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SciTools / cartopy

Cartopy - a cartographic python library with matplotlib support
https://scitools.org.uk/cartopy/docs/latest
BSD 3-Clause "New" or "Revised" License
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Features plotting slowed down on anything than PlateCarree #2102

Open guidocioni opened 2 years ago

guidocioni commented 2 years ago

Description

Saving figures with anything that is not the default PlateCarree takes a long time. Here are my results with a simple plot having ocean, land, borders and a shapefile: times are from start to figure saving.

I know that I'm using high resolution coastlines, but I'm only plotting on a limited area, not the entire globe. Isn't there really a way to speed it up? As we have to produce many plots operationally having something that takes so long hinders our pipeline. Or maybe is it related to the machine capabilities?

Code to reproduce

fig = plt.figure(1, figsize=(15, 10))

logging.info("Creating projection")
ax = plt.axes(projection=ccrs.PlateCarree())
# ax = plt.axes(projection=ccrs.Mercator(central_longitude=5, min_latitude=30, max_latitude=75))

ax.set_extent(extents, ccrs.PlateCarree())

ax.add_feature(cfeature.OCEAN.with_scale('10m'),
                 facecolor='#2081C3', zorder=2)
ax.add_feature(cfeature.LAND.with_scale('10m'),
                 facecolor='lightgray', zorder=0)
ax.add_feature(cfeature.BORDERS.with_scale('10m'),
                    edgecolor='black', zorder=2)

ax.add_feature(ShapelyFeature(
        Reader(f'{SHAPEFILES_DIR}/ITA_adm_shp/ITA_adm1.shp').geometries(),
        ccrs.PlateCarree(),
        edgecolor='black',
        facecolor='none',
        linewidth=1),
        zorder=5)

plt.savefig('test_projection.png', bbox_inches="tight",dpi=100)
Full environment definition ### Operating system CentOS Linux 7 ### conda list ``` _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge affine 2.3.1 pyhd8ed1ab_0 conda-forge alsa-lib 1.2.7.2 h166bdaf_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py310h5764c6d_2 conda-forge asciitree 0.3.3 py_2 conda-forge asttokens 2.0.8 pyhd8ed1ab_0 conda-forge attr 2.5.1 h166bdaf_1 conda-forge attrs 22.1.0 pyh71513ae_1 conda-forge autopep8 1.7.0 pyhd8ed1ab_0 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 py_2 conda-forge backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.11.1 pyha770c72_0 conda-forge bleach 5.0.1 pyhd8ed1ab_0 conda-forge blosc 1.21.1 h83bc5f7_3 conda-forge bokeh 2.4.3 pyhd8ed1ab_3 conda-forge boost-cpp 1.78.0 h75c5d50_1 conda-forge branca 0.5.0 pyhd8ed1ab_0 conda-forge brotli 1.0.9 h166bdaf_7 conda-forge brotli-bin 1.0.9 h166bdaf_7 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xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h7f98852_1007 conda-forge xyzservices 2022.6.0 pyhd8ed1ab_0 conda-forge xz 5.2.6 h166bdaf_0 conda-forge yaml 0.2.5 h7f98852_2 conda-forge zarr 2.12.0 pyhd8ed1ab_0 conda-forge zeromq 4.3.4 h9c3ff4c_1 conda-forge zict 2.2.0 pyhd8ed1ab_0 conda-forge zipp 3.8.1 pyhd8ed1ab_0 conda-forge zlib 1.2.12 h166bdaf_3 conda-forge zstd 1.5.2 h6239696_4 conda-forge ```
guidocioni commented 2 years ago

A similar plot with basemap and Mercator projection is generated in only 8 seconds... Even with all the differences between Cartopy and Basemap I cannot really figure out a drop factor of almost 18 in performance. 

fig = plt.figure(1, figsize=(15, 10))

m = Basemap(projection='merc',
            llcrnrlat=extents[2],
            urcrnrlat=extents[3],
            llcrnrlon=extents[0],
            urcrnrlon=extents[1],
            lat_ts=20,
            resolution='h')

m.fillcontinents(color='lightgray',lake_color='#2081C3')
m.drawmapboundary(fill_color='#2081C3')
m.drawcountries()

ax = plt.gca()

m.readshapefile(f'{SHAPEFILES_DIR}/ITA_adm_shp/ITA_adm1',
                'ITA_adm1',linewidth=0.8,color='black',zorder=5)

plt.savefig('test_projection_bm.png', bbox_inches="tight",dpi=100)

Plot with Cartopy test_projection

Plot with Basemap test_projection_bm

greglucas commented 2 years ago

10m oceans are known to be extremely slow, so I would definitely not recommend using those if you want speed. See this thread for some performance suggestions: https://github.com/SciTools/cartopy/issues/1895

Unfortunately, no one has really had the time to invest in speeding up the code, but we are always open to PRs to help improve the code!

rcomer commented 8 months ago

Currently the entire geometry gets transformed if part of it is in the map view. So in this case we are transforming a lot of ocean boundary points unnecessarily. A solution might be to take the intersection of the geometry with the map extent before transforming. This would be more efficient if you are making a static plot, but less efficient if you want to pan/zoom interactively as you would be transforming new geometries each time the figure is re-drawn. We could make it an option though?

wqshen commented 8 months ago

Currently the entire geometry gets transformed if part of it is in the map view. So in this case we are transforming a lot of ocean boundary points unnecessarily. A solution might be to take the intersection of the geometry with the map extent before transforming. This would be more efficient if you are making a static plot, but less efficient if you want to pan/zoom interactively as you would be transforming new geometries each time the figure is re-drawn. We could make it an option though?

the pre-clip feature you suggested will be useful in static plot, not only in the performance, but also reduce the file size of vector graphics like eps, ps.