Merge pull request #9 from Edu-Py/main

Simulator and RuleX

Rulex/README.md

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+## RuleX process
+
+The RuleX processes can be consulted at the following link:
+https://zenodo.org/records/10078165
+

Simulator_V18/Iteration_Simulations.sh

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+#!/usr/bin/env bash
+
+mkdir Risultati
+
+for i in {19..19..1}
+do
+  ./OPENNESS_RomaTermini_v18_linux.sh
+  mv Risultati R$i 
+  cp input_CNR.xlsx ./R$i/input.xlsx
+  cd R$i
+  mv *_INPUT.csv INPUT.csv
+  mv *OUTPUTDiscesaTreni.csv OUTPUTDiscesaTreni.csv
+  mv *_OUTPUTMask.csv OUTPUTMask.csv
+  mv *OUTPUTMaskCumulata.csv OUTPUTMaskCumulata.csv 
+  mv *_OUTPUTPeople.csv OUTPUTPeople.csv
+  mv *OUTPUTPeopleCumulata.csv OUTPUTPeopleCumulata.csv
+  mv *_OUTPUTPiattaforme.csv OUTPUTPiattaforme.csv
+  mv *_OUTPUTRisk.csv OUTPUTRisk.csv
+  mv *OUTPUTSalitaTreni.csv OUTPUTSalitaTreni.csv
+  mv *OUTPUTArrivoTreni.csv OUTPUTArrivoTreni.csv  
+  mv *OUTPUTDelta.csv OUTPUTDelta.csv  
+  cd ..
+  mkdir Risultati
+  clear
+done

Simulator_V18/Iteration_Simulations_random.sh

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+#!/usr/bin/env bash
+
+mkdir Risultati
+
+for i in {1..2..1}
+do
+  python3 Modify_Excel_28_07.py
+  rm input_CNR.xlsx
+  mv input.xlsx input_CNR.xlsx
+  ./OPENNESS_RomaTermini_v18_linux.sh
+  mv Risultati R$i
+  cp input_CNR.xlsx ./R$i/input.xlsx
+  cd R$i
+  mv *_INPUT.csv INPUT.csv
+  mv *OUTPUTDiscesaTreni.csv OUTPUTDiscesaTreni.csv
+  mv *_OUTPUTMask.csv OUTPUTMask.csv
+  mv *OUTPUTMaskCumulata.csv OUTPUTMaskCumulata.csv 
+  mv *_OUTPUTPeople.csv OUTPUTPeople.csv
+  mv *OUTPUTPeopleCumulata.csv OUTPUTPeopleCumulata.csv
+  mv *_OUTPUTPiattaforme.csv OUTPUTPiattaforme.csv
+  mv *_OUTPUTRisk.csv OUTPUTRisk.csv
+  mv *OUTPUTSalitaTreni.csv OUTPUTSalitaTreni.csv
+  mv *OUTPUTArrivoTreni.csv OUTPUTArrivoTreni.csv
+  mv *OUTPUTDelta.csv OUTPUTDelta.csv  
+  cd ..
+  mkdir Risultati
+  clear
+done

Simulator_V18/Modify_Excel_10_07.py

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+import pandas as pd
+import numpy as np
+np.random.seed(42)
+
+TRAIN_CAPACITY_LOW = np.random.randint(0, 4)/100
+TRAIN_CAPACITY_HIGH = np.random.randint(4, 7)/100
+ARRIVI_LOW = TRAIN_CAPACITY_LOW*400
+ARRIVI_HIGH = TRAIN_CAPACITY_HIGH*400
+PARTENZA_LOW = 4 * ARRIVI_LOW
+PARTENZA_HIGH = 4 * ARRIVI_HIGH
+POSITIVI_LOW = 30
+POSITIVI_HIGH = 61
+NEGOZI_LOW = 10
+NEGOZI_HIGH = 91
+RISPETO_LOW = 10
+RISPETO_HIGH = 91
+FFP2_LOW = 0
+FFP2_HIGH = 101
+CHIRURGICA_LOW = 0
+CHIRURGICA_HIGH = 61
+FREQ_AV_LOW = 30
+FREQ_AV_HIGH = 70
+FREQ_LEON_LOW = 30
+FREQ_LEON_HIGH = 70
+DS_AV_LOW = 3
+DS_AV_HIGH = 11
+DS_LEON_LOW = 3
+DS_LEON_HIGH = 11
+AER_NAT_LOW = 1
+AER_NAT_HIGH = 6
+AER_FINESTRE_LOW = 2
+AER_FINESTRE_HIGH = 6
+PP = 0.15  # Model the same as POSITIVI
+DIST_MIN_LOW = 10
+DIST_MIN_HIGH = 21
+PERCENTAGE_ACTIVE_AV_RAIL_TRACKS = np.random.choice(a=[0.85, 0.89, 0.95, 1])
+PERCENTAGE_ACTIVE_LEONARDO_RAIL_TRACKS = np.random.choice(a=[0.7, 1])
+
+#%% Mask
+#ffp2 = np.random.randint(FFP2_LOW, FFP2_HIGH)
+#chirurgica = np.random.randint(CHIRURGICA_LOW, CHIRURGICA_HIGH)
+
+#while ffp2 + chirurgica > 100:
+#    ffp2 = np.random.randint(FFP2_LOW, FFP2_HIGH)
+#    chirurgica = np.random.randint(CHIRURGICA_LOW, CHIRURGICA_HIGH)
+
+#%%  Read Sheets
+
+sheet_1 = pd.read_excel('input_CNR.xlsx', sheet_name=0, usecols=[0,1,2], engine='openpyxl', dtype=object)
+sheet_2 = pd.read_excel('input_CNR.xlsx', sheet_name=1, usecols=[0,1,2], engine='openpyxl', dtype=object)
+sheet_3 = pd.read_excel('input_CNR.xlsx', sheet_name=2, usecols=[0,1], engine='openpyxl', dtype=object)
+
+#sheet_3[sheet_3.columns[1]] = sheet_3[sheet_3.columns[1]].astype(str).str.lower()
+
+
+
+#%% Sheet 3
+sheet_3[sheet_3.columns[1]] = 'false'
+
+#AV_rail_tracks = np.random.choice(a=['true', 'false'], size=18, p=[PERCENTAGE_ACTIVE_AV_RAIL_TRACKS, 1-PERCENTAGE_ACTIVE_AV_RAIL_TRACKS])
+
+#AV_rail_tracks = np.random.choice(a=np.arange(0,14,1), size=3,replace=False)
+#sheet_3[sheet_3.columns[1]].iloc[AV_rail_tracks] = 'true'
+sheet_3[sheet_3.columns[1]].iloc[0:2] = 'true'
+
+#AV_rail_tracks = np.random.choice(a=np.arange(16,19,1), size=1,replace=False)
+#sheet_3[sheet_3.columns[1]].iloc[AV_rail_tracks] = 'true'
+
+#Leonardo_rail_tracks = np.random.choice(a=np.arange(20,22,1), size=2,replace=True)
+#sheet_3[sheet_3.columns[1]].iloc[Leonardo_rail_tracks] = 'true'
+sheet_3[sheet_3.columns[1]].iloc[20:22] = 'true'
+
+
+#sheet_3[sheet_3.columns[1]].iloc[0:14] = AV_rail_tracks[0:14]
+#sheet_3[sheet_3.columns[1]].iloc[14:16] = ['false', 'false']
+#sheet_3[sheet_3.columns[1]].iloc[16:20] = AV_rail_tracks[14:18]
+
+#Leonardo_rail_tracks = np.random.choice(a=['true', 'false'], size=2, p=[PERCENTAGE_ACTIVE_LEONARDO_RAIL_TRACKS, 1-PERCENTAGE_ACTIVE_LEONARDO_RAIL_TRACKS])
+
+#sheet_3[sheet_3.columns[1]].iloc[20:22] = Leonardo_rail_tracks
+
+# To set arrivi and partenze
+number_active_rail_tracks = sum(sheet_3[sheet_3.columns[1]]=='true')
+
+#%% Sheet 1
+
+sheet_1[sheet_1.columns[1]].iloc[0:4] = ARRIVI_LOW 
+
+sheet_1[sheet_1.columns[1]].iloc[4:8] = ARRIVI_HIGH 
+
+sheet_1[sheet_1.columns[1]].iloc[8:12] = ARRIVI_LOW 
+
+sheet_1[sheet_1.columns[1]].iloc[12:16] = PARTENZA_LOW * number_active_rail_tracks
+
+sheet_1[sheet_1.columns[1]].iloc[16:20] = PARTENZA_HIGH * number_active_rail_tracks
+
+sheet_1[sheet_1.columns[1]].iloc[20:24] = PARTENZA_LOW * number_active_rail_tracks
+
+#%% Sheet 2
+
+sheet_2[sheet_2.columns[1]].iloc[0:2] = np.zeros(2, dtype=int)
+
+sheet_2[sheet_2.columns[1]].iloc[2] = np.random.randint(POSITIVI_LOW, POSITIVI_HIGH)
+
+sheet_2[sheet_2.columns[1]].iloc[3] = sheet_2[sheet_2.columns[1]].iloc[2]
+
+#sheet_2[sheet_2.columns[1]].iloc[4] = ffp2
+#sheet_2[sheet_2.columns[1]].iloc[5] = chirurgica
+
+sheet_2[sheet_2.columns[1]].iloc[4] = FFP2_LOW 
+sheet_2[sheet_2.columns[1]].iloc[5] = CHIRURGICA_LOW
+
+
+sheet_2[sheet_2.columns[1]].iloc[6] = 16
+sheet_2[sheet_2.columns[1]].iloc[7] = 15
+sheet_2[sheet_2.columns[1]].iloc[8] = 16
+sheet_2[sheet_2.columns[1]].iloc[9] = 15
+sheet_2[sheet_2.columns[1]].iloc[10] = 15
+sheet_2[sheet_2.columns[1]].iloc[11] = 15
+sheet_2[sheet_2.columns[1]].iloc[12] = 15
+sheet_2[sheet_2.columns[1]].iloc[13] = 15
+sheet_2[sheet_2.columns[1]].iloc[14] = 15
+sheet_2[sheet_2.columns[1]].iloc[15] = 16
+sheet_2[sheet_2.columns[1]].iloc[16] = 15
+sheet_2[sheet_2.columns[1]].iloc[17] = 16
+sheet_2[sheet_2.columns[1]].iloc[18] = 16
+sheet_2[sheet_2.columns[1]].iloc[19] = 15
+sheet_2[sheet_2.columns[1]].iloc[20] = 16
+sheet_2[sheet_2.columns[1]].iloc[21] = 15
+sheet_2[sheet_2.columns[1]].iloc[22] = 15
+sheet_2[sheet_2.columns[1]].iloc[23] = 15
+sheet_2[sheet_2.columns[1]].iloc[24] = 15
+sheet_2[sheet_2.columns[1]].iloc[25] = 15
+sheet_2[sheet_2.columns[1]].iloc[26] = 15
+sheet_2[sheet_2.columns[1]].iloc[27] = 16
+sheet_2[sheet_2.columns[1]].iloc[28] = 15
+sheet_2[sheet_2.columns[1]].iloc[29] = 16
+
+sheet_2[sheet_2.columns[1]].iloc[30:54] = 5
+
+#sheet_2[sheet_2.columns[1]].iloc[54] = np.random.randint(AER_NAT_LOW, AER_NAT_HIGH)
+
+sheet_2[sheet_2.columns[1]].iloc[54] = AER_NAT_LOW
+
+
+#sheet_2[sheet_2.columns[1]].iloc[55] = np.random.randint(AER_FINESTRE_LOW, AER_FINESTRE_HIGH)/10
+
+sheet_2[sheet_2.columns[1]].iloc[55] = AER_FINESTRE_LOW/10
+
+
+sheet_2[sheet_2.columns[1]].iloc[56] = PP
+
+sheet_2[sheet_2.columns[1]].iloc[57] = np.random.randint(DIST_MIN_LOW, DIST_MIN_HIGH)/10
+
+sheet_2[sheet_2.columns[1]].iloc[58:60] = np.zeros(2, dtype=int)
+
+#sheet_2[sheet_2.columns[1]].iloc[60] = 0.1
+
+sheet_2[sheet_2.columns[1]].iloc[60] = np.random.randint(NEGOZI_LOW, NEGOZI_HIGH)/100
+
+#sheet_2[sheet_2.columns[1]].iloc[61] = 0.1
+
+sheet_2[sheet_2.columns[1]].iloc[60] = np.random.randint(RISPETO_LOW, RISPETO_HIGH)/100
+
+sheet_2[sheet_2.columns[1]].iloc[62] = 3
+
+#%% Write File
+
+with pd.ExcelWriter('input.xlsx', engine = 'openpyxl') as writer:
+    sheet_1.to_excel(writer, sheet_name = 'Foglio1', engine = 'openpyxl', index = False)
+    sheet_2.to_excel(writer, sheet_name = 'Foglio2', engine = 'openpyxl', index = False)
+    sheet_3.to_excel(writer, sheet_name = 'Foglio3', engine = 'openpyxl', index = False)