def time_series_in_cdp():
global TEST_TS_1_ID, TEST_TS_2_ID
client = CogniteClient()
try:
ts_list = [TimeSeries(name=TEST_TS_1_NAME)]
client.time_series.post_time_series(ts_list)
log.warning("Posted sdk test time series 1")
client.datapoints.post_datapoints(
name=TEST_TS_1_NAME,
datapoints=[Datapoint(timestamp=i, value=i) for i in range(TEST_TS_START, TEST_TS_END, int(3.6e6))],
)
log.warning("Posted datapoints to sdk test time series 1")
TEST_TS_1_ID = client.time_series.get_time_series(prefix=TEST_TS_1_NAME).to_json()[0]["id"]
except APIError as e:
log.warning("Posting test time series 1 failed with code {}".format(e.code))
try:
ts_list = [TimeSeries(name=TEST_TS_2_NAME)]
client.time_series.post_time_series(ts_list)
log.warning("Posted sdk test time series 2")
client.datapoints.post_datapoints(
name=TEST_TS_2_NAME,
datapoints=[Datapoint(timestamp=i, value=i) for i in range(TEST_TS_START, TEST_TS_END, int(3.6e6))],
)
log.warning("Posted datapoints to sdk test time series 2")
TEST_TS_2_ID = client.time_series.get_time_series(prefix=TEST_TS_2_NAME).to_json()[0]["id"]
except APIError as e:
log.warning("Posting test time series 2 failed with code {}".format(e.code))
TEST_TS_1_ID = client.time_series.get_time_series(prefix=TEST_TS_1_NAME).to_json()[0]["id"]
TEST_TS_2_ID = client.time_series.get_time_series(prefix=TEST_TS_2_NAME).to_json()[0]["id"]
yield TEST_TS_1_ID, TEST_TS_2_ID
def post_data(df):
prefix = "".join(
random.choice(string.ascii_uppercase + string.digits)
for _ in range(5))
ts_names = {
"x": "tutorial_{}_x".format(prefix),
"y": "tutorial_{}_y".format(prefix)
}
df.rename(columns=ts_names, inplace=True)
client = CogniteClient()
client.time_series.post_time_series(
[TimeSeries(name=name) for name in df.columns if name != "timestamp"])
client.time_series.post_time_series(
[TimeSeries(name="tutorial_{}_mean_x_y".format(prefix))])
created_time_series = []
while len(created_time_series) != 3:
created_time_series = client.time_series.get_time_series(
prefix="tutorial_" + prefix)
sleep(0.5)
client.datapoints.post_datapoints_frame(df)
ts_ids = {ts.name.split("_", 2)[-1]: ts.id for ts in created_time_series}
return ts_ids
def post_data(df):
time_series_to_post = [TimeSeries(name=name) for name in df.columns if name != "timestamp"]
# Create a time series for the prediction output as well
time_series_to_post.append(TimeSeries(name="{}_predicted_prod_rate".format(prefix)))
client.time_series.post_time_series(time_series_to_post)
created_time_series = []
while len(created_time_series) != 5:
created_time_series = client.time_series.get_time_series(prefix=prefix)
sleep(0.5)
client.datapoints.post_datapoints_frame(df)
ts_dict = {"_".join(ts.name.split("_")[1:]): ts.id for ts in created_time_series}
print(ts_dict)
def new_ts_id():
name = "test_ts_{}".format(randint(1, 2 ** 53 - 1))
stable_time_series.post_time_series([TimeSeries(name)])
res = stable_time_series.get_time_series(prefix=name)
while len(res) == 0:
res = stable_time_series.get_time_series(prefix=name)
sleep(0.5)
yield res[0].id
def test_post_datapoints_frame(self):
data = pd.DataFrame()
data["timestamp"] = [int(1537208777557 + 1000 * i) for i in range(0, 100)]
X = data["timestamp"].values.astype(float)
data["X"] = X ** 2
data["Y"] = 1.0 / (1 + X)
for name in data.drop(["timestamp"], axis=1).columns:
ts = TimeSeries(name=name, description="To be deleted")
try:
client.time_series.post_time_series([ts])
except:
pass
res = client.datapoints.post_datapoints_frame(data)
assert res is None
def update_cdp_timeseries(client,
name,
metadata,
asset_name=None,
asset_id=None):
"""Insert timeseries metadata into CDP."""
vargs = {"is_string": False}
if asset_id:
vargs["asset_id"] = asset_id
if "Unit" in metadata and metadata["Unit"]:
vargs["unit"] = metadata["Unit"]
if "Type" in metadata:
vargs["description"] = metadata["Type"]
if asset_name:
metadata["assetName"] = asset_name
client.time_series.post_time_series(
[TimeSeries(name=name, metadata=metadata, **vargs)])
def update_cdp_timeseries(client,
name,
metadata,
asset_name=None,
asset_id=None):
"""Insert timeseries metadata into CDP."""
vargs = {"is_string": False}
if asset_id:
vargs["asset_id"] = asset_id
if "unit_string" in metadata and metadata["unit_string"]:
vargs["unit"] = metadata["unit_string"]
if "NI_CM_Reason" in metadata:
vargs["description"] = metadata["NI_CM_Reason"]
if asset_name:
metadata["assetName"] = asset_name
metadata.update(
{k: str(v)
for k, v in metadata.items() if isinstance(v, date)})
client.time_series.post_time_series(
[TimeSeries(name=name, metadata=metadata, **vargs)])
def test_update_timeseries(self):
tso = TimeSeries(TS_NAME, unit="celsius")
res = timeseries.update_time_series([tso])
assert res is None
def test_post_timeseries(self):
tso = TimeSeries(TS_NAME)
res = timeseries.post_time_series([tso])
assert res is None
def datapoints_fixture():
tso = TimeSeries(TS_NAME)
client.time_series.post_time_series([tso])
yield
client.time_series.delete_time_series(TS_NAME)
def new_ts_id():
name = "test_ts_{}".format(randint(1, 2 ** 53 - 1))
stable_time_series.post_time_series([TimeSeries(name)])
yield stable_time_series.get_time_series(prefix=name).to_json()[0]["id"]