feat(plotnine): implement streamline-basic

plots/streamline-basic/implementations/plotnine.py

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+""" pyplots.ai
+streamline-basic: Basic Streamline Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    coord_fixed,
+    element_rect,
+    element_text,
+    geom_path,
+    geom_point,
+    ggplot,
+    labs,
+    scale_color_gradient,
+    theme,
+    theme_minimal,
+)
+from scipy.integrate import solve_ivp
+from scipy.interpolate import RegularGridInterpolator
+
+
+# Set seed for reproducibility
+np.random.seed(42)
+
+# Create grid for vector field
+nx, ny = 40, 40
+x = np.linspace(-3, 3, nx)
+y = np.linspace(-3, 3, ny)
+X, Y = np.meshgrid(x, y)
+
+# Define vector field: vortex flow with a dipole effect
+# u = -y, v = x creates circular streamlines (vortex)
+U = -Y
+V = X
+
+# Create interpolators for the velocity field
+u_interp = RegularGridInterpolator((y, x), U, bounds_error=False, fill_value=0)
+v_interp = RegularGridInterpolator((y, x), V, bounds_error=False, fill_value=0)
+
+# Compute streamlines from various starting points
+streamlines_data = []
+arrow_data = []
+streamline_id = 0
+
+# Create varied starting points - grid on one side and scattered points
+# This shows field topology better than just radial starting points
+start_points = []
+
+# Grid of starting points on left side
+for sx in np.linspace(-2.8, -1.5, 4):
+    for sy in np.linspace(-2.5, 2.5, 6):
+        start_points.append((sx, sy))
+
+# Some radial starting points to show circular nature
+for r in [0.6, 1.3, 2.2]:
+    for angle in np.linspace(0, 2 * np.pi, 6, endpoint=False):
+        start_points.append((r * np.cos(angle), r * np.sin(angle)))
+
+for x0, y0 in start_points:
+    # Integrate forward - inline velocity calculation (no function definition)
+    try:
+        result = solve_ivp(
+            lambda t, pos: [u_interp([pos[1], pos[0]])[0], v_interp([pos[1], pos[0]])[0]],
+            [0, 4],
+            [x0, y0],
+            max_step=0.05,
+            dense_output=True,
+        )
+        if result.success and len(result.t) > 2:
+            t_eval = np.linspace(0, result.t[-1], 100)
+            trajectory = result.sol(t_eval)
+
+            # Calculate velocity magnitude at each point
+            for j in range(len(t_eval)):
+                px, py = trajectory[0, j], trajectory[1, j]
+                # Keep points within bounds
+                if -3 <= px <= 3 and -3 <= py <= 3:
+                    speed = np.sqrt(px**2 + py**2)  # For vortex: speed = r
+                    streamlines_data.append({"x": px, "y": py, "streamline": streamline_id, "order": j, "speed": speed})
+
+            # Add arrow marker at ~60% along the streamline to show direction
+            arrow_idx = int(len(t_eval) * 0.6)
+            if arrow_idx < len(t_eval):
+                ax, ay = trajectory[0, arrow_idx], trajectory[1, arrow_idx]
+                if -3 <= ax <= 3 and -3 <= ay <= 3:
+                    arrow_speed = np.sqrt(ax**2 + ay**2)
+                    arrow_data.append({"x": ax, "y": ay, "speed": arrow_speed})
+
+            streamline_id += 1
+    except Exception:
+        pass
+
+# Convert to DataFrames
+df = pd.DataFrame(streamlines_data)
+df_arrows = pd.DataFrame(arrow_data)
+
+# Create the plot using plotnine's native geom_path
+# Use 1:1 canvas (12x12) for circular pattern - avoids empty horizontal space
+plot = (
+    ggplot(df, aes(x="x", y="y", group="streamline", color="speed"))
+    + geom_path(size=1.2, alpha=0.8)
+    + geom_point(
+        data=df_arrows,
+        mapping=aes(x="x", y="y", color="speed"),
+        shape=">",
+        size=4,
+        inherit_aes=False,
+        show_legend=False,
+    )
+    + scale_color_gradient(low="#306998", high="#FFD43B", name="Flow Speed")
+    + labs(x="X Position", y="Y Position", title="streamline-basic · plotnine · pyplots.ai")
+    + coord_fixed(ratio=1)
+    + theme_minimal()
+    + theme(
+        figure_size=(12, 12),
+        plot_title=element_text(size=24, weight="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=14),
+        panel_background=element_rect(fill="white"),
+        plot_background=element_rect(fill="white"),
+    )
+)
+
+# Save the plot
+plot.save("plot.png", dpi=300, verbose=False)

plots/streamline-basic/metadata/plotnine.yaml

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+library: plotnine
+specification_id: streamline-basic
+created: '2025-12-31T00:15:01Z'
+updated: '2025-12-31T00:25:15Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608708992
+issue: 2861
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/streamline-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/streamline-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent vortex flow visualization with concentric circular streamlines that
+    clearly demonstrate field topology
+  - Effective color gradient (blue to yellow) encoding flow speed with values increasing
+    toward the outer regions
+  - Arrow markers positioned along streamlines clearly indicate counter-clockwise
+    flow direction
+  - Well-balanced 1:1 aspect ratio canvas that perfectly suits circular streamline
+    patterns
+  - Good use of plotnine grammar of graphics with geom_path for streamlines and geom_point
+    for directional arrows
+  weaknesses:
+  - Axis labels could include units or more descriptive context (e.g., meters, arbitrary
+    units)