nel-lab / mesmerize-core

High level pandas-based API for batch analysis of Calcium Imaging data using CaImAn
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gSig_filter used twice in demo #241

Closed zhouyi0812 closed 1 year ago

zhouyi0812 commented 1 year ago

Hello!

May I ask why we are going to use high pass filter twice in the demo? If we are going to see the effect from different gSig_filt, the second filter won't make them not obvious? Thank you!

First time is to find out the best parameter for motion correction, and the second is to better see the result with the same filter.

1) Nested loop to iterate over gSig_filt values from 6 to 9 for gsig in range(6, 9): Deep copy the base parameters current_params = deepcopy(base_params)

     Assign the "gSig_filt"
    current_params["main"]["gSig_filt"] = (gsig, gsig)

2) filt = {subplot_ix: lambda frame: high_pass_filter_space(frame, (7, 7)) for subplot_ix in range(len(movies))}

kushalkolar commented 1 year ago

Can you elaborate more? I don't exactly understand what you mean.

On Wed, Nov 1, 2023, 13:07 Yi Zhou @.***> wrote:

Hello!

May I ask why we are going to use high pass filter twice in the demo? If we are going to see the effect from different gSig_filt, the second filter won't make them not obvious? Thank you!

First time is to find out the best parameter for motion correction, and the second is to better see the result with the same filter.

1.

Nested loop to iterate over gSig_filt values from 6 to 9 for gsig in range(6, 9): Deep copy the base parameters current_params = deepcopy(base_params)

 Assign the "gSig_filt"
current_params["main"]["gSig_filt"] = (gsig, gsig)

2.

filt = {subplot_ix: lambda frame: high_pass_filter_space(frame, (7, 7)) for subplot_ix in range(len(movies))}

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kushalkolar commented 1 year ago

closing due to inactivity