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Add missing descriptions for templates #44

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47cc6ae
add missing descriptions for templates
Zhimin-arya Sep 1, 2026
b0a9105
add demo_cdm
Zhimin-arya Sep 10, 2026
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2 changes: 1 addition & 1 deletion flows/Load_DICOM_Vocab.json
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"id": "13660611-484f-48ab-80fb-ba90dc8b68aa",
"data": {
"name": "TransformConceptRelationshipDf",
"description": "Describe the task of node python_node_1",
"description": "Transform concept relationship vocabulary data into the OMOP-compatible column structure.",
"python_code": "import pickle\nimport numpy as np\n\nfrom pathlib import Path\nfrom typing import Any, Dict\n\ndef exec(myinput: Dict[str, Any]) -> pd.DataFrame:\n \"\"\"\n Combine multiple DICOM concept relationship dataframes into a single staging dataframe.\n \n This function processes three sources of DICOM data:\n 1. Cs_values_maps_to_value (Code String values mapped to values)\n 2. Cs_values_maps_to (Code String values mappings)\n 3. Part3_to_part16_relationship_via_CID (from pickle file in storage)\n \n The function downloads a pickle file containing concept relationships, converts\n date columns to datetime format, assigns default date ranges to the other datasets,\n and combines all three into a single dataframe.\n \n Args:\n myinput: Dictionary containing workflow data with the following expected keys:\n - \"UploadAttributes-CodeStringValues\": Result containing cs_values_maps_to_value DataFrame\n - \"UploadCs_values_maps_to\": Result containing cs_values_maps_to DataFrame\n - \"UploadAttributes-CID-ValueSets\": Result containing node_id and filename for pickle file\n \n Returns:\n pd.DataFrame: Combined dataframe with all concept relationships, including properly\n formatted valid_start_date and valid_end_date columns.\n \"\"\"\n cs_values_maps_to_value_df: pd.DataFrame = myinput.get(\"UploadAttributes-CodeStringValues\").result\n cs_values_maps_to_df: pd.DataFrame = myinput.get(\"UploadCs_values_maps_to\").result\n \n node_id: str = myinput.get(\"UploadAttributes-CID-ValueSets\").result.get(\"node_id\")\n filename: str = myinput.get(\"UploadAttributes-CID-ValueSets\").result.get(\"filename\")\n \n # Download the pickle file from remote storage\n download_file_from_storage(node_id, filename)\n\n filepath: Path = Path(DOWNLOADS_DIR) / filename\n\n concept_relationship_staging: pd.DataFrame = pd.read_pickle(filepath)\n\n # Convert date columns from integer (YYYYMMDD) to datetime using vectorized operations\n concept_relationship_staging['valid_start_date'] = pd.to_datetime(\n concept_relationship_staging['valid_start_date'], \n format='%Y%m%d', \n errors='coerce'\n )\n\n concept_relationship_staging['valid_end_date'] = pd.to_datetime(\n concept_relationship_staging['valid_end_date'], \n format='%Y%m%d', \n errors='coerce'\n )\n\n # Assign constant dates to all rows (vectorized - no loops needed)\n cs_values_maps_to_value_df['valid_start_date'] = pd.to_datetime('1993-01-01')\n cs_values_maps_to_value_df['valid_end_date'] = pd.to_datetime('2099-12-31')\n\n cs_values_maps_to_df['valid_start_date'] = pd.to_datetime('1993-01-01')\n cs_values_maps_to_df['valid_end_date'] = pd.to_datetime('2099-12-31')\n\n # Combine the three dataframes into one\n combined_df: pd.DataFrame = pd.concat([concept_relationship_staging, cs_values_maps_to_value_df, cs_values_maps_to_df], ignore_index=True)\n\n return combined_df\n\n\ndef download_file_from_storage(node_id: str, filename: str) -> Path:\n \"\"\"\n Download a file from Supabase storage to the downloads directory.\n \n Args:\n node_id: The storage node identifier where the file is located.\n filename: The name of the file to download.\n \n Returns:\n Path: The path to the downloaded file.\n \"\"\"\n storage_api = SupabaseStorageAPI()\n return storage_api.download_file_to_path(node_id, filename, Path(DOWNLOADS_DIR))"
},
"type": "python_node",
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2 changes: 1 addition & 1 deletion flows/artemis.json
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"positionAbsolute": { "x": -420, "y": 80 },
"data": {
"name": "README",
"description": "Describe the ARTEMIS flow and supported conditions.",
"description": "Explain the ARTEMIS flow, its configurable inputs, and supported conditions.",
"python_code": "def exec(myinput):\n \"\"\"\n ARTEMIS provides an interface for utilizing a modified Temporal Smith-Waterman (TSW) algorithm \n that is capable of analysing conditions in the following list\n [1] \"ER Breast cancer\" \n [2] \"Prostate cancer\" \n [3] \"undefined\" \n [4] \"Mantle cell lymphoma\" \n [5] \"Acute myeloid leukemia\" \n [6] \"KRAS Colorectal cancer\" \n [7] \"Non-small cell lung cancer\" \n [8] \"Non-small cell lung cancer squamous\" \n [9] \"Kaposi sarcoma\" \n [10] \"Acute promyelocytic leukemia\" \n [11] \"Immune thrombocytopenia\" \n [12] \"Acute myeloid leukemia pediatric\" \n [13] \"Castleman disease\" \n [14] \"Endometrial cancer\" \n [15] \"Myelodysplastic syndrome\" \n [16] \"Hepatocellular carcinoma\" \n [17] \"Thyroid cancer differentiated\" \n [18] \"Chronic myeloid leukemia\" \n [19] \"T-cell acute lymphoblastic leukemia pediatric\" \n [20] \"Extranodal NK- and T-cell lymphoma nasal type\" \n [21] \"Antiphospholipid antibody syndrome\" \n [22] \"Venous thromboembolism\" \n [23] \"Systemic mastocytosis\" \n [24] \"Clear cell renal cell carcinoma\" \n [25] \"Thyroid cancer medullary\" \n [26] \"IDH Acute myeloid leukemia\" \n [27] \"Graft versus host disease\" \n [28] \"VHL Renal cell carcinoma\" \n [29] \"Chronic lymphocytic leukemia\" \n [30] \"Transformed lymphoma\" \n [31] \"Renal cell carcinoma\" \n [32] \"Ovarian cancer\" \n [33] \"Cutaneous T-cell lymphoma\" \n [34] \"NRAS Melanoma\" \n [35] \"Testicular cancer\" \n [36] \"HIV-associated lymphoma\" \n [37] \"BCR::ABL1 B-cell acute lymphoblastic leukemia\" \n [38] \"Multiple myeloma\" \n [39] \"Waldenstroem macroglobulinemia\" \n [40] \"Chronic myeloid leukemia pediatric\" \n [41] \"Gastric cancer\" \n [42] \"Colorectal cancer\" \n [43] \"Pancreatic cancer\" \n [44] \"ERBB2 Breast cancer\" \n [45] \"Rectal cancer\" \n [46] \"TNBC Breast cancer\" \n [47] \"Small cell lung cancer\" \n [48] \"Glioblastoma\" \n [49] \"Peripheral T-cell lymphoma\" \n [50] \"CNS lymphoma\" \n [51] \"Head and neck cancer\" \n [52] \"ROS1 Non-small cell lung cancer\" \n [53] \"Diffuse large B-cell lymphoma\" \n [54] \"Breast cancer\" \n [55] \"Classical Hodgkin lymphoma\" \n [56] \"Osteosarcoma\" \n [57] \"Cutaneous squamous cell carcinoma\" \n [58] \"Adrenocortical carcinoma\" \n [59] \"B-cell acute lymphoblastic leukemia pediatric\" \n [60] \"Melanoma\" \n [61] \"Light-chain (AL) amyloidosis\" \n [62] \"B-cell acute lymphoblastic leukemia\" \n [63] \"Rhabdomyosarcoma\" \n [64] \"BCR::ABL1 B-cell acute lymphoblastic leukemia pediatric\" \n [65] \"Non-Hodgkin lymphoma pediatric\" \n [66] \"FLT3 Acute myeloid leukemia\" \n [67] \"NPM1 Acute myeloid leukemia\" \n [68] \"Thyroid cancer\" \n [69] \"Indolent lymphoma\" \n [70] \"Neuroblastoma\" \n [71] \"Papillary renal cell carcinoma\" \n [72] \"Pancreatic NET\" \n [73] \"Myelofibrosis\" \n [74] \"Penile cancer\" \n [75] \"Cholangiocarcinoma\" \n [76] \"Non-small cell lung cancer nonsquamous\" \n [77] \"Sickle cell anemia\" \n [78] \"Marginal zone lymphoma\" \n [79] \"T-cell prolymphocytic leukemia\" \n [80] \"Uveal melanoma\" \n [81] \"Neuroendocrine tumor\" \n [82] \"ER|ERBB2 Breast cancer\" \n [83] \"Malignant solid neoplasm NTRK-mutated\" \n [84] \"Low-grade serous ovarian cancer\" \n [85] \"Gastrointestinal stromal tumor\" \n [86] \"WHIM syndrome\" \n [87] \"Plasma cell leukemia\" \n [88] \"Gestational trophoblastic neoplasia\" \n [89] \"Langerhans cell histiocytosis\" \n [90] \"Malignant solid neoplasm ERBB3-mutated\" \n [91] \"Desmoid tumor\" \n [92] \"Thymoma\" \n [93] \"MALT lymphoma\" \n [94] \"Warm autoimmune hemolytic anemia\" \n [95] \"Burkitt lymphoma\" \n [96] \"Soft tissue sarcoma\" \n [97] \"Polycythemia vera\" \n [98] \"Cutaneous basal cell carcinoma\" \n [99] \"Follicular lymphoma\" \n [100] \"Smoldering multiple myeloma\" \n [101] \"Adult T-cell leukemia-lymphoma\" \n [102] \"Malignant solid neoplasm SMO-mutated or PTCH-1-mutated (Hedgehog)\"\n [103] \"Low-grade glioma\" \n [104] \"Post-transplant lymphoproliferative disorder\" \n [105] \"Primary mediastinal B-cell lymphoma\" \n [106] \"Ewing sarcoma\" \n [107] \"RAS Colorectal cancer\" \n [108] \"Urothelial carcinoma\" \n [109] \"NK- and T-cell lymphoma\" \n [110] \"Hairy cell leukemia\" \n [111] \"Gallbladder cancer\" \n [112] \"Hodgkin lymphoma nodular lymphocyte-predominant\" \n [113] \"Classical Hodgkin lymphoma pediatric\" \n [114] \"Nasopharyngeal carcinoma\" \n [115] \"Esophageal squamous cell carcinoma\" \n [116] \"Wilms tumor\" \n [117] \"Esophageal cancer\" \n [118] \"Sarcomatoid renal cell carcinoma\" \n [119] \"Anaplastic large cell lymphoma pediatric\" \n [120] \"Carcinoma of unknown primary\" \n [121] \"Hepatoblastoma\" \n [122] \"Bladder cancer\" \n [123] \"Vulvar cancer\" \n [124] \"Paroxysmal nocturnal hemoglobinuria\" \n [125] \"Mismatch repair deficient malignancy\" \n [126] \"PEComa\" \n [127] \"High-grade B-cell lymphoma\" \n [128] \"Blastic plasmacytoid dendritic cell neoplasm\" \n [129] \"Cervical cancer\" \n [130] \"Medulloblastoma\" \n [131] \"Osteosarcoma pediatric\" \n [132] \"Pheochromocytoma\" \n [133] \"RET Non-small cell lung cancer\" \n [134] \"Low-grade glioma pediatric\" \n [135] \"Malignant pleural mesothelioma\" \n [136] \"Leiomyosarcoma\" \n [137] \"Colon cancer\" \n [138] \"Hypereosinophilic syndrome\" \n [139] \"Malignant solid neoplasm ERBB2-mutated\" \n [140] \"KMT2A Acute lymphoblastic leukemia infant\" \n [141] \"PIK3CA Breast cancer\" \n [142] \"Essential thrombocythemia\" \n [143] \"Merkel cell carcinoma\" \n [144] \"T-cell acute lymphoblastic leukemia\" \n [145] \"Rosai-Dorfman-Destombes disease\" \n \"\"\" \n\n"
}
},
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