edv-dwh-connector 0.9.3

This module helps to connect to our data warehouse

This project helps to connect to our data warehouse.

Requirements

• Python 3.8.x or later.

Quick start

Synchronize tags

Synchronize tags from PI server to Data warehouse on period [01/01/2022 05:00:00 - 31/01/2022 05:00:00] can be easily achieved by the code below:

from datetime import datetime

from edv_dwh_connector.pg_dwh import PgDwh
from edv_dwh_connector.pi_web_api_client import PiWebAPIClient
from edv_dwh_connector.pi.rest.rest_sync_pi_tags import RestSyncPITags
from edv_dwh_connector.utils.periods import HourIntervals
from edv_dwh_connector.pi.sync_pi_data import SyncPIDataRestToDwh
from edv_dwh_connector.pi.db.pg_pi_tag import PgPITags

# We should firstly declare the Data warehouse pool connection.
dwh = PgDwh.from_connection(
    name="dwh_db_name", host="dwh_server_name_or_ip",
    user="dwh_user", password="dwh_password", port=5432
)
# Next, we should declare the PI server REST API client.
client = PiWebAPIClient(
    base_url="base/url/of/pi/server", username="admin_user",
    password="admin_password", session_timeout=2.5
)
# Finally, we can synchronize tags measures.
# The code below will automatically synchronize tags (create new tags),
# split period provided into hour intervals and synchronize PI measures
# on these intervals.
SyncPIDataRestToDwh(
    tags=RestSyncPITags(
        server_id="F1DSmN2338899MpX8PREOtdbEZ56sypOOOKRZLVNSVi1QSS1ISTGt", # Fake server ID
        client=client,
        codes=['AI162003_SCLD', 'AI162007_SCLD', 'AI162014_SCLD'],
        target=PgPITags(dwh)
    ),
    periods=HourIntervals(
        datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
    ),
    client=client, dwh=dwh
).synchronize()

To store data to a CSV file in fact_pi_measure format, you could do this:

SyncPIDataRestToCSVDwhLike(
    tags=RestSyncPITags(
        server_id="F1DSmN2338899MpX8PREOtdbEZ56sypOOOKRZLVNSVi1QSS1ISTGt", # Fake server ID
        client=client,
        codes=['AI162003_SCLD', 'AI162007_SCLD', 'AI162014_SCLD'],
        target=PgPITags(dwh)
    ),
    periods=HourIntervals(
        datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
    ),
    client=client,
    file="path/of/csv/file/where/to/store"
).synchronize()

after importing SyncPIDataRestToCSVDwhLike from edv_dwh_connector.pi.sync_pi_data. It is very useful when you want to recover data of DWH table fact_pi_measure on a long period.

N.B. You could also fetch on day intervals by using class DayIntervals instead of HourIntervals. But, HourIntervals could be faster than DayIntervals depending on the size of data to be imported.

Read tags and measures from DWH

Read tags

To get all tags, just do this:

tags = PgCachedPITags(dwh).items()

Read measures

To read measures of a tag on a period, just do this:

tag = ... # get PI tag here
measures = PgCachedPIMeasures(tag, dwh).items(
    datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
)
# or use only tag code
measures = PgCachedPIMeasures.of_tag_code("AI162014_SCLD", dwh).items(
    datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
)

Synchronize blend proposals

We easily synchronize by this code below:

SyncBlendProposals(
    src=ExcelBlendProposals(
        file="path/of/excel/file/name",
        start_date=date.fromisoformat("2022-10-18"),
        end_date=date.fromisoformat("2022-10-20")
    ),
    target=PgBlendProposals(dwh)
).synchronize()

Read blend proposals from DWH

blends = PgBlendProposals(dwh).items(
    date(2022, 10, 18), datetime(2022, 10, 20)
)

Development environment

It is recommended to start by creating a virtual environment. You could do it by following commands:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

N.B. We activate an environment on Windows by executing:

.venv\Scripts\activate.bat

How to contribute

Please read contributing rules.

Fork repository, make changes, send us a pull request. We will review your changes and apply them to the master branch shortly, provided they don't violate our quality standards. To avoid frustration, before sending us your pull request please run these commands:

sh pyqulice.sh # Linux
pyqulice.bat # Windows
pytest tests/unit/