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:

create a conda environment:

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

clone npc_sessions from github:

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

pip install all dependencies:

cd npc_sessions
pip install -e .

Hierarchy of required packages

Current NWB components

key data types

DynamicTable: for general tabular data. Cells can contain arrays (e.g. spike_times in a table of units)
- can be accessed as a pandas dataframe
TimeIntervals: subclass of DynamicTable which always has a start_time and stop_time column, plus other user-defined columns
TimeSeries: has a vector array of data, a units string, and either:
- timestamps (same length as data)
- or starting_time and rate
ElectricalSeries: subclass of TimeSeries with units fixed as volts
Events: (added via an NWB extension) like TimeSeries with timestamps only, without values for data (think lick times)

all of the above also have a description, as well as other type-specific attributes

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: