semvec/pypi-packages huggingface dataset:
orm-converter 0.2.2b0
A utility that allows you to convert ORM models.
License: MIT
Keywords: python,django,orm,conversion,orm-framework,tortoise-orm,django-orm,admin-panel

[![PyPI version](https://badge.fury.io/py/orm-converter.svg)](https://badge.fury.io/py/orm-converter)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)

# Orm-Converter

## Installation
```bash
pip install orm-converter
```

or

```bash
pip install git+https://github.com/MaxZayats/orm-converter
```

***

## Available conversions
1. `TortoiseORM` -> `DjangoORM`

***

## Usage Examples
### 1. Simple Usage
```python
from orm_converter.tortoise_to_django import ConvertedModel
from tortoise import fields
from tortoise.models import Model as TortoiseModel


class ExampleModel(TortoiseModel, ConvertedModel):
    example_field = fields.IntField()


ExampleModel.DjangoModel  # <- Converted Django Model
```

### 2. Redefining fields/attributes

```python
from orm_converter.tortoise_to_django import (ConvertedModel,
                                              RedefinedAttributes)
from tortoise.models import Model as TortoiseModel

from custom_django_fields import CustomDjangoField
from custom_tortoise_fields import CustomTortoiseField


class ExampleModel(TortoiseModel, ConvertedModel):
    custom_field = CustomTortoiseField()

    class RedefinedAttributes(RedefinedAttributes):
        """
        In this class you can redefine your tortoise attributes to django attributes.
        You can use this if you have a custom fields
        Or if `orm_converter` converts fields incorrectly.
        """

        custom_field = CustomDjangoField()
```

### 3. Adding custom converters
```python
from orm_converter.tortoise_to_django import (BaseTortoiseFieldConverter,
                                              ConvertedModel, Converter)
from tortoise.models import Model as TortoiseModel

from custom_django_fields import CustomDjangoField
from custom_tortoise_fields import CustomTortoiseField


class MyCustomFieldConverter(BaseTortoiseFieldConverter):
    ORIGINAL_FIELD_TYPE = CustomTortoiseField
    CONVERTED_FIELD_TYPE = CustomDjangoField

    def _reformat_kwargs(self):
        super()._reformat_kwargs()
        # change field kwargs here

        self._original_field_kwargs["custom_kwarg"] = "Django"


Converter.add_converters(MyCustomFieldConverter)


class ExampleModel(TortoiseModel, ConvertedModel):
    custom_field = CustomTortoiseField(custom_kwarg="Tortoise")
```
pandas-quant-data-provider 0.2.7
Augment pandas DataFrame with methods to fetch time series data for quant finance
License: MIT
Keywords: pandas,ml,util,quant

# Pandas ML Quant Data Provider

An independent module used to fetch time series and other data used for quant finance studies.

## Installation

Data can always be fetched instant without any local data base. However if you need to screen across assets
for matching criteria then a local database is needed. Developing models often depend on some premise. Like 
for a crash detection model you would want to use instruments which indeed suffered a crash. Therefore some
data is stored into a data base using dolthub. Querying this data requires you to install [dolt][dolt] by
following their [installation instructions][dolt].  

```shell script
pip install pandas-quant-data-provider
 
```
   
**!! NOTE !!**
We need to keep data libraries like yfincance up do date alsmost every minute. Whenever there is a change in the api
the library has to react. This happens more often as we whish, especially with yahoo finance. This is why this library
gets _pip installed -U_ on every import. This means for server based solutions on every restart. Make sure you use
all pandas-ml* libraries inside a virtual env.


 
Example:

```python
# monkey patch pandas 
from pandas_quant_data_provider import pd, YAHOO, INVESTING, CRYPTO_COMPARE

# fetch data from various data sources 
#   * fetches all available dates
#   * caches data for 10 minutes
df = pd.fetch_timeseries({
    YAHOO: ["SPY", "DIA"],
    INVESTING: ["index::NYSE Tick Index::united states", "bond::U.S. 30Y::united states"],
    CRYPTO_COMPARE: ["BTC"]
})

df.tail()
```

PS If you are not familiar with pandas MultiIndex, you can watch this video:
[How do I use the MultiIndex in pandas?](https://www.youtube.com/watch?v=tcRGa2soc-c)

 
## Installation
Follow the instructions on [https://github.com/KIC/pandas-ml-quant](https://github.com/KIC/pandas-ml-quant)

## Documentation
Check out the notebooks at [https://github.com/KIC/pandas-ml-quant/blob/master/notebooks](https://github.com/KIC/pandas-ml-quant/blob/master/notebooks)


[dolt]: https://www.dolthub.com/blog/2020-02-03-dolt-and-dolthub-getting-started/
pandas-ml-quant 0.2.7
Augment pandas DataFrame with methods for quant analysis
License: MIT
Keywords: pandas,ml,util,quant

# Pandas ML Quant

The Pandas Machine Learning Quant module is not only a pure python re-implementation of the famous 
[TA-Lib][e1]. There are also useful tools to plot data as well as to 
train neural networks while you never need to leave the world of pandas. 

## Installation
Follow the instructions on [https://github.com/KIC/pandas-ml-quant][ghl1]

## Documentation
Check out the [notebooks][ghl2]

[e1]: http://mrjbq7.github.io/ta-lib/
[ghl1]: ../
[ghl2]: ./examples
pandas-ta-quant-plot 0.2.7
Augment pandas DataFrame with methods to fetch time series data for quant finance
License: MIT
Keywords: pandas,ml,util,quant

# Pandas ML Quant Plot

An independent module used to plot time series data for quant finance studies. 
 
![showcase][ghi1]
 
## Installation
Follow the instructions on [https://github.com/KIC/pandas-ml-quant](https://github.com/KIC/pandas-ml-quant)

## Documentation
Check out the [notebooks][ghl1]

[ghl1]: ./examples
[ghi1]: ../.readme/videos/ta_plot.gif
pandas-ml-utils-torch 0.2.7
Augment pandas DataFrame with methods for machine learning
License: MIT
Keywords: pandas,ml,util,quant

# pytorch extension for [pandas-ml-utils][ghl1]

Adds a `PytorchModel` to the pandas ml utils suite. While a regular class extending `nn.Module` is sufficient,
there is also a special class `PytorchNN` which can be extended as well. Using this class has the following 
advantages:

 * allows to use L1, L2 regularization -> [example][ghl2] 
 * allows different forward path for training and prediction (useful i.e. for reparameterization trick) -> [example][ghl3]
 * allows to implement auto-encoders easily by just providing the encode/decode functions
 * added loss functions like `SoftDTW` (fit time series) loss or `HeteroscedasticityLoss` (fit Normal Distribution) -> [example][ghl3]

<br/><br/>

![Fitting Example][ghi1]

[ghl1]: ../pandas-ml-utils
[ghl2]: ./examples/regression_with_regularization.ipynb
[ghl3]: ./examples/probabilistic_model.ipynb
[ghi1]: ../.readme/videos/probabilistic-model-fit.gif
pandas-ml-utils 0.2.7
Augment pandas DataFrame with methods for machine learning
License: MIT
Keywords: pandas,ml,util,quant

# Pandas ML Utils

Pandas Machine Learning Utilities is part of a bigger set of libraries for a convenient experience. Usually exploring
statistical models start with a pandas `DataFrame`. 

But soon enough you will find yourself converting your data frames to numpy, splitting arrays, applying min
max scalers, lagging and concatenating columns etc. As a result your notebook looks messy and became and 
unreadable beast. Yet the mess becomes only worse once you start to deploy your research into a productive
application. The untested hard coded data pipelines need be be maintained at two places. 

The aim of this library is to conveniently operate with data frames without and abstract away the ugly unreproducible 
data pipelines. The only thing you need is the original unprocessed data frame where you started.
The data pipeline becomes a part of your model and gets saved that way. Going into production is as easy as 
this:

```python

import pandas as pd
import pandas_ml_utils  # monkey patch the `DataFrame`
from pandas_ml_utils import Model
# alternatively as a one liner `from pandas_ml_utils import pd, Model` 

model = Model.load('your_saved.model')
df = pd.read_csv('your_raw_data.csv')
df_prediction = df.model.predict(model)

# do something with your prediction
df_prediction.plot()
``` 


is intended to help you through your journey of statistical or machine learning models, 
while you never need to leave the world of pandas.

## Installation
The basic implementation supports [scikit learn][e1] classifiers and regressors.
```shell script

pip install pandas-ml-utils
```

Additional machine learning libraries are available as an add on:
```shell script

pip install pandas-ml-utils-torch  # pytorch implementation
pip install pandas-ml-utils-keras  # keras + tensorflow 1.x implementation
```

Note that the keras/tensorflow version is currently stalled as I focus on pytorch recently. This might change
with PyMC4 and tensorflow probability
 
## Example
You will find some demo projects in the [examples][ghl1] directory. But It might also be worth it to check
the unit tests and the [integration tests][ghl2]. Here is how classification challenge
might look like:
  
![Classification Example][ghi1]


[e1]: https://scikit-learn.org/stable/
[ghl1]: ./examples/
[ghl2]: ../pandas-ml-1ntegration-test
[ghi1]: ../.readme/images/classification.png
ixtranslate 0.0.1
ixtranslate is a free and unlimited python library
License: MIT
Requires Python: >=3.7
Keywords: translate

# ixranslate

[![python](https://img.shields.io/static/v1?label=python+&message=3.7%2B&color=blue)](https://img.shields.io/static/v1?label=python+&message=3.7%2B&color=blue)[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)[![PyPI version](https://badge.fury.io/py/ixtranslate.svg)](https://badge.fury.io/py/ixtranslate)

`ixtranslate` is a `free` and `unlimited` python translate library (uses Google translate)

## Install it

```shell
#to install:
pip install ixtranslate

# to upgrade:
pip install ixabolfazl -U

```

## Use it

The quality from this service is not as good as web google translate. There is nothing we can do about it.

It's unclear whether your ip will be blocked if you relentlessly use the service. Please feedback should you find out any information.

```python
from ixabolfazl import translate

# Translate to English by default
translate("Hallo Welt!")  # 'Hello world!'

translate("Hello world!", to_lang="fa")  # '!Ø³Ù„Ø§Ù… Ø¯Ù†ÛŒØ§'
translate("Hello world!", to_lang="de")  # 'Hallo Welt!'
translate("Hello world!",src="en" , to_lang="fa")  # '!Ø³Ù„Ø§Ù… Ø¯Ù†ÛŒØ§'
```

## Requirements

```
Python >=3.7
requests
json
```


## Disclaimer

``ixtranslate`` makes use of a translate interface floating around the net and is for study and research purpose only. The interface may become invalid without notice, which would render ``ixtranslate`` completely useless.

## Licenses

This project is licensed under the MIT License - see the LICENSE [LICENSE](https://github.com/ixabolfazl/ixtranslate/LICENSE) file for details


## Change log
```
0.0.1 (19/4/2021)
-------------------
- First Release
```