diff --git a/.idea/emodpy-snt.iml b/.idea/emodpy-snt.iml
index d9e6024..1d956bf 100644
--- a/.idea/emodpy-snt.iml
+++ b/.idea/emodpy-snt.iml
@@ -2,7 +2,9 @@
-
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 8d93904..ea2c556 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
-
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index 584c775..6a4f902 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,7 +2,9 @@
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 9661ac7..07be948 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -1,6 +1,7 @@
-
+
+
\ No newline at end of file
diff --git a/Jenkinsfile b/Jenkinsfile
index 2cde2ae..e18df44 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -59,8 +59,8 @@ podTemplate(
def wheelFile = sh(returnStdout: true, script: "find ./dist -name '*.whl'").toString().trim()
//def wheelFile = sh(returnStdout: true, script: "python3 ./.github/scripts/get_wheel_filename.py --package-file package_setup.py").toString().trim()
echo "This is the package file: ${wheelFile}"
- sh "pip3 install $wheelFile --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple"
-
+ //sh "pip3 install $wheelFile --index-url=https://packages.idmod.org/api/pypi/pypi-production/simple"
+ sh "pip3 install $wheelFile --index-url=https://stitova@idmod.org:tWIoY8i6DuYX!tWp@packages.idmod.org/api/pypi/pypi-staging/simple --pre"
//sh "pip3 install dataclasses"
sh 'pip3 install keyrings.alt'
sh "pip3 freeze"
@@ -108,7 +108,7 @@ podTemplate(
echo "Running examples"
dir('examples') {
sh 'pip3 install snakemake'
- sh 'snakemake --cores=10 --config python_version=python3'
+ sh 'snakemake --cores=1 --config python_version=python3'
}
}
diff --git a/data/example_files/simulation_inputs/larval_habitats/larval_habitat_multipliers_v1.csv b/data/example_files/simulation_inputs/larval_habitats/larval_habitat_multipliers_v1.csv
index a6d4f03..85ea04a 100644
--- a/data/example_files/simulation_inputs/larval_habitats/larval_habitat_multipliers_v1.csv
+++ b/data/example_files/simulation_inputs/larval_habitats/larval_habitat_multipliers_v1.csv
@@ -4,16 +4,16 @@ AA,1,1.5848931924611125
AA,2,1.5848931924611125
AA,3,1.5848931924611125
AA,4,1.5848931924611125
-BB,0,0.3981071705534972
-BB,1,0.3981071705534972
-BB,2,0.3981071705534972
-BB,3,0.3981071705534972
-BB,4,0.3981071705534972
-CC,0,0.3981071705534972
-CC,1,0.3981071705534972
-CC,2,0.3981071705534972
-CC,3,0.3981071705534972
-CC,4,0.3981071705534972
+BB,0,0.3981071705534971
+BB,1,0.3981071705534971
+BB,2,0.3981071705534971
+BB,3,0.3981071705534971
+BB,4,0.3981071705534971
+CC,0,0.3981071705534971
+CC,1,0.3981071705534971
+CC,2,0.3981071705534971
+CC,3,0.3981071705534971
+CC,4,0.3981071705534971
DD,0,1.5848931924611125
DD,1,1.5848931924611125
DD,2,1.5848931924611125
@@ -24,13 +24,13 @@ EE,1,0.1
EE,2,0.1
EE,3,0.1
EE,4,0.1
-FF,0,25.118864315095795
+FF,0,6.30957344480193
FF,1,25.118864315095795
-FF,2,25.118864315095795
-FF,3,25.118864315095795
+FF,2,6.30957344480193
+FF,3,6.30957344480193
FF,4,25.118864315095795
GG,0,6.30957344480193
GG,1,6.30957344480193
GG,2,6.30957344480193
-GG,3,25.118864315095795
+GG,3,6.30957344480193
GG,4,6.30957344480193
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run0.csv
new file mode 100644
index 0000000..37ea293
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run0.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-186.53927596010544
+0.1,-173.99270893245284
+0.3981071705534971,-43.328227823219095
+1.5848931924611125,-36.99050230882267
+6.30957344480193,-63.36162503360811
+25.118864315095795,-107.3877951284203
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run1.csv
new file mode 100644
index 0000000..9700a53
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run1.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-187.96837742501248
+0.1,-169.88709236992327
+0.3981071705534971,-49.83678889213934
+1.5848931924611125,-34.59288010625551
+6.30957344480193,-68.88023543974077
+25.118864315095795,-109.24930340285187
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run2.csv
new file mode 100644
index 0000000..f9c014a
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run2.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-184.72220058141465
+0.1,-177.14934300759796
+0.3981071705534971,-52.487039231733434
+1.5848931924611125,-33.83493750639718
+6.30957344480193,-66.37750093306386
+25.118864315095795,-109.31340763089884
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run3.csv
new file mode 100644
index 0000000..4e0b576
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run3.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-183.47705926267008
+0.1,-174.5735622262191
+0.3981071705534971,-46.80607847423926
+1.5848931924611125,-33.31510489048787
+6.30957344480193,-63.13828873477496
+25.118864315095795,-106.39338101279372
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run4.csv
new file mode 100644
index 0000000..e1823fe
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/AA_run4.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-184.45584172722283
+0.1,-175.89478428984603
+0.3981071705534971,-44.62845603284677
+1.5848931924611125,-32.934052221396996
+6.30957344480193,-67.5044917321784
+25.118864315095795,-107.62870247898059
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run0.csv
new file mode 100644
index 0000000..449c7cb
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run0.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-27.033116866266937
+0.1,-27.356654998702652
+0.3981071705534971,-8.485222803619763
+1.5848931924611125,-36.874366108000686
+6.30957344480193,-70.89105429326037
+25.118864315095795,-116.53092179312557
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run1.csv
new file mode 100644
index 0000000..173bae4
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run1.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-26.97143185031564
+0.1,-22.27926965982806
+0.3981071705534971,-9.316039939631992
+1.5848931924611125,-34.43602847985585
+6.30957344480193,-70.97168387938336
+25.118864315095795,-117.13331939317959
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run2.csv
new file mode 100644
index 0000000..1711edc
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run2.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-26.80327572636361
+0.1,-24.683700927392238
+0.3981071705534971,-9.092472534654917
+1.5848931924611125,-34.760006129154135
+6.30957344480193,-69.47273092803562
+25.118864315095795,-114.81930669831922
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run3.csv
new file mode 100644
index 0000000..999ec25
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run3.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-26.811226179147525
+0.1,-23.857127563115682
+0.3981071705534971,-9.051516152425393
+1.5848931924611125,-33.65637336876125
+6.30957344480193,-68.92247219646424
+25.118864315095795,-116.60216094848033
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run4.csv
new file mode 100644
index 0000000..6262d60
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/BB_run4.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-27.17112028549036
+0.1,-22.824627237911955
+0.3981071705534971,-8.662181215167038
+1.5848931924611125,-30.40498344885691
+6.30957344480193,-68.58206853965248
+25.118864315095795,-118.99564023167181
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run0.csv
new file mode 100644
index 0000000..96badf4
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run0.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-173.55736663503012
+0.1,-176.40241934866935
+0.3981071705534971,-68.77171027877739
+1.5848931924611125,-101.14599112571022
+6.30957344480193,-123.1568561675831
+25.118864315095795,-166.1208407207837
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run1.csv
new file mode 100644
index 0000000..1f11468
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run1.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-179.41269285253657
+0.1,-170.638777924878
+0.3981071705534971,-71.65817539777709
+1.5848931924611125,-101.40499060383036
+6.30957344480193,-127.22801602148229
+25.118864315095795,-167.59705648855106
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run2.csv
new file mode 100644
index 0000000..591f192
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run2.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-178.11526268026773
+0.1,-179.4486602904799
+0.3981071705534971,-81.93241394747929
+1.5848931924611125,-100.30025278982157
+6.30957344480193,-121.18038215910656
+25.118864315095795,-158.80202843669053
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run3.csv
new file mode 100644
index 0000000..defa1d8
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run3.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-181.84368195171282
+0.1,-177.04291191645098
+0.3981071705534971,-65.31578731455102
+1.5848931924611125,-106.3231530635635
+6.30957344480193,-119.41683306074492
+25.118864315095795,-164.76795794522286
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run4.csv
new file mode 100644
index 0000000..cc3b12a
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/CC_run4.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-173.4211960238954
+0.1,-174.25585497637712
+0.3981071705534971,-71.7669950804875
+1.5848931924611125,-102.35741991027953
+6.30957344480193,-120.02040205670255
+25.118864315095795,-164.0832913651684
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run0.csv
new file mode 100644
index 0000000..65b62c8
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run0.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-159.82702309561319
+0.1,-153.35259280040827
+0.3981071705534971,-38.39998383076363
+1.5848931924611125,-30.500651619162
+6.30957344480193,-41.761525098297625
+25.118864315095795,-65.48557119778889
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run1.csv
new file mode 100644
index 0000000..2ad9e2a
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run1.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-159.3648492153534
+0.1,-145.3103088819371
+0.3981071705534971,-46.02593146845811
+1.5848931924611125,-30.521665855256288
+6.30957344480193,-41.84082765868743
+25.118864315095795,-73.8452958663022
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run2.csv
new file mode 100644
index 0000000..be118d1
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run2.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-158.824883567293
+0.1,-140.49395099979847
+0.3981071705534971,-48.19606890857813
+1.5848931924611125,-31.0333996146328
+6.30957344480193,-40.852238605119965
+25.118864315095795,-74.09121043983532
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run3.csv
new file mode 100644
index 0000000..748aa5c
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run3.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-161.3797659552356
+0.1,-142.98849957960556
+0.3981071705534971,-43.6127655689952
+1.5848931924611125,-32.63237622024735
+6.30957344480193,-40.95657805794622
+25.118864315095795,-70.84064244293268
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run4.csv
new file mode 100644
index 0000000..78c2420
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/DD_run4.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-160.3892279968004
+0.1,-152.45620330340898
+0.3981071705534971,-42.30347028002507
+1.5848931924611125,-31.06034277200979
+6.30957344480193,-38.77199487420694
+25.118864315095795,-72.35240770470205
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run0.csv
new file mode 100644
index 0000000..06ee04e
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run0.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-37.854650365159614
+0.1,-17.13012477925531
+0.3981071705534971,-31.267075636078516
+1.5848931924611125,-92.85118040666566
+6.30957344480193,-111.40001559250322
+25.118864315095795,-139.4825035598942
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run1.csv
new file mode 100644
index 0000000..f22b61e
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run1.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-37.47865655154237
+0.1,-19.556382086146186
+0.3981071705534971,-32.27834172900975
+1.5848931924611125,-87.55260192051992
+6.30957344480193,-114.8709116380478
+25.118864315095795,-139.12566874570803
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run2.csv
new file mode 100644
index 0000000..cf6d320
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run2.csv
@@ -0,0 +1,7 @@
+Habitat_Multiplier,score
+0.0251188643150957,-37.740776471961
+0.1,-26.957675795469186
+0.3981071705534971,-32.90366613324568
+1.5848931924611125,-86.76201218468486
+6.30957344480193,-110.48589128043955
+25.118864315095795,-143.77322788173774
diff --git a/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run/EE_run3.csv
new file mode 100644
index 0000000..3b8e786
--- /dev/null
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/FF_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/FF_run3.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/FF_run3.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/FF_run3.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/FF_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/FF_run4.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/FF_run4.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/FF_run4.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run0.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run0.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run0.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run0.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run1.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run1.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run1.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run1.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run2.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run2.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run2.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run2.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run3.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run3.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run3.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run3.csv
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run4.csv b/data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run4.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/GG_run4.csv
rename to data/example_files/simulation_output/baseline_calibration/LL_allxLH_each_admin_run_old/GG_run4.csv
diff --git a/data/example_files/simulation_output/baseline_calibration/compare_xLH_sweep_with_DHS_loglikelihoods_SS.png b/data/example_files/simulation_output/baseline_calibration/compare_xLH_sweep_with_DHS_loglikelihoods_SS.png
new file mode 100644
index 0000000..5150203
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diff --git a/data/example_files/simulation_output/baseline_calibration/compare_xLH_sweep_with_DHS_timeseries_SS.png b/data/example_files/simulation_output/baseline_calibration/compare_xLH_sweep_with_DHS_timeseries_SS.png
new file mode 100644
index 0000000..1d87a24
Binary files /dev/null and b/data/example_files/simulation_output/baseline_calibration/compare_xLH_sweep_with_DHS_timeseries_SS.png differ
diff --git a/data/example_files/simulation_output/baseline_calibration/monthly_U5_PfPR.csv b/data/example_files/simulation_output/baseline_calibration/monthly_U5_PfPR.csv
new file mode 100644
index 0000000..5b813c8
--- /dev/null
+++ b/data/example_files/simulation_output/baseline_calibration/monthly_U5_PfPR.csv
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+8,1016.266666667,0.02660062975597,2014,0,0.39810717055349715,GG
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+1,1008.633333333,0.03413860339073,2015,0,0.39810717055349715,GG
+2,1013.2,0.03085932359521,2015,0,0.39810717055349715,GG
+3,1007.433333333,0.03434470436423,2015,0,0.39810717055349715,GG
+4,1002.9,0.0259912919201,2015,0,0.39810717055349715,GG
+5,1004.833333333,0.01778072648864,2015,0,0.39810717055349715,GG
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+7,1016.833333333,0.03481396492378,2015,0,0.39810717055349715,GG
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+12,1042.866666667,0.05532826184236,2015,0,0.39810717055349715,GG
+1,1045.766666667,0.03879131737481,2016,0,0.39810717055349715,GG
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+4,970.4333333333,0.5801188472504,2015,0,25.118864315095795,GG
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+3,1018.1,0.03676783551059,2016,1,0.39810717055349715,BB
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+4,980.7,0.0,2017,1,0.1,CC
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+10,999.3666666667,0.0,2017,1,0.1,CC
+11,1005.133333333,0.0,2017,1,0.1,CC
+12,1010.766666667,0.0,2017,1,0.1,CC
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+2,991.4,0.2291036245041,2010,1,1.5848931924611125,BB
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+9,1003.533333333,0.6016076529595,2010,1,1.5848931924611125,BB
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+11,1008.966666667,0.5324259143018,2010,1,1.5848931924611125,BB
+12,1004.4,0.3804924996681,2010,1,1.5848931924611125,BB
+1,1008.3,0.2667856788654,2011,1,1.5848931924611125,BB
+2,1005.9,0.226795241409,2011,1,1.5848931924611125,BB
+3,990.8,0.2565267124209,2011,1,1.5848931924611125,BB
+4,985.9,0.2481996145654,2011,1,1.5848931924611125,BB
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+6,987.8,0.245899979753,2011,1,1.5848931924611125,BB
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+6,1032.533333333,0.6468233471074,2013,1,25.118864315095795,CC
+7,1029.233333333,0.7253619198756,2013,1,25.118864315095795,CC
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+10,1021.633333333,0.6516036412281,2013,1,25.118864315095795,CC
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+12,1010.533333333,0.6306240928882,2013,1,25.118864315095795,CC
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+5,972.9666666667,0.602795573675,2014,1,25.118864315095795,CC
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+7,972.2333333333,0.7253745671478,2014,1,25.118864315095795,CC
+8,975.4666666667,0.7012028430837,2014,1,25.118864315095795,CC
+9,975.8666666667,0.6468779887963,2014,1,25.118864315095795,CC
+10,975.1,0.6472498547158,2014,1,25.118864315095795,CC
+11,976.2333333333,0.6554102502817,2014,1,25.118864315095795,CC
+12,982.1,0.6035026982996,2014,1,25.118864315095795,CC
+1,979.7333333333,0.56614044638,2015,1,25.118864315095795,CC
+2,984.6666666667,0.5879485443466,2015,1,25.118864315095795,CC
+3,991.5666666667,0.657377214509,2015,1,25.118864315095795,CC
+4,983.2333333333,0.6532189714208,2015,1,25.118864315095795,CC
+5,982.8,0.5756681590015,2015,1,25.118864315095795,CC
+6,978.3333333333,0.6147529812606,2015,1,25.118864315095795,CC
+7,978.3,0.7040103581042,2015,1,25.118864315095795,CC
+8,969.2666666667,0.6806520393425,2015,1,25.118864315095795,CC
+9,960.8333333333,0.6339982653946,2015,1,25.118864315095795,CC
+10,959.4666666667,0.6247915508616,2015,1,25.118864315095795,CC
+11,967.2666666667,0.6034185677855,2015,1,25.118864315095795,CC
+12,967.4,0.5619530011715,2015,1,25.118864315095795,CC
+1,976.7333333333,0.5480854549178,2016,1,25.118864315095795,CC
+2,978.3,0.6030869876316,2016,1,25.118864315095795,CC
+3,982.4333333333,0.6472703830625,2016,1,25.118864315095795,CC
+4,984.8,0.6164703493095,2016,1,25.118864315095795,CC
+5,985.5666666667,0.5529475428687,2016,1,25.118864315095795,CC
+6,985.5666666667,0.5896438597085,2016,1,25.118864315095795,CC
+7,984.9666666667,0.697045585299,2016,1,25.118864315095795,CC
+8,976.9,0.6483433991879,2016,1,25.118864315095795,CC
+9,979.0666666667,0.6011507558219,2016,1,25.118864315095795,CC
+10,979.0666666667,0.621782650143,2016,1,25.118864315095795,CC
+11,976.4,0.6223542264099,2016,1,25.118864315095795,CC
+12,970.3666666667,0.576757926557,2016,1,25.118864315095795,CC
+1,964.8666666667,0.5383127202377,2017,1,25.118864315095795,CC
+2,959.8333333333,0.5868379927071,2017,1,25.118864315095795,CC
+3,961.7,0.6197705452151,2017,1,25.118864315095795,CC
+4,971.9,0.6077099838804,2017,1,25.118864315095795,CC
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+6,981.9,0.5800998064976,2017,1,25.118864315095795,CC
+7,979.1666666667,0.659914893617,2017,1,25.118864315095795,CC
+8,971.0,0.6308959835221,2017,1,25.118864315095795,CC
+9,969.5333333333,0.6079213367256,2017,1,25.118864315095795,CC
+10,969.6333333333,0.5864072329747,2017,1,25.118864315095795,CC
+11,971.0333333333,0.5855274449899,2017,1,25.118864315095795,CC
+12,972.0,0.5496570644719,2017,1,25.118864315095795,CC
+1,1038.1,0.01236232861317,2010,1,0.1,DD
+2,1042.6,0.008344523307117,2010,1,0.1,DD
+3,1050.666666667,0.01132614213198,2010,1,0.1,DD
+4,1056.833333333,0.007601324712191,2010,1,0.1,DD
+5,1056.433333333,0.005300855078408,2010,1,0.1,DD
+6,1062.133333333,0.004770273663068,2010,1,0.1,DD
+7,1060.266666667,0.004967303822938,2010,1,0.1,DD
+8,1056.133333333,0.00912132306527,2010,1,0.1,DD
+9,1057.433333333,0.008731834946254,2010,1,0.1,DD
+10,1053.333333333,0.01449367088608,2010,1,0.1,DD
+11,1047.933333333,0.01692219606845,2010,1,0.1,DD
+12,1048.8,0.01106025934401,2010,1,0.1,DD
+1,1042.833333333,0.007479622822439,2011,1,0.1,DD
+2,1044.166666667,0.006256983240223,2011,1,0.1,DD
+3,1040.166666667,0.007947444319821,2011,1,0.1,DD
+4,1038.966666667,0.00644871506946,2011,1,0.1,DD
+5,1038.833333333,0.004524306112626,2011,1,0.1,DD
+6,1043.866666667,0.005492400051092,2011,1,0.1,DD
+7,1040.933333333,0.007493275265787,2011,1,0.1,DD
+8,1042.5,0.004924060751399,2011,1,0.1,DD
+9,1046.866666667,0.01114436731835,2011,1,0.1,DD
+10,1042.933333333,0.01284837637433,2011,1,0.1,DD
+11,1041.766666667,0.01791827984513,2011,1,0.1,DD
+12,1045.233333333,0.01342602927576,2011,1,0.1,DD
+1,1045.6,0.009946442234124,2012,1,0.1,DD
+2,1048.766666667,0.006769856657026,2012,1,0.1,DD
+3,1048.366666667,0.004546755270103,2012,1,0.1,DD
+4,1050.8,0.002157086664129,2012,1,0.1,DD
+5,1050.433333333,0.001808777329991,2012,1,0.1,DD
+6,1050.933333333,0.00250570921086,2012,1,0.1,DD
+7,1047.833333333,0.003403849212661,2012,1,0.1,DD
+8,1047.933333333,0.002767351612698,2012,1,0.1,DD
+9,1051.3,0.005263324772504,2012,1,0.1,DD
+10,1040.1,0.009454219145595,2012,1,0.1,DD
+11,1031.566666667,0.0110511519695,2012,1,0.1,DD
+12,1027.3,0.008663486810085,2012,1,0.1,DD
+1,1022.2,0.005641426987543,2013,1,0.1,DD
+2,1020.6,0.003298713175256,2013,1,0.1,DD
+3,1030.2,0.003494467093768,2013,1,0.1,DD
+4,1014.966666667,0.002561660481461,2013,1,0.1,DD
+5,1011.566666667,0.004119023297196,2013,1,0.1,DD
+6,1018.9,0.0008833055255668,2013,1,0.1,DD
+7,1025.8,0.002729576915578,2013,1,0.1,DD
+8,1021.533333333,0.002675716243555,2013,1,0.1,DD
+9,1016.866666667,0.005441552481479,2013,1,0.1,DD
+10,1021.433333333,0.008941683255556,2013,1,0.1,DD
+11,1022.833333333,0.008473195372332,2013,1,0.1,DD
+12,1020.333333333,0.005227049983665,2013,1,0.1,DD
+1,1016.433333333,0.001902075886269,2014,1,0.1,DD
+2,1017.6,0.0005241090146751,2014,1,0.1,DD
+3,1011.766666667,6.589134517181e-05,2014,1,0.1,DD
+4,1007.066666667,0.0008605851979346,2014,1,0.1,DD
+5,1006.266666667,0.0003975089439512,2014,1,0.1,DD
+6,999.8666666667,0.0005667422322976,2014,1,0.1,DD
+7,1004.0,0.000464807436919,2014,1,0.1,DD
+8,1008.1,0.001190358099395,2014,1,0.1,DD
+9,997.6333333333,0.0004677737311638,2014,1,0.1,DD
+10,987.7,0.000978704734906,2014,1,0.1,DD
+11,983.0666666667,0.0001017225010172,2014,1,0.1,DD
+12,983.4,0.0002033760423022,2014,1,0.1,DD
+1,982.4333333333,0.00115359820853,2015,1,0.1,DD
+2,982.4,0.001696525515744,2015,1,0.1,DD
+3,990.9,0.002018367140983,2015,1,0.1,DD
+4,997.8666666667,0.0009687332977018,2015,1,0.1,DD
+5,1003.866666667,9.961482268562e-05,2015,1,0.1,DD
+6,1007.733333333,3.307753373908e-05,2015,1,0.1,DD
+7,1009.733333333,0.0,2015,1,0.1,DD
+8,1008.6,0.0,2015,1,0.1,DD
+9,1005.766666667,0.0,2015,1,0.1,DD
+10,1011.333333333,0.0,2015,1,0.1,DD
+11,1007.033333333,0.0,2015,1,0.1,DD
+12,1004.033333333,0.0,2015,1,0.1,DD
+1,998.8,0.0,2016,1,0.1,DD
+2,1005.666666667,0.0,2016,1,0.1,DD
+3,1018.6,0.0,2016,1,0.1,DD
+4,1024.033333333,0.0,2016,1,0.1,DD
+5,1024.933333333,0.0,2016,1,0.1,DD
+6,1029.8,0.0,2016,1,0.1,DD
+7,1036.333333333,0.0,2016,1,0.1,DD
+8,1040.2,0.0,2016,1,0.1,DD
+9,1040.133333333,0.0,2016,1,0.1,DD
+10,1036.966666667,0.0,2016,1,0.1,DD
+11,1032.333333333,0.0,2016,1,0.1,DD
+12,1037.1,0.0,2016,1,0.1,DD
+1,1044.3,0.0,2017,1,0.1,DD
+2,1041.5,0.0,2017,1,0.1,DD
+3,1043.266666667,0.0,2017,1,0.1,DD
+4,1045.866666667,0.0,2017,1,0.1,DD
+5,1054.4,0.0,2017,1,0.1,DD
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+8,1063.266666667,0.0,2017,1,0.1,DD
+9,1070.333333333,0.0,2017,1,0.1,DD
+10,1072.533333333,0.0,2017,1,0.1,DD
+11,1070.933333333,0.0,2017,1,0.1,DD
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+1,1001.5,0.2423697786653,2010,1,1.5848931924611125,DD
+2,990.7333333333,0.222966153018,2010,1,1.5848931924611125,DD
+3,987.6,0.2403469690833,2010,1,1.5848931924611125,DD
+4,987.3666666667,0.2652172445225,2010,1,1.5848931924611125,DD
+5,986.0,0.2375591615957,2010,1,1.5848931924611125,DD
+6,994.3,0.2548526601629,2010,1,1.5848931924611125,DD
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+8,994.0,0.4987927565392,2010,1,1.5848931924611125,DD
+9,996.9,0.5993914468185,2010,1,1.5848931924611125,DD
+10,1008.266666667,0.6262893414441,2010,1,1.5848931924611125,DD
+11,1019.7,0.5231930960086,2010,1,1.5848931924611125,DD
+12,1024.066666667,0.3737386888874,2010,1,1.5848931924611125,DD
+1,1025.666666667,0.2510237244069,2011,1,1.5848931924611125,DD
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+3,1018.066666667,0.2213345556938,2011,1,1.5848931924611125,DD
+4,1011.0,0.2330365974283,2011,1,1.5848931924611125,DD
+5,1008.933333333,0.2015660103079,2011,1,1.5848931924611125,DD
+6,1004.3,0.2254638388264,2011,1,1.5848931924611125,DD
+7,998.7,0.3942124762191,2011,1,1.5848931924611125,DD
+8,996.6333333333,0.5204521890364,2011,1,1.5848931924611125,DD
+9,997.9333333333,0.6039481595297,2011,1,1.5848931924611125,DD
+10,997.3333333333,0.620922459893,2011,1,1.5848931924611125,DD
+11,1000.6,0.5122926244253,2011,1,1.5848931924611125,DD
+12,999.8666666667,0.3641818909188,2011,1,1.5848931924611125,DD
+1,997.2666666667,0.2600106959021,2012,1,1.5848931924611125,DD
+2,999.5,0.2489911622478,2012,1,1.5848931924611125,DD
+3,1005.5,0.2479363500746,2012,1,1.5848931924611125,DD
+4,1000.066666667,0.2476834877675,2012,1,1.5848931924611125,DD
+5,989.9,0.2289793581843,2012,1,1.5848931924611125,DD
+6,989.3333333333,0.2342991913747,2012,1,1.5848931924611125,DD
+7,985.2,0.3991406144269,2012,1,1.5848931924611125,DD
+8,984.3,0.5243658776118,2012,1,1.5848931924611125,DD
+9,982.4666666667,0.6034471059239,2012,1,1.5848931924611125,DD
+10,978.3666666667,0.6072024803244,2012,1,1.5848931924611125,DD
+11,976.3333333333,0.5090474564698,2012,1,1.5848931924611125,DD
+12,978.3666666667,0.3566147661068,2012,1,1.5848931924611125,DD
+1,979.1,0.2553024886801,2013,1,1.5848931924611125,DD
+2,980.0666666667,0.2196449221141,2013,1,1.5848931924611125,DD
+3,976.2333333333,0.2282924164305,2013,1,1.5848931924611125,DD
+4,968.4333333333,0.2256221388497,2013,1,1.5848931924611125,DD
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+6,958.5666666667,0.1989428660848,2013,1,1.5848931924611125,DD
+7,962.9,0.3549693633815,2013,1,1.5848931924611125,DD
+8,960.0,0.4654513888889,2013,1,1.5848931924611125,DD
+9,974.0,0.5616358658453,2013,1,1.5848931924611125,DD
+10,980.9,0.5759676487579,2013,1,1.5848931924611125,DD
+11,985.5,0.4940639269406,2013,1,1.5848931924611125,DD
+12,988.8666666667,0.352389941347,2013,1,1.5848931924611125,DD
+1,991.3,0.2343051212213,2014,1,1.5848931924611125,DD
+2,988.0333333333,0.1988124557201,2014,1,1.5848931924611125,DD
+3,979.3333333333,0.1995234853642,2014,1,1.5848931924611125,DD
+4,980.1,0.2116110600959,2014,1,1.5848931924611125,DD
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+7,983.7666666667,0.3096262663911,2014,1,1.5848931924611125,DD
+8,982.7,0.3894711848309,2014,1,1.5848931924611125,DD
+9,983.1333333333,0.4818946226351,2014,1,1.5848931924611125,DD
+10,989.0,0.535187057634,2014,1,1.5848931924611125,DD
+11,991.7666666667,0.4649615164857,2014,1,1.5848931924611125,DD
+12,987.3,0.3344137209224,2014,1,1.5848931924611125,DD
+1,989.9333333333,0.2314633982086,2015,1,1.5848931924611125,DD
+2,987.7333333333,0.1886811555076,2015,1,1.5848931924611125,DD
+3,980.1666666667,0.1791532052372,2015,1,1.5848931924611125,DD
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+5,983.8666666667,0.1354519582599,2015,1,1.5848931924611125,DD
+6,993.6,0.1488526570048,2015,1,1.5848931924611125,DD
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+8,995.6333333333,0.3262245137099,2015,1,1.5848931924611125,DD
+9,1000.266666667,0.4072580645161,2015,1,1.5848931924611125,DD
+10,992.5,0.4784550797649,2015,1,1.5848931924611125,DD
+11,986.0333333333,0.4261181163585,2015,1,1.5848931924611125,DD
+12,980.8,0.30906742795,2015,1,1.5848931924611125,DD
+1,980.4666666667,0.2053443938261,2016,1,1.5848931924611125,DD
+2,991.5333333333,0.1702077590264,2016,1,1.5848931924611125,DD
+3,994.2666666667,0.1667225425774,2016,1,1.5848931924611125,DD
+4,988.9,0.1586274311525,2016,1,1.5848931924611125,DD
+5,983.5,0.1322826639553,2016,1,1.5848931924611125,DD
+6,986.7,0.1470896253505,2016,1,1.5848931924611125,DD
+7,988.7,0.2506658575233,2016,1,1.5848931924611125,DD
+8,990.9,0.3450062232987,2016,1,1.5848931924611125,DD
+9,989.9,0.4458026063239,2016,1,1.5848931924611125,DD
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+9,997.3666666667,0.3776945957689,2011,1,1.5848931924611125,GG
+10,1000.5,0.4191570881226,2011,1,1.5848931924611125,GG
+11,991.2666666667,0.3746048826417,2011,1,1.5848931924611125,GG
+12,985.1333333333,0.2651417743791,2011,1,1.5848931924611125,GG
+1,983.1666666667,0.1783692151212,2012,1,1.5848931924611125,GG
+2,985.9,0.1463299185178,2012,1,1.5848931924611125,GG
+3,986.3,0.150866876204,2012,1,1.5848931924611125,GG
+4,977.5666666667,0.1543969720735,2012,1,1.5848931924611125,GG
+5,972.7333333333,0.1234322527585,2012,1,1.5848931924611125,GG
+6,975.1333333333,0.1354002871402,2012,1,1.5848931924611125,GG
+7,976.7,0.2179447800416,2012,1,1.5848931924611125,GG
+8,980.6,0.2740499014209,2012,1,1.5848931924611125,GG
+9,990.5333333333,0.3579889621753,2012,1,1.5848931924611125,GG
+10,987.4666666667,0.422258979206,2012,1,1.5848931924611125,GG
+11,988.3,0.3849370973726,2012,1,1.5848931924611125,GG
+12,990.5,0.2861181221605,2012,1,1.5848931924611125,GG
+1,988.7333333333,0.1994808172072,2013,1,1.5848931924611125,GG
+2,983.6666666667,0.159742460183,2013,1,1.5848931924611125,GG
+3,972.0333333333,0.1454682624053,2013,1,1.5848931924611125,GG
+4,959.7,0.1591122225696,2013,1,1.5848931924611125,GG
+5,959.1666666667,0.1373066898349,2013,1,1.5848931924611125,GG
+6,957.0333333333,0.1606701264324,2013,1,1.5848931924611125,GG
+7,952.3666666667,0.2350985264779,2013,1,1.5848931924611125,GG
+8,955.1,0.2971765609186,2013,1,1.5848931924611125,GG
+9,956.3333333333,0.3771348902056,2013,1,1.5848931924611125,GG
+10,960.4666666667,0.424411744291,2013,1,1.5848931924611125,GG
+11,962.3333333333,0.3633183235192,2013,1,1.5848931924611125,GG
+12,955.8,0.275197042617,2013,1,1.5848931924611125,GG
+1,958.8,0.1826588791545,2014,1,1.5848931924611125,GG
+2,951.3,0.1536143522898,2014,1,1.5848931924611125,GG
+3,944.9,0.1455885984408,2014,1,1.5848931924611125,GG
+4,943.9,0.1387152593848,2014,1,1.5848931924611125,GG
+5,940.4333333333,0.1257221848084,2014,1,1.5848931924611125,GG
+6,938.0333333333,0.1296684552788,2014,1,1.5848931924611125,GG
+7,938.5,0.2354821523708,2014,1,1.5848931924611125,GG
+8,937.2666666667,0.3115442065581,2014,1,1.5848931924611125,GG
+9,936.7333333333,0.3820724503594,2014,1,1.5848931924611125,GG
+10,936.6666666667,0.4514234875445,2014,1,1.5848931924611125,GG
+11,941.6666666667,0.4025486725664,2014,1,1.5848931924611125,GG
+12,948.7,0.2950704472787,2014,1,1.5848931924611125,GG
+1,947.8,0.1766898783147,2015,1,1.5848931924611125,GG
+2,946.2666666667,0.1431943074539,2015,1,1.5848931924611125,GG
+3,941.8,0.1461739930629,2015,1,1.5848931924611125,GG
+4,931.1333333333,0.1637431087564,2015,1,1.5848931924611125,GG
+5,933.7333333333,0.1364058260745,2015,1,1.5848931924611125,GG
+6,927.7666666667,0.1488520820609,2015,1,1.5848931924611125,GG
+7,918.9,0.2385823629702,2015,1,1.5848931924611125,GG
+8,919.9333333333,0.3096963548083,2015,1,1.5848931924611125,GG
+9,916.3666666667,0.3849259757739,2015,1,1.5848931924611125,GG
+10,916.4666666667,0.4476976794937,2015,1,1.5848931924611125,GG
+11,922.7,0.3878472598533,2015,1,1.5848931924611125,GG
+12,921.3,0.2684250515576,2015,1,1.5848931924611125,GG
+1,925.2,0.1944444444444,2016,1,1.5848931924611125,GG
+2,924.0333333333,0.1635583131922,2016,1,1.5848931924611125,GG
+3,935.1333333333,0.1528837242461,2016,1,1.5848931924611125,GG
+4,945.3,0.1433407383899,2016,1,1.5848931924611125,GG
+5,944.2,0.1180540845866,2016,1,1.5848931924611125,GG
+6,938.3,0.1360616718178,2016,1,1.5848931924611125,GG
+7,945.8,0.2240431380842,2016,1,1.5848931924611125,GG
+8,960.5,0.3039736248482,2016,1,1.5848931924611125,GG
+9,961.1,0.3611833662817,2016,1,1.5848931924611125,GG
+10,960.9,0.4186006174767,2016,1,1.5848931924611125,GG
+11,952.3,0.3913682663026,2016,1,1.5848931924611125,GG
+12,952.5666666667,0.286944045911,2016,1,1.5848931924611125,GG
+1,950.4666666667,0.1767202076173,2017,1,1.5848931924611125,GG
+2,946.2,0.14225322342,2017,1,1.5848931924611125,GG
+3,940.2,0.1379493724739,2017,1,1.5848931924611125,GG
+4,938.0666666667,0.1391869803141,2017,1,1.5848931924611125,GG
+5,943.4666666667,0.1265898812889,2017,1,1.5848931924611125,GG
+6,943.5666666667,0.1315575652665,2017,1,1.5848931924611125,GG
+7,947.0333333333,0.2283270564218,2017,1,1.5848931924611125,GG
+8,943.4666666667,0.2952232899943,2017,1,1.5848931924611125,GG
+9,940.2,0.4007303410622,2017,1,1.5848931924611125,GG
+10,945.1666666667,0.4647152177746,2017,1,1.5848931924611125,GG
+11,949.8666666667,0.3892827063448,2017,1,1.5848931924611125,GG
+12,954.9666666667,0.2699919717966,2017,1,1.5848931924611125,GG
+1,1037.466666667,0.01253052306901,2010,1,0.1,GG
+2,1041.433333333,0.008353871267164,2010,1,0.1,GG
+3,1046.433333333,0.009269582390979,2010,1,0.1,GG
+4,1043.9,0.004693936200786,2010,1,0.1,GG
+5,1040.7,0.003427180423433,2010,1,0.1,GG
+6,1047.566666667,0.004550227511376,2010,1,0.1,GG
+7,1053.133333333,0.00240552003545,2010,1,0.1,GG
+8,1065.0,0.0004381846635368,2010,1,0.1,GG
+9,1066.0,0.002001250781739,2010,1,0.1,GG
+10,1061.166666667,0.003895084027014,2010,1,0.1,GG
+11,1057.2,0.002774624795056,2010,1,0.1,GG
+12,1062.0,0.00172630257376,2010,1,0.1,GG
+1,1060.2,0.001414827391058,2011,1,0.1,GG
+2,1046.933333333,0.0007959755476312,2011,1,0.1,GG
+3,1043.4,0.0002875215641173,2011,1,0.1,GG
+4,1043.466666667,0.0002555583950933,2011,1,0.1,GG
+5,1052.033333333,6.336934824625e-05,2011,1,0.1,GG
+6,1063.266666667,0.0,2011,1,0.1,GG
+7,1060.333333333,0.0,2011,1,0.1,GG
+8,1060.2,0.0,2011,1,0.1,GG
+9,1056.133333333,0.0,2011,1,0.1,GG
+10,1051.866666667,0.0,2011,1,0.1,GG
+11,1061.033333333,0.0,2011,1,0.1,GG
+12,1067.166666667,0.0,2011,1,0.1,GG
+1,1077.1,0.0,2012,1,0.1,GG
+2,1076.733333333,0.0002786205188533,2012,1,0.1,GG
+3,1078.7,0.0,2012,1,0.1,GG
+4,1075.733333333,0.0,2012,1,0.1,GG
+5,1073.066666667,0.0,2012,1,0.1,GG
+6,1077.5,0.0,2012,1,0.1,GG
+7,1085.0,0.0,2012,1,0.1,GG
+8,1090.2,0.0,2012,1,0.1,GG
+9,1096.166666667,0.0,2012,1,0.1,GG
+10,1101.6,0.0,2012,1,0.1,GG
+11,1094.3,0.0,2012,1,0.1,GG
+12,1086.3,0.0,2012,1,0.1,GG
+1,1089.866666667,0.0,2013,1,0.1,GG
+2,1095.3,0.0,2013,1,0.1,GG
+3,1091.666666667,0.0,2013,1,0.1,GG
+4,1083.466666667,0.0,2013,1,0.1,GG
+5,1082.033333333,0.0,2013,1,0.1,GG
+6,1087.966666667,0.0,2013,1,0.1,GG
+7,1097.366666667,0.0,2013,1,0.1,GG
+8,1097.8,0.0,2013,1,0.1,GG
+9,1094.3,0.0,2013,1,0.1,GG
+10,1096.433333333,0.0,2013,1,0.1,GG
+11,1090.7,0.0,2013,1,0.1,GG
+12,1087.933333333,0.0,2013,1,0.1,GG
+1,1087.966666667,0.0,2014,1,0.1,GG
+2,1088.033333333,0.0,2014,1,0.1,GG
+3,1085.1,0.0,2014,1,0.1,GG
+4,1079.7,0.0,2014,1,0.1,GG
+5,1082.866666667,0.0,2014,1,0.1,GG
+6,1082.4,0.0,2014,1,0.1,GG
+7,1076.866666667,0.0,2014,1,0.1,GG
+8,1077.066666667,0.0,2014,1,0.1,GG
+9,1073.7,0.0,2014,1,0.1,GG
+10,1069.466666667,0.0,2014,1,0.1,GG
+11,1065.366666667,0.0,2014,1,0.1,GG
+12,1056.433333333,0.0,2014,1,0.1,GG
+1,1048.966666667,0.0,2015,1,0.1,GG
+2,1054.133333333,0.0,2015,1,0.1,GG
+3,1051.2,0.0,2015,1,0.1,GG
+4,1043.966666667,0.0,2015,1,0.1,GG
+5,1042.866666667,0.0,2015,1,0.1,GG
+6,1041.8,0.0,2015,1,0.1,GG
+7,1050.333333333,0.0,2015,1,0.1,GG
+8,1054.266666667,0.0,2015,1,0.1,GG
+9,1045.4,0.0,2015,1,0.1,GG
+10,1036.133333333,0.0,2015,1,0.1,GG
+11,1047.133333333,0.0,2015,1,0.1,GG
+12,1048.866666667,0.0,2015,1,0.1,GG
+1,1058.466666667,0.0,2016,1,0.1,GG
+2,1054.533333333,0.0,2016,1,0.1,GG
+3,1047.266666667,0.0,2016,1,0.1,GG
+4,1048.866666667,0.0,2016,1,0.1,GG
+5,1046.266666667,0.0,2016,1,0.1,GG
+6,1053.233333333,0.0,2016,1,0.1,GG
+7,1059.366666667,0.0,2016,1,0.1,GG
+8,1052.866666667,0.0,2016,1,0.1,GG
+9,1043.1,0.0,2016,1,0.1,GG
+10,1037.533333333,0.0,2016,1,0.1,GG
+11,1040.166666667,0.0,2016,1,0.1,GG
+12,1039.233333333,0.0,2016,1,0.1,GG
+1,1033.733333333,0.0,2017,1,0.1,GG
+2,1040.833333333,0.0,2017,1,0.1,GG
+3,1036.7,0.0,2017,1,0.1,GG
+4,1028.966666667,0.0,2017,1,0.1,GG
+5,1028.333333333,0.0,2017,1,0.1,GG
+6,1027.366666667,0.0,2017,1,0.1,GG
+7,1026.433333333,0.0,2017,1,0.1,GG
+8,1023.766666667,0.0,2017,1,0.1,GG
+9,1029.133333333,0.0,2017,1,0.1,GG
+10,1027.433333333,0.0,2017,1,0.1,GG
+11,1032.433333333,0.0,2017,1,0.1,GG
+12,1029.866666667,0.0,2017,1,0.1,GG
+1,1043.666666667,0.5681890769722,2010,1,25.118864315095795,GG
+2,1045.333333333,0.5541135204082,2010,1,25.118864315095795,GG
+3,1051.6,0.5710663116521,2010,1,25.118864315095795,GG
+4,1054.866666667,0.5637363331859,2010,1,25.118864315095795,GG
+5,1053.5,0.5440278436956,2010,1,25.118864315095795,GG
+6,1051.3,0.5717999936586,2010,1,25.118864315095795,GG
+7,1048.733333333,0.6184921492594,2010,1,25.118864315095795,GG
+8,1047.5,0.617661097852,2010,1,25.118864315095795,GG
+9,1052.4,0.5777904472317,2010,1,25.118864315095795,GG
+10,1058.133333333,0.5791645665323,2010,1,25.118864315095795,GG
+11,1058.466666667,0.5597719972287,2010,1,25.118864315095795,GG
+12,1055.466666667,0.5077374936837,2010,1,25.118864315095795,GG
+1,1051.933333333,0.4869129856138,2011,1,25.118864315095795,GG
+2,1049.2,0.5541682551785,2011,1,25.118864315095795,GG
+3,1039.533333333,0.5790418777657,2011,1,25.118864315095795,GG
+4,1038.066666667,0.5822683193115,2011,1,25.118864315095795,GG
+5,1030.133333333,0.534655707999,2011,1,25.118864315095795,GG
+6,1029.966666667,0.5547752354445,2011,1,25.118864315095795,GG
+7,1028.9,0.6247448731655,2011,1,25.118864315095795,GG
+8,1023.066666667,0.605467222729,2011,1,25.118864315095795,GG
+9,1025.666666667,0.5828729281768,2011,1,25.118864315095795,GG
+10,1024.966666667,0.5643110345052,2011,1,25.118864315095795,GG
+11,1020.933333333,0.5593901005616,2011,1,25.118864315095795,GG
+12,1012.7,0.5120963760245,2011,1,25.118864315095795,GG
+1,1000.333333333,0.5104965011663,2012,1,25.118864315095795,GG
+2,996.4333333333,0.5667882112869,2012,1,25.118864315095795,GG
+3,997.5666666667,0.5763691649681,2012,1,25.118864315095795,GG
+4,995.0,0.5639195979899,2012,1,25.118864315095795,GG
+5,995.2666666667,0.5256212740304,2012,1,25.118864315095795,GG
+6,992.2666666667,0.5511623219565,2012,1,25.118864315095795,GG
+7,993.9,0.6199148136969,2012,1,25.118864315095795,GG
+8,984.7333333333,0.6134655744364,2012,1,25.118864315095795,GG
+9,982.5,0.5742154368109,2012,1,25.118864315095795,GG
+10,988.4666666667,0.5686922506239,2012,1,25.118864315095795,GG
+11,998.2666666667,0.5417724055029,2012,1,25.118864315095795,GG
+12,997.8,0.5096211665664,2012,1,25.118864315095795,GG
+1,993.4,0.5044963425273,2013,1,25.118864315095795,GG
+2,988.8,0.5539037216828,2013,1,25.118864315095795,GG
+3,983.2666666667,0.569258932809,2013,1,25.118864315095795,GG
+4,975.5666666667,0.5846516554481,2013,1,25.118864315095795,GG
+5,978.2,0.5401076807742,2013,1,25.118864315095795,GG
+6,979.8333333333,0.5591086919544,2013,1,25.118864315095795,GG
+7,975.7333333333,0.5936048100574,2013,1,25.118864315095795,GG
+8,969.9333333333,0.60718949756,2013,1,25.118864315095795,GG
+9,970.3666666667,0.5777541135653,2013,1,25.118864315095795,GG
+10,972.1666666667,0.5691753814504,2013,1,25.118864315095795,GG
+11,968.5,0.5633109619687,2013,1,25.118864315095795,GG
+12,955.1,0.5373957351761,2013,1,25.118864315095795,GG
+1,941.7,0.5169374535415,2014,1,25.118864315095795,GG
+2,934.7,0.5383545522628,2014,1,25.118864315095795,GG
+3,938.5333333333,0.6045247904532,2014,1,25.118864315095795,GG
+4,936.0,0.594943019943,2014,1,25.118864315095795,GG
+5,934.2,0.5313994148291,2014,1,25.118864315095795,GG
+6,938.7,0.5552359646319,2014,1,25.118864315095795,GG
+7,948.2,0.6339028334388,2014,1,25.118864315095795,GG
+8,949.1333333333,0.618599424036,2014,1,25.118864315095795,GG
+9,950.0666666667,0.5871166935654,2014,1,25.118864315095795,GG
+10,942.8,0.5786663838212,2014,1,25.118864315095795,GG
+11,945.9333333333,0.5570159983085,2014,1,25.118864315095795,GG
+12,941.6666666667,0.5239646017699,2014,1,25.118864315095795,GG
+1,941.3666666667,0.516660174923,2015,1,25.118864315095795,GG
+2,946.4666666667,0.541205888568,2015,1,25.118864315095795,GG
+3,941.6666666667,0.5820530973451,2015,1,25.118864315095795,GG
+4,936.8333333333,0.5891122576054,2015,1,25.118864315095795,GG
+5,933.0666666667,0.5232209202629,2015,1,25.118864315095795,GG
+6,930.5333333333,0.5411950136123,2015,1,25.118864315095795,GG
+7,926.1666666667,0.6104012956631,2015,1,25.118864315095795,GG
+8,929.7666666667,0.6118380955795,2015,1,25.118864315095795,GG
+9,927.7333333333,0.5833213567117,2015,1,25.118864315095795,GG
+10,924.5,0.5724896340364,2015,1,25.118864315095795,GG
+11,919.6666666667,0.5565784704603,2015,1,25.118864315095795,GG
+12,923.6666666667,0.5130999639119,2015,1,25.118864315095795,GG
+1,928.7666666667,0.509277536518,2016,1,25.118864315095795,GG
+2,937.1333333333,0.552073699936,2016,1,25.118864315095795,GG
+3,943.8666666667,0.5720087582992,2016,1,25.118864315095795,GG
+4,950.7,0.5617965709477,2016,1,25.118864315095795,GG
+5,954.6666666667,0.5189245810056,2016,1,25.118864315095795,GG
+6,950.8666666667,0.5670265722499,2016,1,25.118864315095795,GG
+7,953.1,0.6207813101109,2016,1,25.118864315095795,GG
+8,962.3666666667,0.6144920508469,2016,1,25.118864315095795,GG
+9,965.7333333333,0.5779028027061,2016,1,25.118864315095795,GG
+10,976.1,0.5789365843664,2016,1,25.118864315095795,GG
+11,975.1666666667,0.5401469834216,2016,1,25.118864315095795,GG
+12,976.2666666667,0.5167986888828,2016,1,25.118864315095795,GG
+1,980.1333333333,0.498809685757,2017,1,25.118864315095795,GG
+2,969.6666666667,0.5372980405638,2017,1,25.118864315095795,GG
+3,974.4,0.5853174603175,2017,1,25.118864315095795,GG
+4,974.0333333333,0.5872488963417,2017,1,25.118864315095795,GG
+5,974.0333333333,0.5384825981315,2017,1,25.118864315095795,GG
+6,978.8666666667,0.5466866444187,2017,1,25.118864315095795,GG
+7,978.1,0.6305762873598,2017,1,25.118864315095795,GG
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+1,997.3,0.1283131120693,2010,2,0.39810717055349715,BB
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+3,1002.366666667,0.08293704898407,2012,2,0.39810717055349715,BB
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+3,1013.133333333,0.07817332368231,2013,2,0.39810717055349715,BB
+4,1014.9,0.06575360462443,2013,2,0.39810717055349715,BB
+5,1008.566666667,0.05473113659649,2013,2,0.39810717055349715,BB
+6,1006.733333333,0.04830805906894,2013,2,0.39810717055349715,BB
+7,1008.066666667,0.06811718801667,2013,2,0.39810717055349715,BB
+8,1013.833333333,0.1032385336183,2013,2,0.39810717055349715,BB
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+10,1014.9,0.1762078365685,2013,2,0.39810717055349715,BB
+11,1007.1,0.165557872439,2013,2,0.39810717055349715,BB
+12,1005.966666667,0.1177971437092,2013,2,0.39810717055349715,BB
+1,1001.933333333,0.07595315722936,2014,2,0.39810717055349715,BB
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+4,1003.366666667,0.05837015381549,2014,2,0.39810717055349715,BB
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+6,1011.733333333,0.0385147601476,2014,2,0.39810717055349715,BB
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+9,1022.933333333,0.1024830552659,2014,2,0.39810717055349715,BB
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+11,1006.033333333,0.1505251648388,2014,2,0.39810717055349715,BB
+12,1012.333333333,0.1234112611129,2014,2,0.39810717055349715,BB
+1,1020.2,0.08857740312357,2015,2,0.39810717055349715,BB
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+3,1020.033333333,0.06303715564851,2015,2,0.39810717055349715,BB
+4,1012.9,0.04863922071939,2015,2,0.39810717055349715,BB
+5,1016.0,0.03464566929134,2015,2,0.39810717055349715,BB
+6,1025.166666667,0.03885547065518,2015,2,0.39810717055349715,BB
+7,1025.766666667,0.04832158060638,2015,2,0.39810717055349715,BB
+8,1032.9,0.05750798722045,2015,2,0.39810717055349715,BB
+9,1035.466666667,0.07043523049189,2015,2,0.39810717055349715,BB
+10,1039.8,0.1035134961852,2015,2,0.39810717055349715,BB
+11,1040.433333333,0.1031621439785,2015,2,0.39810717055349715,BB
+12,1049.533333333,0.08565711744902,2015,2,0.39810717055349715,BB
+1,1060.7,0.06260016969926,2016,2,0.39810717055349715,BB
+2,1063.166666667,0.04790719548519,2016,2,0.39810717055349715,BB
+3,1060.833333333,0.04053417124902,2016,2,0.39810717055349715,BB
+4,1059.033333333,0.03490604639451,2016,2,0.39810717055349715,BB
+5,1048.466666667,0.02635594836905,2016,2,0.39810717055349715,BB
+6,1043.7,0.03177796940372,2016,2,0.39810717055349715,BB
+7,1036.0,0.04028314028314,2016,2,0.39810717055349715,BB
+8,1028.166666667,0.06033392770303,2016,2,0.39810717055349715,BB
+9,1028.233333333,0.07439945537654,2016,2,0.39810717055349715,BB
+10,1016.866666667,0.08536025699862,2016,2,0.39810717055349715,BB
+11,1008.1,0.07419898819562,2016,2,0.39810717055349715,BB
+12,1001.266666667,0.05839270257674,2016,2,0.39810717055349715,BB
+1,1003.866666667,0.03596095098951,2017,2,0.39810717055349715,BB
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+3,993.3333333333,0.02805369127517,2017,2,0.39810717055349715,BB
+4,991.4666666667,0.02551775147929,2017,2,0.39810717055349715,BB
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+6,990.4333333333,0.01639013226534,2017,2,0.39810717055349715,BB
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+8,996.3333333333,0.03154901304784,2017,2,0.39810717055349715,BB
+9,1003.433333333,0.05338338371591,2017,2,0.39810717055349715,BB
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+11,1016.3,0.04759093443537,2017,2,0.39810717055349715,BB
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+1,999.8666666667,0.2511001466862,2010,2,1.5848931924611125,BB
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+3,1007.233333333,0.2540622828209,2012,2,1.5848931924611125,BB
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+3,1043.3,0.2067797693217,2013,2,1.5848931924611125,BB
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+1,1050.0,0.2186031746032,2014,2,1.5848931924611125,BB
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+8,1038.366666667,0.7176976662065,2012,2,25.118864315095795,BB
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+1,1043.6,0.6173501980325,2013,2,25.118864315095795,BB
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+5,985.6333333333,0.0001014576076296,2011,2,0.1,CC
+6,982.3333333333,0.0001357312521208,2011,2,0.1,CC
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+9,1007.1,0.0006288683679211,2011,2,0.1,CC
+10,1011.033333333,0.001120965348983,2011,2,0.1,CC
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+1,1024.466666667,0.0004880588273573,2012,2,0.1,CC
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+3,1010.233333333,0.0002969610980961,2012,2,0.1,CC
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+7,1004.666666667,0.0,2012,2,0.1,CC
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+11,1013.5,0.0008551225127446,2012,2,0.1,CC
+12,1019.033333333,0.001243008079553,2012,2,0.1,CC
+1,1024.066666667,0.0002929496777554,2013,2,0.1,CC
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+3,1037.366666667,0.0,2013,2,0.1,CC
+4,1043.466666667,0.0,2013,2,0.1,CC
+5,1038.033333333,0.0,2013,2,0.1,CC
+6,1029.033333333,0.0,2013,2,0.1,CC
+7,1026.8,0.0,2013,2,0.1,CC
+8,1019.533333333,0.0,2013,2,0.1,CC
+9,1019.133333333,0.0,2013,2,0.1,CC
+10,1017.7,0.0,2013,2,0.1,CC
+11,1013.833333333,0.0,2013,2,0.1,CC
+12,1019.0,0.0,2013,2,0.1,CC
+1,1021.366666667,0.0,2014,2,0.1,CC
+2,1018.833333333,0.0,2014,2,0.1,CC
+3,1017.066666667,0.0,2014,2,0.1,CC
+4,1020.966666667,0.0,2014,2,0.1,CC
+5,1027.733333333,0.0,2014,2,0.1,CC
+6,1021.033333333,0.0,2014,2,0.1,CC
+7,1019.066666667,0.0,2014,2,0.1,CC
+8,1014.6,0.0,2014,2,0.1,CC
+9,1016.233333333,0.0,2014,2,0.1,CC
+10,1017.066666667,0.0,2014,2,0.1,CC
+11,1013.2,0.0,2014,2,0.1,CC
+12,1018.3,0.0,2014,2,0.1,CC
+1,1018.033333333,0.0,2015,2,0.1,CC
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+3,1016.033333333,0.0,2015,2,0.1,CC
+4,1013.7,0.0,2015,2,0.1,CC
+5,1003.766666667,0.0,2015,2,0.1,CC
+6,1004.533333333,0.0,2015,2,0.1,CC
+7,998.7666666667,0.0,2015,2,0.1,CC
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+9,994.8,0.0,2015,2,0.1,CC
+10,1001.4,0.0,2015,2,0.1,CC
+11,1002.666666667,0.0,2015,2,0.1,CC
+12,1003.8,0.0,2015,2,0.1,CC
+1,1004.433333333,0.0,2016,2,0.1,CC
+2,1011.1,0.0,2016,2,0.1,CC
+3,1013.266666667,0.0,2016,2,0.1,CC
+4,1010.966666667,0.0,2016,2,0.1,CC
+5,1011.233333333,0.0,2016,2,0.1,CC
+6,1010.533333333,0.0,2016,2,0.1,CC
+7,1016.833333333,0.0,2016,2,0.1,CC
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+10,1028.366666667,0.0,2016,2,0.1,CC
+11,1035.566666667,0.0,2016,2,0.1,CC
+12,1033.933333333,0.0,2016,2,0.1,CC
+1,1040.466666667,0.0,2017,2,0.1,CC
+2,1043.566666667,0.0,2017,2,0.1,CC
+3,1036.033333333,0.0,2017,2,0.1,CC
+4,1029.2,0.0,2017,2,0.1,CC
+5,1021.533333333,0.0,2017,2,0.1,CC
+6,1021.233333333,0.0,2017,2,0.1,CC
+7,1024.7,0.0,2017,2,0.1,CC
+8,1028.0,0.0,2017,2,0.1,CC
+9,1034.466666667,0.0,2017,2,0.1,CC
+10,1036.033333333,0.0,2017,2,0.1,CC
+11,1033.366666667,0.0,2017,2,0.1,CC
+12,1034.766666667,0.0,2017,2,0.1,CC
+1,998.7,0.1277327192016,2010,2,0.39810717055349715,CC
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+12,984.6,0.202146387704,2012,2,0.39810717055349715,CC
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+6,998.5333333333,0.05474696221124,2013,2,0.39810717055349715,CC
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+1,999.8666666667,0.1050473396453,2015,2,0.39810717055349715,CC
+2,991.4333333333,0.06714184850217,2015,2,0.39810717055349715,CC
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+12,991.8,0.06711702628218,2015,2,0.39810717055349715,CC
+1,991.1666666667,0.04422397847654,2016,2,0.39810717055349715,CC
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+1,999.5333333333,0.2482158340559,2010,2,1.5848931924611125,CC
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+3,980.4,0.234836121311,2013,2,1.5848931924611125,CC
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+5,992.7666666667,0.2055535036766,2013,2,1.5848931924611125,CC
+6,998.4,0.2373464209402,2013,2,1.5848931924611125,CC
+7,1001.366666667,0.3863053826437,2013,2,1.5848931924611125,CC
+8,997.1333333333,0.4845557264157,2013,2,1.5848931924611125,CC
+9,999.0666666667,0.5607900707327,2013,2,1.5848931924611125,CC
+10,996.2,0.5827477748779,2013,2,1.5848931924611125,CC
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+12,980.6333333333,0.36354056902,2013,2,1.5848931924611125,CC
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+3,980.9,0.2244197505692,2014,2,1.5848931924611125,CC
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+10,988.2666666667,0.5824001618996,2014,2,1.5848931924611125,CC
+11,991.1333333333,0.4839577587946,2014,2,1.5848931924611125,CC
+12,987.1,0.3506905750853,2014,2,1.5848931924611125,CC
+1,979.9,0.2302956083954,2015,2,1.5848931924611125,CC
+2,980.0666666667,0.1786613155568,2015,2,1.5848931924611125,CC
+3,975.4,0.1689904996241,2015,2,1.5848931924611125,CC
+4,973.6666666667,0.16857240671,2015,2,1.5848931924611125,CC
+5,981.3666666667,0.1407221222105,2015,2,1.5848931924611125,CC
+6,989.4666666667,0.1253874140951,2015,2,1.5848931924611125,CC
+7,987.7,0.2147750666532,2015,2,1.5848931924611125,CC
+8,973.0333333333,0.2801890993799,2015,2,1.5848931924611125,CC
+9,971.9333333333,0.3730022635297,2015,2,1.5848931924611125,CC
+10,966.1,0.4508504985681,2015,2,1.5848931924611125,CC
+11,972.9666666667,0.436911165165,2015,2,1.5848931924611125,CC
+12,973.4,0.317272789535,2015,2,1.5848931924611125,CC
+1,972.4666666667,0.2131692603003,2016,2,1.5848931924611125,CC
+2,974.9333333333,0.1588826586433,2016,2,1.5848931924611125,CC
+3,975.2333333333,0.1544587620057,2016,2,1.5848931924611125,CC
+4,974.6333333333,0.1472006566572,2016,2,1.5848931924611125,CC
+5,970.7,0.1252704234058,2016,2,1.5848931924611125,CC
+6,968.6333333333,0.1323170102206,2016,2,1.5848931924611125,CC
+7,982.3666666667,0.2407790709511,2016,2,1.5848931924611125,CC
+8,991.8333333333,0.3174256427491,2016,2,1.5848931924611125,CC
+9,992.7666666667,0.3991874559312,2016,2,1.5848931924611125,CC
+10,992.9666666667,0.4800429688811,2016,2,1.5848931924611125,CC
+11,994.8333333333,0.444127994639,2016,2,1.5848931924611125,CC
+12,996.2333333333,0.3235520460401,2016,2,1.5848931924611125,CC
+1,1002.433333333,0.199248495328,2017,2,1.5848931924611125,CC
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+3,1014.533333333,0.1569522933368,2017,2,1.5848931924611125,CC
+4,1013.566666667,0.1609168941362,2017,2,1.5848931924611125,CC
+5,1011.4,0.1324237031178,2017,2,1.5848931924611125,CC
+6,1015.3,0.1444564824847,2017,2,1.5848931924611125,CC
+7,1028.8,0.2610484707102,2017,2,1.5848931924611125,CC
+8,1036.666666667,0.3324437299035,2017,2,1.5848931924611125,CC
+9,1039.533333333,0.4246777400115,2017,2,1.5848931924611125,CC
+10,1039.266666667,0.4684072102123,2017,2,1.5848931924611125,CC
+11,1039.7,0.4236799076657,2017,2,1.5848931924611125,CC
+12,1042.266666667,0.2983241652808,2017,2,1.5848931924611125,CC
+1,1029.166666667,0.614995951417,2010,2,25.118864315095795,CC
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+11,1000.533333333,0.6356276652452,2014,2,25.118864315095795,CC
+12,999.5666666667,0.6072297995798,2014,2,25.118864315095795,CC
+1,1001.666666667,0.5515141430948,2015,2,25.118864315095795,CC
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+5,989.0666666667,0.5699312483149,2015,2,25.118864315095795,CC
+6,984.4,0.607476635514,2015,2,25.118864315095795,CC
+7,987.2,0.7096164235548,2015,2,25.118864315095795,CC
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+9,1002.166666667,0.6214867786463,2015,2,25.118864315095795,CC
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+12,1000.3,0.5765270418874,2015,2,25.118864315095795,CC
+1,998.6333333333,0.5522547481558,2016,2,25.118864315095795,CC
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+1,1008.4,0.5368570673013,2017,2,25.118864315095795,CC
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+11,1023.133333333,0.587411220434,2017,2,25.118864315095795,CC
+12,1021.966666667,0.5403307348576,2017,2,25.118864315095795,CC
+1,999.5666666667,0.1275886217361,2010,2,0.39810717055349715,DD
+2,1001.633333333,0.1009351392725,2010,2,0.39810717055349715,DD
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+10,974.9,0.251957465723,2010,2,0.39810717055349715,DD
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+12,970.7333333333,0.1827140993064,2010,2,0.39810717055349715,DD
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+3,976.4666666667,0.09356864887008,2011,2,0.39810717055349715,DD
+4,978.6,0.09442060085837,2011,2,0.39810717055349715,DD
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+1,981.9333333333,0.1215289564804,2013,2,0.39810717055349715,DD
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+11,1038.4,0.1692026194145,2013,2,0.39810717055349715,DD
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+5,1024.433333333,0.04386164708945,2014,2,0.39810717055349715,DD
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+11,1015.7,0.135308982311,2014,2,0.39810717055349715,DD
+12,1024.0,0.1206380208333,2014,2,0.39810717055349715,DD
+1,1029.133333333,0.07844788495174,2015,2,0.39810717055349715,DD
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+3,1036.533333333,0.04839850784667,2015,2,0.39810717055349715,DD
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+6,1050.033333333,0.02923716707406,2015,2,0.39810717055349715,DD
+7,1050.133333333,0.03716988318944,2015,2,0.39810717055349715,DD
+8,1043.966666667,0.04450972253265,2015,2,0.39810717055349715,DD
+9,1042.133333333,0.06525076765609,2015,2,0.39810717055349715,DD
+10,1049.4,0.08913029667747,2015,2,0.39810717055349715,DD
+11,1045.366666667,0.08909154682568,2015,2,0.39810717055349715,DD
+12,1049.166666667,0.07625099285147,2015,2,0.39810717055349715,DD
+1,1050.233333333,0.0492906338274,2016,2,0.39810717055349715,DD
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+3,1055.1,0.03111869333081,2016,2,0.39810717055349715,DD
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+6,1050.4,0.02186468646865,2016,2,0.39810717055349715,DD
+7,1046.733333333,0.03964715623209,2016,2,0.39810717055349715,DD
+8,1040.466666667,0.04491574293586,2016,2,0.39810717055349715,DD
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+1,1039.033333333,0.03169612781111,2017,2,0.39810717055349715,DD
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+12,1029.133333333,0.05571030640669,2017,2,0.39810717055349715,DD
+1,993.7333333333,0.0016436334362,2010,2,0.025118864315095794,EE
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+6,1009.833333333,0.1501898002971,2015,2,1.5848931924611125,DD
+7,1006.066666667,0.2475316413757,2015,2,1.5848931924611125,DD
+8,1013.066666667,0.3225190839695,2015,2,1.5848931924611125,DD
+9,1004.866666667,0.4019107012539,2015,2,1.5848931924611125,DD
+10,1013.3,0.4691601697424,2015,2,1.5848931924611125,DD
+11,1022.433333333,0.4210217455091,2015,2,1.5848931924611125,DD
+12,1021.2,0.307122339731,2015,2,1.5848931924611125,DD
+1,1005.9,0.194717831461,2016,2,1.5848931924611125,DD
+2,1008.0,0.1501653439153,2016,2,1.5848931924611125,DD
+3,1004.6,0.150142677019,2016,2,1.5848931924611125,DD
+4,1011.9,0.1596336923938,2016,2,1.5848931924611125,DD
+5,1013.433333333,0.1313686149393,2016,2,1.5848931924611125,DD
+6,1012.833333333,0.1307553068948,2016,2,1.5848931924611125,DD
+7,1015.9,0.2450044295698,2016,2,1.5848931924611125,DD
+8,1020.633333333,0.318462392632,2016,2,1.5848931924611125,DD
+9,1021.366666667,0.4113769132861,2016,2,1.5848931924611125,DD
+10,1024.633333333,0.4613357623865,2016,2,1.5848931924611125,DD
+11,1020.433333333,0.4261914872766,2016,2,1.5848931924611125,DD
+12,1021.966666667,0.3069571740761,2016,2,1.5848931924611125,DD
+1,1033.566666667,0.1874737962396,2017,2,1.5848931924611125,DD
+2,1033.4,0.1549577446616,2017,2,1.5848931924611125,DD
+3,1037.266666667,0.1431004563275,2017,2,1.5848931924611125,DD
+4,1034.7,0.1470957765536,2017,2,1.5848931924611125,DD
+5,1035.3,0.1416336649602,2017,2,1.5848931924611125,DD
+6,1040.066666667,0.1401512723543,2017,2,1.5848931924611125,DD
+7,1037.1,0.2280718670652,2017,2,1.5848931924611125,DD
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+9,1020.233333333,0.3724964877316,2017,2,1.5848931924611125,DD
+10,1023.666666667,0.4228915662651,2017,2,1.5848931924611125,DD
+11,1020.033333333,0.3649553936146,2017,2,1.5848931924611125,DD
+12,1023.8,0.2807514488507,2017,2,1.5848931924611125,DD
+1,1019.3,0.006965564603159,2010,2,0.1,DD
+2,1021.233333333,0.005255083722297,2010,2,0.1,DD
+3,1019.6,0.004838498757683,2010,2,0.1,DD
+4,1010.766666667,0.004122283415229,2010,2,0.1,DD
+5,1011.666666667,0.002240527182867,2010,2,0.1,DD
+6,1007.966666667,0.002513310625351,2010,2,0.1,DD
+7,999.5,0.0064032016008,2010,2,0.1,DD
+8,995.9666666667,0.00478597007932,2010,2,0.1,DD
+9,996.8333333333,0.00805885303461,2010,2,0.1,DD
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+11,995.2,0.01256028938907,2010,2,0.1,DD
+12,997.1,0.008056697756828,2010,2,0.1,DD
+1,994.9333333333,0.005561511659073,2011,2,0.1,DD
+2,998.5666666667,0.004272791000434,2011,2,0.1,DD
+3,1005.766666667,0.005733602890001,2011,2,0.1,DD
+4,1008.066666667,0.004298657496197,2011,2,0.1,DD
+5,1011.366666667,0.003164035463564,2011,2,0.1,DD
+6,1009.1,0.002741717041588,2011,2,0.1,DD
+7,1007.166666667,0.003607479728612,2011,2,0.1,DD
+8,1003.333333333,0.003023255813953,2011,2,0.1,DD
+9,1010.8,0.004715736710197,2011,2,0.1,DD
+10,1016.433333333,0.005771816482471,2011,2,0.1,DD
+11,1022.3,0.006358211875183,2011,2,0.1,DD
+12,1021.033333333,0.00424406646861,2011,2,0.1,DD
+1,1017.1,0.004031068724806,2012,2,0.1,DD
+2,1011.933333333,0.00227287700112,2012,2,0.1,DD
+3,1005.966666667,0.002087544318897,2012,2,0.1,DD
+4,1004.3,0.0009293371834445,2012,2,0.1,DD
+5,1003.533333333,0.001228990898824,2012,2,0.1,DD
+6,1001.233333333,0.001298398641675,2012,2,0.1,DD
+7,996.9333333333,0.002039588070082,2012,2,0.1,DD
+8,996.5333333333,0.002174203906877,2012,2,0.1,DD
+9,994.3666666667,0.006637390633904,2012,2,0.1,DD
+10,995.9666666667,0.01087720472573,2012,2,0.1,DD
+11,986.7666666667,0.01094483667196,2012,2,0.1,DD
+12,984.6,0.004367255738371,2012,2,0.1,DD
+1,982.5,0.005326547921968,2013,2,0.1,DD
+2,988.9333333333,0.004415531886207,2013,2,0.1,DD
+3,991.7666666667,0.004402917352872,2013,2,0.1,DD
+4,987.0,0.005200945626478,2013,2,0.1,DD
+5,985.8,0.00344897545141,2013,2,0.1,DD
+6,986.1333333333,0.002264737696052,2013,2,0.1,DD
+7,999.7333333333,0.003934382502001,2013,2,0.1,DD
+8,1004.433333333,0.004612882885873,2013,2,0.1,DD
+9,996.1,0.007897466787136,2013,2,0.1,DD
+10,988.7666666667,0.008090887637798,2013,2,0.1,DD
+11,982.9666666667,0.005222286276239,2013,2,0.1,DD
+12,982.9333333333,0.004510309278351,2013,2,0.1,DD
+1,992.6333333333,0.004600557439807,2014,2,0.1,DD
+2,995.7666666667,0.002878853814481,2014,2,0.1,DD
+3,1002.2,0.002095390141688,2014,2,0.1,DD
+4,1002.466666667,0.0007980315222451,2014,2,0.1,DD
+5,1002.6,0.0003989626969878,2014,2,0.1,DD
+6,997.6333333333,0.0001670620468442,2014,2,0.1,DD
+7,992.6,0.0007052186177715,2014,2,0.1,DD
+8,991.7666666667,0.002319093872887,2014,2,0.1,DD
+9,995.9666666667,0.00167341611165,2014,2,0.1,DD
+10,992.8666666667,0.003155845027865,2014,2,0.1,DD
+11,995.2333333333,0.004186622902502,2014,2,0.1,DD
+12,1002.7,0.00232705029753,2014,2,0.1,DD
+1,1010.4,0.001946423858538,2015,2,0.1,DD
+2,1016.6,0.00114761623713,2015,2,0.1,DD
+3,1017.333333333,0.0009174311926606,2015,2,0.1,DD
+4,1013.033333333,0.001908459741371,2015,2,0.1,DD
+5,1017.366666667,0.0005242292192261,2015,2,0.1,DD
+6,1008.033333333,0.0002645415164842,2015,2,0.1,DD
+7,1005.833333333,0.0,2015,2,0.1,DD
+8,1010.266666667,0.0,2015,2,0.1,DD
+9,1010.366666667,0.0,2015,2,0.1,DD
+10,1021.766666667,0.0004567252797442,2015,2,0.1,DD
+11,1016.266666667,0.0008855943321963,2015,2,0.1,DD
+12,1012.233333333,0.0009220535449666,2015,2,0.1,DD
+1,1011.466666667,0.0003295544423939,2016,2,0.1,DD
+2,1013.633333333,0.0,2016,2,0.1,DD
+3,1007.833333333,0.0,2016,2,0.1,DD
+4,1009.366666667,3.302400845415e-05,2016,2,0.1,DD
+5,1010.933333333,0.0,2016,2,0.1,DD
+6,1004.933333333,0.0,2016,2,0.1,DD
+7,994.9666666667,0.0009715568360749,2016,2,0.1,DD
+8,1000.166666667,0.0002332944509248,2016,2,0.1,DD
+9,1013.866666667,0.001117832719621,2016,2,0.1,DD
+10,1017.8,0.003078535403157,2016,2,0.1,DD
+11,1019.8,0.001144015166372,2016,2,0.1,DD
+12,1025.733333333,0.001787339139477,2016,2,0.1,DD
+1,1030.666666667,0.001228978007762,2017,2,0.1,DD
+2,1034.066666667,0.0004190574431049,2017,2,0.1,DD
+3,1032.6,0.00038737168313,2017,2,0.1,DD
+4,1030.933333333,0.0001293326435592,2017,2,0.1,DD
+5,1039.6,0.0,2017,2,0.1,DD
+6,1047.566666667,0.0,2017,2,0.1,DD
+7,1047.0,0.0,2017,2,0.1,DD
+8,1056.633333333,0.0,2017,2,0.1,DD
+9,1059.266666667,0.0,2017,2,0.1,DD
+10,1060.133333333,0.0,2017,2,0.1,DD
+11,1058.266666667,0.0,2017,2,0.1,DD
+12,1057.833333333,0.0,2017,2,0.1,DD
+1,1018.8,0.006707237272608,2010,2,0.1,EE
+2,1018.533333333,0.005825369812803,2010,2,0.1,EE
+3,1021.233333333,0.005809968338937,2010,2,0.1,EE
+4,1016.4,0.003279548734094,2010,2,0.1,EE
+5,1018.733333333,0.002126824160722,2010,2,0.1,EE
+6,1012.166666667,0.002107689774411,2010,2,0.1,EE
+7,1001.6,0.004825612353568,2010,2,0.1,EE
+8,998.2666666667,0.007579804995325,2010,2,0.1,EE
+9,990.4666666667,0.01113953018779,2010,2,0.1,EE
+10,997.1,0.01213519205697,2010,2,0.1,EE
+11,995.4333333333,0.008237618457623,2010,2,0.1,EE
+12,1000.833333333,0.004429641965029,2010,2,0.1,EE
+1,1010.833333333,0.005375103050289,2011,2,0.1,EE
+2,1006.233333333,0.007751681187266,2011,2,0.1,EE
+3,1004.266666667,0.004812798725438,2011,2,0.1,EE
+4,1007.466666667,0.004102699841186,2011,2,0.1,EE
+5,1010.7,0.003825731341315,2011,2,0.1,EE
+6,1010.466666667,0.003727650590486,2011,2,0.1,EE
+7,1016.3,0.004985404572141,2011,2,0.1,EE
+8,1018.6,0.005432292689312,2011,2,0.1,EE
+9,1023.733333333,0.01045194060953,2011,2,0.1,EE
+10,1027.466666667,0.01699974046198,2011,2,0.1,EE
+11,1033.966666667,0.01589348463845,2011,2,0.1,EE
+12,1042.966666667,0.01476557256544,2011,2,0.1,EE
+1,1052.0,0.01058301647655,2012,2,0.1,EE
+2,1047.266666667,0.01164937297091,2012,2,0.1,EE
+3,1044.766666667,0.007976262642376,2012,2,0.1,EE
+4,1041.966666667,0.007005982277104,2012,2,0.1,EE
+5,1038.433333333,0.004879144865663,2012,2,0.1,EE
+6,1039.833333333,0.004199390928033,2012,2,0.1,EE
+7,1036.166666667,0.006080102943542,2012,2,0.1,EE
+8,1029.1,0.006834450814628,2012,2,0.1,EE
+9,1023.2,0.01006645817045,2012,2,0.1,EE
+10,1024.566666667,0.009011940007157,2012,2,0.1,EE
+11,1024.9,0.01170845936189,2012,2,0.1,EE
+12,1020.866666667,0.01113433030758,2012,2,0.1,EE
+1,1019.933333333,0.006078828681613,2013,2,0.1,EE
+2,1019.766666667,0.002811100578564,2013,2,0.1,EE
+3,1010.366666667,0.003035201741942,2013,2,0.1,EE
+4,1007.666666667,0.002381739993384,2013,2,0.1,EE
+5,1010.466666667,0.002770996899123,2013,2,0.1,EE
+6,1005.7,0.003778462762255,2013,2,0.1,EE
+7,1010.766666667,0.003528674603436,2013,2,0.1,EE
+8,1008.266666667,0.006413647183285,2013,2,0.1,EE
+9,996.2333333333,0.01462174189447,2013,2,0.1,EE
+10,990.9,0.01113465872776,2013,2,0.1,EE
+11,990.5333333333,0.01406649616368,2013,2,0.1,EE
+12,992.0333333333,0.01293639326636,2013,2,0.1,EE
+1,994.1,0.007946886631124,2014,2,0.1,EE
+2,993.5333333333,0.004764141448031,2014,2,0.1,EE
+3,988.7,0.004517716867267,2014,2,0.1,EE
+4,986.7333333333,0.003614620633741,2014,2,0.1,EE
+5,990.9,0.001446496451038,2014,2,0.1,EE
+6,991.9666666667,0.001747370543365,2014,2,0.1,EE
+7,995.4666666667,0.001741226895259,2014,2,0.1,EE
+8,1000.466666667,0.003331778503365,2014,2,0.1,EE
+9,996.6666666667,0.004548494983278,2014,2,0.1,EE
+10,998.8333333333,0.005072584682129,2014,2,0.1,EE
+11,989.8,0.006297568532363,2014,2,0.1,EE
+12,990.6666666667,0.006224764468371,2014,2,0.1,EE
+1,983.8666666667,0.003015313728147,2015,2,0.1,EE
+2,992.1666666667,0.002519737947253,2015,2,0.1,EE
+3,994.8333333333,0.002144412799464,2015,2,0.1,EE
+4,998.5333333333,0.0007344104686874,2015,2,0.1,EE
+5,992.9,0.0003692886158391,2015,2,0.1,EE
+6,996.7666666667,0.002173694946995,2015,2,0.1,EE
+7,998.2,0.003539704801977,2015,2,0.1,EE
+8,998.8666666667,0.001301475005006,2015,2,0.1,EE
+9,995.1333333333,0.001272861258123,2015,2,0.1,EE
+10,987.6666666667,0.002294971312859,2015,2,0.1,EE
+11,991.2,0.005616088243207,2015,2,0.1,EE
+12,1003.966666667,0.003818187854842,2015,2,0.1,EE
+1,1008.466666667,0.002974813247835,2016,2,0.1,EE
+2,1011.533333333,0.003493046859553,2016,2,0.1,EE
+3,1009.033333333,0.001453536387962,2016,2,0.1,EE
+4,999.8,0.002533840101354,2016,2,0.1,EE
+5,996.5333333333,0.003043885469628,2016,2,0.1,EE
+6,1002.6,0.002493516856174,2016,2,0.1,EE
+7,1005.266666667,0.001425823993634,2016,2,0.1,EE
+8,1003.866666667,0.0008633284632753,2016,2,0.1,EE
+9,993.0,0.004431017119839,2016,2,0.1,EE
+10,985.9333333333,0.004868483332206,2016,2,0.1,EE
+11,988.6,0.006136624182345,2016,2,0.1,EE
+12,989.0666666667,0.004684551091939,2016,2,0.1,EE
+1,992.3333333333,0.004030903594222,2017,2,0.1,EE
+2,987.3333333333,0.001721809588116,2017,2,0.1,EE
+3,986.6,0.00165551726468,2017,2,0.1,EE
+4,985.2666666667,0.002402056972732,2017,2,0.1,EE
+5,991.0666666667,0.004977801695143,2017,2,0.1,EE
+6,994.1,0.002883680380914,2017,2,0.1,EE
+7,995.8666666667,0.005891016200295,2017,2,0.1,EE
+8,989.2666666667,0.004784688995215,2017,2,0.1,EE
+9,996.1333333333,0.0107415339312,2017,2,0.1,EE
+10,991.4,0.01469302669625,2017,2,0.1,EE
+11,986.6,0.01223055611866,2017,2,0.1,EE
+12,990.6,0.01086883370348,2017,2,0.1,EE
+1,981.2333333333,0.4480076094711,2010,2,6.30957344480193,DD
+2,979.7333333333,0.4510751224823,2010,2,6.30957344480193,DD
+3,981.7666666667,0.5373985672088,2010,2,6.30957344480193,DD
+4,988.9,0.5580948528668,2010,2,6.30957344480193,DD
+5,988.2,0.5183498617014,2010,2,6.30957344480193,DD
+6,983.3,0.5144920166785,2010,2,6.30957344480193,DD
+7,975.4,0.7158772469414,2010,2,6.30957344480193,DD
+8,971.3666666667,0.7154181393912,2010,2,6.30957344480193,DD
+9,977.7666666667,0.680360004091,2010,2,6.30957344480193,DD
+10,979.1333333333,0.6286171444134,2010,2,6.30957344480193,DD
+11,972.3333333333,0.5873842989373,2010,2,6.30957344480193,DD
+12,972.8666666667,0.5096621667923,2010,2,6.30957344480193,DD
+1,974.0666666667,0.4553076449251,2011,2,6.30957344480193,DD
+2,973.8333333333,0.48434023618,2011,2,6.30957344480193,DD
+3,974.7666666667,0.5406080087542,2011,2,6.30957344480193,DD
+4,971.9333333333,0.5697921668153,2011,2,6.30957344480193,DD
+5,976.0,0.5251024590164,2011,2,6.30957344480193,DD
+6,977.6666666667,0.537026934879,2011,2,6.30957344480193,DD
+7,975.3666666667,0.6828201360172,2011,2,6.30957344480193,DD
+8,974.5666666667,0.7170024284297,2011,2,6.30957344480193,DD
+9,972.7,0.6734519036359,2011,2,6.30957344480193,DD
+10,975.3,0.6223384257835,2011,2,6.30957344480193,DD
+11,972.7,0.5968952400535,2011,2,6.30957344480193,DD
+12,981.5666666667,0.4974360715862,2011,2,6.30957344480193,DD
+1,991.0333333333,0.4461336651979,2012,2,6.30957344480193,DD
+2,988.1,0.4608170563033,2012,2,6.30957344480193,DD
+3,989.1666666667,0.544700926706,2012,2,6.30957344480193,DD
+4,991.8666666667,0.569263341847,2012,2,6.30957344480193,DD
+5,990.0666666667,0.5010437007609,2012,2,6.30957344480193,DD
+6,987.5,0.5245569620253,2012,2,6.30957344480193,DD
+7,986.9,0.70388759415,2012,2,6.30957344480193,DD
+8,988.8,0.7314927184466,2012,2,6.30957344480193,DD
+9,990.3333333333,0.6679232581622,2012,2,6.30957344480193,DD
+10,995.8,0.6167235723371,2012,2,6.30957344480193,DD
+11,993.7,0.5735802220657,2012,2,6.30957344480193,DD
+12,985.6666666667,0.506695975651,2012,2,6.30957344480193,DD
+1,983.9333333333,0.4138152991395,2013,2,6.30957344480193,DD
+2,988.4333333333,0.4182038916804,2013,2,6.30957344480193,DD
+3,988.5,0.4843365368403,2013,2,6.30957344480193,DD
+4,1000.433333333,0.5068803518475,2013,2,6.30957344480193,DD
+5,998.2,0.4687103452882,2013,2,6.30957344480193,DD
+6,1000.433333333,0.4797920900943,2013,2,6.30957344480193,DD
+7,996.9333333333,0.7061655744282,2013,2,6.30957344480193,DD
+8,996.7333333333,0.7280449468263,2013,2,6.30957344480193,DD
+9,988.7,0.6547318027039,2013,2,6.30957344480193,DD
+10,990.4666666667,0.5929864710238,2013,2,6.30957344480193,DD
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+3,1002.0,0.251996007984,2017,2,1.5848931924611125,EE
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+1,1029.4,0.6127193834596,2010,2,25.118864315095795,DD
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+11,1035.133333333,0.6755973465576,2012,2,25.118864315095795,DD
+12,1029.8,0.6450119764356,2012,2,25.118864315095795,DD
+1,1024.1,0.6002669010188,2013,2,25.118864315095795,DD
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+3,1010.066666667,0.6797901128638,2013,2,25.118864315095795,DD
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+5,1014.833333333,0.6148792905239,2013,2,25.118864315095795,DD
+6,1023.433333333,0.6396443344299,2013,2,25.118864315095795,DD
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+8,1021.866666667,0.6802257306889,2013,2,25.118864315095795,DD
+9,1024.033333333,0.640050779597,2013,2,25.118864315095795,DD
+10,1024.733333333,0.6599765792727,2013,2,25.118864315095795,DD
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+12,1030.866666667,0.6337386018237,2013,2,25.118864315095795,DD
+1,1036.233333333,0.5703026988773,2014,2,25.118864315095795,DD
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+3,1040.566666667,0.6480763686453,2014,2,25.118864315095795,DD
+4,1036.766666667,0.643281998521,2014,2,25.118864315095795,DD
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+6,1050.233333333,0.6196718189609,2014,2,25.118864315095795,DD
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+8,1044.866666667,0.6640719709054,2014,2,25.118864315095795,DD
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+10,1043.5,0.6425491135601,2014,2,25.118864315095795,DD
+11,1048.133333333,0.6390726370691,2014,2,25.118864315095795,DD
+12,1042.2,0.584052964882,2014,2,25.118864315095795,DD
+1,1038.266666667,0.5292795685116,2015,2,25.118864315095795,DD
+2,1045.0,0.561658692185,2015,2,25.118864315095795,DD
+3,1050.233333333,0.622306154188,2015,2,25.118864315095795,DD
+4,1043.233333333,0.6303799086174,2015,2,25.118864315095795,DD
+5,1042.9,0.5696934829162,2015,2,25.118864315095795,DD
+6,1043.2,0.5752172801636,2015,2,25.118864315095795,DD
+7,1047.133333333,0.645444706182,2015,2,25.118864315095795,DD
+8,1042.366666667,0.6516580857664,2015,2,25.118864315095795,DD
+9,1035.933333333,0.6124267970912,2015,2,25.118864315095795,DD
+10,1039.6,0.6074451712197,2015,2,25.118864315095795,DD
+11,1046.466666667,0.5913868892145,2015,2,25.118864315095795,DD
+12,1045.266666667,0.5373429427897,2015,2,25.118864315095795,DD
+1,1040.7,0.5286505877454,2016,2,25.118864315095795,DD
+2,1030.3,0.5772428742437,2016,2,25.118864315095795,DD
+3,1025.266666667,0.5989661226348,2016,2,25.118864315095795,DD
+4,1028.066666667,0.5883211205499,2016,2,25.118864315095795,DD
+5,1026.066666667,0.5473003703463,2016,2,25.118864315095795,DD
+6,1034.7,0.5747881833704,2016,2,25.118864315095795,DD
+7,1031.0,0.6396378920142,2016,2,25.118864315095795,DD
+8,1033.733333333,0.6233071069264,2016,2,25.118864315095795,DD
+9,1031.133333333,0.6030581237473,2016,2,25.118864315095795,DD
+10,1027.2,0.5994613187954,2016,2,25.118864315095795,DD
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+12,1001.9,0.5462288318861,2016,2,25.118864315095795,DD
+1,1003.533333333,0.5227861555836,2017,2,25.118864315095795,DD
+2,998.8666666667,0.5492224521124,2017,2,25.118864315095795,DD
+3,997.2333333333,0.5717150783835,2017,2,25.118864315095795,DD
+4,991.6,0.5847788086594,2017,2,25.118864315095795,DD
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+6,989.4333333333,0.5764579051983,2017,2,25.118864315095795,DD
+7,985.4333333333,0.6327503974563,2017,2,25.118864315095795,DD
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+9,994.7,0.5969974196575,2017,2,25.118864315095795,DD
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+1,994.9,0.001708714443663,2010,2,0.025118864315095794,FF
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+6,1000.466666667,3.331778503365e-05,2010,2,0.025118864315095794,FF
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+12,1008.866666667,0.0,2012,2,0.1,FF
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+2,1017.7,0.0,2013,2,0.1,FF
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+1,1011.666666667,0.0,2014,2,0.1,FF
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+6,1022.333333333,0.0,2014,2,0.1,FF
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+9,1035.4,0.0,2014,2,0.1,FF
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+11,1036.633333333,0.0,2014,2,0.1,FF
+12,1040.9,0.0,2014,2,0.1,FF
+1,1048.033333333,0.0,2015,2,0.1,FF
+2,1047.633333333,0.0,2015,2,0.1,FF
+3,1045.533333333,0.0,2015,2,0.1,FF
+4,1041.133333333,0.0,2015,2,0.1,FF
+5,1041.166666667,0.0,2015,2,0.1,FF
+6,1036.333333333,0.0,2015,2,0.1,FF
+7,1042.4,0.0,2015,2,0.1,FF
+8,1044.366666667,0.0,2015,2,0.1,FF
+9,1040.133333333,0.0,2015,2,0.1,FF
+10,1031.466666667,0.0,2015,2,0.1,FF
+11,1033.233333333,0.0,2015,2,0.1,FF
+12,1035.866666667,0.0,2015,2,0.1,FF
+1,1042.533333333,0.0,2016,2,0.1,FF
+2,1048.633333333,0.0,2016,2,0.1,FF
+3,1052.8,0.0,2016,2,0.1,FF
+4,1053.533333333,0.0,2016,2,0.1,FF
+5,1066.0,0.0,2016,2,0.1,FF
+6,1070.666666667,0.0,2016,2,0.1,FF
+7,1071.366666667,0.0,2016,2,0.1,FF
+8,1058.866666667,0.0,2016,2,0.1,FF
+9,1058.4,0.0,2016,2,0.1,FF
+10,1051.466666667,0.0,2016,2,0.1,FF
+11,1052.3,0.0,2016,2,0.1,FF
+12,1052.1,0.0,2016,2,0.1,FF
+1,1055.2,0.0,2017,2,0.1,FF
+2,1063.8,0.0,2017,2,0.1,FF
+3,1062.233333333,0.0,2017,2,0.1,FF
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+8,1048.633333333,0.0,2017,2,0.1,FF
+9,1052.033333333,0.0,2017,2,0.1,FF
+10,1056.9,0.0,2017,2,0.1,FF
+11,1047.166666667,0.0,2017,2,0.1,FF
+12,1048.633333333,0.0,2017,2,0.1,FF
+1,994.0666666667,0.001743679163034,2010,2,0.025118864315095794,GG
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+11,992.1666666667,0.0,2010,2,0.025118864315095794,GG
+12,987.9333333333,0.0,2010,2,0.025118864315095794,GG
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+4,977.7333333333,0.0,2011,2,0.025118864315095794,GG
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+10,977.5666666667,0.0,2011,2,0.025118864315095794,GG
+11,981.0333333333,0.0,2011,2,0.025118864315095794,GG
+12,977.1,0.0,2011,2,0.025118864315095794,GG
+1,982.3666666667,0.0,2012,2,0.025118864315095794,GG
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+3,979.8666666667,0.0,2012,2,0.025118864315095794,GG
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+9,972.8333333333,0.0,2012,2,0.025118864315095794,GG
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+11,971.9666666667,0.0,2012,2,0.025118864315095794,GG
+12,972.5333333333,0.0,2012,2,0.025118864315095794,GG
+1,977.4666666667,0.0,2013,2,0.025118864315095794,GG
+2,989.0333333333,0.0,2013,2,0.025118864315095794,GG
+3,999.1666666667,0.0,2013,2,0.025118864315095794,GG
+4,1002.1,0.0,2013,2,0.025118864315095794,GG
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+6,1020.033333333,0.0,2013,2,0.025118864315095794,GG
+7,1022.533333333,0.0,2013,2,0.025118864315095794,GG
+8,1019.466666667,0.0,2013,2,0.025118864315095794,GG
+9,1012.4,0.0,2013,2,0.025118864315095794,GG
+10,1015.9,0.0,2013,2,0.025118864315095794,GG
+11,1022.533333333,0.0,2013,2,0.025118864315095794,GG
+12,1019.1,0.0,2013,2,0.025118864315095794,GG
+1,1014.5,0.0,2014,2,0.025118864315095794,GG
+2,1012.466666667,0.0,2014,2,0.025118864315095794,GG
+3,1011.233333333,0.0,2014,2,0.025118864315095794,GG
+4,1001.8,0.0,2014,2,0.025118864315095794,GG
+5,1001.8,0.0,2014,2,0.025118864315095794,GG
+6,1005.133333333,0.0,2014,2,0.025118864315095794,GG
+7,1015.133333333,0.0,2014,2,0.025118864315095794,GG
+8,1021.533333333,0.0,2014,2,0.025118864315095794,GG
+9,1021.5,0.0,2014,2,0.025118864315095794,GG
+10,1024.4,0.0,2014,2,0.025118864315095794,GG
+11,1023.666666667,0.0,2014,2,0.025118864315095794,GG
+12,1016.7,0.0,2014,2,0.025118864315095794,GG
+1,1021.933333333,0.0,2015,2,0.025118864315095794,GG
+2,1031.366666667,0.0,2015,2,0.025118864315095794,GG
+3,1027.666666667,0.0,2015,2,0.025118864315095794,GG
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+5,1017.666666667,0.0,2015,2,0.025118864315095794,GG
+6,1016.533333333,0.0,2015,2,0.025118864315095794,GG
+7,1012.266666667,0.0,2015,2,0.025118864315095794,GG
+8,1008.866666667,0.0,2015,2,0.025118864315095794,GG
+9,1008.1,0.0,2015,2,0.025118864315095794,GG
+10,1011.266666667,0.0,2015,2,0.025118864315095794,GG
+11,1010.333333333,0.0,2015,2,0.025118864315095794,GG
+12,1010.233333333,0.0,2015,2,0.025118864315095794,GG
+1,1007.5,0.0,2016,2,0.025118864315095794,GG
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+3,1003.666666667,0.0,2016,2,0.025118864315095794,GG
+4,1005.133333333,0.0,2016,2,0.025118864315095794,GG
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+3,1007.766666667,0.0,2017,2,0.025118864315095794,GG
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+10,993.6666666667,0.0,2017,2,0.025118864315095794,GG
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+1,1000.8,0.2472355448974,2010,2,1.5848931924611125,FF
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+3,1002.433333333,0.1827220430286,2010,2,1.5848931924611125,FF
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+1,985.5333333333,0.2232970303727,2012,2,1.5848931924611125,FF
+2,991.6,0.1791717090225,2012,2,1.5848931924611125,FF
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+4,1015.2,0.1842001576044,2013,2,1.5848931924611125,FF
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+6,1006.2,0.184820777844,2014,2,1.5848931924611125,FF
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+8,1000.666666667,0.3886075949367,2014,2,1.5848931924611125,FF
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+12,985.4,0.3571476896015,2014,2,1.5848931924611125,FF
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+1,1011.433333333,0.2305968427644,2016,2,1.5848931924611125,FF
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+6,1004.233333333,0.6302984034255,2011,2,25.118864315095795,FF
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+10,1000.066666667,0.6118258782748,2011,2,25.118864315095795,FF
+11,1000.366666667,0.6139415547633,2011,2,25.118864315095795,FF
+12,995.4,0.5788627687362,2011,2,25.118864315095795,FF
+1,994.8333333333,0.5572122633607,2012,2,25.118864315095795,FF
+2,994.3333333333,0.6058330539725,2012,2,25.118864315095795,FF
+3,999.1333333333,0.6661439914593,2012,2,25.118864315095795,FF
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+7,981.7333333333,0.6958101317398,2016,2,6.30957344480193,EE
+8,987.5333333333,0.7368190103288,2016,2,6.30957344480193,EE
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+12,1000.8,0.5151878497202,2016,2,6.30957344480193,EE
+1,1002.333333333,0.4570668440306,2017,2,6.30957344480193,EE
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+3,988.0333333333,0.5522080901454,2017,2,6.30957344480193,EE
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+8,987.5666666667,0.7224491173592,2017,2,6.30957344480193,EE
+9,988.3,0.6793821039495,2017,2,6.30957344480193,EE
+10,977.7333333333,0.6265171144143,2017,2,6.30957344480193,EE
+11,969.0666666667,0.5870941111723,2017,2,6.30957344480193,EE
+12,975.5,0.5113275243465,2017,2,6.30957344480193,EE
+1,1029.666666667,0.6103269666559,2010,2,25.118864315095795,EE
+2,1029.3,0.6642702160044,2010,2,25.118864315095795,EE
+3,1032.0,0.7061046511628,2010,2,25.118864315095795,EE
+4,1033.633333333,0.6788351768841,2010,2,25.118864315095795,EE
+5,1039.1,0.6077053860713,2010,2,25.118864315095795,EE
+6,1032.133333333,0.659733884511,2010,2,25.118864315095795,EE
+7,1043.5,0.7490816163552,2010,2,25.118864315095795,EE
+8,1048.066666667,0.7043763119394,2010,2,25.118864315095795,EE
+9,1044.933333333,0.6515567181319,2010,2,25.118864315095795,EE
+10,1041.6,0.6694508448541,2010,2,25.118864315095795,EE
+11,1037.333333333,0.671883033419,2010,2,25.118864315095795,EE
+12,1035.8,0.6418549269486,2010,2,25.118864315095795,EE
+1,1032.666666667,0.6041639767592,2011,2,25.118864315095795,EE
+2,1035.733333333,0.6475283213182,2011,2,25.118864315095795,EE
+3,1033.3,0.6990870673248,2011,2,25.118864315095795,EE
+4,1026.2,0.6785227051257,2011,2,25.118864315095795,EE
+5,1022.2,0.6324920106959,2011,2,25.118864315095795,EE
+6,1017.8,0.6699744547062,2011,2,25.118864315095795,EE
+7,1013.533333333,0.7392290995198,2011,2,25.118864315095795,EE
+8,1019.533333333,0.7069901262015,2011,2,25.118864315095795,EE
+9,1017.333333333,0.6835190039318,2011,2,25.118864315095795,EE
+10,1009.6,0.6631669307977,2011,2,25.118864315095795,EE
+11,1007.466666667,0.6656630492324,2011,2,25.118864315095795,EE
+12,1014.933333333,0.6291382028376,2011,2,25.118864315095795,EE
+1,1019.733333333,0.6192141736402,2012,2,25.118864315095795,EE
+2,1026.166666667,0.6746142601917,2012,2,25.118864315095795,EE
+3,1031.466666667,0.6935431747673,2012,2,25.118864315095795,EE
+4,1039.433333333,0.6726742135138,2012,2,25.118864315095795,EE
+5,1043.3,0.6360906099236,2012,2,25.118864315095795,EE
+6,1045.0,0.6616267942584,2012,2,25.118864315095795,EE
+7,1033.633333333,0.7387532651811,2012,2,25.118864315095795,EE
+8,1026.366666667,0.6980286447339,2012,2,25.118864315095795,EE
+9,1029.133333333,0.6762972080067,2012,2,25.118864315095795,EE
+10,1029.366666667,0.6659434603802,2012,2,25.118864315095795,EE
+11,1031.3,0.6704806231617,2012,2,25.118864315095795,EE
+12,1024.166666667,0.6462164361269,2012,2,25.118864315095795,EE
+1,1021.5,0.617457986621,2013,2,25.118864315095795,EE
+2,1020.1,0.6564389112179,2013,2,25.118864315095795,EE
+3,1010.166666667,0.7045372050817,2013,2,25.118864315095795,EE
+4,1004.7,0.6686904880395,2013,2,25.118864315095795,EE
+5,1007.9,0.6230776862784,2013,2,25.118864315095795,EE
+6,1010.966666667,0.652675656962,2013,2,25.118864315095795,EE
+7,1009.5,0.7369654944692,2013,2,25.118864315095795,EE
+8,1008.866666667,0.7066675477433,2013,2,25.118864315095795,EE
+9,1011.466666667,0.6534075929344,2013,2,25.118864315095795,EE
+10,1012.866666667,0.659119331271,2013,2,25.118864315095795,EE
+11,1020.8,0.6654911180773,2013,2,25.118864315095795,EE
+12,1030.633333333,0.6425498884181,2013,2,25.118864315095795,EE
+1,1038.366666667,0.6144907065584,2014,2,25.118864315095795,EE
+2,1036.5,0.6544138929088,2014,2,25.118864315095795,EE
+3,1030.4,0.7053894927536,2014,2,25.118864315095795,EE
+4,1028.133333333,0.6963753080016,2014,2,25.118864315095795,EE
+5,1026.9,0.6240464829422,2014,2,25.118864315095795,EE
+6,1025.466666667,0.6579443505396,2014,2,25.118864315095795,EE
+7,1032.8,0.7408985282727,2014,2,25.118864315095795,EE
+8,1033.7,0.69485021444,2014,2,25.118864315095795,EE
+9,1033.833333333,0.6482024826697,2014,2,25.118864315095795,EE
+10,1030.633333333,0.6672272712572,2014,2,25.118864315095795,EE
+11,1026.0,0.6633528265107,2014,2,25.118864315095795,EE
+12,1019.3,0.6290264560646,2014,2,25.118864315095795,EE
+1,1011.066666667,0.6108730054068,2015,2,25.118864315095795,EE
+2,1002.9,0.6622793897697,2015,2,25.118864315095795,EE
+3,1006.5,0.7075012419275,2015,2,25.118864315095795,EE
+4,998.2666666667,0.6713636970749,2015,2,25.118864315095795,EE
+5,999.7,0.6070821246374,2015,2,25.118864315095795,EE
+6,1001.266666667,0.6646248085758,2015,2,25.118864315095795,EE
+7,997.5666666667,0.7304106659538,2015,2,25.118864315095795,EE
+8,990.9666666667,0.6911769652528,2015,2,25.118864315095795,EE
+9,985.3,0.6701512229778,2015,2,25.118864315095795,EE
+10,987.4333333333,0.6773453060122,2015,2,25.118864315095795,EE
+11,987.4,0.6713591249747,2015,2,25.118864315095795,EE
+12,984.1333333333,0.6391410377998,2015,2,25.118864315095795,EE
+1,993.1666666667,0.6153045813056,2016,2,25.118864315095795,EE
+2,1000.433333333,0.6624129543864,2016,2,25.118864315095795,EE
+3,1003.433333333,0.6944158389529,2016,2,25.118864315095795,EE
+4,1004.433333333,0.6696313012312,2016,2,25.118864315095795,EE
+5,1000.666666667,0.611492338441,2016,2,25.118864315095795,EE
+6,990.4666666667,0.6551120683853,2016,2,25.118864315095795,EE
+7,992.1666666667,0.7363682177054,2016,2,25.118864315095795,EE
+8,995.1333333333,0.6962551081932,2016,2,25.118864315095795,EE
+9,985.6666666667,0.6594521474467,2016,2,25.118864315095795,EE
+10,991.6333333333,0.6543413223974,2016,2,25.118864315095795,EE
+11,989.7666666667,0.6672616441586,2016,2,25.118864315095795,EE
+12,990.0333333333,0.6242214066866,2016,2,25.118864315095795,EE
+1,979.3666666667,0.6160784180253,2017,2,25.118864315095795,EE
+2,971.5666666667,0.6534120149587,2017,2,25.118864315095795,EE
+3,970.5333333333,0.7084077483171,2017,2,25.118864315095795,EE
+4,977.1666666667,0.6766842913184,2017,2,25.118864315095795,EE
+5,974.1333333333,0.6162400766493,2017,2,25.118864315095795,EE
+6,967.0666666667,0.6633806700676,2017,2,25.118864315095795,EE
+7,958.7333333333,0.7456018357555,2017,2,25.118864315095795,EE
+8,958.3333333333,0.7019130434783,2017,2,25.118864315095795,EE
+9,958.6,0.6635371027192,2017,2,25.118864315095795,EE
+10,957.0333333333,0.6691511964056,2017,2,25.118864315095795,EE
+11,951.1333333333,0.6779280857924,2017,2,25.118864315095795,EE
+12,956.0,0.6312412831241,2017,2,25.118864315095795,EE
+1,1020.266666667,0.007351019341349,2010,2,0.1,GG
+2,1023.933333333,0.004915684614884,2010,2,0.1,GG
+3,1025.6,0.00552522100884,2010,2,0.1,GG
+4,1016.066666667,0.003444655862476,2010,2,0.1,GG
+5,1012.566666667,0.004147875037035,2010,2,0.1,GG
+6,1006.533333333,0.003775334481388,2010,2,0.1,GG
+7,996.1,0.003179065020246,2010,2,0.1,GG
+8,986.9333333333,0.003580113482842,2010,2,0.1,GG
+9,982.2666666667,0.003155965793403,2010,2,0.1,GG
+10,985.9333333333,0.002197579281899,2010,2,0.1,GG
+11,985.0666666667,0.002470221981592,2010,2,0.1,GG
+12,991.5333333333,0.0009413030323405,2010,2,0.1,GG
+1,997.1666666667,0.0004011365535684,2011,2,0.1,GG
+2,990.5,0.0,2011,2,0.1,GG
+3,1000.233333333,0.0,2011,2,0.1,GG
+4,996.2333333333,0.0,2011,2,0.1,GG
+5,994.4,0.0,2011,2,0.1,GG
+6,994.5666666667,0.0,2011,2,0.1,GG
+7,993.0,0.0,2011,2,0.1,GG
+8,989.6,0.0,2011,2,0.1,GG
+9,997.7333333333,0.0,2011,2,0.1,GG
+10,1005.466666667,0.0,2011,2,0.1,GG
+11,1006.033333333,0.0,2011,2,0.1,GG
+12,1002.966666667,0.0,2011,2,0.1,GG
+1,1000.3,0.0,2012,2,0.1,GG
+2,996.5333333333,0.0,2012,2,0.1,GG
+3,997.2,0.0,2012,2,0.1,GG
+4,995.3666666667,0.0,2012,2,0.1,GG
+5,995.3,0.0,2012,2,0.1,GG
+6,994.1,0.0,2012,2,0.1,GG
+7,993.7,0.0,2012,2,0.1,GG
+8,996.1666666667,0.0,2012,2,0.1,GG
+9,1002.5,0.0,2012,2,0.1,GG
+10,1007.933333333,0.0,2012,2,0.1,GG
+11,1001.4,0.0,2012,2,0.1,GG
+12,998.8,0.0,2012,2,0.1,GG
+1,1001.633333333,0.0,2013,2,0.1,GG
+2,1001.166666667,0.0,2013,2,0.1,GG
+3,1007.033333333,0.0,2013,2,0.1,GG
+4,1015.866666667,0.0,2013,2,0.1,GG
+5,1025.066666667,0.0,2013,2,0.1,GG
+6,1018.666666667,0.0,2013,2,0.1,GG
+7,1024.766666667,0.0,2013,2,0.1,GG
+8,1025.933333333,0.0,2013,2,0.1,GG
+9,1011.466666667,0.0,2013,2,0.1,GG
+10,1000.833333333,0.0,2013,2,0.1,GG
+11,996.1666666667,0.0,2013,2,0.1,GG
+12,996.6666666667,0.0,2013,2,0.1,GG
+1,999.1,0.0,2014,2,0.1,GG
+2,999.2,0.0,2014,2,0.1,GG
+3,999.0666666667,0.0,2014,2,0.1,GG
+4,1000.333333333,0.0,2014,2,0.1,GG
+5,1009.166666667,0.0,2014,2,0.1,GG
+6,1012.1,0.0,2014,2,0.1,GG
+7,1012.7,0.0,2014,2,0.1,GG
+8,1007.033333333,0.0,2014,2,0.1,GG
+9,1009.566666667,0.0,2014,2,0.1,GG
+10,1010.9,0.0,2014,2,0.1,GG
+11,1013.166666667,0.0,2014,2,0.1,GG
+12,1007.7,0.0,2014,2,0.1,GG
+1,1007.366666667,0.0,2015,2,0.1,GG
+2,1007.8,0.0,2015,2,0.1,GG
+3,1014.966666667,0.0,2015,2,0.1,GG
+4,1017.4,0.0,2015,2,0.1,GG
+5,1023.5,0.0,2015,2,0.1,GG
+6,1022.8,0.0,2015,2,0.1,GG
+7,1016.166666667,0.0,2015,2,0.1,GG
+8,1014.2,0.0,2015,2,0.1,GG
+9,1010.666666667,0.0,2015,2,0.1,GG
+10,1012.933333333,0.0,2015,2,0.1,GG
+11,1014.2,0.0,2015,2,0.1,GG
+12,1009.366666667,0.0,2015,2,0.1,GG
+1,1014.233333333,0.0,2016,2,0.1,GG
+2,1019.433333333,0.0,2016,2,0.1,GG
+3,1018.4,0.0,2016,2,0.1,GG
+4,1016.233333333,0.0,2016,2,0.1,GG
+5,1019.366666667,0.0,2016,2,0.1,GG
+6,1012.333333333,0.0,2016,2,0.1,GG
+7,1005.733333333,0.0,2016,2,0.1,GG
+8,1005.466666667,0.0,2016,2,0.1,GG
+9,1016.833333333,0.0,2016,2,0.1,GG
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+11,1030.3,0.0,2016,2,0.1,GG
+12,1030.366666667,0.0,2016,2,0.1,GG
+1,1035.533333333,0.0,2017,2,0.1,GG
+2,1040.633333333,0.0,2017,2,0.1,GG
+3,1045.966666667,0.0,2017,2,0.1,GG
+4,1043.433333333,0.0,2017,2,0.1,GG
+5,1037.066666667,0.0,2017,2,0.1,GG
+6,1033.933333333,0.0,2017,2,0.1,GG
+7,1034.466666667,0.0,2017,2,0.1,GG
+8,1041.566666667,0.0,2017,2,0.1,GG
+9,1042.1,0.0,2017,2,0.1,GG
+10,1043.233333333,0.0,2017,2,0.1,GG
+11,1041.2,0.0,2017,2,0.1,GG
+12,1035.1,0.0,2017,2,0.1,GG
+1,1001.066666667,0.2284896110815,2010,2,1.5848931924611125,GG
+2,1004.366666667,0.1677010387973,2010,2,1.5848931924611125,GG
+3,1001.233333333,0.1603355861105,2010,2,1.5848931924611125,GG
+4,999.1333333333,0.1506305464736,2010,2,1.5848931924611125,GG
+5,1000.433333333,0.1249125379002,2010,2,1.5848931924611125,GG
+6,995.5333333333,0.1238866938994,2010,2,1.5848931924611125,GG
+7,995.7333333333,0.2186997857525,2010,2,1.5848931924611125,GG
+8,997.1666666667,0.2584990807287,2010,2,1.5848931924611125,GG
+9,1000.166666667,0.336477253791,2010,2,1.5848931924611125,GG
+10,997.7666666667,0.3984899609127,2010,2,1.5848931924611125,GG
+11,1001.033333333,0.3465752056209,2010,2,1.5848931924611125,GG
+12,996.0333333333,0.2598641277066,2010,2,1.5848931924611125,GG
+1,987.6666666667,0.1803577455282,2011,2,1.5848931924611125,GG
+2,989.6,0.1617825383994,2011,2,1.5848931924611125,GG
+3,990.9666666667,0.1388879545225,2011,2,1.5848931924611125,GG
+4,976.9333333333,0.1344342841545,2011,2,1.5848931924611125,GG
+5,971.5666666667,0.1203897485161,2011,2,1.5848931924611125,GG
+6,974.1,0.1181603531465,2011,2,1.5848931924611125,GG
+7,978.2333333333,0.1956588407674,2011,2,1.5848931924611125,GG
+8,976.4,0.2601392871774,2011,2,1.5848931924611125,GG
+9,970.2,0.3251906823335,2011,2,1.5848931924611125,GG
+10,964.3,0.380759791213,2011,2,1.5848931924611125,GG
+11,972.4333333333,0.3351386556062,2011,2,1.5848931924611125,GG
+12,974.1333333333,0.2561251026554,2011,2,1.5848931924611125,GG
+1,976.3333333333,0.1691703653124,2012,2,1.5848931924611125,GG
+2,976.9666666667,0.1440513152956,2012,2,1.5848931924611125,GG
+3,979.4666666667,0.1338483528451,2012,2,1.5848931924611125,GG
+4,982.0333333333,0.1342452734123,2012,2,1.5848931924611125,GG
+5,974.5333333333,0.1162949787933,2012,2,1.5848931924611125,GG
+6,974.8,0.1175625769389,2012,2,1.5848931924611125,GG
+7,970.3,0.1904222061905,2012,2,1.5848931924611125,GG
+8,970.4,0.2515801044243,2012,2,1.5848931924611125,GG
+9,968.3,0.3228682570829,2012,2,1.5848931924611125,GG
+10,969.5,0.3852501289324,2012,2,1.5848931924611125,GG
+11,972.3666666667,0.3529875561345,2012,2,1.5848931924611125,GG
+12,971.9666666667,0.2550841935594,2012,2,1.5848931924611125,GG
+1,975.1666666667,0.1726884293283,2013,2,1.5848931924611125,GG
+2,980.3333333333,0.1503910234614,2013,2,1.5848931924611125,GG
+3,988.0,0.1488866396761,2013,2,1.5848931924611125,GG
+4,992.2333333333,0.1391137837202,2013,2,1.5848931924611125,GG
+5,998.5333333333,0.1241821337962,2013,2,1.5848931924611125,GG
+6,1000.5,0.1228385807096,2013,2,1.5848931924611125,GG
+7,993.4,0.2150862358231,2013,2,1.5848931924611125,GG
+8,995.5333333333,0.2834996316882,2013,2,1.5848931924611125,GG
+9,996.0,0.3844712182062,2013,2,1.5848931924611125,GG
+10,985.8666666667,0.4251420070327,2013,2,1.5848931924611125,GG
+11,975.2666666667,0.3734021464215,2013,2,1.5848931924611125,GG
+12,973.7333333333,0.264138025469,2013,2,1.5848931924611125,GG
+1,975.7333333333,0.172998086909,2014,2,1.5848931924611125,GG
+2,978.3,0.1565981805172,2014,2,1.5848931924611125,GG
+3,979.0333333333,0.1399339484526,2014,2,1.5848931924611125,GG
+4,982.6333333333,0.1285321754469,2014,2,1.5848931924611125,GG
+5,984.0333333333,0.1057890992853,2014,2,1.5848931924611125,GG
+6,979.0333333333,0.1125600081713,2014,2,1.5848931924611125,GG
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+3,998.4666666667,0.1281631835481,2015,2,1.5848931924611125,GG
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+12,991.3333333333,0.2644586415602,2015,2,1.5848931924611125,GG
+1,996.7,0.1687568977626,2016,2,1.5848931924611125,GG
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+6,1000.3,0.1237628711387,2016,2,1.5848931924611125,GG
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+1,997.3,0.1260737324108,2010,2,0.39810717055349715,FF
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+6,1038.3,0.03711194580885,2017,2,0.39810717055349715,GG
+7,1038.233333333,0.06064789546345,2017,2,0.39810717055349715,GG
+8,1032.766666667,0.06180808830649,2017,2,0.39810717055349715,GG
+9,1027.166666667,0.08203796852182,2017,2,0.39810717055349715,GG
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+12,1019.8,0.09380924364254,2017,2,0.39810717055349715,GG
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+12,961.4666666667,0.403411454722,2012,2,6.30957344480193,GG
+1,953.3333333333,0.3203146853147,2013,2,6.30957344480193,GG
+2,955.2,0.3089056393076,2013,2,6.30957344480193,GG
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+5,956.9333333333,0.3672843806604,2013,2,6.30957344480193,GG
+6,960.0666666667,0.383445594056,2013,2,6.30957344480193,GG
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+9,968.1666666667,0.5808917197452,2013,2,6.30957344480193,GG
+10,967.1,0.5643676972392,2013,2,6.30957344480193,GG
+11,971.4333333333,0.4918505301445,2013,2,6.30957344480193,GG
+12,973.3666666667,0.4055340570528,2013,2,6.30957344480193,GG
+1,971.0666666667,0.3211588631059,2014,2,6.30957344480193,GG
+2,976.8333333333,0.3350622760621,2014,2,6.30957344480193,GG
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+5,975.9,0.3651330395874,2014,2,6.30957344480193,GG
+6,967.6666666667,0.3782638649673,2014,2,6.30957344480193,GG
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+8,978.7333333333,0.5747564879777,2014,2,6.30957344480193,GG
+9,982.4333333333,0.5707596783497,2014,2,6.30957344480193,GG
+10,986.1,0.5563330291046,2014,2,6.30957344480193,GG
+11,989.8,0.4875732471206,2014,2,6.30957344480193,GG
+12,984.7333333333,0.3972310608625,2014,2,6.30957344480193,GG
+1,979.1,0.3176046028666,2015,2,6.30957344480193,GG
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+3,984.2666666667,0.3801815226226,2015,2,6.30957344480193,GG
+4,984.5333333333,0.402559588299,2015,2,6.30957344480193,GG
+5,988.5666666667,0.3763023906666,2015,2,6.30957344480193,GG
+6,991.6666666667,0.4005378151261,2015,2,6.30957344480193,GG
+7,996.1333333333,0.5627426047383,2015,2,6.30957344480193,GG
+8,998.7666666667,0.5941327637419,2015,2,6.30957344480193,GG
+9,995.1333333333,0.5743953909024,2015,2,6.30957344480193,GG
+10,996.5666666667,0.539652807974,2015,2,6.30957344480193,GG
+11,996.7333333333,0.4819744498696,2015,2,6.30957344480193,GG
+12,996.1666666667,0.4039150075289,2015,2,6.30957344480193,GG
+1,999.8333333333,0.3068844807468,2016,2,6.30957344480193,GG
+2,993.2333333333,0.3219116018391,2016,2,6.30957344480193,GG
+3,988.7666666667,0.3690793244109,2016,2,6.30957344480193,GG
+4,987.7333333333,0.414214362851,2016,2,6.30957344480193,GG
+5,990.5333333333,0.3680172297752,2016,2,6.30957344480193,GG
+6,993.2333333333,0.3968855925093,2016,2,6.30957344480193,GG
+7,992.0333333333,0.530996942307,2016,2,6.30957344480193,GG
+8,984.0,0.5947493224932,2016,2,6.30957344480193,GG
+9,981.0666666667,0.5778064691492,2016,2,6.30957344480193,GG
+10,982.1333333333,0.5523011132229,2016,2,6.30957344480193,GG
+11,982.7666666667,0.481158633789,2016,2,6.30957344480193,GG
+12,984.7666666667,0.386927529364,2016,2,6.30957344480193,GG
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+12,989.1,0.4017119940687,2017,2,6.30957344480193,GG
+1,981.1333333333,0.001053203777944,2010,3,0.025118864315095794,AA
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+9,1026.266666667,0.5736650643108,2013,2,25.118864315095795,GG
+10,1021.666666667,0.570766721044,2013,2,25.118864315095795,GG
+11,1021.866666667,0.5653705636743,2013,2,25.118864315095795,GG
+12,1020.4,0.5340062720502,2013,2,25.118864315095795,GG
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+3,1024.033333333,0.5930796523551,2014,2,25.118864315095795,GG
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+1,1010.466666667,0.07306854918519,2015,3,0.39810717055349715,AA
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+5,1014.366666667,0.543393250304,2015,3,25.118864315095795,AA
+6,1005.466666667,0.5773770057022,2015,3,25.118864315095795,AA
+7,1010.3,0.6748160612359,2015,3,25.118864315095795,AA
+8,1008.9,0.6471074107113,2015,3,25.118864315095795,AA
+9,1005.433333333,0.597288068163,2015,3,25.118864315095795,AA
+10,1012.4,0.5963387330436,2015,3,25.118864315095795,AA
+11,1014.1,0.5905071820662,2015,3,25.118864315095795,AA
+12,1008.0,0.5659060846561,2015,3,25.118864315095795,AA
+1,994.5,0.5392324451148,2016,3,25.118864315095795,AA
+2,992.6,0.5646114581234,2016,3,25.118864315095795,AA
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+5,998.5333333333,0.522432901589,2016,3,25.118864315095795,AA
+6,998.8333333333,0.5720674119806,2016,3,25.118864315095795,AA
+7,1005.466666667,0.6416257790744,2016,3,25.118864315095795,AA
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+9,1022.166666667,0.6066199249959,2016,3,25.118864315095795,AA
+10,1023.133333333,0.5719684628918,2016,3,25.118864315095795,AA
+11,1018.966666667,0.5579181523766,2016,3,25.118864315095795,AA
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+1,1024.033333333,0.5246248494515,2017,3,25.118864315095795,AA
+2,1028.766666667,0.5399669507177,2017,3,25.118864315095795,AA
+3,1037.1,0.5892713656671,2017,3,25.118864315095795,AA
+4,1044.8,0.5902884124553,2017,3,25.118864315095795,AA
+5,1041.1,0.5430474177953,2017,3,25.118864315095795,AA
+6,1039.766666667,0.5492578463117,2017,3,25.118864315095795,AA
+7,1029.6,0.6118881118881,2017,3,25.118864315095795,AA
+8,1021.8,0.6045866771058,2017,3,25.118864315095795,AA
+9,1021.366666667,0.5861753859208,2017,3,25.118864315095795,AA
+10,1025.7,0.5549380910598,2017,3,25.118864315095795,AA
+11,1026.9,0.5369883468043,2017,3,25.118864315095795,AA
+12,1032.866666667,0.5288840121345,2017,3,25.118864315095795,AA
+1,991.6666666667,0.1253781512605,2010,3,0.39810717055349715,BB
+2,996.4,0.09587849591864,2010,3,0.39810717055349715,BB
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+4,993.1,0.07622595911791,2010,3,0.39810717055349715,BB
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+6,991.5,0.05476550680787,2010,3,0.39810717055349715,BB
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+8,997.1666666667,0.1185358515795,2010,3,0.39810717055349715,BB
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+11,998.6,0.2191401295147,2010,3,0.39810717055349715,BB
+12,996.0333333333,0.1716810013052,2010,3,0.39810717055349715,BB
+1,996.1333333333,0.1225070271717,2011,3,0.39810717055349715,BB
+2,992.4666666667,0.103378786861,2011,3,0.39810717055349715,BB
+3,993.6,0.1060118089104,2011,3,0.39810717055349715,BB
+4,999.3666666667,0.08628798238885,2011,3,0.39810717055349715,BB
+5,1002.9,0.06946521753581,2011,3,0.39810717055349715,BB
+6,1008.6,0.05985193998281,2011,3,0.39810717055349715,BB
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+8,1024.133333333,0.1364731154798,2011,3,0.39810717055349715,BB
+9,1026.8,0.1815348656019,2011,3,0.39810717055349715,BB
+10,1020.8,0.2440569487983,2011,3,0.39810717055349715,BB
+11,1022.466666667,0.2459737888766,2011,3,0.39810717055349715,BB
+12,1019.7,0.1987185773594,2011,3,0.39810717055349715,BB
+1,1024.166666667,0.1260211554109,2012,3,0.39810717055349715,BB
+2,1017.433333333,0.1009730367264,2012,3,0.39810717055349715,BB
+3,1018.3,0.08913548724999,2012,3,0.39810717055349715,BB
+4,1021.433333333,0.08723036256241,2012,3,0.39810717055349715,BB
+5,1014.2,0.07125484782752,2012,3,0.39810717055349715,BB
+6,1005.266666667,0.06459314278135,2012,3,0.39810717055349715,BB
+7,1002.4,0.1183160415004,2012,3,0.39810717055349715,BB
+8,998.7666666667,0.1344324667089,2012,3,0.39810717055349715,BB
+9,995.5666666667,0.1948638966083,2012,3,0.39810717055349715,BB
+10,1001.7,0.238960433929,2012,3,0.39810717055349715,BB
+11,997.9666666667,0.2377500918534,2012,3,0.39810717055349715,BB
+12,988.8666666667,0.1855322591519,2012,3,0.39810717055349715,BB
+1,997.7,0.1270923123183,2013,3,0.39810717055349715,BB
+2,1004.3,0.09967141292442,2013,3,0.39810717055349715,BB
+3,1003.033333333,0.0916220796916,2013,3,0.39810717055349715,BB
+4,999.6666666667,0.06985661887296,2013,3,0.39810717055349715,BB
+5,994.2333333333,0.05582190632648,2013,3,0.39810717055349715,BB
+6,993.4333333333,0.05462537328457,2013,3,0.39810717055349715,BB
+7,994.4,0.07887503352105,2013,3,0.39810717055349715,BB
+8,998.5333333333,0.09383762852183,2013,3,0.39810717055349715,BB
+9,1006.7,0.1320154961756,2013,3,0.39810717055349715,BB
+10,1016.233333333,0.2002492865812,2013,3,0.39810717055349715,BB
+11,1025.1,0.1837544304621,2013,3,0.39810717055349715,BB
+12,1032.266666667,0.136689485921,2013,3,0.39810717055349715,BB
+1,1025.733333333,0.09469647731704,2014,3,0.39810717055349715,BB
+2,1024.233333333,0.07192371529925,2014,3,0.39810717055349715,BB
+3,1014.066666667,0.06564328446519,2014,3,0.39810717055349715,BB
+4,1011.333333333,0.05540540540541,2014,3,0.39810717055349715,BB
+5,1012.5,0.04994238683128,2014,3,0.39810717055349715,BB
+6,1006.1,0.04631746347282,2014,3,0.39810717055349715,BB
+7,1007.233333333,0.06797498097098,2014,3,0.39810717055349715,BB
+8,1007.6,0.08637686912796,2014,3,0.39810717055349715,BB
+9,1012.8,0.1220379146919,2014,3,0.39810717055349715,BB
+10,1013.8,0.1763332675741,2014,3,0.39810717055349715,BB
+11,1015.466666667,0.1607471113445,2014,3,0.39810717055349715,BB
+12,1021.3,0.1248735272039,2014,3,0.39810717055349715,BB
+1,1022.866666667,0.08590236589976,2015,3,0.39810717055349715,BB
+2,1029.866666667,0.05528223718281,2015,3,0.39810717055349715,BB
+3,1038.366666667,0.05377034445122,2015,3,0.39810717055349715,BB
+4,1038.566666667,0.04897775780723,2015,3,0.39810717055349715,BB
+5,1030.833333333,0.0357316087308,2015,3,0.39810717055349715,BB
+6,1034.1,0.02749572897528,2015,3,0.39810717055349715,BB
+7,1031.866666667,0.05481974415299,2015,3,0.39810717055349715,BB
+8,1033.833333333,0.05900370788328,2015,3,0.39810717055349715,BB
+9,1042.4,0.06027756459453,2015,3,0.39810717055349715,BB
+10,1043.033333333,0.0722571985555,2015,3,0.39810717055349715,BB
+11,1049.066666667,0.08963523131673,2015,3,0.39810717055349715,BB
+12,1055.566666667,0.07171503457858,2015,3,0.39810717055349715,BB
+1,1038.233333333,0.0500529746043,2016,3,0.39810717055349715,BB
+2,1033.366666667,0.03864391471243,2016,3,0.39810717055349715,BB
+3,1027.233333333,0.03274166855956,2016,3,0.39810717055349715,BB
+4,1024.333333333,0.03254149040026,2016,3,0.39810717055349715,BB
+5,1020.333333333,0.0250245017968,2016,3,0.39810717055349715,BB
+6,1005.333333333,0.02496684350133,2016,3,0.39810717055349715,BB
+7,1008.433333333,0.03064158926387,2016,3,0.39810717055349715,BB
+8,1009.8,0.03852247969895,2016,3,0.39810717055349715,BB
+9,1008.166666667,0.05167796329972,2016,3,0.39810717055349715,BB
+10,1013.033333333,0.06130104307196,2016,3,0.39810717055349715,BB
+11,1009.3,0.06116450345124,2016,3,0.39810717055349715,BB
+12,1011.266666667,0.04360867558837,2016,3,0.39810717055349715,BB
+1,1013.8,0.02814493325442,2017,3,0.39810717055349715,BB
+2,1012.133333333,0.02957449611382,2017,3,0.39810717055349715,BB
+3,1013.9,0.0268928559687,2017,3,0.39810717055349715,BB
+4,1010.866666667,0.01701510255227,2017,3,0.39810717055349715,BB
+5,1012.866666667,0.01313104719279,2017,3,0.39810717055349715,BB
+6,1018.633333333,0.01335122222586,2017,3,0.39810717055349715,BB
+7,1010.566666667,0.01814163670548,2017,3,0.39810717055349715,BB
+8,1009.566666667,0.02631492059299,2017,3,0.39810717055349715,BB
+9,1008.666666667,0.02653668208857,2017,3,0.39810717055349715,BB
+10,1008.0,0.03730158730159,2017,3,0.39810717055349715,BB
+11,1005.5,0.04485330681253,2017,3,0.39810717055349715,BB
+12,1012.7,0.02755011355782,2017,3,0.39810717055349715,BB
+1,979.6,0.005342316591806,2010,3,0.1,BB
+2,982.2,0.003800990972646,2010,3,0.1,BB
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+4,980.8666666667,0.008258003126487,2010,3,0.1,BB
+5,991.8333333333,0.005780541085532,2010,3,0.1,BB
+6,988.0333333333,0.004149657568908,2010,3,0.1,BB
+7,984.8666666667,0.01272591890611,2010,3,0.1,BB
+8,981.0666666667,0.009139711878228,2010,3,0.1,BB
+9,981.0666666667,0.01280918727915,2010,3,0.1,BB
+10,979.0,0.02219952332312,2010,3,0.1,BB
+11,983.4,0.01501593112331,2010,3,0.1,BB
+12,975.1333333333,0.01661311273672,2010,3,0.1,BB
+1,972.7333333333,0.009903365088068,2011,3,0.1,BB
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+3,972.5333333333,0.005072662462298,2011,3,0.1,BB
+4,968.3666666667,0.00605831124574,2011,3,0.1,BB
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+6,968.7,0.003131344413475,2011,3,0.1,BB
+7,965.1333333333,0.006251295157837,2011,3,0.1,BB
+8,966.0333333333,0.00634898726752,2011,3,0.1,BB
+9,964.3666666667,0.007777124883343,2011,3,0.1,BB
+10,958.1666666667,0.007444773004001,2011,3,0.1,BB
+11,955.7333333333,0.007707868303571,2011,3,0.1,BB
+12,956.4333333333,0.008050744083923,2011,3,0.1,BB
+1,957.8666666667,0.007412305122494,2012,3,0.1,BB
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+4,956.3,0.005855902959322,2012,3,0.1,BB
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+6,954.7666666667,0.001187026498621,2012,3,0.1,BB
+7,951.7333333333,0.003152143457551,2012,3,0.1,BB
+8,960.7333333333,0.005343140656443,2012,3,0.1,BB
+9,954.7333333333,0.003945255219608,2012,3,0.1,BB
+10,949.7666666667,0.007545712982136,2012,3,0.1,BB
+11,949.2666666667,0.004564927312311,2012,3,0.1,BB
+12,949.1666666667,0.006602282704126,2012,3,0.1,BB
+1,959.1333333333,0.006568429832488,2013,3,0.1,BB
+2,961.6333333333,0.006066068147943,2013,3,0.1,BB
+3,961.7666666667,0.005129449277372,2013,3,0.1,BB
+4,966.6,0.003172632595351,2013,3,0.1,BB
+5,972.3,0.002605505845247,2013,3,0.1,BB
+6,984.7,0.002741951863512,2013,3,0.1,BB
+7,990.9,0.002691156187977,2013,3,0.1,BB
+8,996.9333333333,0.001805536980072,2013,3,0.1,BB
+9,990.8,0.00161485668147,2013,3,0.1,BB
+10,987.3666666667,0.001789271125215,2013,3,0.1,BB
+11,988.1666666667,0.002698600101198,2013,3,0.1,BB
+12,987.2,0.003815505132361,2013,3,0.1,BB
+1,996.8666666667,0.002140038788203,2014,3,0.1,BB
+2,993.3333333333,0.001577181208054,2014,3,0.1,BB
+3,986.3666666667,0.001115203947146,2014,3,0.1,BB
+4,977.2666666667,0.0004093048639061,2014,3,0.1,BB
+5,978.9333333333,0.001225824026151,2014,3,0.1,BB
+6,976.8666666667,0.0004435951682249,2014,3,0.1,BB
+7,971.9333333333,0.0002400713354825,2014,3,0.1,BB
+8,975.1666666667,0.0,2014,3,0.1,BB
+9,977.1333333333,0.0,2014,3,0.1,BB
+10,979.3666666667,0.0,2014,3,0.1,BB
+11,980.5333333333,0.001359804188197,2014,3,0.1,BB
+12,985.4333333333,0.00145452085377,2014,3,0.1,BB
+1,978.1333333333,0.0009541984732824,2015,3,0.1,BB
+2,967.2,0.000413564929694,2015,3,0.1,BB
+3,973.4,0.0002397096089309,2015,3,0.1,BB
+4,980.2333333333,0.0,2015,3,0.1,BB
+5,988.3666666667,0.0,2015,3,0.1,BB
+6,983.4666666667,0.0,2015,3,0.1,BB
+7,983.2333333333,0.0,2015,3,0.1,BB
+8,986.0666666667,0.0,2015,3,0.1,BB
+9,992.1333333333,0.0,2015,3,0.1,BB
+10,994.1666666667,0.0,2015,3,0.1,BB
+11,993.6666666667,0.0,2015,3,0.1,BB
+12,995.4333333333,0.0,2015,3,0.1,BB
+1,998.8666666667,0.0,2016,3,0.1,BB
+2,1000.466666667,0.0,2016,3,0.1,BB
+3,1001.533333333,0.0,2016,3,0.1,BB
+4,1002.666666667,0.0,2016,3,0.1,BB
+5,1005.166666667,0.0,2016,3,0.1,BB
+6,1005.966666667,0.0,2016,3,0.1,BB
+7,1012.433333333,0.0,2016,3,0.1,BB
+8,1012.1,0.0,2016,3,0.1,BB
+9,1010.566666667,0.0,2016,3,0.1,BB
+10,1020.1,0.0,2016,3,0.1,BB
+11,1027.1,0.0,2016,3,0.1,BB
+12,1036.3,0.0,2016,3,0.1,BB
+1,1028.433333333,0.0,2017,3,0.1,BB
+2,1028.2,0.0,2017,3,0.1,BB
+3,1030.9,0.0,2017,3,0.1,BB
+4,1040.033333333,0.0,2017,3,0.1,BB
+5,1032.066666667,0.0,2017,3,0.1,BB
+6,1031.466666667,0.0,2017,3,0.1,BB
+7,1037.833333333,0.0,2017,3,0.1,BB
+8,1035.066666667,0.0,2017,3,0.1,BB
+9,1028.433333333,0.0,2017,3,0.1,BB
+10,1021.5,0.0,2017,3,0.1,BB
+11,1017.833333333,0.0,2017,3,0.1,BB
+12,1021.266666667,0.0,2017,3,0.1,BB
+1,1006.533333333,0.2422837461915,2010,3,1.5848931924611125,BB
+2,1000.733333333,0.2318633002465,2010,3,1.5848931924611125,BB
+3,999.8333333333,0.2405067511252,2010,3,1.5848931924611125,BB
+4,999.8333333333,0.2708784797466,2010,3,1.5848931924611125,BB
+5,1004.033333333,0.2312340227748,2010,3,1.5848931924611125,BB
+6,1006.466666667,0.2438563953103,2010,3,1.5848931924611125,BB
+7,1001.533333333,0.3874725421021,2010,3,1.5848931924611125,BB
+8,1003.933333333,0.5048143967063,2010,3,1.5848931924611125,BB
+9,1007.766666667,0.6108226110541,2010,3,1.5848931924611125,BB
+10,1015.233333333,0.6234691532324,2010,3,1.5848931924611125,BB
+11,1014.166666667,0.5149383730485,2010,3,1.5848931924611125,BB
+12,1020.433333333,0.3721294874726,2010,3,1.5848931924611125,BB
+1,1017.4,0.2459537382871,2011,3,1.5848931924611125,BB
+2,1023.3,0.2266197596013,2011,3,1.5848931924611125,BB
+3,1028.533333333,0.2373282343791,2011,3,1.5848931924611125,BB
+4,1029.533333333,0.2461633102376,2011,3,1.5848931924611125,BB
+5,1029.9,0.2218014693983,2011,3,1.5848931924611125,BB
+6,1035.9,0.2307172506999,2011,3,1.5848931924611125,BB
+7,1042.233333333,0.3724693766591,2011,3,1.5848931924611125,BB
+8,1041.333333333,0.4742637644046,2011,3,1.5848931924611125,BB
+9,1038.766666667,0.5942945159324,2011,3,1.5848931924611125,BB
+10,1043.633333333,0.6219297965441,2011,3,1.5848931924611125,BB
+11,1040.266666667,0.5324275826711,2011,3,1.5848931924611125,BB
+12,1030.3,0.3688893202627,2011,3,1.5848931924611125,BB
+1,1028.766666667,0.2494896801996,2012,3,1.5848931924611125,BB
+2,1040.4,0.2083173138536,2012,3,1.5848931924611125,BB
+3,1043.633333333,0.2431249800377,2012,3,1.5848931924611125,BB
+4,1038.9,0.2565534058459,2012,3,1.5848931924611125,BB
+5,1049.8,0.2182320441989,2012,3,1.5848931924611125,BB
+6,1050.4,0.2407336887535,2012,3,1.5848931924611125,BB
+7,1051.833333333,0.3964506417367,2012,3,1.5848931924611125,BB
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+11,1022.133333333,0.5173167231933,2013,3,1.5848931924611125,BB
+12,1024.033333333,0.3762898343153,2013,3,1.5848931924611125,BB
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+6,1021.833333333,0.1883542652096,2014,3,1.5848931924611125,BB
+7,1020.2,0.3037966411815,2014,3,1.5848931924611125,BB
+8,1021.266666667,0.3871662641165,2014,3,1.5848931924611125,BB
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+12,1021.9,0.3262223961901,2014,3,1.5848931924611125,BB
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+12,1020.6,0.2975373963028,2015,3,1.5848931924611125,BB
+1,1022.133333333,0.1986694495173,2016,3,1.5848931924611125,BB
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+11,988.0666666667,0.5883206261386,2011,3,6.30957344480193,BB
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+1,1001.866666667,0.4542853340431,2012,3,6.30957344480193,BB
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+11,1019.733333333,0.6070541317992,2012,3,6.30957344480193,BB
+12,1016.166666667,0.5226176808266,2012,3,6.30957344480193,BB
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+7,1024.666666667,0.6890696161353,2013,3,6.30957344480193,BB
+8,1032.4,0.7235890481725,2013,3,6.30957344480193,BB
+9,1028.066666667,0.6820893586668,2013,3,6.30957344480193,BB
+10,1022.1,0.6234223657176,2013,3,6.30957344480193,BB
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+12,1031.366666667,0.5111987330726,2013,3,6.30957344480193,BB
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+8,1013.666666667,0.7059519894771,2014,3,6.30957344480193,BB
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+1,1014.266666667,0.3989417641646,2015,3,6.30957344480193,BB
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+1,965.7,0.0,2015,3,0.1,CC
+2,962.0333333333,0.0,2015,3,0.1,CC
+3,954.1333333333,0.0,2015,3,0.1,CC
+4,962.1333333333,0.0,2015,3,0.1,CC
+5,973.1333333333,0.0,2015,3,0.1,CC
+6,972.2666666667,0.0,2015,3,0.1,CC
+7,971.7333333333,0.0,2015,3,0.1,CC
+8,975.7,0.0,2015,3,0.1,CC
+9,974.8666666667,0.0,2015,3,0.1,CC
+10,976.9,0.0,2015,3,0.1,CC
+11,983.0333333333,0.0,2015,3,0.1,CC
+12,982.3666666667,0.0,2015,3,0.1,CC
+1,987.8333333333,0.0,2016,3,0.1,CC
+2,991.9333333333,0.0,2016,3,0.1,CC
+3,990.0666666667,0.0,2016,3,0.1,CC
+4,993.4,0.0,2016,3,0.1,CC
+5,995.1666666667,0.0,2016,3,0.1,CC
+6,998.2,0.0,2016,3,0.1,CC
+7,999.9333333333,0.0,2016,3,0.1,CC
+8,999.6666666667,0.0,2016,3,0.1,CC
+9,995.2666666667,0.0,2016,3,0.1,CC
+10,996.2,0.0,2016,3,0.1,CC
+11,999.1333333333,0.0,2016,3,0.1,CC
+12,995.7333333333,0.0,2016,3,0.1,CC
+1,987.8,0.0,2017,3,0.1,CC
+2,992.8666666667,0.0,2017,3,0.1,CC
+3,1001.666666667,0.0,2017,3,0.1,CC
+4,1003.1,0.0,2017,3,0.1,CC
+5,1010.933333333,0.0,2017,3,0.1,CC
+6,1009.733333333,0.0,2017,3,0.1,CC
+7,1014.733333333,0.0,2017,3,0.1,CC
+8,1018.266666667,0.0,2017,3,0.1,CC
+9,1032.366666667,0.0,2017,3,0.1,CC
+10,1037.533333333,0.0,2017,3,0.1,CC
+11,1048.766666667,0.0,2017,3,0.1,CC
+12,1048.566666667,0.0,2017,3,0.1,CC
+1,980.2,0.001020199959192,2010,3,0.025118864315095794,DD
+2,972.4,0.001131221719457,2010,3,0.025118864315095794,DD
+3,978.1333333333,0.0007838058887677,2010,3,0.025118864315095794,DD
+4,986.3333333333,0.0005407232173031,2010,3,0.025118864315095794,DD
+5,990.3666666667,0.001043384605028,2010,3,0.025118864315095794,DD
+6,986.2333333333,0.0003379862777571,2010,3,0.025118864315095794,DD
+7,986.5,0.0003378949146815,2010,3,0.025118864315095794,DD
+8,987.6666666667,3.374957813027e-05,2010,3,0.025118864315095794,DD
+9,996.8666666667,3.343810606567e-05,2010,3,0.025118864315095794,DD
+10,1004.466666667,0.0,2010,3,0.025118864315095794,DD
+11,1006.8,0.0,2010,3,0.025118864315095794,DD
+12,1016.2,0.0,2010,3,0.025118864315095794,DD
+1,1016.433333333,0.0,2011,3,0.025118864315095794,DD
+2,1017.433333333,0.0,2011,3,0.025118864315095794,DD
+3,1020.033333333,0.0,2011,3,0.025118864315095794,DD
+4,1019.166666667,0.0,2011,3,0.025118864315095794,DD
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+6,1008.533333333,0.0,2011,3,0.025118864315095794,DD
+7,1004.533333333,0.0,2011,3,0.025118864315095794,DD
+8,1001.666666667,0.0,2011,3,0.025118864315095794,DD
+9,1003.566666667,0.0,2011,3,0.025118864315095794,DD
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+11,997.8333333333,0.0,2011,3,0.025118864315095794,DD
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+1,991.1333333333,0.0,2012,3,0.025118864315095794,DD
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+6,1005.333333333,0.0,2012,3,0.025118864315095794,DD
+7,1004.866666667,0.0,2012,3,0.025118864315095794,DD
+8,1005.033333333,0.0,2012,3,0.025118864315095794,DD
+9,1005.966666667,0.0,2012,3,0.025118864315095794,DD
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+12,1009.433333333,0.0,2012,3,0.025118864315095794,DD
+1,1015.5,0.0,2013,3,0.025118864315095794,DD
+2,1018.366666667,0.0,2013,3,0.025118864315095794,DD
+3,1022.633333333,0.0,2013,3,0.025118864315095794,DD
+4,1029.7,0.0,2013,3,0.025118864315095794,DD
+5,1034.766666667,0.0,2013,3,0.025118864315095794,DD
+6,1036.166666667,0.0,2013,3,0.025118864315095794,DD
+7,1038.633333333,0.0,2013,3,0.025118864315095794,DD
+8,1036.166666667,0.0,2013,3,0.025118864315095794,DD
+9,1041.033333333,0.0,2013,3,0.025118864315095794,DD
+10,1043.133333333,0.0,2013,3,0.025118864315095794,DD
+11,1047.333333333,0.0,2013,3,0.025118864315095794,DD
+12,1058.066666667,0.0,2013,3,0.025118864315095794,DD
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+2,1048.133333333,0.0,2014,3,0.025118864315095794,DD
+3,1048.033333333,0.0,2014,3,0.025118864315095794,DD
+4,1042.466666667,0.0,2014,3,0.025118864315095794,DD
+5,1041.1,0.0,2014,3,0.025118864315095794,DD
+6,1042.466666667,0.0,2014,3,0.025118864315095794,DD
+7,1046.366666667,0.0,2014,3,0.025118864315095794,DD
+8,1048.133333333,0.0,2014,3,0.025118864315095794,DD
+9,1050.266666667,0.0,2014,3,0.025118864315095794,DD
+10,1038.933333333,0.0,2014,3,0.025118864315095794,DD
+11,1045.166666667,0.0,2014,3,0.025118864315095794,DD
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+1,1051.6,0.0,2015,3,0.025118864315095794,DD
+2,1047.366666667,0.0,2015,3,0.025118864315095794,DD
+3,1046.833333333,0.0,2015,3,0.025118864315095794,DD
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+6,1035.2,0.0,2015,3,0.025118864315095794,DD
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+12,1020.833333333,0.0,2015,3,0.025118864315095794,DD
+1,1015.833333333,0.0,2016,3,0.025118864315095794,DD
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+3,1034.533333333,0.0,2016,3,0.025118864315095794,DD
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+10,1036.266666667,0.0,2016,3,0.025118864315095794,DD
+11,1032.366666667,0.0,2016,3,0.025118864315095794,DD
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+1,1034.733333333,0.0,2017,3,0.025118864315095794,DD
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+7,1032.833333333,0.0,2017,3,0.025118864315095794,DD
+8,1033.666666667,0.0,2017,3,0.025118864315095794,DD
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+1,1008.433333333,0.236869070836,2010,3,1.5848931924611125,CC
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+8,1016.266666667,0.3408226187352,2010,3,1.5848931924611125,CC
+9,1019.233333333,0.4407234195637,2010,3,1.5848931924611125,CC
+10,1021.733333333,0.5178128670234,2010,3,1.5848931924611125,CC
+11,1022.6,0.4694569398266,2010,3,1.5848931924611125,CC
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+1,1022.266666667,0.2491848180514,2011,3,1.5848931924611125,CC
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+6,1045.5,0.2126255380201,2011,3,1.5848931924611125,CC
+7,1046.9,0.3710319355558,2011,3,1.5848931924611125,CC
+8,1047.733333333,0.4704759480784,2011,3,1.5848931924611125,CC
+9,1044.9,0.5618719494688,2011,3,1.5848931924611125,CC
+10,1044.5,0.6024892292963,2011,3,1.5848931924611125,CC
+11,1043.466666667,0.5169307436749,2011,3,1.5848931924611125,CC
+12,1039.966666667,0.3732170902914,2011,3,1.5848931924611125,CC
+1,1032.466666667,0.2438819655195,2012,3,1.5848931924611125,CC
+2,1027.366666667,0.1958729437721,2012,3,1.5848931924611125,CC
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+6,1037.933333333,0.2370736720406,2012,3,1.5848931924611125,CC
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+11,1036.766666667,0.5246117737839,2012,3,1.5848931924611125,CC
+12,1044.266666667,0.3950459652707,2012,3,1.5848931924611125,CC
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+2,1042.566666667,0.2212808133772,2013,3,1.5848931924611125,CC
+3,1047.6,0.2408043782614,2013,3,1.5848931924611125,CC
+4,1054.233333333,0.2483637398425,2013,3,1.5848931924611125,CC
+5,1051.7,0.2289309372128,2013,3,1.5848931924611125,CC
+6,1046.7,0.2477946562211,2013,3,1.5848931924611125,CC
+7,1038.133333333,0.3783714359106,2013,3,1.5848931924611125,CC
+8,1039.6,0.4773310247531,2013,3,1.5848931924611125,CC
+9,1030.766666667,0.5720014228891,2013,3,1.5848931924611125,CC
+10,1028.533333333,0.5965776510241,2013,3,1.5848931924611125,CC
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+12,1029.333333333,0.3694948186528,2013,3,1.5848931924611125,CC
+1,1023.5,0.2474189871356,2014,3,1.5848931924611125,CC
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+3,1031.233333333,0.2174095742962,2014,3,1.5848931924611125,CC
+4,1024.5,0.2203676590207,2014,3,1.5848931924611125,CC
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+6,1039.9,0.2186107638555,2014,3,1.5848931924611125,CC
+7,1044.466666667,0.359641284228,2014,3,1.5848931924611125,CC
+8,1042.566666667,0.4537199859322,2014,3,1.5848931924611125,CC
+9,1043.3,0.5627975334675,2014,3,1.5848931924611125,CC
+10,1034.466666667,0.6018238061481,2014,3,1.5848931924611125,CC
+11,1031.2,0.4992242048099,2014,3,1.5848931924611125,CC
+12,1028.633333333,0.3522149129913,2014,3,1.5848931924611125,CC
+1,1026.933333333,0.2311088029083,2015,3,1.5848931924611125,CC
+2,1030.2,0.1898013330745,2015,3,1.5848931924611125,CC
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+5,1027.166666667,0.1217264319325,2015,3,1.5848931924611125,CC
+6,1025.7,0.130285008612,2015,3,1.5848931924611125,CC
+7,1030.2,0.2018378308419,2015,3,1.5848931924611125,CC
+8,1029.7,0.263345310932,2015,3,1.5848931924611125,CC
+9,1025.4,0.3641505753852,2015,3,1.5848931924611125,CC
+10,1021.633333333,0.4287578713824,2015,3,1.5848931924611125,CC
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+12,1025.633333333,0.3099223244174,2015,3,1.5848931924611125,CC
+1,1022.5,0.1936430317848,2016,3,1.5848931924611125,CC
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+3,1014.8,0.1615096570753,2016,3,1.5848931924611125,CC
+4,1012.666666667,0.1505595786702,2016,3,1.5848931924611125,CC
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+6,990.5,0.1465589769477,2016,3,1.5848931924611125,CC
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+8,989.9333333333,0.3306283251397,2016,3,1.5848931924611125,CC
+9,992.5333333333,0.4353506179473,2016,3,1.5848931924611125,CC
+10,1002.7,0.5235863169443,2016,3,1.5848931924611125,CC
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+12,1005.333333333,0.3520557029178,2016,3,1.5848931924611125,CC
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+6,1030.1,0.1680095783581,2017,3,1.5848931924611125,CC
+7,1029.166666667,0.2771821862348,2017,3,1.5848931924611125,CC
+8,1020.566666667,0.3453963484339,2017,3,1.5848931924611125,CC
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+12,1015.133333333,0.3112891574177,2017,3,1.5848931924611125,CC
+1,990.8333333333,0.1237342304458,2010,3,0.39810717055349715,CC
+2,995.3,0.09528115476071,2010,3,0.39810717055349715,CC
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+8,993.8666666667,0.07774349342635,2010,3,0.39810717055349715,CC
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+10,997.1666666667,0.1329099114157,2010,3,0.39810717055349715,CC
+11,1003.933333333,0.135533567966,2010,3,0.39810717055349715,CC
+12,1005.866666667,0.1161519088017,2010,3,0.39810717055349715,CC
+1,1010.233333333,0.0819942587521,2011,3,0.39810717055349715,CC
+2,1005.5,0.0665340626554,2011,3,0.39810717055349715,CC
+3,1003.733333333,0.05177337938363,2011,3,0.39810717055349715,CC
+4,1002.6,0.04797526431279,2011,3,0.39810717055349715,CC
+5,1001.266666667,0.03961648578467,2011,3,0.39810717055349715,CC
+6,1012.5,0.03934156378601,2011,3,0.39810717055349715,CC
+7,1022.566666667,0.06591257293738,2011,3,0.39810717055349715,CC
+8,1031.9,0.08741157088865,2011,3,0.39810717055349715,CC
+9,1029.8,0.1246844047388,2011,3,0.39810717055349715,CC
+10,1025.233333333,0.179341288162,2011,3,0.39810717055349715,CC
+11,1026.4,0.1722200571577,2011,3,0.39810717055349715,CC
+12,1026.433333333,0.142824667944,2011,3,0.39810717055349715,CC
+1,1027.3,0.1123333008858,2012,3,0.39810717055349715,CC
+2,1028.2,0.09301043895481,2012,3,0.39810717055349715,CC
+3,1028.8,0.08858216692587,2012,3,0.39810717055349715,CC
+4,1024.6,0.08510638297872,2012,3,0.39810717055349715,CC
+5,1013.566666667,0.07455520110501,2012,3,0.39810717055349715,CC
+6,1002.566666667,0.06376965787811,2012,3,0.39810717055349715,CC
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+8,999.0,0.1287954621288,2012,3,0.39810717055349715,CC
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+10,989.4666666667,0.2513138391052,2012,3,0.39810717055349715,CC
+11,990.0,0.2449831649832,2012,3,0.39810717055349715,CC
+12,982.9666666667,0.1797619451321,2012,3,0.39810717055349715,CC
+1,986.8333333333,0.118561053876,2013,3,0.39810717055349715,CC
+2,991.2666666667,0.0962405003699,2013,3,0.39810717055349715,CC
+3,984.4,0.08834484626845,2013,3,0.39810717055349715,CC
+4,982.6666666667,0.06777476255088,2013,3,0.39810717055349715,CC
+5,986.3,0.05431072357971,2013,3,0.39810717055349715,CC
+6,988.7666666667,0.05151198462731,2013,3,0.39810717055349715,CC
+7,997.3666666667,0.07900805454363,2013,3,0.39810717055349715,CC
+8,1007.666666667,0.1119086999669,2013,3,0.39810717055349715,CC
+9,1020.066666667,0.1588131494674,2013,3,0.39810717055349715,CC
+10,1021.833333333,0.2080247920405,2013,3,0.39810717055349715,CC
+11,1016.633333333,0.2026295944129,2013,3,0.39810717055349715,CC
+12,1010.166666667,0.1718198317109,2013,3,0.39810717055349715,CC
+1,1004.6,0.1079700046453,2014,3,0.39810717055349715,CC
+2,1002.5,0.08389027431421,2014,3,0.39810717055349715,CC
+3,996.0666666667,0.07944582022622,2014,3,0.39810717055349715,CC
+4,998.0333333333,0.07187468688421,2014,3,0.39810717055349715,CC
+5,1004.2,0.05619730465379,2014,3,0.39810717055349715,CC
+6,1003.833333333,0.05625103768886,2014,3,0.39810717055349715,CC
+7,994.5,0.08024132730015,2014,3,0.39810717055349715,CC
+8,990.9,0.1045514179029,2014,3,0.39810717055349715,CC
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+11,1004.666666667,0.2000663570007,2014,3,0.39810717055349715,CC
+12,1000.9,0.1522296599727,2014,3,0.39810717055349715,CC
+1,989.1333333333,0.1098604839253,2015,3,0.39810717055349715,CC
+2,988.9666666667,0.07330884087768,2015,3,0.39810717055349715,CC
+3,979.7,0.05658194685448,2015,3,0.39810717055349715,CC
+4,977.7,0.05212914663666,2015,3,0.39810717055349715,CC
+5,976.7333333333,0.04061156235069,2015,3,0.39810717055349715,CC
+6,975.4,0.04059872872668,2015,3,0.39810717055349715,CC
+7,965.9,0.05311108810436,2015,3,0.39810717055349715,CC
+8,956.3666666667,0.06346938064201,2015,3,0.39810717055349715,CC
+9,965.6333333333,0.07891194035003,2015,3,0.39810717055349715,CC
+10,968.0333333333,0.09589890155298,2015,3,0.39810717055349715,CC
+11,969.0333333333,0.08513639021705,2015,3,0.39810717055349715,CC
+12,970.2,0.06507249364392,2015,3,0.39810717055349715,CC
+1,967.5,0.03769164513351,2016,3,0.39810717055349715,CC
+2,970.6666666667,0.02973901098901,2016,3,0.39810717055349715,CC
+3,960.7,0.03695222233788,2016,3,0.39810717055349715,CC
+4,949.7333333333,0.03741401095044,2016,3,0.39810717055349715,CC
+5,946.9,0.03122469813778,2016,3,0.39810717055349715,CC
+6,944.2,0.02651274447504,2016,3,0.39810717055349715,CC
+7,947.4666666667,0.04081058260625,2016,3,0.39810717055349715,CC
+8,945.1,0.04320530455331,2016,3,0.39810717055349715,CC
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+11,946.8333333333,0.09966555183946,2016,3,0.39810717055349715,CC
+12,947.8,0.0791306182739,2016,3,0.39810717055349715,CC
+1,947.8666666667,0.05369953579969,2017,3,0.39810717055349715,CC
+2,950.5,0.03934771173067,2017,3,0.39810717055349715,CC
+3,956.8,0.03605769230769,2017,3,0.39810717055349715,CC
+4,963.6,0.02853881278539,2017,3,0.39810717055349715,CC
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+6,968.9,0.02735060377748,2017,3,0.39810717055349715,CC
+7,969.1333333333,0.03343193231066,2017,3,0.39810717055349715,CC
+8,965.5,0.04881753840842,2017,3,0.39810717055349715,CC
+9,969.9666666667,0.06759682463315,2017,3,0.39810717055349715,CC
+10,974.5333333333,0.09891914078533,2017,3,0.39810717055349715,CC
+11,974.5333333333,0.09194144205774,2017,3,0.39810717055349715,CC
+12,981.5333333333,0.07596957141887,2017,3,0.39810717055349715,CC
+1,958.2,0.4420441104849,2010,3,6.30957344480193,CC
+2,955.5666666667,0.4355879582796,2010,3,6.30957344480193,CC
+3,957.8333333333,0.4913172089786,2010,3,6.30957344480193,CC
+4,955.5666666667,0.5168311996372,2010,3,6.30957344480193,CC
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+11,947.7333333333,0.5475872256612,2010,3,6.30957344480193,CC
+12,952.7666666667,0.4732183465696,2010,3,6.30957344480193,CC
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+4,965.4333333333,0.5532230777198,2011,3,6.30957344480193,CC
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+6,958.3,0.5019305019305,2011,3,6.30957344480193,CC
+7,964.8333333333,0.6978752807048,2011,3,6.30957344480193,CC
+8,967.6,0.7144825685545,2011,3,6.30957344480193,CC
+9,974.9666666667,0.6760573011043,2011,3,6.30957344480193,CC
+10,974.4666666667,0.6189026476021,2011,3,6.30957344480193,CC
+11,977.6666666667,0.5760654619843,2011,3,6.30957344480193,CC
+12,980.2333333333,0.5058319447751,2011,3,6.30957344480193,CC
+1,991.7333333333,0.4500537778973,2012,3,6.30957344480193,CC
+2,991.4333333333,0.4640083380964,2012,3,6.30957344480193,CC
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+9,993.5333333333,0.6811380259008,2012,3,6.30957344480193,CC
+10,997.0333333333,0.6108455083414,2012,3,6.30957344480193,CC
+11,997.1333333333,0.5650865815337,2012,3,6.30957344480193,CC
+12,996.3333333333,0.4904315824691,2012,3,6.30957344480193,CC
+1,1001.8,0.4584747454582,2013,3,6.30957344480193,CC
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+3,1015.3,0.548376506123,2013,3,6.30957344480193,CC
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+6,1018.133333333,0.5350641697224,2013,3,6.30957344480193,CC
+7,1015.2,0.7043932230102,2013,3,6.30957344480193,CC
+8,1013.4,0.7078152753108,2013,3,6.30957344480193,CC
+9,1018.233333333,0.6667103152519,2013,3,6.30957344480193,CC
+10,1012.233333333,0.6184674152863,2013,3,6.30957344480193,CC
+11,1012.166666667,0.570525275811,2013,3,6.30957344480193,CC
+12,1014.633333333,0.5094122671573,2013,3,6.30957344480193,CC
+1,1027.133333333,0.4538521451288,2014,3,6.30957344480193,CC
+2,1026.733333333,0.4716901499903,2014,3,6.30957344480193,CC
+3,1021.6,0.5178478204124,2014,3,6.30957344480193,CC
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+6,1013.633333333,0.5030089776053,2014,3,6.30957344480193,CC
+7,1011.533333333,0.6948856521453,2014,3,6.30957344480193,CC
+8,1009.466666667,0.7119601109497,2014,3,6.30957344480193,CC
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+10,997.8666666667,0.6167490646713,2014,3,6.30957344480193,CC
+11,992.5,0.5586901763224,2014,3,6.30957344480193,CC
+12,990.4333333333,0.4933867330798,2014,3,6.30957344480193,CC
+1,991.8333333333,0.4344143841371,2015,3,6.30957344480193,CC
+2,997.8,0.3986102759404,2015,3,6.30957344480193,CC
+3,995.9666666667,0.438234211319,2015,3,6.30957344480193,CC
+4,1002.933333333,0.4225272533901,2015,3,6.30957344480193,CC
+5,999.2333333333,0.4033425626314,2015,3,6.30957344480193,CC
+6,994.2666666667,0.4093133968084,2015,3,6.30957344480193,CC
+7,990.9333333333,0.6099973089343,2015,3,6.30957344480193,CC
+8,989.4666666667,0.668912545479,2015,3,6.30957344480193,CC
+9,984.1666666667,0.645283657917,2015,3,6.30957344480193,CC
+10,985.4666666667,0.5787444188878,2015,3,6.30957344480193,CC
+11,988.4666666667,0.5267417549066,2015,3,6.30957344480193,CC
+12,988.0333333333,0.4507270335009,2015,3,6.30957344480193,CC
+1,981.1666666667,0.3604552403601,2016,3,6.30957344480193,CC
+2,977.5,0.3788235294118,2016,3,6.30957344480193,CC
+3,978.3,0.4261133258373,2016,3,6.30957344480193,CC
+4,975.0666666667,0.4435594147409,2016,3,6.30957344480193,CC
+5,970.7666666667,0.4085087387975,2016,3,6.30957344480193,CC
+6,966.4666666667,0.4622335655653,2016,3,6.30957344480193,CC
+7,965.2333333333,0.6458887315675,2016,3,6.30957344480193,CC
+8,973.8333333333,0.679719322266,2016,3,6.30957344480193,CC
+9,978.5,0.6319196048373,2016,3,6.30957344480193,CC
+10,970.9666666667,0.5684369528648,2016,3,6.30957344480193,CC
+11,974.6,0.5233258088789,2016,3,6.30957344480193,CC
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+1,975.1666666667,0.359904289865,2017,3,6.30957344480193,CC
+2,978.2,0.3975669597219,2017,3,6.30957344480193,CC
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+4,975.2666666667,0.4401189418279,2017,3,6.30957344480193,CC
+5,984.8,0.4170051448687,2017,3,6.30957344480193,CC
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+8,978.7,0.6312795885699,2017,3,6.30957344480193,CC
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+11,968.7333333333,0.5082926157869,2017,3,6.30957344480193,CC
+12,967.5333333333,0.4263419003652,2017,3,6.30957344480193,CC
+1,979.3333333333,0.005854322668482,2010,3,0.1,DD
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+1,970.2666666667,0.009378864916861,2011,3,0.1,DD
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+7,972.4333333333,0.006478593219758,2011,3,0.1,DD
+8,969.9666666667,0.009656689233307,2011,3,0.1,DD
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+11,970.3666666667,0.02012984782385,2011,3,0.1,DD
+12,971.5333333333,0.01379263020655,2011,3,0.1,DD
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+2,976.4,0.007271609995903,2012,3,0.1,DD
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+4,993.1333333333,0.006444250520239,2012,3,0.1,DD
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+7,1000.366666667,0.002032588051048,2012,3,0.1,DD
+8,998.4333333333,0.009181050312156,2012,3,0.1,DD
+9,998.1333333333,0.01409297355063,2012,3,0.1,DD
+10,1007.7,0.01194138467136,2012,3,0.1,DD
+11,1004.9,0.01137758317577,2012,3,0.1,DD
+12,1002.7,0.009574149795552,2012,3,0.1,DD
+1,1013.833333333,0.008811441722834,2013,3,0.1,DD
+2,1012.4,0.008198340576847,2013,3,0.1,DD
+3,1013.266666667,0.006447792617935,2013,3,0.1,DD
+4,1011.633333333,0.003327951497578,2013,3,0.1,DD
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+6,1016.533333333,0.001770724029381,2013,3,0.1,DD
+7,1019.066666667,0.003696192594531,2013,3,0.1,DD
+8,1021.833333333,0.005415103572011,2013,3,0.1,DD
+9,1021.0,0.003885079986941,2013,3,0.1,DD
+10,1020.166666667,0.008691390295703,2013,3,0.1,DD
+11,1022.133333333,0.006652752413253,2013,3,0.1,DD
+12,1020.666666667,0.003461789679948,2013,3,0.1,DD
+1,1030.366666667,0.003590954676329,2014,3,0.1,DD
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+4,1015.833333333,0.002526661197703,2014,3,0.1,DD
+5,1018.566666667,0.00265078378113,2014,3,0.1,DD
+6,1014.466666667,0.002727212985477,2014,3,0.1,DD
+7,1019.4,0.003139101432215,2014,3,0.1,DD
+8,1023.033333333,0.002150467563781,2014,3,0.1,DD
+9,1024.1,0.007095661231,2014,3,0.1,DD
+10,1022.466666667,0.005835561061485,2014,3,0.1,DD
+11,1021.9,0.003653325504779,2014,3,0.1,DD
+12,1013.9,0.002432850050958,2014,3,0.1,DD
+1,1014.266666667,0.002793479689759,2015,3,0.1,DD
+2,1008.6,0.003172714653976,2015,3,0.1,DD
+3,1016.166666667,0.001869772019026,2015,3,0.1,DD
+4,1019.333333333,0.000654022236756,2015,3,0.1,DD
+5,1017.566666667,0.0007206735021456,2015,3,0.1,DD
+6,1016.5,0.002787342187244,2015,3,0.1,DD
+7,1007.966666667,0.0009259565461821,2015,3,0.1,DD
+8,1011.8,6.588917440864e-05,2015,3,0.1,DD
+9,1016.8,3.278258589038e-05,2015,3,0.1,DD
+10,1013.966666667,0.0002629935237845,2015,3,0.1,DD
+11,1014.1,0.0,2015,3,0.1,DD
+12,1009.633333333,0.0,2015,3,0.1,DD
+1,1003.533333333,0.0,2016,3,0.1,DD
+2,1005.333333333,0.0,2016,3,0.1,DD
+3,1014.533333333,0.0,2016,3,0.1,DD
+4,1014.466666667,0.0,2016,3,0.1,DD
+5,1013.133333333,0.0,2016,3,0.1,DD
+6,1014.333333333,0.0,2016,3,0.1,DD
+7,1009.9,0.0,2016,3,0.1,DD
+8,1011.333333333,6.59195781147e-05,2016,3,0.1,DD
+9,1009.0,0.0009910802775025,2016,3,0.1,DD
+10,1009.7,0.0004621834868443,2016,3,0.1,DD
+11,998.9,0.000133480161511,2016,3,0.1,DD
+12,985.3333333333,0.0,2016,3,0.1,DD
+1,982.5333333333,0.0,2017,3,0.1,DD
+2,978.1,0.0,2017,3,0.1,DD
+3,980.3666666667,0.0,2017,3,0.1,DD
+4,984.6,0.0,2017,3,0.1,DD
+5,990.1666666667,0.0,2017,3,0.1,DD
+6,995.1,0.0,2017,3,0.1,DD
+7,990.3,0.0,2017,3,0.1,DD
+8,992.5666666667,0.0,2017,3,0.1,DD
+9,988.0,0.0,2017,3,0.1,DD
+10,994.5666666667,0.0,2017,3,0.1,DD
+11,1003.433333333,0.0,2017,3,0.1,DD
+12,999.0,0.0,2017,3,0.1,DD
+1,1007.5,0.2351695616212,2010,3,1.5848931924611125,DD
+2,1003.633333333,0.2226908897672,2010,3,1.5848931924611125,DD
+3,1002.066666667,0.2336504557248,2010,3,1.5848931924611125,DD
+4,1008.6,0.2639302002776,2010,3,1.5848931924611125,DD
+5,1006.9,0.2264375806932,2010,3,1.5848931924611125,DD
+6,1010.833333333,0.2404286892003,2010,3,1.5848931924611125,DD
+7,1011.0,0.4119024068579,2010,3,1.5848931924611125,DD
+8,1012.766666667,0.5045255570549,2010,3,1.5848931924611125,DD
+9,1008.033333333,0.6162163949605,2010,3,1.5848931924611125,DD
+10,1012.433333333,0.6229545978336,2010,3,1.5848931924611125,DD
+11,1013.466666667,0.5221681357716,2010,3,1.5848931924611125,DD
+12,1013.9,0.3654206529244,2010,3,1.5848931924611125,DD
+1,1016.833333333,0.2518931322734,2011,3,1.5848931924611125,DD
+2,1011.833333333,0.21495634986,2011,3,1.5848931924611125,DD
+3,1009.466666667,0.2373860784573,2011,3,1.5848931924611125,DD
+4,1011.266666667,0.238578680203,2011,3,1.5848931924611125,DD
+5,1020.766666667,0.2068379975835,2011,3,1.5848931924611125,DD
+6,1021.6,0.2218415557296,2011,3,1.5848931924611125,DD
+7,1020.733333333,0.4044804389001,2011,3,1.5848931924611125,DD
+8,1024.366666667,0.4787673684553,2011,3,1.5848931924611125,DD
+9,1016.533333333,0.5986030954879,2011,3,1.5848931924611125,DD
+10,1022.166666667,0.6094896461764,2011,3,1.5848931924611125,DD
+11,1022.433333333,0.5225116552016,2011,3,1.5848931924611125,DD
+12,1020.4,0.3833464001045,2011,3,1.5848931924611125,DD
+1,1018.366666667,0.2703348499231,2012,3,1.5848931924611125,DD
+2,1024.866666667,0.2348272946074,2012,3,1.5848931924611125,DD
+3,1028.9,0.2362717465254,2012,3,1.5848931924611125,DD
+4,1031.466666667,0.2492567218201,2012,3,1.5848931924611125,DD
+5,1040.033333333,0.2407294637992,2012,3,1.5848931924611125,DD
+6,1047.133333333,0.2334309543516,2012,3,1.5848931924611125,DD
+7,1050.433333333,0.4006917779964,2012,3,1.5848931924611125,DD
+8,1056.9,0.5088150881509,2012,3,1.5848931924611125,DD
+9,1053.4,0.5939497500158,2012,3,1.5848931924611125,DD
+10,1056.133333333,0.6120439338467,2012,3,1.5848931924611125,DD
+11,1044.8,0.5256189382338,2012,3,1.5848931924611125,DD
+12,1035.533333333,0.3729157278053,2012,3,1.5848931924611125,DD
+1,1030.833333333,0.247793047696,2013,3,1.5848931924611125,DD
+2,1028.4,0.210974977311,2013,3,1.5848931924611125,DD
+3,1037.933333333,0.214753677179,2013,3,1.5848931924611125,DD
+4,1037.366666667,0.2269207287684,2013,3,1.5848931924611125,DD
+5,1028.033333333,0.196070166337,2013,3,1.5848931924611125,DD
+6,1023.6,0.198873257783,2013,3,1.5848931924611125,DD
+7,1014.433333333,0.3422271875924,2013,3,1.5848931924611125,DD
+8,1011.4,0.4394898160965,2013,3,1.5848931924611125,DD
+9,999.5333333333,0.543353564997,2013,3,1.5848931924611125,DD
+10,996.0333333333,0.595796660085,2013,3,1.5848931924611125,DD
+11,998.6,0.5304092396021,2013,3,1.5848931924611125,DD
+12,1001.6,0.3870473908413,2013,3,1.5848931924611125,DD
+1,1007.3,0.239087990999,2014,3,1.5848931924611125,DD
+2,1009.866666667,0.2078822286771,2014,3,1.5848931924611125,DD
+3,1015.066666667,0.2025154341258,2014,3,1.5848931924611125,DD
+4,1009.333333333,0.1985468956407,2014,3,1.5848931924611125,DD
+5,1011.666666667,0.1823064250412,2014,3,1.5848931924611125,DD
+6,1015.233333333,0.1861312670322,2014,3,1.5848931924611125,DD
+7,1009.266666667,0.2985335887443,2014,3,1.5848931924611125,DD
+8,999.1333333333,0.387435777674,2014,3,1.5848931924611125,DD
+9,990.1333333333,0.4976434150283,2014,3,1.5848931924611125,DD
+10,989.4,0.5523549626036,2014,3,1.5848931924611125,DD
+11,987.3666666667,0.484014719287,2014,3,1.5848931924611125,DD
+12,990.9333333333,0.3428417653391,2014,3,1.5848931924611125,DD
+1,988.1666666667,0.211334120425,2015,3,1.5848931924611125,DD
+2,989.0,0.1737108190091,2015,3,1.5848931924611125,DD
+3,996.9666666667,0.1519609482096,2015,3,1.5848931924611125,DD
+4,1001.366666667,0.1674045471189,2015,3,1.5848931924611125,DD
+5,1009.366666667,0.1586803606222,2015,3,1.5848931924611125,DD
+6,1014.066666667,0.1626454539478,2015,3,1.5848931924611125,DD
+7,1006.533333333,0.2612597695059,2015,3,1.5848931924611125,DD
+8,1008.566666667,0.3340714545394,2015,3,1.5848931924611125,DD
+9,1009.2,0.4140903686088,2015,3,1.5848931924611125,DD
+10,1004.066666667,0.4564437952327,2015,3,1.5848931924611125,DD
+11,1003.833333333,0.417864851403,2015,3,1.5848931924611125,DD
+12,1010.233333333,0.3000626917874,2015,3,1.5848931924611125,DD
+1,1006.333333333,0.1945677376615,2016,3,1.5848931924611125,DD
+2,1006.5,0.1509521443948,2016,3,1.5848931924611125,DD
+3,1016.6,0.1531575841039,2016,3,1.5848931924611125,DD
+4,1017.666666667,0.1422207664592,2016,3,1.5848931924611125,DD
+5,1013.666666667,0.1186780664255,2016,3,1.5848931924611125,DD
+6,1014.566666667,0.1363143542399,2016,3,1.5848931924611125,DD
+7,1015.766666667,0.2465789387326,2016,3,1.5848931924611125,DD
+8,1021.033333333,0.2975417061147,2016,3,1.5848931924611125,DD
+9,1021.166666667,0.391512975355,2016,3,1.5848931924611125,DD
+10,1023.4,0.4451827242525,2016,3,1.5848931924611125,DD
+11,1016.733333333,0.4076126155662,2016,3,1.5848931924611125,DD
+12,1011.5,0.2951392321635,2016,3,1.5848931924611125,DD
+1,1009.9,0.185298874476,2017,3,1.5848931924611125,DD
+2,1000.2,0.1539692061588,2017,3,1.5848931924611125,DD
+3,989.6666666667,0.1527787133715,2017,3,1.5848931924611125,DD
+4,992.9,0.1511733306476,2017,3,1.5848931924611125,DD
+5,996.7333333333,0.1286536017658,2017,3,1.5848931924611125,DD
+6,996.6,0.1323834370192,2017,3,1.5848931924611125,DD
+7,992.9333333333,0.2088424869075,2017,3,1.5848931924611125,DD
+8,1000.8,0.2865707434053,2017,3,1.5848931924611125,DD
+9,1009.266666667,0.4024043860229,2017,3,1.5848931924611125,DD
+10,1007.5,0.4754011579818,2017,3,1.5848931924611125,DD
+11,1014.533333333,0.42144171376,2017,3,1.5848931924611125,DD
+12,1015.9,0.3041966072776,2017,3,1.5848931924611125,DD
+1,990.9666666667,0.1218338995594,2010,3,0.39810717055349715,DD
+2,993.9,0.09367139551263,2010,3,0.39810717055349715,DD
+3,990.8,0.09208047369129,2010,3,0.39810717055349715,DD
+4,994.3666666667,0.08792866481177,2010,3,0.39810717055349715,DD
+5,989.3,0.07466558846322,2010,3,0.39810717055349715,DD
+6,984.7333333333,0.07521494820933,2010,3,0.39810717055349715,DD
+7,978.3,0.1156768544073,2010,3,0.39810717055349715,DD
+8,981.7333333333,0.1356104848567,2010,3,0.39810717055349715,DD
+9,983.9666666667,0.1874724753549,2010,3,0.39810717055349715,DD
+10,982.9333333333,0.2644126424308,2010,3,0.39810717055349715,DD
+11,983.0333333333,0.2533654335221,2010,3,0.39810717055349715,DD
+12,982.3666666667,0.1840114010383,2010,3,0.39810717055349715,DD
+1,995.7666666667,0.1309208984702,2011,3,0.39810717055349715,DD
+2,1004.833333333,0.1174655830154,2011,3,0.39810717055349715,DD
+3,1010.833333333,0.1031821929101,2011,3,0.39810717055349715,DD
+4,1010.566666667,0.08750865850843,2011,3,0.39810717055349715,DD
+5,1016.1,0.0732539448217,2011,3,0.39810717055349715,DD
+6,1020.633333333,0.07459420621183,2011,3,0.39810717055349715,DD
+7,1023.166666667,0.1120052125753,2011,3,0.39810717055349715,DD
+8,1027.566666667,0.1425049469621,2011,3,0.39810717055349715,DD
+9,1031.033333333,0.201222074941,2011,3,0.39810717055349715,DD
+10,1032.7,0.243923695168,2011,3,0.39810717055349715,DD
+11,1035.433333333,0.2281814377233,2011,3,0.39810717055349715,DD
+12,1040.433333333,0.1716912824785,2011,3,0.39810717055349715,DD
+1,1035.433333333,0.1204326690918,2012,3,0.39810717055349715,DD
+2,1032.266666667,0.08660552828726,2012,3,0.39810717055349715,DD
+3,1039.666666667,0.08669445335043,2012,3,0.39810717055349715,DD
+4,1035.566666667,0.08040686258731,2012,3,0.39810717055349715,DD
+5,1033.166666667,0.06555896112276,2012,3,0.39810717055349715,DD
+6,1034.833333333,0.07147688838782,2012,3,0.39810717055349715,DD
+7,1039.933333333,0.1117699852555,2012,3,0.39810717055349715,DD
+8,1039.533333333,0.1528570512409,2012,3,0.39810717055349715,DD
+9,1037.433333333,0.1983099315619,2012,3,0.39810717055349715,DD
+10,1037.2,0.2387839053863,2012,3,0.39810717055349715,DD
+11,1033.733333333,0.2298787566103,2012,3,0.39810717055349715,DD
+12,1029.466666667,0.1634503302681,2012,3,0.39810717055349715,DD
+1,1027.466666667,0.1096548144303,2013,3,0.39810717055349715,DD
+2,1022.333333333,0.08483860449951,2013,3,0.39810717055349715,DD
+3,1019.6,0.06616973976723,2013,3,0.39810717055349715,DD
+4,1026.6,0.06149749983765,2013,3,0.39810717055349715,DD
+5,1031.7,0.05114535879293,2013,3,0.39810717055349715,DD
+6,1031.5,0.04634028114397,2013,3,0.39810717055349715,DD
+7,1026.3,0.06710188703758,2013,3,0.39810717055349715,DD
+8,1024.4,0.09485227124821,2013,3,0.39810717055349715,DD
+9,1032.733333333,0.1154218578529,2013,3,0.39810717055349715,DD
+10,1045.266666667,0.1728107659927,2013,3,0.39810717055349715,DD
+11,1041.366666667,0.1654556512276,2013,3,0.39810717055349715,DD
+12,1030.066666667,0.1371108666106,2013,3,0.39810717055349715,DD
+1,1026.6,0.1024741866355,2014,3,0.39810717055349715,DD
+2,1027.2,0.06980140186916,2014,3,0.39810717055349715,DD
+3,1022.833333333,0.07081635978491,2014,3,0.39810717055349715,DD
+4,1020.233333333,0.06018231123599,2014,3,0.39810717055349715,DD
+5,1014.433333333,0.04583839910623,2014,3,0.39810717055349715,DD
+6,1018.166666667,0.03630708790309,2014,3,0.39810717055349715,DD
+7,1020.066666667,0.05597673354683,2014,3,0.39810717055349715,DD
+8,1021.0,0.07479595168136,2014,3,0.39810717055349715,DD
+9,1019.066666667,0.1175258406385,2014,3,0.39810717055349715,DD
+10,1012.966666667,0.160090822337,2014,3,0.39810717055349715,DD
+11,1014.7,0.1537400216813,2014,3,0.39810717055349715,DD
+12,1015.833333333,0.1354552912223,2014,3,0.39810717055349715,DD
+1,1012.133333333,0.0799631142142,2015,3,0.39810717055349715,DD
+2,1011.7,0.06447892985404,2015,3,0.39810717055349715,DD
+3,1014.5,0.06055528174799,2015,3,0.39810717055349715,DD
+4,1018.733333333,0.05336692624828,2015,3,0.39810717055349715,DD
+5,1011.766666667,0.0413797647679,2015,3,0.39810717055349715,DD
+6,1008.3,0.04165426956263,2015,3,0.39810717055349715,DD
+7,1006.9,0.06273380342305,2015,3,0.39810717055349715,DD
+8,1007.533333333,0.07169324422682,2015,3,0.39810717055349715,DD
+9,1008.166666667,0.09684245329807,2015,3,0.39810717055349715,DD
+10,995.9666666667,0.1086716422906,2015,3,0.39810717055349715,DD
+11,991.4333333333,0.1091685438591,2015,3,0.39810717055349715,DD
+12,987.4666666667,0.08567377801782,2015,3,0.39810717055349715,DD
+1,982.1333333333,0.05861390171056,2016,3,0.39810717055349715,DD
+2,978.3666666667,0.04296276106436,2016,3,0.39810717055349715,DD
+3,979.9333333333,0.03265528267229,2016,3,0.39810717055349715,DD
+4,978.9666666667,0.0320746365215,2016,3,0.39810717055349715,DD
+5,977.7333333333,0.0245124778399,2016,3,0.39810717055349715,DD
+6,977.8666666667,0.02300927188437,2016,3,0.39810717055349715,DD
+7,976.9333333333,0.03548519175652,2016,3,0.39810717055349715,DD
+8,973.6333333333,0.05433256872882,2016,3,0.39810717055349715,DD
+9,969.3333333333,0.06403026134801,2016,3,0.39810717055349715,DD
+10,973.1333333333,0.09556758237994,2016,3,0.39810717055349715,DD
+11,963.1,0.08590315993493,2016,3,0.39810717055349715,DD
+12,955.7333333333,0.06277901785714,2016,3,0.39810717055349715,DD
+1,951.9666666667,0.03655590181729,2017,3,0.39810717055349715,DD
+2,951.3333333333,0.03377715487036,2017,3,0.39810717055349715,DD
+3,946.9333333333,0.02798507462687,2017,3,0.39810717055349715,DD
+4,940.6,0.02891771209866,2017,3,0.39810717055349715,DD
+5,945.4,0.02669064240886,2017,3,0.39810717055349715,DD
+6,946.4666666667,0.02454743959992,2017,3,0.39810717055349715,DD
+7,951.1666666667,0.03371298405467,2017,3,0.39810717055349715,DD
+8,958.4,0.04879660545353,2017,3,0.39810717055349715,DD
+9,956.9,0.07590483157418,2017,3,0.39810717055349715,DD
+10,958.6333333333,0.1117563197608,2017,3,0.39810717055349715,DD
+11,965.9333333333,0.1036303402581,2017,3,0.39810717055349715,DD
+12,967.7,0.0721986841652,2017,3,0.39810717055349715,DD
+1,1012.833333333,0.6206022708573,2010,3,25.118864315095795,CC
+2,1012.8,0.6481371774618,2010,3,25.118864315095795,CC
+3,1013.3,0.6721273726109,2010,3,25.118864315095795,CC
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+5,1015.266666667,0.6118261212161,2010,3,25.118864315095795,CC
+6,1015.533333333,0.650331517101,2010,3,25.118864315095795,CC
+7,1010.6,0.74661917013,2010,3,25.118864315095795,CC
+8,1003.266666667,0.6995813675327,2010,3,25.118864315095795,CC
+9,1003.466666667,0.6423731065639,2010,3,25.118864315095795,CC
+10,1001.733333333,0.653766804206,2010,3,25.118864315095795,CC
+11,1005.5,0.645682081883,2010,3,25.118864315095795,CC
+12,1009.966666667,0.6175451335028,2010,3,25.118864315095795,CC
+1,1020.933333333,0.6103565365025,2011,3,25.118864315095795,CC
+2,1019.166666667,0.6707767784137,2011,3,25.118864315095795,CC
+3,1016.033333333,0.705652701683,2011,3,25.118864315095795,CC
+4,1018.0,0.6768827766863,2011,3,25.118864315095795,CC
+5,1018.333333333,0.5888379705401,2011,3,25.118864315095795,CC
+6,1023.266666667,0.6528438334745,2011,3,25.118864315095795,CC
+7,1024.266666667,0.7428078625358,2011,3,25.118864315095795,CC
+8,1023.766666667,0.7011037671344,2011,3,25.118864315095795,CC
+9,1019.766666667,0.6530905762756,2011,3,25.118864315095795,CC
+10,1014.233333333,0.6578039241463,2011,3,25.118864315095795,CC
+11,1016.8,0.6623393653291,2011,3,25.118864315095795,CC
+12,1011.166666667,0.6297346299654,2011,3,25.118864315095795,CC
+1,1017.566666667,0.6012382481082,2012,3,25.118864315095795,CC
+2,1021.1,0.6567100838965,2012,3,25.118864315095795,CC
+3,1023.9,0.693980531953,2012,3,25.118864315095795,CC
+4,1026.266666667,0.6738664414707,2012,3,25.118864315095795,CC
+5,1021.566666667,0.6220510979868,2012,3,25.118864315095795,CC
+6,1026.4,0.6614055598857,2012,3,25.118864315095795,CC
+7,1028.133333333,0.7337569705615,2012,3,25.118864315095795,CC
+8,1024.3,0.6889257704449,2012,3,25.118864315095795,CC
+9,1024.2,0.659441515329,2012,3,25.118864315095795,CC
+10,1024.133333333,0.6674261163911,2012,3,25.118864315095795,CC
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+12,985.0,0.4619289340102,2014,3,6.30957344480193,DD
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+12,987.6,0.4275010125557,2015,3,6.30957344480193,DD
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+2,980.0666666667,0.3611999183729,2016,3,6.30957344480193,DD
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+8,996.7666666667,0.004614921579775,2013,3,0.1,EE
+9,992.3333333333,0.008867987907289,2013,3,0.1,EE
+10,993.6666666667,0.0160348876216,2013,3,0.1,EE
+11,992.0,0.01216397849462,2013,3,0.1,EE
+12,994.4666666667,0.006200978749078,2013,3,0.1,EE
+1,1003.133333333,0.006479696949558,2014,3,0.1,EE
+2,1006.266666667,0.007088909500464,2014,3,0.1,EE
+3,1004.4,0.004613035975043,2014,3,0.1,EE
+4,997.8666666667,0.001837252805986,2014,3,0.1,EE
+5,992.8333333333,0.004263891220413,2014,3,0.1,EE
+6,996.3,0.004349426210311,2014,3,0.1,EE
+7,999.2333333333,0.006972011875771,2014,3,0.1,EE
+8,1002.5,0.008445552784705,2014,3,0.1,EE
+9,1004.733333333,0.007829606529096,2014,3,0.1,EE
+10,999.5,0.0104052026013,2014,3,0.1,EE
+11,992.2666666667,0.007827196990056,2014,3,0.1,EE
+12,988.2333333333,0.006847235808008,2014,3,0.1,EE
+1,983.5333333333,0.006846065207077,2015,3,0.1,EE
+2,981.0,0.007611281005776,2015,3,0.1,EE
+3,985.5666666667,0.00717015591707,2015,3,0.1,EE
+4,988.1666666667,0.004688817675831,2015,3,0.1,EE
+5,992.4333333333,0.00382897255903,2015,3,0.1,EE
+6,987.7666666667,0.002429723618938,2015,3,0.1,EE
+7,996.7333333333,0.004380977861013,2015,3,0.1,EE
+8,998.1,0.008683164679558,2015,3,0.1,EE
+9,1006.466666667,0.0142743591442,2015,3,0.1,EE
+10,1009.233333333,0.01585361825808,2015,3,0.1,EE
+11,1008.9,0.02028612019691,2015,3,0.1,EE
+12,1007.7,0.01832555985578,2015,3,0.1,EE
+1,1008.4,0.01778394816872,2016,3,0.1,EE
+2,1022.133333333,0.00971823636838,2016,3,0.1,EE
+3,1033.066666667,0.008002065049045,2016,3,0.1,EE
+4,1033.533333333,0.006321357156679,2016,3,0.1,EE
+5,1039.166666667,0.005100240577386,2016,3,0.1,EE
+6,1044.066666667,0.003320349913799,2016,3,0.1,EE
+7,1043.433333333,0.007123917835351,2016,3,0.1,EE
+8,1038.166666667,0.008123294268743,2016,3,0.1,EE
+9,1024.3,0.01291939210518,2016,3,0.1,EE
+10,1022.666666667,0.02249022164276,2016,3,0.1,EE
+11,1016.033333333,0.02329319904203,2016,3,0.1,EE
+12,1025.1,0.01742919389978,2016,3,0.1,EE
+1,1025.866666667,0.01179490512087,2017,3,0.1,EE
+2,1031.666666667,0.007819063004847,2017,3,0.1,EE
+3,1027.3,0.006262370615529,2017,3,0.1,EE
+4,1023.966666667,0.006673394316221,2017,3,0.1,EE
+5,1022.666666667,0.004041720990874,2017,3,0.1,EE
+6,1027.3,0.003147409065836,2017,3,0.1,EE
+7,1029.7,0.007251302968502,2017,3,0.1,EE
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+10,1027.0,0.0168776371308,2017,3,0.1,EE
+11,1036.833333333,0.01546375180839,2017,3,0.1,EE
+12,1043.033333333,0.01048224729155,2017,3,0.1,EE
+1,977.0333333333,0.001125857186722,2010,3,0.025118864315095794,FF
+2,969.0,0.00171998624011,2010,3,0.025118864315095794,FF
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+11,976.9666666667,0.0,2010,3,0.025118864315095794,FF
+12,988.7333333333,0.0,2010,3,0.025118864315095794,FF
+1,995.7333333333,0.0,2011,3,0.025118864315095794,FF
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+5,998.0333333333,0.0,2011,3,0.025118864315095794,FF
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+9,990.8333333333,0.0,2011,3,0.025118864315095794,FF
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+11,990.4666666667,0.0,2011,3,0.025118864315095794,FF
+12,990.1666666667,0.0,2011,3,0.025118864315095794,FF
+1,981.3666666667,0.0,2012,3,0.025118864315095794,FF
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+7,984.3333333333,0.0,2012,3,0.025118864315095794,FF
+8,985.3333333333,0.0,2012,3,0.025118864315095794,FF
+9,986.8666666667,0.0,2012,3,0.025118864315095794,FF
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+11,993.2666666667,0.0,2012,3,0.025118864315095794,FF
+12,994.2333333333,0.0,2012,3,0.025118864315095794,FF
+1,1007.133333333,0.0,2013,3,0.025118864315095794,FF
+2,1008.633333333,0.0,2013,3,0.025118864315095794,FF
+3,1014.866666667,0.0,2013,3,0.025118864315095794,FF
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+6,1024.7,0.0,2013,3,0.025118864315095794,FF
+7,1024.1,0.0,2013,3,0.025118864315095794,FF
+8,1024.266666667,0.0,2013,3,0.025118864315095794,FF
+9,1023.466666667,0.0,2013,3,0.025118864315095794,FF
+10,1031.8,0.0,2013,3,0.025118864315095794,FF
+11,1038.366666667,0.0,2013,3,0.025118864315095794,FF
+12,1043.033333333,0.0,2013,3,0.025118864315095794,FF
+1,1039.833333333,0.0,2014,3,0.025118864315095794,FF
+2,1047.2,0.0,2014,3,0.025118864315095794,FF
+3,1045.1,0.0,2014,3,0.025118864315095794,FF
+4,1044.433333333,0.0,2014,3,0.025118864315095794,FF
+5,1041.666666667,0.0,2014,3,0.025118864315095794,FF
+6,1036.3,0.0,2014,3,0.025118864315095794,FF
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+11,1030.766666667,0.0,2014,3,0.025118864315095794,FF
+12,1026.233333333,0.0,2014,3,0.025118864315095794,FF
+1,1024.766666667,0.0,2015,3,0.025118864315095794,FF
+2,1024.166666667,0.0,2015,3,0.025118864315095794,FF
+3,1030.5,0.0,2015,3,0.025118864315095794,FF
+4,1028.433333333,0.0,2015,3,0.025118864315095794,FF
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+6,1022.366666667,0.0,2015,3,0.025118864315095794,FF
+7,1018.566666667,0.0,2015,3,0.025118864315095794,FF
+8,1023.766666667,0.0,2015,3,0.025118864315095794,FF
+9,1023.5,0.0,2015,3,0.025118864315095794,FF
+10,1019.233333333,0.0,2015,3,0.025118864315095794,FF
+11,1022.166666667,0.0,2015,3,0.025118864315095794,FF
+12,1018.966666667,0.0,2015,3,0.025118864315095794,FF
+1,1019.366666667,0.0,2016,3,0.025118864315095794,FF
+2,1014.3,0.0,2016,3,0.025118864315095794,FF
+3,1001.866666667,0.0,2016,3,0.025118864315095794,FF
+4,996.4333333333,0.0,2016,3,0.025118864315095794,FF
+5,995.1,0.0,2016,3,0.025118864315095794,FF
+6,998.3666666667,0.0,2016,3,0.025118864315095794,FF
+7,998.9666666667,0.0,2016,3,0.025118864315095794,FF
+8,992.0666666667,0.0,2016,3,0.025118864315095794,FF
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+10,995.1666666667,0.0,2016,3,0.025118864315095794,FF
+11,990.5666666667,0.0,2016,3,0.025118864315095794,FF
+12,988.7333333333,0.0,2016,3,0.025118864315095794,FF
+1,990.1666666667,0.0,2017,3,0.025118864315095794,FF
+2,992.5333333333,0.0,2017,3,0.025118864315095794,FF
+3,988.0666666667,0.0,2017,3,0.025118864315095794,FF
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+6,988.4333333333,0.0,2017,3,0.025118864315095794,FF
+7,990.5666666667,0.0,2017,3,0.025118864315095794,FF
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+9,1001.1,0.0,2017,3,0.025118864315095794,FF
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+11,998.8666666667,0.0,2017,3,0.025118864315095794,FF
+12,991.0666666667,0.0,2017,3,0.025118864315095794,FF
+1,1006.733333333,0.2378319316602,2010,3,1.5848931924611125,EE
+2,1001.7,0.2317726531563,2010,3,1.5848931924611125,EE
+3,999.8333333333,0.2141356892815,2010,3,1.5848931924611125,EE
+4,999.5333333333,0.2409457746949,2010,3,1.5848931924611125,EE
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+6,1004.9,0.2458287723488,2010,3,1.5848931924611125,EE
+7,1008.766666667,0.4022403595149,2010,3,1.5848931924611125,EE
+8,1012.166666667,0.511081837642,2010,3,1.5848931924611125,EE
+9,1015.9,0.6094431866654,2010,3,1.5848931924611125,EE
+10,1028.566666667,0.6100398612957,2010,3,1.5848931924611125,EE
+11,1023.966666667,0.5127445554868,2010,3,1.5848931924611125,EE
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+1,1021.233333333,0.2643861996932,2011,3,1.5848931924611125,EE
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+3,1016.6,0.2462128664175,2011,3,1.5848931924611125,EE
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+5,1014.7,0.2276534936434,2011,3,1.5848931924611125,EE
+6,1013.4,0.239293467535,2011,3,1.5848931924611125,EE
+7,1005.933333333,0.3989329975479,2011,3,1.5848931924611125,EE
+8,993.0,0.5160456529037,2011,3,1.5848931924611125,EE
+9,994.7666666667,0.6131421103776,2011,3,1.5848931924611125,EE
+10,996.9666666667,0.617606740446,2011,3,1.5848931924611125,EE
+11,1000.733333333,0.5054293518087,2011,3,1.5848931924611125,EE
+12,990.7333333333,0.368851355898,2011,3,1.5848931924611125,EE
+1,986.1666666667,0.2407300997127,2012,3,1.5848931924611125,EE
+2,988.1666666667,0.220880418283,2012,3,1.5848931924611125,EE
+3,992.4,0.2401921268306,2012,3,1.5848931924611125,EE
+4,994.8666666667,0.2289754070897,2012,3,1.5848931924611125,EE
+5,1003.7,0.2120487529474,2012,3,1.5848931924611125,EE
+6,1011.866666667,0.2306298590065,2012,3,1.5848931924611125,EE
+7,1016.333333333,0.3882256477534,2012,3,1.5848931924611125,EE
+8,1022.333333333,0.5129768503424,2012,3,1.5848931924611125,EE
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+11,1021.1,0.5186237064604,2012,3,1.5848931924611125,EE
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+1,1029.333333333,0.2602655440415,2013,3,1.5848931924611125,EE
+2,1026.266666667,0.2399636221905,2013,3,1.5848931924611125,EE
+3,1033.166666667,0.2608485239555,2013,3,1.5848931924611125,EE
+4,1038.266666667,0.2584114549891,2013,3,1.5848931924611125,EE
+5,1036.733333333,0.2181531734294,2013,3,1.5848931924611125,EE
+6,1038.533333333,0.2203106945693,2013,3,1.5848931924611125,EE
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+11,1042.033333333,0.5440644892998,2013,3,1.5848931924611125,EE
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+12,1044.766666667,0.3717257441853,2014,3,1.5848931924611125,EE
+1,1045.9,0.2473149121968,2015,3,1.5848931924611125,EE
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+7,1038.166666667,0.4049446139027,2015,3,1.5848931924611125,EE
+8,1040.9,0.5289653184744,2015,3,1.5848931924611125,EE
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+10,1049.333333333,0.6310355781449,2015,3,1.5848931924611125,EE
+11,1039.966666667,0.5143113561332,2015,3,1.5848931924611125,EE
+12,1040.833333333,0.3794075260208,2015,3,1.5848931924611125,EE
+1,1038.333333333,0.2585874799358,2016,3,1.5848931924611125,EE
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+8,1048.7,0.4723943930581,2016,3,1.5848931924611125,EE
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+10,1062.933333333,0.6284182137481,2016,3,1.5848931924611125,EE
+11,1062.833333333,0.5328838011604,2016,3,1.5848931924611125,EE
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+2,1057.266666667,0.248660066839,2017,3,1.5848931924611125,EE
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+4,1053.266666667,0.2648268877777,2017,3,1.5848931924611125,EE
+5,1051.1,0.2371800970412,2017,3,1.5848931924611125,EE
+6,1059.9,0.2486712582948,2017,3,1.5848931924611125,EE
+7,1063.333333333,0.3955799373041,2017,3,1.5848931924611125,EE
+8,1064.6,0.4954286429958,2017,3,1.5848931924611125,EE
+9,1060.866666667,0.602903286621,2017,3,1.5848931924611125,EE
+10,1056.566666667,0.619301511184,2017,3,1.5848931924611125,EE
+11,1055.366666667,0.5326742680269,2017,3,1.5848931924611125,EE
+12,1050.833333333,0.4027914353688,2017,3,1.5848931924611125,EE
+1,957.8,0.4519732720819,2010,3,6.30957344480193,EE
+2,956.5333333333,0.48163507109,2010,3,6.30957344480193,EE
+3,957.5333333333,0.5639142240479,2010,3,6.30957344480193,EE
+4,956.6333333333,0.5611345343043,2010,3,6.30957344480193,EE
+5,951.5666666667,0.5305636319053,2010,3,6.30957344480193,EE
+6,950.7333333333,0.5307131337213,2010,3,6.30957344480193,EE
+7,943.2666666667,0.7288854335995,2010,3,6.30957344480193,EE
+8,939.0666666667,0.7475862558569,2010,3,6.30957344480193,EE
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+6,1009.733333333,0.04585369074343,2014,3,0.39810717055349715,FF
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+8,1017.0,0.09377253359554,2014,3,0.39810717055349715,FF
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+10,1021.033333333,0.1985570174007,2014,3,0.39810717055349715,FF
+11,1020.866666667,0.1938548945341,2014,3,0.39810717055349715,FF
+12,1016.766666667,0.1439202701374,2014,3,0.39810717055349715,FF
+1,1020.266666667,0.09559592263461,2015,3,0.39810717055349715,FF
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+3,1005.266666667,0.06906956694741,2015,3,0.39810717055349715,FF
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+6,988.2,0.04051136746947,2015,3,0.39810717055349715,FF
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+1,1003.233333333,0.08313120909061,2016,3,0.39810717055349715,FF
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+6,999.0666666667,0.05094755104764,2016,3,0.39810717055349715,FF
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+12,1026.033333333,0.002274130145219,2012,3,0.1,FF
+1,1033.6,0.001418988648091,2013,3,0.1,FF
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+4,1031.5,6.463079657457e-05,2013,3,0.1,FF
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+7,1026.533333333,0.0,2013,3,0.1,FF
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+9,1023.733333333,0.0,2013,3,0.1,FF
+10,1021.2,0.0,2013,3,0.1,FF
+11,1019.7,0.0,2013,3,0.1,FF
+12,1019.3,0.0,2013,3,0.1,FF
+1,1032.233333333,0.0,2014,3,0.1,FF
+2,1033.166666667,0.0,2014,3,0.1,FF
+3,1026.633333333,0.0,2014,3,0.1,FF
+4,1021.033333333,0.0,2014,3,0.1,FF
+5,1018.466666667,0.0,2014,3,0.1,FF
+6,1025.333333333,0.0007802340702211,2014,3,0.1,FF
+7,1017.866666667,0.0002947340843594,2014,3,0.1,FF
+8,1010.3,0.0007258570061698,2014,3,0.1,FF
+9,1011.866666667,0.0005600210831467,2014,3,0.1,FF
+10,1015.066666667,0.00134638119007,2014,3,0.1,FF
+11,1013.366666667,0.001677576395513,2014,3,0.1,FF
+12,1014.766666667,0.0007226620241106,2014,3,0.1,FF
+1,1009.7,0.0004951965930474,2015,3,0.1,FF
+2,997.2333333333,0.002038974496106,2015,3,0.1,FF
+3,999.9,0.001300130013001,2015,3,0.1,FF
+4,997.9,0.001269332264422,2015,3,0.1,FF
+5,1001.833333333,0.0002329063383796,2015,3,0.1,FF
+6,1002.566666667,0.0002992319712737,2015,3,0.1,FF
+7,994.2666666667,0.001810379509186,2015,3,0.1,FF
+8,995.2666666667,0.00257887333378,2015,3,0.1,FF
+9,994.8666666667,0.002780942169805,2015,3,0.1,FF
+10,997.6666666667,0.002873371199465,2015,3,0.1,FF
+11,998.6666666667,0.001602136181575,2015,3,0.1,FF
+12,998.3333333333,0.001001669449082,2015,3,0.1,FF
+1,991.4333333333,0.001412096963992,2016,3,0.1,FF
+2,993.5,0.0007045797684952,2016,3,0.1,FF
+3,993.2333333333,0.001375977447394,2016,3,0.1,FF
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+8,991.7333333333,0.002117504705566,2016,3,0.1,FF
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+10,989.2333333333,0.003201132189911,2016,3,0.1,FF
+11,991.0333333333,0.002791698900138,2016,3,0.1,FF
+12,990.3666666667,0.002557975160715,2016,3,0.1,FF
+1,990.1,0.003299330034003,2017,3,0.1,FF
+2,986.2666666667,0.001318101933216,2017,3,0.1,FF
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+10,958.9,0.0,2017,3,0.1,FF
+11,958.9333333333,0.0,2017,3,0.1,FF
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+1,1005.133333333,0.2336671751675,2010,3,1.5848931924611125,FF
+2,1001.733333333,0.2079728470651,2010,3,1.5848931924611125,FF
+3,1003.9,0.2007172029087,2010,3,1.5848931924611125,FF
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+8,1029.266666667,0.4147613187383,2010,3,1.5848931924611125,FF
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+8,1021.466666667,0.4270330244093,2011,3,1.5848931924611125,FF
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+11,1021.733333333,0.4837530993084,2011,3,1.5848931924611125,FF
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+12,1027.366666667,0.3284448914701,2012,3,1.5848931924611125,FF
+1,1022.0,0.2309197651663,2013,3,1.5848931924611125,FF
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+4,1041.033333333,0.2029073676795,2013,3,1.5848931924611125,FF
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+8,1034.2,0.4150389995488,2013,3,1.5848931924611125,FF
+9,1027.233333333,0.4898594931369,2013,3,1.5848931924611125,FF
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+12,1027.366666667,0.3337983842186,2013,3,1.5848931924611125,FF
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+1,1044.366666667,0.2344004340749,2015,3,1.5848931924611125,FF
+2,1043.1,0.1857923497268,2015,3,1.5848931924611125,FF
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+4,1038.9,0.2047999486637,2015,3,1.5848931924611125,FF
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+6,1033.633333333,0.2065206875423,2015,3,1.5848931924611125,FF
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+8,1029.966666667,0.4230557623224,2015,3,1.5848931924611125,FF
+9,1030.2,0.4978321361548,2015,3,1.5848931924611125,FF
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+11,1030.666666667,0.479786545925,2015,3,1.5848931924611125,FF
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+5,1021.6,0.1760963194988,2016,3,1.5848931924611125,FF
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+8,1024.333333333,0.4088838268793,2016,3,1.5848931924611125,FF
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+12,1015.133333333,0.3506928482301,2017,3,1.5848931924611125,FF
+1,975.2,0.4130776592836,2010,4,6.30957344480193,GG
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+3,969.2,0.4172169486862,2010,4,6.30957344480193,GG
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+5,968.3666666667,0.3955457643455,2010,4,6.30957344480193,GG
+6,969.1333333333,0.3912774300062,2010,4,6.30957344480193,GG
+7,966.2666666667,0.5521595142818,2010,4,6.30957344480193,GG
+8,965.0666666667,0.6036888643272,2010,4,6.30957344480193,GG
+9,964.3666666667,0.5852545712212,2010,4,6.30957344480193,GG
+10,958.7333333333,0.5438078019609,2010,4,6.30957344480193,GG
+11,958.9666666667,0.4825332823525,2010,4,6.30957344480193,GG
+12,957.5333333333,0.396853025134,2010,4,6.30957344480193,GG
+1,958.0333333333,0.3117497651439,2011,4,6.30957344480193,GG
+2,971.4,0.3201221604557,2011,4,6.30957344480193,GG
+3,974.7333333333,0.3921072430066,2011,4,6.30957344480193,GG
+4,983.7,0.4329233167294,2011,4,6.30957344480193,GG
+5,985.2,0.3831709297605,2011,4,6.30957344480193,GG
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+10,1027.1,0.5572323370006,2013,4,25.118864315095795,GG
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+10,1021.933333333,0.5707156370279,2014,4,25.118864315095795,GG
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+1,1021.166666667,0.4864044393667,2015,4,25.118864315095795,GG
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+5,1024.433333333,0.5237041616503,2015,4,25.118864315095795,GG
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+5,1003.8,0.0,2014,3,0.025118864315095794,GG
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+7,994.9333333333,0.0,2014,3,0.025118864315095794,GG
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+9,1005.0,0.0,2014,3,0.025118864315095794,GG
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+11,1013.633333333,0.0,2014,3,0.025118864315095794,GG
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+12,1039.6,0.0,2015,3,0.025118864315095794,GG
+1,1038.6,0.0,2016,3,0.025118864315095794,GG
+2,1040.766666667,0.0,2016,3,0.025118864315095794,GG
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+11,1038.766666667,0.0,2017,3,0.025118864315095794,GG
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+1,954.9,0.43397214368,2010,3,6.30957344480193,FF
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+1,982.6666666667,0.399762550882,2015,3,6.30957344480193,FF
+2,983.5,0.419827147941,2015,3,6.30957344480193,FF
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+4,974.9333333333,0.5122059628009,2015,3,6.30957344480193,FF
+5,975.5,0.4712113446096,2015,3,6.30957344480193,FF
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+7,975.4333333333,0.6461060041691,2015,3,6.30957344480193,FF
+8,975.0333333333,0.6906430549383,2015,3,6.30957344480193,FF
+9,979.3666666667,0.6523944045472,2015,3,6.30957344480193,FF
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+12,984.4666666667,0.4582176474572,2015,3,6.30957344480193,FF
+1,989.3333333333,0.4065363881402,2016,3,6.30957344480193,FF
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+1,977.6,0.004978177850518,2010,3,0.1,GG
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+6,955.4,0.0,2011,3,0.1,GG
+7,945.8666666667,0.0008457851705667,2011,3,0.1,GG
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+9,951.4333333333,0.0009109063518201,2011,3,0.1,GG
+10,952.7333333333,0.001994262123014,2011,3,0.1,GG
+11,949.8333333333,0.000456220389542,2011,3,0.1,GG
+12,953.0666666667,0.0003497481813095,2011,3,0.1,GG
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+2,957.9333333333,3.479713271626e-05,2012,3,0.1,GG
+3,964.4333333333,0.0,2012,3,0.1,GG
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+5,967.4333333333,0.0,2012,3,0.1,GG
+6,971.9333333333,0.0,2012,3,0.1,GG
+7,981.6666666667,0.0,2012,3,0.1,GG
+8,983.1666666667,0.0,2012,3,0.1,GG
+9,970.4333333333,0.0,2012,3,0.1,GG
+10,966.3333333333,0.0,2012,3,0.1,GG
+11,961.8333333333,0.0,2012,3,0.1,GG
+12,956.2666666667,0.0,2012,3,0.1,GG
+1,962.6333333333,0.0,2013,3,0.1,GG
+2,969.3,0.0,2013,3,0.1,GG
+3,975.1666666667,0.0,2013,3,0.1,GG
+4,976.2333333333,0.0,2013,3,0.1,GG
+5,970.9333333333,0.0,2013,3,0.1,GG
+6,976.8333333333,0.0,2013,3,0.1,GG
+7,987.4,0.0,2013,3,0.1,GG
+8,993.3,0.0,2013,3,0.1,GG
+9,993.8,0.0,2013,3,0.1,GG
+10,990.7666666667,0.0,2013,3,0.1,GG
+11,987.0666666667,0.0,2013,3,0.1,GG
+12,991.4333333333,0.0,2013,3,0.1,GG
+1,1002.866666667,0.0,2014,3,0.1,GG
+2,996.6,0.0,2014,3,0.1,GG
+3,995.6,0.0,2014,3,0.1,GG
+4,995.6,0.0,2014,3,0.1,GG
+5,989.3,0.0,2014,3,0.1,GG
+6,994.4333333333,0.0,2014,3,0.1,GG
+7,991.2666666667,0.0,2014,3,0.1,GG
+8,993.5666666667,0.0,2014,3,0.1,GG
+9,985.8,0.0,2014,3,0.1,GG
+10,978.6333333333,0.0,2014,3,0.1,GG
+11,981.6666666667,0.0,2014,3,0.1,GG
+12,980.2,0.0,2014,3,0.1,GG
+1,975.8333333333,0.0,2015,3,0.1,GG
+2,974.6666666667,0.0,2015,3,0.1,GG
+3,983.6333333333,0.0,2015,3,0.1,GG
+4,986.6666666667,0.0,2015,3,0.1,GG
+5,990.2,0.0,2015,3,0.1,GG
+6,986.0666666667,0.0,2015,3,0.1,GG
+7,985.4333333333,0.0,2015,3,0.1,GG
+8,987.2333333333,0.0,2015,3,0.1,GG
+9,988.5333333333,0.0,2015,3,0.1,GG
+10,989.4666666667,0.0,2015,3,0.1,GG
+11,986.6666666667,0.0,2015,3,0.1,GG
+12,980.9,0.0,2015,3,0.1,GG
+1,993.3333333333,0.0,2016,3,0.1,GG
+2,993.9333333333,0.0,2016,3,0.1,GG
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+4,1008.1,0.0,2016,3,0.1,GG
+5,1015.8,0.0,2016,3,0.1,GG
+6,1005.333333333,0.0,2016,3,0.1,GG
+7,1007.5,0.0,2016,3,0.1,GG
+8,1004.633333333,0.0,2016,3,0.1,GG
+9,1002.333333333,0.0,2016,3,0.1,GG
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+11,1008.433333333,0.0,2016,3,0.1,GG
+12,1007.166666667,0.0,2016,3,0.1,GG
+1,1004.533333333,0.0,2017,3,0.1,GG
+2,1005.6,0.0,2017,3,0.1,GG
+3,1002.033333333,0.0,2017,3,0.1,GG
+4,999.6666666667,0.0,2017,3,0.1,GG
+5,997.3666666667,0.0,2017,3,0.1,GG
+6,1001.633333333,0.0,2017,3,0.1,GG
+7,1004.4,0.0,2017,3,0.1,GG
+8,1004.4,0.0,2017,3,0.1,GG
+9,1010.7,0.0,2017,3,0.1,GG
+10,1021.4,0.0,2017,3,0.1,GG
+11,1026.8,0.0,2017,3,0.1,GG
+12,1029.766666667,0.0,2017,3,0.1,GG
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+1,956.6,0.5705275628964,2012,3,25.118864315095795,FF
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+3,976.0666666667,0.6533365207295,2014,3,25.118864315095795,FF
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+1,1043.066666667,0.5714559631855,2016,3,25.118864315095795,FF
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+11,1042.9,0.04513056541055,2011,3,0.39810717055349715,GG
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+1,1033.066666667,0.02245740836345,2012,3,0.39810717055349715,GG
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+8,1016.266666667,0.02292705326686,2012,3,0.39810717055349715,GG
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+11,1014.3,0.02872260015117,2012,3,0.39810717055349715,GG
+12,1007.3,0.02223766504517,2012,3,0.39810717055349715,GG
+1,1012.266666667,0.01735379346681,2013,3,0.39810717055349715,GG
+2,1020.333333333,0.01127082652728,2013,3,0.39810717055349715,GG
+3,1017.466666667,0.01038527060674,2013,3,0.39810717055349715,GG
+4,1006.133333333,0.01116485555261,2013,3,0.39810717055349715,GG
+5,1008.6,0.00670896952872,2013,3,0.39810717055349715,GG
+6,1013.166666667,0.007369633163349,2013,3,0.39810717055349715,GG
+7,1008.133333333,0.009125777013623,2013,3,0.39810717055349715,GG
+8,1007.233333333,0.01409802429096,2013,3,0.39810717055349715,GG
+9,1009.866666667,0.02204911539477,2013,3,0.39810717055349715,GG
+10,1019.733333333,0.02785041841004,2013,3,0.39810717055349715,GG
+11,1019.033333333,0.03061725164371,2013,3,0.39810717055349715,GG
+12,1015.933333333,0.02713432639937,2013,3,0.39810717055349715,GG
+1,1012.833333333,0.01214415007405,2014,3,0.39810717055349715,GG
+2,1010.733333333,0.008970384539278,2014,3,0.39810717055349715,GG
+3,1009.133333333,0.01687917024509,2014,3,0.39810717055349715,GG
+4,1006.6,0.01341148420425,2014,3,0.39810717055349715,GG
+5,1001.4,0.01601091804807,2014,3,0.39810717055349715,GG
+6,997.7666666667,0.01393111281863,2014,3,0.39810717055349715,GG
+7,1001.233333333,0.01328361687252,2014,3,0.39810717055349715,GG
+8,999.5666666667,0.02057558275253,2014,3,0.39810717055349715,GG
+9,998.0666666667,0.02428027519872,2014,3,0.39810717055349715,GG
+10,995.7,0.04653342706973,2014,3,0.39810717055349715,GG
+11,998.8666666667,0.05162517519856,2014,3,0.39810717055349715,GG
+12,1004.5,0.04038493446159,2014,3,0.39810717055349715,GG
+1,1000.133333333,0.02799626716438,2015,3,0.39810717055349715,GG
+2,995.3333333333,0.02073007367716,2015,3,0.39810717055349715,GG
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+4,1000.533333333,0.02388726012793,2015,3,0.39810717055349715,GG
+5,1000.233333333,0.01832905655347,2015,3,0.39810717055349715,GG
+6,997.4333333333,0.02566587574775,2015,3,0.39810717055349715,GG
+7,1000.366666667,0.02705674585985,2015,3,0.39810717055349715,GG
+8,997.5333333333,0.03097640847424,2015,3,0.39810717055349715,GG
+9,998.1333333333,0.0450173657494,2015,3,0.39810717055349715,GG
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+11,992.6,0.05799583585197,2015,3,0.39810717055349715,GG
+12,981.4,0.04833231438082,2015,3,0.39810717055349715,GG
+1,978.7,0.0357617247369,2016,3,0.39810717055349715,GG
+2,966.8333333333,0.03878641613515,2016,3,0.39810717055349715,GG
+3,960.1666666667,0.03933344905398,2016,3,0.39810717055349715,GG
+4,959.1,0.03089702151322,2016,3,0.39810717055349715,GG
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+9,975.1,0.5931015622329,2010,3,6.30957344480193,GG
+10,966.5,0.5759613726505,2010,3,6.30957344480193,GG
+11,957.1333333333,0.5020547468134,2010,3,6.30957344480193,GG
+12,949.2,0.397597977244,2010,3,6.30957344480193,GG
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+2,948.1666666667,0.3555985234663,2011,3,6.30957344480193,GG
+3,949.6666666667,0.389820989821,2011,3,6.30957344480193,GG
+4,947.9,0.4169567816577,2011,3,6.30957344480193,GG
+5,943.9,0.3695306706219,2011,3,6.30957344480193,GG
+6,945.5333333333,0.3921596277233,2011,3,6.30957344480193,GG
+7,942.9333333333,0.5588235294118,2011,3,6.30957344480193,GG
+8,946.8,0.5957611603999,2011,3,6.30957344480193,GG
+9,957.1666666667,0.5959951244994,2011,3,6.30957344480193,GG
+10,962.8666666667,0.5576057605761,2011,3,6.30957344480193,GG
+11,963.4,0.502179779946,2011,3,6.30957344480193,GG
+12,966.4,0.4072502759382,2011,3,6.30957344480193,GG
+1,969.5333333333,0.3259643814894,2012,3,6.30957344480193,GG
+2,970.0333333333,0.342599910656,2012,3,6.30957344480193,GG
+3,972.3,0.403099180637,2012,3,6.30957344480193,GG
+4,969.4,0.406471356853,2012,3,6.30957344480193,GG
+5,968.0333333333,0.3595950552667,2012,3,6.30957344480193,GG
+6,967.2666666667,0.3900337721414,2012,3,6.30957344480193,GG
+7,972.4,0.5674619498149,2012,3,6.30957344480193,GG
+8,972.5666666667,0.5957089488296,2012,3,6.30957344480193,GG
+9,975.3666666667,0.6044906189125,2012,3,6.30957344480193,GG
+10,975.8,0.553118808499,2012,3,6.30957344480193,GG
+11,974.1333333333,0.4690665206679,2012,3,6.30957344480193,GG
+12,969.1,0.3851683692773,2012,3,6.30957344480193,GG
+1,978.8,0.3382713526767,2013,3,6.30957344480193,GG
+2,991.9666666667,0.3499445545885,2013,3,6.30957344480193,GG
+3,1003.433333333,0.3817227518852,2013,3,6.30957344480193,GG
+4,1001.133333333,0.3983152427249,2013,3,6.30957344480193,GG
+5,1006.8,0.3391272679115,2013,3,6.30957344480193,GG
+6,1013.966666667,0.3893947861534,2013,3,6.30957344480193,GG
+7,1012.8,0.5547985781991,2013,3,6.30957344480193,GG
+8,1011.7,0.610918915357,2013,3,6.30957344480193,GG
+9,1004.833333333,0.57847072483,2013,3,6.30957344480193,GG
+10,998.0666666667,0.5586467169862,2013,3,6.30957344480193,GG
+11,999.6333333333,0.4892460568875,2013,3,6.30957344480193,GG
+12,1000.133333333,0.3964471403813,2013,3,6.30957344480193,GG
+1,1004.633333333,0.3315969342049,2014,3,6.30957344480193,GG
+2,1001.466666667,0.343396352017,2014,3,6.30957344480193,GG
+3,990.4333333333,0.3804058829469,2014,3,6.30957344480193,GG
+4,994.4666666667,0.4205939532077,2014,3,6.30957344480193,GG
+5,998.0666666667,0.3691136196647,2014,3,6.30957344480193,GG
+6,991.6,0.4048675541213,2014,3,6.30957344480193,GG
+7,992.4333333333,0.5590971685756,2014,3,6.30957344480193,GG
+8,999.3666666667,0.6123211367199,2014,3,6.30957344480193,GG
+9,1001.666666667,0.5884193011647,2014,3,6.30957344480193,GG
+10,999.9,0.5573557355736,2014,3,6.30957344480193,GG
+11,994.7333333333,0.5082434153207,2014,3,6.30957344480193,GG
+12,997.1,0.3922709189984,2014,3,6.30957344480193,GG
+1,993.7,0.313172989836,2015,3,6.30957344480193,GG
+2,990.3,0.3332996734996,2015,3,6.30957344480193,GG
+3,980.6333333333,0.3883544648017,2015,3,6.30957344480193,GG
+4,977.8,0.4200927251653,2015,3,6.30957344480193,GG
+5,967.0666666667,0.3745346753068,2015,3,6.30957344480193,GG
+6,965.5,0.3937510788883,2015,3,6.30957344480193,GG
+7,968.4,0.5580682913397,2015,3,6.30957344480193,GG
+8,978.2666666667,0.6062764072509,2015,3,6.30957344480193,GG
+9,983.5,0.5964073885782,2015,3,6.30957344480193,GG
+10,988.0,0.5507422402159,2015,3,6.30957344480193,GG
+11,987.4,0.4805212342178,2015,3,6.30957344480193,GG
+12,990.1333333333,0.3890048478319,2015,3,6.30957344480193,GG
+1,998.6333333333,0.3342234386996,2016,3,6.30957344480193,GG
+2,998.3333333333,0.3492821368948,2016,3,6.30957344480193,GG
+3,989.7,0.380990872655,2016,3,6.30957344480193,GG
+4,988.2666666667,0.4143955747437,2016,3,6.30957344480193,GG
+5,991.1,0.3853966972724,2016,3,6.30957344480193,GG
+6,990.7666666667,0.4018100460923,2016,3,6.30957344480193,GG
+7,984.6,0.5367323447762,2016,3,6.30957344480193,GG
+8,982.7666666667,0.5935284740359,2016,3,6.30957344480193,GG
+9,984.1,0.5681333197846,2016,3,6.30957344480193,GG
+10,989.1333333333,0.5504145042798,2016,3,6.30957344480193,GG
+11,986.7333333333,0.5076008377812,2016,3,6.30957344480193,GG
+12,980.8666666667,0.4125603208047,2016,3,6.30957344480193,GG
+1,974.7,0.3160972606956,2017,3,6.30957344480193,GG
+2,974.4,0.3254652435687,2017,3,6.30957344480193,GG
+3,975.7666666667,0.4010863252827,2017,3,6.30957344480193,GG
+4,975.3,0.440308964763,2017,3,6.30957344480193,GG
+5,979.4,0.3836022054319,2017,3,6.30957344480193,GG
+6,984.5,0.4149991535466,2017,3,6.30957344480193,GG
+7,983.6,0.5679476752067,2017,3,6.30957344480193,GG
+8,983.1666666667,0.6011188337006,2017,3,6.30957344480193,GG
+9,975.4,0.5921331419589,2017,3,6.30957344480193,GG
+10,972.3333333333,0.542509427494,2017,3,6.30957344480193,GG
+11,969.8333333333,0.4635504382196,2017,3,6.30957344480193,GG
+12,967.4666666667,0.3867488974642,2017,3,6.30957344480193,GG
+1,933.2666666667,0.006679048503465,2010,4,0.1,AA
+2,933.3,0.00778599235687,2010,4,0.1,AA
+3,936.5333333333,0.01220814350797,2010,4,0.1,AA
+4,933.2333333333,0.006107797263993,2010,4,0.1,AA
+5,934.7666666667,0.002995399921549,2010,4,0.1,AA
+6,933.2333333333,0.003714683716112,2010,4,0.1,AA
+7,935.3666666667,0.004490217739924,2010,4,0.1,AA
+8,938.7,0.004722843649018,2010,4,0.1,AA
+9,927.3333333333,0.008806613946801,2010,4,0.1,AA
+10,916.9666666667,0.01406812315969,2010,4,0.1,AA
+11,912.7,0.0163982323509,2010,4,0.1,AA
+12,912.6666666667,0.01132213294375,2010,4,0.1,AA
+1,922.8,0.008055194336079,2011,4,0.1,AA
+2,927.2666666667,0.009418362211518,2011,4,0.1,AA
+3,918.0666666667,0.00664439764723,2011,4,0.1,AA
+4,914.3333333333,0.007728764126868,2011,4,0.1,AA
+5,917.7,0.005012531328321,2011,4,0.1,AA
+6,920.0333333333,0.002354987138147,2011,4,0.1,AA
+7,925.4333333333,0.005114721031589,2011,4,0.1,AA
+8,925.6666666667,0.0049693914296,2011,4,0.1,AA
+9,931.0666666667,0.008484891880281,2011,4,0.1,AA
+10,935.8,0.01517418251763,2011,4,0.1,AA
+11,940.9666666667,0.01643699741401,2011,4,0.1,AA
+12,945.6,0.01124506486182,2011,4,0.1,AA
+1,943.5333333333,0.005051932452484,2012,4,0.1,AA
+2,935.2666666667,0.005809394825005,2012,4,0.1,AA
+3,937.5,0.004231111111111,2012,4,0.1,AA
+4,940.8666666667,0.00605824417204,2012,4,0.1,AA
+5,942.0333333333,0.005590743427338,2012,4,0.1,AA
+6,945.1333333333,0.007088946885801,2012,4,0.1,AA
+7,946.9333333333,0.005456209518446,2012,4,0.1,AA
+8,942.9666666667,0.004948920074941,2012,4,0.1,AA
+9,946.3666666667,0.005529921453982,2012,4,0.1,AA
+10,953.0333333333,0.005001573921864,2012,4,0.1,AA
+11,960.0333333333,0.00711780840943,2012,4,0.1,AA
+12,953.9666666667,0.007372724413851,2012,4,0.1,AA
+1,958.0,0.004906054279749,2013,4,0.1,AA
+2,963.4,0.002214379627707,2013,4,0.1,AA
+3,962.1666666667,0.0005543045210462,2013,4,0.1,AA
+4,955.6,0.0002092925910423,2013,4,0.1,AA
+5,947.6333333333,0.0,2013,4,0.1,AA
+6,948.9,0.0,2013,4,0.1,AA
+7,948.4666666667,0.0,2013,4,0.1,AA
+8,955.4,0.001395576024004,2013,4,0.1,AA
+9,955.4,0.002198032237806,2013,4,0.1,AA
+10,957.7666666667,0.002505829533985,2013,4,0.1,AA
+11,953.7666666667,0.001083423618635,2013,4,0.1,AA
+12,955.5,0.002337345194488,2013,4,0.1,AA
+1,969.1,0.00103188525436,2014,4,0.1,AA
+2,971.2333333333,0.002059237395751,2014,4,0.1,AA
+3,967.7666666667,0.003616574243103,2014,4,0.1,AA
+4,967.6,0.001929171834091,2014,4,0.1,AA
+5,968.8666666667,0.001754627399711,2014,4,0.1,AA
+6,967.0,0.0007583591864874,2014,4,0.1,AA
+7,979.4333333333,0.0001361331382092,2014,4,0.1,AA
+8,978.6666666667,0.0001702997275204,2014,4,0.1,AA
+9,973.7,0.0002396357536544,2014,4,0.1,AA
+10,970.1333333333,0.0002405167674546,2014,4,0.1,AA
+11,974.9666666667,0.00211973058908,2014,4,0.1,AA
+12,978.5666666667,0.002077868992063,2014,4,0.1,AA
+1,982.1,0.001798866374775,2015,4,0.1,AA
+2,978.3666666667,0.0006132670096419,2015,4,0.1,AA
+3,982.5,0.0002374893977947,2015,4,0.1,AA
+4,986.1666666667,0.0001352036504986,2015,4,0.1,AA
+5,986.3666666667,0.0006758811800885,2015,4,0.1,AA
+6,992.7666666667,3.35762011886e-05,2015,4,0.1,AA
+7,1004.0,0.000332005312085,2015,4,0.1,AA
+8,1007.966666667,0.0,2015,4,0.1,AA
+9,1002.466666667,0.0,2015,4,0.1,AA
+10,997.1,0.0,2015,4,0.1,AA
+11,1001.0,0.0002997002997003,2015,4,0.1,AA
+12,1015.033333333,0.0,2015,4,0.1,AA
+1,1027.233333333,0.0,2016,4,0.1,AA
+2,1034.4,0.0,2016,4,0.1,AA
+3,1042.0,0.0,2016,4,0.1,AA
+4,1038.0,0.0,2016,4,0.1,AA
+5,1042.133333333,0.0,2016,4,0.1,AA
+6,1040.8,0.0,2016,4,0.1,AA
+7,1045.033333333,0.0,2016,4,0.1,AA
+8,1044.033333333,0.0,2016,4,0.1,AA
+9,1047.3,0.0,2016,4,0.1,AA
+10,1048.566666667,0.0,2016,4,0.1,AA
+11,1049.0,0.0,2016,4,0.1,AA
+12,1053.1,0.0,2016,4,0.1,AA
+1,1053.4,0.0,2017,4,0.1,AA
+2,1046.666666667,0.0,2017,4,0.1,AA
+3,1044.133333333,0.0,2017,4,0.1,AA
+4,1037.633333333,0.0,2017,4,0.1,AA
+5,1044.733333333,0.0,2017,4,0.1,AA
+6,1045.866666667,0.0,2017,4,0.1,AA
+7,1046.9,0.0,2017,4,0.1,AA
+8,1033.633333333,0.0,2017,4,0.1,AA
+9,1029.333333333,0.0,2017,4,0.1,AA
+10,1028.3,0.0,2017,4,0.1,AA
+11,1029.366666667,0.0,2017,4,0.1,AA
+12,1029.7,0.0,2017,4,0.1,AA
+1,1008.433333333,0.223349750438,2010,3,1.5848931924611125,GG
+2,1006.033333333,0.185580331997,2010,3,1.5848931924611125,GG
+3,1007.266666667,0.1700972930042,2010,3,1.5848931924611125,GG
+4,1010.8,0.1543332014246,2010,3,1.5848931924611125,GG
+5,1010.066666667,0.1278133456538,2010,3,1.5848931924611125,GG
+6,1014.666666667,0.1332457293035,2010,3,1.5848931924611125,GG
+7,1012.6,0.228059780104,2010,3,1.5848931924611125,GG
+8,1018.166666667,0.2754296938943,2010,3,1.5848931924611125,GG
+9,1017.3,0.3543694092205,2010,3,1.5848931924611125,GG
+10,1016.9,0.4023011112204,2010,3,1.5848931924611125,GG
+11,1013.0,0.3707469562356,2010,3,1.5848931924611125,GG
+12,1008.0,0.2774470899471,2010,3,1.5848931924611125,GG
+1,1000.033333333,0.1578947368421,2011,3,1.5848931924611125,GG
+2,993.8666666667,0.1375436007513,2011,3,1.5848931924611125,GG
+3,993.5666666667,0.1379541718388,2011,3,1.5848931924611125,GG
+4,996.1,0.1280326607101,2011,3,1.5848931924611125,GG
+5,996.7,0.1032072505936,2011,3,1.5848931924611125,GG
+6,995.8333333333,0.116820083682,2011,3,1.5848931924611125,GG
+7,994.1666666667,0.1947359597653,2011,3,1.5848931924611125,GG
+8,993.2,0.2460061753255,2011,3,1.5848931924611125,GG
+9,986.9,0.3255649001925,2011,3,1.5848931924611125,GG
+10,987.9333333333,0.4092381402254,2011,3,1.5848931924611125,GG
+11,987.6,0.3820372620494,2011,3,1.5848931924611125,GG
+12,985.8,0.283492256712,2011,3,1.5848931924611125,GG
+1,981.1,0.1813950327863,2012,3,1.5848931924611125,GG
+2,983.0666666667,0.1524481215245,2012,3,1.5848931924611125,GG
+3,993.2333333333,0.1376648655905,2012,3,1.5848931924611125,GG
+4,995.7666666667,0.1260335419944,2012,3,1.5848931924611125,GG
+5,998.2666666667,0.1103579537866,2012,3,1.5848931924611125,GG
+6,997.8666666667,0.1105358097274,2012,3,1.5848931924611125,GG
+7,1005.633333333,0.2079286685008,2012,3,1.5848931924611125,GG
+8,1007.766666667,0.266728409354,2012,3,1.5848931924611125,GG
+9,1012.8,0.3554173249078,2012,3,1.5848931924611125,GG
+10,1011.366666667,0.3892422794239,2012,3,1.5848931924611125,GG
+11,1006.833333333,0.3532858798212,2012,3,1.5848931924611125,GG
+12,1003.566666667,0.2653867871259,2012,3,1.5848931924611125,GG
+1,990.0666666667,0.1826139653895,2013,3,1.5848931924611125,GG
+2,986.5666666667,0.1408588708315,2013,3,1.5848931924611125,GG
+3,988.6,0.1305212758783,2013,3,1.5848931924611125,GG
+4,989.7,0.1499781078441,2013,3,1.5848931924611125,GG
+5,993.9666666667,0.1092927328214,2013,3,1.5848931924611125,GG
+6,996.3666666667,0.1193670335553,2013,3,1.5848931924611125,GG
+7,992.4333333333,0.2014241090921,2013,3,1.5848931924611125,GG
+8,1000.3,0.2668866006865,2013,3,1.5848931924611125,GG
+9,995.1,0.3482397079021,2013,3,1.5848931924611125,GG
+10,986.6666666667,0.3923648648649,2013,3,1.5848931924611125,GG
+11,983.6666666667,0.3574720433751,2013,3,1.5848931924611125,GG
+12,992.1,0.2648254544233,2013,3,1.5848931924611125,GG
+1,997.4333333333,0.1727433746616,2014,3,1.5848931924611125,GG
+2,1003.766666667,0.1604290505762,2014,3,1.5848931924611125,GG
+3,1001.233333333,0.1432566501315,2014,3,1.5848931924611125,GG
+4,998.3,0.1360980333233,2014,3,1.5848931924611125,GG
+5,996.7333333333,0.1173500100328,2014,3,1.5848931924611125,GG
+6,992.1666666667,0.1325382160255,2014,3,1.5848931924611125,GG
+7,982.1333333333,0.1975291881618,2014,3,1.5848931924611125,GG
+8,983.9333333333,0.2849109018226,2014,3,1.5848931924611125,GG
+9,978.5666666667,0.3700650611438,2014,3,1.5848931924611125,GG
+10,977.3333333333,0.4141200545703,2014,3,1.5848931924611125,GG
+11,973.1,0.394032816086,2014,3,1.5848931924611125,GG
+12,972.5,0.2868551842331,2014,3,1.5848931924611125,GG
+1,968.6333333333,0.1859664819849,2015,3,1.5848931924611125,GG
+2,961.6666666667,0.1643674176776,2015,3,1.5848931924611125,GG
+3,959.8666666667,0.1541533546326,2015,3,1.5848931924611125,GG
+4,964.6,0.1566797981892,2015,3,1.5848931924611125,GG
+5,965.1,0.1236486719856,2015,3,1.5848931924611125,GG
+6,970.9333333333,0.1304586652019,2015,3,1.5848931924611125,GG
+7,974.1,0.224720254594,2015,3,1.5848931924611125,GG
+8,975.7666666667,0.2834694086701,2015,3,1.5848931924611125,GG
+9,974.3666666667,0.3846943313605,2015,3,1.5848931924611125,GG
+10,979.7,0.423599060937,2015,3,1.5848931924611125,GG
+11,983.2666666667,0.3845006441115,2015,3,1.5848931924611125,GG
+12,984.9666666667,0.2721242681647,2015,3,1.5848931924611125,GG
+1,975.2333333333,0.1664217110435,2016,3,1.5848931924611125,GG
+2,973.8666666667,0.1556681270537,2016,3,1.5848931924611125,GG
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+1,1053.9,0.4991618433121,2016,3,25.118864315095795,GG
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+9,946.6666666667,0.003380281690141,2013,4,0.1,BB
+10,961.1,0.009468317552804,2013,4,0.1,BB
+11,968.9,0.009632917053704,2013,4,0.1,BB
+12,967.5333333333,0.009026390132984,2013,4,0.1,BB
+1,970.2666666667,0.003950803902707,2014,4,0.1,BB
+2,975.0333333333,0.003384499675225,2014,4,0.1,BB
+3,978.5666666667,0.002997581496747,2014,4,0.1,BB
+4,978.3666666667,0.001158393240435,2014,4,0.1,BB
+5,976.5333333333,0.001945658110322,2014,4,0.1,BB
+6,977.5,0.0008866155157715,2014,4,0.1,BB
+7,987.7333333333,0.0001687365010799,2014,4,0.1,BB
+8,985.5,0.0,2014,4,0.1,BB
+9,982.0666666667,0.0,2014,4,0.1,BB
+10,989.6,0.0,2014,4,0.1,BB
+11,994.7333333333,0.0002010589102607,2014,4,0.1,BB
+12,990.0333333333,0.0003366890003704,2014,4,0.1,BB
+1,991.4,0.001781991796113,2015,4,0.1,BB
+2,994.5333333333,0.001206596058453,2015,4,0.1,BB
+3,993.2333333333,0.0005369668087391,2015,4,0.1,BB
+4,998.8333333333,0.0007008176205573,2015,4,0.1,BB
+5,1005.633333333,0.0005303457191156,2015,4,0.1,BB
+6,1018.033333333,0.0007203431452801,2015,4,0.1,BB
+7,1020.333333333,0.0007840574975498,2015,4,0.1,BB
+8,1010.6,0.0003958044725905,2015,4,0.1,BB
+9,1020.1,3.267653498023e-05,2015,4,0.1,BB
+10,1016.366666667,0.0,2015,4,0.1,BB
+11,1018.8,0.0,2015,4,0.1,BB
+12,1024.8,0.0,2015,4,0.1,BB
+1,1019.833333333,0.0,2016,4,0.1,BB
+2,1015.633333333,0.0,2016,4,0.1,BB
+3,1007.2,0.0,2016,4,0.1,BB
+4,1005.433333333,0.0,2016,4,0.1,BB
+5,1007.1,0.0,2016,4,0.1,BB
+6,1010.333333333,0.0,2016,4,0.1,BB
+7,1014.0,0.0,2016,4,0.1,BB
+8,1008.5,0.0,2016,4,0.1,BB
+9,1009.9,0.0,2016,4,0.1,BB
+10,1001.2,0.0,2016,4,0.1,BB
+11,997.1666666667,0.0,2016,4,0.1,BB
+12,998.8333333333,0.0,2016,4,0.1,BB
+1,997.6,0.0,2017,4,0.1,BB
+2,998.4333333333,0.0,2017,4,0.1,BB
+3,995.5666666667,0.0,2017,4,0.1,BB
+4,1001.1,0.0,2017,4,0.1,BB
+5,1002.7,0.0,2017,4,0.1,BB
+6,1003.1,0.0,2017,4,0.1,BB
+7,1003.8,0.0,2017,4,0.1,BB
+8,1005.133333333,0.0,2017,4,0.1,BB
+9,1002.9,0.0,2017,4,0.1,BB
+10,998.6666666667,0.0,2017,4,0.1,BB
+11,991.3333333333,0.0,2017,4,0.1,BB
+12,987.5333333333,0.0,2017,4,0.1,BB
+1,998.1,0.001770029723141,2010,4,0.025118864315095794,EE
+2,991.9333333333,0.001881846898313,2010,4,0.025118864315095794,EE
+3,994.2,0.001240528398042,2010,4,0.025118864315095794,EE
+4,993.3,0.0005369307694889,2010,4,0.025118864315095794,EE
+5,991.5,6.723819129265e-05,2010,4,0.025118864315095794,EE
+6,996.3333333333,0.0001003680160589,2010,4,0.025118864315095794,EE
+7,996.4666666667,0.0,2010,4,0.025118864315095794,EE
+8,989.1333333333,0.0,2010,4,0.025118864315095794,EE
+9,991.9666666667,0.0,2010,4,0.025118864315095794,EE
+10,993.9333333333,0.0,2010,4,0.025118864315095794,EE
+11,1002.133333333,0.0,2010,4,0.025118864315095794,EE
+12,1012.333333333,0.0,2010,4,0.025118864315095794,EE
+1,1025.666666667,0.0,2011,4,0.025118864315095794,EE
+2,1021.466666667,0.0,2011,4,0.025118864315095794,EE
+3,1017.233333333,0.0,2011,4,0.025118864315095794,EE
+4,1007.133333333,0.0,2011,4,0.025118864315095794,EE
+5,1002.0,0.0,2011,4,0.025118864315095794,EE
+6,1010.833333333,0.0,2011,4,0.025118864315095794,EE
+7,1014.666666667,0.0,2011,4,0.025118864315095794,EE
+8,1011.233333333,0.0,2011,4,0.025118864315095794,EE
+9,1009.333333333,0.0,2011,4,0.025118864315095794,EE
+10,1003.3,0.0,2011,4,0.025118864315095794,EE
+11,1007.6,0.0,2011,4,0.025118864315095794,EE
+12,1009.633333333,0.0,2011,4,0.025118864315095794,EE
+1,1012.8,0.0,2012,4,0.025118864315095794,EE
+2,1013.366666667,0.0,2012,4,0.025118864315095794,EE
+3,1012.033333333,0.0,2012,4,0.025118864315095794,EE
+4,1000.866666667,0.0,2012,4,0.025118864315095794,EE
+5,990.4,0.0,2012,4,0.025118864315095794,EE
+6,986.5333333333,0.0,2012,4,0.025118864315095794,EE
+7,986.9,0.0,2012,4,0.025118864315095794,EE
+8,984.9666666667,0.0,2012,4,0.025118864315095794,EE
+9,992.7,0.0,2012,4,0.025118864315095794,EE
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+11,994.3666666667,0.0,2012,4,0.025118864315095794,EE
+12,1000.3,0.0,2012,4,0.025118864315095794,EE
+1,997.8,0.0,2013,4,0.025118864315095794,EE
+2,995.4,0.0,2013,4,0.025118864315095794,EE
+3,998.5666666667,0.0,2013,4,0.025118864315095794,EE
+4,1004.366666667,0.0,2013,4,0.025118864315095794,EE
+5,1012.866666667,0.0,2013,4,0.025118864315095794,EE
+6,1012.833333333,0.0,2013,4,0.025118864315095794,EE
+7,1005.0,0.0,2013,4,0.025118864315095794,EE
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+9,1002.366666667,0.0,2013,4,0.025118864315095794,EE
+10,1003.7,0.0,2013,4,0.025118864315095794,EE
+11,1005.033333333,0.0,2013,4,0.025118864315095794,EE
+12,998.0,0.0,2013,4,0.025118864315095794,EE
+1,990.5333333333,0.0,2014,4,0.025118864315095794,EE
+2,990.3,0.0,2014,4,0.025118864315095794,EE
+3,993.3,0.0,2014,4,0.025118864315095794,EE
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+5,998.1,0.0,2014,4,0.025118864315095794,EE
+6,1001.566666667,0.0,2014,4,0.025118864315095794,EE
+7,995.4333333333,0.0,2014,4,0.025118864315095794,EE
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+9,987.7333333333,0.0,2014,4,0.025118864315095794,EE
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+11,993.8333333333,0.0,2014,4,0.025118864315095794,EE
+12,996.6666666667,0.0,2014,4,0.025118864315095794,EE
+1,994.6666666667,0.0,2015,4,0.025118864315095794,EE
+2,1001.566666667,0.0,2015,4,0.025118864315095794,EE
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+5,996.1333333333,0.0,2015,4,0.025118864315095794,EE
+6,994.4666666667,0.0,2015,4,0.025118864315095794,EE
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+9,988.8333333333,0.0,2015,4,0.025118864315095794,EE
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+12,996.0666666667,0.0,2015,4,0.025118864315095794,EE
+1,998.1666666667,0.0,2016,4,0.025118864315095794,EE
+2,998.5666666667,0.0,2016,4,0.025118864315095794,EE
+3,990.9333333333,0.0,2016,4,0.025118864315095794,EE
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+8,989.5333333333,0.0,2016,4,0.025118864315095794,EE
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+10,986.9333333333,0.0,2016,4,0.025118864315095794,EE
+11,994.7333333333,0.0,2016,4,0.025118864315095794,EE
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+1,1009.366666667,0.0,2017,4,0.025118864315095794,EE
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+3,1012.6,0.0,2017,4,0.025118864315095794,EE
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+9,999.7666666667,0.0,2017,4,0.025118864315095794,EE
+10,998.8666666667,0.0,2017,4,0.025118864315095794,EE
+11,991.8333333333,0.0,2017,4,0.025118864315095794,EE
+12,987.8666666667,0.0,2017,4,0.025118864315095794,EE
+1,1013.666666667,0.2435054258468,2010,4,1.5848931924611125,EE
+2,1014.366666667,0.2069928691137,2010,4,1.5848931924611125,EE
+3,1013.666666667,0.2394607037159,2010,4,1.5848931924611125,EE
+4,1015.766666667,0.2553407934893,2010,4,1.5848931924611125,EE
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+6,1024.7,0.2313847955499,2010,4,1.5848931924611125,EE
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+8,1023.633333333,0.4768960239669,2010,4,1.5848931924611125,EE
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+10,1026.933333333,0.6263308231628,2010,4,1.5848931924611125,EE
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+4,1019.533333333,0.254724383705,2011,4,1.5848931924611125,EE
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+6,1015.366666667,0.2541610584025,2011,4,1.5848931924611125,EE
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+8,1002.7,0.5159070509624,2011,4,1.5848931924611125,EE
+9,996.5,0.6094330155544,2011,4,1.5848931924611125,EE
+10,994.0666666667,0.6020387633291,2011,4,1.5848931924611125,EE
+11,993.4666666667,0.5165749563817,2011,4,1.5848931924611125,EE
+12,989.0666666667,0.3799541655433,2011,4,1.5848931924611125,EE
+1,989.1333333333,0.2532520051223,2012,4,1.5848931924611125,EE
+2,985.2333333333,0.2172412626451,2012,4,1.5848931924611125,EE
+3,978.1,0.2310602187915,2012,4,1.5848931924611125,EE
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+5,975.8,0.2248753159801,2012,4,1.5848931924611125,EE
+6,970.7,0.2376978812541,2012,4,1.5848931924611125,EE
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+8,970.6,0.4844426128168,2012,4,1.5848931924611125,EE
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+11,960.9,0.508342873001,2012,4,1.5848931924611125,EE
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+3,957.5,0.2264926022628,2013,4,1.5848931924611125,EE
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+6,962.9,0.2093329179216,2013,4,1.5848931924611125,EE
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+8,975.9,0.4665095467432,2013,4,1.5848931924611125,EE
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+10,971.8333333333,0.6004801920768,2013,4,1.5848931924611125,EE
+11,967.4333333333,0.5165558350274,2013,4,1.5848931924611125,EE
+12,957.5666666667,0.3737598774672,2013,4,1.5848931924611125,EE
+1,958.2666666667,0.2461388618339,2014,4,1.5848931924611125,EE
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+3,954.4,0.2347024308466,2014,4,1.5848931924611125,EE
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+6,960.7333333333,0.2047394351537,2014,4,1.5848931924611125,EE
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+11,966.8,0.5145497172804,2014,4,1.5848931924611125,EE
+12,968.6,0.3720145915066,2014,4,1.5848931924611125,EE
+1,973.0,0.259027064063,2015,4,1.5848931924611125,EE
+2,969.9666666667,0.2318980033678,2015,4,1.5848931924611125,EE
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+1,970.7333333333,0.238651191539,2016,4,1.5848931924611125,EE
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+6,1012.133333333,0.2413054933474,2017,4,1.5848931924611125,EE
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+8,1002.566666667,0.5026099677494,2017,4,1.5848931924611125,EE
+9,1005.2,0.5948733253747,2017,4,1.5848931924611125,EE
+10,1008.2,0.6077167228724,2017,4,1.5848931924611125,EE
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+12,1003.666666667,0.3895051477914,2017,4,1.5848931924611125,EE
+1,997.9,0.00173698099342,2010,4,0.025118864315095794,FF
+2,992.7333333333,0.002383990329729,2010,4,0.025118864315095794,FF
+3,996.5,0.002241177454424,2010,4,0.025118864315095794,FF
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+10,1005.733333333,0.0,2010,4,0.025118864315095794,FF
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+12,1012.366666667,0.0,2010,4,0.025118864315095794,FF
+1,1008.433333333,0.0,2011,4,0.025118864315095794,FF
+2,1006.266666667,0.0,2011,4,0.025118864315095794,FF
+3,1005.6,0.0,2011,4,0.025118864315095794,FF
+4,998.2,0.0,2011,4,0.025118864315095794,FF
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+2,1017.066666667,0.0,2014,4,0.025118864315095794,GG
+3,1011.133333333,0.0,2014,4,0.025118864315095794,GG
+4,1009.466666667,0.0,2014,4,0.025118864315095794,GG
+5,1021.266666667,0.0,2014,4,0.025118864315095794,GG
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+12,1025.666666667,0.0,2014,4,0.025118864315095794,GG
+1,1030.766666667,0.0,2015,4,0.025118864315095794,GG
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+7,1028.366666667,0.0,2015,4,0.025118864315095794,GG
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+10,1024.333333333,0.0,2015,4,0.025118864315095794,GG
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+12,1027.266666667,0.0,2015,4,0.025118864315095794,GG
+1,1035.233333333,0.0,2016,4,0.025118864315095794,GG
+2,1032.733333333,0.0,2016,4,0.025118864315095794,GG
+3,1034.133333333,0.0,2016,4,0.025118864315095794,GG
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+10,1014.266666667,0.0,2017,4,0.025118864315095794,GG
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+1,975.3666666667,0.4269163733297,2010,4,6.30957344480193,FF
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+2,1006.6,0.08599907278628,2010,4,0.39810717055349715,FF
+3,1006.533333333,0.07765929262154,2010,4,0.39810717055349715,FF
+4,1000.7,0.06955131408014,2010,4,0.39810717055349715,FF
+5,1000.1,0.05502783055028,2010,4,0.39810717055349715,FF
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+7,1003.033333333,0.06732910172477,2010,4,0.39810717055349715,FF
+8,1003.366666667,0.08484767947909,2010,4,0.39810717055349715,FF
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+10,1008.9,0.1529058050021,2010,4,0.39810717055349715,FF
+11,1007.933333333,0.1514319730141,2010,4,0.39810717055349715,FF
+12,1008.266666667,0.1046680772282,2010,4,0.39810717055349715,FF
+1,1006.766666667,0.07661490580406,2011,4,0.39810717055349715,FF
+2,1000.733333333,0.06192125774432,2011,4,0.39810717055349715,FF
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+9,988.2666666667,0.1226389638424,2011,4,0.39810717055349715,FF
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+11,987.1333333333,0.1421962585264,2011,4,0.39810717055349715,FF
+12,992.0666666667,0.1062428600228,2011,4,0.39810717055349715,FF
+1,996.4333333333,0.07955039641388,2012,4,0.39810717055349715,FF
+2,999.9666666667,0.07630254341811,2012,4,0.39810717055349715,FF
+3,996.0,0.07011378848728,2012,4,0.39810717055349715,FF
+4,999.9666666667,0.05523517450582,2012,4,0.39810717055349715,FF
+5,1008.633333333,0.049704220232,2012,4,0.39810717055349715,FF
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+9,1018.333333333,0.1073977086743,2012,4,0.39810717055349715,FF
+10,1014.633333333,0.1530602187983,2012,4,0.39810717055349715,FF
+11,1006.3,0.1442578422604,2012,4,0.39810717055349715,FF
+12,1005.7,0.1057969573431,2012,4,0.39810717055349715,FF
+1,1006.633333333,0.0786781019239,2013,4,0.39810717055349715,FF
+2,1003.3,0.07146416824479,2013,4,0.39810717055349715,FF
+3,996.6,0.06221151916516,2013,4,0.39810717055349715,FF
+4,995.1666666667,0.05811421872383,2013,4,0.39810717055349715,FF
+5,993.0666666667,0.05028195488722,2013,4,0.39810717055349715,FF
+6,990.3333333333,0.04247728037698,2013,4,0.39810717055349715,FF
+7,985.5666666667,0.06294179321541,2013,4,0.39810717055349715,FF
+8,981.7666666667,0.07754727871524,2013,4,0.39810717055349715,FF
+9,978.3,0.1317591740775,2013,4,0.39810717055349715,FF
+10,970.6666666667,0.1721497252747,2013,4,0.39810717055349715,FF
+11,966.2333333333,0.173250077621,2013,4,0.39810717055349715,FF
+12,961.0333333333,0.1249349658354,2013,4,0.39810717055349715,FF
+1,950.4,0.09504769921437,2014,4,0.39810717055349715,FF
+2,959.7666666667,0.07696315076581,2014,4,0.39810717055349715,FF
+3,971.9666666667,0.06639459515073,2014,4,0.39810717055349715,FF
+4,970.8333333333,0.07004291845494,2014,4,0.39810717055349715,FF
+5,973.5,0.0517377161445,2014,4,0.39810717055349715,FF
+6,978.6666666667,0.03985013623978,2014,4,0.39810717055349715,FF
+7,983.3333333333,0.07223728813559,2014,4,0.39810717055349715,FF
+8,986.6666666667,0.1006418918919,2014,4,0.39810717055349715,FF
+9,984.4666666667,0.1347599376989,2014,4,0.39810717055349715,FF
+10,977.1,0.1730972606011,2014,4,0.39810717055349715,FF
+11,975.6666666667,0.1710625213529,2014,4,0.39810717055349715,FF
+12,974.3,0.1448903486264,2014,4,0.39810717055349715,FF
+1,970.2333333333,0.09035627168722,2015,4,0.39810717055349715,FF
+2,974.1,0.0869178386887,2015,4,0.39810717055349715,FF
+3,977.7333333333,0.07670803218328,2015,4,0.39810717055349715,FF
+4,972.4333333333,0.06571144551469,2015,4,0.39810717055349715,FF
+5,966.7666666667,0.04613315863876,2015,4,0.39810717055349715,FF
+6,965.9,0.04289609000242,2015,4,0.39810717055349715,FF
+7,966.4333333333,0.07515607215535,2015,4,0.39810717055349715,FF
+8,970.9,0.1009029422872,2015,4,0.39810717055349715,FF
+9,970.9333333333,0.1397280966767,2015,4,0.39810717055349715,FF
+10,972.9333333333,0.1762710703029,2015,4,0.39810717055349715,FF
+11,981.8333333333,0.1637752503819,2015,4,0.39810717055349715,FF
+12,990.5333333333,0.1268676807107,2015,4,0.39810717055349715,FF
+1,994.9,0.08453110865414,2016,4,0.39810717055349715,FF
+2,989.9333333333,0.07593103912721,2016,4,0.39810717055349715,FF
+3,985.8,0.06678163251505,2016,4,0.39810717055349715,FF
+4,989.0333333333,0.0643726197297,2016,4,0.39810717055349715,FF
+5,996.8666666667,0.05119374038654,2016,4,0.39810717055349715,FF
+6,999.0,0.04164164164164,2016,4,0.39810717055349715,FF
+7,995.5333333333,0.06361749146186,2016,4,0.39810717055349715,FF
+8,1004.7,0.07776782455791,2016,4,0.39810717055349715,FF
+9,1006.8,0.1174347768507,2016,4,0.39810717055349715,FF
+10,998.7,0.1550015019525,2016,4,0.39810717055349715,FF
+11,1002.5,0.1465336658354,2016,4,0.39810717055349715,FF
+12,1013.666666667,0.1290036172312,2016,4,0.39810717055349715,FF
+1,1003.233333333,0.08951058245008,2017,4,0.39810717055349715,FF
+2,1002.966666667,0.0733822991791,2017,4,0.39810717055349715,FF
+3,1003.066666667,0.06825734414462,2017,4,0.39810717055349715,FF
+4,1010.0,0.06108910891089,2017,4,0.39810717055349715,FF
+5,1015.333333333,0.05164149704531,2017,4,0.39810717055349715,FF
+6,1029.433333333,0.05456076158404,2017,4,0.39810717055349715,FF
+7,1028.8,0.07649688958009,2017,4,0.39810717055349715,FF
+8,1024.433333333,0.1012266944327,2017,4,0.39810717055349715,FF
+9,1029.866666667,0.1340626618332,2017,4,0.39810717055349715,FF
+10,1026.933333333,0.1956634640353,2017,4,0.39810717055349715,FF
+11,1033.333333333,0.1889032258065,2017,4,0.39810717055349715,FF
+12,1038.633333333,0.1331236560865,2017,4,0.39810717055349715,FF
+1,934.3666666667,0.006136063643823,2010,4,0.1,GG
+2,934.8,0.007880473541578,2010,4,0.1,GG
+3,938.1666666667,0.006004618937644,2010,4,0.1,GG
+4,935.3666666667,0.004882220875949,2010,4,0.1,GG
+5,932.5666666667,0.003359902777281,2010,4,0.1,GG
+6,928.5,0.001687309280201,2010,4,0.1,GG
+7,924.7,0.001405861360441,2010,4,0.1,GG
+8,921.4333333333,0.001157616756503,2010,4,0.1,GG
+9,910.8333333333,0.002268984446478,2010,4,0.1,GG
+10,902.7,0.003138731952291,2010,4,0.1,GG
+11,899.0,0.001890989988877,2010,4,0.1,GG
+12,893.3333333333,0.001791044776119,2010,4,0.1,GG
+1,907.5,0.0003673094582185,2011,4,0.1,GG
+2,915.8,0.0001091941471937,2011,4,0.1,GG
+3,924.9,0.0002162395934696,2011,4,0.1,GG
+4,922.1666666667,0.0003976143141153,2011,4,0.1,GG
+5,923.7333333333,0.0,2011,4,0.1,GG
+6,926.6333333333,0.0,2011,4,0.1,GG
+7,930.6333333333,0.002041620401877,2011,4,0.1,GG
+8,934.1666666667,0.00149866190901,2011,4,0.1,GG
+9,938.3,0.001527585349391,2011,4,0.1,GG
+10,941.9666666667,0.002087830425705,2011,4,0.1,GG
+11,946.1,0.0006341824331466,2011,4,0.1,GG
+12,945.0666666667,0.001763544018059,2011,4,0.1,GG
+1,944.0666666667,0.0006355483369819,2012,4,0.1,GG
+2,945.2333333333,0.001481115773883,2012,4,0.1,GG
+3,951.6333333333,0.0009107149112053,2012,4,0.1,GG
+4,962.4333333333,0.0004156132026461,2012,4,0.1,GG
+5,976.3,0.000136570043361,2012,4,0.1,GG
+6,977.1,3.411455668134e-05,2012,4,0.1,GG
+7,981.2333333333,0.0,2012,4,0.1,GG
+8,977.9333333333,0.0,2012,4,0.1,GG
+9,976.1666666667,0.0,2012,4,0.1,GG
+10,982.4666666667,0.0,2012,4,0.1,GG
+11,981.5666666667,0.0,2012,4,0.1,GG
+12,972.9333333333,0.0,2012,4,0.1,GG
+1,977.6333333333,0.0,2013,4,0.1,GG
+2,987.6666666667,0.0,2013,4,0.1,GG
+3,996.7666666667,0.0,2013,4,0.1,GG
+4,997.9,0.0,2013,4,0.1,GG
+5,991.8666666667,0.0,2013,4,0.1,GG
+6,994.4666666667,0.0,2013,4,0.1,GG
+7,994.3666666667,0.0,2013,4,0.1,GG
+8,992.7333333333,0.0,2013,4,0.1,GG
+9,1004.033333333,0.0,2013,4,0.1,GG
+10,1009.933333333,0.0,2013,4,0.1,GG
+11,1004.466666667,0.0,2013,4,0.1,GG
+12,1004.1,0.0,2013,4,0.1,GG
+1,1010.566666667,0.0,2014,4,0.1,GG
+2,1008.266666667,0.0,2014,4,0.1,GG
+3,1013.366666667,0.0,2014,4,0.1,GG
+4,1027.233333333,0.0,2014,4,0.1,GG
+5,1032.566666667,0.0,2014,4,0.1,GG
+6,1028.666666667,0.0,2014,4,0.1,GG
+7,1034.833333333,0.0,2014,4,0.1,GG
+8,1034.8,0.0,2014,4,0.1,GG
+9,1038.366666667,0.0,2014,4,0.1,GG
+10,1042.7,0.0,2014,4,0.1,GG
+11,1042.9,0.0,2014,4,0.1,GG
+12,1040.1,0.0,2014,4,0.1,GG
+1,1044.966666667,0.0,2015,4,0.1,GG
+2,1045.5,0.0,2015,4,0.1,GG
+3,1046.166666667,0.0,2015,4,0.1,GG
+4,1047.8,0.0,2015,4,0.1,GG
+5,1050.266666667,0.0,2015,4,0.1,GG
+6,1046.633333333,0.0,2015,4,0.1,GG
+7,1051.366666667,0.0,2015,4,0.1,GG
+8,1052.8,0.0,2015,4,0.1,GG
+9,1062.666666667,0.0,2015,4,0.1,GG
+10,1061.3,0.0,2015,4,0.1,GG
+11,1055.433333333,0.0,2015,4,0.1,GG
+12,1049.3,0.0,2015,4,0.1,GG
+1,1056.1,0.0,2016,4,0.1,GG
+2,1063.133333333,0.0,2016,4,0.1,GG
+3,1064.833333333,0.0,2016,4,0.1,GG
+4,1068.8,0.0,2016,4,0.1,GG
+5,1071.4,0.0,2016,4,0.1,GG
+6,1067.233333333,0.0,2016,4,0.1,GG
+7,1075.1,0.0,2016,4,0.1,GG
+8,1081.4,0.0,2016,4,0.1,GG
+9,1079.866666667,0.0,2016,4,0.1,GG
+10,1081.533333333,0.0,2016,4,0.1,GG
+11,1078.3,0.0,2016,4,0.1,GG
+12,1076.4,0.0,2016,4,0.1,GG
+1,1066.9,0.0,2017,4,0.1,GG
+2,1060.666666667,0.0,2017,4,0.1,GG
+3,1062.4,0.0,2017,4,0.1,GG
+4,1065.433333333,0.0,2017,4,0.1,GG
+5,1073.1,0.0,2017,4,0.1,GG
+6,1074.9,0.0,2017,4,0.1,GG
+7,1073.033333333,0.0,2017,4,0.1,GG
+8,1073.2,0.0,2017,4,0.1,GG
+9,1073.266666667,0.0,2017,4,0.1,GG
+10,1064.633333333,0.0,2017,4,0.1,GG
+11,1059.5,0.0,2017,4,0.1,GG
+12,1052.366666667,0.0,2017,4,0.1,GG
+1,982.0333333333,0.5818200332643,2010,4,25.118864315095795,FF
+2,986.9,0.5999256932482,2010,4,25.118864315095795,FF
+3,989.5333333333,0.6423229805295,2010,4,25.118864315095795,FF
+4,986.4,0.6292917004596,2010,4,25.118864315095795,FF
+5,975.9333333333,0.5861056083066,2010,4,25.118864315095795,FF
+6,975.7666666667,0.5977180336829,2010,4,25.118864315095795,FF
+7,987.1666666667,0.6622319770387,2010,4,25.118864315095795,FF
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+10,987.3666666667,0.6209783599473,2010,4,25.118864315095795,FF
+11,981.3,0.59679336934,2010,4,25.118864315095795,FF
+12,984.5,0.5716945996276,2010,4,25.118864315095795,FF
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+2,978.5333333333,0.6241994822183,2011,4,25.118864315095795,FF
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+6,984.4,0.6106934850332,2011,4,25.118864315095795,FF
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+8,979.5333333333,0.6708296467706,2011,4,25.118864315095795,FF
+9,974.7666666667,0.6312963786205,2011,4,25.118864315095795,FF
+10,977.5333333333,0.6114369501466,2011,4,25.118864315095795,FF
+11,977.7333333333,0.6037433519705,2011,4,25.118864315095795,FF
+12,973.2666666667,0.5837043633126,2011,4,25.118864315095795,FF
+1,975.4,0.5755929191443,2012,4,25.118864315095795,FF
+2,976.6,0.6072428152092,2012,4,25.118864315095795,FF
+3,983.9666666667,0.6509028083607,2012,4,25.118864315095795,FF
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+7,1004.4,0.6895327226868,2012,4,25.118864315095795,FF
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+11,1007.666666667,0.6261660602051,2012,4,25.118864315095795,FF
+12,1004.166666667,0.5852614107884,2012,4,25.118864315095795,FF
+1,1000.666666667,0.5536975349767,2013,4,25.118864315095795,FF
+2,992.4,0.6038223834475,2013,4,25.118864315095795,FF
+3,994.3,0.6601629286936,2013,4,25.118864315095795,FF
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+6,1001.233333333,0.6102473615874,2013,4,25.118864315095795,FF
+7,1005.066666667,0.6973003449191,2013,4,25.118864315095795,FF
+8,1000.2,0.6718323002066,2013,4,25.118864315095795,FF
+9,997.7666666667,0.6351852470517,2013,4,25.118864315095795,FF
+10,994.8666666667,0.6130469744689,2013,4,25.118864315095795,FF
+11,981.3333333333,0.60625,2013,4,25.118864315095795,FF
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+1,983.8333333333,0.54921226495,2014,4,25.118864315095795,FF
+2,982.0333333333,0.5925121346865,2014,4,25.118864315095795,FF
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+4,967.8666666667,0.6321463011434,2014,4,25.118864315095795,FF
+5,960.6666666667,0.5710964607911,2014,4,25.118864315095795,FF
+6,966.4333333333,0.6055599627496,2014,4,25.118864315095795,FF
+7,965.2666666667,0.6988396988742,2014,4,25.118864315095795,FF
+8,962.0666666667,0.6642644307394,2014,4,25.118864315095795,FF
+9,959.4666666667,0.6206573096165,2014,4,25.118864315095795,FF
+10,957.2333333333,0.6241947278615,2014,4,25.118864315095795,FF
+11,951.7,0.6140940772652,2014,4,25.118864315095795,FF
+12,951.7333333333,0.5743905855982,2014,4,25.118864315095795,FF
+1,958.6,0.5622783225537,2015,4,25.118864315095795,FF
+2,960.9333333333,0.6087137505203,2015,4,25.118864315095795,FF
+3,963.2333333333,0.6608990552653,2015,4,25.118864315095795,FF
+4,967.6666666667,0.6266620737168,2015,4,25.118864315095795,FF
+5,964.3,0.5778284766152,2015,4,25.118864315095795,FF
+6,962.8333333333,0.6089665916566,2015,4,25.118864315095795,FF
+7,962.2666666667,0.686504087571,2015,4,25.118864315095795,FF
+8,964.6333333333,0.6438370365251,2015,4,25.118864315095795,FF
+9,974.2666666667,0.631757219105,2015,4,25.118864315095795,FF
+10,987.6666666667,0.6292271346608,2015,4,25.118864315095795,FF
+11,983.6,0.6019045682527,2015,4,25.118864315095795,FF
+12,975.6666666667,0.5720191322173,2015,4,25.118864315095795,FF
+1,971.7,0.5519193166615,2016,4,25.118864315095795,FF
+2,968.1333333333,0.60139787908,2016,4,25.118864315095795,FF
+3,967.6666666667,0.6544609025146,2016,4,25.118864315095795,FF
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+5,969.3333333333,0.5620701513067,2016,4,25.118864315095795,FF
+6,973.9,0.6000958346168,2016,4,25.118864315095795,FF
+7,970.1666666667,0.689778388593,2016,4,25.118864315095795,FF
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+2,968.2,0.6255938855608,2017,4,25.118864315095795,FF
+3,967.7,0.645103510041,2017,4,25.118864315095795,FF
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+5,962.3666666667,0.5719233833258,2017,4,25.118864315095795,FF
+6,971.1666666667,0.6141753904239,2017,4,25.118864315095795,FF
+7,971.2,0.701674903899,2017,4,25.118864315095795,FF
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+9,980.8333333333,0.6228037383178,2017,4,25.118864315095795,FF
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+11,1003.1,0.6059548732263,2017,4,25.118864315095795,FF
+12,999.0666666667,0.5975243560657,2017,4,25.118864315095795,FF
+1,1012.066666667,0.2318029115342,2010,4,1.5848931924611125,GG
+2,1010.966666667,0.1780474133667,2010,4,1.5848931924611125,GG
+3,1009.4,0.1515421702662,2010,4,1.5848931924611125,GG
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+6,1020.533333333,0.1261758557617,2010,4,1.5848931924611125,GG
+7,1014.266666667,0.2103983173393,2010,4,1.5848931924611125,GG
+8,1008.233333333,0.2569511025887,2010,4,1.5848931924611125,GG
+9,1003.233333333,0.3429245439745,2010,4,1.5848931924611125,GG
+10,1008.0,0.3844907407407,2010,4,1.5848931924611125,GG
+11,1013.166666667,0.3411087349893,2010,4,1.5848931924611125,GG
+12,1024.7,0.2587423961485,2010,4,1.5848931924611125,GG
+1,1026.5,0.1723981165774,2011,4,1.5848931924611125,GG
+2,1030.8,0.1393739490363,2011,4,1.5848931924611125,GG
+3,1031.633333333,0.136288733077,2011,4,1.5848931924611125,GG
+4,1028.866666667,0.1474761873907,2011,4,1.5848931924611125,GG
+5,1030.933333333,0.1121314019659,2011,4,1.5848931924611125,GG
+6,1032.866666667,0.1258632931001,2011,4,1.5848931924611125,GG
+7,1024.733333333,0.1794287944831,2011,4,1.5848931924611125,GG
+8,1023.1,0.2386863454208,2011,4,1.5848931924611125,GG
+9,1021.1,0.3350961381517,2011,4,1.5848931924611125,GG
+10,1021.233333333,0.4057512158501,2011,4,1.5848931924611125,GG
+11,1019.2,0.3756868131868,2011,4,1.5848931924611125,GG
+12,1022.3,0.2623822100492,2011,4,1.5848931924611125,GG
+1,1024.533333333,0.1717529932327,2012,4,1.5848931924611125,GG
+2,1020.2,0.1421616676469,2012,4,1.5848931924611125,GG
+3,1018.833333333,0.1368231637494,2012,4,1.5848931924611125,GG
+4,1016.6,0.1237130303626,2012,4,1.5848931924611125,GG
+5,1020.333333333,0.106566481542,2012,4,1.5848931924611125,GG
+6,1025.566666667,0.1082328468814,2012,4,1.5848931924611125,GG
+7,1031.033333333,0.1899712262778,2012,4,1.5848931924611125,GG
+8,1027.866666667,0.2561291996368,2012,4,1.5848931924611125,GG
+9,1025.0,0.3271544715447,2012,4,1.5848931924611125,GG
+10,1020.0,0.4059477124183,2012,4,1.5848931924611125,GG
+11,1017.433333333,0.3550109753301,2012,4,1.5848931924611125,GG
+12,1012.733333333,0.2585741557501,2012,4,1.5848931924611125,GG
+1,1009.866666667,0.1686361235807,2013,4,1.5848931924611125,GG
+2,1015.266666667,0.1339549543634,2013,4,1.5848931924611125,GG
+3,1021.4,0.1235885386071,2013,4,1.5848931924611125,GG
+4,1019.666666667,0.1149722131415,2013,4,1.5848931924611125,GG
+5,1013.7,0.1036795896222,2013,4,1.5848931924611125,GG
+6,1012.066666667,0.1107963902246,2013,4,1.5848931924611125,GG
+7,1015.433333333,0.1950234710961,2013,4,1.5848931924611125,GG
+8,1018.8,0.2556275356629,2013,4,1.5848931924611125,GG
+9,1015.766666667,0.3388573491287,2013,4,1.5848931924611125,GG
+10,1015.633333333,0.4034592536677,2013,4,1.5848931924611125,GG
+11,1017.633333333,0.3594287398867,2013,4,1.5848931924611125,GG
+12,1013.3,0.259975657094,2013,4,1.5848931924611125,GG
+1,1012.033333333,0.160501959751,2014,4,1.5848931924611125,GG
+2,1013.233333333,0.1401783070698,2014,4,1.5848931924611125,GG
+3,1010.566666667,0.1468483029323,2014,4,1.5848931924611125,GG
+4,1012.133333333,0.1520221314715,2014,4,1.5848931924611125,GG
+5,1015.933333333,0.1262550036092,2014,4,1.5848931924611125,GG
+6,1023.5,0.1280573196548,2014,4,1.5848931924611125,GG
+7,1027.233333333,0.2226693059026,2014,4,1.5848931924611125,GG
+8,1028.933333333,0.29525722431,2014,4,1.5848931924611125,GG
+9,1029.833333333,0.3672115229001,2014,4,1.5848931924611125,GG
+10,1028.666666667,0.4220025923526,2014,4,1.5848931924611125,GG
+11,1029.666666667,0.3745224991907,2014,4,1.5848931924611125,GG
+12,1038.333333333,0.2782664526485,2014,4,1.5848931924611125,GG
+1,1043.566666667,0.1882007218833,2015,4,1.5848931924611125,GG
+2,1041.4,0.1553357659561,2015,4,1.5848931924611125,GG
+3,1042.533333333,0.14161018033,2015,4,1.5848931924611125,GG
+4,1046.3,0.1425021504349,2015,4,1.5848931924611125,GG
+5,1041.6,0.116935483871,2015,4,1.5848931924611125,GG
+6,1048.333333333,0.1258823529412,2015,4,1.5848931924611125,GG
+7,1048.133333333,0.2075753720901,2015,4,1.5848931924611125,GG
+8,1044.0,0.2737228607918,2015,4,1.5848931924611125,GG
+9,1034.4,0.3455465326115,2015,4,1.5848931924611125,GG
+10,1038.866666667,0.4313996021305,2015,4,1.5848931924611125,GG
+11,1036.233333333,0.3843407212018,2015,4,1.5848931924611125,GG
+12,1028.466666667,0.2817138782654,2015,4,1.5848931924611125,GG
+1,1024.9,0.1890590951963,2016,4,1.5848931924611125,GG
+2,1016.533333333,0.1402151101784,2016,4,1.5848931924611125,GG
+3,1013.733333333,0.1413586742075,2016,4,1.5848931924611125,GG
+4,1011.433333333,0.1344626437729,2016,4,1.5848931924611125,GG
+5,1018.933333333,0.1181627846114,2016,4,1.5848931924611125,GG
+6,1023.233333333,0.1218685865068,2016,4,1.5848931924611125,GG
+7,1021.2,0.2021477999739,2016,4,1.5848931924611125,GG
+8,1016.233333333,0.2697215206481,2016,4,1.5848931924611125,GG
+9,1010.033333333,0.3575789577902,2016,4,1.5848931924611125,GG
+10,1012.133333333,0.4171716506389,2016,4,1.5848931924611125,GG
+11,1012.466666667,0.3624152235465,2016,4,1.5848931924611125,GG
+12,1006.166666667,0.2623157197283,2016,4,1.5848931924611125,GG
+1,1006.7,0.1647627562001,2017,4,1.5848931924611125,GG
+2,999.4666666667,0.1413086979723,2017,4,1.5848931924611125,GG
+3,992.2,0.1356245380636,2017,4,1.5848931924611125,GG
+4,995.2333333333,0.1387614294805,2017,4,1.5848931924611125,GG
+5,997.1,0.1329522281282,2017,4,1.5848931924611125,GG
+6,1001.633333333,0.144730273886,2017,4,1.5848931924611125,GG
+7,1005.433333333,0.2384709743726,2017,4,1.5848931924611125,GG
+8,1008.466666667,0.2928207840286,2017,4,1.5848931924611125,GG
+9,1009.3,0.3911291654282,2017,4,1.5848931924611125,GG
+10,1013.866666667,0.4204366123093,2017,4,1.5848931924611125,GG
+11,1005.3,0.3620478132564,2017,4,1.5848931924611125,GG
+12,998.1,0.2675082657048,2017,4,1.5848931924611125,GG
+1,995.8,0.001807591885921,2010,4,0.025118864315095794,CC
+2,987.6,0.002598892939112,2010,4,0.025118864315095794,CC
+3,989.8666666667,0.002727640086207,2010,4,0.025118864315095794,CC
+4,988.9666666667,0.001550439853045,2010,4,0.025118864315095794,CC
+5,987.6333333333,0.0003037564548247,2010,4,0.025118864315095794,CC
+6,991.4333333333,0.0001344854251421,2010,4,0.025118864315095794,CC
+7,993.7,3.354466472108e-05,2010,4,0.025118864315095794,CC
+8,990.0,0.0001010101010101,2010,4,0.025118864315095794,CC
+9,986.5,0.0,2010,4,0.025118864315095794,CC
+10,985.0333333333,0.0,2010,4,0.025118864315095794,CC
+11,997.5333333333,0.0,2010,4,0.025118864315095794,CC
+12,1007.8,0.0,2010,4,0.025118864315095794,CC
+1,1009.633333333,0.0,2011,4,0.025118864315095794,CC
+2,1002.533333333,0.0,2011,4,0.025118864315095794,CC
+3,994.2,0.0,2011,4,0.025118864315095794,CC
+4,984.4333333333,0.0,2011,4,0.025118864315095794,CC
+5,979.7666666667,0.0,2011,4,0.025118864315095794,CC
+6,987.7,0.0,2011,4,0.025118864315095794,CC
+7,997.8,0.0,2011,4,0.025118864315095794,CC
+8,1005.1,0.0,2011,4,0.025118864315095794,CC
+9,1008.033333333,0.0,2011,4,0.025118864315095794,CC
+10,1014.933333333,0.0,2011,4,0.025118864315095794,CC
+11,1015.533333333,0.0,2011,4,0.025118864315095794,CC
+12,1013.0,0.0,2011,4,0.025118864315095794,CC
+1,1005.766666667,0.0,2012,4,0.025118864315095794,CC
+2,1003.6,0.0,2012,4,0.025118864315095794,CC
+3,1005.166666667,0.0,2012,4,0.025118864315095794,CC
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+5,998.6666666667,0.0,2012,4,0.025118864315095794,CC
+6,999.1333333333,0.0,2012,4,0.025118864315095794,CC
+7,999.2333333333,0.0,2012,4,0.025118864315095794,CC
+8,992.5,0.0,2012,4,0.025118864315095794,CC
+9,987.2,0.0,2012,4,0.025118864315095794,CC
+10,985.5,0.0,2012,4,0.025118864315095794,CC
+11,983.1666666667,0.0,2012,4,0.025118864315095794,CC
+12,976.9333333333,0.0,2012,4,0.025118864315095794,CC
+1,975.0333333333,0.0,2013,4,0.025118864315095794,CC
+2,976.0666666667,0.0,2013,4,0.025118864315095794,CC
+3,974.8666666667,0.0,2013,4,0.025118864315095794,CC
+4,973.3333333333,0.0,2013,4,0.025118864315095794,CC
+5,975.1,0.0,2013,4,0.025118864315095794,CC
+6,971.3333333333,0.0,2013,4,0.025118864315095794,CC
+7,964.3666666667,0.0,2013,4,0.025118864315095794,CC
+8,957.1333333333,0.0,2013,4,0.025118864315095794,CC
+9,955.0,0.0,2013,4,0.025118864315095794,CC
+10,957.9666666667,0.0,2013,4,0.025118864315095794,CC
+11,957.1666666667,0.0,2013,4,0.025118864315095794,CC
+12,951.7666666667,0.0,2013,4,0.025118864315095794,CC
+1,954.4333333333,0.0,2014,4,0.025118864315095794,CC
+2,959.7,0.0,2014,4,0.025118864315095794,CC
+3,962.7666666667,0.0,2014,4,0.025118864315095794,CC
+4,962.8,0.0,2014,4,0.025118864315095794,CC
+5,967.2333333333,0.0,2014,4,0.025118864315095794,CC
+6,966.4666666667,0.0,2014,4,0.025118864315095794,CC
+7,961.2666666667,0.0,2014,4,0.025118864315095794,CC
+8,960.5666666667,0.0,2014,4,0.025118864315095794,CC
+9,955.9333333333,0.0,2014,4,0.025118864315095794,CC
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+11,949.6333333333,0.0,2014,4,0.025118864315095794,CC
+12,946.3333333333,0.0,2014,4,0.025118864315095794,CC
+1,946.6,0.0,2015,4,0.025118864315095794,CC
+2,942.5,0.0,2015,4,0.025118864315095794,CC
+3,938.0666666667,0.0,2015,4,0.025118864315095794,CC
+4,934.6333333333,0.0,2015,4,0.025118864315095794,CC
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+6,933.0,0.0,2015,4,0.025118864315095794,CC
+7,935.2666666667,0.0,2015,4,0.025118864315095794,CC
+8,934.0666666667,0.0,2015,4,0.025118864315095794,CC
+9,931.7666666667,0.0,2015,4,0.025118864315095794,CC
+10,930.7333333333,0.0,2015,4,0.025118864315095794,CC
+11,934.1666666667,0.0,2015,4,0.025118864315095794,CC
+12,939.2,0.0,2015,4,0.025118864315095794,CC
+1,941.7666666667,0.0,2016,4,0.025118864315095794,CC
+2,939.1666666667,0.0,2016,4,0.025118864315095794,CC
+3,931.3666666667,0.0,2016,4,0.025118864315095794,CC
+4,921.5333333333,0.0,2016,4,0.025118864315095794,CC
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+6,912.3666666667,0.0,2016,4,0.025118864315095794,CC
+7,901.8666666667,0.0,2016,4,0.025118864315095794,CC
+8,913.4333333333,0.0,2016,4,0.025118864315095794,CC
+9,917.0666666667,0.0,2016,4,0.025118864315095794,CC
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+11,922.2666666667,0.0,2016,4,0.025118864315095794,CC
+12,923.8333333333,0.0,2016,4,0.025118864315095794,CC
+1,930.5333333333,0.0,2017,4,0.025118864315095794,CC
+2,929.2666666667,0.0,2017,4,0.025118864315095794,CC
+3,930.9666666667,0.0,2017,4,0.025118864315095794,CC
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+5,934.0666666667,0.0,2017,4,0.025118864315095794,CC
+6,935.2,0.0,2017,4,0.025118864315095794,CC
+7,938.4333333333,0.0,2017,4,0.025118864315095794,CC
+8,943.6666666667,0.0,2017,4,0.025118864315095794,CC
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+10,953.2333333333,0.0,2017,4,0.025118864315095794,CC
+11,960.4666666667,0.0,2017,4,0.025118864315095794,CC
+12,959.7333333333,0.0,2017,4,0.025118864315095794,CC
+1,1001.033333333,0.1028936765342,2010,4,0.39810717055349715,GG
+2,1005.866666667,0.07270678685048,2010,4,0.39810717055349715,GG
+3,1008.733333333,0.0609345053202,2010,4,0.39810717055349715,GG
+4,1001.3,0.04127966976264,2010,4,0.39810717055349715,GG
+5,998.6333333333,0.02950699289028,2010,4,0.39810717055349715,GG
+6,998.2666666667,0.02631227460932,2010,4,0.39810717055349715,GG
+7,1004.366666667,0.03896319405264,2010,4,0.39810717055349715,GG
+8,1011.266666667,0.05095919309117,2010,4,0.39810717055349715,GG
+9,1014.866666667,0.05678906917165,2010,4,0.39810717055349715,GG
+10,1017.5,0.06692874692875,2010,4,0.39810717055349715,GG
+11,1023.5,0.06038104543234,2010,4,0.39810717055349715,GG
+12,1027.6,0.05345789541975,2010,4,0.39810717055349715,GG
+1,1032.266666667,0.03897571686903,2011,4,0.39810717055349715,GG
+2,1033.366666667,0.03016031740912,2011,4,0.39810717055349715,GG
+3,1021.2,0.0252970361666,2011,4,0.39810717055349715,GG
+4,1021.6,0.02006656225529,2011,4,0.39810717055349715,GG
+5,1015.566666667,0.01647684379821,2011,4,0.39810717055349715,GG
+6,1020.0,0.01281045751634,2011,4,0.39810717055349715,GG
+7,1028.7,0.02060853504423,2011,4,0.39810717055349715,GG
+8,1029.466666667,0.02172646030307,2011,4,0.39810717055349715,GG
+9,1028.2,0.03491538611165,2011,4,0.39810717055349715,GG
+10,1029.066666667,0.04667660015548,2011,4,0.39810717055349715,GG
+11,1032.933333333,0.05043887956628,2011,4,0.39810717055349715,GG
+12,1035.1,0.04221814317457,2011,4,0.39810717055349715,GG
+1,1037.3,0.02622192229827,2012,4,0.39810717055349715,GG
+2,1039.9,0.02067506491009,2012,4,0.39810717055349715,GG
+3,1039.166666667,0.02283881315156,2012,4,0.39810717055349715,GG
+4,1042.166666667,0.01695186310571,2012,4,0.39810717055349715,GG
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+6,986.0,0.0,2016,4,0.1,CC
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+8,980.7666666667,0.0,2016,4,0.1,CC
+9,981.9333333333,0.0,2016,4,0.1,CC
+10,993.2666666667,0.0,2016,4,0.1,CC
+11,1002.033333333,0.0,2016,4,0.1,CC
+12,1007.466666667,0.0,2016,4,0.1,CC
+1,1005.433333333,0.0,2017,4,0.1,CC
+2,1006.333333333,0.0,2017,4,0.1,CC
+3,1001.8,0.0,2017,4,0.1,CC
+4,1002.266666667,0.0,2017,4,0.1,CC
+5,994.4666666667,0.0,2017,4,0.1,CC
+6,994.1666666667,0.0,2017,4,0.1,CC
+7,1000.633333333,0.0,2017,4,0.1,CC
+8,1009.233333333,0.0,2017,4,0.1,CC
+9,1007.566666667,0.0,2017,4,0.1,CC
+10,1008.166666667,0.0,2017,4,0.1,CC
+11,1019.933333333,0.0,2017,4,0.1,CC
+12,1026.2,0.0,2017,4,0.1,CC
+1,1013.633333333,0.2547601039166,2010,4,1.5848931924611125,BB
+2,1010.833333333,0.2229513602638,2010,4,1.5848931924611125,BB
+3,1008.9,0.2277067433178,2010,4,1.5848931924611125,BB
+4,1003.766666667,0.2438481718859,2010,4,1.5848931924611125,BB
+5,1002.9,0.2336889686576,2010,4,1.5848931924611125,BB
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+7,997.0333333333,0.3707331750861,2010,4,1.5848931924611125,BB
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+9,1000.733333333,0.5864366131504,2010,4,1.5848931924611125,BB
+10,1003.333333333,0.6209302325581,2010,4,1.5848931924611125,BB
+11,1008.3,0.5205461337565,2010,4,1.5848931924611125,BB
+12,1023.466666667,0.3745440333507,2010,4,1.5848931924611125,BB
+1,1033.4,0.2415005483517,2011,4,1.5848931924611125,BB
+2,1039.166666667,0.1979149959904,2011,4,1.5848931924611125,BB
+3,1040.9,0.2053351266532,2011,4,1.5848931924611125,BB
+4,1039.9,0.2307914222521,2011,4,1.5848931924611125,BB
+5,1042.6,0.2127693586546,2011,4,1.5848931924611125,BB
+6,1035.166666667,0.2165512799871,2011,4,1.5848931924611125,BB
+7,1027.333333333,0.3595392602206,2011,4,1.5848931924611125,BB
+8,1026.7,0.4795948183501,2011,4,1.5848931924611125,BB
+9,1024.1,0.5784265859454,2011,4,1.5848931924611125,BB
+10,1021.766666667,0.6098587413956,2011,4,1.5848931924611125,BB
+11,1013.133333333,0.535500427716,2011,4,1.5848931924611125,BB
+12,1011.0,0.3870755028025,2011,4,1.5848931924611125,BB
+1,1012.8,0.2582938388626,2012,4,1.5848931924611125,BB
+2,1013.3,0.233593210303,2012,4,1.5848931924611125,BB
+3,1012.966666667,0.235381223469,2012,4,1.5848931924611125,BB
+4,1014.766666667,0.2263246066419,2012,4,1.5848931924611125,BB
+5,1017.066666667,0.2094257996854,2012,4,1.5848931924611125,BB
+6,1012.866666667,0.2145066807082,2012,4,1.5848931924611125,BB
+7,1011.333333333,0.377587343441,2012,4,1.5848931924611125,BB
+8,1010.8,0.4916897506925,2012,4,1.5848931924611125,BB
+9,1013.6,0.6070770849776,2012,4,1.5848931924611125,BB
+10,1009.933333333,0.6216581952604,2012,4,1.5848931924611125,BB
+11,1005.9,0.5096928124068,2012,4,1.5848931924611125,BB
+12,1001.5,0.3474787818273,2012,4,1.5848931924611125,BB
+1,991.0333333333,0.2347381520971,2013,4,1.5848931924611125,BB
+2,989.1666666667,0.1956529064869,2013,4,1.5848931924611125,BB
+3,991.0,0.2127817019845,2013,4,1.5848931924611125,BB
+4,996.4333333333,0.1911484293982,2013,4,1.5848931924611125,BB
+5,1000.033333333,0.1729942335255,2013,4,1.5848931924611125,BB
+6,1002.8,0.1910650179497,2013,4,1.5848931924611125,BB
+7,1009.9,0.2935934250916,2013,4,1.5848931924611125,BB
+8,1014.666666667,0.3892904073587,2013,4,1.5848931924611125,BB
+9,1011.066666667,0.5129895819596,2013,4,1.5848931924611125,BB
+10,1010.433333333,0.5755946293669,2013,4,1.5848931924611125,BB
+11,1010.066666667,0.497425912481,2013,4,1.5848931924611125,BB
+12,1011.6,0.3779161724002,2013,4,1.5848931924611125,BB
+1,1010.0,0.2341914191419,2014,4,1.5848931924611125,BB
+2,1016.066666667,0.1782363361984,2014,4,1.5848931924611125,BB
+3,1022.0,0.1651989562948,2014,4,1.5848931924611125,BB
+4,1022.566666667,0.1670306744467,2014,4,1.5848931924611125,BB
+5,1015.866666667,0.1543509646935,2014,4,1.5848931924611125,BB
+6,1013.933333333,0.1561904135709,2014,4,1.5848931924611125,BB
+7,1015.566666667,0.2887058128467,2014,4,1.5848931924611125,BB
+8,1019.9,0.3514070006863,2014,4,1.5848931924611125,BB
+9,1014.866666667,0.4862379294489,2014,4,1.5848931924611125,BB
+10,1009.0,0.5280145358441,2014,4,1.5848931924611125,BB
+11,1007.633333333,0.4752720897152,2014,4,1.5848931924611125,BB
+12,1007.833333333,0.3314701504878,2014,4,1.5848931924611125,BB
+1,1013.733333333,0.2017953439432,2015,4,1.5848931924611125,BB
+2,1015.7,0.1624495421877,2015,4,1.5848931924611125,BB
+3,1011.3,0.1706054912818,2015,4,1.5848931924611125,BB
+4,1007.8,0.167824303764,2015,4,1.5848931924611125,BB
+5,1006.1,0.145943080542,2015,4,1.5848931924611125,BB
+6,1002.633333333,0.1493733169321,2015,4,1.5848931924611125,BB
+7,1002.833333333,0.2347681568888,2015,4,1.5848931924611125,BB
+8,1002.166666667,0.2989190088142,2015,4,1.5848931924611125,BB
+9,993.3333333333,0.4042281879195,2015,4,1.5848931924611125,BB
+10,990.8333333333,0.4577964676198,2015,4,1.5848931924611125,BB
+11,990.4,0.4214458804523,2015,4,1.5848931924611125,BB
+12,988.7666666667,0.3095101641776,2015,4,1.5848931924611125,BB
+1,999.5666666667,0.2049554807083,2016,4,1.5848931924611125,BB
+2,1005.466666667,0.1573730274499,2016,4,1.5848931924611125,BB
+3,1014.866666667,0.1349931025422,2016,4,1.5848931924611125,BB
+4,1012.166666667,0.1321916680389,2016,4,1.5848931924611125,BB
+5,1015.3,0.1158606651564,2016,4,1.5848931924611125,BB
+6,1016.833333333,0.1223405999017,2016,4,1.5848931924611125,BB
+7,1014.066666667,0.1993951745447,2016,4,1.5848931924611125,BB
+8,1011.633333333,0.284424527991,2016,4,1.5848931924611125,BB
+9,1016.066666667,0.3584738534217,2016,4,1.5848931924611125,BB
+10,1014.766666667,0.4168117465427,2016,4,1.5848931924611125,BB
+11,1011.9,0.379088842771,2016,4,1.5848931924611125,BB
+12,1005.7,0.267906267608,2016,4,1.5848931924611125,BB
+1,998.4666666667,0.1818454964279,2017,4,1.5848931924611125,BB
+2,998.3333333333,0.133489148581,2017,4,1.5848931924611125,BB
+3,1000.1,0.1238542812385,2017,4,1.5848931924611125,BB
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+5,989.3333333333,0.1140498652291,2017,4,1.5848931924611125,BB
+6,989.7333333333,0.1113431227267,2017,4,1.5848931924611125,BB
+7,999.8333333333,0.1886981163527,2017,4,1.5848931924611125,BB
+8,1004.133333333,0.2512614526623,2017,4,1.5848931924611125,BB
+9,1014.833333333,0.3338479224832,2017,4,1.5848931924611125,BB
+10,1012.633333333,0.3876032785806,2017,4,1.5848931924611125,BB
+11,1014.066666667,0.3461310893432,2017,4,1.5848931924611125,BB
+12,1007.866666667,0.2651805794417,2017,4,1.5848931924611125,BB
+1,982.6,0.6104891783703,2010,4,25.118864315095795,BB
+2,985.8,0.660850747278,2010,4,25.118864315095795,BB
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+9,957.9666666667,0.662444761474,2011,4,25.118864315095795,BB
+10,960.6,0.6552501908529,2011,4,25.118864315095795,BB
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+12,959.2666666667,0.623219125721,2011,4,25.118864315095795,BB
+1,970.6333333333,0.6108039424431,2012,4,25.118864315095795,BB
+2,972.0333333333,0.6539899180412,2012,4,25.118864315095795,BB
+3,975.2333333333,0.6991147417712,2012,4,25.118864315095795,BB
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+6,985.1333333333,0.6452933613047,2012,4,25.118864315095795,BB
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+8,994.6666666667,0.6922252010724,2012,4,25.118864315095795,BB
+9,989.8666666667,0.6677330280172,2012,4,25.118864315095795,BB
+10,986.3333333333,0.6502872592092,2012,4,25.118864315095795,BB
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+12,1001.933333333,0.6317785614479,2012,4,25.118864315095795,BB
+1,1013.566666667,0.6078863419607,2013,4,25.118864315095795,BB
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+5,1016.666666667,0.6105573770492,2013,4,25.118864315095795,BB
+6,1024.266666667,0.642345743296,2013,4,25.118864315095795,BB
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+9,1030.266666667,0.6707324964411,2013,4,25.118864315095795,BB
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+12,1037.666666667,0.6084484420173,2013,4,25.118864315095795,BB
+1,1044.4,0.5830141708158,2014,4,25.118864315095795,BB
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+10,1056.5,0.6163432718094,2014,4,25.118864315095795,BB
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+1,1067.133333333,0.5385456362841,2015,4,25.118864315095795,BB
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+3,1076.366666667,0.5970084543681,2016,4,25.118864315095795,BB
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+6,1082.7,0.5633447246082,2016,4,25.118864315095795,BB
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+6,1063.366666667,0.5497633303031,2017,4,25.118864315095795,BB
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+7,993.5,0.6956550914276,2011,4,6.30957344480193,BB
+8,989.3666666667,0.7224487045585,2011,4,6.30957344480193,BB
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+10,994.3333333333,0.6283607106939,2011,4,6.30957344480193,BB
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+2,988.7333333333,0.4603196008361,2012,4,6.30957344480193,BB
+3,993.4,0.5468424937924,2012,4,6.30957344480193,BB
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+7,990.7666666667,0.7185344682569,2012,4,6.30957344480193,BB
+8,987.5,0.7379578059072,2012,4,6.30957344480193,BB
+9,993.8666666667,0.6589415079152,2012,4,6.30957344480193,BB
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+6,1005.3,0.4524022679797,2016,4,6.30957344480193,CC
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+8,1008.933333333,0.6719968283336,2016,4,6.30957344480193,CC
+9,1009.5,0.6169390787519,2016,4,6.30957344480193,CC
+10,1008.3,0.5720519686601,2016,4,6.30957344480193,CC
+11,1003.833333333,0.5220986219492,2016,4,6.30957344480193,CC
+12,1001.3,0.4411598255601,2016,4,6.30957344480193,CC
+1,1002.166666667,0.3450856477632,2017,4,6.30957344480193,CC
+2,1002.1,0.3678940890796,2017,4,6.30957344480193,CC
+3,998.3333333333,0.4382971619366,2017,4,6.30957344480193,CC
+4,995.9666666667,0.4522574383346,2017,4,6.30957344480193,CC
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+11,1024.3,0.5127404080836,2012,4,1.5848931924611125,CC
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+1,1014.2,0.2499507000592,2013,4,1.5848931924611125,CC
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+3,1017.3,0.2299551099315,2013,4,1.5848931924611125,CC
+4,1011.766666667,0.2279840542945,2013,4,1.5848931924611125,CC
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+6,1006.9,0.203297248982,2013,4,1.5848931924611125,CC
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+8,1024.4,0.4927437199011,2013,4,1.5848931924611125,CC
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+10,1022.066666667,0.5953949514056,2013,4,1.5848931924611125,CC
+11,1016.6,0.4984917043741,2013,4,1.5848931924611125,CC
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+3,1014.133333333,0.2040165658691,2014,4,1.5848931924611125,CC
+4,1016.466666667,0.2060077392274,2014,4,1.5848931924611125,CC
+5,1017.933333333,0.1763376776475,2014,4,1.5848931924611125,CC
+6,1024.466666667,0.2033903819874,2014,4,1.5848931924611125,CC
+7,1024.7,0.3344393481019,2014,4,1.5848931924611125,CC
+8,1033.433333333,0.4447311550495,2014,4,1.5848931924611125,CC
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+1,1033.566666667,0.2215306221176,2015,4,1.5848931924611125,CC
+2,1034.1,0.1833800728492,2015,4,1.5848931924611125,CC
+3,1031.166666667,0.1650234362373,2015,4,1.5848931924611125,CC
+4,1026.9,0.1496737754406,2015,4,1.5848931924611125,CC
+5,1029.333333333,0.1263601036269,2015,4,1.5848931924611125,CC
+6,1030.6,0.134452422537,2015,4,1.5848931924611125,CC
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+8,1031.566666667,0.2667786861408,2015,4,1.5848931924611125,CC
+9,1029.066666667,0.3488274164291,2015,4,1.5848931924611125,CC
+10,1030.466666667,0.4276379633823,2015,4,1.5848931924611125,CC
+11,1030.566666667,0.395769317851,2015,4,1.5848931924611125,CC
+12,1026.633333333,0.3036786908666,2015,4,1.5848931924611125,CC
+1,1021.5,0.1969326154348,2016,4,1.5848931924611125,CC
+2,1014.0,0.1648257725181,2016,4,1.5848931924611125,CC
+3,1010.066666667,0.1552042769454,2016,4,1.5848931924611125,CC
+4,1007.0,0.1493545183714,2016,4,1.5848931924611125,CC
+5,1005.333333333,0.1338527851459,2016,4,1.5848931924611125,CC
+6,1010.0,0.1362706270627,2016,4,1.5848931924611125,CC
+7,1014.266666667,0.2432299198107,2016,4,1.5848931924611125,CC
+8,1006.1,0.3105390451579,2016,4,1.5848931924611125,CC
+9,992.9,0.4071574848088,2016,4,1.5848931924611125,CC
+10,984.3,0.5049612245589,2016,4,1.5848931924611125,CC
+11,985.3666666667,0.4610804776564,2016,4,1.5848931924611125,CC
+12,987.0,0.3500168861871,2016,4,1.5848931924611125,CC
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+3,977.8333333333,0.16833134481,2017,4,1.5848931924611125,CC
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+8,974.2333333333,0.3422520272351,2017,4,1.5848931924611125,CC
+9,972.2,0.422169649592,2017,4,1.5848931924611125,CC
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+12,960.9333333333,0.2941584570556,2017,4,1.5848931924611125,CC
+1,1001.833333333,0.1191149559142,2010,4,0.39810717055349715,CC
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+12,978.8,0.1752826590383,2014,4,0.39810717055349715,CC
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+9,987.0,0.6569402228977,2012,4,25.118864315095795,CC
+10,990.0666666667,0.655242071241,2012,4,25.118864315095795,CC
+11,991.0,0.662663975782,2012,4,25.118864315095795,CC
+12,990.1,0.6289600377066,2012,4,25.118864315095795,CC
+1,991.9333333333,0.6025270515492,2013,4,25.118864315095795,CC
+2,987.2666666667,0.6516645283274,2013,4,25.118864315095795,CC
+3,986.0666666667,0.6769995267392,2013,4,25.118864315095795,CC
+4,990.8666666667,0.6584471506425,2013,4,25.118864315095795,CC
+5,986.6333333333,0.6227913105172,2013,4,25.118864315095795,CC
+6,990.9,0.6469875870421,2013,4,25.118864315095795,CC
+7,1004.666666667,0.7262110152621,2013,4,25.118864315095795,CC
+8,1003.966666667,0.6826587868123,2013,4,25.118864315095795,CC
+9,1001.6,0.6508919062833,2013,4,25.118864315095795,CC
+10,1001.8,0.6689292606641,2013,4,25.118864315095795,CC
+11,1000.766666667,0.6499683575925,2013,4,25.118864315095795,CC
+12,1003.866666667,0.6338491167486,2013,4,25.118864315095795,CC
+1,1007.533333333,0.5955137960696,2014,4,25.118864315095795,CC
+2,1008.5,0.6418773756404,2014,4,25.118864315095795,CC
+3,1001.933333333,0.6938585401557,2014,4,25.118864315095795,CC
+4,995.9666666667,0.6658188025034,2014,4,25.118864315095795,CC
+5,992.2,0.6013908486192,2014,4,25.118864315095795,CC
+6,997.6333333333,0.6396805773664,2014,4,25.118864315095795,CC
+7,991.5,0.7024037653387,2014,4,25.118864315095795,CC
+8,985.0333333333,0.6817366586579,2014,4,25.118864315095795,CC
+9,977.4333333333,0.6504450431402,2014,4,25.118864315095795,CC
+10,980.1666666667,0.6361163067506,2014,4,25.118864315095795,CC
+11,977.1,0.6438099136902,2014,4,25.118864315095795,CC
+12,977.6666666667,0.5947153085578,2014,4,25.118864315095795,CC
+1,989.9,0.5760851264437,2015,4,25.118864315095795,CC
+2,987.1,0.5770100969169,2015,4,25.118864315095795,CC
+3,983.6,0.610885183679,2015,4,25.118864315095795,CC
+4,990.0666666667,0.6319439768366,2015,4,25.118864315095795,CC
+5,994.9,0.5898750293162,2015,4,25.118864315095795,CC
+6,992.3,0.613020255971,2015,4,25.118864315095795,CC
+7,987.0666666667,0.7058962582737,2015,4,25.118864315095795,CC
+8,987.7,0.6754412608417,2015,4,25.118864315095795,CC
+9,986.2333333333,0.6212187785176,2015,4,25.118864315095795,CC
+10,993.9333333333,0.6311623851365,2015,4,25.118864315095795,CC
+11,992.5666666667,0.6237700238439,2015,4,25.118864315095795,CC
+12,986.9333333333,0.5851121318563,2015,4,25.118864315095795,CC
+1,989.2666666667,0.5237549700115,2016,4,25.118864315095795,CC
+2,992.1,0.5844168934583,2016,4,25.118864315095795,CC
+3,996.0,0.6444109772423,2016,4,25.118864315095795,CC
+4,998.9666666667,0.6362574660482,2016,4,25.118864315095795,CC
+5,1001.4,0.573996405033,2016,4,25.118864315095795,CC
+6,996.9333333333,0.600207302394,2016,4,25.118864315095795,CC
+7,995.3,0.6755752034562,2016,4,25.118864315095795,CC
+8,995.3,0.663953916742,2016,4,25.118864315095795,CC
+9,995.9666666667,0.6200341376887,2016,4,25.118864315095795,CC
+10,993.9333333333,0.6233147763096,2016,4,25.118864315095795,CC
+11,988.2,0.5956284153005,2016,4,25.118864315095795,CC
+12,988.7,0.5666363237922,2016,4,25.118864315095795,CC
+1,981.8,0.5228831398112,2017,4,25.118864315095795,CC
+2,981.0333333333,0.5764669905882,2017,4,25.118864315095795,CC
+3,984.2666666667,0.6222229748036,2017,4,25.118864315095795,CC
+4,986.6,0.6054463139401,2017,4,25.118864315095795,CC
+5,985.0,0.5548223350254,2017,4,25.118864315095795,CC
+6,983.1,0.5901400332282,2017,4,25.118864315095795,CC
+7,979.2333333333,0.6613677366647,2017,4,25.118864315095795,CC
+8,981.4666666667,0.6299076212471,2017,4,25.118864315095795,CC
+9,986.4,0.6041497702082,2017,4,25.118864315095795,CC
+10,981.3666666667,0.5942053598723,2017,4,25.118864315095795,CC
+11,980.8,0.5883632408918,2017,4,25.118864315095795,CC
+12,983.7666666667,0.5335953647545,2017,4,25.118864315095795,CC
+1,934.1,0.007600899261321,2010,4,0.1,DD
+2,935.9666666667,0.01000747889882,2010,4,0.1,DD
+3,936.5666666667,0.01160266220593,2010,4,0.1,DD
+4,929.8666666667,0.009642959564095,2010,4,0.1,DD
+5,935.7333333333,0.006554573952693,2010,4,0.1,DD
+6,941.6666666667,0.005769911504425,2010,4,0.1,DD
+7,945.7,0.007542913538472,2010,4,0.1,DD
+8,939.1,0.01096794803535,2010,4,0.1,DD
+9,931.6,0.01670960354945,2010,4,0.1,DD
+10,934.8,0.02196548281272,2010,4,0.1,DD
+11,945.3,0.01749003843577,2010,4,0.1,DD
+12,945.3333333333,0.01138928067701,2010,4,0.1,DD
+1,953.4666666667,0.00989372115788,2011,4,0.1,DD
+2,960.7333333333,0.01103323849837,2011,4,0.1,DD
+3,963.7666666667,0.01252031957943,2011,4,0.1,DD
+4,958.8,0.007787512167988,2011,4,0.1,DD
+5,965.6,0.003417564208782,2011,4,0.1,DD
+6,972.3,0.002776920703487,2011,4,0.1,DD
+7,976.5666666667,0.005768508721029,2011,4,0.1,DD
+8,974.1666666667,0.006980325064157,2011,4,0.1,DD
+9,981.6333333333,0.008591123637475,2011,4,0.1,DD
+10,991.8666666667,0.0125688936685,2011,4,0.1,DD
+11,996.7666666667,0.0126408721533,2011,4,0.1,DD
+12,992.4666666667,0.01148653187345,2011,4,0.1,DD
+1,990.9666666667,0.003935551145346,2012,4,0.1,DD
+2,988.3666666667,0.004654143199218,2012,4,0.1,DD
+3,999.1,0.003302972675408,2012,4,0.1,DD
+4,1001.5,0.004692960559161,2012,4,0.1,DD
+5,1006.7,0.004304493228701,2012,4,0.1,DD
+6,1012.133333333,0.00388618100382,2012,4,0.1,DD
+7,1018.133333333,0.006351492928235,2012,4,0.1,DD
+8,1016.966666667,0.007997640040644,2012,4,0.1,DD
+9,1014.4,0.007492113564669,2012,4,0.1,DD
+10,1024.1,0.008007030563422,2012,4,0.1,DD
+11,1019.433333333,0.004185331720237,2012,4,0.1,DD
+12,1016.166666667,0.003739544038052,2012,4,0.1,DD
+1,1016.333333333,0.007576254509675,2013,4,0.1,DD
+2,1019.0,0.003532875368008,2013,4,0.1,DD
+3,1013.8,0.006641678174525,2013,4,0.1,DD
+4,1010.3,0.00517997954403,2013,4,0.1,DD
+5,998.5666666667,0.002603732015889,2013,4,0.1,DD
+6,998.9666666667,0.002068804431246,2013,4,0.1,DD
+7,997.4333333333,0.001804631888514,2013,4,0.1,DD
+8,1001.533333333,0.004226852160021,2013,4,0.1,DD
+9,1011.933333333,0.007675077409579,2013,4,0.1,DD
+10,1016.8,0.00960529766588,2013,4,0.1,DD
+11,1021.0,0.00992491021874,2013,4,0.1,DD
+12,1017.466666667,0.007666098807496,2013,4,0.1,DD
+1,1024.166666667,0.004589096826688,2014,4,0.1,DD
+2,1025.066666667,0.001918574401665,2014,4,0.1,DD
+3,1020.033333333,0.001928041567269,2014,4,0.1,DD
+4,1020.6,0.00127376053302,2014,4,0.1,DD
+5,1027.7,0.001524439687328,2014,4,0.1,DD
+6,1025.1,0.0009429974311449,2014,4,0.1,DD
+7,1028.8,0.004957231726283,2014,4,0.1,DD
+8,1034.3,0.01037738889426,2014,4,0.1,DD
+9,1033.4,0.01099929036836,2014,4,0.1,DD
+10,1039.633333333,0.007983583955882,2014,4,0.1,DD
+11,1039.6,0.006284468385276,2014,4,0.1,DD
+12,1038.166666667,0.002536522716327,2014,4,0.1,DD
+1,1047.733333333,0.003499618223467,2015,4,0.1,DD
+2,1048.133333333,0.001049484798372,2015,4,0.1,DD
+3,1048.133333333,0.0003498282661239,2015,4,0.1,DD
+4,1034.966666667,0.002190086637251,2015,4,0.1,DD
+5,1037.0,0.002057216329155,2015,4,0.1,DD
+6,1036.833333333,0.001768204468735,2015,4,0.1,DD
+7,1035.933333333,0.003089001866272,2015,4,0.1,DD
+8,1024.466666667,0.00331880002603,2015,4,0.1,DD
+9,1021.166666667,0.00159947772156,2015,4,0.1,DD
+10,1012.866666667,0.002402422168104,2015,4,0.1,DD
+11,1009.666666667,0.0004952129415649,2015,4,0.1,DD
+12,1009.166666667,0.0005945499587118,2015,4,0.1,DD
+1,1012.666666667,0.0004279131007242,2016,4,0.1,DD
+2,1012.4,0.0002304754379033,2016,4,0.1,DD
+3,1005.1,0.0,2016,4,0.1,DD
+4,997.2666666667,0.0,2016,4,0.1,DD
+5,1003.733333333,0.0,2016,4,0.1,DD
+6,1006.766666667,0.0,2016,4,0.1,DD
+7,1002.166666667,0.0,2016,4,0.1,DD
+8,998.1,0.0,2016,4,0.1,DD
+9,999.8333333333,0.0,2016,4,0.1,DD
+10,1002.0,0.0,2016,4,0.1,DD
+11,1007.533333333,0.0,2016,4,0.1,DD
+12,1010.333333333,0.0,2016,4,0.1,DD
+1,1022.433333333,0.0,2017,4,0.1,DD
+2,1026.566666667,0.0,2017,4,0.1,DD
+3,1031.466666667,0.0,2017,4,0.1,DD
+4,1035.5,0.0,2017,4,0.1,DD
+5,1033.7,0.0,2017,4,0.1,DD
+6,1034.5,0.0,2017,4,0.1,DD
+7,1032.766666667,0.0,2017,4,0.1,DD
+8,1030.066666667,0.0,2017,4,0.1,DD
+9,1022.3,0.0,2017,4,0.1,DD
+10,1018.766666667,0.0,2017,4,0.1,DD
+11,1022.533333333,0.0,2017,4,0.1,DD
+12,1032.3,0.0,2017,4,0.1,DD
+1,1014.8,0.2510511102352,2010,4,1.5848931924611125,DD
+2,1013.433333333,0.2122158997467,2010,4,1.5848931924611125,DD
+3,1012.766666667,0.2221307968272,2010,4,1.5848931924611125,DD
+4,1010.566666667,0.2421413728271,2010,4,1.5848931924611125,DD
+5,1012.566666667,0.215294466208,2010,4,1.5848931924611125,DD
+6,1009.266666667,0.2207213158069,2010,4,1.5848931924611125,DD
+7,1013.0,0.3847976307996,2010,4,1.5848931924611125,DD
+8,1006.2,0.4870801033592,2010,4,1.5848931924611125,DD
+9,998.9333333333,0.6065803523759,2010,4,1.5848931924611125,DD
+10,996.9666666667,0.6162024808586,2010,4,1.5848931924611125,DD
+11,994.4333333333,0.5361512419133,2010,4,1.5848931924611125,DD
+12,1002.3,0.3898367089029,2010,4,1.5848931924611125,DD
+1,1009.766666667,0.2527316541775,2011,4,1.5848931924611125,DD
+2,1012.966666667,0.2184343018856,2011,4,1.5848931924611125,DD
+3,1006.933333333,0.2395722987288,2011,4,1.5848931924611125,DD
+4,1008.233333333,0.2293781201441,2011,4,1.5848931924611125,DD
+5,1006.3,0.2030540925503,2011,4,1.5848931924611125,DD
+6,1018.266666667,0.2034830430797,2011,4,1.5848931924611125,DD
+7,1018.266666667,0.3876194840906,2011,4,1.5848931924611125,DD
+8,1018.7,0.4537155197801,2011,4,1.5848931924611125,DD
+9,1010.966666667,0.5727851231495,2011,4,1.5848931924611125,DD
+10,1003.933333333,0.5972840162029,2011,4,1.5848931924611125,DD
+11,996.7333333333,0.5007022941609,2011,4,1.5848931924611125,DD
+12,993.9666666667,0.3630571112378,2011,4,1.5848931924611125,DD
+1,995.1,0.2391384450474,2012,4,1.5848931924611125,DD
+2,996.4666666667,0.2136883655583,2012,4,1.5848931924611125,DD
+3,1002.166666667,0.2239481124231,2012,4,1.5848931924611125,DD
+4,1000.566666667,0.2264050371456,2012,4,1.5848931924611125,DD
+5,999.8333333333,0.1949324887481,2012,4,1.5848931924611125,DD
+6,991.4666666667,0.2054532006455,2012,4,1.5848931924611125,DD
+7,988.2333333333,0.3557864202112,2012,4,1.5848931924611125,DD
+8,987.9333333333,0.4640664012416,2012,4,1.5848931924611125,DD
+9,981.2,0.5747723875527,2012,4,1.5848931924611125,DD
+10,977.7666666667,0.6021545699383,2012,4,1.5848931924611125,DD
+11,976.8666666667,0.5070633999864,2012,4,1.5848931924611125,DD
+12,968.0,0.3578512396694,2012,4,1.5848931924611125,DD
+1,968.3,0.2357396123791,2013,4,1.5848931924611125,DD
+2,969.8,0.213514814051,2013,4,1.5848931924611125,DD
+3,963.5333333333,0.2209575866602,2013,4,1.5848931924611125,DD
+4,959.4333333333,0.2023069172776,2013,4,1.5848931924611125,DD
+5,962.6333333333,0.1698466013366,2013,4,1.5848931924611125,DD
+6,967.6,0.1592255753066,2013,4,1.5848931924611125,DD
+7,981.9666666667,0.2953596523982,2013,4,1.5848931924611125,DD
+8,988.2333333333,0.3973420582184,2013,4,1.5848931924611125,DD
+9,988.6333333333,0.4991065106713,2013,4,1.5848931924611125,DD
+10,987.3666666667,0.5586239492252,2013,4,1.5848931924611125,DD
+11,981.6333333333,0.4874189276376,2013,4,1.5848931924611125,DD
+12,983.4,0.3431292793709,2013,4,1.5848931924611125,DD
+1,988.7333333333,0.2346436518104,2014,4,1.5848931924611125,DD
+2,995.1666666667,0.1815441299615,2014,4,1.5848931924611125,DD
+3,997.0,0.1735874289535,2014,4,1.5848931924611125,DD
+4,993.6,0.1655260332797,2014,4,1.5848931924611125,DD
+5,989.6666666667,0.1537891545975,2014,4,1.5848931924611125,DD
+6,994.3666666667,0.1553082363984,2014,4,1.5848931924611125,DD
+7,996.5,0.2825221608965,2014,4,1.5848931924611125,DD
+8,1004.333333333,0.3684367739794,2014,4,1.5848931924611125,DD
+9,1001.5,0.4761524380097,2014,4,1.5848931924611125,DD
+10,996.7,0.5190461857463,2014,4,1.5848931924611125,DD
+11,1003.433333333,0.4606517622828,2014,4,1.5848931924611125,DD
+12,1001.8,0.3288081453384,2014,4,1.5848931924611125,DD
+1,996.3,0.2014788049115,2015,4,1.5848931924611125,DD
+2,990.8666666667,0.1605328668506,2015,4,1.5848931924611125,DD
+3,993.5666666667,0.1592243432751,2015,4,1.5848931924611125,DD
+4,992.7333333333,0.1450876368276,2015,4,1.5848931924611125,DD
+5,991.3666666667,0.1472378198447,2015,4,1.5848931924611125,DD
+6,985.0,0.146937394247,2015,4,1.5848931924611125,DD
+7,985.0333333333,0.2231734966668,2015,4,1.5848931924611125,DD
+8,980.1666666667,0.2908348920252,2015,4,1.5848931924611125,DD
+9,977.2666666667,0.3887031857562,2015,4,1.5848931924611125,DD
+10,976.0333333333,0.4516239199481,2015,4,1.5848931924611125,DD
+11,975.7666666667,0.438390325556,2015,4,1.5848931924611125,DD
+12,980.6,0.3241552790808,2015,4,1.5848931924611125,DD
+1,979.3666666667,0.1988019468364,2016,4,1.5848931924611125,DD
+2,988.4333333333,0.1570498769096,2016,4,1.5848931924611125,DD
+3,982.7666666667,0.1544958111454,2016,4,1.5848931924611125,DD
+4,971.3,0.1382339819486,2016,4,1.5848931924611125,DD
+5,964.4333333333,0.1264300279957,2016,4,1.5848931924611125,DD
+6,967.7666666667,0.1398408707333,2016,4,1.5848931924611125,DD
+7,973.1666666667,0.2211680082206,2016,4,1.5848931924611125,DD
+8,979.8333333333,0.281680557918,2016,4,1.5848931924611125,DD
+9,983.2333333333,0.3690883818693,2016,4,1.5848931924611125,DD
+10,985.6333333333,0.4204741452197,2016,4,1.5848931924611125,DD
+11,989.1666666667,0.3932603201348,2016,4,1.5848931924611125,DD
+12,988.7666666667,0.2795064558541,2016,4,1.5848931924611125,DD
+1,986.2,0.1757588048401,2017,4,1.5848931924611125,DD
+2,980.7,0.1441827266238,2017,4,1.5848931924611125,DD
+3,977.4666666667,0.1510366934934,2017,4,1.5848931924611125,DD
+4,982.5,0.1681764206955,2017,4,1.5848931924611125,DD
+5,981.9666666667,0.137581044842,2017,4,1.5848931924611125,DD
+6,986.3333333333,0.136093274755,2017,4,1.5848931924611125,DD
+7,987.7,0.2285781782593,2017,4,1.5848931924611125,DD
+8,988.7666666667,0.2863162862826,2017,4,1.5848931924611125,DD
+9,997.7,0.3758644883231,2017,4,1.5848931924611125,DD
+10,995.5666666667,0.4255532862356,2017,4,1.5848931924611125,DD
+11,997.0666666667,0.3795132388339,2017,4,1.5848931924611125,DD
+12,1000.233333333,0.2752691038758,2017,4,1.5848931924611125,DD
+1,1000.266666667,0.1161690215942,2010,4,0.39810717055349715,DD
+2,1006.366666667,0.09314033983638,2010,4,0.39810717055349715,DD
+3,1006.533333333,0.08865412637435,2010,4,0.39810717055349715,DD
+4,1007.0,0.0737173121483,2010,4,0.39810717055349715,DD
+5,1005.5,0.06534062655395,2010,4,0.39810717055349715,DD
+6,999.7333333333,0.05521472392638,2010,4,0.39810717055349715,DD
+7,1003.766666667,0.09275063925879,2010,4,0.39810717055349715,DD
+8,996.9333333333,0.1465828540859,2010,4,0.39810717055349715,DD
+9,994.3666666667,0.1767959505213,2010,4,0.39810717055349715,DD
+10,994.4666666667,0.2345645907354,2010,4,0.39810717055349715,DD
+11,1002.933333333,0.223112204201,2010,4,0.39810717055349715,DD
+12,1012.866666667,0.1739287829922,2010,4,0.39810717055349715,DD
+1,1009.133333333,0.1117130210742,2011,4,0.39810717055349715,DD
+2,1010.633333333,0.09581450575547,2011,4,0.39810717055349715,DD
+3,1011.066666667,0.08990505077146,2011,4,0.39810717055349715,DD
+4,1009.533333333,0.08152281582249,2011,4,0.39810717055349715,DD
+5,1006.3,0.07151611514128,2011,4,0.39810717055349715,DD
+6,1016.8,0.0595987411487,2011,4,0.39810717055349715,DD
+7,1023.9,0.08539245369014,2011,4,0.39810717055349715,DD
+8,1025.6,0.1210673426937,2011,4,0.39810717055349715,DD
+9,1029.966666667,0.1594873620506,2011,4,0.39810717055349715,DD
+10,1032.366666667,0.212295373091,2011,4,0.39810717055349715,DD
+11,1034.266666667,0.2029779553951,2011,4,0.39810717055349715,DD
+12,1037.766666667,0.1525712266727,2011,4,0.39810717055349715,DD
+1,1043.133333333,0.1140793762383,2012,4,0.39810717055349715,DD
+2,1051.633333333,0.09566071824781,2012,4,0.39810717055349715,DD
+3,1055.566666667,0.09530425995516,2012,4,0.39810717055349715,DD
+4,1057.8,0.08464107896893,2012,4,0.39810717055349715,DD
+5,1057.0,0.06127404604226,2012,4,0.39810717055349715,DD
+6,1049.333333333,0.05813214739517,2012,4,0.39810717055349715,DD
+7,1050.666666667,0.1002538071066,2012,4,0.39810717055349715,DD
+8,1049.933333333,0.12889707283,2012,4,0.39810717055349715,DD
+9,1049.633333333,0.1598018355616,2012,4,0.39810717055349715,DD
+10,1054.133333333,0.2361497596762,2012,4,0.39810717055349715,DD
+11,1052.7,0.2458440201387,2012,4,0.39810717055349715,DD
+12,1049.866666667,0.1902463804928,2012,4,0.39810717055349715,DD
+1,1047.833333333,0.112008907269,2013,4,0.39810717055349715,DD
+2,1049.066666667,0.07629003558719,2013,4,0.39810717055349715,DD
+3,1046.833333333,0.06390702117497,2013,4,0.39810717055349715,DD
+4,1048.5,0.06068987442378,2013,4,0.39810717055349715,DD
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+6,1055.1,0.05222253814804,2013,4,0.39810717055349715,DD
+7,1050.0,0.07234920634921,2013,4,0.39810717055349715,DD
+8,1041.2,0.09937251888846,2013,4,0.39810717055349715,DD
+9,1037.633333333,0.1642198592952,2013,4,0.39810717055349715,DD
+10,1034.133333333,0.1902398143373,2013,4,0.39810717055349715,DD
+11,1032.166666667,0.1760051671242,2013,4,0.39810717055349715,DD
+12,1024.933333333,0.1321061532457,2013,4,0.39810717055349715,DD
+1,1022.433333333,0.09301339940664,2014,4,0.39810717055349715,DD
+2,1023.266666667,0.07365300671053,2014,4,0.39810717055349715,DD
+3,1035.533333333,0.07123543423679,2014,4,0.39810717055349715,DD
+4,1040.133333333,0.05822971413921,2014,4,0.39810717055349715,DD
+5,1034.9,0.04531838825007,2014,4,0.39810717055349715,DD
+6,1029.7,0.03674209316629,2014,4,0.39810717055349715,DD
+7,1029.9,0.04851603715571,2014,4,0.39810717055349715,DD
+8,1029.866666667,0.06796996374935,2014,4,0.39810717055349715,DD
+9,1031.833333333,0.08302374414473,2014,4,0.39810717055349715,DD
+10,1028.766666667,0.1241940187279,2014,4,0.39810717055349715,DD
+11,1027.266666667,0.1251865792719,2014,4,0.39810717055349715,DD
+12,1020.933333333,0.09791693874886,2014,4,0.39810717055349715,DD
+1,1011.933333333,0.05998418868173,2015,4,0.39810717055349715,DD
+2,1010.8,0.04442026117926,2015,4,0.39810717055349715,DD
+3,1005.033333333,0.04659878610991,2015,4,0.39810717055349715,DD
+4,1005.433333333,0.0333521201472,2015,4,0.39810717055349715,DD
+5,1005.466666667,0.02774830924281,2015,4,0.39810717055349715,DD
+6,1006.466666667,0.02056700006624,2015,4,0.39810717055349715,DD
+7,1007.933333333,0.03849460943184,2015,4,0.39810717055349715,DD
+8,1000.5,0.06430118274196,2015,4,0.39810717055349715,DD
+9,988.2666666667,0.07963437668645,2015,4,0.39810717055349715,DD
+10,983.0,0.1019328585961,2015,4,0.39810717055349715,DD
+11,982.3,0.09905324239031,2015,4,0.39810717055349715,DD
+12,976.4,0.07749556192817,2015,4,0.39810717055349715,DD
+1,978.5666666667,0.06441393875396,2016,4,0.39810717055349715,DD
+2,975.0,0.04478632478632,2016,4,0.39810717055349715,DD
+3,969.7666666667,0.03956278142509,2016,4,0.39810717055349715,DD
+4,962.0333333333,0.0340598038876,2016,4,0.39810717055349715,DD
+5,964.8,0.02387368711996,2016,4,0.39810717055349715,DD
+6,964.5,0.02021772939347,2016,4,0.39810717055349715,DD
+7,967.5,0.03524547803618,2016,4,0.39810717055349715,DD
+8,965.3666666667,0.0392251648769,2016,4,0.39810717055349715,DD
+9,960.1666666667,0.05735115431349,2016,4,0.39810717055349715,DD
+10,962.8333333333,0.08204950666436,2016,4,0.39810717055349715,DD
+11,962.7666666667,0.07616937298757,2016,4,0.39810717055349715,DD
+12,960.7,0.06588945560529,2016,4,0.39810717055349715,DD
+1,953.1666666667,0.04200034971149,2017,4,0.39810717055349715,DD
+2,960.3333333333,0.03491843110031,2017,4,0.39810717055349715,DD
+3,961.2666666667,0.03023788057424,2017,4,0.39810717055349715,DD
+4,958.9333333333,0.03163236929922,2017,4,0.39810717055349715,DD
+5,960.3333333333,0.0242624088858,2017,4,0.39810717055349715,DD
+6,963.6333333333,0.01826420837801,2017,4,0.39810717055349715,DD
+7,960.6,0.03168158789645,2017,4,0.39810717055349715,DD
+8,957.4666666667,0.03300375992202,2017,4,0.39810717055349715,DD
+9,960.5666666667,0.06877884582018,2017,4,0.39810717055349715,DD
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+12,953.7333333333,0.0530896127499,2017,4,0.39810717055349715,DD
+1,976.1,0.4431581463648,2010,4,6.30957344480193,DD
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+3,972.8666666667,0.5635921332146,2010,4,6.30957344480193,DD
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+8,965.7666666667,0.7345459565803,2010,4,6.30957344480193,DD
+9,961.8666666667,0.6703631827003,2010,4,6.30957344480193,DD
+10,952.0333333333,0.624943104233,2010,4,6.30957344480193,DD
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+8,947.1,0.7229085277866,2011,4,6.30957344480193,DD
+9,943.7333333333,0.667031647358,2011,4,6.30957344480193,DD
+10,940.0333333333,0.6130633665473,2011,4,6.30957344480193,DD
+11,947.0333333333,0.5867445707648,2011,4,6.30957344480193,DD
+12,953.3666666667,0.5276039299325,2011,4,6.30957344480193,DD
+1,961.0333333333,0.4661302070688,2012,4,6.30957344480193,DD
+2,968.6,0.4774588753527,2012,4,6.30957344480193,DD
+3,974.2333333333,0.5538714202621,2012,4,6.30957344480193,DD
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+9,965.5666666667,0.6775641246936,2012,4,6.30957344480193,DD
+10,977.9666666667,0.6293329697672,2012,4,6.30957344480193,DD
+11,985.6,0.5740327380952,2012,4,6.30957344480193,DD
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+1,985.7333333333,0.4250980657379,2013,4,6.30957344480193,DD
+2,982.2,0.4436638837983,2013,4,6.30957344480193,DD
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+6,976.0333333333,0.4978996618968,2013,4,6.30957344480193,DD
+7,974.2666666667,0.6704872040509,2013,4,6.30957344480193,DD
+8,966.5666666667,0.7134531158396,2013,4,6.30957344480193,DD
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+10,970.8,0.6098406812251,2013,4,6.30957344480193,DD
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+12,971.5666666667,0.4731533262428,2013,4,6.30957344480193,DD
+1,969.5,0.405604263366,2014,4,6.30957344480193,DD
+2,968.4666666667,0.4191505472568,2014,4,6.30957344480193,DD
+3,966.1333333333,0.4762282638697,2014,4,6.30957344480193,DD
+4,966.0333333333,0.5079534867672,2014,4,6.30957344480193,DD
+5,961.7333333333,0.4626715652294,2014,4,6.30957344480193,DD
+6,958.8333333333,0.4742221449678,2014,4,6.30957344480193,DD
+7,959.8,0.6431548239217,2014,4,6.30957344480193,DD
+8,957.3666666667,0.6775878277219,2014,4,6.30957344480193,DD
+9,960.0,0.6296180555556,2014,4,6.30957344480193,DD
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+11,975.5666666667,0.5468616530563,2014,4,6.30957344480193,DD
+12,978.4,0.4745843554102,2014,4,6.30957344480193,DD
+1,966.2333333333,0.3966605719805,2015,4,6.30957344480193,DD
+2,968.2333333333,0.3877508864943,2015,4,6.30957344480193,DD
+3,966.1666666667,0.4282214938761,2015,4,6.30957344480193,DD
+4,966.8,0.4445593711212,2015,4,6.30957344480193,DD
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+6,978.1333333333,0.412213740458,2015,4,6.30957344480193,DD
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+8,994.0333333333,0.6443445893833,2015,4,6.30957344480193,DD
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+11,983.6333333333,0.5278728523501,2015,4,6.30957344480193,DD
+12,988.1666666667,0.4448304941811,2015,4,6.30957344480193,DD
+1,991.9666666667,0.3516919251319,2016,4,6.30957344480193,DD
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+6,985.0666666667,0.4146250676773,2016,4,6.30957344480193,DD
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+1,977.0333333333,0.3206304800246,2017,4,6.30957344480193,DD
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+6,980.2333333333,0.4003468561907,2017,4,6.30957344480193,DD
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+8,959.0666666667,0.7056513276797,2011,4,25.118864315095795,DD
+9,968.0333333333,0.6609276540064,2011,4,25.118864315095795,DD
+10,978.0666666667,0.6644059709631,2011,4,25.118864315095795,DD
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+1,984.2666666667,0.6131468436738,2012,4,25.118864315095795,DD
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+3,991.2,0.7067863868711,2012,4,25.118864315095795,DD
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+10,981.2,0.6584454409567,2012,4,25.118864315095795,DD
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+12,987.6333333333,0.6148368152823,2012,4,25.118864315095795,DD
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+3,980.8666666667,0.6908176442602,2013,4,25.118864315095795,DD
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+5,997.7666666667,0.602712725086,2013,4,25.118864315095795,DD
+6,1005.233333333,0.6406472792387,2013,4,25.118864315095795,DD
+7,1005.966666667,0.7314689022168,2013,4,25.118864315095795,DD
+8,1010.0,0.6914851485149,2013,4,25.118864315095795,DD
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+1,1011.0,0.5519287833828,2015,4,25.118864315095795,DD
+2,1011.666666667,0.5763426688633,2015,4,25.118864315095795,DD
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+10,1025.333333333,0.5920351105332,2015,4,25.118864315095795,DD
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+12,1025.866666667,0.5614114894723,2015,4,25.118864315095795,DD
+1,1022.766666667,0.5323143108562,2016,4,25.118864315095795,DD
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+3,1026.033333333,0.6008576719405,2016,4,25.118864315095795,DD
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+8,1005.566666667,0.6270427951072,2016,4,25.118864315095795,DD
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+11,1017.233333333,0.5650620965364,2016,4,25.118864315095795,DD
+12,1016.266666667,0.5368341642613,2016,4,25.118864315095795,DD
+1,1020.9,0.5120318673066,2017,4,25.118864315095795,DD
+2,1022.4,0.5666405842462,2017,4,25.118864315095795,DD
+3,1019.9,0.5968885838481,2017,4,25.118864315095795,DD
+4,1020.766666667,0.5775724128923,2017,4,25.118864315095795,DD
+5,1024.866666667,0.5315488193586,2017,4,25.118864315095795,DD
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+7,1030.533333333,0.6457174278691,2017,4,25.118864315095795,DD
+8,1031.1,0.6264507160638,2017,4,25.118864315095795,DD
+9,1028.0,0.5942607003891,2017,4,25.118864315095795,DD
+10,1030.2,0.5756163851679,2017,4,25.118864315095795,DD
+11,1031.133333333,0.5741255576388,2017,4,25.118864315095795,DD
+12,1029.4,0.5288193769834,2017,4,25.118864315095795,DD
diff --git a/data/example_files/simulation_output/calibration/baseline_calibration/monthly_U5_PfPR.csv b/data/example_files/simulation_output/baseline_calibration/monthly_U5_PfPR_old.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/baseline_calibration/monthly_U5_PfPR.csv
rename to data/example_files/simulation_output/baseline_calibration/monthly_U5_PfPR_old.csv
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_0.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_0.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_0.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_0.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_1.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_1.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_1.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_1.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_2.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_2.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_2.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_2.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_3.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_3.png
similarity index 100%
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rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_3.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_4.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_4.png
similarity index 100%
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rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_4.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_5.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_5.png
similarity index 100%
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rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_5.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_6.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_6.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_7.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/AA_sample_7.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/IterationState.json b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/IterationState.json
similarity index 100%
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rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/IterationState.json
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MaxHab.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MaxHab.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MaxHab.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MaxHab.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal1.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal1.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal1.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal1.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal10.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal10.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal10.pdf
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal11.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal11.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal11.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal11.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal12.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal12.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal12.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal12.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal2.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal2.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal2.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal2.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal3.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal3.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal3.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal3.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal4.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal4.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal4.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal4.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal5.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal5.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal5.pdf
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal6.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal6.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal6.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal6.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal7.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal7.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal7.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal7.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal8.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal8.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal8.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal8.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal9.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal9.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal9.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter0/LL_MonthVal9.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_0.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_0.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_0.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_0.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_1.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_1.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_2.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_2.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_3.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_3.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_4.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_4.png
similarity index 100%
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diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_5.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_5.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_5.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_5.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_6.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_6.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_6.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_6.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_7.png b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_7.png
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_7.png
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/AA_sample_7.png
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MaxHab.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MaxHab.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MaxHab.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MaxHab.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal1.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal1.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal1.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal1.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal10.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal10.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal10.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal10.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal11.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal11.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal11.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal11.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal12.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal12.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal12.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal12.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal2.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal2.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal2.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal2.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal3.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal3.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal3.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal3.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal4.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal4.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal4.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal4.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal5.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal5.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal5.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal5.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal6.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal6.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal6.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal6.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal7.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal7.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal7.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal7.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal8.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal8.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal8.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal8.pdf
diff --git a/data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal9.pdf b/data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal9.pdf
similarity index 100%
rename from data/example_files/simulation_output/calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal9.pdf
rename to data/example_files/simulation_output/seasonality_calibration/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/LL_MonthVal9.pdf
diff --git a/dtk_sif.id b/dtk_sif.id
new file mode 100644
index 0000000..c52a3a5
--- /dev/null
+++ b/dtk_sif.id
@@ -0,0 +1 @@
+5de4584d-6b55-ed11-a9ff-b88303911bc1
\ No newline at end of file
diff --git a/examples/Snakefile b/examples/Snakefile
index c68cb71..5f73c91 100644
--- a/examples/Snakefile
+++ b/examples/Snakefile
@@ -6,13 +6,17 @@ from tempfile import mkstemp
from shutil import move, copymode
from os import fdopen, remove
from update_lines import update_parameters_in_file
+import emod_malaria.bootstrap as dtk
+# PLEASE NOTE: This file cannot run on multiple cores, the sharing of the update_parameters_in_file function
+# between rules creates catastrophic issues
dirs = os.listdir()
input_files = list()
config['python_version'] = 0
-def get_command(script="run_simulations.py", python_version: str=None):
+
+def get_command(script="run_simulations.py", python_version: str = None):
# using pushd (i.e. cd into directory) to use script location as base dir, otherwise relative
# paths in script use snakemake dir as base
if python_version:
@@ -23,175 +27,198 @@ def get_command(script="run_simulations.py", python_version: str=None):
command = "python " + script
else:
print("Unknown OS")
- raise Exception
+ raise Exception
return command
-
-#
-# # monique\\calibration\\baseline_calibration\\ run
-baseline_calibration = r"monique\\calibration\\baseline_calibration\\"
+
+
+# doing it manually because... too complicated otherwise? re
+expected_outputs = [r"monique/calibration/baseline_calibration/01_serialize_transmission_sweep/experiment_id.txt",
+ r"monique/calibration/baseline_calibration/02_run_transmission_sweep/experiment_id.txt",
+ r"monthly_U5_PfPR.csv",
+ r"../data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/CC_run3.csv",
+ r"../data/example_files/simulation_inputs/larval_habitats/monthly_habitats_1.csv",
+ r"monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/experiment_id.txt",
+ r"monique/calibration/seasonality_calibration/02_seasonality_calibration/experiment_id.txt",
+ r"monique/run_to_present/experiment_id.txt",
+ r"monique/run_future_scenarios/experiment_id.txt",
+ r"ben/example/run_1960-2004/experiment_id.txt",
+ r"ben/example/run_2005-2022/experiment_id.txt"]
+
+download_folder = r"../download"
+onstart:
+ if not os.path.exists(download_folder + r"/schema.json"):
+ print("should be making download folder\n")
+ dtk.setup(download_folder)
+ os.chdir(os.path.dirname(__file__))
+
+rule all:
+ input: expected_outputs
+ default_target: True
+
+# # monique/calibration/baseline_calibration/ run
+baseline_calibration = r"monique/calibration/baseline_calibration/"
+
rule serialize_transmission_sweep:
- input: baseline_calibration + r"01_serialize_transmission_sweep\\params.py",
- baseline_calibration + r"01_serialize_transmission_sweep\\manifest.py"
+ input: baseline_calibration + r"01_serialize_transmission_sweep/params.py",
+ baseline_calibration + r"01_serialize_transmission_sweep/manifest.py"
output:
- touch(baseline_calibration + r"01_serialize_transmission_sweep\\experiment_id.txt")
+ touch(baseline_calibration + r"01_serialize_transmission_sweep/experiment_id.txt")
run:
- update_parameters_in_file(input[0], {"years = 30": f"years = 10\n",
- "num_seeds = ": "num_seeds = 1\n" })
- update_parameters_in_file(input[1], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
- shell(get_command(script=baseline_calibration + r"01_serialize_transmission_sweep\\run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[0],{"years = 30": f"years = 10\n",
+ "num_seeds = ": "num_seeds = 1\n"})
+ update_parameters_in_file(input[1],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=baseline_calibration + r"01_serialize_transmission_sweep/run_simulations.py",
+ python_version=config['python_version']))
rule run_transmission_sweep:
- input: baseline_calibration + r"01_serialize_transmission_sweep\\experiment_id.txt",
- baseline_calibration + r"02_run_transmission_sweep\\params.py",
- baseline_calibration + r"02_run_transmission_sweep\\manifest.py",
- output: touch(baseline_calibration + r"02_run_transmission_sweep\\experiment_id.txt")
+ input: baseline_calibration + r"01_serialize_transmission_sweep/experiment_id.txt",
+ baseline_calibration + r"02_run_transmission_sweep/params.py",
+ baseline_calibration + r"02_run_transmission_sweep/manifest.py",
+ output: touch(baseline_calibration + r"02_run_transmission_sweep/experiment_id.txt")
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
- update_parameters_in_file(input[1], {"burnin_id = =": f"burnin_id = '{burnin_id}'\n",
- "num_seeds = ": "num_seeds = 1\n",
- "num_burnin_seeds = ": "num_burnin_seeds = 1\n"})
- update_parameters_in_file(input[2], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
- shell(get_command(script=baseline_calibration + r"02_run_transmission_sweep\\run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[1],{"burnin_id = =": f"burnin_id = '{burnin_id}'\n",
+ "num_seeds = ": "num_seeds = 1\n",
+ "num_burnin_seeds = ": "num_burnin_seeds = 1\n"})
+ update_parameters_in_file(input[2],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=baseline_calibration + r"02_run_transmission_sweep/run_simulations.py",
+ python_version=config['python_version']))
rule analyze_ssmt_monthy_U5_PfPR:
- input: baseline_calibration + r"02_run_transmission_sweep\\experiment_id.txt",
- baseline_calibration + r"03_analyze_ssmt_monthly_U5_PfPR.py"
+ input: baseline_calibration + r"02_run_transmission_sweep/experiment_id.txt",
+ baseline_calibration + r"03_analyze_ssmt_monthly_U5_PfPR.py"
output: touch(r"monthly_U5_PfPR.csv")
- default_target: True
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
update_parameters_in_file(input[1],
{"experiments = ": f"experiments = {{'PfPR_sweep_main_example': '{burnin_id}'}}\n"})
shell(get_command(script=baseline_calibration + r"03_analyze_ssmt_monthly_U5_PfPR.py",
- python_version=config['python_version']))
+ python_version=config['python_version']))
rule find_best_xLH_fits:
output:
- touch(r"..\\data\\example_files\\simulation_output\\calibration\\baseline_calibration\\LL_allxLH_each_admin_run\\CC_run3.csv")
- default_target: True
+ touch(r"../data/example_files/simulation_output/calibration/baseline_calibration/LL_allxLH_each_admin_run/CC_run3.csv")
run:
- shell(get_command(script=r"monique\\calibration\\baseline_calibration\\04_find_best_xLH_fits.py",
- python_version=config['python_version']))
+ shell(get_command(script=r"monique/calibration/baseline_calibration/04_find_best_xLH_fits.py",
+ python_version=config['python_version']))
+
+# monique/calibration/baseline_calibration/ run
+seasonality_cal = r"monique/calibration/seasonality_calibration/"
-# monique\\calibration\\baseline_calibration\\ run
-seasonality_cal = r"monique\\calibration\\seasonality_calibration\\"
rule burnin_for_seasonalityCalib:
- input: seasonality_cal + r"01_burnin_for_seasonalityCalib\\params.py",
- seasonality_cal + r"01_burnin_for_seasonalityCalib\\manifest.py"
+ input: seasonality_cal + r"01_burnin_for_seasonalityCalib/params.py",
+ seasonality_cal + r"01_burnin_for_seasonalityCalib/manifest.py"
output:
- touch(seasonality_cal + r"01_burnin_for_seasonalityCalib\\experiment_id.txt")
+ touch(seasonality_cal + r"01_burnin_for_seasonalityCalib/experiment_id.txt")
run:
- update_parameters_in_file(input[0], {"years = 20": f"years = 5\n"})
- update_parameters_in_file(input[1], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
- shell(get_command(script=seasonality_cal + r"01_burnin_for_seasonalityCalib\\run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[0],{"years = 20": f"years = 5\n"})
+ update_parameters_in_file(input[1],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=seasonality_cal + r"01_burnin_for_seasonalityCalib/run_simulations.py",
+ python_version=config['python_version']))
rule seasonality_calibration:
- input: seasonality_cal + r"01_burnin_for_seasonalityCalib\\experiment_id.txt",
- seasonality_cal + r"02_seasonality_calibration\\params.py"
+ input: seasonality_cal + r"01_burnin_for_seasonalityCalib/experiment_id.txt",
+ seasonality_cal + r"02_seasonality_calibration/params.py",
+ seasonality_cal + r"02_seasonality_calibration/manifest.py"
output:
- touch(seasonality_cal + r"02_seasonality_calibration\\experiment_id.txt")
- default_target: True
+ touch(seasonality_cal + r"02_seasonality_calibration/experiment_id.txt")
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
update_parameters_in_file(input[1],{"burnin_ids = {{": f"burnin_ids = {{'AA': '{burnin_id}'}}\n"})
- shell(get_command(script=seasonality_cal + r"01_serialize_transmission_sweep\\run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[2],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=r"monique/calibration/seasonality_calibration/02_seasonality_calibration/run_calibration.py",
+ python_version=config['python_version']))
rule save_best_seasonality_fit:
- input: seasonality_cal + r"\03_save_best_seasonality_fit.py"
+ input: seasonality_cal + r"/03_save_best_seasonality_fit.py"
output:
- touch(r"..\\data\\example_files\\simulation_inputs\\larval_habitats\\monthly_habitats_1.csv")
- default_target: True
+ touch(r"../data/example_files/simulation_inputs/larval_habitats/monthly_habitats_1.csv")
run:
- update_parameters_in_file(input[0], {"USER_PATH = ": f"USER_PATH = r'..\data'\n"})
+ update_parameters_in_file(input[0],{"USER_PATH = ": f"USER_PATH = r'../data'\n"})
shell(get_command(script=seasonality_cal + r"03_save_best_seasonality_fit.py",
- python_version=config['python_version']))
+ python_version=config['python_version']))
# run to present
-run_to_pre = r"monique\\run_to_present\\"
+run_to_pre = r"monique/run_to_present/"
+
rule run_to_present:
- input: baseline_calibration + r"01_serialize_transmission_sweep\\experiment_id.txt",
- run_to_pre + r"params.py", run_to_pre + r"manifest.py"
+ input: baseline_calibration + r"01_serialize_transmission_sweep/experiment_id.txt",
+ run_to_pre + r"params.py",run_to_pre + r"manifest.py"
output: touch(run_to_pre + r"experiment_id.txt")
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
- update_parameters_in_file(input[1], {"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
- "num_seeds = ": "num_seeds = 1\n",
- "num_burnin_seeds = ": "num_burnin_seeds = 1\n",
- "ser_date = 30 * 365": "ser_date = 10 * 365\n"})
- update_parameters_in_file(input[2], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
- shell(get_command(script= run_to_pre + r"run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[1],{"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
+ "num_seeds = ": "num_seeds = 1\n",
+ "num_burnin_seeds = ": "num_burnin_seeds = 1\n",
+ "ser_date = 30 * 365": "ser_date = 10 * 365\n"})
+ update_parameters_in_file(input[2],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=run_to_pre + r"run_simulations.py",
+ python_version=config['python_version']))
+
#
# run to future
-run_to_futu = r"monique\\run_future_scenarios\\"
+run_to_futu = r"monique/run_future_scenarios/"
+
rule run_to_future:
input: run_to_pre + r"experiment_id.txt",
- run_to_futu + r"params.py", run_to_futu + r"manifest.py"
+ run_to_futu + r"params.py",run_to_futu + r"manifest.py"
output: touch(run_to_futu + r"experiment_id.txt")
- default_target: True
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
- update_parameters_in_file(input[1], {"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
- "num_seeds = ": "num_seeds = 1\n",
- "num_burnin_seeds = ": "num_burnin_seeds = 1\n",
- "num_burnin_seeds_calib = 5" : "num_burnin_seeds_calib = 1"})
- update_parameters_in_file(input[2], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
- shell(get_command(script= run_to_futu + r"run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[1],{"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
+ "num_seeds = ": "num_seeds = 1\n",
+ "num_burnin_seeds = ": "num_burnin_seeds = 1\n",
+ "num_burnin_seeds_calib = 5": "num_burnin_seeds_calib = 1"})
+ update_parameters_in_file(input[2],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_id = None": "sif_id = 'dtk_sif.id'\n"})
+ shell(get_command(script=run_to_futu + r"run_simulations.py",
+ python_version=config['python_version']))
# run 1960 to 2004
-run_1960_to_2004 = r"ben\\example\\run_1960-2004\\"
+run_1960_to_2004 = r"ben/example/run_1960-2004/"
+
rule run_1960_2004:
- input: run_1960_to_2004 + r"params.py", run_1960_to_2004 + r"manifest.py"
+ input: run_1960_to_2004 + r"params.py",run_1960_to_2004 + r"manifest.py"
output: touch(run_1960_to_2004 + r"experiment_id.txt")
- default_target: True
run:
- update_parameters_in_file(input[0], {"num_seeds = ": "num_seeds = 1 \n",
- "iopath = ": "iopath = 'ben/IO' \n",
- "years = ": "years = 5 \n"})
- update_parameters_in_file(input[1], {"USER_PATH = ": f"USER_PATH = r'..\data'\n",
- "sif_path = ": "sif_path = 'dtk_sif.id'\n"})
- shell(get_command(script= run_1960_to_2004 + r"run_simulations.py",
- python_version=config['python_version']))
-
-run_2005_to_2022 = r"ben\\example\\run_2005-2022\\"
+ update_parameters_in_file(input[0],{"num_seeds = ": "num_seeds = 1 \n",
+ "iopath = ": "iopath = 'ben/IO' \n",
+ "years = ": "years = 5 \n"})
+ update_parameters_in_file(input[1],{"USER_PATH = ": f"USER_PATH = r'../data'\n",
+ "sif_path = ": "sif_path = 'dtk_sif.id'\n"})
+ shell(get_command(script=run_1960_to_2004 + r"run_simulations.py",
+ python_version=config['python_version']))
+
+run_2005_to_2022 = r"ben/example/run_2005-2022/"
+
rule run_2005_2022:
input: run_1960_to_2004 + r"experiment_id.txt",
- run_2005_to_2022 + r"params.py", run_2005_to_2022 + r"manifest.py"
+ run_2005_to_2022 + r"params.py",run_2005_to_2022 + r"manifest.py"
output: touch(run_2005_to_2022 + r"experiment_id.txt")
- default_target: True
run:
with open(input[0]) as exp_id_file:
burnin_id = exp_id_file.read()
- update_parameters_in_file(input[1], {"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
- "iopath = ": "iopath = 'ben/IO' \n"})
- update_parameters_in_file(input[2], {"BEN_DIR = ": f"BEN_DIR = r'ben'\n",
- "sif_path = ": "sif_path = 'dtk_sif.id'\n"})
- shell(get_command(script= run_2005_to_2022 + r"run_simulations.py",
- python_version=config['python_version']))
+ update_parameters_in_file(input[1],{"burnin_id = ": f"burnin_id = '{burnin_id}'\n",
+ "iopath = ": "iopath = 'ben/IO' \n"})
+ update_parameters_in_file(input[2],{"BEN_DIR = ": f"BEN_DIR = r'ben'\n",
+ "sif_path = ": "sif_path = 'dtk_sif.id'\n"})
+ shell(get_command(script=run_2005_to_2022 + r"run_simulations.py",
+ python_version=config['python_version']))
-
-rule all:
- input: r"monique\\calibration\\baseline_calibration\\02_run_transmission_sweep\\experiment_id.txt"
-
rule clean_output:
run:
- os.remove(r"experiment_id.txt")
- # os.remove(r"monique\\calibration\\baseline_calibration\\02_run_transmission_sweep\\experiment_id.txt")
+ for file in expected_outputs:
+ os.remove(file)
diff --git a/examples/ben/example/run_1960-2004/run_simulations.py b/examples/ben/example/run_1960-2004/run_simulations.py
index 5ce2777..c5c64f9 100644
--- a/examples/ben/example/run_1960-2004/run_simulations.py
+++ b/examples/ben/example/run_1960-2004/run_simulations.py
@@ -42,8 +42,9 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- with open("ben\\example\\run_1960-2004\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ with open("ben\\example\\run_1960-2004\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -87,7 +88,7 @@ def run_experiment(**kwargs):
experiment = _config_experiment(**kwargs)
_pre_run(experiment, **kwargs)
- experiment.run(wait_until_done=True, wait_on_done=False)
+ experiment.run(wait_until_done=True)
_post_run(experiment, **kwargs)
diff --git a/examples/ben/example/run_2005-2022/run_simulations.py b/examples/ben/example/run_2005-2022/run_simulations.py
index 201bac0..9d4af40 100644
--- a/examples/ben/example/run_2005-2022/run_simulations.py
+++ b/examples/ben/example/run_2005-2022/run_simulations.py
@@ -42,8 +42,9 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- with open("ben\\example\\run_2005-2022\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ with open("ben\\example\\run_2005-2022\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -87,7 +88,7 @@ def run_experiment(**kwargs):
experiment = _config_experiment(**kwargs)
_pre_run(experiment, **kwargs)
- experiment.run(wait_until_done=True, wait_on_done=False)
+ experiment.run(wait_until_done=True)
_post_run(experiment, **kwargs)
diff --git a/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/manifest.py b/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/manifest.py
index 6266f86..cfed8a8 100644
--- a/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/manifest.py
+++ b/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/manifest.py
@@ -5,7 +5,7 @@
PROJECT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "..", "..", "..", "..", ".."))
# change to your input folder which contains the required files...
-download_dir = os.path.join(PROJECT_DIR, "download")
+download_dir = os.path.join(PROJECT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
@@ -18,4 +18,4 @@
data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = None
\ No newline at end of file
+sif_id = '../../../../../dtk_sif.id'
diff --git a/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/run_simulations.py b/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/run_simulations.py
index 4a95013..8139ecc 100644
--- a/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/run_simulations.py
+++ b/examples/monique/calibration/baseline_calibration/01_serialize_transmission_sweep/run_simulations.py
@@ -40,9 +40,10 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- # Save experiment id to file to be used by snakefile
- with open("monique\\calibration\\baseline_calibration\\01_serialize_transmission_sweep\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ # Save experiment id to file to be used by snakefile
+ with open("monique\\calibration\\baseline_calibration\\01_serialize_transmission_sweep\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -87,7 +88,7 @@ def run_experiment(**kwargs):
experiment = _config_experiment(**kwargs)
_pre_run(experiment, **kwargs)
- experiment.run(wait_until_done=True, wait_on_done=False)
+ experiment.run(wait_until_done=True)
_post_run(experiment, **kwargs)
@@ -97,7 +98,7 @@ def run_experiment(**kwargs):
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='emod_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
diff --git a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/manifest.py b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/manifest.py
index 4e430ed..d0347a8 100644
--- a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/manifest.py
+++ b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/manifest.py
@@ -5,7 +5,7 @@
PROJECT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "..", "..", "..", "..", ".."))
# change to your input folder which contains the required files...
-download_dir = os.path.join(PROJECT_DIR, "download")
+download_dir = os.path.join(PROJECT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
@@ -18,4 +18,5 @@
data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = r"dtk_sif.id"
+# sif_id = r"dtk_sif.id"
+sif_id = '../../../../../dtk_sif.id'
diff --git a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/params.py b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/params.py
index c5f6366..97d91bc 100644
--- a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/params.py
+++ b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/params.py
@@ -2,7 +2,7 @@
import pandas as pd
import manifest
-burnin_id = 'cab2ccbc-f70a-ee11-aa07-b88303911bc1' # generated from serialize_transmission_sweep
+burnin_id = 'eb89ea6e-2f53-ee11-aa0a-b88303911bc1' # generated from serialize_transmission_sweep
expname = 'PfPR_sweep_main_example'
population_size = 6000 # needs to match burnin simulation population size
num_seeds = 1
diff --git a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/run_simulations.py b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/run_simulations.py
index 272925f..c876617 100644
--- a/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/run_simulations.py
+++ b/examples/monique/calibration/baseline_calibration/02_run_transmission_sweep/run_simulations.py
@@ -39,9 +39,10 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
+ if experiment.succeeded:
# Save experiment id to file to be used by snakefile
- with open(r"monique\\calibration\\baseline_calibration\\02_run_transmission_sweep\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ with open(r"monique\\calibration\\baseline_calibration\\02_run_transmission_sweep\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -85,7 +86,7 @@ def run_experiment(**kwargs):
experiment = _config_experiment(**kwargs)
_pre_run(experiment, **kwargs)
- experiment.run(wait_until_done=True, wait_on_done=False)
+ experiment.run(wait_until_done=True)
_post_run(experiment, **kwargs)
@@ -95,7 +96,7 @@ def run_experiment(**kwargs):
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='emod_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
diff --git a/examples/monique/calibration/baseline_calibration/03_analyze_ssmt_monthly_U5_PfPR.py b/examples/monique/calibration/baseline_calibration/03_analyze_ssmt_monthly_U5_PfPR.py
index 4877e70..3a6ccb8 100644
--- a/examples/monique/calibration/baseline_calibration/03_analyze_ssmt_monthly_U5_PfPR.py
+++ b/examples/monique/calibration/baseline_calibration/03_analyze_ssmt_monthly_U5_PfPR.py
@@ -5,8 +5,8 @@
import tempfile
from idmtools.core import ItemType
+experiments = {'PfPR_sweep_main_example': 'e369e323-5153-ee11-aa0a-b88303911bc1'}
-experiments = {'PfPR_sweep_main_example': '20c71292-a74d-ee11-aa0a-b88303911bc1'}
start_year = 2010
end_year = 2017
diff --git a/examples/monique/calibration/baseline_calibration/04_find_best_xLH_fits.py b/examples/monique/calibration/baseline_calibration/04_find_best_xLH_fits.py
index d0034f4..645a802 100644
--- a/examples/monique/calibration/baseline_calibration/04_find_best_xLH_fits.py
+++ b/examples/monique/calibration/baseline_calibration/04_find_best_xLH_fits.py
@@ -12,9 +12,10 @@
mpl.rcParams['pdf.fonttype'] = 42
# Specify data location
-USER_PATH = None
-USER_PATH = r"..\data"
-_, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
+
+# USER_PATH = r"..\data"
+# _, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
+_, project_path = load_box_paths(country_name='Example')
def load_ref_data():
@@ -214,7 +215,7 @@ def save_plots(working_dir, fig, name):
if __name__ == "__main__":
expt_name = 'baseline_calibration'
- working_dir = os.path.join(project_path, 'simulation_output', 'calibration', expt_name)
+ working_dir = os.path.join(project_path, 'simulation_output', 'baseline_calibration', expt_name)
if not os.path.exists(os.path.join(working_dir, 'LL_allxLH_each_admin_run')):
os.mkdir(os.path.join(working_dir, 'LL_allxLH_each_admin_run'))
@@ -224,4 +225,4 @@ def save_plots(working_dir, fig, name):
all_df = pd.read_csv(os.path.join(working_dir, 'monthly_U5_PfPR.csv'))
save_best_hab(all_df, working_dir, habitats_fname, create_plot=False)
- # plot_outputs_separate(all_df, working_dir)
+ plot_outputs_separate(all_df, working_dir)
diff --git a/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/manifest.py b/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/manifest.py
index 9a7c058..231fa05 100644
--- a/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/manifest.py
+++ b/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/manifest.py
@@ -5,17 +5,16 @@
PROJECT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "..", "..", "..", "..", ".."))
# change to your input folder which contains the required files...
-download_dir = os.path.join(PROJECT_DIR, "download")
+download_dir = os.path.join(PROJECT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
# user test data directory
-USER_PATH = None
# specify user data for testing
# USER_PATH = r'C:\Projects\emodpy-snt\data'
# load project path
-data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
+data_path, project_path = load_box_paths(country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = None
+sif_id = '../../../../../dtk_sif.id'
diff --git a/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/run_simulations.py b/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/run_simulations.py
index 25499e0..d23f9d5 100644
--- a/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/run_simulations.py
+++ b/examples/monique/calibration/seasonality_calibration/01_burnin_for_seasonalityCalib/run_simulations.py
@@ -1,5 +1,7 @@
import manifest
import params
+import pathlib
+import os
from idmtools.core.platform_factory import Platform
from idmtools.entities.experiment import Experiment
from idmtools.entities.templated_simulation import TemplatedSimulations
@@ -38,8 +40,9 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- with open("monique\\calibration\\seasonality_calibration\\01_burnin_for_seasonalityCalib\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ with open("monique\\calibration\\seasonality_calibration\\01_burnin_for_seasonalityCalib\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -84,24 +87,23 @@ def run_experiment(**kwargs):
experiment = _config_experiment(**kwargs)
_pre_run(experiment, **kwargs)
- experiment.run(wait_until_done=True, wait_on_done=False)
+ experiment.run(wait_until_done=True)
_post_run(experiment, **kwargs)
-
if __name__ == "__main__":
"""
- show_warnings_once=True: show api warnings for only one simulation
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='emod_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
- # import emod_malaria.bootstrap as dtk
+ import emod_malaria.bootstrap as dtk
# import pathlib
# import os
- # dtk.setup(pathlib.Path(manifest.eradication_path).parent)
- # os.chdir(os.path.dirname(__file__))
- # print("...done.")
+ dtk.setup(pathlib.Path(manifest.eradication_path).parent)
+ os.chdir(os.path.dirname(__file__))
+ print("...done.")
run_experiment(show_warnings_once=True)
diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json
new file mode 100644
index 0000000..f9cb67d
--- /dev/null
+++ b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/CalibManager.json
@@ -0,0 +1,396 @@
+{
+ "name": "seasonality_calibration_noMassITN_AA_round1_maxInc100",
+ "directory": "C:\\Users\\moniqueam\\Documents\\emodpy-snt\\examples\\monique\\calibration\\seasonality_calibration\\02_seasonality_calibration\\Calibra_results\\seasonality_calibration_noMassITN_AA_round1_maxInc100",
+ "location": "CALCULON",
+ "suites": [
+ {
+ "id": "b70611c1-cb4d-ee11-aa0a-b88303911bc1"
+ }
+ ],
+ "iteration": 1,
+ "param_names": [
+ "MonthVal1",
+ "MonthVal2",
+ "MonthVal3",
+ "MonthVal4",
+ "MonthVal5",
+ "MonthVal6",
+ "MonthVal7",
+ "MonthVal8",
+ "MonthVal9",
+ "MonthVal10",
+ "MonthVal11",
+ "MonthVal12",
+ "MaxHab"
+ ],
+ "sites": {
+ "AA": [
+ "ChannelByMultiYearSeasonCohortInsetAnalyzer"
+ ]
+ },
+ "results": {
+ "sample": [
+ 7,
+ 3,
+ 0,
+ 2,
+ 1,
+ 1,
+ 3,
+ 4,
+ 5,
+ 0,
+ 7,
+ 2,
+ 6,
+ 6,
+ 4,
+ 5
+ ],
+ "iteration": [
+ 0,
+ 1,
+ 1,
+ 0,
+ 1,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 1,
+ 1,
+ 0,
+ 1,
+ 1,
+ 1
+ ],
+ "total": [
+ -134.22421230502815,
+ -139.15893808991987,
+ -139.61498097689721,
+ -140.5949497032943,
+ -141.1080508777211,
+ -141.51931733339833,
+ -141.54964207425726,
+ -141.86136954546944,
+ -142.07410217245865,
+ -142.30265034106318,
+ -142.60307284898443,
+ -142.7608660186244,
+ -143.0525415016367,
+ -143.24778977768742,
+ -143.47464755194358,
+ -144.8070662482837
+ ],
+ "ChannelByMultiYearSeasonCohortInsetAnalyzer": [
+ -134.22421230502815,
+ -139.15893808991987,
+ -139.61498097689721,
+ -140.5949497032943,
+ -141.1080508777211,
+ -141.51931733339833,
+ -141.54964207425726,
+ -141.86136954546944,
+ -142.07410217245865,
+ -142.30265034106318,
+ -142.60307284898443,
+ -142.7608660186244,
+ -143.0525415016367,
+ -143.24778977768742,
+ -143.47464755194358,
+ -144.8070662482837
+ ],
+ "MonthVal1": [
+ 0.014130732965460146,
+ 0.010224659788231137,
+ 0.007237572888799553,
+ 0.021315667848185588,
+ 1e-05,
+ 0.011843033821507386,
+ 0.0036636394373384526,
+ 1e-05,
+ 0.006107785687358823,
+ 0.0075,
+ 0.014006903214861963,
+ 0.0073192115393199714,
+ 0.0034319192189143713,
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+}
\ No newline at end of file
diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv
new file mode 100644
index 0000000..2bc91cb
--- /dev/null
+++ b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/_plots/LL_all.csv
@@ -0,0 +1,17 @@
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diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/Calibra_results/seasonality_calibration_noMassITN_AA_round1_maxInc100/iter1/IterationState.json
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diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/manifest.py b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/manifest.py
index 5a99a68..6592ad7 100644
--- a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/manifest.py
+++ b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/manifest.py
@@ -5,7 +5,7 @@
PROJECT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, "..", "..", "..", "..", ".."))
# change to your input folder which contains the required files...
-download_dir = os.path.join(PROJECT_DIR, "download")
+download_dir = os.path.join(PROJECT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
@@ -18,7 +18,7 @@
data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = None
+sif_id = '../../../../../dtk_sif.id'
# Specify local folder for calibration results, for example
directory = os.path.join(CURRENT_DIR, 'Calibra_results')
diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/params.py b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/params.py
index b014022..eb726c4 100644
--- a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/params.py
+++ b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/params.py
@@ -15,7 +15,9 @@
# specify name of archetype's representative admin
rep_admin = 'AA'
# burn-in id should be listed for each of the archetypes
-burnin_ids = {'AA': '7c9195e8-3d0a-ee11-aa07-b88303911bc1'}
+burnin_ids = {
+ 'AA': '7fbc7c53-c44d-ee11-aa0a-b88303911bc1'
+}
# set whether this is the first or second set of calibrations for this archetype
round_number = 1 # <-- user needs to specify which round this is to pick up from prior round. Starts at round_number=1
@@ -45,7 +47,7 @@
scen_df = pd.read_csv(scenario_fname)
# row matching this main calibration (not the burnin)
scen_index = scen_df[scen_df['ScenarioName'] == 'seasonality_calibration_noMassITN'].index[0]
-expname = '%s_%s_round%i_maxInc%i' % (scen_df.at[scen_index, 'ScenarioName'], rep_admin, round_number, max_incidence)
+expname = '%s_%s_round%i_maxInc%i_v2' % (scen_df.at[scen_index, 'ScenarioName'], rep_admin, round_number, max_incidence)
demographics_file = os.path.join('demographics_and_climate', '_entire_country',
f'demographics_each_admin_{simulation_pop}.json')
@@ -60,10 +62,10 @@
sim_runs_per_param_set = 2 # outside of testing, generally 5
sim_runs_per_param_set_second = 5 # 10
sim_runs_per_param_set_fourth = 10 # 30
-max_iterations = 2 # outside of testing, generally 10
-max_iterations_second = 20
-max_iterations_fourth = 20
-samples_per_iteration = 8 # outside of testing, generally 40 # must be at least 8
+max_iterations = 5 # outside of testing, generally 10
+max_iterations_second = 5 # 20
+max_iterations_fourth = 5 # 20
+samples_per_iteration = 15 # outside of testing, generally 40 # must be at least 8
#############################################################################################################
# set up the parameters that will be used to calibrate vector larval habitat seasonality
@@ -78,7 +80,7 @@
# if the second round, read in the best values from the previous round and use those as the starting point
if later_round:
- round1_fname = os.path.join('%s_%s_round%i_maxInc%i' % (
+ round1_fname = os.path.join('%s_%s_round%i_maxInc%i_v2' % (
scen_df.at[scen_index, 'ScenarioName'], rep_admin, (round_number - 1), max_incidence), '_plots', 'LL_all.csv')
round1_df = pd.read_csv(round1_fname)
diff --git a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/run_calibration.py b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/run_calibration.py
index 1f0e908..0026053 100644
--- a/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/run_calibration.py
+++ b/examples/monique/calibration/seasonality_calibration/02_seasonality_calibration/run_calibration.py
@@ -158,6 +158,23 @@ def get_manager(**kwargs):
return calib_manager
+def _post_run(suite_id, **kwargs):
+ """
+ Add extra work after run calibration.
+ Args:
+ suite_id:
+ kwargs: additional parameters
+ Return:
+ None
+ """
+ from idmtools.core import ItemType
+ suite = platform.get_item(suite_id, ItemType.SUITE)
+
+ if all([exp.succeeded for exp in suite.experiments]):
+ with open(r"monique\\calibration\\seasonality_calibration\\02_seasonality_calibration\\experiment_id.txt", "w") as fd:
+ fd.write("Calibration good.")
+
+
def run_calibration(directory: str = '.', **kwargs):
"""
Get configured calibration and run.
@@ -176,7 +193,7 @@ def run_calibration(directory: str = '.', **kwargs):
calib_manager = get_manager(**kwargs)
_pre_run(**kwargs)
calib_manager.run_calibration(**kwargs)
-
+ _post_run(calib_manager.suite_id, **kwargs)
if __name__ == "__main__":
"""
@@ -184,7 +201,7 @@ def run_calibration(directory: str = '.', **kwargs):
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='idm_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
diff --git a/examples/monique/calibration/seasonality_calibration/03_save_best_seasonality_fit.py b/examples/monique/calibration/seasonality_calibration/03_save_best_seasonality_fit.py
index 4b7902a..42bfa3f 100644
--- a/examples/monique/calibration/seasonality_calibration/03_save_best_seasonality_fit.py
+++ b/examples/monique/calibration/seasonality_calibration/03_save_best_seasonality_fit.py
@@ -3,8 +3,7 @@
import pandas as pd
from snt.load_paths import load_box_paths
-USER_PATH = r'C:\Projects\emodpy-hbhi_zdu\data'
-_, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
+_, project_path = load_box_paths(country_name='Example')
admin_names = ['AA']
constant_habitat = 4 # must match what was used for seasonality calibration
diff --git a/examples/monique/run_future_scenarios/manifest.py b/examples/monique/run_future_scenarios/manifest.py
index 8419c1f..77074e7 100644
--- a/examples/monique/run_future_scenarios/manifest.py
+++ b/examples/monique/run_future_scenarios/manifest.py
@@ -6,7 +6,7 @@
# change to your input folder which contains the required files...
input_dir = os.path.join(ROOT_DIR, "inputs")
-download_dir = os.path.join(ROOT_DIR, "download")
+download_dir = os.path.join(ROOT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
@@ -19,4 +19,4 @@
data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = None
+sif_id = '../../../dtk_sif.id'
diff --git a/examples/monique/run_future_scenarios/run_simulations.py b/examples/monique/run_future_scenarios/run_simulations.py
index 08e7d23..c296453 100644
--- a/examples/monique/run_future_scenarios/run_simulations.py
+++ b/examples/monique/run_future_scenarios/run_simulations.py
@@ -39,8 +39,9 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- with open("monique\\run_future_scenarios\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ with open("monique\\run_future_scenarios\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -94,7 +95,7 @@ def run_experiment(**kwargs):
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='emod_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
diff --git a/examples/monique/run_to_present/manifest.py b/examples/monique/run_to_present/manifest.py
index d4f755a..b55f201 100644
--- a/examples/monique/run_to_present/manifest.py
+++ b/examples/monique/run_to_present/manifest.py
@@ -6,7 +6,7 @@
# change to your input folder which contains the required files...
input_dir = os.path.join(ROOT_DIR, "inputs")
-download_dir = os.path.join(ROOT_DIR, "download")
+download_dir = os.path.join(ROOT_DIR, "downloads")
schema_file = os.path.join(download_dir, "schema.json")
eradication_path = os.path.join(download_dir, "Eradication")
@@ -19,4 +19,4 @@
data_path, project_path = load_box_paths(user_path=USER_PATH, country_name='Example')
input_dir = os.path.join(project_path, "simulation_inputs")
-sif_id = None
+sif_id = '../../../dtk_sif.id'
diff --git a/examples/monique/run_to_present/params.py b/examples/monique/run_to_present/params.py
index 317c8ce..afcce37 100644
--- a/examples/monique/run_to_present/params.py
+++ b/examples/monique/run_to_present/params.py
@@ -16,7 +16,7 @@
f'demographics_each_admin_{population_size}.json')
use_arch_burnin = True
-burnin_id = 'cab2ccbc-f70a-ee11-aa07-b88303911bc1' # <- on IDMCloud # generated from serialize_transmission_sweep (1960-2010)
+burnin_id = 'eb89ea6e-2f53-ee11-aa0a-b88303911bc1' # 'cab2ccbc-f70a-ee11-aa07-b88303911bc1' # <- on IDMCloud # generated from serialize_transmission_sweep (1960-2010)
scenario_fname = os.path.join(manifest.project_path, 'simulation_inputs', '_intervention_file_references',
'Interventions_to_present.csv')
diff --git a/examples/monique/run_to_present/run_simulations.py b/examples/monique/run_to_present/run_simulations.py
index f2c7293..6859354 100644
--- a/examples/monique/run_to_present/run_simulations.py
+++ b/examples/monique/run_to_present/run_simulations.py
@@ -39,8 +39,9 @@ def _post_run(experiment: Experiment, **kwargs):
Return:
None
"""
- with open("monique\\run_to_present\\experiment_id.txt", "w") as fd:
- fd.write(experiment.uid.hex)
+ if experiment.succeeded:
+ with open("monique\\run_to_present\\experiment_id.txt", "w") as fd:
+ fd.write(experiment.uid.hex)
pass
@@ -94,7 +95,7 @@ def run_experiment(**kwargs):
- show_warnings_once=False: show api warnings for all simulations
- show_warnings_once=None: not show api warnings
"""
- platform = Platform('CALCULON', node_group='idm_48cores')
+ platform = Platform('CALCULON', node_group='emod_abcd')
# platform = Platform('IDMCLOUD', node_group='emod_abcd')
# If you don't have Eradication, un-comment out the following to download Eradication
diff --git a/examples/update_lines.py b/examples/update_lines.py
index d188bcd..909b3f0 100644
--- a/examples/update_lines.py
+++ b/examples/update_lines.py
@@ -7,12 +7,12 @@ def update_parameters_in_file(filepath, parameters: dict = None):
Helper function that updates file parameters to run examples in snakemake
Args:
filepath:
- parameters: a dictionary formatted as: {"what to find in line": "what to replace the line with"}
+ parameters: a dictionary formatted as: {"what to find in line": "what to replace the entire line with"}
Returns:
"""
- temp = "temp.ptxt"
+ temp = "temp.txt"
with open(temp, 'w') as new_file:
with open(filepath) as old_file:
for line in old_file:
diff --git a/idmtools.ini b/idmtools.ini
new file mode 100644
index 0000000..9a64f88
--- /dev/null
+++ b/idmtools.ini
@@ -0,0 +1,34 @@
+[COMMON]
+# Number of threads idm-tools will use for analysis and other multi-threaded activities
+max_threads = 16
+
+# How many simulations per threads during simulation creation
+sims_per_thread = 20
+
+# Maximum number of LOCAL simulation ran simultaneously
+max_local_sims = 6
+
+
+[SLURM]
+type = COMPS
+endpoint = https://comps.idmod.org
+environment = Calculon
+priority = Normal
+simulation_root = $COMPS_PATH(USER)\output
+num_retries = 0
+node_group = idm_abcd
+num_cores = 1
+exclusive = False
+
+[Local]
+type = Local
+
+[Logging]
+level = DEBUG
+console = off
+
+
+# This is a test we used to validate loading local from section block
+[Custom_Local]
+type = Local
+
diff --git a/r_utilities/IPTp_mortality_postprocessing/get_MiP_protection_coverage.R b/r_utilities/IPTp_mortality_postprocessing/get_MiP_protection_coverage.R
index e4242e4..4b77fe1 100644
--- a/r_utilities/IPTp_mortality_postprocessing/get_MiP_protection_coverage.R
+++ b/r_utilities/IPTp_mortality_postprocessing/get_MiP_protection_coverage.R
@@ -56,7 +56,7 @@ get_IPTp_coverages = function(iptp_estimates_filename, iptp_dose_number_filename
## - - - - - - - - - - - - - - - - - - - - ##
if (!future_projection_flag){
- if(coverage_string == 'noCoverage'){
+ if(coverage_string %in% c('noCoverage', 'none')){
project_coverage = rep(0, length(iptp_coverage_df[,dim(iptp_coverage_df)[2]]))
project_dose_number = iptp_dose_number[,dim(iptp_dose_number)[2]]
@@ -89,14 +89,37 @@ get_IPTp_coverages = function(iptp_estimates_filename, iptp_dose_number_filename
iptp_relative_risk = iptp_relative_risk_estimateBetterProtection
} else if(coverage_string != 'curCoverage') warning('coverage string not recognized for historical simulations... assuming estimated true coverage')
+
+ # make sure that values are included for all years
+ if (first_year < min(as.numeric(gsub('X','', colnames(iptp_coverage_df)[-1])))){
+ warning('The first year of the IPTp coverage dataframe is after the first year of the simulation. Need to add more IPTp data/assumptions.')
+ }
+ if(last_year > max(as.numeric(gsub('X','', colnames(iptp_coverage_df)[-1])))){
+ warning('The simulation continues for more years than supplied in the IPTp coverage dataframe. Assuming continuation of lastest coverage. To use a different assumption, need to update IPTp input file.')
+ # use coverage from latest year for projections
+ project_coverage = iptp_coverage_df[,dim(iptp_coverage_df)[2]]
+ project_dose_number = iptp_dose_number[,dim(iptp_dose_number)[2]]
+ for(yy in (max(as.numeric(gsub('X','', colnames(iptp_coverage_df)[-1])))+1):last_year){
+ # update iptp coverage (>=1 dose)
+ new_coverage_df = data.frame(project_coverage)
+ colnames(new_coverage_df) = yy
+ iptp_coverage_df = cbind(iptp_coverage_df, new_coverage_df)
+
+ # update iptp doses (of covered individuals, how many doses received?)
+ new_dose_df = data.frame(project_dose_number)
+ colnames(new_dose_df) = yy
+ iptp_dose_number = cbind(iptp_dose_number, new_dose_df)
+ }
+ }
+
## - - - - - - - - - - - - - - - - - - - - ##
# simulations of future transmission
## - - - - - - - - - - - - - - - - - - - - ##
} else{
constant_future_values = TRUE
# replace entries with projection scenario
- if(coverage_string == 'noCoverage'){
+ if(coverage_string %in% c('noCoverage', 'none')){
project_coverage = rep(0, length(iptp_coverage_df[,dim(iptp_coverage_df)[2]]))
project_dose_number = iptp_dose_number[,dim(iptp_dose_number)[2]]
iptp_coverage_df = data.frame('admin_name' = admin_names)
@@ -411,6 +434,36 @@ get_IPTp_coverages = function(iptp_estimates_filename, iptp_dose_number_filename
}
# remove first column
iptp_dose_number = iptp_dose_number[,-1]
+ } else if (coverage_string == 'stopAfterThreeYears'){
+ # keep current values for 2 years, then discontinue
+ constant_future_values = FALSE
+
+ # use coverage from latest year for first two years of projections
+ num_years_cur_coverage = 3
+ project_coverage = iptp_coverage_df[,dim(iptp_coverage_df)[2]]
+ project_0_coverage = rep(0, length(iptp_coverage_df[,dim(iptp_coverage_df)[2]]))
+ project_dose_number = iptp_dose_number[,dim(iptp_dose_number)[2]]
+ iptp_coverage_df = data.frame('admin_name' = admin_names)
+ iptp_dose_number = data.frame('doses' = c(1,2,3))
+ for(yy in first_year:last_year){
+ if(yy < (first_year+num_years_cur_coverage)){
+ # update iptp coverage (>=1 dose)
+ new_coverage_df = data.frame(project_coverage)
+ colnames(new_coverage_df) = yy
+ iptp_coverage_df = cbind(iptp_coverage_df, new_coverage_df)
+ } else{
+ # update iptp coverage (>=1 dose)
+ new_coverage_df = data.frame(project_0_coverage)
+ colnames(new_coverage_df) = yy
+ iptp_coverage_df = cbind(iptp_coverage_df, new_coverage_df)
+ }
+ # update iptp doses (of covered individuals, how many doses received?)
+ new_dose_df = data.frame(project_dose_number)
+ colnames(new_dose_df) = yy
+ iptp_dose_number = cbind(iptp_dose_number, new_dose_df)
+ }
+ # remove first column
+ iptp_dose_number = iptp_dose_number[,-1]
} else warning(print('coverage string not recognized'))
if (constant_future_values){
diff --git a/r_utilities/IPTp_mortality_postprocessing/malariaInPregnancy_functions.R b/r_utilities/IPTp_mortality_postprocessing/malariaInPregnancy_functions.R
index ddfeebd..3b88354 100644
--- a/r_utilities/IPTp_mortality_postprocessing/malariaInPregnancy_functions.R
+++ b/r_utilities/IPTp_mortality_postprocessing/malariaInPregnancy_functions.R
@@ -46,6 +46,7 @@ get_pop_size_infection_from_sim = function(first_year=2010, last_year=2019, admi
# replace spaces with periods in column names
colnames(sim_output) = gsub(' ', '.', colnames(sim_output))
+ sim_output = setorder(sim_output, admin_name,year,month)
# each row corresponds to a month in the simulation
months = rep(1:12, times=length(first_year:last_year))
@@ -171,6 +172,8 @@ get_pop_size_infection_from_sim = function(first_year=2010, last_year=2019, admi
+
+
calc_mStill_mLBW_death = function(preg_withoutNMStill_1530, preg_withoutNMStill_3050, prob_infect_in_preg_1530, prob_infect_in_preg_3050, f_iptp_t, pm_LBW_iptp, pm_still_iptp, d0, d1, frac_first_second_birth){
#' calculate number of malaria-attributed stillbirths, livebirths, mLBWs, and mLBW deaths with the estimated levels of IPTp
#'
@@ -767,6 +770,13 @@ adjust_sim_output_for_MiP = function(prob_severe_from_MiP=0.57, # probability o
(popUnder15_size + pop1530_size + pop3050_size + popOver50_size)
+ # calculate PfPR for population, unadjusted for IPTp
+ pfpr_allAges_noIPTp = (pfpr_under15 * popUnder15_size +
+ pfpr_1530 * pop1530_size +
+ pfpr_3050 * pop3050_size +
+ pfpr_over50 * popOver50_size) /
+ (popUnder15_size + pop1530_size + pop3050_size + popOver50_size)
+
@@ -875,6 +885,9 @@ adjust_sim_output_for_MiP = function(prob_severe_from_MiP=0.57, # probability o
new_pfpr = as.vector(as.matrix(pfpr_allAges_withIPTp[, ..i_MiP_col]))
adjusted_allAgeMonthly[rows_adjusted_allAge_rr_ds[1:length(pop1530_size$month)], 'PfPR_MiP_adjusted' := new_pfpr]
+ unadjusted_pfpr = as.vector(as.matrix(pfpr_allAges_noIPTp[, ..i_MiP_col]))
+ adjusted_allAgeMonthly[rows_adjusted_allAge_rr_ds[1:length(pop1530_size$month)], 'PfPR_unadjusted' := unadjusted_pfpr]
+
new_severe_m = as.vector(as.matrix(num_severe_from_MiP[, ..i_MiP_col]))
adjusted_allAgeMonthly[rows_adjusted_allAge_rr_ds[1:length(pop1530_size$month)], 'severe_maternal' := new_severe_m]
diff --git a/r_utilities/IPTp_mortality_postprocessing/malariaMortality_functions.R b/r_utilities/IPTp_mortality_postprocessing/malariaMortality_functions.R
index d006727..cce79a9 100644
--- a/r_utilities/IPTp_mortality_postprocessing/malariaMortality_functions.R
+++ b/r_utilities/IPTp_mortality_postprocessing/malariaMortality_functions.R
@@ -5,8 +5,8 @@ library(data.table)
# read in simulation output and calculate updated numbers from multiple sources of mortality
adjust_sim_output_mortality = function(cfr_severe_treated = 0.097, # case fatality rate for severe, treated cases
- cfr_severe_untreated_1 = 0.539, # case fatality rate for severe, treated cases (upper estimate)
- cfr_severe_untreated_2 = 0.177, # case fatality rate for severe, treated cases (lower estimate)
+ cfr_severe_untreated_1 = 0.539, # case fatality rate for severe, untreated cases (upper estimate)
+ cfr_severe_untreated_2 = 0.177, # case fatality rate for severe, untreated cases (lower estimate)
starting_prob_1 = 0.037, # (QD from Ross et al, upper value)
starting_prob_2 = 0.01, # (QD from Ross et al, lower value)
age_shape_param = 0.117, # (aF* from Ross et al)
diff --git a/r_utilities/data_processing/1_DHS_data_extraction.R b/r_utilities/data_processing/1_DHS_data_extraction.R
index b2096d2..9d3a9c5 100644
--- a/r_utilities/data_processing/1_DHS_data_extraction.R
+++ b/r_utilities/data_processing/1_DHS_data_extraction.R
@@ -9,12 +9,13 @@ library(foreign)
# detach("package:plyr", unload=TRUE)
# }
library(dplyr)
-library(rgdal)
+# library(rgdal) # not available for newer version of R
library(raster)
library(sp)
library(pals)
library(prettyGraphs)
library(stringr)
+library(haven)
# function to read in relevant dta file and extract the number of positive and negative results, along with the number tested in each cluster and cluster locations
@@ -46,6 +47,7 @@ get_cluster_level_outputs = function(dta_dir, cur_dta, DHS_file_recode_df, var_i
num_pos = sum(pos),
num_tested = n())
}
+
# match 'hv001' with 'clusterid'
MIS_outputs = merge(MIS_outputs, dta_cluster_0, by.y=DHS_file_recode_df$cluster_id_code[var_index], by.x='clusterid', all=TRUE)
@@ -70,6 +72,90 @@ get_cluster_level_outputs = function(dta_dir, cur_dta, DHS_file_recode_df, var_i
return(MIS_outputs)
}
+
+
+
+
+# function to read in relevant dta file and extract the number of positive and negative results, along with the number tested in each cluster and cluster locations
+get_cluster_level_vacc_outputs = function(dta_dir, cur_dta, DHS_file_recode_df, var_index, MIS_outputs, include_itn_weight=FALSE, alternate_positive_patterns = c('vaccination date on card','vaccination marked on card', 'reported by mother'),
+ survey_month_code = 'v006', survey_year_code='v007', age_month_code='b1', age_year_code='b2', min_age_months_included=12){
+ cur_dta$pos = NA
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == DHS_file_recode_df$pos_pattern[var_index])] = 1
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == DHS_file_recode_df$neg_pattern[var_index])] = 0
+ if(length(alternate_positive_patterns)>0){
+ for(alt_pos_pattern in alternate_positive_patterns){
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == alt_pos_pattern)] = 1
+ }
+ }
+
+ # calculate age at time of survey
+ cur_dta$survey_date = as.Date(paste0(cur_dta[,which(colnames(cur_dta)==survey_year_code)],'-',cur_dta[,which(colnames(cur_dta)==survey_month_code)],'-28'))
+ cur_dta$birth_date = as.Date(paste0(cur_dta[,which(colnames(cur_dta)==age_year_code)],'-',cur_dta[,which(colnames(cur_dta)==age_month_code)],'-01'))
+ cur_dta$age = as.numeric(cur_dta$survey_date - cur_dta$birth_date)
+ # only include entries above the minimum age
+ cur_dta$over_min_age = cur_dta$age > (min_age_months_included*30.4)
+
+ # # compare rates of positivity between all ages and ages over min (for debugging/checking)
+ # sum(cur_dta$pos, na.rm=T)/sum(!is.na(cur_dta$pos)) # fraction with the vaccine among all ages
+ # sum(cur_dta$pos[(cur_dta$over_min_age)], na.rm=T)/sum(!is.na(cur_dta$pos[(cur_dta$over_min_age)])) # fraction with the vaccine among those over the minimum age
+
+ # remove entries for individuals under the age cutoff (i.e., who are to young to have received the vaccine yet)
+ cur_dta$pos[!(cur_dta$over_min_age)] = NA
+
+ dta_cluster_0 = cur_dta %>%
+ filter(!is.na(cur_dta$pos)) %>%
+ group_by_at(DHS_file_recode_df$cluster_id_code[var_index]) %>%
+ summarize(rate = mean(pos, na.rm = TRUE),
+ num_pos = sum(pos),
+ num_tested = n())
+
+ # match 'hv001' with 'clusterid'
+ MIS_outputs = merge(MIS_outputs, dta_cluster_0, by.y=DHS_file_recode_df$cluster_id_code[var_index], by.x='clusterid', all=TRUE)
+ return(MIS_outputs)
+}
+
+
+
+
+
+
+# function to read in relevant dta file and extract the number of positive and negative results, along with the number tested in each cluster and cluster locations
+get_cluster_level_vacc_mics_outputs = function(dta_dir, cur_dta, DHS_file_recode_df, var_index, MIS_outputs, include_itn_weight=FALSE, alternate_positive_patterns = c('vaccination date on card','vaccination marked on card', 'reported by mother'),
+ min_age_months_included=12){
+ cur_dta$pos = NA
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == DHS_file_recode_df$pos_pattern[var_index])] = 1
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == DHS_file_recode_df$neg_pattern[var_index])] = 0
+ if(length(alternate_positive_patterns)>0){
+ for(alt_pos_pattern in alternate_positive_patterns){
+ cur_dta$pos[which(cur_dta[,which(colnames(cur_dta) == DHS_file_recode_df$code[var_index])] == alt_pos_pattern)] = 1
+ }
+ }
+
+ # only include entries above the minimum age
+ cur_dta$over_min_age = cur_dta[[DHS_file_recode_df$age_code[var_index]]] > (min_age_months_included*30.4)
+
+ # # compare rates of positivity between all ages and ages over min (for debugging/checking)
+ # sum(cur_dta$pos, na.rm=T)/sum(!is.na(cur_dta$pos)) # fraction with the vaccine among all ages
+ # sum(cur_dta$pos[(cur_dta$over_min_age)], na.rm=T)/sum(!is.na(cur_dta$pos[(cur_dta$over_min_age)])) # fraction with the vaccine among those over the minimum age
+
+ # remove entries for individuals under the age cutoff (i.e., who are to young to have received the vaccine yet)
+ cur_dta$pos[!(cur_dta$over_min_age)] = NA
+
+ dta_cluster_0 = cur_dta %>%
+ filter(!is.na(cur_dta$pos)) %>%
+ group_by_at(DHS_file_recode_df$cluster_id_code[var_index]) %>%
+ summarize(rate = mean(pos, na.rm = TRUE),
+ num_pos = sum(pos),
+ num_tested = n())
+
+ # match 'hv001' with 'clusterid'
+ MIS_outputs = merge(MIS_outputs, dta_cluster_0, by.y=DHS_file_recode_df$cluster_id_code[var_index], by.x='clusterid', all=TRUE)
+ return(MIS_outputs)
+}
+
+
+
+
match_lga_names = function(lga_name){
lga_name = toupper(lga_name)
lga_name = str_replace_all(lga_name, pattern=' ', replacement='-')
@@ -89,7 +175,7 @@ any_matches = function(df_row, pos_codes){
# create new column describing whether an individual received effective treatment given they received any antimalarial.
# Column value will be 1 if an individual received rectal artesunate, IV artesunate, or an ACT and 0 if they received a different antimalarial
# note: currently does not include individuals who reported country-specific antimalarial ("ml13g", "ml13f"), since it's not clear whether or not those are ACT
-received_art_antimalarial = function(dta_dir, DHS_file_recode_df, var_index, art_codes = c("ml13e", "ml13aa", "ml13ab"), non_art_codes = c("ml13a", "ml13b", "ml13c", "ml13d", "ml13da", "ml13h")){
+received_art_antimalarial = function(dta_dir, DHS_file_recode_df, var_index, art_codes = c("ml13e", "ml13ab"), non_art_codes = c("ml13a", "ml13b", "ml13c", "ml13d", "ml13aa", "ml13da", "ml13h")){
cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
# change columns to strings (the any_matches function does not work as expected if there are factors)
cur_dta = data.frame(lapply(cur_dta, as.character), stringsAsFactors=FALSE)
@@ -117,10 +203,39 @@ received_art_antimalarial = function(dta_dir, DHS_file_recode_df, var_index, art
}
+
+
+
+# create new column describing whether an individual sought any type of treatment (public or private or pharmacy)
+sought_any_treatment = function(dta_dir, year, DHS_file_recode_df, var_index, treat_codes=NA){
+ if(is.na(treat_codes)){
+ if(year !=2013){
+ treat_codes = c("h32a", "h32b", "h32c", "h32d", "h32e", "h32f", "h32g", "h32h", "h32i", "h32j", "h32k", "h32l", "h32m", "h32n", "h32o", "h32p", "h32q", "h32r", "h32na", "h32nb", "h32nc", "h32nd", "h32ne", "h32s", "h32u", "h32v", "h32w", "h32x") # currently not including traditional practitioner "h32t",
+ } else{
+ treat_codes = c("h32a", "h32b", "h32c", "h32d", "h32e", "h32f", "h32g", "h32h", "h32i", "h32j", "h32k", "h32l", "h32m", "h32n", "h32o", "h32p", "h32q", "h32r", "h32na", "h32nb", "h32nc", "h32nd", "h32ne", "h32s", "h32u", "h32t", "h32w", "h32x") # currently not including traditional practitioner "h32v",
+ }
+ }
+ cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
+ # change columns to strings (the any_matches function does not work as expected if there are factors)
+ cur_dta = data.frame(lapply(cur_dta, as.character), stringsAsFactors=FALSE)
+ # find rows where at least one of the treatment seeking behaviors matches the positive code
+ treat_codes = c(treat_codes[which(treat_codes %in% colnames(cur_dta))])
+
+ if(length(treat_codes)>1){
+ cur_dta$sought_treatment = as.numeric(apply(cur_dta[,treat_codes], 1, any_matches, pos_codes=c('yes', 'Yes', 'YES', 'Y', 1, '1', 'T', 'TRUE', 'True') ))
+ cur_dta$responded_sought_treatment = as.numeric(apply(cur_dta[,treat_codes], 1, any_matches, pos_codes=c('yes', 'Yes', 'YES', 'Y', 1, '1', 'T', 'TRUE', 'True', 'no', 'No', 'NO', 'N', 0, '0', 'F', 'FALSE', 'False') ))
+ cur_dta$sought_treatment[cur_dta$responded_sought_treatment != 1] = NA
+ } else{
+ cur_dta$sought_treatment = NA
+ }
+ return(cur_dta)
+}
+
+
#################################################################################################################
# main function to extract DHS data and plot maps of the results
#################################################################################################################
-extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_filename, min_num_total=30, variables=c('mic','itn_all','itn_u5','itn_5_10','itn_10_15','itn_15_20','itn_o20','iptp','cm','blood_test', 'art_given_antimal')){
+extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_filename, min_num_total=30, variables=c('mic', 'rdt','itn_all','itn_u5','itn_5_10','itn_10_15','itn_15_20','itn_o20','iptp','cm','blood_test', 'art_given_antimal')){
####=========================================================================================================####
# iterate through years, creating csvs with cluster-level and admin-level counts and rates for all variables
@@ -147,6 +262,18 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'mic_num_total'
}
+ ### - - - - - - - - - - - - - - - - - - ###
+ # PfPR (RDT)
+ ### - - - - - - - - - - - - - - - - - - ###
+ var_index = which(DHS_file_recode_df$variable == 'rdt')
+ if(!is.na(DHS_file_recode_df$filename[var_index])){
+ cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
+ MIS_outputs=get_cluster_level_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs)
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='rate'] = 'rdt_rate'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = 'rdt_num_true'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'rdt_num_total'
+ }
+
### - - - - - - - - - - - - - - - - - - ###
# ITNs - all ages
### - - - - - - - - - - - - - - - - - - ###
@@ -238,7 +365,7 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
}
### - - - - - - - - - - - - - - - - - - ###
- # Case management - seek treatment
+ # Case management - sought treatment at a facility
### - - - - - - - - - - - - - - - - - - ###
var_index = which(DHS_file_recode_df$variable == 'cm')
if(!is.na(DHS_file_recode_df$filename[var_index])){
@@ -248,7 +375,34 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = 'cm_num_true'
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'cm_num_total'
}
+
+ ### - - - - - - - - - - - - - - - - - - ###
+ # Case management - receive treatment (1-no treatment or advice sought)
+ ### - - - - - - - - - - - - - - - - - - ###
+ var_index = which(DHS_file_recode_df$variable == 'received_treatment')
+ if(!is.na(DHS_file_recode_df$filename[var_index])){
+ cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
+ MIS_outputs=get_cluster_level_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs)
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='rate'] = 'received_treatment_rate'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = 'received_treatment_num_true'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'received_treatment_num_total'
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - ###
+ # Case management - sought advice or treatment (sum across all types of treatment except traditional healers)
+ ### - - - - - - - - - - - - - - - - - - ###
+ var_index = which(DHS_file_recode_df$variable == 'sought_treatment')
+ if(!is.na(DHS_file_recode_df$filename[var_index])){
+ cur_dta = sought_any_treatment(dta_dir, year, DHS_file_recode_df, var_index, treat_codes=NA)
+ MIS_outputs=get_cluster_level_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs)
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='rate'] = 'sought_treatment_rate'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = 'sought_treatment_num_true'
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'sought_treatment_num_total'
+ }
+
+
### - - - - - - - - - - - - - - - - - - ###
# Case management - heel prick or blood test
### - - - - - - - - - - - - - - - - - - ###
@@ -273,8 +427,6 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'iptp_num_total'
}
-
-
### - - - - - - - - - - - - - - - - - - ###
# ACT or artesunate given antimalarial
### - - - - - - - - - - - - - - - - - - ###
@@ -287,7 +439,11 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = 'art_num_total'
}
+
+
+
+
####=========================================================================================================####
# determine which clusters are in which admins
@@ -336,6 +492,7 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
# A previous approach was to assume that the rate of receiving effective treatment is halfway in between a) and b) - i.e., half of the people who seek treatment but don't receive a blood test are nonetheless given ACTs
# The current approach is to use the national fraction of individuals who are given ACTs/artesunate among those who receive any antimalarial, multiplied by the local treatment-seeking rate
use_art_probs = TRUE
+ use_cm_probs = TRUE # set which of the variables for treatment-seeking is used: any facility (TRUE) or any treatment/advice aside from traditional (FALSE)
if(use_art_probs){ # new approach using probability someone who gets an antimalarial gets an ACT
# use the country-wide, cluster-weighted probability of ACT/artesunate use given some type of antimalarial use if cluster weights are available
if('art_rate' %in% colnames(MIS_shape) & 'itn_weights' %in% colnames(MIS_shape)){
@@ -348,8 +505,13 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
use_art_probs = FALSE
}
admin_sums$national_act_rate = national_act_rate
- admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
- admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ if(use_cm_probs){
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
+ admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ } else{
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$sought_treatment_rate
+ admin_sums$effective_treatment_num_total = admin_sums$sought_treatment_num_total
+ }
admin_sums$effective_treatment_num_true = admin_sums$effective_treatment_num_total * admin_sums$effective_treatment_rate
}
if(!use_art_probs){ # old approach using an average of treatment-seeking and blood-test rates
@@ -437,8 +599,13 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
# calculate estimate for effective treatment-seeking rate
if(use_art_probs){ # new approach using probability someone who gets an antimalarial gets an ACT
admin_sums$national_act_rate = national_act_rate
- admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
- admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ if(use_cm_probs){
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
+ admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ } else{
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$sought_treatment_rate
+ admin_sums$effective_treatment_num_total = admin_sums$sought_treatment_num_total
+ }
admin_sums$effective_treatment_num_true = admin_sums$effective_treatment_num_total * admin_sums$effective_treatment_rate
} else{ # old approach using an average of treatment-seeking and blood-test rates
if('cm_num_total' %in% colnames(admin_sums) & 'blood_test_num_total' %in% colnames(admin_sums)){
@@ -495,8 +662,13 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
# calculate estimate for effective treatment-seeking rate
if(use_art_probs){ # new approach using probability someone who gets an antimalarial gets an ACT
admin_sums$national_act_rate = national_act_rate
- admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
- admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ if(use_cm_probs){
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$cm_rate
+ admin_sums$effective_treatment_num_total = admin_sums$cm_num_total
+ } else{
+ admin_sums$effective_treatment_rate = admin_sums$national_act_rate * admin_sums$sought_treatment_rate
+ admin_sums$effective_treatment_num_total = admin_sums$sought_treatment_num_total
+ }
admin_sums$effective_treatment_num_true = admin_sums$effective_treatment_num_total * admin_sums$effective_treatment_rate
} else{ # old approach using an average of treatment-seeking and blood-test rates
if('cm_num_total' %in% colnames(admin_sums) & 'blood_test_num_total' %in% colnames(admin_sums)){
@@ -518,7 +690,8 @@ extract_DHS_data = function(hbhi_dir, dta_dir, years, admin_shape, ds_pop_df_fil
# extract cluster-level data for EPI vaccination coverages for single year
-extract_vaccine_DHS_data = function(hbhi_dir, dta_dir, year, admin_shape, ds_pop_df_filename, min_num_total=30, vaccine_variables=c('vacc_dpt1', 'vacc_dpt2', 'vacc_dpt3'), vaccine_alternate_positive_patterns=c('reported by mother', 'vaccination marked on card')){
+extract_vaccine_DHS_data = function(hbhi_dir, dta_dir, year, admin_shape, ds_pop_df_filename, min_num_total=30, vaccine_variables=c('vacc_dpt1', 'vacc_dpt2', 'vacc_dpt3'), vaccine_alternate_positive_patterns=c('reported by mother', 'vaccination marked on card'),
+ survey_month_code = 'v006', survey_year_code='v007', age_month_code='b1', age_year_code='b2', min_age_months_included=12){
####=========================================================================================================####
# create csvs with cluster-level and admin-level counts and rates for all vaccination variables
@@ -534,7 +707,8 @@ extract_vaccine_DHS_data = function(hbhi_dir, dta_dir, year, admin_shape, ds_pop
var_index = which(DHS_file_recode_df$variable == vaccine_variables[vv])
if(!is.na(DHS_file_recode_df$filename[var_index])){
cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
- MIS_outputs=get_cluster_level_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs, alternate_positive_patterns=vaccine_alternate_positive_patterns)
+ MIS_outputs=get_cluster_level_vacc_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs, alternate_positive_patterns=vaccine_alternate_positive_patterns,
+ survey_month_code=survey_month_code, survey_year_code=survey_year_code, age_month_code=age_month_code, age_year_code=age_year_code, min_age_months_included=DHS_file_recode_df$min_age_months_to_include[var_index])
colnames(MIS_outputs)[colnames(MIS_outputs)=='rate'] = paste0(vaccine_variables[vv], '_rate')
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = paste0(vaccine_variables[vv], '_num_true')
colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = paste0(vaccine_variables[vv], '_num_total')
@@ -665,6 +839,198 @@ extract_vaccine_DHS_data = function(hbhi_dir, dta_dir, year, admin_shape, ds_pop
+
+
+
+
+
+
+
+
+
+
+
+
+# extract cluster-level data for EPI vaccination coverages for single year
+extract_vaccine_MICS_data = function(hbhi_dir, dta_dir, year, admin_shape, ds_pop_df_filename, min_num_total=30, vaccine_variables=c('vacc_dpt1', 'vacc_dpt2', 'vacc_dpt3'), vaccine_alternate_positive_patterns=c('reported by mother', 'vaccination marked on card'),
+ min_age_months_included=12, use_admin_level=FALSE){
+
+ ####=========================================================================================================####
+ # create csvs with cluster-level and admin-level counts and rates for all vaccination variables
+ ####=========================================================================================================####
+
+ DHS_file_recode_df = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/MICS_',year,'_files_recodes_for_sims.csv'))
+ location_index = which(DHS_file_recode_df$variable == 'locations')
+ locations_shp = shapefile(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[location_index], '/', DHS_file_recode_df$filename[location_index]))
+ locations = data.frame(clusterid = locations_shp$HH1, latitude=locations_shp$LATITUDE, longitude=locations_shp$LONGITUDE)
+ MIS_outputs = locations
+
+ for(vv in 1:length(vaccine_variables)){
+ var_index = which(DHS_file_recode_df$variable == vaccine_variables[vv])
+ if(!is.na(DHS_file_recode_df$filename[var_index])){
+ cur_dta = read_sav(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
+ MIS_outputs=get_cluster_level_vacc_mics_outputs(dta_dir=dta_dir, cur_dta=cur_dta, DHS_file_recode_df=DHS_file_recode_df, var_index=var_index, MIS_outputs=MIS_outputs, alternate_positive_patterns=vaccine_alternate_positive_patterns,
+ min_age_months_included=DHS_file_recode_df$min_age_months_to_include[var_index])
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='rate'] = paste0(vaccine_variables[vv], '_rate')
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_pos'] = paste0(vaccine_variables[vv], '_num_true')
+ colnames(MIS_outputs)[colnames(MIS_outputs)=='num_tested'] = paste0(vaccine_variables[vv], '_num_total')
+ }
+ }
+
+ ####=========================================================================================================####
+ # determine which clusters are in which admins
+ ####=========================================================================================================####
+ # turn MIS output data frame into spatial points data frame
+ points_crs = crs(admin_shape)
+ MIS_shape = SpatialPointsDataFrame(MIS_outputs[,c('longitude', 'latitude')],
+ MIS_outputs,
+ proj4string = points_crs)
+ # find which admins each cluster belongs to
+ MIS_locations = over(MIS_shape, admin_shape)
+ MIS_locations$NOMDEP = MIS_locations$GEONAMET
+ MIS_locations$NOMREGION = MIS_locations$GEONAMES
+ if(nrow(MIS_locations) == nrow(MIS_shape)){
+ MIS_shape$NOMDEP = MIS_locations$NOMDEP
+ MIS_shape$NOMREGION = MIS_locations$NOMREGION
+ # MIS_shape$NAME_1 = MIS_locations$NAME_1
+ }
+ write.csv(as.data.frame(MIS_shape), paste0(hbhi_dir, '/estimates_from_DHS/MICS_vaccine_cluster_outputs_', year, '.csv'))
+
+
+ ####=========================================================================================================####
+ # get values for each admin
+ ####=========================================================================================================####
+ # aggregate across clusters to get total number tested and positive within each admin and within each region for all variables
+ MIS_shape = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/MICS_vaccine_cluster_outputs_', year, '.csv'))[,-1]
+ # remove rows without known admin/region
+ MIS_shape = MIS_shape[!is.na(MIS_shape$NOMREGION),]
+
+ # standardize admin and state names
+ archetype_info = read.csv(ds_pop_df_filename)
+ if(use_admin_level){
+ MIS_shape$admin_name = standardize_admin_names_in_vector(target_names=archetype_info$admin_name, origin_names=MIS_shape$NOMDEP)
+ MIS_shape = standardize_admin_names_in_df(target_names_df=archetype_info, origin_names_df=MIS_shape, target_names_col='admin_name', origin_names_col='NOMDEP', additional_id_col='State', possible_suffixes=c(1,2, 3))
+ }
+ MIS_shape$State = MIS_shape$NOMREGION
+ MIS_shape = standardize_state_names_in_df(target_names_df=archetype_info, origin_names_df=MIS_shape, target_names_col='State', origin_names_col='State')
+
+ if(use_admin_level){
+ # admin level values
+ include_cols = c(which(names(MIS_shape) %in% c('NOMREGION','NOMDEP')), grep('num_total', names(MIS_shape)), grep('num_true', names(MIS_shape)))
+ admin_sums = MIS_shape[,include_cols] %>%
+ group_by(NOMREGION, NOMDEP) %>%
+ summarise_all(sum, na.rm = TRUE)
+
+ for(var in vaccine_variables){
+ if(paste0(var,'_num_true') %in% colnames(admin_sums)){
+ admin_sums[[paste0(var, '_rate')]] = admin_sums[[paste0(var,'_num_true')]] / admin_sums[[paste0(var,'_num_total')]]
+ }
+ }
+
+ # add any admins that did not have any DHS clusters (with all NAs and 0s) and record which state and archetype each cluster belongs to
+ colnames(archetype_info)[colnames(archetype_info)=='LGA'] = 'NOMDEP'
+ colnames(archetype_info)[colnames(archetype_info)=='DS'] = 'NOMDEP'
+ colnames(archetype_info)[colnames(archetype_info)=='admin_name'] = 'NOMDEP'
+ colnames(archetype_info)[colnames(archetype_info)=='State'] = 'NOMREGION'
+ # colnames(archetype_info)[colnames(archetype_info)=='NOMDEP'] = 'NOMDEP_target'
+ archetype_info$name_match = sapply(archetype_info$NOMDEP, match_lga_names)
+ archetype_info = archetype_info[,c('name_match', 'NOMDEP', 'NOMREGION', 'Archetype')]
+ admin_sums$name_match = sapply(admin_sums$NOMDEP, match_lga_names)
+ colnames(admin_sums)[colnames(admin_sums) == 'NOMDEP'] = 'NOMDEP_dhs_orig'
+ admin_sums_expanded = merge(admin_sums, archetype_info, all=TRUE)
+ # admin_sums_expanded$NOMDEP[is.na(admin_sums_expanded$NOMDEP)] = admin_sums_expanded$NOMDEP_backup[is.na(admin_sums_expanded$NOMDEP)]
+ # check that all names have been matched successfully
+ if(length(admin_sums$name_match[which(!(admin_sums$name_match %in% archetype_info$name_match))])>0) warning('Not all LGA names from the shapefile were matched with archetype file')
+ if(length(admin_sums$NOMREGION[which(!(admin_sums$NOMREGION %in% archetype_info$NOMREGION))])>0) warning('Not all state names from the shapefile were matched with archetype file')
+ # remove extra columns
+ admin_sums_expanded = admin_sums_expanded[,-c(which(colnames(admin_sums_expanded) %in% c('NOMDEP_dhs_orig', 'name_match')))]
+ # change to zero sample size for admins that were not included in DHS
+ admin_sums_expanded[,grep('num_total', names(admin_sums_expanded))][is.na(admin_sums_expanded[,grep('num_total', names(admin_sums_expanded))])] = 0
+ write.csv(as.data.frame(admin_sums), paste0(hbhi_dir, '/estimates_from_DHS/MICS_vaccine_admin_', year, '.csv'))
+ admin_sums = admin_sums_expanded
+ }
+
+ # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
+ # when the number surveyed in a admin is lower than the threshold, use the region value instead
+ # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
+ # region level values
+ include_cols = c(which(names(MIS_shape) %in% c('NOMREGION', 'State')), grep('num_total', names(MIS_shape)), grep('num_true', names(MIS_shape)))
+ region_sums = MIS_shape[,include_cols] %>%
+ group_by(NOMREGION, State) %>%
+ summarise_all(sum, na.rm = TRUE)
+ for(var in vaccine_variables){
+ if(paste0(var,'_num_true') %in% colnames(region_sums)){
+ region_sums[[paste0(var, '_rate')]] = region_sums[[paste0(var,'_num_true')]] / region_sums[[paste0(var,'_num_total')]]
+ }
+ }
+ write.csv(as.data.frame(region_sums), paste0(hbhi_dir, '/estimates_from_DHS/MICS_vaccine_state_', year, '.csv'))
+
+
+ if(use_admin_level){
+ for(var in vaccine_variables){
+ if(paste0(var,'_num_true') %in% colnames(admin_sums)){
+ for(i_admin in 1:nrow(admin_sums)){
+ if(admin_sums[[paste0(var,'_num_total')]][i_admin]%
+ group_by(Archetype) %>%
+ summarise_all(sum, na.rm = TRUE)
+ for(var in vaccine_variables){
+ if(paste0(var,'_num_true') %in% colnames(arch_sums)){
+ arch_sums[[paste0(var, '_rate')]] = arch_sums[[paste0(var,'_num_true')]] / arch_sums[[paste0(var,'_num_total')]]
+ }
+ }
+
+ for(var in vaccine_variables){
+ if(paste0(var,'_num_true') %in% colnames(admin_sums)){
+ for(i_admin in 1:nrow(admin_sums)){
+ if(admin_sums[[paste0(var,'_num_total')]][i_admin]1){
- for(vv in 2:length(variables2)){
- layout_matrix = rbind(layout_matrix, base_layout_matrix + (vv-1)*max(base_layout_matrix))
+
+ if(separate_plots_for_each_var){
+ for(i_var in 1:length(variables2)){
+ var = variables2[i_var]
+ png(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',var,'_',vacc_string, 'cluster_observations_all_years',plot_suffix,'.png'), width=0.5*7*nyears, height=0.5*6*1, units='in', res=900)
+ base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
+ layout(base_layout_matrix)
+ par(mar=c(0,0,1,0))
+ for(yy in 1:nyears){
+ cluster_obs = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'cluster_outputs_', years[yy], '.csv'))[,-1]
+ cluster_obs$latitude[which(cluster_obs$latitude == 0)] = NA
+ cluster_obs$longitude[which(cluster_obs$longitude == 0)] = NA
+ if(paste0(var,'_num_total') %in% colnames(cluster_obs)){
+ max_survey_size = max(cluster_obs[[paste0(var,'_num_total')]], na.rm=TRUE)
+ plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0.5,0.5,0.5,0.5))
+ points(cluster_obs$longitude, cluster_obs$latitude, col=colors_range_0_to_1[1+round(cluster_obs[[paste0(var,'_rate')]]*100)], pch=20, cex=cluster_obs[[paste0(var,'_num_total')]]/round(max_survey_size/5))#, xlim=c(min(cluster_obs$longitude), max(cluster_obs$longitude)), ylim=c(min(cluster_obs$latitude), max(cluster_obs$latitude)))
+ }else{
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ }
+ }
+ # legend - colorbar
+ legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
+ text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
+ rasterImage(legend_image, 0, 0, 1,1)
+ # legend - survey size
+ plot(rep(0,5), seq(1, max_survey_size, length.out=5), cex=seq(1,max_survey_size, length.out=5)/round(max_survey_size/5), pch=20, axes=FALSE, xlab='', ylab='sample size'); axis(2)
+ dev.off()
}
- }
- layout(layout_matrix)
- par(mar=c(0,0,1,0))
- for(i_var in 1:length(variables2)){
- var = variables2[i_var]
- for(yy in 1:nyears){
- cluster_obs = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'cluster_outputs_', years[yy], '.csv'))[,-1]
- cluster_obs$latitude[which(cluster_obs$latitude == 0)] = NA
- cluster_obs$longitude[which(cluster_obs$longitude == 0)] = NA
- if(paste0(var,'_num_total') %in% colnames(cluster_obs)){
- max_survey_size = max(cluster_obs[[paste0(var,'_num_total')]], na.rm=TRUE)
- plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0.5,0.5,0.5,0.5))
- points(cluster_obs$longitude, cluster_obs$latitude, col=colors_range_0_to_1[1+round(cluster_obs[[paste0(var,'_rate')]]*100)], pch=20, cex=cluster_obs[[paste0(var,'_num_total')]]/round(max_survey_size/5))#, xlim=c(min(cluster_obs$longitude), max(cluster_obs$longitude)), ylim=c(min(cluster_obs$latitude), max(cluster_obs$latitude)))
- }else{
- plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ } else{
+ # pdf(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_cluster_observations_all_years2.pdf'), width=28, height=6*length(variables), useDingbats = FALSE)
+ png(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',vacc_string, 'cluster_observations_all_years',plot_suffix,'.png'), width=0.5*7*nyears, height=0.5*6*length(variables2), units='in', res=900)
+ base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
+ layout_matrix = base_layout_matrix
+ if (length(variables2)>1){
+ for(vv in 2:length(variables2)){
+ layout_matrix = rbind(layout_matrix, base_layout_matrix + (vv-1)*max(base_layout_matrix))
+ }
+ }
+ layout(layout_matrix)
+ par(mar=c(0,0,1,0))
+ for(i_var in 1:length(variables2)){
+ var = variables2[i_var]
+ for(yy in 1:nyears){
+ cluster_obs = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'cluster_outputs_', years[yy], '.csv'))[,-1]
+ cluster_obs$latitude[which(cluster_obs$latitude == 0)] = NA
+ cluster_obs$longitude[which(cluster_obs$longitude == 0)] = NA
+ if(paste0(var,'_num_total') %in% colnames(cluster_obs)){
+ max_survey_size = max(cluster_obs[[paste0(var,'_num_total')]], na.rm=TRUE)
+ plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0.5,0.5,0.5,0.5))
+ points(cluster_obs$longitude, cluster_obs$latitude, col=colors_range_0_to_1[1+round(cluster_obs[[paste0(var,'_rate')]]*100)], pch=20, cex=cluster_obs[[paste0(var,'_num_total')]]/round(max_survey_size/5))#, xlim=c(min(cluster_obs$longitude), max(cluster_obs$longitude)), ylim=c(min(cluster_obs$latitude), max(cluster_obs$latitude)))
+ }else{
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ }
}
+ # legend - colorbar
+ legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
+ text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
+ rasterImage(legend_image, 0, 0, 1,1)
+ # legend - survey size
+ plot(rep(0,5), seq(1, max_survey_size, length.out=5), cex=seq(1,max_survey_size, length.out=5)/round(max_survey_size/5), pch=20, axes=FALSE, xlab='', ylab='sample size'); axis(2)
}
- # legend - colorbar
- legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
- plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
- text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
- rasterImage(legend_image, 0, 0, 1,1)
- # legend - survey size
- plot(rep(0,5), seq(1, max_survey_size, length.out=5), cex=seq(1,max_survey_size, length.out=5)/round(max_survey_size/5), pch=20, axes=FALSE, xlab='', ylab='sample size'); axis(2)
+ dev.off()
}
- dev.off()
}
-
-
-
- ####=========================================================================================================####
- # map of LGA-level DHS results, allowing for aggregation to admin1 level when sample sizes too small
- ####=========================================================================================================####
+ #
+ #
+ #
+ # ####=========================================================================================================####
+ # # map of LGA-level DHS results, allowing for aggregation to admin1 level when sample sizes too small
+ # ####=========================================================================================================####
if(plot_separate_pdfs){
for(yy in 1:length(years)){
pdf(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',vacc_string, 'admin_minN', min_num_total,'_', years[yy], '.pdf'), width=7, height=5, useDingbats = FALSE)
@@ -884,7 +1281,7 @@ plot_extracted_DHS_data = function(hbhi_dir, years, admin_shape, min_num_total=3
layout(matrix(c(1,1,1,2, 1,1,1,3),nrow=2, byrow=TRUE))
admin_colors = colors_range_0_to_1[1+round(admin_sums[[paste0(var,'_rate')]]*100)]
plot(admin_shape, main=var, col=admin_colors)
-
+
# legend - colorbar
legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
@@ -911,51 +1308,153 @@ plot_extracted_DHS_data = function(hbhi_dir, years, admin_shape, min_num_total=3
if(!dir.exists(paste0(hbhi_dir,'/estimates_from_DHS/plots'))) dir.create(paste0(hbhi_dir,'/estimates_from_DHS/plots'))
# use subset of variables
if(!plot_vaccine) {
- variables2 = c(variables[variables %in% c('mic', 'itn_all', 'itn_u5', 'iptp','cm','blood_test')], 'effective_treatment')
+ variables2 = c(variables[variables %in% c('mic', 'rdt', 'itn_all', 'itn_u5', 'iptp','cm','received_treatment','sought_treatment','blood_test','art')], 'effective_treatment')
} else variables2 = variables
nyears = length(years)
- # pdf(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_cluster_observations_all_years2.pdf'), width=28, height=6*length(variables), useDingbats = FALSE)
- png(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',vacc_string, 'admin_minN',min_num_total,'_observations_acrossYears.png'), width=0.5*7*nyears, height=0.5*6*length(variables2), units='in', res=900)
- base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
- layout_matrix = base_layout_matrix
- if (length(variables2)>1){
- for(vv in 2:length(variables2)){
- layout_matrix = rbind(layout_matrix, base_layout_matrix + (vv-1)*max(base_layout_matrix))
- }
- }
- layout(layout_matrix)
- par(mar=c(0,0,1,0))
- for(i_var in 1:length(variables2)){
- var = variables2[i_var]
- for(yy in 1:nyears){
+
+ if(separate_plots_for_each_var){
+ for(i_var in 1:length(variables2)){
+ var = variables2[i_var]
+ png(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',var,'_',vacc_string, 'admin_minN',min_num_total,'_observations_acrossYears',plot_suffix,'.png'), width=0.5*7*nyears, height=0.5*6*length(variables2), units='in', res=900)
+ base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
+ layout(base_layout_matrix)
+ par(mar=c(0,0,1,0))
+ for(yy in 1:nyears){
+
+ # admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_admin_minN',min_num_total,'_includeArch_', years[yy], '.csv'))[,-1]
+ admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'admin_minN', min_num_total,'_', years[yy], '.csv'))[,-1]
+ reorder_admins = match(sapply(admin_shape$NOMDEP, match_lga_names), sapply(admin_sums0$NOMDEP, match_lga_names))
+ admin_sums = admin_sums0[reorder_admins,]
+ if(all(sapply(admin_shape$NOMDEP, match_lga_names) == sapply(admin_sums$NOMDEP, match_lga_names))){
+ if(paste0(var,'_num_total') %in% colnames(admin_sums)){
+ admin_colors = colors_range_0_to_1[1+round(admin_sums[[paste0(var,'_rate')]]*100)]
+ plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0,0,0,0.5), col=admin_colors)
+
+ }else {
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ }
+ } else {
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ warning('during plot generation, order of districts in shapefile and data frame did not match, skipping plotting.')
+ }
+ }
- # admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_admin_minN',min_num_total,'_includeArch_', years[yy], '.csv'))[,-1]
- admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'admin_minN', min_num_total,'_', years[yy], '.csv'))[,-1]
- reorder_admins = match(sapply(admin_shape$NOMDEP, match_lga_names), sapply(admin_sums0$NOMDEP, match_lga_names))
- admin_sums = admin_sums0[reorder_admins,]
- if(all(sapply(admin_shape$NOMDEP, match_lga_names) == sapply(admin_sums$NOMDEP, match_lga_names))){
- if(paste0(var,'_num_total') %in% colnames(admin_sums)){
- admin_colors = colors_range_0_to_1[1+round(admin_sums[[paste0(var,'_rate')]]*100)]
- plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0,0,0,0.5), col=admin_colors)
-
- }else {
+ # legend - colorbar
+ legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
+ text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
+ rasterImage(legend_image, 0, 0, 1,1)
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ dev.off()
+ }
+ } else{
+ # pdf(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_cluster_observations_all_years2.pdf'), width=28, height=6*length(variables), useDingbats = FALSE)
+ png(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_',vacc_string, 'admin_minN',min_num_total,'_observations_acrossYears',plot_suffix,'.png'), width=0.5*7*nyears, height=0.5*6*length(variables2), units='in', res=900)
+ base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
+ layout_matrix = base_layout_matrix
+ if (length(variables2)>1){
+ for(vv in 2:length(variables2)){
+ layout_matrix = rbind(layout_matrix, base_layout_matrix + (vv-1)*max(base_layout_matrix))
+ }
+ }
+ layout(layout_matrix)
+ par(mar=c(0,0,1,0))
+ for(i_var in 1:length(variables2)){
+ var = variables2[i_var]
+ for(yy in 1:nyears){
+
+ # admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_admin_minN',min_num_total,'_includeArch_', years[yy], '.csv'))[,-1]
+ admin_sums0 = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'admin_minN', min_num_total,'_', years[yy], '.csv'))[,-1]
+ reorder_admins = match(sapply(admin_shape$NOMDEP, match_lga_names), sapply(admin_sums0$NOMDEP, match_lga_names))
+ admin_sums = admin_sums0[reorder_admins,]
+ if(all(sapply(admin_shape$NOMDEP, match_lga_names) == sapply(admin_sums$NOMDEP, match_lga_names))){
+ if(paste0(var,'_num_total') %in% colnames(admin_sums)){
+ admin_colors = colors_range_0_to_1[1+round(admin_sums[[paste0(var,'_rate')]]*100)]
+ plot(admin_shape, main=paste0(var, ' - ', years[yy]), border=rgb(0,0,0,0.5), col=admin_colors)
+
+ }else {
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ }
+ } else {
plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ warning('during plot generation, order of districts in shapefile and data frame did not match, skipping plotting.')
}
- } else {
- plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
- warning('during plot generation, order of districts in shapefile and data frame did not match, skipping plotting.')
}
+
+ # legend - colorbar
+ legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
+ text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
+ rasterImage(legend_image, 0, 0, 1,1)
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
+ }
+ dev.off()
+ }
+ }
+}
+
+
+
+
+
+# plot coverage/prevalence values in each cluster and admin, as extracted from DHS
+plot_extracted_DHS_clusters_within_state = function(hbhi_dir, years, admin_shape, state, variables=c('mic', 'rdt','itn_all','itn_u5','itn_5_10','itn_10_15','itn_15_20','itn_o20','iptp','cm','received_treatment','sought_treatment','blood_test'), colors_range_0_to_1=NA, plot_vaccine=FALSE, plot_suffix=''){
+ admin_shape = admin_shape[admin_shape$State==state,]
+ if(any(is.na(colors_range_0_to_1))){
+ colors_range_0_to_1 = add.alpha(pals::parula(101), alpha=0.5)
+ }
+ if(plot_vaccine){
+ vacc_string='vaccine_'
+ } else vacc_string=''
+ if(!dir.exists(paste0(hbhi_dir,'/estimates_from_DHS/plots'))) dir.create(paste0(hbhi_dir,'/estimates_from_DHS/plots'))
+
+ ##=========================================================================================================##
+ # plots of cluster-level DHS results - separated by interventions, each plot panel showing across years
+ ##=========================================================================================================##
+ if(!dir.exists(paste0(hbhi_dir,'/estimates_from_DHS/plots'))) dir.create(paste0(hbhi_dir,'/estimates_from_DHS/plots'))
+ # use subset of variables
+ if(!plot_vaccine) {
+ variables2 = variables[variables %in% c('mic', 'rdt', 'itn_all', 'itn_u5', 'iptp','cm','received_treatment','sought_treatment','blood_test', 'art')]
+ } else variables2 = variables
+ nyears = length(years)
+
+ # pdf(paste0(hbhi_dir, '/estimates_from_DHS/plots/DHS_cluster_observations_all_years2.pdf'), width=28, height=6*length(variables), useDingbats = FALSE)
+ png(paste0(hbhi_dir, '/estimates_from_DHS/plots/',state,'_DHS_',vacc_string, 'cluster_observations_all_years',plot_suffix,'.png'), width=0.5*7*nyears, height=0.5*6*length(variables2), units='in', res=900)
+ base_layout_matrix = matrix(c(rep(1:nyears, each=3), nyears+1, rep(1:nyears, each=3),nyears+2),nrow=2, byrow=TRUE)
+ layout_matrix = base_layout_matrix
+ if (length(variables2)>1){
+ for(vv in 2:length(variables2)){
+ layout_matrix = rbind(layout_matrix, base_layout_matrix + (vv-1)*max(base_layout_matrix))
+ }
+ }
+ layout(layout_matrix)
+ par(mar=c(0,0,1,0))
+ for(i_var in 1:length(variables2)){
+ var = variables2[i_var]
+ for(yy in 1:nyears){
+ cluster_obs = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_',vacc_string, 'cluster_outputs_', years[yy], '.csv'))[,-1]
+ cluster_obs = cluster_obs[cluster_obs$NOMREGION == state,]
+ cluster_obs$latitude[which(cluster_obs$latitude == 0)] = NA
+ cluster_obs$longitude[which(cluster_obs$longitude == 0)] = NA
+ if(paste0(var,'_num_total') %in% colnames(cluster_obs)){
+ max_survey_size = max(cluster_obs[[paste0(var,'_num_total')]], na.rm=TRUE)
+ plot(st_geometry(admin_shape), main=paste0(var, ' - ', years[yy]), border=rgb(0.5,0.5,0.5,0.5))
+ points(cluster_obs$longitude, cluster_obs$latitude, col=colors_range_0_to_1[1+round(cluster_obs[[paste0(var,'_rate')]]*100)], pch=20, cex=cluster_obs[[paste0(var,'_num_total')]]/round(max_survey_size/5))#, xlim=c(min(cluster_obs$longitude), max(cluster_obs$longitude)), ylim=c(min(cluster_obs$latitude), max(cluster_obs$latitude)))
+ }else{
+ plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
}
-
- # legend - colorbar
- legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
- plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
- text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
- rasterImage(legend_image, 0, 0, 1,1)
- plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, xlab=NA, ylab=NA)
}
- dev.off()
+ # legend - colorbar
+ legend_image = as.raster(matrix(rev(colors_range_0_to_1[1+round(seq(0,1,length.out=20)*100)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = var)
+ text(x=1.5, y = seq(0,1,length.out=5), labels = seq(0,1,length.out=5))
+ rasterImage(legend_image, 0, 0, 1,1)
+ # legend - survey size
+ plot(rep(0,5), seq(1, max_survey_size, length.out=5), cex=seq(1,max_survey_size, length.out=5)/round(max_survey_size/5), pch=20, axes=FALSE, xlab='', ylab='sample size'); axis(2)
}
+ dev.off()
+
+
}
diff --git a/r_utilities/data_processing/3_sample_itn_mass_distribution_coverages.R b/r_utilities/data_processing/3_sample_itn_mass_distribution_coverages.R
index 20638cf..01a2506 100644
--- a/r_utilities/data_processing/3_sample_itn_mass_distribution_coverages.R
+++ b/r_utilities/data_processing/3_sample_itn_mass_distribution_coverages.R
@@ -11,6 +11,7 @@ library(pals)
library(prettyGraphs)
library(lubridate)
library(gridExtra)
+library(geofacet)
##############################################################
@@ -126,7 +127,7 @@ aggregate_itn_dhs_data_across_years = function(hbhi_dir, years, itn_variables, m
}
colnames(net_dhs_info)[which(colnames(net_dhs_info)=='NOMDEP')] = 'admin_name'
colnames(net_dhs_info)[which(colnames(net_dhs_info)=='mean_date')] = 'date'
- write.csv(net_dhs_info, paste0(hbhi_dir, '/estimates_from_DHS/DHS_ITN_dates_and_rates.csv'))
+ write.csv(net_dhs_info, net_dhs_filename)
}
return(net_dhs_info)
}
@@ -152,9 +153,9 @@ aggregate_itn_dhs_data_across_years = function(hbhi_dir, years, itn_variables, m
# - net_life_lognormal_mu # for the Expiration_Period_Log_Normal_Mu parameter in the lognormal decay distribution (time before nets discarded, lost, forgotten, etc.).
# - net_life_lognormal_sigma
# single seed and admins may have different mass distributions dates
-create_itn_input_from_DHS_differentDates = function(hbhi_dir, itn_variables, itn_distributions_by_admin_filename, sim_start_year=2010, maximum_coverage=0.9,
+create_itn_input_from_DHS_differentDates = function(hbhi_dir, itn_variables, itn_distributions_by_admin_filename, grid_layout_state_locations, sim_start_year=2010, maximum_coverage=0.9,
seasonality_monthly_scalar, # adjust net usage for seasonality
- years, min_num_total=30, default_first_coverage=0.1, itn_variable_base='itn_u5', save_age_ratio_plots=FALSE, save_timeseries_coverage_plots=FALSE
+ years, min_num_total=30, default_first_coverage=0.1, itn_variable_base='itn_u5', save_age_ratio_plots=FALSE, save_timeseries_coverage_plots=FALSE
){
# get the distribution dates for each admin
itn_distributions_by_admin = read.csv(itn_distributions_by_admin_filename)
@@ -360,6 +361,106 @@ create_itn_input_from_DHS_differentDates = function(hbhi_dir, itn_variables, itn
theme(legend.position='none') +
facet_wrap('State', nrow=5)
ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_rate_timeseries_extrapolation_build0.png'), plot=ggb, width=18, height=15, units='in', dpi=900)
+
+
+ itn_distributions_by_state_raw_filename = gsub('/[^/]*.csv','/uncorrected_mass_dist_dates_state.csv',itn_distributions_by_admin_filename)
+ if(file.exists(itn_distributions_by_state_raw_filename)){
+ llin_info_raw_state = read.csv(itn_distributions_by_state_raw_filename)
+ net_dhs_info$code = net_dhs_info$NOMREGION
+ llin_info_raw_state$code = llin_info_raw_state$State
+ llin_info_raw_state$date = as.Date(llin_info_raw_state$date)
+
+ ggc1 = ggplot(grid_layout_state_locations)+
+ # geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_from_dhs.png'), plot=ggc1, width=9, height=6, units='in', dpi=1600)
+
+ ggc = ggplot(grid_layout_state_locations)+
+ geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_from_dhs_and_raw_dist_dates.png'), plot=ggc, width=9, height=6, units='in', dpi=1600)
+
+ coverage_df$code = coverage_df$State
+ ggd = ggplot(grid_layout_state_locations)+
+ geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_point(data=coverage_df, aes(x=date), y=1, col='blue', shape='|', size=7) +
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_from_dhs_and_estimated_dist_dates.png'), plot=ggd, width=9, height=6, units='in', dpi=1600)
+
+ ggdv2 = ggplot(grid_layout_state_locations)+
+ # geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_vline(data=coverage_df, aes(xintercept=date), color='blue', size=0.3) +
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_from_dhs_and_estimated_dist_dates_v2.png'), plot=ggdv2, width=9, height=6, units='in', dpi=1600)
+
+
+ coverage_timeseries$code = coverage_timeseries$State
+ gge = ggplot(grid_layout_state_locations)+
+ geom_line(data=coverage_timeseries, aes(x=date, y=adjusted_coverage, col=admin_name), linewidth=0.15) +
+ geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_point(data=coverage_df, aes(x=date), y=1, col='blue', shape='|', size=7) +
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_timeseries_from_dhs_and_estimated_dist_dates.png'), plot=gge, width=9, height=6, units='in', dpi=1600)
+
+
+ ggev2 = ggplot(grid_layout_state_locations)+
+ geom_line(data=coverage_timeseries, aes(x=date, y=adjusted_coverage, col=admin_name), linewidth=0.15) +
+ # geom_vline(data=llin_info_raw_state, aes(xintercept=date), color='darkgreen')+
+ geom_vline(data=coverage_df, aes(xintercept=date), color='blue', size=0.3) +
+ geom_hline(aes(yintercept=0), color='black')+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate), color='black', size=0.8)+
+ geom_point(data=net_dhs_info, aes(x=date, y=itn_u5_rate, color=admin_name), size=0.6)+
+ scale_y_continuous(guide = guide_axis(check.overlap = TRUE)) +
+ scale_x_date(guide = guide_axis(check.overlap = TRUE), breaks=as.Date(paste0(c(2012,2016,2020),'/01/01')), labels=c(2012,2016,2020)) +
+ coord_cartesian(xlim=c(as.Date('2010-01-01'), as.Date('2023-01-01'))) +
+ theme_classic()+
+ theme(legend.position='none') +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='fixed')
+ ggsave(filename=paste0(hbhi_dir, '/simulation_inputs/plots/itn_use_timeseries_from_dhs_and_estimated_dist_dates_v2.png'), plot=ggev2, width=9, height=6, units='in', dpi=1600)
+
+
+ }
+
}
}
diff --git a/r_utilities/data_processing/4_create_DHS_reference_monthly_pfpr.R b/r_utilities/data_processing/4_create_DHS_reference_monthly_pfpr.R
index c9e50c3..c3b6332 100644
--- a/r_utilities/data_processing/4_create_DHS_reference_monthly_pfpr.R
+++ b/r_utilities/data_processing/4_create_DHS_reference_monthly_pfpr.R
@@ -2,7 +2,16 @@
# create data frame with a row entry for each admin-month-year present in the DHS dataset, giving the total number tested and the number microscopy positive
-create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_pop_df_filename, pfpr_dhs_ref_years=c(2012, 2016), min_num_total=30){
+create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_pop_df_filename, pfpr_dhs_ref_years=c(2012, 2016), min_num_total=30, pfpr_measure='mic'){
+ if(pfpr_measure=='mic'){
+ pfpr_measure_name = 'microscopy'
+ }else if(pfpr_measure=='rdt'){
+ pfpr_measure_name = 'RDT'
+ }else{
+ warning('Name of PfPR metric not recognized. Assuming microscopy.')
+ pfpr_measure='mic'
+ pfpr_measure_name = 'microscopy'
+ }
for (yy in 1:length(pfpr_dhs_ref_years)){
year = pfpr_dhs_ref_years[yy]
@@ -12,7 +21,7 @@ create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_
locations = data.frame(clusterid = locations_shp$DHSCLUST, latitude=locations_shp$LATNUM, longitude=locations_shp$LONGNUM)
MIS_outputs = locations
- var_index = which(DHS_file_recode_df$variable == 'mic')
+ var_index = which(DHS_file_recode_df$variable == pfpr_measure)
if(!is.na(DHS_file_recode_df$filename[var_index])){
cur_dta = read.dta(paste0(dta_dir, '/', DHS_file_recode_df$folder_dir[var_index], '/', DHS_file_recode_df$filename[var_index]))
@@ -53,7 +62,7 @@ create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_
} else{
ds_pfpr_all_years = rbind(ds_pfpr_all_years, as.data.frame(MIS_shape))
}
- }else warning(paste0('filename not specified for microscpy year ', pfpr_dhs_ref_years[yy]))
+ }else warning(paste0('filename not specified for ', pfpr_measure_name, ' year ', pfpr_dhs_ref_years[yy]))
}
ds_pfpr_all_years = ds_pfpr_all_years[!is.na(ds_pfpr_all_years$admin_name),]
@@ -88,7 +97,7 @@ create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_
admin_pfpr_sums = ds_pfpr_all_years_agg_expanded
admin_pfpr_sums$data_spatial_level = 'admin'
- write.csv(as.data.frame(admin_pfpr_sums), paste0(hbhi_dir, '/estimates_from_DHS/DHS_monthly_microscopy_adminLevelDataOnly.csv'), row.names=FALSE)
+ write.csv(as.data.frame(admin_pfpr_sums), paste0(hbhi_dir, '/estimates_from_DHS/DHS_monthly_', pfpr_measure_name, '_adminLevelDataOnly.csv'), row.names=FALSE)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# when the number surveyed in a admin is lower than the threshold, use the region value instead
@@ -110,7 +119,7 @@ create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_
summarise(total_positive = sum(num_pos),
total_tested = sum(num_tested))
# hist(agg_to_region$total_tested, breaks=60)
- if(any(agg_to_region$total_tested < min_num_total)) warning('Some regions do not have the minimum number of required U5 tested with microscopy, need to modify code to aggregate further.')
+ if(any(agg_to_region$total_tested < min_num_total)) warning('Some regions do not have the minimum number of required U5 tested with ', pfpr_measure_name,', need to modify code to aggregate further.')
admin_pfpr_sums_adjMin = admin_pfpr_sums
# if the total number of U5s surveyed across all months/years/clusters is less than the threshold min_num_total, replace the admin-level data with the aggregated state-level data instead
@@ -132,7 +141,7 @@ create_DHS_reference_monthly_pfpr = function(hbhi_dir, dta_dir, admin_shape, ds_
}
}
admin_pfpr_sums_adjMin = admin_pfpr_sums_adjMin[(admin_pfpr_sums_adjMin$num_tested>0) & !is.na(admin_pfpr_sums_adjMin$year),]
- write.csv(as.data.frame(admin_pfpr_sums_adjMin), paste0(hbhi_dir, '/estimates_from_DHS/DHS_admin_monthly_microscopy.csv'), row.names=FALSE)
+ write.csv(as.data.frame(admin_pfpr_sums_adjMin), paste0(hbhi_dir, '/estimates_from_DHS/DHS_admin_monthly_',pfpr_measure_name,'.csv'), row.names=FALSE)
}
# # plot distribution of aggregated prevalences across LGAs
diff --git a/r_utilities/data_processing/DHS_code_examination.R b/r_utilities/data_processing/DHS_code_examination.R
index 4978407..149bb22 100644
--- a/r_utilities/data_processing/DHS_code_examination.R
+++ b/r_utilities/data_processing/DHS_code_examination.R
@@ -3,7 +3,7 @@
library(foreign)
library(haven)
library(dplyr)
-library(rgdal)
+# library(rgdal)
library(raster)
library(sp)
@@ -44,8 +44,6 @@ if(country =='NGA'){
dta_cur = read.dta(dta_filepaths[dd])
dta_list[[dd]] = dta_cur
}
-
-
}
@@ -102,7 +100,7 @@ non_art_codes = c("ml13a", "ml13b", "ml13c", "ml13d", "ml13da", "ml13h") # count
# household codes
house_codes = c('hhid','hvidx','hv001','hv006','hv007','hv105','hml20','hml32','hml32a')
-house_filenum = 5 # 2010:5, 2013:7, 2015:5, 2018:8, 2021: 5
+house_filenum = 8 # 2010:5, 2013:7, 2015:5, 2018:8, 2021: 5
dta_filepaths[house_filenum]
View(dta_list[[house_filenum]][1:50,house_codes[house_codes %in% colnames(dta_list[[house_filenum]])]])
for(ii in 6:length(house_codes)){
@@ -114,7 +112,7 @@ for(ii in 6:length(house_codes)){
# individual codes
ind_codes = c('caseid','bidx','v001','v012','v014','v006','v007','v105', 'hw16', 'h32z','h47','m49a', 'ml1', 'h3', 'h5', 'h7', 'h9', art_codes, non_art_codes)
-ind_filenum = 1 # 2010:1, 2013:1, 2015:4, 2018:1, 2021: 5
+ind_filenum = 5 # 2010:1, 2013:1, 2015:4, 2018:1, 2021: 5
dta_filepaths[ind_filenum]
View(dta_list[[ind_filenum]][1:50,ind_codes[ind_codes %in% colnames(dta_list[[ind_filenum]])]])
for(ii in 6:length(ind_codes)){
@@ -127,6 +125,14 @@ for(ii in 6:length(ind_codes)){
+# vaccine with birth dates
+ind_codes = c('caseid','bidx','v001','v012','v014','v006','v007','v105', 'hw16', 'h32z','h47','m49a', 'ml1', 'b1','b2','b3', 'h3', 'h5', 'h7', 'h9')
+ind_filenum = 1 # 2010:1, 2013:1, 2015:4, 2018:1, 2021: 5
+dta_filepaths[ind_filenum]
+View(dta_list[[ind_filenum]][1:50,ind_codes[ind_codes %in% colnames(dta_list[[ind_filenum]])]])
+
+
+
dta_filepaths[4]
########################
# PfPR (microscopy)
@@ -167,8 +173,9 @@ find_code_locations(dta_list=dta_list, code_str='h37e') # combination with arte
find_code_locations(dta_list=dta_list, code_str='ml13e') # combination with artemisinin
find_code_locations(dta_list=dta_list, code_str='ml20a') # how long after fever started did first take ACT
# # view all the artesunate versus non-ACT antimalarials
-# art_codes = c("ml13e", "ml13aa", "ml13ab")
+# art_codes = c("ml13e", "ml13aa", "ml13ab") # 'ml13aa' is rectal artesunate, moving to non-ACT
# non_art_codes = c("ml13a", "ml13b", "ml13c", "ml13d", "ml13da", "ml13h") #, "ml13g", "ml13f") # country-specific antimalarial: "ml13g", "ml13f",
+find_code_locations(dta_list=dta_list, code_str='ml13aa') # rectal artesunate
diff --git a/r_utilities/data_processing/plot_map_intervention_coverage_input.R b/r_utilities/data_processing/plot_map_intervention_coverage_input_example.R
similarity index 97%
rename from r_utilities/data_processing/plot_map_intervention_coverage_input.R
rename to r_utilities/data_processing/plot_map_intervention_coverage_input_example.R
index da9f421..6dd8850 100644
--- a/r_utilities/data_processing/plot_map_intervention_coverage_input.R
+++ b/r_utilities/data_processing/plot_map_intervention_coverage_input_example.R
@@ -1,6 +1,6 @@
# plot_map_intervention_coverage_input.R
-library(rgdal)
+# library(rgdal)
library(raster)
library(ggplot2)
library(gridExtra)
@@ -19,9 +19,9 @@ library(prettyGraphs)
user = Sys.getenv("USERNAME")
user_path = file.path("C:/Users",user)
-country = 'BDI' #'SLE' # 'BDI'
-dta_dir = 'C:/Users/moniqueam/Dropbox (IDM)/Malaria Team Folder/data'
-script_dir = 'C:/Users/moniqueam/Documents/malaria-snt-core'
+country = 'NGA' #'SLE' # 'BDI'
+dta_dir = 'C:/Users/moniqueam/IDM Dropbox/Malaria Team Folder/data'
+script_dir = 'C:/Users/moniqueam/Documents/emodpy-snt/r_utilities'
if(country =='BDI'){
@@ -29,9 +29,12 @@ if(country =='BDI'){
admin_shapefile_filepath = (paste0(hbhi_dir, '/SpatialClustering/reference_rasters_shapefiles/bdi_adm2.shp'))
data_dir = 'C:/Users/mambrose/Dropbox (IDM)/Malaria Team Folder/data/Burundi'
} else if(country=='NGA'){
- base_filepath = paste0(user_path, '/Dropbox (IDM)/NU_collaboration')
- hbhi_dir = paste0(user_path, '/Dropbox (IDM)/NU_collaboration/hbhi_nigeria/snt_2022')
- admin_shapefile_filepath = (paste0(base_filepath, '/hbhi_nigeria/SpatialClustering/reference_rasters_shapefiles/NGA_DS_clusteringProjection.shp'))
+ hbhi_dir = 'C:/Users/moniqueam/IDM Dropbox/Malaria Team Folder/projects/snt/Nigeria/snt_2023'
+ admin_shapefile_filepath = paste0(hbhi_dir, '/SpatialClustering/reference_rasters_shapefiles/NGA_DS_clusteringProjection.shp')
+
+ # base_filepath = paste0(user_path, '/IDM Dropbox/NU_collaboration')
+ # hbhi_dir = paste0(user_path, '/IDM Dropbox/Monique Ambrose/NU_collaboration/hbhi_nigeria/snt_2022')
+ # admin_shapefile_filepath = (paste0(base_filepath, '/hbhi_nigeria/SpatialClustering/reference_rasters_shapefiles/NGA_DS_clusteringProjection.shp'))
}
@@ -41,8 +44,8 @@ admin_shapefile = st_read(admin_shapefile_filepath)
base_sim_input_dir = paste0(hbhi_dir, '/simulation_inputs')
intervention_coordinator = read.csv(paste0(base_sim_input_dir, '/_intervention_file_references/Interventions_to_present.csv'))
-intervention_coordinator = read.csv(paste0(base_sim_input_dir, '/_intervention_file_references/Interventions_for_projections.csv'))
-scenario_row = 2
+# intervention_coordinator = read.csv(paste0(base_sim_input_dir, '/_intervention_file_references/Interventions_for_projections.csv'))
+scenario_row = 1 #5
if(scenario_row>nrow(intervention_coordinator)) scenario_row = nrow(intervention_coordinator)
source(paste0(script_dir,'/standardize_admin_names.R'))
@@ -244,7 +247,7 @@ if(file.exists(intervention_filename)){
inter_input = read.csv(intervention_filename)
if('seed' %in% colnames(inter_input)) inter_input = inter_input[inter_input$seed == 1,]
inter_years = sort(unique(inter_input$year))
- inter_years = inter_years[seq(1,length(inter_years), by=2)]
+ inter_years = inter_years[seq(2,length(inter_years), by=2)]
create_coverage_input_maps(inter_input=inter_input, inter_years=inter_years, output_filename=output_filename, colorscale=colorscale, min_value=min_value, max_value=max_value, num_colors=num_colors)
}
diff --git a/r_utilities/data_processing/plot_map_intervention_coverage_input_functions.R b/r_utilities/data_processing/plot_map_intervention_coverage_input_functions.R
new file mode 100644
index 0000000..7554c30
--- /dev/null
+++ b/r_utilities/data_processing/plot_map_intervention_coverage_input_functions.R
@@ -0,0 +1,110 @@
+# plot_map_intervention_coverage_input_functions.R
+
+
+library(raster)
+library(ggplot2)
+library(gridExtra)
+library(grid)
+library(RColorBrewer)
+library(tidyverse)
+library(sf)
+library(reshape2)
+library(pals)
+library(prettyGraphs)
+
+
+
+##########################################################
+# functions for ggplot version
+##########################################################
+
+# function to combine multiple plots for same intervention that share a legend
+grid_arrange_shared_legend_plotlist =function(...,
+ plotlist=NULL,
+ ncol = length(list(...)),
+ nrow = NULL,
+ position = c("bottom", "right")) {
+
+ plots <- c(list(...), plotlist)
+
+ if (is.null(nrow)) nrow = ceiling(length(plots)/ncol)
+
+ position <- match.arg(position)
+ g <- ggplotGrob(plots[[1]] + theme(legend.position = position) + guides(fill = guide_legend(reverse=T)))$grobs
+ legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
+ lheight <- sum(legend$height)
+ lwidth <- sum(legend$width)
+ gl <- lapply(plots, function(x) x + theme(legend.position="none"))
+ gl <- c(gl, ncol = ncol, nrow = nrow)
+
+ combined <- switch(position,
+ "bottom" = arrangeGrob(do.call(arrangeGrob, gl),
+ legend,
+ ncol = 1,
+ heights = unit.c(unit(1, "npc") - lheight, lheight)),
+ "right" = arrangeGrob(do.call(arrangeGrob, gl),
+ legend,
+ ncol = 2,
+ widths = unit.c(unit(1, "npc") - lwidth, lwidth)))
+
+ grid.newpage()
+ grid.draw(combined)
+
+ # return gtable invisibly
+ invisible(combined)
+}
+
+change_legend_size <- function(myPlot, pointSize = 0.5, textSize = 3, spaceLegend = 0.1) {
+ myPlot +
+ guides(shape = guide_legend(override.aes = list(size = pointSize)),
+ color = guide_legend(override.aes = list(size = pointSize))) +
+ theme(legend.title = element_text(size = textSize),
+ legend.text = element_text(size = textSize),
+ legend.key.size = unit(spaceLegend, "lines"))
+}
+
+# function to create and save maps of intervention coverages
+create_coverage_input_maps = function(inter_input, inter_years, output_filename, colorscale, min_value, max_value, num_colors){
+ plot_list = list()
+ for(yy in 1:length(inter_years)){
+ inter_input_cur = inter_input[inter_input$year == inter_years[yy],]
+ inter_input_cur$output_value = inter_input_cur[[coverage_colname]]
+ admin_cur = admin_shapefile %>%
+ left_join(inter_input_cur, by=c('NOMDEP' = 'admin_name')) %>%
+ mutate(binned_values=cut(output_value,
+ breaks=round(seq((min_value), (max_value), length.out = (num_colors+1)),2)))
+ plot_list[[yy]] = ggplot(admin_cur) +
+ geom_sf(aes(fill=binned_values), size=0.1) +
+ scale_fill_manual(values=setNames(colorscale, levels(admin_cur$binned_values)), drop=FALSE, name='coverage', na.value='grey96') +
+ ggtitle(inter_years[yy]) +
+ guides(fill = guide_legend(reverse=T)) +
+ theme_void() +
+ theme(plot.title = element_text(hjust = 0.5))
+ plot_list[[yy]] = change_legend_size(plot_list[[yy]], pointSize=10, textSize=10, spaceLegend=1)
+ }
+
+ gg = grid_arrange_shared_legend_plotlist(plotlist=plot_list, ncol=length(plot_list), position='right')
+ ggsave(output_filename, gg, width = (length(inter_years)+1)*1.8, height=2.3, units='in', dpi=800)
+}
+
+
+
+# function to create and save maps of intervention coverages
+create_maps_state_groups = function(admin_shapefile_filepath, shapefile_admin_colname, admin_group_df, column_name, output_filename, colorscale){
+ admin_shapefile = shapefile(admin_shapefile_filepath)
+ # standardize shapefile names
+ admin_shapefile$NOMDEP = standardize_admin_names_in_vector(target_names=archetype_info$LGA, origin_names=admin_shapefile$NOMDEP)
+
+ admin_group_df$output_value = admin_group_df[[column_name]]
+ admin_cur = admin_shapefile %>%
+ left_join(inter_input_cur, by=c('NOMDEP' = 'admin_name'))
+ gg = ggplot(admin_cur) +
+ geom_sf(aes(fill=binned_values), size=0.1) +
+ scale_fill_manual(values=setNames(colorscale, levels(admin_cur$output_value)), drop=FALSE, name='group', na.value='grey96') +
+ ggtitle(inter_years[yy]) +
+ guides(fill = guide_legend(reverse=T)) +
+ theme_void() +
+ theme(plot.title = element_text(hjust = 0.5))
+
+ ggsave(output_filename, gg, width = (2)*1.8, height=2.3, units='in', dpi=800)
+}
diff --git a/r_utilities/data_processing/setup_inputs/create_sim_input_cm_iptp.R b/r_utilities/data_processing/setup_inputs/create_sim_input_cm_iptp.R
index 4b99c76..7d8b8ee 100644
--- a/r_utilities/data_processing/setup_inputs/create_sim_input_cm_iptp.R
+++ b/r_utilities/data_processing/setup_inputs/create_sim_input_cm_iptp.R
@@ -29,7 +29,7 @@ library(reshape2)
# maximum_coverage = 0.9
# sim_start_year = 2010
-create_cm_input_from_DHS = function(hbhi_dir, cm_variable_name='cm', sim_start_year=2010, adult_multiplier=0.5, severe_multiplier=2, severe_minimum=0.6, maximum_coverage=0.9){
+create_cm_input_from_DHS = function(hbhi_dir, cm_variable_name='cm', sim_start_year=2010, adult_multiplier=0.5, severe_multiplier=2, severe_minimum=0.6, maximum_coverage=0.9, act_adherence_effective_multiplier=1){
# read in coverages for U5
sample_df = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/DHS_sampled_params_',cm_variable_name,'.csv'))[,-1]
# convert from wide to long format to make each seed its own row
@@ -47,6 +47,10 @@ create_cm_input_from_DHS = function(hbhi_dir, cm_variable_name='cm', sim_start_y
# add severe disease coverage
coverage_df$severe_coverage = sapply(sapply((coverage_df$U5_coverage * severe_multiplier), min, maximum_coverage), max, severe_minimum)
+ # decrease the effective coverage to account for imperfect adherence to the full ACT regimine
+ coverage_df$adult_coverage = coverage_df$adult_coverage * act_adherence_effective_multiplier
+ coverage_df$U5_coverage = coverage_df$U5_coverage * act_adherence_effective_multiplier
+
# add in the day of the simulation each intervention should start and the duration
coverage_df$simday = (coverage_df$year - sim_start_year) * 365
coverage_df$duration = 365 # duration in days
diff --git a/r_utilities/plots_results_analyses/plot_counterfactual_timeseries_functions.R b/r_utilities/plots_results_analyses/plot_counterfactual_timeseries_functions.R
new file mode 100644
index 0000000..a604393
--- /dev/null
+++ b/r_utilities/plots_results_analyses/plot_counterfactual_timeseries_functions.R
@@ -0,0 +1,277 @@
+# functions_counterfactual_timeseries.R
+
+
+
+######################################################################
+# create plot panel with all burden metrics, no intervention info
+######################################################################
+
+plot_burden_reduction_attribution = function(sim_output_dir, pop_filepath, district_subset, cur_admins,
+ plot_by_month, min_year, max_year,
+ scenario_filepaths, scenario_names, experiment_names, inter_ordered, scenario_palette, scenario_palette_stacked,
+ burden_metrics='PfPR', burden_metric_names='PfPR (U5)', burden_colnames='PfPR_U5',
+ LLIN2y_flag=FALSE, overwrite_files=FALSE, noInterName='noInter', allInterName='allInter',
+ firstInPrefix='only',lastInPrefix='allInterExcept', create_plots_flag=TRUE){
+
+
+
+ pop_sizes = read.csv(pop_filepath)
+ pop_sizes = pop_sizes[,c('admin_name','pop_size')]
+ # if we include all admins, get list of names from population size dataframe
+ if(cur_admins[1] == 'all'){
+ cur_admins = unique(pop_sizes$admin_name)
+ }
+
+ # create output directories
+ if(!dir.exists(paste0(sim_output_dir, '/_plots'))) dir.create(paste0(sim_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_output_dir, '/_plots/timeseries_dfs'))
+ if(plot_by_month){
+ time_string = 'monthly'
+ } else time_string = 'annual'
+
+
+ # ----- malaria burden ----- #
+
+ for(bb in 1:length(burden_colnames)){
+ burden_metric_name = burden_metric_names[bb]
+ burden_colname = burden_colnames[bb]
+ burden_metric = burden_metrics[bb]
+
+
+ if(grepl('U5', burden_metric_name)){
+ age_plotted = 'U5'
+ } else age_plotted = 'all'
+
+ # check whether burden output already exists for this comparison
+ if(LLIN2y_flag){
+ llin2y_string = '_2yLLIN'
+ } else{
+ llin2y_string = ''
+ }
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create data frame with annual burden estimate for all experiments
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+
+ burden_df_filepath = paste0(sim_output_dir, '/_plots/timeseries_dfs/df_burden_',time_string,'Timeseries_', burden_metric, '_', age_plotted, '_',district_subset, llin2y_string,'.csv')
+ if(file.exists(burden_df_filepath) & !overwrite_files){
+ burden_df = read.csv(burden_df_filepath)
+ } else{
+ # iterate through scenarios, storing relevant output
+ burden_df = data.frame()
+ for(ee in 1:length(scenario_filepaths)){
+ cur_sim_output_agg = get_burden_timeseries_exp(exp_filepath = scenario_filepaths[ee],
+ exp_name = scenario_names[ee], district_subset=district_subset,
+ cur_admins=cur_admins, pop_sizes=pop_sizes, min_year=min_year, max_year=max_year, burden_colname=burden_colname, age_plotted=age_plotted, plot_by_month=plot_by_month)
+ if(nrow(burden_df)==0){
+ burden_df = cur_sim_output_agg
+ } else{
+ burden_df = rbind(burden_df, cur_sim_output_agg)
+ }
+ }
+ write.csv(burden_df, burden_df_filepath, row.names=FALSE)
+ }
+
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # calculate burden averted by intervention when first-in and last-out
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ burden_averted_filepath = paste0(sim_output_dir, '/_plots/timeseries_dfs/df_burden_averted_',time_string,'Timeseries_', burden_metric, '_', age_plotted, '_',district_subset, llin2y_string,'.csv')
+ if(file.exists(burden_averted_filepath) & !overwrite_files){
+ averted_df = read.csv(burden_averted_filepath)
+ } else{
+ ### look at impact attributed to each intervention when it is the first (only) intervention added on top of a simulation without interventions
+ first_in_df = burden_df[grepl(firstInPrefix, burden_df$scenario),]
+ first_in_df$intervention = sapply(first_in_df$scenario, gsub, pattern=firstInPrefix, replacement='')
+ # format the no-intervention reference data frame
+ noInter_df = burden_df[burden_df$scenario == noInterName,]
+ noInter_df = noInter_df %>% rename(ref_mean_burden=mean_burden) %>% # , ref_max_burden=max_burden, ref_min_burden=min_burden
+ dplyr::select(year, ref_mean_burden) # , ref_max_burden, ref_min_burden
+ # get difference between intervention and no-intervention simulation
+ first_in_df = merge(first_in_df, noInter_df, all=TRUE)
+ first_in_df$burden_averted_first_in = first_in_df$ref_mean_burden - first_in_df$mean_burden
+
+ ### look at impact attributed to each intervention when it is the final intervention added (i.e. the first subtracted from a simulation with all interventions)
+ last_in_df = burden_df[grepl(lastInPrefix, burden_df$scenario),]
+ last_in_df$intervention = sapply(last_in_df$scenario, gsub, pattern=lastInPrefix, replacement='')
+ # format the no-intervention reference data frame
+ allInter_df = burden_df[burden_df$scenario == allInterName,]
+ allInter_df = allInter_df %>% rename(ref_mean_burden=mean_burden) %>% # , ref_max_burden=max_burden, ref_min_burden=min_burden
+ dplyr::select(year, ref_mean_burden) # , ref_max_burden, ref_min_burden
+ # get difference between intervention and no-intervention simulation
+ last_in_df = merge(last_in_df, allInter_df, all=TRUE)
+ last_in_df$burden_averted_last_in = last_in_df$mean_burden - last_in_df$ref_mean_burden
+
+ averted_df = merge(first_in_df[,c('intervention','year','burden_averted_first_in')], last_in_df[,c('intervention','year','burden_averted_last_in')], all=TRUE)
+ write.csv(averted_df, burden_averted_filepath, row.names=FALSE)
+ }
+
+ if(create_plots_flag){
+ gg = ggplot(averted_df, aes(x=burden_averted_first_in, y=burden_averted_last_in, color=intervention, size=as.factor(year)))+
+ geom_point()+
+ geom_abline()+
+ ggtitle(paste0(burden_metric_name, ' averted by first-in versus last-out interventions'))+
+ ylab('burden averted when intervention is last-in')+
+ xlab('burden averted when intervention is first-in')+
+ scale_color_manual(values = scenario_palette) +
+ theme_classic()
+ ggsave(paste0(sim_output_dir, '/_plots/firstIn_versus_lastOut_burden_attribution_', burden_metric, '_', age_plotted, '_',district_subset,'.png'), gg, width=8, height=7, units='in')
+ }
+
+ # get average between the two burden-averted calculations
+ averted_df$burden_averted_mean = (averted_df$burden_averted_first_in + averted_df$burden_averted_last_in)/2
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create plots of timeseries (attributed burden averted)
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+
+ # create dataframe with stacked/cumulative burden averted for plotting
+ stacked_df = burden_df[burden_df$scenario %in% c(noInterName, allInterName),c('year','mean_burden','scenario')]
+ stacked_df$interventions_included = 'none'
+ stacked_df$interventions_included[stacked_df$scenario==allInterName] = 'all'
+ stacked_df = stacked_df[,c('year','mean_burden','interventions_included')]
+ stacked_df$ribbon_min = stacked_df$mean_burden
+ cur_inter_combo = ''
+ cur_stacked_values = stacked_df[stacked_df$interventions_included == 'none', c('year','mean_burden', 'ribbon_min')]
+ for(i_inter in 1:length(inter_ordered)){
+ cur_inter_combo = paste0(cur_inter_combo, inter_ordered[i_inter], '_')
+ # get the burden averted by current intervention
+ averted_cur = averted_df[averted_df$intervention==inter_ordered[i_inter],]
+ # calculate new stacked burden value and add rows to stacked_df
+ new_stacked_values = merge(averted_cur, cur_stacked_values, all=TRUE)
+ new_stacked_values$ribbon_min = new_stacked_values$mean_burden
+ new_stacked_values$mean_burden = new_stacked_values$mean_burden - new_stacked_values$burden_averted_mean
+ new_stacked_values$interventions_included = cur_inter_combo
+ stacked_df = merge(stacked_df, new_stacked_values[,c('year','mean_burden', 'ribbon_min','interventions_included')], all=TRUE)
+ # update current burden df
+ cur_stacked_values = new_stacked_values[, c('year','mean_burden', 'ribbon_min')]
+ }
+ # make sure ordering is correct
+ stacked_df$interventions_included = factor(stacked_df$interventions_included, levels=names(scenario_palette_stacked))
+
+ # plot timeseries with burden attributions
+ gg = ggplot(stacked_df, aes(x=year, y=mean_burden, color=interventions_included)) +
+ geom_ribbon(aes(ymin=ribbon_min, ymax=mean_burden, fill=interventions_included), alpha=0.8, color=NA)+
+ scale_fill_manual(values = scenario_palette_stacked) +
+ geom_line(data=stacked_df[stacked_df$interventions_included == 'none',], aes(x=year, y=mean_burden, group=interventions_included), size=1, color='darkgrey') +
+ geom_line(data=stacked_df[stacked_df$interventions_included == cur_inter_combo,], aes(x=year, y=mean_burden, group=interventions_included), size=1, color='black') +
+ scale_color_manual(values = scenario_palette_stacked) +
+ xlab('year') +
+ ylab(burden_metric_name) +
+ ylim(min(0, stacked_df$mean_burden),NA) +
+ coord_cartesian(xlim=c(min_year, max_year))+
+ theme_classic()+
+ theme(legend.position = "right", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = 10))
+ if(create_plots_flag){
+ ggsave(paste0(sim_output_dir, '/_plots/timeseries_burden_attribution_', burden_metric, '_', age_plotted, '_',district_subset,'.png'), gg, width=10*0.8, height=4.5*0.8, units='in')
+ }
+
+ if(burden_colname == 'PfPR_U5'){
+ returned_gg = gg
+ }
+ }
+ return(returned_gg)
+}
+
+
+###################################################################################################################
+# create plot showing intervention coverage from csv inputs (CM, SMC, IPTp) and/or simulation output (ITN use)
+###################################################################################################################
+
+# helper function to get weighted average coverage based on population size in included admins
+get_weighted_coverage = function(coverage_df, admin_info){
+ coverage_df = merge(coverage_df, admin_info[,c('admin_name','pop_size')], by='admin_name', all.x=TRUE)
+ coverage_df$cov_x_pop = coverage_df$coverage * coverage_df$pop_size
+ wmean_coverage = coverage_df %>% group_by(simday, duration) %>%
+ summarise(sum_cov_x_pop = sum(cov_x_pop))
+ total_pop = sum(admin_info$pop_size)
+ wmean_coverage$coverage = wmean_coverage$sum_cov_x_pop/total_pop
+ return(wmean_coverage)
+}
+
+# create main plot of all intervention inputs
+plot_input_intervention_coverages = function(base_sim_input_dir, sim_output_dir, pop_filepath, intervention_coordinator_filepath, cur_admins, sim_start_year, min_year, max_year, indoor_protection_fraction, all_inter_exp_name='NGA_toPresent_allInter'){
+ admin_info = read.csv(pop_filepath)
+ if(cur_admins[1] == 'all'){
+ cur_admins = unique(admin_info$admin_name)
+ admin_info_cur = admin_info
+ } else{
+ admin_info_cur = admin_info[admin_info$admin_name %in% cur_admins,]
+ }
+ intervention_coordinator = read.csv(intervention_coordinator_filepath)
+
+ # CM
+ cm_input_df = read.csv(paste0(base_sim_input_dir, '/', intervention_coordinator$CM_filename[1],'.csv'))
+ cm_input_df = cm_input_df[cm_input_df$admin_name %in% cur_admins,]
+ cm_input_df$coverage = cm_input_df$U5_coverage
+ mean_cm_coverage = get_weighted_coverage(coverage_df=cm_input_df, admin_info=admin_info_cur)
+ mean_cm_coverage$year = mean_cm_coverage$simday/365 + sim_start_year
+ mean_cm_coverage = mean_cm_coverage[(mean_cm_coverage$year>=min_year) & (mean_cm_coverage$year<=max_year),]
+ sim_year_end_to_present = mean_cm_coverage[mean_cm_coverage$duration==-1,]
+ sim_year_end_to_present$duration = round(max_year - sim_year_end_to_present$year)
+
+ # SMC
+ smc_input_df = read.csv(paste0(base_sim_input_dir, '/', intervention_coordinator$SMC_filename[1],'.csv'))
+ smc_input_df = smc_input_df[smc_input_df$admin_name %in% cur_admins,]
+ if(nrow(smc_input_df)>0){
+ smc_input_df$coverage = smc_input_df$coverage_high_access_U5 * smc_input_df$high_access_U5 + smc_input_df$coverage_low_access_U5 * (1-smc_input_df$high_access_U5)
+ # plot average across all four rounds
+ smc_input_df = smc_input_df %>% group_by(admin_name, year) %>%
+ summarise(coverage = mean(coverage),
+ simday = mean(simday))
+ smc_input_df$duration = 0
+ # set to same simday (otherwise will have daily coverage instead of yearly when distributions occur on different days)
+ smc_input_df$simday = (smc_input_df$year-sim_start_year)*365
+ mean_smc_coverage = get_weighted_coverage(coverage_df=smc_input_df, admin_info=admin_info_cur)
+ mean_smc_coverage$year = mean_smc_coverage$simday/365 + sim_start_year
+ mean_smc_coverage = mean_smc_coverage[(mean_smc_coverage$year>=min_year) & (mean_smc_coverage$year<=max_year),]
+ } else mean_smc_coverage = data.frame(year=c(min_year, max_year), coverage=c(0,0))
+
+ # LLIN use
+ cur_net_agg = get_intervention_use_timeseries_exp(exp_filepath = paste0(sim_output_dir, '/', all_inter_exp_name),
+ exp_name = all_inter_exp_name,
+ cur_admins=cur_admins, pop_sizes=admin_info, min_year=min_year, max_year=max_year, indoor_protection_fraction=indoor_protection_fraction, plot_by_month=FALSE)
+
+
+ # create plot
+ # LLIN and CM
+ g_all_inter = ggplot() +
+ geom_line(data=cur_net_agg, aes(x=year, y=coverage, color="ITN use"), size=1) +
+ geom_line(data=mean_cm_coverage, aes(x=year+0.5, y=coverage, color="Effective treatment with ACT"), size=1) +
+ # geom_segment(data=mean_cm_coverage, aes(x=year, xend=year+duration/365, y=coverage), color=rgb(0.1,0.9,0.4), size=1)+
+ geom_segment(data=sim_year_end_to_present, aes(x=year+0.5, xend=year+duration, y=coverage, color="Effective treatment with ACT"), size=1)+
+ scale_color_manual("",
+ breaks=c('Effective treatment with ACT', 'ITN use','SMC (U5)'),
+ values=c('#FFC145', '#A5351F', '#0090A4'))+
+ xlab('year') +
+ ylab(paste0('intervention coverage')) +
+ coord_cartesian(ylim=c(0,0.9))+
+ theme_bw()
+ # SMC
+ if(nrow(mean_smc_coverage)>0){
+ g_all_inter = g_all_inter +
+ geom_line(data=mean_smc_coverage, aes(x=year, y=coverage, color="SMC (U5)"), size=1)
+ }
+ return(g_all_inter)
+}
+
+
+################################################
+# add DHS/MIS U5 prevalence estimate to plot
+################################################
+add_state_prev_points_from_dhs = function(gg, hbhi_dir, dhs_years, state_name){
+ for(dd in 1:length(dhs_years)){
+ dhs_prev = read.csv(paste0(hbhi_dir, '/estimates_from_DHS/state_prev_results_',dhs_years[dd], '.csv'))
+ dhs_prev$state = gsub('-','',gsub(' ', '', toupper(dhs_prev$region)))
+
+ if(toupper(state_name) %in% dhs_prev$state){
+ gg = gg +
+ geom_point(data = dhs_prev[dhs_prev$state==toupper(state_name),], x=dhs_years[dd], aes(y=mic_rate), color='black', size=3)
+ } else{
+ warning(paste0('State name (',state_name,') did not match any name in ',dhs_years[dd],' DHS prevalence file.'))
+ }
+ }
+ return(gg)
+}
diff --git a/r_utilities/plots_results_analyses/plot_sim_input_functions.R b/r_utilities/plots_results_analyses/plot_sim_input_functions.R
new file mode 100644
index 0000000..4f82408
--- /dev/null
+++ b/r_utilities/plots_results_analyses/plot_sim_input_functions.R
@@ -0,0 +1,747 @@
+# plot_sim_input_functions.R
+
+# library(rgdal)
+library(raster)
+library(ggplot2)
+library(gridExtra)
+library(grid)
+library(RColorBrewer)
+library(ggpubr)
+library(cowplot)
+library(tidyverse)
+library(sf)
+library(reshape2)
+library(data.table)
+library(dplyr)
+library(geofacet)
+library(ggpattern)
+
+
+separate_plot_text_size=12
+text_size = 15
+save_plots = TRUE
+
+
+
+
+#####################################################################################################
+# ================================================================================================= #
+# create state-grid-arranged plot panel with intervention timeseries for included scenarios
+# ================================================================================================= #
+#####################################################################################################
+
+##############
+# CM
+##############
+plot_state_grid_cm = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, act_adherence_effective_multiplier=1, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether CM output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_cm_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$CM_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_cm_timeseries_by_state(cm_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_cm_timeseries_by_state(cm_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+ # adjust for ACT adherence to get coverage of individuals taking ACTs (not reduced effective coverage due to not completing the regimen)
+ if(act_adherence_effective_multiplier<1){
+ timeseries_df$mean_coverage = timeseries_df$mean_coverage / act_adherence_effective_multiplier
+ timeseries_df$min_coverage = timeseries_df$min_coverage / act_adherence_effective_multiplier
+ timeseries_df$max_coverage = timeseries_df$max_coverage / act_adherence_effective_multiplier
+ }
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.8) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_CM_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+##############
+# ITN ANC
+##############
+plot_state_grid_itn_anc = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, act_adherence_effective_multiplier=1, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether CM output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_itn_anc_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input ITN ANC coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$ANC_ITN_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_itn_anc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_itn_anc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.5) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('ANC ITN coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('ANC ITN coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_ITN_ANC_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+
+##############
+# SMC
+##############
+
+plot_state_grid_smc = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether SMC output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_smc_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$SMC_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_smc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_smc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.8) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('SMC coverage (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ # geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ # scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('SMC coverage (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_SMC_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+##############
+# ITN mass campaign
+##############
+
+plot_state_grid_itn = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether SMC output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_itn_mass_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$ITN_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_itn_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_itn_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.8) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('ITN initial use rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ # geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ # scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('ITN initial use rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_ITN_mass_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+
+
+
+##############
+# Vaccines
+##############
+
+plot_state_grid_vacc = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether SMC output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_vacc_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$vacc_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_vacc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_vacc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.8) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('EPI malaria vaccine coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ # geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ # scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('EPI malaria vaccine coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_vacc_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+
+
+##############
+# PMC
+##############
+
+plot_state_grid_pmc = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ separate_admin_lines_flag = FALSE, overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ time_string = 'annual'
+ if(separate_admin_lines_flag){
+ separate_admin_string = '_separated_admins'
+ } else{
+ separate_admin_string = ''
+ }
+
+ # check whether SMC output already exists for this comparison
+ timeseries_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_pmc_state_',time_string,'Timeseries', separate_admin_string, '.csv')
+ if(file.exists(timeseries_filepath)){
+ timeseries_df = read.csv(timeseries_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ timeseries_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ input_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$PMC_filename[cur_int_row], '.csv')
+
+ if(separate_admin_lines_flag){
+ cur_timeseries_agg = get_pmc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=FALSE)
+ } else{
+ cur_timeseries_agg = get_pmc_timeseries_by_state(input_filepath=input_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, get_state_level=TRUE)
+ }
+
+ if(nrow(timeseries_df)==0){
+ timeseries_df = cur_timeseries_agg
+ } else{
+ timeseries_df = rbind(timeseries_df, cur_timeseries_agg)
+ }
+ }
+
+ if(any(grepl('to-present', timeseries_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ # join past and future simulation trajectories
+ to_present_df = timeseries_df[timeseries_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ timeseries_df = rbind(timeseries_df, final_to_present_row)
+ }
+ }
+ write.csv(timeseries_df, timeseries_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ timeseries_df$scenario = factor(timeseries_df$scenario, levels=rev(scenario_names))
+ timeseries_df$code = timeseries_df$State
+
+
+ if(separate_admin_lines_flag){
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_line(aes(group_by=admin_name), size=0.8) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('PMC coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+
+ } else{
+ gg = ggplot(timeseries_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ # geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ # scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('PMC coverage')) +
+ coord_cartesian(xlim=c(min_year, max_year), ylim=c(0,1))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_PMC_by_state', separate_admin_string, '.png'), gg, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+
+
+
+
+#####################################################################
+# plot map of admin subsets
+#####################################################################
+plot_included_admin_map = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins, admin_shapefile_filepath, shapefile_admin_colname){
+ admin_pop = read.csv(pop_filepath)
+ admin_shapefile = st_read(admin_shapefile_filepath)
+ admin_shapefile$NOMDEP = standardize_admin_names_in_vector(target_names=admin_pop$admin_name, origin_names=admin_shapefile[[shapefile_admin_colname]])
+
+ admin_in_map = data.frame(admin_name = admin_pop$admin_name, admin_included='no')
+ admin_in_map$admin_included[admin_in_map$admin_name %in% cur_admins] = 'yes'
+ included_colors = c('#006692', 'grey96')
+ names(included_colors) = c('yes', 'no')
+
+ admin_cur = admin_shapefile %>%
+ dplyr::left_join(admin_in_map, by=c('NOMDEP' = 'admin_name'))
+
+ gg_map = ggplot(admin_cur) +
+ geom_sf(aes(fill=admin_included), size=0.5, color='black') +
+ scale_fill_manual(values=included_colors, drop=FALSE, na.value='grey96') +
+ theme_void() +
+ theme(legend.position = 'none')
+ ggsave(paste0(sim_future_output_dir, '/_plots/map_admins_included_', district_subset, '.png'), gg_map, dpi=600, width=4.8, height=4.8, units='in')
+}
+
+
diff --git a/r_utilities/plots_results_analyses/plot_sim_output_functions.R b/r_utilities/plots_results_analyses/plot_sim_output_functions.R
index 906ad7d..ccda0fa 100644
--- a/r_utilities/plots_results_analyses/plot_sim_output_functions.R
+++ b/r_utilities/plots_results_analyses/plot_sim_output_functions.R
@@ -1,6 +1,6 @@
# plot_sim_output_functions.R
-library(rgdal)
+# library(rgdal)
library(raster)
library(ggplot2)
library(gridExtra)
@@ -13,18 +13,19 @@ library(sf)
library(reshape2)
library(data.table)
library(dplyr)
+library(geofacet)
+library(ggpattern)
separate_plot_text_size=12
text_size = 15
-save_plots = FALSE
+save_plots = TRUE
####################################################################################
-# barplots for burden relative to BAU
+# barplots for burden relative to BAU: percent reduction
####################################################################################
-
plot_relative_burden_barplots = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
barplot_start_year, barplot_end_year,
pyr, chw_cov,
@@ -68,15 +69,15 @@ plot_relative_burden_barplots = function(sim_future_output_dir, pop_filepath, di
}
# get factors in the correct order (rather than alphabetical)
- relative_burden_all_df$scenario = factor(relative_burden_all_df$scenario, levels=unique(scenario_names[comparison_start_index:length(scenario_names)]))
+ relative_burden_all_df$scenario = factor(relative_burden_all_df$scenario, levels=scenario_names[comparison_start_index:length(scenario_names)])
# get minimum and maximum reductions - these will be used if they are smaller / greater than the current min/max
- standard_min_y = 0
- standard_max_y = 0.1
+ standard_min_x = 0
+ standard_max_x = 0.1
cur_min = min(relative_burden_all_df[,2:(1+length(burden_colnames))])
cur_max = max(relative_burden_all_df[,2:(1+length(burden_colnames))])
- if(cur_min < standard_min_y) standard_min_y = cur_min
- if(cur_max > standard_max_y) standard_max_y = cur_max
+ if(cur_min < standard_min_x) standard_min_x = cur_min
+ if(cur_max > standard_max_x) standard_max_x = cur_max
gg_list = list()
for(bb in 1:length(burden_colnames)){
@@ -92,7 +93,7 @@ plot_relative_burden_barplots = function(sim_future_output_dir, pop_filepath, di
gg_list[[bb]] = ggplot(rel_burden_agg) +
geom_bar(aes(x=scenario, y=mean_rel, fill=scenario), stat='identity') +
- scale_y_continuous(labels=percent_format(), limits=c(standard_min_y, standard_max_y)) + # turn into percent reduction
+ scale_y_continuous(labels=percent_format(), limits=c(standard_min_x, standard_max_x)) + # turn into percent reduction
ylab('Percent reduction') +
geom_hline(yintercept=0, color='black') +
ggtitle(gsub('\\(births\\)', '', burden_metric_name)) +
@@ -137,6 +138,221 @@ plot_relative_burden_barplots = function(sim_future_output_dir, pop_filepath, di
+
+
+####################################################################################
+# barplots for burden relative to BAU at the state level, displayed in a state grid
+####################################################################################
+
+plot_relative_burden_barplots_by_state = function(sim_future_output_dir, pop_filepath,grid_layout_state_locations,
+ barplot_start_year, barplot_end_year,
+ pyr, chw_cov,
+ scenario_names, experiment_names, scenario_palette, LLIN2y_flag=FALSE, overwrite_files=FALSE, show_error_bar=TRUE, align_seeds=TRUE,
+ burden_metric_subset=c(), include_to_present=TRUE, file_suffix=''){
+
+ admin_pop = read.csv(pop_filepath)
+
+ # burden metrics
+ burden_metrics = c('PfPR', 'PfPR', 'incidence', 'incidence', 'directMortality', 'directMortality', 'allMortality', 'allMortality', 'mLBW_deaths', 'MiP_stillbirths')
+ burden_colnames = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'direct_death_rate_mean_U5', 'direct_death_rate_mean_all', 'all_death_rate_mean_U5', 'all_death_rate_mean_all', 'annual_num_mLBW', 'annual_num_mStill')
+ burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'direct mortality (U5)', 'direct mortality (all ages)', 'mortality (U5)', 'mortality (all ages)', 'mLBW mortality (births)', 'stillbirths (births)')
+ # allow subsetting of which burden metrics plotted (based on burden_metric_subset argument)
+ if((length(burden_metric_subset)>=1)){
+ burden_metrics_subset_indices = which(burden_metrics %in% burden_metric_subset)
+ burden_colnames = burden_colnames[burden_metrics_subset_indices]
+ burden_metric_names = burden_metric_names[burden_metrics_subset_indices]
+ }
+
+ # first comparison name is to-present (skip it), second is BAU (use as reference), comparison scenarios start at the third index
+ if(include_to_present){
+ reference_experiment_name = experiment_names[2]
+ comparison_start_index = 3
+ } else{
+ reference_experiment_name = experiment_names[1]
+ comparison_start_index = 2
+ }
+ # iterate through comparison scenarios, calculating the burden reduction of all metrics relative to BAU (seedwise comparisons, so one output for each run). Combine all scenario reductions into a dataframe (each scenario set in separate rows)
+ relative_burden_all_df = data.frame()
+ for(ss in comparison_start_index:length(scenario_names)){
+ comparison_experiment_name = experiment_names[ss]
+ comparison_scenario_name = scenario_names[ss]
+ relative_burden_df = get_relative_burden_by_state(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name,
+ start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ # only save relevant columns for plotting
+ relative_burden_df = relative_burden_df[,which(colnames(relative_burden_df) %in% c('scenario', 'Run_Number', 'State', burden_colnames))]
+ if(nrow(relative_burden_all_df) == 0){
+ relative_burden_all_df = relative_burden_df
+ }else{
+ relative_burden_all_df = rbind(relative_burden_all_df, relative_burden_df)
+ }
+ }
+
+ # get factors in the correct order (rather than alphabetical)
+ relative_burden_all_df$scenario = factor(relative_burden_all_df$scenario, levels=scenario_names[comparison_start_index:length(scenario_names)])
+
+ # # get minimum and maximum reductions - these will be used if they are smaller / greater than the current min/max
+ # standard_min_y = 0
+ # standard_max_y = 0.1
+ # cur_min = min(relative_burden_all_df[,2:(1+length(burden_colnames))])
+ # cur_max = max(relative_burden_all_df[,2:(1+length(burden_colnames))])
+ # if(cur_min < standard_min_y) standard_min_y = cur_min
+ # if(cur_max > standard_max_y) standard_max_y = cur_max
+
+
+ for(bb in 1:length(burden_colnames)){
+ current_burden_name = burden_colnames[bb]
+ burden_metric_name = burden_metric_names[bb]
+ select_col_names = c(current_burden_name, 'scenario', 'State')
+ # get mean, min, and max among all runs for this burden metric
+ rel_burden_agg = as.data.frame(relative_burden_all_df) %>% dplyr::select(match(select_col_names, names(.))) %>%
+ dplyr::group_by(scenario, State) %>%
+ dplyr::summarise(mean_rel = mean(get(current_burden_name)),
+ max_rel = max(get(current_burden_name)),
+ min_rel = min(get(current_burden_name)))
+
+ rel_burden_agg$code = rel_burden_agg$State
+ gg = ggplot(rel_burden_agg) +
+ geom_bar(aes(x=scenario, y=mean_rel, fill=scenario), stat='identity') +
+ scale_y_continuous(labels=percent_format(), n.breaks=4) + #,limits=c(standard_min_y, standard_max_y)) + # turn into percent reduction
+ ylab('Percent reduction in burden \n ((Baseline - Plan) / Baseline) * 100') +
+ geom_hline(yintercept=0, color='black') +
+ ggtitle(gsub('\\(births\\)', '', burden_metric_name)) +
+ scale_fill_manual(values = scenario_palette) +
+ theme_classic()+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size),
+ axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),axis.line.x=element_blank(),
+ plot.margin=unit(c(0,1,1,0), 'cm')) +
+ facet_geo(~code, grid = grid_layout_state_locations, label="name") #, scales='free')
+
+ if(show_error_bar){
+ gg = gg +
+ geom_errorbar(aes(x=scenario, ymin=min_rel, ymax=max_rel), width=0.4, colour="black", alpha=0.9, size=1)
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/','barplot_percent_reduction_', burden_metric_name,'_stateGrid',file_suffix,'.png'), gg, dpi=600, width=18*.6, height=12*.6, units='in') # , width=18, height=12, units='in'
+ }
+}
+
+
+
+
+####################################################################################
+# barplots for burden reduction relative to BAU: difference
+####################################################################################
+
+plot_difference_burden_barplots = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
+ barplot_start_year, barplot_end_year,
+ pyr, chw_cov,
+ scenario_names, experiment_names, scenario_palette, LLIN2y_flag=FALSE, overwrite_files=FALSE, separate_plots_flag=FALSE, show_error_bar=TRUE, align_seeds=TRUE,
+ include_to_present=TRUE, burden_metric_subset=c()){
+ admin_pop = read.csv(pop_filepath)
+
+ # burden metrics
+ burden_metrics = c('PfPR', 'PfPR', 'incidence', 'incidence', 'directMortality', 'directMortality', 'allMortality', 'allMortality', 'mLBW_deaths', 'MiP_stillbirths')
+ burden_colnames = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'direct_death_rate_mean_U5', 'direct_death_rate_mean_all', 'all_death_rate_mean_U5', 'all_death_rate_mean_all', 'annual_num_mLBW', 'annual_num_mStill')
+ burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'direct mortality (U5)', 'direct mortality (all ages)', 'mortality (U5)', 'mortality (all ages)', 'mLBW mortality (births)', 'stillbirths (births)')
+ # allow subsetting of which burden metrics plotted (based on burden_metric_subset argument)
+ if((length(burden_metric_subset)>=1)){
+ burden_metrics_subset_indices = which(burden_metrics %in% burden_metric_subset)
+ burden_colnames = burden_colnames[burden_metrics_subset_indices]
+ burden_metric_names = burden_metric_names[burden_metrics_subset_indices]
+ }
+
+ # first comparison name is to-present (skip it), second is BAU (use as reference), comparison scenarios start at the third index
+ if(include_to_present){
+ reference_experiment_name = experiment_names[2]
+ comparison_start_index = 3
+ } else{
+ reference_experiment_name = experiment_names[1]
+ comparison_start_index = 2
+ }
+ # iterate through comparison scenarios, calculating the burden reduction of all metrics relative to BAU (seedwise comparisons, so one output for each run). Combine all scenario reductions into a dataframe (each scenario set in separate rows)
+ difference_burden_all_df = data.frame()
+ for(ss in comparison_start_index:length(scenario_names)){
+ comparison_experiment_name = experiment_names[ss]
+ comparison_scenario_name = scenario_names[ss]
+ difference_burden_df = get_difference_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name,
+ start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ # only save relevant columns for plotting
+ difference_burden_df = difference_burden_df[,which(colnames(difference_burden_df) %in% c('scenario', 'Run_Number', burden_colnames))]
+ if(nrow(difference_burden_all_df) == 0){
+ difference_burden_all_df = difference_burden_df
+ }else{
+ difference_burden_all_df = rbind(difference_burden_all_df, difference_burden_df)
+ }
+ }
+
+ # get factors in the correct order (rather than alphabetical)
+ difference_burden_all_df$scenario = factor(difference_burden_all_df$scenario, levels=scenario_names[comparison_start_index:length(scenario_names)])
+
+ # get minimum and maximum reductions - these will be used if they are smaller / greater than the current min/max
+ standard_min_x = 0
+ standard_max_x = 0.1
+ cur_min = min(difference_burden_all_df[,2:(1+length(burden_colnames))])
+ cur_max = max(difference_burden_all_df[,2:(1+length(burden_colnames))])
+ if(cur_min < standard_min_x) standard_min_x = cur_min
+ if(cur_max > standard_max_x) standard_max_x = cur_max
+
+ gg_list = list()
+ for(bb in 1:length(burden_colnames)){
+ current_burden_name = burden_colnames[bb]
+ burden_metric_name = burden_metric_names[bb]
+ select_col_names = c(current_burden_name, 'scenario')
+ # get mean, min, and max among all runs for this burden metric
+ rel_burden_agg = as.data.frame(difference_burden_all_df) %>% dplyr::select(match(select_col_names, names(.))) %>%
+ dplyr::group_by(scenario) %>%
+ dplyr::summarise(mean_rel = mean(get(current_burden_name)),
+ max_rel = max(get(current_burden_name)),
+ min_rel = min(get(current_burden_name)))
+
+ gg_list[[bb]] = ggplot(rel_burden_agg) +
+ geom_bar(aes(x=scenario, y=mean_rel, fill=scenario), stat='identity') +
+ scale_y_continuous(labels=percent_format(), limits=c(standard_min_x, standard_max_x)) + # turn into percent reduction
+ ylab('Burden averted') +
+ geom_hline(yintercept=0, color='black') +
+ ggtitle(gsub('\\(births\\)', '', burden_metric_name)) +
+ scale_fill_manual(values = scenario_palette) +
+ theme_classic()+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size),
+ axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),axis.line.x=element_blank(),
+ plot.margin=unit(c(0,1,1,0), 'cm'))
+ if(show_error_bar){
+ gg_list[[bb]] = gg_list[[bb]] +
+ geom_errorbar(aes(x=scenario, ymin=min_rel, ymax=max_rel), width=0.4, colour="black", alpha=0.9, size=1)
+ }
+ if(separate_plots_flag){
+ separate_plot = gg_list[[bb]] +
+ ylab('Burden averted \n ((Baseline - Plan)') +
+ theme(legend.position='none', plot.title = element_blank(), text=element_text(size =separate_plot_text_size))
+ ggsave(paste0(sim_future_output_dir, '/_plots/','barplot_burden_averted_', burden_metric_name,'_',district_subset,'.png'), separate_plot, dpi=600, width=4, height=3, units='in')
+ }
+ }
+ # for each burden type,
+ # get mean, min, and max among all runs for each burden metric, each saved as a separate column
+ # create barplot for each burden type (using columns of dataframe, separate bar for each scenario)
+
+ gg_list = append(list(ggpubr::as_ggplot(ggpubr::get_legend(gg_list[[1]]))), gg_list)
+ # remove legend from main plots
+ for(bb in 2:(length(burden_colnames)+1)){
+ gg_list[[bb]] = gg_list[[bb]] + theme(legend.position = "none") + theme(text = element_text(size = text_size))
+ }
+
+ if(save_plots){
+ gg_saved = grid.arrange(grobs = gg_list[-1], layout_matrix = matrix(c(1:(length(burden_colnames))), nrow=2, byrow=FALSE))
+ ggsave(paste0(sim_future_output_dir, '/_plots/barplot_burden_averted_', pyr, '_', chw_cov, 'CHW_',district_subset,'.png'), gg_saved, dpi=600, width=14, height=7, units='in')
+ }
+
+ # ----- combine all burden plots ----- #
+ # gg = grid.arrange(grobs = gg_list, layout_matrix = matrix(c(1,1,2:(length(burden_colnames)+1)), ncol=2, byrow=TRUE))
+ gg = grid.arrange(grobs = gg_list, layout_matrix = rbind(matrix(rep(1, length(burden_colnames)/2), nrow=1), matrix(2:(length(burden_colnames)+1), nrow=2, byrow=FALSE)))
+
+ return(gg)
+}
+
+
+
+
+
+
####################################################################################################################################
# barplot of the impact a specific intervention has in relevant admins
# (percent reduction when intervention is included versus matched simulation without the intervention)
@@ -146,9 +362,8 @@ plot_relative_burden_barplots = function(sim_future_output_dir, pop_filepath, di
plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
barplot_start_year, barplot_end_year,
pyr, chw_cov,
- experiment_names_without, experiment_names_with, scenario_palette, intervention_name='PMC', age_group = 'U1', LLIN2y_flag=FALSE, overwrite_files=FALSE, show_error_bar=TRUE, align_seeds=TRUE,
- burden_metric_subset=c(), default_ylim_min=0, default_ylim_max=0.03,
- seed_subset=NA, seed_subset_name=NA){
+ experiment_names_without, experiment_names_with, scenario_palette, scenario_barfill=NA, intervention_name='PMC', age_group = 'U1', LLIN2y_flag=FALSE, overwrite_files=FALSE, show_error_bar=TRUE, align_seeds=TRUE,
+ burden_metric_subset=c(), default_ylim_max=0.03){
admin_pop = read.csv(pop_filepath)
comparison_scenario_name = intervention_name
@@ -162,44 +377,23 @@ plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_
comparison_experiment_name = experiment_names_with[ii]
# set which burden metrics are relevant and get relative burden between simulations
- burden_metrics_base = c('PfPR', 'incidence', 'directMortality', 'allMortality')
- relative_burden_df_u1 = data.frame()
- relative_burden_df_u5 = data.frame()
- relative_burden_df_all = data.frame()
- burden_metrics = c()
- burden_colnames = c()
- burden_metric_names = c()
- if('U1' %in% age_group){
- burden_metrics = c(burden_metrics, burden_metrics_base)
- burden_colnames = c(burden_colnames, 'average_PfPR_U1', 'incidence_U1', 'direct_death_rate_mean_U1', 'all_death_rate_mean_U1')
- burden_metric_names = c(burden_metric_names, 'PfPR (U1)', 'incidence (U1)', 'direct mortality (U1)', 'mortality (U1)')
- relative_burden_df_u1 = get_relative_U1_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
- }
- if ('U5' %in% age_group){
- burden_metrics = c(burden_metrics, burden_metrics_base)
- burden_colnames = c(burden_colnames, 'average_PfPR_U5', 'incidence_U5', 'direct_death_rate_mean_U5', 'all_death_rate_mean_U5')
- burden_metric_names = c(burden_metric_names, 'PfPR (U5)', 'incidence (U5)', 'direct mortality (U5)', 'mortality (U5)')
- relative_burden_df_u5 = get_relative_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
- }
- if ('all' %in% age_group){
- burden_metrics = c(burden_metrics, burden_metrics_base, 'mLBW_deaths', 'MiP_stillbirths')
- burden_colnames = c(burden_colnames, 'average_PfPR_all', 'incidence_all', 'direct_death_rate_mean_all', 'all_death_rate_mean_all', 'annual_num_mLBW', 'annual_num_mStill')
- burden_metric_names = c(burden_metric_names, 'PfPR (all ages)', 'incidence (all ages)', 'direct mortality (all ages)', 'mortality (all ages)', 'mLBW mortality (births)', 'stillbirths (births)')
- relative_burden_df_all = get_relative_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
- }
-
- #merge all data frames together
- df_list = list(relative_burden_df_u1, relative_burden_df_u5, relative_burden_df_all)
- df_list = df_list[sapply(df_list, function(x) dim(x)[1]) > 0]
- if(length(df_list)>1){
- relative_burden_df = Reduce(function(x, y) merge(x, y, all=TRUE), df_list)
- } else{
- relative_burden_df= df_list[[1]]
+ burden_metrics = c('PfPR', 'incidence', 'directMortality', 'allMortality')
+ if(age_group=='U1'){
+ burden_colnames = c('average_PfPR_U1', 'incidence_U1', 'direct_death_rate_mean_U1', 'all_death_rate_mean_U1')
+ burden_metric_names = c('PfPR (U1)', 'incidence (U1)', 'direct mortality (U1)', 'mortality (U1)')
+ relative_burden_df = get_relative_U1_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ } else if (age_group=='U5'){
+ burden_colnames = c('average_PfPR_U5', 'incidence_U5', 'direct_death_rate_mean_U5', 'all_death_rate_mean_U5')
+ burden_metric_names = c('PfPR (U5)', 'incidence (U5)', 'direct mortality (U5)', 'mortality (U5)')
+ relative_burden_df = get_relative_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ }else{
+ burden_metrics = c(burden_metrics, 'mLBW_deaths', 'MiP_stillbirths')
+ burden_colnames = c('average_PfPR_all', 'incidence_all', 'direct_death_rate_mean_all', 'all_death_rate_mean_all', 'annual_num_mLBW', 'annual_num_mStill')
+ burden_metric_names = c('PfPR (all ages)', 'incidence (all ages)', 'direct mortality (all ages)', 'mortality (all ages)', 'mLBW mortality (births)', 'stillbirths (births)')
+ relative_burden_df = get_relative_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
}
-
+
+
# allow subsetting of which burden metrics plotted (based on burden_metric_subset argument)
if((length(burden_metric_subset)>=1)){
burden_metrics_subset_indices = which(burden_metrics %in% burden_metric_subset)
@@ -219,9 +413,7 @@ plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_
dplyr::group_by(scenario) %>%
dplyr::summarise(mean_rel = mean(get(current_burden_name)),
max_rel = max(get(current_burden_name)),
- min_rel = min(get(current_burden_name)),
- min_quant = quantile(get(current_burden_name), probs=0.05),
- max_quant = quantile(get(current_burden_name), probs=0.95))
+ min_rel = min(get(current_burden_name)))
rel_burden_agg_bb$burden_metric = burden_metric_name
rel_burden_agg_bb$scenario_name = experiment_names_with[ii]
if(nrow(rel_burden_agg)<1){
@@ -235,28 +427,50 @@ plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_
rel_burden_agg$burden_metric = gsub('\\(births\\)', '', rel_burden_agg$burden_metric)
rel_burden_agg$burden_metric = factor(rel_burden_agg$burden_metric, levels=gsub('\\(births\\)', '', burden_metric_names))
- rel_burden_agg$scenario_name = factor(rel_burden_agg$scenario_name, levels=unique(experiment_names_with))
+ rel_burden_agg$scenario_name = factor(rel_burden_agg$scenario_name, levels=experiment_names_with)
# get minimum and maximum reductions - these will be used if they are smaller / greater than the current min/max
- standard_min_y = default_ylim_min
- standard_max_y = default_ylim_max
- if(show_error_bar){
- cur_min = min(rel_burden_agg[,2:4])
- cur_max = max(rel_burden_agg[,2:4])
- } else{
- cur_min = min(rel_burden_agg[,2])
- cur_max = max(rel_burden_agg[,2])
+ standard_min_x = 0
+ standard_max_x = default_ylim_max
+ cur_min = min(rel_burden_agg[,2:4])
+ cur_max = max(rel_burden_agg[,2:4])
+ if(cur_min < standard_min_x) standard_min_x = cur_min
+ if(cur_max > standard_max_x) standard_max_x = cur_max
+ if(any(is.na(scenario_barfill))){
+ scenario_barfill = rep('none', length(unique(rel_burden_agg$scenario_name)))
+ names(scenario_barfill) = unique(rel_burden_agg$scenario_name)
}
- if(cur_min < standard_min_y) standard_min_y = cur_min
- if(cur_max > standard_max_y) standard_max_y = cur_max
+ # original without shading:
+ # gg = ggplot(rel_burden_agg) +
+ # geom_bar(aes(x=burden_metric, y=mean_rel, fill=scenario_name), stat='identity', position="dodge") +
+ # scale_y_continuous(labels=percent_format(), limits=c(standard_min_x, standard_max_x)) + # turn into percent reduction
+ # ylab(paste0('Percent reduction in burden \n ((without ', intervention_name, ' - with ', intervention_name, ') / without ', intervention_name, ') * 100')) +
+ # geom_hline(yintercept=0, color='black') +
+ # ggtitle(paste0('Comparison of burden in proposed ', intervention_name, ' districts')) +
+ # scale_fill_manual(values = scenario_palette) +
+ # theme_classic()+
+ # theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size),
+ # axis.title.x=element_blank(), axis.ticks.x=element_blank(), axis.line.x=element_blank(),
+ # plot.margin=unit(c(0,1,1,0), 'cm'))
gg = ggplot(rel_burden_agg) +
- geom_bar(aes(x=burden_metric, y=mean_rel, fill=scenario_name), stat='identity', position="dodge") +
- scale_y_continuous(labels=percent_format(), limits=c(standard_min_y, standard_max_y)) + # turn into percent reduction
+ # geom_bar(aes(x=burden_metric, y=mean_rel, fill=scenario_name, pattern=scenario_name), stat='identity', position="dodge") +
+ scale_y_continuous(labels=percent_format(), limits=c(standard_min_x, standard_max_x)) + # turn into percent reduction
ylab(paste0('Percent reduction in burden \n ((without ', intervention_name, ' - with ', intervention_name, ') / without ', intervention_name, ') * 100')) +
geom_hline(yintercept=0, color='black') +
ggtitle(paste0('Comparison of burden in proposed ', intervention_name, ' districts')) +
scale_fill_manual(values = scenario_palette) +
+ geom_bar_pattern(aes(x=burden_metric, y=mean_rel, fill=scenario_name, pattern=scenario_name), stat='identity', position="dodge", #position = position_dodge(preserve = "single"),
+ # color = "white",
+ pattern_fill = "white",
+ pattern_linetype=0,
+ pattern_angle = 45,
+ pattern_density = 0.35,
+ pattern_spacing = 0.06,# 0.025,
+ pattern_key_scale_factor = 0.6) +
+ scale_pattern_manual(values = scenario_barfill) +
+ guides(pattern = guide_legend(override.aes = list(fill = "white")),
+ fill = guide_legend(override.aes = list(pattern = "none"))) +
theme_classic()+
theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size),
axis.title.x=element_blank(), axis.ticks.x=element_blank(), axis.line.x=element_blank(),
@@ -264,7 +478,7 @@ plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_
if(show_error_bar){
gg = gg +
- geom_errorbar(aes(x=burden_metric, ymin=min_quant, ymax=max_quant, group=scenario_name), position='dodge', colour="black", alpha=0.9, size=1) # width=0.4,
+ geom_errorbar(aes(x=burden_metric, ymin=min_rel, ymax=max_rel, group=scenario_name), position='dodge', colour="black", alpha=0.9, size=1) # width=0.4,
}
if(save_plots){
@@ -279,6 +493,90 @@ plot_barplot_impact_specific_intervention = function(sim_future_output_dir, pop_
+####################################################################################################################################
+# barplot of the impact a specific intervention has in relevant admins
+# (burden reduction when intervention is included versus matched simulation without the intervention)
+####################################################################################################################################
+
+
+table_difference_impact_specific_intervention = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
+ barplot_start_year, barplot_end_year,
+ pyr, chw_cov,
+ experiment_names_without, experiment_names_with, intervention_name='PMC', age_group = 'U1', LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE,
+ burden_metric_subset=c()){
+ admin_pop = read.csv(pop_filepath)
+ comparison_scenario_name = intervention_name
+
+ # iterate through the matched pairs of experiments without / with the intervention
+ rel_burden_agg = data.frame()
+ if(length(experiment_names_without) == length(experiment_names_with)){
+ for(ii in 1:length(experiment_names_without)){
+ # first experiment is without interventions, second experiment is with intervention
+ reference_experiment_name = experiment_names_without[ii]
+ # calculating the burden reduction of all metrics relative to no intervention (seedwise comparisons, so one output for each run).
+ comparison_experiment_name = experiment_names_with[ii]
+
+ # set which burden metrics are relevant and get relative burden between simulations
+ burden_metrics = c('PfPR', 'incidence', 'directMortality', 'allMortality')
+ if(age_group=='U1'){
+ warning('Have not yet added support for burden reduction for U1... need to add relevant functions')
+ # burden_colnames = c('average_PfPR_U1', 'incidence_U1', 'direct_death_rate_mean_U1', 'all_death_rate_mean_U1')
+ # burden_metric_names = c('PfPR (U1)', 'incidence (U1)', 'direct mortality (U1)', 'mortality (U1)')
+ # relative_burden_df = get_relative_U1_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ } else if (age_group=='U5'){
+ burden_colnames = c('average_PfPR_U5', 'incidence_U5', 'direct_death_rate_mean_U5', 'all_death_rate_mean_U5')
+ burden_metric_names = c('PfPR (U5)', 'incidence (U5)', 'direct mortality (U5)', 'mortality (U5)')
+ relative_burden_df = get_difference_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ }else{
+ burden_metrics = c(burden_metrics, 'mLBW_deaths', 'MiP_stillbirths')
+ burden_colnames = c('average_PfPR_all', 'incidence_all', 'direct_death_rate_mean_all', 'all_death_rate_mean_all', 'annual_num_mLBW', 'annual_num_mStill')
+ burden_metric_names = c('PfPR (all ages)', 'incidence (all ages)', 'direct mortality (all ages)', 'mortality (all ages)', 'mLBW mortality (births)', 'stillbirths (births)')
+ relative_burden_df = get_difference_burden(sim_output_filepath=sim_future_output_dir, reference_experiment_name=reference_experiment_name, comparison_experiment_name=comparison_experiment_name, comparison_scenario_name=comparison_scenario_name, start_year=barplot_start_year, end_year=barplot_end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files, align_seeds=align_seeds)
+ }
+
+
+ # allow subsetting of which burden metrics plotted (based on burden_metric_subset argument)
+ if((length(burden_metric_subset)>=1)){
+ burden_metrics_subset_indices = which(burden_metrics %in% burden_metric_subset)
+ burden_colnames = burden_colnames[burden_metrics_subset_indices]
+ burden_metric_names = burden_metric_names[burden_metrics_subset_indices]
+ }
+
+ # only save relevant columns for plotting
+ relative_burden_df = relative_burden_df[,which(colnames(relative_burden_df) %in% c('scenario', 'Run_Number', burden_colnames))]
+
+ for(bb in 1:length(burden_colnames)){
+ current_burden_name = burden_colnames[bb]
+ burden_metric_name = burden_metric_names[bb]
+ select_col_names = c(current_burden_name, 'scenario')
+ # get mean, min, and max among all runs for this burden metric
+ rel_burden_agg_bb = as.data.frame(relative_burden_df) %>% dplyr::select(match(select_col_names, names(.))) %>%
+ dplyr::group_by(scenario) %>%
+ dplyr::summarise(mean_rel = mean(get(current_burden_name)),
+ max_rel = max(get(current_burden_name)),
+ min_rel = min(get(current_burden_name)))
+ rel_burden_agg_bb$burden_metric = burden_metric_name
+ rel_burden_agg_bb$scenario_name = experiment_names_with[ii]
+ if(nrow(rel_burden_agg)<1){
+ rel_burden_agg = rel_burden_agg_bb
+ } else{
+ rel_burden_agg = merge(rel_burden_agg, rel_burden_agg_bb, all=TRUE)
+ }
+ }
+ }
+ }
+
+ rel_burden_agg$burden_metric = gsub('\\(births\\)', '', rel_burden_agg$burden_metric)
+ rel_burden_agg$burden_metric = factor(rel_burden_agg$burden_metric, levels=gsub('\\(births\\)', '', burden_metric_names))
+ rel_burden_agg$scenario_name = factor(rel_burden_agg$scenario_name, levels=experiment_names_with)
+
+ return(rel_burden_agg)
+}
+
+
+
+
+
plot_barplot_impact_two_specific_interventions = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
@@ -348,12 +646,12 @@ plot_barplot_impact_two_specific_interventions = function(sim_future_output_dir,
rel_burden_agg$burden_metric = factor(rel_burden_agg$burden_metric, levels=burden_metric_names)
# get minimum and maximum reductions - these will be used if they are smaller / greater than the current min/max
- standard_min_y = 0
- standard_max_y = 0.2
+ standard_min_x = 0
+ standard_max_x = 0.2
cur_min = min(rel_burden_agg[,2:4])
cur_max = max(rel_burden_agg[,2:4])
- if(cur_min < standard_min_y) standard_min_y = cur_min
- if(cur_max > standard_max_y) standard_max_y = cur_max
+ if(cur_min < standard_min_x) standard_min_x = cur_min
+ if(cur_max > standard_max_x) standard_max_x = cur_max
# create list where each element is a barplot corresponding to one of the interventions in intervention_strings
@@ -361,7 +659,7 @@ plot_barplot_impact_two_specific_interventions = function(sim_future_output_dir,
for(jj in 1:length(intervention_strings)){
gg = ggplot(rel_burden_agg[rel_burden_agg$intervention_info == intervention_strings[jj],]) +
geom_bar(aes(x=burden_metric, y=mean_rel, fill=scenario_name), stat='identity', position="dodge") +
- scale_y_continuous(labels=percent_format(), limits=c(standard_min_y, standard_max_y)) + # turn into percent reduction
+ scale_y_continuous(labels=percent_format(), limits=c(standard_min_x, standard_max_x)) + # turn into percent reduction
ylab(paste0('Percent reduction in burden \n ((without ', intervention_strings[jj], ' - with ', intervention_name, ') / without ', intervention_strings[jj], ') * 100')) +
geom_hline(yintercept=0, color='black') +
ggtitle(paste0('Comparison of burden in proposed ', intervention_name, ' districts')) +
@@ -386,19 +684,22 @@ plot_barplot_impact_two_specific_interventions = function(sim_future_output_dir,
-
-
######################################################################
-# create plot panel with all burden metrics, no intervention info
+# create plot panel with all burden metrics, no intervention info (either showing burden or burden relative to burden in specified year)
######################################################################
plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
- plot_by_month, min_year, max_year, sim_end_years,
- pyr, chw_cov,
+ plot_by_month, min_year, max_year, sim_end_years, relative_year=NA,
+ pyr='', chw_cov='',
scenario_filepaths, scenario_names, experiment_names, scenario_palette, LLIN2y_flag=FALSE, overwrite_files=FALSE,
separate_plots_flag=FALSE, extend_past_timeseries_year=NA, scenario_linetypes=NA, plot_CI=TRUE, include_U1=FALSE,
burden_metric_subset=c()){
+ if (!is.na(relative_year)){ if(relative_year(reference_burden_cur*(1+similarity_threshold))) | any(all_ref_year_burdens<(reference_burden_cur*(1-similarity_threshold)))){
+ warning(paste0('in the reference year, some scenarios have different burdens for ',burden_metric_name))
+ }
+ }
+ # calculate all relative burden values as mean_burden / reference_burden_cur: this will be referred to as 'burden relative to burden in relative_year'
+ burden_df$mean_burden = burden_df$mean_burden / reference_burden_cur
+ burden_df$max_burden = NA
+ burden_df$min_burden = NA
+
+ ylab_add_component = paste0('\n relative to ', relative_year)
+ relative_string = paste0('_relativeTo', relative_year)
+ } else{
+ ylab_add_component = ''
+ relative_string = ''
+ }
+
+
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
# create scenario-comparison plots
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
@@ -537,7 +874,7 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
# subset to relevant scenarios currently being compared
burden_df = burden_df[burden_df$scenario %in% scenario_names,]
# get factors in the correct order (rather than alphabetical)
- burden_df$scenario = factor(burden_df$scenario, levels=unique(scenario_names))
+ burden_df$scenario = factor(burden_df$scenario, levels=rev(scenario_names))
if(is.na(scenario_linetypes[1])){
scenario_linetypes = rep(1, length(unique(burden_df$scenario)))
@@ -548,11 +885,11 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
if(plot_by_month){
gg_list[[bb]] = ggplot(burden_df, aes(x=as.Date(date), y=mean_burden, color=scenario)) +
geom_ribbon(aes(ymin=min_burden, ymax=max_burden, fill=scenario), alpha=0.1, color=NA)+
- scale_fill_manual(values = scenario_palette) +
+ scale_fill_manual(values = rev(scenario_palette)) +
geom_line(size=1) +
- scale_color_manual(values = scenario_palette) +
+ scale_color_manual(values = rev(scenario_palette)) +
xlab('date') +
- ylab(gsub('\\(births\\)', '', burden_metric_name)) +
+ ylab(paste0(gsub('\\(births\\)', '', burden_metric_name),ylab_add_component)) +
xlim(as.Date(paste0(min_year, '-01-01')), as.Date(paste0(max_year, '-01-01'))) +
coord_cartesian(ylim=c(0, NA)) +
theme_classic()+
@@ -560,11 +897,12 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
} else{
gg_list[[bb]] = ggplot(burden_df, aes(x=year, y=mean_burden, color=scenario, linetype=scenario)) +
geom_line(size=1) +
- scale_linetype_manual(values=scenario_linetypes) +
- scale_color_manual(values = scenario_palette) +
+ scale_linetype_manual(values=rev(scenario_linetypes)) +
+ scale_color_manual(values = rev(scenario_palette)) +
xlab('year') +
- ylab(gsub('\\(births\\)', '', burden_metric_name)) +
- xlim(min_year, max_year) +
+ ylab(paste0(gsub('\\(births\\)', '', burden_metric_name), ylab_add_component)) +
+ xlim(min_year, max_year) +
+ scale_x_continuous(breaks= pretty_breaks()) +
coord_cartesian(ylim=c(0, NA)) +
theme_classic()+
theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size))
@@ -576,7 +914,7 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
}
if(separate_plots_flag){
separate_plot = gg_list[[bb]] + theme(legend.position='none', text=element_text(size =separate_plot_text_size))
- ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_', burden_metric_name,'_',district_subset,'.png'), separate_plot, dpi=600, width=4, height=3, units='in')
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_', relative_string, burden_metric_name,'_',district_subset,'.png'), separate_plot, dpi=600, width=4, height=3, units='in')
}
}
# gg_list = append(list(ggpubr::as_ggplot(ggpubr::get_legend(gg_list[[1]])), (ggplot() + theme_void())), gg_list)
@@ -593,9 +931,175 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
gg = grid.arrange(grobs = gg_list, layout_matrix = rbind(matrix(rep(1, ceiling(length(burden_colnames)/nrow_plot)), nrow=1), matrix(2:(num_in_matrix+1), nrow=nrow_plot, byrow=FALSE)))
if(save_plots){
- ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_burden_pyr', pyr, '_', chw_cov, 'CHW_',district_subset,'.png'), gg, dpi=600, width=9, height=3*nrow_plot, units='in')
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_burden', relative_string,'_pyr', pyr, '_', chw_cov, 'CHW_',district_subset,'.png'), gg, dpi=600, width=9, height=3*nrow_plot, units='in')
+ }
+
+ return(gg)
+}
+
+
+
+
+
+######################################################################
+# create state grid plot with timeseries of burden, no intervention info
+######################################################################
+
+plot_simulation_output_burden_by_state = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ min_year, max_year, sim_end_years, relative_year=NA,
+ scenario_filepaths, scenario_names, experiment_names, scenario_palette, LLIN2y_flag=FALSE, overwrite_files=FALSE,
+ extend_past_timeseries_year=NA, scenario_linetypes=NA,filename_suffix=''){
+ if (!is.na(relative_year)){ if(relative_year(compare_burdens[1]*(1-similarity_threshold)))){
+ # connect_future_with_past = FALSE
+ # merge_years = earliest_future_year
+ # if(extend_past_timeseries_year > earliest_future_year){
+ # # check which years (up to a maximum of extend_past_timeseries_year) should be included in the to-present line
+ # yy = earliest_future_year + 1
+ # while(yy <= extend_past_timeseries_year){
+ # compare_burdens = future_df$mean_burden[future_df$year == (yy)]
+ # if(all(compare_burdens<(compare_burdens[1]*1.05)) & all(compare_burdens>(compare_burdens[1]*0.95))){
+ # merge_years = c(merge_years, yy)
+ # yy = yy+1
+ # } else{ # as soon as they don't match for a year, stop trying to match any future years
+ # yy=99999999
+ # }
+ # }
+ # }
+ # # get the mean value from the 'future-projection' rows so that it can be added to the 'to-present' scenario
+ # past_from_future_df = future_df[future_df$year %in% merge_years,]
+ # past_from_future_df_means = past_from_future_df %>% dplyr::select(-scenario) %>% group_by(year) %>%
+ # summarise_all(mean) %>% ungroup()
+ # past_from_future_df_means$scenario = 'to-present'
+ # # delete the old 'future-projection' rows for all but the final of these years
+ # delete_future_years = merge_years[merge_years != max(merge_years)]
+ # if(length(delete_future_years)>0) burden_df = burden_df[-which(burden_df$year %in% merge_years),]
+ # # add the rows to the 'to-present' scenario in the data frame
+ # burden_df = merge(burden_df, past_from_future_df_means, all=TRUE)
+ # }else{
+ connect_future_with_past = TRUE
+ # }
+ }
+ if(connect_future_with_past){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ to_present_df = burden_df[burden_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ burden_df = rbind(burden_df, final_to_present_row)
+ }
+ }
+ }
+
+
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # if plotting burden relative to specified year, calculate relative values
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ if (!is.na(relative_year)){
+ # if the reference year is in the to-present simulation, use the same reference for all scenarios
+ # if the reference year is not in the to-present simulation, use average value across scenarios and check that all scenarios have similar values for the reference year (if they do not, send a warning)
+ if(('to-present' %in% burden_df$scenario) & (relative_year %in% unique(burden_df$year[burden_df$scenario=='to-present']))){
+ # get the burden in the reference year
+ reference_burden_cur = burden_df[burden_df$scenario=='to-present' & burden_df$year == relative_year, c('State','mean_burden')]
+ } else{
+ similarity_threshold = 0.1
+ all_ref_year_burdens = burden_df$mean_burden[burden_df$year == relative_year, c('State','mean_burden')]
+ reference_burden_cur = all_ref_year_burdens %>% group_by(State) %>%
+ summarise(mean_burden = mean(mean_burden)) %>% ungroup()
+ # if(any(all_ref_year_burdens>(reference_burden_cur*(1+similarity_threshold))) | any(all_ref_year_burdens<(reference_burden_cur*(1-similarity_threshold)))){
+ # warning(paste0('in the reference year, some scenarios have different burdens for ',burden_metric_name))
+ # }
+ }
+ # calculate all relative burden values as mean_burden / reference_burden_cur: this will be referred to as 'burden relative to burden in relative_year'
+ colnames(reference_burden_cur)[colnames(reference_burden_cur)=='mean_burden'] = 'ref_mean_burden'
+ burden_df = merge(burden_df, reference_burden_cur, all=TRUE)
+ burden_df$mean_burden = burden_df$mean_burden / burden_df$ref_mean_burden
+
+ burden_ylab = paste0(gsub(' per person', '', burden_metric_name), ' relative to ', relative_year)
+ relative_string = paste0('_relativeTo', relative_year)
+ } else{
+ burden_ylab = burden_metric_name
+ relative_string = ''
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+
+ # get factors in the correct order (rather than alphabetical)
+ burden_df$scenario = factor(burden_df$scenario, levels=rev(scenario_names))
+
+ if(is.na(scenario_linetypes[1])){
+ scenario_linetypes = rep(1, length(unique(burden_df$scenario)))
+ names(scenario_linetypes) = unique(burden_df$scenario)
+ }
+
+ burden_df$code = burden_df$State
+ gg = ggplot(burden_df, aes(x=year, y=mean_burden, color=scenario, linetype=scenario))+
+ geom_line(size=1) +
+ scale_linetype_manual(values=scenario_linetypes) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(burden_ylab) +
+ coord_cartesian(ylim=c(0, ifelse(!is.na(relative_year),2,NA)), xlim=c(min_year, max_year)) +
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name")#, scales='free')
+
+ ggsave(paste0(sim_future_output_dir, '/_plots/Timeseries_burden',relative_string,'_state_grid_',burden_metric,filename_suffix,'.png'), gg, dpi=600, width=12*0.7, height=10*0.7, units='in')
}
-
return(gg)
}
@@ -603,6 +1107,10 @@ plot_simulation_output_burden_all = function(sim_future_output_dir, pop_filepath
+
+
+
+
######################################################################
# create plot panel with selected burden metric and intervention info
######################################################################
@@ -693,17 +1201,19 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
# add the final 'to-present' row to all future simulations for a continuous plot
to_present_df = burden_df[burden_df$scenario == 'to-present',]
- if(plot_by_month){
- final_to_present_row = to_present_df[as.Date(to_present_df$date) == max(as.Date(to_present_df$date)),]
- for(ss in 2:length(scenario_names)){
- final_to_present_row$scenario = scenario_names[ss]
- burden_df = rbind(burden_df, final_to_present_row)
- }
- } else{
- final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
- for(ss in 2:length(scenario_names)){
- final_to_present_row$scenario = scenario_names[ss]
- burden_df = rbind(burden_df, final_to_present_row)
+ if(nrow(to_present_df)>0){
+ if(plot_by_month){
+ final_to_present_row = to_present_df[as.Date(to_present_df$date) == max(as.Date(to_present_df$date)),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ burden_df = rbind(burden_df, final_to_present_row)
+ }
+ } else{
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ burden_df = rbind(burden_df, final_to_present_row)
+ }
}
}
write.csv(burden_df, burden_df_filepath, row.names=FALSE)
@@ -749,16 +1259,18 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
}
} else{
# remove excess year from to-present simulation
- max_to_present_date = max(net_use_df$year[net_use_df$scenario == 'to-present'])
- row_to_remove = intersect(which(net_use_df$scenario == 'to-present'), which(net_use_df$year == max_to_present_date))
- net_use_df = net_use_df[-row_to_remove,]
-
- # join past and future simulation trajectories
- to_present_df = net_use_df[net_use_df$scenario == 'to-present',]
- final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
- for(ss in 2:length(scenario_names)){
- final_to_present_row$scenario = scenario_names[ss]
- net_use_df = rbind(net_use_df, final_to_present_row)
+ if(any(net_use_df$scenario == 'to-present')){
+ max_to_present_date = max(net_use_df$year[net_use_df$scenario == 'to-present'])
+ row_to_remove = intersect(which(net_use_df$scenario == 'to-present'), which(net_use_df$year == max_to_present_date))
+ net_use_df = net_use_df[-row_to_remove,]
+
+ # join past and future simulation trajectories
+ to_present_df = net_use_df[net_use_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ net_use_df = rbind(net_use_df, final_to_present_row)
+ }
}
}
write.csv(net_use_df, llin_df_filepath, row.names=FALSE)
@@ -826,7 +1338,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
# create scenario-comparison plots
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
# get factors in the correct order (rather than alphabetical)
- burden_df$scenario = factor(burden_df$scenario, levels=unique(scenario_names))
+ burden_df$scenario = factor(burden_df$scenario, levels=scenario_names)
# ----- malaria burden ----- #
@@ -838,6 +1350,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0(burden_metric, ' - ', age_plotted)) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size))
} else{
@@ -848,6 +1361,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0(burden_metric, ' - ', age_plotted)) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), text = element_text(size = text_size), legend.text=element_text(size = text_size))
}
@@ -863,6 +1377,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0('LLIN use (all ages)')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -875,6 +1390,15 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
# geom_hline(yintercept=0.39, alpha=0.1)+
xlab('year') +
ylab(paste0('LLIN use (all ages)')) +
+ coord_cartesian(ylim=c(0,NA))+
+ theme_classic()+
+ theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = ggplot() +
+ geom_line(data=net_use_df, aes(x=year, y=coverage), color=rgb(1,0.6,1), size=1) +
+ xlab('year') +
+ ylab(paste0('coverage metric')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
}
@@ -908,6 +1432,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0('Vaccines (primary series + booster) per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -917,8 +1442,13 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0('Vaccines (primary series + booster) per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = g_all_inter +
+ geom_line(data=net_use_df, aes(x=year, y=vacc_per_cap), color=rgb(0,0.3,0), size=1)
+
}
inter_plot_list = append(inter_plot_list, list(g_vacc))
}
@@ -932,6 +1462,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0('PMC doses per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -941,8 +1472,12 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0('PMC doses per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = g_all_inter +
+ geom_line(data=net_use_df, aes(x=year, y=pmc_per_cap), color=rgb(0.0,0.4,1), size=1)
}
inter_plot_list = append(inter_plot_list, list(g_pmc))
}
@@ -955,7 +1490,8 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
geom_point(size=1) +
scale_color_manual(values = scenario_palette) +
xlab('date') +
- ylab(paste0('PMC doses per person')) +
+ ylab(paste0('SMC doses per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -965,8 +1501,12 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0('SMC doses per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = g_all_inter +
+ geom_line(data=net_use_df, aes(x=year, y=smc_per_cap), color=rgb(0.0,0.4,1), size=1)
}
inter_plot_list = append(inter_plot_list, list(g_smc))
}
@@ -980,6 +1520,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0('IRS rounds per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -989,8 +1530,12 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0('IRS per person')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = g_all_inter +
+ geom_line(data=net_use_df, aes(x=year, y=irs_per_cap), color=rgb(1,0,1), size=1)
}
inter_plot_list = append(inter_plot_list, list(g_irs))
}
@@ -1005,6 +1550,7 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('date') +
ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
} else{
@@ -1015,8 +1561,12 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
scale_color_manual(values = scenario_palette) +
xlab('year') +
ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(ylim=c(0,NA))+
theme_classic()+
theme(legend.position = "none", text = element_text(size = text_size))
+
+ g_all_inter = g_all_inter +
+ geom_line(data=cm_df, aes(x=year, y=mean_coverage), color=rgb(0.1,0.9,0.4), size=1)
}
inter_plot_list = append(inter_plot_list, list(g_cm))
@@ -1029,6 +1579,10 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
if(save_plots){
ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_', burden_metric, '_', age_plotted, '_versusInterventions_pyr', pyr, '_', chw_cov, 'CHW_',district_subset,'.png'), gg, dpi=600, width=7, height=4*(2+length(inter_plot_list)), units='in')
+
+ if(!plot_by_month){
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_interventions_pyr', pyr, '_', chw_cov, 'CHW_',district_subset,'.png'), g_all_inter, dpi=600, width=7, height=5, units='in')
+ }
}
return(gg)
}
@@ -1037,6 +1591,120 @@ plot_simulation_intervention_output = function(sim_future_output_dir, pop_filepa
+######################################################################
+# create plot panel with CM timeseries for included scenarios in each state
+######################################################################
+
+plot_state_grid_cm = function(sim_future_output_dir, pop_filepath, grid_layout_state_locations,
+ plot_by_month, min_year, max_year, sim_end_years,
+ scenario_names, scenario_input_references, experiment_names, scenario_palette,
+ overwrite_files=FALSE){
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # combine simulation output from multiple scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # create output directories
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots'))) dir.create(paste0(sim_future_output_dir, '/_plots'))
+ if(!dir.exists(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))) dir.create(paste0(sim_future_output_dir, '/_plots/timeseries_dfs'))
+ if(plot_by_month){
+ time_string = 'monthly'
+ } else time_string = 'annual'
+
+ # check whether CM output already exists for this comparison
+ cm_df_filepath = paste0(sim_future_output_dir, '/_plots/timeseries_dfs/df_cm_state_',time_string,'Timeseries.csv')
+ if(file.exists(cm_df_filepath)){
+ cm_df = read.csv(cm_df_filepath)
+ } else{
+ # iterate through scenarios, storing input CM coverages
+ cm_df = data.frame()
+ for(ee in 1:length(experiment_names)){
+ intervention_csv_filepath = scenario_input_references[ee]
+ intervention_file_info = read.csv(intervention_csv_filepath)
+ experiment_intervention_name = experiment_names[ee]
+ end_year = sim_end_years[ee]
+ cur_int_row = which(intervention_file_info$ScenarioName == experiment_intervention_name)
+ # read in intervention files
+ cm_filepath = paste0(hbhi_dir, '/simulation_inputs/', intervention_file_info$CM_filename[cur_int_row], '.csv')
+
+ cur_cm_agg = get_cm_timeseries_by_state(cm_filepath=cm_filepath, admin_info=admin_info, end_year=end_year, exp_name = scenario_names[ee],
+ min_year=min_year, plot_by_month=plot_by_month)
+
+ if(nrow(cm_df)==0){
+ cm_df = cur_cm_agg
+ } else{
+ cm_df = rbind(cm_df, cur_cm_agg)
+ }
+ }
+
+ if(any(grepl('to-present', cm_df$scenario))){
+ # add the final 'to-present' row to all future simulations for a continuous plot
+ if(plot_by_month){
+ # join past and future simulation trajectories
+ to_present_df = cm_df[cm_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[as.Date(to_present_df$date) == max(as.Date(to_present_df$date)),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ cm_df = rbind(cm_df, final_to_present_row)
+ }
+ } else{
+ # join past and future simulation trajectories
+ to_present_df = cm_df[cm_df$scenario == 'to-present',]
+ final_to_present_row = to_present_df[to_present_df$year == max(to_present_df$year),]
+ for(ss in 2:length(scenario_names)){
+ final_to_present_row$scenario = scenario_names[ss]
+ cm_df = rbind(cm_df, final_to_present_row)
+ }
+ }
+ }
+ write.csv(cm_df, cm_df_filepath, row.names=FALSE)
+ }
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create scenario-comparison plots
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # get factors in the correct order (rather than alphabetical)
+ cm_df$scenario = factor(cm_df$scenario, levels=rev(scenario_names))
+ cm_df$code = cm_df$State
+
+ if(plot_by_month){
+ g_cm = ggplot(cm_df, aes(x=as.Date(date), y=mean_coverage, color=scenario)) +
+ geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('date') +
+ ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year))+
+ theme_bw()+
+ theme(legend.position = "none", text = element_text(size = text_size))+
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ } else{
+ g_cm = ggplot(cm_df, aes(x=year, y=mean_coverage, color=scenario)) +
+ geom_ribbon(aes(ymin=min_coverage, ymax=max_coverage, fill=scenario), alpha=0.1, color=NA)+
+ scale_fill_manual(values = scenario_palette) +
+ geom_line(size=1) +
+ scale_color_manual(values = scenario_palette) +
+ xlab('year') +
+ ylab(paste0('Effective treatment rate (U5)')) +
+ coord_cartesian(xlim=c(min_year, max_year))+
+ scale_x_continuous(breaks= pretty_breaks(), guide = guide_axis(check.overlap = TRUE)) +
+ theme_bw()+
+ # theme(legend.position = "none", text = element_text(size = text_size))+
+ theme(legend.position = "top", legend.box='horizontal', legend.title = element_blank(), legend.text=element_text(size = text_size)) + # legend.position = "none"
+ facet_geo(~code, grid = grid_layout_state_locations, label="name", scales='free')
+ }
+ ggsave(paste0(sim_future_output_dir, '/_plots/',time_string,'Timeseries_CM_by_state.png'), g_cm, dpi=600, width=12, height=10, units='in')
+}
+
+
+
+
+
+
@@ -1074,102 +1742,222 @@ plot_included_admin_map = function(sim_future_output_dir, pop_filepath, district
#####################################################################
# plot maps of burden with and without the intervention
#####################################################################
-# plot_burden_maps = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
-# barplot_start_year, barplot_end_year,
-# pyr, chw_cov,
-# scenario_names, experiment_names, admin_shapefile_filepath, shapefile_admin_colname='NOMDEP', LLIN2y_flag=FALSE,
-# overwrite_files=FALSE){
-#
-#
-# admin_pop = read.csv(pop_filepath)
-# if(!(cur_admins[1] == 'all')){
-# admin_pop=admin_pop[which(admin_pop$admin_name %in% cur_admins),]
-# }
-# admin_shapefile = shapefile(admin_shapefile_filepath)
-#
-# years_included = barplot_end_year - barplot_start_year + 1
-#
-# # burden metrics
-# # burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'death_rate_mean_U5', 'death_rate_mean_all')
-# # burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'mortality (U5)', 'mortality (all ages)')
-# # burden_colnames_for_map = c('pfpr_all', 'incidence_all', 'mortality_rate_all')
-# # burden_metric_names = c('PfPR (all ages)', 'incidence (all ages)', 'mortality (all)')
-# burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'direct_death_rate_mean_U5', 'direct_death_rate_mean_all', 'all_death_rate_mean_U5', 'all_death_rate_mean_all')
-# burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'direct mortality (U5)', 'direct mortality (all ages)', 'mortality (U5)', 'mortality (all ages)')
-#
-#
-# ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
-# # iterate through scenarios, creating dataframe including all burden metrics
-# ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
-# num_scenarios = length(experiment_names)
-# burden_df_all = data.frame()
-# for(ee in 1:num_scenarios){
-# experiment_name = experiment_names[ee]
-# cur_burden_df = get_total_burden(sim_output_filepath=sim_future_output_dir, experiment_name=experiment_name, admin_pop=admin_pop, comparison_start_year=barplot_start_year, comparison_end_year=barplot_end_year, district_subset=district_subset, cur_admins=cur_admins, overwrite_files=overwrite_files)
-# cur_burden_df$scenario_name = scenario_names[ee]
-# if(nrow(burden_df_all) == 0){
-# burden_df_all = cur_burden_df
-# } else{
-# burden_df_all = rbind(burden_df_all, cur_burden_df)
-# }
-# }
-#
-#
-#
-# ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
-# # create maps showing each burden metric for all scenarios
-# ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
-# if(LLIN2y_flag){
-# llin2y_string = '_2yLLIN'
-# } else{
-# llin2y_string = ''
-# }
-# num_colors = 40
-# colorscale = colorRampPalette(brewer.pal(9, 'YlGnBu'))(num_colors)
-#
-#
-#
-# # iterate through burden metrics, creating plots for each
-# for(cc in 1:length(burden_colnames_for_map)){
-#
-# if(save_plots) png(paste0(sim_future_output_dir, '/_plots/map_', burden_colnames_for_map[cc], '_', pyr, '_', chw_cov, 'CHW_', district_subset, llin2y_string, '.png'), res=600, width=(num_scenarios*3+2)*3/4, height=3, units='in')
-# par(mar=c(0,1,2,0))
-# # set layout for panel of maps
-# layout_matrix = matrix(rep(c(rep(1:num_scenarios, each=3),rep((num_scenarios+1),2)),2), nrow=2, byrow=TRUE)
-# layout(mat = layout_matrix)
-#
-# cur_colname = burden_colnames_for_map[cc]
-# min_value = min(burden_df_all[[cur_colname]], na.rm=TRUE)
-# max_value = max(burden_df_all[[cur_colname]], na.rm=TRUE)
-#
-# # iterate through scenarios
-# for(ee in 1:num_scenarios){
-# cur_burden_df = burden_df_all[burden_df_all$scenario_name == scenario_names[ee],]
-# vals_ordered = data.frame('ds_ordered'=admin_shapefile[[shapefile_admin_colname]], 'value'=rep(NA, length(admin_shapefile[[shapefile_admin_colname]])))
-# for (i_ds in 1:length(vals_ordered$ds_ordered)){
-# cur_ds = vals_ordered$ds_ordered[i_ds]
-# if(toupper(cur_ds) %in% toupper(cur_burden_df$admin_name)){
-# vals_ordered$value[i_ds] = cur_burden_df[which(toupper(cur_burden_df$admin_name) == toupper(cur_ds)), cur_colname]
-# }
-# }
-#
-# col_cur = colorscale[sapply(floor((num_colors)*(vals_ordered$value - min_value) / (max_value - min_value))+1, min, num_colors)]
-# col_cur[is.na(col_cur)] = 'grey'
-# plot(admin_shapefile, col=col_cur, border=rgb(0.3,0.3,0.3), main=scenario_names[ee])
-# }
-# # legend
-# legend_label_vals = seq(min_value, max_value, length.out=5)
-# legend_image = as.raster(matrix(rev(colorscale[sapply(floor((num_colors)*(legend_label_vals - min_value) / (max_value - min_value))+1, min, num_colors)]), ncol=1))
-# plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = burden_metric_names[cc])
-# text(x=1.5, y = seq(0,1,length.out=5), labels = round(legend_label_vals,2))
-# rasterImage(legend_image, 0, 0, 1,1)
-# # fourth blank plot
-# # plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, ylab='', xlab='')
-# par(mfrow=c(1,1), mar=c(5,4,4,2))
-# if(save_plots) dev.off()
-# }
-#
-# }
+plot_burden_maps = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
+ barplot_start_year, barplot_end_year,
+ pyr, chw_cov,
+ scenario_names, experiment_names, admin_shapefile_filepath, shapefile_admin_colname='NOMDEP', LLIN2y_flag=FALSE,
+ filename_sufffix='', overwrite_files=FALSE){
+
+
+ admin_pop = read.csv(pop_filepath)
+ if(!(cur_admins[1] == 'all')){
+ admin_pop=admin_pop[which(admin_pop$admin_name %in% cur_admins),]
+ }
+ admin_shapefile = shapefile(admin_shapefile_filepath)
+ # standardize shapefile names
+ admin_shapefile$NOMDEP = standardize_admin_names_in_vector(target_names=archetype_info$LGA, origin_names=admin_shapefile$NOMDEP)
+
+ years_included = barplot_end_year - barplot_start_year + 1
+
+ # burden metrics
+ # burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'death_rate_mean_U5', 'death_rate_mean_all')
+ # burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'mortality (U5)', 'mortality (all ages)')
+ # burden_colnames_for_map = c('pfpr_all', 'incidence_all', 'mortality_rate_all')
+ # burden_metric_names = c('PfPR (all ages)', 'incidence (all ages)', 'mortality (all)')
+ # burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'direct_death_rate_mean_U5', 'direct_death_rate_mean_all', 'all_death_rate_mean_U5', 'all_death_rate_mean_all')
+ # burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'direct mortality (U5)', 'direct mortality (all ages)', 'mortality (U5)', 'mortality (all ages)')
+ burden_colnames_for_map = c('pfpr_u5', 'pfpr_all', 'incidence_u5', 'incidence_all', 'mortality_rate_u5', 'mortality_rate_all')
+ burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'mortality (U5)', 'mortality (all ages)')
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # iterate through scenarios, creating dataframe including all burden metrics
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ num_scenarios = length(experiment_names)
+ burden_df_all = data.frame()
+ for(ee in 1:num_scenarios){
+ experiment_name = experiment_names[ee]
+ cur_burden_df = get_total_burden(sim_output_filepath=sim_future_output_dir, experiment_name=experiment_name, admin_pop=admin_pop, comparison_start_year=barplot_start_year, comparison_end_year=barplot_end_year, district_subset=district_subset, cur_admins=cur_admins, overwrite_files=overwrite_files)
+ cur_burden_df$scenario_name = scenario_names[ee]
+ if(nrow(burden_df_all) == 0){
+ burden_df_all = cur_burden_df
+ } else{
+ burden_df_all = rbind(burden_df_all, cur_burden_df)
+ }
+ }
+
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create maps showing each burden metric for all scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ if(LLIN2y_flag){
+ llin2y_string = '_2yLLIN'
+ } else{
+ llin2y_string = ''
+ }
+ num_colors = 40
+ # colorscale = colorRampPalette(brewer.pal(9, 'YlGnBu'))(num_colors)
+ colorscale = colorRampPalette(brewer.pal(9, 'YlOrRd'))(num_colors)
+
+
+
+ # iterate through burden metrics, creating plots for each
+ for(cc in 1:length(burden_colnames_for_map)){
+
+ if(save_plots) png(paste0(sim_future_output_dir, '/_plots/map_', burden_colnames_for_map[cc], '_', pyr, '_', chw_cov, 'CHW_', district_subset, llin2y_string, filename_sufffix, '.png'), res=600, width=(num_scenarios*3+2)*3/4, height=3, units='in')
+ par(mar=c(0,1,2,0))
+ # set layout for panel of maps
+ layout_matrix = matrix(rep(c(rep(1:num_scenarios, each=3),rep((num_scenarios+1),2)),2), nrow=2, byrow=TRUE)
+ layout(mat = layout_matrix)
+
+ cur_colname = burden_colnames_for_map[cc]
+ min_value = min(min(burden_df_all[[cur_colname]], na.rm=TRUE), 0)
+ max_value = max(max(burden_df_all[[cur_colname]], na.rm=TRUE), 0.65)
+
+ # iterate through scenarios
+ for(ee in 1:num_scenarios){
+ cur_burden_df = burden_df_all[burden_df_all$scenario_name == scenario_names[ee],]
+ vals_ordered = data.frame('ds_ordered'=admin_shapefile[[shapefile_admin_colname]], 'value'=rep(NA, length(admin_shapefile[[shapefile_admin_colname]])))
+ for (i_ds in 1:length(vals_ordered$ds_ordered)){
+ cur_ds = vals_ordered$ds_ordered[i_ds]
+ if(toupper(cur_ds) %in% toupper(cur_burden_df$admin_name)){
+ vals_ordered$value[i_ds] = cur_burden_df[which(toupper(cur_burden_df$admin_name) == toupper(cur_ds)), cur_colname]
+ }
+ }
+
+ col_cur = colorscale[sapply(floor((num_colors)*(vals_ordered$value - min_value) / (max_value - min_value))+1, min, num_colors)]
+ col_cur[is.na(col_cur)] = 'grey'
+ plot(admin_shapefile, col=col_cur, border=rgb(0.3,0.3,0.3), main=scenario_names[ee])
+ }
+ # legend
+ legend_label_vals = seq(min_value, max_value, length.out=5)
+ legend_image = as.raster(matrix(rev(colorscale[sapply(floor((num_colors)*(legend_label_vals - min_value) / (max_value - min_value))+1, min, num_colors)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = burden_metric_names[cc])
+ text(x=1.5, y = seq(0,1,length.out=5), labels = round(legend_label_vals,2))
+ rasterImage(legend_image, 0, 0, 1,1)
+ # fourth blank plot
+ # plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, ylab='', xlab='')
+ par(mfrow=c(1,1), mar=c(5,4,4,2))
+ if(save_plots) dev.off()
+ }
+
+}
+
+
+
+
+
+# plot map with the reduction in burden relative to the first scenario
+plot_burden_relative_reduction_maps = function(sim_future_output_dir, pop_filepath, district_subset, cur_admins,
+ barplot_start_year, barplot_end_year,
+ pyr, chw_cov,
+ scenario_names, experiment_names, admin_shapefile_filepath, shapefile_admin_colname='NOMDEP', LLIN2y_flag=FALSE,
+ overwrite_files=FALSE){
+
+
+ admin_pop = read.csv(pop_filepath)
+ if(!(cur_admins[1] == 'all')){
+ admin_pop=admin_pop[which(admin_pop$admin_name %in% cur_admins),]
+ }
+ admin_shapefile = shapefile(admin_shapefile_filepath)
+ # standardize shapefile names
+ admin_shapefile$NOMDEP = standardize_admin_names_in_vector(target_names=archetype_info$LGA, origin_names=admin_shapefile$NOMDEP)
+
+ years_included = barplot_end_year - barplot_start_year + 1
+
+ # burden metrics
+ # burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'death_rate_mean_U5', 'death_rate_mean_all')
+ # burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'mortality (U5)', 'mortality (all ages)')
+ # burden_colnames_for_map = c('pfpr_all', 'incidence_all', 'mortality_rate_all')
+ # burden_metric_names = c('PfPR (all ages)', 'incidence (all ages)', 'mortality (all)')
+ # burden_colnames_for_map = c('average_PfPR_U5', 'average_PfPR_all', 'incidence_U5', 'incidence_all', 'direct_death_rate_mean_U5', 'direct_death_rate_mean_all', 'all_death_rate_mean_U5', 'all_death_rate_mean_all')
+ # burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'direct mortality (U5)', 'direct mortality (all ages)', 'mortality (U5)', 'mortality (all ages)')
+ burden_colnames_for_map = c('pfpr_u5', 'pfpr_all', 'incidence_u5', 'incidence_all', 'mortality_rate_u5', 'mortality_rate_all')
+ burden_metric_names = c('PfPR (U5)', 'PfPR (all ages)', 'incidence (U5)', 'incidence (all ages)', 'mortality (U5)', 'mortality (all ages)')
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # iterate through scenarios, creating dataframe including all burden metrics
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ num_scenarios = length(experiment_names)
+ burden_df_all = data.frame()
+ for(ee in 1:num_scenarios){
+ experiment_name = experiment_names[ee]
+ cur_burden_df = get_total_burden(sim_output_filepath=sim_future_output_dir, experiment_name=experiment_name, admin_pop=admin_pop, comparison_start_year=barplot_start_year, comparison_end_year=barplot_end_year, district_subset=district_subset, cur_admins=cur_admins, overwrite_files=overwrite_files)
+ cur_burden_df$scenario_name = scenario_names[ee]
+ if(nrow(burden_df_all) == 0){
+ burden_df_all = cur_burden_df
+ } else{
+ burden_df_all = rbind(burden_df_all, cur_burden_df)
+ }
+ }
+
+
+
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ # create maps showing each burden metric for all scenarios
+ ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ###
+ if(LLIN2y_flag){
+ llin2y_string = '_2yLLIN'
+ } else{
+ llin2y_string = ''
+ }
+ num_colors = 40
+ colorscale = colorRampPalette(brewer.pal(9, 'YlGnBu'))(num_colors)
+
+
+
+ # iterate through burden metrics, creating plots for each
+ for(cc in 1:length(burden_colnames_for_map)){
+
+ if(save_plots) png(paste0(sim_future_output_dir, '/_plots/map_rel_reduction_', burden_colnames_for_map[cc], '_', pyr, '_', chw_cov, 'CHW_', district_subset, llin2y_string, '.png'), res=600, width=(num_scenarios*3+2)*3/4, height=3, units='in')
+ par(mar=c(0,1,2,0))
+ # set layout for panel of maps
+ layout_matrix = matrix(rep(c(rep(1:num_scenarios, each=3),rep((num_scenarios+1),2)),2), nrow=2, byrow=TRUE)
+ layout(mat = layout_matrix)
+
+ cur_colname = burden_colnames_for_map[cc]
+
+ # get reference column for this burden metric
+ burden_df_ref = burden_df_all[burden_df_all$scenario_name==scenario_names[1],c('admin_name',cur_colname)]
+ colnames(burden_df_ref)[which(colnames(burden_df_ref)==cur_colname)]='reference_value'
+ burden_df_relative = merge(burden_df_all, burden_df_ref, all=TRUE)
+ burden_df_relative$rel_reduction = (burden_df_relative$reference_value - burden_df_relative[[cur_colname]]) / burden_df_relative$reference_value
+
+ # set minimum and maximum plotted in legend
+ abs_max = max(abs( burden_df_relative$rel_reduction), na.rm=TRUE)
+ min_value = -1 * abs_max
+ max_value = abs_max
+
+ # iterate through scenarios
+ for(ee in 2:num_scenarios){
+ cur_burden_df = burden_df_relative[burden_df_relative$scenario_name == scenario_names[ee],]
+ vals_ordered = data.frame('ds_ordered'=admin_shapefile[[shapefile_admin_colname]], 'value'=rep(NA, length(admin_shapefile[[shapefile_admin_colname]])))
+ for (i_ds in 1:length(vals_ordered$ds_ordered)){
+ cur_ds = vals_ordered$ds_ordered[i_ds]
+ if(toupper(cur_ds) %in% toupper(cur_burden_df$admin_name)){
+ vals_ordered$value[i_ds] = cur_burden_df[which(toupper(cur_burden_df$admin_name) == toupper(cur_ds)), 'rel_reduction']
+ }
+ }
+
+ col_cur = colorscale[sapply(floor((num_colors)*(vals_ordered$value - min_value) / (max_value - min_value))+1, min, num_colors)]
+ col_cur[is.na(col_cur)] = 'grey'
+ plot(admin_shapefile, col=col_cur, border=rgb(0.3,0.3,0.3), main=scenario_names[ee])
+ }
+ # legend
+ legend_label_vals = seq(min_value, max_value, length.out=5)
+ legend_image = as.raster(matrix(rev(colorscale[sapply(floor((num_colors)*(legend_label_vals - min_value) / (max_value - min_value))+1, min, num_colors)]), ncol=1))
+ plot(c(0,2),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = burden_metric_names[cc])
+ text(x=1.5, y = seq(0,1,length.out=5), labels = round(legend_label_vals,2))
+ rasterImage(legend_image, 0, 0, 1,1)
+ # fourth blank plot
+ # plot(NA, ylim=c(0,1), xlim=c(0,1), axes=FALSE, ylab='', xlab='')
+ par(mfrow=c(1,1), mar=c(5,4,4,2))
+ if(save_plots) dev.off()
+ }
+
+}
#
#
#
diff --git a/r_utilities/plots_results_analyses/process_sim_input_functions.R b/r_utilities/plots_results_analyses/process_sim_input_functions.R
new file mode 100644
index 0000000..efc2e43
--- /dev/null
+++ b/r_utilities/plots_results_analyses/process_sim_input_functions.R
@@ -0,0 +1,487 @@
+# process_sim_input_functions.R
+
+
+library(data.table)
+library(dplyr)
+library(tidyverse)
+library(lubridate)
+# library(rgdal)
+library(lubridate)
+
+
+
+
+#####################################################################################################
+# ================================================================================================= #
+# calculate state- or LGA-level intervention timeseries for included scenarios, based on inputs
+# ================================================================================================= #
+#####################################################################################################
+
+##############
+# CM
+##############
+
+# ----- admin- or state-level CM intervention coverage ----- #
+get_cm_timeseries_by_state = function(cm_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(cm_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+ input_df = merge(input_df, admin_info, by='admin_name')
+
+ # CM is sometimes repeated for several years but only listed once; change to repeat the appropriate number of times
+ input_df$years_repeated = input_df$duration/365
+ if(any(input_df$years_repeated>1)){
+ cur_cm_years = unique(input_df$year)
+ for(yy in cur_cm_years){
+ # get first instance of this year
+ cur_year = input_df[input_df$year==yy,]
+ if(cur_year$years_repeated[1]>1){
+ for(rr in 1:(cur_year$years_repeated[1] - 1)){
+ temp_year = cur_year
+ temp_year$year = cur_year$year + rr
+ temp_year$simday = cur_year$simday + rr*365
+ input_df = rbind(input_df, temp_year)
+ }
+ }
+ }
+ }
+ if(any(input_df$duration==-1) & (max(input_df$year)% group_by(year, admin_name, State, seed) %>%
+ summarise_all(mean) %>% ungroup()
+
+ # input_df = input_df[intersect(which(input_df$year >= min_year), which(input_df$year <= end_year)),]
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_U5_cm = input_df$U5_coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied CM coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_U5_cm, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$U5_coverage = input_df_agg_admin$multiplied_U5_cm / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ cm_agg = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, U5_coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ } else{
+ # if there are multiple values in a single year (for a single DS/LGA), take the mean of those values
+ input_df <- input_df %>% group_by(year, admin_name, State, seed) %>%
+ summarise_all(mean) %>% ungroup()
+
+ # input_df = input_df[intersect(which(input_df$year >= min_year), which(input_df$year <= end_year)),]
+ input_df_agg_admin = input_df
+
+ # take average across simulation seeds
+ cm_agg = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, U5_coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ }
+ cm_agg$scenario = exp_name
+ return(cm_agg)
+}
+
+
+# ----- national-level CM intervention coverage ----- #
+get_cm_timeseries_exp = function(cm_filepath, pop_sizes, end_year, exp_name, cur_admins, min_year, plot_by_month=TRUE){
+
+ input_df = read.csv(cm_filepath)
+ # subset to appropriate admins
+ input_df = input_df[input_df$admin_name %in% cur_admins,]
+ if(!('seed' %in% input_df)) input_df$seed = 1
+
+ # CM is sometimes repeated for several years but only listed once; change to repeate the appropriate number of times
+ input_df$years_repeated = input_df$duration/365
+ if(any(input_df$years_repeated>1)){
+ cur_cm_years = unique(input_df$year)
+ for(yy in cur_cm_years){
+ # get first instance of this year
+ cur_year = input_df[input_df$year==yy,]
+ if(cur_year$years_repeated[1]>1){
+ for(rr in 1:(cur_year$years_repeated[1] - 1)){
+ temp_year = cur_year
+ temp_year$year = cur_year$year + rr
+ temp_year$simday = cur_year$simday + rr*365
+ input_df = rbind(input_df, temp_year)
+ }
+ }
+ }
+ }
+ if(any(input_df$duration==-1) & (max(input_df$year)% group_by(year, admin_name, seed) %>%
+ summarise_all(mean) %>% ungroup()
+
+ input_df = input_df[intersect(which(input_df$year >= min_year), which(input_df$year <= end_year)),]
+
+
+ # get population-weighted CM coverage across admins
+ input_df = merge(input_df, pop_sizes, by='admin_name')
+ input_df$multiplied_U5_cm = input_df$U5_coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied CM coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, seed, multiplied_U5_cm, pop_size) %>% group_by(year, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$U5_coverage = input_df_agg_admin$multiplied_U5_cm / input_df_agg_admin$pop_size
+
+
+ # take average, max, and min burdens across simulation seeds
+ if(plot_by_month){
+ # subdivide year values and add dates
+ # date dataframe
+ included_years = unique(input_df_agg_admin$year)
+ all_months = as.Date(paste0(rep(included_years, each=12),'-',c('01','02','03','04','05','06','07','08','09','10','11','12'), '-01' ))
+ date_df = data.frame(year=rep(included_years, each=12), date=all_months)
+ input_df_agg_admin_monthly = merge(input_df_agg_admin, date_df, by='year', all.x=TRUE, all.y=TRUE)
+ cm_agg = as.data.frame(input_df_agg_admin_monthly) %>% dplyr::select(year, date, U5_coverage) %>%
+ dplyr::group_by(year, date) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ } else{
+ cm_agg = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, U5_coverage) %>%
+ dplyr::group_by(year) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ }
+
+ cm_agg$scenario = exp_name
+ return(cm_agg)
+
+}
+
+
+
+
+##############
+# ANC ITN
+##############
+
+# ----- admin- or state-level CM intervention coverage ----- #
+get_itn_anc_timeseries_by_state = function(input_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(input_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+ input_df = merge(input_df, admin_info, by='admin_name')
+
+ # ANC ITN is sometimes repeated for several years but only listed once; change to repeat the appropriate number of times
+ input_df$years_repeated = input_df$duration/365
+ if(any(input_df$years_repeated>1)){
+ cur_years = unique(input_df$year)
+ for(yy in cur_years){
+ # get first instance of this year
+ cur_year = input_df[input_df$year==yy,]
+ if(cur_year$years_repeated[1]>1){
+ for(rr in 1:(cur_year$years_repeated[1] - 1)){
+ temp_year = cur_year
+ temp_year$year = cur_year$year + rr
+ temp_year$simday = cur_year$simday + rr*365
+ input_df = rbind(input_df, temp_year)
+ }
+ }
+ }
+ }
+ if(any(input_df$duration==-1) & (max(input_df$year)% group_by(year, admin_name, State, seed) %>%
+ summarise(coverage=mean(coverage), pop_size=mean(pop_size)) %>% ungroup()
+
+ # input_df = input_df[intersect(which(input_df$year >= min_year), which(input_df$year <= end_year)),]
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_cov = input_df$coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied CM coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_cov, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$coverage = input_df_agg_admin$multiplied_cov / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ input_agg = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ } else{
+ # if there are multiple values in a single year (for a single DS/LGA), take the mean of those values
+ input_df <- input_df %>% group_by(year, admin_name, State, seed) %>%
+ summarise(coverage=mean(coverage)) %>% ungroup()
+
+ # input_df = input_df[intersect(which(input_df$year >= min_year), which(input_df$year <= end_year)),]
+ input_df_agg_admin = input_df
+
+ # take average across simulation seeds
+ input_agg = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ }
+ input_agg$scenario = exp_name
+ return(input_agg)
+}
+
+
+
+##############
+# SMC
+##############
+get_smc_timeseries_by_state = function(input_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(input_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+ input_df$u5_coverage_total = input_df$coverage_high_access_U5 * input_df$high_access_U5 + input_df$coverage_low_access_U5 * (1-input_df$high_access_U5)
+
+ # get mean across rounds within a year
+ input_df <- input_df %>% group_by(year, admin_name, seed) %>%
+ summarise_all(mean) %>% ungroup()
+
+ # create data frame with all admin-years as zeros if SMC isn't specified as given in input file
+ admin_years = merge(admin_info, data.frame('year'=seq(min_year, end_year)), all=TRUE)
+ input_df = merge(input_df, admin_years, by=c('admin_name','year'), all=TRUE)
+ input_df$u5_coverage_total[is.na(input_df$u5_coverage_total)] = 0
+ input_df$U5_coverage = input_df$u5_coverage_total
+
+ # calculate values within state or admin
+ if(get_state_level){
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_U5_cov = input_df$U5_coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied SMC coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_U5_cov, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$U5_coverage = input_df_agg_admin$multiplied_U5_cov / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ input_ave_df = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, U5_coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ } else{
+ # take average across simulation seeds
+ input_ave_df = as.data.frame(input_df) %>% dplyr::select(year, U5_coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ }
+ input_ave_df$scenario = exp_name
+
+ return(input_ave_df)
+}
+
+
+
+
+
+##############
+# ITN mass campaign
+##############
+get_itn_timeseries_by_state = function(input_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(input_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+
+ # get mean across rounds within a year
+ input_df <- input_df %>% group_by(year, admin_name, seed) %>%
+ summarise(itn_u5 = mean(itn_u5)) %>% ungroup()
+
+ # create data frame with all admin-years as zeros if SMC isn't specified as given in input file
+ admin_years = merge(admin_info, data.frame('year'=seq(min_year, end_year)), all=TRUE)
+ input_df = merge(input_df, admin_years, by=c('admin_name','year'), all=TRUE)
+ input_df$itn_u5[is.na(input_df$itn_u5)] = 0
+ input_df$U5_coverage = input_df$itn_u5
+
+ # calculate values within state or admin
+ if(get_state_level){
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_U5_cov = input_df$U5_coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied SMC coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_U5_cov, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$U5_coverage = input_df_agg_admin$multiplied_U5_cov / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ input_ave_df = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, U5_coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ } else{
+ # take average across simulation seeds
+ input_ave_df = as.data.frame(input_df) %>% dplyr::select(year, U5_coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ }
+ input_ave_df$scenario = exp_name
+
+ return(input_ave_df)
+}
+
+
+
+
+
+
+##############
+# EPI vaccine
+##############
+get_vacc_timeseries_by_state = function(input_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(input_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+ input_df = merge(input_df, admin_info, by='admin_name')
+
+ # look just at coverage for primary series
+ input_df = input_df[input_df$vaccine == 'primary',]
+ # true start day of administration is RTSS_day+rtss_touchpoint (due to how things are setup in campaign for birth-triggered delayed vaccine)
+ input_df$simday = input_df$RTSS_day + input_df$rtss_touchpoint
+ input_df$year = round(input_df$simday/365 + min_year)
+
+ # EPI vaccine coverage is specified only once (and then continues for remainder of simulation after the start day)
+ # NOTE: assumes same delivery dates across LGAs
+ df_repeated = input_df
+ for(rr in 1:(end_year - df_repeated$year[1])){
+ temp_year = df_repeated
+ temp_year$year = df_repeated$year + rr
+ temp_year$simday = df_repeated$simday + rr*365
+ input_df = rbind(input_df, temp_year)
+ }
+
+ # # create data frame with all admin-years as zeros before start of vaccine
+ # admin_years = merge(admin_info, data.frame('year'=seq(min_year, vacc_start_year-1), coverage=0), all=TRUE)
+
+ # calculate values within state or admin
+ if(get_state_level){
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_cov = input_df$coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied SMC coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_cov, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$coverage = input_df_agg_admin$multiplied_cov / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ input_ave_df = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ } else{
+ # take average across simulation seeds
+ input_ave_df = as.data.frame(input_df) %>% dplyr::select(year, coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ }
+ input_ave_df$scenario = exp_name
+
+ return(input_ave_df)
+}
+
+
+
+
+
+##############
+# PMC
+##############
+get_pmc_timeseries_by_state = function(input_filepath, admin_info, end_year, exp_name, min_year, get_state_level=TRUE){
+
+ input_df = read.csv(input_filepath)
+ if(!('seed' %in% input_df)) input_df$seed = 1
+
+ # true start day of administration is simday+pmc_touchpoints (due to how things are setup in campaign for birth-triggered delayed vaccine)
+ input_df$simday = input_df$simday + input_df$pmc_touchpoints
+
+ # get average coverage across touchpoints; for simday, take date of earliest touchpoint simday
+ input_df <- input_df %>% group_by(admin_name, seed) %>%
+ summarise(coverage = mean(coverage), simday=min(simday)) %>%
+ ungroup()
+ input_df$year = round(input_df$simday/365 + min_year)
+ input_df = merge(input_df, admin_info, by='admin_name')
+
+ # PMC coverage is specified only once (and then continues for remainder of simulation after the start day)
+ # NOTE: assumes same delivery dates across LGAs
+ df_repeated = input_df
+ for(rr in 1:(end_year - df_repeated$year[1])){
+ temp_year = df_repeated
+ temp_year$year = df_repeated$year + rr
+ temp_year$simday = df_repeated$simday + rr*365
+ input_df = rbind(input_df, temp_year)
+ }
+
+ # # create data frame with all admin-years as zeros before start of vaccine
+ # admin_years = merge(admin_info, data.frame('year'=seq(min_year, vacc_start_year-1), coverage=0), all=TRUE)
+
+ # calculate values within state or admin
+ if(get_state_level){
+ # get population-weighted CM coverage across admins
+ input_df$multiplied_cov = input_df$coverage * input_df$pop_size
+
+ # get sum of population sizes and multiplied SMC coverage across included admins
+ input_df_agg_admin <- input_df %>% dplyr::select(year, State, seed, multiplied_cov, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ input_df_agg_admin$coverage = input_df_agg_admin$multiplied_cov / input_df_agg_admin$pop_size
+
+ # get average within state and across seeds
+ input_ave_df = as.data.frame(input_df_agg_admin) %>% dplyr::select(year, State, coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ } else{
+ # take average across simulation seeds
+ input_ave_df = as.data.frame(input_df) %>% dplyr::select(year, coverage, State, admin_name) %>%
+ dplyr::group_by(year, State, admin_name) %>%
+ dplyr::summarise(mean_coverage = mean(coverage),
+ max_coverage = max(coverage),
+ min_coverage = min(coverage))
+ }
+ input_ave_df$scenario = exp_name
+
+ return(input_ave_df)
+}
diff --git a/r_utilities/plots_results_analyses/process_sim_output_functions.R b/r_utilities/plots_results_analyses/process_sim_output_functions.R
index aa23b2a..3c5146f 100644
--- a/r_utilities/plots_results_analyses/process_sim_output_functions.R
+++ b/r_utilities/plots_results_analyses/process_sim_output_functions.R
@@ -5,7 +5,7 @@ library(data.table)
library(dplyr)
library(tidyverse)
library(lubridate)
-library(rgdal)
+# library(rgdal)
library(lubridate)
@@ -16,107 +16,110 @@ library(lubridate)
# I think the code below isn't used anymore... I'm not updating the direct versus direct+indirect mortality in it for now
-# # function that returns dataframe where each row is an admin and columns contain U5 and all-age total number of cases, total number of deaths, incidence, death rate, and average pfpr within the time period in each admin, averaged over all seeds
-# get_total_burden = function(sim_output_filepath, experiment_name, admin_pop, comparison_start_year, comparison_end_year, district_subset, cur_admins='all', overwrite_files=FALSE){
-# output_filename = paste0(sim_output_filepath, '/', experiment_name, '/totalBurden_', comparison_start_year,'_', comparison_end_year,'_', district_subset,'.csv')
-# if(file.exists(output_filename) & !overwrite_files){
-# burden_means = read.csv(output_filename)
-# } else{
-# burden_df = fread(paste0(sim_output_filepath, '/', experiment_name, '/malariaBurden_withAdjustments.csv'))
-# # subset to appropriate time period
-# burden_df = burden_df[intersect(which(burden_df$year >= comparison_start_year), which(burden_df$year <= comparison_end_year)),]
-# if(!(cur_admins[1] == 'all')){
-# # subset to appropriate admins
-# burden_df = burden_df[burden_df$admin_name %in% cur_admins,]
-# }
-#
-# # PfPR
-# pfpr_u5_means = burden_df[,c('admin_name', 'PfPR_U5')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
-# pfpr_all_means = burden_df[,c('admin_name', 'PfPR_MiP_adjusted')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
-# pfpr_means = merge(pfpr_u5_means, pfpr_all_means, by='admin_name')
-# colnames(pfpr_means)[which(colnames(pfpr_means) == 'PfPR_U5')] = 'pfpr_u5'
-# colnames(pfpr_means)[which(colnames(pfpr_means) == 'PfPR_MiP_adjusted')] = 'pfpr_all'
-#
-#
-# # mortality
-# # divide mortality by total population size in simulation; will later multiply by true population of DS
-# burden_df$mortality_pp_u5 = (burden_df$total_mortality_U5_1*1 + burden_df$total_mortality_U5_2*1) / 2 / burden_df$Statistical_Population # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
-# burden_df$mortality_pp_all = (burden_df$total_mortality_1*1 + burden_df$total_mortality_2*1) / 2 / burden_df$Statistical_Population # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
-# mortality_sums = burden_df[,c('admin_name', 'Run_Number', 'mortality_pp_u5', 'mortality_pp_all')] %>% dplyr::group_by(admin_name, Run_Number) %>% dplyr::summarise_all(sum) %>% dplyr::ungroup()
-# mortality_sums = mortality_sums[,c('admin_name', 'mortality_pp_u5', 'mortality_pp_all')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
-# # multiply by admin population to get total number of deaths
-# mortality_sums = merge(mortality_sums, admin_pop, by='admin_name')
-# mortality_sums$deaths_u5 = mortality_sums$mortality_pp_u5 * mortality_sums$pop_size
-# mortality_sums$deaths_all = mortality_sums$mortality_pp_all * mortality_sums$pop_size
-#
-# # mortality rate (number of deaths in a year in each admin divided by the U5 or all-age population size times 1000)
-# # take sum of deaths within each year
-# burden_df_annual = burden_df %>% dplyr::group_by(admin_name, Run_Number, year) %>%
-# dplyr::summarise(total_mortality_all_1 = sum(total_mortality_1),
-# total_mortality_all_2 = sum(total_mortality_2),
-# New_clinical_cases_all = sum(New_Clinical_Cases),
-# Pop_all = mean(Statistical_Population),
-# total_mortality_U5_1 = sum(total_mortality_U5_1),
-# total_mortality_U5_2 = sum(total_mortality_U5_2),
-# New_clinical_cases_U5 = sum(New_clinical_cases_U5),
-# Pop_U5 = mean(Pop_U5)
-# ) %>%
-# dplyr::ungroup()
-# burden_df_annual$mortality_rate_u5 = (burden_df_annual$total_mortality_U5_1*1 + burden_df_annual$total_mortality_U5_2*1) / 2 / burden_df_annual$Pop_U5 * 1000 # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
-# burden_df_annual$mortality_rate_all = (burden_df_annual$total_mortality_all_1*1 + burden_df_annual$total_mortality_all_2*1) / 2 / burden_df_annual$Pop_all * 1000 # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
-# # remove any NA rows
-# burden_df_annual = burden_df_annual[!is.na(burden_df_annual$mortality_rate_u5),]
-# burden_df_annual = burden_df_annual[!is.na(burden_df_annual$mortality_rate_all),]
-# # get average annual rate across all included years
-# mortality_rate_means = burden_df_annual[,c('admin_name', 'mortality_rate_u5', 'mortality_rate_all')] %>% dplyr::group_by(admin_name) %>%
-# dplyr::summarise(mortality_rate_u5 = mean(mortality_rate_u5),
-# mortality_rate_all = mean(mortality_rate_all)) %>%
-# dplyr::ungroup()
-#
-#
-#
-# # clinical cases
-# # divide number of clinical cases by total population size in simulation; will later multiply by true population of DS to get estimated number of clinical cases in that DS
-# burden_df$cases_pp_u5 = burden_df$New_clinical_cases_U5 / burden_df$Statistical_Population
-# burden_df$cases_pp_all = burden_df$New_Clinical_Cases / burden_df$Statistical_Population
-# case_sums = burden_df[,c('admin_name', 'Run_Number', 'cases_pp_u5', 'cases_pp_all')] %>% dplyr::group_by(admin_name, Run_Number) %>% dplyr::summarise_all(sum) %>% dplyr::ungroup()
-# case_sums = case_sums[,c('admin_name', 'cases_pp_u5', 'cases_pp_all')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
-# # multiply by admin population to get total number of cases
-# case_sums = merge(case_sums, admin_pop, by='admin_name')
-# case_sums$clinical_cases_u5 = case_sums$cases_pp_u5 * case_sums$pop_size
-# case_sums$clinical_cases_all = case_sums$cases_pp_all * case_sums$pop_size
-#
-# # incidence (number of cases in a year in each admin divided by the U5 or all-age population size times 1000)
-# burden_df_annual$incidence_u5 = burden_df_annual$New_clinical_cases_U5 / burden_df_annual$Pop_U5 * 1000
-# burden_df_annual$incidence_all = burden_df_annual$New_clinical_cases_all / burden_df_annual$Pop_all * 1000
-# # remove any NA rows
-# burden_df_annual = burden_df_annual[!is.na(burden_df_annual$incidence_u5),]
-# burden_df_annual = burden_df_annual[!is.na(burden_df_annual$incidence_all),]
-# # get average annual rate across all included years
-# incidence_means = burden_df_annual[,c('admin_name', 'incidence_u5', 'incidence_all')] %>% dplyr::group_by(admin_name) %>%
-# dplyr::summarise(incidence_u5 = mean(incidence_u5),
-# incidence_all = mean(incidence_all)) %>%
-# dplyr::ungroup()
-#
-#
-#
-#
-# # fraction of population U5
-# burden_df$frac_u5 = burden_df$Pop_U5 / burden_df$Statistical_Population
-# frac_u5 = burden_df[,c('admin_name', 'frac_u5')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
-#
-# burden_means = merge(pfpr_means, mortality_sums, by='admin_name')
-# burden_means = merge(burden_means, case_sums, by=c('admin_name', 'pop_size'))
-# burden_means = merge(burden_means, mortality_rate_means, by=c('admin_name'))
-# burden_means = merge(burden_means, incidence_means, by=c('admin_name'))
-# burden_means = merge(burden_means, frac_u5, by=c('admin_name'))
-# burden_means$pop_size_u5 = burden_means$pop_size * burden_means$frac_u5
-#
-# write.csv(burden_means, output_filename, row.names=FALSE)
-# }
-# return(burden_means)
-# }
-#
+# function that returns dataframe where each row is an admin and columns contain U5 and all-age total number of cases, total number of deaths, incidence, death rate, and average pfpr within the time period in each admin, averaged over all seeds
+get_total_burden = function(sim_output_filepath, experiment_name, admin_pop, comparison_start_year, comparison_end_year, district_subset, cur_admins='all', overwrite_files=FALSE){
+ output_filename = paste0(sim_output_filepath, '/', experiment_name, '/totalBurden_', comparison_start_year,'_', comparison_end_year,'_', district_subset,'.csv')
+ if(file.exists(output_filename) & !overwrite_files){
+ burden_means = read.csv(output_filename)
+ } else{
+ burden_df = fread(paste0(sim_output_filepath, '/', experiment_name, '/malariaBurden_withAdjustments.csv'))
+ # subset to appropriate time period
+ burden_df = burden_df[intersect(which(burden_df$year >= comparison_start_year), which(burden_df$year <= comparison_end_year)),]
+ if(!(cur_admins[1] == 'all')){
+ # subset to appropriate admins
+ burden_df = burden_df[burden_df$admin_name %in% cur_admins,]
+ }
+
+ # PfPR
+ pfpr_u5_means = burden_df[,c('admin_name', 'PfPR_U5')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
+ pfpr_all_means = burden_df[,c('admin_name', 'PfPR_MiP_adjusted')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
+ pfpr_means = merge(pfpr_u5_means, pfpr_all_means, by='admin_name')
+ colnames(pfpr_means)[which(colnames(pfpr_means) == 'PfPR_U5')] = 'pfpr_u5'
+ colnames(pfpr_means)[which(colnames(pfpr_means) == 'PfPR_MiP_adjusted')] = 'pfpr_all'
+
+
+ # mortality
+ # divide mortality by total population size in simulation; will later multiply by true population of DS
+ burden_df$mortality_pp_u5 = (burden_df$total_mortality_U5_1*1 + burden_df$total_mortality_U5_2*1) / 2 / burden_df$Statistical_Population # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
+ burden_df$mortality_pp_all = (burden_df$total_mortality_1*1 + burden_df$total_mortality_2*1) / 2 / burden_df$Statistical_Population # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
+ mortality_sums = burden_df[,c('admin_name', 'Run_Number', 'mortality_pp_u5', 'mortality_pp_all')] %>% dplyr::group_by(admin_name, Run_Number) %>% dplyr::summarise_all(sum) %>% dplyr::ungroup()
+ mortality_sums = mortality_sums[,c('admin_name', 'mortality_pp_u5', 'mortality_pp_all')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
+ # multiply by admin population to get total number of deaths
+ mortality_sums = merge(mortality_sums, admin_pop, by='admin_name')
+ mortality_sums$deaths_u5 = mortality_sums$mortality_pp_u5 * mortality_sums$pop_size
+ mortality_sums$deaths_all = mortality_sums$mortality_pp_all * mortality_sums$pop_size
+
+ # mortality rate (number of deaths in a year in each admin divided by the U5 or all-age population size times 1000)
+ # take sum of deaths within each year
+ burden_df_annual = burden_df %>% dplyr::group_by(admin_name, Run_Number, year) %>%
+ dplyr::summarise(total_mortality_all_1 = sum(total_mortality_1),
+ total_mortality_all_2 = sum(total_mortality_2),
+ New_clinical_cases_all = sum(New_Clinical_Cases),
+ Pop_all = mean(Statistical_Population),
+ total_mortality_U5_1 = sum(total_mortality_U5_1),
+ total_mortality_U5_2 = sum(total_mortality_U5_2),
+ New_clinical_cases_U5 = sum(New_clinical_cases_U5),
+ Pop_U5 = mean(Pop_U5)
+ ) %>%
+ dplyr::ungroup()
+ burden_df_annual$mortality_rate_u5 = (burden_df_annual$total_mortality_U5_1*1 + burden_df_annual$total_mortality_U5_2*1) / 2 / burden_df_annual$Pop_U5 * 1000 # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
+ burden_df_annual$mortality_rate_all = (burden_df_annual$total_mortality_all_1*1 + burden_df_annual$total_mortality_all_2*1) / 2 / burden_df_annual$Pop_all * 1000 # weighted average of mortality estimates (1/2 mort_1, 1/2 mort_2)
+ # remove any NA rows
+ burden_df_annual = burden_df_annual[!is.na(burden_df_annual$mortality_rate_u5),]
+ burden_df_annual = burden_df_annual[!is.na(burden_df_annual$mortality_rate_all),]
+ # get average annual rate across all included years
+ mortality_rate_means = burden_df_annual[,c('admin_name', 'mortality_rate_u5', 'mortality_rate_all')] %>% dplyr::group_by(admin_name) %>%
+ dplyr::summarise(mortality_rate_u5 = mean(mortality_rate_u5),
+ mortality_rate_all = mean(mortality_rate_all)) %>%
+ dplyr::ungroup()
+
+
+
+ # clinical cases
+ # divide number of clinical cases by total population size in simulation; will later multiply by true population of DS to get estimated number of clinical cases in that DS
+ burden_df$cases_pp_u5 = burden_df$New_clinical_cases_U5 / burden_df$Statistical_Population
+ burden_df$cases_pp_all = burden_df$New_Clinical_Cases / burden_df$Statistical_Population
+ case_sums = burden_df[,c('admin_name', 'Run_Number', 'cases_pp_u5', 'cases_pp_all')] %>% dplyr::group_by(admin_name, Run_Number) %>% dplyr::summarise_all(sum) %>% dplyr::ungroup()
+ case_sums = case_sums[,c('admin_name', 'cases_pp_u5', 'cases_pp_all')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
+ # multiply by admin population to get total number of cases
+ case_sums = merge(case_sums, admin_pop, by='admin_name')
+ case_sums$clinical_cases_u5 = case_sums$cases_pp_u5 * case_sums$pop_size
+ case_sums$clinical_cases_all = case_sums$cases_pp_all * case_sums$pop_size
+
+ # incidence (number of cases in a year in each admin divided by the U5 or all-age population size times 1000)
+ burden_df_annual$incidence_u5 = burden_df_annual$New_clinical_cases_U5 / burden_df_annual$Pop_U5 * 1000
+ burden_df_annual$incidence_all = burden_df_annual$New_clinical_cases_all / burden_df_annual$Pop_all * 1000
+ # remove any NA rows
+ burden_df_annual = burden_df_annual[!is.na(burden_df_annual$incidence_u5),]
+ burden_df_annual = burden_df_annual[!is.na(burden_df_annual$incidence_all),]
+ # get average annual rate across all included years
+ incidence_means = burden_df_annual[,c('admin_name', 'incidence_u5', 'incidence_all')] %>% dplyr::group_by(admin_name) %>%
+ dplyr::summarise(incidence_u5 = mean(incidence_u5),
+ incidence_all = mean(incidence_all)) %>%
+ dplyr::ungroup()
+
+
+
+
+ # fraction of population U5
+ burden_df$frac_u5 = burden_df$Pop_U5 / burden_df$Statistical_Population
+ frac_u5 = burden_df[,c('admin_name', 'frac_u5')] %>% dplyr::group_by(admin_name) %>% dplyr::summarise_all(mean) %>% dplyr::ungroup()
+
+ burden_means = merge(pfpr_means, mortality_sums)#, by='admin_name')
+ burden_means = merge(burden_means, case_sums)#, by=c('admin_name', 'pop_size'))
+ burden_means = merge(burden_means, mortality_rate_means)#, by=c('admin_name'))
+ burden_means = merge(burden_means, incidence_means)#, by=c('admin_name'))
+ burden_means = merge(burden_means, frac_u5)#, by=c('admin_name'))
+ burden_means$pop_size_u5 = burden_means$pop_size * burden_means$frac_u5
+
+ write.csv(burden_means, output_filename, row.names=FALSE)
+ }
+ return(burden_means)
+}
+
+
+
+
#
#
#
@@ -215,12 +218,11 @@ library(lubridate)
###############################################################################################################################
-# total simulation burden over specified interval, aggregated across all incldued districts and separated by seed
+# total simulation burden over specified interval, aggregated across all included districts and separated by seed
###############################################################################################################################
# total over a time period
-get_cumulative_burden = function(sim_output_filepath, experiment_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE,
- seed_subset=NA, seed_subset_name=''){
+get_cumulative_burden = function(sim_output_filepath, experiment_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE){
#' @description get cumulative U5 and all-age burden over specified years in specified districts (for all malaria metrics, separate values for each seed)
#' @return save and return data frame where each row is a seed and each column is the total over all included years of different burden metrics:
#' sum of:
@@ -240,8 +242,8 @@ get_cumulative_burden = function(sim_output_filepath, experiment_name, start_yea
#' - mLBW
#' - stillbirths
- if(!dir.exists(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))) dir.create(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))
- output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden/cumulativeBurden_', start_year, '_', end_year, '_', district_subset, seed_subset_name, '.csv')
+
+ output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden_', start_year, '_', end_year, '_', district_subset, '.csv')
if(file.exists(output_filename) & !overwrite_files){
df_aggregated = read.csv(output_filename)
}else{
@@ -258,14 +260,10 @@ get_cumulative_burden = function(sim_output_filepath, experiment_name, start_yea
df = merge(cur_file, admin_pop, by='admin_name')
# subset to appropriate admins
df = df[df$admin_name %in% cur_admins,]
- # subset to appropriate seeds if relevant
- if(all(!is.na(seed_subset)) & is.numeric(seed_subset)){
- df = df[(df$Run_Number+1) %in% seed_subset,]
- }
# all age metrics - rescaled to full population
df$positives_all_ages = df$PfPR_MiP_adjusted * df$pop_size
- df$cases_all_ages = df$New_Clinical_Cases * df$pop_size / df$Statistical_Population
+ df$cases_all_ages = df$New_Clinical_Cases * (df$pop_size / df$Statistical_Population)
df$direct_deaths_1_all_ages = df$direct_mortality_nonMiP_1 * df$pop_size / df$Statistical_Population
df$direct_deaths_2_all_ages = df$direct_mortality_nonMiP_2 * df$pop_size / df$Statistical_Population
df$all_deaths_1_all_ages = df$total_mortality_1 * df$pop_size / df$Statistical_Population
@@ -338,13 +336,136 @@ get_cumulative_burden = function(sim_output_filepath, experiment_name, start_yea
+# total over a time period for each state
+get_cumulative_burden_by_state = function(sim_output_filepath, experiment_name, start_year, end_year, admin_pop, LLIN2y_flag=FALSE, overwrite_files=FALSE, mean_across_seeds=FALSE){
+ #' @description get cumulative U5 and all-age burden over specified years in each state (for all malaria metrics, separate values for each seed)
+ #' @return save and return data frame where each row is a seed x state and each column is the total over all included years of different burden metrics:
+ #' sum of:
+ #' - clinical cases (all ages)
+ #' - clinical cases (U5)
+ #' - deaths (all ages) - upper, lower, average parameter estimates
+ #' - deaths (U5) - upper, lower, average parameter estimates
+ #' - mLBW
+ #' - malaria-attributable stillbirths
+ #' average of annual (population weighted):
+ #' - PfPR (all ages)
+ #' - PfPR (U5)
+ #' - incidence (all ages)
+ #' - incidence (U5)
+ #' - death rate (all ages)
+ #' - death rate (U5)
+ #' - mLBW
+ #' - stillbirths
+
+
+ output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden_', start_year, '_', end_year, '_byState.csv')
+ if(file.exists(output_filename) & !overwrite_files){
+ df_aggregated = read.csv(output_filename)
+ }else{
+ cur_file = fread(paste0(sim_output_filepath, '/', experiment_name, '/malariaBurden_withAdjustments.csv'), check.names=TRUE)
+ # filter to relevant years
+ cur_file = cur_file[cur_file$year <= end_year,]
+ cur_file = cur_file[cur_file$year >= start_year,]
+ # merge population sizes in each admin
+ df = merge(cur_file, admin_pop, by='admin_name')
+
+ # all age metrics - rescaled to full population
+ df$positives_all_ages = df$PfPR_MiP_adjusted * df$pop_size
+ df$cases_all_ages = df$New_Clinical_Cases * (df$pop_size / df$Statistical_Population)
+ df$severe_all_ages = df$severe_total * (df$pop_size / df$Statistical_Population)
+ df$direct_deaths_1_all_ages = df$direct_mortality_nonMiP_1 * df$pop_size / df$Statistical_Population
+ df$direct_deaths_2_all_ages = df$direct_mortality_nonMiP_2 * df$pop_size / df$Statistical_Population
+ df$all_deaths_1_all_ages = df$total_mortality_1 * df$pop_size / df$Statistical_Population
+ df$all_deaths_2_all_ages = df$total_mortality_2 * df$pop_size / df$Statistical_Population
+ df$num_mLBW = df$mLBW_births * df$pop_size / df$Statistical_Population
+ df$num_mStillbirths = df$MiP_stillbirths * df$pop_size / df$Statistical_Population
+ # U5 metrics - rescaled to full population
+ df$pop_size_U5 = df$pop_size * (df$Pop_U5 / df$Statistical_Population) # assumes fraction of individual U5 in simulation is same as fraction in full population
+ df$positives_U5 = df$PfPR_U5 * df$pop_size_U5
+ df$cases_U5 = df$New_clinical_cases_U5 * df$pop_size_U5 / df$Pop_U5
+ df$severe_U5 = df$Severe_cases_U5 * df$pop_size_U5 / df$Pop_U5
+ df$direct_deaths_1_U5 = df$direct_mortality_nonMiP_U5_1 * df$pop_size_U5 / df$Pop_U5
+ df$direct_deaths_2_U5 = df$direct_mortality_nonMiP_U5_2 * df$pop_size_U5 / df$Pop_U5
+ df$all_deaths_1_U5 = df$total_mortality_U5_1 * df$pop_size_U5 / df$Pop_U5
+ df$all_deaths_2_U5 = df$total_mortality_U5_2 * df$pop_size_U5 / df$Pop_U5
+
+ df_aggregated = df %>% group_by(Run_Number, State) %>%
+ dplyr::summarize(pop_all_sum = sum(pop_size),
+ pop_U5_sum = sum(pop_size_U5),
+ cases_all_sum = sum(cases_all_ages),
+ cases_U5_sum = sum(cases_U5),
+ severe_all_sum = sum(severe_all_ages),
+ severe_U5_sum = sum(severe_U5),
+ positives_all_sum = sum(positives_all_ages),
+ positives_U5_sum = sum(positives_U5),
+ direct_deaths_1_all_sum = sum(direct_deaths_1_all_ages),
+ direct_deaths_2_all_sum = sum(direct_deaths_2_all_ages),
+ all_deaths_1_all_sum = sum(all_deaths_1_all_ages),
+ all_deaths_2_all_sum = sum(all_deaths_2_all_ages),
+ direct_deaths_1_U5_sum = sum(direct_deaths_1_U5),
+ direct_deaths_2_U5_sum = sum(direct_deaths_2_U5),
+ all_deaths_1_U5_sum = sum(all_deaths_1_U5),
+ all_deaths_2_U5_sum = sum(all_deaths_2_U5),
+ mLBW_sum = sum(num_mLBW),
+ mStill_sum = sum(num_mStillbirths),
+ num_values_grouped = n())
+
+
+ # clinical incidence (annual): sum of number of cases over all months / (pop size) / (number of years) * 1000
+ # = sum of number of cases over all months / (sum of pop size over all months / number of months) / (number of months / 12) * 1000
+ # = sum of number of cases over all months / (sum of pop size over all months / 12) * 1000
+ df_aggregated$incidence_all = (df_aggregated$cases_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$incidence_U5 = (df_aggregated$cases_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$severe_incidence_all = (df_aggregated$severe_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$severe_incidence_U5 = (df_aggregated$severe_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$direct_death_rate_1_all = (df_aggregated$direct_deaths_1_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$direct_death_rate_2_all = (df_aggregated$direct_deaths_2_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$direct_death_rate_mean_all = (df_aggregated$direct_death_rate_1_all + df_aggregated$direct_death_rate_2_all) / 2
+ df_aggregated$all_death_rate_1_all = (df_aggregated$all_deaths_1_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$all_death_rate_2_all = (df_aggregated$all_deaths_2_all_sum / (df_aggregated$pop_all_sum / 12) * 1000)
+ df_aggregated$all_death_rate_mean_all = (df_aggregated$all_death_rate_1_all + df_aggregated$all_death_rate_2_all) / 2
+ df_aggregated$direct_death_rate_1_U5 = (df_aggregated$direct_deaths_1_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$direct_death_rate_2_U5 = (df_aggregated$direct_deaths_2_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$direct_death_rate_mean_U5 = (df_aggregated$direct_death_rate_1_U5 + df_aggregated$direct_death_rate_2_U5) / 2
+ df_aggregated$all_death_rate_1_U5 = (df_aggregated$all_deaths_1_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$all_death_rate_2_U5 = (df_aggregated$all_deaths_2_U5_sum / (df_aggregated$pop_U5_sum / 12) * 1000)
+ df_aggregated$all_death_rate_mean_U5 = (df_aggregated$all_death_rate_1_U5 + df_aggregated$all_death_rate_2_U5) / 2
+ df_aggregated$average_PfPR_all = (df_aggregated$positives_all_sum / (df_aggregated$pop_all_sum))
+ df_aggregated$average_PfPR_U5 = (df_aggregated$positives_U5_sum / (df_aggregated$pop_U5_sum))
+ df_aggregated$annual_num_mLBW = (df_aggregated$mLBW_sum / (end_year - start_year + 1))
+ df_aggregated$annual_num_mStill = (df_aggregated$mStill_sum / (end_year - start_year + 1))
+
+
+ df_aggregated = df_aggregated[,which(colnames(df_aggregated) %in% c('State','Run_Number','cases_all_sum','cases_U5_sum','severe_all_sum','severe_U5_sum',
+ 'mLBW_sum', 'mStill_sum', 'incidence_all', 'incidence_U5', 'severe_incidence_all', 'severe_incidence_U5', 'average_PfPR_all','average_PfPR_U5', 'annual_num_mLBW', 'annual_num_mStill',
+ 'direct_deaths_1_all_sum', 'direct_deaths_2_all_sum', 'direct_deaths_1_U5_sum', 'direct_deaths_2_U5_sum',
+ 'all_deaths_1_all_sum', 'all_deaths_2_all_sum', 'all_deaths_1_U5_sum', 'all_deaths_2_U5_sum',
+ 'direct_death_rate_1_all', 'direct_death_rate_2_all', 'all_death_rate_1_all', 'all_death_rate_2_all',
+ 'direct_death_rate_1_U5', 'direct_death_rate_2_U5', 'all_death_rate_1_U5', 'all_death_rate_2_U5',
+ 'direct_death_rate_mean_all', 'direct_death_rate_mean_U5', 'all_death_rate_mean_all', 'all_death_rate_mean_U5'))]
+
+ if(mean_across_seeds){
+ df_aggregated = df_aggregated %>% group_by(State) %>%
+ summarise_all(mean, na.rm=TRUE)
+ }
+ write.csv(df_aggregated, output_filename, row.names=FALSE)
+ }
+ if(mean_across_seeds){
+ df_aggregated = df_aggregated %>% group_by(State) %>%
+ summarise_all(mean, na.rm=TRUE)
+ }
+ return(df_aggregated)
+}
+
+
+
-get_cumulative_U1_burden = function(sim_output_filepath, experiment_name, start_year, end_year, admin_pop, district_subset=district_subset, cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE,
- seed_subset=NA, seed_subset_name=''){
+
+get_cumulative_U1_burden = function(sim_output_filepath, experiment_name, start_year, end_year, admin_pop, district_subset=district_subset, cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE){
#' @description get cumulative U1 burden over specified years in specified districts (for all malaria metrics, separate values for each seed)
#' @return save and return data frame where each row is a seed and each column is the total over all included years of different burden metrics:
#' sum of:
@@ -355,9 +476,9 @@ get_cumulative_U1_burden = function(sim_output_filepath, experiment_name, start_
#' - incidence (U1)
#' - death rate (U1)
+
- if(!dir.exists(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))) dir.create(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))
- output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden/cumulativeBurden_IPTi_', start_year, '_', end_year, '_', district_subset, seed_subset_name, '.csv')
+ output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden_IPTi_', start_year, '_', end_year, '_', district_subset, '.csv')
if(file.exists(output_filename) & !overwrite_files){
df_aggregated = read.csv(output_filename)
}else{
@@ -374,10 +495,6 @@ get_cumulative_U1_burden = function(sim_output_filepath, experiment_name, start_
df = merge(cur_file, admin_pop, by='admin_name')
# subset to appropriate admins
df = df[df$admin_name %in% cur_admins,]
- # subset to appropriate seeds if relevant
- if(all(!is.na(seed_subset)) & is.numeric(seed_subset)){
- df = df[(df$Run_Number+1) %in% seed_subset,]
- }
# U1 metrics - rescaled to full population
df$pop_size_U1 = df$pop_size * (df$Pop_U1 / df$Statistical_Population) # assumes fraction of individual U1 in simulation is same as fraction in full population
@@ -422,106 +539,18 @@ get_cumulative_U1_burden = function(sim_output_filepath, experiment_name, start_
-# total over a time period
-get_cumulative_burden_each_admin = function(sim_output_filepath, experiment_name, start_year, end_year, district_subset='allDistricts', cur_admins='all', overwrite_files=FALSE){
- #' @description get cumulative U5 and all-age burden over specified years in specified districts (for all malaria metrics, separate values for each seed)
- #' @return save and return data frame where each row is a seed and each column is the total over all included years of different burden metrics:
- #' sum of:
- #' - clinical cases (all ages)
- #' - clinical cases (U5)
- #' - deaths (all ages) - upper, lower, average parameter estimates
- #' - deaths (U5) - upper, lower, average parameter estimates
- #' - mLBW
- #' - malaria-attributable stillbirths
- #' average of annual (population weighted):
- #' - PfPR (all ages)
- #' - PfPR (U5)
- #' - incidence (all ages)
- #' - incidence (U5)
- #' - death rate (all ages)
- #' - death rate (U5)
- #' - mLBW
- #' - stillbirths
-
- if(!dir.exists(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))) dir.create(paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden'))
- output_filename = paste0(sim_output_filepath, '/', experiment_name, '/cumulativeBurden/cumulativeBurden_eachAdmin_', start_year, '_', end_year, '_', district_subset, '.csv')
- if(file.exists(output_filename) & !overwrite_files){
- df_aggregated = read.csv(output_filename)
- }else{
- cur_file = fread(paste0(sim_output_filepath, '/', experiment_name, '/malariaBurden_withAdjustments.csv'), check.names=TRUE)
- # filter to relevant years
- cur_file = cur_file[cur_file$year <= end_year,]
- cur_file = cur_file[cur_file$year >= start_year,]
- # if we include all admins, get list of names from population size dataframe
- if(cur_admins[1] == 'all'){
- cur_admins = unique(cur_file$admin_name)
- }
- # subset to appropriate admins
- df = cur_file[cur_file$admin_name %in% cur_admins,]
-
- # all age metrics - rescaled by simulated population size where relevant; also multiply by 12 to get numbers per year instead of per month
- df$pfpr_all_ages = df$PfPR_MiP_adjusted
- df$cases_pp_all_ages = df$New_Clinical_Cases / df$Statistical_Population * 12
- df$direct_deaths_1_pp_all_ages = df$direct_mortality_nonMiP_1 / df$Statistical_Population * 12
- df$direct_deaths_2_pp_all_ages = df$direct_mortality_nonMiP_2 / df$Statistical_Population * 12
- df$all_deaths_1_pp_all_ages = df$total_mortality_1 / df$Statistical_Population * 12
- df$all_deaths_2_pp_all_ages = df$total_mortality_2 / df$Statistical_Population * 12
- df$mLBW_pp = df$mLBW_births / df$Statistical_Population * 12
- df$mStillbirths_pp = df$MiP_stillbirths / df$Statistical_Population * 12
- # U5 metrics - rescaled by simulated population size where relevant
- df$pfpr_U5 = df$PfPR_U5
- df$cases_pp_U5 = df$New_clinical_cases_U5 / df$Pop_U5 * 12
- df$direct_deaths_1_pp_U5 = df$direct_mortality_nonMiP_U5_1 / df$Pop_U5 * 12
- df$direct_deaths_2_pp_U5 = df$direct_mortality_nonMiP_U5_2 / df$Pop_U5 * 12
- df$all_deaths_1_pp_U5 = df$total_mortality_U5_1 / df$Pop_U5 * 12
- df$all_deaths_2_pp_U5 = df$total_mortality_U5_2 / df$Pop_U5 * 12
-
- # aggregate across months/years (all values aside from pfpr are in average number per person per month)
- df_aggregated = df %>% group_by(Run_Number, admin_name) %>%
- dplyr::summarize_all(mean) %>% ungroup()
-
- df_aggregated = df_aggregated[,which(colnames(df_aggregated) %in% c(
- 'admin_name',
- 'Run_Number',
- 'pfpr_all_ages',
- 'cases_pp_all_ages',
- 'direct_deaths_1_pp_all_ages',
- 'direct_deaths_2_pp_all_ages',
- 'all_deaths_1_pp_all_ages',
- 'all_deaths_2_pp_all_ages',
- 'mLBW_pp',
- 'mStillbirths_pp',
- 'pfpr_U5',
- 'cases_pp_U5',
- 'direct_deaths_1_pp_U5',
- 'direct_deaths_2_pp_U5',
- 'all_deaths_1_pp_U5',
- 'all_deaths_2_pp_U5'
- ))]
- write.csv(df_aggregated, output_filename, row.names=FALSE)
- }
- return(df_aggregated)
-}
-
-
-
-
-
####################################################################################
# relative simulation burden between two experiments over specified time interval
####################################################################################
get_relative_burden = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all',
- LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE,
- seed_subset=NA, seed_subset_name='' ){
+ LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE ){
#' @description get relative change in U5 and all-age burden when comparing between two simulations in specified years and in specified districts (for all malaria metrics, separate values for each seed)
#' @return data frame where each row is a seed and each column is the relative change of different burden metrics, calculated as (reference-comparison) / reference:
- reference_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
- comparison_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
+ reference_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+ comparison_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
if(align_seeds){ # compare one run seed against the matching run seed in the other experiment
# align seeds
@@ -545,16 +574,47 @@ get_relative_burden = function(sim_output_filepath, reference_experiment_name, c
+# get relative burden by state for grid plot
+get_relative_burden_by_state = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all',
+ LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE ){
+ #' @description get relative change in U5 and all-age burden when comparing between two simulations in specified years and in specified districts (for all malaria metrics, separate values for each seed)
+ #' @return data frame where each row is a seed and each column is the relative change of different burden metrics, calculated as (reference-comparison) / reference:
+
+ reference_df = get_cumulative_burden_by_state(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+ comparison_df = get_cumulative_burden_by_state(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+
+ if(align_seeds){ # compare one run seed against the matching run seed in the other experiment
+ # align seeds and states into same order
+ reference_df = reference_df[order(reference_df$State, reference_df$Run_Number),]
+ comparison_df = comparison_df[order(comparison_df$State, comparison_df$Run_Number),]
+ } else{ # compare the averages across all seeds from one experiment against the average from the other experiment
+ reference_df = reference_df %>% group_by(State) %>% summarise_all(mean)
+ comparison_df = comparison_df %>% group_by(State) %>% summarise_all(mean)
+ # align states into same order
+ reference_df = reference_df[order(reference_df$State),]
+ comparison_df = comparison_df[order(comparison_df$State),]
+ }
+ relative_burden_df = data.frame('State'=reference_df$State, 'Run_Number'=reference_df$Run_Number)
+ # iterate through burden indicators, calculating relative burden and adding to dataframe
+ burden_indicators = colnames(reference_df)[-which(colnames(reference_df) %in% c('State', 'Run_Number'))]
+ for(bb in 1:length(burden_indicators)){
+ # relative_burden_cur = (comparison_df[[burden_indicators[bb]]] - reference_df[[burden_indicators[bb]]]) / reference_df[[burden_indicators[bb]]]
+ relative_burden_cur = (reference_df[[burden_indicators[bb]]] - comparison_df[[burden_indicators[bb]]]) / reference_df[[burden_indicators[bb]]]
+ relative_burden_df[[burden_indicators[bb]]] = relative_burden_cur
+ }
+ relative_burden_df$scenario = comparison_scenario_name
+ return(relative_burden_df)
+}
+
+
+
-get_relative_U1_burden = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE,
- seed_subset=NA, seed_subset_name=''){
+get_relative_U1_burden = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all', LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE){
#' @description get relative change in U1 burden when comparing between two simulations in specified years and in specified districts (for all malaria metrics, separate values for each seed)
#' @return data frame where each row is a seed and each column is the relative change of different burden metrics, calculated as (reference-comparison) / reference:
- reference_df = get_cumulative_U1_burden(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
- comparison_df = get_cumulative_U1_burden(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files,
- seed_subset=seed_subset, seed_subset_name=seed_subset_name)
+ reference_df = get_cumulative_U1_burden(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+ comparison_df = get_cumulative_U1_burden(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
if(align_seeds){ # compare one run seed against the matching run seed in the other experiment
# align seeds
@@ -580,6 +640,74 @@ get_relative_U1_burden = function(sim_output_filepath, reference_experiment_name
+####################################################################################
+# absolute difference in simulation burden between two experiments over specified time interval
+####################################################################################
+
+get_difference_burden = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all',
+ LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE ){
+ #' @description get relative change in U5 and all-age burden when comparing between two simulations in specified years and in specified districts (for all malaria metrics, separate values for each seed)
+ #' @return data frame where each row is a seed and each column is the relative change of different burden metrics, calculated as (reference-comparison) / reference:
+
+ reference_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+ comparison_df = get_cumulative_burden(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, district_subset=district_subset, cur_admins=cur_admins, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+
+ if(align_seeds){ # compare one run seed against the matching run seed in the other experiment
+ # align seeds
+ reference_df = reference_df[order(reference_df$Run_Number),]
+ comparison_df = comparison_df[order(comparison_df$Run_Number),]
+ } else{ # compare the averages across all seeds from one experiment against the average from the other experiment
+ reference_df = reference_df %>% summarise_all(mean)
+ comparison_df = comparison_df %>% summarise_all(mean)
+ }
+ difference_burden_df = data.frame('Run_Number' = reference_df$Run_Number)
+ # iterate through burden indicators, calculating relative burden and adding to dataframe
+ burden_indicators = colnames(reference_df)[-which(colnames(reference_df) == 'Run_Number')]
+ for(bb in 1:length(burden_indicators)){
+ difference_burden_cur = (reference_df[[burden_indicators[bb]]] - comparison_df[[burden_indicators[bb]]])
+ difference_burden_df[[burden_indicators[bb]]] = difference_burden_cur
+ }
+ difference_burden_df$scenario = comparison_scenario_name
+ return(difference_burden_df)
+}
+
+
+
+# get relative burden by state for grid plot
+get_difference_burden_by_state = function(sim_output_filepath, reference_experiment_name, comparison_experiment_name, comparison_scenario_name, start_year, end_year, admin_pop, district_subset='allDistricts', cur_admins='all',
+ LLIN2y_flag=FALSE, overwrite_files=FALSE, align_seeds=TRUE ){
+ #' @description get relative change in U5 and all-age burden when comparing between two simulations in specified years and in specified districts (for all malaria metrics, separate values for each seed)
+ #' @return data frame where each row is a seed and each column is the relative change of different burden metrics, calculated as (reference-comparison) / reference:
+
+ reference_df = get_cumulative_burden_by_state(sim_output_filepath=sim_output_filepath, experiment_name=reference_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+ comparison_df = get_cumulative_burden_by_state(sim_output_filepath=sim_output_filepath, experiment_name=comparison_experiment_name, start_year=start_year, end_year=end_year, admin_pop=admin_pop, LLIN2y_flag=LLIN2y_flag, overwrite_files=overwrite_files)
+
+ if(align_seeds){ # compare one run seed against the matching run seed in the other experiment
+ # align seeds and states into same order
+ reference_df = reference_df[order(reference_df$State, reference_df$Run_Number),]
+ comparison_df = comparison_df[order(comparison_df$State, comparison_df$Run_Number),]
+ } else{ # compare the averages across all seeds from one experiment against the average from the other experiment
+ reference_df = reference_df %>% group_by(State) %>% summarise_all(mean)
+ comparison_df = comparison_df %>% group_by(State) %>% summarise_all(mean)
+ # align states into same order
+ reference_df = reference_df[order(reference_df$State),]
+ comparison_df = comparison_df[order(comparison_df$State),]
+ }
+ difference_burden_df = data.frame('State'=reference_df$State, 'Run_Number'=reference_df$Run_Number)
+ # iterate through burden indicators, calculating relative burden and adding to dataframe
+ burden_indicators = colnames(reference_df)[-which(colnames(reference_df) %in% c('State', 'Run_Number'))]
+ for(bb in 1:length(burden_indicators)){
+ difference_burden_cur = (reference_df[[burden_indicators[bb]]] - comparison_df[[burden_indicators[bb]]])
+ difference_burden_df[[burden_indicators[bb]]] = difference_burden_cur
+ }
+ difference_burden_df$scenario = comparison_scenario_name
+ return(difference_burden_df)
+}
+
+
+
+
+
####################################################################################
# timeseries of simulation burden and/or interventions
@@ -638,7 +766,7 @@ get_burden_timeseries_exp = function(exp_filepath, exp_name, district_subset, cu
} else if((grepl('mLBW', burden_colname)) | (grepl('stillbirth', burden_colname))){
# rescale to number present in full admin (with true population instead of simulated population size)
- cur_sim_output$true_burden = cur_sim_output[[burden_colname]] * cur_sim_output$true_population / cur_sim_output$population
+ cur_sim_output$true_burden = cur_sim_output[[burden_colname]] * (cur_sim_output$true_population / cur_sim_output$population)
# take sum of the number of mLBWs or stillbirths and births across all included admins
select_col_names = c('true_burden', 'month', 'year', 'date', 'true_population', 'Run_Number')
cur_sim_output_agg_admin = as.data.frame(cur_sim_output) %>% dplyr::select(match(select_col_names, names(.))) %>%
@@ -651,7 +779,7 @@ get_burden_timeseries_exp = function(exp_filepath, exp_name, district_subset, cu
}
}else{
# rescale case (or death) numbers to number present in full admin (with true population instead of simulated population size)
- cur_sim_output$true_burden = cur_sim_output[[burden_colname]] * cur_sim_output$true_population / cur_sim_output$population
+ cur_sim_output$true_burden = cur_sim_output[[burden_colname]] * (cur_sim_output$true_population / cur_sim_output$population)
# take sum of the number of cases (or deaths) and population sizes across all included admins
select_col_names = c('true_burden', 'month', 'year', 'date', 'true_population', 'Run_Number')
cur_sim_output_agg_admin = as.data.frame(cur_sim_output) %>% dplyr::select(match(select_col_names, names(.))) %>%
@@ -680,13 +808,154 @@ get_burden_timeseries_exp = function(exp_filepath, exp_name, district_subset, cu
}
cur_sim_output_agg$scenario = exp_name
+ write.csv(cur_sim_output_agg, output_filename, row.names=FALSE)
}
- write.csv(cur_sim_output_agg, output_filename, row.names=FALSE)
return(cur_sim_output_agg)
}
+get_burden_timeseries_by_lga = function(exp_filepath, exp_name, pop_filepath, overwrite_files=FALSE){
+ #' @description subset simulation output to appropriate admin and time period, and calculate annual mean burden (for a set of malaria burden metric) across all runs
+ #' @return data frame where each row is a time point and there are columns for the mean, minimum, and maximum burden value across seeds, and also a column for the scenario name
+ # Assumes malariaBurden_withAdjustments.csv contains the following burden colnames: c('PfPR_U5', 'PfPR_MiP_adjusted', 'New_clinical_cases_U5', 'New_Clinical_Cases', 'direct_mortality_nonMiP_U5_mean', 'direct_mortality_nonMiP_mean', 'total_mortality_U5_mean', 'total_mortality_mean')
+
+ # check whether file already exists, otherwise create new dataframe
+ output_filename = paste0(exp_filepath, '/timeseries_burden_annual_by_LGA.csv')
+ if(file.exists(output_filename) & !overwrite_files){
+ cur_sim_output_agg = read.csv(output_filename)
+ } else{
+ # read in information about LGAs
+ admin_info = read.csv(pop_filepath)
+ admin_info = admin_info[,c('admin_name','pop_size','State')]
+
+ # read in simulation information, subset to appropriate years
+ cur_sim_output = fread(paste0(exp_filepath, '/malariaBurden_withAdjustments.csv'))
+
+ # merge to get real-world population sizes in each admin and the State each admin belongs to
+ cur_sim_output = merge(cur_sim_output, admin_info, by='admin_name')
+ # get simulation population denominator and the real-world population size in each admin
+ cur_sim_output$true_population_U5 = cur_sim_output$pop_size * cur_sim_output$Pop_U5 / cur_sim_output$Statistical_Population
+ cur_sim_output$true_population_all = cur_sim_output$pop_size
+
+ # process simulation output to get total numbers in each state:
+ # - get total numbers in each admin, scaled to appropriate LGA population size from simulation population size
+ # - subset to relevant columns
+ # - get total within each admin-year-Run (across months)
+ cur_sim_output_a = cur_sim_output %>% mutate(
+ positives_U5 = PfPR_U5 * true_population_U5,
+ positives_all = PfPR_MiP_adjusted * true_population_all,
+ num_cases_U5 = New_clinical_cases_U5 / Pop_U5 * true_population_U5,
+ num_cases_all = New_Clinical_Cases / Statistical_Population * true_population_all,
+ num_direct_mortality_U5 = direct_mortality_nonMiP_U5_mean / Pop_U5 * true_population_U5,
+ num_direct_mortality_all = direct_mortality_nonMiP_mean / Statistical_Population * true_population_all,
+ num_total_mortality_U5 = total_mortality_U5_mean / Pop_U5 * true_population_U5,
+ num_total_mortality_all = total_mortality_mean / Statistical_Population * true_population_all
+ ) %>%
+ dplyr::select(Run_Number, admin_name, State, year, true_population_U5, true_population_all, positives_U5, positives_all, num_cases_U5, num_cases_all, num_direct_mortality_U5, num_direct_mortality_all, num_total_mortality_U5, num_total_mortality_all) %>%
+ group_by(Run_Number, admin_name, State, year) %>%
+ summarise_all(sum) %>%
+ ungroup() %>%
+ mutate( # take average population over 12 months rather than sum of population sizes in each month
+ true_population_U5 = true_population_U5 / 12,
+ true_population_all = true_population_all / 12
+ )
+ # process simulation output to get per-capita values and average across runs:
+ # - get per-capita or rate values
+ # - find average across runs
+ cur_sim_output_agg = cur_sim_output_a %>% mutate(
+ PfPR_U5 = positives_U5 / true_population_U5 / 12, # divide by number of months in year to get population-weighted average prevalence across all months rather than sum of positives across months
+ PfPR_all = positives_all / true_population_all / 12,
+ incidence_pp_U5 = num_cases_U5 / true_population_U5,
+ incidence_pp_all = num_cases_all / true_population_all,
+ direct_mortality_pp_U5 = num_direct_mortality_U5 / true_population_U5,
+ direct_mortality_pp_all = num_direct_mortality_all / true_population_all,
+ total_mortality_pp_U5 = num_total_mortality_U5 / true_population_U5,
+ total_mortality_pp_all = num_total_mortality_all / true_population_all
+ ) %>%
+ group_by(admin_name, State, year) %>%
+ summarise_all(mean) %>%
+ ungroup()
+
+ # save result
+ cur_sim_output_agg$scenario = exp_name
+ write.csv(cur_sim_output_agg, output_filename, row.names=FALSE)
+ }
+ return(cur_sim_output_agg)
+}
+
+
+#' # PROBLEM: need to fix how U5 values are calculated to use simulated population U5 sizes instead of full population sizes rescaled
+#' get_burden_timeseries_by_state = function(exp_filepath, exp_name, pop_filepath, overwrite_files=FALSE){
+#' #' @description subset simulation output to appropriate admin and time period, and calculate annual mean burden (for a set of malaria burden metric) across all runs
+#' #' @return data frame where each row is a time point and there are columns for the mean, minimum, and maximum burden value across seeds, and also a column for the scenario name
+#' # Assumes malariaBurden_withAdjustments.csv contains the following burden colnames: c('PfPR_U5', 'PfPR_MiP_adjusted', 'New_clinical_cases_U5', 'New_Clinical_Cases', 'direct_mortality_nonMiP_U5_mean', 'direct_mortality_nonMiP_mean', 'total_mortality_U5_mean', 'total_mortality_mean')
+#'
+#' # check whether file already exists, otherwise create new dataframe
+#' output_filename = paste0(exp_filepath, '/timeseries_burden_annual_by_state.csv')
+#' if(file.exists(output_filename) & !overwrite_files){
+#' cur_sim_output_agg = read.csv(output_filename)
+#' } else{
+#' # read in information about LGAs
+#' admin_info = read.csv(pop_filepath)
+#' admin_info = admin_info[,c('admin_name','pop_size','State')]
+#'
+#' # read in simulation information, subset to appropriate years
+#' cur_sim_output = fread(paste0(exp_filepath, '/malariaBurden_withAdjustments.csv'))
+#'
+#' # merge to get real-world population sizes in each admin and the State each admin belongs to
+#' cur_sim_output = merge(cur_sim_output, admin_info, by='admin_name')
+#' # get simulation population denominator and the real-world population size in each admin
+#' cur_sim_output$true_population_U5 = cur_sim_output$pop_size * cur_sim_output$Pop_U5 / cur_sim_output$Statistical_Population
+#' cur_sim_output$true_population_all = cur_sim_output$pop_size
+#'
+#' # process simulation output to get total numbers in each state:
+#' # - get total numbers in each admin, scaled to appropriate LGA population size from simulation population size
+#' # - subset to relevant columns
+#' # - get total within each state-year-Run (across months and LGAs)
+#' cur_sim_output_a = cur_sim_output %>% mutate(
+#' positives_U5 = PfPR_U5 * true_population_U5,
+#' positives_all = PfPR_MiP_adjusted * true_population_all,
+#' num_cases_U5 = New_clinical_cases_U5 / Pop_U5 * true_population_U5,
+#' num_cases_all = New_Clinical_Cases / Statistical_Population * true_population_all,
+#' num_direct_mortality_U5 = direct_mortality_nonMiP_U5_mean / Pop_U5 * true_population_U5,
+#' num_direct_mortality_all = direct_mortality_nonMiP_mean / Statistical_Population * true_population_all,
+#' num_total_mortality_U5 = total_mortality_U5_mean / Pop_U5 * true_population_U5,
+#' num_total_mortality_all = total_mortality_mean / Statistical_Population * true_population_all
+#' ) %>%
+#' dplyr::select(Run_Number, State, year, true_population_U5, true_population_all, positives_U5, positives_all, num_cases_U5, num_cases_all, num_direct_mortality_U5, num_direct_mortality_all, num_total_mortality_U5, num_total_mortality_all) %>%
+#' group_by(Run_Number, State, year) %>%
+#' summarise_all(sum) %>%
+#' ungroup() %>%
+#' mutate( # take average population over 12 months rather than sum of population sizes in each month
+#' true_population_U5 = true_population_U5 / 12,
+#' true_population_all = true_population_all / 12
+#' )
+#' # process simulation output to get per-capita values and average across runs:
+#' # - get per-capita or rate values
+#' # - find average across runs
+#' cur_sim_output_agg = cur_sim_output_a %>% mutate(
+#' PfPR_U5 = positives_U5 / true_population_U5 / 12, # divide by number of months in year to get population-weighted average prevalence across all months rather than sum of positives across months
+#' PfPR_all = positives_all / true_population_all / 12,
+#' incidence_pp_U5 = num_cases_U5 / true_population_U5,
+#' incidence_pp_all = num_cases_all / true_population_all,
+#' direct_mortality_pp_U5 = num_direct_mortality_U5 / true_population_U5,
+#' direct_mortality_pp_all = num_direct_mortality_all / true_population_all,
+#' total_mortality_pp_U5 = num_total_mortality_U5 / true_population_U5,
+#' total_mortality_pp_all = num_total_mortality_all / true_population_all
+#' ) %>%
+#' group_by(State, year) %>%
+#' summarise_all(mean) %>%
+#' ungroup()
+#'
+#' # save result
+#' cur_sim_output_agg$scenario = exp_name
+#' write.csv(cur_sim_output_agg, output_filename, row.names=FALSE)
+#' }
+#' return(cur_sim_output_agg)
+#' }
+
+
get_intervention_use_timeseries_exp = function(exp_filepath, exp_name, cur_admins, pop_sizes, min_year, max_year, indoor_protection_fraction, plot_by_month=TRUE){
#' @description subset simulation output to appropriate admin(s) and time period, and calculate monthly net usage, net distribution, and IRS coverage across all runs
#' @return data frame where each row is a time point and there are columns for the mean, minimum, and maximum ITN coverage across seeds, as well as columns for mean nets distributed and IRS coverages, and also a column for the scenario name
@@ -715,7 +984,7 @@ get_intervention_use_timeseries_exp = function(exp_filepath, exp_name, cur_admin
interv_all = merge(interv_all, pop_sizes, by='admin_name')
colnames(interv_all)[colnames(interv_all) == 'pop_size'] = 'true_population'
interv_pop_scaled = as.data.frame(interv_all)
- interv_pop_scaled[,intervention_columns] = interv_pop_scaled[,intervention_columns]* interv_pop_scaled$true_population / interv_pop_scaled$Statistical.Population
+ interv_pop_scaled[,intervention_columns] = interv_pop_scaled[,intervention_columns]* (interv_pop_scaled$true_population / interv_pop_scaled$Statistical.Population)
# get sum of numbers across all included admins (keeping runs and months separate)
interv_sums = interv_pop_scaled %>% dplyr::select(one_of('year', 'date', 'Run_Number', 'true_population', intervention_columns)) %>% dplyr::group_by(date, year, Run_Number) %>%
@@ -760,6 +1029,81 @@ get_intervention_use_timeseries_exp = function(exp_filepath, exp_name, cur_admin
}
+# ----- CM intervention coverage ----- #
+get_cm_timeseries_by_state = function(cm_filepath, admin_info, end_year, exp_name, min_year, plot_by_month=TRUE){
+
+ cm_input = read.csv(cm_filepath)
+ if(!('seed' %in% cm_input)) cm_input$seed = 1
+ cm_input = merge(cm_input, admin_info, by='admin_name')
+
+ # CM is sometimes repeated for several years but only listed once; change to repeate the appropriate number of times
+ cm_input$years_repeated = cm_input$duration/365
+ if(any(cm_input$years_repeated>1)){
+ cur_cm_years = unique(cm_input$year)
+ for(yy in cur_cm_years){
+ # get first instance of this year
+ cur_year = cm_input[cm_input$year==yy,]
+ if(cur_year$years_repeated[1]>1){
+ for(rr in 1:(cur_year$years_repeated[1] - 1)){
+ temp_year = cur_year
+ temp_year$year = cur_year$year + rr
+ temp_year$simday = cur_year$simday + rr*365
+ cm_input = rbind(cm_input, temp_year)
+ }
+ }
+ }
+ }
+ if(any(cm_input$duration==-1) & (max(cm_input$year)% group_by(year, admin_name, State, seed) %>%
+ summarise_all(mean) %>% ungroup()
+
+ # cm_input = cm_input[intersect(which(cm_input$year >= min_year), which(cm_input$year <= end_year)),]
+
+
+ # get population-weighted CM coverage across admins
+ cm_input$multiplied_U5_cm = cm_input$U5_coverage * cm_input$pop_size
+
+ # get sum of population sizes and multiplied CM coverage across included admins
+ cm_input_agg_admin <- cm_input %>% dplyr::select(year, State, seed, multiplied_U5_cm, pop_size) %>% group_by(year, State, seed) %>%
+ summarise_all(sum) %>% ungroup()
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
+ cm_input_agg_admin$U5_coverage = cm_input_agg_admin$multiplied_U5_cm / cm_input_agg_admin$pop_size
+
+
+ # take average, max, and min burdens across simulation seeds
+ if(plot_by_month){
+ # subdivide year values and add dates
+ # date dataframe
+ included_years = unique(cm_input_agg_admin$year)
+ all_months = as.Date(paste0(rep(included_years, each=12),'-',c('01','02','03','04','05','06','07','08','09','10','11','12'), '-01' ))
+ date_df = data.frame(year=rep(included_years, each=12), date=all_months)
+ cm_input_agg_admin_monthly = merge(cm_input_agg_admin, date_df, by='year', all.x=TRUE, all.y=TRUE)
+ cm_agg = as.data.frame(cm_input_agg_admin_monthly) %>% dplyr::select(year, State, date, U5_coverage) %>%
+ dplyr::group_by(year, State, date) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ } else{
+ cm_agg = as.data.frame(cm_input_agg_admin) %>% dplyr::select(year, State, U5_coverage) %>%
+ dplyr::group_by(year, State) %>%
+ dplyr::summarise(mean_coverage = mean(U5_coverage),
+ max_coverage = max(U5_coverage),
+ min_coverage = min(U5_coverage))
+ }
+ cm_agg$scenario = exp_name
+ return(cm_agg)
+}
+
+
# ----- CM intervention coverage ----- #
get_cm_timeseries_exp = function(cm_filepath, pop_sizes, end_year, exp_name, cur_admins, min_year, plot_by_month=TRUE){
@@ -808,7 +1152,7 @@ get_cm_timeseries_exp = function(cm_filepath, pop_sizes, end_year, exp_name, cur
# get sum of population sizes and multiplied CM coverage across included admins
cm_input_agg_admin <- cm_input %>% dplyr::select(year, seed, multiplied_U5_cm, pop_size) %>% group_by(year, seed) %>%
summarise_all(sum) %>% ungroup()
- # get population-weighted U5 coverage across all included admin by dividing by su of population sizes
+ # get population-weighted U5 coverage across all included admin by dividing by sum of population sizes
cm_input_agg_admin$U5_coverage = cm_input_agg_admin$multiplied_U5_cm / cm_input_agg_admin$pop_size
@@ -839,46 +1183,3 @@ get_cm_timeseries_exp = function(cm_filepath, pop_sizes, end_year, exp_name, cur
}
-
-
-get_intervention_per_cap_each_admin = function(exp_filepath, exp_name, cur_admins, min_year, max_year){
- #' @description subset simulation output to appropriate admin(s) and time period, and total interventions distributed per person in each seed
- #' similar to get_intervention_timeseries_exp, but doesn't include all intervention measures and is disaggregated by admin and seed
- #' @return data frame where each row is an admin-seed and columns are the number of interventions distributed per person over the specified time period
-
- # read in simulation information, merge to single dataframe, subset to appropriate years
- interv_dist_all = fread(paste0(exp_filepath, '/monthly_Event_Count.csv'))
- sim_pop_all = fread(paste0(exp_filepath, '/All_Age_monthly_Cases.csv'))
- intervention_distribution_columns_orig = c('Received_IRS', 'Received_Vaccine', 'Received_Campaign_Drugs', 'Received_PMC_VaccDrug', 'Bednet_Got_New_One')
- intervention_distribution_columns_new_name = c('irs_per_cap', 'vacc_per_cap', 'smc_per_cap', 'pmc_per_cap', 'new_net_per_cap')
- intervention_distribution_columns = intervention_distribution_columns_orig[intervention_distribution_columns_orig %in% colnames(interv_dist_all)]
- intervention_columns = c(intervention_distribution_columns)
- interv_dist_all = interv_dist_all %>% dplyr::select(one_of(c('admin_name', 'date', 'Run_Number', intervention_distribution_columns)))
- sim_pop_all = sim_pop_all %>% dplyr::select(one_of(c('admin_name', 'date', 'Run_Number', 'Statistical Population')))
- interv_all = merge(interv_dist_all, sim_pop_all, all=TRUE)
- interv_all$date = as.Date(interv_all$date)
- interv_all$year = lubridate::year(interv_all$date)
- interv_all = interv_all[intersect(which(interv_all$year >= min_year), which(interv_all$year <= max_year)),]
-
- # subset to appropriate admins
- interv_all = interv_all[interv_all$admin_name %in% cur_admins,]
- colnames(interv_all) = gsub(' ','.',colnames(interv_all))
-
- # rescale numbers to interventions per individual in simulated population
- interv_pop_scaled = as.data.frame(interv_all)
- interv_pop_scaled[,intervention_columns] = interv_pop_scaled[,intervention_columns] / interv_pop_scaled$Statistical.Population
-
- # get sum of numbers across all included admins (keeping runs and months separate)
- interv_sums = interv_pop_scaled %>% dplyr::select(one_of('admin_name', 'Run_Number', intervention_columns)) %>% dplyr::group_by(admin_name, Run_Number) %>%
- dplyr::summarise_all(sum) %>% ungroup()
- interv_per_capita = interv_sums
-
- # update column names for plotting
- for(cc in intervention_columns){
- colnames(interv_per_capita)[colnames(interv_per_capita)==cc] = intervention_distribution_columns_new_name[which(intervention_distribution_columns_orig==cc)]
- }
- interv_per_capita$scenario = exp_name
- return(interv_per_capita)
-}
-
-
diff --git a/r_utilities/standardize_admin_names.R b/r_utilities/standardize_admin_names.R
index faae9ab..976d188 100644
--- a/r_utilities/standardize_admin_names.R
+++ b/r_utilities/standardize_admin_names.R
@@ -5,9 +5,25 @@
# Goal: the names of LGAs are often different across different files and we want to make sure that they are used consistently
# This function takes a 'target' naming system and a 'origin' set of names and matches the origin LGA names with the target names.
# It then updates the origin names so that the same LGA names can be used consistently across all files.
+# NOTE OF CAUTION: some NGA admin2 names appear in multiple admin1s, so they must be renamed to end in a 1 or 2, depending on the admin1. Currently done manually. Includes:
+# 'BASSA'
+# 'IFELODUN'
+# 'IREPODUN'
+# 'NASARAWA'
+# 'OBI'
+# 'SURULERE'
library(stringr)
+# if there are admin names that shared across multiple admins (without their numeric distintuishers) in the inputs, will need to have user modify
+duplicate_names = c('BASSA', 'IFELODUN', 'IREPODUN', 'NASARAWA', 'OBI', 'SURULERE') # LGA
+duplicate_names_state = c()
+
create_reference_name_match = function(lga_name){
+ #' alter string lga_name to standardized admin2 (LGA) format, also check for and fix common misspellings
+ #' @param lga_name the name of the admin2 that should be changed to standardized format
+
+ lga_name = str_replace_all(lga_name, pattern=" State", replacement='')
+ lga_name = str_replace_all(lga_name, pattern=" state", replacement='')
lga_name = str_replace_all(lga_name, pattern=' ', replacement='-')
lga_name = str_replace_all(lga_name, pattern='/', replacement='-')
lga_name = str_replace_all(lga_name, pattern='_', replacement='-')
@@ -17,6 +33,12 @@ create_reference_name_match = function(lga_name){
# first value (the name) is the one that is replaced by the second value
replace_list = list(
# NGA
+ # 'IFELODUN' = 'IFELODUN1', 'IFELODUN2',
+ # 'IREPODUN' = 'IREPODUN1', 'IREPODUN2',
+ # 'BASSA' = 'BASSA1', 'BASSA2',
+ # 'NASARAWA' = 'NASARAWA1', 'NASARAWA2',
+ # 'SURULERE' = 'SURULERE1', 'SURULERE2',
+ # 'OBI' = 'OBI2',
'GANYE' = 'GANAYE',
'GIREI' = 'GIRERI',
'DAMBAM' = 'DAMBAN',
@@ -37,15 +59,9 @@ create_reference_name_match = function(lga_name){
'DAN' = 'DAN-MUSA',
'DUTSIN' = 'DUTSIN-MA',
'MATAZU' = 'MATAZUU',
- 'BASSA' = 'BASSA1',
- 'IFELODUN' = 'IFELODUN1',
- 'IREPODUN' = 'IREPODUN1',
'PATIGI' = 'PATEGI',
- 'NASARAWA' = 'NASARAWA2',
- 'OBI' = 'OBI2',
'MUNYA' = 'MUYA',
'PAIKORO' = 'PAILORO',
- 'SURULERE' = 'SURULERE2',
'BARKIN-LADI' = 'BARIKIN-LADI',
'DANGE-SHUNI' = 'DANGE-SHNSI',
'GWADABAWA' = 'GAWABAWA',
@@ -55,13 +71,28 @@ create_reference_name_match = function(lga_name){
'BARDE' = 'BADE',
'BORSARI' = 'BURSARI',
'TARMUWA' = 'TARMUA',
+ 'EGBADO-NORTH' = 'YEWA-NORTH',
+ 'EGBADO-SOUTH' = 'YEWA-SOUTH',
+ 'ILEMEJI' = 'ILEJEMEJE',
+ 'GBOYIN' = 'AIYEKIRE-(GBOYIN)',
+ 'UMU-NEOCHI' = 'UMU-NNEOCHI',
+ 'IBADAN-NORTH-EAST' = 'IBADAN-CENTRAL-(IBADAN-NORTH-EAST)',
+ 'NAFADA' = 'NAFADA-(BAJOGA)',
+
# BDI
'BUJUMBURA-CENTRE' = 'ZONE-CENTRE',
'BUJUMBURA-NORD' = 'ZONE-NORD',
- 'BUJUMBURA-SUD' = 'ZONE-SUD'
+ 'BUJUMBURA-SUD' = 'ZONE-SUD',
# 'Bukinanyana' = 'Cibitoke', # Bukinanyana is a commune in Cibitoke province - may now be a new DS
# 'Gisuru' = 'Ruyigi', # Gisuru is a commune in Ruyigi province - may now be a new DS
# 'Rutovu' = 'Bururi' # Rutovu is a commune in Bururi province - may now be a new DS
+
+ # state names
+ 'AKWA-LBOM' = 'AKWA-IBOM',
+ 'FEDERAL-CAPITAL-TERRITORY' = 'FCT-ABUJA',
+ 'ABUJA-FCT' = 'FCT-ABUJA',
+ 'FCT' = 'FCT-ABUJA',
+ 'CROSS-RIVER' = 'CRORIVER'
)
if(lga_name %in% names(replace_list)){
@@ -70,7 +101,34 @@ create_reference_name_match = function(lga_name){
return(lga_name)
}
-standardize_admin_names_in_df = function(target_names_df, origin_names_df, target_names_col='admin_name', origin_names_col='admin_name'){
+standardize_admin_names_in_df = function(target_names_df, origin_names_df, target_names_col='admin_name', origin_names_col='admin_name', unique_entries_flag=FALSE, additional_id_col='State', possible_suffixes=c(1,2,3)){
+ #' given a dataframe with a column containing admin names, update the admin names to match the standardized naming conventions
+ #' @param target_names_df dataframe containing a column with the admin names that should be used as the standard
+ #' @param origin_names_df dataframe containing a column with admin names that user would like to update to the standard
+ #' @param target_names_col name of column in target_names_df where the standard admin names are found
+ #' @param origin_names_col name of column in origin_names_df where the to-be-updated admin names are found
+ #' @return the origin_names_df data frame, with the admin names replaced with the standardized version
+
+ if(unique_entries_flag){
+ duplicates = unique(origin_names_df[[origin_names_col]][which(duplicated(origin_names_df[[origin_names_col]]))])
+ if(length(duplicates)>0){
+ for(dd in 1:length(duplicates)){
+ duplicate_rows = which(origin_names_df[[origin_names_col]] == duplicates[dd])
+ # see whether the duplicated names can be distinguished based on value in additional_id_col
+ possible_match_rows = as.numeric(sapply(paste0(duplicates[dd], possible_suffixes), grep, target_names_df[[target_names_col]]))
+ possible_match_rows = possible_match_rows[!is.na(possible_match_rows)]
+ if(length(possible_match_rows) == length(duplicate_rows)){
+ origin_additional_ids = origin_names_df[[additional_id_col]][duplicate_rows]
+ target_additional_ids = target_names_df[[additional_id_col]][possible_match_rows]
+ if(all(origin_additional_ids %in% target_additional_ids)){
+ origin_names_df[[origin_names_col]][duplicate_rows] = target_names_df[[target_names_col]][possible_match_rows][match(origin_additional_ids, target_additional_ids)]
+ } else stop('PROBLEM ENCOUNTERED: Some of the input admin names are not unique identifiers of the admin. Need to add numeric value to distintuish, determined by admin region, following the system from the standardization base file.')
+ } else stop('PROBLEM ENCOUNTERED: Some of the input admin names are not unique identifiers of the admin. Need to add numeric value to distintuish, determined by admin region, following the system from the standardization base file.')
+ }
+ }
+ } else if(any(duplicate_names %in% toupper(origin_names_df[[origin_names_col]]))){
+ stop('PROBLEM ENCOUNTERED: Some of the input admin names are not unique identifiers of the admin. Need to add numeric value to distintuish, determined by admin region, following the system from the standardization base file.')
+ }
target_names_df$matched_name = sapply(target_names_df[[target_names_col]], create_reference_name_match)
origin_names_df$matched_name = sapply(origin_names_df[[origin_names_col]], create_reference_name_match)
@@ -79,6 +137,7 @@ standardize_admin_names_in_df = function(target_names_df, origin_names_df, targe
if(!all(origin_names_df$matched_name %in% target_names_df$matched_name)){
warning('Some of the source admin names could not be matched with a target admin name')
View(distinct(origin_names_df[which(!(origin_names_df$matched_name %in% target_names_df$matched_name)), c('matched_name', 'State')]))
+ View(distinct(target_names_df[which(!(target_names_df$matched_name %in% origin_names_df$matched_name)), c('matched_name', 'State')]))
View(target_names_df[,c('matched_name', 'State')])
}
if('data.table' %in% class(origin_names_df)){ # changes how indexing works for columns in dataframe
@@ -93,7 +152,7 @@ standardize_admin_names_in_df = function(target_names_df, origin_names_df, targe
if(target_names_col %in% colnames(origin_names_df)){
origin_names_df = origin_names_df[,-which(colnames(origin_names_df)==target_names_col)]
}
-
+
# add the updated admin names to the dataframe, under the original admin column name
target_names_df = target_names_df[,c('matched_name', target_names_col)]
updated_names_df = merge(origin_names_df, target_names_df, all.x=TRUE, by='matched_name')
@@ -105,26 +164,85 @@ standardize_admin_names_in_df = function(target_names_df, origin_names_df, targe
-standardize_admin_names_in_vector = function(target_names, origin_names){
+standardize_state_names_in_df = function(target_names_df, origin_names_df, target_names_col='State', origin_names_col='State'){
+ #' given a dataframe with a column containing admin names, update the admin names to match the standardized naming conventions
+ #' @param target_names_df dataframe containing a column with the admin names that should be used as the standard
+ #' @param origin_names_df dataframe containing a column with admin names that user would like to update to the standard
+ #' @param target_names_col name of column in target_names_df where the standard admin names are found
+ #' @param origin_names_col name of column in origin_names_df where the to-be-updated admin names are found
+ #' @return the origin_names_df data frame, with the admin names replaced with the standardized version
- target_matched_name = sapply(target_names, create_reference_name_match)
- origin_matched_name = sapply(origin_names, create_reference_name_match)
+ if(any(duplicate_names_state %in% toupper(origin_names_df[[origin_names_col]]))){
+ stop('PROBLEM ENCOUNTERED: Some of the input admin names are not unique identifiers of the admin. Need to add numeric value to distintuish, determined by admin region, following the system from the standardization base file.')
+ } else{
+ target_names_df$matched_name = sapply(target_names_df[[target_names_col]], create_reference_name_match)
+ origin_names_df$matched_name = sapply(origin_names_df[[origin_names_col]], create_reference_name_match)
+
+ # check whether all names from the origin source are now matched to one of the target names
+ if(!all(origin_names_df$matched_name %in% target_names_df$matched_name)){
+ warning('Some of the source admin names could not be matched with a target admin name')
+ View(distinct(origin_names_df[which(!(origin_names_df$matched_name %in% target_names_df$matched_name)), c('matched_name', 'State')]))
+ View(target_names_df[,c('matched_name', 'State')])
+ }
+ if('data.table' %in% class(origin_names_df)){ # changes how indexing works for columns in dataframe
+ origin_names_df = as.data.frame(origin_names_df)
+ was_data_table=TRUE
+ } else was_data_table = FALSE
+ if('data.table' %in% class(target_names_df)){
+ target_names_df = as.data.frame(target_names_df)
+ }
+ # remove the original admin name column from the origin dataframe, and also the target name column if applicable (for merging)
+ origin_names_df = origin_names_df[,-(which(colnames(origin_names_df)==origin_names_col))]
+ if(target_names_col %in% colnames(origin_names_df)){
+ origin_names_df = origin_names_df[,-which(colnames(origin_names_df)==target_names_col)]
+ }
+
+ # add the updated admin names to the dataframe, under the original admin column name
+ target_names_df = distinct(target_names_df[,c('matched_name', target_names_col)])
+ updated_names_df = merge(origin_names_df, target_names_df, all.x=TRUE, by='matched_name') # only include States in the origin df (i.e., don't include empty placeholders if they weren't in the original)
+ colnames(updated_names_df)[colnames(updated_names_df)==target_names_col] = origin_names_col
+
+ if(was_data_table) updated_names_df = as.data.table(updated_names_df)
+ return(updated_names_df)
+ }
+}
+
+
+
+
+
+standardize_admin_names_in_vector = function(target_names, origin_names){
+ #' given a vector of admin names, update the admin names to match the standardized naming conventions provided in target_names
+ #' @param target_names set of admin names that should be used as the standard
+ #' @param origin_names current set of admin names that user would like to update to the standard
+ #' @return a vector with updated admin names, in the same order as origin_names
- # check whether all names from the origin source are now matched to one of the target names
- if(!all(origin_matched_name %in% target_matched_name)){
- warning('Some of the source admin names could not be matched with a target admin name')
- }
- # create dataframes to merge
- target_df = data.frame('matched_name'=target_matched_name, 'target_name'=target_names)
- origin_df = data.frame('matched_name'=origin_matched_name, 'original_name'=origin_names)
-
- # add the updated admin names to the dataframe, under the original admin column name
- updated_names_df = merge(origin_df, target_df, all.x=TRUE, by='matched_name', sort=FALSE)
- if(all(updated_names_df$original_name == origin_names)){
- return(updated_names_df$target_name)
+ if(any(duplicate_names %in% toupper(origin_names))){
+ stop('PROBLEM ENCOUNTERED: Some of the input admin names are not unique identifiers of the admin. Need to add numeric value to distintuish, determined by admin region, following the system from the standardization base file.')
} else{
- warning('merge did not maintain original order, need to fix standardize_admin_names_in_vector function.')
- return(NA)
+ target_matched_name = sapply(target_names, create_reference_name_match)
+ origin_matched_name = sapply(origin_names, create_reference_name_match)
+
+ # check whether all names from the origin source are now matched to one of the target names
+ if(!all(origin_matched_name %in% target_matched_name)){
+ warning('Some of the source admin names could not be matched with a target admin name')
+ }
+
+ # create dataframes to merge
+ target_df = data.frame('matched_name'=target_matched_name, 'target_name'=target_names)
+ origin_df = data.frame('matched_name'=origin_matched_name, 'original_name'=origin_names)
+
+ # add the updated admin names to the dataframe, under the original admin column name
+ updated_names_df = merge(origin_df, target_df, all.x=TRUE, by='matched_name', sort=FALSE)
+ if(all(updated_names_df$original_name == origin_names)){
+ return(updated_names_df$target_name)
+ } else{
+ warning('merge did not maintain original order, need to fix standardize_admin_names_in_vector function or use standardize_admin_names_in_df')
+ return(NA)
+ }
}
}
+
+
+
diff --git a/requirements.txt b/requirements.txt
index ca5a4c5..e5e0cc3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
-i https://packages.idmod.org/api/pypi/pypi-production/simple
-emodpy-malaria~=3.1.1
+emodpy-malaria
+requests==2.29.0
idmtools_calibra~=1.0.9
idmtools-platform-comps~=1.7.7
scipy
diff --git a/snt/analyzers/analyze_helpers.py b/snt/analyzers/analyze_helpers.py
index dc5516d..c0ad902 100644
--- a/snt/analyzers/analyze_helpers.py
+++ b/snt/analyzers/analyze_helpers.py
@@ -71,8 +71,8 @@ def __init__(self, expt_name, sweep_variables=None, working_dir=".", start_year=
self.start_year = start_year
self.end_year = end_year
- def filter(self, simulation):
- return simulation.status.name == 'Succeeded'
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
def map(self, data, simulation):
@@ -315,6 +315,72 @@ def reduce(self, all_data):
adf.to_csv(os.path.join(self.working_dir, self.expt_name, 'All_Age_monthly_prevalence.csv'), index=False)
+class monthlyEventAnalyzerITN(IAnalyzer):
+
+ @classmethod
+ def monthparser(self, x):
+ if x == 0:
+ return 12
+ else:
+ return datetime.datetime.strptime(str(x), '%j').month
+
+ def __init__(self, expt_name, channels=None, sweep_variables=None, working_dir=".", start_year=2020, end_year=2026, output_file_suffix=''):
+ super(monthlyEventAnalyzerITN, self).__init__(working_dir=working_dir,
+ filenames=["output/ReportEventCounter.json"]
+ )
+ self.sweep_variables = sweep_variables or ["admin_name", "Run_Number"]
+ if channels is None:
+ self.channels = ['Received_Treatment', 'Received_Severe_Treatment', 'Received_NMF_Treatment',
+ 'Received_Self_Medication', 'Bednet_Using', # 'Bednet_Got_New_One',
+ # currently removed 'Bednet_Got_New_One', since length is 1 longer than expected for unknown reasons
+ 'Received_Campaign_Drugs', 'Received_IRS', 'Received_Vaccine', 'Received_PMC_VaccDrug']
+ else:
+ self.channels = channels
+ self.expt_name = expt_name
+ self.start_year = start_year
+ self.end_year = end_year
+ self.output_file_suffix = output_file_suffix
+
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
+
+ def map(self, data, simulation):
+
+ channels_in_expt = [x for x in self.channels if x in data[self.filenames[0]]['Channels'].keys()]
+
+ simdata = pd.DataFrame({x: data[self.filenames[0]]['Channels'][x]['Data'] for x in channels_in_expt})
+ simdata['Time'] = simdata.index + 1
+
+ simdata['Day'] = simdata['Time'] % 365
+ simdata['month'] = simdata['Day'].apply(lambda x: self.monthparser((x + 1) % 365))
+ simdata['year'] = simdata['Time'].apply(lambda x: int(x / 365) + self.start_year)
+
+ for missing_channel in [x for x in self.channels if x not in channels_in_expt]:
+ simdata[missing_channel] = 0
+
+ for sweep_var in self.sweep_variables:
+ if sweep_var in simulation.tags.keys():
+ simdata[sweep_var] = simulation.tags[sweep_var]
+ return simdata
+
+ def reduce(self, all_data):
+
+ selected = [data for sim, data in all_data.items()]
+ if len(selected) == 0:
+ print("No data have been returned... Exiting...")
+ return
+
+ if not os.path.exists(os.path.join(self.working_dir, self.expt_name)):
+ os.mkdir(os.path.join(self.working_dir, self.expt_name))
+
+ adf = pd.concat(selected).reset_index(drop=True, )
+ adf['date'] = adf.apply(lambda x: datetime.date(x['year'], x['month'], 1), axis=1)
+
+ df = adf.groupby(['admin_name', 'date', 'Run_Number'])[self.channels].agg(np.sum).reset_index()
+ df.to_csv(os.path.join(self.working_dir, self.expt_name, 'monthly_Event_Count%s.csv' % self.output_file_suffix), index=False)
+
+
+
class monthlyEventAnalyzer(IAnalyzer):
@classmethod
@@ -324,14 +390,14 @@ def monthparser(self, x):
else:
return datetime.datetime.strptime(str(x), '%j').month
- def __init__(self, expt_name, channels=None, sweep_variables=None, working_dir=".", start_year=2020, end_year=2026):
+ def __init__(self, expt_name, channels=None, sweep_variables=None, working_dir=".", start_year=2020, end_year=2026, output_file_suffix=''):
super(monthlyEventAnalyzer, self).__init__(working_dir=working_dir,
filenames=["output/ReportEventCounter.json"]
)
self.sweep_variables = sweep_variables or ["admin_name", "Run_Number"]
if channels is None:
self.channels = ['Received_Treatment', 'Received_Severe_Treatment', 'Received_NMF_Treatment',
- 'Received_Self_Medication', 'Bednet_Using', 'Bednet_Got_New_One',
+ 'Received_Self_Medication', 'Bednet_Using', # 'Bednet_Got_New_One',
# currently removed 'Bednet_Got_New_One', since length is 1 longer than expected for unknown reasons
'Received_Campaign_Drugs', 'Received_IRS', 'Received_Vaccine', 'Received_PMC_VaccDrug']
else:
@@ -339,9 +405,10 @@ def __init__(self, expt_name, channels=None, sweep_variables=None, working_dir="
self.expt_name = expt_name
self.start_year = start_year
self.end_year = end_year
+ self.output_file_suffix = output_file_suffix
- def filter(self, simulation):
- return simulation.status.name == 'Succeeded'
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
def map(self, data, simulation):
@@ -376,7 +443,7 @@ def reduce(self, all_data):
adf['date'] = adf.apply(lambda x: datetime.date(x['year'], x['month'], 1), axis=1)
df = adf.groupby(['admin_name', 'date', 'Run_Number'])[self.channels].agg(np.sum).reset_index()
- df.to_csv(os.path.join(self.working_dir, self.expt_name, 'monthly_Event_Count.csv'), index=False)
+ df.to_csv(os.path.join(self.working_dir, self.expt_name, 'monthly_Event_Count%s.csv' % self.output_file_suffix), index=False)
class monthlySevereTreatedByAgeAnalyzer(IAnalyzer):
@@ -540,8 +607,8 @@ def __init__(self, expt_name, sweep_variables=None, working_dir=".", start_year=
self.end_year = end_year
self.output_filename = output_filename
- def filter(self, simulation):
- return simulation.status.name == 'Succeeded'
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
def map(self, data, simulation):
@@ -650,8 +717,8 @@ def __init__(self, expt_name, sweep_variables=None, working_dir=".", start_year=
self.end_year = end_year
self.output_filename = output_filename
- def filter(self, simulation):
- return simulation.status.name == 'Succeeded'
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
def map(self, data, simulation):
@@ -777,8 +844,8 @@ def __init__(self, expt_name, sweep_variables=None, working_dir=".", start_year=
self.end_year = end_year
self.output_filename = output_filename
- def filter(self, simulation):
- return simulation.status.name == 'Succeeded'
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
def map(self, data, simulation):
@@ -1017,8 +1084,8 @@ def reduce(self, all_data):
output_filename='newInfections_PfPR_cases_monthly_byAgeGroup.csv')
]
- am = AnalyzeManager(platform=platform, ids=[expid, ItemType.EXPERIMENT], analyzers=analyzers,
- force_analyze=True)
+ am = AnalyzeManager(platform=platform, ids=[(expid, ItemType.EXPERIMENT)], analyzers=analyzers,
+ analyze_failed_items=True)
am.analyze()
elif include_LLINp:
diff --git a/snt/analyzers/analyze_monthly_pfpr_u5.py b/snt/analyzers/analyze_monthly_pfpr_u5.py
index 1d82bb4..a1cff04 100644
--- a/snt/analyzers/analyze_monthly_pfpr_u5.py
+++ b/snt/analyzers/analyze_monthly_pfpr_u5.py
@@ -1,5 +1,7 @@
import os
import pandas as pd
+import numpy as np
+import datetime
from idmtools.entities import IAnalyzer
@@ -59,3 +61,108 @@ def reduce(self, all_data):
# all_df = all_df.groupby([self.mult_param, 'month', 'admin_name'])[['PfPR U5', 'Pop']].agg(np.mean).reset_index()
# all_df = all_df.sort_values(by=[self.mult_param, 'month', 'archetype', 'DS_Name_for_ITN'])
all_df.to_csv(os.path.join(self.working_dir, 'monthly_U5_PfPR.csv'), index=False)
+
+
+
+
+class monthlyU5PrevalenceAnalyzer(IAnalyzer):
+
+ @classmethod
+ def monthparser(self, x):
+ if x == 0:
+ return 12
+ else:
+ return datetime.datetime.strptime(str(x), '%j').month
+
+ def __init__(self, expt_name, sweep_variables=None, working_dir=".", start_year=2020, end_year=2026):
+ super(monthlyU5PrevalenceAnalyzer, self).__init__(working_dir=working_dir,
+ filenames=["output/ReportMalariaFiltered__RDT_mic_PfPR_U5.json"]
+ )
+ self.sweep_variables = sweep_variables or ["admin_name", "Run_Number"]
+ self.inset_channels = ['Statistical Population', 'New Clinical Cases', 'True Prevalence',
+ 'PCR Parasite Prevalence', 'Blood Smear Parasite Prevalence', 'PfHRP2 Prevalence']
+ self.expt_name = expt_name
+ self.start_year = start_year
+ self.end_year = end_year
+
+ # added to bypass failed cases
+ # def filter(self, simulation):
+ # return simulation.status.name == 'Succeeded'
+
+ def map(self, data, simulation):
+ d = pd.DataFrame({x: data[self.filenames[0]]['Channels'][x]['Data'] for x in self.inset_channels})
+ d['Time'] = d.index
+ simdata = d
+ simdata['Day'] = simdata['Time'] % 365
+ simdata['month'] = simdata['Day'].apply(lambda x: self.monthparser((x + 1) % 365))
+ simdata['year'] = simdata['Time'].apply(lambda x: int(x / 365) + self.start_year)
+
+ for sweep_var in self.sweep_variables:
+ if sweep_var in simulation.tags.keys():
+ simdata[sweep_var] = simulation.tags[sweep_var]
+ return simdata
+
+ def reduce(self, all_data):
+
+ selected = [data for sim, data in all_data.items()]
+ if len(selected) == 0:
+ print("No data have been returned... Exiting...")
+ return
+
+ if not os.path.exists(os.path.join(self.working_dir, self.expt_name)):
+ os.mkdir(os.path.join(self.working_dir, self.expt_name))
+
+ adf = pd.concat(selected).reset_index(drop=True)
+ # adf['date'] = adf.apply(lambda x: datetime.date(x['year'], x['month'], 1), axis=1)
+
+ sum_channels = ['New Clinical Cases']
+ mean_channels = ['Statistical Population', 'True Prevalence', 'PCR Parasite Prevalence',
+ 'Blood Smear Parasite Prevalence', 'PfHRP2 Prevalence']
+
+ df = adf.groupby(['year', 'month']+self.sweep_variables)[sum_channels].agg(np.sum).reset_index()
+ pdf = adf.groupby(['year', 'month']+self.sweep_variables)[mean_channels].agg(np.mean).reset_index()
+
+ adf = pd.merge(left=pdf, right=df, on=['year', 'month']+self.sweep_variables)
+ adf.to_csv(os.path.join(self.working_dir, self.expt_name, 'monthly_U5_PfPR_mic_RDT.csv'), index=False)
+
+
+
+
+
+
+
+
+if __name__ == "__main__":
+ from idmtools.analysis.analyze_manager import AnalyzeManager
+ from idmtools.core import ItemType
+ from idmtools.core.platform_factory import Platform
+ from snt.load_paths import load_box_paths
+
+ platform = Platform('Calculon')
+
+ data_path, project_path = load_box_paths(country_name='Nigeria')
+
+ working_dir = os.path.join(project_path, 'simulation_outputs', 'baseline_calibration')
+ start_year = 2010 # simulation starts in January of this year
+ end_year = 2021 # simulation ends in December of this year
+
+
+ expt_ids = {
+ 'PfPR_sweep_main_NGA_v1_testRDT': '58ddf0c3-90a9-ee11-9eff-b88303912b51'
+ }
+
+ for expname, expid in expt_ids.items():
+ print('running expt %s' % expname)
+
+ sweep_variables = ["Run_Number",
+ "Habitat_Multiplier",
+ "admin_name",
+ ]
+ analyzers = [MonthlyPfPRU5Analyzer(expname, sweep_variables, working_dir, start_year, end_year),
+ monthlyU5PrevalenceAnalyzer(expname, sweep_variables, working_dir, start_year, end_year)]
+
+
+ am = AnalyzeManager(platform=platform, ids=[(expid, ItemType.EXPERIMENT)], analyzers=analyzers,
+ analyze_failed_items=True)
+
+ am.analyze()
diff --git a/snt/calibration/ChannelByMultiYearSeasonCohortInsetAnalyzer.py b/snt/calibration/ChannelByMultiYearSeasonCohortInsetAnalyzer.py
index 4197037..9ba5b6a 100644
--- a/snt/calibration/ChannelByMultiYearSeasonCohortInsetAnalyzer.py
+++ b/snt/calibration/ChannelByMultiYearSeasonCohortInsetAnalyzer.py
@@ -22,7 +22,7 @@ def monthparser(self, x):
else:
return datetime.datetime.strptime(str(x), '%j').month
- def __init__(self, site, weight=1, compare_fn=ll_calculators.gamma_poisson_pandas, **kwargs):
+ def __init__(self, site, weight=1, compare_fn=ll_calculators.negative_square_diff_obs_sim, **kwargs):
super().__init__(reference_data=site.get_reference_data('entomology_by_season'),
weight=weight,
filenames=['output/ReportEventCounter.json',
@@ -49,8 +49,6 @@ def map(self, data, simulation):
simdata = pd.DataFrame(simdata)
simdata[self.comparison_channel] = simdata[self.case_channel] + simdata[self.nmf_channel]
- # inflate pop for undercounted denom
- # simdata[self.population_channel] = simdata[self.population_channel] # *1.2
simdata = simdata[-365:].reset_index(drop=True)
simdata['Time'] = simdata.index
@@ -64,6 +62,10 @@ def map(self, data, simulation):
s2 = simdata.groupby('Month')['Observations'].agg(np.sum).reset_index()
simdata = pd.merge(left=s1, right=s2, on='Month')
simdata = simdata[['Month', 'Trials', 'Observations']]
+
+ # add in the simulation id for debugging
+ # simdata['sim_id'] = simulation.id.hex
+
simdata = simdata.set_index(['Month'])
return simdata
@@ -95,6 +97,9 @@ def reduce(self, all_data):
data = combined.groupby(level=['sample', 'Counts'], axis=1).mean()
compare_results = data.groupby(level='sample', axis=1).apply(self.compare)
+ # Make sure index is sorted in correct order
+ compare_results.index = compare_results.index.astype(int)
+ compare_results = compare_results.sort_index(ascending=True)
head, tail = os.path.split(self.working_dir)
iteration = int(tail.split('r')[-1])
@@ -129,3 +134,8 @@ def reduce(self, all_data):
plt.close(fig)
return compare_results
+
+
+
+
+
diff --git a/snt/calibration/helpers_seasonality_calibration.py b/snt/calibration/helpers_seasonality_calibration.py
index 83db70a..048689b 100644
--- a/snt/calibration/helpers_seasonality_calibration.py
+++ b/snt/calibration/helpers_seasonality_calibration.py
@@ -128,11 +128,10 @@ def get_spline_values4(hfca, project_path):
hdf = habitat_scales(project_path)
df = pd.DataFrame({'Name': ['MonthVal%d' % x for x in range(1, 13)],
- 'Guess': [0.0075] * 12,
+ 'Guess': [0.01] * 12,
'Min': [0.00001] * 12,
- 'Max': [0.1] * 12}) # !0.01
-
- df = pd.concat([df, pd.DataFrame({'Name': ['MaxHab'], 'Guess': [10], 'Min': [8], 'Max': [12.5]})])
+ 'Max': [1] * 12}) # !0.01
+ df = pd.concat([df, pd.DataFrame({'Name': ['MaxHab'], 'Guess': [11], 'Min': [8], 'Max': [12.2]})])
df['Dynamic'] = True
a = hdf.at[hfca, 'arabiensis_scale_factor']
@@ -152,13 +151,13 @@ def get_spline_values4_constantMaxHab(hfca, project_path):
hdf = habitat_scales(project_path)
df = pd.DataFrame({'Name': ['MonthVal%d' % x for x in range(1, 13)],
- 'Guess': [0.0075] * 12,
+ 'Guess': [0.01] * 12,
'Min': [0.00001] * 12,
'Max': [1] * 12}) # !0.01
df['Dynamic'] = True
df = pd.concat(
- [df, pd.DataFrame({'Name': ['MaxHab'], 'Guess': [10], 'Min': [10], 'Max': [10], 'Dynamic': [False]})])
+ [df, pd.DataFrame({'Name': ['MaxHab'], 'Guess': [12], 'Min': [12], 'Max': [12], 'Dynamic': [False]})])
a = hdf.at[hfca, 'arabiensis_scale_factor']
f = hdf.at[hfca, 'funestus_scale_factor']
diff --git a/snt/calibration/seasonalityIncidenceAnalyzer.py b/snt/calibration/seasonalityIncidenceAnalyzer.py
new file mode 100644
index 0000000..a601c52
--- /dev/null
+++ b/snt/calibration/seasonalityIncidenceAnalyzer.py
@@ -0,0 +1,102 @@
+import os
+import datetime
+import logging
+import pandas as pd
+import numpy as np
+from idmtools.entities import IAnalyzer
+
+logger = logging.getLogger(__name__)
+
+
+class seasonalityIncidenceAnalyzer(IAnalyzer):
+ """
+ Get incidence across months from simulations (to be compared against reference datasets)
+ """
+
+ @classmethod
+ def monthparser(self, x):
+ if x == 0:
+ return 12
+ else:
+ return datetime.datetime.strptime(str(x), '%j').month
+
+ def __init__(self, working_dir="."):
+ super().__init__(working_dir=working_dir, filenames=['output/ReportEventCounter.json',
+ 'output/ReportMalariaFiltered.json'])
+
+ self.population_channel = 'Statistical Population'
+ self.case_channel = 'Received_Treatment'
+ self.prev_channel = 'PfHRP2 Prevalence'
+ self.nmf_channel = 'Received_NMF_Treatment'
+ self.comparison_channel = 'Treated Cases NMF Adjusted'
+ self.working_dir = working_dir
+
+ def map(self, data, simulation):
+ """
+ Extract data from output data and accumulate in same bins as reference.
+ """
+
+ # Load data from simulation
+ simdata = {self.case_channel: data[self.filenames[0]]['Channels'][self.case_channel]['Data'][-365:],
+ self.nmf_channel: data[self.filenames[0]]['Channels'][self.nmf_channel]['Data'][-365:],
+ self.population_channel: data[self.filenames[1]]['Channels'][self.population_channel]['Data'][-365:],
+ self.prev_channel: data[self.filenames[1]]['Channels'][self.prev_channel]['Data'][-365:]}
+
+ simdata = pd.DataFrame(simdata)
+ simdata[self.comparison_channel] = simdata[self.case_channel] + simdata[self.nmf_channel]
+
+ simdata = simdata[-365:].reset_index(drop=True)
+ simdata['Time'] = simdata.index
+ simdata['Day'] = simdata['Time'] % 365
+ simdata['Month'] = simdata['Day'].apply(lambda x: self.monthparser((x + 1) % 365))
+
+ simdata = simdata.rename(columns={self.population_channel: 'Trials',
+ self.comparison_channel: 'Observations'})
+
+ s1 = simdata.groupby('Month')['Trials'].agg(np.mean).reset_index()
+ s2 = simdata.groupby('Month')['Observations'].agg(np.sum).reset_index()
+ simdata = pd.merge(left=s1, right=s2, on='Month')
+ simdata = simdata[['Month', 'Trials', 'Observations']]
+ simdata['CasesPer1000'] = simdata['Observations'] / simdata['Trials'] * 1000
+
+ # add in the simulation id for debugging
+ # simdata['sim_id'] = simulation.id.hex
+
+ # simdata = simdata.set_index(['Month'])
+
+ return simdata
+
+ def reduce(self, all_data):
+ """
+ Calculate the mean output result for each experiment. Note that we assume the setup is that each experiment
+ uses a single set of parameters and we compare its mean incidence against the reference.
+ """
+ # selected = list(all_data.values())
+ #
+ # # Stack selected_data from each parser, adding unique (sim_id) and shared (sample) levels to MultiIndex
+ # combine_levels = ['sample', 'sim_id', 'Counts']
+ # combined = pd.concat(selected, axis=1,
+ # keys=[(s.tags.get('__sample_index__'), s.id) for s in all_data.keys()],
+ # names=combine_levels)
+ #
+ # data = combined.groupby(level=['sample', 'Counts'], axis=1).mean()
+ # compare_results = data.groupby(level='sample', axis=1).apply(self.compare)
+ # # Make sure index is sorted in correct order
+ # compare_results.index = compare_results.index.astype(int)
+ # compare_results = compare_results.sort_index(ascending=True)
+
+ selected = [data for sim, data in all_data.items()]
+ if len(selected) == 0:
+ print("No data have been returned... Exiting...")
+ return
+ # if not os.path.exists(os.path.join(self.working_dir, self.expt_name)):
+ # os.mkdir(os.path.join(self.working_dir, self.expt_name))
+ adf = pd.concat(selected).reset_index(drop=True)
+ adf = adf.groupby(['Month']).agg(np.mean).reset_index()
+ adf.to_csv(os.path.join(self.working_dir, 'All_Age_monthly_prevalence.csv'), index=False)
+ return adf
+
+
+
+
+
diff --git a/snt/helpers_add_interventions.py b/snt/helpers_add_interventions.py
index e539838..b1a9d9a 100644
--- a/snt/helpers_add_interventions.py
+++ b/snt/helpers_add_interventions.py
@@ -1,4 +1,6 @@
import warnings
+import os
+import math
import pandas as pd
import numpy as np
from emodpy_malaria.interventions.treatment_seeking import add_treatment_seeking
@@ -12,8 +14,19 @@
from emodpy_malaria.interventions.adherentdrug import adherent_drug
from snt.helpers_sim_setup import update_smc_access_ips
from emodpy_malaria.interventions.vaccine import add_scheduled_vaccine, add_triggered_vaccine
-from emodpy_malaria.interventions.common import add_triggered_campaign_delay_event
-from emod_api.interventions.common import BroadcastEvent, DelayedIntervention
+from emodpy_malaria.interventions.common import add_triggered_campaign_delay_event, add_campaign_event
+from emod_api.interventions.common import BroadcastEvent, PropertyValueChanger, DelayedIntervention, change_individual_property_scheduled
+
+def tryread_intervention_csv_from_scen_df(project_path, scen_df, scen_index, intervention_colname):
+ if (pd.isna(scen_df.at[scen_index, intervention_colname])) or ((scen_df.at[scen_index, intervention_colname] == '')):
+ df = pd.DataFrame()
+ else:
+ try:
+ df = pd.read_csv(os.path.join(project_path, 'simulation_inputs', '%s.csv' % scen_df.at[scen_index, intervention_colname]))
+ except IOError:
+ print(f"WARNING: Cannot read intervention file for: {intervention_colname}.")
+ df = pd.DataFrame()
+ return df
def add_hfca_hs(campaign, hs_df, hfca, seed_index=0):
@@ -115,7 +128,7 @@ def add_hfca_irs(campaign, irs_df, hfca, seed_index=0):
target_age_min=0,
target_age_max=100,
killing_initial_effect=row['initial_kill'],
- killing_decay_time_constant=row['initial_kill'])
+ killing_decay_time_constant=row['mean_duration']) # !! this was 'initial_kill' before, but I think that was an error in the translation from dtktools?
return len(irs_df)
@@ -743,7 +756,6 @@ def add_epi_rtss(campaign, rtss_df):
vaccine_decay_time_constant=decay_t,
efficacy_is_multiplicative=False)
else:
-
add_triggered_vaccine(campaign,
start_day=start_days[0],
trigger_condition_list=[event_name],
@@ -874,6 +886,262 @@ def add_ds_vaccpmc(campaign, pmc_df, hfca):
return len(pmc_df)
+
+
+
+
+
+
+
+#####################
+# ################### vaccine campaigns created with triggered event ############
+#####################
+
+def get_concentration_at_time(tt, initial_concentration, fast_frac, k1, k2):
+ # calculate the concentration at a specified time
+ concentration_at_tt = initial_concentration * (fast_frac * math.exp(-1 * tt / k1) + (1 - fast_frac) * math.exp(-1 * tt / k2))
+ return concentration_at_tt
+
+
+def get_time_efficacy_values(initial_concentration, max_efficacy, fast_frac, k1, k2, hh, nn, total_time):
+ # get concentration and efficacy through time
+ concentration_through_time = [get_concentration_at_time(tt, initial_concentration, fast_frac, k1, k2) for tt in range(total_time)]
+ # efficacy_through_time = [max_efficacy * (1 - math.exp(mm * cc)) for cc in concentration_through_time]
+ efficacy_through_time = [max_efficacy / (1 + math.pow((hh / cc), nn)) for cc in concentration_through_time]
+ return [[i for i in range(total_time)], efficacy_through_time]
+
+
+def get_vacc_params_from_pkpd_df(row):
+ # extract the parameters describing PKPD from the row of an input file
+ initial_concentration = row['initial_concentration']
+ max_efficacy = row['max_efficacy']
+ fast_frac = row['fast_frac']
+ k1 = row['k1']
+ k2 = row['k2']
+ hh = row['hh']
+ nn = row['nn']
+ total_time = row['total_time']
+ time_efficacy_values = get_time_efficacy_values(initial_concentration, max_efficacy, fast_frac, k1, k2, hh, nn, total_time)
+ return time_efficacy_values
+
+
+def add_triggered_vacc(campaign, vacc_char_df, my_ds=''):
+ # set up vaccines to be distributed when triggered by the 'event_add_new_vaccine' event
+ if my_ds != '':
+ if 'admin_name' in vacc_char_df.columns:
+ vacc_char_df = vacc_char_df[vacc_char_df['admin_name'].str.upper() == my_ds.upper()]
+ if 'vacc_type' in vacc_char_df.columns:
+ row_initial = vacc_char_df[vacc_char_df['vacc_type'] == 'initial'].iloc[0]
+ row_boost = vacc_char_df[vacc_char_df['vacc_type'] == 'booster'].iloc[0]
+ else:
+ row_initial = vacc_char_df.iloc[0]
+ row_boost = vacc_char_df.iloc[0]
+
+ """Set vaccine properties (e.g., initial efficacy, waning)"""
+ # TODO: currently, assumes that boost has same waning type as initial dose. Should probably change this.
+ try:
+ waning_type = row_initial['vacc_waning_type']
+ except:
+ waning_type = 'exponential'
+
+ if waning_type == 'pkpd':
+ time_efficacy_values_initial = get_vacc_params_from_pkpd_df(row_initial)
+ time_efficacy_values_boost = get_vacc_params_from_pkpd_df(row_boost)
+ time_efficacy_initial = time_efficacy_values_initial[1][0]
+ time_efficacy_multipliers = [time_efficacy_values_initial[1][yy] / time_efficacy_initial for yy in
+ range(len(time_efficacy_values_initial[1]))]
+ time_efficacy_boost_initial = time_efficacy_values_boost[1][0]
+ time_efficacy_boost_multipliers = [time_efficacy_values_boost[1][yy] / time_efficacy_boost_initial for yy in
+ range(len(time_efficacy_values_boost[1]))]
+ else:
+ raise ValueError("Unknown vaccine decay type. Only 'pkpd' currently supported.")
+
+ # vaccine is added in response to broadcast event
+ # initial vaccine
+ add_triggered_vaccine(campaign,
+ start_day=1,
+ vaccine_type="AcquisitionBlocking",
+ trigger_condition_list=['event_add_new_vaccine'],
+ listening_duration=-1,
+ demographic_coverage=1,
+ repetitions=1,
+ timesteps_between_repetitions=-1,
+ ind_property_restrictions=[{'VaccineStatus': 'None'}],
+ vaccine_initial_effect=time_efficacy_initial,
+ vaccine_linear_times=time_efficacy_values_initial[0],
+ vaccine_linear_values=time_efficacy_boost_multipliers,
+ vaccine_expire_at_end=True,
+ disqualifying_properties=[{"vaccine_selected": "Yes"}],
+ efficacy_is_multiplicative=True)
+
+
+ # booster vaccines
+ add_triggered_vaccine(campaign,
+ start_day=1,
+ vaccine_type="AcquisitionBlocking",
+ trigger_condition_list=['event_add_new_vaccine'],
+ listening_duration=-1,
+ demographic_coverage=1,
+ repetitions=1,
+ timesteps_between_repetitions=-1,
+ ind_property_restrictions=[{'VaccineStatus': 'GotVaccine'}],
+ vaccine_initial_effect=time_efficacy_boost_initial,
+ vaccine_linear_times=time_efficacy_values_boost[0],
+ vaccine_linear_values=time_efficacy_multipliers,
+ vaccine_expire_at_end=True,
+ disqualifying_properties=[{"vaccine_selected": "Yes"}],
+ efficacy_is_multiplicative=True)
+
+
+def change_vacc_ips(campaign):
+ # update VaccineStatus IP to 'Received_Vaccine' if individual receives a vaccine (note: it looks like the add_triggered_vaccine function may now support doing this internally)
+ change_individual_property_triggered(campaign,
+ new_ip_key='VaccineStatus',
+ new_ip_value='GotVaccine',
+ ip_restrictions=[{'VaccineStatus': 'None'}],
+ triggers=['Received_Vaccine'],
+ blackout=False)
+
+
+def add_pkpd_vacc(campaign, vacc_df, my_ds='', cohort_month_shift=0):
+ # add delivery of vaccine on a particular day, which works for seasonal distribution and for age-based ONLY in cohort simulations
+
+ # Sequence of vaccine events:
+ # - on start_day, select people to receive vaccine (change their IP vaccine_selected to True). This will remove old vaccines
+ # - on start_day+1, create a campaign which changes IP vaccine_selected to False (allowing new vaccines to be given) and broadcasts an event that triggers a node-level intervention where the new vaccine is distributed to these same individuals. The vaccine should have Disqualifying_Properties set to {“vaccine_selected”: “True”}. Also change VaccineStatus IP to ReceivedVaccine.
+ if my_ds != '':
+ if 'admin_name' in vacc_df.columns:
+ vacc_df = vacc_df[vacc_df['admin_name'].str.upper() == my_ds.upper()]
+
+ """Note: for cohort-EPI model, we use campaign-style deployment targeted to specific ages (since births disabled in cohort simulation)"""
+ for r, row in vacc_df.iterrows():
+ # calculate vaccine delivery day, given cohort month shift. If EPI type, don't adjust for cohort month
+ # (because vaccine is given according to individual's age instead of in a mass campaign)
+ start_day0 = row['simday']
+ if row['deploy_type'] == 'EPI_cohort':
+ start_day = start_day0
+ elif 'season_cohort' in row['deploy_type']:
+ start_day = start_day0 - round(30.4 * cohort_month_shift)
+ elif 'season' in row['deploy_type']:
+ start_day = start_day0
+ else:
+ print('WARNING: vaccine delivery name not recognized, age-based vaccination.')
+ start_day = start_day0
+ if start_day == 0:
+ start_day = 1 # avoid issue with vaccines not being given if set to begin on day 0
+ if start_day > 0:
+ cov_high = row['coverage_high_access']
+ cov_low = row['coverage_low_access']
+ # Select people to receive vaccine (change their IP vaccine_selected to True)
+ """Set group of individuals to receive vaccine and change IPs accordingly"""
+
+ # high-access coverage
+ change_individual_property_scheduled(campaign,
+ start_day=start_day,
+ coverage=cov_high,
+ new_ip_key='vaccine_selected',
+ new_ip_value='Yes',
+ target_age_min=row['agemin'],
+ target_age_max=row['agemax'],
+ ip_restrictions=[{'SMCAccess': 'High'}])
+
+ # low-access coverage
+ change_individual_property_scheduled(campaign,
+ start_day=start_day,
+ coverage=cov_low,
+ new_ip_key='vaccine_selected',
+ new_ip_value='Yes',
+ target_age_min=row['agemin'],
+ target_age_max=row['agemax'],
+ ip_restrictions=[{'SMCAccess': 'Low'}])
+
+ # On start_day+1, create a campaign which changes IP vaccine_selected to No (allowing new vaccines to be given) and broadcasts an event that triggers a node-level intervention where the new vaccine is distributed to these same individuals. The vaccine should have Disqualifying_Properties set to {“vaccine_selected”: “Yes”}. Also change VaccineStatus IP to ReceivedVaccine.
+ broadcast = BroadcastEvent(campaign, "event_add_new_vaccine")
+ change = PropertyValueChanger(campaign, Target_Property_Key="vaccine_selected", Target_Property_Value="No")
+ add_campaign_event(campaign=campaign,
+ start_day=start_day+1,
+ demographic_coverage=1,
+ node_ids=None, # all nodes will get intervention
+ repetitions=1,
+ timesteps_between_repetitions=-1,
+ ind_property_restrictions=[{'vaccine_selected': 'Yes'}],
+ individual_intervention=[change, broadcast])
+
+ change_vacc_ips(campaign)
+ return len(vacc_df)
+
+
+def add_pkpd_epi_vacc(campaign, epi_vacc_df, hfca):
+ # add delivery of vaccine a particular number of days after birth, which works for age-based distribution in simulations with vital dynamics
+
+ # Sequence of vaccine events:
+ # - When someone is born, it begins a countdown until the specified touchpoint(s), when an 'epi_touchpoint' event is broadcast (each will have a unique event name).
+ # - Any time an individual has an 'epi_touchpoint' event, it causes them to be eligible for an IP change to 'vaccine_selected:Yes' (whether or not this occurs depends on their access group and the coverage associated with that access group)
+ # - Also have a daily campaign with 100% coverage that applies only to individuals with vaccine_selected:Yes. It should broadcast the 'event_add_new_vaccine' event and change the 'vaccine_selected' IP to 'No'
+ # - The 'event_add_new_vaccine' will trigger a node-level intervention, created in add_triggered_vacc(), where the new vaccine is distributed to these individuals. The vaccine should have Disqualifying_Properties set to {“vaccine_selected”: “True”} so that old vaccines are removed when new ones are given. It also changes VaccineStatus IP to ReceivedVaccine.
+
+ if hfca != '':
+ if 'admin_name' in epi_vacc_df.columns:
+ epi_vacc_df = epi_vacc_df[epi_vacc_df['admin_name'].str.upper() == hfca.upper()]
+ if len(epi_vacc_df) == 0:
+ return 0
+
+ # iterate through touchpoints in dataframe, since each one may have a different coverage
+ for r, row in epi_vacc_df.iterrows():
+ cur_touchpoint = row['epi_touchpoint']
+ cur_touchpoint_event = f'epi_touchpoint{cur_touchpoint}'
+ cov_high = row['coverage_high_access']
+ cov_low = row['coverage_low_access']
+ start_epi = row['simday'] # EPI vaccines are only given after this day of the simulation
+
+ # have a campaign to broadcast the current EPI touchpoint event
+ broadcast_event = BroadcastEvent(campaign, cur_touchpoint_event)
+ add_triggered_campaign_delay_event(campaign, start_day=0,
+ trigger_condition_list=['Births'],
+ delay_period_constant=cur_touchpoint,
+ demographic_coverage=1,
+ individual_intervention=broadcast_event)
+
+ # Select people to receive vaccine on this EPI touchpoint day (change their IP vaccine_selected to Yes), with coverage depending on access group
+ # high-access coverage
+ change_individual_property_triggered(campaign,
+ start_day=start_epi,
+ coverage=cov_high,
+ new_ip_key='vaccine_selected',
+ new_ip_value='Yes',
+ ip_restrictions=[{'SMCAccess': 'High'}],
+ triggers=[cur_touchpoint_event],
+ blackout=False)
+ # low-access coverage
+ change_individual_property_triggered(campaign,
+ start_day=start_epi,
+ coverage=cov_low,
+ new_ip_key='vaccine_selected',
+ new_ip_value='Yes',
+ ip_restrictions=[{'SMCAccess': 'Low'}],
+ triggers=[cur_touchpoint_event],
+ blackout=False)
+
+ # Whenever an individual has the 'vaccine_selected:Yes' IP, change the IP vaccine_selected to No (allowing new vaccines to be given) and broadcast an event that triggers a node-level intervention where the new vaccine is distributed to these same individuals. The vaccine should have Disqualifying_Properties set to {“vaccine_selected”: “Yes”}. Also change VaccineStatus IP to ReceivedVaccine.
+ broadcast = BroadcastEvent(campaign, "event_add_new_vaccine")
+ change = PropertyValueChanger(campaign, Target_Property_Key="vaccine_selected", Target_Property_Value="No")
+ add_campaign_event(campaign=campaign,
+ start_day=0,
+ demographic_coverage=1,
+ repetitions=-1,
+ timesteps_between_repetitions=1,
+ ind_property_restrictions=[{'vaccine_selected': 'Yes'}],
+ individual_intervention=[change, broadcast])
+
+ change_vacc_ips(campaign)
+ return len(epi_vacc_df)
+
+
+#####################
+# ################### main function to coordinate adding all interventions ############
+#####################
+
+
def add_all_interventions(campaign, hfca, seed_index=1, hs_df=pd.DataFrame(), nmf_df=pd.DataFrame(),
itn_df=pd.DataFrame(),
itn_anc_df=pd.DataFrame(), itn_use_seasonality=pd.DataFrame(),
@@ -881,6 +1149,7 @@ def add_all_interventions(campaign, hfca, seed_index=1, hs_df=pd.DataFrame(), nm
itn_anc_adult_birthday_years=None, itn_epi_df=pd.DataFrame(),
itn_chw_df=pd.DataFrame(), itn_chw_annual_df=pd.DataFrame(),
irs_df=pd.DataFrame(), smc_df=pd.DataFrame(), pmc_df=pd.DataFrame(), vacc_df=pd.DataFrame(),
+ vacc_char_df=pd.DataFrame(), vacc_df_2=pd.DataFrame(), epi_vacc_df=pd.DataFrame(), use_same_access_ips_all_ages=False,
sp_resist_day1_multiply=1, adherence_multiplier=1, use_smc_vaccine_proxy=False):
event_list = []
@@ -889,7 +1158,7 @@ def add_all_interventions(campaign, hfca, seed_index=1, hs_df=pd.DataFrame(), nm
if has_irs > 0:
event_list.append('Received_IRS')
if not smc_df.empty:
- has_smc = update_smc_access_ips(campaign, smc_df=smc_df, hfca=hfca)
+ has_smc = update_smc_access_ips(campaign, smc_df=smc_df, hfca=hfca, use_same_access_ips_all_ages=use_same_access_ips_all_ages)
if use_smc_vaccine_proxy:
has_smc = add_hfca_vaccsmc(campaign, smc_df, hfca,
effective_coverage_resistance_multiplier=sp_resist_day1_multiply,
@@ -908,6 +1177,18 @@ def add_all_interventions(campaign, hfca, seed_index=1, hs_df=pd.DataFrame(), nm
has_vacc = add_ds_rtss(campaign, rtss_df=vacc_df, hfca=hfca)
if has_vacc > 0:
event_list = event_list + ['Received_Vaccine']
+ if not vacc_df_2.empty:
+ has_pkpd_vacc = update_smc_access_ips(campaign, smc_df=vacc_df_2, hfca=hfca, use_same_access_ips_all_ages=use_same_access_ips_all_ages)
+ add_triggered_vacc(campaign, vacc_char_df, hfca)
+ has_vacc = add_pkpd_vacc(campaign, vacc_df_2, hfca)
+ if has_vacc > 0:
+ event_list = event_list + ['Received_Vaccine']
+ event_list = event_list + ['event_add_new_vaccine']
+ if not epi_vacc_df.empty:
+ has_pkpd_vacc = update_smc_access_ips(campaign, smc_df=epi_vacc_df, hfca=hfca,
+ use_same_access_ips_all_ages=use_same_access_ips_all_ages)
+ add_triggered_vacc(campaign, vacc_char_df, hfca)
+ has_vacc = add_pkpd_epi_vacc(campaign, epi_vacc_df, hfca)
if not (itn_df.empty and itn_anc_df.empty and itn_epi_df.empty and itn_chw_df.empty and itn_chw_annual_df.empty):
has_itn = add_hfca_itns(campaign=campaign, itn_df=itn_df, itn_anc_df=itn_anc_df,
itn_anc_adult_birthday_years=itn_anc_adult_birthday_years, itn_epi_df=itn_epi_df,
@@ -929,3 +1210,5 @@ def add_all_interventions(campaign, hfca, seed_index=1, hs_df=pd.DataFrame(), nm
event_list.append('Received_NMF_Treatment')
return {"events": event_list}
+
+
diff --git a/snt/helpers_sim_setup.py b/snt/helpers_sim_setup.py
index 3cdb573..e7a3e94 100644
--- a/snt/helpers_sim_setup.py
+++ b/snt/helpers_sim_setup.py
@@ -3,7 +3,7 @@
import numpy as np
import emodpy_malaria.malaria_config as malaria_config
from emodpy_malaria.malaria_config import configure_linear_spline, set_species_param, add_species
-from emod_api.interventions.common import change_individual_property_scheduled
+from emod_api.interventions.common import change_individual_property_scheduled, change_individual_property_at_age
import emod_api.config.default_from_schema_no_validation as dfs
@@ -54,6 +54,8 @@ def habitat_scales(project_path):
def set_input_files(config, hfca, archetype_hfca, population_size=1000):
config.parameters.Climate_Model = "CLIMATE_CONSTANT"
+ config.parameters.Base_Air_Temperature = 25
+ config.parameters.Base_Land_Temperature = 25
config.parameters["DS"] = hfca
config.parameters["Archetype"] = archetype_hfca
return {'DS': hfca}
@@ -162,74 +164,83 @@ def load_master_csv(project_path):
return df
-def update_smc_access_ips(campaign, hfca, smc_df):
- # done
+def update_smc_access_ips(campaign, hfca, smc_df, use_same_access_ips_all_ages=False):
df = smc_df[smc_df['admin_name'] == hfca]
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- # # Original approach was to set IPs at the beginning of the simulation and then update individuals' IPs at their birthday.
- # # However, this does not work as expected because individuals born before the start of the simulation are missed by the birthday-triggered IP change
- # # (it appears that it is birth-triggered with a delay, so it doesn't get applied to anyone who was already born when the simulation begins).
- #
- # # set IPs at beginning of simulation (does not use IPs from burnin since SMC coverage may change)
- # change_individual_property(cb, 'SMCAccess', 'Low', target={'agemin': 0, 'agemax': 5}, coverage=1, blackout_flag=False)
- # change_individual_property(cb, 'SMCAccess', 'High', target={'agemin': 0, 'agemax': 5}, coverage=df['high_access_U5'].values[0], blackout_flag=False)
- # change_individual_property(cb, 'SMCAccess', 'Low', target={'agemin': 5, 'agemax': 120}, coverage=1, blackout_flag=False)
- # change_individual_property(cb, 'SMCAccess', 'High', target={'agemin': 5, 'agemax': 120}, coverage=df['high_access_5_10'].values[0], blackout_flag=False)
- #
- # # set how IPs change as individuals are born or age
- # change_individual_property_at_age(cb, 'SMCAccess', 'Low', 1, coverage=1)
- # change_individual_property_at_age(cb, 'SMCAccess', 'High', 2, coverage=df['high_access_U5'].values[0])
- # change_individual_property_at_age(cb, 'SMCAccess', 'Low', 365*5, coverage=1)
- # change_individual_property_at_age(cb, 'SMCAccess', 'High', (365*5+1), coverage=df['high_access_5_10'].values[0])
- # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
- # change SMCAccess property of newborns (important for those born after the first SMC round in a year)
- if df.shape[0] > 0:
- # SVET - no blackout_flag available, but result is same as blackout_flag=False
+ if use_same_access_ips_all_ages:
+ # Set IPs at the beginning of the simulation and then update individuals' IPs at their birthday.
+ # However, individuals born before the start of the simulation are missed by the birthday-triggered IP change
+ # (it is birth-triggered with a delay, so it doesn't get applied to anyone who was already born when the simulation begins).
+ # I believe this approach only works if we use the same access IPs for individuals of all ages (therefore, it does not support different high-access fractions for U5 versus 5-10
+ if 'high_access' in df.columns:
+ high_access_fraction = df['high_access'].values[0]
+ else:
+ high_access_fraction = df['high_access_U5'].values[0]
+
+ # set IPs at beginning of simulation (does not use IPs from burnin since SMC coverage may change)
change_individual_property_scheduled(campaign, coverage=1,
- new_ip_key='SMCAccess', new_ip_value="Low",
- target_age_min=0, target_age_max=5)
+ new_ip_key='SMCAccess', new_ip_value="Low")
change_individual_property_scheduled(campaign, coverage=df['high_access_U5'].values[0],
- new_ip_key='SMCAccess', new_ip_value="High",
- target_age_min=0, target_age_max=5)
+ new_ip_key='SMCAccess', new_ip_value="High")
- # before the first SMC round in each year, change the SMCAccess IP for the U5 and O5 age groups
- # simdays of the first rounds (change IPs one week before)
- first_round_days = df.loc[df['round'] == 1, 'simday'].values
- change_ip_days = [first_round_days[yy] - 7 for yy in range(len(first_round_days))]
+ # set how IPs change as individuals are born or age
+ change_individual_property_at_age(campaign, 'SMCAccess', 'Low', 1, coverage=1)
+ change_individual_property_at_age(campaign, 'SMCAccess', 'High', 2, coverage=high_access_fraction)
- for rr in change_ip_days:
- change_individual_property_scheduled(campaign, start_day=rr, coverage=1,
+ else: # change the SMCAccess each year, but this does mean scrambling who is in low versus high access group between years
+ # change SMCAccess property of newborns (important for those born after the first SMC round in a year)
+ if df.shape[0] > 0:
+ change_individual_property_scheduled(campaign, coverage=1,
new_ip_key='SMCAccess', new_ip_value="Low",
target_age_min=0, target_age_max=5)
- change_individual_property_scheduled(campaign, start_day=rr, coverage=df['high_access_U5'].values[0],
+ change_individual_property_scheduled(campaign, coverage=df['high_access_U5'].values[0],
new_ip_key='SMCAccess', new_ip_value="High",
target_age_min=0, target_age_max=5)
- change_individual_property_scheduled(campaign, start_day=rr, coverage=1,
- new_ip_key='SMCAccess', new_ip_value="Low",
- target_age_min=5, target_age_max=120)
- change_individual_property_scheduled(campaign, start_day=rr, coverage=df['high_access_5_10'].values[0],
- new_ip_key='SMCAccess', new_ip_value="High",
- target_age_min=5, target_age_max=120)
+ # before the first SMC round in each year, change the SMCAccess IP for the U5 and O5 age groups
+ # simdays of the first rounds (change IPs one week before)
+ first_round_days = df.loc[df['round'] == 1, 'simday'].values
+ change_IP_days = [first_round_days[yy] - 7 for yy in range(len(first_round_days))]
+
+ for rr in change_IP_days:
+ change_individual_property_scheduled(campaign, start_day=rr, coverage=1,
+ new_ip_key='SMCAccess', new_ip_value="Low",
+ target_age_min=0, target_age_max=5)
+ change_individual_property_scheduled(campaign, start_day=rr, coverage=df['high_access_U5'].values[0],
+ new_ip_key='SMCAccess', new_ip_value="High",
+ target_age_min=0, target_age_max=5)
+ change_individual_property_scheduled(campaign, start_day=rr, coverage=1,
+ new_ip_key='SMCAccess', new_ip_value="Low",
+ target_age_min=5, target_age_max=120)
+ change_individual_property_scheduled(campaign, start_day=rr, coverage=df['high_access_5_10'].values[0],
+ new_ip_key='SMCAccess', new_ip_value="High",
+ target_age_min=5, target_age_max=120)
return {'admin_name': hfca}
+
+
def set_drug_params(config):
# Amodaquine
malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_Cmax", 270)
malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_Decay_T1", 0.7)
malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_Decay_T2", 15.9)
- malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_PKPD_C50", 55)
+ malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_PKPD_C50", 150) # param2: 55)
malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_Vd", 1)
- malaria_config.set_drug_param(config, 'Amodiaquine', "Max_Drug_IRBC_Kill", 0.2)
+ malaria_config.set_drug_param(config, 'Amodiaquine', "Max_Drug_IRBC_Kill", 0.23) # param2: 0.2)
# SulfadoxinePyrimethamine
malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Drug_Decay_T1", 11.5)
malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Drug_Decay_T2", 11.5)
- malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Drug_PKPD_C50", 0.9)
- malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Max_Drug_IRBC_Kill", 0.28)
+ malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Drug_PKPD_C50", 3) # param2: 0.9)
+ malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Max_Drug_IRBC_Kill", 0.28) # param2: 0.28)
+
+
+def update_smc_drug_params(config, row):
+ malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Drug_PKPD_C50", row['sp_c50'])
+ malaria_config.set_drug_param(config, 'SulfadoxinePyrimethamine', "Max_Drug_IRBC_Kill", row['sp_kill'])
+ malaria_config.set_drug_param(config, 'Amodiaquine', "Drug_PKPD_C50", row['aq_c50'])
+ malaria_config.set_drug_param(config, 'Amodiaquine', "Max_Drug_IRBC_Kill", row['sp_kill'])
if __name__ == "__main__":
diff --git a/snt/load_paths.py b/snt/load_paths.py
index 35098e4..2f2f2ee 100644
--- a/snt/load_paths.py
+++ b/snt/load_paths.py
@@ -7,21 +7,23 @@ def load_box_paths(user_path=None, country_name='Example'):
if country_name == 'Example':
home_path = user_path
- data_path = home_path
- project_path = os.path.join(home_path, 'example_files')
+ data_path = os.path.join(user_path, 'Documents', 'emodpy-snt', 'data', 'example_files')
+ project_path = os.path.join(user_path, 'Documents', 'emodpy-snt', 'data', 'example_files')
elif country_name == 'SierraLeone':
- home_path = os.path.join(user_path, 'Dropbox (IDM)', 'Malaria Team Folder')
+ home_path = os.path.join(user_path, 'IDM Dropbox', 'Malaria Team Folder')
data_path = os.path.join(home_path, 'data')
project_path = os.path.join(home_path, 'projects', 'SierraLeone_hbhi')
elif country_name == 'Burundi':
- home_path = os.path.join(user_path, 'Dropbox (IDM)', 'Malaria Team Folder')
+ home_path = os.path.join(user_path, 'IDM Dropbox', 'Malaria Team Folder')
data_path = os.path.join(home_path, 'data')
project_path = os.path.join(home_path, 'projects', 'burundi_hbhi', 'snt_2023')
elif country_name == 'Nigeria':
- home_path = os.path.join(user_path, 'Dropbox (IDM)', 'NU_collaboration')
- data_path = os.path.join(home_path, 'hbhi_nigeria', 'snt_2022')
- project_path = os.path.join(home_path, 'hbhi_nigeria', 'snt_2022')
-
+ # home_path = os.path.join(user_path, 'Dropbox (IDM)', 'NU_collaboration')
+ # data_path = os.path.join(home_path, 'hbhi_nigeria', 'snt_2022')
+ # project_path = os.path.join(home_path, 'hbhi_nigeria', 'snt_2022')
+ home_path = os.path.join(user_path, 'IDM Dropbox', 'Malaria Team Folder', 'projects', 'snt')
+ data_path = os.path.join(home_path, 'Nigeria', 'snt_2024')
+ project_path = os.path.join(home_path, 'Nigeria', 'snt_2024')
return data_path, project_path