semvec/pypi-packages huggingface dataset:
hydromt_fiat 0.2.0
hydroMT plugin for fiat models.
Requires Python: >=3.8

hydroMT-fiat: FIAT plugin for hydroMT
#########################################

.. image:: https://codecov.io/gh/Deltares/hydromt_fiat/branch/main/graph/badge.svg
    :target: https://codecov.io/gh/Deltares/hydromt_fiat

.. image:: https://img.shields.io/badge/docs-latest-brightgreen.svg
    :target: https://deltares.github.io/hydromt_fiat/latest
    :alt: Latest developers docs

.. image:: https://img.shields.io/badge/docs-stable-brightgreen.svg
    :target: https://deltares.github.io/hydromt_fiat/stable
    :alt: Stable docs last release

.. image:: https://badge.fury.io/py/hydromt_fiat.svg
    :target: https://pypi.org/project/hydromt_fiat/
    :alt: Latest PyPI version

.. image:: https://mybinder.org/badge_logo.svg
    :target: https://mybinder.org/v2/gh/Deltares/hydromt_fiat/main?urlpath=lab/tree/examples


hydroMT_ is a python package, developed by Deltares, to build and analysis hydro models.
It provides a generic model api with attributes to access the model schematization,
(dynamic) forcing data, results and states. This plugin provides an implementation 
for the FIAT_ model.


.. _hydromt: https://deltares.github.io/hydromt
.. _FIAT: https://storymaps.arcgis.com/stories/687a256881b94bf6ad20677543bb8cf2


Installation
------------

hydroMT-fiat will soon be availble from pypi and conda-forge (in progress).

To install hydromt_fiat using pip do:

.. code-block:: console

  pip install hydromt_fiat

We recommend installing a hydromt-fiat environment including the hydromt_fiat package
based on the environment.yml file. This environment will install all package dependencies 
including the core of hydroMT_.

.. code-block:: console

  conda env create -f binder/environment.yml
  conda activate hydromt-fiat
  pip install hydromt_fiat

Documentation
-------------

Learn more about the hydroMT_fiat plugin in its `online documentation <https://deltares.github.io/hydromt_fiat/>`_

Contributing
------------

You can find information about contributing to hydroMT at our `Contributing page <https://deltares.github.io/hydromt_fiat/latest/contributing.html>`_.

License
-------

Copyright (c) 2021, Deltares

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General 
Public License as published by the Free Software Foundation, either version 3 of the License, or (at your 
option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the 
implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 
for more details.

You should have received a copy of the GNU General Public License along with this program. If not, 
see <https://www.gnu.org/licenses/>.
gpxpr 0.0.3
GPX parser and renderer
Requires Python: >=3.9

# gpxpr
![GitHub Workflow Status](https://img.shields.io/github/workflow/status/kruhlmann/gpxpr/gpxpr%20lint?label=linting&style=for-the-badge)
![GitHub Workflow Status](https://img.shields.io/github/workflow/status/kruhlmann/gpxpr/gpxpr%20tests?label=tests&style=for-the-badge)
![PyPI](https://img.shields.io/pypi/v/gpxpr?style=for-the-badge)
![GitHub](https://img.shields.io/github/license/kruhlmann/gpxpr?style=for-the-badge)

GPX file parser and renderer.

## Installation

### From PIP

```sh
pip install gpxpr
```

### Manual

```
git clone https://github.com/Kruhlmann/gpxpr
cd gpxpr
make install
```

## Usage

```
usage: gpxpr [-h] -r RENDERER -w WALKING -v RUNNING -a {time,distance} [-o DESTINATION] target

positional arguments:
  target                File to process

optional arguments:
  -h, --help            show this help message and exit
  -r RENDERER, --renderer RENDERER
                        Renderer to use [stdout, matplot]
  -w WALKING, --min-walking-speed WALKING
                        Slowest min/km where a runner is considered 'walking'
  -v RUNNING, --min-running-speed RUNNING
                        Slowest min/km when a runner is considered 'running'
  -a {time,distance}, --aggregation-value {time,distance}
                        Value to base the aggregation on
  -o DESTINATION, --output DESTINATION
                        Output location. Default is /dev/stdout
```

## Developing

### Windows

The recommended way to develop on Windows is the make use of the [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install) and then referring to the UNIX-like section.

### UNIX-like

Auto-running tests on code change is achievable with [entr](https://archlinux.org/packages/community/x86_64/entr/)

```sh
find src tests -name "*.py" | entr make test
```

Similarly for linting on file change:


```sh
find src tests -name "*.py" | entr make lint
```
nbgrader-unittest-sebastian-stigler 0.0.3
Drop-in replacement for the unittest module to use them in tests for nbgrader assignments.
Requires Python: >=3.6

# nbgrader_unittest

Drop-in replacement for the unittest module to use them in tests for
[nbgrader][nbgrader] assignments.

You can use this module just like the [unittest][unittest] module. Just don't
call the `main` function to run the tests. Use the `run_nbgrader_test`
function instead.

If your tests all pass, no output will be generated and nbgrader will assign
full marks to this cell.  If one of your tests fail, the usual unittest output
will be created and printed to `sys.stderr` and no marks will be assigned to
this cell in nbgrader.

## Example

You prepare a new jupyter notebook assignment with parts, that should be
autograded with [nbgrader][nbgrader].Your assignment could be something like
this:

> Write a function that returns a list of numbers, such that $x_i=i^2$, for
> $1 \leq i \leq n$. Make sure it handles the case where $n<1$ by raising a
> `ValueError`.

Your code cell with the "Autograded answer" will look like this:

```python
def squares(n):
    """Compute the squares of numbers from 1 to n, such that the 
    ith element of the returned list equals i^2.
    
    """
    ### BEGIN SOLUTION
    if n < 1:
        raise ValueError("n must be greater than or equal to 1")
    return [x**2 for x in range(1, n + 1)]
    ### END SOLUTION
```

In a new code cell you put your "Autograder test":

```python
import nbgrader_unittest

class SquaresTests(nbgrader_unittest.TestCase):

    def test_square_of_one(self):
        self.assertEqual(squares(1), [1])
   
    def test_squares_up_to_ten(self):
       self.assertEqual(
           squares(10),
           [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
       )

    def test_negative_argument(self):
        with self.assertRaises(ValueError):
            squares(-4)

nbgrader_unittest.run_nbgrader_test(SquaresTests)
```


[nbgrader]: https://nbgrader.readthedocs.io/en/stable/
[unittest]: https://docs.python.org/3/library/unittest.html