Library for the Piscada Cloud including authentication and data access.
Project description
Picada Cloud
Library for the Piscada Cloud including authentication and data access.
Features
- Login to Piscada Cloud and retrieve credentials
- Persist credentialss locally
- Read historic values for multiple tags as a Pandas DataFrame
- Possible apply time-based linear interpolation to measurements
- Utils to add fractional representations of periods: day, week, year
Install
Install from PyPI:
pip install piscada-cloud
or
poetry add piscada-cloud
Install from local source:
pip install --editable path/to/piscada_cloud
or
poetry add path/to/piscada_cloud
Usage
Authentication
To log-in interactively and persist the retrieved credentials on disk (under $HOME/.piscada_credentials
) simply run:
python -m piscada_cloud.auth
or
poetry run python -m piscada_cloud.auth
Any future invocation, e.g. credentials = piscada_cloud.auth.persisted_login()
will return the credentials on disk without user interaction.
credentials = piscada_cloud.auth.login(username, password, host)
can be used to retrieve the credentials programmatically.
Getting Data
The credentials retrieved through the login can be used to get the host and acccesss-token for the historical data API:
from piscada_cloud import auth
credentials = auth.login_persisted()
host, token = auth.get_historian_credentials(credentials)
The host and token can be used to retrieve historic data as a Pandas DataFrame.
The get_historic_values
method takes a row of parameters:
- controller: e.g.
0798ac4a-4d4f-4648-95f0-12676b3411d5
- start date as ISO8601 string: e.g.
2019-08-01T00:00Z
- end date as ISO8601 string: e.g.
2019-08-01T00:00Z
- a list of tags: e.g.
["oBU136003RT90_MV|linear", "oBU136003QD40_A1"]
which can optionally include the suffix|linear
to enable linear time-based interpolation on this tag. - Endpoint to which we send the historian queries. e.g.
historian.piscada.online
. Optional. - Access token, associated with the endpoint, used for authentication. Optional.
from piscada_cloud.data import get_historic_values
data = get_historic_values(
"0798ac4a-4d4f-4648-95f0-12676b3411d5",
"2019-08-01T00:00Z",
"2019-08-31T23:59Z",
[
"oBU136003RT90_MV|linear",
"oBU136003QD40_A1",
],
)
Write Data
In this example the column oCU135001RT90_MV
is selected and the average value is calculated using the method .mean()
.
To write the result back to the Piscada Cloud, the data
module offers the write_value
function. It takes three arguments: controller_id
, target_tag
, and value
.
The target_tag
must use the prefix py_
as this is the only namespace allowed for writing data via the API.
mean = data_frame["oCU135001RT90_MV"].mean()
print(mean)
response = write_value("0798ac4a-4d4f-4648-95f0-12676b3411d5", "py_oCU135001RT90_MV_1h_mean", mean)
if response.ok:
print("OK")
else:
print(response.text)
The response
returned by the write_value
method allows to check if the writing of data was successful response.ok == True
.
Manipulations
In order to support analysis in the context of periodic patters, the manipulations
allow you to add fractional representations of day, week, and year as additional columns in the DataFrame:
- 00:00:00 -> 0.0 --- 23:59:59 -> 1.0
- Monday 00:00:00 -> 0.0 --- Sunday 23:59:59 -> 1.0
- 1st Jan. 00:00:00 -> 0.0 --- 31st Dec. 23:59:59 -> 1.0
from piscada_cloud import manipulations
manipulations.add_weekdays(data)
manipulations.add_day_fraction(data)
manipulations.add_week_fraction(data)
manipulations.add_year_fraction(data)
Development
Enable the provided git pre commit hook: ln -s ./qa.sh .git/hooks/pre-commit
Requirements
The package will support the two latest version of Python.
Authors
- Tim Jagenberg tim.jagenberg@piscada.com
- Filip Henrik Larsen filip.larsen@piscada.com
License
© Piscada AS 2019
Project details
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