From ba956c767b9377b67f7b37c518717a2f4ba96739 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Tue, 27 Feb 2024 17:00:30 +0000
Subject: [PATCH 01/86] chore: small updates to accomodate GWAS Catalog for feb
 release

---
 poetry.lock                                   | 5151 +++++++++--------
 pyproject.toml                                |    1 +
 src/airflow/dags/gwas_curation_update.py      |    2 +-
 .../datasource/gwas_catalog/study_index.py    |   17 +-
 utils/update_GWAS_Catalog_data.sh             |   37 +-
 5 files changed, 2647 insertions(+), 2561 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 64ac6a99d..b27acb272 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -27,87 +27,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
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+version = "3.9.3"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -137,13 +137,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "alembic"
-version = "1.12.1"
+version = "1.13.1"
 description = "A database migration tool for SQLAlchemy."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "alembic-1.12.1-py3-none-any.whl", hash = "sha256:47d52e3dfb03666ed945becb723d6482e52190917fdb47071440cfdba05d92cb"},
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+    {file = "alembic-1.13.1.tar.gz", hash = "sha256:4932c8558bf68f2ee92b9bbcb8218671c627064d5b08939437af6d77dc05e595"},
 ]
 
 [package.dependencies]
@@ -152,7 +152,7 @@ SQLAlchemy = ">=1.3.0"
 typing-extensions = ">=4"
 
 [package.extras]
-tz = ["python-dateutil"]
+tz = ["backports.zoneinfo"]
 
 [[package]]
 name = "annotated-types"
@@ -177,38 +177,40 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.0.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "apache-airflow"
-version = "2.8.0"
+version = "2.8.2"
 description = "Programmatically author, schedule and monitor data pipelines"
 optional = false
 python-versions = "<3.12,~=3.8"
 files = [
-    {file = "apache-airflow-2.8.0.tar.gz", hash = "sha256:5917ee148125892764f4306ec76a62c66a5801218a99edfd3fe46e968cb1a344"},
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+    {file = "apache_airflow-2.8.2.tar.gz", hash = "sha256:a993e272d254ec80852553042dca5641e24a416a49ac7341c947295dee0c4b20"},
 ]
 
 [package.dependencies]
-alembic = ">=1.6.3,<2.0"
+alembic = ">=1.13.1,<2.0"
+apache-airflow-providers-common-io = "*"
 apache-airflow-providers-common-sql = "*"
 apache-airflow-providers-ftp = "*"
 apache-airflow-providers-http = "*"
@@ -227,14 +229,13 @@ cryptography = ">=0.9.3"
 deprecated = ">=1.2.13"
 dill = ">=0.2.2"
 flask = ">=2.2,<2.3"
-flask-appbuilder = "4.3.10"
+flask-appbuilder = "4.3.11"
 flask-caching = ">=1.5.0"
 flask-login = ">=0.6.2"
-flask-session = ">=0.4.0"
+flask-session = ">=0.4.0,<0.6"
 flask-wtf = ">=0.15"
 fsspec = ">=2023.10.0"
 google-re2 = ">=1.0"
-graphviz = ">=0.12"
 gunicorn = ">=20.1.0"
 httpx = "*"
 itsdangerous = ">=2.0"
@@ -243,7 +244,6 @@ jsonschema = ">=4.18.0"
 lazy-object-proxy = "*"
 linkify-it-py = ">=2.0.0"
 lockfile = ">=0.12.2"
-markdown = ">=3.0"
 markdown-it-py = ">=2.1.0"
 markupsafe = ">=1.1.1"
 marshmallow-oneofschema = ">=2.0.1"
@@ -252,16 +252,16 @@ opentelemetry-api = ">=1.15.0"
 opentelemetry-exporter-otlp = "*"
 packaging = ">=14.0"
 pathspec = ">=0.9.0"
-pendulum = ">=2.0,<3.0"
+pendulum = ">=2.1.2,<4.0"
 pluggy = ">=1.0"
 psutil = ">=4.2.0"
-pydantic = ">=2.3.0"
 pygments = ">=2.0.1"
 pyjwt = ">=2.0.0"
 python-daemon = ">=3.0.0"
 python-dateutil = ">=2.3"
 python-nvd3 = ">=0.15.0"
 python-slugify = ">=5.0"
+requests = ">=2.27.0,<3"
 rfc3339-validator = ">=0.1.4"
 rich = ">=12.4.4"
 rich-argparse = ">=1.0.0"
@@ -271,17 +271,17 @@ sqlalchemy-jsonfield = ">=1.0"
 tabulate = ">=0.7.5"
 tenacity = ">=6.2.0,<8.2.0 || >8.2.0"
 termcolor = ">=1.1.0"
-typing-extensions = ">=4.0.0"
 unicodecsv = ">=0.14.1"
-universal-pathlib = ">=0.1.4"
+universal-pathlib = ">=0.1.4,<0.2.0"
 werkzeug = ">=2.0,<3"
 
 [package.extras]
-aiobotocore = ["aiobotocore (>=2.1.1)"]
+aiobotocore = ["aiobotocore (>=2.7.0)"]
 airbyte = ["apache-airflow-providers-airbyte"]
 alibaba = ["apache-airflow-providers-alibaba"]
-all = ["PyGithub (!=1.58)", "PyOpenSSL", "adal (>=1.2.7)", "adlfs (>=2023.10.0)", "aiobotocore (>=2.1.1)", "aiofiles (>=23.2.0)", "aiohttp", "aiohttp (>=3.6.3,<4)", "alibabacloud-adb20211201 (>=1.0.0)", "alibabacloud-tea-openapi (>=0.3.7)", "amqp", "analytics-python (>=1.2.9)", "apache-airflow (>=2.6.0)", "apache-airflow (>=2.7.0)", "apache-airflow (>=2.8.0)", "apache-airflow-providers-airbyte", "apache-airflow-providers-alibaba", "apache-airflow-providers-amazon", "apache-airflow-providers-apache-beam", "apache-airflow-providers-apache-cassandra", "apache-airflow-providers-apache-drill", "apache-airflow-providers-apache-druid", "apache-airflow-providers-apache-flink", "apache-airflow-providers-apache-hdfs", "apache-airflow-providers-apache-hive", "apache-airflow-providers-apache-impala", "apache-airflow-providers-apache-kafka", "apache-airflow-providers-apache-kylin", "apache-airflow-providers-apache-livy", "apache-airflow-providers-apache-pig", "apache-airflow-providers-apache-pinot", "apache-airflow-providers-apache-spark", "apache-airflow-providers-apprise", "apache-airflow-providers-arangodb", "apache-airflow-providers-asana", "apache-airflow-providers-atlassian-jira", "apache-airflow-providers-celery", "apache-airflow-providers-cloudant", "apache-airflow-providers-cncf-kubernetes", "apache-airflow-providers-cohere", "apache-airflow-providers-common-io", "apache-airflow-providers-common-sql", "apache-airflow-providers-databricks", "apache-airflow-providers-datadog", "apache-airflow-providers-dbt-cloud", "apache-airflow-providers-dingding", "apache-airflow-providers-discord", "apache-airflow-providers-docker", "apache-airflow-providers-elasticsearch", "apache-airflow-providers-exasol", "apache-airflow-providers-facebook", "apache-airflow-providers-ftp", "apache-airflow-providers-github", "apache-airflow-providers-google", "apache-airflow-providers-grpc", "apache-airflow-providers-hashicorp", "apache-airflow-providers-http", "apache-airflow-providers-imap", "apache-airflow-providers-influxdb", "apache-airflow-providers-jdbc", "apache-airflow-providers-jenkins", "apache-airflow-providers-microsoft-azure", "apache-airflow-providers-microsoft-mssql", "apache-airflow-providers-microsoft-psrp", "apache-airflow-providers-microsoft-winrm", "apache-airflow-providers-mongo", "apache-airflow-providers-mysql", "apache-airflow-providers-neo4j", "apache-airflow-providers-odbc", "apache-airflow-providers-openai", "apache-airflow-providers-openfaas", "apache-airflow-providers-openlineage", "apache-airflow-providers-opensearch", "apache-airflow-providers-opsgenie", "apache-airflow-providers-oracle", "apache-airflow-providers-pagerduty", "apache-airflow-providers-papermill", "apache-airflow-providers-pgvector", "apache-airflow-providers-pinecone", "apache-airflow-providers-postgres", "apache-airflow-providers-presto", "apache-airflow-providers-redis", "apache-airflow-providers-salesforce", "apache-airflow-providers-samba", "apache-airflow-providers-segment", "apache-airflow-providers-sendgrid", "apache-airflow-providers-sftp", "apache-airflow-providers-singularity", "apache-airflow-providers-slack", "apache-airflow-providers-smtp", "apache-airflow-providers-snowflake", "apache-airflow-providers-sqlite", "apache-airflow-providers-ssh", "apache-airflow-providers-tableau", "apache-airflow-providers-tabular", "apache-airflow-providers-telegram", "apache-airflow-providers-trino", "apache-airflow-providers-vertica", "apache-airflow-providers-weaviate", "apache-airflow-providers-yandex", "apache-airflow-providers-zendesk", "apache-beam (>=2.47.0)", "apprise", "asana (>=0.10,<4.0.0)", "asgiref", "asgiref (>=3.5.2)", "atlasclient (>=0.1.2)", "atlassian-python-api (>=1.14.2)", "attrs (>=22.2)", "authlib (>=1.0.0)", "azure-batch (>=8.0.0)", "azure-cosmos (>=4.0.0)", "azure-datalake-store (>=0.0.45)", "azure-identity (>=1.3.1)", "azure-keyvault-secrets (>=4.1.0)", "azure-kusto-data (>=4.1.0)", "azure-mgmt-containerinstance (>=9.0.0)", "azure-mgmt-containerregistry (>=8.0.0)", "azure-mgmt-cosmosdb", "azure-mgmt-datafactory (>=2.0.0)", "azure-mgmt-datalake-store (>=0.5.0)", "azure-mgmt-resource (>=2.2.0)", "azure-mgmt-storage (>=16.0.0)", "azure-servicebus (>=7.6.1)", "azure-storage-blob (>=12.14.0)", "azure-storage-file-datalake (>=12.9.1)", "azure-storage-file-share", "azure-synapse-artifacts (>=0.17.0)", "azure-synapse-spark", "bcrypt (>=2.0.0)", "blinker (>=1.1)", "boto3 (>=1.28.0)", "botocore (>=1.31.0)", "cassandra-driver (>=3.13.0)", "celery (>=5.3.0,!=5.3.2,!=5.3.3,<6)", "cgroupspy (>=0.2.2)", "cloudant (>=2.0)", "cohere (>=4.27)", "confluent-kafka (>=1.8.2)", "cryptography (>=2.0.0)", "databricks-sql-connector (>=2.0.0,!=2.9.0,<3.0.0)", "datadog (>=0.14.0)", "dnspython (>=1.13.0)", "docker (>=5.0.3)", "elasticsearch (>=8.10,<9)", "eventlet (>=0.33.3)", "facebook-business (>=6.0.2)", "flask-appbuilder[oauth] (==4.3.10)", "flask-bcrypt (>=0.7.1)", "flower (>=1.0.0)", "gcloud-aio-auth (>=4.0.0,<5.0.0)", "gcloud-aio-bigquery (>=6.1.2)", "gcloud-aio-storage", "gcsfs (>=2023.10.0)", "gevent (>=0.13)", "google-ads (>=22.1.0)", "google-api-core (>=2.11.0)", "google-api-python-client (>=1.6.0)", "google-auth (>=1.0.0)", "google-auth (>=1.0.0,<3.0.0)", "google-auth-httplib2 (>=0.0.1)", "google-cloud-aiplatform (>=1.22.1)", "google-cloud-automl (>=2.12.0)", "google-cloud-batch (>=0.13.0)", "google-cloud-bigquery-datatransfer (>=3.13.0)", "google-cloud-bigtable (>=2.17.0)", "google-cloud-build (>=3.22.0)", "google-cloud-compute (>=1.10.0)", "google-cloud-container (>=2.17.4)", "google-cloud-datacatalog (>=3.11.1)", "google-cloud-dataflow-client (>=0.8.6)", "google-cloud-dataform (>=0.5.0)", "google-cloud-dataplex (>=1.10.0)", "google-cloud-dataproc (>=5.8.0)", "google-cloud-dataproc-metastore (>=1.12.0)", "google-cloud-dlp (>=3.12.0)", "google-cloud-kms (>=2.15.0)", "google-cloud-language (>=2.9.0)", "google-cloud-logging (>=3.5.0)", "google-cloud-memcache (>=1.7.0)", "google-cloud-monitoring (>=2.18.0)", "google-cloud-orchestration-airflow (>=1.10.0)", "google-cloud-os-login (>=2.9.1)", "google-cloud-pubsub (>=2.19.0)", "google-cloud-redis (>=2.12.0)", "google-cloud-run (>=0.9.0)", "google-cloud-secret-manager (>=2.16.0)", "google-cloud-spanner (>=3.11.1)", "google-cloud-speech (>=2.18.0)", "google-cloud-storage (>=2.7.0)", "google-cloud-storage-transfer (>=1.4.1)", "google-cloud-tasks (>=2.13.0)", "google-cloud-texttospeech (>=2.14.1)", "google-cloud-translate (>=3.11.0)", "google-cloud-videointelligence (>=2.11.0)", "google-cloud-vision (>=3.4.0)", "google-cloud-workflows (>=1.10.0)", "google-re2 (>=1.0)", "greenlet (>=0.4.9)", "grpcio (>=1.15.0)", "grpcio-gcp (>=0.2.2)", "grpcio-status", "hdfs[avro,dataframe,kerberos] (>=2.0.4)", "hmsclient (>=0.1.0)", "httpx", "hvac (>=0.10)", "impyla (>=0.18.0,<1.0)", "inflection (>=0.5.1)", "influxdb-client (>=1.19.0)", "ipykernel", "jaydebeapi (>=1.1.1)", "json-merge-patch (>=0.2)", "jsonpath-ng (>=1.5.3)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)", "kylinpy (>=2.6)", "ldap3 (>=2.5.1)", "looker-sdk (>=22.2.0)", "mysql-connector-python (>=8.0.11)", "mysqlclient (>=1.3.6)", "neo4j (>=4.2.1)", "openai[datalib] (>=1.0)", "openlineage-integration-common (>=0.28.0)", "openlineage-python (>=0.28.0)", "opensearch-py (>=2.2.0)", "opentelemetry-exporter-prometheus", "opsgenie-sdk (>=2.1.5)", "oracledb (>=1.0.0)", "oss2 (>=2.14.0)", "pandas (>=0.17.1)", "pandas-gbq", "papermill[all] (>=1.2.1)", "paramiko (>=2.6.0)", "paramiko (>=2.8.0)", "pdpyras (>=4.1.2)", "pgvector (>=0.2.3)", "pinecone-client (>=2.2.4)", "pinotdb (>0.4.7)", "plyvel", "presto-python-client (>=0.8.4)", "proto-plus (>=1.19.6)", "psycopg2-binary (>=2.8.0)", "pyarrow (>=9.0.0)", "pyarrow-hotfix", "pydruid (>=0.4.1)", "pyexasol (>=0.5.1)", "pyhive[hive-pure-sasl] (>=0.7.0)", "pykerberos (>=1.1.13)", "pymongo (>=3.6.0)", "pymssql (>=2.1.8)", "pyodbc", "pypsrp (>=0.8.0)", "pyspark", "python-arango (>=7.3.2)", "python-dotenv (>=0.21.0)", "python-jenkins (>=1.0.0)", "python-ldap", "python-telegram-bot (>=20.0.0)", "python3-saml (>=1.16.0)", "pywinrm (>=0.4)", "redis (>=4.5.2,!=4.5.5,<5.0.0)", "redshift-connector (>=2.0.888)", "requests (>=2.26.0)", "requests (>=2.27,<3)", "requests-kerberos (>=0.10.0)", "requests-toolbelt", "s3fs (>=2023.10.0)", "scrapbook[all]", "sendgrid (>=6.0.0)", "sentry-sdk (>=1.32.0,!=1.33.0)", "simple-salesforce (>=1.0.0)", "slack-sdk (>=3.0.0)", "smbprotocol (>=1.5.0)", "snowflake-connector-python (>=2.7.8)", "snowflake-sqlalchemy (>=1.1.0)", "spython (>=0.0.56)", "sqlalchemy-bigquery (>=1.2.1)", "sqlalchemy-drill (>=1.1.0)", "sqlalchemy-redshift (>=0.8.6)", "sqlalchemy-spanner (>=1.6.2)", "sqlparse (>=0.4.2)", "sshtunnel (>=0.3.2)", "statsd (>=3.3.0)", "tableauserverclient", "thrift (>=0.9.2)", "thrift-sasl (>=0.2.0)", "trino (>=0.318.0)", "vertica-python (>=0.5.1)", "virtualenv", "watchtower (>=2.0.1,<4)", "weaviate-client (>=3.24.2)", "yandexcloud (>=0.228.0)", "zenpy (>=2.0.24)"]
-all-dbs = ["aiohttp (>=3.6.3,<4)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-apache-cassandra", "apache-airflow-providers-apache-drill", "apache-airflow-providers-apache-druid", "apache-airflow-providers-apache-hdfs", "apache-airflow-providers-apache-hive", "apache-airflow-providers-apache-impala", "apache-airflow-providers-apache-pinot", "apache-airflow-providers-arangodb", "apache-airflow-providers-cloudant", "apache-airflow-providers-common-sql (>=1.3.1)", "apache-airflow-providers-common-sql (>=1.8.1)", "apache-airflow-providers-databricks", "apache-airflow-providers-exasol", "apache-airflow-providers-influxdb", "apache-airflow-providers-microsoft-mssql", "apache-airflow-providers-mongo", "apache-airflow-providers-mysql", "apache-airflow-providers-neo4j", "apache-airflow-providers-postgres", "apache-airflow-providers-presto", "apache-airflow-providers-trino", "apache-airflow-providers-vertica", "cassandra-driver (>=3.13.0)", "cloudant (>=2.0)", "databricks-sql-connector (>=2.0.0,!=2.9.0,<3.0.0)", "dnspython (>=1.13.0)", "hdfs[avro,dataframe,kerberos] (>=2.0.4)", "hmsclient (>=0.1.0)", "impyla (>=0.18.0,<1.0)", "influxdb-client (>=1.19.0)", "mysql-connector-python (>=8.0.11)", "mysqlclient (>=1.3.6)", "neo4j (>=4.2.1)", "pandas (>=0.17.1)", "pinotdb (>0.4.7)", "presto-python-client (>=0.8.4)", "psycopg2-binary (>=2.8.0)", "pydruid (>=0.4.1)", "pyexasol (>=0.5.1)", "pyhive[hive-pure-sasl] (>=0.7.0)", "pymongo (>=3.6.0)", "pymssql (>=2.1.8)", "python-arango (>=7.3.2)", "requests (>=2.26.0)", "requests (>=2.27,<3)", "sqlalchemy-drill (>=1.1.0)", "thrift (>=0.9.2)", "trino (>=0.318.0)", "vertica-python (>=0.5.1)"]
+all = ["apache-airflow[aiobotocore]", "apache-airflow[airbyte]", "apache-airflow[alibaba]", "apache-airflow[all-core]", "apache-airflow[amazon]", "apache-airflow[apache-atlas]", "apache-airflow[apache-beam]", "apache-airflow[apache-cassandra]", "apache-airflow[apache-drill]", "apache-airflow[apache-druid]", "apache-airflow[apache-flink]", "apache-airflow[apache-hdfs]", "apache-airflow[apache-hive]", "apache-airflow[apache-impala]", "apache-airflow[apache-kafka]", "apache-airflow[apache-kylin]", "apache-airflow[apache-livy]", "apache-airflow[apache-pig]", "apache-airflow[apache-pinot]", "apache-airflow[apache-spark]", "apache-airflow[apache-sqoop]", "apache-airflow[apache-webhdfs]", "apache-airflow[apprise]", "apache-airflow[arangodb]", "apache-airflow[asana]", "apache-airflow[async]", "apache-airflow[atlassian-jira]", "apache-airflow[celery]", "apache-airflow[cgroups]", "apache-airflow[cloudant]", "apache-airflow[cncf-kubernetes]", "apache-airflow[cohere]", "apache-airflow[common-io]", "apache-airflow[common-sql]", "apache-airflow[daskexecutor]", "apache-airflow[databricks]", "apache-airflow[datadog]", "apache-airflow[dbt-cloud]", "apache-airflow[deprecated-api]", "apache-airflow[dingding]", "apache-airflow[discord]", "apache-airflow[docker]", "apache-airflow[elasticsearch]", "apache-airflow[exasol]", "apache-airflow[facebook]", "apache-airflow[ftp]", "apache-airflow[github-enterprise]", "apache-airflow[github]", "apache-airflow[google-auth]", "apache-airflow[google]", "apache-airflow[graphviz]", "apache-airflow[grpc]", "apache-airflow[hashicorp]", "apache-airflow[http]", "apache-airflow[imap]", "apache-airflow[influxdb]", "apache-airflow[jdbc]", "apache-airflow[jenkins]", "apache-airflow[kerberos]", "apache-airflow[ldap]", "apache-airflow[leveldb]", "apache-airflow[microsoft-azure]", "apache-airflow[microsoft-mssql]", "apache-airflow[microsoft-psrp]", "apache-airflow[microsoft-winrm]", "apache-airflow[mongo]", "apache-airflow[mysql]", "apache-airflow[neo4j]", "apache-airflow[odbc]", "apache-airflow[openai]", "apache-airflow[openfaas]", "apache-airflow[openlineage]", "apache-airflow[opensearch]", "apache-airflow[opsgenie]", "apache-airflow[oracle]", "apache-airflow[otel]", "apache-airflow[pagerduty]", "apache-airflow[pandas]", "apache-airflow[papermill]", "apache-airflow[password]", "apache-airflow[pgvector]", "apache-airflow[pinecone]", "apache-airflow[plexus]", "apache-airflow[postgres]", "apache-airflow[presto]", "apache-airflow[pydantic]", "apache-airflow[rabbitmq]", "apache-airflow[redis]", "apache-airflow[s3fs]", "apache-airflow[salesforce]", "apache-airflow[samba]", "apache-airflow[saml]", "apache-airflow[segment]", "apache-airflow[sendgrid]", "apache-airflow[sentry]", "apache-airflow[sftp]", "apache-airflow[singularity]", "apache-airflow[slack]", "apache-airflow[smtp]", "apache-airflow[snowflake]", "apache-airflow[sqlite]", "apache-airflow[ssh]", "apache-airflow[statsd]", "apache-airflow[tableau]", "apache-airflow[tabular]", "apache-airflow[telegram]", "apache-airflow[trino]", "apache-airflow[vertica]", "apache-airflow[virtualenv]", "apache-airflow[weaviate]", "apache-airflow[yandex]", "apache-airflow[zendesk]"]
+all-core = ["apache-airflow[aiobotocore]", "apache-airflow[apache-atlas]", "apache-airflow[apache-webhdfs]", "apache-airflow[async]", "apache-airflow[cgroups]", "apache-airflow[deprecated-api]", "apache-airflow[github-enterprise]", "apache-airflow[google-auth]", "apache-airflow[graphviz]", "apache-airflow[kerberos]", "apache-airflow[ldap]", "apache-airflow[leveldb]", "apache-airflow[otel]", "apache-airflow[pandas]", "apache-airflow[password]", "apache-airflow[pydantic]", "apache-airflow[rabbitmq]", "apache-airflow[s3fs]", "apache-airflow[saml]", "apache-airflow[sentry]", "apache-airflow[statsd]", "apache-airflow[virtualenv]"]
+all-dbs = ["apache-airflow[apache-cassandra]", "apache-airflow[apache-drill]", "apache-airflow[apache-druid]", "apache-airflow[apache-hdfs]", "apache-airflow[apache-hive]", "apache-airflow[apache-impala]", "apache-airflow[apache-pinot]", "apache-airflow[arangodb]", "apache-airflow[cloudant]", "apache-airflow[databricks]", "apache-airflow[exasol]", "apache-airflow[influxdb]", "apache-airflow[microsoft-mssql]", "apache-airflow[mongo]", "apache-airflow[mysql]", "apache-airflow[neo4j]", "apache-airflow[postgres]", "apache-airflow[presto]", "apache-airflow[trino]", "apache-airflow[vertica]"]
 amazon = ["apache-airflow-providers-amazon"]
 apache-atlas = ["atlasclient (>=0.1.2)"]
 apache-beam = ["apache-airflow-providers-apache-beam"]
@@ -290,7 +290,7 @@ apache-drill = ["apache-airflow-providers-apache-drill"]
 apache-druid = ["apache-airflow-providers-apache-druid"]
 apache-flink = ["apache-airflow-providers-apache-flink"]
 apache-hdfs = ["apache-airflow-providers-apache-hdfs"]
-apache-hive = ["apache-airflow-providers-apache-hive (>=5.1.0)"]
+apache-hive = ["apache-airflow-providers-apache-hive"]
 apache-impala = ["apache-airflow-providers-apache-impala"]
 apache-kafka = ["apache-airflow-providers-apache-kafka"]
 apache-kylin = ["apache-airflow-providers-apache-kylin"]
@@ -303,53 +303,49 @@ apprise = ["apache-airflow-providers-apprise"]
 arangodb = ["apache-airflow-providers-arangodb"]
 asana = ["apache-airflow-providers-asana"]
 async = ["eventlet (>=0.33.3)", "gevent (>=0.13)", "greenlet (>=0.4.9)"]
-atlas = ["apache-airflow-providers-apache-atlas"]
+atlas = ["apache-airflow[apache-atlas]"]
 atlassian-jira = ["apache-airflow-providers-atlassian-jira"]
-aws = ["apache-airflow-providers-amazon"]
-azure = ["apache-airflow-providers-microsoft-azure"]
-cassandra = ["apache-airflow-providers-apache-cassandra"]
-celery = ["apache-airflow (>=2.6.0)", "apache-airflow-providers-celery", "celery (>=5.3.0,!=5.3.2,!=5.3.3,<6)", "flower (>=1.0.0)", "google-re2 (>=1.0)"]
+aws = ["apache-airflow[amazon]"]
+azure = ["apache-airflow[microsoft-azure]"]
+cassandra = ["apache-airflow[apache-cassandra]"]
+celery = ["apache-airflow-providers-celery"]
 cgroups = ["cgroupspy (>=0.2.2)"]
 cloudant = ["apache-airflow-providers-cloudant"]
-cncf-kubernetes = ["aiofiles (>=23.2.0)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-cncf-kubernetes", "asgiref (>=3.5.2)", "cryptography (>=2.0.0)", "google-re2 (>=1.0)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)"]
+cncf-kubernetes = ["apache-airflow-providers-cncf-kubernetes"]
 cohere = ["apache-airflow-providers-cohere"]
 common-io = ["apache-airflow-providers-common-io"]
 common-sql = ["apache-airflow-providers-common-sql"]
 databricks = ["apache-airflow-providers-databricks"]
 datadog = ["apache-airflow-providers-datadog"]
 dbt-cloud = ["apache-airflow-providers-dbt-cloud"]
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"trino (>=0.318.0)", "twine (>=4.0.2)", "types-aiofiles", "types-certifi", "types-croniter", "types-deprecated", "types-docutils", "types-markdown", "types-paramiko", "types-protobuf", "types-pymysql", "types-python-dateutil", "types-python-slugify", "types-pytz", "types-pyyaml", "types-redis", "types-requests", "types-setuptools", "types-tabulate", "types-termcolor", "types-toml", "vertica-python (>=0.5.1)", "virtualenv", "watchtower (>=2.0.1,<4)", "weaviate-client (>=3.24.2)", "yamllint (>=1.33.0)", "yandexcloud (>=0.228.0)", "zenpy (>=2.0.24)"]
 dingding = ["apache-airflow-providers-dingding"]
 discord = ["apache-airflow-providers-discord"]
-doc = ["astroid (>=2.12.3,<3.0)", "checksumdir", "click (>=8.0,!=8.1.4,!=8.1.5)", "docutils (<0.17.0)", "eralchemy2", "sphinx (>=5.2.0)", "sphinx-airflow-theme", "sphinx-argparse (>=0.1.13)", "sphinx-autoapi (>=2.0.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-jinja (>=2.0)", "sphinx-rtd-theme (>=0.1.6)", "sphinxcontrib-httpdomain (>=1.7.0)", "sphinxcontrib-redoc (>=1.6.0)", "sphinxcontrib-spelling (>=7.3)"]
-doc-gen = ["eralchemy2"]
 docker = ["apache-airflow-providers-docker"]
-druid = ["apache-airflow-providers-apache-druid"]
+druid = ["apache-airflow[apache-druid]"]
 elasticsearch = ["apache-airflow-providers-elasticsearch"]
 exasol = ["apache-airflow-providers-exasol"]
 facebook = ["apache-airflow-providers-facebook"]
 ftp = ["apache-airflow-providers-ftp"]
-gcp = ["apache-airflow-providers-google"]
-gcp-api = ["apache-airflow-providers-google"]
+gcp = ["apache-airflow[google]"]
+gcp-api = ["apache-airflow[google]"]
 github = ["apache-airflow-providers-github"]
-github-enterprise = ["authlib (>=1.0.0)", "flask-appbuilder[oauth] (==4.3.10)"]
+github-enterprise = ["apache-airflow[fab]", "authlib (>=1.0.0)"]
 google = ["apache-airflow-providers-google"]
-google-auth = ["authlib (>=1.0.0)", "flask-appbuilder[oauth] (==4.3.10)"]
+google-auth = ["apache-airflow[fab]", "authlib (>=1.0.0)"]
+graphviz = ["graphviz (>=0.12)"]
 grpc = ["apache-airflow-providers-grpc"]
 hashicorp = ["apache-airflow-providers-hashicorp"]
-hdfs = ["apache-airflow-providers-apache-hdfs"]
-hive = ["apache-airflow-providers-apache-hive"]
+hdfs = ["apache-airflow[apache-hdfs]"]
+hive = ["apache-airflow[apache-hive]"]
 http = ["apache-airflow-providers-http"]
 imap = ["apache-airflow-providers-imap"]
 influxdb = ["apache-airflow-providers-influxdb"]
 jdbc = ["apache-airflow-providers-jdbc"]
 jenkins = ["apache-airflow-providers-jenkins"]
 kerberos = ["pykerberos (>=1.1.13)", "requests-kerberos (>=0.10.0)", "thrift-sasl (>=0.2.0)"]
-kubernetes = ["aiofiles (>=23.2.0)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-cncf-kubernetes", "asgiref (>=3.5.2)", "cryptography (>=2.0.0)", "google-re2 (>=1.0)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)"]
+kubernetes = ["apache-airflow[cncf-kubernetes]"]
 ldap = ["ldap3 (>=2.5.1)", "python-ldap"]
 leveldb = ["plyvel"]
 microsoft-azure = ["apache-airflow-providers-microsoft-azure"]
@@ -357,7 +353,7 @@ microsoft-mssql = ["apache-airflow-providers-microsoft-mssql"]
 microsoft-psrp = ["apache-airflow-providers-microsoft-psrp"]
 microsoft-winrm = ["apache-airflow-providers-microsoft-winrm"]
 mongo = ["apache-airflow-providers-mongo"]
-mssql = ["apache-airflow-providers-microsoft-mssql"]
+mssql = ["apache-airflow[microsoft-mssql]"]
 mysql = ["apache-airflow-providers-mysql"]
 neo4j = ["apache-airflow-providers-neo4j"]
 odbc = ["apache-airflow-providers-odbc"]
@@ -369,17 +365,18 @@ opsgenie = ["apache-airflow-providers-opsgenie"]
 oracle = ["apache-airflow-providers-oracle"]
 otel = ["opentelemetry-exporter-prometheus"]
 pagerduty = ["apache-airflow-providers-pagerduty"]
-pandas = ["pandas (>=0.17.1)", "pyarrow (>=9.0.0)", "pyarrow-hotfix"]
+pandas = ["pandas (>=1.2.5)"]
 papermill = ["apache-airflow-providers-papermill"]
 password = ["bcrypt (>=2.0.0)", "flask-bcrypt (>=0.7.1)"]
 pgvector = ["apache-airflow-providers-pgvector"]
 pinecone = ["apache-airflow-providers-pinecone"]
-pinot = ["apache-airflow-providers-apache-pinot"]
+pinot = ["apache-airflow[apache-pinot]"]
 postgres = ["apache-airflow-providers-postgres"]
 presto = ["apache-airflow-providers-presto"]
+pydantic = ["pydantic (>=2.3.0)"]
 rabbitmq = ["amqp"]
 redis = ["apache-airflow-providers-redis"]
-s3 = ["apache-airflow-providers-amazon"]
+s3 = ["apache-airflow[amazon]"]
 s3fs = ["s3fs (>=2023.10.0)"]
 salesforce = ["apache-airflow-providers-salesforce"]
 samba = ["apache-airflow-providers-samba"]
@@ -392,7 +389,7 @@ singularity = ["apache-airflow-providers-singularity"]
 slack = ["apache-airflow-providers-slack"]
 smtp = ["apache-airflow-providers-smtp"]
 snowflake = ["apache-airflow-providers-snowflake"]
-spark = ["apache-airflow-providers-apache-spark"]
+spark = ["apache-airflow[apache-spark]"]
 sqlite = ["apache-airflow-providers-sqlite"]
 ssh = ["apache-airflow-providers-ssh"]
 statsd = ["statsd (>=3.3.0)"]
@@ -403,43 +400,61 @@ trino = ["apache-airflow-providers-trino"]
 vertica = ["apache-airflow-providers-vertica"]
 virtualenv = ["virtualenv"]
 weaviate = ["apache-airflow-providers-weaviate"]
-webhdfs = ["hdfs[avro,dataframe,kerberos] (>=2.0.4)"]
-winrm = ["apache-airflow-providers-microsoft-winrm"]
+webhdfs = ["apache-airflow[apache-webhdfs]"]
+winrm = ["apache-airflow[microsoft-winrm]"]
 yandex = ["apache-airflow-providers-yandex"]
 zendesk = ["apache-airflow-providers-zendesk"]
 
+[[package]]
+name = "apache-airflow-providers-common-io"
+version = "1.3.0"
+description = "Provider package apache-airflow-providers-common-io for Apache Airflow"
+optional = false
+python-versions = "~=3.8"
+files = [
+    {file = "apache_airflow_providers_common_io-1.3.0-py3-none-any.whl", hash = "sha256:a67c6dd3cb419c68fc1a9ed62f0f434426852e15a46c3159f367b3961332955d"},
+    {file = "apache_airflow_providers_common_io-1.3.0.tar.gz", hash = "sha256:7172620a2370031970df2212a9f694a5ff82240f7e498b8b7dfdbae7e6c882d6"},
+]
+
+[package.dependencies]
+apache-airflow = ">=2.8.0"
+
+[package.extras]
+openlineage = ["apache-airflow-providers-openlineage"]
+
 [[package]]
 name = "apache-airflow-providers-common-sql"
-version = "1.8.0"
-description = "Provider for Apache Airflow. Implements apache-airflow-providers-common-sql package"
+version = "1.11.0"
+description = "Provider package apache-airflow-providers-common-sql for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache-airflow-providers-common-sql-1.8.0.tar.gz", hash = "sha256:4d8d99f33f7b5a3e313ef12c5a2f10d1612477ed2e784d0ffb721447e076e3f7"},
-    {file = "apache_airflow_providers_common_sql-1.8.0-py3-none-any.whl", hash = "sha256:45117fa38f5a2e83cdb9a8d3bdfdbd602bfdc7a894d087b26e6ebb94df79ec32"},
+    {file = "apache_airflow_providers_common_sql-1.11.0-py3-none-any.whl", hash = "sha256:0c200bd17ff89fac03bce8404a3a7cec021dd99e136effe0d6b68c4642c925c5"},
+    {file = "apache_airflow_providers_common_sql-1.11.0.tar.gz", hash = "sha256:7667358efdf2ece87bfd136c786d729bc6f3274f8b87566ce9b432ab14b6aa33"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.5.0"
+apache-airflow = ">=2.6.0"
+more-itertools = ">=9.0.0"
 sqlparse = ">=0.4.2"
 
 [package.extras]
 openlineage = ["apache-airflow-providers-openlineage"]
-pandas = ["pandas (>=0.17.1)"]
+pandas = ["pandas (>=1.2.5)"]
 
 [[package]]
 name = "apache-airflow-providers-ftp"
-version = "3.6.0"
-description = "Provider for Apache Airflow. Implements apache-airflow-providers-ftp package"
+version = "3.7.0"
+description = "Provider package apache-airflow-providers-ftp for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache-airflow-providers-ftp-3.6.0.tar.gz", hash = "sha256:160f12e9f78ffbcdca4bdffbac93c1f04641fd3f1f7d236c7bfe79cd0a68d14b"},
-    {file = "apache_airflow_providers_ftp-3.6.0-py3-none-any.whl", hash = "sha256:03264207293710c3821f5ccd548c5e89a3c5ca7562bc4ff629e6f73d112a71a4"},
+    {file = "apache_airflow_providers_ftp-3.7.0-py3-none-any.whl", hash = "sha256:817936fa2f9f5d7f00f50bfe0dfdf83a3ffbcd863cb92b862a2ab5a40da1b8c4"},
+    {file = "apache_airflow_providers_ftp-3.7.0.tar.gz", hash = "sha256:cc6e7602fb9b92255b83343673923339c8fe7d22c8c738ba372ce38a70cce1cc"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.5.0"
+apache-airflow = ">=2.6.0"
 
 [package.extras]
 openlineage = ["apache-airflow-providers-openlineage"]
@@ -538,49 +553,49 @@ trino = ["apache-airflow-providers-trino"]
 
 [[package]]
 name = "apache-airflow-providers-http"
-version = "4.6.0"
-description = "Provider for Apache Airflow. Implements apache-airflow-providers-http package"
+version = "4.9.1"
+description = "Provider package apache-airflow-providers-http for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache-airflow-providers-http-4.6.0.tar.gz", hash = "sha256:c9b08782031daa8c18ba62c2c8ae687659968d636fbf6b9fbed488d1fe4f0162"},
-    {file = "apache_airflow_providers_http-4.6.0-py3-none-any.whl", hash = "sha256:ca3cf6c209cb27144fd4d676ee5feef96a1ed67002f13bf4a8b74d859ef8b56c"},
+    {file = "apache_airflow_providers_http-4.9.1-py3-none-any.whl", hash = "sha256:3b7c002899193acccf19bc29fa98e2c91b0b5f2fa05524bc3042fab0dc1700fb"},
+    {file = "apache_airflow_providers_http-4.9.1.tar.gz", hash = "sha256:2bb670f9e89a4f4e50ce71faf8002aff729516feceba99f9947acbb5d7163754"},
 ]
 
 [package.dependencies]
-aiohttp = "*"
-apache-airflow = ">=2.5.0"
+aiohttp = ">=3.9.2"
+apache-airflow = ">=2.6.0"
 asgiref = "*"
-requests = ">=2.26.0"
-requests-toolbelt = "*"
+requests = ">=2.27.0,<3"
+requests_toolbelt = "*"
 
 [[package]]
 name = "apache-airflow-providers-imap"
-version = "3.4.0"
-description = "Provider for Apache Airflow. Implements apache-airflow-providers-imap package"
+version = "3.5.0"
+description = "Provider package apache-airflow-providers-imap for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache-airflow-providers-imap-3.4.0.tar.gz", hash = "sha256:347f78efa0e1353f90be78244fad0136d7c0cb1af25b9a3ba296d4e31fb53f6c"},
-    {file = "apache_airflow_providers_imap-3.4.0-py3-none-any.whl", hash = "sha256:4972793b0dbb25d5372fe1bf174329958075985c0beca013459ec89fcbbca620"},
+    {file = "apache_airflow_providers_imap-3.5.0-py3-none-any.whl", hash = "sha256:f5f26c662cff27a532c6a7c3ed0a8bc9c7fcae14634424e82e85150e631a2adb"},
+    {file = "apache_airflow_providers_imap-3.5.0.tar.gz", hash = "sha256:d50929801cb9b7abcea33799e8b3cd64b756bd42530a44281febaffcdb30998f"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.5.0"
+apache-airflow = ">=2.6.0"
 
 [[package]]
 name = "apache-airflow-providers-sqlite"
-version = "3.5.0"
-description = "Provider for Apache Airflow. Implements apache-airflow-providers-sqlite package"
+version = "3.7.1"
+description = "Provider package apache-airflow-providers-sqlite for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache-airflow-providers-sqlite-3.5.0.tar.gz", hash = "sha256:6f47c25d7fb026fa8b8b4dc8edfee1491998255048255625716fceaae2c67024"},
-    {file = "apache_airflow_providers_sqlite-3.5.0-py3-none-any.whl", hash = "sha256:7baba67c9ddf75b31d8197e22ca491c2e1cf9e4c6306c1788c150b5457c3a182"},
+    {file = "apache_airflow_providers_sqlite-3.7.1-py3-none-any.whl", hash = "sha256:49af9ce1012e2d52ef89dc5d5832ff9bc245330635aa4f011590dc19113a380f"},
+    {file = "apache_airflow_providers_sqlite-3.7.1.tar.gz", hash = "sha256:0dd8967ac1f3fb518cef278611a2f8bbae19421e215e0bf4ede01542d4af86cf"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.5.0"
+apache-airflow = ">=2.6.0"
 apache-airflow-providers-common-sql = ">=1.3.1"
 
 [package.extras]
@@ -588,13 +603,13 @@ common-sql = ["apache-airflow-providers-common-sql"]
 
 [[package]]
 name = "apispec"
-version = "6.3.0"
+version = "6.5.0"
 description = "A pluggable API specification generator. Currently supports the OpenAPI Specification (f.k.a. the Swagger specification)."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "apispec-6.3.0-py3-none-any.whl", hash = "sha256:95a0b9355785df998bb0e9b939237a30ee4c7428fd6ef97305eae3da06b9b339"},
-    {file = "apispec-6.3.0.tar.gz", hash = "sha256:6cb08d92ce73ff0b3bf46cb2ea5c00d57289b0f279fb0256a3df468182ba5344"},
+    {file = "apispec-6.5.0-py3-none-any.whl", hash = "sha256:708a9a6e6ccd5e69cd73bf75b41e7ed5ea8866f8a4539f419db3df642789bb1e"},
+    {file = "apispec-6.5.0.tar.gz", hash = "sha256:c03a4d848ae70e9bb2269dd4e4d30d8ec7ec069d24ef9e9b422e3cbd1a9fe794"},
 ]
 
 [package.dependencies]
@@ -602,12 +617,10 @@ packaging = ">=21.3"
 PyYAML = {version = ">=3.10", optional = true, markers = "extra == \"yaml\""}
 
 [package.extras]
-dev = ["PyYAML (>=3.10)", "flake8 (==5.0.4)", "flake8-bugbear (==22.9.23)", "marshmallow (>=3.13.0)", "mypy (==0.982)", "openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)", "pre-commit (>=2.4,<3.0)", "pytest", "tox", "types-PyYAML"]
-docs = ["marshmallow (>=3.13.0)", "pyyaml (==6.0)", "sphinx (==5.2.3)", "sphinx-issues (==3.0.1)", "sphinx-rtd-theme (==1.0.0)"]
-lint = ["flake8 (==5.0.4)", "flake8-bugbear (==22.9.23)", "mypy (==0.982)", "pre-commit (>=2.4,<3.0)", "types-PyYAML"]
+dev = ["apispec[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
+docs = ["apispec[marshmallow]", "pyyaml (==6.0.1)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-rtd-theme (==2.0.0)"]
 marshmallow = ["marshmallow (>=3.18.0)"]
-tests = ["PyYAML (>=3.10)", "marshmallow (>=3.13.0)", "openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)", "pytest"]
-validation = ["openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)"]
+tests = ["apispec[marshmallow,yaml]", "openapi-spec-validator (==0.7.1)", "pytest"]
 yaml = ["PyYAML (>=3.10)"]
 
 [[package]]
@@ -623,24 +636,24 @@ files = [
 
 [[package]]
 name = "appnope"
-version = "0.1.3"
+version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
 [[package]]
 name = "argcomplete"
-version = "3.1.2"
+version = "3.2.2"
 description = "Bash tab completion for argparse"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "argcomplete-3.1.2-py3-none-any.whl", hash = "sha256:d97c036d12a752d1079f190bc1521c545b941fda89ad85d15afa909b4d1b9a99"},
-    {file = "argcomplete-3.1.2.tar.gz", hash = "sha256:d5d1e5efd41435260b8f85673b74ea2e883affcbec9f4230c582689e8e78251b"},
+    {file = "argcomplete-3.2.2-py3-none-any.whl", hash = "sha256:e44f4e7985883ab3e73a103ef0acd27299dbfe2dfed00142c35d4ddd3005901d"},
+    {file = "argcomplete-3.2.2.tar.gz", hash = "sha256:f3e49e8ea59b4026ee29548e24488af46e30c9de57d48638e24f54a1ea1000a2"},
 ]
 
 [package.extras]
@@ -694,21 +707,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "avro"
@@ -737,17 +751,17 @@ files = [
 
 [[package]]
 name = "azure-core"
-version = "1.29.5"
+version = "1.30.0"
 description = "Microsoft Azure Core Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.29.5.tar.gz", hash = "sha256:52983c89d394c6f881a121e5101c5fa67278ca3b1f339c8fb2ef39230c70e9ac"},
-    {file = "azure_core-1.29.5-py3-none-any.whl", hash = "sha256:0fa04b7b1f7d44a4fb8468c4093deb2ea01fdf4faddbf802ed9205615f99d68c"},
+    {file = "azure-core-1.30.0.tar.gz", hash = "sha256:6f3a7883ef184722f6bd997262eddaf80cfe7e5b3e0caaaf8db1695695893d35"},
+    {file = "azure_core-1.30.0-py3-none-any.whl", hash = "sha256:3dae7962aad109610e68c9a7abb31d79720e1d982ddf61363038d175a5025e89"},
 ]
 
 [package.dependencies]
-requests = ">=2.18.4"
+requests = ">=2.21.0"
 six = ">=1.11.0"
 typing-extensions = ">=4.6.0"
 
@@ -803,13 +817,13 @@ msrest = ">=0.6.21"
 
 [[package]]
 name = "azure-storage-blob"
-version = "12.18.3"
+version = "12.19.0"
 description = "Microsoft Azure Blob Storage Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-storage-blob-12.18.3.tar.gz", hash = "sha256:d8ced0deee3367fa3d4f3d1a03cd9edadf4440c0a371f503d623fa6c807554ee"},
-    {file = "azure_storage_blob-12.18.3-py3-none-any.whl", hash = "sha256:c278dde2ac41857a68d615c9f2b36d894ba877a7e84d62795603c7e79d0bb5e9"},
+    {file = "azure-storage-blob-12.19.0.tar.gz", hash = "sha256:26c0a4320a34a3c2a1b74528ba6812ebcb632a04cd67b1c7377232c4b01a5897"},
+    {file = "azure_storage_blob-12.19.0-py3-none-any.whl", hash = "sha256:7bbc2c9c16678f7a420367fef6b172ba8730a7e66df7f4d7a55d5b3c8216615b"},
 ]
 
 [package.dependencies]
@@ -823,13 +837,13 @@ aio = ["azure-core[aio] (>=1.28.0,<2.0.0)"]
 
 [[package]]
 name = "babel"
-version = "2.13.1"
+version = "2.14.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.13.1-py3-none-any.whl", hash = "sha256:7077a4984b02b6727ac10f1f7294484f737443d7e2e66c5e4380e41a3ae0b4ed"},
-    {file = "Babel-2.13.1.tar.gz", hash = "sha256:33e0952d7dd6374af8dbf6768cc4ddf3ccfefc244f9986d4074704f2fbd18900"},
+    {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
+    {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
 ]
 
 [package.extras]
@@ -848,42 +862,45 @@ files = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.2"
+version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
-    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
 [[package]]
 name = "blinker"
-version = "1.6.3"
+version = "1.7.0"
 description = "Fast, simple object-to-object and broadcast signaling"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "blinker-1.6.3-py3-none-any.whl", hash = "sha256:296320d6c28b006eb5e32d4712202dbcdcbf5dc482da298c2f44881c43884aaa"},
-    {file = "blinker-1.6.3.tar.gz", hash = "sha256:152090d27c1c5c722ee7e48504b02d76502811ce02e1523553b4cf8c8b3d3a8d"},
+    {file = "blinker-1.7.0-py3-none-any.whl", hash = "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9"},
+    {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
 ]
 
 [[package]]
 name = "bokeh"
-version = "3.3.0"
+version = "3.3.4"
 description = "Interactive plots and applications in the browser from Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "bokeh-3.3.0-py3-none-any.whl", hash = "sha256:65e36824c99fd46530c559263c6d14eabed6945370cd3beffc2eeedb62d6db6d"},
-    {file = "bokeh-3.3.0.tar.gz", hash = "sha256:cdbe268f842c139ba8fa0fb43c0c55c172c8215ec5a69a2629482c63c9d4039c"},
+    {file = "bokeh-3.3.4-py3-none-any.whl", hash = "sha256:ad7b6f89d0a7c2be01eff1db0ca24e2755ac41de14539db919a62e791809c309"},
+    {file = "bokeh-3.3.4.tar.gz", hash = "sha256:73b7982dc2b8df15bf660cdddc8d3825e829195c438015a5d09824f1a7028368"},
 ]
 
 [package.dependencies]
@@ -899,32 +916,32 @@ xyzservices = ">=2021.09.1"
 
 [[package]]
 name = "boto3"
-version = "1.28.75"
+version = "1.34.50"
 description = "The AWS SDK for Python"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
 files = [
-    {file = "boto3-1.28.75-py3-none-any.whl", hash = "sha256:b959decd588982bc919bfcbebc7916926b05003a0093e7f2845362da4f5dd9fc"},
-    {file = "boto3-1.28.75.tar.gz", hash = "sha256:63d772a784e8e35ee51974eb1c20dff5faa51b007d22c5647783f18966bf4042"},
+    {file = "boto3-1.34.50-py3-none-any.whl", hash = "sha256:8d709365231234bc4f0ca98fdf33a25eeebf78072853c6aa3d259f0f5cf09877"},
+    {file = "boto3-1.34.50.tar.gz", hash = "sha256:290952be7899560039cb0042e8a2354f61a7dead0d0ca8bea6ba901930df0468"},
 ]
 
 [package.dependencies]
-botocore = ">=1.31.75,<1.32.0"
+botocore = ">=1.34.50,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.7.0,<0.8.0"
+s3transfer = ">=0.10.0,<0.11.0"
 
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.31.75"
+version = "1.34.50"
 description = "Low-level, data-driven core of boto 3."
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
 files = [
-    {file = "botocore-1.31.75-py3-none-any.whl", hash = "sha256:fa078c4aa9a5777b3ede756540e62fec551e13d39cf7abf9a37bb81981496d68"},
-    {file = "botocore-1.31.75.tar.gz", hash = "sha256:d704ea9867b2227de0350bc2a5ca2543349e164ecb5d15edbfacbb05f2056482"},
+    {file = "botocore-1.34.50-py3-none-any.whl", hash = "sha256:fda510559dbe796eefdb59561cc81be1b99afba3dee53fd23db9a3d587adc0ab"},
+    {file = "botocore-1.34.50.tar.gz", hash = "sha256:33ab82cb96c4bb684f0dbafb071808e4817d83debc88b223e7d988256370c6d7"},
 ]
 
 [package.dependencies]
@@ -933,7 +950,7 @@ python-dateutil = ">=2.1,<3.0.0"
 urllib3 = {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.16.26)"]
+crt = ["awscrt (==0.19.19)"]
 
 [[package]]
 name = "bracex"
@@ -959,49 +976,49 @@ files = [
 
 [[package]]
 name = "cachetools"
-version = "5.3.2"
+version = "5.3.3"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
-    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
 ]
 
 [[package]]
 name = "cattrs"
-version = "23.1.2"
+version = "23.2.3"
 description = "Composable complex class support for attrs and dataclasses."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "cattrs-23.1.2-py3-none-any.whl", hash = "sha256:b2bb14311ac17bed0d58785e5a60f022e5431aca3932e3fc5cc8ed8639de50a4"},
-    {file = "cattrs-23.1.2.tar.gz", hash = "sha256:db1c821b8c537382b2c7c66678c3790091ca0275ac486c76f3c8f3920e83c657"},
+    {file = "cattrs-23.2.3-py3-none-any.whl", hash = "sha256:0341994d94971052e9ee70662542699a3162ea1e0c62f7ce1b4a57f563685108"},
+    {file = "cattrs-23.2.3.tar.gz", hash = "sha256:a934090d95abaa9e911dac357e3a8699e0b4b14f8529bcc7d2b1ad9d51672b9f"},
 ]
 
 [package.dependencies]
-attrs = ">=20"
-exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
-typing_extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+attrs = ">=23.1.0"
+exceptiongroup = {version = ">=1.1.1", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.1.0,<4.6.3 || >4.6.3", markers = "python_version < \"3.11\""}
 
 [package.extras]
-bson = ["pymongo (>=4.2.0,<5.0.0)"]
-cbor2 = ["cbor2 (>=5.4.6,<6.0.0)"]
-msgpack = ["msgpack (>=1.0.2,<2.0.0)"]
-orjson = ["orjson (>=3.5.2,<4.0.0)"]
-pyyaml = ["PyYAML (>=6.0,<7.0)"]
-tomlkit = ["tomlkit (>=0.11.4,<0.12.0)"]
-ujson = ["ujson (>=5.4.0,<6.0.0)"]
+bson = ["pymongo (>=4.4.0)"]
+cbor2 = ["cbor2 (>=5.4.6)"]
+msgpack = ["msgpack (>=1.0.5)"]
+orjson = ["orjson (>=3.9.2)"]
+pyyaml = ["pyyaml (>=6.0)"]
+tomlkit = ["tomlkit (>=0.11.8)"]
+ujson = ["ujson (>=5.7.0)"]
 
 [[package]]
 name = "certifi"
-version = "2023.7.22"
+version = "2024.2.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"},
-    {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
+    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
+    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
 ]
 
 [[package]]
@@ -1081,112 +1098,112 @@ files = [
 
 [[package]]
 name = "chardet"
-version = "4.0.0"
-description = "Universal encoding detector for Python 2 and 3"
+version = "5.2.0"
+description = "Universal encoding detector for Python 3"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.7"
 files = [
-    {file = "chardet-4.0.0-py2.py3-none-any.whl", hash = "sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5"},
-    {file = "chardet-4.0.0.tar.gz", hash = "sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa"},
+    {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
+    {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
 ]
 
 [[package]]
 name = "charset-normalizer"
-version = "3.3.1"
+version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 [[package]]
@@ -1245,22 +1262,20 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 
 [[package]]
 name = "comm"
-version = "0.1.4"
+version = "0.2.1"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 traitlets = ">=4"
 
 [package.extras]
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 test = ["pytest"]
-typing = ["mypy (>=0.990)"]
 
 [[package]]
 name = "commonmark"
@@ -1278,17 +1293,17 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
 
 [[package]]
 name = "configupdater"
-version = "3.1.1"
+version = "3.2"
 description = "Parser like ConfigParser but for updating configuration files"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.extras]
-testing = ["flake8", "pytest", "pytest-cov", "pytest-virtualenv", "pytest-xdist", "sphinx"]
+testing = ["flake8", "pytest", "pytest-cov", "pytest-randomly", "pytest-xdist", "sphinx"]
 
 [[package]]
 name = "connexion"
@@ -1321,134 +1336,126 @@ tests = ["MarkupSafe (>=0.23)", "aiohttp (>=2.3.10,<4)", "aiohttp-jinja2 (>=0.14
 
 [[package]]
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-version = "1.1.1"
+version = "1.2.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -1459,12 +1466,13 @@ toml = ["tomli"]
 
 [[package]]
 name = "cron-descriptor"
-version = "1.4.0"
+version = "1.4.3"
 description = "A Python library that converts cron expressions into human readable strings."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -1487,49 +1495,48 @@ pytz = ">2021.1"
 
 [[package]]
 name = "cryptography"
-version = "38.0.4"
+version = "41.0.7"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
-python-versions = ">=3.6"
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 ]
 
 [package.dependencies]
 cffi = ">=1.12"
 
 [package.extras]
-docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1)", "sphinx-rtd-theme"]
+docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
 docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
-pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"]
-sdist = ["setuptools-rust (>=0.11.4)"]
+nox = ["nox"]
+pep8test = ["black", "check-sdist", "mypy", "ruff"]
+sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["hypothesis (>=1.11.4,!=3.79.2)", "iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pytz"]
+test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "darglint"
@@ -1544,13 +1551,13 @@ files = [
 
 [[package]]
 name = "db-dtypes"
-version = "1.1.1"
+version = "1.2.0"
 description = "Pandas Data Types for SQL systems (BigQuery, Spanner)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1561,40 +1568,44 @@ pyarrow = ">=3.0.0"
 
 [[package]]
 name = "dbldatagen"
-version = "0.3.5"
+version = "0.3.6"
 description = "Databricks Labs -  PySpark Synthetic Data Generator"
 optional = false
 python-versions = ">=3.8.10"
 files = [
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 ]
 
 [[package]]
 name = "debugpy"
-version = "1.8.0"
+version = "1.8.1"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -1645,47 +1656,49 @@ tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "dill"
-version = "0.3.7"
+version = "0.3.8"
 description = "serialize all of Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.7"
+version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "dnspython"
-version = "2.4.2"
+version = "2.6.1"
 description = "DNS toolkit"
 optional = false
-python-versions = ">=3.8,<4.0"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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-doh = ["h2 (>=4.1.0)", "httpcore (>=0.17.3)", "httpx (>=0.24.1)"]
-doq = ["aioquic (>=0.9.20)"]
-idna = ["idna (>=2.1,<4.0)"]
-trio = ["trio (>=0.14,<0.23)"]
-wmi = ["wmi (>=1.5.1,<2.0.0)"]
+dev = ["black (>=23.1.0)", "coverage (>=7.0)", "flake8 (>=7)", "mypy (>=1.8)", "pylint (>=3)", "pytest (>=7.4)", "pytest-cov (>=4.1.0)", "sphinx (>=7.2.0)", "twine (>=4.0.0)", "wheel (>=0.42.0)"]
+dnssec = ["cryptography (>=41)"]
+doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
+doq = ["aioquic (>=0.9.25)"]
+idna = ["idna (>=3.6)"]
+trio = ["trio (>=0.23)"]
+wmi = ["wmi (>=1.5.1)"]
 
 [[package]]
 name = "docker-pycreds"
@@ -1751,13 +1764,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.3"
+version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -1809,19 +1822,19 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "flake8"
-version = "6.1.0"
+version = "7.0.0"
 description = "the modular source code checker: pep8 pyflakes and co"
 optional = false
 python-versions = ">=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
 mccabe = ">=0.7.0,<0.8.0"
 pycodestyle = ">=2.11.0,<2.12.0"
-pyflakes = ">=3.1.0,<3.2.0"
+pyflakes = ">=3.2.0,<3.3.0"
 
 [[package]]
 name = "flask"
@@ -1846,13 +1859,13 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "flask-appbuilder"
-version = "4.3.10"
+version = "4.3.11"
 description = "Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more."
 optional = false
 python-versions = "~=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1921,13 +1934,13 @@ Flask = "*"
 
 [[package]]
 name = "flask-jwt-extended"
-version = "4.5.3"
+version = "4.6.0"
 description = "Extended JWT integration with Flask"
 optional = false
 python-versions = ">=3.7,<4"
 files = [
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 ]
 
 [package.dependencies]
@@ -1940,13 +1953,13 @@ asymmetric-crypto = ["cryptography (>=3.3.1)"]
 
 [[package]]
 name = "flask-limiter"
-version = "3.5.0"
+version = "3.5.1"
 description = "Rate limiting for flask applications"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2027,83 +2040,99 @@ email = ["email-validator"]
 
 [[package]]
 name = "frozenlist"
-version = "1.4.0"
+version = "1.4.1"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"},
 ]
 
 [[package]]
 name = "fsspec"
-version = "2023.10.0"
+version = "2024.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
-    {file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
+    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
+    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
 ]
 
 [package.extras]
@@ -2121,7 +2150,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -2130,45 +2159,34 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
 
-[[package]]
-name = "future"
-version = "0.18.3"
-description = "Clean single-source support for Python 3 and 2"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "future-0.18.3.tar.gz", hash = "sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307"},
-]
-
 [[package]]
 name = "gcloud-aio-auth"
-version = "4.1.5"
+version = "4.2.3"
 description = "Python Client for Google Cloud Auth"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "gcloud_aio_auth-4.1.5-py3-none-any.whl", hash = "sha256:606431bd670effc4b67456877747d400c4dd0d8d20bbca98c72f09041359985f"},
-    {file = "gcloud_aio_auth-4.1.5.tar.gz", hash = "sha256:12b8308f32a83b76ae04129800df8eb09e8803219c2ebabccad2364f39744f79"},
+    {file = "gcloud_aio_auth-4.2.3-py3-none-any.whl", hash = "sha256:e4adadd36e35eeeb8537b926840372c3080c2f5a6909d44aa1bacbced2260bb1"},
+    {file = "gcloud_aio_auth-4.2.3.tar.gz", hash = "sha256:8e12297c5b45cfc20d629b83e1233f83a1c7d5f830f24f31bc5bb8816c0cda1b"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.3.0,<4.0.0"
 backoff = ">=1.0.0,<3.0.0"
-chardet = {version = ">=2.0,<4.1", markers = "python_version >= \"3.7\""}
-cryptography = ">=2.0.0,<39.0.0"
-future = ">=0.17.0,<0.19.0"
+chardet = ">=2.0,<6.0"
+cryptography = ">=2.0.0,<42.0.0"
 pyjwt = ">=1.5.3,<3.0.0"
-six = ">=1.11.0,<2.0.0"
+setuptools = ">=66.0.0,<67.0.0"
 
 [[package]]
 name = "gcloud-aio-bigquery"
-version = "7.0.0"
+version = "7.1.0"
 description = "Python Client for Google Cloud BigQuery"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "gcloud_aio_bigquery-7.0.0-py3-none-any.whl", hash = "sha256:188b96b16b0fdbef2a0bc15ffce3f5a930ecc36de537fcfa5794fc397506fdd8"},
-    {file = "gcloud_aio_bigquery-7.0.0.tar.gz", hash = "sha256:2cd1c7c9dcb68122ff6187f2b533941c86d954e5312d942c8107aac4d20ea2ec"},
+    {file = "gcloud_aio_bigquery-7.1.0-py3-none-any.whl", hash = "sha256:524ae3cc14c1af6977a358829cc673b4471159caa7d62bba7f2d9334262bcd4a"},
+    {file = "gcloud_aio_bigquery-7.1.0.tar.gz", hash = "sha256:4a3c775c2677c0588e9caeb2df40d81a54b31c174e562a527cb08e023c4408a3"},
 ]
 
 [package.dependencies]
@@ -2176,13 +2194,13 @@ gcloud-aio-auth = ">=3.1.0,<6.0.0"
 
 [[package]]
 name = "gcloud-aio-storage"
-version = "9.0.0"
+version = "9.2.0"
 description = "Python Client for Google Cloud Storage"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "gcloud_aio_storage-9.0.0-py3-none-any.whl", hash = "sha256:223583018b0ad68f22201593970c638e00fb86f40d9bb9187b17910a02ab118c"},
-    {file = "gcloud_aio_storage-9.0.0.tar.gz", hash = "sha256:99e6d30c65809ceafe059d496dbf10a4186edb1850fe9634988e096966503046"},
+    {file = "gcloud_aio_storage-9.2.0-py3-none-any.whl", hash = "sha256:0a8cc27223cea05ad27117fa574ce8697ec32a252382cbfdfe54df0678e92d03"},
+    {file = "gcloud_aio_storage-9.2.0.tar.gz", hash = "sha256:47be865222d22fdf873cd5a4af9a285d7b64b176d4d92f1773ecacd52a6a8c6f"},
 ]
 
 [package.dependencies]
@@ -2193,19 +2211,19 @@ rsa = ">=3.1.4,<5.0.0"
 
 [[package]]
 name = "gcsfs"
-version = "2023.10.0"
+version = "2024.2.0"
 description = "Convenient Filesystem interface over GCS"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "gcsfs-2023.10.0-py2.py3-none-any.whl", hash = "sha256:f3ad41652a596ca56e44778e041f428d022add3eff62b97a47302014affd270f"},
-    {file = "gcsfs-2023.10.0.tar.gz", hash = "sha256:1915e6d766faad9a295e815b13c59966b925a274108b8a56f673623bf4aef83f"},
+    {file = "gcsfs-2024.2.0-py2.py3-none-any.whl", hash = "sha256:20bf70cc81d580474dd299d55e1ffcf8b3e81721aeb562e148ca0a3c900d0421"},
+    {file = "gcsfs-2024.2.0.tar.gz", hash = "sha256:f7cffd7cae2fb50c56ef883f8aef9792be045b5059f06c1902c3a6151509f506"},
 ]
 
 [package.dependencies]
 aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1"
 decorator = ">4.1.2"
-fsspec = "2023.10.0"
+fsspec = "2024.2.0"
 google-auth = ">=1.2"
 google-auth-oauthlib = "*"
 google-cloud-storage = "*"
@@ -2248,20 +2266,20 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.40"
+version = "3.1.42"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
-    {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
+    {file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
+    {file = "GitPython-3.1.42.tar.gz", hash = "sha256:2d99869e0fef71a73cbd242528105af1d6c1b108c60dfabd994bf292f76c3ceb"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
 
 [[package]]
 name = "google"
@@ -2304,30 +2322,30 @@ tests = ["nox (>=2020.12.31,<2022.6)"]
 
 [[package]]
 name = "google-analytics-admin"
-version = "0.22.5"
+version = "0.22.6"
 description = "Google Analytics Admin API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-analytics-admin-0.22.5.tar.gz", hash = "sha256:2de1671ea945c23b3587323ed1dc6e6c78d02a7507b562d425de1dc7fb27b08f"},
-    {file = "google_analytics_admin-0.22.5-py2.py3-none-any.whl", hash = "sha256:54c6e00baeb9be61492b4efcd4859ca1b94b181ce454ba70ff5d73a630dd5661"},
+    {file = "google-analytics-admin-0.22.6.tar.gz", hash = "sha256:9299b30a51c1da4bd77406211efe10f30f672ef7b36f3d223a0248ebf2916d05"},
+    {file = "google_analytics_admin-0.22.6-py2.py3-none-any.whl", hash = "sha256:f4122133bbd66693f641d548a6d3a8ffcd29eabca17392bf2b7e6b3d83aaee5e"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-api-core"
-version = "2.12.0"
+version = "2.17.1"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.12.0.tar.gz", hash = "sha256:c22e01b1e3c4dcd90998494879612c38d0a3411d1f7b679eb89e2abe3ce1f553"},
-    {file = "google_api_core-2.12.0-py3-none-any.whl", hash = "sha256:ec6054f7d64ad13b41e43d96f735acbd763b0f3b695dabaa2d579673f6a6e160"},
+    {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"},
+    {file = "google_api_core-2.17.1-py3-none-any.whl", hash = "sha256:610c5b90092c360736baccf17bd3efbcb30dd380e7a6dc28a71059edb8bd0d8e"},
 ]
 
 [package.dependencies]
@@ -2345,13 +2363,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.106.0"
+version = "2.119.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-python-client-2.106.0.tar.gz", hash = "sha256:f6a3862be2f6e5e0536d7bd47b5af3f24ac0b9147c76c830cafb3329d71d5724"},
-    {file = "google_api_python_client-2.106.0-py2.py3-none-any.whl", hash = "sha256:c47c0dae5dd20aa43e4ea184566fe59d0c8fd0b86dd223b29040d8ea4f7ed6ea"},
+    {file = "google-api-python-client-2.119.0.tar.gz", hash = "sha256:ff9ef7539eaf7e088a481b25d1af4704210b07863e1d51b5ee498b910a3a46a3"},
+    {file = "google_api_python_client-2.119.0-py2.py3-none-any.whl", hash = "sha256:84e43bdb58dd8d2301669513863996378ffe9a3bf6d23b5ccd4f1e021323dbeb"},
 ]
 
 [package.dependencies]
@@ -2363,13 +2381,13 @@ uritemplate = ">=3.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.23.4"
+version = "2.28.1"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.23.4.tar.gz", hash = "sha256:79905d6b1652187def79d491d6e23d0cbb3a21d3c7ba0dbaa9c8a01906b13ff3"},
-    {file = "google_auth-2.23.4-py2.py3-none-any.whl", hash = "sha256:d4bbc92fe4b8bfd2f3e8d88e5ba7085935da208ee38a134fc280e7ce682a05f2"},
+    {file = "google-auth-2.28.1.tar.gz", hash = "sha256:34fc3046c257cedcf1622fc4b31fc2be7923d9b4d44973d481125ecc50d83885"},
+    {file = "google_auth-2.28.1-py2.py3-none-any.whl", hash = "sha256:25141e2d7a14bfcba945f5e9827f98092716e99482562f15306e5b026e21aa72"},
 ]
 
 [package.dependencies]
@@ -2386,13 +2404,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-auth-httplib2"
-version = "0.1.1"
+version = "0.2.0"
 description = "Google Authentication Library: httplib2 transport"
 optional = false
 python-versions = "*"
 files = [
-    {file = "google-auth-httplib2-0.1.1.tar.gz", hash = "sha256:c64bc555fdc6dd788ea62ecf7bccffcf497bf77244887a3f3d7a5a02f8e3fc29"},
-    {file = "google_auth_httplib2-0.1.1-py2.py3-none-any.whl", hash = "sha256:42c50900b8e4dcdf8222364d1f0efe32b8421fb6ed72f2613f12f75cc933478c"},
+    {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
+    {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
 ]
 
 [package.dependencies]
@@ -2419,17 +2437,18 @@ tool = ["click (>=6.0.0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.36.0"
+version = "1.42.1"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.36.0.tar.gz", hash = "sha256:c35eb179fa3107c262024a63bb4a3dc1887678424a0b9f49f0da3f5179896186"},
-    {file = "google_cloud_aiplatform-1.36.0-py2.py3-none-any.whl", hash = "sha256:06adb1605208a9bdbadd8c4deeaeb6511a7f59220ef606e2d95ccc1da705ebda"},
+    {file = "google-cloud-aiplatform-1.42.1.tar.gz", hash = "sha256:679068e068e29059d673a6410483fea762286fa07739d684fb1b4626698e0805"},
+    {file = "google_cloud_aiplatform-1.42.1-py2.py3-none-any.whl", hash = "sha256:9f25ebd306807972cf05a578abc16695c4f72d4a2dd7e7b1624dbe247937ba24"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.32.0,<2.0.dev0 || >=2.8.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.8.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 google-cloud-bigquery = ">=1.15.0,<4.0.0dev"
 google-cloud-resource-manager = ">=1.3.3,<3.0.0dev"
 google-cloud-storage = ">=1.32.0,<3.0.0dev"
@@ -2443,7 +2462,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
+full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (==5.3.1)"]
@@ -2451,25 +2470,26 @@ prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<0.103.1)", "httpx (>=0.23.
 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)"]
-tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"]
-testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
-vizier = ["google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)"]
+tensorboard = ["tensorflow (>=2.3.0,<2.15.0)"]
+testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
+vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-appengine-logging"
-version = "1.3.2"
+version = "1.4.2"
 description = "Google Cloud Appengine Logging API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-appengine-logging-1.3.2.tar.gz", hash = "sha256:a2989fca0e88463b56432aa821e64b81c3d171ee37b84771189b48e8b97cd496"},
-    {file = "google_cloud_appengine_logging-1.3.2-py2.py3-none-any.whl", hash = "sha256:6ac6261567b56611f6891fa650f76db8a48d528762e5c2a09230b41d82ee2be0"},
+    {file = "google-cloud-appengine-logging-1.4.2.tar.gz", hash = "sha256:134de64a84017cfe26a4b3a325bcc9b4a2dba05f9c9d3902ee24b4b1fa3e28af"},
+    {file = "google_cloud_appengine_logging-1.4.2-py2.py3-none-any.whl", hash = "sha256:08d46bacf7e6bb4cb963dc5c0ab6995f2cd3fec69c7a2862405e98dce0c194bc"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
@@ -2489,17 +2509,18 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 
 [[package]]
 name = "google-cloud-automl"
-version = "2.12.0"
+version = "2.13.2"
 description = "Google Cloud Automl API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-automl-2.12.0.tar.gz", hash = "sha256:22f7a61e04b4a9b03d504ef2db6683df462a032da53fe3fbb2c36f525074abb5"},
-    {file = "google_cloud_automl-2.12.0-py2.py3-none-any.whl", hash = "sha256:e20e9aec2f368bda29b039d8109384379a6cd9410ac5ee81acdab3afbf89ad8e"},
+    {file = "google-cloud-automl-2.13.2.tar.gz", hash = "sha256:d90273ab8b74a28d7c81b237cc5cf92b189d3a47d2b90dac8cd36722527ba24e"},
+    {file = "google_cloud_automl-2.13.2-py2.py3-none-any.whl", hash = "sha256:bb36c140955ae14b26969054e8bcdfe9fa81e6f99e430d832579d6884647b8f7"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2510,77 +2531,77 @@ storage = ["google-cloud-storage (>=1.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-batch"
-version = "0.17.2"
+version = "0.17.12"
 description = "Google Cloud Batch API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-batch-0.17.2.tar.gz", hash = "sha256:381ac83d18b46354cb5c51d33dbfcede62b039f3f13960ac9691cb4178f3c5b7"},
-    {file = "google_cloud_batch-0.17.2-py2.py3-none-any.whl", hash = "sha256:c614f17d47095fd0b41e3f7de2e66f6ec5f1168d682b8338dfa3c18803234be0"},
+    {file = "google-cloud-batch-0.17.12.tar.gz", hash = "sha256:ff351f5a4a46283fa7e8353f71cdb1fb750d4d407f7a14eae752801a22b76fe9"},
+    {file = "google_cloud_batch-0.17.12-py2.py3-none-any.whl", hash = "sha256:4d6d0f6c392c5ed2e1fe59d38f1151b1414abcf9daa50ffb25a5981984fd0b63"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.13.0"
+version = "3.17.2"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.13.0.tar.gz", hash = "sha256:794ccfc93ccb0e0ad689442f896f9c82de56da0fe18a195531bb37096c2657d6"},
-    {file = "google_cloud_bigquery-3.13.0-py2.py3-none-any.whl", hash = "sha256:eda3dbcff676e17962c54e5224e415b55e4f6833a5c896c6c8902b69e7dba4b4"},
+    {file = "google-cloud-bigquery-3.17.2.tar.gz", hash = "sha256:6e1cf669a40e567ab3289c7b5f2056363da9fcb85d9a4736ee90240d4a7d84ea"},
+    {file = "google_cloud_bigquery-3.17.2-py2.py3-none-any.whl", hash = "sha256:cdadf5283dca55a1a350bacf8c8a7466169d3cf46c5a0a3abc5e9aa0b0a51dee"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
 google-cloud-core = ">=1.6.0,<3.0.0dev"
 google-resumable-media = ">=0.6.0,<3.0dev"
-grpcio = ">=1.47.0,<2.0dev"
 packaging = ">=20.0.0"
-proto-plus = ">=1.15.0,<2.0.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 python-dateutil = ">=2.7.2,<3.0dev"
 requests = ">=2.21.0,<3.0.0dev"
 
 [package.extras]
-all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
+all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "importlib-metadata (>=1.0.0)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
+bigquery-v2 = ["proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)"]
 bqstorage = ["google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "pyarrow (>=3.0.0)"]
 geopandas = ["Shapely (>=1.8.4,<3.0.0dev)", "geopandas (>=0.9.0,<1.0dev)"]
 ipython = ["ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)"]
 ipywidgets = ["ipykernel (>=6.0.0)", "ipywidgets (>=7.7.0)"]
 opentelemetry = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)"]
-pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)"]
+pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "importlib-metadata (>=1.0.0)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)"]
 tqdm = ["tqdm (>=4.7.4,<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery-datatransfer"
-version = "3.13.0"
+version = "3.15.0"
 description = "Google Cloud Bigquery Datatransfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-datatransfer-3.13.0.tar.gz", hash = "sha256:27a845cb22605a512c05ce28c2890d2ef97ad4edfc9817af555a73d8024ec3ba"},
-    {file = "google_cloud_bigquery_datatransfer-3.13.0-py2.py3-none-any.whl", hash = "sha256:f8a73ba135fc6f6df4516187b5d88c7d1fcd68ecb1b0ed259ff55b89c5a0974a"},
+    {file = "google-cloud-bigquery-datatransfer-3.15.0.tar.gz", hash = "sha256:fcb0613c9a2b210bf28889df372ecf25c5723af1f6d7b144aedc220b65d366f4"},
+    {file = "google_cloud_bigquery_datatransfer-3.15.0-py2.py3-none-any.whl", hash = "sha256:edf78337b1e2f96ed1c582e617b2545ea176cc6e90f8ff983bf2da5164d586b1"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-bigquery-storage"
-version = "2.22.0"
+version = "2.24.0"
 description = "Google Cloud Bigquery Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-storage-2.22.0.tar.gz", hash = "sha256:f6d8c7b3ab9b574c66977fcee9d336e334ad1a3843a722be19123640e7808ea3"},
-    {file = "google_cloud_bigquery_storage-2.22.0-py2.py3-none-any.whl", hash = "sha256:7f11b2ae590a5b3874fb6ddf705a66a070340db238f971cf7b53349eee9ca317"},
+    {file = "google-cloud-bigquery-storage-2.24.0.tar.gz", hash = "sha256:b4af5b9aacd8396b8407d1b877601a376d8eea6d192823a8a7881bd2fdc076ce"},
+    {file = "google_cloud_bigquery_storage-2.24.0-py2.py3-none-any.whl", hash = "sha256:7981eb2758cba56603058d11bb1eeeebf2e1c18097a7118a894510a16e02be52"},
 ]
 
 [package.dependencies]
@@ -2590,23 +2611,23 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [package.extras]
 fastavro = ["fastavro (>=0.21.2)"]
-pandas = ["pandas (>=0.21.1)"]
+pandas = ["importlib-metadata (>=1.0.0)", "pandas (>=0.21.1)"]
 pyarrow = ["pyarrow (>=0.15.0)"]
 
 [[package]]
 name = "google-cloud-bigtable"
-version = "2.21.0"
+version = "2.23.0"
 description = "Google Cloud Bigtable API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigtable-2.21.0.tar.gz", hash = "sha256:d9f0efbf940ca392f07d13787fc2da76d1e168dedafae0f8f1b4208e0c1132dc"},
-    {file = "google_cloud_bigtable-2.21.0-py2.py3-none-any.whl", hash = "sha256:219d39f2787b76394b045603bbd0b13dfc37645ca855022e646b8ecbfbe9ad1f"},
+    {file = "google-cloud-bigtable-2.23.0.tar.gz", hash = "sha256:c3cabb6619f5f2eea11b3db72731e84d28b168055c9cc17c2d2daff15cb82379"},
+    {file = "google_cloud_bigtable-2.23.0-py2.py3-none-any.whl", hash = "sha256:d89ed580733865007ae89639b7d961024a2385aa8c2828cf3aff3f5889782402"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-cloud-core = ">=1.4.1,<3.0.0dev"
+google-api-core = {version = ">=2.16.0,<3.0.0dev", extras = ["grpc"]}
+google-cloud-core = ">=1.4.4,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
@@ -2616,62 +2637,65 @@ libcst = ["libcst (>=0.2.5)"]
 
 [[package]]
 name = "google-cloud-build"
-version = "3.22.0"
+version = "3.23.2"
 description = "Google Cloud Build API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-build-3.22.0.tar.gz", hash = "sha256:72a8330c31108cc47ce510956196a98c158eeecd796004928bf239a2e1ea300e"},
-    {file = "google_cloud_build-3.22.0-py2.py3-none-any.whl", hash = "sha256:b2906d59d02066f859a5e3a0ef9437dffc08f11a1f0f9381c92c986c73ab7ae0"},
+    {file = "google-cloud-build-3.23.2.tar.gz", hash = "sha256:52ee4e94298401f7ba6ef32f9a7e3bdaa478867fd0ef6f93017b88a3832aa7e2"},
+    {file = "google_cloud_build-3.23.2-py2.py3-none-any.whl", hash = "sha256:233580a601a266f643cdb68db844f9568ba63065e3cdcc252e48e55292dd4a78"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-compute"
-version = "1.14.1"
+version = "1.17.0"
 description = "Google Cloud Compute API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-compute-1.14.1.tar.gz", hash = "sha256:acd987647d7c826aa97b4418141c740ead5e8811d3349315f2f89a30c01c7f4b"},
-    {file = "google_cloud_compute-1.14.1-py2.py3-none-any.whl", hash = "sha256:b40d6aeeb2c5ce373675c869f1404a1bc19b9763b746ad8f2d91ed1148893d6f"},
+    {file = "google-cloud-compute-1.17.0.tar.gz", hash = "sha256:74fb3b8527b461c0f796e36a1e417a4fc7d31c2e3fbb71cc270b30e931d62f8e"},
+    {file = "google_cloud_compute-1.17.0-py2.py3-none-any.whl", hash = "sha256:f281f1960f236d3be6614002af11becfe088600f2d3fa7da92dade28fac42f0e"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-container"
-version = "2.32.0"
+version = "2.41.0"
 description = "Google Cloud Container API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-container-2.32.0.tar.gz", hash = "sha256:694fb8da77963653e1c70fa60928b4a11faf8e1f1580a39025053a3e1bcce6de"},
-    {file = "google_cloud_container-2.32.0-py2.py3-none-any.whl", hash = "sha256:a5555c6c19679332086859838fbaee468cb206e5e76365bb5027d5305a2c5bf4"},
+    {file = "google-cloud-container-2.41.0.tar.gz", hash = "sha256:e43505690ab322f37ff15a3b309341aec31a0781c034eec46f69eecf1de70c97"},
+    {file = "google_cloud_container-2.41.0-py2.py3-none-any.whl", hash = "sha256:fb7f53ba8e750ee1ccefae971b0123880753b41dab27ef8e05711d62b6d38b3e"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-core"
-version = "2.3.3"
+version = "2.4.1"
 description = "Google Cloud API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-core-2.3.3.tar.gz", hash = "sha256:37b80273c8d7eee1ae816b3a20ae43585ea50506cb0e60f3cf5be5f87f1373cb"},
-    {file = "google_cloud_core-2.3.3-py2.py3-none-any.whl", hash = "sha256:fbd11cad3e98a7e5b0343dc07cb1039a5ffd7a5bb96e1f1e27cee4bda4a90863"},
+    {file = "google-cloud-core-2.4.1.tar.gz", hash = "sha256:9b7749272a812bde58fff28868d0c5e2f585b82f37e09a1f6ed2d4d10f134073"},
+    {file = "google_cloud_core-2.4.1-py2.py3-none-any.whl", hash = "sha256:a9e6a4422b9ac5c29f79a0ede9485473338e2ce78d91f2370c01e730eab22e61"},
 ]
 
 [package.dependencies]
@@ -2679,167 +2703,176 @@ google-api-core = ">=1.31.6,<2.0.dev0 || >2.3.0,<3.0.0dev"
 google-auth = ">=1.25.0,<3.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.38.0,<2.0dev)"]
+grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
 name = "google-cloud-datacatalog"
-version = "3.16.0"
+version = "3.18.2"
 description = "Google Cloud Datacatalog API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-datacatalog-3.16.0.tar.gz", hash = "sha256:703e414396248fa98474bbab9ea962f4c96a3caf1184c903bfc9453d274b0ea2"},
-    {file = "google_cloud_datacatalog-3.16.0-py2.py3-none-any.whl", hash = "sha256:06f43501ce57742f394508bca6b0eb14e6dd5aa7e0d51cc0fd9d259295d46c9f"},
+    {file = "google-cloud-datacatalog-3.18.2.tar.gz", hash = "sha256:512a3c95d51b7ec02bbe3c39f8c3ae6c50ce957915e063e8c07615b111c11eb9"},
+    {file = "google_cloud_datacatalog-3.18.2-py2.py3-none-any.whl", hash = "sha256:1de2f9c214779b90f558d952daf2dd9d02cc8e6c89b0d5df338bf934abc7c524"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataflow-client"
-version = "0.8.6"
+version = "0.8.9"
 description = "Google Cloud Dataflow Client API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataflow-client-0.8.6.tar.gz", hash = "sha256:8753fff57edf640a984015dbd6b3a0f427a779244f9b0ad8981f77d82272798a"},
-    {file = "google_cloud_dataflow_client-0.8.6-py2.py3-none-any.whl", hash = "sha256:2d7a6e0586f50421567252283c330dd81cecafa139a12c6021e9da0ca2bf619c"},
+    {file = "google-cloud-dataflow-client-0.8.9.tar.gz", hash = "sha256:f024118695f2e5b8ca2f57e22c7e479238bd6a7bd9c24bf5f60c0893ed234fb7"},
+    {file = "google_cloud_dataflow_client-0.8.9-py2.py3-none-any.whl", hash = "sha256:2dbfc4333dbaf825fd1a1baaf6899f240bde80510e73ea23e29525681d279c4e"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataform"
-version = "0.5.3"
+version = "0.5.8"
 description = "Google Cloud Dataform API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataform-0.5.3.tar.gz", hash = "sha256:89ed0dce20ce34f74cb038d614dd71191451fb70ea60888b6dbfc4265cea7a48"},
-    {file = "google_cloud_dataform-0.5.3-py2.py3-none-any.whl", hash = "sha256:aa265043272b0b925fe5e40ea928a4793e1e30f37fff4169a38dfde9d7e32dc9"},
+    {file = "google-cloud-dataform-0.5.8.tar.gz", hash = "sha256:40009ed80dfb152f675db4395b86b45bf5ae9a032c7f37c98b464066ff6ff54e"},
+    {file = "google_cloud_dataform-0.5.8-py2.py3-none-any.whl", hash = "sha256:3d1810b42e1bb533af7e891e3e478b5fa36b412542d6054d77c38f55cb71e3a4"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataplex"
-version = "1.11.0"
+version = "1.12.2"
 description = "Google Cloud Dataplex API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataplex-1.11.0.tar.gz", hash = "sha256:c71b16166e03a04386d2379754912d2ea081ad8fb9dd7547a65d91ee2f2cd4b4"},
-    {file = "google_cloud_dataplex-1.11.0-py2.py3-none-any.whl", hash = "sha256:92f616319fbd1fe03b1e9a401930c927edc22795002dd600d42cbe8a3c7cc1dd"},
+    {file = "google-cloud-dataplex-1.12.2.tar.gz", hash = "sha256:fdd89ce585954a8e75972be5a00475b1ff1d2f26dadb29ef574deaa654aa2236"},
+    {file = "google_cloud_dataplex-1.12.2-py2.py3-none-any.whl", hash = "sha256:c6ae7a85276e9eaa4072e27ffd90aef52fa0bb2d1d21db6a1af8322b1278b758"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc"
-version = "5.9.0"
+version = "5.9.2"
 description = "Google Cloud Dataproc API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataproc-5.9.0.tar.gz", hash = "sha256:7e51f9c9005bc5f1bcb23627171de9cd6246a44775118a48cc632861280a76df"},
-    {file = "google_cloud_dataproc-5.9.0-py2.py3-none-any.whl", hash = "sha256:be7bdf2efe9af2a54632dd476dc3c13a1bb848bcebc337c1e83d2f961bffc106"},
+    {file = "google-cloud-dataproc-5.9.2.tar.gz", hash = "sha256:1352e3cc4e146db1f09fa4287559248c8b3d9c0cfecea56c25c3f4f63d58e4f8"},
+    {file = "google_cloud_dataproc-5.9.2-py2.py3-none-any.whl", hash = "sha256:a728e35db70132dc5d2e6b27627825775f5a0a2cd7024fa1149669d3de4b436d"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc-metastore"
-version = "1.13.0"
+version = "1.15.2"
 description = "Google Cloud Dataproc Metastore API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataproc-metastore-1.13.0.tar.gz", hash = "sha256:58066b89d782bcf6985afb29822fb2a1d9a67c20210c87e3a990f69e6354e52c"},
-    {file = "google_cloud_dataproc_metastore-1.13.0-py2.py3-none-any.whl", hash = "sha256:76e462a42537077ba4f9b1c4c8ec5d91d4fc4bac6d3f31a81446f50d39ef850f"},
+    {file = "google-cloud-dataproc-metastore-1.15.2.tar.gz", hash = "sha256:61c4832bce9ba8288c81d48a2661a853e3d852210ebec15b7e505bcf9c95d26e"},
+    {file = "google_cloud_dataproc_metastore-1.15.2-py2.py3-none-any.whl", hash = "sha256:ab6bba67d94665126aff5ddff697255eab5f12d23a4d6dfe7f5c2d20e2aa0712"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dlp"
-version = "3.12.3"
+version = "3.15.2"
 description = "Google Cloud Dlp API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dlp-3.12.3.tar.gz", hash = "sha256:73880f2a8bfa60049576fb94d85698aa2367d32ac901b76278fe50b75663440b"},
-    {file = "google_cloud_dlp-3.12.3-py2.py3-none-any.whl", hash = "sha256:db263305466d0fb22b756043c8a7e1cc2842e3ed6b59fb800b9e9af63a8b0ec8"},
+    {file = "google-cloud-dlp-3.15.2.tar.gz", hash = "sha256:4edcdae94b950942549a81fd8455565734df5f8913312f4443e8b1a189bdc4e8"},
+    {file = "google_cloud_dlp-3.15.2-py2.py3-none-any.whl", hash = "sha256:4d5e0d2b055bfabba61ee2565c3461055a82f22d44f9aec3f73275f9d6b69b27"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-kms"
-version = "2.19.2"
+version = "2.21.2"
 description = "Google Cloud Kms API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-kms-2.19.2.tar.gz", hash = "sha256:17a5034592e85ef0031d25bbe5894bdb2d719460855980bfeb68aa4e8ffc12bd"},
-    {file = "google_cloud_kms-2.19.2-py2.py3-none-any.whl", hash = "sha256:0cc0b9d16338a46b7fc5beba7e96a88df0f836586eb8650e9acd36eb249b52d9"},
+    {file = "google-cloud-kms-2.21.2.tar.gz", hash = "sha256:f06ad9dfc801544fba1183788b964f6b017483a66091aa686b51abd074806c84"},
+    {file = "google_cloud_kms-2.21.2-py2.py3-none-any.whl", hash = "sha256:98a092fb7501d0c7d4a64e409a8a0510916f7f982fafaae9572835ac8080ba0f"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-language"
-version = "2.11.1"
+version = "2.13.2"
 description = "Google Cloud Language API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-language-2.11.1.tar.gz", hash = "sha256:5f184409f040c0c73057c2616e6bd2e86e45aeb646180d19c189df4aa3d02dba"},
-    {file = "google_cloud_language-2.11.1-py2.py3-none-any.whl", hash = "sha256:beb5dd1344a6ed00dd04b1eb05a967e7f83fc1b51471f9bd51f238f63f46f9d4"},
+    {file = "google-cloud-language-2.13.2.tar.gz", hash = "sha256:813ad3dddca97890710098e76ad48c37ea63ea3a14a0f6d7f4246c6fb15cb2a5"},
+    {file = "google_cloud_language-2.13.2-py2.py3-none-any.whl", hash = "sha256:74ececa7918f183ed7652b3b6ba57adb3541e199803bd9ce8faa2f048997a2db"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-logging"
-version = "3.8.0"
+version = "3.9.0"
 description = "Stackdriver Logging API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-logging-3.8.0.tar.gz", hash = "sha256:fdd916e59a84aa8c02e8148d7fdd3b3b623c57b0c1ff71f43297ce8e50fc1eab"},
-    {file = "google_cloud_logging-3.8.0-py2.py3-none-any.whl", hash = "sha256:c868b276b021cf5f32b6e8356b6cb3666357d149ad0fd798076043a5ec7ed988"},
+    {file = "google-cloud-logging-3.9.0.tar.gz", hash = "sha256:4decb1b0bed4a0e3c0e58a376646e6002d6be7cad039e3466822e8665072ea33"},
+    {file = "google_cloud_logging-3.9.0-py2.py3-none-any.whl", hash = "sha256:094a2db068ff7f38c9e0c1017673fa49c0768fbae02769e03e06baa30f138b87"},
 ]
 
 [package.dependencies]
@@ -2853,33 +2886,35 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-cloud-memcache"
-version = "1.7.3"
+version = "1.9.2"
 description = "Google Cloud Memcache API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-memcache-1.7.3.tar.gz", hash = "sha256:a73f44ec5bfc398b4c9d94d5e77621c54795bcade19960242dec2cd91b854bcb"},
-    {file = "google_cloud_memcache-1.7.3-py2.py3-none-any.whl", hash = "sha256:054e2adde4e99fca6a2e3f9ef733a2159083923c0a41af51b7d5e9d38ca9de12"},
+    {file = "google-cloud-memcache-1.9.2.tar.gz", hash = "sha256:d6ab1600fad9617731666ebae780d7e4c15ea58eb3c573296d533380567c2018"},
+    {file = "google_cloud_memcache-1.9.2-py2.py3-none-any.whl", hash = "sha256:4c22b5d15f8d53969a1a5bc2171bf9deda446e96b42ddee8db1f30dd6e370ca2"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-monitoring"
-version = "2.18.0"
+version = "2.19.2"
 description = "Google Cloud Monitoring API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-monitoring-2.18.0.tar.gz", hash = "sha256:06cc1d7fb75c5e50b54bcc004941ea4b20a7a9f09ed7e6e753514fd8c436096a"},
-    {file = "google_cloud_monitoring-2.18.0-py2.py3-none-any.whl", hash = "sha256:548a1ac0b88c38694e14e1359e89c7eb19b27423906f456bb590f3f0962bb562"},
+    {file = "google-cloud-monitoring-2.19.2.tar.gz", hash = "sha256:3881b026204e0648707461a9783ef7e84c6c716bd067a3864b1cb3d4c9f5dc73"},
+    {file = "google_cloud_monitoring-2.19.2-py2.py3-none-any.whl", hash = "sha256:4d60247403d36576c561ed3cc684131d32a455e9bdc188c14cc83d7cb6b28fa2"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2888,49 +2923,52 @@ pandas = ["pandas (>=0.23.2)"]
 
 [[package]]
 name = "google-cloud-orchestration-airflow"
-version = "1.10.0"
+version = "1.12.0"
 description = "Google Cloud Orchestration Airflow API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-orchestration-airflow-1.10.0.tar.gz", hash = "sha256:2aa88b4882d3c5dd0f7ecc572d37972d51f2cf69530783c524344d018de0b1bf"},
-    {file = "google_cloud_orchestration_airflow-1.10.0-py2.py3-none-any.whl", hash = "sha256:363e22e3b52316eb556899ffb3b1d2122bb4fce3937e4f7cef42202ec1a15e20"},
+    {file = "google-cloud-orchestration-airflow-1.12.0.tar.gz", hash = "sha256:2e7b6f507b3ef9796638faed6d0cdb402db134bbb60b3f46cb115b16b44e88ee"},
+    {file = "google_cloud_orchestration_airflow-1.12.0-py2.py3-none-any.whl", hash = "sha256:683135931bd714027cbc984cad49ff811759b40e5e522dbb5a19c052f4640ebf"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-os-login"
-version = "2.11.0"
+version = "2.14.2"
 description = "Google Cloud Os Login API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-os-login-2.11.0.tar.gz", hash = "sha256:e3219d00b75ea44182871681985e9db3f1e38641bb84519dd85d3dbb62b97656"},
-    {file = "google_cloud_os_login-2.11.0-py2.py3-none-any.whl", hash = "sha256:1b0679d772fbef486230208a65b3a96d513b599000beca1dc8ff5bde4838607f"},
+    {file = "google-cloud-os-login-2.14.2.tar.gz", hash = "sha256:9b26fe84e8d3cd2499e3b306283909fb57d1a8f40377822416085fd174345294"},
+    {file = "google_cloud_os_login-2.14.2-py2.py3-none-any.whl", hash = "sha256:24c64b06b4a2e07d35b47eb9074d26c3769dd00f4f22b352b8b9aeee3893b2a1"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-pubsub"
-version = "2.19.0"
+version = "2.19.7"
 description = "Google Cloud Pub/Sub API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-pubsub-2.19.0.tar.gz", hash = "sha256:6a98c33f7eb5f7de2ae52efa059b2b5f75b2ccd9f0f11f2edcefdda8d14e425c"},
-    {file = "google_cloud_pubsub-2.19.0-py2.py3-none-any.whl", hash = "sha256:0cc444e5b2220a703106668829315a724cfb4304d6772725035993bb2fc81388"},
+    {file = "google-cloud-pubsub-2.19.7.tar.gz", hash = "sha256:da5f1eb217c0727befa7c85b9b95e6a89b32b422d548c9cf9a1e029410270b87"},
+    {file = "google_cloud_pubsub-2.19.7-py2.py3-none-any.whl", hash = "sha256:443cb58e2440729876f3b0d6046c30c7a873aeb247e5e17f161fe967c91b53fb"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 grpcio = ">=1.51.3,<2.0dev"
 grpcio-status = ">=1.33.2"
@@ -2942,86 +2980,92 @@ libcst = ["libcst (>=0.3.10)"]
 
 [[package]]
 name = "google-cloud-redis"
-version = "2.13.2"
+version = "2.15.2"
 description = "Google Cloud Redis API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-redis-2.13.2.tar.gz", hash = "sha256:5c485730355d96723d64ae637f1b2264f35b57c301ec0ef2c6d30b2f06dca14e"},
-    {file = "google_cloud_redis-2.13.2-py2.py3-none-any.whl", hash = "sha256:86378e8ed1c5a1c621eb2731d734436f443dffdbbafc6cd5d24511c94d6c38bc"},
+    {file = "google-cloud-redis-2.15.2.tar.gz", hash = "sha256:baaf93114e4acb752e6a06b5f58e7c50bf683eb0e138645fece76e0a0159c188"},
+    {file = "google_cloud_redis-2.15.2-py2.py3-none-any.whl", hash = "sha256:ec239088ba8901014d716c5e763227cb9978bc442dc8e3ae23d2bfb9fb7bd8d3"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-resource-manager"
-version = "1.10.4"
+version = "1.12.2"
 description = "Google Cloud Resource Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-resource-manager-1.10.4.tar.gz", hash = "sha256:456b25ddda3d4cd27488a72736bbc3af04d713ae2fe3655c01b66a339d28d679"},
-    {file = "google_cloud_resource_manager-1.10.4-py2.py3-none-any.whl", hash = "sha256:2ba56ba8e5280cd425bd63620da48b78da2cd299ece58a71f0f2ce3a32d56f99"},
+    {file = "google-cloud-resource-manager-1.12.2.tar.gz", hash = "sha256:2ede446a5087b236f0e1fb39cca3791bae97eb0d9125057401454b190d5572ee"},
+    {file = "google_cloud_resource_manager-1.12.2-py2.py3-none-any.whl", hash = "sha256:45abbb8911195cc831cc77c8e3be84decc271686579b332d4142af507f423ebf"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-run"
-version = "0.10.0"
+version = "0.10.4"
 description = "Google Cloud Run API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-run-0.10.0.tar.gz", hash = "sha256:e597dff8da3d6e0f43bdaaa38c80933d8e81e8eac8c1b5e119812af99539d734"},
-    {file = "google_cloud_run-0.10.0-py2.py3-none-any.whl", hash = "sha256:3f3d317200e890c20e744e692d69b241ca4a25ec161f02babbc77c1df68c7a4d"},
+    {file = "google-cloud-run-0.10.4.tar.gz", hash = "sha256:e278e91822c26a895190e4193b84950cb9aa3f437f6895c806620a1890e2cc90"},
+    {file = "google_cloud_run-0.10.4-py2.py3-none-any.whl", hash = "sha256:179045cc576e3aa2446ae2a11537a1af6c032407cd28f6bb58b1f8abdee2b796"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-secret-manager"
-version = "2.16.4"
+version = "2.18.2"
 description = "Google Cloud Secret Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-secret-manager-2.16.4.tar.gz", hash = "sha256:371dc72f9145af323e8a813c8e50380e6ac4bd6a5dbcd42dcf3162d8f37e5080"},
-    {file = "google_cloud_secret_manager-2.16.4-py2.py3-none-any.whl", hash = "sha256:5031c45dd84dc584d91ee0baae2bbd5df6710efe0c42719ee370a3ab62aaf618"},
+    {file = "google-cloud-secret-manager-2.18.2.tar.gz", hash = "sha256:a00d62115a70083e86b1d44ca7ebcae041b36a44cc62ea57de4005731f8d3c88"},
+    {file = "google_cloud_secret_manager-2.18.2-py2.py3-none-any.whl", hash = "sha256:bb2f2b23a588f9eeae51df4a028d6198a603231a056f838e6a476b5aecadbbca"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-spanner"
-version = "3.40.1"
+version = "3.42.0"
 description = "Google Cloud Spanner API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-spanner-3.40.1.tar.gz", hash = "sha256:616b07c86cdae6c78bad27b8ab39d8ce7a273511f2b91fe260f170d926653c2e"},
-    {file = "google_cloud_spanner-3.40.1-py2.py3-none-any.whl", hash = "sha256:0c5825bb5956df83ee1d732e1851467be97bbc4502ccd91ed0c5e7fb4b3f4ccb"},
+    {file = "google-cloud-spanner-3.42.0.tar.gz", hash = "sha256:13b6aba86059fd03f36c032c4143274d6883d39e2d32bf753e0ad3d2dcd08e12"},
+    {file = "google_cloud_spanner-3.42.0-py2.py3-none-any.whl", hash = "sha256:53a68d3cce1df986729244e829a607cd5b97c8f1dd721d8af0cfdd12413fb09f"},
 ]
 
 [package.dependencies]
+deprecated = ">=1.2.14"
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-cloud-core = ">=1.4.1,<3.0dev"
+google-cloud-core = ">=1.4.4,<3.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
+grpc-interceptor = ">=0.15.4"
 proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 sqlparse = ">=0.4.4"
@@ -3032,29 +3076,30 @@ tracing = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20
 
 [[package]]
 name = "google-cloud-speech"
-version = "2.21.1"
+version = "2.25.0"
 description = "Google Cloud Speech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-speech-2.21.1.tar.gz", hash = "sha256:4935375eaaf18656e53e2631770b9d579e80cb4ec1b3d46dd23346f789aa016a"},
-    {file = "google_cloud_speech-2.21.1-py2.py3-none-any.whl", hash = "sha256:36089039640c5ab79cefd32af7a482043a67144d393ec752c6b57c1fdc02a95d"},
+    {file = "google-cloud-speech-2.25.0.tar.gz", hash = "sha256:070b7a73735d871cdcf5590238e9c4ff59bb5f524172876ba55646b5c5eb86b8"},
+    {file = "google_cloud_speech-2.25.0-py2.py3-none-any.whl", hash = "sha256:3741c1b0cea6e86b389572c25c6d4aee93d77579651edc79fe9e22d7a574ecc5"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.13.0"
+version = "2.14.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.13.0.tar.gz", hash = "sha256:f62dc4c7b6cd4360d072e3deb28035fbdad491ac3d9b0b1815a12daea10f37c7"},
-    {file = "google_cloud_storage-2.13.0-py2.py3-none-any.whl", hash = "sha256:ab0bf2e1780a1b74cf17fccb13788070b729f50c252f0c94ada2aae0ca95437d"},
+    {file = "google-cloud-storage-2.14.0.tar.gz", hash = "sha256:2d23fcf59b55e7b45336729c148bb1c464468c69d5efbaee30f7201dd90eb97e"},
+    {file = "google_cloud_storage-2.14.0-py2.py3-none-any.whl", hash = "sha256:8641243bbf2a2042c16a6399551fbb13f062cbc9a2de38d6c0bb5426962e9dbd"},
 ]
 
 [package.dependencies]
@@ -3070,116 +3115,123 @@ protobuf = ["protobuf (<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-storage-transfer"
-version = "1.9.2"
+version = "1.11.2"
 description = "Google Cloud Storage Transfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-transfer-1.9.2.tar.gz", hash = "sha256:c36800cf425e23cca5bed51320dce6eec313b68db8dd4677be6e3b49c891207d"},
-    {file = "google_cloud_storage_transfer-1.9.2-py2.py3-none-any.whl", hash = "sha256:d30b519b57eee0d337da6d9051442337625128e950e6b487b4fd63f4942f7cb7"},
+    {file = "google-cloud-storage-transfer-1.11.2.tar.gz", hash = "sha256:391b31934a9bed3d46f27d2d5daca1bda40b87ce3731e86b177342092a9fe5bf"},
+    {file = "google_cloud_storage_transfer-1.11.2-py2.py3-none-any.whl", hash = "sha256:fc2d3478ac998a76bd4c5eb660b65b4452aea15049e010009862df643d807e1a"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-tasks"
-version = "2.14.2"
+version = "2.16.2"
 description = "Google Cloud Tasks API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-tasks-2.14.2.tar.gz", hash = "sha256:3efb280e7a635f57860a06a346131c117042e820ad483afd256800fae1b4d701"},
-    {file = "google_cloud_tasks-2.14.2-py2.py3-none-any.whl", hash = "sha256:801a671c6dbd2269179037e93490f87e2df59ddfcb59551128bab27e7dd09f94"},
+    {file = "google-cloud-tasks-2.16.2.tar.gz", hash = "sha256:2f5c15c581151d913d840fca348dc9a5fbd1cc16ec511e3f66b2fc6a01f06e38"},
+    {file = "google_cloud_tasks-2.16.2-py2.py3-none-any.whl", hash = "sha256:c47355f7de04f7ebf00d352ef9995fa32533eb24ff56b3a0af613367b3073a27"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-texttospeech"
-version = "2.14.2"
+version = "2.16.2"
 description = "Google Cloud Texttospeech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-texttospeech-2.14.2.tar.gz", hash = "sha256:a23738366988faf4d265200f57c5de4312efa3d14bb434b92a6e541713f5d906"},
-    {file = "google_cloud_texttospeech-2.14.2-py2.py3-none-any.whl", hash = "sha256:3a4e7ca71a1d3f90de77170a013b37f18775858dac01c80793752b8c160d6555"},
+    {file = "google-cloud-texttospeech-2.16.2.tar.gz", hash = "sha256:eaac809d9a7d07c5c9eb58613916542cb2fa52c14f9f31da7ff4989f717f8e0c"},
+    {file = "google_cloud_texttospeech-2.16.2-py2.py3-none-any.whl", hash = "sha256:59de4d6021d39502572e9d6acdf851c2466f9140357dce249641d137bf63f062"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-translate"
-version = "3.12.1"
+version = "3.15.2"
 description = "Google Cloud Translate API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-translate-3.12.1.tar.gz", hash = "sha256:661cb8879aa3c63a2f725a3ea68f90199bd354c95ca169cc2e83a5105987d29e"},
-    {file = "google_cloud_translate-3.12.1-py2.py3-none-any.whl", hash = "sha256:2a0f6aa398a64d7836d0f2880dfd023be0de05f8b688ac537825391014fc114a"},
+    {file = "google-cloud-translate-3.15.2.tar.gz", hash = "sha256:6d4c2447bc2f88ec5e0d3a6778c3d85fab36da214b9384b10659b29e8db9f594"},
+    {file = "google_cloud_translate-3.15.2-py2.py3-none-any.whl", hash = "sha256:f20d5ea4bbe686e3bfd897f3afb6ddaf91738e86233ab0b8343f43a9cbc5e672"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 google-cloud-core = ">=1.4.4,<3.0.0dev"
-proto-plus = ">=1.22.0,<2.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-videointelligence"
-version = "2.11.4"
+version = "2.13.2"
 description = "Google Cloud Videointelligence API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-videointelligence-2.11.4.tar.gz", hash = "sha256:07ace299a63f5b3d4441375634853b55ce8f90c62c6a24f8a47eb054149f6f99"},
-    {file = "google_cloud_videointelligence-2.11.4-py2.py3-none-any.whl", hash = "sha256:4d62f7b074f6f1eae4efd8697b5a93fb59b3b22f9934e383b4f6fbed69f49eb6"},
+    {file = "google-cloud-videointelligence-2.13.2.tar.gz", hash = "sha256:c077e25599a107f18c7e057ca661f37600b1b71c65d90d6cf5c42043dadc8db1"},
+    {file = "google_cloud_videointelligence-2.13.2-py2.py3-none-any.whl", hash = "sha256:11a220d00254edf5cac55515aadfee23131d9ba45fe4114f9113200f780158e7"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-vision"
-version = "3.4.5"
+version = "3.7.1"
 description = "Google Cloud Vision API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-vision-3.4.5.tar.gz", hash = "sha256:0df8241ab277199b919ca3837a7de85059363fea0e3d660060870be7ceffc047"},
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+    {file = "google-cloud-vision-3.7.1.tar.gz", hash = "sha256:868be6df5bb5491c6f31bedf600af23661c02776ca564c151c42c63e0b3465db"},
+    {file = "google_cloud_vision-3.7.1-py2.py3-none-any.whl", hash = "sha256:55bb95304ccfe6d8b7a7de7fe6cb3f580d87dcbf971bb8225d1beb0e17a2d75c"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-workflows"
-version = "1.12.1"
+version = "1.14.2"
 description = "Google Cloud Workflows API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-workflows-1.12.1.tar.gz", hash = "sha256:fad66cece6279f7be4fd919c1cc50f2a4a7ebf8bba97feaba75b9addece0b64b"},
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+    {file = "google-cloud-workflows-1.14.2.tar.gz", hash = "sha256:2f580bdebf87cf2c2fb1b7d6936f11b61c26f25ab3d12f5e913716521f0570b3"},
+    {file = "google_cloud_workflows-1.14.2-py2.py3-none-any.whl", hash = "sha256:23e1e00f8ff7dd16a984c0b6c7e530565a613df97d249da1a3ee548212cfb0e6"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
@@ -3426,67 +3478,17 @@ files = [
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 test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
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+version = "0.41.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
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 ]
 
 [package.dependencies]
@@ -3616,13 +3603,13 @@ colorama = ">=0.4"
 
 [[package]]
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+version = "0.13.0"
 description = "IAM API client library"
 optional = false
 python-versions = ">=3.7"
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@@ -3630,71 +3617,88 @@ googleapis-common-protos = {version = ">=1.56.0,<2.0.0dev", extras = ["grpc"]}
 grpcio = ">=1.44.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
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+
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+    {file = "grpcio-1.62.0-cp39-cp39-win_amd64.whl", hash = "sha256:b71c65427bf0ec6a8b48c68c17356cb9fbfc96b1130d20a07cb462f4e4dcdcd5"},
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+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.62.0)"]
 
 [[package]]
 name = "grpcio-gcp"
@@ -3806,24 +3810,24 @@ uvloop = {version = ">=0.19.0,<1", markers = "sys_platform != \"win32\""}
 
 [[package]]
 name = "httpcore"
-version = "0.18.0"
+version = "1.0.4"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-0.18.0-py3-none-any.whl", hash = "sha256:adc5398ee0a476567bf87467063ee63584a8bce86078bf748e48754f60202ced"},
-    {file = "httpcore-0.18.0.tar.gz", hash = "sha256:13b5e5cd1dca1a6636a6aaea212b19f4f85cd88c366a2b82304181b769aab3c9"},
+    {file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
+    {file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
 ]
 
 [package.dependencies]
-anyio = ">=3.0,<5.0"
 certifi = "*"
 h11 = ">=0.13,<0.15"
-sniffio = "==1.*"
 
 [package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.25.0)"]
 
 [[package]]
 name = "httplib2"
@@ -3841,18 +3845,19 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0
 
 [[package]]
 name = "httpx"
-version = "0.25.0"
+version = "0.27.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.25.0-py3-none-any.whl", hash = "sha256:181ea7f8ba3a82578be86ef4171554dd45fec26a02556a744db029a0a27b7100"},
-    {file = "httpx-0.25.0.tar.gz", hash = "sha256:47ecda285389cb32bb2691cc6e069e3ab0205956f681c5b2ad2325719751d875"},
+    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
+    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
 ]
 
 [package.dependencies]
+anyio = "*"
 certifi = "*"
-httpcore = ">=0.18.0,<0.19.0"
+httpcore = "==1.*"
 idna = "*"
 sniffio = "*"
 
@@ -3894,13 +3899,13 @@ packaging = "*"
 
 [[package]]
 name = "identify"
-version = "2.5.31"
+version = "2.5.35"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.31-py2.py3-none-any.whl", hash = "sha256:90199cb9e7bd3c5407a9b7e81b4abec4bb9d249991c79439ec8af740afc6293d"},
-    {file = "identify-2.5.31.tar.gz", hash = "sha256:7736b3c7a28233637e3c36550646fc6389bedd74ae84cb788200cc8e2dd60b75"},
+    {file = "identify-2.5.35-py2.py3-none-any.whl", hash = "sha256:c4de0081837b211594f8e877a6b4fad7ca32bbfc1a9307fdd61c28bfe923f13e"},
+    {file = "identify-2.5.35.tar.gz", hash = "sha256:10a7ca245cfcd756a554a7288159f72ff105ad233c7c4b9c6f0f4d108f5f6791"},
 ]
 
 [package.extras]
@@ -3908,48 +3913,48 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.8.0"
+version = "6.11.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
-    {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"},
+    {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"},
+    {file = "importlib_metadata-6.11.0.tar.gz", hash = "sha256:1231cf92d825c9e03cfc4da076a16de6422c863558229ea0b22b675657463443"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.2"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.0-py3-none-any.whl", hash = "sha256:aa50258bbfa56d4e33fbd8aa3ef48ded10d1735f11532b8df95388cc6bdb7e83"},
-    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
+    {file = "importlib_resources-6.1.2-py3-none-any.whl", hash = "sha256:9a0a862501dc38b68adebc82970140c9e4209fc99601782925178f8386339938"},
+    {file = "importlib_resources-6.1.2.tar.gz", hash = "sha256:308abf8474e2dba5f867d279237cd4076482c3de7104a40b41426370e891549b"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "inflection"
@@ -4000,13 +4005,13 @@ tests = ["pytest", "pytest-cov", "pytest-mock"]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.0"
+version = "6.29.3"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
-    {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"},
+    {file = "ipykernel-6.29.3-py3-none-any.whl", hash = "sha256:5aa086a4175b0229d4eca211e181fb473ea78ffd9869af36ba7694c947302a21"},
+    {file = "ipykernel-6.29.3.tar.gz", hash = "sha256:e14c250d1f9ea3989490225cc1a542781b095a18a19447fcf2b5eaf7d0ac5bd2"},
 ]
 
 [package.dependencies]
@@ -4029,7 +4034,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -4141,13 +4146,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
-    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
+    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
+    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
 ]
 
 [package.dependencies]
@@ -4204,13 +4209,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.19.2"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.19.2-py3-none-any.whl", hash = "sha256:eee9e502c788e89cb166d4d37f43084e3b64ab405c795c03d343a4dbc2c810fc"},
-    {file = "jsonschema-4.19.2.tar.gz", hash = "sha256:c9ff4d7447eed9592c23a12ccee508baf0dd0d59650615e847feb6cdca74f392"},
+    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
+    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
 ]
 
 [package.dependencies]
@@ -4225,27 +4230,27 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.7.1"
+version = "2023.12.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"},
-    {file = "jsonschema_specifications-2023.7.1.tar.gz", hash = "sha256:c91a50404e88a1f6ba40636778e2ee08f6e24c5613fe4c53ac24578a5a7f72bb"},
+    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
 ]
 
 [package.dependencies]
-referencing = ">=0.28.0"
+referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter-client"
-version = "8.5.0"
+version = "8.6.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.5.0-py3-none-any.whl", hash = "sha256:c3877aac7257ec68d79b5c622ce986bd2a992ca42f6ddc9b4dd1da50e89f7028"},
-    {file = "jupyter_client-8.5.0.tar.gz", hash = "sha256:e8754066510ce456358df363f97eae64b50860f30dc1fe8c6771440db3be9a63"},
+    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
+    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
 ]
 
 [package.dependencies]
@@ -4261,13 +4266,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
-version = "5.5.0"
+version = "5.7.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.5.0-py3-none-any.whl", hash = "sha256:e11e02cd8ae0a9de5c6c44abf5727df9f2581055afe00b22183f621ba3585805"},
-    {file = "jupyter_core-5.5.0.tar.gz", hash = "sha256:880b86053bf298a8724994f95e99b99130659022a4f7f45f563084b6223861d3"},
+    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
+    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
 ]
 
 [package.dependencies]
@@ -4281,58 +4286,59 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "lazy-object-proxy"
-version = "1.9.0"
+version = "1.10.0"
 description = "A fast and thorough lazy object proxy."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "lazy-object-proxy-1.9.0.tar.gz", hash = "sha256:659fb5809fa4629b8a1ac5106f669cfc7bef26fbb389dda53b3e010d1ac4ebae"},
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 [[package]]
 name = "limits"
-version = "3.6.0"
+version = "3.9.0"
 description = "Rate limiting utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4355,13 +4361,13 @@ rediscluster = ["redis (>=4.2.0,!=4.5.2,!=4.5.3)"]
 
 [[package]]
 name = "linkify-it-py"
-version = "2.0.2"
+version = "2.0.3"
 description = "Links recognition library with FULL unicode support."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4386,13 +4392,13 @@ files = [
 
 [[package]]
 name = "looker-sdk"
-version = "23.16.0"
+version = "24.2.0"
 description = "Looker REST API"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
@@ -4496,13 +4502,13 @@ source = ["Cython (>=3.0.7)"]
 
 [[package]]
 name = "mako"
-version = "1.2.4"
+version = "1.3.2"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4515,13 +4521,13 @@ testing = ["pytest"]
 
 [[package]]
 name = "markdown"
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 ]
 
 [package.extras]
@@ -4554,111 +4560,109 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
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 ]
 
 [[package]]
 name = "marshmallow"
-version = "3.20.1"
+version = "3.21.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-3.20.1-py3-none-any.whl", hash = "sha256:684939db93e80ad3561392f47be0230743131560a41c5110684c16e21ade0a5c"},
-    {file = "marshmallow-3.20.1.tar.gz", hash = "sha256:5d2371bbe42000f2b3fb5eaa065224df7d8f8597bc19a1bbfa5bfe7fba8da889"},
+    {file = "marshmallow-3.21.0-py3-none-any.whl", hash = "sha256:e7997f83571c7fd476042c2c188e4ee8a78900ca5e74bd9c8097afa56624e9bd"},
+    {file = "marshmallow-3.21.0.tar.gz", hash = "sha256:20f53be28c6e374a711a16165fb22a8dc6003e3f7cda1285e3ca777b9193885b"},
 ]
 
 [package.dependencies]
 packaging = ">=17.0"
 
 [package.extras]
-dev = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)", "pytest", "pytz", "simplejson", "tox"]
-docs = ["alabaster (==0.7.13)", "autodocsumm (==0.2.11)", "sphinx (==7.0.1)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"]
-lint = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)"]
+dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
+docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-version-warning (==1.1.2)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
 name = "marshmallow-oneofschema"
-version = "3.0.1"
+version = "3.1.1"
 description = "marshmallow multiplexing schema"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-oneofschema-3.0.1.tar.gz", hash = "sha256:62cd2099b29188c92493c2940ee79d1bf2f2619a71721664e5a98ec2faa58237"},
-    {file = "marshmallow_oneofschema-3.0.1-py2.py3-none-any.whl", hash = "sha256:bd29410a9f2f7457a2b428286e2a80ef76b8ddc3701527dc1f935a88914b02f2"},
+    {file = "marshmallow_oneofschema-3.1.1-py3-none-any.whl", hash = "sha256:ff4cb2a488785ee8edd521a765682c2c80c78b9dc48894124531bdfa1ec9303b"},
+    {file = "marshmallow_oneofschema-3.1.1.tar.gz", hash = "sha256:68b4a57d0281a04ac25d4eb7a4c5865a57090a0a8fd30fd6362c8e833ac6a6d9"},
 ]
 
 [package.dependencies]
 marshmallow = ">=3.0.0,<4.0.0"
 
 [package.extras]
-dev = ["flake8 (==3.9.2)", "flake8-bugbear (==21.4.3)", "mock", "pre-commit (>=2.7,<3.0)", "pytest", "tox"]
-lint = ["flake8 (==3.9.2)", "flake8-bugbear (==21.4.3)", "pre-commit (>=2.7,<3.0)"]
-tests = ["mock", "pytest"]
+dev = ["marshmallow-oneofschema[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
+tests = ["pytest"]
 
 [[package]]
 name = "marshmallow-sqlalchemy"
@@ -4793,17 +4797,18 @@ mkdocs = ">=1.2.3"
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "0.5.0"
+version = "1.0.0"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_autorefs-0.5.0-py3-none-any.whl", hash = "sha256:7930fcb8ac1249f10e683967aeaddc0af49d90702af111a5e390e8b20b3d97ff"},
-    {file = "mkdocs_autorefs-0.5.0.tar.gz", hash = "sha256:9a5054a94c08d28855cfab967ada10ed5be76e2bfad642302a610b252c3274c0"},
+    {file = "mkdocs_autorefs-1.0.0-py3-none-any.whl", hash = "sha256:2b6d288f0582589d1be7c99ce4470c8e7c5077892014051ff0d4ff574a73dbe8"},
+    {file = "mkdocs_autorefs-1.0.0.tar.gz", hash = "sha256:1b20db41cade632b07b7a73dee818977b52e869c9deb438f6f20e2896ff01859"},
 ]
 
 [package.dependencies]
 Markdown = ">=3.3"
+markupsafe = ">=2.0.1"
 mkdocs = ">=1.1"
 
 [[package]]
@@ -4853,13 +4858,13 @@ requests = "*"
 
 [[package]]
 name = "mkdocs-git-revision-date-localized-plugin"
-version = "1.2.2"
+version = "1.2.4"
 description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "mkdocs-git-revision-date-localized-plugin-1.2.2.tar.gz", hash = "sha256:0c43a9aac1fa69df99a823f833cc223bac9967b60d5261a857761c7c6e3b30de"},
-    {file = "mkdocs_git_revision_date_localized_plugin-1.2.2-py3-none-any.whl", hash = "sha256:85c7fe9ab06e7a63c4e522c26fee8b51d357cb8cbe605064501ad80f4f31cb94"},
+    {file = "mkdocs-git-revision-date-localized-plugin-1.2.4.tar.gz", hash = "sha256:08fd0c6f33c8da9e00daf40f7865943113b3879a1c621b2bbf0fa794ffe997d3"},
+    {file = "mkdocs_git_revision_date_localized_plugin-1.2.4-py3-none-any.whl", hash = "sha256:1f94eb510862ef94e982a2910404fa17a1657ecf29f45a07b0f438c00767fc85"},
 ]
 
 [package.dependencies]
@@ -4870,13 +4875,13 @@ pytz = "*"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.3"
+version = "9.5.11"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.3-py3-none-any.whl", hash = "sha256:76c93a8525cceb0b395b9cedab3428bf518cf6439adef2b940f1c1574b775d89"},
-    {file = "mkdocs_material-9.5.3.tar.gz", hash = "sha256:5899219f422f0a6de784232d9d40374416302ffae3c160cacc72969fcc1ee372"},
+    {file = "mkdocs_material-9.5.11-py3-none-any.whl", hash = "sha256:788ee0f3e036dca2dc20298d65e480297d348a44c9d7b2ee05c5262983e66072"},
+    {file = "mkdocs_material-9.5.11.tar.gz", hash = "sha256:7af7f8af0dea16175558f3fb9245d26c83a17199baa5f157755e63d7437bf971"},
 ]
 
 [package.dependencies]
@@ -4893,19 +4898,19 @@ regex = ">=2022.4"
 requests = ">=2.26,<3.0"
 
 [package.extras]
-git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
-imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
+git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
+imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
 
 [[package]]
 name = "mkdocs-material-extensions"
-version = "1.3"
+version = "1.3.1"
 description = "Extension pack for Python Markdown and MkDocs Material."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material_extensions-1.3-py3-none-any.whl", hash = "sha256:0297cc48ba68a9fdd1ef3780a3b41b534b0d0df1d1181a44676fda5f464eeadc"},
-    {file = "mkdocs_material_extensions-1.3.tar.gz", hash = "sha256:f0446091503acb110a7cab9349cbc90eeac51b58d1caa92a704a81ca1e24ddbd"},
+    {file = "mkdocs_material_extensions-1.3.1-py3-none-any.whl", hash = "sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31"},
+    {file = "mkdocs_material_extensions-1.3.1.tar.gz", hash = "sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443"},
 ]
 
 [[package]]
@@ -4924,21 +4929,23 @@ mkdocs = ">=1.2"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.23.0"
+version = "0.24.0"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.23.0-py3-none-any.whl", hash = "sha256:051fa4014dfcd9ed90254ae91de2dbb4f24e166347dae7be9a997fe16316c65e"},
-    {file = "mkdocstrings-0.23.0.tar.gz", hash = "sha256:d9c6a37ffbe7c14a7a54ef1258c70b8d394e6a33a1c80832bce40b9567138d1c"},
+    {file = "mkdocstrings-0.24.0-py3-none-any.whl", hash = "sha256:f4908560c10f587326d8f5165d1908817b2e280bbf707607f601c996366a2264"},
+    {file = "mkdocstrings-0.24.0.tar.gz", hash = "sha256:222b1165be41257b494a9d29b14135d2b7ca43f38161d5b10caae03b87bd4f7e"},
 ]
 
 [package.dependencies]
+click = ">=7.0"
 Jinja2 = ">=2.11.1"
 Markdown = ">=3.3"
 MarkupSafe = ">=1.1"
-mkdocs = ">=1.2"
+mkdocs = ">=1.4"
 mkdocs-autorefs = ">=0.3.1"
+platformdirs = ">=2.2.0"
 pymdown-extensions = ">=6.3"
 
 [package.extras]
@@ -4961,41 +4968,53 @@ files = [
 griffe = ">=0.37"
 mkdocstrings = ">=0.20"
 
+[[package]]
+name = "more-itertools"
+version = "10.2.0"
+description = "More routines for operating on iterables, beyond itertools"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "more-itertools-10.2.0.tar.gz", hash = "sha256:8fccb480c43d3e99a00087634c06dd02b0d50fbf088b380de5a41a015ec239e1"},
+    {file = "more_itertools-10.2.0-py3-none-any.whl", hash = "sha256:686b06abe565edfab151cb8fd385a05651e1fdf8f0a14191e4439283421f8684"},
+]
+
 [[package]]
 name = "msal"
-version = "1.24.1"
-description = "The Microsoft Authentication Library (MSAL) for Python library"
+version = "1.27.0"
+description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
 optional = false
 python-versions = ">=2.7"
 files = [
-    {file = "msal-1.24.1-py2.py3-none-any.whl", hash = "sha256:ce4320688f95c301ee74a4d0e9dbcfe029a63663a8cc61756f40d0d0d36574ad"},
-    {file = "msal-1.24.1.tar.gz", hash = "sha256:aa0972884b3c6fdec53d9a0bd15c12e5bd7b71ac1b66d746f54d128709f3f8f8"},
+    {file = "msal-1.27.0-py2.py3-none-any.whl", hash = "sha256:572d07149b83e7343a85a3bcef8e581167b4ac76befcbbb6eef0c0e19643cdc0"},
+    {file = "msal-1.27.0.tar.gz", hash = "sha256:3109503c038ba6b307152b0e8d34f98113f2e7a78986e28d0baf5b5303afda52"},
 ]
 
 [package.dependencies]
-cryptography = ">=0.6,<44"
+cryptography = ">=0.6,<45"
 PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
 requests = ">=2.0.0,<3"
 
 [package.extras]
-broker = ["pymsalruntime (>=0.13.2,<0.14)"]
+broker = ["pymsalruntime (>=0.13.2,<0.15)"]
 
 [[package]]
 name = "msal-extensions"
-version = "1.0.0"
+version = "1.1.0"
 description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
-    {file = "msal-extensions-1.0.0.tar.gz", hash = "sha256:c676aba56b0cce3783de1b5c5ecfe828db998167875126ca4b47dc6436451354"},
-    {file = "msal_extensions-1.0.0-py2.py3-none-any.whl", hash = "sha256:91e3db9620b822d0ed2b4d1850056a0f133cba04455e62f11612e40f5502f2ee"},
+    {file = "msal-extensions-1.1.0.tar.gz", hash = "sha256:6ab357867062db7b253d0bd2df6d411c7891a0ee7308d54d1e4317c1d1c54252"},
+    {file = "msal_extensions-1.1.0-py3-none-any.whl", hash = "sha256:01be9711b4c0b1a151450068eeb2c4f0997df3bba085ac299de3a66f585e382f"},
 ]
 
 [package.dependencies]
 msal = ">=0.4.1,<2.0.0"
+packaging = "*"
 portalocker = [
-    {version = ">=1.0,<3", markers = "python_version >= \"3.5\" and platform_system != \"Windows\""},
-    {version = ">=1.6,<3", markers = "python_version >= \"3.5\" and platform_system == \"Windows\""},
+    {version = ">=1.0,<3", markers = "platform_system != \"Windows\""},
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 [[package]]
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 [[package]]
@@ -5278,13 +5313,13 @@ PyYAML = ">=5.1.0"
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.20.0"
+version = "1.23.0"
 description = "OpenTelemetry Python API"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -5293,91 +5328,88 @@ importlib-metadata = ">=6.0,<7.0"
 
 [[package]]
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-version = "1.20.0"
+version = "1.23.0"
 description = "OpenTelemetry Collector Exporters"
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 [package.dependencies]
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+opentelemetry-exporter-otlp-proto-http = "1.23.0"
 
 [[package]]
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 [package.dependencies]
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+opentelemetry-proto = "1.23.0"
 
 [[package]]
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 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
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+opentelemetry-proto = "1.23.0"
+opentelemetry-sdk = ">=1.23.0,<1.24.0"
 
 [package.extras]
 test = ["pytest-grpc"]
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.20.0"
+version = "1.23.0"
 description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
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 [package.dependencies]
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+opentelemetry-proto = "1.23.0"
+opentelemetry-sdk = ">=1.23.0,<1.24.0"
 requests = ">=2.7,<3.0"
 
 [package.extras]
-test = ["responses (==0.22.0)"]
+test = ["responses (>=0.22.0,<0.25)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.20.0"
+version = "1.23.0"
 description = "OpenTelemetry Python Proto"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 [package.dependencies]
@@ -5385,29 +5417,29 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
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-version = "1.20.0"
+version = "1.23.0"
 description = "OpenTelemetry Python SDK"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 [package.dependencies]
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-opentelemetry-semantic-conventions = "0.41b0"
+opentelemetry-api = "1.23.0"
+opentelemetry-semantic-conventions = "0.44b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.41b0"
+version = "0.44b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
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@@ -5426,61 +5458,61 @@ dev = ["black", "mypy", "pytest"]
 
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-version = "3.9.10"
+version = "3.9.15"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -5567,6 +5599,7 @@ parquet = ["pyarrow (>=10.0.1)"]
 performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"]
 plot = ["matplotlib (>=3.6.3)"]
 postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
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 spss = ["pyreadstat (>=1.2.0)"]
 sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
@@ -5574,13 +5607,13 @@ xml = ["lxml (>=4.9.2)"]
 
 [[package]]
 name = "pandas-gbq"
-version = "0.19.2"
+version = "0.21.0"
 description = "Google BigQuery connector for pandas"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -5591,6 +5624,7 @@ google-auth-oauthlib = ">=0.7.0"
 google-cloud-bigquery = ">=3.3.5,<4.0.0dev"
 google-cloud-bigquery-storage = ">=2.16.2,<3.0.0dev"
 numpy = ">=1.16.6"
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 pandas = ">=1.1.4"
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 name = "pep8-naming"
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 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "3.11.0"
+version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
 
 [[package]]
 name = "plotly"
-version = "5.18.0"
+version = "5.19.0"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5800,13 +5917,13 @@ tenacity = ">=6.2.0"
 
 [[package]]
 name = "pluggy"
-version = "1.3.0"
+version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -5834,13 +5951,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "pre-commit"
-version = "3.6.0"
+version = "3.6.2"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -5880,13 +5997,13 @@ dev = ["nose", "pipreqs", "twine"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.41"
+version = "3.0.43"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -5894,13 +6011,13 @@ wcwidth = "*"
 
 [[package]]
 name = "proto-plus"
-version = "1.22.3"
+version = "1.23.0"
 description = "Beautiful, Pythonic protocol buffers."
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "proto-plus-1.23.0.tar.gz", hash = "sha256:89075171ef11988b3fa157f5dbd8b9cf09d65fffee97e29ce403cd8defba19d2"},
+    {file = "proto_plus-1.23.0-py3-none-any.whl", hash = "sha256:a829c79e619e1cf632de091013a4173deed13a55f326ef84f05af6f50ff4c82c"},
 ]
 
 [package.dependencies]
@@ -5942,27 +6059,27 @@ files = [
 
 [[package]]
 name = "psutil"
-version = "5.9.6"
+version = "5.9.8"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 files = [
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 ]
 
 [package.dependencies]
@@ -6350,28 +6440,29 @@ flake8 = ["flake8 (>=4)"]
 
 [[package]]
 name = "pyflakes"
-version = "3.1.0"
+version = "3.2.0"
 description = "passive checker of Python programs"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "pygments"
-version = "2.16.1"
+version = "2.17.2"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "Pygments-2.16.1.tar.gz", hash = "sha256:1daff0494820c69bc8941e407aa20f577374ee88364ee10a98fdbe0aece96e29"},
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 ]
 
 [package.extras]
 plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyjwt"
@@ -6395,12 +6486,13 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pyliftover"
-version = "0.4"
+version = "0.4.1"
 description = "Pure-python implementation of UCSC ``liftOver`` genome coordinate conversion."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -6423,20 +6515,20 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pyopenssl"
-version = "23.2.0"
+version = "24.0.0"
 description = "Python wrapper module around the OpenSSL library"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-cryptography = ">=38.0.0,<40.0.0 || >40.0.0,<40.0.1 || >40.0.1,<42"
+cryptography = ">=41.0.5,<43"
 
 [package.extras]
-docs = ["sphinx (!=5.2.0,!=5.2.0.post0)", "sphinx-rtd-theme"]
+docs = ["sphinx (!=5.2.0,!=5.2.0.post0,!=7.2.5)", "sphinx-rtd-theme"]
 test = ["flaky", "pretend", "pytest (>=3.0.1)"]
 
 [[package]]
@@ -6474,13 +6566,13 @@ sql = ["pandas (>=1.0.5)", "pyarrow (>=1.0.0)"]
 
 [[package]]
 name = "pytest"
-version = "8.0.1"
+version = "8.0.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6587,13 +6679,13 @@ six = ">=1.5"
 
 [[package]]
 name = "python-gitlab"
-version = "3.15.0"
-description = "Interact with GitLab API"
+version = "4.4.0"
+description = "A python wrapper for the GitLab API"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -6602,7 +6694,7 @@ requests-toolbelt = ">=0.10.1"
 
 [package.extras]
 autocompletion = ["argcomplete (>=1.10.0,<3)"]
-yaml = ["PyYaml (>=5.2)"]
+yaml = ["PyYaml (>=6.0.1)"]
 
 [[package]]
 name = "python-json-logger"
@@ -6631,13 +6723,13 @@ python-slugify = ">=1.2.5"
 
 [[package]]
 name = "python-semantic-release"
-version = "9.1.0"
+version = "9.1.1"
 description = "Automatic Semantic Versioning for Python projects"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6661,13 +6753,13 @@ test = ["coverage[toml] (>=6,<8)", "pytest (>=7,<8)", "pytest-clarity (>=1.0.1)"
 
 [[package]]
 name = "python-slugify"
-version = "8.0.1"
+version = "8.0.4"
 description = "A Python slugify application that also handles Unicode"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -6678,24 +6770,13 @@ unidecode = ["Unidecode (>=1.1.1)"]
 
 [[package]]
 name = "pytz"
-version = "2023.3.post1"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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-]
-
-[[package]]
-name = "pytzdata"
-version = "2020.1"
-description = "The Olson timezone database for Python."
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
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+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
 ]
 
 [[package]]
@@ -6746,7 +6827,6 @@ files = [
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@@ -6797,104 +6877,104 @@ pyyaml = "*"
 
 [[package]]
 name = "pyzmq"
-version = "25.1.1"
+version = "25.1.2"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
@@ -7113,110 +7198,110 @@ rich = ">=11.0.0"
 
 [[package]]
 name = "rpds-py"
-version = "0.10.6"
+version = "0.18.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
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-numpy = ">=1.19.5"
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@@ -7382,13 +7447,13 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
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 [package.dependencies]
@@ -7724,7 +7789,7 @@ greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platfo
 
 [package.extras]
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 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2)"]
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 mysql-connector = ["mysql-connector-python"]
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 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
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 postgresql-psycopg2cffi = ["psycopg2cffi"]
 pymysql = ["pymysql", "pymysql (<1)"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
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 [package.dependencies]
@@ -7770,13 +7835,13 @@ tests = ["packaging", "pytz"]
 
 [[package]]
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 [[package]]
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 [package.extras]
@@ -7919,13 +7984,13 @@ files = [
 
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 description = "threadpoolctl"
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 python-versions = ">=3.8"
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 [[package]]
@@ -7952,49 +8017,49 @@ files = [
 
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 optional = false
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 python-versions = ">= 3.8"
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 description = "Traitlets Python configuration system"
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 python-versions = ">=3.8"
 files = [
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+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "typer"
@@ -8030,24 +8095,24 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2023.3"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
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 ]
 
 [[package]]
 name = "uc-micro-py"
-version = "1.0.2"
+version = "1.0.3"
 description = "Micro subset of unicode data files for linkify-it-py projects."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -8155,19 +8220,19 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
 
 [[package]]
 name = "virtualenv"
-version = "20.24.6"
+version = "20.25.1"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 distlib = ">=0.3.7,<1"
 filelock = ">=3.12.2,<4"
-platformdirs = ">=3.9.1,<4"
+platformdirs = ">=3.9.1,<5"
 
 [package.extras]
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
@@ -8175,13 +8240,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "wandb"
-version = "0.16.2"
+version = "0.16.3"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -8207,42 +8272,45 @@ launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identi
 media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]
+reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
 [[package]]
 name = "watchdog"
-version = "3.0.0"
+version = "4.0.0"
 description = "Filesystem events monitoring"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-MarkupSafe = "*"
+markupsafe = "*"
 
 [package.extras]
 email = ["email-validator"]
@@ -8449,85 +8512,101 @@ dev = ["doc8", "flake8", "flake8-import-order", "rstcheck[sphinx]", "sphinx"]
 
 [[package]]
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+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
 ]
 
 [package.dependencies]
@@ -8552,4 +8631,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "8d85933db4bf8f4ca92ef227e9a767d26f4f7d2b360083449cfafc54878885fc"
+content-hash = "6f20e245eeac06bbf87c036df5db1198b74d90609a907d5b781eb023a1d7dd81"
diff --git a/pyproject.toml b/pyproject.toml
index 3f4f44545..2b4150996 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,7 @@ omegaconf = "^2.3.0"
 typing-extensions = "^4.9.0"
 scikit-learn = "^1.3.2"
 pandas = "^2.1.4"
+fsspec = ">=2022.11.0"
 
 [tool.poetry.dev-dependencies]
 pre-commit = "^3.6.0"
diff --git a/src/airflow/dags/gwas_curation_update.py b/src/airflow/dags/gwas_curation_update.py
index 1ef0f39f9..830007e6d 100644
--- a/src/airflow/dags/gwas_curation_update.py
+++ b/src/airflow/dags/gwas_curation_update.py
@@ -18,7 +18,7 @@
 ):
     update_gwas_curation = common.submit_step(
         cluster_name=CLUSTER_NAME,
-        step_id="gwas_catalog_curation_update",
+        step_id="ot_gwas_catalog_study_curation",
         task_id="gwas_catalog_curation_update",
         other_args=[
             f"step.gwas_catalog_study_curation_out=gs://genetics_etl_python_playground/input/v2d/GWAS_Catalog_study_curation_{RUN_DATE}.tsv",
diff --git a/src/gentropy/datasource/gwas_catalog/study_index.py b/src/gentropy/datasource/gwas_catalog/study_index.py
index cb6d3338a..4d317b44b 100644
--- a/src/gentropy/datasource/gwas_catalog/study_index.py
+++ b/src/gentropy/datasource/gwas_catalog/study_index.py
@@ -549,12 +549,12 @@ def annotate_ancestries(
         )
 
         # Parsing cohort information:
-        cohorts = ancestry_lut.select(
-            f.col("STUDY ACCESSION").alias("studyId"),
-            GWASCatalogStudyIndexParser.parse_cohorts(f.col("COHORT(S)")).alias(
-                "cohorts"
-            ),
-        ).distinct()
+        # cohorts = ancestry_lut.select(
+        #     f.col("STUDY ACCESSION").alias("studyId"),
+        #     GWASCatalogStudyIndexParser.parse_cohorts(f.col("COHORT(S)")).alias(
+        #         "cohorts"
+        #     ),
+        # ).distinct()
 
         # Get a high resolution dataset on experimental stage:
         ancestry_stages = (
@@ -644,8 +644,9 @@ def annotate_ancestries(
         ).select(
             "studyId", "discoverySamples", "ldPopulationStructure", "replicationSamples"
         )
-        self.df = self.df.join(parsed_ancestry_lut, on="studyId", how="left").join(
-            cohorts, on="studyId", how="left"
+        self.df = (
+            self.df.join(parsed_ancestry_lut, on="studyId", how="left")
+            # .join(cohorts, on="studyId", how="left")
         )
         return self
 
diff --git a/utils/update_GWAS_Catalog_data.sh b/utils/update_GWAS_Catalog_data.sh
index 1e380d30c..00ee44d8f 100755
--- a/utils/update_GWAS_Catalog_data.sh
+++ b/utils/update_GWAS_Catalog_data.sh
@@ -15,7 +15,7 @@ get_release_url(){
 
 # Function to get the Ensembl and EFO version which used to ground GWAS data:
 get_release_info(){
-    curl -s https://www.ebi.ac.uk/gwas/api/search/stats | jq -r '"\(.ensemblbuild) \(.efoversion)"'
+    curl -s "${1}" | jq -r '"\(.ensemblbuild) \(.efoversion)"'
 }
 
 logging(){
@@ -41,6 +41,18 @@ upload_file_to_gcp(){
     fi
 }
 
+fetch_from_ftp(){
+    URL=${1}
+    TARGET=${2}
+    wget -q ${URL} -O ${TARGET}
+    if [ $? -ne 0 ]; then
+        logging "Failed to fetch ${URL}"
+        return
+    else
+        logging "File ${TARGET} saved."
+    fi
+}
+
 # Resources:
 export BASE_URL=ftp://ftp.ebi.ac.uk/pub/databases/gwas
 export RELEASE_INFO_URL=https://www.ebi.ac.uk/gwas/api/search/stats
@@ -71,7 +83,7 @@ read YEAR MONTH DAY < <(get_release_url)
 logging "Most recent GWAS Catalog release: ${YEAR}/${MONTH}/${DAY}"
 
 # Capturing release metadata:
-read ENSEMBL EFO < <(get_release_info)
+read ENSEMBL EFO < <(get_release_info ${RELEASE_INFO_URL})
 logging "Genes were mapped to v${ENSEMBL} Ensembl release."
 logging "Diseases were mapped to ${EFO} EFO release."
 
@@ -80,26 +92,19 @@ RELEASE_URL=${BASE_URL}/releases/${YEAR}/${MONTH}/${DAY}
 logging "Datafiles are fetching from ${RELEASE_URL}"
 
 # Fetching files while assigning properly dated and annotated names:
-wget -q ${RELEASE_URL}/gwas-catalog-associations_ontology-annotated.tsv -O ${ASSOCIATION_FILE}
-logging "File ${ASSOCIATION_FILE} saved."
+fetch_from_ftp ${RELEASE_URL}/gwas-catalog-associations_ontology-annotated.tsv ${ASSOCIATION_FILE}
 
-wget -q ${RELEASE_URL}/gwas-catalog-download-studies-v1.0.3.txt -O ${PUBLISHED_STUDIES_FILE}
-logging "File ${PUBLISHED_STUDIES_FILE} saved."
+fetch_from_ftp ${RELEASE_URL}/gwas-catalog-download-studies-v1.0.3.1.txt ${PUBLISHED_STUDIES_FILE}
 
-wget -q ${RELEASE_URL}/gwas-catalog-unpublished-studies-v1.0.3.tsv -O ${UNPUBLISHED_STUDIES_FILE}
-logging "File ${UNPUBLISHED_STUDIES_FILE} saved."
+fetch_from_ftp ${RELEASE_URL}/gwas-catalog-unpublished-studies-v1.0.3.1.tsv ${UNPUBLISHED_STUDIES_FILE}
 
-wget -q ${RELEASE_URL}/gwas-catalog-download-ancestries-v1.0.3.txt -O ${PUBLISHED_ANCESTRIES_FILE}
-logging "File ${PUBLISHED_ANCESTRIES_FILE} saved."
+fetch_from_ftp ${RELEASE_URL}/gwas-catalog-download-ancestries-v1.0.3.1.txt ${PUBLISHED_ANCESTRIES_FILE}
 
-wget -q ${RELEASE_URL}/gwas-catalog-unpublished-ancestries-v1.0.3.tsv -O ${UNPUBLISHED_ANCESTRIES_FILE}
-logging "File ${UNPUBLISHED_ANCESTRIES_FILE} saved."
+fetch_from_ftp ${RELEASE_URL}/gwas-catalog-unpublished-ancestries-v1.0.3.1.tsv ${UNPUBLISHED_ANCESTRIES_FILE}
 
-wget -q ${BASE_URL}/summary_statistics/harmonised_list.txt -O ${HARMONISED_LIST_FILE}
-logging "File ${HARMONISED_LIST_FILE} saved."
+fetch_from_ftp ${BASE_URL}/summary_statistics/harmonised_list.txt ${HARMONISED_LIST_FILE}
 
-wget -q ${GWAS_CATALOG_STUDY_CURATION_URL} -O ${GWAS_CATALOG_STUDY_CURATION_FILE}
-logging "In-house GWAS Catalog study curation file fetched from GitHub."
+fetch_from_ftp ${GWAS_CATALOG_STUDY_CURATION_URL} ${GWAS_CATALOG_STUDY_CURATION_FILE}
 
 logging "Copying files to GCP..."
 

From 655a5f36638badf961f97b1e575e92ee0e2e6e2e Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Wed, 28 Feb 2024 14:52:18 +0000
Subject: [PATCH 02/86] fix: updating cohort parser to new GWAS Catalog format

---
 .../datasource/gwas_catalog/study_index.py         | 14 ++------------
 1 file changed, 2 insertions(+), 12 deletions(-)

diff --git a/src/gentropy/datasource/gwas_catalog/study_index.py b/src/gentropy/datasource/gwas_catalog/study_index.py
index 4d317b44b..641254754 100644
--- a/src/gentropy/datasource/gwas_catalog/study_index.py
+++ b/src/gentropy/datasource/gwas_catalog/study_index.py
@@ -305,6 +305,7 @@ def _parse_study_table(
                 parse_efos(f.col("MAPPED BACKGROUND TRAIT URI")).alias(
                     "backgroundTraitFromSourceMappedIds"
                 ),
+                cls.parse_cohorts(f.col("COHORTS")).alias("cohorts"),
             ),
             _schema=StudyIndexGWASCatalog.get_schema(),
         )
@@ -548,14 +549,6 @@ def annotate_ancestries(
             )  # studyId has not been split yet
         )
 
-        # Parsing cohort information:
-        # cohorts = ancestry_lut.select(
-        #     f.col("STUDY ACCESSION").alias("studyId"),
-        #     GWASCatalogStudyIndexParser.parse_cohorts(f.col("COHORT(S)")).alias(
-        #         "cohorts"
-        #     ),
-        # ).distinct()
-
         # Get a high resolution dataset on experimental stage:
         ancestry_stages = (
             ancestry.groupBy("studyId")
@@ -644,10 +637,7 @@ def annotate_ancestries(
         ).select(
             "studyId", "discoverySamples", "ldPopulationStructure", "replicationSamples"
         )
-        self.df = (
-            self.df.join(parsed_ancestry_lut, on="studyId", how="left")
-            # .join(cohorts, on="studyId", how="left")
-        )
+        self.df = self.df.join(parsed_ancestry_lut, on="studyId", how="left")
         return self
 
     def annotate_sumstats_info(

From d4dd97eaf75448596e971752e9af2f7f4ab8bb2d Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Mon, 4 Mar 2024 11:57:21 +0000
Subject: [PATCH 03/86] fix: updating study index generation from gwas flat
 files

---
 src/gentropy/datasource/gwas_catalog/study_index.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/gentropy/datasource/gwas_catalog/study_index.py b/src/gentropy/datasource/gwas_catalog/study_index.py
index 641254754..e8b49b0da 100644
--- a/src/gentropy/datasource/gwas_catalog/study_index.py
+++ b/src/gentropy/datasource/gwas_catalog/study_index.py
@@ -305,7 +305,7 @@ def _parse_study_table(
                 parse_efos(f.col("MAPPED BACKGROUND TRAIT URI")).alias(
                     "backgroundTraitFromSourceMappedIds"
                 ),
-                cls.parse_cohorts(f.col("COHORTS")).alias("cohorts"),
+                cls.parse_cohorts(f.col("COHORT")).alias("cohorts"),
             ),
             _schema=StudyIndexGWASCatalog.get_schema(),
         )

From c2e3e91cb3a883b672d212265ac4c453fd2fcced Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Thu, 7 Mar 2024 23:36:33 +0000
Subject: [PATCH 04/86] chore: resolve merge commit

---
 .pre-commit-config.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 51d4e05a8..14be15497 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.0
+    rev: v0.3.1
     hooks:
       - id: ruff
         args:
@@ -59,7 +59,7 @@ repos:
         exclude: "CHANGELOG.md"
 
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.11.0
+    rev: v9.13.0
     hooks:
       - id: commitlint
         additional_dependencies: ["@commitlint/config-conventional"]

From 9b64cf8f2d3adfe1fd248799bba9cfdb9bc1ac25 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Thu, 7 Mar 2024 23:31:20 +0000
Subject: [PATCH 05/86] chore: bumping for 24.03 release

---
 config/datasets/ot_gcp.yaml            |   8 +-
 src/airflow/dags/common_airflow.py     |   6 +-
 src/airflow/dags/finngen_preprocess.py |   2 +-
 src/airflow/dags/test_DAG.py           | 170 +++++++++++++++++++++++++
 4 files changed, 177 insertions(+), 9 deletions(-)
 create mode 100644 src/airflow/dags/test_DAG.py

diff --git a/config/datasets/ot_gcp.yaml b/config/datasets/ot_gcp.yaml
index ebc9fc9c2..566e2ec50 100644
--- a/config/datasets/ot_gcp.yaml
+++ b/config/datasets/ot_gcp.yaml
@@ -1,10 +1,10 @@
 # Release specific configuration:
-release_version: "24.01"
+release_version: "24.03"
 dev_version: XX.XX
 release_folder: gs://genetics_etl_python_playground/releases/${datasets.release_version}
 
 inputs: gs://genetics_etl_python_playground/input
-static_assets: gs://genetics_etl_python_playground/static_assetss
+static_assets: gs://genetics_etl_python_playground/static_assets
 outputs: gs://genetics_etl_python_playground/output/python_etl/parquet/${datasets.dev_version}
 
 ## Datasets:
@@ -36,9 +36,9 @@ anderson: ${datasets.static_assets}/andersson2014/enhancer_tss_associations.bed
 javierre: ${datasets.static_assets}/javierre_2016_preprocessed
 jung: ${datasets.static_assets}/jung2019_pchic_tableS3.csv
 thurman: ${datasets.static_assets}/thurman2012/genomewideCorrs_above0.7_promoterPlusMinus500kb_withGeneNames_32celltypeCategories.bed8.gz
-target_index: ${datasets.release_folder}/targets # OTP 23.12 data
+target_index: ${datasets.static_assets}/targets # OTP 23.12 data
+gene_interactions: ${datasets.static_assets}/interaction # OTP 23.12 data
 
-gene_interactions: ${datasets.release_folder}/interaction # OTP 23.12 data
 finngen_finemapping_results_path: ${datasets.inputs}/Finngen_susie_finemapping_r10/full
 finngen_finemapping_summaries_path: ${datasets.inputs}/Finngen_susie_finemapping_r10/Finngen_susie_credset_summary_r10.tsv
 
diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py
index e3dc56ccb..b6069b282 100644
--- a/src/airflow/dags/common_airflow.py
+++ b/src/airflow/dags/common_airflow.py
@@ -302,10 +302,8 @@ def generate_dag(cluster_name: str, tasks: list[DataprocSubmitJobOperator]) -> A
         Any: Airflow DAG.
     """
     return (
-        create_cluster(cluster_name)
-        >> install_dependencies(cluster_name)
-        >> tasks
-        >> delete_cluster(cluster_name)
+        create_cluster(cluster_name) >> install_dependencies(cluster_name) >> tasks
+        # >> delete_cluster(cluster_name)
     )
 
 
diff --git a/src/airflow/dags/finngen_preprocess.py b/src/airflow/dags/finngen_preprocess.py
index 38aeb2510..d4fc9446d 100644
--- a/src/airflow/dags/finngen_preprocess.py
+++ b/src/airflow/dags/finngen_preprocess.py
@@ -26,7 +26,7 @@
 )
 FINNGEN_FM_SUMMARIES = "gs://genetics_etl_python_playground/input/Finngen_susie_finemapping_r10/Finngen_susie_credset_summary_r10.tsv"
 FINNGEN_PREFIX = "FINNGEN_R10_"
-FINNGEN_FM_OUT = "gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/finngen_susie_processed"
+FINNGEN_FM_OUT = f"{FINNGEN_BUCKET}/credible_set_datasets/finngen_susie"
 
 with DAG(
     dag_id=Path(__file__).stem,
diff --git a/src/airflow/dags/test_DAG.py b/src/airflow/dags/test_DAG.py
new file mode 100644
index 000000000..3f9a8ee13
--- /dev/null
+++ b/src/airflow/dags/test_DAG.py
@@ -0,0 +1,170 @@
+"""Test DAG to prototype data transfer."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import common_airflow as common
+from airflow.models.dag import DAG
+from airflow.operators.python import ShortCircuitOperator
+from airflow.providers.google.cloud.hooks.gcs import GCSHook
+from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator
+from airflow.utils.task_group import TaskGroup
+
+CLUSTER_NAME = "otg-etl"
+SOURCE_CONFIG_FILE_PATH = Path(__file__).parent / "configs" / "dag.yaml"
+
+# Release specific variables:
+RELEASE_VERSION = "24.03"
+RELEASE_BUCKET_NAME = "genetics_etl_python_playground"
+
+# Datasource paths:
+GWAS_CATALOG_BUCKET_NAME = "gwas_catalog_data"
+EQTL_BUCKET_NAME = "eqtl_catalog_data"
+FINNGEN_BUCKET_NAME = "finngen_data"
+FINNGEN_RELEASE = "r10"
+
+# Files to move:
+DATA_TO_MOVE = {
+    # GWAS Catalog summary study index:
+    "gwas_catalog_study_index": {
+        "source_bucket": GWAS_CATALOG_BUCKET_NAME,
+        "source_object": "study_index",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/study_index/gwas_catalog",
+    },
+    # PICS credible sets from GWAS Catalog curated associations:
+    "gwas_catalog_curated_credible_set": {
+        "source_bucket": GWAS_CATALOG_BUCKET_NAME,
+        "source_object": "credible_set_datasets/gwas_catalog_curated",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_curated",
+    },
+    # PICS credible sets from GWAS Catalog summary statistics:
+    "gwas_catalog_sumstats_credible_set": {
+        "source_bucket": GWAS_CATALOG_BUCKET_NAME,
+        "source_object": "credible_set_datasets/gwas_catalog_summary_stats",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_summary_statistics",
+    },
+    # GWAS Catalog manifest files:
+    "gwas_catalog_manifests": {
+        "source_bucket": GWAS_CATALOG_BUCKET_NAME,
+        "source_object": "manifests",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/manifests",
+    },
+    # eQTL Catalog study index:
+    "eqtl_catalog_study_index": {
+        "source_bucket": EQTL_BUCKET_NAME,
+        "source_object": "study_index",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/study_index/eqtl_catalog",
+    },
+    # eQTL Catalog SuSiE credible sets:
+    "eqtl_catalog_susie_credible_set": {
+        "source_bucket": EQTL_BUCKET_NAME,
+        "source_object": "credible_set_datasets/susie",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/eqtl_catalog_susie",
+    },
+    # Finngen study index:
+    "finngen_study_index": {
+        "source_bucket": FINNGEN_BUCKET_NAME,
+        "source_object": f"{FINNGEN_RELEASE}/study_index",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/study_index/finngen",
+    },
+    # Finngen summary statistics:
+    "finngen_PICS_credible_set": {
+        "source_bucket": FINNGEN_BUCKET_NAME,
+        "source_object": f"{FINNGEN_RELEASE}/credible_set_datasets/finngen_pics",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/finngen_pics",
+    },
+    # Finngen SuSiE credible sets:
+    "finngen_susie_credible_set": {
+        "source_bucket": FINNGEN_BUCKET_NAME,
+        "source_object": f"{FINNGEN_RELEASE}/credible_set_datasets/finngen_susie_processed",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/finngen_susie",
+    },
+    # L2G gold standard:
+    "gold_standard": {
+        "source_bucket": "genetics_etl_python_playground",
+        "source_object": "input/l2g/gold_standard/curation.json",
+        "destination_bucket": RELEASE_BUCKET_NAME,
+        "destination_object": f"releases/{RELEASE_VERSION}/locus_to_gene_gold_standard.json",
+    },
+}
+
+
+# Test if release folder exists:
+def test_release_folder_exists() -> bool:
+    """This function tests if the release folder exists.
+
+    Returns:
+        bool: False if the release folder exists, True otherwise.
+    """
+    hook = GCSHook(gcp_conn_id="google_cloud_default")
+    return (
+        False
+        if hook.exists(RELEASE_BUCKET_NAME, f"releases/{RELEASE_VERSION}")
+        else True
+    )
+
+
+# This operator meant to fail the DAG if the release folder exists:
+test_folder = ShortCircuitOperator(
+    task_id="test_release_folder_exists",
+    python_callable=test_release_folder_exists,
+)
+
+with DAG(
+    dag_id=Path(__file__).stem,
+    description="Open Targets Genetics ETL workflow",
+    default_args=common.shared_dag_args,
+    **common.shared_dag_kwargs,
+):
+    # Compiling tasks for moving data to the right place:
+    with TaskGroup(group_id="data_transfer") as data_transfer:
+        # Defining the tasks to execute in the task group:
+        [
+            GCSToGCSOperator(
+                task_id=f"move_{data_name}",
+                source_bucket=data["source_bucket"],
+                source_object=data["source_object"],
+                destination_bucket=data["destination_bucket"],
+                destination_object=data["destination_object"],
+            )
+            for data_name, data in DATA_TO_MOVE.items()
+        ]
+
+    with TaskGroup(group_id="genetics_etl") as genetics_etl:
+        # Parse and define all steps and their prerequisites.
+        tasks = {}
+        steps = common.read_yaml_config(SOURCE_CONFIG_FILE_PATH)
+        for step in steps:
+            # Define task for the current step.
+            step_id = step["id"]
+            this_task = common.submit_step(
+                cluster_name=CLUSTER_NAME,
+                step_id=step_id,
+                task_id=step_id,
+            )
+            # Chain prerequisites.
+            tasks[step_id] = this_task
+            for prerequisite in step.get("prerequisites", []):
+                this_task.set_upstream(tasks[prerequisite])
+
+        common.generate_dag(cluster_name=CLUSTER_NAME, tasks=list(tasks.values()))
+
+    # DAG description:
+    (
+        # Test if release folder exists:
+        test_folder
+        # Run data transfer:
+        >> data_transfer
+        # Once datasets are transferred, run the rest of the steps:
+        >> genetics_etl
+    )

From 9ee712a19cfec8ed2832206eb12407dec1956576 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Thu, 7 Mar 2024 23:45:15 +0000
Subject: [PATCH 06/86] fix: reverting some changes

---
 .pre-commit-config.yaml            | 4 ++--
 src/airflow/dags/common_airflow.py | 6 ++++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 14be15497..51d4e05a8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.1
+    rev: v0.3.0
     hooks:
       - id: ruff
         args:
@@ -59,7 +59,7 @@ repos:
         exclude: "CHANGELOG.md"
 
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.13.0
+    rev: v9.11.0
     hooks:
       - id: commitlint
         additional_dependencies: ["@commitlint/config-conventional"]
diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py
index b6069b282..e3dc56ccb 100644
--- a/src/airflow/dags/common_airflow.py
+++ b/src/airflow/dags/common_airflow.py
@@ -302,8 +302,10 @@ def generate_dag(cluster_name: str, tasks: list[DataprocSubmitJobOperator]) -> A
         Any: Airflow DAG.
     """
     return (
-        create_cluster(cluster_name) >> install_dependencies(cluster_name) >> tasks
-        # >> delete_cluster(cluster_name)
+        create_cluster(cluster_name)
+        >> install_dependencies(cluster_name)
+        >> tasks
+        >> delete_cluster(cluster_name)
     )
 
 

From 6e62c4bfcb3f52c01df28bf6b03f454faff8bed5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?= <irene.lopezs@protonmail.com>
Date: Fri, 8 Mar 2024 10:29:57 +0100
Subject: [PATCH 07/86] chore: add tuqtl features to L2Gfeaturematrix schema

---
 .../assets/schemas/l2g_feature_matrix.json    | 24 +++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/src/gentropy/assets/schemas/l2g_feature_matrix.json b/src/gentropy/assets/schemas/l2g_feature_matrix.json
index a1b58040b..114936168 100644
--- a/src/gentropy/assets/schemas/l2g_feature_matrix.json
+++ b/src/gentropy/assets/schemas/l2g_feature_matrix.json
@@ -125,6 +125,30 @@
       "name": "sqtlColocLlrMaximumNeighborhood",
       "nullable": true,
       "type": "float"
+    },
+    {
+      "metadata": {},
+      "name": "tuqtlColocClppMaximum",
+      "nullable": true,
+      "type": "float"
+    },
+    {
+      "metadata": {},
+      "name": "tuqtlColocClppMaximumNeighborhood",
+      "nullable": true,
+      "type": "float"
+    },
+    {
+      "metadata": {},
+      "name": "tuqtlColocLlrMaximum",
+      "nullable": true,
+      "type": "float"
+    },
+    {
+      "metadata": {},
+      "name": "tuqtlColocLlrMaximumNeighborhood",
+      "nullable": true,
+      "type": "float"
     }
   ],
   "type": "struct"

From fda650b5b2384b3c14cff1d8ca2872a68d8fb329 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?= <irene.lopezs@protonmail.com>
Date: Fri, 8 Mar 2024 10:33:27 +0100
Subject: [PATCH 08/86] chore: add sqtl, tuqtl, pqtl features to l2g inclusion
 list

---
 src/gentropy/config.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 7202ee578..28935e637 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -213,13 +213,17 @@ class LocusToGeneConfig(StepConfig):
             # max clpp for each (study, locus) aggregating over all eQTLs
             "eqtlColocClppMaximumNeighborhood",
             # max clpp for each (study, locus, gene) aggregating over all pQTLs
-            # "pqtlColocClppMaximum",
+            "pqtlColocClppMaximum",
             # max clpp for each (study, locus) aggregating over all pQTLs
-            # "pqtlColocClppMaximumNeighborhood",
+            "pqtlColocClppMaximumNeighborhood",
             # max clpp for each (study, locus, gene) aggregating over all sQTLs
-            # "sqtlColocClppMaximum",
+            "sqtlColocClppMaximum",
             # max clpp for each (study, locus) aggregating over all sQTLs
-            # "sqtlColocClppMaximumNeighborhood",
+            "sqtlColocClppMaximumNeighborhood",
+            # max clpp for each (study, locus) aggregating over all tuQTLs
+            "tuqtlColocClppMaximum",
+            # max clpp for each (study, locus, gene) aggregating over all tuQTLs
+            "tuqtlColocClppMaximumNeighborhood",
             # # max log-likelihood ratio value for each (study, locus, gene) aggregating over all eQTLs
             # "eqtlColocLlrLocalMaximum",
             # # max log-likelihood ratio value for each (study, locus) aggregating over all eQTLs

From 02dc66f7f358e74bbeb5d1fce475870e9d3da750 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Wed, 6 Mar 2024 17:16:47 +0000
Subject: [PATCH 09/86] feat: extract credible sets and studies from all eQTL
 Catalogue finemapping results (#518)

* feat: dataflow decompress prototype (#501)

* chore: commit susie results gist

* feat(study_index): add `tissueFromSourceId` to schema and make `traitFromSource` nullable

* fix: bug and linting fixes in new eqtl ingestion step

* perf: config bugfixes and performance improvements

* perf: remove data persistance to avoid executor failure

* perf: load susie results for studies of interest only

* perf: collect locus for leads only and optimise partitioning cols

* feat: parametrise methods to include

* feat: run full dag

* test: add tests

* fix: reorder test inputs

* docs: update eqtl catalogue docs

* fix: correct typos in tests docstrings

* refactor: change mqtl_quantification_methods to mqtl_quantification_methods_blacklist

* feat: studyId is based on measured trait and not on gene

* feat: credible set lead is the variant with highest pip

* feat(studies): change logic in _identify_study_type to extract qtl type based on quantization method

* refactor: externalise reading logic to source classes

* chore: add mqtl_quantification_methods_blacklist to yaml config

* docs: update docs

* fix(dag): pass bucket name to GCSDeleteBucketOperator

* refactor(coloc): move get_logsum function to common utils

* feat(studylocus): add calculate_credible_set_log10bf and use it for eqtlcat credible sets

* fix: credible sets dataset is too large and cant be broadcasted

* fix(dag): use GCSDeleteObjectsOperator instead of GCSDeleteBucketOperator

* fix: correct typo

* fix: correct typo
---
 config/step/ot_eqtl_catalogue.yaml            |  1 +
 .../eqtl_catalogue/_eqtl_catalogue.md         |  2 +-
 src/airflow/dags/eqtl_preprocess.py           | 14 ++--
 src/gentropy/common/utils.py                  | 23 ++++++
 src/gentropy/config.py                        |  6 +-
 src/gentropy/dataset/study_locus.py           | 24 ++++++
 .../datasource/eqtl_catalogue/finemapping.py  | 80 ++++++++++++++-----
 .../datasource/eqtl_catalogue/study_index.py  | 63 ++++++++++++---
 .../datasource/finngen/finemapping.py         |  5 +-
 src/gentropy/eqtl_catalogue.py                | 31 +++----
 src/gentropy/method/colocalisation.py         | 27 +------
 11 files changed, 187 insertions(+), 89 deletions(-)

diff --git a/config/step/ot_eqtl_catalogue.yaml b/config/step/ot_eqtl_catalogue.yaml
index 06076d228..7d4441864 100644
--- a/config/step/ot_eqtl_catalogue.yaml
+++ b/config/step/ot_eqtl_catalogue.yaml
@@ -4,6 +4,7 @@ defaults:
 eqtl_catalogue_paths_imported: ???
 eqtl_catalogue_study_index_out: ???
 eqtl_catalogue_credible_sets_out: ???
+mqtl_quantification_methods_blacklist: []
 session:
   extended_spark_conf:
     "spark.sql.shuffle.partitions": "3200"
diff --git a/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md b/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md
index c2e7c79ce..2e95f561c 100644
--- a/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md
+++ b/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md
@@ -4,4 +4,4 @@ title: eQTL Catalogue
 
 The [eQTL Catalogue](https://www.ebi.ac.uk/eqtl/) aims to provide unified gene, protein expression and splicing Quantitative Trait Loci (QTLs) from all available human public studies.
 
-It serves as the ultimate resource of mQTLs that we use for colocalization and target prioritization.
+It serves as the ultimate resource of molecular QTLs that we use for colocalization and target prioritization.
diff --git a/src/airflow/dags/eqtl_preprocess.py b/src/airflow/dags/eqtl_preprocess.py
index 40a3c1d6a..ec346d137 100644
--- a/src/airflow/dags/eqtl_preprocess.py
+++ b/src/airflow/dags/eqtl_preprocess.py
@@ -9,10 +9,10 @@
 from airflow.providers.google.cloud.operators.dataflow import (
     DataflowTemplatedJobStartOperator,
 )
-from airflow.providers.google.cloud.operators.gcs import GCSDeleteBucketOperator
+from airflow.providers.google.cloud.operators.gcs import GCSDeleteObjectsOperator
 
 CLUSTER_NAME = "otg-preprocess-eqtl"
-AUTOSCALING = "do-ld-explosion"
+AUTOSCALING = "eqtl-preprocess"
 PROJECT_ID = "open-targets-genetics-dev"
 
 EQTL_CATALOG_SUSIE_LOCATION = "gs://eqtl_catalog_data/ebi_ftp/susie"
@@ -52,11 +52,10 @@
         ],
     )
 
-    delete_decompressed_job = GCSDeleteBucketOperator(
+    delete_decompressed_job = GCSDeleteObjectsOperator(
         task_id="delete_decompressed_files",
-        bucket_name=TEMP_DECOMPRESS_LOCATION,
-        force=True,
-        user_project=PROJECT_ID,
+        bucket_name=TEMP_DECOMPRESS_LOCATION.split("/")[2],
+        prefix=f"{TEMP_DECOMPRESS_LOCATION.split('/')[-1]}/",
     )
 
     (
@@ -69,6 +68,5 @@
         )
         >> common.install_dependencies(CLUSTER_NAME)
         >> ingestion_job
-        >> delete_decompressed_job
-        >> common.delete_cluster(CLUSTER_NAME)
+        >> [delete_decompressed_job, common.delete_cluster(CLUSTER_NAME)]
     )
diff --git a/src/gentropy/common/utils.py b/src/gentropy/common/utils.py
index 05704a299..4cdda9ad2 100644
--- a/src/gentropy/common/utils.py
+++ b/src/gentropy/common/utils.py
@@ -6,6 +6,7 @@
 from typing import TYPE_CHECKING, Tuple
 
 import hail as hl
+import numpy as np
 from pyspark.sql import functions as f
 from pyspark.sql import types as t
 
@@ -13,6 +14,7 @@
 
 if TYPE_CHECKING:
     from hail.table import Table
+    from numpy.typing import NDArray
     from pyspark.sql import Column
 
 
@@ -325,3 +327,24 @@ def parse_efos(efo_uri: Column) -> Column:
     """
     colname = efo_uri._jc.toString()
     return f.array_sort(f.expr(f"regexp_extract_all(`{colname}`, '([A-Z]+_[0-9]+)')"))
+
+
+def get_logsum(arr: NDArray[np.float64]) -> float:
+    """Calculates logarithm of the sum of exponentials of a vector. The max is extracted to ensure that the sum is not Inf.
+
+    This function emulates scipy's logsumexp expression.
+
+    Args:
+        arr (NDArray[np.float64]): input array
+
+    Returns:
+        float: logsumexp of the input array
+
+    Example:
+        >>> l = [0.2, 0.1, 0.05, 0]
+        >>> round(get_logsum(l), 6)
+        1.476557
+    """
+    themax = np.max(arr)
+    result = themax + np.log(np.sum(np.exp(arr - themax)))
+    return float(result)
diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 5e82055cb..7202ee578 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -113,11 +113,7 @@ class EqtlCatalogueConfig(StepConfig):
     eqtl_catalogue_paths_imported: str = MISSING
     eqtl_catalogue_study_index_out: str = MISSING
     eqtl_catalogue_credible_sets_out: str = MISSING
-    mqtl_quantification_methods: list[str] = field(
-        default_factory=lambda: [
-            "ge",
-        ]
-    )
+    mqtl_quantification_methods_blacklist: list[str] = field(default_factory=lambda: [])
     _target_: str = "gentropy.eqtl_catalogue.EqtlCatalogueStep"
 
 
diff --git a/src/gentropy/dataset/study_locus.py b/src/gentropy/dataset/study_locus.py
index 1867dfa94..4ff9f0172 100644
--- a/src/gentropy/dataset/study_locus.py
+++ b/src/gentropy/dataset/study_locus.py
@@ -6,12 +6,14 @@
 from typing import TYPE_CHECKING
 
 import pyspark.sql.functions as f
+from pyspark.sql.types import FloatType
 
 from gentropy.common.schemas import parse_spark_schema
 from gentropy.common.spark_helpers import (
     calculate_neglog_pvalue,
     order_array_of_structs_by_field,
 )
+from gentropy.common.utils import get_logsum
 from gentropy.dataset.dataset import Dataset
 from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
 from gentropy.method.clump import LDclumping
@@ -220,6 +222,28 @@ def assign_study_locus_id(study_id_col: Column, variant_id_col: Column) -> Colum
         variant_id_col = f.coalesce(variant_id_col, f.rand().cast("string"))
         return f.xxhash64(study_id_col, variant_id_col).alias("studyLocusId")
 
+    @classmethod
+    def calculate_credible_set_log10bf(cls: type[StudyLocus], logbfs: Column) -> Column:
+        """Calculate Bayes factor for the entire credible set. The Bayes factor is calculated as the logsumexp of the logBF values of the variants in the locus.
+
+        Args:
+            logbfs (Column): Array column with the logBF values of the variants in the locus.
+
+        Returns:
+            Column: log10 Bayes factor for the entire credible set.
+
+        Examples:
+            >>> spark.createDataFrame([([0.2, 0.1, 0.05, 0.0],)]).toDF("logBF").select(f.round(StudyLocus.calculate_credible_set_log10bf(f.col("logBF")), 7).alias("credibleSetlog10BF")).show()
+            +------------------+
+            |credibleSetlog10BF|
+            +------------------+
+            |         1.4765565|
+            +------------------+
+            <BLANKLINE>
+        """
+        logsumexp_udf = f.udf(lambda x: get_logsum(x), FloatType())
+        return logsumexp_udf(logbfs).cast("double").alias("credibleSetlog10BF")
+
     @classmethod
     def get_schema(cls: type[StudyLocus]) -> StructType:
         """Provides the schema for the StudyLocus dataset.
diff --git a/src/gentropy/datasource/eqtl_catalogue/finemapping.py b/src/gentropy/datasource/eqtl_catalogue/finemapping.py
index af814d94f..ff55ead72 100644
--- a/src/gentropy/datasource/eqtl_catalogue/finemapping.py
+++ b/src/gentropy/datasource/eqtl_catalogue/finemapping.py
@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
 import pyspark.sql.functions as f
 from pyspark.sql import Column, DataFrame, Window
@@ -13,17 +14,21 @@
     StructType,
 )
 
+from gentropy.common.session import Session
 from gentropy.common.utils import parse_pvalue
 from gentropy.dataset.study_locus import StudyLocus
 from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex
 
+if TYPE_CHECKING:
+    from pyspark.sql import DataFrame
+
 
 @dataclass
 class EqtlCatalogueFinemapping:
     """SuSIE finemapping dataset for eQTL Catalogue.
 
     Credible sets from SuSIE are extracted and transformed into StudyLocus objects:
-    - A study ID is defined as a triad between: the publication, the tissue, and the measured gene (e.g. Braineac2_substantia_nigra_ENSG00000248275)
+    - A study ID is defined as a triad between: the publication, the tissue, and the measured trait (e.g. Braineac2_substantia_nigra_ENSG00000248275)
     - Each row in the `credible_sets.tsv.gz` files is represented by molecular_trait_id/variant/rsid trios relevant for a given tissue. Each have their own finemapping statistics
     - log Bayes Factors are available for all variants in the `lbf_variable.txt` files
     """
@@ -135,7 +140,7 @@ def parse_susie_results(
                 cls._extract_dataset_id_from_file_path(f.input_file_name()),
             )
             .join(
-                f.broadcast(
+                (
                     credible_sets.withColumn(
                         "dataset_id",
                         cls._extract_dataset_id_from_file_path(f.input_file_name()),
@@ -176,18 +181,18 @@ def parse_susie_results(
                 f.col("logBF"),
                 f.lit("SuSie").alias("finemappingMethod"),
                 # Study metadata
+                f.col("molecular_trait_id").alias("traitFromSource"),
                 f.col("gene_id").alias("geneId"),
-                f.array(f.col("molecular_trait_id")).alias("traitFromSourceMappedIds"),
                 f.col("dataset_id"),
                 f.concat_ws(
                     "_",
                     f.col("study_label"),
                     f.col("sample_group"),
-                    f.col("gene_id"),
+                    f.col("molecular_trait_id"),
                 ).alias("studyId"),
-                f.col("tissue_id").alias("c"),
+                f.col("tissue_id").alias("tissueFromSourceId"),
                 EqtlCatalogueStudyIndex._identify_study_type(
-                    f.col("study_label")
+                    f.col("quant_method")
                 ).alias("studyType"),
                 f.col("study_label").alias("projectId"),
                 f.concat_ws(
@@ -220,19 +225,12 @@ def from_susie_results(
             field.name
             for field in StudyLocus.get_schema().fields
             if field.name in processed_finemapping_df.columns
-        ] + ["studyLocusId", "locus"]
+        ] + ["locus"]
         return StudyLocus(
             _df=(
                 processed_finemapping_df.withColumn(
                     "isLead",
-                    f.row_number().over(
-                        lead_w.orderBy(
-                            *[
-                                f.col("pValueExponent").asc(),
-                                f.col("pValueMantissa").asc(),
-                            ]
-                        )
-                    )
+                    f.row_number().over(lead_w.orderBy(f.desc("posteriorProbability")))
                     == f.lit(1),
                 )
                 .withColumn(
@@ -255,13 +253,59 @@ def from_susie_results(
                 )
                 .filter(f.col("isLead"))
                 .drop("isLead")
-                .withColumn(
-                    "studyLocusId",
+                .select(
+                    *study_locus_cols,
                     StudyLocus.assign_study_locus_id(
                         f.col("studyId"), f.col("variantId")
                     ),
+                    StudyLocus.calculate_credible_set_log10bf(
+                        f.col("locus.logBF")
+                    ).alias("credibleSetlog10BF"),
                 )
-                .select(study_locus_cols)
             ),
             _schema=StudyLocus.get_schema(),
         ).annotate_credible_sets()
+
+    @classmethod
+    def read_credible_set_from_source(
+        cls: type[EqtlCatalogueFinemapping],
+        session: Session,
+        credible_set_path: str | list[str],
+    ) -> DataFrame:
+        """Load raw credible sets from eQTL Catalogue.
+
+        Args:
+            session (Session): Spark session.
+            credible_set_path (str | list[str]): Path to raw table(s) containing finemapping results for any variant belonging to a credible set.
+
+        Returns:
+            DataFrame: Credible sets DataFrame.
+        """
+        return session.spark.read.csv(
+            credible_set_path,
+            sep="\t",
+            header=True,
+            schema=cls.raw_credible_set_schema,
+        )
+
+    @classmethod
+    def read_lbf_from_source(
+        cls: type[EqtlCatalogueFinemapping],
+        session: Session,
+        lbf_path: str | list[str],
+    ) -> DataFrame:
+        """Load raw log Bayes Factors from eQTL Catalogue.
+
+        Args:
+            session (Session): Spark session.
+            lbf_path (str | list[str]): Path to raw table(s) containing Log Bayes Factors for each variant.
+
+        Returns:
+            DataFrame: Log Bayes Factors DataFrame.
+        """
+        return session.spark.read.csv(
+            lbf_path,
+            sep="\t",
+            header=True,
+            schema=cls.raw_lbf_schema,
+        )
diff --git a/src/gentropy/datasource/eqtl_catalogue/study_index.py b/src/gentropy/datasource/eqtl_catalogue/study_index.py
index 611bcdff7..71cec1ec0 100644
--- a/src/gentropy/datasource/eqtl_catalogue/study_index.py
+++ b/src/gentropy/datasource/eqtl_catalogue/study_index.py
@@ -2,11 +2,14 @@
 
 from __future__ import annotations
 
+from itertools import chain
 from typing import TYPE_CHECKING
 
+import pandas as pd
 import pyspark.sql.functions as f
 from pyspark.sql.types import IntegerType, StringType, StructField, StructType
 
+from gentropy.common.session import Session
 from gentropy.dataset.study_index import StudyIndex
 
 if TYPE_CHECKING:
@@ -23,7 +26,7 @@ class EqtlCatalogueStudyIndex:
 
         - in the same publication (e.g. Alasoo_2018)
         - in the same cell type or tissue (e.g. monocytes)
-        - and for the same gene associated with the measured molecular trait (e.g. ENSG00000141510)
+        - and for the same measured molecular trait (e.g. ENSG00000141510)
 
     """
 
@@ -40,24 +43,45 @@ class EqtlCatalogueStudyIndex:
             StructField("quant_method", StringType(), True),
         ]
     )
-    raw_studies_metadata_path = "https://raw.githubusercontent.com/eQTL-Catalogue/eQTL-Catalogue-resources/master/data_tables/dataset_metadata.tsv"
+    raw_studies_metadata_path = "https://raw.githubusercontent.com/eQTL-Catalogue/eQTL-Catalogue-resources/19929ff6a99bf402194292a14f96f9615b35f65f/data_tables/dataset_metadata.tsv"
 
     @classmethod
     def _identify_study_type(
-        cls: type[EqtlCatalogueStudyIndex], study_label_col: Column
+        cls: type[EqtlCatalogueStudyIndex], quantification_method_col: Column
     ) -> Column:
-        """Identify the study type based on the study label.
+        """Identify the study type based on the method to quantify the trait.
 
         Args:
-            study_label_col (Column): column with the study label that identifies the supporting publication.
+            quantification_method_col (Column): column with the label of the method to quantify the trait. Available methods are [here](https://www.ebi.ac.uk/eqtl/Methods/)
 
         Returns:
             Column: The study type.
+
+        Examples:
+            >>> df = spark.createDataFrame([("ge",), ("exon",), ("tx",)], ["quant_method"])
+            >>> df.withColumn("study_type", EqtlCatalogueStudyIndex._identify_study_type(f.col("quant_method"))).show()
+            +------------+----------+
+            |quant_method|study_type|
+            +------------+----------+
+            |          ge|      eqtl|
+            |        exon|      eqtl|
+            |          tx|      eqtl|
+            +------------+----------+
+            <BLANKLINE>
         """
-        return f.when(
-            study_label_col == "Sun_2018",
-            f.lit("pqtl"),
-        ).otherwise(f.lit("eqtl"))
+        method_to_study_type_mapping = {
+            "ge": "eqtl",
+            "exon": "eqtl",
+            "tx": "eqtl",
+            "microarray": "eqtl",
+            "leafcutter": "sqtl",
+            "aptamer": "pqtl",
+            "txrev": "tuqtl",
+        }
+        map_expr = f.create_map(
+            *[f.lit(x) for x in chain(*method_to_study_type_mapping.items())]
+        )
+        return map_expr.getItem(quantification_method_col)
 
     @classmethod
     def get_studies_of_interest(
@@ -100,3 +124,24 @@ def from_susie_results(
             _df=processed_finemapping_df.select(study_index_cols).distinct(),
             _schema=StudyIndex.get_schema(),
         )
+
+    @classmethod
+    def read_studies_from_source(
+        cls: type[EqtlCatalogueStudyIndex],
+        session: Session,
+        mqtl_quantification_methods_blacklist: list[str],
+    ) -> DataFrame:
+        """Read raw studies metadata from eQTL Catalogue.
+
+        Args:
+            session (Session): Spark session.
+            mqtl_quantification_methods_blacklist (list[str]): Molecular trait quantification methods that we don't want to ingest. Available options in https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/data_tables/dataset_metadata.tsv
+
+        Returns:
+            DataFrame: raw studies metadata.
+        """
+        pd.DataFrame.iteritems = pd.DataFrame.items
+        return session.spark.createDataFrame(
+            pd.read_csv(cls.raw_studies_metadata_path, sep="\t"),
+            schema=cls.raw_studies_metadata_schema,
+        ).filter(~(f.col("quant_method").isin(mqtl_quantification_methods_blacklist)))
diff --git a/src/gentropy/datasource/finngen/finemapping.py b/src/gentropy/datasource/finngen/finemapping.py
index 8fd12b0ba..a31ddf511 100644
--- a/src/gentropy/datasource/finngen/finemapping.py
+++ b/src/gentropy/datasource/finngen/finemapping.py
@@ -253,10 +253,7 @@ def from_finngen_susie_finemapping(
         toploci_df = get_top_ranked_in_window(
             processed_finngen_finemapping_df,
             Window.partitionBy("studyId", "region", "credibleSetIndex").orderBy(
-                *[
-                    f.col("pValueExponent").asc(),
-                    f.col("pValueMantissa").asc(),
-                ]
+                f.desc("posteriorProbability")
             ),
         ).select(
             "variantId",
diff --git a/src/gentropy/eqtl_catalogue.py b/src/gentropy/eqtl_catalogue.py
index bfbfd79c2..7adc5d8a2 100644
--- a/src/gentropy/eqtl_catalogue.py
+++ b/src/gentropy/eqtl_catalogue.py
@@ -2,9 +2,6 @@
 
 from __future__ import annotations
 
-import pandas as pd
-import pyspark.sql.functions as f
-
 from gentropy.common.session import Session
 from gentropy.datasource.eqtl_catalogue.finemapping import EqtlCatalogueFinemapping
 from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex
@@ -19,7 +16,7 @@ class EqtlCatalogueStep:
     def __init__(
         self,
         session: Session,
-        mqtl_quantification_methods: list[str],
+        mqtl_quantification_methods_blacklist: list[str],
         eqtl_catalogue_paths_imported: str,
         eqtl_catalogue_study_index_out: str,
         eqtl_catalogue_credible_sets_out: str,
@@ -28,39 +25,33 @@ def __init__(
 
         Args:
             session (Session): Session object.
-            mqtl_quantification_methods (list[str]): Molecular trait quantification methods to ingest. Available options in https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/data_tables/dataset_metadata.tsv
+            mqtl_quantification_methods_blacklist (list[str]): Molecular trait quantification methods that we don't want to ingest. Available options in https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/data_tables/dataset_metadata.tsv
             eqtl_catalogue_paths_imported (str): Input eQTL Catalogue fine mapping results path.
             eqtl_catalogue_study_index_out (str): Output eQTL Catalogue study index path.
             eqtl_catalogue_credible_sets_out (str): Output eQTL Catalogue credible sets path.
         """
         # Extract
-        pd.DataFrame.iteritems = pd.DataFrame.items
-        studies_metadata = session.spark.createDataFrame(
-            pd.read_csv(EqtlCatalogueStudyIndex.raw_studies_metadata_path, sep="\t"),
-            schema=EqtlCatalogueStudyIndex.raw_studies_metadata_schema,
-        ).filter(f.col("quant_method").isin(list(mqtl_quantification_methods)))
+        studies_metadata = EqtlCatalogueStudyIndex.read_studies_from_source(
+            session, list(mqtl_quantification_methods_blacklist)
+        )
 
         # Load raw data only for the studies we are interested in ingestion. This makes the proces much lighter.
         studies_to_ingest = EqtlCatalogueStudyIndex.get_studies_of_interest(
             studies_metadata
         )
-        credible_sets_df = session.spark.read.csv(
-            [
+        credible_sets_df = EqtlCatalogueFinemapping.read_credible_set_from_source(
+            session,
+            credible_set_path=[
                 f"{eqtl_catalogue_paths_imported}/{qtd_id}.credible_sets.tsv"
                 for qtd_id in studies_to_ingest
             ],
-            sep="\t",
-            header=True,
-            schema=EqtlCatalogueFinemapping.raw_credible_set_schema,
         )
-        lbf_df = session.spark.read.csv(
-            [
+        lbf_df = EqtlCatalogueFinemapping.read_lbf_from_source(
+            session,
+            lbf_path=[
                 f"{eqtl_catalogue_paths_imported}/{qtd_id}.lbf_variable.txt"
                 for qtd_id in studies_to_ingest
             ],
-            sep="\t",
-            header=True,
-            schema=EqtlCatalogueFinemapping.raw_lbf_schema,
         )
 
         # Transform
diff --git a/src/gentropy/method/colocalisation.py b/src/gentropy/method/colocalisation.py
index fd56398f0..899343998 100644
--- a/src/gentropy/method/colocalisation.py
+++ b/src/gentropy/method/colocalisation.py
@@ -10,6 +10,7 @@
 from pyspark.ml.linalg import DenseVector, Vectors, VectorUDT
 from pyspark.sql.types import DoubleType
 
+from gentropy.common.utils import get_logsum
 from gentropy.dataset.colocalisation import Colocalisation
 
 if TYPE_CHECKING:
@@ -105,28 +106,6 @@ class Coloc:
 
     """
 
-    @staticmethod
-    def _get_logsum(log_bf: NDArray[np.float64]) -> float:
-        """Calculates logsum of vector.
-
-        This function calculates the log of the sum of the exponentiated
-        logs taking out the max, i.e. insuring that the sum is not Inf
-
-        Args:
-            log_bf (NDArray[np.float64]): log bayes factor
-
-        Returns:
-            float: logsum
-
-        Example:
-            >>> l = [0.2, 0.1, 0.05, 0]
-            >>> round(Coloc._get_logsum(l), 6)
-            1.476557
-        """
-        themax = np.max(log_bf)
-        result = themax + np.log(np.sum(np.exp(log_bf - themax)))
-        return float(result)
-
     @staticmethod
     def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector:
         """Calculate posterior probabilities for each hypothesis.
@@ -142,7 +121,7 @@ def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector:
             >>> Coloc._get_posteriors(l)
             DenseVector([0.279, 0.2524, 0.2401, 0.2284])
         """
-        diff = all_bfs - Coloc._get_logsum(all_bfs)
+        diff = all_bfs - get_logsum(all_bfs)
         bfs_posteriors = np.exp(diff)
         return Vectors.dense(bfs_posteriors)
 
@@ -166,7 +145,7 @@ def colocalise(
             Colocalisation: Colocalisation results
         """
         # register udfs
-        logsum = f.udf(Coloc._get_logsum, DoubleType())
+        logsum = f.udf(get_logsum, DoubleType())
         posteriors = f.udf(Coloc._get_posteriors, VectorUDT())
         return Colocalisation(
             _df=(

From 0b0db5ebcccd5cef0a0f683c2ce11952093f145c Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 15:09:21 +0000
Subject: [PATCH 10/86] fix: fixing merge conflict

---
 poetry.lock | 759 +++++++++++++++++++++++++---------------------------
 1 file changed, 367 insertions(+), 392 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index b27acb272..320208a14 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -424,13 +424,13 @@ openlineage = ["apache-airflow-providers-openlineage"]
 
 [[package]]
 name = "apache-airflow-providers-common-sql"
-version = "1.11.0"
+version = "1.11.1"
 description = "Provider package apache-airflow-providers-common-sql for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_common_sql-1.11.0-py3-none-any.whl", hash = "sha256:0c200bd17ff89fac03bce8404a3a7cec021dd99e136effe0d6b68c4642c925c5"},
-    {file = "apache_airflow_providers_common_sql-1.11.0.tar.gz", hash = "sha256:7667358efdf2ece87bfd136c786d729bc6f3274f8b87566ce9b432ab14b6aa33"},
+    {file = "apache_airflow_providers_common_sql-1.11.1-py3-none-any.whl", hash = "sha256:eb8531b9eedcfdcdd972f12a0bffadc289ade9c3587a5bb5bd146f3240d5eb02"},
+    {file = "apache_airflow_providers_common_sql-1.11.1.tar.gz", hash = "sha256:5b8c4d267677758dcb28b1a8976f10f30e50a4b568abda93a667ad6ff938060b"},
 ]
 
 [package.dependencies]
@@ -440,7 +440,7 @@ sqlparse = ">=0.4.2"
 
 [package.extras]
 openlineage = ["apache-airflow-providers-openlineage"]
-pandas = ["pandas (>=1.2.5)"]
+pandas = ["pandas (>=1.2.5,<2.2)"]
 
 [[package]]
 name = "apache-airflow-providers-ftp"
@@ -461,13 +461,13 @@ openlineage = ["apache-airflow-providers-openlineage"]
 
 [[package]]
 name = "apache-airflow-providers-google"
-version = "10.15.0"
+version = "10.16.0"
 description = "Provider package apache-airflow-providers-google for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_google-10.15.0-py3-none-any.whl", hash = "sha256:70d2d4feb66f06cd750ea673344f3be20d2d575c9645f7e2b030c73551a297fc"},
-    {file = "apache_airflow_providers_google-10.15.0.tar.gz", hash = "sha256:ff48fa0a29abec2645a8008c47d0a84a759b7203412707a5e2fc01558c4052a3"},
+    {file = "apache_airflow_providers_google-10.16.0-py3-none-any.whl", hash = "sha256:d9e3f04cc9c41ba077f8607e062a975ff3e18a583c51ac56d808455a2074e6ae"},
+    {file = "apache_airflow_providers_google-10.16.0.tar.gz", hash = "sha256:dde9ce5b6873558c92438fad3832a63345072736c69222de3246a1d7e6f109f1"},
 ]
 
 [package.dependencies]
@@ -524,7 +524,7 @@ grpcio-gcp = ">=0.2.2"
 httpx = "*"
 json-merge-patch = ">=0.2"
 looker-sdk = ">=22.2.0"
-pandas = ">=1.2.5"
+pandas = ">=1.2.5,<2.2"
 pandas-gbq = "*"
 proto-plus = ">=1.19.6"
 PyOpenSSL = "*"
@@ -553,13 +553,13 @@ trino = ["apache-airflow-providers-trino"]
 
 [[package]]
 name = "apache-airflow-providers-http"
-version = "4.9.1"
+version = "4.10.0"
 description = "Provider package apache-airflow-providers-http for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_http-4.9.1-py3-none-any.whl", hash = "sha256:3b7c002899193acccf19bc29fa98e2c91b0b5f2fa05524bc3042fab0dc1700fb"},
-    {file = "apache_airflow_providers_http-4.9.1.tar.gz", hash = "sha256:2bb670f9e89a4f4e50ce71faf8002aff729516feceba99f9947acbb5d7163754"},
+    {file = "apache_airflow_providers_http-4.10.0-py3-none-any.whl", hash = "sha256:3c9af9a64954973a3bc31d33adf145b9b4273e5194603bee86c4fad6c51d872f"},
+    {file = "apache_airflow_providers_http-4.10.0.tar.gz", hash = "sha256:f8280633053f25d7d11ef33f26aa7225fb081400a0dd97c6af9e0a9f59b26566"},
 ]
 
 [package.dependencies]
@@ -647,13 +647,13 @@ files = [
 
 [[package]]
 name = "argcomplete"
-version = "3.2.2"
+version = "3.2.3"
 description = "Bash tab completion for argparse"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "argcomplete-3.2.2-py3-none-any.whl", hash = "sha256:e44f4e7985883ab3e73a103ef0acd27299dbfe2dfed00142c35d4ddd3005901d"},
-    {file = "argcomplete-3.2.2.tar.gz", hash = "sha256:f3e49e8ea59b4026ee29548e24488af46e30c9de57d48638e24f54a1ea1000a2"},
+    {file = "argcomplete-3.2.3-py3-none-any.whl", hash = "sha256:c12355e0494c76a2a7b73e3a59b09024ca0ba1e279fb9ed6c1b82d5b74b6a70c"},
+    {file = "argcomplete-3.2.3.tar.gz", hash = "sha256:bf7900329262e481be5a15f56f19736b376df6f82ed27576fa893652c5de6c23"},
 ]
 
 [package.extras]
@@ -751,13 +751,13 @@ files = [
 
 [[package]]
 name = "azure-core"
-version = "1.30.0"
+version = "1.30.1"
 description = "Microsoft Azure Core Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.30.0.tar.gz", hash = "sha256:6f3a7883ef184722f6bd997262eddaf80cfe7e5b3e0caaaf8db1695695893d35"},
-    {file = "azure_core-1.30.0-py3-none-any.whl", hash = "sha256:3dae7962aad109610e68c9a7abb31d79720e1d982ddf61363038d175a5025e89"},
+    {file = "azure-core-1.30.1.tar.gz", hash = "sha256:26273a254131f84269e8ea4464f3560c731f29c0c1f69ac99010845f239c1a8f"},
+    {file = "azure_core-1.30.1-py3-none-any.whl", hash = "sha256:7c5ee397e48f281ec4dd773d67a0a47a0962ed6fa833036057f9ea067f688e74"},
 ]
 
 [package.dependencies]
@@ -817,13 +817,13 @@ msrest = ">=0.6.21"
 
 [[package]]
 name = "azure-storage-blob"
-version = "12.19.0"
+version = "12.19.1"
 description = "Microsoft Azure Blob Storage Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-storage-blob-12.19.0.tar.gz", hash = "sha256:26c0a4320a34a3c2a1b74528ba6812ebcb632a04cd67b1c7377232c4b01a5897"},
-    {file = "azure_storage_blob-12.19.0-py3-none-any.whl", hash = "sha256:7bbc2c9c16678f7a420367fef6b172ba8730a7e66df7f4d7a55d5b3c8216615b"},
+    {file = "azure-storage-blob-12.19.1.tar.gz", hash = "sha256:13e16ba42fc54ac2c7e8f976062173a5c82b9ec0594728e134aac372965a11b0"},
+    {file = "azure_storage_blob-12.19.1-py3-none-any.whl", hash = "sha256:c5530dc51c21c9564e4eb706cd499befca8819b10dd89716d3fc90d747556243"},
 ]
 
 [package.dependencies]
@@ -916,17 +916,17 @@ xyzservices = ">=2021.09.1"
 
 [[package]]
 name = "boto3"
-version = "1.34.50"
+version = "1.34.58"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "boto3-1.34.50-py3-none-any.whl", hash = "sha256:8d709365231234bc4f0ca98fdf33a25eeebf78072853c6aa3d259f0f5cf09877"},
-    {file = "boto3-1.34.50.tar.gz", hash = "sha256:290952be7899560039cb0042e8a2354f61a7dead0d0ca8bea6ba901930df0468"},
+    {file = "boto3-1.34.58-py3-none-any.whl", hash = "sha256:d213a6fea9db6d537b1e65924133d8279ada79a40bc840d4930e1b64be869d4c"},
+    {file = "boto3-1.34.58.tar.gz", hash = "sha256:09e3d17c718bc938a76774f31bc557b20733c0f5f9135a3e7782b55f3459cbdd"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.50,<1.35.0"
+botocore = ">=1.34.58,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -935,13 +935,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.50"
+version = "1.34.58"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "botocore-1.34.50-py3-none-any.whl", hash = "sha256:fda510559dbe796eefdb59561cc81be1b99afba3dee53fd23db9a3d587adc0ab"},
-    {file = "botocore-1.34.50.tar.gz", hash = "sha256:33ab82cb96c4bb684f0dbafb071808e4817d83debc88b223e7d988256370c6d7"},
+    {file = "botocore-1.34.58-py3-none-any.whl", hash = "sha256:8086a6ea27d0f658505ac81e6d7314e013561469c40db9b5a4692127eb5c97cf"},
+    {file = "botocore-1.34.58.tar.gz", hash = "sha256:d75216952886dc513ea1b5e2979a6af08feed2f537e3fc102e4a0a2ead563a35"},
 ]
 
 [package.dependencies]
@@ -1480,13 +1480,13 @@ dev = ["polib"]
 
 [[package]]
 name = "croniter"
-version = "2.0.1"
+version = "2.0.2"
 description = "croniter provides iteration for datetime object with cron like format"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "croniter-2.0.1-py2.py3-none-any.whl", hash = "sha256:4cb064ce2d8f695b3b078be36ff50115cf8ac306c10a7e8653ee2a5b534673d7"},
-    {file = "croniter-2.0.1.tar.gz", hash = "sha256:d199b2ec3ea5e82988d1f72022433c5f9302b3b3ea9e6bfd6a1518f6ea5e700a"},
+    {file = "croniter-2.0.2-py2.py3-none-any.whl", hash = "sha256:78bf110a2c7dbbfdd98b926318ae6c64a731a4c637c7befe3685755110834746"},
+    {file = "croniter-2.0.2.tar.gz", hash = "sha256:8bff16c9af4ef1fb6f05416973b8f7cb54997c02f2f8365251f9bf1dded91866"},
 ]
 
 [package.dependencies]
@@ -2322,18 +2322,18 @@ tests = ["nox (>=2020.12.31,<2022.6)"]
 
 [[package]]
 name = "google-analytics-admin"
-version = "0.22.6"
+version = "0.22.7"
 description = "Google Analytics Admin API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-analytics-admin-0.22.6.tar.gz", hash = "sha256:9299b30a51c1da4bd77406211efe10f30f672ef7b36f3d223a0248ebf2916d05"},
-    {file = "google_analytics_admin-0.22.6-py2.py3-none-any.whl", hash = "sha256:f4122133bbd66693f641d548a6d3a8ffcd29eabca17392bf2b7e6b3d83aaee5e"},
+    {file = "google-analytics-admin-0.22.7.tar.gz", hash = "sha256:9546afaddf7ee275ec4729de6da8b15f27d4d245ee8896f80800e22572fc7987"},
+    {file = "google_analytics_admin-0.22.7-py2.py3-none-any.whl", hash = "sha256:09463653ecb42306c34fb07c9ff27193737aae8e63c52b8f695bf705e8589b46"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2363,13 +2363,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.119.0"
+version = "2.121.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-python-client-2.119.0.tar.gz", hash = "sha256:ff9ef7539eaf7e088a481b25d1af4704210b07863e1d51b5ee498b910a3a46a3"},
-    {file = "google_api_python_client-2.119.0-py2.py3-none-any.whl", hash = "sha256:84e43bdb58dd8d2301669513863996378ffe9a3bf6d23b5ccd4f1e021323dbeb"},
+    {file = "google-api-python-client-2.121.0.tar.gz", hash = "sha256:df863ece4db8b36ce1053ebd983e43fbc5b664209eed78e82cc84ae56ddac6c0"},
+    {file = "google_api_python_client-2.121.0-py2.py3-none-any.whl", hash = "sha256:bb4da677150dd16c45818620baca8a63208c6f4180a0691ad1c1708b384c10be"},
 ]
 
 [package.dependencies]
@@ -2437,13 +2437,13 @@ tool = ["click (>=6.0.0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.42.1"
+version = "1.43.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.42.1.tar.gz", hash = "sha256:679068e068e29059d673a6410483fea762286fa07739d684fb1b4626698e0805"},
-    {file = "google_cloud_aiplatform-1.42.1-py2.py3-none-any.whl", hash = "sha256:9f25ebd306807972cf05a578abc16695c4f72d4a2dd7e7b1624dbe247937ba24"},
+    {file = "google-cloud-aiplatform-1.43.0.tar.gz", hash = "sha256:bd73af311a827e79bd2094a508e9343a2fa71b8ae5d8fa1178d0a96d034b421b"},
+    {file = "google_cloud_aiplatform-1.43.0-py2.py3-none-any.whl", hash = "sha256:8aa1bdcde1bec85ea53db26b638744f7bad91f3af27c2ab8a3ea32079fb709e3"},
 ]
 
 [package.dependencies]
@@ -2462,33 +2462,33 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
+full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (==5.3.1)"]
 prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<0.103.1)", "httpx (>=0.23.0,<0.25.0)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
-ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)"]
+ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"]
 tensorboard = ["tensorflow (>=2.3.0,<2.15.0)"]
-testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
+testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-appengine-logging"
-version = "1.4.2"
+version = "1.4.3"
 description = "Google Cloud Appengine Logging API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-appengine-logging-1.4.2.tar.gz", hash = "sha256:134de64a84017cfe26a4b3a325bcc9b4a2dba05f9c9d3902ee24b4b1fa3e28af"},
-    {file = "google_cloud_appengine_logging-1.4.2-py2.py3-none-any.whl", hash = "sha256:08d46bacf7e6bb4cb963dc5c0ab6995f2cd3fec69c7a2862405e98dce0c194bc"},
+    {file = "google-cloud-appengine-logging-1.4.3.tar.gz", hash = "sha256:fb504e6199fe8de85baa9d31cecf6776877851fe58867de603317ec7cc739987"},
+    {file = "google_cloud_appengine_logging-1.4.3-py2.py3-none-any.whl", hash = "sha256:8e30af51d853f219caf29e8b8b342b9ce8214b29f334dafae38d39aaaff7d372"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2509,18 +2509,18 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 
 [[package]]
 name = "google-cloud-automl"
-version = "2.13.2"
+version = "2.13.3"
 description = "Google Cloud Automl API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-automl-2.13.2.tar.gz", hash = "sha256:d90273ab8b74a28d7c81b237cc5cf92b189d3a47d2b90dac8cd36722527ba24e"},
-    {file = "google_cloud_automl-2.13.2-py2.py3-none-any.whl", hash = "sha256:bb36c140955ae14b26969054e8bcdfe9fa81e6f99e430d832579d6884647b8f7"},
+    {file = "google-cloud-automl-2.13.3.tar.gz", hash = "sha256:891a9082eaedeb17bc5bb724fc150b7702c684d7420f9f22ae3f7c5fc611c71a"},
+    {file = "google_cloud_automl-2.13.3-py2.py3-none-any.whl", hash = "sha256:8117943ad1534f1d11d40e0937ade7965c56a6d7fd2c2314e56942361727a6a2"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2531,30 +2531,30 @@ storage = ["google-cloud-storage (>=1.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-batch"
-version = "0.17.12"
+version = "0.17.14"
 description = "Google Cloud Batch API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-batch-0.17.12.tar.gz", hash = "sha256:ff351f5a4a46283fa7e8353f71cdb1fb750d4d407f7a14eae752801a22b76fe9"},
-    {file = "google_cloud_batch-0.17.12-py2.py3-none-any.whl", hash = "sha256:4d6d0f6c392c5ed2e1fe59d38f1151b1414abcf9daa50ffb25a5981984fd0b63"},
+    {file = "google-cloud-batch-0.17.14.tar.gz", hash = "sha256:03c29c384f5872cf2e3aa4f2f7446f5bbecd1aea035f19cd7c595b245df96179"},
+    {file = "google_cloud_batch-0.17.14-py2.py3-none-any.whl", hash = "sha256:0bebf878e3f4cb470ba788dc9d9d78a11db2c16bea7cc9da9bfde24522bc1914"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.17.2"
+version = "3.18.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.17.2.tar.gz", hash = "sha256:6e1cf669a40e567ab3289c7b5f2056363da9fcb85d9a4736ee90240d4a7d84ea"},
-    {file = "google_cloud_bigquery-3.17.2-py2.py3-none-any.whl", hash = "sha256:cdadf5283dca55a1a350bacf8c8a7466169d3cf46c5a0a3abc5e9aa0b0a51dee"},
+    {file = "google-cloud-bigquery-3.18.0.tar.gz", hash = "sha256:74f0fc6f0ba9477f808d25924dc8a052c55f7ca91064e83e16d3ee5fb7ca77ab"},
+    {file = "google_cloud_bigquery-3.18.0-py2.py3-none-any.whl", hash = "sha256:3520552075502c69710d37b1e9600c84e6974ad271914677d16bfafa502857fb"},
 ]
 
 [package.dependencies]
@@ -2578,42 +2578,21 @@ tqdm = ["tqdm (>=4.7.4,<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery-datatransfer"
-version = "3.15.0"
+version = "3.15.1"
 description = "Google Cloud Bigquery Datatransfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-datatransfer-3.15.0.tar.gz", hash = "sha256:fcb0613c9a2b210bf28889df372ecf25c5723af1f6d7b144aedc220b65d366f4"},
-    {file = "google_cloud_bigquery_datatransfer-3.15.0-py2.py3-none-any.whl", hash = "sha256:edf78337b1e2f96ed1c582e617b2545ea176cc6e90f8ff983bf2da5164d586b1"},
+    {file = "google-cloud-bigquery-datatransfer-3.15.1.tar.gz", hash = "sha256:d80d2fe940451de3f47ec53bd5edb601abbb1df4189cde1fa386d80f71bea762"},
+    {file = "google_cloud_bigquery_datatransfer-3.15.1-py2.py3-none-any.whl", hash = "sha256:24da33d0296cfb3f606df3f2c387e7d73f4678c5f9d9aec82db8b93a4482e6e0"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
-[[package]]
-name = "google-cloud-bigquery-storage"
-version = "2.24.0"
-description = "Google Cloud Bigquery Storage API client library"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "google-cloud-bigquery-storage-2.24.0.tar.gz", hash = "sha256:b4af5b9aacd8396b8407d1b877601a376d8eea6d192823a8a7881bd2fdc076ce"},
-    {file = "google_cloud_bigquery_storage-2.24.0-py2.py3-none-any.whl", hash = "sha256:7981eb2758cba56603058d11bb1eeeebf2e1c18097a7118a894510a16e02be52"},
-]
-
-[package.dependencies]
-google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.22.0,<2.0.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
-
-[package.extras]
-fastavro = ["fastavro (>=0.21.2)"]
-pandas = ["importlib-metadata (>=1.0.0)", "pandas (>=0.21.1)"]
-pyarrow = ["pyarrow (>=0.15.0)"]
-
 [[package]]
 name = "google-cloud-bigtable"
 version = "2.23.0"
@@ -2637,53 +2616,53 @@ libcst = ["libcst (>=0.2.5)"]
 
 [[package]]
 name = "google-cloud-build"
-version = "3.23.2"
+version = "3.23.3"
 description = "Google Cloud Build API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-build-3.23.2.tar.gz", hash = "sha256:52ee4e94298401f7ba6ef32f9a7e3bdaa478867fd0ef6f93017b88a3832aa7e2"},
-    {file = "google_cloud_build-3.23.2-py2.py3-none-any.whl", hash = "sha256:233580a601a266f643cdb68db844f9568ba63065e3cdcc252e48e55292dd4a78"},
+    {file = "google-cloud-build-3.23.3.tar.gz", hash = "sha256:6e8dd88cb816f30bb55cec1e4d2285d570a223d34df516fb017a495ed605720e"},
+    {file = "google_cloud_build-3.23.3-py2.py3-none-any.whl", hash = "sha256:b01060544bd0fd349f66c2af4c79312ec08d644237a27615e1a75eeee910189e"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-compute"
-version = "1.17.0"
+version = "1.18.0"
 description = "Google Cloud Compute API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-compute-1.17.0.tar.gz", hash = "sha256:74fb3b8527b461c0f796e36a1e417a4fc7d31c2e3fbb71cc270b30e931d62f8e"},
-    {file = "google_cloud_compute-1.17.0-py2.py3-none-any.whl", hash = "sha256:f281f1960f236d3be6614002af11becfe088600f2d3fa7da92dade28fac42f0e"},
+    {file = "google-cloud-compute-1.18.0.tar.gz", hash = "sha256:412237183877eb2838aa6e08cdaa6cf17f399451a166e3a9aa33ae894cc15a1d"},
+    {file = "google_cloud_compute-1.18.0-py2.py3-none-any.whl", hash = "sha256:fd827495ffc3a006a70f984133cb12191572497d96a522056a75414db918f37d"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-container"
-version = "2.41.0"
+version = "2.43.0"
 description = "Google Cloud Container API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-container-2.41.0.tar.gz", hash = "sha256:e43505690ab322f37ff15a3b309341aec31a0781c034eec46f69eecf1de70c97"},
-    {file = "google_cloud_container-2.41.0-py2.py3-none-any.whl", hash = "sha256:fb7f53ba8e750ee1ccefae971b0123880753b41dab27ef8e05711d62b6d38b3e"},
+    {file = "google-cloud-container-2.43.0.tar.gz", hash = "sha256:45f1a142fd70af97cc30795e31a9b048a436a29bf79e9a464af51ee14745893d"},
+    {file = "google_cloud_container-2.43.0-py2.py3-none-any.whl", hash = "sha256:b5c28d28df17be70fb12f5141f93b455fa261e693d65add989ae018d521a4b05"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2707,160 +2686,160 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
 name = "google-cloud-datacatalog"
-version = "3.18.2"
+version = "3.18.3"
 description = "Google Cloud Datacatalog API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-datacatalog-3.18.2.tar.gz", hash = "sha256:512a3c95d51b7ec02bbe3c39f8c3ae6c50ce957915e063e8c07615b111c11eb9"},
-    {file = "google_cloud_datacatalog-3.18.2-py2.py3-none-any.whl", hash = "sha256:1de2f9c214779b90f558d952daf2dd9d02cc8e6c89b0d5df338bf934abc7c524"},
+    {file = "google-cloud-datacatalog-3.18.3.tar.gz", hash = "sha256:77831301982c795e6223cdc0ee54a46defd21205fd65f434a4b1df6014ad7e9e"},
+    {file = "google_cloud_datacatalog-3.18.3-py2.py3-none-any.whl", hash = "sha256:8116f0bca6709b4cd984c85cd83f952bc0b1bb915244dfe478c9d425fce09057"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataflow-client"
-version = "0.8.9"
+version = "0.8.10"
 description = "Google Cloud Dataflow Client API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataflow-client-0.8.9.tar.gz", hash = "sha256:f024118695f2e5b8ca2f57e22c7e479238bd6a7bd9c24bf5f60c0893ed234fb7"},
-    {file = "google_cloud_dataflow_client-0.8.9-py2.py3-none-any.whl", hash = "sha256:2dbfc4333dbaf825fd1a1baaf6899f240bde80510e73ea23e29525681d279c4e"},
+    {file = "google-cloud-dataflow-client-0.8.10.tar.gz", hash = "sha256:6aa8178743bce4b6c9142b492e649db84f55db70a88ec29a3d889054c4752144"},
+    {file = "google_cloud_dataflow_client-0.8.10-py2.py3-none-any.whl", hash = "sha256:4b0dce886ec65fbf3195f87e956d559f6d4bd88a03b527aa8498c303e2d4d39a"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataform"
-version = "0.5.8"
+version = "0.5.9"
 description = "Google Cloud Dataform API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataform-0.5.8.tar.gz", hash = "sha256:40009ed80dfb152f675db4395b86b45bf5ae9a032c7f37c98b464066ff6ff54e"},
-    {file = "google_cloud_dataform-0.5.8-py2.py3-none-any.whl", hash = "sha256:3d1810b42e1bb533af7e891e3e478b5fa36b412542d6054d77c38f55cb71e3a4"},
+    {file = "google-cloud-dataform-0.5.9.tar.gz", hash = "sha256:2733c3fbda4f0134629e2d6554db93e1840683567043906268718b1a874eb9ac"},
+    {file = "google_cloud_dataform-0.5.9-py2.py3-none-any.whl", hash = "sha256:c94e61358a68de389803bb06b936f815108211b3324e1f143db9a8bd2bb6a081"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataplex"
-version = "1.12.2"
+version = "1.12.3"
 description = "Google Cloud Dataplex API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataplex-1.12.2.tar.gz", hash = "sha256:fdd89ce585954a8e75972be5a00475b1ff1d2f26dadb29ef574deaa654aa2236"},
-    {file = "google_cloud_dataplex-1.12.2-py2.py3-none-any.whl", hash = "sha256:c6ae7a85276e9eaa4072e27ffd90aef52fa0bb2d1d21db6a1af8322b1278b758"},
+    {file = "google-cloud-dataplex-1.12.3.tar.gz", hash = "sha256:c0692b6713fce6180df6d59bfb972d06f9fae264f33e7f2ba781d785f64ab8c3"},
+    {file = "google_cloud_dataplex-1.12.3-py2.py3-none-any.whl", hash = "sha256:2ef7943b331ff254d2a8d2395c6a3b666837a73d32da1c4572a07453c0069dd3"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc"
-version = "5.9.2"
+version = "5.9.3"
 description = "Google Cloud Dataproc API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataproc-5.9.2.tar.gz", hash = "sha256:1352e3cc4e146db1f09fa4287559248c8b3d9c0cfecea56c25c3f4f63d58e4f8"},
-    {file = "google_cloud_dataproc-5.9.2-py2.py3-none-any.whl", hash = "sha256:a728e35db70132dc5d2e6b27627825775f5a0a2cd7024fa1149669d3de4b436d"},
+    {file = "google-cloud-dataproc-5.9.3.tar.gz", hash = "sha256:97d647891e7f4cd25f6b839ae57cd355809df2ca396653ed24af62b4ef02f412"},
+    {file = "google_cloud_dataproc-5.9.3-py2.py3-none-any.whl", hash = "sha256:034615e8aca7f2b917a29ee1ca317fa65f8fa878018f2b80a2487e09e32b297a"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc-metastore"
-version = "1.15.2"
+version = "1.15.3"
 description = "Google Cloud Dataproc Metastore API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dataproc-metastore-1.15.2.tar.gz", hash = "sha256:61c4832bce9ba8288c81d48a2661a853e3d852210ebec15b7e505bcf9c95d26e"},
-    {file = "google_cloud_dataproc_metastore-1.15.2-py2.py3-none-any.whl", hash = "sha256:ab6bba67d94665126aff5ddff697255eab5f12d23a4d6dfe7f5c2d20e2aa0712"},
+    {file = "google-cloud-dataproc-metastore-1.15.3.tar.gz", hash = "sha256:1f8a5a66e43a4d1c119e9be225ba66b041f432d046b5c4da6717fbd56fcfb4f5"},
+    {file = "google_cloud_dataproc_metastore-1.15.3-py2.py3-none-any.whl", hash = "sha256:871dcea73ae2ab2ecfb6da1fe1bc82206eee817b744755f7b24aaef554b8d896"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dlp"
-version = "3.15.2"
+version = "3.16.0"
 description = "Google Cloud Dlp API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-dlp-3.15.2.tar.gz", hash = "sha256:4edcdae94b950942549a81fd8455565734df5f8913312f4443e8b1a189bdc4e8"},
-    {file = "google_cloud_dlp-3.15.2-py2.py3-none-any.whl", hash = "sha256:4d5e0d2b055bfabba61ee2565c3461055a82f22d44f9aec3f73275f9d6b69b27"},
+    {file = "google-cloud-dlp-3.16.0.tar.gz", hash = "sha256:0d69a1f7a5ef88756c99e56b8af4c39eae40d2106883f0e2794c5cb3642696d5"},
+    {file = "google_cloud_dlp-3.16.0-py2.py3-none-any.whl", hash = "sha256:0a1a008cb95a31f29e19dc7a822c4fd27b28dc941164e7bd6034170fd486e55b"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-kms"
-version = "2.21.2"
+version = "2.21.3"
 description = "Google Cloud Kms API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-kms-2.21.2.tar.gz", hash = "sha256:f06ad9dfc801544fba1183788b964f6b017483a66091aa686b51abd074806c84"},
-    {file = "google_cloud_kms-2.21.2-py2.py3-none-any.whl", hash = "sha256:98a092fb7501d0c7d4a64e409a8a0510916f7f982fafaae9572835ac8080ba0f"},
+    {file = "google-cloud-kms-2.21.3.tar.gz", hash = "sha256:5e87d5a7b7d8f5b5c9f0f648f352a7886951a8b4c72703eb9ce7ba8076fc2533"},
+    {file = "google_cloud_kms-2.21.3-py2.py3-none-any.whl", hash = "sha256:910bd58bce0bd2b6c413f8a32b7a5a0d77e34ab7204ce18e2dcdadeec6914253"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-language"
-version = "2.13.2"
+version = "2.13.3"
 description = "Google Cloud Language API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-language-2.13.2.tar.gz", hash = "sha256:813ad3dddca97890710098e76ad48c37ea63ea3a14a0f6d7f4246c6fb15cb2a5"},
-    {file = "google_cloud_language-2.13.2-py2.py3-none-any.whl", hash = "sha256:74ececa7918f183ed7652b3b6ba57adb3541e199803bd9ce8faa2f048997a2db"},
+    {file = "google-cloud-language-2.13.3.tar.gz", hash = "sha256:569d35260af906de25b8e2a76b6364e05809b8453afd89da738d4a4fcb90846f"},
+    {file = "google_cloud_language-2.13.3-py2.py3-none-any.whl", hash = "sha256:b060de20d8ed2b20b7b7ebd8a46ae240df36c9977db383db5155276d2b1f6b9a"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2886,35 +2865,35 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-cloud-memcache"
-version = "1.9.2"
+version = "1.9.3"
 description = "Google Cloud Memcache API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-memcache-1.9.2.tar.gz", hash = "sha256:d6ab1600fad9617731666ebae780d7e4c15ea58eb3c573296d533380567c2018"},
-    {file = "google_cloud_memcache-1.9.2-py2.py3-none-any.whl", hash = "sha256:4c22b5d15f8d53969a1a5bc2171bf9deda446e96b42ddee8db1f30dd6e370ca2"},
+    {file = "google-cloud-memcache-1.9.3.tar.gz", hash = "sha256:ee5ff95afe47efc1a0746ba13b2f56996bc018f158f6b0cad69d835b0447d6e8"},
+    {file = "google_cloud_memcache-1.9.3-py2.py3-none-any.whl", hash = "sha256:d1574670dcdb861cc772e2b797f7e5fb83b9dd7ef2ce34fbe4ff54d454a816d3"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-monitoring"
-version = "2.19.2"
+version = "2.19.3"
 description = "Google Cloud Monitoring API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-monitoring-2.19.2.tar.gz", hash = "sha256:3881b026204e0648707461a9783ef7e84c6c716bd067a3864b1cb3d4c9f5dc73"},
-    {file = "google_cloud_monitoring-2.19.2-py2.py3-none-any.whl", hash = "sha256:4d60247403d36576c561ed3cc684131d32a455e9bdc188c14cc83d7cb6b28fa2"},
+    {file = "google-cloud-monitoring-2.19.3.tar.gz", hash = "sha256:37641e537986fd2227f8738b879d605a8cdf4c315cdc60e86e109347ab1ca1d7"},
+    {file = "google_cloud_monitoring-2.19.3-py2.py3-none-any.whl", hash = "sha256:448016170deb4f16fdb57d12f639a0b61e865f7a22ec9a85b3e12295046b62b0"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2923,47 +2902,47 @@ pandas = ["pandas (>=0.23.2)"]
 
 [[package]]
 name = "google-cloud-orchestration-airflow"
-version = "1.12.0"
+version = "1.12.1"
 description = "Google Cloud Orchestration Airflow API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-orchestration-airflow-1.12.0.tar.gz", hash = "sha256:2e7b6f507b3ef9796638faed6d0cdb402db134bbb60b3f46cb115b16b44e88ee"},
-    {file = "google_cloud_orchestration_airflow-1.12.0-py2.py3-none-any.whl", hash = "sha256:683135931bd714027cbc984cad49ff811759b40e5e522dbb5a19c052f4640ebf"},
+    {file = "google-cloud-orchestration-airflow-1.12.1.tar.gz", hash = "sha256:659a72f506a9441f1938664e98cdced9a4a852dc5d8c533a5c2f822cefe72eb1"},
+    {file = "google_cloud_orchestration_airflow-1.12.1-py2.py3-none-any.whl", hash = "sha256:9dab139528860c3d10b9affe6a7e34848f10e15f5800554792c1bab37f713e3d"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-os-login"
-version = "2.14.2"
+version = "2.14.3"
 description = "Google Cloud Os Login API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-os-login-2.14.2.tar.gz", hash = "sha256:9b26fe84e8d3cd2499e3b306283909fb57d1a8f40377822416085fd174345294"},
-    {file = "google_cloud_os_login-2.14.2-py2.py3-none-any.whl", hash = "sha256:24c64b06b4a2e07d35b47eb9074d26c3769dd00f4f22b352b8b9aeee3893b2a1"},
+    {file = "google-cloud-os-login-2.14.3.tar.gz", hash = "sha256:b1a15ec7ee306de890750822dc4aab04c64e5bcf76447590f45551bf1afcf1e8"},
+    {file = "google_cloud_os_login-2.14.3-py2.py3-none-any.whl", hash = "sha256:de19bb2ade36e982256050972b5f68be34a0f4c70365c76a7cd91de3f0e7985f"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-pubsub"
-version = "2.19.7"
+version = "2.20.1"
 description = "Google Cloud Pub/Sub API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-pubsub-2.19.7.tar.gz", hash = "sha256:da5f1eb217c0727befa7c85b9b95e6a89b32b422d548c9cf9a1e029410270b87"},
-    {file = "google_cloud_pubsub-2.19.7-py2.py3-none-any.whl", hash = "sha256:443cb58e2440729876f3b0d6046c30c7a873aeb247e5e17f161fe967c91b53fb"},
+    {file = "google-cloud-pubsub-2.20.1.tar.gz", hash = "sha256:b6d06f1827968273c42b57a09f642462649c9504dc0f8756f99770f4e3e755ad"},
+    {file = "google_cloud_pubsub-2.20.1-py2.py3-none-any.whl", hash = "sha256:06dd62181e2f248f32b9077f4dc07b413191a84fc06d7323b208602d887207bc"},
 ]
 
 [package.dependencies]
@@ -2980,84 +2959,84 @@ libcst = ["libcst (>=0.3.10)"]
 
 [[package]]
 name = "google-cloud-redis"
-version = "2.15.2"
+version = "2.15.3"
 description = "Google Cloud Redis API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-redis-2.15.2.tar.gz", hash = "sha256:baaf93114e4acb752e6a06b5f58e7c50bf683eb0e138645fece76e0a0159c188"},
-    {file = "google_cloud_redis-2.15.2-py2.py3-none-any.whl", hash = "sha256:ec239088ba8901014d716c5e763227cb9978bc442dc8e3ae23d2bfb9fb7bd8d3"},
+    {file = "google-cloud-redis-2.15.3.tar.gz", hash = "sha256:e6a231e45107038cfe498dfad1f6dad2ca7bdca3a931323730be03ab7a000a8f"},
+    {file = "google_cloud_redis-2.15.3-py2.py3-none-any.whl", hash = "sha256:df79f6f8a81f948d2d5343a76e7c9108ab56eab9279be0763da6968f497b2bdb"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-resource-manager"
-version = "1.12.2"
+version = "1.12.3"
 description = "Google Cloud Resource Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-resource-manager-1.12.2.tar.gz", hash = "sha256:2ede446a5087b236f0e1fb39cca3791bae97eb0d9125057401454b190d5572ee"},
-    {file = "google_cloud_resource_manager-1.12.2-py2.py3-none-any.whl", hash = "sha256:45abbb8911195cc831cc77c8e3be84decc271686579b332d4142af507f423ebf"},
+    {file = "google-cloud-resource-manager-1.12.3.tar.gz", hash = "sha256:809851824119834e4f2310b2c4f38621c1d16b2bb14d5b9f132e69c79d355e7f"},
+    {file = "google_cloud_resource_manager-1.12.3-py2.py3-none-any.whl", hash = "sha256:92be7d6959927b76d90eafc4028985c37975a46ded5466a018f02e8649e113d4"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-run"
-version = "0.10.4"
+version = "0.10.5"
 description = "Google Cloud Run API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-run-0.10.4.tar.gz", hash = "sha256:e278e91822c26a895190e4193b84950cb9aa3f437f6895c806620a1890e2cc90"},
-    {file = "google_cloud_run-0.10.4-py2.py3-none-any.whl", hash = "sha256:179045cc576e3aa2446ae2a11537a1af6c032407cd28f6bb58b1f8abdee2b796"},
+    {file = "google-cloud-run-0.10.5.tar.gz", hash = "sha256:d6c546693ad1232794b75c617871f2e46283c0cd0c4cc3886b85b8f0a3ba575f"},
+    {file = "google_cloud_run-0.10.5-py2.py3-none-any.whl", hash = "sha256:0f68125f13d1a87f74e1e78c37fa6afb4b094d76dad2b9f951d5526648e8939c"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-secret-manager"
-version = "2.18.2"
+version = "2.18.3"
 description = "Google Cloud Secret Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-secret-manager-2.18.2.tar.gz", hash = "sha256:a00d62115a70083e86b1d44ca7ebcae041b36a44cc62ea57de4005731f8d3c88"},
-    {file = "google_cloud_secret_manager-2.18.2-py2.py3-none-any.whl", hash = "sha256:bb2f2b23a588f9eeae51df4a028d6198a603231a056f838e6a476b5aecadbbca"},
+    {file = "google-cloud-secret-manager-2.18.3.tar.gz", hash = "sha256:1db2f409324536e34f985081d389e3974ca3a3668df7845cad0be03ab8c0fa7d"},
+    {file = "google_cloud_secret_manager-2.18.3-py2.py3-none-any.whl", hash = "sha256:4d4af82bddd9099ebdbe79e0c6b68f6c6cabea8323a3c1275bcead8f56310fb7"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-spanner"
-version = "3.42.0"
+version = "3.43.0"
 description = "Google Cloud Spanner API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-spanner-3.42.0.tar.gz", hash = "sha256:13b6aba86059fd03f36c032c4143274d6883d39e2d32bf753e0ad3d2dcd08e12"},
-    {file = "google_cloud_spanner-3.42.0-py2.py3-none-any.whl", hash = "sha256:53a68d3cce1df986729244e829a607cd5b97c8f1dd721d8af0cfdd12413fb09f"},
+    {file = "google-cloud-spanner-3.43.0.tar.gz", hash = "sha256:0662e95fe314674a3b8b2fa3c19e81e544d94f584ca6965b98c95083a8864222"},
+    {file = "google_cloud_spanner-3.43.0-py2.py3-none-any.whl", hash = "sha256:b83f286459547b551b9fec5e13becadd7c7786d94e61a6df1007966536cf35a1"},
 ]
 
 [package.dependencies]
@@ -3076,35 +3055,35 @@ tracing = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20
 
 [[package]]
 name = "google-cloud-speech"
-version = "2.25.0"
+version = "2.25.1"
 description = "Google Cloud Speech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-speech-2.25.0.tar.gz", hash = "sha256:070b7a73735d871cdcf5590238e9c4ff59bb5f524172876ba55646b5c5eb86b8"},
-    {file = "google_cloud_speech-2.25.0-py2.py3-none-any.whl", hash = "sha256:3741c1b0cea6e86b389572c25c6d4aee93d77579651edc79fe9e22d7a574ecc5"},
+    {file = "google-cloud-speech-2.25.1.tar.gz", hash = "sha256:5b7470a949f5a77c40511c3664ac339fe0a4704482f9b6b3a2ba554b7d2b2cdc"},
+    {file = "google_cloud_speech-2.25.1-py2.py3-none-any.whl", hash = "sha256:b08ab9648774949295a869a69473af9b9a65f038436a2a1a560ec5ba28d2b227"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.14.0"
+version = "2.15.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.14.0.tar.gz", hash = "sha256:2d23fcf59b55e7b45336729c148bb1c464468c69d5efbaee30f7201dd90eb97e"},
-    {file = "google_cloud_storage-2.14.0-py2.py3-none-any.whl", hash = "sha256:8641243bbf2a2042c16a6399551fbb13f062cbc9a2de38d6c0bb5426962e9dbd"},
+    {file = "google-cloud-storage-2.15.0.tar.gz", hash = "sha256:7560a3c48a03d66c553dc55215d35883c680fe0ab44c23aa4832800ccc855c74"},
+    {file = "google_cloud_storage-2.15.0-py2.py3-none-any.whl", hash = "sha256:5d9237f88b648e1d724a0f20b5cde65996a37fe51d75d17660b1404097327dd2"},
 ]
 
 [package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
-google-auth = ">=2.23.3,<3.0dev"
+google-api-core = ">=2.15.0,<3.0.0dev"
+google-auth = ">=2.26.1,<3.0dev"
 google-cloud-core = ">=2.3.0,<3.0dev"
 google-crc32c = ">=1.0,<2.0dev"
 google-resumable-media = ">=2.6.0"
@@ -3115,122 +3094,122 @@ protobuf = ["protobuf (<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-storage-transfer"
-version = "1.11.2"
+version = "1.11.3"
 description = "Google Cloud Storage Transfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-transfer-1.11.2.tar.gz", hash = "sha256:391b31934a9bed3d46f27d2d5daca1bda40b87ce3731e86b177342092a9fe5bf"},
-    {file = "google_cloud_storage_transfer-1.11.2-py2.py3-none-any.whl", hash = "sha256:fc2d3478ac998a76bd4c5eb660b65b4452aea15049e010009862df643d807e1a"},
+    {file = "google-cloud-storage-transfer-1.11.3.tar.gz", hash = "sha256:c4564749492f41957f44c41b76f61f72c12b519424b841895bfe2d932c6f1490"},
+    {file = "google_cloud_storage_transfer-1.11.3-py2.py3-none-any.whl", hash = "sha256:698e1b21c9dc710f543b2e6dc3c70334b872d4a80fafc0b2718fc91b6f45858e"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-tasks"
-version = "2.16.2"
+version = "2.16.3"
 description = "Google Cloud Tasks API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-tasks-2.16.2.tar.gz", hash = "sha256:2f5c15c581151d913d840fca348dc9a5fbd1cc16ec511e3f66b2fc6a01f06e38"},
-    {file = "google_cloud_tasks-2.16.2-py2.py3-none-any.whl", hash = "sha256:c47355f7de04f7ebf00d352ef9995fa32533eb24ff56b3a0af613367b3073a27"},
+    {file = "google-cloud-tasks-2.16.3.tar.gz", hash = "sha256:d891fe7006db4d6122838aa6de4aca6e0077bab224edcae684666ae3e303c45f"},
+    {file = "google_cloud_tasks-2.16.3-py2.py3-none-any.whl", hash = "sha256:a1a8473f5c76907525b51ae4c20182a42333b5c3e1dc5782d7af6c2f58241a0c"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-texttospeech"
-version = "2.16.2"
+version = "2.16.3"
 description = "Google Cloud Texttospeech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-texttospeech-2.16.2.tar.gz", hash = "sha256:eaac809d9a7d07c5c9eb58613916542cb2fa52c14f9f31da7ff4989f717f8e0c"},
-    {file = "google_cloud_texttospeech-2.16.2-py2.py3-none-any.whl", hash = "sha256:59de4d6021d39502572e9d6acdf851c2466f9140357dce249641d137bf63f062"},
+    {file = "google-cloud-texttospeech-2.16.3.tar.gz", hash = "sha256:fabc315032d137da0710bb4c268734d336212d8fa8316b23b277dd3a84ce721c"},
+    {file = "google_cloud_texttospeech-2.16.3-py2.py3-none-any.whl", hash = "sha256:5d1e23f9270908a5d7ecf2af04105fbd3a7ddde60fe48506e397bd18c1ece499"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-translate"
-version = "3.15.2"
+version = "3.15.3"
 description = "Google Cloud Translate API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-translate-3.15.2.tar.gz", hash = "sha256:6d4c2447bc2f88ec5e0d3a6778c3d85fab36da214b9384b10659b29e8db9f594"},
-    {file = "google_cloud_translate-3.15.2-py2.py3-none-any.whl", hash = "sha256:f20d5ea4bbe686e3bfd897f3afb6ddaf91738e86233ab0b8343f43a9cbc5e672"},
+    {file = "google-cloud-translate-3.15.3.tar.gz", hash = "sha256:ed587a1e60cf847c3b1add6b28256e2d1722fa73a4285334f5ec5ca933e616f2"},
+    {file = "google_cloud_translate-3.15.3-py2.py3-none-any.whl", hash = "sha256:2e92cd286fb25d5cf9fc0e8710f524fdf80656b6b64947d244e91696e69346ad"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 google-cloud-core = ">=1.4.4,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-videointelligence"
-version = "2.13.2"
+version = "2.13.3"
 description = "Google Cloud Videointelligence API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-videointelligence-2.13.2.tar.gz", hash = "sha256:c077e25599a107f18c7e057ca661f37600b1b71c65d90d6cf5c42043dadc8db1"},
-    {file = "google_cloud_videointelligence-2.13.2-py2.py3-none-any.whl", hash = "sha256:11a220d00254edf5cac55515aadfee23131d9ba45fe4114f9113200f780158e7"},
+    {file = "google-cloud-videointelligence-2.13.3.tar.gz", hash = "sha256:1460721d8120661c477c01af0edd3db03845857f52146a4a39f16b4acfb36f6d"},
+    {file = "google_cloud_videointelligence-2.13.3-py2.py3-none-any.whl", hash = "sha256:2eddfc42c4ef60378b638a5f4dfe466fadefae5cd6b1134eee377b5937be2c9e"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-vision"
-version = "3.7.1"
+version = "3.7.2"
 description = "Google Cloud Vision API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
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 [[package]]
 name = "google-cloud-workflows"
-version = "1.14.2"
+version = "1.14.3"
 description = "Google Cloud Workflows API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
 google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -3589,13 +3568,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "0.41.0"
+version = "0.41.3"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3943,18 +3922,18 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.2"
+version = "6.1.3"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "inflection"
@@ -4038,13 +4017,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.22.1"
+version = "8.22.2"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -4629,13 +4608,13 @@ files = [
 
 [[package]]
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-version = "3.21.0"
+version = "3.21.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4797,13 +4776,13 @@ mkdocs = ">=1.2.3"
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "1.0.0"
+version = "1.0.1"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4875,13 +4854,13 @@ pytz = "*"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.11"
+version = "9.5.13"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4929,13 +4908,13 @@ mkdocs = ">=1.2"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.24.0"
+version = "0.24.1"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5538,82 +5517,77 @@ files = [
 
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 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
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 setuptools = "*"
 
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@@ -6497,13 +6471,13 @@ files = [
 
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 [[package]]
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 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
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+    {file = "SQLAlchemy-1.4.52.tar.gz", hash = "sha256:80e63bbdc5217dad3485059bdf6f65a7d43f33c8bde619df5c220edf03d87296"},
 ]
 
 [package.dependencies]
@@ -7810,13 +7784,13 @@ sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "sqlalchemy-bigquery"
-version = "1.9.0"
+version = "1.10.0"
 description = "SQLAlchemy dialect for BigQuery"
 optional = false
 python-versions = ">=3.8, <3.13"
 files = [
-    {file = "sqlalchemy-bigquery-1.9.0.tar.gz", hash = "sha256:549b250ad4c75fe9efaff4ee32e08deb488f1886affbb159d8c149b6b537524f"},
-    {file = "sqlalchemy_bigquery-1.9.0-py2.py3-none-any.whl", hash = "sha256:b1a4c2f5b672ca7bb02e1357d6f3aeabbb19a67e986f2ccd2654fb005705e98e"},
+    {file = "sqlalchemy-bigquery-1.10.0.tar.gz", hash = "sha256:f7537fa283687c31b6f8bf9d3c76d6a1fd5d1a1df8e814116a991b2339656c1e"},
+    {file = "sqlalchemy_bigquery-1.10.0-py2.py3-none-any.whl", hash = "sha256:ea59c9625848c92469463956f93ea1179242f35e582bf1010dec794ab7042046"},
 ]
 
 [package.dependencies]
@@ -8240,13 +8214,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "wandb"
-version = "0.16.3"
+version = "0.16.4"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.16.3-py3-none-any.whl", hash = "sha256:b8907ddd775c27dc6c12687386a86b5d6acf291060f9ae680bbc61cc8fc03237"},
-    {file = "wandb-0.16.3.tar.gz", hash = "sha256:d789acda32053b18b7a160d0595837e45a3c8a79d25e1fe1f051875303f480ec"},
+    {file = "wandb-0.16.4-py3-none-any.whl", hash = "sha256:bb9eb5aa2c2c85e11c76040c4271366f54d4975167aa6320ba86c3f2d97fe5fa"},
+    {file = "wandb-0.16.4.tar.gz", hash = "sha256:8752c67d1347a4c29777e64dc1e1a742a66c5ecde03aebadf2b0d62183fa307c"},
 ]
 
 [package.dependencies]
@@ -8267,8 +8241,9 @@ async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
 gcp = ["google-cloud-storage"]
+importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "tomli", "typing-extensions"]
 media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]

From 8f91f3a2f8d8ed291038260a95302104f4ec66c0 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Thu, 7 Mar 2024 09:51:54 +0000
Subject: [PATCH 11/86] revert: make setup dev not to update pre-commits (#524)

---
 utils/install_dependencies.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/utils/install_dependencies.sh b/utils/install_dependencies.sh
index e17290f1f..33c5cf8b1 100644
--- a/utils/install_dependencies.sh
+++ b/utils/install_dependencies.sh
@@ -30,7 +30,6 @@ poetry install --sync
 
 echo "Setting up pre-commit..."
 poetry run pre-commit install
-poetry run pre-commit autoupdate
 poetry run pre-commit install --hook-type commit-msg
 
 echo "Activating the Poetry environment..."

From af16331ab5acaf721c6e0272f4c910c158b4e518 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Thu, 7 Mar 2024 09:57:55 +0000
Subject: [PATCH 12/86] chore: update ruff pre-commit and rules (#522)

* chore: update ruff pre-commit and rules

* refactor: to address None type and remove typeignore

---------

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>
---
 .pre-commit-config.yaml                                      | 2 +-
 pyproject.toml                                               | 5 ++---
 tests/airflow/test_dag.py                                    | 1 +
 tests/gentropy/common/test_session.py                        | 1 +
 tests/gentropy/conftest.py                                   | 1 +
 tests/gentropy/dataset/test_colocalisation.py                | 1 +
 tests/gentropy/dataset/test_dataset.py                       | 1 +
 tests/gentropy/dataset/test_gene_index.py                    | 1 +
 tests/gentropy/dataset/test_intervals.py                     | 1 +
 tests/gentropy/dataset/test_l2g.py                           | 5 +++--
 tests/gentropy/dataset/test_ld_index.py                      | 1 +
 tests/gentropy/dataset/test_study_index.py                   | 1 +
 tests/gentropy/dataset/test_study_locus.py                   | 1 +
 tests/gentropy/dataset/test_study_locus_overlap.py           | 1 +
 tests/gentropy/dataset/test_study_locus_overlaps.py          | 1 +
 tests/gentropy/dataset/test_summary_statistics.py            | 1 +
 tests/gentropy/dataset/test_v2g.py                           | 1 +
 tests/gentropy/dataset/test_variant_annotation.py            | 1 +
 tests/gentropy/dataset/test_variant_index.py                 | 1 +
 .../gwas_catalog/test_gwas_catalog_study_splitter.py         | 1 +
 tests/gentropy/datasource/intervals/test_andersson.py        | 1 +
 tests/gentropy/datasource/intervals/test_javierre.py         | 1 +
 tests/gentropy/datasource/intervals/test_jung.py             | 1 +
 tests/gentropy/datasource/intervals/test_thurman.py          | 1 +
 .../datasource/open_targets/test_l2g_gold_standard.py        | 1 +
 tests/gentropy/datasource/open_targets/test_target.py        | 1 +
 tests/gentropy/docs/test_applying_methods.py                 | 1 +
 tests/gentropy/docs/test_create_dataset.py                   | 1 +
 tests/gentropy/docs/test_creating_spark_session.py           | 1 +
 tests/gentropy/docs/test_inspect_dataset.py                  | 1 +
 tests/gentropy/method/test_window_based_clumping.py          | 1 +
 tests/gentropy/step/test_clump_step.py                       | 1 +
 tests/gentropy/test_cli.py                                   | 1 +
 tests/gentropy/test_schemas.py                               | 1 +
 tests/gentropy/test_spark_helpers.py                         | 1 +
 35 files changed, 38 insertions(+), 6 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 45736c8af..104a97062 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,7 +3,7 @@ ci:
   autofix_commit_msg: "chore: pre-commit auto fixes [...]"
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.0
     hooks:
       - id: ruff
         args:
diff --git a/pyproject.toml b/pyproject.toml
index 2b4150996..4ede53f24 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -159,13 +159,14 @@ module = [
 ignore_missing_imports = true
 
 
-[tool.ruff]
+[tool.ruff.lint]
 select = [
   "B002",    # Python does not support the unary prefix increment
   "B007",    # Loop control variable {name} not used within loop body
   "B014",    # Exception handler with duplicate exception
   "B023",    # Function definition does not bind loop variable {name}
   "B026",    # Star-arg unpacking after a keyword argument is strongly discouraged
+  "B904",    # Checks for raise statements in exception handlers that lack a from clause
   "C",       # complexity
   "COM818",  # Trailing comma on bare tuple prohibited
   "D",       # docstrings
@@ -180,7 +181,6 @@ select = [
   "N804",    # First argument of a class method should be named cls
   "N805",    # First argument of a method should be named self
   "N815",    # Variable {name} in class scope should not be mixedCase
-  "PGH001",  # No builtin eval() allowed
   "PGH004",  # Use specific rule codes when using noqa
   "PLC0414", # Useless import alias. Import alias does not rename original package.
   "PLC",     # pylint
@@ -219,7 +219,6 @@ select = [
   "T20",     # flake8-print
   "TID251",  # Banned imports
   "TRY004",  # Prefer TypeError exception for invalid type
-  "TRY200",  # Use raise from to specify exception cause
   "TRY302",  # Remove exception handler; error is immediately re-raised
   "UP",      # pyupgrade
   "W",       # pycodestyle
diff --git a/tests/airflow/test_dag.py b/tests/airflow/test_dag.py
index 8676d70f4..49edc76ee 100644
--- a/tests/airflow/test_dag.py
+++ b/tests/airflow/test_dag.py
@@ -1,4 +1,5 @@
 """Check for airflow import errors. Inspiration from https://garystafford.medium.com/devops-for-dataops-building-a-ci-cd-pipeline-for-apache-airflow-dags-975e4a622f83."""
+
 from __future__ import annotations
 
 import pytest
diff --git a/tests/gentropy/common/test_session.py b/tests/gentropy/common/test_session.py
index b7d0156d2..2ea7ccc1b 100644
--- a/tests/gentropy/common/test_session.py
+++ b/tests/gentropy/common/test_session.py
@@ -1,4 +1,5 @@
 """Tests GWAS Catalog study splitter."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py
index c7af5a589..13409ac16 100644
--- a/tests/gentropy/conftest.py
+++ b/tests/gentropy/conftest.py
@@ -1,4 +1,5 @@
 """Unit test configuration."""
+
 from __future__ import annotations
 
 from pathlib import Path
diff --git a/tests/gentropy/dataset/test_colocalisation.py b/tests/gentropy/dataset/test_colocalisation.py
index 2937efbca..1651aa2d4 100644
--- a/tests/gentropy/dataset/test_colocalisation.py
+++ b/tests/gentropy/dataset/test_colocalisation.py
@@ -1,4 +1,5 @@
 """Test colocalisation dataset."""
+
 from __future__ import annotations
 
 from gentropy.dataset.colocalisation import Colocalisation
diff --git a/tests/gentropy/dataset/test_dataset.py b/tests/gentropy/dataset/test_dataset.py
index 340906322..88f44ed5b 100644
--- a/tests/gentropy/dataset/test_dataset.py
+++ b/tests/gentropy/dataset/test_dataset.py
@@ -1,4 +1,5 @@
 """Test Dataset class."""
+
 from __future__ import annotations
 
 import pyspark.sql.functions as f
diff --git a/tests/gentropy/dataset/test_gene_index.py b/tests/gentropy/dataset/test_gene_index.py
index 68c279cab..999f089b3 100644
--- a/tests/gentropy/dataset/test_gene_index.py
+++ b/tests/gentropy/dataset/test_gene_index.py
@@ -1,4 +1,5 @@
 """Tests on LD index."""
+
 from __future__ import annotations
 
 from gentropy.dataset.gene_index import GeneIndex
diff --git a/tests/gentropy/dataset/test_intervals.py b/tests/gentropy/dataset/test_intervals.py
index bfa12ad69..26d79acd1 100644
--- a/tests/gentropy/dataset/test_intervals.py
+++ b/tests/gentropy/dataset/test_intervals.py
@@ -1,4 +1,5 @@
 """Tests on LD index."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/dataset/test_l2g.py b/tests/gentropy/dataset/test_l2g.py
index 045b98d62..a6a14c346 100644
--- a/tests/gentropy/dataset/test_l2g.py
+++ b/tests/gentropy/dataset/test_l2g.py
@@ -1,4 +1,5 @@
 """Tests on L2G datasets."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
@@ -167,8 +168,8 @@ def test_calculate_feature_missingness_rate(spark: SparkSession) -> None:
     expected_missingness = {"distanceTssMean": 0.0, "distanceTssMinimum": 1.0}
     observed_missingness = fm.calculate_feature_missingness_rate()
     assert isinstance(observed_missingness, dict)
-    assert len(observed_missingness) == len(
-        fm.features_list  # type: ignore
+    assert fm.features_list is not None and len(observed_missingness) == len(
+        fm.features_list
     ), "Missing features in the missingness rate dictionary."
     assert (
         observed_missingness == expected_missingness
diff --git a/tests/gentropy/dataset/test_ld_index.py b/tests/gentropy/dataset/test_ld_index.py
index c3eff6a4d..dc2b49c09 100644
--- a/tests/gentropy/dataset/test_ld_index.py
+++ b/tests/gentropy/dataset/test_ld_index.py
@@ -1,4 +1,5 @@
 """Tests on LD index."""
+
 from __future__ import annotations
 
 from gentropy.dataset.ld_index import LDIndex
diff --git a/tests/gentropy/dataset/test_study_index.py b/tests/gentropy/dataset/test_study_index.py
index 1d43b3b07..6e327c086 100644
--- a/tests/gentropy/dataset/test_study_index.py
+++ b/tests/gentropy/dataset/test_study_index.py
@@ -1,4 +1,5 @@
 """Test study index dataset."""
+
 from __future__ import annotations
 
 from gentropy.dataset.study_index import StudyIndex
diff --git a/tests/gentropy/dataset/test_study_locus.py b/tests/gentropy/dataset/test_study_locus.py
index 037ede068..c12597d54 100644
--- a/tests/gentropy/dataset/test_study_locus.py
+++ b/tests/gentropy/dataset/test_study_locus.py
@@ -1,4 +1,5 @@
 """Test study locus dataset."""
+
 from __future__ import annotations
 
 from typing import Any
diff --git a/tests/gentropy/dataset/test_study_locus_overlap.py b/tests/gentropy/dataset/test_study_locus_overlap.py
index ca49fc067..53d87226c 100644
--- a/tests/gentropy/dataset/test_study_locus_overlap.py
+++ b/tests/gentropy/dataset/test_study_locus_overlap.py
@@ -1,4 +1,5 @@
 """Test study locus overlap dataset."""
+
 from __future__ import annotations
 
 from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
diff --git a/tests/gentropy/dataset/test_study_locus_overlaps.py b/tests/gentropy/dataset/test_study_locus_overlaps.py
index 6a812fb39..ee89eec84 100644
--- a/tests/gentropy/dataset/test_study_locus_overlaps.py
+++ b/tests/gentropy/dataset/test_study_locus_overlaps.py
@@ -1,4 +1,5 @@
 """Test colocalisation dataset."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
diff --git a/tests/gentropy/dataset/test_summary_statistics.py b/tests/gentropy/dataset/test_summary_statistics.py
index b148994b6..9885181a8 100644
--- a/tests/gentropy/dataset/test_summary_statistics.py
+++ b/tests/gentropy/dataset/test_summary_statistics.py
@@ -1,4 +1,5 @@
 """Test study index dataset."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/dataset/test_v2g.py b/tests/gentropy/dataset/test_v2g.py
index e53395fe4..24a917508 100644
--- a/tests/gentropy/dataset/test_v2g.py
+++ b/tests/gentropy/dataset/test_v2g.py
@@ -1,4 +1,5 @@
 """Tests V2G dataset."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/dataset/test_variant_annotation.py b/tests/gentropy/dataset/test_variant_annotation.py
index 563d3c05c..05fa0cd60 100644
--- a/tests/gentropy/dataset/test_variant_annotation.py
+++ b/tests/gentropy/dataset/test_variant_annotation.py
@@ -1,4 +1,5 @@
 """Tests variant annotation dataset."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/dataset/test_variant_index.py b/tests/gentropy/dataset/test_variant_index.py
index c79a04092..a41db3031 100644
--- a/tests/gentropy/dataset/test_variant_index.py
+++ b/tests/gentropy/dataset/test_variant_index.py
@@ -1,4 +1,5 @@
 """Tests on variant index generation."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py
index 2955cee91..58bcb53c8 100644
--- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py
+++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py
@@ -1,4 +1,5 @@
 """Tests GWAS Catalog study splitter."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
diff --git a/tests/gentropy/datasource/intervals/test_andersson.py b/tests/gentropy/datasource/intervals/test_andersson.py
index b7283597b..09204967d 100644
--- a/tests/gentropy/datasource/intervals/test_andersson.py
+++ b/tests/gentropy/datasource/intervals/test_andersson.py
@@ -1,4 +1,5 @@
 """Test Andersson Intervals."""
+
 from __future__ import annotations
 
 import pytest
diff --git a/tests/gentropy/datasource/intervals/test_javierre.py b/tests/gentropy/datasource/intervals/test_javierre.py
index be8c528e2..a6a74e94e 100644
--- a/tests/gentropy/datasource/intervals/test_javierre.py
+++ b/tests/gentropy/datasource/intervals/test_javierre.py
@@ -1,4 +1,5 @@
 """Test JavierreIntervals."""
+
 from __future__ import annotations
 
 import pytest
diff --git a/tests/gentropy/datasource/intervals/test_jung.py b/tests/gentropy/datasource/intervals/test_jung.py
index 81506ae55..44062f680 100644
--- a/tests/gentropy/datasource/intervals/test_jung.py
+++ b/tests/gentropy/datasource/intervals/test_jung.py
@@ -1,4 +1,5 @@
 """Test Jung Intervals."""
+
 from __future__ import annotations
 
 import pytest
diff --git a/tests/gentropy/datasource/intervals/test_thurman.py b/tests/gentropy/datasource/intervals/test_thurman.py
index f89c9d3da..853adb380 100644
--- a/tests/gentropy/datasource/intervals/test_thurman.py
+++ b/tests/gentropy/datasource/intervals/test_thurman.py
@@ -1,4 +1,5 @@
 """Test Thurman."""
+
 from __future__ import annotations
 
 import pytest
diff --git a/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py b/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py
index 448e2a50c..0690781f5 100644
--- a/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py
+++ b/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py
@@ -1,4 +1,5 @@
 """Test Open Targets L2G gold standards data source."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/datasource/open_targets/test_target.py b/tests/gentropy/datasource/open_targets/test_target.py
index 33bb6ddc9..c0a92fcd7 100644
--- a/tests/gentropy/datasource/open_targets/test_target.py
+++ b/tests/gentropy/datasource/open_targets/test_target.py
@@ -1,4 +1,5 @@
 """Test Open Targets target data source."""
+
 from __future__ import annotations
 
 from gentropy.dataset.gene_index import GeneIndex
diff --git a/tests/gentropy/docs/test_applying_methods.py b/tests/gentropy/docs/test_applying_methods.py
index 255752f46..cbfdb9155 100644
--- a/tests/gentropy/docs/test_applying_methods.py
+++ b/tests/gentropy/docs/test_applying_methods.py
@@ -1,4 +1,5 @@
 """Testing applying methods docs."""
+
 from typing import Any
 
 import pytest
diff --git a/tests/gentropy/docs/test_create_dataset.py b/tests/gentropy/docs/test_create_dataset.py
index 663254753..2daae0c6a 100644
--- a/tests/gentropy/docs/test_create_dataset.py
+++ b/tests/gentropy/docs/test_create_dataset.py
@@ -1,4 +1,5 @@
 """Testing creating dataset docs."""
+
 from typing import Any
 
 import pytest
diff --git a/tests/gentropy/docs/test_creating_spark_session.py b/tests/gentropy/docs/test_creating_spark_session.py
index 6e1cf1ca5..197d22d56 100644
--- a/tests/gentropy/docs/test_creating_spark_session.py
+++ b/tests/gentropy/docs/test_creating_spark_session.py
@@ -1,4 +1,5 @@
 """Testing creating spark session docs."""
+
 from gentropy.common.session import Session
 
 from docs.src_snippets.howto.python_api.a_creating_spark_session import (
diff --git a/tests/gentropy/docs/test_inspect_dataset.py b/tests/gentropy/docs/test_inspect_dataset.py
index daefa9793..38e258158 100644
--- a/tests/gentropy/docs/test_inspect_dataset.py
+++ b/tests/gentropy/docs/test_inspect_dataset.py
@@ -1,4 +1,5 @@
 """Testing inspecting dataset docs."""
+
 from gentropy.dataset.summary_statistics import SummaryStatistics
 from pyspark.sql.types import StructType
 
diff --git a/tests/gentropy/method/test_window_based_clumping.py b/tests/gentropy/method/test_window_based_clumping.py
index 59bf0277a..03546df9d 100644
--- a/tests/gentropy/method/test_window_based_clumping.py
+++ b/tests/gentropy/method/test_window_based_clumping.py
@@ -1,4 +1,5 @@
 """Test window-based clumping."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
diff --git a/tests/gentropy/step/test_clump_step.py b/tests/gentropy/step/test_clump_step.py
index 701afc950..ab40de901 100644
--- a/tests/gentropy/step/test_clump_step.py
+++ b/tests/gentropy/step/test_clump_step.py
@@ -1,4 +1,5 @@
 """Test clump step."""
+
 from __future__ import annotations
 
 import tempfile
diff --git a/tests/gentropy/test_cli.py b/tests/gentropy/test_cli.py
index 92effa840..5675713ce 100644
--- a/tests/gentropy/test_cli.py
+++ b/tests/gentropy/test_cli.py
@@ -1,4 +1,5 @@
 """Test the command-line interface (CLI)."""
+
 from unittest.mock import patch
 
 import pytest
diff --git a/tests/gentropy/test_schemas.py b/tests/gentropy/test_schemas.py
index 768a3d4df..417eb4657 100644
--- a/tests/gentropy/test_schemas.py
+++ b/tests/gentropy/test_schemas.py
@@ -1,4 +1,5 @@
 """Tests on spark schemas."""
+
 from __future__ import annotations
 
 import json
diff --git a/tests/gentropy/test_spark_helpers.py b/tests/gentropy/test_spark_helpers.py
index 76abcbe56..a3790c62e 100644
--- a/tests/gentropy/test_spark_helpers.py
+++ b/tests/gentropy/test_spark_helpers.py
@@ -1,4 +1,5 @@
 """Tests on helper spark functions."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any

From f6e0ad0d0196a727e6ffc17936b95bb2154e455b Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Thu, 7 Mar 2024 11:39:22 +0000
Subject: [PATCH 13/86] fix: pr labeller patterns (#523)

* fix: pr labeler patterns

* fix: change bug for fix

* revert: as per reviewers will
---
 .github/labeler.yml | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index 197d1f923..060922825 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -1,4 +1,4 @@
-version: 1
+version: "1"
 labels:
   - label: "size-XS"
     size:
@@ -25,39 +25,39 @@ labels:
       above: 999
   - label: "airflow"
     files:
-      - "src/airflow/.*"
+      - "^src/airflow/.*"
   - label: "Documentation"
     files:
-      - "docs/.*"
+      - "^docs/.*"
   - label: "Dataset"
     files:
-      - "src/gentropy/dataset/.*"
+      - "^src/gentropy/dataset/.*"
   - label: "Method"
     files:
-      - "src/gentropy/method/.*"
+      - "^src/gentropy/method/.*"
   - label: "Datasource"
     files:
-      - "src/gentropy/datasource/.*"
+      - "^src/gentropy/datasource/.*"
   - label: "Step"
     files:
-      - "src/gentropy/[a-zA-Z]\\w+\\.py"
+      - "^src/gentropy/[a-zA-Z]\\w+\\.py"
   - label: "Feature"
-    title: "feat.*"
+    title: "^feat.*"
   - label: "Bug"
-    title: "fix.*"
+    title: "^fix.*"
   - label: "Refactor"
-    title: "refactor.*"
+    title: "^refactor.*"
   - label: "Chore"
-    title: "chore.*"
+    title: "^chore.*"
   - label: "CI"
-    title: "ci.*"
+    title: "^ci.*"
   - label: "Test"
-    title: "test.*"
+    title: "^test.*"
   - label: "Documentation"
-    title: "docs.*"
+    title: "^docs.*"
   - label: "Performance"
-    title: "perf.*"
+    title: "^perf.*"
   - label: "Build"
-    title: "build.*"
+    title: "^build.*"
   - label: "Revert"
-    title: "revert.*"
+    title: "^revert.*"

From 8257ea3fe4c55a765cd288e6cc5b9c06ebedd136 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 15:10:46 +0000
Subject: [PATCH 14/86] fix: fixing merge conflict

---
 .pre-commit-config.yaml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 104a97062..51d4e05a8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,9 @@
+default_language_version:
+  python: python3.10
 ci:
   autoupdate_commit_msg: "chore: pre-commit autoupdate"
   autofix_commit_msg: "chore: pre-commit auto fixes [...]"
+  skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.3.0
@@ -99,3 +102,10 @@ repos:
     rev: 0.4.1
     hooks:
       - id: pydoclint
+
+  - repo: https://github.com/python-poetry/poetry
+    rev: "1.8.2"
+    hooks:
+      - id: poetry-check
+      - id: poetry-lock
+        args: ["--no-update"]

From d70a8efc3ae0621a4877d907eb1fe36aa1f313fd Mon Sep 17 00:00:00 2001
From: xyg123 <33658607+xyg123@users.noreply.github.com>
Date: Thu, 7 Mar 2024 12:36:57 +0000
Subject: [PATCH 15/86] feat(coloc): single SNP case (#511)

* feat: include single snp overlaps in coloc calcs

* chore: added unit test for coloc

* feat: include single snp overlaps in coloc calcs

* chore: added unit test for coloc

* fix: check for differences in test results

* fix: revert changes to single snp coloc tests for other pr

* fix: revert single snp change

* chore: restructure test folder

* fix: compare differences in results rather than exact match

* chore: test description

* fix: allow for coloc when only one snp is overlapping

* chore: include tests for h0 and h1/2

* fix: added pseudocount variable to coloc

* fix: pseudocount as a Coloc attribute

---------

Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 src/gentropy/method/colocalisation.py         |  11 +-
 .../method/test_colocalisation_method.py      | 134 ++++++++++++++++++
 2 files changed, 142 insertions(+), 3 deletions(-)

diff --git a/src/gentropy/method/colocalisation.py b/src/gentropy/method/colocalisation.py
index 899343998..3e4e91c74 100644
--- a/src/gentropy/method/colocalisation.py
+++ b/src/gentropy/method/colocalisation.py
@@ -104,8 +104,12 @@ class Coloc:
 
         Coloc requires the availability of Bayes factors (BF) for each variant in the credible set (`logBF` column).
 
+    Attributes:
+        PSEUDOCOUNT (float): Pseudocount to avoid log(0). Defaults to 1e-10.
     """
 
+    PSEUDOCOUNT: float = 1e-10
+
     @staticmethod
     def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector:
         """Calculate posterior probabilities for each hypothesis.
@@ -137,6 +141,7 @@ def colocalise(
 
         Args:
             overlapping_signals (StudyLocusOverlap): overlapping peaks
+
             priorc1 (float): Prior on variant being causal for trait 1. Defaults to 1e-4.
             priorc2 (float): Prior on variant being causal for trait 2. Defaults to 1e-4.
             priorc12 (float): Prior on variant being causal for traits 1 and 2. Defaults to 1e-5.
@@ -188,12 +193,12 @@ def colocalise(
                 .withColumn("lH2bf", f.log(f.col("priorc2")) + f.col("logsum2"))
                 # h3
                 .withColumn("sumlogsum", f.col("logsum1") + f.col("logsum2"))
-                # exclude null H3/H4s: due to sumlogsum == logsum12
-                .filter(f.col("sumlogsum") != f.col("logsum12"))
                 .withColumn("max", f.greatest("sumlogsum", "logsum12"))
                 .withColumn(
                     "logdiff",
-                    (
+                    f.when(
+                        f.col("sumlogsum") == f.col("logsum12"), Coloc.PSEUDOCOUNT
+                    ).otherwise(
                         f.col("max")
                         + f.log(
                             f.exp(f.col("sumlogsum") - f.col("max"))
diff --git a/tests/gentropy/method/test_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py
index a4fe07646..f90d54b3f 100644
--- a/tests/gentropy/method/test_colocalisation_method.py
+++ b/tests/gentropy/method/test_colocalisation_method.py
@@ -41,6 +41,140 @@ def test_coloc_colocalise(
         assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
 
 
+def test_single_snp_coloc(
+    spark: SparkSession,
+    threshold: float = 1e-5,
+) -> None:
+    """Test edge case of coloc where only one causal SNP is present in the StudyLocusOverlap."""
+    test_overlap_df = spark.createDataFrame(
+        [
+            {
+                "leftStudyLocusId": 1,
+                "rightStudyLocusId": 2,
+                "chromosome": "1",
+                "tagVariantId": "snp",
+                "left_logBF": 10.3,
+                "right_logBF": 10.5,
+            }
+        ]
+    )
+    test_overlap = StudyLocusOverlap(
+        test_overlap_df.select(
+            "leftStudyLocusId",
+            "rightStudyLocusId",
+            "chromosome",
+            "tagVariantId",
+            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
+        ),
+        StudyLocusOverlap.get_schema(),
+    )
+    test_result = Coloc.colocalise(test_overlap)
+
+    expected = spark.createDataFrame(
+        [
+            {
+                "h0": 9.254841951638903e-5,
+                "h1": 2.7517068829182966e-4,
+                "h2": 3.3609423764447284e-4,
+                "h3": 9.254841952564387e-13,
+                "h4": 0.9992961866536217,
+            }
+        ]
+    )
+    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
+    for col in difference.columns:
+        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
+
+
+def test_single_snp_coloc_one_negative(
+    spark: SparkSession,
+    threshold: float = 1e-5,
+) -> None:
+    """Test edge case of coloc where only one causal SNP is present (On one side!) in the StudyLocusOverlap."""
+    test_overlap_df = spark.createDataFrame(
+        [
+            {
+                "leftStudyLocusId": 1,
+                "rightStudyLocusId": 2,
+                "chromosome": "1",
+                "tagVariantId": "snp",
+                "left_logBF": 18.3,
+                "right_logBF": 0.01,
+            }
+        ]
+    )
+    test_overlap = StudyLocusOverlap(
+        test_overlap_df.select(
+            "leftStudyLocusId",
+            "rightStudyLocusId",
+            "chromosome",
+            "tagVariantId",
+            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
+        ),
+        StudyLocusOverlap.get_schema(),
+    )
+    test_result = Coloc.colocalise(test_overlap)
+    test_result.df.show(1, False)
+    expected = spark.createDataFrame(
+        [
+            {
+                "h0": 1.0246538505087709e-4,
+                "h1": 0.9081680002273896,
+                "h2": 1.0349517929098209e-8,
+                "h3": 1.0246538506112363e-12,
+                "h4": 0.09172952403701702,
+            }
+        ]
+    )
+    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
+    for col in difference.columns:
+        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
+
+
+def test_single_snp_coloc_both_negative(
+    spark: SparkSession,
+    threshold: float = 1e-5,
+) -> None:
+    """Test edge case of coloc where only one non-causal SNP overlaps in the StudyLocusOverlap."""
+    test_overlap_df = spark.createDataFrame(
+        [
+            {
+                "leftStudyLocusId": 1,
+                "rightStudyLocusId": 2,
+                "chromosome": "1",
+                "tagVariantId": "snp",
+                "left_logBF": 0.03,
+                "right_logBF": 0.01,
+            }
+        ]
+    )
+    test_overlap = StudyLocusOverlap(
+        test_overlap_df.select(
+            "leftStudyLocusId",
+            "rightStudyLocusId",
+            "chromosome",
+            "tagVariantId",
+            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
+        ),
+        StudyLocusOverlap.get_schema(),
+    )
+    test_result = Coloc.colocalise(test_overlap)
+    expected = spark.createDataFrame(
+        [
+            {
+                "h0": 0.9997855774090624,
+                "h1": 1.0302335812225042e-4,
+                "h2": 1.0098335895103664e-4,
+                "h3": 9.9978557750904e-9,
+                "h4": 1.0405876008495098e-5,
+            }
+        ]
+    )
+    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
+    for col in difference.columns:
+        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
+
+
 def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None:
     """Test eCAVIAR."""
     assert isinstance(ECaviar.colocalise(mock_study_locus_overlap), Colocalisation)

From 4d7294628c731b680d98694bfa7c799de72c3544 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 14:57:35 +0000
Subject: [PATCH 16/86] fix: sorting out merge conflicts

---
 poetry.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 320208a14..918f8e47f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"

From 45750c51ade294b51a5bdb9f102e312d2d69a216 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 15:02:47 +0000
Subject: [PATCH 17/86] fix: removing fsspec from dependencies

---
 poetry.lock    | 4 ++--
 pyproject.toml | 1 -
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 918f8e47f..a894ef843 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -8606,4 +8606,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "6f20e245eeac06bbf87c036df5db1198b74d90609a907d5b781eb023a1d7dd81"
+content-hash = "8d85933db4bf8f4ca92ef227e9a767d26f4f7d2b360083449cfafc54878885fc"
diff --git a/pyproject.toml b/pyproject.toml
index 4ede53f24..d94d8412b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,7 +33,6 @@ omegaconf = "^2.3.0"
 typing-extensions = "^4.9.0"
 scikit-learn = "^1.3.2"
 pandas = "^2.1.4"
-fsspec = ">=2022.11.0"
 
 [tool.poetry.dev-dependencies]
 pre-commit = "^3.6.0"

From 50af4d2343c2763e8f2bf51c1468012eb903e90a Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 15:32:52 +0000
Subject: [PATCH 18/86] fix: fixing lock file

---
 poetry.lock | 5099 +++++++++++++++++++++++++--------------------------
 1 file changed, 2497 insertions(+), 2602 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index db10c1be1..f1b435867 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -27,87 +27,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.9.3"
+version = "3.9.1"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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+    {file = "aiohttp-3.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:9b05d33ff8e6b269e30a7957bd3244ffbce2a7a35a81b81c382629b80af1a8bf"},
+    {file = "aiohttp-3.9.1.tar.gz", hash = "sha256:8fc49a87ac269d4529da45871e2ffb6874e87779c3d0e2ccd813c0899221239d"},
 ]
 
 [package.dependencies]
@@ -137,13 +137,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "alembic"
-version = "1.13.1"
+version = "1.12.1"
 description = "A database migration tool for SQLAlchemy."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "alembic-1.13.1-py3-none-any.whl", hash = "sha256:2edcc97bed0bd3272611ce3a98d98279e9c209e7186e43e75bbb1b2bdfdbcc43"},
-    {file = "alembic-1.13.1.tar.gz", hash = "sha256:4932c8558bf68f2ee92b9bbcb8218671c627064d5b08939437af6d77dc05e595"},
+    {file = "alembic-1.12.1-py3-none-any.whl", hash = "sha256:47d52e3dfb03666ed945becb723d6482e52190917fdb47071440cfdba05d92cb"},
+    {file = "alembic-1.12.1.tar.gz", hash = "sha256:bca5877e9678b454706347bc10b97cb7d67f300320fa5c3a94423e8266e2823f"},
 ]
 
 [package.dependencies]
@@ -152,7 +152,7 @@ SQLAlchemy = ">=1.3.0"
 typing-extensions = ">=4"
 
 [package.extras]
-tz = ["backports.zoneinfo"]
+tz = ["python-dateutil"]
 
 [[package]]
 name = "annotated-types"
@@ -177,40 +177,38 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.3.0"
+version = "4.0.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+    {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
+    {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.22)"]
 
 [[package]]
 name = "apache-airflow"
-version = "2.8.2"
+version = "2.8.0"
 description = "Programmatically author, schedule and monitor data pipelines"
 optional = false
 python-versions = "<3.12,~=3.8"
 files = [
-    {file = "apache_airflow-2.8.2-py3-none-any.whl", hash = "sha256:41ccb84bb4a42dfd7e138850b90e376751a54312750a920ebf978096cad18af0"},
-    {file = "apache_airflow-2.8.2.tar.gz", hash = "sha256:a993e272d254ec80852553042dca5641e24a416a49ac7341c947295dee0c4b20"},
+    {file = "apache-airflow-2.8.0.tar.gz", hash = "sha256:5917ee148125892764f4306ec76a62c66a5801218a99edfd3fe46e968cb1a344"},
+    {file = "apache_airflow-2.8.0-py3-none-any.whl", hash = "sha256:4ee532b97ef6520ad8e1ffda325aabe265f007a14166570d5f710d4999c9ad1c"},
 ]
 
 [package.dependencies]
-alembic = ">=1.13.1,<2.0"
-apache-airflow-providers-common-io = "*"
+alembic = ">=1.6.3,<2.0"
 apache-airflow-providers-common-sql = "*"
 apache-airflow-providers-ftp = "*"
 apache-airflow-providers-http = "*"
@@ -229,13 +227,14 @@ cryptography = ">=0.9.3"
 deprecated = ">=1.2.13"
 dill = ">=0.2.2"
 flask = ">=2.2,<2.3"
-flask-appbuilder = "4.3.11"
+flask-appbuilder = "4.3.10"
 flask-caching = ">=1.5.0"
 flask-login = ">=0.6.2"
-flask-session = ">=0.4.0,<0.6"
+flask-session = ">=0.4.0"
 flask-wtf = ">=0.15"
 fsspec = ">=2023.10.0"
 google-re2 = ">=1.0"
+graphviz = ">=0.12"
 gunicorn = ">=20.1.0"
 httpx = "*"
 itsdangerous = ">=2.0"
@@ -244,6 +243,7 @@ jsonschema = ">=4.18.0"
 lazy-object-proxy = "*"
 linkify-it-py = ">=2.0.0"
 lockfile = ">=0.12.2"
+markdown = ">=3.0"
 markdown-it-py = ">=2.1.0"
 markupsafe = ">=1.1.1"
 marshmallow-oneofschema = ">=2.0.1"
@@ -252,16 +252,16 @@ opentelemetry-api = ">=1.15.0"
 opentelemetry-exporter-otlp = "*"
 packaging = ">=14.0"
 pathspec = ">=0.9.0"
-pendulum = ">=2.1.2,<4.0"
+pendulum = ">=2.0,<3.0"
 pluggy = ">=1.0"
 psutil = ">=4.2.0"
+pydantic = ">=2.3.0"
 pygments = ">=2.0.1"
 pyjwt = ">=2.0.0"
 python-daemon = ">=3.0.0"
 python-dateutil = ">=2.3"
 python-nvd3 = ">=0.15.0"
 python-slugify = ">=5.0"
-requests = ">=2.27.0,<3"
 rfc3339-validator = ">=0.1.4"
 rich = ">=12.4.4"
 rich-argparse = ">=1.0.0"
@@ -271,17 +271,17 @@ sqlalchemy-jsonfield = ">=1.0"
 tabulate = ">=0.7.5"
 tenacity = ">=6.2.0,<8.2.0 || >8.2.0"
 termcolor = ">=1.1.0"
+typing-extensions = ">=4.0.0"
 unicodecsv = ">=0.14.1"
-universal-pathlib = ">=0.1.4,<0.2.0"
+universal-pathlib = ">=0.1.4"
 werkzeug = ">=2.0,<3"
 
 [package.extras]
-aiobotocore = ["aiobotocore (>=2.7.0)"]
+aiobotocore = ["aiobotocore (>=2.1.1)"]
 airbyte = ["apache-airflow-providers-airbyte"]
 alibaba = ["apache-airflow-providers-alibaba"]
-all = ["apache-airflow[aiobotocore]", "apache-airflow[airbyte]", "apache-airflow[alibaba]", "apache-airflow[all-core]", "apache-airflow[amazon]", "apache-airflow[apache-atlas]", "apache-airflow[apache-beam]", "apache-airflow[apache-cassandra]", "apache-airflow[apache-drill]", "apache-airflow[apache-druid]", "apache-airflow[apache-flink]", "apache-airflow[apache-hdfs]", "apache-airflow[apache-hive]", "apache-airflow[apache-impala]", "apache-airflow[apache-kafka]", "apache-airflow[apache-kylin]", "apache-airflow[apache-livy]", "apache-airflow[apache-pig]", "apache-airflow[apache-pinot]", "apache-airflow[apache-spark]", "apache-airflow[apache-sqoop]", "apache-airflow[apache-webhdfs]", "apache-airflow[apprise]", "apache-airflow[arangodb]", "apache-airflow[asana]", "apache-airflow[async]", "apache-airflow[atlassian-jira]", "apache-airflow[celery]", "apache-airflow[cgroups]", "apache-airflow[cloudant]", "apache-airflow[cncf-kubernetes]", "apache-airflow[cohere]", "apache-airflow[common-io]", "apache-airflow[common-sql]", "apache-airflow[daskexecutor]", "apache-airflow[databricks]", "apache-airflow[datadog]", "apache-airflow[dbt-cloud]", "apache-airflow[deprecated-api]", "apache-airflow[dingding]", "apache-airflow[discord]", "apache-airflow[docker]", "apache-airflow[elasticsearch]", "apache-airflow[exasol]", "apache-airflow[facebook]", "apache-airflow[ftp]", "apache-airflow[github-enterprise]", "apache-airflow[github]", "apache-airflow[google-auth]", "apache-airflow[google]", "apache-airflow[graphviz]", "apache-airflow[grpc]", "apache-airflow[hashicorp]", "apache-airflow[http]", "apache-airflow[imap]", "apache-airflow[influxdb]", "apache-airflow[jdbc]", "apache-airflow[jenkins]", "apache-airflow[kerberos]", "apache-airflow[ldap]", "apache-airflow[leveldb]", "apache-airflow[microsoft-azure]", "apache-airflow[microsoft-mssql]", "apache-airflow[microsoft-psrp]", "apache-airflow[microsoft-winrm]", "apache-airflow[mongo]", "apache-airflow[mysql]", "apache-airflow[neo4j]", "apache-airflow[odbc]", "apache-airflow[openai]", "apache-airflow[openfaas]", "apache-airflow[openlineage]", "apache-airflow[opensearch]", "apache-airflow[opsgenie]", "apache-airflow[oracle]", "apache-airflow[otel]", "apache-airflow[pagerduty]", "apache-airflow[pandas]", "apache-airflow[papermill]", "apache-airflow[password]", "apache-airflow[pgvector]", "apache-airflow[pinecone]", "apache-airflow[plexus]", "apache-airflow[postgres]", "apache-airflow[presto]", "apache-airflow[pydantic]", "apache-airflow[rabbitmq]", "apache-airflow[redis]", "apache-airflow[s3fs]", "apache-airflow[salesforce]", "apache-airflow[samba]", "apache-airflow[saml]", "apache-airflow[segment]", "apache-airflow[sendgrid]", "apache-airflow[sentry]", "apache-airflow[sftp]", "apache-airflow[singularity]", "apache-airflow[slack]", "apache-airflow[smtp]", "apache-airflow[snowflake]", "apache-airflow[sqlite]", "apache-airflow[ssh]", "apache-airflow[statsd]", "apache-airflow[tableau]", "apache-airflow[tabular]", "apache-airflow[telegram]", "apache-airflow[trino]", "apache-airflow[vertica]", "apache-airflow[virtualenv]", "apache-airflow[weaviate]", "apache-airflow[yandex]", "apache-airflow[zendesk]"]
-all-core = ["apache-airflow[aiobotocore]", "apache-airflow[apache-atlas]", "apache-airflow[apache-webhdfs]", "apache-airflow[async]", "apache-airflow[cgroups]", "apache-airflow[deprecated-api]", "apache-airflow[github-enterprise]", "apache-airflow[google-auth]", "apache-airflow[graphviz]", "apache-airflow[kerberos]", "apache-airflow[ldap]", "apache-airflow[leveldb]", "apache-airflow[otel]", "apache-airflow[pandas]", "apache-airflow[password]", "apache-airflow[pydantic]", "apache-airflow[rabbitmq]", "apache-airflow[s3fs]", "apache-airflow[saml]", "apache-airflow[sentry]", "apache-airflow[statsd]", "apache-airflow[virtualenv]"]
-all-dbs = ["apache-airflow[apache-cassandra]", "apache-airflow[apache-drill]", "apache-airflow[apache-druid]", "apache-airflow[apache-hdfs]", "apache-airflow[apache-hive]", "apache-airflow[apache-impala]", "apache-airflow[apache-pinot]", "apache-airflow[arangodb]", "apache-airflow[cloudant]", "apache-airflow[databricks]", "apache-airflow[exasol]", "apache-airflow[influxdb]", "apache-airflow[microsoft-mssql]", "apache-airflow[mongo]", "apache-airflow[mysql]", "apache-airflow[neo4j]", "apache-airflow[postgres]", "apache-airflow[presto]", "apache-airflow[trino]", "apache-airflow[vertica]"]
+all = ["PyGithub (!=1.58)", "PyOpenSSL", "adal (>=1.2.7)", "adlfs (>=2023.10.0)", "aiobotocore (>=2.1.1)", "aiofiles (>=23.2.0)", "aiohttp", "aiohttp (>=3.6.3,<4)", "alibabacloud-adb20211201 (>=1.0.0)", "alibabacloud-tea-openapi (>=0.3.7)", "amqp", "analytics-python (>=1.2.9)", "apache-airflow (>=2.6.0)", "apache-airflow (>=2.7.0)", "apache-airflow (>=2.8.0)", "apache-airflow-providers-airbyte", "apache-airflow-providers-alibaba", "apache-airflow-providers-amazon", "apache-airflow-providers-apache-beam", "apache-airflow-providers-apache-cassandra", "apache-airflow-providers-apache-drill", "apache-airflow-providers-apache-druid", "apache-airflow-providers-apache-flink", "apache-airflow-providers-apache-hdfs", "apache-airflow-providers-apache-hive", "apache-airflow-providers-apache-impala", "apache-airflow-providers-apache-kafka", "apache-airflow-providers-apache-kylin", "apache-airflow-providers-apache-livy", "apache-airflow-providers-apache-pig", "apache-airflow-providers-apache-pinot", "apache-airflow-providers-apache-spark", "apache-airflow-providers-apprise", "apache-airflow-providers-arangodb", "apache-airflow-providers-asana", "apache-airflow-providers-atlassian-jira", "apache-airflow-providers-celery", "apache-airflow-providers-cloudant", "apache-airflow-providers-cncf-kubernetes", "apache-airflow-providers-cohere", "apache-airflow-providers-common-io", "apache-airflow-providers-common-sql", "apache-airflow-providers-databricks", "apache-airflow-providers-datadog", "apache-airflow-providers-dbt-cloud", "apache-airflow-providers-dingding", "apache-airflow-providers-discord", "apache-airflow-providers-docker", "apache-airflow-providers-elasticsearch", "apache-airflow-providers-exasol", "apache-airflow-providers-facebook", "apache-airflow-providers-ftp", "apache-airflow-providers-github", "apache-airflow-providers-google", "apache-airflow-providers-grpc", "apache-airflow-providers-hashicorp", "apache-airflow-providers-http", "apache-airflow-providers-imap", "apache-airflow-providers-influxdb", "apache-airflow-providers-jdbc", "apache-airflow-providers-jenkins", "apache-airflow-providers-microsoft-azure", "apache-airflow-providers-microsoft-mssql", "apache-airflow-providers-microsoft-psrp", "apache-airflow-providers-microsoft-winrm", "apache-airflow-providers-mongo", "apache-airflow-providers-mysql", "apache-airflow-providers-neo4j", "apache-airflow-providers-odbc", "apache-airflow-providers-openai", "apache-airflow-providers-openfaas", "apache-airflow-providers-openlineage", "apache-airflow-providers-opensearch", "apache-airflow-providers-opsgenie", "apache-airflow-providers-oracle", "apache-airflow-providers-pagerduty", "apache-airflow-providers-papermill", "apache-airflow-providers-pgvector", "apache-airflow-providers-pinecone", "apache-airflow-providers-postgres", "apache-airflow-providers-presto", "apache-airflow-providers-redis", "apache-airflow-providers-salesforce", "apache-airflow-providers-samba", "apache-airflow-providers-segment", "apache-airflow-providers-sendgrid", "apache-airflow-providers-sftp", "apache-airflow-providers-singularity", "apache-airflow-providers-slack", "apache-airflow-providers-smtp", "apache-airflow-providers-snowflake", "apache-airflow-providers-sqlite", "apache-airflow-providers-ssh", "apache-airflow-providers-tableau", "apache-airflow-providers-tabular", "apache-airflow-providers-telegram", "apache-airflow-providers-trino", "apache-airflow-providers-vertica", "apache-airflow-providers-weaviate", "apache-airflow-providers-yandex", "apache-airflow-providers-zendesk", "apache-beam (>=2.47.0)", "apprise", "asana (>=0.10,<4.0.0)", "asgiref", "asgiref (>=3.5.2)", "atlasclient (>=0.1.2)", "atlassian-python-api (>=1.14.2)", "attrs (>=22.2)", "authlib (>=1.0.0)", "azure-batch (>=8.0.0)", "azure-cosmos (>=4.0.0)", "azure-datalake-store (>=0.0.45)", "azure-identity (>=1.3.1)", "azure-keyvault-secrets (>=4.1.0)", "azure-kusto-data (>=4.1.0)", "azure-mgmt-containerinstance (>=9.0.0)", "azure-mgmt-containerregistry (>=8.0.0)", "azure-mgmt-cosmosdb", "azure-mgmt-datafactory (>=2.0.0)", "azure-mgmt-datalake-store (>=0.5.0)", "azure-mgmt-resource (>=2.2.0)", "azure-mgmt-storage (>=16.0.0)", "azure-servicebus (>=7.6.1)", "azure-storage-blob (>=12.14.0)", "azure-storage-file-datalake (>=12.9.1)", "azure-storage-file-share", "azure-synapse-artifacts (>=0.17.0)", "azure-synapse-spark", "bcrypt (>=2.0.0)", "blinker (>=1.1)", "boto3 (>=1.28.0)", "botocore (>=1.31.0)", "cassandra-driver (>=3.13.0)", "celery (>=5.3.0,!=5.3.2,!=5.3.3,<6)", "cgroupspy (>=0.2.2)", "cloudant (>=2.0)", "cohere (>=4.27)", "confluent-kafka (>=1.8.2)", "cryptography (>=2.0.0)", "databricks-sql-connector (>=2.0.0,!=2.9.0,<3.0.0)", "datadog (>=0.14.0)", "dnspython (>=1.13.0)", "docker (>=5.0.3)", "elasticsearch (>=8.10,<9)", "eventlet (>=0.33.3)", "facebook-business (>=6.0.2)", "flask-appbuilder[oauth] (==4.3.10)", "flask-bcrypt (>=0.7.1)", "flower (>=1.0.0)", "gcloud-aio-auth (>=4.0.0,<5.0.0)", "gcloud-aio-bigquery (>=6.1.2)", "gcloud-aio-storage", "gcsfs (>=2023.10.0)", "gevent (>=0.13)", "google-ads (>=22.1.0)", "google-api-core (>=2.11.0)", "google-api-python-client (>=1.6.0)", "google-auth (>=1.0.0)", "google-auth (>=1.0.0,<3.0.0)", "google-auth-httplib2 (>=0.0.1)", "google-cloud-aiplatform (>=1.22.1)", "google-cloud-automl (>=2.12.0)", "google-cloud-batch (>=0.13.0)", "google-cloud-bigquery-datatransfer (>=3.13.0)", "google-cloud-bigtable (>=2.17.0)", "google-cloud-build (>=3.22.0)", "google-cloud-compute (>=1.10.0)", "google-cloud-container (>=2.17.4)", "google-cloud-datacatalog (>=3.11.1)", "google-cloud-dataflow-client (>=0.8.6)", "google-cloud-dataform (>=0.5.0)", "google-cloud-dataplex (>=1.10.0)", "google-cloud-dataproc (>=5.8.0)", "google-cloud-dataproc-metastore (>=1.12.0)", "google-cloud-dlp (>=3.12.0)", "google-cloud-kms (>=2.15.0)", "google-cloud-language (>=2.9.0)", "google-cloud-logging (>=3.5.0)", "google-cloud-memcache 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(>=0.228.0)", "zenpy (>=2.0.24)"]
+all-dbs = ["aiohttp (>=3.6.3,<4)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-apache-cassandra", "apache-airflow-providers-apache-drill", "apache-airflow-providers-apache-druid", "apache-airflow-providers-apache-hdfs", "apache-airflow-providers-apache-hive", "apache-airflow-providers-apache-impala", "apache-airflow-providers-apache-pinot", "apache-airflow-providers-arangodb", "apache-airflow-providers-cloudant", "apache-airflow-providers-common-sql (>=1.3.1)", "apache-airflow-providers-common-sql (>=1.8.1)", "apache-airflow-providers-databricks", "apache-airflow-providers-exasol", "apache-airflow-providers-influxdb", "apache-airflow-providers-microsoft-mssql", "apache-airflow-providers-mongo", "apache-airflow-providers-mysql", "apache-airflow-providers-neo4j", "apache-airflow-providers-postgres", "apache-airflow-providers-presto", "apache-airflow-providers-trino", "apache-airflow-providers-vertica", "cassandra-driver (>=3.13.0)", "cloudant (>=2.0)", "databricks-sql-connector (>=2.0.0,!=2.9.0,<3.0.0)", "dnspython (>=1.13.0)", "hdfs[avro,dataframe,kerberos] (>=2.0.4)", "hmsclient (>=0.1.0)", "impyla (>=0.18.0,<1.0)", "influxdb-client (>=1.19.0)", "mysql-connector-python (>=8.0.11)", "mysqlclient (>=1.3.6)", "neo4j (>=4.2.1)", "pandas (>=0.17.1)", "pinotdb (>0.4.7)", "presto-python-client (>=0.8.4)", "psycopg2-binary (>=2.8.0)", "pydruid (>=0.4.1)", "pyexasol (>=0.5.1)", "pyhive[hive-pure-sasl] (>=0.7.0)", "pymongo (>=3.6.0)", "pymssql (>=2.1.8)", "python-arango (>=7.3.2)", "requests (>=2.26.0)", "requests (>=2.27,<3)", "sqlalchemy-drill (>=1.1.0)", "thrift (>=0.9.2)", "trino (>=0.318.0)", "vertica-python (>=0.5.1)"]
 amazon = ["apache-airflow-providers-amazon"]
 apache-atlas = ["atlasclient (>=0.1.2)"]
 apache-beam = ["apache-airflow-providers-apache-beam"]
@@ -290,7 +290,7 @@ apache-drill = ["apache-airflow-providers-apache-drill"]
 apache-druid = ["apache-airflow-providers-apache-druid"]
 apache-flink = ["apache-airflow-providers-apache-flink"]
 apache-hdfs = ["apache-airflow-providers-apache-hdfs"]
-apache-hive = ["apache-airflow-providers-apache-hive"]
+apache-hive = ["apache-airflow-providers-apache-hive (>=5.1.0)"]
 apache-impala = ["apache-airflow-providers-apache-impala"]
 apache-kafka = ["apache-airflow-providers-apache-kafka"]
 apache-kylin = ["apache-airflow-providers-apache-kylin"]
@@ -303,49 +303,53 @@ apprise = ["apache-airflow-providers-apprise"]
 arangodb = ["apache-airflow-providers-arangodb"]
 asana = ["apache-airflow-providers-asana"]
 async = ["eventlet (>=0.33.3)", "gevent (>=0.13)", "greenlet (>=0.4.9)"]
-atlas = ["apache-airflow[apache-atlas]"]
+atlas = ["apache-airflow-providers-apache-atlas"]
 atlassian-jira = ["apache-airflow-providers-atlassian-jira"]
-aws = ["apache-airflow[amazon]"]
-azure = ["apache-airflow[microsoft-azure]"]
-cassandra = ["apache-airflow[apache-cassandra]"]
-celery = ["apache-airflow-providers-celery"]
+aws = ["apache-airflow-providers-amazon"]
+azure = ["apache-airflow-providers-microsoft-azure"]
+cassandra = ["apache-airflow-providers-apache-cassandra"]
+celery = ["apache-airflow (>=2.6.0)", "apache-airflow-providers-celery", "celery (>=5.3.0,!=5.3.2,!=5.3.3,<6)", "flower (>=1.0.0)", "google-re2 (>=1.0)"]
 cgroups = ["cgroupspy (>=0.2.2)"]
 cloudant = ["apache-airflow-providers-cloudant"]
-cncf-kubernetes = ["apache-airflow-providers-cncf-kubernetes"]
+cncf-kubernetes = ["aiofiles (>=23.2.0)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-cncf-kubernetes", "asgiref (>=3.5.2)", "cryptography (>=2.0.0)", "google-re2 (>=1.0)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)"]
 cohere = ["apache-airflow-providers-cohere"]
 common-io = ["apache-airflow-providers-common-io"]
 common-sql = ["apache-airflow-providers-common-sql"]
 databricks = ["apache-airflow-providers-databricks"]
 datadog = ["apache-airflow-providers-datadog"]
 dbt-cloud = ["apache-airflow-providers-dbt-cloud"]
-deprecated-api = ["requests (>=2.27.0,<3)"]
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"google-cloud-dataform (>=0.5.0)", "google-cloud-dataplex (>=1.10.0)", "google-cloud-dataproc (>=5.8.0)", "google-cloud-dataproc-metastore (>=1.12.0)", "google-cloud-dlp (>=3.12.0)", "google-cloud-kms (>=2.15.0)", "google-cloud-language (>=2.9.0)", "google-cloud-logging (>=3.5.0)", "google-cloud-memcache (>=1.7.0)", "google-cloud-monitoring (>=2.18.0)", "google-cloud-orchestration-airflow (>=1.10.0)", "google-cloud-os-login (>=2.9.1)", "google-cloud-pubsub (>=2.19.0)", "google-cloud-redis (>=2.12.0)", "google-cloud-run (>=0.9.0)", "google-cloud-secret-manager (>=2.16.0)", "google-cloud-spanner (>=3.11.1)", "google-cloud-speech (>=2.18.0)", "google-cloud-storage (>=2.7.0)", "google-cloud-storage-transfer (>=1.4.1)", "google-cloud-tasks (>=2.13.0)", "google-cloud-texttospeech (>=2.14.1)", "google-cloud-translate (>=3.11.0)", "google-cloud-videointelligence (>=2.11.0)", "google-cloud-vision (>=3.4.0)", "google-cloud-workflows (>=1.10.0)", "google-re2 (>=1.0)", "greenlet (>=0.4.9)", "grpcio (>=1.15.0)", "grpcio-gcp (>=0.2.2)", "grpcio-status", "hdfs[avro,dataframe,kerberos] (>=2.0.4)", "hmsclient (>=0.1.0)", "httpx", "hvac (>=0.10)", "impyla (>=0.18.0,<1.0)", "inflection (>=0.5.1)", "influxdb-client (>=1.19.0)", "ipdb", "ipykernel", "jaydebeapi (>=1.1.1)", "json-merge-patch (>=0.2)", "jsonpath-ng (>=1.5.3)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)", "kylinpy (>=2.6)", "ldap3 (>=2.5.1)", "looker-sdk (>=22.2.0)", "mongomock", "moto[cloudformation,glue] (>=4.2.9)", "mypy (==1.2.0)", "mypy-boto3-appflow (>=1.28.0)", "mypy-boto3-rds (>=1.28.0)", "mypy-boto3-redshift-data (>=1.28.0)", "mypy-boto3-s3 (>=1.28.0)", "mysql-connector-python (>=8.0.11)", "mysqlclient (>=1.3.6)", "neo4j (>=4.2.1)", "openai[datalib] (>=1.0)", "openlineage-integration-common (>=0.28.0)", "openlineage-python (>=0.28.0)", "opensearch-py (>=2.2.0)", "opentelemetry-exporter-prometheus", "opsgenie-sdk (>=2.1.5)", "oracledb (>=1.0.0)", "oss2 (>=2.14.0)", "pandas (>=0.17.1)", "pandas-gbq", "papermill[all] (>=1.2.1)", "paramiko (>=2.6.0)", "paramiko (>=2.8.0)", "pdpyras (>=4.1.2)", "pgvector (>=0.2.3)", "pinecone-client (>=2.2.4)", "pinotdb (>0.4.7)", "pipdeptree", "plyvel", "pre-commit", "presto-python-client (>=0.8.4)", "proto-plus (>=1.19.6)", "psycopg2-binary (>=2.8.0)", "pyarrow (>=9.0.0)", "pyarrow-hotfix", "pydruid (>=0.4.1)", "pyexasol (>=0.5.1)", "pygithub", "pyhive[hive-pure-sasl] (>=0.7.0)", "pyiceberg (>=0.5.0)", "pykerberos (>=1.1.13)", "pymongo (>=3.6.0)", "pymssql (>=2.1.8)", "pyodbc", "pypsrp (>=0.8.0)", "pyspark", "pytest (>=7.1)", "pytest-asyncio (!=0.23.0,!=0.23.1)", "pytest-cov", "pytest-httpx", "pytest-icdiff", "pytest-instafail", "pytest-mock", "pytest-rerunfailures", "pytest-timeouts", "pytest-xdist", "python-arango (>=7.3.2)", "python-dotenv (>=0.21.0)", "python-jenkins (>=1.0.0)", "python-ldap", "python-telegram-bot (>=20.0.0)", "python3-saml (>=1.16.0)", "pywinrm", "pywinrm (>=0.4)", "redis (>=4.5.2,!=4.5.5,<5.0.0)", "redshift-connector (>=2.0.888)", "requests (>=2.26.0)", "requests (>=2.27,<3)", "requests-kerberos (>=0.10.0)", "requests-mock", "requests-toolbelt", "restructuredtext-lint", "rich-click (>=1.7.0)", "ruff (>=0.0.219)", "s3fs (>=2023.10.0)", "scrapbook[all]", "semver", "sendgrid (>=6.0.0)", "sentry-sdk (>=1.32.0,!=1.33.0)", "simple-salesforce (>=1.0.0)", "slack-sdk (>=3.0.0)", "smbprotocol (>=1.5.0)", "snowflake-connector-python (>=2.7.8)", "snowflake-sqlalchemy (>=1.1.0)", "sphinx (>=5.2.0)", "sphinx-airflow-theme", "sphinx-argparse (>=0.1.13)", "sphinx-autoapi (>=2.0.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-jinja (>=2.0)", "sphinx-rtd-theme (>=0.1.6)", "sphinxcontrib-httpdomain (>=1.7.0)", "sphinxcontrib-redoc (>=1.6.0)", "sphinxcontrib-spelling (>=7.3)", "spython (>=0.0.56)", "sqlalchemy-bigquery (>=1.2.1)", "sqlalchemy-drill (>=1.1.0)", "sqlalchemy-redshift (>=0.8.6)", "sqlalchemy-spanner (>=1.6.2)", "sqlparse (>=0.4.2)", "sshtunnel (>=0.3.2)", "statsd (>=3.3.0)", "tableauserverclient", "thrift (>=0.9.2)", "thrift-sasl (>=0.2.0)", "time-machine", "towncrier", "trino (>=0.318.0)", "twine", "types-Deprecated", "types-Markdown", "types-PyMySQL", "types-PyYAML", "types-aiofiles", "types-certifi", "types-croniter", "types-docutils", "types-paramiko", "types-protobuf", "types-python-dateutil", "types-python-slugify", "types-pytz", "types-redis", "types-requests", "types-setuptools", "types-tabulate", "types-termcolor", "types-toml", "vertica-python (>=0.5.1)", "virtualenv", "watchtower (>=2.0.1,<4)", "weaviate-client (>=3.24.2)", "wheel", "yamllint", "yandexcloud (>=0.228.0)", "zenpy (>=2.0.24)"]
+devel-hadoop = ["aiobotocore (>=2.1.1)", "aioresponses", "apache-airflow (>=2.6.0)", "apache-airflow-providers-apache-hdfs", "apache-airflow-providers-apache-hive", "apache-airflow-providers-common-sql", "apache-airflow-providers-presto", "apache-airflow-providers-trino", "astroid (>=2.12.3,<3.0)", "aws-xray-sdk", "backports.zoneinfo (>=0.2.1)", "bcrypt (>=2.0.0)", "beautifulsoup4 (>=4.7.1)", "black", "blinker", "cgroupspy (>=0.2.2)", "checksumdir", "click (>=8.0)", "click (>=8.0,!=8.1.4,!=8.1.5)", "coverage (>=7.2)", "cryptography (>=2.0.0)", "deltalake (>=0.12.0)", "docutils (<0.17.0)", "duckdb (>=0.9.0)", "eralchemy2", "filelock", "flask-bcrypt (>=0.7.1)", "gitpython", "hdfs[avro,dataframe,kerberos] (>=2.0.4)", "hmsclient (>=0.1.0)", "impyla (>=0.18.0,<1.0)", "ipdb", "kubernetes (>=21.7.0,<24)", "mongomock", "moto[cloudformation,glue] (>=4.2.9)", "mypy (==1.2.0)", "mypy-boto3-appflow (>=1.28.0)", "mypy-boto3-rds (>=1.28.0)", "mypy-boto3-redshift-data (>=1.28.0)", "mypy-boto3-s3 (>=1.28.0)", "mysql-connector-python (>=8.0.11)", "mysqlclient (>=1.3.6)", "pandas (>=0.17.1)", "pipdeptree", "pre-commit", "presto-python-client (>=0.8.4)", "pyarrow (>=9.0.0)", "pyarrow-hotfix", "pygithub", "pyhive[hive-pure-sasl] (>=0.7.0)", "pyiceberg (>=0.5.0)", "pykerberos (>=1.1.13)", "pytest (>=7.1)", "pytest-asyncio (!=0.23.0,!=0.23.1)", "pytest-cov", "pytest-httpx", "pytest-icdiff", "pytest-instafail", "pytest-mock", "pytest-rerunfailures", "pytest-timeouts", "pytest-xdist", "python3-saml (>=1.16.0)", "pywinrm", "requests-kerberos (>=0.10.0)", "requests-mock", "restructuredtext-lint", "rich-click (>=1.7.0)", "ruff (>=0.0.219)", "s3fs (>=2023.10.0)", "semver", "sphinx (>=5.2.0)", "sphinx-airflow-theme", "sphinx-argparse (>=0.1.13)", "sphinx-autoapi (>=2.0.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-jinja (>=2.0)", "sphinx-rtd-theme (>=0.1.6)", "sphinxcontrib-httpdomain (>=1.7.0)", "sphinxcontrib-redoc (>=1.6.0)", "sphinxcontrib-spelling (>=7.3)", "thrift (>=0.9.2)", "thrift-sasl (>=0.2.0)", "time-machine", "towncrier", "twine", "types-Deprecated", "types-Markdown", "types-PyMySQL", "types-PyYAML", "types-aiofiles", "types-certifi", "types-croniter", "types-docutils", "types-paramiko", "types-protobuf", "types-python-dateutil", "types-python-slugify", "types-pytz", "types-redis", "types-requests", "types-setuptools", "types-tabulate", "types-termcolor", "types-toml", "wheel", "yamllint"]
 dingding = ["apache-airflow-providers-dingding"]
 discord = ["apache-airflow-providers-discord"]
+doc = ["astroid (>=2.12.3,<3.0)", "checksumdir", "click (>=8.0,!=8.1.4,!=8.1.5)", "docutils (<0.17.0)", "eralchemy2", "sphinx (>=5.2.0)", "sphinx-airflow-theme", "sphinx-argparse (>=0.1.13)", "sphinx-autoapi (>=2.0.0)", "sphinx-copybutton", "sphinx-design (>=0.5.0)", "sphinx-jinja (>=2.0)", "sphinx-rtd-theme (>=0.1.6)", "sphinxcontrib-httpdomain (>=1.7.0)", "sphinxcontrib-redoc (>=1.6.0)", "sphinxcontrib-spelling (>=7.3)"]
+doc-gen = ["eralchemy2"]
 docker = ["apache-airflow-providers-docker"]
-druid = ["apache-airflow[apache-druid]"]
+druid = ["apache-airflow-providers-apache-druid"]
 elasticsearch = ["apache-airflow-providers-elasticsearch"]
 exasol = ["apache-airflow-providers-exasol"]
 facebook = ["apache-airflow-providers-facebook"]
 ftp = ["apache-airflow-providers-ftp"]
-gcp = ["apache-airflow[google]"]
-gcp-api = ["apache-airflow[google]"]
+gcp = ["apache-airflow-providers-google"]
+gcp-api = ["apache-airflow-providers-google"]
 github = ["apache-airflow-providers-github"]
-github-enterprise = ["apache-airflow[fab]", "authlib (>=1.0.0)"]
+github-enterprise = ["authlib (>=1.0.0)", "flask-appbuilder[oauth] (==4.3.10)"]
 google = ["apache-airflow-providers-google"]
-google-auth = ["apache-airflow[fab]", "authlib (>=1.0.0)"]
-graphviz = ["graphviz (>=0.12)"]
+google-auth = ["authlib (>=1.0.0)", "flask-appbuilder[oauth] (==4.3.10)"]
 grpc = ["apache-airflow-providers-grpc"]
 hashicorp = ["apache-airflow-providers-hashicorp"]
-hdfs = ["apache-airflow[apache-hdfs]"]
-hive = ["apache-airflow[apache-hive]"]
+hdfs = ["apache-airflow-providers-apache-hdfs"]
+hive = ["apache-airflow-providers-apache-hive"]
 http = ["apache-airflow-providers-http"]
 imap = ["apache-airflow-providers-imap"]
 influxdb = ["apache-airflow-providers-influxdb"]
 jdbc = ["apache-airflow-providers-jdbc"]
 jenkins = ["apache-airflow-providers-jenkins"]
 kerberos = ["pykerberos (>=1.1.13)", "requests-kerberos (>=0.10.0)", "thrift-sasl (>=0.2.0)"]
-kubernetes = ["apache-airflow[cncf-kubernetes]"]
+kubernetes = ["aiofiles (>=23.2.0)", "apache-airflow (>=2.6.0)", "apache-airflow-providers-cncf-kubernetes", "asgiref (>=3.5.2)", "cryptography (>=2.0.0)", "google-re2 (>=1.0)", "kubernetes (>=21.7.0,<24)", "kubernetes-asyncio (>=18.20.1,<25)"]
 ldap = ["ldap3 (>=2.5.1)", "python-ldap"]
 leveldb = ["plyvel"]
 microsoft-azure = ["apache-airflow-providers-microsoft-azure"]
@@ -353,7 +357,7 @@ microsoft-mssql = ["apache-airflow-providers-microsoft-mssql"]
 microsoft-psrp = ["apache-airflow-providers-microsoft-psrp"]
 microsoft-winrm = ["apache-airflow-providers-microsoft-winrm"]
 mongo = ["apache-airflow-providers-mongo"]
-mssql = ["apache-airflow[microsoft-mssql]"]
+mssql = ["apache-airflow-providers-microsoft-mssql"]
 mysql = ["apache-airflow-providers-mysql"]
 neo4j = ["apache-airflow-providers-neo4j"]
 odbc = ["apache-airflow-providers-odbc"]
@@ -365,18 +369,17 @@ opsgenie = ["apache-airflow-providers-opsgenie"]
 oracle = ["apache-airflow-providers-oracle"]
 otel = ["opentelemetry-exporter-prometheus"]
 pagerduty = ["apache-airflow-providers-pagerduty"]
-pandas = ["pandas (>=1.2.5)"]
+pandas = ["pandas (>=0.17.1)", "pyarrow (>=9.0.0)", "pyarrow-hotfix"]
 papermill = ["apache-airflow-providers-papermill"]
 password = ["bcrypt (>=2.0.0)", "flask-bcrypt (>=0.7.1)"]
 pgvector = ["apache-airflow-providers-pgvector"]
 pinecone = ["apache-airflow-providers-pinecone"]
-pinot = ["apache-airflow[apache-pinot]"]
+pinot = ["apache-airflow-providers-apache-pinot"]
 postgres = ["apache-airflow-providers-postgres"]
 presto = ["apache-airflow-providers-presto"]
-pydantic = ["pydantic (>=2.3.0)"]
 rabbitmq = ["amqp"]
 redis = ["apache-airflow-providers-redis"]
-s3 = ["apache-airflow[amazon]"]
+s3 = ["apache-airflow-providers-amazon"]
 s3fs = ["s3fs (>=2023.10.0)"]
 salesforce = ["apache-airflow-providers-salesforce"]
 samba = ["apache-airflow-providers-samba"]
@@ -389,7 +392,7 @@ singularity = ["apache-airflow-providers-singularity"]
 slack = ["apache-airflow-providers-slack"]
 smtp = ["apache-airflow-providers-smtp"]
 snowflake = ["apache-airflow-providers-snowflake"]
-spark = ["apache-airflow[apache-spark]"]
+spark = ["apache-airflow-providers-apache-spark"]
 sqlite = ["apache-airflow-providers-sqlite"]
 ssh = ["apache-airflow-providers-ssh"]
 statsd = ["statsd (>=3.3.0)"]
@@ -400,74 +403,56 @@ trino = ["apache-airflow-providers-trino"]
 vertica = ["apache-airflow-providers-vertica"]
 virtualenv = ["virtualenv"]
 weaviate = ["apache-airflow-providers-weaviate"]
-webhdfs = ["apache-airflow[apache-webhdfs]"]
-winrm = ["apache-airflow[microsoft-winrm]"]
+webhdfs = ["hdfs[avro,dataframe,kerberos] (>=2.0.4)"]
+winrm = ["apache-airflow-providers-microsoft-winrm"]
 yandex = ["apache-airflow-providers-yandex"]
 zendesk = ["apache-airflow-providers-zendesk"]
 
-[[package]]
-name = "apache-airflow-providers-common-io"
-version = "1.3.0"
-description = "Provider package apache-airflow-providers-common-io for Apache Airflow"
-optional = false
-python-versions = "~=3.8"
-files = [
-    {file = "apache_airflow_providers_common_io-1.3.0-py3-none-any.whl", hash = "sha256:a67c6dd3cb419c68fc1a9ed62f0f434426852e15a46c3159f367b3961332955d"},
-    {file = "apache_airflow_providers_common_io-1.3.0.tar.gz", hash = "sha256:7172620a2370031970df2212a9f694a5ff82240f7e498b8b7dfdbae7e6c882d6"},
-]
-
-[package.dependencies]
-apache-airflow = ">=2.8.0"
-
-[package.extras]
-openlineage = ["apache-airflow-providers-openlineage"]
-
 [[package]]
 name = "apache-airflow-providers-common-sql"
-version = "1.11.1"
-description = "Provider package apache-airflow-providers-common-sql for Apache Airflow"
+version = "1.8.0"
+description = "Provider for Apache Airflow. Implements apache-airflow-providers-common-sql package"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_common_sql-1.11.1-py3-none-any.whl", hash = "sha256:eb8531b9eedcfdcdd972f12a0bffadc289ade9c3587a5bb5bd146f3240d5eb02"},
-    {file = "apache_airflow_providers_common_sql-1.11.1.tar.gz", hash = "sha256:5b8c4d267677758dcb28b1a8976f10f30e50a4b568abda93a667ad6ff938060b"},
+    {file = "apache-airflow-providers-common-sql-1.8.0.tar.gz", hash = "sha256:4d8d99f33f7b5a3e313ef12c5a2f10d1612477ed2e784d0ffb721447e076e3f7"},
+    {file = "apache_airflow_providers_common_sql-1.8.0-py3-none-any.whl", hash = "sha256:45117fa38f5a2e83cdb9a8d3bdfdbd602bfdc7a894d087b26e6ebb94df79ec32"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.6.0"
-more-itertools = ">=9.0.0"
+apache-airflow = ">=2.5.0"
 sqlparse = ">=0.4.2"
 
 [package.extras]
 openlineage = ["apache-airflow-providers-openlineage"]
-pandas = ["pandas (>=1.2.5,<2.2)"]
+pandas = ["pandas (>=0.17.1)"]
 
 [[package]]
 name = "apache-airflow-providers-ftp"
-version = "3.7.0"
-description = "Provider package apache-airflow-providers-ftp for Apache Airflow"
+version = "3.6.0"
+description = "Provider for Apache Airflow. Implements apache-airflow-providers-ftp package"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_ftp-3.7.0-py3-none-any.whl", hash = "sha256:817936fa2f9f5d7f00f50bfe0dfdf83a3ffbcd863cb92b862a2ab5a40da1b8c4"},
-    {file = "apache_airflow_providers_ftp-3.7.0.tar.gz", hash = "sha256:cc6e7602fb9b92255b83343673923339c8fe7d22c8c738ba372ce38a70cce1cc"},
+    {file = "apache-airflow-providers-ftp-3.6.0.tar.gz", hash = "sha256:160f12e9f78ffbcdca4bdffbac93c1f04641fd3f1f7d236c7bfe79cd0a68d14b"},
+    {file = "apache_airflow_providers_ftp-3.6.0-py3-none-any.whl", hash = "sha256:03264207293710c3821f5ccd548c5e89a3c5ca7562bc4ff629e6f73d112a71a4"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.6.0"
+apache-airflow = ">=2.5.0"
 
 [package.extras]
 openlineage = ["apache-airflow-providers-openlineage"]
 
 [[package]]
 name = "apache-airflow-providers-google"
-version = "10.16.0"
+version = "10.15.0"
 description = "Provider package apache-airflow-providers-google for Apache Airflow"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_google-10.16.0-py3-none-any.whl", hash = "sha256:d9e3f04cc9c41ba077f8607e062a975ff3e18a583c51ac56d808455a2074e6ae"},
-    {file = "apache_airflow_providers_google-10.16.0.tar.gz", hash = "sha256:dde9ce5b6873558c92438fad3832a63345072736c69222de3246a1d7e6f109f1"},
+    {file = "apache_airflow_providers_google-10.15.0-py3-none-any.whl", hash = "sha256:70d2d4feb66f06cd750ea673344f3be20d2d575c9645f7e2b030c73551a297fc"},
+    {file = "apache_airflow_providers_google-10.15.0.tar.gz", hash = "sha256:ff48fa0a29abec2645a8008c47d0a84a759b7203412707a5e2fc01558c4052a3"},
 ]
 
 [package.dependencies]
@@ -524,7 +509,7 @@ grpcio-gcp = ">=0.2.2"
 httpx = "*"
 json-merge-patch = ">=0.2"
 looker-sdk = ">=22.2.0"
-pandas = ">=1.2.5,<2.2"
+pandas = ">=1.2.5"
 pandas-gbq = "*"
 proto-plus = ">=1.19.6"
 PyOpenSSL = "*"
@@ -553,49 +538,49 @@ trino = ["apache-airflow-providers-trino"]
 
 [[package]]
 name = "apache-airflow-providers-http"
-version = "4.10.0"
-description = "Provider package apache-airflow-providers-http for Apache Airflow"
+version = "4.6.0"
+description = "Provider for Apache Airflow. Implements apache-airflow-providers-http package"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_http-4.10.0-py3-none-any.whl", hash = "sha256:3c9af9a64954973a3bc31d33adf145b9b4273e5194603bee86c4fad6c51d872f"},
-    {file = "apache_airflow_providers_http-4.10.0.tar.gz", hash = "sha256:f8280633053f25d7d11ef33f26aa7225fb081400a0dd97c6af9e0a9f59b26566"},
+    {file = "apache-airflow-providers-http-4.6.0.tar.gz", hash = "sha256:c9b08782031daa8c18ba62c2c8ae687659968d636fbf6b9fbed488d1fe4f0162"},
+    {file = "apache_airflow_providers_http-4.6.0-py3-none-any.whl", hash = "sha256:ca3cf6c209cb27144fd4d676ee5feef96a1ed67002f13bf4a8b74d859ef8b56c"},
 ]
 
 [package.dependencies]
-aiohttp = ">=3.9.2"
-apache-airflow = ">=2.6.0"
+aiohttp = "*"
+apache-airflow = ">=2.5.0"
 asgiref = "*"
-requests = ">=2.27.0,<3"
-requests_toolbelt = "*"
+requests = ">=2.26.0"
+requests-toolbelt = "*"
 
 [[package]]
 name = "apache-airflow-providers-imap"
-version = "3.5.0"
-description = "Provider package apache-airflow-providers-imap for Apache Airflow"
+version = "3.4.0"
+description = "Provider for Apache Airflow. Implements apache-airflow-providers-imap package"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_imap-3.5.0-py3-none-any.whl", hash = "sha256:f5f26c662cff27a532c6a7c3ed0a8bc9c7fcae14634424e82e85150e631a2adb"},
-    {file = "apache_airflow_providers_imap-3.5.0.tar.gz", hash = "sha256:d50929801cb9b7abcea33799e8b3cd64b756bd42530a44281febaffcdb30998f"},
+    {file = "apache-airflow-providers-imap-3.4.0.tar.gz", hash = "sha256:347f78efa0e1353f90be78244fad0136d7c0cb1af25b9a3ba296d4e31fb53f6c"},
+    {file = "apache_airflow_providers_imap-3.4.0-py3-none-any.whl", hash = "sha256:4972793b0dbb25d5372fe1bf174329958075985c0beca013459ec89fcbbca620"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.6.0"
+apache-airflow = ">=2.5.0"
 
 [[package]]
 name = "apache-airflow-providers-sqlite"
-version = "3.7.1"
-description = "Provider package apache-airflow-providers-sqlite for Apache Airflow"
+version = "3.5.0"
+description = "Provider for Apache Airflow. Implements apache-airflow-providers-sqlite package"
 optional = false
 python-versions = "~=3.8"
 files = [
-    {file = "apache_airflow_providers_sqlite-3.7.1-py3-none-any.whl", hash = "sha256:49af9ce1012e2d52ef89dc5d5832ff9bc245330635aa4f011590dc19113a380f"},
-    {file = "apache_airflow_providers_sqlite-3.7.1.tar.gz", hash = "sha256:0dd8967ac1f3fb518cef278611a2f8bbae19421e215e0bf4ede01542d4af86cf"},
+    {file = "apache-airflow-providers-sqlite-3.5.0.tar.gz", hash = "sha256:6f47c25d7fb026fa8b8b4dc8edfee1491998255048255625716fceaae2c67024"},
+    {file = "apache_airflow_providers_sqlite-3.5.0-py3-none-any.whl", hash = "sha256:7baba67c9ddf75b31d8197e22ca491c2e1cf9e4c6306c1788c150b5457c3a182"},
 ]
 
 [package.dependencies]
-apache-airflow = ">=2.6.0"
+apache-airflow = ">=2.5.0"
 apache-airflow-providers-common-sql = ">=1.3.1"
 
 [package.extras]
@@ -603,13 +588,13 @@ common-sql = ["apache-airflow-providers-common-sql"]
 
 [[package]]
 name = "apispec"
-version = "6.5.0"
+version = "6.3.0"
 description = "A pluggable API specification generator. Currently supports the OpenAPI Specification (f.k.a. the Swagger specification)."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "apispec-6.5.0-py3-none-any.whl", hash = "sha256:708a9a6e6ccd5e69cd73bf75b41e7ed5ea8866f8a4539f419db3df642789bb1e"},
-    {file = "apispec-6.5.0.tar.gz", hash = "sha256:c03a4d848ae70e9bb2269dd4e4d30d8ec7ec069d24ef9e9b422e3cbd1a9fe794"},
+    {file = "apispec-6.3.0-py3-none-any.whl", hash = "sha256:95a0b9355785df998bb0e9b939237a30ee4c7428fd6ef97305eae3da06b9b339"},
+    {file = "apispec-6.3.0.tar.gz", hash = "sha256:6cb08d92ce73ff0b3bf46cb2ea5c00d57289b0f279fb0256a3df468182ba5344"},
 ]
 
 [package.dependencies]
@@ -617,10 +602,12 @@ packaging = ">=21.3"
 PyYAML = {version = ">=3.10", optional = true, markers = "extra == \"yaml\""}
 
 [package.extras]
-dev = ["apispec[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["apispec[marshmallow]", "pyyaml (==6.0.1)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-rtd-theme (==2.0.0)"]
+dev = ["PyYAML (>=3.10)", "flake8 (==5.0.4)", "flake8-bugbear (==22.9.23)", "marshmallow (>=3.13.0)", "mypy (==0.982)", "openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)", "pre-commit (>=2.4,<3.0)", "pytest", "tox", "types-PyYAML"]
+docs = ["marshmallow (>=3.13.0)", "pyyaml (==6.0)", "sphinx (==5.2.3)", "sphinx-issues (==3.0.1)", "sphinx-rtd-theme (==1.0.0)"]
+lint = ["flake8 (==5.0.4)", "flake8-bugbear (==22.9.23)", "mypy (==0.982)", "pre-commit (>=2.4,<3.0)", "types-PyYAML"]
 marshmallow = ["marshmallow (>=3.18.0)"]
-tests = ["apispec[marshmallow,yaml]", "openapi-spec-validator (==0.7.1)", "pytest"]
+tests = ["PyYAML (>=3.10)", "marshmallow (>=3.13.0)", "openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)", "pytest"]
+validation = ["openapi-spec-validator (<0.5)", "prance[osv] (>=0.11)"]
 yaml = ["PyYAML (>=3.10)"]
 
 [[package]]
@@ -636,24 +623,24 @@ files = [
 
 [[package]]
 name = "appnope"
-version = "0.1.4"
+version = "0.1.3"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = ">=3.6"
+python-versions = "*"
 files = [
-    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
-    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
 ]
 
 [[package]]
 name = "argcomplete"
-version = "3.2.3"
+version = "3.1.2"
 description = "Bash tab completion for argparse"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 files = [
-    {file = "argcomplete-3.2.3-py3-none-any.whl", hash = "sha256:c12355e0494c76a2a7b73e3a59b09024ca0ba1e279fb9ed6c1b82d5b74b6a70c"},
-    {file = "argcomplete-3.2.3.tar.gz", hash = "sha256:bf7900329262e481be5a15f56f19736b376df6f82ed27576fa893652c5de6c23"},
+    {file = "argcomplete-3.1.2-py3-none-any.whl", hash = "sha256:d97c036d12a752d1079f190bc1521c545b941fda89ad85d15afa909b4d1b9a99"},
+    {file = "argcomplete-3.1.2.tar.gz", hash = "sha256:d5d1e5efd41435260b8f85673b74ea2e883affcbec9f4230c582689e8e78251b"},
 ]
 
 [package.extras]
@@ -707,22 +694,21 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "23.1.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
+    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
+dev = ["attrs[docs,tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "avro"
@@ -751,17 +737,17 @@ files = [
 
 [[package]]
 name = "azure-core"
-version = "1.30.1"
+version = "1.29.5"
 description = "Microsoft Azure Core Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.30.1.tar.gz", hash = "sha256:26273a254131f84269e8ea4464f3560c731f29c0c1f69ac99010845f239c1a8f"},
-    {file = "azure_core-1.30.1-py3-none-any.whl", hash = "sha256:7c5ee397e48f281ec4dd773d67a0a47a0962ed6fa833036057f9ea067f688e74"},
+    {file = "azure-core-1.29.5.tar.gz", hash = "sha256:52983c89d394c6f881a121e5101c5fa67278ca3b1f339c8fb2ef39230c70e9ac"},
+    {file = "azure_core-1.29.5-py3-none-any.whl", hash = "sha256:0fa04b7b1f7d44a4fb8468c4093deb2ea01fdf4faddbf802ed9205615f99d68c"},
 ]
 
 [package.dependencies]
-requests = ">=2.21.0"
+requests = ">=2.18.4"
 six = ">=1.11.0"
 typing-extensions = ">=4.6.0"
 
@@ -817,13 +803,13 @@ msrest = ">=0.6.21"
 
 [[package]]
 name = "azure-storage-blob"
-version = "12.19.1"
+version = "12.18.3"
 description = "Microsoft Azure Blob Storage Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-storage-blob-12.19.1.tar.gz", hash = "sha256:13e16ba42fc54ac2c7e8f976062173a5c82b9ec0594728e134aac372965a11b0"},
-    {file = "azure_storage_blob-12.19.1-py3-none-any.whl", hash = "sha256:c5530dc51c21c9564e4eb706cd499befca8819b10dd89716d3fc90d747556243"},
+    {file = "azure-storage-blob-12.18.3.tar.gz", hash = "sha256:d8ced0deee3367fa3d4f3d1a03cd9edadf4440c0a371f503d623fa6c807554ee"},
+    {file = "azure_storage_blob-12.18.3-py3-none-any.whl", hash = "sha256:c278dde2ac41857a68d615c9f2b36d894ba877a7e84d62795603c7e79d0bb5e9"},
 ]
 
 [package.dependencies]
@@ -837,13 +823,13 @@ aio = ["azure-core[aio] (>=1.28.0,<2.0.0)"]
 
 [[package]]
 name = "babel"
-version = "2.14.0"
+version = "2.13.1"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
-    {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
+    {file = "Babel-2.13.1-py3-none-any.whl", hash = "sha256:7077a4984b02b6727ac10f1f7294484f737443d7e2e66c5e4380e41a3ae0b4ed"},
+    {file = "Babel-2.13.1.tar.gz", hash = "sha256:33e0952d7dd6374af8dbf6768cc4ddf3ccfefc244f9986d4074704f2fbd18900"},
 ]
 
 [package.extras]
@@ -862,45 +848,42 @@ files = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.3"
+version = "4.12.2"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
-    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
-cchardet = ["cchardet"]
-chardet = ["chardet"]
-charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
 [[package]]
 name = "blinker"
-version = "1.7.0"
+version = "1.6.3"
 description = "Fast, simple object-to-object and broadcast signaling"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "blinker-1.7.0-py3-none-any.whl", hash = "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9"},
-    {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
+    {file = "blinker-1.6.3-py3-none-any.whl", hash = "sha256:296320d6c28b006eb5e32d4712202dbcdcbf5dc482da298c2f44881c43884aaa"},
+    {file = "blinker-1.6.3.tar.gz", hash = "sha256:152090d27c1c5c722ee7e48504b02d76502811ce02e1523553b4cf8c8b3d3a8d"},
 ]
 
 [[package]]
 name = "bokeh"
-version = "3.3.4"
+version = "3.3.0"
 description = "Interactive plots and applications in the browser from Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "bokeh-3.3.4-py3-none-any.whl", hash = "sha256:ad7b6f89d0a7c2be01eff1db0ca24e2755ac41de14539db919a62e791809c309"},
-    {file = "bokeh-3.3.4.tar.gz", hash = "sha256:73b7982dc2b8df15bf660cdddc8d3825e829195c438015a5d09824f1a7028368"},
+    {file = "bokeh-3.3.0-py3-none-any.whl", hash = "sha256:65e36824c99fd46530c559263c6d14eabed6945370cd3beffc2eeedb62d6db6d"},
+    {file = "bokeh-3.3.0.tar.gz", hash = "sha256:cdbe268f842c139ba8fa0fb43c0c55c172c8215ec5a69a2629482c63c9d4039c"},
 ]
 
 [package.dependencies]
@@ -916,32 +899,32 @@ xyzservices = ">=2021.09.1"
 
 [[package]]
 name = "boto3"
-version = "1.34.58"
+version = "1.28.75"
 description = "The AWS SDK for Python"
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">= 3.7"
 files = [
-    {file = "boto3-1.34.58-py3-none-any.whl", hash = "sha256:d213a6fea9db6d537b1e65924133d8279ada79a40bc840d4930e1b64be869d4c"},
-    {file = "boto3-1.34.58.tar.gz", hash = "sha256:09e3d17c718bc938a76774f31bc557b20733c0f5f9135a3e7782b55f3459cbdd"},
+    {file = "boto3-1.28.75-py3-none-any.whl", hash = "sha256:b959decd588982bc919bfcbebc7916926b05003a0093e7f2845362da4f5dd9fc"},
+    {file = "boto3-1.28.75.tar.gz", hash = "sha256:63d772a784e8e35ee51974eb1c20dff5faa51b007d22c5647783f18966bf4042"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.58,<1.35.0"
+botocore = ">=1.31.75,<1.32.0"
 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.10.0,<0.11.0"
+s3transfer = ">=0.7.0,<0.8.0"
 
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.58"
+version = "1.31.75"
 description = "Low-level, data-driven core of boto 3."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">= 3.7"
 files = [
-    {file = "botocore-1.34.58-py3-none-any.whl", hash = "sha256:8086a6ea27d0f658505ac81e6d7314e013561469c40db9b5a4692127eb5c97cf"},
-    {file = "botocore-1.34.58.tar.gz", hash = "sha256:d75216952886dc513ea1b5e2979a6af08feed2f537e3fc102e4a0a2ead563a35"},
+    {file = "botocore-1.31.75-py3-none-any.whl", hash = "sha256:fa078c4aa9a5777b3ede756540e62fec551e13d39cf7abf9a37bb81981496d68"},
+    {file = "botocore-1.31.75.tar.gz", hash = "sha256:d704ea9867b2227de0350bc2a5ca2543349e164ecb5d15edbfacbb05f2056482"},
 ]
 
 [package.dependencies]
@@ -950,7 +933,7 @@ python-dateutil = ">=2.1,<3.0.0"
 urllib3 = {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.19.19)"]
+crt = ["awscrt (==0.16.26)"]
 
 [[package]]
 name = "bracex"
@@ -976,49 +959,49 @@ files = [
 
 [[package]]
 name = "cachetools"
-version = "5.3.3"
+version = "5.3.2"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
-    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
+    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
 ]
 
 [[package]]
 name = "cattrs"
-version = "23.2.3"
+version = "23.1.2"
 description = "Composable complex class support for attrs and dataclasses."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "cattrs-23.2.3-py3-none-any.whl", hash = "sha256:0341994d94971052e9ee70662542699a3162ea1e0c62f7ce1b4a57f563685108"},
-    {file = "cattrs-23.2.3.tar.gz", hash = "sha256:a934090d95abaa9e911dac357e3a8699e0b4b14f8529bcc7d2b1ad9d51672b9f"},
+    {file = "cattrs-23.1.2-py3-none-any.whl", hash = "sha256:b2bb14311ac17bed0d58785e5a60f022e5431aca3932e3fc5cc8ed8639de50a4"},
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-typing-extensions = {version = ">=4.1.0,<4.6.3 || >4.6.3", markers = "python_version < \"3.11\""}
+attrs = ">=20"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
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-cbor2 = ["cbor2 (>=5.4.6)"]
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-pyyaml = ["pyyaml (>=6.0)"]
-tomlkit = ["tomlkit (>=0.11.8)"]
-ujson = ["ujson (>=5.7.0)"]
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+msgpack = ["msgpack (>=1.0.2,<2.0.0)"]
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+tomlkit = ["tomlkit (>=0.11.4,<0.12.0)"]
+ujson = ["ujson (>=5.4.0,<6.0.0)"]
 
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 name = "certifi"
-version = "2024.2.2"
+version = "2023.7.22"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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 [[package]]
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+description = "Universal encoding detector for Python 2 and 3"
 optional = false
-python-versions = ">=3.7"
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 [[package]]
 name = "connexion"
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 [package.dependencies]
 cffi = ">=1.12"
 
 [package.extras]
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 docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
-nox = ["nox"]
-pep8test = ["black", "check-sdist", "mypy", "ruff"]
-sdist = ["build"]
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 ssh = ["bcrypt (>=3.1.5)"]
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 [[package]]
 name = "darglint"
@@ -1551,13 +1544,13 @@ files = [
 
 [[package]]
 name = "db-dtypes"
-version = "1.2.0"
+version = "1.1.1"
 description = "Pandas Data Types for SQL systems (BigQuery, Spanner)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1568,44 +1561,40 @@ pyarrow = ">=3.0.0"
 
 [[package]]
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 description = "Databricks Labs -  PySpark Synthetic Data Generator"
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -1656,49 +1645,47 @@ tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "dill"
-version = "0.3.8"
+version = "0.3.7"
 description = "serialize all of Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
-profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.8"
+version = "0.3.7"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "dnspython"
-version = "2.6.1"
+version = "2.4.2"
 description = "DNS toolkit"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8,<4.0"
 files = [
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 ]
 
 [package.extras]
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-dnssec = ["cryptography (>=41)"]
-doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
-doq = ["aioquic (>=0.9.25)"]
-idna = ["idna (>=3.6)"]
-trio = ["trio (>=0.23)"]
-wmi = ["wmi (>=1.5.1)"]
+dnssec = ["cryptography (>=2.6,<42.0)"]
+doh = ["h2 (>=4.1.0)", "httpcore (>=0.17.3)", "httpx (>=0.24.1)"]
+doq = ["aioquic (>=0.9.20)"]
+idna = ["idna (>=2.1,<4.0)"]
+trio = ["trio (>=0.14,<0.23)"]
+wmi = ["wmi (>=1.5.1,<2.0.0)"]
 
 [[package]]
 name = "docker-pycreds"
@@ -1764,13 +1751,13 @@ idna = ">=2.0.0"
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.1.3"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -1822,19 +1809,19 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "flake8"
-version = "7.0.0"
+version = "6.1.0"
 description = "the modular source code checker: pep8 pyflakes and co"
 optional = false
 python-versions = ">=3.8.1"
 files = [
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+    {file = "flake8-6.1.0.tar.gz", hash = "sha256:d5b3857f07c030bdb5bf41c7f53799571d75c4491748a3adcd47de929e34cd23"},
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 [package.dependencies]
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 pycodestyle = ">=2.11.0,<2.12.0"
-pyflakes = ">=3.2.0,<3.3.0"
+pyflakes = ">=3.1.0,<3.2.0"
 
 [[package]]
 name = "flask"
@@ -1859,13 +1846,13 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "flask-appbuilder"
-version = "4.3.11"
+version = "4.3.10"
 description = "Simple and rapid application development framework, built on top of Flask. includes detailed security, auto CRUD generation for your models, google charts and much more."
 optional = false
 python-versions = "~=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1934,13 +1921,13 @@ Flask = "*"
 
 [[package]]
 name = "flask-jwt-extended"
-version = "4.6.0"
+version = "4.5.3"
 description = "Extended JWT integration with Flask"
 optional = false
 python-versions = ">=3.7,<4"
 files = [
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+    {file = "Flask-JWT-Extended-4.5.3.tar.gz", hash = "sha256:061ef3d25ed5743babe4964ab38f36d870e6d2fd8a126bab5d77ddef8a01932b"},
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 ]
 
 [package.dependencies]
@@ -1953,13 +1940,13 @@ asymmetric-crypto = ["cryptography (>=3.3.1)"]
 
 [[package]]
 name = "flask-limiter"
-version = "3.5.1"
+version = "3.5.0"
 description = "Rate limiting for flask applications"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2040,99 +2027,83 @@ email = ["email-validator"]
 
 [[package]]
 name = "frozenlist"
-version = "1.4.1"
+version = "1.4.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2024.2.0"
+version = "2023.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2150,7 +2121,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -2159,34 +2130,45 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
 
+[[package]]
+name = "future"
+version = "0.18.3"
+description = "Clean single-source support for Python 3 and 2"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
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+]
+
 [[package]]
 name = "gcloud-aio-auth"
-version = "4.2.3"
+version = "4.1.5"
 description = "Python Client for Google Cloud Auth"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
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 ]
 
 [package.dependencies]
 aiohttp = ">=3.3.0,<4.0.0"
 backoff = ">=1.0.0,<3.0.0"
-chardet = ">=2.0,<6.0"
-cryptography = ">=2.0.0,<42.0.0"
+chardet = {version = ">=2.0,<4.1", markers = "python_version >= \"3.7\""}
+cryptography = ">=2.0.0,<39.0.0"
+future = ">=0.17.0,<0.19.0"
 pyjwt = ">=1.5.3,<3.0.0"
-setuptools = ">=66.0.0,<67.0.0"
+six = ">=1.11.0,<2.0.0"
 
 [[package]]
 name = "gcloud-aio-bigquery"
-version = "7.1.0"
+version = "7.0.0"
 description = "Python Client for Google Cloud BigQuery"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "gcloud_aio_bigquery-7.1.0-py3-none-any.whl", hash = "sha256:524ae3cc14c1af6977a358829cc673b4471159caa7d62bba7f2d9334262bcd4a"},
-    {file = "gcloud_aio_bigquery-7.1.0.tar.gz", hash = "sha256:4a3c775c2677c0588e9caeb2df40d81a54b31c174e562a527cb08e023c4408a3"},
+    {file = "gcloud_aio_bigquery-7.0.0-py3-none-any.whl", hash = "sha256:188b96b16b0fdbef2a0bc15ffce3f5a930ecc36de537fcfa5794fc397506fdd8"},
+    {file = "gcloud_aio_bigquery-7.0.0.tar.gz", hash = "sha256:2cd1c7c9dcb68122ff6187f2b533941c86d954e5312d942c8107aac4d20ea2ec"},
 ]
 
 [package.dependencies]
@@ -2194,13 +2176,13 @@ gcloud-aio-auth = ">=3.1.0,<6.0.0"
 
 [[package]]
 name = "gcloud-aio-storage"
-version = "9.2.0"
+version = "9.0.0"
 description = "Python Client for Google Cloud Storage"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "gcloud_aio_storage-9.2.0-py3-none-any.whl", hash = "sha256:0a8cc27223cea05ad27117fa574ce8697ec32a252382cbfdfe54df0678e92d03"},
-    {file = "gcloud_aio_storage-9.2.0.tar.gz", hash = "sha256:47be865222d22fdf873cd5a4af9a285d7b64b176d4d92f1773ecacd52a6a8c6f"},
+    {file = "gcloud_aio_storage-9.0.0-py3-none-any.whl", hash = "sha256:223583018b0ad68f22201593970c638e00fb86f40d9bb9187b17910a02ab118c"},
+    {file = "gcloud_aio_storage-9.0.0.tar.gz", hash = "sha256:99e6d30c65809ceafe059d496dbf10a4186edb1850fe9634988e096966503046"},
 ]
 
 [package.dependencies]
@@ -2211,19 +2193,19 @@ rsa = ">=3.1.4,<5.0.0"
 
 [[package]]
 name = "gcsfs"
-version = "2024.2.0"
+version = "2023.10.0"
 description = "Convenient Filesystem interface over GCS"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "gcsfs-2024.2.0-py2.py3-none-any.whl", hash = "sha256:20bf70cc81d580474dd299d55e1ffcf8b3e81721aeb562e148ca0a3c900d0421"},
-    {file = "gcsfs-2024.2.0.tar.gz", hash = "sha256:f7cffd7cae2fb50c56ef883f8aef9792be045b5059f06c1902c3a6151509f506"},
+    {file = "gcsfs-2023.10.0-py2.py3-none-any.whl", hash = "sha256:f3ad41652a596ca56e44778e041f428d022add3eff62b97a47302014affd270f"},
+    {file = "gcsfs-2023.10.0.tar.gz", hash = "sha256:1915e6d766faad9a295e815b13c59966b925a274108b8a56f673623bf4aef83f"},
 ]
 
 [package.dependencies]
 aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1"
 decorator = ">4.1.2"
-fsspec = "2024.2.0"
+fsspec = "2023.10.0"
 google-auth = ">=1.2"
 google-auth-oauthlib = "*"
 google-cloud-storage = "*"
@@ -2266,20 +2248,20 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.42"
+version = "3.1.40"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
-    {file = "GitPython-3.1.42.tar.gz", hash = "sha256:2d99869e0fef71a73cbd242528105af1d6c1b108c60dfabd994bf292f76c3ceb"},
+    {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
+    {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
 
 [[package]]
 name = "google"
@@ -2322,30 +2304,30 @@ tests = ["nox (>=2020.12.31,<2022.6)"]
 
 [[package]]
 name = "google-analytics-admin"
-version = "0.22.7"
+version = "0.22.5"
 description = "Google Analytics Admin API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-analytics-admin-0.22.7.tar.gz", hash = "sha256:9546afaddf7ee275ec4729de6da8b15f27d4d245ee8896f80800e22572fc7987"},
-    {file = "google_analytics_admin-0.22.7-py2.py3-none-any.whl", hash = "sha256:09463653ecb42306c34fb07c9ff27193737aae8e63c52b8f695bf705e8589b46"},
+    {file = "google-analytics-admin-0.22.5.tar.gz", hash = "sha256:2de1671ea945c23b3587323ed1dc6e6c78d02a7507b562d425de1dc7fb27b08f"},
+    {file = "google_analytics_admin-0.22.5-py2.py3-none-any.whl", hash = "sha256:54c6e00baeb9be61492b4efcd4859ca1b94b181ce454ba70ff5d73a630dd5661"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-api-core"
-version = "2.17.1"
+version = "2.12.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"},
-    {file = "google_api_core-2.17.1-py3-none-any.whl", hash = "sha256:610c5b90092c360736baccf17bd3efbcb30dd380e7a6dc28a71059edb8bd0d8e"},
+    {file = "google-api-core-2.12.0.tar.gz", hash = "sha256:c22e01b1e3c4dcd90998494879612c38d0a3411d1f7b679eb89e2abe3ce1f553"},
+    {file = "google_api_core-2.12.0-py3-none-any.whl", hash = "sha256:ec6054f7d64ad13b41e43d96f735acbd763b0f3b695dabaa2d579673f6a6e160"},
 ]
 
 [package.dependencies]
@@ -2363,13 +2345,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.121.0"
+version = "2.106.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-python-client-2.121.0.tar.gz", hash = "sha256:df863ece4db8b36ce1053ebd983e43fbc5b664209eed78e82cc84ae56ddac6c0"},
-    {file = "google_api_python_client-2.121.0-py2.py3-none-any.whl", hash = "sha256:bb4da677150dd16c45818620baca8a63208c6f4180a0691ad1c1708b384c10be"},
+    {file = "google-api-python-client-2.106.0.tar.gz", hash = "sha256:f6a3862be2f6e5e0536d7bd47b5af3f24ac0b9147c76c830cafb3329d71d5724"},
+    {file = "google_api_python_client-2.106.0-py2.py3-none-any.whl", hash = "sha256:c47c0dae5dd20aa43e4ea184566fe59d0c8fd0b86dd223b29040d8ea4f7ed6ea"},
 ]
 
 [package.dependencies]
@@ -2381,13 +2363,13 @@ uritemplate = ">=3.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.28.1"
+version = "2.23.4"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.28.1.tar.gz", hash = "sha256:34fc3046c257cedcf1622fc4b31fc2be7923d9b4d44973d481125ecc50d83885"},
-    {file = "google_auth-2.28.1-py2.py3-none-any.whl", hash = "sha256:25141e2d7a14bfcba945f5e9827f98092716e99482562f15306e5b026e21aa72"},
+    {file = "google-auth-2.23.4.tar.gz", hash = "sha256:79905d6b1652187def79d491d6e23d0cbb3a21d3c7ba0dbaa9c8a01906b13ff3"},
+    {file = "google_auth-2.23.4-py2.py3-none-any.whl", hash = "sha256:d4bbc92fe4b8bfd2f3e8d88e5ba7085935da208ee38a134fc280e7ce682a05f2"},
 ]
 
 [package.dependencies]
@@ -2404,13 +2386,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-auth-httplib2"
-version = "0.2.0"
+version = "0.1.1"
 description = "Google Authentication Library: httplib2 transport"
 optional = false
 python-versions = "*"
 files = [
-    {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
-    {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
+    {file = "google-auth-httplib2-0.1.1.tar.gz", hash = "sha256:c64bc555fdc6dd788ea62ecf7bccffcf497bf77244887a3f3d7a5a02f8e3fc29"},
+    {file = "google_auth_httplib2-0.1.1-py2.py3-none-any.whl", hash = "sha256:42c50900b8e4dcdf8222364d1f0efe32b8421fb6ed72f2613f12f75cc933478c"},
 ]
 
 [package.dependencies]
@@ -2437,18 +2419,17 @@ tool = ["click (>=6.0.0)"]
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.43.0"
+version = "1.36.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.43.0.tar.gz", hash = "sha256:bd73af311a827e79bd2094a508e9343a2fa71b8ae5d8fa1178d0a96d034b421b"},
-    {file = "google_cloud_aiplatform-1.43.0-py2.py3-none-any.whl", hash = "sha256:8aa1bdcde1bec85ea53db26b638744f7bad91f3af27c2ab8a3ea32079fb709e3"},
+    {file = "google-cloud-aiplatform-1.36.0.tar.gz", hash = "sha256:c35eb179fa3107c262024a63bb4a3dc1887678424a0b9f49f0da3f5179896186"},
+    {file = "google_cloud_aiplatform-1.36.0-py2.py3-none-any.whl", hash = "sha256:06adb1605208a9bdbadd8c4deeaeb6511a7f59220ef606e2d95ccc1da705ebda"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.8.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
+google-api-core = {version = ">=1.32.0,<2.0.dev0 || >=2.8.dev0,<3.0.0dev", extras = ["grpc"]}
 google-cloud-bigquery = ">=1.15.0,<4.0.0dev"
 google-cloud-resource-manager = ">=1.3.3,<3.0.0dev"
 google-cloud-storage = ">=1.32.0,<3.0.0dev"
@@ -2462,34 +2443,33 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
+full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (==5.3.1)"]
 prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<0.103.1)", "httpx (>=0.23.0,<0.25.0)", "starlette (>=0.17.1)", "uvicorn[standard] (>=0.16.0)"]
 preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
 private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
-ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"]
-tensorboard = ["tensorflow (>=2.3.0,<2.15.0)"]
-testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<2.15.0)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
-vizier = ["google-vizier (>=0.1.6)"]
+ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "pandas (>=1.0.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)"]
+tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"]
+testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<0.103.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "ipython", "kfp", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pyarrow (>=10.0.1)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (==5.3.1)", "ray[default] (>=2.4,<2.5)", "ray[default] (>=2.5,<2.5.1)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<=2.12.0)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost", "xgboost-ray"]
+vizier = ["google-vizier (==0.0.11)", "google-vizier (==0.0.4)", "google-vizier (>=0.0.14)", "google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-appengine-logging"
-version = "1.4.3"
+version = "1.3.2"
 description = "Google Cloud Appengine Logging API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-appengine-logging-1.4.3.tar.gz", hash = "sha256:fb504e6199fe8de85baa9d31cecf6776877851fe58867de603317ec7cc739987"},
-    {file = "google_cloud_appengine_logging-1.4.3-py2.py3-none-any.whl", hash = "sha256:8e30af51d853f219caf29e8b8b342b9ce8214b29f334dafae38d39aaaff7d372"},
+    {file = "google-cloud-appengine-logging-1.3.2.tar.gz", hash = "sha256:a2989fca0e88463b56432aa821e64b81c3d171ee37b84771189b48e8b97cd496"},
+    {file = "google_cloud_appengine_logging-1.3.2-py2.py3-none-any.whl", hash = "sha256:6ac6261567b56611f6891fa650f76db8a48d528762e5c2a09230b41d82ee2be0"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
@@ -2509,18 +2489,17 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 
 [[package]]
 name = "google-cloud-automl"
-version = "2.13.3"
+version = "2.12.0"
 description = "Google Cloud Automl API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-automl-2.13.3.tar.gz", hash = "sha256:891a9082eaedeb17bc5bb724fc150b7702c684d7420f9f22ae3f7c5fc611c71a"},
-    {file = "google_cloud_automl-2.13.3-py2.py3-none-any.whl", hash = "sha256:8117943ad1534f1d11d40e0937ade7965c56a6d7fd2c2314e56942361727a6a2"},
+    {file = "google-cloud-automl-2.12.0.tar.gz", hash = "sha256:22f7a61e04b4a9b03d504ef2db6683df462a032da53fe3fbb2c36f525074abb5"},
+    {file = "google_cloud_automl-2.12.0-py2.py3-none-any.whl", hash = "sha256:e20e9aec2f368bda29b039d8109384379a6cd9410ac5ee81acdab3afbf89ad8e"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2531,82 +2510,103 @@ storage = ["google-cloud-storage (>=1.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-batch"
-version = "0.17.14"
+version = "0.17.2"
 description = "Google Cloud Batch API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-batch-0.17.14.tar.gz", hash = "sha256:03c29c384f5872cf2e3aa4f2f7446f5bbecd1aea035f19cd7c595b245df96179"},
-    {file = "google_cloud_batch-0.17.14-py2.py3-none-any.whl", hash = "sha256:0bebf878e3f4cb470ba788dc9d9d78a11db2c16bea7cc9da9bfde24522bc1914"},
+    {file = "google-cloud-batch-0.17.2.tar.gz", hash = "sha256:381ac83d18b46354cb5c51d33dbfcede62b039f3f13960ac9691cb4178f3c5b7"},
+    {file = "google_cloud_batch-0.17.2-py2.py3-none-any.whl", hash = "sha256:c614f17d47095fd0b41e3f7de2e66f6ec5f1168d682b8338dfa3c18803234be0"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.18.0"
+version = "3.13.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.18.0.tar.gz", hash = "sha256:74f0fc6f0ba9477f808d25924dc8a052c55f7ca91064e83e16d3ee5fb7ca77ab"},
-    {file = "google_cloud_bigquery-3.18.0-py2.py3-none-any.whl", hash = "sha256:3520552075502c69710d37b1e9600c84e6974ad271914677d16bfafa502857fb"},
+    {file = "google-cloud-bigquery-3.13.0.tar.gz", hash = "sha256:794ccfc93ccb0e0ad689442f896f9c82de56da0fe18a195531bb37096c2657d6"},
+    {file = "google_cloud_bigquery-3.13.0-py2.py3-none-any.whl", hash = "sha256:eda3dbcff676e17962c54e5224e415b55e4f6833a5c896c6c8902b69e7dba4b4"},
 ]
 
 [package.dependencies]
-google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
+google-api-core = {version = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev", extras = ["grpc"]}
 google-cloud-core = ">=1.6.0,<3.0.0dev"
 google-resumable-media = ">=0.6.0,<3.0dev"
+grpcio = ">=1.47.0,<2.0dev"
 packaging = ">=20.0.0"
+proto-plus = ">=1.15.0,<2.0.0dev"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 python-dateutil = ">=2.7.2,<3.0dev"
 requests = ">=2.21.0,<3.0.0dev"
 
 [package.extras]
-all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "importlib-metadata (>=1.0.0)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
-bigquery-v2 = ["proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)"]
+all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
 bqstorage = ["google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "pyarrow (>=3.0.0)"]
 geopandas = ["Shapely (>=1.8.4,<3.0.0dev)", "geopandas (>=0.9.0,<1.0dev)"]
 ipython = ["ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)"]
 ipywidgets = ["ipykernel (>=6.0.0)", "ipywidgets (>=7.7.0)"]
 opentelemetry = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)"]
-pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "importlib-metadata (>=1.0.0)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)"]
+pandas = ["db-dtypes (>=0.3.0,<2.0.0dev)", "pandas (>=1.1.0)", "pyarrow (>=3.0.0)"]
 tqdm = ["tqdm (>=4.7.4,<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery-datatransfer"
-version = "3.15.1"
+version = "3.13.0"
 description = "Google Cloud Bigquery Datatransfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-datatransfer-3.15.1.tar.gz", hash = "sha256:d80d2fe940451de3f47ec53bd5edb601abbb1df4189cde1fa386d80f71bea762"},
-    {file = "google_cloud_bigquery_datatransfer-3.15.1-py2.py3-none-any.whl", hash = "sha256:24da33d0296cfb3f606df3f2c387e7d73f4678c5f9d9aec82db8b93a4482e6e0"},
+    {file = "google-cloud-bigquery-datatransfer-3.13.0.tar.gz", hash = "sha256:27a845cb22605a512c05ce28c2890d2ef97ad4edfc9817af555a73d8024ec3ba"},
+    {file = "google_cloud_bigquery_datatransfer-3.13.0-py2.py3-none-any.whl", hash = "sha256:f8a73ba135fc6f6df4516187b5d88c7d1fcd68ecb1b0ed259ff55b89c5a0974a"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
+[[package]]
+name = "google-cloud-bigquery-storage"
+version = "2.22.0"
+description = "Google Cloud Bigquery Storage API client library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "google-cloud-bigquery-storage-2.22.0.tar.gz", hash = "sha256:f6d8c7b3ab9b574c66977fcee9d336e334ad1a3843a722be19123640e7808ea3"},
+    {file = "google_cloud_bigquery_storage-2.22.0-py2.py3-none-any.whl", hash = "sha256:7f11b2ae590a5b3874fb6ddf705a66a070340db238f971cf7b53349eee9ca317"},
+]
+
+[package.dependencies]
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+
+[package.extras]
+fastavro = ["fastavro (>=0.21.2)"]
+pandas = ["pandas (>=0.21.1)"]
+pyarrow = ["pyarrow (>=0.15.0)"]
+
 [[package]]
 name = "google-cloud-bigtable"
-version = "2.23.0"
+version = "2.21.0"
 description = "Google Cloud Bigtable API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigtable-2.23.0.tar.gz", hash = "sha256:c3cabb6619f5f2eea11b3db72731e84d28b168055c9cc17c2d2daff15cb82379"},
-    {file = "google_cloud_bigtable-2.23.0-py2.py3-none-any.whl", hash = "sha256:d89ed580733865007ae89639b7d961024a2385aa8c2828cf3aff3f5889782402"},
+    {file = "google-cloud-bigtable-2.21.0.tar.gz", hash = "sha256:d9f0efbf940ca392f07d13787fc2da76d1e168dedafae0f8f1b4208e0c1132dc"},
+    {file = "google_cloud_bigtable-2.21.0-py2.py3-none-any.whl", hash = "sha256:219d39f2787b76394b045603bbd0b13dfc37645ca855022e646b8ecbfbe9ad1f"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=2.16.0,<3.0.0dev", extras = ["grpc"]}
-google-cloud-core = ">=1.4.4,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-cloud-core = ">=1.4.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
@@ -2616,65 +2616,62 @@ libcst = ["libcst (>=0.2.5)"]
 
 [[package]]
 name = "google-cloud-build"
-version = "3.23.3"
+version = "3.22.0"
 description = "Google Cloud Build API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-build-3.23.3.tar.gz", hash = "sha256:6e8dd88cb816f30bb55cec1e4d2285d570a223d34df516fb017a495ed605720e"},
-    {file = "google_cloud_build-3.23.3-py2.py3-none-any.whl", hash = "sha256:b01060544bd0fd349f66c2af4c79312ec08d644237a27615e1a75eeee910189e"},
+    {file = "google-cloud-build-3.22.0.tar.gz", hash = "sha256:72a8330c31108cc47ce510956196a98c158eeecd796004928bf239a2e1ea300e"},
+    {file = "google_cloud_build-3.22.0-py2.py3-none-any.whl", hash = "sha256:b2906d59d02066f859a5e3a0ef9437dffc08f11a1f0f9381c92c986c73ab7ae0"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-compute"
-version = "1.18.0"
+version = "1.14.1"
 description = "Google Cloud Compute API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-compute-1.18.0.tar.gz", hash = "sha256:412237183877eb2838aa6e08cdaa6cf17f399451a166e3a9aa33ae894cc15a1d"},
-    {file = "google_cloud_compute-1.18.0-py2.py3-none-any.whl", hash = "sha256:fd827495ffc3a006a70f984133cb12191572497d96a522056a75414db918f37d"},
+    {file = "google-cloud-compute-1.14.1.tar.gz", hash = "sha256:acd987647d7c826aa97b4418141c740ead5e8811d3349315f2f89a30c01c7f4b"},
+    {file = "google_cloud_compute-1.14.1-py2.py3-none-any.whl", hash = "sha256:b40d6aeeb2c5ce373675c869f1404a1bc19b9763b746ad8f2d91ed1148893d6f"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-container"
-version = "2.43.0"
+version = "2.32.0"
 description = "Google Cloud Container API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-container-2.43.0.tar.gz", hash = "sha256:45f1a142fd70af97cc30795e31a9b048a436a29bf79e9a464af51ee14745893d"},
-    {file = "google_cloud_container-2.43.0-py2.py3-none-any.whl", hash = "sha256:b5c28d28df17be70fb12f5141f93b455fa261e693d65add989ae018d521a4b05"},
+    {file = "google-cloud-container-2.32.0.tar.gz", hash = "sha256:694fb8da77963653e1c70fa60928b4a11faf8e1f1580a39025053a3e1bcce6de"},
+    {file = "google_cloud_container-2.32.0-py2.py3-none-any.whl", hash = "sha256:a5555c6c19679332086859838fbaee468cb206e5e76365bb5027d5305a2c5bf4"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-core"
-version = "2.4.1"
+version = "2.3.3"
 description = "Google Cloud API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-core-2.4.1.tar.gz", hash = "sha256:9b7749272a812bde58fff28868d0c5e2f585b82f37e09a1f6ed2d4d10f134073"},
-    {file = "google_cloud_core-2.4.1-py2.py3-none-any.whl", hash = "sha256:a9e6a4422b9ac5c29f79a0ede9485473338e2ce78d91f2370c01e730eab22e61"},
+    {file = "google-cloud-core-2.3.3.tar.gz", hash = "sha256:37b80273c8d7eee1ae816b3a20ae43585ea50506cb0e60f3cf5be5f87f1373cb"},
+    {file = "google_cloud_core-2.3.3-py2.py3-none-any.whl", hash = "sha256:fbd11cad3e98a7e5b0343dc07cb1039a5ffd7a5bb96e1f1e27cee4bda4a90863"},
 ]
 
 [package.dependencies]
@@ -2682,176 +2679,167 @@ google-api-core = ">=1.31.6,<2.0.dev0 || >2.3.0,<3.0.0dev"
 google-auth = ">=1.25.0,<3.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
+grpc = ["grpcio (>=1.38.0,<2.0dev)"]
 
 [[package]]
 name = "google-cloud-datacatalog"
-version = "3.18.3"
+version = "3.16.0"
 description = "Google Cloud Datacatalog API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-datacatalog-3.18.3.tar.gz", hash = "sha256:77831301982c795e6223cdc0ee54a46defd21205fd65f434a4b1df6014ad7e9e"},
-    {file = "google_cloud_datacatalog-3.18.3-py2.py3-none-any.whl", hash = "sha256:8116f0bca6709b4cd984c85cd83f952bc0b1bb915244dfe478c9d425fce09057"},
+    {file = "google-cloud-datacatalog-3.16.0.tar.gz", hash = "sha256:703e414396248fa98474bbab9ea962f4c96a3caf1184c903bfc9453d274b0ea2"},
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 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
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 [[package]]
 name = "google-cloud-dataflow-client"
-version = "0.8.10"
+version = "0.8.6"
 description = "Google Cloud Dataflow Client API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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 proto-plus = ">=1.22.3,<2.0.0dev"
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 [[package]]
 name = "google-cloud-dataform"
-version = "0.5.9"
+version = "0.5.3"
 description = "Google Cloud Dataform API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
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 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
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 [[package]]
 name = "google-cloud-dataplex"
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 description = "Google Cloud Dataplex API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc"
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 description = "Google Cloud Dataproc API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-dataproc-metastore"
-version = "1.15.3"
+version = "1.13.0"
 description = "Google Cloud Dataproc Metastore API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
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 [[package]]
 name = "google-cloud-dlp"
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 description = "Google Cloud Dlp API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
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-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-kms"
-version = "2.21.3"
+version = "2.19.2"
 description = "Google Cloud Kms API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google_cloud_kms-2.19.2-py2.py3-none-any.whl", hash = "sha256:0cc0b9d16338a46b7fc5beba7e96a88df0f836586eb8650e9acd36eb249b52d9"},
 ]
 
 [package.dependencies]
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-language"
-version = "2.13.3"
+version = "2.11.1"
 description = "Google Cloud Language API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google_cloud_language-2.11.1-py2.py3-none-any.whl", hash = "sha256:beb5dd1344a6ed00dd04b1eb05a967e7f83fc1b51471f9bd51f238f63f46f9d4"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-logging"
-version = "3.9.0"
+version = "3.8.0"
 description = "Stackdriver Logging API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-logging-3.9.0.tar.gz", hash = "sha256:4decb1b0bed4a0e3c0e58a376646e6002d6be7cad039e3466822e8665072ea33"},
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+    {file = "google-cloud-logging-3.8.0.tar.gz", hash = "sha256:fdd916e59a84aa8c02e8148d7fdd3b3b623c57b0c1ff71f43297ce8e50fc1eab"},
+    {file = "google_cloud_logging-3.8.0-py2.py3-none-any.whl", hash = "sha256:c868b276b021cf5f32b6e8356b6cb3666357d149ad0fd798076043a5ec7ed988"},
 ]
 
 [package.dependencies]
@@ -2865,35 +2853,33 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-cloud-memcache"
-version = "1.9.3"
+version = "1.7.3"
 description = "Google Cloud Memcache API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-memcache-1.9.3.tar.gz", hash = "sha256:ee5ff95afe47efc1a0746ba13b2f56996bc018f158f6b0cad69d835b0447d6e8"},
-    {file = "google_cloud_memcache-1.9.3-py2.py3-none-any.whl", hash = "sha256:d1574670dcdb861cc772e2b797f7e5fb83b9dd7ef2ce34fbe4ff54d454a816d3"},
+    {file = "google-cloud-memcache-1.7.3.tar.gz", hash = "sha256:a73f44ec5bfc398b4c9d94d5e77621c54795bcade19960242dec2cd91b854bcb"},
+    {file = "google_cloud_memcache-1.7.3-py2.py3-none-any.whl", hash = "sha256:054e2adde4e99fca6a2e3f9ef733a2159083923c0a41af51b7d5e9d38ca9de12"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-monitoring"
-version = "2.19.3"
+version = "2.18.0"
 description = "Google Cloud Monitoring API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-monitoring-2.19.3.tar.gz", hash = "sha256:37641e537986fd2227f8738b879d605a8cdf4c315cdc60e86e109347ab1ca1d7"},
-    {file = "google_cloud_monitoring-2.19.3-py2.py3-none-any.whl", hash = "sha256:448016170deb4f16fdb57d12f639a0b61e865f7a22ec9a85b3e12295046b62b0"},
+    {file = "google-cloud-monitoring-2.18.0.tar.gz", hash = "sha256:06cc1d7fb75c5e50b54bcc004941ea4b20a7a9f09ed7e6e753514fd8c436096a"},
+    {file = "google_cloud_monitoring-2.18.0-py2.py3-none-any.whl", hash = "sha256:548a1ac0b88c38694e14e1359e89c7eb19b27423906f456bb590f3f0962bb562"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2902,52 +2888,49 @@ pandas = ["pandas (>=0.23.2)"]
 
 [[package]]
 name = "google-cloud-orchestration-airflow"
-version = "1.12.1"
+version = "1.10.0"
 description = "Google Cloud Orchestration Airflow API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-orchestration-airflow-1.12.1.tar.gz", hash = "sha256:659a72f506a9441f1938664e98cdced9a4a852dc5d8c533a5c2f822cefe72eb1"},
-    {file = "google_cloud_orchestration_airflow-1.12.1-py2.py3-none-any.whl", hash = "sha256:9dab139528860c3d10b9affe6a7e34848f10e15f5800554792c1bab37f713e3d"},
+    {file = "google-cloud-orchestration-airflow-1.10.0.tar.gz", hash = "sha256:2aa88b4882d3c5dd0f7ecc572d37972d51f2cf69530783c524344d018de0b1bf"},
+    {file = "google_cloud_orchestration_airflow-1.10.0-py2.py3-none-any.whl", hash = "sha256:363e22e3b52316eb556899ffb3b1d2122bb4fce3937e4f7cef42202ec1a15e20"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-os-login"
-version = "2.14.3"
+version = "2.11.0"
 description = "Google Cloud Os Login API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-os-login-2.14.3.tar.gz", hash = "sha256:b1a15ec7ee306de890750822dc4aab04c64e5bcf76447590f45551bf1afcf1e8"},
-    {file = "google_cloud_os_login-2.14.3-py2.py3-none-any.whl", hash = "sha256:de19bb2ade36e982256050972b5f68be34a0f4c70365c76a7cd91de3f0e7985f"},
+    {file = "google-cloud-os-login-2.11.0.tar.gz", hash = "sha256:e3219d00b75ea44182871681985e9db3f1e38641bb84519dd85d3dbb62b97656"},
+    {file = "google_cloud_os_login-2.11.0-py2.py3-none-any.whl", hash = "sha256:1b0679d772fbef486230208a65b3a96d513b599000beca1dc8ff5bde4838607f"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-pubsub"
-version = "2.20.1"
+version = "2.19.0"
 description = "Google Cloud Pub/Sub API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-pubsub-2.20.1.tar.gz", hash = "sha256:b6d06f1827968273c42b57a09f642462649c9504dc0f8756f99770f4e3e755ad"},
-    {file = "google_cloud_pubsub-2.20.1-py2.py3-none-any.whl", hash = "sha256:06dd62181e2f248f32b9077f4dc07b413191a84fc06d7323b208602d887207bc"},
+    {file = "google-cloud-pubsub-2.19.0.tar.gz", hash = "sha256:6a98c33f7eb5f7de2ae52efa059b2b5f75b2ccd9f0f11f2edcefdda8d14e425c"},
+    {file = "google_cloud_pubsub-2.19.0-py2.py3-none-any.whl", hash = "sha256:0cc444e5b2220a703106668829315a724cfb4304d6772725035993bb2fc81388"},
 ]
 
 [package.dependencies]
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<3.0.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
 grpcio = ">=1.51.3,<2.0dev"
 grpcio-status = ">=1.33.2"
@@ -2959,92 +2942,86 @@ libcst = ["libcst (>=0.3.10)"]
 
 [[package]]
 name = "google-cloud-redis"
-version = "2.15.3"
+version = "2.13.2"
 description = "Google Cloud Redis API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-redis-2.15.3.tar.gz", hash = "sha256:e6a231e45107038cfe498dfad1f6dad2ca7bdca3a931323730be03ab7a000a8f"},
-    {file = "google_cloud_redis-2.15.3-py2.py3-none-any.whl", hash = "sha256:df79f6f8a81f948d2d5343a76e7c9108ab56eab9279be0763da6968f497b2bdb"},
+    {file = "google-cloud-redis-2.13.2.tar.gz", hash = "sha256:5c485730355d96723d64ae637f1b2264f35b57c301ec0ef2c6d30b2f06dca14e"},
+    {file = "google_cloud_redis-2.13.2-py2.py3-none-any.whl", hash = "sha256:86378e8ed1c5a1c621eb2731d734436f443dffdbbafc6cd5d24511c94d6c38bc"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-resource-manager"
-version = "1.12.3"
+version = "1.10.4"
 description = "Google Cloud Resource Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-resource-manager-1.12.3.tar.gz", hash = "sha256:809851824119834e4f2310b2c4f38621c1d16b2bb14d5b9f132e69c79d355e7f"},
-    {file = "google_cloud_resource_manager-1.12.3-py2.py3-none-any.whl", hash = "sha256:92be7d6959927b76d90eafc4028985c37975a46ded5466a018f02e8649e113d4"},
+    {file = "google-cloud-resource-manager-1.10.4.tar.gz", hash = "sha256:456b25ddda3d4cd27488a72736bbc3af04d713ae2fe3655c01b66a339d28d679"},
+    {file = "google_cloud_resource_manager-1.10.4-py2.py3-none-any.whl", hash = "sha256:2ba56ba8e5280cd425bd63620da48b78da2cd299ece58a71f0f2ce3a32d56f99"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-run"
-version = "0.10.5"
+version = "0.10.0"
 description = "Google Cloud Run API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-run-0.10.5.tar.gz", hash = "sha256:d6c546693ad1232794b75c617871f2e46283c0cd0c4cc3886b85b8f0a3ba575f"},
-    {file = "google_cloud_run-0.10.5-py2.py3-none-any.whl", hash = "sha256:0f68125f13d1a87f74e1e78c37fa6afb4b094d76dad2b9f951d5526648e8939c"},
+    {file = "google-cloud-run-0.10.0.tar.gz", hash = "sha256:e597dff8da3d6e0f43bdaaa38c80933d8e81e8eac8c1b5e119812af99539d734"},
+    {file = "google_cloud_run-0.10.0-py2.py3-none-any.whl", hash = "sha256:3f3d317200e890c20e744e692d69b241ca4a25ec161f02babbc77c1df68c7a4d"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-secret-manager"
-version = "2.18.3"
+version = "2.16.4"
 description = "Google Cloud Secret Manager API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-secret-manager-2.18.3.tar.gz", hash = "sha256:1db2f409324536e34f985081d389e3974ca3a3668df7845cad0be03ab8c0fa7d"},
-    {file = "google_cloud_secret_manager-2.18.3-py2.py3-none-any.whl", hash = "sha256:4d4af82bddd9099ebdbe79e0c6b68f6c6cabea8323a3c1275bcead8f56310fb7"},
+    {file = "google-cloud-secret-manager-2.16.4.tar.gz", hash = "sha256:371dc72f9145af323e8a813c8e50380e6ac4bd6a5dbcd42dcf3162d8f37e5080"},
+    {file = "google_cloud_secret_manager-2.16.4-py2.py3-none-any.whl", hash = "sha256:5031c45dd84dc584d91ee0baae2bbd5df6710efe0c42719ee370a3ab62aaf618"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-spanner"
-version = "3.43.0"
+version = "3.40.1"
 description = "Google Cloud Spanner API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-spanner-3.43.0.tar.gz", hash = "sha256:0662e95fe314674a3b8b2fa3c19e81e544d94f584ca6965b98c95083a8864222"},
-    {file = "google_cloud_spanner-3.43.0-py2.py3-none-any.whl", hash = "sha256:b83f286459547b551b9fec5e13becadd7c7786d94e61a6df1007966536cf35a1"},
+    {file = "google-cloud-spanner-3.40.1.tar.gz", hash = "sha256:616b07c86cdae6c78bad27b8ab39d8ce7a273511f2b91fe260f170d926653c2e"},
+    {file = "google_cloud_spanner-3.40.1-py2.py3-none-any.whl", hash = "sha256:0c5825bb5956df83ee1d732e1851467be97bbc4502ccd91ed0c5e7fb4b3f4ccb"},
 ]
 
 [package.dependencies]
-deprecated = ">=1.2.14"
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-cloud-core = ">=1.4.4,<3.0dev"
+google-cloud-core = ">=1.4.1,<3.0dev"
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-grpc-interceptor = ">=0.15.4"
 proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 sqlparse = ">=0.4.4"
@@ -3055,35 +3032,34 @@ tracing = ["opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20
 
 [[package]]
 name = "google-cloud-speech"
-version = "2.25.1"
+version = "2.21.1"
 description = "Google Cloud Speech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-speech-2.25.1.tar.gz", hash = "sha256:5b7470a949f5a77c40511c3664ac339fe0a4704482f9b6b3a2ba554b7d2b2cdc"},
-    {file = "google_cloud_speech-2.25.1-py2.py3-none-any.whl", hash = "sha256:b08ab9648774949295a869a69473af9b9a65f038436a2a1a560ec5ba28d2b227"},
+    {file = "google-cloud-speech-2.21.1.tar.gz", hash = "sha256:4935375eaaf18656e53e2631770b9d579e80cb4ec1b3d46dd23346f789aa016a"},
+    {file = "google_cloud_speech-2.21.1-py2.py3-none-any.whl", hash = "sha256:36089039640c5ab79cefd32af7a482043a67144d393ec752c6b57c1fdc02a95d"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.15.0"
+version = "2.13.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.15.0.tar.gz", hash = "sha256:7560a3c48a03d66c553dc55215d35883c680fe0ab44c23aa4832800ccc855c74"},
-    {file = "google_cloud_storage-2.15.0-py2.py3-none-any.whl", hash = "sha256:5d9237f88b648e1d724a0f20b5cde65996a37fe51d75d17660b1404097327dd2"},
+    {file = "google-cloud-storage-2.13.0.tar.gz", hash = "sha256:f62dc4c7b6cd4360d072e3deb28035fbdad491ac3d9b0b1815a12daea10f37c7"},
+    {file = "google_cloud_storage-2.13.0-py2.py3-none-any.whl", hash = "sha256:ab0bf2e1780a1b74cf17fccb13788070b729f50c252f0c94ada2aae0ca95437d"},
 ]
 
 [package.dependencies]
-google-api-core = ">=2.15.0,<3.0.0dev"
-google-auth = ">=2.26.1,<3.0dev"
+google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev"
+google-auth = ">=2.23.3,<3.0dev"
 google-cloud-core = ">=2.3.0,<3.0dev"
 google-crc32c = ">=1.0,<2.0dev"
 google-resumable-media = ">=2.6.0"
@@ -3094,123 +3070,116 @@ protobuf = ["protobuf (<5.0.0dev)"]
 
 [[package]]
 name = "google-cloud-storage-transfer"
-version = "1.11.3"
+version = "1.9.2"
 description = "Google Cloud Storage Transfer API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-transfer-1.11.3.tar.gz", hash = "sha256:c4564749492f41957f44c41b76f61f72c12b519424b841895bfe2d932c6f1490"},
-    {file = "google_cloud_storage_transfer-1.11.3-py2.py3-none-any.whl", hash = "sha256:698e1b21c9dc710f543b2e6dc3c70334b872d4a80fafc0b2718fc91b6f45858e"},
+    {file = "google-cloud-storage-transfer-1.9.2.tar.gz", hash = "sha256:c36800cf425e23cca5bed51320dce6eec313b68db8dd4677be6e3b49c891207d"},
+    {file = "google_cloud_storage_transfer-1.9.2-py2.py3-none-any.whl", hash = "sha256:d30b519b57eee0d337da6d9051442337625128e950e6b487b4fd63f4942f7cb7"},
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-tasks"
-version = "2.16.3"
+version = "2.14.2"
 description = "Google Cloud Tasks API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-texttospeech"
-version = "2.16.3"
+version = "2.14.2"
 description = "Google Cloud Texttospeech API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-texttospeech-2.16.3.tar.gz", hash = "sha256:fabc315032d137da0710bb4c268734d336212d8fa8316b23b277dd3a84ce721c"},
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+    {file = "google_cloud_texttospeech-2.14.2-py2.py3-none-any.whl", hash = "sha256:3a4e7ca71a1d3f90de77170a013b37f18775858dac01c80793752b8c160d6555"},
 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-translate"
-version = "3.15.3"
+version = "3.12.1"
 description = "Google Cloud Translate API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 google-cloud-core = ">=1.4.4,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-videointelligence"
-version = "2.13.3"
+version = "2.11.4"
 description = "Google Cloud Videointelligence API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "google_cloud_videointelligence-2.11.4-py2.py3-none-any.whl", hash = "sha256:4d62f7b074f6f1eae4efd8697b5a93fb59b3b22f9934e383b4f6fbed69f49eb6"},
 ]
 
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-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-vision"
-version = "3.7.2"
+version = "3.4.5"
 description = "Google Cloud Vision API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
 name = "google-cloud-workflows"
-version = "1.14.3"
+version = "1.12.1"
 description = "Google Cloud Workflows API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
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-proto-plus = ">=1.22.3,<2.0.0dev"
+google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+proto-plus = ">=1.22.0,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
 [[package]]
@@ -3461,13 +3430,13 @@ files = [
 
 [[package]]
 name = "google-resumable-media"
-version = "2.7.0"
+version = "2.6.0"
 description = "Utilities for Google Media Downloads and Resumable Uploads"
 optional = false
 python-versions = ">= 3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3479,13 +3448,13 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.62.0"
+version = "1.61.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3495,86 +3464,101 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
+[[package]]
+name = "graphviz"
+version = "0.20.1"
+description = "Simple Python interface for Graphviz"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+dev = ["flake8", "pep8-naming", "tox (>=3)", "twine", "wheel"]
+docs = ["sphinx (>=5)", "sphinx-autodoc-typehints", "sphinx-rtd-theme"]
+test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=3)"]
+
 [[package]]
 name = "greenlet"
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+version = "3.0.1"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
 files = [
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@@ -3582,13 +3566,13 @@ colorama = ">=0.4"
 
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 description = "IAM API client library"
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+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.59.2)"]
 
 [[package]]
 name = "grpcio-gcp"
@@ -3789,24 +3756,24 @@ uvloop = {version = ">=0.19.0,<1", markers = "sys_platform != \"win32\""}
 
 [[package]]
 name = "httpcore"
-version = "1.0.4"
+version = "0.18.0"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
-    {file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
+    {file = "httpcore-0.18.0-py3-none-any.whl", hash = "sha256:adc5398ee0a476567bf87467063ee63584a8bce86078bf748e48754f60202ced"},
+    {file = "httpcore-0.18.0.tar.gz", hash = "sha256:13b5e5cd1dca1a6636a6aaea212b19f4f85cd88c366a2b82304181b769aab3c9"},
 ]
 
 [package.dependencies]
+anyio = ">=3.0,<5.0"
 certifi = "*"
 h11 = ">=0.13,<0.15"
+sniffio = "==1.*"
 
 [package.extras]
-asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.25.0)"]
 
 [[package]]
 name = "httplib2"
@@ -3824,19 +3791,18 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0
 
 [[package]]
 name = "httpx"
-version = "0.27.0"
+version = "0.25.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
-    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+    {file = "httpx-0.25.0-py3-none-any.whl", hash = "sha256:181ea7f8ba3a82578be86ef4171554dd45fec26a02556a744db029a0a27b7100"},
+    {file = "httpx-0.25.0.tar.gz", hash = "sha256:47ecda285389cb32bb2691cc6e069e3ab0205956f681c5b2ad2325719751d875"},
 ]
 
 [package.dependencies]
-anyio = "*"
 certifi = "*"
-httpcore = "==1.*"
+httpcore = ">=0.18.0,<0.19.0"
 idna = "*"
 sniffio = "*"
 
@@ -3878,13 +3844,13 @@ packaging = "*"
 
 [[package]]
 name = "identify"
-version = "2.5.35"
+version = "2.5.31"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.35-py2.py3-none-any.whl", hash = "sha256:c4de0081837b211594f8e877a6b4fad7ca32bbfc1a9307fdd61c28bfe923f13e"},
-    {file = "identify-2.5.35.tar.gz", hash = "sha256:10a7ca245cfcd756a554a7288159f72ff105ad233c7c4b9c6f0f4d108f5f6791"},
+    {file = "identify-2.5.31-py2.py3-none-any.whl", hash = "sha256:90199cb9e7bd3c5407a9b7e81b4abec4bb9d249991c79439ec8af740afc6293d"},
+    {file = "identify-2.5.31.tar.gz", hash = "sha256:7736b3c7a28233637e3c36550646fc6389bedd74ae84cb788200cc8e2dd60b75"},
 ]
 
 [package.extras]
@@ -3892,48 +3858,48 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
+    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.11.0"
+version = "6.8.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"},
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 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.3"
+version = "6.1.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.3-py3-none-any.whl", hash = "sha256:4c0269e3580fe2634d364b39b38b961540a7738c02cb984e98add8b4221d793d"},
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+    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
 
 [[package]]
 name = "inflection"
@@ -3984,13 +3950,13 @@ tests = ["pytest", "pytest-cov", "pytest-mock"]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.3"
+version = "6.29.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.3-py3-none-any.whl", hash = "sha256:5aa086a4175b0229d4eca211e181fb473ea78ffd9869af36ba7694c947302a21"},
-    {file = "ipykernel-6.29.3.tar.gz", hash = "sha256:e14c250d1f9ea3989490225cc1a542781b095a18a19447fcf2b5eaf7d0ac5bd2"},
+    {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
+    {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"},
 ]
 
 [package.dependencies]
@@ -4013,17 +3979,17 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.22.2"
+version = "8.22.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.22.2-py3-none-any.whl", hash = "sha256:3c86f284c8f3d8f2b6c662f885c4889a91df7cd52056fd02b7d8d6195d7f56e9"},
-    {file = "ipython-8.22.2.tar.gz", hash = "sha256:2dcaad9049f9056f1fef63514f176c7d41f930daa78d05b82a176202818f2c14"},
+    {file = "ipython-8.22.1-py3-none-any.whl", hash = "sha256:869335e8cded62ffb6fac8928e5287a05433d6462e3ebaac25f4216474dd6bc4"},
+    {file = "ipython-8.22.1.tar.gz", hash = "sha256:39c6f9efc079fb19bfb0f17eee903978fe9a290b1b82d68196c641cecb76ea22"},
 ]
 
 [package.dependencies]
@@ -4125,13 +4091,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.2"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
+    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
 ]
 
 [package.dependencies]
@@ -4188,13 +4154,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.21.1"
+version = "4.19.2"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
-    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
+    {file = "jsonschema-4.19.2-py3-none-any.whl", hash = "sha256:eee9e502c788e89cb166d4d37f43084e3b64ab405c795c03d343a4dbc2c810fc"},
+    {file = "jsonschema-4.19.2.tar.gz", hash = "sha256:c9ff4d7447eed9592c23a12ccee508baf0dd0d59650615e847feb6cdca74f392"},
 ]
 
 [package.dependencies]
@@ -4209,27 +4175,27 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.12.1"
+version = "2023.7.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
-    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+    {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"},
+    {file = "jsonschema_specifications-2023.7.1.tar.gz", hash = "sha256:c91a50404e88a1f6ba40636778e2ee08f6e24c5613fe4c53ac24578a5a7f72bb"},
 ]
 
 [package.dependencies]
-referencing = ">=0.31.0"
+referencing = ">=0.28.0"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.5.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.5.0-py3-none-any.whl", hash = "sha256:c3877aac7257ec68d79b5c622ce986bd2a992ca42f6ddc9b4dd1da50e89f7028"},
+    {file = "jupyter_client-8.5.0.tar.gz", hash = "sha256:e8754066510ce456358df363f97eae64b50860f30dc1fe8c6771440db3be9a63"},
 ]
 
 [package.dependencies]
@@ -4245,13 +4211,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.5.0"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4265,59 +4231,58 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
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 ]
 
 [[package]]
 name = "limits"
-version = "3.9.0"
+version = "3.6.0"
 description = "Rate limiting utilities"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "limits-3.9.0.tar.gz", hash = "sha256:7b44aa4d05c539276928372681190136914958cccbb99c30ecc5df72a179661a"},
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 ]
 
 [package.dependencies]
@@ -4340,13 +4305,13 @@ rediscluster = ["redis (>=4.2.0,!=4.5.2,!=4.5.3)"]
 
 [[package]]
 name = "linkify-it-py"
-version = "2.0.3"
+version = "2.0.2"
 description = "Links recognition library with FULL unicode support."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4371,13 +4336,13 @@ files = [
 
 [[package]]
 name = "looker-sdk"
-version = "24.2.0"
+version = "23.16.0"
 description = "Looker REST API"
 optional = false
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
@@ -4481,13 +4446,13 @@ source = ["Cython (>=3.0.7)"]
 
 [[package]]
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+version = "1.2.4"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
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+python-versions = ">=3.7"
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 [package.dependencies]
@@ -4500,13 +4465,13 @@ testing = ["pytest"]
 
 [[package]]
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+version = "3.5.1"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
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 ]
 
 [[package]]
 name = "marshmallow"
-version = "3.21.1"
+version = "3.20.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-3.21.1-py3-none-any.whl", hash = "sha256:f085493f79efb0644f270a9bf2892843142d80d7174bbbd2f3713f2a589dc633"},
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 ]
 
 [package.dependencies]
 packaging = ">=17.0"
 
 [package.extras]
-dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-version-warning (==1.1.2)"]
+dev = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)", "pytest", "pytz", "simplejson", "tox"]
+docs = ["alabaster (==0.7.13)", "autodocsumm (==0.2.11)", "sphinx (==7.0.1)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"]
+lint = ["flake8 (==6.0.0)", "flake8-bugbear (==23.7.10)", "mypy (==1.4.1)", "pre-commit (>=2.4,<4.0)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
 name = "marshmallow-oneofschema"
-version = "3.1.1"
+version = "3.0.1"
 description = "marshmallow multiplexing schema"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 files = [
-    {file = "marshmallow_oneofschema-3.1.1-py3-none-any.whl", hash = "sha256:ff4cb2a488785ee8edd521a765682c2c80c78b9dc48894124531bdfa1ec9303b"},
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+    {file = "marshmallow-oneofschema-3.0.1.tar.gz", hash = "sha256:62cd2099b29188c92493c2940ee79d1bf2f2619a71721664e5a98ec2faa58237"},
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 ]
 
 [package.dependencies]
 marshmallow = ">=3.0.0,<4.0.0"
 
 [package.extras]
-dev = ["marshmallow-oneofschema[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
-tests = ["pytest"]
+dev = ["flake8 (==3.9.2)", "flake8-bugbear (==21.4.3)", "mock", "pre-commit (>=2.7,<3.0)", "pytest", "tox"]
+lint = ["flake8 (==3.9.2)", "flake8-bugbear (==21.4.3)", "pre-commit (>=2.7,<3.0)"]
+tests = ["mock", "pytest"]
 
 [[package]]
 name = "marshmallow-sqlalchemy"
@@ -4776,18 +4743,17 @@ mkdocs = ">=1.2.3"
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "1.0.1"
+version = "0.5.0"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_autorefs-1.0.1-py3-none-any.whl", hash = "sha256:aacdfae1ab197780fb7a2dac92ad8a3d8f7ca8049a9cbe56a4218cd52e8da570"},
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+    {file = "mkdocs_autorefs-0.5.0-py3-none-any.whl", hash = "sha256:7930fcb8ac1249f10e683967aeaddc0af49d90702af111a5e390e8b20b3d97ff"},
+    {file = "mkdocs_autorefs-0.5.0.tar.gz", hash = "sha256:9a5054a94c08d28855cfab967ada10ed5be76e2bfad642302a610b252c3274c0"},
 ]
 
 [package.dependencies]
 Markdown = ">=3.3"
-markupsafe = ">=2.0.1"
 mkdocs = ">=1.1"
 
 [[package]]
@@ -4837,13 +4803,13 @@ requests = "*"
 
 [[package]]
 name = "mkdocs-git-revision-date-localized-plugin"
-version = "1.2.4"
+version = "1.2.2"
 description = "Mkdocs plugin that enables displaying the localized date of the last git modification of a markdown file."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 files = [
-    {file = "mkdocs-git-revision-date-localized-plugin-1.2.4.tar.gz", hash = "sha256:08fd0c6f33c8da9e00daf40f7865943113b3879a1c621b2bbf0fa794ffe997d3"},
-    {file = "mkdocs_git_revision_date_localized_plugin-1.2.4-py3-none-any.whl", hash = "sha256:1f94eb510862ef94e982a2910404fa17a1657ecf29f45a07b0f438c00767fc85"},
+    {file = "mkdocs-git-revision-date-localized-plugin-1.2.2.tar.gz", hash = "sha256:0c43a9aac1fa69df99a823f833cc223bac9967b60d5261a857761c7c6e3b30de"},
+    {file = "mkdocs_git_revision_date_localized_plugin-1.2.2-py3-none-any.whl", hash = "sha256:85c7fe9ab06e7a63c4e522c26fee8b51d357cb8cbe605064501ad80f4f31cb94"},
 ]
 
 [package.dependencies]
@@ -4854,13 +4820,13 @@ pytz = "*"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.13"
+version = "9.5.3"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.13-py3-none-any.whl", hash = "sha256:5cbe17fee4e3b4980c8420a04cc762d8dc052ef1e10532abd4fce88e5ea9ce6a"},
-    {file = "mkdocs_material-9.5.13.tar.gz", hash = "sha256:d8e4caae576312a88fd2609b81cf43d233cdbe36860d67a68702b018b425bd87"},
+    {file = "mkdocs_material-9.5.3-py3-none-any.whl", hash = "sha256:76c93a8525cceb0b395b9cedab3428bf518cf6439adef2b940f1c1574b775d89"},
+    {file = "mkdocs_material-9.5.3.tar.gz", hash = "sha256:5899219f422f0a6de784232d9d40374416302ffae3c160cacc72969fcc1ee372"},
 ]
 
 [package.dependencies]
@@ -4877,19 +4843,19 @@ regex = ">=2022.4"
 requests = ">=2.26,<3.0"
 
 [package.extras]
-git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
-imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
+git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
+imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
 
 [[package]]
 name = "mkdocs-material-extensions"
-version = "1.3.1"
+version = "1.3"
 description = "Extension pack for Python Markdown and MkDocs Material."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material_extensions-1.3.1-py3-none-any.whl", hash = "sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31"},
-    {file = "mkdocs_material_extensions-1.3.1.tar.gz", hash = "sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443"},
+    {file = "mkdocs_material_extensions-1.3-py3-none-any.whl", hash = "sha256:0297cc48ba68a9fdd1ef3780a3b41b534b0d0df1d1181a44676fda5f464eeadc"},
+    {file = "mkdocs_material_extensions-1.3.tar.gz", hash = "sha256:f0446091503acb110a7cab9349cbc90eeac51b58d1caa92a704a81ca1e24ddbd"},
 ]
 
 [[package]]
@@ -4908,23 +4874,21 @@ mkdocs = ">=1.2"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.24.1"
+version = "0.23.0"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.24.1-py3-none-any.whl", hash = "sha256:b4206f9a2ca8a648e222d5a0ca1d36ba7dee53c88732818de183b536f9042b5d"},
-    {file = "mkdocstrings-0.24.1.tar.gz", hash = "sha256:cc83f9a1c8724fc1be3c2fa071dd73d91ce902ef6a79710249ec8d0ee1064401"},
+    {file = "mkdocstrings-0.23.0-py3-none-any.whl", hash = "sha256:051fa4014dfcd9ed90254ae91de2dbb4f24e166347dae7be9a997fe16316c65e"},
+    {file = "mkdocstrings-0.23.0.tar.gz", hash = "sha256:d9c6a37ffbe7c14a7a54ef1258c70b8d394e6a33a1c80832bce40b9567138d1c"},
 ]
 
 [package.dependencies]
-click = ">=7.0"
 Jinja2 = ">=2.11.1"
 Markdown = ">=3.3"
 MarkupSafe = ">=1.1"
-mkdocs = ">=1.4"
+mkdocs = ">=1.2"
 mkdocs-autorefs = ">=0.3.1"
-platformdirs = ">=2.2.0"
 pymdown-extensions = ">=6.3"
 
 [package.extras]
@@ -4947,53 +4911,41 @@ files = [
 griffe = ">=0.37"
 mkdocstrings = ">=0.20"
 
-[[package]]
-name = "more-itertools"
-version = "10.2.0"
-description = "More routines for operating on iterables, beyond itertools"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "more-itertools-10.2.0.tar.gz", hash = "sha256:8fccb480c43d3e99a00087634c06dd02b0d50fbf088b380de5a41a015ec239e1"},
-    {file = "more_itertools-10.2.0-py3-none-any.whl", hash = "sha256:686b06abe565edfab151cb8fd385a05651e1fdf8f0a14191e4439283421f8684"},
-]
-
 [[package]]
 name = "msal"
-version = "1.27.0"
-description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
+version = "1.24.1"
+description = "The Microsoft Authentication Library (MSAL) for Python library"
 optional = false
 python-versions = ">=2.7"
 files = [
-    {file = "msal-1.27.0-py2.py3-none-any.whl", hash = "sha256:572d07149b83e7343a85a3bcef8e581167b4ac76befcbbb6eef0c0e19643cdc0"},
-    {file = "msal-1.27.0.tar.gz", hash = "sha256:3109503c038ba6b307152b0e8d34f98113f2e7a78986e28d0baf5b5303afda52"},
+    {file = "msal-1.24.1-py2.py3-none-any.whl", hash = "sha256:ce4320688f95c301ee74a4d0e9dbcfe029a63663a8cc61756f40d0d0d36574ad"},
+    {file = "msal-1.24.1.tar.gz", hash = "sha256:aa0972884b3c6fdec53d9a0bd15c12e5bd7b71ac1b66d746f54d128709f3f8f8"},
 ]
 
 [package.dependencies]
-cryptography = ">=0.6,<45"
+cryptography = ">=0.6,<44"
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 requests = ">=2.0.0,<3"
 
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+version = "1.0.0"
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 optional = false
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 portalocker = [
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-    {version = ">=1.6,<3", markers = "platform_system == \"Windows\""},
+    {version = ">=1.0,<3", markers = "python_version >= \"3.5\" and platform_system != \"Windows\""},
+    {version = ">=1.6,<3", markers = "python_version >= \"3.5\" and platform_system == \"Windows\""},
 ]
 
 [[package]]
@@ -5019,101 +4971,85 @@ async = ["aiodns", "aiohttp (>=3.0)"]
 
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 [[package]]
@@ -5292,13 +5228,13 @@ PyYAML = ">=5.1.0"
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Python API"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -5307,88 +5243,91 @@ importlib-metadata = ">=6.0,<7.0"
 
 [[package]]
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-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Collector Exporters"
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-python-versions = ">=3.8"
+python-versions = ">=3.7"
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+opentelemetry-exporter-otlp-proto-grpc = "1.20.0"
+opentelemetry-exporter-otlp-proto-http = "1.20.0"
 
 [[package]]
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 [package.dependencies]
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+backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
+opentelemetry-proto = "1.20.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
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 ]
 
 [package.dependencies]
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 deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
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-opentelemetry-proto = "1.23.0"
-opentelemetry-sdk = ">=1.23.0,<1.24.0"
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+opentelemetry-proto = "1.20.0"
+opentelemetry-sdk = ">=1.20.0,<1.21.0"
 
 [package.extras]
 test = ["pytest-grpc"]
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
+backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
 deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.23.0"
-opentelemetry-proto = "1.23.0"
-opentelemetry-sdk = ">=1.23.0,<1.24.0"
+opentelemetry-exporter-otlp-proto-common = "1.20.0"
+opentelemetry-proto = "1.20.0"
+opentelemetry-sdk = ">=1.20.0,<1.21.0"
 requests = ">=2.7,<3.0"
 
 [package.extras]
-test = ["responses (>=0.22.0,<0.25)"]
+test = ["responses (==0.22.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Python Proto"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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+    {file = "opentelemetry_proto-1.20.0-py3-none-any.whl", hash = "sha256:512c3d2c6864fb7547a69577c3907348e6c985b7a204533563cb4c4c5046203b"},
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 ]
 
 [package.dependencies]
@@ -5396,29 +5335,29 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.23.0"
+version = "1.20.0"
 description = "OpenTelemetry Python SDK"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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+    {file = "opentelemetry_sdk-1.20.0-py3-none-any.whl", hash = "sha256:f2230c276ff4c63ea09b3cb2e2ac6b1265f90af64e8d16bbf275c81a9ce8e804"},
+    {file = "opentelemetry_sdk-1.20.0.tar.gz", hash = "sha256:702e432a457fa717fd2ddfd30640180e69938f85bb7fec3e479f85f61c1843f8"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.23.0"
-opentelemetry-semantic-conventions = "0.44b0"
+opentelemetry-api = "1.20.0"
+opentelemetry-semantic-conventions = "0.41b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.44b0"
+version = "0.41b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -5437,61 +5376,61 @@ dev = ["black", "mypy", "pytest"]
 
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 name = "orjson"
-version = "3.9.15"
+version = "3.9.10"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
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 pytz = ">=2020.1"
-tzdata = ">=2022.1"
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 [[package]]
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@@ -5971,13 +5830,13 @@ dev = ["nose", "pipreqs", "twine"]
 
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 [package.dependencies]
@@ -6414,29 +6300,28 @@ flake8 = ["flake8 (>=4)"]
 
 [[package]]
 name = "pyflakes"
-version = "3.2.0"
+version = "3.1.0"
 description = "passive checker of Python programs"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "pygments"
-version = "2.17.2"
+version = "2.16.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
 plugins = ["importlib-metadata"]
-windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyjwt"
@@ -6460,24 +6345,23 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pyliftover"
-version = "0.4.1"
+version = "0.4"
 description = "Pure-python implementation of UCSC ``liftOver`` genome coordinate conversion."
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.7.1"
+version = "10.7"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6489,31 +6373,31 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pyopenssl"
-version = "24.0.0"
+version = "23.2.0"
 description = "Python wrapper module around the OpenSSL library"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
-cryptography = ">=41.0.5,<43"
+cryptography = ">=38.0.0,<40.0.0 || >40.0.0,<40.0.1 || >40.0.1,<42"
 
 [package.extras]
-docs = ["sphinx (!=5.2.0,!=5.2.0.post0,!=7.2.5)", "sphinx-rtd-theme"]
+docs = ["sphinx (!=5.2.0,!=5.2.0.post0)", "sphinx-rtd-theme"]
 test = ["flaky", "pretend", "pytest (>=3.0.1)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.2"
+version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 ]
 
 [package.extras]
@@ -6639,13 +6523,13 @@ test = ["coverage", "docutils", "testscenarios (>=0.4)", "testtools"]
 
 [[package]]
 name = "python-dateutil"
-version = "2.9.0.post0"
+version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -6653,13 +6537,13 @@ six = ">=1.5"
 
 [[package]]
 name = "python-gitlab"
-version = "4.4.0"
-description = "A python wrapper for the GitLab API"
+version = "3.15.0"
+description = "Interact with GitLab API"
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -6668,7 +6552,7 @@ requests-toolbelt = ">=0.10.1"
 
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 [[package]]
@@ -7172,110 +7062,110 @@ rich = ">=11.0.0"
 
 [[package]]
 name = "rpds-py"
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+version = "0.10.6"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
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 description = "An Amazon S3 Transfer Manager"
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 ]
 
 [package.dependencies]
 joblib = ">=1.2.0"
-numpy = ">=1.19.5,<2.0"
+numpy = ">=1.19.5"
 scipy = ">=1.6.0"
 threadpoolctl = ">=2.0.0"
 
@@ -7421,13 +7331,13 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
 [[package]]
 name = "sentry-sdk"
-version = "1.41.0"
+version = "1.33.1"
 description = "Python client for Sentry (https://sentry.io)"
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 python-versions = "*"
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 ]
 
 [package.dependencies]
@@ -7453,7 +7363,7 @@ huey = ["huey (>=2)"]
 loguru = ["loguru (>=0.5)"]
 opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
 opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
-pure-eval = ["asttokens", "executing", "pure-eval"]
+pure-eval = ["asttokens", "executing", "pure_eval"]
 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
@@ -7566,72 +7476,72 @@ test = ["pytest"]
 
 [[package]]
 name = "setuptools"
-version = "66.1.1"
+version = "68.2.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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-python-versions = ">=3.7"
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 ]
 
 [package.extras]
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 name = "shapely"
-version = "2.0.3"
+version = "2.0.2"
 description = "Manipulation and analysis of geometric objects"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
 docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
@@ -7672,13 +7582,13 @@ files = [
 
 [[package]]
 name = "sniffio"
-version = "1.3.1"
+version = "1.3.0"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -7705,7 +7615,7 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "1.4.52"
+version = "1.4.50"
 description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
@@ -7742,7 +7652,7 @@ greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platfo
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2)"]
@@ -7752,24 +7662,24 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)", "sqlalchemy2-stubs"]
 mysql = ["mysqlclient (>=1.4.0)", "mysqlclient (>=1.4.0,<2)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx_oracle (>=7)", "cx_oracle (>=7,<8)"]
+oracle = ["cx-oracle (>=7)", "cx-oracle (>=7,<8)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
 postgresql-pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
 pymysql = ["pymysql", "pymysql (<1)"]
-sqlcipher = ["sqlcipher3_binary"]
+sqlcipher = ["sqlcipher3-binary"]
 
 [[package]]
 name = "sqlalchemy-bigquery"
-version = "1.10.0"
+version = "1.8.0"
 description = "SQLAlchemy dialect for BigQuery"
 optional = false
-python-versions = ">=3.8, <3.13"
+python-versions = ">=3.7, <3.12"
 files = [
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-    {file = "sqlalchemy_bigquery-1.10.0-py2.py3-none-any.whl", hash = "sha256:ea59c9625848c92469463956f93ea1179242f35e582bf1010dec794ab7042046"},
+    {file = "sqlalchemy-bigquery-1.8.0.tar.gz", hash = "sha256:fdc58cc23415b9e251dc90a97052a4831894c15b1b54911909d746696eba6f38"},
+    {file = "sqlalchemy_bigquery-1.8.0-py2.py3-none-any.whl", hash = "sha256:d755a9f820c373858cc799fd4b03d334fb1c372e21a6d224f82eb4f10db00124"},
 ]
 
 [package.dependencies]
@@ -7788,13 +7698,13 @@ tests = ["packaging", "pytz"]
 
 [[package]]
 name = "sqlalchemy-jsonfield"
-version = "1.0.2"
+version = "1.0.1.post0"
 description = "SQLALchemy JSONField implementation for storing dicts at SQL"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -7912,13 +7822,13 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
 
 [[package]]
 name = "termcolor"
-version = "2.4.0"
+version = "2.3.0"
 description = "ANSI color formatting for output in terminal"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -7937,13 +7847,13 @@ files = [
 
 [[package]]
 name = "threadpoolctl"
-version = "3.3.0"
+version = "3.2.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
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 ]
 
 [[package]]
@@ -7970,49 +7880,49 @@ files = [
 
 [[package]]
 name = "tomlkit"
-version = "0.12.4"
+version = "0.12.3"
 description = "Style preserving TOML library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.3.3"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">= 3.8"
 files = [
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 ]
 
 [[package]]
 name = "traitlets"
-version = "5.14.1"
+version = "5.13.0"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
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+    {file = "traitlets-5.13.0-py3-none-any.whl", hash = "sha256:baf991e61542da48fe8aef8b779a9ea0aa38d8a54166ee250d5af5ecf4486619"},
+    {file = "traitlets-5.13.0.tar.gz", hash = "sha256:9b232b9430c8f57288c1024b34a8f0251ddcc47268927367a0dd3eeaca40deb5"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.6.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "typer"
@@ -8048,24 +7958,24 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2024.1"
+version = "2023.3"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
-    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
+    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
 ]
 
 [[package]]
 name = "uc-micro-py"
-version = "1.0.3"
+version = "1.0.2"
 description = "Micro subset of unicode data files for linkify-it-py projects."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "uc-micro-py-1.0.3.tar.gz", hash = "sha256:d321b92cff673ec58027c04015fcaa8bb1e005478643ff4a500882eaab88c48a"},
-    {file = "uc_micro_py-1.0.3-py3-none-any.whl", hash = "sha256:db1dffff340817673d7b466ec86114a9dc0e9d4d9b5ba229d9d60e5c12600cd5"},
+    {file = "uc-micro-py-1.0.2.tar.gz", hash = "sha256:30ae2ac9c49f39ac6dce743bd187fcd2b574b16ca095fa74cd9396795c954c54"},
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 ]
 
 [package.extras]
@@ -8173,19 +8083,19 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
 
 [[package]]
 name = "virtualenv"
-version = "20.25.1"
+version = "20.24.6"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"},
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+    {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"},
+    {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"},
 ]
 
 [package.dependencies]
 distlib = ">=0.3.7,<1"
 filelock = ">=3.12.2,<4"
-platformdirs = ">=3.9.1,<5"
+platformdirs = ">=3.9.1,<4"
 
 [package.extras]
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
@@ -8193,13 +8103,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "wandb"
-version = "0.16.4"
+version = "0.16.2"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "wandb-0.16.2.tar.gz", hash = "sha256:e40cd79ea6272fe4762a80b9f47b172e141daeb3b56eb9d1e192ebd10752e64e"},
 ]
 
 [package.dependencies]
@@ -8220,51 +8130,47 @@ async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
 gcp = ["google-cloud-storage"]
-importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "tomli", "typing-extensions"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"]
 media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]
-reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
 [[package]]
 name = "watchdog"
-version = "4.0.0"
+version = "3.0.0"
 description = "Filesystem events monitoring"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
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-python-versions = ">=3.8"
+python-versions = ">=3.7"
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 [package.dependencies]
-markupsafe = "*"
+MarkupSafe = "*"
 
 [package.extras]
 email = ["email-validator"]
@@ -8466,101 +8377,85 @@ dev = ["doc8", "flake8", "flake8-import-order", "rstcheck[sphinx]", "sphinx"]
 
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+    {file = "yarl-1.9.2-cp39-cp39-win32.whl", hash = "sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623"},
+    {file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
+    {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
 ]
 
 [package.dependencies]

From a7c919a52f7bbccfbcf4a98351f23a15fa8a56fc Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Fri, 8 Mar 2024 15:45:33 +0000
Subject: [PATCH 19/86] fix: updating gwas catalog test files following schema
 changes

---
 tests/gentropy/conftest.py                    |  6 +++---
 .../data_samples/gwas_catalog_ancestries.tsv  | 20 +++++++++++++++++++
 ...g_ancestries_sample_v1.0.3-r2022-11-29.tsv | 20 -------------------
 ...1-29.tsv => gwas_catalog_associations.tsv} |  0
 .../data_samples/gwas_catalog_studies.tsv     | 20 +++++++++++++++++++
 ...was_catalog_studies_sample-r2022-11-29.tsv | 20 -------------------
 6 files changed, 43 insertions(+), 43 deletions(-)
 create mode 100644 tests/gentropy/data_samples/gwas_catalog_ancestries.tsv
 delete mode 100644 tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv
 rename tests/gentropy/data_samples/{gwas_catalog_associations_sample_e107_r2022-11-29.tsv => gwas_catalog_associations.tsv} (100%)
 create mode 100644 tests/gentropy/data_samples/gwas_catalog_studies.tsv
 delete mode 100644 tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv

diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py
index 13409ac16..78bd567da 100644
--- a/tests/gentropy/conftest.py
+++ b/tests/gentropy/conftest.py
@@ -414,7 +414,7 @@ def mock_ld_index(spark: SparkSession) -> LDIndex:
 def sample_gwas_catalog_studies(spark: SparkSession) -> DataFrame:
     """Sample GWAS Catalog studies."""
     return spark.read.csv(
-        "tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv",
+        "tests/gentropy/data_samples/gwas_catalog_studies.tsv",
         sep="\t",
         header=True,
     )
@@ -424,7 +424,7 @@ def sample_gwas_catalog_studies(spark: SparkSession) -> DataFrame:
 def sample_gwas_catalog_ancestries_lut(spark: SparkSession) -> DataFrame:
     """Sample GWAS ancestries sample data."""
     return spark.read.csv(
-        "tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv",
+        "tests/gentropy/data_samples/gwas_catalog_ancestries.tsv",
         sep="\t",
         header=True,
     )
@@ -444,7 +444,7 @@ def sample_gwas_catalog_harmonised_sumstats_list(spark: SparkSession) -> DataFra
 def sample_gwas_catalog_associations(spark: SparkSession) -> DataFrame:
     """Sample GWAS raw associations sample data."""
     return spark.read.csv(
-        "tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv",
+        "tests/gentropy/data_samples/gwas_catalog_associations.tsv",
         sep="\t",
         header=True,
     )
diff --git a/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv b/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv
new file mode 100644
index 000000000..48423eeee
--- /dev/null
+++ b/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv
@@ -0,0 +1,20 @@
+STUDY ACCESSION	PUBMED ID	FIRST AUTHOR	DATE	INITIAL SAMPLE DESCRIPTION	REPLICATION SAMPLE DESCRIPTION	STAGE	NUMBER OF INDIVIDUALS	BROAD ANCESTRAL CATEGORY	COUNTRY OF ORIGIN	COUNTRY OF RECRUITMENT	ADDITIONAL ANCESTRY DESCRIPTION	ANCESTRY DESCRIPTOR	FOUNDER/GENETICALLY ISOLATED POPULATION	NUMBER OF CASES	NUMBER OF CONTROLS	SAMPLE DESCRIPTION
+GCST004795	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	205	Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry	NR	U.S.
+GCST004795	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	356	European	NR	U.S.
+GCST004796	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	356	European	NR	U.S.
+GCST004796	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	205	Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry	NR	U.S.
+GCST004797	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	205	Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry	NR	U.S.
+GCST004797	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	356	European	NR	U.S.
+GCST004794	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	356	European	NR	U.S.
+GCST004794	28763065	Xia K	2017-08-01	295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs.	NA	initial	205	Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry	NR	U.S.
+GCST005522	23459209	Faraco J	2013-01-01	1,886 European ancestry cases, 10,421 European ancestry controls	NA	initial	12307	European	NR	Canada, U.S., Australia, Austria, France, Germany, Netherlands, Switzerland, Argentina, Israel, Turkey, Czech Republic, Poland, Slovakia, Denmark, Finland, Norway, U.K., Italy, Portugal, Spain
+GCST004692	27455348	van Rheenen W	2016-07-25	12,577 European ancestry cases, 23,475 European ancestry controls	2,579 European ancestry cases, 2,767 European ancestry controls	initial	36052	European	NR	U.S., Belgium, France, Germany, Netherlands, Switzerland, Finland, Republic of Ireland, Sweden, U.K., Italy, Portugal, Spain
+GCST004692	27455348	van Rheenen W	2016-07-25	12,577 European ancestry cases, 23,475 European ancestry controls	2,579 European ancestry cases, 2,767 European ancestry controls	replication	5346	European	NR	Australia, Belgium, France, Germany, Netherlands, Turkey, Republic of Ireland, Italy
+GCST005134	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	replication	64	Greater Middle Eastern (Middle Eastern, North African or Persian)	NR	France
+GCST005134	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	replication	431	European	NR	France
+GCST005134	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	initial	448	European	NR	France
+GCST005134	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	initial	47	Greater Middle Eastern (Middle Eastern, North African or Persian)	NR	France
+GCST005135	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	initial	448	European	NR	France
+GCST005135	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	initial	47	Greater Middle Eastern (Middle Eastern, North African or Persian)	NR	France
+GCST005135	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	replication	431	European	NR	France
+GCST005135	28800628	Salem JE	2017-08-11	448 European ancestry individuals, 47 North African ancestry individuals	431 European ancestry individuals, 64 North African ancestry individuals	replication	64	Greater Middle Eastern (Middle Eastern, North African or Persian)	NR	France
diff --git a/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv
deleted file mode 100644
index 92d87d92b..000000000
--- a/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-STUDY ACCESSION	PUBMED ID	FIRST AUTHOR	DATE	INITIAL SAMPLE DESCRIPTION	REPLICATION SAMPLE DESCRIPTION	STAGE	NUMBER OF INDIVIDUALS	BROAD ANCESTRAL CATEGORY	COUNTRY OF ORIGIN	COUNTRY OF RECRUITMENT	ADDITIONAL ANCESTRY DESCRIPTION	ANCESTRY DESCRIPTOR	FOUNDER/GENETICALLY ISOLATED POPULATION	NUMBER OF CASES	NUMBER OF CONTROLS	SAMPLE DESCRIPTION	COHORT(S)	COHORT-SPECIFIC REFERENCE
-GCST008644	26546613	Gutierrez-Achury J	2016-01-01	371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases,  4,418 European ancestry rheumatoid arthritis cases,  509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls	NA	initial	32475	NR	NR	U.S., Netherlands, U.K.
-GCST008644	26546613	Gutierrez-Achury J	2016-01-01	371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases,  4,418 European ancestry rheumatoid arthritis cases,  509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls	NA	initial	13329	European	NR	Sweden, Poland, Italy, Spain
-GCST008644	26546613	Gutierrez-Achury J	2016-01-01	371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases,  4,418 European ancestry rheumatoid arthritis cases,  509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls	NA	initial	880	South Asian	NR	India
-GCST004026	27911795	Schumann G	2016-11-28	up to 70,460 European ancestry drinker individuals	up to 35,438 European ancestry drinker individuals	replication	35438	European	NR	Finland, Sweden, Italy, Netherlands, U.K., Austria, France, Republic of Ireland
-GCST004026	27911795	Schumann G	2016-11-28	up to 70,460 European ancestry drinker individuals	up to 35,438 European ancestry drinker individuals	initial	70460	European	NR	Finland, U.S., Australia, Iceland, Netherlands, Germany, U.K., Switzerland, Estonia, NR
-GCST004027	27911795	Schumann G	2016-11-28	up to 74,711 European ancestry heavy and light/non-drinker individuals	up to 31,021 European ancestry heavy and light/non-drinker individuals	initial	74711	European	NR	Finland, U.S., Australia, Iceland, Netherlands, Germany, U.K., Switzerland, Estonia, NR, France
-GCST004027	27911795	Schumann G	2016-11-28	up to 74,711 European ancestry heavy and light/non-drinker individuals	up to 31,021 European ancestry heavy and light/non-drinker individuals	replication	31021	European	NR	Finland, U.S., Italy, Netherlands, U.K., Austria, Republic of Ireland
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	23	South Asian	NR	U.S.
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	329	Hispanic or Latin American	NR	U.S.
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	21	African American or Afro-Caribbean	NR	U.S.
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	273	European	NR	U.S.
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	22	Other	NR	U.S.
-GCST004281	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	122	Asian unspecified	NR	U.S.
-GCST004284	28235828	Traglia M	2017-04-03	764 fetuses	NA	initial	764	European, South Asian, Asian unspecified, African American or Afro-Caribbean, Hispanic or Latin American, Other	NR	U.S.
-GCST004285	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	329	Hispanic or Latin American	NR	U.S.
-GCST004285	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	273	European	NR	U.S.
-GCST004285	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	23	South Asian	NR	U.S.
-GCST004285	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	21	African American or Afro-Caribbean	NR	U.S.
-GCST004285	28235828	Traglia M	2017-04-03	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	initial	22	Other	NR	U.S.
diff --git a/tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_associations.tsv
similarity index 100%
rename from tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv
rename to tests/gentropy/data_samples/gwas_catalog_associations.tsv
diff --git a/tests/gentropy/data_samples/gwas_catalog_studies.tsv b/tests/gentropy/data_samples/gwas_catalog_studies.tsv
new file mode 100644
index 000000000..aca9ed666
--- /dev/null
+++ b/tests/gentropy/data_samples/gwas_catalog_studies.tsv
@@ -0,0 +1,20 @@
+DATE ADDED TO CATALOG	PUBMED ID	FIRST AUTHOR	DATE	JOURNAL	LINK	STUDY	DISEASE/TRAIT	INITIAL SAMPLE SIZE	REPLICATION SAMPLE SIZE	PLATFORM [SNPS PASSING QC]	ASSOCIATION COUNT	MAPPED_TRAIT	MAPPED_TRAIT_URI	STUDY ACCESSION	GENOTYPING TECHNOLOGY	SUBMISSION DATE	STATISTICAL MODEL	BACKGROUND TRAIT	MAPPED BACKGROUND TRAIT	MAPPED BACKGROUND TRAIT URI	COHORT	FULL SUMMARY STATISTICS	SUMMARY STATS LOCATION
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints	12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls	1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls	Affymetrix [at least 12428592] (imputed)	2	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST004695	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004695
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Sleep duration	112,411 European ancestry male individuals	NA	Affymetrix [at least 12428592] (imputed)	3	sleep duration	http://www.ebi.ac.uk/efo/EFO_0005271	GCST004694	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints (sex interaction)	12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls	NA	Affymetrix [at least 12428592] (imputed)	0	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST004700	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints (continuous)	12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls	NA	Affymetrix [at least 12428592] (imputed)	1	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST004701	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints (dichotomous)	32,384 European ancestry cases,  27,128 European ancestry controls	NA	Affymetrix [at least 12428592] (imputed)	1	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST004702	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Chronotype	101,185 European ancestry individuals	NA	Affymetrix [at least 12428592] (imputed)	9	circadian rhythm	http://www.ebi.ac.uk/efo/EFO_0004354	GCST004696	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Ease of getting up in the morning	112,866 European ancestry individuals	NA	Affymetrix [at least 12428592] (imputed)	5	chronotype measurement	http://www.ebi.ac.uk/efo/EFO_0008328	GCST004697	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Daytime nap	113,054 European ancestry cases and controls	NA	Affymetrix [at least 12428592] (imputed)	4	daytime rest measurement	http://www.ebi.ac.uk/efo/EFO_0007828	GCST004693	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Snoring	105,377 European ancestry cases and controls	NA	Affymetrix [at least 12428592] (imputed)	1	snoring measurement	http://www.ebi.ac.uk/efo/EFO_0008341	GCST004698	Genome-wide genotyping array							no	NA
+2017-09-11	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Daytime sleepiness	112,717 European ancestry cases and controls	NA	Affymetrix [at least 12428592] (imputed)	6	excessive daytime sleepiness measurement	http://www.ebi.ac.uk/efo/EFO_0007875	GCST004699	Genome-wide genotyping array							no	NA
+2018-11-02	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints	19,521 European ancestry female cases, 39,846 European ancestry female controls	1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls	Affymetrix [at least 12428592] (imputed)	1	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST006487	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006487
+2018-11-02	28604731	Hammerschlag AR	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604731	Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.	Insomnia complaints	12,863 European ancestry male cases, 40,776 European ancestry male controls	1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls	Affymetrix [at least 12428592] (imputed)	2	insomnia	http://www.ebi.ac.uk/efo/EFO_0004698	GCST006488	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006488
+2017-09-14	28610988	Kerr KF	2017-06-10	Heart Rhythm	www.ncbi.nlm.nih.gov/pubmed/28610988	Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts.	Heart rate	13,184 Hispanic/Latino individuals	7,073 European ancestry individuals, 4,771 African American individuals	Affymetrix, Illumina [16967914] (imputed)	2	heart rate	http://www.ebi.ac.uk/efo/EFO_0004326	GCST004715	Genome-wide genotyping array							no	NA
+2017-09-14	28610988	Kerr KF	2017-06-10	Heart Rhythm	www.ncbi.nlm.nih.gov/pubmed/28610988	Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts.	Heart rate variability traits (RMSSD)	13,767 Hispanic/Latino individuals	4,730 European ancestry individuals, 2,908 African American individuals	Affymetrix, Illumina [17209892] (imputed)	2	heart rate variability measurement	http://www.ebi.ac.uk/efo/EFO_0008003	GCST004716	Genome-wide genotyping array							no	NA
+2017-09-14	28610988	Kerr KF	2017-06-10	Heart Rhythm	www.ncbi.nlm.nih.gov/pubmed/28610988	Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts.	Heart rate variability traits (SDNN)	13,184 Hispanic/Latino individuals	7,073 European ancestry individuals, 2,908 African American individuals	Affymetrix, Illumina [17209740] (imputed)	3	heart rate variability measurement	http://www.ebi.ac.uk/efo/EFO_0008003	GCST004714	Genome-wide genotyping array							no	NA
+2017-09-18	28604730	McKay JD	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604730	Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.	Lung cancer	29,266 European ancestry cases, 56,450 European ancestry controls	NA	Illumina [10439017] (imputed)	135	lung carcinoma	http://www.ebi.ac.uk/efo/EFO_0001071	GCST004748	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004748
+2017-09-18	28604730	McKay JD	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604730	Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.	Lung adenocarcinoma	11,273 European ancestry cases,  55,483 European ancestry controls	NA	Illumina [10439017] (imputed)	79	lung adenocarcinoma	http://www.ebi.ac.uk/efo/EFO_0000571	GCST004744	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004744
+2017-09-18	28604730	McKay JD	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604730	Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.	Squamous cell lung carcinoma	7,426 European ancestry cases, 55,627 European ancestry controls	NA	Illumina [10439017] (imputed)	101	squamous cell lung carcinoma	http://www.ebi.ac.uk/efo/EFO_0000708	GCST004750	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004750
+2017-09-18	28604730	McKay JD	2017-06-12	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/28604730	Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.	Small cell lung carcinoma	2,664 European ancestry cases,  21,444 European ancestry controls	NA	Illumina [10439017] (imputed)	50	small cell lung carcinoma	http://www.ebi.ac.uk/efo/EFO_0000702	GCST004746	Genome-wide genotyping array							yes	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004746
diff --git a/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv
deleted file mode 100644
index 7db8f5302..000000000
--- a/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv
+++ /dev/null
@@ -1,20 +0,0 @@
-DATE ADDED TO CATALOG	PUBMED ID	FIRST AUTHOR	DATE	JOURNAL	LINK	STUDY	DISEASE/TRAIT	INITIAL SAMPLE SIZE	REPLICATION SAMPLE SIZE	PLATFORM [SNPS PASSING QC]	ASSOCIATION COUNT	MAPPED_TRAIT	MAPPED_TRAIT_URI	STUDY ACCESSION	GENOTYPING TECHNOLOGY	SUMMARY STATS LOCATION	SUBMISSION DATE	STATISTICAL MODEL	BACKGROUND TRAIT	MAPPED BACKGROUND TRAIT	MAPPED BACKGROUND TRAIT URI
-2019-09-11	26546613	Gutierrez-Achury J	2016-01-01	Hum Mol Genet	www.ncbi.nlm.nih.gov/pubmed/26546613	Functional implications of disease-specific variants in loci jointly associated with coeliac disease and rheumatoid arthritis.	Celiac disease and Rheumatoid arthritis	371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases,  4,418 European ancestry rheumatoid arthritis cases,  509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls	NA	Illumina [109572]	24	rheumatoid arthritis, celiac disease	http://www.ebi.ac.uk/efo/EFO_0000685, http://www.ebi.ac.uk/efo/EFO_0001060	GCST008644	Targeted genotyping array [Immunochip]
-2017-05-12	27911795	Schumann G	2016-11-28	Proc Natl Acad Sci U S A	www.ncbi.nlm.nih.gov/pubmed/27911795	KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.	Alcohol consumption	up to 70,460 European ancestry drinker individuals	up to 35,438 European ancestry drinker individuals	Affymetrix, Illumina, Perlegen [at least 316407] (imputed)	3	alcohol consumption measurement	http://www.ebi.ac.uk/efo/EFO_0007878	GCST004026	Genome-wide genotyping array
-2017-05-12	27911795	Schumann G	2016-11-28	Proc Natl Acad Sci U S A	www.ncbi.nlm.nih.gov/pubmed/27911795	KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.	Alcohol consumption (heavy vs. light/non-drinkers)	up to 74,711 European ancestry heavy and light/non-drinker individuals	up to 31,021 European ancestry heavy and light/non-drinker individuals	Affymetrix, Illumina, Perlegen [at least 316407] (imputed)	2	alcohol consumption measurement	http://www.ebi.ac.uk/efo/EFO_0007878	GCST004027	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of organochlorine pesticides	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	Affymetrix [629686]	2	gestational serum measurement, organochlorine pesticide measurement	http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007960	GCST004281	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of PCBs (fetal genetic effect)	764 fetuses	NA	Affymetrix [622716]	11	polychlorinated biphenyls measurement, gestational serum measurement, fetal genotype effect measurement	http://www.ebi.ac.uk/efo/EFO_0007042, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959	GCST004284	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of PBDEs	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	Affymetrix [629686]	9	polybrominated biphenyl measurement, gestational serum measurement, polybrominated diphenyl ether measurement	http://www.ebi.ac.uk/efo/EFO_0007961, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007962	GCST004285	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of PBDEs (fetal genetic effect)	764 fetuses	NA	Affymetrix [629686]	11	polybrominated biphenyl measurement, gestational serum measurement, fetal genotype effect measurement, polybrominated diphenyl ether measurement	http://www.ebi.ac.uk/efo/EFO_0007961, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959, http://www.ebi.ac.uk/efo/EFO_0007962	GCST004286	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of organochlorine pesticides (fetal genetic effect)	764 fetuses	NA	Affymetrix [629686]	0	gestational serum measurement, fetal genotype effect measurement, organochlorine pesticide measurement	http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959, http://www.ebi.ac.uk/efo/EFO_0007960	GCST004282	Genome-wide genotyping array
-2017-07-03	28235828	Traglia M	2017-04-03	G3 (Bethesda)	www.ncbi.nlm.nih.gov/pubmed/28235828	Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants.	Midgestational circulating levels of PCBs	329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers	NA	Affymetrix [629686]	25	polychlorinated biphenyls measurement, gestational serum measurement	http://www.ebi.ac.uk/efo/EFO_0007042, http://www.ebi.ac.uk/efo/EFO_0007964	GCST004283	Genome-wide genotyping array
-2016-09-12	26325155	Brehm JM	2015-09-01	Am J Respir Crit Care Med	www.ncbi.nlm.nih.gov/pubmed/26325155	A Genome-Wide Association Study of Post-bronchodilator Lung Function in Children with Asthma.	Post-bronchodilator lung function in asthma (FEV1)	447 Puerto Rican ancestry cases	568 European, black or Hispanic cases, 2,414 Hispanic cases	Illumina [NR]	0	pulmonary function measurement, forced expiratory volume, response to bronchodilator	http://www.ebi.ac.uk/efo/EFO_0003892, http://www.ebi.ac.uk/efo/EFO_0004314, http://purl.obolibrary.org/obo/GO_0097366	GCST003110	Genome-wide genotyping array					asthma	http://purl.obolibrary.org/obo/MONDO_0004979
-2016-09-12	26325155	Brehm JM	2015-09-01	Am J Respir Crit Care Med	www.ncbi.nlm.nih.gov/pubmed/26325155	A Genome-Wide Association Study of Post-bronchodilator Lung Function in Children with Asthma.	Post-bronchodilator lung function in asthma (FEV1/FVC)	447 Puerto Rican ancestry cases	568 European, black or Hispanic cases, 2,414 Hispanic cases	Illumina [NR]	0	pulmonary function measurement, response to bronchodilator, FEV/FEC ratio	http://www.ebi.ac.uk/efo/EFO_0003892, http://purl.obolibrary.org/obo/GO_0097366, http://www.ebi.ac.uk/efo/EFO_0004713	GCST003107	Genome-wide genotyping array					asthma	http://purl.obolibrary.org/obo/MONDO_0004979
-2016-07-27	26237429	Aminkeng F	2015-08-03	Nat Genet	www.ncbi.nlm.nih.gov/pubmed/26237429	A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer.	Anthracycline-induced cardiotoxicity in childhood cancer	32 European ancestry cases with cardiotoxicity, 248 European ancestry cases without cardiotoxicity	22 European ancestry cases with cardiotoxicity, 74 European ancestry cases without cardiotoxicity, 2 African cases with cardiotoxicity, 9 African cases without cardiotoxicity, 5 Hispanic cases with cardiotoxicity, 18 Hispanic cases without cardiotoxicity, 8 East Asian cases with cardiotoxicity, 23 East Asian cases without cardiotoxicity, 4 Aboriginal Canadian cases with cardiotoxicity, 11 Aboriginal Canadian cases without cardiotoxicity	Illumina [657694]	2	cardiotoxicity, response to anthracycline-based chemotherapy	http://www.ebi.ac.uk/efo/EFO_1001482, http://www.ebi.ac.uk/efo/EFO_0005257	GCST003062	Genome-wide genotyping array					childhood cancer	http://www.ebi.ac.uk/efo/EFO_1000654
-2017-02-25	27387956	Murk W	2016-07-07	BMC Genet	www.ncbi.nlm.nih.gov/pubmed/27387956	Genome-wide search identifies a gene-gene interaction between 20p13 and 2q14 in asthma.	Asthma (SNP x SNP interaction)	802 European ancestry cases, 823 European ancestry controls	754 European ancestry cases, 57 European and unknown ancestry cases, 2,573 cases, 880 European ancestry controls, 68 European and unknown ancestry controls, 2,145 controls	Illumina [301547]	0	asthma	http://purl.obolibrary.org/obo/MONDO_0004979	GCST003682	Genome-wide genotyping array
-2016-12-09	27008869	Chen MM	2016-03-23	Hum Mol Genet	www.ncbi.nlm.nih.gov/pubmed/27008869	GWAS meta-analysis of 16 852 women identifies new susceptibility locus for endometrial cancer.	Endometrial cancer	4,907 European ancestry cases, 11,945 European ancestry controls	NA	Illumina [9486271] (imputed)	4	endometrial carcinoma	http://www.ebi.ac.uk/efo/EFO_1001512	GCST003436	Genome-wide genotyping array
-2016-12-01	27016271	Kornilov SA	2016-03-25	Pediatrics	www.ncbi.nlm.nih.gov/pubmed/27016271	Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population.	Developmental language disorder	149 isolated population cases, 210 isolated population controls	NA	Illumina [223580]	4	specific language impairment	http://www.ebi.ac.uk/efo/EFO_1001510	GCST003396	Genome-wide genotyping array
-2016-12-01	27016271	Kornilov SA	2016-03-25	Pediatrics	www.ncbi.nlm.nih.gov/pubmed/27016271	Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population.	Developmental language disorder (linguistic errors)	149 isolated population cases, 210 isolated population controls	NA	Illumina [223580]	9	linguistic error measurement, specific language impairment	http://www.ebi.ac.uk/efo/EFO_0007798, http://www.ebi.ac.uk/efo/EFO_1001510	GCST003397	Genome-wide genotyping array
-2016-12-01	27016271	Kornilov SA	2016-03-25	Pediatrics	www.ncbi.nlm.nih.gov/pubmed/27016271	Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population.	Developmental language disorder (syntactic complexity)	149 isolated population cases, 210 isolated population controls	NA	Illumina [223580]	3	syntactic complexity measurement, specific language impairment	http://www.ebi.ac.uk/efo/EFO_0007799, http://www.ebi.ac.uk/efo/EFO_1001510	GCST003398	Genome-wide genotyping array
-2016-12-13	27005419	Borne Y	2016-03-22	Hum Mol Genet	www.ncbi.nlm.nih.gov/pubmed/27005419	Genome wide association study identifies two loci associated with cadmium in erythrocytes among never-smokers.	Erythrocyte cadmium concentration	2,704 ever-smoker individuals, 1,728 never-smoker individuals	NA	Illumina [658884]	5	erythrocyte cadmium measurement	http://www.ebi.ac.uk/efo/EFO_0007807	GCST003449	Genome-wide genotyping array
-2016-12-13	27005419	Borne Y	2016-03-22	Hum Mol Genet	www.ncbi.nlm.nih.gov/pubmed/27005419	Genome wide association study identifies two loci associated with cadmium in erythrocytes among never-smokers.	Erythrocyte cadmium concentration in never smokers	1,728 individuals	NA	Illumina [658884]	7	erythrocyte cadmium measurement	http://www.ebi.ac.uk/efo/EFO_0007807	GCST003448	Genome-wide genotyping array

From 8f0c6d57d92a2f3a42b432a296d154aef5d9a481 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 11 Mar 2024 09:24:17 +0000
Subject: [PATCH 20/86] build(deps-dev): bump ruff from 0.2.0 to 0.3.2 (#531)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.0 to 0.3.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.2.0...v0.3.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 160 +++++++++++++++++++++++++++++++++++++++++++------
 pyproject.toml |   2 +-
 2 files changed, 142 insertions(+), 20 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index f1b435867..baa3259c2 100644
--- a/poetry.lock
+++ b/poetry.lock
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 ]
 
 [[package]]
@@ -7620,30 +7721,51 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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     {file = "SQLAlchemy-1.4.50.tar.gz", hash = "sha256:3b97ddf509fc21e10b09403b5219b06c5b558b27fc2453150274fa4e70707dbf"},
 ]
 
@@ -8480,4 +8602,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "8d85933db4bf8f4ca92ef227e9a767d26f4f7d2b360083449cfafc54878885fc"
+content-hash = "22fb188b8c3280c467d702419d19bcef44e4f35fa35de4f89ba7930455d721ac"
diff --git a/pyproject.toml b/pyproject.toml
index d94d8412b..40b4ea630 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,7 +41,7 @@ pep8-naming = "^0.13.2"
 interrogate = "^1.5.0"
 isort = "^5.13.2"
 darglint = "^1.8.1"
-ruff = "^0.2.0"
+ruff = "^0.3.2"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs = "^1.5.3"

From 317a09c25ed2c9e4e73a7abf1da4680a61d72356 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 11 Mar 2024 09:47:12 +0000
Subject: [PATCH 21/86] build(deps-dev): bump mypy from 1.8.0 to 1.9.0 (#532)

Bumps [mypy](https://github.com/python/mypy) from 1.8.0 to 1.9.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.8.0...1.9.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 58 +++++++++++++++++++++++++-------------------------
 pyproject.toml |  2 +-
 2 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index baa3259c2..3044f36c4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5154,38 +5154,38 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.8.0"
+version = "1.9.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "mypy-1.9.0-py3-none-any.whl", hash = "sha256:a260627a570559181a9ea5de61ac6297aa5af202f06fd7ab093ce74e7181e43e"},
+    {file = "mypy-1.9.0.tar.gz", hash = "sha256:3cc5da0127e6a478cddd906068496a97a7618a21ce9b54bde5bf7e539c7af974"},
 ]
 
 [package.dependencies]
@@ -8602,4 +8602,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "22fb188b8c3280c467d702419d19bcef44e4f35fa35de4f89ba7930455d721ac"
+content-hash = "3e83722356bcba15ea8f788d4bc43f05820f1577adef4c558aa30676f5bc7c08"
diff --git a/pyproject.toml b/pyproject.toml
index 40b4ea630..5680f5d39 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ pandas = "^2.1.4"
 
 [tool.poetry.dev-dependencies]
 pre-commit = "^3.6.0"
-mypy = "^1.8"
+mypy = "^1.9"
 pep8-naming = "^0.13.2"
 interrogate = "^1.5.0"
 isort = "^5.13.2"

From 5cd46028d8a28b2299f0520e72d2cf6fadfaf738 Mon Sep 17 00:00:00 2001
From: Joris Louwen <62542407+louwenjjr@users.noreply.github.com>
Date: Mon, 11 Mar 2024 17:22:00 +0100
Subject: [PATCH 22/86] fix: pin version of commitlint (#533)

---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 51d4e05a8..a8a2bf4c9 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -62,7 +62,7 @@ repos:
     rev: v9.11.0
     hooks:
       - id: commitlint
-        additional_dependencies: ["@commitlint/config-conventional"]
+        additional_dependencies: ["@commitlint/config-conventional@18.6.3"]
         stages: [commit-msg]
 
   - repo: https://github.com/pre-commit/mirrors-mypy

From 06c611780e3ecb95b7d4b8b40a192dd8b984560b Mon Sep 17 00:00:00 2001
From: Joris Louwen <62542407+louwenjjr@users.noreply.github.com>
Date: Tue, 12 Mar 2024 11:20:10 +0100
Subject: [PATCH 23/86] fix: read function for thurman data to include first
 line (#534)

---
 src/gentropy/datasource/intervals/thurman.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/gentropy/datasource/intervals/thurman.py b/src/gentropy/datasource/intervals/thurman.py
index 88ed5b6ad..a8113e5a6 100644
--- a/src/gentropy/datasource/intervals/thurman.py
+++ b/src/gentropy/datasource/intervals/thurman.py
@@ -41,7 +41,7 @@ def read(spark: SparkSession, path: str) -> DataFrame:
                 t.StructField("score", t.FloatType(), False),
             ]
         )
-        return spark.read.csv(path, sep="\t", header=True, schema=thurman_schema)
+        return spark.read.csv(path, sep="\t", header=False, schema=thurman_schema)
 
     @classmethod
     def parse(

From 43dd479f896f651093f2511de277b208d1d2a9b2 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Tue, 12 Mar 2024 15:35:22 +0000
Subject: [PATCH 24/86] feat(sumstat imputation): adding class for sumstat
 imputation (#490)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* feat(sumstat imputation): adding class for sumstat imputation

* feat: added test

* docs: update summary statistics imputation description

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update docs/python_api/methods/sumstat_imputation.md

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* fix: changes according to comment

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* Update src/gentropy/method/sumstat_imputation.py

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>

* fix: fixing tests

---------

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 docs/python_api/methods/sumstat_imputation.md |  28 +++
 src/gentropy/method/sumstat_imputation.py     | 172 ++++++++++++++++++
 .../method/test_sumstat_imputation.py         |  31 ++++
 3 files changed, 231 insertions(+)
 create mode 100644 docs/python_api/methods/sumstat_imputation.md
 create mode 100644 src/gentropy/method/sumstat_imputation.py
 create mode 100644 tests/gentropy/method/test_sumstat_imputation.py

diff --git a/docs/python_api/methods/sumstat_imputation.md b/docs/python_api/methods/sumstat_imputation.md
new file mode 100644
index 000000000..6e64d35b1
--- /dev/null
+++ b/docs/python_api/methods/sumstat_imputation.md
@@ -0,0 +1,28 @@
+---
+title: Summary Statistics Imputation
+---
+
+Summary statistics imputation leverages linkage disequilibrium (LD) information to compute Z-scores of missing SNPs from neighbouring observed SNPs
+SNPs by taking advantage of the Linkage Disequilibrium.
+
+We implemented the basic model from RAISS (Robust and Accurate Imputation from Summary Statistics) package (see the original [paper](https://academic.oup.com/bioinformatics/article/35/22/4837/5512360)).
+
+The full repository for the RAISS package can be found [here](https://gitlab.pasteur.fr/statistical-genetics/raiss).
+
+The original model was suggested in 2014 by Bogdan Pasaniuc et al. [here](https://pubmed.ncbi.nlm.nih.gov/24990607/).
+
+It represents the following formula:
+
+E(z*i|z_t) = M*{i,t} \cdot (M\_{t,t})^{-1} \cdot z_t
+
+Where:
+
+- E(z_i|z_t) represents the expected z-score of SNP 'i' given the observed z-scores at known SNP indexes 't'.
+
+- M\_{i,t} represents the LD (Linkage Disequilibrium) matrix between SNP 'i' and the known SNPs at indexes 't'.
+
+- (M\_{t,t})^{-1} represents the inverse of the LD matrix of the known SNPs at indexes 't'.
+
+- z_t represents the vector of observed z-scores at the known SNP indexes 't'.
+
+:::gentropy.method.sumstat_imputation.SummaryStatisticsImputation
diff --git a/src/gentropy/method/sumstat_imputation.py b/src/gentropy/method/sumstat_imputation.py
new file mode 100644
index 000000000..8375fa84f
--- /dev/null
+++ b/src/gentropy/method/sumstat_imputation.py
@@ -0,0 +1,172 @@
+"""RAISS summary statstics imputation model."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import scipy.linalg
+
+
+class SummaryStatisticsImputation:
+    """Implementation of RAISS summary statstics imputation model."""
+
+    @staticmethod
+    def raiss_model(
+        z_scores_known: np.ndarray,
+        ld_matrix_known: np.ndarray,
+        ld_matrix_known_missing: np.ndarray,
+        lamb: float = 0.01,
+        rtol: float = 0.01,
+    ) -> dict[str, Any]:
+        """Compute the imputation of the z-score using the RAISS model.
+
+        Args:
+            z_scores_known (np.ndarray): the vector of known Z scores
+            ld_matrix_known (np.ndarray) : the matrix of known LD correlations
+            ld_matrix_known_missing (np.ndarray): LD matrix of known SNPs with other unknown SNPs in large matrix (similar to ld[unknowns, :][:,known])
+            lamb (float): size of the small value added to the diagonal of the covariance matrix before inversion. Defaults to 0.01.
+            rtol (float): threshold to filter eigenvectos by its eigenvalue. It makes an inversion biased but much more numerically robust. Default to 0.01.
+
+        Returns:
+            dict[str, Any]:
+                - var (np.ndarray): variance of the imputed SNPs
+                - mu (np.ndarray): the estimation of the zscore of the imputed SNPs
+                - ld_score (np.ndarray): the linkage disequilibrium score of the imputed SNPs
+                - condition_number (np.ndarray): the condition number of the correlation matrix
+                - correct_inversion (np.ndarray): a boolean array indicating if the inversion was successful
+                - imputation_r2 (np.ndarray): the R2 of the imputation
+        """
+        sig_t_inv = SummaryStatisticsImputation._invert_sig_t(
+            ld_matrix_known, lamb, rtol
+        )
+        if sig_t_inv is None:
+            return {
+                "var": None,
+                "mu": None,
+                "ld_score": None,
+                "condition_number": None,
+                "correct_inversion": None,
+                "imputation_r2": None,
+            }
+        else:
+            condition_number = np.array(
+                [np.linalg.cond(ld_matrix_known)] * ld_matrix_known_missing.shape[0]
+            )
+            correct_inversion = np.array(
+                [
+                    SummaryStatisticsImputation._check_inversion(
+                        ld_matrix_known, sig_t_inv
+                    )
+                ]
+                * ld_matrix_known_missing.shape[0]
+            )
+
+            var, ld_score = SummaryStatisticsImputation._compute_var(
+                ld_matrix_known_missing, sig_t_inv, lamb
+            )
+
+            mu = SummaryStatisticsImputation._compute_mu(
+                ld_matrix_known_missing, sig_t_inv, z_scores_known
+            )
+            var_norm = SummaryStatisticsImputation._var_in_boundaries(var, lamb)
+
+            R2 = (1 + lamb) - var_norm
+
+            mu = mu / np.sqrt(R2)
+            return {
+                "var": var,
+                "mu": mu,
+                "ld_score": ld_score,
+                "condition_number": condition_number,
+                "correct_inversion": correct_inversion,
+                "imputation_r2": 1 - var,
+            }
+
+    @staticmethod
+    def _compute_mu(
+        sig_i_t: np.ndarray, sig_t_inv: np.ndarray, zt: np.ndarray
+    ) -> np.ndarray:
+        """Compute the estimation of z-score from neighborring snp.
+
+        Args:
+            sig_i_t (np.ndarray) : correlation matrix with line corresponding to unknown Snp (snp to impute) and column to known SNPs
+            sig_t_inv (np.ndarray): inverse of the correlation matrix of known matrix
+            zt (np.ndarray): Zscores of known snp
+        Returns:
+            np.ndarray: a vector of length i containing the estimate of zscore
+
+        """
+        return np.dot(sig_i_t, np.dot(sig_t_inv, zt))
+
+    @staticmethod
+    def _compute_var(
+        sig_i_t: np.ndarray, sig_t_inv: np.ndarray, lamb: float
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Compute the expected variance of the imputed SNPs.
+
+        Args:
+            sig_i_t (np.ndarray) : correlation matrix with line corresponding to unknown Snp (snp to impute) and column to known SNPs
+            sig_t_inv (np.ndarray): inverse of the correlation matrix of known matrix
+            lamb (float): regularization term added to matrix
+
+        Returns:
+            tuple[np.ndarray, np.ndarray]: a tuple containing the variance and the ld score
+        """
+        var = (1 + lamb) - np.einsum(
+            "ij,jk,ki->i", sig_i_t, sig_t_inv, sig_i_t.transpose()
+        )
+        ld_score = (sig_i_t**2).sum(1)
+
+        return var, ld_score
+
+    @staticmethod
+    def _check_inversion(sig_t: np.ndarray, sig_t_inv: np.ndarray) -> bool:
+        """Check if the inversion is correct.
+
+        Args:
+            sig_t (np.ndarray): the correlation matrix
+            sig_t_inv (np.ndarray): the inverse of the correlation matrix
+        Returns:
+            bool: True if the inversion is correct, False otherwise
+        """
+        return np.allclose(sig_t, np.dot(sig_t, np.dot(sig_t_inv, sig_t)))
+
+    @staticmethod
+    def _var_in_boundaries(var: np.ndarray, lamb: float) -> np.ndarray:
+        """Forces the variance to be in the 0 to 1+lambda boundary. Theoritically we shouldn't have to do that.
+
+        Args:
+            var (np.ndarray): the variance of the imputed SNPs
+            lamb (float): regularization term added to the diagonal of the sig_t matrix
+
+        Returns:
+            np.ndarray: the variance of the imputed SNPs
+        """
+        id_neg = np.where(var < 0)
+        var[id_neg] = 0
+        id_inf = np.where(var > (0.99999 + lamb))
+        var[id_inf] = 1
+
+        return var
+
+    @staticmethod
+    def _invert_sig_t(sig_t: np.ndarray, lamb: float, rtol: float) -> np.ndarray:
+        """Invert the correlation matrix. If the provided regularization values are not enough to stabilize the inversion process for the given matrix, the function calls itself recursively, increasing lamb and rtol by 10%.
+
+        Args:
+            sig_t (np.ndarray): the correlation matrix
+            lamb (float): regularization term added to the diagonal of the sig_t matrix
+            rtol (float): threshold to filter eigenvector with a eigenvalue under rtol make inversion biased but much more numerically robust
+
+        Returns:
+            np.ndarray: the inverse of the correlation matrix
+        """
+        try:
+            np.fill_diagonal(sig_t, (1 + lamb))
+            sig_t_inv = scipy.linalg.pinv(sig_t, rtol=rtol, atol=0)
+            return sig_t_inv
+        except np.linalg.LinAlgError:
+            return SummaryStatisticsImputation._invert_sig_t(
+                sig_t, lamb * 1.1, rtol * 1.1
+            )
diff --git a/tests/gentropy/method/test_sumstat_imputation.py b/tests/gentropy/method/test_sumstat_imputation.py
new file mode 100644
index 000000000..aea59f76b
--- /dev/null
+++ b/tests/gentropy/method/test_sumstat_imputation.py
@@ -0,0 +1,31 @@
+"""Test of sumstat imputation functions."""
+
+from __future__ import annotations
+
+import numpy as np
+from gentropy.method.sumstat_imputation import SummaryStatisticsImputation
+
+
+class TestSSImp:
+    """Test of RAISS sumstat imputation main function."""
+
+    def test_sumstat_imputation(
+        self: TestSSImp, sample_data_for_carma: list[np.ndarray]
+    ) -> None:
+        """Test of RAISS."""
+        ld = sample_data_for_carma[0]
+        z = sample_data_for_carma[1]
+
+        unknowns = [5]
+        known = [index for index in list(range(21)) if index not in unknowns]
+        sig_t = ld[known, :][:, known]
+        sig_i_t = ld[unknowns, :][:, known]
+        zt = z[known]
+
+        _l = SummaryStatisticsImputation.raiss_model(
+            zt, sig_t, sig_i_t, lamb=0.01, rtol=0.01
+        )
+        assert (
+            np.round(_l["imputation_r2"][0], decimals=4) == 0.9304
+            and np.round(_l["mu"][0], decimals=4) == 9.7215
+        )

From db73c2332b482466c04689b7db1561943728d74a Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Wed, 13 Mar 2024 10:08:39 +0000
Subject: [PATCH 25/86] chore: apply suggestions from code review
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 src/airflow/dags/test_DAG.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/airflow/dags/test_DAG.py b/src/airflow/dags/test_DAG.py
index 3f9a8ee13..6e3fe17dd 100644
--- a/src/airflow/dags/test_DAG.py
+++ b/src/airflow/dags/test_DAG.py
@@ -38,14 +38,14 @@
         "source_bucket": GWAS_CATALOG_BUCKET_NAME,
         "source_object": "credible_set_datasets/gwas_catalog_curated",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_curated",
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_pics_from_curation",
     },
     # PICS credible sets from GWAS Catalog summary statistics:
     "gwas_catalog_sumstats_credible_set": {
         "source_bucket": GWAS_CATALOG_BUCKET_NAME,
         "source_object": "credible_set_datasets/gwas_catalog_summary_stats",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_summary_statistics",
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_pics_from_summary_statistics",
     },
     # GWAS Catalog manifest files:
     "gwas_catalog_manifests": {
@@ -55,18 +55,18 @@
         "destination_object": f"releases/{RELEASE_VERSION}/manifests",
     },
     # eQTL Catalog study index:
-    "eqtl_catalog_study_index": {
+    "eqtl_catalogue_study_index": {
         "source_bucket": EQTL_BUCKET_NAME,
         "source_object": "study_index",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/study_index/eqtl_catalog",
+        "destination_object": f"releases/{RELEASE_VERSION}/study_index/eqtl_catalogue",
     },
     # eQTL Catalog SuSiE credible sets:
-    "eqtl_catalog_susie_credible_set": {
+    "eqtl_catalogue_susie_credible_set": {
         "source_bucket": EQTL_BUCKET_NAME,
         "source_object": "credible_set_datasets/susie",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/eqtl_catalog_susie",
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/eqtl_catalogue_susie",
     },
     # Finngen study index:
     "finngen_study_index": {
@@ -100,7 +100,7 @@
 
 
 # Test if release folder exists:
-def test_release_folder_exists() -> bool:
+def test_release_folder_absent() -> bool:
     """This function tests if the release folder exists.
 
     Returns:
@@ -109,13 +109,13 @@ def test_release_folder_exists() -> bool:
     hook = GCSHook(gcp_conn_id="google_cloud_default")
     return (
         False
-        if hook.exists(RELEASE_BUCKET_NAME, f"releases/{RELEASE_VERSION}")
+        return not hook.exists(RELEASE_BUCKET_NAME, f"releases/{RELEASE_VERSION}")
         else True
     )
 
 
 # This operator meant to fail the DAG if the release folder exists:
-test_folder = ShortCircuitOperator(
+absent_release_folder = ShortCircuitOperator(
     task_id="test_release_folder_exists",
     python_callable=test_release_folder_exists,
 )
@@ -161,7 +161,7 @@ def test_release_folder_exists() -> bool:
 
     # DAG description:
     (
-        # Test if release folder exists:
+        # Test that the release folder doesn't exist:
         test_folder
         # Run data transfer:
         >> data_transfer

From 6fa81a6df682436c60810a363070a5f7277545cb Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Wed, 13 Mar 2024 10:52:05 +0000
Subject: [PATCH 26/86] refactor: tidying up the ETL DAG

---
 src/airflow/dags/common_airflow.py            | 19 ++++++++++
 src/airflow/dags/dag_genetics_etl.py          | 35 -------------------
 src/airflow/dags/eqtl_preprocess.py           |  8 ++---
 .../dags/{test_DAG.py => genetics_etl.py}     | 29 +++++----------
 4 files changed, 32 insertions(+), 59 deletions(-)
 delete mode 100644 src/airflow/dags/dag_genetics_etl.py
 rename src/airflow/dags/{test_DAG.py => genetics_etl.py} (90%)

diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py
index e3dc56ccb..cfacacd13 100644
--- a/src/airflow/dags/common_airflow.py
+++ b/src/airflow/dags/common_airflow.py
@@ -6,6 +6,7 @@
 
 import pendulum
 import yaml
+from airflow.providers.google.cloud.hooks.gcs import GCSHook
 from airflow.providers.google.cloud.operators.dataproc import (
     ClusterGenerator,
     DataprocCreateClusterOperator,
@@ -59,6 +60,24 @@
 }
 
 
+# Test if release folder exists:
+def tests_cloud_folder_exist(
+    bucket_name: str, path: str, connection_id: str = "google_cloud_default"
+) -> bool:
+    """This function tests if a folder exist in Google Cloud.
+
+    Args:
+        bucket_name (str): Name of the GCP bucket.
+        path (str): Path to the folder in the bucket.
+        connection_id (str): Connection ID to use. Defaults to "google_cloud_default".
+
+    Returns:
+        bool: True if folder exists, False otherwise.
+    """
+    hook = GCSHook(gcp_conn_id=connection_id)
+    return hook.exists(bucket_name, path)
+
+
 def create_cluster(
     cluster_name: str,
     master_machine_type: str = "n1-highmem-16",
diff --git a/src/airflow/dags/dag_genetics_etl.py b/src/airflow/dags/dag_genetics_etl.py
deleted file mode 100644
index 1d27b8bba..000000000
--- a/src/airflow/dags/dag_genetics_etl.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Airflow DAG for the ETL part of the pipeline."""
-from __future__ import annotations
-
-from pathlib import Path
-
-import common_airflow as common
-from airflow.models.dag import DAG
-
-CLUSTER_NAME = "otg-etl"
-SOURCE_CONFIG_FILE_PATH = Path(__file__).parent / "configs" / "dag.yaml"
-
-
-with DAG(
-    dag_id=Path(__file__).stem,
-    description="Open Targets Genetics ETL workflow",
-    default_args=common.shared_dag_args,
-    **common.shared_dag_kwargs,
-):
-    # Parse and define all steps and their prerequisites.
-    tasks = {}
-    steps = common.read_yaml_config(SOURCE_CONFIG_FILE_PATH)
-    for step in steps:
-        # Define task for the current step.
-        step_id = step["id"]
-        this_task = common.submit_step(
-            cluster_name=CLUSTER_NAME,
-            step_id=step_id,
-            task_id=step_id,
-        )
-        # Chain prerequisites.
-        tasks[step_id] = this_task
-        for prerequisite in step.get("prerequisites", []):
-            this_task.set_upstream(tasks[prerequisite])
-    # Construct the DAG with all tasks.
-    dag = common.generate_dag(cluster_name=CLUSTER_NAME, tasks=list(tasks.values()))
diff --git a/src/airflow/dags/eqtl_preprocess.py b/src/airflow/dags/eqtl_preprocess.py
index ec346d137..5433d8968 100644
--- a/src/airflow/dags/eqtl_preprocess.py
+++ b/src/airflow/dags/eqtl_preprocess.py
@@ -15,11 +15,11 @@
 AUTOSCALING = "eqtl-preprocess"
 PROJECT_ID = "open-targets-genetics-dev"
 
-EQTL_CATALOG_SUSIE_LOCATION = "gs://eqtl_catalog_data/ebi_ftp/susie"
-TEMP_DECOMPRESS_LOCATION = "gs://eqtl_catalog_data/susie_decompressed_tmp"
+EQTL_CATALOG_SUSIE_LOCATION = "gs://eqtl_catalogue_data/ebi_ftp/susie"
+TEMP_DECOMPRESS_LOCATION = "gs://eqtl_catalogue_data/susie_decompressed_tmp"
 DECOMPRESS_FAILED_LOG = f"{TEMP_DECOMPRESS_LOCATION}.log"
-STUDY_INDEX_PATH = "gs://eqtl_catalog_data/study_index"
-CREDIBLE_SET_PATH = "gs://eqtl_catalog_data/credible_set_datasets/susie"
+STUDY_INDEX_PATH = "gs://eqtl_catalogue_data/study_index"
+CREDIBLE_SET_PATH = "gs://eqtl_catalogue_data/credible_set_datasets/susie"
 
 with DAG(
     dag_id=Path(__file__).stem,
diff --git a/src/airflow/dags/test_DAG.py b/src/airflow/dags/genetics_etl.py
similarity index 90%
rename from src/airflow/dags/test_DAG.py
rename to src/airflow/dags/genetics_etl.py
index 6e3fe17dd..9c44a440e 100644
--- a/src/airflow/dags/test_DAG.py
+++ b/src/airflow/dags/genetics_etl.py
@@ -7,7 +7,6 @@
 import common_airflow as common
 from airflow.models.dag import DAG
 from airflow.operators.python import ShortCircuitOperator
-from airflow.providers.google.cloud.hooks.gcs import GCSHook
 from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator
 from airflow.utils.task_group import TaskGroup
 
@@ -20,7 +19,7 @@
 
 # Datasource paths:
 GWAS_CATALOG_BUCKET_NAME = "gwas_catalog_data"
-EQTL_BUCKET_NAME = "eqtl_catalog_data"
+EQTL_BUCKET_NAME = "eqtl_catalogue_data"
 FINNGEN_BUCKET_NAME = "finngen_data"
 FINNGEN_RELEASE = "r10"
 
@@ -99,25 +98,15 @@
 }
 
 
-# Test if release folder exists:
-def test_release_folder_absent() -> bool:
-    """This function tests if the release folder exists.
-
-    Returns:
-        bool: False if the release folder exists, True otherwise.
-    """
-    hook = GCSHook(gcp_conn_id="google_cloud_default")
-    return (
-        False
-        return not hook.exists(RELEASE_BUCKET_NAME, f"releases/{RELEASE_VERSION}")
-        else True
-    )
-
-
 # This operator meant to fail the DAG if the release folder exists:
-absent_release_folder = ShortCircuitOperator(
+ensrure_absent_release_folder = ShortCircuitOperator(
     task_id="test_release_folder_exists",
-    python_callable=test_release_folder_exists,
+    # The short-circuit operator will fail the DAG if the test condition returns True (i.e. the release folder exists)
+    python_callable=not common.tests_cloud_folder_exist(
+        bucket_name=RELEASE_BUCKET_NAME,
+        path=f"releases/{RELEASE_VERSION}",
+        connection_id="google_cloud_default",
+    ),
 )
 
 with DAG(
@@ -162,7 +151,7 @@ def test_release_folder_absent() -> bool:
     # DAG description:
     (
         # Test that the release folder doesn't exist:
-        test_folder
+        ensrure_absent_release_folder
         # Run data transfer:
         >> data_transfer
         # Once datasets are transferred, run the rest of the steps:

From 8b312efabd4fe1cb5e9475dbe22d0f5f5286b52f Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Wed, 13 Mar 2024 11:53:27 +0000
Subject: [PATCH 27/86] ci: bugfix in PR labeller (#537)

---
 .github/labeler.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index 060922825..fdb0e58b6 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -1,4 +1,4 @@
-version: "1"
+version: 1
 labels:
   - label: "size-XS"
     size:

From 1095c67a8d9d713615bc8890a5c1926b53815e48 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 13 Mar 2024 12:12:43 +0000
Subject: [PATCH 28/86] chore: pre-commit autoupdate (#539)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* chore: pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.3.0 → v0.3.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.3.0...v0.3.2)
- [github.com/alessandrojcm/commitlint-pre-commit-hook: v9.11.0 → v9.13.0](https://github.com/alessandrojcm/commitlint-pre-commit-hook/compare/v9.11.0...v9.13.0)
- [github.com/pre-commit/mirrors-mypy: v1.8.0 → v1.9.0](https://github.com/pre-commit/mirrors-mypy/compare/v1.8.0...v1.9.0)
- [github.com/python-poetry/poetry: 1.8.2 → 1.8.0](https://github.com/python-poetry/poetry/compare/1.8.2...1.8.0)

* ci: keep poetry s latest release

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 .pre-commit-config.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a8a2bf4c9..b1b9b1a43 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.0
+    rev: v0.3.2
     hooks:
       - id: ruff
         args:
@@ -59,14 +59,14 @@ repos:
         exclude: "CHANGELOG.md"
 
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.11.0
+    rev: v9.13.0
     hooks:
       - id: commitlint
         additional_dependencies: ["@commitlint/config-conventional@18.6.3"]
         stages: [commit-msg]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.8.0"
+    rev: "v1.9.0"
     hooks:
       - id: mypy
         args:

From cae2e40f2a43ff8eb107e0e5ea06091927fabe93 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Thu, 14 Mar 2024 10:23:50 +0000
Subject: [PATCH 29/86] fix(dag): sorting out the checks for the existence of
 GCP folders

---
 poetry.lock                        | 124 +----------------------------
 src/airflow/dags/common_airflow.py |  23 +++---
 src/airflow/dags/genetics_etl.py   |  15 ++--
 3 files changed, 19 insertions(+), 143 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 3044f36c4..31758ef5d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -3426,106 +3426,6 @@ files = [
     {file = "google_re2-1.1-3-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d140c7b9395b4d1e654127aa1c99bcc603ed01000b7bc7e28c52562f1894ec12"},
     {file = "google_re2-1.1-3-cp39-cp39-win32.whl", hash = "sha256:80c5fc200f64b2d903eeb07b8d6cefc620a872a0240c7caaa9aca05b20f5568f"},
     {file = "google_re2-1.1-3-cp39-cp39-win_amd64.whl", hash = "sha256:9eb6dbcee9b5dc4069bbc0634f2eb039ca524a14bed5868fdf6560aaafcbca06"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0db114d7e1aa96dbcea452a40136d7d747d60cbb61394965774688ef59cccd4e"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:82133958e003a1344e5b7a791b9a9dd7560b5c8f96936dbe16f294604524a633"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:9e74fd441d1f3d917d3303e319f61b82cdbd96b9a5ba919377a6eef1504a1e2b"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:734a2e7a4541c57253b5ebee24f3f3366ba3658bcad01da25fb623c78723471a"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:d88d5eecbc908abe16132456fae13690d0508f3ac5777f320ef95cb6cab9a961"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:b91db80b171ecec435a07977a227757dd487356701a32f556fa6fca5d0a40522"},
-    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b23129887a64bb9948af14c84705273ed1a40054e99433b4acccab4dcf6a226"},
-    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5dc1a0cc7cd19261dcaf76763e2499305dbb7e51dc69555167cdb8af98782698"},
-    {file = "google_re2-1.1-4-cp310-cp310-win32.whl", hash = "sha256:3b2ab1e2420b5dd9743a2d6bc61b64e5f708563702a75b6db86637837eaeaf2f"},
-    {file = "google_re2-1.1-4-cp310-cp310-win_amd64.whl", hash = "sha256:92efca1a7ef83b6df012d432a1cbc71d10ff42200640c0f9a5ff5b343a48e633"},
-    {file = "google_re2-1.1-4-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:854818fd4ce79787aca5ba459d6e5abe4ca9be2c684a5b06a7f1757452ca3708"},
-    {file = "google_re2-1.1-4-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:4ceef51174b6f653b6659a8fdaa9c38960c5228b44b25be2a3bcd8566827554f"},
-    {file = "google_re2-1.1-4-cp311-cp311-macosx_13_0_arm64.whl", hash = "sha256:ee49087c3db7e6f5238105ab5299c09e9b77516fe8cfb0a37e5f1e813d76ecb8"},
-    {file = "google_re2-1.1-4-cp311-cp311-macosx_13_0_x86_64.whl", hash = "sha256:dc2312854bdc01410acc5d935f1906a49cb1f28980341c20a68797ad89d8e178"},
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 ]
 
diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py
index cfacacd13..9d4f18503 100644
--- a/src/airflow/dags/common_airflow.py
+++ b/src/airflow/dags/common_airflow.py
@@ -6,7 +6,6 @@
 
 import pendulum
 import yaml
-from airflow.providers.google.cloud.hooks.gcs import GCSHook
 from airflow.providers.google.cloud.operators.dataproc import (
     ClusterGenerator,
     DataprocCreateClusterOperator,
@@ -14,7 +13,7 @@
     DataprocSubmitJobOperator,
 )
 from airflow.utils.trigger_rule import TriggerRule
-from google.cloud import dataproc_v1
+from google.cloud import dataproc_v1, storage
 
 if TYPE_CHECKING:
     from pathlib import Path
@@ -60,22 +59,20 @@
 }
 
 
-# Test if release folder exists:
-def tests_cloud_folder_exist(
-    bucket_name: str, path: str, connection_id: str = "google_cloud_default"
-) -> bool:
-    """This function tests if a folder exist in Google Cloud.
+def check_gcp_folder_exists(bucket_name: str, folder_path: str) -> bool:
+    """Check if a folder exists in a Google Cloud bucket.
 
     Args:
-        bucket_name (str): Name of the GCP bucket.
-        path (str): Path to the folder in the bucket.
-        connection_id (str): Connection ID to use. Defaults to "google_cloud_default".
+        bucket_name (str): The name of the Google Cloud bucket.
+        folder_path (str): The path of the folder to check.
 
     Returns:
-        bool: True if folder exists, False otherwise.
+        bool: True if the folder exists, False otherwise.
     """
-    hook = GCSHook(gcp_conn_id=connection_id)
-    return hook.exists(bucket_name, path)
+    client = storage.Client()
+    bucket = client.get_bucket(bucket_name)
+    blobs = bucket.list_blobs(prefix=folder_path)
+    return any(blobs)
 
 
 def create_cluster(
diff --git a/src/airflow/dags/genetics_etl.py b/src/airflow/dags/genetics_etl.py
index 9c44a440e..55f343648 100644
--- a/src/airflow/dags/genetics_etl.py
+++ b/src/airflow/dags/genetics_etl.py
@@ -99,14 +99,15 @@
 
 
 # This operator meant to fail the DAG if the release folder exists:
-ensrure_absent_release_folder = ShortCircuitOperator(
+ensure_release_folder_not_exists = ShortCircuitOperator(
     task_id="test_release_folder_exists",
-    # The short-circuit operator will fail the DAG if the test condition returns True (i.e. the release folder exists)
-    python_callable=not common.tests_cloud_folder_exist(
-        bucket_name=RELEASE_BUCKET_NAME,
-        path=f"releases/{RELEASE_VERSION}",
-        connection_id="google_cloud_default",
+    python_callable=lambda bucket, path: not common.check_gcp_folder_exists(
+        bucket, path
     ),
+    op_kwargs={
+        "bucket": RELEASE_BUCKET_NAME,
+        "path": f"releases/{RELEASE_VERSION}",
+    },
 )
 
 with DAG(
@@ -151,7 +152,7 @@
     # DAG description:
     (
         # Test that the release folder doesn't exist:
-        ensrure_absent_release_folder
+        ensure_release_folder_not_exists
         # Run data transfer:
         >> data_transfer
         # Once datasets are transferred, run the rest of the steps:

From 01abe9678bb3620648cea4b2a9ad1d48d9f468d1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 18 Mar 2024 12:30:45 +0000
Subject: [PATCH 30/86] build(deps-dev): bump deptry from 0.12.0 to 0.14.0
 (#548)

Bumps [deptry](https://github.com/fpgmaas/deptry) from 0.12.0 to 0.14.0.
- [Release notes](https://github.com/fpgmaas/deptry/releases)
- [Changelog](https://github.com/fpgmaas/deptry/blob/main/CHANGELOG.md)
- [Commits](https://github.com/fpgmaas/deptry/compare/0.12.0...0.14.0)

---
updated-dependencies:
- dependency-name: deptry
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 142 ++++++++++++++++++++++++++++++++++++++++++++++---
 pyproject.toml |   2 +-
 2 files changed, 135 insertions(+), 9 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 31758ef5d..f725d304a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -1627,21 +1627,25 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
 [[package]]
 name = "deptry"
-version = "0.12.0"
+version = "0.14.0"
 description = "A command line utility to check for unused, missing and transitive dependencies in a Python project."
 optional = false
-python-versions = ">=3.8,<4.0"
+python-versions = ">=3.8, <4.0"
 files = [
-    {file = "deptry-0.12.0-py3-none-any.whl", hash = "sha256:69c801a6ae1b39c7b8e0daf40dbe8b75f1f161277d206dd8f921f32cd22dad91"},
-    {file = "deptry-0.12.0.tar.gz", hash = "sha256:ac3cd32d149c92a9af12f63cd9486ddd1760f0277ed0cf306c6ef0388f57ff0a"},
+    {file = "deptry-0.14.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:951d40e40cca6b538d8b1992e9532c082757598c73249469d3f6f90cf3344c9f"},
+    {file = "deptry-0.14.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:59e0808507ffb57a0ce9b8c40ecda7b4a235a0627f00485d8e6104bf71dc99f8"},
+    {file = "deptry-0.14.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5e6b7485c3ece10eb94b2787258282275687a101587c6f88f65a6eadc65c4a4"},
+    {file = "deptry-0.14.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d5a8299b9627dc32352caf99c5e8fdff0227539ccbe1535a6145649b5caa394"},
+    {file = "deptry-0.14.0-cp38-abi3-win_amd64.whl", hash = "sha256:d55654025b567739a57f9b6b8467a21c65a30c21e834b2d8cb225618b320874c"},
+    {file = "deptry-0.14.0.tar.gz", hash = "sha256:3415c65c0734f6dfafa1321c77c1c9fafb6d6f88d0f8441e5b2cfb5b3b41f71b"},
 ]
 
 [package.dependencies]
 chardet = ">=4.0.0"
-click = ">=8.0.0,<9.0.0"
+click = ">=8.0.0,<9"
 colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\""}
 pathspec = ">=0.9.0"
-tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
+tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "dill"
@@ -3426,6 +3430,106 @@ files = [
     {file = "google_re2-1.1-3-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d140c7b9395b4d1e654127aa1c99bcc603ed01000b7bc7e28c52562f1894ec12"},
     {file = "google_re2-1.1-3-cp39-cp39-win32.whl", hash = "sha256:80c5fc200f64b2d903eeb07b8d6cefc620a872a0240c7caaa9aca05b20f5568f"},
     {file = "google_re2-1.1-3-cp39-cp39-win_amd64.whl", hash = "sha256:9eb6dbcee9b5dc4069bbc0634f2eb039ca524a14bed5868fdf6560aaafcbca06"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0db114d7e1aa96dbcea452a40136d7d747d60cbb61394965774688ef59cccd4e"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:82133958e003a1344e5b7a791b9a9dd7560b5c8f96936dbe16f294604524a633"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:9e74fd441d1f3d917d3303e319f61b82cdbd96b9a5ba919377a6eef1504a1e2b"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:734a2e7a4541c57253b5ebee24f3f3366ba3658bcad01da25fb623c78723471a"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:d88d5eecbc908abe16132456fae13690d0508f3ac5777f320ef95cb6cab9a961"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:b91db80b171ecec435a07977a227757dd487356701a32f556fa6fca5d0a40522"},
+    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b23129887a64bb9948af14c84705273ed1a40054e99433b4acccab4dcf6a226"},
+    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5dc1a0cc7cd19261dcaf76763e2499305dbb7e51dc69555167cdb8af98782698"},
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@@ -7620,30 +7725,51 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:128a948bd40780667114b0297e2cc6d657b71effa942e0a368d8cc24293febb3"},
     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f2d526aeea1bd6a442abc7c9b4b00386fd70253b80d54a0930c0a216230a35be"},
+    {file = "SQLAlchemy-1.4.50-cp39-cp39-win32.whl", hash = "sha256:a7c9b9dca64036008962dd6b0d9fdab2dfdbf96c82f74dbd5d86006d8d24a30f"},
+    {file = "SQLAlchemy-1.4.50-cp39-cp39-win_amd64.whl", hash = "sha256:df200762efbd672f7621b253721644642ff04a6ff957236e0e2fe56d9ca34d2c"},
     {file = "SQLAlchemy-1.4.50.tar.gz", hash = "sha256:3b97ddf509fc21e10b09403b5219b06c5b558b27fc2453150274fa4e70707dbf"},
 ]
 
@@ -8480,4 +8606,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "3e83722356bcba15ea8f788d4bc43f05820f1577adef4c558aa30676f5bc7c08"
+content-hash = "7e49947f0842c795031610eba766ccca04acb4944d62c6bc2dc522fb4c702cc4"
diff --git a/pyproject.toml b/pyproject.toml
index 5680f5d39..e83666536 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,7 +74,7 @@ apache-airflow = "^2.8.0"
 apache-airflow-providers-google = "^10.13.1"
 pydoclint = ">=0.3.8,<0.5.0"
 prettier = "^0.0.7"
-deptry = "^0.12.0"
+deptry = ">=0.12,<0.15"
 python-semantic-release = ">=8.7,<10.0"
 yamllint = "^1.33.0"
 

From eaefc98c9346866e75eecdef309293a8a6af4083 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 18 Mar 2024 12:41:41 +0000
Subject: [PATCH 31/86] build(deps-dev): bump mkdocstrings-python from 1.8.0 to
 1.9.0 (#549)

Bumps [mkdocstrings-python](https://github.com/mkdocstrings/python) from 1.8.0 to 1.9.0.
- [Release notes](https://github.com/mkdocstrings/python/releases)
- [Changelog](https://github.com/mkdocstrings/python/blob/main/CHANGELOG.md)
- [Commits](https://github.com/mkdocstrings/python/compare/1.8.0...1.9.0)

---
updated-dependencies:
- dependency-name: mkdocstrings-python
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index f725d304a..8e2164c5d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5002,17 +5002,18 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.8.0"
+version = "1.9.0"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings_python-1.8.0-py3-none-any.whl", hash = "sha256:4209970cc90bec194568682a535848a8d8489516c6ed4adbe58bbc67b699ca9d"},
-    {file = "mkdocstrings_python-1.8.0.tar.gz", hash = "sha256:1488bddf50ee42c07d9a488dddc197f8e8999c2899687043ec5dd1643d057192"},
+    {file = "mkdocstrings_python-1.9.0-py3-none-any.whl", hash = "sha256:fad27d7314b4ec9c0359a187b477fb94c65ef561fdae941dca1b717c59aae96f"},
+    {file = "mkdocstrings_python-1.9.0.tar.gz", hash = "sha256:6e1a442367cf75d30cf69774cbb1ad02aebec58bfff26087439df4955efecfde"},
 ]
 
 [package.dependencies]
 griffe = ">=0.37"
+markdown = ">=3.3,<3.6"
 mkdocstrings = ">=0.20"
 
 [[package]]

From b3a56645a25bb1e74575012d246a193f0d6c6a0a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Mon, 18 Mar 2024 15:15:15 +0000
Subject: [PATCH 32/86] chore(l2g): log annotated gold standards in w&b (#546)

* refactor(l2g): select features of interest in data outside trainer

* chore(l2g): log annotated gold standards in w&b

* fix: update test_train
---
 src/gentropy/l2g.py                         | 27 ++++++++++++---------
 src/gentropy/method/l2g/model.py            | 11 ++++++---
 src/gentropy/method/l2g/trainer.py          | 10 +++-----
 tests/gentropy/method/test_locus_to_gene.py |  3 +--
 4 files changed, 27 insertions(+), 24 deletions(-)

diff --git a/src/gentropy/l2g.py b/src/gentropy/l2g.py
index d00a91596..0d85bd0ef 100644
--- a/src/gentropy/l2g.py
+++ b/src/gentropy/l2g.py
@@ -132,17 +132,21 @@ def __init__(
                 colocalisation=coloc,
             )
 
-            # Join and fill null values with 0
-            data = L2GFeatureMatrix(
-                _df=fm.df.join(
-                    f.broadcast(
-                        gold_standards.df.drop("variantId", "studyId", "sources")
+            data = (
+                # Annotate gold standards with features
+                L2GFeatureMatrix(
+                    _df=fm.df.join(
+                        f.broadcast(
+                            gold_standards.df.drop("variantId", "studyId", "sources")
+                        ),
+                        on=["studyLocusId", "geneId"],
+                        how="inner",
                     ),
-                    on=["studyLocusId", "geneId"],
-                    how="inner",
-                ),
-                _schema=L2GFeatureMatrix.get_schema(),
-            ).fill_na()
+                    _schema=L2GFeatureMatrix.get_schema(),
+                )
+                .fill_na()
+                .select_features(list(features_list))
+            )
 
             # Instantiate classifier
             estimator = SparkXGBClassifier(
@@ -165,9 +169,8 @@ def __init__(
             else:
                 # Train model
                 LocusToGeneTrainer.train(
-                    data=data,
+                    gold_standard_data=data,
                     l2g_model=l2g_model,
-                    features_list=list(features_list),
                     model_path=model_path,
                     evaluate=True,
                     wandb_run_name=wandb_run_name,
diff --git a/src/gentropy/method/l2g/model.py b/src/gentropy/method/l2g/model.py
index 5df189d33..f8d892e07 100644
--- a/src/gentropy/method/l2g/model.py
+++ b/src/gentropy/method/l2g/model.py
@@ -207,7 +207,7 @@ def evaluate(
         results: DataFrame,
         hyperparameters: dict[str, Any],
         wandb_run_name: str | None,
-        training_data: L2GFeatureMatrix | None = None,
+        gold_standard_data: L2GFeatureMatrix | None = None,
     ) -> None:
         """Perform evaluation of the model predictions for the test set and track the results with W&B.
 
@@ -215,7 +215,7 @@ def evaluate(
             results (DataFrame): Dataframe containing the predictions
             hyperparameters (dict[str, Any]): Hyperparameters used for the model
             wandb_run_name (str | None): Descriptive name for the run to be tracked with W&B
-            training_data (L2GFeatureMatrix | None): Training data used for the model. If provided, the ratio of positive to negative labels will be logged to W&B
+            gold_standard_data (L2GFeatureMatrix | None): Feature matrix for the associations in the gold standard. If provided, the ratio of positive to negative labels will be logged to W&B
         """
         binary_evaluator = BinaryClassificationEvaluator(
             rawPredictionCol="rawPrediction", labelCol="label"
@@ -224,7 +224,7 @@ def evaluate(
             labelCol="label", predictionCol="prediction"
         )
 
-        if wandb_run_name and training_data:
+        if wandb_run_name and gold_standard_data:
             run = wandb_init(
                 project=self.wandb_l2g_project_name,
                 config=hyperparameters,
@@ -232,7 +232,10 @@ def evaluate(
             )
             if isinstance(run, Run):
                 self.log_to_wandb(
-                    results, training_data, [binary_evaluator, multi_evaluator], run
+                    results,
+                    gold_standard_data,
+                    [binary_evaluator, multi_evaluator],
+                    run,
                 )
                 run.finish()
 
diff --git a/src/gentropy/method/l2g/trainer.py b/src/gentropy/method/l2g/trainer.py
index 8d2b3d7aa..1638a0417 100644
--- a/src/gentropy/method/l2g/trainer.py
+++ b/src/gentropy/method/l2g/trainer.py
@@ -22,9 +22,8 @@ class LocusToGeneTrainer:
     @classmethod
     def train(
         cls: type[LocusToGeneTrainer],
-        data: L2GFeatureMatrix,
+        gold_standard_data: L2GFeatureMatrix,
         l2g_model: LocusToGeneModel,
-        features_list: list[str],
         evaluate: bool,
         wandb_run_name: str | None = None,
         model_path: str | None = None,
@@ -33,9 +32,8 @@ def train(
         """Train the Locus to Gene model.
 
         Args:
-            data (L2GFeatureMatrix): Feature matrix containing the data
+            gold_standard_data (L2GFeatureMatrix): Feature matrix for the associations in the gold standard
             l2g_model (LocusToGeneModel): Model to fit to the data on
-            features_list (list[str]): List of features to use for the model
             evaluate (bool): Whether to evaluate the model on a test set
             wandb_run_name (str | None): Descriptive name for the run to be tracked with W&B
             model_path (str | None): Path to save the model to
@@ -44,7 +42,7 @@ def train(
         Returns:
             LocusToGeneModel: Trained model
         """
-        train, test = data.select_features(features_list).train_test_split(fraction=0.8)
+        train, test = gold_standard_data.train_test_split(fraction=0.8)
 
         model = l2g_model.add_pipeline_stage(l2g_model.estimator).fit(train)
 
@@ -53,7 +51,7 @@ def train(
                 results=model.predict(test),
                 hyperparameters=hyperparams,
                 wandb_run_name=wandb_run_name,
-                training_data=train,
+                gold_standard_data=gold_standard_data,
             )
         if model_path:
             l2g_model.save(model_path)
diff --git a/tests/gentropy/method/test_locus_to_gene.py b/tests/gentropy/method/test_locus_to_gene.py
index d97f4dc4d..db06c66bb 100644
--- a/tests/gentropy/method/test_locus_to_gene.py
+++ b/tests/gentropy/method/test_locus_to_gene.py
@@ -67,9 +67,8 @@ def test_train(
     ) -> None:
         """Test the training function."""
         trained_model = LocusToGeneTrainer.train(
-            mock_l2g_feature_matrix.fill_na(),
+            mock_l2g_feature_matrix.fill_na().select_features(["distanceTssMean"]),
             model,
-            features_list=["distanceTssMean"],
             evaluate=False,
         )
         # Check that `model` is a PipelineModel object and not None

From 77976b59a080f3c287336b7a2045d8521d6293a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Mon, 18 Mar 2024 15:40:56 +0000
Subject: [PATCH 33/86] perf(l2g): streamline feature generation (#544)

* refactor(l2g): streamline coloc feature factory

* perf(l2g): make joins in _get_vep_features lighter

* fix(l2g): use weighted scores for _get_vep_features

* refactor(l2g): minor improvements

* perf(l2g): adapt session to set partition number for shuffling to 800

---------

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>
---
 src/gentropy/config.py                      |   1 +
 src/gentropy/dataset/l2g_feature_matrix.py  |  33 +++--
 src/gentropy/dataset/l2g_prediction.py      |   8 +-
 src/gentropy/l2g.py                         |   8 +-
 src/gentropy/method/l2g/feature_factory.py  | 137 ++++++++------------
 tests/gentropy/conftest.py                  |   5 +
 tests/gentropy/method/test_locus_to_gene.py |  24 +---
 7 files changed, 95 insertions(+), 121 deletions(-)

diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 28935e637..6a2469c0c 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -182,6 +182,7 @@ class LocusToGeneConfig(StepConfig):
                 "spark.dynamicAllocation.enabled": "false",
                 "spark.driver.memory": "48g",
                 "spark.executor.memory": "48g",
+                "spark.sql.shuffle.partitions": "800",
             }
         }
     )
diff --git a/src/gentropy/dataset/l2g_feature_matrix.py b/src/gentropy/dataset/l2g_feature_matrix.py
index fa84499dc..948477600 100644
--- a/src/gentropy/dataset/l2g_feature_matrix.py
+++ b/src/gentropy/dataset/l2g_feature_matrix.py
@@ -5,6 +5,8 @@
 from functools import reduce
 from typing import TYPE_CHECKING, Type
 
+from pyspark.sql.functions import col
+
 from gentropy.common.schemas import parse_spark_schema
 from gentropy.common.spark_helpers import convert_from_long_to_wide
 from gentropy.dataset.dataset import Dataset
@@ -40,7 +42,7 @@ def __post_init__(self: L2GFeatureMatrix) -> None:
     def generate_features(
         cls: Type[L2GFeatureMatrix],
         features_list: list[str],
-        study_locus: StudyLocus,
+        credible_set: StudyLocus,
         study_index: StudyIndex,
         variant_gene: V2G,
         colocalisation: Colocalisation,
@@ -49,7 +51,7 @@ def generate_features(
 
         Args:
             features_list (list[str]): List of features to generate
-            study_locus (StudyLocus): Study locus dataset
+            credible_set (StudyLocus): Credible set dataset
             study_index (StudyIndex): Study index dataset
             variant_gene (V2G): Variant to gene dataset
             colocalisation (Colocalisation): Colocalisation dataset
@@ -60,13 +62,24 @@ def generate_features(
         Raises:
             ValueError: If the feature matrix is empty
         """
+        coloc_methods = (
+            colocalisation.df.select("colocalisationMethod")
+            .distinct()
+            .toPandas()["colocalisationMethod"]
+            .tolist()
+        )
         if features_dfs := [
             # Extract features
-            ColocalisationFactory._get_coloc_features(
-                study_locus, study_index, colocalisation
-            ).df,
-            StudyLocusFactory._get_tss_distance_features(study_locus, variant_gene).df,
-            StudyLocusFactory._get_vep_features(study_locus, variant_gene).df,
+            ColocalisationFactory._get_max_coloc_per_credible_set(
+                credible_set,
+                study_index,
+                colocalisation.filter(col("colocalisationMethod") == method),
+                method,
+            ).df
+            for method in coloc_methods
+        ] + [
+            StudyLocusFactory._get_tss_distance_features(credible_set, variant_gene).df,
+            StudyLocusFactory._get_vep_features(credible_set, variant_gene).df,
         ]:
             fm = reduce(
                 lambda x, y: x.unionByName(y),
@@ -162,8 +175,6 @@ def train_test_split(
         """
         train, test = self._df.randomSplit([fraction, 1 - fraction], seed=42)
         return (
-            L2GFeatureMatrix(
-                _df=train, _schema=L2GFeatureMatrix.get_schema()
-            ).persist(),
-            L2GFeatureMatrix(_df=test, _schema=L2GFeatureMatrix.get_schema()).persist(),
+            L2GFeatureMatrix(_df=train, _schema=L2GFeatureMatrix.get_schema()),
+            L2GFeatureMatrix(_df=test, _schema=L2GFeatureMatrix.get_schema()),
         )
diff --git a/src/gentropy/dataset/l2g_prediction.py b/src/gentropy/dataset/l2g_prediction.py
index e24688da3..c4eccdf45 100644
--- a/src/gentropy/dataset/l2g_prediction.py
+++ b/src/gentropy/dataset/l2g_prediction.py
@@ -44,7 +44,7 @@ def from_credible_set(
         cls: Type[L2GPrediction],
         model_path: str,
         features_list: list[str],
-        study_locus: StudyLocus,
+        credible_set: StudyLocus,
         study_index: StudyIndex,
         v2g: V2G,
         coloc: Colocalisation,
@@ -54,7 +54,7 @@ def from_credible_set(
         Args:
             model_path (str): Path to the fitted model
             features_list (list[str]): List of features to use for the model
-            study_locus (StudyLocus): Study locus dataset
+            credible_set (StudyLocus): Credible set dataset
             study_index (StudyIndex): Study index dataset
             v2g (V2G): Variant to gene dataset
             coloc (Colocalisation): Colocalisation dataset
@@ -64,7 +64,7 @@ def from_credible_set(
         """
         fm = L2GFeatureMatrix.generate_features(
             features_list=features_list,
-            study_locus=study_locus,
+            credible_set=credible_set,
             study_index=study_index,
             variant_gene=v2g,
             colocalisation=coloc,
@@ -73,7 +73,7 @@ def from_credible_set(
         gwas_fm = L2GFeatureMatrix(
             _df=(
                 fm.df.join(
-                    study_locus.filter_by_study_type("gwas", study_index).df,
+                    credible_set.filter_by_study_type("gwas", study_index).df,
                     on="studyLocusId",
                 )
             ),
diff --git a/src/gentropy/l2g.py b/src/gentropy/l2g.py
index 0d85bd0ef..d0acd2b95 100644
--- a/src/gentropy/l2g.py
+++ b/src/gentropy/l2g.py
@@ -73,7 +73,9 @@ def __init__(
             session, study_index_path, recursiveFileLookup=True
         )
         v2g = V2G.from_parquet(session, variant_gene_path)
-        coloc = Colocalisation.from_parquet(session, colocalisation_path)
+        coloc = Colocalisation.from_parquet(
+            session, colocalisation_path, recursiveFileLookup=True
+        )
 
         if run_mode == "predict":
             if not model_path or not predictions_path:
@@ -91,7 +93,7 @@ def __init__(
             and gene_interactions_path
         ):
             # Process gold standard and L2G features
-            gs_curation = session.spark.read.json(gold_standard_curation_path).persist()
+            gs_curation = session.spark.read.json(gold_standard_curation_path)
             interactions = session.spark.read.parquet(gene_interactions_path)
             study_locus_overlap = StudyLocus(
                 # We just extract overlaps of associations in the gold standard. This parsing is a duplication of the one in the gold standard curation,
@@ -126,7 +128,7 @@ def __init__(
 
             fm = L2GFeatureMatrix.generate_features(
                 features_list=features_list,
-                study_locus=credible_set,
+                credible_set=credible_set,
                 study_index=studies,
                 variant_gene=v2g,
                 colocalisation=coloc,
diff --git a/src/gentropy/method/l2g/feature_factory.py b/src/gentropy/method/l2g/feature_factory.py
index a3dba6688..ff378e170 100644
--- a/src/gentropy/method/l2g/feature_factory.py
+++ b/src/gentropy/method/l2g/feature_factory.py
@@ -25,8 +25,8 @@ class ColocalisationFactory:
     """Feature extraction in colocalisation."""
 
     @staticmethod
-    def _get_max_coloc_per_study_locus(
-        study_locus: StudyLocus,
+    def _get_max_coloc_per_credible_set(
+        credible_set: StudyLocus,
         studies: StudyIndex,
         colocalisation: Colocalisation,
         colocalisation_method: str,
@@ -34,7 +34,7 @@ def _get_max_coloc_per_study_locus(
         """Get the maximum colocalisation posterior probability for each pair of overlapping study-locus per type of colocalisation method and QTL type.
 
         Args:
-            study_locus (StudyLocus): Study locus dataset
+            credible_set (StudyLocus): Study locus dataset
             studies (StudyIndex): Study index dataset
             colocalisation (Colocalisation): Colocalisation dataset
             colocalisation_method (str): Colocalisation method to extract the max from
@@ -57,8 +57,8 @@ def _get_max_coloc_per_study_locus(
             coloc_score_col_name = "clpp"
             coloc_feature_col_template = "ColocClppMaximum"
 
-        colocalising_study_locus = (
-            study_locus.df.select("studyLocusId", "studyId")
+        colocalising_credible_sets = (
+            credible_set.df.select("studyLocusId", "studyId")
             # annotate studyLoci with overlapping IDs on the left - to just keep GWAS associations
             .join(
                 colocalisation.df.selectExpr(
@@ -72,7 +72,7 @@ def _get_max_coloc_per_study_locus(
             )
             # bring study metadata to just keep QTL studies on the right
             .join(
-                study_locus.df.selectExpr(
+                credible_set.df.selectExpr(
                     "studyLocusId as rightStudyLocusId", "studyId as right_studyId"
                 ),
                 on="rightStudyLocusId",
@@ -98,52 +98,48 @@ def _get_max_coloc_per_study_locus(
 
         # Max PP calculation per studyLocus AND type of QTL
         local_max = get_record_with_maximum_value(
-            colocalising_study_locus,
+            colocalising_credible_sets,
             ["studyLocusId", "right_studyType", "geneId"],
             "coloc_score",
-        ).persist()
+        )
 
         intercept = 0.0001
         neighbourhood_max = (
-            (
-                local_max.selectExpr(
-                    "studyLocusId", "coloc_score as coloc_local_max", "geneId"
-                )
-                .join(
-                    # Add maximum in the neighborhood
-                    get_record_with_maximum_value(
-                        colocalising_study_locus.withColumnRenamed(
-                            "coloc_score", "coloc_neighborhood_max"
-                        ),
-                        ["studyLocusId", "right_studyType"],
-                        "coloc_neighborhood_max",
-                    ).drop("geneId"),
-                    on="studyLocusId",
-                )
-                .withColumn(
-                    f"{coloc_feature_col_template}Neighborhood",
-                    f.log10(
-                        f.abs(
-                            f.col("coloc_local_max")
-                            - f.col("coloc_neighborhood_max")
-                            + f.lit(intercept)
-                        )
+            local_max.selectExpr(
+                "studyLocusId", "coloc_score as coloc_local_max", "geneId"
+            )
+            .join(
+                # Add maximum in the neighborhood
+                get_record_with_maximum_value(
+                    colocalising_credible_sets.withColumnRenamed(
+                        "coloc_score", "coloc_neighborhood_max"
                     ),
-                )
+                    ["studyLocusId", "right_studyType"],
+                    "coloc_neighborhood_max",
+                ).drop("geneId"),
+                on="studyLocusId",
             )
-            .drop("coloc_neighborhood_max", "coloc_local_max")
-            .persist()
-        )
+            .withColumn(
+                f"{coloc_feature_col_template}Neighborhood",
+                f.log10(
+                    f.abs(
+                        f.col("coloc_local_max")
+                        - f.col("coloc_neighborhood_max")
+                        + f.lit(intercept)
+                    )
+                ),
+            )
+        ).drop("coloc_neighborhood_max", "coloc_local_max")
 
         # Split feature per molQTL
         local_dfs = []
         nbh_dfs = []
         qtl_types: list[str] = (
-            colocalising_study_locus.select("right_studyType")
+            colocalising_credible_sets.select("right_studyType")
             .distinct()
             .toPandas()["right_studyType"]
             .tolist()
-        ) or ["eqtl", "pqtl", "sqtl"]
+        )
         for qtl_type in qtl_types:
             filtered_local_max = (
                 local_max.filter(f.col("right_studyType") == qtl_type)
@@ -190,46 +186,18 @@ def _get_max_coloc_per_study_locus(
             _schema=L2GFeature.get_schema(),
         )
 
-    @staticmethod
-    def _get_coloc_features(
-        study_locus: StudyLocus, studies: StudyIndex, colocalisation: Colocalisation
-    ) -> L2GFeature:
-        """Calls _get_max_coloc_per_study_locus for both methods and concatenates the results.
-
-        !!! note "Colocalisation features are only available for the eCAVIAR results for now."
-
-        Args:
-            study_locus (StudyLocus): Study locus dataset
-            studies (StudyIndex): Study index dataset
-            colocalisation (Colocalisation): Colocalisation dataset
-
-        Returns:
-            L2GFeature: Stores the features with the max coloc probabilities for each pair of study-locus
-        """
-        coloc_clpp = ColocalisationFactory._get_max_coloc_per_study_locus(
-            study_locus,
-            studies,
-            colocalisation,
-            "eCAVIAR",
-        )
-
-        return L2GFeature(
-            _df=coloc_clpp.df,
-            _schema=L2GFeature.get_schema(),
-        )
-
 
 class StudyLocusFactory(StudyLocus):
     """Feature extraction in study locus."""
 
     @staticmethod
     def _get_tss_distance_features(
-        study_locus: StudyLocus, distances: V2G
+        credible_set: StudyLocus, distances: V2G
     ) -> L2GFeature:
         """Joins StudyLocus with the V2G to extract the minimum distance to a gene TSS of all variants in a StudyLocus credible set.
 
         Args:
-            study_locus (StudyLocus): Study locus dataset
+            credible_set (StudyLocus): Credible set dataset
             distances (V2G): Dataframe containing the distances of all variants to all genes TSS within a region
 
         Returns:
@@ -237,7 +205,7 @@ def _get_tss_distance_features(
 
         """
         wide_df = (
-            study_locus.filter_credible_set(CredibleInterval.IS95)
+            credible_set.filter_credible_set(CredibleInterval.IS95)
             .df.select(
                 "studyLocusId",
                 "variantId",
@@ -321,32 +289,33 @@ def _aggregate_vep_feature(
 
         credible_set_w_variant_consequences = (
             credible_set.filter_credible_set(CredibleInterval.IS95)
-            .df.withColumn("variantInLocusId", f.explode(f.col("locus.variantId")))
-            .withColumn(
-                "variantInLocusPosteriorProbability",
-                f.explode(f.col("locus.posteriorProbability")),
+            .df.withColumn("variantInLocus", f.explode_outer("locus"))
+            .select(
+                f.col("studyLocusId"),
+                f.col("variantId"),
+                f.col("studyId"),
+                f.col("variantInLocus.variantId").alias("variantInLocusId"),
+                f.col("variantInLocus.posteriorProbability").alias(
+                    "variantInLocusPosteriorProbability"
+                ),
             )
             .join(
                 # Join with V2G to get variant consequences
-                v2g.df.filter(
-                    f.col("datasourceId") == "variantConsequence"
-                ).withColumnRenamed("variantId", "variantInLocusId"),
+                v2g.df.filter(f.col("datasourceId") == "variantConsequence").selectExpr(
+                    "variantId as variantInLocusId", "geneId", "score"
+                ),
                 on="variantInLocusId",
             )
-            .withColumn(
-                "weightedScore",
-                f.col("score") * f.col("variantInLocusPosteriorProbability"),
-            )
             .select(
                 "studyLocusId",
                 "variantId",
                 "studyId",
                 "geneId",
-                "score",
-                "weightedScore",
+                (f.col("score") * f.col("variantInLocusPosteriorProbability")).alias(
+                    "weightedScore"
+                ),
             )
             .distinct()
-            .persist()
         )
 
         return L2GFeature(
@@ -357,14 +326,14 @@ def _aggregate_vep_feature(
                         # Calculate overall max VEP score for all genes in the vicinity
                         credible_set_w_variant_consequences.transform(
                             _aggregate_vep_feature,
-                            f.max("score"),
+                            f.max("weightedScore"),
                             ["studyLocusId"],
                             "vepMaximumNeighborhood",
                         ),
                         # Calculate overall max VEP score per gene
                         credible_set_w_variant_consequences.transform(
                             _aggregate_vep_feature,
-                            f.max("score"),
+                            f.max("weightedScore"),
                             ["studyLocusId", "geneId"],
                             "vepMaximum",
                         ),
diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py
index 78bd567da..ec106f975 100644
--- a/tests/gentropy/conftest.py
+++ b/tests/gentropy/conftest.py
@@ -84,6 +84,11 @@ def mock_colocalisation(spark: SparkSession) -> Colocalisation:
         .withColumnSpec("h4", percentNulls=0.1)
         .withColumnSpec("log2h4h3", percentNulls=0.1)
         .withColumnSpec("clpp", percentNulls=0.1)
+        .withColumnSpec(
+            "colocalisationMethod",
+            percentNulls=0.0,
+            values=["COLOC", "eCAVIAR"],
+        )
     )
     return Colocalisation(_df=data_spec.build(), _schema=coloc_schema)
 
diff --git a/tests/gentropy/method/test_locus_to_gene.py b/tests/gentropy/method/test_locus_to_gene.py
index db06c66bb..3976406c9 100644
--- a/tests/gentropy/method/test_locus_to_gene.py
+++ b/tests/gentropy/method/test_locus_to_gene.py
@@ -87,7 +87,7 @@ class TestColocalisationFactory:
             "eCAVIAR",
         ],
     )
-    def test_get_max_coloc_per_study_locus(
+    def test_get_max_coloc_per_credible_set(
         self: TestColocalisationFactory,
         mock_study_locus: StudyLocus,
         mock_study_index: StudyIndex,
@@ -95,7 +95,7 @@ def test_get_max_coloc_per_study_locus(
         colocalisation_method: str,
     ) -> None:
         """Test the function that extracts the maximum log likelihood ratio for each pair of overlapping study-locus returns the right data type."""
-        coloc_features = ColocalisationFactory._get_max_coloc_per_study_locus(
+        coloc_features = ColocalisationFactory._get_max_coloc_per_credible_set(
             mock_study_locus,
             mock_study_index,
             mock_colocalisation,
@@ -103,9 +103,9 @@ def test_get_max_coloc_per_study_locus(
         )
         assert isinstance(
             coloc_features, L2GFeature
-        ), "Unexpected model type returned from _get_max_coloc_per_study_locus"
+        ), "Unexpected model type returned from _get_max_coloc_per_credible_set"
 
-    def test_get_max_coloc_per_study_locus_semantic(
+    def test_get_max_coloc_per_credible_set_semantic(
         self: TestColocalisationFactory,
         spark: SparkSession,
     ) -> None:
@@ -182,7 +182,7 @@ def test_get_max_coloc_per_study_locus_semantic(
             L2GFeature.get_schema(),
         )
         # Test
-        coloc_features = ColocalisationFactory._get_max_coloc_per_study_locus(
+        coloc_features = ColocalisationFactory._get_max_coloc_per_credible_set(
             credset,
             studies,
             coloc,
@@ -190,20 +190,6 @@ def test_get_max_coloc_per_study_locus_semantic(
         )
         assert coloc_features.df.collect() == expected_coloc_features_df.collect()
 
-    def test_get_coloc_features(
-        self: TestColocalisationFactory,
-        mock_study_locus: StudyLocus,
-        mock_study_index: StudyIndex,
-        mock_colocalisation: Colocalisation,
-    ) -> None:
-        """Test the function that calls all the methods to produce colocalisation features."""
-        coloc_features = ColocalisationFactory._get_coloc_features(
-            mock_study_locus, mock_study_index, mock_colocalisation
-        )
-        assert isinstance(
-            coloc_features, L2GFeature
-        ), "Unexpected model type returned from _get_coloc_features"
-
 
 class TestStudyLocusFactory:
     """Test the StudyLocusFactory methods."""

From 923c622f0825a9763b75bef861f91e46825bd240 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 19 Mar 2024 13:14:57 +0000
Subject: [PATCH 34/86] chore: pre-commit autoupdate (#550)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* chore: pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.3.2 → v0.3.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.3.2...v0.3.3)
- [github.com/python-poetry/poetry: 1.8.2 → 1.8.0](https://github.com/python-poetry/poetry/compare/1.8.2...1.8.0)

* fix: poetry lock adjustments

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <dogcaesar@gmail.com>
---
 .pre-commit-config.yaml |   4 +-
 poetry.lock             | 124 +---------------------------------------
 2 files changed, 3 insertions(+), 125 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b1b9b1a43..56714becf 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.2
+    rev: v0.3.3
     hooks:
       - id: ruff
         args:
@@ -104,7 +104,7 @@ repos:
       - id: pydoclint
 
   - repo: https://github.com/python-poetry/poetry
-    rev: "1.8.2"
+    rev: "1.8.0"
     hooks:
       - id: poetry-check
       - id: poetry-lock
diff --git a/poetry.lock b/poetry.lock
index 8e2164c5d..01cafdeec 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
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From 160051c762142d9a83439fa461e0491da05f86ba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Tue, 19 Mar 2024 14:09:09 +0000
Subject: [PATCH 35/86] feat(l2g): distance features based on weighted score
 (#545)

* refactor(l2g): streamline coloc feature factory

* perf(l2g): make joins in _get_vep_features lighter

* fix(l2g): use weighted scores for _get_vep_features

* refactor(l2g): minor improvements

* perf(l2g): adapt session to set partition number for shuffling to 800

* feat(l2g): distance features based on weighted score

* docs: update docs

* refactor(l2g): select features of interest in data outside trainer

* chore(l2g): log annotated gold standards in w&b

* fix: update test_train

* fix(l2g): typo in _get_tss_distance_features with no consequences
---
 src/gentropy/method/l2g/feature_factory.py | 32 +++++++++++++---------
 1 file changed, 19 insertions(+), 13 deletions(-)

diff --git a/src/gentropy/method/l2g/feature_factory.py b/src/gentropy/method/l2g/feature_factory.py
index ff378e170..5373be108 100644
--- a/src/gentropy/method/l2g/feature_factory.py
+++ b/src/gentropy/method/l2g/feature_factory.py
@@ -191,37 +191,43 @@ class StudyLocusFactory(StudyLocus):
     """Feature extraction in study locus."""
 
     @staticmethod
-    def _get_tss_distance_features(
-        credible_set: StudyLocus, distances: V2G
-    ) -> L2GFeature:
-        """Joins StudyLocus with the V2G to extract the minimum distance to a gene TSS of all variants in a StudyLocus credible set.
+    def _get_tss_distance_features(credible_set: StudyLocus, v2g: V2G) -> L2GFeature:
+        """Joins StudyLocus with the V2G to extract a score that is based on the distance to a gene TSS of any variant weighted by its posterior probability in a credible set.
 
         Args:
             credible_set (StudyLocus): Credible set dataset
-            distances (V2G): Dataframe containing the distances of all variants to all genes TSS within a region
+            v2g (V2G): Dataframe containing the distances of all variants to all genes TSS within a region
 
         Returns:
-            L2GFeature: Stores the features with the minimum distance among all variants in the credible set and a gene TSS.
+            L2GFeature: Stores the features with the score of weighting the distance to the TSS by the posterior probability of the variant
 
         """
         wide_df = (
             credible_set.filter_credible_set(CredibleInterval.IS95)
-            .df.select(
+            .df.withColumn("variantInLocus", f.explode_outer("locus"))
+            .select(
                 "studyLocusId",
                 "variantId",
-                f.explode("locus.variantId").alias("tagVariantId"),
+                f.col("variantInLocus.variantId").alias("variantInLocusId"),
+                f.col("variantInLocus.posteriorProbability").alias(
+                    "variantInLocusPosteriorProbability"
+                ),
             )
             .join(
-                distances.df.selectExpr(
-                    "variantId as tagVariantId", "geneId", "distance"
+                v2g.df.filter(f.col("datasourceId") == "canonical_tss").selectExpr(
+                    "variantId as variantInLocusId", "geneId", "score"
                 ),
-                on="tagVariantId",
+                on="variantInLocusId",
                 how="inner",
             )
+            .withColumn(
+                "weightedScore",
+                f.col("score") * f.col("variantInLocusPosteriorProbability"),
+            )
             .groupBy("studyLocusId", "geneId")
             .agg(
-                f.min("distance").alias("distanceTssMinimum"),
-                f.mean("distance").alias("distanceTssMean"),
+                f.min("weightedScore").alias("distanceTssMinimum"),
+                f.mean("weightedScore").alias("distanceTssMean"),
             )
         )
 

From ad50c158d45004c36dbeee0860c9a96ea052f2d7 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Wed, 20 Mar 2024 10:34:56 +0000
Subject: [PATCH 36/86] perf(clump): refactored window based clumping (#492)

* perf: refactored window based clumping

* fix: gwas catalog clump step adjusted

* fix: broadcast logic

* revert: coalesce instruction

* fix: add alias

* docs: enhance docs

* test: accidentally removed test

* test: rescue missing test

* chore: remove unused step

* refactor: increased modularisation

* feat: restructure data model

* test: structural test for annotate function

* fix: prevent locus column could be duplicated

* chore: up-to-date with pre-commit
---
 .../howto/python_api/c_applying_methods.py    |   2 +-
 src/gentropy/clump.py                         |  70 -----------
 src/gentropy/config.py                        |   5 +-
 src/gentropy/dataset/study_locus.py           |  70 +++++++++++
 src/gentropy/dataset/summary_statistics.py    |  31 ++---
 src/gentropy/method/window_based_clumping.py  | 115 ++----------------
 src/gentropy/window_based_clumping.py         |  39 +++---
 tests/gentropy/dataset/test_study_locus.py    |  11 ++
 .../method/test_window_based_clumping.py      |  18 ++-
 9 files changed, 147 insertions(+), 214 deletions(-)
 delete mode 100644 src/gentropy/clump.py

diff --git a/docs/src_snippets/howto/python_api/c_applying_methods.py b/docs/src_snippets/howto/python_api/c_applying_methods.py
index 12eaf61ac..d0bec9edc 100644
--- a/docs/src_snippets/howto/python_api/c_applying_methods.py
+++ b/docs/src_snippets/howto/python_api/c_applying_methods.py
@@ -23,7 +23,7 @@ def apply_class_method_clumping(summary_stats: SummaryStatistics) -> StudyLocus:
     from gentropy.method.window_based_clumping import WindowBasedClumping
 
     clumped_summary_statistics = WindowBasedClumping.clump(
-        summary_stats, window_length=500_000
+        summary_stats, distance=250_000
     )
     # --8<-- [end:apply_class_method_clumping]
     return clumped_summary_statistics
diff --git a/src/gentropy/clump.py b/src/gentropy/clump.py
deleted file mode 100644
index 9ea3306ac..000000000
--- a/src/gentropy/clump.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Step to run clump associations from summary statistics or study locus."""
-from __future__ import annotations
-
-from typing import Optional
-
-from gentropy.common.session import Session
-from gentropy.dataset.ld_index import LDIndex
-from gentropy.dataset.study_index import StudyIndex
-from gentropy.dataset.study_locus import StudyLocus
-from gentropy.dataset.summary_statistics import SummaryStatistics
-
-
-class ClumpStep:
-    """Perform clumping of an association dataset to identify independent signals.
-
-    Two types of clumping are supported and are applied based on the input dataset:
-    - Clumping of summary statistics based on a window-based approach.
-    - Clumping of study locus based on LD.
-
-    Both approaches yield a StudyLocus dataset.
-    """
-
-    def __init__(
-        self,
-        session: Session,
-        input_path: str,
-        clumped_study_locus_path: str,
-        study_index_path: Optional[str] = None,
-        ld_index_path: Optional[str] = None,
-        locus_collect_distance: Optional[int] = None,
-    ) -> None:
-        """Run the clumping step.
-
-        Args:
-            session (Session): Session object.
-            input_path (str): Input path for the study locus or summary statistics files.
-            clumped_study_locus_path (str): Output path for the clumped study locus dataset.
-            study_index_path (Optional[str]): Input path for the study index dataset.
-            ld_index_path (Optional[str]): Input path for the LD index dataset.
-            locus_collect_distance (Optional[int]): Distance in base pairs to collect variants around the study locus.
-
-        Raises:
-            ValueError: If study index and LD index paths are not provided for study locus.
-        """
-        input_cols = session.spark.read.parquet(
-            input_path, recursiveFileLookup=True
-        ).columns
-        if "studyLocusId" in input_cols:
-            if study_index_path is None or ld_index_path is None:
-                raise ValueError(
-                    "Study index and LD index paths are required for clumping study locus."
-                )
-            study_locus = StudyLocus.from_parquet(session, input_path)
-            ld_index = LDIndex.from_parquet(session, ld_index_path)
-            study_index = StudyIndex.from_parquet(session, study_index_path)
-
-            clumped_study_locus = study_locus.annotate_ld(
-                study_index=study_index, ld_index=ld_index
-            ).clump()
-        else:
-            sumstats = SummaryStatistics.from_parquet(
-                session, input_path, recursiveFileLookup=True
-            ).coalesce(4000)
-            clumped_study_locus = sumstats.window_based_clumping(
-                locus_collect_distance=locus_collect_distance
-            )
-
-        clumped_study_locus.df.write.mode(session.write_mode).parquet(
-            clumped_study_locus_path
-        )
diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 6a2469c0c..127d90844 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -321,9 +321,10 @@ class WindowBasedClumpingStep(StepConfig):
 
     summary_statistics_input_path: str = MISSING
     study_locus_output_path: str = MISSING
+    distance: int = 500_000
+    collect_locus: bool = False
+    collect_locus_distance: int = 500_000
     inclusion_list_path: str | None = None
-    locus_collect_distance: str | None = None
-
     _target_: str = "gentropy.window_based_clumping.WindowBasedClumpingStep"
 
 
diff --git a/src/gentropy/dataset/study_locus.py b/src/gentropy/dataset/study_locus.py
index 4ff9f0172..185aeb569 100644
--- a/src/gentropy/dataset/study_locus.py
+++ b/src/gentropy/dataset/study_locus.py
@@ -1,4 +1,5 @@
 """Study locus dataset."""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -24,6 +25,7 @@
 
     from gentropy.dataset.ld_index import LDIndex
     from gentropy.dataset.study_index import StudyIndex
+    from gentropy.dataset.summary_statistics import SummaryStatistics
 
 
 class StudyLocusQualityCheck(Enum):
@@ -427,6 +429,74 @@ def annotate_credible_sets(self: StudyLocus) -> StudyLocus:
         )
         return self
 
+    def annotate_locus_statistics(
+        self: StudyLocus,
+        summary_statistics: SummaryStatistics,
+        collect_locus_distance: int,
+    ) -> StudyLocus:
+        """Annotates study locus with summary statistics in the specified distance around the position.
+
+        Args:
+            summary_statistics (SummaryStatistics): Summary statistics to be used for annotation.
+            collect_locus_distance (int): distance from variant defining window for inclusion of variants in locus.
+
+        Returns:
+            StudyLocus: Study locus annotated with summary statistics in `locus` column. If no statistics are found, the `locus` column will be empty.
+        """
+        # The clumps will be used several times (persisting)
+        self.df.persist()
+        # Renaming columns:
+        sumstats_renamed = summary_statistics.df.selectExpr(
+            *[f"{col} as tag_{col}" for col in summary_statistics.df.columns]
+        ).alias("sumstat")
+
+        locus_df = (
+            sumstats_renamed
+            # Joining the two datasets together:
+            .join(
+                f.broadcast(
+                    self.df.alias("clumped").select(
+                        "position", "chromosome", "studyId", "studyLocusId"
+                    )
+                ),
+                on=[
+                    (f.col("sumstat.tag_studyId") == f.col("clumped.studyId"))
+                    & (f.col("sumstat.tag_chromosome") == f.col("clumped.chromosome"))
+                    & (
+                        f.col("sumstat.tag_position")
+                        >= (f.col("clumped.position") - collect_locus_distance)
+                    )
+                    & (
+                        f.col("sumstat.tag_position")
+                        <= (f.col("clumped.position") + collect_locus_distance)
+                    )
+                ],
+                how="inner",
+            )
+            .withColumn(
+                "locus",
+                f.struct(
+                    f.col("tag_variantId").alias("variantId"),
+                    f.col("tag_beta").alias("beta"),
+                    f.col("tag_pValueMantissa").alias("pValueMantissa"),
+                    f.col("tag_pValueExponent").alias("pValueExponent"),
+                    f.col("tag_standardError").alias("standardError"),
+                ),
+            )
+            .groupBy("studyLocusId")
+            .agg(
+                f.collect_list(f.col("locus")).alias("locus"),
+            )
+        )
+
+        self.df = self.df.drop("locus").join(
+            locus_df,
+            on="studyLocusId",
+            how="left",
+        )
+
+        return self
+
     def annotate_ld(
         self: StudyLocus, study_index: StudyIndex, ld_index: LDIndex
     ) -> StudyLocus:
diff --git a/src/gentropy/dataset/summary_statistics.py b/src/gentropy/dataset/summary_statistics.py
index 442672c58..6cde03988 100644
--- a/src/gentropy/dataset/summary_statistics.py
+++ b/src/gentropy/dataset/summary_statistics.py
@@ -9,7 +9,6 @@
 from gentropy.common.schemas import parse_spark_schema
 from gentropy.common.utils import parse_region, split_pvalue
 from gentropy.dataset.dataset import Dataset
-from gentropy.method.window_based_clumping import WindowBasedClumping
 
 if TYPE_CHECKING:
     from pyspark.sql.types import StructType
@@ -59,34 +58,24 @@ def window_based_clumping(
         self: SummaryStatistics,
         distance: int = 500_000,
         gwas_significance: float = 5e-8,
-        baseline_significance: float = 0.05,
-        locus_collect_distance: int | None = None,
     ) -> StudyLocus:
-        """Generate study-locus from summary statistics by distance based clumping + collect locus.
+        """Generate study-locus from summary statistics using window-based clumping.
+
+        For more info, see [`WindowBasedClumping`][gentropy.method.window_based_clumping.WindowBasedClumping]
 
         Args:
             distance (int): Distance in base pairs to be used for clumping. Defaults to 500_000.
             gwas_significance (float, optional): GWAS significance threshold. Defaults to 5e-8.
-            baseline_significance (float, optional): Baseline significance threshold for inclusion in the locus. Defaults to 0.05.
-            locus_collect_distance (int | None): The distance to collect locus around semi-indices. If not provided, locus is not collected.
 
         Returns:
-            StudyLocus: Clumped study-locus containing variants based on window.
+            StudyLocus: Clumped study-locus optionally containing variants based on window.
         """
-        return (
-            WindowBasedClumping.clump_with_locus(
-                self,
-                window_length=distance,
-                p_value_significance=gwas_significance,
-                p_value_baseline=baseline_significance,
-                locus_window_length=locus_collect_distance,
-            )
-            if locus_collect_distance
-            else WindowBasedClumping.clump(
-                self,
-                window_length=distance,
-                p_value_significance=gwas_significance,
-            )
+        from gentropy.method.window_based_clumping import WindowBasedClumping
+
+        return WindowBasedClumping.clump(
+            self,
+            distance=distance,
+            gwas_significance=gwas_significance,
         )
 
     def exclude_region(self: SummaryStatistics, region: str) -> SummaryStatistics:
diff --git a/src/gentropy/method/window_based_clumping.py b/src/gentropy/method/window_based_clumping.py
index a2ae12419..57a24c559 100644
--- a/src/gentropy/method/window_based_clumping.py
+++ b/src/gentropy/method/window_based_clumping.py
@@ -151,22 +151,21 @@ def _prune_peak(position: NDArray[np.float64], window_size: int) -> DenseVector:
 
         return DenseVector(is_lead)
 
-    @classmethod
+    @staticmethod
     def clump(
-        cls: type[WindowBasedClumping],
-        summary_stats: SummaryStatistics,
-        window_length: int,
-        p_value_significance: float = 5e-8,
+        summary_statistics: SummaryStatistics,
+        distance: int = 500_000,
+        gwas_significance: float = 5e-8,
     ) -> StudyLocus:
-        """Clump summary statistics by distance.
+        """Clump significant signals from summary statistics based on window.
 
         Args:
-            summary_stats (SummaryStatistics): summary statistics to clump
-            window_length (int): window length in basepair
-            p_value_significance (float): only more significant variants are considered
+            summary_statistics (SummaryStatistics): Summary statistics to be used for clumping.
+            distance (int): Distance in base pairs to be used for clumping. Defaults to 500_000.
+            gwas_significance (float): GWAS significance threshold. Defaults to 5e-8.
 
         Returns:
-            StudyLocus: clumped summary statistics
+            StudyLocus: clumped summary statistics (without locus collection)
         """
         # Create window for locus clusters
         # - variants where the distance between subsequent variants is below the defined threshold.
@@ -177,9 +176,9 @@ def clump(
 
         return StudyLocus(
             _df=(
-                summary_stats
+                summary_statistics
                 # Dropping snps below significance - all subsequent steps are done on significant variants:
-                .pvalue_filter(p_value_significance)
+                .pvalue_filter(gwas_significance)
                 .df
                 # Clustering summary variants for efficient windowing (complexity reduction):
                 .withColumn(
@@ -188,7 +187,7 @@ def clump(
                         f.col("studyId"),
                         f.col("chromosome"),
                         f.col("position"),
-                        window_length,
+                        distance,
                     ),
                 )
                 # Within each cluster variants are ranked by significance:
@@ -213,7 +212,7 @@ def clump(
                         fml.vector_to_array(
                             f.udf(WindowBasedClumping._prune_peak, VectorUDT())(
                                 fml.array_to_vector(f.col("collectedPositions")),
-                                f.lit(window_length),
+                                f.lit(distance),
                             )
                         ),
                     ),
@@ -245,91 +244,3 @@ def clump(
             ),
             _schema=StudyLocus.get_schema(),
         )
-
-    @classmethod
-    def clump_with_locus(
-        cls: type[WindowBasedClumping],
-        summary_stats: SummaryStatistics,
-        window_length: int,
-        p_value_significance: float = 5e-8,
-        p_value_baseline: float = 0.05,
-        locus_window_length: int | None = None,
-    ) -> StudyLocus:
-        """Clump significant associations while collecting locus around them.
-
-        Args:
-            summary_stats (SummaryStatistics): Input summary statistics dataset
-            window_length (int): Window size in  bp, used for distance based clumping.
-            p_value_significance (float): GWAS significance threshold used to filter peaks. Defaults to 5e-8.
-            p_value_baseline (float): Least significant threshold. Below this, all snps are dropped. Defaults to 0.05.
-            locus_window_length (int | None): The distance for collecting locus around the semi indices. Defaults to None.
-
-        Returns:
-            StudyLocus: StudyLocus after clumping with information about the `locus`
-        """
-        # If no locus window provided, using the same value:
-        if locus_window_length is None:
-            locus_window_length = window_length
-
-        # Run distance based clumping on the summary stats:
-        clumped_dataframe = WindowBasedClumping.clump(
-            summary_stats,
-            window_length=window_length,
-            p_value_significance=p_value_significance,
-        ).df.alias("clumped")
-
-        # Get list of columns from clumped dataset for further propagation:
-        clumped_columns = clumped_dataframe.columns
-
-        # Dropping variants not meeting the baseline criteria:
-        sumstats_baseline = summary_stats.pvalue_filter(p_value_baseline).df
-
-        # Renaming columns:
-        sumstats_baseline_renamed = sumstats_baseline.selectExpr(
-            *[f"{col} as tag_{col}" for col in sumstats_baseline.columns]
-        ).alias("sumstat")
-
-        study_locus_df = (
-            sumstats_baseline_renamed
-            # Joining the two datasets together:
-            .join(
-                f.broadcast(clumped_dataframe),
-                on=[
-                    (f.col("sumstat.tag_studyId") == f.col("clumped.studyId"))
-                    & (f.col("sumstat.tag_chromosome") == f.col("clumped.chromosome"))
-                    & (
-                        f.col("sumstat.tag_position")
-                        >= (f.col("clumped.position") - locus_window_length)
-                    )
-                    & (
-                        f.col("sumstat.tag_position")
-                        <= (f.col("clumped.position") + locus_window_length)
-                    )
-                ],
-                how="right",
-            )
-            .withColumn(
-                "locus",
-                f.struct(
-                    f.col("tag_variantId").alias("variantId"),
-                    f.col("tag_beta").alias("beta"),
-                    f.col("tag_pValueMantissa").alias("pValueMantissa"),
-                    f.col("tag_pValueExponent").alias("pValueExponent"),
-                    f.col("tag_standardError").alias("standardError"),
-                ),
-            )
-            .groupby("studyLocusId")
-            .agg(
-                *[
-                    f.first(col).alias(col)
-                    for col in clumped_columns
-                    if col != "studyLocusId"
-                ],
-                f.collect_list(f.col("locus")).alias("locus"),
-            )
-        )
-
-        return StudyLocus(
-            _df=study_locus_df,
-            _schema=StudyLocus.get_schema(),
-        )
diff --git a/src/gentropy/window_based_clumping.py b/src/gentropy/window_based_clumping.py
index fcc680ef7..bce9edd37 100644
--- a/src/gentropy/window_based_clumping.py
+++ b/src/gentropy/window_based_clumping.py
@@ -1,4 +1,5 @@
 """Step to run window based clumping on summary statistics datasts."""
+
 from __future__ import annotations
 
 from gentropy.common.session import Session
@@ -13,8 +14,10 @@ def __init__(
         session: Session,
         summary_statistics_input_path: str,
         study_locus_output_path: str,
+        distance: int = 500_000,
+        collect_locus: bool = False,
+        collect_locus_distance: int = 500_000,
         inclusion_list_path: str | None = None,
-        locus_collect_distance: int | None = None,
     ) -> None:
         """Run window-based clumping step.
 
@@ -22,8 +25,10 @@ def __init__(
             session (Session): Session object.
             summary_statistics_input_path (str): Path to the harmonized summary statistics dataset.
             study_locus_output_path (str): Output path for the resulting study locus dataset.
+            distance (int): Distance, within which tagging variants are collected around the semi-index. Optional.
+            collect_locus (bool): Whether to collect locus around semi-indices. Optional.
+            collect_locus_distance (int): Distance, within which tagging variants are collected around the semi-index. Optional.
             inclusion_list_path (str | None): Path to the inclusion list (list of white-listed study identifier). Optional.
-            locus_collect_distance (int | None): Distance, within which tagging variants are collected around the semi-index. Optional.
         """
         # If inclusion list path is provided, only these studies will be read:
         if inclusion_list_path:
@@ -35,16 +40,22 @@ def __init__(
             # If no inclusion list is provided, read all summary stats in folder:
             study_ids_to_ingest = [summary_statistics_input_path]
 
-        (
-            SummaryStatistics.from_parquet(
-                session,
-                study_ids_to_ingest,
-                recursiveFileLookup=True,
-            )
-            .coalesce(4000)
-            # Applying window based clumping:
-            .window_based_clumping(locus_collect_distance=locus_collect_distance)
-            # Save resulting study locus dataset:
-            .df.write.mode(session.write_mode)
-            .parquet(study_locus_output_path)
+        ss = SummaryStatistics.from_parquet(
+            session,
+            study_ids_to_ingest,
+            recursiveFileLookup=True,
+        )
+
+        # Clumping:
+        study_locus = ss.window_based_clumping(
+            distance=distance,
         )
+
+        # Optional locus collection:
+        if collect_locus:
+            # Collecting locus around semi-indices:
+            study_locus = study_locus.annotate_locus_statistics(
+                ss, collect_locus_distance=collect_locus_distance
+            )
+
+        study_locus.df.write.mode(session.write_mode).parquet(study_locus_output_path)
diff --git a/tests/gentropy/dataset/test_study_locus.py b/tests/gentropy/dataset/test_study_locus.py
index c12597d54..1401b9dd3 100644
--- a/tests/gentropy/dataset/test_study_locus.py
+++ b/tests/gentropy/dataset/test_study_locus.py
@@ -10,6 +10,7 @@
 from gentropy.dataset.study_index import StudyIndex
 from gentropy.dataset.study_locus import CredibleInterval, StudyLocus
 from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
+from gentropy.dataset.summary_statistics import SummaryStatistics
 from pyspark.sql import Column, SparkSession
 from pyspark.sql.types import (
     ArrayType,
@@ -214,6 +215,16 @@ def test_filter_by_study_type(
     assert observed.df.count() == expected_sl_count
 
 
+def test_annotate_locus_statistics(
+    mock_study_locus: StudyLocus, mock_summary_statistics: SummaryStatistics
+) -> None:
+    """Test annotate locus statistics returns a StudyLocus."""
+    assert isinstance(
+        mock_study_locus.annotate_locus_statistics(mock_summary_statistics, 100),
+        StudyLocus,
+    )
+
+
 def test_filter_credible_set(mock_study_locus: StudyLocus) -> None:
     """Test credible interval filter."""
     assert isinstance(
diff --git a/tests/gentropy/method/test_window_based_clumping.py b/tests/gentropy/method/test_window_based_clumping.py
index 03546df9d..cd583bac2 100644
--- a/tests/gentropy/method/test_window_based_clumping.py
+++ b/tests/gentropy/method/test_window_based_clumping.py
@@ -8,12 +8,12 @@
 from gentropy.method.window_based_clumping import WindowBasedClumping
 from pyspark.ml import functions as fml
 from pyspark.ml.linalg import VectorUDT
+from pyspark.sql import SparkSession
 from pyspark.sql import functions as f
 from pyspark.sql.window import Window
 
 if TYPE_CHECKING:
     from gentropy.dataset.summary_statistics import SummaryStatistics
-    from pyspark.sql import SparkSession
 
 
 def test_window_based_clump__return_type(
@@ -21,7 +21,14 @@ def test_window_based_clump__return_type(
 ) -> None:
     """Test window-based clumping."""
     assert isinstance(
-        WindowBasedClumping.clump_with_locus(mock_summary_statistics, 250_000),
+        WindowBasedClumping.clump(mock_summary_statistics, distance=250_000),
+        StudyLocus,
+    )
+    assert isinstance(
+        WindowBasedClumping.clump(
+            mock_summary_statistics,
+            distance=250_000,
+        ),
         StudyLocus,
     )
 
@@ -44,7 +51,10 @@ def test_window_based_clump_with_locus__correctness(
 ) -> None:
     """Test window-based clumping."""
     clumped = sample_summary_statistics.window_based_clumping(
-        distance=250_000, locus_collect_distance=250_000
+        distance=250_000,
+    )
+    clumped = clumped.annotate_locus_statistics(
+        sample_summary_statistics, collect_locus_distance=250_000
     )
 
     # Asserting the presence of locus key:
@@ -57,7 +67,7 @@ def test_window_based_clump_with_locus__correctness(
     assert (clumped.df.filter(f.col("variantId") == "18_12843138_T_C").count()) == 1
 
     # Assert the number of variants in the locus:
-    assert (clumped.df.select(f.explode_outer("locus").alias("loci")).count()) == 132
+    assert (clumped.df.select(f.explode_outer("locus").alias("loci")).count()) == 218
 
 
 def test_prune_peak(spark: SparkSession) -> None:

From 650bb2ee9f159b1a296b888daa4997837b881112 Mon Sep 17 00:00:00 2001
From: xyg123 <33658607+xyg123@users.noreply.github.com>
Date: Wed, 20 Mar 2024 13:50:02 +0000
Subject: [PATCH 37/86] feat: notebook to run qc metrics each release (#541)

* feat: notebook to run qc metrics each release

* feat: added additional checks as discussed

* feat: additional checks

* fix: remove matplotlib from dependencies

* fix: address comments
---
 notebooks/Release_QC_metrics.ipynb | 1103 ++++++++++++++++++++++++++++
 1 file changed, 1103 insertions(+)
 create mode 100644 notebooks/Release_QC_metrics.ipynb

diff --git a/notebooks/Release_QC_metrics.ipynb b/notebooks/Release_QC_metrics.ipynb
new file mode 100644
index 000000000..0052a3cc8
--- /dev/null
+++ b/notebooks/Release_QC_metrics.ipynb
@@ -0,0 +1,1103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook Title: Exploratory Data Analysis for Release QC Metrics\n",
+    "\n",
+    "## Description:\n",
+    "This notebook provides an exploratory data analysis for release quality control (QC) metrics.  The notebook utilizes PySpark and pandas for data processing and visualization.\n",
+    "\n",
+    "## Notebook Workflow:\n",
+    "1. Import necessary modules and set up the release path and version.\n",
+    "2. Load and analyze the variant index data:\n",
+    "   - Count the number of unique variants.\n",
+    "3. Load and analyze the variant-to-gene (v2g) data:\n",
+    "   - Count the number of unique variants and total variant-to-gene assignments.\n",
+    "   - Count the number of v2g assignments where the score is > 0.8.\n",
+    "   - Plot a histogram/density plot for the \"score\" column.\n",
+    "4. Load and analyze the study index data for different data sources (FinnGen, GWASCat, eQTLcat):\n",
+    "   - Count the number of unique studies for each data source.\n",
+    "5. Analyze the credible sets for each datasource (Finngen, gwascat, eqtlcat):\n",
+    "   - Analyze the credible sets:\n",
+    "     - Count the number of unique credible sets and unique study IDs.\n",
+    "     - Plot a scatter plot of the credible set size vs. the top posterior probability.\n",
+    "     - Count the number of credible sets with a top SNP posterior probability > 0.9..\n",
+    "6. Analyze colocalization data:\n",
+    "   - Count the total number of colocalizations and the number with clpp > 0.8.\n",
+    "   - Calculate the average number of overlaps per credible set.\n",
+    "7. Analyze locus-to-gene (L2G) predictions:\n",
+    "   - Load the locus-to-gene predictions data.\n",
+    "   - How many Studylocus contains a \"good\" l2g prediction? (l2g_score > 0.5)\n",
+    "   - How does l2g perform based on different datasource inputs? (impossible to tell)\n",
+    "   \n",
+    "Note: This notebook assumes that the necessary data files are available in the specified release path and version.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup steps\n",
+    "##### 1. Import necessary modules and set up the release path and version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting default log level to \"WARN\".\n",
+      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/03/20 13:24:56 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"notebook for release qc metrics.\"\"\"\n",
+    "\n",
+    "import sys\n",
+    "from gentropy.common.session import Session\n",
+    "from pyspark.sql import functions as f\n",
+    "\n",
+    "sys.path.append(\"../../gentropy/src/\")\n",
+    "release_path=\"../../otg_releases\"\n",
+    "release_ver=\"2403\"\n",
+    "\n",
+    "session=Session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Load and analyze the variant index data:\n",
+    "   - Count the number of unique variants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 3:>                                                          (0 + 8) / 9]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variant index contains  5468737  unique variants.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "variant_index_path = f\"{release_path}/{release_ver}/variant_index\"\n",
+    "variant_index=session.spark.read.parquet(variant_index_path, recursiveFileLookup=True)\n",
+    "\n",
+    "# How many variants?\n",
+    "print(\"Variant index contains \", variant_index.select(f.col(\"variantId\")).distinct().count(), \" unique variants.\")\n",
+    "\n",
+    "# How many variants with MAF>=0.01 for EUR population?\n",
+    "#variant_index.filter(variant_index[\"alleleFrequencies.populationName\"] > 0.05).show(10, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### 3. Load and analyze the variant-to-gene (v2g) data:\n",
+    "   - Count the number of unique variants and total variant-to-gene assignments.\n",
+    "   - Count the number of v2g assignments where the score is > 0.8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique variants in v2g release:  5090991 , total variant to gene assignments:  105771851 , number of v2g assignments where score > 0.8:  23176515 ( 4.552 %)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of v2g_score: Mean:  0.5909395615801637 L.quart:  0.29 Median:  0.62 U.quart:  0.94\n"
+     ]
+    }
+   ],
+   "source": [
+    "#v2g_path='gs://genetics_etl_python_playground/releases/24.03/variant_to_gene'\n",
+    "v2g_path=f\"{release_path}/{release_ver}/variant_to_gene\"\n",
+    "v2g=session.spark.read.parquet(v2g_path, recursiveFileLookup=True)\n",
+    "\n",
+    "#How many variants?\n",
+    "print(\"Unique variants in v2g release: \", v2g.select(f.col(\"variantId\")).distinct().count(), \", total variant to gene assignments: \", v2g.count(), \", number of v2g assignments where score > 0.8: \", v2g.filter(v2g[\"score\"] > 0.8).count(), \"(\", round( v2g.filter(v2g[\"score\"] > 0.8).count()/v2g.select(f.col(\"variantId\")).distinct().count(), 3), \"%)\")\n",
+    "sample_size_quartiles = v2g.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of v2g_score: Mean: \", v2g.select(f.mean(v2g[\"score\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "#v2g.select().toPandas().plot.hist()\n",
+    "#v2g.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   - Plot a histogram/density plot for the \"score\" column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The histogram/density plot for “score”\n",
+    "# Out of mem error:\n",
+    "#v2g.select(f.col(\"score\")).toPandas().plot.hist(bins=10, alpha=0.5, label=\"v2g scores\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 5. Analyze the credible sets:\n",
+    "For each datasource and finemapping methods:\n",
+    "- Count the number of unique credible sets and unique study IDs.\n",
+    "- Plot a scatter plot of the credible set size vs. the top posterior probability.\n",
+    "- Count the number of credible sets with a top SNP posterior probability > 0.9.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique finngen susie CSs:  13966\n",
+      "Ingested  13966  Credible sets from 2408 finngen studies\n",
+      "Summary of finngen sample sizes: Mean:  355917.896179402 L.quart:  316100.0 Median:  392089.0 U.quart:  409683.0\n",
+      "Number of finngen studies with at least one CS:  1194\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of CS with top SNP PP > 0.9:  1898\n",
+      "Summary of finngen credible set sizes: L.quart:  4.0 Median:  14.0 U.quart:  41.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# study_index='gs://genetics_etl_python_playground/releases/24.03/study_index'\n",
+    "# Study Index. It has different files for different datasource (FinnGen, GWASCat, eQTLcat).\n",
+    "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n",
+    "\n",
+    "finngen_index_path=f\"{release_path}/{release_ver}/study_index/finngen\"\n",
+    "# finngen susie:\n",
+    "finngen_susie_path=f\"{release_path}/{release_ver}/credible_set/finngen_susie\"\n",
+    "\n",
+    "finngen_susie=session.spark.read.parquet(finngen_susie_path, recursiveFileLookup=True)\n",
+    "print(\"Number of unique finngen susie CSs: \", finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\").distinct().count())\n",
+    "\n",
+    "# FinnGen:\n",
+    "finngen_index=session.spark.read.parquet(finngen_index_path, recursiveFileLookup=True)\n",
+    "# Number of CSs, studies.\n",
+    "print(\"Ingested \", finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\").distinct().count(), \" Credible sets from\", finngen_index.select(f.col(\"studyId\")).distinct().count(), \"finngen studies\")\n",
+    "sample_size_quartiles = finngen_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of finngen sample sizes: Mean: \", finngen_index.select(f.mean(finngen_index[\"nSamples\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "# Number of unique studyids with at leas one CS.\n",
+    "print(\"Number of finngen studies with at least one CS: \", finngen_susie.select(\"studyId\").distinct().count())\n",
+    "# Number of CSs with at leas one SNP with PIP>0.9\n",
+    "print(\"Number of CS with top SNP PP > 0.9: \", finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).filter(f.col(\"top_PP\") > 0.9).count())\n",
+    "# The descriptive summary of 99% CS size and histogram/density plot\n",
+    "\n",
+    "credset_size_quartiles = finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of finngen credible set sizes: L.quart: \", credset_size_quartiles[0], \"Median: \", credset_size_quartiles[1], \"U.quart: \", credset_size_quartiles[2])\n",
+    "\n",
+    "finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", xlim=[0, 500], alpha=0.05, label=\"finngen susie CSs\", title=\"finngen susie credsets\")\n",
+    "\n",
+    "finngen_susie_fm=finngen_susie.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n",
+    "# The histogram/density plot for total sample size\n",
+    "#finngen_index.select(f.col(\"nSamples\")).toPandas().plot.hist(bins=10, alpha=0.5, label=\"FinnGen sample size\", title=\"FinnGen sample sizes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique finngen pics CSs:  19967  in  1342  studies.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of finngen_pics CS with top SNP PP > 0.9:  7239\n",
+      "Summary of finngen_pics sample sizes: L.quart:  316100.0 Median:  392089.0 U.quart:  409683.0\n",
+      "Summary of finngen_pics credset sizes: Mean:  36.04337156307908 L.quart:  1.0 Median:  7.0 U.quart:  30.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# finngen (PICs):\n",
+    "finngen_index_path=f\"{release_path}/{release_ver}/study_index/finngen\"\n",
+    "finngen_index=session.spark.read.parquet(finngen_index_path, recursiveFileLookup=True)\n",
+    "finngen_pics_path=f\"{release_path}/{release_ver}/credible_set/finngen_pics\"\n",
+    "# Number of CSs.\n",
+    "finngen_pics=session.spark.read.parquet(finngen_pics_path, recursiveFileLookup=True)\n",
+    "#gwascat_sumstats.printSchema()\n",
+    "print(\"Number of unique finngen pics CSs: \", finngen_pics.select(\"studyLocusId\").distinct().count(), \" in \", finngen_pics.select(\"studyId\").distinct().count(), \" studies.\")\n",
+    "# keep only credible sets snps\n",
+    "\n",
+    "finngen_pics_fm=finngen_pics.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n",
+    "finngen_pics_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, xlim=[0, 500], label=\"finngen PICS CS\", title=\"finngen_pics CS\")\n",
+    "print(\"Number of finngen_pics CS with top SNP PP > 0.9: \", finngen_pics_fm.filter(f.col(\"top_PP\") > 0.9).distinct().count())\n",
+    "sample_size_quartiles = finngen_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of finngen_pics sample sizes: L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "\n",
+    "sample_size_quartiles = finngen_pics_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of finngen_pics credset sizes: Mean: \", finngen_pics_fm.select(f.mean(finngen_pics_fm[\"credset_size\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are  9966  common loci between finngen susie and finngen pics\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"There are \", finngen_susie.join(finngen_pics, on=[\"studyId\", \"studyLocusId\"], how=\"inner\").count(), \" common loci between finngen susie and finngen pics\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlation coef:  0.6244595901320829\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "finngen_matching=finngen_pics_fm.withColumnRenamed(\"top_PP\", \"pics_PP\").join(finngen_susie_fm.withColumnRenamed(\"top_PP\", \"susie_PP\"), on=[\"studyId\", \"studyLocusId\"], how=\"inner\")\n",
+    "finngen_matching.select(\"pics_PP\", \"susie_PP\").toPandas().plot.scatter(x=\"susie_PP\", y=\"pics_PP\", alpha=0.05, title=\"finngen_pics vs finngen_susie CS\")\n",
+    "print(\"correlation coef: \", finngen_matching.stat.corr(\"pics_PP\", \"susie_PP\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique eQTLcat studies:  1801315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unqiue eQTLcat tissues:  76\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ingested  2055350  Credible sets from 1801315 eQTL catalog studies\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of eQTL catalog sample sizes: Mean:  336.1455965527037 L.quart:  190.0 Median:  322.0 U.quart:  483.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of eQTL catalog studies with at least one CS:  1801315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of CS with top SNP PP > 0.9:  386227\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 146:===============================================>       (32 + 5) / 37]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of eQTL credible set sizes: L.quart:  3.0 Median:  10.0 U.quart:  27.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "eqtl_index_path=f\"{release_path}/{release_ver}/study_index/eqtl_catalogue\"\n",
+    "# eQTLcat:\n",
+    "# Number of studies\n",
+    "eqtl_index=session.spark.read.parquet(eqtl_index_path, recursiveFileLookup=True)\n",
+    "print(\"Number of unique eQTLcat studies: \", eqtl_index.select(f.col(\"studyId\")).distinct().count())\n",
+    "# Number of tissues, list of tissues\n",
+    "print(\"Number of unqiue eQTLcat tissues: \", eqtl_index.select(f.col(\"tissueFromSourceId\")).distinct().count())\n",
+    "#eqtl_index.select(f.col(\"tissueFromSourceId\")).distinct().show(truncate=False)\n",
+    "\n",
+    "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n",
+    "# eqtl catalog susie:\n",
+    "eqtlcat_susie_path=f\"{release_path}/{release_ver}/credible_set/eqtl_catalogue_susie\"\n",
+    "# Number of CSs.\n",
+    "eqtlcat_susie=session.spark.read.parquet(eqtlcat_susie_path, recursiveFileLookup=True)\n",
+    "\n",
+    "\n",
+    "# Number of CSs, studies.\n",
+    "print(\"Ingested \", eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\").distinct().count(), \" Credible sets from\", eqtl_index.select(f.col(\"studyId\")).distinct().count(), \"eQTL catalog studies\")\n",
+    "sample_size_quartiles = eqtl_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of eQTL catalog sample sizes: Mean: \", eqtl_index.select(f.mean(eqtl_index[\"nSamples\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "# Number of unique studyids with at leas one CS.\n",
+    "print(\"Number of eQTL catalog studies with at least one CS: \", eqtlcat_susie.select(\"studyId\").distinct().count())\n",
+    "# Number of CSs with at leas one SNP with PIP>0.9\n",
+    "print(\"Number of CS with top SNP PP > 0.9: \", eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).filter(f.col(\"top_PP\") > 0.9).count())\n",
+    "# The descriptive summary of 99% CS size and histogram/density plot\n",
+    "\n",
+    "credset_size_quartiles = eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of eQTL credible set sizes: L.quart: \", credset_size_quartiles[0], \"Median: \", credset_size_quartiles[1], \"U.quart: \", credset_size_quartiles[2])\n",
+    "\n",
+    "# Out of mem error:\n",
+    "#eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", xlim=[0, 500], alpha=0.05, label=\"finngen susie CSs\", title=\"finngen susie credsets\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique gwascat studies:  79858\n",
+      "Number of unique SUMSTATS gwascat studies:  18435\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique gwas catalog sumstats CSs:  247782  in  9679  studies.\n",
+      "Summary of SUMSTATS gwas sample sizes: L.quart:  88329.0 Median:  357580.0 U.quart:  445573.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of SUMSTATS CS with top SNP PP > 0.9:  77721\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of SUMSTATS gwascat pics credset sizes: Mean:  39.7659733230078 L.quart:  1.0 Median:  8.0 U.quart:  35.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique gwas catalog curated CSs:  531198  in  35662  studies.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of CURATED CS with top SNP PP > 0.9:  92000\n",
+      "Summary of CURATED gwas sample sizes: L.quart:  687.0 Median:  4960.0 U.quart:  21282.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of CURATED gwascat pics credset sizes: Mean:  35.42119007174911 L.quart:  1.0 Median:  8.0 U.quart:  34.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gwascat_path=f\"{release_path}/{release_ver}/study_index/gwas_catalog\"\n",
+    "# Gwas Catalog:\n",
+    "gwascat_index=session.spark.read.parquet(gwascat_path, recursiveFileLookup=True)\n",
+    "# Number of GWAS curated studies\n",
+    "print(\"Number of unique gwascat studies: \", gwascat_index.select(f.col(\"studyId\")).distinct().count())\n",
+    "# Number of studies with full GWAS sumstats\n",
+    "print(\"Number of unique SUMSTATS gwascat studies: \", gwascat_index.filter(f.col(\"hasSumstats\") == True).select(f.col(\"studyId\")).distinct().count())\n",
+    "#gwascat_index\n",
+    "# The histogram/density plot for total sample size separately for curated studies and full GWAS\n",
+    "#gwascat_index.filter(f.col(\"hasSumstats\") == True).select(f.col(\"nSamples\")).toPandas().plot.hist(bins=25, alpha=0.5, label=\"Sumstats GWAScat sample size\", title=\"Sumstats GWAScat sample size\")\n",
+    "#gwascat_index.filter(f.col(\"hasSumstats\") == False).select(f.col(\"nSamples\")).toPandas().plot.hist(bins=25, alpha=0.5, label=\"Sumstats GWAScat sample size\", title=\"Curated GWAScat sample size\")\n",
+    "\n",
+    "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n",
+    "# gwas catalog sumstats (PICs):\n",
+    "gwascat_sumstats_path=f\"{release_path}/{release_ver}/credible_set/gwas_catalog_PICSed_summary_statistics\"\n",
+    "# Number of CSs.\n",
+    "gwascat_sumstats=session.spark.read.parquet(gwascat_sumstats_path, recursiveFileLookup=True)\n",
+    "\n",
+    "print(\"Number of unique gwas catalog sumstats CSs: \", gwascat_sumstats.select(\"studyLocusId\").distinct().count(), \" in \", gwascat_sumstats.select(\"studyId\").distinct().count(), \" studies.\")\n",
+    "\n",
+    "\n",
+    "sample_size_quartiles = gwascat_index.join(gwascat_sumstats, how=\"inner\", on=\"studyId\").stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of SUMSTATS gwas sample sizes: L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "#\n",
+    "\n",
+    "\n",
+    "gwascat_sumstats_fm=gwascat_sumstats.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n",
+    "gwascat_sumstats_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, label=\"gwascat sumstats PICS CS\", title=\"gwascat sumstats PICS CS\")\n",
+    "print(\"Number of SUMSTATS CS with top SNP PP > 0.9: \", gwascat_sumstats_fm.filter(f.col(\"top_PP\") > 0.9).distinct().count())\n",
+    "\n",
+    "sample_size_quartiles = gwascat_sumstats_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of SUMSTATS gwascat pics credset sizes: Mean: \", gwascat_sumstats_fm.select(f.mean(gwascat_sumstats_fm[\"credset_size\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "\n",
+    "\n",
+    "# gwas catalog curated (PICs):\n",
+    "gwascat_curated_path=f\"{release_path}/{release_ver}/credible_set/gwas_catalog_PICSed_curated_associations\"\n",
+    "# Number of CSs.\n",
+    "gwascat_curated=session.spark.read.parquet(gwascat_curated_path, recursiveFileLookup=True)\n",
+    "#gwascat_sumstats.printSchema()\n",
+    "print(\"Number of unique gwas catalog curated CSs: \", gwascat_curated.select(\"studyLocusId\").distinct().count(), \" in \", gwascat_curated.select(\"studyId\").distinct().count(), \" studies.\")\n",
+    "# keep only credible sets snps\n",
+    "\n",
+    "gwascat_curated_fm=gwascat_curated.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n",
+    "gwascat_curated_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, label=\"gwascat curated PICS CS\", title=\"gwascat curated PICS CS\")\n",
+    "print(\"Number of CURATED CS with top SNP PP > 0.9: \", gwascat_curated_fm.filter(f.col(\"top_PP\") > 0.9).distinct().count())\n",
+    "sample_size_quartiles = gwascat_index.join(gwascat_sumstats, how=\"anti\", on=\"studyId\").stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of CURATED gwas sample sizes: L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])\n",
+    "\n",
+    "sample_size_quartiles = gwascat_curated_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of CURATED gwascat pics credset sizes: Mean: \", gwascat_curated_fm.select(f.mean(gwascat_curated_fm[\"credset_size\"])).collect()[0][0], \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 6. Analyze colocalization data:\n",
+    "   - Count the total number of colocalizations and the number with clpp > 0.8.\n",
+    "   - Calculate the average number of overlaps per credible set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of colocalisations:  46733065  , of which,  4031821  > 0.8 clpp ( 8.6 %)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 360:============================================>          (12 + 3) / 15]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average number of overlaps per CS:  90.79159130338489\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "coloc_path=f\"{release_path}/{release_ver}/colocalisation\"\n",
+    "coloc=session.spark.read.parquet(coloc_path, recursiveFileLookup=True)\n",
+    "\n",
+    "print(\"Number of colocalisations: \", coloc.count(), \" , of which, \", coloc.filter(f.col(\"clpp\") > 0.8).count(), \" > 0.8 clpp (\", round((coloc.filter(f.col(\"clpp\") > 0.8).count()/coloc.count()), 3)*100, \"%)\")\n",
+    "Avg_overlaps=coloc.groupBy(\"leftStudyLocusId\").count().agg(f.avg(\"count\")).collect()[0][0]\n",
+    "print(\"Average number of overlaps per CS: \", Avg_overlaps)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 7. Analyze locus-to-gene (L2G) predictions:\n",
+    "   - Histogram of l2g score distribution.\n",
+    "   - Number of CS with at least one gene with L2G>=0.5\n",
+    "   - Number of CS with more than one gene with L2G>=0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A total of  10561869 l2g predictions were computed.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 8591772  UNIQUE locus to gene predictions for 607372  unique studyloci\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Where  357875  studyloci contains at least one gene with score > 0.5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Of these,  122485  studyloci contains more than one gene with score > 0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ZMkWjRo1ScnKyT44fFBRUYPv06dOKjIwscHvovLCwMJ+c82IoKgAAlEIul0txcXGKi4vT3/72N9WqVUvLli1TTEyMkpOT1a5du0Kvuemmm3To0CEdOnTIc1Vl+/btOnXqlBo0aHDRczVv3lxHjx6Vv7+/YmJiiustXRBFBQCAUuabb75RcnKy7rrrLoWHh+ubb77RiRMndNNNN2nMmDEaPHiwwsPDFR8fr4yMDK1du1ZPPfWUOnTooMaNG6tfv36aPHmycnNz9eSTT+qOO+5Qy5YtL3q+Dh06qHXr1urevbsmTJig2NhYHTlyRF988YV69OhxyddeLYoKAAAXYPOnKENCQrR69WpNnjxZ6enpqlWrliZOnKj4+HhJ0rlz5/Taa69pxIgRqlq1qu677z5Jv12F+eyzz/TUU0/p9ttvV7ly5XT33XdrypQplzyfy+XSwoULNWrUKA0cOFAnTpxQRESEbr/9dlWvXr1Y36vLGGOK9QzFKD09XaGhoUpLS1NISIjPj8/HkwHg2nbu3DmlpKQU+L4Q+Mal5rYoP7/5wjcAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAlHml+HMl1vLVnFJUAABlVvny5SVJZ86ccTjJtef8nJ6fY2/xPSoAgDLLz89PYWFhOn78uCSpYsWKcrlcDqcq3YwxOnPmjI4fP66wsDDPb1/2FkUFAFCmRURESJKnrMA3wsLCPHN7NSgqAIAyzeVyKTIyUuHh4crJyXE6zjWhfPnyV30l5TyKCgAA+u02kK9+uMJ3WEwLAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtR4tKTEyMXC5XoUdiYqKTsQAAgCUc/R6V9evXKy8vz7O9bds2dezYUb169XIwFQAAsIWjRaVatWoFtsePH6+6devqjjvucCgRAACwiTXfTJudna0PPvhAw4cPv+gvhMrKylJWVpZnOz09vaTiAQAAB1izmHb+/Pk6deqUBgwYcNExSUlJCg0N9Tyio6NLLiAAAChx1hSVd999V/Hx8YqKirromJEjRyotLc3zOHToUAkmBAAAJc2KWz8HDhzQ8uXLNXfu3EuOc7vdcrvdJZQKAAA4zYorKjNmzFB4eLi6dOnidBQAAGARx4tKfn6+ZsyYoYSEBPn7W3GBBwAAWMLxorJ8+XIdPHhQDz/8sNNRAACAZRy/hHHXXXfJGON0DAAAYCHHr6gAAABcDEUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANZyvKj89NNPevDBB1WlShUFBgaqcePG+u6775yOBQAALODv5MlPnjypuLg4tWvXTosWLVK1atW0e/duVapUyclYAADAEo4WlZdfflnR0dGaMWOGZ1/t2rUvOj4rK0tZWVme7fT09GLNBwAAnOXorZ/PP/9cLVu2VK9evRQeHq6bb75Zb7/99kXHJyUlKTQ01POIjo4uwbQAAKCkOVpU9u3bp2nTpqlevXpasmSJnnjiCT399NN6//33Lzh+5MiRSktL8zwOHTpUwokBAEBJcvTWT35+vlq2bKlx48ZJkm6++WZt27ZN06dPV0JCQqHxbrdbbre7pGMCAACHOHpFJTIyUg0aNCiw76abbtLBgwcdSgQAAGziaFGJi4vTzp07C+zbtWuXatWq5VAiAABgE0eLyrBhw7Ru3TqNGzdOe/bs0UcffaS///3vSkxMdDIWAACwhKNF5ZZbbtG8efP0j3/8Q40aNdKLL76oyZMnq1+/fk7GAgAAlnB0Ma0k3XPPPbrnnnucjgEAACzk+FfoAwAAXAxFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACs5WhRGTNmjFwuV4FH/fr1nYwEAAAs4u90gIYNG2r58uWebX9/xyMBAABLON4K/P39FRER4XQMAABgIcfXqOzevVtRUVGqU6eO+vXrp4MHD150bFZWltLT0ws8AADAtcvRonLrrbdq5syZWrx4saZNm6aUlBS1bdtWGRkZFxyflJSk0NBQzyM6OrqEEwMAgJLkMsYYp0Ocd+rUKdWqVUuTJk3SoEGDCj2flZWlrKwsz3Z6erqio6OVlpamkJAQn+d5bdkunx+zuA3rGOt0BAAALik9PV2hoaFX9PPb8TUq/y4sLEyxsbHas2fPBZ93u91yu90lnAoAADjF8TUq/+706dPau3evIiMjnY4CAAAs4GhRGTFihL788kvt379fX331lXr06CE/Pz/17dvXyVgAAMASjt76OXz4sPr27atffvlF1apVU5s2bbRu3TpVq1bNyVgAAMASjhaV2bNnO3l6AABgOavWqAAAAPw7igoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLW8Kir79u3zdQ4AAIBCvCoqN9xwg9q1a6cPPvhA586d83UmAAAASV4WlY0bN6pJkyYaPny4IiIi9Pjjj+vbb7/1dTYAAFDGeVVUmjVrptdff11HjhzRe++9p9TUVLVp00aNGjXSpEmTdOLECV/nBAAAZdBVLab19/dXz549NWfOHL388svas2ePRowYoejoaPXv31+pqam+ygkAAMqgqyoq3333nZ588klFRkZq0qRJGjFihPbu3atly5bpyJEj6tatm69yAgCAMsir3548adIkzZgxQzt37lTnzp01a9Ysde7cWeXK/dZ7ateurZkzZyomJsaXWQEAQBnjVVGZNm2aHn74YQ0YMECRkZEXHBMeHq533333qsIBAICyzauisnv37suOCQgIUEJCgjeHBwAAkOTlGpUZM2Zozpw5hfbPmTNH77///lWHAgAAkLwsKklJSapatWqh/eHh4Ro3btxVhwIAAJC8LCoHDx5U7dq1C+2vVauWDh48eNWhAAAAJC+LSnh4uL7//vtC+7ds2aIqVapcdSgAAADJy6LSt29fPf3001q5cqXy8vKUl5enFStWaOjQobr//vt9nREAAJRRXn3q58UXX9T+/fvVvn17+fv/doj8/Hz179+fNSoAAMBnvCoqAQEB+vjjj/Xiiy9qy5YtCgwMVOPGjVWrVi1f5wMAAGWYV0XlvNjYWMXGxvoqCwAAQAFeFZW8vDzNnDlTycnJOn78uPLz8ws8v2LFCp+EAwAAZZtXRWXo0KGaOXOmunTpokaNGsnlcvk6FwAAgHdFZfbs2frnP/+pzp07+zoPAACAh1cfTw4ICNANN9zg6ywAAAAFeFVUnn32Wb3++usyxvg6DwAAgIdXt37WrFmjlStXatGiRWrYsKHKly9f4Pm5c+f6JBwAACjbvCoqYWFh6tGjh6+zAAAAFOBVUZkxY4avcwAAABTi1RoVScrNzdXy5cv11ltvKSMjQ5J05MgRnT592mfhAABA2ebVFZUDBw7o7rvv1sGDB5WVlaWOHTsqODhYL7/8srKysjR9+nRf5wQAAGWQV1dUhg4dqpYtW+rkyZMKDAz07O/Ro4eSk5N9Fg4AAJRtXhWVf/3rX/rLX/6igICAAvtjYmL0008/eRVk/PjxcrlceuaZZ7x6PQAAuPZ4VVTy8/OVl5dXaP/hw4cVHBxc5OOtX79eb731lpo0aeJNHAAAcI3yqqjcddddmjx5smfb5XLp9OnTGj16dJG/Vv/06dPq16+f3n77bVWqVMmbOAAA4BrlVVGZOHGi1q5dqwYNGujcuXN64IEHPLd9Xn755SIdKzExUV26dFGHDh0uOzYrK0vp6ekFHgAA4Nrl1ad+atasqS1btmj27Nn6/vvvdfr0aQ0aNEj9+vUrsLj2cmbPnq2NGzdq/fr1VzQ+KSlJL7zwgjeRAQBAKeRVUZEkf39/Pfjgg16f+NChQxo6dKiWLVumChUqXNFrRo4cqeHDh3u209PTFR0d7XUGAABgN6+KyqxZsy75fP/+/S97jA0bNuj48eNq3ry5Z19eXp5Wr16tN954Q1lZWfLz8yvwGrfbLbfb7U1kAABQCnlVVIYOHVpgOycnR2fOnFFAQIAqVqx4RUWlffv22rp1a4F9AwcOVP369fWnP/2pUEkBAABlj1dF5eTJk4X27d69W0888YSee+65KzpGcHCwGjVqVGBfUFCQqlSpUmg/AAAom7z+XT+/V69ePY0fP77Q1RYAAABveb2Y9oIH8/fXkSNHvH79qlWrfBcGAACUel4Vlc8//7zAtjFGqampeuONNxQXF+eTYAAAAF4Vle7duxfYdrlcqlatmu68805NnDjRF7kAAAC8Kyr5+fm+zgEAAFCIzxbTAgAA+JpXV1T+/dthL2fSpEnenAIAAMC7orJp0yZt2rRJOTk5uvHGGyVJu3btkp+fX4FvmnW5XL5JCQAAyiSvikrXrl0VHBys999/X5UqVZL025fADRw4UG3bttWzzz7r05AAAKBs8mqNysSJE5WUlOQpKZJUqVIljR07lk/9AAAAn/GqqKSnp+vEiROF9p84cUIZGRlXHQoAAEDysqj06NFDAwcO1Ny5c3X48GEdPnxYn376qQYNGqSePXv6OiMAACijvFqjMn36dI0YMUIPPPCAcnJyfjuQv78GDRqkV155xacBAQBA2eVVUalYsaLefPNNvfLKK9q7d68kqW7dugoKCvJpOAAAULZd1Re+paamKjU1VfXq1VNQUJCMMb7KBQAA4F1R+eWXX9S+fXvFxsaqc+fOSk1NlSQNGjSIjyYDAACf8aqoDBs2TOXLl9fBgwdVsWJFz/4+ffpo8eLFPgsHAADKNq/WqCxdulRLlixRzZo1C+yvV6+eDhw44JNgAAAAXl1RyczMLHAl5bxff/1Vbrf7qkMBAABIXhaVtm3batasWZ5tl8ul/Px8TZgwQe3atfNZOAAAULZ5detnwoQJat++vb777jtlZ2fr+eef1w8//KBff/1Va9eu9XVGAABQRnl1RaVRo0batWuX2rRpo27duikzM1M9e/bUpk2bVLduXV9nBAAAZVSRr6jk5OTo7rvv1vTp0zVq1KjiyAQAACDJiysq5cuX1/fff18cWQAAAArw6tbPgw8+qHfffdfXWQAAAArwajFtbm6u3nvvPS1fvlwtWrQo9Dt+Jk2a5JNwAACgbCtSUdm3b59iYmK0bds2NW/eXJK0a9euAmNcLpfv0gEAgDKtSEWlXr16Sk1N1cqVKyX99pX5//3f/63q1asXSzgAAFC2FWmNyu9/O/KiRYuUmZnp00AAAADnebWY9rzfFxcAAABfKlJRcblchdagsCYFAAAUlyKtUTHGaMCAAZ5fPHju3DkNHjy40Kd+5s6d67uEAACgzCpSUUlISCiw/eCDD/o0DAAAwL8rUlGZMWNGceUAAAAo5KoW0wIAABQnigoAALAWRQUAAFiLogIAAKzlaFGZNm2amjRpopCQEIWEhKh169ZatGiRk5EAAIBFHC0qNWvW1Pjx47VhwwZ99913uvPOO9WtWzf98MMPTsYCAACWKNLHk32ta9euBbZfeuklTZs2TevWrVPDhg0Ljc/KylJWVpZnOz09vdgzAgAA51izRiUvL0+zZ89WZmamWrdufcExSUlJCg0N9Tyio6NLOCUAAChJjheVrVu36rrrrpPb7dbgwYM1b948NWjQ4IJjR44cqbS0NM/j0KFDJZwWAACUJEdv/UjSjTfeqM2bNystLU2ffPKJEhIS9OWXX16wrLjdbs/vGQIAANc+x4tKQECAbrjhBklSixYttH79er3++ut66623HE4GAACc5vitn9/Lz88vsGAWAACUXY5eURk5cqTi4+N1/fXXKyMjQx999JFWrVqlJUuWOBkLAABYwtGicvz4cfXv31+pqakKDQ1VkyZNtGTJEnXs2NHJWAAAwBKOFpV3333XydMDAADLWbdGBQAA4DyKCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYy9GikpSUpFtuuUXBwcEKDw9X9+7dtXPnTicjAQAAizhaVL788kslJiZq3bp1WrZsmXJycnTXXXcpMzPTyVgAAMAS/k6efPHixQW2Z86cqfDwcG3YsEG33367Q6kAAIAtHC0qv5eWliZJqly58gWfz8rKUlZWlmc7PT29RHIBAABnWLOYNj8/X88884zi4uLUqFGjC45JSkpSaGio5xEdHV3CKQEAQEmypqgkJiZq27Ztmj179kXHjBw5UmlpaZ7HoUOHSjAhAAAoaVbc+hkyZIgWLFig1atXq2bNmhcd53a75Xa7SzAZAABwkqNFxRijp556SvPmzdOqVatUu3ZtJ+MAAADLOFpUEhMT9dFHH+mzzz5TcHCwjh49KkkKDQ1VYGCgk9EAAIAFHF2jMm3aNKWlpekPf/iDIiMjPY+PP/7YyVgAAMASjt/6AQAAuBhrPvUDAADwexQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrWfG7fuA7ry3b5XSEIhvWMdbpCAAAS3FFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1KCoAAMBaFBUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1nK0qKxevVpdu3ZVVFSUXC6X5s+f72QcAABgGUeLSmZmppo2baqpU6c6GQMAAFjK38mTx8fHKz4+3skIAADAYo4WlaLKyspSVlaWZzs9Pd3BNAAAoLiVqsW0SUlJCg0N9Tyio6OdjgQAAIpRqSoqI0eOVFpamudx6NAhpyMBAIBiVKpu/bjdbrndbqdjAACAElKqrqgAAICyxdErKqdPn9aePXs82ykpKdq8ebMqV66s66+/3sFkAADABo4Wle+++07t2rXzbA8fPlySlJCQoJkzZzqUCgAA2MLRovKHP/xBxhgnIwAAAIuxRgUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtSgqAADAWhQVAABgLYoKAACwFkUFAABYi6ICAACsRVEBAADWoqgAAABrUVQAAIC1/J0OALy2bJfTEYpsWMdYpyMAQJnAFRUAAGAtigoAALAWRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2+mRYAgBLCN3EXHUUF8EJp/MumNHL6L0gAzuPWDwAAsBZXVADAh0rj1bbSeOWqNM4zvGNFUZk6dapeeeUVHT16VE2bNtWUKVPUqlUrp2MBcBg/jEoG8wybOX7r5+OPP9bw4cM1evRobdy4UU2bNlWnTp10/Phxp6MBAACHOV5UJk2apEcffVQDBw5UgwYNNH36dFWsWFHvvfee09EAAIDDHL31k52drQ0bNmjkyJGefeXKlVOHDh309ddfFxqflZWlrKwsz3ZaWpokKT09vVjyncs8XSzHBQCgtCiOn7Hnj2mMuexYR4vKzz//rLy8PFWvXr3A/urVq+vHH38sND4pKUkvvPBCof3R0dHFlhEAgLLsP4vx2BkZGQoNDb3kGCsW016pkSNHavjw4Z7t/Px8/frrr6pSpYpcLpdPz5Wenq7o6GgdOnRIISEhPj02/g/zXDKY55LBPJcc5rpkFNc8G2OUkZGhqKioy451tKhUrVpVfn5+OnbsWIH9x44dU0RERKHxbrdbbre7wL6wsLDijKiQkBD+IygBzHPJYJ5LBvNccpjrklEc83y5KynnObqYNiAgQC1atFBycrJnX35+vpKTk9W6dWsHkwEAABs4futn+PDhSkhIUMuWLdWqVStNnjxZmZmZGjhwoNPRAACAwxwvKn369NGJEyf0t7/9TUePHlWzZs20ePHiQgtsS5rb7dbo0aML3WqCbzHPJYN5LhnMc8lhrkuGDfPsMlfy2SAAAAAHOP6FbwAAABdDUQEAANaiqAAAAGtRVAAAgLUoKgAAwFpluqhMnTpVMTExqlChgm699VZ9++23lxw/Z84c1a9fXxUqVFDjxo21cOHCEkpauhVlnt9++221bdtWlSpVUqVKldShQ4fL/nvBb4r65/m82bNny+VyqXv37sUb8BpR1Hk+deqUEhMTFRkZKbfbrdjYWP7uuAJFnefJkyfrxhtvVGBgoKKjozVs2DCdO3euhNKWTqtXr1bXrl0VFRUll8ul+fPnX/Y1q1atUvPmzeV2u3XDDTdo5syZxZ5TpoyaPXu2CQgIMO+995754YcfzKOPPmrCwsLMsWPHLjh+7dq1xs/Pz0yYMMFs377d/OUvfzHly5c3W7duLeHkpUtR5/mBBx4wU6dONZs2bTI7duwwAwYMMKGhoebw4cMlnLx0Keo8n5eSkmJq1Khh2rZta7p161YyYUuxos5zVlaWadmypencubNZs2aNSUlJMatWrTKbN28u4eSlS1Hn+cMPPzRut9t8+OGHJiUlxSxZssRERkaaYcOGlXDy0mXhwoVm1KhRZu7cuUaSmTdv3iXH79u3z1SsWNEMHz7cbN++3UyZMsX4+fmZxYsXF2vOMltUWrVqZRITEz3beXl5JioqyiQlJV1wfO/evU2XLl0K7Lv11lvN448/Xqw5S7uizvPv5ebmmuDgYPP+++8XV8RrgjfznJuba2677TbzzjvvmISEBIrKFSjqPE+bNs3UqVPHZGdnl1TEa0JR5zkxMdHceeedBfYNHz7cxMXFFWvOa8mVFJXnn3/eNGzYsMC+Pn36mE6dOhVjMmPK5K2f7OxsbdiwQR06dPDsK1eunDp06KCvv/76gq/5+uuvC4yXpE6dOl10PLyb5987c+aMcnJyVLly5eKKWep5O8//9V//pfDwcA0aNKgkYpZ63szz559/rtatWysxMVHVq1dXo0aNNG7cOOXl5ZVU7FLHm3m+7bbbtGHDBs/toX379mnhwoXq3LlziWQuK5z6Oej4V+g74eeff1ZeXl6hr+mvXr26fvzxxwu+5ujRoxccf/To0WLLWdp5M8+/96c//UlRUVGF/uPA//FmntesWaN3331XmzdvLoGE1wZv5nnfvn1asWKF+vXrp4ULF2rPnj168sknlZOTo9GjR5dE7FLHm3l+4IEH9PPPP6tNmzYyxig3N1eDBw/Wf/7nf5ZE5DLjYj8H09PTdfbsWQUGBhbLecvkFRWUDuPHj9fs2bM1b948VahQwek414yMjAw99NBDevvtt1W1alWn41zT8vPzFR4err///e9q0aKF+vTpo1GjRmn69OlOR7umrFq1SuPGjdObb76pjRs3au7cufriiy/04osvOh0NPlAmr6hUrVpVfn5+OnbsWIH9x44dU0RExAVfExERUaTx8G6ez3v11Vc1fvx4LV++XE2aNCnOmKVeUed579692r9/v7p27erZl5+fL0ny9/fXzp07Vbdu3eINXQp58+c5MjJS5cuXl5+fn2ffTTfdpKNHjyo7O1sBAQHFmrk08mae//rXv+qhhx7SI488Iklq3LixMjMz9dhjj2nUqFEqV47/J/eFi/0cDAkJKbarKVIZvaISEBCgFi1aKDk52bMvPz9fycnJat269QVf07p16wLjJWnZsmUXHQ/v5lmSJkyYoBdffFGLFy9Wy5YtSyJqqVbUea5fv762bt2qzZs3ex733nuv2rVrp82bNys6Orok45ca3vx5jouL0549ezxFUJJ27dqlyMhISspFeDPPZ86cKVRGzpdDw+/d9RnHfg4W61Jdi82ePdu43W4zc+ZMs337dvPYY4+ZsLAwc/ToUWOMMQ899JD585//7Bm/du1a4+/vb1599VWzY8cOM3r0aD6efAWKOs/jx483AQEB5pNPPjGpqameR0ZGhlNvoVQo6jz/Hp/6uTJFneeDBw+a4OBgM2TIELNz506zYMECEx4ebsaOHevUWygVijrPo0ePNsHBweYf//iH2bdvn1m6dKmpW7eu6d27t1NvoVTIyMgwmzZtMps2bTKSzKRJk8ymTZvMgQMHjDHG/PnPfzYPPfSQZ/z5jyc/99xzZseOHWbq1Kl8PLm4TZkyxVx//fUmICDAtGrVyqxbt87z3B133GESEhIKjP/nP/9pYmNjTUBAgGnYsKH54osvSjhx6VSUea5Vq5aRVOgxevTokg9eyhT1z/O/o6hcuaLO81dffWVuvfVW43a7TZ06dcxLL71kcnNzSzh16VOUec7JyTFjxowxdevWNRUqVDDR0dHmySefNCdPniz54KXIypUrL/j37fm5TUhIMHfccUeh1zRr1swEBASYOnXqmBkzZhR7TpcxXBcDAAB2KpNrVAAAQOlAUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa/0/d9/p6Gd4cAgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l2g_path=f\"{release_path}/{release_ver}/locus_to_gene_predictions\"\n",
+    "l2g=session.spark.read.parquet(l2g_path, recursiveFileLookup=True)\n",
+    "l2g.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores\")\n",
+    "print(\"A total of \", l2g.select(\"studyLocusId\", \"geneId\").count(), \"l2g predictions were computed.\")\n",
+    "print(\"There are\", l2g.select(\"studyLocusId\", \"geneId\").distinct().count(), \" UNIQUE locus to gene predictions for\", l2g.select(\"studyLocusId\").distinct().count(), \" unique studyloci\")\n",
+    "print(\"Where \", l2g.filter(f.col(\"score\") > 0.5).select(\"studyLocusId\").distinct().count(), \" studyloci contains at least one gene with score > 0.5\")\n",
+    "print(\"Of these, \", l2g.filter(f.col(\"score\") > 0.5).groupBy(\"studyLocusId\").count().filter(f.col(\"count\") > 1).count(), \" studyloci contains more than one gene with score > 0.5\")\n",
+    "\n",
+    "# There are duplicated l2g predictions studyLocusId with finngen pics and susie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are definitely duplicated studylocusIDs in the l2g predictions, and still around 20% of studylocus contains more than 1 gene with score>0.5. It is not possible to separate out these predictions based on whether they came from pics or susie,  as the l2g outputs only contains the studylocusID (duplicated between pics and susie). \n",
+    "\n",
+    "If 20% is too high then it implies finngen pics and susie l2g are pointing (confidently) at different genes for the same studylocus.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 7. Analyze locus-to-gene (L2G) predictions:\n",
+    "   - Consider only the top gene assignments from l2g:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'l2g scores (top gene assignment)'}, ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyspark.sql import Window\n",
+    "\n",
+    "window = Window.partitionBy(l2g['studyLocusId']).orderBy(l2g['score'].desc())\n",
+    "l2g = l2g.withColumn('rn', f.row_number().over(window))\n",
+    "l2g_max_scores = l2g.filter(l2g['rn'] == 1).drop('rn')\n",
+    "l2g_max_scores.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores (top gene assignment)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 7. Analyze locus-to-gene (L2G) predictions:\n",
+    "   - Consider only the top gene assignments from l2g:\n",
+    "   - How does this look when separated by datasource?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of finngen PICS l2g scores: mean: 0.5615633023783144 L.quart:  0.2371208220720291 Median:  0.6145811676979065 U.quart:  0.8626531362533569\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l2g_finngen_pics=l2g_max_scores.join(finngen_pics.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n",
+    "l2g_finngen_pics.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores (top gene assignment), finngen_pics\")\n",
+    "\n",
+    "sample_size_quartiles = l2g_finngen_pics.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of finngen PICS l2g scores: mean:\",  l2g_finngen_pics.select(f.mean(l2g_finngen_pics[\"score\"])).collect()[0][0],  \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of l2g_finngen_susie: mean: 0.5593829938336615 L.quart:  0.2241434007883072 Median:  0.6244931817054749 U.quart:  0.8736903071403503\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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azwm3bt1S27ZtdeTIEa1Zs0bVq1fP0OfO/4M7H/sXL17M0hkQe/Py8lLhwoUzXHEoKUNbQECADMNQhQoVcnS8M+NBz6H0s2NXrlyxWSf9LN7dAgICNGzYMA0bNkxHjx5VnTp1NHnyZH333Xf37Z+Trz94OD4yQ64ICgqSdHveyZ3c3NwkZXwR8fX1VZ06dTRnzhybZX/++adWr16t1q1bZ9jH559/bnN/+vTpkvTAK0nq1aunkiVL6uuvv1ZKSoq1fd68eRneUDp16qT//e9/+vrrrzNs5+bNmxk+wnsYJycndezYUf/5z3/u+dMP58+ff+j6ku1fuvHx8Zo9e7ZNv8uXL2f4a7hOnTqSZD3lf/HiRZvljo6O1kBzv48FsjJ2eeVRx/R+ihQpYv0I8l527typGjVqWD/Ce1Spqal6+eWXtXXrVi1cuND6/LlbcHCwnJ2dM3zVwmeffZYjdeQVJycnhYSEaOnSpTpz5oy1/dixY1qxYoVN3w4dOsjJyUnjxo3L8Lg2DCPDYzknZOY5VK5cOTk5OenXX3+16ffFF1/Y3L9x40aGKwADAgJUrFixB34El9OvP3g4zhAhV1SsWFE1a9bUmjVrbL67w9XVVdWrV9ePP/6oKlWqqESJEqpZs6Zq1qypjz76SK1atVJQUJD69Oljvezew8Pjnr+rFB0drRdeeEEtW7bU1q1b9d1336lLly568skn71uXxWLR2LFj9eabb6p58+bq1KmTTp48qYiICAUEBNj8JdatWzctWLBA/fv31/r169WwYUOlpqbq0KFDWrBggVatWqV69eplaVwmTpyo9evXq379+nrttddUvXp1Xbp0Sbt27dKaNWt06dKl+67bokUL61md119/XdeuXdPXX38tLy8vnT171tpvzpw5+uKLL/Tiiy8qICBAV69e1ddffy13d3drsOzbt68uXbqk5s2bq0yZMjp16pSmT5+uOnXq3PfS66yMXV56lDG9n8DAQP34448aOnSonn76aRUtWlRt27aVJCUnJ2vjxo0ZJjZv2LBBzZo105gxY7L8O2DDhg3TTz/9pLZt2+rSpUsZzhqkTxL29vbWoEGDNHnyZOtjf8+ePVqxYoVKlSpVoM4kjB07VqtXr1bDhg31xhtvKDU1VZ999plq1qypqKgoa7+AgAC9//77GjlypPVrHooVK6bo6GgtWbJE/fr101tvvZWjtWXmOeTh4aGXXnpJ06dPl4ODgwICArRs2bIMc3uOHDmi4OBgderUSdWrV5ezs7OWLFmiuLg4mwsJ7pYbrz94iDy+qg0mMmXKFKNo0aIZLhXfsmWLERgYaFgslgyX965Zs8Zo2LCh4erqari7uxtt27Y1Dhw4YLN++iXABw4cMP7xj38YxYoVM4oXL24MHDjQuHnzZqZqmzZtmlGuXDnDxcXFeOaZZ4zNmzcbgYGBRsuWLW36JSUlGZMmTTJq1KhhuLi4GMWLFzcCAwONcePGGfHx8dZ+koywsLAM+ylXrlyGS6Hj4uKMsLAww9/f3yhUqJDh4+NjBAcHG1999dVD6/7pp5+M2rVrG4ULFzbKly9vTJo0yZg1a5YhyYiOjjYMwzB27dplvPLKK0bZsmUNFxcXw8vLy3j++eeNHTt2WLezaNEio0WLFoaXl5dhsViMsmXLGq+//rpx9uxZa5/7XVacmbFLX3fhwoU26959SbJh3P/S73Llyhlt2rTJ0H6vsc7MmGalpmvXrhldunQxPD09DUk2l+CvWLHCkGQcPXrUZjs///yzIcmYOXNmhprvdQx3Pu7TLxe/3+1OKSkpxrvvvmv4+PgYrq6uRvPmzY2DBw8aJUuWNPr37//Qfd8pq5fdnz9/3qbf7NmzbR576etm9rmwdu1a46mnnjIsFosREBBgfPPNN8awYcOMwoULZ1j/P//5j/Hss88aRYoUMYoUKWJUrVrVCAsLMw4fPvzQ4+nRo8cDv0bhbpl5DhnG7a8R6dixo+Hm5mYUL17ceP31140///zT5vF04cIFIywszKhatapRpEgRw8PDw6hfv76xYMECm23dfdm9YWT+9Qc5w8EwsjHLDciE+Ph4VaxYUeHh4erTp0+ObXfs2LEaN26czp8/n2OTWtPS0lS6dGl16NDhnqeocX9mG7v27dvLwcFBS5YssWkfPny4vv/+ex07dkwuLi55WtOVK1dUvHhxvf/++xo1alSe7juntW/fXvv379fRo0ftXQpMhjlEyDUeHh4aPny4PvroI+vVQPnBrVu3MswPmDt3ri5dusQPzj6E2cfu4MGDWrZsmcaPH59h2fr16/Xuu+/mehi6efNmhrZPPvlEUsH7weS7j+Xo0aP65ZdfCtxx4PHAGSIUOI96hmjDhg0aMmSIXnrpJZUsWVK7du3St99+q2rVqmnnzp38wOIDMHb2FxERoYiICLVu3VpFixbVpk2b9P3336tFixZatWqVJD30O25cXV1zbEL4o/D19VXPnj1VsWJFnTp1SjNmzFBiYqJ2796d4XvGcsr58+fveWl8OovFYvPlsDAPJlXDdMqXLy9/f39NmzZNly5dUokSJdS9e3dNnDiRN/SHYOzsr3bt2nJ2dlZ4eLgSEhKsE63ff/99ax9fX98HbqNHjx42Pz5qLy1bttT333+v2NhYubi4KCgoSB9++GGuhSFJevrpp+97abx0+0sZ7/7BVpgDZ4gA4DGzZs2aBy738/O75/ccmcHmzZvv+bFjuuLFiyswMDAPK0J+QSACAACmx6RqAABgeswhyoS0tDSdOXNGxYoVK1BffAYAgJkZhqGrV6/Kz89Pjo4PPgdEIMqEM2fO8EN6AAAUUKdPn1aZMmUe2IdAlAnpv9Z++vRpubu727kaAACQGQkJCfL397e+jz8IgSgT0j8mc3d3JxABAFDAZGa6C5OqAQCA6RGIAACA6RGIAACA6TGHKIcYhqGUlJQH/kYOssbJyUnOzs581QEAINcRiHJAUlKSzp49qxs3bti7lMeOm5ubfH19+Z0sAECuIhA9orS0NEVHR8vJyUl+fn6yWCyc0cgBhmEoKSlJ58+fV3R0tCpXrvzQL9UCACC7CESPKCkpSWlpafL395ebm5u9y3msuLq6qlChQjp16pSSkpJUuHBhe5cEAHhM8Sd3DuHsRe5gXAEAeYF3GwAAYHoEIgAAYHrMIcpFUyOP5On+hjxXJU/3BwDA44IzRAAAwPTsGohmzJih2rVrW380NSgoSCtWrLAuv3XrlsLCwlSyZEkVLVpUHTt2VFxcnM02YmJi1KZNG7m5ucnLy0tvv/22UlJSbPps2LBBdevWlYuLiypVqqSIiIi8ODz8/5KSkuxdAgAAD2TXQFSmTBlNnDhRO3fu1I4dO9S8eXO1a9dO+/fvlyQNGTJEP//8sxYuXKiNGzfqzJkz6tChg3X91NRUtWnTRklJSdqyZYvmzJmjiIgIjR492tonOjpabdq0UbNmzRQVFaXBgwerb9++WrVqVZ4fb36zaNEi1apVS66uripZsqRCQkJ0/fp1SdKsWbNUo0YNubi4yNfXVwMHDrSuFxMTo3bt2qlo0aJyd3dXp06dbILq2LFjVadOHX3zzTeqUKGC9XL5K1euqG/fvipdurTc3d3VvHlz7dmzJ28PGgCAe7DrHKK2bdva3P/ggw80Y8YMbdu2TWXKlNG3336r+fPnq3nz5pKk2bNnq1q1atq2bZsaNGig1atX68CBA1qzZo28vb1Vp04djR8/XiNGjNDYsWNlsVg0c+ZMVahQQZMnT5YkVatWTZs2bdLUqVMVGhqa58ecX5w9e1avvPKKwsPD9eKLL+rq1av67bffZBiGZsyYoaFDh2rixIlq1aqV4uPjtXnzZkm3v4gyPQxt3LhRKSkpCgsL08svv6wNGzZYt3/s2DH95z//0eLFi+Xk5CRJeumll+Tq6qoVK1bIw8NDX375pYKDg3XkyBGVKFHCHsMAAI+lvJ7DmhPsPQ8230yqTk1N1cKFC3X9+nUFBQVp586dSk5OVkhIiLVP1apVVbZsWW3dulUNGjTQ1q1bVatWLXl7e1v7hIaG6o033tD+/fv11FNPaevWrTbbSO8zePDg+9aSmJioxMRE6/2EhIScO9B84uzZs0pJSVGHDh1Urlw5SVKtWrUkSe+//76GDRumQYMGWfs//fTTkqS1a9dq3759io6Olr+/vyRp7ty5qlGjhv744w9rv6SkJM2dO1elS5eWJG3atEnbt2/XuXPn5OLiIkn6+OOPtXTpUi1atEj9+vXLmwMHAOAe7D6pet++fSpatKhcXFzUv39/LVmyRNWrV1dsbKwsFos8PT1t+nt7eys2NlaSFBsbaxOG0penL3tQn4SEBN28efOeNU2YMEEeHh7WW/ob/+PkySefVHBwsGrVqqWXXnpJX3/9tS5fvqxz587pzJkzCg4Ovud6Bw8elL+/v82YVK9eXZ6enjp48KC1rVy5ctYwJEl79uzRtWvXrPPB0m/R0dE6fvx47h0oAACZYPczRE888YSioqIUHx+vRYsWqUePHtq4caNdaxo5cqSGDh1qvZ+QkPDYhSInJydFRkZqy5YtWr16taZPn65Ro0Zp7dq1ObL9IkWK2Ny/du2afH19bT5WS3d36AUAIK/ZPRBZLBZVqlRJkhQYGKg//vhDn376qV5++WUlJSXpypUrNm+YcXFx8vHxkST5+Pho+/btNttLn9x7Z5+7r0yLi4uTu7u7XF1d71mTi4uL9WOdx5mDg4MaNmyohg0bavTo0SpXrpwiIyNVvnx5rV27Vs2aNcuwTrVq1XT69GmdPn3aGhIPHDigK1euqHr16vfdV926dRUbGytnZ2eVL18+tw4JAIBssftHZndLS0tTYmKiAgMDVahQIZszFocPH1ZMTIyCgoIkSUFBQdq3b5/OnTtn7RMZGSl3d3frm3NQUFCGsx6RkZHWbZjV77//rg8//FA7duxQTEyMFi9erPPnz6tatWoaO3asJk+erGnTpuno0aPatWuXpk+fLkkKCQlRrVq11LVrV+3atUvbt29X9+7d1aRJE9WrV++++wsJCVFQUJDat2+v1atX6+TJk9qyZYtGjRqlHTt25NVhAwBwT3Y9QzRy5Ei1atVKZcuW1dWrVzV//nxt2LBBq1atkoeHh/r06aOhQ4eqRIkScnd315tvvqmgoCA1aNBAktSiRQtVr15d3bp1U3h4uGJjY/XOO+8oLCzMeoanf//++uyzzzR8+HD17t1b69at04IFC7R8+fJcPz57z5h/EHd3d/3666/65JNPlJCQoHLlymny5Mlq1aqVpNvfATV16lS99dZbKlWqlP7xj39Iun1W6b///a/efPNNNW7cWI6OjmrZsqU1MN2Pg4ODfvnlF40aNUq9evXS+fPn5ePjo8aNG2eY4wUAQF5zMAzDsNfO+/Tpo7Vr1+rs2bPy8PBQ7dq1NWLECD333HOSbr8pDxs2TN9//70SExMVGhqqL774wvpxmCSdOnVKb7zxhjZs2KAiRYqoR48emjhxopyd/y/rbdiwQUOGDNGBAwdUpkwZvfvuu+rZs2em60xISJCHh4fi4+Pl7u5us+zWrVuKjo62+b4d5BzGFwCyjsvub3vQ+/fd7BqICgoCkf0wvgCQdQSi27ISiPLdHCIAAIC8RiACAACmRyACAACmRyDKIUzFyh2MKwAgLxCIHlGhQoUkSTdu3LBzJY+n9HFNH2cAAHKD3b+puqBzcnKSp6en9csh3dzc5ODgYOeqCj7DMHTjxg2dO3dOnp6ecnJysndJAIDHGIEoB6R/L9Kd35iNnOHp6WnzvVMAAOQGAlEOcHBwkK+vr7y8vJScnGzvch4bhQoV4swQACBPEIhykJOTE2/gAAAUQEyqBgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApkcgAgAApmfXQDRhwgQ9/fTTKlasmLy8vNS+fXsdPnzYpk/Tpk3l4OBgc+vfv79Nn5iYGLVp00Zubm7y8vLS22+/rZSUFJs+GzZsUN26deXi4qJKlSopIiIitw8PAAAUEHYNRBs3blRYWJi2bdumyMhIJScnq0WLFrp+/bpNv9dee01nz5613sLDw63LUlNT1aZNGyUlJWnLli2aM2eOIiIiNHr0aGuf6OhotWnTRs2aNVNUVJQGDx6svn37atWqVXl2rAAAIP9ytufOV65caXM/IiJCXl5e2rlzpxo3bmxtd3Nzk4+Pzz23sXr1ah04cEBr1qyRt7e36tSpo/Hjx2vEiBEaO3asLBaLZs6cqQoVKmjy5MmSpGrVqmnTpk2aOnWqQkNDc+8AAQBAgZCv5hDFx8dLkkqUKGHTPm/ePJUqVUo1a9bUyJEjdePGDeuyrVu3qlatWvL29ra2hYaGKiEhQfv377f2CQkJsdlmaGiotm7des86EhMTlZCQYHMDAACPL7ueIbpTWlqaBg8erIYNG6pmzZrW9i5duqhcuXLy8/PT3r17NWLECB0+fFiLFy+WJMXGxtqEIUnW+7GxsQ/sk5CQoJs3b8rV1dVm2YQJEzRu3LgcP0YAAJA/5ZtAFBYWpj///FObNm2yae/Xr5/137Vq1ZKvr6+Cg4N1/PhxBQQE5EotI0eO1NChQ633ExIS5O/vnyv7AgAA9pcvPjIbOHCgli1bpvXr16tMmTIP7Fu/fn1J0rFjxyRJPj4+iouLs+mTfj993tH9+ri7u2c4OyRJLi4ucnd3t7kBAIDHl10DkWEYGjhwoJYsWaJ169apQoUKD10nKipKkuTr6ytJCgoK0r59+3Tu3Dlrn8jISLm7u6t69erWPmvXrrXZTmRkpIKCgnLoSAAAQEFm10AUFham7777TvPnz1exYsUUGxur2NhY3bx5U5J0/PhxjR8/Xjt37tTJkyf1008/qXv37mrcuLFq164tSWrRooWqV6+ubt26ac+ePVq1apXeeecdhYWFycXFRZLUv39/nThxQsOHD9ehQ4f0xRdfaMGCBRoyZIjdjh0AAOQfdg1EM2bMUHx8vJo2bSpfX1/r7ccff5QkWSwWrVmzRi1atFDVqlU1bNgwdezYUT///LN1G05OTlq2bJmcnJwUFBSkV199Vd27d9d7771n7VOhQgUtX75ckZGRevLJJzV58mR98803XHIPAAAkSQ6GYRj2LiK/S0hIkIeHh+Lj45lPBADI96ZGHrF3CVk25LkqOb7NrLx/54tJ1QAAAPZEIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZn10A0YcIEPf300ypWrJi8vLzUvn17HT582KbPrVu3FBYWppIlS6po0aLq2LGj4uLibPrExMSoTZs2cnNzk5eXl95++22lpKTY9NmwYYPq1q0rFxcXVapUSREREbl9eAAAoICwayDauHGjwsLCtG3bNkVGRio5OVktWrTQ9evXrX2GDBmin3/+WQsXLtTGjRt15swZdejQwbo8NTVVbdq0UVJSkrZs2aI5c+YoIiJCo0ePtvaJjo5WmzZt1KxZM0VFRWnw4MHq27evVq1alafHCwAA8icHwzAMexeR7vz58/Ly8tLGjRvVuHFjxcfHq3Tp0po/f77+8Y9/SJIOHTqkatWqaevWrWrQoIFWrFih559/XmfOnJG3t7ckaebMmRoxYoTOnz8vi8WiESNGaPny5frzzz+t++rcubOuXLmilStXPrSuhIQEeXh4KD4+Xu7u7rlz8AAA5JCpkUfsXUKWDXmuSo5vMyvv3/lqDlF8fLwkqUSJEpKknTt3Kjk5WSEhIdY+VatWVdmyZbV161ZJ0tatW1WrVi1rGJKk0NBQJSQkaP/+/dY+d24jvU/6Nu6WmJiohIQEmxsAAHh85ZtAlJaWpsGDB6thw4aqWbOmJCk2NlYWi0Wenp42fb29vRUbG2vtc2cYSl+evuxBfRISEnTz5s0MtUyYMEEeHh7Wm7+/f44cIwAAyJ/yTSAKCwvTn3/+qR9++MHepWjkyJGKj4+33k6fPm3vkgAAQC5ytncBkjRw4EAtW7ZMv/76q8qUKWNt9/HxUVJSkq5cuWJzliguLk4+Pj7WPtu3b7fZXvpVaHf2ufvKtLi4OLm7u8vV1TVDPS4uLnJxccmRYwMAAPmfXc8QGYahgQMHasmSJVq3bp0qVKhgszwwMFCFChXS2rVrrW2HDx9WTEyMgoKCJElBQUHat2+fzp07Z+0TGRkpd3d3Va9e3drnzm2k90nfBgAAMDe7niEKCwvT/Pnz9d///lfFihWzzvnx8PCQq6urPDw81KdPHw0dOlQlSpSQu7u73nzzTQUFBalBgwaSpBYtWqh69erq1q2bwsPDFRsbq3feeUdhYWHWszz9+/fXZ599puHDh6t3795at26dFixYoOXLl9vt2AEAQP5h1zNEM2bMUHx8vJo2bSpfX1/r7ccff7T2mTp1qp5//nl17NhRjRs3lo+PjxYvXmxd7uTkpGXLlsnJyUlBQUF69dVX1b17d7333nvWPhUqVNDy5csVGRmpJ598UpMnT9Y333yj0NDQPD1eAACQP+Wr7yHKr/geIgBAQcL3EN1WYL+HCAAAwB4IRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPSyFYhOnDiR03UAAADYTbYCUaVKldSsWTN99913unXrVk7XBAAAkKeyFYh27dql2rVra+jQofLx8dHrr7+u7du353RtAAAAeSJbgahOnTr69NNPdebMGc2aNUtnz57Vs88+q5o1a2rKlCk6f/58TtcJAACQax5pUrWzs7M6dOighQsXatKkSTp27Jjeeust+fv7q3v37jp79mxO1QkAAJBrHikQ7dixQwMGDJCvr6+mTJmit956S8ePH1dkZKTOnDmjdu3a5VSdAAAAucY5OytNmTJFs2fP1uHDh9W6dWvNnTtXrVu3lqPj7XxVoUIFRUREqHz58jlZKwAAQK7IViCaMWOGevfurZ49e8rX1/eefby8vPTtt98+UnEAAAB5IVuB6OjRow/tY7FY1KNHj+xsHgAAIE9law7R7NmztXDhwgztCxcu1Jw5cx65KAAAgLyUrTNEEyZM0Jdffpmh3cvLS/369ePMUBZNjTxi7xKybMhzVexdAgAAOSZbZ4hiYmJUoUKFDO3lypVTTEzMIxcFAACQl7IViLy8vLR3794M7Xv27FHJkiUfuSgAAIC8lK1A9Morr+j//b//p/Xr1ys1NVWpqalat26dBg0apM6dO+d0jQAAALkqW3OIxo8fr5MnTyo4OFjOzrc3kZaWpu7du+vDDz/M0QIBAAByW7YCkcVi0Y8//qjx48drz549cnV1Va1atVSuXLmcrg8AACDXZSsQpatSpYqqVOFqIwAAULBlKxClpqYqIiJCa9eu1blz55SWlmazfN26dTlSHAAAQF7IViAaNGiQIiIi1KZNG9WsWVMODg45XRcAAECeyVYg+uGHH7RgwQK1bt06p+sBAADIc9m67N5isahSpUo5XQsAAIBdZCsQDRs2TJ9++qkMw8jpegAAAPJctj4y27Rpk9avX68VK1aoRo0aKlSokM3yxYsX50hxAAAAeSFbgcjT01MvvvhiTtcCAABgF9kKRLNnz87pOgAAAOwmW3OIJCklJUVr1qzRl19+qatXr0qSzpw5o2vXruVYcQAAAHkhW2eITp06pZYtWyomJkaJiYl67rnnVKxYMU2aNEmJiYmaOXNmTtcJAACQa7J1hmjQoEGqV6+eLl++LFdXV2v7iy++qLVr1+ZYcQAAAHkhW2eIfvvtN23ZskUWi8WmvXz58vrf//6XI4UBAADklWwForS0NKWmpmZo/+uvv1SsWLFHLgoAgPxiauQRe5eAPJCtj8xatGihTz75xHrfwcFB165d05gxY/g5DwAAUOBk6wzR5MmTFRoaqurVq+vWrVvq0qWLjh49qlKlSun777/P6RoBAAByVbYCUZkyZbRnzx798MMP2rt3r65du6Y+ffqoa9euNpOsAQAACoJsBSJJcnZ21quvvpqTtQAAANhFtgLR3LlzH7i8e/fu2SoGAADAHrIViAYNGmRzPzk5WTdu3JDFYpGbmxuBCAAAFCjZusrs8uXLNrdr167p8OHDevbZZ5lUDQAACpxs/5bZ3SpXrqyJEydmOHsEAACQ3+VYIJJuT7Q+c+ZMpvv/+uuvatu2rfz8/OTg4KClS5faLO/Zs6ccHBxsbi1btrTpc+nSJXXt2lXu7u7y9PRUnz59MvzA7N69e9WoUSMVLlxY/v7+Cg8Pz/YxAgCAx0+25hD99NNPNvcNw9DZs2f12WefqWHDhpnezvXr1/Xkk0+qd+/e6tChwz37tGzZUrNnz7bed3FxsVnetWtXnT17VpGRkUpOTlavXr3Ur18/zZ8/X5KUkJCgFi1aKCQkRDNnztS+ffvUu3dveXp6ql+/fpmuFQAAPL6yFYjat29vc9/BwUGlS5dW8+bNNXny5Exvp1WrVmrVqtUD+7i4uMjHx+eeyw4ePKiVK1fqjz/+UL169SRJ06dPV+vWrfXxxx/Lz89P8+bNU1JSkmbNmiWLxaIaNWooKipKU6ZMIRABAABJ2fzILC0tzeaWmpqq2NhYzZ8/X76+vjla4IYNG+Tl5aUnnnhCb7zxhi5evGhdtnXrVnl6elrDkCSFhITI0dFRv//+u7VP48aNbX6INjQ0VIcPH9bly5fvuc/ExEQlJCTY3AAAwOMrR+cQ5bSWLVtq7ty5Wrt2rSZNmqSNGzeqVatW1h+WjY2NlZeXl806zs7OKlGihGJjY619vL29bfqk30/vc7cJEybIw8PDevP398/pQwMAAPlItj4yGzp0aKb7TpkyJTu7kCR17tzZ+u9atWqpdu3aCggI0IYNGxQcHJzt7T7MyJEjbY4xISGBUAQAwGMsW4Fo9+7d2r17t5KTk/XEE09Iko4cOSInJyfVrVvX2s/BwSFnqvz/VaxYUaVKldKxY8cUHBwsHx8fnTt3zqZPSkqKLl26ZJ135OPjo7i4OJs+6ffvNzfJxcUlw+RtAADw+MpWIGrbtq2KFSumOXPmqHjx4pJuf1ljr1691KhRIw0bNixHi0z3119/6eLFi9Z5SkFBQbpy5Yp27typwMBASdK6deuUlpam+vXrW/uMGjVKycnJKlSokCQpMjJSTzzxhLV2AABgbtmaQzR58mRNmDDBJlAUL15c77//fpauMrt27ZqioqIUFRUlSYqOjlZUVJRiYmJ07do1vf3229q2bZtOnjyptWvXql27dqpUqZJCQ0MlSdWqVVPLli312muvafv27dq8ebMGDhyozp07y8/PT5LUpUsXWSwW9enTR/v379ePP/6oTz/9NEsf+wEAgMdbtgJRQkKCzp8/n6H9/Pnzunr1aqa3s2PHDj311FN66qmnJN2em/TUU09p9OjRcnJy0t69e/XCCy+oSpUq6tOnjwIDA/Xbb7/ZfJw1b948Va1aVcHBwWrdurWeffZZffXVV9blHh4eWr16taKjoxUYGKhhw4Zp9OjRXHIPAACssvWR2YsvvqhevXpp8uTJeuaZZyRJv//+u95+++37fsHivTRt2lSGYdx3+apVqx66jRIlSli/hPF+ateurd9++y3TdQEAAHPJViCaOXOm3nrrLXXp0kXJycm3N+TsrD59+uijjz7K0QIBAAByW7YCkZubm7744gt99NFHOn78uCQpICBARYoUydHiAAAA8sIjfTHj2bNndfbsWVWuXFlFihR54MdfAAAA+VW2AtHFixcVHBysKlWqqHXr1jp79qwkqU+fPrl2yT0AAEBuyVYgGjJkiAoVKqSYmBi5ublZ219++WWtXLkyx4oDAADIC9maQ7R69WqtWrVKZcqUsWmvXLmyTp06lSOFAQAA5JVsnSG6fv26zZmhdJcuXeInLwAAQIGTrUDUqFEjzZ0713rfwcFBaWlpCg8PV7NmzXKsOAAAgLyQrY/MwsPDFRwcrB07digpKUnDhw/X/v37denSJW3evDmnawQAAMhV2TpDVLNmTR05ckTPPvus2rVrp+vXr6tDhw7avXu3AgICcrpGAACAXJXlM0TJyclq2bKlZs6cqVGjRuVGTQAAAHkqy2eIChUqpL179+ZGLQAAAHaRrTlEr776qr799ltNnDgxp+tBATE18oi9S8iyIc9VsXcJAIB8KluBKCUlRbNmzdKaNWsUGBiY4TfMpkyZkiPFAQAA5IUsBaITJ06ofPny+vPPP1W3bl1J0pEjtmcKHBwccq46AACAPJClQFS5cmWdPXtW69evl3T7pzqmTZsmb2/vXCkOAAAgL2RpUvXdv2a/YsUKXb9+PUcLAgAAyGvZ+h6idHcHJAAAgIIoS4HIwcEhwxwh5gwBAICCLktziAzDUM+ePa0/4Hrr1i31798/w1VmixcvzrkKAQAAclmWAlGPHj1s7r/66qs5WgwAIHMK4neBSXwfGPKvLAWi2bNn51YdAAAAdvNIk6oBAAAeBwQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgeln6HiIAAB5FQf1CSTz+OEMEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMj0AEAABMz66B6Ndff1Xbtm3l5+cnBwcHLV261Ga5YRgaPXq0fH195erqqpCQEB09etSmz6VLl9S1a1e5u7vL09NTffr00bVr12z67N27V40aNVLhwoXl7++v8PDw3D40AABQgNg1EF2/fl1PPvmkPv/883suDw8P17Rp0zRz5kz9/vvvKlKkiEJDQ3Xr1i1rn65du2r//v2KjIzUsmXL9Ouvv6pfv37W5QkJCWrRooXKlSunnTt36qOPPtLYsWP11Vdf5frxAQCAgsHZnjtv1aqVWrVqdc9lhmHok08+0TvvvKN27dpJkubOnStvb28tXbpUnTt31sGDB7Vy5Ur98ccfqlevniRp+vTpat26tT7++GP5+flp3rx5SkpK0qxZs2SxWFSjRg1FRUVpypQpNsEJAACYV76dQxQdHa3Y2FiFhIRY2zw8PFS/fn1t3bpVkrR161Z5enpaw5AkhYSEyNHRUb///ru1T+PGjWWxWKx9QkNDdfjwYV2+fPme+05MTFRCQoLNDQAAPL7ybSCKjY2VJHl7e9u0e3t7W5fFxsbKy8vLZrmzs7NKlChh0+de27hzH3ebMGGCPDw8rDd/f/9HPyAAAJBv5dtAZE8jR45UfHy89Xb69Gl7lwQAAHKRXecQPYiPj48kKS4uTr6+vtb2uLg41alTx9rn3LlzNuulpKTo0qVL1vV9fHwUFxdn0yf9fnqfu7m4uMjFxSVHjgP5x9TII/YuIcuGPFfF3iUAgCnk2zNEFSpUkI+Pj9auXWttS0hI0O+//66goCBJUlBQkK5cuaKdO3da+6xbt05paWmqX7++tc+vv/6q5ORka5/IyEg98cQTKl68eB4dDQAAyM/sGoiuXbumqKgoRUVFSbo9kToqKkoxMTFycHDQ4MGD9f777+unn37Svn371L17d/n5+al9+/aSpGrVqqlly5Z67bXXtH37dm3evFkDBw5U586d5efnJ0nq0qWLLBaL+vTpo/379+vHH3/Up59+qqFDh9rpqAEAQH5j14/MduzYoWbNmlnvp4eUHj16KCIiQsOHD9f169fVr18/XblyRc8++6xWrlypwoULW9eZN2+eBg4cqODgYDk6Oqpjx46aNm2adbmHh4dWr16tsLAwBQYGqlSpUho9ejSX3AMAACsHwzAMexeR3yUkJMjDw0Px8fFyd3fP8e0XxLktyBvMIcL98LqBx01uvN5l5f07384hAgAAyCsEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHoEIgAAYHrO9i4AAOyNX44HQCACkKMIFwAKIj4yAwAApkcgAgAApkcgAgAApsccIiAfYz4OAOQNzhABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTIxABAADTy9eBaOzYsXJwcLC5Va1a1br81q1bCgsLU8mSJVW0aFF17NhRcXFxNtuIiYlRmzZt5ObmJi8vL7399ttKSUnJ60MBAAD5mLO9C3iYGjVqaM2aNdb7zs7/V/KQIUO0fPlyLVy4UB4eHho4cKA6dOigzZs3S5JSU1PVpk0b+fj4aMuWLTp79qy6d++uQoUK6cMPP8zzYwEAAPlTvg9Ezs7O8vHxydAeHx+vb7/9VvPnz1fz5s0lSbNnz1a1atW0bds2NWjQQKtXr9aBAwe0Zs0aeXt7q06dOho/frxGjBihsWPHymKx5PXhAACAfChff2QmSUePHpWfn58qVqyorl27KiYmRpK0c+dOJScnKyQkxNq3atWqKlu2rLZu3SpJ2rp1q2rVqiVvb29rn9DQUCUkJGj//v333WdiYqISEhJsbgAA4PGVrwNR/fr1FRERoZUrV2rGjBmKjo5Wo0aNdPXqVcXGxspiscjT09NmHW9vb8XGxkqSYmNjbcJQ+vL0ZfczYcIEeXh4WG/+/v45e2AAACBfydcfmbVq1cr679q1a6t+/foqV66cFixYIFdX11zb78iRIzV06FDr/YSEBEIRAACPsXx9huhunp6eqlKlio4dOyYfHx8lJSXpypUrNn3i4uKsc458fHwyXHWWfv9e85LSubi4yN3d3eYGAAAeXwUqEF27dk3Hjx+Xr6+vAgMDVahQIa1du9a6/PDhw4qJiVFQUJAkKSgoSPv27dO5c+esfSIjI+Xu7q7q1avnef0AACB/ytcfmb311ltq27atypUrpzNnzmjMmDFycnLSK6+8Ig8PD/Xp00dDhw5ViRIl5O7urjfffFNBQUFq0KCBJKlFixaqXr26unXrpvDwcMXGxuqdd95RWFiYXFxc7Hx0AAAgv8jXgeivv/7SK6+8oosXL6p06dJ69tlntW3bNpUuXVqSNHXqVDk6Oqpjx45KTExUaGiovvjiC+v6Tk5OWrZsmd544w0FBQWpSJEi6tGjh9577z17HRIAAMiHHAzDMOxdRH6XkJAgDw8PxcfH58p8oqmRR3J8mwAAFCRDnquS49vMyvt3gZpDBAAAkBsIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPRMFYg+//xzlS9fXoULF1b9+vW1fft2e5cEAADyAdMEoh9//FFDhw7VmDFjtGvXLj355JMKDQ3VuXPn7F0aAACwM9MEoilTpui1115Tr169VL16dc2cOVNubm6aNWuWvUsDAAB25mzvAvJCUlKSdu7cqZEjR1rbHB0dFRISoq1bt2bon5iYqMTEROv9+Ph4SVJCQkKu1Hfr+rVc2S4AAAVFbrzHpm/TMIyH9jVFILpw4YJSU1Pl7e1t0+7t7a1Dhw5l6D9hwgSNGzcuQ7u/v3+u1QgAgJn9Kxe3ffXqVXl4eDywjykCUVaNHDlSQ4cOtd5PS0vTpUuXVLJkSTk4OOTovhISEuTv76/Tp0/L3d09R7eN/8M45w3GOe8w1nmDcc4buTXOhmHo6tWr8vPze2hfUwSiUqVKycnJSXFxcTbtcXFx8vHxydDfxcVFLi4uNm2enp65WaLc3d15suUBxjlvMM55h7HOG4xz3siNcX7YmaF0pphUbbFYFBgYqLVr11rb0tLStHbtWgUFBdmxMgAAkB+Y4gyRJA0dOlQ9evRQvXr19Mwzz+iTTz7R9evX1atXL3uXBgAA7Mw0gejll1/W+fPnNXr0aMXGxqpOnTpauXJlhonWec3FxUVjxozJ8BEdchbjnDcY57zDWOcNxjlv5IdxdjAycy0aAADAY8wUc4gAAAAehEAEAABMj0AEAABMj0AEAABMj0AEAABMj0CUBz7//HOVL19ehQsXVv369bV9+/YH9l+4cKGqVq2qwoULq1atWvrll1/yqNKCLSvj/PXXX6tRo0YqXry4ihcvrpCQkIf+v+C2rD6e0/3www9ycHBQ+/btc7fAx0RWx/nKlSsKCwuTr6+vXFxcVKVKFV47MimrY/3JJ5/oiSeekKurq/z9/TVkyBDdunUrj6otmH799Ve1bdtWfn5+cnBw0NKlSx+6zoYNG1S3bl25uLioUqVKioiIyN0iDeSqH374wbBYLMasWbOM/fv3G6+99prh6elpxMXF3bP/5s2bDScnJyM8PNw4cOCA8c477xiFChUy9u3bl8eVFyxZHecuXboYn3/+ubF7927j4MGDRs+ePQ0PDw/jr7/+yuPKC5asjnO66Oho429/+5vRqFEjo127dnlTbAGW1XFOTEw06tWrZ7Ru3drYtGmTER0dbWzYsMGIiorK48oLnqyO9bx58wwXFxdj3rx5RnR0tLFq1SrD19fXGDJkSB5XXrD88ssvxqhRo4zFixcbkowlS5Y8sP+JEycMNzc3Y+jQocaBAweM6dOnG05OTsbKlStzrUYCUS575plnjLCwMOv91NRUw8/Pz5gwYcI9+3fq1Mlo06aNTVv9+vWN119/PVfrLOiyOs53S0lJMYoVK2bMmTMnt0p8LGRnnFNSUoy///3vxjfffGP06NGDQJQJWR3nGTNmGBUrVjSSkpLyqsTHRlbHOiwszGjevLlN29ChQ42GDRvmap2Pk8wEouHDhxs1atSwaXv55ZeN0NDQXKuLj8xyUVJSknbu3KmQkBBrm6Ojo0JCQrR169Z7rrN161ab/pIUGhp63/7I3jjf7caNG0pOTlaJEiVyq8wCL7vj/N5778nLy0t9+vTJizILvOyM808//aSgoCCFhYXJ29tbNWvW1IcffqjU1NS8KrtAys5Y//3vf9fOnTutH6udOHFCv/zyi1q3bp0nNZuFPd4LTfPTHfZw4cIFpaamZvh5EG9vbx06dOie68TGxt6zf2xsbK7VWdBlZ5zvNmLECPn5+WV4AuL/ZGecN23apG+//VZRUVF5UOHjITvjfOLECa1bt05du3bVL7/8omPHjmnAgAFKTk7WmDFj8qLsAik7Y92lSxdduHBBzz77rAzDUEpKivr3769//etfeVGyadzvvTAhIUE3b96Uq6trju+TM0QwvYkTJ+qHH37QkiVLVLhwYXuX89i4evWqunXrpq+//lqlSpWydzmPtbS0NHl5eemrr75SYGCgXn75ZY0aNUozZ860d2mPnQ0bNujDDz/UF198oV27dmnx4sVavny5xo8fb+/S8Ig4Q5SLSpUqJScnJ8XFxdm0x8XFycfH557r+Pj4ZKk/sjfO6T7++GNNnDhRa9asUe3atXOzzAIvq+N8/PhxnTx5Um3btrW2paWlSZKcnZ11+PBhBQQE5G7RBVB2Hs++vr4qVKiQnJycrG3VqlVTbGyskpKSZLFYcrXmgio7Y/3uu++qW7du6tu3rySpVq1aun79uvr166dRo0bJ0ZHzDDnhfu+F7u7uuXJ2SOIMUa6yWCwKDAzU2rVrrW1paWlau3atgoKC7rlOUFCQTX9JioyMvG9/ZG+cJSk8PFzjx4/XypUrVa9evbwotUDL6jhXrVpV+/btU1RUlPX2wgsvqFmzZoqKipK/v39ell9gZOfx3LBhQx07dswaOCXpyJEj8vX1JQw9QHbG+saNGxlCT3oQNfit9Bxjl/fCXJuuDcMwbl/S6eLiYkRERBgHDhww+vXrZ3h6ehqxsbGGYRhGt27djH/+85/W/ps3bzacnZ2Njz/+2Dh48KAxZswYLrvPhKyO88SJEw2LxWIsWrTIOHv2rPV29epVex1CgZDVcb4bV5llTlbHOSYmxihWrJgxcOBA4/Dhw8ayZcsMLy8v4/3337fXIRQYWR3rMWPGGMWKFTO+//5748SJE8bq1auNgIAAo1OnTvY6hALh6tWrxu7du43du3cbkowpU6YYu3fvNk6dOmUYhmH885//NLp162btn37Z/dtvv20cPHjQ+Pzzz7ns/nEwffp0o2zZsobFYjGeeeYZY9u2bdZlTZo0MXr06GHTf8GCBUaVKlUMi8Vi1KhRw1i+fHkeV1wwZWWcy5UrZ0jKcBszZkzeF17AZPXxfCcCUeZldZy3bNli1K9f33BxcTEqVqxofPDBB0ZKSkoeV10wZWWsk5OTjbFjxxoBAQFG4cKFDX9/f2PAgAHG5cuX877wAmT9+vX3fM1NH9sePXoYTZo0ybBOnTp1DIvFYlSsWNGYPXt2rtboYBic4wMAAObGHCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6BCIAAGB6/x+XTWngYcWmgwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l2g_finngen_susie=l2g_max_scores.join(finngen_susie.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n",
+    "l2g_finngen_susie.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_finngen_susie\")\n",
+    "\n",
+    "sample_size_quartiles = l2g_finngen_susie.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of l2g_finngen_susie: mean:\",  l2g_finngen_susie.select(f.mean(l2g_finngen_susie[\"score\"])).collect()[0][0],  \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of l2g_gwas_curated PICS l2g scores: mean: 0.5761322645683256 L.quart:  0.22704362869262695 Median:  0.653652012348175 U.quart:  0.8937538862228394\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4lJQUVa9eXWPGjEl3+Z1v+Le/+d0u9Q04dQLUF1980ebsw+1Sry9Lz7Fjx9SsWTNVqlRJY8aMkb+/v5ycnLRkyRKNHTvW3L6dnZ2+//57/f777/r111+1fPlyvfzyyxo9erR+//13FS5cWF5eXtq1a5eWL1+upUuXaunSpZoxY4a6detm6V1t9xuT+/XLjjHNaE33khrAPD09M7xORsTExKhFixa6cuWKNmzYID8/P0u3n1W1atWSi4uL1q9fr1KlSsnLy0sVKlRQw4YNNXnyZMXHx2vDhg1q166duc6VK1fUuHFjubm56T//+Y/Kli0rFxcX7dixQwMGDLCZEHj06NHq0aOHfv75Z61YsUJvvvmmea1ketfqZdW9rhlKz+1npFJt2LBBbdq0UaNGjTR58mT5+vqqQIECmjFjhubMmXPfGh70OJBX5dQxJb8hVMESlSpVknTrLsDbDxB3O6iVLl1aknT48OE0yw4dOiRPT08VKlTIpv3o0aM2/5P8888/lZKSctezJrfv588//1STJk3M9qSkJJ08edKm1rJly2r37t1q1qyZJYGtRIkSKlKkiJKTk23uisyoX3/9VfHx8frll19szrbc+TFkqrp166pu3br6+OOPNWfOHHXt2lVz587VK6+8IunWbdOtW7dW69atlZKSor59++qLL77QBx98kO7ZisyMXU550DG9m/v9vk+cOCF7e3vzrKUVbt68qdatW+vIkSNauXJluh873f47uP21/88//2TqTFvqtlJSUnTixAmbsxd33p0p3XqtpN7pWapUKfMj04YNGyo+Pl6zZ89WdHS0GjVqZK6zdu1a/fPPP/rxxx9t2k+cOJFuPdWrV1f16tU1ePBgbdq0SfXr19fUqVP10Ucf3fe5PPbYY5J035nuixYtqitXrqRpTz0DmBE//PCDXFxctHz5cjk7O5vtM2bMSNM3vddRZl6zpUuX1r59+2QYhs220jtOZtTRo0dt/n6vXbumc+fOqWXLlnddp2zZstqyZYsSExNtPjq8U2aPKY8CPv6DJYKCgiTdug7ndgULFpSkNAc2X19f1ahRQ7NmzbJZtm/fPq1YsSLdP/hJkybZ/Dxx4kRJt27rvptatWqpePHi+vLLL5WUlGS2z549O82bUseOHfX333/ryy+/TLOdGzdupPk48n4cHBzUoUMH/fDDD+ke/C9cuHDf9SXbMyoxMTFpDuaXL19Oc9alRo0akmR+BPjPP//YLLe3tzdD0d1u187M2OWUBx3TuylUqNA9P8rZvn27qlatmuE70+4nOTlZnTp10ubNm7VgwQLz7+dOzZo1k6OjY5ppND7//PNM7zMkJESSNHnyZJv21L+jOzVs2FBbtmzRmjVrzFDl6empypUrm3d7pbZL6b9eExIS0uwvNjbW5vUk3QpY9vb2GZ46oESJEmrUqJGmT5+u06dP2yy7ff9ly5ZVTEyM9uzZY7adO3dOP/30U4b2I916XnZ2djZnt06ePJnuzOmFChVKc6zLzGu2ZcuWOnv2rM10DdevX9e0adMyXO+dpk2bZnOt5ZQpU5SUlHTP42aHDh108eLFdF9nqeOblWPKo4AzVbDEY489pmrVqmnlypU28yO5urqqSpUqmjdvnipUqKBixYqpWrVqqlatmkaNGqUWLVooKChIvXr1MqdUcHd3T/d70k6cOKE2bdro6aef1ubNm/Xdd9+pS5cueuKJJ+5al5OTk4YNG6Y33nhDTZs2VceOHXXy5EnNnDlTZcuWtfnf4EsvvaT58+frtdde05o1a1S/fn0lJyfr0KFDmj9/vpYvX65atWplalxGjhypNWvWqE6dOurdu7eqVKmiS5cuaceOHVq5cqUuXbp013WbN29u/k/w1Vdf1bVr1/Tll1/Ky8tL586dM/vNmjVLkydPVrt27VS2bFldvXpVX375pdzc3Mxw+sorr+jSpUtq2rSpSpYsqVOnTmnixImqUaOGKleu/MBjl5MeZEzvJjAwUPPmzVN4eLiefPJJFS5cWK1bt5YkJSYmat26dWku8F67dq2aNGmioUOHZvp7/d5++2398ssvat26tS5duqTvvvvOZnnqdUje3t7q16+fRo8ebb72d+/eraVLl8rT0zNTv4PAwEB16NBB48aN0z///GNOqZD61Th3bqthw4b6+OOPdebMGZvw1KhRI33xxRcqU6aMzUd19erVU9GiRdW9e3e9+eabsrOz07fffpsm8K9evVphYWF6/vnnVaFCBSUlJenbb781w0dGTZgwQQ0aNFDNmjXVp08fBQQE6OTJk1q8eLF27dolSercubMGDBigdu3a6c0339T169c1ZcoUVahQIUMXmUtSq1atNGbMGD399NPq0qWLzp8/r0mTJqlcuXI2YS11jFeuXKkxY8bIz89PAQEBqlOnToZfs71799bnn3+ubt26afv27fL19dW3335r/uc0KxISEtSsWTN17NhRhw8f1uTJk9WgQQNzuoz0dOvWTd98843Cw8P1xx9/qGHDhoqLi9PKlSvVt29fPfvss1k6pjwScvp2Qzy8xowZYxQuXDjN7cebNm0yAgMDDScnpzS3bq9cudKoX7++4erqari5uRmtW7c2Dhw4YLN+6u3PBw4cMJ577jmjSJEiRtGiRY2wsDDjxo0bGaptwoQJRunSpQ1nZ2ejdu3axsaNG43AwEDj6aeftumXkJBgfPrpp0bVqlUNZ2dno2jRokZgYKDx4YcfGjExMWY/SUZoaGia/ZQuXTrNLdXR0dFGaGio4e/vbxQoUMDw8fExmjVrZkybNu2+df/yyy/G448/bri4uBhlypQxPv30U/N25xMnThiGcevW9hdeeMEoVaqU4ezsbHh5eRnPPPOMsW3bNnM733//vdG8eXPDy8vLcHJyMkqVKmW8+uqrxrlz58w+d06pkJmxS133ztvkT5w4kWZKhMaNGxtVq1ZNd+xatWqVpj29sc7ImGampmvXrhldunQxPDw8DEk2t+EvXbrUkGQcPXrUZju//vqrIcmYOnVqmprTew63v+5Tp5W42+N2SUlJxgcffGD4+PgYrq6uRtOmTY2DBw8axYsXN1577bX77vt2cXFxRmhoqFGsWDGjcOHCRtu2bY3Dhw8bkoyRI0fa9I2NjTUcHByMIkWK2Nw2/9133xmSjJdeeinN9jdu3GjUrVvXcHV1Nfz8/Iz33nvPnBYj9XV1/Phx4+WXXzbKli1ruLi4GMWKFTOaNGlirFy5MlPPxTAMY9++fUa7du0MDw8Pw8XFxahYsaLxwQcf2PRZsWKFUa1aNcPJycmoWLGi8d133911SoX0/qYNwzC+/vpro3z58oazs7NRqVIlY8aMGelu49ChQ0ajRo0MV1fXNFNeZPQ4cOrUKaNNmzZGwYIFDU9PT6Nfv37GsmXLsjylwrp164w+ffoYRYsWNQoXLmx07drVZhobw0g7pYJh3JpGYtCgQUZAQIBZ73PPPWccO3bMMIyMHVMeRYQqWObKlStGsWLFjK+++srS7aYevC5cuGDZNpOTk41ixYoZr7zyimXbfFQ8amP37LPPppknyjAM49133zVKlixpzr+Uky5fvmxIMj766KMH3lbqvFHfffedBZUhr0gNVfeavw7W45oqWMbd3V3vvfeeRo0aZXOnT267efNmmo8gvvnmG126dIkveb6PR33sDh48qEWLFmn48OFplq1Zs0YffPCBzcXL2eHGjRtp2saNGycp819Sfrdt2dvb21xcDiBruKYKlhowYIAGDBiQ22XY+P3339W/f389//zzKl68uHbs2KGvv/5a1apV0/PPP5/b5eVpj/rYVa5cOc1F1am2bt2aIzXMmzdPM2fOVMuWLVW4cGH99ttv+u9//6vmzZurfv36km7NdXUvrq6ucnd3V0REhLZv364mTZrI0dHRvBW+T58+aaYMyS0xMTHphr/b+fj45FA1ec+NGzfuOz/W7XOHIWcRqvDQK1OmjPz9/TVhwgRdunRJxYoVU7du3TRy5Ei+mf0+GLvc9/jjj8vR0VERERGKjY01L16/feoBX1/fe26je/fumjlzpurVq6fIyEgNHz5c165dU6lSpTRs2DCbLyHObf369bvvPEd3nj19lMybN8+cNPRu7jbtCrKfnfEovzoB4CGwcuXKey738/NLdx6svOjAgQM6e/bsPftYOUdZfnPu3Dnt37//nn0CAwMzPeM+rEGoAgAAsAAXqgMAAFiAa6pyUEpKis6ePasiRYrk2sSJAAAgcwzD0NWrV+Xn5yd7+7ufjyJU5aCzZ8/mmTtsAABA5pw5c+aeX/pNqMpBRYoUkXTrl+Lm5pbL1QAAgIyIjY2Vv7+/+T5+N4SqHJT6kZ+bmxuhCgCAfOZ+l+5woToAAIAFCFUAAAAWIFQBAABYgGuq8hjDMJSUlKTk5OTcLuWh4ODgIEdHR6awAABkO0JVHpKQkKBz587p+vXruV3KQ6VgwYLy9fXlu+oAANmKUJVHpKSk6MSJE3JwcJCfn5+cnJw4u/KADMNQQkKCLly4oBMnTqh8+fL3nLQNAIAHQajKIxISEpSSkiJ/f38VLFgwt8t5aLi6uqpAgQI6deqUEhIS5OLiktslAQAeUvy3PY/hTIr1GFMAQE7g3QYAAMAChCoAAAALcE1VHjc28kiO7q//vyrk6P4AAHhYcKYKAADAAoQq5BsJCQm5XQIAAHdFqMID+/7771W9enW5urqqePHiCg4OVlxcnCRp+vTpqlq1qpydneXr66uwsDBzvdOnT+vZZ59V4cKF5ebmpo4dOyo6OtpcPmzYMNWoUUNfffWVAgICzOkQrly5oldeeUUlSpSQm5ubmjZtqt27d+fskwYA4A5cU4UHcu7cOb3wwguKiIhQu3btdPXqVW3YsEGGYWjKlCkKDw/XyJEj1aJFC8XExGjjxo2Sbk12mhqo1q1bp6SkJIWGhqpTp05au3atuf0///xTP/zwg3788Uc5ODhIkp5//nm5urpq6dKlcnd31xdffKFmzZrpyJEjKlasWG4MAwA8dHL6ml4r5PZ1wYQqPJBz584pKSlJ7du3V+nSpSVJ1atXlyR99NFHevvtt9WvXz+z/5NPPilJWrVqlfbu3asTJ07I399fkvTNN9+oatWq2rp1q9kvISFB33zzjUqUKCFJ+u233/THH3/o/PnzcnZ2liR99tlnWrhwob7//nv16dMnZ544AAB3IFThgTzxxBNq1qyZqlevrpCQEDVv3lzPPfecEhMTdfbsWTVr1izd9Q4ePCh/f38zUElSlSpV5OHhoYMHD5qhqnTp0magkqTdu3fr2rVrKl68uM32bty4oWPHjmXDMwQAIGMIVXggDg4OioyM1KZNm7RixQpNnDhRgwYN0qpVqyzZfqFChWx+vnbtmnx9fW0+Ikzl4eFhyT4BAMgKQhUemJ2dnerXr6/69etryJAhKl26tCIjI1WmTBmtWrVKTZo0SbNO5cqVdebMGZ05c8Y8W3XgwAFduXJFVapUueu+atasqaioKDk6OqpMmTLZ9ZQAAMg0QhUeyJYtW7Rq1So1b95cXl5e2rJliy5cuKDKlStr2LBheu211+Tl5aUWLVro6tWr2rhxo9544w0FBwerevXq6tq1q8aNG6ekpCT17dtXjRs3Vq1ate66v+DgYAUFBalt27aKiIhQhQoVdPbsWS1evFjt2rW757oAAGQnQlUel9t3MtyPm5ub1q9fr3Hjxik2NlalS5fW6NGj1aJFC0nSzZs3NXbsWL3zzjvy9PTUc889J+nW2a2ff/5Zb7zxhho1aiR7e3s9/fTTmjhx4j33Z2dnpyVLlmjQoEHq2bOnLly4IB8fHzVq1Eje3t7Z/nwBALgbO8MwjNwu4lERGxsrd3d3xcTEyM3NzWbZzZs3deLECZv5mGANxhYAMo8pFf7nXu/ft2PyTwAAAAsQqgAAACxAqAIAALAAoQoAAMAChKo8hvsGrMeYAgByAqEqjyhQoIAk6fr167lcycMndUxTxxgAgOzAPFV5hIODgzw8PHT+/HlJUsGCBWVnZ5fLVeVvhmHo+vXrOn/+vDw8POTg4JDbJQEAHmKEqjzEx8dHksxgBWt4eHiYYwsAQHYhVOUhdnZ28vX1lZeXlxITE3O7nIdCgQIFOEMFAMgRhKo8yMHBgSAAAEA+w4XqAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYIFcDVXr169X69at5efnJzs7Oy1cuNBmuWEYGjJkiHx9feXq6qrg4GAdPXrUps+lS5fUtWtXubm5ycPDQ7169dK1a9ds+uzZs0cNGzaUi4uL/P39FRERkaaWBQsWqFKlSnJxcVH16tW1ZMmSTNcCAAAeXbkaquLi4vTEE09o0qRJ6S6PiIjQhAkTNHXqVG3ZskWFChVSSEiIbt68afbp2rWr9u/fr8jISC1atEjr169Xnz59zOWxsbFq3ry5Spcure3bt2vUqFEaNmyYpk2bZvbZtGmTXnjhBfXq1Us7d+5U27Zt1bZtW+3bty9TtQAAgEeXnWEYRm4XIUl2dnb66aef1LZtW0m3zgz5+fnp7bff1jvvvCNJiomJkbe3t2bOnKnOnTvr4MGDqlKlirZu3apatWpJkpYtW6aWLVvqr7/+kp+fn6ZMmaJBgwYpKipKTk5OkqT3339fCxcu1KFDhyRJnTp1UlxcnBYtWmTWU7duXdWoUUNTp07NUC0ZERsbK3d3d8XExMjNzc2ScQMAIDuMjTyS2yVkWv9/VciW7Wb0/TvPXlN14sQJRUVFKTg42Gxzd3dXnTp1tHnzZknS5s2b5eHhYQYqSQoODpa9vb22bNli9mnUqJEZqCQpJCREhw8f1uXLl80+t+8ntU/qfjJSS3ri4+MVGxtr8wAAAA+nPBuqoqKiJEne3t427d7e3uayqKgoeXl52Sx3dHRUsWLFbPqkt43b93G3Prcvv18t6RkxYoTc3d3Nh7+//32eNQAAyK/ybKh6GAwcOFAxMTHm48yZM7ldEgAAyCZ5NlT5+PhIkqKjo23ao6OjzWU+Pj46f/68zfKkpCRdunTJpk9627h9H3frc/vy+9WSHmdnZ7m5udk8AADAwynPhqqAgAD5+Pho1apVZltsbKy2bNmioKAgSVJQUJCuXLmi7du3m31Wr16tlJQU1alTx+yzfv16JSYmmn0iIyNVsWJFFS1a1Oxz+35S+6TuJyO1AACAR1uuhqpr165p165d2rVrl6RbF4Tv2rVLp0+flp2dnd566y199NFH+uWXX7R3715169ZNfn5+5h2ClStX1tNPP63evXvrjz/+0MaNGxUWFqbOnTvLz89PktSlSxc5OTmpV69e2r9/v+bNm6fx48crPDzcrKNfv35atmyZRo8erUOHDmnYsGHatm2bwsLCJClDtQAAgEebY27ufNu2bWrSpIn5c2rQ6d69u2bOnKn33ntPcXFx6tOnj65cuaIGDRpo2bJlcnFxMdeZPXu2wsLC1KxZM9nb26tDhw6aMGGCudzd3V0rVqxQaGioAgMD5enpqSFDhtjMZVWvXj3NmTNHgwcP1r///W+VL19eCxcuVLVq1cw+GakFAAA8uvLMPFWPAuapAgDkF8xT9T/5fp4qAACA/IRQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABbI06EqOTlZH3zwgQICAuTq6qqyZctq+PDhMgzD7GMYhoYMGSJfX1+5uroqODhYR48etdnOpUuX1LVrV7m5ucnDw0O9evXStWvXbPrs2bNHDRs2lIuLi/z9/RUREZGmngULFqhSpUpycXFR9erVtWTJkux54gAAIN/J06Hq008/1ZQpU/T555/r4MGD+vTTTxUREaGJEyeafSIiIjRhwgRNnTpVW7ZsUaFChRQSEqKbN2+afbp27ar9+/crMjJSixYt0vr169WnTx9zeWxsrJo3b67SpUtr+/btGjVqlIYNG6Zp06aZfTZt2qQXXnhBvXr10s6dO9W2bVu1bdtW+/bty5nBAAAAeZqdcftpnzzmmWeekbe3t77++muzrUOHDnJ1ddV3330nwzDk5+ent99+W++8844kKSYmRt7e3po5c6Y6d+6sgwcPqkqVKtq6datq1aolSVq2bJlatmypv/76S35+fpoyZYoGDRqkqKgoOTk5SZLef/99LVy4UIcOHZIkderUSXFxcVq0aJFZS926dVWjRg1NnTo1Q88nNjZW7u7uiomJkZubmyVjBABAdhgbeSS3S8i0/v+qkC3bzej7d54+U1WvXj2tWrVKR47c+sXu3r1bv/32m1q0aCFJOnHihKKiohQcHGyu4+7urjp16mjz5s2SpM2bN8vDw8MMVJIUHBwse3t7bdmyxezTqFEjM1BJUkhIiA4fPqzLly+bfW7fT2qf1P2kJz4+XrGxsTYPAADwcHLM7QLu5f3331dsbKwqVaokBwcHJScn6+OPP1bXrl0lSVFRUZIkb29vm/W8vb3NZVFRUfLy8rJZ7ujoqGLFitn0CQgISLON1GVFixZVVFTUPfeTnhEjRujDDz/M7NMGAAD5UJ4+UzV//nzNnj1bc+bM0Y4dOzRr1ix99tlnmjVrVm6XliEDBw5UTEyM+Thz5kxulwQAALJJnj5T9e677+r9999X586dJUnVq1fXqVOnNGLECHXv3l0+Pj6SpOjoaPn6+prrRUdHq0aNGpIkHx8fnT9/3ma7SUlJunTpkrm+j4+PoqOjbfqk/ny/PqnL0+Ps7CxnZ+fMPm0AAJAP5ekzVdevX5e9vW2JDg4OSklJkSQFBATIx8dHq1atMpfHxsZqy5YtCgoKkiQFBQXpypUr2r59u9ln9erVSklJUZ06dcw+69evV2JiotknMjJSFStWVNGiRc0+t+8ntU/qfgAAwKMtT4eq1q1b6+OPP9bixYt18uRJ/fTTTxozZozatWsnSbKzs9Nbb72ljz76SL/88ov27t2rbt26yc/PT23btpUkVa5cWU8//bR69+6tP/74Qxs3blRYWJg6d+4sPz8/SVKXLl3k5OSkXr16af/+/Zo3b57Gjx+v8PBws5Z+/fpp2bJlGj16tA4dOqRhw4Zp27ZtCgsLy/FxAQAAeU+e/vhv4sSJ+uCDD9S3b1+dP39efn5+evXVVzVkyBCzz3vvvae4uDj16dNHV65cUYMGDbRs2TK5uLiYfWbPnq2wsDA1a9ZM9vb26tChgyZMmGAud3d314oVKxQaGqrAwEB5enpqyJAhNnNZ1atXT3PmzNHgwYP173//W+XLl9fChQtVrVq1nBkMAACQp+XpeaoeNsxTBQDIL5in6n8einmqAAAA8gtCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFggS6Hq+PHjVtcBAACQr2UpVJUrV05NmjTRd999p5s3b1pdEwAAQL6TpVC1Y8cOPf744woPD5ePj49effVV/fHHH1bXBgAAkG9kKVTVqFFD48eP19mzZzV9+nSdO3dODRo0ULVq1TRmzBhduHDB6joBAADytAe6UN3R0VHt27fXggUL9Omnn+rPP//UO++8I39/f3Xr1k3nzp2zqk4AAIA87YFC1bZt29S3b1/5+vpqzJgxeuedd3Ts2DFFRkbq7NmzevbZZ62qEwAAIE9zzMpKY8aM0YwZM3T48GG1bNlS33zzjVq2bCl7+1sZLSAgQDNnzlSZMmWsrBUAACDPylKomjJlil5++WX16NFDvr6+6fbx8vLS119//UDFAQAA5BdZClVHjx69bx8nJyd17949K5sHAADId7J0TdWMGTO0YMGCNO0LFizQrFmzHrgoAACA/CZLoWrEiBHy9PRM0+7l5aVPPvnkgYsCAADIb7IUqk6fPq2AgIA07aVLl9bp06cfuCgAAID8JkuhysvLS3v27EnTvnv3bhUvXvyBiwIAAMhvshSqXnjhBb355ptas2aNkpOTlZycrNWrV6tfv37q3Lmz1TUCAADkeVm6+2/48OE6efKkmjVrJkfHW5tISUlRt27duKYKAAA8krIUqpycnDRv3jwNHz5cu3fvlqurq6pXr67SpUtbXR8AAEC+kKVQlapChQqqUKGCVbUAAADkW1kKVcnJyZo5c6ZWrVql8+fPKyUlxWb56tWrLSkOAAAgv8hSqOrXr59mzpypVq1aqVq1arKzs7O6LgAAgHwlS6Fq7ty5mj9/vlq2bGl1PQAAAPlSlqZUcHJyUrly5ayuBQAAIN/KUqh6++23NX78eBmGYXU9AAAA+VKWPv777bfftGbNGi1dulRVq1ZVgQIFbJb/+OOPlhQHAACQX2QpVHl4eKhdu3ZW1wIAAJBvZenjvxkzZtzzYaW///5bL774oooXL25OMrpt2zZzuWEYGjJkiHx9feXq6qrg4GAdPXrUZhuXLl1S165d5ebmJg8PD/Xq1UvXrl2z6bNnzx41bNhQLi4u8vf3V0RERJpaFixYoEqVKsnFxUXVq1fXkiVLLH2uAAAg/8pSqJKkpKQkrVy5Ul988YWuXr0qSTp79myasPIgLl++rPr166tAgQJaunSpDhw4oNGjR6to0aJmn4iICE2YMEFTp07Vli1bVKhQIYWEhOjmzZtmn65du2r//v2KjIzUokWLtH79evXp08dcHhsbq+bNm6t06dLavn27Ro0apWHDhmnatGlmn02bNumFF15Qr169tHPnTrVt21Zt27bVvn37LHu+AAAg/7IzsnC1+alTp/T000/r9OnTio+P15EjR/TYY4+pX79+io+P19SpUy0p7v3339fGjRu1YcOGdJcbhiE/Pz+9/fbbeueddyRJMTEx8vb21syZM9W5c2cdPHhQVapU0datW1WrVi1J0rJly9SyZUv99ddf8vPz05QpUzRo0CBFRUXJycnJ3PfChQt16NAhSVKnTp0UFxenRYsWmfuvW7euatSokeHnGxsbK3d3d8XExMjNzS3L4wIAQHYbG3kkt0vItP7/yp5vecno+3eWzlT169dPtWrV0uXLl+Xq6mq2t2vXTqtWrcrKJtP1yy+/qFatWnr++efl5eWl//u//9OXX35pLj9x4oSioqIUHBxstrm7u6tOnTravHmzJGnz5s3y8PAwA5UkBQcHy97eXlu2bDH7NGrUyAxUkhQSEqLDhw/r8uXLZp/b95PaJ3U/6YmPj1dsbKzNAwAAPJyyFKo2bNigwYMH24QQSSpTpoz+/vtvSwqTpOPHj2vKlCkqX768li9frtdff11vvvmmZs2aJUmKioqSJHl7e9us5+3tbS6LioqSl5eXzXJHR0cVK1bMpk9627h9H3frk7o8PSNGjJC7u7v58Pf3z9TzBwAA+UeWQlVKSoqSk5PTtP/1118qUqTIAxd1+35q1qypTz75RP/3f/+nPn36qHfv3pZ9vJjdBg4cqJiYGPNx5syZ3C4JAABkkyyFqubNm2vcuHHmz3Z2drp27ZqGDh1q6VfX+Pr6qkqVKjZtlStX1unTpyVJPj4+kqTo6GibPtHR0eYyHx8fnT9/3mZ5UlKSLl26ZNMnvW3cvo+79Uldnh5nZ2e5ubnZPAAAwMMpS6Fq9OjR2rhxo6pUqaKbN2+qS5cu5kd/n376qWXF1a9fX4cPH7ZpO3LkiEqXLi1JCggIkI+Pj811XLGxsdqyZYuCgoIkSUFBQbpy5Yq2b99u9lm9erVSUlJUp04ds8/69euVmJho9omMjFTFihXNOw2DgoLSXC8WGRlp7gcAADzasjT5Z8mSJbV7927NnTtXe/bs0bVr19SrVy917drV5sL1B9W/f3/Vq1dPn3zyiTp27Kg//vhD06ZNM6c6sLOz01tvvaWPPvpI5cuXV0BAgD744AP5+fmpbdu2km6d2Xr66afNjw0TExMVFhamzp07y8/PT5LUpUsXffjhh+rVq5cGDBigffv2afz48Ro7dqxZS79+/dS4cWONHj1arVq10ty5c7Vt2zabaRcAAMCjK0tTKuSkRYsWaeDAgTp69KgCAgIUHh6u3r17m8sNw9DQoUM1bdo0XblyRQ0aNNDkyZNVocL/bqu8dOmSwsLC9Ouvv8re3l4dOnTQhAkTVLhwYbPPnj17FBoaqq1bt8rT01NvvPGGBgwYYFPLggULNHjwYJ08eVLly5dXREREpj7uZEoFAEB+wZQK/5PR9+8shapvvvnmnsu7deuW2U0+EghVAID8glD1Pxl9/87Sx3/9+vWz+TkxMVHXr1+Xk5OTChYsSKgCAACPnCxdqH758mWbx7Vr13T48GE1aNBA//3vf62uEQAAIM/L8nf/3al8+fIaOXJkmrNYAAAAjwLLQpV0a6bys2fPWrlJAACAfCFL11T98ssvNj8bhqFz587p888/V/369S0pDAAAID/JUqhKnQMqlZ2dnUqUKKGmTZtq9OjRVtQFAACQr2QpVKWkpFhdBwAAQL5m6TVVAAAAj6osnakKDw/PcN8xY8ZkZRcAAAD5SpZC1c6dO7Vz504lJiaqYsWKkm590bGDg4Nq1qxp9rOzs7OmSgAAgDwuS6GqdevWKlKkiGbNmqWiRYtKujUhaM+ePdWwYUO9/fbblhYJAACQ12XpmqrRo0drxIgRZqCSpKJFi+qjjz7i7j8AAPBIylKoio2N1YULF9K0X7hwQVevXn3gogAAAPKbLH38165dO/Xs2VOjR49W7dq1JUlbtmzRu+++q/bt21taIAAA+d3YyCO5XQJyQJZC1dSpU/XOO++oS5cuSkxMvLUhR0f16tVLo0aNsrRAAACA/CBLoapgwYKaPHmyRo0apWPHjkmSypYtq0KFCllaHDIuv/4vqP+/KuR2CQAAWOKBJv88d+6czp07p/Lly6tQoUIyDMOqugAAAPKVLIWqf/75R82aNVOFChXUsmVLnTt3TpLUq1cvplMAAACPpCyFqv79+6tAgQI6ffq0ChYsaLZ36tRJy5Yts6w4AACA/CJL11StWLFCy5cvV8mSJW3ay5cvr1OnTllSGAAAQH6SpTNVcXFxNmeoUl26dEnOzs4PXBQAAEB+k6VQ1bBhQ33zzTfmz3Z2dkpJSVFERISaNGliWXEAAAD5RZY+/ouIiFCzZs20bds2JSQk6L333tP+/ft16dIlbdy40eoaAQAA8rwsnamqVq2ajhw5ogYNGujZZ59VXFyc2rdvr507d6ps2bJW1wgAAJDnZfpMVWJiop5++mlNnTpVgwYNyo6aAAAA8p1Mn6kqUKCA9uzZkx21AAAA5FtZ+vjvxRdf1Ndff211LQAAAPlWli5UT0pK0vTp07Vy5UoFBgam+c6/MWPGWFIcAABAfpGpUHX8+HGVKVNG+/btU82aNSVJR47YfpGvnZ2dddUBAADkE5kKVeXLl9e5c+e0Zs0aSbe+lmbChAny9vbOluIAAADyi0xdU2UYhs3PS5cuVVxcnKUFAQAA5EdZuqYq1Z0hC8issZFH7t8pj+n/rwq5XQIAIA/K1JkqOzu7NNdMcQ0VAABAJs9UGYahHj16mF+afPPmTb322mtp7v778ccfrasQAAAgH8hUqOrevbvNzy+++KKlxQAAAORXmQpVM2bMyK46AAAA8rUszagOAAAAW4QqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsIBjbheQGSNHjtTAgQPVr18/jRs3TpJ08+ZNvf3225o7d67i4+MVEhKiyZMny9vb21zv9OnTev3117VmzRoVLlxY3bt314gRI+To+L+nv3btWoWHh2v//v3y9/fX4MGD1aNHD5v9T5o0SaNGjVJUVJSeeOIJTZw4UbVr186Jpw4A2WJs5JHcLiHT+v+rQm6XAKQr35yp2rp1q7744gs9/vjjNu39+/fXr7/+qgULFmjdunU6e/as2rdvby5PTk5Wq1atlJCQoE2bNmnWrFmaOXOmhgwZYvY5ceKEWrVqpSZNmmjXrl1666239Morr2j58uVmn3nz5ik8PFxDhw7Vjh079MQTTygkJETnz5/P/icPAADyvHwRqq5du6auXbvqyy+/VNGiRc32mJgYff311xozZoyaNm2qwMBAzZgxQ5s2bdLvv/8uSVqxYoUOHDig7777TjVq1FCLFi00fPhwTZo0SQkJCZKkqVOnKiAgQKNHj1blypUVFham5557TmPHjjX3NWbMGPXu3Vs9e/ZUlSpVNHXqVBUsWFDTp0/P2cEAAAB5Ur4IVaGhoWrVqpWCg4Nt2rdv367ExESb9kqVKqlUqVLavHmzJGnz5s2qXr26zceBISEhio2N1f79+80+d247JCTE3EZCQoK2b99u08fe3l7BwcFmn/TEx8crNjbW5gEAAB5Oef6aqrlz52rHjh3aunVrmmVRUVFycnKSh4eHTbu3t7eioqLMPrcHqtTlqcvu1Sc2NlY3btzQ5cuXlZycnG6fQ4cO3bX2ESNG6MMPP8zYEwUAAPlanj5TdebMGfXr10+zZ8+Wi4tLbpeTaQMHDlRMTIz5OHPmTG6XBAAAskmeDlXbt2/X+fPnVbNmTTk6OsrR0VHr1q3ThAkT5OjoKG9vbyUkJOjKlSs260VHR8vHx0eS5OPjo+jo6DTLU5fdq4+bm5tcXV3l6ekpBweHdPukbiM9zs7OcnNzs3kAAICHU54OVc2aNdPevXu1a9cu81GrVi117drV/HeBAgW0atUqc53Dhw/r9OnTCgoKkiQFBQVp7969NnfpRUZGys3NTVWqVDH73L6N1D6p23ByclJgYKBNn5SUFK1atcrsAwAAHm15+pqqIkWKqFq1ajZthQoVUvHixc32Xr16KTw8XMWKFZObm5veeOMNBQUFqW7dupKk5s2bq0qVKnrppZcUERGhqKgoDR48WKGhoXJ2dpYkvfbaa/r888/13nvv6eWXX9bq1as1f/58LV682NxveHi4unfvrlq1aql27doaN26c4uLi1LNnzxwaDQAAkJfl6VCVEWPHjpW9vb06dOhgM/lnKgcHBy1atEivv/66goKCVKhQIXXv3l3/+c9/zD4BAQFavHix+vfvr/Hjx6tkyZL66quvFBISYvbp1KmTLly4oCFDhigqKko1atTQsmXL0ly8DgAAHk12hmEYuV3EoyI2Nlbu7u6KiYmx/Pqq/Dgrcn7FbM54mOTHY0d+/BvMj+OcH2XXayOj7995+poqAACA/IJQBQAAYAFCFQAAgAXy/YXqAIBHC9cnIa/iTBUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAHH3C4AyG/GRh7J7RIyrf+/KuR2CQDw0ONMFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAG+UBlAnsQXVwPIbzhTBQAAYAFCFQAAgAUIVQAAABbgmioAsEh+vA4MgHU4UwUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAe7+Ax4B3JUGANmPM1UAAAAWIFQBAABYgFAFAABgAUIVAACABfJ0qBoxYoSefPJJFSlSRF5eXmrbtq0OHz5s0+fmzZsKDQ1V8eLFVbhwYXXo0EHR0dE2fU6fPq1WrVqpYMGC8vLy0rvvvqukpCSbPmvXrlXNmjXl7OyscuXKaebMmWnqmTRpksqUKSMXFxfVqVNHf/zxh+XPGQAA5E95OlStW7dOoaGh+v333xUZGanExEQ1b95ccXFxZp/+/fvr119/1YIFC7Ru3TqdPXtW7du3N5cnJyerVatWSkhI0KZNmzRr1izNnDlTQ4YMMfucOHFCrVq1UpMmTbRr1y699dZbeuWVV7R8+XKzz7x58xQeHq6hQ4dqx44deuKJJxQSEqLz58/nzGAAAIA8zc4wDCO3i8ioCxcuyMvLS+vWrVOjRo0UExOjEiVKaM6cOXruueckSYcOHVLlypW1efNm1a1bV0uXLtUzzzyjs2fPytvbW5I0depUDRgwQBcuXJCTk5MGDBigxYsXa9++fea+OnfurCtXrmjZsmWSpDp16ujJJ5/U559/LklKSUmRv7+/3njjDb3//vsZqj82Nlbu7u6KiYmRm5ublUPDLfMAgEde/39VyJbtZvT9O0+fqbpTTEyMJKlYsWKSpO3btysxMVHBwcFmn0qVKqlUqVLavHmzJGnz5s2qXr26GagkKSQkRLGxsdq/f7/Z5/ZtpPZJ3UZCQoK2b99u08fe3l7BwcFmn/TEx8crNjbW5gEAAB5O+SZUpaSk6K233lL9+vVVrVo1SVJUVJScnJzk4eFh09fb21tRUVFmn9sDVery1GX36hMbG6sbN27o4sWLSk5OTrdP6jbSM2LECLm7u5sPf3//zD9xAACQL+SbUBUaGqp9+/Zp7ty5uV1Khg0cOFAxMTHm48yZM7ldEgAAyCb54mtqwsLCtGjRIq1fv14lS5Y02318fJSQkKArV67YnK2Kjo6Wj4+P2efOu/RS7w68vc+ddwxGR0fLzc1Nrq6ucnBwkIODQ7p9UreRHmdnZzk7O2f+CQMAgHwnT5+pMgxDYWFh+umnn7R69WoFBATYLA8MDFSBAgW0atUqs+3w4cM6ffq0goKCJElBQUHau3evzV16kZGRcnNzU5UqVcw+t28jtU/qNpycnBQYGGjTJyUlRatWrTL7AACAR1uePlMVGhqqOXPm6Oeff1aRIkXM65fc3d3l6uoqd3d39erVS+Hh4SpWrJjc3Nz0xhtvKCgoSHXr1pUkNW/eXFWqVNFLL72kiIgIRUVFafDgwQoNDTXPIr322mv6/PPP9d577+nll1/W6tWrNX/+fC1evNisJTw8XN27d1etWrVUu3ZtjRs3TnFxcerZs2fODwwAAMhz8nSomjJliiTpqaeesmmfMWOGevToIUkaO3as7O3t1aFDB8XHxyskJESTJ082+zo4OGjRokV6/fXXFRQUpEKFCql79+76z3/+Y/YJCAjQ4sWL1b9/f40fP14lS5bUV199pZCQELNPp06ddOHCBQ0ZMkRRUVGqUaOGli1blubidQAA8GjKV/NU5XfMUwUAQPZhnioAAICHAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoyqRJkyapTJkycnFxUZ06dfTHH3/kdkkAACAPIFRlwrx58xQeHq6hQ4dqx44deuKJJxQSEqLz58/ndmkAACCXEaoyYcyYMerdu7d69uypKlWqaOrUqSpYsKCmT5+e26UBAIBc5pjbBeQXCQkJ2r59uwYOHGi22dvbKzg4WJs3b053nfj4eMXHx5s/x8TESJJiY2Mtr+9m3DXLtwkAQH6SHe+vt2/XMIx79iNUZdDFixeVnJwsb29vm3Zvb28dOnQo3XVGjBihDz/8ME27v79/ttQIAMCj7N/ZvP2rV6/K3d39rssJVdlo4MCBCg8PN39OSUnRpUuXVLx4cdnZ2Vm2n9jYWPn7++vMmTNyc3OzbLuwxTjnHMY6ZzDOOYNxzjnZNdaGYejq1avy8/O7Zz9CVQZ5enrKwcFB0dHRNu3R0dHy8fFJdx1nZ2c5OzvbtHl4eGRXiXJzc+MPNgcwzjmHsc4ZjHPOYJxzTnaM9b3OUKXiQvUMcnJyUmBgoFatWmW2paSkaNWqVQoKCsrFygAAQF7AmapMCA8PV/fu3VWrVi3Vrl1b48aNU1xcnHr27JnbpQEAgFxGqMqETp066cKFCxoyZIiioqJUo0YNLVu2LM3F6znN2dlZQ4cOTfNRI6zFOOccxjpnMM45g3HOObk91nbG/e4PBAAAwH1xTRUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhV+cSkSZNUpkwZubi4qE6dOvrjjz/u2X/BggWqVKmSXFxcVL16dS1ZsiSHKs3fMjPOX375pRo2bKiiRYuqaNGiCg4Ovu/vBf+T2dd0qrlz58rOzk5t27bN3gIfEpkd5ytXrig0NFS+vr5ydnZWhQoVOH5kQGbHedy4capYsaJcXV3l7++v/v376+bNmzlUbf60fv16tW7dWn5+frKzs9PChQvvu87atWtVs2ZNOTs7q1y5cpo5c2b2Fmkgz5s7d67h5ORkTJ8+3di/f7/Ru3dvw8PDw4iOjk63/8aNGw0HBwcjIiLCOHDggDF48GCjQIECxt69e3O48vwls+PcpUsXY9KkScbOnTuNgwcPGj169DDc3d2Nv/76K4crz38yO9apTpw4Yfy///f/jIYNGxrPPvtszhSbj2V2nOPj441atWoZLVu2NH777TfjxIkTxtq1a41du3blcOX5S2bHefbs2Yazs7Mxe/Zs48SJE8by5csNX19fo3///jlcef6yZMkSY9CgQcaPP/5oSDJ++umne/Y/fvy4UbBgQSM8PNw4cOCAMXHiRMPBwcFYtmxZttVIqMoHateubYSGhpo/JycnG35+fsaIESPS7d+xY0ejVatWNm116tQxXn311WytM7/L7DjfKSkpyShSpIgxa9as7CrxoZGVsU5KSjLq1atnfPXVV0b37t0JVRmQ2XGeMmWK8dhjjxkJCQk5VeJDIbPjHBoaajRt2tSmLTw83Khfv3621vkwyUioeu+994yqVavatHXq1MkICQnJtrr4+C+PS0hI0Pbt2xUcHGy22dvbKzg4WJs3b053nc2bN9v0l6SQkJC79kfWxvlO169fV2JioooVK5ZdZT4UsjrW//nPf+Tl5aVevXrlRJn5XlbG+ZdfflFQUJBCQ0Pl7e2tatWq6ZNPPlFycnJOlZ3vZGWc69Wrp+3bt5sfER4/flxLlixRy5Ytc6TmR0VuvBfyNTV53MWLF5WcnJzmq3C8vb116NChdNeJiopKt39UVFS21ZnfZWWc7zRgwAD5+fml+SOGrayM9W+//aavv/5au3btyoEKHw5ZGefjx49r9erV6tq1q5YsWaI///xTffv2VWJiooYOHZoTZec7WRnnLl266OLFi2rQoIEMw1BSUpJee+01/fvf/86Jkh8Zd3svjI2N1Y0bN+Tq6mr5PjlTBVhg5MiRmjt3rn766Se5uLjkdjkPlatXr+qll17Sl19+KU9Pz9wu56GWkpIiLy8vTZs2TYGBgerUqZMGDRqkqVOn5nZpD5W1a9fqk08+0eTJk7Vjxw79+OOPWrx4sYYPH57bpeEBcaYqj/P09JSDg4Oio6Nt2qOjo+Xj45PuOj4+Ppnqj6yNc6rPPvtMI0eO1MqVK/X4449nZ5kPhcyO9bFjx3Ty5Em1bt3abEtJSZEkOTo66vDhwypbtmz2Fp0PZeU17evrqwIFCsjBwcFsq1y5sqKiopSQkCAnJ6dsrTk/yso4f/DBB3rppZf0yiuvSJKqV6+uuLg49enTR4MGDZK9Pec7rHC390I3N7dsOUslcaYqz3NyclJgYKBWrVpltqWkpGjVqlUKCgpKd52goCCb/pIUGRl51/7I2jhLUkREhIYPH65ly5apVq1aOVFqvpfZsa5UqZL27t2rXbt2mY82bdqoSZMm2rVrl/z9/XOy/HwjK6/p+vXr688//zRDqyQdOXJEvr6+BKq7yMo4X79+PU1wSg2yt67BhhVy5b0w2y6Bh2Xmzp1rODs7GzNnzjQOHDhg9OnTx/Dw8DCioqIMwzCMl156yXj//ffN/hs3bjQcHR2Nzz77zDh48KAxdOhQplTIgMyO88iRIw0nJyfj+++/N86dO2c+rl69mltPId/I7Fjfibv/Miaz43z69GmjSJEiRlhYmHH48GFj0aJFhpeXl/HRRx/l1lPIFzI7zkOHDjWKFCli/Pe//zWOHz9urFixwihbtqzRsWPH3HoK+cLVq1eNnTt3Gjt37jQkGWPGjDF27txpnDp1yjAMw3j//feNl156yeyfOqXCu+++axw8eNCYNGkSUyrglokTJxqlSpUynJycjNq1axu///67uaxx48ZG9+7dbfrPnz/fqFChguHk5GRUrVrVWLx4cQ5XnD9lZpxLly5tSErzGDp0aM4Xng9l9jV9O0JVxmV2nDdt2mTUqVPHcHZ2Nh577DHj448/NpKSknK46vwnM+OcmJhoDBs2zChbtqzh4uJi+Pv7G3379jUuX76c84XnI2vWrEn3mJs6tt27dzcaN26cZp0aNWoYTk5OxmOPPWbMmDEjW2u0MwzONQIAADworqkCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAAL/H9j5VcqElnFuAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l2g_gwas_curated=l2g_max_scores.join(gwascat_curated.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n",
+    "l2g_gwas_curated.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_gwas_curated_pics\")\n",
+    "\n",
+    "sample_size_quartiles = l2g_gwas_curated.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of l2g_gwas_curated PICS l2g scores: mean:\",  l2g_gwas_curated.select(f.mean(l2g_gwas_curated[\"score\"])).collect()[0][0],  \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary of l2g_gwas_sumstats PICS l2g scores: mean: 0.5751686123272477 L.quart:  0.23706066608428955 Median:  0.647024929523468 U.quart:  0.8808161020278931\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l2g_gwas_sumstats=l2g_max_scores.join(gwascat_sumstats.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n",
+    "l2g_gwas_sumstats.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_gwas_sumstats\")\n",
+    "\n",
+    "sample_size_quartiles = l2g_gwas_sumstats.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)\n",
+    "print(\"Summary of l2g_gwas_sumstats PICS l2g scores: mean:\",  l2g_gwas_sumstats.select(f.mean(l2g_gwas_sumstats[\"score\"])).collect()[0][0],  \"L.quart: \", sample_size_quartiles[0], \"Median: \", sample_size_quartiles[1], \"U.quart: \", sample_size_quartiles[2])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gentropy-NMtW8s8F-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From dee3085f56a2aeb2c6870b3ea8ca263ed0892e1d Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Thu, 21 Mar 2024 09:30:38 +0000
Subject: [PATCH 38/86] fix: small fixes in susie defaults (#552)

---
 src/gentropy/method/susie_inf.py        |  4 ++--
 tests/gentropy/method/test_susie_inf.py | 10 +++++++---
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/gentropy/method/susie_inf.py b/src/gentropy/method/susie_inf.py
index d493285a0..522f77193 100644
--- a/src/gentropy/method/susie_inf.py
+++ b/src/gentropy/method/susie_inf.py
@@ -34,7 +34,7 @@ def susie_inf(  # noqa: C901
         ssq_range: tuple[float, float] = (0, 1),
         pi0: np.ndarray | None = None,
         est_sigmasq: bool = True,
-        est_tausq: bool = True,
+        est_tausq: bool = False,
         sigmasq: float = 1,
         tausq: float = 0,
         sigmasq_range: tuple[float, float] | None = None,
@@ -399,7 +399,7 @@ def g(x: float) -> float:
     def cred_inf(
         PIP: np.ndarray,
         n: int = 100_000,
-        coverage: float = 0.9,
+        coverage: float = 0.99,
         purity: float = 0.5,
         LD: np.ndarray | None = None,
         V: np.ndarray | None = None,
diff --git a/tests/gentropy/method/test_susie_inf.py b/tests/gentropy/method/test_susie_inf.py
index d8e855a5b..b671bf274 100644
--- a/tests/gentropy/method/test_susie_inf.py
+++ b/tests/gentropy/method/test_susie_inf.py
@@ -16,7 +16,7 @@ def test_SUSIE_inf_lbf_moments(
         ld = sample_data_for_susie_inf[0]
         z = sample_data_for_susie_inf[1]
         lbf_moments = sample_data_for_susie_inf[2]
-        susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, method="moments")
+        susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, est_tausq=True, method="moments")
         lbf_calc = susie_output["lbf_variable"][:, 0]
         assert np.allclose(
             lbf_calc, lbf_moments
@@ -29,7 +29,7 @@ def test_SUSIE_inf_lbf_mle(
         ld = sample_data_for_susie_inf[0]
         z = sample_data_for_susie_inf[1]
         lbf_mle = sample_data_for_susie_inf[3]
-        susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, method="MLE")
+        susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, est_tausq=True, method="MLE")
         lbf_calc = susie_output["lbf_variable"][:, 0]
         assert np.allclose(
             lbf_calc, lbf_mle, atol=1e-1
@@ -41,6 +41,10 @@ def test_SUSIE_inf_cred(
         """Test of SuSiE-inf credible set generator."""
         ld = sample_data_for_susie_inf[0]
         z = sample_data_for_susie_inf[1]
-        susie_output = SUSIE_inf.susie_inf(z=z, LD=ld)
+        susie_output = SUSIE_inf.susie_inf(
+            z=z,
+            LD=ld,
+            est_tausq=True,
+        )
         cred = SUSIE_inf.cred_inf(susie_output["PIP"], LD=ld)
         assert cred[0] == [5]

From 512a80aa13de404ead7940b0fe95e035241e8dc7 Mon Sep 17 00:00:00 2001
From: xyg123 <33658607+xyg123@users.noreply.github.com>
Date: Thu, 21 Mar 2024 11:23:52 +0000
Subject: [PATCH 39/86] test(method): improved performance in coloc tests
 (#536)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix: pytest for coloc unit tests

* fix: removed unused coloc tests

* test(coloc): add coloc semantic test (#538)

* test(coloc): define fixtures and parametrise coloc tests

* test(coloc): compare dfs with assert_frame_equal

* fix: remove unused threshold variable

---------

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 .../coloc_test_data.snappy.parquet            | Bin 11927 -> 0 bytes
 .../method/test_colocalisation_method.py      | 233 ++++++------------
 2 files changed, 81 insertions(+), 152 deletions(-)
 delete mode 100644 tests/gentropy/data_samples/coloc_test_data.snappy.parquet

diff --git a/tests/gentropy/data_samples/coloc_test_data.snappy.parquet b/tests/gentropy/data_samples/coloc_test_data.snappy.parquet
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/tests/gentropy/method/test_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py
index f90d54b3f..37613354c 100644
--- a/tests/gentropy/method/test_colocalisation_method.py
+++ b/tests/gentropy/method/test_colocalisation_method.py
@@ -2,11 +2,14 @@
 
 from __future__ import annotations
 
+from typing import Any
+
+import pytest
 from gentropy.dataset.colocalisation import Colocalisation
 from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
 from gentropy.method.colocalisation import Coloc, ECaviar
+from pandas.testing import assert_frame_equal
 from pyspark.sql import SparkSession
-from pyspark.sql import functions as f
 
 
 def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:
@@ -14,165 +17,91 @@ def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:
     assert isinstance(Coloc.colocalise(mock_study_locus_overlap), Colocalisation)
 
 
-def test_coloc_colocalise(
-    spark: SparkSession,
-    threshold: float = 1e-5,
-) -> None:
-    """Compare COLOC results with R implementation, using provided sample dataset from R package (StudyLocusOverlap)."""
-    test_overlap_df = spark.read.parquet(
-        "tests/gentropy/data_samples/coloc_test_data.snappy.parquet", header=True
-    )
-    test_overlap = StudyLocusOverlap(test_overlap_df, StudyLocusOverlap.get_schema())
-    test_result = Coloc.colocalise(test_overlap)
-
-    expected = spark.createDataFrame(
-        [
-            {
-                "h0": 1.3769995397857477e-18,
-                "h1": 2.937336451601565e-10,
-                "h2": 8.593226431647826e-12,
-                "h3": 8.338916748775843e-4,
-                "h4": 0.9991661080227981,
-            }
-        ]
-    )
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
-
-
-def test_single_snp_coloc(
-    spark: SparkSession,
-    threshold: float = 1e-5,
-) -> None:
-    """Test edge case of coloc where only one causal SNP is present in the StudyLocusOverlap."""
-    test_overlap_df = spark.createDataFrame(
-        [
-            {
-                "leftStudyLocusId": 1,
-                "rightStudyLocusId": 2,
-                "chromosome": "1",
-                "tagVariantId": "snp",
-                "left_logBF": 10.3,
-                "right_logBF": 10.5,
-            }
-        ]
-    )
-    test_overlap = StudyLocusOverlap(
-        test_overlap_df.select(
-            "leftStudyLocusId",
-            "rightStudyLocusId",
-            "chromosome",
-            "tagVariantId",
-            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
+@pytest.mark.parametrize(
+    "observed_data, expected_data",
+    [
+        # associations with a single overlapping SNP
+        (
+            # observed overlap
+            [
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+            ],
+            # expected coloc
+            [
+                {
+                    "h0": 9.254841951638903e-5,
+                    "h1": 2.7517068829182966e-4,
+                    "h2": 3.3609423764447284e-4,
+                    "h3": 9.254841952564387e-13,
+                    "h4": 0.9992961866536217,
+                },
+            ],
         ),
-        StudyLocusOverlap.get_schema(),
-    )
-    test_result = Coloc.colocalise(test_overlap)
-
-    expected = spark.createDataFrame(
-        [
-            {
-                "h0": 9.254841951638903e-5,
-                "h1": 2.7517068829182966e-4,
-                "h2": 3.3609423764447284e-4,
-                "h3": 9.254841952564387e-13,
-                "h4": 0.9992961866536217,
-            }
-        ]
-    )
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
-
-
-def test_single_snp_coloc_one_negative(
+        # associations with multiple overlapping SNPs
+        (
+            # observed overlap
+            [
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp1",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+                {
+                    "leftStudyLocusId": 1,
+                    "rightStudyLocusId": 2,
+                    "chromosome": "1",
+                    "tagVariantId": "snp2",
+                    "statistics": {"left_logBF": 10.3, "right_logBF": 10.5},
+                },
+            ],
+            # expected coloc
+            [
+                {
+                    "h0": 4.6230151407950416e-5,
+                    "h1": 2.749086942648107e-4,
+                    "h2": 3.357742374172504e-4,
+                    "h3": 9.983447421747411e-4,
+                    "h4": 0.9983447421747356,
+                },
+            ],
+        ),
+    ],
+)
+def test_coloc_semantic(
     spark: SparkSession,
-    threshold: float = 1e-5,
+    observed_data: list[Any],
+    expected_data: list[Any],
 ) -> None:
-    """Test edge case of coloc where only one causal SNP is present (On one side!) in the StudyLocusOverlap."""
-    test_overlap_df = spark.createDataFrame(
-        [
-            {
-                "leftStudyLocusId": 1,
-                "rightStudyLocusId": 2,
-                "chromosome": "1",
-                "tagVariantId": "snp",
-                "left_logBF": 18.3,
-                "right_logBF": 0.01,
-            }
-        ]
+    """Test our COLOC with the implementation in R."""
+    observed_overlap = StudyLocusOverlap(
+        _df=spark.createDataFrame(observed_data, schema=StudyLocusOverlap.get_schema()),
+        _schema=StudyLocusOverlap.get_schema(),
     )
-    test_overlap = StudyLocusOverlap(
-        test_overlap_df.select(
-            "leftStudyLocusId",
-            "rightStudyLocusId",
-            "chromosome",
-            "tagVariantId",
-            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
-        ),
-        StudyLocusOverlap.get_schema(),
+    observed_coloc_pdf = (
+        Coloc.colocalise(observed_overlap)
+        .df.select("h0", "h1", "h2", "h3", "h4")
+        .toPandas()
     )
-    test_result = Coloc.colocalise(test_overlap)
-    test_result.df.show(1, False)
-    expected = spark.createDataFrame(
-        [
-            {
-                "h0": 1.0246538505087709e-4,
-                "h1": 0.9081680002273896,
-                "h2": 1.0349517929098209e-8,
-                "h3": 1.0246538506112363e-12,
-                "h4": 0.09172952403701702,
-            }
-        ]
+    expected_coloc_pdf = (
+        spark.createDataFrame(expected_data)
+        .select("h0", "h1", "h2", "h3", "h4")
+        .toPandas()
     )
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
 
-
-def test_single_snp_coloc_both_negative(
-    spark: SparkSession,
-    threshold: float = 1e-5,
-) -> None:
-    """Test edge case of coloc where only one non-causal SNP overlaps in the StudyLocusOverlap."""
-    test_overlap_df = spark.createDataFrame(
-        [
-            {
-                "leftStudyLocusId": 1,
-                "rightStudyLocusId": 2,
-                "chromosome": "1",
-                "tagVariantId": "snp",
-                "left_logBF": 0.03,
-                "right_logBF": 0.01,
-            }
-        ]
-    )
-    test_overlap = StudyLocusOverlap(
-        test_overlap_df.select(
-            "leftStudyLocusId",
-            "rightStudyLocusId",
-            "chromosome",
-            "tagVariantId",
-            f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
-        ),
-        StudyLocusOverlap.get_schema(),
-    )
-    test_result = Coloc.colocalise(test_overlap)
-    expected = spark.createDataFrame(
-        [
-            {
-                "h0": 0.9997855774090624,
-                "h1": 1.0302335812225042e-4,
-                "h2": 1.0098335895103664e-4,
-                "h3": 9.9978557750904e-9,
-                "h4": 1.0405876008495098e-5,
-            }
-        ]
+    assert_frame_equal(
+        observed_coloc_pdf,
+        expected_coloc_pdf,
+        check_exact=False,
+        check_dtype=True,
     )
-    difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
-    for col in difference.columns:
-        assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
 
 
 def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None:

From 232b1e0b3685a5af842191b5a6c871d51630a04b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Fri, 22 Mar 2024 13:59:05 +0000
Subject: [PATCH 40/86] perf(l2g): optimise extraction of features from
 colocalisation results (#553)

* perf(l2g): rewrite ColocalisationFactory._get_max_coloc_per_credible_set

* perf(l2g): more elegant rewrite ColocalisationFactory._get_max_coloc_per_credible_set

* chore: adapt tests to changes

* refactor: import colocalisation attributes
---
 src/gentropy/dataset/l2g_feature_matrix.py  |  15 +-
 src/gentropy/method/colocalisation.py       |   9 +-
 src/gentropy/method/l2g/feature_factory.py  | 185 +++++++++-----------
 tests/gentropy/method/test_locus_to_gene.py |  20 +--
 4 files changed, 94 insertions(+), 135 deletions(-)

diff --git a/src/gentropy/dataset/l2g_feature_matrix.py b/src/gentropy/dataset/l2g_feature_matrix.py
index 948477600..e5be1a019 100644
--- a/src/gentropy/dataset/l2g_feature_matrix.py
+++ b/src/gentropy/dataset/l2g_feature_matrix.py
@@ -5,8 +5,6 @@
 from functools import reduce
 from typing import TYPE_CHECKING, Type
 
-from pyspark.sql.functions import col
-
 from gentropy.common.schemas import parse_spark_schema
 from gentropy.common.spark_helpers import convert_from_long_to_wide
 from gentropy.dataset.dataset import Dataset
@@ -62,22 +60,13 @@ def generate_features(
         Raises:
             ValueError: If the feature matrix is empty
         """
-        coloc_methods = (
-            colocalisation.df.select("colocalisationMethod")
-            .distinct()
-            .toPandas()["colocalisationMethod"]
-            .tolist()
-        )
         if features_dfs := [
             # Extract features
             ColocalisationFactory._get_max_coloc_per_credible_set(
+                colocalisation,
                 credible_set,
                 study_index,
-                colocalisation.filter(col("colocalisationMethod") == method),
-                method,
-            ).df
-            for method in coloc_methods
-        ] + [
+            ).df,
             StudyLocusFactory._get_tss_distance_features(credible_set, variant_gene).df,
             StudyLocusFactory._get_vep_features(credible_set, variant_gene).df,
         ]:
diff --git a/src/gentropy/method/colocalisation.py b/src/gentropy/method/colocalisation.py
index 3e4e91c74..eaf5b9de6 100644
--- a/src/gentropy/method/colocalisation.py
+++ b/src/gentropy/method/colocalisation.py
@@ -26,6 +26,9 @@ class ECaviar:
     It extends [CAVIAR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142122/#bib18) framework to explicitly estimate the posterior probability that the same variant is causal in 2 studies while accounting for the uncertainty of LD. eCAVIAR computes the colocalization posterior probability (**CLPP**) by utilizing the marginal posterior probabilities. This framework allows for **multiple variants to be causal** in a single locus.
     """
 
+    METHOD_NAME: str = "eCAVIAR"
+    METHOD_METRIC: str = "clpp"
+
     @staticmethod
     def _get_clpp(left_pp: Column, right_pp: Column) -> Column:
         """Calculate the colocalisation posterior probability (CLPP).
@@ -81,7 +84,7 @@ def colocalise(
                     f.count("*").alias("numberColocalisingVariants"),
                     f.sum(f.col("clpp")).alias("clpp"),
                 )
-                .withColumn("colocalisationMethod", f.lit("eCAVIAR"))
+                .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME))
             ),
             _schema=Colocalisation.get_schema(),
         )
@@ -108,6 +111,8 @@ class Coloc:
         PSEUDOCOUNT (float): Pseudocount to avoid log(0). Defaults to 1e-10.
     """
 
+    METHOD_NAME: str = "COLOC"
+    METHOD_METRIC: str = "llr"
     PSEUDOCOUNT: float = 1e-10
 
     @staticmethod
@@ -253,7 +258,7 @@ def colocalise(
                     "lH3bf",
                     "lH4bf",
                 )
-                .withColumn("colocalisationMethod", f.lit("COLOC"))
+                .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME))
             ),
             _schema=Colocalisation.get_schema(),
         )
diff --git a/src/gentropy/method/l2g/feature_factory.py b/src/gentropy/method/l2g/feature_factory.py
index 5373be108..f037b57b4 100644
--- a/src/gentropy/method/l2g/feature_factory.py
+++ b/src/gentropy/method/l2g/feature_factory.py
@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 from functools import reduce
+from itertools import chain
 from typing import TYPE_CHECKING
 
 import pyspark.sql.functions as f
@@ -12,6 +13,7 @@
 )
 from gentropy.dataset.l2g_feature import L2GFeature
 from gentropy.dataset.study_locus import CredibleInterval, StudyLocus
+from gentropy.method.colocalisation import Coloc, ECaviar
 
 if TYPE_CHECKING:
     from pyspark.sql import Column, DataFrame
@@ -24,164 +26,135 @@
 class ColocalisationFactory:
     """Feature extraction in colocalisation."""
 
+    @classmethod
+    def _add_colocalisation_metric(cls: type[ColocalisationFactory]) -> Column:
+        """Expression that adds a `colocalisationMetric` column to the colocalisation dataframe in preparation for feature extraction.
+
+        Returns:
+            Column: The expression that adds a `colocalisationMetric` column with the derived metric
+        """
+        method_metric_map = {
+            ECaviar.METHOD_NAME: ECaviar.METHOD_METRIC,
+            Coloc.METHOD_NAME: Coloc.METHOD_METRIC,
+        }
+        map_expr = f.create_map(*[f.lit(x) for x in chain(*method_metric_map.items())])
+        return map_expr[f.col("colocalisationMethod")].alias("colocalisationMetric")
+
     @staticmethod
     def _get_max_coloc_per_credible_set(
+        colocalisation: Colocalisation,
         credible_set: StudyLocus,
         studies: StudyIndex,
-        colocalisation: Colocalisation,
-        colocalisation_method: str,
     ) -> L2GFeature:
         """Get the maximum colocalisation posterior probability for each pair of overlapping study-locus per type of colocalisation method and QTL type.
 
         Args:
+            colocalisation (Colocalisation): Colocalisation dataset
             credible_set (StudyLocus): Study locus dataset
             studies (StudyIndex): Study index dataset
-            colocalisation (Colocalisation): Colocalisation dataset
-            colocalisation_method (str): Colocalisation method to extract the max from
 
         Returns:
             L2GFeature: Stores the features with the max coloc probabilities for each pair of study-locus
-
-        Raises:
-            ValueError: If the colocalisation method is not supported
         """
-        if colocalisation_method not in ["COLOC", "eCAVIAR"]:
-            raise ValueError(
-                f"Colocalisation method {colocalisation_method} not supported"
-            )
-        if colocalisation_method == "COLOC":
-            coloc_score_col_name = "log2h4h3"
-            coloc_feature_col_template = "ColocLlrMaximum"
-
-        elif colocalisation_method == "eCAVIAR":
-            coloc_score_col_name = "clpp"
-            coloc_feature_col_template = "ColocClppMaximum"
+        colocalisation_df = colocalisation.df.select(
+            f.col("leftStudyLocusId").alias("studyLocusId"),
+            "rightStudyLocusId",
+            f.coalesce("log2h4h3", "clpp").alias("score"),
+            ColocalisationFactory._add_colocalisation_metric(),
+        )
 
         colocalising_credible_sets = (
             credible_set.df.select("studyLocusId", "studyId")
             # annotate studyLoci with overlapping IDs on the left - to just keep GWAS associations
             .join(
-                colocalisation.df.selectExpr(
-                    "leftStudyLocusId as studyLocusId",
-                    "rightStudyLocusId",
-                    "colocalisationMethod",
-                    f"{coloc_score_col_name} as coloc_score",
-                ),
+                colocalisation_df,
                 on="studyLocusId",
                 how="inner",
             )
             # bring study metadata to just keep QTL studies on the right
             .join(
-                credible_set.df.selectExpr(
-                    "studyLocusId as rightStudyLocusId", "studyId as right_studyId"
+                credible_set.df.join(
+                    studies.df.select("studyId", "studyType", "geneId"), "studyId"
+                ).selectExpr(
+                    "studyLocusId as rightStudyLocusId",
+                    "studyType as right_studyType",
+                    "geneId",
                 ),
                 on="rightStudyLocusId",
                 how="inner",
             )
-            .join(
-                f.broadcast(
-                    studies.df.selectExpr(
-                        "studyId as right_studyId",
-                        "studyType as right_studyType",
-                        "geneId",
-                    )
-                ),
-                on="right_studyId",
-                how="inner",
-            )
-            .filter(
-                (f.col("colocalisationMethod") == colocalisation_method)
-                & (f.col("right_studyType") != "gwas")
+            .filter(f.col("right_studyType") != "gwas")
+            .select(
+                "studyLocusId",
+                "right_studyType",
+                "geneId",
+                "score",
+                "colocalisationMetric",
             )
-            .select("studyLocusId", "right_studyType", "geneId", "coloc_score")
         )
 
-        # Max PP calculation per studyLocus AND type of QTL
-        local_max = get_record_with_maximum_value(
-            colocalising_credible_sets,
-            ["studyLocusId", "right_studyType", "geneId"],
-            "coloc_score",
+        # Max PP calculation per credible set AND type of QTL AND colocalisation method
+        local_max = (
+            get_record_with_maximum_value(
+                colocalising_credible_sets,
+                ["studyLocusId", "right_studyType", "geneId", "colocalisationMetric"],
+                "score",
+            )
+            .select(
+                "*",
+                f.col("score").alias("max_score"),
+                f.lit("Local").alias("score_type"),
+            )
+            .drop("score")
         )
 
-        intercept = 0.0001
         neighbourhood_max = (
             local_max.selectExpr(
-                "studyLocusId", "coloc_score as coloc_local_max", "geneId"
+                "studyLocusId", "max_score as local_max_score", "geneId"
             )
             .join(
                 # Add maximum in the neighborhood
                 get_record_with_maximum_value(
                     colocalising_credible_sets.withColumnRenamed(
-                        "coloc_score", "coloc_neighborhood_max"
+                        "score", "tmp_nbh_max_score"
                     ),
-                    ["studyLocusId", "right_studyType"],
-                    "coloc_neighborhood_max",
+                    ["studyLocusId", "right_studyType", "colocalisationMetric"],
+                    "tmp_nbh_max_score",
                 ).drop("geneId"),
                 on="studyLocusId",
             )
+            .withColumn("score_type", f.lit("Neighborhood"))
             .withColumn(
-                f"{coloc_feature_col_template}Neighborhood",
+                "max_score",
                 f.log10(
                     f.abs(
-                        f.col("coloc_local_max")
-                        - f.col("coloc_neighborhood_max")
-                        + f.lit(intercept)
+                        f.col("local_max_score")
+                        - f.col("tmp_nbh_max_score")
+                        + f.lit(0.0001)  # intercept
                     )
                 ),
             )
-        ).drop("coloc_neighborhood_max", "coloc_local_max")
-
-        # Split feature per molQTL
-        local_dfs = []
-        nbh_dfs = []
-        qtl_types: list[str] = (
-            colocalising_credible_sets.select("right_studyType")
-            .distinct()
-            .toPandas()["right_studyType"]
-            .tolist()
-        )
-        for qtl_type in qtl_types:
-            filtered_local_max = (
-                local_max.filter(f.col("right_studyType") == qtl_type)
-                .withColumnRenamed(
-                    "coloc_score",
-                    f"{qtl_type}{coloc_feature_col_template}",
-                )
-                .drop("right_studyType")
-            )
-            local_dfs.append(filtered_local_max)
-
-            filtered_neighbourhood_max = (
-                neighbourhood_max.filter(f.col("right_studyType") == qtl_type)
-                .withColumnRenamed(
-                    f"{coloc_feature_col_template}Neighborhood",
-                    f"{qtl_type}{coloc_feature_col_template}Neighborhood",
-                )
-                .drop("right_studyType")
-            )
-            nbh_dfs.append(filtered_neighbourhood_max)
-
-        wide_dfs = reduce(
-            lambda x, y: x.unionByName(y, allowMissingColumns=True),
-            local_dfs + nbh_dfs,
-        )
+        ).drop("tmp_nbh_max_score", "local_max_score")
 
         return L2GFeature(
-            _df=convert_from_wide_to_long(
-                wide_dfs.groupBy("studyLocusId", "geneId").agg(
-                    *(
-                        f.first(f.col(c), ignorenulls=True).alias(c)
-                        for c in wide_dfs.columns
-                        if c
-                        not in [
-                            "studyLocusId",
-                            "geneId",
-                        ]
-                    )
-                ),
-                id_vars=("studyLocusId", "geneId"),
-                var_name="featureName",
-                value_name="featureValue",
+            _df=(
+                # Combine local and neighborhood metrics
+                local_max.unionByName(
+                    neighbourhood_max, allowMissingColumns=True
+                ).select(
+                    "studyLocusId",
+                    "geneId",
+                    # Feature name is a concatenation of the QTL type, colocalisation metric and if it's local or in the vicinity
+                    f.concat_ws(
+                        "",
+                        f.col("right_studyType"),
+                        f.lit("Coloc"),
+                        f.initcap(f.col("colocalisationMetric")),
+                        f.lit("Maximum"),
+                        f.regexp_replace(f.col("score_type"), "Local", ""),
+                    ).alias("featureName"),
+                    f.col("max_score").cast("float").alias("featureValue"),
+                )
             ),
             _schema=L2GFeature.get_schema(),
         )
diff --git a/tests/gentropy/method/test_locus_to_gene.py b/tests/gentropy/method/test_locus_to_gene.py
index 3976406c9..898252f9f 100644
--- a/tests/gentropy/method/test_locus_to_gene.py
+++ b/tests/gentropy/method/test_locus_to_gene.py
@@ -80,30 +80,21 @@ def test_train(
 class TestColocalisationFactory:
     """Test the ColocalisationFactory methods."""
 
-    @pytest.mark.parametrize(
-        "colocalisation_method",
-        [
-            "COLOC",
-            "eCAVIAR",
-        ],
-    )
     def test_get_max_coloc_per_credible_set(
         self: TestColocalisationFactory,
         mock_study_locus: StudyLocus,
         mock_study_index: StudyIndex,
         mock_colocalisation: Colocalisation,
-        colocalisation_method: str,
     ) -> None:
         """Test the function that extracts the maximum log likelihood ratio for each pair of overlapping study-locus returns the right data type."""
         coloc_features = ColocalisationFactory._get_max_coloc_per_credible_set(
+            mock_colocalisation,
             mock_study_locus,
             mock_study_index,
-            mock_colocalisation,
-            colocalisation_method,
         )
         assert isinstance(
             coloc_features, L2GFeature
-        ), "Unexpected model type returned from _get_max_coloc_per_credible_set"
+        ), "Unexpected type returned from _get_max_coloc_per_credible_set"
 
     def test_get_max_coloc_per_credible_set_semantic(
         self: TestColocalisationFactory,
@@ -169,8 +160,10 @@ def test_get_max_coloc_per_credible_set_semantic(
                         "colocalisationMethod": "eCAVIAR",
                         "numberColocalisingVariants": 1,
                         "clpp": 0.81,  # 0.9*0.9
+                        "log2h4h3": None,
                     }
-                ]
+                ],
+                schema=Colocalisation.get_schema(),
             ),
             _schema=Colocalisation.get_schema(),
         )
@@ -183,10 +176,9 @@ def test_get_max_coloc_per_credible_set_semantic(
         )
         # Test
         coloc_features = ColocalisationFactory._get_max_coloc_per_credible_set(
+            coloc,
             credset,
             studies,
-            coloc,
-            "eCAVIAR",
         )
         assert coloc_features.df.collect() == expected_coloc_features_df.collect()
 

From 8f9d2680edb25545f71d9df518989f6e817ac233 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Fri, 22 Mar 2024 18:01:05 +0000
Subject: [PATCH 41/86] fix(coloc): handle cases when the bayes factors are
 null (#556)

* fix(coloc): fillna doesnt fill nested data

* test(coloc): added test_coloc_no_logbf (semantic)

* revert(ecaviar): revert accidental changes
---
 src/gentropy/method/colocalisation.py         | 18 +++----
 .../method/test_colocalisation_method.py      | 52 +++++++++++++++++++
 2 files changed, 61 insertions(+), 9 deletions(-)

diff --git a/src/gentropy/method/colocalisation.py b/src/gentropy/method/colocalisation.py
index eaf5b9de6..18d97fdf8 100644
--- a/src/gentropy/method/colocalisation.py
+++ b/src/gentropy/method/colocalisation.py
@@ -159,24 +159,24 @@ def colocalise(
         posteriors = f.udf(Coloc._get_posteriors, VectorUDT())
         return Colocalisation(
             _df=(
-                overlapping_signals.df
+                overlapping_signals.df.select("*", "statistics.*")
                 # Before summing log_BF columns nulls need to be filled with 0:
-                .fillna(0, subset=["statistics.left_logBF", "statistics.right_logBF"])
+                .fillna(0, subset=["left_logBF", "right_logBF"])
                 # Sum of log_BFs for each pair of signals
                 .withColumn(
                     "sum_log_bf",
-                    f.col("statistics.left_logBF") + f.col("statistics.right_logBF"),
+                    f.col("left_logBF") + f.col("right_logBF"),
                 )
                 # Group by overlapping peak and generating dense vectors of log_BF:
                 .groupBy("chromosome", "leftStudyLocusId", "rightStudyLocusId")
                 .agg(
                     f.count("*").alias("numberColocalisingVariants"),
-                    fml.array_to_vector(
-                        f.collect_list(f.col("statistics.left_logBF"))
-                    ).alias("left_logBF"),
-                    fml.array_to_vector(
-                        f.collect_list(f.col("statistics.right_logBF"))
-                    ).alias("right_logBF"),
+                    fml.array_to_vector(f.collect_list(f.col("left_logBF"))).alias(
+                        "left_logBF"
+                    ),
+                    fml.array_to_vector(f.collect_list(f.col("right_logBF"))).alias(
+                        "right_logBF"
+                    ),
                     fml.array_to_vector(f.collect_list(f.col("sum_log_bf"))).alias(
                         "sum_log_bf"
                     ),
diff --git a/tests/gentropy/method/test_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py
index 37613354c..e58b0e562 100644
--- a/tests/gentropy/method/test_colocalisation_method.py
+++ b/tests/gentropy/method/test_colocalisation_method.py
@@ -10,6 +10,7 @@
 from gentropy.method.colocalisation import Coloc, ECaviar
 from pandas.testing import assert_frame_equal
 from pyspark.sql import SparkSession
+from pyspark.sql.types import DoubleType, LongType, StringType, StructField, StructType
 
 
 def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:
@@ -104,6 +105,57 @@ def test_coloc_semantic(
     )
 
 
+def test_coloc_no_logbf(
+    spark: SparkSession,
+    minimum_expected_h0: float = 0.99,
+    maximum_expected_h4: float = 1e-5,
+) -> None:
+    """Test COLOC output when the input data has irrelevant logBF."""
+    observed_overlap = StudyLocusOverlap(
+        (
+            spark.createDataFrame(
+                [
+                    {
+                        "leftStudyLocusId": 1,
+                        "rightStudyLocusId": 2,
+                        "chromosome": "1",
+                        "tagVariantId": "snp",
+                        "statistics": {
+                            "left_logBF": None,
+                            "right_logBF": None,
+                        },  # irrelevant for COLOC
+                    }
+                ],
+                schema=StructType(
+                    [
+                        StructField("leftStudyLocusId", LongType(), False),
+                        StructField("rightStudyLocusId", LongType(), False),
+                        StructField("chromosome", StringType(), False),
+                        StructField("tagVariantId", StringType(), False),
+                        StructField(
+                            "statistics",
+                            StructType(
+                                [
+                                    StructField("left_logBF", DoubleType(), True),
+                                    StructField("right_logBF", DoubleType(), True),
+                                ]
+                            ),
+                        ),
+                    ]
+                ),
+            )
+        ),
+        StudyLocusOverlap.get_schema(),
+    )
+    observed_coloc_df = Coloc.colocalise(observed_overlap).df
+    assert (
+        observed_coloc_df.select("h0").collect()[0]["h0"] > minimum_expected_h0
+    ), "COLOC should return a high h0 (no association) when the input data has irrelevant logBF."
+    assert (
+        observed_coloc_df.select("h4").collect()[0]["h4"] < maximum_expected_h4
+    ), "COLOC should return a low h4 (traits are associated) when the input data has irrelevant logBF."
+
+
 def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None:
     """Test eCAVIAR."""
     assert isinstance(ECaviar.colocalise(mock_study_locus_overlap), Colocalisation)

From 255c42d4b6ad7cbf86a5fa0090bbbe7aaef4e53b Mon Sep 17 00:00:00 2001
From: Joris Louwen <62542407+louwenjjr@users.noreply.github.com>
Date: Tue, 26 Mar 2024 17:24:03 +0100
Subject: [PATCH 42/86] fix(sumstats): correct study id for dir of finngen
 studies (#551)

---
 src/gentropy/datasource/finngen/summary_stats.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/gentropy/datasource/finngen/summary_stats.py b/src/gentropy/datasource/finngen/summary_stats.py
index 0d77f7d5c..08403bed5 100644
--- a/src/gentropy/datasource/finngen/summary_stats.py
+++ b/src/gentropy/datasource/finngen/summary_stats.py
@@ -50,7 +50,6 @@ def from_source(
         Returns:
             SummaryStatistics: Processed summary statistics dataset
         """
-        study_id = raw_file.split("/")[-1].split(".")[0].upper()
         processed_summary_stats_df = (
             spark.read.schema(cls.raw_schema)
             .option("delimiter", "\t")
@@ -59,7 +58,11 @@ def from_source(
             .filter(f.col("pos").cast(t.IntegerType()).isNotNull())
             .select(
                 # From the full path, extracts just the filename, and converts to upper case to get the study ID.
-                f.lit(study_id).alias("studyId"),
+                f.upper(
+                    f.regexp_extract(
+                        f.input_file_name(), r"([^/]+)(\.tsv\.gz|\.gz|\.tsv)", 1
+                    )
+                ).alias("studyId"),
                 # Add variant information.
                 f.concat_ws(
                     "_",

From d76ebbef03a130a43d9d89a5daa21c24f9a88907 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Tue, 2 Apr 2024 14:52:29 +0100
Subject: [PATCH 43/86] feat: add the step class for fine-mapping (#554)

* feat: add the step class for fine-mapping

* test: adding test for susie to studylocus converter

* chore: fix the class description

* chore: answering comments

---------

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>
---
 src/gentropy/susie_finemapper.py        | 140 ++++++++++++++++++++++++
 tests/gentropy/method/test_susie_inf.py |  29 +++++
 2 files changed, 169 insertions(+)
 create mode 100644 src/gentropy/susie_finemapper.py

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
new file mode 100644
index 000000000..bd02716c2
--- /dev/null
+++ b/src/gentropy/susie_finemapper.py
@@ -0,0 +1,140 @@
+"""Step to run a finemapping using."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pyspark.sql.functions as f
+from pyspark.sql import DataFrame, Window
+
+from gentropy.common.session import Session
+from gentropy.dataset.study_locus import StudyLocus
+
+
+class SusieFineMapperStep:
+    """SuSie finemaping. It has generic methods to run SuSie fine mapping for a study locus.
+
+    This class/step is the temporary solution of the fine-mapping warpper for the development purposes.
+    In the future this step will be refactored and moved to the methods module.
+    """
+
+    @staticmethod
+    def susie_inf_to_studylocus(
+        susie_output: dict[str, Any],
+        session: Session,
+        _studyId: str,
+        _region: str,
+        variant_index: DataFrame,
+        cs_lbf_thr: float = 2,
+    ) -> StudyLocus:
+        """Convert SuSiE-inf output to studyLocus DataFrame.
+
+        Args:
+            susie_output (dict[str, Any]): SuSiE-inf output dictionary
+            session (Session): Spark session
+            _studyId (str): study ID
+            _region (str): region
+            variant_index (DataFrame): DataFrame with variant information
+            cs_lbf_thr (float): credible set logBF threshold, default is 2
+
+        Returns:
+            StudyLocus: StudyLocus object with fine-mapped credible sets
+        """
+        variants = np.array(
+            [row["variantId"] for row in variant_index.select("variantId").collect()]
+        ).reshape(-1, 1)
+        PIPs = susie_output["PIP"]
+        lbfs = susie_output["lbf_variable"]
+        mu = susie_output["mu"]
+        susie_result = np.hstack((variants, PIPs, lbfs, mu))
+
+        L_snps = PIPs.shape[1]
+
+        # Extracting credible sets
+        order_creds = list(enumerate(susie_output["lbf"]))
+        order_creds.sort(key=lambda x: x[1], reverse=True)
+        cred_sets = None
+        counter = 0
+        for i, cs_lbf_value in order_creds:
+            if counter > 0 and cs_lbf_value < cs_lbf_thr:
+                counter += 1
+                continue
+            counter += 1
+            sorted_arr = susie_result[
+                susie_result[:, i + 1].astype(float).argsort()[::-1]
+            ]
+            cumsum_arr = np.cumsum(sorted_arr[:, i + 1].astype(float))
+            filter_row = np.argmax(cumsum_arr >= 0.99)
+            if filter_row == 0 and cumsum_arr[0] < 0.99:
+                filter_row = len(cumsum_arr)
+            filter_row += 1
+            filtered_arr = sorted_arr[:filter_row]
+            cred_set = filtered_arr[:, [0, i + 1, i + L_snps + 1, i + 2 * L_snps + 1]]
+            win = Window.rowsBetween(
+                Window.unboundedPreceding, Window.unboundedFollowing
+            )
+            cred_set = (
+                session.spark.createDataFrame(
+                    cred_set.tolist(),
+                    ["variantId", "posteriorProbability", "logBF", "beta"],
+                )
+                .join(
+                    variant_index.select(
+                        "variantId",
+                        "chromosome",
+                        "position",
+                    ),
+                    "variantId",
+                )
+                .sort(f.desc("posteriorProbability"))
+                .withColumn(
+                    "locus",
+                    f.collect_list(
+                        f.struct(
+                            f.col("variantId").cast("string").alias("variantId"),
+                            f.col("posteriorProbability")
+                            .cast("double")
+                            .alias("posteriorProbability"),
+                            f.col("logBF").cast("double").alias("logBF"),
+                            f.col("beta").cast("double").alias("beta"),
+                        )
+                    ).over(win),
+                )
+                .limit(1)
+                .withColumns(
+                    {
+                        "studyId": f.lit(_studyId),
+                        "region": f.lit(_region),
+                        "credibleSetIndex": f.lit(counter),
+                        "credibleSetlog10BF": f.lit(cs_lbf_value * 0.4342944819),
+                        "finemappingMethod": f.lit("SuSiE-inf"),
+                    }
+                )
+                .withColumn(
+                    "studyLocusId",
+                    StudyLocus.assign_study_locus_id(
+                        f.col("studyId"), f.col("variantId")
+                    ),
+                )
+                .select(
+                    "studyLocusId",
+                    "studyId",
+                    "region",
+                    "credibleSetIndex",
+                    "locus",
+                    "variantId",
+                    "chromosome",
+                    "position",
+                    "finemappingMethod",
+                    "credibleSetlog10BF",
+                )
+            )
+            if cred_sets is None:
+                cred_sets = cred_set
+            else:
+                cred_sets = cred_sets.unionByName(cred_set)
+        return StudyLocus(
+            _df=cred_sets,
+            _schema=StudyLocus.get_schema(),
+        )
diff --git a/tests/gentropy/method/test_susie_inf.py b/tests/gentropy/method/test_susie_inf.py
index b671bf274..393f786d7 100644
--- a/tests/gentropy/method/test_susie_inf.py
+++ b/tests/gentropy/method/test_susie_inf.py
@@ -3,7 +3,11 @@
 from __future__ import annotations
 
 import numpy as np
+from gentropy.common.session import Session
+from gentropy.dataset.study_locus import StudyLocus
+from gentropy.dataset.summary_statistics import SummaryStatistics
 from gentropy.method.susie_inf import SUSIE_inf
+from gentropy.susie_finemapper import SusieFineMapperStep
 
 
 class TestSUSIE_inf:
@@ -48,3 +52,28 @@ def test_SUSIE_inf_cred(
         )
         cred = SUSIE_inf.cred_inf(susie_output["PIP"], LD=ld)
         assert cred[0] == [5]
+
+    def test_SUSIE_inf_convert_to_study_locus(
+        self: TestSUSIE_inf,
+        sample_data_for_susie_inf: list[np.ndarray],
+        sample_summary_statistics: SummaryStatistics,
+        session: Session,
+    ) -> None:
+        """Test of SuSiE-inf credible set generator."""
+        ld = sample_data_for_susie_inf[0]
+        z = sample_data_for_susie_inf[1]
+        susie_output = SUSIE_inf.susie_inf(
+            z=z,
+            LD=ld,
+            est_tausq=False,
+        )
+        gwas_df = sample_summary_statistics._df.limit(21)
+        L1 = SusieFineMapperStep.susie_inf_to_studylocus(
+            susie_output=susie_output,
+            session=session,
+            _studyId="sample_id",
+            _region="sample_region",
+            variant_index=gwas_df,
+            cs_lbf_thr=2,
+        )
+        assert isinstance(L1, StudyLocus), "L1 is not an instance of StudyLocus"

From 56067e7562e3369dcee8e109093519ba66512567 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Wed, 3 Apr 2024 13:37:46 +0100
Subject: [PATCH 44/86] feat: LD index and block matrix extraction for a
 studyLocus (#463)

* test: adding test for pairwiseLD

* feat: adding ld matrix extraction

* chore: merge from dev

* feat: index and block matrix extraction for studyLocus

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* chore: updating some test files to gentropy

* chore: updating tests

* chore: updating pairwise_ld_schema for tests

* chore: updating pairwise_ld tests

* chore: fix ld_pairwise tests

* chore: fix pairwise_ld tests

* chore: fix tests

* chore: fix tests

* chore: fixing typing for tests

* chore: fixing tests

* chore: fixing ld tests

* Update src/gentropy/dataset/study_index.py

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>

* feat: moving functions to their appropriate locations and improving logic

* fix: optimise conversion of BM to NumPy

* feat: updating get_locus_index to allow for just chromosome and position inputs

* fix: suggested changes

* Update study_index.py

* fix: changes to datasource/gnomad/ld.py

* feat: updated method for ld_index extraction

* fix: sorting idx in hail

---------

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 src/gentropy/assets/schemas/pairwise_ld.json |  23 ++++
 src/gentropy/dataset/pairwise_ld.py          | 104 ++++++++++++++
 src/gentropy/datasource/gnomad/ld.py         | 135 ++++++++++++++-----
 tests/gentropy/dataset/test_pairwise_ld.py   | 102 ++++++++++++++
 4 files changed, 332 insertions(+), 32 deletions(-)
 create mode 100644 src/gentropy/assets/schemas/pairwise_ld.json
 create mode 100644 src/gentropy/dataset/pairwise_ld.py
 create mode 100644 tests/gentropy/dataset/test_pairwise_ld.py

diff --git a/src/gentropy/assets/schemas/pairwise_ld.json b/src/gentropy/assets/schemas/pairwise_ld.json
new file mode 100644
index 000000000..bac781ac3
--- /dev/null
+++ b/src/gentropy/assets/schemas/pairwise_ld.json
@@ -0,0 +1,23 @@
+{
+  "fields": [
+    {
+      "metadata": {},
+      "name": "variantIdI",
+      "nullable": false,
+      "type": "string"
+    },
+    {
+      "metadata": {},
+      "name": "variantIdJ",
+      "nullable": false,
+      "type": "string"
+    },
+    {
+      "metadata": {},
+      "name": "r",
+      "nullable": false,
+      "type": "double"
+    }
+  ],
+  "type": "struct"
+}
diff --git a/src/gentropy/dataset/pairwise_ld.py b/src/gentropy/dataset/pairwise_ld.py
new file mode 100644
index 000000000..9650efa32
--- /dev/null
+++ b/src/gentropy/dataset/pairwise_ld.py
@@ -0,0 +1,104 @@
+"""Pairwise LD dataset."""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from math import sqrt
+from typing import TYPE_CHECKING
+
+import numpy as np
+from pyspark.sql import functions as f
+from pyspark.sql import types as t
+
+from gentropy.common.schemas import parse_spark_schema
+from gentropy.dataset.dataset import Dataset
+
+if TYPE_CHECKING:
+    from pyspark.sql.types import StructType
+
+
+@dataclass
+class PairwiseLD(Dataset):
+    """Pairwise variant correlation dataset.
+
+    This class captures logic applied on pairwise linkage data + by validation ensuring data quality.
+    """
+
+    dimension: tuple[int, int] = field(init=False)
+
+    def __post_init__(self: PairwiseLD) -> None:
+        """Validating the dataset upon creation.
+
+        - Besides the schema, a pairwise LD table is expected have rows being a square number.
+        """
+        row_count = self.df.count()
+
+        assert (
+            int(sqrt(row_count)) == sqrt(row_count)
+        ), f"The number of rows in a pairwise LD table has to be square. Got: {row_count}"
+
+        self.dimension = (int(sqrt(row_count)), int(sqrt(row_count)))
+
+    @classmethod
+    def get_schema(cls: type[PairwiseLD]) -> StructType:
+        """Provide the schema for the StudyIndex dataset.
+
+        Returns:
+            StructType: The schema of the StudyIndex dataset.
+        """
+        return parse_spark_schema("pairwise_ld.json")
+
+    def overlap_with_locus(self: PairwiseLD, locus_variants: list[str]) -> PairwiseLD:
+        """Subset pairwise LD table with locus.
+
+        Args:
+            locus_variants (list[str]): List of variants found in the locus.
+
+        Returns:
+            PairwiseLD: _description_
+        """
+        return PairwiseLD(
+            _df=(
+                self.df.filter(
+                    f.col("variantIdI").isin(locus_variants)
+                    & f.col("variantIdJ").isin(locus_variants)
+                )
+            ),
+            _schema=PairwiseLD.get_schema(),
+        )
+
+    def r_to_numpy_matrix(self) -> np.ndarray:
+        """Convert pairwise LD to a numpy square matrix.
+
+        Returns:
+            np.ndarray: 2D square matrix with r values.
+        """
+        return np.array(
+            self.df.select(
+                f.split("variantIdI", "_")[1].cast(t.IntegerType()).alias("position_i"),
+                f.split("variantIdJ", "_")[1].cast(t.IntegerType()).alias("position_j"),
+                "r",
+            )
+            .orderBy(f.col("position_i").asc(), f.col("position_j").asc())
+            .select("r")
+            .collect()
+        ).reshape(self.dimension)
+
+    def get_variant_list(self) -> list[str]:
+        """Return a list of unique variants from the dataset.
+
+        Returns:
+            list[str]: list of variant identifiers sorted by position.
+        """
+        return [
+            row["variantId"]
+            for row in (
+                self.df.select(
+                    f.col("variantIdI").alias("variantId"),
+                    f.split(f.col("variantIdI"), "_")[1]
+                    .cast(t.IntegerType())
+                    .alias("position"),
+                )
+                .orderBy(f.col("position").asc())
+                .collect()
+            )
+        ]
diff --git a/src/gentropy/datasource/gnomad/ld.py b/src/gentropy/datasource/gnomad/ld.py
index 1d2d6c18f..a19cbb06b 100644
--- a/src/gentropy/datasource/gnomad/ld.py
+++ b/src/gentropy/datasource/gnomad/ld.py
@@ -7,6 +7,7 @@
 from typing import TYPE_CHECKING
 
 import hail as hl
+import numpy as np
 import pyspark.sql.functions as f
 from hail.linalg import BlockMatrix
 from pyspark.sql import Window
@@ -16,7 +17,7 @@
 from gentropy.dataset.ld_index import LDIndex
 
 if TYPE_CHECKING:
-    from pyspark.sql import DataFrame
+    from pyspark.sql import DataFrame, Row
 
 
 @dataclass
@@ -34,6 +35,7 @@ class GnomADLDMatrix:
 
     ld_matrix_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.adj.ld.bm"
     ld_index_raw_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.ld.variant_indices.ht"
+    liftover_ht_path: str = "gs://gcp-public-data--gnomad/release/2.1.1/liftover_grch38/ht/genomes/gnomad.genomes.r2.1.1.sites.liftover_grch38.ht"
     grch37_to_grch38_chain_path: str = (
         "gs://hail-common/references/grch37_to_grch38.over.chain.gz"
     )
@@ -218,9 +220,9 @@ def _resolve_variant_indices(
             DataFrame: Dataframe with variant IDs instead of `i` and `j` indices
         """
         ld_index_i = ld_index.selectExpr(
-            "idx as i", "variantId as variantId_i", "chromosome"
+            "idx as i", "variantId as variantIdI", "chromosome"
         )
-        ld_index_j = ld_index.selectExpr("idx as j", "variantId as variantId_j")
+        ld_index_j = ld_index.selectExpr("idx as j", "variantId as variantIdJ")
         return (
             ld_matrix.join(ld_index_i, on="i", how="inner")
             .join(ld_index_j, on="j", how="inner")
@@ -238,35 +240,35 @@ def _transpose_ld_matrix(ld_matrix: DataFrame) -> DataFrame:
             DataFrame: Square LD matrix without diagonal duplicates
 
         Examples:
-            >>> df = spark.createDataFrame(
-            ...     [
-            ...         (1, 1, 1.0, "1", "AFR"),
-            ...         (1, 2, 0.5, "1", "AFR"),
-            ...         (2, 2, 1.0, "1", "AFR"),
-            ...     ],
-            ...     ["variantId_i", "variantId_j", "r", "chromosome", "population"],
-            ... )
-            >>> GnomADLDMatrix._transpose_ld_matrix(df).show()
-            +-----------+-----------+---+----------+----------+
-            |variantId_i|variantId_j|  r|chromosome|population|
-            +-----------+-----------+---+----------+----------+
-            |          1|          2|0.5|         1|       AFR|
-            |          1|          1|1.0|         1|       AFR|
-            |          2|          1|0.5|         1|       AFR|
-            |          2|          2|1.0|         1|       AFR|
-            +-----------+-----------+---+----------+----------+
-            <BLANKLINE>
+        >>> df = spark.createDataFrame(
+        ...     [
+        ...         (1, 1, 1.0, "1", "AFR"),
+        ...         (1, 2, 0.5, "1", "AFR"),
+        ...         (2, 2, 1.0, "1", "AFR"),
+        ...     ],
+        ...     ["variantIdI", "variantIdJ", "r", "chromosome", "population"],
+        ... )
+        >>> GnomADLDMatrix._transpose_ld_matrix(df).show()
+        +----------+----------+---+----------+----------+
+        |variantIdI|variantIdJ|  r|chromosome|population|
+        +----------+----------+---+----------+----------+
+        |         1|         2|0.5|         1|       AFR|
+        |         1|         1|1.0|         1|       AFR|
+        |         2|         1|0.5|         1|       AFR|
+        |         2|         2|1.0|         1|       AFR|
+        +----------+----------+---+----------+----------+
+        <BLANKLINE>
         """
         ld_matrix_transposed = ld_matrix.selectExpr(
-            "variantId_i as variantId_j",
-            "variantId_j as variantId_i",
+            "variantIdI as variantIdJ",
+            "variantIdJ as variantIdI",
             "r",
             "chromosome",
             "population",
         )
-        return ld_matrix.filter(
-            f.col("variantId_i") != f.col("variantId_j")
-        ).unionByName(ld_matrix_transposed)
+        return ld_matrix.filter(f.col("variantIdI") != f.col("variantIdJ")).unionByName(
+            ld_matrix_transposed
+        )
 
     def as_ld_index(
         self: GnomADLDMatrix,
@@ -307,8 +309,8 @@ def as_ld_index(
             GnomADLDMatrix._transpose_ld_matrix(
                 reduce(lambda df1, df2: df1.unionByName(df2), ld_indices_unaggregated)
             )
-            .withColumnRenamed("variantId_i", "variantId")
-            .withColumnRenamed("variantId_j", "tagVariantId")
+            .withColumnRenamed("variantIdI", "variantId")
+            .withColumnRenamed("variantIdJ", "tagVariantId")
         )
         return LDIndex(
             _df=self._aggregate_ld_index_across_populations(ld_index_unaggregated),
@@ -345,7 +347,6 @@ def get_ld_variants(
                 & (f.col("position") <= end)
             )
             .select("chromosome", "position", "variantId", "idx")
-            .persist()
         )
 
         if ld_index_df.limit(1).count() == 0:
@@ -395,7 +396,7 @@ def _extract_square_matrix(
             .join(
                 ld_index_df.select(
                     f.col("idx").alias("idx_i"),
-                    f.col("variantId").alias("variantId_i"),
+                    f.col("variantId").alias("variantIdI"),
                 ),
                 on="idx_i",
                 how="inner",
@@ -403,12 +404,12 @@ def _extract_square_matrix(
             .join(
                 ld_index_df.select(
                     f.col("idx").alias("idx_j"),
-                    f.col("variantId").alias("variantId_j"),
+                    f.col("variantId").alias("variantIdJ"),
                 ),
                 on="idx_j",
                 how="inner",
             )
-            .select("variantId_i", "variantId_j", "r")
+            .select("variantIdI", "variantIdJ", "r")
         )
 
     def get_ld_matrix_slice(
@@ -448,3 +449,73 @@ def get_ld_matrix_slice(
                 .alias("r"),
             )
         )
+
+    def get_locus_index(
+        self: GnomADLDMatrix,
+        study_locus_row: Row,
+        window_size: int = 1_000_000,
+        major_population: str = "nfe",
+    ) -> DataFrame:
+        """Extract hail matrix index from StudyLocus rows.
+
+        Args:
+            study_locus_row (Row): Study-locus row
+            window_size (int): Window size to extract from gnomad matrix
+            major_population (str): Major population to extract from gnomad matrix, default is "nfe"
+
+        Returns:
+            DataFrame: Returns the index of the gnomad matrix for the locus
+
+        """
+        chromosome = str("chr" + study_locus_row["chromosome"])
+        start = study_locus_row["position"] - window_size // 2
+        end = study_locus_row["position"] + window_size // 2
+
+        liftover_ht = hl.read_table(self.liftover_ht_path)
+        liftover_ht = (
+            liftover_ht.filter(
+                (liftover_ht.locus.contig == chromosome)
+                & (liftover_ht.locus.position >= start)
+                & (liftover_ht.locus.position <= end)
+            )
+            .key_by()
+            .select("locus", "alleles", "original_locus")
+            .key_by("original_locus", "alleles")
+            .naive_coalesce(20)
+        )
+
+        hail_index = hl.read_table(
+            self.ld_index_raw_template.format(POP=major_population)
+        )
+
+        joined_index = (
+            liftover_ht.join(hail_index, how="inner").order_by("idx").to_spark()
+        )
+
+        return joined_index
+
+    @staticmethod
+    def get_numpy_matrix(
+        locus_index: DataFrame,
+        gnomad_ancestry: str = "nfe",
+    ) -> np.ndarray:
+        """Extract the LD block matrix for a locus.
+
+        Args:
+            locus_index (DataFrame): hail matrix variant index table
+            gnomad_ancestry (str): GnomAD major ancestry label eg. `nfe`
+
+        Returns:
+            np.ndarray: LD block matrix for the locus
+        """
+        idx = [row["idx"] for row in locus_index.select("idx").collect()]
+
+        half_matrix = (
+            BlockMatrix.read(
+                GnomADLDMatrix.ld_matrix_template.format(POP=gnomad_ancestry)
+            )
+            .filter(idx, idx)
+            .to_numpy()
+        )
+
+        return (half_matrix + half_matrix.T) - np.diag(np.diag(half_matrix))
diff --git a/tests/gentropy/dataset/test_pairwise_ld.py b/tests/gentropy/dataset/test_pairwise_ld.py
new file mode 100644
index 000000000..11ebf75ca
--- /dev/null
+++ b/tests/gentropy/dataset/test_pairwise_ld.py
@@ -0,0 +1,102 @@
+"""Testing pairwise LD dataset."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import pytest
+from gentropy.dataset.pairwise_ld import PairwiseLD
+from pyspark.sql import functions as f
+from pyspark.sql.window import Window
+
+if TYPE_CHECKING:
+    from pyspark.sql import SparkSession
+
+
+class TestPairwiseLD:
+    """Test suit for pairwise LD dataset and associated methods."""
+
+    variants = [
+        "1_8_A_C",
+        "1_9_A_C",
+        "1_10_A_C",
+        "1_99_A_C",
+    ]
+
+    @pytest.fixture(scope="class")
+    def mock_pairwise_ld(self: TestPairwiseLD, spark: SparkSession) -> PairwiseLD:
+        """Generate a mock pairwise LD dataset.
+
+        Args:
+            spark (SparkSession): _description_
+
+        Returns:
+            PairwiseLD: _description_
+        """
+        spark = spark.builder.getOrCreate()
+
+        data = [(v1, v2) for v1 in self.variants for v2 in self.variants]
+        return PairwiseLD(
+            _df=(
+                spark.createDataFrame(data, ["variantIdI", "variantIdJ"])
+                .withColumn(
+                    "r",
+                    f.row_number()
+                    .over(Window.partitionBy(f.lit("x")).orderBy("variantIdI"))
+                    .cast("double"),
+                )
+                .withColumn(
+                    "r",
+                    f.when(f.col("variantIdI") == f.col("variantIdJ"), 1.0).otherwise(
+                        f.col("r")
+                    ),
+                )
+                .persist()
+            ),
+            _schema=PairwiseLD.get_schema(),
+        )
+
+    @staticmethod
+    def test_pairwise_ld__type(mock_pairwise_ld: PairwiseLD) -> None:
+        """Testing type."""
+        assert isinstance(mock_pairwise_ld, PairwiseLD)
+
+    def test_pariwise_ld__get_variants(
+        self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD
+    ) -> None:
+        """Testing function that returns list of variants from the LD table.
+
+        Args:
+            mock_pairwise_ld (PairwiseLD): _description_
+        """
+        variant_set_expected = set(self.variants)
+        variant_set_from_data = set(mock_pairwise_ld.get_variant_list())
+
+        assert variant_set_from_data == variant_set_expected
+
+    def test_pairwise_ld__r_to_numpy_matrix__type(
+        self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD
+    ) -> None:
+        """Testing the returned numpy array."""
+        assert isinstance(mock_pairwise_ld.r_to_numpy_matrix(), np.ndarray)
+
+    def test_pairwise_ld__r_to_numpy_matrix__dimensions(
+        self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD
+    ) -> None:
+        """Testing the returned numpy array."""
+        assert mock_pairwise_ld.r_to_numpy_matrix().shape == (
+            len(self.variants),
+            len(self.variants),
+        )
+
+    def test_pairwise_ld__overlap_with_locus(
+        self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD
+    ) -> None:
+        """Testing the returned numpy array."""
+        variant_subset = self.variants[1:3]
+
+        assert (
+            mock_pairwise_ld.overlap_with_locus(variant_subset).df.count()
+            == len(variant_subset) ** 2
+        )

From e1d20f38e3a470d3b7340d054cee717640bd6870 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Fri, 5 Apr 2024 12:50:51 +0100
Subject: [PATCH 45/86] feat: the FineMapper function for one locus (#564)

* test: adding test for pairwiseLD

* feat: adding ld matrix extraction

* chore: merge from dev

* feat: index and block matrix extraction for studyLocus

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* chore: updating some test files to gentropy

* chore: updating tests

* chore: updating pairwise_ld_schema for tests

* chore: updating pairwise_ld tests

* chore: fix ld_pairwise tests

* chore: fix pairwise_ld tests

* chore: fix tests

* chore: fix tests

* chore: fixing typing for tests

* chore: fixing tests

* chore: fixing ld tests

* Update src/gentropy/dataset/study_index.py

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>

* feat: moving functions to their appropriate locations and improving logic

* fix: optimise conversion of BM to NumPy

* feat: updating get_locus_index to allow for just chromosome and position inputs

* fix: suggested changes

* Update study_index.py

* fix: changes to datasource/gnomad/ld.py

* feat: add the draft of finemapper fucntion

* feat: updated method for ld_index extraction

* fix: changing input

* fix: adding fillter by studyId

* fix: sorting idx in hail

* feat: add fine-mapping of one study_locus_row

* fix: small fix in majpop

* fix: small fixes in function

* fix: using more spark before converting to pandas

* fix: fix in test

---------

Co-authored-by: Daniel Suveges <daniel.suveges@protonmail.com>
Co-authored-by: Daniel Considine <dc16@sanger.ac.uk>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
---
 src/gentropy/susie_finemapper.py        | 139 ++++++++++++++++++++++--
 tests/gentropy/method/test_susie_inf.py |   5 +-
 2 files changed, 135 insertions(+), 9 deletions(-)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index bd02716c2..03e1c2a0b 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -5,11 +5,17 @@
 from typing import Any
 
 import numpy as np
+import pandas as pd
 import pyspark.sql.functions as f
-from pyspark.sql import DataFrame, Window
+from pyspark.sql import DataFrame, Row, Window
+from pyspark.sql.types import IntegerType, StringType, StructField, StructType
 
 from gentropy.common.session import Session
+from gentropy.dataset.study_index import StudyIndex
 from gentropy.dataset.study_locus import StudyLocus
+from gentropy.dataset.summary_statistics import SummaryStatistics
+from gentropy.datasource.gnomad.ld import GnomADLDMatrix
+from gentropy.method.susie_inf import SUSIE_inf
 
 
 class SusieFineMapperStep:
@@ -19,12 +25,129 @@ class SusieFineMapperStep:
     In the future this step will be refactored and moved to the methods module.
     """
 
+    @staticmethod
+    def susie_finemapper_one_studylocus_row(
+        GWAS: SummaryStatistics,
+        session: Session,
+        study_locus_row: Row,
+        study_index: StudyIndex,
+        window: int = 1_000_000,
+        L: int = 10,
+    ) -> StudyLocus:
+        """Susie fine-mapper function that uses Summary Statstics, chromosome and position as inputs.
+
+        Args:
+            GWAS (SummaryStatistics): GWAS summary statistics
+            session (Session): Spark session
+            study_locus_row (Row): StudyLocus row
+            study_index (StudyIndex): StudyIndex object
+            window (int): window size for fine-mapping
+            L (int): number of causal variants
+
+        Returns:
+            StudyLocus: StudyLocus object with fine-mapped credible sets
+        """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
+        chromosome = study_locus_row["chromosome"]
+        position = study_locus_row["position"]
+        studyId = study_locus_row["studyId"]
+
+        study_index_df = study_index._df
+        study_index_df = study_index_df.filter(f.col("studyId") == studyId)
+        major_population = study_index_df.select(
+            "studyId",
+            f.array_max(f.col("ldPopulationStructure"))
+            .getItem("ldPopulation")
+            .alias("majorPopulation"),
+        ).collect()[0]["majorPopulation"]
+
+        region = (
+            chromosome
+            + ":"
+            + str(int(position - window / 2))
+            + "-"
+            + str(int(position + window / 2))
+        )
+
+        gwas_df = (
+            GWAS.df.withColumn("z", f.col("beta") / f.col("standardError"))
+            .withColumn("chromosome", f.split(f.col("variantId"), "_")[0])
+            .withColumn("position", f.split(f.col("variantId"), "_")[1])
+            .filter(f.col("studyId") == studyId)
+            .filter(f.col("z").isNotNull())
+        )
+
+        ld_index = (
+            GnomADLDMatrix()
+            .get_locus_index(
+                study_locus_row=study_locus_row,
+                window_size=window,
+                major_population=major_population,
+            )
+            .withColumn(
+                "variantId",
+                f.concat(
+                    f.lit(chromosome),
+                    f.lit("_"),
+                    f.col("`locus.position`"),
+                    f.lit("_"),
+                    f.col("alleles").getItem(0),
+                    f.lit("_"),
+                    f.col("alleles").getItem(1),
+                ).cast("string"),
+            )
+        )
+
+        # Filtering out the variants that are not in the LD matrix, we don't need them
+        gwas_index = gwas_df.join(
+            ld_index.select("variantId", "alleles", "idx"), on="variantId"
+        ).sort("idx")
+
+        gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
+            gwas_index, gnomad_ancestry=major_population
+        )
+
+        pd_df = gwas_index.toPandas()
+        z_to_fm = np.array(pd_df["z"])
+        ld_to_fm = gnomad_ld
+
+        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=L)
+
+        schema = StructType(
+            [
+                StructField("variantId", StringType(), True),
+                StructField("chromosome", StringType(), True),
+                StructField("position", IntegerType(), True),
+            ]
+        )
+        pd_df["position"] = pd_df["position"].astype(int)
+        variant_index = session.spark.createDataFrame(
+            pd_df[
+                [
+                    "variantId",
+                    "chromosome",
+                    "position",
+                ]
+            ],
+            schema=schema,
+        )
+
+        return SusieFineMapperStep.susie_inf_to_studylocus(
+            susie_output=susie_output,
+            session=session,
+            studyId=studyId,
+            region=region,
+            variant_index=variant_index,
+        )
+
     @staticmethod
     def susie_inf_to_studylocus(
         susie_output: dict[str, Any],
         session: Session,
-        _studyId: str,
-        _region: str,
+        studyId: str,
+        region: str,
         variant_index: DataFrame,
         cs_lbf_thr: float = 2,
     ) -> StudyLocus:
@@ -33,8 +156,8 @@ def susie_inf_to_studylocus(
         Args:
             susie_output (dict[str, Any]): SuSiE-inf output dictionary
             session (Session): Spark session
-            _studyId (str): study ID
-            _region (str): region
+            studyId (str): study ID
+            region (str): region
             variant_index (DataFrame): DataFrame with variant information
             cs_lbf_thr (float): credible set logBF threshold, default is 2
 
@@ -44,6 +167,7 @@ def susie_inf_to_studylocus(
         variants = np.array(
             [row["variantId"] for row in variant_index.select("variantId").collect()]
         ).reshape(-1, 1)
+
         PIPs = susie_output["PIP"]
         lbfs = susie_output["lbf_variable"]
         mu = susie_output["mu"]
@@ -74,6 +198,7 @@ def susie_inf_to_studylocus(
             win = Window.rowsBetween(
                 Window.unboundedPreceding, Window.unboundedFollowing
             )
+
             cred_set = (
                 session.spark.createDataFrame(
                     cred_set.tolist(),
@@ -104,8 +229,8 @@ def susie_inf_to_studylocus(
                 .limit(1)
                 .withColumns(
                     {
-                        "studyId": f.lit(_studyId),
-                        "region": f.lit(_region),
+                        "studyId": f.lit(studyId),
+                        "region": f.lit(region),
                         "credibleSetIndex": f.lit(counter),
                         "credibleSetlog10BF": f.lit(cs_lbf_value * 0.4342944819),
                         "finemappingMethod": f.lit("SuSiE-inf"),
diff --git a/tests/gentropy/method/test_susie_inf.py b/tests/gentropy/method/test_susie_inf.py
index 393f786d7..0129a5934 100644
--- a/tests/gentropy/method/test_susie_inf.py
+++ b/tests/gentropy/method/test_susie_inf.py
@@ -68,11 +68,12 @@ def test_SUSIE_inf_convert_to_study_locus(
             est_tausq=False,
         )
         gwas_df = sample_summary_statistics._df.limit(21)
+
         L1 = SusieFineMapperStep.susie_inf_to_studylocus(
             susie_output=susie_output,
             session=session,
-            _studyId="sample_id",
-            _region="sample_region",
+            studyId="sample_id",
+            region="sample_region",
             variant_index=gwas_df,
             cs_lbf_thr=2,
         )

From b76fd07909a717572e9038a5b02a53f907c61f43 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Fri, 5 Apr 2024 20:43:59 +0100
Subject: [PATCH 46/86] feat: susie_finemapper_ss_gathered() (#567)

* feat: susie_finemapper_ss_gathered()

* fix: docstring updates
---
 src/gentropy/susie_finemapper.py | 119 ++++++++++++++++++++++++++++++-
 1 file changed, 117 insertions(+), 2 deletions(-)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index 03e1c2a0b..c5d86b1a8 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -34,7 +34,7 @@ def susie_finemapper_one_studylocus_row(
         window: int = 1_000_000,
         L: int = 10,
     ) -> StudyLocus:
-        """Susie fine-mapper function that uses Summary Statstics, chromosome and position as inputs.
+        """Susie fine-mapper for StudyLocus row with SummaryStatistics object.
 
         Args:
             GWAS (SummaryStatistics): GWAS summary statistics
@@ -151,7 +151,7 @@ def susie_inf_to_studylocus(
         variant_index: DataFrame,
         cs_lbf_thr: float = 2,
     ) -> StudyLocus:
-        """Convert SuSiE-inf output to studyLocus DataFrame.
+        """Convert SuSiE-inf output to StudyLocus DataFrame.
 
         Args:
             susie_output (dict[str, Any]): SuSiE-inf output dictionary
@@ -263,3 +263,118 @@ def susie_inf_to_studylocus(
             _df=cred_sets,
             _schema=StudyLocus.get_schema(),
         )
+
+    @staticmethod
+    def susie_finemapper_ss_gathered(
+        session: Session,
+        study_locus_row: Row,
+        study_index: StudyIndex,
+        window: int = 1_000_000,
+        L: int = 10,
+    ) -> StudyLocus:
+        """Susie fine-mapper for StudyLocus row with locus annotated summary statistics.
+
+        Args:
+            session (Session): Spark session
+            study_locus_row (Row): StudyLocus row
+            study_index (StudyIndex): StudyIndex object
+            window (int): window size for fine-mapping
+            L (int): number of causal variants
+
+        Returns:
+            StudyLocus: StudyLocus object with fine-mapped credible sets
+        """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
+        chromosome = study_locus_row["chromosome"]
+        position = study_locus_row["position"]
+        studyId = study_locus_row["studyId"]
+
+        study_index_df = study_index._df
+        study_index_df = study_index_df.filter(f.col("studyId") == studyId)
+        major_population = study_index_df.select(
+            "studyId",
+            f.array_max(f.col("ldPopulationStructure"))
+            .getItem("ldPopulation")
+            .alias("majorPopulation"),
+        ).collect()[0]["majorPopulation"]
+
+        region = (
+            chromosome
+            + ":"
+            + str(int(position - window / 2))
+            + "-"
+            + str(int(position + window / 2))
+        )
+
+        gwas_df = (
+            session.spark.createDataFrame(study_locus_row.locus)
+            .withColumn("z", f.col("beta") / f.col("standardError"))
+            .withColumn("chromosome", f.split(f.col("variantId"), "_")[0])
+            .withColumn("position", f.split(f.col("variantId"), "_")[1])
+            .filter(f.col("z").isNotNull())
+        )
+
+        ld_index = (
+            GnomADLDMatrix()
+            .get_locus_index(
+                study_locus_row=study_locus_row,
+                window_size=window,
+                major_population=major_population,
+            )
+            .withColumn(
+                "variantId",
+                f.concat(
+                    f.lit(chromosome),
+                    f.lit("_"),
+                    f.col("`locus.position`"),
+                    f.lit("_"),
+                    f.col("alleles").getItem(0),
+                    f.lit("_"),
+                    f.col("alleles").getItem(1),
+                ).cast("string"),
+            )
+        )
+
+        # Filtering out the variants that are not in the LD matrix, we don't need them
+        gwas_index = gwas_df.join(
+            ld_index.select("variantId", "alleles", "idx"), on="variantId"
+        ).sort("idx")
+
+        gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
+            gwas_index, gnomad_ancestry=major_population
+        )
+
+        pd_df = gwas_index.toPandas()
+        z_to_fm = np.array(pd_df["z"])
+        ld_to_fm = gnomad_ld
+
+        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=L)
+
+        schema = StructType(
+            [
+                StructField("variantId", StringType(), True),
+                StructField("chromosome", StringType(), True),
+                StructField("position", IntegerType(), True),
+            ]
+        )
+        pd_df["position"] = pd_df["position"].astype(int)
+        variant_index = session.spark.createDataFrame(
+            pd_df[
+                [
+                    "variantId",
+                    "chromosome",
+                    "position",
+                ]
+            ],
+            schema=schema,
+        )
+
+        return SusieFineMapperStep.susie_inf_to_studylocus(
+            susie_output=susie_output,
+            session=session,
+            studyId=studyId,
+            region=region,
+            variant_index=variant_index,
+        )

From 86600b0df666f4ed2874f446c3831fe2baa8ff13 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Tue, 9 Apr 2024 12:24:09 +0100
Subject: [PATCH 47/86] feat: add FM step with carma and sumstat imputation
 (#568)

* feat: add fm step with carma and sumstat imputation

* fix: adding log

* fix: fixing carma

* fix: resolving conflict

* fix: resolve conflicts with dev v2

* fix: siliencing FutureWarning in Carma
---
 src/gentropy/method/carma.py     |  19 +-
 src/gentropy/susie_finemapper.py | 296 ++++++++++++++++++++++++++++++-
 2 files changed, 310 insertions(+), 5 deletions(-)

diff --git a/src/gentropy/method/carma.py b/src/gentropy/method/carma.py
index 75cb32c79..af8816706 100644
--- a/src/gentropy/method/carma.py
+++ b/src/gentropy/method/carma.py
@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import concurrent.futures
+import warnings
 from itertools import combinations
 from math import floor, lgamma
 from typing import Any
@@ -32,6 +33,8 @@ def time_limited_CARMA_spike_slab_noEM(
                 - B_list: A dataframe containing the marginal likelihoods and the corresponding model space or None.
                 - Outliers: A list of outlier SNPs or None.
         """
+        # Ignore pandas future warnings
+        warnings.simplefilter(action="ignore", category=FutureWarning)
         try:
             # Execute CARMA.CARMA_spike_slab_noEM with a timeout
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
@@ -854,9 +857,19 @@ def _MCS_modified(  # noqa: C901
                         sec_sample = np.random.choice(
                             range(0, 3), 1, p=np.exp(aa) / np.sum(np.exp(aa))
                         )
-                        S = set_gamma[sec_sample[0]][
-                            int(set_star["gamma_set_index"][sec_sample[0]])
-                        ].tolist()
+                        if set_gamma[sec_sample[0]] is not None:
+                            S = set_gamma[sec_sample[0]][
+                                int(set_star["gamma_set_index"][sec_sample[0]])
+                            ].tolist()
+                        else:
+                            sec_sample = np.random.choice(
+                                range(1, 3),
+                                1,
+                                p=np.exp(aa)[[1, 2]] / np.sum(np.exp(aa)[[1, 2]]),
+                            )
+                            S = set_gamma[sec_sample[0]][
+                                int(set_star["gamma_set_index"][sec_sample[0]])
+                            ].tolist()
 
                 for item in conditional_S:
                     if item not in S:
diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index c5d86b1a8..aba5b4c40 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import time
 from typing import Any
 
 import numpy as np
@@ -15,6 +16,8 @@
 from gentropy.dataset.study_locus import StudyLocus
 from gentropy.dataset.summary_statistics import SummaryStatistics
 from gentropy.datasource.gnomad.ld import GnomADLDMatrix
+from gentropy.method.carma import CARMA
+from gentropy.method.sumstat_imputation import SummaryStatisticsImputation
 from gentropy.method.susie_inf import SUSIE_inf
 
 
@@ -150,6 +153,7 @@ def susie_inf_to_studylocus(
         region: str,
         variant_index: DataFrame,
         cs_lbf_thr: float = 2,
+        sum_pips: float = 0.99,
     ) -> StudyLocus:
         """Convert SuSiE-inf output to StudyLocus DataFrame.
 
@@ -160,6 +164,7 @@ def susie_inf_to_studylocus(
             region (str): region
             variant_index (DataFrame): DataFrame with variant information
             cs_lbf_thr (float): credible set logBF threshold, default is 2
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
 
         Returns:
             StudyLocus: StudyLocus object with fine-mapped credible sets
@@ -189,8 +194,8 @@ def susie_inf_to_studylocus(
                 susie_result[:, i + 1].astype(float).argsort()[::-1]
             ]
             cumsum_arr = np.cumsum(sorted_arr[:, i + 1].astype(float))
-            filter_row = np.argmax(cumsum_arr >= 0.99)
-            if filter_row == 0 and cumsum_arr[0] < 0.99:
+            filter_row = np.argmax(cumsum_arr >= sum_pips)
+            if filter_row == 0 and cumsum_arr[0] < sum_pips:
                 filter_row = len(cumsum_arr)
             filter_row += 1
             filtered_arr = sorted_arr[:filter_row]
@@ -378,3 +383,290 @@ def susie_finemapper_ss_gathered(
             region=region,
             variant_index=variant_index,
         )
+
+    @staticmethod
+    def susie_finemapper_from_prepared_dataframes(
+        GWAS_df: DataFrame,
+        ld_index: DataFrame,
+        gnomad_ld: np.ndarray,
+        L: int,
+        session: Session,
+        studyId: str,
+        region: str,
+        susie_est_tausq: bool = False,
+        run_carma: bool = False,
+        run_sumstat_imputation: bool = False,
+        carma_time_limit: int = 600,
+        imputed_r2_threshold: float = 0.8,
+        ld_score_threshold: float = 4,
+        sum_pips: float = 0.99,
+    ) -> dict[str, Any]:
+        """Susie fine-mapper function that uses LD, z-scores, variant info and other options for Fine-Mapping.
+
+        Args:
+            GWAS_df (DataFrame): GWAS DataFrame with mandotary columns: z, variantId
+            ld_index (DataFrame): LD index DataFrame
+            gnomad_ld (np.ndarray): GnomAD LD matrix
+            L (int): number of causal variants
+            session (Session): Spark session
+            studyId (str): study ID
+            region (str): region
+            susie_est_tausq (bool): estimate tau squared, default is False
+            run_carma (bool): run CARMA, default is False
+            run_sumstat_imputation (bool): run summary statistics imputation, default is False
+            carma_time_limit (int): CARMA time limit, default is 600 seconds
+            imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
+            ld_score_threshold (float): LD score threshold ofr imputation, default is 4
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+
+        Returns:
+            dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
+        """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
+        start_time = time.time()
+        GWAS_df = GWAS_df.toPandas()
+        ld_index = ld_index.toPandas()
+        ld_index = ld_index.reset_index()
+
+        N_gwas = len(GWAS_df)
+        N_ld = len(ld_index)
+
+        # Filtering out the variants that are not in the LD matrix, we don't need them
+        df_columns = ["variantId", "z"]
+        GWAS_df = GWAS_df.merge(ld_index, on="variantId", how="inner")
+        GWAS_df = GWAS_df[df_columns].reset_index()
+        N_after_merge = len(GWAS_df)
+
+        merged_df = GWAS_df.merge(
+            ld_index, left_on="variantId", right_on="variantId", how="inner"
+        )
+        indices = merged_df["index_y"].values
+
+        ld_to_fm = gnomad_ld[indices][:, indices]
+        z_to_fm = GWAS_df["z"].values
+
+        if run_carma:
+            carma_output = CARMA.time_limited_CARMA_spike_slab_noEM(
+                z=z_to_fm, ld=ld_to_fm, sec_threshold=carma_time_limit
+            )
+            if carma_output["Outliers"] != [] and carma_output["Outliers"] is not None:
+                GWAS_df.drop(carma_output["Outliers"], inplace=True)
+                GWAS_df = GWAS_df.reset_index()
+                ld_index = ld_index.reset_index()
+                merged_df = GWAS_df.merge(
+                    ld_index, left_on="variantId", right_on="variantId", how="inner"
+                )
+                indices = merged_df["index_y"].values
+
+                ld_to_fm = gnomad_ld[indices][:, indices]
+                z_to_fm = GWAS_df["z"].values
+                N_outliers = len(carma_output["Outliers"])
+            else:
+                N_outliers = 0
+        else:
+            N_outliers = 0
+
+        if run_sumstat_imputation:
+            known = indices
+            unknown = [
+                index for index in list(range(len(gnomad_ld))) if index not in known
+            ]
+            sig_t = gnomad_ld[known, :][:, known]
+            sig_i_t = gnomad_ld[unknown, :][:, known]
+            zt = z_to_fm
+
+            sumstat_imp_res = SummaryStatisticsImputation.raiss_model(
+                z_scores_known=zt,
+                ld_matrix_known=sig_t,
+                ld_matrix_known_missing=sig_i_t,
+                lamb=0.01,
+                rtol=0.01,
+            )
+
+            bool_index = (sumstat_imp_res["imputation_r2"] >= imputed_r2_threshold) * (
+                sumstat_imp_res["ld_score"] >= ld_score_threshold
+            )
+            if sum(bool_index) >= 1:
+                indices = np.where(bool_index)[0]
+                index_to_add = [unknown[i] for i in indices]
+                index_to_fm = np.concatenate((known, index_to_add))
+
+                ld_to_fm = gnomad_ld[index_to_fm][:, index_to_fm]
+
+                snp_info_to_add = pd.DataFrame(
+                    {
+                        "variantId": ld_index.iloc[index_to_add, :]["variantId"],
+                        "z": sumstat_imp_res["mu"][indices],
+                    }
+                )
+                GWAS_df = pd.concat([GWAS_df, snp_info_to_add], ignore_index=True)
+                z_to_fm = GWAS_df["z"].values
+
+                N_imputed = len(indices)
+            else:
+                N_imputed = 0
+        else:
+            N_imputed = 0
+
+        susie_output = SUSIE_inf.susie_inf(
+            z=z_to_fm, LD=ld_to_fm, L=L, est_tausq=susie_est_tausq
+        )
+
+        schema = StructType([StructField("variantId", StringType(), True)])
+        variant_index = (
+            session.spark.createDataFrame(
+                GWAS_df[["variantId"]],
+                schema=schema,
+            )
+            .withColumn(
+                "chromosome", f.split(f.col("variantId"), "_")[0].cast("string")
+            )
+            .withColumn("position", f.split(f.col("variantId"), "_")[1].cast("int"))
+        )
+
+        study_locus = SusieFineMapperStep.susie_inf_to_studylocus(
+            susie_output=susie_output,
+            session=session,
+            studyId=studyId,
+            region=region,
+            variant_index=variant_index,
+            sum_pips=sum_pips,
+        )
+
+        end_time = time.time()
+
+        log_df = pd.DataFrame(
+            {
+                "N_gwas": N_gwas,
+                "N_ld": N_ld,
+                "N_overlap": N_after_merge,
+                "N_outliers": N_outliers,
+                "N_imputed": N_imputed,
+                "N_final_to_fm": len(ld_to_fm),
+                "eleapsed_time": end_time - start_time,
+            },
+            index=[0],
+        )
+
+        return {
+            "study_locus": study_locus,
+            "log": log_df,
+        }
+
+    @staticmethod
+    def susie_finemapper_one_studylocus_row_v2_dev(
+        GWAS: SummaryStatistics,
+        session: Session,
+        study_locus_row: Row,
+        study_index: StudyIndex,
+        window: int = 1_000_000,
+        L: int = 10,
+        susie_est_tausq: bool = False,
+        run_carma: bool = False,
+        run_sumstat_imputation: bool = False,
+        carma_time_limit: int = 600,
+        imputed_r2_threshold: float = 0.8,
+        ld_score_threshold: float = 4,
+        sum_pips: float = 0.99,
+    ) -> dict[str, Any]:
+        """Susie fine-mapper function that uses Summary Statstics, chromosome and position as inputs.
+
+        Args:
+            GWAS (SummaryStatistics): GWAS summary statistics
+            session (Session): Spark session
+            study_locus_row (Row): StudyLocus row
+            study_index (StudyIndex): StudyIndex object
+            window (int): window size for fine-mapping
+            L (int): number of causal variants
+            susie_est_tausq (bool): estimate tau squared, default is False
+            run_carma (bool): run CARMA, default is False
+            run_sumstat_imputation (bool): run summary statistics imputation, default is False
+            carma_time_limit (int): CARMA time limit, default is 600 seconds
+            imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
+            ld_score_threshold (float): LD score threshold ofr imputation, default is 4
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+
+        Returns:
+            dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
+        """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
+        chromosome = study_locus_row["chromosome"]
+        position = study_locus_row["position"]
+        studyId = study_locus_row["studyId"]
+
+        study_index_df = study_index._df
+        study_index_df = study_index_df.filter(f.col("studyId") == studyId)
+        major_population = study_index_df.select(
+            "studyId",
+            f.array_max(f.col("ldPopulationStructure"))
+            .getItem("ldPopulation")
+            .alias("majorPopulation"),
+        ).collect()[0]["majorPopulation"]
+
+        region = (
+            chromosome
+            + ":"
+            + str(int(position - window / 2))
+            + "-"
+            + str(int(position + window / 2))
+        )
+        gwas_df = (
+            GWAS.df.withColumn("z", f.col("beta") / f.col("standardError"))
+            .withColumn(
+                "chromosome", f.split(f.col("variantId"), "_")[0].cast("string")
+            )
+            .withColumn("position", f.split(f.col("variantId"), "_")[1].cast("int"))
+            .filter(f.col("studyId") == studyId)
+            .filter(f.col("z").isNotNull())
+            .filter(f.col("chromosome") == chromosome)
+            .filter(f.col("position") >= position - window / 2)
+            .filter(f.col("position") <= position + window / 2)
+        )
+
+        ld_index = (
+            GnomADLDMatrix()
+            .get_locus_index(
+                study_locus_row=study_locus_row,
+                window_size=window,
+                major_population=major_population,
+            )
+            .withColumn(
+                "variantId",
+                f.concat(
+                    f.lit(chromosome),
+                    f.lit("_"),
+                    f.col("`locus.position`"),
+                    f.lit("_"),
+                    f.col("alleles").getItem(0),
+                    f.lit("_"),
+                    f.col("alleles").getItem(1),
+                ).cast("string"),
+            )
+        )
+
+        gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
+            ld_index, gnomad_ancestry=major_population
+        )
+
+        out = SusieFineMapperStep.susie_finemapper_from_prepared_dataframes(
+            GWAS_df=gwas_df,
+            ld_index=ld_index,
+            gnomad_ld=gnomad_ld,
+            L=L,
+            session=session,
+            studyId=studyId,
+            region=region,
+            susie_est_tausq=susie_est_tausq,
+            run_carma=run_carma,
+            run_sumstat_imputation=run_sumstat_imputation,
+            carma_time_limit=carma_time_limit,
+            imputed_r2_threshold=imputed_r2_threshold,
+            ld_score_threshold=ld_score_threshold,
+            sum_pips=sum_pips,
+        )
+
+        return out

From dc4e3674d30baf736fd540871c1fc00179777290 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Wed, 10 Apr 2024 13:23:44 +0100
Subject: [PATCH 48/86] fix: adding deduplication for GWAS in locus (#573)

---
 src/gentropy/susie_finemapper.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index aba5b4c40..db62fe359 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -427,6 +427,11 @@ def susie_finemapper_from_prepared_dataframes(
 
         start_time = time.time()
         GWAS_df = GWAS_df.toPandas()
+        N_gwas_before_dedupl = len(GWAS_df)
+
+        GWAS_df = GWAS_df.drop_duplicates(subset="variantId", keep=False)
+        GWAS_df = GWAS_df.reset_index()
+
         ld_index = ld_index.toPandas()
         ld_index = ld_index.reset_index()
 
@@ -539,6 +544,7 @@ def susie_finemapper_from_prepared_dataframes(
 
         log_df = pd.DataFrame(
             {
+                "N_gwas_before_dedupl": N_gwas_before_dedupl,
                 "N_gwas": N_gwas,
                 "N_ld": N_ld,
                 "N_overlap": N_after_merge,

From a5b62f2359aa8285ce638329400c39a55101d622 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Wed, 10 Apr 2024 14:02:51 +0100
Subject: [PATCH 49/86] feat: add benchmarking for fine-mapping using Alzheimer
 as example (#572)

* feat: add benchmarking for fine-mapping using Alzheimer as example

* fix: small fix in notebook
---
 notebooks/FineMapping_AlzheimierDisease.ipynb | 1734 +++++++++++++++++
 1 file changed, 1734 insertions(+)
 create mode 100644 notebooks/FineMapping_AlzheimierDisease.ipynb

diff --git a/notebooks/FineMapping_AlzheimierDisease.ipynb b/notebooks/FineMapping_AlzheimierDisease.ipynb
new file mode 100644
index 000000000..8a785cc3f
--- /dev/null
+++ b/notebooks/FineMapping_AlzheimierDisease.ipynb
@@ -0,0 +1,1734 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-mapping of Alzheimer's disease GWAS summary statistics using GentroPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook presents an example of fine-mapping of the GWAS catalog study for Alzheimer's disease ([link to study](https://genetics.opentargets.org/Study/GCST90012877/associations)). The study itself is a good benchmarking example for fine-mapping - relatively large number of SNPs, very strong signal on the 19th chromosome (APOE). It's worth noting that usually very strong signals are excluded from fine-mapping due to instability.\n",
+    "\n",
+    "Also, we excluded MHC region (6:28M-34M) from fine-mapping because it has a huge density of the variants.\n",
+    "\n",
+    "To execute it on your local machine (not dataproc) you need to install https://github.com/broadinstitute/install-gcs-connector."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your browser has been opened to visit:\n",
+      "\n",
+      "    https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=764086051850-6qr4p6gpi6hn506pt8ejuq83di341hur.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8085%2F&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fsqlservice.login+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Faccounts.reauth&state=2Jvk4c7unAsigRvEKhceIxcrpGmeK8&access_type=offline&code_challenge=84guS6MmOY7qgvNpHLxoJbhRDBUAEUS93teMwQboD3Q&code_challenge_method=S256\n",
+      "\n",
+      "\n",
+      "Credentials saved to file: [/Users/yt4/.config/gcloud/application_default_credentials.json]\n",
+      "\n",
+      "These credentials will be used by any library that requests Application Default Credentials (ADC).\n",
+      "\n",
+      "Quota project \"open-targets-genetics-dev\" was added to ADC which can be used by Google client libraries for billing and quota. Note that some services may still bill the project owning the resource.\n",
+      "\n",
+      "\n",
+      "Updates are available for some Google Cloud CLI components.  To install them,\n",
+      "please run:\n",
+      "  $ gcloud components update\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gcloud auth application-default login"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "        .bk-notebook-logo {\n",
+       "            display: block;\n",
+       "            width: 20px;\n",
+       "            height: 20px;\n",
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+       "        }\n",
+       "    </style>\n",
+       "    <div>\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
+       "        <span id=\"a8e9a6a6-96f7-4efd-a426-b2299499ef03\">Loading BokehJS ...</span>\n",
+       "    </div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? 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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a8e9a6a6-96f7-4efd-a426-b2299499ef03\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a8e9a6a6-96f7-4efd-a426-b2299499ef03\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
+      "application/vnd.bokehjs_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/04/09 10:40:45 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting default log level to \"WARN\".\n",
+      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
+      "pip-installed Hail requires additional configuration options in Spark referring\n",
+      "  to the path to the Hail Python module directory HAIL_DIR,\n",
+      "  e.g. /path/to/python/site-packages/hail:\n",
+      "    spark.jars=HAIL_DIR/backend/hail-all-spark.jar\n",
+      "    spark.driver.extraClassPath=HAIL_DIR/backend/hail-all-spark.jar\n",
+      "    spark.executor.extraClassPath=./hail-all-spark.jarRunning on Apache Spark version 3.3.4\n",
+      "SparkUI available at http://192.168.0.232:4040\n",
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.127-bb535cd096c5\n",
+      "LOGGING: writing to /dev/null\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import hail as hl\n",
+    "import pyspark.sql.functions as f\n",
+    "import pandas as pd\n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "pd.set_option('display.expand_frame_repr', False)\n",
+    "\n",
+    "from gentropy.common.session import Session\n",
+    "from gentropy.dataset.study_index import StudyIndex\n",
+    "from gentropy.dataset.summary_statistics import SummaryStatistics\n",
+    "from gentropy.dataset.study_index import StudyIndex\n",
+    "from gentropy.method.window_based_clumping import WindowBasedClumping\n",
+    "from gentropy.susie_finemapper import SusieFineMapperStep\n",
+    "\n",
+    "hail_dir = os.path.dirname(hl.__file__)\n",
+    "session = Session(hail_home=hail_dir, start_hail=True, extended_spark_conf={\"spark.driver.memory\": \"12g\",\n",
+    "    \"spark.kryoserializer.buffer.max\": \"500m\",\"spark.driver.maxResultSize\":\"3g\"})\n",
+    "hl.init(sc=session.spark.sparkContext, log=\"/dev/null\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the data and clumping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of SNPs in GWAS:  10607272\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 7:>                                                          (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of clumps:  33\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "path_gwas1=\"gs://gwas_catalog_data/harmonised_summary_statistics/GCST90012877.parquet\"\n",
+    "path_si=\"gs://gwas_catalog_data/study_index\"\n",
+    "\n",
+    "gwas1 = SummaryStatistics.from_parquet(session, path_gwas1)\n",
+    "study_index = StudyIndex.from_parquet(session, path_si)\n",
+    "\n",
+    "slt=WindowBasedClumping.clump(gwas1,gwas_significance=5e-8,distance=1e6)\n",
+    "slt_df=slt._df\n",
+    "\n",
+    "print(\"Number of SNPs in GWAS: \",gwas1._df.count())\n",
+    "print(\"Number of clumps: \",slt_df.count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 15:===================================================>     (9 + 1) / 10]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n",
+      "|     studyId|       variantId|chromosome| position|            beta|sampleSize|pValueMantissa|pValueExponent|effectAlleleFrequencyFromSource|   standardError|        studyLocusId|qualityControls|\n",
+      "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n",
+      "|GCST90012877| 1_161185602_G_A|         1|161185602| 0.0609052805639|      null|         4.302|            -8|                        0.23499| 0.0111181765833| 6360456299763482946|             []|\n",
+      "|GCST90012877| 1_207577223_T_C|         1|207577223| -0.122752564739|      null|         1.403|           -23|                       0.822818| 0.0122652043685|-6742466305250328444|             []|\n",
+      "|GCST90012877| 10_11678309_A_G|        10| 11678309| 0.0668997305692|      null|         1.085|           -11|                       0.380517|0.00984571382836| 3672202482976347473|             []|\n",
+      "|GCST90012877| 10_59886075_G_T|        10| 59886075|-0.0523916765294|      null|         3.802|            -8|                       0.480668|0.00952612570169|  760299597568413738|             []|\n",
+      "|GCST90012877| 10_80520381_T_G|        10| 80520381| 0.0701098772587|      null|         2.736|            -9|                       0.793475| 0.0117897597766|-6168361428432361140|             []|\n",
+      "|GCST90012877|11_121564878_T_C|        11|121564878| -0.186386086749|      null|         5.586|           -14|                       0.037005| 0.0247938672944|-7548659272243096830|             []|\n",
+      "|GCST90012877| 11_47370397_G_A|        11| 47370397| 0.0634588530202|      null|         6.911|           -11|                       0.387521| 0.0097291000298| 1916491992423016132|             []|\n",
+      "|GCST90012877| 11_60328267_T_C|        11| 60328267|-0.0892048800109|      null|         9.335|           -20|                       0.371215|0.00980658024905| 3318332793803757311|             []|\n",
+      "|GCST90012877| 11_86156833_A_G|        11| 86156833|  0.103281644827|      null|         5.214|           -26|                       0.629462|0.00979200684254| 3806751464721795080|             []|\n",
+      "|GCST90012877| 14_52924962_A_G|        14| 52924962|  0.102404628268|      null|          3.69|           -10|                       0.092233| 0.0163413709974|-8640267085448358001|             []|\n",
+      "|GCST90012877| 14_92472511_G_A|        14| 92472511|-0.0762776811698|      null|         7.454|           -14|                       0.339674| 0.0101980809801| 8895835730818824947|             []|\n",
+      "|GCST90012877| 15_50707194_C_G|        15| 50707194|-0.0722934881552|      null|         1.639|            -9|                       0.197469| 0.0119888249532|-4585712009512019667|             []|\n",
+      "|GCST90012877| 15_58730416_T_C|        15| 58730416|-0.0675867539589|      null|         2.674|           -11|                       0.319058|  0.010142839928|-9173595866829505633|             []|\n",
+      "|GCST90012877| 15_63277703_C_T|        15| 63277703| 0.0849598934189|      null|         1.052|            -8|                       0.139487| 0.0148475601067|-6181511576673508209|             []|\n",
+      "|GCST90012877| 16_31115000_C_A|        16| 31115000|-0.0620662164665|      null|         4.466|            -9|                       0.281382| 0.0105807514538|-3612515273077152914|             []|\n",
+      "|GCST90012877|  17_5229833_T_C|        17|  5229833|-0.0849787931131|      null|         1.352|            -9|                       0.875068| 0.0140203927902|-7070596043624425654|             []|\n",
+      "|GCST90012877| 17_63483402_T_C|        17| 63483402| 0.0542810764988|      null|         1.215|            -8|                       0.529632|0.00952697587266| 7171154626284587162|             []|\n",
+      "|GCST90012877|  19_1050875_A_G|        19|  1050875|-0.0772974277902|      null|         2.415|           -13|                       0.674169| 0.0105546077307| 6109438569946056978|             []|\n",
+      "|GCST90012877| 19_44892009_G_A|        19| 44892009|  0.352722374032|      null|         1.995|          -277|                       0.605067|0.00991069396551| 6814727764900576662|             []|\n",
+      "|GCST90012877| 19_51224706_C_A|        19| 51224706|-0.0582180344342|      null|         1.295|            -8|                       0.325551|  0.010237506551|-8288099943480320096|             []|\n",
+      "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n",
+      "only showing top 20 rows\n",
+      "\n",
+      "None\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "print(slt_df.show())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fine-mapping without outliers detection and imputation using 2M as window size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-04-09 10:41:57.354 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:42:15.499 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:42:28.284 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:44:41.305 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:44:51.854 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:45:03.059 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:47:04.871 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:47:17.310 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:47:29.113 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:50:32.790 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:50:46.191 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:50:57.958 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:53:22.698 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:53:34.535 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:53:45.816 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:57:23.189 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:57:34.563 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:57:43.988 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 10:59:39.834 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 10:59:52.878 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:00:06.629 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:02:11.433 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:02:22.101 Hail: INFO: Ordering unsorted dataset with network shuffle\n",
+      "2024-04-09 11:02:32.320 Hail: INFO: wrote table with 175330 rows in 8 partitions to /tmp/__iruid_20813-EyC6kjgQ1hAjFSiH1Xp7sB\n",
+      "2024-04-09 11:02:35.350 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:04:16.225 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:04:27.837 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:04:41.879 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:07:09.950 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:07:21.139 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:07:33.197 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:09:56.240 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:10:08.288 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:10:20.802 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:14:07.114 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:14:20.204 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:14:32.464 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:16:41.133 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:16:53.191 Hail: INFO: Ordering unsorted dataset with network shuffle\n",
+      "2024-04-09 11:17:03.328 Hail: INFO: wrote table with 211068 rows in 9 partitions to /tmp/__iruid_35318-By6CsozcY2JvH6dhwjdBPU\n",
+      "2024-04-09 11:17:10.133 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:19:10.141 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:19:21.964 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:19:34.636 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:21:47.445 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:21:59.127 Hail: INFO: Ordering unsorted dataset with network shuffle\n",
+      "2024-04-09 11:22:15.902 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:23:18.294 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:23:32.131 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:23:48.719 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:26:51.703 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:27:02.820 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:27:18.412 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:29:02.997 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:29:19.516 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:29:41.224 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:33:19.553 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:33:32.903 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:33:49.144 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:36:16.552 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:36:28.952 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:36:46.964 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:40:40.837 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:40:51.976 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:41:04.014 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:43:54.259 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:44:05.534 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:44:20.087 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:46:45.605 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:46:59.301 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:47:13.181 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:49:50.219 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:50:02.311 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:50:16.072 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:52:34.864 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:52:46.513 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:53:00.919 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:55:50.580 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:56:02.124 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:56:16.907 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:59:15.457 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 11:59:27.380 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 11:59:40.184 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:03:31.055 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:03:42.554 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:03:53.915 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:06:33.123 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:06:45.813 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:06:58.340 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:09:23.153 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:09:33.531 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:09:43.693 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:11:13.739 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:11:26.087 Hail: INFO: Ordering unsorted dataset with network shuffle\n",
+      "2024-04-09 12:11:38.950 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:13:17.020 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:13:29.727 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:13:45.213 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:20:03.844 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-09 12:20:13.834 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-09 12:20:24.282 Hail: INFO: Coerced sorted dataset\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  1:160185602-162185602 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/p5/4t9crp1563l792qz8xz_3x5h0000gq/T/ipykernel_46905/1319014212.py:29: FutureWarning:\n",
+      "\n",
+      "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "\n",
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  1:206577223-208577223 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  10:10678309-12678309 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  10:58886075-60886075 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  10:79520381-81520381 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  11:120564878-122564878 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  11:46370397-48370397 ; number of CSs:  6 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  11:59328267-61328267 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  11:85156833-87156833 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  14:51924962-53924962 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  14:91472511-93472511 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  15:49707194-51707194 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  15:57730416-59730416 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  15:62277703-64277703 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  16:30115000-32115000 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  17:4229833-6229833 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  17:62483402-64483402 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  19:50875-2050875 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  19:43892009-45892009 ; number of CSs:  10 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  19:50224706-52224706 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  2:104749599-106749599 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  2:126135234-128135234 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  2:232117202-234117202 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  2:64381229-66381229 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  20:55423488-57423488 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 1199:=====================>                                  (3 + 5) / 8]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  21:25775872-27775872 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  4:10025995-12025995 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  6:39974457-41974457 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  6:46627419-48627419 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 1403:===================================>                    (5 + 3) / 8]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  7:142410495-144410495 ; number of CSs:  2 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  7:99374211-101374211 ; number of CSs:  1 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 1505:>                                                       (0 + 8) / 8]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Region:  8:26610986-28610986 ; number of CSs:  3 ; log:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n",
+    "\n",
+    "columns = ['N_gwas', 'N_ld', 'N_overlap', 'N_outliers', 'N_imputed', 'N_final_to_fm', 'eleapsed_time']\n",
+    "logs = pd.DataFrame(columns=columns)\n",
+    "\n",
+    "for i in range(0,df.count()):\n",
+    "    if i!=27:\n",
+    "        one_row = df.filter(df.row_index == i).first()\n",
+    "\n",
+    "        res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "            GWAS=gwas1,\n",
+    "            session=session,\n",
+    "            study_locus_row=one_row,\n",
+    "            study_index=study_index,\n",
+    "            window= 2_000_000,\n",
+    "            L=10,\n",
+    "            susie_est_tausq=False,\n",
+    "            run_carma=False,\n",
+    "            run_sumstat_imputation=False,\n",
+    "            carma_time_limit=600,\n",
+    "            imputed_r2_threshold=0.8,\n",
+    "            ld_score_threshold=4\n",
+    "        )\n",
+    "\n",
+    "        sl=res[\"study_locus\"]\n",
+    "        #print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "        print(\"Region: \",sl._df.collect()[0]['region'], \"; number of CSs: \",sl._df.count(), \"; log:\")\n",
+    "        #print(res[\"log\"])\n",
+    "        logs=pd.concat([logs,res[\"log\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  N_gwas   N_ld N_overlap N_outliers N_imputed N_final_to_fm  eleapsed_time\n",
+      "0   7120  10431      6456          0         0          6456      56.839336\n",
+      "0   7128   8657      5769          0         0          5769      46.149004\n",
+      "0   9203  12106      7930          0         0          7930      93.531924\n",
+      "0   8351  10014      6995          0         0          6995      74.174323\n",
+      "0   9388  12551      8337          0         0          8337     120.602071\n",
+      "0   6560   8729      5758          0         0          5758      45.064894\n",
+      "0   5005   7701      3954          0         0          3954      55.229344\n",
+      "0   7012   8940      5815          0         0          5815      38.824251\n",
+      "0   8661  10303      7291          0         0          7291      68.802810\n",
+      "0   8081   9966      6771          0         0          6771      64.327746\n",
+      "0   8375  11213      7467          0         0          7467     141.808555\n",
+      "0   7377   9622      6369          0         0          6369      51.198955\n",
+      "0   8181  10864      7116          0         0          7116      49.033224\n",
+      "0   7976  10135      6704          0         0          6704      58.357743\n",
+      "0   3369   6542      2972          0         0          2972      17.138722\n",
+      "0   9006  12776      7969          0         0          7969      83.551872\n",
+      "0   4565   7018      3887          0         0          3887      37.801989\n",
+      "0   8278  13741      7852          0         0          7852     105.623508\n",
+      "0   7582  10448      6100          0         0          6100      58.572944\n",
+      "0   9145  12706      8242          0         0          8242     129.442009\n",
+      "0   8795  11311      7584          0         0          7584      86.336201\n",
+      "0   7852  10028      7041          0         0          7041      67.886754\n",
+      "0   8393  10850      7195          0         0          7195      72.375809\n",
+      "0   7639  10031      6520          0         0          6520      61.900982\n",
+      "0   8899  11509      7922          0         0          7922      86.535298\n",
+      "0   8908  11309      7889          0         0          7889      93.595320\n",
+      "0  10654  12663      8990          0         0          8990     133.390712\n",
+      "0   9073  10228      7398          0         0          7398      79.774280\n",
+      "0   8033   9785      6822          0         0          6822      63.950340\n",
+      "0   4570   5516      3162          0         0          3162      27.943313\n",
+      "0   5716   8785      4760          0         0          4760      30.024706\n",
+      "0   9243  10989      7869          0         0          7869     108.898056\n"
+     ]
+    }
+   ],
+   "source": [
+    "pd.set_option('display.max_rows', None)\n",
+    "print(logs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6653.3125\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary = logs['N_overlap'].mean()\n",
+    "print(summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-mapping of APOE locus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Row(studyId='GCST90012877', variantId='19_44892009_G_A', chromosome='19', position=44892009, beta=0.352722374032, sampleSize=None, pValueMantissa=1.9950000047683716, pValueExponent=-277, effectAlleleFrequencyFromSource=0.6050670146942139, standardError=0.00991069396551, studyLocusId=6814727764900576662, qualityControls=[], row_index=18)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n",
+    "one_row = df.filter(df.row_index == 18).first()\n",
+    "one_row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Without CARMA, without imputation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-04-08 21:34:03.208 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "2024-04-08 21:34:19.253 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 21:34:34.941 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 21:37:16.576 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "2024-04-08 21:37:28.867 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 21:37:44.733 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 21:52:03.198 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "2024-04-08 21:52:15.100 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 21:52:30.553 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:21:27.877 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "2024-04-08 22:21:40.137 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:21:55.249 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:23:21.795 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "2024-04-08 22:23:35.031 Hail: INFO: Coerced sorted dataset\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|              region|credibleSetIndex|               locus|      variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               1|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf| 2135.710824756712|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               2|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf| 955.4948390766739|   1|\n",
+      "| 8324745608044585165|GCST90012877|19:43892009-45892009|               3|[{19_44917947_C_T...|19_44917947_C_T|        19|44917947|        SuSiE-inf| 690.0307437138443|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               4|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf|425.33378303492805|   1|\n",
+      "| 2240477384494621278|GCST90012877|19:43892009-45892009|               5|[{19_44891079_T_C...|19_44891079_T_C|        19|44891079|        SuSiE-inf|395.31055398960274|   1|\n",
+      "| 1029535804909934921|GCST90012877|19:43892009-45892009|               6|[{19_44894695_T_C...|19_44894695_T_C|        19|44894695|        SuSiE-inf| 333.9497424582455|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               7|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf|  261.573648706883|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               8|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf|186.66554412409607|   1|\n",
+      "|-7370952295217410456|GCST90012877|19:43892009-45892009|               9|[{19_44922505_T_G...|19_44922505_T_G|        19|44922505|        SuSiE-inf| 78.06352464083552|   1|\n",
+      "| 3925446284512644964|GCST90012877|19:43892009-45892009|              10|[{19_44913574_T_G...|19_44913574_T_G|        19|44913574|        SuSiE-inf|55.346197523194675|   1|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0    7582  10448       6100           0          0           6100      66.112839\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 2_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=False,\n",
+    "    run_carma=False,\n",
+    "    run_sumstat_imputation=False,\n",
+    "    carma_time_limit=1000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With CARMA, without imputation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|              region|credibleSetIndex|               locus|      variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               1|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf|1995.6574121818223|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               2|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf| 721.2637360279233|   1|\n",
+      "| 7760477027903907683|GCST90012877|19:43892009-45892009|               3|[{19_44911142_C_A...|19_44911142_C_A|        19|44911142|        SuSiE-inf|248.39159334060017|   1|\n",
+      "|-1172224975892516254|GCST90012877|19:43892009-45892009|               4|[{19_44894255_A_C...|19_44894255_A_C|        19|44894255|        SuSiE-inf| 96.16160678286879|   1|\n",
+      "| 8852802213660052283|GCST90012877|19:43892009-45892009|               5|[{19_44862190_G_A...|19_44862190_G_A|        19|44862190|        SuSiE-inf| 55.80518621838019|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               6|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf| 53.24772075097935|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               7|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf| 45.65754067281976|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               8|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf|  39.3840804563262|   1|\n",
+      "| 6986973025714240626|GCST90012877|19:43892009-45892009|               9|[{19_44873060_C_G...|19_44873060_C_G|        19|44873060|        SuSiE-inf| 38.54912041595975|   1|\n",
+      "| 3640651426400620880|GCST90012877|19:43892009-45892009|              10|[{19_44845920_G_C...|19_44845920_G_C|        19|44845920|        SuSiE-inf|35.378479810047224|   2|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0    7582  10448       6100         151          0           5949     783.939477\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 2_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=False,\n",
+    "    run_carma=True,\n",
+    "    run_sumstat_imputation=False,\n",
+    "    carma_time_limit=1000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Without CARMA, with imputation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-04-08 22:25:15.739 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-08 22:25:30.625 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:25:46.020 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:32:35.094 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-08 22:32:47.616 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:33:02.484 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:51:33.149 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n",
+      "    'age_index_dict' -> 'age_index_dict_1'\n",
+      "    'freq_index_dict' -> 'freq_index_dict_1'\n",
+      "    'faf_index_dict' -> 'faf_index_dict_1'\n",
+      "    'freq_meta' -> 'freq_meta_1'\n",
+      "    'rf' -> 'rf_1'\n",
+      "    'age_distribution' -> 'age_distribution_1'\n",
+      "    'popmax_index_dict' -> 'popmax_index_dict_1'\n",
+      "2024-04-08 22:51:45.708 Hail: INFO: Coerced sorted dataset\n",
+      "2024-04-08 22:52:00.731 Hail: INFO: Coerced sorted dataset\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|              region|credibleSetIndex|               locus|        variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n",
+      "|-1350283509846281677|GCST90012877|19:43892009-45892009|               1|[{19_44909967_TGG...|19_44909967_TGG_T|        19|44909967|        SuSiE-inf| 2310.665662473933|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               2|[{19_44921094_A_T...|  19_44921094_A_T|        19|44921094|        SuSiE-inf| 903.6138342773536|   1|\n",
+      "| 8324745608044585165|GCST90012877|19:43892009-45892009|               3|[{19_44917947_C_T...|  19_44917947_C_T|        19|44917947|        SuSiE-inf| 700.3080514793324|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               4|[{19_44921094_A_T...|  19_44921094_A_T|        19|44921094|        SuSiE-inf|431.79459690536703|   1|\n",
+      "| 1029535804909934921|GCST90012877|19:43892009-45892009|               5|[{19_44894695_T_C...|  19_44894695_T_C|        19|44894695|        SuSiE-inf|402.50010763388156|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               6|[{19_44921094_A_T...|  19_44921094_A_T|        19|44921094|        SuSiE-inf|225.93101254172214|   1|\n",
+      "|  -60207296485035224|GCST90012877|19:43892009-45892009|               7|[{19_44888997_C_T...|  19_44888997_C_T|        19|44888997|        SuSiE-inf| 191.4947272198485|   1|\n",
+      "|-1350283509846281677|GCST90012877|19:43892009-45892009|               8|[{19_44909967_TGG...|19_44909967_TGG_T|        19|44909967|        SuSiE-inf|105.04460057482835|   1|\n",
+      "|-4078755027603845519|GCST90012877|19:43892009-45892009|               9|[{19_44918393_G_A...|  19_44918393_G_A|        19|44918393|        SuSiE-inf| 63.30243818120949|   1|\n",
+      "| 3925446284512644964|GCST90012877|19:43892009-45892009|              10|[{19_44913574_T_G...|  19_44913574_T_G|        19|44913574|        SuSiE-inf|54.079307276192694|   1|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0    7582  10448       6100           0        681           6781     334.328722\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 2_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=False,\n",
+    "    run_carma=False,\n",
+    "    run_sumstat_imputation=True,\n",
+    "    carma_time_limit=10000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With CARMA, with imputation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|              region|credibleSetIndex|               locus|      variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "| 3030414938485808431|GCST90012877|19:43892009-45892009|               1|[{19_44895007_C_T...|19_44895007_C_T|        19|44895007|        SuSiE-inf|2680.9099711333456|   1|\n",
+      "|-2201142982564351776|GCST90012877|19:43892009-45892009|               2|[{19_44900601_A_G...|19_44900601_A_G|        19|44900601|        SuSiE-inf| 2103.873956796136|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               3|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf|1968.8126348567705|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               4|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf|1089.9033376410644|   1|\n",
+      "| 7760477027903907683|GCST90012877|19:43892009-45892009|               5|[{19_44911142_C_A...|19_44911142_C_A|        19|44911142|        SuSiE-inf|188.55568384844716|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               6|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf| 83.57344085238768|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               7|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf| 82.01732099119907|   1|\n",
+      "| 8852802213660052283|GCST90012877|19:43892009-45892009|               8|[{19_44862190_G_A...|19_44862190_G_A|        19|44862190|        SuSiE-inf| 45.92126992319222|   1|\n",
+      "|-1611304699666037367|GCST90012877|19:43892009-45892009|               9|[{19_44821259_C_T...|19_44821259_C_T|        19|44821259|        SuSiE-inf|37.363613067645254|   1|\n",
+      "| 3556335645959991344|GCST90012877|19:43892009-45892009|              10|[{19_45017701_G_T...|19_45017701_G_T|        19|45017701|        SuSiE-inf|30.736039473626658|   4|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0    7582  10448       6100         152        715           6663    1036.467428\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 2_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=False,\n",
+    "    run_carma=True,\n",
+    "    run_sumstat_imputation=True,\n",
+    "    carma_time_limit=10000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With CARMA, with imputation, with estimation of infinitisimal effects (susie_est_tausq=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|              region|credibleSetIndex|               locus|      variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|               1|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf| 1105.297844890198|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               2|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf|1042.0949995382389|   1|\n",
+      "|-2201142982564351776|GCST90012877|19:43892009-45892009|               3|[{19_44900601_A_G...|19_44900601_A_G|        19|44900601|        SuSiE-inf| 760.0654878716481|   1|\n",
+      "| 3030414938485808431|GCST90012877|19:43892009-45892009|               4|[{19_44895007_C_T...|19_44895007_C_T|        19|44895007|        SuSiE-inf| 388.8928142354868|   1|\n",
+      "| -251577639520141451|GCST90012877|19:43892009-45892009|               5|[{19_44899220_C_T...|19_44899220_C_T|        19|44899220|        SuSiE-inf| 259.5645544847559|   1|\n",
+      "| 7760477027903907683|GCST90012877|19:43892009-45892009|               6|[{19_44911142_C_A...|19_44911142_C_A|        19|44911142|        SuSiE-inf|231.66277856324325|   1|\n",
+      "| 4133344777320628094|GCST90012877|19:43892009-45892009|               7|[{19_44904531_G_A...|19_44904531_G_A|        19|44904531|        SuSiE-inf|143.22657752219786|   1|\n",
+      "|-1764089385585984368|GCST90012877|19:43892009-45892009|               8|[{19_44893642_T_C...|19_44893642_T_C|        19|44893642|        SuSiE-inf| 87.72507299242906|   1|\n",
+      "|-1158278093713046158|GCST90012877|19:43892009-45892009|               9|[{19_44921094_A_T...|19_44921094_A_T|        19|44921094|        SuSiE-inf|  71.4171763690986|   1|\n",
+      "|-6417720984991662128|GCST90012877|19:43892009-45892009|              10|[{19_44908684_T_C...|19_44908684_T_C|        19|44908684|        SuSiE-inf| 43.36071977593145|   1|\n",
+      "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0    7582  10448       6100         151        720           6669    1229.515921\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 2_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=True,\n",
+    "    run_carma=True,\n",
+    "    run_sumstat_imputation=True,\n",
+    "    carma_time_limit=10000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-mapping of MHC region using 1Mb window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Row(studyId='GCST90012877', variantId='6_32592248_A_G', chromosome='6', position=32592248, beta=-0.103604380043, sampleSize=None, pValueMantissa=2.877000093460083, pValueExponent=-15, effectAlleleFrequencyFromSource=0.21086899936199188, standardError=0.0131209374957, studyLocusId=5718491981995302674, qualityControls=[], row_index=27)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n",
+    "one_row = df.filter(df.row_index == 27).first()\n",
+    "one_row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 1541:==========================================>             (6 + 2) / 8]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|        studyLocusId|     studyId|             region|credibleSetIndex|               locus|      variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n",
+      "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "|-3446214959021623473|GCST90012877|6:32092248-33092248|               1|[{6_32557997_G_A,...| 6_32557997_G_A|         6|32557997|        SuSiE-inf| 4323.908142062261|   1|\n",
+      "| -439738150050389281|GCST90012877|6:32092248-33092248|               2|[{6_32558002_G_T,...| 6_32558002_G_T|         6|32558002|        SuSiE-inf|3428.8321277074765|   1|\n",
+      "| 5831857384024844796|GCST90012877|6:32092248-33092248|               3|[{6_32557987_C_A,...| 6_32557987_C_A|         6|32557987|        SuSiE-inf|1699.8680349563335|   1|\n",
+      "|-1087057043201011402|GCST90012877|6:32092248-33092248|               4|[{6_32557977_T_C,...| 6_32557977_T_C|         6|32557977|        SuSiE-inf| 965.9753305300063|   1|\n",
+      "| 6919234179916081233|GCST90012877|6:32092248-33092248|               5|[{6_32649735_C_T,...| 6_32649735_C_T|         6|32649735|        SuSiE-inf| 369.9698233117616|   1|\n",
+      "| 7781006900918060896|GCST90012877|6:32092248-33092248|               6|[{6_32652962_C_T,...| 6_32652962_C_T|         6|32652962|        SuSiE-inf| 328.6834447478274|   1|\n",
+      "|-7512794333418509403|GCST90012877|6:32092248-33092248|               7|[{6_32591896_T_G,...| 6_32591896_T_G|         6|32591896|        SuSiE-inf|326.52393082050276|   1|\n",
+      "| 4056478719932360430|GCST90012877|6:32092248-33092248|               8|[{6_32621456_GC_G...|6_32621456_GC_G|         6|32621456|        SuSiE-inf|263.48518383939836|   1|\n",
+      "| 8380896542014789747|GCST90012877|6:32092248-33092248|               9|[{6_32648039_G_A,...| 6_32648039_G_A|         6|32648039|        SuSiE-inf|176.62947310155317|   1|\n",
+      "| 9053545161380162736|GCST90012877|6:32092248-33092248|              10|[{6_32700030_C_T,...| 6_32700030_C_T|         6|32700030|        SuSiE-inf| 69.83226092797517|   2|\n",
+      "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n",
+      "\n",
+      "None\n",
+      "   N_gwas   N_ld  N_overlap  N_outliers  N_imputed  N_final_to_fm  eleapsed_time\n",
+      "0   19311  22318      13188           0          0          13188     298.784288\n"
+     ]
+    }
+   ],
+   "source": [
+    "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n",
+    "    GWAS=gwas1,\n",
+    "    session=session,\n",
+    "    study_locus_row=one_row,\n",
+    "    study_index=study_index,\n",
+    "    window= 1_000_000,\n",
+    "    L=10,\n",
+    "    susie_est_tausq=False,\n",
+    "    run_carma=False,\n",
+    "    run_sumstat_imputation=False,\n",
+    "    carma_time_limit=10000,\n",
+    "    imputed_r2_threshold=0.8,\n",
+    "    ld_score_threshold=4\n",
+    ")\n",
+    "sl=res[\"study_locus\"]\n",
+    "print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n",
+    "print(res[\"log\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gentropy-krNFZEZg-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ecd806331813bb28eada4d4a56c983e5a419b833 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Wed, 10 Apr 2024 14:41:07 +0100
Subject: [PATCH 50/86] fix: removing all duplicated variants in sumstats for
 finemapping functions (#574)

---
 src/gentropy/susie_finemapper.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index db62fe359..d37298436 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -81,6 +81,10 @@ def susie_finemapper_one_studylocus_row(
             .filter(f.col("studyId") == studyId)
             .filter(f.col("z").isNotNull())
         )
+        # Remove ALL duplicated variants from GWAS DataFrame - we don't know which is correct
+        variant_counts = gwas_df.groupBy("variantId").count()
+        unique_variants = variant_counts.filter(f.col("count") == 1)
+        gwas_df = gwas_df.join(unique_variants, on="variantId", how="left_semi")
 
         ld_index = (
             GnomADLDMatrix()
@@ -320,6 +324,10 @@ def susie_finemapper_ss_gathered(
             .withColumn("position", f.split(f.col("variantId"), "_")[1])
             .filter(f.col("z").isNotNull())
         )
+        # Remove ALL duplicated variants from GWAS DataFrame - we don't know which is correct
+        variant_counts = gwas_df.groupBy("variantId").count()
+        unique_variants = variant_counts.filter(f.col("count") == 1)
+        gwas_df = gwas_df.join(unique_variants, on="variantId", how="left_semi")
 
         ld_index = (
             GnomADLDMatrix()

From 7ed4703fcb3589af82f9fc76425f0691a080dee4 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Sun, 14 Apr 2024 16:42:34 +0100
Subject: [PATCH 51/86] feat: adding notebook for mapping EFOs for the FinnGen
 study index (#575)

---
 notebooks/Mapping_EFO_finngen.ipynb | 768 ++++++++++++++++++++++++++++
 1 file changed, 768 insertions(+)
 create mode 100644 notebooks/Mapping_EFO_finngen.ipynb

diff --git a/notebooks/Mapping_EFO_finngen.ipynb b/notebooks/Mapping_EFO_finngen.ipynb
new file mode 100644
index 000000000..9bd82d8d4
--- /dev/null
+++ b/notebooks/Mapping_EFO_finngen.ipynb
@@ -0,0 +1,768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mapping EFOs for the FinnGen study index using old study index from the previos prod"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook adds EFOs from previos prod version of study_index to the new FinnGen study_index using trait name as a matching key.\n",
+    "\n",
+    "The rsulting study index has 1542 rows with not null EFOs (out of 2408 rows).\n",
+    "\n",
+    "The new study index is saved here:\n",
+    "\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your browser has been opened to visit:\n",
+      "\n",
+      "    https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=764086051850-6qr4p6gpi6hn506pt8ejuq83di341hur.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8085%2F&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fsqlservice.login+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Faccounts.reauth&state=XHb8Uk43SsVjvFRqwgrX4Tgg2tTOHS&access_type=offline&code_challenge=OkiqDAkHXDGEgJQbX8r0ZYKfZ7gcgfXS8mfZc5a913Y&code_challenge_method=S256\n",
+      "\n",
+      "\n",
+      "Credentials saved to file: [/Users/yt4/.config/gcloud/application_default_credentials.json]\n",
+      "\n",
+      "These credentials will be used by any library that requests Application Default Credentials (ADC).\n",
+      "\n",
+      "Quota project \"open-targets-genetics-dev\" was added to ADC which can be used by Google client libraries for billing and quota. Note that some services may still bill the project owning the resource.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gcloud auth application-default login"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
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+       "            display: block;\n",
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+      ]
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+     "output_type": "display_data"
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+      "application/vnd.bokehjs_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/04/14 16:03:28 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting default log level to \"WARN\".\n",
+      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
+      "pip-installed Hail requires additional configuration options in Spark referring\n",
+      "  to the path to the Hail Python module directory HAIL_DIR,\n",
+      "  e.g. /path/to/python/site-packages/hail:\n",
+      "    spark.jars=HAIL_DIR/backend/hail-all-spark.jar\n",
+      "    spark.driver.extraClassPath=HAIL_DIR/backend/hail-all-spark.jar\n",
+      "    spark.executor.extraClassPath=./hail-all-spark.jarRunning on Apache Spark version 3.3.4\n",
+      "SparkUI available at http://192.168.0.232:4040\n",
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.127-bb535cd096c5\n",
+      "LOGGING: writing to /dev/null\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import hail as hl\n",
+    "import pyspark.sql.functions as f\n",
+    "import pandas as pd\n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "pd.set_option('display.expand_frame_repr', False)\n",
+    "\n",
+    "from gentropy.common.session import Session\n",
+    "from gentropy.dataset.study_index import StudyIndex\n",
+    "\n",
+    "\n",
+    "hail_dir = os.path.dirname(hl.__file__)\n",
+    "session = Session(hail_home=hail_dir, start_hail=True, extended_spark_conf={\"spark.driver.memory\": \"12g\",\n",
+    "    \"spark.kryoserializer.buffer.max\": \"500m\",\"spark.driver.maxResultSize\":\"3g\"})\n",
+    "hl.init(sc=session.spark.sparkContext, log=\"/dev/null\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_si=\"gs://genetics_etl_python_playground/releases/24.03/study_index/finngen/study_index\"\n",
+    "path_si_old=\"gs://genetics-portal-dev-analysis/yt4/study_index.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "si_old=session.spark.read.csv(path_si_old, header=True,sep=\"\\t\")\n",
+    "si_new=StudyIndex.from_parquet(session=session, path=path_si)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n",
+      "|            study_id|   ancestry_initial|ancestry_replication|n_cases|n_initial|n_replication|pmid|pub_author|  pub_date|pub_journal|pub_title|has_sumstats|num_assoc_loci| source|      trait_reported|       trait_efos|      trait_category|\n",
+      "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n",
+      "|FINNGEN_R6_M13_MU...|['European=253458']|                  []|  108.0|   253458|          0.0|null|FINNGEN_R6|2022-01-24|       null|     null|        True|             0|FINNGEN|Multifocal fibros...|['MONDO_0009230']|immune system dis...|\n",
+      "|FINNGEN_R6_M13_MU...|['European=199528']|                  []| 1804.0|   199528|          0.0|null|FINNGEN_R6|2022-01-24|       null|     null|        True|             0|FINNGEN|Disorders of muscles|  ['EFO_0002970']|musculoskeletal o...|\n",
+      "|FINNGEN_R6_M13_MU...|['European=197821']|                  []|   97.0|   197821|          0.0|null|FINNGEN_R6|2022-01-24|       null|     null|        True|             0|FINNGEN|\"\"\"Muscle wasting...|  ['EFO_0009851']|  biological process|\n",
+      "|FINNGEN_R6_M13_MU...|['European=198253']|                  []|  529.0|   198253|          0.0|null|FINNGEN_R6|2022-01-24|       null|     null|        True|             0|FINNGEN|Other specified d...|  ['EFO_0002970']|musculoskeletal o...|\n",
+      "|FINNGEN_R6_M13_MU...|['European=198179']|                  []|  455.0|   198179|          0.0|null|FINNGEN_R6|2022-01-24|       null|     null|        True|             1|FINNGEN|       Muscle strain|  ['EFO_0010686']|injury, poisoning...|\n",
+      "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "si_old.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|             studyId|  projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|  cohorts|ldPopulationStructure|   discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Actinomycosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   101|   363227|  363328|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|          Amoebiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   160|   367214|  367374|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Anogenital herpes...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1986|   400197|  402183|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Aspergillosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   211|   403213|  403424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Atypical virus in...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   282|   409849|  410131|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "si_new_df=si_new.df\n",
+    "si_new_df.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "57246\n",
+      "2408\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(si_old.count())\n",
+    "print(si_new_df.count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------------+\n",
+      "|      trait_reported|       trait_efos|\n",
+      "+--------------------+-----------------+\n",
+      "|Multifocal fibros...|['MONDO_0009230']|\n",
+      "|Disorders of muscles|  ['EFO_0002970']|\n",
+      "|\"\"\"Muscle wasting...|  ['EFO_0009851']|\n",
+      "|Other specified d...|  ['EFO_0002970']|\n",
+      "|       Muscle strain|  ['EFO_0010686']|\n",
+      "+--------------------+-----------------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "si_old=si_old.select(\"trait_reported\",\"trait_efos\")\n",
+    "si_old.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql.functions import lower\n",
+    "\n",
+    "si_old = si_old.withColumn(\"trait_reported_low\", lower(si_old[\"trait_reported\"])).select(\"trait_reported_low\",\"trait_efos\")\n",
+    "si_new_df= si_new_df.withColumn(\"trait_reported_low\", lower(si_new_df[\"traitFromSource\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2408"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "si_old = si_old.dropDuplicates(['trait_reported_low'])\n",
+    "joined_df = si_new_df.join(si_old, \"trait_reported_low\", how='left')\n",
+    "joined_df.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n",
+      "|  trait_reported_low|             studyId|  projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|  cohorts|ldPopulationStructure|   discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|          trait_efos|\n",
+      "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n",
+      "|       actinomycosis|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Actinomycosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   101|   363227|  363328|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|                null|\n",
+      "|          amoebiasis|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|          Amoebiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   160|   367214|  367374|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0007144']|\n",
+      "|anogenital herpes...|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Anogenital herpes...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1986|   400197|  402183|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0007282']|\n",
+      "|       aspergillosis|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Aspergillosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   211|   403213|  403424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0007157']|\n",
+      "|atypical virus in...|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Atypical virus in...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   282|   409849|  410131|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|   ['MONDO_0024318']|\n",
+      "|bacterial infecti...|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial infecti...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 20226|   363227|  383453|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|                null|\n",
+      "|bacterial, viral ...|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial, viral ...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2852|   409329|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|                null|\n",
+      "|other bacterial i...|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Other bacterial i...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  6145|   367214|  373359|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0000771']|\n",
+      "|         candidiasis|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|         Candidiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  4306|   403213|  407519|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|   ['MONDO_0002026']|\n",
+      "|other sexually tr...|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|Other sexually tr...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2186|   400197|  402383|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|['MONDO_0021681',...|\n",
+      "|             cholera|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|             Cholera|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1385|   367214|  368599|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_1001235']|\n",
+      "|dengue fever [cla...|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|Dengue fever [cla...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|    53|   409137|  409190|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|                null|\n",
+      "|     dermatophytosis|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|     Dermatophytosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  3921|   403213|  407134|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|   ['MONDO_0004678']|\n",
+      "|      early syphilis|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|      Early syphilis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   308|   400197|  400505|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0007504']|\n",
+      "|infectious mononu...| FINNGEN_R10_AB1_EBV|FINNGEN_R10|     gwas|Infectious mononu...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2979|   400974|  403953|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_0007326']|\n",
+      "|        enterobiasis|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|        Enterobiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   112|   411658|  411770|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|                null|\n",
+      "|          erysipelas|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|          Erysipelas|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 22261|   363227|  385488|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|     ['EFO_1001462']|\n",
+      "|diarrhoea and gas...|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Diarrhoea and gas...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 32210|   367214|  399424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|   ['MONDO_0045031']|\n",
+      "|gonococcal infection|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Gonococcal infection|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   954|   400197|  401151|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|       ['DOID_7551']|\n",
+      "|       helminthiases|FINNGEN_R10_AB1_H...|FINNGEN_R10|     gwas|       Helminthiases|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   523|   411658|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|['EFO_0007245', '...|\n",
+      "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "joined_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1542\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 22:===========================================>              (3 + 1) / 4]\r"
+     ]
+    }
+   ],
+   "source": [
+    "num_non_null_rows = joined_df.filter(joined_df.trait_efos.isNotNull()).count()\n",
+    "print(num_non_null_rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n",
+      "|     studyId|projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|    publicationTitle|publicationFirstAuthor|publicationDate|  publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|             cohorts|ldPopulationStructure|    discoverySamples|  replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n",
+      "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n",
+      "|  GCST000102|     GCST|     gwas|Endothelial funct...|           [EFO_0004298]|  null|              null|17903301|Genome-wide assoc...|              Vasan RS|     2007-09-19|       BMC Med Genet|                              null|Up to 1,238 Europ...|     0|        0|    1238|              [null]|         [{nfe, 1.0}]|  [{1238, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST000272|     GCST|     gwas|              Height|           [EFO_0004339]|  null|              null|19030899|Genome-wide assoc...|                Lei SF|     2008-11-23|           Hum Genet|                              null|618 Chinese ances...|     0|        0|     618|              [null]|         [{eas, 1.0}]| [{618, East Asian}]|[{2953, East Asian}]|           null|         null|                null|      false|\n",
+      "|  GCST000436|     GCST|     gwas|Acenocoumarol mai...|            [GO_0061476]|  null|              null|19578179|A genome-wide ass...|            Teichert M|     2009-07-04|       Hum Mol Genet|                              null|1,451 European an...|     0|        0|    1451|              [null]|         [{nfe, 1.0}]|  [{1451, European}]|         [{287, NR}]|           null|         null|                null|      false|\n",
+      "|  GCST000514|     GCST|     gwas|Response to antip...|            [GO_0097332]|  null|              null|19875103|Genomewide associ...|               Aberg K|     2009-10-27|     Biol Psychiatry|                              null|421 European ance...|   738|        0|     738|              [null]| [{afr, 0.28997289...|[{214, African Am...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST000550|     GCST|     gwas|   Metabolite levels|           [EFO_0004725]|  null|              null|20037589|A genome-wide per...|               Illig T|     2009-12-27|           Nat Genet|                              null|1,029 European an...|     0|        0|    1029|              [null]|         [{nfe, 1.0}]|  [{1029, European}]|  [{1202, European}]|           null|         null|                null|      false|\n",
+      "|  GCST000708|     GCST|     gwas|           Freckling|           [EFO_0003963]|  null|              null|20585627|Web-based, partic...|            Eriksson N|     2010-06-24|          PLoS Genet|                              null|9,126 European an...|     0|        0|    9126|              [null]|         [{nfe, 1.0}]|  [{9126, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST000754|     GCST|     gwas|Personality dimen...|           [EFO_0004365]|  null|              null|20691247|A genome-wide ass...|            Verweij KJ|     2010-08-04|        Biol Psychol|                              null|5,117 European an...|     0|        0|    5117|              [null]|         [{nfe, 1.0}]|  [{5117, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST000880|     GCST|     gwas|Menarche (age at ...|           [EFO_0004703]|  null|              null|21102462|Thirty new loci f...|               Elks CE|     2010-11-21|           Nat Genet|                              null|86,142 European a...|     0|        0|   87802|              [null]|         [{nfe, 1.0}]| [{87802, European}]| [{14731, European}]|           null|         null|                null|      false|\n",
+      "|  GCST001031|     GCST|     gwas|Large B-cell lymp...|           [EFO_0000403]|  null|              null|21471979|Common variants o...|               Kumar V|     2011-04-07|         J Hum Genet|                              null|74 Japanese ances...|    74|      934|    1008|              [null]|         [{eas, 1.0}]|[{1008, East Asian}]|[{3634, East Asian}]|           null|         null|                null|      false|\n",
+      "|  GCST001032|     GCST|     gwas|Caffeine consumption|           [EFO_0004330]|  null|              null|21490707|Genome-wide meta-...|           Cornelis MC|     2011-04-07|          PLoS Genet|                              null|47,431 European a...|     0|        0|   47431|              [null]|         [{nfe, 1.0}]| [{47431, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST001059|     GCST|     gwas|    Neutrophil count|           [EFO_0004833]|  null|              null|21507922|Duffy-null-associ...|            Ramsuran V|     2011-05-01|     Clin Infect Dis|                              null|115 African ances...|     0|        0|     115|              [null]|         [{afr, 1.0}]|[{115, Sub-Sahara...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST002187|     GCST|     gwas|Systolic blood pr...|           [EFO_0006335]|  null|              null|24058526|Genome-wide meta-...|           Bhatnagar P|     2013-09-13|            PLoS One|                   [MONDO_0011382]|1617 African Amer...|  1617|        0|    1617|              [null]|         [{afr, 1.0}]|[{1617, African A...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST002623|     GCST|     gwas|   L-arginine levels|           [EFO_0006524]|  null|              null|25245031|Genome-wide assoc...|            Luneburg N|     2014-09-21|Circ Cardiovasc G...|                              null|3,747 European an...|     0|        0|    6739|              [null]|         [{nfe, 1.0}]|[{3747, European}...|  [{1159, European}]|           null|         null|                null|      false|\n",
+      "|  GCST003261|     GCST|     gwas|Ischemic stroke (...|            [HP_0002140]|  null|              null|26708676|Loci associated w...|              Pulit SL|     2015-12-18|       Lancet Neurol|                              null|up to 8,062 Europ...|  9510|    32473|   41983|              [null]| [{amr, 0.06647928...|[{2791, Hispanic ...|[{256, African Am...|           null|         null|                null|      false|\n",
+      "|  GCST003427|     GCST|     gwas|Alzheimer disease...|    [EFO_0004847, MON...|  null|              null|26830138|Family-based asso...|              Herold C|     2016-02-02|      Mol Psychiatry|                              null|2,478 European an...|  2478|      979|    3457|              [null]|         [{nfe, 1.0}]|  [{3524, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST003665|     GCST|     gwas|Free cholesterol ...|    [EFO_0004611, EFO...|  null|              null|27005778|Genome-wide study...|            Kettunen J|     2016-03-23|          Nat Commun|                              null|21,555 European a...|     0|        0|   21555|[EGCUT, ERF, FTC,...|         [{nfe, 1.0}]| [{21555, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST003773|     GCST|     gwas|Loneliness (multi...|           [EFO_0007865]|  null|              null|27629369|Genome-Wide Assoc...|                 Gao J|     2016-09-15|Neuropsychopharma...|                              null|8,490 European an...|     0|        0|   10760|              [null]| [{nfe, 0.80529739...|[{8490, European}...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST003791|     GCST|     gwas|Response to metfo...|    [EFO_0006952, GO_...|  null|              null|28173075|Metformin pharmac...|                 Niu N|     2016-09-11|       Hum Mol Genet|                              null|up to 96 African ...|     0|        0|     288|              [null]| [{afr, 0.33333333...|[{96, African Ame...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST003824|     GCST|     gwas|Depression in res...|    [EFO_0007006, EFO...|  null|              null|27723809|Genome-Wide Assoc...|           Matsunami K|     2016-10-10|            PLoS One|                     [EFO_0004220]|45 Japanese ances...|    45|      179|     224|              [null]|         [{eas, 1.0}]| [{224, East Asian}]| [{160, East Asian}]|           null|         null|                null|      false|\n",
+      "|  GCST003837|     GCST|     gwas|          Chronotype|           [EFO_0004354]|  null|              null|27494321|Genome-Wide Assoc...|              Jones SE|     2016-08-05|          PLoS Genet|                              null|127,898 British i...|     0|        0|  127898|              [null]|         [{nfe, 1.0}]|[{127898, European}]|       [{89283, NR}]|             []|           []|ftp://ftp.ebi.ac....|       true|\n",
+      "|  GCST004678|     GCST|     gwas|Psychosis pronene...|           [EFO_0008337]|  null|              null|28525603|Genome-Wide Assoc...|       Ortega-Alonso A|     2017-05-19|      Schizophr Bull|                              null|3,967 Finnish anc...|     0|        0|    3967|              [null]|         [{nfe, 1.0}]|  [{3967, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST005189|     GCST|     gwas|             Tanning|           [EFO_0004279]|  null|              null|29195075|An Unexpectedly C...|             Martin AR|     2017-11-30|                Cell|                              null|216 Sub-Saharan A...|     0|        0|     216|              [null]|         [{afr, 1.0}]|[{216, Sub-Sahara...|[{240, Sub-Sahara...|           null|         null|                null|      false|\n",
+      "|  GCST005437|     GCST|     gwas|Random C-peptide ...|           [EFO_0005187]|  null|              null|29404672|Meta-genome-wide ...|           Roshandel D|     2018-02-05|        Diabetologia|                   [MONDO_0005147]|1,497 European an...|     0|        0|    1497|              [null]|         [{nfe, 1.0}]|  [{1497, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST005503|     GCST|     gwas|Medium HDL partic...|           [EFO_0004612]|  null|              null|29084231|Common, low-frequ...|              Davis JP|     2017-10-30|          PLoS Genet|                              null|8,372 Finnish anc...|     0|        0|    8372|              [null]|         [{nfe, 1.0}]|  [{8372, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST005669|     GCST|     gwas|Delta-6 desaturas...|    [EFO_0007765, EFO...|  null|              null|29246731|A common variant ...|      de Toro-Martin J|     2017-11-02|      J Clin Lipidol|                              null|81 extreme respon...|     0|        0|     141|              [null]|         [{nfe, 1.0}]|         [{141, NR}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST005749|     GCST|     gwas|Digit length rati...|           [EFO_0004841]|  null|              null|29659830|Genome-wide assoc...|         Warrington NM|     2018-04-12|       Hum Mol Genet|                              null|14,382 European a...|     0|        0|   15661|              [null]|         [{nfe, 1.0}]|[{14382, European...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST006420|     GCST|     gwas|Affective disorde...|    [EFO_0004247, EFO...|  null|              null|30116032|Genetics of suici...|           Erlangsen A|     2018-08-16|      Mol Psychiatry|                              null|4,302 European an...|  4302|    13294|   17596|              [null]|         [{nfe, 1.0}]| [{17596, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST006484|     GCST|     gwas|     Type 2 diabetes|         [MONDO_0005148]|  null|              null|30130595|Pilot genome-wide...|     Dominguez-Cruz MG|     2018-08-18|                Gene|                              null|45 Maya ancestry ...|    45|       47|      92|              [null]|         [{amr, 1.0}]|[{92, Native Amer...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST006496|     GCST|     gwas|Glomerular filtra...|    [EFO_0006829, EFO...|  null|              null|30160337|Genome Wide Assoc...|               Asleh R|     2018-08-30|     Clin Transplant|                              null|243 European ance...|     0|        0|     251|              [null]| [{nfe, 0.99601593...|[{243, European},...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST006739|     GCST|     gwas|Proportion of mis...|           [EFO_0006923]|  null|              null|30188897|Detecting past an...|               Jeong C|     2018-09-06|          PLoS Genet|                              null|981 Tibetan ances...|     0|        0|     981|              [null]|         [{nfe, 1.0}]|         [{981, NR}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST006907|     GCST|     gwas|Ischemic stroke (...|           [EFO_0005524]|  null|              null|29531354|Multiancestry gen...|               Malik R|     2018-03-12|           Nat Genet|                              null|4,373 European an...|  4373|   406111|  410484|              [null]|         [{nfe, 1.0}]|[{150765, European}]|                  []|             []|           []|ftp://ftp.ebi.ac....|       true|\n",
+      "|  GCST006960|     GCST|     gwas|Inflammatory bowe...|           [EFO_0003767]|  null|              null|26490195|Inherited determi...|             Cleynen I|     2015-10-18|              Lancet|                              null|16,902 European a...| 29838|        0|   29838|              [null]|         [{nfe, 1.0}]| [{29838, European}]|  [{6182, European}]|           null|         null|                null|      false|\n",
+      "|  GCST007217|     GCST|     gwas|RR interval (hear...|           [EFO_0004831]|  null|              null|30679814|Genome-wide assoc...|          van Setten J|     2019-01-24|     Eur J Hum Genet|                              null|2,006 Erasmus Ruc...|     0|        0|   28698|              [null]|         [{nfe, 1.0}]| [{28698, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST008154|     GCST|     gwas|      Trunk fat mass|           [EFO_0005409]|  null|              null|28552196|Whole-Genome Sequ...|         Tachmazidou I|     2017-06-01|      Am J Hum Genet|                              null|3,399 whole genom...|     0|        0|   16237|              [null]|         [{nfe, 1.0}]|[{3538, NR}, {128...| [{10667, European}]|           null|         null|                null|      false|\n",
+      "|  GCST008483|     GCST|     gwas|  Ulcerative colitis|           [EFO_0000729]|  null|              null|26398853|Identification of...|                 Ye BD|     2016-01-01|   Inflamm Bowel Dis|                              null|705 Korean ancest...|   705|     1178|    1883|              [null]|         [{eas, 1.0}]|[{1883, South Asi...|[{3674, South Asi...|           null|         null|                null|      false|\n",
+      "|  GCST008671|     GCST|     gwas|Phlegm x occupati...|    [EFO_0007939, EFO...|  null|              null|30449631|Genome-wide inter...|                Zeng X|     2018-11-15|         Environ Int|                              null|1,702 Dutch ances...|  1702|     6274|    7976|              [null]|         [{nfe, 1.0}]|  [{7976, European}]|  [{6789, European}]|           null|         null|                null|      false|\n",
+      "|  GCST008675|     GCST|     gwas|Maximum habitual ...|           [EFO_0007878]|  null|              null|31151762|Genome-wide Assoc...|           Gelernter J|     2019-04-08|     Biol Psychiatry|                              null|126,936 European ...|     0|        0|  143965|              [null]| [{afr, 0.11828569...|[{17029, African ...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST008775|     GCST|     gwas|Birth weight or w...|    [EFO_0004342, EFO...|  null|              null|30858448|Genetic overlap b...|        Tekola-Ayele F|     2019-03-11|             Sci Rep|                              null|153,781 European ...|     0|        0|  378240|              [null]|         [{nfe, 1.0}]|[{246502, Europea...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST008870|     GCST|     gwas|Keratinocyte canc...|           [EFO_0010176]|  null|              null|31174203|Combined analysis...|           Liyanage UE|     2019-06-07|       Hum Mol Genet|                              null|at least 18,538 E...| 18538|   340302|  358840|              [null]|         [{nfe, 1.0}]|[{358840, European}]|                  []|           null|         null|                null|      false|\n",
+      "|  GCST009173|     GCST|     gwas|Response to (pegy...|           [EFO_0007859]|  null|              null|30715261|Genome Wide Assoc...|            Brouwer WP|     2019-02-02|     Clin Infect Dis|                     [EFO_0004239]|121 Asian, Europe...|     0|        0|     509|              [null]| [{nfe, 0.5}, {afr...|[{127, European},...|                  []|           null|         null|                null|      false|\n",
+      "|  GCST009364|     GCST|     gwas|Triglyceride leve...|    [EFO_0004530, EFO...|  null|              null|31719535|Multi-ancestry sl...|             Noordam R|     2019-11-12|          Nat Commun|                              null|at least 2,926 Af...|     0|    49886|   61990|              [null]| [{eas, 0.03837715...|[{2096, East Asia...|[{12579, Hispanic...|           null|         null|                null|      false|\n",
+      "|  GCST009391|     GCST|     gwas|Metabolite levels...|           [EFO_0005132]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_2|     GCST|     gwas|   Metabolite levels|                      []|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_3|     GCST|     gwas|   Metabolite levels|    [EFO_0004468, EFO...|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_4|     GCST|     gwas|   Metabolite levels|           [EFO_0004518]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_5|     GCST|     gwas|   Metabolite levels|           [EFO_0004761]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_6|     GCST|     gwas|   Metabolite levels|           [EFO_0004846]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_7|     GCST|     gwas|   Metabolite levels|           [EFO_0005001]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_8|     GCST|     gwas|   Metabolite levels|           [EFO_0005002]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "|GCST009391_9|     GCST|     gwas|   Metabolite levels|           [EFO_0005058]|  null|              null|23823483|A genome-wide ass...|               Rhee EP|     2013-07-02|          Cell Metab|                              null|2,076 European an...|     0|        0|    2076|              [null]|         [{nfe, 1.0}]|  [{2076, European}]|                  []|           null|         null|                null|      false|\n",
+      "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n",
+      "only showing top 50 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_tmp=\"gs://gwas_catalog_data/study_index\"\n",
+    "tmp=StudyIndex.from_parquet(session=session, path=path_tmp)\n",
+    "tmp.df.show(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined_df=joined_df.withColumn(\"traitFromSourceMappedIds\",joined_df[\"trait_efos\"]).drop(\"trait_efos\",\"trait_reported_low\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|             studyId|  projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|  cohorts|ldPopulationStructure|   discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Actinomycosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   101|   363227|  363328|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|          Amoebiasis|         ['EFO_0007144']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   160|   367214|  367374|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Anogenital herpes...|         ['EFO_0007282']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1986|   400197|  402183|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Aspergillosis|         ['EFO_0007157']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   211|   403213|  403424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Atypical virus in...|       ['MONDO_0024318']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   282|   409849|  410131|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial infecti...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 20226|   363227|  383453|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial, viral ...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2852|   409329|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Other bacterial i...|         ['EFO_0000771']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  6145|   367214|  373359|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|         Candidiasis|       ['MONDO_0002026']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  4306|   403213|  407519|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|Other sexually tr...|    ['MONDO_0021681',...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2186|   400197|  402383|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|             Cholera|         ['EFO_1001235']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1385|   367214|  368599|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|Dengue fever [cla...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|    53|   409137|  409190|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|     Dermatophytosis|       ['MONDO_0004678']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  3921|   403213|  407134|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|      Early syphilis|         ['EFO_0007504']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   308|   400197|  400505|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "| FINNGEN_R10_AB1_EBV|FINNGEN_R10|     gwas|Infectious mononu...|         ['EFO_0007326']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2979|   400974|  403953|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|        Enterobiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   112|   411658|  411770|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|          Erysipelas|         ['EFO_1001462']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 22261|   363227|  385488|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Diarrhoea and gas...|       ['MONDO_0045031']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 32210|   367214|  399424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Gonococcal infection|           ['DOID_7551']|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   954|   400197|  401151|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_H...|FINNGEN_R10|     gwas|       Helminthiases|    ['EFO_0007245', '...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   523|   411658|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "joined_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "string\n"
+     ]
+    }
+   ],
+   "source": [
+    "column_type = dict(joined_df.dtypes)[\"traitFromSourceMappedIds\"]\n",
+    "print(column_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql.functions import from_json\n",
+    "from pyspark.sql.types import ArrayType, StringType\n",
+    "\n",
+    "# Assuming joined_df is your DataFrame\n",
+    "joined_df = joined_df.withColumn(\n",
+    "    \"traitFromSourceMappedIds\",\n",
+    "    from_json(\"traitFromSourceMappedIds\", ArrayType(StringType()))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|             studyId|  projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|  cohorts|ldPopulationStructure|   discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Actinomycosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   101|   363227|  363328|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|          Amoebiasis|           [EFO_0007144]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   160|   367214|  367374|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Anogenital herpes...|           [EFO_0007282]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1986|   400197|  402183|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Aspergillosis|           [EFO_0007157]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   211|   403213|  403424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Atypical virus in...|         [MONDO_0024318]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   282|   409849|  410131|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial infecti...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 20226|   363227|  383453|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial, viral ...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2852|   409329|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Other bacterial i...|           [EFO_0000771]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  6145|   367214|  373359|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|         Candidiasis|         [MONDO_0002026]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  4306|   403213|  407519|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|Other sexually tr...|    [MONDO_0021681, E...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2186|   400197|  402383|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|             Cholera|           [EFO_1001235]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1385|   367214|  368599|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|Dengue fever [cla...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|    53|   409137|  409190|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|     Dermatophytosis|         [MONDO_0004678]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  3921|   403213|  407134|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|      Early syphilis|           [EFO_0007504]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   308|   400197|  400505|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "| FINNGEN_R10_AB1_EBV|FINNGEN_R10|     gwas|Infectious mononu...|           [EFO_0007326]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2979|   400974|  403953|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|        Enterobiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   112|   411658|  411770|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|          Erysipelas|           [EFO_1001462]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 22261|   363227|  385488|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Diarrhoea and gas...|         [MONDO_0045031]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 32210|   367214|  399424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Gonococcal infection|             [DOID_7551]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   954|   400197|  401151|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_H...|FINNGEN_R10|     gwas|       Helminthiases|    [EFO_0007245, EFO...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   523|   411658|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "joined_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "array<string>\n"
+     ]
+    }
+   ],
+   "source": [
+    "column_type = dict(joined_df.dtypes)[\"traitFromSourceMappedIds\"]\n",
+    "print(column_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "si=StudyIndex(_df=joined_df, _schema=StudyIndex.get_schema())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|             studyId|  projectId|studyType|     traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds|   initialSampleSize|nCases|nControls|nSamples|  cohorts|ldPopulationStructure|   discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Actinomycosis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   101|   363227|  363328|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|          Amoebiasis|           [EFO_0007144]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   160|   367214|  367374|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Anogenital herpes...|           [EFO_0007282]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1986|   400197|  402183|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|       Aspergillosis|           [EFO_0007157]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   211|   403213|  403424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_A...|FINNGEN_R10|     gwas|Atypical virus in...|         [MONDO_0024318]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   282|   409849|  410131|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial infecti...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 20226|   363227|  383453|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Bacterial, viral ...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2852|   409329|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_B...|FINNGEN_R10|     gwas|Other bacterial i...|           [EFO_0000771]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  6145|   367214|  373359|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|         Candidiasis|         [MONDO_0002026]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  4306|   403213|  407519|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|Other sexually tr...|    [MONDO_0021681, E...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2186|   400197|  402383|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_C...|FINNGEN_R10|     gwas|             Cholera|           [EFO_1001235]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  1385|   367214|  368599|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|Dengue fever [cla...|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|    53|   409137|  409190|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_D...|FINNGEN_R10|     gwas|     Dermatophytosis|         [MONDO_0004678]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  3921|   403213|  407134|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|      Early syphilis|           [EFO_0007504]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   308|   400197|  400505|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "| FINNGEN_R10_AB1_EBV|FINNGEN_R10|     gwas|Infectious mononu...|           [EFO_0007326]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|  2979|   400974|  403953|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|        Enterobiasis|                    null|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   112|   411658|  411770|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_E...|FINNGEN_R10|     gwas|          Erysipelas|           [EFO_1001462]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 22261|   363227|  385488|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Diarrhoea and gas...|         [MONDO_0045031]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...| 32210|   367214|  399424|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_G...|FINNGEN_R10|     gwas|Gonococcal infection|             [DOID_7551]|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   954|   400197|  401151|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "|FINNGEN_R10_AB1_H...|FINNGEN_R10|     gwas|       Helminthiases|    [EFO_0007245, EFO...|  null|              null|    null|            null|                  null|           null|              null|                              null|377,277 (210,870 ...|   523|   411658|  412181|[FinnGen]|         [{fin, 1.0}]|[{377277, Finnish}]|              null|           null|         null|gs://finngen-publ...|       true|\n",
+      "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "si.df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2408"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "si.df.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "si.df.write.parquet(path=\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_to_study_index=\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\"\n",
+    "si=StudyIndex.from_parquet(session=session, path=path_to_study_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gentropy-krNFZEZg-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 900dd649779c2727e44c30b11671b0e3c7261036 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:33:16 +0100
Subject: [PATCH 52/86] feat: adding init to finemapping step (#577)

* feat: adding init to finemapping step

* fix: removing some commented lines

* chore: fixing indents

* fix: schema

* feat: changing output path to include studyLocusId mapped

---------

Co-authored-by: Yakov <yt4@sanger.ac.uk>
---
 src/gentropy/susie_finemapper.py | 51 ++++++++++++++++++++++++++++++--
 1 file changed, 49 insertions(+), 2 deletions(-)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index d37298436..7b6f81b3a 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -28,6 +28,47 @@ class SusieFineMapperStep:
     In the future this step will be refactored and moved to the methods module.
     """
 
+    def __init__(
+        self,
+        session: Session,
+        study_locus_to_finemap: str,
+        study_locus_collected_path: str,
+        study_index_path: str,
+        output_path: str,
+        locus_radius: int = 500_000,
+        locus_L: int = 10,
+    ) -> None:
+        """Run fine-mapping on a studyLocusId from a collected studyLocus table.
+
+        Args:
+            session (Session): Spark session
+            study_locus_to_finemap (str): path to the study locus to fine-map
+            study_locus_collected_path (str): path to the collected study locus
+            study_index_path (str): path to the study index
+            output_path (str): path to the output
+            locus_radius (int): Radius of base-pair window around the locus, default is 500_000
+            locus_L (int): Maximum number of causal variants in locus, default is 10
+        """
+        # Read studyLocus
+        study_locus = (
+            StudyLocus.from_parquet(session, study_locus_collected_path)
+            .df.filter(f.col("studyLocusId") == study_locus_to_finemap)
+            .collect()[0]
+        )
+        study_index = StudyIndex.from_parquet(session, study_index_path)
+        # Run fine-mapping
+        result = self.susie_finemapper_ss_gathered(
+            session,
+            study_locus,
+            study_index,
+            locus_radius * 2,
+            locus_L,
+        )
+        # Write result
+        result.df.write.mode(session.write_mode).parquet(
+            output_path + "/" + study_locus_to_finemap
+        )
+
     @staticmethod
     def susie_finemapper_one_studylocus_row(
         GWAS: SummaryStatistics,
@@ -317,9 +358,15 @@ def susie_finemapper_ss_gathered(
             + str(int(position + window / 2))
         )
 
+        schema = StudyLocus.get_schema()
+        gwas_df = session.spark.createDataFrame([study_locus_row], schema=schema)
+        exploded_df = gwas_df.select(f.explode("locus").alias("locus"))
+
+        result_df = exploded_df.select(
+            "locus.variantId", "locus.beta", "locus.standardError"
+        )
         gwas_df = (
-            session.spark.createDataFrame(study_locus_row.locus)
-            .withColumn("z", f.col("beta") / f.col("standardError"))
+            result_df.withColumn("z", f.col("beta") / f.col("standardError"))
             .withColumn("chromosome", f.split(f.col("variantId"), "_")[0])
             .withColumn("position", f.split(f.col("variantId"), "_")[1])
             .filter(f.col("z").isNotNull())

From 78fcf1b85a72751fb6a7a006b3769d8170348aed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Tue, 23 Apr 2024 13:55:35 +0100
Subject: [PATCH 53/86] feat: dockerise gentropy python package (#579)

Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 Dockerfile | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Dockerfile

diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 000000000..1221ec637
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,8 @@
+FROM python:3.10
+
+RUN pip install poetry==1.7.1
+
+COPY . .
+RUN poetry install --without dev,docs,tests
+
+ENTRYPOINT ["poetry", "run", "gentropy"]

From 82b8a7c539d6ee669a73c09db1d30783eec41d69 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Tue, 23 Apr 2024 14:27:50 +0100
Subject: [PATCH 54/86] feat: updating step config file (#580)

---
 src/gentropy/config.py           | 19 +++++++++++++++++++
 src/gentropy/susie_finemapper.py |  6 +++---
 2 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 127d90844..0a76b2c84 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -328,6 +328,24 @@ class WindowBasedClumpingStep(StepConfig):
     _target_: str = "gentropy.window_based_clumping.WindowBasedClumpingStep"
 
 
+@dataclass
+class FinemapperConfig(StepConfig):
+    """SuSiE fine-mapper step configuration."""
+
+    session: Any = field(
+        default_factory=lambda: {
+            "start_hail": True,
+        }
+    )
+    study_locus_to_finemap: str = MISSING
+    study_locus_collected_path: str = MISSING
+    study_index_path: str = MISSING
+    output_path: str = MISSING
+    locus_radius: int = MISSING
+    locus_l: int = MISSING
+    _target_: str = "gentropy.susie_finemapping.SusieFineMapperStep"
+
+
 @dataclass
 class Config:
     """Application configuration."""
@@ -385,3 +403,4 @@ def register_config() -> None:
     cs.store(group="step", name="variant_index", node=VariantIndexConfig)
     cs.store(group="step", name="variant_to_gene", node=VariantToGeneConfig)
     cs.store(group="step", name="window_based_clumping", node=WindowBasedClumpingStep)
+    cs.store(group="step", name="susie_finemapping", node=FinemapperConfig)
diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index 7b6f81b3a..5a0fa31f3 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -36,7 +36,7 @@ def __init__(
         study_index_path: str,
         output_path: str,
         locus_radius: int = 500_000,
-        locus_L: int = 10,
+        locus_l: int = 10,
     ) -> None:
         """Run fine-mapping on a studyLocusId from a collected studyLocus table.
 
@@ -47,7 +47,7 @@ def __init__(
             study_index_path (str): path to the study index
             output_path (str): path to the output
             locus_radius (int): Radius of base-pair window around the locus, default is 500_000
-            locus_L (int): Maximum number of causal variants in locus, default is 10
+            locus_l (int): Maximum number of causal variants in locus, default is 10
         """
         # Read studyLocus
         study_locus = (
@@ -62,7 +62,7 @@ def __init__(
             study_locus,
             study_index,
             locus_radius * 2,
-            locus_L,
+            locus_l,
         )
         # Write result
         result.df.write.mode(session.write_mode).parquet(

From b0c4530f8925a3d8123d678623d91e5743368ac2 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Tue, 23 Apr 2024 16:22:38 +0100
Subject: [PATCH 55/86] fix: update error in config.py (#583)

---
 src/gentropy/config.py           | 2 +-
 src/gentropy/susie_finemapper.py | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 0a76b2c84..8c876bb6d 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -343,7 +343,7 @@ class FinemapperConfig(StepConfig):
     output_path: str = MISSING
     locus_radius: int = MISSING
     locus_l: int = MISSING
-    _target_: str = "gentropy.susie_finemapping.SusieFineMapperStep"
+    _target_: str = "gentropy.susie_finemapper.SusieFineMapperStep"
 
 
 @dataclass
diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index 5a0fa31f3..ceb5fbb34 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -5,6 +5,7 @@
 import time
 from typing import Any
 
+import hail as hl
 import numpy as np
 import pandas as pd
 import pyspark.sql.functions as f
@@ -49,6 +50,7 @@ def __init__(
             locus_radius (int): Radius of base-pair window around the locus, default is 500_000
             locus_l (int): Maximum number of causal variants in locus, default is 10
         """
+        hl.init(sc=session.spark.sparkContext, log="/dev/null")
         # Read studyLocus
         study_locus = (
             StudyLocus.from_parquet(session, study_locus_collected_path)

From 28a067cd19573eaae49078df777fd72dbcd4dcc4 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Tue, 23 Apr 2024 18:01:36 +0100
Subject: [PATCH 56/86] feat: changing locus window to locus radius to be
 consistent with other functions (#582)

---
 src/gentropy/datasource/gnomad/ld.py |  8 ++--
 src/gentropy/susie_finemapper.py     | 55 ++++++++++++++--------------
 2 files changed, 32 insertions(+), 31 deletions(-)

diff --git a/src/gentropy/datasource/gnomad/ld.py b/src/gentropy/datasource/gnomad/ld.py
index a19cbb06b..02cb15c7f 100644
--- a/src/gentropy/datasource/gnomad/ld.py
+++ b/src/gentropy/datasource/gnomad/ld.py
@@ -453,14 +453,14 @@ def get_ld_matrix_slice(
     def get_locus_index(
         self: GnomADLDMatrix,
         study_locus_row: Row,
-        window_size: int = 1_000_000,
+        radius: int = 500_000,
         major_population: str = "nfe",
     ) -> DataFrame:
         """Extract hail matrix index from StudyLocus rows.
 
         Args:
             study_locus_row (Row): Study-locus row
-            window_size (int): Window size to extract from gnomad matrix
+            radius (int): Locus radius to extract from gnomad matrix
             major_population (str): Major population to extract from gnomad matrix, default is "nfe"
 
         Returns:
@@ -468,8 +468,8 @@ def get_locus_index(
 
         """
         chromosome = str("chr" + study_locus_row["chromosome"])
-        start = study_locus_row["position"] - window_size // 2
-        end = study_locus_row["position"] + window_size // 2
+        start = study_locus_row["position"] - radius
+        end = study_locus_row["position"] + radius
 
         liftover_ht = hl.read_table(self.liftover_ht_path)
         liftover_ht = (
diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index ceb5fbb34..24635934e 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -37,7 +37,7 @@ def __init__(
         study_index_path: str,
         output_path: str,
         locus_radius: int = 500_000,
-        locus_l: int = 10,
+        max_causal_snps: int = 10,
     ) -> None:
         """Run fine-mapping on a studyLocusId from a collected studyLocus table.
 
@@ -48,8 +48,9 @@ def __init__(
             study_index_path (str): path to the study index
             output_path (str): path to the output
             locus_radius (int): Radius of base-pair window around the locus, default is 500_000
-            locus_l (int): Maximum number of causal variants in locus, default is 10
+            max_causal_snps (int): Maximum number of causal variants in locus, default is 10
         """
+        # Initialise Hail
         hl.init(sc=session.spark.sparkContext, log="/dev/null")
         # Read studyLocus
         study_locus = (
@@ -63,8 +64,8 @@ def __init__(
             session,
             study_locus,
             study_index,
-            locus_radius * 2,
-            locus_l,
+            locus_radius,
+            max_causal_snps,
         )
         # Write result
         result.df.write.mode(session.write_mode).parquet(
@@ -77,7 +78,7 @@ def susie_finemapper_one_studylocus_row(
         session: Session,
         study_locus_row: Row,
         study_index: StudyIndex,
-        window: int = 1_000_000,
+        radius: int = 1_000_000,
         L: int = 10,
     ) -> StudyLocus:
         """Susie fine-mapper for StudyLocus row with SummaryStatistics object.
@@ -87,7 +88,7 @@ def susie_finemapper_one_studylocus_row(
             session (Session): Spark session
             study_locus_row (Row): StudyLocus row
             study_index (StudyIndex): StudyIndex object
-            window (int): window size for fine-mapping
+            radius (int): window size for fine-mapping
             L (int): number of causal variants
 
         Returns:
@@ -112,9 +113,9 @@ def susie_finemapper_one_studylocus_row(
         region = (
             chromosome
             + ":"
-            + str(int(position - window / 2))
+            + str(int(position - radius))
             + "-"
-            + str(int(position + window / 2))
+            + str(int(position + radius))
         )
 
         gwas_df = (
@@ -133,7 +134,7 @@ def susie_finemapper_one_studylocus_row(
             GnomADLDMatrix()
             .get_locus_index(
                 study_locus_row=study_locus_row,
-                window_size=window,
+                radius=radius,
                 major_population=major_population,
             )
             .withColumn(
@@ -321,8 +322,8 @@ def susie_finemapper_ss_gathered(
         session: Session,
         study_locus_row: Row,
         study_index: StudyIndex,
-        window: int = 1_000_000,
-        L: int = 10,
+        radius: int = 1_000_000,
+        max_causal_snps: int = 10,
     ) -> StudyLocus:
         """Susie fine-mapper for StudyLocus row with locus annotated summary statistics.
 
@@ -330,8 +331,8 @@ def susie_finemapper_ss_gathered(
             session (Session): Spark session
             study_locus_row (Row): StudyLocus row
             study_index (StudyIndex): StudyIndex object
-            window (int): window size for fine-mapping
-            L (int): number of causal variants
+            radius (int): window size for fine-mapping
+            max_causal_snps (int): number of causal variants
 
         Returns:
             StudyLocus: StudyLocus object with fine-mapped credible sets
@@ -355,9 +356,9 @@ def susie_finemapper_ss_gathered(
         region = (
             chromosome
             + ":"
-            + str(int(position - window / 2))
+            + str(int(position - radius))
             + "-"
-            + str(int(position + window / 2))
+            + str(int(position + radius))
         )
 
         schema = StudyLocus.get_schema()
@@ -382,7 +383,7 @@ def susie_finemapper_ss_gathered(
             GnomADLDMatrix()
             .get_locus_index(
                 study_locus_row=study_locus_row,
-                window_size=window,
+                radius=radius,
                 major_population=major_population,
             )
             .withColumn(
@@ -412,7 +413,7 @@ def susie_finemapper_ss_gathered(
         z_to_fm = np.array(pd_df["z"])
         ld_to_fm = gnomad_ld
 
-        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=L)
+        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=max_causal_snps)
 
         schema = StructType(
             [
@@ -624,8 +625,8 @@ def susie_finemapper_one_studylocus_row_v2_dev(
         session: Session,
         study_locus_row: Row,
         study_index: StudyIndex,
-        window: int = 1_000_000,
-        L: int = 10,
+        radius: int = 1_000_000,
+        max_causal_snps: int = 10,
         susie_est_tausq: bool = False,
         run_carma: bool = False,
         run_sumstat_imputation: bool = False,
@@ -641,8 +642,8 @@ def susie_finemapper_one_studylocus_row_v2_dev(
             session (Session): Spark session
             study_locus_row (Row): StudyLocus row
             study_index (StudyIndex): StudyIndex object
-            window (int): window size for fine-mapping
-            L (int): number of causal variants
+            radius (int): Radius in base-pairs of window for fine-mapping
+            max_causal_snps (int): maximum number of causal variants
             susie_est_tausq (bool): estimate tau squared, default is False
             run_carma (bool): run CARMA, default is False
             run_sumstat_imputation (bool): run summary statistics imputation, default is False
@@ -673,9 +674,9 @@ def susie_finemapper_one_studylocus_row_v2_dev(
         region = (
             chromosome
             + ":"
-            + str(int(position - window / 2))
+            + str(int(position - radius))
             + "-"
-            + str(int(position + window / 2))
+            + str(int(position + radius))
         )
         gwas_df = (
             GWAS.df.withColumn("z", f.col("beta") / f.col("standardError"))
@@ -686,15 +687,15 @@ def susie_finemapper_one_studylocus_row_v2_dev(
             .filter(f.col("studyId") == studyId)
             .filter(f.col("z").isNotNull())
             .filter(f.col("chromosome") == chromosome)
-            .filter(f.col("position") >= position - window / 2)
-            .filter(f.col("position") <= position + window / 2)
+            .filter(f.col("position") >= position - radius)
+            .filter(f.col("position") <= position + radius)
         )
 
         ld_index = (
             GnomADLDMatrix()
             .get_locus_index(
                 study_locus_row=study_locus_row,
-                window_size=window,
+                radius=radius,
                 major_population=major_population,
             )
             .withColumn(
@@ -719,7 +720,7 @@ def susie_finemapper_one_studylocus_row_v2_dev(
             GWAS_df=gwas_df,
             ld_index=ld_index,
             gnomad_ld=gnomad_ld,
-            L=L,
+            L=max_causal_snps,
             session=session,
             studyId=studyId,
             region=region,

From e2f8e870725b282d430b5e7f2fb140d30d66ecc1 Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Wed, 24 Apr 2024 10:29:22 +0200
Subject: [PATCH 57/86] fix: minor updates and bug fixes (#543)

---
 config/step/ot_variant_index.yaml           | 2 +-
 src/airflow/dags/genetics_etl.py            | 8 ++++----
 src/airflow/dags/gwas_catalog_preprocess.py | 8 ++------
 src/gentropy/pics.py                        | 8 ++++++--
 4 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/config/step/ot_variant_index.yaml b/config/step/ot_variant_index.yaml
index 1625c7126..3834196b2 100644
--- a/config/step/ot_variant_index.yaml
+++ b/config/step/ot_variant_index.yaml
@@ -2,5 +2,5 @@ defaults:
   - variant_index
 
 variant_annotation_path: ${datasets.variant_annotation}
-credible_set_path: ${datasets.study_locus}
+credible_set_path: ${datasets.credible_set}
 variant_index_path: ${datasets.variant_index}
diff --git a/src/airflow/dags/genetics_etl.py b/src/airflow/dags/genetics_etl.py
index 55f343648..ae510131c 100644
--- a/src/airflow/dags/genetics_etl.py
+++ b/src/airflow/dags/genetics_etl.py
@@ -35,16 +35,16 @@
     # PICS credible sets from GWAS Catalog curated associations:
     "gwas_catalog_curated_credible_set": {
         "source_bucket": GWAS_CATALOG_BUCKET_NAME,
-        "source_object": "credible_set_datasets/gwas_catalog_curated",
+        "source_object": "credible_set_datasets/gwas_catalog_PICSed_curated_associations",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_pics_from_curation",
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_PICSed_curated_associations",
     },
     # PICS credible sets from GWAS Catalog summary statistics:
     "gwas_catalog_sumstats_credible_set": {
         "source_bucket": GWAS_CATALOG_BUCKET_NAME,
-        "source_object": "credible_set_datasets/gwas_catalog_summary_stats",
+        "source_object": "credible_set_datasets/gwas_catalog_PICSed_summary_statistics",
         "destination_bucket": RELEASE_BUCKET_NAME,
-        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_pics_from_summary_statistics",
+        "destination_object": f"releases/{RELEASE_VERSION}/credible_set/gwas_catalog_PICSed_summary_statistics",
     },
     # GWAS Catalog manifest files:
     "gwas_catalog_manifests": {
diff --git a/src/airflow/dags/gwas_catalog_preprocess.py b/src/airflow/dags/gwas_catalog_preprocess.py
index 36130c87e..1814ddf2d 100644
--- a/src/airflow/dags/gwas_catalog_preprocess.py
+++ b/src/airflow/dags/gwas_catalog_preprocess.py
@@ -45,12 +45,8 @@
 WINDOW_BASED_CLUMPED = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_summary_stats_window_clumped"
 LD_BASED_CLUMPED = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_summary_stats_ld_clumped"
 # Credible sets:
-CURATED_CREDIBLE_SETS = (
-    f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_curated"
-)
-SUMMARY_STATISTICS_CREDIBLE_SETS = (
-    f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_summary_stats"
-)
+CURATED_CREDIBLE_SETS = f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_PICSed_curated_associations"
+SUMMARY_STATISTICS_CREDIBLE_SETS = f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_PICSed_summary_statistics"
 
 
 def upload_harmonized_study_list(
diff --git a/src/gentropy/pics.py b/src/gentropy/pics.py
index c2ed9bf66..80421b9ae 100644
--- a/src/gentropy/pics.py
+++ b/src/gentropy/pics.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from gentropy.common.session import Session
-from gentropy.dataset.study_locus import StudyLocus
+from gentropy.dataset.study_locus import CredibleInterval, StudyLocus
 from gentropy.method.pics import PICS
 
 
@@ -28,6 +28,10 @@ def __init__(
             session, study_locus_ld_annotated_in
         )
         # PICS
-        picsed_sl = PICS.finemap(study_locus_ld_annotated).annotate_credible_sets()
+        picsed_sl = (
+            PICS.finemap(study_locus_ld_annotated)
+            .annotate_credible_sets()
+            .filter_credible_set(credible_interval=CredibleInterval.IS99)
+        )
         # Write
         picsed_sl.df.write.mode(session.write_mode).parquet(picsed_study_locus_out)

From cf184f8d8a3d88c02e056995e74549f141cc6348 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Wed, 24 Apr 2024 09:43:46 +0100
Subject: [PATCH 58/86] fix: updating config.py argument for finemapper (#584)

---
 src/gentropy/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 8c876bb6d..068bd42bf 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -342,7 +342,7 @@ class FinemapperConfig(StepConfig):
     study_index_path: str = MISSING
     output_path: str = MISSING
     locus_radius: int = MISSING
-    locus_l: int = MISSING
+    max_causal_snps: int = MISSING
     _target_: str = "gentropy.susie_finemapper.SusieFineMapperStep"
 
 

From bcc9a364a475136dbd7350bc6c9a7ca4da252971 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Wed, 24 Apr 2024 09:55:51 +0100
Subject: [PATCH 59/86] feat(sumstat qc): adding methods for QC of summary
 statistics (#455)

* feat(sumstat qc): class for GWAS sumstat QC

* feat: small fixes

* feat: modifying sumarry_statistics class

* feat: adding qc_pz_check

* fix: test fix

* feat: adding se_n check

* chore: fixing functions

* feat: adding gc lambda check

* test: adding tests

* fix: fixing tests

* fix: fixing tests

* fix: fixing tests and adding rounding

* chore: pre-commit auto fixes [...]

* feat(sumstat qc): adding new method class

* test: fix tests

* fix(sumstat qc): fixing tests

* fix: remove number_of_snps from susmtat class

* test: add multiple studyId test

* docs: adding documentation for sumstat QC

* fix: fixes according to comments

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .../methods/sumstat_quality_controls.md       |  18 ++
 src/gentropy/dataset/summary_statistics.py    |  26 ++
 .../method/sumstat_quality_controls.py        | 285 ++++++++++++++++++
 tests/gentropy/method/test_qc_of_sumstats.py  |  63 ++++
 4 files changed, 392 insertions(+)
 create mode 100644 docs/python_api/methods/sumstat_quality_controls.md
 create mode 100644 src/gentropy/method/sumstat_quality_controls.py
 create mode 100644 tests/gentropy/method/test_qc_of_sumstats.py

diff --git a/docs/python_api/methods/sumstat_quality_controls.md b/docs/python_api/methods/sumstat_quality_controls.md
new file mode 100644
index 000000000..dfc5c9d16
--- /dev/null
+++ b/docs/python_api/methods/sumstat_quality_controls.md
@@ -0,0 +1,18 @@
+---
+title: QC of GWAS Summary Statistics
+---
+
+This class consists of several general quality control checks for GWAS with full summary statistics.
+There are several checks included:
+
+1. Genomic control lambda (median of the distribution of Chi2 statistics divided by expected for Chi2 with df=1). Lambda should be reasonably close to 1. Ideally not bigger than 2.
+
+2. P-Z check: the linear regression between log10 of reported p-values and log10 of p-values inferred from betas and standard errors. Intercept of the regression should be close to 0, slope close to 1.
+
+3. Mean beta check: mean of beta. Should be close to 0.
+
+4. The N_eff check: It estimates the ratio between effective sample size and the expected one and checks its distribution. It is possible to conduct only if the effective allele frequency is provided in the study. The median ratio is always close to 1, standard error should be close to 0.
+
+5. Number of SNPs and number of significant SNPs.
+
+:::gentropy.method.sumstat_quality_controls.SummaryStatisticsQC
diff --git a/src/gentropy/dataset/summary_statistics.py b/src/gentropy/dataset/summary_statistics.py
index 6cde03988..6244d5879 100644
--- a/src/gentropy/dataset/summary_statistics.py
+++ b/src/gentropy/dataset/summary_statistics.py
@@ -103,3 +103,29 @@ def exclude_region(self: SummaryStatistics, region: str) -> SummaryStatistics:
             ),
             _schema=SummaryStatistics.get_schema(),
         )
+
+    def sanity_filter(self: SummaryStatistics) -> SummaryStatistics:
+        """The function filters the summary statistics by sanity filters.
+
+        The function filters the summary statistics by the following filters:
+            - The p-value should not be eqaul 1.
+            - The beta and se should not be equal 0.
+            - The p-value, beta and se should not be NaN.
+
+        Returns:
+            SummaryStatistics: The filtered summary statistics.
+        """
+        gwas_df = self._df
+        gwas_df = gwas_df.dropna(
+            subset=["beta", "standardError", "pValueMantissa", "pValueExponent"]
+        )
+
+        gwas_df = gwas_df.filter((f.col("beta") != 0) & (f.col("standardError") != 0))
+        gwas_df = gwas_df.filter(
+            f.col("pValueMantissa") * 10 ** f.col("pValueExponent") != 1
+        )
+
+        return SummaryStatistics(
+            _df=gwas_df,
+            _schema=SummaryStatistics.get_schema(),
+        )
diff --git a/src/gentropy/method/sumstat_quality_controls.py b/src/gentropy/method/sumstat_quality_controls.py
new file mode 100644
index 000000000..2858f4813
--- /dev/null
+++ b/src/gentropy/method/sumstat_quality_controls.py
@@ -0,0 +1,285 @@
+"""Summary statistics qulity control methods."""
+from __future__ import annotations
+
+import numpy as np
+import pyspark.sql.functions as f
+import pyspark.sql.types as t
+import scipy as sc
+from pyspark.sql import DataFrame
+from pyspark.sql.functions import expr, log10, row_number
+from pyspark.sql.window import Window
+from scipy.stats import chi2
+
+from gentropy.dataset.summary_statistics import SummaryStatistics
+
+
+class SummaryStatisticsQC:
+    """Summary statistics QC methods.
+
+    This module contains methods for quality control of GWAS summary statistics.
+    The list of methods includes:
+
+        - sumstat_qc_beta_check: This is the mean beta check. The mean beta should be close to 0.
+
+        - sumstat_qc_pz_check: This is the PZ check. It runs a linear regression between reported p-values and p-values inferred from z-scores.
+
+        - sumstat_n_eff_check: This is the effective sample size check. It estimates the ratio between the effective sample size and the expected one and checks its distribution.
+
+        - gc_lambda_check: This is the genomic control lambda check.
+
+        - number_of_snps: This function calculates the number of SNPs and the number of SNPs with a p-value less than 5e-8.
+    """
+
+    @staticmethod
+    def sumstat_qc_beta_check(
+        gwas_for_qc: SummaryStatistics,
+    ) -> DataFrame:
+        """The mean beta check for QC of GWAS summary statstics.
+
+        Args:
+            gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the mean beta for each study.
+        """
+        gwas_df = gwas_for_qc._df
+        qc_c = gwas_df.groupBy("studyId").agg(
+            f.mean("beta").alias("mean_beta"),
+        )
+        return qc_c
+
+    @staticmethod
+    def _calculate_logpval(z2: float) -> float:
+        """Calculate negative log10-pval from Z-score.
+
+        Args:
+            z2 (float): Z-score squared.
+
+        Returns:
+            float: log10-pval.
+
+        Examples:
+            >>> SummaryStatisticsQC._calculate_logpval(1.0)
+            0.49851554582799334
+        """
+        logpval = -np.log10(sc.stats.chi2.sf((z2), 1))
+        return float(logpval)
+
+    @staticmethod
+    def sumstat_qc_pz_check(
+        gwas_for_qc: SummaryStatistics,
+        limit: int = 10_000_000,
+    ) -> DataFrame:
+        """The PZ check for QC of GWAS summary statstics. It runs linear regression between reported p-values and p-values infered from z-scores.
+
+        Args:
+            gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class.
+            limit (int): The limit for the number of variants to be used for the estimation.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the results of the linear regression for each study.
+        """
+        gwas_df = gwas_for_qc._df
+
+        calculate_logpval_udf = f.udf(
+            SummaryStatisticsQC._calculate_logpval, t.DoubleType()
+        )
+
+        window = Window.partitionBy("studyId").orderBy("studyId")
+
+        gwas_df = (
+            gwas_df.withColumn("row_num", row_number().over(window))
+            .filter(f.col("row_num") <= limit)
+            .drop("row_num")
+        )
+
+        qc_c = (
+            gwas_df.withColumn("zscore", f.col("beta") / f.col("standardError"))
+            .withColumn("new_logpval", calculate_logpval_udf(f.col("zscore") ** 2))
+            .withColumn("log_mantissa", log10("pValueMantissa"))
+            .withColumn(
+                "diffpval",
+                -f.col("log_mantissa") - f.col("pValueExponent") - f.col("new_logpval"),
+            )
+            .groupBy("studyId")
+            .agg(
+                f.mean("diffpval").alias("mean_diff_pz"),
+                f.stddev("diffpval").alias("se_diff_pz"),
+            )
+            .select("studyId", "mean_diff_pz", "se_diff_pz")
+        )
+
+        return qc_c
+
+    @staticmethod
+    def sumstat_n_eff_check(
+        gwas_for_qc: SummaryStatistics,
+        n_total: int = 100_000,
+        limit: int = 10_000_000,
+        min_count: int = 100,
+    ) -> DataFrame:
+        """The effective sample size check for QC of GWAS summary statstics.
+
+        It estiamtes the ratio between effective sample size and the expected one and checks it's distribution.
+        It is possible to conduct only if the effective allele frequency is provided in the study.
+        The median rartio is always close to 1, but standard error could be inflated.
+
+        Args:
+            gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class.
+            n_total (int): The reported sample size of the study. The QC metrics is robust toward the sample size.
+            limit (int): The limit for the number of variants to be used for the estimation.
+            min_count (int): The minimum number of variants to be used for the estimation.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the effective sample size ratio for each study.
+        """
+        gwas_df = gwas_for_qc._df
+
+        gwas_df = gwas_df.dropna(subset=["effectAlleleFrequencyFromSource"])
+
+        counts_df = gwas_df.groupBy("studyId").count()
+
+        # Join the original DataFrame with the counts DataFrame
+        df_with_counts = gwas_df.join(counts_df, on="studyId")
+
+        # Filter the DataFrame to keep only the groups with count greater than or equal to min_count
+        filtered_df = df_with_counts.filter(f.col("count") >= min_count).drop("count")
+
+        window = Window.partitionBy("studyId").orderBy("studyId")
+        gwas_df = (
+            filtered_df.withColumn("row_num", row_number().over(window))
+            .filter(f.col("row_num") <= limit)
+            .drop("row_num")
+        )
+
+        gwas_df = gwas_df.withColumn(
+            "var_af",
+            2
+            * (
+                f.col("effectAlleleFrequencyFromSource")
+                * (1 - f.col("effectAlleleFrequencyFromSource"))
+            ),
+        ).withColumn(
+            "pheno_var",
+            ((f.col("standardError") ** 2) * n_total * f.col("var_af"))
+            + ((f.col("beta") ** 2) * f.col("var_af")),
+        )
+
+        window = Window.partitionBy("studyId").orderBy("studyId")
+
+        # Calculate the median of 'pheno_var' for each 'studyId' and add it as a new column
+        gwas_df = gwas_df.withColumn(
+            "pheno_median", expr("percentile_approx(pheno_var, 0.5)").over(window)
+        )
+
+        gwas_df = gwas_df.withColumn(
+            "N_hat_ratio",
+            (
+                (f.col("pheno_median") - ((f.col("beta") ** 2) * f.col("var_af")))
+                / ((f.col("standardError") ** 2) * f.col("var_af") * n_total)
+            ),
+        )
+
+        qc_c = (
+            gwas_df.groupBy("studyId")
+            .agg(
+                f.stddev("N_hat_ratio").alias("se_N"),
+            )
+            .select("studyId", "se_N")
+        )
+
+        return qc_c
+
+    @staticmethod
+    def gc_lambda_check(
+        gwas_for_qc: SummaryStatistics,
+        limit: int = 10_000_000,
+    ) -> DataFrame:
+        """The genomic control lambda check for QC of GWAS summary statstics.
+
+        Args:
+            gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class.
+            limit (int): The limit for the number of variants to be used for the estimation.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the genomic control lambda for each study.
+        """
+        gwas_df = gwas_for_qc._df
+        window = Window.partitionBy("studyId").orderBy("studyId")
+        gwas_df = (
+            gwas_df.withColumn("row_num", row_number().over(window))
+            .filter(f.col("row_num") <= limit)
+            .drop("row_num")
+        )
+
+        qc_c = (
+            gwas_df.select("studyId", "beta", "standardError")
+            .withColumn("Z2", (f.col("beta") / f.col("standardError")) ** 2)
+            .groupBy("studyId")
+            .agg(f.expr("percentile_approx(Z2, 0.5)").alias("gc_lambda"))
+            .withColumn("gc_lambda", f.col("gc_lambda") / chi2.ppf(0.5, df=1))
+            .select("studyId", "gc_lambda")
+        )
+
+        return qc_c
+
+    @staticmethod
+    def number_of_snps(
+        gwas_for_qc: SummaryStatistics, pval_threhod: float = 5e-8
+    ) -> DataFrame:
+        """The function caluates number of SNPs and number of SNPs with p-value less than 5e-8.
+
+        Args:
+            gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class.
+            pval_threhod (float): The threshold for the p-value.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the number of SNPs and number of SNPs with p-value less than threshold.
+        """
+        gwas_df = gwas_for_qc._df
+
+        snp_counts = gwas_df.groupBy("studyId").agg(
+            f.count("*").alias("n_variants"),
+            f.sum(
+                (
+                    f.log10(f.col("pValueMantissa")) + f.col("pValueExponent")
+                    <= np.log10(pval_threhod)
+                ).cast("int")
+            ).alias("n_variants_sig"),
+        )
+
+        return snp_counts
+
+    @staticmethod
+    def get_quality_control_metrics(
+        gwas: SummaryStatistics,
+        limit: int = 100_000_000,
+        min_count: int = 100_000,
+        n_total: int = 100_000,
+    ) -> DataFrame:
+        """The function calculates the quality control metrics for the summary statistics.
+
+        Args:
+            gwas (SummaryStatistics): The instance of the SummaryStatistics class.
+            limit (int): The limit for the number of variants to be used for the estimation.
+            min_count (int): The minimum number of variants to be used for the estimation.
+            n_total (int): The total sample size.
+
+        Returns:
+            DataFrame: PySpark DataFrame with the quality control metrics for the summary statistics.
+        """
+        qc1 = SummaryStatisticsQC.sumstat_qc_beta_check(gwas_for_qc=gwas)
+        qc2 = SummaryStatisticsQC.sumstat_qc_pz_check(gwas_for_qc=gwas, limit=limit)
+        qc3 = SummaryStatisticsQC.sumstat_n_eff_check(
+            gwas_for_qc=gwas, n_total=n_total, limit=limit, min_count=min_count
+        )
+        qc4 = SummaryStatisticsQC.gc_lambda_check(gwas_for_qc=gwas, limit=limit)
+        qc5 = SummaryStatisticsQC.number_of_snps(gwas_for_qc=gwas)
+        df = (
+            qc1.join(qc2, on="studyId", how="outer")
+            .join(qc3, on="studyId", how="outer")
+            .join(qc4, on="studyId", how="outer")
+            .join(qc5, on="studyId", how="outer")
+        )
+
+        return df
diff --git a/tests/gentropy/method/test_qc_of_sumstats.py b/tests/gentropy/method/test_qc_of_sumstats.py
new file mode 100644
index 000000000..8480fce8d
--- /dev/null
+++ b/tests/gentropy/method/test_qc_of_sumstats.py
@@ -0,0 +1,63 @@
+"""Test of the qc of summary statistics."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pyspark.sql.functions as f
+from gentropy.dataset.summary_statistics import SummaryStatistics
+from gentropy.method.sumstat_quality_controls import SummaryStatisticsQC
+from pyspark.sql.functions import rand, when
+
+
+def test_qc_functions(
+    sample_summary_statistics: SummaryStatistics,
+) -> None:
+    """Test all sumstat qc functions."""
+    gwas = sample_summary_statistics.sanity_filter()
+    QC = SummaryStatisticsQC.get_quality_control_metrics(
+        gwas=gwas, limit=100000, min_count=100, n_total=100000
+    )
+    QC = QC.toPandas()
+
+    assert QC["n_variants"].iloc[0] == 1663
+    assert QC["n_variants_sig"].iloc[0] == 29
+    assert np.round(QC["gc_lambda"].iloc[0], 4) == 1.916
+    assert np.round(QC["mean_beta"].iloc[0], 4) == 0.0013
+    assert np.round(QC["mean_diff_pz"].iloc[0], 6) == 0
+    assert np.round(QC["se_diff_pz"].iloc[0], 6) == 0
+    assert pd.isna(QC["se_N"].iloc[0])
+
+
+def test_neff_check_eaf(
+    sample_summary_statistics: SummaryStatistics,
+) -> None:
+    """Test N_eff check using mock EAFs."""
+    gwas = sample_summary_statistics.sanity_filter()
+    gwas_df = gwas._df
+    gwas_df = gwas_df.withColumn("effectAlleleFrequencyFromSource", f.lit(0.5))
+    gwas._df = gwas_df
+
+    QC = SummaryStatisticsQC.get_quality_control_metrics(
+        gwas=gwas, limit=100000, min_count=100, n_total=100000
+    )
+    QC = QC.toPandas()
+    assert np.round(QC["se_N"].iloc[0], 4) == 0.5586
+
+
+def test_several_studyid(
+    sample_summary_statistics: SummaryStatistics,
+) -> None:
+    """Test stability when several studyIds are present."""
+    gwas = sample_summary_statistics.sanity_filter()
+    gwas_df = gwas._df
+    gwas_df = gwas_df.withColumn(
+        "studyId", when(rand() < 0.5, "new_value").otherwise(gwas_df["studyId"])
+    )
+    gwas._df = gwas_df
+
+    QC = SummaryStatisticsQC.get_quality_control_metrics(
+        gwas=gwas, limit=100000, min_count=100, n_total=100000
+    )
+    QC = QC.toPandas()
+    assert QC.shape == (2, 8)

From 05d21bc69331d7408dbfbacda54eab73ed67295b Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Thu, 25 Apr 2024 14:05:10 +0100
Subject: [PATCH 60/86] feat:
 susie_finemapper_one_studylocus_row_v3_dev_ss_gathered (#586)

---
 src/gentropy/susie_finemapper.py | 121 +++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index 24635934e..41e1ddcfe 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -734,3 +734,124 @@ def susie_finemapper_one_studylocus_row_v2_dev(
         )
 
         return out
+
+    @staticmethod
+    def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
+        session: Session,
+        study_locus_row: Row,
+        study_index: StudyIndex,
+        radius: int = 1_000_000,
+        max_causal_snps: int = 10,
+        susie_est_tausq: bool = False,
+        run_carma: bool = False,
+        run_sumstat_imputation: bool = False,
+        carma_time_limit: int = 600,
+        imputed_r2_threshold: float = 0.8,
+        ld_score_threshold: float = 4,
+        sum_pips: float = 0.99,
+    ) -> dict[str, Any]:
+        """Susie fine-mapper function that uses study-locus row with collected locus, chromosome and position as inputs.
+
+        Args:
+            session (Session): Spark session
+            study_locus_row (Row): StudyLocus row with collected locus
+            study_index (StudyIndex): StudyIndex object
+            radius (int): Radius in base-pairs of window for fine-mapping
+            max_causal_snps (int): maximum number of causal variants
+            susie_est_tausq (bool): estimate tau squared, default is False
+            run_carma (bool): run CARMA, default is False
+            run_sumstat_imputation (bool): run summary statistics imputation, default is False
+            carma_time_limit (int): CARMA time limit, default is 600 seconds
+            imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
+            ld_score_threshold (float): LD score threshold ofr imputation, default is 4
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+
+        Returns:
+            dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
+        """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
+        chromosome = study_locus_row["chromosome"]
+        position = study_locus_row["position"]
+        studyId = study_locus_row["studyId"]
+
+        study_index_df = study_index._df
+        study_index_df = study_index_df.filter(f.col("studyId") == studyId)
+        major_population = study_index_df.select(
+            "studyId",
+            f.array_max(f.col("ldPopulationStructure"))
+            .getItem("ldPopulation")
+            .alias("majorPopulation"),
+        ).collect()[0]["majorPopulation"]
+
+        region = (
+            chromosome
+            + ":"
+            + str(int(position - radius))
+            + "-"
+            + str(int(position + radius))
+        )
+
+        schema = StudyLocus.get_schema()
+        gwas_df = session.spark.createDataFrame([study_locus_row], schema=schema)
+        exploded_df = gwas_df.select(f.explode("locus").alias("locus"))
+
+        result_df = exploded_df.select(
+            "locus.variantId", "locus.beta", "locus.standardError"
+        )
+        gwas_df = (
+            result_df.withColumn("z", f.col("beta") / f.col("standardError"))
+            .withColumn(
+                "chromosome", f.split(f.col("variantId"), "_")[0].cast("string")
+            )
+            .withColumn("position", f.split(f.col("variantId"), "_")[1].cast("int"))
+            .filter(f.col("chromosome") == chromosome)
+            .filter(f.col("position") >= position - radius)
+            .filter(f.col("position") <= position + radius)
+            .filter(f.col("z").isNotNull())
+        )
+
+        ld_index = (
+            GnomADLDMatrix()
+            .get_locus_index(
+                study_locus_row=study_locus_row,
+                radius=radius,
+                major_population=major_population,
+            )
+            .withColumn(
+                "variantId",
+                f.concat(
+                    f.lit(chromosome),
+                    f.lit("_"),
+                    f.col("`locus.position`"),
+                    f.lit("_"),
+                    f.col("alleles").getItem(0),
+                    f.lit("_"),
+                    f.col("alleles").getItem(1),
+                ).cast("string"),
+            )
+        )
+
+        gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
+            ld_index, gnomad_ancestry=major_population
+        )
+
+        out = SusieFineMapperStep.susie_finemapper_from_prepared_dataframes(
+            GWAS_df=gwas_df,
+            ld_index=ld_index,
+            gnomad_ld=gnomad_ld,
+            L=max_causal_snps,
+            session=session,
+            studyId=studyId,
+            region=region,
+            susie_est_tausq=susie_est_tausq,
+            run_carma=run_carma,
+            run_sumstat_imputation=run_sumstat_imputation,
+            carma_time_limit=carma_time_limit,
+            imputed_r2_threshold=imputed_r2_threshold,
+            ld_score_threshold=ld_score_threshold,
+            sum_pips=sum_pips,
+        )
+
+        return out

From a88f16caab044e2104063e2144b370fbaf61b520 Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Fri, 26 Apr 2024 11:14:54 +0100
Subject: [PATCH 61/86] feat: functionality added to StudyLocus.find_overlaps()
 for finding within-study overlaps (#587)

* feat: functionality added to StudyLocus.find_overlaps() for finding within-study overlaps

* feat: removal of secondary credible sets at the same region as overlaps

* fix: defining the join condition to make the code tidier
---
 src/gentropy/dataset/study_locus.py           | 50 ++++++++++++++-----
 .../dataset/test_study_locus_overlaps.py      | 49 ++++++++++++++++--
 2 files changed, 84 insertions(+), 15 deletions(-)

diff --git a/src/gentropy/dataset/study_locus.py b/src/gentropy/dataset/study_locus.py
index 185aeb569..77c663800 100644
--- a/src/gentropy/dataset/study_locus.py
+++ b/src/gentropy/dataset/study_locus.py
@@ -82,31 +82,52 @@ class StudyLocus(Dataset):
     """
 
     @staticmethod
-    def _overlapping_peaks(credset_to_overlap: DataFrame) -> DataFrame:
+    def _overlapping_peaks(
+        credset_to_overlap: DataFrame, intra_study_overlap: bool = False
+    ) -> DataFrame:
         """Calculate overlapping signals (study-locus) between GWAS-GWAS and GWAS-Molecular trait.
 
         Args:
             credset_to_overlap (DataFrame): DataFrame containing at least `studyLocusId`, `studyType`, `chromosome` and `tagVariantId` columns.
+            intra_study_overlap (bool): When True, finds intra-study overlaps for credible set deduplication. Default is False.
 
         Returns:
             DataFrame: containing `leftStudyLocusId`, `rightStudyLocusId` and `chromosome` columns.
         """
         # Reduce columns to the minimum to reduce the size of the dataframe
         credset_to_overlap = credset_to_overlap.select(
-            "studyLocusId", "studyType", "chromosome", "tagVariantId"
+            "studyLocusId",
+            "studyId",
+            "studyType",
+            "chromosome",
+            "region",
+            "tagVariantId",
         )
+        # Define join condition - if intra_study_overlap is True, finds overlaps within the same study. Otherwise finds gwas vs everything overlaps for coloc.
+        join_condition = (
+            [
+                f.col("left.studyId") == f.col("right.studyId"),
+                f.col("left.chromosome") == f.col("right.chromosome"),
+                f.col("left.tagVariantId") == f.col("right.tagVariantId"),
+                f.col("left.studyLocusId") > f.col("right.studyLocusId"),
+                f.col("left.region") != f.col("right.region"),
+            ]
+            if intra_study_overlap
+            else [
+                f.col("left.chromosome") == f.col("right.chromosome"),
+                f.col("left.tagVariantId") == f.col("right.tagVariantId"),
+                (f.col("right.studyType") != "gwas")
+                | (f.col("left.studyLocusId") > f.col("right.studyLocusId")),
+                f.col("left.studyType") == f.lit("gwas"),
+            ]
+        )
+
         return (
             credset_to_overlap.alias("left")
-            .filter(f.col("studyType") == "gwas")
-            # Self join with complex condition. Left it's all gwas and right can be gwas or molecular trait
+            # Self join with complex condition.
             .join(
                 credset_to_overlap.alias("right"),
-                on=[
-                    f.col("left.chromosome") == f.col("right.chromosome"),
-                    f.col("left.tagVariantId") == f.col("right.tagVariantId"),
-                    (f.col("right.studyType") != "gwas")
-                    | (f.col("left.studyLocusId") > f.col("right.studyLocusId")),
-                ],
+                on=join_condition,
                 how="inner",
             )
             .select(
@@ -305,7 +326,9 @@ def filter_credible_set(
         )
         return self
 
-    def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverlap:
+    def find_overlaps(
+        self: StudyLocus, study_index: StudyIndex, intra_study_overlap: bool = False
+    ) -> StudyLocusOverlap:
         """Calculate overlapping study-locus.
 
         Find overlapping study-locus that share at least one tagging variant. All GWAS-GWAS and all GWAS-Molecular traits are computed with the Molecular traits always
@@ -313,6 +336,7 @@ def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverla
 
         Args:
             study_index (StudyIndex): Study index to resolve study types.
+            intra_study_overlap (bool): If True, finds intra-study overlaps for credible set deduplication. Default is False.
 
         Returns:
             StudyLocusOverlap: Pairs of overlapping study-locus with aligned tags.
@@ -322,8 +346,10 @@ def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverla
             .withColumn("locus", f.explode("locus"))
             .select(
                 "studyLocusId",
+                "studyId",
                 "studyType",
                 "chromosome",
+                "region",
                 f.col("locus.variantId").alias("tagVariantId"),
                 f.col("locus.logBF").alias("logBF"),
                 f.col("locus.posteriorProbability").alias("posteriorProbability"),
@@ -335,7 +361,7 @@ def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverla
         )
 
         # overlapping study-locus
-        peak_overlaps = self._overlapping_peaks(loci_to_overlap)
+        peak_overlaps = self._overlapping_peaks(loci_to_overlap, intra_study_overlap)
 
         # study-locus overlap by aligning overlapping variants
         return self._align_overlapping_tags(loci_to_overlap, peak_overlaps)
diff --git a/tests/gentropy/dataset/test_study_locus_overlaps.py b/tests/gentropy/dataset/test_study_locus_overlaps.py
index ee89eec84..8e732fc5c 100644
--- a/tests/gentropy/dataset/test_study_locus_overlaps.py
+++ b/tests/gentropy/dataset/test_study_locus_overlaps.py
@@ -30,46 +30,89 @@ def test_study_locus_overlap_from_associations(
 
 
 @pytest.mark.parametrize(
-    ("observed", "expected"),
+    ("observed", "intrastudy", "expected"),
     [
         (
             # observed - input DataFrame representing gwas and nongwas data to find overlapping signals
             [
                 {
                     "studyLocusId": 1,
+                    "studyId": "A",
                     "studyType": "gwas",
                     "chromosome": "1",
                     "tagVariantId": "A",
                 },
                 {
                     "studyLocusId": 2,
+                    "studyId": "B",
                     "studyType": "eqtl",
                     "chromosome": "1",
                     "tagVariantId": "A",
                 },
                 {
                     "studyLocusId": 3,
+                    "studyId": "C",
                     "studyType": "gwas",
                     "chromosome": "1",
                     "tagVariantId": "B",
                 },
             ],
+            # intrastudy - bool of whether or not to use inter-study or intra-study logic
+            False,
             # expected - output DataFrame with overlapping signals
             [
                 {"leftStudyLocusId": 1, "rightStudyLocusId": 2, "chromosome": "1"},
             ],
         ),
+        (
+            # observed - input DataFrame representing intra-study data to find overlapping signals in the same study
+            [
+                {
+                    "studyLocusId": 1,
+                    "studyId": "A",
+                    "studyType": "gwas",
+                    "chromosome": "1",
+                    "region": "X",
+                    "tagVariantId": "A",
+                },
+                {
+                    "studyLocusId": 2,
+                    "studyId": "A",
+                    "studyType": "gwas",
+                    "chromosome": "1",
+                    "region": "Y",
+                    "tagVariantId": "A",
+                },
+                {
+                    "studyLocusId": 3,
+                    "studyId": "B",
+                    "studyType": "gwas",
+                    "chromosome": "1",
+                    "region": "X",
+                    "tagVariantId": "A",
+                },
+            ],
+            # intrastudy - bool of whether or not to use inter-study or intra-study logic
+            True,
+            # expected - output DataFrame with overlapping signals
+            [{"leftStudyLocusId": 2, "rightStudyLocusId": 1, "chromosome": "1"}],
+        ),
     ],
 )
 def test_overlapping_peaks(
-    spark: SparkSession, observed: list[dict[str, Any]], expected: list[dict[str, Any]]
+    spark: SparkSession,
+    observed: list[dict[str, Any]],
+    intrastudy: bool,
+    expected: list[dict[str, Any]],
 ) -> None:
     """Test overlapping signals between GWAS-GWAS and GWAS-Molecular trait to make sure that mQTLs are always on the right."""
     mock_schema = t.StructType(
         [
             t.StructField("studyLocusId", t.LongType()),
+            t.StructField("studyId", t.StringType()),
             t.StructField("studyType", t.StringType()),
             t.StructField("chromosome", t.StringType()),
+            t.StructField("region", t.StringType()),
             t.StructField("tagVariantId", t.StringType()),
         ]
     )
@@ -81,6 +124,6 @@ def test_overlapping_peaks(
         ]
     )
     observed_df = spark.createDataFrame(observed, mock_schema)
-    result_df = StudyLocus._overlapping_peaks(observed_df)
+    result_df = StudyLocus._overlapping_peaks(observed_df, intrastudy)
     expected_df = spark.createDataFrame(expected, expected_schema)
     assert result_df.collect() == expected_df.collect()

From da2c75d5ab582109e9dd9072bf2f0d4b3038f39b Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Fri, 26 Apr 2024 17:21:12 +0100
Subject: [PATCH 62/86] feat: github action to upload docker image to registry
 (#588)

---
 .github/workflows/artifact.yml | 39 ++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 .github/workflows/artifact.yml

diff --git a/.github/workflows/artifact.yml b/.github/workflows/artifact.yml
new file mode 100644
index 000000000..a33bd1e55
--- /dev/null
+++ b/.github/workflows/artifact.yml
@@ -0,0 +1,39 @@
+name: Build and Push to Artifact Registry
+
+"on":
+  push:
+    branches: ["dev"]
+
+env:
+  PROJECT_ID: open-targets-genetics-dev
+  REGION: europe-west1
+  GAR_LOCATION: europe-west1-docker.pkg.dev/open-targets-genetics-dev/gentropy-app-dev/
+  IMAGE_NAME: gentropy-app
+
+jobs:
+  build-push-artifact:
+    runs-on: ubuntu-latest
+    steps:
+      - name: "Checkout"
+        uses: "actions/checkout@v3"
+
+      - id: "auth"
+        uses: "google-github-actions/auth@v1"
+        with:
+          credentials_json: "${{ secrets.SERVICE_ACCOUNT_KEY }}"
+
+      - name: "Set up Cloud SDK"
+        uses: "google-github-actions/setup-gcloud@v1"
+
+      - name: "Use gcloud CLI"
+        run: "gcloud info"
+
+      - name: "Docker auth"
+        run: |-
+          gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev --quiet
+
+      - name: Build image
+        run: docker build . --tag "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.github.ref_name }}"
+
+      - name: Push image
+        run: docker push "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.github.ref_name }}"

From df75870a9b902a4b6b9f842797d5183b87a83e26 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Fri, 26 Apr 2024 17:31:16 +0100
Subject: [PATCH 63/86] feat: lighter dockerfile (#585)

---
 Dockerfile | 29 +++++++++++++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 1221ec637..deb43bcd8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,8 +1,33 @@
-FROM python:3.10
+FROM python:3.10-bullseye
+
+
+RUN apt-get update && \
+    apt-get install -y openjdk-11-jdk && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+
+RUN java -version
+
+# Set environment variables for Java
+ENV JAVA_HOME /usr/lib/jvm/java-11-openjdk-amd64
+ENV PATH=$PATH:$JAVA_HOME/bin
 
 RUN pip install poetry==1.7.1
 
-COPY . .
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+WORKDIR /app
+
+COPY pyproject.toml poetry.lock ./
+RUN touch README.md
+
+RUN poetry config installer.max-workers 10
+RUN poetry install --without dev,docs,tests --no-root --no-interaction --no-ansi -vvv && rm -rf $POETRY_CACHE_DIR
+
+COPY src ./src
+
 RUN poetry install --without dev,docs,tests
 
 ENTRYPOINT ["poetry", "run", "gentropy"]

From b83a8aa8fba4521a7465ecb9f4889112094e0950 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Fri, 26 Apr 2024 17:44:21 +0100
Subject: [PATCH 64/86] fix: docker action fixes (#589)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 .github/workflows/artifact.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/artifact.yml b/.github/workflows/artifact.yml
index a33bd1e55..849aa8268 100644
--- a/.github/workflows/artifact.yml
+++ b/.github/workflows/artifact.yml
@@ -7,7 +7,7 @@ name: Build and Push to Artifact Registry
 env:
   PROJECT_ID: open-targets-genetics-dev
   REGION: europe-west1
-  GAR_LOCATION: europe-west1-docker.pkg.dev/open-targets-genetics-dev/gentropy-app-dev/
+  GAR_LOCATION: europe-west1-docker.pkg.dev/open-targets-genetics-dev
   IMAGE_NAME: gentropy-app
 
 jobs:
@@ -23,7 +23,7 @@ jobs:
           credentials_json: "${{ secrets.SERVICE_ACCOUNT_KEY }}"
 
       - name: "Set up Cloud SDK"
-        uses: "google-github-actions/setup-gcloud@v1"
+        uses: "google-github-actions/setup-gcloud"
 
       - name: "Use gcloud CLI"
         run: "gcloud info"
@@ -33,7 +33,7 @@ jobs:
           gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev --quiet
 
       - name: Build image
-        run: docker build . --tag "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.github.ref_name }}"
+        run: docker build . --tag "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.ref_name }}"
 
       - name: Push image
-        run: docker push "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.github.ref_name }}"
+        run: docker push "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.ref_name }}"

From c89cd57315a3431d0ad9072eb36aef954ddc32aa Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Fri, 26 Apr 2024 17:50:21 +0100
Subject: [PATCH 65/86] fix: docker action fixes (#590)

---
 .github/workflows/artifact.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/artifact.yml b/.github/workflows/artifact.yml
index 849aa8268..e9ab0157d 100644
--- a/.github/workflows/artifact.yml
+++ b/.github/workflows/artifact.yml
@@ -17,13 +17,13 @@ jobs:
       - name: "Checkout"
         uses: "actions/checkout@v3"
 
-      - id: "auth"
-        uses: "google-github-actions/auth@v1"
+      - name: "auth"
+        uses: "google-github-actions/auth@v2"
         with:
           credentials_json: "${{ secrets.SERVICE_ACCOUNT_KEY }}"
 
       - name: "Set up Cloud SDK"
-        uses: "google-github-actions/setup-gcloud"
+        uses: "google-github-actions/setup-gcloud@v2"
 
       - name: "Use gcloud CLI"
         run: "gcloud info"

From 70e5e2615700518324d414d11165efe025798284 Mon Sep 17 00:00:00 2001
From: David Ochoa <ochoa@ebi.ac.uk>
Date: Fri, 26 Apr 2024 18:09:52 +0100
Subject: [PATCH 66/86] fix: docker action fixes v3 (#591)

---
 .github/workflows/artifact.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/artifact.yml b/.github/workflows/artifact.yml
index e9ab0157d..ce7b47b6c 100644
--- a/.github/workflows/artifact.yml
+++ b/.github/workflows/artifact.yml
@@ -33,7 +33,7 @@ jobs:
           gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev --quiet
 
       - name: Build image
-        run: docker build . --tag "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.ref_name }}"
+        run: docker build . --tag "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}/gentropy:${{ github.ref_name }}"
 
       - name: Push image
-        run: docker push "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}:${{ github.ref_name }}"
+        run: docker push "${{ env.GAR_LOCATION }}/${{ env.IMAGE_NAME }}/gentropy:${{ github.ref_name }}"

From d16321522cf8cfd5c7a1a9ac5eada527f73c67a1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 16:34:50 +0100
Subject: [PATCH 67/86] build(deps-dev): bump deptry from 0.14.0 to 0.16.1
 (#570)

Bumps [deptry](https://github.com/fpgmaas/deptry) from 0.14.0 to 0.16.1.
- [Release notes](https://github.com/fpgmaas/deptry/releases)
- [Changelog](https://github.com/fpgmaas/deptry/blob/main/CHANGELOG.md)
- [Commits](https://github.com/fpgmaas/deptry/compare/0.14.0...0.16.1)

---
updated-dependencies:
- dependency-name: deptry
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 poetry.lock    | 198 ++++++++++++++++++++++++++++++++++++++++++++++---
 pyproject.toml |   2 +-
 2 files changed, 187 insertions(+), 13 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 01cafdeec..40c3283b4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -1627,24 +1627,26 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
 [[package]]
 name = "deptry"
-version = "0.14.0"
+version = "0.16.1"
 description = "A command line utility to check for unused, missing and transitive dependencies in a Python project."
 optional = false
-python-versions = ">=3.8, <4.0"
+python-versions = ">=3.8"
 files = [
-    {file = "deptry-0.14.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:951d40e40cca6b538d8b1992e9532c082757598c73249469d3f6f90cf3344c9f"},
-    {file = "deptry-0.14.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:59e0808507ffb57a0ce9b8c40ecda7b4a235a0627f00485d8e6104bf71dc99f8"},
-    {file = "deptry-0.14.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5e6b7485c3ece10eb94b2787258282275687a101587c6f88f65a6eadc65c4a4"},
-    {file = "deptry-0.14.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d5a8299b9627dc32352caf99c5e8fdff0227539ccbe1535a6145649b5caa394"},
-    {file = "deptry-0.14.0-cp38-abi3-win_amd64.whl", hash = "sha256:d55654025b567739a57f9b6b8467a21c65a30c21e834b2d8cb225618b320874c"},
-    {file = "deptry-0.14.0.tar.gz", hash = "sha256:3415c65c0734f6dfafa1321c77c1c9fafb6d6f88d0f8441e5b2cfb5b3b41f71b"},
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+    {file = "deptry-0.16.1.tar.gz", hash = "sha256:39fb62da4a8f4d17ed282310f7bcaadec55a95a8c471b01e0fcdf5351a7ac323"},
 ]
 
 [package.dependencies]
-chardet = ">=4.0.0"
 click = ">=8.0.0,<9"
 colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\""}
-pathspec = ">=0.9.0"
 tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 
 [[package]]
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@@ -7625,30 +7778,51 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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     {file = "SQLAlchemy-1.4.50.tar.gz", hash = "sha256:3b97ddf509fc21e10b09403b5219b06c5b558b27fc2453150274fa4e70707dbf"},
 ]
 
@@ -8485,4 +8659,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "7e49947f0842c795031610eba766ccca04acb4944d62c6bc2dc522fb4c702cc4"
+content-hash = "0219fa88667d94b340e7a707f86a732ddbb5a6c1e2ac91b4f0b4b97f134b33a4"
diff --git a/pyproject.toml b/pyproject.toml
index e83666536..cd9d6ffad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,7 +74,7 @@ apache-airflow = "^2.8.0"
 apache-airflow-providers-google = "^10.13.1"
 pydoclint = ">=0.3.8,<0.5.0"
 prettier = "^0.0.7"
-deptry = ">=0.12,<0.15"
+deptry = ">=0.12,<0.17"
 python-semantic-release = ">=8.7,<10.0"
 yamllint = "^1.33.0"
 

From 1c341c2d6b4feb6cc99b852291e5e3c275ac979c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 16:52:44 +0100
Subject: [PATCH 68/86] build(deps-dev): bump lxml from 5.1.0 to 5.2.1 (#569)

Bumps [lxml](https://github.com/lxml/lxml) from 5.1.0 to 5.2.1.
- [Release notes](https://github.com/lxml/lxml/releases)
- [Changelog](https://github.com/lxml/lxml/blob/master/CHANGES.txt)
- [Commits](https://github.com/lxml/lxml/compare/lxml-5.1.0...lxml-5.2.1)

---
updated-dependencies:
- dependency-name: lxml
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 poetry.lock | 238 ++++++++++++++++++++++++++++++++++------------------
 1 file changed, 158 insertions(+), 80 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 40c3283b4..cdcfee68c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4509,96 +4509,174 @@ typing-extensions = ">=4.1.1"
 
 [[package]]
 name = "lxml"
-version = "5.1.0"
+version = "5.2.1"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = false
 python-versions = ">=3.6"
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 ]
 
 [package.extras]
 cssselect = ["cssselect (>=0.7)"]
+html-clean = ["lxml-html-clean"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=3.0.7)"]
+source = ["Cython (>=3.0.10)"]
 
 [[package]]
 name = "mako"

From dce499fb258ea05ae48963e37e30ef16e2ca85bf Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 17:33:04 +0100
Subject: [PATCH 69/86] chore: pre-commit autoupdate (#561)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* chore: pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.3.3 → v0.4.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.3.3...v0.4.1)
- [github.com/pre-commit/pre-commit-hooks: v4.5.0 → v4.6.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.5.0...v4.6.0)
- [github.com/alessandrojcm/commitlint-pre-commit-hook: v9.13.0 → v9.15.0](https://github.com/alessandrojcm/commitlint-pre-commit-hook/compare/v9.13.0...v9.15.0)
- [github.com/econchick/interrogate: 1.5.0 → 1.7.0](https://github.com/econchick/interrogate/compare/1.5.0...1.7.0)

* fix: poetry lock is not up-to-date

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
Co-authored-by: David Ochoa <dogcaesar@gmail.com>
---
 .pre-commit-config.yaml |   8 +-
 poetry.lock             | 174 +---------------------------------------
 2 files changed, 5 insertions(+), 177 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 56714becf..11bb80eb8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.3
+    rev: v0.4.1
     hooks:
       - id: ruff
         args:
@@ -15,7 +15,7 @@ repos:
         files: ^((gentropy|utils|tests)/.+)?[^/]+\.py$
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -59,7 +59,7 @@ repos:
         exclude: "CHANGELOG.md"
 
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.13.0
+    rev: v9.15.0
     hooks:
       - id: commitlint
         additional_dependencies: ["@commitlint/config-conventional@18.6.3"]
@@ -82,7 +82,7 @@ repos:
           - "--disallow-untyped-defs"
 
   - repo: https://github.com/econchick/interrogate
-    rev: 1.5.0
+    rev: 1.7.0
     hooks:
       - id: interrogate
         args: [--verbose]
diff --git a/poetry.lock b/poetry.lock
index cdcfee68c..cf0598674 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
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 ]
 
 [[package]]
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@@ -7856,51 +7705,30 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 ]
 

From e58ad7f2770d0356f551d85b15bbe224633e3384 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 17:47:26 +0100
Subject: [PATCH 70/86] build(deps-dev): bump pre-commit from 3.6.0 to 3.7.0
 (#559)

Bumps [pre-commit](https://github.com/pre-commit/pre-commit) from 3.6.0 to 3.7.0.
- [Release notes](https://github.com/pre-commit/pre-commit/releases)
- [Changelog](https://github.com/pre-commit/pre-commit/blob/main/CHANGELOG.md)
- [Commits](https://github.com/pre-commit/pre-commit/compare/v3.6.0...v3.7.0)

---
updated-dependencies:
- dependency-name: pre-commit
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <dogcaesar@gmail.com>
---
 poetry.lock    | 8 ++++----
 pyproject.toml | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index cf0598674..63a995aa6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5869,13 +5869,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "pre-commit"
-version = "3.6.0"
+version = "3.7.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "pre_commit-3.6.0-py2.py3-none-any.whl", hash = "sha256:c255039ef399049a5544b6ce13d135caba8f2c28c3b4033277a788f434308376"},
-    {file = "pre_commit-3.6.0.tar.gz", hash = "sha256:d30bad9abf165f7785c15a21a1f46da7d0677cb00ee7ff4c579fd38922efe15d"},
+    {file = "pre_commit-3.7.0-py2.py3-none-any.whl", hash = "sha256:5eae9e10c2b5ac51577c3452ec0a490455c45a0533f7960f993a0d01e59decab"},
+    {file = "pre_commit-3.7.0.tar.gz", hash = "sha256:e209d61b8acdcf742404408531f0c37d49d2c734fd7cff2d6076083d191cb060"},
 ]
 
 [package.dependencies]
@@ -8565,4 +8565,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "0219fa88667d94b340e7a707f86a732ddbb5a6c1e2ac91b4f0b4b97f134b33a4"
+content-hash = "73a1b27bf8f5994bbf2546a1eb9d7440abf77a52c3cae64bd385d46cf0f4a430"
diff --git a/pyproject.toml b/pyproject.toml
index cd9d6ffad..e1a6727e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ scikit-learn = "^1.3.2"
 pandas = "^2.1.4"
 
 [tool.poetry.dev-dependencies]
-pre-commit = "^3.6.0"
+pre-commit = "^3.7.0"
 mypy = "^1.9"
 pep8-naming = "^0.13.2"
 interrogate = "^1.5.0"

From 60c000316634fd4fae3c33d0ef865cf08bbdf99d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 20:05:09 +0100
Subject: [PATCH 71/86] build(deps-dev): bump pytest-cov from 4.1.0 to 5.0.0
 (#560)

Bumps [pytest-cov](https://github.com/pytest-dev/pytest-cov) from 4.1.0 to 5.0.0.
- [Changelog](https://github.com/pytest-dev/pytest-cov/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-cov/compare/v4.1.0...v5.0.0)

---
updated-dependencies:
- dependency-name: pytest-cov
  dependency-type: direct:development
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <dogcaesar@gmail.com>
---
 poetry.lock    | 12 ++++++------
 pyproject.toml |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 63a995aa6..cdc596175 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6531,13 +6531,13 @@ testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygm
 
 [[package]]
 name = "pytest-cov"
-version = "4.1.0"
+version = "5.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"},
-    {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"},
+    {file = "pytest-cov-5.0.0.tar.gz", hash = "sha256:5837b58e9f6ebd335b0f8060eecce69b662415b16dc503883a02f45dfeb14857"},
+    {file = "pytest_cov-5.0.0-py3-none-any.whl", hash = "sha256:4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652"},
 ]
 
 [package.dependencies]
@@ -6545,7 +6545,7 @@ coverage = {version = ">=5.2.1", extras = ["toml"]}
 pytest = ">=4.6"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "pytest-sugar"
@@ -8565,4 +8565,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "73a1b27bf8f5994bbf2546a1eb9d7440abf77a52c3cae64bd385d46cf0f4a430"
+content-hash = "3eea1f8207eb94aa82e58ee036fa3cfa4dc4f30d3bd76089204d1fec7f609fe6"
diff --git a/pyproject.toml b/pyproject.toml
index e1a6727e2..03f8e0265 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,7 +58,7 @@ pymdown-extensions = "^10.7"
 
 
 [tool.poetry.group.tests.dependencies]
-pytest-cov = "^4.1.0"
+pytest-cov = ">=4.1,<6.0"
 pytest-sugar = ">=0.9.5,<1.1.0"
 dbldatagen = "^0.3.1"
 pyparsing = "^3.1.1"

From c468fe039074851abbc95328ac3393ed0326250a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 29 Apr 2024 20:15:51 +0100
Subject: [PATCH 72/86] build(deps-dev): bump python-semantic-release from
 9.1.0 to 9.4.1 (#571)

Bumps [python-semantic-release](https://github.com/python-semantic-release/python-semantic-release) from 9.1.0 to 9.4.1.
- [Release notes](https://github.com/python-semantic-release/python-semantic-release/releases)
- [Changelog](https://github.com/python-semantic-release/python-semantic-release/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python-semantic-release/python-semantic-release/compare/v9.1.0...v9.4.1)

---
updated-dependencies:
- dependency-name: python-semantic-release
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock | 240 ++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 216 insertions(+), 24 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index cdc596175..a25908623 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -1203,6 +1203,25 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "click-option-group"
+version = "0.5.6"
+description = "Option groups missing in Click"
+optional = false
+python-versions = ">=3.6,<4"
+files = [
+    {file = "click-option-group-0.5.6.tar.gz", hash = "sha256:97d06703873518cc5038509443742b25069a3c7562d1ea72ff08bfadde1ce777"},
+    {file = "click_option_group-0.5.6-py3-none-any.whl", hash = "sha256:38a26d963ee3ad93332ddf782f9259c5bdfe405e73408d943ef5e7d0c3767ec7"},
+]
+
+[package.dependencies]
+Click = ">=7.0,<9"
+
+[package.extras]
+docs = ["Pallets-Sphinx-Themes", "m2r2", "sphinx"]
+tests = ["pytest"]
+tests-cov = ["coverage", "coveralls", "pytest", "pytest-cov"]
+
 [[package]]
 name = "clickclick"
 version = "20.10.2"
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 [[package]]
@@ -6622,13 +6791,13 @@ six = ">=1.5"
 
 [[package]]
 name = "python-gitlab"
-version = "3.15.0"
-description = "Interact with GitLab API"
+version = "4.4.0"
+description = "A python wrapper for the GitLab API"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -6637,7 +6806,7 @@ requests-toolbelt = ">=0.10.1"
 
 [package.extras]
 autocompletion = ["argcomplete (>=1.10.0,<3)"]
-yaml = ["PyYaml (>=5.2)"]
+yaml = ["PyYaml (>=6.0.1)"]
 
 [[package]]
 name = "python-json-logger"
@@ -6666,33 +6835,34 @@ python-slugify = ">=1.2.5"
 
 [[package]]
 name = "python-semantic-release"
-version = "9.1.0"
+version = "9.4.1"
 description = "Automatic Semantic Versioning for Python projects"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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-dotty-dict = ">=1.3.0,<2"
-gitpython = ">=3.0.8,<4"
-importlib-resources = ">=5.7,<7"
-jinja2 = ">=3.1.2,<4"
-pydantic = ">=2,<3"
-python-gitlab = ">=2,<5"
-requests = ">=2.25,<3"
-rich = ">=12.5.1"
-shellingham = ">=1.5.0.post1"
+click = ">=8.0,<9.0"
+click-option-group = ">=0.5,<1.0"
+dotty-dict = ">=1.3,<2.0"
+gitpython = ">=3.0,<4.0"
+importlib-resources = ">=6.0,<7.0"
+jinja2 = ">=3.1,<4.0"
+pydantic = ">=2.0,<3.0"
+python-gitlab = ">=4.0,<5.0"
+requests = ">=2.25,<3.0"
+rich = ">=12.5,<13.0"
+shellingham = ">=1.5,<2.0"
 tomlkit = ">=0.11,<1.0"
 
 [package.extras]
-dev = ["pre-commit", "ruff (==0.1.11)", "tox"]
-docs = ["Sphinx (<=6.0.0)", "furo (>=2023.3.27)", "sphinx-autobuild (==2021.03.14)", "sphinxcontrib-apidoc (==0.3.0)"]
-mypy = ["mypy", "types-requests"]
-test = ["coverage[toml] (>=6,<8)", "pytest (>=7,<8)", "pytest-clarity (>=1.0.1)", "pytest-cov (>=4,<5)", "pytest-env (>=1.0,<2.0)", "pytest-lazy-fixture (>=0.6.3,<0.7.0)", "pytest-mock (>=3,<4)", "pytest-pretty (>=1.2.0,<2)", "pytest-xdist (>=2,<4)", "requests-mock (>=1.10.0,<2)", "responses (==0.23.3)", "types-pytest-lazy-fixture (>=0.6.3.3)"]
+dev = ["pre-commit (>=3.5,<4.0)", "ruff (==0.3.5)", "tox (>=4.11,<5.0)"]
+docs = ["Sphinx (>=6.0,<7.0)", "furo (>=2023.3,<2024.0)", "sphinx-autobuild (==2024.2.4)", "sphinxcontrib-apidoc (==0.5.0)"]
+mypy = ["mypy (==1.9.0)", "types-requests (>=2.31.0,<2.32.0)"]
+test = ["coverage[toml] (>=7.0,<8.0)", "pytest (>=7.0,<8.0)", "pytest-clarity (>=1.0,<2.0)", "pytest-cov (>=5.0,<6.0)", "pytest-env (>=1.0,<2.0)", "pytest-lazy-fixture (>=0.6.3,<0.7.0)", "pytest-mock (>=3.0,<4.0)", "pytest-pretty (>=1.2,<2.0)", "pytest-xdist (>=3.0,<4.0)", "requests-mock (>=1.10,<2.0)", "responses (>=0.25.0,<0.26.0)", "types-pytest-lazy-fixture (>=0.6.3,<0.7.0)"]
 
 [[package]]
 name = "python-slugify"
@@ -6781,6 +6951,7 @@ files = [
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@@ -7705,30 +7876,51 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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     {file = "SQLAlchemy-1.4.50.tar.gz", hash = "sha256:3b97ddf509fc21e10b09403b5219b06c5b558b27fc2453150274fa4e70707dbf"},
 ]
 

From 23a5faec8343d2f05716eb43213d27a42e80416e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 30 Apr 2024 10:13:41 +0100
Subject: [PATCH 73/86] chore: pre-commit autoupdate (#593)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* chore: pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.4.1 → v0.4.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.4.1...v0.4.2)
- [github.com/alessandrojcm/commitlint-pre-commit-hook: v9.15.0 → v9.16.0](https://github.com/alessandrojcm/commitlint-pre-commit-hook/compare/v9.15.0...v9.16.0)
- [github.com/pre-commit/mirrors-mypy: v1.9.0 → v1.10.0](https://github.com/pre-commit/mirrors-mypy/compare/v1.9.0...v1.10.0)

* chore: it bumps poetry version

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: David Ochoa <dogcaesar@gmail.com>
---
 .pre-commit-config.yaml |   6 +-
 poetry.lock             | 174 +---------------------------------------
 2 files changed, 4 insertions(+), 176 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 11bb80eb8..8f57e85b5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.1
+    rev: v0.4.2
     hooks:
       - id: ruff
         args:
@@ -59,14 +59,14 @@ repos:
         exclude: "CHANGELOG.md"
 
   - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
-    rev: v9.15.0
+    rev: v9.16.0
     hooks:
       - id: commitlint
         additional_dependencies: ["@commitlint/config-conventional@18.6.3"]
         stages: [commit-msg]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.9.0"
+    rev: "v1.10.0"
     hooks:
       - id: mypy
         args:
diff --git a/poetry.lock b/poetry.lock
index a25908623..cb7ff6fd0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -3451,156 +3451,6 @@ files = [
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     {file = "google_re2-1.1-3-cp39-cp39-win32.whl", hash = "sha256:80c5fc200f64b2d903eeb07b8d6cefc620a872a0240c7caaa9aca05b20f5568f"},
     {file = "google_re2-1.1-3-cp39-cp39-win_amd64.whl", hash = "sha256:9eb6dbcee9b5dc4069bbc0634f2eb039ca524a14bed5868fdf6560aaafcbca06"},
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-    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:82133958e003a1344e5b7a791b9a9dd7560b5c8f96936dbe16f294604524a633"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:9e74fd441d1f3d917d3303e319f61b82cdbd96b9a5ba919377a6eef1504a1e2b"},
-    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:734a2e7a4541c57253b5ebee24f3f3366ba3658bcad01da25fb623c78723471a"},
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From 690d158815de9b43b0b66536d2f20d4e120f4086 Mon Sep 17 00:00:00 2001
From: Yakov <yt4@sanger.ac.uk>
Date: Tue, 30 Apr 2024 12:32:54 +0100
Subject: [PATCH 74/86] feat: add purity qc metrics to fine-mapping (#592)

* feat: add purity qc metrics to fine-mapping

* fix: adding cs_lbf_thr to susie_finemapper_one_studylocus_row

* fix: schema
---
 src/gentropy/assets/schemas/study_locus.json |  18 +
 src/gentropy/config.py                       |  14 +
 src/gentropy/susie_finemapper.py             | 340 ++++++++++++++++---
 tests/gentropy/method/test_susie_inf.py      |  12 +-
 4 files changed, 345 insertions(+), 39 deletions(-)

diff --git a/src/gentropy/assets/schemas/study_locus.json b/src/gentropy/assets/schemas/study_locus.json
index e0f366d68..bf18a73b8 100644
--- a/src/gentropy/assets/schemas/study_locus.json
+++ b/src/gentropy/assets/schemas/study_locus.json
@@ -42,6 +42,12 @@
       "nullable": true,
       "type": "double"
     },
+    {
+      "metadata": {},
+      "name": "zScore",
+      "nullable": true,
+      "type": "double"
+    },
     {
       "metadata": {},
       "name": "pValueMantissa",
@@ -100,6 +106,18 @@
       "nullable": true,
       "type": "double"
     },
+    {
+      "metadata": {},
+      "name": "purityMeanR2",
+      "nullable": true,
+      "type": "double"
+    },
+    {
+      "metadata": {},
+      "name": "purityMinR2",
+      "nullable": true,
+      "type": "double"
+    },
     {
       "metadata": {},
       "name": "sampleSize",
diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 068bd42bf..018c70135 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -343,6 +343,20 @@ class FinemapperConfig(StepConfig):
     output_path: str = MISSING
     locus_radius: int = MISSING
     max_causal_snps: int = MISSING
+    primary_signal_pval_threshold: float = MISSING
+    secondary_signal_pval_threshold: float = MISSING
+    purity_mean_r2_threshold: float = MISSING
+    purity_min_r2_threshold: float = MISSING
+    cs_lbf_th: float = MISSING
+    sum_pips: float = MISSING
+    logging: bool = MISSING
+    susie_est_tausq: bool = MISSING
+    run_carma: bool = MISSING
+    run_sumstat_imputation: bool = MISSING
+    carma_time_limit: int = MISSING
+    imputed_r2_threshold: float = MISSING
+    ld_score_threshold: float = MISSING
+    output_path_log: str = MISSING
     _target_: str = "gentropy.susie_finemapper.SusieFineMapperStep"
 
 
diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index 41e1ddcfe..f61ec5748 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -9,10 +9,19 @@
 import numpy as np
 import pandas as pd
 import pyspark.sql.functions as f
+import scipy as sc
 from pyspark.sql import DataFrame, Row, Window
-from pyspark.sql.types import IntegerType, StringType, StructField, StructType
+from pyspark.sql.functions import row_number
+from pyspark.sql.types import (
+    DoubleType,
+    IntegerType,
+    StringType,
+    StructField,
+    StructType,
+)
 
 from gentropy.common.session import Session
+from gentropy.common.spark_helpers import neglog_pvalue_to_mantissa_and_exponent
 from gentropy.dataset.study_index import StudyIndex
 from gentropy.dataset.study_locus import StudyLocus
 from gentropy.dataset.summary_statistics import SummaryStatistics
@@ -38,6 +47,20 @@ def __init__(
         output_path: str,
         locus_radius: int = 500_000,
         max_causal_snps: int = 10,
+        primary_signal_pval_threshold: float = 1,
+        secondary_signal_pval_threshold: float = 1,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0,
+        cs_lbf_thr: float = 2,
+        sum_pips: float = 0.99,
+        logging: bool = False,
+        susie_est_tausq: bool = False,
+        run_carma: bool = False,
+        run_sumstat_imputation: bool = False,
+        carma_time_limit: int = 600,
+        imputed_r2_threshold: float = 0.9,
+        ld_score_threshold: float = 5,
+        output_path_log: str = "~/",
     ) -> None:
         """Run fine-mapping on a studyLocusId from a collected studyLocus table.
 
@@ -49,6 +72,20 @@ def __init__(
             output_path (str): path to the output
             locus_radius (int): Radius of base-pair window around the locus, default is 500_000
             max_causal_snps (int): Maximum number of causal variants in locus, default is 10
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1), default is 5e-8
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals, default is 1e-7
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets, default is 0
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets, default is 0.25
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            logging (bool): enable logging, default is False, runs diffrent FM wrapper
+            susie_est_tausq (bool): estimate tau squared, default is False
+            run_carma (bool): run CARMA, default is False
+            run_sumstat_imputation (bool): run summary statistics imputation, default is False
+            carma_time_limit (int): CARMA time limit, default is 600 seconds
+            imputed_r2_threshold (float): imputed R2 threshold, default is 0.9
+            ld_score_threshold (float): LD score threshold ofr imputation, default is 5
+            output_path_log (str): path to the output log
         """
         # Initialise Hail
         hl.init(sc=session.spark.sparkContext, log="/dev/null")
@@ -60,17 +97,54 @@ def __init__(
         )
         study_index = StudyIndex.from_parquet(session, study_index_path)
         # Run fine-mapping
-        result = self.susie_finemapper_ss_gathered(
-            session,
-            study_locus,
-            study_index,
-            locus_radius,
-            max_causal_snps,
-        )
-        # Write result
-        result.df.write.mode(session.write_mode).parquet(
-            output_path + "/" + study_locus_to_finemap
-        )
+        if logging:
+            result_logging = (
+                self.susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
+                    session=session,
+                    study_locus_row=study_locus,
+                    study_index=study_index,
+                    radius=locus_radius,
+                    max_causal_snps=max_causal_snps,
+                    primary_signal_pval_threshold=primary_signal_pval_threshold,
+                    secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+                    purity_mean_r2_threshold=purity_mean_r2_threshold,
+                    purity_min_r2_threshold=purity_min_r2_threshold,
+                    cs_lbf_thr=cs_lbf_thr,
+                    sum_pips=sum_pips,
+                    susie_est_tausq=susie_est_tausq,
+                    run_carma=run_carma,
+                    run_sumstat_imputation=run_sumstat_imputation,
+                    carma_time_limit=carma_time_limit,
+                    imputed_r2_threshold=imputed_r2_threshold,
+                    ld_score_threshold=ld_score_threshold,
+                )
+            )
+            # Write result
+            result_logging["study_locus"].df.write.mode(session.write_mode).parquet(
+                output_path + "/" + study_locus_to_finemap
+            )
+            # Write log
+            result_logging["log"].df.write.mode(session.write_mode).parquet(
+                output_path_log + "/" + study_locus_to_finemap
+            )
+        else:
+            result = self.susie_finemapper_ss_gathered(
+                session=session,
+                study_locus_row=study_locus,
+                study_index=study_index,
+                radius=locus_radius,
+                max_causal_snps=max_causal_snps,
+                primary_signal_pval_threshold=primary_signal_pval_threshold,
+                secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+                purity_mean_r2_threshold=purity_mean_r2_threshold,
+                purity_min_r2_threshold=purity_min_r2_threshold,
+                cs_lbf_thr=cs_lbf_thr,
+                sum_pips=sum_pips,
+            )
+            # Write result
+            result.df.write.mode(session.write_mode).parquet(
+                output_path + "/" + study_locus_to_finemap
+            )
 
     @staticmethod
     def susie_finemapper_one_studylocus_row(
@@ -79,7 +153,13 @@ def susie_finemapper_one_studylocus_row(
         study_locus_row: Row,
         study_index: StudyIndex,
         radius: int = 1_000_000,
-        L: int = 10,
+        max_causal_snps: int = 10,
+        primary_signal_pval_threshold: float = 5e-8,
+        secondary_signal_pval_threshold: float = 1e-7,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0.25,
+        sum_pips: float = 0.99,
+        cs_lbf_thr: float = 2,
     ) -> StudyLocus:
         """Susie fine-mapper for StudyLocus row with SummaryStatistics object.
 
@@ -89,7 +169,13 @@ def susie_finemapper_one_studylocus_row(
             study_locus_row (Row): StudyLocus row
             study_index (StudyIndex): StudyIndex object
             radius (int): window size for fine-mapping
-            L (int): number of causal variants
+            max_causal_snps (int): number of causal variants
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
 
         Returns:
             StudyLocus: StudyLocus object with fine-mapped credible sets
@@ -164,24 +250,19 @@ def susie_finemapper_one_studylocus_row(
         z_to_fm = np.array(pd_df["z"])
         ld_to_fm = gnomad_ld
 
-        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=L)
+        susie_output = SUSIE_inf.susie_inf(z=z_to_fm, LD=ld_to_fm, L=max_causal_snps)
 
         schema = StructType(
             [
                 StructField("variantId", StringType(), True),
                 StructField("chromosome", StringType(), True),
                 StructField("position", IntegerType(), True),
+                StructField("z", DoubleType(), True),
             ]
         )
         pd_df["position"] = pd_df["position"].astype(int)
         variant_index = session.spark.createDataFrame(
-            pd_df[
-                [
-                    "variantId",
-                    "chromosome",
-                    "position",
-                ]
-            ],
+            pd_df[["variantId", "chromosome", "position", "z"]],
             schema=schema,
         )
 
@@ -191,6 +272,13 @@ def susie_finemapper_one_studylocus_row(
             studyId=studyId,
             region=region,
             variant_index=variant_index,
+            ld_matrix=ld_to_fm,
+            primary_signal_pval_threshold=primary_signal_pval_threshold,
+            secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+            purity_mean_r2_threshold=purity_mean_r2_threshold,
+            purity_min_r2_threshold=purity_min_r2_threshold,
+            sum_pips=sum_pips,
+            cs_lbf_thr=cs_lbf_thr,
         )
 
     @staticmethod
@@ -200,8 +288,13 @@ def susie_inf_to_studylocus(
         studyId: str,
         region: str,
         variant_index: DataFrame,
+        ld_matrix: np.ndarray,
         cs_lbf_thr: float = 2,
         sum_pips: float = 0.99,
+        primary_signal_pval_threshold: float = 1,
+        secondary_signal_pval_threshold: float = 1,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0,
     ) -> StudyLocus:
         """Convert SuSiE-inf output to StudyLocus DataFrame.
 
@@ -211,12 +304,20 @@ def susie_inf_to_studylocus(
             studyId (str): study ID
             region (str): region
             variant_index (DataFrame): DataFrame with variant information
-            cs_lbf_thr (float): credible set logBF threshold, default is 2
+            ld_matrix (np.ndarray): LD matrix used for fine-mapping
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
             sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
 
         Returns:
             StudyLocus: StudyLocus object with fine-mapped credible sets
         """
+        # PLEASE DO NOT REMOVE THIS LINE
+        pd.DataFrame.iteritems = pd.DataFrame.items
+
         variants = np.array(
             [row["variantId"] for row in variant_index.select("variantId").collect()]
         ).reshape(-1, 1)
@@ -231,7 +332,7 @@ def susie_inf_to_studylocus(
         # Extracting credible sets
         order_creds = list(enumerate(susie_output["lbf"]))
         order_creds.sort(key=lambda x: x[1], reverse=True)
-        cred_sets = None
+
         counter = 0
         for i, cs_lbf_value in order_creds:
             if counter > 0 and cs_lbf_value < cs_lbf_thr:
@@ -308,10 +409,108 @@ def susie_inf_to_studylocus(
                     "credibleSetlog10BF",
                 )
             )
-            if cred_sets is None:
+            if counter == 1:
                 cred_sets = cred_set
             else:
                 cred_sets = cred_sets.unionByName(cred_set)
+
+        # Calulating purity
+        variant_index_df = variant_index.toPandas()
+        cred_sets_variantId = cred_sets.select("locus.variantId").toPandas()
+
+        lead_variantId_list = (
+            cred_sets.select("variantId").toPandas()["variantId"].tolist()
+        )
+        cred_set_index = (
+            cred_sets.select("credibleSetIndex").toPandas()["credibleSetIndex"].tolist()
+        )
+        vlist_series = pd.Series(lead_variantId_list)
+        ind = vlist_series.map(variant_index_df.set_index("variantId").index.get_loc)
+        z_values = variant_index_df.iloc[ind]["z"].tolist()
+        z_values_array = np.array(z_values)
+        pval = sc.stats.chi2.sf((z_values_array**2), 1)
+
+        # sometimes pval is 0, we need to avoid it
+        pval[pval < 1e-322] = 1e-322
+
+        neglogpval = -np.log10(pval)
+        neglogpval = neglogpval.tolist()
+
+        list_purity_mean_r2 = []
+        list_purity_min_r2 = []
+        for _, row in cred_sets_variantId.iterrows():
+            row = row.iloc[0]
+            vlist_series = pd.Series(row)
+            ind = vlist_series.map(
+                variant_index_df.set_index("variantId").index.get_loc
+            )
+            # print(variant_index_df.iloc[ind,0]==vlist)
+            squared_matrix = ld_matrix[ind, :][:, ind] ** 2
+            purity_mean_r2 = np.mean(squared_matrix)
+            purity_min_r2 = np.min(squared_matrix)
+            list_purity_mean_r2.append(purity_mean_r2)
+            list_purity_min_r2.append(purity_min_r2)
+
+        cred_sets = cred_sets.drop("pValueMantissa", "pValueExponent")
+
+        df = pd.DataFrame(
+            {
+                "credibleSetIndex": cred_set_index,
+                "purityMeanR2": purity_mean_r2,
+                "purityMinR2": purity_min_r2,
+                "zScore": z_values,
+                "neglogpval": neglogpval,
+            }
+        )
+        schema = StructType(
+            [
+                StructField("credibleSetIndex", IntegerType(), True),
+                StructField("purityMeanR2", DoubleType(), True),
+                StructField("purityMinR2", DoubleType(), True),
+                StructField("zScore", DoubleType(), True),
+                StructField("neglogpval", DoubleType(), True),
+            ]
+        )
+
+        df_spark = session.spark.createDataFrame(df, schema=schema)
+
+        cred_sets = cred_sets.join(df_spark, on="credibleSetIndex")
+
+        mantissa, exponent = neglog_pvalue_to_mantissa_and_exponent(
+            cred_sets.neglogpval
+        )
+
+        cred_sets = cred_sets.withColumn("pValueMantissa", mantissa)
+        cred_sets = cred_sets.withColumn("pValueExponent", exponent)
+
+        cred_sets = cred_sets.withColumn(
+            "pValueMantissa", f.col("pValueMantissa").cast("float")
+        )
+
+        cred_sets = cred_sets.filter(
+            (f.col("neglogpval") >= -np.log10(secondary_signal_pval_threshold))
+            | (f.col("credibleSetIndex") == 1)
+        )
+
+        cred_sets = cred_sets.filter(
+            (f.col("neglogpval") >= -np.log10(primary_signal_pval_threshold))
+            | (f.col("credibleSetIndex") > 1)
+        )
+
+        cred_sets = cred_sets.drop("neglogpval")
+
+        cred_sets = cred_sets.filter(
+            (f.col("credibleSetlog10BF") >= cs_lbf_thr * 0.4342944819)
+            | (f.col("credibleSetIndex") == 1)
+        )
+
+        cred_sets = cred_sets.filter(f.col("purityMeanR2") >= purity_mean_r2_threshold)
+        cred_sets = cred_sets.filter(f.col("purityMinR2") >= purity_min_r2_threshold)
+
+        window = Window.partitionBy("studyLocusId").orderBy("credibleSetIndex")
+        cred_sets = cred_sets.withColumn("rank", row_number().over(window))
+        cred_sets = cred_sets.filter(cred_sets["rank"] == 1).drop("rank")
+
         return StudyLocus(
             _df=cred_sets,
             _schema=StudyLocus.get_schema(),
@@ -324,6 +523,12 @@ def susie_finemapper_ss_gathered(
         study_index: StudyIndex,
         radius: int = 1_000_000,
         max_causal_snps: int = 10,
+        primary_signal_pval_threshold: float = 5e-8,
+        secondary_signal_pval_threshold: float = 1e-7,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0.25,
+        cs_lbf_thr: float = 2,
+        sum_pips: float = 0.99,
     ) -> StudyLocus:
         """Susie fine-mapper for StudyLocus row with locus annotated summary statistics.
 
@@ -333,6 +538,12 @@ def susie_finemapper_ss_gathered(
             study_index (StudyIndex): StudyIndex object
             radius (int): window size for fine-mapping
             max_causal_snps (int): number of causal variants
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets
+            sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
 
         Returns:
             StudyLocus: StudyLocus object with fine-mapped credible sets
@@ -420,17 +631,12 @@ def susie_finemapper_ss_gathered(
                 StructField("variantId", StringType(), True),
                 StructField("chromosome", StringType(), True),
                 StructField("position", IntegerType(), True),
+                StructField("z", DoubleType(), True),
             ]
         )
         pd_df["position"] = pd_df["position"].astype(int)
         variant_index = session.spark.createDataFrame(
-            pd_df[
-                [
-                    "variantId",
-                    "chromosome",
-                    "position",
-                ]
-            ],
+            pd_df[["variantId", "chromosome", "position", "z"]],
             schema=schema,
         )
 
@@ -440,6 +646,13 @@ def susie_finemapper_ss_gathered(
             studyId=studyId,
             region=region,
             variant_index=variant_index,
+            ld_matrix=ld_to_fm,
+            primary_signal_pval_threshold=primary_signal_pval_threshold,
+            secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+            purity_mean_r2_threshold=purity_mean_r2_threshold,
+            purity_min_r2_threshold=purity_min_r2_threshold,
+            cs_lbf_thr=cs_lbf_thr,
+            sum_pips=sum_pips,
         )
 
     @staticmethod
@@ -458,6 +671,11 @@ def susie_finemapper_from_prepared_dataframes(
         imputed_r2_threshold: float = 0.8,
         ld_score_threshold: float = 4,
         sum_pips: float = 0.99,
+        primary_signal_pval_threshold: float = 5e-8,
+        secondary_signal_pval_threshold: float = 1e-7,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0.25,
+        cs_lbf_thr: float = 2,
     ) -> dict[str, Any]:
         """Susie fine-mapper function that uses LD, z-scores, variant info and other options for Fine-Mapping.
 
@@ -476,6 +694,11 @@ def susie_finemapper_from_prepared_dataframes(
             imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
             ld_score_threshold (float): LD score threshold ofr imputation, default is 4
             sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
 
         Returns:
             dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
@@ -577,10 +800,15 @@ def susie_finemapper_from_prepared_dataframes(
             z=z_to_fm, LD=ld_to_fm, L=L, est_tausq=susie_est_tausq
         )
 
-        schema = StructType([StructField("variantId", StringType(), True)])
+        schema = StructType(
+            [
+                StructField("variantId", StringType(), True),
+                StructField("z", DoubleType(), True),
+            ]
+        )
         variant_index = (
             session.spark.createDataFrame(
-                GWAS_df[["variantId"]],
+                GWAS_df[["variantId", "z"]],
                 schema=schema,
             )
             .withColumn(
@@ -596,6 +824,12 @@ def susie_finemapper_from_prepared_dataframes(
             region=region,
             variant_index=variant_index,
             sum_pips=sum_pips,
+            ld_matrix=ld_to_fm,
+            primary_signal_pval_threshold=primary_signal_pval_threshold,
+            secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+            purity_mean_r2_threshold=purity_mean_r2_threshold,
+            purity_min_r2_threshold=purity_min_r2_threshold,
+            cs_lbf_thr=cs_lbf_thr,
         )
 
         end_time = time.time()
@@ -631,9 +865,14 @@ def susie_finemapper_one_studylocus_row_v2_dev(
         run_carma: bool = False,
         run_sumstat_imputation: bool = False,
         carma_time_limit: int = 600,
-        imputed_r2_threshold: float = 0.8,
-        ld_score_threshold: float = 4,
+        imputed_r2_threshold: float = 0.9,
+        ld_score_threshold: float = 5,
         sum_pips: float = 0.99,
+        primary_signal_pval_threshold: float = 5e-8,
+        secondary_signal_pval_threshold: float = 1e-7,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0.25,
+        cs_lbf_thr: float = 2,
     ) -> dict[str, Any]:
         """Susie fine-mapper function that uses Summary Statstics, chromosome and position as inputs.
 
@@ -651,6 +890,11 @@ def susie_finemapper_one_studylocus_row_v2_dev(
             imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
             ld_score_threshold (float): LD score threshold ofr imputation, default is 4
             sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
 
         Returns:
             dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
@@ -731,6 +975,11 @@ def susie_finemapper_one_studylocus_row_v2_dev(
             imputed_r2_threshold=imputed_r2_threshold,
             ld_score_threshold=ld_score_threshold,
             sum_pips=sum_pips,
+            primary_signal_pval_threshold=primary_signal_pval_threshold,
+            secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+            purity_mean_r2_threshold=purity_mean_r2_threshold,
+            purity_min_r2_threshold=purity_min_r2_threshold,
+            cs_lbf_thr=cs_lbf_thr,
         )
 
         return out
@@ -746,9 +995,14 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
         run_carma: bool = False,
         run_sumstat_imputation: bool = False,
         carma_time_limit: int = 600,
-        imputed_r2_threshold: float = 0.8,
-        ld_score_threshold: float = 4,
+        imputed_r2_threshold: float = 0.9,
+        ld_score_threshold: float = 5,
         sum_pips: float = 0.99,
+        primary_signal_pval_threshold: float = 5e-8,
+        secondary_signal_pval_threshold: float = 1e-7,
+        purity_mean_r2_threshold: float = 0,
+        purity_min_r2_threshold: float = 0.25,
+        cs_lbf_thr: float = 2,
     ) -> dict[str, Any]:
         """Susie fine-mapper function that uses study-locus row with collected locus, chromosome and position as inputs.
 
@@ -765,6 +1019,11 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
             imputed_r2_threshold (float): imputed R2 threshold, default is 0.8
             ld_score_threshold (float): LD score threshold ofr imputation, default is 4
             sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
+            primary_signal_pval_threshold (float): p-value threshold for the lead variant from the primary signal (credibleSetIndex==1)
+            secondary_signal_pval_threshold (float): p-value threshold for the lead variant from the secondary signals
+            purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets
+            purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets
+            cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2
 
         Returns:
             dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
@@ -852,6 +1111,11 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
             imputed_r2_threshold=imputed_r2_threshold,
             ld_score_threshold=ld_score_threshold,
             sum_pips=sum_pips,
+            primary_signal_pval_threshold=primary_signal_pval_threshold,
+            secondary_signal_pval_threshold=secondary_signal_pval_threshold,
+            purity_mean_r2_threshold=purity_mean_r2_threshold,
+            purity_min_r2_threshold=purity_min_r2_threshold,
+            cs_lbf_thr=cs_lbf_thr,
         )
 
         return out
diff --git a/tests/gentropy/method/test_susie_inf.py b/tests/gentropy/method/test_susie_inf.py
index 0129a5934..45d79bcae 100644
--- a/tests/gentropy/method/test_susie_inf.py
+++ b/tests/gentropy/method/test_susie_inf.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import numpy as np
+import pyspark.sql.functions as f
 from gentropy.common.session import Session
 from gentropy.dataset.study_locus import StudyLocus
 from gentropy.dataset.summary_statistics import SummaryStatistics
@@ -67,7 +68,10 @@ def test_SUSIE_inf_convert_to_study_locus(
             LD=ld,
             est_tausq=False,
         )
-        gwas_df = sample_summary_statistics._df.limit(21)
+        gwas_df = sample_summary_statistics._df.withColumn(
+            "z", f.col("beta") / f.col("standardError")
+        ).filter(f.col("z").isNotNull())
+        gwas_df = gwas_df.limit(21)
 
         L1 = SusieFineMapperStep.susie_inf_to_studylocus(
             susie_output=susie_output,
@@ -76,5 +80,11 @@ def test_SUSIE_inf_convert_to_study_locus(
             region="sample_region",
             variant_index=gwas_df,
             cs_lbf_thr=2,
+            ld_matrix=ld,
+            primary_signal_pval_threshold=1,
+            secondary_signal_pval_threshold=1,
+            purity_mean_r2_threshold=0,
+            purity_min_r2_threshold=0,
+            sum_pips=0.99,
         )
         assert isinstance(L1, StudyLocus), "L1 is not an instance of StudyLocus"

From 962b79c3cdac1c7b1fb7acd64218f3e85608f47d Mon Sep 17 00:00:00 2001
From: Daniel-Considine <113430683+Daniel-Considine@users.noreply.github.com>
Date: Wed, 1 May 2024 09:32:52 +0100
Subject: [PATCH 75/86] feat: check for if no overlapping variants in LD index
 (#594)

* feat: check for if no overlapping variants in LD index

* fix: removing unnecessary else statement
---
 src/gentropy/susie_finemapper.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py
index f61ec5748..cc4c7a6a1 100644
--- a/src/gentropy/susie_finemapper.py
+++ b/src/gentropy/susie_finemapper.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import logging
 import time
 from typing import Any
 
@@ -142,9 +143,10 @@ def __init__(
                 sum_pips=sum_pips,
             )
             # Write result
-            result.df.write.mode(session.write_mode).parquet(
-                output_path + "/" + study_locus_to_finemap
-            )
+            if result is not None:
+                result.df.write.mode(session.write_mode).parquet(
+                    output_path + "/" + study_locus_to_finemap
+                )
 
     @staticmethod
     def susie_finemapper_one_studylocus_row(
@@ -529,7 +531,7 @@ def susie_finemapper_ss_gathered(
         purity_min_r2_threshold: float = 0.25,
         cs_lbf_thr: float = 2,
         sum_pips: float = 0.99,
-    ) -> StudyLocus:
+    ) -> StudyLocus | None:
         """Susie fine-mapper for StudyLocus row with locus annotated summary statistics.
 
         Args:
@@ -546,7 +548,7 @@ def susie_finemapper_ss_gathered(
             sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set)
 
         Returns:
-            StudyLocus: StudyLocus object with fine-mapped credible sets
+            StudyLocus | None: StudyLocus object with fine-mapped credible sets, or None
         """
         # PLEASE DO NOT REMOVE THIS LINE
         pd.DataFrame.iteritems = pd.DataFrame.items
@@ -615,7 +617,9 @@ def susie_finemapper_ss_gathered(
         gwas_index = gwas_df.join(
             ld_index.select("variantId", "alleles", "idx"), on="variantId"
         ).sort("idx")
-
+        if gwas_index.rdd.isEmpty():
+            logging.warning("No overlapping variants in the LD Index")
+            return None
         gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
             gwas_index, gnomad_ancestry=major_population
         )

From 85c3aa8271d4a8b1dff2ef78d95b14016ee00c16 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 7 May 2024 16:39:51 +0100
Subject: [PATCH 76/86] chore: pre-commit autoupdate (#601)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.4.2 → v0.4.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.4.2...v0.4.3)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8f57e85b5..00d914dc0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ ci:
   skip: [poetry-lock]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.4.3
     hooks:
       - id: ruff
         args:

From a9ef7c0e9af810c50b974a03b2240120f26ff26a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 May 2024 16:57:49 +0100
Subject: [PATCH 77/86] build(deps-dev): bump pymdown-extensions from 10.7 to
 10.8.1 (#600)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Bumps [pymdown-extensions](https://github.com/facelessuser/pymdown-extensions) from 10.7 to 10.8.1.
- [Release notes](https://github.com/facelessuser/pymdown-extensions/releases)
- [Commits](https://github.com/facelessuser/pymdown-extensions/compare/10.7...10.8.1)

---
updated-dependencies:
- dependency-name: pymdown-extensions
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 poetry.lock | 216 ++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 194 insertions(+), 22 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index cb7ff6fd0..2c948e7d4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiodns"
@@ -3451,6 +3451,156 @@ files = [
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+    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0db114d7e1aa96dbcea452a40136d7d747d60cbb61394965774688ef59cccd4e"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:82133958e003a1344e5b7a791b9a9dd7560b5c8f96936dbe16f294604524a633"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:9e74fd441d1f3d917d3303e319f61b82cdbd96b9a5ba919377a6eef1504a1e2b"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_13_0_x86_64.whl", hash = "sha256:734a2e7a4541c57253b5ebee24f3f3366ba3658bcad01da25fb623c78723471a"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:d88d5eecbc908abe16132456fae13690d0508f3ac5777f320ef95cb6cab9a961"},
+    {file = "google_re2-1.1-4-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:b91db80b171ecec435a07977a227757dd487356701a32f556fa6fca5d0a40522"},
+    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b23129887a64bb9948af14c84705273ed1a40054e99433b4acccab4dcf6a226"},
+    {file = "google_re2-1.1-4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5dc1a0cc7cd19261dcaf76763e2499305dbb7e51dc69555167cdb8af98782698"},
+    {file = "google_re2-1.1-4-cp310-cp310-win32.whl", hash = "sha256:3b2ab1e2420b5dd9743a2d6bc61b64e5f708563702a75b6db86637837eaeaf2f"},
+    {file = "google_re2-1.1-4-cp310-cp310-win_amd64.whl", hash = "sha256:92efca1a7ef83b6df012d432a1cbc71d10ff42200640c0f9a5ff5b343a48e633"},
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+    {file = "google_re2-1.1-4-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:4ceef51174b6f653b6659a8fdaa9c38960c5228b44b25be2a3bcd8566827554f"},
+    {file = "google_re2-1.1-4-cp311-cp311-macosx_13_0_arm64.whl", hash = "sha256:ee49087c3db7e6f5238105ab5299c09e9b77516fe8cfb0a37e5f1e813d76ecb8"},
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 [[package]]
@@ -3577,13 +3727,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "0.38.0"
+version = "0.44.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -4475,7 +4625,6 @@ files = [
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@@ -4568,13 +4717,13 @@ testing = ["pytest"]
 
 [[package]]
 name = "markdown"
-version = "3.5.1"
+version = "3.6"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -4977,21 +5126,23 @@ mkdocs = ">=1.2"
 
 [[package]]
 name = "mkdocstrings"
-version = "0.23.0"
+version = "0.25.1"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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 Jinja2 = ">=2.11.1"
 Markdown = ">=3.3"
 MarkupSafe = ">=1.1"
-mkdocs = ">=1.2"
+mkdocs = ">=1.4"
 mkdocs-autorefs = ">=0.3.1"
+platformdirs = ">=2.2.0"
 pymdown-extensions = ">=6.3"
 
 [package.extras]
@@ -5001,19 +5152,18 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.9.0"
+version = "1.10.0"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-griffe = ">=0.37"
-markdown = ">=3.3,<3.6"
-mkdocstrings = ">=0.20"
+griffe = ">=0.44"
+mkdocstrings = ">=0.24.2"
 
 [[package]]
 name = "msal"
@@ -6459,17 +6609,17 @@ files = [
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.7"
+version = "10.8.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-markdown = ">=3.5"
+markdown = ">=3.6"
 pyyaml = "*"
 
 [package.extras]
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@@ -7725,30 +7876,51 @@ description = "Database Abstraction Library"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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     {file = "SQLAlchemy-1.4.50-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c1db0221cb26d66294f4ca18c533e427211673ab86c1fbaca8d6d9ff78654293"},
     {file = "SQLAlchemy-1.4.50-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b7dbe6369677a2bea68fe9812c6e4bbca06ebfa4b5cde257b2b0bf208709131"},
     {file = "SQLAlchemy-1.4.50-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:a9bddb60566dc45c57fd0a5e14dd2d9e5f106d2241e0a2dc0c1da144f9444516"},
     {file = "SQLAlchemy-1.4.50-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:82dd4131d88395df7c318eeeef367ec768c2a6fe5bd69423f7720c4edb79473c"},
+    {file = "SQLAlchemy-1.4.50-cp36-cp36m-win32.whl", hash = "sha256:1b9c4359d3198f341480e57494471201e736de459452caaacf6faa1aca852bd8"},
+    {file = "SQLAlchemy-1.4.50-cp36-cp36m-win_amd64.whl", hash = "sha256:35e4520f7c33c77f2636a1e860e4f8cafaac84b0b44abe5de4c6c8890b6aaa6d"},
+    {file = "SQLAlchemy-1.4.50-cp37-cp37m-macosx_11_0_x86_64.whl", hash = "sha256:f5b1fb2943d13aba17795a770d22a2ec2214fc65cff46c487790192dda3a3ee7"},
     {file = "SQLAlchemy-1.4.50-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:273505fcad22e58cc67329cefab2e436006fc68e3c5423056ee0513e6523268a"},
     {file = "SQLAlchemy-1.4.50-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3257a6e09626d32b28a0c5b4f1a97bced585e319cfa90b417f9ab0f6145c33c"},
     {file = "SQLAlchemy-1.4.50-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:d69738d582e3a24125f0c246ed8d712b03bd21e148268421e4a4d09c34f521a5"},
     {file = "SQLAlchemy-1.4.50-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:34e1c5d9cd3e6bf3d1ce56971c62a40c06bfc02861728f368dcfec8aeedb2814"},
+    {file = "SQLAlchemy-1.4.50-cp37-cp37m-win32.whl", hash = "sha256:7b4396452273aedda447e5aebe68077aa7516abf3b3f48408793e771d696f397"},
+    {file = "SQLAlchemy-1.4.50-cp37-cp37m-win_amd64.whl", hash = "sha256:752f9df3dddbacb5f42d8405b2d5885675a93501eb5f86b88f2e47a839cf6337"},
+    {file = "SQLAlchemy-1.4.50-cp38-cp38-macosx_11_0_x86_64.whl", hash = "sha256:35c7ed095a4b17dbc8813a2bfb38b5998318439da8e6db10a804df855e3a9e3a"},
     {file = "SQLAlchemy-1.4.50-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f1fcee5a2c859eecb4ed179edac5ffbc7c84ab09a5420219078ccc6edda45436"},
     {file = "SQLAlchemy-1.4.50-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fbaf6643a604aa17e7a7afd74f665f9db882df5c297bdd86c38368f2c471f37d"},
     {file = "SQLAlchemy-1.4.50-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:2e70e0673d7d12fa6cd363453a0d22dac0d9978500aa6b46aa96e22690a55eab"},
     {file = "SQLAlchemy-1.4.50-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b881ac07d15fb3e4f68c5a67aa5cdaf9eb8f09eb5545aaf4b0a5f5f4659be18"},
+    {file = "SQLAlchemy-1.4.50-cp38-cp38-win32.whl", hash = "sha256:8a219688297ee5e887a93ce4679c87a60da4a5ce62b7cb4ee03d47e9e767f558"},
+    {file = "SQLAlchemy-1.4.50-cp38-cp38-win_amd64.whl", hash = "sha256:a648770db002452703b729bdcf7d194e904aa4092b9a4d6ab185b48d13252f63"},
+    {file = "SQLAlchemy-1.4.50-cp39-cp39-macosx_11_0_x86_64.whl", hash = "sha256:4be4da121d297ce81e1ba745a0a0521c6cf8704634d7b520e350dce5964c71ac"},
     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f6997da81114daef9203d30aabfa6b218a577fc2bd797c795c9c88c9eb78d49"},
     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bdb77e1789e7596b77fd48d99ec1d2108c3349abd20227eea0d48d3f8cf398d9"},
     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:128a948bd40780667114b0297e2cc6d657b71effa942e0a368d8cc24293febb3"},
     {file = "SQLAlchemy-1.4.50-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f2d526aeea1bd6a442abc7c9b4b00386fd70253b80d54a0930c0a216230a35be"},
+    {file = "SQLAlchemy-1.4.50-cp39-cp39-win32.whl", hash = "sha256:a7c9b9dca64036008962dd6b0d9fdab2dfdbf96c82f74dbd5d86006d8d24a30f"},
+    {file = "SQLAlchemy-1.4.50-cp39-cp39-win_amd64.whl", hash = "sha256:df200762efbd672f7621b253721644642ff04a6ff957236e0e2fe56d9ca34d2c"},
     {file = "SQLAlchemy-1.4.50.tar.gz", hash = "sha256:3b97ddf509fc21e10b09403b5219b06c5b558b27fc2453150274fa4e70707dbf"},
 ]
 

From d34b7d7d8ef6363d2b44ad84cfb9b40611505ce0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 May 2024 17:06:02 +0100
Subject: [PATCH 78/86] build(deps-dev): bump interrogate from 1.5.0 to 1.7.0
 (#599)

Bumps [interrogate](https://github.com/econchick/interrogate) from 1.5.0 to 1.7.0.
- [Release notes](https://github.com/econchick/interrogate/releases)
- [Changelog](https://github.com/econchick/interrogate/blob/master/docs/changelog.rst)
- [Commits](https://github.com/econchick/interrogate/compare/1.5.0...1.7.0)

---
updated-dependencies:
- dependency-name: interrogate
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 27 ++++++++-------------------
 pyproject.toml |  2 +-
 2 files changed, 9 insertions(+), 20 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 2c948e7d4..dc947f937 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4100,13 +4100,13 @@ files = [
 
 [[package]]
 name = "interrogate"
-version = "1.5.0"
+version = "1.7.0"
 description = "Interrogate a codebase for docstring coverage."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "interrogate-1.5.0-py3-none-any.whl", hash = "sha256:a4ccc5cbd727c74acc98dee6f5e79ef264c0bcfa66b68d4e123069b2af89091a"},
-    {file = "interrogate-1.5.0.tar.gz", hash = "sha256:b6f325f0aa84ac3ac6779d8708264d366102226c5af7d69058cecffcff7a6d6c"},
+    {file = "interrogate-1.7.0-py3-none-any.whl", hash = "sha256:b13ff4dd8403369670e2efe684066de9fcb868ad9d7f2b4095d8112142dc9d12"},
+    {file = "interrogate-1.7.0.tar.gz", hash = "sha256:a320d6ec644dfd887cc58247a345054fc4d9f981100c45184470068f4b3719b0"},
 ]
 
 [package.dependencies]
@@ -4115,13 +4115,13 @@ click = ">=7.1"
 colorama = "*"
 py = "*"
 tabulate = "*"
-toml = "*"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
 
 [package.extras]
-dev = ["cairosvg", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "sphinx", "sphinx-autobuild", "wheel"]
+dev = ["cairosvg", "coverage[toml]", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "sphinx", "sphinx-autobuild", "wheel"]
 docs = ["sphinx", "sphinx-autobuild"]
 png = ["cairosvg"]
-tests = ["pytest", "pytest-cov", "pytest-mock"]
+tests = ["coverage[toml]", "pytest", "pytest-cov", "pytest-mock"]
 
 [[package]]
 name = "ipykernel"
@@ -8133,17 +8133,6 @@ files = [
     {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
 ]
 
-[[package]]
-name = "toml"
-version = "0.10.2"
-description = "Python Library for Tom's Obvious, Minimal Language"
-optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
-    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
-]
-
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -8757,4 +8746,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "3eea1f8207eb94aa82e58ee036fa3cfa4dc4f30d3bd76089204d1fec7f609fe6"
+content-hash = "cf95c8bf122b74c6c479b7c9f61b7879651a1daed6e2b2905379302e0c43dc9d"
diff --git a/pyproject.toml b/pyproject.toml
index 03f8e0265..ae016f53e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,7 @@ pandas = "^2.1.4"
 pre-commit = "^3.7.0"
 mypy = "^1.9"
 pep8-naming = "^0.13.2"
-interrogate = "^1.5.0"
+interrogate = "^1.7.0"
 isort = "^5.13.2"
 darglint = "^1.8.1"
 ruff = "^0.3.2"

From 7af1cbcc3a0c247ab7615a8f6aff3f14f2d1409c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 May 2024 17:31:19 +0100
Subject: [PATCH 79/86] build(deps-dev): bump pytest from 8.1.0 to 8.2.0 (#598)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.1.0 to 8.2.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.1.0...8.2.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index dc947f937..51ff5e7eb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6004,13 +6004,13 @@ tenacity = ">=6.2.0"
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pluggy-1.4.0-py3-none-any.whl", hash = "sha256:7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981"},
-    {file = "pluggy-1.4.0.tar.gz", hash = "sha256:8c85c2876142a764e5b7548e7d9a0e0ddb46f5185161049a79b7e974454223be"},
+    {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
+    {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
 ]
 
 [package.extras]
@@ -6678,13 +6678,13 @@ sql = ["pandas (>=1.0.5)", "pyarrow (>=1.0.0)"]
 
 [[package]]
 name = "pytest"
-version = "8.1.0"
+version = "8.2.0"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.1.0-py3-none-any.whl", hash = "sha256:ee32db7af8de4629a455806befa90559f307424c07b8413ccfc30bf5b221dd7e"},
-    {file = "pytest-8.1.0.tar.gz", hash = "sha256:f8fa04ab8f98d185113ae60ea6d79c22f8143b14bc1caeced44a0ab844928323"},
+    {file = "pytest-8.2.0-py3-none-any.whl", hash = "sha256:1733f0620f6cda4095bbf0d9ff8022486e91892245bb9e7d5542c018f612f233"},
+    {file = "pytest-8.2.0.tar.gz", hash = "sha256:d507d4482197eac0ba2bae2e9babf0672eb333017bcedaa5fb1a3d42c1174b3f"},
 ]
 
 [package.dependencies]
@@ -6692,11 +6692,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.4,<2.0"
+pluggy = ">=1.5,<2.0"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"

From 9f6e6b97d21b2d3ba4f74ecb690c2b2af4d61c29 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 May 2024 09:56:37 +0100
Subject: [PATCH 80/86] build(deps-dev): bump pytest-xdist from 3.5.0 to 3.6.1
 (#597)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.5.0 to 3.6.1.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.5.0...v3.6.1)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 51ff5e7eb..5530ba88f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1790,13 +1790,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "execnet"
-version = "2.0.2"
+version = "2.1.1"
 description = "execnet: rapid multi-Python deployment"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "execnet-2.0.2-py3-none-any.whl", hash = "sha256:88256416ae766bc9e8895c76a87928c0012183da3cc4fc18016e6f050e025f41"},
-    {file = "execnet-2.0.2.tar.gz", hash = "sha256:cc59bc4423742fd71ad227122eb0dd44db51efb3dc4095b45ac9a08c770096af"},
+    {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+    {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
 ]
 
 [package.extras]
@@ -6737,18 +6737,18 @@ dev = ["black", "flake8", "pre-commit"]
 
 [[package]]
 name = "pytest-xdist"
-version = "3.5.0"
+version = "3.6.1"
 description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "pytest-xdist-3.5.0.tar.gz", hash = "sha256:cbb36f3d67e0c478baa57fa4edc8843887e0f6cfc42d677530a36d7472b32d8a"},
-    {file = "pytest_xdist-3.5.0-py3-none-any.whl", hash = "sha256:d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24"},
+    {file = "pytest_xdist-3.6.1-py3-none-any.whl", hash = "sha256:9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7"},
+    {file = "pytest_xdist-3.6.1.tar.gz", hash = "sha256:ead156a4db231eec769737f57668ef58a2084a34b2e55c4a8fa20d861107300d"},
 ]
 
 [package.dependencies]
-execnet = ">=1.1"
-pytest = ">=6.2.0"
+execnet = ">=2.1"
+pytest = ">=7.0.0"
 
 [package.extras]
 psutil = ["psutil (>=3.0)"]

From d89ec861cf551e54ddf92cb57a86f840c7c5d786 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 May 2024 10:20:31 +0100
Subject: [PATCH 81/86] build(deps-dev): bump ruff from 0.3.2 to 0.4.3 (#596)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.2 to 0.4.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.2...v0.4.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 38 +++++++++++++++++++-------------------
 pyproject.toml |  2 +-
 2 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 5530ba88f..74886899e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -7440,28 +7440,28 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.3.2"
+version = "0.4.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.3.2-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:77f2612752e25f730da7421ca5e3147b213dca4f9a0f7e0b534e9562c5441f01"},
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-    {file = "ruff-0.3.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8439338a6303585d27b66b4626cbde89bb3e50fa3cae86ce52c1db7449330a7"},
-    {file = "ruff-0.3.2-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:de8b480d8379620cbb5ea466a9e53bb467d2fb07c7eca54a4aa8576483c35d36"},
-    {file = "ruff-0.3.2-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:b74c3de9103bd35df2bb05d8b2899bf2dbe4efda6474ea9681280648ec4d237d"},
-    {file = "ruff-0.3.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:f380be9fc15a99765c9cf316b40b9da1f6ad2ab9639e551703e581a5e6da6745"},
-    {file = "ruff-0.3.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:0ac06a3759c3ab9ef86bbeca665d31ad3aa9a4b1c17684aadb7e61c10baa0df4"},
-    {file = "ruff-0.3.2-py3-none-win32.whl", hash = "sha256:9bd640a8f7dd07a0b6901fcebccedadeb1a705a50350fb86b4003b805c81385a"},
-    {file = "ruff-0.3.2-py3-none-win_amd64.whl", hash = "sha256:0c1bdd9920cab5707c26c8b3bf33a064a4ca7842d91a99ec0634fec68f9f4037"},
-    {file = "ruff-0.3.2-py3-none-win_arm64.whl", hash = "sha256:5f65103b1d76e0d600cabd577b04179ff592064eaa451a70a81085930e907d0b"},
-    {file = "ruff-0.3.2.tar.gz", hash = "sha256:fa78ec9418eb1ca3db392811df3376b46471ae93792a81af2d1cbb0e5dcb5142"},
+    {file = "ruff-0.4.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:b70800c290f14ae6fcbb41bbe201cf62dfca024d124a1f373e76371a007454ce"},
+    {file = "ruff-0.4.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:08a0d6a22918ab2552ace96adeaca308833873a4d7d1d587bb1d37bae8728eb3"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eba1f14df3c758dd7de5b55fbae7e1c8af238597961e5fb628f3de446c3c40c5"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:819fb06d535cc76dfddbfe8d3068ff602ddeb40e3eacbc90e0d1272bb8d97113"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0bfc9e955e6dc6359eb6f82ea150c4f4e82b660e5b58d9a20a0e42ec3bb6342b"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:510a67d232d2ebe983fddea324dbf9d69b71c4d2dfeb8a862f4a127536dd4cfb"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dc9ff11cd9a092ee7680a56d21f302bdda14327772cd870d806610a3503d001f"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:29efff25bf9ee685c2c8390563a5b5c006a3fee5230d28ea39f4f75f9d0b6f2f"},
+    {file = "ruff-0.4.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18b00e0bcccf0fc8d7186ed21e311dffd19761cb632241a6e4fe4477cc80ef6e"},
+    {file = "ruff-0.4.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:262f5635e2c74d80b7507fbc2fac28fe0d4fef26373bbc62039526f7722bca1b"},
+    {file = "ruff-0.4.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:7363691198719c26459e08cc17c6a3dac6f592e9ea3d2fa772f4e561b5fe82a3"},
+    {file = "ruff-0.4.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:eeb039f8428fcb6725bb63cbae92ad67b0559e68b5d80f840f11914afd8ddf7f"},
+    {file = "ruff-0.4.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:927b11c1e4d0727ce1a729eace61cee88a334623ec424c0b1c8fe3e5f9d3c865"},
+    {file = "ruff-0.4.3-py3-none-win32.whl", hash = "sha256:25cacda2155778beb0d064e0ec5a3944dcca9c12715f7c4634fd9d93ac33fd30"},
+    {file = "ruff-0.4.3-py3-none-win_amd64.whl", hash = "sha256:7a1c3a450bc6539ef00da6c819fb1b76b6b065dec585f91456e7c0d6a0bbc725"},
+    {file = "ruff-0.4.3-py3-none-win_arm64.whl", hash = "sha256:71ca5f8ccf1121b95a59649482470c5601c60a416bf189d553955b0338e34614"},
+    {file = "ruff-0.4.3.tar.gz", hash = "sha256:ff0a3ef2e3c4b6d133fbedcf9586abfbe38d076041f2dc18ffb2c7e0485d5a07"},
 ]
 
 [[package]]
@@ -8746,4 +8746,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "cf95c8bf122b74c6c479b7c9f61b7879651a1daed6e2b2905379302e0c43dc9d"
+content-hash = "78e9e4e7f1c8fc05f25d4b4e7e7e252f272ddc8e3568b6eb94ad7968cc57a675"
diff --git a/pyproject.toml b/pyproject.toml
index ae016f53e..113b0ac1d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,7 +41,7 @@ pep8-naming = "^0.13.2"
 interrogate = "^1.7.0"
 isort = "^5.13.2"
 darglint = "^1.8.1"
-ruff = "^0.3.2"
+ruff = "^0.4.3"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs = "^1.5.3"

From bc1a1124b0744895f2ce246c23b62861ea600288 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Irene=20L=C3=B3pez?=
 <45119610+ireneisdoomed@users.noreply.github.com>
Date: Wed, 15 May 2024 09:20:58 +0100
Subject: [PATCH 82/86] feat(airflow): include COLOC as a node in the DAG
 (#530)

* feat: split colocalisation step into two nodes: coloc and ecaviar

* feat: filter credible sets to be colocalised based on method

* fix(dag): update coloc dependencies

* revert(config): revert colocalisation output location

* feat(coloc): _get_colocalisation_class to import methods dynamically

* refactor(step): remove method enum

* refactor(step): do not hardcode method value

* fix(config): typo in static_assets

* refactor(dag): uncomment steps
---
 ...tion.yaml => ot_colocalisation_coloc.yaml} |  1 +
 config/step/ot_colocalisation_ecaviar.yaml    |  7 +++
 src/airflow/dags/configs/dag.yaml             |  9 ++-
 src/gentropy/colocalisation.py                | 56 +++++++++++++++++--
 src/gentropy/config.py                        |  1 +
 5 files changed, 66 insertions(+), 8 deletions(-)
 rename config/step/{ot_colocalisation.yaml => ot_colocalisation_coloc.yaml} (84%)
 create mode 100644 config/step/ot_colocalisation_ecaviar.yaml

diff --git a/config/step/ot_colocalisation.yaml b/config/step/ot_colocalisation_coloc.yaml
similarity index 84%
rename from config/step/ot_colocalisation.yaml
rename to config/step/ot_colocalisation_coloc.yaml
index 4433595ef..f01335514 100644
--- a/config/step/ot_colocalisation.yaml
+++ b/config/step/ot_colocalisation_coloc.yaml
@@ -4,3 +4,4 @@ defaults:
 credible_set_path: ${datasets.credible_set}
 study_index_path: ${datasets.study_index}
 coloc_path: ${datasets.colocalisation}
+colocalisation_method: Coloc
diff --git a/config/step/ot_colocalisation_ecaviar.yaml b/config/step/ot_colocalisation_ecaviar.yaml
new file mode 100644
index 000000000..d57887c93
--- /dev/null
+++ b/config/step/ot_colocalisation_ecaviar.yaml
@@ -0,0 +1,7 @@
+defaults:
+  - colocalisation
+
+credible_set_path: ${datasets.credible_set}
+study_index_path: ${datasets.study_index}
+coloc_path: ${datasets.colocalisation}
+colocalisation_method: ECaviar
diff --git a/src/airflow/dags/configs/dag.yaml b/src/airflow/dags/configs/dag.yaml
index 0b634caaa..b6d343343 100644
--- a/src/airflow/dags/configs/dag.yaml
+++ b/src/airflow/dags/configs/dag.yaml
@@ -4,15 +4,18 @@
   prerequisites:
     - "ot_variant_index"
     - "ot_gene_index"
-- id: "ot_colocalisation"
+- id: "ot_colocalisation_ecaviar"
+- id: "ot_colocalisation_coloc"
 - id: "ot_locus_to_gene_train"
   prerequisites:
     - "ot_variant_index"
     - "ot_variant_to_gene"
-    - "ot_colocalisation"
+    - "ot_colocalisation_ecaviar"
+    - "ot_colocalisation_coloc"
 - id: "ot_locus_to_gene_predict"
   prerequisites:
     - "ot_locus_to_gene_train"
     - "ot_variant_index"
     - "ot_variant_to_gene"
-    - "ot_colocalisation"
+    - "ot_colocalisation_ecaviar"
+    - "ot_colocalisation_coloc"
diff --git a/src/gentropy/colocalisation.py b/src/gentropy/colocalisation.py
index 010827713..1d71ed447 100644
--- a/src/gentropy/colocalisation.py
+++ b/src/gentropy/colocalisation.py
@@ -1,10 +1,15 @@
 """Step to generate colocalisation results."""
 from __future__ import annotations
 
+import inspect
+from importlib import import_module
+
+from pyspark.sql.functions import col
+
 from gentropy.common.session import Session
 from gentropy.dataset.study_index import StudyIndex
 from gentropy.dataset.study_locus import CredibleInterval, StudyLocus
-from gentropy.method.colocalisation import ECaviar
+from gentropy.method.colocalisation import Coloc
 
 
 class ColocalisationStep:
@@ -19,6 +24,7 @@ def __init__(
         credible_set_path: str,
         study_index_path: str,
         coloc_path: str,
+        colocalisation_method: str,
     ) -> None:
         """Run Colocalisation step.
 
@@ -27,10 +33,18 @@ def __init__(
             credible_set_path (str): Input credible sets path.
             study_index_path (str): Input study index path.
             coloc_path (str): Output Colocalisation path.
+            colocalisation_method (str): Colocalisation method.
         """
+        colocalisation_class = self._get_colocalisation_class(colocalisation_method)
         # Extract
-        credible_set = StudyLocus.from_parquet(
-            session, credible_set_path, recursiveFileLookup=True
+        credible_set = (
+            StudyLocus.from_parquet(
+                session, credible_set_path, recursiveFileLookup=True
+            ).filter(col("finemappingMethod") == "SuSie")
+            if colocalisation_class is Coloc
+            else StudyLocus.from_parquet(
+                session, credible_set_path, recursiveFileLookup=True
+            )
         )
         si = StudyIndex.from_parquet(
             session, study_index_path, recursiveFileLookup=True
@@ -40,7 +54,39 @@ def __init__(
         overlaps = credible_set.filter_credible_set(
             CredibleInterval.IS95
         ).find_overlaps(si)
-        ecaviar_results = ECaviar.colocalise(overlaps)
+        colocalisation_results = colocalisation_class.colocalise(overlaps)  # type: ignore
 
         # Load
-        ecaviar_results.df.write.mode(session.write_mode).parquet(coloc_path)
+        colocalisation_results.df.write.mode(session.write_mode).parquet(
+            f"{coloc_path}/{colocalisation_method.lower()}"
+        )
+
+    @classmethod
+    def _get_colocalisation_class(cls: type[ColocalisationStep], method: str) -> type:
+        """Get colocalisation class.
+
+        Args:
+            method (str): Colocalisation method.
+
+        Returns:
+            type: Colocalisation class.
+
+        Raises:
+            ValueError: if method not available.
+
+        Examples:
+            >>> ColocalisationStep._get_colocalisation_class("ECaviar")
+            <class 'gentropy.method.colocalisation.ECaviar'>
+        """
+        module_name = "gentropy.method.colocalisation"
+        module = import_module(module_name)
+
+        available_methods = []
+        for class_name, class_obj in inspect.getmembers(module, inspect.isclass):
+            if class_obj.__module__ == module_name:
+                available_methods.append(class_name)
+                if class_name == method:
+                    return class_obj
+        raise ValueError(
+            f"Method {method} is not supported. Available: {(', ').join(available_methods)}"
+        )
diff --git a/src/gentropy/config.py b/src/gentropy/config.py
index 018c70135..59452fed8 100644
--- a/src/gentropy/config.py
+++ b/src/gentropy/config.py
@@ -37,6 +37,7 @@ class ColocalisationConfig(StepConfig):
     credible_set_path: str = MISSING
     study_index_path: str = MISSING
     coloc_path: str = MISSING
+    colocalisation_method: str = MISSING
     _target_: str = "gentropy.colocalisation.ColocalisationStep"
 
 

From 1576cf4d4da005805b07f4b32279bfc014456408 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 May 2024 09:36:15 +0100
Subject: [PATCH 83/86] build(deps): bump typing-extensions from 4.10.0 to
 4.11.0 (#602)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Bumps [typing-extensions](https://github.com/python/typing_extensions) from 4.10.0 to 4.11.0.
- [Release notes](https://github.com/python/typing_extensions/releases)
- [Changelog](https://github.com/python/typing_extensions/blob/main/CHANGELOG.md)
- [Commits](https://github.com/python/typing_extensions/compare/4.10.0...4.11.0)

---
updated-dependencies:
- dependency-name: typing-extensions
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Irene López <45119610+ireneisdoomed@users.noreply.github.com>
---
 poetry.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 74886899e..a371b4a41 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -8213,13 +8213,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]

From 8bc09c8d663de7d8e3bacdea06f8b8756daead09 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 May 2024 09:44:58 +0100
Subject: [PATCH 84/86] build(deps-dev): bump ipython from 8.22.1 to 8.24.0
 (#603)

Bumps [ipython](https://github.com/ipython/ipython) from 8.22.1 to 8.24.0.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/8.22.1...8.24.0)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index a371b4a41..84f95e18c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4158,13 +4158,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.22.1"
+version = "8.24.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.22.1-py3-none-any.whl", hash = "sha256:869335e8cded62ffb6fac8928e5287a05433d6462e3ebaac25f4216474dd6bc4"},
-    {file = "ipython-8.22.1.tar.gz", hash = "sha256:39c6f9efc079fb19bfb0f17eee903978fe9a290b1b82d68196c641cecb76ea22"},
+    {file = "ipython-8.24.0-py3-none-any.whl", hash = "sha256:d7bf2f6c4314984e3e02393213bab8703cf163ede39672ce5918c51fe253a2a3"},
+    {file = "ipython-8.24.0.tar.gz", hash = "sha256:010db3f8a728a578bb641fdd06c063b9fb8e96a9464c63aec6310fbcb5e80501"},
 ]
 
 [package.dependencies]
@@ -4178,18 +4178,20 @@ prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5.13.0"
+typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["ipython[black,doc,kernel,nbconvert,nbformat,notebook,parallel,qtconsole,terminal]", "ipython[test,test-extra]"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
 doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath"]
+test = ["pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]

From c06632763a534ee6ec0401101479b474a097e698 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 May 2024 09:56:59 +0100
Subject: [PATCH 85/86] build(deps-dev): bump mypy from 1.9.0 to 1.10.0 (#604)

Bumps [mypy](https://github.com/python/mypy) from 1.9.0 to 1.10.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/1.9.0...v1.10.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 poetry.lock    | 58 +++++++++++++++++++++++++-------------------------
 pyproject.toml |  2 +-
 2 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 84f95e18c..8a3664c64 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5310,38 +5310,38 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.9.0"
+version = "1.10.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "mypy-1.10.0.tar.gz", hash = "sha256:3d087fcbec056c4ee34974da493a826ce316947485cef3901f511848e687c131"},
 ]
 
 [package.dependencies]
@@ -8748,4 +8748,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10, <3.11"
-content-hash = "78e9e4e7f1c8fc05f25d4b4e7e7e252f272ddc8e3568b6eb94ad7968cc57a675"
+content-hash = "f3118a34b55508e9d5876d1aefaa7754a8307277591c650fcd8c119033290552"
diff --git a/pyproject.toml b/pyproject.toml
index 113b0ac1d..818d0685a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ pandas = "^2.1.4"
 
 [tool.poetry.dev-dependencies]
 pre-commit = "^3.7.0"
-mypy = "^1.9"
+mypy = "^1.10"
 pep8-naming = "^0.13.2"
 interrogate = "^1.7.0"
 isort = "^5.13.2"

From bb9f9c64a11c7d275c54a06b6a1c90db6410b3cd Mon Sep 17 00:00:00 2001
From: Daniel Suveges <daniel.suveges@protonmail.com>
Date: Wed, 15 May 2024 21:01:33 +0100
Subject: [PATCH 86/86] refactor: moving all variant coordinates to GnomAD
 (#566)

* refactor: removing gnomAD variant id. GonomadID is the variant id

* refactor: converting ld index coordinates to gnomad

* refactor: movig GWAS Catalog to gnomad coordinates

* test: fixing doctest

* refactor: touching DAG for preparing GnomAD dataset

* fix: adding updated lockfile

* fix: replace hyphens with underscores in variant identifiers

* fix: removing commented line

---------

Co-authored-by: David Ochoa <ochoa@ebi.ac.uk>
---
 src/airflow/dags/common_airflow.py            |  6 +--
 ...dag_preprocess.py => gnomad_preprocess.py} |  3 +-
 .../assets/schemas/variant_annotation.json    |  6 ---
 src/gentropy/common/utils.py                  | 36 +------------
 src/gentropy/datasource/gnomad/ld.py          | 24 +++------
 src/gentropy/datasource/gnomad/variants.py    | 44 ++--------------
 .../datasource/gwas_catalog/associations.py   | 51 +++++++++++++++++--
 7 files changed, 65 insertions(+), 105 deletions(-)
 rename src/airflow/dags/{dag_preprocess.py => gnomad_preprocess.py} (94%)

diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py
index 9d4f18503..f3ec89bb2 100644
--- a/src/airflow/dags/common_airflow.py
+++ b/src/airflow/dags/common_airflow.py
@@ -127,9 +127,9 @@ def create_cluster(
             # Create a disk config section if it does not exist.
             cluster_config[worker_section].setdefault("disk_config", {})
             # Specify the number of local SSDs.
-            cluster_config[worker_section]["disk_config"][
-                "num_local_ssds"
-            ] = num_local_ssds
+            cluster_config[worker_section]["disk_config"]["num_local_ssds"] = (
+                num_local_ssds
+            )
 
     # Return the cluster creation operator.
     return DataprocCreateClusterOperator(
diff --git a/src/airflow/dags/dag_preprocess.py b/src/airflow/dags/gnomad_preprocess.py
similarity index 94%
rename from src/airflow/dags/dag_preprocess.py
rename to src/airflow/dags/gnomad_preprocess.py
index 4439914c5..22e7fa056 100644
--- a/src/airflow/dags/dag_preprocess.py
+++ b/src/airflow/dags/gnomad_preprocess.py
@@ -1,4 +1,5 @@
 """Airflow DAG for the Preprocess part of the pipeline."""
+
 from __future__ import annotations
 
 from pathlib import Path
@@ -6,7 +7,7 @@
 import common_airflow as common
 from airflow.models.dag import DAG
 
-CLUSTER_NAME = "otg-preprocess"
+CLUSTER_NAME = "gnomad-preprocess"
 
 ALL_STEPS = [
     "ot_ld_index",
diff --git a/src/gentropy/assets/schemas/variant_annotation.json b/src/gentropy/assets/schemas/variant_annotation.json
index 826eabaf9..ab8767389 100644
--- a/src/gentropy/assets/schemas/variant_annotation.json
+++ b/src/gentropy/assets/schemas/variant_annotation.json
@@ -19,12 +19,6 @@
       "nullable": false,
       "metadata": {}
     },
-    {
-      "name": "gnomadVariantId",
-      "type": "string",
-      "nullable": false,
-      "metadata": {}
-    },
     {
       "name": "referenceAllele",
       "type": "string",
diff --git a/src/gentropy/common/utils.py b/src/gentropy/common/utils.py
index 4cdda9ad2..3ec361a50 100644
--- a/src/gentropy/common/utils.py
+++ b/src/gentropy/common/utils.py
@@ -1,4 +1,5 @@
 """Common functions in the Genetics datasets."""
+
 from __future__ import annotations
 
 import sys
@@ -208,41 +209,6 @@ def parse_pvalue(pv: Column) -> list[Column]:
     ]
 
 
-def convert_gnomad_position_to_ensembl(
-    position: Column, reference: Column, alternate: Column
-) -> Column:
-    """Convert GnomAD variant position to Ensembl variant position.
-
-    For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs,
-    the position is unchanged. More info about the problem: https://www.biostars.org/p/84686/
-
-    Args:
-        position (Column): Position of the variant in GnomAD's coordinates system.
-        reference (Column): The reference allele in GnomAD's coordinates system.
-        alternate (Column): The alternate allele in GnomAD's coordinates system.
-
-    Returns:
-        Column: The position of the variant in the Ensembl genome.
-
-    Examples:
-        >>> d = [(1, "A", "C"), (2, "AA", "C"), (3, "A", "AA")]
-        >>> df = spark.createDataFrame(d).toDF("position", "reference", "alternate")
-        >>> df.withColumn("new_position", convert_gnomad_position_to_ensembl(f.col("position"), f.col("reference"), f.col("alternate"))).show()
-        +--------+---------+---------+------------+
-        |position|reference|alternate|new_position|
-        +--------+---------+---------+------------+
-        |       1|        A|        C|           1|
-        |       2|       AA|        C|           3|
-        |       3|        A|       AA|           4|
-        +--------+---------+---------+------------+
-        <BLANKLINE>
-
-    """
-    return f.when(
-        (f.length(reference) > 1) | (f.length(alternate) > 1), position + 1
-    ).otherwise(position)
-
-
 def _liftover_loci(
     variant_index: Table, chain_path: str, dest_reference_genome: str
 ) -> Table:
diff --git a/src/gentropy/datasource/gnomad/ld.py b/src/gentropy/datasource/gnomad/ld.py
index 02cb15c7f..471b87cae 100644
--- a/src/gentropy/datasource/gnomad/ld.py
+++ b/src/gentropy/datasource/gnomad/ld.py
@@ -1,4 +1,5 @@
 """Step to import filtered version of a LD matrix (block matrix)."""
+
 from __future__ import annotations
 
 import sys
@@ -13,7 +14,7 @@
 from pyspark.sql import Window
 
 from gentropy.common.spark_helpers import get_top_ranked_in_window, get_value_from_row
-from gentropy.common.utils import _liftover_loci, convert_gnomad_position_to_ensembl
+from gentropy.common.utils import _liftover_loci
 from gentropy.dataset.ld_index import LDIndex
 
 if TYPE_CHECKING:
@@ -177,24 +178,15 @@ def _process_variant_indices(
             ld_index_38.to_spark()
             # Filter out variants where the liftover failed
             .filter(f.col("`locus_GRCh38.position`").isNotNull())
-            .withColumn(
-                "chromosome", f.regexp_replace("`locus_GRCh38.contig`", "chr", "")
-            )
-            .withColumn(
-                "position",
-                convert_gnomad_position_to_ensembl(
-                    f.col("`locus_GRCh38.position`"),
-                    f.col("`alleles`").getItem(0),
-                    f.col("`alleles`").getItem(1),
-                ),
-            )
             .select(
-                "chromosome",
-                "position",
+                f.regexp_replace("`locus_GRCh38.contig`", "chr", "").alias(
+                    "chromosome"
+                ),
+                f.col("`locus_GRCh38.position`").alias("position"),
                 f.concat_ws(
                     "_",
-                    f.col("chromosome"),
-                    f.col("position"),
+                    f.regexp_replace("`locus_GRCh38.contig`", "chr", ""),
+                    f.col("`locus_GRCh38.position`"),
                     f.col("`alleles`").getItem(0),
                     f.col("`alleles`").getItem(1),
                 ).alias("variantId"),
diff --git a/src/gentropy/datasource/gnomad/variants.py b/src/gentropy/datasource/gnomad/variants.py
index 58f5f8093..b06b4ba6c 100644
--- a/src/gentropy/datasource/gnomad/variants.py
+++ b/src/gentropy/datasource/gnomad/variants.py
@@ -1,4 +1,5 @@
 """Import gnomAD variants dataset."""
+
 from __future__ import annotations
 
 from dataclasses import dataclass, field
@@ -9,7 +10,7 @@
 from gentropy.dataset.variant_annotation import VariantAnnotation
 
 if TYPE_CHECKING:
-    from hail.expr.expressions import Int32Expression, StringExpression
+    pass
 
 
 @dataclass
@@ -39,29 +40,6 @@ class GnomADVariants:
         ]
     )
 
-    @staticmethod
-    def _convert_gnomad_position_to_ensembl_hail(
-        position: Int32Expression,
-        reference: StringExpression,
-        alternate: StringExpression,
-    ) -> Int32Expression:
-        """Convert GnomAD variant position to Ensembl variant position in hail table.
-
-        For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs, the position is unchanged.
-        More info about the problem: https://www.biostars.org/p/84686/
-
-        Args:
-            position (Int32Expression): Position of the variant in the GnomAD genome.
-            reference (StringExpression): The reference allele.
-            alternate (StringExpression): The alternate allele
-
-        Returns:
-            Int32Expression: The position of the variant according to Ensembl genome.
-        """
-        return hl.if_else(
-            (reference.length() > 1) | (alternate.length() > 1), position + 1, position
-        )
-
     def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation:
         """Generate variant annotation dataset from gnomAD.
 
@@ -93,7 +71,7 @@ def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation:
         return VariantAnnotation(
             _df=(
                 ht.select(
-                    gnomadVariantId=hl.str("-").join(
+                    variantId=hl.str("_").join(
                         [
                             ht.locus.contig.replace("chr", ""),
                             hl.str(ht.locus.position),
@@ -102,21 +80,7 @@ def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation:
                         ]
                     ),
                     chromosome=ht.locus.contig.replace("chr", ""),
-                    position=GnomADVariants._convert_gnomad_position_to_ensembl_hail(
-                        ht.locus.position, ht.alleles[0], ht.alleles[1]
-                    ),
-                    variantId=hl.str("_").join(
-                        [
-                            ht.locus.contig.replace("chr", ""),
-                            hl.str(
-                                GnomADVariants._convert_gnomad_position_to_ensembl_hail(
-                                    ht.locus.position, ht.alleles[0], ht.alleles[1]
-                                )
-                            ),
-                            ht.alleles[0],
-                            ht.alleles[1],
-                        ]
-                    ),
+                    position=ht.locus.position,
                     chromosomeB37=ht.locus_GRCh37.contig.replace("chr", ""),
                     positionB37=ht.locus_GRCh37.position,
                     referenceAllele=ht.alleles[0],
diff --git a/src/gentropy/datasource/gwas_catalog/associations.py b/src/gentropy/datasource/gwas_catalog/associations.py
index 5ff499f2c..658facea7 100644
--- a/src/gentropy/datasource/gwas_catalog/associations.py
+++ b/src/gentropy/datasource/gwas_catalog/associations.py
@@ -1,4 +1,5 @@
 """Study Locus for GWAS Catalog data source."""
+
 from __future__ import annotations
 
 import importlib.resources as pkg_resources
@@ -31,6 +32,40 @@
 class GWASCatalogCuratedAssociationsParser:
     """GWAS Catalog curated associations parser."""
 
+    @staticmethod
+    def convert_gnomad_position_to_ensembl(
+        position: Column, reference: Column, alternate: Column
+    ) -> Column:
+        """Convert GnomAD variant position to Ensembl variant position.
+
+        For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs,
+        the position is unchanged. More info about the problem: https://www.biostars.org/p/84686/
+
+        Args:
+            position (Column): Position of the variant in GnomAD's coordinates system.
+            reference (Column): The reference allele in GnomAD's coordinates system.
+            alternate (Column): The alternate allele in GnomAD's coordinates system.
+
+        Returns:
+            Column: The position of the variant in the Ensembl genome.
+
+        Examples:
+            >>> d = [(1, "A", "C"), (2, "AA", "C"), (3, "A", "AA")]
+            >>> df = spark.createDataFrame(d).toDF("position", "reference", "alternate")
+            >>> df.withColumn("new_position", GWASCatalogCuratedAssociationsParser.convert_gnomad_position_to_ensembl(f.col("position"), f.col("reference"), f.col("alternate"))).show()
+            +--------+---------+---------+------------+
+            |position|reference|alternate|new_position|
+            +--------+---------+---------+------------+
+            |       1|        A|        C|           1|
+            |       2|       AA|        C|           3|
+            |       3|        A|       AA|           4|
+            +--------+---------+---------+------------+
+            <BLANKLINE>
+        """
+        return f.when(
+            (f.length(reference) > 1) | (f.length(alternate) > 1), position + 1
+        ).otherwise(position)
+
     @staticmethod
     def _parse_pvalue(pvalue: Column) -> tuple[Column, Column]:
         """Parse p-value column.
@@ -178,7 +213,8 @@ def _map_to_variant_annotation_variants(
         gwas_associations_subset = gwas_associations.select(
             "studyLocusId",
             f.col("CHR_ID").alias("chromosome"),
-            f.col("CHR_POS").cast(IntegerType()).alias("position"),
+            # The positions from GWAS Catalog are from ensembl that causes discrepancy for indels:
+            f.col("CHR_POS").cast(IntegerType()).alias("ensemblPosition"),
             # List of all SNPs associated with the variant
             GWASCatalogCuratedAssociationsParser._collect_rsids(
                 f.split(f.col("SNPS"), "; ").getItem(0),
@@ -194,6 +230,11 @@ def _map_to_variant_annotation_variants(
         va_subset = variant_annotation.df.select(
             "variantId",
             "chromosome",
+            # Calculate the position in Ensembl coordinates for indels:
+            GWASCatalogCuratedAssociationsParser.convert_gnomad_position_to_ensembl(
+                f.col("position"), f.col("referenceAllele"), f.col("alternateAllele")
+            ).alias("ensemblPosition"),
+            # Keeping GnomAD position:
             "position",
             f.col("rsIds").alias("rsIdsGnomad"),
             "referenceAllele",
@@ -202,9 +243,11 @@ def _map_to_variant_annotation_variants(
             variant_annotation.max_maf().alias("maxMaf"),
         ).join(
             f.broadcast(
-                gwas_associations_subset.select("chromosome", "position").distinct()
+                gwas_associations_subset.select(
+                    "chromosome", "ensemblPosition"
+                ).distinct()
             ),
-            on=["chromosome", "position"],
+            on=["chromosome", "ensemblPosition"],
             how="inner",
         )
 
@@ -213,7 +256,7 @@ def _map_to_variant_annotation_variants(
         filtered_associations = (
             gwas_associations_subset.join(
                 f.broadcast(va_subset),
-                on=["chromosome", "position"],
+                on=["chromosome", "ensemblPosition"],
                 how="left",
             )
             .withColumn(