diff --git a/examples/Waste Heat Recovery/analysis.py b/examples/Waste Heat Recovery/analysis.py
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+# Copyright (C) 2023
+# Liam Dauchat
+# University of Liege .
+# All rights reserved.
+
+#parsing
+import os
+import json as js
+import re
+#treatment
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import pandas as pd
+#reporting
+import pprint
+from PyPDF2 import PdfMerger
+from tabulate import tabulate
+import subprocess
+
+
+
+
+def save_pretty_dictionnary(data,file_name,save=False):
+ """
+ PRE: data is a dictionary, save is a boolean indicating if we want to save the dictionary in a file by default file_name+_pretty
+ POST: print the dictionary in a pretty way
+ """
+
+ pretty = js.dumps(data, indent=4, sort_keys=True)
+ # print(data) # to be used with causion as supply vectors are very long
+ if save:
+ with open(file_name+"_pretty", 'w') as f:
+ pprint.pprint(data, f)
+
+def data_description(data):
+ print("data description ================")
+ print("keys: ",data.keys())
+ print("version: ",data['version'])
+ # print("model: ",data['model'])
+ print("solver: ",data['solver'])
+ print("end of data description ================ \n")
+
+def model_description(model):
+ print("model description ================")
+ print("model keys: ",model.keys())
+ print("model nodes: ",model['nodes'].keys())
+ print("model hyperedges: ",model['hyperedges'].keys())
+ print("end of model description ================ \n")
+
+def solution_description(solution):
+ print("solution description ================")
+ print("solution keys: ",solution.keys())
+ print("solution status: ",solution['status'])
+ print("solution objective: ",solution['objective'])
+ print("solution elements: ",solution['elements'].keys())
+ for key in solution["elements"].keys():
+ print("solution elements ",key," len : ",len(solution['elements'][key]))
+ print("end of solution description ================ \n")
+
+
+def make_node_report_old(node_name, node_data,destination):
+ """
+ OUTDATED
+ PRE:
+ - node_data is the node data stored in a dictionary
+ - node_name is a string representing the name of the node
+ - destination is a string representing the destination folder for all node reports
+ POST: save all nodes' variables on a single PDF file
+ """
+ # Calculate the number of rows and columns based on the number of plots
+ num_plots = len(node_data.keys())
+ num_cols = 2
+ num_rows = (num_plots + num_cols - 1) // num_cols
+
+ output_filename = node_name + "_DATA.pdf"
+ output_path = os.path.join(destination, output_filename)
+ pdf_pages = PdfPages(output_path)
+ fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 8))
+ axes = axes.flatten()
+
+ for idx, key in enumerate(node_data.keys()):
+ ax = axes[idx]
+ ax.plot(node_data[str(key)]["values"])
+ ax.set_ylabel(str(key))
+ ax.set_xlabel("time")
+ ax.set_title(node_name + " " + str(key))
+
+ # Hide any empty subplots
+ for idx in range(num_plots, num_rows * num_cols):
+ fig.delaxes(axes[idx])
+
+ fig.suptitle(node_name+"_DATA", fontsize=16, fontweight="bold")
+ fig.tight_layout(rect=[0, 0, 1, 0.95]) # Add some space for the title
+ pdf_pages.savefig(fig)
+ plt.close(fig)
+ pdf_pages.close()
+
+
+
+def make_node_report(node_name, node_data, destination):
+ """
+ PRE:
+ - node_data is the node data stored in a dictionary
+ - node_name is a string representing the name of the node
+ - destination is a string representing the destination folder for all node reports
+ POST: save all nodes' variables and objectives on a single PDF file
+ """
+ # Separate the keys into two groups
+ data_tech = node_data["variables"]
+ data_obj = node_data["objectives"]
+ capacity_keys = []
+ other_keys = []
+ objective_keys = ["unnamed"] #list(data_obj.keys())
+
+ for key in data_tech.keys():
+ if "capacity" in key:
+ capacity_keys.append(key)
+ else:
+ other_keys.append(key)
+
+ # Calculate the number of rows and columns based on the number of plots
+ num_plots = len(other_keys) + 2 # +2 for capacities and objectives
+ num_cols = 1
+ num_rows = (num_plots + num_cols - 1) // num_cols
+
+ output_filename = node_name + "_DATA.pdf"
+ output_path = os.path.join(destination, output_filename)
+ pdf_pages = PdfPages(output_path)
+ fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 8))
+ axes = axes.flatten()
+
+ # Plot the capacities
+ if capacity_keys:
+ capacity_ax = axes[0]
+ capacity_values = []
+ capacity_labels = []
+ for key in capacity_keys:
+ values = data_tech[str(key)]["values"]
+ capacity_values.extend(values)
+ capacity_labels.append(key)
+
+ capacity_ax.stem(capacity_labels, capacity_values, basefmt=" ",markerfmt=" ", orientation="horizontal")
+ capacity_ax.set_title("Capacities")
+ for label, value in zip(capacity_labels, capacity_values):
+ capacity_ax.text(value , label, str(round(value, 2)), va='center', ha="left")
+
+
+ # Plot the objective
+ if objective_keys:
+ objective_ax = axes[1]
+ objective_values = []
+ objective_labels = ["CAPEX","OPEX"]
+ for key in objective_keys:
+ values = data_obj[str(key)]
+ objective_values.extend(values)
+
+ objective_ax.stem(objective_labels, objective_values, basefmt=" ",markerfmt=" ", orientation="horizontal")
+ objective_ax.set_title("Objectives")
+ for label, value in zip(objective_labels, objective_values):
+ objective_ax.text(value, label, str(round(value,2)), va='center')
+
+ # Plot the remaining keys
+ for idx, key in enumerate(other_keys):
+ ax = axes[idx+2] if capacity_keys else axes[idx]
+ values = data_tech[str(key)]["values"]
+ non_zero_values = [val for val in values if val != 0]
+ ax.plot(non_zero_values)
+ ax.set_ylabel(str(key))
+ ax.set_xlabel("time")
+ ax.set_title(node_name + " " + str(key))
+
+ # Hide any empty subplots
+ for idx in range(num_plots, num_rows * num_cols):
+ fig.delaxes(axes[idx])
+
+ fig.suptitle(node_name+"_DATA", fontsize=16, fontweight="bold")
+ fig.tight_layout(rect=[0, 0, 1, 0.95]) # Add some space for the title
+
+ pdf_pages.savefig(fig)
+ plt.close(fig)
+ pdf_pages.close()
+
+def make_node_statistics(node_name, node_variables_data,stats_df):
+ """
+ PRE :
+ - node_variables_data is the dictionnary containings all the items variables - values
+ - node_name is a string representing the name of the node
+ - stats_df is a panda dataframe containing the statistics of all nodes
+ POST : write/update stats dataframe for each variable the following statistics : mean, min, max, std, 25%, 50%, 75%
+ """
+ for key in node_variables_data.keys():
+ values = node_variables_data[str(key)]["values"]
+ stats_df.loc[node_name+"_"+key] = [np.mean(values), np.min(values), np.max(values), np.std(values),
+ np.percentile(values, 25), np.percentile(values, 50), np.percentile(values, 75)]
+
+def make_system_statistics(data):
+ """
+ PRE :
+ - data is a dictionary containing the solution of the model
+ POST :
+ - returns a panda dataframe containing for each variable of each node of the system the following
+ statistics : mean, min, max, std, 25%, 50%, 75%
+ """
+ stats = pd.DataFrame(columns=["mean", "min", "max", "std", "25%", "50%", "75%"])
+ for key in data["solution"]["elements"].keys():
+ make_node_statistics(key,data["solution"]["elements"][key]["variables"],stats)
+ return stats
+
+
+def make_system_report(data,destination,report_name, merge):
+ """
+ PRE:
+ - data is a dictionary containing the solution of the model
+ - destination is a string representing the destination folder name in the results folder for the report
+ POST: save a pdf file at destination agregating all the nodes' plots
+ """
+
+ if not os.path.exists(destination):
+ os.makedirs(destination)
+ # os.makedirs(os.path.join(destination, r"\report"))
+
+ #Node report generation
+ for key,val in data.items():
+ if key == "elements":
+ for k,v in val.items():
+ make_node_report(k,v,destination)
+ else:
+ print(key,val)
+ if merge : merge_pdfs(destination, report_name)
+
+def merge_pdfs(destination, report_name):
+ #PDF aggregation
+ merger = PdfMerger()
+ for filename in os.listdir(destination):
+ if filename.endswith(".pdf"):
+ filepath = os.path.join(destination, filename)
+ merger.append(filepath)
+ output_path = os.path.join(destination, report_name+".pdf")
+ merger.write(output_path)
+ merger.close()
+
+def compile_tex_to_pdf(file_name):
+ # Run LaTeX compiler to generate the PDF
+ process = subprocess.Popen(['pdflatex', file_name], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+ output, error = process.communicate()
+
+ if process.returncode == 0:
+ print('PDF generated successfully.')
+ else:
+ print('Error occurred during PDF generation:')
+ print(error.decode())
+
+
+def make_acronym(string):
+ parts = string.split('_')
+ new_name=""
+ for part in parts:
+ if part.islower():
+ new_name +='_'+part
+ else : new_name +=part[0]
+ return new_name
+
+def make_statistics_report(data, destination, report_name):
+ """
+ This function is not yet finished, tex file is generated but is too large and table is too long to fit on an A4 page
+ PRE:
+ - data is a dictionary containing the solution of the model
+ - destination is a string representing the destination folder name in the results folder for the report
+ - report_name is a string representing the name of the report
+ POST: save a tex file at destination aggregating all the nodes' statistics
+ """
+
+
+ statistics_data=make_system_statistics(data)
+ df = pd.DataFrame.from_dict(statistics_data)
+ df.index = [make_acronym(index) for index in df.index]
+
+ #save the statistics in a csv file
+ csv_stats_path = os.path.join(destination, "statistics" + ".csv")
+ statistics_data.to_csv(csv_stats_path)
+ # Save the DataFrame to a LaTeX table with custom styling
+ table = tabulate(df, headers='keys', tablefmt='latex')
+
+ # Define a LaTeX template with custom styling for the document and save it to a .tex file
+ latex_template = r'''
+ \documentclass[10pt]{article}
+ \usepackage{booktabs}
+ \usepackage{array}
+ \usepackage{lscape}
+ \begin{document}
+ \pagenumbering{gobble}
+ \centering
+ \renewcommand{\arraystretch}{1.5}
+
+ \begin{landscape}
+ \begin{tabular}{>{\raggedright\arraybackslash}p{1cm}cc}
+ %s
+ \end{tabular}
+ \end{landscape}
+ \end{document}
+ '''
+ tex_path = os.path.join(destination, report_name + ".tex")
+ with open(tex_path, 'w') as f:
+ f.write(latex_template % table)
+
+def make_system_capacity_report(data,destination,report_name):
+ """
+ PRE:
+ - data is a dictionary containing the solution of the model
+ - destination is a string representing the destination folder name in the results folder for the report
+ - report_name is a string representing the name of the report
+ POST:
+ - saves a table containing node_name, capacity_name, value in a csv file at destination
+ """
+ capacity_data = pd.DataFrame(columns=["node_name","capacity_name","value"])
+ for key in data["solution"]["elements"].keys():
+ for k,v in data["solution"]["elements"][key]["variables"].items():
+ if "capacity" in k:
+ capacity_data.loc[len(capacity_data)] = [key,k,v["values"][-1]]
+ capacity_data.to_csv(os.path.join(destination,"Capacity_"+report_name+".csv"))
+
+def make_system_cost_report(data,destination,report_name):
+ """
+ PRE:
+ - data is a dictionary containing the solution of the model
+ - destination is a string representing the destination folder name in the results folder for the report
+ - report_name is a string representing the name of the report
+ POST:
+ - saves a table containing node_name, capex, opex and sorted by greatest capex in a csv file at destination
+ """
+ cost_data = pd.DataFrame(columns=["node_name","capex","opex"])
+ for key in data["solution"]["elements"].keys():
+ cost_data.loc[len(cost_data)] = [key,data["solution"]["elements"][key]["objectives"]["unnamed"][0],data["solution"]["elements"][key]["objectives"]["unnamed"][1]]
+ cost_data.to_csv(os.path.join(destination,"Cost_"+report_name+".csv"))
+
+def main():
+
+ #=============================ANALYSIS PARAMETERS====================================
+ gboml_file = "RREHA_ref_5y"
+ time_horizon = 5 #years
+
+ #=============================load data====================================
+ gboml_path = r"\results\GBOML"
+ path = gboml_path + "\\" + gboml_file
+ report_folder = r"\results"+"\\"+gboml_file
+ report_name = "Report_"+gboml_file
+
+ root_path = os.path.abspath(os.path.dirname(__file__))
+ file_name = root_path+path
+ with open(file_name) as f:
+ data = js.load(f)
+ # save_pretty_dictionnary(data,file_name,True)
+
+ #=============================Variables=============================
+ installed_capacity= 1e7 #MWh
+ yearly_cost = data.get("solution").get("objective") #M€
+ print("Methane cost {} [€/MWh]".format(1e6*(yearly_cost/installed_capacity/time_horizon)))
+ units = {"Power":"[GW]",
+ "Energy":"[GWh]",
+ "Cost":"[M€]",
+ "Mass":"[kt]",
+ "MassFlow":"[kt/h]"}
+
+ #=============================data_description=============================
+
+ # data_description(data)
+ # model_description(data['model'])
+ # save_pretty_dictionnary(data,True)
+ # solution_description(data['solution'])
+ # make_technology_plots("DIRECT_AIR_CAPTURE_PLANTS",data["solution"]["elements"]["DIRECT_AIR_CAPTURE_PLANTS"]["variables"]) #build a node report
+ make_system_report(data["solution"],root_path+report_folder,report_name, True)
+ # merge_pdfs(root_path+report_folder, report_name)
+ make_statistics_report(data,root_path+report_folder,"statistics_report")
+ make_system_capacity_report(data,root_path+report_folder,report_name)
+ make_system_cost_report(data,root_path+report_folder,report_name)
+if __name__ == "__main__":
+ main()
+
+
+