Space Weather Data¶

import os
import pathlib
import sys
import plotly.graph_objects as go
import brahe as bh
import numpy as np

# Add plots directory to path for importing brahe_theme
sys.path.insert(0, str(pathlib.Path(__file__).parent))
from brahe_theme import get_theme_colors, save_themed_html

# ------------------------------
# Configuration
# ------------------------------

SCRIPT_NAME = pathlib.Path(__file__).stem
OUTDIR = pathlib.Path(os.getenv("BRAHE_FIGURE_OUTPUT_DIR", "./docs/figures/"))

# Ensure output directory exists
os.makedirs(OUTDIR, exist_ok=True)

# ------------------------------

# Initialize space weather data
sw = bh.FileSpaceWeatherProvider.from_default_file()
bh.set_global_space_weather_provider(sw)

## Generate plot data

# Get range of dates stored in space weather data
mjd_min = bh.get_global_sw_mjd_min()
mjd_max = bh.get_global_sw_mjd_max()
mjd_last_obs = bh.get_global_sw_mjd_last_observed()
mjd_last_daily = bh.get_global_sw_mjd_last_daily_predicted()

print("Space Weather MJD Range:", mjd_min, "to", mjd_max)
print("Last Observed MJD:", mjd_last_obs)
print("Last Daily Predicted MJD:", mjd_last_daily)

# Split data into three regions: observed, daily predicted, monthly predicted
days_observed = np.arange(mjd_min, mjd_last_obs + 1, 1)
days_daily_predicted = np.arange(mjd_last_obs, mjd_last_daily + 1, 1)
days_monthly_predicted = np.arange(mjd_last_daily, mjd_max + 1, 1)

# Get F10.7 observed flux values for each region
f107_observed = [bh.get_global_f107_observed(mjd) for mjd in days_observed]
f107_daily_predicted = [
    bh.get_global_f107_observed(mjd) for mjd in days_daily_predicted
]
f107_monthly_predicted = [
    bh.get_global_f107_observed(mjd) for mjd in days_monthly_predicted
]


# Create hover text with year-month-day format
def mjd_to_date_str(mjd):
    """Convert MJD to YYYY-MM-DD string."""
    epoch = bh.Epoch.from_mjd(mjd, bh.TimeSystem.UTC)
    dt = epoch.to_datetime()
    return f"{int(dt[0]):04d}-{int(dt[1]):02d}-{int(dt[2]):02d}"


hover_observed = [mjd_to_date_str(mjd) for mjd in days_observed]
hover_daily_predicted = [mjd_to_date_str(mjd) for mjd in days_daily_predicted]
hover_monthly_predicted = [mjd_to_date_str(mjd) for mjd in days_monthly_predicted]

# Get year range for x-axis tick labels
epoch_min = bh.Epoch.from_mjd(mjd_min, bh.TimeSystem.UTC)
epoch_max = bh.Epoch.from_mjd(mjd_max, bh.TimeSystem.UTC)
year_min = epoch_min.to_datetime()[0]
year_max = epoch_max.to_datetime()[0]


## Create figure with theme support


def create_figure(theme):
    """Create figure with theme-specific colors."""
    colors = get_theme_colors(theme)

    fig = go.Figure()

    # Plot observed data (solid line) - use primary color
    fig.add_trace(
        go.Scatter(
            x=days_observed,
            y=f107_observed,
            mode="lines",
            line=dict(color=colors["primary"], width=1),
            name="Observed",
            showlegend=True,
            text=hover_observed,
            hovertemplate="%{text}<br>F10.7: %{y:.1f} sfu<extra></extra>",
        )
    )

    # Plot daily predicted data (dashed line) - use error color
    fig.add_trace(
        go.Scatter(
            x=days_daily_predicted,
            y=f107_daily_predicted,
            mode="lines",
            line=dict(color=colors["error"], width=1, dash="dash"),
            name="Daily Predicted",
            showlegend=True,
            text=hover_daily_predicted,
            hovertemplate="%{text}<br>F10.7: %{y:.1f} sfu<extra></extra>",
        )
    )

    # Plot monthly predicted data (dotted line) - use secondary color
    fig.add_trace(
        go.Scatter(
            x=days_monthly_predicted,
            y=f107_monthly_predicted,
            mode="lines",
            line=dict(color=colors["secondary"], width=1, dash="dot"),
            name="Monthly Predicted",
            showlegend=True,
            text=hover_monthly_predicted,
            hovertemplate="%{text}<br>F10.7: %{y:.1f} sfu<extra></extra>",
        )
    )

    # Create custom tick values and labels for x-axis (years)
    # Generate tick positions every 10 years
    tick_mjds = []
    tick_labels = []
    for year in range(1960, year_max + 1, 10):
        if year >= year_min:
            epoch = bh.Epoch.from_datetime(
                year, 1, 1, 0, 0, 0.0, 0.0, bh.TimeSystem.UTC
            )
            tick_mjds.append(epoch.mjd())
            tick_labels.append(str(year))

    # Configure axes (theme-agnostic settings)
    fig.update_xaxes(
        tickmode="array",
        tickvals=tick_mjds,
        ticktext=tick_labels,
        title_text="Year",
        range=[mjd_min, mjd_max],
        showgrid=False,
    )

    fig.update_yaxes(
        title_text="F10.7 Solar Flux [sfu]",
        showgrid=False,
    )

    return fig


# Generate and save both themed versions
light_path, dark_path = save_themed_html(create_figure, OUTDIR / SCRIPT_NAME)
print(f"✓ Generated {light_path}")
print(f"✓ Generated {dark_path}")