Impact7: minumal app with streamlit and example data

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "streamlit_app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run streamlit_app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.streamlit/config.toml

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+[theme]
+
+# Primary accent for interactive elements
+primaryColor = '#36c2e2'#'#7792E3'
+
+# Background color for the main content area
+backgroundColor = '#FFFFFF' #'#270AA0' #'#498069' #'#273346'
+
+# Background color for sidebar and most interactive widgets
+secondaryBackgroundColor = '#36C3E2' #'#B9F1C0'
+
+# Color used for almost all text
+textColor = '#16065B'# '#FFFFFF'
+
+# Font family for all text in the app, except code blocks
+# Accepted values (serif | sans serif | monospace) 
+# Default: "sans serif"
+font = "sans serif"

CreateSampleDataset.py

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+#%%
+import pandas as pd
+import random
+from random import randint
+#from Parameters import *
+from MergeDatasets import *
+
+TotBeneficiaries = 2000
+months = ['January', 'February', 'March', 'April','May', 'June'
+          , 'July','August','September','October', 'November', 'December']
+
+years = ['2022','2023']
+
+n_max_activity_year = {'2022':3,'2023':5}
+activities_names = ['hackaton', 'workshop','networking','deploy(impact)','coffee']
+
+# Seed for reproducibility
+random.seed(1)
+
+# Generate number of activities for each month
+n_activities =[random.randint(1, 5) for month in months]
+
+# Helper function to Generate supporter enterprises
+def GenerateNSupporterEnterprises(months,n_activities):
+    random.seed(1)
+    supporters = [0]*len(months)
+    for i in range(len(months)):
+        if i==0: 
+            supporters[i]=randint(1,5)
+        else:
+            supporters[i]=round(supporters[i-1]+(n_activities[i]*0.5),0)
+    return supporters
+
+#print(GenerateNSupporterEnterprises(months,n_activities))
+#%%
+# Helper function to Generate total donations
+def GenerateTotDonations(months,n_activities):
+    random.seed(1)
+    contributions = [0]*len(months)
+    nsup = GenerateNSupporterEnterprises(months,n_activities)
+    for i in range(len(months)):
+        contribution=0
+        for n in range(0,int(nsup[i])):
+            contribution+=2000*random.random()
+        contributions[i] = round(contribution,0)
+    return contributions
+
+def GenerateActivityID(months, n_activities):
+    act_id = [0]*len(months)
+    for j in range(len(months)):
+        act_id[j] = []
+        for i in range(n_activities[j]):
+             act_id[j].append(i)
+    return act_id
+
+# Organization table
+staff_data = {
+    'Month': months
+    ,'N_staff': [random.randint(5, 10) for _ in range(len(months))]
+    ,'Tot_Donations (CHF)': GenerateTotDonations(months,n_activities)
+    ,'N_Esupporters':GenerateNSupporterEnterprises(months,n_activities)
+    , 'N_volunteers':[randint(4, 15) for i in range(len(months))]
+    ,'N_activities': n_activities
+    ,'Activity_id':GenerateActivityID(months, n_activities)
+}
+df_org = pd.DataFrame.from_dict(staff_data)
+df_org.head()
+df_org.to_csv("data/OrganizationTable.csv")
+#%%
+'''
+dict_org = {'N_staff': {month: randint(5, 10) for month in months}
+            , 'N_Esupporters': GenerateNSupporterEnterprises()
+            , 'Tot_Donations (CHF)': GenerateTotDonations()
+            , 'N_volunteers': {month: randint(0, 10) for month in months}
+            ,'N_activities': n_activities
+            ,'Activity_id': {month:[i for i in range(0,n_activities[month])] for month in months}
+            }
+
+df_org = pd.DataFrame.from_dict(dict_org)
+df_org.index.name='Month'
+df_org.head()
+df_org.to_csv("data/OrganizationTable.csv")
+'''
+
+
+#%%
+# Activities table
+activities_duration = [24,4,10,2,2]
+activities_tperw = [2,1,1,3,1]
+activities_budget = [20000,12000,5000,12000,500]
+activities_cost = [18000,9034,3250,11060,450]
+activities_npart = [120,50,100,60,20]
+activities_nwaitlist = [20,10,1,12,0]
+activities_nvolunteers = [6,4,3,6,4]
+activities_nstaff = [5,6,6,6,2]
+
+#%%    
+def checkPartID(partID, rnd):
+    if rnd in partID:
+        return 'True'
+    else:
+        return 'False'
+
+def GenerateParticipantID(nmax, npart):
+    partID = [-99]*npart
+    for i in range(npart):
+        rnd =  randint(0,nmax)
+        check = checkPartID(partID, rnd)
+        while(check=='True'):
+            rnd =  randint(0,nmax)
+            check = checkPartID(partID, rnd)    
+        partID[i] = rnd    
+    return partID
+
+#%%
+def DefineDropOut(act_id):
+    rnd = random.uniform(0,1)
+    threshold={'0':0.7,'1':0.9,'2':0.85,'3':0.85,'4':0.9}
+    if rnd<threshold[str(act_id)]:
+        return 0 #Not dropped-out
+    if rnd>=threshold[str(act_id)]:
+        return 1 #Dropped-out
+
+def GenerateParticipantDropOut(npart, act_id):
+    partDO = [-99]*npart
+    for i in range(0,npart):
+        partDO[i] = DefineDropOut(act_id)
+    return partDO
+
+#%%
+avg_participation = [0.77,0.82,0.93,0.94,0.95]
+
+#%%
+
+
+data = []
+
+# Populate the list with dictionaries for each month
+for j, month in enumerate(months):
+    if j<12:
+        n_act_month = n_activities[j]
+
+        #Create a dictionary for the current month
+        month_data = {'Month': [month] * n_act_month}
+        print(month_data)
+        month_data['Activity_id'] =  [i for i in range(len(month_data['Month']))]
+        month_data['Tot_budget'] = [activities_budget[month_data['Activity_id'][i]]+randint(-2,2)*1000 for i in range(len(month_data['Month']))]
+        month_data['Tot_cost'] = [activities_cost[month_data['Activity_id'][i]]+randint(-2,2)*random.uniform(150,200) for i in range(len(month_data['Month']))]
+        month_data['n_beneficiaries'] = [activities_npart[month_data['Activity_id'][i]]+randint(-15,0) for i in range(len(month_data['Month']))]
+        month_data['n_volunteers'] = [activities_nvolunteers[month_data['Activity_id'][i]]+randint(-2,2) for i in range(len(month_data['Month']))]
+        month_data['n_staff'] = [activities_nstaff[month_data['Activity_id'][i]]+randint(-2,2) for i in range(len(month_data['Month']))]
+        month_data['beneficiary_id'] =  [ GenerateParticipantID(TotBeneficiaries, activities_npart[month_data['Activity_id'][i]]) for i in range(len(month_data['Month']))]
+        month_data['beneficiary_dropout'] = [GenerateParticipantDropOut(activities_npart[month_data['Activity_id'][i]],i) for i in range(len(month_data['Month']))]
+
+        # Append the dictionary to the data list
+        data.append(month_data)
+
+activities = data
+'''
+              ,'Activity_id': [i for i in range(0,5)]
+              ,'name':[activities_names[i] for i in range(0,5)]
+              ,'Tot. duration (hours)': [activities_duration[i] for i in range(5)]
+              ,'times_per_week' : [activities_tperw[i] for i in range(5)]
+              ,'Tot_budget' : [activities_budget[i] for i in range(5)]
+              ,'Tot_cost' : [activities_cost[i] for i in range(5)]
+}
+'''
+'''
+for j in range(len(months)):
+    for i in range(0,5):
+        activity = {}
+        activity['id'] = i
+        activity['name'] = activities_names[i]
+        activity['Tot. duration (hours)'] = activities_duration[i]
+        activity['times_per_week'] = activities_tperw[i]
+        activity['Tot_budget'] = activities_budget[i]
+        activity['Tot_cost'] = activities_cost[i]
+        #activity['n_beneficiaries'] = activities_npart[i]
+        activity['n_volunteers'] = activities_nvolunteers[i]
+        activity['n_staff'] = activities_nstaff[i]
+        activity['n_waitlist'] = activities_nwaitlist[i]
+        activity['beneficiary_id'] = GenerateParticipantID(TotBeneficiaries, activities_npart[i])
+        activity['beneficiary_dropout'] = GenerateParticipantDropOut(activities_npart[i],i)
+        activity['average_participation'] = avg_participation[i]
+        activities['Month'].append(activity)
+'''
+
+# %%
+df_act = pd.DataFrame.from_dict(activities)
+column_list = []
+for column in df_act.columns:
+    column_list.append(column)
+df_act_dummy = df_act.explode(column_list).reset_index(drop=True)
+
+df_act_dummy.head()
+
+
+# %%
+df_act_dummy.to_csv("data/ActivitiesTable.csv")
+# %%
+def GenerateGender():
+    rnd = random.uniform(0,1)
+    if rnd<0.7:
+        return 'F'
+    if rnd>=0.7 and rnd<0.88:
+        return 'M'
+    if rnd>=0.88 and rnd<0.95:
+        return 'nonbinary'
+    if rnd>0.95:
+        return 'NS'
+
+def GenerateAge():
+    rnd = random.uniform(0,1)
+    if rnd<0.2:
+        return randint(20,30)
+    if rnd>=0.2 and rnd<0.65:
+        return randint(30,40)
+    if rnd>=0.65 and rnd<0.85:
+        return randint(40,50)
+    if rnd>=0.85 and rnd<0.97:
+        return randint(50,60)
+    if rnd>=0.97:
+        return randint(60,90)
+
+def GenerateEducation():
+    rnd = random.uniform(0,1)
+    if rnd<0.1:
+        return 'HighSchool'
+    if rnd>=0.1 and rnd<0.4:
+        return 'Bachelor'
+    if rnd>=0.4 and rnd<0.75:
+        return 'Master'
+    if rnd>=0.75:
+        return 'PhD'
+
+def GenerateEmploymentStatus():
+    rnd = random.uniform(0,1)
+    if rnd<0.5:
+        return 'Employed'
+    if rnd>=0.5:
+        return 'Unemployed'
+
+#%%
+start_fields = {'Business':0.1,'Construction':0.01,'Education':0.13,'Engineering':0.1,'Farming':0.01,'Health':0.02,'Hospitality':0.04,'Law':0.02,'Management':0.02,'Media':0.05,'Administration':0.02,'Science':0.18,'IT':0.3}
+end_fields = {'Business':0.09,'Construction':0.008,'Education':0.09,'Engineering':0.05,'Farming':0.009,'Health':0.018,'Hospitality':0.036,'Law':0.016,'Management':0.02,'Media':0.046,'Administration':0.019,'Science':0.14,'IT':0.458}
+
+def check_sum_to_one(di):
+    sum = 0
+    for key in di.keys():
+        sum+=di[key]
+    print(sum)
+    return
+
+#check_sum_to_one(start_fields)
+#check_sum_to_one(end_fields)
+#%%
+def GenerateStartField(start_fields):
+    rnd = random.uniform(0,1)
+    #print(rnd)
+    thr = [0]*(len(start_fields)+1)
+    for i,key in enumerate(start_fields):
+        thr[i+1]=thr[i]+start_fields[key]
+    #print(thr)
+    for i,key in enumerate(start_fields):
+        if rnd>thr[i] and rnd<=thr[i+1]:
+            #print(rnd, thr[i],thr[i+1])
+            return key
+
+
+GenerateStartField(start_fields)
+#%%
+def GenerateEndField(start,start_fields,end_fields):
+    end = ''
+    if start!='IT':
+        rnd = random.uniform(0,1)
+        ratio = end_fields[start]/start_fields[start]
+        #print(ratio)
+        if rnd<=1-ratio:
+            end = 'IT'
+        else:
+            end = start
+    else:
+        end = start
+    return end
+
+
+
+
+#%%
+#Generate participants
+participants = []
+for i in range(0,TotBeneficiaries):
+    participant = {}
+    participant['id'] = i
+    participant['gender'] = GenerateGender()
+    participant['age'] = GenerateAge()
+    participant['education'] = GenerateEducation()
+    #participant['employment_status'] = GenerateEmploymentStatus()
+    participant['start_field'] = GenerateStartField(start_fields)
+    participant['end_field'] = GenerateEndField(participant['start_field'],start_fields,end_fields)
+    participants.append(participant)
+
+df_part = pd.DataFrame.from_dict(participants)
+df_part.head()
+df_part.to_csv("data/ParticipantsTable.csv")
+
+#%%
+MergeTables(df_org, df_act_dummy, df_part)
+# %%
+
+#%%
+'''
+def MergeTables(df_org, df_act_dummy, df_part):
+    df_org_dummy = df_org.explode('Activity_id')
+    df_act_dummy = df_act_dummy.explode(['beneficiary_id','beneficiary_dropout']).reset_index(drop=True)
+    df_1 = pd.merge(df_org_dummy, df_act_dummy, how="left", on=["Month","Activity_id"])
+    df_tot = pd.merge(df_1, df_part, how="left", left_on="beneficiary_id", right_on = "id")
+    return df_tot
+df_tot = MergeTables(df_org, df_act_dummy, df_part)
+df_tot.head()
+'''
+# %%