From cde0f4ac5b692c6122b100b0bfdb2da9cebd48fb Mon Sep 17 00:00:00 2001 From: haozhu233 Date: Sat, 11 May 2024 15:02:35 +0000 Subject: [PATCH] deploy: 25b57c622f8f9a85e4a910567fb72eb6948de8eb --- _sources/quick_tour.md | 6 ++---- quick_tour.html | 6 ++---- searchindex.js | 2 +- 3 files changed, 5 insertions(+), 9 deletions(-) diff --git a/_sources/quick_tour.md b/_sources/quick_tour.md index 03d49cf..be162ee 100644 --- a/_sources/quick_tour.md +++ b/_sources/quick_tour.md @@ -162,11 +162,9 @@ You can also apply the clustering information to your visual. ```python >>> gene_group_dict = dict() ->>> gene_group_dict = {g:[ ->>> 'red', 'blue', 'yellow', 'green' ->>> ][c] for g, c in zip(nxg.nodes(), node_labels)} +>>> gene_group_dict = {g:str(c) for g, c in zip(nxg.nodes(), node_labels)} >>> g = grn.visualize_local_neighborhood( ->>> 'HIST1H1D', k=40, node_color_dict=gene_group_dict +>>> 'HIST1H1D', k=40, node_group_dict=gene_group_dict >>> ) >>> g.show('view.html') ``` diff --git a/quick_tour.html b/quick_tour.html index b19f171..d3cc2a3 100644 --- a/quick_tour.html +++ b/quick_tour.html @@ -498,11 +498,9 @@

(Optional) Step 3. Node clustering
>>> gene_group_dict = dict()
->>> gene_group_dict = {g:[
->>>     'red', 'blue', 'yellow', 'green'
->>> ][c] for g, c in zip(nxg.nodes(), node_labels)}
+>>> gene_group_dict = {g:str(c) for g, c in zip(nxg.nodes(), node_labels)}
 >>> g = grn.visualize_local_neighborhood(
->>>     'HIST1H1D', k=40, node_color_dict=gene_group_dict
+>>>     'HIST1H1D', k=40, node_group_dict=gene_group_dict
 >>>     )
 >>> g.show('view.html')
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