semvec/pypi-packages huggingface dataset:
linuxmedia 0.1.4
Linuxmedia is a wrapper for 60 above 60+ Linux Commands

## Linuxmedia is a wrapper for 60 above 60+ Linux Commands

#### Prerequisites


On Ubuntu/Debian:

```
sudo apt install update
sudo apt install upgrade
```



#### Usage

To get all available info about Linuxmedia Command and Its usage :
```
linuxmedia <command> [Options..]

```
lifedata 0.1.1
A Python Framework for Tracable Active Learning Projects.
Requires Python: >=3.10

# LIFEDATA

![Process Outline of a Active Learning Project with LIFEDATA](docs/process_outline.jpg)

## Usage

Create a project specific conda environment (e.g. `conda create -n myproject python=3.10`) or use your conda base environment

Install lifedata in your environment via `pip install lifedata`

After lifedata installation use `lifedata --help` to get help for lifedata commands

Generate your LIFEDATA project by using the [project template](https://github.com/ds-lab/lifedata-project-template) `lifedata init <project instance name>`

Further steps can be found in the project's README.

## Custom Development

### Setup

To create your local development environment (e.g. for integration with your IDE) run:

```bash
conda env create
```

This will create you a local environment called `lifedata` with all necessary dependencies installed.

### Run development server

To start the project related services run:

```bash
docker-compose up
```

You can then access the frontend at http://localhost:3000/ in your browser. The API is available at http://localhost:8000/ and is also proxied via the frontend (to avoid CORS issues).

Storybook can be accessed at http://localhost:3001

### Tests

There are tests for the python code as well as tests for the TypeScript based frontend code.

#### Python Tests

To always keep the tests running and see if everything is still working as expected, run::

    ptw

To run the tests once, just do: `py.test`

#### TypeScript Tests

Run `yarn test` in the `webui` directory.

### Database Migrations

If you want to adjust the database tables, you also need to prepare a migration script using `alembic` in order to provide executable instructions on how to alter the database.

For most cases it's sufficient to change the table definitions in the code and then run::

    docker-compose run --rm webapi alembic revision --autogenerate -m "your change message here"

Now inspect the auto generated file if everything is as it is supposed to be.

Then run `alembic upgrade head` in the `api` container to apply these changes to your local database.

## Citation

coming soon
tank-forecaster 0.3.63
small package for generating forecasts for gas tanks
Requires Python: >=3.6
Keywords: tank_forecaster

===============
Tank Forecaster
===============

Small package for generating forecasts of fuel tank demand.


Features
--------

- tank_forecaster.validation
    - clean and format sales data
    - clean and format tank reading data
    - use sales data to impute missing values in tank readings
- tank_forecaster.decompositions
    - create seasonal decomposition curves (yearly / monthly / weekly)
    - generate generic decomposition curves for instances of no data
- tank_forecaster.forecaster
    - create short term forecasts with generalized additive models (prophet)
    - create long term forecasts with ARIMA type models
- tank_forecaster.events
    - define holidays and special events that effect fuel demand


Credits
-------

This package was created with Cookiecutter


=======
History
=======

0.1.0 (2020-05-11)
------------------

* First release on PyPI.
faciesteller 1.0.5
This package is created to classify facies using FMI image and depth CSV data, output prediction result with csv format as well as image.

# faciesteller
faciesteller is a Python package as Techlog plug-in, which automate facies classifaction from borehole image. 


## installation
To install faciesteller, you need to navigate to Python3 folder of Techlog using command prompt.
The usual Techlog Python3 folder address likes this: "C:\Program Files\Schlumberger\Techlog Version\Python3".
Code in: python.exe -m pip install faciesteller and hit execute, that is it.


## usage
The model has been trained with Resnet50 using local FMI image and geolgical interpretation. It might be used in same geological area with caution, while local expertise is very important and advised though. 

To successfully predict faices, need to have the training model (model.h5) in the following address:
C:\Users\username\AppData\Roaming\Schlumberger\Techlog\model\model.h5

As for pratical manipulation, it requires to open a new python eiditor in Techlog and paste below coding inside, add three rows for inputs in paramters area. The rows are CSV path for depth data, img path for FMI image and well name.

Code to use in python script area:

from faciesteller.faciesteller import FaciesClassifier

fc = FaciesClassifier(csv_path, img_path, well_name)
df_csv, img_array = fc.load_data()
print("DataFrame shape: ", df_csv.shape, 
	"\nImagearray shape: ", img_array.shape)
	
df_output = fc.imagetochunk(df_csv, img_array)

df_predict = fc.predict(df_output)
print(df_predict)


## contributing
If you'd like to contribute to faciesteller, please contact me.


## license
My Package is licensed under the MIT License. See LICENSE for more information.