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Small fix Datamanager #18

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Small fix Datamanager #18

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38792c0
fix associate_ROI to_image
zinelya Feb 20, 2026
52ef3e0
medimage -> mediml
zinelya Feb 20, 2026
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2 changes: 1 addition & 1 deletion MEDiml/wrangling/DataManager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -456,7 +456,7 @@ def load_mask(_id, file, medscan):
path_roi_data = self.paths._path_to_niftis

for file in self.__nifti.stack_path_roi:
_id = image_file.name.split("(")[0] if ("(") in image_file.name else image_file.name # id is PatientID__ImagingScanName
_id = file.name.split("(")[0] if ("(") in file.name else file.name # id is PatientID__ImagingScanName
load_mask(_id, file, medscan)
roi_index += 1
else:
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16 changes: 6 additions & 10 deletions notebooks/tutorial/DataManager-Tutorial.ipynb
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Expand Up @@ -31,7 +31,7 @@
"## Introduction\n",
"\n",
"\n",
"This notebook is a tutorial for the *DataManager* class to give a detailed introduction & explanation on how to use this Python class. The *DataManager* class is the main object used in the *MEDimage* package when it comes to processing raw data in NIfTI and DICOM formats. This class can:\n",
"This notebook is a tutorial for the *DataManager* class to give a detailed introduction & explanation on how to use this Python class. The *DataManager* class is the main object used in the *MEDiml* package when it comes to processing raw data in NIfTI and DICOM formats. This class can:\n",
" - Create ``MEDscan`` class objects from the raw data and makes the manipulation of these objects easy.\n",
" - Help find the proper dimension and re-segmentation ranges options for radiomics analysis by running some pre-computation checks.\n",
"\n",
Expand All @@ -43,7 +43,7 @@
"id": "95b2b171",
"metadata": {},
"source": [
"The ``DataManager`` class is one of the first operation done in the radiomics analysis workflow, because it helps create the ``MEDscan`` class objects which is the main asset used in the *MEDimage* package.\n",
"The ``DataManager`` class is one of the first operation done in the radiomics analysis workflow, because it helps create the ``MEDscan`` class objects which is the main asset used in the *MEDiml* package.\n",
"\n",
"<img src=\"images/MEDimageFlowchart.png\"/>\n",
"\n",
Expand All @@ -66,7 +66,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "af05f061",
"metadata": {},
"outputs": [
Expand All @@ -81,11 +81,7 @@
"source": [
"import os\n",
"import sys\n",
"\n",
"MODULE_DIR = os.path.dirname(os.path.abspath('../MEDimage/'))\n",
"sys.path.append(os.path.dirname(MODULE_DIR))\n",
"\n",
"import MEDimage"
"import MEDiml"
]
},
{
Expand Down Expand Up @@ -591,15 +587,15 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "235fa073",
"metadata": {},
"outputs": [],
"source": [
"path_dicoms_data = Path(os.getcwd()) / \"data\" / \"DICOM-STS\"\n",
"path_save = Path(os.getcwd()) / \"data\" / \"npy\"\n",
"path_save.mkdir() if not path_save.exists() else path_save\n",
"dm = MEDimage.wrangling.DataManager(path_to_dicoms=path_dicoms_data,\n",
"dm = MEDiml.wrangling.DataManager(path_to_dicoms=path_dicoms_data,\n",
" path_save=path_save,\n",
" path_csv=path_csv,\n",
" n_batch=2)"
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