2. LBM Suite2p: Demo Pipeline#
Example dataset collected by kevin barber with Dr. Alipasha Vaziri @rockefeller university.
Animal: mk301 Date: 2025-03-01 Virus: jGCaMP8s Framerate: 17hz FOV: 900um x 900um Resolution: 2um x 2um x 16um
import lbm_suite2p_python.volume
%load_ext autoreload
%autoreload 2
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import suite2p
import mbo_utilities as mbo
import lbm_suite2p_python as lsp
import fastplotlib as fpl
import matplotlib as mpl
mpl.rcParams.update({
'axes.spines.left': True,
'axes.spines.bottom': True,
'axes.spines.top': False,
'axes.spines.right': False,
'legend.frameon': False,
'figure.subplot.wspace': .01,
'figure.subplot.hspace': .01,
'figure.figsize': (18, 13),
'ytick.major.left': True,
})
jet = mpl.cm.get_cmap('jet')
jet.set_bad(color='k')
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
2.1. Assembly#
To preview data, run
scan = mbo.read_scan(files, join_contiguous=True)
widget = mbo.run_gui(scan)
widget.show()
scan = mbo.read_scan(r"D:\W2_DATA\kbarber\2025-02-27\mk301\green\*", join_contiguous=True)
animal_path = Path(r"D:\W2_DATA\kbarber\2025-02-27\mk301")
assembled_path = animal_path.joinpath("assembled")
save_path = animal_path.joinpath("results")
print(f"Saving raw tiffs to: {assembled_path}")
print(f"Saving suite2p results to: {save_path}")
Saving raw tiffs to: D:\W2_DATA\kbarber\2025-02-27\mk301\assembled
Saving suite2p results to: D:\W2_DATA\kbarber\2025-02-27\mk301\results
2.2. Save assembled files#
mbo.save_as(scan, assembled_path,)
Reading tiff series data...
Reading tiff pages...
Raw tiff fully read.
Time elapsed: 27 minutes 41 seconds.
input_files = mbo.get_files(assembled_path, str_contains='tif', max_depth=3)
input_files = [Path(x) for x in input_files]
input_files[:3]
[WindowsPath('D:/W2_DATA/kbarber/2025-02-27/mk301/assembled/plane_01.tiff'),
WindowsPath('D:/W2_DATA/kbarber/2025-02-27/mk301/assembled/plane_02.tiff'),
WindowsPath('D:/W2_DATA/kbarber/2025-02-27/mk301/assembled/plane_03.tiff')]
metadata = mbo.get_metadata(input_files[0])
ops = suite2p.default_ops()
ops = mbo.params_from_metadata(metadata, ops)
Ops provided. Setting pipeline to suite2p
2.3. Run sutie2p on each z-plane (same parameters)#
output_ops = lsp.run_plane(ops, input_files[-1], save_path=save_path, replot=True)
2.4. Run the entire volume#
Because we already ran the first z-plane, it will be skipped
all_ops = lsp.run_volume(ops, input_files, save_path=save_path, replot=True)
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_01.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_01.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_01...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_02.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_02.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_02...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_03.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_03.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_03...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_04.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_04.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_04...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_05.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_05.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_05...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_06.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_06.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_06...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_07.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_07.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_07...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_08.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_08.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_08...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_09.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_09.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_09...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_10.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_10.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_10...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_11.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_11.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_11...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_12.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_12.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_12...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_13.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_13.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_13...
Processing D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_14.tiff ---------------
D:\W2_DATA\kbarber\2025-02-27\mk301\assembled\plane_14.tiff already has segmentation results. Skipping execution.
Generating missing plots for plane_14...
running volumetric statistics
if not isinstance(output_ops, dict):
output_ops = load_ops(output_ops)["ops_path"]
zplane = Path(output_ops["tiff_list"][0]).name
plot_registration(output_ops, Path(output_ops["save_path"]).joinpath('registration.png'))
plot_segmentation(output_ops, Path(output_ops["save_path"]).joinpath('segmentation.png'), fig_label=zplane)
plot_traces(output_ops, Path(output_ops["save_path"]).joinpath('traces.png'))
all_ops.append(output_ops)
ops['classifier_path']
''
ops_files = mbo.get_files(save_path.parent, 'ops', 4)
ops_files[:3]
['D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_01\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_02\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_03\\plane0\\ops.npy']
ops0 = lsp.load_ops(ops_files[0])
plot_traces(ops0, './test.png')
volume_stats_file = lbm_suite2p_python.volume.get_volume_stats(ops_files)
volume_stats_file # runs stats and gives back the filename
'D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\volume_stats.npy'
# plane, acc, rej, means, stds, ops-file
print(np.load(volume_stats_file, allow_pickle=True))
[( 1, 601, 601, 1504.95532227, 635.71911621, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_01_demo\\plane0\\ops.npy')
( 2, 709, 709, 1729.17712402, 759.26446533, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_02_demo\\plane0\\ops.npy')
( 3, 840, 840, 1682.04052734, 771.42297363, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_03_demo\\plane0\\ops.npy')
( 4, 937, 937, 1581.18676758, 803.08453369, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_04_demo\\plane0\\ops.npy')
( 5, 1163, 1163, 1454.5435791 , 746.58349609, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_05_demo\\plane0\\ops.npy')
( 6, 1306, 1306, 1436.0411377 , 701.63793945, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_06_demo\\plane0\\ops.npy')
( 7, 537, 537, 55.10420609, 25.497509 , 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_07_demo\\plane0\\ops.npy')
( 8, 1572, 1572, 1194.73181152, 573.11859131, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_08_demo\\plane0\\ops.npy')
( 9, 1697, 1697, 1068.63830566, 499.10424805, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_09_demo\\plane0\\ops.npy')
(10, 1678, 1678, 1031.76330566, 505.93670654, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_10_demo\\plane0\\ops.npy')
(11, 1598, 1598, 913.86999512, 463.93460083, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_11_demo\\plane0\\ops.npy')
(12, 1128, 1128, 723.86236572, 390.0612793 , 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_12_demo\\plane0\\ops.npy')
(13, 730, 730, 606.41137695, 329.38095093, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_13_demo\\plane0\\ops.npy')
(14, 398, 398, 480.65631104, 255.62088013, 'D:\\W2_DATA\\kbarber\\2025-02-10\\mk303\\results\\plane_14_demo\\plane0\\ops.npy')]
stat_files = mbo.get_files(save_path.parent, 'stat.npy', max_depth=5)
stat_files[:3]
['D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_01\\plane0\\stat.npy',
'D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_02\\plane0\\stat.npy',
'D:\\W2_DATA\\kbarber\\2025-02-17\\mk303\\results\\plane_03\\plane0\\stat.npy']
import fastplotlib as fpl
ops_files = mbo.get_files(save_path.parent, 'ops.npy', max_depth=5)
ops_files
['D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_9\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_2_overlap_0_6\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_2_overlap_0_75\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_2_overlap_0_9\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_overlap_0_6\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_overlap_0_75\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_1_overlap_0_9\\suite2p\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_01\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_02\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_03\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_04\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_05\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_06\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_07\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_08\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_09\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_10\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_11\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_12\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_13\\plane0\\ops.npy',
'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\results\\plane_14\\plane0\\ops.npy']
lsp.load_ops(ops_files[0])
{'suite2p_version': '0.14.4',
'look_one_level_down': False,
'fast_disk': 'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75',
'delete_bin': False,
'mesoscan': False,
'bruker': False,
'bruker_bidirectional': False,
'h5py': [],
'h5py_key': 'data',
'nwb_file': '',
'nwb_driver': '',
'nwb_series': '',
'save_path0': 'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75',
'save_folder': 'suite2p',
'subfolders': [],
'move_bin': False,
'nplanes': 1,
'nchannels': 1,
'functional_chan': 1,
'tau': 1.5,
'fs': 17.06863078416647,
'force_sktiff': False,
'frames_include': -1,
'multiplane_parallel': False,
'ignore_flyback': [],
'preclassify': 0.0,
'save_mat': False,
'save_NWB': False,
'combined': True,
'aspect': np.float64(1.0),
'do_bidiphase': 0,
'bidiphase': 0,
'bidi_corrected': False,
'do_registration': True,
'two_step_registration': False,
'keep_movie_raw': False,
'nimg_init': 300,
'batch_size': 500,
'maxregshift': 0.1,
'align_by_chan': 1,
'reg_tif': False,
'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_reg': 42,
'pre_smooth': 0,
'spatial_taper': 40,
'roidetect': True,
'spikedetect': True,
'sparse_mode': True,
'spatial_scale': 0,
'connected': True,
'nbinned': 5000,
'max_iterations': 20,
'threshold_scaling': 0.8,
'max_overlap': 0.6,
'high_pass': 100,
'spatial_hp_detect': 25,
'denoise': False,
'anatomical_only': 0,
'diameter': 0,
'cellprob_threshold': 0.0,
'flow_threshold': 1.5,
'spatial_hp_cp': 0,
'pretrained_model': 'cyto',
'soma_crop': True,
'neuropil_extract': True,
'inner_neuropil_radius': 2,
'min_neuropil_pixels': 350,
'lam_percentile': 50.0,
'allow_overlap': False,
'use_builtin_classifier': False,
'classifier_path': '',
'chan2_thres': 0.65,
'baseline': 'maximin',
'win_baseline': 60.0,
'sig_baseline': 10.0,
'prctile_baseline': 8.0,
'neucoeff': 0.7,
'dx': 2.0,
'dy': 2.0,
'tiff_list': ['D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\assembled\\plane_10.tiff'],
'data_path': ['D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\assembled'],
'save_path': 'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75\\suite2p\\plane0',
'input_format': 'tif',
'ops_path': 'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75\\suite2p\\plane0\\ops.npy',
'reg_file': 'D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\grid_search\\thrscale_0_8_overlap_0_75\\suite2p\\plane0\\data.bin',
'first_tiffs': array([ True]),
'frames_per_folder': array([57868], dtype=int32),
'filelist': ['D:\\W2_DATA\\kbarber\\2025-02-27\\mk301\\assembled\\plane_10.tiff'],
'nframes': 57868,
'frames_per_file': array([57868]),
'meanImg': array([[181.91132 , 120.80995 , 37.601868, ..., 617.23883 , 384.737 ,
270.3948 ],
[181.19084 , 113.28088 , 31.626112, ..., 665.2934 , 451.35962 ,
310.2186 ],
[204.64474 , 117.56807 , 28.590752, ..., 683.74695 , 456.2078 ,
326.21167 ],
...,
[ 81.983635, 75.61998 , 56.666134, ..., 237.1744 , 174.09166 ,
126.1574 ],
[102.84038 , 95.08476 , 72.190575, ..., 356.91528 , 251.56357 ,
175.65007 ],
[120.40256 , 109.67453 , 79.66536 , ..., 459.09015 , 329.7626 ,
212.41444 ]], dtype=float32),
'Ly': 448,
'Lx': 448,
'yrange': [6, 442],
'xrange': [6, 442],
'date_proc': datetime.datetime(2025, 2, 27, 19, 0, 52, 485820, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=68400), 'Eastern Standard Time')),
'refImg': array([[ 20, 18, 12, ..., 651, 336, 140],
[ 9, 13, 7, ..., 762, 502, 135],
[ 13, 21, 5, ..., 733, 420, 161],
...,
[ 17, 15, 12, ..., 189, 110, 69],
[ 52, 48, 36, ..., 354, 189, 98],
[ 18, 35, 28, ..., 537, 307, 105]], dtype=int16),
'rmin': np.int16(20),
'rmax': np.int16(4053),
'yoff': array([-1, -1, -1, ..., -1, -1, -1], dtype=int32),
'xoff': array([ 0, -1, -1, ..., -2, -2, -2], dtype=int32),
'corrXY': array([0.03640025, 0.03399996, 0.03471949, ..., 0.02637114, 0.02503825,
0.02377436], dtype=float32),
'yoff1': array([[ 0.5, 0.4, 0.3, ..., 0.1, 0.2, 0.1],
[ 0.4, 0.5, 0.4, ..., -0.3, -0.5, -0.4],
[ 0. , 0. , 0. , ..., -0.7, -0.4, -0.4],
...,
[-1.3, -0.8, -0.4, ..., 0.7, 0.7, 0.8],
[-0.6, -0.2, -0.4, ..., 0.9, 1. , 1.1],
[-0.9, -0.2, -0.5, ..., 0.6, 1. , 0.9]], dtype=float32),
'xoff1': array([[-0.8, -0.5, -1. , ..., -0.6, -0.3, 0. ],
[ 0.3, 0.5, -0.1, ..., -0.1, 0.3, 0.8],
[ 0.1, 0.2, -0.5, ..., -0.3, 0.2, 0.6],
...,
[-1.4, -1.2, -1.2, ..., -0.9, -0.3, 0.2],
[-1.1, -0.6, -1.8, ..., -1.4, -0.5, -0.2],
[-1.1, -0.9, -3.6, ..., -1.4, -0.5, -0.2]], dtype=float32),
'corrXY1': array([[0.04104537, 0.03616329, 0.03442401, ..., 0.03657242, 0.03728601,
0.03416988],
[0.04013292, 0.03346131, 0.03129024, ..., 0.03413978, 0.03284104,
0.03109973],
[0.03959985, 0.03243906, 0.02836499, ..., 0.0323982 , 0.03230157,
0.03357244],
...,
[0.03120196, 0.02492863, 0.01786365, ..., 0.02900613, 0.03183422,
0.02177376],
[0.03109105, 0.02438505, 0.01833273, ..., 0.02649263, 0.02784955,
0.02478352],
[0.03132045, 0.02543353, 0.01892262, ..., 0.02573716, 0.0309259 ,
0.02549327]], dtype=float32),
'badframes': array([False, False, False, ..., False, False, False]),
'spatscale_pix': array([6]),
'meanImgE': array([[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., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]], dtype=float32),
'tPC': array([[ 0.03801467, 0.04167883, 0.04417367, ..., 0.01885787,
-0.0020818 , -0.02587652],
[ 0.02916383, 0.03726072, 0.00695184, ..., 0.02878292,
-0.03131345, -0.00467559],
[ 0.01840586, 0.02908189, -0.02043437, ..., -0.00108247,
-0.00686843, -0.00301439],
...,
[-0.02388801, 0.00893071, 0.00217036, ..., -0.00959489,
-0.00224079, 0.0014706 ],
[-0.02478324, 0.00718862, -0.01112887, ..., -0.00156723,
-0.02470737, -0.00945794],
[-0.02351212, 0.00718434, -0.01127633, ..., -0.00225101,
-0.00482763, -0.00064703]], dtype=float32),
'regPC': array([[[[ 10.143333 , 8.906667 , 6.12 , ..., 810.4767 ,
815.67 , 745.71 ],
[ 13.423333 , 11.67 , 5.01 , ..., 812.43 ,
811.9 , 768.70667 ],
[ 9.123333 , 8.803333 , 2.68 , ..., 838.98 ,
770.7267 , 729.58 ],
...,
[ 2.75 , 2.0033333, 11.156667 , ..., 198.33667 ,
182.24333 , 187.13333 ],
[ 8.97 , 7.8166666, 6.99 , ..., 204.44 ,
199.15 , 186.45334 ],
[ 12.716666 , 9.09 , 6.59 , ..., 201.16667 ,
193.4 , 185.82666 ]],
[[ 13.446667 , 13.366667 , 6.4766665, ..., 993.4767 ,
1013.36664 , 995.03 ],
[ 4.59 , 7.74 , 7.9533334, ..., 1011.75 ,
963.75336 , 971.14667 ],
[ 14.803333 , 13.723333 , 11.59 , ..., 1098.99 ,
1001.74335 , 1007.79333 ],
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