semvec/pypi-packages huggingface dataset:
bykml 1.5.0
the bykml library
Requires Python: >=3.9
Keywords: feed, bykml, tutorial

<h2>Introduction</h2>

<p>bykml is a library to easily read, write and update KML files. 
It's built around using dictionaries to easily replace values and add kml elements.</p>

<h2>Documentation</h2>
<p>kmlfactory file inside the bykml package has a KmlFactory class and a placemark_template function 
used to create placemarks and add to the 
</p>

<h2>Install</h2>
<p>You can install the package with ```pip install bykml```</p>

<h2>Dependencies</h2>
<ul>
    <li><a href="https://pypi.org/project/xmltodict/">xmltodict</a></li>
</ul>

<h2>Limitations</h2>
<p>Due to the nature of dictionaries of only being able to have one key per value.
This subsequently means that lists have to be used to have multiple items within the same kml tag.
For example if we have a 'Folder' tag which will contain a number of placemarks, 
we cannot have a nested folder with multiple placemarks inside.
This is currently being reviewed for a solution.</p>

<h2>Changelog</h2>
<h3>1.5.0 (29/04/2024)</h3>
<ul>
    <li>Updated placemark template to accept images</li>
    <li>Updated function comments</li>
</ul>

<h3>1.4.0 (22/01/2024)</h3>
<ul>
    <li>Added kmlfactory</li>
</ul>

<h3>1.3.0 (22/01/2024)</h3>
<ul>
    <li>Updated README</li>
</ul>

<h3>1.2.0 (22/01/2024)</h3>
<ul>
    <li>Updated maintainers</li>
</ul>

<h3>1.1.0 (21/01/2024)</h3>
<ul>
    <li>Initial release</li>
</ul>
era5-torch 0.1.1
PyTorch-compatible wrapper for Era5
Requires Python: >=3.9

# ERA5-Torch
This repository provides a wrapper around [ERA5](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview) files that is compatible with PyTorch. 

## Installation
Install the requirements via `pip install -r requirements.txt`.

## Usage Examples
The main functionality is exposed through the Era5 class, which is a valid PyTorch Dataset and can thus be used with standard PyTorch dataloaders. 

Assume that you have downloaded some NetCDF files (for example, follow [this tutorial](https://github.com/joaohenry23/Download_ERA5_with_python) containing the temperature ("t") and your data lives at `path/to/dataset.nc`. You can then load the data with:

```
from era5_torch import Era5
ds = Era5("/path/to/dataset.nc", "t", normalize=False)
ds[0]
```

which returns a single sample of the temperature values with shape `(1, num_lat, num_long)`. By default, the samples are all located at a single point in time and a single pressure level. 

If your model uses coordinate information, you can pass in the coordinates desired coordinates to the dataset class, which are appended in the passed order to the first axis of the return sample:

```
from era5_torch import Era5, Coordinate
ds = Era5("/path/to/dataset.nc", "t", normalize=False, normalize_coords=True, coords=[Coordinate.X, Coordinate.Y, Coordinate.Z])
ds[0]
```

which returns a sample with shape `(4, num_lat, num_long)`, and `ds[0][0]` is the temperature, `ds[0][1]` the x-values of the temperature, et cetera.

To access the dataset in different ways (for example with 3D chunks of shape `(time, lat, long)`), we provide the Chunker class. This can be passed to the Era5 class.

```
from era5_torch import Era5, Coordinate, ContinuousChunker
ds = Era5("/Users/almico/datasets/era5_temperatures_2008.nc", "t", normalize=False,chunker=ContinuousChunker((3,3,3)))
ds[0]
```

Returns a sample of shape `(3,3,3)` with values varying over `(time, lat, long)`. Alternatively, to get patches, you can also create a Chunker with `ContinuousChunker((3,3))`. All chunkers have different behaviour, the `ContinuousChunker` for example returns chunks starting from the first position in the dataset (usually the (0,0) coordinate) and creates as many chunks in an orderly manner as possible by traversing the coordinates one after another. Refer to the documentation of the Chunkers to get more information.# era5_torch