EOmaps - Interactive maps in python!
EOmaps is a Python package to visualize and analyze geographical datasets.
It is built on top of matplotlib and cartopy and aims to provide an
intuitive and easy-to-use interface to handle the following tasks:
- Speed up and simplify the creation and comparison of maps
- Visualize small datasets as well as millions of datapoints
- Handle 1D and 2D datasets and create plots from NetCDF, GeoTIFF or CSV files
- Take care of re-projecting the data
- Compare or overlay different plot-layers and WebMap services
- Use the maps as interactive data-analysis widgets (e.g. execute functions if you click on the map)
- Provide a versatile set of tools to customize the maps
- Arrange multiple maps in one figure
- Get a nice colorbar with a histogram on top
- Export high resolution images
🔨 Installation
To install EOmaps (and all its dependencies) via the conda package-manager, simply use:
conda install -c conda-forge eomaps... to get a huge speedup, use mamba to solve the dependencies!
conda install -c conda-forge mamba
mamba install -c conda-forge eomapsFor more information, have a look at the installation instructions or checkout the quickstart guide
📖 Documentation
Make sure to have a look at the
✔️ Citation
Did EOmaps help in your research?
Consider supporting the development and add a citation to your publication!
🚀 Contribute
Found a bug or got an idea for an interesting feature?
Open an issue or start a discussion, and I'll see what I can do!
Interested in actively contributing to the library?
- Any contributions are welcome! (new features, enhancements, fixes, documentation updates, outreach etc.)
- Have a look at this
🌟 overview project to get an overview of existing ideas that could use some help. - Get in touch by opening a discussion in the
🐜 Contribution section!
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🌳 Basic usage
Checkout the
from eomaps import Maps
# initialize Maps object
m = Maps(crs=Maps.CRS.Orthographic())
# add map-features from NaturalEarth
m.add_feature.preset.coastline()
m.add_feature.cultural_50m.admin_0_countries(fc="none", ec="g")
# assign a dataset
m.set_data(data=[1, 2, 3, 4], x=[45, 46, 47, 42], y=[23, 24, 25, 26], crs=4326)
# set the shape you want to use to represent the data-points
m.set_shape.geod_circles(radius=10000) # (e.g. geodetic circles with 10km radius)
# (optionally) classify the data
m.set_classify_specs(scheme=Maps.CLASSIFIERS.Quantiles, k=5)
# plot the data
m.plot_map(cmap="viridis", vmin=2, vmax=4)
# add a colorbar with a colored histogram on top
m.add_colorbar(histbins=200)
# add a scalebar
m.add_scalebar()
# add a compass (or north-arrow)
m.add_compass()
# add imagery from a open-access WebMap services
m.add_wms.OpenStreetMap.add_layer.default()
# use callback functions to interact with the map
m.cb.pick.attach.annotate()
# use multiple layers to compare and analyze different datasets
m3 = m.new_layer(layer="layer 2")
m3.add_feature.preset.ocean()
# attach a callback to peek on layer 1 if you click on the map
m.cb.click.attach.peek_layer(layer="layer 2", how=0.4)
# attach a callback to show an annotation while you move the mouse
# (and simultaneously press "a" on the keyboard)
m.cb.move.attach.annotate(modifier="a")
# attach callbacks to switch between the layers with the keyboard
m.cb.keypress.attach.switch_layer(layer=0, key="0")
m.cb.keypress.attach.switch_layer(layer="layer 2", key="1")
# get a clickable widget to switch between the available plot-layers
m.util.layer_selector()
# add zoomed-in "inset-maps" to highlight areas on th map
m_inset = m.new_inset_map((10, 45))
m_inset.add_feature.preset.coastline(fc="g")
# ---- plot data directly from GeoTIFF / NetCDF or CSV files
m4 = m.new_layer_from_file.GeoTIFF(...)
m4 = m.new_layer_from_file.NetCDF(...)
m4 = m.new_layer_from_file.CSV(...)🌼 Thanks to
- Jakob Quast for designing the nice logo!