update draw.py
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Hanzhang Ma
parent
58a7662a8b
commit
d4fde202d0
115
draw.py
115
draw.py
@ -1,5 +1,6 @@
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from matplotlib.ticker import FuncFormatter
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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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@ -7,7 +8,6 @@ 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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@ -15,25 +15,43 @@ def read_data(file_name: str):
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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 = df.T
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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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def draw_results(results, filename, title_benefit, 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.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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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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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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# print(new_Data)
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df = pd.concat([df, new_Data])
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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x,pos):
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return '{:.0f}'.format(x/1000)
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cmap = sns.color_palette("coolwarm", as_cmap=True)
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plt.figure(figsize=figure_size)
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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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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.title(title_benefit)
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plt.gca().invert_yaxis()
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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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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@ -41,43 +59,84 @@ def draw_results(results, filename, title, annot_benefit=False, figure_size=(10,
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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.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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# print(new_Data)
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df = pd.concat([df, new_Data])
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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x, pos):
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return '{:.0f}'.format(x / 1000000)
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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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ax = sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.title(title_cost)
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plt.gca().invert_yaxis()
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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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 = (4 * 24 * 365 - df) / (4 * 24 * 365)
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df = df.astype(float)
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df.index = df.index / 1000
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df.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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print('unmet index')
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print(df.index)
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print('unmet columns')
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print(df.columns)
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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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# max_scale = max(abs(min_value/1000), abs(max_value/1000))
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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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# print(new_Data)
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df = pd.concat([df, new_Data])
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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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ax = sns.heatmap(df, 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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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x, pos):
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return '{:.0f}%'.format(x * 100)
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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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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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@ -99,19 +158,19 @@ 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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draw_cost(costs_df,
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f'plots/costs-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[-1])}.png',
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f'Costs for ESS-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[-1])}MWh', annot_cost=False)
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draw_overload(overload_df,
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f'plots/overload-ess-{overload_df.columns[-1]}-pv-{overload_df.index[-1]}.png',
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f'Overload for ess-{overload_df.columns[-1]}MW pv-{overload_df.index[-1]}MWh',
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annot_unmet=False)
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draw_results(results_df,
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f'plots/results-{results_df.columns[-1]}-pv-{results_df.index[-1]}.png',
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f'Results for ess-{results_df.columns[-1]}MW pv-{results_df.index[-1]}MWh', annot_benefit=False)
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