Tutorial 2 - Data organization inside Imaging+¶
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[1]:
ipython3
import imagingplus as ip
imported imagingplus successfully
version: 0.2-beta
This notebook demonstrates the basics of how data is organized inside of a trial object.
Importing data analysis objects¶
The first step to begin using the data analysis objects is to import the previously created objects. In general, it is advised to import th high-level Experiment object first, and then use the .load_trial method to load an individual -Trial object.
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[24]:
ipython3
# import experiment obj and TwoPhotonImagingTrial object
expobj = ip.import_obj(pkl_path='/mnt/qnap_share/Data/imagingplus-example/RL109_analysis.pkl')
print(expobj)
trialobj = expobj.load_trial(trialID='t-013')
|- Loaded imagingplus.Experiment object (expID: RL109)109_analysis.pkl ...
imagingplus Experiment object (last saved: Sun Oct 23 13:04:11 2022), expID: RL109
file path: /mnt/qnap_share/Data/imagingplus-example/RL109_analysis.pkl
trials in Experiment object:
t-005: awake spont. 2p imaging + LFP
t-006: awake spont. 2p imaging + LFP
t-013: all optical trial with LFP
|- Loaded TwoPhotonImagingTrial.alloptical experimental trial object ...
paq data¶
The paq sub-module is used to retrieve and store data from a .paq file for each trial. This temporal-data was saved into trialobj.tmdata:
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[25]:
ipython3
# show all attributes saved in `trialobj.tmdata`
print(trialobj.tmdata.data)
frame_clock x_galvo_uncaging slm2packio markpoints2packio packio2slm packio2markpoints pycontrol_rsync voltage stim_start_times
0 0.005525 -1.167153 3.338084 0.005854 -0.001710 0.000264 1.320265 0.055181 False
1 0.005854 -1.166824 3.339728 0.007827 0.000264 -0.000065 1.237395 0.053866 False
2 0.005854 -1.165509 3.336768 0.007169 -0.000394 0.000592 1.158142 0.056168 False
3 0.005525 -1.166495 3.334138 0.006183 -0.000065 0.000264 1.084809 0.054524 False
4 0.005854 -1.166824 3.331178 0.007169 0.002565 0.000592 1.018710 0.055510 False
... ... ... ... ... ... ... ... ... ...
11864763 0.006512 -1.165837 3.331836 0.008156 0.001579 0.000921 0.022296 3.461073 False
11864764 0.007498 -1.165180 3.333151 0.006512 0.000592 0.000592 0.018679 3.461402 False
11864765 0.006512 -1.164851 3.335782 0.007827 -0.001052 0.000264 0.018350 3.461731 False
11864766 0.008485 -1.164851 3.341701 0.008156 -0.000065 0.000592 0.020323 3.461073 False
11864767 0.006512 -1.164851 3.344990 0.009142 -0.001052 0.000592 0.019008 3.461402 False
[11864768 rows x 9 columns]
We can see that the ‘frame_clock’ paq channel was used as the primary channel for retrieving imaging frame timestamps synchronized to the paq clock. There are a number of other channels associated with this .paq file (however their data is not saved in this object to save space). Data from any of the other channels can stored directly to the .paq object using the .storePaqChannel() object method. In this case, the ‘voltage’ paq channel was stored in its entirety under .paq.voltage.
Suite2p data¶
Suite2p is the primary Ca2+ imaging library that is integrated into the analysis pipeline. The dedicated suite2p submodule handles accessing suite2p functionality, as well as the data imported from Suite2p processing. Suite2p related data and methods are accessed using trialobj.Suite2p.
Some example functionality is shown below, refer to the reference documentation for more extensive information.
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[26]:
ipython3
trialobj.Suite2p
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[26]:
Suite2p Results (trial level) Object, 16368 key_frames x 640 s2p ROIs
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[27]:
ipython3
# the ROIs x raw data:
trialobj.Suite2p.imdata
# NOTE: we recommend working with the data that is stored in the anndata table (`trialobj.data`)
# for your processing/analysis work.
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[27]:
array([[352.13678 , 411.9472 , 280.92416 , ..., 401.3014 , 515.2566 ,
541.41565 ],
[192.22421 , 395.29306 , 330.7496 , ..., 257.25806 , 285.31506 ,
126.660484],
[336.64996 , 539.26746 , 219.30368 , ..., 423.15295 , 433.1515 ,
220.52742 ],
...,
[308.56497 , 303.55536 , 413.3554 , ..., 482.61044 , 386.2576 ,
283.1643 ],
[133.96815 , 122.96908 , 84.63106 , ..., 109.2256 , 187.91866 ,
159.50813 ],
[252.49574 , 240.2455 , 273.2785 , ..., 181.601 , 229.0061 ,
278.74188 ]], dtype=float32)
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[28]:
ipython3
# meta-information about ROIs from the Suite2p stat file:
trialobj.Suite2p.stat
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[28]:
array([{'ypix': array([102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 103, 103, 104,
104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105,
105, 105, 106, 106, 106, 106, 106, 106, 106, 106, 107, 107, 107,
107, 107, 107, 107, 108, 108, 108, 108, 108]), 'xpix': array([457, 458, 459, 460, 461, 456, 457, 458, 459, 460, 461, 462, 455,
456, 457, 458, 459, 460, 461, 462, 455, 456, 457, 458, 459, 460,
461, 462, 455, 456, 457, 458, 459, 460, 461, 462, 456, 457, 458,
459, 460, 461, 462, 457, 458, 459, 460, 461]), 'lam': array([0.00638468, 0.00895854, 0.01136301, 0.01110086, 0.00705759,
0.01243491, 0.02065784, 0.02732179, 0.03081292, 0.02857405,
0.01981334, 0.00643057, 0.01076856, 0.02522881, 0.03536875,
0.03719155, 0.03523667, 0.03170755, 0.02582202, 0.01195719,
0.01138049, 0.02936709, 0.0388124 , 0.03670786, 0.03174627,
0.02781939, 0.0247726 , 0.01484124, 0.0064307 , 0.02178741,
0.03363076, 0.03584319, 0.03252564, 0.02755288, 0.02219192,
0.01259714, 0.00933054, 0.01957686, 0.02685436, 0.02868195,
0.02438695, 0.01593173, 0.00604195, 0.00614118, 0.01241217,
0.01551214, 0.01438082, 0.00855111], dtype=float32), 'footprint': 1.0, 'mrs': 0.90981513, 'mrs0': 2.6042028761153, 'compact': 1.0019696625843746, 'med': [105.0, 459.0], 'npix': 48, 'overlap': array([ True, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, True, False, False, False, False, False, False,
False, True, True, False, False, False, False, False, False,
True, True, True, False, False, False, False, True, True,
True, True, False]), 'radius': 3.5656038064243396, 'aspect_ratio': 1.0513974755946096, 'npix_norm': 0.649175, 'skew': 3.0169547, 'std': 353.67505},
{'ypix': array([46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47, 47, 47, 47, 48, 48,
48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50,
50, 50, 50, 50, 51, 51, 51, 51, 51, 52, 52, 52]), 'xpix': array([116, 117, 118, 119, 120, 121, 114, 115, 116, 117, 118, 119, 120,
121, 122, 115, 116, 117, 118, 119, 120, 121, 122, 115, 116, 117,
118, 119, 120, 121, 122, 116, 117, 118, 119, 120, 121, 122, 117,
118, 119, 120, 121, 118, 119, 120]), 'lam': array([0.00909591, 0.01456937, 0.01832514, 0.01890799, 0.01570023,
0.00973547, 0.00744942, 0.01453985, 0.02419673, 0.03284908,
0.03678438, 0.03534257, 0.02903387, 0.01822114, 0.00731372,
0.01591995, 0.03092505, 0.04184085, 0.0448999 , 0.04109605,
0.03287527, 0.02178643, 0.00951811, 0.00878189, 0.02346953,
0.03750538, 0.04307739, 0.03926133, 0.02999047, 0.01967636,
0.00941749, 0.01269261, 0.02711541, 0.03495914, 0.03259644,
0.0242609 , 0.01472905, 0.00662102, 0.01389002, 0.02195401,
0.02241964, 0.01594619, 0.00838999, 0.00760095, 0.00864918,
0.00606909], dtype=float32), 'footprint': 1.0, 'mrs': 0.9120764, 'mrs0': 2.543516044642053, 'compact': 1.0284258197493805, 'med': [48.5, 119.0], 'npix': 46, 'overlap': array([ True, True, True, False, False, False, True, True, True,
False, False, False, False, False, True, True, False, False,
False, False, False, True, True, True, False, False, False,
False, True, True, True, True, False, False, True, True,
True, True, True, True, True, True, True, True, True,
True]), 'radius': 3.538467745454827, 'aspect_ratio': 1.0744280065263727, 'npix_norm': 0.62212604, 'skew': 3.7846518, 'std': 422.92258},
{'ypix': array([18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20,
20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22,
22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24,
24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26]), 'xpix': array([202, 203, 204, 205, 200, 201, 202, 203, 204, 205, 206, 207, 200,
201, 202, 203, 204, 205, 206, 207, 208, 200, 201, 202, 203, 204,
205, 206, 207, 208, 209, 199, 200, 201, 202, 203, 204, 205, 206,
207, 208, 200, 201, 202, 203, 204, 205, 206, 207, 200, 201, 202,
203, 204, 205, 206, 207, 201, 202, 203, 204, 205, 206, 207, 202,
203, 204, 205]), 'lam': array([0.00545189, 0.00608802, 0.00620215, 0.00528778, 0.00509016,
0.0122368 , 0.0166328 , 0.01625221, 0.01300093, 0.00930335,
0.00649809, 0.0054801 , 0.00835997, 0.01898179, 0.02681964,
0.02716714, 0.02171787, 0.01509764, 0.01041566, 0.00822917,
0.00693172, 0.0117227 , 0.02400276, 0.03096121, 0.03138037,
0.02795716, 0.02170095, 0.01506931, 0.01117996, 0.00849509,
0.00498517, 0.00457927, 0.01367252, 0.02446236, 0.02843685,
0.02762569, 0.02674694, 0.02412982, 0.01838652, 0.01173578,
0.00783563, 0.01189346, 0.02102235, 0.02426873, 0.02397901,
0.02280454, 0.0210674 , 0.01677865, 0.00966797, 0.00772996,
0.01501356, 0.0198556 , 0.02045688, 0.01869032, 0.01662539,
0.01329887, 0.00737635, 0.00898902, 0.01389659, 0.0156041 ,
0.01357497, 0.01125927, 0.00867181, 0.0049869 , 0.00614371,
0.00747479, 0.00685853, 0.00570034], dtype=float32), 'footprint': 1.0, 'mrs': 1.0885593, 'mrs0': 3.0920896689992627, 'compact': 1.0096624260641558, 'med': [22.0, 204.0], 'npix': 68, 'overlap': array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, False, False, False,
False, False, False, True, True, False, False, False, False,
False, True, True, True, False, False, False, False, True,
True, True, True, True, True, True, False, False, True,
True, True, True, True, True, False, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True]), 'radius': 4.124214681698544, 'aspect_ratio': 1.0274750851745456, 'npix_norm': 0.91966456, 'skew': 3.6033483, 'std': 342.36813},
...,
{'ypix': array([158, 159, 159, 159, 159, 160, 160, 160, 160, 160, 161, 161, 161,
162, 162, 162, 162, 162, 162, 162, 162, 163, 163, 163, 163, 163,
163, 163, 163, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164,
164, 164, 164, 164, 164, 164, 164, 165, 165, 165, 165, 165, 165,
165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 166,
166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166,
166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166,
167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167,
167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167,
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168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168,
168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168,
168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168,
168, 168, 168, 168, 168, 169, 169, 169, 169, 169, 169, 169, 169,
169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169,
169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169,
169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 170, 170, 170,
170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170,
170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170,
170, 170, 170, 170, 170, 170, 171, 171, 171, 171, 171, 171, 171,
171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171,
171, 171, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172,
172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 173, 173, 173,
173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173,
173, 173, 173, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174,
174, 174, 174, 174, 175, 175]), 'xpix': array([375, 374, 375, 376, 377, 373, 374, 375, 376, 387, 374, 379, 380,
374, 375, 376, 379, 382, 385, 386, 388, 372, 373, 374, 375, 376,
378, 379, 386, 373, 374, 378, 379, 380, 384, 386, 387, 388, 389,
390, 392, 393, 401, 402, 403, 404, 353, 354, 355, 356, 373, 374,
382, 384, 385, 386, 387, 388, 392, 401, 402, 403, 404, 405, 352,
353, 354, 355, 356, 370, 373, 374, 377, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 404, 405, 406, 409, 410, 411,
354, 355, 357, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374,
375, 376, 377, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389,
390, 391, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413,
353, 354, 355, 356, 357, 359, 365, 366, 367, 368, 369, 370, 371,
372, 373, 374, 375, 376, 377, 380, 381, 382, 383, 384, 385, 386,
388, 389, 390, 391, 392, 403, 404, 406, 408, 409, 410, 411, 412,
413, 414, 415, 416, 417, 354, 355, 356, 357, 358, 359, 360, 361,
362, 363, 364, 365, 366, 367, 369, 370, 371, 372, 373, 376, 377,
379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 404,
405, 406, 407, 408, 413, 414, 415, 416, 417, 418, 355, 356, 359,
360, 361, 362, 363, 364, 366, 367, 369, 370, 371, 377, 378, 379,
380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 404,
405, 406, 407, 416, 417, 418, 356, 367, 369, 370, 377, 378, 379,
380, 381, 382, 383, 387, 388, 389, 390, 391, 392, 403, 404, 416,
417, 418, 381, 382, 383, 387, 388, 389, 390, 391, 393, 395, 396,
397, 398, 399, 401, 403, 404, 405, 416, 417, 418, 359, 360, 380,
382, 387, 388, 389, 390, 391, 392, 393, 395, 396, 399, 400, 402,
403, 404, 405, 360, 390, 391, 392, 393, 394, 395, 396, 397, 398,
399, 400, 401, 402, 393, 394]), 'lam': array([0.00286928, 0.00259462, 0.00337227, 0.00255347, 0.00204948,
0.00202108, 0.00264209, 0.00276468, 0.00159292, 0.0017551 ,
0.00349023, 0.00286867, 0.00200605, 0.00511696, 0.00287608,
0.0017088 , 0.00151836, 0.00200302, 0.00164775, 0.00255818,
0.00152779, 0.00192849, 0.00335887, 0.00512549, 0.00234283,
0.00192926, 0.00196653, 0.00231145, 0.00387329, 0.00285975,
0.00298356, 0.00185528, 0.00229166, 0.00263537, 0.00209626,
0.00185905, 0.00425079, 0.00405806, 0.00191003, 0.0024535 ,
0.00249094, 0.00259834, 0.00277398, 0.00334289, 0.00350523,
0.00205348, 0.00302739, 0.00420143, 0.0031908 , 0.00126848,
0.00183293, 0.0029247 , 0.00197722, 0.00344929, 0.00318677,
0.00247438, 0.00310556, 0.00413478, 0.00176862, 0.00222995,
0.00326012, 0.00488892, 0.00418905, 0.00214313, 0.00250975,
0.00294531, 0.00288882, 0.0027946 , 0.0018857 , 0.00146898,
0.003711 , 0.00363824, 0.00154039, 0.00310719, 0.00375308,
0.00255617, 0.00466113, 0.00475768, 0.0030186 , 0.00474222,
0.00290538, 0.004514 , 0.00193983, 0.00285148, 0.00237405,
0.00505675, 0.00420587, 0.00221289, 0.00348542, 0.00489987,
0.00199475, 0.00183996, 0.00206761, 0.00302848, 0.00154587,
0.00251421, 0.00325534, 0.00324607, 0.00380017, 0.00604871,
0.00524036, 0.00550321, 0.00475682, 0.0037554 , 0.00470549,
0.00431793, 0.00338732, 0.00151919, 0.00159598, 0.00318731,
0.004536 , 0.00518949, 0.00561012, 0.00387813, 0.00329935,
0.00384294, 0.00238 , 0.00236514, 0.00192933, 0.00227059,
0.00360231, 0.00306801, 0.00418943, 0.00454379, 0.00372138,
0.002403 , 0.00354666, 0.00386164, 0.003181 , 0.00245785,
0.00255984, 0.00366211, 0.00436258, 0.00235788, 0.00183096,
0.00173962, 0.00151972, 0.00326573, 0.00403507, 0.00260203,
0.00447684, 0.00431312, 0.00344967, 0.00557541, 0.00342687,
0.00158354, 0.00394798, 0.00276107, 0.00271234, 0.00308114,
0.003789 , 0.00350815, 0.00290263, 0.00340051, 0.00217004,
0.00336243, 0.00159389, 0.00297907, 0.00402694, 0.00370309,
0.00189018, 0.00422567, 0.00462089, 0.00237474, 0.00265822,
0.00387684, 0.00313393, 0.0031415 , 0.00460251, 0.00364379,
0.00282508, 0.00214486, 0.00274323, 0.00315781, 0.00296068,
0.00282563, 0.00210713, 0.0016407 , 0.00155725, 0.00292419,
0.00161113, 0.00187676, 0.00170153, 0.00286219, 0.00260381,
0.00237066, 0.00381711, 0.00229396, 0.0043518 , 0.00322419,
0.00283945, 0.00257016, 0.00179217, 0.0015092 , 0.00209903,
0.00221834, 0.00235991, 0.00261474, 0.00332405, 0.00258864,
0.002608 , 0.00157223, 0.0016706 , 0.0020885 , 0.00282937,
0.00343391, 0.00242269, 0.00662414, 0.00623758, 0.00523879,
0.00461471, 0.00328737, 0.00286894, 0.00381225, 0.00226984,
0.00238015, 0.00494135, 0.00242791, 0.00132498, 0.00289301,
0.00220604, 0.00326554, 0.00390699, 0.00538431, 0.00660804,
0.00181801, 0.00395399, 0.00194908, 0.00214654, 0.00397877,
0.00191753, 0.00191654, 0.00249725, 0.00191403, 0.00348469,
0.0025085 , 0.00253094, 0.00373572, 0.00212011, 0.00132894,
0.00190789, 0.00326894, 0.00351297, 0.00348651, 0.0038919 ,
0.00185133, 0.00382373, 0.00376294, 0.00448563, 0.00410995,
0.00243404, 0.00267941, 0.00238115, 0.00217249, 0.00210481,
0.0014822 , 0.00162536, 0.00144151, 0.0023181 , 0.00149256,
0.00183859, 0.00408302, 0.00452229, 0.00208426, 0.00525849,
0.00521683, 0.00460148, 0.0049272 , 0.00447842, 0.00195348,
0.00327713, 0.00339903, 0.00256586, 0.00375898, 0.0021151 ,
0.00260367, 0.00298934, 0.00345007, 0.00504905, 0.00679261,
0.00553208, 0.00556962, 0.0051293 , 0.00482718, 0.00153104,
0.00187431, 0.00337595, 0.00319663, 0.00225396, 0.0020286 ,
0.00243751, 0.00402626, 0.00299196, 0.00178289, 0.00192062,
0.00151216, 0.00169066, 0.00303077, 0.00178265, 0.00131673,
0.0019633 , 0.00383657, 0.00185625, 0.00405352, 0.003759 ,
0.00313889, 0.00364831, 0.00576573, 0.0017102 , 0.00263339,
0.00242149, 0.00277657, 0.00511749, 0.00442813, 0.00205553,
0.0018405 , 0.00177784, 0.00203138, 0.00323508, 0.00316913,
0.00243073, 0.00238249, 0.00385366, 0.00318298, 0.00193085,
0.00209014, 0.00362567, 0.00206467, 0.00271361, 0.00130027,
0.00184112], dtype=float32), 'footprint': 2.0, 'mrs': 4.998543, 'mrs0': 6.843361744187066, 'compact': 2.0948364712531085, 'med': [168.0, 385.0], 'npix': 331, 'overlap': array([ True, False, False, True, True, False, True, True, False,
True, False, False, True, False, True, False, True, True,
True, True, True, True, True, False, True, False, False,
True, True, True, False, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, False, False, True, False, True, True,
True, True, True, True, True, True, False, True, True,
True, True, True, True, False, False, True, False, False,
True, True, True, True, True, True, True, False, False,
False, True, False, True, True, True, True, True, True,
True, True, False, False, True, True, True, True, True,
False, False, False, False, False, False, False, False, True,
True, True, True, True, True, True, True, False, False,
True, True, True, True, True, True, True, True, True,
True, True, True, True, False, False, False, True, True,
True, True, True, True, True, True, False, False, False,
False, False, False, False, False, True, True, True, True,
True, True, True, True, False, False, False, False, True,
True, True, True, True, True, True, True, True, True,
True, True, False, False, True, True, True, True, True,
True, True, True, True, True, True, True, True, False,
False, False, False, False, False, False, True, True, True,
True, True, True, True, True, True, True, True, False,
True, True, True, True, True, True, True, True, True,
True, True, False, False, True, False, False, False, False,
True, False, False, False, False, False, False, False, True,
False, True, True, True, True, True, True, True, True,
True, False, False, True, True, True, True, True, True,
True, False, False, False, False, True, False, False, False,
False, False, True, True, False, True, True, True, True,
True, True, True, True, True, False, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, False, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True]), 'radius': 33.09026240792262, 'aspect_ratio': 1.6760580112361614, 'npix_norm': 4.4766026, 'skew': 2.9793897, 'std': 56.44806},
{'ypix': array([274, 274, 274, 274, 275, 275, 275, 275, 276, 276, 276, 276, 277,
277, 277, 277, 278, 278, 278, 279, 279, 279, 279, 280, 280, 280,
280, 281, 281, 281, 281, 282, 282, 282, 282, 282, 282, 282, 282,
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284, 284, 284, 284, 284, 284, 285, 285, 285, 285, 285, 285, 285,
285, 285, 286, 286, 286, 286, 286, 286, 286, 286, 287, 287, 287,
287, 287, 287, 287, 287, 287, 288, 288, 288, 288, 288, 288, 288,
288, 288, 288, 289, 289, 289, 289, 289, 289, 289, 289, 290, 290,
290, 290, 290, 290, 290, 291, 291, 291, 291, 291, 291, 292, 292,
292, 292, 292, 292, 292, 292, 292, 292, 293, 293, 293, 293, 293,
293, 293, 293, 293, 293, 293, 293, 293, 293, 294, 294, 294, 294,
294, 294, 294, 294, 294, 294, 294, 294, 295, 295, 295, 295, 295,
295, 295, 295, 295, 295, 295, 295, 296, 296, 296, 296, 296]), 'xpix': array([235, 236, 237, 238, 235, 236, 237, 238, 235, 236, 237, 238, 235,
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228, 229, 233, 234, 235, 236, 237, 238, 239, 240, 241, 229, 230,
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241, 242, 245, 246, 247, 248, 249, 250, 238, 239, 240, 241, 242,
243, 244, 245, 246, 247, 248, 249, 242, 243, 244, 248, 249]), 'lam': array([0.00353741, 0.00517016, 0.00329727, 0.00242688, 0.00562091,
0.00851122, 0.00902197, 0.00581402, 0.00508556, 0.00906205,
0.01027361, 0.00704236, 0.00266805, 0.00577183, 0.00894417,
0.00258301, 0.00346624, 0.00572842, 0.00828662, 0.00369061,
0.00778687, 0.00929033, 0.00555683, 0.00537309, 0.00639053,
0.01002211, 0.00271699, 0.00966459, 0.00380201, 0.00461136,
0.00312464, 0.00320701, 0.00406449, 0.00522045, 0.00784463,
0.00931368, 0.00349013, 0.00940471, 0.00486539, 0.00372914,
0.00348873, 0.00299941, 0.00649019, 0.01081055, 0.00591648,
0.00761427, 0.00786332, 0.00745158, 0.00659136, 0.00329301,
0.00506582, 0.00632644, 0.00559625, 0.00547724, 0.0035666 ,
0.00537009, 0.00703207, 0.00474662, 0.00293929, 0.00527441,
0.00700889, 0.00802964, 0.00545722, 0.00701146, 0.00783141,
0.00954959, 0.00472145, 0.00286008, 0.00413494, 0.00316838,
0.00655688, 0.00773258, 0.01021765, 0.00886467, 0.0038621 ,
0.00265623, 0.00318746, 0.00749208, 0.01182964, 0.01098835,
0.00886356, 0.00513412, 0.00592422, 0.00542556, 0.00280691,
0.00454984, 0.00804457, 0.0096524 , 0.00539598, 0.00430585,
0.00582052, 0.00585476, 0.00655159, 0.00419438, 0.00528989,
0.00720624, 0.00521403, 0.0070284 , 0.00683123, 0.00418202,
0.00422283, 0.00337052, 0.00919068, 0.00937859, 0.00727854,
0.0053387 , 0.0047962 , 0.0045184 , 0.00378519, 0.00492048,
0.01271848, 0.00960135, 0.00512651, 0.00453599, 0.00539573,
0.00272184, 0.00689939, 0.00851619, 0.00394244, 0.00283469,
0.00492722, 0.00332688, 0.00271739, 0.00385867, 0.0044151 ,
0.00225622, 0.00788738, 0.00966464, 0.00583506, 0.00456629,
0.00366349, 0.00343617, 0.00392625, 0.0041062 , 0.00613991,
0.00736859, 0.00892439, 0.00740234, 0.00635011, 0.00564566,
0.01279525, 0.01160507, 0.0078163 , 0.00567653, 0.00653419,
0.00338925, 0.00434683, 0.0054269 , 0.00842457, 0.0101354 ,
0.00415056, 0.00570583, 0.00666986, 0.00296249, 0.00338989,
0.00395845, 0.00390827, 0.00890002, 0.00437463, 0.00502896,
0.00769272, 0.01021333, 0.00910969, 0.0031401 , 0.00529241,
0.00510558, 0.00368369, 0.00422071], dtype=float32), 'footprint': 1.0, 'mrs': 2.4965265, 'mrs0': 4.877157380553088, 'compact': 1.468066186248697, 'med': [287.5, 238.0], 'npix': 168, 'overlap': array([False, False, False, False, True, True, False, False, True,
True, True, False, True, True, False, False, True, True,
False, True, True, True, False, True, True, False, False,
True, True, True, False, True, False, True, True, True,
True, False, False, True, True, False, False, False, True,
False, False, False, False, False, True, True, True, False,
False, False, False, True, True, True, False, False, False,
False, False, False, True, True, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, True,
True, True, True, False, False, True, True, True, True,
True, False, True, True, True, True, True, False, True,
True, True, True, True, True, True, True, True, False,
True, True, True, True, True, True, True, True, True,
True, True, True, True, False, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, False]), 'radius': 13.957618205848593, 'aspect_ratio': 1.339115841204737, 'npix_norm': 2.2721124, 'skew': 1.6962737, 'std': 55.597923},
{'ypix': array([380, 381, 381, 381, 381, 381, 381, 381, 381, 381, 381, 381, 382,
382, 382, 382, 382, 382, 382, 382, 382, 382, 383, 383, 383, 383,
383, 383, 383, 383, 383, 384, 384, 384, 384, 384, 384, 384, 384,
384, 385, 385, 385, 385, 385, 385, 385, 385, 385, 385, 386, 386,
386, 386, 386, 386, 386, 386, 386, 386, 386, 387, 387, 387, 387,
387, 387, 387, 387, 387, 387, 387, 388, 388, 388, 388, 388, 388,
388, 388, 388, 388, 389, 389, 389, 389, 389, 389, 389, 390, 390,
390, 390, 390, 391, 391, 391, 391, 391, 391, 391, 392, 392, 392,
392, 392, 392, 392, 392, 393, 393, 393, 393, 393, 393, 393, 394,
394, 394, 394, 394, 394, 394, 395, 395, 395, 395, 395, 395, 395,
396, 396, 396, 396, 397]), 'xpix': array([165, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 161,
162, 167, 168, 169, 170, 171, 172, 173, 174, 162, 167, 168, 169,
170, 171, 172, 173, 174, 166, 167, 168, 169, 170, 171, 172, 173,
174, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 164, 165,
166, 167, 168, 169, 170, 171, 172, 173, 174, 164, 165, 166, 167,
168, 169, 170, 171, 172, 173, 174, 165, 166, 167, 168, 169, 170,
171, 172, 173, 174, 167, 168, 169, 170, 171, 172, 173, 167, 168,
170, 171, 172, 166, 167, 168, 169, 170, 171, 172, 164, 165, 166,
167, 168, 169, 170, 171, 164, 165, 166, 167, 168, 169, 170, 164,
165, 166, 167, 168, 169, 170, 164, 165, 166, 167, 168, 169, 170,
166, 167, 168, 170, 167]), 'lam': array([0.00340487, 0.00443801, 0.00432576, 0.00364362, 0.00372115,
0.0040224 , 0.00417633, 0.00480787, 0.00354271, 0.00403816,
0.0050534 , 0.00306647, 0.00317431, 0.00347661, 0.00582939,
0.00717845, 0.00530369, 0.00492711, 0.00470956, 0.00557796,
0.00620364, 0.00479258, 0.00335542, 0.00475656, 0.00652052,
0.00544199, 0.00550956, 0.00730126, 0.00841345, 0.00802831,
0.0042955 , 0.00464875, 0.0083406 , 0.00900917, 0.00706007,
0.00674814, 0.00993268, 0.01159688, 0.00853468, 0.00410526,
0.0044767 , 0.00742816, 0.00709389, 0.00519973, 0.00671921,
0.00670468, 0.00825069, 0.01028707, 0.0091009 , 0.00554293,
0.00396461, 0.00787717, 0.00824603, 0.0078269 , 0.00689538,
0.00743007, 0.00990708, 0.01138136, 0.0117935 , 0.00800649,
0.0052063 , 0.0050209 , 0.00850611, 0.0077141 , 0.00610797,
0.0056674 , 0.00711334, 0.01013461, 0.01220826, 0.01165707,
0.01071399, 0.00704673, 0.00452671, 0.00680357, 0.00701252,
0.00787713, 0.00966987, 0.00868987, 0.00850217, 0.00997268,
0.00815679, 0.00552057, 0.00465902, 0.00710784, 0.00624458,
0.00395763, 0.00561889, 0.00919538, 0.00613715, 0.00527674,
0.00642221, 0.00384698, 0.00716806, 0.0092556 , 0.00563079,
0.00597326, 0.00725889, 0.00778292, 0.00925728, 0.00931448,
0.00463286, 0.00385788, 0.00715826, 0.00858624, 0.0110799 ,
0.01386848, 0.01385103, 0.01151467, 0.00747484, 0.00697996,
0.01026383, 0.01158813, 0.01415514, 0.01577739, 0.01696804,
0.01635207, 0.00626897, 0.01167264, 0.00914658, 0.00947039,
0.01374428, 0.01636752, 0.01416822, 0.00490234, 0.00620987,
0.00580414, 0.00667666, 0.01042749, 0.00996509, 0.00810222,
0.00406754, 0.00441266, 0.00561097, 0.00375401, 0.00403797],
dtype=float32), 'footprint': 2.0, 'mrs': 1.7417334, 'mrs0': 4.367111321475383, 'compact': 1.1438356678731552, 'med': [387.0, 169.0], 'npix': 135, 'overlap': array([ True, True, True, True, False, False, False, False, False,
False, False, True, True, True, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, True, True,
False, False, False, False, False, False, False, False, True,
True, False, False, False, False, False, True, True, False,
False, True, True, True, False, False, True, False, False,
True, True, False, False, True, True, True, True, False,
True, False, True, True, True, True, True, False, False,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True]), 'radius': 8.848792719685099, 'aspect_ratio': 1.2544510943588814, 'npix_norm': 1.8258046, 'skew': 1.5335176, 'std': 60.688213}],
dtype=object)
none
[29]:
ipython3
# meta-information about the Suite2p run:
trialobj.Suite2p.output_ops
none
[29]:
{'suite2p_version': '0.9.3',
'look_one_level_down': False,
'fast_disk': '/mnt/sandbox/pshah/suite2p_tmp/suite2p/plane0',
'delete_bin': True,
'mesoscan': False,
'bruker': False,
'h5py': [],
'h5py_key': 'data',
'save_path0': '/home/pshah/mnt/qnap/Data/2020-12-19',
'save_folder': '/home/pshah/mnt/qnap/Analysis/2020-12-19/suite2p/alloptical-2p-1x-alltrials',
'subfolders': [],
'move_bin': False,
'nplanes': 1,
'nchannels': 1,
'functional_chan': 1,
'tau': 1.26,
'fs': 30.0,
'force_sktiff': False,
'frames_include': -1,
'multiplane_parallel': False,
'preclassify': 0.0,
'save_mat': True,
'save_NWB': False,
'combined': True,
'aspect': 1.0,
'do_bidiphase': False,
'bidiphase': 0,
'bidi_corrected': True,
'do_registration': True,
'two_step_registration': False,
'keep_movie_raw': False,
'nimg_init': 200,
'batch_size': 2000,
'maxregshift': 0.1,
'align_by_chan': 1,
'reg_tif': True,
'reg_tif_chan2': False,
'subpixel': 10,
'smooth_sigma_time': 0,
'smooth_sigma': 1.15,
'th_badframes': 1.0,
'norm_frames': True,
'force_refImg': False,
'pad_fft': False,
'nonrigid': True,
'block_size': (128, 128),
'snr_thresh': 1.2,
'maxregshiftNR': 5,
'1Preg': False,
'spatial_hp': 42,
'spatial_hp_reg': 42,
'spatial_hp_detect': 25,
'pre_smooth': 0,
'spatial_taper': 40,
'roidetect': True,
'spikedetect': True,
'anatomical_only': False,
'sparse_mode': True,
'diameter': 4.5,
'spatial_scale': 0,
'connected': True,
'nbinned': 5000,
'max_iterations': 20,
'threshold_scaling': 1.0,
'max_overlap': 0.75,
'high_pass': 100,
'use_builtin_classifier': False,
'neuropil_extract': True,
'inner_neuropil_radius': 2,
'min_neuropil_pixels': 350,
'allow_overlap': False,
'chan2_thres': 0.65,
'baseline': 'maximin',
'win_baseline': 60.0,
'sig_baseline': 10.0,
'prctile_baseline': 8.0,
'neucoeff': 0.7,
'num_workers': 50,
'num_workers_roi': 0,
'navg_frames_svd': 5000,
'nsvd_for_roi': 1000,
'ratio_neuropil': 6.0,
'ratio_neuropil_to_cell': 3,
'tile_factor': 1.0,
'outer_neuropil_radius': inf,
'data_path': ['/home/pshah/mnt/qnap/Data/2020-12-19'],
'tiff_list': ['/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-005/2020-12-19_t-005_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-006/2020-12-19_t-006_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-007/2020-12-19_t-007_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-008/2020-12-19_t-008_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-011/2020-12-19_t-011_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-012/2020-12-19_t-012_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-013/2020-12-19_t-013_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-014/2020-12-19_t-014_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-016/2020-12-19_t-016_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-017/2020-12-19_t-017_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-018/2020-12-19_t-018_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-019/2020-12-19_t-019_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-020/2020-12-19_t-020_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-021/2020-12-19_t-021_Cycle00001_Ch3.tif'],
'input_format': 'tif',
'save_path': '/home/pshah/mnt/qnap/Analysis/2020-12-19/suite2p/alloptical-2p-1x-alltrials/plane0',
'ops_path': '/home/pshah/mnt/qnap/Analysis/2020-12-19/suite2p/alloptical-2p-1x-alltrials/plane0/ops.npy',
'reg_file': '/mnt/sandbox/pshah/suite2p_tmp/suite2p/plane0/data.bin',
'first_tiffs': array([ True, False, False, False, False, False, False, False, False,
False, False, False, False, False]),
'frames_per_folder': array([256018], dtype=int32),
'filelist': ['/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-005/2020-12-19_t-005_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-006/2020-12-19_t-006_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-007/2020-12-19_t-007_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-008/2020-12-19_t-008_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-011/2020-12-19_t-011_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-012/2020-12-19_t-012_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-013/2020-12-19_t-013_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-014/2020-12-19_t-014_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-016/2020-12-19_t-016_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-017/2020-12-19_t-017_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-018/2020-12-19_t-018_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-019/2020-12-19_t-019_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-020/2020-12-19_t-020_Cycle00001_Ch3.tif',
'/home/pshah/mnt/qnap/Data/2020-12-19/2020-12-19_t-021/2020-12-19_t-021_Cycle00001_Ch3.tif'],
'nframes': 256018,
'frames_per_file': array([14880, 23173, 16368, 16368, 16368, 16368, 16368, 16368, 15436,
17520, 26784, 16368, 20015, 23634]),
'meanImg': array([[ 50.12349604, 50.23252739, 48.01559696, ..., 121.3560685 ,
95.51667508, 71.15855232],
[ 51.21065841, 50.49309602, 48.47367467, ..., 121.16339316,
96.34339423, 72.14198395],
[ 54.0642524 , 53.15367606, 50.94001476, ..., 122.28726252,
97.78234619, 74.57712429],
...,
[ 48.55685746, 48.13359499, 47.3793314 , ..., 110.28305276,
85.49449971, 66.07143804],
[ 48.142719 , 47.56769193, 46.87753913, ..., 112.38966948,
88.19436885, 68.4536527 ],
[ 47.91820708, 47.66211425, 46.95750661, ..., 114.25616458,
90.47362592, 70.60839945]]),
'Ly': 512,
'Lx': 512,
'yrange': [9, 503],
'xrange': [8, 504],
'date_proc': ('2021-04-12 01:24:09.523948',),
'refImg': array([[ 35, 40, 64, ..., 162, 107, 99],
[ 43, 49, 41, ..., 198, 108, 113],
[ 43, 43, 80, ..., 198, 174, 117],
...,
[ 71, 55, 41, ..., 107, 87, 55],
[ 35, 54, 49, ..., 104, 78, 60],
[ 53, 36, 41, ..., 86, 52, 50]], dtype=int16),
'yblock': [array([ 0, 128]),
array([ 0, 128]),
array([ 0, 128]),
array([ 0, 128]),
array([ 0, 128]),
array([ 0, 128]),
array([ 76, 204]),
array([ 76, 204]),
array([ 76, 204]),
array([ 76, 204]),
array([ 76, 204]),
array([ 76, 204]),
array([153, 281]),
array([153, 281]),
array([153, 281]),
array([153, 281]),
array([153, 281]),
array([153, 281]),
array([230, 358]),
array([230, 358]),
array([230, 358]),
array([230, 358]),
array([230, 358]),
array([230, 358]),
array([307, 435]),
array([307, 435]),
array([307, 435]),
array([307, 435]),
array([307, 435]),
array([307, 435]),
array([384, 512]),
array([384, 512]),
array([384, 512]),
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0. , 18.946835 , 71.625206 , 47.389957 , 33.905098 ,
36.503983 , 36.663177 , 67.34269 , 60.24297 , 59.704277 ,
43.837112 , 54.632523 , 72.18209 , 43.334446 , 36.803596 ,
12.46697 , 31.010822 , 32.08981 , 24.267569 , 26.31773 ,
14.205252 ],
[ 12.415798 , 22.179655 , 40.28706 , 70.733246 , 45.976845 ,
15.367744 , 10.765518 , 19.17331 , 20.989594 , 0. ,
16.940937 , 22.014372 , 60.527145 , 48.575287 , 44.873466 ,
36.98361 , 32.90341 , 56.18464 , 36.599037 , 32.541187 ,
22.995617 , 33.40358 , 41.268433 , 29.26771 , 42.17862 ,
29.699398 , 49.418747 , 34.054085 , 31.691277 , 39.46365 ,
26.664072 ],
[ 20.650377 , 31.5019 , 19.80476 , 17.834845 , 0. ,
11.243249 , 21.004097 , 33.937267 , 34.570484 , 14.8726425,
19.466679 , 24.94124 , 64.13373 , 62.453747 , 56.201878 ,
39.288147 , 49.11496 , 49.559643 , 51.781067 , 52.13033 ,
69.369606 , 67.112434 , 59.32581 , 41.05275 , 51.665318 ,
41.544285 , 59.189323 , 36.45694 , 37.24715 , 37.08217 ,
30.871262 ],
[ 73.68833 , 72.83313 , 46.720818 , 22.842861 , 10.706368 ,
26.221462 , 29.644278 , 60.330868 , 33.24753 , 15.645941 ,
19.726418 , 30.158867 , 58.787712 , 62.116245 , 77.67166 ,
74.466675 , 88.32849 , 65.13905 , 42.919373 , 31.354347 ,
43.751274 , 44.326294 , 56.69054 , 41.900967 , 60.17963 ,
49.41677 , 65.14522 , 43.092133 , 29.558516 , 25.482983 ,
30.73054 ],
[ 13.020716 , 27.34724 , 40.140205 , 20.155624 , 32.174774 ,
56.04063 , 53.90798 , 67.00694 , 20.946318 , 30.36067 ,
50.926823 , 56.70052 , 65.39784 , 57.883133 , 74.21626 ,
81.24211 , 107.921875 , 95.31582 , 63.307045 , 36.27327 ,
40.20667 , 46.316204 , 55.129314 , 44.933735 , 51.42051 ,
45.037663 , 52.088474 , 40.505413 , 36.676994 , 50.82425 ,
47.347218 ],
[ 0. , 14.254572 , 12.756462 , 27.333847 , 88.414406 ,
56.557953 , 42.787914 , 34.31327 , 33.14932 , 62.234585 ,
89.91793 , 79.488884 , 83.20377 , 53.7662 , 69.51782 ,
56.061245 , 69.1291 , 61.856358 , 21.744175 , 25.660513 ,
0. , 33.505833 , 53.59767 , 53.598907 , 56.61656 ,
46.451965 , 40.940727 , 34.242485 , 27.516453 , 26.344011 ,
0. ],
[ 0. , 0. , 18.546104 , 36.564713 , 45.84534 ,
0. , 0. , 18.260416 , 87.69083 , 74.11969 ,
111.75257 , 66.37363 , 52.041443 , 12.619892 , 21.150396 ,
22.890997 , 51.84068 , 72.282394 , 25.026175 , 0. ,
0. , 21.175545 , 23.335567 , 32.922916 , 45.002266 ,
54.604027 , 42.660576 , 27.103464 , 19.974123 , 21.852142 ,
0. ],
[ 26.09762 , 0. , 14.429226 , 0. , 0. ,
0. , 13.387272 , 51.04614 , 102.92584 , 81.83338 ,
78.45017 , 54.45624 , 48.19962 , 20.947622 , 13.310457 ,
11.487244 , 18.69836 , 49.52861 , 18.11824 , 15.17062 ,
22.62213 , 38.891678 , 52.026188 , 57.005016 , 62.450703 ,
56.280834 , 56.293064 , 41.676464 , 45.163937 , 17.143925 ,
0. ],
[ 69.2023 , 45.039143 , 55.902893 , 11.05204 , 0. ,
0. , 29.517735 , 87.63849 , 113.8917 , 51.189655 ,
42.392963 , 36.309063 , 31.309158 , 30.220482 , 32.288418 ,
30.90326 , 34.732376 , 34.15919 , 28.271446 , 19.721619 ,
30.168718 , 41.933464 , 42.43278 , 30.222301 , 49.654083 ,
40.82311 , 60.85302 , 55.470646 , 56.86636 , 29.072601 ,
0. ],
[ 51.31906 , 39.408813 , 83.5 , 49.80349 , 60.469414 ,
48.037983 , 45.27676 , 75.21179 , 31.432724 , 40.231922 ,
33.33812 , 28.125282 , 51.62141 , 30.795444 , 19.669012 ,
15.096413 , 34.983948 , 35.408955 , 57.472977 , 101.00325 ,
47.860912 , 62.187714 , 25.056152 , 47.735107 , 34.697876 ,
46.761932 , 36.07154 , 38.886765 , 16.448402 , 0. ,
0. ],
[ 36.575233 , 15.004146 , 65.10667 , 35.94373 , 43.60637 ,
28.609274 , 10.350796 , 84.091576 , 46.850124 , 57.395676 ,
56.469635 , 14.328652 , 44.849834 , 15.821119 , 40.797646 ,
25.726791 , 28.025984 , 23.909845 , 32.93551 , 30.149992 ,
30.56912 , 42.578205 , 38.922474 , 58.99219 , 19.601097 ,
20.381102 , 0. , 0. , 0. , 0. ,
0. ],
[ 43.74546 , 32.38469 , 47.736877 , 33.801086 , 24.081137 ,
12.182896 , 0. , 19.05047 , 12.183497 , 27.165195 ,
24.599052 , 16.693968 , 20.222195 , 17.012455 , 52.180542 ,
33.2982 , 32.71403 , 35.016582 , 36.288067 , 26.527443 ,
40.014294 , 33.828037 , 26.525133 , 19.702091 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[ 49.69824 , 39.516964 , 31.188408 , 26.918964 , 0. ,
0. , 0. , 18.505878 , 12.468141 , 14.049125 ,
50.826515 , 0. , 19.151745 , 15.160233 , 41.003994 ,
24.800297 , 25.76484 , 28.67309 , 47.285294 , 23.727394 ,
21.46927 , 28.968851 , 0. , 14.954822 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[ 49.704754 , 47.20818 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
17.729279 , 17.599442 , 50.23716 , 39.016808 , 42.02624 ,
42.25864 , 58.123146 , 41.62897 , 49.37666 , 18.239134 ,
0. , 11.025133 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[ 62.078747 , 60.143616 , 23.276524 , 24.717758 , 22.978292 ,
10.960012 , 11.229352 , 12.934702 , 53.534874 , 54.600044 ,
45.671627 , 29.377514 , 45.58333 , 31.234728 , 55.60468 ,
42.841145 , 78.4407 , 50.08436 , 21.816065 , 0. ,
0. , 14.923934 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. ],
[ 37.65311 , 34.476532 , 21.753775 , 21.979464 , 28.30353 ,
0. , 12.026232 , 0. , 31.215992 , 39.304512 ,
64.60065 , 11.7430525, 24.881798 , 20.760872 , 73.585915 ,
54.400772 , 51.92013 , 20.653893 , 10.239027 , 16.149317 ,
0. , 0. , 0. , 0. , 0. ,
0. , 10.042473 , 0. , 11.338436 , 15.213412 ,
15.844808 ],
[ 0. , 19.078972 , 27.77968 , 30.989094 , 54.33296 ,
0. , 13.461093 , 0. , 11.069255 , 0. ,
21.951103 , 0. , 15.719333 , 23.344418 , 76.51987 ,
35.987278 , 25.11747 , 0. , 15.156668 , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 28.900726 , 55.672203 , 54.48396 ,
34.622555 ],
[ 17.740337 , 23.67508 , 41.108585 , 28.646248 , 34.675606 ,
0. , 0. , 0. , 0. , 0. ,
25.658663 , 11.66374 , 25.59551 , 0. , 18.080381 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 27.093662 , 0. ,
0. ],
[ 13.67132 , 22.655464 , 71.59811 , 28.375526 , 23.769104 ,
0. , 0. , 12.166921 , 0. , 17.340382 ,
15.418265 , 20.931238 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 46.89845 ,
48.228558 , 37.01589 , 35.522602 , 26.637213 , 0. ,
0. ],
[ 0. , 0. , 35.08596 , 0. , 21.843304 ,
0. , 26.014463 , 39.36237 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 10.372042 , 0. ,
0. ],
[ 0. , 0. , 0. , 0. , 17.737555 ,
13.628258 , 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=float32)],
'spatscale_pix': array([12]),
'meanImgE': 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=float32),
'timing': {'registration': 11319.762184858322,
'registration_metrics': 34.16234278678894,
'detection': 671.0295331478119,
'extraction': 867.5214445590973,
'classification': 0.08369636535644531,
'deconvolution': 24.275188446044922,
'total_plane_runtime': 12946.64022898674}}
Annotated Data¶
The AnnData library is the primary protocol that is used to store imaging data in an efficient, multi-functional format. It is created using the anndata sub-module and can be accessed using trialobj.data. By default, trialobj.data is a data array generated from Suite2p processed data. For all guidance on AnnData objects, visit: https://anndata.readthedocs.io/en/latest/index.html.
The AnnData object is built around the raw Flu matrix of each trialobj . In keeping with AnnData conventions, the data structure is organized in n observations (obs) x m variables (var), where observations are suite2p ROIs and variables are imaging frame timepoints.
none
[30]:
ipython3
print(trialobj.data) # this is the anndata object for this trial
Annotated Data of n_obs × n_vars = 640 × 16368
available attributes:
.X (primary datamatrix) of .data_label:
|- suite2p raw - neuropil corrected
.obs (obs metadata):
|- 'ypix', 'xpix', 'lam', 'footprint', 'mrs', 'mrs0', 'compact', 'med', 'npix', 'overlap', 'radius', 'aspect_ratio', 'npix_norm', 'skew', 'std'
.var (vars metadata):
|- 'frame_clock', 'x_galvo_uncaging', 'slm2packio', 'markpoints2packio', 'packio2slm', 'packio2markpoints', 'pycontrol_rsync', 'voltage'
.obsm:
|- 'ypix', 'xpix'
storage of Flu data¶
The raw data is stored in .X
none
[31]:
ipython3
print(trialobj.data.X)
print('shape: ', trialobj.data.X.shape)
[[352.13678 411.9472 280.92416 ... 401.3014 515.2566 541.41565 ]
[192.22421 395.29306 330.7496 ... 257.25806 285.31506 126.660484]
[336.64996 539.26746 219.30368 ... 423.15295 433.1515 220.52742 ]
...
[308.56497 303.55536 413.3554 ... 482.61044 386.2576 283.1643 ]
[133.96815 122.96908 84.63106 ... 109.2256 187.91866 159.50813 ]
[252.49574 240.2455 273.2785 ... 181.601 229.0061 278.74188 ]]
shape: (640, 16368)
Processed data is added to trialobj.data as a unique layers key.
none
[34]:
ipython3
trialobj.data.layers
# NOTE: we haven't added any layers to this dataset yet.
none
[34]:
Layers with keys:
The entire AnnData data object is built according to the dimensions of the original Flu data input.
observations (Suite2p ROIs metadata and associated processing info)¶
For instance, the metadata for each suite2p ROI stored in Suite2p’s stat.npy output is added to trialobject.data under obs and obsm (1D and >1-D observations annotations, respectively).
none
[35]:
ipython3
trialobj.data.obs
none
[35]:
| ypix | xpix | lam | footprint | mrs | ... | radius | aspect_ratio | npix_norm | skew | std | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [102, 102, 102, 102, 102, 103, 103, 103, 103, ... | [457, 458, 459, 460, 461, 456, 457, 458, 459, ... | [0.0063846777, 0.008958542, 0.011363007, 0.011... | 1.0 | 0.909815 | ... | 3.565604 | 1.051397 | 0.649175 | 3.016955 | 353.675049 |
| 1 | [46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 4... | [116, 117, 118, 119, 120, 121, 114, 115, 116, ... | [0.009095913, 0.014569374, 0.01832514, 0.01890... | 1.0 | 0.912076 | ... | 3.538468 | 1.074428 | 0.622126 | 3.784652 | 422.922577 |
| 2 | [18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 1... | [202, 203, 204, 205, 200, 201, 202, 203, 204, ... | [0.00545189, 0.006088022, 0.0062021483, 0.0052... | 1.0 | 1.088559 | ... | 4.124215 | 1.027475 | 0.919665 | 3.603348 | 342.368134 |
| 3 | [43, 44, 45, 46, 46, 47, 47, 47, 48, 48, 48, 4... | [352, 352, 352, 352, 353, 352, 353, 354, 351, ... | [0.0036495698, 0.0043396214, 0.0031816224, 0.0... | 1.0 | 1.561322 | ... | 8.133019 | 1.348522 | 1.325399 | 3.187822 | 357.666168 |
| 4 | [156, 156, 156, 156, 156, 157, 157, 157, 157, ... | [382, 383, 384, 385, 386, 380, 381, 382, 383, ... | [0.013304887, 0.02187323, 0.023734575, 0.01969... | 1.0 | 0.869808 | ... | 3.62042 | 1.139261 | 0.554504 | 2.59998 | 263.609039 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1241 | [290, 291, 291, 291, 291, 291, 291, 291, 291, ... | [299, 298, 299, 300, 301, 302, 305, 306, 307, ... | [0.0029507184, 0.0032565512, 0.005071437, 0.00... | 2.0 | 2.336259 | ... | 11.46564 | 1.251471 | 2.447931 | 2.540658 | 94.641136 |
| 1242 | [354, 354, 355, 355, 355, 355, 355, 356, 356, ... | [309, 310, 308, 309, 310, 311, 312, 307, 308, ... | [0.00252066, 0.0021455055, 0.007094776, 0.0078... | 2.0 | 2.386548 | ... | 10.831544 | 1.129317 | 2.934812 | 2.229956 | 79.882561 |
| 1246 | [15, 15, 16, 16, 16, 17, 17, 17, 17, 17, 17, 1... | [488, 489, 486, 487, 489, 486, 487, 488, 489, ... | [0.010669279, 0.007242187, 0.013514522, 0.0124... | 2.0 | 2.238235 | ... | 13.789857 | 1.460301 | 1.636462 | 4.38241 | 55.825489 |
| 1250 | [472, 472, 472, 473, 473, 473, 473, 473, 473, ... | [55, 56, 67, 55, 56, 57, 63, 64, 65, 66, 67, 6... | [0.0023643558, 0.0034383552, 0.0021977199, 0.0... | 2.0 | 2.079421 | ... | 10.794925 | 1.331867 | 2.583175 | 1.233372 | 64.879417 |
| 1251 | [342, 342, 342, 343, 343, 343, 343, 343, 344, ... | [128, 129, 130, 126, 127, 128, 129, 130, 124, ... | [0.010067902, 0.007827523, 0.005219734, 0.0070... | 2.0 | 1.777428 | ... | 8.44572 | 1.100779 | 1.744658 | 2.057152 | 62.520458 |
640 rows × 15 columns
none
[36]:
ipython3
trialobj.data.obsm
none
[36]:
AxisArrays with keys: ypix, xpix
The .obsm includes the ypix and xpix outputs for each suite2p ROI which represent the pixel locations of the ROI mask.
none
[37]:
ipython3
print('ypix:', trialobj.data.obsm['ypix'][:5], '\n\nxpix: \t', trialobj.data.obsm['xpix'][:5])
ypix: [array([102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 103, 103, 104,
104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105,
105, 105, 106, 106, 106, 106, 106, 106, 106, 106, 107, 107, 107,
107, 107, 107, 107, 108, 108, 108, 108, 108])
array([46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47, 47, 47, 47, 48, 48,
48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50,
50, 50, 50, 50, 51, 51, 51, 51, 51, 52, 52, 52])
array([18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20,
20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22,
22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24,
24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26])
array([43, 44, 45, 46, 46, 47, 47, 47, 48, 48, 48, 48, 48, 49, 49, 49, 49,
49, 49, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51, 51, 52,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53,
53, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55,
55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 57,
57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 59, 59])
array([156, 156, 156, 156, 156, 157, 157, 157, 157, 157, 157, 157, 157,
158, 158, 158, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159,
159, 159, 159, 160, 160, 160, 160, 160, 160, 160, 161, 161, 161,
161, 161])]
xpix: [array([457, 458, 459, 460, 461, 456, 457, 458, 459, 460, 461, 462, 455,
456, 457, 458, 459, 460, 461, 462, 455, 456, 457, 458, 459, 460,
461, 462, 455, 456, 457, 458, 459, 460, 461, 462, 456, 457, 458,
459, 460, 461, 462, 457, 458, 459, 460, 461])
array([116, 117, 118, 119, 120, 121, 114, 115, 116, 117, 118, 119, 120,
121, 122, 115, 116, 117, 118, 119, 120, 121, 122, 115, 116, 117,
118, 119, 120, 121, 122, 116, 117, 118, 119, 120, 121, 122, 117,
118, 119, 120, 121, 118, 119, 120])
array([202, 203, 204, 205, 200, 201, 202, 203, 204, 205, 206, 207, 200,
201, 202, 203, 204, 205, 206, 207, 208, 200, 201, 202, 203, 204,
205, 206, 207, 208, 209, 199, 200, 201, 202, 203, 204, 205, 206,
207, 208, 200, 201, 202, 203, 204, 205, 206, 207, 200, 201, 202,
203, 204, 205, 206, 207, 201, 202, 203, 204, 205, 206, 207, 202,
203, 204, 205])
array([352, 352, 352, 352, 353, 352, 353, 354, 351, 352, 353, 354, 355,
350, 351, 352, 353, 354, 355, 350, 351, 352, 353, 354, 355, 356,
349, 350, 351, 352, 353, 354, 355, 349, 350, 351, 352, 353, 354,
355, 357, 358, 359, 360, 361, 350, 351, 352, 353, 354, 355, 356,
357, 358, 359, 360, 361, 352, 353, 354, 355, 356, 357, 358, 359,
360, 361, 354, 355, 356, 357, 358, 359, 360, 361, 362, 354, 355,
356, 357, 358, 359, 360, 361, 355, 356, 357, 358, 359, 360, 361,
357, 358, 359, 360, 361, 358, 359])
array([382, 383, 384, 385, 386, 380, 381, 382, 383, 384, 385, 386, 387,
380, 381, 382, 383, 384, 385, 386, 387, 380, 381, 382, 383, 384,
385, 386, 387, 380, 381, 382, 383, 384, 385, 386, 381, 382, 383,
384, 385])]
variables (temporal synchronization of paq channels and imaging)¶
And the temporal synchronization data of the experiment collected in .paq output is added to the variables annotations under var. These variables are timed to the imaging frame clock timings. The total # of variables is the number of imaging frames in the original Flu data input.
none
[38]:
ipython3
trialobj.data.var
none
[38]:
| frame_clock | x_galvo_uncaging | slm2packio | markpoints2packio | packio2slm | packio2markpoints | pycontrol_rsync | voltage | |
|---|---|---|---|---|---|---|---|---|
| 139577 | 4.972792 | -1.165180 | 3.329534 | 0.007827 | 0.000264 | 0.000592 | 0.017035 | -0.116807 |
| 140252 | 4.974765 | -1.164851 | 3.325917 | 0.007827 | -0.000394 | -0.000394 | 0.019666 | -0.196060 |
| 140925 | 4.971806 | -1.165180 | 3.337755 | 0.008156 | -0.000065 | 0.000592 | 0.021310 | -0.210858 |
| 141595 | 4.970819 | -1.164851 | 3.331507 | 0.007827 | 0.000592 | 0.000264 | 0.021310 | -0.204939 |
| 142267 | 4.975094 | -1.166166 | 3.340715 | 0.006841 | 0.000264 | 0.001250 | 0.021639 | -0.234206 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11136216 | 4.974436 | -1.165837 | 3.337426 | 0.006841 | -0.000065 | 0.002565 | 0.021639 | 2.423554 |
| 11136886 | 4.975751 | -1.164851 | 3.334138 | 0.012102 | -0.000065 | -0.000065 | 0.017035 | 2.473211 |
| 11137559 | 4.960953 | -1.164851 | 3.337097 | 0.008485 | 0.000264 | 0.000921 | 0.019994 | 2.473211 |
| 11138232 | 4.971477 | -1.167153 | 3.319340 | 0.006183 | -0.000065 | -0.000065 | 0.005525 | 2.466963 |
| 11138904 | 4.975751 | -1.164851 | 3.329863 | 0.009471 | 0.000264 | 0.000592 | 0.017693 | 2.453151 |
16368 rows × 8 columns
Creating or Modifying AnnData arrays of trialobj¶
There are a number of helper functions to create anndata arrays or modify existing anndata arrays.
none
[39]:
ipython3
# creating new anndata object. This is identical to the base AnnData library.
# the example below is from the Getting Started Tutorial for AnnData:
# any given anndata object is created from constituent data arrays.
# 1) Primary data matrix
import numpy as np
import pandas as pd
n_rois, n_frames = 10, 10000
X = np.random.random((n_rois, n_frames)) # create random data matrix
df = pd.DataFrame(X, columns=range(n_frames), index=np.arange(n_rois, dtype=int).astype(str))
df # show the dataframe
none
[39]:
| 0 | 1 | 2 | 3 | 4 | ... | 9995 | 9996 | 9997 | 9998 | 9999 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.760647 | 0.110865 | 0.013329 | 0.935462 | 0.540991 | ... | 0.473592 | 0.368700 | 0.200333 | 0.580795 | 0.397233 |
| 1 | 0.971282 | 0.036780 | 0.315767 | 0.216254 | 0.759866 | ... | 0.982392 | 0.137152 | 0.935467 | 0.262601 | 0.867271 |
| 2 | 0.227014 | 0.163650 | 0.851788 | 0.527026 | 0.444399 | ... | 0.996837 | 0.685767 | 0.307295 | 0.282857 | 0.553372 |
| 3 | 0.054008 | 0.280003 | 0.651034 | 0.933380 | 0.087784 | ... | 0.899018 | 0.389161 | 0.996816 | 0.672480 | 0.860496 |
| 4 | 0.474105 | 0.987455 | 0.986814 | 0.977501 | 0.322008 | ... | 0.571053 | 0.896681 | 0.143181 | 0.967625 | 0.332282 |
| 5 | 0.473564 | 0.460252 | 0.806648 | 0.583812 | 0.419692 | ... | 0.226740 | 0.973925 | 0.974531 | 0.315231 | 0.431784 |
| 6 | 0.891266 | 0.953494 | 0.120490 | 0.178379 | 0.894506 | ... | 0.089454 | 0.563263 | 0.897826 | 0.295088 | 0.194042 |
| 7 | 0.829370 | 0.295575 | 0.703529 | 0.639097 | 0.606885 | ... | 0.408049 | 0.901431 | 0.242319 | 0.211567 | 0.089038 |
| 8 | 0.271850 | 0.935636 | 0.120800 | 0.487115 | 0.638711 | ... | 0.700857 | 0.314190 | 0.338174 | 0.822562 | 0.709709 |
| 9 | 0.486074 | 0.555504 | 0.725748 | 0.992619 | 0.433843 | ... | 0.968497 | 0.225324 | 0.896016 | 0.659566 | 0.660780 |
10 rows × 10000 columns
none
[40]:
ipython3
#2) Observations matrix
obs_meta = pd.DataFrame({
'cell_type': np.random.choice(['exc', 'int'], n_rois),
},
index=np.arange(n_rois, dtype=int).astype(str), # these are the same IDs of observations as above!
)
obs_meta
none
[40]:
| cell_type | |
|---|---|
| 0 | int |
| 1 | exc |
| 2 | exc |
| 3 | exc |
| 4 | exc |
| 5 | int |
| 6 | int |
| 7 | int |
| 8 | exc |
| 9 | exc |
none
[41]:
ipython3
#3) Variables matrix
var_meta = pd.DataFrame({
'exp_group': np.random.choice(['A','B', 'C'], n_frames),
},
index=np.arange(n_frames, dtype=int).astype(str), # these are the same IDs of observations as above!
)
var_meta
none
[41]:
| exp_group | |
|---|---|
| 0 | A |
| 1 | C |
| 2 | B |
| 3 | C |
| 4 | B |
| ... | ... |
| 9995 | B |
| 9996 | C |
| 9997 | C |
| 9998 | A |
| 9999 | A |
10000 rows × 1 columns
none
[43]:
ipython3
#4) Creating a new anndata attribute for the trialobj
import imagingplus.processing.anndata as ad # from the processing module, import anndata submodule
trialobj.new_anndata = ad.AnnotatedData(X=df,obs=obs_meta, var=var_meta)
print(trialobj.new_anndata)
Created AnnData object:
Annotated Data of n_obs (# ROIs) × n_vars (# Frames) = 10 × 10000
Annotated Data of n_obs × n_vars = 10 × 10000
available attributes:
.X (primary datamatrix)
.obs (obs metadata):
|- 'cell_type'
.var (vars metadata):
|- 'exp_group'
none
[44]:
ipython3
# adding an 'obs' to existing anndata object
new_obs = pd.DataFrame({
'cell_loc_x': np.random.random_integers(0, 512, n_rois),
'cell_loc_y': np.random.random_integers(0, 512, n_rois),
},
index=np.arange(n_rois, dtype=int).astype(str), # these are the same IDs of observations as above!
)
cell_loc_x = np.random.random_integers(0, 512, n_rois)
cell_loc_y = np.random.random_integers(0, 512, n_rois)
trialobj.new_anndata.add_obs(obs_name='cell_loc_x', values=cell_loc_x)
trialobj.new_anndata.add_obs(obs_name='cell_loc_y', values=cell_loc_y)
print(trialobj.new_anndata)
Annotated Data of n_obs × n_vars = 10 × 10000
available attributes:
.X (primary datamatrix)
.obs (obs metadata):
|- 'cell_type', 'cell_loc_x', 'cell_loc_y'
.var (vars metadata):
|- 'exp_group'
none
[45]:
ipython3
# deleting an 'obs' to existing anndata object
# uses the pop method
trialobj.new_anndata.del_obs('cell_type')
print(trialobj.new_anndata)
Annotated Data of n_obs × n_vars = 10 × 10000
available attributes:
.X (primary datamatrix)
.obs (obs metadata):
|- 'cell_loc_x', 'cell_loc_y'
.var (vars metadata):
|- 'exp_group'
Note: adding and deleting an ‘var’ to existing anndata object can be done in the exact same manner as demonstrated above for ‘obs’ using .add_var() and .del_var() methods on an anndata object.