semvec/pypi-packages huggingface dataset:
PyAmdecoder 0.0.3
AndroidManifest.xml PyDecoder
License: MIT

# Py_Amdecoder
# - AndroidManifest.xml decoder -


Pypi URL:
https://pypi.org/project/PyAmdecoder/

install:

```sh
pip install PyAmdecoder==0.0.2
```

usage: 
```sh
from Py_Amdecoder.PyAmdecoder import PyAmdecoder 
PyAmdecoder("AndroidManifest_encode.xml")
```

output:
```sh
<?xml version="1.0" encoding="utf-8"?>
<manifest 
	xmlns:android="http://schemas.android.com/apk/res/android"
	android:versionCode="1"
	android:versionName="1.0"
	android:compileSdkVersion="30"
	android:compileSdkVersionCodename="11"
	package="com.example.myapplication"
	platformBuildVersionCode="30"
	platformBuildVersionName="11"
	>
	<uses-sdk
		android:minSdkVersion="16"
		android:targetSdkVersion="30"
		>
	</uses-sdk>
	<application
		android:theme="@7f1101bf"
		android:label="@7f10001c"
		android:icon="@7f0d0000"
		android:allowBackup="true"
		android:supportsRtl="true"
		android:roundIcon="@7f0d0001"
		android:appComponentFactory="androidx.core.app.CoreComponentFactory"
		>
		<activity
			android:theme="@7f1101c1"
			android:label="@7f10001c"
			android:name="com.example.myapplication.MainActivity"
			>
			<intent-filter
				>
				<action
					android:name="android.intent.action.MAIN"
					>
				</action>
				<category
					android:name="android.intent.category.LAUNCHER"
					>
				</category>
			</intent-filter>
		</activity>
	</application>
</manifest>
```
eda-fe-model 0.3.2
A python package to handle EDA and feature extraction and also return the best hyperparameters for a tabular classification problem.
License: MIT
Keywords: EDA,Feature_selection,Model_hyper_parametrs

# EDA FEATURE_EXTRACTOR MODEL

A python package to do EDA, feature selection and display the best hyperparameters for a pre-built classification model.

Useful for datasets with no NaNs or null values present.
Can be used for normal classification tasks, next update will work for Regression type problems and incorporate a sorted arrangement of variables.

before utilizing the package, ensure no Null or NaN values remain.

    pip install eda-fe-model

    pip install eda-fe-model==0.3.2

## Using the library
    from eda_fe_model import package

    package.EDA()
    package.feature_extraction()

Use to_categorical from keras.utils, to One Hot Encode the labels
    
    package.build_best_model()
    package.model_create()


## EDA
package.EDA accepts the following:

            dataset = pandas dataframe
            columns_drop = columns to drop as a list. Accepts None
            one_hot_encode = True/False
            label_encode = True/False
            normalize = True/False
            standardize = True/False
            target_varaible = single target, y, as array
            test_size = percentage of the dataset to be used for testing purposes
            random_state

    If the dataset only consists of categorical variables, set normalize or standardize to True.

    returns the splitted dataset: x_train, x_test, y_train, y_test (respectively)


## FEATURE EXTRACTION
package.feature_extraction accepts the following:

            train_X = train dataset consisting of predictors
            train_Y = train labels
            test_X = test dataset consisting of predictors
            test_Y = test labels
            rfe = True/False; Do you want to use Random Feature Extractor
            dim_out = Used only if rfe=True; output dimension; number of features to be selected 
            distribution = Distibution of the dataset you want to use for GLM

    If rfe is False, set dim_out and distribution to be None, to return the input x and y for train and test datasets.
    Try changing the distribution if error due to convergence appear.

    returns x_train and x_test datasets with the user entered dimension/predictors


## BUILD BEST MODEL
package.build_best_model accepts the follwoing:

            x = train dataset consisting of predictors
            y = One HOt Encoded training labels

    returns a RandomizedSearchCV object.

    Best Score: results.best_score_
    Best Parameters: results.best_params_

## CREATING THE MODEL
package.model_create accpets the best parameters from the build_best_model() and runs the model for a user specified epochs.

            x = the new train dataset consisting of just the predictors.
            y = One Hot encoded training labels
MatrixDemos 0.3
A collection of demos for the Adafruit 32x32 matrix and a Raspberry Pi
License: MIT
Requires Python: >=3.6

# MatrixDemos

*A collection of demos for a Raspberry Pi and the Adafruit 32x32 matrix*

### Software Dependencies

* Python >= 3.6

* Pillow
* pygame (for joystick support)

### Hardware Dependencies

* Raspberry Pi 2, 3, or 4
* [Adafruit RGB Matrix HAT](https://www.adafruit.com/product/2345)
* [Adafruit 32x32 RGB LED Matrix](https://www.adafruit.com/product/1484)
* [5V 4A power supply](https://www.adafruit.com/product/1466)
* USB joystick (optional)

## Instructions

1. [Assemble your Matrix HAT](https://learn.adafruit.com/adafruit-rgb-matrix-plus-real-time-clock-hat-for-raspberry-pi/assembly)
2. [Wire up and configure the Raspberry Pi](https://learn.adafruit.com/adafruit-rgb-matrix-plus-real-time-clock-hat-for-raspberry-pi/driving-matrices)
3. Install MatrixDemos

```bash
sudo pip3 install MatrixDemos
```

Now, the MatrixDemos are installed. You can test by running this, which lists the possible demo commands:

```bash
python3 -m matrixdemos
```

## Usage

Execute any of the following console commands.

* `matrix_digitalclock`: Shows the time and date. Run with `-sdn` to only display time.
* `matrix_analogclock`: Displays a simple analog clock, use command line options to change colors
* `matrix_slideshow path/to/images/`: Display images from a directory.  Use `-l` to change the slide time in seconds, and `-b` for the background color
* `matrix_snakegame` The classic "snake game" now on your matrix! **Requires a USB joystick!**
* `matrix_stars` Displays some twinkling stars. Use `-s` to adjust speed
* `matrix_textswarm` Displays text flying about. Use `-c` to edit the text color, and `-b` for the background color.
* `matrix_animations [[PATH]/[DEMO]]` Shows an animation from images in a directory. Use `-f` to adjust framerate, and `-l` and `-n` to control looping. Use `-g` to specify an animated gif or video
* `matrix_scrolling [[TEXT]/[PATH]]` Scrolls text or an image from left to right. Use `-c` and `-f` to adjust text color and font, `-s` for speed, `-r` to repeat, and `-b` to bounce.

Visit the [Gallery](https://github.com/nachomonkey/MatrixDemos/blob/master/extra/gallery.md) to see a visualization of each demo.

### Setting Colors:

When setting the color on (ie. `-c` in `matrix_textswarm`) you can specify either the color's name (RED) or its hex code ("#ff000").

Example of setting the color of the analog clock:
`matrix_analogclock -c "#333" -s Red -m white -h "#aaaaaa" -b "#001"`

# License

This software is licensed under the MIT License. See the [LICENSE](https://github.com/nachomonkey/MatrixDemos/blob/master/LICENSE) file for more details.
dbbackup 0.1.13
Package dbbackup to manage server database backups
License: MIT
Requires Python: >=3.6,<4.0
Keywords: backup,restore,databases,Mysql,PostgreSQL

# dbbackup to manage server databases backups:
- Mysql: this release
- PostgreSQL: next release
- Other: futures releases (need some help...)

Installation:

pip install dbbackup

dbbackup-tools is instaled with dbbackup dependencies

Run it:
- python
- import dbbackup_tools.util
- Enter your folder name to hold dbbackup configuration
- cd to your new dbbackup folder

Configuration:
- ajust the file .env to match your local configration

- To logging on smtp, rename and adjust:

loggin_config_example.yaml to loggin_config.yaml

- To backup Mysql databases, rename and adjust:

mysql-backup-script_example.config to mysql-backup-script.config

Work with it:
- python
- import dbbackup
- from dbbackup import check_dbbackup
dbbackup-tools 0.1.5
Package dbbackup to manage server database backups
License: MIT
Requires Python: >=3.6,<4.0
Keywords: backup,restore,databases,Mysql,PostgreSQL

# dbbackup-tools 
Tool to create a new dbbackup folder

pip install dbbackup

dbbackup-tools is instaled with dbbackup dependencies

Run it:
- python
- import dbbackup_tools.util
- Enter your folder name to hold dbbackup configuration
- cd to your new dbbackup folder

Configuration:
- ajust the file .env to match your local configration

- To logging on smtp, rename and adjust:

loggin_config_example.yaml to loggin_config.yaml

- To backup Mysql databases, rename and adjust:

mysql-backup-script_example.config to mysql-backup-script.config

Work with it:
- python
- import dbbackup
- from dbbackup import check_dbbackup