Polars 1xx

.gitignore

@@ -47,3 +47,8 @@ share/python-wheels/
 *.egg
 target/
 *.out
+
+# Local directories
+stubs/
+test_files/
+test_scripts/

docs/source/notebooks/human_cerebellum.ipynb

@@ -803,7 +803,7 @@
    "id": "405b984c-4131-4d17-a253-5d056caa922e",
    "metadata": {},
    "source": [
-    "Next, let's calculate the QC metrics using the `pycistopic qc` command."
+    "Next, let's calculate the QC metrics using the `pycistopic qc run` command."
    ]
   },
   {
@@ -813,7 +813,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pycistopic qc \\\n",
+    "!pycistopic qc run \\\n",
     "    --fragments data/fragments.tsv.gz \\\n",
     "    --regions outs/consensus_peak_calling/consensus_regions.bed \\\n",
     "    --tss outs/qc/tss.bed \\\n",
@@ -841,11 +841,11 @@
     "\n",
     "pycistopic_qc_commands_filename = \"pycistopic_qc_commands.txt\"\n",
     "\n",
-    "# Create text file with all pycistopic qc command lines.\n",
+    "# Create text file with all pycistopic qc run command lines.\n",
     "with open(pycistopic_qc_commands_filename, \"w\") as fh:\n",
     "    for sample, fragment_filename in fragments_dict.items():\n",
     "        print(\n",
-    "            \"pycistopic qc\",\n",
+    "            \"pycistopic qc run\",\n",
     "            f\"--fragments {fragment_filename}\",\n",
     "            f\"--regions {regions_bed_filename}\",\n",
     "            f\"--tss {tss_bed_filename}\",\n",
@@ -935,7 +935,7 @@
     "\n",
     "**Note:**\n",
     "\n",
-    "The `pycistopic qc` command will determine automatic thresholds for the minimum number of unique number of fragments and the minumum TSS enrichment.\n",
+    "The `pycistopic qc run` command will determine automatic thresholds for the minimum number of unique number of fragments and the minumum TSS enrichment.\n",
     "In case you want to change these thresholds or want to threhold based on FRIP, you can provide manually defined thresholds using the parameters:\n",
     "- unique_fragments_threshold\n",
     "- tss_enrichment_threshold\n",

pyproject.toml

@@ -31,13 +31,14 @@ classifiers = [
   "Topic :: Scientific/Engineering :: Bio-Informatics",
 ]
 dependencies = [
-  "numpy >= 1.20.3",
+  "numpy >= 1.20.3, < 2",
   "pandas == 1.5",
-  "polars >= 0.18.3",
+  "polars >= 1",
   "pyarrow >= 8.0.0",
   "pyranges < 0.0.128",
+  "numba",
   "ray",
-  "scatac_fragment_tools",
+  "scatac_fragment_tools >= 0.1.2",
   "scikit-learn",
   "lda",
   "matplotlib < 3.7",
@@ -144,3 +145,7 @@ max-doc-length = 88
 
 [tool.ruff.lint.flake8-tidy-imports]
 ban-relative-imports = "all"
+
+[tool.mypy]
+mypy_path = "$MYPY_CONFIG_FILE_DIR/stubs"
+plugins = ["numpy.typing.mypy_plugin"]

src/pycisTopic/cistopic_class.py

@@ -14,10 +14,10 @@
     get_position_index,
     non_zero_rows,
     prepare_tag_cells,
-    read_fragments_from_file,
     region_names_to_coordinates,
     subset_list,
 )
+from pycisTopic.fragments import read_fragments_to_pyranges
 from scipy import sparse
 
 if TYPE_CHECKING:
@@ -813,7 +813,10 @@ def create_cistopic_object_from_fragments(
         if path_to_fragments is not None:
             log.info("Using fragments of provided pandas data frame")
     else:
-        fragments = read_fragments_from_file(path_to_fragments, use_polars=use_polars)
+        fragments = read_fragments_to_pyranges(
+            fragments_bed_filename=path_to_fragments,
+            engine = "polars"
+        )
 
     if "Score" not in fragments.df:
         fragments_df = fragments.df