imhr.Webgazer.plot¶

Functions

imhr.Webgazer.plot.bokeh_trial(config, df, stim_bounds, roi_bounds, flt)[source]¶

Create single subject trial bokeh plots.

Parameters:	df : pandas.core.frame.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.

imhr.Webgazer.plot.bokeh_calibration(config, df, cxy, event, monitorSize=[1920, 1080])[source]¶

Create calibration matrix, using pandas and bokeh.

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

imhr.Webgazer.plot.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.core.frame.DataFrame Pandas dataframe of raw data. meta : pandas.core.frame.DataFrame Metadata for each chart. drop : pandas.core.frame.DataFrame Participants to be dropped. y : str Variable of interest. clip : int Clip value for single subject plot.
Returns:	odp Bokeh or seaborn plot.

imhr.Webgazer.plot.density_plot(config, df, title)[source]¶

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

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

imhr.Webgazer.plot.corr_matrix(config, df, path, title, method, footnote=None)[source]¶

Create correlation matrix using bokeh and pandas

Parameters:	config : dict Configuration data. df : pandas.core.frame.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.

imhr.Webgazer.plot.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.core.frame.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.

imhr.Webgazer.plot.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.core.frame.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.

imhr.Webgazer.plot.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.core.frame.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.

imhr.Webgazer.plot.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.core.frame.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.

imhr.Webgazer.plot.logit_plot(config, df, path, param)[source]¶

Create logistic regression plot using seaborn and pandas

Parameters:	df : pandas.core.frame.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.

imhr.Webgazer.plot.html(config, 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:

df : pandas.core.frame.DataFrame Pandas dataframe of analysis results data. raw_data : pandas.core.frame.DataFrame Pandas dataframe of raw data. name : str The name of csv file created (source=logit only). 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 or None, optional Additional properties, relevent for specific content types. Here’s a list of available properties: 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.