A couple of things I'd like to do downstream of localizations on the emitter set

[cleterrier]

Hi,

Thank you for this great tool. There are a couple of things I'd like to do downstream of localizations before exporting the emitter set as a CSV file:

1. Sort Z localizations to only keep those within a certain range (let's say -300 - 300 nm). Something similar to this in the Fit notebook:
   em_sub = emitter[(emitter.xyz_sig_nm[:, 0] <= sigma_x_high_threshold) * (emitter.xyz_sig_nm[:, 1] <= sigma_x_high_threshold) * (emitter.xyz_sig_nm[:, 2] <= sigma_z_high_threshold)]
   ... but filtering on Z coordinates rather than x,y,z uncertainties.

2. Get the histogram of the Z coordinates, and shift the Z coordinates so that the maximum of the Z histogram is at the value Z=0 (i.e. determine the mode M of the Z histogram, and shift the Z coordinates to Z' = Z-M).

I can do both outside of DECODE but it requires working on the CSV files and it is much slower than directly working on the emitter set in DECODE. Thank you for your help!

[Haydnspass]

Hey,

(1) have you looked at the Introduction notebook? In general, decode supports flexible emitter slicing, i.e. you can put anything into the brackets that is an array of bools or integers. The logic in the brackets in your example notebook that you posted above will just compute a boolean matrix.

That means you can do

em_filtered = em[(em.xyz_nm[:, 2] >= -300) * (em.xyz_nm[:, 2] <= 300)]

(2)

# possibly clone the emitterset first if you want both versions (i.e. em_cloned = em.clone())
em.xyz[:, 2] -= em.xyz[:,2].max()

[ASpeiser]

Was just about to answer myself so here the extended version:
Untitled1.pdf

[cleterrier]

It works great, thanks Artur and Lucas