Mouse Spatial Tuning¶

This notebook plots the spatial tuning curves from Zhao et al. It corresponds to Figure 8 from the paper.

Note: The easiest way to use this is as a colab notebook, which allows you to dive in with no setup.

Load Dependencies - Colab Only¶

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from os.path import exists
if not exists('opponency.zip'):
    !wget -O opponency.zip https://github.com/ecs-vlc/opponency/archive/master.zip
    !unzip -qq opponency.zip
    !mv opponency-master/* ./
    !rm -r opponency-master

Generate Plots¶

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import pandas as pd
import numpy as np

drifting = pd.read_csv('zhao_drifting.csv', names=['response', 'angle'], header=None)
drifting['angle'] = (drifting['angle'] * np.pi) / 180
drifting = drifting.sort_values(by='angle')
flashing = pd.read_csv('zhao_flashing.csv', names=['response', 'angle'], header=None)
flashing['angle'] = (flashing['angle'] * np.pi) / 180
flashing = flashing.sort_values(by='angle')

%matplotlib inline
import matplotlib.pyplot as plt

plt.polar(drifting['angle'], drifting['response'])
plt.gca().set_yticklabels([])
plt.gca().spines['polar'].set_visible(False)
plt.savefig("figures/zhao_drifting.pdf", bbox_inches='tight')

plt.figure()
plt.polar(flashing['angle'], flashing['response'])
plt.gca().set_yticklabels([])
plt.gca().spines['polar'].set_visible(False)
plt.savefig("figures/zhao_flashing.pdf", bbox_inches='tight')

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