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@@ -36,6 +36,12 @@ python build_cost_matrix.py -s craddock -t shen -task rest1
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The output will be stored in `cost_source_target.csv` with `n` rows and`m` columns indicating number of ROIs in source and target respectively.
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## 2. Finding mappings
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Now, we can specify two atlases and the cost matrix derived from previous step to obtain optimal transport mapping between these two.
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```console
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python build_mapping.py -s craddock -t shen -task rest1
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```
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The output will be stored in `T_source_target.csv` with `n` rows and`m` columns indicating number of ROIs in source and target respectively. Each row is a probability distribution exhibiting optimum assignment of values from the appropriate node to target nodes.
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## 3. Carot: Transforming source(s) to a target atlas
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To run a simple script with source `brainnetome` and target `shen` using `rest1` with `euclidean` cost measure, and saving it:
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```console

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