Loading of Stringer spontaneous data¶

includes some visualizations

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# @title Install dependencies
!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 umap import UMAP
from scipy.ndimage import uniform_filter1d
from scipy.stats import zscore
from sklearn.decomposition import PCA

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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 retrieval
import os, requests

fname = "stringer_spontaneous.npy"
url = "https://osf.io/dpqaj/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 Data loading

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

dict_keys(['sresp', 'run', 'beh_svd_time', 'beh_svd_mask', 'stat', 'pupilArea', 'pupilCOM', 'xyz'])

dat has fields:

dat['sresp']: neurons by timebins, a.k.a. the neural response data (11983 by 7018). Timebin = 1.2 sec.
dat['run']: timebins by 1, a.k.a. the running speed of the animal in a.u.
dat['xyz']: three-dimensional position of each neuron in the brain.
dat['pupilArea']: timebins by 1, see FaceMap for details on the pupil tracker
dat['pupilCOM']: timebins by 2, correspond to X and Y position. See FaceMap for details on the pupil tracker.
dat['beh_svd_time']: timebins by 1,000. Timecourses of behavioral SVDs extracted from face movies. See FaceMap for details.
dat['beh_svd_mask']: 240 by 320 by 1,000. Spatial masks of behavioral SVDs. Roughly normalized to each other.
dat['beh_svd_mask'] @ dat['beh_svd_time'][T, :]: reconstruction of the face movie (its motion energy) for frame T
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']))

(11983, 7018)
11983

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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 properties of behavioral data using plot and scatter
ax = plt.subplot(1, 5, 1)
plt.plot(dat['pupilArea'][:500, 0])
ax.set(xlabel='timepoints', ylabel='pupil area')

ax = plt.subplot(1, 5, 2)
plt.plot(dat['pupilCOM'][:500, :])
ax.set(xlabel='timepoints', ylabel='pupil XY position')

ax = plt.subplot(1, 5, 3)
plt.plot(dat['beh_svd_time'][:500, 0])
ax.set(xlabel='timepoints', ylabel='face SVD #0')

ax = plt.subplot(1, 5, 4)
plt.plot(dat['beh_svd_time'][:500, 1])
ax.set(xlabel='timepoints', ylabel='face SVD #1')

ax = plt.subplot(1, 5, 5)
plt.scatter(dat['beh_svd_time'][:, 0], dat['beh_svd_time'][:, 1], s=1)
ax.set(xlabel='face SVD #0', ylabel='face SVD #1')

plt.show()