def _test_dataConversion_aggregate(self):
# Run the data conversion on the same data and look for expected records
log.debug("Run the conversion process...")
convOptions = ConversionOptions()
convOptions.startDate = TEST_START_DATE
convOptions.aggregate = True
self.converter.convertSourceItems(convOptions)
# Just query back for the same data, de-normalizing the data back to a general table
testQuery = \
"""
select
pi.external_id as pi_external_id,
pi.patient_id,
pi.encounter_id,
cic.description as cic_description,
ci.external_id as ci_external_id,
ci.name,
ci.description as ci_description,
pi.item_date
from
%s.patient_item as pi,
%s.clinical_item as ci,
%s.clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '%s'
order by
pi.external_id desc, ci.external_id desc
""" % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
expectedData = \
[]
actualData = DBUtil.execute(testQuery)
self.assertEqualTable(expectedData, actualData)
def convertSourceItems(self, convOptions, conn=None):
"""Primary run function to process the contents of the raw source
table and convert them into equivalent patient_item, clinical_item, and clinical_item_category entries.
Should look for redundancies after the fact to catch repeated conversions.
startDate - If provided, only return items whose ordering_date is on or after that date.
endDate - If provided, only return items whose ordering_date is before that date.
"""
log.info("Conversion for items dated %s to %s" %
(convOptions.startDate, convOptions.endDate))
progress = ProgressDots()
extConn = conn is not None
if not extConn:
conn = self.connFactory.connection()
try:
# Next round for medications directly from order_med table not addressed in medmix TODO (nodir) seems like an unrelated comment?
category = self.categoryFromSourceItem(conn)
for sourceItem in self.querySourceItems(convOptions):
log.debug('sourceItem: {}'.format(sourceItem))
self.convertSourceItem(category, sourceItem, conn=conn)
progress.Update()
finally:
conn.close()
progress.PrintStatus()
def generate_test_and_expected_data(self, test_data_size, aggregate):
for curr_row in range(test_data_size):
patient_id = 'JC' + format(curr_row, '06')
test_data_row = self.generate_test_data_row(curr_row, self.starrUtil.random_period(), patient_id)
self.test_data.append(test_data_row)
self.generate_expected_data_rows(test_data_row, self.expected_data, aggregate)
# pi.external_id desc (ci.external_id is always None, so not included here)
self.expected_data.sort(key=lambda tup: (-tup[0]))
log.debug('test_data: {}'.format(self.test_data))
log.debug('expected_data: {}'.format(self.expected_data))
def test_dataConversion(self):
# Run the data conversion on the same data and look for expected records
log.debug("Run the conversion process...")
convOptions = ConversionOptions()
convOptions.startDate = TEST_START_DATE
#self.converter.convertSourceItems(convOptions)
tempDir = '/tmp'
self.converter.convertAndUpload(convOptions,
tempDir=tempDir,
datasetId=TEST_DEST_DATASET)
# Just query back for the same data, de-normalizing the data back to a general table
testQuery = \
"""
select
pi.external_id as pi_external_id,
pi.patient_id,
pi.encounter_id,
cic.description as cic_description,
ci.external_id as ci_external_id,
ci.name,
ci.description as ci_description,
pi.item_date
from
%s.patient_item as pi,
%s.clinical_item as ci,
%s.clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '%s'
order by
pi.external_id desc, ci.external_id desc
""" % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
self.starrUtil.dumpClinicalTablesToCsv(tempDir)
self.starrUtil.uploadClinicalTablesCsvToBQ(tempDir, TEST_DEST_DATASET)
self.starrUtil.removeClinicalTablesCsv(tempDir)
self.starrUtil.removeClinicalTablesAddedLines(TEST_SOURCE_TABLE)
expectedData = STARRExpectedData().treatmentteam_expected
bqCursor = self.bqConn.cursor()
bqCursor.execute(testQuery)
actualData = [row.values() for row in bqCursor.fetchall()]
#print('actual data %s' % actualData)
self.assertEqualTable(expectedData, actualData)
def querySourceItems(self, convOptions):
"""Query the database for list of all source clinical items (medications, etc.)
and yield the results one at a time. If startDate provided, only return items whose
occurrence date is on or after that date.
"""
# TODO need to figure out how to pass date to query in BQ using SQLQuery object
query = "SELECT {} FROM {}".format(', '.join(self.HEADERS),
SOURCE_TABLE)
if convOptions.startDate is not None:
query += ' WHERE trtmnt_tm_begin_dt_jittered >= @startDate '
if convOptions.endDate is not None:
query += ' WHERE ' if convOptions.startDate is None else 'AND'
query += ' trtmnt_tm_begin_dt_jittered < @endDate'
query += ' ORDER BY trtmnt_tm_begin_dt_jittered'
query += ';'
query_params = [
bigquery.ScalarQueryParameter(
'startDate',
'TIMESTAMP',
convOptions.startDate,
),
bigquery.ScalarQueryParameter(
'endDate',
'TIMESTAMP',
convOptions.endDate,
)
]
query_job = self.bqClient.queryBQ(str(query),
query_params=query_params,
location='US',
batch_mode=False,
verbose=True)
for row in query_job: # API request - fetches results
rowModel = RowItemModel(list(row.values()), self.HEADERS)
log.debug("rowModel: {}".format(rowModel))
yield self.normalizeRowModel(
rowModel, convOptions
) # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
def test_batchDataConversion(self):
# Run the data conversion on the same data and look for expected records
log.debug(
"Run the batch conversion process, and upload to test dataset in BigQuery..."
)
self.converter.convertItemsByBatch(self.pat_id_csv,
10,
datasetId=TEST_DEST_DATASET,
startBatch=4)
# Just query back for the same data, de-normalizing the data back to a general table
testQuery = \
"""
select
pi.external_id as pi_external_id,
pi.patient_id,
pi.encounter_id,
cic.description as cic_description,
ci.external_id as ci_external_id,
ci.name,
ci.description as ci_description,
pi.item_date
from
%s.patient_item as pi,
%s.clinical_item as ci,
%s.clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '%s'
order by
pi.patient_id desc, ci.name
""" % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
expectedData = STARRExpectedData().demographics_expected
bqCursor = self.bqConn.cursor()
bqCursor.execute(testQuery)
actualData = [row.values() for row in bqCursor.fetchall()]
#print('actual data %s' % actualData)
self.assertEqualTable(expectedData, actualData)
def test_dataConversion(self, name, aggregation):
log.info("Generating test source data")
self.generate_test_and_expected_data(self.TEST_DATA_SIZE,
aggregate=aggregation)
self.starrUtil.dump_test_data_to_csv(self.converter.HEADERS,
self.test_data,
self.test_data_csv)
self.starrUtil.upload_csv_to_bigquery(
'starr_datalake2018', 'treatment_team', TEST_DEST_DATASET,
'starr_treatment_team', self.test_data_csv, self.converter.HEADERS)
log.debug("Run the conversion process...")
conv_options = STARRTreatmentTeamConversion.ConversionOptions()
conv_options.aggregate = aggregation
temp_dir = tempfile.gettempdir()
self.converter.convertAndUpload(conv_options,
tempDir=temp_dir,
targetDatasetId=TEST_DEST_DATASET)
# Just query back for the same data, de-normalizing the data back to a general table
test_query = \
"""
select
pi.external_id as pi_external_id,
pi.patient_id,
pi.encounter_id,
cic.description as cic_description,
ci.external_id as ci_external_id,
ci.name,
ci.description as ci_description,
pi.item_date,
pi.item_date_utc
from
%s.patient_item as pi,
%s.clinical_item as ci,
%s.clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '%s'
order by
pi.external_id desc, ci.external_id desc
""" % (TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
bq_cursor = self.bqConn.cursor()
bq_cursor.execute(test_query)
actual_data = [row.values() for row in bq_cursor.fetchall()]
log.debug('actual data: {}'.format(actual_data))
log.debug('expected data: {}'.format(self.expected_data))
self.assertEqualTable(self.expected_data, actual_data)
def querySourceItems(self,
startDate=None,
endDate=None,
progress=None,
conn=None):
"""Query the database for list of all source clinical items (orders, etc.)
and yield the results one at a time. If startDate provided, only return items whose order_time is on or after that date.
Ignore entries with instantiated_time not null, as those represent child orders spawned from an original order,
whereas we are more interested in the decision making to enter the original order.
"""
extConn = conn is not None
if not extConn:
conn = self.connFactory.connection()
# Column headers to query for that map to respective fields in analysis table
queryHeaders = [
"op.order_proc_id_coded", "jc_uid", "op.pat_enc_csn_id_coded",
"op.order_type", "op.proc_id", "op.proc_code", "description",
"order_time_jittered", "order_time_jittered_utc", "ordering_mode",
"protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
"ss_sg_key", "ss_sg_name"
]
headers = [
"order_proc_id_coded", "jc_uid", "pat_enc_csn_id_coded",
"order_type", "proc_id", "proc_code", "description",
"order_time_jittered", "order_time_jittered_utc", "ordering_mode",
"protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
"ss_sg_key", "ss_sg_name"
]
# TODO original query - need to figure out how to pass date to query in BQ using SQLQuery object
# query = SQLQuery()
# for header in queryHeaders:
# query.addSelect(header)
# query.addFrom("stride_order_proc as op left outer join stride_orderset_order_proc as os on op.order_proc_id = os.order_proc_id")
# query.addWhere("order_time is not null") # Rare cases of "comment" orders with no date/time associated
# query.addWhere("instantiated_time is null")
# query.addWhere("(stand_interval is null or stand_interval not like '%%PRN')") # Ignore PRN orders to simplify somewhat
# if startDate is not None:
# query.addWhereOp("order_time", ">=", startDate)
# if endDate is not None:
# query.addWhereOp("order_time", "<", endDate)
query = "SELECT {} FROM {} as op left outer join {} as os on op.order_proc_id_coded = os.order_proc_id_coded" \
.format(', '.join(queryHeaders), SOURCE_TABLE, ORDERSET_TABLE)
query += " where order_time_jittered is not null" # Rare cases of "comment" orders with no date/time associated
query += " and (stand_interval is NULL or stand_interval not like '%PRN')" # Ignore PRN orders to simplify somewhat
if startDate is not None:
query += " and order_time_jittered >= @startDate"
if endDate is not None:
query += " and order_time_jittered < @endDate"
query += " order by order_time_jittered"
# query += " order by op.order_proc_id_coded, jc_uid, op.pat_enc_csn_id_coded, op.proc_id"
query += ';'
query_params = [
bigquery.ScalarQueryParameter(
'startDate',
'DATETIME',
startDate,
),
bigquery.ScalarQueryParameter(
'endDate',
'DATETIME',
endDate,
)
]
# TODO Query to get an estimate of how long the process will be
# if progress is not None:
# progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0]
query_job = self.bqClient.queryBQ(query,
query_params=query_params,
verbose=True)
for row in query_job: # API request - fetches results
rowModel = RowItemModel(list(row.values()), headers)
log.debug("rowModel: {}".format(rowModel))
yield rowModel # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
if not extConn:
conn.close()
def test_data_conversion(self):
log.info("Generating test source data")
self.generate_test_and_expected_data(self.TEST_DATA_SIZE)
# upload proc_orderset
self.starrUtil.dump_test_data_to_csv(self.PROC_ORDERSET_HEADER,
self.orderset_data,
self.orderset_data_csv)
self.starrUtil.upload_csv_to_bigquery('starr_datalake2018',
'proc_orderset',
TEST_DEST_DATASET,
'starr_proc_orderset',
self.orderset_data_csv,
self.PROC_ORDERSET_HEADER)
# upload order_proc
self.starrUtil.dump_test_data_to_csv(self.ORDER_PROC_HEADER,
self.test_data,
self.test_data_csv)
self.starrUtil.upload_csv_to_bigquery('starr_datalake2018',
'order_proc', TEST_DEST_DATASET,
'starr_order_proc',
self.test_data_csv,
self.ORDER_PROC_HEADER)
log.debug("Run the conversion process...")
# temp_dir = tempfile.gettempdir()
# self.converter.convertAndUpload(tempDir=temp_dir, target_dataset_id=TEST_DEST_DATASET, removeCsvs=True)
self.converter.main(["test_order_proc"])
# Just query back for the same data, de-normalizing the data back to a general table
test_query = \
"""
select
pi.external_id as pi_external_id,
pi.patient_id,
pi.encounter_id,
cic.description as cic_description,
ci.external_id as ci_external_id,
ci.name,
ci.description as ci_description,
pi.item_date,
pi.item_date_utc
from
{}.patient_item as pi,
{}.clinical_item as ci,
{}.clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '{}'
order by
pi.external_id desc, pi.patient_id, pi.encounter_id, ci.external_id desc
""".format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_SOURCE_TABLE)
bq_cursor = self.bqConn.cursor()
bq_cursor.execute(test_query)
# remove timezone info in pi.item_date from coming from bigquery - we're storing datetime without timezone
actual_data = [
row.values()[:7] + (
row.values()[7].replace(tzinfo=None),
row.values()[8],
) for row in bq_cursor.fetchall()
]
log.debug('actual data: {}'.format(actual_data))
log.debug('expected data: {}'.format(self.expected_data))
self.assertEqualTable(self.expected_data, actual_data)
# Query for orderset links
test_orderset_query = \
"""
select
pi.external_id as pi_external_id,
ci.external_id as ci_external_id,
cic.description as cic_description,
ci.name as ci_name,
ci.description,
ic.external_id,
ic.name,
ic.section,
ic.subgroup
from
{}.patient_item as pi,
{}.clinical_item as ci,
{}.clinical_item_category as cic,
{}.patient_item_collection_link as picl,
{}.item_collection_item as ici,
{}.item_collection as ic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '{}' and
pi.patient_item_id = picl.patient_item_id and
picl.item_collection_item_id = ici.item_collection_item_id and
ici.item_collection_id = ic.item_collection_id
order by
pi.external_id, ci.external_id, cic.description, ci.name, ci.description
""".format(TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET, TEST_DEST_DATASET,
TEST_DEST_DATASET, TEST_SOURCE_TABLE)
bq_cursor.execute(test_orderset_query)
actual_orderset_data = [row.values() for row in bq_cursor.fetchall()]
log.debug('actual orderset data: {}'.format(actual_orderset_data))
log.debug('expected orderset data: {}'.format(
self.expected_orderset_data))
self.assertEqualTable(self.expected_orderset_data,
actual_orderset_data)
def querySourceItems(self,
rxcuiDataByMedId,
convOptions,
progress=None,
conn=None):
"""Query the database for list of all source clinical items (medications, etc.)
and yield the results one at a time. If startDate provided, only return items whose
order_time_jittered is on or after that date.
"""
# Column headers to query for that map to respective fields in analysis table
queryHeaders = [
"med.order_med_id_coded", "jc_uid", "med.pat_enc_csn_id_coded",
"med.medication_id", "med.med_description", "order_time_jittered",
"order_time_jittered_utc", "med_route", "number_of_times",
"protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
"ss_sg_key", "ss_sg_name", "ordering_mode"
]
headers = [
"order_med_id_coded", "jc_uid", "pat_enc_csn_id_coded",
"medication_id", "med_description", "order_time_jittered",
"order_time_jittered_utc", "med_route", "number_of_times",
"protocol_id", "protocol_name", "ss_section_id", "ss_section_name",
"ss_sg_key", "ss_sg_name", "ordering_mode"
]
# TODO original query - need to figure out how to pass date to query in BQ using SQLQuery object
# query = SQLQuery()
# for header in queryHeaders:
# query.addSelect(header)
# query.addFrom("stride_order_med as med left outer join stride_orderset_order_med as os on med.order_med_id = os.order_med_id") # Grab order set links if they exist
# query.addWhere("med.medication_id <> %s" % TEMPLATE_MEDICATION_ID)
# query.addWhere("freq_name not like '%%PRN'") # Ignore PRN orders
# if convOptions.startDate is not None:
# query.addWhereOp("ordering_date",">=", convOptions.startDate)
# if convOptions.endDate is not None:
# query.addWhereOp("ordering_date","<", convOptions.endDate)
query = "SELECT {} FROM {} as med left outer join {} as os on med.order_med_id_coded = os.order_med_id_coded".format(
', '.join(queryHeaders), SOURCE_TABLE, ORDERSET_TABLE)
# TODO only 20 records with medication_id = TEMPLATE_MEDICATION_ID (whereas stride has 67041 such rows)
query += " where med.medication_id <> {}".format(
TEMPLATE_MEDICATION_ID)
query += " and (freq_name is NULL or freq_name not like '%PRN')" # Ignore PRN orders
if convOptions.startDate is not None:
query += " and order_time_jittered >= @startDate"
if convOptions.endDate is not None:
query += " and order_time_jittered < @endDate"
query += " order by order_time_jittered, med.order_med_id_coded, jc_uid, med.pat_enc_csn_id_coded, med.medication_id"
query += ';'
query_params = [
bigquery.ScalarQueryParameter(
'startDate',
'DATETIME',
convOptions.startDate,
),
bigquery.ScalarQueryParameter(
'endDate',
'DATETIME',
convOptions.endDate,
)
]
# TODO Query to get an estimate of how long the process will be
# if progress is not None:
# progress.total = DBUtil.execute(query.totalQuery(), conn=conn)[0][0]
query_job = self.bqClient.queryBQ(query,
query_params=query_params,
verbose=True)
for row in query_job: # API request - fetches results
rowModel = RowItemModel(row.values(), headers)
log.debug("rowModel: {}".format(rowModel))
for normalizedModel in self.normalizeMedData(
rxcuiDataByMedId, rowModel, convOptions):
yield normalizedModel # Yield one row worth of data at a time to avoid having to keep the whole result set in memory
def querySourceItems(self, patientIds=None, progress=None, debug=False):
"""Query the database for list of all patient demographics
and yield the results one at a time. If patientIds provided, only return items
matching those IDs.
"""
# Column headers to query for that map to respective fields in analysis table
headers = ["rit_uid", "birth_date_jittered", "gender", "death_date_jittered", "canonical_race",
"canonical_ethnicity"]
'''
query = SQLQuery()
for header in headers:
query.addSelect( header )
query.addFrom(SOURCE_TABLE + " as dem")
if patientIds is not None:
query.addWhereIn("dem.rit_uid", patientIds)
'''
query = '''
SELECT rit_uid,birth_date_jittered,gender,death_date_jittered,canonical_race,canonical_ethnicity
FROM %s as dem
WHERE dem.rit_uid IN UNNEST(@pat_ids)
ORDER BY rit_uid;
''' % SOURCE_TABLE
query_params = [
bigquery.ArrayQueryParameter('pat_ids', 'STRING', patientIds)
]
if debug:
print(query)
print(query_params)
query_job = self.bqClient.queryBQ(str(query), query_params=query_params, location='US', batch_mode=False,
verbose=True)
previous_rit_uid = None
rows_fetched = 0
for row in query_job: # API request - fetches results
rows_fetched += 1
# Row values can be accessed by field name or index
# assert row[0] == row.name == row["name"]
rowModel = RowItemModel(row.values(), headers)
# skip this record if we already processed this rit_uid
if rowModel["rit_uid"] == previous_rit_uid:
continue
if rowModel["birth_date_jittered"] is None:
# Blank values, doesn't make sense. Skip it
log.warning(rowModel)
else:
# Record birth at resolution of year
rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
rowModel["name"] = "Birth"
rowModel["description"] = "Birth Year"
yield rowModel
# Record another at resolution of decade
decade = (rowModel["birth_date_jittered"].year / 10) * 10
rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
rowModel["name"] = "Birth%ds" % decade
rowModel["description"] = "Birth Decade %ds" % decade
yield rowModel
# Summarize race and ethnicity information into single field of interest
raceEthnicity = self.summarizeRaceEthnicity(rowModel["canonical_race"], rowModel["canonical_ethnicity"])
rowModel["itemDate"] = datetime(rowModel["birth_date_jittered"].year, 1, 1)
rowModel["name"] = "Race" + (raceEthnicity.translate(None, " ()-/")) # Strip off punctuation
rowModel["description"] = "Race/Ethnicity: %s" % raceEthnicity
yield rowModel
gender = rowModel["gender"].title()
rowModel["name"] = gender
rowModel["description"] = "%s Gender" % gender
yield rowModel
if rowModel["death_date_jittered"] is not None:
rowModel["name"] = "Death"
rowModel["description"] = "Death Date"
rowModel["itemDate"] = rowModel["death_date_jittered"]
yield rowModel
previous_rit_uid = rowModel["rit_uid"]
progress.Update()
log.debug("fetched {} rows".format(rows_fetched))
def _test_dataConversion(self):
# Run the data conversion on the same data and look for expected records
log.debug("Run the conversion process...")
self.converter.convertSourceItems(self.patientIds)
# Just query back for the same data, de-normalizing the data back to a general table
testQuery = \
"""
select
pi.external_id,
pi.patient_id,
pi.encounter_id,
cic.description,
ci.external_id,
ci.name,
ci.description,
pi.item_date
from
patient_item as pi,
clinical_item as ci,
clinical_item_category as cic
where
pi.clinical_item_id = ci.clinical_item_id and
ci.clinical_item_category_id = cic.clinical_item_category_id and
cic.source_table = '%s'
order by
pi.patient_id desc, ci.name
""" % TEST_SOURCE_TABLE
expectedData = \
[
[None, 15437801L, None, 'Demographics', None, 'Birth', 'Birth Year', datetime(1963, 1, 1, 0, 0)],
[None, 15437801L, None, 'Demographics', None, 'Birth1960s', 'Birth Decade 1960s',
datetime(1963, 1, 1, 0, 0)],
[None, 15437801L, None, 'Demographics', None, 'Male', 'Male Gender', datetime(1963, 1, 1, 0, 0)],
[None, 15437801L, None, 'Demographics', None, 'RaceOther', 'Race/Ethnicity: Other',
datetime(1963, 1, 1, 0, 0)],
[None, 15350146L, None, 'Demographics', None, 'Birth', 'Birth Year', datetime(2007, 1, 1, 0, 0)],
[None, 15350146L, None, 'Demographics', None, 'Birth2000s', 'Birth Decade 2000s',
datetime(2007, 1, 1, 0, 0)],
[None, 15350146L, None, 'Demographics', None, 'Female', 'Female Gender', datetime(2007, 1, 1, 0, 0)],
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[None, 13414764L, None, 'Demographics', None, 'Birth1920s', 'Birth Decade 1920s',
datetime(1929, 1, 1, 0, 0)],
[None, 13414764L, None, 'Demographics', None, 'Female', 'Female Gender', datetime(1929, 1, 1, 0, 0)],
[None, 13414764L, None, 'Demographics', None, 'RaceWhiteNonHispanicLatino',
'Race/Ethnicity: White (Non-Hispanic/Latino)', datetime(1929, 1, 1, 0, 0)],
[None, 13411182L, None, 'Demographics', None, 'Birth', 'Birth Year', datetime(1961, 1, 1, 0, 0)],
[None, 13411182L, None, 'Demographics', None, 'Birth1960s', 'Birth Decade 1960s',
datetime(1961, 1, 1, 0, 0)],
[None, 13411182L, None, 'Demographics', None, 'Male', 'Male Gender', datetime(1961, 1, 1, 0, 0)],
[None, 13411182L, None, 'Demographics', None, 'RaceWhiteNonHispanicLatino',
'Race/Ethnicity: White (Non-Hispanic/Latino)', datetime(1961, 1, 1, 0, 0)],
[None, 13406787L, None, 'Demographics', None, 'Birth', 'Birth Year', datetime(1954, 1, 1, 0, 0)],
[None, 13406787L, None, 'Demographics', None, 'Birth1950s', 'Birth Decade 1950s',
datetime(1954, 1, 1, 0, 0)],
[None, 13406787L, None, 'Demographics', None, 'Female', 'Female Gender', datetime(1954, 1, 1, 0, 0)],
[None, 13406787L, None, 'Demographics', None, 'RaceBlack', 'Race/Ethnicity: Black',
datetime(1954, 1, 1, 0, 0)],
[None, 13386163L, None, 'Demographics', None, 'Birth', 'Birth Year', datetime(1983, 1, 1, 0, 0)],
[None, 13386163L, None, 'Demographics', None, 'Birth1980s', 'Birth Decade 1980s',
datetime(1983, 1, 1, 0, 0)],
[None, 13386163L, None, 'Demographics', None, 'Male', 'Male Gender', datetime(1983, 1, 1, 0, 0)],
[None, 13386163L, None, 'Demographics', None, 'RaceWhiteNonHispanicLatino',
'Race/Ethnicity: White (Non-Hispanic/Latino)', datetime(1983, 1, 1, 0, 0)]
]
actualData = DBUtil.execute(testQuery)
self.assertEqualTable(expectedData, actualData)
