visualization

Visualization class

class idpet.visualization.Visualization(analysis)

Bases: object

Visualization class for ensemble analysis.

Parameters:

analysis (EnsembleAnalysis): An instance of EnsembleAnalysis providing data for visualization.

alpha_angles(bins=50, save=False, ax=None)

Alpha angles: Angles between four consecutive Cα atoms along the protein backbone. This method calculates the alpha angles for each ensemble in the analysis and plots their distribution

Return type:

Axes

Parameters

binsint

The number of bins for the histogram. Default is 50.

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axplt.Axes, optional

The axes on which to plot. Default is None, which creates a new figure and axes.

Returns

plt.Axes

The Axes object containing the plot.

asphericity(bins=50, hist_range=None, violin_plot=True, summary_stat='mean', dpi=96, save=False, color='lightblue', multiple_hist_ax=False, x_ticks_rotation=45, ax=None)

Plot asphericity distribution in each ensemble. Asphericity is calculated based on the gyration tensor.

Return type:

Axes

Parameters

binsint, optional

The number of bins for the histogram. Default is 50.

hist_range: Tuple, optional

A tuple with a min and max value for the histogram range. Default is None, which corresponds to using the min a max value across all data.

violin_plotbool, optional

If True, a violin plot is visualized. Default is True.

summary_statstr, optional

Specifies whether to display the “mean”, “median”, or “both” as reference lines on the plots. This applies when violin_plot is True or when multiple_hist_ax is True for histograms.

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

colorstr, optional

Color of the violin plot. Default is lightblue.

multiple_hist_axbool, optional

If True, each histogram will be plotted on separate axes. Default is False.

x_ticks_rotationint, optional

The rotation angle of the x-axis tick labels for the violin plot. Default is 45

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The axes on which to plot. Default is None, which creates a new figure and axes.

Returns

plt.Axes

The Axes object containing the plot.

comparison_matrix(score, featurization_params={}, bootstrap_iters=None, bootstrap_frac=1.0, bootstrap_replace=True, confidence_level=0.95, significance_level=0.05, bins=50, random_seed=None, verbose=False, ax=None, figsize=(6.0, 5.0), dpi=100, cmap='viridis_r', title=None, cbar_label=None, textcolors=('black', 'white'))

Generates and visualizes the pairwise comparison matrix for the ensembles. This function computes the comparison matrix using the specified score type and feature. It then visualizes the matrix using a heatmap.

Return type:

dict

Parameters:

score, featurization_params, bootstrap_iters, bootstrap_frac, bootstrap_replace, bins, random_seed, verbose:

See the documentation of EnsembleAnalysis.comparison_scores for more information about these arguments.

ax: Union[None, plt.Axes], optional

Axes object where to plot the comparison heatmap. If None (the default value) is provided, a new Figure will be created.

figsize: Tuple[int], optional

The size of the figure for the heatmap. Default is (6.00, 5.0). Only takes effect if ax is not None.

dpi: int, optional

DPIs of the figure for the heatmap. Default is 100. Only takes effect if ax is not None.

confidence_level, significance_level, cmap, title, cbar_label, textcolors:

See the documentation of dpet.visualization.plot_comparison_matrix for more information about these arguments.

Returns:

results: dict

A dictionary containing the following keys:

ax: the Axes object with the comparison matrix heatmap. scores: comparison matrix. See EnsembleAnalysis.comparison_scores

for more information.

codes: codes of the ensembles that were compared. fig: Figure object, only returned when a new figure is created

inside this function.

Notes:

The comparison matrix is annotated with the scores, and the axes are labeled with the ensemble labels.

contact_prob_maps(log_scale=True, avoid_zero_count=False, threshold=0.8, dpi=96, color='Blues', save=False, ax=None)

Plot the contact probability map based on the threshold.

Return type:

Union[List[Axes], ndarray]

Parameters

log_scalebool, optional

If True, use log scale range. Default is True.

avoid_zero_count: bool, optional

If True, avoid contacts with zero counts by adding to all contacts a pseudo count of 1e-6.

thresholdfloat, optional

Determining the threshold for calculating the contact frequencies. Default is 0.8 [nm].

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

colorstr, optional

The colormap to use for the contact probability map. Default is ‘Blues’.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

axUnion[None, List[plt.Axes], np.ndarray], optional

The axes on which to plot. If None, new axes will be created. Default is None.

Returns

Union[List[plt.Axes], np.ndarray]

Returns a list or array of Axes objects representing the subplot grid.

dimensionality_reduction_scatter(color_by='rg', save=False, ax=None, kde_by_ensemble=False, dpi=96, size=10, plotly=False, cmap_label='viridis', n_components=2)

Plot the results of dimensionality reduction using the method specified in the analysis.

Return type:

List[Axes]

Parameters

color_bystr, optional

The feature extraction method used for coloring points in the scatter plot. Options are “rg”, “prolateness”, “asphericity”, “sasa”, and “end_to_end”. Default is “rg”.

savebool, optional

If True, the plot will be saved in the data directory. Default is False.

axUnion[None, List[plt.Axes]], optional

A list of Axes objects to plot on. Default is None, which creates new axes.

kde_by_ensemblebool, optional

If True, the KDE plot will be generated for each ensemble separately. If False, a single KDE plot will be generated for the concatenated ensembles. Default is False.

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

sizeint, optional

The size of the points in the scatter plot. Default is 10.

plotlybool, optional

If True, the plot will be generated using Plotly. Default is False.

cmap_labelstr, optional

The colormap to use for the feature-colored labels. Default is ‘viridis’.

n_componentsint, optional

The number of components for dimensionality reduction.

Returns

List[plt.Axes]

List containing Axes objects for the scatter plot of original labels, clustering labels, and feature-colored labels, respectively.

Raises

NotImplementedError

If the dimensionality reduction method specified in the analysis is not supported.

distance_maps(min_sep=2, max_sep=None, distance_type='both', get_names=True, inverse=False, color='plasma', dpi=96, save=False, ax=None)

Plot CA and/or COM distance maps for one or more protein ensembles.

Return type:

List[Axes]

Parameters

min_sepint, default=2

Minimum sequence separation between residues to consider.

max_sepint or None, optional

Maximum sequence separation. If None, no upper limit is applied.

distance_type{‘ca’, ‘com’, ‘both’}, default=’both’

Specifies which type of distance map(s) to plot.

get_namesbool, default=True

Whether to return feature names from featurization (used internally).

inversebool, default=False

If True, compute and plot 1/distance instead of distance.

colorstr, default=’plasma’

Colormap to use for the distance maps.

dpiint, default=96

The DPI (dots per inch) of the output figure.

savebool, default=False

If True, the plot will be saved as an image file in the specified directory.

axmatplotlib Axes or array-like, optional

Axes on which to plot. If None, a new figure and axes will be created.

Returns

List[matplotlib.axes.Axes]

List of axes objects used for plotting.

end_to_end_distances(rg_norm=False, bins=50, hist_range=None, violin_plot=True, summary_stat='mean', dpi=96, save=False, color='lightblue', multiple_hist_ax=False, x_ticks_rotation=45, ax=None)

Plot end-to-end distance distributions.

Return type:

Union[Axes, List[Axes]]

Parameters

rg_norm: bool, optional

Normalize end-to-end distances on the average radius of gyration.

binsint, optional

The number of bins for the histogram. Default is 50.

hist_range: Tuple, optional

A tuple with a min and max value for the histogram. Default is None, which corresponds to using the min a max value across all data.

violin_plotbool, optional

If True, a violin plot is visualized. Default is True.

summary_stat: str, optional

Specifies whether to display the “mean”, “median”, or “both” as reference lines on the plots. This applies when violin_plot is True or when multiple_hist_ax is True for histograms.

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The axes on which to plot. Default is None, which creates a new figure and axes.

color: str, optional

Change the color of the violin plot. Default is lightblue.

multiple_hist_ax: bool, optional

If True, it will plot each histogram in a different axis.

x_ticks_rotation: int, optional

The rotation angle of the x-axis tick labels for the violin plot. Default is 45

Returns

Union[plt.Axes, List[plt.Axes]]

The Axes object or a list of Axes objects containing the plot(s).

global_sasa(bins=50, hist_range=None, violin_plot=True, summary_stat='mean', save=False, dpi=96, color='lightblue', multiple_hist_ax=False, x_ticks_rotation=45, ax=None)

Plot the distribution of SASA for each conformation within the ensembles.

Return type:

Axes

Parameters

binsint, optional

The number of bins for the histogram. Default is 50.

hist_range: Tuple, optional

A tuple with a min and max value for the histogram. Default is None, which corresponds to using the min a max value across all data.

violin_plotbool, optional

If True, a violin plot is visualized. Default is True.

summary_stat: str, optional

Specifies whether to display the “mean”, “median”, or “both” as reference lines on the plots. This applies when violin_plot is True or when multiple_hist_ax is True for histograms.

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

colorstr, optional

Color of the violin plot. Default is lightblue.

multiple_hist_axbool, optional

If True, it will plot each histogram in a different axis.

x_ticks_rotationint, optional

The rotation angle of the x-axis tick labels for the violin plot. Default is 45.

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The matplotlib Axes object on which to plot. If None, a new Axes object will be created. Default is None.

Returns

plt.Axes

The Axes object containing the plot.

pca_1d_histograms(save=False, sel_components=0, bins=30, ax=None, dpi=96)

Plot 1D histogram when the dimensionality reduction method is “pca” or “kpca”.

Return type:

List[Axes]

Parameters

save: bool, optional

If True the plot will be saved in the data directory. Default is False.

dim: int, optional

To select the specific component (dimension) for which you want to visualize the histogram distribution. Default is 0 (first principal component in PCA).

n_bins: int, optional

Number of bins in the histograms.

ax: Union[None, List[plt.Axes]], optional

A list of Axes objects to plot on. Default is None, which creates new axes.

dpiint, optional

For changing the quality and dimension of the output figure. Default is 96.

Returns

List[plt.Axes]

A list of plt.Axes objects representing the subplots created.

pca_2d_landscapes(save=False, sel_components=[0, 1], ax=None, dpi=96)

Plot 2D landscapes when the dimensionality reduction method is “pca” or “kpca”.

Return type:

List[Axes]

Parameters

save: bool, optional

If True the plot will be saved in the data directory. Default is False.

sel_components: List[int], optional

Indices of the selected principal components to analyze, starting from 0. The default components are the first and second.

ax: Union[None, List[plt.Axes]], optional

A list of Axes objects to plot on. Default is None, which creates new axes.

dpiint, optional

For changing the quality and dimension of the output figure. Default is 96.

Returns

List[plt.Axes]

A list of plt.Axes objects representing the subplots created.

pca_cumulative_explained_variance(save=False, dpi=96, ax=None)

Plot the cumulative variance. Only applicable when the dimensionality reduction method is “pca”.

Return type:

Axes

Parameters

save: bool, optional

If True, the plot will be saved in the data directory. Default is False.

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

ax: Union[None, plt.Axes], optional

An Axes object to plot on. Default is None, which creates a new axes.

Returns

plt.Axes, cumvar

The Axes object for the cumulative explained variance plot and a numpy array with the cumulative variance.

pca_residue_correlation(sel_components=[0, 1, 2], save=False, ax=None, dpi=96, cmap='RdBu', cmap_range=None, scale_loadings=False)

Plot the loadings (weights) of each pair of residues for a list of principal components (PCs).

Return type:

List[Axes]

Parameters

sel_componentsList[int], optional

A list of indices specifying the PC to include in the plot.

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axUnion[None, List[plt.Axes]], optional

A list of Axes objects to plot on. Default is None, which creates new axes.

dpiint, optional

For changing the quality and dimension of the output figure. Default is 96.

cmap: str, optional

Matplotlib colormap name.

cmap_range: Union[None, Tuple[float]], optional

Range of the colormap. Defaults to ‘None’, the range will be identified automatically. If a tuple, the first and second elements are the min. and max. of the range.

scale_loadings: bool, optional

Scale loadings by explained variance. Some definitions use correlation coefficients as loadings, when input features are standardized.

Returns

List[plt.Axes]

A list of plt.Axes objects representing the subplots created.

Notes

This method generates a correlation plot showing the weights of pairwise residue distances for selected PCA dimensions. The plot visualizes the correlation between residues based on the PCA weights.

The analysis is only valid on PCA and kernel PCA dimensionality reduction with ‘ca_dist’ feature extraction.

pca_rg_correlation(save=False, ax=None, dpi=96, sel_components=0)

Examine and plot the correlation between PC dimension 1 and the amount of Rg. Typically high correlation can be detected in case of IDPs/IDRs .

Return type:

List[Axes]

Parameters

savebool, optional

If True, the plot will be saved in the data directory. Default is False.

ax: Union[None, List[plt.Axes]], optional

A list of Axes objects to plot on. Default is None, which creates new axes.

dpiint, optional

For changing the quality and dimension of the output figure. Default is 96.

sel_components: int, optional

Index of the selected principal component to analyze, defaults to 0 (first principal component).

Returns

List[plt.Axes], dict

A list of plt.Axes objects representing the subplots created and a dictionary with all the raw data being plotted.

per_residue_mean_sasa(probe_radius=0.14, n_sphere_points=960, figsize=(15, 5), dpi=96, size=3, auto_xticks=True, xtick_interval=5, pointer=None, save=False, ax=None)

Plot the average solvent-accessible surface area (SASA) for each residue among all conformations in an ensemble. This function uses the Shrake–Rupley algorithm as implemented in MDTraj (mdtraj.shrake_rupley) to compute the solvent-accessible surface area.

Return type:

Axes

Parameters

probe_radius: float, optional

The radius of the probe sphere used in the Shrake–Rupley algorithm. Default is 0.14 nm.

n_sphere_points: int, optional

The number of points representing the surface of each atom, higher values leads to more accuracy. Default is 960.

figsize: Tuple[int, int], optional

Tuple specifying the size of the figure. Default is (15, 5).

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

size: int, optional

The size of the marker points. Default is 3.

auto_xticks: bool, optional

If True, use matplotlib default xticks. Default is True.

xtick_interval: int, optional

If auto_xticks is False, this parameter defines the interval between displayed residue indices on the x-axis. Always start with 1, followed by every xtick_interval residues (e.g., 1, 5, 10, 15, … if `xtick_interval`=5).

pointer: List[int], optional

List of desired residues to highlight with vertical dashed lines. Default is None.

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axUnion[None, plt.Axes], optional

The matplotlib Axes object on which to plot. If None, a new Axes object will be created. Default is None.

Returns

plt.Axes

Axes object containing the plot.

plot_histogram_grid(feature='ca_dist', ids=None, n_rows=2, n_cols=3, subplot_width=2.0, subplot_height=2.2, bins=None, dpi=90, save=False)

Plot a grid if histograms for distance or angular features. Can only be used when analyzing ensembles of proteins with same number of residues. The function will create a new matplotlib figure for histogram grid.

Return type:

Axes

Parameters

feature: str, optional

Feature to analyze. Must be one of ca_dist (Ca-Ca distances), a_angle (alpha angles), phi or psi (phi or psi backbone angles).

ids: Union[list, List[list]], optional

Residue indices (integers starting from zero) to define the residues to analyze. For angular features it must be a 1d list with N indices of the residues. For distance features it must be 2d list/array of shape (N, 2) in which N is the number of residue pairs to analyze are 2 their indices. Each of the N indices (or pair of indices) will be plotted in an histogram of the grid. If this argument is not provided, random indices will be sampled, which is useful for quickly comparing the distance or angle distributions of multiple ensembles.

n_rows: int, optional

Number of rows in the histogram grid.

n_cols: int, optional

Number of columns in the histogram grid.

subplot_width: int, optional

Use to specify the Matplotlib width of the figure. The size of the figure will be calculated as: figsize = (n_cols*subplot_width, n_rows*subplot_height).

subplot_height: int, optional

See the subplot_width argument.

bins: Union[str, int], optional

Number of bins in all the histograms.

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

save: bool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

Returns

ax: plt.Axes

The Axes object for the histogram grid.

plot_rama_grid(ids=None, n_rows=2, n_cols=3, subplot_width=2.0, subplot_height=2.2, dpi=90, save=False)

Plot a grid if Ramachandran plots for different residues. Can only be be used when analyzing ensembles of proteins with same number of residues. The function will create a new matplotlib figure for the scatter plot grid.

Return type:

Axes

Parameters

ids: Union[list, List[list]], optional

Residue indices (integers starting from zero) to define the residues to analyze. For angular features it must be a 1d list with N indices of the residues. Each of the N indices will be plotted in an scatter plot in the grid. If this argument is not provided, random indices will be sampled, which is useful for quickly comparing features of multiple ensembles.

n_rows: int, optional

Number of rows in the scatter grid.

n_cols: int, optional

Number of columns in the scatter grid.

subplot_width: int, optional

Use to specify the Matplotlib width of the figure. The size of the figure will be calculated as: figsize = (n_cols*subplot_width, n_rows*subplot_height).

subplot_height: int, optional

See the subplot_width argument.

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

Returns

ax: plt.Axes

The Axes object for the scatter plot grid.

prolateness(bins=50, hist_range=None, violin_plot=True, summary_stat='mean', dpi=96, save=False, color='lightblue', multiple_hist_ax=False, x_ticks_rotation=45, ax=None)

Plot prolateness distribution in each ensemble. Prolateness is calculated based on the gyration tensor.

Return type:

Axes

Parameters

binsint, optional

The number of bins for the histogram. Default is 50.

hist_rangeTuple, optional

A tuple with a min and max value for the histogram. Default is None, which corresponds to using the min a max value across all data.

violin_plotbool, optional

If True, a violin plot is visualized. Default is True.

summary_statstr, optional

Specifies whether to display the “mean”, “median”, or “both” as reference lines on the plots. This applies when violin_plot is True or when multiple_hist_ax is True for histograms.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

colorstr, optional

Color of the violin plot. Default is lightblue.

multiple_hist_axbool, optional

If True, each histogram will be plotted on separate axes. Default is False.

x_ticks_rotationint, optional

The rotation angle of the x-axis tick labels for the violin plot. Default is 45

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The axes on which to plot. Default is None, which creates a new figure and axes.

Returns

plt.Axes

The Axes object containing the plot.

radius_of_gyration(bins=50, hist_range=None, multiple_hist_ax=False, violin_plot=True, x_ticks_rotation=45, summary_stat='mean', color='lightblue', dpi=96, save=False, ax=None)

Plot the distribution of the radius of gyration (Rg) within each ensemble.

Return type:

Union[Axes, List[Axes]]

Parameters

binsint, optional

The number of bins for the histogram. Default is 50.

hist_rangeTuple, optional

A tuple with a min and max value for the histogram. Default is None, which corresponds to using the min and max value across all data.

multiple_hist_ax: bool, optional

If True, it will plot each histogram in a different axis.

violin_plotbool, optional

If True, a violin plot is visualized. Default is True.

x_ticks_rotationint, optional

The rotation angle of the x-axis tick labels for the violin plot. Default is 45

summary_stat: str, optional

Specifies whether to display the “mean”, “median”, or “both” as reference lines on the plots. This applies when violin_plot is True or when multiple_hist_ax is True for histograms.

colorstr, optional

Color of the violin plot. Default is lightblue.

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The axes on which to plot. If None, new axes will be created. Default is None.

Returns

Union[plt.Axes, List[plt.Axes]]

Returns a single Axes object or a list of Axes objects containing the plot(s).

Notes

This method plots the distribution of the radius of gyration (Rg) within each ensemble in the analysis.

The Rg values are binned according to the specified number of bins (bins) and range (hist_range) and displayed as histograms. Additionally, dashed lines representing the mean and median Rg values are overlaid on each histogram.

ramachandran_plots(two_d_hist=True, bins=(-180, 180, 80), dpi=96, color='viridis', log_scale=True, save=False, ax=None)

Ramachandran plot. If two_d_hist=True it returns a 2D histogram for each ensemble. If two_d_hist=False it returns a simple scatter plot for all ensembles in one plot.

Return type:

Union[List[Axes], Axes]

Parameters

two_d_histbool, optional

If True, it returns a 2D histogram for each ensemble. Default is True.

binstuple, optional

You can customize the bins for 2D histogram. Default is (-180, 180, 80).

log_scalebool, optional

If True, the histogram will be plotted on a logarithmic scale. Default is True.

colorstr, optional

The colormap to use for the 2D histogram. Default is ‘viridis’.

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

axUnion[None, plt.Axes, np.ndarray, List[plt.Axes]], optional

The axes on which to plot. If None, new axes will be created. Default is None.

Returns

Union[List[plt.Axes], plt.Axes]

If two_d_hist=True, returns a list of Axes objects representing the subplot grid for each ensemble. If two_d_hist=False, returns a single Axes object representing the scatter plot for all ensembles.

relative_dssp_content(dssp_code='H', dpi=96, auto_xticks=False, xtick_interval=5, figsize=(10, 5), save=False, ax=None)

Plot the relative ss content in each ensemble for each residue.

Return type:

Axes

Parameters

dssp_codestr, optional

The selected dssp code , it could be selected between ‘H’ for Helix, ‘C’ for Coil and ‘E’ for strand. It works based on the simplified DSSP codes

dpiint, optional

The DPI (dots per inch) of the output figure. Default is 96.

auto_xticks: bool, optional

If True, use matplotlib default xticks.

xtick_interval: int, optional

If auto_xticks is False, this parameter defines the interval between displayed residue indices on the x-axis. Residue 1 is always included,followed by every xtick_interval residues (e.g., 1, 5, 10, 15 if `xtick_interval`=5).

figsizeTuple[float, float], optional

The size of the figure in inches. Default is (10, 5).

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

axplt.Axes, optional

The axes on which to plot. Default is None, which creates a new figure and axes.

Returns

plt.Axes

The Axes object containing the plot.

rg_vs_asphericity(dpi=96, save=False, size=4, ax=None, verbose=True)

Plots the Rg versus Asphericity and calculates the pearson correlation coefficient to evaluate the correlation between Rg and Asphericity.

Return type:

Axes

Parameters

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

size: int, optional

The size of the scatter points. Default is 4.

ax: plt.Axes, optional

The axes on which to plot. Default is None, which creates a new figure and axes.

verbose: bool, optional

Verbosity for the output of the method. Showin the Pearson correlation coefficient for each ensemble.

Returns

plt.Axes

The Axes object containing the plot.

rg_vs_prolateness(dpi=96, size=4, save=False, ax=None, verbose=True)

Plot the Rg versus Prolateness and get the Pearson correlation coefficient to evaluate the correlation between Rg and Prolateness.

Return type:

Axes

Parameters

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

size: int, optional

The size of the scatter marker points. Default is 4.

save: bool, optional

If True, the plot will be saved as an image file in the specified directory. Default is False.

ax: plt.Axes, optional

The axes on which to plot. Default is None, which creates a new figure and axes.

verbose: bool, optional

Verbosity for the output of the method.

Returns

plt.Axes

The Axes object containing the plot.

site_specific_flexibility(pointer=None, auto_xticks=False, xtick_interval=5, dpi=96, figsize=(15, 5), save=False, ax=None)

Generate a plot of the site-specific flexibility parameter.

This plot shows the site-specific measure of disorder, which is sensitive to local flexibility based on the circular variance of the Ramachandran angles φ and ψ for each residue in the ensemble. The score ranges from 0 for identical dihedral angles for all conformers at the residue i to 1 for a uniform distribution of dihedral angles at the residue i. (For more information about this method look at here https://onlinelibrary.wiley.com/doi/full/10.1002/pro.4906)

Return type:

Axes

Parameters

pointer: List[int], optional

A list of desired residues. Vertical dashed lines will be added to point to these residues. Default is None.

auto_xticks: bool, optional

If True, use matplotlib default xticks.

xtick_interval: int, optional

If auto_xticks is False, this parameter defines the interval between displayed residue indices on the x-axis. Always start with 1, followed by every xtick_interval residues (e.g., 1, 5, 10, 15, … if `xtick_interval`=5).

figsize: Tuple[int, int], optional

The size of the figure. Default is (15, 5).

dpi: int, optional

The DPI (dots per inch) of the output figure. Default is 96.

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axUnion[None, plt.Axes], optional

The matplotlib Axes object on which to plot. If None, a new Axes object will be created. Default is None.

Returns

plt.Axes

The matplotlib Axes object containing the plot.

site_specific_order(pointer=None, auto_xticks=True, xtick_interval=5, dpi=96, figsize=(15, 5), save=False, ax=None)

Generate a plot of the site-specific order parameter. The function computes and plots per-residue order parameters that quantify how consistently each residue’s backbone orientation is aligned with the rest of the chain across all conformers in an ensemble. The result is a per-residue value between 0 and 1: values near 1 indicate high orientational order (rigid or structured regions), while values near 0 reflect disorder (flexible or unstructured regions). This measure captures long-range orientational correlations in the backbone and is particularly useful for detecting weakly ordered segments in intrinsically disordered proteins. (For more information about this method look at here https://onlinelibrary.wiley.com/doi/full/10.1002/pro.4906)

Return type:

Axes

Parameters

pointer: List[int], optional

A list of desired residues. Vertical dashed lines will be added to point to these residues. Default is None.

auto_xticks: bool, optional

If True, use matplotlib default xticks.

xtick_interval: int, optional

If auto_xticks is False, this parameter defines the interval between displayed residue indices on the x-axis. Always start with 1, followed by every xtick_interval residues (e.g., 1, 5, 10, 15, … if `xtick_interval`=5).

figsize: Tuple[int, int], optional

The size of the figure. Default is (15, 5).

savebool, optional

If True, the plot will be saved as an image file. Default is False.

axUnion[None, plt.Axes], optional

The matplotlib Axes object on which to plot. If None, a new Axes object will be created. Default is None.

Returns

plt.Axes

The matplotlib Axes object containing the plot.

idpet.visualization.plot_comparison_matrix(ax, comparison_out, codes, confidence_level=0.95, significance_level=0.05, cmap='viridis_r', title='New Comparison', cbar_label='score', textcolors=('black', 'white'))

Plot a matrix with all-vs-all comparison scores of M ensembles as a heatmap. If plotting the results of a regular all-vs-all analysis (no bootstraping involved), it will just plot the M x M comparison scores, with empty values on the diagonal. If plotting the results of an all-vs-all analysis with bootstrapping it will plot the M x M confidence intervals for the scores. The intervals are obtained by using the ‘percentile’ method. Additionally, it will plot an asterisk for those non-diagonal entries in for which the inter-ensemble scores are significantly higher than the intra-ensemble scores according to a Mann–Whitney U test.

Parameters

ax: plt.Axes

Axes object where the heatmap should be created.

comparison_out: dict

A dictionary containing the output of the comparison_scores method of the dpet.ensemble_analysis.EnsembleAnalysis class. It must contain the following key-value pairs: scores: NumPy array with shape (M, M, B) containing the comparison scores for M ensembles and B bootstrap iterations. If no bootstrap analysis was performed, B = 1, otherwise it will be B > 1. p_values (optional): used only when a bootstrap analysis was performed. A (M, M) NumPy array storiging the p-values obtained by comparing with a statistical test the inter-ensemble and intra-ensemble comparison scores.

codes: List[str]

List of strings with the codes of the ensembles.

confidence_level: float, optional

Condifence level for the bootstrap intervals of the comparison scores.

significance_level: float, optional

Significance level for the statistical test used to compare inter and intra-ensemble comparison scores.

cmap: str, optional

Matplotlib colormap name to use in the heatmap.

title: str, optional

Title of the heatmap.

cbar_label: str, optional

Label of the colorbar.

textcolors: Union[str, tuple], optional

Color of the text for each cell of the heatmap, specified as a string. By providing a tuple with two elements, the two colors will be applied to cells with color intensity above/below a certain threshold, so that ligher text can be plotted in darker cells and darker text can be plotted in lighter cells.

Returns

ax: plt.Axes

The same updated Axes object from the input. The comparison_out will be updated to store confidence intervals if performing a bootstrap analysis.

Notes

The comparison matrix is annotated with the scores, and the axes are labeled with the ensemble labels.

idpet.visualization.plot_histogram(ax, data, labels, bins=50, range=None, title='Histogram', xlabel='x', ylabel='Density', location=None)

Plot an histogram for different features.

Parameters

ax: plt.Axes

Matplotlib axis object where the histograms will be for plotted.

data: List[np.array]

List of NumPy array storing the data to be plotted.

labels: List[str]

List of strings with the labels of the arrays.

bins:

Number of bins.

range: Tuple, optional

A tuple with a min and max value for the histogram. Default is None, which corresponds to using the min a max value across all data.

title: str, optional

Title of the axis object.

xlabel: str, optional

Label of the horizontal axis.

ylabel: str, optional

Label of the vertical axis.

Returns

plt.Axes

Axis objects for the histogram plot of original labels.

idpet.visualization.plot_violins(ax, data, labels, summary_stat='mean', title='Histogram', xlabel='x', color='blue', x_ticks_rotation=45)

Make a violin plot.

Parameters

ax: plt.Axes

Matplotlib axis object where the histograms will be for plotted.

data: List[np.array]

List of NumPy array storing the data to be plotted.

labels: List[str]

List of strings with the labels of the arrays.

summary_stat: str, optional

Select between “median” or “mean” to show in violin plot. Default value is “mean”

title: str, optional

Title of the axis object.

xlabel: str, optional

Label of the horizontal axis.

Returns

plt.Axes

Axis objects for the histogram plot of original labels.