semvec/pypi-packages huggingface dataset:
slackblockkit 1.0.2
Python wrapper for the Slack Block Kit API

# slackblockkit


## What is it?

`slackblockkit` is Python API for building messages in the fancy new Slack Block Kit API.

Based on [slackblocks](https://github.com/nicklambourne/slackblocks)

## Requirements
`slackblockkit` requires Python >= 3.7

## Installation

```bash
pip install slackblockkit
```

## Usage

```python
from slackblocks import Message, SectionBlock


block = SectionBlock("Hello, world!")
message = Message(channel="#general", blocks=block)
message.json()

```

Will produce the following JSON string:
```json
{
    "channel": "#general",
    "mrkdwn": true,
    "blocks": [
        {
            "type": "section",
            "block_id": "992ceb6b-9ad4-496b-b8e6-1bd8a632e8b3",
            "text": {
                "type": "mrkdwn",
                "text": "Hello, world!",
                "verbatim": false
            }
        }
    ]
}
```
Which can be sent as payload to the Slack message API HTTP endpoints.

Of more practical use is the ability to unpack the objects directly into 
the Python Slack Client to send messages:
```python
from os import environ
from slack import WebClient
from slackblocks import Message, SectionBlock


client = WebClient(token=environ["SLACK_API_TOKEN"])
block = SectionBlock("Hello, world!")
message = Message(channel="#general", blocks=block)

response = client.chat_postMessage(**message)
```

Note the `**` operator in front of the `message` object.
pdfkivygui 0.1.0
Package that enables the user to use Pandas Dataframe inside Kivy Apps.
Requires Python: >=3.7
Keywords: Kivy,Pandas DataFrame,Graphs

# DataframeGUIKivy

Port of Dataframe GUI module to a Kivy widget. Original module can be found at this repository: https://github.com/bluenote10/PandasDataFrameGUI

# Updates
This fork has been updated to use the current version of all required libraries.

* matplotlib _png is no longer needed to be imported and was removed
*  pandas.DataFrame.from_items was [deprecated since version 0.23.0](https://github.com/pandas-dev/pandas/blob/v0.23.4/pandas/core/frame.py#L1422-L1507) 
and has been replaced with pandas.DataFrame.from_dict
* kivy.garden.matplotlib was giving me trouble so I just included the source in garden.matplotlib
* added in requirements.txt so all requirements can be installed through pip.

## Demo Run Instructions

```sh
$ python demo.py
```

## Demo Screenshots

<p align="center">
  <img src="https://raw.githubusercontent.com/MichaelStott/DataframeGUIKivy/master/docs/sc1.png">
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/MichaelStott/DataframeGUIKivy/master/docs/sc2.png">
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/MichaelStott/DataframeGUIKivy/master/docs/sc3.png">
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/MichaelStott/DataframeGUIKivy/master/docs/sc4.png">
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/MichaelStott/DataframeGUIKivy/master/docs/sc5.png">
</p>
MLProto 0.2.0
Modular Neural Network Protyping for Stock Market Prediction
Requires Python: >=3.6

# MLProto: Modular Prototyping Tool for LSTM Machine Learning Models

## Usage

### Video overview coming

### The ProtoMake Script

The ProtoMake script combines the Proto and Data modules into one to create an easy, convenient, and modular neural network prototyping tool for LSTM machine learning models. The script will take the user's desired parameters and create, train, and evaluate a model fitting said parameters. This allows the user to quickly analyze model prototypes, make adjustments, and iterate on model designs.

#### Arguments

    Positional:
        key ---- User's Alpha_Vantage API key
        identifier ---- Ticker symbols to create models for
        target ---- Column number of target values

    Optional:
        -depth ---- number of layers to include in the neural network (def: 1)
        -node_counts ---- list of node counts for layers (len(node_counts) must equal depth)
        -batch ---- batch size of input data set (def: [100])
        -test_size ---- proportion of dataset to use as validation (def: .2)
        -loss ---- identifier string of keras-supported loss function to be used in training (def: mse)
        -learning_rate ---- learning rate to be used by the Adam optimizer
        -epochs ---- maximum number of epochs to train the model (def: 100)
        -model_in ---- file path of pre-made model to load
        --early_stop ---- flag deciding whether to apply early stopping (patience 5) to the training phase
        --plots ---- flag deciding whether to save loss, input, and prediction graphs
        --normalize ---- flag deciding whether or not to normalize input data

#### Usage Example

    ProtoMake test.csv test_model --early_stop --plots

The above command will create, train, and evaluate a model for the data in test.csv. It saves a model test_model.h5 in directory ./models/ and input, loss, and prediction graphs in the directory ./plots/ for analysis.

### The Proto Module

The Proto module contains the core functionality of the machine-learning portion of the package. It holds all of the model manipulation methods.

    ``` Python
    from MLProto import *

    """ Data operations (assign data, pred_data)
    ______________________________________________
    """

    stkr = Proto('test', data)
    stkr.train(25, True, True))
    stkr.evaluate()
    stkr.predict_data(pred_data)
    ```

The above code will take prepared data, create a Proto instance "test" for the data given, train for 25 epochs, save the model to the models folder as train.h5 and predict data points based on the user's prepared prediction data.

### The Data module

This module includes the data operation helper functions used by MLProto.

single_step_data takes a full dataset and creates a timeseries dataset from it for input into an LSTM model.

## Contributions

Please send pull requests! I am a full-time student, so development and support for MLProto will likely be slow with me working alone. I welcome any and all efforts to contribute!

## License

[GNU LGPLv3](https://choosealicense.com/licenses/lgpl-3.0/)
bluff 1.0.2
Bluff is a pythonic poker framework
Requires Python: >=3.6
Keywords: poker

# bluff
Bluff is a pythonic poker framework.

[![PyPi Version](https://img.shields.io/pypi/v/bluff.svg)](https://pypi.python.org/pypi/bluff/)
[![MIT License](https://img.shields.io/github/license/matheusccouto/bluff)](https://github.com/matheusccouto/bluff/blob/master/LICENSE)

Currently, bluff covers the following poker variants:
* Texas Hold'em
* Chinese Poker

## Getting Started
### Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Bluff.
```bash
pip install bluff
```
### Usage
#### Evaluating hands
```python
import bluff

# Hands can be created by passing Card instances as arguments.
hand1 = bluff.Hand(
    bluff.Card("5d"),
    bluff.Card("4s"),
    bluff.Card("5s"),
    bluff.Card("4c"),
    bluff.Card("5h"),
)

hand1.name
>>> "full_house"
```
#### Comparing hands values
```python
import bluff

# Hands can also be created by passing their string representations.
hand1 = bluff.Hand("5d", "4s", "5s", "4c", "5h")
# Concatenated strings are also accepted.
hand2 = bluff.Hand("Jh", "Td", "Js", "5s", "8d")

hand1.value > hand2.value
>>> True
```

#### Drawing a card for a player
```python
import bluff

player = bluff.Player(name="Chris Moneymaker", chips=10000)
deck = bluff.Deck()
card = deck.draw()
player.hand.add(card)
```
#### Evaluating equity with Monte Carlo
```python
from bluff.holdem import equity

equity.equity(("QQ", "AKo"), times=10000)
```
#### Evaluating a hand equity against several ranges
```python
from bluff.holdem import equity

# Ranges descriptions.
equity.eval_ranges("JTs", ("KQs ATo 99", "AKs QQ"), times=10000)
# Ranges percentages.
equity.eval_ranges("JTs", (10, 30), times=10000)
```

For more information see the [documentation](https://matheusccouto.github.io/bluff/).

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.


## Author

* **Matheus Couto** - [Linkedin](https://www.linkedin.com/in/matheusccouto/)

## License
This project is licensed under the [MIT](https://choosealicense.com/licenses/mit/) License.