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params-example.ini
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[io]
; don't forget the slash (/) at the end of the directory strings
input-directory = ./PATH-TO-DATA/
input-filename = INPUT-FILE-NAME.csv
output-directory = ./outputs/
output-filename = OUTPUT-FILE-NAME.csv
separator = ,
na-flag = NA
; 0 - outputs a dataset with only the new columns
; 1 - outputs the same input catalog but with the new columns added (coordinates in the auxiliary space, matching weights, selection labels)
copy-initial-data = 0
; save umap model object: 1 (yes) or 0 (no)
save-umap-object = 1
; use extension .sav
umap-object-filename = FILE-example-umap.sav
; -------------------------------------------------------------------------------------------------------------------------------------------
[data-description]
filters = u j378 j395 j410 j430 g j515 r j660 i j861 z
filters-error = eu ej378 ej395 ej410 ej430 eg ej515 er ej660 ei ej861 ez
GC-flag-column-name = GCs
; -------------------------------------------------------------------------------------------------------------------------------------------
[auxiliary-space]
; select the method used to reduce dimensionality
; options are: 'PCA', 'MLPCA', 'UMAP'
method-dim-reduction = UMAP
; number of dimensios to reduce to.
; for now, this number is only important in case you choose UMAP in the parameter above
N-dim-reduction = 2
; UMAP main parameters:
; check umap documentation to understand the meaning of these parameters
n-neighbors = 50
min-dist = 0.1
metric-umap = euclidean
; -------------------------------------------------------------------------------------------------------------------------------------------
[matching]
; for now, we use the MatchIt library available in the R language
; check MatchIt documentation to understand how it works, the meaning of its parameters and which options are available
; there are other matching parameters that are not displayed here. If you want to use some of them, I encourage you to edit the code
; yourself and send a pull request
; number of dimension of the space in which matching is performed
N-dim-matching = 2
; MatchIt parameters:
distance = euclidean
discard = none
method-matching = nearest
; number of points that will be matched with each object in the treatment group (confirmed GCs)
ratio = 50
; replace must be 1 (True) or 0 (False).
replace = 1
weight-cut = 1