python-eigen-ingenuity 0.4.32b1

A python library used to query data from the Eigen Ingenuity system

CONTENTS

• About
• Installation
• Getting Started
• Historian
  • Data Formats
    • Examples
  • General Functions
  • Read Functions
  • Write Functions
• Asset Model
• License

ABOUT

The python-eigen-ingenuity library is used to query data from the Eigen Ingenuity system for use in the python environment, and to upload data to the

python-eigen-ingenuity requires Python3

INSTALLATION

python-eigen-ingenuity supports python 3.9 onwards. It may work for earlier versions of python3, but these are not tested.

Install python3, then in the terminal run:

pip install C:\path\to\python-eigen-ingenuity -r requirements.txt

Third party libraries should be automatically installed through requirements.txt.

GETTING STARTED

Begin by Importing the module with

Go to the examples folder and copy example-python-to-eigen-ingenuity.py modify the eigen-ingenuity server and tags then run python example-python-to-eigen-ingenuity.py

To use this module, you must first set an Ingenuity server to query, and a datasource within the server.

For example, for a historian with Ingenuity instance "https://demo.eigen.co/" and datasource "Demo-influxdb",

server = eigen.EigenServer("https://demo.eigen.co/")
demo = eigen.get_historian("Demo-influxdb",server)

Alternatively, it is possible to set the Ingenuity instance as the environmental variable "EIGENSERVER",

os.environ["EIGENSERVER"] = "https://demo.eigen.co/"
demo = get_historian("Demo-influxdb")

Alternatively, it is possible to set the Ingenuity instance as the environmental variable "EIGENSERVER",

os.environ["EIGENSERVER"] = "https://demo.eigen.co/"
demo = get_historian("Demo-influxdb")

If the datasource of interest is the default datasource for the ingenuity instance, it can be omitted:

os.environ["EIGENSERVER"] = "https://demo.eigen.co/"
demo = get_historian()

With the datasource set, the historian data can be queried with functions such as,

demo.getInterpolatedRange(tag,start,end,points)
demo.getCurrentDataPoints(tag)
demo.listDataTags()

Where:

• tag is the name of the tag to query
• start is the epoch timestamp (ms) of the beginning of the query window
• end is the epoch timestamp (ms) of the end of the query window
• points is the number of points to be returned

Each function will return an list, each element consisting of list of a value, timestamp and status, corresponding to a single point of data

To convert a Datetime (UTC or Local) to epoch, or vice-versa, you can use this tool: https://www.epochconverter.com/

Historian

DATA FORMAT

Once the server and datasource have been configured, the historian data can be queried through functions we define in the EXAMPLE FUNCTIONS section.

These functions can be used to query a single tag, or multiple tags at once. A tag in ingenuity with the form "datasource/tagname", we query with, for example:

datasource = eigen.get_historian("datasource")
tagdata = datasource.getCurrentDataPoints("tagname")

Functions have multiple options on how to return the data, that can be specified using the "output" parameter:

• The Raw Response. (output="raw")
• A preformatted python dict (default: output="json")
• a pandas dataframe (default: output="df")

Example:

---

x = influx.getInterpolatedRange("DEMO_02TI301.PV","1 hour ago","now",3)

Raw:

---

{'items': {'DEMO_02TI301.PV': [{'value': 38.0, 'timestamp': 1701166741139, 'status': 'OK'}, {'value': 37.5, 'timestamp': 1701168541139, 'status': 'OK'}, {'value': 38.0, 'timestamp': 1701170341139, 'status': 'OK'}]}, 'unknown': []}

Json

---

[{'value': 35.88444444444445, 'timestamp': 1701166983980, 'status': 'OK'}, {'value': 33.5, 'timestamp': 1701168783980, 'status': 'OK'}, {'value': 34.0, 'timestamp': 1701170583980, 'status': 'OK'}]

Df

---
                         DEMO_02TI301.PV
2023-11-28 11:23:39.201             38.0
2023-11-28 10:53:39.201             36.0
2023-11-28 10:23:39.201             33.0

CSV

---

DEMO_02TI301.PV,37.1718,1701167341282,OK
DEMO_02TI301.PV,35.5,1701169141282,OK
DEMO_02TI301.PV,37.0,1701170941283,OK

The CSV output type allows for 2 additional optional parameters:

• multi-csv: Creates a separate csv for each tag queried, rather than placing them all in one. Also puts tag in filename rather than in row
• filepath: Specify a directory to write the csv files to

Query Multiple tags

if multiple tags are queried in a single request, the data will be returned as a dictionary, with the tag IDs as its keys, the individual dictionary entries will retain the same format returned when querying a single tag

---

FUNCTIONS

Data Queries

General Functions:

Simple Functions to check server defaults

list_historians

Find all historians on the instance

from eigeningenuity import list_historians
list_historians(eigenserver)

Where:

• (Optional) eigenserver is the ingenuity instance of interest (If omitted will look for environmental variable EIGENSERVER)

Returns a list of strings

get_default_historian_name

Find the name of the default historian of the instance, if one exists

from eigeningenuity import get_default_historian_name
get_default_historian_name(eigenserver)

Where:

• (Optional) eigenserver is the ingenuity instance of interest (If omitted will look for environmental variable EIGENSERVER)

Returns a string, or None

Read Functions

The following functions are designed to help the user pull and process data from historians into a python environment

getCurrentDataPoints

Find the most recent raw datapoint for each tag

demo.getCurrentDataPoints(tags,output)

Where:

• tags is a list of IDs of tags to query
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns one datapoint object per tag

countPoints

Find the number of datapoints in the given time frame

demo.countPoints(tag, start, end, output)

Where:

• tags is a list of IDs of tags to query
• start is the datetime object (or epoch timestamp in ms) of the beginning of the query window
• end is the datetime object (or epoch timestamp in ms) of the end of the query window
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns one integer per tag

getInterpolatedRange

Find a number of interpolated points of a tag, equally spaced over a set timeframe

demo.getInterpolatedRange(tag, start, end, count, output)

Where:

• tags is a list of IDs of the tags to query
• start is the datetime object (or epoch timestamp in ms) of the beginning of the query window
• end is the datetime object (or epoch timestamp in ms) of the end of the query window
• count is the total number of points to be returned
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns a list of count-many datapoints per tag

getInterpolatedpoints

Find datapoints at given timestamps

demo.getInterpolatedPoints(tags, timestamps, output)

Where:

• tags is a list of IDs of the tags to query
• timestamps is a list of timestamps at which to query data
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns a list of datapoints (one at each timestamp) per tag

getRawDataPoints

Find the first n Raw datapoints from a time window

demo.getRawDataPoints(tags, start, end, count, output)

Where:

• tags is a list of IDs of the tags to query
• start is the datetime object (or epoch timestamp in ms) of the beginning of the query window
• end is the datetime object (or epoch timestamp in ms) of the end of the query window
• (Optional) count is the maximum number of raw datapoints to return. (default is 1000)
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns a list of count-many datapoints per tag

getAggregates

Finds a set of aggregate values for tags over a timeframe

demo.getAggregates(tags, start, end, count, aggfields, output)

Where:

• tags is a list of IDs of the tags to query
• start is the datetime object (or epoch timestamp in ms) of the beginning of the query window
• end is the datetime object (or epoch timestamp in ms) of the end of the query window
• (Optional) count is the number of divisions to split the time window into (i.e. if time window is one day, and count is 2, we return separate sets of aggregate data for first and second half of day). omit for count=1
• (Optional) aggfields is a list of aggregate functions to calculate, a subset of ["min","max","avg","var","stddev","numgood","numbad"]. Leave blank to return all aggregates.
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns a list of count-many Aggregate Data Sets per tag

getAggregateIntervals

A variation of getAggregates which finds aggregates on fixed length intervals dividing the overall window

demo.getAggregateInterval(tags, start, end, interval, aggfields, output)

Where:

• tags is a list of IDs of the tags to query
• start is the datetime object (or epoch timestamp in ms) of the beginning of the query window
• end is the datetime object (or epoch timestamp in ms) of the end of the query window
• (Optional) interval is the length of the sub-intervals over which aggregates are calculated, it accepts values such as ["1s","1m","1h","1d","1M","1y"] being 1 second, 1 minute, 1 hour etc. Default is whole time window.
• (Optional) aggfields is a list of aggregate functions to calculate, a subset of ["min","max","avg","var","stddev","numgood","numbad"]. Default is all Aggregates.
• output (optional) See DATA FORMAT section
  • multi-csv (optional) See DATA FORMAT section
  • filepath (optional) See DATA FORMAT section

Returns a list of Aggregate Data Sets (One per interval) per tag

listDataTags

Find all tags in datasource, or all tags in datasource that match a search parameter

demo.listDataTags(match)

Where:

• (optional) match is the regex wildcard to match tags to (i.e. DEMO* will match all tags beginning with DEMO, *DEMO* will match all tags containing DEMO, and *DEMO will match all tags ending with DEMO) (Leave blank to return all tags in historian)

Returns a list of strings

getMetaData

Find units, unitMultiplier and description of each tag

demo.getMetaData(tags, output)

Where:

• tags is a list of IDs of tags to query
• output (optional) Does Not Accept CSV. Otherwise, See DATA FORMAT section

Returns a dict with keys [units, unitMultiplier, description] per tag

Write Functions

The following functions are intended for users to update/create historian tags using data processed/loaded in python.

createDataTag

Creates a datatag with a specified ID, Unit type/label, and Description

demo.createDataTag(Name, Units, Description)

Where:

• Name is the unique ID/Identifier of the tag
• Units is the unit specifier of the data in the tag e.g. "m/s","Days" etc. (This will be shown on axis in ingenuity trends)
• Description is text/metadata describing the content/purpose of the tag (This will show up in search bar for ingenuity trends)

Returns a boolean representing success/failure to create tag

writeDataPoints

Writes sets of datapoints to the historian

from eigeningenuity.historian import DataPoint

dataPoints = []
point = DataPoint(value, timestamp, "OK")
dataPoint = {tagName: point}

dataPointList.append(dataPoint)

demo.writeDataPoints(dataPointList)

Where:

• value if the value of the datapoint at the timestamp
• timestamp is the datetime object (or epoch timestamp in ms) of the point
• "OK" is the status we give to a point that contains non-null data

Returns a boolean representing success/failure to write data

Asset Model

Currently the AM tools only support direct queries using,

executeRawQuery

Executes a cypher query directly against our asset model

from eigeningenuity import get_assetmodel, EigenServer

demo = EigenServer("demo.eigen.co")
am = get_assetmodel(demo)

wells = demo.executeRawQuery("Match (n:Well) return n limit 25")

LICENSE

Apache License 2.0

Copyright 2022 Eigen Ltd.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.