pandas-xyz 0.0.5
Geospatial calculation accessor for pandas DataFrames.
License: MIT

# pandas-xyz

Geospatial calculation accessor for pandas DataFrames.

## Example Usage

pandas-xyz provides the `.xyz` DataFrame accessor:

```python
>>> import pandas as pd
>>> import pandas_xyz

>>> df = pd.DataFrame.from_dict({
...   'lat': [40.0, 40.1, 40.3],
...   'lon': [-105.0, -105.0, -105.0]
... })

>>> df['displacement'] = df.xyz.ds_from_xy()
>>> df['displacement']
0        0.000000
1    11119.492664
2    22238.985329
dtype: float64

>>> df.xyz.s_from_ds()
0        0.000000
1    11119.492664
2    33358.477993
dtype: float64
```

## Dependencies and Installation

[Pandas](https://pandas.pydata.org/), [NumPy](https://numpy.org/), and [SciPy](https://www.scipy.org/) are required.

The package is available on [PyPi](https://pypi.org/project/pandas-xyz) and can be installed with `pip`:

```
$ pip install pandas-xyz
```

## License 

[![License](http://img.shields.io/:license-mit-blue.svg)](http://badges.mit-license.org)

This project is licensed under the MIT License. See
[LICENSE](https://github.com/aaron-schroeder/pandas-xyz/blob/master/LICENSE)
file for details.

## Documentation

The official documentation is hosted at readthedocs: https://pandas-xyz.readthedocs.io/en/stable/

## The Elevation Smoothing Algorithm

The algorithm I apply to filter elevation time series is based on a paper produced by
the National Renewable Energy Laboratory. Their algorithm is meant to smooth the elevation
time series of a moving vehicle for more reasonable estimates of road grades for energy consumption
models. This actually isn't that different from my end goal of smoothing elevation series for
more reasonable estimates of elevation gain and energy consumption by ambulating humans!
The paper is included in the 
[resources folder](https://github.com/aaron-schroeder/pandas-xyz/blob/master/resources/elevation_filtering_nrel.pdf)

## Project Status

### Complete

- Implement an algorithm to smooth noisy elevation time series.
- Implement a smoothing algorithm for elevation series as a function of
  distance (similar to how the completed time-smoother works.)

### Current Activities

- Implement an algorithm to smooth GPS position and speed data. 
  Most GPS-enabled activity trackers filter their speed and distance
  timeseries to remove measurement noise. I want to try and figure out
  how they do it, then replicate their techniques, and compare the
  smoothed position data.
- Develop and document a variety of elevation gain/loss algorithms.  
- Describe the algorithms in more detail. Maybe in a wiki?
- Provide references to papers and other resources where I got inspiration
  for each algorithm.

### Future Work

- Benchmark algorithm performance (speed, accuracy, and consistency):
   - Generate dummy time series of (distance, elevation) data to check
     smoothing algorithm.
   - Generate series of GPS points to obtain elevation coordinates from
     various DEMs (using [`elevation-query` package](https://github.com/aaron-schroeder/elevation-query))
     to compare elevation datasets with and without smoothing.
- Create gain/loss algorithms to emulate those employed by 
  Strava / TrainingPeaks / Garmin.

## Contact

Reach out to me at one of the following places!

- Website: [trailzealot.com](https://trailzealot.com)
- LinkedIn: [linkedin.com/in/aarondschroeder](https://www.linkedin.com/in/aarondschroeder/)
- Twitter: [@trailzealot](https://twitter.com/trailzealot)
- Instagram: [@trailzealot](https://instagram.com/trailzealot)
- GitHub: [github.com/aaron-schroeder](https://github.com/aaron-schroeder)