From 9f80e7cba82406acebf48462fdfb65b822900530 Mon Sep 17 00:00:00 2001
From: Hilmar Magnusson <hilmarmag@protonmail.com>
Date: Thu, 14 Nov 2024 08:24:02 +0100
Subject: [PATCH] add reqs

---
 ekg.py           | 140 +++++++++++++++++++++++++++++++++++++++++++++++
 requirements.txt |   5 ++
 2 files changed, 145 insertions(+)
 create mode 100644 ekg.py
 create mode 100644 requirements.txt

diff --git a/ekg.py b/ekg.py
new file mode 100644
index 0000000..2096a7e
--- /dev/null
+++ b/ekg.py
@@ -0,0 +1,140 @@
+import pandas as pd #handling dataframes
+import numpy as np #numpy for array management
+import matplotlib.pyplot as plt #plotting
+import plotly.express as px #generate beautiful interactive plots
+from datetime import timedelta, date #for later datetimes processing
+
+from itertools import product
+import datetime
+import openpyxl
+
+#pyomo.environ provides the framework for build the model
+import pyomo.environ as pyo
+
+#SolverFactory allows to call the solver to solve
+from pyomo.opt import SolverFactory
+
+#store the solver in a variable to call it later, we need to tell google colab
+#the specific path on which the solver was installed
+opt_glpk = pyo.SolverFactory('glpk', executable='/usr/bin/glpsol')
+
+activities =  {0:'Start',
+               1: 'A',
+               2: 'B',
+               3: 'C',
+               4: 'D',
+               5: 'E',
+               6: 'F',
+               7: 'G',
+               8: 'H',
+               9: 'I',
+               10: 'J',
+               11:'End'}
+p = [0, 5, 2, 5, 6, 5, 2, 3, 2, 4, 3, 0] # activity durations
+
+u = [[0, 0, 0], # list of resource consumptions
+     [1, 0, 0],
+     [1, 0, 0],
+     [0, 1, 0],
+     [0, 0, 1],
+     [0, 1, 0],
+     [0, 1, 0],
+     [0, 0, 1],
+     [0, 0, 1],
+     [0, 0, 1],
+     [0, 0, 1],
+     [0, 0, 0]]
+
+E = [[0, 1], # list of precedence constraints
+    [0, 2],
+    [1, 3],
+    [2, 4],
+    [3, 5],
+    [3, 6],
+    [3, 7],
+    [5, 8],
+    [6, 9],
+    [8, 10],
+    [9, 10],
+    [4, 11],
+    [7, 11],
+    [10, 11]]
+
+c = [1,1,1] # max resource capacity
+
+
+n = len(p) - 2
+ph = sum(p)
+(R, J, T) = (range(len(c)), range(len(p)), range(ph))
+
+model = pyo.ConcreteModel() # create empty model (like empty canvas)
+
+model.J = pyo.RangeSet(0,len(p)-1) # set for the activities
+model.T = pyo.RangeSet(0,ph) # set for the days in the planning horizon
+model.Xs = pyo.Var(model.J,model.T,within = pyo.Binary) # variables
+Xs = model.Xs
+
+# Objective Function Eq. (1)
+# Next we proceed to create our objective function. Following the exact formula described by equation (1).
+
+# sum of the dates for the final activity (the dummy end)
+Z = sum([t*Xs[(n+1,t)] for t in model.T]) 
+# we want to minimize this objective function
+model.obj = pyo.Objective(expr = Z,sense=pyo.minimize) 
+
+# Constraint (2): Only one start time
+
+model.one_xs = pyo.ConstraintList() #create a list of constraints
+for j in model.J:
+  #add constraints to the list created above
+  model.one_xs.add(expr = sum(Xs[(j,t)] for t in model.T)==1) 
+
+# Constraint (3): Precedence constraints
+
+model.pc = pyo.ConstraintList() # precedence constraints
+for (j,s) in  E:
+  model.pc.add(expr = sum(t*Xs[(s,t)] - t*Xs[(j,t)] for t in model.T) >= p[j]) 
+
+# Constraint (4): Resource capacity constraints
+
+model.rl = pyo.ConstraintList() #resource level constraints
+print(R)
+print(T)
+for (r, t) in product(R, T):
+  model.rl.add(expr = sum(u[j][r]*Xs[(j,t2)] for j in model.J for t2 in range(max(0, t - p[j] + 1), t + 1)) <= c[r])
+
+# solve
+
+opt_glpk.options['tmlim'] = 60
+opt_glpk.options["mipgap"] = 0
+results = opt_glpk.solve(model,tee=True) # ask the solver to solve the model
+results.write()
+
+SCHEDULE = {}
+for (j, t) in product(J, T):
+
+  if pyo.value(Xs[(j, t)]) >= 0.99: #only get the x values == 1
+    a = activities[j]
+    SCHEDULE[a] = {'s':t,'f':t+p[j]}
+    print(f'Activity {j}, begins at t={t} and finishes at {t+p[j]}')
+
+out = pd.DataFrame(SCHEDULE).T
+SCHEDULE = out.T.to_dict()
+
+#start_date = datetime.date(2023, 1, 11)
+start_date = datetime.date.today()
+
+SCHEDULE_DF = pd.DataFrame(SCHEDULE).T
+SCHEDULE_DF['Start_Date'] = [str(start_date + datetime.timedelta(days=s)) for s in SCHEDULE_DF['s']]
+SCHEDULE_DF['Finish_Date'] =[str(start_date + datetime.timedelta(days=f)) for f in SCHEDULE_DF['f']]
+
+SCHEDULE_DF.to_csv('output_schedule.csv') #export csv file
+SCHEDULE_DF.to_excel('output_schedule.xlsx') #export excel file
+
+fig = px.timeline(SCHEDULE_DF, 
+                  x_start="Start_Date", 
+                  x_end="Finish_Date", 
+                  y=SCHEDULE_DF.index,title='Project Gantt Chart')
+fig.update_yaxes(autorange="reversed") # otherwise tasks are listed from the bottom up
+fig.write_html("Gantt_chart.html")
+fig.show()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..dc6e7ff
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+pandas
+pyomo
+matplotlib
+plotly
+openpyxl