[DC-3632] Remove five cases of unused imports

data_steward/analytics/cdr_ops/ad_hoc_analyses/cdr_person_id.py

@@ -1,7 +1,6 @@
 # # Person
 # ## Person ID validation
 
-import bq_utils
 import utils.bq
 from notebooks.parameters import RDR_DATASET_ID, EHR_DATASET_ID
 
@@ -13,7 +12,7 @@
 hpo_ids = utils.bq.query("""
 SELECT REPLACE(table_id, '_person', '') AS hpo_id
 FROM `{EHR_DATASET_ID}.__TABLES__`
-WHERE table_id LIKE '%person' 
+WHERE table_id LIKE '%person'
 AND table_id NOT LIKE '%unioned_ehr_%' AND table_id NOT LIKE '\\\_%'
 """.format(EHR_DATASET_ID=EHR_DATASET_ID)).hpo_id.tolist()
 
@@ -31,7 +30,7 @@
 (SELECT COUNT(1) AS n
  FROM {EHR_DATASET_ID}.{h}_person e
  WHERE NOT EXISTS(
-  SELECT 1 
+  SELECT 1
   FROM {RDR_DATASET_ID}.person r
   WHERE r.person_id = e.person_id)) not_in_rdr
  ON TRUE
@@ -63,31 +62,31 @@
 RDR_EHR_NAME_MATCH_QUERY = '''
 WITH
   rdr_first_name AS
-  (SELECT DISTINCT person_id, 
-   FIRST_VALUE(value_as_string) 
+  (SELECT DISTINCT person_id,
+   FIRST_VALUE(value_as_string)
      OVER (PARTITION BY person_id, observation_source_value ORDER BY value_as_string) val
   FROM {RDR_DATASET_ID}.observation
   WHERE observation_source_value = 'PIIName_First'),
 
   rdr_last_name AS
-  (SELECT DISTINCT person_id, 
-   FIRST_VALUE(value_as_string) 
+  (SELECT DISTINCT person_id,
+   FIRST_VALUE(value_as_string)
      OVER (PARTITION BY person_id, observation_source_value ORDER BY value_as_string) val
   FROM {RDR_DATASET_ID}.observation
   WHERE observation_source_value = 'PIIName_Last'),
 
   rdr_name AS
-  (SELECT 
+  (SELECT
      f.person_id person_id,
-     f.val       first_name, 
+     f.val       first_name,
      l.val       last_name
    FROM rdr_first_name f JOIN rdr_last_name l USING (person_id))
 
  SELECT
    '{HPO_ID}'                 hpo_id,
-   rdr.person_id              rdr_person_id, 
-   rdr.first_name             rdr_first_name, 
-   rdr.last_name              rdr_last_name, 
+   rdr.person_id              rdr_person_id,
+   rdr.first_name             rdr_first_name,
+   rdr.last_name              rdr_last_name,
    pii.person_id              pii_person_id,
    pii.first_name             pii_first_name,
    pii.middle_name            pii_middle_name,
@@ -97,7 +96,7 @@
  FROM rdr_name rdr
  JOIN `{EHR_DATASET_ID}.{HPO_ID}_pii_name` pii
    ON  pii.first_name = rdr.first_name
-   AND pii.last_name  = rdr.last_name 
+   AND pii.last_name  = rdr.last_name
  LEFT JOIN `{EHR_DATASET_ID}.{HPO_ID}_person` p
    ON pii.person_id = p.person_id
 '''

data_steward/analytics/cdr_ops/ad_hoc_analyses/cohort_testing.py

@@ -21,7 +21,6 @@
 #     - Record count (condition_occurrence)
 #
 # - We want to determine if these fluctations are potentially caused by OMOP vocabulary issues. If this is the case, we should be able to determine similar trends in AoU data.
-import bq_utils
 import utils.bq
 from notebooks import parameters
 
@@ -42,8 +41,8 @@
 q4_2018_hypo_obs_card_query = """
 SELECT
 DISTINCT
-co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons, 
-COUNT(DISTINCT co.condition_occurrence_id) as num_records, 
+co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons,
+COUNT(DISTINCT co.condition_occurrence_id) as num_records,
 ROUND(COUNT(DISTINCT co.condition_occurrence_id) / COUNT(DISTINCT p.person_id), 2) as records_per_capita
 
 FROM
@@ -190,8 +189,8 @@
 q2_2019_hypo_obs_card_query = """
 SELECT
 DISTINCT
-co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons, 
-COUNT(DISTINCT co.condition_occurrence_id) as num_records, 
+co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons,
+COUNT(DISTINCT co.condition_occurrence_id) as num_records,
 ROUND(COUNT(DISTINCT co.condition_occurrence_id) / COUNT(DISTINCT p.person_id), 2) as records_per_capita
 
 FROM
@@ -339,14 +338,14 @@
 SELECT
 DISTINCT
 q4.*, q2.*, (SUM(q2.num_persons) - SUM(q4.old_num_persons)) as person_difference,
-(SUM(q2.num_records) - SUM(q4.old_num_records)) as record_difference 
+(SUM(q2.num_records) - SUM(q4.old_num_records)) as record_difference
 FROM
 
     (SELECT
     DISTINCT
-    co.condition_concept_id as old_condition_concept_id, c.concept_name as old_concept_name, 
-    COUNT(DISTINCT p.person_id) AS old_num_persons, 
-    COUNT(DISTINCT co.condition_occurrence_id) as old_num_records, 
+    co.condition_concept_id as old_condition_concept_id, c.concept_name as old_concept_name,
+    COUNT(DISTINCT p.person_id) AS old_num_persons,
+    COUNT(DISTINCT co.condition_occurrence_id) as old_num_records,
     ROUND(COUNT(DISTINCT co.condition_occurrence_id) / COUNT(DISTINCT p.person_id), 2) as old_records_per_capita
 
     FROM
@@ -378,13 +377,13 @@
 
     GROUP BY 1, 2
     ORDER BY old_num_persons DESC) q4
-    
+
     LEFT JOIN
-    
+
     (SELECT
     DISTINCT
-    co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons, 
-    COUNT(DISTINCT co.condition_occurrence_id) as num_records, 
+    co.condition_concept_id, c.concept_name, COUNT(DISTINCT p.person_id) AS num_persons,
+    COUNT(DISTINCT co.condition_occurrence_id) as num_records,
     ROUND(COUNT(DISTINCT co.condition_occurrence_id) / COUNT(DISTINCT p.person_id), 2) as records_per_capita
 
     FROM
@@ -416,10 +415,10 @@
 
     GROUP BY 1, 2
     ORDER BY num_persons DESC) q2
-    
+
     ON
     q4.old_condition_concept_id = q2.condition_concept_id
-    
+
     GROUP BY 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
     ORDER BY old_num_persons DESC
 

data_steward/analytics/cdr_ops/ad_hoc_analyses/deid_race.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 # +
-import bq_utils
 import utils.bq
 from notebooks import render, parameters
 import pandas as pd
@@ -27,19 +26,19 @@
 
 MULTIRACIAL_DIST_QUERY = """
 WITH race_combo AS
-(SELECT o.person_id, 
-  o.questionnaire_response_id, 
+(SELECT o.person_id,
+  o.questionnaire_response_id,
   STRING_AGG(REPLACE(c.concept_code, 'WhatRaceEthnicity_', ''), ' ' ORDER BY value_source_value) selected_races
  FROM {DATASET}.observation o
- JOIN {VOCAB}.concept c ON o.value_source_concept_id = c.concept_id  
+ JOIN {VOCAB}.concept c ON o.value_source_concept_id = c.concept_id
  WHERE observation_source_concept_id = 1586140
  GROUP BY person_id, questionnaire_response_id)
- 
-SELECT 
-  selected_races, 
+
+SELECT
+  selected_races,
   (LENGTH(selected_races) - LENGTH(REPLACE(selected_races, ' ', '')) + 1) AS selected_count,
   COUNT(DISTINCT person_id) row_count
-FROM race_combo 
+FROM race_combo
 GROUP BY selected_races
 ORDER BY selected_count, selected_races
 """

data_steward/analytics/cdr_ops/ad_hoc_analyses/ehr_ops_row_counts.py

@@ -16,7 +16,6 @@
 
 # +
 import datetime
-import bq_utils
 import utils.bq
 from notebooks.parameters import RDR_PROJECT_ID, RDR_DATASET_ID, EHR_DATASET_ID
 
@@ -65,17 +64,17 @@
 # ## EHR Site Submission Counts
 
 utils.bq.query('''
-SELECT 
+SELECT
   l.Org_ID AS org_id,
   l.HPO_ID AS hpo_id,
   l.Site_Name AS site_name,
-  table_id AS table_id, 
+  table_id AS table_id,
   row_count AS row_count
 FROM `{EHR_DATASET_ID}.__TABLES__` AS t
-JOIN `lookup_tables.hpo_site_id_mappings` AS l  
+JOIN `lookup_tables.hpo_site_id_mappings` AS l
   ON STARTS_WITH(table_id,lower(l.HPO_ID))=true
 WHERE table_id like '%person%' AND
-NOT(table_id like '%unioned_ehr_%') AND 
+NOT(table_id like '%unioned_ehr_%') AND
 l.hpo_id <> ''
 ORDER BY Display_Order
 '''.format(EHR_DATASET_ID=EHR_DATASET_ID))
@@ -84,7 +83,7 @@
 hpo_ids = utils.bq.query("""
 SELECT REPLACE(table_id, '_person', '') AS hpo_id
 FROM `{EHR_DATASET_ID}.__TABLES__`
-WHERE table_id LIKE '%person' 
+WHERE table_id LIKE '%person'
 AND table_id NOT LIKE '%unioned_ehr_%' AND table_id NOT LIKE '\\\_%'
 """.format(EHR_DATASET_ID=EHR_DATASET_ID)).hpo_id.tolist()