From d791ac481a21094e98f11c925cda2d0e64e13e8a Mon Sep 17 00:00:00 2001
From: Hanzhang ma <harvey.ma@eng2move.com>
Date: Thu, 9 May 2024 23:49:48 +0200
Subject: [PATCH] add draw.py

---
 draw.py | 122 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 draw.py

diff --git a/draw.py b/draw.py
new file mode 100644
index 0000000..a744a92
--- /dev/null
+++ b/draw.py
@@ -0,0 +1,122 @@
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import numpy as np
+import pandas as pd
+import os
+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)
+    for key, value in data.items():
+        for subkey, subvalue in value.items():
+            data[key][subkey] = float(subvalue)
+    df = pd.DataFrame.from_dict(data, orient='index')
+    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)):
+    df= results
+    df = df.astype(float)
+    df.index = df.index / 1000
+    df.columns = df.columns / 1000
+    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)
+    cmap = sns.color_palette("coolwarm", as_cmap=True)
+    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}"))
+    # ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
+    plt.title(title)
+    plt.gca().invert_yaxis()
+    plt.xlabel('ESS Capacity (MWh)')
+    plt.ylabel('PV Capacity (MW)')
+    plt.savefig(filename)
+
+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.columns = df.columns / 1000
+
+    plt.figure(figsize=figure_size)
+    sns.heatmap(df/1000000,  fmt=".1f", cmap='viridis', annot=annot_cost)
+    plt.title(title_cost)
+    plt.gca().invert_yaxis()
+    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.index = df.index / 1000
+    df.columns = df.columns / 1000
+    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)
+    cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
+    ax = sns.heatmap(df/(4*24*365), 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%}'))
+
+    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))]
+
+
+split_files = [f.split('-') for f in file_list]
+
+for f in split_files:
+    print(f[-1])
+costs_files = [f for f in split_files if f[-1].endswith('costs.json')]
+print(f'find costs files: {costs_files}')
+overload_files = [f for f in split_files if f[-1].endswith('overload_cnt.json')]
+print(f'find overload files: {overload_files}')
+results_files = [f for f in split_files if f[-1].endswith('results.json')]
+print(f'find results files: {results_files}')
+
+costs_dfs = [read_data(directory + '-'.join(f)) for f in costs_files]
+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))
+    # costs_df.index = pd.to_numeric(costs_df.columns )
+    # costs_df.columns = pd.to_numeric(costs_df.index)
+    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)
+    # overload_df.index = pd.to_numeric(overload_df.columns, errors='coerce')
+    # overload_df.columns = pd.to_numeric(overload_df.columns, errors='coerce')
+    print(overload_df)
+    # 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')
+    # results_df.columns = pd.to_numeric(results_df.columns, errors='coerce')
+    # 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)
+
+
+
+
+
+
+
+