semvec/pypi-packages huggingface dataset:
pyvisauto 1.0.3
pyvisauto - a vision-based automation tool
Requires Python: >=3.4.*, <3.8

# pyvisauto

**pyvisauto** is a Python visual automation tool. Inspired by Sikuli, pyvisauto provides Python-native easy-to-use abstractions for complex interactions with on-screen visual elements by wrapping [OpenCV](https://opencv.org/) (specifically [opencv-contrib-python-headless](https://pypi.org/project/opencv-contrib-python-headless/)), [pyautogui](https://github.com/asweigart/pyautogui), [pytesseract](https://pypi.org/project/pytesseract/), and [numpy](https://numpy.org/).

Features include:

* OpenCV and numpy-driven image matching of on-screen elements
* TesseractOCR support
* Methods to find an image match (`find`), find all matches (`find_all`), check if a match exists (`exists`), wait until an image match occurs (`wait`), and wait for an image match to disappear (`wait_vanish`)
* Methods to click and hover over regions and matches (`click` and `hover`, respectively) with random x and y coordinates within the region
* Sub-region and cached matching for faster performance
* Method to save screenshots of matches and regions to a file (`screenshot`)

## Requirements

pyvisauto has been tested on Python 3.7. The `opencv-contrib-python-headless` package limits availability to Python 2.7 and 3.4 ~ 3.7. While pyvisauto should be compatible with Python 3, **Python 3.8 is currently not supported.**

## Installation and Usage

1. Install OS-specific dependencies:
    * Windows: No extra dependencies needed
    * Linux: `python3-xlib`
    * OSX: `pyobjc-core` and `pyobjc`, in that order
2. Install pyvisauto using pip: `pip install pyvisauto`
3. Import pyvisauto: `import pyvisauto`
4. Read the Quick Start and API docs

## Quick Start

* Define a full-screen region and assign it to `r`:

    ```
    r = pyvisauto.Region()
    ```

* Define a region with the upper-left corner at x: 50px and y: 100px, with a width of 500px and height of 300px and assign it to `r`:

    ```
    r = pyvisauto.Region(50, 100, 500, 300)
    ```

* Find the image `asset1.png` in the defined region, with a similarity score of 0.9:

    ```
    match1 = r.find('asset1.png', 0.8)
    ```

* If there has been no visual changes in the defined region, subsequent `find` actions can be expedited by passing in `cached=True`:

    ```
    match2 = r.find('asset2.png', 0.9, cached=True)
    ```

* `find_all` and `exists` can be used in a similar fashion as `find`.

* Hover over a random point in the first returned match:

    ```
    match1.hover()
    ```

* Click a random point in the second returned match:

    ```
    match2.click()
    ```

* One can use `wait` and `vanish` to wait for on-screen changes, detected by the presence or disappearance of an image on-screen, respectively:

    ```
    r.wait('wait_asset1.png', 30, 0.8)
    ```

    The above code will wait for `wait_asset1.png` in the previously defined region `r`, with a minimum similarity score of 0.8, waiting a maximum of 30 seconds before throwing a `FindFailed` exception. `vanish`, on the other hand, throws a `VanishFailed` exception. Both exceptions are defined in the `pyvisauto` module.
SnowFlakeToDiagramERScript 0.0.1
Converts SnowFlake diagrams to script for dbdiagram.io
Requires Python: >=3.6

# SNOWFLAKE TO DIAGRAM SCRIPTS

This project transform a set of tables from SNOWFLAKE to a full ``Entity Relation Diagram``, your time is so valious to waste time drawing table by table, relation by relation, just follow this steps.

1. Download Tables Specification
2. Add Facts-Dimension tags (optional)
3. Create Script
4. Print or save the final script to a file
5. Paste the generated code in https://dbdiagram.io
6. Fix it in your own way
7. Be happy. !!! :)

## Installation

```bash
pip install SnowFlakeToDiagramScript==0.0.1
```

## Usage
You can find a Example Script in the rootfolder of this REPO

1. Download Tables Specification   
![Query Columns Name](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/SnowflakeDownload1.png)   
![Download File](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/SnowflakeDownload1.png)   
**NOTE: You have to download all CSV in the same folder**

2. Add Facts-Dimension tags on CSV optional
![Download File](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/FactsDimensionTags.png)  
Add a last column in csv file with title ``FoD`` and Facts or Dimension in every field

3, 4. Create script using library

```python
from SnowFlakeToDiagram.Converter import Converter
import os

currentDir      = os.getcwd()
src_path        = f"{currentDir}/DataExample/OriginTables"
target_path     = f"{currentDir}/DataExample/target"

# Create the relations Array
# [ SOURCE TABLENAME, SOURCE FIELD, RELATION, TARGET TABLENAME, TARGET FIELD  ]
relations = [
    ["SNOWFLAKE_SAMPLE_DATA__TPCH_SF1__NATION", "N_NATIONKEY", "<", "SNOWFLAKE_SAMPLE_DATA__TPCH_SF1__SUPPLIER", "S_NATIONKEY"]
]

Converter(src_path, target_path, saveToFile=True, print_script=True, relations = relations)
```

Note the correct format to create a relation
```python
relations = [ # List of relations
    [ SOURCE TABLENAME, SOURCE FIELD, RELATION, TARGET TABLENAME, TARGET FIELD  ]    # Specific relation
]
```
Set the correct source folder of your CSV Files and your target folder

5. Paste the generated code in https://dbdiagram.io   
![Paste Code in https://dbdiagram.io](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/PasteCode.png)   

6. Fix it in your own way
You can edit your schema as you want, addign colors, or new relations

## Next steps
1. Query the tables from SNOWFLAKE using SQNOWFLAKE Python CONNECTOR   

## Contributors

- GermÃ¡n AndrÃ©s JejÃ©n CortÃ©s   [Twitter](https://twitter.com/andres_jejen)
SnowFlakeToDiagramIOScript 0.0.1
Converts SnowFlake diagrams to script for dbdiagram.io
Requires Python: >=3.6

# SNOWFLAKE TO DIAGRAM SCRIPTS

This project transform a set of tables from SNOWFLAKE to a full ``Entity Relation Diagram``, your time is so valious to waste time drawing table by table, relation by relation, just follow this steps.

1. Download Tables Specification
2. Add Facts-Dimension tags (optional)
3. Create Script
4. Print or save the final script to a file
5. Paste the generated code in https://dbdiagram.io
6. Fix it in your own way
7. Be happy. !!! :)

## Installation

```bash
pip install SnowFlakeToDiagramScript==0.0.1
```

## Usage
You can find a Example Script in the rootfolder of this REPO

1. Download Tables Specification   
![Query Columns Name](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/SnowflakeDownload1.png)   
![Download File](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/SnowflakeDownload1.png)   
**NOTE: You have to download all CSV in the same folder**

2. Add Facts-Dimension tags on CSV optional
![Download File](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/FactsDimensionTags.png)  
Add a last column in csv file with title ``FoD`` and Facts or Dimension in every field

3, 4. Create script using library

```python
from SnowFlakeToDiagram.Converter import Converter
import os

currentDir      = os.getcwd()
src_path        = f"{currentDir}/DataExample/OriginTables"
target_path     = f"{currentDir}/DataExample/target"

# Create the relations Array
# [ SOURCE TABLENAME, SOURCE FIELD, RELATION, TARGET TABLENAME, TARGET FIELD  ]
relations = [
    ["SNOWFLAKE_SAMPLE_DATA__TPCH_SF1__NATION", "N_NATIONKEY", "<", "SNOWFLAKE_SAMPLE_DATA__TPCH_SF1__SUPPLIER", "S_NATIONKEY"]
]

Converter(src_path, target_path, saveToFile=True, print_script=True, relations = relations)
```

Note the correct format to create a relation
```python
relations = [ # List of relations
    [ SOURCE TABLENAME, SOURCE FIELD, RELATION, TARGET TABLENAME, TARGET FIELD  ]    # Specific relation
]
```
Set the correct source folder of your CSV Files and your target folder

5. Paste the generated code in https://dbdiagram.io   
![Paste Code in https://dbdiagram.io](https://raw.githubusercontent.com/AndresJejen/SnowFlakeToDiagramScript/master/Images/PasteCode.png)   

6. Fix it in your own way
You can edit your schema as you want, addign colors, or new relations

## Next steps
1. Query the tables from SNOWFLAKE using SQNOWFLAKE Python CONNECTOR   

## Contributors

- GermÃ¡n AndrÃ©s JejÃ©n CortÃ©s   [Twitter](https://twitter.com/andres_jejen)
PyDSALib 0.2
A library of data structures, implemented in Python for Python
Requires Python: >=3.6

# PyDSALib

PyDSALib is a data structures library, written in Python for Python. It aims to provide an efficient implementation and a 
convenient interface for each data structure, as well as a selection of useful methods and algorithms to manipulate them.

PyDSALib is very much still in development - see the TODO section. We welcome open-source contributions to help improve
this library.

Data structures currently supported include:

- Queue
- Deque
- Stack
- Linked List
- Binary Heap
- Priority Queue
- Binary Search Tree
- AVL Tree
- Graph

Documentation (PDF)is included in the `/docs` folder

# Installation

Prerequisites:
- Python3.6 or later

Installation using pip:

```pip install PyDSALib==0.1```

Clone from GitHub:

```git clone https://github.com/HerrHruby/PyDSALib.git```
SnowFlakeToDiagramScript 0.0.1
Converts SnowFlake diagrams to script for dbdiagram.io
Requires Python: >=3.6

# SNOWFLAKE TO DIAGRAM SCRIPTS

This project transform a set of tables from SNOWFLAKE to a full ``Entity Relation Diagram``, your time is so valious to waste time drawing table by table, relation by relation, just follow this steps.

1. Download Tables Specification
2. Add Facts-Dimension tags (optional)
3. Create Script
4. Print or save the final script to a file
5. Paste the generated code in https://dbdiagram.io
6. Fix it in your own way
7. Be happy. !!! :)

## Installation
py-mcc-f1 0.1.0
MCC-F1 Curve
Requires Python: >=3.6

# MCC-F1 Python package

Recently, the MCC-F1 curve has been proposed as an alternative, better way of assessing the performance of score-based binary classifiers [1].

This Python package implements a function to compute the MCC-F1 curve, namely `mcc_f1_curve`, similarly to the `precision_recall_curve` and `roc_curve` functions of [scikit-learn](https://github.com/scikit-learn).


## Installation

```console
pip install py-mcc-f1
```

## Usage

```python
from mcc_f1 import mcc_f1_curve, plot_mcc_f1_curve

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Load data and train model
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = LogisticRegression().fit(X_train, y_train)

# Calculate MCC-F1 metric
# @TODO

# Get predictions and MCC-F1 curve points
y_score = clf.predict_proba(X_test)[:,1]
mcc, f1, thresholds = mcc_f1_curve(y_test, y_score)

# Plot MCC-F1 curve
plot_mcc_f1_curve(clf, X_test, y_test)
```

Please refer to the function's docstring for further comments and details.


## Future enhancements

- [x] Function to plot the MCC-F1 curve, (e.g., `plot_mcc_f1_curve`), similar to `sklearn/metrics/_plot/precision_recall_curve.py` and `sklearn/metrics/_plot/roc_curve.py`;
- [ ] Function to compute the MCC-F1 metric, as defined in section 2.2 of the original paper.


## Contributing

If you would like to contribute to this package, please follow the [common community guidelines](https://github.com/MarcDiethelm/contributing).

Please, also keep in mind that the main goal of this project is to be of similar implementation and quality as scikit-learn. Pull requests should pass the existing unit-tests, and add new ones when necessary.

To run the tests:

```console
make test
```

## License

This package is distributed under the [MIT license](./LICENSE).

## References

1. [[2006.11278] The MCC-F1 curve: a performance evaluation technique for binary classification](https://arxiv.org/abs/2006.11278)
pykkachu 0.0.1
An Actor Model which packages Pykka and FSMs
Requires Python: >=3.6

# Pykkachu

A convenience library on top of Pykka and PySM to help with common Actor Model patterns.
[Github](https://github.com/amcknight/pykkachu)

## Roadmap
### v0.1
- Expand to work with all Actor types instead of just ThreadingActors
- Better encapsulate Pykka and PySM so they are not imported by users
- Make Fleet abstract and take a subactor selection algorithm as param
- Unit tests
### Beyond
- Create a config loader for wiring up simple Actors
- Add simple patterns like "Retry" and "When"

## Changelog
### v0.0.1
- Made it exist by pulling it from [gab](https://github.com/amcknight/gab)
- Factored it into classes
- Pulled a Message class in from [gab](https://github.com/amcknight/gab)

## Deployment

```commandline
rm -rf dist
pipenv shell
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*
exit
```