pymaid.sparseness¶
- pymaid.sparseness(x, which='LTS')[source]¶
Calculate sparseness.
Sparseness comes in two flavors:
Lifetime kurtosis (LTK) quantifies the widths of tuning curves (according to Muench & Galizia, 2016):
where is the number of observations, the value of observation , and and the mean and the standard deviation of the observations’ values, respectively. LTK is assuming a normal, or at least symmetric distribution.
Lifetime sparseness (LTS) quantifies selectivity (Bhandawat et al., 2007):
where is the number of observations, and is the value of an observation.
Notes
NaN
values will be ignored. You can use that to e.g. ignore zero values in a large connectivity matrix by changing these values toNaN
before passing it topymaid.sparseness
.- Parameters
x (DataFrame | array-like) – (N, M) dataset with N (rows) observations for M (columns) neurons. One-dimensional data will be converted to two dimensions (N rows, 1 column).
which ("LTS" | "LTK") – Determines whether lifetime sparseness (LTS) or lifetime kurtosis (LTK) is returned.
- Returns
pandas.Series
if input was pandas DataFrame, elsenumpy.array
.- Return type
sparseness
Examples
Calculate sparseness of olfactory inputs to group of neurons:
>>> import numpy as np >>> import matplotlib.pyplot as plt >>> # Generate adjacency matrix >>> adj = pymaid.adjacency_matrix(s='annotation:WTPN2017_excitatory_uPN_right', ... t='annotation:ASB LHN') >>> # Calculate lifetime sparseness >>> S = pymaid.sparseness(adj, which='LTS') >>> # Plot distribution >>> ax = S.plot.hist(bins=np.arange(0, 1, .1)) >>> ax.set_xlabel('LTS') >>> plt.show()