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edv-dwh-connector-0.9.3
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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تبلیغات ما
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This module helps to connect to our data warehouse
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | edv-dwh-connector-0.9.3 |
| نام | edv-dwh-connector |
| نسخه کتابخانه | 0.9.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Endeavour Mining |
| ایمیل نویسنده | github@endeavourmining.com |
| آدرس صفحه اصلی | https://github.com/endeavourmining/edv-dwh-connector |
| آدرس اینترنتی | https://pypi.org/project/edv-dwh-connector/ |
| مجوز | - |
[](https://github.com/endeavourmining/edv-dwh-connector/blob/master/LICENSE.txt)
[](https://codecov.io/gh/endeavourmining/edv-dwh-connector)
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:
```python
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:
```python
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.
### Synchronize partially a CSV file for a tag
To synchronize a CSV file with the latest data from interpolated data from DWH, we do like this:
```python
dwh = ...
tag = "AI56222_SCDL"
CsvWithLatestPIMeasuresDf(
path="my/path/data.csv",
tag=tag,
origin=PgMinuteInterpolatedPIMeasuresDf(tag, dwh)
).frame(
datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
)
```
If `data.csv` contains data for period `[01/01/2022 - 24/01/2022]`, this instruction will only add to the CSV file data of the last week.
### Read tags and measures from DWH
#### Read tags
To get all tags, just do this:
```python
tags = PgCachedPITags(dwh).items()
```
#### Read measures
To read measures of a tag on a period, just do this:
```python
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("AI162014_SCLD", dwh).items(
datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
)
# or use a data frame (by tag or tag code)
dt = PgPIMeasuresDf(tag, dwh).frame(
datetime(2022, 1, 1, 5, 0, 0), datetime(2022, 1, 31, 5, 0, 0)
)
```
### Synchronize blend proposals
We easily synchronize by this code below:
```python
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
```python
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:
```shell
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
**N.B.** We activate an environment on Windows by executing:
```shell
.venv\Scripts\activate.bat
```
## How to contribute
Please read [contributing rules](https://github.com/endeavourmining/.github/blob/master/CONTRIBUTING.md).
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:
```shell
sh pyqulice.sh # Linux
pyqulice.bat # Windows
pytest tests/unit/
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl edv-dwh-connector-0.9.3:
pip install edv-dwh-connector-0.9.3.whl
نصب پکیج tar.gz edv-dwh-connector-0.9.3:
pip install edv-dwh-connector-0.9.3.tar.gz