"""Generate all baseline SVG examples for the charted documentation.

Run from the repo root:
    python docs/generate_examples.py

All output SVGs are written to docs/examples/.
"""

import math
import os
from datetime import date

OUTPUT_DIR = os.path.join(os.path.dirname(__file__), "examples")
os.makedirs(OUTPUT_DIR, exist_ok=True)

from charted.charts import (
    AreaChart,
    BarChart,
    BoxPlot,
    BubbleChart,
    ColumnChart,
    ComboChart,
    GanttChart,
    HeatmapChart,
    Histogram,
    LineChart,
    PieChart,
    PolarAreaChart,
    RadarChart,
    ScatterChart,
)
from charted.utils.series_style import SeriesStyle


def save(name: str, svg: str) -> None:
    path = os.path.join(OUTPUT_DIR, name)
    with open(path, "w") as f:
        f.write(svg)
    print(f"  wrote {name}")


# ---------------------------------------------------------------------------
# Bar charts
# ---------------------------------------------------------------------------

# Single-series bar
save(
    "bar.svg",
    BarChart(
        title="Profit/Loss by Region ($M)",
        data=[-12, 34, -8, 52, -5, 28, 41, -19, 15, 60],
        labels=[
            "North",
            "South",
            "East",
            "West",
            "Central",
            "Pacific",
            "Atlantic",
            "Mountain",
            "Plains",
            "Metro",
        ],
        width=700,
        height=500,
    ).html,
)

# Multi-series bar (grouped / side-by-side)
save(
    "bar_multi.svg",
    BarChart(
        title="Revenue vs Expenses by Quarter ($K)",
        data=[
            [120, -45, 180, -30, 210, -60],
            [-80, -20, -95, -15, -110, -25],
        ],
        labels=["Q1 Prod", "Q1 Ops", "Q2 Prod", "Q2 Ops", "Q3 Prod", "Q3 Ops"],
        width=700,
        height=500,
    ).html,
)

# Stacked bar
save(
    "bar_stacked.svg",
    BarChart(
        title="Budget by Department ($K)",
        data=[
            [100, -50, 120],
            [80, 60, -40],
        ],
        labels=["Q1", "Q2", "Q3"],
        series_names=["Revenue", "Expenses"],
        x_stacked=True,
        width=700,
        height=400,
    ).html,
)

# Side-by-side bar
save(
    "bar_sidebyside.svg",
    BarChart(
        title="Revenue vs Expenses by Quarter ($K)",
        data=[
            [120, 180, 210],
            [-80, -95, -110],
        ],
        labels=["Q1", "Q2", "Q3"],
        series_names=["Revenue", "Expenses"],
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Column charts
# ---------------------------------------------------------------------------

# Multi-series column (default stacking)
save(
    "column.svg",
    ColumnChart(
        title="Year-over-Year Growth Rate (%) by Segment",
        data=[
            [12, -8, 22, 18, -5, 30],
            [-3, -15, 5, -2, -20, 8],
            [9, -23, 17, 16, -25, 38],
        ],
        labels=["Q1", "Q2", "Q3", "Q4", "Q5", "Q6"],
        width=700,
        height=500,
        theme={
            "v_padding": 0.12,
            "h_padding": 0.10,
        },
    ).html,
)

# Stacked column
save(
    "column_stacked.svg",
    ColumnChart(
        title="Year-over-Year Growth by Segment",
        data=[
            [12, 22, 30],
            [-8, -15, -20],
            [4, 7, 10],
        ],
        labels=["Q1", "Q2", "Q3"],
        series_names=["Revenue", "Costs", "Net"],
        width=700,
        height=400,
    ).html,
)

# Side-by-side column
save(
    "column_sidebyside.svg",
    ColumnChart(
        title="Sales Performance by Region",
        data=[
            [45, 52, 38, 61],
            [38, 46, 52, 49],
            [52, 39, 46, 51],
        ],
        labels=["Q1", "Q2", "Q3", "Q4"],
        series_names=["North", "South", "East"],
        width=700,
        height=400,
        y_stacked=False,
    ).html,
)

# ---------------------------------------------------------------------------
# Line charts
# ---------------------------------------------------------------------------

n = 20

# Multi-series line
save(
    "line.svg",
    LineChart(
        title="Signal Analysis: Raw vs Filtered vs Baseline",
        data=[
            [math.sin(i * 0.5) * 30 + (i % 7 - 3) * 5 for i in range(n)],
            [math.sin(i * 0.5) * 25 for i in range(n)],
            [math.sin(i * 0.5) * 10 - 5 for i in range(n)],
        ],
        labels=[str(i) for i in range(n)],
        width=700,
        height=400,
    ).html,
)

# XY mode line (temperature anomaly)
years = list(range(1990, 2010))
anomalies = [
    -15,
    -5,
    10,
    20,
    5,
    25,
    15,
    30,
    10,
    20,
    40,
    25,
    45,
    30,
    50,
    35,
    60,
    55,
    45,
    70,
]
# Baseline as a moving average trend instead of flat zero
baseline = [round(5 + 2 * math.sin(i * 0.4) + i * 0.5, 1) for i in range(len(years))]
save(
    "xy_line.svg",
    LineChart(
        title="Temperature Anomaly vs 5-Year Rolling Baseline (1990-2009)",
        data=[anomalies, baseline],
        x_data=years,
        labels=[str(y) for y in years],
        width=700,
        height=400,
    ).html,
)


# Single-series line
save(
    "line_single.svg",
    LineChart(
        title="Monthly Active Users (K)",
        data=[[42, 48, 55, 61, 58, 70, 80, 78, 85, 92, 88, 100]],
        labels=[
            "Jan",
            "Feb",
            "Mar",
            "Apr",
            "May",
            "Jun",
            "Jul",
            "Aug",
            "Sep",
            "Oct",
            "Nov",
            "Dec",
        ],
        series_names=["MAU"],
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Scatter charts
# ---------------------------------------------------------------------------

# Multi-series scatter: two distinct clusters with noise
import random

random.seed(42)
cluster_a_x = [30 + random.gauss(0, 8) for _ in range(20)]
cluster_a_y = [40 + random.gauss(0, 8) for _ in range(20)]
cluster_b_x = [70 + random.gauss(0, 10) for _ in range(20)]
cluster_b_y = [20 + random.gauss(0, 10) for _ in range(20)]
save(
    "scatter.svg",
    ScatterChart(
        title="Cluster Analysis: Two Distinct Populations",
        x_data=[cluster_a_x, cluster_b_x],
        y_data=[cluster_a_y, cluster_b_y],
        series_names=["Cluster A", "Cluster B"],
        width=700,
        height=400,
    ).html,
)

# Single-series scatter: quadratic relationship with spread
random.seed(1)
x_vals = [i for i in range(5, 95, 5)]
y_vals = [round(10 + (v - 50) ** 2 / 50 + random.gauss(0, 4), 1) for v in x_vals]
save(
    "scatter_single.svg",
    ScatterChart(
        title="U-Shaped Response Curve: Signal vs Input",
        x_data=x_vals,
        y_data=y_vals,
        series_names=["Observations"],
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Pie / Doughnut charts
# ---------------------------------------------------------------------------

# Basic pie
save(
    "pie.svg",
    PieChart(
        title="Market Share by Product Line",
        data=[35, 28, 18, 12, 7],
        labels=["Product A", "Product B", "Product C", "Product D", "Other"],
        width=600,
        height=500,
    ).html,
)

# Doughnut
save(
    "pie_doughnut.svg",
    PieChart(
        title="Operating System Market Share",
        data=[72, 15, 8, 5],
        labels=["Windows", "macOS", "Linux", "Other"],
        inner_radius=0.5,
        width=600,
        height=500,
    ).html,
)

# ---------------------------------------------------------------------------
# Radar charts
# ---------------------------------------------------------------------------

# Single-series radar
save(
    "radar.svg",
    RadarChart(
        title="Character Stats Comparison",
        data=[20, 35, 30, 45, 25],
        labels=["Speed", "Power", "Endurance", "Defense", "Skill"],
        width=600,
        height=500,
    ).html,
)

# Multi-series radar
save(
    "radar_multi.svg",
    RadarChart(
        title="Player Comparison",
        data=[[20, 35, 30, 45, 25], [30, 25, 40, 35, 30]],
        labels=["Speed", "Power", "Endurance", "Defense", "Skill"],
        series_names=["Player A", "Player B"],
        width=600,
        height=500,
    ).html,
)

# ---------------------------------------------------------------------------
# Area charts
# ---------------------------------------------------------------------------

# Single-series area: CPU temperature over a day
temps = [42 + 10 * math.sin(i * 0.6) + (hash(str(i)) % 5 - 2) * 1.5 for i in range(24)]
save(
    "area.svg",
    AreaChart(
        title="CPU Temperature (°C): 24-hour Cycle",
        data=[round(t, 1) for t in temps],
        labels=[f"{h}:00" for h in range(24)],
        width=700,
        height=400,
    ).html,
)

# Multi-series area
save(
    "area_multi.svg",
    AreaChart(
        title="Multi-series Area: Revenue by Channel",
        data=[
            [30, 50, 45, 60, 70, 80, 65, 55],
            [20, 35, 30, 45, 50, 55, 40, 35],
        ],
        labels=["Q1", "Q2", "Q3", "Q4", "Q5", "Q6", "Q7", "Q8"],
        series_names=["Online", "Retail"],
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Box plot
# ---------------------------------------------------------------------------

# Box plot with clear quartiles and outliers
random.seed(42)
box_a = [round(random.gauss(50, 10), 1) for _ in range(50)] + [95, 5, 102]
box_b = [round(random.gauss(70, 15), 1) for _ in range(50)] + [120, 30, 130]
box_c = [round(random.gauss(30, 8), 1) for _ in range(50)] + [55, 8, 60]
save(
    "boxplot.svg",
    BoxPlot(
        title="Test Scores by Group: with Outliers",
        data=[box_a, box_b, box_c],
        labels=["Group A", "Group B", "Group C"],
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Histogram
# ---------------------------------------------------------------------------

# Histogram: 10-bin normal distribution (bell curve)
random.seed(42)
hist_data = [random.gauss(50, 15) for _ in range(500)]
save(
    "histogram.svg",
    Histogram(
        title="Exam Scores: Normal Distribution (500 Students, 10 Bins)",
        data=hist_data,
        bins=10,
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Heatmap
# ---------------------------------------------------------------------------

# Heatmap: monthly temperature matrix (°C)
save(
    "heatmap.svg",
    HeatmapChart(
        title="Average Temperature (°C): Monthly by City",
        data=[
            [35, 36, 38, 40, 43, 45, 47, 46, 44, 41, 38, 36],
            [22, 24, 28, 32, 36, 40, 42, 41, 38, 33, 27, 23],
            [15, 18, 22, 27, 32, 37, 40, 39, 35, 29, 22, 17],
            [5, 8, 14, 20, 26, 32, 35, 34, 29, 22, 14, 7],
            [-2, 2, 10, 18, 25, 31, 34, 33, 27, 19, 10, 3],
        ],
        x_labels=[
            "Jan",
            "Feb",
            "Mar",
            "Apr",
            "May",
            "Jun",
            "Jul",
            "Aug",
            "Sep",
            "Oct",
            "Nov",
            "Dec",
        ],
        y_labels=["Dubai", "Sydney", "Tokyo", "Berlin", "Moscow"],
        width=700,
        height=450,
        low_color="#5fab9e",
        high_color="#f58b51",
        show_values=True,
        value_format=".0f",
    ).html,
)

# ---------------------------------------------------------------------------
# Gantt chart
# ---------------------------------------------------------------------------

# Gantt: software project timeline
save(
    "gantt.svg",
    GanttChart(
        title="Software Project Timeline: Q1 2026",
        data=[(0, 2), (1, 4), (3, 6), (5, 8), (6, 9)],
        labels=["Design", "Frontend", "Backend", "Testing", "Deployment"],
        width=700,
        height=400,
        dependencies=[(0, 1), (0, 2), (2, 3), (3, 4)],
        show_today_line=True,
        x_position=4.5,
    ).html,
)

# ---------------------------------------------------------------------------
# Log and time scales
# ---------------------------------------------------------------------------

# Log y-scale: values spanning several orders of magnitude
save(
    "line_log_y.svg",
    LineChart(
        title="Requests per Second (log scale)",
        data=[12, 140, 1300, 9800, 75000],
        labels=["v1", "v2", "v3", "v4", "v5"],
        y_scale="log",
        width=700,
        height=400,
    ).html,
)

# Time x-axis: date-typed x_data with a time scale
save(
    "line_time_x.svg",
    LineChart(
        title="Active Users Over Time",
        data=[120, 180, 210, 260, 240, 310],
        x_data=[
            date(2024, 1, 1),
            date(2024, 3, 1),
            date(2024, 5, 1),
            date(2024, 7, 1),
            date(2024, 9, 1),
            date(2024, 11, 1),
        ],
        x_scale="time",
        width=700,
        height=400,
    ).html,
)

# ---------------------------------------------------------------------------
# Showcase examples: feature-rich, real-domain charts
# ---------------------------------------------------------------------------

# Combo dual-axis: bar revenue on the primary axis, line conversion rate on a
# secondary axis that scales to its own range.
save(
    "combo_dual_axis.svg",
    ComboChart(
        title="Revenue vs Conversion Rate by Quarter",
        series=[
            {
                "data": [320, 410, 380, 520, 610, 700],
                "type": "bar",
                "axis": "primary",
                "name": "Revenue ($K)",
            },
            {
                "data": [2.1, 2.6, 2.4, 3.1, 3.5, 3.9],
                "type": "line",
                "axis": "secondary",
                "name": "Conversion Rate (%)",
            },
        ],
        labels=["Q1", "Q2", "Q3", "Q4", "Q5", "Q6"],
        legend="bottom",
        width=700,
        height=450,
    ).html,
)

# Bubble chart: GDP vs life expectancy, bubble size = population, hue = literacy.
bubble_x = [3.2, 8.5, 12.1, 21.4, 35.0, 48.2, 62.5, 71.0]
bubble_y = [58, 64, 67, 71, 74, 78, 81, 83]
bubble_sizes = [12, 45, 90, 30, 140, 55, 200, 70]
bubble_hue = [55, 68, 74, 80, 86, 90, 95, 98]
save(
    "bubble.svg",
    BubbleChart(
        title="GDP per Capita vs Life Expectancy",
        x_data=bubble_x,
        y_data=bubble_y,
        sizes=bubble_sizes,
        hue=bubble_hue,
        size_legend=True,
        size_legend_title="Population (M)",
        hue_title="Literacy %",
        x_label="GDP per capita ($K)",
        y_label="Life expectancy (years)",
        width=720,
        height=460,
    ).html,
)

# Radar: team skill assessment, dark theme with a legend.
save(
    "radar_dark.svg",
    RadarChart(
        title="Engineering Team Skill Coverage",
        data=[
            [80, 65, 70, 55, 90, 60],
            [55, 85, 60, 80, 50, 75],
        ],
        labels=[
            "Backend",
            "Frontend",
            "DevOps",
            "Testing",
            "Data",
            "Design",
        ],
        series_names=["Team Alpha", "Team Beta"],
        theme="dark",
        width=620,
        height=520,
    ).html,
)

# Box plot: request latency (ms) by service. NOTE: BoxPlot's constructor does
# not accept reference_lines (or h_lines), so the SLA marker requested in the
# spec cannot be drawn through the public API: see the EX-GENERATE summary.
random.seed(7)
svc_auth = [round(random.gauss(45, 8), 1) for _ in range(60)] + [110, 95, 12]
svc_search = [round(random.gauss(120, 25), 1) for _ in range(60)] + [260, 240, 40]
svc_cart = [round(random.gauss(70, 12), 1) for _ in range(60)] + [180, 160, 20]
svc_pay = [round(random.gauss(95, 18), 1) for _ in range(60)] + [220, 200, 35]
save(
    "boxplot_latency.svg",
    BoxPlot(
        title="Request Latency (ms) by Service",
        data=[svc_auth, svc_search, svc_cart, svc_pay],
        labels=["Auth", "Search", "Cart", "Payment"],
        width=720,
        height=440,
    ).html,
)

# Column with value labels and a milestone reference line.
save(
    "column_value_labels.svg",
    ColumnChart(
        title="New Signups by Month",
        data=[1200, 1850, 2100, 2640, 3010, 3550],
        labels=["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
        value_labels="number",
        reference_lines=[{"value": 3000, "axis": "y", "label": "Target"}],
        width=720,
        height=460,
    ).html,
)

# Stacked bar with percentage value labels and a legend.
save(
    "bar_stacked_pct.svg",
    BarChart(
        title="Traffic Source Mix by Quarter",
        data=[
            [40, 45, 38, 42],
            [35, 30, 34, 31],
            [25, 25, 28, 27],
        ],
        labels=["Q1", "Q2", "Q3", "Q4"],
        series_names=["Organic", "Paid", "Referral"],
        x_stacked=True,
        # Data is already in percent units (40 == 40%), so disable the
        # default percent_scale (which assumes fractional data and would
        # render "4000%"). The bare "percent" shorthand is a foot-gun here.
        value_labels={"format": "percent", "percent_scale": False},
        legend="bottom",
        width=720,
        height=460,
    ).html,
)

# Pie with percentage value labels and a right-hand legend.
save(
    "pie_value_labels.svg",
    PieChart(
        title="Cloud Spend by Service",
        data=[42, 26, 18, 9, 5],
        labels=["Compute", "Storage", "Network", "Database", "Other"],
        value_labels="percent",
        legend="right",
        width=720,
        height=500,
    ).html,
)

# Line forecast: a solid actual series and a dashed model forecast, styled via
# per-series SeriesStyle. Both series are fully numeric (LineChart has no gap /
# None support); the dashed forecast tracks the actuals early then projects the
# trend forward through year-end.
save(
    "line_forecast.svg",
    LineChart(
        title="Monthly Active Users (K): Actual vs Forecast",
        data=[
            [42, 48, 55, 61, 58, 70, 80, 78, 85, 92, 88, 100],
            [40, 47, 53, 60, 63, 71, 79, 84, 90, 96, 103, 110],
        ],
        labels=[
            "Jan",
            "Feb",
            "Mar",
            "Apr",
            "May",
            "Jun",
            "Jul",
            "Aug",
            "Sep",
            "Oct",
            "Nov",
            "Dec",
        ],
        series_names=["Actual", "Forecast"],
        series_styles=[
            SeriesStyle().with_stroke_width(2.5).to_dict(),
            SeriesStyle().with_stroke_dasharray("6,4").with_stroke_width(2.5).to_dict(),
        ],
        legend="bottom",
        width=720,
        height=440,
    ).html,
)

# Accessible column: high-contrast theme plus per-category hatch patterns so the
# chart stays readable without relying on hue alone.
save(
    "column_accessible.svg",
    ColumnChart(
        title="Defects Closed by Severity",
        data=[
            [12, 18, 9, 14],
            [22, 15, 19, 11],
            [6, 9, 4, 7],
        ],
        labels=["Sprint 1", "Sprint 2", "Sprint 3", "Sprint 4"],
        series_names=["Critical", "Major", "Minor"],
        theme="high-contrast",
        category_patterns=True,
        legend="bottom",
        width=720,
        height=460,
    ).html,
)

# Polar area: commits by weekday on a dark theme.
save(
    "polar_area.svg",
    PolarAreaChart(
        title="Commits by Weekday",
        data=[58, 72, 81, 76, 64, 22, 15],
        labels=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
        theme="dark",
        legend="right",
        width=640,
        height=540,
    ).html,
)

print("Done. All examples written to docs/examples/")