Update release example (#341)

* update example scenario version

* add input EIR output in example scenario

* remove obsoletes build status links from README

* add example plot script in python

README.md

@@ -1,6 +1,3 @@
-[![Linux Build Status](https://travis-ci.org/SwissTPH/openmalaria.svg?branch=master)](https://travis-ci.org/SwissTPH/openmalaria)
-[![Windows Build Status](https://ci.appveyor.com/api/projects/status/dhardy/openmalaria)](https://ci.appveyor.com/project/dhardy/openmalaria)
-
 Open Malaria
 ============
 

util/example/example_scenario.xml

@@ -1,5 +1,5 @@
 <?xml version='1.0' encoding='UTF-8'?>
-<om:scenario xmlns:om="http://openmalaria.org/schema/scenario_43" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" name="Example Scenario" schemaVersion="43" xsi:schemaLocation="http://openmalaria.org/schema/scenario_43 scenario_43.xsd">
+<om:scenario xmlns:om="http://openmalaria.org/schema/scenario_44" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" name="Example Scenario" schemaVersion="44" xsi:schemaLocation="http://openmalaria.org/schema/scenario_44 scenario_44.xsd">
   <demography maximumAgeYrs="90" name="Ifakara" popSize="1000">
     <ageGroup lowerbound="0">
       <group poppercent="3.474714994" upperbound="1"/>
@@ -47,6 +47,7 @@
       <option name="nUncomp" value="true"/>
       <option name="nSevere" value="true"/>
       <option name="nDirDeaths" value="false"/>
+      <option name="inputEIR" value="true"/>
       <option name="simulatedEIR" value="true"/>
     </SurveyOptions>
     <surveys detectionLimit="40">

util/example/plot.py

@@ -0,0 +1,204 @@
+import os
+from math import *
+import numpy as np
+from matplotlib import pyplot as plt
+import matplotlib
+import pandas as pd
+
+names = {
+	0: 'nHost',
+	1: 'nInfect',
+	2: 'nExpectd',
+	3: 'nPatent',
+	4: 'sumLogPyrogenThres',
+	5: 'sumlogDens',
+	6: 'totalInfs',
+	7: 'nTransmit',
+	8: 'totalPatentInf',
+	9: 'contrib',
+	10: 'sumPyrogenThresh',
+	11: 'nTreatments1',
+	12: 'nTreatments2',
+	13: 'nTreatments3',
+	14: 'nUncomp',
+	15: 'nSevere',
+	16: 'nSeq',
+	17: 'nHospitalDeaths',
+	18: 'nIndDeaths',
+	19: 'nDirDeaths',
+	20: 'nEPIVaccinations',
+	21: 'allCauseIMR',
+	22: 'nMassVaccinations',
+	23: 'nHospitalRecovs',
+	24: 'nHospitalSeqs',
+	25: 'nIPTDoses',
+	26: 'annAvgK',
+	27: 'nNMFever',
+	30: 'innoculationsPerAgeGroup',
+	28: 'innoculationsPerDayOfYear',
+	29: 'kappaPerDayOfYear',
+	31: 'Vector_Nv0',
+	32: 'Vector_Nv',
+	33: 'Vector_Ov',
+	34: 'Vector_Sv',
+	35: 'Vector_EIR_Input',
+	36: 'Vector_EIR_Simulated',
+	39: 'Clinical_RDTs',
+	40: 'Clinical_DrugUsage',
+	41: 'Clinical_FirstDayDeaths',
+	42: 'Clinical_HospitalFirstDayDeaths',
+	43: 'nNewInfections',
+	44: 'nMassITNs',
+	45: 'nEPI_ITNs',
+	46: 'nMassIRS',
+	47: 'nMassVA',
+	48: 'Clinical_Microscopy',
+	49: 'Clinical_DrugUsageIV',
+	50: 'nAddedToCohort',
+	51: 'nRemovedFromCohort',
+	52: 'nMDAs',
+	53: 'nNmfDeaths',
+	54: 'nAntibioticTreatments',
+	55: 'nMassScreenings',
+	56: 'nMassGVI',
+	57: 'nCtsIRS',
+	58: 'nCtsGVI',
+	59: 'nCtsMDA',
+	60: 'nCtsScreenings',
+	61: 'nSubPopRemovalTooOld',
+	62: 'nSubPopRemovalFirstEvent',
+	63: 'nPQTreatments',
+	64: 'nTreatDiagnostics',
+	65: 'nMassRecruitOnly',
+	66: 'nCtsRecruitOnly',
+	67: 'nTreatDeployments',
+	68: 'sumAge',
+	69: 'nInfectByGenotype',
+	70: 'nPatentByGenotype',
+	71: 'logDensByGenotype',
+	72: 'nHostDrugConcNonZero',
+	73: 'sumLogDrugConcNonZero',
+	74:	'expectedDirectDeaths',
+	75:	'expectedHospitalDeaths',
+	76:	'expectedIndirectDeaths',
+	77:	'expectedSequelae',
+	78:	'expectedSevere',
+	79:	'innoculationsPerVector'	
+}
+
+def load(scenario):
+	df = pd.read_csv(scenario, sep="\t", header=None)
+	df.columns = ['survey', 'group', 'code', 'value']
+	return df
+
+def extract(df, surveys, groups, code):
+	data = []
+
+	# use only survey 2 (post intervention)
+	for survey in surveys:
+		y = np.array(len(groups), dtype=float)
+		for group in groups:
+			v = df[(df['survey'] == survey) & (df['group'] == group) & (df['code'] == code)]['value'].values
+			if len(v) > 1:
+				print('Too many values found for survey', survey, ' code ', code, ' and group ', group)
+			elif len(v) == 0:
+				print('No values found for survey', survey, ' code ', code, ' and group ', group)
+			else:
+				y += v[0]
+		data.append(y)
+
+	return np.array(data)
+
+def plot(df, codes, groups, labels, title, norm_code=None):
+	fig = plt.figure(figsize=(6,4))
+	ax = fig.add_subplot(1,1,1)
+
+	for i in range(len(codes)):
+		surveys = np.arange(max(df['survey']))+1
+		data = extract(df, surveys, groups, codes[i])
+
+		if norm_code is not None:
+			nHost = extract(df, surveys, groups, norm_code)
+			data /= nHost
+
+		ax.plot(surveys, data, marker='o', linewidth=1, markersize=4, label=labels[i])
+
+	ax.set_title(title)
+	ax.set_xlabel("time steps")
+	ax.set_ylabel(title)
+	ax.legend(loc="upper left")
+	plt.tight_layout()
+
+df = load(f'output.txt')
+
+def plot_by_group(df, codes, groups, labels, title, norm_code=None):
+	fig = plt.figure(figsize=(6,4))
+	ax = fig.add_subplot(1,1,1)
+
+	for i in range(len(codes)):
+		surveys = np.arange(max(df['survey']))+1
+		for j in range(len(groups)):
+			data = extract(df, surveys, [groups[j]], codes[i])
+
+			if norm_code is not None:
+				nHost = extract(df, surveys, [groups[j]], norm_code)
+				data /= nHost
+
+			ax.plot(surveys, data, marker='o', linewidth=1, markersize=4, label=labels[i][j])
+
+	ax.set_title(title)
+	ax.set_xlabel("time steps")
+	ax.set_ylabel(title)
+	ax.legend(loc="upper left")
+	plt.tight_layout()
+
+def plot_cts(df, field, label):
+	fig = plt.figure(figsize=(6,4))
+	ax = fig.add_subplot(1,1,1)
+
+	data = df[field].values
+	ax.plot(np.arange(len(data)), data, marker='o', markersize=2, label=label)
+
+	ax.set_title(field)
+	ax.set_xlabel("time steps")
+	ax.set_ylabel(field)
+	ax.legend(loc="upper right")
+
+# Normal output 
+# =============
+
+# The file is organized in 4 columns:
+# survey, group, code, value
+# There is one value per survey, group and code
+# Groups are typically age groups (specified in xml)
+# Note 1: some outputs do not have groups
+# Note 2: sometimes instead of age, groups will indicate phenotype or other things depending on the output code (see wiki)
+
+# Example scenario has 4 surveys per year and 2 age groups (< 5y and < 90y).
+# Monitoring period is 20 years. A GVI interventions is deployed after 10 years
+# The transmission is seasonal and you can observe a peak every year
+# Simulated EIR, new infections, Malaria episodes and others are expected to decrease after the intervention is deployed
+# Note: increase population size and run multiple seeds for better results
+df = load(f'output.txt')
+
+# Entomological Innoculation Rate (input and simulated), no age groups
+plot(df, codes=[35,36], groups=[0], labels=['Input EIR', 'Simulated EIR'], title="EIR")
+
+# Number of Severe Episodes per person (divided by number of hosts, which is code 0)
+plot(df, codes=[15], groups=[1,2], labels=['Severe Epiosdes'], title="Severe Episodes per person per survey", norm_code=0)
+
+# Number of Uncomplicated Episodes per person and by age group
+plot_by_group(df, codes=[14], groups=[1,2], labels=[['age < 5', 'age < 90']], title="Unomplicated Episodes per person per survey", norm_code=0)
+
+# Continous output
+# ================
+
+# The file contains one column per output (specified in the xml file)
+
+df = pd.read_csv(f'ctsout.txt', sep="\t", header=1)
+
+plot_cts(df, ["immunity Y"], label="immunity Y")
+plot_cts(df, ["immunity h"], label="immunity h")
+plot_cts(df, ["new infections"], label="new infections")
+
+plt.show()