semvec/pypi-packages huggingface dataset:
agrimetscraper 0.4.2
A package to scrape AgriMet weather data

# agrimetscraper


### introduction

agrimetscraper crawls AgriMet weather data and summarise for later data usage.

### basic usage

Python virtual environment is preferred

`pip install agrimetscraper`

`startproject -p <yourproject> -t <select database type: mongodb or sql or atlas>`

It will create a directory for your project, this folder will contain yourproject-config, yourproject-log, yourproject-database, yourproject-stations

>If you choose 'atlas', the program will prompt you for your atlas connection string and the password 


cd into your project, change .ini file if needed


**There are two baseurls for agrimet API: one is for daily, the other is for instant, are all defined in ini file**

run project: python runproject.py -f "instant"|"daily" -n dbtable

set scheduler in .ini file (this program uses crontab)

**For instant data, program will aggregate 15 minutes into 1 hour interval and average the parameters
nacha3 0.0.2
NACHA File Generation

=====
nacha
=====

.. image:: https://travis-ci.org/DisruptiveLabs/nacha.png
   :target: https://travis-ci.org/DisruptiveLabs/nacha

.. image:: https://coveralls.io/repos/DisruptiveLabs/nacha/badge.png?branch=master
  :target: https://coveralls.io/r/DisruptiveLabs/nacha?branch=master

`NACHA <http://www.regaltek.com/docs/NACHA Format.pdf>`_ is a fixed sized
record format used to represent financial transactions composed like this:

.. code::

    FileHeader
        CompanyBatchHeader
            EntryDetail
                EntryDetailAddendum
                ...
            ...
        CompanyBatchControl
        ...
    FileControl

which we express using `bryl <https://github.com/balanced/bryl/>`_. Writing is
done like this:

.. code:: python

    with open('sample.nacha', 'w') as fo:
        writer = nacha.Writer(fo)
        with writer.begin_file(
             ...
             ):
             with writer.begin_company_batch(
                  ...
                  ):
                 writer.entry(...):
                 ...
            ...

Reading is done by iterating records like this:

.. code:: python

    with open('sample.nacha', 'r') as fo:
        reader = Reader(fo, include_terminal=True)
        for record, terminal in reader:
            ...

Or structured like this:

.. code:: python

    with open('sample.nacha', 'r') as fo:
        reader = Reader(fo)
        reader.file_header()
        for company_batch_header in reader.company_batches():
            for entry_detail, entry_addenda in reader.entries():
                ...
            reader.company_batch_control()
        reader.file_control()

===
use
===

.. code:: bash

   $ pip install nacha3

===
dev
===

.. code:: bash

   $ git clone https://github.com/rockxraj68/nacha3.git
   $ cd nacha
   $ mkvirtualenv nacha
   (nacha)$ pip install -e .[tests]
   (nacha)$ py.test tests.py --cov=nacha --cov-report term-missing
fast-package-file 1.1
Package a directory to a file, with fast file access and compression support

[![PyPI](https://img.shields.io/pypi/v/fast-package-file.svg)](https://pypi.org/project/fast-package-file/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fast-package-file.svg)](https://pypi.org/project/fast-package-file/)
[![Build Status](https://travis-ci.com/Kataiser/fast-package-file.svg?branch=master)](https://travis-ci.com/Kataiser/fast-package-file)
[![Documentation Status](https://readthedocs.org/projects/fast-package-file/badge/?version=latest)](https://fast-package-file.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/Kataiser/fast-package-file/branch/master/graph/badge.svg)](https://codecov.io/gh/Kataiser/fast-package-file)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/5018ac2a202145eca3d0ede57904815e)](https://app.codacy.com/app/Kataiser/fast-package-file?utm_source=github.com&utm_medium=referral&utm_content=Kataiser/fast-package-file&utm_campaign=Badge_Grade_Dashboard)

# fast_package_file
Package a directory to a file, with fast file access and compression support

```python
import fast_package_file

# Package a directory into a file
fast_package_file.build('a_directory', 'a_package.file')

# Prepare a package file
data_package = fast_package_file.PackagedDataFile('a_package.file')

# Load a file from the packed directory and save it
with open('any.file', 'wb') as any_file:
  data_package.load_file('path\\to\\any.file')

# Or just get the raw binary data
from PIL import Image
i = Image.open(io.BytesIO(data_package.load_file('image.png')))

# Some other useful functions
data_package.load_bulk(prefix='audio\\sfx\\', postfix='.wav')
fast_package_file.oneshot('a_package.file', 'path\\to\\any.file')
fast_package_file.oneshot_bulk('a_package.file', prefix='audio\\sfx\\', postfix='.wav')
```

## Installation
From PyPI:
```shell
pip install fast-package-file
```
Or from Github:
```shell
pip install git+git://github.com/Kataiser/fast-package-file.git@master#egg=fast_package_file
```

## Documentation
More details will be available at https://fast-package-file.readthedocs.io/en/latest/

## Features
-   Is fast because only the data needed is loaded from the package file, total package size is irrelevant
-   Obfuscates files from (most) users
-   Like a .zip file, but doesn't decompress the entire thing when reading just one file
-   Includes the entire directory and subdirectories, not just surface-level files
-   Files are compressed with Gzip, but only if compression improves file size (per file) and is enabled (per package file)
-   Pretty good error handling when loading package files, just catch fast_package_file.PackageDataError
-   Inspired by video game packaging, such as UE4's .pak or GTA V's .rpf formats
-   Cross-platform, has CI for Linux, MacOS, and Windows

## Contribute
-   Issue Tracker: https://github.com/Kataiser/fast-package-file/issues
-   Source Code: https://github.com/Kataiser/fast-package-file

## License
The project is licensed under the MIT license.
pymean 0.30
The pyMEAN package is designed to facilitate semi-automated enrichment analysis for metabolomic experiments.
Requires Python: >= 3.6
Keywords: metabolomics,enrichment analysis,omics

# pyMEAN: Metabolomic Enrichment ANalysis in Python

The pyMEAN package is designed to facilitate semi-automated enrichment analysis for metabolomic experiments.

## Installation

pyMEAN requires Python 3+ and is unfortunately not compatible with Python 2. If you are still using Python 2, a clever workaround is to install Python 3 and use that instead.

The easiest way of installing pyMEAN is using pip:

```
pip install pymean
```

Alternatively, you can use git and pip in unison to get the development branch:

```
pip install https://github.com/KeironO/pyMEAN
```

## Usage

Here's a starting template to get you started:

```python
# Import pyMEAN module into Python.
from pymean import EnrichmentAnalysis

# A compound list of inchikeys.
compound_list = [
    "WDJHALXBUFZDSR-UHFFFAOYSA-N", # acetoacetic acid
    "UCMIRNVEIXFBKS-UHFFFAOYSA-N", # beta-alanine
    "CVSVTCORWBXHQV-UHFFFAOYSA-N", # creatine
    "FFDGPVCHZBVARC-UHFFFAOYSA-N", # dimethylglycine
    "VZCYOOQTPOCHFL-OWOJBTEDSA-N", # fumaric acid
    "DHMQDGOQFOQNFH-UHFFFAOYSA-N", # glycine
    "FFFHZYDWPBMWHY-UHFFFAOYSA-N", # l-homocysteine
    "XUJNEKJLAYXESH-REOHCLBHSA-N", # l-cysteine
    "COLNVLDHVKWLRT-QMMMGPOBSA-N", # l-phenylalanine
    "BTNMPGBKDVTSJY-UHFFFAOYSA-N" # phenylpyruvic acid
]


# Create an EnrichmentAnalysis object for the analysis of hsa
ea = EnrichmentAnalysis(compound_list, organism="hsa")

# Run the analysis
ea.run_analysis(pvalue_cutoff=0.05)

# Obtain results (in the format of a pandas dataframe)
resuklts = ea.results

```

If you'd like to plot out your results, take inspiration from the following method:

```python
def plot_enrichment_analysis_results(results: pd.DataFrame, adj_method:str):
    fold_enrichment = np.abs(np.log(results["%s adj. p-value" % (adj_method)]))
    plt.figure()
    plt.title("Metabolite Sets Enrichment Overview")
    plt.barh(results["Pathway Name"], fold_enrichment, height=0.5)
    plt.xlabel("Fold Enrichment")

    plt.yticks(fontsize=6)
    plt.tight_layout()

    plt.show()

plot_enrichment_analysis_results(results, "fdr_bh")

```

Which will return the following chart:

![Plot of results](https://github.com/AberystwythSystemsBiology/pyMEAN/raw/master/example.png)

## License

Code released under the [GPLv3](https://github.com/AberystwythSystemsBiology/pyMEAN/blob/master/LICENSE).
python-libfprint 0.0.1
A fprint library wrapper

# Requirements

- Cython >= 0.27
- libfprint == 0.99.0

# Install

```
pip install git+git://github.com/jdiego/python-libfprint.git
```

# Usage

```python
import fprint

fprint.init()
devices = fprint.DiscoveredDevices()

if len(devices) > 0:
    dev = devices[0].open_device()
    print_data = dev.enroll_finger_loop()
    print_data = fprint.PrintData.from_data(print_data.data)
    result = dev.verify_finger_loop(print_data)
    assert result is True
    dev.close()
```
robotframework-openpyxllib2 0.1
Robotframework library for excel xlsx file format

Robotframework-Openpyxllib library

This Robotframework Openpyxl library that provides keywords to Open, Read, Write and Save XLSX files. This library uses Openpyxl python module to read and write datas in xlsx files.

Pre-requisities:
	- Python - 2.7 (Not tested with Python 3.0)
	- Robotframework - 3.0.2 (Not tested with other versions)
	- Openpyxl

Installation:

pip install robotframework-openpyxllib

Example:

| Library    |    OpenPyxlLibrary    |
| Open Excel | <Filename with path>  |