semvec/pypi-packages huggingface dataset:
marutils 1.0.0
Utilities and tools for working with MAR regional climate model outputs through xarray
Requires Python: >=3.6

# marutils - Utilities for working with MAR RCM outputs

This package contains utilities/tools that make it easier to open MAR RCM outputs.

* Load outputs straight into an xarray Dataset with standard dimensions names `(time, y, x)`.
* Provide a path with a wildcard expression (`*`) to automatically load several years of MAR files at once, concatenating them along the time axis.
* Plays nicely with Dask - chunking options default to 365-366 days (but can be changed at will).
* Loaded Datasets are automatically "geo-aware" using the rioxarray `.rio` accessor - no more trying to work out MAR's geo-referencing manually!
* Helper functions for turning X-hourly variables into continuous time series.
* Helper functions for masking.


## QuickStart

```python
import marutils
# Open a time series for the 21st century.
mar_outputs = marutils.open_dataset('MARv3.11.2/MAR*20*.nc')

# Check out the coordinates.
print(mar_outputs.coords)
Coordinates:
  * x            (x) float32 -760000.0 -740000.0 -720000.0 ... 660000.0 680000.0
  * y            (y) float32 -1180000.0 -1160000.0 ... 1480000.0 1500000.0
  * SECTOR       (SECTOR) float32 1.0 2.0
  * SECTOR1_1    (SECTOR1_1) float32 1.0
  * time         (time) datetime64[ns] 2019-01-01T12:00:00 ... 2019-12-31T12:...
  * ATMLAY3_3    (ATMLAY3_3) float32 0.99974793
    spatial_ref  int64 0

# What is proj.4 string for the geo-referencing of this dataset?
print(mar_outputs.rio.crs.to_proj4())
+proj=stere +lat_0=70.5 +lon_0=-40 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs=True
```

## Installation

At the moment, manually:

	git clone <repository>
	cd <repository>
	pip install -e .

Soon to be available via pyPI.


## Working with X-hourly outputs

To return an X-hourly variable with a single, continuous hourly time variable:

```python
hourly_air_temperatures = marutils.xhourly.Xhourly_to_time(mar_outputs.TTH)
```

## Useful notes

### Pixel corners vs centres

It's useful to remember that netCDF file geo-referencing treats X,Y coordinates as pixel (cell) centres. In contrast, the rasterio/GDAL data model treats X,Y coordinates as pixel upper-left corners.

In the QuickStart section above we loaded a 20 km resolution dataset. The minimum and maximum X coordinates are -760000 and 680000 m respectively. These correspond to the centres of the min/max pixels.

Through the `.rio` accessor we can check on the bounds/complete spatial extent of the file:

```python
# What is the centre of the top-left cell? (xmin, ymin, xmax, ymax)
mar_outputs.rio.bounds()
(-770000.0, 1510000.0, 690000.0, -1190000.0)
```

We can see that these bounds account for the 20 km pixel size of this dataset.


## Caveats

The geo-referencing capabilities have so far been tested only for Greenland datasets. If you have problems trying to load other domains, please post an issue on this repository.
helloworld-praalinepy 0.0.1
Hello world for python packaging

# Hello World

This is an example project demostrating how to publish a Python module to PyPI.

## Installation

Run the following to install:

```python
pip install helloworld-praalinepy
```

## Usage

```python
from helloworld import say_hello

# Generate "Hello, World!"
say_hello()

# Generate "Hello, Everybody!"
say_hello("Everybody")
```

## Development

To install the development tools for testing, run the following in a virtualenv:

```bash
$ pip install -e .[dev]
```
gelviz 0.9
A python package for plotting genomic elements in a genome browser style
Requires Python: >=3.0

# gelviz: A python python package for plotting genomic elements.

gelviz is a python package for plotting a wide range of genomic elements in a genome browser-like fashion. It comes with basic plotting functions that can be used for plotting different kind of genomic elements.

# Example

The following example was created using published genomics and epigenomics data from [10.5281/zenodo.4557352](https://zenodo.org/record/4557352). The data originates from the publication [F. Haller, M. Bieg, R. Will, et al.. Enhancer hijacking activates oncogenic transcription factor NR4A3 in acinic cell carcinomas of the salivary glands. Nature Communications volume 10, Article number: 368 (2019)](https://www.nature.com/articles/s41467-018-08069-x)

![example](jupyter_notebooks/pics/salivary_gland_locus_acicc.jpeg)

Please check the [jupyter notebook](jupyter_notebooks/gelviz_example.ipynb) for details on how the above figure was created.

# Acknowledgements
This package was implemented during my time at the *German Cancer Research Center* in the group of *Theoretical Bioinformatics* headed by Prof. Dr. Roland Eils, where i was part of the core bioinformatics team of the *Heidelberg Institute of Personalized Oncology (HIPO)*. Further refinment and final upload to PyPI was done during my time at *Charite, Universitaetsmedizin Berlin, Berlin Institute of Health (BIH) in the Department of Digital Health* headed by Prof. Roland Eils.

# License
Copyright (c) 2021, Matthias Bieg

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.