a3a38e85d2
AI-powered tool that generates publication-quality SVG charts matching PIMCO's InDesign style. Upload Excel/CSV data, write a plain-English brief, then iterate with natural language edits until the chart is exactly right. - Claude Opus 4.6 interprets briefs into structured ChartSpec JSON - Deterministic SVG renderer via drawsvg (no visual hallucinations) - Roboto/Roboto Condensed fonts base64-embedded in SVG - FastAPI + HTMX web frontend with live preview - Conversational refinement: "make lines thicker", "change title", etc. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
"""Test script: render a multi-line chart matching PIMCO Chart 1 (bond yields) using synthetic data."""
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import sys
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sys.path.insert(0, ".")
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from app.models.chart_spec import ChartSpec, PanelSpec, AxisSpec, SeriesSpec
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from app.renderer.engine import render_chart
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def generate_bond_yield_data() -> pd.DataFrame:
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"""Generate synthetic 10-year government bond yield data resembling the PDF chart."""
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np.random.seed(42)
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dates = pd.date_range("2020-08-01", "2025-08-31", freq="B") # Business days
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def random_walk(start, drift, vol, n):
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returns = np.random.normal(drift, vol, n)
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values = start + np.cumsum(returns)
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# Smooth with rolling average
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series = pd.Series(values)
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return series.rolling(window=10, min_periods=1).mean().values
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n = len(dates)
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data = pd.DataFrame({
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"Date": dates,
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"U.S.": random_walk(0.7, 0.0025, 0.03, n),
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"Australia": random_walk(0.9, 0.0025, 0.03, n),
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"U.K.": random_walk(0.3, 0.0028, 0.035, n),
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"Germany": random_walk(-0.4, 0.0020, 0.03, n),
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"Japan": random_walk(0.0, 0.0008, 0.015, n),
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})
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# Clamp values to reasonable ranges
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for col in ["U.S.", "Australia", "U.K.", "Germany", "Japan"]:
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data[col] = data[col].clip(-1, 6)
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return data
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def main():
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# Generate data
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df = generate_bond_yield_data()
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# Create chart spec matching the PDF Chart 1
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spec = ChartSpec(
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layout="single",
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panels=[
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PanelSpec(
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title="10-year government bond yields",
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subtitle=None,
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x_axis=AxisSpec(
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date_format="%b %Y",
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),
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y_axis=AxisSpec(
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suffix="%",
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min_val=-1,
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max_val=6,
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tick_interval=1,
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),
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series=[
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SeriesSpec(label="U.S.", data_column="U.S.", color_index=0),
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SeriesSpec(label="Australia", data_column="Australia", color_index=1),
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SeriesSpec(label="U.K.", data_column="U.K.", color_index=2),
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SeriesSpec(label="Germany", data_column="Germany", color_index=3),
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SeriesSpec(label="Japan", data_column="Japan", color_index=4),
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],
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)
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],
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)
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# Render
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svg = render_chart(spec, {"_default": df})
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# Save
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output_path = "output/test_bond_yields.svg"
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with open(output_path, "w") as f:
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f.write(svg)
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print(f"SVG saved to {output_path}")
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print(f"SVG size: {len(svg):,} bytes")
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if __name__ == "__main__":
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main()
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