update the csv file output code and add a progress bar in the code

main.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[39]:
+# In[83]:
 
 
 import os
@@ -28,7 +28,7 @@ folder_path = 'plots'
 clear_folder_make_ess_pv(folder_path)
 
 
-# In[40]:
+# In[84]:
 
 
 import matplotlib.pyplot as plt
@@ -39,7 +39,7 @@ from EnergySystem import EnergySystem
 from config import pv_config, grid_config, ess_config
 
 
-# In[41]:
+# In[85]:
 
 
 import json
@@ -53,7 +53,7 @@ with open('config.json', 'r') as f:
     
 
 time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
-print(time_interval)
+# print(time_interval)
 
 pv_loss = js_data["pv"]["loss"]
 pv_cost_per_kW = js_data["pv"]["cost_per_kW"]
@@ -116,7 +116,7 @@ ess_capacities = np.linspace(ess_begin, ess_end, ess_groups)
 # overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
 
 
-# In[42]:
+# In[86]:
 
 
 hour_demand = []
@@ -132,7 +132,7 @@ plt.savefig('plots/demand.png')
 plt.close()
 
 
-# In[43]:
+# In[87]:
 
 
 def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):
@@ -171,7 +171,7 @@ def draw_results(results, filename, title_benefit, annot_benefit=False, figure_s
     plt.savefig(filename)
 
 
-# In[44]:
+# In[88]:
 
 
 def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)):
@@ -184,7 +184,7 @@ def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, fig
     if 0 in df.index and 0 in df.columns:
         df.loc[0,0] = 100
     df[df > 80] = 100
-    print(df)
+    # print(df)
 
     df = df.astype(float)
     df.index = df.index / 1000
@@ -193,7 +193,7 @@ def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, fig
     df.columns = df.columns.map(int)
     min_value = df.min().min()
     max_value = df.max().max()
-    print(max_value)
+    # print(max_value)
     max_scale = max(abs(min_value), abs(max_value))
 
     df[df.columns[-1] + 1] = df.iloc[:, -1] 
@@ -222,7 +222,7 @@ def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, fig
     plt.close()
 
 
-# In[45]:
+# In[89]:
 
 
 def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
@@ -257,17 +257,17 @@ def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10
     plt.close()
 
 
-# In[46]:
+# In[90]:
 
 
 def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10), days=365, granularity=15):
     df = overload_cnt
-    print(days, granularity)
+    # print(days, granularity)
     coef = 60 / granularity * days * 24
-    print(coef)
-    print(df)
+    # print(coef)
+    # print(df)
     df = ( coef - df) / coef
-    print(df)
+    # print(df)
 
     df = df.astype(float)
     df.index = df.index / 1000
@@ -309,7 +309,7 @@ def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure
     plt.close()
 
 
-# In[47]:
+# In[91]:
 
 
 def cal_profit(es: EnergySystem, saved_money, days):
@@ -317,7 +317,7 @@ def cal_profit(es: EnergySystem, saved_money, days):
     return profit
 
 
-# In[48]:
+# In[92]:
 
 
 def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price, time_interval, data, days, storage=0):
@@ -357,9 +357,11 @@ def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacit
             energySystem.time)
 
 
-# In[49]:
+
+# In[93]:
 
 
+from tqdm import tqdm
 months_results = []
 months_costs = []
 months_overload = []
@@ -368,7 +370,7 @@ months_gen_energy = []
 months_gen_energy2 = []
 months_ess_rest = pd.DataFrame(30, index=pv_capacities, columns= ess_capacities)
 months_csv_data = {}
-for index, month_data in enumerate(months_data):
+for index, month_data in tqdm(enumerate(months_data), total=len(months_data), position=0, leave= True):
     results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
     costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
     overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
@@ -381,7 +383,7 @@ for index, month_data in enumerate(months_data):
     pv_generates = {}
     grid_need_powers = {}
     times = {}
-    for pv_capacity in pv_capacities:
+    for pv_capacity in tqdm(pv_capacities, total=len(pv_capacities), desc=f'generating pv for month {index + 1}',position=1, leave=False):
         factory_demands[pv_capacity] = {}
         buy_prices[pv_capacity] = {}
         sell_prices[pv_capacity] = {}
@@ -496,7 +498,7 @@ draw_overload(overload_cnt=annual_overload,
                 figure_size=figure_size)
 
 
-# In[50]:
+# In[94]:
 
 
 def collapse_months_csv_data(months_csv_data, column_name,pv_capacies, ess_capacities):
@@ -513,7 +515,7 @@ def collapse_months_csv_data(months_csv_data, column_name,pv_capacies, ess_capac
     return data 
 
 
-# In[51]:
+# In[102]:
 
 
 annual_pv_gen = collapse_months_csv_data(months_csv_data, "pv_generate", pv_capacities, ess_capacities)
@@ -523,16 +525,21 @@ annual_sell_price = collapse_months_csv_data(months_csv_data, "sell_price", pv_c
 annual_factory_demand = collapse_months_csv_data(months_csv_data, "factory_demand", pv_capacities, ess_capacities)
 annual_grid_need_power = collapse_months_csv_data(months_csv_data, "grid_need_power", pv_capacities, ess_capacities)
 
+from datetime import datetime, timedelta
+
 for pv_capacity in pv_capacities:
     for ess_capacity in ess_capacities:
         with open(f'data/annual_data-pv-{pv_capacity}-ess-{ess_capacity}.csv', 'w') as f:
-            f.write("time,pv_generate,factory_demand,buy_price,sell_price,grid_need_power\n")
+            f.write("date, time,pv_generate (kW),factory_demand (kW),buy_price (USD/MWh),sell_price (USD/MWh),grid_need_power (kW)\n")
+            start_date = datetime(2023, 1, 1, 0, 0, 0)
             for i in range(len(annual_time[pv_capacity][ess_capacity])):
-                f.write(f"{annual_time[pv_capacity][ess_capacity][i]}, {int(annual_pv_gen[pv_capacity][ess_capacity][i])}, {int(annual_factory_demand[pv_capacity][ess_capacity][i])}, {int(annual_buy_price[pv_capacity][ess_capacity][i]*1000)}, {int(annual_sell_price[pv_capacity][ess_capacity][i]*1000)}, {int(annual_grid_need_power[pv_capacity][ess_capacity][i])}\n")
+                current_date = start_date + timedelta(hours=i)
+                formate_date = current_date.strftime("%Y-%m-%d")
+                f.write(f"{formate_date},{annual_time[pv_capacity][ess_capacity][i]},{int(annual_pv_gen[pv_capacity][ess_capacity][i])},{int(annual_factory_demand[pv_capacity][ess_capacity][i])},{int(annual_buy_price[pv_capacity][ess_capacity][i]*1000)},{int(annual_sell_price[pv_capacity][ess_capacity][i]*1000)},{int(annual_grid_need_power[pv_capacity][ess_capacity][i])} \n")
 
 
 
-# In[52]:
+# In[96]:
 
 
 def save_data(data, filename):
@@ -540,7 +547,7 @@ def save_data(data, filename):
     data.to_json(filename + '.json')
 
 
-# In[53]:
+# In[97]:
 
 
 if not os.path.isdir('data'):
@@ -551,13 +558,13 @@ save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_
 save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')
 
 
-# In[54]:
+# In[98]:
 
 
 draw_results(annual_result, 'plots/test.png', 'test', False)
 
 
-# In[55]:
+# In[99]:
 
 
 draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png',  title_roi, 365, annot_benefit, figure_size)