"""MBO Default ops"""
from mbo_utilities.metadata import get_param, get_voxel_size
def s2p_ops():
""" default options to run pipeline """
return {
# file input/output settings
"look_one_level_down":
False, # whether to look in all subfolders when searching for tiffs
"fast_disk": [], # used to store temporary binary file, defaults to save_path0
"delete_bin": False, # whether to delete binary file after processing
"mesoscan": False, # for reading in scanimage mesoscope files
"bruker": False, # whether or not single page BRUKER tiffs!
"bruker_bidirectional":
False, # bidirectional multiplane in bruker: 0, 1, 2, 2, 1, 0 (True) vs 0, 1, 2, 0, 1, 2 (False)
"h5py": [], # take h5py as input (deactivates data_path)
"h5py_key": "data", # key in h5py where data array is stored
"nwb_file": "", # take nwb file as input (deactivates data_path)
"nwb_driver": "", # driver for nwb file (nothing if file is local)
"nwb_series":
"", # TwoPhotonSeries name, defaults to first TwoPhotonSeries in nwb file
"save_path0": '', # pathname where you'd like to store results, defaults to first item in data_path
"save_folder": [], # directory you"d like suite2p results to be saved to
"subfolders": [
], # subfolders you"d like to search through when look_one_level_down is set to True
"move_bin":
False, # if 1, and fast_disk is different than save_disk, binary file is moved to save_disk
# main settings
"nplanes": 1, # each tiff has these many planes in sequence
"nchannels": 1, # each tiff has these many channels per plane
"functional_chan":
1, # this channel is used to extract functional ROIs (1-based)
"tau": 1.3, # this is the main parameter for deconvolution
"fs":
10., # sampling rate (PER PLANE e.g. for 12 plane recordings it will be around 2.5)
"force_sktiff": False, # whether or not to use scikit-image for tiff reading
"frames_include": -1,
"multiplane_parallel": False, # whether or not to run on server
"ignore_flyback": [],
# output settings
"preclassify":
0.0, # apply classifier before signal extraction with probability 0.3
"save_mat": False, # whether to save output as matlab files
"save_NWB": False, # whether to save output as NWB file
"combined":
True, # combine multiple planes into a single result /single canvas for GUI
"aspect":
1.0, # um/pixels in X / um/pixels in Y (for correct aspect ratio in GUI)
# bidirectional phase offset
"do_bidiphase":
False, # whether or not to compute bidirectional phase offset (applies to 2P recordings only)
"bidiphase":
0, # Bidirectional Phase offset from line scanning (set by user). Applied to all frames in recording.
"bidi_corrected":
False, # Whether to do bidirectional correction during registration
# registration settings
"do_registration": True, # whether to register data (2 forces re-registration)
"two_step_registration":
False,
# whether or not to run registration twice (useful for low SNR data). Set keep_movie_raw to True if setting this parameter to True.
"keep_movie_raw":
False, # whether to keep binary file of non-registered frames.
"nimg_init": 300, # subsampled frames for finding reference image
"batch_size": 500, # number of frames per batch
"maxregshift":
0.1, # max allowed registration shift, as a fraction of frame max(width and height)
"align_by_chan":
1, # when multi-channel, you can align by non-functional channel (1-based)
"reg_tif": False, # whether to save registered tiffs
"reg_tif_chan2": False, # whether to save channel 2 registered tiffs
"subpixel": 10, # precision of subpixel registration (1/subpixel steps)
"smooth_sigma_time": 0, # gaussian smoothing in time
"smooth_sigma":
1.15, # ~1 good for 2P recordings, recommend 3-5 for 1P recordings
"th_badframes":
1.0,
# this parameter determines which frames to exclude when determining cropping - set it smaller to exclude more frames
"norm_frames": True, # normalize frames when detecting shifts
"force_refImg": False, # if True, use refImg stored in ops if available
"pad_fft": False, # if True, pads image during FFT part of registration
# non rigid registration settings
"nonrigid": True, # whether to use nonrigid registration
"block_size": [128,
128], # block size to register (** keep this a multiple of 2 **)
"snr_thresh":
1.2,
# if any nonrigid block is below this threshold, it gets smoothed until above this threshold. 1.0 results in no smoothing
"maxregshiftNR":
5, # maximum pixel shift allowed for nonrigid, relative to rigid
# 1P settings
"1Preg": False, # whether to perform high-pass filtering and tapering
"spatial_hp_reg":
42, # window for spatial high-pass filtering before registration
"pre_smooth":
0, # whether to smooth before high-pass filtering before registration
"spatial_taper":
40,
# how much to ignore on edges (important for vignetted windows, for FFT padding do not set BELOW 3*ops["smooth_sigma"])
# cell detection settings with suite2p
"roidetect": True, # whether or not to run ROI extraction
"spikedetect": True, # whether or not to run spike deconvolution
"sparse_mode": True, # whether or not to run sparse_mode
"spatial_scale":
1, # 0: multi-scale; 1: 6 pixels, 2: 12 pixels, 3: 24 pixels, 4: 48 pixels
"connected":
True, # whether or not to keep ROIs fully connected (set to 0 for dendrites)
"nbinned": 5000, # max number of binned frames for cell detection
"max_iterations": 20, # maximum number of iterations to do cell detection
"threshold_scaling":
1.0, # adjust the automatically determined threshold by this scalar multiplier
"max_overlap":
0.75, # cells with more overlap than this get removed during triage, before refinement
"high_pass":
100, # running mean subtraction across bins with a window of size "high_pass" (use low values for 1P)
"spatial_hp_detect":
25, # window for spatial high-pass filtering for neuropil subtraction before detection
"denoise": False, # denoise binned movie for cell detection in sparse_mode
# cell detection settings with cellpose (used if anatomical_only > 0)
"anatomical_only":
3,
# run cellpose to get masks on 1: max_proj / mean_img; 2: mean_img; 3: mean_img enhanced, 4: max_proj
"diameter": 6, # use diameter for cellpose, if 0 estimate diameter
"cellprob_threshold": -6, # cellprob_threshold for cellpose
"flow_threshold": 0, # flow_threshold for cellpose
"spatial_hp_cp": 0.5, # high-pass image spatially by a multiple of the diameter
# cellpose model: 'cpsam' (default) or path to custom model trained with lsp.train_cellpose()
# note: only cpsam-based models are currently supported
"pretrained_model": "cpsam",
# classification parameters
"soma_crop":
True, # crop dendrites for cell classification stats like compactness
# ROI extraction parameters
"neuropil_extract":
True, # whether or not to extract neuropil; if False, Fneu is set to zero
"inner_neuropil_radius":
2, # number of pixels to keep between ROI and neuropil donut
"min_neuropil_pixels": 350, # minimum number of pixels in the neuropil
"lam_percentile":
50., # percentile of lambda within area to ignore when excluding cell pixels for neuropil extraction
"allow_overlap":
False, # pixels that are overlapping are thrown out (False) or added to both ROIs (True)
"use_builtin_classifier":
False, # whether or not to use built-in classifier for cell detection (overrides
# classifier specified in classifier_path if set to True)
"classifier_path": "", # path to classifier
# channel 2 detection settings (stat[n]["chan2"], stat[n]["not_chan2"])
"chan2_thres": 0.65, # minimum for detection of brightness on channel 2
# deconvolution settings
"baseline": "maximin", # baselining mode (can also choose "prctile")
"win_baseline": 60., # window for maximin
"sig_baseline": 10., # smoothing constant for gaussian filter
"prctile_baseline": 8., # optional (whether to use a percentile baseline)
"neucoeff": 0.7, # neuropil coefficient
}
[docs]
def default_ops(metadata=None, ops=None):
"""
Returns default ops for Suite2P processing on Light Beads Microscopy datasets.
Main changes to defaults:
anatomical_only=3
diameter=6
spatial_hp_cp=0.5
cellprob_threshold=-6
flow_threshold=0
spatial_scale=1
tau=1.3
Parameters
----------
metadata : dict, optional
Metadata dictionary containing information about the dataset.
ops : dict, str or Path, optional
Path to or dict of suite2p ops.
Returns
-------
dict
Default ops for Suite2P processing.
Examples
--------
>>> import lbm_suite2p_python as lsp
>>> metadata = mbo.get_metadata("D://demo//raw_data//raw_file_00001.tif") # noqa
>>> lsp.run_plane(
>>> ops=ops,
>>> input_tiff="D://demo//raw_data//raw_file_00001.tif",
>>> save_path="D://demo//results",
>>> save_folder="v1"
>>> )
"""
if ops is None:
print("Importing suite2p packages...")
ops = s2p_ops()
if metadata is not None:
fs = get_param(metadata, "fs")
if fs is not None:
ops["fs"] = fs
voxel = get_voxel_size(metadata)
if voxel.dx != 1.0 or voxel.dy != 1.0:
ops["dx"] = voxel.dx
ops["dy"] = voxel.dy
ops["nplanes"] = 1
ops["nchannels"] = 1
return ops
