def test_staircase_history_naive(client, tsh):
# each days we insert 7 data points
from datetime import datetime
for idx, idate in enumerate(
pd.date_range(start=utcdt(2015, 1, 1),
end=utcdt(2015, 1, 4),
freq='D')):
series = genserie(start=idate.tz_convert(None), freq='H', repeat=7)
client.update('staircase-naive', series, 'Babar', insertion_date=idate)
series = client.staircase('staircase-naive',
pd.Timedelta(hours=3),
from_value_date=datetime(2015, 1, 1, 4),
to_value_date=datetime(2015, 1, 2, 5))
assert series.name == 'staircase-naive'
assert_df(
"""
2015-01-01 04:00:00 4.0
2015-01-01 05:00:00 5.0
2015-01-01 06:00:00 6.0
2015-01-02 03:00:00 3.0
2015-01-02 04:00:00 4.0
2015-01-02 05:00:00 5.0
""", series)
# series = client.staircase(
# 'staircase-naive',
# pd.Timedelta(hours=3),
# from_value_date=datetime(2015, 1, 1, 4),
# to_value_date=datetime(2015, 1, 2, 5)
# )
hist = client.history('staircase-naive')
assert len(hist) == 4
hist = client.history('staircase-naive',
from_insertion_date=datetime(2015, 1, 2),
to_insertion_date=datetime(2015, 1, 3))
assert len(hist) == 2
hist = client.history('staircase-naive',
from_value_date=datetime(2015, 1, 1, 3),
to_value_date=datetime(2015, 1, 2, 1))
assert all(series.name == 'staircase-naive' for series in hist.values())
assert_hist(
"""
insertion_date value_date
2015-01-01 00:00:00+00:00 2015-01-01 03:00:00 3.0
2015-01-01 04:00:00 4.0
2015-01-01 05:00:00 5.0
2015-01-01 06:00:00 6.0
2015-01-02 00:00:00+00:00 2015-01-01 03:00:00 3.0
2015-01-01 04:00:00 4.0
2015-01-01 05:00:00 5.0
2015-01-01 06:00:00 6.0
2015-01-02 00:00:00 0.0
2015-01-02 01:00:00 1.0
""", hist)
def test_custom_history(engine, tsh):
@func('made-up-series')
def madeup(base: int, coeff: float = 1.) -> pd.Series:
return pd.Series(np.array([base, base + 1, base + 2]) * coeff,
index=pd.date_range(dt(2019, 1, 1),
periods=3,
freq='D'))
@finder('made-up-series')
def find(cn, tsh, tree):
return {
tree[0]: {
'index_type': 'datetime64[ns]',
'index_dtype': '|M8[ns]',
'tzaware': False,
'value_type': 'float64',
'value_dtype': '<f8'
}
}
@history('made-up-series')
def madeup_history(__interpreter__, base, coeff):
hist = {}
for i in (1, 2, 3):
hist[pd.Timestamp(f'2020-1-{i}', tz='utc')] = pd.Series(
np.array([base, base + 1, base + 2]) * coeff,
index=pd.date_range(dt(2019, 1, i), periods=3, freq='D'))
return hist
tsh.register_formula(
engine, 'made-up',
'(+ 3 (add (made-up-series 1 #:coeff 2.) (made-up-series 2 #:coeff .5)))',
False)
assert_df("""
2019-01-01 6.0
2019-01-02 8.5
2019-01-03 11.0
""", tsh.get(engine, 'made-up'))
hist = tsh.history(engine, 'made-up')
assert_hist(
"""
insertion_date value_date
2020-01-01 00:00:00+00:00 2019-01-01 6.0
2019-01-02 8.5
2019-01-03 11.0
2020-01-02 00:00:00+00:00 2019-01-01 6.0
2019-01-02 8.5
2019-01-03 11.0
2020-01-03 00:00:00+00:00 2019-01-01 6.0
2019-01-02 8.5
2019-01-03 11.0
""", hist)
def test_local_formula_remote_series(mapi):
rtsh = timeseries('test-mapi-2')
rtsh.update(
mapi.engine,
pd.Series(
[1, 2, 3],
index=pd.date_range(pd.Timestamp('2020-1-1'), periods=3, freq='H'),
),
'remote-series',
'Babar',
insertion_date=pd.Timestamp('2020-1-1', tz='UTC')
)
mapi.register_formula(
'test-localformula-remoteseries',
'(+ 1 (series "remote-series"))'
)
ts = mapi.get('test-localformula-remoteseries')
assert_df("""
2020-01-01 00:00:00 2.0
2020-01-01 01:00:00 3.0
2020-01-01 02:00:00 4.0
""", ts)
hist = mapi.history('test-localformula-remoteseries')
assert_hist("""
insertion_date value_date
2020-01-01 00:00:00+00:00 2020-01-01 00:00:00 2.0
2020-01-01 01:00:00 3.0
2020-01-01 02:00:00 4.0
""", hist)
f = mapi.formula('test-localformula-remoteseries')
assert f == '(+ 1 (series "remote-series"))'
none = mapi.formula('nosuchformula')
assert none is None
# altsource formula
rtsh.register_formula(
mapi.engine,
'remote-formula-remote-series',
'(+ 2 (series "remote-series"))'
)
f = mapi.formula('remote-formula-remote-series')
assert f == '(+ 2 (series "remote-series"))'
assert_df("""
2020-01-01 00:00:00 3.0
2020-01-01 01:00:00 4.0
2020-01-01 02:00:00 5.0
""", mapi.get('remote-formula-remote-series'))
def test_base_universal_api(pgapi, httpapi):
series = pd.Series([1, 2, 3],
index=pd.date_range(utcdt(2020, 1, 1),
periods=3,
freq='D'))
pgapi.update('api-test',
series,
'Babar',
insertion_date=utcdt(2019, 1, 1),
metadata={'about': 'test'})
out = httpapi.get('api-test')
assert_df(
"""
2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
""", out)
series[utcdt(2020, 1, 4)] = 4
pgapi.update('api-test', series, 'Babar', insertion_date=utcdt(2019, 1, 2))
out = pgapi.get('api-test',
from_value_date=utcdt(2020, 1, 2),
to_value_date=utcdt(2020, 1, 3))
assert_df(
"""
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
""", out)
series[utcdt(2019, 12, 31)] = 0
pgapi.replace('api-test',
series,
'Babar',
insertion_date=utcdt(2019, 1, 3))
out = httpapi.get('api-test')
assert_df(
"""
2019-12-31 00:00:00+00:00 0.0
2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2020-01-04 00:00:00+00:00 4.0
""", out)
assert httpapi.type('api-test') == pgapi.type('api-test')
assert httpapi.interval('api-test') == pgapi.interval('api-test')
out = httpapi.get('api-test', revision_date=utcdt(2019, 1, 1))
assert_df(
"""
2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
""", out)
hist = httpapi.history('api-test')
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2019-01-02 00:00:00+00:00 2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2020-01-04 00:00:00+00:00 4.0
2019-01-03 00:00:00+00:00 2019-12-31 00:00:00+00:00 0.0
2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2020-01-04 00:00:00+00:00 4.0
""", hist)
hist = pgapi.history('api-test', diffmode=True)
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2020-01-01 00:00:00+00:00 1.0
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2019-01-02 00:00:00+00:00 2020-01-04 00:00:00+00:00 4.0
2019-01-03 00:00:00+00:00 2019-12-31 00:00:00+00:00 0.0
""", hist)
assert pgapi.exists('api-test')
assert not pgapi.exists('i-dont-exist')
assert httpapi.exists('api-test')
assert not httpapi.exists('i-dont-exist')
ival = pgapi.interval('api-test')
assert ival.left == pd.Timestamp('2019-12-31 00:00:00+0000', tz='UTC')
assert ival.right == pd.Timestamp('2020-01-04 00:00:00+0000', tz='UTC')
meta = pgapi.metadata('api-test', all=True)
assert meta == {
'tzaware': True,
'index_type': 'datetime64[ns, UTC]',
'value_type': 'float64',
'index_dtype': '|M8[ns]',
'value_dtype': '<f8'
}
meta = httpapi.metadata('api-test')
assert meta == {}
pgapi.update_metadata('api-test', {'desc': 'a metadata test'})
meta = pgapi.metadata('api-test')
assert meta == {'desc': 'a metadata test'}
assert pgapi.type('api-test') == 'primary'
st = pgapi.staircase('api-test', delta=timedelta(days=366))
assert_df(
"""
2020-01-02 00:00:00+00:00 2.0
2020-01-03 00:00:00+00:00 3.0
2020-01-04 00:00:00+00:00 4.0
""", st)
pgapi.rename('api-test', 'api-test2')
assert pgapi.exists('api-test2')
assert not pgapi.exists('api-test')
assert httpapi.exists('api-test2')
assert not httpapi.exists('api-test')
pgapi.delete('api-test2')
assert not pgapi.exists('api-test')
assert not httpapi.exists('api-test')
pgapi.delete('api-test2')
def test_local_formula_remote_series(mapihttp, engine):
from tshistory_formula.tsio import timeseries as pgseries
mapi = mapihttp
assert repr(mapi) == (
'timeseries(uri=postgresql://localhost:5433/postgres,'
'ns=ns-test-local,'
'sources=[source(uri=http://test-uri2,ns=ns-test-remote), '
'source(uri=http://unavailable,ns=ns-test-unavailable-remote)])')
assert len(mapi.othersources.sources) == 2
assert mapi.namespace == 'ns-test-local'
assert mapi.uri == 'postgresql://localhost:5433/postgres'
assert mapi.othersources.sources[0].namespace == 'ns-test-remote'
assert mapi.othersources.sources[0].uri == 'http://test-uri2'
series = pd.Series(
[1, 2, 3],
index=pd.date_range(pd.Timestamp('2020-1-1'), periods=3, freq='H'),
)
mapi.update('local-series', series, 'Babar')
rtsh = pgseries('ns-test-remote')
rtsh.update(engine,
series,
'remote-series',
'Celeste',
insertion_date=pd.Timestamp('2020-1-1', tz='UTC'))
cat = mapi.catalog(allsources=True)
assert dict(cat) == {
('db://localhost:5433/postgres', 'ns-test-local'):
[('local-series', 'primary')],
('db://localhost:5433/postgres', 'ns-test-remote'):
[['remote-series', 'primary']]
}
mapi.register_formula('test-localformula-remoteseries',
'(+ 1 (series "remote-series"))')
ts = mapi.get('test-localformula-remoteseries')
assert_df(
"""
2020-01-01 00:00:00 2.0
2020-01-01 01:00:00 3.0
2020-01-01 02:00:00 4.0
""", ts)
hist = mapi.history('test-localformula-remoteseries')
assert_hist(
"""
insertion_date value_date
2020-01-01 00:00:00+00:00 2020-01-01 00:00:00 2.0
2020-01-01 01:00:00 3.0
2020-01-01 02:00:00 4.0
""", hist)
ival = mapi.interval('remote-series')
assert (ival.left, ival.right) == (pd.Timestamp('2020-01-01 00:00:00'),
pd.Timestamp('2020-01-01 02:00:00'))
with pytest.raises(ValueError) as err:
mapi.interval('test-localformula-remoteseries')
assert err.value.args[
0] == 'no interval for series: test-localformula-remoteseries'
meta = mapi.metadata('remote-series', all=True)
assert meta == {
'index_dtype': '<M8[ns]',
'index_type': 'datetime64[ns]',
'tzaware': False,
'value_dtype': '<f8',
'value_type': 'float64'
}
meta = mapi.metadata('test-localformula-remoteseries', all=True)
assert meta == {
'index_dtype': '<M8[ns]',
'index_type': 'datetime64[ns]',
'tzaware': False,
'value_dtype': '<f8',
'value_type': 'float64'
}
def test_ifunc(engine, tsh):
@func('shifted')
def shifted(__interpreter__, name: str, days: int = 0) -> pd.Series:
args = __interpreter__.getargs.copy()
fromdate = args.get('from_value_date')
todate = args.get('to_value_date')
if fromdate:
args['from_value_date'] = fromdate + timedelta(days=days)
if todate:
args['to_value_date'] = todate + timedelta(days=days)
return __interpreter__.get(name, args)
@finder('shifted')
def find_series(cn, tsh, stree):
return {stree[1]: tsh.metadata(cn, stree[1])}
tsh.register_formula(engine, 'shifting',
'(+ 0 (shifted "shiftme" #:days -1))', False)
ts = pd.Series([1, 2, 3, 4, 5],
index=pd.date_range(dt(2019, 1, 1), periods=5, freq='D'))
tsh.update(engine,
ts,
'shiftme',
'Babar',
insertion_date=utcdt(2019, 1, 1))
ts = tsh.get(engine, 'shifting')
assert_df(
"""
2019-01-01 1.0
2019-01-02 2.0
2019-01-03 3.0
2019-01-04 4.0
2019-01-05 5.0
""", ts)
ts = tsh.get(engine,
'shifting',
from_value_date=dt(2019, 1, 3),
to_value_date=dt(2019, 1, 4))
assert_df("""
2019-01-02 2.0
2019-01-03 3.0
""", ts)
# now, history
ts = pd.Series([1, 2, 3, 4, 5],
index=pd.date_range(dt(2019, 1, 2), periods=5, freq='D'))
tsh.update(engine,
ts,
'shiftme',
'Babar',
insertion_date=utcdt(2019, 1, 2))
hist = tsh.history(engine, 'shifting')
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2019-01-01 1.0
2019-01-02 2.0
2019-01-03 3.0
2019-01-04 4.0
2019-01-05 5.0
2019-01-02 00:00:00+00:00 2019-01-01 1.0
2019-01-02 1.0
2019-01-03 2.0
2019-01-04 3.0
2019-01-05 4.0
2019-01-06 5.0
""", hist)
hist = tsh.history(engine,
'shifting',
from_value_date=dt(2019, 1, 3),
to_value_date=dt(2019, 1, 4))
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2019-01-03 3.0
2019-01-04 4.0
2019-01-02 00:00:00+00:00 2019-01-03 2.0
2019-01-04 3.0
""", hist)
# cleanup
FUNCS.pop('shifted')
def test_history(engine, tsh):
tsh.register_formula(engine, 'h-addition',
'(add (series "ha") (series "hb"))', False)
for day in (1, 2, 3):
idate = utcdt(2019, 1, day)
for name in 'ab':
ts = pd.Series([day] * 3,
index=pd.date_range(dt(2018, 1, 1),
periods=3,
freq='D'))
tsh.update(engine, ts, 'h' + name, 'Babar', insertion_date=idate)
h = tsh.history(engine, 'h-addition')
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2018-01-01 2.0
2018-01-02 2.0
2018-01-03 2.0
2019-01-02 00:00:00+00:00 2018-01-01 4.0
2018-01-02 4.0
2018-01-03 4.0
2019-01-03 00:00:00+00:00 2018-01-01 6.0
2018-01-02 6.0
2018-01-03 6.0
""", h)
dates = tsh.insertion_dates(engine, 'h-addition')
assert dates == [(1, pd.Timestamp('2019-01-01 00:00:00+0000', tz='UTC')),
(2, pd.Timestamp('2019-01-02 00:00:00+0000', tz='UTC')),
(3, pd.Timestamp('2019-01-03 00:00:00+0000', tz='UTC'))]
h = tsh.history(engine,
'h-addition',
from_insertion_date=utcdt(2019, 1, 2),
to_insertion_date=utcdt(2019, 1, 2),
from_value_date=dt(2018, 1, 2),
to_value_date=dt(2018, 1, 2))
assert_hist(
"""
insertion_date value_date
2019-01-02 00:00:00+00:00 2018-01-02 4.0
""", h)
# let's add a priority
tsh.register_formula(engine, 'h-priority',
'(priority (series "hz") (series "h-addition"))',
False)
for day in (1, 2, 3):
idate = utcdt(2019, 1, day)
ts = pd.Series([41 + day] * 3,
index=pd.date_range(dt(2018, 1, 3), periods=3,
freq='D'))
tsh.update(engine, ts, 'hz', 'Babar', insertion_date=idate)
h = tsh.history(engine, 'h-priority')
assert_hist(
"""
insertion_date value_date
2019-01-01 00:00:00+00:00 2018-01-01 2.0
2018-01-02 2.0
2018-01-03 42.0
2018-01-04 42.0
2018-01-05 42.0
2019-01-02 00:00:00+00:00 2018-01-01 4.0
2018-01-02 4.0
2018-01-03 43.0
2018-01-04 43.0
2018-01-05 43.0
2019-01-03 00:00:00+00:00 2018-01-01 6.0
2018-01-02 6.0
2018-01-03 44.0
2018-01-04 44.0
2018-01-05 44.0
""", h)
def test_base(client):
# insert
series_in = genserie(utcdt(2018, 1, 1), 'H', 3)
res = client.patch('/series/state',
params={
'name': 'test',
'series': util.tojson(series_in),
'author': 'Babar',
'insertion_date': utcdt(2018, 1, 1, 10),
'tzaware': util.tzaware_serie(series_in)
})
assert res.status_code == 201
# catalog
res = client.get('/series/catalog')
assert res.status_code == 200
assert res.json == {
'db://localhost:5433/postgres!tsh': [['test-naive', 'primary'],
['test', 'primary']]
}
# metadata
res = client.get('/series/metadata?name=test')
meta = res.json
assert meta == {}
res = client.get('/series/metadata?name=test&all=1')
meta = res.json
assert meta == {
'index_dtype': '|M8[ns]',
'index_type': 'datetime64[ns, UTC]',
'tzaware': True,
'value_dtype': '<f8',
'value_type': 'float64'
}
res = client.put('/series/metadata',
params={
'metadata':
json.dumps({
'freq': 'D',
'description': 'banana spot price'
}),
'name':
'test'
})
assert res.status_code == 200
res = client.get('/series/metadata?name=test')
meta2 = res.json
assert meta2 == {'freq': 'D', 'description': 'banana spot price'}
# metadata: delete by uploading an empty dict
res = client.put('/series/metadata',
params={
'metadata': json.dumps({}),
'name': 'test'
})
assert res.status_code == 200
res = client.get('/series/metadata?name=test')
meta2 = res.json
assert meta2 == {}
# get
res = client.get('/series/state?name=test')
series = util.fromjson(res.body, 'test', meta['tzaware'])
assert_df(
"""
2018-01-01 00:00:00+00:00 0.0
2018-01-01 01:00:00+00:00 1.0
2018-01-01 02:00:00+00:00 2.0
""", series)
# reinsert
series_in = genserie(utcdt(2018, 1, 1, 3), 'H', 1, [3])
res = client.patch('/series/state',
params={
'name': 'test',
'series': util.tojson(series_in),
'author': 'Babar',
'insertion_date': utcdt(2018, 1, 1, 13),
'tzaware': util.tzaware_serie(series_in)
})
assert res.status_code == 200
res = client.get('/series/state?name=test')
series = util.fromjson(res.body, 'test', meta['tzaware'])
assert_df(
"""
2018-01-01 00:00:00+00:00 0.0
2018-01-01 01:00:00+00:00 1.0
2018-01-01 02:00:00+00:00 2.0
2018-01-01 03:00:00+00:00 3.0
""", series)
# checkout a past state
res = client.get('/series/state',
params={
'name': 'test',
'insertion_date': utcdt(2018, 1, 1, 10)
})
series = util.fromjson(res.body, 'test', meta['tzaware'])
assert_df(
"""
2018-01-01 00:00:00+00:00 0.0
2018-01-01 01:00:00+00:00 1.0
2018-01-01 02:00:00+00:00 2.0
""", series)
# checkout too far in the past
res = client.get('/series/state',
params={
'name': 'test',
'insertion_date': utcdt(2018, 1, 1, 0)
})
assert res.json is None
# history
res = client.get('/series/history?name=test')
df = pd.read_json(res.body)
# we real client would need to handle timestamp
# tz-awareness
assert_df(
"""
2018-01-01 10:00:00 2018-01-01 13:00:00
2018-01-01 00:00:00 0.0 0
2018-01-01 01:00:00 1.0 1
2018-01-01 02:00:00 2.0 2
2018-01-01 03:00:00 NaN 3
""", df)
res = client.get('/series/history?name=test&format=tshpack')
meta, hist = util.unpack_history(res.body)
assert_hist(
"""
insertion_date value_date
2018-01-01 10:00:00+00:00 2018-01-01 00:00:00+00:00 0.0
2018-01-01 01:00:00+00:00 1.0
2018-01-01 02:00:00+00:00 2.0
2018-01-01 13:00:00+00:00 2018-01-01 00:00:00+00:00 0.0
2018-01-01 01:00:00+00:00 1.0
2018-01-01 02:00:00+00:00 2.0
2018-01-01 03:00:00+00:00 3.0
""", hist)
# diff mode
res = client.get('/series/history',
params={
'name': 'test',
'diffmode': True
})
df = pd.read_json(res.body)
assert_df(
"""
2018-01-01 10:00:00 2018-01-01 13:00:00
2018-01-01 00:00:00 0.0 NaN
2018-01-01 01:00:00 1.0 NaN
2018-01-01 02:00:00 2.0 NaN
2018-01-01 03:00:00 NaN 3.0
""", df)
# empty range
res = client.get('/series/history',
params={
'name': 'test',
'from_insertion_date': utcdt(2018, 1, 1, 11),
'to_insertion_date': utcdt(2018, 1, 1, 12),
})
df = pd.read_json(res.body)
assert len(df) == 0
# insertion dates subset
res = client.get('/series/history',
params={
'name': 'test',
'from_insertion_date': utcdt(2018, 1, 1, 10),
'to_insertion_date': utcdt(2018, 1, 1, 12),
})
df = pd.read_json(res.body)
assert_df(
"""
2018-01-01 10:00:00
2018-01-01 00:00:00 0
2018-01-01 01:00:00 1
2018-01-01 02:00:00 2
""", df)
# value dates subset
res = client.get('/series/history',
params={
'name': 'test',
'from_value_date': utcdt(2018, 1, 1, 2),
'to_value_date': utcdt(2018, 1, 1, 3),
})
df = pd.read_json(res.body)
assert_df(
"""
2018-01-01 10:00:00 2018-01-01 13:00:00
2018-01-01 02:00:00 2.0 2
2018-01-01 03:00:00 NaN 3
""", df)
# state/get from from/to value date restriction
res = client.get('/series/state',
params={
'name': 'test',
'from_value_date': utcdt(2018, 1, 1, 1),
'to_value_date': utcdt(2018, 1, 1, 2)
})
assert res.json == {
'2018-01-01T01:00:00.000Z': 1.0,
'2018-01-01T02:00:00.000Z': 2.0
}
series_in = genserie(utcdt(2019, 1, 1), 'H', 3)
res = client.patch('/series/state',
params={
'name': 'test',
'series': util.tojson(series_in),
'author': 'Babar',
'insertion_date': utcdt(2018, 1, 2),
'tzaware': util.tzaware_serie(series_in),
'replace': True
})
assert res.status_code == 200
res = client.get('/series/state', params={'name': 'test'})
assert res.json == {
'2019-01-01T00:00:00.000Z': 0.0,
'2019-01-01T01:00:00.000Z': 1.0,
'2019-01-01T02:00:00.000Z': 2.0
}
res = client.get('/series/metadata',
params={
'name': 'test',
'type': 'interval'
})
assert res.json == [
True, '2019-01-01T00:00:00+00:00', '2019-01-01T02:00:00+00:00'
]
res = client.get('/series/metadata',
params={
'name': 'test',
'type': 'type'
})
assert res.json == 'primary'