diff --git a/preprocessing/configs_precheck_example.ipynb b/preprocessing/configs_precheck_example.ipynb new file mode 100644 index 000000000..7d95d8f5b --- /dev/null +++ b/preprocessing/configs_precheck_example.ipynb @@ -0,0 +1,3412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pre-run config checks\n", + "Move through this section to check that your config looks all good " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import gempyor\n", + "\n", + "import pathlib\n", + "from gempyor import seir, model_info, file_paths, compartments\n", + "from gempyor import outcomes\n", + "from gempyor.utils import config, Timer, read_df, profile\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "import os\n", + "os.chdir('/Users/saraloo/Documents/flepi_repos/COVID19_USA/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up model info" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "config_SMH_R18_allBoo_highIE_blk1.yml\n" + ] + } + ], + "source": [ + "config_filepath = \"config_SMH_R18_allBoo_highIE_blk1.yml\"\n", + "\n", + "# config.clear()\n", + "# config.read(user=False)\n", + "config.set_file(config_filepath)\n", + "print(config_filepath)\n", + "\n", + "s = model_info.ModelInfo(\n", + " config=config,\n", + " nslots=1,\n", + " seir_modifiers_scenario=\"None\",\n", + " write_csv=False,\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check model components are set up properly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compartments and transitions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mc_infection_stagemc_vaccination_stagemc_variant_typemc_age_stratamc_name
0EunvaccinatedALLage0to17E_unvaccinated_ALL_age0to17
1EunvaccinatedALLage18to64LRE_unvaccinated_ALL_age18to64LR
2EunvaccinatedALLage18to64HRE_unvaccinated_ALL_age18to64HR
3EunvaccinatedALLage65to100E_unvaccinated_ALL_age65to100
4EvaccinatedALLage0to17E_vaccinated_ALL_age0to17
..................
115X0unvaccinatedALLage65to100X0_unvaccinated_ALL_age65to100
116X0vaccinatedALLage0to17X0_vaccinated_ALL_age0to17
117X0vaccinatedALLage18to64LRX0_vaccinated_ALL_age18to64LR
118X0vaccinatedALLage18to64HRX0_vaccinated_ALL_age18to64HR
119X0vaccinatedALLage65to100X0_vaccinated_ALL_age65to100
\n", + "

120 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " mc_infection_stage mc_vaccination_stage mc_variant_type mc_age_strata \\\n", + "0 E unvaccinated ALL age0to17 \n", + "1 E unvaccinated ALL age18to64LR \n", + "2 E unvaccinated ALL age18to64HR \n", + "3 E unvaccinated ALL age65to100 \n", + "4 E vaccinated ALL age0to17 \n", + ".. ... ... ... ... \n", + "115 X0 unvaccinated ALL age65to100 \n", + "116 X0 vaccinated ALL age0to17 \n", + "117 X0 vaccinated ALL age18to64LR \n", + "118 X0 vaccinated ALL age18to64HR \n", + "119 X0 vaccinated ALL age65to100 \n", + "\n", + " mc_name \n", + "0 E_unvaccinated_ALL_age0to17 \n", + "1 E_unvaccinated_ALL_age18to64LR \n", + "2 E_unvaccinated_ALL_age18to64HR \n", + "3 E_unvaccinated_ALL_age65to100 \n", + "4 E_vaccinated_ALL_age0to17 \n", + ".. ... \n", + "115 X0_unvaccinated_ALL_age65to100 \n", + "116 X0_vaccinated_ALL_age0to17 \n", + "117 X0_vaccinated_ALL_age18to64LR \n", + "118 X0_vaccinated_ALL_age18to64HR \n", + "119 X0_vaccinated_ALL_age65to100 \n", + "\n", + "[120 rows x 5 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = compartments.Compartments(seir_config=config[\"seir\"], compartments_config=config[\"compartments\"])\n", + "# test.get_transition_array()\n", + "test.get_compartments_explicitDF()\n", + "# test.parse_transitions(seir_config=config[\"seir\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0
01*1*1*1
1alpha*1*1*1
2sigma_OMICRON*1*1*1
33*gamma*1*1*1
4epsilon+omegaph4*1*1*1
......
88eta_X9toX9_highIE*1*1*nuage65to100
89eta_X10toX10_highIE*1*1*nuage0to17
90eta_X10toX10_highIE*1*1*nuage18to64LR
91eta_X10toX10_highIE*1*1*nuage18to64HR
92eta_X10toX10_highIE*1*1*nuage65to100
\n", + "

93 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " 0\n", + "0 1*1*1*1\n", + "1 alpha*1*1*1\n", + "2 sigma_OMICRON*1*1*1\n", + "3 3*gamma*1*1*1\n", + "4 epsilon+omegaph4*1*1*1\n", + ".. ...\n", + "88 eta_X9toX9_highIE*1*1*nuage65to100\n", + "89 eta_X10toX10_highIE*1*1*nuage0to17\n", + "90 eta_X10toX10_highIE*1*1*nuage18to64LR\n", + "91 eta_X10toX10_highIE*1*1*nuage18to64HR\n", + "92 eta_X10toX10_highIE*1*1*nuage65to100\n", + "\n", + "[93 rows x 1 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sav = test.get_transition_array()\n", + "\n", + "# these are the transition rates\n", + "df = pd.DataFrame(sav[0])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " gempyor >> Running ***DETERMINISTIC*** simulation;\n", + " gempyor >> ModelInfo USA_inference_all; index: 1; run_id: test_run_id,\n", + " gempyor >> prefix: test_prefix/;\n" + ] + } + ], + "source": [ + "# check that things work in gempyor by setting up a gempyor object\n", + "\n", + "gempyor_simulator = gempyor.GempyorInference(\n", + " config_filepath=config_filepath,\n", + " run_id=\"test_run_id\",\n", + " prefix=\"test_prefix/\",\n", + " stoch_traj_flag=False,\n", + " autowrite_seir=True,\n", + " # spatial_path_prefix=\"../tests/npi/\", # prefix where to find the folder indicated in spatial_setup$\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Input parameters and timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['sigma', 'sigma_OMICRON', 'alpha', 'r0', 'gamma', 'epsilon',\n", + " 'chi_OMICRON', 'omegaph4', 'zeta_r18', 'theta0', 'theta1',\n", + " 'theta2', 'theta3', 'theta4', 'theta5', 'theta6', 'theta7',\n", + " 'theta8', 'theta9', 'theta10'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "params_draw_arr = (\n", + " gempyor_simulator.get_seir_parameters()\n", + ") # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=) or (bypass_FN=)\n", + "\n", + "params_const = gempyor_simulator.get_seir_parametersDF()\n", + "# len(params_const)\n", + "params_const = params_const['parameter'].to_numpy()\n", + "params_const\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", + " 26, 27, 28, 29, 30, 31])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params_names = gempyor_simulator.modinf.parameters.pnames\n", + "params_names = np.array(params_names)\n", + "params_ts = np.setdiff1d(params_names, params_const)\n", + "which_ts = np.where(~np.isin(params_names, params_const))[0]\n", + "which_ts" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['sigma', 'sigma_OMICRON', 'alpha', 'r0', 'gamma', 'epsilon',\n", + " 'chi_OMICRON', 'omegaph4', 'zeta_r18', 'nuage0to17',\n", + " 'nuage18to64LR', 'nuage18to64HR', 'nuage65to100',\n", + " 'eta_X0toX3_highIE', 'eta_X0toX4_highIE', 'eta_X0toX5_highIE',\n", + " 'eta_X1toX4_highIE', 'eta_X1toX5_highIE', 'eta_X2toX4_highIE',\n", + " 'eta_X2toX5_highIE', 'eta_X2toX6_highIE', 'eta_X3toX5_highIE',\n", + " 'eta_X3toX6_highIE', 'eta_X4toX6_highIE', 'eta_X4toX7_highIE',\n", + " 'eta_X5toX7_highIE', 'eta_X6toX7_highIE', 'eta_X6toX8_highIE',\n", + " 'eta_X7toX8_highIE', 'eta_X8toX9_highIE', 'eta_X9toX9_highIE',\n", + " 'eta_X10toX10_highIE', 'theta0', 'theta1', 'theta2', 'theta3',\n", + " 'theta4', 'theta5', 'theta6', 'theta7', 'theta8', 'theta9',\n", + " 'theta10'], dtype='" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot all parameters\n", + "fig, axes = plt.subplots(math.ceil(len(params_draw_arr)/2),2, figsize=(5*2,len(params_draw_arr)), sharex=True)\n", + "\n", + "for i, c in enumerate(params_draw_arr):\n", + " ax = axes.flat[i]\n", + " ax.set_title(params_names[i])\n", + " ax.grid()\n", + " ax.plot(gempyor_simulator.modinf.dates,c)\n", + "\n", + "fig.autofmt_xdate()\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifiers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check that parameters and modififiers all read in properly\n", + "npi_seir = (\n", + " gempyor_simulator.get_seir_npi()\n", + ") # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=) or (bypass_FN=)\n", + "npi_outcome = (\n", + " gempyor_simulator. get_outcome_npi()\n", + ") # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=) or (bypass_FN=)\n", + "# param_reduc = gempyor_simulator.get_seir_parameter_reduced(\n", + "# npi_seir=npi_seir\n", + "# ) # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=) or (bypass_FN=)\n", + "param_reduc = gempyor_simulator.get_seir_parameter_reduced(\n", + " npi_seir=npi_seir, p_draw=params_draw_arr\n", + ") # could also accept (load_ID=True, sim_id2load=XXX) or (bypass_DF=) or (bypass_FN=)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['r0', 'chi_omicron', 'epsilon', 'zeta'])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# What is getting reduced?\n", + "npi_seir.reductions.keys()\n", + "# npi_outcome.reductions.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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45237 rows × 45 columns

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2025-08-20 \n", + "45236 0.45 0.35 0.25 0.15 0.05 06000 2025-08-21 \n", + "\n", + "[45237 rows x 45 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_reduc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot reductions\n", + "param_reduc = param_reduc[param_reduc[\"subpop\"] == \"06000\"]\n", + "param_reduc = param_reduc.set_index(\"date\")\n", + "# print(param_reduc)\n", + "param_reduc = param_reduc.drop(\"subpop\",axis=1)\n", + "param_reduc = param_reduc.filter(items=npi_seir.reductions.keys())\n", + "\n", + "fig, axes = plt.subplots(math.ceil(len(npi_seir.reductions.keys())/2),2, figsize=(5*2,len(npi_seir.reductions.keys())), sharex=True)\n", + "\n", + "for i, c in enumerate(param_reduc.columns):\n", + " ax = axes.flat[i]\n", + " ax.set_title(c)\n", + " ax.grid()\n", + " ax.plot(param_reduc[c])\n", + "\n", + "# for i, c in enumerate(hosp.columns):\n", + "# ax = axes.flat[i]\n", + "# ax.set_title(c)\n", + "# ax.grid()\n", + "# ax.plot(hosp[c])\n", + "fig.autofmt_xdate()\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check one simulation " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded subpops in loaded relative probablity file: 51 Intersect with seir simulation: 51 kept\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "model_output/test_prefix/init/000000000.test_run_id.init.parquet", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mgempyor_simulator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mone_simulation\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_id2write\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/flepi_repos/flepiMoP/flepimop/gempyor_pkg/src/gempyor/inference.py:603\u001b[0m, in \u001b[0;36mGempyorInference.one_simulation\u001b[0;34m(self, sim_id2write, load_ID, sim_id2load, parallel)\u001b[0m\n\u001b[1;32m 601\u001b[0m seeding_data, seeding_amounts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodinf\u001b[38;5;241m.\u001b[39mseeding\u001b[38;5;241m.\u001b[39mget_from_file(sim_id2load, modinf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodinf)\n\u001b[1;32m 602\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 603\u001b[0m initial_conditions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodinf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitial_conditions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_from_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 604\u001b[0m \u001b[43m \u001b[49m\u001b[43msim_id2write\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodinf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodinf\u001b[49m\n\u001b[1;32m 605\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 606\u001b[0m seeding_data, seeding_amounts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodinf\u001b[38;5;241m.\u001b[39mseeding\u001b[38;5;241m.\u001b[39mget_from_config(\n\u001b[1;32m 607\u001b[0m sim_id2write, modinf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodinf\n\u001b[1;32m 608\u001b[0m )\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlastsim_seeding_data \u001b[38;5;241m=\u001b[39m seeding_data\n", + "File \u001b[0;32m~/Documents/flepi_repos/flepiMoP/flepimop/gempyor_pkg/src/gempyor/initial_conditions.py:87\u001b[0m, in \u001b[0;36mInitialConditions.get_from_config\u001b[0;34m(self, sim_id, modinf)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialConditionsFolderDraw\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFromFile\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialConditionsFolderDraw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 87\u001b[0m ic_df \u001b[38;5;241m=\u001b[39m \u001b[43mmodinf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_simID\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mftype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitial_conditions_config\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minitial_file_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msim_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msim_id\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFromFile\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 91\u001b[0m ic_df \u001b[38;5;241m=\u001b[39m read_df(\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath_prefix \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitial_conditions_config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minitial_conditions_file\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(),\n\u001b[1;32m 93\u001b[0m )\n", + "File \u001b[0;32m~/Documents/flepi_repos/flepiMoP/flepimop/gempyor_pkg/src/gempyor/model_info.py:291\u001b[0m, in \u001b[0;36mModelInfo.read_simID\u001b[0;34m(self, ftype, sim_id, input, extension_override)\u001b[0m\n\u001b[1;32m 284\u001b[0m fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_filename(\n\u001b[1;32m 285\u001b[0m ftype\u001b[38;5;241m=\u001b[39mftype,\n\u001b[1;32m 286\u001b[0m sim_id\u001b[38;5;241m=\u001b[39msim_id,\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 288\u001b[0m extension_override\u001b[38;5;241m=\u001b[39mextension_override,\n\u001b[1;32m 289\u001b[0m )\n\u001b[1;32m 290\u001b[0m \u001b[38;5;66;03m# print(f\"Readings {fname}\")\u001b[39;00m\n\u001b[0;32m--> 291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mread_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/flepi_repos/flepiMoP/flepimop/gempyor_pkg/src/gempyor/utils.py:69\u001b[0m, in \u001b[0;36mread_df\u001b[0;34m(fname, extension)\u001b[0m\n\u001b[1;32m 67\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(fname, converters\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubpop\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28mstr\u001b[39m(x)}, skipinitialspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m extension \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparquet\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 69\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparquet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto_pandas()\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid extension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mextension\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Must be \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcsv\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparquet\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/flepimop-env/lib/python3.11/site-packages/pyarrow/parquet/core.py:1776\u001b[0m, in \u001b[0;36mread_table\u001b[0;34m(source, columns, use_threads, schema, use_pandas_metadata, read_dictionary, memory_map, buffer_size, partitioning, filesystem, filters, use_legacy_dataset, ignore_prefixes, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit, page_checksum_verification)\u001b[0m\n\u001b[1;32m 1770\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 1771\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124muse_legacy_dataset\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is deprecated as of pyarrow 15.0.0 \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mand will be removed in a future version.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1776\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mParquetDataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1777\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1778\u001b[0m \u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1779\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mpage_checksum_verification\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpage_checksum_verification\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1791\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 1793\u001b[0m \u001b[38;5;66;03m# fall back on ParquetFile for simple cases when pyarrow.dataset\u001b[39;00m\n\u001b[1;32m 1794\u001b[0m \u001b[38;5;66;03m# module is not available\u001b[39;00m\n\u001b[1;32m 1795\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filters \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/flepimop-env/lib/python3.11/site-packages/pyarrow/parquet/core.py:1354\u001b[0m, in \u001b[0;36mParquetDataset.__init__\u001b[0;34m(self, path_or_paths, filesystem, schema, filters, read_dictionary, memory_map, buffer_size, partitioning, ignore_prefixes, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit, page_checksum_verification, use_legacy_dataset)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m partitioning \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhive\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1351\u001b[0m partitioning \u001b[38;5;241m=\u001b[39m ds\u001b[38;5;241m.\u001b[39mHivePartitioning\u001b[38;5;241m.\u001b[39mdiscover(\n\u001b[1;32m 1352\u001b[0m infer_dictionary\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m-> 1354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilesystem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilesystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1355\u001b[0m \u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparquet_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1356\u001b[0m \u001b[43m \u001b[49m\u001b[43mpartitioning\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpartitioning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1357\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_prefixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_prefixes\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/flepimop-env/lib/python3.11/site-packages/pyarrow/dataset.py:782\u001b[0m, in \u001b[0;36mdataset\u001b[0;34m(source, schema, format, filesystem, partitioning, partition_base_dir, exclude_invalid_files, ignore_prefixes)\u001b[0m\n\u001b[1;32m 771\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 772\u001b[0m schema\u001b[38;5;241m=\u001b[39mschema,\n\u001b[1;32m 773\u001b[0m filesystem\u001b[38;5;241m=\u001b[39mfilesystem,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 778\u001b[0m selector_ignore_prefixes\u001b[38;5;241m=\u001b[39mignore_prefixes\n\u001b[1;32m 779\u001b[0m )\n\u001b[1;32m 781\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_path_like(source):\n\u001b[0;32m--> 782\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_filesystem_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 783\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(source, (\u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mlist\u001b[39m)):\n\u001b[1;32m 784\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(_is_path_like(elem) \u001b[38;5;28;01mfor\u001b[39;00m elem \u001b[38;5;129;01min\u001b[39;00m source):\n", + "File \u001b[0;32m/opt/anaconda3/envs/flepimop-env/lib/python3.11/site-packages/pyarrow/dataset.py:465\u001b[0m, in \u001b[0;36m_filesystem_dataset\u001b[0;34m(source, schema, filesystem, partitioning, format, partition_base_dir, exclude_invalid_files, selector_ignore_prefixes)\u001b[0m\n\u001b[1;32m 463\u001b[0m fs, paths_or_selector \u001b[38;5;241m=\u001b[39m _ensure_multiple_sources(source, filesystem)\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 465\u001b[0m fs, paths_or_selector \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_single_source\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilesystem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 467\u001b[0m options \u001b[38;5;241m=\u001b[39m FileSystemFactoryOptions(\n\u001b[1;32m 468\u001b[0m partitioning\u001b[38;5;241m=\u001b[39mpartitioning,\n\u001b[1;32m 469\u001b[0m partition_base_dir\u001b[38;5;241m=\u001b[39mpartition_base_dir,\n\u001b[1;32m 470\u001b[0m exclude_invalid_files\u001b[38;5;241m=\u001b[39mexclude_invalid_files,\n\u001b[1;32m 471\u001b[0m selector_ignore_prefixes\u001b[38;5;241m=\u001b[39mselector_ignore_prefixes\n\u001b[1;32m 472\u001b[0m )\n\u001b[1;32m 473\u001b[0m factory \u001b[38;5;241m=\u001b[39m FileSystemDatasetFactory(fs, paths_or_selector, \u001b[38;5;28mformat\u001b[39m, options)\n", + "File \u001b[0;32m/opt/anaconda3/envs/flepimop-env/lib/python3.11/site-packages/pyarrow/dataset.py:441\u001b[0m, in \u001b[0;36m_ensure_single_source\u001b[0;34m(path, filesystem)\u001b[0m\n\u001b[1;32m 439\u001b[0m paths_or_selector \u001b[38;5;241m=\u001b[39m [path]\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 441\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(path)\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filesystem, paths_or_selector\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: model_output/test_prefix/init/000000000.test_run_id.init.parquet" + ] + } + ], + "source": [ + "gempyor_simulator.one_simulation(sim_id2write=0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Outputs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "config_name = config_filepath\n", + "run_id = \"test_prefix\"\n", + "data_dir = \"../COVID19_USA\"\n", + "fs_results_path = \"model_output\" \n", + "\n", + "def get_all_filenames(file_type, model_output_path=\"model_output/\", finals_only=False, intermediates_only=True, ignore_chimeric=True) -> list:\n", + " \"\"\"\n", + " fuzzy list of all filenames of a specific type in a directory:\n", + " \"\"\"\n", + " if file_type==\"seed\":\n", + " ext=\"csv\"\n", + " else: \n", + " ext=\"parquet\"\n", + " l = []\n", + " for f in Path(str(model_output_path)).rglob(f'*.{ext}'):\n", + " f = str(f)\n", + " if file_type in f:\n", + " if (finals_only and \"final\" in f) or (intermediates_only and \"intermediate\" in f) or (not finals_only and not intermediates_only):\n", + " if not (ignore_chimeric and \"chimeric\" in f):\n", + " l.append(str(f))\n", + " return l\n", + "class RunInfo():\n", + " \"\"\" Store the information to reproduce and buid a run\"\"\"\n", + " def __init__(self, run_id, config_path=None, folder_path=None):\n", + " self.run_id = run_id\n", + " self.config_path = config_path\n", + " self.folder_path = folder_path\n", + " self.gempyor_inference=None\n", + "\n", + " def get_all_filenames(self, file_type, finals_only=False, intermediates_only=True, ignore_chimeric=True) -> list:\n", + " return get_all_filenames(file_type, model_output_path=self.folder_path, finals_only=finals_only, intermediates_only=intermediates_only, ignore_chimeric=ignore_chimeric)\n", + "\n", + "\n", + "run_info = RunInfo(run_id = run_id, \n", + " config_path= f\"{data_dir}/{config_name}\",\n", + " folder_path = f\"{fs_results_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hosp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " subpop incidI_unvaccinated_ALL_age0to17ph4 \\\n", + "date \n", + "2023-03-19 06000 67703.372077 \n", + "2023-03-20 06000 67703.372077 \n", + "2023-03-21 06000 89071.590211 \n", + "2023-03-22 06000 89071.590211 \n", + "2023-03-23 06000 101598.390896 \n", + "... ... ... \n", + "2025-08-17 06000 25663.239460 \n", + "2025-08-18 06000 25663.239460 \n", + "2025-08-19 06000 26641.744551 \n", + "2025-08-20 06000 26641.744551 \n", + "2025-08-21 06000 27668.327017 \n", + "\n", + " incidI_vaccinated_ALL_age0to17ph4 \\\n", + "date \n", + "2023-03-19 8830.199826 \n", + "2023-03-20 8830.199826 \n", + "2023-03-21 11747.868603 \n", + "2023-03-22 11747.868603 \n", + "2023-03-23 13543.781288 \n", + "... ... \n", + "2025-08-17 46.474430 \n", + "2025-08-18 46.474430 \n", + "2025-08-19 48.246277 \n", + "2025-08-20 48.246277 \n", + "2025-08-21 50.105157 \n", + "\n", + " incidI_unvaccinated_ALL_age18to64HRph4 \\\n", + "date \n", + "2023-03-19 29981.927328 \n", + "2023-03-20 29981.927328 \n", + "2023-03-21 39443.655611 \n", + "2023-03-22 39443.655611 \n", + "2023-03-23 44993.339338 \n", + "... ... \n", + "2025-08-17 10754.773575 \n", + "2025-08-18 10754.773575 \n", + "2025-08-19 11164.993833 \n", + "2025-08-20 11164.993833 \n", + "2025-08-21 11595.374124 \n", + "\n", + " incidI_vaccinated_ALL_age18to64HRph4 \\\n", + "date \n", + "2023-03-19 12396.769227 \n", + "2023-03-20 12396.769227 \n", + "2023-03-21 16469.562446 \n", + "2023-03-22 16469.562446 \n", + "2023-03-23 18964.135538 \n", + "... ... \n", + "2025-08-17 3779.819021 \n", + "2025-08-18 3779.819021 \n", + "2025-08-19 3924.243596 \n", + "2025-08-20 3924.243596 \n", + "2025-08-21 4075.772059 \n", + "\n", + " incidI_unvaccinated_ALL_age18to64LRph4 \\\n", + "date \n", + "2023-03-19 117712.295961 \n", + "2023-03-20 117712.295961 \n", + "2023-03-21 154860.066610 \n", + "2023-03-22 154860.066610 \n", + "2023-03-23 176648.726365 \n", + "... ... \n", + "2025-08-17 56992.347027 \n", + "2025-08-18 56992.347027 \n", + "2025-08-19 59165.387329 \n", + "2025-08-20 59165.387329 \n", + "2025-08-21 61445.196634 \n", + "\n", + " incidI_vaccinated_ALL_age18to64LRph4 \\\n", + "date \n", + "2023-03-19 48671.059477 \n", + "2023-03-20 48671.059477 \n", + "2023-03-21 64661.287041 \n", + "2023-03-22 64661.287041 \n", + "2023-03-23 74455.251349 \n", + "... ... \n", + "2025-08-17 92.049231 \n", + "2025-08-18 92.049231 \n", + "2025-08-19 95.558627 \n", + "2025-08-20 95.558627 \n", + "2025-08-21 99.240403 \n", + "\n", + " incidI_unvaccinated_ALL_age65to100ph4 \\\n", + "date \n", + "2023-03-19 19986.180045 \n", + "2023-03-20 19986.180045 \n", + "2023-03-21 26294.230407 \n", + "2023-03-22 26294.230407 \n", + "2023-03-23 29998.356600 \n", + "... ... \n", + "2025-08-17 10209.281387 \n", + "2025-08-18 10209.281387 \n", + "2025-08-19 10598.744494 \n", + "2025-08-20 10598.744494 \n", + "2025-08-21 11007.348941 \n", + "\n", + " incidI_vaccinated_ALL_age65to100ph4 incidI_ALL ... \\\n", + "date ... \n", + "2023-03-19 28014.475170 333296.279111 ... \n", + "2023-03-20 28014.475170 333296.279111 ... \n", + "2023-03-21 37172.729062 439720.989991 ... \n", + "2023-03-22 37172.729062 439720.989991 ... \n", + "2023-03-23 42756.490584 502958.471957 ... \n", + "... ... ... ... \n", + "2025-08-17 6673.481148 114211.465279 ... \n", + "2025-08-18 6673.481148 114211.465279 ... \n", + "2025-08-19 6928.124761 118567.043470 ... \n", + "2025-08-20 6928.124761 118567.043470 ... \n", + "2025-08-21 7195.283765 123136.648099 ... \n", + "\n", + " incidD_unvaccinated_ALL_age0to17ph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 0.094008 \n", + "2025-08-18 0.094008 \n", + "2025-08-19 0.095329 \n", + "2025-08-20 0.095329 \n", + "2025-08-21 0.097117 \n", + "\n", + " incidD_vaccinated_ALL_age0to17ph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 0.000073 \n", + "2025-08-18 0.000073 \n", + "2025-08-19 0.000074 \n", + "2025-08-20 0.000074 \n", + "2025-08-21 0.000075 \n", + "\n", + " incidD_unvaccinated_ALL_age18to64HRph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 10.787713 \n", + "2025-08-18 10.787713 \n", + "2025-08-19 10.939477 \n", + "2025-08-20 10.939477 \n", + "2025-08-21 11.144787 \n", + "\n", + " incidD_vaccinated_ALL_age18to64HRph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 1.623804 \n", + "2025-08-18 1.623804 \n", + "2025-08-19 1.646764 \n", + "2025-08-20 1.646764 \n", + "2025-08-21 1.677785 \n", + "\n", + " incidD_unvaccinated_ALL_age18to64LRph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 6.148122 \n", + "2025-08-18 6.148122 \n", + "2025-08-19 6.234524 \n", + "2025-08-20 6.234524 \n", + "2025-08-21 6.351440 \n", + "\n", + " incidD_vaccinated_ALL_age18to64LRph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 0.004256 \n", + "2025-08-18 0.004256 \n", + "2025-08-19 0.004316 \n", + "2025-08-20 0.004316 \n", + "2025-08-21 0.004396 \n", + "\n", + " incidD_unvaccinated_ALL_age65to100ph4 \\\n", + "date \n", + "2023-03-19 0.000000 \n", + "2023-03-20 0.000000 \n", + "2023-03-21 0.000000 \n", + "2023-03-22 0.000000 \n", + "2023-03-23 0.000000 \n", + "... ... \n", + "2025-08-17 81.523202 \n", + "2025-08-18 81.523202 \n", + "2025-08-19 82.670507 \n", + "2025-08-20 82.670507 \n", + "2025-08-21 84.222464 \n", + "\n", + " incidD_vaccinated_ALL_age65to100ph4 incidD_ALL incidD \n", + "date \n", + "2023-03-19 0.000000 0.000000 0.000000 \n", + "2023-03-20 0.000000 0.000000 0.000000 \n", + "2023-03-21 0.000000 0.000000 0.000000 \n", + "2023-03-22 0.000000 0.000000 0.000000 \n", + "2023-03-23 0.000000 0.000000 0.000000 \n", + "... ... ... ... \n", + "2025-08-17 22.835823 123.017001 123.017001 \n", + "2025-08-18 22.835823 123.017001 123.017001 \n", + "2025-08-19 23.157481 124.748471 124.748471 \n", + "2025-08-20 23.157481 124.748471 124.748471 \n", + "2025-08-21 23.592482 127.090547 127.090547 \n", + "\n", + "[887 rows x 41 columns]\n" + ] + } + ], + "source": [ + "hosp = gempyor.read_df(\"./model_output/test_prefix/hosp/000000000.test_run_id.hosp.parquet\")\n", + "hosp = hosp[hosp[\"subpop\"] == \"06000\"]\n", + "hosp = hosp.set_index(\"date\")\n", + "print(hosp)\n", + "hosp = hosp.drop(\"subpop\",axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['incidH', 'incidD'], dtype='object')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in data\n", + "data = read_df(\"data/us_data.csv\")\n", + "data = data[data[\"subpop\"] == \"06000\"]\n", + "data = data.set_index(\"date\")\n", + "data = data.drop(\"subpop\",axis=1)\n", + "data = data[['incidH', 'incidD']]\n", + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot hosp files for sanity check\n", + "hosp = hosp[['incidH', 'incidD','incidI', 'incidC']]\n", + "\n", + "fig, axes = plt.subplots(4, 1, figsize=(7, 7), sharex=True) \n", + "\n", + "for i, c in enumerate(hosp.columns):\n", + " ax = axes.flat[i]\n", + " ax.set_title(c)\n", + " ax.grid()\n", + " ax.plot(hosp[c])\n", + "# add points from data\n", + "for i, c in enumerate(data.columns):\n", + " ax = axes.flat[i]\n", + " ax.scatter(data.index, data[c], color=\"red\", label=\"data\")\n", + " ax.legend()\n", + "\n", + "fig.autofmt_xdate()\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SEIR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mc_value_typemc_infection_stagemc_vaccination_stagemc_variant_typemc_age_stratamc_name56000500001100002000...370001300039000170004200036000120004800006000date
0incidenceEunvaccinatedALLage0to17E_unvaccinated_ALL_age0to17468.413982565.724162562.2710391112.399808...29389.13621426249.89976841575.9123128165.10913618894.39980834917.30331132112.180910104372.054530121441.1075872023-03-19
1incidenceEunvaccinatedALLage18to64LRE_unvaccinated_ALL_age18to64LR858.7400461071.8536561119.7377161847.391617...52109.75573247193.14421072800.21549113691.24616334221.98671766297.67755168347.510656168342.179541211118.8093622023-03-19
2incidenceEunvaccinatedALLage18to64HRE_unvaccinated_ALL_age18to64HR224.160390374.642506330.699740583.386826...18213.80126113779.91032523623.9109874371.08386711899.28918218917.84629317193.80430852000.98739853773.0468012023-03-19
3incidenceEunvaccinatedALLage65to100E_unvaccinated_ALL_age65to100191.254538227.302460165.489741268.828760...11481.4948459190.69725315630.0854312564.7206837970.92230313899.82537819475.61483329605.90846435841.8942572023-03-19
4incidenceEvaccinatedALLage0to17E_vaccinated_ALL_age0to1719.731266144.351001147.60667376.235808...2566.3384412324.9590333608.178902995.2107091718.7220572532.8838541305.4728797034.61954216035.9899822023-03-19
..................................................................
212875prevalenceX0unvaccinatedALLage65to100X0_unvaccinated_ALL_age65to10030.97133914.38890717.46943529.409762...496.334868412.526007555.860940492.853011552.9131801010.0772421279.0683291246.1638191371.9436012025-08-21
212876prevalenceX0vaccinatedALLage0to17X0_vaccinated_ALL_age0to170.0384200.1247530.1478190.055036...0.6479060.4183920.8158471.3154180.9603972.3632901.0906832.3280074.2899772025-08-21
212877prevalenceX0vaccinatedALLage18to64LRX0_vaccinated_ALL_age18to64LR0.0701380.2347510.3799300.137478...1.6536290.9815701.7709382.6749982.3484535.4133882.6634584.0597448.4969292025-08-21
212878prevalenceX0vaccinatedALLage18to64HRX0_vaccinated_ALL_age18to64HR3.4124258.52967412.8320048.051759...103.35209242.845115118.690756175.310285125.630680179.73162043.138946261.909977338.0063422025-08-21
212879prevalenceX0vaccinatedALLage65to100X0_vaccinated_ALL_age65to10010.91963824.84965812.80978310.133536...202.662897115.729849265.531988303.676120264.248691360.649550395.245070376.325635661.6774212025-08-21
\n", + "

212880 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " mc_value_type mc_infection_stage mc_vaccination_stage mc_variant_type \\\n", + "0 incidence E unvaccinated ALL \n", + "1 incidence E unvaccinated ALL \n", + "2 incidence E unvaccinated ALL \n", + "3 incidence E unvaccinated ALL \n", + "4 incidence E vaccinated ALL \n", + "... ... ... ... ... \n", + "212875 prevalence X0 unvaccinated ALL \n", + "212876 prevalence X0 vaccinated ALL \n", + "212877 prevalence X0 vaccinated ALL \n", + "212878 prevalence X0 vaccinated ALL \n", + "212879 prevalence X0 vaccinated ALL \n", + "\n", + " mc_age_strata mc_name 56000 50000 \\\n", + "0 age0to17 E_unvaccinated_ALL_age0to17 468.413982 565.724162 \n", + "1 age18to64LR E_unvaccinated_ALL_age18to64LR 858.740046 1071.853656 \n", + "2 age18to64HR E_unvaccinated_ALL_age18to64HR 224.160390 374.642506 \n", + "3 age65to100 E_unvaccinated_ALL_age65to100 191.254538 227.302460 \n", + "4 age0to17 E_vaccinated_ALL_age0to17 19.731266 144.351001 \n", + "... ... ... ... ... \n", + "212875 age65to100 X0_unvaccinated_ALL_age65to100 30.971339 14.388907 \n", + "212876 age0to17 X0_vaccinated_ALL_age0to17 0.038420 0.124753 \n", + "212877 age18to64LR X0_vaccinated_ALL_age18to64LR 0.070138 0.234751 \n", + "212878 age18to64HR X0_vaccinated_ALL_age18to64HR 3.412425 8.529674 \n", + "212879 age65to100 X0_vaccinated_ALL_age65to100 10.919638 24.849658 \n", + "\n", + " 11000 02000 ... 37000 13000 \\\n", + "0 562.271039 1112.399808 ... 29389.136214 26249.899768 \n", + "1 1119.737716 1847.391617 ... 52109.755732 47193.144210 \n", + "2 330.699740 583.386826 ... 18213.801261 13779.910325 \n", + "3 165.489741 268.828760 ... 11481.494845 9190.697253 \n", + "4 147.606673 76.235808 ... 2566.338441 2324.959033 \n", + "... ... ... ... ... ... \n", + "212875 17.469435 29.409762 ... 496.334868 412.526007 \n", + "212876 0.147819 0.055036 ... 0.647906 0.418392 \n", + "212877 0.379930 0.137478 ... 1.653629 0.981570 \n", + "212878 12.832004 8.051759 ... 103.352092 42.845115 \n", + "212879 12.809783 10.133536 ... 202.662897 115.729849 \n", + "\n", + " 39000 17000 42000 36000 12000 \\\n", + "0 41575.912312 8165.109136 18894.399808 34917.303311 32112.180910 \n", + "1 72800.215491 13691.246163 34221.986717 66297.677551 68347.510656 \n", + "2 23623.910987 4371.083867 11899.289182 18917.846293 17193.804308 \n", + "3 15630.085431 2564.720683 7970.922303 13899.825378 19475.614833 \n", + "4 3608.178902 995.210709 1718.722057 2532.883854 1305.472879 \n", + "... ... ... ... ... ... \n", + "212875 555.860940 492.853011 552.913180 1010.077242 1279.068329 \n", + "212876 0.815847 1.315418 0.960397 2.363290 1.090683 \n", + "212877 1.770938 2.674998 2.348453 5.413388 2.663458 \n", + "212878 118.690756 175.310285 125.630680 179.731620 43.138946 \n", + "212879 265.531988 303.676120 264.248691 360.649550 395.245070 \n", + "\n", + " 48000 06000 date \n", + "0 104372.054530 121441.107587 2023-03-19 \n", + "1 168342.179541 211118.809362 2023-03-19 \n", + "2 52000.987398 53773.046801 2023-03-19 \n", + "3 29605.908464 35841.894257 2023-03-19 \n", + "4 7034.619542 16035.989982 2023-03-19 \n", + "... ... ... ... \n", + "212875 1246.163819 1371.943601 2025-08-21 \n", + "212876 2.328007 4.289977 2025-08-21 \n", + "212877 4.059744 8.496929 2025-08-21 \n", + "212878 261.909977 338.006342 2025-08-21 \n", + "212879 376.325635 661.677421 2025-08-21 \n", + "\n", + "[212880 rows x 58 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# plot SEIR file for sanity check\n", + "seir = gempyor.read_df(\"./model_output/test_prefix/seir/000000000.test_run_id.seir.parquet\")\n", + "seir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loaded 1 seir files\n" + ] + } + ], + "source": [ + "seir_filenames = run_info.get_all_filenames(\"seir\", finals_only=False, intermediates_only=False)\n", + "print(f\"loaded {len(seir_filenames)} seir files\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "comp_to_plot = \"I1\" #[\"S\", \"E\", \"I1\", \"I2\", \"I3\", \"R\", \"W\"] \n", + "vt= 'incidence'\n", + "\n", + "def get_comp_to_plot(out_df, comp_to_plot=\"I1\", vt = \"incidence\", vacc = \"vaccinated\"):\n", + " # sum accross other meta-compartments\n", + " df = out_df[(out_df['mc_value_type'] == vt) & (out_df['mc_infection_stage'] == comp_to_plot) & (out_df['mc_vaccination_stage'] == vacc) ].reset_index(drop=True)\n", + " return clean_all_mc(df).groupby('date').sum()\n", + "\n", + "def clean_all_mc(df):\n", + " return df.drop([c for c in df.columns if ('mc_' in c)], axis=1)\n", + "\n", + "node_names_toplot = [\"06000\",\"12000\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# cmp_to_plot = [[\"E\", \"incidence\"],[\"I1\", \"incidence\"],[\"I3\", \"incidence\"]]\n", + "cmp_to_plot = [[\"X10\", \"prevalence\", \"vaccinated\"],[\"X10\", \"prevalence\", \"unvaccinated\"]]\n", + "\n", + "fig, axes = plt.subplots(len(node_names_toplot),len(cmp_to_plot), figsize=(5*len(cmp_to_plot),len(node_names_toplot)*3), sharex=True)\n", + "\n", + "for sm in range(len(seir_filenames)):\n", + " dfl = gempyor.read_df(seir_filenames[sm])\n", + " \n", + " for k, c in enumerate(cmp_to_plot):\n", + " df = get_comp_to_plot(dfl, comp_to_plot=c[0], vt = c[1], vacc = c[2])\n", + " for idp, nn in enumerate(node_names_toplot):\n", + " ax = axes[idp,k]\n", + " ax.plot(df[nn], lw=0.5)\n", + " if sm == 0:\n", + " ax.set_title(f\"{nn}, {c[1]} in {c[0]} and {c[2]}\")\n", + " ax.grid()\n", + " #ax.set_ylim(0)\n", + "fig.autofmt_xdate()\n", + "# plt.savefig(f\"some_comp_for_{len(seir_filenames)}slots.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "flepimop-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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}