Building a Dataset Using XArray
Building a Dataset Using XArray
Authors
Dr. Nicholas Del Grosso
|
Dr. Sangeetha Nandakumar
|
Dr. Ole Bialas
|
Dr. Atle E. Rimehaug
In the previous notebook, you learned how to make DataArrays with Xarray. But Xarray has another level of organization, referred to as a Dataset, which is a collection of DataArrays.
- The
DataArray: A multidimensional array with labels describing each of its dimensions. - The
Dataset: A collection ofDataArraysand their relationships.
We’ll be working with a good amount of data organized in Xarray, mainly to load data, select the variables we’re interested in, and convert the data into the structure we want, so let’s take a little time to familiarize ourselves with some of these features
Setup
Import Libraries
%matplotlib inline
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[3], line 1 ----> 1 get_ipython().run_line_magic('matplotlib', 'inline') 3 import xarray as xr 4 import numpy as np File ~/.local/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2511, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth) 2509 kwargs['local_ns'] = self.get_local_scope(stack_depth) 2510 with self.builtin_trap: -> 2511 result = fn(*args, **kwargs) 2513 # The code below prevents the output from being displayed 2514 # when using magics with decorator @output_can_be_silenced 2515 # when the last Python token in the expression is a ';'. 2516 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False): File ~/.local/lib/python3.12/site-packages/IPython/core/magics/pylab.py:103, in PylabMagics.matplotlib(self, line) 98 print( 99 "Available matplotlib backends: %s" 100 % _list_matplotlib_backends_and_gui_loops() 101 ) 102 else: --> 103 gui, backend = self.shell.enable_matplotlib(args.gui) 104 self._show_matplotlib_backend(args.gui, backend) File ~/.local/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3789, in InteractiveShell.enable_matplotlib(self, gui) 3786 import matplotlib_inline.backend_inline 3788 from IPython.core import pylabtools as pt -> 3789 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select) 3791 if gui != None: 3792 # If we have our first gui selection, store it 3793 if self.pylab_gui_select is None: File ~/.local/lib/python3.12/site-packages/IPython/core/pylabtools.py:338, in find_gui_and_backend(gui, gui_select) 321 def find_gui_and_backend(gui=None, gui_select=None): 322 """Given a gui string return the gui and mpl backend. 323 324 Parameters (...) 335 'WXAgg','Qt4Agg','module://matplotlib_inline.backend_inline','agg'). 336 """ --> 338 import matplotlib 340 if _matplotlib_manages_backends(): 341 backend_registry = matplotlib.backends.registry.backend_registry ModuleNotFoundError: No module named 'matplotlib'
Download Data
import owncloud
from pathlib import Path
Path('data').mkdir(exist_ok=True, parents=True)
owncloud.Client.from_public_link('https://uni-bonn.sciebo.de/s/c4T8MndMZTtTtme', folder_pasword = 'ibots').get_file('/', f'data/steinmetz_2017-12-05_Lederberg.nc')TrueSection 1: Working with XArray Datasets
Datasets describe the relationships between multiple data variables, often connected by their coordinates. For example, there could be different behavioral measurements taken at the same time points, or different data might be related to the same subject. xr.Dataset objects help in this way to describe a more-complete picture of an experiment.
Let’s explore Datasets by looking at the data from a session of the Steinmetz et al, 2019 paper. In this notebook, we’ll be exploring the spike counts data recorded during a specific session and investigate differences between brain regions and their relation to behavioral, task, or performance variables. This data, stored in the netCDF format (the .nc extension), can be loaded as an XArray Dataset.
| Code | Description |
|---|---|
xr.load_dataset("file.nc") |
Load a netCDF file as an XArray Dataset. |
dset['variable_name'] |
Access a specific variable (DataArray) from the dataset. |
dset[['var1', 'var2']] |
Access multiple variables from the dataset simultaneously. |
dset['variable'].loc[start:end] |
Select data using location-based indexing for specific range. |
sum(boolean_array).item() |
Count True values in boolean array and extract as scalar. |
len(array) |
Get the length of an array. |
Run the following code cell to load the dataset into Python using XArray. Explore the dataset. Note that different variables are related to different sets of coordinates:
Exercises
dset = xr.load_dataset(f"data/steinmetz_2017-12-05_Lederberg.nc")
dset<xarray.Dataset> Size: 125MB
Dimensions: (trial: 340, time: 250, cell: 698,
waveform_component: 3, sample: 82, probe: 384,
brain_area_lfp: 12, spike_id: 3185888)
Coordinates:
* trial (trial) int32 1kB 1 2 3 4 5 6 ... 336 337 338 339 340
* time (time) float64 2kB 0.01 0.02 0.03 0.04 ... 2.48 2.49 2.5
* cell (cell) int32 3kB 1 2 3 4 5 6 ... 693 694 695 696 697 698
* waveform_component (waveform_component) int32 12B 1 2 3
* probe (probe) int32 2kB 1 2 3 4 5 6 ... 380 381 382 383 384
* brain_area_lfp (brain_area_lfp) <U5 240B 'DG' 'LGd' ... 'MD' 'VISam'
* spike_id (spike_id) int32 13MB 1 2 3 ... 3185886 3185887 3185888
Dimensions without coordinates: sample
Data variables: (12/31)
contrast_left (trial) int8 340B 0 0 0 100 25 50 0 0 ... 0 0 0 0 0 0 0
contrast_right (trial) int8 340B 0 0 0 50 50 50 ... 100 100 100 100 100
gocue (trial) float64 3kB 0.4884 0.5617 ... 0.7894 0.7892
stim_onset (trial) float64 3kB 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5
feedback_type (trial) float64 3kB 1.0 -1.0 1.0 1.0 ... -1.0 -1.0 -1.0
feedback_time (trial) float64 3kB 2.028 0.9821 2.175 ... 2.331 2.316
... ...
waveform_w (cell, sample, waveform_component) float32 687kB 0.0 ...
waveform_u (cell, waveform_component, probe) float32 3MB 0.0 ......
lfp (brain_area_lfp, trial, time) float64 8MB -57.29 ... ...
spike_time (spike_id) float32 13MB 0.01967 0.09227 ... 2.355 2.475
spike_cell (spike_id) uint32 13MB 1 1 1 1 1 ... 698 698 698 698 698
spike_trial (spike_id) uint32 13MB 1 1 1 1 1 ... 450 450 450 450 450xarray.Dataset
- trial: 340
- time: 250
- cell: 698
- waveform_component: 3
- sample: 82
- probe: 384
- brain_area_lfp: 12
- spike_id: 3185888
- trial(trial)int321 2 3 4 5 6 ... 336 337 338 339 340
array([ 1, 2, 3, ..., 338, 339, 340], dtype=int32)
- time(time)float640.01 0.02 0.03 ... 2.48 2.49 2.5
array([0.01, 0.02, 0.03, ..., 2.48, 2.49, 2.5 ])
- cell(cell)int321 2 3 4 5 6 ... 694 695 696 697 698
array([ 1, 2, 3, ..., 696, 697, 698], dtype=int32)
- waveform_component(waveform_component)int321 2 3
array([1, 2, 3], dtype=int32)
- probe(probe)int321 2 3 4 5 6 ... 380 381 382 383 384
array([ 1, 2, 3, ..., 382, 383, 384], dtype=int32)
- brain_area_lfp(brain_area_lfp)<U5'DG' 'LGd' 'SUB' ... 'MD' 'VISam'
array(['DG', 'LGd', 'SUB', 'VISp', 'ACA', 'MOs', 'PL', 'CA1', 'DG', 'LH', 'MD', 'VISam'], dtype='<U5') - spike_id(spike_id)int321 2 3 4 ... 3185886 3185887 3185888
array([ 1, 2, 3, ..., 3185886, 3185887, 3185888], dtype=int32)
- contrast_left(trial)int80 0 0 100 25 50 0 ... 0 0 0 0 0 0 0
array([ 0, 0, 0, 100, 25, 50, 0, 0, 100, 25, 0, 25, 50, 0, 0, 0, 25, 100, 100, 100, 50, 25, 25, 25, 0, 0, 50, 100, 0, 0, 100, 0, 0, 0, 50, 50, 0, 50, 50, 100, 0, 0, 25, 25, 0, 100, 50, 25, 50, 0, 0, 25, 100, 0, 0, 100, 50, 0, 25, 50, 0, 0, 25, 100, 0, 0, 0, 0, 0, 100, 100, 50, 0, 25, 0, 50, 25, 50, 25, 25, 25, 100, 0, 0, 25, 0, 0, 0, 50, 100, 25, 0, 0, 0, 0, 0, 0, 25, 100, 0, 0, 0, 0, 50, 50, 0, 0, 50, 100, 50, 50, 25, 0, 0, 0, 0, 0, 0, 25, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 50, 25, 25, 50, 100, 0, 0, 25, 0, 0, 100, 100, 0, 0, 100, 0, 100, 50, 25, 50, 0, 100, 50, 25, 100, 100, 100, 50, 0, 50, 25, 25, 25, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 100, 0, 0, 50, 0, 0, 0, 0, 0, 50, 25, 0, 25, 25, 0, 0, 0, 0, 50, 25, 0, 0, 100, 25, 0, 0, 0, 50, 25, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 100, 100, 50, 50, 100, 50, 0, 25, 25, 25, 100, 0, 100, 0, 0, 0, 25, 50, 25, 25, 50, 0, 50, 100, 25, 0, 100, 0, 50, 25, 100, 50, 50, 50, 0, 0, 100, 100, 50, 50, 0, 25, 0, 100, 100, 25, 0, 100, 50, 0, 0, 0, 100, 100, 50, 50, 0, 50, 100, 25, 25, 50, 100, 50, 100, 25, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 100, 0, 0, 0, 25, 100, 100, 100, 0, 0, 0, 0, 25, 100, 0, 0, 100, 25, 100, 25, 25, 50, 100, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) - contrast_right(trial)int80 0 0 50 50 ... 100 100 100 100 100
array([ 0, 0, 0, 50, 50, 50, 50, 100, 25, 100, 50, 100, 0, 0, 0, 0, 0, 50, 0, 0, 100, 0, 25, 100, 25, 0, 25, 25, 0, 0, 50, 0, 0, 0, 0, 25, 100, 0, 50, 50, 50, 0, 0, 50, 25, 50, 0, 100, 0, 100, 100, 0, 100, 100, 100, 0, 0, 100, 100, 25, 0, 0, 50, 25, 0, 100, 100, 0, 0, 0, 25, 0, 0, 50, 0, 25, 25, 25, 100, 0, 25, 0, 0, 0, 0, 50, 0, 0, 25, 25, 100, 0, 0, 0, 0, 0, 0, 100, 0, 0, 50, 100, 100, 0, 25, 0, 0, 0, 0, 0, 25, 100, 0, 0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0, 100, 100, 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 50, 100, 0, 0, 0, 0, 50, 100, 0, 25, 25, 0, 0, 25, 0, 100, 25, 100, 0, 50, 0, 100, 100, 25, 0, 50, 100, 25, 0, 50, 100, 50, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 100, 0, 0, 50, 25, 0, 0, 0, 100, 25, 50, 25, 100, 0, 0, 0, 0, 100, 50, 0, 0, 25, 50, 50, 0, 0, 0, 100, 25, 0, 0, 25, 0, 0, 0, 0, 0, 0, 0, 0, 100, 100, 50, 50, 25, 25, 50, 100, 100, 0, 25, 0, 0, 0, 0, 0, 100, 100, 100, 0, 100, 50, 50, 0, 100, 25, 50, 0, 100, 0, 0, 0, 25, 50, 50, 50, 25, 50, 100, 0, 0, 100, 0, 50, 0, 100, 25, 0, 25, 50, 25, 100, 25, 0, 50, 25, 25, 0, 50, 100, 0, 0, 25, 25, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 100, 50, 0, 100, 100, 0, 25, 0, 100, 50, 50, 0, 0, 50, 25, 0, 0, 25, 100, 25, 0, 25, 50, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], dtype=int8) - gocue(trial)float640.4884 0.5617 ... 0.7894 0.7892
array([0.48841865, 0.56171624, 0.6277027 , 0.77353535, 0.61263709, 0.72839327, 0.77820739, 0.66233874, 0.63386641, 0.44806527, 0.72309279, 0.48826985, 0.56320355, 0.48741344, 0.40065609, 0.40867261, 0.40519758, 0.45826623, 0.61785096, 0.72780273, 0.6359332 , 0.66807363, 0.66320184, 0.5058879 , 0.76738597, 0.6770692 , 0.62228154, 0.67822337, 0.59281896, 0.77301021, 0.76107631, 0.79780037, 0.42827087, 0.6431434 , 0.71314721, 0.61172721, 0.76718867, 0.40236408, 0.45802182, 0.46126396, 0.63226761, 0.72263371, 0.6418007 , 0.74508493, 0.51705922, 0.77714175, 0.59683823, 0.56767474, 0.43744805, 0.59697573, 0.47711398, 0.4820389 , 0.7669893 , 0.48216235, 0.62250457, 0.7473069 , 0.78727484, 0.66693145, 0.67125 , 0.52177099, 0.41740057, 0.5669238 , 0.60699578, 0.62739084, 0.70705385, 0.63129674, 0.79720542, 0.57208664, 0.78635025, 0.51704805, 0.63723618, 0.49176417, 0.53180061, 0.62708314, 0.44227212, 0.55142033, 0.53169049, 0.75719256, 0.42145264, 0.54966724, 0.53634796, 0.77579902, 0.53710938, 0.69714292, 0.58019525, 0.62705861, 0.74681771, 0.74183868, 0.4270687 , 0.74197311, 0.45669958, 0.61687268, 0.59668802, 0.62129444, 0.69696077, 0.50667484, 0.56113018, 0.41629755, 0.77705928, 0.51608511, ... 0.41069624, 0.63567106, 0.66570524, 0.71051356, 0.67978922, 0.6591758 , 0.45460895, 0.52062908, 0.5801893 , 0.62012709, 0.71477602, 0.58591776, 0.65907354, 0.73929954, 0.6257914 , 0.55991738, 0.69002588, 0.60469953, 0.56554717, 0.71460434, 0.6950691 , 0.52516365, 0.61495259, 0.75344787, 0.42905262, 0.59459707, 0.56548625, 0.43189034, 0.48555692, 0.66416638, 0.49997099, 0.66033448, 0.77180753, 0.50448264, 0.48456124, 0.69431648, 0.54973633, 0.57073016, 0.59482885, 0.72096502, 0.64981071, 0.59484637, 0.46018813, 0.69214446, 0.6742718 , 0.46417024, 0.69456219, 0.52826072, 0.5947646 , 0.62995266, 0.69882236, 0.57850464, 0.76453035, 0.75957599, 0.50986484, 0.40022378, 0.47402451, 0.77469096, 0.61014063, 0.75925778, 0.60908615, 0.75835371, 0.60483325, 0.60963295, 0.66916011, 0.62968463, 0.69432738, 0.40507228, 0.59356458, 0.58959661, 0.50473541, 0.67970194, 0.51677721, 0.64535707, 0.68420267, 0.69940944, 0.488297 , 0.66372588, 0.45982753, 0.77415393, 0.77894818, 0.61375407, 0.48408199, 0.73298968, 0.50836249, 0.56847091, 0.52441248, 0.41031817, 0.45388966, 0.40969042, 0.42125827, 0.76423648, 0.68824672, 0.53006278, 0.434965 , 0.77343027, 0.68478504, 0.65960524, 0.78944758, 0.78920937]) - stim_onset(trial)float640.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5
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array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) - wheel(trial, time)int80 0 -1 0 0 -1 0 ... 1 0 -1 -2 -2 -2
array([[ 0, 0, -1, ..., 0, 0, 0], [-1, -1, -1, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], ..., [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., 0, 0, 0], [ 0, 0, 0, ..., -2, -2, -2]], dtype=int8) - licks(trial, time)int80 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
array([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], dtype=int8) - pupil_x(trial, time)float64-0.03687 -0.06081 ... -0.6049
array([[-0.03687325, -0.06080917, -0.09111937, ..., 0.18874694, 0.21005926, 0.13812765], [ 0.27035386, 0.41271519, 0.57465343, ..., -0.15011777, 0.09788839, 0.31645547], [ 0.05982958, 0.24078953, 0.30618341, ..., 0.26449726, 0.34041753, 0.46878237], ..., [-0.28792338, -0.45475543, -0.68214447, ..., -0.56089523, -0.74167388, -0.69821645], [-0.57530686, -0.64454211, -0.50760033, ..., -0.68730709, -0.49327275, -0.43123595], [-0.92819876, -1.00284926, -0.8915857 , ..., -0.8400229 , -0.71173101, -0.60486285]]) - pupil_y(trial, time)float64-0.7333 -0.6722 ... -0.8551 -0.6559
array([[-0.73330929, -0.67215452, -0.69929956, ..., -0.48343186, -0.553119 , -0.33521189], [-0.61031102, -0.61433116, -0.6945977 , ..., -0.21308693, -0.38743376, -0.45208842], [ 0.52273196, 0.4997616 , 0.3911043 , ..., 0.76265024, 0.60712037, 0.71520343], ..., [-1.54045432, -1.43085199, -1.3787897 , ..., -1.1521952 , -1.29758356, -1.27988791], [-1.00602112, -0.90223187, -0.81425065, ..., -0.40731512, -0.49983651, -0.42592426], [-0.55938251, -0.42983043, -0.5606555 , ..., -0.73331289, -0.85507582, -0.65585751]]) - pupil_area(trial, time)float640.07904 0.07208 ... 0.00741 0.01596
array([[0.0790378 , 0.07208427, 0.08898566, ..., 0.04742573, 0.03847304, 0.03930904], [0.07455263, 0.08073489, 0.07686962, ..., 0.03609075, 0.03387179, 0.04783916], [0.03029546, 0.03232989, 0.03613245, ..., 0.03221604, 0.03299808, 0.02791235], ..., [0.06048629, 0.06249252, 0.06813664, ..., 0.07211884, 0.07244322, 0.0665155 ], [0.02274222, 0.02415935, 0.02242029, ..., 0.02580303, 0.02671467, 0.02228576], [0.01973291, 0.0202247 , 0.02414676, ..., 0.01473611, 0.00740969, 0.01595974]]) - face(trial, time)float641.32 1.32 1.32 ... 1.246 2.116
array([[1.31961002, 1.31961002, 1.31961002, ..., 0.0502838 , 0.00434887, 0.00434887], [2.21411806, 2.21411806, 2.0532099 , ..., 0.53219288, 0.53219288, 0.63901198], [0.41178356, 0.41178356, 0.35008398, ..., 0.35225841, 0.35225841, 0.72517395], ..., [0.66727963, 0.66727963, 0.83009042, ..., 0.70641945, 0.70641945, 0.57106089], [0.22559759, 0.22559759, 0.2386442 , ..., 0.01331841, 0.01331841, 0.01359022], [0.2386442 , 0.2386442 , 0.17531379, ..., 1.24622285, 1.24622285, 2.11572489]]) - spike_count(cell, trial, time)int80 1 0 0 0 0 0 0 ... 0 0 0 0 1 0 0 0
array([[[0, 1, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], [[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], [[0, 0, 1, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., ... ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], [[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], [[0, 0, 0, ..., 1, 0, 0], [0, 0, 0, ..., 1, 0, 0], [1, 0, 0, ..., 0, 1, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 1, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]]], dtype=int8) - trough_to_peak(cell)int824 20 23 17 19 ... 17 20 20 20 13
array([24, 20, 23, 17, 19, 22, 18, 22, 19, 12, 17, 8, 17, 19, 17, 14, 17, 19, 12, 18, 15, 23, 19, 18, 25, 21, 22, 13, 20, 15, 19, 12, 18, 17, 14, 20, 17, 21, 24, 20, 17, 9, 8, 16, 15, 10, 17, 18, 18, 18, 25, 14, 17, 19, 18, 18, 16, 15, 17, 23, 17, 13, 14, 17, 11, 20, 15, 24, 15, 20, 17, 8, 18, 16, 14, 17, 20, 18, 27, 12, 16, 17, 19, 19, 16, 18, 16, 18, 13, 24, 23, 18, 19, 18, 19, 12, 19, 15, 18, 17, 11, 15, 16, 17, 16, 11, 20, 14, 19, 25, 20, 19, 20, 15, 5, 5, 18, 17, 20, 16, 18, 21, 17, 17, 17, 18, 16, 15, 18, 9, 14, 19, 17, 7, 18, 18, 24, 6, 8, 13, 18, 5, 5, 10, 12, 15, 19, 16, 16, 17, 17, 15, 19, 15, 6, 15, 24, 18, 10, 8, 16, 17, 19, 18, 15, 15, 22, 10, 18, 22, 16, 8, 15, 21, 12, 17, 12, 18, 9, 8, 19, 19, 11, 19, 22, 16, 19, 18, 31, 17, 16, 18, 18, 17, 24, 13, 20, 15, 13, 20, 19, 15, 17, 19, 18, 25, 17, 17, 19, 11, 21, 18, 20, 18, 17, 18, 8, 15, 15, 16, 16, 25, 17, 20, 14, 17, 22, 14, 23, 19, 23, 18, 24, 17, 7, 16, 21, 14, 9, 19, 19, 24, 14, 15, 17, 19, 14, 22, 15, 23, 17, 17, 23, 18, 18, 18, 13, 17, 18, 21, 12, 18, 16, 17, 26, 14, 21, 24, 23, 20, 21, 24, 22, 20, 22, 29, 20, 14, 23, 28, 14, 15, 17, 15, 13, 17, 23, 18, 17, 13, 14, 17, 20, 13, 17, 18, 24, 14, 15, 24, 23, 13, 19, 21, 21, 22, 19, 14, 12, 18, 22, 18, 13, 13, 16, 17, 13, 19, 18, 27, 29, 20, 13, 13, 14, 16, 13, 14, 23, 13, 13, 13, 13, 18, 13, 17, 7, 25, 18, 13, ... 28, 23, 8, 16, 25, 13, 20, 12, 21, 24, 18, 26, 13, 21, 20, 14, 23, 13, 20, 25, 26, 12, 14, 22, 24, 21, 26, 13, 20, 13, 18, 15, 13, 20, 14, 15, 15, 22, 22, 19, 22, 17, 14, 15, 15, 18, 15, 18, 23, 19, 13, 19, 23, 14, 18, 17, 17, 16, 22, 16, 20, 16, 16, 17, 29, 20, 16, 20, 22, 5, 12, 17, 17, 19, 16, 16, 14, 20, 14, 7, 42, 25, 16, 13, 18, 17, 25, 17, 21, 17, 21, 12, 15, 15, 23, 9, 16, 7, 15, 17, 12, 16, 23, 19, 17, 15, 20, 18, 10, 19, 15, 17, 21, 26, 13, 16, 17, 16, 21, 27, 16, 28, 41, 15, 9, 17, 19, 11, 21, 15, 17, 18, 20, 24, 25, 23, 20, 20, 10, 34, 17, 19, 17, 13, 14, 18, 8, 20, 16, 9, 27, 21, 20, 16, 18, 17, 14, 17, 29, 14, 16, 19, 22, 12, 8, 16, 17, 17, 13, 18, 15, 14, 20, 25, 21, 7, 16, 10, 16, 22, 13, 16, 18, 24, 25, 22, 22, 18, 7, 22, 33, 17, 8, 17, 15, 15, 20, 34, 15, 18, 14, 19, 15, 19, 17, 14, 17, 25, 22, 23, 15, 13, 28, 15, 15, 13, 20, 17, 18, 7, 18, 8, 16, 14, 10, 9, 16, 20, 16, 7, 9, 11, 17, 17, 19, 15, 19, 16, 14, 15, 11, 10, 15, 16, 6, 15, 22, 13, 16, 15, 19, 12, 15, 16, 19, 8, 14, 18, 14, 21, 10, 14, 20, 16, 17, 17, 14, 21, 16, 14, 24, 16, 9, 17, 15, 14, 18, 19, 31, 18, 28, 21, 14, 18, 20, 14, 16, 29, 22, 13, 16, 18, 19, 16, 17, 17, 32, 18, 15, 17, 15, 21, 22, 14, 15, 15, 24, 30, 13, 19, 23, 20, 17, 20, 28, 15, 27, 15, 15, 17, 20, 20, 20, 13], dtype=int8) - ccf_ap(cell)float649.07e+03 8.92e+03 ... 6.41e+03
array([9069.9, 8919.6, 9096.6, 8073.8, 8968.9, 9276.7, 8574.3, 8111.4, 8889.8, 8975.9, 8607.9, 8243. , 9156.9, 9021.4, 8555.5, 8975.9, 8975.9, 8889.8, 8555.5, 8611.8, 8205.4, 9077.8, 8547.6, 8882. , 8875. , 9265.7, 8111.4, 9051.1, 8566.4, 8600.1, 8499.1, 8524.9, 8566.4, 8581.3, 8476.4, 8979.9, 8919.6, 8581.3, 9213.2, 8055.1, 8574.3, 8908.6, 8273.6, 8536.7, 8562.5, 7687. , 8555.5, 8983.8, 8889.8, 8555.5, 8574.3, 8975.9, 8536.7, 8562.5, 8036.3, 9115.4, 8536.7, 8224.2, 9096.6, 8882. , 8593.1, 8938.4, 7668.2, 9100.5, 8160.8, 8532.7, 8656.5, 8040.2, 8656.5, 8055.1, 9096.6, 7641.6, 8547.6, 8570.3, 8123.2, 8562.5, 8600.1, 8600.1, 8077.8, 7687. , 8574.3, 8555.5, 8506.1, 8547.6, 8593.1, 8607.9, 8536.7, 8055.1, 8975.9, 9107.5, 8581.3, 8547.6, 8547.6, 9149. , 8562.5, 8852.2, 8292.4, 8536.7, 8555.5, 8430.9, 8987.7, 8562.5, 8555.5, 8506.1, 8010.4, 8637.7, 9100.5, 8167.8, 8524.9, 9107.5, 8957.2, 8547.6, 8600.1, 8506.1, 8637.7, 8611.8, 8547.6, 8536.7, 9107.5, 8562.5, 9013.5, 9265.7, 9111.4, 8908.6, 8574.3, 8574.3, 8213.3, 8194.5, 9111.4, 9077.8, 7829.5, 8547.6, 8562.5, 9013.5, 8055.1, 9115.4, 9276.7, 8938.4, 7626.7, 7916.5, 8585.2, 8957.2, 8630.6, 8081.7, 8524.9, 8630.6, 8611.8, 8600.1, 8618.9, 8100.5, 9088.7, 8574.3, 8593.1, 8618.9, 7709.7, 8111.4, 8788. , 8562.5, 7679.2, 8288.4, ... 6344.2, 6366.3, 6444.2, 7101.5, 7361.5, 7361.5, 6366.3, 6609.4, 6355.2, 7261.5, 6858.9, 6444.2, 6505.2, 7173.1, 7228.3, 6399.4, 7095.7, 6543.6, 7322.5, 6538.4, 7135.2, 6350. , 7372. , 7145.7, 6405.8, 6892. , 7350.4, 7162. , 6421.5, 6476.8, 6388.4, 6521. , 6394.7, 6443.6, 6405.8, 6537.8, 6355.2, 7239.9, 7057.3, 6548.9, 6455.2, 7228.8, 7084.6, 6405.8, 6421.5, 7422.5, 7228.3, 6582.6, 6388.4, 7151.5, 6450. , 6914.1, 6875.2, 6372.1, 7046.7, 6670.5, 6399.4, 6621. , 6704.1, 6682. , 6643.1, 6704.1, 6676.3, 6444.2, 7334.1, 6421.5, 6438.9, 6472.1, 6427.9, 6494.2, 6852.6, 6653.6, 6421.5, 6626.8, 6903.1, 6366.3, 7300.4, 6996.2, 6394.2, 6366.3, 6483.1, 6642.6, 6399.4, 6476.8, 7272.5, 7162. , 6461. , 7305.7, 6394.7, 7316.7, 6830.5, 6383.7, 7345.1, 7228.3, 6427.9, 6527.3, 6410.5, 7267.8, 6510.5, 6416.8, 7272.5, 6498.9, 6819.4, 6443.6, 6465.8, 6416.8, 6886.2, 6450. , 7135.2, 7073.6, 7212.5, 6510.5, 6421.5, 7334.1, 7095.7, 7400.4, 6355.2, 7029.4, 6465.8, 6847.3, 6576.3, 6516.3, 7323. , 6394.7, 6355.2, 7283.6, 7095.7, 6505.2, 6394.7, 6355.2, 7135.2, 7360.9, 7135.2, 7068.3, 6432.6, 6355.2, 6465.8, 7073.6, 6444.2, 7345.1, 7118.3, 7294.6, 7261.5, 7073.6, 7051.5, 6394.7, 7118.3, 6405.8, 6355.2, 6444.2, 7367.2, 7338.8, 7073.6, 6410.5]) - ccf_dv(cell)float641.104e+03 1.381e+03 ... 3.57e+03
array([1103.5, 1380.7, 1034.3, 2939.5, 1259.4, 722.5, 1986.9, 2870.2, 1415.3, 1276.7, 1934.9, 2627.7, 913. , 1172.8, 2021.5, 1276.7, 1276.7, 1415.3, 2021.5, 1917.6, 2697. , 1068.9, 2056.2, 1449.9, 1432.6, 722.5, 2870.2, 1138.2, 2021.5, 1969.6, 2125.4, 2108.1, 2021.5, 2004.2, 2177.4, 1259.4, 1380.7, 2004.2, 809.1, 2974.2, 1986.9, 1380.7, 2541.1, 2056.2, 2038.8, 3632.3, 2021.5, 1242.1, 1415.3, 2021.5, 1986.9, 1276.7, 2056.2, 2038.8, 3008.8, 999.6, 2056.2, 2662.4, 1034.3, 1449.9, 1952.2, 1346. , 3667. , 1016.9, 2749. , 2073.5, 1865.6, 2991.5, 1865.6, 2974.2, 1034.3, 3736.3, 2056.2, 2004.2, 2818.3, 2038.8, 1969.6, 1969.6, 2922.2, 3632.3, 1986.9, 2021.5, 2142.8, 2056.2, 1952.2, 1934.9, 2056.2, 2974.2, 1276.7, 1034.3, 2004.2, 2056.2, 2056.2, 947.7, 2038.8, 1484.6, 2506.5, 2056.2, 2021.5, 2281.3, 1224.8, 2038.8, 2021.5, 2142.8, 3026.1, 1900.3, 1016.9, 2766.3, 2108.1, 1034.3, 1311.4, 2056.2, 1969.6, 2142.8, 1900.3, 1917.6, 2056.2, 2056.2, 1034.3, 2038.8, 1207.5, 722.5, 1016.9, 1380.7, 1986.9, 1986.9, 2662.4, 2697. , 1016.9, 1068.9, 3389.8, 2056.2, 2038.8, 1207.5, 2974.2, 999.6, 722.5, 1346. , 3753.6, 3199.3, 1986.9, 1311.4, 1883. , 2904.9, 2108.1, 1883. , 1917.6, 1969.6, 1934.9, 2870.2, 1068.9, 1986.9, 1952.2, 1934.9, 3580.4, 2870.2, 1623.2, 2038.8, 3667. , 2523.8, ... 3791.1, 3717.5, 3533.4, 1305.7, 477.3, 477.3, 3717.5, 2907.4, 3754.3, 735. , 2189.4, 3533.4, 3367.7, 1029.6, 845.5, 3607. , 1287.3, 3202. , 569.3, 3257.2, 1268.9, 3809.5, 366.8, 1158.5, 3699. , 2079. , 514.1, 1066.4, 3533.4, 3349.3, 3643.8, 3202. , 3735.9, 3459.7, 3699. , 3183.6, 3754.3, 882.3, 1453. , 3146.7, 3496.5, 919.1, 1324.1, 3699. , 3533.4, 311.6, 845.5, 3109.9, 3643.8, 1176.9, 3551.8, 2005.3, 2097.4, 3735.9, 1563.5, 2741.7, 3607. , 2944.2, 2704.9, 2778.5, 2870.6, 2704.9, 2760.1, 3533.4, 606.2, 3533.4, 3588.6, 3478.1, 3625.4, 3404.5, 2097.4, 2760.1, 3533.4, 2962.6, 2042.1, 3717.5, 643. , 1618.7, 3662.2, 3717.5, 3441.3, 2797. , 3607. , 3349.3, 698.2, 1066.4, 3514.9, 587.7, 3735.9, 550.9, 2171. , 3772.7, 569.3, 845.5, 3625.4, 3294. , 3570.2, 827.1, 3312.4, 3662.2, 698.2, 3275.6, 2207.8, 3459.7, 3386.1, 3662.2, 2060.5, 3551.8, 1268.9, 1361. , 1011.2, 3312.4, 3533.4, 606.2, 1287.3, 385.2, 3754.3, 1508.2, 3386.1, 2152.6, 3017.9, 3330.8, 643. , 3735.9, 3754.3, 661.4, 1287.3, 3367.7, 3735.9, 3754.3, 1268.9, 403.6, 1268.9, 1416.2, 3496.5, 3754.3, 3386.1, 1361. , 3533.4, 569.3, 1287.3, 624.6, 735. , 1361. , 1434.6, 3735.9, 1287.3, 3699. , 3754.3, 3533.4, 495.7, 477.3, 1361. , 3570.2]) - ccf_lr(cell)float643.854e+03 3.8e+03 ... 4.956e+03
array([3854.3, 3799.6, 3898.1, 3491.8, 3868.6, 3929.6, 3725. , 3505.5, 3822.8, 3820.1, 3720.2, 3553.3, 3937. , 3870.7, 3718.2, 3820.1, 3820.1, 3822.8, 3718.2, 3738.7, 3539.7, 3891.2, 3681.2, 3785.9, 3834.4, 3959.6, 3505.5, 3847.5, 3688.1, 3683.3, 3697.6, 3655.9, 3688.1, 3676.5, 3672.3, 3838.6, 3799.6, 3676.5, 3957.6, 3484.9, 3725. , 3829.7, 3615.5, 3711.3, 3669.6, 3385. , 3718.2, 3857. , 3822.8, 3718.2, 3725. , 3820.1, 3711.3, 3669.6, 3478.1, 3904.9, 3711.3, 3546.5, 3898.1, 3785.9, 3731.8, 3806.4, 3378.2, 3916.5, 3574.5, 3692.9, 3703.8, 3496.6, 3703.8, 3484.9, 3898.1, 3334.4, 3681.2, 3706.5, 3560.8, 3669.6, 3683.3, 3683.3, 3510.2, 3385. , 3725. , 3718.2, 3649.1, 3681.2, 3731.8, 3720.2, 3711.3, 3484.9, 3820.1, 3868. , 3676.5, 3681.2, 3681.2, 3900.1, 3669.6, 3809.1, 3622.4, 3711.3, 3718.2, 3621.7, 3875.5, 3669.6, 3718.2, 3649.1, 3519.8, 3697. , 3916.5, 3526. , 3655.9, 3868. , 3813.3, 3681.2, 3683.3, 3649.1, 3697. , 3738.7, 3681.2, 3711.3, 3868. , 3669.6, 3833.8, 3959.6, 3886.5, 3829.7, 3725. , 3725. , 3576.6, 3569.7, 3886.5, 3891.2, 3402.9, 3681.2, 3669.6, 3833.8, 3484.9, 3904.9, 3929.6, 3806.4, 3346.1, 3485.6, 3694.9, 3813.3, 3745.5, 3528.7, 3655.9, 3745.5, 3738.7, 3683.3, 3690.2, 3535.5, 3861.2, 3725. , 3731.8, 3690.2, 3410.3, 3505.5, 3751.7, 3669.6, 3348.1, 3603.9, ... 5021.9, 4999.8, 4967.1, 4287.2, 4049.9, 4049.9, 4999.8, 4756.6, 5010.8, 4104.6, 4575.1, 4967.1, 4928.7, 4193. , 4137.8, 4966.6, 4270.4, 4867.7, 4066.2, 4895.6, 4298.8, 5038.7, 3994.1, 4243. , 5028.2, 4541.9, 4060.9, 4204.1, 4944.5, 4889.3, 4977.7, 4845. , 5039.2, 4922.4, 5028.2, 4850.8, 5010.8, 4171.4, 4331.4, 4839.8, 4956.1, 4182.5, 4281.4, 5028.2, 4944.5, 4011.4, 4137.8, 4851.4, 4977.7, 4259.8, 4984. , 4519.8, 4536.1, 5016.6, 4387.2, 4718.2, 4966.6, 4790.3, 4729.8, 4751.9, 4768.2, 4729.8, 4735.1, 4967.1, 4099.9, 4944.5, 4995. , 4961.9, 5006.1, 4939.8, 4513.5, 4712.4, 4944.5, 4807.2, 4530.9, 4999.8, 4088.3, 4369.8, 4994.5, 4999.8, 4950.8, 4723.5, 4966.6, 4889.3, 4093.5, 4204.1, 4972.9, 4060.4, 5039.2, 4049.3, 4535.6, 5050.3, 4088.8, 4137.8, 5006.1, 4906.6, 4955.6, 4166.2, 4900.8, 5017.1, 4093.5, 4867.2, 4546.7, 4922.4, 4900.3, 5017.1, 4525.1, 4984. , 4298.8, 4292.5, 4221.4, 4900.8, 4944.5, 4099.9, 4270.4, 4033.5, 5010.8, 4336.7, 4900.3, 4541.4, 4789.8, 4917.7, 4110.9, 5039.2, 5010.8, 4082.5, 4270.4, 4928.7, 5039.2, 5010.8, 4298.8, 4005.1, 4298.8, 4320.4, 4933.5, 5010.8, 4900.3, 4292.5, 4967.1, 4088.8, 4293. , 4071.4, 4104.6, 4292.5, 4314.6, 5039.2, 4293. , 5028.2, 5010.8, 4967.1, 4066.7, 4027.2, 4292.5, 4955.6]) - brain_area(cell)<U5'VISp' 'VISp' 'VISp' ... 'CA1' 'MD'
array(['VISp', 'VISp', 'VISp', 'DG', 'VISp', 'VISp', 'SUB', 'DG', 'VISp', 'VISp', 'SUB', 'DG', 'VISp', 'VISp', 'SUB', 'VISp', 'VISp', 'VISp', 'SUB', 'SUB', 'DG', 'VISp', 'SUB', 'VISp', 'VISp', 'VISp', 'DG', 'VISp', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'VISp', 'VISp', 'SUB', 'VISp', 'DG', 'SUB', 'VISp', 'DG', 'SUB', 'SUB', 'LGd', 'SUB', 'VISp', 'VISp', 'SUB', 'SUB', 'VISp', 'SUB', 'SUB', 'DG', 'VISp', 'SUB', 'DG', 'VISp', 'VISp', 'SUB', 'VISp', 'LGd', 'VISp', 'DG', 'SUB', 'SUB', 'DG', 'SUB', 'DG', 'VISp', 'LGd', 'SUB', 'SUB', 'DG', 'SUB', 'SUB', 'SUB', 'DG', 'LGd', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'DG', 'VISp', 'VISp', 'SUB', 'SUB', 'SUB', 'VISp', 'SUB', 'VISp', 'DG', 'SUB', 'SUB', 'SUB', 'VISp', 'SUB', 'SUB', 'SUB', 'DG', 'SUB', 'VISp', 'DG', 'SUB', 'VISp', 'VISp', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'VISp', 'SUB', 'VISp', 'VISp', 'VISp', 'VISp', 'SUB', 'SUB', 'DG', 'DG', 'VISp', 'VISp', 'LGd', 'SUB', 'SUB', 'VISp', 'DG', 'VISp', 'VISp', 'VISp', 'LGd', 'DG', 'SUB', 'VISp', 'SUB', 'DG', 'SUB', 'SUB', 'SUB', 'SUB', 'SUB', 'DG', 'VISp', 'SUB', 'SUB', 'SUB', 'LGd', 'DG', 'SUB', 'SUB', 'LGd', 'DG', 'SUB', 'SUB', 'SUB', 'SUB', 'DG', 'SUB', 'DG', 'SUB', 'SUB', 'SUB', 'SUB', 'LGd', 'SUB', 'VISp', 'SUB', 'SUB', 'SUB', 'SUB', 'LGd', 'DG', 'VISp', 'VISp', ... 'DG', 'VISam', 'MD', 'DG', 'MD', 'DG', 'VISam', 'CA1', 'VISam', 'VISam', 'MD', 'MD', 'VISam', 'MD', 'MD', 'CA1', 'LH', 'MD', 'VISam', 'VISam', 'VISam', 'DG', 'MD', 'MD', 'MD', 'CA1', 'VISam', 'MD', 'MD', 'MD', 'CA1', 'VISam', 'VISam', 'MD', 'LH', 'MD', 'VISam', 'DG', 'MD', 'MD', 'VISam', 'VISam', 'MD', 'CA1', 'MD', 'VISam', 'MD', 'CA1', 'MD', 'VISam', 'CA1', 'MD', 'DG', 'VISam', 'VISam', 'MD', 'MD', 'MD', 'MD', 'MD', 'MD', 'MD', 'MD', 'MD', 'VISam', 'CA1', 'MD', 'MD', 'VISam', 'CA1', 'MD', 'MD', 'VISam', 'VISam', 'MD', 'MD', 'CA1', 'MD', 'DG', 'DG', 'MD', 'CA1', 'LH', 'MD', 'LH', 'LH', 'LH', 'LH', 'LH', 'LH', 'MD', 'VISam', 'MD', 'MD', 'MD', 'MD', 'MD', 'DG', 'LH', 'MD', 'LH', 'DG', 'MD', 'VISam', 'CA1', 'MD', 'MD', 'MD', 'LH', 'MD', 'MD', 'VISam', 'VISam', 'MD', 'VISam', 'MD', 'VISam', 'DG', 'MD', 'VISam', 'VISam', 'MD', 'MD', 'MD', 'VISam', 'MD', 'MD', 'VISam', 'MD', 'DG', 'MD', 'MD', 'MD', 'DG', 'MD', 'CA1', 'CA1', 'VISam', 'MD', 'MD', 'VISam', 'CA1', 'VISam', 'MD', 'CA1', 'MD', 'DG', 'LH', 'MD', 'VISam', 'MD', 'MD', 'VISam', 'CA1', 'MD', 'MD', 'MD', 'CA1', 'VISam', 'CA1', 'CA1', 'MD', 'MD', 'MD', 'CA1', 'MD', 'VISam', 'CA1', 'VISam', 'VISam', 'CA1', 'CA1', 'MD', 'CA1', 'MD', 'MD', 'MD', 'VISam', 'VISam', 'CA1', 'MD'], dtype='<U5') - brain_groups(cell)<U17'visual cortex' ... 'thalamus'
array(['visual cortex', 'visual cortex', 'visual cortex', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'visual cortex', 'visual cortex', 'visual cortex', 'hippocampus', 'hippocampus', 'hippocampus', 'visual cortex', 'hippocampus', 'visual cortex', 'visual cortex', 'visual cortex', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'hippocampus', 'thalamus', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'visual cortex', 'thalamus', 'visual cortex', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'visual cortex', 'thalamus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'thalamus', ... 'thalamus', 'thalamus', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'hippocampus', 'thalamus', 'visual cortex', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'thalamus', 'thalamus', 'thalamus', 'visual cortex', 'visual cortex', 'thalamus', 'visual cortex', 'thalamus', 'visual cortex', 'hippocampus', 'thalamus', 'visual cortex', 'visual cortex', 'thalamus', 'thalamus', 'thalamus', 'visual cortex', 'thalamus', 'thalamus', 'visual cortex', 'thalamus', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'hippocampus', 'thalamus', 'hippocampus', 'hippocampus', 'visual cortex', 'thalamus', 'thalamus', 'visual cortex', 'hippocampus', 'visual cortex', 'thalamus', 'hippocampus', 'thalamus', 'hippocampus', 'thalamus', 'thalamus', 'visual cortex', 'thalamus', 'thalamus', 'visual cortex', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'hippocampus', 'visual cortex', 'hippocampus', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'hippocampus', 'thalamus', 'visual cortex', 'hippocampus', 'visual cortex', 'visual cortex', 'hippocampus', 'hippocampus', 'thalamus', 'hippocampus', 'thalamus', 'thalamus', 'thalamus', 'visual cortex', 'visual cortex', 'hippocampus', 'thalamus'], dtype='<U17') - waveform_w(cell, sample, waveform_component)float320.0 0.0 0.0 ... -0.0124 0.03969
array([[[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ], ..., [-0.4481609 , -0.13800871, 0.0395868 ], [-0.42316234, -0.12405451, 0.02057694], [-0.37062392, -0.10515313, 0.01265119]], [[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ], ..., [-0.02701856, 0.06971366, 0.00617525], [-0.00638989, 0.06602961, 0.01049682], [ 0.01408696, 0.05259689, 0.01120061]], [[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ], ..., ... ..., [-0.06039745, 0.04920603, -0.03191666], [-0.03636225, 0.03228572, -0.0282197 ], [-0.01095693, 0.02940689, -0.01048632]], [[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ], ..., [-0.31517556, 0.1144239 , -0.04443172], [-0.2860381 , 0.11826571, -0.04293353], [-0.2682851 , 0.12098445, -0.02821838]], [[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ], ..., [-0.00454372, -0.00972404, 0.03496319], [ 0.01556368, -0.00708604, 0.03618595], [ 0.04895331, -0.01240014, 0.03969195]]], dtype=float32) - waveform_u(cell, waveform_component, probe)float320.0 0.0 0.0 ... -0.01824 -0.01266
array([[[ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ]], [[ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ]], [[ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ]], ... [[ 0.05271564, 0.04852932, 0.04062656, ..., 0.04546255, 0.04898005, 0.05257358], [-0.09452463, -0.08259631, -0.07637203, ..., -0.09408229, -0.08096855, -0.08106099], [ 0.01066507, 0.0676906 , 0.01226856, ..., 0.09427451, 0.06466331, 0.08417223]], [[ 0. , 0.0172444 , 0. , ..., 0.01804066, 0. , 0.02229728], [ 0. , 0.00105885, 0. , ..., -0.0059192 , 0. , -0.00219953], [ 0. , 0.00052403, 0. , ..., -0.01789685, 0. , -0.02679556]], [[ 0.00581419, 0.0047881 , 0.00665496, ..., 0. , 0.0043255 , 0.00438046], [-0.00903494, 0.00174251, -0.00360132, ..., 0. , -0.01243377, 0.00495622], [-0.01273568, -0.01582302, -0.02444749, ..., 0. , -0.01824343, -0.0126649 ]]], dtype=float32) - lfp(brain_area_lfp, trial, time)float64-57.29 -45.63 ... -18.44 -15.9
array([[[-5.72920408e+01, -4.56320408e+01, -4.30820408e+01, ..., 1.76779592e+01, -1.84920408e+01, 1.37779592e+01], [-4.42469388e+00, -3.89446939e+01, -4.50246939e+01, ..., 3.33953061e+01, 8.30253061e+01, 1.57530612e+00], [-5.88420408e+01, -5.01204082e+00, -5.55204082e+00, ..., 5.89795918e+00, 1.20479592e+01, -6.28204082e+00], ..., [-2.02538776e+01, 3.95361224e+01, 1.13761224e+01, ..., 1.33561224e+01, 8.27612245e+00, 4.86612245e+00], [ 1.83336735e+01, -4.53963265e+01, -1.18663265e+01, ..., 5.72836735e+01, 6.66836735e+01, -2.53863265e+01], [ 7.20938776e+00, -1.27406122e+01, 4.65693878e+01, ..., -2.62806122e+01, -1.95806122e+01, -4.79306122e+01]], [[-3.14265306e+01, -4.05665306e+01, -3.83065306e+01, ..., -3.14653061e+00, -4.58865306e+01, -2.84065306e+01], [-1.27657143e+01, 1.42857143e-02, -2.93457143e+01, ..., 3.69942857e+01, 4.23142857e+01, 9.15428571e+00], [ 7.59387755e+00, 1.04738776e+01, -6.86122449e-01, ..., 4.09387755e+00, 6.37387755e+00, 6.83387755e+00], ... [-4.75484694e+00, 3.29515306e+00, 6.38265306e+00, ..., 2.73265306e+00, 3.00765306e+00, 9.33265306e+00], [ 2.12500000e+00, 5.01250000e+00, -2.50000000e-01, ..., -1.75000000e-01, -6.35000000e+00, -8.71250000e+00], [ 4.14974490e+00, 2.79974490e+00, -1.76275510e+00, ..., -1.61377551e+01, -8.92525510e+00, 7.24744898e-01]], [[-4.82970522e+00, -4.51859410e+00, -6.26303855e+00, ..., -5.85192744e+00, -1.07630385e+01, -9.56303855e+00], [ 6.22902494e-01, -8.54875283e-01, 2.41179138e+00, ..., -1.33265306e+00, -2.34376417e+00, 5.56235828e-01], [ 7.31360544e+00, 9.13832200e-02, -2.50861678e+00, ..., 3.40249433e+00, -1.57528345e+00, -2.75283447e-01], ..., [-2.47619048e+00, 2.94603175e+00, 9.46031746e-01, ..., -2.22063492e+00, 3.07936508e+00, 1.83492063e+00], [ 1.24081633e+00, 3.67414966e+00, 2.29705215e-01, ..., 2.37414966e+00, 3.40748299e+00, 7.29705215e-01], [-4.02018141e+00, 1.13151927e-01, -6.40907029e+00, ..., -1.38535147e+01, -1.84424036e+01, -1.58979592e+01]]]) - spike_time(spike_id)float320.01967 0.09227 ... 2.355 2.475
array([0.01966781, 0.09226781, 0.19510114, ..., 2.313836 , 2.3547363 , 2.4751368 ], dtype=float32) - spike_cell(spike_id)uint321 1 1 1 1 1 ... 698 698 698 698 698
array([ 1, 1, 1, ..., 698, 698, 698], dtype=uint32)
- spike_trial(spike_id)uint321 1 1 1 1 1 ... 450 450 450 450 450
array([ 1, 1, 1, ..., 450, 450, 450], dtype=uint32)
- trialPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 331, 332, 333, 334, 335, 336, 337, 338, 339, 340], dtype='int32', name='trial', length=340)) - timePandasIndex
PandasIndex(Index([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, ... 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5], dtype='float64', name='time', length=250)) - cellPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 689, 690, 691, 692, 693, 694, 695, 696, 697, 698], dtype='int32', name='cell', length=698)) - waveform_componentPandasIndex
PandasIndex(Index([1, 2, 3], dtype='int32', name='waveform_component'))
- probePandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 375, 376, 377, 378, 379, 380, 381, 382, 383, 384], dtype='int32', name='probe', length=384)) - brain_area_lfpPandasIndex
PandasIndex(Index(['DG', 'LGd', 'SUB', 'VISp', 'ACA', 'MOs', 'PL', 'CA1', 'DG', 'LH', 'MD', 'VISam'], dtype='object', name='brain_area_lfp')) - spike_idPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 3185879, 3185880, 3185881, 3185882, 3185883, 3185884, 3185885, 3185886, 3185887, 3185888], dtype='int32', name='spike_id', length=3185888))
Example: Get the reaction time for each trial in the session.
dset['reaction_time']<xarray.DataArray 'reaction_time' (trial: 340)> Size: 3kB
array([ 740., 160., 730., 210., 160., 220., 170., 130., 100.,
140., 180., 140., 220., 260., 130., inf, 150., 90.,
50., 220., 120., 1430., 340., 220., 140., 80., 210.,
190., 130., inf, 130., 480., inf, 210., 210., 220.,
180., 110., 200., 180., 210., 60., 130., 140., 140.,
120., 160., 150., 200., 110., 140., 190., 110., 190.,
190., 180., 150., 170., 130., 10., 1940., 80., 190.,
230., 1040., 90., 190., 130., inf, 130., 180., 70.,
inf, 100., inf, 190., 150., 240., 120., 0., 240.,
180., 1220., inf, 220., 210., 280., 480., 240., 190.,
220., 120., 1390., 320., 100., 420., 460., 140., 160.,
inf, 300., 170., 160., 160., 250., 510., 750., 150.,
0., 160., 10., 20., 240., 470., 750., 780., 1390.,
inf, 170., 160., 240., 1400., 310., 180., 690., 460.,
270., 20., 580., 390., 150., 190., 290., 40., 460.,
150., 680., 130., 180., 460., inf, 240., 220., 230.,
160., 210., 1190., 740., 90., 110., 270., 190., 210.,
inf, inf, 170., 1540., 90., 140., 60., 200., 180.,
170., 200., 200., 150., 180., 40., 220., 200., 10.,
270., 190., 10., 150., inf, 210., 1750., 320., 330.,
630., 650., 860., 290., 1800., 1220., inf, 140., 170.,
140., 140., 130., 10., 110., 620., 310., 220., 130.,
100., 80., 370., 970., 140., 1960., 500., 180., 290.,
1030., inf, 230., 180., 210., 210., inf, 230., 250.,
220., 260., inf, 70., 130., 450., 190., 500., 330.,
420., 810., 170., 180., 160., 200., 200., 190., 330.,
140., 230., 20., 260., 250., 200., 380., 830., inf,
270., 200., 230., 200., 310., 170., 60., 140., 80.,
70., 220., 300., 200., 230., 190., 30., 120., 190.,
110., 60., 200., 300., 140., 110., inf, 210., 60.,
210., 160., 60., 280., 80., 220., 230., 570., 140.,
280., 200., 170., 240., 190., 220., 80., 180., 190.,
80., 40., 240., 30., 250., 260., 210., 380., 80.,
590., 160., 880., 830., 1930., 250., 190., 190., 340.,
inf, 110., 180., 240., 180., inf, 150., 210., 190.,
220., 240., 240., 270., 190., 330., 50., 250., 250.,
800., 320., 230., inf, inf, inf, inf, inf, inf,
inf, inf, inf, inf, inf, inf, inf])
Coordinates:
* trial (trial) int32 1kB 1 2 3 4 5 6 7 8 ... 334 335 336 337 338 339 340xarray.DataArray
'reaction_time'
- trial: 340
- 740.0 160.0 730.0 210.0 160.0 220.0 170.0 ... inf inf inf inf inf inf
array([ 740., 160., 730., 210., 160., 220., 170., 130., 100., 140., 180., 140., 220., 260., 130., inf, 150., 90., 50., 220., 120., 1430., 340., 220., 140., 80., 210., 190., 130., inf, 130., 480., inf, 210., 210., 220., 180., 110., 200., 180., 210., 60., 130., 140., 140., 120., 160., 150., 200., 110., 140., 190., 110., 190., 190., 180., 150., 170., 130., 10., 1940., 80., 190., 230., 1040., 90., 190., 130., inf, 130., 180., 70., inf, 100., inf, 190., 150., 240., 120., 0., 240., 180., 1220., inf, 220., 210., 280., 480., 240., 190., 220., 120., 1390., 320., 100., 420., 460., 140., 160., inf, 300., 170., 160., 160., 250., 510., 750., 150., 0., 160., 10., 20., 240., 470., 750., 780., 1390., inf, 170., 160., 240., 1400., 310., 180., 690., 460., 270., 20., 580., 390., 150., 190., 290., 40., 460., 150., 680., 130., 180., 460., inf, 240., 220., 230., 160., 210., 1190., 740., 90., 110., 270., 190., 210., inf, inf, 170., 1540., 90., 140., 60., 200., 180., 170., 200., 200., 150., 180., 40., 220., 200., 10., 270., 190., 10., 150., inf, 210., 1750., 320., 330., 630., 650., 860., 290., 1800., 1220., inf, 140., 170., 140., 140., 130., 10., 110., 620., 310., 220., 130., 100., 80., 370., 970., 140., 1960., 500., 180., 290., 1030., inf, 230., 180., 210., 210., inf, 230., 250., 220., 260., inf, 70., 130., 450., 190., 500., 330., 420., 810., 170., 180., 160., 200., 200., 190., 330., 140., 230., 20., 260., 250., 200., 380., 830., inf, 270., 200., 230., 200., 310., 170., 60., 140., 80., 70., 220., 300., 200., 230., 190., 30., 120., 190., 110., 60., 200., 300., 140., 110., inf, 210., 60., 210., 160., 60., 280., 80., 220., 230., 570., 140., 280., 200., 170., 240., 190., 220., 80., 180., 190., 80., 40., 240., 30., 250., 260., 210., 380., 80., 590., 160., 880., 830., 1930., 250., 190., 190., 340., inf, 110., 180., 240., 180., inf, 150., 210., 190., 220., 240., 240., 270., 190., 330., 50., 250., 250., 800., 320., 230., inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]) - trial(trial)int321 2 3 4 5 6 ... 336 337 338 339 340
array([ 1, 2, 3, ..., 338, 339, 340], dtype=int32)
- trialPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 331, 332, 333, 334, 335, 336, 337, 338, 339, 340], dtype='int32', name='trial', length=340))
Exercise: Get the response type for each trial:
Solution
dset['response_type']<xarray.DataArray 'response_type' (trial: 340)> Size: 3kB
array([ 0., 1., 0., 1., -1., -1., -1., -1., 1., -1., -1., -1., 1.,
0., -1., 0., 1., 1., 1., 1., 1., -1., 1., -1., -1., 0.,
1., 1., -1., 0., 1., -1., 0., 0., -1., -1., -1., 1., 1.,
1., -1., 0., 1., -1., -1., 1., 1., -1., 1., -1., -1., 1.,
-1., -1., -1., 1., 1., -1., -1., -1., 0., 0., -1., 1., 0.,
-1., -1., 1., 0., 1., 1., 1., 0., 1., 0., 1., -1., 1.,
-1., 1., 1., 1., -1., 0., 1., -1., -1., 0., 1., 1., -1.,
1., -1., 1., -1., -1., 0., -1., 1., 0., -1., -1., -1., 1.,
1., -1., 0., 1., 1., 1., 1., -1., 0., 1., -1., 1., 0.,
0., -1., -1., -1., -1., 0., 1., 0., 0., 1., -1., -1., 0.,
-1., -1., 1., -1., 1., -1., 1., -1., 1., -1., 0., 1., -1.,
-1., 1., 1., -1., 0., -1., -1., 0., 1., 1., 0., 0., 1.,
0., 1., 1., -1., 1., -1., 1., 1., -1., 1., 1., 1., 1.,
-1., 1., 1., -1., -1., -1., 0., -1., 1., 0., -1., -1., -1.,
-1., -1., -1., -1., 0., 1., -1., 0., 1., -1., -1., 1., 1.,
0., -1., -1., -1., -1., -1., -1., -1., -1., 0., -1., -1., -1.,
0., 1., -1., -1., -1., 0., 1., -1., 1., -1., 0., -1., -1.,
0., -1., -1., -1., -1., 0., 1., 1., 1., 1., 1., -1., -1.,
-1., 1., -1., 1., -1., 1., 1., -1., 0., 1., -1., 1., -1.,
1., -1., 1., 1., 1., -1., 1., -1., 1., -1., 1., 1., 1.,
1., -1., -1., 1., 1., -1., 1., 0., 1., -1., 1., 1., 1.,
-1., 1., 1., -1., -1., -1., 1., 1., 1., 1., -1., 1., 1.,
-1., -1., 1., 1., 1., 1., -1., -1., 1., 1., 0., 1., 1.,
1., -1., 0., -1., 1., -1., -1., 0., -1., 1., 1., 1., 0.,
-1., -1., -1., 0., 1., -1., -1., 1., 1., 1., -1., 1., 1.,
1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0.])
Coordinates:
* trial (trial) int32 1kB 1 2 3 4 5 6 7 8 ... 334 335 336 337 338 339 340xarray.DataArray
'response_type'
- trial: 340
- 0.0 1.0 0.0 1.0 -1.0 -1.0 -1.0 -1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
array([ 0., 1., 0., 1., -1., -1., -1., -1., 1., -1., -1., -1., 1., 0., -1., 0., 1., 1., 1., 1., 1., -1., 1., -1., -1., 0., 1., 1., -1., 0., 1., -1., 0., 0., -1., -1., -1., 1., 1., 1., -1., 0., 1., -1., -1., 1., 1., -1., 1., -1., -1., 1., -1., -1., -1., 1., 1., -1., -1., -1., 0., 0., -1., 1., 0., -1., -1., 1., 0., 1., 1., 1., 0., 1., 0., 1., -1., 1., -1., 1., 1., 1., -1., 0., 1., -1., -1., 0., 1., 1., -1., 1., -1., 1., -1., -1., 0., -1., 1., 0., -1., -1., -1., 1., 1., -1., 0., 1., 1., 1., 1., -1., 0., 1., -1., 1., 0., 0., -1., -1., -1., -1., 0., 1., 0., 0., 1., -1., -1., 0., -1., -1., 1., -1., 1., -1., 1., -1., 1., -1., 0., 1., -1., -1., 1., 1., -1., 0., -1., -1., 0., 1., 1., 0., 0., 1., 0., 1., 1., -1., 1., -1., 1., 1., -1., 1., 1., 1., 1., -1., 1., 1., -1., -1., -1., 0., -1., 1., 0., -1., -1., -1., -1., -1., -1., -1., 0., 1., -1., 0., 1., -1., -1., 1., 1., 0., -1., -1., -1., -1., -1., -1., -1., -1., 0., -1., -1., -1., 0., 1., -1., -1., -1., 0., 1., -1., 1., -1., 0., -1., -1., 0., -1., -1., -1., -1., 0., 1., 1., 1., 1., 1., -1., -1., -1., 1., -1., 1., -1., 1., 1., -1., 0., 1., -1., 1., -1., 1., -1., 1., 1., 1., -1., 1., -1., 1., -1., 1., 1., 1., 1., -1., -1., 1., 1., -1., 1., 0., 1., -1., 1., 1., 1., -1., 1., 1., -1., -1., -1., 1., 1., 1., 1., -1., 1., 1., -1., -1., 1., 1., 1., 1., -1., -1., 1., 1., 0., 1., 1., 1., -1., 0., -1., 1., -1., -1., 0., -1., 1., 1., 1., 0., -1., -1., -1., 0., 1., -1., -1., 1., 1., 1., -1., 1., 1., 1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) - trial(trial)int321 2 3 4 5 6 ... 336 337 338 339 340
array([ 1, 2, 3, ..., 338, 339, 340], dtype=int32)
- trialPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 331, 332, 333, 334, 335, 336, 337, 338, 339, 340], dtype='int32', name='trial', length=340))
Exercise: Get the contrast levels of both the left and right stimulus for each trial (note: you’ll need double-square brackets for this (e.g. dset[['a', 'b']])).
Solution
dset[['contrast_left', 'contrast_right']]<xarray.Dataset> Size: 2kB
Dimensions: (trial: 340)
Coordinates:
* trial (trial) int32 1kB 1 2 3 4 5 6 7 ... 335 336 337 338 339 340
Data variables:
contrast_left (trial) int8 340B 0 0 0 100 25 50 0 0 ... 0 0 0 0 0 0 0 0
contrast_right (trial) int8 340B 0 0 0 50 50 50 ... 100 100 100 100 100 100xarray.Dataset
- trial: 340
- trial(trial)int321 2 3 4 5 6 ... 336 337 338 339 340
array([ 1, 2, 3, ..., 338, 339, 340], dtype=int32)
- contrast_left(trial)int80 0 0 100 25 50 0 ... 0 0 0 0 0 0 0
array([ 0, 0, 0, 100, 25, 50, 0, 0, 100, 25, 0, 25, 50, 0, 0, 0, 25, 100, 100, 100, 50, 25, 25, 25, 0, 0, 50, 100, 0, 0, 100, 0, 0, 0, 50, 50, 0, 50, 50, 100, 0, 0, 25, 25, 0, 100, 50, 25, 50, 0, 0, 25, 100, 0, 0, 100, 50, 0, 25, 50, 0, 0, 25, 100, 0, 0, 0, 0, 0, 100, 100, 50, 0, 25, 0, 50, 25, 50, 25, 25, 25, 100, 0, 0, 25, 0, 0, 0, 50, 100, 25, 0, 0, 0, 0, 0, 0, 25, 100, 0, 0, 0, 0, 50, 50, 0, 0, 50, 100, 50, 50, 25, 0, 0, 0, 0, 0, 0, 25, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 50, 25, 25, 50, 100, 0, 0, 25, 0, 0, 100, 100, 0, 0, 100, 0, 100, 50, 25, 50, 0, 100, 50, 25, 100, 100, 100, 50, 0, 50, 25, 25, 25, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 100, 0, 0, 50, 0, 0, 0, 0, 0, 50, 25, 0, 25, 25, 0, 0, 0, 0, 50, 25, 0, 0, 100, 25, 0, 0, 0, 50, 25, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 100, 100, 50, 50, 100, 50, 0, 25, 25, 25, 100, 0, 100, 0, 0, 0, 25, 50, 25, 25, 50, 0, 50, 100, 25, 0, 100, 0, 50, 25, 100, 50, 50, 50, 0, 0, 100, 100, 50, 50, 0, 25, 0, 100, 100, 25, 0, 100, 50, 0, 0, 0, 100, 100, 50, 50, 0, 50, 100, 25, 25, 50, 100, 50, 100, 25, 0, 50, 0, 0, 0, 0, 0, 0, 0, 0, 100, 0, 0, 0, 25, 100, 100, 100, 0, 0, 0, 0, 25, 100, 0, 0, 100, 25, 100, 25, 25, 50, 100, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8) - contrast_right(trial)int80 0 0 50 50 ... 100 100 100 100 100
array([ 0, 0, 0, 50, 50, 50, 50, 100, 25, 100, 50, 100, 0, 0, 0, 0, 0, 50, 0, 0, 100, 0, 25, 100, 25, 0, 25, 25, 0, 0, 50, 0, 0, 0, 0, 25, 100, 0, 50, 50, 50, 0, 0, 50, 25, 50, 0, 100, 0, 100, 100, 0, 100, 100, 100, 0, 0, 100, 100, 25, 0, 0, 50, 25, 0, 100, 100, 0, 0, 0, 25, 0, 0, 50, 0, 25, 25, 25, 100, 0, 25, 0, 0, 0, 0, 50, 0, 0, 25, 25, 100, 0, 0, 0, 0, 0, 0, 100, 0, 0, 50, 100, 100, 0, 25, 0, 0, 0, 0, 0, 25, 100, 0, 0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 50, 0, 0, 0, 0, 0, 0, 100, 100, 0, 100, 0, 0, 0, 0, 0, 0, 0, 0, 50, 100, 0, 0, 0, 0, 50, 100, 0, 25, 25, 0, 0, 25, 0, 100, 25, 100, 0, 50, 0, 100, 100, 25, 0, 50, 100, 25, 0, 50, 100, 50, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 100, 0, 0, 50, 25, 0, 0, 0, 100, 25, 50, 25, 100, 0, 0, 0, 0, 100, 50, 0, 0, 25, 50, 50, 0, 0, 0, 100, 25, 0, 0, 25, 0, 0, 0, 0, 0, 0, 0, 0, 100, 100, 50, 50, 25, 25, 50, 100, 100, 0, 25, 0, 0, 0, 0, 0, 100, 100, 100, 0, 100, 50, 50, 0, 100, 25, 50, 0, 100, 0, 0, 0, 25, 50, 50, 50, 25, 50, 100, 0, 0, 100, 0, 50, 0, 100, 25, 0, 25, 50, 25, 100, 25, 0, 50, 25, 25, 0, 50, 100, 0, 0, 25, 25, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 100, 50, 0, 100, 100, 0, 25, 0, 100, 50, 50, 0, 0, 50, 25, 0, 0, 25, 100, 25, 0, 25, 50, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], dtype=int8)
- trialPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... 331, 332, 333, 334, 335, 336, 337, 338, 339, 340], dtype='int32', name='trial', length=340))
Exercise: Get the response times for trials 10 through 16 (tip: .loc).
Solution
dset['response_time'].loc[10:16]<xarray.DataArray 'response_time' (trial: 7)> Size: 56B
array([0.55870603, 0.97381392, 0.65443056, 0.76496615, 1.99861344,
0.69717263, 1.90782541])
Coordinates:
* trial (trial) int32 28B 10 11 12 13 14 15 16xarray.DataArray
'response_time'
- trial: 7
- 0.5587 0.9738 0.6544 0.765 1.999 0.6972 1.908
array([0.55870603, 0.97381392, 0.65443056, 0.76496615, 1.99861344, 0.69717263, 1.90782541]) - trial(trial)int3210 11 12 13 14 15 16
array([10, 11, 12, 13, 14, 15, 16], dtype=int32)
- trialPandasIndex
PandasIndex(Index([10, 11, 12, 13, 14, 15, 16], dtype='int32', name='trial'))
Section 2: Spike Count Analysis For Specific Brain Regions: Practicing Logical Indexing on Multidimensional Numpy Arrays
Different areas in the brain are commonly believed to specialize in specific aspects of behavior or task. This makes analyzing spiking activity across brain regions an essential step in many neuroscience data analysis pipelines. Over the next couple of exercises let’s focus on how the spike counts of neurons differ across different brain regions.
| Code | Description |
|---|---|
np.unique(data) |
Finds the unique elements of an array. |
bool_array = var_name == specific_value |
Creates a boolean array where each element is True if the corresponding element in var_name equals specific_value; otherwise False. |
var_name[bool_array] |
Uses a boolean array to filter elements of var_name, selecting only those that correspond to True in the boolean array. |
var_name.mean(axis=0) or np.mean(var_name, axis=0) |
Compute the mean across the first dimension, i.e. across rows. |
var_name.mean(axis=(0, 1)) or np.mean(var_name, axis=(0, 1)) |
Compute the mean across the first and second dimensions |
var_name.sum(axis=N) |
Sum along axis N (0=rows, 1=columns, etc.). |
array.values |
Get the underlying numpy array from an XArray object. |
array.item() |
Extract a scalar value from a single-element array. |
plt.imshow(data) |
For a 2D array, make a heat map. |
plt.plot(x, y) |
Make a line plot. |
plt.xticks(positions, labels) |
Customize x-axis tick positions and labels. |
data.sum(dim='name') |
Sum along named dimension in XArray. |
data.plot.imshow(), data.plot.line() |
Built-in XArray plotting methods. |
For the exercises below, we’ll use the spike_count and brain_groups variables and their associated coordinate data to explore some of the spiking data.
Exercises
Example: What is the total number of neurons recorded from the visual cortex?
sum(dset['brain_groups'] == 'visual cortex').item()145(dset['brain_groups']== 'visual cortex').sum().item()145Exercise: What is the total number of neurons recorded from thalamus?
Solution
(dset['brain_groups']== 'thalamus').sum().item()155Exercise: What is the proportion of neurons recorded from hippocampus?
Solution
(dset['brain_groups']== 'hippocampus').sum().item()/len(dset['brain_groups'])0.3151862464183381Exercise: How many non-zero spike count measurements were there?
Solution
np.sum(dset["spike_count"] > 0).item()2301519Exercise: What proportion of cells had more than 300 spikes measured across all trials and time?
Solution
np.mean(np.sum(dset["spike_count"], axis=(1, 2)) > 300).item()0.7263610315186246Exercise: (extra): What proportion of trials had more than 6000 spikes across all cells measured?
Solution
np.mean(np.sum(dset["spike_count"], axis=(0, 2)) > 6000).item()0.9941176470588236Exercise: Compute the population spike count (i.e. average spike count across all neurons) for neurons in thalamus, and visualize it using the plt.imshow() plotting function.
Solution
mask = dset['brain_groups'] == 'thalamus'
pop_firing_rate = dset['spike_count'][mask,:,:].mean(axis=0)
plt.imshow(pop_firing_rate, cmap = 'bwr')Exercise: From the spike counts, extract out all the trials and time points, but only the cells from the thalamus brain area. Plot the total spike counts across trials as a heat map.
Solution
time = dset['spike_count'].time.values
mask = dset['brain_groups'] == 'thalamus'
data = dset['spike_count'][mask, :, :]
total_spikes = data.sum(axis=1)
plt.imshow(total_spikes);
plt.xticks(np.arange(len(time))[::30], time[::30]);Exercise: From the spike counts, extract out all the trials and time points, but only the cells from the Visual cortex brain area. Plot the total spike counts across trials as a heat map.
Solution
mask = dset['brain_groups'] == 'visual cortex'
data = dset['spike_count'][mask, :, :]
total_spikes = data.sum(axis=1)
plt.imshow(total_spikes);
plt.xticks(np.arange(len(time))[::30], time[::30]);Exercise: For each trial and time point, show the total activity across all cells in the visual cortex
Solution
mask = dset['brain_groups'] == 'visual cortex'
data = dset['spike_count'][mask, :, :]
total_spikes = data.sum(axis=0)
plt.imshow(total_spikes);
plt.xticks(np.arange(len(time))[::30], time[::30]);Exercise: For each trial and time point, show the total activity across all cells in the hippocampus.
Solution
mask = dset['brain_groups'] == 'hippocampus'
data = dset['spike_count'][mask, :, :]
total_spikes = data.sum(axis=0)
plt.imshow(total_spikes);
plt.xticks(np.arange(len(time))[::30], time[::30]);Example: Make a line plot showing the average number of total spikes for all cells in the thalamus.
mask = dset['brain_groups'] == 'thalamus'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data);
plt.xlabel('time (secs)')
plt.ylabel('average num spikes');Exercise: Make a line plot showing the average number of total spikes for all cells in the hippocampus.
Solution
mask = dset['brain_groups'] == 'hippocampus'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data);
plt.xlabel('time (secs)')
plt.ylabel('total spikes');Exercise: Make a line plot showing the average number of total spikes for all cells in the visual cortex.
Solution
mask = dset['brain_groups'] == 'visual cortex'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data);
plt.xlabel('time (secs)')
plt.ylabel('average num spikes');Exercise: Make a plot showing the average spike counts for all three brain areas (visual cortex, thalamus, and hippocampus)
Solution
mask = dset['brain_groups'] == 'visual cortex'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data, label='Visual Cortex');
mask = dset['brain_groups'] == 'thalamus'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data, label='Thalamus');
mask = dset['brain_groups'] == 'hippocampus'
data = dset['spike_count'][mask, :, :].sum(axis=0).mean(axis=0)
plt.plot(time, data, label='Hippocampus');
plt.xlabel('time (secs)')
plt.ylabel('average num spikes');
plt.legend();Section 3: Demo: Simplifying this process using XArray
Exercises
Example: This can also be done without creating a bunch of variables. Here are two examples:
dset['spike_count'][dset['brain_groups'] == 'visual cortex'].sum(dim='cell').plot.imshow();dset['spike_count'][dset['brain_groups'] == 'visual cortex'].sum(dim=('cell', 'trial')).plot.line();