def run_end_to_end():
print('BQ_NONCE: {}'.format(settings.BQ_NONCE))
call_command('migrate')
path = os.path.join(settings.APPS_ROOT, 'frontend', 'management',
'commands', 'measure_definitions')
# No MeasureGlobals or MeasureValues are generated for the ghost branded
# generics measure, because both numerator and denominator are computed
# from a view (vw__ghost_generic_measure) which has no data. Rather than
# populate this view, it is simpler to pretend it doesn't exist.
num_measures = len(os.listdir(path)) - 1
shutil.rmtree(settings.PIPELINE_DATA_BASEDIR, ignore_errors=True)
with open(settings.PIPELINE_IMPORT_LOG_PATH, 'w') as f:
f.write('{}')
for blob in StorageClient().bucket().list_blobs():
blob.delete()
for dataset_key in DATASETS:
BQClient(dataset_key).create_dataset()
client = BQClient('hscic')
client.create_table('bnf', schemas.BNF_SCHEMA)
client.create_table('ccgs', schemas.CCG_SCHEMA)
client.create_table('ppu_savings', schemas.PPU_SAVING_SCHEMA)
client.create_table(
'practice_statistics',
schemas.PRACTICE_STATISTICS_SCHEMA
)
client.create_table(
'practice_statistics_all_years',
schemas.PRACTICE_STATISTICS_SCHEMA
)
client.create_table('practices', schemas.PRACTICE_SCHEMA)
client.create_table('prescribing', schemas.PRESCRIBING_SCHEMA)
client.create_table('presentation', schemas.PRESENTATION_SCHEMA)
client.create_table('tariff', schemas.TARIFF_SCHEMA)
client.create_table('bdz_adq', schemas.BDZ_ADQ_SCHEMA)
client = BQClient('measures')
# This is enough of a schema to allow the practice_data_all_low_priority
# table to be created, since it references these fields. Once populated by
# import_measures, the tables in the measures dataset will have several
# more fields. But we don't need to specify exactly what they are, as BQ
# will work it out when the data is inserted with insert_rows_from_query.
measures_schema = build_schema(
('month', 'DATE'),
('practice_id', 'STRING'),
('numerator', 'INTEGER'),
('denominator', 'INTEGER'),
)
path = os.path.join(settings.APPS_ROOT, 'frontend', 'management',
'commands', 'measure_definitions', '*.json')
for path in glob.glob(path):
measure_id = os.path.splitext(os.path.basename(path))[0]
client.create_table('practice_data_' + measure_id, measures_schema)
client.create_table('ccg_data_' + measure_id, measures_schema)
client.create_table('global_data_' + measure_id, measures_schema)
# Although there are no model instances, we call upload_model to create the
# tables in BQ that might be required by certain measure views.
client = BQClient('dmd')
client.upload_model(TariffPrice)
client.upload_model(VMPP)
call_command('generate_presentation_replacements')
path = os.path.join(settings.APPS_ROOT, 'frontend', 'management',
'commands', 'replace_matviews.sql')
with open(path) as f:
with connection.cursor() as c:
c.execute(f.read())
copy_tree(
os.path.join(e2e_path, 'data-1'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 9, under_test=True)
# We expect one MeasureGlobal per measure per month.
assert_count_equal(num_measures, MeasureGlobal)
# We expect one MeasureValue for each organisation per measure per month
# (There are 4 practices, 2 CCGs, 2 STPs, and 2 regional teams).
assert_count_equal(10 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(2, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(2, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(4, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(1, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(2, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")
copy_tree(
os.path.join(e2e_path, 'data-2'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 10, under_test=True)
# We expect one MeasureGlobal per measure per month
assert_count_equal(2 * num_measures, MeasureGlobal)
# We expect one MeasureValue for each organisation per measure per month
assert_count_equal(20 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(4, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(8, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(8, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(2, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(4, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")
def run_end_to_end():
print('BQ_NONCE: {}'.format(settings.BQ_NONCE))
num_measures = 56
shutil.rmtree(settings.PIPELINE_DATA_BASEDIR, ignore_errors=True)
with open(settings.PIPELINE_IMPORT_LOG_PATH, 'w') as f:
f.write('{}')
for blob in StorageClient().bucket().list_blobs():
blob.delete()
for dataset_key in DATASETS:
BQClient(dataset_key).create_dataset()
client = BQClient('hscic')
client.create_table('bnf', schemas.BNF_SCHEMA)
client.create_table('ccgs', schemas.CCG_SCHEMA)
client.create_table('ppu_savings', schemas.PPU_SAVING_SCHEMA)
client.create_table(
'practice_statistics',
schemas.PRACTICE_STATISTICS_SCHEMA
)
client.create_table(
'practice_statistics_all_years',
schemas.PRACTICE_STATISTICS_SCHEMA
)
client.create_table('practices', schemas.PRACTICE_SCHEMA)
client.create_table('prescribing', schemas.PRESCRIBING_SCHEMA)
client.create_table('presentation', schemas.PRESENTATION_SCHEMA)
client.create_table('tariff', schemas.TARIFF_SCHEMA)
call_command('generate_presentation_replacements')
path = os.path.join(settings.SITE_ROOT, 'frontend', 'management',
'commands', 'replace_matviews.sql')
with open(path) as f:
with connection.cursor() as c:
c.execute(f.read())
copy_tree(
os.path.join(e2e_path, 'data-1'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 9, under_test=True)
# We expect one MeasureGlobal per measure per month. If this assert fails,
# check that num_measures is still correct.
assert_count_equal(num_measures, MeasureGlobal)
# We expect one MeasureValue for each CCG or Practice per measure per month
assert_count_equal(6 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(2, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(2, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(4, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(1, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(2, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")
copy_tree(
os.path.join(e2e_path, 'data-2'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 10, under_test=True)
# We expect one MeasureGlobal per measure per month
assert_count_equal(2 * num_measures, MeasureGlobal)
# We expect one MeasureValue for each CCG or Practice per measure per month
assert_count_equal(12 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(4, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(8, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(8, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(2, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(4, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")
def run_end_to_end():
print("BQ_NONCE: {}".format(settings.BQ_NONCE))
call_command("migrate")
# No MeasureGlobals or MeasureValues are generated for the ghost branded
# generics measure, because both numerator and denominator are computed
# from a view (vw__ghost_generic_measure) which has no data. Rather than
# populate this view, it is simpler to pretend it doesn't exist.
num_measures = (len(
glob.glob(os.path.join(settings.MEASURE_DEFINITIONS_PATH, "*.json"))) -
1)
shutil.rmtree(settings.PIPELINE_DATA_BASEDIR, ignore_errors=True)
with open(settings.PIPELINE_IMPORT_LOG_PATH, "w") as f:
f.write("{}")
for blob in StorageClient().bucket().list_blobs():
blob.delete()
for dataset_key in DATASETS:
BQClient(dataset_key).create_dataset()
client = BQClient("hscic")
client.create_table("bnf", schemas.BNF_SCHEMA)
client.create_table("ccgs", schemas.CCG_SCHEMA)
client.create_table("stps", schemas.STP_SCHEMA)
client.create_table("regional_teams", schemas.REGIONAL_TEAM_SCHEMA)
client.create_table("ppu_savings", schemas.PPU_SAVING_SCHEMA)
client.create_table("practice_statistics",
schemas.PRACTICE_STATISTICS_SCHEMA)
client.create_table("practice_statistics_all_years",
schemas.PRACTICE_STATISTICS_SCHEMA)
client.create_table("practices", schemas.PRACTICE_SCHEMA)
client.create_table("prescribing", schemas.PRESCRIBING_SCHEMA)
client.create_table("presentation", schemas.PRESENTATION_SCHEMA)
client.create_table("tariff", schemas.TARIFF_SCHEMA)
client.create_table("bdz_adq", schemas.BDZ_ADQ_SCHEMA)
client = BQClient("measures")
# This is enough of a schema to allow the practice_data_all_low_priority
# table to be created, since it references these fields. Once populated by
# import_measures, the tables in the measures dataset will have several
# more fields. But we don't need to specify exactly what they are, as BQ
# will work it out when the data is inserted with insert_rows_from_query.
measures_schema = build_schema(
("month", "DATE"),
("practice_id", "STRING"),
("numerator", "INTEGER"),
("denominator", "INTEGER"),
)
for path in glob.glob(
os.path.join(settings.MEASURE_DEFINITIONS_PATH, "*.json")):
measure_id = os.path.splitext(os.path.basename(path))[0]
client.create_table("practice_data_" + measure_id, measures_schema)
client.create_table("ccg_data_" + measure_id, measures_schema)
client.create_table("global_data_" + measure_id, measures_schema)
# Although there are no model instances, we call upload_model to create the
# dm+d tables in BQ that are required by certain measure views.
client = BQClient("dmd")
for model in apps.get_app_config("dmd2").get_models():
client.upload_model(model)
call_command("generate_presentation_replacements")
copy_tree(os.path.join(e2e_path, "data-1"), os.path.join(e2e_path, "data"))
runner.run_all(2017, 9, under_test=True)
# We expect one MeasureGlobal per measure per month.
assert_count_equal(num_measures, MeasureGlobal)
# We expect one MeasureValue for each organisation per measure per month
# (There are 4 practices, 2 CCGs, 2 STPs, and 2 regional teams).
assert_count_equal(10 * num_measures, MeasureValue)
copy_tree(os.path.join(e2e_path, "data-2"), os.path.join(e2e_path, "data"))
runner.run_all(2017, 10, under_test=True)
# We expect one MeasureGlobal per measure per month
assert_count_equal(2 * num_measures, MeasureGlobal)
# We expect one MeasureValue for each organisation per measure per month
assert_count_equal(20 * num_measures, MeasureValue)
def run_end_to_end():
print('BQ_NONCE: {}'.format(settings.BQ_NONCE))
num_measures = 57
shutil.rmtree(settings.PIPELINE_DATA_BASEDIR, ignore_errors=True)
with open(settings.PIPELINE_IMPORT_LOG_PATH, 'w') as f:
f.write('{}')
for blob in StorageClient().bucket().list_blobs():
blob.delete()
for dataset_key in DATASETS:
BQClient(dataset_key).create_dataset()
client = BQClient('hscic')
client.create_table('bnf', schemas.BNF_SCHEMA)
client.create_table('ccgs', schemas.CCG_SCHEMA)
client.create_table('ppu_savings', schemas.PPU_SAVING_SCHEMA)
client.create_table('practice_statistics',
schemas.PRACTICE_STATISTICS_SCHEMA)
client.create_table('practice_statistics_all_years',
schemas.PRACTICE_STATISTICS_SCHEMA)
client.create_table('practices', schemas.PRACTICE_SCHEMA)
client.create_table('prescribing', schemas.PRESCRIBING_SCHEMA)
client.create_table('presentation', schemas.PRESENTATION_SCHEMA)
client.create_table('tariff', schemas.TARIFF_SCHEMA)
client = BQClient('measures')
# This is enough of a schema to allow the practice_data_all_low_priority
# table to be created, since it references these fields. Once populated by
# import_measures, the tables in the measures dataset will have several
# more fields. But we don't need to specify exactly what they are, as BQ
# will work it out when the data is inserted with insert_rows_from_query.
measures_schema = build_schema(
('month', 'DATE'),
('practice_id', 'STRING'),
('numerator', 'INTEGER'),
('denominator', 'INTEGER'),
)
path = os.path.join(settings.SITE_ROOT, 'frontend', 'management',
'commands', 'measure_definitions', '*.json')
for path in glob.glob(path):
measure_id = os.path.splitext(os.path.basename(path))[0]
client.create_table('practice_data_' + measure_id, measures_schema)
client.create_table('ccg_data_' + measure_id, measures_schema)
client.create_table('global_data_' + measure_id, measures_schema)
call_command('generate_presentation_replacements')
path = os.path.join(settings.SITE_ROOT, 'frontend', 'management',
'commands', 'replace_matviews.sql')
with open(path) as f:
with connection.cursor() as c:
c.execute(f.read())
copy_tree(
os.path.join(e2e_path, 'data-1'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 9, under_test=True)
# We expect one MeasureGlobal per measure per month. If this assert fails,
# check that num_measures is still correct.
assert_count_equal(num_measures, MeasureGlobal)
# We expect one MeasureValue for each CCG or Practice per measure per month
assert_count_equal(6 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(2, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(2, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(4, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(1, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(2, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")
copy_tree(
os.path.join(e2e_path, 'data-2'),
os.path.join(e2e_path, 'data'),
)
runner.run_all(2017, 10, under_test=True)
# We expect one MeasureGlobal per measure per month
assert_count_equal(2 * num_measures, MeasureGlobal)
# We expect one MeasureValue for each CCG or Practice per measure per month
assert_count_equal(12 * num_measures, MeasureValue)
# We expect one statistic per CCG per month
assert_raw_count_equal(4, 'vw__ccgstatistics')
# We expect one chemical summary per CCG per month
assert_raw_count_equal(4, 'vw__chemical_summary_by_ccg',
"chemical_id = '1001030C0'")
# We expect one chemical summary per practice per month
assert_raw_count_equal(8, 'vw__chemical_summary_by_practice',
"chemical_id = '1001030C0'")
# We expect one summary per practice per month
assert_raw_count_equal(8, 'vw__practice_summary')
# We expect one presentation summary per month
assert_raw_count_equal(2, 'vw__presentation_summary',
"presentation_code = '1001030C0AAAAAA'")
# We expect one presentation summary per CCG per month
assert_raw_count_equal(4, 'vw__presentation_summary_by_ccg',
"presentation_code = '1001030C0AAAAAA'")