_cacheddepths(X::LFPMatrix, method)

Depths cached in X's metadata by addchanneldepths, in the array's current Chan lookup order, or nothing if X carries no cache for method. The keys must match the ones addchanneldepths writes.

Get the lfp data for a probe, providing indices for channels and times. See function below for indexing by channel ids and time values/intervals

For flashes alignment, trail=false will return only the data from within the flash period. trail=onset will return the data from the onset of the flash to the onset of the flash through to the onset of the next flash. trail=offset will return the data from the offset of the flash to the onset of the next flash.

A function to easily grab formatted spike data for a given session, using some sensible default parameters

If you want to downsample the LFP data, its quicker to use this function and then perform slicing afterwards (since getlfp() has to check all of the time coordinates you supply, which can be slow).

Now we can overload getlfp() to index by structure

This is the one you should be using. Get lfp data by channel id and time intervals or vector. Also, throw error if you try to access an invalid time interval.

`getprobes()`

Read the probe data from the EcephysProjectCache object returned by ecephyscache() and return it as a DataFrame.

Returns

A DataFrame containing the probe data.

getsessiontable — Read and return the session table from the EcephysProjectCache

`getsessiontable()`

Read the session table data from the EcephysProjectCache object returned by ecephyscache and returns it as a DataFrame.

We combine the spike times and spike amplitudes into one sparse array, using a given bin width.

loaddataframe(file, dir=datadir)::DataFrame

Load a CSV file into a DataFrame.

Arguments:

• file: A string representing the name of the CSV file to be loaded.
• dir (optional): A string representing the directory containing the CSV file. Default is datadir.

Returns:

A DataFrame object containing the contents of the CSV file.

Example:

using DataFrames
df = loaddataframe("mydata.csv", "/path/to/my/data/")

Adjust the times of LFP matrix Y so that they match the matrix X

sortbydepth(LFP::AbstractToolsArray, depths::AbstractVector)

Permute LFP along its Chan dimension so that channels run from the surface downwards, given each channel's depth in the array's current Chan lookup order.

The data and the lookup are permuted together, explicitly. An earlier implementation splatted an Array{Any} of indices into getindex, which permuted the lookup but left the data in place; every LFP the package returned was then mirrored against its own channel labels, since _getlfp calls this on every read.

Construct a sparse array of spike counts from a Dict of spike times

visual_cortex_svg()

Path to an SVG schematic of mouse visual cortex areas.

 datapaths()

Get the paths to the visual behavior neuropixels data on Amazon S3 as a tree-like dictionary.

filetree(paths::Vector{String})

Creates a dictionary-like object representing the directory and file structure from a list of file paths.

Arguments:

• paths: A vector of file paths to include in the tree.

Returns:

• dict_tree : A dictionary-like object representing the directory and file structure.

getmanifesturl(manifest_version="0.5.0")

Return the URL for the neuropixels visual behavior project manifest file hosted on a given hostname server and version manifest_version.

Arguments

• manifest_version::String: The version of the manifest file. Default value is "0.5.0".

Other manifest version numbers can be identified here: https://s3.console.aws.amazon.com/s3/buckets/visual-behavior-neuropixels-data?prefix=visual-behavior-neuropixels%2Fmanifests%2F&region=us-west-2

getsessionfiles()

Return a dictionary mapping session IDs to their corresponding file paths.