coco_pipe.viz.dim_reduction#

Matplotlib visualization helpers for dimensionality reduction outputs.

Functions#

`plot_embedding`(X_emb[, labels, metadata, dims, title, ...])	Plot an explicit 2D or 3D embedding.
`plot_metrics`(scores[, title, figsize, ax, plot_type, ...])	Plot tidy metric observations using one shared entrypoint.
`plot_loss_history`(loss_history[, title, figsize, ax, ...])	Plot reducer loss history. Linear reducers usually do not expose this.
`plot_eigenvalues`(values[, title, ylabel, figsize, ax, ...])	Plot explained variance curves from linear reducers (PCA, TruncatedSVD).
`plot_shepard_diagram`(X_orig, X_emb[, sample_size, ...])	Plot original vs embedded pairwise distances.
`plot_streamlines`(X_emb, V_emb[, grid_density, title, ...])	Plot a velocity field on a 2D embedding.
`plot_feature_importance`(scores[, title, top_n, ...])	Plot feature-importance scores as horizontal bars.
`plot_feature_correlation_heatmap`(correlations[, ...])	Plot feature-to-dimension correlations as a heatmap.
`plot_trajectory_metric_series`(series[, times, labels, ...])	Plot evaluated trajectory metric time series.
`plot_trajectory`(X[, times, values, labels, color_map, ...])	Plot prepared trajectory tensors of shape `trajectory x time x dim`.
`plot_coranking_matrix`(coranking_matrix[, title, ...])	Heatmap of a co-ranking matrix produced by `DimReduction.score()`.
`plot_trajectory_separation`(separation[, times, top_n, ...])	Pairwise label-separation timecourses for trajectory embeddings.
`plot_component_loadings`(components[, feature_names, ...])	Heatmap of component loadings from linear reducers.
`plot_phase_portrait`(X, times, labels[, component_idx, ...])	Plot a phase portrait (amplitude vs velocity) for condition-mean trajectories.
`plot_scree`(evr[, title, figsize, ax, axes_kws])	Creates a scree plot.

Module Contents#

coco_pipe.viz.dim_reduction.plot_embedding(X_emb, labels=None, metadata=None, dims=(0, 1), title='Embedding', figsize=(10, 8), cmap=None, palette='deep', s=40, alpha=0.8, label_kind='categorical', metrics=None, metric_name=None, ax=None, random_state=None)#

Plot an explicit 2D or 3D embedding.

Parameters:

X_emb (numpy.ndarray) – Embedding array with shape (n_samples, n_dimensions).
labels (numpy.ndarray | None) – Optional label array aligned with samples used for color encoding.
metadata (dict[str, Any] | None) – Optional column-oriented metadata aligned with samples.
dims (tuple[int, int] | tuple[int, int, int]) – Column indices to use as plot axes. Two indices produce a 2D plot; three produce a 3D plot.
title (str) – Axes title.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
cmap (str | None) – Colormap used when label_kind="continuous".
palette (str) – Seaborn palette name used when label_kind="categorical".
s (int) – Scatter marker size.
alpha (float) – Scatter point opacity.
label_kind (coco_pipe.viz.theme.ColorKind) – "categorical" to color by class, "continuous" to apply a colormap to numeric labels.
metrics (dict[str, Any] | None) – Optional metrics mapping used to annotate the plot when metric_name is provided.
metric_name (str | None) – Name of the metric from metrics to display as an annotation.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
random_state (int | None) – Accepted for API compatibility; not used internally.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_embedding: Interactive Plotly version.
plot_metrics: Quality metric overview for the embedding run.
plot_shepard_diagram: Distance-preservation diagnostic.
plot_eigenvalues: Explained variance for linear reducers.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X_emb = rng.normal(size=(50, 2))
>>> labels = np.arange(50) % 5
>>> fig, ax = viz.plot_embedding(X_emb, labels=labels)

coco_pipe.viz.dim_reduction.plot_metrics(scores, title='Quality Metrics', figsize=(8, 6), ax=None, plot_type='bar', metric=None, scope=None, method=None, axes_kws=None)#

Plot tidy metric observations using one shared entrypoint.

Parameters:

scores (Any) – Metric source: a tidy DataFrame, {metric: value} mapping, list of records, or any object exposing to_frame() or metrics_.
title (str) – Axes title.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
plot_type (Literal['bar', 'grouped_bar', 'lollipop', 'box', 'boxen', 'violin', 'raincloud', 'strip', 'swarm', 'heatmap', 'line', 'dumbbell', 'slopegraph']) – Visualization style. "bar" / "grouped_bar" / "lollipop" aggregate to global scalars; "box" / "boxen" / "violin" / "raincloud" / "strip" / "swarm" show per-observation distributions; "heatmap" produces a method x metric grid; "line" plots metrics across a numeric scope axis; "dumbbell" / "slopegraph" require exactly two methods.
metric (str | None) – Optional metric name used to filter rows before plotting.
scope (str | None) – Optional scope name used to filter rows before plotting.
method (str | collections.abc.Sequence[str] | None) – Optional method name or names used to filter rows before plotting.
axes_kws (dict | None)

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_metrics: Interactive Plotly version.
plot_embedding: 2D or 3D scatter of the embedding points.
plot_eigenvalues: Per-component explained variance.
plot_coranking_matrix: Neighbourhood-preservation co-ranking heatmap.

Examples

>>> from coco_pipe.viz import dim_reduction as viz
>>> fig, ax = viz.plot_metrics({"trustworthiness": 0.92, "continuity": 0.88})

coco_pipe.viz.dim_reduction.plot_loss_history(loss_history, title='Training Loss', figsize=(8, 5), ax=None, scope=None, axes_kws=None)#

Plot reducer loss history. Linear reducers usually do not expose this.

Parameters:

loss_history (collections.abc.Sequence[float] | numpy.ndarray) – Sequence of per-epoch loss values from an iterative reducer.
title (str) – Axes title.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
scope (str | None) – Optional scope tag, accepted as "train" or "val".
axes_kws (dict | None)

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

Raises:

ValueError – If loss_history is empty or scope is not "train" or "val".

See also

coco_pipe.viz.interactive.dim_reduction.plot_loss_history: Interactive Plotly version.
plot_eigenvalues: Explained variance for linear reducers.
plot_metrics: Scalar quality metric overview.

Examples

>>> from coco_pipe.viz import dim_reduction as viz
>>> fig, ax = viz.plot_loss_history([1.0, 0.7, 0.4, 0.25, 0.18])

coco_pipe.viz.dim_reduction.plot_eigenvalues(values, title='Scree Plot', ylabel='Explained Variance', figsize=(8, 5), ax=None, max_components=None, condition_colors=None, axes_kws=None)#

Plot explained variance curves from linear reducers (PCA, TruncatedSVD).

Parameters:

values (dict[str, np.ndarray]) –
Mapping of label → array. Array shapes:
- 1-D (n_pcs,) — pre-averaged curve, no SEM band.
- 2-D (n_subjects, n_pcs) — per-subject data; mean ± SEM band drawn.
max_components (int, optional) – Cap the number of components shown.
condition_colors (dict[str, str], optional) – Per-label hex colour overrides.
title (str)
ylabel (str)
figsize (tuple[float, float] | None)
ax (matplotlib.pyplot.Axes | None)
axes_kws (dict | None)

Return type:

tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_eigenvalues: Interactive Plotly version.
plot_metrics: Scalar quality metric overview.
plot_loss_history: Iterative training loss curve.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> evals = {"PCA": np.array([0.50, 0.30, 0.12, 0.05, 0.03])}
>>> fig, ax = viz.plot_eigenvalues(evals)

coco_pipe.viz.dim_reduction.plot_shepard_diagram(X_orig, X_emb, sample_size=1000, title='Shepard Diagram', ax=None, random_state=None, distances=None, figsize=None)#

Plot original vs embedded pairwise distances.

Parameters:

X_orig (numpy.ndarray) – Original high-dimensional data with shape (n_samples, n_features). Ignored when distances is provided.
X_emb (numpy.ndarray) – Low-dimensional embedding with shape (n_samples, n_dims). Ignored when distances is provided.
sample_size (int) – Number of point pairs to sample for distance computation.
title (str) – Axes title.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
random_state (int | None) – Random seed for reproducible distance sampling.
distances (dict[str, numpy.ndarray] | None) – Pre-computed distance dict with "original" and "embedded" keys. When both keys are present, X_orig and X_emb are not used.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_shepard_diagram: Interactive Plotly version.
plot_embedding: Scatter of the low-dimensional embedding.
plot_coranking_matrix: Neighbourhood-rank preservation heatmap.
plot_metrics: Scalar quality metrics such as trustworthiness.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X_orig = rng.normal(size=(60, 10))
>>> X_emb = rng.normal(size=(60, 2))
>>> fig, ax = viz.plot_shepard_diagram(X_orig, X_emb, sample_size=200)

coco_pipe.viz.dim_reduction.plot_streamlines(X_emb, V_emb, grid_density=25, title='Velocity Streamlines', ax=None, random_state=None, figsize=None)#

Plot a velocity field on a 2D embedding.

Parameters:

X_emb (numpy.ndarray) – 2D embedding coordinates with shape (n_samples, 2).
V_emb (numpy.ndarray) – Velocity vectors with the same shape as X_emb, as returned by coco_pipe.dim_reduction.evaluation.velocity.compute_velocity_fields.
grid_density (int) – Number of grid points per axis used for velocity interpolation.
title (str) – Axes title.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
random_state (int | None) – Accepted for API compatibility; not used internally.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_streamlines: Interactive Plotly version.
plot_embedding: Scatter of the underlying embedding points.
plot_trajectory: Plotted trajectory paths over the embedding.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X_emb = rng.normal(size=(80, 2))
>>> V_emb = rng.normal(size=(80, 2)) * 0.2
>>> fig, ax = viz.plot_streamlines(X_emb, V_emb)

coco_pipe.viz.dim_reduction.plot_feature_importance(scores, title='Feature Importance', top_n=20, figsize=(8, 6), ax=None, analysis=None, method=None, dimension=None)#

Plot feature-importance scores as horizontal bars.

Parameters:

scores (Any) – Feature-score source: a raw {feature: score} mapping, interpretation payload, tidy interpretation DataFrame, or any object exposing interpretation_.
title (str) – Axes title.
top_n (int) – Maximum number of features to display, ranked by score magnitude.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
analysis (str | None) – Interpretation analysis to select when multiple analyses are present.
method (str | None) – Method name to select when multiple methods are present.
dimension (str | None) – Dimension label to select when multiple dimensions are present.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_feature_importance: Interactive Plotly version.
plot_feature_correlation_heatmap: Feature-to-dimension correlation heatmap.
plot_component_loadings: Linear-reducer component loading matrix.

Examples

>>> from coco_pipe.viz import dim_reduction as viz
>>> scores = {"alpha": 0.72, "beta": 0.55, "gamma": 0.31}
>>> fig, ax = viz.plot_feature_importance(scores)

coco_pipe.viz.dim_reduction.plot_feature_correlation_heatmap(correlations, title='Feature Correlation', top_n=25, figsize=(10, 8), ax=None, method=None)#

Plot feature-to-dimension correlations as a heatmap.

Parameters:

correlations (Any) – Correlation source: a raw correlation payload, tidy interpretation DataFrame, or any object exposing interpretation_.
title (str) – Axes title.
top_n (int | None) – Maximum number of features to show, ranked by peak absolute correlation across dimensions.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
method (str | None) – Method name to select when multiple methods are present.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_feature_correlation_heatmap: Interactive Plotly version.
plot_feature_importance: Ranked feature-importance bar chart.
plot_component_loadings: Linear-reducer component loading matrix.

Examples

>>> from coco_pipe.viz import dim_reduction as viz
>>> payload = {
...     "correlation": {"D1": {"F1": 0.6, "F2": -0.3}, "D2": {"F1": 0.1, "F2": 0.8}}
... }
>>> fig, ax = viz.plot_feature_correlation_heatmap(payload)

coco_pipe.viz.dim_reduction.plot_trajectory_metric_series(series, times=None, labels=None, color_map=None, linestyle_map=None, smooth_window=1, title='Trajectory Metric', ylabel='Value', figsize=(10, 6), ax=None)#

Plot evaluated trajectory metric time series.

Parameters:

series (Any) – Metric values: a 1D array, 2D (trajectory, time) array, or a {name: timecourse} mapping. 2D arrays are averaged across trajectories per unique label.
times (numpy.ndarray | None) – Explicit time axis aligned with the time dimension.
labels (numpy.ndarray | None) – Trajectory labels aligned with the first axis of 2D inputs.
title (str) – Axes title.
ylabel (str) – Y-axis label.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
color_map (dict[str, str] | None)
linestyle_map (dict[str, str] | None)
smooth_window (int)

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_trajectory_metric_series: Interactive Plotly version.
plot_trajectory: Raw trajectory paths in embedding space.
plot_trajectory_separation: Pairwise label-separation timecourses.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> series = rng.normal(size=(3, 20))
>>> fig, ax = viz.plot_trajectory_metric_series(series)

coco_pipe.viz.dim_reduction.plot_trajectory(X, times=None, values=None, labels=None, color_map=None, linestyle_map=None, smooth_window=1, downsample=1, speed_mode='linecollection', add_start_end_markers=False, show_markers=True, xlim=None, ylim=None, linewidth=2.5, title='Trajectory Plot', dimensions=2, figsize=(10, 8), ax=None, cmap=None, axis_labels=None, axes_kws=None, showlegend=True)#

Plot prepared trajectory tensors of shape trajectory x time x dim.

Parameters:

X (np.ndarray of shape (n_trajectories, n_times, n_dims)) – Each trajectory along the first axis — can be individual subjects, pre-averaged conditions, or any other grouping.
times (np.ndarray, optional) – Time stamps for the time axis. Defaults to integer indices.
values (np.ndarray of shape (n_trajectories, n_times), optional) – Per-point scalar values (e.g. speed) used for colour encoding.
labels (array-like of length n_trajectories, optional) – Label per trajectory used for colouring and legend.
color_map (dict[str, str], optional) – Optional mapping of label to hex color string.
linestyle_map (dict[str, str], optional) – Optional mapping of label to Matplotlib linestyle string.
smooth_window (int, default=1) – Moving-average window applied before plotting.
downsample (int, default=1) – Keep every downsample-th time point.
speed_mode ({"linecollection", "alpha"}, default="linecollection") – Colour-encoding style when values is provided (2D only). "linecollection" colours each segment by value; "alpha" modulates transparency and lightness of the base colour.
add_start_end_markers (bool, default=False) – Draw a circle (●) at the start and a cross (✕) at the end of each trajectory instead of a marker on every point.
show_markers (bool, default=True) – If True, draws markers at each sampled time point unless add_start_end_markers is True.
xlim (tuple[float, float], optional) – Fixed axis limits. Auto-scaled when None.
ylim (tuple[float, float], optional) – Fixed axis limits. Auto-scaled when None.
linewidth (float, default=2.5)
title (str, default="Trajectory Plot")
dimensions (int, default=2) – Number of spatial dimensions to render (2 or 3).
figsize (tuple[float, float], optional)
ax (matplotlib.axes.Axes, optional)
cmap (str, optional) – Colormap name for value encoding. Defaults to the theme sequential map.
axis_labels (list[str], optional) – Custom axis labels (e.g. ["PC1", "PC2"]).
axes_kws (dict, optional) – Additional kwargs passed to ax.set().
showlegend (bool, default=True) – Whether to draw the legend.

Return type:

tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_trajectory: Interactive Plotly version.
plot_trajectory_separation: Pairwise label-separation timecourses.
plot_trajectory_metric_series: Metric time series for trajectories.
plot_streamlines: Velocity field overlay on the embedding.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X = rng.normal(size=(3, 20, 2))
>>> labels = np.array(["A", "B", "C"])
>>> fig, ax = viz.plot_trajectory(X, labels=labels)

coco_pipe.viz.dim_reduction.plot_coranking_matrix(coranking_matrix, title='Co-Ranking Matrix', max_k=None, ax=None, figsize=None)#

Heatmap of a co-ranking matrix produced by DimReduction.score().

Parameters:

coranking_matrix (numpy.ndarray) – Square co-ranking matrix with shape (n_samples-1, n_samples-1).
title (str) – Axes title.
max_k (int | None) – Crop the matrix to the top-left max_k x max_k corner. Defaults to min(n, 50).
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_coranking_matrix: Interactive Plotly version.
plot_shepard_diagram: Continuous distance-preservation scatter.
plot_metrics: Scalar quality metric overview.
plot_embedding: Low-dimensional scatter being diagnosed.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> Q = np.random.default_rng(42).integers(0, 10, size=(15, 15)).astype(float)
>>> fig, ax = viz.plot_coranking_matrix(Q)

coco_pipe.viz.dim_reduction.plot_trajectory_separation(separation, times=None, top_n=None, color_map=None, linestyle_map=None, smooth_window=1, ax=None, figsize=None)#

Pairwise label-separation timecourses for trajectory embeddings.

Parameters:

separation (dict) – Mapping of (label_a, label_b) tuples (or any hashable key) to 1D separation timecourses, as returned by DimReduction.evaluate_trajectory.
times (numpy.ndarray | None) – Explicit time axis aligned with the separation arrays.
top_n (int | None) – Keep only the top_n pairs ranked by peak separation.
color_map (dict[tuple, str] | None) – Optional mapping of pair key to color.
linestyle_map (dict[tuple, str] | None) – Optional mapping of pair key to linestyle.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.
smooth_window (int)

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_trajectory_separation: Interactive Plotly version.
plot_trajectory: Raw trajectory paths in embedding space.
plot_trajectory_metric_series: Metric time series for trajectories.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> sep = {
...     ("A", "B"): np.linspace(0.1, 0.9, 20),
...     ("A", "C"): np.linspace(0.3, 0.6, 20),
... }
>>> fig, ax = viz.plot_trajectory_separation(sep)

coco_pipe.viz.dim_reduction.plot_component_loadings(components, feature_names=None, n_components=None, center=0.0, ax=None, figsize=None)#

Heatmap of component loadings from linear reducers.

Parameters:

components (numpy.ndarray) – Loading matrix with shape (n_features, n_components).
feature_names (list[str] | None) – Optional feature names for row labels. Defaults to ["Feature 0", "Feature 1", ...].
n_components (int | None) – Crop to this many components (columns).
center (float) – Colormap center value for the diverging palette.
ax (matplotlib.pyplot.Axes | None) – Existing Matplotlib axes to draw into.
figsize (tuple[float, float] | None) – Figure size used when creating new axes.

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_component_loadings: Interactive Plotly version.
plot_feature_importance: Ranked feature-importance bar chart.
plot_feature_correlation_heatmap: Feature-to-dimension correlation heatmap.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> components = rng.normal(size=(12, 3))
>>> fig, ax = viz.plot_component_loadings(components)

coco_pipe.viz.dim_reduction.plot_phase_portrait(X, times, labels, component_idx=0, title='Phase Portrait', figsize=(8, 10), ax=None, axes_kws=None)#

Plot a phase portrait (amplitude vs velocity) for condition-mean trajectories.

Parameters:

X (np.ndarray) – Trajectory array with shape (n_conditions, n_times, n_components).
times (np.ndarray) – One-dimensional time axis aligned with the time dimension of X.
labels (sequence) – Condition labels, one per trajectory (first axis of X).
component_idx (int, default=0) – Index of the component to extract for the portrait.
title (str, default="Phase Portrait") – Axes title.
figsize (tuple[float, float], optional) – Figure size used when creating new axes.
ax (matplotlib.axes.Axes, optional) – Existing Matplotlib axes to draw into.
axes_kws (dict | None)

Returns:

The created or reused figure and axes.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]

See also

coco_pipe.viz.interactive.dim_reduction.plot_phase_portrait: Interactive Plotly version.
plot_trajectory: Full trajectory geometry in 2D or 3D space.
plot_trajectory_metric_series: Scalar metric timecourses per trajectory.

Examples

>>> import numpy as np
>>> from coco_pipe.viz import dim_reduction as viz
>>> rng = np.random.default_rng(42)
>>> X = rng.normal(size=(3, 20, 5))
>>> times = np.linspace(0, 1, 20)
>>> fig, ax = viz.plot_phase_portrait(X, times, labels=["A", "B", "C"])

coco_pipe.viz.dim_reduction.plot_scree(evr, title='Scree Plot', figsize=(8, 10), ax=None, axes_kws=None)#

Creates a scree plot. Plots individual explained variance as bars and cumulative variance as a line.

Parameters:

evr (numpy.ndarray)
title (str | None)
figsize (tuple[float, float] | None)
ax (matplotlib.pyplot.Axes | None)
axes_kws (dict | None)

Return type:

tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]