feat(plotly): implement line-interactive

plots/line-interactive/implementations/plotly.py

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+""" pyplots.ai
+line-interactive: Interactive Line Chart with Hover and Zoom
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+
+# Data - Server CPU usage over 7 days (hourly readings)
+np.random.seed(42)
+n_points = 168  # 7 days * 24 hours
+
+dates = pd.date_range("2024-01-01", periods=n_points, freq="h")
+
+# Simulate realistic CPU usage pattern with daily cycles and some anomalies
+base = 35  # base CPU usage
+daily_pattern = 20 * np.sin(np.linspace(0, 7 * 2 * np.pi, n_points))  # daily cycle
+weekly_trend = np.linspace(0, 10, n_points)  # slight upward trend
+noise = np.random.normal(0, 5, n_points)
+
+# Add some random spikes (anomalies)
+spikes = np.zeros(n_points)
+spike_indices = [45, 92, 120, 155]
+for idx in spike_indices:
+    spikes[idx] = np.random.uniform(20, 35)
+
+cpu_usage = base + daily_pattern + weekly_trend + noise + spikes
+cpu_usage = np.clip(cpu_usage, 5, 100)  # Keep within 5-100%
+
+# Create figure with interactive features
+fig = go.Figure()
+
+fig.add_trace(
+    go.Scatter(
+        x=dates,
+        y=cpu_usage,
+        mode="lines",
+        name="CPU Usage",
+        line={"color": "#306998", "width": 2.5},
+        hovertemplate="<b>%{x|%Y-%m-%d %H:%M}</b><br>CPU Usage: %{y:.1f}%<extra></extra>",
+    )
+)
+
+# Layout with interactive features
+fig.update_layout(
+    title={
+        "text": "Server Metrics · line-interactive · plotly · pyplots.ai",
+        "font": {"size": 28},
+        "x": 0.5,
+        "xanchor": "center",
+    },
+    xaxis={
+        "title": {"text": "Date & Time", "font": {"size": 22}},
+        "tickfont": {"size": 16},
+        "rangeslider": {"visible": True, "thickness": 0.08},
+        "rangeselector": {
+            "buttons": [
+                {"count": 1, "label": "1d", "step": "day", "stepmode": "backward"},
+                {"count": 3, "label": "3d", "step": "day", "stepmode": "backward"},
+                {"step": "all", "label": "All"},
+            ],
+            "font": {"size": 14},
+            "bgcolor": "#f0f0f0",
+            "activecolor": "#FFD43B",
+        },
+        "showgrid": True,
+        "gridwidth": 1,
+        "gridcolor": "rgba(0,0,0,0.1)",
+    },
+    yaxis={
+        "title": {"text": "CPU Usage (%)", "font": {"size": 22}},
+        "tickfont": {"size": 16},
+        "range": [0, 105],
+        "showgrid": True,
+        "gridwidth": 1,
+        "gridcolor": "rgba(0,0,0,0.1)",
+    },
+    template="plotly_white",
+    hovermode="x unified",
+    dragmode="zoom",
+    margin={"l": 80, "r": 40, "t": 100, "b": 80},
+)
+
+# Add modebar buttons for interactivity
+fig.update_layout(
+    modebar={
+        "add": ["zoom", "pan", "zoomIn", "zoomOut", "resetScale"],
+        "remove": ["lasso", "select"],
+        "bgcolor": "rgba(255,255,255,0.8)",
+    }
+)
+
+# Save as PNG (static snapshot)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save as HTML (interactive version)
+fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

plots/line-interactive/metadata/plotly.yaml

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+library: plotly
+specification_id: line-interactive
+created: '2025-12-30T17:48:10Z'
+updated: '2025-12-30T17:55:50Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602450418
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-interactive/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-interactive/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-interactive/plotly/plot.html
+quality_score: 93
+review:
+  strengths:
+  - Excellent use of Plotly interactive features (rangeslider, rangeselector, hover
+    templates)
+  - Well-crafted realistic data with daily cycles, trend, noise, and anomaly spikes
+  - Perfect title format following pyplots conventions
+  - Clean visual design with appropriate text sizes and layout
+  - Good use of hovertemplate for formatted datetime and percentage display
+  weaknesses:
+  - Missing visible legend in the plot (trace has name but legend not shown)
+  - Pandas import is unnecessary - could use numpy for date generation