URL: /sdk/python/styling

---
title: 'Chart Styling'
description: 'Create charts with Open Electricity branding and colors'
icon: 'palette'
---

The Open Electricity Python SDK includes a comprehensive styling module that helps you create professional charts matching the Open Electricity brand guidelines.

## Installation

The styling module requires additional visualization dependencies:

<CodeGroup>

```bash pip
pip install openelectricity[charts]
```

```bash uv
uv add openelectricity[charts]
```

</CodeGroup>

Or install the dependencies separately:

```bash
pip install matplotlib seaborn pillow
```

## Quick Start

<CodeGroup>

```python Basic Setup
from openelectricity import styles
import matplotlib.pyplot as plt

# Apply Open Electricity styling globally
styles.set_openelectricity_style()

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

# Your plotting code here
ax.plot(data)

# Format with Open Electricity branding
styles.format_chart(
    ax,
    title="My Energy Chart",
    ylabel="Generation (MW)",
    add_logo=True  # Adds watermark
)

plt.show()
```

```python With Data
from openelectricity import OEClient, styles
from openelectricity.types import DataMetric
import pandas as pd

# Fetch data
with OEClient() as client:
    response = client.get_network_data(
        network_code="NEM",
        metrics=[DataMetric.POWER],
        interval="1h",
        primary_grouping="fueltech"
    )

# Create styled chart
fig, ax = styles.create_styled_figure()

# Plot with fuel technology colors
for fueltech in fueltechs:
    color = styles.get_fueltech_color(fueltech)
    ax.plot(data[fueltech], color=color, label=fueltech)

styles.format_chart(ax, title="Generation by Technology")
plt.show()
```

</CodeGroup>

## Fuel Technology Colors

The module includes the official color palette for all Australian energy fuel technologies.

### Color Functions

<CodeGroup>

```python Single Color
# Get color for a specific fuel technology
color = styles.get_fueltech_color("solar_rooftop")
# Returns: "#FFD700" (gold)
```

```python Multiple Colors
# Get colors for multiple fuel technologies
fueltechs = ["solar", "wind", "coal", "gas"]
colors = styles.get_fueltech_palette(fueltechs)
# Returns: ["#FFA500", "#4A8E3C", "#2C2C2C", "#D2691E"]
```

</CodeGroup>

### Available Colors

<Tabs>
  <Tab title="Renewables">
    | Technology | Color | Hex Code |
    |------------|-------|----------|
    | Solar Rooftop | 🟡 Gold | `#FFD700` |
    | Solar Utility | 🟠 Orange | `#FFA500` |
    | Wind | 🟢 Forest Green | `#4A8E3C` |
    | Hydro | 🔵 Light Blue | `#4A90E2` |
  </Tab>
  
  <Tab title="Storage">
    | Technology | Color | Hex Code |
    |------------|-------|----------|
    | Battery Discharging | 🟣 Indigo | `#6366F1` |
    | Battery Charging | 🟪 Purple | `#8B5CF6` |
    | Pumps | 🔷 Cyan | `#06B6D4` |
  </Tab>
  
  <Tab title="Fossil Fuels">
    | Technology | Color | Hex Code |
    |------------|-------|----------|
    | Coal Black | ⚫ Very Dark Gray | `#2C2C2C` |
    | Coal Brown | 🟤 Dark Brown | `#654321` |
    | Gas CCGT | 🟫 Chocolate | `#D2691E` |
    | Gas OCGT | 🟨 Burlesque | `#DEB887` |
    | Gas Steam | 🟧 Sandy Brown | `#F4A460` |
  </Tab>
  
  <Tab title="Other">
    | Technology | Color | Hex Code |
    |------------|-------|----------|
    | Distillate | 🔴 Crimson | `#DC143C` |
    | Bioenergy | 🟫 Tan | `#8B7355` |
    | Imports/Exports | ⚪ Gray | `#9CA3AF` |
  </Tab>
</Tabs>

## Logo Watermark

Add the official Open Electricity logo as a watermark to your charts.

```python
# 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
)
```

<Note>
The logo is automatically downloaded and cached from the official Open Electricity platform on first use.
</Note>

## Chart Formatting

The `format_chart()` function applies consistent Open Electricity branding:

```python
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
)
```

### Features Applied

- **Typography**: DM Sans font family with appropriate sizes
- **Colors**: Clean white background with subtle gray gridlines
- **Grid**: Light gray (30% alpha) horizontal and vertical lines
- **Spines**: Only left and bottom axes visible (cleaner look)
- **Logo**: Optional watermark in bottom-right corner

## Complete Examples

### Stacked Area Chart

Recreate the Open Electricity website's generation chart:

```python
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()
```

### Daily Emissions Chart

```python
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()
```

## Style Configuration

The module applies these consistent style settings:

| Element | Configuration |
|---------|--------------|
| **Font Family** | DM Sans, fallback to system sans-serif |
| **Background** | Clean white (#FFFFFF) |
| **Text Color** | Dark gray (#1A1A1A) |
| **Grid** | Light gray (#E0E0E0) at 30% opacity |
| **Grid Style** | Solid lines, 0.5pt width |
| **Spines** | Only bottom and left visible |
| **Title Size** | 14pt bold |
| **Label Size** | 11pt regular |
| **Tick Size** | 10pt regular |

## Best Practices

<AccordionGroup>
  <Accordion title="Color Usage">
    - Always use the provided fuel technology colors for energy data
    - Use `get_fueltech_color()` for consistent coloring
    - Group similar technologies with color families
  </Accordion>
  
  <Accordion title="Watermark Placement">
    - Position in bottom-right corner for minimal interference
    - Use 15-20% size for optimal visibility
    - Set 30% transparency to avoid obscuring data
  </Accordion>
  
  <Accordion title="Chart Layout">
    - Stack fossil fuels at bottom, renewables on top
    - Use thousands separators for large numbers
    - Include clear time labels on x-axis
    - Add units to axis labels (MW, MWh, tCO₂)
  </Accordion>
  
  <Accordion title="Performance">
    - Logo is downloaded once and cached
    - Apply global styling once at start
    - Use vectorized operations for large datasets
  </Accordion>
</AccordionGroup>

## API Reference

### Core Functions

| Function | Description |
|----------|------------|
| `set_openelectricity_style()` | Apply global matplotlib/seaborn styling |
| `create_styled_figure(figsize, dpi)` | Create pre-styled figure and axes |
| `format_chart(ax, **kwargs)` | Apply Open Electricity formatting to axes |
| `add_watermark(ax, **kwargs)` | Add logo watermark to axes |

### Color Functions

| Function | Description |
|----------|------------|
| `get_fueltech_color(fueltech)` | Get hex color for a fuel technology |
| `get_fueltech_palette(fueltechs)` | Get list of colors for multiple technologies |
| `get_color_map()` | Get complete fuel technology color dictionary |
| `get_brand_colors()` | Get Open Electricity brand color palette |

## Troubleshooting

<Warning>
If the logo fails to download, ensure you have internet connectivity. The module will continue without the watermark if the logo is unavailable.
</Warning>

<Info>
For high-DPI displays, increase the `dpi` parameter in `create_styled_figure()` to 150 or 200 for sharper output.
</Info>

## Related Resources

- [Python SDK Overview](/sdk/python/overview)
- [Python SDK Reference](/sdk/python/reference)
- [Example Scripts](https://github.com/opennem/openelectricity-python/tree/main/examples)