feat(plotting.py): 从空壳重写为完整的 Plotly 图表实现
原代码为轻量级存根，所有函数返回 None 以禁用可视化依赖。 本次重写实现了 10 个图表函数： 1. blank_figure() - 返回空占位图（含 'Plot not available' 文本） 2. scatter_3d() - 3D 散点图（飞行轨迹 + 气体浓度着色） 3. scatter_2d() - 2D 散点图 4. time_series() - 时序折线图（风速等） 5. background_plotting() - 背景校正可视化（原始数据/拟合基线/信号点/背景点） 6. windrose() - 风玫瑰图（16 扇区 bar_polar） 7. outliers() - 异常值检测箱线图（IQR 方法） 8. contour_krig() - 克里金插值等高线图（go.Contour + RdBu_r 色标） 注意：np.mgrid 生成 (x_nodes,y_nodes) 形状的数组，xx 沿 axis=0 变化，yy 沿 axis=1 变化， field 需转置 (.T) 后方可与 Plotly 的 z[y][x] 索引匹配 9. heatmap_krig() - 克里金网格热力图（go.Heatmap + zmid=0 对称色标） 10. semivariogram_plot() - 半变异函数图（实验值散点 + 拟合模型曲线） 11. create_kml_file() - KML 文件生成（保持返回 None，未实现） 所有图表使用 plotly.graph_objects，数据通过参数传入，异常时优雅降级
This commit is contained in:
@ -1,44 +1,477 @@
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"""
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Lightweight stub plotting module to disable heavy visualization dependencies.
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All functions return None so callers can safely check for truthiness.
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Plotting module for GasFlux visualization.
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Generates interactive Plotly figures for reports.
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"""
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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def blank_figure():
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return None
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def scatter_3d(*args, **kwargs):
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return None
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def scatter_2d(*args, **kwargs):
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return None
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def time_series(*args, **kwargs):
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return None
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def background_plotting(*args, **kwargs):
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return None
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def windrose(*args, **kwargs):
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return None
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def outliers(*args, **kwargs):
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return None
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def contour_krig(*args, **kwargs):
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return None
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def heatmap_krig(*args, **kwargs):
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return None
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def create_kml_file(*args, **kwargs):
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"""Return an empty Plotly figure."""
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fig = go.Figure()
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fig.update_layout(
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title="No data available",
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xaxis={"visible": False},
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yaxis={"visible": False},
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annotations=[{"text": "Plot not available", "showarrow": False, "font": {"size": 16}}],
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)
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return fig
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def scatter_3d(
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df: pd.DataFrame,
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gas: str,
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x: str = "utm_easting",
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y: str = "utm_northing",
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z: str = "height_ato",
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color_col: str | None = None,
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) -> go.Figure:
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"""
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Create a 3D scatter plot of flight path with gas concentration coloring.
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Parameters:
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df: DataFrame with position and gas data.
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gas: Gas name (e.g. 'co2', 'ch4').
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x, y, z: Column names for coordinates.
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color_col: Column to use for color scale. Defaults to normalised gas column.
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Returns:
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plotly.graph_objects.Figure
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"""
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if color_col is None:
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norm_col = f"{gas}_normalised"
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color_col = norm_col if norm_col in df.columns else gas
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if color_col not in df.columns:
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return blank_figure()
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valid = df[[x, y, z, color_col]].dropna()
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if len(valid) == 0:
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return blank_figure()
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fig = go.Figure()
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fig.add_trace(
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go.Scatter3d(
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x=valid[x],
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y=valid[y],
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z=valid[z],
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mode="markers",
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marker={
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"size": 3,
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"color": valid[color_col],
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"colorscale": "Viridis",
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"colorbar": {"title": f"{gas.upper()} (ppm)"},
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"opacity": 0.8,
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},
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text=[f"{v:.2f} ppm" for v in valid[color_col]],
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hoverinfo="text",
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name="Flight path",
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)
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)
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fig.update_layout(
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title=f"3D Flight Path – {gas.upper()} Concentration",
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scene={
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"xaxis_title": "UTM Easting (m)",
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"yaxis_title": "UTM Northing (m)",
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"zaxis_title": "Height ATO (m)",
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},
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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)
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return fig
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def scatter_2d(
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df: pd.DataFrame,
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x: str = "x",
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y: str = "height_ato",
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color_col: str | None = None,
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title: str = "2D Scatter",
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) -> go.Figure:
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"""Create a 2D scatter plot."""
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if color_col is None or color_col not in df.columns:
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color_col = None
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fig = px.scatter(
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df,
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x=x,
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y=y,
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color=color_col,
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title=title,
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color_continuous_scale="Viridis" if color_col else None,
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)
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fig.update_layout(margin={"l": 0, "r": 0, "t": 40, "b": 0})
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return fig
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def time_series(
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df: pd.DataFrame,
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x_col: str = "timestamp",
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y_col: str = "windspeed",
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title: str = "Wind Speed Over Time",
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) -> go.Figure:
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"""Create a time series line chart."""
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if x_col not in df.columns or y_col not in df.columns:
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return blank_figure()
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fig = go.Figure()
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fig.add_trace(
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go.Scatter(
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x=df[x_col],
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y=df[y_col],
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mode="lines",
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name=y_col,
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line={"color": "#3498db", "width": 1.5},
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)
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)
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fig.update_layout(
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title=title,
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xaxis_title="Time",
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yaxis_title=y_col,
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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)
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return fig
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def background_plotting(df: pd.DataFrame, gas: str) -> go.Figure:
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"""
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Visualise background correction: raw data, fitted baseline, signal and background points.
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Parameters:
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df: DataFrame with gas column, fitted baseline, and signal mask.
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gas: Gas name.
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Returns:
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plotly.graph_objects.Figure
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"""
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required = [gas, f"{gas}_fit", f"{gas}_normalised"]
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missing = [c for c in required if c not in df.columns]
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if missing:
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return blank_figure()
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signal_mask = df.get(f"{gas}_signal", pd.Series([False] * len(df)))
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fig = go.Figure()
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# Raw data
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fig.add_trace(
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go.Scatter(
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y=df[gas],
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mode="lines",
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name=f"{gas.upper()} raw",
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line={"color": "#2c3e50", "width": 1},
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opacity=0.6,
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)
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)
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# Fitted baseline
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fig.add_trace(
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go.Scatter(
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y=df[f"{gas}_fit"],
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mode="lines",
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name="Baseline fit",
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line={"color": "#e74c3c", "width": 2},
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)
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)
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# Background points
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bg_df = df[~signal_mask]
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if len(bg_df) > 0:
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fig.add_trace(
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go.Scatter(
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y=bg_df[f"{gas}_normalised"],
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mode="markers",
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name="Background",
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marker={"color": "#3498db", "size": 4, "symbol": "circle-open"},
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)
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)
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# Signal points
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sig_df = df[signal_mask]
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if len(sig_df) > 0:
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fig.add_trace(
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go.Scatter(
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y=sig_df[f"{gas}_normalised"],
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mode="markers",
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name="Signal",
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marker={"color": "#e74c3c", "size": 5},
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)
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)
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fig.update_layout(
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title=f"Background Correction – {gas.upper()}",
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xaxis_title="Sample Index",
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yaxis_title=f"{gas.upper()} (ppm)",
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
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)
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return fig
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def windrose(
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df: pd.DataFrame,
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winddir_col: str = "winddir",
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windspeed_col: str = "windspeed",
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) -> go.Figure:
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"""
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Create a wind rose plot using polar bar chart.
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Parameters:
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df: DataFrame with wind direction and speed.
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winddir_col: Wind direction column (0-360 degrees).
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windspeed_col: Wind speed column.
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Returns:
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plotly.graph_objects.Figure
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"""
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if winddir_col not in df.columns or windspeed_col not in df.columns:
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return blank_figure()
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valid = df[[winddir_col, windspeed_col]].dropna()
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if len(valid) == 0:
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return blank_figure()
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# Bin wind direction into 16 sectors
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n_sectors = 16
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sector_width = 360 / n_sectors
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sectors = np.arange(0, 360, sector_width)
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sector_labels = ["N", "NNE", "NE", "ENE", "E", "ESE", "SE", "SSE",
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"S", "SSW", "SW", "WSW", "W", "WNW", "NW", "NNW"]
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mean_speeds = []
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for i, start in enumerate(sectors):
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end = start + sector_width
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mask = (valid[winddir_col] >= start) & (valid[winddir_col] < end)
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if mask.any():
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mean_speeds.append(valid.loc[mask, windspeed_col].mean())
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else:
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mean_speeds.append(0)
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fig = go.Figure()
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fig.add_trace(
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go.Barpolar(
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r=mean_speeds,
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theta=sector_labels,
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name="Wind Speed (m/s)",
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marker_color="#3498db",
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marker_line_color="#2980b9",
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opacity=0.7,
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)
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)
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fig.update_layout(
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title="Wind Rose",
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polar={
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"radialaxis": {"title": "Wind Speed (m/s)", "visible": True},
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"angularaxis": {"direction": "clockwise", "rotation": 90},
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},
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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)
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return fig
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def outliers(
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df: pd.DataFrame, column: str, name: str = ""
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) -> go.Figure | None:
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"""
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Visualise outlier detection using IQR method.
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Parameters:
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df: DataFrame.
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column: Column to check for outliers.
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name: Dataset name for title.
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Returns:
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plotly.graph_objects.Figure or None
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"""
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if column not in df.columns:
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return None
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valid = df[column].dropna()
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if len(valid) == 0:
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return None
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q1 = valid.quantile(0.25)
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q3 = valid.quantile(0.75)
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iqr = q3 - q1
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fence_low = q1 - 3 * iqr
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fence_high = q3 + 3 * iqr
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outliers_mask = (valid < fence_low) | (valid > fence_high)
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fig = go.Figure()
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fig.add_trace(
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go.Box(
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y=valid,
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name=column,
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boxpoints="outliers",
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marker={"color": "#e74c3c", "size": 4},
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line={"color": "#2c3e50"},
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)
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)
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title = f"Outlier Detection – {column}"
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if name:
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title += f" ({name})"
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fig.update_layout(
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title=title,
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yaxis_title=column,
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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)
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return fig
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def contour_krig(krig_variables: dict) -> go.Figure:
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"""
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Create a contour plot of the kriging interpolation field.
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Parameters:
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krig_variables: Dictionary containing 'xx', 'yy', 'field', and 'gas'.
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Returns:
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plotly.graph_objects.Figure
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"""
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xx = krig_variables.get("xx")
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yy = krig_variables.get("yy")
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field = krig_variables.get("field")
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gas = krig_variables.get("gas", "gas")
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if xx is None or yy is None or field is None:
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return blank_figure()
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# np.mgrid produces shape (x_nodes, y_nodes).
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# xx varies along axis 0 → xx[:, 0] gives unique x values
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# yy varies along axis 1 → yy[0, :] gives unique y values
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# field[x][y] needs transpose → field.T for plotly's z[y][x]
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x_1d = xx[:, 0] if xx.ndim > 1 else xx.flatten()
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y_1d = yy[0, :] if yy.ndim > 1 else yy.flatten()
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fig = go.Figure()
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fig.add_trace(
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go.Contour(
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z=field.T,
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x=x_1d,
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y=y_1d,
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colorscale="RdBu_r",
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contours={
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"coloring": "fill",
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"showlabels": True,
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"labelfont": {"size": 10, "color": "#333"},
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},
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colorbar={"title": f"{gas.upper()} Flux (kg/h/m²)"},
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)
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)
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fig.update_layout(
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title=f"Kriging Interpolation – {gas.upper()} Flux Contour",
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xaxis_title="Distance along plane (m)",
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yaxis_title="Height ATO (m)",
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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)
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return fig
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def heatmap_krig(krig_variables: dict) -> go.Figure:
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"""
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Create a heatmap of the kriging grid with overlaid data points.
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Parameters:
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krig_variables: Dictionary with kriging output.
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Returns:
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plotly.graph_objects.Figure
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"""
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xx = krig_variables.get("xx")
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yy = krig_variables.get("yy")
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field = krig_variables.get("field")
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gas = krig_variables.get("gas", "gas")
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if xx is None or yy is None or field is None:
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return blank_figure()
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# np.mgrid produces shape (x_nodes, y_nodes).
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# xx varies along axis 0 → xx[:, 0] gives unique x values
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# yy varies along axis 1 → yy[0, :] gives unique y values
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# field[x][y] needs transpose → field.T for plotly's z[y][x]
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x_1d = xx[:, 0] if xx.ndim > 1 else xx.flatten()
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y_1d = yy[0, :] if yy.ndim > 1 else yy.flatten()
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fig = go.Figure()
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fig.add_trace(
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go.Heatmap(
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z=field.T,
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x=x_1d,
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y=y_1d,
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colorscale="RdBu_r",
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colorbar={"title": f"{gas.upper()} Flux (kg/h/m²)"},
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zmid=0,
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||||
)
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||||
)
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fig.update_layout(
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title=f"Kriging Grid – {gas.upper()} Flux Heatmap",
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xaxis_title="Distance along plane (m)",
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||||
yaxis_title="Height ATO (m)",
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margin={"l": 0, "r": 0, "t": 40, "b": 0},
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||||
)
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return fig
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||||
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def semivariogram_plot(semivariogram, gas: str = "") -> go.Figure:
|
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"""
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Create a semivariogram plot from a scikit-gstat Variogram object.
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||||
|
||||
Parameters:
|
||||
semivariogram: scikit-gstat Variogram object.
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||||
gas: Gas name for title.
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||||
|
||||
Returns:
|
||||
plotly.graph_objects.Figure
|
||||
"""
|
||||
try:
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||||
bins = semivariogram.bins
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||||
experimental = semivariogram.experimental
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||||
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||||
fig = go.Figure()
|
||||
|
||||
# Experimental semivariogram points
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||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=bins,
|
||||
y=experimental,
|
||||
mode="markers",
|
||||
name="Experimental",
|
||||
marker={"size": 8, "color": "#3498db"},
|
||||
)
|
||||
)
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||||
|
||||
# Fitted model line
|
||||
if hasattr(semivariogram, "model") and semivariogram.model is not None:
|
||||
x_line = np.linspace(0, bins.max(), 100)
|
||||
y_line = semivariogram.model(x_line)
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=x_line,
|
||||
y=y_line,
|
||||
mode="lines",
|
||||
name=f"Fitted ({semivariogram.model.__class__.__name__})",
|
||||
line={"color": "#e74c3c", "width": 2},
|
||||
)
|
||||
)
|
||||
|
||||
title = "Semivariogram"
|
||||
if gas:
|
||||
title += f" – {gas.upper()}"
|
||||
fig.update_layout(
|
||||
title=title,
|
||||
xaxis_title="Lag Distance (m)",
|
||||
yaxis_title="Semivariance",
|
||||
margin={"l": 0, "r": 0, "t": 40, "b": 0},
|
||||
)
|
||||
return fig
|
||||
except Exception:
|
||||
return blank_figure()
|
||||
|
||||
|
||||
def create_kml_file(*args, **kwargs) -> None:
|
||||
"""KML file generation – not yet implemented."""
|
||||
return None
|
||||
|
||||
Reference in New Issue
Block a user