qa.plot

Prebuilt vizualization functions.

Module Contents

Functions

format_plot(→ None)

General plotting parameters for the Kulik Lab.

heatmap(→ None)

Generates formatted heat maps.

get_parity_plot(→ None)

General set up to create an attractive parity plot.

get_charge_distributions(→ None)

Creates a charge distribution plot with one residue on each axis.

plot_feature_importance(→ None)

Creates a plot with the features on the x-axis and their importance on the y

esp_separate_barchart(→ None)

Plots the ESP for one single charge scheme as a barchart with error bars.

esp_combined_barchart(→ None)

Plots the ESP for all charge schemes as a barchart with error bars.

plot_rmsd(rmsd_list, labels)

Plots a bar plot of the RMSD for each analog with its standard error.

time_coupling_plot(→ None)

Compares the charge fluctuations of two residues against time

esp_dist_plot(esp_choice[, xlim, ylim, color_map, ...])

Creates a scatter plot for a distance and the corresponding ESP.

esp_kde_dist_plot(esp_choice[, xlim, ylim, color_map])

Creates a KDE plot for a distance and the corresponding ESP.
qa.plot.format_plot() → None

General plotting parameters for the Kulik Lab.

qa.plot.heatmap(data: str, residues: List[str], delete: List[int] = [], out_file: str = 'heatmap', v=[-0.4, 0.4]) → None

Generates formatted heat maps.

Uses the Kulik Lab figure formatting standards.

Parameters:

  • data (str) – The name of the data file, which can be a data or a dat file.

  • delete (List[int]) – A list of the amino acids you would like removed indexed at zero.

  • out_file (str) – The name you would like the image saved as.

Examples

heatmap(data=”cacovar.dat”, protein=”mc6sa”, delete=[0,15,16,27,28,29], out_file=”matrix_geom.svg”)

qa.plot.get_parity_plot(x: List[int], y: List[int]) → None

General set up to create an attractive parity plot.

Parameters:

  • x (list[int]) – List of x data points.

  • y (list[int]) – List of y data points.

qa.plot.get_charge_distributions(charge_df, out_file, res_x, res_y, ext, axes_range) → None

Creates a charge distribution plot with one residue on each axis.

Parameters:

charge_pd (pd.DataFrame) – A dataframe with two columns, each corresponding to a residue.

See also

qa.analyze.get_joint_qres

qa.plot.plot_feature_importance(models: List[str], template: List[str], mutations: List[str], by_atom=False) → None

Creates a plot with the features on the x-axis and their importance on the y

Parameters:

  • models (List[str]) – A list of the different models that were trained and tested.

  • template (str) – The name of the template pdb for the protein of interest.

See also

qa.predict.run_ml

qa.plot.esp_separate_barchart() → None

Plots the ESP for one single charge scheme as a barchart with error bars.

The ESP analysis outputs multicolumn dataframes for components. This allows us to compare metal-centered ESP contributions for components.

qa.plot.esp_combined_barchart(schemes, width=6.5, height=4) → None

Plots the ESP for all charge schemes as a barchart with error bars.

The ESP analysis outputs multicolumn dataframes for components. This allows us to compare metal-centered ESP contributions for components. This combined version also allows us to compare different charge schemes.

Paramers

schemesList of strings

A list of the names of the files that you would like to plot.

Notes

After running qa.manage.collect_esp_components(first, last, step), csv files for each charge scheme can be generate in the replicate directory. Those files can then be moved to another directory and run this script.

qa.plot.plot_rmsd(rmsd_list, labels)

Plots a bar plot of the RMSD for each analog with its standard error.

Parameters:

  • rmsd_list (List[List[float]]) – List of lists where each list represents an analog, and each list contains RMSDs for each frame.

  • labels (List[str]) – List of labels for the x-axis, corresponding to the analog names.

See also

qa.analyze.compute_rmsd, qa.analyze.get_rmsd

qa.plot.time_coupling_plot(charge_df, out_file, res_x, res_y, ext) → None

Compares the charge fluctuations of two residues against time

Parameters:

charge_df (pd.DataFrame) – A dataframe with two columns, each corresponding to a residue.

See also

qa.analyze.td_coupling

qa.plot.esp_dist_plot(esp_choice, xlim=None, ylim=None, color_map='viridis', custom_colors=None)

Creates a scatter plot for a distance and the corresponding ESP.

Parameters:

  • esp_choice (int) – Column index for the esp_data to be plotted.

  • xlim (tuple, optional) – Limits for x-axis in the form (xmin, xmax). If not provided, defaults to None.

  • ylim (tuple, optional) – Limits for y-axis in the form (ymin, ymax). If not provided, defaults to None.

  • color_map (str, optional) – Name of the Matplotlib color map. Defaults to ‘viridis’.

  • custom_colors (list of str, optional) – List of color hex codes for custom color map. If provided, will override color_map.

qa.plot.esp_kde_dist_plot(esp_choice, xlim=None, ylim=None, color_map='viridis')

Creates a KDE plot for a distance and the corresponding ESP.

Parameters:

  • esp_choice (int) – Column index for the esp_data to be plotted.

  • xlim (tuple, optional) – Limits for x-axis in the form (xmin, xmax). If not provided, defaults to None.

  • ylim (tuple, optional) – Limits for y-axis in the form (ymin, ymax). If not provided, defaults to None.

  • color_map (str, optional) – Name of the Matplotlib color map. Defaults to ‘viridis’.