[MAIN] Change workdir files, add docstring in functions

etc/tools/plots.py

@@ -0,0 +1,103 @@
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+def stacked_plot(data):
+    """Create a stacked plot with mean power on top and a 2D heatmap below.
+
+    Parameters
+    ----------
+    data : array-like
+        Input data array. Will be squeezed to remove singleton dimensions.
+        Rows are interpreted as samples and columns as range/frequency bins.
+
+    Returns
+    -------
+    plotly.graph_objects.Figure
+        A two-row figure: the top panel shows the mean absolute power across
+        columns, and the bottom panel shows a heatmap of the absolute values.
+    """
+    data = np.squeeze(data)
+    mean_dp = np.mean(np.abs(data), axis=1)
+
+    fig = make_subplots(rows=2, cols=1, row_heights=[0.3, 0.7], shared_xaxes=True,
+                        vertical_spacing=0.01)
+
+    fig.add_trace(go.Scatter(y=mean_dp, name='Mean Power'), row=1, col=1)
+    fig.add_trace(go.Heatmap(z=np.abs(data).T, showscale=False, name='Heat Map'), row=2, col=1)
+
+    fig.update_layout(title='Mean DP Power and 2D Map', autosize=True)
+    fig.update_xaxes(visible=False, row=2, col=1)
+    fig.update_yaxes(visible=False, row=2, col=1)
+
+    return fig
+
+
+def noise_mean(data):
+    """Estimate the noise floor as the trimmed mean of absolute values.
+
+    Sorts the flattened absolute data and discards the bottom 10% and top 10%
+    before computing the mean, making the estimate robust to outliers and
+    strong targets.
+
+    Parameters
+    ----------
+    data : array-like
+        Input data array of any shape.
+
+    Returns
+    -------
+    float
+        Mean of the central 80% of the sorted absolute values.
+    """
+    sorted_data = np.sort(np.abs(data.flatten()))
+    cutoff_up_index = int(len(sorted_data) * 0.9)
+    cutoff_down_index = int(len(sorted_data) * 0.1)
+    trimmed_data = sorted_data[cutoff_down_index:cutoff_up_index]
+
+    return np.mean(trimmed_data)
+
+
+def calculate_cdf(data):
+    """Compute the empirical cumulative distribution function (CDF) of the data.
+
+    Parameters
+    ----------
+    data : array-like
+        Input data array of any shape. Will be flattened before processing.
+
+    Returns
+    -------
+    tuple[numpy.ndarray, numpy.ndarray]
+        A ``(sorted_data, cdf)`` tuple where ``sorted_data`` contains the
+        sorted values and ``cdf`` contains the corresponding CDF probabilities
+        in the range (0, 1].
+    """
+    sorted_data = np.sort(data.flatten())
+    cdf = np.arange(1, len(sorted_data) + 1) / len(sorted_data)
+    return (sorted_data, cdf)
+
+
+def plot_cdfs(data_list, labels):
+    """Plot the empirical CDFs of multiple datasets on a single figure.
+
+    Parameters
+    ----------
+    data_list : list of array-like
+        List of data arrays to plot. Each array can have any shape and will
+        be flattened internally by :func:`calculate_cdf`.
+    labels : list of str
+        Legend labels corresponding to each entry in ``data_list``.
+
+    Returns
+    -------
+    plotly.graph_objects.Figure
+        A figure with one CDF line per dataset.
+    """
+    fig = go.Figure()
+    for data, label in zip(data_list, labels):
+        sorted_data, cdf = calculate_cdf(data)
+        fig.add_trace(go.Scatter(x=sorted_data, y=cdf, mode='lines', name=label))
+    fig.update_layout(title='CDF of Data', xaxis_title='Value', yaxis_title='CDF', autosize=True)
+    return fig