Merge branch 'main' into claude/improve-test-coverage-zo0aK

Author: MarkusNeusinger

plots/scatter-lag/implementations/altair.py

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+""" pyplots.ai
+scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis
+Library: altair 6.0.0 | Python 3.14.3
+Quality: 90/100 | Created: 2026-04-12
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - synthetic AR(1) process with moderate autocorrelation
+np.random.seed(42)
+n_points = 500
+lag = 1
+phi = 0.85
+noise = np.random.normal(0, 1, n_points)
+values = np.zeros(n_points)
+values[0] = noise[0]
+for i in range(1, n_points):
+    values[i] = phi * values[i - 1] + noise[i]
+
+y_t = values[:-lag]
+y_t_lag = values[lag:]
+r_value = np.corrcoef(y_t, y_t_lag)[0, 1]
+
+df = pd.DataFrame({"y_t": y_t, "y_t_lag": y_t_lag, "time_index": np.arange(n_points - lag)})
+
+# Reference line (y = x diagonal)
+margin = 0.5
+axis_min = min(df["y_t"].min(), df["y_t_lag"].min()) - margin
+axis_max = max(df["y_t"].max(), df["y_t_lag"].max()) + margin
+ref_df = pd.DataFrame({"x": [axis_min, axis_max], "y": [axis_min, axis_max]})
+
+# Annotation for correlation coefficient
+annot_df = pd.DataFrame({"x": [axis_max - 0.3], "y": [axis_min + 0.5], "label": [f"r = {r_value:.3f}"]})
+
+# Reference line
+reference_line = (
+    alt.Chart(ref_df).mark_line(strokeDash=[8, 6], strokeWidth=1.5, color="#aaaaaa").encode(x="x:Q", y="y:Q")
+)
+
+# Scatter points with reduced size/opacity to prevent overplotting
+points = (
+    alt.Chart(df)
+    .mark_point(size=45, filled=True, strokeWidth=0.5, stroke="white", opacity=0.45)
+    .encode(
+        x=alt.X("y_t:Q", title="y(t)", scale=alt.Scale(domain=[axis_min, axis_max]), axis=alt.Axis(tickCount=10)),
+        y=alt.Y(
+            "y_t_lag:Q", title="y(t + 1)", scale=alt.Scale(domain=[axis_min, axis_max]), axis=alt.Axis(tickCount=10)
+        ),
+        color=alt.Color(
+            "time_index:Q",
+            scale=alt.Scale(scheme="viridis"),
+            legend=alt.Legend(
+                title="Time Index",
+                titleFontSize=16,
+                labelFontSize=16,
+                gradientLength=280,
+                gradientThickness=14,
+                orient="right",
+                offset=10,
+            ),
+        ),
+        tooltip=[
+            alt.Tooltip("y_t:Q", title="y(t)", format=".2f"),
+            alt.Tooltip("y_t_lag:Q", title="y(t+1)", format=".2f"),
+            alt.Tooltip("time_index:Q", title="Time Index"),
+        ],
+    )
+)
+
+# Correlation annotation
+annotation = (
+    alt.Chart(annot_df)
+    .mark_text(align="right", baseline="bottom", fontSize=20, fontWeight="bold", color="#333333")
+    .encode(x="x:Q", y="y:Q", text="label:N")
+)
+
+chart = (
+    (reference_line + points + annotation)
+    .properties(
+        width=1600,
+        height=900,
+        title=alt.Title(
+            "scatter-lag · altair · pyplots.ai",
+            fontSize=28,
+            subtitle=f"AR(1) process (φ = {phi}) | lag = {lag}",
+            subtitleFontSize=18,
+            subtitleColor="#666666",
+        ),
+    )
+    .configure_axis(
+        labelFontSize=18,
+        titleFontSize=22,
+        grid=True,
+        gridOpacity=0.15,
+        gridWidth=0.5,
+        domainWidth=0,
+        tickSize=6,
+        tickWidth=0.8,
+        tickColor="#999999",
+        labelColor="#444444",
+        titleColor="#333333",
+    )
+    .configure_view(strokeWidth=0)
+    .configure_title(anchor="start", offset=10)
+)
+
+# Save
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/scatter-lag/implementations/bokeh.py

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+""" pyplots.ai
+scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis
+Library: bokeh 3.9.0 | Python 3.14.3
+Quality: 85/100 | Created: 2026-04-12
+"""
+
+import numpy as np
+from bokeh.io import export_png, save
+from bokeh.models import ColorBar, ColumnDataSource, Label, LinearColorMapper
+from bokeh.palettes import Viridis256
+from bokeh.plotting import figure
+from bokeh.resources import CDN
+from bokeh.transform import linear_cmap
+
+
+# Data - AR(1) process with moderate positive autocorrelation
+np.random.seed(42)
+n_obs = 500
+phi = 0.85
+noise = np.random.normal(0, 1, n_obs)
+series = np.zeros(n_obs)
+series[0] = noise[0]
+for i in range(1, n_obs):
+    series[i] = phi * series[i - 1] + noise[i]
+
+lag = 1
+y_t = series[:-lag]
+y_t_lag = series[lag:]
+time_index = np.arange(len(y_t))
+
+correlation = np.corrcoef(y_t, y_t_lag)[0, 1]
+
+source = ColumnDataSource(data={"y_t": y_t, "y_t_lag": y_t_lag, "time_index": time_index})
+
+# Plot
+color_mapper = LinearColorMapper(palette=Viridis256, low=time_index.min(), high=time_index.max())
+
+p = figure(
+    width=4800, height=2700, title="scatter-lag · bokeh · pyplots.ai", x_axis_label="y(t)", y_axis_label="y(t + 1)"
+)
+
+p.scatter(
+    x="y_t",
+    y="y_t_lag",
+    source=source,
+    size=20,
+    fill_color=linear_cmap("time_index", palette=Viridis256, low=time_index.min(), high=time_index.max()),
+    line_color="white",
+    line_width=0.8,
+    fill_alpha=0.8,
+)
+
+# Diagonal reference line (y = x)
+axis_min = min(y_t.min(), y_t_lag.min()) - 0.5
+axis_max = max(y_t.max(), y_t_lag.max()) + 0.5
+p.line(
+    [axis_min, axis_max], [axis_min, axis_max], line_color="#AAAAAA", line_dash="dashed", line_width=3, line_alpha=0.5
+)
+
+# Color bar for time index
+color_bar = ColorBar(
+    color_mapper=color_mapper,
+    title="Time Index",
+    title_text_font_size="22pt",
+    title_standoff=20,
+    major_label_text_font_size="18pt",
+    label_standoff=12,
+    width=50,
+    padding=40,
+)
+p.add_layout(color_bar, "right")
+
+# Correlation annotation
+corr_label = Label(
+    x=axis_min + 0.3, y=axis_max - 0.5, text=f"r = {correlation:.3f}", text_font_size="24pt", text_color="#333333"
+)
+p.add_layout(corr_label)
+
+# Style
+p.title.text_font_size = "32pt"
+p.title.text_font_style = "normal"
+p.xaxis.axis_label_text_font_size = "24pt"
+p.yaxis.axis_label_text_font_size = "24pt"
+p.xaxis.major_label_text_font_size = "20pt"
+p.yaxis.major_label_text_font_size = "20pt"
+
+p.xaxis.minor_tick_line_color = None
+p.yaxis.minor_tick_line_color = None
+p.xaxis.major_tick_line_color = None
+p.yaxis.major_tick_line_color = None
+
+p.xgrid.grid_line_alpha = 0.2
+p.ygrid.grid_line_alpha = 0.2
+p.xgrid.grid_line_width = 1
+p.ygrid.grid_line_width = 1
+
+p.outline_line_color = None
+p.background_fill_color = "#FFFFFF"
+p.border_fill_color = "#FFFFFF"
+
+p.toolbar_location = None
+
+# Save
+export_png(p, filename="plot.png")
+save(p, filename="plot.html", resources=CDN, title="scatter-lag · bokeh · pyplots.ai")