Plotting¶
This module contains functions that help visualize the scenewalk model.
The sw_plot module is used for making dynamical video files.
The nb_plot module are visualization functions that are useful for taking a quick look at data or settings before running estimations.
Example: Dynamic Visualization¶
The scenewalk model evolves continuously over time and therefore can be visualized as a video. Since we are visualizing all the in between steps this code is separate from the core model code however and needs to be changed separately in some places when new mechanisms are added.
Dynamic plotting Notebook
Full Plotting Doc¶
Visualizing Scenewalk
Lisa Schwetlick 2019
University of Potsdam
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scenewalk.plotting.sw_plot.plot_3_maps(ufinal_map=None, inhib_map=None, att_map=None)¶ Makes a nice plot of the three output maps (attention, inhibition, and final)
- Parameters
- ufinal_maparray
final SceneWalk map
- inhib_maparray
SceneWalk inhibition map
- att_maparray
SceneWalk attention map
Notes
For example see demo/detailed_look_at_sw.ipynb
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scenewalk.plotting.sw_plot.plot_corrsac_dynamic_shifts_image(sw, fix_density_map, x_path, y_path, dur_path, filename, image, speed=100)¶ Make dynamic video plot of model evolution given a scanpath when the corrective saccade modification
- Parameters
- swscenewalk model object
scenewalk model object
- fix_density_maparray
empirical density
- x_path, y_path, dur_patharrays
list of datapoints, one for each fixation for x and y coordinates and duration
- filenamestr
where to save. must end in “.mp4”
- imagearray
base picture
- speedint
frames per second (?)
- Saves Result
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scenewalk.plotting.sw_plot.plot_dynamic_shifts(sw, fix_density_map, x_path, y_path, dur_path, filename)¶ Make dynamic video plot of model evolution given a scanpath when the presaccadic attention shift is switched on.
- Parameters
- swscenewalk model object
scenewalk model object
- fix_density_maparray
empirical density
- x_path, y_path, dur_patharrays
list of datapoints, one for each fixation for x and y coordinates and duration
- filenamestr
where to save. must end in “.mp4”
- Saves Result
-
scenewalk.plotting.sw_plot.plot_dynamic_shifts_image(sw, fix_density_map, x_path, y_path, dur_path, filename, image, speed=100)¶ Make dinamic video plot of model evolution given a scanpath when the presaccadic attention shift is switched on.
- Parameters
- swscenewalk model object
scenewalk model object
- fix_density_maparray
empirical density
- x_path, y_path, dur_patharrays
list of datapoints, one for each fixation for x and y coordinates and duration
- filenamestr
where to save. must end in “.mp4”
- imagearray
base picture
- speedint
frames per second (?)
- Saves Result
Some useful visualization functions for Notebooks
-
scenewalk.plotting.nb_plots.plot_path_on_map(dens, x_path, y_path, sw)¶ plots a scanpath on top of the underlying image density
- Parameters
- densarray
empirical densities
- x_path, y_patharrays
fixation locations
- swscenewalk model object
scenewalk model object which includes the data range
- Returns
- fig, ax
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scenewalk.plotting.nb_plots.priors_plot(priors, xlimsd=1, show_bounds=False)¶ Makes a simple plot of the priors in the dictionary
- Parameters
- Priorsdict
dictionary of priors where the key is the name and the value is an an instance of scipy.stats.truncated normal or some other distribution
- xlimsdfloat
determines how large the xlims are. mean +_ sd*xlimsd
- show_boundsbool
if true, makes xlims contingent on bounds, not on mean
- Returns
- fig, ax