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SI_tools.py
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import spikeinterface.core as si
from spikeinterface.extractors import read_nwb
from spikeinterface.preprocessing import common_reference as spre_common_reference
from spikeinterface.sortingcomponents.peak_detection import detect_peaks
from spikeinterface.sortingcomponents.peak_selection import select_peaks
from spikeinterface.sortingcomponents.peak_localization import localize_peaks
from probeinterface.plotting import plot_probe
from probeinterface import Probe
from pynwb import NWBHDF5IO
import matplotlib.pyplot as plt
import os
import shutil
import numpy
import pickle
####################################################################################################################################################################################
# SOME DEFAULT PARAMETERS
####################################################################################################################################################################################
n_cpus = os.cpu_count()
n_jobs = n_cpus - 2
job_kwargs = dict(chunk_duration="1s", n_jobs=n_jobs, progress_bar=True)
# si.set_global_job_kwargs(**job_kwargs)
supported_probes_manufacturer = ['PLX', 'FHC']
######################################################################################################
# GENERAL PARAMETER FOR "PEAKS LOCATIONS"
ms_before_default = 0.6
ms_after_default = 1.5
nearest_chans_default = 3 # Number of neighbor channels to search for the same waveform
peak_sign_default = 'both' # (“neg” | “pos” | “both”)
###########################################################
# DETECT PEAKS:
peak_detect_threshold_default = 5 # MAD: Median Amplitude Deviations
######################################################################################################
# Figures size when is saved
fig_width_global = 12 # inches
fig_height_global = 8 # inches
fig_dpi = 120
####################################################################################################################################################################################
####################################################################################################################################################################################
# MAIN FUNCTIONS
####################################################################################################################################################################################
####################################################################################################################################################################################
########################################################################################################################
# SET UP A TEMPORARY DIRECTORY TO PROCESS DATA
########################################################################################################################
def set_SItempdir_environ():
# CONFIRM or ADD TEMPORAL DIR ('SI_PROCESSOR_TEMPDIR') into the variables of the environment
if not os.environ.get('SI_PROCESSOR_TEMPDIR'):
os.environ['SI_PROCESSOR_TEMPDIR'] = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/SI_tempdir')
print('SI_PROCESSOR_TEMPDIR variable was created')
# CONFIRM THAT EXISTS or CREATE THE TEMPORAL DIR ('SI_PROCESSOR_TEMPDIR')
if not os.path.exists(os.environ.get('SI_PROCESSOR_TEMPDIR')):
os.mkdir(os.environ.get('SI_PROCESSOR_TEMPDIR'))
print(' Temporal folder was created ')
########################################################################################################################
# CLEAR TEMPORARY DIRECTORY
########################################################################################################################
def clear_SItempdir(verbose=True):
tempdir_path = os.environ.get('SI_PROCESSOR_TEMPDIR')
if tempdir_path is not None:
# Clear tempDir
for root, _, files in os.walk(tempdir_path, topdown=False):
if root != tempdir_path:
remove_dir = True
else:
remove_dir = False
for fname in files:
tempFile_path = os.path.join(root, fname)
if os.path.isfile(tempFile_path):
try:
os.remove(tempFile_path)
except:
remove_dir = False
if verbose:
print('Warning:\nUnable to delete the file:\n{}\nprobably is still open or in use\n\n'.format(tempFile_path))
if remove_dir:
try:
os.rmdir(root)
except:
if verbose:
print('Warning:\nUnable to delete the folder:\n{}\nprobably it is not an empty folder\n\n'.format(root))
########################################################################################################################
# GET GENERAL INFORMATION ABOUT THE PROBE FROM A GIVEN ELECTRODE GROUP
########################################################################################################################
def getProbeInfo(electrodeGroup_dataFrame, nwbFile):
groupName_u = electrodeGroup_dataFrame.group_name.unique()
# Double check this electrodes were recorded with the same probe:
if groupName_u.size>1:
raise Exception('ElectrodeGroup should have a unique name, but more than one name was found {}'.format(
groupName_u
))
groupName = groupName_u[0]
nameDevice = nwbFile.electrode_groups[groupName].device.name.upper()
if 'PLX' in nameDevice:
# PLX-UP-16-15SE(150)
# [0:3] = PLX prefix
# [4:6] = Tip Profile
# [7:9] = Number of Channels
# [10:12] = electrode diameter in microns
# [12:14] = Electrode configuration (SE=single, ST=stereotrode, TT=tetrode)
# [15:-1] = electrode spacing e.g.: 15/100 intra/inter spacing
diameterElec = int(nameDevice[10:12])/1000
num_shanks = 1
shank_ids = numpy.zeros(int(nameDevice[7:9])).astype(int)
elif 'FHC' in nameDevice:
# FHC-UE1234567890
# UE = metal microelectrode
# 1 = Material
# 2= lenght range
# 3= shank diameter (microns)
FHC_diameter = {
'C': 75,
'D': 100,
'E': 125,
'F': 200,
'G': 250,
'H': 500,
'X': 'special'
}
diameterElec = FHC_diameter[nameDevice[8]]
if type(diameterElec)==str:
diameterElec = 50
diameterElec = diameterElec/1000
num_shanks = 1
shank_ids = numpy.zeros(1).astype(int)
else:
print('WARNING¡¡¡\nDevice name "{}" was not recognized\nelectrodeProbes should start with:\n{}'.format(nameDevice, supported_probes_manufacturer))
diameterElec = 50
num_shanks = 0
shank_ids = numpy.zeros(1).astype(int)
return {'probeName': nameDevice, 'radius': diameterElec, 'radius_units': 'mm', 'num_shanks': num_shanks, 'shank_ids': shank_ids}
########################################################################################################################
# CREATE A SPIKEINTERFACE-PROBE USING ELECTRODE-GROUP INFORMATION
########################################################################################################################
def constructProbe_2d(si_recordingObj_nwb, probeInfo_dict, showPlot=False):
"""
It will construct a 2D probe (probeInterface) using:
From NWB : locations, re_id, frontEnd_electrode_id
From probeInfo: manufacturer, radius,
Coordinates extracted from NWB:
x = AnteriorPosterior (ap) ZERO = Bregma (+x is posterior)
y = DorsalVentral (dv) ZERO = top of the brain (+y is inferior)
z = MedialLateral (ml) ZERO = middline (+z is right)
rel_x = x-axis (width) locations of the contacts on the Probe
rel_y = y-axis (length) locations of the contacts on the Probe
rel_z = z-axis (zero-values)
When loading an NWB into a sipkeinterface RecordingObject, relative coordinates (rel_x, rel_y, rel_z)
are used to create the "property" "location".
Using the "location" will provide the contact positions in the "probe"-coordinate system
Probe plane will be X (ap), Y(dv) coordinates
"""
# Check the electrode/probe is supported by this version:
# PLEXON-SE(single shank with multiple electrode/contacts)
# FHC-UE (single electrode)
probeExist = any([p in probeInfo_dict['probeName'] for p in supported_probes_manufacturer])
if not probeExist:
raise Exception(
'Device name was not recognized:{}\
\nCurrent electrodeProbes should start with: {}'.format
(probeInfo_dict['probeName'],
supported_probes_manufacturer)
)
# GET CONTACTS_IDs:
# These correspond to the labels of the contacts on the probe (defined by Probe-manufacturer)
# In the YAULAB's NWB-scheme. The probe-contactID corresponds to the property "rel_id"
# Contacts ids are converted to strings. Contact ids must be **unique** for the **Probe** and also for the **ProbeGroup**
contact_ids_nwb = si_recordingObj_nwb.get_property('rel_id')
# GET DEVICE CHANNEL INDICES:
# Correspond to the channel indexes in the recording system (i.e., Ripple)
# The connections between contact_ids (Probe) with the recording device depends on the Headstage manufacturer (i.e., Ripple)
# In the YAULAB's NWB-scheme. The device-channel ID corresponds to the property "frontEnd_electrode_id"-1 (zero index for pyhton)
# CHECK TODO
# WARNING¡¡¡¡
# For multiple contact probes, it will assume that no more than one electrodeProbe was connected in a the same fronEnd
if si_recordingObj_nwb.get_num_channels()>1:
channel_indices_nwb = si_recordingObj_nwb.get_property('frontEnd_electrode_id') -1
else:
# For single contact electrodes (e.g., FHC), force the channel_index to ZERO
channel_indices_nwb = numpy.array([0])
# GET LOCATIONS IN "um"
# Check if the recording Object has the location_units defined
if 'location_units' in si_recordingObj_nwb.get_annotation_keys():
if si_recordingObj_nwb.get_annotation('location_units') == 'mm':
locationsProbe_nwb = si_recordingObj_nwb.get_property('location')*1000
elif si_recordingObj_nwb.get_annotation('location_units') == 'um':
locationsProbe_nwb = si_recordingObj_nwb.get_property('location')
else:
raise Exception('Units of the Locations in the RecordingObject is not recognized: {}'.format(
si_recordingObj_nwb.get_annotation('location_units')
))
else:
# If not Assume units in 'mm' (default units from NWB format)
locationsProbe_nwb = si_recordingObj_nwb.get_property('location')*1000
# Get Radius of the contacts
if probeInfo_dict['radius_units']=='mm':
radiusContact = probeInfo_dict['radius']*1000
elif probeInfo_dict['radius_units']=='um':
radiusContact = probeInfo_dict['radius']
else:
raise Exception('Units of the ElectrodeContact radius is not recognized: {}'.format(
probeInfo_dict['radius_units']
))
# PolygonProbe for a single linear probe.
if probeInfo_dict['num_shanks']==1:
marginPol = radiusContact*5
# (x, y) coordinates of the points that will define the contour of the 2D probe
#
# E---D
# | |
# A C
# \ /
# B
#
polygonProbe2d = [
(-marginPol, 0), # A
(0, -marginPol), # B
(marginPol, 0), # C
(marginPol, max(locationsProbe_nwb[:, 1])+marginPol), # D
(-marginPol, max(locationsProbe_nwb[:, 1])+marginPol) # E
]
else:
raise Exception('Probe with {} shanks is not supported: {}'.format(
probeInfo_dict['shankNum']
))
probeObj = Probe(ndim=2, si_units='um', model_name=probeInfo_dict['probeName'])
probeObj.set_contacts(
positions=locationsProbe_nwb[:, 0:2],
contact_ids=contact_ids_nwb,
shapes='square',
shape_params={'width':radiusContact},
)
probeObj.set_shank_ids(probeInfo_dict['shank_ids'])
probeObj.set_planar_contour(contour_polygon=polygonProbe2d)
probeObj.set_device_channel_indices(channel_indices=channel_indices_nwb)
if showPlot:
plot_probe(probe=probeObj, with_contact_id=True, with_device_index=True)
plt.show()
return probeObj
########################################################################################################################################################
# Set up NWB file Paths to be sorted. Sorting must be done for each raw-ElectrodeGroup
# from all sessions where the probe(s) was not move (i.e., the same neurons were recorded). To choose those sessions,
# it will search for NWB files with the same "expYEAR-MONTH-DAY" prefix and will check that coordinates and electrode
# configuration match across sessions. If a session has a mismatch on coordinates or electrode configuration, that
# session will be named as -subX.
########################################################################################################################################################
def getUnique_electrodeGroups(parentRecordingFolder, sessionYear, sessionMonth, sessionDay):
# Get filePaths of all NWB files recorded on the same Day
filesDate = []
for root, _, files in os.walk(parentRecordingFolder):
for name in files:
nameSplit = os.path.splitext(name)
if nameSplit[1]=='.nwb' and '-noNEV' not in nameSplit[0]:
fileName = nameSplit[0]
yearFile = int(fileName[3:7])
monthFile = int(fileName[8:10])
dayFile = int(fileName[11:13])
if yearFile==sessionYear and monthFile==sessionMonth and dayFile==sessionDay:
filesDate.append(os.path.join(root, name))
# Check for electrodeGroups on each file
elecGroups = {}
for f in filesDate:
# Open the file in read mode "r"
io = NWBHDF5IO(f, mode="r")
nwbfile = io.read()
elecGroups_f = [k for k in nwbfile.acquisition.keys() if 'raw-' in k]
fParts = os.path.split(f)
for e in elecGroups_f:
electrodes_df = nwbfile.acquisition[e].electrodes.to_dataframe()
probeInfo = getProbeInfo(electrodeGroup_dataFrame=electrodes_df, nwbFile = nwbfile)
elecGroups_keys = list(elecGroups.keys())
if elecGroups_keys.count(e)==1:
elecGroups[e]['pathFiles'].append(f)
elecGroups[e]['fileNames'].append(fParts[1])
elecGroups[e]['electrodes'].append(electrodes_df)
elecGroups[e]['probeInfo'].append(probeInfo)
else:
elecGroups[e] = {
'pathFiles': [f],
'fileNames': [fParts[1]],
'electrodes': [electrodes_df],
'probeInfo': [probeInfo],
}
io.close()
uniqueElectrodesNames = []
uniqueElectrodesPaths = []
uniqueProbeInfo = []
uniqueElectrodesFilePaths = []
uniqueElectrodesFileNames = []
for g_name, g in elecGroups.items():
# Check that across files this ElectrodeGroup has the same:
# coordinates, and electrode configuration
nFiles = len(g['fileNames'])
electrodesDF_unique = [g['electrodes'][0]]
probes_unique = [g['probeInfo'][0]]
filesMatch = [[0]]
groupNames = [g_name]
groupPath = [g_name]
count = 1
for t in range(1, nFiles):
testDF = g['electrodes'][t]
testProbe = g['probeInfo'][t]
test_is_NEW = True
n_u = len(electrodesDF_unique)
for u in range(n_u):
uDF = electrodesDF_unique[u]
probe_equal = probes_unique[u]['probeName']==testProbe['probeName']
x_equal = testDF['x'].equals(uDF['x'])
y_equal = testDF['y'].equals(uDF['y'])
z_equal = testDF['z'].equals(uDF['z'])
port_equal = testDF['port_id'].equals(uDF['port_id'])
FE_equal = testDF['frontEnd_id'].equals(uDF['frontEnd_id'])
FEchannel_equal = testDF['frontEnd_electrode_id'].equals(uDF['frontEnd_electrode_id'])
relID_equal = testDF['rel_id'].equals(uDF['rel_id'])
if x_equal and y_equal and z_equal and port_equal and FE_equal and FEchannel_equal and relID_equal and probe_equal:
test_is_NEW = False
filesMatch[u].append(t)
if test_is_NEW:
electrodesDF_unique.append(testDF)
probes_unique.append(testProbe)
filesMatch.append([t])
groupNames.append(g_name + '-sub' + str(count))
groupPath.append(g_name)
count +=1
for u in range(len(groupNames)):
uniqueElectrodesNames.append(groupNames[u])
uniqueElectrodesPaths.append(groupPath[u])
uniqueProbeInfo.append(probes_unique[u])
nFiles_u = len(filesMatch[u])
filePaths_u = []
files_u = []
for f_u in range(nFiles_u):
filePaths_u.append(g['pathFiles'][filesMatch[u][f_u]])
files_u.append(g['fileNames'][filesMatch[u][f_u]])
uniqueElectrodesFilePaths.append(filePaths_u)
uniqueElectrodesFileNames.append(files_u)
electrodeGroups = []
for g in range(len(uniqueElectrodesNames)):
########################################################################################################################
# Ensure Files are sorted by time
file_index = numpy.argsort(numpy.array([float(os.path.splitext(f)[0][14:20]) for f in uniqueElectrodesFileNames[g]]))
fileNames_sorted = []
filePaths_sorted = []
fileSamples_sorted = []
fileDuration_sorted = []
for i in file_index:
fileNames_sorted.append(uniqueElectrodesFileNames[g][i])
fP = [fP for fP in uniqueElectrodesFilePaths[g] if uniqueElectrodesFileNames[g][i] in fP][0]
filePaths_sorted.append(fP)
# Open the file in read mode "r"
io = NWBHDF5IO(fP, mode="r")
nwbfile = io.read()
nSamples = nwbfile.acquisition[uniqueElectrodesPaths[g]].data.shape[0] # first dimension is time
fs = nwbfile.acquisition[uniqueElectrodesPaths[g]].rate # fSampling
fileSamples_sorted.append(nSamples)
fileDuration_sorted.append(nSamples/fs)
io.close()
########################################################################################################################
electrodeGroups.append({
'electrodeName': uniqueElectrodesNames[g],
'electrodePath': uniqueElectrodesPaths[g],
'probeInfo': uniqueProbeInfo[g],
'fileNames': fileNames_sorted,
'fileNamePaths': filePaths_sorted,
'fileSamples': numpy.array(fileSamples_sorted),
'fileDurations': numpy.array(fileDuration_sorted)
})
return electrodeGroups
#####################################################################################################################################################
# PRINT INFORMATION ABOUT HOW MANY ELECTRODE GROUPS AND SESSIONS WERE FOUND FOR A GIVEN EXPERIMENTAL DAY
#####################################################################################################################################################
def print_recordingInfo(parentRecordingFolder, sessionYear, sessionMonth, sessionDay):
electrodeGroups = getUnique_electrodeGroups(parentRecordingFolder, sessionYear, sessionMonth, sessionDay)
# Electrode Group Preprocessing
nGroups = len(electrodeGroups)
print('\nThere are {} electrode group(s)\n'.format(nGroups))
# Print some information from each electrodeGroup
for eg_ in electrodeGroups:
print('ElectrodeGroup Name: "{}" (nwbPath: {})'.format(eg_['electrodeName'], 'acquisition/' + eg_['electrodePath']))
print('\tProbe Info: ')
for k, v, in eg_['probeInfo'].items():
print('\t\t{} : {}'.format(k, v))
print('\tNumber of sessions = {}\n\t\tSessionsInfo:'.format(len(eg_['fileNames'])))
for n in range(len(eg_['fileNames'])):
print('\t\t\tSessIndx: {} (SessName: {})'.format(n, eg_['fileNames'][n]))
######################################################################################################
# Print some Recording information about the session:
print('\t\t\tRecInfo: \n\t\t\t',
read_nwb(
file_path=eg_['fileNamePaths'][n],
electrical_series_path='acquisition/' + eg_['electrodePath'],
load_recording=True,
load_sorting=False,
),
'\n')
print('\n')
####################################################################################################################################################################################
# Construct Dictionary with general information about the ElectrodeGroup & Sessions
####################################################################################################################################################################################
def select_electrodeGroup_and_session_info(electrodeGroups, sessionYear, sessionMonth, sessionDay, electrodeGroup_Name, session_index=None):
#######################################################################################################################
# Get selected ElectrodeGroup & Sessions
#######################################################################################################################
eg_i = [i for i in range(len(electrodeGroups)) if electrodeGroups[i]['electrodeName']==electrodeGroup_Name]
if len(eg_i)==0:
raise Exception("electrodeGroup Name : '{}' was not found\nAvailable electrodeGroups:\n\t{}".format(electrodeGroup_Name, [electrodeGroups[i]['electrodeName'] for i in range(len(electrodeGroups))]))
else:
# Check indexes to create sufix of the selected sessions
sess_indexes = [n for n in range(len(electrodeGroups[eg_i[0]]['fileNames']))]
consecutiveSessions = True
if session_index is None:
session_index_in = sess_indexes
else:
if len(session_index)==0:
session_index_in = sess_indexes
else:
session_index_in = session_index
if len(session_index)>len(sess_indexes):
raise Exception('session index of length = {} is out of bounds (nSession = {})\nSessions index available = {}'.format(len(session_index), len(sess_indexes), sess_indexes))
elif len(session_index)>1:
# Check all indexes are within boundary
prev_i = 0
start_i = True
for i in session_index:
if i not in sess_indexes:
raise Exception('session index = {} is out of bounds\nSessions index available: {}'.format(i, sess_indexes))
if prev_i>i:
print('\n\nWARNING¡¡¡ Multiple Sessions selected, but indexes are not in consecutive order: {} should go before {}\nCheck your session-index = {}\nIt will concatenate non-consecutive recordings¡¡¡\n\n'.format(i, prev_i, session_index))
consecutiveSessions = False
if not start_i and (i-prev_i)>1:
print('\n\nWARNING¡¡¡ Multiple Sessions selected, but there is a gap between session "{}" and "{}"\nCheck your session-index = {}\nIt will concatenate non-consecutive recordings¡¡¡\n\n'.format(prev_i, i, session_index))
consecutiveSessions = False
prev_i = i
start_i = False
if session_index_in[0]==session_index_in[-1]:
consecutiveSessions = False
sessID = 'exp{}-{:02d}-{:02d}'.format(sessionYear, sessionMonth, sessionDay) + '-sess'
if consecutiveSessions:
sessID += '-{}_{}'.format(session_index_in[0], session_index_in[-1])
else:
for i in session_index_in:
sessID += '-' + str(i)
electrodeGroup_sessions = {
'electrodeName': electrodeGroups[eg_i[0]]['electrodeName'],
'electrodePath': electrodeGroups[eg_i[0]]['electrodePath'],
'probeInfo': electrodeGroups[eg_i[0]]['probeInfo'],
'sessID': sessID,
'fileNames': [electrodeGroups[eg_i[0]]['fileNames'][i] for i in session_index_in],
'fileNamePaths': [electrodeGroups[eg_i[0]]['fileNamePaths'][i] for i in session_index_in],
'fileNamePaths_read': [],
'fileSamples': electrodeGroups[eg_i[0]]['fileSamples'][numpy.array(session_index_in)],
'fileDurations': electrodeGroups[eg_i[0]]['fileDurations'][numpy.array(session_index_in)]
}
return electrodeGroup_sessions
####################################################################################################################################################################################
# Return SpikeInterface Recording Object for a given ElectrodeGroup & Sessions
####################################################################################################################################################################################
def get_si_recording(parentRecordingFolder, sessionYear, sessionMonth, sessionDay, electrodeGroup_Name, session_index=None, localProcess_NWB=False, reset_tempdir=True):
electrodeGroups = getUnique_electrodeGroups(parentRecordingFolder, sessionYear, sessionMonth, sessionDay)
electrodeGroup_sessions = select_electrodeGroup_and_session_info(electrodeGroups, sessionYear, sessionMonth, sessionDay, electrodeGroup_Name, session_index=session_index)
# Check & clear SI temporal folder (it will be used to save peaks, peaks locations, and NWB files if localProcess_NWB=True)
folder_temp = os.environ.get('SI_PROCESSOR_TEMPDIR')
if folder_temp is None:
set_SItempdir_environ()
folder_temp = os.environ.get('SI_PROCESSOR_TEMPDIR')
if reset_tempdir:
clear_SItempdir()
#######################################################################################################################
# Copy files if need it & get updated FILE paths to read
#######################################################################################################################
# Check if the NWB files exists in the same Disk as this "code". Otherwise copy NWB-files to the same disk for "speed"
copyFiles = False
if localProcess_NWB:
for f in electrodeGroup_sessions['fileNamePaths']:
filePath, _ = os.path.split(os.path.abspath(f))
if filePath[0:2].lower()!=os.path.abspath(__file__)[0:2].lower():
copyFiles = True
# If need it, copy NWBfiles. Update paths to read files
for f in electrodeGroup_sessions['fileNamePaths']:
fPath, fName = os.path.split(os.path.abspath(f))
if copyFiles:
print('..... copying file {}'.format(fName))
shutil.copy2(os.path.join(fPath, fName), os.path.join(folder_temp, fName))
electrodeGroup_sessions['fileNamePaths_read'].append(os.path.join(folder_temp, fName))
else:
electrodeGroup_sessions['fileNamePaths_read'].append(os.path.join(fPath, fName))
###################################################
# LOAD session(s)
###################################################
if len(electrodeGroup_sessions['fileNamePaths_read'])>1:
si_recording_List = []
for f in electrodeGroup_sessions['fileNamePaths_read']:
print('Concatenating sessions :\n', f, '\n')
si_recording_List.append(read_nwb(
file_path=f,
electrical_series_path='acquisition/' + electrodeGroup_sessions['electrodePath'],
load_recording=True,
load_sorting=False,
))
si_recording_raw = si.concatenate_recordings(si_recording_List)
else:
f = electrodeGroup_sessions['fileNamePaths_read'][0]
print('loading session : ', f)
si_recording_raw = read_nwb(
file_path=f,
electrical_series_path='acquisition/' + electrodeGroup_sessions['electrodePath'],
load_recording=True,
load_sorting=False,
)
if not si_recording_raw.has_probe():
print('Attaching probe to the recording ......')
probe_from_nwb = constructProbe_2d(si_recordingObj_nwb = si_recording_raw, probeInfo_dict = electrodeGroup_sessions['probeInfo'])
si_recording_rawProbe = si_recording_raw.set_probe(probe_from_nwb)
else:
si_recording_rawProbe = si_recording_raw
del si_recording_raw
###################################################
# Order channels by location
###################################################
print('Sorting channels by depth ......')
chan_ordered_index, chan_orig_index = si.order_channels_by_depth(recording=si_recording_rawProbe, dimensions=("x", "y"))
si_recording = si_recording_rawProbe.channel_slice(channel_ids=[si_recording_rawProbe.channel_ids[i] for i in chan_ordered_index])
si_recording.set_property(key='to_raw_index', values = chan_orig_index)
del si_recording_rawProbe
# CONFIRM LOCATIONS ARE IN "um"
if si_recording.has_probe():
units_probe = numpy.unique(si_recording._properties['contact_vector']['si_units'])
if len(units_probe)>1:
raise Exception('more than one type of units were found in the location of the contacts within the probe : {}', units_probe)
else:
si_recording.annotate(location_units=units_probe[0])
# Check if the recording Object has the location_units defined
if 'location_units' in si_recording.get_annotation_keys():
if si_recording.get_annotation('location_units') == 'mm':
si_recording._properties['location'] *=1000
si_recording._annotations['location_units'] = 'um'
if si_recording.get_annotation('location_units') != 'mm' and si_recording.get_annotation('location_units') != 'um':
raise Exception('Units of the Locations in the RecordingObject is not recognized: {}'.format(
si_recording.get_annotation('location_units')
))
else:
# If not Assume units in 'mm' (default units from NWB format)
si_recording._properties['location'] *=1000
si_recording.annotate(location_units='um')
#################################################################################################################
# GET CHANNEL-CONTACT SPACING ("y" coordinate)
contact_locations = si_recording.get_property('location')
if len(contact_locations)>1:
step_chan = numpy.mean(numpy.absolute(numpy.diff(contact_locations[:, 1])))
else:
step_chan = 50 # Default from SipkeInterface
si_recording.annotate(y_contacts_distance_um = step_chan)
#################################################################################################################
# ADD CONCATENATION INFORMATION
nSessions = len(electrodeGroup_sessions['fileNames'])
nConcatenations = nSessions-1
if nConcatenations>0:
concatenationSamples = numpy.array(electrodeGroup_sessions['fileSamples'][:-1]).cumsum()
concatenationTimes = concatenationSamples/si_recording.sampling_frequency
else:
concatenationSamples = numpy.array([])
concatenationTimes = numpy.array([])
si_recording.annotate(nSessions = nSessions)
si_recording.annotate(sessionSamples = numpy.array(electrodeGroup_sessions['fileSamples']))
si_recording.annotate(nConcatenations = nConcatenations)
si_recording.annotate(concatenationSamples = concatenationSamples)
si_recording.annotate(concatenationTimes = concatenationTimes)
si_recording.annotate(fileNamePaths = electrodeGroup_sessions['fileNamePaths'])
return si_recording, electrodeGroup_sessions
#########################################################################################################################################
# PLOT TO INSPECT THE RECORDING AROUND CONCATENATION OF DIFFERENT SESSIONS
#########################################################################################################################################
def plot_concatenations(si_recording_dict, plot_windows_secs=0.01, sampleChans=False, showPlots=True, savePlots=False, folderPlots=None):
recNames = list(si_recording_dict.keys())
nRecs = len(recNames)
si_recording = si_recording_dict[recNames[0]]
nConcatenations = si_recording.get_annotation('nConcatenations')
samples_radius = int(si_recording.sampling_frequency*plot_windows_secs)
median_recs = int(numpy.median(numpy.arange(nRecs)))
color_line_concat = (0.25, 0.25, 0.25, 1)
if nConcatenations>0:
if folderPlots is None:
if 'savePath' in si_recording._annotations:
preproPlots_folder = os.path.join(si_recording.get_annotation('savePath'), 'preproPlots')
else:
savePlots = False
preproPlots_folder = None
print('Concatenation Plots will not be saved')
else:
preproPlots_folder = folderPlots
# maxChans = 9
concatenationSamples = si_recording.get_annotation('concatenationSamples')
concatenationTimes = si_recording.get_annotation('concatenationTimes')
nChans = si_recording.get_num_channels()
if nChans>9 and sampleChans:
chans_range = numpy.arange(nChans)
median_ch = numpy.median(chans_range).round().astype(dtype='int')-1
chans_index = numpy.concatenate([chans_range[0:3], chans_range[median_ch-1:median_ch+2], chans_range[-3::]])
else:
chans_index = range(nChans)
steps = []
for c in range(nConcatenations):
traces = si_recording.get_traces(start_frame=concatenationSamples[c]-samples_radius, end_frame=concatenationSamples[c]+samples_radius)
meanTraces = traces.mean(axis=0)
stdTraces = traces.std(axis=0)
steps.append(max([abs(max(meanTraces + stdTraces)), abs(min(meanTraces - stdTraces))]))
del traces, meanTraces, stdTraces
step = max(steps)
for c in range(nConcatenations):
sample_start = int(concatenationSamples[c]-samples_radius)
sample_end = int(concatenationSamples[c]+samples_radius)
traces_time = si_recording.sample_index_to_time(numpy.array(range(sample_start, sample_end))) - concatenationTimes[c]
fig, axis = plt.subplots(ncols=nRecs, sharex=True, sharey=True)
if nRecs>1:
for r in range(nRecs):
traces = si_recording_dict[recNames[r]].get_traces(start_frame=sample_start, end_frame=sample_end)
axis[r].plot((0, 0), (-step, step*len(chans_index)), color=color_line_concat, linestyle='dotted')
count_ch = 0
for ch in chans_index:
axis[r].plot(traces_time, traces[:, ch]+(step*count_ch), alpha=0.75)
count_ch +=1
axis[r].set_title(recNames[r])
axis[median_recs].set_xlabel('time[s]')
axis[r].set_xlim(-plot_windows_secs, plot_windows_secs)
axis[r].set_rasterized(True)
fig.suptitle('Concatenation: {} [{} s]'.format(c+1, concatenationTimes[c]))
else:
traces = si_recording.get_traces(start_frame=sample_start, end_frame=sample_end)
axis.plot((0, 0), (-step, step*len(chans_index)), color=color_line_concat, linestyle='dotted')
count_ch = 0
for ch in chans_index:
axis.plot(traces_time, traces[:, ch]+(step*count_ch), alpha=0.75)
count_ch +=1
axis.set_title(recNames[0])
axis.set_xlabel('time[s]')
axis.set_xlim(-plot_windows_secs, plot_windows_secs)
axis.set_rasterized(True)
fig.suptitle('Concatenation: {} [{} s]'.format(c+1, concatenationTimes[c]))
if savePlots:
_, sess_electrodeGroup_Name = os.path.split(si_recording.get_annotation('savePath'))
fig.set_figheight(fig_height_global)
fig.set_figwidth(fig_width_global)
fig.savefig(os.path.join(preproPlots_folder, '{}-Concat-{}.eps'.format(sess_electrodeGroup_Name, c+1)), dpi=fig_dpi, format='eps')
if showPlots:
plt.show()
else:
plt.close(fig=fig)
else:
print('This recording do not have concatenated sessions (nSess = {})'.format(nConcatenations))
#########################################################################################################################################
# PLOT TO INSPECT FOR NOISE AT SPECIFIC FREQUENCIES
#########################################################################################################################################
def plotPSD_randomChunks(si_recording, compare_CMR=True, plot_by_channel=True, chan_radius=None, showPlots=True, savePlots=False, folderPlots=None):
nChans = si_recording.get_num_channels()
# Create PSD folder if savePlots & plot_by_channel
if folderPlots is None:
if 'savePath' in si_recording._annotations:
preproPlots_folder = os.path.join(si_recording.get_annotation('savePath'), 'preproPlots')
else:
savePlots = False
preproPlots_folder = None
print('PSD-Plots will not be saved')
else:
preproPlots_folder = folderPlots
if 'savePath' in si_recording._annotations:
_, sess_electrodeGroup_Name = os.path.split(si_recording.get_annotation('savePath'))
else:
sess_electrodeGroup_Name = 'electrodeGroup'
if plot_by_channel and savePlots and nChans>1 and preproPlots_folder is not None:
if compare_CMR:
psd_chans_folder = os.path.join(preproPlots_folder, 'CMR-PSD_by_chan')
else:
psd_chans_folder = os.path.join(preproPlots_folder, 'PSD_by_chan')
if not os.path.isdir(psd_chans_folder):
os.mkdir(psd_chans_folder)
# Get Chunks of data
data_chunk = si.get_random_data_chunks(
recording = si_recording,
num_chunks_per_segment=50,
chunk_size=10000,
seed=0
)
##########################################################
# Apply Common Median Reference to compare
if compare_CMR and nChans>1:
if chan_radius is None:
recObj_cmr = spre_common_reference(recording=si_recording, reference='global', operator='median')
else:
step_chan = si_recording.get_annotation('y_contacts_distance_um') # GET CHANNEL-CONTACT SPACING ("y" coordinate)
recObj_cmr = spre_common_reference(recording=si_recording, reference='local', operator='median', local_radius=(chan_radius[0] * step_chan, chan_radius[1] * step_chan ))
# Get Chunks of data
data_chunk_cmr = si.get_random_data_chunks(
recording = recObj_cmr,
num_chunks_per_segment=50,
chunk_size=10000,
seed=0
)
# Plot the power spectral density for each channel to inspect specific noise frequencies
if nChans==1:
fig, ax = plt.subplots(ncols=1, figsize=(10, 7))
p, f = ax.psd(data_chunk[:, 0], Fs=si_recording.sampling_frequency, color="b")
ax.set_rasterized(True)
if savePlots:
fig.set_figheight(fig_height_global)
fig.set_figwidth(fig_width_global)
fig.savefig(os.path.join(preproPlots_folder, '{}-PSD.eps'.format(sess_electrodeGroup_Name)), dpi=fig_dpi, format='eps')
if showPlots:
plt.show()
else:
plt.close(fig=fig)
else:
if compare_CMR:
fig, axis = plt.subplots(ncols=2)
for tr in data_chunk.T:
p, f = axis[0].psd(tr, Fs=si_recording.sampling_frequency, color="b")
axis[0].set_title('filtered-PSD')
axis[0].set_rasterized(True)
for tr in data_chunk_cmr.T:
p, f = axis[1].psd(tr, Fs=si_recording.sampling_frequency, color="r")
axis[1].set_title('CMR-PSD')
axis[1].set_rasterized(True)
fig.suptitle('All channels')
if savePlots:
fig.set_figheight(fig_height_global)
fig.set_figwidth(fig_width_global)
fig.savefig(os.path.join(preproPlots_folder, '{}-CMR-PSD.eps'.format(sess_electrodeGroup_Name)), dpi=fig_dpi, format='eps')
if showPlots:
plt.show()
else:
plt.close(fig=fig)
else:
fig, axis = plt.subplots(ncols=1)
for tr in data_chunk.T:
p, f = axis.psd(tr, Fs=si_recording.sampling_frequency, color="b")
axis.set_title('filtered-PSD')
axis.set_rasterized(True)
fig.suptitle('All channels')
if savePlots:
fig.set_figheight(fig_height_global)
fig.set_figwidth(fig_width_global)
fig.savefig(os.path.join(preproPlots_folder, '{}-PSD.eps'.format(sess_electrodeGroup_Name)), dpi=fig_dpi, format='eps')
if showPlots:
plt.show()
else:
plt.close(fig=fig)
if plot_by_channel:
chanIDs = si_recording.get_channel_ids()
deviceIDs = [si_recording.get_channel_property(chanID, 'contact_vector')['device_channel_indices'] for chanID in chanIDs]
for ch in range(nChans):
if compare_CMR:
fig, axis = plt.subplots(ncols=2)
for tr in data_chunk.T:
p, f = axis[0].psd(tr, Fs=si_recording.sampling_frequency, color="k", alpha=0.05)
p, f = axis[0].psd(data_chunk[:, ch], Fs=si_recording.sampling_frequency, color="b", linewidth=1.5)
axis[0].set_title('filtered-PSD')
axis[0].set_rasterized(True)
for tr in data_chunk_cmr.T:
p, f = axis[1].psd(tr, Fs=si_recording.sampling_frequency, color="m", alpha=0.05)
p, f = axis[1].psd(data_chunk_cmr[:, ch], Fs=si_recording.sampling_frequency, color="r", linewidth=1.5)
axis[1].set_title('CMR-PSD')
axis[1].set_rasterized(True)
fig.suptitle('ch={} (devID={})'.format(chanIDs[ch], deviceIDs[ch]))
if savePlots:
fig.set_figheight(fig_height_global)
fig.set_figwidth(fig_width_global)
fig.savefig(os.path.join(psd_chans_folder, '{}-CMR-PSD-dev{}-id{}.eps'.format(sess_electrodeGroup_Name, deviceIDs[ch], chanIDs[ch])), dpi=fig_dpi, format='eps')
if showPlots:
plt.show()
else:
plt.close(fig=fig)
else:
fig, axis = plt.subplots(ncols=1)
for tr in data_chunk.T:
p, f = axis.psd(tr, Fs=si_recording.sampling_frequency, color="k", alpha=0.05)
p, f = axis.psd(data_chunk[:, ch], Fs=si_recording.sampling_frequency, color="b", linewidth=1.5)
axis.set_title('filtered-PSD')
axis.set_rasterized(True)
fig.suptitle('ch={} (devID={})'.format(chanIDs[ch], deviceIDs[ch]))