrix, SERVICE_RADIUS, facility_constraints=facility_capacity )\n" + "lscp_from_cost_matrix = LSCP.from_cost_matrix(\n", + " cost_matrix, SERVICE_RADIUS, facility_capacity_arr=facility_capacity, demand_quantity_arr=demand_quantity)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -431,45 +453,100 @@ "MINIMIZE\n", "1*x_0_ + 1*x_1_ + 1*x_2_ + 1*x_3_ + 1*x_4_ + 0\n", "SUBJECT TO\n", - "_C1: x_1_ + x_3_ + x_4_ >= 1\n", + "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ + z_5_0_ + z_6_0_\n", + " + z_7_0_ + z_8_0_ + z_9_0_ <= 0\n", "\n", - "_C2: x_1_ + x_3_ + x_4_ >= 1\n", + "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ + z_5_1_ + z_6_1_\n", + " + z_7_1_ + z_8_1_ + z_9_1_ <= 0\n", "\n", - "_C3: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", + "_C3: - 10 x_2_ + z_0_2_ + z_1_2_ + z_2_2_ + z_3_2_ + z_4_2_ + z_5_2_ + z_6_2_\n", + " + z_7_2_ + z_8_2_ + z_9_2_ <= 0\n", "\n", - "_C4: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", + "_C4: - 10 x_3_ + z_0_3_ + z_1_3_ + z_2_3_ + z_3_3_ + z_4_3_ + z_5_3_ + z_6_3_\n", + " + z_7_3_ + z_8_3_ + z_9_3_ <= 0\n", "\n", - "_C5: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", + "_C5: - 10 x_4_ + z_0_4_ + z_1_4_ + z_2_4_ + z_3_4_ + z_4_4_ + z_5_4_ + z_6_4_\n", + " + z_7_4_ + z_8_4_ + z_9_4_ <= 0\n", "\n", - "_C6: x_0_ + x_2_ + x_3_ >= 1\n", + "_C6: x_1_ + x_3_ + x_4_ >= 1\n", "\n", - "_C7: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", + "_C7: x_1_ + x_3_ + x_4_ >= 1\n", "\n", "_C8: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", "\n", - "_C9: x_0_ + x_2_ + x_3_ + x_4_ >= 1\n", + "_C9: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", "\n", - "_C10: x_1_ + x_3_ + x_4_ >= 1\n", + "_C10: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", "\n", - "_C11: - 10 x_0_ >= -4.9\n", + "_C11: x_0_ + x_2_ + x_3_ >= 1\n", "\n", - "_C12: - 10 x_1_ >= -6.4\n", + "_C12: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", "\n", - "_C13: - 10 x_2_ >= -4.9\n", + "_C13: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", "\n", - "_C14: - 10 x_3_ >= -10\n", + "_C14: x_0_ + x_2_ + x_3_ + x_4_ >= 1\n", "\n", - "_C15: - 10 x_4_ >= -4.9\n", + "_C15: x_1_ + x_3_ + x_4_ >= 1\n", "\n", "VARIABLES\n", "0 <= x_0_ <= 1 Integer\n", "0 <= x_1_ <= 1 Integer\n", "0 <= x_2_ <= 1 Integer\n", "0 <= x_3_ <= 1 Integer\n", - "0 <= x_4_ <= 1 Integer" + "0 <= x_4_ <= 1 Integer\n", + "z_0_0_ <= 1 Continuous\n", + "z_0_1_ <= 1 Continuous\n", + "z_0_2_ <= 1 Continuous\n", + "z_0_3_ <= 1 Continuous\n", + "z_0_4_ <= 1 Continuous\n", + "z_1_0_ <= 1 Continuous\n", + "z_1_1_ <= 1 Continuous\n", + "z_1_2_ <= 1 Continuous\n", + "z_1_3_ <= 1 Continuous\n", + "z_1_4_ <= 1 Continuous\n", + "z_2_0_ <= 1 Continuous\n", + "z_2_1_ <= 1 Continuous\n", + "z_2_2_ <= 1 Continuous\n", + "z_2_3_ <= 1 Continuous\n", + "z_2_4_ <= 1 Continuous\n", + "z_3_0_ <= 1 Continuous\n", + "z_3_1_ <= 1 Continuous\n", + "z_3_2_ <= 1 Continuous\n", + "z_3_3_ <= 1 Continuous\n", + "z_3_4_ <= 1 Continuous\n", + "z_4_0_ <= 1 Continuous\n", + "z_4_1_ <= 1 Continuous\n", + "z_4_2_ <= 1 Continuous\n", + "z_4_3_ <= 1 Continuous\n", + "z_4_4_ <= 1 Continuous\n", + "z_5_0_ <= 1 Continuous\n", + "z_5_1_ <= 1 Continuous\n", + "z_5_2_ <= 1 Continuous\n", + "z_5_3_ <= 1 Continuous\n", + "z_5_4_ <= 1 Continuous\n", + "z_6_0_ <= 1 Continuous\n", + "z_6_1_ <= 1 Continuous\n", + "z_6_2_ <= 1 Continuous\n", + "z_6_3_ <= 1 Continuous\n", + "z_6_4_ <= 1 Continuous\n", + "z_7_0_ <= 1 Continuous\n", + "z_7_1_ <= 1 Continuous\n", + "z_7_2_ <= 1 Continuous\n", + "z_7_3_ <= 1 Continuous\n", + "z_7_4_ <= 1 Continuous\n", + "z_8_0_ <= 1 Continuous\n", + "z_8_1_ <= 1 Continuous\n", + "z_8_2_ <= 1 Continuous\n", + "z_8_3_ <= 1 Continuous\n", + "z_8_4_ <= 1 Continuous\n", + "z_9_0_ <= 1 Continuous\n", + "z_9_1_ <= 1 Continuous\n", + "z_9_2_ <= 1 Continuous\n", + "z_9_3_ <= 1 Continuous\n", + "z_9_4_ <= 1 Continuous" ] }, - 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" + ], + "text/plain": [ + " id geometry comp_label predefined_loc\n", + "0 0 POINT (9.00000 3.25259) 0 0\n", + "1 1 POINT (0.91963 6.00000) 0 0\n", + "2 2 POINT (5.31010 4.00000) 0 0\n", + "3 3 POINT (5.18758 6.00000) 0 0\n", + "4 4 POINT (6.51169 10.00000) 0 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "facilities_snapped['predefined_loc'] = numpy.array([0, 0, 0, 0, 1])\n", "facilities_snapped" @@ -547,12 +705,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for -=: 'float' and 'str'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/erinolson/spopt/notebooks/lscp_capacity.ipynb Cell 39'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m lscp_from_geodataframe \u001b[39m=\u001b[39m LSCP\u001b[39m.\u001b[39;49mfrom_geodataframe(\n\u001b[1;32m 2\u001b[0m gdf_demand\u001b[39m=\u001b[39;49mclients_snapped, gdf_fac\u001b[39m=\u001b[39;49mfacilities_snapped, demand_col\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m, facility_col\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m, service_radius\u001b[39m=\u001b[39;49mSERVICE_RADIUS, distance_metric\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39meuclidean\u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m 3\u001b[0m )\n\u001b[1;32m 4\u001b[0m lscp_from_geodataframe \u001b[39m=\u001b[39m lscp_from_geodataframe\u001b[39m.\u001b[39msolve(solver)\n", + "File \u001b[0;32m~/spopt/spopt/locate/coverage.py:285\u001b[0m, in \u001b[0;36mLSCP.from_geodataframe\u001b[0;34m(cls, gdf_demand, gdf_fac, demand_col, facility_col, service_radius, predefined_facility_col, distance_metric, name)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 280\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mgeodataframes crs are different: gdf_demand-\u001b[39m\u001b[39m{\u001b[39;00mgdf_demand\u001b[39m.\u001b[39mcrs\u001b[39m}\u001b[39;00m\u001b[39m, gdf_fac-\u001b[39m\u001b[39m{\u001b[39;00mgdf_fac\u001b[39m.\u001b[39mcrs\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 281\u001b[0m )\n\u001b[1;32m 283\u001b[0m distances \u001b[39m=\u001b[39m cdist(dem_data, fac_data, distance_metric)\n\u001b[0;32m--> 285\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49mfrom_cost_matrix(\n\u001b[1;32m 286\u001b[0m distances, service_radius, predefined_facilities_arr, name\n\u001b[1;32m 287\u001b[0m )\n", + "File \u001b[0;32m~/spopt/spopt/locate/coverage.py:160\u001b[0m, in \u001b[0;36mLSCP.from_cost_matrix\u001b[0;34m(cls, cost_matrix, service_radius, predefined_facilities_arr, facility_capacity_arr, demand_quantity_arr, name)\u001b[0m\n\u001b[1;32m 156\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39madd_client_assign_integer_variable(\n\u001b[1;32m 157\u001b[0m lscp, r_cli, r_fac, \u001b[39m\"\u001b[39m\u001b[39mz[\u001b[39m\u001b[39m{i}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{j}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m, pulp\u001b[39m.\u001b[39mLpContinuous)\n\u001b[1;32m 159\u001b[0m \u001b[39mif\u001b[39;00m facility_capacity_arr \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39;49madd_facility_capacity_constraint(\n\u001b[1;32m 161\u001b[0m lscp, lscp\u001b[39m.\u001b[39;49mproblem, lscp\u001b[39m.\u001b[39;49maij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli\n\u001b[1;32m 162\u001b[0m )\n\u001b[1;32m 164\u001b[0m lscp\u001b[39m.\u001b[39m__add_obj()\n\u001b[1;32m 165\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39madd_set_covering_constraint(\n\u001b[1;32m 166\u001b[0m lscp, lscp\u001b[39m.\u001b[39mproblem, lscp\u001b[39m.\u001b[39maij, r_fac, r_cli\n\u001b[1;32m 167\u001b[0m )\n", + "File \u001b[0;32m~/spopt/spopt/locate/base.py:464\u001b[0m, in \u001b[0;36mFacilityModelBuilder.add_facility_capacity_constraint\u001b[0;34m(obj, model, ni, cl_ni, dq_ni, range_facility, range_client)\u001b[0m\n\u001b[1;32m 457\u001b[0m ni_t \u001b[39m=\u001b[39m ni\u001b[39m.\u001b[39mtranspose() \u001b[39m#may not even need this any more\u001b[39;00m\n\u001b[1;32m 459\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m range_facility:\n\u001b[1;32m 460\u001b[0m \u001b[39m#Demand at (i) multiplied by the fraction of demand (i) assigned to facility (j) must be <= to facility (j)'s capacity. a_i(Z_i_j) <= C_j(X_j)\u001b[39;00m\n\u001b[1;32m 461\u001b[0m \u001b[39m#zij = sum(ni_t[j]) # sum of demand pts assigned to a facility.\u001b[39;00m\n\u001b[1;32m 462\u001b[0m model \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m 463\u001b[0m \u001b[39m#pulp.lpSum([ ni_t[j][i] * zij for i in range_client ])\u001b[39;00m\n\u001b[0;32m--> 464\u001b[0m pulp\u001b[39m.\u001b[39;49mlpSum([ dq_ni[i] \u001b[39m*\u001b[39;49m cli_assn_vars[i][j] \u001b[39mfor\u001b[39;49;00m i \u001b[39min\u001b[39;49;00m range_client ])\n\u001b[1;32m 465\u001b[0m \u001b[39m<\u001b[39;49m\u001b[39m=\u001b[39;49m cl_ni[j] \u001b[39m*\u001b[39;49m fac_vars[j]\n\u001b[1;32m 466\u001b[0m )\n\u001b[1;32m 467\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 468\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[1;32m 469\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mbefore setting constraints must set facility variable and demand quantity variable\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m#might want to update this message later\u001b[39;00m\n\u001b[1;32m 470\u001b[0m )\n", + "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:1022\u001b[0m, in \u001b[0;36mLpAffineExpression.__le__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1021\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__le__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[0;32m-> 1022\u001b[0m \u001b[39mreturn\u001b[39;00m LpConstraint(\u001b[39mself\u001b[39;49m \u001b[39m-\u001b[39;49m other, const\u001b[39m.\u001b[39mLpConstraintLE)\n", + "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:943\u001b[0m, in \u001b[0;36mLpAffineExpression.__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 942\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__sub__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[0;32m--> 943\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcopy()\u001b[39m.\u001b[39;49msubInPlace(other)\n", + "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:910\u001b[0m, in \u001b[0;36mLpAffineExpression.subInPlace\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maddterm(other, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 909\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(other, LpAffineExpression):\n\u001b[0;32m--> 910\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconstant \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m other\u001b[39m.\u001b[39mconstant\n\u001b[1;32m 911\u001b[0m \u001b[39mfor\u001b[39;00m v, x \u001b[39min\u001b[39;00m other\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 912\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maddterm(v, \u001b[39m-\u001b[39mx)\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -=: 'float' and 'str'" + ] + } + ], "source": [ "lscp_from_geodataframe = LSCP.from_geodataframe(\n", - " clients_snapped, facilities_snapped, \"geometry\", \"geometry\", SERVICE_RADIUS, distance_metric=\"euclidean\"\n", + " gdf_demand=clients_snapped, gdf_fac=facilities_snapped, demand_col=\"geometry\", facility_col=\"geometry\", service_radius=SERVICE_RADIUS, distance_metric=\"euclidean\"\n", ")\n", "lscp_from_geodataframe = lscp_from_geodataframe.solve(solver)" ] @@ -608,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -709,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { From f3d878ecd3bc505b2f0348e9d60518f993284e24 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 2 Sep 2022 10:54:19 -0400 Subject: [PATCH 15/34] remove dev notes --- spopt/locate/base.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 99b5dae8..c6aabb2c 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -457,8 +457,6 @@ def add_facility_capacity_constraint( ni_t = ni.transpose() #may not even need this any more for j in range_facility: - #Demand at (i) multiplied by the fraction of demand (i) assigned to facility (j) must be <= to facility (j)'s capacity. a_i(Z_i_j) <= C_j(X_j) - #zij = sum(ni_t[j]) # sum of demand pts assigned to a facility. model += ( #pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) pulp.lpSum([ dq_ni[i] * cli_assn_vars[i][j] for i in range_client ]) From 6ca8e6885e9dfcbd24296110d36dfe150fb97437 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Tue, 6 Sep 2022 11:08:26 -0400 Subject: [PATCH 16/34] rerun notebook --- notebooks/lscp_capacity.ipynb | 40 ++++++++++++++++++++++++++--------- 1 file changed, 30 insertions(+), 10 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 583388af..6c3dab81 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -149,7 +149,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -210,7 +210,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -292,7 +292,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -557,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -573,16 +573,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -784,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -885,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -905,6 +905,26 @@ "plot_results(lscp_from_cost_matrix, facility_points)" ] }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[], [], [], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], []]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lscp_from_cost_matrix.fac2cli" + ] + }, { "cell_type": "markdown", "metadata": {}, From 7b3e52fb9eacb3b98dc02c5b0608c8efca4333fb Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Tue, 6 Sep 2022 11:09:19 -0400 Subject: [PATCH 17/34] add default value --- spopt/locate/base.py | 27 +++++++++++---------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index c6aabb2c..1fdde651 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -208,7 +208,7 @@ def add_client_integer_variable(obj: T_FacModel, range_client, var_name) -> None @staticmethod def add_client_assign_integer_variable( - obj: T_FacModel, range_client, range_facility, var_name, lp_category + obj: T_FacModel, range_client, range_facility, var_name, lp_category=pulp.LpInteger ) -> None: """ @@ -230,23 +230,18 @@ def add_client_assign_integer_variable( ------- None """ - if lp_category != pulp.LpBinary: - cli_assgn_vars = [ - [ - pulp.LpVariable( - var_name.format(i=i, j=j), lowBound=0, upBound=1, cat=lp_category - ) - for j in range_facility - ] - for i in range_client - ] - setattr(obj, "cli_assgn_vars", cli_assgn_vars) + cli_assgn_vars = [ + [ + pulp.LpVariable( + var_name.format(i=i, j=j), lowBound=0, upBound=1, cat=lp_category + ) + for j in range_facility + ] + for i in range_client + ] - else: - #error message indicating improper pulp variable parameter - #what kind of error should we create here? This is a message for developers, not users. - pass + setattr(obj, "cli_assgn_vars", cli_assgn_vars) @staticmethod def add_weight_continuous_variable(obj: T_FacModel) -> None: From d9af1eb3b4c72821110ad0d571b66bd2718005ae Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Tue, 6 Sep 2022 11:09:47 -0400 Subject: [PATCH 18/34] add changes from meeting with levi --- spopt/locate/coverage.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 1c63fabc..76bc42bb 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -132,19 +132,20 @@ def from_cost_matrix( model = pulp.LpProblem(name, pulp.LpMinimize) lscp = LSCP(name, model) + #use raise value error here this will stop everything + #!raise ValueError("message") if demand_quantity_arr is not None and facility_capacity_arr is None: warnings.warn( - "Demand quantities supplied with no facility capacities. Model cannot solve for capacities without facility capacity values.", + "Demand quantities supplied with no facility capacities. Model cannot satisfy clients with different demands without facility capacities.", Warning, ) lscp.aij = np.zeros(cost_matrix.shape) lscp.aij[cost_matrix <= service_radius] = 1 - if demand_quantity_arr is None and facility_capacity_arr is not None: + if (demand_quantity_arr is None) and (facility_capacity_arr is not None): demand_quantity_arr = np.ones(cost_matrix.shape[0]) - #if capacities exist, create later? FacilityModelBuilder.add_facility_integer_variable(lscp, r_fac, "x[{i}]") if predefined_facilities_arr is not None: @@ -154,17 +155,23 @@ def from_cost_matrix( if demand_quantity_arr is not None: FacilityModelBuilder.add_client_assign_integer_variable( - lscp, r_cli, r_fac, "z[{i}_{j}]", pulp.LpContinuous) + lscp, r_cli, r_fac, "z[{i}_{j}]", lp_category=pulp.LpContinuous) - if facility_capacity_arr is not None: FacilityModelBuilder.add_facility_capacity_constraint( lscp, lscp.problem, lscp.aij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli ) + #add demand satisfaction constraint 7.19 + #check all demands covered by a facility + #loop demand + #loop facility + + else: + FacilityModelBuilder.add_set_covering_constraint( + lscp, lscp.problem, lscp.aij, r_fac, r_cli + ) + lscp.__add_obj() - FacilityModelBuilder.add_set_covering_constraint( - lscp, lscp.problem, lscp.aij, r_fac, r_cli - ) return lscp From 2331606bceb7ae55d6f81812f056c331747dcc7d Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 11:31:58 -0400 Subject: [PATCH 19/34] add raise ValueError for dem quant w/ no fac cap. --- spopt/locate/coverage.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 76bc42bb..5a37348a 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -60,8 +60,8 @@ def from_cost_matrix( cost_matrix: np.array, service_radius: float, predefined_facilities_arr: np.array = None, - facility_capacity_arr: np.array = None, #one-dimensional - demand_quantity_arr: np.array = None, #one-dimensional + facility_capacity_arr: np.array = None, + demand_quantity_arr: np.array = None, name: str = "LSCP", ): """ @@ -132,12 +132,9 @@ def from_cost_matrix( model = pulp.LpProblem(name, pulp.LpMinimize) lscp = LSCP(name, model) - #use raise value error here this will stop everything - #!raise ValueError("message") if demand_quantity_arr is not None and facility_capacity_arr is None: - warnings.warn( - "Demand quantities supplied with no facility capacities. Model cannot satisfy clients with different demands without facility capacities.", - Warning, + raise ValueError( + "Demand quantities supplied with no facility capacities. Model cannot satisfy clients with different demands without facility capacities." ) lscp.aij = np.zeros(cost_matrix.shape) From b06948520e6a4ab63572416985b2641f659ea57f Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 12:19:22 -0400 Subject: [PATCH 20/34] add client demand satisfaction constraint --- spopt/locate/base.py | 39 +++++++++++++++++++++++++++++++++++---- 1 file changed, 35 insertions(+), 4 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 1fdde651..42988506 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -449,13 +449,44 @@ def add_facility_capacity_constraint( fac_vars = getattr(obj, "fac_vars") cli_assn_vars = getattr(obj, "cli_assgn_vars") - ni_t = ni.transpose() #may not even need this any more - for j in range_facility: model += ( - #pulp.lpSum([ ni_t[j][i] * zij for i in range_client ]) pulp.lpSum([ dq_ni[i] * cli_assn_vars[i][j] for i in range_client ]) - <= cl_ni[j] * fac_vars[j] #my concern is that fac_vars[j] isn't correct. Shows up as zero in the LP problem formulation, but I thought this would just be a decision variable that is assigned 0 or 1 depending on if a facility is selected? + <= cl_ni[j] * fac_vars[j] + ) + else: + raise AttributeError( + "before setting constraints must set facility variable and demand quantity variable" #might want to update this message later + ) + + @staticmethod + def add_client_demand_satisfaction_constraint( + obj: T_FacModel, model, range_client, range_facility + ) -> None: + """ + + Parameters + ---------- + obj: T_FacModel + bounded type of LocateSolver class + model: pulp.LpProblem + optimization model problem + range_client: range + range of demand points quantity + range_facility: range + range of facility points quantity + + Returns + ------- + None + """ + print('add satisfaction constraint') + if hasattr(obj, "fac_vars") and hasattr(obj, "cli_assgn_vars"): + cli_assn_vars = getattr(obj, "cli_assgn_vars") + + for i in range_client: + model += ( + pulp.lpSum([cli_assn_vars[i][j] for j in range_facility]) == 1 ) else: raise AttributeError( From e23357c261061351586ad039993bf187d16f45c6 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 12:19:58 -0400 Subject: [PATCH 21/34] add client demand satisfaction constraint --- spopt/locate/coverage.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 5a37348a..d7df809a 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -158,10 +158,8 @@ def from_cost_matrix( lscp, lscp.problem, lscp.aij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli ) - #add demand satisfaction constraint 7.19 - #check all demands covered by a facility - #loop demand - #loop facility + FacilityModelBuilder.add_client_demand_satisfaction_constraint( + lscp, lscp.problem, r_cli, r_fac) else: FacilityModelBuilder.add_set_covering_constraint( From fcb48551fbff90a010c6d9dc30c0db0d36fc478e Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 13:54:26 -0400 Subject: [PATCH 22/34] add capacities to from_geodataframe --- spopt/locate/coverage.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index d7df809a..7633b3ad 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -179,6 +179,8 @@ def from_geodataframe( facility_col: str, service_radius: float, predefined_facility_col: str = None, + facility_capacity_col: str = None, + demand_quantity_col: str = None, distance_metric: str = "euclidean", name: str = "LSCP", ): @@ -249,6 +251,18 @@ def from_geodataframe( >>> lscp_from_geodataframe.fac2cli """ + demand_quantity_arr = None + if demand_quantity_col is not None: + demand_quantity_arr = gdf_demand[demand_quantity_col].to_numpy() + + facility_capacity_arr = None + if facility_capacity_col is not None: + facility_capacity_arr = gdf_fac[facility_capacity_col].to_numpy() + + if demand_quantity_arr is not None and facility_capacity_arr is None: + raise ValueError( + "Demand quantities supplied with no facility capacities. Model cannot satisfy clients with different demands without facility capacities." + ) predefined_facilities_arr = None if predefined_facility_col is not None: @@ -285,7 +299,7 @@ def from_geodataframe( distances = cdist(dem_data, fac_data, distance_metric) return cls.from_cost_matrix( - distances, service_radius, predefined_facilities_arr, name + distances, service_radius, predefined_facilities_arr, facility_capacity_arr, demand_quantity_arr, name ) def facility_client_array(self) -> None: From 458aaf9fb9f878bd7ac4d05ab885449ee69c0afd Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 13:54:44 -0400 Subject: [PATCH 23/34] remove dev notes --- spopt/locate/base.py | 1 - 1 file changed, 1 deletion(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 42988506..e856cd89 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -480,7 +480,6 @@ def add_client_demand_satisfaction_constraint( ------- None """ - print('add satisfaction constraint') if hasattr(obj, "fac_vars") and hasattr(obj, "cli_assgn_vars"): cli_assn_vars = getattr(obj, "cli_assgn_vars") From 971721cf9aaf8f7b5c8b1f55cb2dea6d0e4418b6 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 15:05:26 -0400 Subject: [PATCH 24/34] run black --- spopt/locate/base.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index e856cd89..3c245be6 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -208,7 +208,11 @@ def add_client_integer_variable(obj: T_FacModel, range_client, var_name) -> None @staticmethod def add_client_assign_integer_variable( - obj: T_FacModel, range_client, range_facility, var_name, lp_category=pulp.LpInteger + obj: T_FacModel, + range_client, + range_facility, + var_name, + lp_category=pulp.LpInteger, ) -> None: """ @@ -451,12 +455,12 @@ def add_facility_capacity_constraint( for j in range_facility: model += ( - pulp.lpSum([ dq_ni[i] * cli_assn_vars[i][j] for i in range_client ]) + pulp.lpSum([dq_ni[i] * cli_assn_vars[i][j] for i in range_client]) <= cl_ni[j] * fac_vars[j] ) else: raise AttributeError( - "before setting constraints must set facility variable and demand quantity variable" #might want to update this message later + "before setting constraints must set facility variable and demand quantity variable" # might want to update this message later ) @staticmethod @@ -484,12 +488,10 @@ def add_client_demand_satisfaction_constraint( cli_assn_vars = getattr(obj, "cli_assgn_vars") for i in range_client: - model += ( - pulp.lpSum([cli_assn_vars[i][j] for j in range_facility]) == 1 - ) + model += pulp.lpSum([cli_assn_vars[i][j] for j in range_facility]) == 1 else: raise AttributeError( - "before setting constraints must set facility variable and demand quantity variable" #might want to update this message later + "before setting constraints must set facility variable and demand quantity variable" # might want to update this message later ) @staticmethod From fbe25570c912df004e5f731ecee9bb190512a16a Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 15:05:44 -0400 Subject: [PATCH 25/34] run black --- spopt/locate/coverage.py | 33 +++++++++++++++++++++++---------- 1 file changed, 23 insertions(+), 10 deletions(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 7633b3ad..76023dc4 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -60,8 +60,8 @@ def from_cost_matrix( cost_matrix: np.array, service_radius: float, predefined_facilities_arr: np.array = None, - facility_capacity_arr: np.array = None, - demand_quantity_arr: np.array = None, + facility_capacity_arr: np.array = None, + demand_quantity_arr: np.array = None, name: str = "LSCP", ): """ @@ -131,7 +131,7 @@ def from_cost_matrix( model = pulp.LpProblem(name, pulp.LpMinimize) lscp = LSCP(name, model) - + if demand_quantity_arr is not None and facility_capacity_arr is None: raise ValueError( "Demand quantities supplied with no facility capacities. Model cannot satisfy clients with different demands without facility capacities." @@ -152,14 +152,22 @@ def from_cost_matrix( if demand_quantity_arr is not None: FacilityModelBuilder.add_client_assign_integer_variable( - lscp, r_cli, r_fac, "z[{i}_{j}]", lp_category=pulp.LpContinuous) + lscp, r_cli, r_fac, "z[{i}_{j}]", lp_category=pulp.LpContinuous + ) FacilityModelBuilder.add_facility_capacity_constraint( - lscp, lscp.problem, lscp.aij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli + lscp, + lscp.problem, + lscp.aij, + facility_capacity_arr, + demand_quantity_arr, + r_fac, + r_cli, ) FacilityModelBuilder.add_client_demand_satisfaction_constraint( - lscp, lscp.problem, r_cli, r_fac) + lscp, lscp.problem, r_cli, r_fac + ) else: FacilityModelBuilder.add_set_covering_constraint( @@ -179,8 +187,8 @@ def from_geodataframe( facility_col: str, service_radius: float, predefined_facility_col: str = None, - facility_capacity_col: str = None, - demand_quantity_col: str = None, + facility_capacity_col: str = None, + demand_quantity_col: str = None, distance_metric: str = "euclidean", name: str = "LSCP", ): @@ -254,7 +262,7 @@ def from_geodataframe( demand_quantity_arr = None if demand_quantity_col is not None: demand_quantity_arr = gdf_demand[demand_quantity_col].to_numpy() - + facility_capacity_arr = None if facility_capacity_col is not None: facility_capacity_arr = gdf_fac[facility_capacity_col].to_numpy() @@ -299,7 +307,12 @@ def from_geodataframe( distances = cdist(dem_data, fac_data, distance_metric) return cls.from_cost_matrix( - distances, service_radius, predefined_facilities_arr, facility_capacity_arr, demand_quantity_arr, name + distances, + service_radius, + predefined_facilities_arr, + facility_capacity_arr, + demand_quantity_arr, + name, ) def facility_client_array(self) -> None: From 24e6b8b0d019d1b5db4e6f204ad4d70b86c845c3 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Thu, 8 Sep 2022 15:13:26 -0400 Subject: [PATCH 26/34] add erin as author --- notebooks/lscp_capacity.ipynb | 457 ++++++++++++++++++++++------------ 1 file changed, 291 insertions(+), 166 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 6c3dab81..51b81e8f 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -10,7 +10,7 @@ "source": [ "# Location Set Covering Problem (LSCP)\n", "\n", - "*Authors:* [Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", + "*Authors:* [Erin Olson](https://github.com/erinrolson),[Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", "\n", "Location Set Covering is a problem realized by Toregas, et al. (1971). He figured out that emergency services must have placed according to a response time, since, there is a allowable maximum service time when it's discussed how handle an emergency activity. Therefore he proprosed a model named LSCP that:\n", "\n", @@ -76,8 +76,8 @@ "metadata": {}, "outputs": [], "source": [ - "CLIENT_COUNT = 10 # quantity demand points\n", - "FACILITY_COUNT = 5 # quantity supply points\n", + "CLIENT_COUNT = 5 # quantity demand points\n", + "FACILITY_COUNT = 2 # quantity supply points\n", "\n", "SERVICE_RADIUS = 8 # maximum service radius in meters\n", "\n", @@ -149,7 +149,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -210,7 +210,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -219,7 +219,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -233,8 +233,8 @@ "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "street.plot(ax=ax, alpha=0.8, zorder=1, label='streets')\n", - "facility_points.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=5)')\n", - "client_points.plot(ax=ax, color='black', label='clients points ($n$=100)')\n", + "facility_points.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=2)')\n", + "client_points.plot(ax=ax, color='black', label='clients points ($n$=5)')\n", "plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1))" ] }, @@ -292,7 +292,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -301,7 +301,7 @@ }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -315,8 +315,8 @@ "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "street.plot(ax=ax, alpha=0.8, zorder=1, label='streets')\n", - "facilities_snapped.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=5)')\n", - "clients_snapped.plot(ax=ax, color='black', label='clients points ($n$=100)')\n", + "facilities_snapped.plot(ax=ax, color='red', zorder=2, label='facility candidate sites ($n$=2)')\n", + "clients_snapped.plot(ax=ax, color='black', label='clients points ($n$=5)')\n", "plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1))" ] }, @@ -361,16 +361,11 @@ { "data": { "text/plain": [ - "array([[12.60302601, 3.93598651, 8.16571655, 6.04319467, 5.65607701],\n", - " [13.10096347, 4.43392397, 8.66365401, 6.54113213, 5.15813955],\n", - " [ 6.9095462 , 4.2425067 , 2.47223674, 0.34971486, 5.34955682],\n", - " [ 2.98196832, 7.84581224, 3.45534114, 3.57786302, 6.25374871],\n", - " [ 7.5002892 , 6.32806975, 4.55779979, 6.43527791, 11.75939222],\n", - " [ 0.60209077, 11.42987132, 5.03940023, 7.16192211, 9.8378078 ],\n", - " [ 5.37335867, 6.20113923, 2.43086927, 4.30834738, 9.6324617 ],\n", - " [ 5.40801577, 5.41976478, 3.02929369, 1.15181557, 4.85108725],\n", - " [ 3.68807115, 8.51585171, 2.12538061, 4.24790249, 7.94717417],\n", - " [14.22503627, 4.60274429, 9.78772681, 7.66520493, 4.98931924]])" + "array([[12.60302601, 3.93598651],\n", + " [13.10096347, 4.43392397],\n", + " [ 6.9095462 , 4.2425067 ],\n", + " [ 2.98196832, 7.84581224],\n", + " [ 7.5002892 , 6.32806975]])" ] }, "execution_count": 11, @@ -390,7 +385,7 @@ { "data": { "text/plain": [ - "array([10, 10, 10, 10, 10])" + "array([10, 10])" ] }, "execution_count": 12, @@ -399,7 +394,7 @@ } ], "source": [ - "facility_capacity = numpy.array([10, 10, 10, 10, 10])\n", + "facility_capacity = numpy.array([10, 10])\n", "facility_capacity" ] }, @@ -411,7 +406,7 @@ { "data": { "text/plain": [ - "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" + "array([1, 1, 1, 1, 1])" ] }, "execution_count": 13, @@ -420,7 +415,7 @@ } ], "source": [ - "demand_quantity = numpy.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "demand_quantity = numpy.array([1, 1, 1, 1, 1])\n", "demand_quantity" ] }, @@ -451,99 +446,35 @@ "text/plain": [ "LSCP:\n", "MINIMIZE\n", - "1*x_0_ + 1*x_1_ + 1*x_2_ + 1*x_3_ + 1*x_4_ + 0\n", + "1*x_0_ + 1*x_1_ + 0\n", "SUBJECT TO\n", - "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ + z_5_0_ + z_6_0_\n", - " + z_7_0_ + z_8_0_ + z_9_0_ <= 0\n", + "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ <= 0\n", "\n", - "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ + z_5_1_ + z_6_1_\n", - " + z_7_1_ + z_8_1_ + z_9_1_ <= 0\n", + "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ <= 0\n", "\n", - "_C3: - 10 x_2_ + z_0_2_ + z_1_2_ + z_2_2_ + z_3_2_ + z_4_2_ + z_5_2_ + z_6_2_\n", - " + z_7_2_ + z_8_2_ + z_9_2_ <= 0\n", + "_C3: z_0_0_ + z_0_1_ = 1\n", "\n", - "_C4: - 10 x_3_ + z_0_3_ + z_1_3_ + z_2_3_ + z_3_3_ + z_4_3_ + z_5_3_ + z_6_3_\n", - " + z_7_3_ + z_8_3_ + z_9_3_ <= 0\n", + "_C4: z_1_0_ + z_1_1_ = 1\n", "\n", - "_C5: - 10 x_4_ + z_0_4_ + z_1_4_ + z_2_4_ + z_3_4_ + z_4_4_ + z_5_4_ + z_6_4_\n", - " + z_7_4_ + z_8_4_ + z_9_4_ <= 0\n", + "_C5: z_2_0_ + z_2_1_ = 1\n", "\n", - "_C6: x_1_ + x_3_ + x_4_ >= 1\n", + "_C6: z_3_0_ + z_3_1_ = 1\n", "\n", - "_C7: x_1_ + x_3_ + x_4_ >= 1\n", - "\n", - "_C8: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", - "\n", - "_C9: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", - "\n", - "_C10: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", - "\n", - "_C11: x_0_ + x_2_ + x_3_ >= 1\n", - "\n", - "_C12: x_0_ + x_1_ + x_2_ + x_3_ >= 1\n", - "\n", - "_C13: x_0_ + x_1_ + x_2_ + x_3_ + x_4_ >= 1\n", - "\n", - "_C14: x_0_ + x_2_ + x_3_ + x_4_ >= 1\n", - "\n", - "_C15: x_1_ + x_3_ + x_4_ >= 1\n", + "_C7: z_4_0_ + z_4_1_ = 1\n", "\n", "VARIABLES\n", "0 <= x_0_ <= 1 Integer\n", "0 <= x_1_ <= 1 Integer\n", - "0 <= x_2_ <= 1 Integer\n", - "0 <= x_3_ <= 1 Integer\n", - "0 <= x_4_ <= 1 Integer\n", "z_0_0_ <= 1 Continuous\n", "z_0_1_ <= 1 Continuous\n", - "z_0_2_ <= 1 Continuous\n", - "z_0_3_ <= 1 Continuous\n", - "z_0_4_ <= 1 Continuous\n", "z_1_0_ <= 1 Continuous\n", "z_1_1_ <= 1 Continuous\n", - "z_1_2_ <= 1 Continuous\n", - "z_1_3_ <= 1 Continuous\n", - "z_1_4_ <= 1 Continuous\n", "z_2_0_ <= 1 Continuous\n", "z_2_1_ <= 1 Continuous\n", - "z_2_2_ <= 1 Continuous\n", - "z_2_3_ <= 1 Continuous\n", - "z_2_4_ <= 1 Continuous\n", "z_3_0_ <= 1 Continuous\n", "z_3_1_ <= 1 Continuous\n", - "z_3_2_ <= 1 Continuous\n", - "z_3_3_ <= 1 Continuous\n", - "z_3_4_ <= 1 Continuous\n", "z_4_0_ <= 1 Continuous\n", - "z_4_1_ <= 1 Continuous\n", - "z_4_2_ <= 1 Continuous\n", - "z_4_3_ <= 1 Continuous\n", - "z_4_4_ <= 1 Continuous\n", - "z_5_0_ <= 1 Continuous\n", - "z_5_1_ <= 1 Continuous\n", - "z_5_2_ <= 1 Continuous\n", - "z_5_3_ <= 1 Continuous\n", - "z_5_4_ <= 1 Continuous\n", - "z_6_0_ <= 1 Continuous\n", - "z_6_1_ <= 1 Continuous\n", - "z_6_2_ <= 1 Continuous\n", - "z_6_3_ <= 1 Continuous\n", - "z_6_4_ <= 1 Continuous\n", - "z_7_0_ <= 1 Continuous\n", - "z_7_1_ <= 1 Continuous\n", - "z_7_2_ <= 1 Continuous\n", - "z_7_3_ <= 1 Continuous\n", - "z_7_4_ <= 1 Continuous\n", - "z_8_0_ <= 1 Continuous\n", - "z_8_1_ <= 1 Continuous\n", - "z_8_2_ <= 1 Continuous\n", - "z_8_3_ <= 1 Continuous\n", - "z_8_4_ <= 1 Continuous\n", - "z_9_0_ <= 1 Continuous\n", - "z_9_1_ <= 1 Continuous\n", - "z_9_2_ <= 1 Continuous\n", - "z_9_3_ <= 1 Continuous\n", - "z_9_4_ <= 1 Continuous" + "z_4_1_ <= 1 Continuous" ] }, "execution_count": 15, @@ -557,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -573,16 +504,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -650,26 +581,163 @@ " 1\n", " POINT (0.91963 6.00000)\n", " 0\n", + " 1\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " id geometry comp_label predefined_loc\n", + "0 0 POINT (9.00000 3.25259) 0 0\n", + "1 1 POINT (0.91963 6.00000) 0 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "facilities_snapped['predefined_loc'] = numpy.array([0, 1])\n", + "facilities_snapped" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idgeometrycomp_labelpredefined_loccapacity
00POINT (9.00000 3.25259)0010
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\n", + "
" + ], + "text/plain": [ + " id geometry comp_label predefined_loc capacity\n", + "0 0 POINT (9.00000 3.25259) 0 0 10\n", + "1 1 POINT (0.91963 6.00000) 0 1 10" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "facilities_snapped['capacity'] = numpy.array([10, 10])\n", + "facilities_snapped" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -678,22 +746,22 @@ "" ], "text/plain": [ - " id geometry comp_label predefined_loc\n", - "0 0 POINT (9.00000 3.25259) 0 0\n", - "1 1 POINT (0.91963 6.00000) 0 0\n", - "2 2 POINT (5.31010 4.00000) 0 0\n", - "3 3 POINT (5.18758 6.00000) 0 0\n", - "4 4 POINT (6.51169 10.00000) 0 1" + " id geometry comp_label dem_quantity\n", + "0 0 POINT (2.00000 8.85562) 0 1\n", + "1 1 POINT (2.00000 9.35355) 0 1\n", + "2 2 POINT (5.00000 6.16214) 0 1\n", + "3 3 POINT (7.76544 5.00000) 0 1\n", + "4 4 POINT (3.00000 1.75230) 0 1" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "facilities_snapped['predefined_loc'] = numpy.array([0, 0, 0, 0, 1])\n", - "facilities_snapped" + "clients_snapped['dem_quantity'] = numpy.array([1, 1, 1, 1, 1])\n", + "clients_snapped" ] }, { @@ -705,31 +773,71 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "lscp_from_geodataframe = LSCP.from_geodataframe(\n", + " gdf_demand=clients_snapped, gdf_fac=facilities_snapped, demand_col=\"geometry\", facility_col=\"geometry\", service_radius=SERVICE_RADIUS, facility_capacity_col='capacity',demand_quantity_col='dem_quantity',distance_metric=\"euclidean\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for -=: 'float' and 'str'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/erinolson/spopt/notebooks/lscp_capacity.ipynb Cell 39'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m lscp_from_geodataframe \u001b[39m=\u001b[39m LSCP\u001b[39m.\u001b[39;49mfrom_geodataframe(\n\u001b[1;32m 2\u001b[0m gdf_demand\u001b[39m=\u001b[39;49mclients_snapped, gdf_fac\u001b[39m=\u001b[39;49mfacilities_snapped, demand_col\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m, facility_col\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mgeometry\u001b[39;49m\u001b[39m\"\u001b[39;49m, service_radius\u001b[39m=\u001b[39;49mSERVICE_RADIUS, distance_metric\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39meuclidean\u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m 3\u001b[0m )\n\u001b[1;32m 4\u001b[0m lscp_from_geodataframe \u001b[39m=\u001b[39m lscp_from_geodataframe\u001b[39m.\u001b[39msolve(solver)\n", - "File \u001b[0;32m~/spopt/spopt/locate/coverage.py:285\u001b[0m, in \u001b[0;36mLSCP.from_geodataframe\u001b[0;34m(cls, gdf_demand, gdf_fac, demand_col, facility_col, service_radius, predefined_facility_col, distance_metric, name)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 280\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mgeodataframes crs are different: gdf_demand-\u001b[39m\u001b[39m{\u001b[39;00mgdf_demand\u001b[39m.\u001b[39mcrs\u001b[39m}\u001b[39;00m\u001b[39m, gdf_fac-\u001b[39m\u001b[39m{\u001b[39;00mgdf_fac\u001b[39m.\u001b[39mcrs\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 281\u001b[0m )\n\u001b[1;32m 283\u001b[0m distances \u001b[39m=\u001b[39m cdist(dem_data, fac_data, distance_metric)\n\u001b[0;32m--> 285\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49mfrom_cost_matrix(\n\u001b[1;32m 286\u001b[0m distances, service_radius, predefined_facilities_arr, name\n\u001b[1;32m 287\u001b[0m )\n", - "File \u001b[0;32m~/spopt/spopt/locate/coverage.py:160\u001b[0m, in \u001b[0;36mLSCP.from_cost_matrix\u001b[0;34m(cls, cost_matrix, service_radius, predefined_facilities_arr, facility_capacity_arr, demand_quantity_arr, name)\u001b[0m\n\u001b[1;32m 156\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39madd_client_assign_integer_variable(\n\u001b[1;32m 157\u001b[0m lscp, r_cli, r_fac, \u001b[39m\"\u001b[39m\u001b[39mz[\u001b[39m\u001b[39m{i}\u001b[39;00m\u001b[39m_\u001b[39m\u001b[39m{j}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m, pulp\u001b[39m.\u001b[39mLpContinuous)\n\u001b[1;32m 159\u001b[0m \u001b[39mif\u001b[39;00m facility_capacity_arr \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39;49madd_facility_capacity_constraint(\n\u001b[1;32m 161\u001b[0m lscp, lscp\u001b[39m.\u001b[39;49mproblem, lscp\u001b[39m.\u001b[39;49maij, facility_capacity_arr, demand_quantity_arr, r_fac, r_cli\n\u001b[1;32m 162\u001b[0m )\n\u001b[1;32m 164\u001b[0m lscp\u001b[39m.\u001b[39m__add_obj()\n\u001b[1;32m 165\u001b[0m FacilityModelBuilder\u001b[39m.\u001b[39madd_set_covering_constraint(\n\u001b[1;32m 166\u001b[0m lscp, lscp\u001b[39m.\u001b[39mproblem, lscp\u001b[39m.\u001b[39maij, r_fac, r_cli\n\u001b[1;32m 167\u001b[0m )\n", - "File \u001b[0;32m~/spopt/spopt/locate/base.py:464\u001b[0m, in \u001b[0;36mFacilityModelBuilder.add_facility_capacity_constraint\u001b[0;34m(obj, model, ni, cl_ni, dq_ni, range_facility, range_client)\u001b[0m\n\u001b[1;32m 457\u001b[0m ni_t \u001b[39m=\u001b[39m ni\u001b[39m.\u001b[39mtranspose() \u001b[39m#may not even need this any more\u001b[39;00m\n\u001b[1;32m 459\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m range_facility:\n\u001b[1;32m 460\u001b[0m \u001b[39m#Demand at (i) multiplied by the fraction of demand (i) assigned to facility (j) must be <= to facility (j)'s capacity. a_i(Z_i_j) <= C_j(X_j)\u001b[39;00m\n\u001b[1;32m 461\u001b[0m \u001b[39m#zij = sum(ni_t[j]) # sum of demand pts assigned to a facility.\u001b[39;00m\n\u001b[1;32m 462\u001b[0m model \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m 463\u001b[0m \u001b[39m#pulp.lpSum([ ni_t[j][i] * zij for i in range_client ])\u001b[39;00m\n\u001b[0;32m--> 464\u001b[0m pulp\u001b[39m.\u001b[39;49mlpSum([ dq_ni[i] \u001b[39m*\u001b[39;49m cli_assn_vars[i][j] \u001b[39mfor\u001b[39;49;00m i \u001b[39min\u001b[39;49;00m range_client ])\n\u001b[1;32m 465\u001b[0m \u001b[39m<\u001b[39;49m\u001b[39m=\u001b[39;49m cl_ni[j] \u001b[39m*\u001b[39;49m fac_vars[j]\n\u001b[1;32m 466\u001b[0m )\n\u001b[1;32m 467\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 468\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[1;32m 469\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mbefore setting constraints must set facility variable and demand quantity variable\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m#might want to update this message later\u001b[39;00m\n\u001b[1;32m 470\u001b[0m )\n", - "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:1022\u001b[0m, in \u001b[0;36mLpAffineExpression.__le__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 1021\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__le__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[0;32m-> 1022\u001b[0m \u001b[39mreturn\u001b[39;00m LpConstraint(\u001b[39mself\u001b[39;49m \u001b[39m-\u001b[39;49m other, const\u001b[39m.\u001b[39mLpConstraintLE)\n", - "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:943\u001b[0m, in \u001b[0;36mLpAffineExpression.__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 942\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__sub__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[0;32m--> 943\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcopy()\u001b[39m.\u001b[39;49msubInPlace(other)\n", - "File \u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/pulp/pulp.py:910\u001b[0m, in \u001b[0;36mLpAffineExpression.subInPlace\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maddterm(other, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 909\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(other, LpAffineExpression):\n\u001b[0;32m--> 910\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconstant \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m other\u001b[39m.\u001b[39mconstant\n\u001b[1;32m 911\u001b[0m \u001b[39mfor\u001b[39;00m v, x \u001b[39min\u001b[39;00m other\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 912\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maddterm(v, \u001b[39m-\u001b[39mx)\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -=: 'float' and 'str'" - ] + "data": { + "text/plain": [ + "LSCP:\n", + "MINIMIZE\n", + "1*x_0_ + 1*x_1_ + 0\n", + "SUBJECT TO\n", + "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ <= 0\n", + "\n", + "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ <= 0\n", + "\n", + "_C3: z_0_0_ + z_0_1_ = 1\n", + "\n", + "_C4: z_1_0_ + z_1_1_ = 1\n", + "\n", + "_C5: z_2_0_ + z_2_1_ = 1\n", + "\n", + "_C6: z_3_0_ + z_3_1_ = 1\n", + "\n", + "_C7: z_4_0_ + z_4_1_ = 1\n", + "\n", + "VARIABLES\n", + "0 <= x_0_ <= 1 Integer\n", + "0 <= x_1_ <= 1 Integer\n", + "z_0_0_ <= 1 Continuous\n", + "z_0_1_ <= 1 Continuous\n", + "z_1_0_ <= 1 Continuous\n", + "z_1_1_ <= 1 Continuous\n", + "z_2_0_ <= 1 Continuous\n", + "z_2_1_ <= 1 Continuous\n", + "z_3_0_ <= 1 Continuous\n", + "z_3_1_ <= 1 Continuous\n", + "z_4_0_ <= 1 Continuous\n", + "z_4_1_ <= 1 Continuous" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "lscp_from_geodataframe = LSCP.from_geodataframe(\n", - " gdf_demand=clients_snapped, gdf_fac=facilities_snapped, demand_col=\"geometry\", facility_col=\"geometry\", service_radius=SERVICE_RADIUS, distance_metric=\"euclidean\"\n", - ")\n", + "lscp_from_cost_matrix.problem" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ "lscp_from_geodataframe = lscp_from_geodataframe.solve(solver)" ] }, @@ -742,9 +850,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lscp_from_geodataframe" ] @@ -758,12 +877,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "lscp_preselected_from_geodataframe = LSCP.from_geodataframe(\n", - " clients_snapped, facilities_snapped, \"geometry\", \"geometry\", SERVICE_RADIUS, predefined_facility_col=\"predefined_loc\", distance_metric=\"euclidean\"\n", + " clients_snapped, facilities_snapped, \"geometry\", \"geometry\", SERVICE_RADIUS, predefined_facility_col=\"predefined_loc\", facility_capacity_col='capacity', demand_quantity_col='dem_quantity', distance_metric=\"euclidean\"\n", ")\n", "lscp_preselected_from_geodataframe = lscp_preselected_from_geodataframe.solve(solver)" ] @@ -784,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -885,12 +1004,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -905,38 +1024,31 @@ "plot_results(lscp_from_cost_matrix, facility_points)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSCP built from geodataframe" + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { + "image/png": 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/benxk5OTS1evXu2r1Woje/fuXdW/f/8LABAfH181f/78vGHDhuk0Go2Mjo6u3LhxY/YfjRcQEGCKi4urCA8PjxoxYkRpSkpKrouLixw8eHCZl5eXqblPS2RkZHR+6qmngjUaDRwdHeWqVatO/dGYzzzzzJmRI0dqzWYznJyc5IoVK06XlJQ4XGscABg+fHjpd999556UlFS+f//+zoMGDbrQ8FxWVlbnuLi4i02Pac727dvdt2zZ0i08PPyiTqeLBIAXXnjhzD333FP6zTffeI4cObK0JeO0NQYJG5B9OhtDeg2+bJtPr0D89O4OlToiImq9M2fO7G/4fufOnUeb22fu3Lnn5s6de8VbEZWVlXsbf236/ZdffnnZR0pNJhP27Nnj/umnnx5HM5KTk8uSk5MPXq1Oc2POmDGjeMaMGcXNjHXFOI09+uijBUuXLg1ISkoqX7NmTW7j53Jzc/df7bim/vznP1dIKTOae279+vVdly5dmtvcc9bGj3/agNCQUBSdvPytuqKTeQgNCVWnISKidiQjI8OlZ8+eMcOGDSuLiYmpVrufIUOGXExMTCxr7QWprqaqqkqMHTu2pH///qrPFeCKhE2YNWUaFq5cAiQB0mRG5dkSnPjP71j0yJNqt0ZEZPPi4uKqrueVvjXMmzfvilUWS3FxcZGPPPJIm41/vRgkbEBCQgIWoe5ciUPbv4OzUye8uvY9nh9BREQ2j0HCRiQkJCAhIQGJiYmXHhMREdk6niNBREREijFIEBERkWIMEkRERKQYgwQREREp1m5OtjQYDJdORLRWPQBWrWlvde1prmrVtae5qlXXYDBAr9dbrR6RrWk3QYKIiJTx6x7QvzDvrMX+vfcN9DcW/J6/z1LjdTT/+7//6+vq6mq+1rUedu3a1TknJ6fTPffc06LLXB85cqTTHXfcEX706NEDlusUmDt3btCnn37arayszKHxVT6vR7sJEnq9HqmpqVar1/CKxpo1G9d94YUXkPL+OmSfzkZoSChmTZnWph8JVWO+av+M7aGuPc1VrbrWXnVRojDvrOPQL/9qsfH+M+ZVm/ndUVtbCycni95jrNUef/zxwj/aJz093TU9Pd2tpUGirSQlJZU89thjBX379o1WOgbPkbBBxSV1d/90vqkXhjw/Cc439cLClUuQlpamdmtERC2ycuXKblqtNjIiIiIyKSmpFwBkZWV1uvHGG7VarTbyxhtv1B49erTTuXPnHIKCgmJMprqbfpaXl2sCAgL6VVdXiwMHDjgPGzYsPCoqqm9cXFzE3r17XQAgOTk5dPr06cGDBg3SzpkzJ3jHjh2usbGxur59+0bGxsbq9u3b59ww1m233Ram1Wojb7/99rB+/frpdu7c6QoAmzZt8tTr9brIyMi+o0ePDistLb3q78OgoKCY2bNnB8XExPSNiYnpm5mZ6Xy1+QDA3/72t+4LFy70B4CBAwdGNBwbGhoavW3bNveqqirxP//zP92//PJLb51OF/n22297f/XVV+46nS5Sp9NF9u3bN7K4uPiKfoxGI+68885QrVYbOWrUqLDy8nLN559/7nHLLbf0bthn8+bNnrfeemvvxsdda5+RI0de6NmzZ62iP+R6DBI2qLCi+NLdQDWODvALD0ZY0iCkvL9O7daIiP5Qenq6y7JlywLT0tKyjhw5cjAlJeU0ADz88MMhkyZNOpeVlXXwnnvuOTd79uwe3bp1M+l0usqvv/7aAwA2bNjQJSEhodTZ2VlOnz6956pVq04fOHDg0NKlS3Nnz54d0lDj+PHjLj/99FPW22+/ndu/f/+q//u//zt86NChg88999yZxx9/PBgAli5d6uvl5WXKyso6+Pzzz/9+8OBBNwDIy8tzfPnllwN37tyZdfDgwUM33HBD5Ysvvuh/rTl5enqa9u/ff2jWrFkFc+fO7XG1+TR3rNFoFPv37z/0yiuv5CxatKi7i4uLfOqpp34fM2ZM8eHDhw/OmDGjePny5QErVqw4dfjw4YO//PLLYXd3d3PTcbKzs10efvjhwqysrIMeHh7mpUuX+o4ZM6b82LFjLr///rsjAKxbt67bAw88UNT4uJbs0xoMEjaourYGPr0CL9vm0ysQ2aez1WmIiOg6fPvtt55jxowpDgwMNAKAv7+/CQD27t3rNnPmzPMAMHv27PMZGRnuADBhwoTi9evXewPAJ5980nXixInFpaWlmr1797pPmDCht06ni5wzZ07PgoKCS+9h3HnnncUNtws/f/68w2233dY7PDw86vHHH++RlZXlAgC7du1yv/fee88DwIABA6q0Wm0lAKSmprodP37cZeDAgTqdThe5YcOGbqdPn+50rTlNnTr1PADMmDHj/N69e92vNZ+mJkyYUAwAgwcPvpCbm9tsnT/96U8Vjz32WI/Fixf7FRUVOTT3dk1AQEDNrbfeegEApkyZcm7Xrl3uGo0Gd99997m33367a1FRkcOePXvcJ0yYcNnbJS3ZpzVs5n0u+i9np04oOpkHv/DgS9t4N1Aiai+klBBCyJbuf++995YsWrQo6OzZsw6ZmZmuY8aMKSsrK9N4eHgYDx8+3Owtuxu/Yn/iiSeCEhISyrdv3378yJEjnUaMGBHR0MfV+hs6dGhZ09uGX4tG89/X3dczN6DuJlsA4OjoCJPJJJrb5+WXX85PSkoq/fzzz7sMHjy477Zt27JiY2OrGu8jxOWHNjyePXv2udtvv72Pi4uLHDNmTHFzIaQl+yjFFQkb5OvujRNbdqPgaC7MRhMKjubixJbdmDVlmtqtERH9oVGjRpV98cUXXfPz8x0A4OzZsw4AEBsbe2HNmjXeAJCSktI1Pj6+AgC6dOli7t+//4VZs2aFjBw5stTR0RFdu3Y1BwcH16xbt84bAMxmM37++efOzdUrKytzCA4Orqkf16dh++DBgys2bNjgDdTdajwrK6szACQmJl5IT093bzjXoby8XPPbb785X2tO7733XlcAWLt2rXdsbOyFa82nJTw9PU0VFRWXfgcfOHDAeeDAgRdfeuml/JiYmAuZmZkuTY/Jy8vr9P3337sBwEcffdR18ODBFQAQGhpa6+/vX7t8+fLAGTNmNPuWRUv2UYorEjbI28sLLzzyJFLeX4ef3t2B0JBQLHrkSd7Ii4gU8Q30N1rykxa+gf7Gaz0fHx9fNX/+/Lxhw4bpNBqNjI6Orty4cWP2m2++eXrq1Kmhr7/+ekC3bt2M7733XnbDMXfffXfxtGnTwrZu3XqkYdv69etPzJgxo+crr7wSaDQaxfjx48/feOONF5vWe+KJJ/KnT5/ea8WKFQHDhg0ra9j+97//vfDuu+8O1Wq1kdHR0ZUREREXvb29Td27dzempKRkT5w4MaympkYAwHPPPXemX79+1VebU3V1tejXr5/ObDaLDRs2nACAa83nj4wePbp82bJlgTqdLnL+/Pl5//nPf9x37drlqdFopFarvXjXXXdd8dZDWFhY1bp167rNmTOnZ69evaofe+yxS58OmThx4rk33njDMS4urqrpcdfa5+GHHw7evHlz16qqKo2/v3+/++67r+gf//jH7y2dBwCIqy39tIX4+HiZnp5+3cfxY3Mds649zVWtuvY0V7XqtramECJDShlvuY6Affv2Zffv39+irzrbI6PRiJqaGuHq6ioPHDjgfOutt2qPHz+e2fBWQ0sFBQXFpKenH2o458MW3X///SGxsbGVf/3rX6/6596Sfa5m3759Pv379w9t7jmuSBARUYdUXl6uGTZsWERtba2QUuLVV189db0hoj2Iiorq27lzZ3NKSkpOa/ZRikGCiIg6JG9vb3NmZuahlu5/yy239M7JybnsXImXXnop98yZM/st353lHDhw4A/n2JJ9lGKQICIiArB9+/bjavfQHvFTG0RERKQYgwQREREpxiBBREQoLS3VzJzzcPC17jlB1Bz+hSEiImzbts3jqx+3dd22bZtHW9YZNmxYuIeHh/6mm27qc7V99u7d69Jw86oDBw5c80JRTX344Yddnn766QDg8ptnzZs3r/uWLVs8AGDRokV+5eXlbfb7r3Hd61FUVOSwZMkSX2vVsxQGCSIiwpZtW73cdYFiy7atXm1Z57HHHstPSUm55qWpP/30U6/Ro0eXHDp06GBUVNRVLxLVnPvuu6/05Zdfzm+6/bXXXvs9KSmpHABSUlL8G19V0lacO3fOYe3atX5q93G9bO4HSUREbW/ytPt79uzbO6bhv92Zezxj7h9Zsztzj2fj7ZOn3d/zesd+9NFHu7/44ouXfiHOnTs3aPHixX4AMG7cuHJPT88r7mzZ4OOPP+6yevVq/w8//NBn0KBBWgC4+eabe0dFRfXt06dP1LJlyy5dAvuzzz7zjIyM7BsRERF54403agFgxYoV3e6///6QpuMmJyeHvvPOO96LFy/2KygocEpISNAOGjRI++qrr/o89NBDl+7auXz5cp/p06cHNz7WaDQiOTk5NDw8PEqr1Ua+8MILfkDdZa2bu815Y1fbJycnx/GWW27pHRERERkRERG5fft2t/nz5wfn5OQ463S6yFmzZgUDwLPPPusfHR3dV6vVRv71r3/t3jDuE088ERAaGho9ePBg7dGjR69r1cbS+PFPIiI7NPOB6YXp+w3uYQ8NRzddj0u/2Icsvd8IAEWHcjQn1+3ErAdnFF59lObNmTOnaPz48b2fffbZApPJhC1btnj/+uuvLbqOwT333FO6e/fuQnd3d9OiRYvOAsCHH36Y7e/vb6qoqBCxsbGRkydPLjabzeKRRx4JTU1NPazT6Woa7ufxR5555pmCN9980z8tLS0rMDDQWFZWpomKioqsrq7OdXZ2lh988IFPSkrKqcbH/Pzzz655eXlOR48ePQDUvQUBANOnT++5evXqUzExMdU//vij2+zZs0N++eWXrMbHXm2fhx9+OGTYsGHlCxcuPG40GlFaWuqwfPny3DvuuKNzw43KNm3a5Hns2DGX33777ZCUEjfffHOfb775xt3d3d28efPmrvv37z9YW1sLvV4fGRsbW9mS+bcFBgkiIjs0fPjwytXLV56cOf+RXnhouKZxmGgIEW//442Tw4YNu+5fUBERETVeXl7Gn376qXNeXp5TVFRUZUBAgElpr6+88or/V1995QUA+fn5TgcOHHA5e/as48CBA8t1Ol0N8N9blV8vT09P85AhQ8o//vjjLjExMVW1tbVi4MCBl93PQ6fTVefk5DhPnTq1x5gxY0rHjx9f1vg25w37Ndy3o8G19tm1a5fHZ599dhKouytot27dTA0BpcG2bds8d+7c6RkZGRkJAJWVlZrDhw+7lJeXa2677bYSDw8PMwDceuutJUrmbikMEkREdmr48OGVr734v6fmPvf30G6vTL60/di/0sQ/X1yarSRENHjwwQeL1qxZ41NQUOD04IMPnlM6ztatWz3S0tI80tPTD3t4eJgHDhwYcfHiRU39rcqVDnuZmTNnFr300ksBWq22avLkyVfch8LX19eUmZl5cPPmzZ6rVq3y+/jjj7umpKScvtZtzgHAZDLhj/a5Fikl5s2bl/f3v//9sp4WLVrkZ6m5WwLPkSAismOlpaUOHiE+yN6+12H3Ux86ZG/f6+DewwelpaUteqvgaqZMmVKyY8eOLvv27XNLTk6+4k6WLVVSUuLQpUsXk4eHh3nv3r0u+/btcwOAm2666cLu3bs9Dh8+3An4763KW8LNzc3U+GOuI0aMuJCXl9dp8+bN3R566KHzTffPy8tzNJlMeOCBB0oWL158Zv/+/a4tuc35tfYZMmRI+dKlS32BunMwzp8/r+nSpYvpwoULl/oaPXp02fvvv+/T0OvJkyedzpw54zhixIiKr776yquiokIUFxdrtm/f3qYnyP4RBgkiIju2ZdtWr7OHTzt4HamqfOul1056HamqLDhy2qG1n95wcXGRgwcPLhs7dux5R8f/Ln7HxcVFTJkyJeznn3/29Pf377dx40bPa42TnJxcajQahVarjXz66ae79+/f/wIAdO/e3bhixYrs8ePH94mIiIgcP358WEt7mzp1atHo0aPDG07mBICkpKTi+Pj4Cl9f3yveIsnOznYaOnRohE6ni5w2bVqvRYsW5TWTKXIAABhoSURBVAJ1tzl/5513fCIiIiLDw8OjNm7ceMXP7Gr7vPnmm6fT0tI86m9xHrlnz57OAQEBpri4uIrw8PCoWbNmBd95551lEyZMOD9gwACdVquNHD9+fO+SkhKHoUOHVo4fP/58dHR01B133NF74MCBFS2de1vgbcRtqKa91bWnuapV157mqlbd9n4b8en/b1aPuJjYCzNnzDjv4OAAk8mElNUpXfdm/ub29htvKb5TpMlkQlRUVOSnn356PCYm5ro+wqmGm266qc+8efPOjhs3rlztXmwRbyNORETNWvPG5beVdnBwwJzZc84DuGKJv6UyMjJcxo0bFz569OhiWw8RRUVFDvHx8X379u1byRChDIMEERFZVFxcXFVubq5N33q7gY+Pjyk7OztT7T7as1adIyGE+KsQ4oAQIlMIsV4IccXFOIiIiKjjUhwkhBBBAP4CIF5KGQ3AAcBESzVGRESKmc1ms+18PpDatfq/S1e9GmlrP7XhCKCzEMIRgCuA31s5HhERtV5mYWFhF4YJai2z2SwKCwu7ALjq2z+Kz5GQUp4RQiwDcBrARQDfSSm/UzreHzEYDJfOjrYGg8EAAFataW917WmuatW1p7mqVddgMECv11utXksYjcbp+fn5a/Lz86PBj/lT65gBZBqNxulX20FxkBBCeAMYB6AXgBIAnwohJkspP2iy30wAMwEgJOSK+6gQEZGFxcXFFQAYq3YfZB9a86mNmwGclFIWAoAQYhOAwQAuCxJSytUAVgN115FQWkyv17erz4azrm3WtLe69jRXtepae9WFyNa0ZsnrNIA/CSFcRd1Fv0cCaNHd3YiIiKhjUBwkpJS7AXwGYA+A/fVjrbZQX0RERNQOtOqCVFLK5wA8Z6FeiIiIqJ3h2bxERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIo5qt1ASxkMBiQmJlq1HgCr1rS3uvY0V7Xq2tNc1aprMBig1+utVo/I1nBFgoiIiBRrNysSer0eqampVqvX8IrGmjXtra49zVWtuvY0V7XqWnvVhcjWcEWCiIiIFGOQICIiIsUYJIiIiEgxBgkiIiJSjEGCiIiIFGOQICIiIsUYJIiIiEgxBgkiIiJSjEGCiIiIFGOQICIiIsUYJIiIiEgxBgkiIiJSjEGCiIiIFGOQICIiIsUYJIiIiEgxBolmnD9/Xu0WiIiI2gUGiSby8/Mx4tabUVNTo3YrRERENo9BookffvgBhZUlKC4pUbsVIiIim8cg0cTn325F4FAdSi6Wq90KERGRzWOQaOTcuXM4kHUYUcnDcNFYjdraWrVbIiIismmOajdgS3bs2IEukd3h5OoMD10gSn7n2xvtXVpaGlLeX4fs09kIDQnFrCnT1G6JiKhD4YpEI1u2bYVXvxAAgFe/Hijm2xvtWlpaGhauXALnm3phyPOT4HxTLyxcuYTnvxARWRCDRL3S0lLs2W+AX3QoAMCjtz8u1FxEaWmpuo2RYinvr0NY0iD4hQdD4+gAv/BghCUNQmFFsdqtERF1GAwS9dLS0uCpDYSjsxNqyyvg0MkRHuEBSEtLU7s1Uij7dDZ8egVets2nVyCqa/nRXiIiS7GLcyS2fL4F72x4H1JefZ/8vDx0HRsDU1UVyk+fgqODI7z0IVi8fAnWfPTuVY8TAnhw4hQkjUtqg86pNUJDQlF0Mg9+4cGXthWdzIOzUycVuyIi6lhaFSSEEF4A1gCIBiABTJNS/myJxixp4ICBeG/Dh9j/+1FE3D0UnTw6X7FPiEMfdAnxw8WCQgCAszTDKzIIAXHRMJvMV+xfU34RRz75D/oFazFwwMA2nwNdv1lTpmHhyiVAUt1KRNHJPJzYshu+7t5qt0ZE1GG09q2N1wFsk1LqAPQHcKj1LVle9+7d8fF7H2F28lSc+uQXmKpq0TUsEF3DAuEdGgC3rp3R2dMZNSWlqC6pe//cxSzRWQKdPZ3h1rUzvEMDLh1jqqrFqU9+wZy7HsDH732E7t27qzxDak5CQgIWPfIkqnecxE/Pf4TqHSex6JEn4e3lpXZrREQdhpDXWu+/1oFCeALYByBMtnCQ+Ph4mZ6eft21EhMTYTAYoNfrr/vYpsrKypBdkAvvoeEIvCkSDg4aeBtN0ECiYRICdcsrUgIaAZghUOzoAJPJjLwfD6D4p2MI9QuGp6dnq/tpymAwAIBF5mrrde1prmrVtae5qlW34d+m1NRURccLITKklPGW7YrIelqzIhEGoBDAO0KIvUKINUIIt6Y7CSFmCiHShRDphYWFrShnGZ6enujbMxxVu3NwdPUOXCy5gPOODqgWAkBdiGj8tVoInHd0wMXiCzi6+kdU/V8u+vYMb5MQQURE1N605hwJRwA3AJgrpdwthHgdwJMAnm28k5RyNYDVQN2KhNJirUn8zZFS4oMPP8TSt15D0LQEdIsIQsmRLJiN/72apRQCgZGRcMrKxYnPM7D8b4tw36RJEEJcY+TWSUxMBACLztVW69rTXNWqa09zVatuQ00ie9WaFYlcALlSyt31jz9DXbBoF4QQmDJ5MhIGDkF53nmYa2pgNhoBoQGEgASggYS5pgYVeeeROGgoJt93X5uGCCIiovZGcZCQUuYDyBFCRNRvGgngoEW6spLq6mqk/fwfBOp7o7qsDICEs7cXvHU6VAkNBIDqsjIExvZB6q5/89biRERETbT2UxtzAXwohPgNgB7Ay61vyXp++eUXOAd5wdnTFY4uLvDo2ROu/v4oPV2IciFQ6uAARxcXOHu6wrl7F/zyyy9qt0xERGRTWnUdCSmlAUC7Pdt467dfwzOm7mJFTu7uqC6rxG9vbkPtqRIUOFYi5K4BcHJ3BwB4xARj67dfY/jw4Wq2TEREZFPs9hLZtbW1+D7tRwTEhgEACg6eguGVzzF56FjsTv0JbgVGZK3YjoKDpwAAgTf0xvbUH3hrcSIiokbs4hLZzfn111/h6OsOZw9XHNm0C6b9BVi3fBUGDBgAAAgK7A6PsjIUfbIXxf3OIHzsIDj4uOHXX3/F4MGDVe6eiIjINtjtisTX330D4e+Gvcu/QKTRD19/9sWlENHA09MTX332OfrW+mLP8i+gCXDDN9u3qdQxERGR7bHLIGEymfDND9+hND0bf79vDt5a8Qa8rnLZZG9vb6T8cxUev28OStNP4ZsfvoPJZLJyx0RERLbJLoNEWVkZYrSR+HL9Jky6994/vDaEEAKT7r0XX67fhOjwvigrK7NSp0RERLbNLs+R8Pb2xr/WvnPdx2m1WkXHERERdVR2uSJBRERElsEgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQUTXLS0tDZOmT8XgWxMwafpUpKWlqd0SEamEQYKIrktaWhoWrlwC55t6Ycjzk+B8Uy8sXLmEYYLITjFIENF1SXl/HcKSBsEvPBgaRwf4hQcjLGkQUt5fp3ZrRKQCBgkiui7Zp7Ph0yvwsm0+vQKRfTpbnYaISFUMEkR0XUJDQlF0Mu+ybUUn8xAaEqpOQ0SkKgYJIrous6ZMw4ktu1FwNBdmowkFR3NxYstuzJoyTe3WiEgFjmo3QETtS0JCAhah7lyJn97dgdCQUCx65EkkJCSo3RoRqYBBgoiuW0JCAoMDEQFoR0HCYDAgMTHRqvUAWLWmvdW1p7mqVdee5qpWXYPBAL1eb7V6RLaG50gQERGRYu1mRUKv1yM1NdVq9Rpe0Vizpr3Vtae5qlXXnuaqVl1rr7oQ2RquSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIq1OkgIIRyEEHuFEFst0RARERG1H5ZYkXgUwCELjENERETtjGNrDhZCBAO4HcBLAP5mkY6uwmAwIDExsS1LXFEPgFVr2ltde5qrWnXtaa5q1TUYDNDr9VarR2RrWrsi8RqAxwGYr7aDEGKmECJdCJFeWFjYynJERERkSxSvSAgh7gBQIKXMEEIkXm0/KeVqAKsBID4+Xiqtp9frkZqaqvTw69bwisaaNe2trj3NVa269jRXtepae9WFyNa0ZkViCICxQohsABsAjBBCfGCRroiIiKhdUBwkpJRPSSmDpZShACYC+FFKOdlinREREZHN43UkiIiISLFWfWqjgZQyFUCqJcYiIiKi9oMrEkRERKQYgwQREREpxiBBREREijFIEBERkWIMEkRERKQYgwQREREpxiBBREREijFIEBERkWIMEkRERKQYgwQREREpxiBBREREijFIEBERkWIMEkTU4UgpcWbnTkgp1W6FqMNjkCCiDqfkyBGkzZ6NkqwstVsh6vAYJIiowzm1bRsA4HT9VyJqO45qN0BE1Fqm6mqc/vZbRFy8CAA4sXkzAOD45s1w79EDAKBxdETIn/8MB2dn1fok6ogYJIio3TNevAjDq69ieEUFjABqamoAADWlpUh/6SWYqqrQ2c8P3YcPZ5AgsjC+tUFE7Z6zlxdu/+ILZHfqBAAw1wcJc00NIASCR4zAHV9+CWcvLzXbJOqQGCSIqEPo5OGB7Z6eqNZc/s9aJ09PDFuxAk7u7ip1RtSxMUgQUYfhYTbD1WyGg4sLNE5OcHBxQVVhIS7k5qrdGlGHxSBBRB1GWFUVNADCxo9H8k8/ISwpCdJsxunvvlO7NaIOiydbElGHUeToiK+7dMEHzzwDABjw7LMIuukmQAiVOyPquBgkiKjDONPMJzK6Dx2qQidE9qPdBAmDwYDExESr1gNg1Zr2Vtee5qpWXXuaq1p1DQYD9Hq91eoR2RqeI0FERESKtZsVCb1ej9TUVKvVa3hFY82a9lbXnuaqVl17mqtada296kJka7giQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKcYgQURERIoxSBAREZFiDBJERESkGIMEERERKaY4SAghegghdgghDgkhDgghHrVkY0RERGT7HFtxrBHAfCnlHiGEB4AMIcR2KeVBC/VGRERENk7xioSUMk9Kuaf++3IAhwAEWaoxIiIisn2tWZG4RAgRCiAWwG5LjNccg8GAxMTEthq+2XoArFrT3ura01zVqmtPc1WrrsFggF6vt1o9IlvT6pMthRDuADYCmCelLGvm+ZlCiHQhRHphYWFryxEREZENadWKhBDCCXUh4kMp5abm9pFSrgawGgDi4+Ol0lp6vR6pqalKD79uDa9orFnT3ura01zVqmtPc1WrrrVXXYhsTWs+tSEArAVwSEr5D8u1RERERO1Fa97aGAJgCoARQghD/X+3WagvIiIiagcUv7UhpfwPAGHBXshK0tLSkPL+OmSfzkZoSChmTZmmdktERNROWeRTG9R+pKWlYeHKJQhLGoQhvQaj6GQeFq5cgpKSEnh7eandHhERtTO8RLadSXl/HcKSBsEvPBgaRwf4hQcjLGkQCiuK1W6NiIjaIQYJO5N9Ohs+vQIv2+bTKxDVtTUqdURERO0Zg4SdCQ0JRdHJvMu2FZ3Mg7NTJ5U6IiKi9oxBws7MmjINJ7bsRsHRXJiNJhQczcWJLbvh6+6tdmtERNQOMUjYmYSEBCx65ElU7ziJn57/CNU7TmLRI0/yREsiIlKEn9qwQwkJCUhISFC7DSIi6gC4IkFERESKMUgQERGRYgwSREREpBiDBBERESnGIEFERESKMUgQERGRYgwSREREpBiDBBERESnGIEFERESKMUgQERGRYgwSREREpBiDBBERESnGIEFERESKMUgQERGRYgwSREREpBiDBBERESnmqHYDLWUwGJCYmGjVegCsWtPe6trTXNWqa09zVauuwWCAXq+3Wj0iW8MVCSIiIlKs3axI6PV6pKamWq1ewysaa9a0t7r2NFe16trTXNWqa+1VFyJbwxUJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUqxVQUIIMUoIcUQIcUwI8aSlmiIiIqL2QXGQEEI4AHgDwGgAkQDuFUJEWqoxIiIisn2OrTh2IIBjUsoTACCE2ABgHICDlmisKYPBgMTExLYY+qr1AFi1pr3Vtae5qlXXnuaqVl2DwQC9Xm+1ekS2RkgplR0oxF0ARkkpp9c/ngJgkJTykSb7zQQwEwBCQkLiTp06paietf9BIiJqqdTUVMXHCiEypJTxluuGyLpasyIhmtl2RSqRUq4GsBoA4uPjlaUWtO5/VCIiImobrTnZMhdAj0aPgwH83rp2iIiIqD1pTZD4FUC4EKKXEKITgIkAvrBMW0RERNQeKH5rQ0ppFEI8AuBbAA4A1kkpD1isMyIiIrJ5rTlHAlLKrwF8baFeiIiIqJ3hlS2JiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMQYJIiIiUoxBgoiIiBRjkCAiIiLFGCSIiIhIMcW3EVdUTIhCAMruIw74ACiyYDu2jHPtuOxpvpxry/SUUvpashkia7JqkGgNIUS6lDJe7T6sgXPtuOxpvpwrkX3gWxtERESkGIMEERERKdaegsRqtRuwIs6147Kn+XKuRHag3ZwjQURERLanPa1IEBERkY2x+SAhhBglhDgihDgmhHhS7X7akhCihxBihxDikBDigBDiUbV7amtCCAchxF4hxFa1e2lLQggvIcRnQojD9X++N6rdU1sRQvy1/u9vphBivRDCRe2eLEkIsU4IUSCEyGy0rasQYrsQ4mj9V281eySyJpsOEkIIBwBvABgNIBLAvUKISHW7alNGAPOllH0B/AnA/+vg8wWARwEcUrsJK3gdwDYppQ5Af3TQOQshggD8BUC8lDIagAOAiep2ZXH/AjCqybYnAfwgpQwH8EP9YyK7YNNBAsBAAMeklCeklDUANgAYp3JPbUZKmSel3FP/fTnqftkEqdtV2xFCBAO4HcAatXtpS0IITwDDAawFAClljZSyRN2u2pQjgM5CCEcArgB+V7kfi5JS7gRwvsnmcQDerf/+XQBJVm2KSEW2HiSCAOQ0epyLDvyLtTEhRCiAWAC71e2kTb0G4HEAZrUbaWNhAAoBvFP/Ns4aIYSb2k21BSnlGQDLAJwGkAegVEr5nbpdWYW/lDIPqHtBAMBP5X6IrMbWg4RoZluH/5iJEMIdwEYA86SUZWr30xaEEHcAKJBSZqjdixU4ArgBwJtSylgAF9BBl77rzw0YB6AXgO4A3IQQk9Xtiojakq0HiVwAPRo9DkYHWyZtSgjhhLoQ8aGUcpPa/bShIQDGCiGyUfeW1QghxAfqttRmcgHkSikbVpc+Q12w6IhuBnBSSlkopawFsAnAYJV7soazQohAAKj/WqByP0RWY+tB4lcA4UKIXkKITqg7aesLlXtqM0IIgbr30Q9JKf+hdj9tSUr5lJQyWEoZiro/1x+llB3ylauUMh9AjhAion7TSAAHVWypLZ0G8CchhGv93+eR6KAnljbxBYCp9d9PBfC5ir0QWZWj2g1ci5TSKIR4BMC3qDv7e52U8oDKbbWlIQCmANgvhDDUb3taSvm1ij2RZcwF8GF9ID4B4EGV+2kTUsrdQojPAOxB3aeQ9qKDXfVRCLEeQCIAHyFELoDnACwB8IkQ4iHUhakJ6nVIZF28siUREREpZutvbRAREZENY5AgIiIixRgkiIiISDEGCSIiIlKMQYKIiIgUY5AgIiIixRgkiIiISDEGCSIiIlLs/wNJwIr2wgAnDgAAAABJRU5ErkJggg==", "text/plain": [ - "[[], [], [], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], []]" + "
" ] }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], - "source": [ - "lscp_from_cost_matrix.fac2cli" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LSCP built from geodataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "plot_results(lscp_from_geodataframe, facility_points)" ] @@ -959,9 +1071,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plot_results(lscp_preselected_from_geodataframe, facility_points)" ] From 7e85b8c48e7ab1ea306dbac23496175c2afda754 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 9 Sep 2022 12:54:45 -0400 Subject: [PATCH 27/34] add formulation --- notebooks/lscp_capacity.ipynb | 151 +++++++++++++++++++++++++--------- 1 file changed, 114 insertions(+), 37 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 51b81e8f..73374922 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -8,33 +8,58 @@ } }, "source": [ - "# Location Set Covering Problem (LSCP)\n", + "# Capacitated Location Set Covering Problem (System Optimal) (CLSCP-SO)\n", "\n", "*Authors:* [Erin Olson](https://github.com/erinrolson),[Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", "\n", - "Location Set Covering is a problem realized by Toregas, et al. (1971). He figured out that emergency services must have placed according to a response time, since, there is a allowable maximum service time when it's discussed how handle an emergency activity. Therefore he proprosed a model named LSCP that:\n", + "Capacitated Location Set Covering (System Optimal) builds off of the [LSCP](https://pysal.org/spopt/notebooks/lscp.html), but allows for the assignment of a facility's capacity and the amount of demand at a demand point to be used in the siting of facilities. CLSCP can be described as follows:\n", "\n", - "_Minimize the number of facilities needed and locate them so that every demand area is covered within a predefined maximal service distance or time._ Church L., Murray, A. (2018)\n", + "_Locate just enough facilities and associated capacity such that all demand is served within the capacity limits of each facility, given the coverage capabilities of each facility._ Church L., Murray, A. (2018)\n", "\n", - "**LSCP can be written as:**\n", + "**CLSCP-SO can be written as:**\n", "\n", - "$\\begin{array} \\displaystyle \\textbf{Minimize} & \\sum_{j=1}^{n}{x_j} && (1) \\\\\n", - "\\displaystyle \\textbf{Subject to:} & \\sum_{j\\in N_i}{x_j} \\geq 1 & \\forall i & (2) \\\\\n", - " & x_j \\in {0,1} & \\forall j & (3) \\\\ \\end{array}$\n", - " \n", - "$\\begin{array} \\displaystyle \\textbf{Where:}\\\\ & & \\displaystyle i & \\small = & \\textrm{index referencing nodes of the network as demand} \\\\\n", - "& & j & \\small = & \\textrm{index referencing nodes of the network as potential facility sites} \\\\\n", - "& & S & \\small = & \\textrm{maximal acceptable service distance or time standard} \\\\\n", - "& & d_{ij} & \\small = & \\textrm{shortest distance or travel time between nodes} \\quad i \\quad \\textrm{and} \\quad j \\\\\n", - "& & N_i & \\small = & \\{j | d_{ij} < S\\} \\\\\n", + "\\begin{equation*}\n", + "\\textbf{Minimize }\\sum_{j \\in J} x_j\n", + "\\end{equation*}\n", + "\n", + "_Subject to:_\n", + "\\begin{equation*}\n", + "\\sum_{j\\in N_i} z_{ij} = 1 \\quad \\forall i \\in I\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + "\\sum_{i\\in I} a_i z_{ij} \\leq C_jx_j \\quad \\forall j \\in J\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + "x_j \\in \\{0,1\\} \\quad \\forall j \\in J\n", + "\\end{equation*}\n", + "\n", + "\\begin{equation*}\n", + "z_{ij} \\geq 0 \\quad \\forall i \\in I, j \\in N_i\n", + "\\end{equation*}\n", + "\n", + "_Where:_\n", + "\n", + "\\begin{array}{lclll}\n", + "& & i & \\small = & \\textrm{index of demand points/areas/objects (entire set denoted} \\quad I \\textrm{ )}\\\\\n", + "& & j & \\small = & \\textrm{index of potential facility sites (entire set denoted} \\quad J \\textrm{ )} \\\\\n", + "& & S & \\small = & \\textrm{desired maximal service standard (travel distance or time)} \\\\\n", + "& & d_{ij} & \\small = & \\textrm{shortest distance or travel time between demand} \\quad i \\quad \\textrm{and potential facility} \\quad j \\\\\n", + "& & N_i & \\small = & \\{j | d_{ij} \\leq S\\} \\\\\n", + "& & \\Psi_j & \\small = & \\{i | d_{ij} \\leq S\\} \\\\\n", + "& & p & \\small = & \\textrm{number of facilities to be located} \\\\\n", + "& & a_i & \\small = & \\textrm{amount of demand at} \\quad i \\\\\n", + "& & C_j & \\small = & \\textrm{capacity of potential facility} \\quad j \\\\\n", + "& & Z_{ij} & \\small = & \\textrm{fraction of demand} \\quad i \\quad \\textrm{that is assigned to facility} \\quad j \\\\\n", "& & x_j & \\small = & \\begin{cases} \n", - " 1, \\text{if a facility is located at node} \\quad j\\\\\n", - " 0, \\text{otherwise} \\\\\n", - " \\end{cases} \\end{array}$\n", + " 1, \\quad \\text{if a facility is located at} \\quad j\\\\\n", + " 0, \\quad \\text{otherwise} \\\\\n", + " \\end{cases} \\end{array}\n", " \n", "_This excerpt above was quoted from Church L., Murray, A. (2018)_\n", "\n", - "This tutorial solves LSCP using `spopt.locate.coverage.LSCP` instance that depends on a array 2D representing the costs between facilities candidate sites and demand points. For that it uses a lattice 10x10 with simulated points to calculate the costs." + "This tutorial solves CLSCP-SO using `spopt.locate.coverage.LSCP` instance that depends on a array 2D representing the costs between facilities candidate sites and demand points. For that it uses a lattice 10x10 with simulated points to calculate the costs." ] }, { @@ -149,7 +174,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -210,7 +235,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -292,7 +317,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -379,43 +404,43 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([10, 10])" + "array([8, 8])" ] }, - "execution_count": 12, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "facility_capacity = numpy.array([10, 10])\n", + "facility_capacity = numpy.array([8, 8])\n", "facility_capacity" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 1, 1, 1, 1])" + "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 13, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "demand_quantity = numpy.array([1, 1, 1, 1, 1])\n", + "demand_quantity = numpy.arange(5) + 1\n", "demand_quantity" ] }, @@ -428,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -438,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -448,9 +473,9 @@ "MINIMIZE\n", "1*x_0_ + 1*x_1_ + 0\n", "SUBJECT TO\n", - "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ <= 0\n", + "_C1: - 5 x_0_ + z_0_0_ + 2 z_1_0_ + 3 z_2_0_ + 4 z_3_0_ + 5 z_4_0_ <= 0\n", "\n", - "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ <= 0\n", + "_C2: - 15 x_1_ + z_0_1_ + 2 z_1_1_ + 3 z_2_1_ + 4 z_3_1_ + 5 z_4_1_ <= 0\n", "\n", "_C3: z_0_0_ + z_0_1_ = 1\n", "\n", @@ -477,7 +502,7 @@ "z_4_1_ <= 1 Continuous" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -488,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -903,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1004,12 +1029,64 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] From 059f2797df1f46086bcf1420b941f2671e8f1aaa Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 9 Sep 2022 13:04:01 -0400 Subject: [PATCH 28/34] remove dev notes --- notebooks/lscp_capacity.ipynb | 188 ++-------------------------------- 1 file changed, 10 insertions(+), 178 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 73374922..8b67be15 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -57,9 +57,9 @@ " 0, \\quad \\text{otherwise} \\\\\n", " \\end{cases} \\end{array}\n", " \n", - "_This excerpt above was quoted from Church L., Murray, A. (2018)_\n", + "_The excerpt above was quoted from Church L., Murray, A. (2018)_\n", "\n", - "This tutorial solves CLSCP-SO using `spopt.locate.coverage.LSCP` instance that depends on a array 2D representing the costs between facilities candidate sites and demand points. For that it uses a lattice 10x10 with simulated points to calculate the costs." + "This tutorial solves CLSCP-SO using `spopt.locate.coverage.LSCP` instance that depends on a array 2D representing the costs between facilities candidate sites and demand points. For that it uses a lattice 10x10 with simulated points to calculate the costs. A numpy array representing facility capacities as well as demand quantities is also required." ] }, { @@ -224,7 +224,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Plotting the 100 client and 5 facility points we can see that the function generates dummy points to an area of 10x10 which is the area created by our lattice created on previous cells." + "Plotting the 5 client and 2 facility points we can see that the function generates dummy points to an area of 10x10 which is the area created by our lattice created on previous cells." ] }, { @@ -375,7 +375,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The expected result here is a Dijkstra distance between clients and facilities points, so we our case an array 2D 100x5." + "The expected result here is a Dijkstra distance between clients and facilities points, so we our case an array 2D 5x2." ] }, { @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -434,13 +434,13 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "demand_quantity = numpy.arange(5) + 1\n", + "demand_quantity = numpy.arange(1, 6)\n", "demand_quantity" ] }, @@ -448,7 +448,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With ``LSCP.from_cost_matrix`` we model LSC problem to cover all demand points with $p$ facility points within `max_coverage` meters as service radius using cost matrix calculated previously." + "With ``LSCP.from_cost_matrix`` we model CLSC problem to cover all demand points with $p$ facility points within `max_coverage` meters as service radius using cost matrix calculated previously." ] }, { @@ -458,65 +458,7 @@ "outputs": [], "source": [ "lscp_from_cost_matrix = LSCP.from_cost_matrix(\n", - " cost_matrix, SERVICE_RADIUS, facility_capacity_arr=facility_capacity, demand_quantity_arr=demand_quantity)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LSCP:\n", - "MINIMIZE\n", - "1*x_0_ + 1*x_1_ + 0\n", - "SUBJECT TO\n", - "_C1: - 5 x_0_ + z_0_0_ + 2 z_1_0_ + 3 z_2_0_ + 4 z_3_0_ + 5 z_4_0_ <= 0\n", - "\n", - "_C2: - 15 x_1_ + z_0_1_ + 2 z_1_1_ + 3 z_2_1_ + 4 z_3_1_ + 5 z_4_1_ <= 0\n", - "\n", - "_C3: z_0_0_ + z_0_1_ = 1\n", - "\n", - "_C4: z_1_0_ + z_1_1_ = 1\n", - "\n", - "_C5: z_2_0_ + z_2_1_ = 1\n", - "\n", - "_C6: z_3_0_ + z_3_1_ = 1\n", - "\n", - "_C7: z_4_0_ + z_4_1_ = 1\n", - "\n", - "VARIABLES\n", - "0 <= x_0_ <= 1 Integer\n", - "0 <= x_1_ <= 1 Integer\n", - "z_0_0_ <= 1 Continuous\n", - "z_0_1_ <= 1 Continuous\n", - "z_1_0_ <= 1 Continuous\n", - "z_1_1_ <= 1 Continuous\n", - "z_2_0_ <= 1 Continuous\n", - "z_2_1_ <= 1 Continuous\n", - "z_3_0_ <= 1 Continuous\n", - "z_3_1_ <= 1 Continuous\n", - "z_4_0_ <= 1 Continuous\n", - "z_4_1_ <= 1 Continuous" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lscp_from_cost_matrix.problem" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ + " cost_matrix, SERVICE_RADIUS, facility_capacity_arr=facility_capacity, demand_quantity_arr=demand_quantity)\n", "lscp_from_cost_matrix = lscp_from_cost_matrix.solve(solver)" ] }, @@ -804,65 +746,7 @@ "source": [ "lscp_from_geodataframe = LSCP.from_geodataframe(\n", " gdf_demand=clients_snapped, gdf_fac=facilities_snapped, demand_col=\"geometry\", facility_col=\"geometry\", service_radius=SERVICE_RADIUS, facility_capacity_col='capacity',demand_quantity_col='dem_quantity',distance_metric=\"euclidean\"\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LSCP:\n", - "MINIMIZE\n", - "1*x_0_ + 1*x_1_ + 0\n", - "SUBJECT TO\n", - "_C1: - 10 x_0_ + z_0_0_ + z_1_0_ + z_2_0_ + z_3_0_ + z_4_0_ <= 0\n", - "\n", - "_C2: - 10 x_1_ + z_0_1_ + z_1_1_ + z_2_1_ + z_3_1_ + z_4_1_ <= 0\n", - "\n", - "_C3: z_0_0_ + z_0_1_ = 1\n", - "\n", - "_C4: z_1_0_ + z_1_1_ = 1\n", - "\n", - "_C5: z_2_0_ + z_2_1_ = 1\n", - "\n", - "_C6: z_3_0_ + z_3_1_ = 1\n", - "\n", - "_C7: z_4_0_ + z_4_1_ = 1\n", - "\n", - "VARIABLES\n", - "0 <= x_0_ <= 1 Integer\n", - "0 <= x_1_ <= 1 Integer\n", - "z_0_0_ <= 1 Continuous\n", - "z_0_1_ <= 1 Continuous\n", - "z_1_0_ <= 1 Continuous\n", - "z_1_1_ <= 1 Continuous\n", - "z_2_0_ <= 1 Continuous\n", - "z_2_1_ <= 1 Continuous\n", - "z_3_0_ <= 1 Continuous\n", - "z_3_1_ <= 1 Continuous\n", - "z_4_0_ <= 1 Continuous\n", - "z_4_1_ <= 1 Continuous" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lscp_from_cost_matrix.problem" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ + ")\n", "lscp_from_geodataframe = lscp_from_geodataframe.solve(solver)" ] }, @@ -1027,58 +911,6 @@ "### LSCP built from cost matrix" ] }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.25, 0.75, 0.0, 1.0]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#lscp_from_cost_matrix.problem.variables()[].varValue\n", - "[v.varValue for v in lscp_from_cost_matrix.problem.variables()]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[x_0_,\n", - " x_1_,\n", - " z_0_0_,\n", - " z_0_1_,\n", - " z_1_0_,\n", - " z_1_1_,\n", - " z_2_0_,\n", - " z_2_1_,\n", - " z_3_0_,\n", - " z_3_1_,\n", - " z_4_0_,\n", - " z_4_1_]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lscp_from_cost_matrix.problem.variables()" - ] - }, { "cell_type": "code", "execution_count": 31, From 45f486b9246b4478d6e5df6096cf4e5a03e57e89 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 9 Sep 2022 13:32:24 -0400 Subject: [PATCH 29/34] add CLSCP cost matrix plots --- notebooks/lscp_capacity.ipynb | 228 +++++++++++++++++++--------------- 1 file changed, 130 insertions(+), 98 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 8b67be15..bdba782b 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -10,7 +10,7 @@ "source": [ "# Capacitated Location Set Covering Problem (System Optimal) (CLSCP-SO)\n", "\n", - "*Authors:* [Erin Olson](https://github.com/erinrolson),[Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", + "*Authors:* [Erin Olson](https://github.com/erinrolson), [Germano Barcelos](https://github.com/gegen07), [James Gaboardi](https://github.com/jGaboardi), [Levi J. Wolf](https://github.com/ljwolf), [Qunshan Zhao](https://github.com/qszhao)\n", "\n", "Capacitated Location Set Covering (System Optimal) builds off of the [LSCP](https://pysal.org/spopt/notebooks/lscp.html), but allows for the assignment of a facility's capacity and the amount of demand at a demand point to be used in the siting of facilities. CLSCP can be described as follows:\n", "\n", @@ -64,18 +64,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 163, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/geo_env/lib/python3.8/site-packages/spaghetti/network.py:36: FutureWarning: The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.\n", - " warnings.warn(f\"{dep_msg}\", FutureWarning)\n" - ] - } - ], + "outputs": [], "source": [ "from spopt.locate.coverage import LSCP\n", "from spopt.locate.util import simulated_geo_points\n", @@ -97,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 164, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -168,16 +159,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" }, @@ -210,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 168, "metadata": {}, "outputs": [], "source": [ @@ -229,16 +220,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" }, @@ -286,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 170, "metadata": {}, "outputs": [], "source": [ @@ -311,16 +302,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" }, @@ -361,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -393,7 +384,7 @@ " [ 7.5002892 , 6.32806975]])" ] }, - "execution_count": 11, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -402,30 +393,46 @@ "cost_matrix" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By manipulating the facility capacity array we can demonstrate how capacity is accounted for and generates different results." + ] + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([8, 8])" + "(array([ 5, 15]), array([15, 5]), array([8, 8]))" ] }, - "execution_count": 28, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "facility_capacity = numpy.array([8, 8])\n", - "facility_capacity" + "facility_capacity = numpy.array([5, 15])\n", + "facility_capacity_switched = numpy.array([15, 5])\n", + "facility_capacity_even = numpy.array([8, 8])\n", + "facility_capacity, facility_capacity_switched, facility_capacity_even" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The sum of demand quantity is 15." ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 175, "metadata": {}, "outputs": [ { @@ -434,7 +441,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 47, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -453,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -463,30 +470,25 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 177, "metadata": {}, + "outputs": [], "source": [ - "Expected result is an instance of LSCP." + "lscp_from_cost_matrix_switched = LSCP.from_cost_matrix(\n", + " cost_matrix, SERVICE_RADIUS, facility_capacity_arr=facility_capacity_switched, demand_quantity_arr=demand_quantity)\n", + "lscp_from_cost_matrix_switched = lscp_from_cost_matrix_switched.solve(solver)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 178, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "lscp_from_cost_matrix" + "lscp_from_cost_matrix_even = LSCP.from_cost_matrix(\n", + " cost_matrix, SERVICE_RADIUS, facility_capacity_arr=facility_capacity_even, demand_quantity_arr=demand_quantity)\n", + "lscp_from_cost_matrix_even = lscp_from_cost_matrix_even.solve(solver)" ] }, { @@ -505,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -560,7 +562,7 @@ "1 1 POINT (0.91963 6.00000) 0 1" ] }, - "execution_count": 18, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -630,7 +632,7 @@ "1 1 POINT (0.91963 6.00000) 0 1 10" ] }, - "execution_count": 19, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -642,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -721,7 +723,7 @@ "4 4 POINT (3.00000 1.75230) 0 1" ] }, - "execution_count": 20, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -740,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ @@ -750,33 +752,6 @@ "lscp_from_geodataframe = lscp_from_geodataframe.solve(solver)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Expected result is an instance of LSCP." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lscp_from_geodataframe" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -786,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ @@ -812,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -839,7 +814,7 @@ "\n", "dv_colors = { f\"y{i}\":dv_colors_arr[i] for i in range(len(dv_colors_arr))}\n", "\n", - "def plot_results(model, facility_points):\n", + "def plot_results(model, facility_points, title):\n", " arr_points = []\n", " fac_sites = []\n", " \n", @@ -900,7 +875,7 @@ " label=f\"y{fac_sites[i]} facility selected\",\n", " ))\n", "\n", - " plt.title(\"LSCP\", fontweight=\"bold\")\n", + " plt.title(f\"{title}\", fontweight=\"bold\")\n", " plt.legend(handles = legend_elements, loc='upper left', bbox_to_anchor=(1.05, 1))" ] }, @@ -913,12 +888,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 185, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -930,24 +905,24 @@ } ], "source": [ - "plot_results(lscp_from_cost_matrix, facility_points)" + "plot_results(lscp_from_cost_matrix, facility_points, \"CLSCP - Original\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### LSCP built from geodataframe" + "Below you may notice the plot only shows three demand points; this is because the two other demand points fall outside the 8 meter maximal service distance." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 186, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -958,6 +933,63 @@ "output_type": "display_data" } ], + "source": [ + "plot_results(lscp_from_cost_matrix_switched, facility_points, \"CLSCP - Capacities Switched\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Demand nodes are split between facility points to accomodate facility capacity." + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(lscp_from_cost_matrix_even, facility_points, \"CLSCP - Even Capacities\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSCP built from geodataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "plot_results() missing 1 required positional argument: 'title'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/erinolson/spopt/notebooks/lscp_capacity.ipynb Cell 52'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_results(lscp_from_geodataframe, facility_points)\n", + "\u001b[0;31mTypeError\u001b[0m: plot_results() missing 1 required positional argument: 'title'" + ] + } + ], "source": [ "plot_results(lscp_from_geodataframe, facility_points)" ] @@ -980,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [ { From a978925949b98826eb7c0e1b11d8fc9ed9d37ad6 Mon Sep 17 00:00:00 2001 From: Erin Olson Date: Fri, 9 Sep 2022 14:17:16 -0400 Subject: [PATCH 30/34] add geodataframe plots --- notebooks/lscp_capacity.ipynb | 131 +++++++++++++++++----------------- 1 file changed, 66 insertions(+), 65 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index bdba782b..5384fc15 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 203, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ @@ -159,16 +159,16 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 167, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" }, @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ @@ -220,16 +220,16 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 209, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 169, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" }, @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 210, "metadata": {}, "outputs": [], "source": [ @@ -302,16 +302,16 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 211, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 171, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" }, @@ -352,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 212, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ " [ 7.5002892 , 6.32806975]])" ] }, - "execution_count": 173, + "execution_count": 213, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 214, "metadata": {}, "outputs": [ { @@ -411,7 +411,7 @@ "(array([ 5, 15]), array([15, 5]), array([8, 8]))" ] }, - "execution_count": 174, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 215, "metadata": {}, "outputs": [ { @@ -441,7 +441,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 175, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -562,7 +562,7 @@ "1 1 POINT (0.91963 6.00000) 0 1" ] }, - "execution_count": 179, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -574,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -612,7 +612,7 @@ "
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POINT (9.00000 3.25259)001015
1POINT (0.91963 6.00000)01105
\n", @@ -628,23 +628,23 @@ ], "text/plain": [ " id geometry comp_label predefined_loc capacity\n", - "0 0 POINT (9.00000 3.25259) 0 0 10\n", - "1 1 POINT (0.91963 6.00000) 0 1 10" + "0 0 POINT (9.00000 3.25259) 0 0 15\n", + "1 1 POINT (0.91963 6.00000) 0 1 5" ] }, - "execution_count": 180, + "execution_count": 220, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "facilities_snapped['capacity'] = numpy.array([10, 10])\n", + "facilities_snapped['capacity'] = numpy.array([15, 5])\n", "facilities_snapped" ] }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 221, "metadata": {}, "outputs": [ { @@ -687,28 +687,28 @@ " 1\n", " POINT (2.00000 9.35355)\n", " 0\n", - " 1\n", + " 2\n", " \n", " \n", " 2\n", " 2\n", " POINT (5.00000 6.16214)\n", " 0\n", - " 1\n", + " 3\n", " \n", " \n", " 3\n", " 3\n", " POINT (7.76544 5.00000)\n", " 0\n", - " 1\n", + " 4\n", " \n", " \n", " 4\n", " 4\n", " POINT (3.00000 1.75230)\n", " 0\n", - " 1\n", + " 5\n", " \n", " \n", "\n", @@ -717,19 +717,19 @@ "text/plain": [ " id geometry comp_label dem_quantity\n", "0 0 POINT (2.00000 8.85562) 0 1\n", - "1 1 POINT (2.00000 9.35355) 0 1\n", - "2 2 POINT (5.00000 6.16214) 0 1\n", - "3 3 POINT (7.76544 5.00000) 0 1\n", - "4 4 POINT (3.00000 1.75230) 0 1" + "1 1 POINT (2.00000 9.35355) 0 2\n", + "2 2 POINT (5.00000 6.16214) 0 3\n", + "3 3 POINT (7.76544 5.00000) 0 4\n", + "4 4 POINT (3.00000 1.75230) 0 5" ] }, - "execution_count": 181, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "clients_snapped['dem_quantity'] = numpy.array([1, 1, 1, 1, 1])\n", + "clients_snapped['dem_quantity'] = numpy.arange(5) + 1\n", "clients_snapped" ] }, @@ -742,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 222, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 223, "metadata": {}, "outputs": [], "source": [ @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -888,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -917,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 226, "metadata": {}, "outputs": [ { @@ -946,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 227, "metadata": {}, "outputs": [ { @@ -975,23 +975,24 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 230, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "plot_results() missing 1 required positional argument: 'title'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/erinolson/spopt/notebooks/lscp_capacity.ipynb Cell 52'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_results(lscp_from_geodataframe, facility_points)\n", - "\u001b[0;31mTypeError\u001b[0m: plot_results() missing 1 required positional argument: 'title'" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "plot_results(lscp_from_geodataframe, facility_points)" + "plot_results(lscp_from_geodataframe, facility_points, 'CLSCP')" ] }, { @@ -1012,12 +1013,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1029,7 +1030,7 @@ } ], "source": [ - "plot_results(lscp_preselected_from_geodataframe, facility_points)" + "plot_results(lscp_preselected_from_geodataframe, facility_points, 'CLSCP - Preselected Facility')" ] }, { From 32a99f112249c192c3171f9cb1dc74e2ffe90875 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 11 Dec 2022 23:33:46 +0000 Subject: [PATCH 31/34] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- spopt/locate/coverage.py | 1 - 1 file changed, 1 deletion(-) diff --git a/spopt/locate/coverage.py b/spopt/locate/coverage.py index 68643bd0..36972950 100644 --- a/spopt/locate/coverage.py +++ b/spopt/locate/coverage.py @@ -344,7 +344,6 @@ def from_geodataframe( """ # noqa - demand_quantity_arr = None if demand_quantity_col is not None: demand_quantity_arr = gdf_demand[demand_quantity_col].to_numpy() From acf9c8690973fb4c450d006944f29ea7b7892bc9 Mon Sep 17 00:00:00 2001 From: Levi John Wolf Date: Wed, 14 Dec 2022 15:54:26 +0000 Subject: [PATCH 32/34] Update spopt/locate/base.py --- spopt/locate/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 4223f833..5f7ef68e 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -557,7 +557,7 @@ def add_client_demand_satisfaction_constraint( model += pulp.lpSum([cli_assn_vars[i][j] for j in range_facility]) == 1 else: raise AttributeError( - "before setting constraints must set facility variable and demand quantity variable" # might want to update this message later +"The facility variable and demand quantity variable most both be set in order to add a client demand satisfaction constraint." ) @staticmethod From 0771adc561e64ad1e26dde194ccada15e17002b3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 14 Dec 2022 15:56:08 +0000 Subject: [PATCH 33/34] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- spopt/locate/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spopt/locate/base.py b/spopt/locate/base.py index 5f7ef68e..2e38b175 100644 --- a/spopt/locate/base.py +++ b/spopt/locate/base.py @@ -557,7 +557,7 @@ def add_client_demand_satisfaction_constraint( model += pulp.lpSum([cli_assn_vars[i][j] for j in range_facility]) == 1 else: raise AttributeError( -"The facility variable and demand quantity variable most both be set in order to add a client demand satisfaction constraint." + "The facility variable and demand quantity variable most both be set in order to add a client demand satisfaction constraint." ) @staticmethod From 20b59655c98d8aec3cd5a5254423f740e398cafd Mon Sep 17 00:00:00 2001 From: ljwolf Date: Wed, 14 Dec 2022 16:18:26 +0000 Subject: [PATCH 34/34] update writing in the lscp_capacity notebook --- notebooks/lscp_capacity.ipynb | 146 +++++++++++++++++++--------------- 1 file changed, 80 insertions(+), 66 deletions(-) diff --git a/notebooks/lscp_capacity.ipynb b/notebooks/lscp_capacity.ipynb index 5384fc15..517ee6d6 100644 --- a/notebooks/lscp_capacity.ipynb +++ b/notebooks/lscp_capacity.ipynb @@ -64,9 +64,18 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/lw17329/opt/anaconda3/envs/analysis/lib/python3.10/site-packages/spaghetti/network.py:36: FutureWarning: The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.\n", + " warnings.warn(f\"{dep_msg}\", FutureWarning)\n" + ] + } + ], "source": [ "from spopt.locate.coverage import LSCP\n", "from spopt.locate.util import simulated_geo_points\n", @@ -88,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -159,24 +168,24 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 207, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": { @@ -201,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -220,24 +229,24 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 209, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" + "
" ] }, "metadata": { @@ -277,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -302,24 +311,24 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 211, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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v05G33nrrjJdffnlEYmLikZSUlFRJevDBB3f94z/+46HXX399yKxZs04Kol1hrA3IDF+XZGVl2eLi4m5vl1tUKkl6eF5GoFvqMzVDQkLU0e/CGKOmpm4dOvRJMDzGwVg3mMbqVV1/axpjSqy1WYHs6dNPP90+ZcqUfV3eYMWKaC1dGquqqgGKiTmmJUt2BfJ8md7Q2NioyZMnp/7+97//W3p6+lGv++lv3n///UGPPvpozMsvv9ztj9l31aWXXnrWo48+WjFlypQOf3+ffvrpyClTpiR0dB8zM31EXFycystPCsOKi4vzoBsAQeXWW6tdCy9tlZSURFx11VWJl1122QGCTM+YOXPmkY8++qimoaFB/n7XTEfq6+vNnDlzDnYWZE6HMNNH5OfnKycn52uHmiIjI5Wfn+9hVwDQ92VmZtZXVFR85nUf/d2iRYtOOrE6UCIiImz7j553BycA9xHZ2dkqKCjQkJFjJWMUHx+vgoICzpcBAOA0mJnpQ7Kzs1U2KF1S759rAACAq5iZAQAATiPMAAAApxFmAACA0wgzAADAac6cALxuW/WJL6fqrXqSerVmsNUNprF6VTeYxupV3XXbqjV9QnSv1QNwMmZmAACA05yZmZk+Idqpryj3RWFhoZY/0nzV7L/18lWzXfwaeOr2zZrBVre3Z59cFxkZObWuru5jSZo6dWrKxx9/vKm7+9i3b1/oqlWrou+77769ge/wf52uP1/6OHz4sPnWt76V9MEHH2z295t0Y2Nj0wcPHtzYek2lsrKyjfX19eb8889P+uCDDzaHh4f7tX+XMDPTR3DVbADBxpcgI0n79+8Pffrpp0cHup/2TtefL3386le/GjlnzpwDgbokwNq1a7ds2rRpQ1lZ2Uap+Zt0L7zwwppVq1YF1bFPwkwfcaqrZgNAT1qxYkX0uHHj0kNCQjLHjRuXvmLFioC8EP76178ekZSUlJqcnJw6d+7cCe3vj4yMnNr68/Lly6PT09PPTklJSV24cGF8Q0ODNm/ePGDixImTFyxYED9p0qTJM2fOTDx8+LBZvHjx+J07dw5MSUlJveWWW8bX1NSEXHTRRZOSk5NTExMTJz/11FPD29bZvHnzgAkTJkyeN29eQlJSUurs2bMn1tbWnnj9e+CBB8YkJiZOTkxMnLx06dLR7fsLVB+S9Lvf/W7Ed7/73YOtty+55JKz7rrrrnGZmZnJI0eOnPLyyy9H+fu4X3PNNQdfeOEFwgx6344dO7q1HAACYcWKFdH33HNPfGVl5QBrrSorKwfcc8898f4GmuLi4ohly5aNXbt27ZbNmzdvWLlyZadPZuvXr4948cUXo4uLizdt2rRpQ0hIiF2xYsUISdqxY0fEXXfdteeLL774fOjQoY3PPvvs8Mcee6zizDPPPLpp06YNK1eurCgqKhoSExNzfPPmzRu2bt36+bx582ra19i+fXvErbfeunfLli0boqKimh599NFRkvTee+9Frl69ekRJScnG4uLijc8+++yo999/f1D77QPRR319vdm5c+fA5OTkY63LtmzZMmjYsGGNJSUlmx977LHy5557boQkZWZmJqekpKS2/9c+7MyaNStx8uTJZy9btmxk67Jzzz33SGlp6eCu/7bcR5jpIzq7OjZXzQbQk5YuXRpbX1//tdeC+vr6kKVLl8b6s9833nhjyJVXXnlg7NixDZI0ZsyYxs7WXbNmTVRZWVnklClTzk5JSUn961//OuTLL78cKEmxsbFHZ8yYcUSSpk6dWrd9+/aB7befNm3akffee2/IbbfdFrtmzZozRowYcVKtmJiYY5deeulXknT99dfv/+///u8zJOndd9894/LLLz84ZMiQpqFDhzZdccUVB955552TZkcC0UdVVVVYVFRUQ+vt2trakNra2tAlS5bslqTjx4+boUOHNkpSSUnJ5k2bNm1o/2/u3Lm1rdu///77mzZs2LDxzTff3PrUU0+Nfv3118+QpLCwMIWHh9sDBw4EzWt80Ay0r8vPz1dkZOTXlnHVbAA9raqqakB3lneVtVbGGNvFdc211167v/UFe/v27WW//OUv/y5JAwYMOLGP0NBQ29DQYNpvn5GRcXT9+vUb0tPTj+Tl5cXee++9Y9uvY4zp8La1XWoxIH0MHjy46dixYyded9evXx+RlpZW13r+TGlp6aC0tLQjUtdmZhISEo5LUmxsbMMVV1xx8IMPPjgxG3P8+HETGRnZtcH1A4SZPoKrZgPwQkxMzLHuLO+q2bNn17zyyivRVVVVoZK0e/fu0FOt++qrrw7ftWtXWOu6W7Zs6TRMDR06tPGrr7468fq1ffv28KioqKbbb7+9etGiRbs/+eSTyPbbVFZWDnj77bcHS9Lq1aujZ8yYcViSLr744sOvvfbasNra2pCampqQ1157bfi3vvWt2vbbB6KPUaNGNTY2Npq6ujojSZ988smg9PT0EydLlpWVRU6bNq1OOv3MTE1NTUjrzEtNTU3IO++8MyQjI+OIJFVVVYUOHz68YeDAgUETZpz5aHYw4KrZAHrbkiVLdt1zzz3xbQ81RURENC1ZsmSXP/vNysqqX7x4ceU3v/nNlJCQEJuWllb30ksvbe9o3czMzPr7779/16xZs5KampoUHh5un3jiiR3jx48/3tH6MTExjZmZmYcTExMnX3zxxYcuvfTSmtzc3PGtH1Fevnx5efttJk6cWP/b3/52xO233x4/YcKEo/fee+9eSTr//PPrFi5cuH/atGlnS9L111+/d+bMmUe6MkZf+rjgggsOvfnmm2fMnTu39rPPPht03nnnfdV635YtWwZlZmZ2qXZFRUXY1VdfPUmSGhsbzfz58/dfc801NZL0+uuvD5k1a9ahruynvyDMAEAQu/XWW6ul5nNnqqqqBsTExBxbsmTJrtbl/rjzzjv333nnnfvbLmv9jpn2P998880Hbr755gPt97F169bPW39eunTp7taf//jHP25ru978+fM3nKqXkJAQrV69usOTkB944IHdDzzwwO72y1v7S05OPhaoPu6+++49jz76aMzcuXNrV61aVdH2voqKis9OtW1bqampxzZv3txhreeffz760Ucfrejovv6KMAMAQe7WW2+tDkR4wenNnDnzyEcffVTT0NCgQH3XTFv19fVmzpw5B6dMmXI04DvvwwgzAIB+rf3MitcWLVq0//Rr+SYiIsLecccdPbb/vooTgAEAgNMIMwAAwGmEGQAA4DTCDAD0P01NTU0nfakb4KqWv+emzu4nzABA/1O2d+/eoQQa9AdNTU1m7969QyWVdbYOn2YCgH6moaHh+1VVVauqqqrSxJtWuK9JUllDQ8P3O1uBMAMA/UxmZuYeSXO87gPoLSR2AADgNMIMAABwGmEGAAA4jTADAACcRpgBAABOI8wAAACnEWYAAIDT/Aozxph7jDGfG2PKjDHPG2MiAtUYAABAV/gcZowxsZLukpRlrU2TFCppQaAaAwAA6Ap/DzOFSRpkjAmTFCnp7/63BAAA0HXGWuv7xsbcLSlf0hFJb1prs0+1flZWli0uLu52ndyiUq3bVq3pE6J9a9QH67ZVS1Kv1gy2usE0Vq/qBtNYvarb+tz08LwMn7Y3xpRYa7MC3BYQVPw5zDRc0lWSJkgaJ2mwMea6DtbLMcYUG2OK9+7d63unAAAAHfDnQpPflrTNWrtXkowxRZJmSHqu7UrW2gJJBVLzzIyvxfx55+OL3KJSSerVmsFWN5jG6lXdYBqrV3VbawLwjj/nzOyQ9A/GmEhjjJE0S9LGwLQFAADQNT6HGWvth5JelLRe0mct+yoIUF8AAABd4s9hJllrfyrppwHqBQAAoNv4BmAAAOA0wgwAAHAaYQYAADiNMAMAAJxGmAEAAE4jzAAAAKcRZgAAgNMIMwAAwGmEGQAA4DTCDAAAcBphBgAAOI0wAwAAnEaYAQAATiPMAAAApxFmAACA0wgzAADAaYQZAADgNMIMAABwGmEGAAA4jTADAACcRpgBAABOI8wAAACnEWYAAIDTjLW214plZWXZ4uLibm+XW1SqdduqNX1CdA901bF126olqVdrBlvdYBqrV3WDaaxe1W19bnp4XoZP2xtjSqy1WQFuCwgqzMwAAACnhXndQFf5887HF7lFpZLUqzWDrW4wjdWrusE0Vq/qttYE4B1mZgAAgNMIMwAAwGmEGQAA4DTCDAAAcBphBgAAOI0wAwAAnEaYAQAATiPMAAAApxFmAACA0wgzAADAaYQZAADgNMIMAABwGmEGAAA4jTADAACcRpgBAABOI8wAAACnEWYAAIDTCDMAAMBphBkAAOA0wgwAAHAaYQZBpbCwUMtvma1HrjlHCQkJKiws9LolAICfwrxuAOgthYWFysnJUV1dnSSpvLxcOTk5kqTs7GwvWwMA+IGZGQSNvLy8E0GmVV1dnfLy8jzqCAAQCIQZBI0dO3Z0azkAwA2EGQSNuLi4bi0HALjBrzBjjBlmjHnRGLPJGLPRGPONQDXmicJCKSFBCgnRD2+ZrSl/+ZPXHSGA8vPzFRkZ+bVlkZGRys/P96gjAEAg+Dsz86+S1lhrUyRNkbTR/5Y8Ulgo5eRI5eWStRq+r1LzVixtXo5+ITs7WwUFBRoycqxkjOLj41VQUMDJvwDgOGOt9W1DY4ZI+lTSRNvFnWRlZdni4uJu18otKtW6bdWaPiG629t21Q9vma3h+ypPWn5g5Fj9y8o1PVa3vXXbqiWpR8faV+oG01i9qhtMY/Wqbutz08PzMnza3hhTYq3NCnBbQFDxZ2ZmoqS9kv7NGPOxMWaVMWZw+5WMMTnGmGJjTPHevXv9KNezhu2v6tZyAADQN/jzPTNhkqZJutNa+6Ex5l8l3SfpJ21XstYWSCqQmmdmfC3mzzufLomLaz7E1I6Ji+vZuu3kFpVKUq/W9KpuMI3Vq7rBNFav6rbWBOAdf2ZmKiRVWGs/bLn9oprDjZvy86V2J4ceGxjRvBwAAPRZPocZa22VpJ3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"text/plain": [ - "
" + "
" ] }, "metadata": { @@ -352,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -384,7 +393,7 @@ " [ 7.5002892 , 6.32806975]])" ] }, - "execution_count": 213, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -394,15 +403,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "By manipulating the facility capacity array we can demonstrate how capacity is accounted for and generates different results." + "By manipulating the facility capacity array we can demonstrate how capacity is accounted for and generate different results. We'll do this using three different \"scenarios\" where the two sites have different capacities. In the first case, the first supply facility has a smaller capacity to service demand points. In the second case, the *second* supply has a smaller capacity. Finally, we provide a case where both sites have the same capacity. " ] }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -411,7 +421,7 @@ "(array([ 5, 15]), array([15, 5]), array([8, 8]))" ] }, - "execution_count": 214, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -424,15 +434,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The sum of demand quantity is 15." + "The sum of demand quantity is 15, so the problem should be feasible in all cases. We know this because the \"capacity\" of the system is always greater than the demand in the system. " ] }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -441,7 +452,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 215, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -482,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -562,7 +573,7 @@ "1 1 POINT (0.91963 6.00000) 0 1" ] }, - "execution_count": 219, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -574,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -632,7 +643,7 @@ "1 1 POINT (0.91963 6.00000) 0 1 5" ] }, - "execution_count": 220, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -644,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -723,7 +734,7 @@ "4 4 POINT (3.00000 1.75230) 0 5" ] }, - "execution_count": 221, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -742,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -787,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -796,6 +807,7 @@ "\n", "dv_colors_arr = [\n", " \"darkcyan\",\n", + " \"mediumvioletred\",\n", " \"mediumseagreen\",\n", " \"cyan\",\n", " \"darkslategray\",\n", @@ -804,7 +816,6 @@ " \"darkgoldenrod\",\n", " \"peachpuff\",\n", " \"coral\",\n", - " \"mediumvioletred\",\n", " \"blueviolet\",\n", " \"fuchsia\",\n", " \"thistle\",\n", @@ -854,7 +865,7 @@ " label = f\"coverage_points by y{fac_sites[i]}\"\n", " legend_elements.append(Patch(facecolor=dv_colors[l], edgecolor=\"k\", label=label))\n", "\n", - " gdf.plot(ax=ax, zorder=3, alpha=0.7, edgecolor=\"k\", color=dv_colors[l], label=label)\n", + " gdf.plot(ax=ax, zorder=3, alpha=0.4, edgecolor=\"k\", color=dv_colors[l], label=label)\n", " facility_points.iloc[[fac_sites[i]]].plot(ax=ax,\n", " marker=\"*\",\n", " markersize=200 * 3.0,\n", @@ -888,14 +899,14 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": { @@ -917,14 +928,14 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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y/JHdekRERC1eWlpaSVpa2l7bZUOGDDk3e/bsCx88z507t9P2/alTpxZOnTq1sGZdo0aNKh01atS+mstnz5590ra+vXv3XlKGml5DLv98H8D3AKJFJFdE7oU5QFwvIocAXG95TURERC6mIVdt3FHHW8Ps3BciIiJqYfiNYERERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBRERNYu7cuR179OgRP2rUqCv+uuq+ffvGAOZbjddcdurUKbeXXnop2B59bIo6y8rK5Oqrr4623gZdq19++cWjf//+UT169Ijv1atX/PPPP98RAMrLyyUlJSW6qqrKHt1tNAYJIiLC2d9+89h8223RZ/Py7HYPpuXLlwd/+umnhz7++ON6b7hVm507d+6va9np06fdli9fXufNIrW0Y886X3311Q6jRo0qtN6QTCsPDw8sXLgw99dff93z008/7Vu+fHnH7du3e3t7e6vU1NSSZcuWtbdHfxuLQYKIiLDrlVc6ndm712/Xyy93tkd9d955Z7fc3FyvUaNG9Xruuec6XnfddT3j4+Nje/XqFb9gwYIO1nKvvfZaUFRUVFx0dHTcmDFjLsxc2M5E1Fw2a9asLjk5OV4xMTFx999/f5eHHnqos/XTOgDMmDEjbO7cuReFgpKSEp1er+8VHR0dFxkZGf/mm28G1lcnACxevLh9YmJibExMTNydd94ZXl1dXWc9tj788MOg2267rcj6+vrrr+/55z//uXNycnJ0hw4d+mzYsMG/IdswPDy86pprrjkHAIGBgaaePXueP3bsmCcA3HrrrUWrVq1qFkGiye/+SUREzdeqpKSrTFVVYn2dvXFjcPbGjcE6Dw813mDYobXe995771hGRkZARkbGwU6dOlXn5+efDgkJMZaVlUnfvn3jJkyYUJibm+uxYMGCTt9///1+Sxm3htS9cOHC3JtvvrnN/v379wLAgQMHPMeOHdvzqaeeOmk0GrFhw4bAn3766aL7bqxbt65taGhoVXp6+i+AeQaivjp37NjhvWbNmvaZmZn7vby81IQJE7r961//CvLz8zPWV095ebnk5OR4RUdHV1qXHTx4sM2AAQPKtm/ffuCdd95p9+677waNGTOmFACSk5Ojz549e8m4X3rppRxrGesY9+7d65OamloGAFdfffX5n3/+2bch26upMUgQEbmwkZ9+ujvzhRe65H33XTtTZaVO5+lp6jRoUFHK7Nk59mxn3rx5IZs2bWoHACdOnPDYs2eP9w8//OA7cuTIwk6dOlUDQEhIiFFL3dHR0ZXt2rWr/vbbb9vk5eV5xMfHnwsNDb2orquuuur87Nmzu06bNi1s9OjRxcOHDy+rr87Nmzf7Z2Vl+fTp0ycWAMrLy3UdO3asnjRp0un66jlx4oS7v7//hZMjSktLdaWlpW5PP/10PgBUVVVJQEDAhb5t3779wOXGV1xcrLvlllt6vvTSSznt27c3AYC7uzs8PDxUYWGhLjAw0NSQ7dRUGCSIiFyYb+fOVR6+vkZTVZVO5+GhTFVVOg9fX6OvZeduDxs3bvTPyMjwz8zM3O/v72/q169f9Pnz53VKKYiIskcbkydPPrVs2bIOJ0+e9Jg8efLpmu/37t27YseOHXvXrl0bMHv27LAvv/yyZMGCBXl11aeUknHjxp1+/fXXj9d8r756fH19TZWVlTqbst4JCQnnrOdL/Pzzz20SEhLOW9+/3IxERUWF3HTTTT3HjRt3ZuLEiUW2ZaqqqsTHx8cu268xGCSIiFxceWGhR8TNNxdE3XFHwcH33w8uP33aw571FxUVuQUEBBj9/f1NO3fu9N61a5cvAAwfPrzk1ltv7fXEE0/kh4aGGvPz890aMisREBBgPHv27EXn+N19991FL7zwQlh1dbWkpaX9WnOd7Oxsj44dO1ZPnz79jL+/v+mdd94Jqq/O4cOHl9xyyy29nnjiifywsLDq/Px8t+LiYjdPT09VXz3BwcFGo9Eo586dEx8fH2UwGNokJiaes76flZXlk5aWdiEQ1DcjYTKZMH78+PCoqKjyZ599Nt/2vRMnTrgFBgZWe3l5MUgQEZFzDX3zzcPWnzv06XPM3vWnpaUVL126NDgqKiquZ8+e5X369DkLACkpKeWzZs3KGzx4cIxOp1MJCQnn1q5dm325+kJDQ43JycllkZGR8UOHDi1esmRJrre3txo4cGBJu3btjLVdLbF9+/Y2jz/+eBedTgd3d3e1ePHio5er88knnzw+bNiwKJPJBA8PD7Vo0aJjRUVFbvXVAwBDhgwp/uKLL/zGjBlTunv37jb9+/c/a33v4MGDbZKTk8/XXKc2W7Zs8duwYUNQZGTk+ZiYmDgAeO65547ffvvtxZ999lnbYcOGFTeknqbGIEFERE3i+PHju60/b9269VBtZWbMmHF6xowZlxyKOHfu3E7b55o/f/LJJxddUmo0GrFjxw6/1atXH0Yt0tLSStLS0vbW1U5tdU6dOrVw6tSphbXUdUk9th566KGT8+fPDx0zZkzpsmXLcm3fy83N3V3XejX98Y9/LFNKba/tvffff7/9/Pnzc2t7z9F4+ScREbVo27dv9w4PD08cPHhwSWJiYoWz+zNo0KDzer2+pLFfSFWX8vJyGTVqVFGfPn2cPlaAMxJERNTCJScnl1/JJ31HmDlz5iWzLPbi7e2tHnzwwSar/0pxRoKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizVrM5Z8GgwF6vd6h7QFwaJuu1q4rjdVZ7brSWJ3VrsFgQFJSksPaI2puWkyQICIibTqEhvY5nZ9vt//vg0JCqk+dOLHLXvW1Nv/4xz+CfXx8TPV918N3333XJicnx/P2229v0NdcHzhwwPPmm2+OPHTo0B779RT45ptvfO69996I8vJy3dChQ4tXrFiRo9Nd2cGKFhMkkpKSkJ6e7rD2rJ9oHNmmq7XrSmN1VruuNFZntevoWRctTufnu2P+fPvV98gjzWbfUVVVBQ8Pu95jrNEeffTRgsuVyczM9MnMzPRtaJBoKtOnTw9fvHjx0aFDh57V6/WRa9asaXvbbbeVXEkdPEeCiIjs7rXXXguKioqKi46OjhszZkx3ADh48KDngAEDoqKiouIGDBgQdejQIc/Tp0+7hYWFJRqN5pt+lpaW6kJDQ3tXVFTInj17vAYPHhwZHx8fm5ycHL1z505vAEhLS4uYMmVKl/79+0dNnz69y9dff+3Tt2/fmNjY2Li+ffvG7Nq1y8ta14033tgjKioq7qabburRu3fvmK1bt/oAwLp169omJSXFxMXFxY4YMaJHcXFxnfvDsLCwxGnTpoUlJibGJiYmxmZlZXnVNR4A+Mtf/tL56aefDgGAfv36RVvXjYiISNi8ebNfeXm5/P3vf+/8ySefBMbExMS9+eabgZs2bfKLiYmJi4mJiYuNjY0rLCy8pD/V1dW45ZZbIqKiouKGDx/eo7S0VPfRRx/5X3/99T2tZdavX9/2hhtu6Gm7Xl1ljh496lFWVqa77rrrzup0Otx1112nN2zYEHilv2sGCSIisqvMzEzvBQsWdMrIyDh44MCBvUuWLDkGAA888EC3O++88/TBgwf33n777aenTZvWNSgoyBgTE3Pu008/9QeAVatWBaSmphZ7eXmpKVOmhC9evPjYnj179s2fPz932rRp3axtHD582Pvbb789+Oabb+b26dOn/Mcff9y/b9++vc8888zxRx99tAsAzJ8/P7hdu3bGgwcP7n322Wd/27t3ry8A5OXlub/44oudtm7denDv3r37rrrqqnPPP/98SH1jatu2rXH37t377r///pMzZszoWtd4alu3urpadu/evW/evHk5c+bM6ezt7a0ef/zx30aOHFm4f//+vVOnTi1cuHBh6KJFi47u379/7w8//LDfz8/PVLOe7Oxs7wceeKDg4MGDe/39/U3z588PHjlyZOkvv/zi/dtvv7kDwIoVK4ImTZp0yna9usocPXrUo1OnTlXWcuHh4ZV5eXlXPL3DIEFERHb1+eeftx05cmRhp06dqgEgJCTECAA7d+70ve+++84AwLRp085s377dDwDGjRtX+P777wcCwIcffth+/PjxhcXFxbqdO3f6jRs3rmdMTEzc9OnTw0+ePHlhJ3fLLbcUWm8XfubMGbcbb7yxZ2RkZPyjjz7a9eDBg94A8N133/ndcccdZwDg6quvLo+KijoHAOnp6b6HDx/27tevX0xMTEzcqlWrgo4dO+ZZ35gmTpx4BgCmTp16ZufOnX71jaemcePGFQLAwIEDz+bm5tbazh/+8Ieyv/71r13nzp3b8dSpU261Ha4JDQ2tvOGGG84CwN133336u+++89PpdLjttttOv/nmm+1PnTrltmPHDr9x48ZddLikrjJKqUvaEJH6NkOtms1xLiIiah2UUhCRS/dSdbjjjjuK5syZE5afn++WlZXlM3LkyJKSkhKdv79/9f79+2u9ZbftJ/bHHnssLDU1tXTLli2HDxw44Dl06NBoaz/q6t8111xTUvO24fWxPQHxSsYGmG+yBQDu7u4wGo217qlffPHFE2PGjCn+6KOPAgYOHBi7efPmg3379i23LVNzJ299PW3atNM33XRTL29vbzVy5MjC2kJIbWUiIiKqbGcgjh496hkaGlp1ycqXwRkJIiKyq+HDh5d8/PHH7U+cOOEGAPn5+W4A0Ldv37PLli0LBIAlS5a0T0lJKQOAgIAAU58+fc7ef//93YYNG1bs7u6O9u3bm7p06VK5YsWKQAAwmUz4/vvv29TWXklJiVuXLl0qLfV2sC4fOHBg2apVqwIB863GDx482AYA9Hr92czMTD/ruQ6lpaW6n3/+2au+Mf373/9uDwDLly8P7Nu379n6xtMQbdu2NZaVlV3YB+/Zs8erX79+51944YUTiYmJZ7OysrxrrpOXl+f55Zdf+gLAe++9137gwIFlABAREVEVEhJStXDhwk5Tp049VXO9usqEh4dX+fr6mr766itfk8mElStXBo0ePbqooWOw4owEEVErFxQSUm3PKy2CQkKq63s/JSWlfNasWXmDBw+O0el0KiEh4dzatWuz33jjjWMTJ06MeOWVV0KDgoKq//3vf2db17ntttsK77nnnh4bN248YF32/vvv/zp16tTwefPmdaqurpaxY8eeGTBgwPma7T322GMnpkyZ0n3RokWhgwcPvnDFwSOPPFJw2223RURFRcUlJCSci46OPh8YGGjs3Llz9ZIlS7LHjx/fo7KyUgDgmWeeOd67d++KusZUUVEhvXv3jjGZTLJq1apfAaC+8VzOiBEjShcsWNApJiYmbtasWXn/+9///L777ru2Op1ORUVFnb/11lsvuZqjR48e5StWrAiaPn16ePfu3Sv++te/Xrg6ZPz48adff/119+Tk5PKa69VXZvHixUfvvffe7uXl5XLttdeW1Dws0hBS19RPU0hJSVGZmZlXvB4vm2ud7brSWJ3VriuN1VntNrZNEdmulEqxX4+AXbt2Zffp06fWT6aupLq6GpWVleLj46P27NnjdcMNN0QdPnw4y3qooaHCwsISMzMz91nP+WiO/vSnP3Xr27fvuYcffrjO33tDytRl165dHfr06RNR23uckSAiolaptLRUN3jw4OiqqipRSuGf//zn0SsNES1BfHx8bJs2bUxLlizJaUwZrRgkiIioVQoMDDRlZWXta2j566+/vmdOTs5F50q88MILucePH99t/97Zz549ey47xoaU0YpBgoiICMCWLVsOO7sPLRGv2iAiIiLNGCSIiIhIMwYJIiJCcXGxbsoDD3Sp754TRLXhHwwREWHz5s3+G7Zsab9582b/pmzn1VdfDQoPD08IDw9PePXVV4NqK7Nz505v682r9uzZU+8XRdW0cuXKgCeeeCIUuPjmWTNnzuy8YcMGfwCYM2dOx9LS0ibb/9m2eyVOnTrl9tJLLwU7qj17YZAgIiKs2bSpnSk8XNZs2tSuqdrIz893mzdvXucff/xxX2Zm5r558+Z1LigocKtZbvXq1e1GjBhRtG/fvr3x8fF1fklUbe66667iF1988UTN5S+//PJvY8aMKZHBysoAAB5eSURBVAWAJUuWhNh+q2Rzcfr0abfly5d3dHY/rlSz25BERNT07pg0KbxzZGSi9fG/XbvaBo8aVfm/Xbva2i6/Y9Kk8Cut+6GHHur8/PPPX9ghzpgxI2zu3LkdN2zYEDBkyJCSkJAQY3BwsHHIkCEl69atC7Bd94MPPghYunRpyMqVKzv0798/CgCuu+66nvHx8bG9evWKX7BgwYWvwF6zZk3buLi42Ojo6LgBAwZEAcCiRYuC/vSnP3VDDWlpaRFvvfVW4Ny5czuePHnSIzU1Nap///5R//znPzvce++9F+7auXDhwg5TpkzpYrtudXU10tLSIiIjI+OjoqLinnvuuY6A+Wuta7vNua26yuTk5Lhff/31PaOjo+Oio6PjtmzZ4jtr1qwuOTk5XjExMXH3339/FwB46qmnQhISEmKjoqLiHn744c7Weh977LHQiIiIhIEDB0YdOnToimZt7I2XfxIRuaBp99xTsG3XLj8ZORJtune/cAOsoJkzqwHg3K+/6rBxI6bfe29B3bXUbvr06afGjh3b86mnnjppNBqxYcOGwJ9++mnf4sWLO1jviQEAYWFhlcePH7/oDlO333578bZt2wr8/PyMc+bMyQeAlStXZoeEhBjLysqkb9++cRMmTCg0mUzy4IMPRqSnp++PiYmptN7P43KefPLJk2+88UZIRkbGwU6dOlWXlJTo4uPj4yoqKnK9vLzUu+++22HJkiVHbdf5/vvvffLy8jwOHTq0BzAfggCAKVOmhC9duvRoYmJixX//+1/fadOmdfvhhx8O2q5bV5kHHnig2+DBg0uffvrpw9XV1SguLnZbuHBh7s0339zGeqOydevWtf3ll1+8f/75531KKVx33XW9PvvsMz8/Pz/T+vXr2+/evXtvVVUVkpKS4vr27XvuSn9P9sIgQUTkgoYMGXLu7VdeOTLpoYe6nx85UmcbJqwh4p1Fi44MHjz4indQ0dHRle3atav+9ttv2+Tl5XnEx8efCw0NNWq9bfW8efNCNlkOuZw4ccJjz5493vn5+e79+vUrjYmJqQR+v1X5lWrbtq1p0KBBpR988EFAYmJieVVVlfTr1++i+3nExMRU5OTkeE2cOLHryJEji8eOHVtie5tzaznrfTus6ivz3Xff+a9Zs+YIYL4raFBQkNEaUKw2b97cduvWrW3j4uLiAODcuXO6/fv3e5eWlupuvPHGIn9/fxMA3HDDDVd8oy17YpAgInJRQ4YMObf4738/OuXxxyPa/PnPF5ZXbtwoy/7+92wtIcJq8uTJp5YtW9bh5MmTHpMnTz4NAF26dKnKyMi4cDLn8ePHPVNTU0vrq2fjxo3+GRkZ/pmZmfv9/f1N/fr1iz5//rzOcqtyrd27yH333XfqhRdeCI2KiiqfMGHCJfehCA4ONmZlZe1dv35928WLF3f84IMP2i9ZsuRYfbc5BwCj0YjLlamPUgozZ87Me+SRRy7q05w5czraa+z2wHMkiIhcWHFxsZtbaChKvv/erfjVV91Kvv/ezS00FMXFxQ06VFCXu+++u+jrr78O2LVrl29aWloxAIwZM6Y4IyOjbUFBgVtBQYFbRkZG2zFjxtR7t8mioiK3gIAAo7+/v2nnzp3eu3bt8gWAa6+99uy2bdv89+/f7wn8fqvyhvD19TXaXuY6dOjQs3l5eZ7r168Puvfee8/ULJ+Xl+duNBoxadKkorlz5x7fvXu3T0Nuc15fmUGDBpXOnz8/GDCfg3HmzBldQECA8ezZsxf6NWLEiJL//Oc/Hax9PXLkiMfx48fdhw4dWrZp06Z2ZWVlUlhYqNuyZUuTnSDbEAwSREQubM2mTe1Kfv3VLb6g4Nw7CxYciS8oOFfy669ujb16w9vbWw0cOLBk1KhRZ9zdzZPfISEhxkceeeS35OTk2OTk5NhHH330t8sdkkhLSyuurq6WqKiouCeeeKJznz59zgJA586dqxctWpQ9duzYXtHR0XFjx47t0dC+TZw48dSIESMirSdzAsCYMWMKU1JSyoKDgy/pT3Z2tsc111wTHRMTE3fPPfd0nzNnTi5gvs35W2+91SE6OjouMjIyfu3atZdss7rKvPHGG8cyMjL8Lbc4j9uxY0eb0NBQY3JycllkZGT8/fff3+WWW24pGTdu3Jmrr746JioqKm7s2LE9i4qK3K655ppzY8eOPZOQkBB/88039+zXr19ZQ8feFHgb8WbUpqu160pjdVa7rjRWZ7Xb0m8jPnnatK5X9+lz9v6pU8+4ubnBaDTiX0uWtN++e7fvijfe0HynSKPRiPj4+LjVq1cfTkxMvKJLOJ3h2muv7TVz5sz80aNH13uoxVXxNuJERFSrt2qEBTc3N/zf9OlnAFwyxd9Q27dv9x49enTkiBEjCpt7iDh16pRbSkpKbGxs7DmGCG0YJIiIyK6Sk5PLc3Nzm/Wtt606dOhgzM7OznJ2P1qyRp0jISIPi8geEckSkfdF5JIv4yAiIqLWS3OQEJEwAH8GkKKUSgDgBmC8vTpGRESamUwmU/O5PpBaNMvfkqmu9xt71YY7gDYi4g7AB8BvjayPiIgaL6ugoCCAYYIay2QySUFBQQCAOg//aD5HQil1XEQWADgG4DyAL5RSX2it73IMBsOFs6MdwWAwAIBD23S1dl1prM5q15XG6qx2DQYDkpKSHNZeQ1RXV085ceLEshMnTiSAl/lT45gAZFVXV0+pq4DmICEigQBGA+gOoAjAahGZoJR6t0a5+wDcBwDdul1yHxUiIrKz5OTkkwBGObsf5Boac9XGdQCOKKUKAEBE1gEYCOCiIKGUWgpgKWD+HgmtjSUlJbWoa8PZbvNs09XadaWxOqtdR8+6EDU3jZnyOgbgDyLiI+Yv/R4GYJ99ukVEREQtgeYgoZTaBmANgB0AdlvqWmqnfhEREVEL0KgvpFJKPQPgGTv1hYiIiFoYns1LREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKSZu7M70FAGgwF6vd6h7QFwaJuu1q4rjdVZ7brSWJ3VrsFgQFJSksPaI2puOCNBREREmrWYGYmkpCSkp6c7rD3rJxpHtulq7brSWJ3VriuN1VntOnrWhai54YwEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkmbuzO0DUlDIyMvDa22/jyNGj6B4ejgcnTXJ2l4iIWhUGCWq1MjIyMGvhQvjq9Qi58UaczMnBrIULUVVUhMB27ZzdPSKiVoGHNqjVeu3tt+Gr16NtRAR0bm5oGxEBX70evxUXO7trREStBoNEDUopHN+6FVDK2V2hRjpy9Cj8una9aJlf164or6x0Uo+IiFofBokaig4cQMa0aWhvNDq7K9RI3cPDUZaTc9GyspwceHt6OqlHREStT6OChIi0E5E1IrJfRPaJyAB7dcxZjm7eDADoUV7u5J5QYz04aRLOpqejJDsbJqMRJdnZOJuejs4BAc7uGhFRq9HYky1fAbBZKXWriHgC8LFDnxzKWFGBY59/DlN1NQDg1/XrAQDRFRUodXPD4XXroHN3R7c//hFuXl7O7CpdodTUVCyE+VyJIxs3mq/amDULzzzzjLO7RkTUaojSeC6AiLQFsAtAD9XASlJSUlRmZuYVt6XX62EwGJCUlHTF616Ol8mEWwsL4WMyoRqAwJyuqpSCSSl46nQ4p9NhTWAgKnRNfyTIYDAAQJOMtbm160pjdVa7rjRWZ7Vr/b8pPT1d0/oisl0plWLfXhE5TmP2jD0AFAB4S0R2isgyEfGtWUhE7hORTBHJLCgoaERzTaNCp8OHgYHIthw3t07ReIgAALI9PfGhg0IEERFRS9OYQxvuAK4CMEMptU1EXgHwNwBP2RZSSi0FsBQwz0hobawxib8hlFLYMGwYzufnX1hW5e6OJ3bswGxLqHAEvV4PAE061ubSriuN1VntutJYndWutU0iV9WYj9m5AHKVUtssr9fAHCxapLO5uSgvKICbtzd0Hh6oAuBjMuFsbq6zu0ZERNRsaQ4SSqkTAHJEJNqyaBiAvXbplRMc/fxzKJMJPcaORdq33+KAtzd0AI598YWzu0ZERNRsNfaqjRkAVlqu2PgVwOTGd8k5AmNjoV+yBJ2vuQYA8J2/P455eUEfE+PknhERETVfjQoSSikDgFZxtnHnQYMuWZbr6VnrciIiIjLjpQhERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBDRFcvIyMC4yZORMnQoxk2ejIyMDGd3iYichEGCiK5IRkYGZi1ciJOJiQiZNg0nExMxa+FChgkiF8UgQURX5LW334avXo+2ERHQubmhbUQEfPV6vPb2287uGhE5AYMEEV2RI0ePwq9r14uW+XXtiiNHjzqpR0TkTAwSRHRFuoeHoywn56JlZTk56B4e7qQeEZEzMUgQ0RV5cNIknE1PR0l2NkxGI0qys3E2PR0PTprk7K4RkRO4O7sDRNSypKamYiHM50oc2bgR3cPD8eCsWUhNTXV214jICRgkiOiKpaamMjgQEYAWFCQMBgP0er1D2wPg0DZdrV1XGquz2nWlsTqrXYPBgKSkJIe1R9Tc8BwJIiIi0qzFzEgkJSUhPT3dYe1ZP9E4sk1Xa9eVxuqsdl1prM5q19GzLkTNDWckiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRgkiIiISDMGCSIiItKMQYKIiIg0Y5AgIiIizRodJETETUR2ishGe3SIiIiIWg57zEg8BGCfHeohIiKiFsa9MSuLSBcANwF4AcBf7NKjOhgMBuj1+qZs4pL2ADi0TVdr15XG6qx2XWmszmrXYDAgKSnJYe0RNTeNnZF4GcCjAEx1FRCR+0QkU0QyCwoKGtkcERERNSeaZyRE5GYAJ5VS20VEX1c5pdRSAEsBICUlRWltLykpCenp6VpXv2LWTzSObNPV2nWlsTqrXVcaq7PadfSsC1Fz05gZiUEARolINoBVAIaKyLt26RURERG1CJqDhFLqcaVUF6VUBIDxAP6rlJpgt54RERFRs8fvkSAiIiLNGnXVhpVSKh1Auj3qIiIiopaDMxJERESkGYMEERERacYgQURERJoxSBAREZFmDBJE1CqdOXPG2V0gcgkMEkTU6pw4cQL6669Hfn6+s7tC1OoxSBBRq/PVV1/heEkJvvrqK2d3hajVY5AgolZn7aefwjspCWs/+8zZXSFq9RgkiKhVqaqqws8HDqDjDTdg1969OH36tLO7RNSqMUgQUatSVFQEiYiAm7c3pHt3h9+BlMjVMEgQUatSUFYGiYwEAEhkJFZv2uTkHhG1bgwSRNRqVFdXo7SiAn69egEA/Hr1wvZdu1BcXOzknhG1XgwSRNRqFBUVAV27QufpiarSUug8PSHduiEjI8PZXSNqtexy908ioqa24aOP8ObKlTApVWeZ7FOngKFDYSwvR+mxYwjo2ROIjMQz//gH3vjPf+pcTyeCqXfdhTGjRzdF14laNQYJImoR+l19Nd567z38mJ2NtjfcAHdf30vKqKAguAUFocJyKKOypAT+CQmoCArCMZPpkvLVZ8+i5Isv0K97d/S7+uomHwNRa8RDG0TUInTu3Blr3n0Xf73zThi//BKmigq06dLF/AgLg87PDz7t26ONUqgsKgIAVBQWorKoCNKmDXR+fmgTFnZhHVNFBYxffolH7roLa1euROfOnZ08QqKWiTMSRNRieHh4YNbMmbhmwAD836OPoujYMQQMGQIAOJ+fD3+TCQq4cPjDZDTiXF4elFLQubvD098fAFCckYF2v/6KxYsWoV+/fs4aDlGrwBkJImpx+vfvjy/Wr8cQLy8UvfsuqkpKENCrFypEzAWs51EoBYjA098fAZGRqCouRtG772KItze2bNjAEEFkBwwSRNQitW/fHssXL8azkyahYuVKnM/NRYmbG2qeCSFubvDr1g3njx1DxXvv4bnJk7F88WIEBgY6pd9ErQ2DBBG1WCKCuydMwLABA1BRUAA3AG6W5RCBiMBUVQVTZSUqCgpw3cCBmHDXXeb3icguGCSIqEWrqKjA199+C/+YGHhZrszwDAxEYEwMPC2zDpUlJfCPjcV/v/0WlZWVzuwuUavDIEFELdoPP/wAU3Aw3P38UC2CYjc3+ISEoDwvDz4hIfAPD4ebtzfc/fxgCgrCDz/84OwuE7UqDBJE1KJ9/NlnqO7RAwBQKYKK8nIUrV4NWbfO/AzAw88PAFDdowc+5q3FieyqxVz+aTAYoNfrHdoeAIe26WrtutJYndVuax+ryWTC9gMHgDvvxMkDB3Du8GGor76CBAaifUgIjh88iAO7dkGuuw5uYWEweXrijRUr8OO330Kns8/nKIPBgKSkJLvURdQScUaCiFqs0tJSqKAgiLc3qrdtAz79FD7nzqFLp07Q6XTo2rkzYoOD4bZ5M6q3bYN4e0O1b4/S0lJnd52o1WgxMxJJSUlIT093WHvWT1KObNPV2nWlsTqr3dY+1ieefhqnDhyA548/4tqYGOw/fx7u7u6XtFtYWIhHZs/G1z/+iIrERIyIjcWLzz1nlz44eraHqLnhjAQRtUhGoxEbt2yB+/79eH7KFLz52mtwd6/9s1FgYCDefP11PD9lCjwOHMCmLVtgNBod3GOi1olBgohapJKSEvSNjcXnq1fjzjvuuOx3Q4gI7rzjDny+ejWSYmJQUlLioJ4StW4t5tAGEZGtwMBAvLNixRWvFxUVpWk9IqodZySIiIhIMwYJIiIi0oxBgoiIiDRjkCAiIiLNGCSIiIhIMwYJIiIi0oxBgoiIiDRjkCAiIiLNGCSIiIhIMwYJIiIi0oxBgoiIiDRjkCAiIiLNGCSIiIhIMwYJIiIi0oxBgoiIiDRjkCAiIiLNGCSIiIhIMwYJIiIi0oxBgoiIiDRjkCAiIiLNGCSIiIhIM81BQkS6isjXIrJPRPaIyEP27BgRERE1f+6NWLcawCyl1A4R8QewXUS2KKX22qlvRERE1MxpnpFQSuUppXZYfi4FsA9AmL06RkRERM1fY2YkLhCRCAB9AWyzR321MRgM0Ov1TVV9re0BcGibrtauK43VWe260lid1a7BYEBSUpLD2iNqbhp9sqWI+AFYC2CmUqqklvfvE5FMEcksKChobHNERETUjDRqRkJEPGAOESuVUutqK6OUWgpgKQCkpKQorW0lJSUhPT1d6+pXzPqJxpFtulq7rjRWZ7XrSmN1VruOnnUham4ac9WGAFgOYJ9S6v/Zr0tERETUUjTm0MYgAHcDGCoiBsvjRjv1i4iIiFoAzYc2lFL/AyB27As5SEZGBl57+20cOXoU3cPD8eCkSc7uEhERtVB2uWqDWo6MjAzMWrgQvno9Qm68ESdzcjBr4UJUFRUhsF07Z3ePiIhaGH5Ftot57e234avXo21EBHRubmgbEQFfvR6/FRc7u2tERNQCMUi4mCNHj8Kva9eLlvl17Yryykon9YiIiFoyBgkX0z08HGU5ORctK8vJgbenp5N6RERELRmDhIt5cNIknE1PR0l2NkxGI0qys3E2PR2dAwKc3TUiImqBGCRcTGpqKhbOmoWOu3cj/4030HH3biycNYsnWhIRkSa8asMFpaamIjU11dndICKiVoAzEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWYMEkRERKQZgwQRERFpxiBBREREmjFIEBERkWbuzu5AQxkMBuj1eoe2B8Chbbpau640Vme160pjdVa7BoMBSUlJDmuPqLnhjAQRERFp1mJmJJKSkpCenu6w9qyfaBzZpqu160pjdVa7rjRWZ7Xr6FkXouaGMxJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkGYMEERERacYgQURERJoxSBAREZFmDBJERESkWaOChIgMF5EDIvKLiPzNXp0iIiKilkFzkBARNwCvAxgBIA7AHSISZ6+OERERUfPn3oh1+wH4RSn1KwCIyCoAowHstUfHajIYDNDr9U1RdZ3tAXBom67WriuN1VntutJYndWuwWBAUlKSw9ojam5EKaVtRZFbAQxXSk2xvL4bQH+l1IM1yt0H4D4A6NatW/LRo0c1tefo/5CIiBoqPT1d87oisl0plWK/3hA5VmNmJKS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"text/plain": [ - "
" + "
" ] }, "metadata": { @@ -938,22 +949,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Demand nodes are split between facility points to accomodate facility capacity." + "In the \"even supply capacity\" case, demand nodes are split between facility points to accomodate facility capacity. This means that the two very pink sites on the top left are served exclusively by facility 1, but the three demand sites in the center are served by a mix of facility y1 and y0. " ] }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": { @@ -975,14 +987,14 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": { @@ -1013,14 +1025,14 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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