70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
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from pyomo.environ import SolverFactory
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solver = SolverFactory('glpk')
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import pandas as pd
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import pyomo.environ as pyo
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# Create a model
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model = pyo.ConcreteModel()
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## Define variables
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#model.x = pyo.Var(within=pyo.NonNegativeReals)
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#model.y = pyo.Var(within=pyo.NonNegativeReals)
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## Define objective
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#model.obj = pyo.Objective(expr=model.x + model.y, sense=pyo.minimize)
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## Define constraints
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#model.con1 = pyo.Constraint(expr=model.x + 2 * model.y >= 4)
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#model.con2 = pyo.Constraint(expr=model.x - model.y <= 1)
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## Select solver
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#solver = pyo.SolverFactory('glpk')
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## Solve the problem
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#result = solver.solve(model)
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## Display results
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#print('Status:', result.solver.status)
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#print('Termination Condition:', result.solver.termination_condition)
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#print('Optimal x:', pyo.value(model.x))
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#print('Optimal y:', pyo.value(model.y))
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#print('Optimal Objective:', pyo.value(model.obj))
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class Scheduler:
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def __init__(self, case_file_path, session_file_path, patient_file_path):
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"""
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Read case and session data into Pandas DataFrames
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Args:
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case_file_path (str): path to case data in CSV format
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session_file_path (str): path to theatre session data in CSV format
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"""
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try:
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self.df_cases = pd.read_csv(case_file_path)
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except FileNotFoundError:
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print("Case data not found.")
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try:
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self.df_sessions = pd.read_csv(session_file_path)
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except FileNotFoundError:
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print("Session data not found")
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try:
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self.df_patients = pd.read_csv(patient_file_path)
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except FileNotFoundError:
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print("Patient data not found")
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#self.model = self.create_model()
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def date2int(date):
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d0 = pd.to_datetime("25/11/2024", dayfirst=True)
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delta = (pd.to_datetime(date, dayfirst=True) - d0).days
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day = delta%7
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week = int((delta - day)/7)
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return week,day
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path = '/home/hmag/code/pyomo/data'
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my = Scheduler(path+'/cases.csv', path+'/sessions.csv', path+'/patients.csv')
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print(my.df_cases)
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print(my.df_sessions)
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#print(my.df_patients)
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#for i in my.df_patients:
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# print(i)
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print(my.df_cases['Date'][0])
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date = my.df_cases['Date'][0]
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print(date2int(date))
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