semvec/pypi-packages huggingface dataset:
PE-Oudin 0.3
Calculate potential evapotranspiration from temperature
Requires Python: >=3.6

# potEvap

Compute PE (Potential Evapotranspiration) using the formula from Oudin et al. (2005). 

This is a pure python implementation of the R - AirGR PE_Oudin function.

Original source found at https://rdrr.io/cran/airGR/src/R/PE_Oudin.R

AirGR package can be found at https://cran.r-project.org/package=airGR 


Changes from the original (R package) include:
 - Support single day as well as multiple days (list, numpy array, pandas etc.). 
 - Get latitude in degrees or radians (default degrees).
 - Can choose output units (useful for GR2M and GR2A)
 - Can calculate single hourly value

## Usage:

`from from PE_Oudin.PE_Oudin import PE_Oudin`

For single value:
```
temp = 20  # Degrees celcius
date = datetime(2018,1,1)
lat = 32  # Degrees (but can be set to radians)
latUnit = 'deg'  # Optional, and default. Can also be 'rad'
out_units = 'mm/day'  # Optional, and default. Can also be 'mm/hour', mm/month' or 'mm/year'

PE_Oudin.pe_oudin(temp, date, lat, latUnit, out_units)
```

For multiple values (list, pandas series etc.):
```
temp = [20, 25] # Degrees celcius
date = [datetime(2018,1,1), datetime(2018,1,1)]
lat = 32 # Degrees (but can be set to radians)
latUnit = 'deg'  # Optional, and default. Can also be 'rad'
out_units = 'mm/day'  # Optional, and default. Can also be 'mm/hour', 'mm/month' or 'mm/year'

PE_Oudin.pe_oudin(temp, date, lat, latUnit, out_units)
```
lawu 3.0.0
Doing fun stuff with JVM ClassFiles.
Keywords: java,disassembly,disassembler,assembly

![lawu](lawu.png)

# lawu

[![license](https://img.shields.io/github/license/tktech/lawu.svg?style=for-the-badge)](LICENCE)

Lawu is a human-friendly library for assembling, disassembling, and exploring
JVM class files. It's highly suitable for automation tasks.

## Documentation

API documentation & examples are available at http://lawu.tkte.ch

## Licence

Lawu is available under the MIT licence. See LICENCE.
intake-hive 0.1.1
Intake Hive DataSource Plugin.
Requires Python: >=3.6

.. image:: https://travis-ci.org/zillow/intake-hive.svg?branch=master
    :target: https://travis-ci.org/zillow/intake-hive

.. image:: https://coveralls.io/repos/github/zillow/intake-hive/badge.svg?branch=master
    :target: https://coveralls.io/github/zillow/intake-hive?branch=master


Welcome to the Intake Hive plugin
==================================================
This `Intake <https://intake.readthedocs.io/en/latest/quickstart.html>`_ plugin 
:

Example where the Hive table is user_events_hive partitioned by userid:

.. code-block:: yaml

    sources:
      user_events_hive:
        driver: hive
        args:
          urlpath: 'user_events_yaml_catalog?userid={{userid}}'



.. code-block:: python

  import pandas as pd
  import intake

  catalog = intake.open_catalog(catalog_path)

  # Reads partition userid=42
  pandas_df: pd.DataFrame = catalog.entity.user.user_events_partitioned(userid="42").read()