brainbox.io.deprecated.one¶
Functions
From an eid, get brain locations from Alyx database analysis. |
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From an eid, hits the Alyx database and downloads a standard default set of dataset types From a local session Path (pathlib.Path), loads a standard default set of dataset types to perform analysis: ‘clusters.channels’, ‘clusters.depths’, ‘clusters.metrics’, ‘spikes.clusters’, ‘spikes.times’, ‘probes.description’ |
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From an eid, hits the Alyx database and downloads the standard set of datasets needed for LFP |
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For a given eid load in the passive receptive field mapping protocol data |
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From an eid, hits the Alyx database and downloads a standard default set of dataset types From a local session Path (pathlib.Path), loads a standard default set of dataset types to perform analysis: ‘clusters.channels’, ‘clusters.depths’, ‘clusters.metrics’, ‘spikes.clusters’, ‘spikes.times’, ‘probes.description’ |
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For a given eid, get spikes, clusters and channels information, and merges clusters and channels information before returning all three variables. |
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Generate a pandas dataframe of per-trial timing information about a given session. |
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Return the calculated reaction times for session. |
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Takes (default and any extra) values in given keys from channels and assign them to clusters. |
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load_lfp
(eid, one=None, dataset_types=None)[source]¶ From an eid, hits the Alyx database and downloads the standard set of datasets needed for LFP
- Parameters
eid –
dataset_types – additional dataset types to add to the list
- Returns
spikeglx.Reader
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load_channel_locations
(eid, one=None, probe=None, aligned=False)[source]¶ From an eid, get brain locations from Alyx database analysis.
- Parameters
eid – session eid or dictionary returned by one.alyx.rest(‘sessions’, ‘read’, id=eid)
dataset_types – additional spikes/clusters objects to add to the standard list
- Returns
channels
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load_ephys_session
(eid, one=None, dataset_types=None)[source]¶ From an eid, hits the Alyx database and downloads a standard default set of dataset types From a local session Path (pathlib.Path), loads a standard default set of dataset types
- to perform analysis:
‘clusters.channels’, ‘clusters.depths’, ‘clusters.metrics’, ‘spikes.clusters’, ‘spikes.times’, ‘probes.description’
- Parameters
eid – experiment UUID or pathlib.Path of the local session
one – one instance
dataset_types – additional spikes/clusters objects to add to the standard default list
- Returns
spikes, clusters, trials (dict of bunch, 1 bunch per probe)
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load_spike_sorting
(eid, one=None, probe=None, dataset_types=None, force=False)[source]¶ From an eid, hits the Alyx database and downloads a standard default set of dataset types From a local session Path (pathlib.Path), loads a standard default set of dataset types
- to perform analysis:
‘clusters.channels’, ‘clusters.depths’, ‘clusters.metrics’, ‘spikes.clusters’, ‘spikes.times’, ‘probes.description’
- Parameters
eid – experiment UUID or pathlib.Path of the local session
one –
probe – name of probe to load in, if not given all probes for session will be loaded
dataset_types – additional spikes/clusters objects to add to the standard default list
force – by default function looks for data on local computer and loads this in. If you
want to connect to database and make sure files are still the same set force=True :return: spikes, clusters (dict of bunch, 1 bunch per probe)
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merge_clusters_channels
(dic_clus, channels, keys_to_add_extra=None)[source]¶ Takes (default and any extra) values in given keys from channels and assign them to clusters. If channels does not contain any data, the new keys are added to clusters but left empty.
- Parameters
dic_clus – dict of bunch, 1 bunch per probe, containing cluster information
channels – dict of bunch, 1 bunch per probe, containing channels information
keys_to_add_extra – Any extra keys contained in channels (will be added to default
[‘acronym’, ‘atlas_id’]) :return: clusters (dict of bunch, 1 bunch per probe), with new keys values.
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load_spike_sorting_with_channel
(eid, one=None, probe=None, dataset_types=None, aligned=False, force=False)[source]¶ For a given eid, get spikes, clusters and channels information, and merges clusters and channels information before returning all three variables.
- Parameters
eid –
one –
dataset_types – additional dataset_types to load
aligned – whether to get the latest user aligned channel when not resolved or use
histology track :return: spikes, clusters, channels (dict of bunch, 1 bunch per probe)
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load_passive_rfmap
(eid, one=None)[source]¶ For a given eid load in the passive receptive field mapping protocol data
- Parameters
eid – eid or pathlib.Path of the local session
one –
- Returns
rf_map
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load_wheel_reaction_times
(eid, one=None)[source]¶ Return the calculated reaction times for session. Reaction times are defined as the time between the go cue (onset tone) and the onset of the first substantial wheel movement. A movement is considered sufficiently large if its peak amplitude is at least 1/3rd of the distance to threshold (~0.1 radians).
Negative times mean the onset of the movement occurred before the go cue. Nans may occur if there was no detected movement withing the period, or when the goCue_times or feedback_times are nan.
- Parameters
eid (str) – Session UUID
one (oneibl.ONE) – An instance of ONE for loading data. If None a new one is instantiated using the defaults.
- Returns
reaction times
- Return type
array-like
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load_trials_df
(eid, one=None, maxlen=None, t_before=0.0, t_after=0.0, ret_wheel=False, ret_abswheel=False, wheel_binsize=0.02)[source]¶ Generate a pandas dataframe of per-trial timing information about a given session. Each row in the frame will correspond to a single trial, with timing values indicating timing session-wide (i.e. time in seconds since session start). Can optionally return a resampled wheel velocity trace of either the signed or absolute wheel velocity.
The resulting dataframe will have a new set of columns, trial_start and trial_end, which define via t_before and t_after the span of time assigned to a given trial. (useful for bb.modeling.glm)
- Parameters
eid (str) – Session UUID string to pass to ONE
one (oneibl.one.OneAlyx, optional) – one object to use for loading. Will generate internal one if not used, by default None
maxlen (float, optional) – Maximum trial length for inclusion in df. Trials where feedback - response is longer than this value will not be included in the dataframe, by default None
t_before (float, optional) – Time before stimulus onset to include for a given trial, as defined by the trial_start column of the dataframe. If zero, trial_start will be identical to stimOn, by default 0.
t_after (float, optional) – Time after feedback to include in the trail, as defined by the trial_end column of the dataframe. If zero, trial_end will be identical to feedback, by default 0.
ret_wheel (bool, optional) – Whether to return the time-resampled wheel velocity trace, by default False
ret_abswheel (bool, optional) – Whether to return the time-resampled absolute wheel velocity trace, by default False
wheel_binsize (float, optional) – Time bins to resample wheel velocity to, by default 0.02
- Returns
Dataframe with trial-wise information. Indices are the actual trial order in the original data, preserved even if some trials do not meet the maxlen criterion. As a result will not have a monotonic index. Has special columns trial_start and trial_end which define start and end times via t_before and t_after
- Return type
pandas.DataFrame