semvec/pypi-packages huggingface dataset:
easy-rasa 1.0
A package that converts .csv file to rasa compatible file

# Easy-Rasa

## Why?
To make it easier to create rasa`nlu.md` and `domain.yml` file.
## What is it?
This package helps you convert a preformatted `.csv` file to Rasa acceptable file formats.

The standard format to be followed while creating a `.csv` is shown in `nlu_sample_format_for_conversion.csv` file. This CSV file is converted  to rasa format `nlu.md` and `domain.yml` file.

Run command `download_csv()` to download sample file that you can fill.

```
from easy-rasa import download_csv
download_csv()
```
This will download a preloaded file called `nlu_sample_format_for_conversion.csv`. Search the directory where you ran the `download_csv()` command to find the file.
Fill the `.csv` file by follwing these instructions:
- First rows consist of intent names. Add any number of intents in the first row.
- The succeeding rows. i.e. after the intent names row, add all your training phrases/sentences.
- save the file

### STEPS TO CREATE `nlu.md` & `domain.yml` file:

- Call the function/method `create_rasa_nlu(csv_path, create_files_path)`. Don't forget to specify path to the CSV file using the path parameter, and path where the output files need to be created via the `create_files_path` parameter.

```
from easy-rasa import create_rasa_files
create_rasa_files(csv_path, create_files_path)
#csv_path and create_files_path can both be ./
```
Check the directory (create_files_path) to file respective `nlu.md` and `domain.yml` files.
gaugetomator 0.0.2
Automation framework developed by Dale
Requires Python: >=3.10

# [QA-QC] Gautomator Guideline
########################

## Unittest
```shell
behave
```

### Definition
```python
api_obj = APIFactory.generate_data(api_name='Service_Liveness').generate
```

The `api_obj` will have the content of 
```python
{
    'endpoint': str ,
    'content_type': str,
    'method': str,
    'request': RequestObj(),
    'response': ResponseObj()
}
```

### Usage

```python
from gautomator.cores.utils.common import ObjectSetupUtil
from gautomator.cores.factory.swagger_factory import APIFactory, APIConfiguration

class TestAPI(APIConfiguration):
    operation_id = 'Test_API' # the operation_id of the api , this info is listed in the swagger.json file

    def __init__(self, token=None):
        super().__init__()
        self.api_obj = self.factory_api.generate_data(api_name=self.operation_id).generate

    def test(self, **kwargs):
        body = ObjectSetupUtil.setup_object(self.api_obj.request, **kwargs)
        params = RequestFactory.create_request(request_type=self.api_obj.method, body=body, token=self._token)
        return RequestUtil.request(url=, data=params)
        
```
density-calculator 0.2.1
density_calculator: Calculate mFo-DFc density with Clipper through Gemmi!
Requires Python: >=3.9

# Density Calculator

Density Calculator is a library for calculating a $mFo-DFc$ maps from `gemmi` in-memory objects.

## Installation 
    
    pip install density-calculator

## Usage

#### Function Signature: 

    density_calculator.calculate(structure: gemmi.Structure, mtz: gemmi.Mtz, column_names: List[str]) -> gemmi.Mtz

#### Example Use:

    import density_calculator
    import gemmi
    
    mtz: gemmi.Mtz = gemmi.read_mtz_file("reflections.mtz")
    st: gemmi.Structure = gemmi.read_structure("model.pdb")
    
    # Calculate the difference map using density_calculator
    recalculated_mtz: gemmi.Mtz = density_calculator.calculate(st, mtz, ["FP", "SIGFP"])

    # Transform the difference_mtz into a map for calculation use
    best_map: gemmi.FloatGrid = recalculated_mtz.transform_f_phi_to_map("FWT", "PHWT")
    
    # Transform the difference_mtz into a map for calculation use
    difference_map: gemmi.FloatGrid = recalculated_mtz.transform_f_phi_to_map("DELFWT", "PHDELWT")
    

## Development 

    pip install --no-build-isolation --config-settings=editable.rebuild=true -Cbuild-dir=build -ve .
csvtorasa 0.1.2
A package that converts .csv file to rasa compatible file

# csvtorasa

## Why?
To make it easier to create rasa`nlu.md` and `domain.yml` file.
## What is it?
This package helps you convert a preformatted `.csv` file to Rasa acceptable file formats.

The standard format to be followed while creating a `.csv` is shown in `nlu_sample_format_for_conversion.csv` file. This CSV file is converted  to rasa format `nlu.md` and `domain.yml` file.

Run command `download_csv()` to download sample file that you can fill.

```
from csv-to-rasa import download_csv
download_csv()
```
This will download a preloaded file called `nlu_sample_format_for_conversion.csv`. Search the directory where you ran the `download_csv()` command to find the file.
Fill the `.csv` file by follwing these instructions:
- First rows consist of intent names. Add any number of intents in the first row.
- The succeeding rows. i.e. after the intent names row, add all your training phrases/sentences.
- save the file

### STEPS TO CREATE `nlu.md` & `domain.yml` file:

- Open csvtorasa.py and call the function/method create_rasa_nlu(path, nlu_md_path). Don't forget to specify path to the CSV file using the path parameter, and path where the output nlu.md file needs to be created via the nlu_md_path parameter.

```
from csv-to-rasa import create_rasa_files
create_rasa_files(csv_path, create_files_path)
#csv_path and create_files_path can both be ./
```
Check the directory (create_files_path) to file respective `nlu.md` and `domain.yml` files.