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Merge pull request #33 from theislab/notebook_fix
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notebooks
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@tothmarcella
tothmarcella committed May 24, 2023
2 parents 0b73f17 + 0099716 commit 647ffdd
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Showing 5 changed files with 170 additions and 195 deletions.
71 changes: 33 additions & 38 deletions docs/notebooks/creating_data_objects_from_anndata.ipynb
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},
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},
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{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'squidpy'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mwarnings\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39manndata\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mad\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msquidpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msq\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgpu_spatial_graph_pipeline\u001b[39;00m \u001b[39mimport\u001b[39;00m transforms\n\u001b[1;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mgpu_spatial_graph_pipeline\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39manndata2data\u001b[39;00m \u001b[39mimport\u001b[39;00m AnnData2DataByCategory\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'squidpy'"
]
}
],
"outputs": [],
"source": [
"import warnings\n",
"import anndata as ad\n",
"import squidpy as sq\n",
"from gpu_spatial_graph_pipeline import transforms\n",
"from gpu_spatial_graph_pipeline.anndata2data import AnnData2DataByCategory\n",
"from .utils.datasets import DatasetHartmann\n",
"from utils.datasets import DatasetHartmann\n",
"\n",
"warnings.filterwarnings('ignore')\n"
]
},
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"## All NCEM Datasets"
]
},
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"metadata": {
"tags": []
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{
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"metadata": {},
"outputs": [
{
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" obsm: 'spatial', 'node_types'"
]
},
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
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]
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},
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},
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"execution_count": 12,
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"outputs": [
{
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"Name: point, Length: 63747, dtype: object"
]
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
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"execution_count": 15,
"metadata": {},
"outputs": [
{
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" Data(features=[618, 88], labels=[618, 36])]"
]
},
"execution_count": 14,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
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},
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"outputs": [
{
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" torch.Size([1338, 88]))"
]
},
"execution_count": 18,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
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]
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},
{
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"execution_count": 23,
"execution_count": 17,
"metadata": {},
"outputs": [
{
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" obsp: 'connectivities', 'distances'"
]
},
"execution_count": 23,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
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},
{
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},
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"execution_count": 19,
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"outputs": [
{
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" Data(edge_index=[2, 6270], features=[1045, 10], labels=[1045, 36])]"
]
},
"execution_count": 27,
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
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},
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"metadata": {},
"outputs": [
{
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" torch.Size([1023, 36]))"
]
},
"execution_count": 30,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
"version": "3.10.8"
},
"vscode": {
"interpreter": {
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