LFP Extraction¶

Overview¶

Developer Note: if you may make a PR in the future, be sure to copy this notebook, and use the gitignore prefix temp to avoid future conflicts.

This is one notebook in a multi-part series on Spyglass.

• To set up your Spyglass environment and database, see the Setup notebook
• For additional info on DataJoint syntax, including table definitions and inserts, see the Insert Data notebook

Imports¶

In [2]:

import os
import copy
import datajoint as dj
import numpy as np
import pandas as pd

# change to the upper level folder to detect dj_local_conf.json
if os.path.basename(os.getcwd()) == "notebooks":
    os.chdir("..")
dj.config.load("dj_local_conf.json")  # load config for database connection info

import spyglass.common as sgc
import spyglass.lfp as lfp

# ignore datajoint+jupyter async warnings
import warnings

warnings.simplefilter("ignore", category=DeprecationWarning)
warnings.simplefilter("ignore", category=ResourceWarning)

import os import copy import datajoint as dj import numpy as np import pandas as pd # change to the upper level folder to detect dj_local_conf.json if os.path.basename(os.getcwd()) == "notebooks": os.chdir("..") dj.config.load("dj_local_conf.json") # load config for database connection info import spyglass.common as sgc import spyglass.lfp as lfp # ignore datajoint+jupyter async warnings import warnings warnings.simplefilter("ignore", category=DeprecationWarning) warnings.simplefilter("ignore", category=ResourceWarning)

[2024-01-12 08:39:21,871][INFO]: Connecting sambray@lmf-db.cin.ucsf.edu:3306
[2024-01-12 08:39:21,915][INFO]: Connected sambray@lmf-db.cin.ucsf.edu:3306
WARNING:root:Populate: Entry in DataAcquisitionDeviceSystem with primary keys {'data_acquisition_device_system': 'SpikeGadgets'} already exists.
WARNING:root:Populate: Entry in DataAcquisitionDeviceAmplifier with primary keys {'data_acquisition_device_amplifier': 'Intan'} already exists.

Select data¶

First, we select the NWB file, which corresponds to the dataset we want to extract LFP from.

In [3]:

nwb_file_name = "minirec20230622_.nwb"

nwb_file_name = "minirec20230622_.nwb"

Create Filters¶

Next, we create the standard LFP Filters. This only needs to be done once.

In [4]:

sgc.FirFilterParameters().create_standard_filters()
sgc.FirFilterParameters()

sgc.FirFilterParameters().create_standard_filters() sgc.FirFilterParameters()

Out[4]:

filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	filter_type	filter_low_stop lowest frequency for stop band for low frequency side of filter	filter_low_pass lowest frequency for pass band of low frequency side of filter	filter_high_pass highest frequency for pass band for high frequency side of filter	filter_high_stop highest frequency for stop band of high frequency side of filter	filter_comments comments about the filter	filter_band_edges numpy array containing the filter bands (redundant with individual parameters)	filter_coeff numpy array containing the filter coefficients
Delta 0.5-4 Hz	1000	lowpass	0.25	0.5	4.0	5.0	delta filter for 1 KHz data	=BLOB=	=BLOB=
Delta 0.5-4 Hz pass, 0.25-4.5 Hz stop	1000	lowpass	0.25	0.5	4.0	4.5	revised delta filter for 1 KHz data	=BLOB=	=BLOB=
Epilepsy project 0.2-40 Hz	2000	bandpass	0.1	0.2	40.0	41.0	Sharp wave filter	=BLOB=	=BLOB=
Epilepsy project 140-800 Hz	2000	bandpass	135.0	140.0	800.0	825.0	Extended ripple filter	=BLOB=	=BLOB=
Epilepsy project 140-800 Hz ripple	2000	bandpass	135.0	140.0	800.0	825.0	Extended ripple filter	=BLOB=	=BLOB=
Epilepsy project 40-600 Hz	2000	bandpass	39.0	40.0	600.0	625.0	HFE filter	=BLOB=	=BLOB=
Fast Gamma 65-100 Hz	1000	bandpass	55.0	65.0	100.0	110.0	slow gamma filter for 1 Khz data	=BLOB=	=BLOB=
LFP 0-400 Hz	1000	lowpass	0.0	0.0	400.0	425.0	LFP filter for referencing	=BLOB=	=BLOB=
LFP 0-400 Hz	20000	lowpass	0.0	0.0	400.0	425.0	standard LFP filter for 20 KHz data	=BLOB=	=BLOB=
LFP 0-400 Hz	29995	lowpass	0.0	0.0	400.0	425.0	adapted LFP filter for Banner20220105	=BLOB=	=BLOB=
LFP 0-400 Hz	30000	lowpass	0.0	0.0	400.0	425.0	standard LFP filter for 20 KHz data	=BLOB=	=BLOB=
LFP 0-800 Hz	30000	lowpass	0.0	0.0	800.0	825.0	LFP filter 0-800 Hz	=BLOB=	=BLOB=

...

Total: 31

Electrode Group¶

Now, we create an LFP electrode group, or the set of electrodes we want to filter for LFP data. We can grab all electrodes and brain regions as a data frame.

In [5]:

electrodes_df = (
    pd.DataFrame(
        (sgc.Electrode & {"nwb_file_name": nwb_file_name, "probe_electrode": 0})
        * sgc.BrainRegion
    )
    .loc[:, ["nwb_file_name", "electrode_id", "region_name"]]
    .sort_values(by="electrode_id")
)
electrodes_df

electrodes_df = ( pd.DataFrame( (sgc.Electrode & {"nwb_file_name": nwb_file_name, "probe_electrode": 0}) * sgc.BrainRegion ) .loc[:, ["nwb_file_name", "electrode_id", "region_name"]] .sort_values(by="electrode_id") ) electrodes_df

Out[5]:

	nwb_file_name	electrode_id	region_name
0	minirec20230622_.nwb	0	corpus callosum and associated subcortical whi...
1	minirec20230622_.nwb	4	corpus callosum and associated subcortical whi...
12	minirec20230622_.nwb	8	corpus callosum and associated subcortical whi...
23	minirec20230622_.nwb	12	corpus callosum and associated subcortical whi...
26	minirec20230622_.nwb	16	corpus callosum and associated subcortical whi...
27	minirec20230622_.nwb	20	corpus callosum and associated subcortical whi...
28	minirec20230622_.nwb	24	corpus callosum and associated subcortical whi...
29	minirec20230622_.nwb	28	corpus callosum and associated subcortical whi...
30	minirec20230622_.nwb	32	corpus callosum and associated subcortical whi...
31	minirec20230622_.nwb	36	corpus callosum and associated subcortical whi...
2	minirec20230622_.nwb	40	corpus callosum and associated subcortical whi...
3	minirec20230622_.nwb	44	corpus callosum and associated subcortical whi...
4	minirec20230622_.nwb	48	corpus callosum and associated subcortical whi...
5	minirec20230622_.nwb	52	corpus callosum and associated subcortical whi...
6	minirec20230622_.nwb	56	corpus callosum and associated subcortical whi...
7	minirec20230622_.nwb	60	corpus callosum and associated subcortical whi...
8	minirec20230622_.nwb	64	corpus callosum and associated subcortical whi...
9	minirec20230622_.nwb	68	corpus callosum and associated subcortical whi...
10	minirec20230622_.nwb	72	corpus callosum and associated subcortical whi...
11	minirec20230622_.nwb	76	corpus callosum and associated subcortical whi...
13	minirec20230622_.nwb	80	corpus callosum and associated subcortical whi...
14	minirec20230622_.nwb	84	corpus callosum and associated subcortical whi...
15	minirec20230622_.nwb	88	corpus callosum and associated subcortical whi...
16	minirec20230622_.nwb	92	corpus callosum and associated subcortical whi...
17	minirec20230622_.nwb	96	corpus callosum and associated subcortical whi...
18	minirec20230622_.nwb	100	corpus callosum and associated subcortical whi...
19	minirec20230622_.nwb	104	corpus callosum and associated subcortical whi...
20	minirec20230622_.nwb	108	corpus callosum and associated subcortical whi...
21	minirec20230622_.nwb	112	corpus callosum and associated subcortical whi...
22	minirec20230622_.nwb	116	corpus callosum and associated subcortical whi...
24	minirec20230622_.nwb	120	corpus callosum and associated subcortical whi...
25	minirec20230622_.nwb	124	corpus callosum and associated subcortical whi...

For a larger dataset, we might want to filter by region, but our example data only has one electrode.

lfp_electrode_ids = electrodes_df.loc[
    electrodes_df.region_name == "ca1"
].electrode_id

In [6]:

lfp_electrode_ids = [0]
lfp_electrode_group_name = "test"
lfp_eg_key = {
    "nwb_file_name": nwb_file_name,
    "lfp_electrode_group_name": lfp_electrode_group_name,
}

lfp.lfp_electrode.LFPElectrodeGroup.create_lfp_electrode_group(
    nwb_file_name=nwb_file_name,
    group_name=lfp_electrode_group_name,
    electrode_list=lfp_electrode_ids,
)

lfp_electrode_ids = [0] lfp_electrode_group_name = "test" lfp_eg_key = { "nwb_file_name": nwb_file_name, "lfp_electrode_group_name": lfp_electrode_group_name, } lfp.lfp_electrode.LFPElectrodeGroup.create_lfp_electrode_group( nwb_file_name=nwb_file_name, group_name=lfp_electrode_group_name, electrode_list=lfp_electrode_ids, )

We can verify the electrode list as follows

In [7]:

lfp.lfp_electrode.LFPElectrodeGroup.LFPElectrode() & {
    "nwb_file_name": nwb_file_name
}

lfp.lfp_electrode.LFPElectrodeGroup.LFPElectrode() & { "nwb_file_name": nwb_file_name }

Out[7]:

nwb_file_name name of the NWB file	lfp_electrode_group_name the name of this group of electrodes	electrode_group_name electrode group name from NWBFile	electrode_id the unique number for this electrode
minirec20230622_.nwb	test	0	0

Total: 1

IntervalList¶

Recall from the previous notebook that IntervalList selects time frames from the experiment. We can select the interval and subset to the first n seconds...

In [8]:

sgc.IntervalList & {"nwb_file_name": nwb_file_name}

sgc.IntervalList & {"nwb_file_name": nwb_file_name}

Out[8]:

Time intervals used for analysis

nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	valid_times numpy array with start/end times for each interval	pipeline type of interval list (e.g. 'position', 'spikesorting_recording_v1')
minirec20230622_.nwb	01_s1	=BLOB=
minirec20230622_.nwb	01_s1_first9	=BLOB=
minirec20230622_.nwb	02_s2	=BLOB=
minirec20230622_.nwb	pos 0 valid times	=BLOB=
minirec20230622_.nwb	pos 1 valid times	=BLOB=
minirec20230622_.nwb	raw data valid times	=BLOB=

Total: 6

In [9]:

n = 9
orig_interval_list_name = "01_s1"
interval_list_name = orig_interval_list_name + f"_first{n}"

valid_times = (
    sgc.IntervalList
    & {
        "nwb_file_name": nwb_file_name,
        "interval_list_name": orig_interval_list_name,
    }
).fetch1("valid_times")

interval_key = {
    "nwb_file_name": nwb_file_name,
    "interval_list_name": interval_list_name,
    "valid_times": np.asarray([[valid_times[0, 0], valid_times[0, 0] + n]]),
}

sgc.IntervalList.insert1(
    interval_key,
    skip_duplicates=True,
)

n = 9 orig_interval_list_name = "01_s1" interval_list_name = orig_interval_list_name + f"_first{n}" valid_times = ( sgc.IntervalList & { "nwb_file_name": nwb_file_name, "interval_list_name": orig_interval_list_name, } ).fetch1("valid_times") interval_key = { "nwb_file_name": nwb_file_name, "interval_list_name": interval_list_name, "valid_times": np.asarray([[valid_times[0, 0], valid_times[0, 0] + n]]), } sgc.IntervalList.insert1( interval_key, skip_duplicates=True, )

In [10]:

sgc.IntervalList() & {
    "nwb_file_name": nwb_file_name,
    "interval_list_name": interval_list_name,
}

sgc.IntervalList() & { "nwb_file_name": nwb_file_name, "interval_list_name": interval_list_name, }

Out[10]:

Time intervals used for analysis

nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	valid_times numpy array with start/end times for each interval	pipeline type of interval list (e.g. 'position', 'spikesorting_recording_v1')
minirec20230622_.nwb	01_s1_first9	=BLOB=

Total: 1

LFPSelection¶

LFPSelection combines the data, interval and filter

In [23]:

lfp_s_key = copy.deepcopy(lfp_eg_key)

lfp_s_key.update(
    {
        "target_interval_list_name": interval_list_name,
        "filter_name": "LFP 0-400 Hz",
        "filter_sampling_rate": 30_000,  # sampling rate of the data (Hz)
        "target_sampling_rate": 1_000,  # smpling rate of the lfp output (Hz)
    }
)

lfp.v1.LFPSelection.insert1(lfp_s_key, skip_duplicates=True)

lfp_s_key = copy.deepcopy(lfp_eg_key) lfp_s_key.update( { "target_interval_list_name": interval_list_name, "filter_name": "LFP 0-400 Hz", "filter_sampling_rate": 30_000, # sampling rate of the data (Hz) "target_sampling_rate": 1_000, # smpling rate of the lfp output (Hz) } ) lfp.v1.LFPSelection.insert1(lfp_s_key, skip_duplicates=True)

In [24]:

lfp.v1.LFPSelection() & lfp_s_key

lfp.v1.LFPSelection() & lfp_s_key

Out[24]:

nwb_file_name name of the NWB file	lfp_electrode_group_name the name of this group of electrodes	target_interval_list_name descriptive name of this interval list	filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	target_sampling_rate the desired output sampling rate, in HZ
minirec20230622_.nwb	test	01_s1_first9	LFP 0-400 Hz	30000	1000.0

Total: 1

Populate LFP¶

LFPV1 has a similar populate command as we've seen before.

Notes:

• For full recordings, this takes ~2h when done locally, for all electrodes
• This populate also inserts into the LFP Merge Table, LFPOutput. For more on Merge Tables, see our documentation. In short, this collects different LFP processing streams into one table.

In [25]:

lfp.v1.LFPV1().populate(lfp_s_key)

lfp.v1.LFPV1().populate(lfp_s_key)

We can now look at the LFP table to see the data we've extracted

In [26]:

lfp.LFPOutput.LFPV1() & lfp_s_key

lfp.LFPOutput.LFPV1() & lfp_s_key

Out[26]:

merge_id	nwb_file_name name of the NWB file	lfp_electrode_group_name the name of this group of electrodes	target_interval_list_name descriptive name of this interval list	filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter
1e7fbe35-034b-ed8e-7965-a0467ae5c0a4	minirec20230622_.nwb	test	01_s1_first9	LFP 0-400 Hz	30000

Total: 1

In [27]:

lfp_key = {"merge_id": (lfp.LFPOutput.LFPV1() & lfp_s_key).fetch1("merge_id")}
lfp_key

lfp_key = {"merge_id": (lfp.LFPOutput.LFPV1() & lfp_s_key).fetch1("merge_id")} lfp_key

Out[27]:

{'merge_id': UUID('1e7fbe35-034b-ed8e-7965-a0467ae5c0a4')}

In [28]:

lfp.LFPOutput & lfp_key

lfp.LFPOutput & lfp_key

Out[28]:

merge_id	source
1e7fbe35-034b-ed8e-7965-a0467ae5c0a4	LFPV1

Total: 1

From the Merge Table, we can get the keys for the LFP data we want to see

In [31]:

lfp_df = (lfp.LFPOutput & lfp_key).fetch1_dataframe()
lfp_df

lfp_df = (lfp.LFPOutput & lfp_key).fetch1_dataframe() lfp_df

Out[31]:

	0
time
1.687475e+09	21
1.687475e+09	10
1.687475e+09	37
1.687475e+09	81
1.687475e+09	61
...	...
1.687475e+09	-125
1.687475e+09	-126
1.687475e+09	-123
1.687475e+09	-129
1.687475e+09	-122

9000 rows × 1 columns

LFP Band¶

Now that we've created the LFP object we can perform a second level of filtering for a band of interest, in this case the theta band. We first need to create the filter.

In [32]:

lfp_sampling_rate = lfp.LFPOutput.merge_get_parent(lfp_key).fetch1(
    "lfp_sampling_rate"
)

filter_name = "Theta 5-11 Hz"

sgc.common_filter.FirFilterParameters().add_filter(
    filter_name,
    lfp_sampling_rate,
    "bandpass",
    [4, 5, 11, 12],
    "theta filter for 1 Khz data",
)

sgc.common_filter.FirFilterParameters() & {
    "filter_name": filter_name,
    "filter_sampling_rate": lfp_sampling_rate,
}

lfp_sampling_rate = lfp.LFPOutput.merge_get_parent(lfp_key).fetch1( "lfp_sampling_rate" ) filter_name = "Theta 5-11 Hz" sgc.common_filter.FirFilterParameters().add_filter( filter_name, lfp_sampling_rate, "bandpass", [4, 5, 11, 12], "theta filter for 1 Khz data", ) sgc.common_filter.FirFilterParameters() & { "filter_name": filter_name, "filter_sampling_rate": lfp_sampling_rate, }

Out[32]:

filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	filter_type	filter_low_stop lowest frequency for stop band for low frequency side of filter	filter_low_pass lowest frequency for pass band of low frequency side of filter	filter_high_pass highest frequency for pass band for high frequency side of filter	filter_high_stop highest frequency for stop band of high frequency side of filter	filter_comments comments about the filter	filter_band_edges numpy array containing the filter bands (redundant with individual parameters)	filter_coeff numpy array containing the filter coefficients
Theta 5-11 Hz	1000	lowpass	4.0	5.0	11.0	12.0	theta filter for 1 KHz data	=BLOB=	=BLOB=

Total: 1

In [33]:

sgc.IntervalList()

sgc.IntervalList()

Out[33]:

Time intervals used for analysis

nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	valid_times numpy array with start/end times for each interval	pipeline type of interval list (e.g. 'position', 'spikesorting_recording_v1')
aj8020230628_.nwb	02_lineartrack	=BLOB=
aj8020230628_.nwb	04_lineartrack	=BLOB=
aj8020230628_.nwb	06_lineartrack	=BLOB=
aj8020230628_.nwb	pos 0 valid times	=BLOB=
aj8020230628_.nwb	pos 1 valid times	=BLOB=
aj8020230628_.nwb	pos 2 valid times	=BLOB=
aj8020230628_.nwb	raw data valid times	=BLOB=
aj8420230703_.nwb	01_lineartrack	=BLOB=
aj8420230703_.nwb	pos 0 valid times	=BLOB=
aj8420230703_.nwb	raw data valid times	=BLOB=
aj8620230628_.nwb	02_lineartrack	=BLOB=
aj8620230628_.nwb	04_lineartrack	=BLOB=

...

Total: 130046

We can specify electrodes of interest, and desired sampling rate.

In [34]:

from spyglass.lfp.analysis.v1 import lfp_band

lfp_band_electrode_ids = [0]  # assumes we've filtered these electrodes
lfp_band_sampling_rate = 100  # desired sampling rate

lfp_band.LFPBandSelection().set_lfp_band_electrodes(
    nwb_file_name=nwb_file_name,
    lfp_merge_id=lfp_key["merge_id"],
    electrode_list=lfp_band_electrode_ids,
    filter_name=filter_name,  # defined above
    interval_list_name=interval_list_name,  # Defined in IntervalList above
    reference_electrode_list=[-1],  # -1 means no ref electrode for all channels
    lfp_band_sampling_rate=lfp_band_sampling_rate,
)

lfp_band.LFPBandSelection()

from spyglass.lfp.analysis.v1 import lfp_band lfp_band_electrode_ids = [0] # assumes we've filtered these electrodes lfp_band_sampling_rate = 100 # desired sampling rate lfp_band.LFPBandSelection().set_lfp_band_electrodes( nwb_file_name=nwb_file_name, lfp_merge_id=lfp_key["merge_id"], electrode_list=lfp_band_electrode_ids, filter_name=filter_name, # defined above interval_list_name=interval_list_name, # Defined in IntervalList above reference_electrode_list=[-1], # -1 means no ref electrode for all channels lfp_band_sampling_rate=lfp_band_sampling_rate, ) lfp_band.LFPBandSelection()

Out[34]:

lfp_merge_id	filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	nwb_file_name name of the NWB file	target_interval_list_name descriptive name of this interval list	lfp_band_sampling_rate the sampling rate for this band	min_interval_len the minimum length of a valid interval to filter
001d08a1-5365-3c20-716b-f431ab136a55	Theta 5-11 Hz	1000	Totoro20220602_.nwb	pos 4 valid times	100	1.0
00213375-550b-ae22-394b-82da3cdfddb3	Theta 5-11 Hz	1000	Yoshi20220518_.nwb	pos 15 valid times	100	1.0
004560d5-417b-ff99-5a13-99a37a3eb998	ripple_150_250	1000	j1620210715_.nwb	09_s5	1000	1.0
004560d5-417b-ff99-5a13-99a37a3eb998	theta_5_11	1000	j1620210715_.nwb	09_s5	1000	1.0
006b9516-88d1-dd58-75e6-e519fb7dfea8	ripple_150_250	1000	senor20201103_.nwb	15_s8	1000	1.0
006b9516-88d1-dd58-75e6-e519fb7dfea8	theta_5_11	1000	senor20201103_.nwb	15_s8	1000	1.0
0087e094-8238-32b8-9e8d-ecb7d9352b3b	Fast Gamma 65-100 Hz	1000	Winnie20220714_.nwb	pos 9 valid times	1000	1.0
0087e094-8238-32b8-9e8d-ecb7d9352b3b	ms_stim_125ms_period	1000	Winnie20220714_.nwb	pos 9 valid times	200	1.0
0087e094-8238-32b8-9e8d-ecb7d9352b3b	Ripple 150-250 Hz	1000	Winnie20220714_.nwb	pos 9 valid times	1000	1.0
0087e094-8238-32b8-9e8d-ecb7d9352b3b	Slow Gamma 25-55 Hz	1000	Winnie20220714_.nwb	pos 9 valid times	1000	1.0
0087e094-8238-32b8-9e8d-ecb7d9352b3b	Theta 5-11 Hz	1000	Winnie20220714_.nwb	pos 9 valid times	100	1.0
0093a2c5-d720-d0c4-6cc4-35f1f34a0a01	ripple_150_250	1000	senor20201118_.nwb	08_r4 noPrePostTrialTimes	1000	1.0

...

Total: 8457

Next we add an entry for the LFP Band and the electrodes we want to filter

In [39]:

lfp_band_key = (
    lfp_band.LFPBandSelection
    & {
        "lfp_merge_id": lfp_key["merge_id"],
        "filter_name": filter_name,
        "lfp_band_sampling_rate": lfp_band_sampling_rate,
    }
).fetch1("KEY")
lfp_band_key

lfp_band_key = ( lfp_band.LFPBandSelection & { "lfp_merge_id": lfp_key["merge_id"], "filter_name": filter_name, "lfp_band_sampling_rate": lfp_band_sampling_rate, } ).fetch1("KEY") lfp_band_key

Out[39]:

{'lfp_merge_id': UUID('1e7fbe35-034b-ed8e-7965-a0467ae5c0a4'),
 'filter_name': 'Theta 5-11 Hz',
 'filter_sampling_rate': 1000,
 'nwb_file_name': 'minirec20230622_.nwb',
 'target_interval_list_name': '01_s1_first9',
 'lfp_band_sampling_rate': 100}

Check to make sure it worked

In [40]:

lfp_band.LFPBandSelection() & lfp_band_key

lfp_band.LFPBandSelection() & lfp_band_key

Out[40]:

lfp_merge_id	filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	nwb_file_name name of the NWB file	target_interval_list_name descriptive name of this interval list	lfp_band_sampling_rate the sampling rate for this band	min_interval_len the minimum length of a valid interval to filter
1e7fbe35-034b-ed8e-7965-a0467ae5c0a4	Theta 5-11 Hz	1000	minirec20230622_.nwb	01_s1_first9	100	1.0

Total: 1

In [42]:

lfp_band.LFPBandV1().populate(lfp_band.LFPBandSelection() & lfp_band_key)
lfp_band.LFPBandV1() & lfp_band_key

lfp_band.LFPBandV1().populate(lfp_band.LFPBandSelection() & lfp_band_key) lfp_band.LFPBandV1() & lfp_band_key

Out[42]:

lfp_merge_id	filter_name descriptive name of this filter	filter_sampling_rate sampling rate for this filter	nwb_file_name name of the NWB file	target_interval_list_name descriptive name of this interval list	lfp_band_sampling_rate the sampling rate for this band	analysis_file_name name of the file	interval_list_name descriptive name of this interval list	lfp_band_object_id the NWB object ID for loading this object from the file
1e7fbe35-034b-ed8e-7965-a0467ae5c0a4	Theta 5-11 Hz	1000	minirec20230622_.nwb	01_s1_first9	100	minirec20230622_DCP0O105KG.nwb	01_s1_first9 lfp band 100Hz	277bdc87-98b9-4eff-a33e-00b64b4c3bb2

Total: 1

Plotting¶

Now we can plot the original signal, the LFP filtered trace, and the theta filtered trace together. Get the three electrical series objects and the indices of the electrodes we band pass filtered

Note: Much of the code below could be replaced by a function calls that would return the data from each electrical series.

Note: If you see an error Qt: Session Management Error, try running the following unix command: export -n SESSION_MANAGER. See also

In [43]:

orig_eseries = (sgc.Raw() & {"nwb_file_name": nwb_file_name}).fetch_nwb()[0][
    "raw"
]
orig_elect_indices = sgc.get_electrode_indices(
    orig_eseries, lfp_band_electrode_ids
)
orig_timestamps = np.asarray(orig_eseries.timestamps)

orig_eseries = (sgc.Raw() & {"nwb_file_name": nwb_file_name}).fetch_nwb()[0][ "raw" ] orig_elect_indices = sgc.get_electrode_indices( orig_eseries, lfp_band_electrode_ids ) orig_timestamps = np.asarray(orig_eseries.timestamps)

In [44]:

lfp_eseries = lfp.LFPOutput().fetch_nwb(lfp_key)[0]["lfp"]
lfp_elect_indices = sgc.get_electrode_indices(
    lfp_eseries, lfp_band_electrode_ids
)
lfp_timestamps = np.asarray(lfp_eseries.timestamps)

lfp_eseries = lfp.LFPOutput().fetch_nwb(lfp_key)[0]["lfp"] lfp_elect_indices = sgc.get_electrode_indices( lfp_eseries, lfp_band_electrode_ids ) lfp_timestamps = np.asarray(lfp_eseries.timestamps)

In [45]:

lfp_band_eseries = (lfp_band.LFPBandV1 & lfp_band_key).fetch_nwb()[0][
    "lfp_band"
]
lfp_band_elect_indices = sgc.get_electrode_indices(
    lfp_band_eseries, lfp_band_electrode_ids
)
lfp_band_timestamps = np.asarray(lfp_band_eseries.timestamps)

lfp_band_eseries = (lfp_band.LFPBandV1 & lfp_band_key).fetch_nwb()[0][ "lfp_band" ] lfp_band_elect_indices = sgc.get_electrode_indices( lfp_band_eseries, lfp_band_electrode_ids ) lfp_band_timestamps = np.asarray(lfp_band_eseries.timestamps)

Get a list of times for the first run epoch and then select a 2 second interval 100 seconds from the beginning

In [46]:

plottimes = [valid_times[0][0] + 1, valid_times[0][0] + 8]

plottimes = [valid_times[0][0] + 1, valid_times[0][0] + 8]

In [47]:

# get the time indices for each dataset
orig_time_ind = np.where(
    np.logical_and(
        orig_timestamps > plottimes[0], orig_timestamps < plottimes[1]
    )
)[0]

lfp_time_ind = np.where(
    np.logical_and(lfp_timestamps > plottimes[0], lfp_timestamps < plottimes[1])
)[0]
lfp_band_time_ind = np.where(
    np.logical_and(
        lfp_band_timestamps > plottimes[0],
        lfp_band_timestamps < plottimes[1],
    )
)[0]

# get the time indices for each dataset orig_time_ind = np.where( np.logical_and( orig_timestamps > plottimes[0], orig_timestamps < plottimes[1] ) )[0] lfp_time_ind = np.where( np.logical_and(lfp_timestamps > plottimes[0], lfp_timestamps < plottimes[1]) )[0] lfp_band_time_ind = np.where( np.logical_and( lfp_band_timestamps > plottimes[0], lfp_band_timestamps < plottimes[1], ) )[0]

In [48]:

import matplotlib.pyplot as plt

plt.plot(
    orig_eseries.timestamps[orig_time_ind],
    orig_eseries.data[orig_time_ind, orig_elect_indices[0]],
    "k-",
)
plt.plot(
    lfp_eseries.timestamps[lfp_time_ind],
    lfp_eseries.data[lfp_time_ind, lfp_elect_indices[0]],
    "b-",
)
plt.plot(
    lfp_band_eseries.timestamps[lfp_band_time_ind],
    lfp_band_eseries.data[lfp_band_time_ind, lfp_band_elect_indices[0]],
    "r-",
)
plt.xlabel("Time (sec)")
plt.ylabel("Amplitude (AD units)")

# Uncomment to see plot
# plt.show()

import matplotlib.pyplot as plt plt.plot( orig_eseries.timestamps[orig_time_ind], orig_eseries.data[orig_time_ind, orig_elect_indices[0]], "k-", ) plt.plot( lfp_eseries.timestamps[lfp_time_ind], lfp_eseries.data[lfp_time_ind, lfp_elect_indices[0]], "b-", ) plt.plot( lfp_band_eseries.timestamps[lfp_band_time_ind], lfp_band_eseries.data[lfp_band_time_ind, lfp_band_elect_indices[0]], "r-", ) plt.xlabel("Time (sec)") plt.ylabel("Amplitude (AD units)") # Uncomment to see plot # plt.show()

Out[48]:

Text(0, 0.5, 'Amplitude (AD units)')

Next Steps¶

Next, we'll use look at Theta bands within LFP data.