semvec/pypi-packages huggingface dataset:
pycxx 0.0.4
simple crypting library ;)
License: MIT
Requires Python: >=3.0

# Cxx
### simple crypting library

## installation
```bash
pip install pycxx
```

## usage
```python
import pycxx

c = pycxx.Cxx(key="password", expires=0)
"""${expires} is the Time in milliseconds for the 
encrypted password to be destroyed
setting ${encrypted} to 0 implies the data would
not be destroyed
"""

# to encrypt;
data = dict(
    name="rubbie kelvin",
    country="Nigeria"
)
encrypted = c.encrypt(**data) # =>str


# to decrypt
data2 = pycxx.Cxx.decrypt(encrypted, key="password") # => dict
```

`made with ❤️ by rubbie`
tf-fourier-features 0.0.2.1
Tensorflow 2.0 implementation of Fourier Features mapping networks.
License: MIT
Requires Python: >=3.0.0


# Tensorflow Fourier Feature Mapping Networks
Tensorflow 2.0 implementation of Fourier Feature Mapping networks from the paper [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://arxiv.org/abs/2006.10739).

# Installation

 - Pip install

```bash
$ pip install --upgrade tf_fourier_features
```

 - Pip install (test support)

```bash
$ pip install --upgrade tf_fourier_features[tests]
```

# Usage

```python
from tf_fourier_features import FourierFeatureProjection
from tf_fourier_features import FourierFeatureMLP

# You should use FourierFeatureProjection right after the input layer.
ip = tf.keras.layers.Input(shape=[2])
x = FourierFeatureProjection(gaussian_projection = 256, gaussian_scale = 1.0)(ip)
x = tf.keras.layers.Dense(256, activation='relu')(x)
x = tf.keras.layers.Dense(3, activation='sigmoid')(x)

model = tf.keras.Model(inputs=ip, outputs=x)

# Or directly use the model class to build a multi layer Fourier Feature Mapping Network
model = FourierFeatureMLP(units=256, final_units=3, final_activation='sigmoid', num_layers=4,
                          gaussian_projection=256, gaussian_scale=10.0)
```

# Results on Image Inpainting task
A partial implementation of the image inpainting task is available as the `train_inpainting_fourier.py` and `eval_inpainting_fourier.py` scripts inside the `scripts` directory.

Weight files are made available in the repository under the `Release` tab of the project. Extract the weights and place the `checkpoints` folder at the scripts directory.

These weights generates the following output after 2000 epochs of training with batch size 8192 while using only 10% of the available pixels in the image during training phase.

<img src="https://github.com/titu1994/tf_fourier_features/blob/master/images/celtic_knot_10pct_kernel20.png?raw=true" height=100% width=100%>

------

If we train for using only 20% of the available pixels in the image during training phase - 

<img src="https://github.com/titu1994/tf_fourier_features/blob/master/images/celtic_knot_20pct_kernel20.png?raw=true" height=100% width=100%>

------

If we train for using only 30% of the available pixels in the image during training phase - .

<img src="https://github.com/titu1994/tf_fourier_features/blob/master/images/celtic_knot_30pct_kernel20.png?raw=true" height=100% width=100%>

# Citation

```
@misc{tancik2020fourier,
    title={Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains},
    author={Matthew Tancik and Pratul P. Srinivasan and Ben Mildenhall and Sara Fridovich-Keil and Nithin Raghavan and Utkarsh Singhal and Ravi Ramamoorthi and Jonathan T. Barron and Ren Ng},
    year={2020},
    eprint={2006.10739},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

# Requirements
 - Tensorflow 2.0+
 - Matplotlib to visualize eval result
pyOMT5 0.0.29
pyOMT5 - Python Open Metatrader 5 module
License: MIT
Keywords: stocks,market,finance,metatrader,quotes,shares

pyOMT5 - Python Open MetaTrader 5
===================================

Python module to request data from MetaTrader 5

To get started:
- install visual c++ 2010 redistributable (x86 or x64 according with your os) 
- install visual c++ 2015 redistributable (x86 or x64 according with your os) 
- create a new directory called pyOMT5 inside your MT5 Scrips folder. Ex: C:\Users\MyUser\AppData\Roaming\MetaQuotes\Terminal\83D4764E0403A8685E84D6FCAB361AAB\MQL5\Scripts\pyOMT5
- download DLL files according your OS (32|64 Bits) from [ZMQ](https://github.com/dingmaotu/mql-zmq/tree/master/Library/VC2010) to the new direcotry called pyOMT5
- copy the files pyOMT5Server.mq5 and pyOMT5Server.ex5 from util directory called pyOMT5 to this new directory and compile it using MetaEditor if necessary.
- run pyOMT5Server as a expert. Remember to have enabled auto trading option and allow DLLs imports when asked.

This module provides data in pandas representation because we believe that whether in finance, scientific fields, or data science, a familiarity with Pandas is known by everyone.

This library uses ZeroMQ and create a internal tcp server on MetaTrader, with the purpose of exchange data with MT5.


Install
---------
To install the package use:
```bash
pip install pyOMT5
```

If you want to install from source, then use:
```bash
git clone https://github.com/paulorodriguesxv/pyOMT5.git
pip install -e pyOMT5
```

Usage
-------
```python

from datetime import datetime
from pyomt5.api import MT5TimeFrame
from pyomt5.stock import StockPriceHistory

c = StockPriceHistory()

start_date = datetime(2019, 1, 1)
end_date = datetime(2019, 5, 2)
data = c.get_price_from(symbol='PETR4',
                        from_date=start_date,
                        to_date=end_date,
                        timeframe=MT5TimeFrame.Daily)

print(data)

start_date = datetime(2019, 1, 2, 10, 0)
end_date = datetime(2019, 1, 2, 12, 0, 0)
data = c.get_price_from(symbol='PETR4',
                        from_date=start_date,
                        to_date=end_date,
                        timeframe=MT5TimeFrame.M5)

print(data)

```

Contributing
-------------
Contributing is always welcome, so, feel free to getting in touch and contribute.

TODOs
-------------
-   Add test for library.
-   Create sphinx docs
-   Create travis
-   Extend Api

Star if you like it.
---------------------
If you like or use this project, consider showing your support by starring it.