data-visualization — Автоматизированные графики для анализа данных GPU

--- name: data-visualization description: Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output. --- # Data Visualization Skill Create publication-quality analytical charts using matplotlib and seaborn in a headless GPU sandbox. Charts are saved as PNG files to `/workspace/` for retrieval. ## When to Use This Skill Use this skill when: - Visualizing results from cuDF analysis or cuML models - Creating charts (bar, line, scatter, heatmap, histogram, box plot) - Building multi-panel analysis summaries - The user asks for visual output, plots, graphs, or charts - Presenting statistical findings with figures ## Initialization (REQUIRED) MUST call `matplotlib.use('Agg')` BEFORE importing pyplot. This enables headless rendering. ```python import matplotlib matplotlib.use('Agg') # Headless backend — MUST be before pyplot import import matplotlib.pyplot as plt import numpy as np # Publication-quality defaults plt.rcParams.update({ 'figure.dpi': 100, 'savefig.dpi': 300, 'font.size': 11, 'axes.labelsize': 12, 'axes.titlesize': 14, 'xtick.labelsize': 10, 'ytick.labelsize': 10, 'legend.fontsize': 10, 'figure.constrained_layout.use': True, }) # Colorblind-safe palette (Okabe-Ito) COLORS = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC', '#CA9161', '#FBAFE4', '#949494', '#ECE133', '#56B4E9'] ``` ## Saving Charts Always save to `/workspace/` with these settings: ```python plt.savefig('/workspace/chart_name.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` - `dpi=300` for print quality - `bbox_inches='tight'` removes excess whitespace - `facecolor='white'` ensures white background - Always call `plt.close()` after saving to free memory ## Displaying Charts (REQUIRED) After saving any chart, you MUST call `read_file` on it to display it inline in the conversation: ``` read_file("/workspace/chart_name.png") ``` Users cannot see charts unless you do this. Every chart you save MUST be followed by a `read_file` call. ## Quick Reference ### Bar Chart (from groupby results) ```python # After: result = to_pd(df.groupby("category")["value"].mean()) fig, ax = plt.subplots(figsize=(8, 5)) bars = ax.bar(result.index, result.values, color=COLORS[:len(result)], edgecolor='black', linewidth=0.8) for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}', ha='center', va='bottom', fontsize=9) ax.set_ylabel('Mean Value', fontweight='bold') ax.set_xlabel('Category', fontweight='bold') ax.set_title('Average Value by Category', fontweight='bold') ax.grid(axis='y', alpha=0.3, linestyle='--') ax.set_axisbelow(True) plt.savefig('/workspace/bar_chart.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Line Chart (trends over time) ```python fig, ax = plt.subplots(figsize=(10, 5)) for i, col in enumerate(columns_to_plot): ax.plot(df["date"], df[col], label=col, color=COLORS[i], linewidth=2, marker='o', markersize=3, markevery=max(1, len(df)//20)) ax.set_ylabel('Values', fontweight='bold') ax.set_xlabel('Date', fontweight='bold') ax.set_title('Trends Over Time', fontweight='bold') ax.legend(frameon=True, shadow=False) ax.grid(True, alpha=0.3, linestyle='--') ax.set_axisbelow(True) plt.xticks(rotation=45, ha='right') plt.savefig('/workspace/line_chart.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Scatter Plot — Continuous Color (correlations) ```python fig, ax = plt.subplots(figsize=(8, 6)) scatter = ax.scatter(df["x"], df["y"], c=df["value"], cmap='viridis', s=40, alpha=0.7, edgecolors='black', linewidth=0.3) plt.colorbar(scatter, ax=ax, label='Value') # Optional: trend line z = np.polyfit(df["x"], df["y"], 1) ax.plot(df["x"].sort_values(), np.poly1d(z)(df["x"].sort_values()), "r--", linewidth=2, label=f'y={z[0]:.2f}x+{z[1]:.2f}') ax.set_xlabel('X', fontweight='bold') ax.set_ylabel('Y', fontweight='bold') ax.set_title('Correlation Analysis', fontweight='bold') ax.legend() ax.grid(True, alpha=0.3, linestyle='--') plt.savefig('/workspace/scatter_correlation.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Scatter Plot — Categorical Color (clusters) ```python fig, ax = plt.subplots(figsize=(8, 6)) for i, label in enumerate(sorted(df["cluster"].unique())): mask = df["cluster"] == label ax.scatter(df.loc[mask, "x"], df.loc[mask, "y"], c=COLORS[i], label=f'Cluster {label}', s=40, alpha=0.7) ax.set_xlabel('X', fontweight='bold') ax.set_ylabel('Y', fontweight='bold') ax.set_title('Cluster Visualization', fontweight='bold') ax.legend() ax.grid(True, alpha=0.3, linestyle='--') plt.savefig('/workspace/scatter_clusters.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Heatmap (correlation matrix or confusion matrix) ```python import seaborn as sns fig, ax = plt.subplots(figsize=(8, 7)) # corr_matrix = to_pd(df[numeric_cols].corr()) sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='RdBu_r', center=0, square=True, linewidths=1, vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'}, ax=ax) ax.set_title('Correlation Matrix', fontweight='bold') plt.savefig('/workspace/heatmap.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Histogram with KDE ```python fig, ax = plt.subplots(figsize=(8, 5)) ax.hist(df["value"], bins=30, color=COLORS[0], alpha=0.7, edgecolor='black', linewidth=0.5, density=True, label='Distribution') # Add KDE curve from scipy.stats import gaussian_kde kde = gaussian_kde(df["value"].dropna()) x_range = np.linspace(df["value"].min(), df["value"].max(), 200) ax.plot(x_range, kde(x_range), color=COLORS[1], linewidth=2, label='KDE') ax.set_xlabel('Value', fontweight='bold') ax.set_ylabel('Density', fontweight='bold') ax.set_title('Value Distribution', fontweight='bold') ax.legend() ax.grid(axis='y', alpha=0.3, linestyle='--') plt.savefig('/workspace/histogram.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Box Plot (compare groups) ```python fig, ax = plt.subplots(figsize=(8, 5)) groups = [df[df["group"] == g]["value"].values for g in group_names] bp = ax.boxplot(groups, labels=group_names, patch_artist=True, widths=0.6, showmeans=True, meanprops=dict(marker='D', markerfacecolor='red', markersize=6)) for i, patch in enumerate(bp['boxes']): patch.set_facecolor(COLORS[i % len(COLORS)]) patch.set_alpha(0.7) ax.set_ylabel('Value', fontweight='bold') ax.set_title('Distribution by Group', fontweight='bold') ax.grid(axis='y', alpha=0.3, linestyle='--') ax.set_axisbelow(True) plt.savefig('/workspace/boxplot.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Multi-Panel Analysis Summary Use this to create a single image with multiple charts — the most effective way to present a complete analysis. ```python fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Top-left: Distribution axes[0, 0].hist(df["value"], bins=30, color=COLORS[0], alpha=0.7, edgecolor='black', linewidth=0.5) axes[0, 0].set_title('Value Distribution', fontweight='bold') axes[0, 0].set_xlabel('Value') axes[0, 0].grid(axis='y', alpha=0.3, linestyle='--') # Top-right: Scatter axes[0, 1].scatter(df["x"], df["y"], c=COLORS[0], s=30, alpha=0.5) axes[0, 1].set_title('X vs Y', fontweight='bold') axes[0, 1].set_xlabel('X') axes[0, 1].set_ylabel('Y') axes[0, 1].grid(True, alpha=0.3, linestyle='--') # Bottom-left: Bar chart group_means = df.groupby("category")["value"].mean() axes[1, 0].bar(group_means.index, group_means.values, color=COLORS[:len(group_means)]) axes[1, 0].set_title('Mean by Category', fontweight='bold') axes[1, 0].set_xlabel('Category') axes[1, 0].grid(axis='y', alpha=0.3, linestyle='--') # Bottom-right: Box plot axes[1, 1].boxplot([df[df["category"] == c]["value"].values for c in categories], labels=categories, patch_artist=True) axes[1, 1].set_title('Distribution by Category', fontweight='bold') axes[1, 1].grid(axis='y', alpha=0.3, linestyle='--') fig.suptitle('Analysis Summary', fontsize=16, fontweight='bold') plt.savefig('/workspace/analysis_summary.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Feature Importance Chart (from cuML model) ```python fig, ax = plt.subplots(figsize=(8, max(4, len(feature_names) * 0.35))) # importances = to_pd(model.feature_importances_) sorted_idx = np.argsort(importances) ax.barh(np.array(feature_names)[sorted_idx], importances[sorted_idx], color=COLORS[0], edgecolor='black', linewidth=0.5) ax.set_xlabel('Importance', fontweight='bold') ax.set_title('Feature Importances', fontweight='bold') ax.grid(axis='x', alpha=0.3, linestyle='--') ax.set_axisbelow(True) plt.savefig('/workspace/feature_importance.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ### Confusion Matrix (from cuML classification) ```python import seaborn as sns fig, ax = plt.subplots(figsize=(7, 6)) # cm = confusion_matrix(to_pd(y_test), to_pd(predictions)) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', square=True, xticklabels=class_names, yticklabels=class_names, linewidths=1, cbar_kws={'label': 'Count'}, ax=ax) ax.set_xlabel('Predicted', fontweight='bold') ax.set_ylabel('Actual', fontweight='bold') ax.set_title('Confusion Matrix', fontweight='bold') plt.savefig('/workspace/confusion_matrix.png', dpi=300, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline ``` ## Style Rules - Use `COLORS` palette (colorblind-safe) — never rely on color alone to distinguish elements - No pie charts (bar charts are always clearer) - No 3D plots (distort data perception) - Grid lines at `alpha=0.3, linestyle='--'` with `ax.set_axisbelow(True)` - Bold axis labels and titles (`fontweight='bold'`) - White background for all exports - 1-4 charts per analysis is typical; use multi-panel for more ## Output Guidelines - Save all charts to `/workspace/` as PNG - Print file paths after saving so the agent can reference them - For multi-panel summaries, use `figsize=(14, 10)` for 2×2 layouts - Keep chart titles descriptive but concise - Include units in axis labels when applicable