npc_sessions

neuropixels cloud sessions

Tools for accessing data and metadata for behavior and epyhys sessions from the Mindscope Neuropixels team - in the cloud.

quickstart

Make a conda environment with python>=3.9 and simply pip install the npc_sessions package:

conda create -n npc_sessions python>=3.9
conda activate npc_sessions
pip install npc_sessions

>>> from npc_sessions import DynamicRoutingSession, get_sessions;

# each object is used to get metadata and paths for a session:         
>>> session = DynamicRoutingSession('668755_2023-08-31')  
>>> session.is_ephys                                
True
>>> session.stim_paths[0].stem                      
'DynamicRouting1_668755_20230831_131418'
# data is processed on-demand to generate individual pynwb modules:
>>> session.subject                                 # doctest: +SKIP
  subject pynwb.file.Subject at 0x1999418231888
  Fields:
    age: P205D
    age__reference: birth
    date_of_birth: 2023-02-06 20:23:02-08:00
    genotype: wt/wt
    sex: M
    species: Mus musculus
    strain: C57BL6J(NP)
    subject_id: 668755

# a full NWBFile instance can also be generated with all currently-available data:
>>> session.nwb                                     # doctest: +SKIP
root pynwb.file.NWBFile at 0x...
Fields:
  acquisition: {
    lick spout <class 'ndx_events.events.Events'>
  }
  devices: {
    18005102491 <class 'pynwb.device.Device'>,
    18005114452 <class 'pynwb.device.Device'>,
    18005123131 <class 'pynwb.device.Device'>,
    18194810652 <class 'pynwb.device.Device'>,
    19192719021 <class 'pynwb.device.Device'>,
    19192719061 <class 'pynwb.device.Device'>
  }
   ...  

# loop over all currently-tracked sessions using the session-generator:
>>> all(s.session_start_time.year >= 2022 for s in get_sessions()) # doctest: +SKIP
True
>>> trials_dfs = {}
>>> for session in get_sessions():                  # doctest: +SKIP
...     trials_dfs[session.id] = session.trials[:]

to develop with conda

To install with the intention of contributing to this package:

1) create a conda environment:

conda create -n npc_sessions python>=3.9
conda activate npc_sessions

2) clone npc_sessions from github:

git clone git@github.com:AllenInstitute/npc_sessions.git

3) pip install all dependencies:

cd npc_sessions
pip install -e .

Hierarchy of required packages

Current NWB components

key data types

(the following all have a description field, as well as other type-specific attributes)

• DynamicTable: for general tabular data

  • e.g. nwb.units
  • each column in the table is stored as a vector, which can be accessed individually (fast)
  • can be accessed as a pandas dataframe with nwb.units[:], but requires reading all data in all columns (slow)
  • as well as individual values, cells in the table can contain multidimensional arrays. These are represented differently depending on location:
  • in the pandas dataframe, these are represented as one would expect:
    • nwb.units[:].spike_times.iloc[0] is a 1-D array
    • nwb.units[:].waveform_mean.iloc[0] is a 2-D array (time x channels)
  • in the non-dataframe memory representation, there are sometimes two components, where the *_index is the one that should be used:
    • nwb.units.spike_times is a 1-D array of all spike times for all units, in chronological order (float)
    • nwb.units.spike_times_index is a list (len = num units) of arrays (len = num spikes for each unit)
  • on disk, these columns separated columns are different again, for example:
    • /nwb/units/spike_times is a 1-D array of all spike times for all units, in chronological order (float)
    • /nwb/units/spike_times_index is a 1-D array of values corresponding to the end of each unit's times in /nwb/units/spike_times:
    • the first unit's spike times are in spike_times[: spike_times_index[0]]
    • the second unit's are in spike_times[spike_times_index[0]: spike_times_index[1]]

• TimeIntervals: for tabular data where each row is an interval of time

  • a subclass of DynamicTable which must have a start_time and stop_time column, plus any other user-defined columns

• TimeSeries: for general array data

  • has an array of data (1-D or N-D, with time as first dimension)
  • has units as a string
  • has either:
  • timestamps (same length as data)
  • starting_time and rate (assumed to be constant)

• ElectricalSeries: for ephys array data

  • a subclass of TimeSeries with units fixed as volts

• Events: an NWB extension for discrete event times

  • like the TimeSeries class, but only has timestamps, without values for data (think: lick times)

• session metadata (multiple attributes)

• subject (multiple attributes)

• devices: DynamicTable

  • physical probes (model, serial number)
  • currently only neuropixels probes

• electrode_groups: DynamicTable

  • represents the group of channels on one probe inserted in the brain
  • has session-specific info, like position relative to other probes or stereotactic coords

• electrodes: DynamicTable

  • individual channels on a probe
  • has CCF coords

• units: DynamicTable

  • metrics, links to electrodes via peak_channel

• epochs: TimeIntervals

  • start/stop time of each stim block
  • has a list of tags (includes TaskControl subclass name)

• intervals: Mapping[str, TimeIntervals]

  • 1x table per stim epoch with trials
  • behavior performance table (each block an interval)

• trials: TimeIntervals

  • same as intervals[DynamicRouting1]

• invalid_times: TimeIntervals

• acquisition: Mapping[str, Any] raw data

  • if is_ephys:
  • raw AP: Mapping[str, ElectricalSeries]
  • raw LFP: Mapping[str, ElectricalSeries]
  • if is_sync:
  • lick_sensor_rising_edges: Events
  • lick_sensor_falling_edges: Events
  • if is_task:
  • rewards: Events
  • if is_video:
  • video frame times: 1x Events per camera

• processing: Mapping[str, Any] processed/filtered data

  • behavior: Mapping[str, Any]
    • licks: Events
    • from sync or stim file
    • running_speed: TimeSeries
    • from stim file, enhanced with sync info if available
  • ecephys: Mapping[str, Any]

• analysis: Mapping[str, Any] derived data, results

  • if is_ephys:
  • all_spike_histograms: 1x TimeSeries per probe
  • drift_maps: ImageSeries
  • if is_task:
  • performance: TimeIntervals

Todo:

• filtered LFP
• stimulus templates (vis, aud, opto)
• OptogeneticStimulusSite
• analysis -> RFMaps
• per-unit response metric for each stim modality