Merge pull request #470 from celprov/enh/mriqc_classifier

ENH: notebook to calibrate regressor to flag HCPh T1w images for exclusion

code/qc/from_mongodb_to_dataframe.py

@@ -0,0 +1,67 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2023 The Axon Lab <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+
+# !! To run this script, you need to have run docker-compose up in the qkay repository to have running containers !!
+
+from pymongo.mongo_client import MongoClient
+
+# Path PyMongo to allow using PyMongoArrow’s functionality directly to Collection instances of PyMongo
+from pymongoarrow.monkey import patch_all
+
+patch_all()
+
+# Connect to the MongoDB database
+client = MongoClient("localhost", 27017)
+
+# Send a ping to confirm a successful connection
+try:
+    client.admin.command("ping")
+    print("Pinged your deployment. You successfully connected to MongoDB!")
+except Exception as e:
+    print(e)
+
+# List available databases
+database_names = client.list_database_names()
+for db_name in database_names:
+    print(db_name)
+
+# Select the database and extract all ratings
+db = client.data_base_qkay
+ratings = db.ratings
+df = ratings.find_pandas_all({"dataset": "mriqc-23.2.0-withoutdwi"})
+
+# Keep only lines for which "subject" contains T1w
+modality = "bold"
+df = df[df["subject"].str.contains(modality)]
+
+# Drop columns we don't need
+df = df.drop(columns=["_id", "md5sum"])
+
+# Replace newline characters in the 'comments' column with a space
+# so that comments do not get split into multiple lines
+df["comments"] = df["comments"].str.replace("\n", ", ")
+
+# Save dataframe to csv so we can load it in R
+df.to_csv(f"data/desc-ratings_{modality}.tsv", sep="\t", index=False)
+
+# Close the connection
+client.close()