Chart Styling

pip install matplotlib seaborn pillow

# Add watermark with defaults (15% size, 30% opacity)
styles.add_watermark(ax)

# Customize watermark
styles.add_watermark(
    ax,
    position=(0.98, 0.02),  # Bottom-right (x, y in 0-1 range)
    size=0.20,              # 20% of figure width
    alpha=0.3               # 30% transparency
)

styles.format_chart(
    ax,
    title="NEM Generation by Technology",
    xlabel="Date",
    ylabel="Power (MW)",
    add_logo=True,           # Add watermark
    logo_position=(0.98, 0.02),
    logo_size=0.15,          # 15% of figure width
    logo_alpha=0.3           # 30% transparency
)

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from openelectricity import OEClient, styles
from openelectricity.types import DataMetric

# Apply styling
styles.set_openelectricity_style()

# Fetch data
with OEClient() as client:
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.POWER],
        interval="30m",
        date_start=datetime.now() - timedelta(days=3),
        primary_grouping="fueltech"
    )

# Process to DataFrame
data = []
for timeseries in response.data:
    for result in timeseries.results:
        fueltech = result.name.replace("power.", "")
        for dp in result.data:
            if dp.value and dp.value >= 0:
                data.append({
                    "timestamp": dp.timestamp,
                    "fueltech": fueltech,
                    "power": dp.value
                })

df = pd.DataFrame(data)
pivot_df = df.pivot_table(
    index="timestamp",
    columns="fueltech",
    values="power",
    fill_value=0
)

# Create chart
fig, ax = styles.create_styled_figure(figsize=(14, 8))

# Order fuel technologies (fossil fuels bottom, renewables top)
fuel_order = [
    "coal_black", "coal_brown",  # Coal at bottom
    "gas_steam", "gas_ccgt", "gas_ocgt",  # Gas
    "hydro", "battery_charging",  # Storage
    "battery_discharging", "wind",  # Renewables
    "solar_utility", "solar_rooftop"  # Solar on top
]

# Filter to available columns
ordered_cols = [col for col in fuel_order if col in pivot_df.columns]
colors = styles.get_fueltech_palette(ordered_cols)

# Create stacked area chart
ax.stackplot(
    pivot_df.index,
    *[pivot_df[col].values for col in ordered_cols],
    labels=[col.replace("_", " ").title() for col in ordered_cols],
    colors=colors,
    alpha=0.9
)

# Format chart
styles.format_chart(
    ax,
    title=f"NEM Generation - {pivot_df.iloc[-1].sum():,.0f} MW",
    ylabel="Generation (MW)",
    add_logo=True,
    logo_size=0.20
)

# Add legend
ax.legend(loc='upper left', frameon=False, ncol=5)

plt.tight_layout()
plt.show()

from openelectricity import OEClient, styles
from openelectricity.types import DataMetric
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Setup styling
styles.set_openelectricity_style()

with OEClient() as client:
    # Get emissions data
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.EMISSIONS],
        interval="1d",
        date_start=datetime.now() - timedelta(days=30),
        primary_grouping="network_region"
    )

# Create styled figure
fig, ax = styles.create_styled_figure(figsize=(12, 6))

# Plot each region with brand colors
regions = ["NSW1", "QLD1", "VIC1", "SA1", "TAS1"]
colors = plt.cm.Set2(range(len(regions)))

for i, region in enumerate(regions):
    # Filter data for region
    region_data = [...]  # Process response
    ax.plot(dates, values, label=region, color=colors[i], linewidth=2)

# Format with branding
styles.format_chart(
    ax,
    title="Daily Emissions by Region",
    xlabel="Date",
    ylabel="Emissions (tCO₂)",
    add_logo=True
)

ax.legend(loc='upper right')
plt.show()