from datetime import datetime, timedelta

import pandas as pd

import matplotlib.pyplot as plt

# from codegreen_core.tools.carbon_intensity import calculate_from_energy_data

# from codegreen_core.tools.carbon_emission import calculate_carbon_footprint_job

from codegreen_core.data import energy

from codegreen_core.tools.loadshift_time import predict_optimal_time

import matplotlib.dates as mdates

Color = {

"red": "#D6A99A",

"green": "#99D19C",

"blue": "#3DA5D9",

"yellow": "#E2C044",

"black": "#0F1A20",

}

def plot_percentage_clean(df, country, save_fig_path=None):

df["startTimeUTC"] = pd.to_datetime(df["startTimeUTC"])

df["percentNonRenewable"] = round(

((df["total"] - df["renewableTotal"]) / df["total"]) * 100

)

df["hour"] = df["startTimeUTC"].dt.strftime("%H:%M")

date_start = df["startTimeUTC"].min().strftime("%Y-%m-%d")

date_end = df["startTimeUTC"].max().strftime("%Y-%m-%d")

time_range_label = f"Time ({date_start} - {date_end})"

# Create the plot

fig, ax = plt.subplots(figsize=(12, 4))

# Bar width

bar_width = 0.85

bar_positions = range(len(df))

# Plot each bar

for i, (index, row) in enumerate(df.iterrows()):

hour = row["hour"]

renewable = row["percentRenewable"]

non_renewable = row["percentNonRenewable"]

# Plotting bars for renewable and non-renewable

ax.bar(i, renewable, bar_width, color=Color["green"], edgecolor=Color["green"])

ax.bar(

i,

non_renewable,

bar_width,

bottom=renewable,

color=Color["red"],

edgecolor=Color["red"],

)

# Set x-ticks to be the hours

if len(df) > 74:

ax.set_xticks([]) # Hide x-ticks if too many entries

ax.set_xlabel("") # Remove x-label if too many entries

else:

ax.set_xticks(bar_positions)

ax.set_xticklabels(df["hour"], rotation=90, fontsize=7)

ax.set_xlabel(time_range_label)

ax.set_ylabel("Percentage")

ax.set_title(

"Energy Generation Breakdown: Renewable and Non-Renewable by Hour ("

+ country

+ ")"

)

# ax.legend()

if save_fig_path:

plt.savefig(save_fig_path, dpi=300, bbox_inches="tight")

plt.tight_layout()

plt.show()

def plot_multiple_percentage_clean(dfs, labels, save_fig_path=None):

num_dfs = len(dfs)

num_cols = 2 # Number of columns in the subplot grid

num_rows = (num_dfs + num_cols - 1) // num_cols # Compute number of rows needed

fig, axes = plt.subplots(

num_rows, num_cols, figsize=(15 * num_rows, 5 * num_rows), squeeze=False

)

fig.suptitle(

"Energy Generation Breakdown: Renewable and Non-Renewable by Hour",

fontsize=17,

y=1,

) # Adjust y for positioning

# Flatten the axes array for easy iteration

axes = axes.flatten()

for i, (df, label) in enumerate(zip(dfs, labels)):

ax = axes[i]

# Ensure 'startTimeUTC' is in datetime format

df["startTimeUTC"] = pd.to_datetime(df["startTimeUTC"])

df["percentNonRenewable"] = round(

((df["total"] - df["renewableTotal"]) / df["total"]) * 100

)

df["hour"] = df["startTimeUTC"].dt.strftime("%H:%M")

date_start = df["startTimeUTC"].min().strftime("%Y-%m-%d")

date_end = df["startTimeUTC"].max().strftime("%Y-%m-%d")

time_range_label = f"Time ({date_start} - {date_end})"

# Bar width

bar_width = 0.85

bar_positions = range(len(df))

# Plot each bar

for index, row in df.iterrows():

hour = row["hour"]

renewable = row["percentRenewable"]

non_renewable = row["percentNonRenewable"]

# Plotting bars for renewable and non-renewable

ax.bar(

index,

renewable,

bar_width,

color=Color["green"],

edgecolor=Color["green"],

)

ax.bar(

index,

non_renewable,

bar_width,

bottom=renewable,

color=Color["red"],

edgecolor=Color["red"],

)

# Set x-ticks to be the hours

if len(df) > 74:

ax.set_xticks([]) # Hide x-ticks if too many entries

ax.set_xlabel("") # Remove x-label if too many entries

else:

ax.set_xticks(bar_positions)

ax.set_xticklabels(df["hour"], rotation=90, fontsize=7)

ax.set_xlabel(time_range_label)

ax.set_ylabel("Percentage")

ax.set_title(label)

# Hide any unused subplots

for j in range(i + 1, len(axes)):

axes[j].axis("off")

if save_fig_path:

plt.savefig(save_fig_path, dpi=300, bbox_inches="tight")

plt.tight_layout()

plt.show()

def show_clean_energy(country, start, end, save_fig_path=None):

"""note that these plots are based on actual energy production and not the forecasts"""

d = energy(country, start, end)

actual1 = d["data"]

plot_percentage_clean(actual1, country, save_fig_path)

def show_clean_energy_multiple(countries, start, end, save_fig_path=None):

data = []

for c in countries:

data.append(energy(c, start, end)["data"])

plot_multiple_percentage_clean(data, countries, save_fig_path)