update draw.py

draw.py

@@ -1,5 +1,6 @@
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
+from matplotlib.ticker import FuncFormatter
 import numpy as np
 import pandas as pd
 import os
@@ -7,7 +8,6 @@ import seaborn as sns
 import json
 from matplotlib.colors import LinearSegmentedColormap
 
-
 def read_data(file_name: str):
     with open(file_name, 'r') as f:
         data = json.load(f)
@@ -15,25 +15,43 @@ def read_data(file_name: str):
         for subkey, subvalue in value.items():
             data[key][subkey] = float(subvalue)
     df = pd.DataFrame.from_dict(data, orient='index')
+    df = df.T
     df.index = pd.to_numeric(df.index)
     df.columns = pd.to_numeric(df.columns)
     return df
 
-def draw_results(results, filename, title, annot_benefit=False, figure_size=(10, 10)):
+def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):
-    df= results
+    df=results
     df = df.astype(float)
     df.index = df.index / 1000
+    df.index = df.index.map(int)
     df.columns = df.columns / 1000
+    df.columns = df.columns.map(int)
     min_value = df.min().min()
     max_value = df.max().max()
     max_scale = max(abs(min_value/1000), abs(max_value/1000))
-    plt.figure(figsize=figure_size)
+
+    df[df.columns[-1] + 1] = df.iloc[:, -1] 
+    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
+    for i in df.columns:
+        new_Data[i] = df[i].iloc[-1]
+    # print(new_Data)
+    df = pd.concat([df, new_Data])
+
+    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
+
+    def fmt(x,pos):
+        return '{:.0f}'.format(x/1000)
+
     cmap = sns.color_palette("coolwarm", as_cmap=True)
+    plt.figure(figsize=figure_size)
     ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
-    ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f"{x:.2f}"))
+    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)
-    # ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
+    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
-    plt.title(title)
+    plt.title(title_benefit)
     plt.gca().invert_yaxis()
+    plt.xlim(0, df.shape[1] - 1)
+    plt.ylim(0, df.shape[0] - 1)
     plt.xlabel('ESS Capacity (MWh)')
     plt.ylabel('PV Capacity (MW)')
     plt.savefig(filename)
@@ -41,43 +59,84 @@ def draw_results(results, filename, title, annot_benefit=False, figure_size=(10,
 def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
     df = costs
     df = df.astype(int)
-    print(df.index)
     df.index = df.index / 1000
-    print(df.columns)
+    df.index = df.index.map(int)
     df.columns = df.columns / 1000
+    df.columns = df.columns.map(int)
+
+    df[df.columns[-1] + 1] = df.iloc[:, -1] 
+    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
+    for i in df.columns:
+        new_Data[i] = df[i].iloc[-1]
+    # print(new_Data)
+    df = pd.concat([df, new_Data])
+    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
+
+    def fmt(x, pos):
+        return '{:.0f}'.format(x / 1000000)
 
     plt.figure(figsize=figure_size)
-    sns.heatmap(df/1000000,  fmt=".1f", cmap='viridis', annot=annot_cost)
+    ax = sns.heatmap(df/1000000,  fmt=".1f", cmap='viridis', annot=annot_cost)
+    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)
+    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
     plt.title(title_cost)
     plt.gca().invert_yaxis()
+    plt.xlim(0, df.shape[1] - 1)
+    plt.ylim(0, df.shape[0] - 1)
     plt.xlabel('ESS Capacity (MWh)')
     plt.ylabel('PV Capacity (MW)')
-    print(filename)
     plt.savefig(filename)
 
+
 def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10)):
     df = overload_cnt
-    df = df.astype(int)
+    df = (4 * 24 * 365 - df) / (4 * 24 * 365)
+    df = df.astype(float)
     df.index = df.index / 1000
+    df.index = df.index.map(int)
     df.columns = df.columns / 1000
+    df.columns = df.columns.map(int)
+    print('unmet index')
+    print(df.index)
+    print('unmet columns')
+    print(df.columns)
     min_value = df.min().min()
     max_value = df.max().max()
-    max_scale = max(abs(min_value/1000), abs(max_value/1000))
+    # max_scale = max(abs(min_value/1000), abs(max_value/1000))
+
+
+    df[df.columns[-1] + 1] = df.iloc[:, -1] 
+    new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
+    for i in df.columns:
+        new_Data[i] = df[i].iloc[-1]
+    # print(new_Data)
+    df = pd.concat([df, new_Data])
+
 
     plt.figure(figsize=figure_size)
     cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
-    ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
+    ax = sns.heatmap(df, fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
+
     cbar = ax.collections[0].colorbar
     cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
     cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
     cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
+    X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
 
+    def fmt(x, pos):
+        return '{:.0f}%'.format(x * 100)
+    CS = ax.contour(X, Y, df,  colors='black', alpha=0.5)
+
+    ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
+
+    plt.xlim(0, df.shape[1] - 1)
+    plt.ylim(0, df.shape[0] - 1)
     plt.title(title_unmet)
-    plt.gca().invert_yaxis()
     plt.xlabel('ESS Capacity (MWh)')
     plt.ylabel('PV Capacity (MW)')
     plt.savefig(filename)
 
+
 directory = 'data/'
 
 file_list = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
@@ -99,19 +158,19 @@ overload_dfs = [read_data(directory + '-'.join(f)) for f in overload_files]
 results_dfs = [read_data(directory + '-'.join(f)) for f in results_files]
 
 for costs_df, overload_df, results_df in zip(costs_dfs, overload_dfs, results_dfs):
-    # print(costs_df.index)
+
-    # print(pd.to_numeric(costs_df.columns))
+    draw_cost(costs_df, 
-    # costs_df.index = pd.to_numeric(costs_df.columns )
+              f'plots/costs-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[-1])}.png', 
-    # costs_df.columns = pd.to_numeric(costs_df.index)
+              f'Costs for ESS-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[-1])}MWh', annot_cost=False)
-    print(costs_df)
+
-    draw_cost(costs_df, f'plots/costs-{int(costs_df.columns[-1])}.png', f'Costs for PV-{int(costs_df.columns[-1])}MW ESS-{int(costs_df.index[-1])}MWh', annot_cost=True)
+    draw_overload(overload_df, 
-    # overload_df.index = pd.to_numeric(overload_df.columns, errors='coerce')
+                  f'plots/overload-ess-{overload_df.columns[-1]}-pv-{overload_df.index[-1]}.png', 
-    # overload_df.columns = pd.to_numeric(overload_df.columns, errors='coerce')
+                  f'Overload for ess-{overload_df.columns[-1]}MW pv-{overload_df.index[-1]}MWh', 
-    print(overload_df)
+                  annot_unmet=False)
-    # draw_overload(overload_df, f'plots/overload-{overload_df.columns[-1]}', f'Overload for PV-{overload_df.columns[-1]}MW ESS-{overload_df.index[-1]}MWh', annot_unmet=True)
+
-    # results_df.index = pd.to_numeric(results_df.columns, errors='coerce')
+    draw_results(results_df, 
-    # results_df.columns = pd.to_numeric(results_df.columns, errors='coerce')
+                 f'plots/results-{results_df.columns[-1]}-pv-{results_df.index[-1]}.png', 
-    # draw_results(results_df, f'plots/results-{results_df.columns[-1]}', f'Results for PV-{results_df.columns[-1]}MW ESS-{results_df.index[-1]}MWh', annot_benefit=True)
+                 f'Results for ess-{results_df.columns[-1]}MW pv-{results_df.index[-1]}MWh', annot_benefit=False)