sources#

Explicit registry of datakit data sources.

class mesofield.datakit.sources.MesoMetadataSource[source]#

Bases: MetadataJSON

Load mesoscope camera metadata JSON files as a table.

class mesofield.datakit.sources.PupilMetadataSource[source]#

Bases: MetadataJSON

Load pupil camera metadata JSON files as a table.

class mesofield.datakit.sources.Suite2pV2[source]#

Bases: TimeseriesSource

Load Suite2p outputs using nidaq pulse alignment.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_timeseries(path, *, context=None)[source]#

Load Suite2p outputs and return timeline, payload, and metadata.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, StreamPayload, dict]

class mesofield.datakit.sources.TreadmillSource[source]#

Bases: TimeseriesSource

Load treadmill samples aligned to the experiment window.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_timeseries(path, *, context=None)[source]#

Return (timeline, value, meta).

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.DataqueueSource[source]#

Bases: TimeseriesSource

Load the dataqueue CSV as a time-indexed table.

build_timeseries(path, *, context=None)[source]#

Read *_dataqueue.csv and return a time-indexed table.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.WheelEncoder[source]#

Bases: TimeseriesSource

Load wheel encoder streams recorded alongside nidaq pulses.

The raw CSV emitted by the behavioral rig contains incremental click counts, elapsed time in seconds, and instantaneous speed estimates. The loader converts this information into a strictly increasing timeline anchored to acquisition start, computes cumulative distance, and exposes rich metadata for downstream alignment against the nidaq-driven master clock.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_timeseries(path, *, context=None)[source]#

Return (timeline, value, meta).

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.Psychopy[source]#

Bases: IntervalSeriesSource

Load Psychopy trial windows as an interval table.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_intervals(path, *, context=None)[source]#

Return an intervals table with start/stop columns and meta.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[DataFrame, dict]

class mesofield.datakit.sources.MousePortalSource[source]#

Bases: TimeseriesSource

Load MousePortal corridor frames aligned to the experiment window.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_timeseries(path, *, context=None)[source]#

Return (timeline, value, meta).

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.MousePortalTrials[source]#

Bases: IntervalSeriesSource

Per-trial intervals from the MousePortal log, on the camera clock.

Collapses the per-frame state == "TRIAL_RUNNING" runs into one row per (block, trial) with start_s/stop_s already aligned to the master (camera) timeline via MousePortalSource. This makes event-triggered analyses trivial: each row’s start_s is a trial onset on the same clock as the widefield/pupil frames, so a peri-event window is a direct time slice (see mesofield.datakit.epoch.event_triggered_average).

Emits the inter-trial intervals too (phase column: trial | iti) so stops/ITIs are available as events as well.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_intervals(path, *, context=None)[source]#

Return an intervals table with start/stop columns and meta.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[DataFrame, dict]

class mesofield.datakit.sources.MesoMeanSource[source]#

Bases: TimeseriesSource

Load mean fluorescence CSV.

The CSV is expected to contain at least Slice and Mean columns.

build_timeseries(path, *, context=None)[source]#

Read a mesoscope CSV and return a normalized table.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.MesoDFFSource[source]#

Bases: TimeseriesSource

Load DFF fluorescence CSV.

The CSV is expected to contain at least Slice and Mean columns.

build_timeseries(path, *, context=None)[source]#

Read a mesoscope CSV and return a normalized table.

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.MesoMapSource[source]#

Bases: TimeseriesSource

Load mesomap traces and attach optional mask/region metadata.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue',)#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.

build_timeseries(path, *, context=None)[source]#

Return (timeline, value, meta).

Parameters:

path (Path)
context (LoadContext | None)

Return type:

tuple[ndarray, DataFrame, dict]

class mesofield.datakit.sources.PupilDLCSource[source]#

Bases: TimeseriesSource

Load DeepLabCut HDF5 output and compute pupil diameters.

requires: ClassVar[Tuple[str, ...]] = ('dataqueue', 'pupil_metadata')#: Tag names of upstream sources whose loaded streams should be made available via LoadContext.dependencies. Soft contract: a missing or failed dependency yields None in dependencies[tag]; sources are responsible for either degrading gracefully or raising.