| Name | pyhgtmap JSON |
| Version |
3.7
JSON |
| download |
| home_page | None |
| Summary | Creates OpenStreetMap suitable contour lines from NASA SRTM data |
| upload_time | 2024-01-11 21:27:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
openstreetmap
srtm
countour
elevation
osm
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
[](https://www.python.org)
[](https://github.com/pypa/hatch)
[](https://github.com/charliermarsh/ruff)
[](https://github.com/python/mypy)
[](https://deepsource.io/gh/agrenott/pyhgtmap/?ref=repository-badge)
[](https://app.codacy.com/gh/agrenott/pyhgtmap/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[](https://codeclimate.com/github/agrenott/pyhgtmap/maintainability)
pyhgtmap is a fork of the original  tool,
which doesn't seem to be maintained anymore.
This is a little program which lets you easily generate OSM contour lines from
NASA SRTM data. It was initially created as replacement for srtm2osm which
stopped working when the NASA changed the server and started distributing the
raw SRTM data via http instead of ftp. In the meanwhile, srtm2osm is working
again due to the efforts of bomm.
However, pyhgtmap has some advantages compared to srtm2osm. One is that you
won't need a C# runtime environment installed on your machine. Another
important thing is that pyhgtmap generates already tiled data. Furthermore,
pyhgtmap seems to slightly outperform srtm2osm. If you are using a multi-core
machine and are running a POSIX compliant operating system you can use a very
simple form of parallelization and the advance will be dramatical.
Note that the intended use is not to upload generated contour OSM data to the
OSM servers but to use it for fancy maps.
# Sources
pyhgtmap supports several HGT data sources. Those are identified by a 4-character "nickname" + one digit resolution suffix, which can be specified with the `--source` parameter of pyhgtmap.
## SRTM
[NASA Shuttle Radar Topography Mission v3.0](https://www.earthdata.nasa.gov/news/nasa-shuttle-radar-topography-mission-srtm-version-3-0-global-1-arc-second-data-released-over-asia-and-australia)
*Available for 1" and 3" resolutions.*
This source requires creating an earthexplorer account on https://ers.cr.usgs.gov/register/.
## VIEW
[VIEWFINDER PANORAMAS DIGITAL ELEVATION DATA](http://viewfinderpanoramas.org/dem3.html)
*Available for 1" and 3" resolutions.*
## SONN
[Sonny's LiDAR Digital Terrain Models (DTM) of European countries](https://sonny.4lima.de/)
*Available for 1" and 3" resolutions.*
This source require usage of Google Drive API. To use it, you have to generate API client OAuth secret as described in [Google drive's documentation](https://developers.google.com/drive/api/quickstart/python?hl=en#set_up_your_environment) and save the client secrets JSON file in pyhgtmap config directory (`~/.pyhgtmap/client-secret.json`).
# Installation
For ubuntu-like system:
## Ligther deployment (without GeoTiff support)
GDAL dependency is required only to add GeoTiff support, and is quite painful to install.
If you don't need GeoTiff support, simply install the default version of pyhgtmap:
```bash
sudo apt update
sudo apt install python3 python3-pip python3-venv
python3 -m venv my_venv
# Switch to venv
. ./my_venv/bin/activate
# Install pyhgtmap with dependencies from PyPi
pip install pyhgtmap
```
## With GeoTiff optional support
```bash
sudo apt update
sudo apt install python3 python3-pip python3-venv
# Install GDAL via system package, as it's painful to install through PIP
sudo apt install python3-gdal
# Create virtual env (allow --system-site-packages to use system's GDAL)
python3 -m venv --system-site-packages my_venv
# Switch to venv
. ./my_venv/bin/activate
# Install pyhgtmap with dependencies from PyPi
pip install pyhgtmap[geotiff]
```
## Install directly from GitHub (useful to test non-released fixes)
```bash
sudo apt update
sudo apt install python3 python3-pip python3-venv git
python3 -m venv my_venv
# Switch to venv
. ./my_venv/bin/activate
# Install latest pyhgtmap development version from github
pip install -U git+https://github.com/agrenott/pyhgtmap.git
```
# Usage
For a detailed help, run `pyhgtmap --help` on the console.
## Example output
Generating contours for France [PACA region](https://download.geofabrik.de/europe/france/provence-alpes-cote-d-azur.html) with a 10m step and 0.00001 RDP Epsilon (taking less than a minute on Intel 13600K via Windows WSL 1):
```
> pyhgtmap --polygon=provence-alpes-cote-d-azur/provence-alpes-cote-d-azur.poly --step=10 --pbf --hgtdir=work/hgt --source=view1,view3 --simplifyContoursEpsilon=0.00001 -j16
...
> du -shc lon*.pbf |tail -3
196K lon7.00_7.75lat44.69_44.75_view1.osm.pbf
168K lon7.00_7.75lat44.75_44.88_view1.osm.pbf
81M total
> ls -l lon*.pbf |wc -l
104
```
Contour lines displayed over OSM map using QGis:
## A word on contour simplification
pyhgtmap now uses very efficient [pybind11-rdp](https://github.com/cubao/pybind11-rdp) Ramer-Douglas-Peucker Algorithm library for contour simplification. This makes RDP activation the best solution in most cases, as the slight overhead in computing performance is compensated by the reduced number of points to write (which is now the most time consuming part). It also reduces the final file size.
Epsilon value must be chosen with care to get the proper tradeoff between efficiency and quality.
Here is an example originating from a "view1" source with 10m step (lon6.00_7.00lat43.00_43.25_view1.osm.pbf):
| RDP Epsilon values | Disabled | 0 | 0.00001 | 0.0001 |
| :---------------------------------: | :------: | :----: | :---------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |
| Visual details | | |  |  |
| | | | Blue lines (Epsilon=0.00001) are almost indistinguishable from red ones (Epsilon=0) | Clear difference appears for green lines (Epsilon = 0.0001) |
| File size (1 tile, PBF format, KiB) | 1840 | 1717 | 1424 | 716 |
| Number of nodes (1 tile) | 869685 | 761085 | 559678 | 210744 |
# Development
This project uses .
(Mini)Conda can be used to easily setup given version of Python and GDAL:
```bash
conda create -n python39 -c conda-forge python=3.9
conda activate python39
conda install -c conda-forge gdal hatch
hatch -e geotiff shell
# To start VSCode using the proper python env
code .
```
The one-liner for formatting and local validation:
```bash
hatch run fmt && hatch run all
```
## Profiling
```bash
pip install yappi
python -m yappi -f callgrind -o yappi_ex1.out ../../pyhgtmap/main.py --pbf --log=DEBUG N43E006.hgt
```
Then open `yappi_ex1.out` with some callgrind viewer (eg. QCacheGrind).
Raw data
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"description": "[](https://www.python.org)\n\n\n[](https://github.com/pypa/hatch)\n[](https://github.com/charliermarsh/ruff)\n[](https://github.com/python/mypy)\n[](https://deepsource.io/gh/agrenott/pyhgtmap/?ref=repository-badge)\n[](https://app.codacy.com/gh/agrenott/pyhgtmap/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)\n[](https://codeclimate.com/github/agrenott/pyhgtmap/maintainability)\n\npyhgtmap is a fork of the original  tool,\nwhich doesn't seem to be maintained anymore.\n\nThis is a little program which lets you easily generate OSM contour lines from\nNASA SRTM data. It was initially created as replacement for srtm2osm which\nstopped working when the NASA changed the server and started distributing the\nraw SRTM data via http instead of ftp. In the meanwhile, srtm2osm is working\nagain due to the efforts of bomm.\n\nHowever, pyhgtmap has some advantages compared to srtm2osm. One is that you\nwon't need a C# runtime environment installed on your machine. Another\nimportant thing is that pyhgtmap generates already tiled data. Furthermore,\npyhgtmap seems to slightly outperform srtm2osm. If you are using a multi-core\nmachine and are running a POSIX compliant operating system you can use a very\nsimple form of parallelization and the advance will be dramatical.\n\nNote that the intended use is not to upload generated contour OSM data to the\nOSM servers but to use it for fancy maps.\n\n# Sources\n\npyhgtmap supports several HGT data sources. Those are identified by a 4-character \"nickname\" + one digit resolution suffix, which can be specified with the `--source` parameter of pyhgtmap.\n\n## SRTM\n\n[NASA Shuttle Radar Topography Mission v3.0](https://www.earthdata.nasa.gov/news/nasa-shuttle-radar-topography-mission-srtm-version-3-0-global-1-arc-second-data-released-over-asia-and-australia)\n\n*Available for 1\" and 3\" resolutions.*\n\nThis source requires creating an earthexplorer account on https://ers.cr.usgs.gov/register/.\n\n## VIEW\n\n[VIEWFINDER PANORAMAS DIGITAL ELEVATION DATA](http://viewfinderpanoramas.org/dem3.html)\n\n*Available for 1\" and 3\" resolutions.*\n\n## SONN\n\n[Sonny's LiDAR Digital Terrain Models (DTM) of European countries](https://sonny.4lima.de/)\n\n*Available for 1\" and 3\" resolutions.*\n\nThis source require usage of Google Drive API. To use it, you have to generate API client OAuth secret as described in [Google drive's documentation](https://developers.google.com/drive/api/quickstart/python?hl=en#set_up_your_environment) and save the client secrets JSON file in pyhgtmap config directory (`~/.pyhgtmap/client-secret.json`).\n\n# Installation\n\nFor ubuntu-like system:\n\n## Ligther deployment (without GeoTiff support)\n\nGDAL dependency is required only to add GeoTiff support, and is quite painful to install.\nIf you don't need GeoTiff support, simply install the default version of pyhgtmap:\n\n```bash\nsudo apt update\nsudo apt install python3 python3-pip python3-venv\npython3 -m venv my_venv\n# Switch to venv\n. ./my_venv/bin/activate\n# Install pyhgtmap with dependencies from PyPi\npip install pyhgtmap\n```\n\n## With GeoTiff optional support\n\n```bash\nsudo apt update\nsudo apt install python3 python3-pip python3-venv\n# Install GDAL via system package, as it's painful to install through PIP\nsudo apt install python3-gdal \n# Create virtual env (allow --system-site-packages to use system's GDAL)\npython3 -m venv --system-site-packages my_venv\n# Switch to venv\n. ./my_venv/bin/activate\n# Install pyhgtmap with dependencies from PyPi\npip install pyhgtmap[geotiff]\n```\n\n## Install directly from GitHub (useful to test non-released fixes)\n\n```bash\nsudo apt update\nsudo apt install python3 python3-pip python3-venv git\npython3 -m venv my_venv\n# Switch to venv\n. ./my_venv/bin/activate\n# Install latest pyhgtmap development version from github\npip install -U git+https://github.com/agrenott/pyhgtmap.git\n```\n\n# Usage\n\nFor a detailed help, run `pyhgtmap --help` on the console.\n\n## Example output\n\nGenerating contours for France [PACA region](https://download.geofabrik.de/europe/france/provence-alpes-cote-d-azur.html) with a 10m step and 0.00001 RDP Epsilon (taking less than a minute on Intel 13600K via Windows WSL 1):\n\n```\n> pyhgtmap --polygon=provence-alpes-cote-d-azur/provence-alpes-cote-d-azur.poly --step=10 --pbf --hgtdir=work/hgt --source=view1,view3 --simplifyContoursEpsilon=0.00001 -j16\n...\n> du -shc lon*.pbf |tail -3\n196K lon7.00_7.75lat44.69_44.75_view1.osm.pbf\n168K lon7.00_7.75lat44.75_44.88_view1.osm.pbf\n81M total\n> ls -l lon*.pbf |wc -l\n104\n```\n\nContour lines displayed over OSM map using QGis:\n\n\n\n\n\n## A word on contour simplification\n\npyhgtmap now uses very efficient [pybind11-rdp](https://github.com/cubao/pybind11-rdp) Ramer-Douglas-Peucker Algorithm library for contour simplification. This makes RDP activation the best solution in most cases, as the slight overhead in computing performance is compensated by the reduced number of points to write (which is now the most time consuming part). It also reduces the final file size.\n\nEpsilon value must be chosen with care to get the proper tradeoff between efficiency and quality.\n\nHere is an example originating from a \"view1\" source with 10m step (lon6.00_7.00lat43.00_43.25_view1.osm.pbf):\n\n| RDP Epsilon values | Disabled | 0 | 0.00001 | 0.0001 |\n| :---------------------------------: | :------: | :----: | :---------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |\n| Visual details | | |  |  |\n| | | | Blue lines (Epsilon=0.00001) are almost indistinguishable from red ones (Epsilon=0) | Clear difference appears for green lines (Epsilon = 0.0001) |\n| File size (1 tile, PBF format, KiB) | 1840 | 1717 | 1424 | 716 |\n| Number of nodes (1 tile) | 869685 | 761085 | 559678 | 210744 |\n\n# Development\n\nThis project uses .\n\n(Mini)Conda can be used to easily setup given version of Python and GDAL:\n```bash\nconda create -n python39 -c conda-forge python=3.9\nconda activate python39\nconda install -c conda-forge gdal hatch\nhatch -e geotiff shell\n# To start VSCode using the proper python env\ncode .\n```\nThe one-liner for formatting and local validation:\n```bash\nhatch run fmt && hatch run all\n```\n\n## Profiling\n\n```bash\npip install yappi\npython -m yappi -f callgrind -o yappi_ex1.out ../../pyhgtmap/main.py --pbf --log=DEBUG N43E006.hgt\n```\n\nThen open `yappi_ex1.out` with some callgrind viewer (eg. 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