semvec/pypi-packages huggingface dataset:
kada-ge-store-plugin 0.1.9
Plugin package for great_expectations to store validation results in a different format in Azure storage.
Requires Python: >=3.8,<4.0

# ge-kada-plugin

### Steps to use the KadaStoreValidationResultsAction from the plugin

1. Install the plugin by `pip install kada-ge-store-plugin`. You can put `kada-ge-store-plugin` in the requirements.txt file for CI/CD operations. You'll also need the following two Azure modules:

    * azure.identity
    * azure.storage.blob

These will be installed as dependencies so you won't have to install them.

### Note

```
This version of kada-plugin only works with Great Expectations version `0.15.41`.
```

2. In your required `checkpoint`, add the following action to your checkpoint `.yml` file.

```yml
  - name: store_kada_validation_result
    action:
      class_name: KadaStoreValidationResultsAction
      module_name: kada_ge_store_plugin.kada_store_validation
      container: ${VALIDATIONS_CONTAINER}
      prefix: lz/ge_landing/landing
      connection_string: DefaultEndpointsProtocol=https;AccountName=${STORAGE_ACCOUNT_NAME};AccountKey=${STORAGE_ACCOUNT_KEY};EndpointSuffix=core.windows.net
      account_url: ${SAS_URL}
      access_key: ${SAS_TOKEN}
```
### Note

New changes made to the library allow connection parameters like host, database and schema name to be appended to the validation results. Simply add the following parameters in the action described above. Following example shows how it can be done using secrets in Github actions:

```yml
  - name: store_kada_validation_result
    action:
      class_name: KadaStoreValidationResultsAction
      module_name: kada_ge_store_plugin.kada_store_validation
      ...
      ...
      ...
      host: ${SNOWFLAKE_HOST}
      database: ${SNOWFLAKE_DATABASE}
      schema: ${SNOWFLAKE_HOST}
```

You should change the prefix to your desired nested blob storage. Preference will be given to the `connection_string` as a connection method to the Azure storage account. If you are using storage access url and token, remove the `connection_string`. 

3. In your uncommited/config_variables.yml file or if you are using environment variables, add the following variables related to the azure storage account:

    * VALIDATIONS_CONTAINER

    Either:

    * STORAGE_ACCOUNT_NAME
    * STORAGE_ACCOUNT_KEY

    Or:

    * SAS_URL
    * SAS_TOKEN
    
4. Add/Change the `run_name_template` in your checkpoint `.yml` file to `'%Y%m%d%H%M%S'`.

### Note

If your checkpoint's `action_list` contains a `StoreValidationResultAction`, the validation results will get stored in the given validtions store inside `great_expectations.yml` file and also in the Azure Storage container with the custom `.json` filename. Basically, you'll be storing validation results in 2 different places.

Since this is a new plugin, it might be worthwhile keeping the `StoreValidationResultAction` to make sure the validation results are getting stored even with a failure in this plugin.

Added tags to facilitate version releasing and CI/CD operations
simplepycli 1.0.1
A really simple library for interactive python CLIs.
Requires Python: >=3.8,<4.0

# PyCLI
PyCLI is a really simple library for interactive python CLIs. I mostly built
it for personal use on side projects, but feel free to use it.

## Install Instructions
To install:
```python
pip install simplepycli
```

## Basic API Example
The API is really simple, so here is a comprehensive example of how it works. This is also available at example.py:

```python
from simplepycli import PyCLIApp

# The only paramater for PyCLIApp is the marker to use in front of the terminal; defaults to "> "
app = PyCLIApp("> ")

@app.command("egg", "Prints egg to the console")
def egg(params):
  print("egg")

@app.command("echotwice", "Echos the string passed in as a paramater twice")
def echotwice(params):
  for i in range(2):
    print(params)

app.run()
```

This outputs a terminal that behaves like this
```
> egg
egg
> echotwice abc
abc
abc
> help
exit : Exit the current CLI session and close the program.
help : List available commands and their help messages.
clear : Clear the current command line.
egg : Prints egg to the console
echotwice : Echos the string passed in as a paramater twice
> |
```
pykoa 1.7.2
KOA archive access client
Requires Python: >= 3.6

KOA Table Access Protocol (TAP) Client
======================================

Version 1 of PyKOA offers access to public raw science and calibration files 
acquired with the HIRES echelle-spectrograph, and for Keck Observatory PIs, 
secure access to their protected data with the KOA credentials already assigned
to them. The remaining Keck instruments are expected to be available before the
end of 2020. Queries made through PyKOA are underpinned by nexsciTAP, a NExScI 
Python-based server that implements the IVOA Table Access Protocol (TAP). 
This design enables a rich variety of searches, including cone, box, polygon, 
or all-sky spatial searches; temporal searches; searches on program 
infortmation; and complex searches on multiple attributes.

The Keck Observatory Archive (KOA) is a collaboration between the NASA 
Exoplanet Science Institute (NExScI) and the W. M. Keck Observatory (WMKO). 
NExScI is sponsored by NASA's Exoplanet Exploration Program, and operated by 
the California Institute of Technology in coordination with the Jet Propulsion 
Laboratory (JPL).

If you used PyKOA in your research, please acknowledge KOA as follows: 


    "This research has made use of the Keck Observatory Archive (KOA), which 
    is operated by the W. M. Keck Observatory and the NASA Exoplanet Science 
    Institute (NExScI), under contract with the National Aeronautics and 
    Space Administration." 


Please also acknowledge the PI(s) of datasets that have been accesses 
through PyKOA.