diff --git a/cross-registration.ipynb b/cross-registration.ipynb index d36ec99e..e5230914 100644 --- a/cross-registration.ipynb +++ b/cross-registration.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -124,9 +124,724 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var 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This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 769 ms, sys: 17.3 ms, total: 786 ms\n", + "Wall time: 774 ms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n", + "/Users/jonny/git/forks/minian/minian/cross_registration.py:507: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " node_df = node_df.groupby(\"session\").apply(maj_deg)\n" + ] + }, + { + "data": { + "text/html": [ + "
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"execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "custom_arr_optimize() got an unexpected keyword argument 'flush'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m mappings_meta_fill\u001b[38;5;241m.\u001b[39mto_pickle(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dpath, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmappings.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 2\u001b[0m cents\u001b[38;5;241m.\u001b[39mto_pickle(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dpath, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcents.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m 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273\u001b[0m \u001b[43m \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchunkmanager_store_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msources \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[0;32m~/git/forks/minian/.venv/lib/python3.9/site-packages/xarray/namedarray/daskmanager.py:249\u001b[0m, in \u001b[0;36mDaskManager.store\u001b[0;34m(self, sources, targets, **kwargs)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mstore\u001b[39m(\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 243\u001b[0m sources: Any \u001b[38;5;241m|\u001b[39m Sequence[Any],\n\u001b[1;32m 244\u001b[0m targets: Any,\n\u001b[1;32m 245\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 246\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 247\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m store\n\u001b[0;32m--> 249\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[43msources\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mmap_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/git/forks/minian/.venv/lib/python3.9/site-packages/dask/base.py:402\u001b[0m, in \u001b[0;36mcompute_as_if_collection\u001b[0;34m(cls, dsk, keys, scheduler, get, **kwargs)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compute a graph as if it were of type cls.\u001b[39;00m\n\u001b[1;32m 399\u001b[0m \n\u001b[1;32m 400\u001b[0m \u001b[38;5;124;03mAllows for applying the same optimizations and default scheduler.\"\"\"\u001b[39;00m\n\u001b[1;32m 401\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(scheduler\u001b[38;5;241m=\u001b[39mscheduler, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcls\u001b[39m, get\u001b[38;5;241m=\u001b[39mget)\n\u001b[0;32m--> 402\u001b[0m dsk2 \u001b[38;5;241m=\u001b[39m \u001b[43moptimization_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m schedule(dsk2, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[0;31mTypeError\u001b[0m: custom_arr_optimize() got an unexpected keyword argument 'flush'" + ] + } + ], "source": [ "mappings_meta_fill.to_pickle(os.path.join(dpath, \"mappings.pkl\"))\n", "cents.to_pickle(os.path.join(dpath, \"cents.pkl\"))\n", @@ -466,7 +1817,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -480,7 +1831,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.9" } }, "nbformat": 4, diff --git a/docs/ext/nbsplit.py b/docs/ext/nbsplit.py index 1c8850bd..f76cf790 100644 --- a/docs/ext/nbsplit.py +++ b/docs/ext/nbsplit.py @@ -23,7 +23,7 @@ def is_hv_init(cell): def split_and_parse(app): for src_nb, out_dir in app.config.nbsplit_dict.items(): if not os.path.exists(src_nb): - warnings.warn("notebook not found: {}".format(src_nb), RuntimeWarning) + warnings.warn(f"notebook not found: {src_nb}", RuntimeWarning) continue srcpath = os.path.split(src_nb)[0] outpath = os.path.join(os.path.abspath(app.srcdir), out_dir) @@ -33,7 +33,7 @@ def split_and_parse(app): if os.path.exists(img_path): shutil.copytree(img_path, os.path.join(outpath, "img")) # read source notebook - with open(src_nb, mode="r") as notebook: + with open(src_nb) as notebook: jnote = json.load(notebook) cells = jnote["cells"] # split and process cells @@ -46,7 +46,7 @@ def split_and_parse(app): hv_init["execution_count"] = None # write notebooks for isplit, start in enumerate(split): - fname = "notebook_{}.ipynb".format(isplit) + fname = f"notebook_{isplit}.ipynb" meta = jnote["metadata"].copy() meta["name"] = fname try: diff --git a/docs/source/conf.py b/docs/source/conf.py index 7486c11c..9a8c24d4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ def _as_gufunc(pyfunc): # -- Project information ----------------------------------------------------- project = "MiniAn" -copyright = "2018-%s, MiniAn Developers" % datetime.datetime.now().year +copyright = f"2018-{datetime.datetime.now().year}, MiniAn Developers" # -- General configuration --------------------------------------------------- diff --git a/minian/__init__.py b/minian/__init__.py index d274ad0f..35b74411 100755 --- a/minian/__init__.py +++ b/minian/__init__.py @@ -1,8 +1,12 @@ import dask as da import os +from importlib.metadata import version from .utilities import custom_arr_optimize, custom_delay_optimize -__version__ = "1.2.1" +try: + __version__ = version("minian") +except: + __version__ = "0.0.0" da.config.set( array_optimize=custom_arr_optimize, delayed_optimize=custom_delay_optimize @@ -28,3 +32,4 @@ # os.environ["LD_PRELOAD"] = "~/.conda/envs/minian-dev/lib/libjemalloc.so" # alternatively one can limit the malloc pool, which is the default for minian os.environ["MALLOC_MMAP_THRESHOLD_"] = "16384" + diff --git a/minian/cnmf.py b/minian/cnmf.py index c6efeb24..6e44f32e 100644 --- a/minian/cnmf.py +++ b/minian/cnmf.py @@ -1,7 +1,7 @@ import functools as fct import os import warnings -from typing import List, Optional, Tuple, Union +from typing import Optional, Union import cv2 import cvxpy as cvx @@ -235,7 +235,7 @@ def update_spatial( normalize=True, size_thres=(9, None), in_memory=False, -) -> Tuple[xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray]: +) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray]: """ Update spatial components given the input data and temporal dynamic for each cell. @@ -387,7 +387,7 @@ def update_spatial( f=f_in, ) else: - cur_blk = darr.array(sparse.zeros((cur_sub.shape))) + cur_blk = darr.array(sparse.zeros(cur_sub.shape)) A_new[hblk, wblk, 0] = cur_blk A_new = darr.block(A_new.tolist()) else: @@ -437,7 +437,7 @@ def update_spatial( ) else: mask = (A_new.sum(["height", "width"]) > 0).compute() - print("{} out of {} units dropped".format(len(mask) - mask.sum().values, len(mask))) + print(f"{len(mask) - mask.sum().values} out of {len(mask)} units dropped") A_new = A_new.sel(unit_id=mask) if normalize: norm_fac = A_new.max(["height", "width"]).compute() @@ -673,7 +673,7 @@ def update_temporal( post_scal=False, scs_fallback=False, concurrent_update=False, -) -> Tuple[ +) -> tuple[ xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray ]: """ @@ -889,10 +889,10 @@ def update_temporal( # issue a warning if expected memory demand is larger than 1G if mem_demand > 1e3: warnings.warn( - "{} cells will be updated togeter, " - "which takes roughly {} MB of memory. " + f"{cur_YrA.shape[0]} cells will be updated togeter, " + f"which takes roughly {mem_demand} MB of memory. " "Consider merging the units " - "or changing jac_thres".format(cur_YrA.shape[0], mem_demand) + "or changing jac_thres" ) if not warm_start: cur_C = None @@ -992,7 +992,7 @@ def update_temporal( int_ds["g"], ) mask = (S_new.sum("frame") > 0).compute() - print("{} out of {} units dropped".format((~mask).sum().values, len(Ymask))) + print(f"{(~mask).sum().values} out of {len(Ymask)} units dropped") C_new, S_new, b0_new, c0_new, g = ( C_new[mask], S_new[mask], @@ -1098,7 +1098,7 @@ def update_temporal_block( med_wd=None, concurrent=False, **kwargs -) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ Update temporal components given residule traces of a group of cells. @@ -1203,7 +1203,7 @@ def update_temporal_block( def update_temporal_cvxpy( y: np.ndarray, g: np.ndarray, sn: np.ndarray, A=None, bseg=None, **kwargs -) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ Solve the temporal update optimization problem using `cvxpy` @@ -1378,7 +1378,7 @@ def update_temporal_cvxpy( raise ValueError except (cvx.SolverError, ValueError): warnings.warn( - "problem status is {}, returning zero".format(prob.status), + f"problem status is {prob.status}, returning zero", RuntimeWarning, ) return [np.zeros(c.shape, dtype=float)] * 4 @@ -1395,10 +1395,10 @@ def update_temporal_cvxpy( def unit_merge( A: xr.DataArray, C: xr.DataArray, - add_list: Optional[List[xr.DataArray]] = None, + add_list: Optional[list[xr.DataArray]] = None, thres_corr=0.9, noise_freq: Optional[float] = None, -) -> Tuple[xr.DataArray, xr.DataArray, Optional[List[xr.DataArray]]]: +) -> tuple[xr.DataArray, xr.DataArray, Optional[list[xr.DataArray]]]: """ Merge cells given spatial footprints and temporal components @@ -1416,8 +1416,8 @@ def unit_merge( Spatial footprints of the cells. C : xr.DataArray Temporal component of cells. - add_list : List[xr.DataArray], optional - List of additional variables to be merged. By default `None`. + add_list : list[xr.DataArray], optional + list of additional variables to be merged. By default `None`. thres_corr : float, optional The threshold of correlation. Any pair of spatially overlapping cells with correlation higher than this threshold will be transitively grouped @@ -1433,8 +1433,8 @@ def unit_merge( Merged spatial footprints of cells. C_merge : xr.DataArray Merged temporal components of cells. - add_list : List[xr.DataArray], optional - List of additional merged variables. Only returned if input `add_list` + add_list : list[xr.DataArray], optional + list of additional merged variables. Only returned if input `add_list` is not `None`. """ print("computing spatial overlap") @@ -1799,7 +1799,7 @@ def construct_comput(edf, pxs): npxs.append(len(pixels)) pixels = set() eg_ls = [] - print("pixel recompute ratio: {}".format(sum(npxs) / G.number_of_nodes())) + print(f"pixel recompute ratio: {sum(npxs) / G.number_of_nodes()}") print("computing correlations") corr_ls = da.compute(corr_ls)[0] corr = pd.Series(np.concatenate(corr_ls), index=np.concatenate(idx_ls), name="corr") @@ -1847,7 +1847,7 @@ def adj_corr( ) -def adj_list(G: nx.Graph) -> List[np.ndarray]: +def adj_list(G: nx.Graph) -> list[np.ndarray]: """ Generate adjacency list representation from graph. @@ -1858,7 +1858,7 @@ def adj_list(G: nx.Graph) -> List[np.ndarray]: Returns ------- - adj_ls : List[np.ndarray] + adj_ls : list[np.ndarray] The adjacency list representation of graph. """ gdict = nx.to_dict_of_dicts(G) @@ -1936,7 +1936,7 @@ def idx_corr(X: np.ndarray, ridx: np.ndarray, cidx: np.ndarray) -> np.ndarray: def update_background( Y: xr.DataArray, A: xr.DataArray, C: xr.DataArray, b: xr.DataArray = None -) -> Tuple[xr.DataArray, xr.DataArray]: +) -> tuple[xr.DataArray, xr.DataArray]: """ Update background terms given spatial and temporal components of cells. diff --git a/minian/cross_registration.py b/minian/cross_registration.py index 58fa3cc3..fcd89262 100755 --- a/minian/cross_registration.py +++ b/minian/cross_registration.py @@ -1,5 +1,5 @@ import itertools as itt -from typing import Iterable +from collections.abc import Iterable import dask as da import networkx as nx diff --git a/minian/initialization.py b/minian/initialization.py index d2e8a368..95ca00dc 100755 --- a/minian/initialization.py +++ b/minian/initialization.py @@ -1,7 +1,7 @@ import functools as fct import itertools as itt import os -from typing import Optional, Tuple, Union +from typing import Optional, Union import cv2 import dask as da @@ -192,7 +192,7 @@ def gmm_refine( n_components=2, valid_components=1, mean_mask=True, -) -> Tuple[pd.DataFrame, xr.DataArray, GaussianMixture]: +) -> tuple[pd.DataFrame, xr.DataArray, GaussianMixture]: """ Filter seeds by fitting a GMM to peak-to-peak values. @@ -282,7 +282,7 @@ def pnr_refine( thres: Union[float, str] = 1.5, q=(0.1, 99.9), med_wnd: Optional[int] = None, -) -> Tuple[pd.DataFrame, xr.DataArray, Optional[GaussianMixture]]: +) -> tuple[pd.DataFrame, xr.DataArray, Optional[GaussianMixture]]: """ Filter seeds by thresholding peak-to-noise ratio. @@ -389,7 +389,7 @@ def ptp_q(a: np.ndarray, q: tuple) -> float: a : np.ndarray Input array. q : tuple - Tuple specifying low and high percentile values. + tuple specifying low and high percentile values. Returns ------- diff --git a/minian/install.py b/minian/install.py index b00534d6..89dfd332 100644 --- a/minian/install.py +++ b/minian/install.py @@ -1,6 +1,7 @@ import argparse import os import requests +from importlib.metadata import version NOTEBOOK_FILES = [ "pipeline.ipynb", @@ -13,7 +14,10 @@ DEMO_FILES = [f"demo_movies/msCam{i}.avi" for i in range(1, 11)] + [ f"demo_data/session{i}/minian.nc" for i in range(1, 3) ] -VERSION = "1.2.1" +try: + VERSION = version("minian") +except: + VERSION = "0.0.0" def _get_file(filename: str, version: str): @@ -57,7 +61,7 @@ def main(): "-v", action="store", default=VERSION, - help="Git repo branch or tag name, default {}".format(VERSION), + help=f"Git repo branch or tag name, default {VERSION}", ) args = parser.parse_args() diff --git a/minian/motion_correction.py b/minian/motion_correction.py index b3a09a90..546cbe47 100755 --- a/minian/motion_correction.py +++ b/minian/motion_correction.py @@ -1,7 +1,7 @@ import functools as fct import itertools as itt import warnings -from typing import Optional, Tuple +from typing import Optional import cv2 import dask as da @@ -94,7 +94,7 @@ def estimate_motion( Amount of zero padding when checking for the quality of frames, specified in pixels. Only used if `circ_thres is not None`. See `circ_thres` for more detail. By default `100`. - mesh_size : Tuple[int, int], optional + mesh_size : tuple[int, int], optional Number of control points for the BSpline mesh in each dimension, specified in the order ("height", "width"). If not `None` then the experimental non-rigid motion estimation is enabled. By default `None` @@ -179,7 +179,7 @@ def estimate_motion( def est_motion_part( varr: darr.Array, npart: int, chunk_nfm: int, alt_error=5, **kwargs -) -> Tuple[darr.Array, darr.Array]: +) -> tuple[darr.Array, darr.Array]: """ Construct dask graph for the recursive motion estimation algorithm. @@ -273,10 +273,10 @@ def est_motion_chunk( upsample=100, max_sh=100, circ_thres: Optional[float] = None, - mesh_size: Optional[Tuple[int, int]] = None, + mesh_size: Optional[tuple[int, int]] = None, niter=100, bin_thres: Optional[float] = None, -) -> Tuple[np.ndarray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray]: """ Carry out motion estimation per chunk. @@ -302,7 +302,7 @@ def est_motion_chunk( circ_thres : float, optional The circularity threshold to check whether a frame can serve as a good template for estimating motion. By default `None`. - mesh_size : Tuple[int, int], optional + mesh_size : tuple[int, int], optional Number of control points for the BSpline mesh in each dimension. By default `None`. niter : int, optional @@ -378,8 +378,8 @@ def est_motion_chunk( prop_good = len(good_idxs) / len(good_fm) if prop_good < 0.9: warnings.warn( - "only {} of the frames are good." - "Consider lowering your circularity threshold".format(prop_good) + f"only {prop_good} of the frames are good." + "Consider lowering your circularity threshold" ) # use good frame closest to center as template mid = good_idxs[np.abs(good_idxs - varr.shape[0] / 2).argmin()] @@ -457,7 +457,7 @@ def est_motion_chunk( else: tmp = varr.mean(axis=0) else: - raise ValueError("does not understand aggregation: {}".format(aggregation)) + raise ValueError(f"does not understand aggregation: {aggregation}") if alt_error: if varr.ndim > 3: tmp0 = varr[0][0] @@ -479,7 +479,7 @@ def est_motion_perframe( upsample: int, src_ma: Optional[np.ndarray] = None, dst_ma: Optional[np.ndarray] = None, - mesh_size: Optional[Tuple[int, int]] = None, + mesh_size: Optional[tuple[int, int]] = None, niter=100, ) -> np.ndarray: """ @@ -499,7 +499,7 @@ def est_motion_perframe( dst_ma : np.ndarray, optional Boolean mask for `dst`. Only used if `mesh_size is not None`. By default `None`. - mesh_size : Tuple[int, int], optional + mesh_size : tuple[int, int], optional Number of control points for the BSpline mesh in each dimension. By default `None`. niter : int, optional @@ -515,11 +515,10 @@ def est_motion_perframe( -------- estimate_motion : for detailed explanation of parameters """ - sh = phase_cross_correlation( + sh, error, phasediff = phase_cross_correlation( src, dst, upsample_factor=upsample, - return_error=False, ) if mesh_size is None: return -sh @@ -668,7 +667,7 @@ def get_mask(fm, bin_thres, bin_wnd): def apply_transform( - varr: xr.DataArray, trans: xr.DataArray, fill=0, mesh_size: Tuple[int, int] = None + varr: xr.DataArray, trans: xr.DataArray, fill=0, mesh_size: tuple[int, int] = None ) -> xr.DataArray: """ Apply necessary transform to correct for motion. @@ -688,7 +687,7 @@ def apply_transform( fill : int, optional Values used to fill in missing pixels (outside field of view). By default `0`. - mesh_size : Tuple[int, int], optional + mesh_size : tuple[int, int], optional `mesh_size` parameter used when estimating motion. Only used if `trans.ndim > 2`. If `None` and `trans.ndim > 2` then one will be computed using :func:`get_mesh_size`. By default `None`. @@ -727,7 +726,7 @@ def transform_perframe( tx_coef: np.ndarray, fill=0, param: Optional[np.ndarray] = None, - mesh_size: Optional[Tuple[int, int]] = None, + mesh_size: Optional[tuple[int, int]] = None, ) -> np.ndarray: """ Transform a single frame. @@ -746,7 +745,7 @@ def transform_perframe( param : np.ndarray, optional Fixed parameters defining the BSpline transform. Only used if `tx_coef.ndim > 1`. By default `None`. - mesh_size : Tuple[int, int], optional + mesh_size : tuple[int, int], optional `mesh_size` parameter used to estimate motion. If `None` and `tx_coef.ndim > 1`, then one will be computed using :func:`get_mesh_size`. By default `None`. @@ -770,7 +769,7 @@ def transform_perframe( return sitk.GetArrayFromImage(fm) -def get_bspline_param(img: np.ndarray, mesh_size: Tuple[int, int]) -> np.ndarray: +def get_bspline_param(img: np.ndarray, mesh_size: tuple[int, int]) -> np.ndarray: """ Compute fixed parameters for the BSpline transform given a frame and mesh size. @@ -778,7 +777,7 @@ def get_bspline_param(img: np.ndarray, mesh_size: Tuple[int, int]) -> np.ndarray ---------- img : np.ndarray Input frame. - mesh_size : Tuple[int, int] + mesh_size : tuple[int, int] Number of control points for the BSpline mesh. Returns diff --git a/minian/preprocessing.py b/minian/preprocessing.py index 606406d4..256c6711 100755 --- a/minian/preprocessing.py +++ b/minian/preprocessing.py @@ -97,7 +97,7 @@ def stripe_correction(varr, reduce_dim="height", on="mean"): elif on == "perframe": temp = varr else: - raise NotImplementedError("on {} not understood".format(on)) + raise NotImplementedError(f"on {on} not understood") mean1d = temp.mean(dim=reduce_dim) varr_sc = varr - mean1d return varr_sc.rename(varr.name + "_Stripe_Corrected") @@ -145,7 +145,7 @@ def denoise(varr: xr.DataArray, method: str, **kwargs) -> xr.DataArray: elif method == "bilateral": func = cv2.bilateralFilter else: - raise NotImplementedError("denoise method {} not understood".format(method)) + raise NotImplementedError(f"denoise method {method} not understood") res = xr.apply_ufunc( func, varr, diff --git a/minian/utilities.py b/minian/utilities.py index a728a0c9..01707c00 100755 --- a/minian/utilities.py +++ b/minian/utilities.py @@ -8,7 +8,7 @@ from os.path import isdir, isfile from os.path import join as pjoin from pathlib import Path -from typing import Callable, List, Optional, Union +from typing import Callable, Optional, Union from uuid import uuid4 import _operator @@ -78,7 +78,7 @@ def load_videos( `vlist`, and the list of DataArray before concatenation `varr_list`. The function should output another valide DataArray. In other words, the function should have signature `f(varr: xr.DataArray, vpath: str, vlist: - List[str], varr_list: List[xr.DataArray]) -> xr.DataArray`. By default + list[str], varr_list: list[xr.DataArray]) -> xr.DataArray`. By default `None` Returns @@ -102,10 +102,10 @@ def load_videos( ) if not vlist: raise FileNotFoundError( - "No data with pattern {}" - " found in the specified folder {}".format(pattern, vpath) + f"No data with pattern {pattern}" + f" found in the specified folder {vpath}" ) - print("loading {} videos in folder {}".format(len(vlist), vpath)) + print(f"loading {len(vlist)} videos in folder {vpath}") file_extension = os.path.splitext(vlist[0])[1] if file_extension in (".avi", ".mkv"): @@ -271,7 +271,7 @@ def load_avi_perframe(fname: str, fid: int) -> np.ndarray: if ret: return np.flip(cv2.cvtColor(fm, cv2.COLOR_RGB2GRAY), axis=0) else: - print("frame read failed for frame {}".format(fid)) + print(f"frame read failed for frame {fid}") return np.zeros((h, w)) @@ -346,10 +346,10 @@ def open_minian( def open_minian_mf( dpath: str, - index_dims: List[str], + index_dims: list[str], result_format="xarray", pattern=r"minian$", - sub_dirs: List[str] = [], + sub_dirs: list[str] = [], exclude=True, **kwargs, ) -> Union[xr.Dataset, pd.DataFrame]: @@ -367,8 +367,8 @@ def open_minian_mf( ---------- dpath : str The root folder containing all datasets to be loaded. - index_dims : List[str] - List of dimensions that can be used to index and merge multiple + index_dims : list[str] + list of dimensions that can be used to index and merge multiple datasets. All loaded datasets should have unique coordinates in the listed dimensions. result_format : str, optional @@ -381,7 +381,7 @@ def open_minian_mf( pointing to the loaded minian dataset objects. By default `"xarray"`. pattern : regexp, optional Pattern of minian dataset directory names. By default `r"minian$"`. - sub_dirs : List[str], optional + sub_dirs : list[str], optional A list of sub-directories under `dpath`. Useful if only a subset of datasets under `dpath` should be recursively loaded. By default `[]`. exclude : bool, optional @@ -408,24 +408,24 @@ def open_minian_mf( nextdir = os.path.abspath(nextdir) cur_path = Path(nextdir) dir_tag = bool( - ( + (any([Path(epath) in cur_path.parents for epath in sub_dirs])) or nextdir in sub_dirs - ) + ) if exclude == dir_tag: continue flist = list(filter(lambda f: re.search(pattern, f), filelist + dirlist)) if flist: - print("opening dataset under {}".format(nextdir)) + print(f"opening dataset under {nextdir}") if len(flist) > 1: - warnings.warn("multiple dataset found: {}".format(flist)) + warnings.warn(f"multiple dataset found: {flist}") fname = flist[-1] - print("opening {}".format(fname)) + print(f"opening {fname}") minian = open_minian(dpath=os.path.join(nextdir, fname), **kwargs) key = tuple([np.array_str(minian[d].values) for d in index_dims]) minian_dict[key] = minian - print(["{}: {}".format(d, v) for d, v in zip(index_dims, key)]) + print([f"{d}: {v}" for d, v in zip(index_dims, key)]) if result_format == "xarray": return xrconcat_recursive(minian_dict, index_dims) @@ -434,7 +434,7 @@ def open_minian_mf( minian_df.index.set_names(index_dims, inplace=True) return minian_df.to_frame() else: - raise NotImplementedError("format {} not understood".format(result_format)) + raise NotImplementedError(f"format {result_format} not understood") def save_minian( @@ -551,7 +551,7 @@ def save_minian( return arr -def xrconcat_recursive(var: Union[dict, list], dims: List[str]) -> xr.Dataset: +def xrconcat_recursive(var: Union[dict, list], dims: list[str]) -> xr.Dataset: """ Recursively concatenate `xr.DataArray` over multiple dimensions. @@ -564,7 +564,7 @@ def xrconcat_recursive(var: Union[dict, list], dims: List[str]) -> xr.Dataset: identify each `xr.DataArray`. If a `list` then each `xr.DataArray` should contain valid coordinates for each dimensions specified in `dims`. - dims : List[str] + dims : list[str] Dimensions to be concatenated over. Returns @@ -583,7 +583,7 @@ def xrconcat_recursive(var: Union[dict, list], dims: List[str]) -> xr.Dataset: elif type(var) is list: var_dict = {tuple([np.asscalar(v[d]) for d in dims]): v for v in var} else: - raise NotImplementedError("type {} not supported".format(type(var))) + raise NotImplementedError(f"type {type(var)} not supported") try: var_dict = {k: v.to_dataset() for k, v in var_dict.items()} except AttributeError: @@ -612,7 +612,7 @@ def update_meta(dpath, pattern=r"^minian\.nc$", meta_dict=None, backend="netcdf" elif backend == "zarr": fnames = filter(lambda fn: re.search(pattern, fn), dirnames) else: - raise NotImplementedError("backend {} not supported".format(backend)) + raise NotImplementedError(f"backend {backend} not supported") for fname in fnames: f_path = os.path.join(dirpath, fname) pathlist = os.path.normpath(dirpath).split(os.sep) @@ -629,7 +629,7 @@ def update_meta(dpath, pattern=r"^minian\.nc$", meta_dict=None, backend="netcdf" new_ds.to_netcdf(f_path, mode="a") elif backend == "zarr": new_ds.to_zarr(f_path, mode="w") - print("updated: {}".format(f_path)) + print(f"updated: {f_path}") def get_chk(arr: xr.DataArray) -> dict: @@ -694,7 +694,7 @@ def get_optimal_chk( arr : xr.DataArray The input array to estimate for chunk size. dim_grp : list, optional - List of tuples specifying which dimensions are usually chunked together + list of tuples specifying which dimensions are usually chunked together during computation. For each tuple in the list, it is assumed that only dimensions in the tuple will be chunked while all other dimensions in the input `arr` will not be chunked. Each dimensions in the input `arr` @@ -723,11 +723,11 @@ def get_optimal_chk( dg_dict = {d: "auto" for d in dg} dr_dict = {d: -1 for d in d_rest} dg_dict.update(dr_dict) - with da.config.set({"array.chunk-size": "{}MiB".format(csize)}): + with da.config.set({"array.chunk-size": f"{csize}MiB"}): arr_chk = arr.chunk(dg_dict) chk = get_chunksize(arr_chk) chk_compute.update({d: chk[d] for d in dg}) - with da.config.set({"array.chunk-size": "{}MiB".format(csize)}): + with da.config.set({"array.chunk-size": f"{csize}MiB"}): arr_chk = arr.chunk({d: "auto" for d in dims}) chk_store_da = get_chunksize(arr_chk) chk_store = dict() @@ -757,7 +757,7 @@ def get_chunksize(arr: xr.DataArray) -> dict: return {d: s for d, s in zip(dims, sz)} -def factors(x: int) -> List[int]: +def factors(x: int) -> list[int]: """ Compute all factors of an interger. @@ -768,8 +768,8 @@ def factors(x: int) -> List[int]: Returns ------- - factors : List[int] - List of factors of `x`. + factors : list[int] + list of factors of `x`. """ return [i for i in range(1, x + 1) if x % i == 0] @@ -813,7 +813,7 @@ def factors(x: int) -> List[int]: darr.core._vindex_transpose, ] """ -List of fast functions that should be inlined during optimization. +list of fast functions that should be inlined during optimization. See Also ------- @@ -867,9 +867,9 @@ def custom_arr_optimize( keys : list Output task keys. fast_funcs : list, optional - List of fast functions to be inlined. By default :const:`FAST_FUNCTIONS`. + list of fast functions to be inlined. By default :const:`FAST_FUNCTIONS`. inline_patterns : list, optional - List of patterns of task keys to be inlined. By default `[]`. + list of patterns of task keys to be inlined. By default `[]`. rename_dict : dict, optional Dictionary mapping old task keys to new ones. Only used during fusing of tasks. By default `None`. @@ -877,7 +877,7 @@ def custom_arr_optimize( Dictionary mapping old task key substrings to new ones. Applied at the end of optimization to all task keys. By default `None`. keep_patterns : list, optional - List of patterns of task keys that should be preserved during + list of patterns of task keys that should be preserved during optimization. By default `[]`. Returns @@ -948,7 +948,7 @@ def rewrite_key(key: Union[str, tuple], rwdict: dict) -> str: elif typ is str: k = key else: - raise ValueError("key must be either str or tuple: {}".format(key)) + raise ValueError(f"key must be either str or tuple: {key}") for pat, repl in rwdict.items(): k = re.sub(pat, repl, k) if typ is tuple: @@ -970,7 +970,7 @@ def custom_fused_keys_renamer( Parameters ---------- keys : list - List of task keys that should be fused together. + list of task keys that should be fused together. max_fused_key_length : int, optional Used to limit the maximum string length for each renamed key. If `None`, there is no limit. By default `120`. @@ -1070,7 +1070,7 @@ def check_key(key: Union[str, tuple], pat: str) -> bool: return bool(re.search(pat, key[0])) -def check_pat(key: Union[str, tuple], pat_ls: List[str]) -> bool: +def check_pat(key: Union[str, tuple], pat_ls: list[str]) -> bool: """ Check whether `key` contains any pattern in a list. @@ -1078,8 +1078,8 @@ def check_pat(key: Union[str, tuple], pat_ls: List[str]) -> bool: ---------- key : Union[str, tuple] Input key. If a `tuple` then the first element will be used to check. - pat_ls : List[str] - List of pattern to check. + pat_ls : list[str] + list of pattern to check. Returns ------- @@ -1092,7 +1092,7 @@ def check_pat(key: Union[str, tuple], pat_ls: List[str]) -> bool: return False -def inline_pattern(dsk: dict, pat_ls: List[str], inline_constants: bool) -> dict: +def inline_pattern(dsk: dict, pat_ls: list[str], inline_constants: bool) -> dict: """ Inline tasks whose keys match certain patterns. @@ -1100,8 +1100,8 @@ def inline_pattern(dsk: dict, pat_ls: List[str], inline_constants: bool) -> dict ---------- dsk : dict Input dask graph. - pat_ls : List[str] - List of patterns to check. + pat_ls : list[str] + list of patterns to check. inline_constants : bool Whether to inline constants. @@ -1139,9 +1139,9 @@ def custom_delay_optimize( keys : list Output task keys. fast_functions : list, optional - List of fast functions to be inlined. By default `[]`. + list of fast functions to be inlined. By default `[]`. inline_patterns : list, optional - List of patterns of task keys to be inlined. By default `[]`. + list of patterns of task keys to be inlined. By default `[]`. Returns ------- @@ -1170,7 +1170,7 @@ def unique_keys(keys: list) -> np.ndarray: Parameters ---------- keys : list - List of dask keys. + list of dask keys. Returns ------- @@ -1195,7 +1195,7 @@ def get_keys_pat(pat: str, keys: list, return_all=False) -> Union[list, str]: pat : str Pattern to check. keys : list - List of keys to be filtered. + list of keys to be filtered. return_all : bool, optional Whether to return all keys matching `pat`. If `False` then only the first match will be returned. By default `False`. @@ -1277,7 +1277,7 @@ def local_extreme(fm: np.ndarray, k: np.ndarray, etype="max", diff=0) -> np.ndar elif etype == "min": fm_ext = (fm == fm_min).astype(np.uint8) else: - raise ValueError("Don't understand {}".format(etype)) + raise ValueError(f"Don't understand {etype}") return cv2.bitwise_and(fm_ext, fm_diff).astype(np.uint8) diff --git a/minian/visualization.py b/minian/visualization.py index 5c0f70f8..a8e5969d 100755 --- a/minian/visualization.py +++ b/minian/visualization.py @@ -2,7 +2,7 @@ import itertools as itt import os from collections import OrderedDict -from typing import Callable, List, Optional, Tuple, Union +from typing import Callable, Optional, Union from uuid import uuid4 import colorcet as cc @@ -91,17 +91,17 @@ class VArrayViewer: def __init__( self, - varr: Union[xr.DataArray, List[xr.DataArray], xr.Dataset], + varr: Union[xr.DataArray, list[xr.DataArray], xr.Dataset], framerate=30, summary=["mean"], - meta_dims: List[str] = None, + meta_dims: list[str] = None, datashading=True, layout=False, ): """ Parameters ---------- - varr : Union[xr.DataArray, List[xr.DataArray], xr.Dataset] + varr : Union[xr.DataArray, list[xr.DataArray], xr.Dataset] Input array, list of arrays, or dataset to be visualized. Each array should contain dimensions "height", "width" and "frame". If a dataset, then the dimensions specified in `meta_dims` will be used @@ -112,10 +112,10 @@ def __init__( framerate : int, optional The framerate of playback when using the toolbar. By default `30`. summary : list, optional - List of summary statistics to plot. The statistics should be one of + list of summary statistics to plot. The statistics should be one of `{"mean", "max", "min", "diff"}`. By default `["mean"]`. - meta_dims : List[str], optional - List of dimension names that can uniquely identify each input array + meta_dims : list[str], optional + list of dimension names that can uniquely identify each input array in `varr`. Only used if `varr` is a `xr.Dataset`. By default `None`. datashading : bool, optional Whether to use datashading on the summary statistics. By default @@ -141,7 +141,7 @@ def __init__( self.ds = varr else: raise NotImplementedError( - "video array of type {} not supported".format(type(varr)) + f"video array of type {type(varr)} not supported" ) try: self.meta_dicts = OrderedDict( @@ -179,7 +179,7 @@ def __init__( try: summ = {k: summ_all[k] for k in summary} except KeyError: - print("{} Not understood for specifying summary".format(summary)) + print(f"{summary} Not understood for specifying summary") if summ: print("computing summary") sum_list = [] @@ -509,7 +509,7 @@ def __init__( self.strm_f.add_subscriber(self.callback_f) self.strm_uid = Selection1D() self.strm_uid.add_subscriber(self.callback_uid) - Stream_usub = Stream.define("Stream_usub", usub=param.List()) + Stream_usub = Stream.define("Stream_usub", usub=param.list()) self.strm_usub = Stream_usub() self.strm_usub.add_subscriber(self.callback_usub) self.usub_sel = self.strm_usub.usub @@ -715,7 +715,7 @@ def _temp_comp_wgt(self): ntabs = np.ceil(len(cur_idxs) / 5) sub_idxs = np.array_split(cur_idxs, ntabs) idxs_dict = OrderedDict( - [("group{}".format(i), g.tolist()) for i, g in enumerate(sub_idxs)] + [(f"group{i}", g.tolist()) for i, g in enumerate(sub_idxs)] ) def_idxs = list(idxs_dict.values())[0] wgt_grp = pnwgt.Select( @@ -816,7 +816,7 @@ def callback_ulab(value, uid): sel.param.watch(cb, "value") wgt_check = { uid: pnwgt.Checkbox( - name="Unit ID: {}".format(uid), value=False, height=50, width=100 + name=f"Unit ID: {uid}", value=False, height=50, width=100 ) for uid in usub } @@ -1061,7 +1061,7 @@ def __init__( sess = list(mappings["session"].columns) self.sess_rgb = {"r": sess[0], "g": sess[0], "b": sess[0]} wgt_sess = { - c: pnwgt.Select(name="session{}".format(c.upper()), options=sess) + c: pnwgt.Select(name=f"session{c.upper()}", options=sess) for c in ["r", "g", "b"] } for wname, w in wgt_sess.items(): @@ -1199,7 +1199,7 @@ def show(self) -> pn.layout.Row: def write_vid_blk(arr, vpath, options): uid = uuid4() - vname = "{}.mp4".format(uid) + vname = f"{uid}.mp4" fpath = os.path.join(vpath, vname) if len(arr.shape) == 2: arr = np.expand_dims(arr, axis=0) @@ -1249,7 +1249,7 @@ def write_video( ffmpeg.output """ if not vname: - vname = "{}.mp4".format(uuid4()) + vname = f"{uuid4()}.mp4" fname = os.path.join(vpath, vname) if norm: arr_opt = fct.partial( @@ -1266,7 +1266,7 @@ def write_video( arr = arr.clip(0, 255).astype(np.uint8) w, h = arr.sizes["width"], arr.sizes["height"] process = ( - ffmpeg.input("pipe:", format="rawvideo", pix_fmt="gray", s="{}x{}".format(w, h)) + ffmpeg.input("pipe:", format="rawvideo", pix_fmt="gray", s=f"{w}x{h}") .filter("pad", int(np.ceil(w / 2) * 2), int(np.ceil(h / 2) * 2)) .output(fname, pix_fmt="yuv420p", vcodec="libx264", r=30, **options) .overwrite_output() @@ -1288,7 +1288,7 @@ def concat_video_recursive(vlist, vname=None): vpath = os.path.dirname(vlist[0]) streams = [ffmpeg.input(p) for p in vlist] if vname is None: - vname = "{}.mp4".format(uuid4()) + vname = f"{uuid4()}.mp4" fpath = os.path.join(vpath, vname) ffmpeg.concat(*streams).output(fpath).run(overwrite_output=True) for vp in vlist: @@ -1392,7 +1392,7 @@ def generate_videos( def datashade_ndcurve( - ovly: hv.NdOverlay, kdim: Optional[Union[str, List[str]]] = None, spread=False + ovly: hv.NdOverlay, kdim: Optional[Union[str, list[str]]] = None, spread=False ) -> hv.Overlay: """ Apply datashading to an overlay of curves with legends. @@ -1401,7 +1401,7 @@ def datashade_ndcurve( ---------- ovly : hv.NdOverlay The input overlay of curves. - kdim : Union[str, List[str]], optional + kdim : Union[str, list[str]], optional Key dimensions of the overlay. If `None` then the first key dimension of `ovly` will be used. By default `None`. spread : bool, optional @@ -1539,7 +1539,7 @@ def convolve_G(s: np.ndarray, g: np.ndarray) -> np.ndarray: def construct_pulse_response( g: np.ndarray, length=500 -) -> Tuple[np.ndarray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray]: """ Construct a model pulse response corresponding to certain AR coefficients. @@ -1818,7 +1818,7 @@ def gaussian(x, mu, sig): def visualize_spatial_update( A_dict: dict, C_dict: dict, - kdims: Optional[Union[str, List[str]]] = None, + kdims: Optional[Union[str, list[str]]] = None, norm=True, datashading=True, ) -> hv.HoloMap: @@ -1842,7 +1842,7 @@ def visualize_spatial_update( format as `A_dict`. The temporal activities of cells are not expected to change across different runs of spatial update, except the number of cells may be different due to dropping of cells in the update process. - kdims : Union[str, List[str]], optional + kdims : Union[str, list[str]], optional Names of key dimensions identifying the parameter space. Should have same length as the keys in `A_dict` and `C_dict`. If `None` then a dimension names "dummy" will be created and the visualization can be @@ -1930,7 +1930,7 @@ def visualize_temporal_update( g_dict: dict, sig_dict: dict, A_dict: dict, - kdims: Optional[Union[str, List[str]]] = None, + kdims: Optional[Union[str, list[str]]] = None, norm=True, datashading=True, ) -> hv.HoloMap: @@ -1972,7 +1972,7 @@ def visualize_temporal_update( `C_dict`. The spatial footprints of cells are note expected to change across different runs of temporal update, except the number of cells may be different due to dropping of cells in the update process. - kdims : Union[str, List[str]], optional + kdims : Union[str, list[str]], optional Names of key dimensions identifying the parameter space. 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359412d2..063bc33d 100644 --- a/pipeline.ipynb +++ b/pipeline.ipynb @@ -2739,9 +2739,7 @@ " ):\n", " ks = (cur_p, cur_sprs, cur_add, cur_noise)\n", " print(\n", - " \"p:{}, sparse penalty:{}, additional lag:{}, noise frequency:{}\".format(\n", - " cur_p, cur_sprs, cur_add, cur_noise\n", - " )\n", + " f\"p:{cur_p}, sparse penalty:{cur_sprs}, additional lag:{cur_add}, noise frequency:{cur_noise}\"\n", " )\n", " cur_C, cur_S, cur_b0, cur_c0, cur_g, cur_mask = update_temporal(\n", " A_sub,\n", @@ -3546,9 +3544,7 @@ " ):\n", " ks = (cur_p, cur_sprs, cur_add, cur_noise)\n", " print(\n", - " \"p:{}, sparse penalty:{}, additional lag:{}, noise frequency:{}\".format(\n", - " cur_p, cur_sprs, cur_add, cur_noise\n", - " )\n", + " f\"p:{cur_p}, sparse penalty:{cur_sprs}, additional lag:{cur_add}, noise frequency:{cur_noise}\"\n", " )\n", " cur_C, cur_S, cur_b0, cur_c0, cur_g, cur_mask = update_temporal(\n", " A_sub,\n", diff --git a/pyproject.toml b/pyproject.toml index 7529c33e..01dd8b32 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,17 +1,90 @@ -[build-system] -requires = [ - "setuptools>=42", - "wheel" +[project] +name = "minian" +version = "1.3.0" +description = "MiniAn is an analysis pipeline and visualization tool inspired by both CaImAn and MIN1PIPE package specifically for Miniscope data." +authors = [ + {name = "MiniAn Developers", email = "denisecailab@gmail.com"}, +] +dependencies = [ + "bokeh>=1.4.0", + "cvxpy>=1.1.11", + "dask>=2021.2.0", + "datashader>=0.12.1", + "distributed>=2021.2.0", + "ecos>=2.0.7", + "ffmpeg-python>=0.2.0", + "holoviews>=1.12.7", + "jinja2>=2.11.3", + "jupyter", + "matplotlib", + "medpy>=0.4.0", + "natsort", + "netcdf4", + "networkx>=2.4", + "numba>=0.52.0", + "numpy>=1.20.2", + "opencv-python>=4.2.0.34", + "pandas>=1.2.3", + "panel>=0.8.0", + "pyfftw>=0.12.0", + "pymetis>=2020.1", + "rechunker>=0.3.3", + "scikit-image>=0.18.1", + "scikit-learn>=0.22.1", + "scipy>=1.4.1", + "scs", + "simpleitk>=2.0.2", + "sk-video", + "sparse>=0.11.2", + "statsmodels>=0.11.1", + "tifffile", + "xarray>=0.17.0", + "zarr", +] +requires-python = ">=3.9" +readme = "README.md" +license = {text = "GPL-3.0"} +classifiers = [ + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", +] + +[project.urls] +Homepage = "https://github.com/DeniseCaiLab/minian" +Documentation = "https://minian.readthedocs.io/" + +[project.scripts] +minian-install = "minian.install:main" + +[project.optional-dependencies] +test = [ + "pytest>=8.4.1", ] -build-backend = "setuptools.build_meta" -[tool] +[build-system] +requires = ["pdm-backend"] +build-backend = "pdm.backend" + +[tool.pdm] +distribution = true + [tool.commitizen] name = "cz_conventional_commits" -version = "1.2.1" +version = "1.3.0" tag_format = "v$version" changelog_incremental = true version_files = [ "minian/__init__.py:__version__", - "setup.py:version", "minian/install.py:VERSION" -] \ No newline at end of file +] + +[tool.ruff] +target-version = "py39" +select = ["UP"] +fixable = ["ALL"] + +[tool.pytest.ini_options] +testpaths = [ + "minian/test" +] + + diff --git a/setup.py b/setup.py deleted file mode 100644 index 5abcc06f..00000000 --- a/setup.py +++ /dev/null @@ -1,42 +0,0 @@ -import setuptools - -NAME = "minian" -REQUIREMENTS = "requirements/requirements-base.txt" - -with open("README.md", "r") as fh: - long_description = fh.read() - - -def _get_requirements(requirement_file): - with open(requirement_file) as f: - reqs = [] - for l in f.read().splitlines(): - reqs.append(l) - return reqs - - -setuptools.setup( - name=NAME, - version="1.2.1", - author="MiniAn Developers", - author_email="denisecailab@gmail.com", - description="MiniAn is an analysis pipeline and visualization tool inspired by both CaImAn and MIN1PIPE package specifically for Miniscope data.", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/DeniseCaiLab/minian", - install_requires=_get_requirements(REQUIREMENTS), - packages=setuptools.find_packages(), - project_urls={ - "Documentation": "https://minian.readthedocs.io/", - }, - classifiers=[ - "Programming Language :: Python :: 3", - "Operating System :: OS Independent", - ], - entry_points={ - "console_scripts": [ - "minian-install = minian.install:main", - ], - }, - python_requires=">=3.8", -)