Dashboard: Stadium Night design system, 5-page Streamlit app, warehouse exporter
- src/export/warehouse.py: reads scattered pipeline artifacts → 6 Parquet files (players, fixtures, predictions, lineups, votes, model_metrics) - dashboard/warehouse.py: read-only cached Parquet loader - .streamlit/config.toml: dark theme base, server config - dashboard/viz/template.py: Plotly 'fantabeto_dark' template (single source of truth) - Semantic palette: pitch_green #00D084, gold #FFC94D, red #FF4D5E, sky #38BDF8 - Space Grotesk headers, Inter body, tabular numerals - All 10 chart colors banned from default palette - dashboard/viz/components.py: KPI cards, role chips, section headers, CSS injection - dashboard/viz/charts.py: 10 pure chart functions (df → Figure) - percentile radar, fixture heatmap, regression comparison, bonus donut - card risk gauge, budget waterfall, value scatter, error violins - dashboard/viz/pitch.py: SVG pitch component — dark turf gradient, player badges sized by FV, gold captain ring, bench strip, formation label - 5 pages: - 01_matchday: KPI sparklines, fixture heatmap, start/sit grid, bump chart - 02_players: search, radar, regression, bonus/malus, card risk, news feed - 03_auction: budget slider, waterfall, value scatter, grid auction heatmap - 04_lineup: SVG pitch, what-if toggles, opponent mirror, MCTS captain - 05_lab: error violins, feature importance, calibration curve, backtest - dashboard/app.py: multi-page Streamlit entry with sidebar navigation - dashboard/tests/test_dashboard.py: 18 unit tests (warehouse, template, charts, pitch) - DASHBOARD.md: full architecture docs, design system reference - 49 total tests passing (31 existing + 18 dashboard)
This commit is contained in:
ramseshk
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"""Pure chart functions. df in → plotly Figure out. Unit-testable."""
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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from .template import (
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PITCH_GREEN, GOLD, RED, SKY, VIOLET, BG, CARD_BG, BORDER,
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TEXT, TEXT_SECONDARY, GRIDLINE, WHITE, ROLE_COLORS,
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FANTABETO_TEMPLATE, HEATMAP_COLORS, DISCRETE_10, insight_caption,
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)
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# ─────────────────────────────────────────────────────────────────
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# KPI sparkline row
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# ─────────────────────────────────────────────────────────────────
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def kpi_sparkline(values: list, label: str, color: str = SKY) -> go.Figure:
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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y=values, mode="lines", line=dict(color=color, width=2),
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fill="tozeroy", fillcolor=f"rgba({_hex_to_rgb(color)},0.15)",
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showlegend=False, hoverinfo="skip",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=60, width=180,
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margin=dict(l=0, r=0, t=0, b=0),
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xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Fixture difficulty heatmap
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# ─────────────────────────────────────────────────────────────────
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def fixture_heatmap(df: pd.DataFrame) -> go.Figure:
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"""Square matrix: teams × matchdays, colored by opponent FV strength.
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Args:
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df: columns ['team', 'matchday', 'opp_strength']
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"""
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pivot = df.pivot(index="team", columns="matchday", values="opp_strength")
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fig = go.Figure(data=go.Heatmap(
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z=pivot.values,
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x=[f"GW {c}" for c in pivot.columns],
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y=pivot.index,
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colorscale=HEATMAP_COLORS,
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zmid=np.mean([pivot.min().min(), pivot.max().max()]),
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hovertemplate="%{y} vs opponent<br>GW %{x}: difficulty %{z:.1f}<extra></extra>",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=440,
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xaxis=dict(side="top", tickangle=-45),
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yaxis=dict(autorange="reversed"),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Percentile radar chart
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# ─────────────────────────────────────────────────────────────────
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def percentile_radar(player_row: pd.Series, metrics: list, labels: list,
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role_avg: pd.Series = None) -> go.Figure:
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"""Single-player percentile radar across N metrics.
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Args:
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player_row: Series with metric values (raw).
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metrics: column names to plot.
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labels: display labels for each metric.
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role_avg: optional league-average Series to normalize against.
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"""
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values = []
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for m in metrics:
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if role_avg is not None and m in role_avg.index and role_avg[m] > 0:
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pct = min(100, max(0, (player_row.get(m, 0) / role_avg[m]) * 50))
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else:
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pct = 50
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values.append(pct)
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values.append(values[0])
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labels_closed = labels + [labels[0]]
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fig = go.Figure()
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fig.add_trace(go.Scatterpolar(
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r=values, theta=labels_closed,
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fill="toself", fillcolor=f"rgba({_hex_to_rgb(SKY)},0.2)",
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line=dict(color=SKY, width=2),
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name=player_row.get("player", "Player"),
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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polar=dict(
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radialaxis=dict(range=[0, 100], showticklabels=False, gridcolor=GRIDLINE),
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angularaxis=dict(gridcolor=GRIDLINE, tickfont=dict(size=9, color=TEXT_SECONDARY)),
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bgcolor=BG,
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),
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height=350,
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showlegend=False,
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margin=dict(l=40, r=40, t=20, b=20),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Regression chart (actual vs xG)
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# ─────────────────────────────────────────────────────────────────
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def regression_chart(df: pd.DataFrame, player: str,
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xg_col="goals_p90", goal_col="goals_season",
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games_col="games_season") -> go.Figure:
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"""Rolling actual goals vs expected with divergence shading.
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Uses season-level totals as static chart.
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"""
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=["Actual Goals", "Expected (xG * 1.2)"],
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y=[df[goal_col].iloc[0], df[xg_col].iloc[0] * 1.2 * df[games_col].iloc[0]],
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marker_color=[PITCH_GREEN, SKY], texttemplate="%{y:.1f}",
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textposition="outside", textfont=dict(color=TEXT, size=13),
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showlegend=False,
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=200,
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margin=dict(l=10, r=10, t=10, b=10),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Bonus/malus donut
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# ─────────────────────────────────────────────────────────────────
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def bonus_donut(goals: float, assists: float, cards_malus: float) -> go.Figure:
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bonus = goals * 3 + assists * 1
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malus = abs(cards_malus)
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fig = go.Figure(data=[go.Pie(
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labels=["Goal Bonus", "Assist Bonus", "Card Malus"],
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values=[goals * 3, assists, malus if malus > 0 else 0.01],
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hole=0.55,
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marker_colors=[PITCH_GREEN, SKY, RED],
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textinfo="label+value",
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textfont=dict(color=TEXT, size=10),
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hovertemplate="%{label}: %{value:.1f}<extra></extra>",
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)])
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=200,
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showlegend=False,
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margin=dict(l=0, r=0, t=10, b=10),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Card risk gauge
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# ─────────────────────────────────────────────────────────────────
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def card_gauge(yellow_per_game: float, red_per_game: float) -> go.Figure:
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ypct = min(100, yellow_per_game / 0.5 * 100)
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rpct = min(100, red_per_game / 0.1 * 100)
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fig = go.Figure()
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fig.add_trace(go.Indicator(
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mode="gauge+number",
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value=ypct,
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title={"text": "Yellow Risk", "font": {"size": 11, "color": TEXT_SECONDARY}},
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gauge={
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"axis": {"range": [0, 100], "tickcolor": TEXT_SECONDARY},
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"bar": {"color": GOLD},
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"bgcolor": CARD_BG,
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"borderwidth": 0,
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"steps": [
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{"range": [0, 30], "color": f"rgba({_hex_to_rgb(PITCH_GREEN)},0.15)"},
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{"range": [30, 70], "color": f"rgba({_hex_to_rgb(GOLD)},0.15)"},
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{"range": [70, 100], "color": f"rgba({_hex_to_rgb(RED)},0.15)"},
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],
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},
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number={"font": {"size": 22, "color": WHITE}},
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domain={"row": 0, "column": 0},
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))
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fig.add_trace(go.Indicator(
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mode="gauge+number",
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value=rpct,
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title={"text": "Red Risk", "font": {"size": 11, "color": TEXT_SECONDARY}},
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gauge={
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"axis": {"range": [0, 100], "tickcolor": TEXT_SECONDARY},
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"bar": {"color": RED},
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"bgcolor": CARD_BG,
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"borderwidth": 0,
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"steps": [
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{"range": [0, 30], "color": f"rgba({_hex_to_rgb(PITCH_GREEN)},0.15)"},
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{"range": [30, 70], "color": f"rgba({_hex_to_rgb(RED)},0.15)"},
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{"range": [70, 100], "color": f"rgba({_hex_to_rgb(RED)},0.3)"},
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],
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},
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number={"font": {"size": 22, "color": WHITE}},
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domain={"row": 0, "column": 1},
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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grid={"rows": 1, "columns": 2},
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height=180,
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margin=dict(l=10, r=10, t=30, b=10),
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Budget waterfall
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# ─────────────────────────────────────────────────────────────────
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def budget_waterfall(allocations: dict) -> go.Figure:
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"""allocations: {'GK': amount, 'DEF': amount, 'MID': amount, 'FWD': amount}"""
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measures = ["relative", "relative", "relative", "relative", "total"]
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labels = list(allocations.keys()) + ["Total"]
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values = list(allocations.values()) + [sum(allocations.values())]
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fig = go.Figure(go.Waterfall(
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measure=measures, x=labels, y=values,
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connector=dict(line=dict(color=BORDER, width=1)),
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decreasing=dict(marker=dict(color=RED)),
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increasing=dict(marker=dict(color=PITCH_GREEN)),
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totals=dict(marker=dict(color=SKY)),
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text=[f"{v:.0f} cr" for v in values],
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textposition="outside",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=280,
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showlegend=False,
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Value scatter (FV vs price)
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# ─────────────────────────────────────────────────────────────────
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def value_scatter(df: pd.DataFrame) -> go.Figure:
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"""Scatter: fv_avg vs qi, bubble = games_season, labeled steals."""
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df = df.copy()
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df["value_ratio"] = df["fv_avg"] / df["qi"].clip(lower=1)
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fig = go.Figure()
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for role, color in ROLE_COLORS.items():
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rdf = df[df["role"] == role]
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if rdf.empty:
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continue
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fig.add_trace(go.Scatter(
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x=rdf["qi"], y=rdf["fv_avg"],
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mode="markers+text",
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marker=dict(
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size=rdf["games_season"].clip(lower=5) / 2,
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color=color, opacity=0.7,
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line=dict(width=1, color=BORDER),
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),
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text=rdf["player"].where(rdf["value_ratio"] > rdf["value_ratio"].quantile(0.9), ""),
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textposition="top center",
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textfont=dict(size=9, color=TEXT),
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name=f"{role} ({len(rdf)})",
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hovertemplate=(
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"<b>%{text}</b><br>"
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"FV: %{y:.2f}<br>Price: %{x:.0f}<br>"
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"Games: %{marker.size:.0f}<extra></extra>"
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),
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))
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# Isoline: cost per FV point
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x_range = [df["qi"].min(), df["qi"].max()]
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for cpp in [3, 5, 8]:
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fig.add_trace(go.Scatter(
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x=x_range, y=[x / cpp for x in x_range],
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mode="lines", line=dict(dash="dash", color=TEXT_SECONDARY, width=0.5),
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name=f"{cpp} cr/FV", showlegend=False,
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=500,
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xaxis_title="Quotazione Iniziale (cr)",
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yaxis_title="Fantavoto Avg (25/26)",
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hovermode="closest",
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Error violin by role
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# ─────────────────────────────────────────────────────────────────
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def error_violins(df: pd.DataFrame) -> go.Figure:
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"""Violins of prediction error by role."""
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roles = ["P", "D", "C", "A"]
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fig = go.Figure()
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for i, role in enumerate(roles):
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rdf = df[df["role"] == role]
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if rdf.empty or "error" not in rdf.columns:
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continue
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fig.add_trace(go.Violin(
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y=rdf["error"], name=role,
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marker=dict(color=ROLE_COLORS.get(role, SKY)),
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box_visible=True, meanline_visible=True,
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side="positive" if i % 2 == 0 else "negative",
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))
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fig.update_layout(
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template=FANTABETO_TEMPLATE,
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height=300,
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xaxis=dict(title="Role"),
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yaxis=dict(title="Prediction Error (FV)"),
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violingap=0, violinmode="overlay",
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)
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return fig
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# ─────────────────────────────────────────────────────────────────
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# Helpers
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# ─────────────────────────────────────────────────────────────────
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def _hex_to_rgb(hex_color: str) -> str:
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h = hex_color.lstrip("#")
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return ",".join(str(int(h[i:i+2], 16)) for i in (0, 2, 4))
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