diff --git a/interactive_simulation/interactive_sim_bep_investigating_correlation.ipynb b/interactive_simulation/interactive_sim_bep_investigating_correlation.ipynb new file mode 100644 index 0000000..3768bbf --- /dev/null +++ b/interactive_simulation/interactive_sim_bep_investigating_correlation.ipynb @@ -0,0 +1,1778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interactive sim\n", + "install environment in a terminal:\n", + "\n", + "1) conda activate gates_bep\n", + "\n", + "2) then type in this command: ipython kernel install --name gates_bep --user" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import display, HTML\n", + "display(HTML(\"\")) #makes the display bars longer and spread out 100% across the screen width" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from numpy.random import randn\n", + "from numpy.random import seed\n", + "from numpy import mean\n", + "from numpy import std\n", + "from numpy import cov\n", + "from matplotlib import pyplot\n", + "from scipy.stats import pearsonr\n", + "from scipy.stats import spearmanr\n", + "from scipy.stats import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from vivarium import InteractiveContext" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "branches india.yaml mali.yaml model_spec.in\tpakistan.yaml tanzania.yaml\r\n" + ] + } + ], + "source": [ + "!make_specs #uses the model_spec.in to create the yaml files \n", + "!ls vivarium_gates_bep/src/vivarium_gates_bep/model_specifications" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/ihme/homes/nicoly/vivarium_gates_bep/src/vivarium_gates_bep/model_specifications\n" + ] + } + ], + "source": [ + "%cd vivarium_gates_bep/src/vivarium_gates_bep/model_specifications\n", + "# ! changes locally\n", + "# % changes in python (the running interpreter)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-07-16 12:32:31.546 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.1.population_manager.metrics as modifier to metrics\n", + "2020-07-16 12:32:31.571 | DEBUG | vivarium.framework.artifact.manager:_load_artifact:66 - Running simulation from artifact located at /share/costeffectiveness/artifacts/vivarium_gates_bep/india.hdf.\n", + "2020-07-16 12:32:31.573 | DEBUG | vivarium.framework.artifact.manager:_load_artifact:67 - Artifact base filter terms are ['draw == 0', \"location == 'India' | location == 'Global'\"].\n", + "2020-07-16 12:32:31.574 | DEBUG | vivarium.framework.artifact.manager:_load_artifact:68 - Artifact additional filter terms are None.\n", + "2020-07-16 12:32:32.279 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline protein_energy_malnutrition.disability_weight\n", + "2020-07-16 12:32:32.280 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering disability_weight.1.protein_energy_malnutrition.disability_weight as modifier to disability_weight\n", + "2020-07-16 12:32:32.890 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering cause_specific_mortality_rate.1.risk_attributable_disease.cause.protein_energy_malnutrition.risk_factor.child_wasting.adjust_cause_specific_mortality_rate as modifier to cause_specific_mortality_rate\n", + "2020-07-16 12:32:33.504 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline protein_energy_malnutrition.excess_mortality_rate\n", + "2020-07-16 12:32:33.507 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline protein_energy_malnutrition.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:32:33.508 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering mortality_rate.1.risk_attributable_disease.cause.protein_energy_malnutrition.risk_factor.child_wasting.adjust_mortality_rate as modifier to mortality_rate\n", + "2020-07-16 12:32:33.513 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline child_wasting.propensity\n", + "2020-07-16 12:32:33.514 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline child_wasting.exposure\n", + "2020-07-16 12:32:47.075 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.incidence_rate.1.risk_effect.risk_factor.child_wasting.cause.diarrheal_diseases.incidence_rate.adjust_target as modifier to diarrheal_diseases.incidence_rate\n", + "2020-07-16 12:32:47.078 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.incidence_rate.paf.1.lookup_table_18 as modifier to diarrheal_diseases.incidence_rate.paf\n", + "2020-07-16 12:32:47.801 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering measles.incidence_rate.1.risk_effect.risk_factor.child_wasting.cause.measles.incidence_rate.adjust_target as modifier to measles.incidence_rate\n", + "2020-07-16 12:32:47.803 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering measles.incidence_rate.paf.1.lookup_table_20 as modifier to measles.incidence_rate.paf\n", + "2020-07-16 12:32:48.518 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.incidence_rate.1.risk_effect.risk_factor.child_wasting.cause.lower_respiratory_infections.incidence_rate.adjust_target as modifier to lower_respiratory_infections.incidence_rate\n", + "2020-07-16 12:32:48.519 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.incidence_rate.paf.1.lookup_table_22 as modifier to lower_respiratory_infections.incidence_rate.paf\n", + "2020-07-16 12:32:48.520 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline child_stunting.propensity\n", + "2020-07-16 12:32:48.521 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline child_stunting.exposure\n", + "2020-07-16 12:32:59.219 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.incidence_rate.2.risk_effect.risk_factor.child_stunting.cause.diarrheal_diseases.incidence_rate.adjust_target as modifier to diarrheal_diseases.incidence_rate\n", + "2020-07-16 12:32:59.221 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.incidence_rate.paf.2.lookup_table_37 as modifier to diarrheal_diseases.incidence_rate.paf\n", + "2020-07-16 12:32:59.950 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering measles.incidence_rate.2.risk_effect.risk_factor.child_stunting.cause.measles.incidence_rate.adjust_target as modifier to measles.incidence_rate\n", + "2020-07-16 12:32:59.953 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering measles.incidence_rate.paf.2.lookup_table_39 as modifier to measles.incidence_rate.paf\n", + "2020-07-16 12:33:00.804 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.incidence_rate.2.risk_effect.risk_factor.child_stunting.cause.lower_respiratory_infections.incidence_rate.adjust_target as modifier to lower_respiratory_infections.incidence_rate\n", + "2020-07-16 12:33:00.806 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.incidence_rate.paf.2.lookup_table_41 as modifier to lower_respiratory_infections.incidence_rate.paf\n", + "2020-07-16 12:33:01.515 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline cause_specific_mortality_rate\n", + "2020-07-16 12:33:04.997 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline affected_unmodeled.csmr\n", + "2020-07-16 12:33:04.999 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline affected_unmodeled.csmr.population_attributable_fraction\n", + "2020-07-16 12:33:05.000 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline mortality_rate\n", + "2020-07-16 12:33:05.001 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline all_causes.mortality_hazard\n", + "2020-07-16 12:33:05.002 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline all_causes.mortality_hazard.population_attributable_fraction\n", + "2020-07-16 12:33:06.124 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering cause_specific_mortality_rate.2.disease_model.diarrheal_diseases.adjust_cause_specific_mortality_rate as modifier to cause_specific_mortality_rate\n", + "2020-07-16 12:33:06.126 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.2.disease_model.diarrheal_diseases.metrics as modifier to metrics\n", + "2020-07-16 12:33:06.127 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.3.state.susceptible_to_diarrheal_diseases.metrics as modifier to metrics\n", + "2020-07-16 12:33:06.720 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.incidence_rate\n", + "2020-07-16 12:33:06.723 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.incidence_rate.paf\n", + "2020-07-16 12:33:06.724 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.4.state.diarrheal_diseases.metrics as modifier to metrics\n", + "2020-07-16 12:33:07.316 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.dwell_time\n", + "2020-07-16 12:33:07.918 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.disability_weight\n", + "2020-07-16 12:33:07.921 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering disability_weight.2.diarrheal_diseases.disability_weight as modifier to disability_weight\n", + "2020-07-16 12:33:08.529 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.excess_mortality_rate\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-07-16 12:33:08.531 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:33:08.532 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering mortality_rate.2.state.diarrheal_diseases.adjust_mortality_rate as modifier to mortality_rate\n", + "2020-07-16 12:33:09.157 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.remission_rate\n", + "2020-07-16 12:33:09.160 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline diarrheal_diseases.remission_rate.paf\n", + "2020-07-16 12:33:09.750 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering cause_specific_mortality_rate.3.disease_model.measles.adjust_cause_specific_mortality_rate as modifier to cause_specific_mortality_rate\n", + "2020-07-16 12:33:09.751 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.5.disease_model.measles.metrics as modifier to metrics\n", + "2020-07-16 12:33:09.752 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.6.state.susceptible_to_measles.metrics as modifier to metrics\n", + "2020-07-16 12:33:10.329 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.incidence_rate\n", + "2020-07-16 12:33:10.332 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.incidence_rate.paf\n", + "2020-07-16 12:33:10.333 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.7.state.measles.metrics as modifier to metrics\n", + "2020-07-16 12:33:10.974 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.dwell_time\n", + "2020-07-16 12:33:11.626 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.disability_weight\n", + "2020-07-16 12:33:11.629 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering disability_weight.3.measles.disability_weight as modifier to disability_weight\n", + "2020-07-16 12:33:12.200 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.excess_mortality_rate\n", + "2020-07-16 12:33:12.201 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline measles.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:33:12.202 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering mortality_rate.3.state.measles.adjust_mortality_rate as modifier to mortality_rate\n", + "2020-07-16 12:33:12.203 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.8.state.recovered_from_measles.metrics as modifier to metrics\n", + "2020-07-16 12:33:12.854 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering cause_specific_mortality_rate.4.disease_model.lower_respiratory_infections.adjust_cause_specific_mortality_rate as modifier to cause_specific_mortality_rate\n", + "2020-07-16 12:33:12.858 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.9.disease_model.lower_respiratory_infections.metrics as modifier to metrics\n", + "2020-07-16 12:33:12.859 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.10.state.susceptible_to_lower_respiratory_infections.metrics as modifier to metrics\n", + "2020-07-16 12:33:13.480 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.incidence_rate\n", + "2020-07-16 12:33:13.483 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.incidence_rate.paf\n", + "2020-07-16 12:33:13.485 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.11.state.lower_respiratory_infections.metrics as modifier to metrics\n", + "2020-07-16 12:33:14.242 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.dwell_time\n", + "2020-07-16 12:33:14.828 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.disability_weight\n", + "2020-07-16 12:33:14.830 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering disability_weight.4.lower_respiratory_infections.disability_weight as modifier to disability_weight\n", + "2020-07-16 12:33:15.431 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.excess_mortality_rate\n", + "2020-07-16 12:33:15.432 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:33:15.433 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering mortality_rate.4.state.lower_respiratory_infections.adjust_mortality_rate as modifier to mortality_rate\n", + "2020-07-16 12:33:16.095 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.remission_rate\n", + "2020-07-16 12:33:16.097 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline lower_respiratory_infections.remission_rate.paf\n", + "2020-07-16 12:33:16.727 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline low_birth_weight_and_short_gestation.raw_exposure\n", + "2020-07-16 12:33:16.729 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline low_birth_weight_and_short_gestation.exposure\n", + "2020-07-16 12:33:18.031 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering affected_unmodeled.csmr.1.risk_effect.risk_factor.low_birth_weight_and_short_gestation.cause.affected_unmodeled.csmr.adjust_target as modifier to affected_unmodeled.csmr\n", + "2020-07-16 12:33:18.032 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering affected_unmodeled.csmr.population_attributable_fraction.1.lookup_table_80 as modifier to affected_unmodeled.csmr.population_attributable_fraction\n", + "2020-07-16 12:33:19.156 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.excess_mortality_rate.1.risk_effect.risk_factor.low_birth_weight_and_short_gestation.cause.lower_respiratory_infections.excess_mortality_rate.adjust_target as modifier to lower_respiratory_infections.excess_mortality_rate\n", + "2020-07-16 12:33:19.160 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering lower_respiratory_infections.excess_mortality_rate.population_attributable_fraction.1.lookup_table_82 as modifier to lower_respiratory_infections.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:33:20.208 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.excess_mortality_rate.1.risk_effect.risk_factor.low_birth_weight_and_short_gestation.cause.diarrheal_diseases.excess_mortality_rate.adjust_target as modifier to diarrheal_diseases.excess_mortality_rate\n", + "2020-07-16 12:33:20.212 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering diarrheal_diseases.excess_mortality_rate.population_attributable_fraction.1.lookup_table_84 as modifier to diarrheal_diseases.excess_mortality_rate.population_attributable_fraction\n", + "2020-07-16 12:33:20.870 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.12.mortality_observer.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.967 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline disability_weight\n", + "2020-07-16 12:33:20.969 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.13.disability_observer.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.972 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.14.disease_observer.diarrheal_diseases.metrics as modifier to metrics\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-07-16 12:33:20.973 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.15.disease_observer.measles.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.975 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.16.disease_observer.lower_respiratory_infections.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.976 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.17.disease_observer.protein_energy_malnutrition.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.977 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.18.risk_observer.child_growth_failure.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.977 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.19.risk_observer.low_birth_weight_and_short_gestation.metrics as modifier to metrics\n", + "2020-07-16 12:33:20.979 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering metrics.20.risk.maternal_malnutrition.metrics as modifier to metrics\n", + "2020-07-16 12:33:45.590 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering low_birth_weight_and_short_gestation.exposure.1.risk_effect.maternal_malnutrition.adjust_birth_weight as modifier to low_birth_weight_and_short_gestation.exposure\n", + "2020-07-16 12:33:45.594 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering child_wasting.exposure.1.risk_effect.maternal_malnutrition.adjust_wasting as modifier to child_wasting.exposure\n", + "2020-07-16 12:33:45.595 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering child_stunting.exposure.1.risk_effect.maternal_malnutrition.adjust_stunting as modifier to child_stunting.exposure\n", + "2020-07-16 12:33:45.694 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering low_birth_weight_and_short_gestation.exposure.2.treatment_effect.maternal_supplementation.adjust_lbwsg as modifier to low_birth_weight_and_short_gestation.exposure\n", + "2020-07-16 12:33:45.695 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering child_stunting.exposure.2.treatment_effect.maternal_supplementation.adjust_cgf as modifier to child_stunting.exposure\n", + "2020-07-16 12:33:45.696 | DEBUG | vivarium.framework.values:register_value_modifier:373 - Registering child_wasting.exposure.2.treatment_effect.maternal_supplementation.adjust_cgf as modifier to child_wasting.exposure\n", + "2020-07-16 12:33:45.698 | DEBUG | vivarium.framework.values:_register_value_producer:323 - Registering value pipeline metrics\n" + ] + } + ], + "source": [ + "sim = InteractiveContext('/ihme/homes/nicoly/vivarium_gates_bep/src/vivarium_gates_bep/model_specifications/india.yaml') #gives before time-steps" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-07-16 13:07:15.302 | DEBUG | vivarium.framework.engine:step:140 - 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trackedmother_malnourishedscenario_maternal_supplementation_typebaseline_maternal_supplementation_typeprevious_protein_energy_malnutritionprevious_lower_respiratory_infectionsprevious_measlesprevious_diarrheal_diseasesyears_lived_with_disabilitycause_of_death...diarrheal_diseasesdiarrheal_diseases_event_timediarrheal_diseases_event_countsusceptible_to_diarrheal_diseases_event_timesusceptible_to_diarrheal_diseases_event_countchild_stunting_propensitychild_wasting_propensityprotein_energy_malnutrition_event_timesusceptible_to_protein_energy_malnutrition_event_timeprotein_energy_malnutrition
0Truenormalnonenonesusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.013717not_dead...susceptible_to_diarrheal_diseases2020-10-0922020-10-1520.3324960.773213NaTNaTsusceptible_to_protein_energy_malnutrition
1Truenormalnonenoneprotein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.025985not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.3933000.1234032020-07-01NaTprotein_energy_malnutrition
2Truemalnourishednonenoneprotein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.031990not_dead...susceptible_to_diarrheal_diseases2020-11-2532020-11-2830.9389840.1007282020-07-01NaTprotein_energy_malnutrition
3Truemalnourishedifaifasusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.000000not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.6091670.578352NaTNaTsusceptible_to_protein_energy_malnutrition
4Truenormalnonenonesusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.000000not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.1930160.973632NaTNaTsusceptible_to_protein_energy_malnutrition
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5 rows × 42 columns

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" + ], + "text/plain": [ + " tracked mother_malnourished scenario_maternal_supplementation_type \\\n", + "0 True normal none \n", + "1 True normal none \n", + "2 True malnourished none \n", + "3 True malnourished ifa \n", + "4 True normal none \n", + "\n", + " baseline_maternal_supplementation_type \\\n", + "0 none \n", + "1 none \n", + "2 none \n", + "3 ifa \n", + "4 none \n", + "\n", + " previous_protein_energy_malnutrition \\\n", + "0 susceptible_to_protein_energy_malnutrition \n", + "1 protein_energy_malnutrition \n", + "2 protein_energy_malnutrition \n", + "3 susceptible_to_protein_energy_malnutrition \n", + "4 susceptible_to_protein_energy_malnutrition \n", + "\n", + " previous_lower_respiratory_infections previous_measles \\\n", + "0 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "1 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "2 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "3 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "4 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "\n", + " previous_diarrheal_diseases years_lived_with_disability \\\n", + "0 susceptible_to_diarrheal_diseases 0.013717 \n", + "1 susceptible_to_diarrheal_diseases 0.025985 \n", + "2 susceptible_to_diarrheal_diseases 0.031990 \n", + "3 susceptible_to_diarrheal_diseases 0.000000 \n", + "4 susceptible_to_diarrheal_diseases 0.000000 \n", + "\n", + " cause_of_death ... diarrheal_diseases \\\n", + "0 not_dead ... susceptible_to_diarrheal_diseases \n", + "1 not_dead ... susceptible_to_diarrheal_diseases \n", + "2 not_dead ... susceptible_to_diarrheal_diseases \n", + "3 not_dead ... susceptible_to_diarrheal_diseases \n", + "4 not_dead ... susceptible_to_diarrheal_diseases \n", + "\n", + " diarrheal_diseases_event_time diarrheal_diseases_event_count \\\n", + "0 2020-10-09 2 \n", + "1 NaT 0 \n", + "2 2020-11-25 3 \n", + "3 NaT 0 \n", + "4 NaT 0 \n", + "\n", + " susceptible_to_diarrheal_diseases_event_time \\\n", + "0 2020-10-15 \n", + "1 NaT \n", + "2 2020-11-28 \n", + "3 NaT \n", + "4 NaT \n", + "\n", + " susceptible_to_diarrheal_diseases_event_count child_stunting_propensity \\\n", + "0 2 0.332496 \n", + "1 0 0.393300 \n", + "2 3 0.938984 \n", + "3 0 0.609167 \n", + "4 0 0.193016 \n", + "\n", + " child_wasting_propensity protein_energy_malnutrition_event_time \\\n", + "0 0.773213 NaT \n", + "1 0.123403 2020-07-01 \n", + "2 0.100728 2020-07-01 \n", + "3 0.578352 NaT \n", + "4 0.973632 NaT \n", + "\n", + " susceptible_to_protein_energy_malnutrition_event_time \\\n", + "0 NaT \n", + "1 NaT \n", + "2 NaT \n", + "3 NaT \n", + "4 NaT \n", + "\n", + " protein_energy_malnutrition \n", + "0 susceptible_to_protein_energy_malnutrition \n", + "1 protein_energy_malnutrition \n", + "2 protein_energy_malnutrition \n", + "3 susceptible_to_protein_energy_malnutrition \n", + "4 susceptible_to_protein_energy_malnutrition \n", + "\n", + "[5 rows x 42 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#co-variate population (the population has been initialized, no time steps taken yet)\n", + "\n", + "pop = sim.get_population()\n", + "pop.head()\n", + "#10,000 simulants were specified in the yaml file" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 alive\n", + "1 alive\n", + "2 alive\n", + "3 alive\n", + "4 alive\n", + "Name: alive, dtype: object" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.alive.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3251.607412\n", + "1 2764.606407\n", + "2 3157.065097\n", + "3 3306.544096\n", + "4 4329.265723\n", + "Name: birth_weight, dtype: float64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.birth_weight.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['metrics',\n", + " 'protein_energy_malnutrition.disability_weight',\n", + " 'disability_weight',\n", + " 'cause_specific_mortality_rate',\n", + " 'protein_energy_malnutrition.excess_mortality_rate',\n", + " 'protein_energy_malnutrition.excess_mortality_rate.population_attributable_fraction',\n", + " 'mortality_rate',\n", + " 'child_wasting.exposure',\n", + " 'child_wasting.propensity',\n", + " 'diarrheal_diseases.incidence_rate',\n", + " 'diarrheal_diseases.incidence_rate.paf',\n", + " 'measles.incidence_rate',\n", + " 'measles.incidence_rate.paf',\n", + " 'lower_respiratory_infections.incidence_rate',\n", + " 'lower_respiratory_infections.incidence_rate.paf',\n", + " 'child_stunting.propensity',\n", + " 'child_stunting.exposure',\n", + " 'affected_unmodeled.csmr',\n", + " 'affected_unmodeled.csmr.population_attributable_fraction',\n", + " 'all_causes.mortality_hazard',\n", + " 'all_causes.mortality_hazard.population_attributable_fraction',\n", + " 'diarrheal_diseases.dwell_time',\n", + " 'diarrheal_diseases.disability_weight',\n", + " 'diarrheal_diseases.excess_mortality_rate',\n", + " 'diarrheal_diseases.excess_mortality_rate.population_attributable_fraction',\n", + " 'diarrheal_diseases.remission_rate',\n", + " 'diarrheal_diseases.remission_rate.paf',\n", + " 'measles.dwell_time',\n", + " 'measles.disability_weight',\n", + " 'measles.excess_mortality_rate',\n", + " 'measles.excess_mortality_rate.population_attributable_fraction',\n", + " 'lower_respiratory_infections.dwell_time',\n", + " 'lower_respiratory_infections.disability_weight',\n", + " 'lower_respiratory_infections.excess_mortality_rate',\n", + " 'lower_respiratory_infections.excess_mortality_rate.population_attributable_fraction',\n", + " 'lower_respiratory_infections.remission_rate',\n", + " 'lower_respiratory_infections.remission_rate.paf',\n", + " 'low_birth_weight_and_short_gestation.raw_exposure',\n", + " 'low_birth_weight_and_short_gestation.exposure']" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#list the names of the pipeline\n", + "sim.list_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "#interested pipelines : 'child_stunting.exposure', 'child_wasting.exposure'\n", + "#this 'get_value' gets the wasting pipline values -not totall sure what this means! get some clarification\n", + "# a pipeline is a 'python method thingie'\n", + "\n", + "pipe_wasting = sim.get_value('child_wasting.exposure')\n", + "pipe_stunting = sim.get_value('child_stunting.exposure')\n", + "pipe_lbwsg = sim.get_value('low_birth_weight_and_short_gestation.exposure')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 11.110376\n", + "1 7.714334\n", + "2 6.684088\n", + "3 9.452161\n", + "4 13.675476\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#the skip_post_processor skips the conversion of the z-scores to cats 1-4 (this is a method, and shouldnt need to be re-run)\n", + "\n", + "type(pipe_wasting(pop.index, skip_post_processor=True))\n", + "pipe_wasting(pop.index, skip_post_processor=True).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 8.828346\n", + "1 9.205418\n", + "2 12.057564\n", + "3 9.456805\n", + "4 8.013094\n", + "dtype: float64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#stunting pipeline\n", + "\n", + "pipe_stunting(pop.index, skip_post_processor=True).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " birth_weight gestation_time\n", + "0 3357.876944 38.696547\n", + "1 2876.793907 40.506541\n", + "2 2996.381503 38.588442\n", + "3 3171.544096 37.822455\n", + "4 4435.535254 37.854704" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# birthweight\n", + "\n", + "#pipe_lbwsg(pd.Index(range(8)), skip_post_processor=True).head(10) #tells the pipeline to select the first 8 indexes\n", + "pipe_lbwsg(pop.index, skip_post_processor=True).head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "Name: alive, dtype: bool" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create mask; read more about masking here ->http://danielandreasen.github.io/:about/2015/01/19/masks-in-python/\n", + "\n", + "#(pop.alive == 'alive').all()\n", + "\n", + "mask_alive = pop.alive == 'alive'\n", + "\n", + "#mask_alive[:5] = False\n", + "mask_alive.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0Truenormalnonenonesusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.013717not_dead...susceptible_to_diarrheal_diseases2020-10-0922020-10-1520.3324960.773213NaTNaTsusceptible_to_protein_energy_malnutrition
1Truenormalnonenoneprotein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.025985not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.3933000.1234032020-07-01NaTprotein_energy_malnutrition
2Truemalnourishednonenoneprotein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.031990not_dead...susceptible_to_diarrheal_diseases2020-11-2532020-11-2830.9389840.1007282020-07-01NaTprotein_energy_malnutrition
3Truemalnourishedifaifasusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.000000not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.6091670.578352NaTNaTsusceptible_to_protein_energy_malnutrition
4Truenormalnonenonesusceptible_to_protein_energy_malnutritionsusceptible_to_lower_respiratory_infectionssusceptible_to_measlessusceptible_to_diarrheal_diseases0.000000not_dead...susceptible_to_diarrheal_diseasesNaT0NaT00.1930160.973632NaTNaTsusceptible_to_protein_energy_malnutrition
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5 rows × 42 columns

\n", + "
" + ], + "text/plain": [ + " tracked mother_malnourished scenario_maternal_supplementation_type \\\n", + "0 True normal none \n", + "1 True normal none \n", + "2 True malnourished none \n", + "3 True malnourished ifa \n", + "4 True normal none \n", + "\n", + " baseline_maternal_supplementation_type \\\n", + "0 none \n", + "1 none \n", + "2 none \n", + "3 ifa \n", + "4 none \n", + "\n", + " previous_protein_energy_malnutrition \\\n", + "0 susceptible_to_protein_energy_malnutrition \n", + "1 protein_energy_malnutrition \n", + "2 protein_energy_malnutrition \n", + "3 susceptible_to_protein_energy_malnutrition \n", + "4 susceptible_to_protein_energy_malnutrition \n", + "\n", + " previous_lower_respiratory_infections previous_measles \\\n", + "0 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "1 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "2 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "3 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "4 susceptible_to_lower_respiratory_infections susceptible_to_measles \n", + "\n", + " previous_diarrheal_diseases years_lived_with_disability \\\n", + "0 susceptible_to_diarrheal_diseases 0.013717 \n", + "1 susceptible_to_diarrheal_diseases 0.025985 \n", + "2 susceptible_to_diarrheal_diseases 0.031990 \n", + "3 susceptible_to_diarrheal_diseases 0.000000 \n", + "4 susceptible_to_diarrheal_diseases 0.000000 \n", + "\n", + " cause_of_death ... diarrheal_diseases \\\n", + "0 not_dead ... susceptible_to_diarrheal_diseases \n", + "1 not_dead ... susceptible_to_diarrheal_diseases \n", + "2 not_dead ... susceptible_to_diarrheal_diseases \n", + "3 not_dead ... susceptible_to_diarrheal_diseases \n", + "4 not_dead ... susceptible_to_diarrheal_diseases \n", + "\n", + " diarrheal_diseases_event_time diarrheal_diseases_event_count \\\n", + "0 2020-10-09 2 \n", + "1 NaT 0 \n", + "2 2020-11-25 3 \n", + "3 NaT 0 \n", + "4 NaT 0 \n", + "\n", + " susceptible_to_diarrheal_diseases_event_time \\\n", + "0 2020-10-15 \n", + "1 NaT \n", + "2 2020-11-28 \n", + "3 NaT \n", + "4 NaT \n", + "\n", + " susceptible_to_diarrheal_diseases_event_count child_stunting_propensity \\\n", + "0 2 0.332496 \n", + "1 0 0.393300 \n", + "2 3 0.938984 \n", + "3 0 0.609167 \n", + "4 0 0.193016 \n", + "\n", + " child_wasting_propensity protein_energy_malnutrition_event_time \\\n", + "0 0.773213 NaT \n", + "1 0.123403 2020-07-01 \n", + "2 0.100728 2020-07-01 \n", + "3 0.578352 NaT \n", + "4 0.973632 NaT \n", + "\n", + " susceptible_to_protein_energy_malnutrition_event_time \\\n", + "0 NaT \n", + "1 NaT \n", + "2 NaT \n", + "3 NaT \n", + "4 NaT \n", + "\n", + " protein_energy_malnutrition \n", + "0 susceptible_to_protein_energy_malnutrition \n", + "1 protein_energy_malnutrition \n", + "2 protein_energy_malnutrition \n", + "3 susceptible_to_protein_energy_malnutrition \n", + "4 susceptible_to_protein_energy_malnutrition \n", + "\n", + "[5 rows x 42 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop[mask_alive].head()\n", + "\n", + "#pop[bolean series] --> if you pass a bolean series inside square brackets of a data frame, you get a dataframe where bolean is true\n", + "# the boleans series is a different object, so make sure they have matching indexes" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "#another way to do it\n", + "alive_pop = pop.loc[mask_alive, :]\n", + "dead_pop = pop.loc[~mask_alive, :]\n", + "\n", + "# pop.loc[mask_alive, ['mother_malnourished', 'tracked']].head() \n", + "\n", + "# two dimensions of dataframe where loc[row,columns]\n", + "# here I'm saying i only want the rows defined by my ask, and the colon means all columns \n", + "# .loc makes it more clear which filters I am using for rows and columns" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", + " ...\n", + " 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999],\n", + " dtype='int64', length=9736)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alive_pop.index" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 50, 120, 167, 202, 231, 278, 421, 494, 509, 671,\n", + " ...\n", + " 9613, 9666, 9677, 9690, 9712, 9786, 9851, 9862, 9941, 9943],\n", + " dtype='int64', length=264)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dead_pop.index" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", + " ...\n", + " 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999],\n", + " dtype='int64', length=9820)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop.index[mask_alive]\n", + "\n", + "#by default, this is giving us a pd index data frame with all the values where mask_alive = alive is TRUE\n", + "\n", + "#pop.index[mask_alive[~mask_alive].index] # ~ means mask_alive= False (~ this is the complement operator)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", + " ...\n", + " 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999],\n", + " dtype='int64', length=9736)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alive_index = alive_pop.index\n", + "dead_index = dead_pop.index\n", + "alive_index" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9736" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#get the dataframe with individual data to look at correlation between birthweight and wasting/stunting z scores\n", + "\n", + "df1 = pd.DataFrame({'stunting': pipe_stunting(alive_index, skip_post_processor=True),\n", + " 'wasting': pipe_wasting(alive_index, skip_post_processor=True),\n", + " 'lbwsg': pipe_lbwsg(alive_index, skip_post_processor=True).birth_weight,\n", + " 'birth_weight': pop.loc[alive_index].birth_weight,\n", + " 'mom_status': pop.loc[alive_index].mother_malnourished,\n", + " 'ifa_status': pop.loc[alive_index].baseline_maternal_supplementation_type,\n", + " 'alive_status': pop.loc[alive_index].alive })\n", + "len(df1)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 10.990586\n", + "1 7.905348\n", + "2 7.051595\n", + "3 9.493579\n", + "4 13.244393\n", + "Name: wasting, dtype: float64\n", + "9.70663162873969\n" + ] + } + ], + "source": [ + "print(df1.wasting.head())\n", + "print(df1.wasting.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2743.7891465354837\n", + "3017.6108235139723\n", + "273.82167697848854\n" + ] + } + ], + "source": [ + "gb = df1.groupby('mom_status')\n", + "\n", + "print(gb.get_group('malnourished').lbwsg.mean())\n", + "print(gb.get_group('normal').lbwsg.mean())\n", + "print(gb.get_group('normal').lbwsg.mean()-gb.get_group('malnourished').lbwsg.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#get individual birthweight and individual LAZ or WLZ\n", + "pyplot.scatter(df1.lbwsg, df1.wasting)\n", + "pyplot.scatter(df1.lbwsg, df1.stunting)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "mask_mommal = df1.mom_status == 'malnourished'" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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b9dD6LdHCCmTN++PVDNGpvboQWNOsJFlU9D+NIsp1k5hA39Ck55GFdAL6DFaXkVyu67THjVTrRqSLU0UuhPgfAP6Hy2MydthYDToll9TSMM0APPHOZTz7+ruthsdhX76Nn3b8+em2cwNQNowGFhTN/pfPtPmiy5Vq6n7PWEo8R5g3DaGJQZ5IasnHoVjwWok8flZnuVJFnqij/rpuxZUkkiuNOu22xpdsctp96BROvHO51djbBWyRDxg2VoMuRlm3OWoji+4Y/sZssPPQ4ZNlbLltRet7Ngq1Ni/wyKFT2H3oVMuqVinMHIAr1260xWEHyeJufD1FJR5V2sCWYDdA/1qqQhN1dVgAfSSXbvU5NtrlOu0SVHtWB49faLvPk8LVD3uI65A/Hz/k7sldmwE06neoQq88RbbL4ZNlrTwuZDcJ54rj3vHD3F5767LyM/NArOJbg0ipWMD9d5SQl/RijUslFG2jutbPvv5u5ASii+QKhw4GffzbDrwaK8rJJbpIHpdhi2yR94i0kwuMj694dqPCAF3IbrIZlTQkkImmXKkqVyZxKQ63Z4yqFFqUH94mkmt5wcO12bmWm0zXMLpbkSa6PQ2Xkwlb5D3CNrnA1gI2Ob4qftlHFwboIjHCJMkpmCgCoGU1OjQemRS4MlNrSwJSXS7ddYzKfwDaE76WLR3quJ8FOm2VpPs/Ns+ibqJyOZmwRd4j4oQJ2ljASeK4fXJEWLvnSIef3VT2qN16041Zma99cqoc2UyZyQ4qdSbEQuEy2XdU+Q8ydG3mSsWCk6iRqGcxfM8PezmlC89l2CIr8h6RNEwwKmFHdfygci4Oe9rMQd+aMG0kHJTddVbd5FS5LbqkWPCwbd0KfPWty9Ybk8XmEtxVhAaTDN0ero37QXVfuiyFG7UaDd/zKoa9nNPNVnat9AibGg9xEhtUzX7rQkS2LJOtdoM3q4nsJu4Xm/rq4y9Mt8lbqdbwtfNX8DPrVkjlLRY8PLR1dVvtjqd2bcbbB3bi1GN3Y9dHVznd3GPSwfe1m7gxtm9YafV6HHTPok2CV9XxRjtb5F0mXE1u6VAOV6u1WGGCOh+baeU/YCH7rtSUQdUVR9VIWCZ71ORjW19dZj3X5oUyMuXq9RqOvPE9VGZqKA57uHZjrtV9x09SSTWxpocQqeuoxGXZkjyuzSbbcNZlvcrw8oT3rrdvXvr3CNB5/6ncMDbumSjjQvcs2q4eXMKKvIuElVfQTZBGe6mgb3ltM6NQhq/E/eWnKjkjvAmpWxpGTT4u6qvrEGJhfMPJPqY1R/oRL0fY9bFVzqNQZmp1jES44qKwGXNVclK1VsfuQ6cwlF9431fwKmvY9P4xMS50z6LqubHp5RoXdq10EdXSq1Ktacu3jo2W2uJ8ZRmQUURZAOGQv6Qd2VUx6lGV8oIWu7+kzjl2gQyqEgeAm28acpox6BOcGLuBbrUk0FlLplqrK11lywtmKfkq4+Jzz023XDsAOqKofANk+4aV0ufms1tXt5UPXjrkXu2yIu8iOssgKvRQlgFpk7Aju8mCqEL+EtWGVuhfXTjarcVCRx3sQXWBpMGVmVqrnstioy6E1Hi4NjtnlLCmi3UP1lIHFoydYEDA4ZNl3H9HqeO52XLbirbywVGGWxy4+XIXMamoV5L45Wwb06oKTS1bkse8EB0bLWk01o1TPdCXI1z7hGFMIAAFRbifSeTKur1fMjIafGvc9Jl02Vg69ebLg0DaVcpMshRlfrk4dbtl57g2W4eXp7bYVr/zvOu6E6Z+yfBGK9DdJTwzOAioyy74Lj3/mZY966YrP929LXuvGw0pWJE36UY/Pv84URZneNPPNmpFd4PU6u3tvW7MpVNvJCpG3Se80er7IZnFRdyyvjaoCnX5r5tu5t6qschlz2Q3GlKwj7xJt/rxjY2WMPWFu/HUrs2tJZqMcqWq9W/rNh9tbpC4v/HRydNYt/dLWLPnCNbt/RIenVwIC5ucKuO963PGx7LJNmUGj6d2bcapx+7WPg+uUBXqqtbqEALafSRg4bmTFuzKE67dmOuIeXcRPBAFK/Im3e7H59eI0N28/gbLweMX2nblozYfVclAKsqVKtbsOYI1e45g8/5XIjdhHp08jWeOX2jbfH3m+IWWMp84eq6jSa+OcNcgZvFQKhbaQvtU1ThdonKhXK3WOjb5w0ll/nMXjiTLEVCvC1SqtY4m02k1lg7CrpUmaS9/VP737RtWRsY1++/5u/ImLeEASGsxR1Gp1jD+/DROvHMZx85eapMX0PezfPb1d/H42EaryU/WNWj3oVMDHSLINPCvvUkrP5eoKhLe2pxUTBRsOJJMZrf4oYt+ffw0OgP5sEXeJM3lTziczp+tH5083dHxPgq/eUJUuvLYaAmnHltw4RAavSlNLJ7avMDB4xfa5B1/fhrjL0xrHzb/pjad/PJEHZbJ2GgJn9262uj7pnh8l2cOv7IhgNaz0Q0KXh4P3rkq8bNumo4fDl10GXIYhC3yJnH78ZmgK6ofN0Zatxmri77x3SJRhKUydZX4Pv2ori8AMC+EdHy33LYCh77+rsOiVrbJ4Uza+JUNj5291LVa8yPDHh67pxGh5befi/usx3G5RhW6SwLHkSfANFxx7Z4jqamRcCyqLIbcj88GEBn+6IKCl8f9d5TwB9Pf07p2RoY9DC8Z6hi/uB3sdahKpTKLB9N8CZPnOsk9KssVMUUVR86KPCaqpJvgrO+juuguOqITgPMHdkaeS5fEkAbFgocbc/NWk0badrOXd9dcmOlPopJwHp083bFnJZsAZJ+zIW4SHicEOUblI7syU+tweax5v3wjdeuHRvCNC1cTWchhf7SL6JtGBija5PJyBFBnjQsVcWKC01axQznCj77vpq5NZkz20D0Hk1NlqXIOhuj6m7JJjQ7XbhbeBoqJad2Uyakyvqootfr2D6p44r6NxnWxVS2rTApM3VosGG9CXputd4RLTfziJkx8ZlNHy7V+quldrc1jfMf6rsQrM510IbIwEt0zMHH0nFI5+3tSvhHgwujIVGYnEa0C8LsAfgyN3/e0EOK3kx63GyRJydc1lAUWLpLu5rhYqWJstKSs/x3E96uF5QXa/d4yV01wR378+enIjUv/eVMtQX0ZSsUC1ry/EKtLT6/Y++IbeOK+jxiNA+OWXg83AW3hjuHnXqdY/SqHLikOm1VlNMGFa2UOwOeEEN8govcBOElEXxZCfMvBsZ0Sburw3vW51sNsm5IfVTfFn/l1N4d/IaMmBT92XIbKxRNsLuCXzRwbLRkVpBLN48qiYcJlDPrNTVGtzeORQ6eQo8ZyNJ0CBUy38F0cJvtN/rvhe3j8+Wnsf/mM0hghmFXgtHW3uNyeTOxaEUJ8Twjxjebffwrg2wDSiXpPQDiW+8pMrcMis0lX97O1ipJax0ELWLeUuzpTa7Qx27EeXl697rz5psZ8K4tFVynS4E3iJ/lMTpWNC1LJjmvTyirrzAtW4oNAMFkuilKxIL2Ha/NC+1x8dutqI7fQz6xbIdUHKq46rC3j1EdORGsAjAJ4XfLew0R0gohOXLpk3nrJFaZKyMZvJUu68dNvgYUIEtU9MI/GUn9stIRlS9SLo8pMTRmLbuqjrs0L7H3xDaUsYQjoSF7gOihMv+IbV7YryJFhD1tuW2HkFnr7B9U2fQDo95BclqNwFrVCRDcDOAzgESHED8PvCyGeBvA00Ag/dHVeU0yVUJzBDaf1hkOTdD+2WpvH5FRZOzvr+gHWhUDByxtNUjYNX2XulSgXEMNkFd+4snV/+EaUCcGetmF9IEvCc9kU2olFTkQeGkr8oBDiRRfHdI2JgnaRkq8KYdKx76Uz2nZm4zvWK+X3VwA2SzpTLlaqmJwqY/Qfv4I1e46wEmf6Gl3ggQqbpsqqlnIumkJHkViRExEB+PcAvi2E+K3kIsUn3N4s6BpQlZ0sFjynFcni3CyVak3p4/N9c7paMGOjJSxbGr24sg3/Wl7wMP7CNDd6YPqeOEW5/OfLtOdnrS5f8fZLY4ltAP4KgNNE5MfR/boQ4ksOjm1MVGOINGupBHHtR54XDZmDHe5l8ked18sTPrZmxDhUMIfGZgwH6DGDwMVK1SqTOtix6tqsWW39a7PqCLa0G0skVuRCiP8DZZvd7qFrDOErO9MSlWFs4s3T8COXmy4OnQzLNR1WSsVCq5CVqWLmiA5mkLBp4l0seLhYqWLi6DnMzM4lLusgC1XmxhIK0lq+qErQqspRmjZ18N05I4ZJAVEyqFzsI8MeXttzV1erzDFMPxNsDmHjVlTtU3FjCQvSWr6YWPpBgi4clWUeLNwzOVU2aqQQJUNFccNVmrHqvFHJMOmRAzA7V8eaPUcAdBbPi+sNsDn/QJBWY4g4lr7fxu2hrauV9VGCn43bSCEog2rCKg57rb0ChmHcMOzlWhZ2seBhHsBMILz3ykwN4y80kvB0QRiuGBiLPK3NzLiW/uRUGYe+/m6Hpe13RRn9x6+0lm3FgodlS/LKzRJVLe2gDNs3rJTGqt6o1Y3ix21SnePC7R2YQaFam8e3mqvqzftfkX6mVhfY//IZXK/NK4MwXMH1yCPQNWrQXYigojYhBwCa5gdhJRiWIWkzhm4o2W3rVuBr569wsSpmIPCDCEw6bsm+q6uLroLrkVsSLrC1dCiHq9WasaVvG3s9D6AwlMP1uXlpMZ3gS8WCh333LvjfXPjAXapW1aTwmqKcL8P0I+VKNZYSB9yHKbMilxC2wq/M1FDw8nhy1+ZUNyxMU+iv3ViIa/VlzQolTuNnBpBhL9fmA0+KyxhyYIA2O12ii1QxJY2UeZ/avMDnnpturRqyElaYz5HT+hEMkwW2rVuBb/2TTzhLlnEdQw6wIpfiIiZ93723N9qjpURdCG0Z216wNE84fPKPei0Gwzhh2MthZNjDV9+6jG0HXnXWCMJ1DDnArhUAnZmbqizJ4HIoKtPS//sRg+4/ccmKJe7jcunJML1mpjbfuqfLlSq8HCkjyGzYfegUJo6ec1oiZNErclmNFi9P8HLUFl0RXA5F1XUJKnkOuWOYwcBVtFUwOxtwE4bY94o8Sd9NQO4Pr9UFRoY9DC8Zkh43yoeuawHXa3hiYZhsoMsQt6WvFXmUZWyCyu9dmalh6gt3W33HL7TjWokTGm2kXITvCTQ2YlUFthiG6R6uwhD7erNTZRl/7rlp43RYVRiQLjxI9Z6/ZDKBsNAUOQoBtzHYrMQZJhu4CkPsa0Wua39mUqkQiFejxbTCoQ4B4MYcbw4yzGLFZRhiXytyk9ksKv7bpMRkuOgNgNZ3GIZhbCkWPKdhiH3tI5cVbJcR5YcKl5j0FffFShXLCx6uBYrL+1b+E/dtxGt77sLaPUd485BhGCtcr8b72iIPW9N5RXcFGz9UuJFEpVrr6BAStPJdp9oWC16qWaEMw/Qe20zxKPpakQMLtb/PH9iJB+9cFVn/OwrTqBPfynfhL/chACvft4Q3IxlmEZC15suZYHKq3NGTktCo/23jhzIdXN8SD9dBT+JmEQC+8/1rCY7AMEy/kKnmy1lBZkkLAM++/i4OHr+AW5u1g4+dvaRNHjJpnlzw8ti+YWXLjx48VtK64AzDDD7cfFmBSSjiM8cvRDZRNnGV3H9HCYdPlqXHGt+xPlaVtPTKazEME0S1l9YtRobdRqwAA6TI4yxTZBsO/gaq6mKXigVpR/pgum0c9wpHvjBMd6gL0VPDaXjJkPPqhwOjyONuOsos+bHREr74wCZ4+fbL7eUJ4zvWR5a5HXFU7pJhmHTopeGUhuvViSInoo8T0TkiepOI9rg4pi1joyXcf0fJetmkteRDV7tWFzjxzmVtWv/kVBnvXZ+Tvs8wDAMAj0667eqVWJETUR7AvwLwCQAfBvAgEX046XFt8aNWbDrA6zYcJo6ek5atPHj8ArZvWClN69++YSU+99w0NxdmGEbLM8cvRNaBssGFRf4xAG8KIb4rhJgF8HsAPu3guFao4r/zRK3U+4e2rtam4gdRuU8EgGNnL3Wk9fsboDYTCcMwixeXCUEuwg9LAN4N/PuPANwZ/hARPQzgYQBYvXq1g9O2o1K880Lg/IGdyu+p6pnrwhAvVqodaf3bDrya2RrkDLK2ByIAACAASURBVMNkj75MCBJCPA3gaQDYsmWLc7NVpXh1PnBVPfMT71zGzKzazy07psuLwjDM4OMyIciFa6UMYFXg3x9svtZV4pSjVdUzf+b4BVyZkafJq47puuYKwzCDzfYNK50dy4Ui/zqAnyCitUS0BMAvAXjJwXGtMClH6+NXN7QNA9Idc3zH+o5wRYZhGBXHzl5ydqzErhUhxBwR/V0ARwHkAfyOEOJMYsliEPZbywi7U0whAK/tuUt6vImj5zgtn2EYK1y6Y53EkQshviSE+EkhxDohxG+4OGZaxO2pKYCO1nHBkrcMwzA2cNGsBOhmwTyRNnwwuBl67OwlVuAMw8TCzxJ3xcCk6JuimwXfd9NQpJ+7WqvjYLP4FsMwTByWOa63sugUua4mS6VaA0SjVopOnXPKD8MwSbjquHnMolPkwegWGbV5geElQzh/YGdmmysXvEV32RhmoHAdrrwoNYLfHk5ldQfbuHk5+5DCYsHrsPpjHKaDPBGe2rUZ12tuG7cyDNM9CHDqHwcWqSL30VUxBBoK/+ab7PeDr1ZrHTHtv/XA5kTlbQmNOsoTR89hOTdnZphEFLxcz2qSC8B5PfJFF7USZHzH+o6Y8nDmZkWR4anj1mJBGtO++9Cp2LL6fvlypQovT/ByxFUWGcaSn/jRZfiV7T+BvS+e7tleVxou20VrkfuJPNVavVXDXJa5aevL0pUFcOUXq9UFV1lkmBj80ZXr2P/ymZ4VuPNybsMOfRalIg8n8tSFaCngoBKfnCrj2g3zJhF5Im1pXJe1FdgYZxh7qrW6so5SN0hrd2tRKnJVsaxgfWBf2VcMw4QKXh5ffGCT1vflsrZCFL1uMMswTCf1eeG0DrnPovSRR/XcBPSNKuaFQHHYgxCNjU2/jjnQSOMP1zZ/dPI0nn393a66Q9j1wjDZJI2S14tSkZvULrdtVCGrbb770Ck8kmCDk2GYwaOYQnP2gXWt+KVq1+450lHsyqR2eVRoYhiZBc82McMwYd67Pue0XycwoIo8uJkpsFDsyh88k9rlpo0q4tY2ZxhmcVJLwU8+kK4V3Wamr6yjapf778n6efrErW3OMMzixrWffCAVuclmpglRyj5ubXOGYRY3XGvFAFv/dly44TLDMHHgWisGxGnEHAfTiSGNlNxhL4dhroLIMH3HsJdzXmtlIDWBTSPmJOhqm/sUCx5e23OXc2U+smwpvvVPPpHZUrsMw3Ti5Qn/9L6POD/uQPrIgWj/tl9rRbaRqXsvfA4A2ubLlWoNa/YccfCL2vHdOuzeYZj+4eal6ajcgVXkOmTJO3tfPN16X/WeSpkHJ4DP//5pXJtNfwPUd+uokpsYhukORI2Vt0kNlyszNa0+ictAulai0IUnmtRhUTE2WjJq+pC0CkqwcauJe4dhmPQQAvjwLe8zVqam+sSGRaXIo5J3ypWq8j1TF4ZpjZO3D+zE2zHayY0Me5j4zKa2ePgn7tsI0xpZXEqLYdzz1bcu4y9vXW38edcu0USKnIgmiOgsEb1BRL9PREVXgrkmXLpWhk7JmUaomFQd9I9lWya3VCxg6gt3dyQl7X/5DExrZHHZAIZxj0CjuulTuzYbfd51vZWkFvmXAfw5IcRHAPxfAHuTi5QOUck7BLWSswldfPDOVZGfGd+x3rpMLtBYMawJ1I6ZnCpj/IXpntZXZhimwcVKFWOjJaOWjq7rrSRS5EKIV4QQvkl5HMAHk4uUDrqlTKlY0FqqNqGLj49txENbVyut+4e2rsbYaClRVqi/Afv53z+NWp1tbIbJArcWC5icKhutjl3XW3HpI//rAP5Q9SYRPUxEJ4joxKVL9g0WdNUMTVC5RkrFgjbOu9Tsv2nD42Mbcf7ATjy1a3NbLPtTuzbj8bGNAPQTi8mMXq3VuxIdwzCMGWveX7BaZbv0k0cqciL6ChF9U/LfpwOf+TyAOQAHVccRQjwthNgihNiycqVdy7OoaoYmRGV7ppENOjZawmt77sL5Azvx2p67jHqB+n7wfmXYy6FYcF9vmWGyzvHvXrFaZbssGRIZRy6E+AXd+0T0VwF8CsDPC5FOWxqTaoZRRFUzNKl2aENUwtHMbOcmZ3DiKBnEh+v8+l4OMIiETIVTj93d2oRl/z2zWLDpyuW6ZEiihCAi+jiAXwPwF4UQM25E6qRb1Qyj3jfFJuHIp1jwsO/e21vnH9+xHuPPT6Om6LJc8PK4/44SDn3t3bbPeDnCx9aO4LW3Lif+HXGYqc3j0cnTeHxsIyaOnmNFzvQ1eSJjBW362RzZ7buZkDSz818CWArgy9QIuzsuhPhbiaUKYdKazRWm6fk6opKKZMuvZUuH2s7j/73vpTMtnxtRI/mgFJLL7weaJ8KaDwz3TIn7HDx+AVtuW8EZp0zfUxcCBS/f9sx6eQIEOoysJUOEuXlEBiD8yE2e87pPiRS5EOLPuBJEx/iO9R1WbBrVDHWWtM3Ax1lByJSeSb2YwyfLLSugLgS+8/1rxnKmhUBjMrOxZgC9q4hheoFvNIWNOwAdrsNqbR5ejjAy7KEyU1Pey1ctQo5N6YtaK6791ypc+OIB9QqiOOzhh9U5qXIjNBSzzXn2v3wms40t4ljjrMSZLEEAtm9YqTSoZK7D2rzA8JIhTH3hbmUWeRqehL5Q5IDeOnXhDgHc+eJlKwgvT3jvulyJAwtWrKnck1PlzPufh70cZnq148owCREADp8sY8ttK6TPZVQ5j255EoA+UuQqXLlDAGB5wZPGgNrOoLIVxLUbc5HxpeEJIzhBFYc93KjVW4rRtmZK2G3h5QkTn9mEE+9cxsHjF1KxhlmJM/2OakU+OVVWugJ9fdEtTwIwAIrclTtkcqqMa5KQQC9HsWbQ8ApirUFN8uCEEZ6gwta3TvFuW7cCx797pbUB+uCdq7DlthVSP9/hk2V2aTCMBtmKfOLoOeVzs33DQp6Mq0i4KPpekbtyh0wcPSfdbb75piEnF8KkbnhlZrblJ4+bwl8seDj4N39a+l74d2w78GrkOXgDkkmTZUvymJ2bV4bZZgHZilz3LB87a5+5npS+V+SuQhNVir/iyA8t85eFuTZbb7mF4mwWFrw89t17u/HnTSa77D5ezCBwbbaObreetY2mCq/IdW4VQP5cudrHU9H39chdpdarFL+rHeZgH1EdvlvIpBxukDh9SdPYPWcYW7q9lbL1QyMde0yqp61Y6Iz51rlVgM7nykWJkSj6XpG7arScRq0VmawmjZgvVqqx0n2DJQBMCowFfXkMs1h47a3LbYqYAPzMuhXS53/fvbd3PE+61bJMZyTpOmZK37tWADcbCt3cYY5ys/gzuql7Jbi5G1UeIBxJY0ueCFs/NIK3f6DupuSzbEke80KgytErTIYRAN7+QRVP3LdRGhAQfp5UbpU8kdSIdLWPp2MgFLkrurXDLEu/92mryPjCtHG9cf+mUM3++146gxtz8203ZBzqQuAbF67iifsa5Xh3HzqlXGZWa3UI0Vie/vB6DbL9LN1DMS8Ecpb+TIaJg98UwiQgQKDzvi14eaUnoBslRvretdKvjI2WcOqxuztqlvs3w9hoCROf2WRUmxxYuCmUm7bVmlUUjO8zlPnqgyuAz2r6FM6Lxs1eqcqVOKDeMKoLgfMHduKLD2yCl+NOo0y65IikLkjV8yQAY3duN9y2lFLlWS1btmwRJ06c6Pp5XZD27rPqnI8cOqV8P2gNRPnwbCgVC7jY3KAJQwDOH9gJANi8/xWrlnU253dZeGtJnjDLHZUWHTkAP6JI9gviW9nBonSq58lvSGOKK71BRCeFEFvCr7NFbkE3dp9lRPUBvP+OhSWhavY3teyDlCtVLFc0iQguC21CHm3P7xJW4oPPtnUrEFxEDns5/NauzUb3qH93BJ9rV9a0rsmMC1iRW9CN3WcVj91zuzJEKpiAoIri0X1fh6xSWzjbdWy0xF2BmEzwjQtX23pmisBdn7dw0QXdhy6i4tKGNzst6Mbus4qx0ZLSvWJy/rHRUqy6KrLPBhvHtjZu7709MuHJJblmbXa2sRmfPJHW0KpbZo/6z1W3giCSsGgUuQsfVZq7zybyqXzGuhotwfDDx8c2tmquJHVbhIuThcM3lxc8EDUyY5drolbikuGMbiZEVCYlEfDkA5sT35eqc8Q1tPopYW5RbHaGlRugDxdK+zhxj6v7HADtgxDenFm754gzazbcsUiGy/Mx6eLlyTjsNQq/JeHhk2Xlas3fOJfd36aMDHsYXjKk3JgE5Hsu/iRjE07YSxb1Zqcr33Za/jJT+VTnB9DahFURtEomp8rIWZYA0FGuVDH+wjQ2739FmU3aT9bNYoZg74LQI/DM8Qu4rlHOwdC/pUP2Kqng5bHzI7dIE9z8jUnVpuUXH9iEtw/sxJOKMGAbTDOq02BRWOQqazAYQtdLdNaqHwKocweZhBzmifDFBzYBkDd/dk3YorG1tsIWEldhdMtQjjAnUdhLh3K4Mdf9TFwvRwBF97sMUyx4+NSmW6QWf44aLjh/xQiYZ27bumLTWq2HUVnki8JH3s3mzXFQyUdYWA6GfdLBG83k1q8LgUcOnUqkEMNNaHWEa8LLSiDoJp/Pbl2NY2cvtT67fcNKPHP8QkzJmTBz8wJ5AoJ6c9u6FT1r3B23jO2NuXn8wfT3pPelf0j/2Xnivo2Rsd+TU+WOjGuTZjWu+iLEZVG4VrqRWZUEmXxAp8L1b4xwPLsNcZX4sJczqt4YxHfn+EvO3c2omyd3bdYWDysWPDw+1njonty1GQBw8PiFWOGTjJqw8dsrJZ6Eaq1ulIxm4kr1nyvZ8aK+38uINmCRKPJ+iAW9ybAo88VKNXbTCR1RSnKpl28lNTy1a7N04glza7GgTaJSTbB+8kb4u+xaYZIQpVSjnivd99Mugx3FonCtANmNBbX1Hd/a9JmroOZnZmbnjJszeznCkqEcrs2qZQg22Ai6SVTuEUJjpaFbcvrLXJUvMo0Jixk8RoY9XK/NR94rUUo1StHrvt/NRssyFo0izyo2ysq/MVQKNBhiGDVB+NUFlxc8XJud0ypxoBHru6bZd7RY8LDv3tvx2p67lOcpeDltZUSTZItuLUuZ/qXg5fHYPY0VXDCH4drsXNvGqYlS1e3bRH2/m2WwZThR5ET0OQD/HMBKIcSfuDjmYsFUWYVrHUfN/v7n9r98psMyDxfZMvExBveiKtUaxp+fbjtP+CGaiahBLuuiEn4ITPqcMunhT/ZJXVo5AMG7wXbDPVjMavuGlW2b4EFlGc65sFWqqj4BI8MeHrvn9sjv93LVnzj8kIhWAfh3ADYAuMNEkfdz9UMTbG4ik9BBVXKQi1AqXegjAdp64LIKcHF+jyp0KyqRxCUc3tiOlyNM/OImZxU1iwUPV6s168nZD5vtloLsRXVTG9IMP3wSwK8B+K8OjtX36FLkwzfE5FRZmsTg5QnLlgy1bnzZzWQz++s+q3qwfCW9tulOkSFbTZj478O/R+VHP/LG97B0KNd6b2TYgxBQriD8LD0/ftgUf9Lop/DGh7auTnWSC07eJo3Do1i2dAinHrsbgHqyz0J2pepZybqCT6TIiejTAMpCiGmKyBQkoocBPAwAq1ermxH0O6bxpI9OnpYWsDJdxrkiapNGZ0HJNn+Kw550k1VXv1ml/MPHuTJTw7CX60ghDz/wusknTI4aZYAfH9uIY2cv9Y0rJzzJxeGhrauVRdTmBToMEJu8hTDBa6y65+6/o6R0m/QSG+OsV0QqciL6CoAfl7z1eQC/DuBukxMJIZ4G8DTQcK1YyNhXmMSTTk6VlQ/Q8JKhrlsggHqTZnzHemnLuXApW6Dxu967Ll9h6DaKbJbbM7V5eDnCsiX51gbt0qEcTrxzufUbdO6gYS/X5r+fF8Dhk2VsuW2FE8uzW5hGJOk49PV3tT6loAEStFTjuFqCk36vNwZt6XWyjwmRilwI8Quy14loI4C1AHxr/IMAvkFEHxNC/LFTKfsIkyzSiaPnIqM5uonO9SLbNPWjVsLfmTh6Tpqht0wyOQWXqsVhD16OjLP7avMCc4Eom0q11uYWkSlxwkK26ExojMPhkJ97bjqyT+hTuzbHKgucJUzS4cuVKrYdeLWlaCenypiZtWvaLYv4yGo4sIxeJ/uYENu1IoQ4DeBH/X8T0dsAtiz2qBWTeNI4iQW9ZnjJECozap89oO8Xuu3Aq23p9kH/7pWZGrw8tW2IXbsxp42mMVGeftRFUGaV28WffFVRQUFKxUJLEbkqC5xlypUqxp+fxol3Lhv75WVj369kvcQHwHHkzjFZNupqq2SlbICPjX/QtGaMzIqt1UXbhliSkqY+880GziYyAo3eo8GVRrjmBrAwKYc3vwad2ryw2gyWjX2/0utkHxOcKXIhxBpXx+p3opaNshvDX/oDaLNee23N2PgHVb8rrLRN3Eo6ZWqKTMGO71iv7LRUqdY6mmVMTpXb3Eq+Pz5omQ6yNR6XQZrc+sGnvyhqrWQNWe2XJ3dtxpbbVvSkubMOG/+g7HfZ+I/DD//YaAmnHrsbT4VqRS9bEl3nRWUxRT18suJI1wObo5VqDQePX+iLDdFuEY5Xi2Ot9rKWdxRZDz0E2LXSM2RW+7YDr2Zud9zWPxj+XTYxw6qHP3xMXXihKlbdhuAkJVuR9OvmZhq4CBvMcnhflmULwoq8C5jO6FncHU/qH0wjZjgqiSmKYsHTumuCk1SWIhPSwssTbl46ZBTSWCx4WLZ0yKl16iK8Ly2ruR9CDwFW5KnjYrPQpC5J3Jsq6lhJ/YNp+BeTTi777r0d489PS8Mdw/Hxug3cQbDMR4Y97PzILUYbmX6JYdcKLKkBk6bVnEXjSgYr8pRJulkYVlAub1rTYyWN+XUdM+xqcpFupIYcvlErClcbnVGd5sO4mEj8XpeHvv5u5GdNGmzHJWl4X5pWcz+EHgK82Zk6STcLw7UmXDWSdn2sbqHqNuRHmJhumI2NlrBsaacdU6uLtt+vuiZ+ByNZk42Cl0ex4Bn/poKXt1LiOWr03FTh5c16KVVrdfyX1y9EJgb5Lqu0XAlRHbyirmuaVnPWu4v5sEWeMkk3C8O4vGl7vWxM2uA2uIIAYL1Sifr9vnzlShV5IggAf3z1Oh45dAoTR891NMHwy/gSNZKcTK3mJ+7bqEwqCh9j2ZI8vHxO6eP3a/WYZp2aJNOWK1Ws3XMktYgN3QpLds13HzqFRw6daq0S0rSa+yH0EGBFnjqukwlc3rSuH4CwYtbVjo7jIlKtIPa/fAY/rM51WLVRy2vd7w/L5x/b/39YXpnSMbWxddmkwWN4edIqcQD4YbWRPv/42MZW1mlU/RkT/HDYcB16V6gMGF3UkH8NZOWOXVrN/VBOgF0rKWPiLrHB5VLP5bFkvTmfOX5BGRMfx62jq5KoUlLhYmXBJfr2DSuVv3//y2ciY8XD8qq+Q2gkEsnwY+LD90leUk20VheRyVF1ITD+wjQmp8oYG230WD1/YCe++MAmoz6rUdTmBfa9dCbxcUyJWh1Wa3UcO3sp8z1504Yt8i7gckZ3udRzeSyTlnVBCzmOWydOxyB/dSFbARw+WW7btMwTtSx80+qCQTeM6jsCjQlibl6gHvBl5HOE3/hLG1v/Dt4nNqV4w9TqAvtfPiONPlJltdoQN9MWsHenmVzzi5VqX1jNacKKvA9xPTG4OJapX93/XBy3jm2Z2eDqQrUCOHb2UsdxbUrELm9uakZtEF+t1vDkrs3GSixpmzvZbxgbLSl98eEiV4AbpR8kjjvN5JrniForkMUKu1YYJ5j61f3PxXfrLFi0OWo0eZYR7nGqWwHYNMAO43tATDqwB10dUVEgsvFxgWrcv/jAppZcANo2kcOMDJtH5ASJ404LupyAznIAQMOd1OtSFr2GFTnjBBPFE1TUtnsHk1NljD8/jWqoKcRsXcALheL5iilccVLGrcVCoiidStPyjZrIZmbnrOqIjI2WcP8dpZavPEd2D6sq/DGsGH130sTRc9r9Cx8vT62u9bbEjZLyJ8C3D+zEk7s2S/cPsh42mzasyBknyBTzQ1tXaxW1jYWqalpRnxe4+aahyAlBtwJIEqYWXGHouDJTsyqENjlVxuGT5dYm7rwA8obx4V6OsO9etbIdGy21xiMchTM5VdYq1onPxG+ErJtMTRkbLWHeYGN7scE+8h7QD9XU4pDmhpPuIa3M1DD1BX3HwaiN3Ti1z8MrDNNNUpOsQ5lVXKsLZQaobSMHnZtDV8uml6UVfPol27KbsCLvMv1STS1r2DaBlqGaaMJKXhdt7StSWcr6Y/fcbjwhhMMiwxOMauKSKXG/+bT/G3ZLEpZ05w9SrlQxImm95yIu21WUVD80eug2JBIkCcRly5Yt4sSJE10/bxZQlXU1rdyXBv2wQvB95GH3ipcn7ProKqfd1zfvf0UaYlcseK0ORjo5g2Opalk3MuxheMkQypWqtKTvTV7OyLr3MzmBzlWFr+BlYxHVQNnLE5YtGWq13svaPdEP92waENFJIcSW8OtskXeZXqfFh+mXFYKs0JVfuS/crSep/LLqiFF+56CcwfPKWtZ5ecJ71+daijpsSlVrdSwdyqHg5SOt++FmY2vbWvZRYX3h1ntZY7HHjYdhi7zLZM0iz5o8tqjkN7GedUxOlTuqIxIBn71zNR4f26j55sL3/ZjtHLXXNDGtwfLUrs1WyUkqfHdQnggP3rmqJX9QRhkE4PyBnV2zfnXnWawWeBiVRc5RK10ma9XUsrZCsEUlZ6VaSxxX/MPr7QpUCOCZ4xfw6KQ6xhpoL1cAdBamMjWdTrxzua3NXFyCdWKC8vtRQyVNNIms9EIaMdu683RLhn6GFXmXcV17JSkuQsK6gaqUqU5OWVyxaanbiaPnlJUBn31dX787SYJR+Dxp9AYNy68zLrpV6lh3nn4st9xt2EfeA7Lk3+uHCACdH398x3plKnnYWrfZD9CtSKKqCLpazcStVuj3LVW5TMLH1UWT7DYcWxk27pA4K8N+WTV2A1bki5x+qLess8he23OX0o8cttZtOsnoFKEss9D0uzboYsZVSj64t7Fu75eU3w+jMi7ixmzbbqJHnYfjxvUkdq0Q0d8jorNEdIaIftOFUEx3scmw7AVR1tpj99xutO9gY/WN71gPVROeB+9cpZXXtk6K7CEseHk8eOcq6e968M5VHWUJgEY0TPA3q+SMkj9I3D0dW3eI7jxZ21fKIoksciLaDuDTADYJIW4Q0Y+6EYthFoiy1kxXFTbWpf/dX3/xDcw0NxxNo1aC8vjlcYOWcY4a9cmv1+bbqg3K5A82hwi/Hg7FfOye9sbIvpzPvv6uNGrFhLgrNltXicl5srxq7DWJwg+J6DkATwshvmLzvcUcfsjYI4vF1iW7pH0cW3p13l7S72GtWSWt8MOfBPAXiOh1IvqfRPRRjQAPE9EJIjpx6dKlhKdlFhOuIn16FTG0GKMu2B3SXSJdK0T0FQA/Lnnr883vrwCwFcBHATxHRB8SEjNfCPE0gKeBhkWeRGhm8eEq0qcXEUP9Hqsfh37YRB8kIhW5EOIXVO8R0d8G8GJTcX+NiOYBfAAAm9wDjutMOxfHUx0jmMGoK3qVFou1Wl+WwmwHnaThh5MAtgM4RkQ/CWAJgD9JLBWTaVzUZwkq3eKwh/euz7Vqm8Q9nkymE+9cbqvFEq6/bXOOqN+hmoB0sfquJhlOYV/cJPWR/w6ADxHRNwH8HoBflrlVmMEiqc83nHJ9ZabWUdXQ1oeskkmXHZnUT22aOq7yzQNoS+WXNXlwKQczuCSyyIUQswAeciQL0yck9fmaprDb+JBt6nfHPUcYmwQjmZtBVrEw6jhJ5WAGE661wliTtD6LqfK08SGrPmuShRmXpBNa1OeSHmeQN1OZdliRM9YkDS0zUZ62oWoqmWTZkXHPESbphBb1uaTHGfTNVGYBVuSMNUnjsWVK18sTigUvdny3SqbHxzZ2dI1HzHOY/A6byUGXyp/0OByzvbjgxhJMTxiUKIukv4OjVhgbVJmdrMgZhmH6BO4QxDAMM6CwImcYhulzWJEzDMP0OazIGYZh+hxW5AzDMH1OT6JWiOgSgHcsvvIBZK8YVxZlArIpVxZlArIpF8tkThblSlum24QQK8Mv9kSR20JEJ2QhN70kizIB2ZQrizIB2ZSLZTIni3L1SiZ2rTAMw/Q5rMgZhmH6nH5R5E/3WgAJWZQJyKZcWZQJyKZcLJM5WZSrJzL1hY+cYRiGUdMvFjnDMAyjgBU5wzBMn5MJRU5EbxPRaSI6RUQnmq+tIKIvE9F3mv8fab5ORPQviOhNInqDiH7KoRy/Q0Tfb/Yg9V+zloOIfrn5+e8Q0S+nINM+Iio3x+sUEX0y8N7epkzniGhH4PWPN197k4j2JJRpFREdI6JvEdEZIvrV5uu9HiuVXD0bLyK6iYi+RkTTTZn2N19fS0SvN49/iIiWNF9f2vz3m83310TJ6liu/0hE5wNjtbn5eleuYfN4eSKaIqI/aP67p2OlkKnn49SGEKLn/wF4G8AHQq/9JoA9zb/3APhnzb8/CeAPARCArQBedyjHzwL4KQDfjCsHgBUAvtv8/0jz7xHHMu0D8A8kn/0wgGkASwGsBfAWgHzzv7cAfAjAkuZnPpxAplsA/FTz7/cB+L/Nc/d6rFRy9Wy8mr/55ubfHoDXm2PwHIBfar7+bwD87ebffwfAv2n+/UsADulkTTBWKrn+I4DPSD7flWvYPObfB/BfAPxB8989HSuFTD0fp+B/mbDIFXwawH9q/v2fAIwFXv9d0eA4gCIR3eLihEKI/wXgckI5dgD4shDishDiCoAvA/i4Y5lUfBrA7wkhbgghzgN4E8DHmv+9KYT4rmg0zP695mfjyvQ9IcQ3mn//KYBvAyih92OlkktF6uPV/M3vNf/pNf8TAO4C8ELz9fBY+WP4AoCfJyLSyBoLjVwqeKAi8AAAAztJREFUunINieiDAHYC+HfNfxN6PFZhmSLoyjiFyYoiFwBeIaKTRPRw87UfE0J8r/n3HwP4sebfJQDvBr77R9A/rEmxlaNb8v3d5tLtd3wXRi9kai5nR9Gw6DIzViG5gB6OV3NZfgrA99F4gN8CUBFCzEmO3zp38/2rAN7vWiaZXEIIf6x+ozlWTxLR0rBcofO7luspAL8GYL757/ej92MVlsmnl+PURlYU+Z8XQvwUgE8A+BUi+tngm6KxNul5nGRW5ADwrwGsA7AZwPcAfLEXQhDRzQAOA3hECPHD4Hu9HCuJXD0dLyFEXQixGcAH0bAMN3Tz/CrCchHRnwOwFw35PoqGG+AfdkseIvoUgO8LIU5265xRaGTq2TjJyIQiF0KUm///PoDfR+Nm/3++y6T5/+83P14GsCrw9Q82X0sLWzlSl08I8f+aD+E8gH+LhWVj12QiIg8NZXlQCPFi8+Wej5VMriyMV1OOCoBjAH4ajSX3kOT4rXM3318O4AdpyRSS6+NN95QQQtwA8B/Q3bHaBuBeInobDXfWXQB+G70dqw6ZiOiZHo9TJ66c7XH/A7AMwPsCf38VDd/RBNo3zn6z+fdOtG8mfM2xPGvQvrFoJQcas/N5NDY0Rpp/r3As0y2Bv3ej4Q8EgNvRvsnzXTQ27oaaf6/Fwubd7QnkIQC/C+Cp0Os9HSuNXD0bLwArARSbfxcA/G8AnwLwPNo38P5O8+9fQfsG3nM6WROMlUquWwJj+RSAA92+35vH/TksbCz2dKwUMmVinFqyuTpQgsH5UHPQpwGcAfD55uvvB/DfAXwHwFf8H90coH+Fhp/xNIAtDmV5Fo2ldw0NH9bfiCMHgL+OxgbLmwD+Wgoy/efmOd8A8BLaFdXnmzKdA/CJwOufRCOK4y1/jBPI9OfRcJu8AeBU879PZmCsVHL1bLwAfATAVPPc3wTwhcB9/7Xm734ewNLm6zc1//1m8/0PRcnqWK5Xm2P1TQDPYCGypSvXMHDMn8OC0uzpWClkysQ4+f9xij7DMEyfkwkfOcMwDBMfVuQMwzB9DityhmGYPocVOcMwTJ/DipxhGKbPYUXOMAzT57AiZxiG6XP+PwsSkf3C5YqhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.scatter(df1.lbwsg, df1.wasting-10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "correlation formula\n", + "\n", + "cov(X, Y) = (sum (x - mean(X)) * (y - mean(Y)) ) * 1/(n-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "bw = df1.lbwsg\n", + "laz = df1.stunting\n", + "wlz = df1.wasting\n", + "n = len(df1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.57624136e+05 4.56507017e+01]\n", + " [4.56507017e+01 4.19289692e+00]]\n" + ] + } + ], + "source": [ + "#Covariance\n", + "\n", + "covariance_laz = cov(bw, laz)\n", + "print(covariance_laz)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.57624136e+05 1.93543580e+01]\n", + " [1.93543580e+01 2.97911280e+00]]\n" + ] + } + ], + "source": [ + "covariance_wlz = cov(bw, wlz)\n", + "print(covariance_wlz)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spearman correlation coefficient for birthweight and laz at 6 months:\n", + "SpearmanrResult(correlation=0.03518313962319648, pvalue=0.0005162293690268319)\n", + "spearman correlation coefficient for birthweight and wlz at 6 months:\n", + "SpearmanrResult(correlation=0.015019215647108964, pvalue=0.1383783776283048)\n" + ] + } + ], + "source": [ + "print('spearman correlation coefficient for birthweight and laz at 6 months:')\n", + "print(stats.spearmanr(bw, laz))\n", + "\n", + "print('spearman correlation coefficient for birthweight and wlz at 6 months:')\n", + "print(stats.spearmanr(bw, wlz))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pearson formula\n", + "\t\n", + "Pearson's correlation coefficient = covariance(X, Y) / (stdv(X) * stdv(Y))\n", + "\n", + "Spearman formula\n", + "\n", + "Spearman's correlation coefficient = covariance(rank(X), rank(Y)) / (stdv(rank(X)) * stdv(rank(Y)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gates_bep", + "language": "python", + "name": "gates_bep" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}