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>
214 lines
7.6 KiB
Python
214 lines
7.6 KiB
Python
"""Test all three chart types from the PIMCO reference PDF."""
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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 app.models.chart_spec import (
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ChartSpec, PanelSpec, AxisSpec, SeriesSpec, ShadedFillSpec, AnnotationSpec,
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)
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from app.renderer.engine import render_chart
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def test_chart1_bond_yields():
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"""Chart 1: Multi-line time series (bond yields across 5 countries)."""
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np.random.seed(42)
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dates = pd.date_range("2020-08-01", "2025-08-31", freq="B")
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n = len(dates)
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def walk(start, drift, vol):
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r = np.random.normal(drift, vol, n)
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v = start + np.cumsum(r)
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return pd.Series(v).rolling(10, min_periods=1).mean().values
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df = pd.DataFrame({
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"Date": dates,
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"U.S.": walk(0.7, 0.0025, 0.03),
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"Australia": walk(0.9, 0.0025, 0.03),
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"U.K.": walk(0.3, 0.0028, 0.035),
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"Germany": walk(-0.4, 0.0020, 0.03),
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"Japan": walk(0.0, 0.0008, 0.015),
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})
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spec = ChartSpec(
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layout="single",
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panels=[PanelSpec(
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title="10-year government bond yields",
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x_axis=AxisSpec(date_format="%b %Y"),
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y_axis=AxisSpec(suffix="%", min_val=-1, max_val=6, tick_interval=1),
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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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svg = render_chart(spec, {"_default": df})
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with open("output/chart1_bond_yields.svg", "w") as f:
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f.write(svg)
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print("Chart 1 (Bond Yields) saved.")
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def test_chart2_gdp_investment():
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"""Chart 2: Two-line chart with grey ellipse annotation."""
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np.random.seed(99)
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dates = pd.date_range("1994-01-01", "2024-12-31", freq="QS")
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n = len(dates)
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tech = np.random.normal(0.5, 0.35, n)
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tech = pd.Series(tech).rolling(2, min_periods=1).mean().values.copy()
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nontech = np.random.normal(0.4, 0.4, n)
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nontech = pd.Series(nontech).rolling(2, min_periods=1).mean().values.copy()
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# Add recession dips
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for start_idx in [24, 32, 56]: # ~2000, 2002, 2008
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tech[start_idx:start_idx+4] -= 0.7
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nontech[start_idx:start_idx+4] -= 0.8
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df = pd.DataFrame({"Date": dates, "Tech investment": tech, "Non-tech investment": nontech})
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spec = ChartSpec(
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layout="single",
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panels=[PanelSpec(
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title="Quarterly contribution to real GDP growth",
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x_axis=AxisSpec(date_format="%Y"),
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y_axis=AxisSpec(label="Percentage points", min_val=-1.0, max_val=1.5, tick_interval=0.5),
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series=[
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SeriesSpec(label="Tech investment", data_column="Tech investment", color_index=0),
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SeriesSpec(label="Non-tech investment", data_column="Non-tech investment", color_index=2),
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],
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annotations=[
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AnnotationSpec(
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type="ellipse",
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x_start="2023-06-01",
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x_end="2024-12-31",
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y_start=-0.3,
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y_end=1.2,
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),
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],
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)],
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)
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svg = render_chart(spec, {"_default": df})
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with open("output/chart2_gdp_investment.svg", "w") as f:
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f.write(svg)
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print("Chart 2 (GDP Investment) saved.")
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def test_chart3_dual_panel():
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"""Chart 3: Dual-panel with trend lines and shaded fills."""
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np.random.seed(77)
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dates = pd.date_range("2022-01-01", "2026-01-31", freq="MS")
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n = len(dates)
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# Left panel: PIMCO Global IP
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trend_left = np.linspace(93, 101, n)
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ip_data = trend_left + np.random.normal(0, 1.0, n)
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ip_data = pd.Series(ip_data).rolling(3, min_periods=1).mean().values.copy()
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# Post-election dip
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election_idx = 34 # ~Oct 2024
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ip_data[election_idx+2:election_idx+5] -= 2.5
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election_level_left = [100.0] * n
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# Right panel: World Exports
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trend_right = np.linspace(93, 106, n)
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exports = trend_right + np.random.normal(0, 2.0, n)
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exports = pd.Series(exports).rolling(3, min_periods=1).mean().values.copy()
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# Tariff spike
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exports[election_idx+3:election_idx+6] += 15
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exports[election_idx+6:election_idx+8] -= 8
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election_level_right = [100.0] * n
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df_left = pd.DataFrame({
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"Date": dates,
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"PIMCO Global IP": ip_data,
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"Trend": trend_left,
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"Election Level": election_level_left,
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})
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df_right = pd.DataFrame({
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"Date": dates,
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"World Exports to U.S.": exports,
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"Trend": trend_right,
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"Election Level": election_level_right,
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})
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spec = ChartSpec(
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layout="dual_panel",
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panels=[
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PanelSpec(
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title="PIMCO global industrial production",
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x_axis=AxisSpec(date_format="%b '%y"),
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y_axis=AxisSpec(min_val=90, max_val=106, tick_interval=2),
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series=[
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SeriesSpec(
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label="PIMCO global IP (Oct. 2024 = 100)",
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data_column="PIMCO Global IP",
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color_index=0,
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shaded_fill=ShadedFillSpec(
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reference_series="Two-year pre-U.S. election trend",
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above_color="blue",
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below_color="pink",
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),
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),
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SeriesSpec(
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label="Two-year pre-U.S. election trend",
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data_column="Trend",
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color_index=1,
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line_style="dashed",
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),
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SeriesSpec(
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label="Pre-U.S. election level",
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data_column="Election Level",
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color_index=2,
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line_style="dotted",
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),
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],
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),
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PanelSpec(
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title="World exports to U.S.",
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x_axis=AxisSpec(date_format="%b '%y"),
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y_axis=AxisSpec(min_val=90, max_val=130, tick_interval=5),
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series=[
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SeriesSpec(
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label="World exports to U.S. (Oct. 2024 = 100)",
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data_column="World Exports to U.S.",
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color_index=0,
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shaded_fill=ShadedFillSpec(
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reference_series="Two-year pre-U.S. election trend",
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above_color="blue",
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below_color="pink",
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),
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),
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SeriesSpec(
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label="Two-year pre-U.S. election trend",
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data_column="Trend",
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color_index=1,
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line_style="dashed",
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),
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SeriesSpec(
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label="Pre-U.S. election level",
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data_column="Election Level",
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color_index=2,
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line_style="dotted",
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),
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],
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),
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],
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)
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svg = render_chart(spec, {"_panel_0": df_left, "_panel_1": df_right})
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with open("output/chart3_dual_panel.svg", "w") as f:
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f.write(svg)
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print("Chart 3 (Dual Panel) saved.")
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if __name__ == "__main__":
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test_chart1_bond_yields()
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test_chart2_gdp_investment()
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test_chart3_dual_panel()
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print("\nAll test charts saved to output/")
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