123 lines
4.7 KiB
Python
123 lines
4.7 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import numpy as np
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import pandas as pd
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import os
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import seaborn as sns
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import json
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from matplotlib.colors import LinearSegmentedColormap
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def read_data(file_name: str):
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with open(file_name, 'r') as f:
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data = json.load(f)
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for key, value in data.items():
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for subkey, subvalue in value.items():
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data[key][subkey] = float(subvalue)
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df = pd.DataFrame.from_dict(data, orient='index')
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df.index = pd.to_numeric(df.index)
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df.columns = pd.to_numeric(df.columns)
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return df
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def draw_results(results, filename, title, annot_benefit=False, figure_size=(10, 10)):
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df= results
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df = df.astype(float)
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df.index = df.index / 1000
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df.columns = df.columns / 1000
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min_value = df.min().min()
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max_value = df.max().max()
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max_scale = max(abs(min_value/1000), abs(max_value/1000))
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plt.figure(figsize=figure_size)
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cmap = sns.color_palette("coolwarm", as_cmap=True)
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ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
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ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f"{x:.2f}"))
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# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
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plt.title(title)
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plt.gca().invert_yaxis()
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
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df = costs
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df = df.astype(int)
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print(df.index)
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df.index = df.index / 1000
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print(df.columns)
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df.columns = df.columns / 1000
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plt.figure(figsize=figure_size)
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sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
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plt.title(title_cost)
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plt.gca().invert_yaxis()
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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print(filename)
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plt.savefig(filename)
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def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10)):
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df = overload_cnt
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df = df.astype(int)
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df.index = df.index / 1000
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df.columns = df.columns / 1000
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min_value = df.min().min()
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max_value = df.max().max()
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max_scale = max(abs(min_value/1000), abs(max_value/1000))
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plt.figure(figsize=figure_size)
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cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
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ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
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cbar = ax.collections[0].colorbar
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cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
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cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
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cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
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plt.title(title_unmet)
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plt.gca().invert_yaxis()
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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directory = 'data/'
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file_list = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
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split_files = [f.split('-') for f in file_list]
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for f in split_files:
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print(f[-1])
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costs_files = [f for f in split_files if f[-1].endswith('costs.json')]
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print(f'find costs files: {costs_files}')
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overload_files = [f for f in split_files if f[-1].endswith('overload_cnt.json')]
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print(f'find overload files: {overload_files}')
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results_files = [f for f in split_files if f[-1].endswith('results.json')]
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print(f'find results files: {results_files}')
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costs_dfs = [read_data(directory + '-'.join(f)) for f in costs_files]
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overload_dfs = [read_data(directory + '-'.join(f)) for f in overload_files]
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results_dfs = [read_data(directory + '-'.join(f)) for f in results_files]
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for costs_df, overload_df, results_df in zip(costs_dfs, overload_dfs, results_dfs):
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# print(costs_df.index)
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# print(pd.to_numeric(costs_df.columns))
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# costs_df.index = pd.to_numeric(costs_df.columns )
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# costs_df.columns = pd.to_numeric(costs_df.index)
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print(costs_df)
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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)
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# overload_df.index = pd.to_numeric(overload_df.columns, errors='coerce')
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# overload_df.columns = pd.to_numeric(overload_df.columns, errors='coerce')
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print(overload_df)
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# 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)
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# results_df.index = pd.to_numeric(results_df.columns, errors='coerce')
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# results_df.columns = pd.to_numeric(results_df.columns, errors='coerce')
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# 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)
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