I have 8 numpy arrays of the dimensionalty (number_of_buildings, number_of_weeks) which in my example is (2, 20). They look like that:
import numpy as np
result_multiple_buildings_simulationObjective_costs_Euro_OPT = np.array([
[181.1, 172.59, 170.02, 173.83, 181.21, 160.95, 182.24, 186.1, 168.4, 153.3, 173.47, 175.36, 175.05, 171.96, 182.94, 163.36, 178.0, 174.27, 174.02, 177.51],
[109.0, 107.78, 106.47, 104.33, 113.67, 98.19, 112.45, 114.94, 102.31, 96.45, 107.67, 107.0, 105.11, 103.22, 116.03, 101.46, 106.54, 106.89, 108.24, 108.84]
])
result_multiple_buildings_negativeScore_PhysicalLimit_OPT = np.array([
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
])
result_multiple_buildings_simulationObjective_costs_Euro_CC = np.array([
[226.29, 217.3, 214.17, 219.22, 226.2, 204.82, 226.94, 232.28, 213.46, 197.07, 218.78, 221.09, 219.95, 216.0, 228.37, 207.02, 223.6, 219.31, 219.67, 222.41],
[148.25, 146.07, 145.98, 143.81, 151.92, 135.71, 151.11, 154.02, 141.29, 136.91, 146.03, 146.73, 145.5, 144.33, 154.31, 143.15, 144.81, 146.57, 146.92, 146.29]
])
result_multiple_buildings_negativeScore_PhysicalLimit_CC = np.array([
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0]
])
result_multiple_buildings_simulationObjective_costs_Euro_PSC = np.array([
[217.38, 206.57, 203.58, 209.52, 215.38, 192.08, 216.79, 221.89, 201.99, 183.88, 207.59, 211.56, 208.42, 205.82, 218.36, 197.26, 212.61, 210.27, 209.26, 212.08],
[137.93, 135.81, 134.24, 133.09, 140.28, 125.58, 139.31, 142.51, 130.13, 123.97, 134.83, 135.38, 133.45, 132.09, 143.84, 130.38, 132.86, 135.51, 135.75, 135.66]
])
result_multiple_buildings_negativeScore_total_overall_PSC = np.array([
[0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
])
result_multiple_buildings_simulationObjective_costs_Euro_ANN = np.array([
[226.29, 217.3, 214.17, 219.22, 226.2, 204.82, 226.94, 232.28, 213.46, 197.07, 218.78, 221.09, 219.95, 216.0, 228.37, 207.02, 223.6, 219.31, 219.67, 222.41],
[148.25, 146.07, 145.98, 143.81, 151.92, 135.71, 151.11, 154.02, 141.29, 136.91, 146.03, 146.73, 145.5, 144.33, 154.31, 0.0, 0.0, 0.0, 0.0, 0.0]
])
result_multiple_buildings_negativeScore_PhysicalLimit_ANN = np.array([
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0]
])
What I want is to have a csv file for each building, that lists all values of the week for that specific building of those 8 arrays. So it should in total have 9 columns: "Week (index)", "Opt_cost", "Opt_neg", "CC_cost", "CC_neg", "PSC_cost", "PSC_neg", "ANN_cost", "ANN_neg". The rows should be the weeks, so in my case 20 entries. Further, there should be an additional row which calculates the average of all 8 columns excluding the week index (rounded, to the first decimal value).
So in my case there should be 2 csv files with the name "Building_1.csv" and "Building_2.csv" but actually the number of buildings should be variable:
I tried the following code but it did not work:
datasets = [
("OPT", result_multiple_buildings_simulationObjective_costs_Euro_OPT,
result_multiple_buildings_negativeScore_PhysicalLimit_OPT),
("CC", result_multiple_buildings_simulationObjective_costs_Euro_CC,
result_multiple_buildings_negativeScore_PhysicalLimit_CC),
("PSC", result_multiple_buildings_simulationObjective_costs_Euro_PSC,
result_multiple_buildings_negativeScore_PhysicalLimit_PSC),
("ANN", result_multiple_buildings_simulationObjective_costs_Euro_ML,
result_multiple_buildings_negativeScore_PhysicalLimit_ML)
]
# Iterate through datasets and create CSV files for each building
for label, cost_array, neg_array in datasets:
for building_index in range(cost_array.shape[0]):
# Create a new CSV file for each building
csv_file_name = f"{folderPath_WholeSimulation}/Results_HH{building_index + 1}.csv"
# Combine data of the arrays and calculate averages
data = np.column_stack((cost_array[building_index, :], neg_array[building_index, :]))
week_index = np.arange(1, data.shape[0] + 1).reshape(-1, 1)
data = np.column_stack((data, week_index))
avg_data = np.mean(data, axis=0).reshape(1, -1)
# column descriptions
column_descriptions = [f"{label}", f"{label}_neg", f"CC", f"CC_neg", f"PSC", f"PSC_neg", f"ANN", f"ANN_neg",
"Week Index"]
# Write data and averages to the CSV file
with open(csv_file_name, 'w') as csv_file:
csv_file.write(";".join(column_descriptions) + "\n")
# Write data rows
for row in data:
csv_file.write(";".join(map(str, row)) + "\n")
# Calculate average row
avg_row = np.append(avg_data, ["Average"])
csv_file.write(";".join(map(str, avg_row)) + "\n")
Do you have any idea how I can do that?
You can use pandas to compute the average and save it to the CSV, e.g.:
import pandas as pd
data = {
"Euro_OPT": result_multiple_buildings_simulationObjective_costs_Euro_OPT,
"PhysicalLimit_OPT": result_multiple_buildings_negativeScore_PhysicalLimit_OPT,
"Euro_CC": result_multiple_buildings_simulationObjective_costs_Euro_CC,
"PhysicalLimit_CC": result_multiple_buildings_negativeScore_PhysicalLimit_CC,
"Euro_PSC": result_multiple_buildings_simulationObjective_costs_Euro_PSC,
"PhysicalLimit_PSC": result_multiple_buildings_negativeScore_total_overall_PSC,
"Euro_ANN": result_multiple_buildings_simulationObjective_costs_Euro_ANN,
"PhysicalLimit_ANN": result_multiple_buildings_negativeScore_PhysicalLimit_ANN,
}
df = pd.DataFrame([dict(zip(data, row)) for row in zip(*data.values())])
df = df.explode(df.columns.to_list())
for idx, g in df.groupby(level=0):
g = g.reset_index().rename(columns={"index": "Week"})
g["Week"] = np.arange(1, len(g) + 1)
mean = g.mean().to_frame().T.astype(float).round(2)
mean["Week"] = "Averages"
g = pd.concat([g, mean])
g.to_csv(f"Building_{idx+1}.csv", index=False)
This creates two CSV files:
Week,Euro_OPT,PhysicalLimit_OPT,Euro_CC,PhysicalLimit_CC,Euro_PSC,PhysicalLimit_PSC,Euro_ANN,PhysicalLimit_ANN
1,181.1,1.0,226.29,0.0,217.38,0.0,226.29,0.0
2,172.59,0.0,217.3,0.0,206.57,0.0,217.3,1.0
3,170.02,0.0,214.17,0.0,203.58,2.0,214.17,0.0
4,173.83,0.0,219.22,1.0,209.52,0.0,219.22,0.0
5,181.21,0.0,226.2,0.0,215.38,0.0,226.2,0.0
6,160.95,0.0,204.82,0.0,192.08,0.0,204.82,0.0
7,182.24,0.0,226.94,0.0,216.79,0.0,226.94,0.0
8,186.1,7.0,232.28,0.0,221.89,0.0,232.28,0.0
9,168.4,0.0,213.46,0.0,201.99,0.0,213.46,0.0
10,153.3,0.0,197.07,0.0,183.88,1.0,197.07,0.0
11,173.47,0.0,218.78,0.0,207.59,1.0,218.78,0.0
12,175.36,0.0,221.09,0.0,211.56,1.0,221.09,0.0
13,175.05,0.0,219.95,0.0,208.42,0.0,219.95,0.0
14,171.96,0.0,216.0,0.0,205.82,0.0,216.0,0.0
15,182.94,0.0,228.37,0.0,218.36,0.0,228.37,0.0
16,163.36,0.0,207.02,0.0,197.26,0.0,207.02,0.0
17,178.0,0.0,223.6,0.0,212.61,0.0,223.6,0.0
18,174.27,0.0,219.31,0.0,210.27,0.0,219.31,0.0
19,174.02,0.0,219.67,0.0,209.26,0.0,219.67,0.0
20,177.51,0.0,222.41,0.0,212.08,0.0,222.41,0.0
Averages,173.78,0.4,218.7,0.05,208.11,0.25,218.7,0.05
Week,Euro_OPT,PhysicalLimit_OPT,Euro_CC,PhysicalLimit_CC,Euro_PSC,PhysicalLimit_PSC,Euro_ANN,PhysicalLimit_ANN
1,109.0,0.0,148.25,0.0,137.93,0.0,148.25,0.0
2,107.78,3.0,146.07,0.0,135.81,0.0,146.07,0.0
3,106.47,0.0,145.98,0.0,134.24,0.0,145.98,0.0
4,104.33,0.0,143.81,0.0,133.09,0.0,143.81,0.0
5,113.67,0.0,151.92,0.0,140.28,0.0,151.92,0.0
6,98.19,0.0,135.71,2.0,125.58,0.0,135.71,0.0
7,112.45,0.0,151.11,0.0,139.31,0.0,151.11,0.0
8,114.94,0.0,154.02,0.0,142.51,0.0,154.02,0.0
9,102.31,0.0,141.29,0.0,130.13,0.0,141.29,3.0
10,96.45,0.0,136.91,0.0,123.97,0.0,136.91,0.0
11,107.67,0.0,146.03,0.0,134.83,0.0,146.03,0.0
12,107.0,0.0,146.73,0.0,135.38,0.0,146.73,0.0
13,105.11,2.0,145.5,0.0,133.45,0.0,145.5,0.0
14,103.22,0.0,144.33,0.0,132.09,0.0,144.33,0.0
15,116.03,1.0,154.31,2.0,143.84,0.0,154.31,0.0
16,101.46,0.0,143.15,0.0,130.38,0.0,0.0,4.0
17,106.54,0.0,144.81,0.0,132.86,0.0,0.0,0.0
18,106.89,0.0,146.57,0.0,135.51,0.0,0.0,0.0
19,108.24,0.0,146.92,0.0,135.75,0.0,0.0,0.0
20,108.84,0.0,146.29,0.0,135.66,0.0,0.0,0.0
Averages,106.83,0.3,145.99,0.2,134.63,0.0,109.6,0.35