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updating notebook params
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carsen-stringer committed Oct 16, 2024
1 parent 7ee6eac commit 09731da
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Showing 3 changed files with 6 additions and 7 deletions.
3 changes: 1 addition & 2 deletions notebooks/rastermap_largescale.ipynb
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Expand Up @@ -103,8 +103,7 @@
"source": [
"model = Rastermap(n_clusters=100, # number of clusters to compute\n",
" n_PCs=128, # number of PCs to use\n",
" locality=0.75, # locality in sorting to find sequences (this is a value from 0-1)\n",
" time_lag_window=5, # use future timepoints to compute correlation\n",
" locality=0., # locality in sorting to find sequences (this is a value from 0-1)\n",
" grid_upsample=10, # default value, 10 is good for large recordings\n",
" ).fit(spks)\n",
"y = model.embedding # neurons x 1\n",
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6 changes: 3 additions & 3 deletions notebooks/rastermap_singleneurons.ipynb
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Expand Up @@ -27,7 +27,7 @@
"outputs": [],
"source": [
"!pip install numpy>=1.24 # (required for google colab)\n",
"!pip install rastermap \n",
"!pip install rastermap\n",
"!pip install matplotlib"
]
},
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"source": [
"model = Rastermap(n_clusters=None, # None turns off clustering and sorts single neurons \n",
" n_PCs=64, # use fewer PCs than neurons\n",
" locality=0.15, # some locality in sorting (this is a value from 0-1)\n",
" locality=0.1, # some locality in sorting (this is a value from 0-1)\n",
" time_lag_window=15, # use future timepoints to compute correlation\n",
" grid_upsample=0, # 0 turns off upsampling since we're using single neurons\n",
" ).fit(spks)\n",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
"version": "3.9.16"
},
"orig_nbformat": 4,
"vscode": {
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4 changes: 2 additions & 2 deletions paper/fig4.ipynb
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Expand Up @@ -63,8 +63,8 @@
"\n",
"model = Rastermap(n_clusters=None, \n",
" n_PCs=64, \n",
" locality=0.1, # 0.1<-include nan's, #0.05 <- interp nan's #0.25 <- remove nan's\n",
" time_lag_window=15, # 15<- include nan's, #10 <- interp nan's # 20 <- remove nan's\n",
" locality=0.1, \n",
" time_lag_window=15, \n",
" symmetric=False,\n",
" grid_upsample=0,\n",
" ).fit(spks)\n",
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