Loading of Stringer orientations data¶

includes some visualizations

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# @title Install depedencies
!pip install umap-learn --quiet

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  Building wheel for umap-learn (setup.py) ... done
  Building wheel for pynndescent (setup.py) ... done

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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import zscore
from sklearn.decomposition import PCA
from umap import UMAP

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#@title Data retrieval
import os, requests

fname = "stringer_orientations.npy"
url = "https://osf.io/ny4ut/download"

if not os.path.isfile(fname):
  try:
    r = requests.get(url)
  except requests.ConnectionError:
    print("!!! Failed to download data !!!")
  else:
    if r.status_code != requests.codes.ok:
      print("!!! Failed to download data !!!")
    else:
      with open(fname, "wb") as fid:
        fid.write(r.content)

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# @title Figure settings
from matplotlib import rcParams

rcParams['figure.figsize'] = [20, 4]
rcParams['font.size'] = 15
rcParams['axes.spines.top'] = False
rcParams['axes.spines.right'] = False
rcParams['figure.autolayout'] = True

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# @title Data loading

dat = np.load('stringer_orientations.npy', allow_pickle=True).item()
print(dat.keys())

dict_keys(['sresp', 'istim', 'stat', 'u_spont', 'v_spont', 'mean_spont', 'std_spont', 'stimtimes', 'frametimes', 'camtimes', 'run', 'info', 'xyz'])

dat has fields:

dat['sresp']: neurons by stimuli, a.k.a. the neural response data (23589 by 4598)
dat['xyz']: three-dimensional position of each neuron in the brain.
dat['run']: 1 by stimuli, a.k.a. the running speed of the animal in a.u.
dat['istim']: 1 by stimuli, goes from 0 to 2*np.pi, the orientations shown on each trial
dat['u_spont']: neurons by 128, the weights for the top 128 principal components of spontaneous activity. Unit norm.
dat['v_spont']: 128 by 910, the timecourses for the top 128 PCs of spont activity.
dat['u_spont'] @ dat['v_spont']: a reconstruction of the spontaneous activity for 910 timepoints interspersed throughout the recording.
dat['stat']: 1 by neurons, some statistics for each neuron, see Suite2p for full documentation.

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print(dat['sresp'].shape)
print(len(dat['stat']))

(23589, 4598)
23589

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# @title plot the 3D positions of all neurons
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

x, y, z = dat['xyz']

zunq, iunq = np.unique(z, return_inverse=True)
xc = np.linspace(0.0, 1.0, len(zunq))
cmap = cm.get_cmap('jet')(xc)

fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x[::-1], y[::-1], z[::-1], 'o', s=4, c=cmap[iunq])
ax.set(xlabel='horizontal(um)', ylabel='vertical(um)', zlabel='depth (um)')
plt.show()

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# @title Basic data properties using plot, hist and scatter
ax = plt.subplot(1, 5, 1)
plt.hist(dat['istim'])
ax.set(xlabel='orientations', ylabel='# trials')

ax = plt.subplot(1, 5, 2)
plt.scatter(dat['istim'], dat['sresp'][1000], s=1)
ax.set(xlabel='orientation', ylabel='neural response')

ax = plt.subplot(1, 5, 3)
plt.plot(dat['run'][:1000])
ax.set(xlabel='timepoints', ylabel='running')

ax = plt.subplot(1, 5, 4)
plt.scatter(dat['run'], dat['sresp'][20998], s=1)
ax.set(xlabel='running', ylabel='neural response')

plt.show()

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# @title take PCA after preparing data by z-score

Z = zscore(dat['sresp'], axis=1)
X = PCA(n_components=200).fit_transform(Z.T)

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# @title plot PCs as function of stimulus orientation
for j in range(5):
  ax = plt.subplot(1, 5, j + 1)
  plt.scatter(dat['istim'], X[:, j], s=1)
  ax.set(xlabel='orientation', ylabel='PC%d'%j)
plt.show()