semvec/pypi-packages huggingface dataset:
welllogs 0.1.0.1b0
WellLogs is a package to visualize Oil And Gas Well logs in order to make interpretetion and petrophysics analysis
Requires Python: >=3.6

# WellLogs - Python

## Introduction.
This package is being designed among others to provide Petroleum Engineering tools in a modern programming language. This package is part of the project 7G which  proposes to make basic but powerful engineering software packages that cover the main topics of the Oil and Gas development phases which could be applied to any case study by suitable engineers.

There are five topics in which the project is going to be focused on:

<br>-Geoscience* (Current Package)
<br>-Reservoir
<br>-Production
<br>-Economics
<br>-Integration

<br> The package will always be in permanent development and open to suggestions to enhance the program. As the code has been written so far by a code enthusiastic Petroleum Engineer I hope to learn as much as possible to get better and usefull programs.

## WellLogs.jl Description
WellLogs.jl is a package to visualize Oil And Gas Well logs in order to make interpretetion and petrophysics analysis via PyPlot plots.

<br> The visualization of logs is based on Tracks functions. Each log track is implemented through a single function that plot the desired data. For example, the Lithology Track is composed generally by GammaRay and Sp logs, so LithoTrack function can plot the GammaRay and SP logs with optional features like add Well tops (Formations or Units), Gr clean and Gr shale to Vshale estimations, etc...
ttgmodsampleproject 1.3.1
A sample Python project
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4
Keywords: sample setuptools development

# A sample Python project

A sample project that exists as an aid to the [Python Packaging User
Guide][packaging guide]'s [Tutorial on Packaging and Distributing
Projects][distribution tutorial].

This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.

[The source for this project is available here][src].

Most of the configuration for a Python project is done in the `setup.py` file,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.

----

This is the README file for the project.

The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.

Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/tutorials/packaging-projects/
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
py-test-utility 0.0.2
useful package for pipelines testing and data mocking for new generation data warehouse
Requires Python: >=3.6

# py-test-utility 

Contains a collection of class and functions which aim to help developers implement and test data pipelines against the new generation data warehouses and storing system i.e. BigQuery and GCS

## Installation
py-test-utility can be installed via pip

```python 
pip install py-test-utility
```
## mockdata - class csv_mock(csv,schema) 
Extract the equivalent json from csv with nested and repeated records structures

### Args

- csv
    - path and file name of the csv
    - mandatory
    - nested fields shall be separated by a dot "."  (i.e. item.id, item.quantity)

|order | item.id | item.quantity | delivery.address | delivey.postcode |
|---|---|---|---|---|
| A0001 | item1 | 5 | address1 | e13bp |
| | item2 | 1 | | |
| | item3 | 3 | | |
| A0002 | item4 | 4  | address4 | e13bp |
| | item1 | 4 | | |
| | item3 | 2 | | |

- schema 
    - path and schema file name of the table schema
    - required if the CSV contain nested and repeated records
    - json format i.e. 
```json
[  
    {
      "mode": "NULLABLE", 
      "name": "order", 
      "type": "STRING"
    },  
    {
      "fields": [
        {
          "mode": "NULLABLE", 
          "name": "id", 
          "type": "STRING"
        },
        {
          "mode": "NULLABLE", 
          "name": "quantity", 
          "type": "STRING"
        }
      ], 
      "mode": "REPEATED", 
      "name": "item", 
      "type": "RECORD"
    }, 
    {
      "fields": [
        {
          "mode": "NULLABLE", 
          "name": "address", 
          "type": "STRING"
        }, 
        {
          "mode": "NULLABLE", 
          "name": "postcode", 
          "type": "STRING"
        }
      ], 
      "mode": "NULLABLE", 
      "name": "delivery", 
      "type": "RECORD"
    }
  ]
```

### Methods
- to_json()
    - if successfuls return the json extracted from the csv


### Usage

```python 
>>> from mockdata import mockdata as md
>>> mockdata_csv = md.csv_mock(
...     csv="mockdata/test/data/csv/repeated_records.csv", 
...     schema="mockdata/test/schema/repeated_records_schema.json") # initialise the object
>>> mockdata_json = mockdata_csv.to_json() # return the equivalent json
```