Update IntervalsModel/network-data/POLYMOD/Duration-priors.py,...

Update IntervalsModel/network-data/POLYMOD/Duration-priors.py, IntervalsModel/network-data/POLYMOD/Frequency-distribution.py, IntervalsModel/network-data/POLYMOD/AALPoisPriors.csv, IntervalsModel/network-data/POLYMOD/AALfreqdist.csv files

IntervalsModel/network-data/POLYMOD/AALfreqdist.csv

 ,Age_in,Age_out,location,0,1,2
 0,Y,Y,workschool,0.77870480731029,0.19355052310952192,0.027744669580188053
 1,Y,Y,rest,0.37273777435502503,0.33153638814016173,0.29572583750481324
-2,Y,M,workschool,0.4929277756915759,0.3358367755664038,0.1712354487420203
-3,Y,M,rest,0.24455087479229792,0.26116704134493207,0.49428208386277
-4,Y,O,workschool,0.5075757575757576,0.21212121212121213,0.2803030303030303
-5,Y,O,rest,0.20387453874538744,0.3274907749077491,0.46863468634686345
-6,M,M,workschool,0.4924376199616123,0.2583493282149712,0.24921305182341652
-7,M,M,rest,0.1579316799251287,0.25869599126501325,0.583372328809858
-8,M,O,workschool,0.3647859922178988,0.24610894941634243,0.3891050583657587
-9,M,O,rest,0.1687344913151365,0.2924937965260546,0.5387717121588089
-10,O,O,workschool,0.2,0.2545454545454545,0.5454545454545455
-11,O,O,rest,0.1589895988112927,0.35066864784546803,0.4903417533432392
+2,Y,M,workschool,0.4884488448844885,0.3536067892503536,0.15794436586515795
+3,Y,M,rest,0.2386593470095348,0.25874024848309735,0.5026004045073678
+4,Y,O,workschool,0.35185185185185186,0.2037037037037037,0.4444444444444444
+5,Y,O,rest,0.1974025974025974,0.34545454545454546,0.45714285714285713
+6,M,Y,workschool,0.49799839871897517,0.31571924206031493,0.1862823592207099
+7,M,Y,rest,0.25687518887881533,0.26624357812027805,0.4768812330009066
+8,M,M,workschool,0.4924376199616123,0.2583493282149712,0.24921305182341652
+9,M,M,rest,0.1579316799251287,0.25869599126501325,0.583372328809858
+10,M,O,workschool,0.40144927536231884,0.2246376811594203,0.3739130434782608
+11,M,O,rest,0.19285274183610598,0.30560690080098585,0.5015403573629083
+12,O,Y,workschool,0.6153846153846154,0.21794871794871795,0.16666666666666669
+13,O,Y,rest,0.2197452229299363,0.28343949044585987,0.4968152866242038
+14,O,M,workschool,0.28994082840236685,0.28994082840236685,0.42011834319526625
+15,O,M,rest,0.14428482198625858,0.27920049968769517,0.5765146783260462
+16,O,O,workschool,0.2,0.2545454545454545,0.5454545454545455
+17,O,O,rest,0.1589895988112927,0.35066864784546803,0.4903417533432392

IntervalsModel/network-data/POLYMOD/Duration-priors.py

@@ -16,7 +16,6 @@ import numpy as np
 import pandas as pd
 import math
 from itertools import product
-import matplotlib.pyplot as plt
 
 # Read the cleaned survey as a dataframe
 Contact = pd.read_csv("AALContact_data.csv")
@@ -36,7 +35,6 @@ def symage_row(row):
         if symage == (agesrc, agetar) or symage == (agetar, agesrc):
             return symage
 Contact['symage'] = Contact.apply(symage_row, axis=1)
-#print(Contact[Contact['symage']==('M', 'M')].head(10))
 
 # Dictionary separating contact data by symage-location
 ContAAL = {}
@@ -46,10 +44,7 @@ for symage, loc in list(product(SymAge,Locales)):
     x,y,z = symage[0], symage[1], loc
     C = Contact[Contact['cnt_'+z] == True][Contact['symage']==symage]
     ContAAL[(x,y,z)] = C[columns].copy(deep=True)
-test1 = ('Y', 'Y', 'home')
-test2 = ('Y', 'M', 'home')
-print(ContAAL[test1].head(15))
-print(ContAAL[test2].head(25))
+
 
 # Duration distributions stratified by SymAge-location
 DurFreqAAL = {}
@@ -97,26 +92,4 @@ dfkeys = pd.DataFrame([pd.Series(x) for x in df.col1])
 dfkeys.columns = ["Age_in", "Age_out", "location"]
 dfvals = pd.DataFrame([pd.Series(x) for x in df.col2])
 dfout = dfkeys.join(dfvals)
-dfout.to_csv("AALPoisPriors.csv")
-
+dfout.to_csv("AALPoisPriors.csv")
\ No newline at end of file
-# Determine frequency distributions accord to to symage-locales, excluding hh
-freqdistAAL={}
-locales_nohh = ['workschool', 'rest']
-for symage, loc in list(product(SymAge, locales_nohh)):
-    x, y, z = symage[0],symage[1],loc
-    C = ContAAL[(x,y,z)]
-    tot = len(C)
-    freqfull = np.full(5,0.)
-    for i in range(1,6):
-        count = len(C[C['frequency_multi']==i])
-        freqfull[i-1] = (count)/tot
-    freqdistAAL[(x,y,z)] = [freqfull[0], freqfull[1], np.sum(freqfull[2:])]
-    
-
-# Save them to csv
-df = pd.DataFrame(list(freqdistAAL.items()), columns=['col1','col2'])
-dfkeys = pd.DataFrame([pd.Series(x) for x in df.col1])
-dfkeys.columns = ["Age_in", "Age_out", "location"]
-dfvals = pd.DataFrame([pd.Series(x) for x in df.col2])
-dfout = dfkeys.join(dfvals)
-dfout.to_csv("AALfreqdist.csv")

IntervalsModel/network-data/POLYMOD/Frequency-distribution.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on 2021-03-24
+
+Determines the fraction of interactions which are daily, 3x/week or singular
+ in the ABM. Daily are those with frequency_mult =1, 3x/week are 2 and
+ singular are 3-5. 
+
+
+
+@author: mark
+"""
+import numpy as np
+import pandas as pd
+from itertools import product
+
+# Read the cleaned survey as a dataframe
+Contact = pd.read_csv("AALContact_data.csv")
+
+
+# Specify AAL categories
+Ages = ['Y', 'M', 'O']
+Locales = ['home', 'workschool', 'rest']
+
+# Dictionary separating contact data by symage-location
+ContAAL = {}
+columns = ['part_id', 'cont_id', 'age_source', 'age_target', 
+           'duration_multi', 'frequency_multi']
+for x,y,z in list(product(Ages,Ages,Locales)):
+    #x,y,z = symage[0], symage[1], loc
+    C = Contact[Contact['cnt_'+z] == True][Contact['age_source']==x][Contact['age_target']==y]
+    ContAAL[(x,y,z)] = C[columns].copy(deep=True)
+    
+# Determine frequency distributions accord to AAL, excluding hh
+freqdistAAL={}
+locales_nohh = ['workschool', 'rest']
+for x,y, z in list(product(Ages, Ages, locales_nohh)):
+    #x, y, z = symage[0],symage[1],loc
+    C = ContAAL[(x,y,z)]
+    tot = len(C)
+    freqfull = np.full(5,0.)
+    for i in range(1,6):
+        count = len(C[C['frequency_multi']==i])
+        freqfull[i-1] = (count)/tot
+    freqdistAAL[(x,y,z)] = [freqfull[0], freqfull[1], np.sum(freqfull[2:])]
+    
+
+# Save them to csv
+df = pd.DataFrame(list(freqdistAAL.items()), columns=['col1','col2'])
+dfkeys = pd.DataFrame([pd.Series(x) for x in df.col1])
+dfkeys.columns = ["Age_in", "Age_out", "location"]
+dfvals = pd.DataFrame([pd.Series(x) for x in df.col2])
+dfout = dfkeys.join(dfvals)
+dfout.to_csv("AALfreqdist.csv")  
\ No newline at end of file