imhr.data.plot¶

Classes

class imhr.data.plot.Plot(isLibrary=False)[source]¶

Bases: object

Hub for creating data visualizations using pandas, seaborn, and bokeh.

Methods

Methods

bokeh_calibration(config, df, cxy, event[, …])	Create calibration matrix, using pandas and bokeh.
bokeh_trial(config, df, stim_bounds, …)	Create single subject trial bokeh plots.
boxplot(config, df[, path, x, y, title, …])	Creates boxplot using seaborn and pandas.
cooks_plot(config, y, model, path, df)	Create cooks distance plot plot using seaborn, pandas, and statsmodel.
corr_matrix(config, df, path, title, method)	Create correlation matrix using bokeh and pandas.
density_plot(config, df, title)	Create density plot (draws kernel density estimate), using seaborn and pandas.
html([destination, df, raw_data, name, …])	Create HTML output.
logit_plot(config, df, path, param)	Create logistic regression plot using seaborn and pandas
onset_diff_plot(config, df, meta, drop, y[, …])	Plot onset differences using pandas and seaborn.
qq_plot(config, y, residuals, path)	Create probability plot using seaborn, pandas, and rpy2.
residual_plot(config, y, residuals, path)	Create probability plot using seaborn, pandas, and rpy2.
single_subject(config, df, path)	Create single subject scatterplot using seaborn and pandas.

bokeh_calibration(config, df, cxy, event[, …])	Create calibration matrix, using pandas and bokeh.
bokeh_trial(config, df, stim_bounds, …)	Create single subject trial bokeh plots.
boxplot(config, df[, path, x, y, title, …])	Creates boxplot using seaborn and pandas.
cooks_plot(config, y, model, path, df)	Create cooks distance plot plot using seaborn, pandas, and statsmodel.
corr_matrix(config, df, path, title, method)	Create correlation matrix using bokeh and pandas.
density_plot(config, df, title)	Create density plot (draws kernel density estimate), using seaborn and pandas.
html([destination, df, raw_data, name, …])	Create HTML output.
logit_plot(config, df, path, param)	Create logistic regression plot using seaborn and pandas
onset_diff_plot(config, df, meta, drop, y[, …])	Plot onset differences using pandas and seaborn.
qq_plot(config, y, residuals, path)	Create probability plot using seaborn, pandas, and rpy2.
residual_plot(config, y, residuals, path)	Create probability plot using seaborn, pandas, and rpy2.
single_subject(config, df, path)	Create single subject scatterplot using seaborn and pandas.

classmethod bokeh_trial(config, df, stim_bounds, roi_bounds, flt)[source]¶

Create single subject trial bokeh plots.

Parameters:	df : pandas.DataFrame Pandas dataframe of participant sample data. stim_bounds : dict Stimulus bounds on screen. roi_bounds : dict ROI bounds on screen. flt : str Filter type.

classmethod bokeh_calibration(config, df, cxy, event, monitorSize=[1920, 1080])[source]¶

Create calibration matrix, using pandas and bokeh.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. cxy : pandas.DataFrame Pandas dataframe of calibration points. event : string calibration, or validation. monitorSize : list Monitor size, in pixels.

classmethod onset_diff_plot(config, df, meta, drop, y, clip=None)[source]¶

Plot onset differences using pandas and seaborn.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. meta : pandas.DataFrame Metadata for each chart. drop : pandas.DataFrame Participants to be dropped. y : str Variable of interest. clip : int Clip value for single subject plot.
Returns:	odp Bokeh or seaborn plot.

classmethod density_plot(config, df, title)[source]¶

Create density plot (draws kernel density estimate), using seaborn and pandas.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. title : str Chart title.
Returns:	cm Bokeh or seaborn plot.

classmethod corr_matrix(config, df, path, title, method, footnote=None)[source]¶

Create correlation matrix using bokeh and pandas.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. path : str The directory path to save the bokeh or plot. method : str Spearman or Pearsons correlation coefficient. title : str Chart title. footnote : str Chart footnote.
Returns:	cm Bokeh plot.

classmethod single_subject(config, df, path)[source]¶

Create single subject scatterplot using seaborn and pandas.

Parameters:	df : pandas.DataFrame Pandas dataframe of raw data. path : str The directory path to save the bokeh or seaborn plot.
Returns:	cm Bokeh or seaborn plot.

classmethod boxplot(config, df, path=None, x=None, y=None, title=None, plots=None, cat='analysis')[source]¶

Creates boxplot using seaborn and pandas.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. path : str Path to save data. drift : str X-axis. drift : str Y-axis. title : str Plot title. plots : dict Dictionary of plots metadata. cat : str Type of plot.

classmethod cooks_plot(config, y, model, path, df)[source]¶

Create cooks distance plot plot using seaborn, pandas, and statsmodel.

Parameters:	config : dict Configuration data. df : pandas.DataFrame Pandas dataframe of raw data. path : str The directory path save the seaborn plot. y : str The predictor variable. model : dict statsmodel model.
Returns:	res seaborn plot.

classmethod residual_plot(config, y, residuals, path)[source]¶

Create probability plot using seaborn, pandas, and rpy2.

Parameters:	config : dict Configuration data. y : str Predictor variable. residuals : pandas.DataFrame Pandas dataframe of residuals vs fitted, qq data, and raw data. path : str The directory path save the seaborn plot.
Returns:	lmp seaborn plot.

classmethod qq_plot(config, y, residuals, path)[source]¶

Create probability plot using seaborn, pandas, and rpy2.

Parameters:	config : dict Configuration data. y : str Predictor variable. residuals : pandas.DataFrame Pandas dataframe of residuals vs fitted, qq data, and raw data. path : str The directory path save the seaborn plot.
Returns:	lmp seaborn plot.

classmethod logit_plot(config, df, path, param)[source]¶

Create logistic regression plot using seaborn and pandas

Parameters:	df : pandas.DataFrame Pandas dataframe of raw data. path : str The directory path save the seaborn plot. param : dict x, y, groupby parameters.
Returns:	lmp seaborn plot.

classmethod html(destination=None, df=None, raw_data=None, name=None, path=None, plots=None, source=None, title=None, intro=None, footnote=None, script='', **kwargs)[source]¶

Create HTML output.

Parameters:

destination : str Path to save file to. df : pandas.DataFrame Pandas dataframe of analysis results data. raw_data : pandas.DataFrame Pandas dataframe of raw data. name : str (py::if source is logit) The name of csv file created. path : str The directory path of the html file. plots : dict If generating seaborn images, the list of plots used. source : str The type of data being recieved. trial : str (If Bokeh) Trial Number. session : str (If Bokeh) Session Number. bokeh_type : str (If Bokeh) Control directory location. If trial, create trial plots. title : str The title of the table or figure. intro : str The introduction of the group of figures or tables. footnote : str The footnote of the table or figure. metadata : dict Additional data to be included. **kwargs : str, int, or None, optional Additional properties, relevent for specific content types. Here’s a list of available properties: class: kwargs widths: 25 50 header-rows: 1 Property Description short, long : str Short (aoi) and long form (Area of Interest) label of html page. This is primarily used for constructing metadata tags in html. display : str (For bokeh) The type of calibration/validation display. trial : str (For bokeh) The trial number for the eyetracking task. session : int (For bokeh) The session number for the eyetracking task. day : str (For bokeh) The day the eyetracking task was run.

Returns:

html : str String of html code.