def handle(self, *args, **kwargs):
client = Client("hscic")
try:
client.create_storage_backed_table(
"raw_prescribing_v1",
RAW_PRESCRIBING_SCHEMA_V1,
"hscic/prescribing_v1/20*Detailed_Prescribing_Information.csv",
)
except Conflict:
pass
try:
client.create_storage_backed_table(
"raw_prescribing_v2",
RAW_PRESCRIBING_SCHEMA_V2,
# This pattern may change once the data is published via the
# new Open Data Portal.
"hscic/prescribing_v2/20*.csv",
)
except Conflict:
pass
for table_name in [
"all_prescribing",
"normalised_prescribing",
"normalised_prescribing_standard",
"raw_prescribing_normalised",
]:
self.recreate_table(client, table_name)
client = Client("measures")
for table_name in [
"dmd_objs_with_form_route",
"dmd_objs_hospital_only",
"opioid_total_ome",
"practice_data_all_low_priority",
"pregabalin_total_mg",
"vw__median_price_per_unit",
"vw__ghost_generic_measure",
"vw__herbal_list",
# This references pregabalin_total_mg, so must come afterwards
"gaba_total_ddd",
]:
self.recreate_table(client, table_name)
# cmpa_products is a table that has been created and managed by Rich.
schema = build_schema(
("bnf_code", "STRING"), ("bnf_name", "STRING"), ("type", "STRING")
)
client.get_or_create_table("cmpa_products", schema)
def handle(self, *args, **options):
path = options["filename"]
head, filename = os.path.split(path)
converted_path = "{}_formatted.CSV".format(os.path.splitext(path)[0])
_, year_and_month = os.path.split(head)
logger.info("path: %s", path)
logger.info("converted_path: %s", converted_path)
logger.info("year_and_month: %s", year_and_month)
date = year_and_month + "_01"
try:
datetime.datetime.strptime(date, "%Y_%m_%d")
except ValueError:
message = ("The file path must have a YYYY_MM "
"date component in the containing directory: ")
message += path
raise CommandError(message)
hscic_dataset_client = Client("hscic")
tmp_dataset_client = Client("tmp_eu")
# Check that we haven't already processed data for this month
sql = """SELECT COUNT(*)
FROM {dataset}.prescribing
WHERE month = TIMESTAMP('{date}')""".format(
dataset=hscic_dataset_client.dataset_id,
date=date.replace("_", "-"))
try:
results = hscic_dataset_client.query(sql)
assert results.rows[0][0] == 0
except NotFound:
pass
# Create BQ table backed backed by uploaded source CSV file
raw_data_table_name = "raw_prescribing_data_{}".format(year_and_month)
gcs_path = "hscic/prescribing/{}/{}".format(year_and_month, filename)
logger.info("raw_data_table_name: %s", raw_data_table_name)
logger.info("gcs_path: %s", gcs_path)
raw_data_table = tmp_dataset_client.create_storage_backed_table(
raw_data_table_name, RAW_PRESCRIBING_SCHEMA, gcs_path)
# Append aggregated data to prescribing table
sql = """
SELECT
Area_Team_Code AS sha,
LEFT(PCO_Code, 3) AS pct,
Practice_Code AS practice,
BNF_Code AS bnf_code,
BNF_Description AS bnf_name,
SUM(Items) AS items,
SUM(NIC) AS net_cost,
SUM(Actual_Cost) AS actual_cost,
SUM(Quantity * Items) AS quantity,
TIMESTAMP('%s') AS month,
FROM %s
WHERE Practice_Code NOT LIKE '%%998' -- see issue #349
GROUP BY
bnf_code, bnf_name, pct,
practice, sha
""" % (
date.replace("_", "-"),
raw_data_table.qualified_name,
)
logger.info("sql: %s", sql)
prescribing_table = hscic_dataset_client.get_table("prescribing")
prescribing_table.insert_rows_from_query(
sql, legacy=True, write_disposition="WRITE_APPEND")
# Write aggregated data to new table, for download
sql = """
SELECT
LEFT(PCO_Code, 3) AS pct_id,
Practice_Code AS practice_code,
BNF_Code AS presentation_code,
SUM(Items) AS total_items,
SUM(NIC) AS net_cost,
SUM(Actual_Cost) AS actual_cost,
SUM(Quantity * Items) AS quantity,
'%s' AS processing_date,
FROM %s
WHERE Practice_Code NOT LIKE '%%998' -- see issue #349
GROUP BY
presentation_code, pct_id, practice_code
""" % (
date,
raw_data_table.qualified_name,
)
fmtd_data_table_name = "formatted_prescribing_%s" % year_and_month
logger.info("sql: %s", sql)
logger.info("fmtd_data_table_name: %s", fmtd_data_table_name)
fmtd_data_table = tmp_dataset_client.get_table(fmtd_data_table_name)
fmtd_data_table.insert_rows_from_query(sql, legacy=True)
# Export new table to storage, and download
exporter = TableExporter(fmtd_data_table, gcs_path + "_formatted-")
exporter.export_to_storage(print_header=False)
with tempfile.NamedTemporaryFile(dir=head) as f:
exporter.download_from_storage_and_unzip(f)
# Sort the output.
#
# Why? Because this is equivalent to CLUSTERing the table on
# loading, but less resource-intensive than doing it in
# Postgres. And the table is too big to sort within BigQuery.
subprocess.call(
"ionice -c 2 nice -n 10 sort -k3,3 -k1,1 -k2,2 -t, %s > %s" %
(f.name, converted_path),
shell=True,
)
def handle(self, *args, **options):
path = options["filename"]
head, filename = os.path.split(path)
_, year_and_month = os.path.split(head)
logger.info("path: %s", path)
logger.info("year_and_month: %s", year_and_month)
date = year_and_month + "_01"
try:
datetime.datetime.strptime(date, "%Y_%m_%d")
except ValueError:
message = ("The file path must have a YYYY_MM "
"date component in the containing directory: ")
message += path
raise CommandError(message)
hscic_dataset_client = Client("hscic")
tmp_dataset_client = Client("tmp_eu")
# Check that we haven't already processed data for this month
sql = """SELECT COUNT(*)
FROM {hscic}.prescribing_v2
WHERE month = TIMESTAMP('{date}')"""
try:
results = hscic_dataset_client.query(
sql, substitutions={"date": date.replace("_", "-")})
assert results.rows[0][0] == 0
except NotFound:
pass
# Create BQ table backed backed by uploaded source CSV file
raw_data_table_name = "raw_prescribing_data_{}".format(year_and_month)
gcs_path = "hscic/prescribing_v2/{}/{}".format(year_and_month,
filename)
logger.info("raw_data_table_name: %s", raw_data_table_name)
logger.info("gcs_path: %s", gcs_path)
raw_data_table = tmp_dataset_client.create_storage_backed_table(
raw_data_table_name, RAW_PRESCRIBING_SCHEMA_V2, gcs_path)
# Append aggregated data to prescribing table
sql = """
SELECT
AREA_TEAM_CODE AS sha,
LEFT(PCO_CODE, 3) AS pct,
PRACTICE_CODE AS practice,
BNF_CODE AS bnf_code,
BNF_DESCRIPTION AS bnf_name,
SUM(ITEMS) AS items,
SUM(NIC) AS net_cost,
SUM(ACTUAL_COST) AS actual_cost,
SUM(TOTAL_QUANTITY) AS quantity,
TIMESTAMP('%s') AS month,
FROM %s
WHERE PRACTICE_CODE NOT LIKE '%%998' -- see issue #349
GROUP BY
bnf_code, bnf_name, pct,
practice, sha
""" % (
date.replace("_", "-"),
raw_data_table.qualified_name,
)
logger.info("sql: %s", sql)
prescribing_table = hscic_dataset_client.get_table("prescribing_v2")
prescribing_table.insert_rows_from_query(
sql, legacy=True, write_disposition="WRITE_APPEND")
ImportLog.objects.create(current_at=date.replace("_", "-"),
filename=filename,
category="prescribing")
def handle(self, *args, **options):
path = options['filename']
head, filename = os.path.split(path)
converted_path = '{}_formatted.CSV'.format(os.path.splitext(path)[0])
_, year_and_month = os.path.split(head)
logger.info('path: %s', path)
logger.info('converted_path: %s', converted_path)
logger.info('year_and_month: %s', year_and_month)
date = year_and_month + '_01'
try:
datetime.datetime.strptime(date, '%Y_%m_%d')
except ValueError:
message = ('The file path must have a YYYY_MM '
'date component in the containing directory: ')
message += path
raise CommandError(message)
hscic_dataset_client = Client('hscic')
tmp_dataset_client = Client('tmp_eu')
# Check that we haven't already processed data for this month
sql = '''SELECT COUNT(*)
FROM {dataset}.prescribing
WHERE month = TIMESTAMP('{date}')'''.format(
dataset=hscic_dataset_client.dataset_id,
date=date.replace('_', '-'),
)
try:
results = hscic_dataset_client.query(sql)
assert results.rows[0][0] == 0
except NotFound:
pass
# Create BQ table backed backed by uploaded source CSV file
raw_data_table_name = 'raw_prescribing_data_{}'.format(year_and_month)
gcs_path = 'hscic/prescribing/{}/{}'.format(year_and_month, filename)
logger.info('raw_data_table_name: %s', raw_data_table_name)
logger.info('gcs_path: %s', gcs_path)
schema = [
{'name': 'Regional_Office_Name', 'type': 'string'},
{'name': 'Regional_Office_Code', 'type': 'string'},
{'name': 'Area_Team_Name', 'type': 'string'},
{'name': 'Area_Team_Code', 'type': 'string', 'mode': 'required'},
{'name': 'PCO_Name', 'type': 'string'},
{'name': 'PCO_Code', 'type': 'string'},
{'name': 'Practice_Name', 'type': 'string'},
{'name': 'Practice_Code', 'type': 'string', 'mode': 'required'},
{'name': 'BNF_Code', 'type': 'string', 'mode': 'required'},
{'name': 'BNF_Description', 'type': 'string', 'mode': 'required'},
{'name': 'Items', 'type': 'integer', 'mode': 'required'},
{'name': 'Quantity', 'type': 'integer', 'mode': 'required'},
{'name': 'ADQ_Usage', 'type': 'float'},
{'name': 'NIC', 'type': 'float', 'mode': 'required'},
{'name': 'Actual_Cost', 'type': 'float', 'mode': 'required'},
]
raw_data_table = tmp_dataset_client.create_storage_backed_table(
raw_data_table_name,
schema,
gcs_path
)
# Append aggregated data to prescribing table
sql = '''
SELECT
Area_Team_Code AS sha,
LEFT(PCO_Code, 3) AS pct,
Practice_Code AS practice,
BNF_Code AS bnf_code,
BNF_Description AS bnf_name,
SUM(Items) AS items,
SUM(NIC) AS net_cost,
SUM(Actual_Cost) AS actual_cost,
SUM(Quantity * Items) AS quantity,
TIMESTAMP('%s') AS month,
FROM %s
WHERE Practice_Code NOT LIKE '%%998' -- see issue #349
GROUP BY
bnf_code, bnf_name, pct,
practice, sha
''' % (date.replace('_', '-'), raw_data_table.qualified_name)
logger.info('sql: %s', sql)
prescribing_table = hscic_dataset_client.get_table('prescribing')
prescribing_table.insert_rows_from_query(
sql,
legacy=True,
write_disposition='WRITE_APPEND'
)
# Write aggregated data to new table, for download
sql = '''
SELECT
LEFT(PCO_Code, 3) AS pct_id,
Practice_Code AS practice_code,
BNF_Code AS presentation_code,
SUM(Items) AS total_items,
SUM(NIC) AS net_cost,
SUM(Actual_Cost) AS actual_cost,
SUM(Quantity * Items) AS quantity,
'%s' AS processing_date,
FROM %s
WHERE Practice_Code NOT LIKE '%%998' -- see issue #349
GROUP BY
presentation_code, pct_id, practice_code
''' % (date, raw_data_table.qualified_name)
fmtd_data_table_name = 'formatted_prescribing_%s' % year_and_month
logger.info('sql: %s', sql)
logger.info('fmtd_data_table_name: %s', fmtd_data_table_name)
fmtd_data_table = tmp_dataset_client.get_table(fmtd_data_table_name)
fmtd_data_table.insert_rows_from_query(sql, legacy=True)
# Export new table to storage, and download
exporter = TableExporter(fmtd_data_table, gcs_path + '_formatted-')
exporter.export_to_storage(print_header=False)
with tempfile.NamedTemporaryFile(dir=head) as f:
exporter.download_from_storage_and_unzip(f)
# Sort the output.
#
# Why? Because this is equivalent to CLUSTERing the table on
# loading, but less resource-intensive than doing it in
# Postgres. And the table is too big to sort within BigQuery.
subprocess.call(
"ionice -c 2 nice -n 10 sort -k3,3 -k1,1 -k2,2 -t, %s > %s" % (
f.name, converted_path),
shell=True)
def test_the_lot(self):
client = Client('test')
schema = build_schema(
('a', 'INTEGER'),
('b', 'STRING'),
)
headers = ['a', 'b']
rows = [
(1, 'apple'),
(2, 'banana'),
(3, 'coconut'),
]
t1 = client.get_or_create_table('t1', schema)
t1_qname = t1.qualified_name
# Test Table.insert_rows_from_csv
t1.insert_rows_from_csv('gcutils/tests/test_table.csv')
self.assertEqual(sorted(t1.get_rows()), rows)
# Test Table.insert_rows_from_query
t2 = client.get_table('t2')
sql = 'SELECT * FROM {} WHERE a > 1'.format(t1_qname)
t2.insert_rows_from_query(sql)
self.assertEqual(sorted(t2.get_rows()), rows[1:])
# Test Client.query
sql = 'SELECT * FROM {} WHERE a > 2'.format(t1_qname)
results = client.query(sql)
self.assertEqual(sorted(results.rows), rows[2:])
# Test Client.query_into_dataframe
sql = 'SELECT * FROM {} WHERE a > 2'.format(t1_qname)
df = client.query_into_dataframe(sql)
self.assertEqual(df.values.tolist(), [list(rows[2])])
# Test TableExporter.export_to_storage and
# TableExporter.download_from_storage_and_unzip
t1_exporter = TableExporter(t1, self.storage_prefix + 'test_table-')
t1_exporter.export_to_storage()
with tempfile.NamedTemporaryFile(mode='r+') as f:
t1_exporter.download_from_storage_and_unzip(f)
f.seek(0)
reader = csv.reader(f)
data = [reader.next()] + sorted(reader)
self.assertEqual(data, [map(str, row) for row in [headers] + rows])
# Test Table.insert_rows_from_storage
storage_path = self.storage_prefix + 'test_table.csv'
self.upload_to_storage('gcutils/tests/test_table.csv', storage_path)
t2.insert_rows_from_storage(storage_path)
self.assertEqual(sorted(t2.get_rows()), rows)
# Test Client.create_storage_backed_table
storage_path = self.storage_prefix + 'test_table_headers.csv'
self.upload_to_storage(
'gcutils/tests/test_table_headers.csv',
storage_path
)
schema = [
{'name': 'a', 'type': 'integer'},
{'name': 'b', 'type': 'string'},
]
t3 = client.create_storage_backed_table(
't3',
schema,
storage_path
)
results = client.query('SELECT * FROM {}'.format(t3.qualified_name))
self.assertEqual(sorted(results.rows), rows)
self.upload_to_storage(
'gcutils/tests/test_table_headers_2.csv',
storage_path
)
results = client.query('SELECT * FROM {}'.format(t3.qualified_name))
self.assertEqual(sorted(results.rows), rows + [(4, u'damson')])
# Test Client.create_table_with_view
sql = 'SELECT * FROM {{project}}.{} WHERE a > 1'.format(t1_qname)
t4 = client.create_table_with_view('t4', sql, False)
results = client.query('SELECT * FROM {}'.format(t4.qualified_name))
self.assertEqual(sorted(results.rows), rows[1:])
# Test Client.insert_rows_from_pg
PCT.objects.create(code='ABC', name='CCG 1')
PCT.objects.create(code='XYZ', name='CCG 2')
def transformer(row):
return [ord(row[0][0]), row[1]]
t1.insert_rows_from_pg(PCT, ['code', 'name'], transformer)
self.assertEqual(sorted(t1.get_rows()), [(65, 'CCG 1'), (88, 'CCG 2')])
# Test Table.delete_all_rows
t1.delete_all_rows()
self.assertEqual(list(t1.get_rows()), [])
def test_the_lot(self):
client = Client("test")
archive_client = Client("archive")
orig_schema = build_schema(("a", "STRING"), ("b", "INTEGER"))
schema = build_schema(("a", "INTEGER"), ("b", "STRING"))
headers = ["a", "b"]
rows = [(1, "apple"), (2, "banana"), (3, "coconut")]
t1 = client.get_or_create_table("t1", orig_schema)
t1_qname = t1.qualified_name
# Test Table.insert_rows_from_csv
t1.insert_rows_from_csv("gcutils/tests/test_table.csv", schema)
self.assertEqual(sorted(t1.get_rows()), rows)
# Test Table.insert_rows_from_query
t2 = client.get_table("t2")
sql = "SELECT * FROM {} WHERE a > 1".format(t1_qname)
t2.insert_rows_from_query(sql)
self.assertEqual(sorted(t2.get_rows()), rows[1:])
# Test Client.query
sql = "SELECT * FROM {} WHERE a > 2".format(t1_qname)
results = client.query(sql)
self.assertEqual(sorted(results.rows), rows[2:])
# Test Client.query_into_dataframe
sql = "SELECT * FROM {} WHERE a > 2".format(t1_qname)
df = client.query_into_dataframe(sql)
self.assertEqual(df.values.tolist(), [list(rows[2])])
# Test TableExporter.export_to_storage and
# TableExporter.download_from_storage_and_unzip
t1_exporter = TableExporter(t1, self.storage_prefix + "test_table-")
t1_exporter.export_to_storage()
with tempfile.NamedTemporaryFile(mode="r+") as f:
t1_exporter.download_from_storage_and_unzip(f)
f.seek(0)
reader = csv.reader(f)
data = [next(reader)] + sorted(reader)
self.assertEqual(data,
[list(map(str, row)) for row in [headers] + rows])
# Test Table.insert_rows_from_storage
storage_path = self.storage_prefix + "test_table.csv"
self.upload_to_storage("gcutils/tests/test_table.csv", storage_path)
t2.insert_rows_from_storage(storage_path)
self.assertEqual(sorted(t2.get_rows()), rows)
# Test Client.create_storage_backed_table
storage_path = self.storage_prefix + "test_table_headers.csv"
self.upload_to_storage("gcutils/tests/test_table_headers.csv",
storage_path)
schema = build_schema(("a", "INTEGER"), ("b", "STRING"))
t3 = client.create_storage_backed_table("t3", schema, storage_path)
results = client.query("SELECT * FROM {}".format(t3.qualified_name))
self.assertEqual(sorted(results.rows), rows)
self.upload_to_storage("gcutils/tests/test_table_headers_2.csv",
storage_path)
results = client.query("SELECT * FROM {}".format(t3.qualified_name))
self.assertEqual(sorted(results.rows), rows + [(4, "damson")])
# Test Client.create_table_with_view
sql = "SELECT * FROM {{project}}.{} WHERE a > 1".format(t1_qname)
t4 = client.create_table_with_view("t4", sql, False)
results = client.query("SELECT * FROM {}".format(t4.qualified_name))
self.assertEqual(sorted(results.rows), rows[1:])
# Test Table.copy_to_new_dataset
t1.copy_to_new_dataset("archive")
t1_archived = archive_client.get_table("t1")
self.assertEqual(sorted(t1_archived.get_rows()), rows)
self.assertEqual(sorted(t1.get_rows()), rows)
# Test Table.move_to_new_dataset
t2.move_to_new_dataset("archive")
t2_archived = archive_client.get_table("t2")
self.assertEqual(sorted(t2_archived.get_rows()), rows)
with self.assertRaises(NotFound):
list(t2.get_rows())
# Test Client.insert_rows_from_pg
PCT.objects.create(code="ABC", name="CCG 1")
PCT.objects.create(code="XYZ", name="CCG 2")
def transformer(row):
return [ord(row[0][0]), row[1]]
t1.insert_rows_from_pg(
PCT,
build_schema(("code", "INTEGER"), ("name", "STRING")),
transformer=transformer,
)
self.assertEqual(sorted(t1.get_rows()), [(65, "CCG 1"), (88, "CCG 2")])
# Test Table.delete_all_rows
t1.delete_all_rows()
self.assertEqual(list(t1.get_rows()), [])