consistent/data_visualization/sample_00237/prompt.md

VeriRender — Causal Consistency Evaluation

Sample: sample_00237

Before sending: attach clean.png from this folder as the image, then paste everything below the horizontal rule into the chat.

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You are evaluating a scientific visualization for causal consistency.

The following specification is the symbolic generator — it fully specifies what the output plot should look like:

import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5906
n_points  = 40
slope     = 1.016841
intercept = 1.151654
noise_std = 0.678537

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()

Domain: Data visualization
Plot family: Scatter plot with linear regression line

I am showing you an image that claims to be the output of this generator.

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Your Task

1. Read the specification carefully. Reason about what the plot should look like (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is causally consistent with the generator.

If the image is not consistent, classify the inconsistency using exactly one of these labels:

Label	Meaning
colormap_inversion	The colormap used is different from what the code specifies
axis_swap	Axes or data dimensions are transposed or mirrored
sign_inversion	Values are negated — peaks and troughs (or bar directions) are swapped
amplitude_scale	The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes
phase_shift	The pattern is shifted from its correct position
frequency_doubling	The number of oscillations or cycles is wrong
dc_offset	The curves or point cloud are shifted away from their correct baseline
wrong_petal_count	The number of petals/lobes differs from what the formula produces
symmetry_mismatch	The image contains asymmetry that the code cannot produce
bar_order_swap	Two bars have had their heights swapped — a bar's value label contradicts the code's heights list
coefficient_scale	Polynomial coefficients are scaled but the formula in the spec is unchanged
wrong_gravity	Trajectory uses a different gravitational constant than the spec
wrong_launch_angle	Trajectory uses a different launch angle than the spec
wrong_iteration_depth	L-system rendered with a different iteration count than the spec
wrong_angle	L-system rendered with a different turn angle than the spec

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Response Format

Respond with only this JSON object and nothing else:

{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "confidence": "low | medium | high",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}