https://github.com/aaron-schroeder/py-elevation
Python library for working with elevation and grade time series.
https://github.com/aaron-schroeder/py-elevation
elevation gis pandas scipy
Last synced: 7 months ago
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Python library for working with elevation and grade time series.
- Host: GitHub
- URL: https://github.com/aaron-schroeder/py-elevation
- Owner: aaron-schroeder
- License: mit
- Created: 2021-01-22T17:42:04.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-02-12T19:12:26.000Z (over 4 years ago)
- Last Synced: 2025-02-06T07:30:09.937Z (8 months ago)
- Topics: elevation, gis, pandas, scipy
- Language: Python
- Homepage:
- Size: 1.32 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# py-elevation
> Python library for working with elevation and grade time series.
[](http://badges.mit-license.org)
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Background](#background)
- [Introduction](#introduction)
- [The Elevation Smoothing Algorithm](#the-elevation-smoothing-algorithm)
- [Dependencies and Installation](#dependencies-and-installation)
- [Example](#example)
- [Project Status](#project-status)
- [Contact](#contact)
- [License](#license)
---
## Background
This project originated as the part of my
[spatialfriend package](https://github.com/aaron-schroeder/spatialfriend) that
handled elevation smoothing and statistics like gain and loss. Lately, I've
been interested in keeping my work in more self-contained modules with lighter
dependencies, so I split it out.
---
## Introduction
Determining one's elevation on Earth's surface has become a lot easier thanks
to high-accuracy consumer GPS products and digital elevation models (DEMs) of
Earth's topography. Still, there are errors in GPS location and in every Earth
surface model. When working with elevation and position time series, for example
calculating instantaneous slopes during a trail running workout, stricter
requirements are placed on the data. Even with a perfectly accurate DEM,
inaccurate GPS data can yield totally unreasonable elevation profiles and path
slopes, documenting work or elevation gain that the runner did not actually do.
The same can be said for a perfectly accurate GPS trace on an inaccurate DEM.
The goal of this project is to wrangle messy or unreasonable elevation data, and
return stats and time series that actually match the athlete's experience.
No more unreasonably steep slopes or noisy data in your elevation profile
making running power calculations meaningless (if you are into that kind of
thing). No more wondering if those elevation measurements you read on GPS device
or barometric altimeter are accurate. No more apples to oranges data comparisons
because of differences between devices or datasets.
---
## 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]()
---
## Dependencies and Installation
[Pandas](http://pandas.pydata.org/) and [SciPy](https://www.scipy.org/) are required.
To install (since I am not on pypi yet), first clone this repo.
```
git clone https://github.com/aaron-schroeder/py-elevation.git
```
Now you have a local version of this package that you can install with `pip`
(the `setup.py` file is configured to make this work).
Activate whatever virtual environment where you wish to install `elevation`,
and then:
```
pip install ${local_path_to_py-elevation_dir}
```
---
## Example
`py-elevation` provides the `elevation` package.
ADAPT THIS
```python
import numpy as np
import pandas as pd
import elevation
# Generate some distance coordinates that would result from moving
# at a variable speed.
num_samples = 600
distance_series = pd.Series(
[2.0 * i + math.cos(0.01 * i) for i in range(num_samples)]
)
# Generate some noisy elevation coordinates with a lower-frequency
# (genuine) signal.
noise_mean = 0.0
std = 0.5
noise = np.random.normal(noise_mean, std, size=num_samples)
signal = pd.Series(
[1600.0 + 100.0 * math.sin(0.01 * i) for i in range(num_samples)]
)
elevation_series = signal + noise
# Smooth with either algorithm
elev_dist_smooth = elevation.dist_smooth(distance_series, elevation_series)
elev_time_smooth = elevation.time_smooth(elevation_series)
```
---
## 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
- 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 upcoming `elevation-query` package) to compare elevation
datasets with and without smoothing.
- Create gain/loss algorithms to emulate those employed by
Strava / TrainingPeaks / Garmin.
---
## Contact
You can get in touch with me at the following places:
- GitHub: github.com/aaron-schroeder
- LinkedIn: linkedin.com/in/aarondschroeder
- Twitter: @trailzealot
- Instagram: @trailzealot
---
## License
[](http://badges.mit-license.org)
This project is licensed under the MIT License. See
[LICENSE](https://github.com/aaron-schroeder/py-activityreaders/blob/master/LICENSE)
file for details.