Module gonioanalysis.drosom.plotting.basics

Most commonly needed functions to plot the data.

Functions

def compare_3d_vectormaps(manalyser1, manalyser2, axes=None, illustrate=True, total_error=True, compact=False, animation=None, animation_type=None, animation_variable=None, optimal_ranges=None, pulsation_length=1, biphasic=False, kwargs1={}, kwargs2={}, kwargsD={}, **kwargs)

Calls get 3d vectors for both analysers. Arrow rotation option only affects manalyser2

Note! If total_error is true, sets attributes pupil_compare_errors and pupil_compare_rotations to the last ax in axes

manalyser1,manalyser2 : objects Analyser objects axes : list of objects List of Matplotlib Axes objects. Expected base length == 3 which is modified by options orientation, total_error and compact.

illustrate : bool Wheter to plot the illustrative plot. Increases the axes requirement by 1 total_error: bool Whether to plot the total error plot Increases the axes requirement by 1 compact : bool Join vectorplots in one. Decreases axes requirement by 1

animation : list For total error x-axis limits kwargs1,kwargs2 : dict List of keyword arguments to pass to plot_3d_vectormap() kwargsD : dict List of keywords arguments to pass to plot_3d_differencemap()

Returns [axes]

def compare_3d_vectormaps_compact(*args, **kwargs)

Wrapper for compare_3d_vectormaps but compact=True, total_error=False, illustrate=False

def compare_3d_vectormaps_manyviews(*args, axes=None, column_titles=['Microsaccades', 'Rhabdomere orientation', 'Difference', 'Mean microsaccade'], row_titles=['Dorsal\nview', 'Anterior\nview', 'Ventral\nview'], **kwargs)

Just with different views rendered

First axes gets attributes .orientation_ax and .error_ax

def plot_1d_magnitude(manalyser, image_folder=None, i_repeat=None, mean_repeats=False, mean_imagefolders=False, mean_eyes=False, color_eyes=False, gray_repeats=False, progressive_colors=False, show_mean=False, show_std=False, show_label=True, milliseconds=False, microns=False, phase=False, derivative=False, label='EYE-ANGLE-IREPEAT', ax=None)

Plots 1D displacement magnitude over time, separately for each eye.

Arguments

manalyser : object

MAnalyser object instance

image_folder : string or None

Image folder to plot the data from. If None (default), plot all image folders.

mean_repeats : bool

Wheter to take mean of the repeats or plot each repeat separately

mean_imagefolders : bool

If True and image_folder is None (plotting all image folders), takes the mean of all image folders.

mean_eyes : bool

Wheter to take a mean over the left and right eyes

label : string

Label to show. If None, no label. Otherwise EYE gets replaced with eye ANGLE gets replaced by image folder name IREPEAT gets reaplaced by the number of repeat

Returns

ax Matplotlib axes traces What has been plotted N_repeats The total number of repeats (independent of i_repeat)

def plot_2d_vectormap(manalyser, ax=None)

Plots a 2-dimensional vector map.

Arguments

manalyser : object

Instance of MAnalyser class or MAverager class (having get2DVectors method)

ax : object

Matplotlib axes object

def plot_3d_differencemap(manalyser1, manalyser2, ax=None, stats_map=False, elev=10, azim=70, colinear=True, direction=False, colorbar=True, colorbar_text=True, colorbar_ax=None, reverse_errors=False, colorbar_text_positions=[[1.1, 0.95, 'left', 'top'], [1.1, 0.5, 'left', 'center'], [1.1, 0.05, 'left', 'bottom']], i_frame=0, arrow_rotations=[0], pitch_rot=None, yaw_rot=None, roll_rot=None, hide_axes=False, hide_text=False, guidance=False, **kwargs)

Plots 3d heatmap presenting the diffrerence in the vector orientations for two analyser objects, by putting the get_3d_vectors of both analysers to field_error and making a surface plot.

Notes

• Errors (differences) are calculated at manalyser1's points.
• arrow_rotations, pitch_rot only affects manalyser2

manalyser1, manalyser2 : object Analyser objects to plot the difference with 3d vectors ax : object or None Matplotlib Axes object stats_map : bool If true, plot p-vals. colorbar : bool Whether to add the colors explaining colorbar colorbar_text: bool Wheter to add the text annotations on the colorbar colorbar_ax : object Optional Axes where to put the colorbar arrow_rotations : list Arrow rotations, for the second manalyser i_frame : int Neglected here hide_axes : bool hide_text : bool guidance : bool

def plot_3d_vectormap(manalyser, arrow_rotations=[0], rhabdomeres=False, repeats_separately=False, vertical_hardborder=False, elev=None, azim=None, pitch_rot=None, roll_rot=None, yaw_rot=None, animation=None, animation_type=None, animation_variable=None, i_frame=0, ax=None, *, points, vectors, color='black', mutation_scale=6, scale_length=1, i_pulsframe=None, guidance=False, camerapos=None, draw_sphere=True, vrot_lines=False, hide_axes=False, hide_text=False, **kwargs)

Plot a 3D vectormap, where the arrows point movement (MAnalyser, FAnalyser) or feature (OAnalyser) directions.

Arguments

manalyser : object

Analyser object

arrow_rotations : list of int

Rotation of arrows in the plane of the arrows (ie. radially).

rhabdomeres : bool

If True, draw rhabdomere pattern where the arrows are.

repeats_separately : bool

If True, and repeat data exists, draw each repeat with it's own arrow.

vertical_hardborder : bool

For MAverager, interpolate dorsal and ventral separetly

elev, azim : float or None

Plot elevation and azim

animation : None

 

animation_type : None

 

animation_variable : None

 

i_frame : int

 

ax : object

Maptlotlib ax object. If not specified, creates a new figure.

kwargs : dict

Variable keyword arguments are passed to vector_plot function.

Returns

ax : object

Matplotlib Axes object

vectors : list of objects

List of arrow artist drawn on the figure.

def plot_magnitude_probability(manalysers, wanted_imagefolders=None, ax=None, mean_repeats=False, mean_imagefolders=False, microns=True, milliseconds=True, xpoints=100)

With many analysers and image folders, calculate probablity of the magnitude response (2D histogram).

Arguments

manalysers : list of objs

Analyser objects

wanted_imagefolders : None or dict

Keys are analyser names, items lists of image folder names to incldue. If None, use all image folders.

def plot_xy_trajectory(manalysers, wanted_imagefolders=None, i_repeat=None, mean_repeats=True, ax=None)

A (x,y) 2D plot of the movement trajectory where time is encoded in color.

manalysers : list wanted_imagefolders : dict {specimen_name: [image_folder1, …]} i_repeat : int mean_repeats : bool