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View Code? Open in Web Editor NEW๐ฆ๐ฆ๐ฆ Sort spikes from extra-cellular recordings using neural networks. Fully automated.
License: GNU General Public License v3.0
๐ฆ๐ฆ๐ฆ Sort spikes from extra-cellular recordings using neural networks. Fully automated.
License: GNU General Public License v3.0
and out of the pref file.
for example, when an electrode pops out, or if there is noise
so turn it on after clusterCallback and after predict
and see if they load.
this simply loops over all files, and checks them. if they don't load, it is marked as such
Given some time series with labelled spikes,
open problems:
though rescaling the remaining channels is going to be painful-- we will have to do a complete redraw on the figure
right now, we assume a very specific structure for self.metadata that is true only for ABF files
and refuses to close.
with units in ms
this should happen right after the findSpikes step
as it's possible to scroll past the end of the data stream
in before and after preference settings
i think we can optimizing saving to .crabsort files -- using savefast?
and not a particular file should be stored elsewhere
like
Right now, it's totally ignored.
and not slow to a crawl on large files
-- only find spikes in the visible trace. we can find all the spikes later
it's silly otherwise
otherwise they appear as ghostly white apparations
empty .crabsort files are too big
that prints all the automate info in a human-friendly fashion
because the items in that list can change over time, their order can change, etc
if you encounter a 1GB file, it's silly to load it into memory. maybe first convert it to a non-ABF format, then load it piecemeal?
turn on when switching to a channel with no automate_info and no spikes
turn off when switching to a channel with automate_info
should have:
as a full path, moving the directory, or transferring to a different computer breaks everything.
maybe use relative paths?
this way, different combinations of methods can be used to weed out spurious spikes
it's too easy to click right now
use a different marker for different units, and paint them in green when correctly classified, and in red when incorrectly classified. maybe also show the training data in black?
with different settings -- so first we pass everything through, then use time...
but why? this can be done in one step
-- checks that every named channel in every file contains at least one spike
as -ve training data to automate_info
when moved to low values, and millions of spikes are found.
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