def main():
configure_session(api_key=os.getenv("COGNITE_API_KEY"),
project="akerbp",
debug=True)
tags_d03 = []
tags_d02 = []
for root, subdirs, files in os.walk("../tags"):
for file in files:
if file in ("well_tags.csv", "routing.csv", "output.csv",
"riser_tags.csv", "template_tags.csv"):
with open(os.path.join(root, file)) as f:
df = pd.read_csv(f)
placements = ["T3 WGM", "Template", "Riser"]
placements_d03 = ["WellD03"] + placements
placements_d02 = ["WellD02"] + placements
df = df[~df["tag"].isin(EXCLUDE_TAGS)]
tags_d03.append(df[df["placement"].isin(placements_d03)])
tags_d02.append(df[df["placement"].isin(placements_d02)])
tags_d02_concat = pd.concat(tags_d02, ignore_index=True)
tags_d03_concat = pd.concat(tags_d03, ignore_index=True)
tags_d02_concat = tags_d02_concat.drop_duplicates(subset="tag")
tags_d03_concat = tags_d03_concat.drop_duplicates(subset="tag")
d02_input_time_series = []
d03_input_time_series = []
for tag in tags_d02_concat["tag"]:
aggregate = "step" if ("ESV" in tag or "18HV" in tag) else "avg"
missing_data_strategy = "ffill" if (
"ESV" in tag or "18HV" in tag) else "linearInterpolation"
ts = TimeSeries(name=tag,
missing_data_strategy=missing_data_strategy,
aggregates=[aggregate])
d02_input_time_series.append(ts)
for tag in tags_d03_concat["tag"]:
aggregate = "step" if ("ESV" in tag or "18HV" in tag) else "avg"
missing_data_strategy = "ffill" if (
"ESV" in tag or "18HV" in tag) else "linearInterpolation"
ts = TimeSeries(name=tag,
missing_data_strategy=missing_data_strategy,
aggregates=[aggregate])
d03_input_time_series.append(ts)
d02_tsds = TimeSeriesDataSpec(
time_series=d02_input_time_series,
aggregates=["avg"],
granularity="10s",
start=int(datetime(2017, 3, 1).timestamp() * 1e3),
label="d2",
)
d03_tsds = TimeSeriesDataSpec(
time_series=d03_input_time_series,
aggregates=["avg"],
granularity="10s",
start=int(datetime(2017, 3, 1).timestamp() * 1e3),
label="d3",
)
data_spec = DataSpec(time_series_data_specs=[d02_tsds, d03_tsds])
dts = DataTransferService(data_spec, num_of_processes=10)
print(data_spec.to_JSON())
df_dict = dts.get_dataframes()
for label, df in df_dict.items():
df.to_csv(f"../data/{label}.csv")
print(df.shape)
from cognite.v05.timeseries import post_time_series, get_timeseries
from cognite.v05.dto import TimeSeries
from bysykkel import Station, find_or_create_asset, find_or_create_root_assets
import oslobysykkelsdk as oslo
import bergenbysykkelsdk as bergen
import trondheimbysykkelsdk as trondheim
parser = argparse.ArgumentParser()
parser.add_argument('--apikey', type=str, required=True)
parser.add_argument('--project', type=str, required=True)
parser.add_argument('--delete_timeseries', action='store_true')
parser.add_argument('--create_timeseries', action='store_true')
args = parser.parse_args()
# Set API key and project for current session
configure_session(api_key=args.apikey, project=args.project)
def delete_timeseries(cities):
# TODO: implement
print('delete_timeseries is not yet implemented')
def create_timeseries(cities):
timeseries = []
for city, data in cities.items():
city_id = data['asset_id']
assets = get_asset_subtree(asset_id=city_id).to_json()
for asset in assets:
timeseries_bikes = TimeSeries(name=asset['name'] + '_bikes',
import tools.lstm_network as lstm
import tools.knn as knn
import tools.constants as constants
import tools.plotting as plotting
import seaborn as sns
from sklearn import preprocessing
from sklearn.metrics import mean_squared_error, r2_score
from cognite.config import configure_session
from cognite.v05.timeseries import get_datapoints_frame
from statsmodels.tsa.stattools import adfuller
from pandas.plotting import scatter_matrix
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from pandas.plotting import autocorrelation_plot
configure_session(os.environ.get('PUBLIC_DATA_KEY'), 'publicdata')
input_tags = {
'VAL_23-FT-92512:X.Value|average': 'Gas inflow from separators',
'VAL_23-PT-92532:X.Value|average': 'Suction pressure',
'VAL_23-TT-92533:X.Value|average': 'Suction temperature'
}
output_tags = {
'VAL_23-FT-92537-01:X.Value|average': 'Discharge mass flow',
'VAL_23-FT-92537-04:X.Value|average': 'Discharge volume flow',
'VAL_23-PT-92539:X.Value|average': 'Discharge pressure',
'VAL_23-TT-92539:X.Value|average': 'Discharge temperature'
}
control_tags = {
TimeseriesWithDatapoints(
locks_asset_name,
[Datapoint(timestamp, num_locks)]))
timestamp = int(time.time() * 1000)
log(' Posting %d data points for %s at %d' %
(len(datapoints), city, timestamp))
post_multi_tag_datapoints(datapoints)
log(' Data points posted to CDP.')
except Exception as e:
log(' Error fetching availaility for %s: %s' % (city, str(e)))
log('Initializing bysykkel sampler ...')
# Set API key and project for current session
configure_session(api_key=os.getenv('COGNITE_API_KEY'),
project=os.getenv('COGNITE_PROJECT'))
# Find root assets
cities = {
'Oslo': {
'stations': oslo.get_stations(),
'get_availability': oslo.get_availability
},
'Bergen': {
'stations': bergen.get_stations(),
'get_availability': bergen.get_availability
},
'Trondheim': {
'stations': trondheim.get_stations(),
'get_availability': trondheim.get_availability
}
import time
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from cognite._utils import APIError
from cognite.config import configure_session, set_number_of_retries
from cognite.data_transfer_service import DataSpec, DataTransferService, TimeSeries, TimeSeriesDataSpec
from cognite.v05.dto import Datapoint
from cognite.v05.dto import TimeSeries as TimeSeriesDTO
from cognite.v05.timeseries import post_datapoints, post_time_series
from cognite.v06 import models
from virtual_metering.data_fetcher import EXCLUDE_TAGS
configure_session(api_key=os.getenv("COGNITE_API_KEY"), project="akerbp", debug=True)
set_number_of_retries(3)
try:
ts = TimeSeriesDTO(name="SKAP_18FI381-VFlLGas/Y/10sSAMP_calc_D02_2", asset_id=8129784932439587)
res = post_time_series([ts])
print(res)
except APIError as e:
print(e)
print("øladfhjaoøsidfhjapsoidfjaspodifhjaspoid")
pass
def main():
output_columns = [
"SKAP_18FI381-VFlLGas/Y/10sSAMP|average",
def unset_config_variables():
configure_session('', '')
yield (TEST_API_KEY, TEST_PROJECT)
configure_session(TEST_API_KEY, TEST_PROJECT)
def configure_test_session():
configure_session(TEST_API_KEY, TEST_PROJECT)
yield
configure_session('', '') # teardown
import os
import matplotlib.pyplot as plt
from cognite.client.stable.time_series import get_datapoints_frame
from cognite.config import configure_session
# Set API key and project for current session. The project is Open Industrial Data.
configure_session(api_key=os.getenv("COGNITE_API_KEY"), project="publicdata")
# Retrieve one year of daily aggregates for a time series
ts = "VAL_23-PT-92512:X.Value"
dataframe = get_datapoints_frame([ts],
start="52w-ago",
aggregates=["avg", "min", "max"],
granularity="1d",
processes=1)
# Plot the dataframe
dataframe.plot(x="timestamp")
plt.show()