spkit.mea.compute_cv

spkit.mea.compute_cv(Ax, Ax_bad, eD=700, esp=1e-10, cv_pad=nan, cv_thr=100, arr_agg='mean', plots=1, verbose=True, flip=False, silent_mode=False, **kwargs)

Compute Conduction Velocity

Compute Conduction Velocity

Given Activation Time Matrix as MEA -Grid form, computing Conduction Velocity

Parameters:
Ax: MEA Grid 8x8

  • Interpolated Activation time matrix as MEA grid form.

  • Ax should not have any NaN values. Use ‘fill_nans_2d’ to fill NaN values or replace Bad channel values

Ax_bad: MEA Grid 8x8

  • Matrix of 1 and np.nan values indicating bad channels.

  • np.nan value corresponds to channel location indicate bad channel

eD: scaler, (default=700 mm)

  • Inter-Node Distance

  • distance between two nodes in horizontal and vertical axis on MEA electrode-plate distance is given in mm

esp: scalar, default =1e-10

  • epsilon

  • to avoid diving by zero, esp is used

cv_thr: scalar, default=100 cm/s

  • threshold on conduction velocity to exclude

  • any electrodes shows cv>=cv_thr is replaced by ‘cv_pad’ (np.nan)

cv_pad: scalar default=np.nan

  • replacement value

  • any cv value above cv_thr is replaced by cv_pad, to avoid including in computation

arr_agg: str, {‘mean’,’median’} default=’mean’

  • method to aggregate the directional arrows, mean or median

plots: int, default=1

  • if 1, plot two figures for CV maps, two figures for directional compass

  • if 2, also plot activation matrix and conduction velocity matrix with more details

verbose: boot, default=True

  • verbosity

  • print information

flip=False:

  • KEEP IT FALSE, DEV. MODE

Returns:
CV_df: pandas Dataframe,

  • including ‘CV_mean’,’CV_median’,’CV_sd’,’inv_speed’,’avg_angle’ computations

  • for ‘Interpolated values’ and ‘Original Values’. Original values exclude the Bad channels

Ax_theta: MEA Grid 8x8

  • Angle of direction for cv at each elctrodes, as a Featire Matix Form

CV0: MEA Grid 8x8

  • Conducntion Velocity matrix, that includes inpterpolated values

CVMEA Grid 8x8

  • Conducntion Velocity matrix, excluding Bad Channels values

Examples

#sp.mea.compute_cv
import numpy as np
import matplotlib.pyplot as plt
import spkit as sp
Ax = sp.create_signal_2d(n=8,sg_winlen=5, sg_polyorder=1,seed=2)*30
Ax_bad = np.ones([8,8])
Ax_bad[(Ax>25)] = np.nan
Ax_bad[np.isnan(Ax)] = np.nan

CV_df, Ax_theta, CV0, CV = sp.mea.compute_cv(Ax,Ax_bad,eD=700,esp=1e-10,cv_pad=np.nan,cv_thr=100,arr_agg='mean',
        plots=1,verbose=True,flip=False,silent_mode=False)

_ = sp.direction_flow_map(X_theta=Ax_theta,X=Ax,arr_pivot='mid',heatmap_prop =dict(vmin=0,vmax=20,cmap='jet'),)
../../_images/spkit-mea-compute_cv-1_00.png
../../_images/spkit-mea-compute_cv-1_01.png
../../_images/spkit-mea-compute_cv-1_02.png

Examples using spkit.mea.compute_cv

MEA: Step-wise Analysis: Example

MEA: Step-wise Analysis: Example