Cartopy-0.14.2/0000755001374000021030000000000012706121712012746 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/0000755001374000021030000000000012706121712013514 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/Cartopy.egg-info/0000755001374000021030000000000012706121712016627 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/Cartopy.egg-info/PKG-INFO0000644001374000021030000000427512706121710017732 0ustar itpeavd00000000000000Metadata-Version: 1.1 Name: Cartopy Version: 0.14.2 Summary: A cartographic python library with matplotlib support for visualisation Home-page: http://scitools.org.uk/cartopy/docs/latest/ Author: UK Met Office Author-email: UNKNOWN License: LGPLv3 Download-URL: https://github.com/SciTools/cartopy Description: ======= Cartopy ======= .. image:: http://scitools.org.uk/cartopy/docs/latest/_static/cartopy.png Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: * object oriented projection definitions * point, line, polygon and image transformations between projections * integration to expose advanced mapping in matplotlib with a simple and intuitive interface * powerful vector data handling by integrating shapefile reading with Shapely capabilities Documentation can be found at http://scitools.org.uk/cartopy/docs/latest/. ---- Code ---- Cartopy is licenced under GNU Lesser General Public License (LGPLv3). Development occurs at https://github.com/SciTools/cartopy. Keywords: cartography map transform projection proj.4 geos shapely shapefile Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+) Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: Microsoft :: Windows Classifier: Operating System :: POSIX Classifier: Operating System :: POSIX :: AIX Classifier: Operating System :: POSIX :: Linux Classifier: Programming Language :: C++ Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: GIS Classifier: Topic :: Scientific/Engineering :: Visualization 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0ustar itpeavd00000000000000 Cartopy-0.14.2/lib/Cartopy.egg-info/requires.txt0000644001374000021030000000030212706121710021220 0ustar itpeavd00000000000000numpy>=1.6 shapely>=1.5.6 pyshp>=1.1.4 six>=1.3.0 setuptools>=0.7.2 [epsg] pyepsg>=0.2.0 [ows] OWSLib>=0.8.7 pillow>=1.7.8 [plotting] matplotlib>=1.3.0 GDAL>=1.10.0 pillow>=1.7.8 scipy>=0.10 Cartopy-0.14.2/lib/Cartopy.egg-info/top_level.txt0000644001374000021030000000001012706121710021346 0ustar itpeavd00000000000000cartopy Cartopy-0.14.2/lib/cartopy/0000755001374000021030000000000012706121712015175 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/examples/0000755001374000021030000000000012706121712017013 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/examples/__init__.py0000644001374000021030000000000012520135744021116 0ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/examples/always_circular_stereo.py0000755001374000021030000000244112520135744024142 0ustar itpeavd00000000000000__tags__ = ['Lines and polygons'] import matplotlib.path as mpath import matplotlib.pyplot as plt import numpy as np import cartopy.crs as ccrs import cartopy.feature def main(): fig = plt.figure(figsize=[10, 5]) ax1 = plt.subplot(1, 2, 1, projection=ccrs.SouthPolarStereo()) ax2 = plt.subplot(1, 2, 2, projection=ccrs.SouthPolarStereo(), sharex=ax1, sharey=ax1) fig.subplots_adjust(bottom=0.05, top=0.95, left=0.04, right=0.95, wspace=0.02) # Limit the map to -60 degrees latitude and below. ax1.set_extent([-180, 180, -90, -60], ccrs.PlateCarree()) ax1.add_feature(cartopy.feature.LAND) ax1.add_feature(cartopy.feature.OCEAN) ax1.gridlines() ax2.gridlines() ax2.add_feature(cartopy.feature.LAND) ax2.add_feature(cartopy.feature.OCEAN) # Compute a circle in axes coordinates, which we can use as a boundary # for the map. We can pan/zoom as much as we like - the boundary will be # permanently circular. theta = np.linspace(0, 2*np.pi, 100) center, radius = [0.5, 0.5], 0.5 verts = np.vstack([np.sin(theta), np.cos(theta)]).T circle = mpath.Path(verts * radius + center) ax2.set_boundary(circle, transform=ax2.transAxes) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/arrows.py0000644001374000021030000000212512520135744020706 0ustar itpeavd00000000000000__tags__ = ['Vector data'] import matplotlib.pyplot as plt import numpy as np import cartopy import cartopy.crs as ccrs def sample_data(shape=(20, 30)): """ Returns ``(x, y, u, v, crs)`` of some vector data computed mathematically. The returned crs will be a rotated pole CRS, meaning that the vectors will be unevenly spaced in regular PlateCarree space. """ crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) x = np.linspace(311.9, 391.1, shape[1]) y = np.linspace(-23.6, 24.8, shape[0]) x2d, y2d = np.meshgrid(x, y) u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2) v = 20 * np.cos(6 * np.deg2rad(x2d)) return x, y, u, v, crs def main(): ax = plt.axes(projection=ccrs.Orthographic(-10, 45)) ax.add_feature(cartopy.feature.OCEAN, zorder=0) ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black') ax.set_global() ax.gridlines() x, y, u, v, vector_crs = sample_data() ax.quiver(x, y, u, v, transform=vector_crs) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/aurora_forecast.py0000644001374000021030000001374512705644356022573 0ustar itpeavd00000000000000""" Plotting the Aurora Forecast from NOAA on Orthographic Polar Projection ----------------------------------------------------------------------- The National Oceanic and Atmospheric Administration (NOAA) monitors the solar wind conditions using the ACE spacecraft orbiting close to the L1 Lagrangian point of the Sun-Earth system. This data is fed into the OVATION-Prime model to forecast the probability of visible aurora at various locations on Earth. Every five minutes a new forecast is published for the coming 30 minutes. The data is provided as a 1024 by 512 grid of probabilities in percent of visible aurora. The data spaced equally in degrees from -180 to 180 and -90 to 90. """ __tags__ = ["Scalar data"] try: from urllib2 import urlopen except ImportError: from urllib.request import urlopen from io import StringIO import numpy as np from datetime import datetime import cartopy.crs as ccrs import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap import matplotlib.patches as patches def aurora_forecast(): """ Gets the latest Aurora Forecast from http://swpc.noaa.gov. Returns ------- img : numpy array The pixels of the image in a numpy array. img_proj : cartopy CRS The rectangular coordinate system of the image. img_extent : tuple of floats The extent of the image ``(x0, y0, x1, y1)`` referenced in the ``img_proj`` coordinate system. origin : str The origin of the image to be passed through to matplotlib's imshow. dt : datetime Time of forecast validity. """ # GitHub gist to download the example data from url = ('https://gist.githubusercontent.com/belteshassar/' 'c7ea9e02a3e3934a9ddc/raw/aurora-nowcast-map.txt') # To plot the current forecast instead, uncomment the following line # url = 'http://services.swpc.noaa.gov/text/aurora-nowcast-map.txt' response_text = StringIO(urlopen(url).read().decode('utf-8')) img = np.loadtxt(response_text) # Read forecast date and time response_text.seek(0) for line in response_text: if line.startswith('Product Valid At:', 2): dt = datetime.strptime(line[-17:-1], '%Y-%m-%d %H:%M') img_proj = ccrs.PlateCarree() img_extent = (-180, 180, -90, 90) return img, img_proj, img_extent, 'lower', dt def aurora_cmap(): """Return a colormap with aurora like colors""" stops = {'red': [(0.00, 0.1725, 0.1725), (0.50, 0.1725, 0.1725), (1.00, 0.8353, 0.8353)], 'green': [(0.00, 0.9294, 0.9294), (0.50, 0.9294, 0.9294), (1.00, 0.8235, 0.8235)], 'blue': [(0.00, 0.3843, 0.3843), (0.50, 0.3843, 0.3843), (1.00, 0.6549, 0.6549)], 'alpha': [(0.00, 0.0, 0.0), (0.50, 1.0, 1.0), (1.00, 1.0, 1.0)]} return LinearSegmentedColormap('aurora', stops) def sun_pos(dt=None): """This function computes a rough estimate of the coordinates for the point on the surface of the Earth where the Sun is directly overhead at the time dt. Precision is down to a few degrees. This means that the equinoxes (when the sign of the latitude changes) will be off by a few days. The function is intended only for visualization. For more precise calculations consider for example the PyEphem package. Parameters ---------- dt: datetime Defaults to datetime.utcnow() Returns ------- lat, lng: tuple of floats Approximate coordinates of the point where the sun is in zenith at the time dt. """ if dt is None: dt = datetime.utcnow() axial_tilt = 23.4 ref_solstice = datetime(2016, 6, 21, 22, 22) days_per_year = 365.2425 seconds_per_day = 24*60*60.0 days_since_ref = (dt - ref_solstice).total_seconds()/seconds_per_day lat = axial_tilt*np.cos(2*np.pi*days_since_ref/days_per_year) sec_since_midnight = (dt - datetime(dt.year, dt.month, dt.day)).seconds lng = -(sec_since_midnight/seconds_per_day - 0.5)*360 return lat, lng def fill_dark_side(ax, time=None, *args, **kwargs): """ Plot a fill on the dark side of the planet (without refraction). Parameters ---------- ax : matplotlib axes The axes to plot on. time : datetime The time to calculate terminator for. Defaults to datetime.utcnow() **kwargs : Passed on to Matplotlib's ax.fill() """ lat, lng = sun_pos(time) pole_lng = lng if lat > 0: pole_lat = -90 + lat central_rot_lng = 180 else: pole_lat = 90 + lat central_rot_lng = 0 rotated_pole = ccrs.RotatedPole(pole_latitude=pole_lat, pole_longitude=pole_lng, central_rotated_longitude=central_rot_lng) x = np.empty(360) y = np.empty(360) x[:180] = -90 y[:180] = np.arange(-90, 90.) x[180:] = 90 y[180:] = np.arange(90, -90., -1) ax.fill(x, y, transform=rotated_pole, **kwargs) def main(): fig = plt.figure(figsize=[10, 5]) # We choose to plot in an Orthographic projection as it looks natural # and the distortion is relatively small around the poles where # the aurora is most likely. # ax1 for Northern Hemisphere ax1 = plt.subplot(1, 2, 1, projection=ccrs.Orthographic(0, 90)) # ax2 for Southern Hemisphere ax2 = plt.subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90)) img, crs, extent, origin, dt = aurora_forecast() for ax in [ax1, ax2]: ax.coastlines(zorder=3) ax.stock_img() ax.gridlines() fill_dark_side(ax, time=dt, color='black', alpha=0.75) ax.imshow(img, vmin=0, vmax=100, transform=crs, extent=extent, origin=origin, zorder=2, cmap=aurora_cmap()) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/barbs.py0000644001374000021030000000103412520135744020460 0ustar itpeavd00000000000000__tags__ = ['Vector data'] import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.examples.arrows import sample_data def main(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([-90, 75, 10, 60]) ax.stock_img() ax.coastlines() x, y, u, v, vector_crs = sample_data(shape=(10, 14)) ax.barbs(x, y, u, v, length=5, sizes=dict(emptybarb=0.25, spacing=0.2, height=0.5), linewidth=0.95, transform=vector_crs) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/eccentric_ellipse.py0000644001374000021030000000503712700747662023062 0ustar itpeavd00000000000000""" Displaying data on an eccentric ellipse --------------------------------------- This example demonstrates plotting data on an eccentric ellipse. The data plotted is a topography map of the asteroid Vesta. The map is actually an image, which is defined on an equirectangluar projection relative to an ellipse with a semi-major axis of 285 km and a semi-minor axis of 229 km. The image is reprojected on-the-fly onto a geostationary projection with matching eccentricity. """ __tags__ = ['Miscellanea'] try: from urllib2 import urlopen except ImportError: from urllib.request import urlopen from io import BytesIO import cartopy.crs as ccrs import matplotlib.pyplot as plt import numpy as np from PIL import Image def vesta_image(): """ Return an image of Vesta's topography. Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI Returns ------- img : numpy array The pixels of the image in a numpy array. img_proj : cartopy CRS The rectangular coordinate system of the image. img_extent : tuple of floats The extent of the image ``(x0, y0, x1, y1)`` referenced in the ``img_proj`` coordinate system. """ url = 'https://www.nasa.gov/sites/default/files/pia17037.jpg' img_handle = BytesIO(urlopen(url).read()) raw_image = Image.open(img_handle) # The image is extremely high-resolution, which takes a long time to # plot. Sub-sampling reduces the time taken to plot while not # significantly altering the integrity of the result. smaller_image = raw_image.resize([raw_image.size[0] // 10, raw_image.size[1] // 10]) img = np.asarray(smaller_image) # We define the semimajor and semiminor axes, but must also tell the # globe not to use the WGS84 ellipse, which is its default behaviour. img_globe = ccrs.Globe(semimajor_axis=285000., semiminor_axis=229000., ellipse=None) img_proj = ccrs.PlateCarree(globe=img_globe) img_extent = (-895353.906273091, 895353.906273091, 447676.9531365455, -447676.9531365455) return img, img_globe, img_proj, img_extent def main(): img, globe, crs, extent = vesta_image() projection = ccrs.Geostationary(globe=globe) ax = plt.axes(projection=projection) ax.imshow(img, transform=crs, extent=extent) plt.gcf().text(.075, .012, "Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI", bbox={'facecolor': 'w', 'edgecolor': 'k'}) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/effects_of_the_ellipse.py0000644001374000021030000001141412520135744024052 0ustar itpeavd00000000000000""" The effect of badly referencing an ellipse ------------------------------------------ This example demonstrates the effect of referencing your data to an incorrect ellipse. First we define two coordinate systems - one using the World Geodetic System established in 1984 and the other using a spherical globe. Next we extract data from the Natural Earth land dataset and convert the Geodetic coordinates (referenced in WGS84) into the respective coordinate systems that we have defined. Finally, we plot these datasets onto a map assuming that they are both referenced to the WGS84 ellipse and compare how the coastlines are shifted as a result of referencing the incorrect ellipse. """ __tags__ = ['Lines and polygons'] import cartopy.crs as ccrs import cartopy.feature from cartopy.io.img_tiles import MapQuestOpenAerial import matplotlib.pyplot as plt from matplotlib.lines import Line2D as Line from matplotlib.patheffects import Stroke import numpy as np import shapely.geometry as sgeom from shapely.ops import transform as geom_transform def transform_fn_factory(target_crs, source_crs): """ Return a function which can be used by ``shapely.op.transform`` to transform the coordinate points of a geometry. The function explicitly *does not* do any interpolation or clever transformation of the coordinate points, so there is no guarantee that the resulting geometry would make any sense. """ def transform_fn(x, y, z=None): new_coords = target_crs.transform_points(source_crs, np.asanyarray(x), np.asanyarray(y)) return new_coords[:, 0], new_coords[:, 1], new_coords[:, 2] return transform_fn def main(): # Define the two coordinate systems with different ellipses. wgs84 = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84', ellipse='WGS84')) sphere = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84', ellipse='sphere')) # Define the coordinate system of the data we have from Natural Earth and # acquire the 1:10m physical coastline shapefile. geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84')) dataset = cartopy.feature.NaturalEarthFeature(category='physical', name='coastline', scale='10m') # Create a MapQuest map tiler instance, and use its CRS for the GeoAxes. tiler = MapQuestOpenAerial() ax = plt.axes(projection=tiler.crs) plt.title('The effect of incorrectly referencing the Solomon Islands') # Pick the area of interest. In our case, roughly the Solomon Islands, and # get hold of the coastlines for that area. extent = (155, 163, -11.5, -6) ax.set_extent(extent, geodetic) geoms = list(dataset.intersecting_geometries(extent)) # Add the MapQuest aerial imagery at zoom level 7. ax.add_image(tiler, 7) # Transform the geodetic coordinates of the coastlines into the two # projections of differing ellipses. wgs84_geoms = [geom_transform(transform_fn_factory(wgs84, geodetic), geom) for geom in geoms] sphere_geoms = [geom_transform(transform_fn_factory(sphere, geodetic), geom) for geom in geoms] # Using these differently referenced geometries, assume that they are # both referenced to WGS84. ax.add_geometries(wgs84_geoms, wgs84, edgecolor='white', color='none') ax.add_geometries(sphere_geoms, wgs84, edgecolor='gray', color='none') # Create a legend for the coastlines. legend_artists = [Line([0], [0], color=color, linewidth=3) for color in ('white', 'gray')] legend_texts = ['Correct ellipse\n(WGS84)', 'Incorrect ellipse\n(sphere)'] legend = plt.legend(legend_artists, legend_texts, fancybox=True, loc='lower left', framealpha=0.75) legend.legendPatch.set_facecolor('wheat') # Create an inset GeoAxes showing the location of the Solomon Islands. sub_ax = plt.axes([0.7, 0.625, 0.2, 0.2], projection=ccrs.PlateCarree()) sub_ax.set_extent([110, 180, -50, 10], geodetic) # Make a nice border around the inset axes. effect = Stroke(linewidth=4, foreground='wheat', alpha=0.5) sub_ax.outline_patch.set_path_effects([effect]) # Add the land, coastlines and the extent of the Solomon Islands. sub_ax.add_feature(cartopy.feature.LAND) sub_ax.coastlines() extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3]) sub_ax.add_geometries([extent_box], ccrs.PlateCarree(), color='none', edgecolor='blue', linewidth=2) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/eyja_volcano.py0000755001374000021030000000340212520135744022044 0ustar itpeavd00000000000000# -*- coding: utf-8 -*- """ Map tile acquisition -------------------- Demonstrates cartopy's ability to draw map tiles which are downloaded on demand from the MapQuest tile server. Internally these tiles are then combined into a single image and displayed in the cartopy GeoAxes. """ __tags__ = ["Scalar data"] import matplotlib.pyplot as plt from matplotlib.transforms import offset_copy import cartopy.crs as ccrs import cartopy.io.img_tiles as cimgt def main(): # Create a MapQuest open aerial instance. map_quest_aerial = cimgt.MapQuestOpenAerial() # Create a GeoAxes in the tile's projection. ax = plt.axes(projection=map_quest_aerial.crs) # Limit the extent of the map to a small longitude/latitude range. ax.set_extent([-22, -15, 63, 65]) # Add the MapQuest data at zoom level 8. ax.add_image(map_quest_aerial, 8) # Add a marker for the Eyjafjallajökull volcano. plt.plot(-19.613333, 63.62, marker='o', color='yellow', markersize=12, alpha=0.7, transform=ccrs.Geodetic()) # Use the cartopy interface to create a matplotlib transform object # for the Geodetic coordinate system. We will use this along with # matplotlib's offset_copy function to define a coordinate system which # translates the text by 25 pixels to the left. geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax) text_transform = offset_copy(geodetic_transform, units='dots', x=-25) # Add text 25 pixels to the left of the volcano. plt.text(-19.613333, 63.62, u'Eyjafjallajökull', verticalalignment='center', horizontalalignment='right', transform=text_transform, bbox=dict(facecolor='wheat', alpha=0.5, boxstyle='round')) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/favicon.py0000644001374000021030000000310712545000115021005 0ustar itpeavd00000000000000__tags__ = ['Miscellanea'] import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib.textpath import matplotlib.patches from matplotlib.font_manager import FontProperties import numpy as np def main(): plt.figure(figsize=[8, 8]) ax = plt.axes(projection=ccrs.SouthPolarStereo()) ax.coastlines() ax.gridlines() im = ax.stock_img() def on_draw(event=None): """ Hooks into matplotlib's event mechanism to define the clip path of the background image. """ # Clip the image to the current background boundary. im.set_clip_path(ax.background_patch.get_path(), transform=ax.background_patch.get_transform()) # Register the on_draw method and call it once now. plt.gcf().canvas.mpl_connect('draw_event', on_draw) on_draw() # Generate a matplotlib path representing the character "C". fp = FontProperties(family='Bitstream Vera Sans', weight='bold') logo_path = matplotlib.textpath.TextPath((-4.5e7, -3.7e7), 'C', size=1, prop=fp) # Scale the letter up to an appropriate X and Y scale. logo_path._vertices *= np.array([103250000, 103250000]) # Add the path as a patch, drawing black outlines around the text. patch = matplotlib.patches.PathPatch(logo_path, facecolor='white', edgecolor='black', linewidth=10, transform=ccrs.SouthPolarStereo()) ax.add_patch(patch) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/feature_creation.py0000644001374000021030000000217512700747662022725 0ustar itpeavd00000000000000__tags__ = ['Lines and polygons'] import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from matplotlib.offsetbox import AnchoredText def main(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([80, 170, -45, 30]) # Put a background image on for nice sea rendering. ax.stock_img() # Create a feature for States/Admin 1 regions at 1:50m from Natural Earth states_provinces = cfeature.NaturalEarthFeature( category='cultural', name='admin_1_states_provinces_lines', scale='50m', facecolor='none') SOURCE = 'Natural Earth' LICENSE = 'public domain' ax.add_feature(cfeature.LAND) ax.add_feature(cfeature.COASTLINE) ax.add_feature(states_provinces, edgecolor='gray') # Add a text annotation for the license information to the # the bottom right corner. text = AnchoredText(r'$\mathcircled{{c}}$ {}; license: {}' ''.format(SOURCE, LICENSE), loc=4, prop={'size': 12}, frameon=True) ax.add_artist(text) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/features.py0000644001374000021030000000102412520135744021204 0ustar itpeavd00000000000000__tags__ = ['Lines and polygons'] import cartopy import matplotlib.pyplot as plt def main(): ax = plt.axes(projection=cartopy.crs.PlateCarree()) ax.add_feature(cartopy.feature.LAND) ax.add_feature(cartopy.feature.OCEAN) ax.add_feature(cartopy.feature.COASTLINE) ax.add_feature(cartopy.feature.BORDERS, linestyle=':') ax.add_feature(cartopy.feature.LAKES, alpha=0.5) ax.add_feature(cartopy.feature.RIVERS) ax.set_extent([-20, 60, -40, 40]) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/geostationary.py0000644001374000021030000000361312520135744022264 0ustar itpeavd00000000000000""" Reprojecting images from a Geostationary projection --------------------------------------------------- This example demonstrates Cartopy's ability to project images into the desired projection on-the-fly. The image itself is retrieved from a URL and is loaded directly into memory without storing it intermediately into a file. It represents pre-processed data from Moderate-Resolution Imaging Spectroradiometer (MODIS) which has been put into an image in the data's native Geostationary coordinate system - it is then projected by cartopy into a global Miller map. """ __tags__ = ["Scalar data"] try: from urllib2 import urlopen except ImportError: from urllib.request import urlopen from io import BytesIO import cartopy.crs as ccrs import matplotlib.pyplot as plt def geos_image(): """ Return a specific MODIS image by retrieving it from a github gist URL. Returns ------- img : numpy array The pixels of the image in a numpy array. img_proj : cartopy CRS The rectangular coordinate system of the image. img_extent : tuple of floats The extent of the image ``(x0, y0, x1, y1)`` referenced in the ``img_proj`` coordinate system. origin : str The origin of the image to be passed through to matplotlib's imshow. """ url = ('https://gist.github.com/pelson/5871263/raw/' 'EIDA50_201211061300_clip2.png') img_handle = BytesIO(urlopen(url).read()) img = plt.imread(img_handle) img_proj = ccrs.Geostationary(satellite_height=35786000) img_extent = (-5500000, 5500000, -5500000, 5500000) return img, img_proj, img_extent, 'upper' def main(): ax = plt.axes(projection=ccrs.Miller()) ax.coastlines() ax.set_global() img, crs, extent, origin = geos_image() plt.imshow(img, transform=crs, extent=extent, origin=origin, cmap='gray') plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/global_map.py0000755001374000021030000000107412520135744021473 0ustar itpeavd00000000000000__tags__ = ['Lines and polygons'] import matplotlib.pyplot as plt import cartopy.crs as ccrs def main(): ax = plt.axes(projection=ccrs.Robinson()) # make the map global rather than have it zoom in to # the extents of any plotted data ax.set_global() ax.stock_img() ax.coastlines() plt.plot(-0.08, 51.53, 'o', transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.Geodetic()) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/hurricane_katrina.py0000755001374000021030000000623012700747662023076 0ustar itpeavd00000000000000__tags__ = ['Lines and polygons'] import matplotlib.patches as mpatches import matplotlib.pyplot as plt import shapely.geometry as sgeom import cartopy.crs as ccrs import cartopy.io.shapereader as shpreader def sample_data(): """ Returns a list of latitudes and a list of longitudes (lons, lats) for Hurricane Katrina (2005). The data was originally sourced from the HURDAT2 dataset from AOML/NOAA: http://www.aoml.noaa.gov/hrd/hurdat/newhurdat-all.html on 14th Dec 2012. """ lons = [-75.1, -75.7, -76.2, -76.5, -76.9, -77.7, -78.4, -79.0, -79.6, -80.1, -80.3, -81.3, -82.0, -82.6, -83.3, -84.0, -84.7, -85.3, -85.9, -86.7, -87.7, -88.6, -89.2, -89.6, -89.6, -89.6, -89.6, -89.6, -89.1, -88.6, -88.0, -87.0, -85.3, -82.9] lats = [23.1, 23.4, 23.8, 24.5, 25.4, 26.0, 26.1, 26.2, 26.2, 26.0, 25.9, 25.4, 25.1, 24.9, 24.6, 24.4, 24.4, 24.5, 24.8, 25.2, 25.7, 26.3, 27.2, 28.2, 29.3, 29.5, 30.2, 31.1, 32.6, 34.1, 35.6, 37.0, 38.6, 40.1] return lons, lats def main(): ax = plt.axes([0, 0, 1, 1], projection=ccrs.LambertConformal()) ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic()) shapename = 'admin_1_states_provinces_lakes_shp' states_shp = shpreader.natural_earth(resolution='110m', category='cultural', name=shapename) lons, lats = sample_data() # to get the effect of having just the states without a map "background" # turn off the outline and background patches ax.background_patch.set_visible(False) ax.outline_patch.set_visible(False) plt.title('US States which intersect the track ' 'of Hurricane Katrina (2005)') # turn the lons and lats into a shapely LineString track = sgeom.LineString(zip(lons, lats)) # buffer the linestring by two degrees (note: this is a non-physical # distance) track_buffer = track.buffer(2) for state in shpreader.Reader(states_shp).geometries(): # pick a default color for the land with a black outline, # this will change if the storm intersects with our track facecolor = [0.9375, 0.9375, 0.859375] edgecolor = 'black' if state.intersects(track): facecolor = 'red' elif state.intersects(track_buffer): facecolor = '#FF7E00' ax.add_geometries([state], ccrs.PlateCarree(), facecolor=facecolor, edgecolor=edgecolor) ax.add_geometries([track_buffer], ccrs.PlateCarree(), facecolor='#C8A2C8', alpha=0.5) ax.add_geometries([track], ccrs.PlateCarree(), facecolor='none') # make two proxy artists to add to a legend direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red") within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00") labels = ['State directly intersects\nwith track', 'State is within \n2 degrees of track'] plt.legend([direct_hit, within_2_deg], labels, loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/image_tiles.py0000644001374000021030000000075612520135744021663 0ustar itpeavd00000000000000__tags__ = ['Web services'] """ Web tile imagery ---------------- This example demonstrates how imagery from a tile providing web service can be accessed. """ import matplotlib.pyplot as plt from cartopy.io.img_tiles import StamenTerrain def main(): tiler = StamenTerrain() mercator = tiler.crs ax = plt.axes(projection=mercator) ax.set_extent([-90, -73, 22, 34]) ax.add_image(tiler, 6) ax.coastlines('10m') plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/logo.py0000644001374000021030000000266512545000115020330 0ustar itpeavd00000000000000__tags__ = ['Miscellanea'] import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib.textpath import matplotlib.patches from matplotlib.font_manager import FontProperties import numpy as np def main(): plt.figure(figsize=[12, 6]) ax = plt.axes(projection=ccrs.Robinson()) ax.coastlines() ax.gridlines() # generate a matplotlib path representing the word "cartopy" fp = FontProperties(family='Bitstream Vera Sans', weight='bold') logo_path = matplotlib.textpath.TextPath((-175, -35), 'cartopy', size=1, prop=fp) # scale the letters up to sensible longitude and latitude sizes logo_path._vertices *= np.array([80, 160]) # add a background image im = ax.stock_img() # clip the image according to the logo_path. mpl v1.2.0 does not support # the transform API that cartopy makes use of, so we have to convert the # projection into a transform manually plate_carree_transform = ccrs.PlateCarree()._as_mpl_transform(ax) im.set_clip_path(logo_path, transform=plate_carree_transform) # add the path as a patch, drawing black outlines around the text patch = matplotlib.patches.PathPatch(logo_path, facecolor='none', edgecolor='black', transform=ccrs.PlateCarree()) ax.add_patch(patch) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/regridding_arrows.py0000644001374000021030000000314512520135744023107 0ustar itpeavd00000000000000""" Regridding vectors with quiver ------------------------------ This example demonstrates the regridding functionality in quiver (there exists equivalent functionality in :meth:`cartopy.mpl.geoaxes.GeoAxes.barbs`). Regridding can be an effective way of visualising a vector field, particularly if the data is dense or warped. """ __tags__ = ['Vector data'] import matplotlib.pyplot as plt import numpy as np import cartopy.crs as ccrs def sample_data(shape=(20, 30)): """ Returns ``(x, y, u, v, crs)`` of some vector data computed mathematically. The returned CRS will be a North Polar Stereographic projection, meaning that the vectors will be unevenly spaced in a PlateCarree projection. """ crs = ccrs.NorthPolarStereo() scale = 1e7 x = np.linspace(-scale, scale, shape[1]) y = np.linspace(-scale, scale, shape[0]) x2d, y2d = np.meshgrid(x, y) u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale) v = 20 * np.cos(6 * x2d / scale) return x, y, u, v, crs def main(): plt.figure(figsize=(8, 10)) x, y, u, v, vector_crs = sample_data(shape=(50, 50)) ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree()) ax1.coastlines('50m') ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree()) ax1.quiver(x, y, u, v, transform=vector_crs) ax2 = plt.subplot(2, 1, 2, projection=ccrs.PlateCarree()) plt.title('The same vector field regridded') ax2.coastlines('50m') ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree()) ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/rotated_pole.py0000755001374000021030000000206012520135744022053 0ustar itpeavd00000000000000""" Rotated pole boxes ------------------ This example demonstrates the way a box is warped when it is defined in a rotated pole coordinate system. Try changing the ``box_top`` to ``44``, ``46`` and ``75`` to see the effect that including the pole in the polygon has. """ __tags__ = ['Lines and polygons'] import matplotlib.pyplot as plt import cartopy.crs as ccrs def main(): rotated_pole = ccrs.RotatedPole(pole_latitude=45, pole_longitude=180) box_top = 45 x, y = [-44, -44, 45, 45, -44], [-45, box_top, box_top, -45, -45] ax = plt.subplot(211, projection=rotated_pole) ax.stock_img() ax.coastlines() ax.plot(x, y, marker='o', transform=rotated_pole) ax.fill(x, y, color='coral', transform=rotated_pole, alpha=0.4) ax.gridlines() ax = plt.subplot(212, projection=ccrs.PlateCarree()) ax.stock_img() ax.coastlines() ax.plot(x, y, marker='o', transform=rotated_pole) ax.fill(x, y, transform=rotated_pole, color='coral', alpha=0.4) ax.gridlines() plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/srtm_shading.py0000644001374000021030000000304412700747662022064 0ustar itpeavd00000000000000__tags__ = ['Scalar data'] """ This example illustrates the automatic download of STRM data, and adding of shading to create a so-called "Shaded Relief SRTM". Originally contributed by Thomas Lecocq (http://geophysique.be). """ import cartopy.crs as ccrs from cartopy.io import srtm import matplotlib.pyplot as plt from cartopy.io import PostprocessedRasterSource, LocatedImage from cartopy.io.srtm import SRTM3Source, SRTM1Source def shade(located_elevations): """ Given an array of elevations in a LocatedImage, add a relief (shadows) to give a realistic 3d appearance. """ new_img = srtm.add_shading(located_elevations.image, azimuth=135, altitude=15) return LocatedImage(new_img, located_elevations.extent) def plot(Source, name): plt.figure() ax = plt.axes(projection=ccrs.PlateCarree()) # Define a raster source which uses the SRTM data and applies the # shade function when the data is retrieved. shaded_srtm = PostprocessedRasterSource(Source(), shade) # Add the shaded SRTM source to our map with a grayscale colormap. ax.add_raster(shaded_srtm, cmap='Greys') # This data is high resolution, so pick a small area which has some # interesting orography. ax.set_extent([12, 13, 47, 48]) plt.title(name + " Shaded Relief Map") gl = ax.gridlines(draw_labels=True) gl.xlabels_top = False gl.ylabels_left = False def main(): plot(SRTM3Source, 'SRTM3') plot(SRTM1Source, 'SRTM1') plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/star_shaped_boundary.py0000644001374000021030000000173212520135744023574 0ustar itpeavd00000000000000""" Modifying the boundary/neatline of a map in cartopy --------------------------------------------------- This example demonstrates how to modify the boundary/neatline of an axes. We construct a star with coordinates in a Plate Carree coordinate system, and use the star as the outline of the map. Notice how changing the projection of the map represents a *projected* star shaped boundary. """ __tags__ = ['Miscellanea'] import matplotlib.path as mpath import matplotlib.pyplot as plt import cartopy.crs as ccrs def main(): ax = plt.axes([0, 0, 1, 1], projection=ccrs.PlateCarree()) ax.coastlines() # Construct a star in longitudes and latitudes. star_path = mpath.Path.unit_regular_star(5, 0.5) star_path = mpath.Path(star_path.vertices.copy() * 80, star_path.codes.copy()) # Use the star as the boundary. ax.set_boundary(star_path, transform=ccrs.PlateCarree()) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/streamplot.py0000644001374000021030000000101112520135744021554 0ustar itpeavd00000000000000__tags__ = ['Vector data'] import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.examples.arrows import sample_data def main(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([-90, 75, 10, 60]) ax.coastlines() x, y, u, v, vector_crs = sample_data(shape=(80, 100)) magnitude = (u ** 2 + v ** 2) ** 0.5 ax.streamplot(x, y, u, v, transform=vector_crs, linewidth=2, density=2, color=magnitude) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/tick_labels.py0000644001374000021030000000327412520135744021653 0ustar itpeavd00000000000000__tags__ = ['Miscellanea'] """ This example demonstrates adding tick labels to maps on rectangular projections using special tick formatters. """ import cartopy.crs as ccrs from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter import matplotlib.pyplot as plt def main(): plt.figure(figsize=(8, 10)) # Label axes of a Plate Carree projection with a central longitude of 180: ax1 = plt.subplot(211, projection=ccrs.PlateCarree(central_longitude=180)) ax1.set_global() ax1.coastlines() ax1.set_xticks([0, 60, 120, 180, 240, 300, 360], crs=ccrs.PlateCarree()) ax1.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree()) lon_formatter = LongitudeFormatter(zero_direction_label=True) lat_formatter = LatitudeFormatter() ax1.xaxis.set_major_formatter(lon_formatter) ax1.yaxis.set_major_formatter(lat_formatter) # Label axes of a Mercator projection without degree symbols in the labels # and formatting labels to include 1 decimal place: ax2 = plt.subplot(212, projection=ccrs.Mercator()) ax2.set_global() ax2.coastlines() ax2.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) ax2.set_yticks([-78.5, -60, -25.5, 25.5, 60, 80], crs=ccrs.PlateCarree()) lon_formatter = LongitudeFormatter(number_format='.1f', degree_symbol='', dateline_direction_label=True) lat_formatter = LatitudeFormatter(number_format='.1f', degree_symbol='') ax2.xaxis.set_major_formatter(lon_formatter) ax2.yaxis.set_major_formatter(lat_formatter) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/tissot.py0000644001374000021030000000057512700747662020735 0ustar itpeavd00000000000000import matplotlib.pyplot as plt import cartopy.crs as ccrs def main(): ax = plt.axes(projection=ccrs.PlateCarree()) # make the map global rather than have it zoom in to # the extents of any plotted data ax.set_global() ax.stock_img() ax.coastlines() ax.tissot(facecolor='orange', alpha=0.4) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/tube_stations.py0000644001374000021030000000447112520135744022262 0ustar itpeavd00000000000000__tags__ = ['Miscellanea'] """ Produces a map showing London Underground station locations with high resolution background imagery provided by MapQuest. """ from matplotlib.path import Path import matplotlib.pyplot as plt import numpy as np import cartopy.crs as ccrs from cartopy.io.img_tiles import MapQuestOSM def tube_locations(): """ Returns an (n, 2) array of selected London Tube locations in Ordnance Survey GB coordinates. Source: http://www.doogal.co.uk/london_stations.php """ return np.array([[531738., 180890.], [532379., 179734.], [531096., 181642.], [530234., 180492.], [531688., 181150.], [530242., 180982.], [531940., 179144.], [530406., 180380.], [529012., 180283.], [530553., 181488.], [531165., 179489.], [529987., 180812.], [532347., 180962.], [529102., 181227.], [529612., 180625.], [531566., 180025.], [529629., 179503.], [532105., 181261.], [530995., 180810.], [529774., 181354.], [528941., 179131.], [531050., 179933.], [530240., 179718.]]) def main(): imagery = MapQuestOSM() ax = plt.axes(projection=imagery.crs) ax.set_extent((-0.14, -0.1, 51.495, 51.515)) # Construct concentric circles and a rectangle, # suitable for a London Underground logo. theta = np.linspace(0, 2 * np.pi, 100) circle_verts = np.vstack([np.sin(theta), np.cos(theta)]).T concentric_circle = Path.make_compound_path(Path(circle_verts[::-1]), Path(circle_verts * 0.6)) rectangle = Path([[-1.1, -0.2], [1, -0.2], [1, 0.3], [-1.1, 0.3]]) # Add the imagery to the map. ax.add_image(imagery, 14) # Plot the locations twice, first with the red concentric circles, # then with the blue rectangle. xs, ys = tube_locations().T plt.plot(xs, ys, transform=ccrs.OSGB(), marker=concentric_circle, color='red', markersize=9, linestyle='') plt.plot(xs, ys, transform=ccrs.OSGB(), marker=rectangle, color='blue', markersize=11, linestyle='') plt.title('London underground locations') plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/un_flag.py0000644001374000021030000001624212700747662021021 0ustar itpeavd00000000000000__tags__ = ['Miscellanea'] import cartopy.crs as ccrs import cartopy.feature import matplotlib.pyplot as plt from matplotlib.patches import PathPatch import matplotlib.path import matplotlib.ticker from matplotlib.transforms import BboxTransform, Bbox import numpy as np # When drawing the flag, we can either use white filled land, or be a little # more fancy and use the Natural Earth shaded relief imagery. filled_land = True def olive_path(): """ Returns a matplotlib path representing a single olive branch from the UN Flag. The path coordinates were extracted from the SVG at https://commons.wikimedia.org/wiki/File:Flag_of_the_United_Nations.svg. """ olives_verts = np.array( [[0, 2, 6, 9, 30, 55, 79, 94, 104, 117, 134, 157, 177, 188, 199, 207, 191, 167, 149, 129, 109, 87, 53, 22, 0, 663, 245, 223, 187, 158, 154, 150, 146, 149, 154, 158, 181, 184, 197, 181, 167, 153, 142, 129, 116, 119, 123, 127, 151, 178, 203, 220, 237, 245, 663, 280, 267, 232, 209, 205, 201, 196, 196, 201, 207, 211, 224, 219, 230, 220, 212, 207, 198, 195, 176, 197, 220, 239, 259, 277, 280, 663, 295, 293, 264, 250, 247, 244, 240, 240, 243, 244, 249, 251, 250, 248, 242, 245, 233, 236, 230, 228, 224, 222, 234, 249, 262, 275, 285, 291, 295, 296, 295, 663, 294, 293, 292, 289, 294, 277, 271, 269, 268, 265, 264, 264, 264, 272, 260, 248, 245, 243, 242, 240, 243, 245, 247, 252, 256, 259, 258, 257, 258, 267, 285, 290, 294, 297, 294, 663, 285, 285, 277, 266, 265, 265, 265, 277, 266, 268, 269, 269, 269, 268, 268, 267, 267, 264, 248, 235, 232, 229, 228, 229, 232, 236, 246, 266, 269, 271, 285, 285, 663, 252, 245, 238, 230, 246, 245, 250, 252, 255, 256, 256, 253, 249, 242, 231, 214, 208, 208, 227, 244, 252, 258, 262, 262, 261, 262, 264, 265, 252, 663, 185, 197, 206, 215, 223, 233, 242, 237, 237, 230, 220, 202, 185, 663], [8, 5, 3, 0, 22, 46, 46, 46, 35, 27, 16, 10, 18, 22, 28, 38, 27, 26, 33, 41, 52, 52, 52, 30, 8, 595, 77, 52, 61, 54, 53, 52, 53, 55, 55, 57, 65, 90, 106, 96, 81, 68, 58, 54, 51, 50, 51, 50, 44, 34, 43, 48, 61, 77, 595, 135, 104, 102, 83, 79, 76, 74, 74, 79, 84, 90, 109, 135, 156, 145, 133, 121, 100, 77, 62, 69, 67, 80, 92, 113, 135, 595, 198, 171, 156, 134, 129, 124, 120, 123, 126, 129, 138, 149, 161, 175, 188, 202, 177, 144, 116, 110, 105, 99, 108, 116, 126, 136, 147, 162, 173, 186, 198, 595, 249, 255, 261, 267, 241, 222, 200, 192, 183, 175, 175, 175, 175, 199, 221, 240, 245, 250, 256, 245, 233, 222, 207, 194, 180, 172, 162, 153, 154, 171, 184, 202, 216, 233, 249, 595, 276, 296, 312, 327, 327, 327, 327, 308, 284, 262, 240, 240, 239, 239, 242, 244, 247, 265, 277, 290, 293, 296, 300, 291, 282, 274, 253, 236, 213, 235, 252, 276, 595, 342, 349, 355, 357, 346, 326, 309, 303, 297, 291, 290, 297, 304, 310, 321, 327, 343, 321, 305, 292, 286, 278, 270, 276, 281, 287, 306, 328, 342, 595, 379, 369, 355, 343, 333, 326, 318, 328, 340, 349, 366, 373, 379, 595]]).T olives_codes = np.array([1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 79], dtype=np.uint8) return matplotlib.path.Path(olives_verts, olives_codes) def main(): blue = '#4b92db' # We're drawing a flag with a 3:5 aspect ratio. fig = plt.figure(figsize=[10, 6], facecolor=blue) # Put a blue background on the figure. blue_background = PathPatch(matplotlib.path.Path.unit_rectangle(), transform=fig.transFigure, color=blue, zorder=-1) fig.patches.append(blue_background) # Set up the Azimuthal Equidistant and Plate Carree projections # for later use. az_eq = ccrs.AzimuthalEquidistant(central_latitude=90) pc = ccrs.PlateCarree() # Pick a suitable location for the map (which is in an Azimuthal # Equidistant projection). ax = plt.axes([0.25, 0.24, 0.5, 0.54], projection=az_eq) # The background patch and outline patch are not needed in this example. ax.background_patch.set_facecolor('none') ax.outline_patch.set_edgecolor('none') # We want the map to go down to -60 degrees latitude. ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree()) # Importantly, we want the axes to be circular at the -60 latitude # rather than cartopy's default behaviour of zooming in and becoming # square. _, patch_radius = az_eq.transform_point(0, -60, pc) circular_path = matplotlib.path.Path.circle(0, patch_radius) ax.set_boundary(circular_path) if filled_land: ax.add_feature( cartopy.feature.LAND, facecolor='white', edgecolor='none') else: ax.stock_img() gl = ax.gridlines(crs=pc, linewidth=3, color='white', linestyle='-') # Meridians every 45 degrees, and 5 parallels. gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(0, 361, 45)) parallels = np.linspace(-60, 70, 5, endpoint=True) gl.ylocator = matplotlib.ticker.FixedLocator(parallels) # Now add the olive branches around the axes. We do this in normalised # figure coordinates olive_leaf = olive_path() olives_bbox = Bbox.null() olives_bbox.update_from_path(olive_leaf) # The first olive branch goes from left to right. olive1_axes_bbox = Bbox([[0.45, 0.15], [0.725, 0.75]]) olive1_trans = BboxTransform(olives_bbox, olive1_axes_bbox) # THe second olive branch goes from right to left (mirroring the first). olive2_axes_bbox = Bbox([[0.55, 0.15], [0.275, 0.75]]) olive2_trans = BboxTransform(olives_bbox, olive2_axes_bbox) olive1 = PathPatch(olive_leaf, facecolor='white', edgecolor='none', transform=olive1_trans + fig.transFigure) olive2 = PathPatch(olive_leaf, facecolor='white', edgecolor='none', transform=olive2_trans + fig.transFigure) fig.patches.append(olive1) fig.patches.append(olive2) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/waves.py0000755001374000021030000000162612520135744020526 0ustar itpeavd00000000000000__tags__ = ['Scalar data'] import matplotlib.pyplot as plt import numpy as np import cartopy.crs as ccrs def sample_data(shape=(73, 145)): """Returns ``lons``, ``lats`` and ``data`` of some fake data.""" nlats, nlons = shape lats = np.linspace(-np.pi / 2, np.pi / 2, nlats) lons = np.linspace(0, 2 * np.pi, nlons) lons, lats = np.meshgrid(lons, lats) wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons) mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2) lats = np.rad2deg(lats) lons = np.rad2deg(lons) data = wave + mean return lons, lats, data def main(): ax = plt.axes(projection=ccrs.Mollweide()) lons, lats, data = sample_data() ax.contourf(lons, lats, data, transform=ccrs.PlateCarree(), cmap='spectral') ax.coastlines() ax.set_global() plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/wms.py0000644001374000021030000000104312520135744020175 0ustar itpeavd00000000000000__tags__ = ['Web services'] """ Interactive WMS (Web Map Service) --------------------------------- This example demonstrates the interactive pan and zoom capability supported by an OGC web services Web Map Service (WMS) aware axes. """ import cartopy.crs as ccrs import matplotlib.pyplot as plt def main(): ax = plt.axes(projection=ccrs.InterruptedGoodeHomolosine()) ax.coastlines() ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0', layers=['basic']) plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/examples/wmts.py0000644001374000021030000000201712700747662020373 0ustar itpeavd00000000000000__tags__ = ['Web services'] """ Interactive WMTS (Web Map Tile Service) --------------------------------------- This example demonstrates the interactive pan and zoom capability supported by an OGC web services Web Map Tile Service (WMTS) aware axes. The example WMTS layer is a single composite of data sampled over nine days in April 2012 and thirteen days in October 2012 showing the Earth at night. It does not vary over time. The imagery was collected by the Suomi National Polar-orbiting Partnership (Suomi NPP) weather satellite operated by the United States National Oceanic and Atmospheric Administration (NOAA). """ import cartopy.crs as ccrs import matplotlib.pyplot as plt def main(): url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' layer = 'VIIRS_CityLights_2012' ax = plt.axes(projection=ccrs.PlateCarree()) ax.add_wmts(url, layer) ax.set_extent((-15, 25, 35, 60)) plt.title('Suomi NPP Earth at night April/October 2012') plt.show() if __name__ == '__main__': main() Cartopy-0.14.2/lib/cartopy/geodesic/0000755001374000021030000000000012706121712016757 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/geodesic/__init__.py0000644001374000021030000000151712700747662021110 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) from cartopy.geodesic._geodesic import Geodesic Cartopy-0.14.2/lib/cartopy/geodesic/_geodesic.pyx0000644001374000021030000002070412700747662021461 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module defines the Geodesic class which can interface with the Proj.4. geodesic functions. """ from cpython.mem cimport PyMem_Malloc from cpython.mem cimport PyMem_Realloc from cpython.mem cimport PyMem_Free import numpy as np cimport numpy as np cimport cython from cython.parallel cimport prange cdef extern from "geodesic.h": # External imports of Proj4.9 functions cdef struct geod_geodesic: pass ctypedef geod_geodesic* geodesic_t void geod_init(geodesic_t, double, double) void geod_direct(geodesic_t, double, double, double, double, double*, double*, double*) nogil void geod_inverse(geodesic_t, double, double, double, double, double*, double*, double*) nogil cdef class Geodesic: """ Defines an ellipsoid on which to solve geodesic problems. """ cdef geod_geodesic* geod cdef double radius cdef double flattening def __cinit__(self, radius=6378137.0, flattening=1/298.257223563): """ Create an ellipsoid with a given radius and flattening. Kwargs: * radius - Equatorial radius (metres). Defaults to the WGS84 semimajor axis (6378137.0 metres). * flattening - Flattening of ellipsoid. Setting flattening = 0 gives a sphere. Negative flattening gives a prolate ellipsoid. If flattening > 1, set flattening to 1/flattening. Defaults to the WGS84 flattening (1/298.257223563). """ # allocate some memory (filled with random data) self.geod = PyMem_Malloc(sizeof(geod_geodesic)) if not self.geod: raise MemoryError() geod_init(self.geod, radius, flattening) self.radius = radius self.flattening = flattening def __dealloc__(self): # Free allocated memory. PyMem_Free(self.geod) def __str__(self): fmt = self.radius, 1/self.flattening return '' % fmt @cython.boundscheck(False) def direct(self, points, azimuths, distances): """ Solve the direct geodesic problem where the length of the geodesic is specified in terms of distance. Can accept and broadcast length 1 arguments. For example, given a single start point and distance, an array of different azimuths can be supplied to locate multiple endpoints. Args: * points - An n (or 1) by 2 numpy.ndarray, list or tuple of lon-lat points. The starting point(s) from which to travel. * azimuths - A length n (or 1) numpy.ndarray or list of azimuth values (degrees). * distances - A length n (or 1) numpy.ndarray or list of distances values (metres). Returns: An n by 3 np.ndarray of lons, lats, and forward azimuths of the located endpoint(s). """ cdef int n_points, i cdef double[:, :] pts, orig_pts cdef double[:] azims, dists # Create numpy arrays from inputs, and ensure correct shape. Note: # reshape(-1) returns a 1D array from a 0 dimensional array as required # for broadcasting. pts = np.array(points, dtype=np.float64).reshape((-1, 2)) azims = np.array(azimuths, dtype=np.float64).reshape(-1) dists = np.array(distances, dtype=np.float64).reshape(-1) sizes = [pts.shape[0], azims.size, dists.size] n_points = max(sizes) if not all(size in [1, n_points] for size in sizes): raise ValueError("Inputs must have common length n or length one.") # Broadcast any length 1 arrays to the correct size. if pts.shape[0] == 1: orig_pts = pts pts = np.empty([n_points, 2], dtype=np.float64) pts[:, :] = orig_pts if azims.size == 1: azims = np.repeat(azims, n_points) if dists.size == 1: dists = np.repeat(dists, n_points) cdef double[:, :] return_pts = np.empty((n_points, 3), dtype=np.float64) with nogil: for i in prange(n_points): geod_direct(self.geod, pts[i, 1], pts[i, 0], azims[i], dists[i], &return_pts[i, 1], &return_pts[i, 0], &return_pts[i, 2]) return return_pts @cython.boundscheck(False) def inverse(self, points, endpoints): """ Solve the inverse geodesic problem. Can accept and broadcast length 1 arguments. For example, given a single start point, an array of different endpoints can be supplied to find multiple distances. Args: * points - An n (or 1) by 2 numpy.ndarray, list or tuple of lon-lat points. The starting point(s) from which to travel. * endpoints - An n (or 1) by 2 numpy.ndarray, list or tuple of lon-lat points. The point(s) to travel to. Returns: An n by 3 np.ndarray of distances, and the (forward) azimuths of the start and end points. """ cdef int n_points, i cdef double[:, :] pts, epts, orig_pts # Create numpy arrays from inputs, and ensure correct shape. Note: # reshape(-1) returns a 1D array from a 0 dimensional array as required # for broadcasting. pts = np.array(points, dtype=np.float64).reshape((-1, 2)) epts = np.array(endpoints, dtype=np.float64).reshape((-1, 2)) sizes = [pts.shape[0], epts.shape[0]] n_points = max(sizes) if not all(size in [1, n_points] for size in sizes): raise ValueError("Inputs must have common length n or length one.") # Broadcast any length 1 arrays to the correct size. if pts.shape[0] == 1: orig_pts = pts pts = np.empty([n_points, 2], dtype=np.float64) pts[:, :] = orig_pts if epts.shape[0] == 1: orig_pts = epts epts = np.empty([n_points, 2], dtype=np.float64) epts[:, :] = orig_pts cdef double[:, :] results = np.empty((n_points, 3), dtype=np.float64) with nogil: for i in prange(n_points): geod_inverse(self.geod, pts[i, 1], pts[i, 0], epts[i, 1], epts[i, 0], &results[i, 0], &results[i, 1], &results[i, 2]) return results def circle(self, double lon, double lat, double radius, int n_samples=180, endpoint=False): """ Find a geodesic circle of given radius at a given point. Args: * lon - Longitude coordinate of the centre. * lat - Latitude coordinate of the centre. * radius - The radius of the circle (metres). Kwargs: * n_samples - Integer number of sample points of circle. * endpoint - Boolean for whether to repeat endpoint at the end of returned array. Returns: An n_samples by 2 np.ndarray of evenly spaced lon-lat points on the circle. """ cdef int i # Put the input arguments into c-typed values. cdef double[:, :] center = np.array([lon, lat]).reshape((1, 2)) cdef double[:] radius_m = np.asarray(radius).reshape(1) azimuths = np.linspace(360., 0., n_samples, endpoint=endpoint).astype(np.double) return self.direct(center, azimuths, radius_m)[:, 0:2] Cartopy-0.14.2/lib/cartopy/io/0000755001374000021030000000000012706121712015604 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/io/__init__.py0000644001374000021030000003642712700747662017745 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Provides a collection of sub-packages for loading, saving and retrieving various data formats. """ from __future__ import (absolute_import, division, print_function) import collections import os import string import warnings import six if six.PY3: from urllib.request import urlopen else: from urllib2 import urlopen from cartopy import config def fh_getter(fh, mode='r', needs_filename=False): """ Convenience function for opening files. Args: * fh - File handle, filename or (file handle, filename) tuple Kwargs: * mode - Open mode. Defaults to "r". Returns: * (file handle, filename), opened in the given mode. """ if mode != 'r': raise ValueError('Only mode "r" currently supported.') if isinstance(fh, six.string_types): filename = fh fh = open(fh, mode) elif isinstance(fh, tuple): fh, filename = fh if filename is None: try: filename = fh.name except AttributeError: # does this occur? if needs_filename: raise ValueError('filename cannot be determined') else: filename = '' return fh, filename class DownloadWarning(Warning): """Issued when a file is being downloaded by a :class:`Downloader`.""" pass class Downloader(object): """ Represents a resource, that can be configured easily, which knows how to acquire itself (perhaps via HTTP). The key interface method is :meth:`path` - typically *all* external calls will be made to that method. To get hold of an appropriate :class:`Downloader` instance the :func:`Downloader.from_config` static method should be considered. .. note: All ``*_template`` arguments should be formattable using the standard :meth:`string.format` rules. The formatting itself is not done until a call to a subsequent method (such as :meth:`Downloader.path`). Args: ``url_template`` - The template of the full URL representing this resource. ``target_path_template`` - The template of the full path to the file that this Downloader represents. Typically the path will be a subdirectory of ``config['data_dir']``, but this is not a strict requirement. If the file does not exist when calling :meth:`Downloader.path` it will be downloaded to this location. Kwargs: ``pre_downloaded_path_template`` - The template of a full path of a file which has been downloaded outside of this Downloader which should be used as the file that this resource represents. If the file does not exist when :meth:`Downloader.path` is called it will not be downloaded to this location (unlike the ``target_path_template`` argument). """ FORMAT_KEYS = ('config',) """ The minimum keys which should be provided in the ``format_dict`` argument for the ``path``, ``url``, ``target_path``, ``pre_downloaded_path`` and ``acquire_resource`` methods. """ def __init__(self, url_template, target_path_template, pre_downloaded_path_template=''): self.url_template = url_template self.target_path_template = target_path_template self.pre_downloaded_path_template = pre_downloaded_path_template # define a formatter which will process the templates. Subclasses # may override the standard ``''.format`` formatting by defining # their own formatter subclass here. self._formatter = string.Formatter() def url(self, format_dict): """ The full URL that this resource represents. Args: ``format_dict`` - The dictionary which is used to replace certain template variables. Subclasses should document which keys are expected as a minimum in their ``FORMAT_KEYS`` class attribute. """ return self._formatter.format(self.url_template, **format_dict) def target_path(self, format_dict): """ The path on disk of the file that this resource represents, must either exist, or be writable by the current user. This method does not check either of these conditions. Args: ``format_dict`` - The dictionary which is used to replace certain template variables. Subclasses should document which keys are expected as a minimum in their ``FORMAT_KEYS`` class attribute. """ return self._formatter.format(self.target_path_template, **format_dict) def pre_downloaded_path(self, format_dict): """ The path on disk of the file that this resource represents, if it does not exist, then no further action will be taken with this path, and all further processing will be done using :meth:`target_path` instead. Args: ``format_dict`` - The dictionary which is used to replace certain template variables. Subclasses should document which keys are expected as a minimum in their ``FORMAT_KEYS`` class attribute. """ return self._formatter.format(self.pre_downloaded_path_template, **format_dict) def path(self, format_dict): """ Returns the path to a file on disk that this resource represents. If the file doesn't exist in :meth:`pre_downloaded_path` then it will check whether it exists in :meth:`target_path`, otherwise the resource will be downloaded via :meth:`acquire_resouce` from :meth:`url` to :meth:`target_path`. Typically, this is the method that most applications will call, allowing implementors of new Downloaders to specialise :meth:`acquire_resource`. Args: ``format_dict`` - The dictionary which is used to replace certain template variables. Subclasses should document which keys are expected as a minimum in their ``FORMAT_KEYS`` class attribute. """ pre_downloaded_path = self.pre_downloaded_path(format_dict) target_path = self.target_path(format_dict) if (pre_downloaded_path is not None and os.path.exists(pre_downloaded_path)): result_path = pre_downloaded_path elif os.path.exists(target_path): result_path = target_path else: # we need to download the file result_path = self.acquire_resource(target_path, format_dict) return result_path def acquire_resource(self, target_path, format_dict): """ Downloads, via HTTP, the file that this resource represents. Subclasses will typically override this method. Args: ``format_dict`` - The dictionary which is used to replace certain template variables. Subclasses should document which keys are expected as a minimum in their ``FORMAT_KEYS`` class attribute. """ target_dir = os.path.dirname(target_path) if not os.path.isdir(target_dir): os.makedirs(target_dir) url = self.url(format_dict) # try getting the resource (no exception handling, just let it raise) response = self._urlopen(url) with open(target_path, 'wb') as fh: fh.write(response.read()) return target_path def _urlopen(self, url): """ Return a file handle to the given HTTP resource URL. Caller should close the file handle when finished with it. """ warnings.warn('Downloading: {}'.format(url), DownloadWarning) return urlopen(url) @staticmethod def from_config(specification, config_dict=None): """ The ``from_config`` static method implements the logic for acquiring a Downloader (sub)class instance from the config dictionary. Args: ``specification`` - should be iterable, as it will be traversed in reverse order to find the most appropriate Downloader instance for this specification. An example specification is ``('shapefiles', 'natural_earth')`` for the Natural Earth shapefiles. Kwargs: ``config_dict`` - typically this is left as None to use the default ``cartopy.config`` "downloaders" dictionary. Example: >>> from cartopy.io import Downloader >>> >>> dnldr = Downloader('https://example.com/{name}', './{name}.txt') >>> config = {('level_1', 'level_2'): dnldr} >>> d1 = Downloader.from_config(('level_1', 'level_2', 'level_3'), ... config_dict=config) >>> print(d1.url_template) https://example.com/{name} >>> print(d1.url({'name': 'item_name'})) https://example.com/item_name """ spec_depth = len(specification) if config_dict is None: downloaders = config['downloaders'] else: downloaders = config_dict result_downloader = None for i in range(spec_depth, 0, -1): lookup = specification[:i] downloadable_item = downloaders.get(lookup, None) if downloadable_item is not None: result_downloader = downloadable_item break if result_downloader is None: # should never really happen, but could if the user does # some strange things like not having any downloaders defined # in the config... raise ValueError('No generic downloadable item in the config ' 'dictionary for {}'.format(specification)) return result_downloader class LocatedImage(collections.namedtuple('LocatedImage', 'image, extent')): """ Defines an image and associated extent in the form: ``image, (min_x, max_x, min_y, max_y)`` """ class RasterSource(object): """ Defines the cartopy raster fetching interface. A :class:`RasterSource` instance is able to supply images and associated extents (as a sequence of :class:`LocatedImage` instances) through its :meth:`~RasterSource.fetch_raster` method. As a result, further interfacing classes, such as :class:`cartopy.mpl.slippy_image_artist.SlippyImageArtist`, can then make use of the interface for functionality such as interactive image retrieval with pan and zoom functionality. """ def validate_projection(self, projection): """ Raise an error if this raster source cannot provide images in the specified projection. Parameters ---------- projection : :class:`cartopy.crs.Projection` The desired projection of the image. """ raise NotImplementedError() def fetch_raster(self, projection, extent, target_resolution): """ Return a sequence of images with extents given some constraining information. Parameters ---------- projection : :class:`cartopy.crs.Projection` The desired projection of the image. extent : iterable of length 4 The extent of the requested image in projected coordinates. The resulting image may not be defined exactly by these extents, and so the extent of the resulting image is also returned. The extents must be defined in the form ``(min_x, max_x, min_y, max_y)``. target_resolution : iterable of length 2 The desired resolution of the image as ``(width, height)`` in pixels. Returns ------- A sequence of :class:`LocatedImage` instances. """ raise NotImplementedError() class RasterSourceContainer(RasterSource): """ A container which simply calls the appropriate methods on the contained :class:`RasterSource`. """ def __init__(self, contained_source): """ Parameters ---------- contained_source : :class:`RasterSource` instance. The source of the raster that this container is wrapping. """ self._source = contained_source def fetch_raster(self, projection, extent, target_resolution): return self._source.fetch_raster(projection, extent, target_resolution) def validate_projection(self, projection): return self._source.validate_projection(projection) class PostprocessedRasterSource(RasterSourceContainer): """ A :class:`RasterSource` which wraps another, an then applies a post-processing step on the raster fetched from the contained source. """ def __init__(self, contained_source, img_post_process): """ Parameters ---------- contained_source : :class:`RasterSource` instance. The source of the raster that this container is wrapping. img_post_process : callable Called after each `fetch_raster` call which yields a non-None image result. The callable must accept the :class:`LocatedImage` from the contained fetch_raster as its only argument, and must return a single LocatedImage. """ super(PostprocessedRasterSource, self).__init__(contained_source) self._post_fetch_fn = img_post_process def fetch_raster(self, *args, **kwargs): fetch_raster = super(PostprocessedRasterSource, self).fetch_raster located_imgs = fetch_raster(*args, **kwargs) if located_imgs: located_imgs = [self._post_fetch_fn(img) for img in located_imgs] return located_imgs Cartopy-0.14.2/lib/cartopy/io/img_nest.py0000755001374000021030000004575712700747662020024 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import collections import glob import itertools import os.path import numpy as np from PIL import Image import shapely.geometry as sgeom from six.moves import zip _img_class_attrs = ['filename', 'extent', 'origin', 'pixel_size'] class Img(collections.namedtuple('Img', _img_class_attrs)): def __new__(cls, *args, **kwargs): # ensure any lists given as args or kwargs are turned into tuples. new_args = [] for item in args: if isinstance(item, list): item = tuple(item) new_args.append(item) new_kwargs = {} for k, item in kwargs.items(): if isinstance(item, list): item = tuple(item) new_kwargs[k] = item return super(Img, cls).__new__(cls, *new_args, **new_kwargs) def __init__(self, *args, **kwargs): """ Represents a simple geo-located image. Args: * filename: Filename of the image tile. * extent: The (x_lower, x_upper, y_lower, y_upper) extent of the image in units of the native projection. * origin: Name of the origin. * pixel_size: The (x_scale, y_scale) pixel width, in units of the native projection per pixel. .. note:: API is likely to change in the future to include a CRS. """ self._bbox = None def __getstate__(self): """ Override the default to ensure when pickling that any new attributes introduced are included in the pickled object. """ return self.__dict__ def bbox(self): """ Return a :class:`~shapely.geometry.polygon.Polygon` instance for this image's extents. """ if self._bbox is None: x0, x1, y0, y1 = self.extent self._bbox = sgeom.box(x0, y0, x1, y1) return self._bbox @staticmethod def world_files(fname): """ Determine potential world filename combinations, without checking their existence. For example, a '*.tif' file may have one of the following popular conventions for world file extensions '*.tifw', '*.tfw', '*.TIFW' or '*.TFW'. Given the possible world file extensions, the upper case basename combinations are also generated. For example, the file 'map.tif' will generate the following world file variations, 'map.tifw', 'map.tfw', 'map.TIFW', 'map.TFW', 'MAP.tifw', 'MAP.tfw', 'MAP.TIFW' and 'MAP.TFW'. Args: * fname: Name of the file for which to get all the possible world filename combinations. Returns: A list of possible world filename combinations. Examples: >>> from cartopy.io.img_nest import Img >>> Img.world_files('img.png')[:6] ['img.pngw', 'img.pgw', 'img.PNGW', 'img.PGW', 'IMG.pngw', 'IMG.pgw'] >>> Img.world_files('/path/to/img.TIF')[:2] ['/path/to/img.tifw', '/path/to/img.tfw'] >>> Img.world_files('/path/to/img/with_no_extension')[0] '/path/to/img/with_no_extension.w' """ froot, fext = os.path.splitext(fname) # If there was no extension to the filename. if froot == fname: result = ['{}.{}'.format(fname, 'w'), '{}.{}'.format(fname, 'W')] else: fext = fext[1::].lower() if len(fext) < 3: result = ['{}.{}'.format(fname, 'w'), '{}.{}'.format(fname, 'W')] else: fext_types = [fext + 'w', fext[0] + fext[-1] + 'w'] fext_types.extend([ext.upper() for ext in fext_types]) result = ['{}.{}'.format(froot, ext) for ext in fext_types] def _convert_basename(name): dirname, basename = os.path.dirname(name), os.path.basename(name) base, ext = os.path.splitext(basename) if base == base.upper(): result = base.lower() + ext else: result = base.upper() + ext if dirname: result = os.path.join(dirname, result) return result result += [_convert_basename(r) for r in result] return result def __array__(self): return np.array(Image.open(self.filename)) @classmethod def from_world_file(cls, img_fname, world_fname): """ Return an Img instance from the given image filename and worldfile filename. """ im = Image.open(img_fname) with open(world_fname) as world_fh: extent, pix_size = cls.world_file_extent(world_fh, im.size) if hasattr(im, 'close'): im.close() return cls(img_fname, extent, 'lower', pix_size) @staticmethod def world_file_extent(worldfile_handle, im_shape): """ Return the extent ``(x0, x1, y0, y1)`` and pixel size ``(x_width, y_width)`` as defined in the given worldfile file handle and associated image shape ``(x, y)``. """ lines = worldfile_handle.readlines() if len(lines) != 6: raise ValueError('Only world files with 6 lines are supported.') pix_size = (float(lines[0]), float(lines[3])) pix_rotation = (float(lines[1]), float(lines[2])) if pix_rotation != (0., 0.): raise ValueError('Rotated pixels in world files is not currently ' 'supported.') ul_corner = (float(lines[4]), float(lines[5])) min_x, max_x = (ul_corner[0] - pix_size[0]/2., ul_corner[0] + pix_size[0]*im_shape[0] - pix_size[0]/2.) min_y, max_y = (ul_corner[1] - pix_size[1]/2., ul_corner[1] + pix_size[1]*im_shape[1] - pix_size[1]/2.) return (min_x, max_x, min_y, max_y), pix_size class ImageCollection(object): def __init__(self, name, crs, images=None): """ Represents a collection of images at the same logical level. Typically these are images at the same zoom level or resolution. Args: * name: The name of the image collection. * crs: The :class:`~cartopy.crs.Projection` instance. Kwargs: * images: A list of one or more :class:`~cartopy.io.img_nest.Img` instances. """ self.name = name self.crs = crs self.images = images or [] def scan_dir_for_imgs(self, directory, glob_pattern='*.tif', img_class=Img): """ Search the given directory for the associated world files of the image files. Args: * directory: The directory path to search for image files. Kwargs: * glob_pattern: The image filename glob pattern to search with. Defaults to '*.tif'. * img_class The class used to construct each image in the Collection. .. note:: Does not recursively search sub-directories. """ imgs = glob.glob(os.path.join(directory, glob_pattern)) for img in imgs: dirname, fname = os.path.split(img) worlds = img_class.world_files(fname) for fworld in worlds: fworld = os.path.join(dirname, fworld) if os.path.exists(fworld): break else: msg = 'Image file {!r} has no associated world file' raise ValueError(msg.format(img)) self.images.append(img_class.from_world_file(img, fworld)) class NestedImageCollection(object): def __init__(self, name, crs, collections, _ancestry=None): """ Represents a complex nest of ImageCollections. On construction, the image collections are scanned for ancestry, leading to fast image finding capabilities. A complex (and time consuming to create) NestedImageCollection instance can be saved as a pickle file and subsequently be (quickly) restored. There is a simplified creation interface for NestedImageCollection ``from_configuration`` for more detail. Args: * name: The name of the nested image collection. * crs: The native :class:`~cartopy.crs.Projection` of all the image collections. * collections: A list of one or more :class:`~cartopy.io.img_nest.ImageCollection` instances. """ # NOTE: all collections must have the same crs. _names = set([collection.name for collection in collections]) assert len(_names) == len(collections), \ 'The collections must have unique names.' self.name = name self.crs = crs self._collections_by_name = {collection.name: collection for collection in collections} def sort_func(c): return np.max([image.bbox().area for image in c.images]) self._collections = sorted(collections, key=sort_func, reverse=True) self._ancestry = {} """ maps (collection name, image) to a list of children (collection name, image). """ if _ancestry is not None: self._ancestry = _ancestry else: parent_wth_children = zip(self._collections, self._collections[1:]) for parent_collection, collection in parent_wth_children: for parent_image in parent_collection.images: for image in collection.images: if self._is_parent(parent_image, image): # append the child image to the parent's ancestry key = (parent_collection.name, parent_image) self._ancestry.setdefault(key, []).append( (collection.name, image)) # TODO check that the ancestry is in a good state (i.e. that each # collection has child images) @staticmethod def _is_parent(parent, child): """ Returns whether the given Image is the parent of image. Used by __init__. """ result = False pbox = parent.bbox() cbox = child.bbox() if pbox.area > cbox.area: result = pbox.intersects(cbox) and not pbox.touches(cbox) return result def image_for_domain(self, target_domain, target_z): """ Determine the image that provides complete coverage of target location. The composed image is merged from one or more image tiles that overlay the target location and provide complete image coverage of the target location. Args: * target_domain: A :class:`~shapely.geometry.linestring.LineString` instance that specifies the target location requiring image coverage. * target_z: The name of the target :class`~cartopy.io.img_nest.ImageCollection` which specifies the target zoom level (resolution) of the required images. Returns: A tuple containing three items, consisting of the target location :class:`numpy.ndarray` image data, the (x-lower, x-upper, y-lower, y-upper) extent of the image, and the origin for the target location. """ # XXX Copied from cartopy.io.img_tiles if target_z not in self._collections_by_name: # TODO: Handle integer depths also? msg = '{!r} is not one of the possible collections.' raise ValueError(msg.format(target_z)) tiles = [] for tile in self.find_images(target_domain, target_z): try: img, extent, origin = self.get_image(tile) except IOError: continue img = np.array(img) x = np.linspace(extent[0], extent[1], img.shape[1], endpoint=False) y = np.linspace(extent[2], extent[3], img.shape[0], endpoint=False) tiles.append([np.array(img), x, y, origin]) from cartopy.io.img_tiles import _merge_tiles img, extent, origin = _merge_tiles(tiles) return img, extent, origin def find_images(self, target_domain, target_z, start_tiles=None): """ A generator that finds all images that overlap the bounded target location. Args: * target_domain: A :class:`~shapely.geometry.linestring.LineString` instance that specifies the target location requiring image coverage. * target_z: The name of the target :class:`~cartopy.io.img_nest.ImageCollection` which specifies the target zoom level (resolution) of the required images. Kwargs: * start_tiles: A list of one or more tuple pairs, composed of a :class:`~cartopy.io.img_nest.ImageCollection` name and an :class:`~cartopy.io.img_nest.Img` instance, from which to search for the target images. Returns: A generator tuple pair composed of a :class:`~cartopy.io.img_nest.ImageCollection` name and an :class:`~cartopy.io.img_nest.Img` instance. """ # XXX Copied from cartopy.io.img_tiles if target_z not in self._collections_by_name: # TODO: Handle integer depths also? msg = '{!r} is not one of the possible collections.' raise ValueError(msg.format(target_z)) if start_tiles is None: start_tiles = ((self._collections[0].name, img) for img in self._collections[0].images) for start_tile in start_tiles: # recursively drill down to the images at the target zoom domain = start_tile[1].bbox() if target_domain.intersects(domain) and \ not target_domain.touches(domain): if start_tile[0] == target_z: yield start_tile else: for tile in self.subtiles(start_tile): for result in self.find_images(target_domain, target_z, start_tiles=[tile]): yield result def subtiles(self, collection_image): """ Find the higher resolution image tiles that compose this parent image tile. Args: * collection_image: A tuple pair containing the parent :class:`~cartopy.io.img_nest.ImageCollection` name and :class:`~cartopy.io.img_nest.Img` instance. Returns: An iterator of tuple pairs containing the higher resolution child :class:`~cartopy.io.img_nest.ImageCollection` name and :class:`~cartopy.io.img_nest.Img` instance that compose the parent. """ return iter(self._ancestry.get(collection_image, [])) desired_tile_form = 'RGB' def get_image(self, collection_image): """ Retrieve the data of the target image from file. .. note:: The format of the retrieved image file data is controlled by :attr:`~cartopy.io.img_nest.NestedImageCollection.desired_tile_form`, which defaults to 'RGB' format. Args: * collection_image: A tuple pair containing the target :class:`~cartopy.io.img_nest.ImageCollection` name and :class:`~cartopy.io.img_nest.Img` instance. Returns: A tuple containing three items, consisting of the associated image file data, the (x_lower, x_upper, y_lower, y_upper) extent of the image, and the image origin. """ img = collection_image[1] img_data = Image.open(img.filename) img_data = img_data.convert(self.desired_tile_form) return img_data, img.extent, img.origin @classmethod def from_configuration(cls, name, crs, name_dir_pairs, glob_pattern='*.tif', img_class=Img): """ Creates a :class:`~cartopy.io.img_nest.NestedImageCollection` instance given the list of image collection name and directory path pairs. This is very convenient functionality for simple configuration level creation of this complex object. For example, to produce a nested collection of OS map tiles:: files = [['OS 1:1,000,000', '/directory/to/1_to_1m'], ['OS 1:250,000', '/directory/to/1_to_250k'], ['OS 1:50,000', '/directory/to/1_to_50k'], ] r = NestedImageCollection.from_configuration('os', ccrs.OSGB(), files) .. important:: The list of image collection name and directory path pairs must be given in increasing resolution order i.e. from low resolution to high resolution. Args: * name: The name for the :class:`~cartopy.io.img_nest.NestedImageCollection` instance. * crs: The :class:`~cartopy.crs.Projection` of the image collection. * name_dir_pairs: A list of image collection name and directory path pairs. Kwargs: * glob_pattern: The image collection filename glob pattern. Defaults to '*.tif'. * img_class: The class of images created in the image collection. Returns: A :class:`~cartopy.io.img_nest.NestedImageCollection` instance. """ collections = [] for collection_name, collection_dir in name_dir_pairs: collection = ImageCollection(collection_name, crs) collection.scan_dir_for_imgs(collection_dir, glob_pattern=glob_pattern, img_class=img_class) collections.append(collection) return cls(name, crs, collections) Cartopy-0.14.2/lib/cartopy/io/img_tiles.py0000755001374000021030000003552112700747662020157 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Implements image tile identification and fetching from various sources. The matplotlib interface can make use of tile objects (defined below) via the :meth:`cartopy.mpl.geoaxes.GeoAxes.add_image` method. For example, to add a :class:`MapQuest Open Aerial tileset ` to an existing axes at zoom level 2, do ``ax.add_image(MapQuestOpenAerial(), 2)``. An example of using tiles in this way can be found at :ref:`examples-eyja_volcano`. """ from __future__ import (absolute_import, division, print_function) from PIL import Image import shapely.geometry as sgeom import numpy as np import six import cartopy.crs as ccrs class GoogleTiles(object): """ Implements web tile retrieval using the Google WTS coordinate system. A "tile" in this class refers to the coordinates (x, y, z). """ def __init__(self, desired_tile_form='RGB', style="street"): """ :param desired_tile_form: :param style: The style for the Google Maps tiles. One of 'street', 'satellite', 'terrain', and 'only_streets'. Defaults to 'street'. """ # Only streets are partly transparent tiles that can be overlayed over # the satellite map to create the known hybrid style from google. styles = ["street", "satellite", "terrain", "only_streets"] style = style.lower() if style not in styles: msg = "Invalid style '%s'. Valid styles: %s" % \ (style, ", ".join(styles)) raise ValueError(msg) self.style = style # The 'satellite' and 'terrain' styles require pillow with a jpeg # decoder. if self.style in ["satellite", "terrain"] and \ not hasattr(Image.core, "jpeg_decoder") or \ not Image.core.jpeg_decoder: msg = "The '%s' style requires pillow with jpeg decoding support." raise ValueError(msg % self.style) self.imgs = [] self.crs = ccrs.Mercator.GOOGLE self.desired_tile_form = desired_tile_form def image_for_domain(self, target_domain, target_z): tiles = [] for tile in self.find_images(target_domain, target_z): try: img, extent, origin = self.get_image(tile) except IOError: continue img = np.array(img) x = np.linspace(extent[0], extent[1], img.shape[1]) y = np.linspace(extent[2], extent[3], img.shape[0]) tiles.append([img, x, y, origin]) img, extent, origin = _merge_tiles(tiles) return img, extent, origin def _find_images(self, target_domain, target_z, start_tile=(0, 0, 0)): """Target domain is a shapely polygon in native coordinates.""" assert isinstance(target_z, int) and target_z >= 0, ('target_z must ' 'be an integer ' '>=0.') # Recursively drill down to the images at the target zoom. x0, x1, y0, y1 = self._tileextent(start_tile) domain = sgeom.box(x0, y0, x1, y1) if domain.intersects(target_domain): if start_tile[2] == target_z: yield start_tile else: for tile in self._subtiles(start_tile): for result in self._find_images(target_domain, target_z, start_tile=tile): yield result find_images = _find_images def subtiles(self, x_y_z): x, y, z = x_y_z # Google tile specific (i.e. up->down). for xi in range(0, 2): for yi in range(0, 2): yield x * 2 + xi, y * 2 + yi, z + 1 _subtiles = subtiles def tile_bbox(self, x, y, z, y0_at_north_pole=True): """ Returns the ``(x0, x1), (y0, y1)`` bounding box for the given x, y, z tile position. Parameters ---------- x, y, z : int The x, y, z tile coordinates in the Google tile numbering system (with y=0 being at the north pole), unless `y0_at_north_pole` is set to ``False``, in which case `y` is in the TMS numbering system (with y=0 being at the south pole). y0_at_north_pole : bool Whether the numbering of the y coordinate starts at the north pole (as is the convention for Google tiles), or the south pole (as is the convention for TMS). """ n = 2 ** z assert 0 <= x <= (n - 1), ("Tile's x index is out of range. Upper " "limit %s. Got %s" % (n, x)) assert 0 <= y <= (n - 1), ("Tile's y index is out of range. Upper " "limit %s. Got %s" % (n, y)) x0, x1 = self.crs.x_limits y0, y1 = self.crs.y_limits # Compute the box height and width in native coordinates # for this zoom level. box_h = (y1 - y0) / n box_w = (x1 - x0) / n # Compute the native x & y extents of the tile. n_xs = x0 + (x + np.arange(0, 2, dtype=np.float64)) * box_w n_ys = y0 + (y + np.arange(0, 2, dtype=np.float64)) * box_h if y0_at_north_pole: n_ys = -1 * n_ys[::-1] return n_xs, n_ys def tileextent(self, x_y_z): """Returns extent tuple ``(x0,x1,y0,y1)`` in Mercator coordinates.""" x, y, z = x_y_z x_lim, y_lim = self.tile_bbox(x, y, z, y0_at_north_pole=True) return tuple(x_lim) + tuple(y_lim) _tileextent = tileextent def _image_url(self, tile): style_dict = { "street": "m", "satellite": "s", "terrain": "t", "only_streets": "h"} url = ('https://mts0.google.com/vt/lyrs={style}@177000000&hl=en&' 'src=api&x={tile_x}&y={tile_y}&z={tile_z}&s=G'.format( style=style_dict[self.style], tile_x=tile[0], tile_y=tile[1], tile_z=tile[2])) return url def get_image(self, tile): if six.PY3: from urllib.request import urlopen else: from urllib2 import urlopen url = self._image_url(tile) fh = urlopen(url) im_data = six.BytesIO(fh.read()) fh.close() img = Image.open(im_data) img = img.convert(self.desired_tile_form) return img, self.tileextent(tile), 'lower' class MapQuestOSM(GoogleTiles): # http://developer.mapquest.com/web/products/open/map for terms of use def _image_url(self, tile): x, y, z = tile url = 'http://otile1.mqcdn.com/tiles/1.0.0/osm/%s/%s/%s.jpg' % ( z, x, y) return url class MapQuestOpenAerial(GoogleTiles): # http://developer.mapquest.com/web/products/open/map for terms of use # The following attribution should be included in the resulting image: # "Portions Courtesy NASA/JPL-Caltech and U.S. Depart. of Agriculture, # Farm Service Agency" def _image_url(self, tile): x, y, z = tile url = 'http://oatile1.mqcdn.com/tiles/1.0.0/sat/%s/%s/%s.jpg' % ( z, x, y) return url class OSM(GoogleTiles): # http://developer.mapquest.com/web/products/open/map for terms of use def _image_url(self, tile): x, y, z = tile url = 'https://a.tile.openstreetmap.org/%s/%s/%s.png' % (z, x, y) return url class StamenTerrain(GoogleTiles): """ Terrain tiles defined for the continental United States, and include land color and shaded hills. The land colors are a custom palette developed by Gem Spear for the National Atlas 1km land cover data set, which defines twenty-four land classifications including five kinds of forest, combinations of shrubs, grasses and crops, and a few tundras and wetlands. The colors are at their highest contrast when fully zoomed-out to the whole U.S., and they slowly fade out to pale off-white as you zoom in to leave room for foreground data and break up the weirdness of large areas of flat, dark green. Additional info: http://mike.teczno.com/notes/osm-us-terrain-layer/background.html http://maps.stamen.com/#terrain/12/37.6902/-122.3600 https://wiki.openstreetmap.org/wiki/List_of_OSM_based_Services https://github.com/migurski/DEM-Tools """ def _image_url(self, tile): x, y, z = tile url = 'http://tile.stamen.com/terrain-background/%s/%s/%s.png' % ( z, x, y) return url class MapboxTiles(GoogleTiles): """ Implements web tile retrieval from Mapbox. For terms of service, see https://www.mapbox.com/tos/. """ def __init__(self, access_token, map_id): """ Set up a new Mapbox tiles instance. Access to Mapbox web services requires an access token and a map ID. See https://www.mapbox.com/developers/api/ for details. Parameters ---------- access_token: str A valid Mapbox API access token. map_id: str A map ID for a publically accessible map. This is the map whose tiles will be retrieved through this process. """ self.access_token = access_token self.map_id = map_id super(MapboxTiles, self).__init__() def _image_url(self, tile): x, y, z = tile url = ('https://api.tiles.mapbox.com/v4/{mapid}/{z}/{x}/{y}.png?' 'access_token={token}'.format(z=z, y=y, x=x, mapid=self.map_id, token=self.access_token)) return url class QuadtreeTiles(GoogleTiles): """ Implements web tile retrieval using the Microsoft WTS quadkey coordinate system. A "tile" in this class refers to a quadkey such as "1", "14" or "141" where the length of the quatree is the zoom level in Google Tile terms. """ def _image_url(self, tile): url = ('http://ecn.dynamic.t1.tiles.virtualearth.net/comp/' 'CompositionHandler/{tile}?mkt=en-' 'gb&it=A,G,L&shading=hill&n=z'.format(tile=tile)) return url def tms_to_quadkey(self, tms, google=False): quadKey = "" x, y, z = tms # this algorithm works with google tiles, rather than tms, so convert # to those first. if not google: y = (2 ** z - 1) - y for i in range(z, 0, -1): digit = 0 mask = 1 << (i - 1) if (x & mask) != 0: digit += 1 if (y & mask) != 0: digit += 2 quadKey += str(digit) return quadKey def quadkey_to_tms(self, quadkey, google=False): # algorithm ported from # https://msdn.microsoft.com/en-us/library/bb259689.aspx assert isinstance(quadkey, six.string_types), \ 'quadkey must be a string' x = y = 0 z = len(quadkey) for i in range(z, 0, -1): mask = 1 << (i - 1) if quadkey[z - i] == '0': pass elif quadkey[z - i] == '1': x |= mask elif quadkey[z - i] == '2': y |= mask elif quadkey[z - i] == '3': x |= mask y |= mask else: raise ValueError('Invalid QuadKey digit ' 'sequence.' + str(quadkey)) # the algorithm works to google tiles, so convert to tms if not google: y = (2 ** z - 1) - y return (x, y, z) def subtiles(self, quadkey): for i in range(4): yield quadkey + str(i) def tileextent(self, quadkey): x_y_z = self.quadkey_to_tms(quadkey, google=True) return GoogleTiles.tileextent(self, x_y_z) def find_images(self, target_domain, target_z, start_tile=None): """ Find all the quadtree's at the given target zoom, in the given target domain. target_z must be a value >= 1. """ if target_z == 0: raise ValueError('The empty quadtree cannot be returned.') if start_tile is None: start_tiles = ['0', '1', '2', '3'] else: start_tiles = [start_tile] for start_tile in start_tiles: start_tile = self.quadkey_to_tms(start_tile, google=True) for tile in GoogleTiles.find_images(self, target_domain, target_z, start_tile=start_tile): yield self.tms_to_quadkey(tile, google=True) def _merge_tiles(tiles): """Return a single image, merging the given images.""" if not tiles: raise ValueError('A non-empty list of tiles should ' 'be provided to merge.') xset = [set(x) for i, x, y, _ in tiles] yset = [set(y) for i, x, y, _ in tiles] xs = xset[0] xs.update(*xset[1:]) ys = yset[0] ys.update(*yset[1:]) xs = sorted(xs) ys = sorted(ys) other_len = tiles[0][0].shape[2:] img = np.zeros((len(ys), len(xs)) + other_len, dtype=np.uint8) - 1 for tile_img, x, y, origin in tiles: y_first, y_last = y[0], y[-1] yi0, yi1 = np.where((y_first == ys) | (y_last == ys))[0] if origin == 'upper': yi0 = tile_img.shape[0] - yi0 - 1 yi1 = tile_img.shape[0] - yi1 - 1 start, stop, step = yi0, yi1, 1 if yi0 < yi1 else -1 if step == 1 and stop == img.shape[0] - 1: stop = None elif step == -1 and stop == 0: stop = None else: stop += step y_slice = slice(start, stop, step) xi0, xi1 = np.where((x[0] == xs) | (x[-1] == xs))[0] start, stop, step = xi0, xi1, 1 if xi0 < xi1 else -1 if step == 1 and stop == img.shape[1] - 1: stop = None elif step == -1 and stop == 0: stop = None else: stop += step x_slice = slice(start, stop, step) img_slice = (y_slice, x_slice, Ellipsis) if origin == 'lower': tile_img = tile_img[::-1, ::] img[img_slice] = tile_img return img, [min(xs), max(xs), min(ys), max(ys)], 'lower' Cartopy-0.14.2/lib/cartopy/io/ogc_clients.py0000644001374000021030000007401712700747662020474 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Implements RasterSource classes which can retrieve imagery from web services such as WMS and WMTS. The matplotlib interface can make use of RasterSources via the :meth:`cartopy.mpl.geoaxes.GeoAxes.add_raster` method, with additional specific methods which make use of this for WMS and WMTS (:meth:`~cartopy.mpl.geoaxes.GeoAxes.add_wms` and :meth:`~cartopy.mpl.geoaxes.GeoAxes.add_wmts`). An example of using WMTS in this way can be found at :ref:`examples-wmts`. """ from __future__ import (absolute_import, division, print_function) import six import collections import io import math import warnings import weakref from xml.etree import ElementTree import numpy as np from PIL import Image import shapely.geometry as sgeom try: from owslib.wms import WebMapService from owslib.wfs import WebFeatureService import owslib.util import owslib.wmts _OWSLIB_AVAILABLE = True except ImportError: WebMapService = None WebFeatureService = None _OWSLIB_AVAILABLE = False import cartopy.crs as ccrs from cartopy.io import LocatedImage, RasterSource from cartopy.img_transform import warp_array _OWSLIB_REQUIRED = 'OWSLib is required to use OGC web services.' # Hardcode some known EPSG codes for now. # The order given here determines the preferred SRS for WMS retrievals. _CRS_TO_OGC_SRS = collections.OrderedDict( [(ccrs.PlateCarree(), 'EPSG:4326'), (ccrs.Mercator.GOOGLE, 'EPSG:900913')]) # Standard pixel size of 0.28 mm as defined by WMTS. METERS_PER_PIXEL = 0.28e-3 _WGS84_METERS_PER_UNIT = 2 * math.pi * 6378137 / 360 METERS_PER_UNIT = { 'urn:ogc:def:crs:EPSG::27700': 1, 'urn:ogc:def:crs:EPSG::900913': 1, 'urn:ogc:def:crs:OGC:1.3:CRS84': _WGS84_METERS_PER_UNIT, } _URN_TO_CRS = { 'urn:ogc:def:crs:EPSG::27700': ccrs.OSGB(), 'urn:ogc:def:crs:EPSG::4326': ccrs.PlateCarree(), 'urn:ogc:def:crs:EPSG::900913': ccrs.GOOGLE_MERCATOR, 'urn:ogc:def:crs:OGC:1.3:CRS84': ccrs.PlateCarree(), 'urn:ogc:def:crs:EPSG::3031': ccrs.Stereographic(central_latitude=-90, true_scale_latitude=-71) } # XML namespace definitions _MAP_SERVER_NS = '{http://mapserver.gis.umn.edu/mapserver}' _GML_NS = '{http://www.opengis.net/gml}' class WMSRasterSource(RasterSource): """ A WMS imagery retriever which can be added to a map. .. note:: Requires owslib and Pillow to work. .. note:: No caching of retrieved maps is done with this WMSRasterSource. To reduce load on the WMS server it is encouraged to tile map requests and subsequently stitch them together to recreate a single raster, thus allowing for a more aggressive caching scheme, but this WMSRasterSource does not currently implement WMS tile fetching. Whilst not the same service, there is also a WMTSRasterSource which makes use of tiles and comes with built-in caching for fast repeated map retrievals. """ def __init__(self, service, layers, getmap_extra_kwargs=None): """ Parameters ---------- service : string or WebMapService instance The WebMapService instance, or URL of a WMS service, from whence to retrieve the image. layers : string or list of strings The name(s) of layers to use from the WMS service. getmap_extra_kwargs : dict or None Extra keywords to pass through to the service's getmap method. If None, a dictionary with ``{'transparent': True}`` will be defined. """ if WebMapService is None: raise ImportError(_OWSLIB_REQUIRED) if isinstance(service, six.string_types): service = WebMapService(service) if isinstance(layers, six.string_types): layers = [layers] if getmap_extra_kwargs is None: getmap_extra_kwargs = {'transparent': True} if len(layers) == 0: raise ValueError('One or more layers must be defined.') for layer in layers: if layer not in service.contents: raise ValueError('The {!r} layer does not exist in ' 'this service.'.format(layer)) #: The OWSLib WebMapService instance. self.service = service #: The names of the layers to fetch. self.layers = layers #: Extra kwargs passed through to the service's getmap request. self.getmap_extra_kwargs = getmap_extra_kwargs def _native_srs(self, projection): # Return the SRS which corresponds to the given projection when # known, otherwise return None. return _CRS_TO_OGC_SRS.get(projection) def _fallback_proj_and_srs(self): """ Return a :class:`cartopy.crs.Projection` and corresponding SRS string in which the WMS service can supply the requested layers. """ contents = self.service.contents for proj, srs in six.iteritems(_CRS_TO_OGC_SRS): missing = any(srs not in contents[layer].crsOptions for layer in self.layers) if not missing: break if missing: raise ValueError('The requested layers are not available in a ' 'known SRS.') return proj, srs def validate_projection(self, projection): if self._native_srs(projection) is None: self._fallback_proj_and_srs() def _image_and_extent(self, wms_proj, wms_srs, wms_extent, output_proj, output_extent, target_resolution): min_x, max_x, min_y, max_y = wms_extent wms_image = self.service.getmap(layers=self.layers, srs=wms_srs, bbox=(min_x, min_y, max_x, max_y), size=target_resolution, format='image/png', **self.getmap_extra_kwargs) wms_image = Image.open(io.BytesIO(wms_image.read())) if wms_proj == output_proj: extent = output_extent else: # Convert Image to numpy array (flipping so that origin # is 'lower'). img, extent = warp_array(np.asanyarray(wms_image)[::-1], source_proj=wms_proj, source_extent=wms_extent, target_proj=output_proj, target_res=target_resolution, target_extent=output_extent, mask_extrapolated=True) # Convert arrays with masked RGB(A) values to non-masked RGBA # arrays, setting the alpha channel to zero for masked values. # This avoids unsightly grey boundaries appearing when the # extent is limited (i.e. not global). if np.ma.is_masked(img): if img.shape[2:3] == (3,): # RGB old_img = img img = np.zeros(img.shape[:2] + (4,), dtype=img.dtype) img[:, :, 0:3] = old_img img[:, :, 3] = ~ np.any(old_img.mask, axis=2) if img.dtype.kind == 'u': img[:, :, 3] *= 255 elif img.shape[2:3] == (4,): # RGBA img[:, :, 3] = np.where(np.any(img.mask, axis=2), 0, img[:, :, 3]) img = img.data # Convert warped image array back to an Image, undoing the # earlier flip. wms_image = Image.fromarray(img[::-1]) return LocatedImage(wms_image, extent) def fetch_raster(self, projection, extent, target_resolution): min_x, max_x, min_y, max_y = extent target_resolution = [int(np.ceil(val)) for val in target_resolution] wms_srs = self._native_srs(projection) if wms_srs is not None: wms_proj = projection wms_extents = [extent] else: # The SRS for the requested projection is not known, so # attempt to use the fallback and perform the necessary # transformations. wms_proj, wms_srs = self._fallback_proj_and_srs() # Calculate the bounding box(es) in WMS projection. # Start with the requested area. target_box = sgeom.box(min_x, min_y, max_x, max_y) # If the requested area (i.e. target_box) is bigger (or # nearly bigger) than the entire output projection domain # then we erode the request area to avoid re-projection # instabilities near the full-projection limit. buffered_target_box = target_box.buffer(projection.threshold, resolution=1) fudge_mode = buffered_target_box.contains(projection.domain) if fudge_mode: target_box = projection.domain.buffer(-projection.threshold) # Convert the requested area to the WMS server's projection. wms_polys = wms_proj.project_geometry(target_box, projection) wms_extents = [] for wms_poly in wms_polys: min_x, min_y, max_x, max_y = wms_poly.bounds if fudge_mode: # If we shrunk the request area before, then here we # need to re-inflate. radius = min(max_x - min_x, max_y - min_y) / 5.0 radius = min(radius, wms_proj.threshold * 15) wms_poly = wms_poly.buffer(radius, resolution=1) # Prevent the expanded request going beyond the # limits of the projection. wms_poly = wms_proj.domain.intersection(wms_poly) min_x, min_y, max_x, max_y = wms_poly.bounds wms_extents.append((min_x, max_x, min_y, max_y)) located_images = [] for wms_extent in wms_extents: located_images.append(self._image_and_extent(wms_proj, wms_srs, wms_extent, projection, extent, target_resolution)) return located_images class WMTSRasterSource(RasterSource): """ A WMTS imagery retriever which can be added to a map. Uses tile caching for fast repeated map retrievals. .. note:: Requires owslib and Pillow to work. """ _shared_image_cache = weakref.WeakKeyDictionary() """ A nested mapping from WMTS, layer name, tile matrix name, tile row and tile column to the resulting PIL image:: {wmts: {(layer_name, tile_matrix_name): {(row, column): Image}}} This provides a significant boost when producing multiple maps of the same projection or with an interactive figure. """ def __init__(self, wmts, layer_name): """ Args: * wmts - The URL of the WMTS, or an owslib.wmts.WebMapTileService instance. * layer_name - The name of the layer to use. """ if WebMapService is None: raise ImportError(_OWSLIB_REQUIRED) if not (hasattr(wmts, 'tilematrixsets') and hasattr(wmts, 'contents') and hasattr(wmts, 'gettile')): wmts = owslib.wmts.WebMapTileService(wmts) try: layer = wmts.contents[layer_name] except KeyError: raise ValueError('Invalid layer name {!r} for WMTS at {!r}'.format( layer_name, wmts.url)) #: The OWSLib WebMapTileService instance. self.wmts = wmts #: The layer to fetch. self.layer = layer self._matrix_set_name_map = {} def _matrix_set_name(self, projection): key = id(projection) matrix_set_name = self._matrix_set_name_map.get(key) if matrix_set_name is None: wmts = self.wmts if hasattr(self.layer, 'tilematrixsetlinks'): matrix_set_names = self.layer.tilematrixsetlinks.keys() else: matrix_set_names = self.layer.tilematrixsets for tile_matrix_set_name in matrix_set_names: tile_matrix_set = wmts.tilematrixsets[tile_matrix_set_name] crs_urn = tile_matrix_set.crs if crs_urn in _URN_TO_CRS: tms_crs = _URN_TO_CRS[crs_urn] if tms_crs == projection: matrix_set_name = tile_matrix_set_name break if matrix_set_name is None: available_urns = sorted(set( wmts.tilematrixsets[name].crs for name in matrix_set_names)) msg = 'Unable to find tile matrix for projection.' msg += '\n Projection: ' + str(projection) msg += '\n Available tile CRS URNs:' msg += '\n ' + '\n '.join(available_urns) raise ValueError(msg) self._matrix_set_name_map[key] = matrix_set_name return matrix_set_name def validate_projection(self, projection): self._matrix_set_name(projection) def fetch_raster(self, projection, extent, target_resolution): matrix_set_name = self._matrix_set_name(projection) min_x, max_x, min_y, max_y = extent width, height = target_resolution max_pixel_span = min((max_x - min_x) / width, (max_y - min_y) / height) image, extent = self._wmts_images(self.wmts, self.layer, matrix_set_name, extent, max_pixel_span) return [LocatedImage(image, extent)] def _choose_matrix(self, tile_matrices, meters_per_unit, max_pixel_span): # Get the tile matrices in order of increasing resolution. tile_matrices = sorted(tile_matrices, key=lambda tm: tm.scaledenominator, reverse=True) # Find which tile matrix has the appropriate resolution. max_scale = max_pixel_span * meters_per_unit / METERS_PER_PIXEL for tm in tile_matrices: if tm.scaledenominator <= max_scale: return tm return tile_matrices[-1] def _tile_span(self, tile_matrix, meters_per_unit): pixel_span = tile_matrix.scaledenominator * ( METERS_PER_PIXEL / meters_per_unit) tile_span_x = tile_matrix.tilewidth * pixel_span tile_span_y = tile_matrix.tileheight * pixel_span return tile_span_x, tile_span_y def _select_tiles(self, tile_matrix, tile_matrix_limits, tile_span_x, tile_span_y, extent): # Convert the requested extent from CRS coordinates to tile # indices. See annex H of the WMTS v1.0.0 spec. # NB. The epsilons get rid of any tiles which only just # (i.e. one part in a million) intrude into the requested # extent. Since these wouldn't be visible anyway there's nothing # to be gained by spending the time downloading them. min_x, max_x, min_y, max_y = extent matrix_min_x, matrix_max_y = tile_matrix.topleftcorner epsilon = 1e-6 min_col = int((min_x - matrix_min_x) / tile_span_x + epsilon) max_col = int((max_x - matrix_min_x) / tile_span_x - epsilon) min_row = int((matrix_max_y - max_y) / tile_span_y + epsilon) max_row = int((matrix_max_y - min_y) / tile_span_y - epsilon) # Clamp to the limits of the tile matrix. min_col = max(min_col, 0) max_col = min(max_col, tile_matrix.matrixwidth - 1) min_row = max(min_row, 0) max_row = min(max_row, tile_matrix.matrixheight - 1) # Clamp to any layer-specific limits on the tile matrix. if tile_matrix_limits: min_col = max(min_col, tile_matrix_limits.mintilecol) max_col = min(max_col, tile_matrix_limits.maxtilecol) min_row = max(min_row, tile_matrix_limits.mintilerow) max_row = min(max_row, tile_matrix_limits.maxtilerow) return min_col, max_col, min_row, max_row def _wmts_images(self, wmts, layer, matrix_set_name, extent, max_pixel_span): """ Add images from the specified WMTS layer and matrix set to cover the specified extent at an appropriate resolution. The zoom level (aka. tile matrix) is chosen to give the lowest possible resolution which still provides the requested quality. If insufficient resolution is available, the highest available resolution is used. Args: * wmts - The owslib.wmts.WebMapTileService providing the tiles. * layer - The owslib.wmts.ContentMetadata (aka. layer) to draw. * matrix_set_name - The name of the matrix set to use. * extent - Tuple of (left, right, bottom, top) in Axes coordinates. * max_pixel_span - Preferred maximum pixel width or height in Axes coordinates. """ # Find which tile matrix has the appropriate resolution. tile_matrix_set = wmts.tilematrixsets[matrix_set_name] tile_matrices = tile_matrix_set.tilematrix.values() meters_per_unit = METERS_PER_UNIT[tile_matrix_set.crs] tile_matrix = self._choose_matrix(tile_matrices, meters_per_unit, max_pixel_span) # Determine which tiles are required to cover the requested extent. tile_span_x, tile_span_y = self._tile_span(tile_matrix, meters_per_unit) tile_matrix_set_links = getattr(layer, 'tilematrixsetlinks', None) if tile_matrix_set_links is None: tile_matrix_limits = None else: tile_matrix_set_link = tile_matrix_set_links[matrix_set_name] tile_matrix_limits = tile_matrix_set_link.tilematrixlimits.get( tile_matrix.identifier) min_col, max_col, min_row, max_row = self._select_tiles( tile_matrix, tile_matrix_limits, tile_span_x, tile_span_y, extent) # Find the relevant section of the image cache. tile_matrix_id = tile_matrix.identifier cache_by_wmts = WMTSRasterSource._shared_image_cache cache_by_layer_matrix = cache_by_wmts.setdefault(wmts, {}) image_cache = cache_by_layer_matrix.setdefault((layer.id, tile_matrix_id), {}) # To avoid nasty seams between the individual tiles, we # accumulate the tile images into a single image. big_img = None n_rows = 1 + max_row - min_row n_cols = 1 + max_col - min_col # Ignore out-of-range errors if the current version of OWSLib # doesn't provide the regional information. ignore_out_of_range = tile_matrix_set_links is None for row in range(min_row, max_row + 1): for col in range(min_col, max_col + 1): # Get the tile's Image from the cache if possible. img_key = (row, col) img = image_cache.get(img_key) if img is None: try: tile = wmts.gettile( layer=layer.id, tilematrixset=matrix_set_name, tilematrix=tile_matrix_id, row=row, column=col) except owslib.util.ServiceException as exception: if ('TileOutOfRange' in exception.message and ignore_out_of_range): continue raise exception img = Image.open(io.BytesIO(tile.read())) image_cache[img_key] = img if big_img is None: size = (img.size[0] * n_cols, img.size[1] * n_rows) big_img = Image.new('RGBA', size, (255, 255, 255, 255)) top = (row - min_row) * tile_matrix.tileheight left = (col - min_col) * tile_matrix.tilewidth big_img.paste(img, (left, top)) if big_img is None: img_extent = None else: matrix_min_x, matrix_max_y = tile_matrix.topleftcorner min_img_x = matrix_min_x + tile_span_x * min_col max_img_y = matrix_max_y - tile_span_y * min_row img_extent = (min_img_x, min_img_x + n_cols * tile_span_x, max_img_y - n_rows * tile_span_y, max_img_y) return big_img, img_extent class WFSGeometrySource(object): """Web Feature Service (WFS) retrieval for Cartopy.""" def __init__(self, service, features, getfeature_extra_kwargs=None): """ Args: * service: The URL of a WFS, or an instance of :class:`owslib.wfs.WebFeatureService`. * features: The typename(s) of the features from the WFS that will be retrieved and made available as geometries. Kwargs: * getfeature_extra_kwargs: Extra keyword args to pass to the service's `getfeature` call. """ if WebFeatureService is None: raise ImportError(_OWSLIB_REQUIRED) if isinstance(service, six.string_types): service = WebFeatureService(service) if isinstance(features, six.string_types): features = [features] if getfeature_extra_kwargs is None: getfeature_extra_kwargs = {} if not features: raise ValueError('One or more features must be specified.') for feature in features: if feature not in service.contents: raise ValueError('The {!r} feature does not exist in this ' 'service.'.format(feature)) self.service = service self.features = features self.getfeature_extra_kwargs = getfeature_extra_kwargs self._default_urn = None def default_projection(self): """ Return a :class:`cartopy.crs.Projection` in which the WFS service can supply the requested features. """ # Using first element in crsOptions (default). if self._default_urn is None: default_urn = set(self.service.contents[feature].crsOptions[0] for feature in self.features) if len(default_urn) != 1: ValueError('Failed to find a single common default SRS ' 'across all features (typenames).') else: default_urn = default_urn.pop() default_srs = default_urn.id if six.text_type(default_urn) not in _URN_TO_CRS: raise ValueError('Unknown mapping from SRS/CRS_URN {!r} to ' 'cartopy projection.'.format(default_urn)) self._default_urn = default_urn return _URN_TO_CRS[six.text_type(self._default_urn)] def fetch_geometries(self, projection, extent): """ Return any Point, Linestring or LinearRing geometries available from the WFS that lie within the specified extent. Args: * projection: :class:`cartopy.crs.Projection` The projection in which the extent is specified and in which the geometries should be returned. Only the default (native) projection is supported. * extent: four element tuple (min_x, max_x, min_y, max_y) tuple defining the geographic extent of the geometries to obtain. Returns: A list of Shapely geometries. """ if self.default_projection() != projection: raise ValueError('Geometries are only available in projection ' '{!r}.'.format(self.default_projection())) min_x, max_x, min_y, max_y = extent response = self.service.getfeature(typename=self.features, bbox=(min_x, min_y, max_x, max_y), **self.getfeature_extra_kwargs) geoms_by_srs = self._to_shapely_geoms(response) if not geoms_by_srs: geoms = [] elif len(geoms_by_srs) > 1: raise ValueError('Unexpected response from the WFS server. The ' 'geometries are in multiple SRSs, when only one ' 'was expected.') else: srs, geoms = list(geoms_by_srs.items())[0] # Attempt to verify the SRS associated with the geometries (if any) # matches the specified projection. if srs is not None: if srs in _URN_TO_CRS: geom_proj = _URN_TO_CRS[srs] if geom_proj != projection: raise ValueError('The geometries are not in expected ' 'projection. Expected {!r}, got ' '{!r}.'.format(projection, geom_proj)) else: msg = 'Unable to verify matching projections due ' \ 'to incomplete mappings from SRS identifiers ' \ 'to cartopy projections. The geometries have ' \ 'an SRS of {!r}.'.format(srs) warnings.warn(msg) return geoms def _to_shapely_geoms(self, response): """ Convert polygon coordinate strings in WFS response XML to Shapely geometries. Args: * response: (file-like object) WFS response XML data. Returns: A dictionary containing geometries, with key-value pairs of the form {srsname: [geoms]}. """ linear_rings_data = [] linestrings_data = [] points_data = [] tree = ElementTree.parse(response) for node in tree.findall('.//{}msGeometry'.format(_MAP_SERVER_NS)): # Find LinearRing geometries in our msGeometry node. find_str = './/{gml}LinearRing'.format(gml=_GML_NS) if self._node_has_child(node, find_str): data = self._find_polygon_coords(node, find_str) linear_rings_data.extend(data) # Find LineString geometries in our msGeometry node. find_str = './/{gml}LineString'.format(gml=_GML_NS) if self._node_has_child(node, find_str): data = self._find_polygon_coords(node, find_str) linestrings_data.extend(data) # Find Point geometries in our msGeometry node. find_str = './/{gml}Point'.format(gml=_GML_NS) if self._node_has_child(node, find_str): data = self._find_polygon_coords(node, find_str) points_data.extend(data) geoms_by_srs = {} for srs, x, y in linear_rings_data: geoms_by_srs.setdefault(srs, []).append( sgeom.LinearRing(zip(x, y))) for srs, x, y in linestrings_data: geoms_by_srs.setdefault(srs, []).append( sgeom.LineString(zip(x, y))) for srs, x, y in points_data: geoms_by_srs.setdefault(srs, []).append( sgeom.Point(zip(x, y))) return geoms_by_srs def _find_polygon_coords(self, node, find_str): """ Return the x, y coordinate values for all the geometries in a given`node`. Args: * node: :class:`xml.etree.ElementTree.Element` Node of the parsed XML response. * find_str: string A search string used to match subelements that contain the coordinates of interest, for example: './/{http://www.opengis.net/gml}LineString' Returns: A list of (srsName, x_vals, y_vals) tuples. """ data = [] for polygon in node.findall(find_str): feature_srs = polygon.attrib.get('srsName') x, y = [], [] # We can have nodes called `coordinates` or `coord`. coordinates_find_str = '{}coordinates'.format(_GML_NS) coords_find_str = '{}coord'.format(_GML_NS) if self._node_has_child(polygon, coordinates_find_str): points = polygon.findtext(coordinates_find_str) coords = points.strip().split(' ') for coord in coords: x_val, y_val = coord.split(',') x.append(float(x_val)) y.append(float(y_val)) elif self._node_has_child(polygon, coords_find_str): for coord in polygon.findall(coords_find_str): x.append(float(coord.findtext('{}X'.format(_GML_NS)))) y.append(float(coord.findtext('{}Y'.format(_GML_NS)))) else: raise ValueError('Unable to find or parse coordinate values ' 'from the XML.') data.append((feature_srs, x, y)) return data @staticmethod def _node_has_child(node, find_str): """ Return whether `node` contains (at any sub-level), a node with name equal to `find_str`. """ element = node.find(find_str) return element is not None Cartopy-0.14.2/lib/cartopy/io/shapereader.py0000644001374000021030000004374512700747662020472 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Combines the shapefile access of pyshp with the geometry representation of shapely: >>> import os.path >>> import cartopy.io.shapereader as shapereader >>> filename = natural_earth(resolution='110m', ... category='physical', ... name='geography_regions_points') >>> reader = shapereader.Reader(filename) >>> len(reader) 3 >>> records = list(reader.records()) >>> print(type(records[0])) >>> print(sorted(records[0].attributes.keys())) ['comment', 'featurecla', 'lat_y', 'long_x', 'name', 'name_alt', \ 'region', 'scalerank', 'subregion'] >>> print(records[0].attributes['name']) Niagara Falls >>> geoms = list(reader.geometries()) >>> print(type(geoms[0])) """ from __future__ import (absolute_import, division, print_function) import glob import itertools import os import shapely.geometry as sgeom import shapefile import six from cartopy.io import Downloader from cartopy import config __all__ = ['Reader', 'Record'] def _create_point(shape): return sgeom.Point(shape.points[0]) def _create_polyline(shape): if not shape.points: return sgeom.MultiLineString() parts = list(shape.parts) + [None] bounds = zip(parts[:-1], parts[1:]) lines = [shape.points[slice(lower, upper)] for lower, upper in bounds] return sgeom.MultiLineString(lines) def _create_polygon(shape): if not shape.points: return sgeom.MultiPolygon() # Partition the shapefile rings into outer rings/polygons (clockwise) and # inner rings/holes (anti-clockwise). parts = list(shape.parts) + [None] bounds = zip(parts[:-1], parts[1:]) outer_polygons_and_holes = [] inner_polygons = [] for lower, upper in bounds: polygon = sgeom.Polygon(shape.points[slice(lower, upper)]) if polygon.exterior.is_ccw: inner_polygons.append(polygon) else: outer_polygons_and_holes.append((polygon, [])) # Find the appropriate outer ring for each inner ring. # aka. Group the holes with their containing polygons. for inner_polygon in inner_polygons: for outer_polygon, holes in outer_polygons_and_holes: if outer_polygon.contains(inner_polygon): holes.append(inner_polygon.exterior.coords) break polygon_defns = [(outer_polygon.exterior.coords, holes) for outer_polygon, holes in outer_polygons_and_holes] return sgeom.MultiPolygon(polygon_defns) def _make_geometry(geometry_factory, shape): geometry = None if shape.shapeType != shapefile.NULL: geometry = geometry_factory(shape) return geometry # The mapping from shapefile shapeType values to geometry creation functions. GEOMETRY_FACTORIES = { shapefile.POINT: _create_point, shapefile.POLYLINE: _create_polyline, shapefile.POLYGON: _create_polygon, } class Record(object): """ A single logical entry from a shapefile, combining the attributes with their associated geometry. """ def __init__(self, shape, geometry_factory, attributes, fields): self._shape = shape self._geometry_factory = geometry_factory self._bounds = None # if the record defines a bbox, then use that for the shape's bounds, # rather than using the full geometry in the bounds property if hasattr(shape, 'bbox'): self._bounds = tuple(shape.bbox) self._geometry = False """The cached geometry instance for this Record.""" self.attributes = attributes """A dictionary mapping attribute names to attribute values.""" self._fields = fields def __repr__(self): return '>' % (self.geometry, self.attributes) def __str__(self): return 'Record(%s, %s, )' % (self.geometry, self.attributes) @property def bounds(self): """ The bounds of this Record's :meth:`~Record.geometry`. """ if self._bounds is None: self._bounds = self.geometry.bounds return self._bounds @property def geometry(self): """ A shapely.geometry instance for this Record. The geometry may be ``None`` if a null shape is defined in the shapefile. """ if self._geometry is False: self._geometry = _make_geometry(self._geometry_factory, self._shape) return self._geometry class Reader(object): """ Provides an interface for accessing the contents of a shapefile. The primary methods used on a Reader instance are :meth:`~Reader.records` and :meth:`~Reader.geometries`. """ def __init__(self, filename): # Validate the filename/shapefile self._reader = reader = shapefile.Reader(filename) if reader.shp is None or reader.shx is None or reader.dbf is None: raise ValueError("Incomplete shapefile definition " "in '%s'." % filename) # Figure out how to make appropriate shapely geometry instances shapeType = reader.shapeType self._geometry_factory = GEOMETRY_FACTORIES.get(shapeType) if self._geometry_factory is None: raise ValueError('Unsupported shape type: %s' % shapeType) self._fields = self._reader.fields def __len__(self): return self._reader.numRecords def geometries(self): """ Returns an iterator of shapely geometries from the shapefile. This interface is useful for accessing the geometries of the shapefile where knowledge of the associated metadata is desired. In the case where further metadata is needed use the :meth:`~Reader.records` interface instead, extracting the geometry from the record with the :meth:`~Record.geometry` method. """ geometry_factory = self._geometry_factory for i in range(self._reader.numRecords): shape = self._reader.shape(i) yield _make_geometry(geometry_factory, shape) def records(self): """ Returns an iterator of :class:`~Record` instances. """ geometry_factory = self._geometry_factory # Ignore the "DeletionFlag" field which always comes first fields = self._reader.fields[1:] field_names = [field[0] for field in fields] for i in range(self._reader.numRecords): shape_record = self._reader.shapeRecord(i) attributes = dict(zip(field_names, shape_record.record)) yield Record(shape_record.shape, geometry_factory, attributes, fields) def natural_earth(resolution='110m', category='physical', name='coastline'): """ Returns the path to the requested natural earth shapefile, downloading and unziping if necessary. To identify valid components for this function, either browse NaturalEarthData.com, or if you know what you are looking for, go to https://github.com/nvkelso/natural-earth-vector/tree/master/zips to see the actual files which will be downloaded. .. note:: Some of the Natural Earth shapefiles have special features which are described in the name. For example, the 110m resolution "admin_0_countries" data also has a sibling shapefile called "admin_0_countries_lakes" which excludes lakes in the country outlines. For details of what is available refer to the Natural Earth website, and look at the "download" link target to identify appropriate names. """ # get hold of the Downloader (typically a NEShpDownloader instance) # which we can then simply call its path method to get the appropriate # shapefile (it will download if necessary) ne_downloader = Downloader.from_config(('shapefiles', 'natural_earth', resolution, category, name)) format_dict = {'config': config, 'category': category, 'name': name, 'resolution': resolution} return ne_downloader.path(format_dict) class NEShpDownloader(Downloader): """ Specialises :class:`cartopy.io.Downloader` to download the zipped Natural Earth shapefiles and extract them to the defined location (typically user configurable). The keys which should be passed through when using the ``format_dict`` are typically ``category``, ``resolution`` and ``name``. """ FORMAT_KEYS = ('config', 'resolution', 'category', 'name') # Define the NaturalEarth URL template. The natural earth website # returns a 302 status if accessing directly, so we use the naciscdn # URL directly. _NE_URL_TEMPLATE = ('http://naciscdn.org/naturalearth/{resolution}' '/{category}/ne_{resolution}_{name}.zip') def __init__(self, url_template=_NE_URL_TEMPLATE, target_path_template=None, pre_downloaded_path_template='', ): # adds some NE defaults to the __init__ of a Downloader Downloader.__init__(self, url_template, target_path_template, pre_downloaded_path_template) def zip_file_contents(self, format_dict): """ Returns a generator of the filenames to be found in the downloaded natural earth zip file. """ for ext in ['.shp', '.dbf', '.shx']: yield ('ne_{resolution}_{name}' '{extension}'.format(extension=ext, **format_dict)) def acquire_resource(self, target_path, format_dict): """ Downloads the zip file and extracts the files listed in :meth:`zip_file_contents` to the target path. """ from zipfile import ZipFile target_dir = os.path.dirname(target_path) if not os.path.isdir(target_dir): os.makedirs(target_dir) url = self.url(format_dict) shapefile_online = self._urlopen(url) zfh = ZipFile(six.BytesIO(shapefile_online.read()), 'r') for member_path in self.zip_file_contents(format_dict): ext = os.path.splitext(member_path)[1] target = os.path.splitext(target_path)[0] + ext member = zfh.getinfo(member_path) with open(target, 'wb') as fh: fh.write(zfh.open(member).read()) shapefile_online.close() zfh.close() return target_path @staticmethod def default_downloader(): """ Returns a generic, standard, NEShpDownloader instance. Typically, a user will not need to call this staticmethod. To find the path template of the NEShpDownloader: >>> ne_dnldr = NEShpDownloader.default_downloader() >>> print(ne_dnldr.target_path_template) {config[data_dir]}/shapefiles/natural_earth/{category}/\ {resolution}_{name}.shp """ default_spec = ('shapefiles', 'natural_earth', '{category}', '{resolution}_{name}.shp') ne_path_template = os.path.join('{config[data_dir]}', *default_spec) pre_path_template = os.path.join('{config[pre_existing_data_dir]}', *default_spec) return NEShpDownloader(target_path_template=ne_path_template, pre_downloaded_path_template=pre_path_template) # add a generic Natural Earth shapefile downloader to the config dictionary's # 'downloaders' section. _ne_key = ('shapefiles', 'natural_earth') config['downloaders'].setdefault(_ne_key, NEShpDownloader.default_downloader()) def gshhs(scale='c', level=1): """ Returns the path to the requested GSHHS shapefile, downloading and unziping if necessary. """ # Get hold of the Downloader (typically a GSHHSShpDownloader instance) # and call its path method to get the appropriate shapefile (it will # download it if necessary). gshhs_downloader = Downloader.from_config(('shapefiles', 'gshhs', scale, level)) format_dict = {'config': config, 'scale': scale, 'level': level} return gshhs_downloader.path(format_dict) class GSHHSShpDownloader(Downloader): """ Specialises :class:`cartopy.io.Downloader` to download the zipped GSHHS shapefiles and extract them to the defined location. The keys which should be passed through when using the ``format_dict`` are ``scale`` (a single character indicating the resolution) and ``level`` (a number indicating the type of feature). """ FORMAT_KEYS = ('config', 'scale', 'level') _GSHHS_URL_TEMPLATE = ('https://www.ngdc.noaa.gov/mgg/shorelines/data/' 'gshhs/oldversions/version2.2.0/' 'GSHHS_shp_2.2.0.zip') def __init__(self, url_template=_GSHHS_URL_TEMPLATE, target_path_template=None, pre_downloaded_path_template=''): super(GSHHSShpDownloader, self).__init__(url_template, target_path_template, pre_downloaded_path_template) def zip_file_contents(self, format_dict): """ Returns a generator of the filenames to be found in the downloaded GSHHS zip file for the specified resource. """ for ext in ['.shp', '.dbf', '.shx']: yield (os.path.join('GSHHS_shp', '{scale}', 'GSHHS_{scale}_L{level}{extension}' ).format(extension=ext, **format_dict)) def acquire_all_resources(self, format_dict): from zipfile import ZipFile # Download archive. url = self.url(format_dict) shapefile_online = self._urlopen(url) zfh = ZipFile(six.BytesIO(shapefile_online.read()), 'r') shapefile_online.close() # Iterate through all scales and levels and extract relevant files. modified_format_dict = dict(format_dict) scales = ('c', 'l', 'i', 'h', 'f') levels = (1, 2, 3, 4) for scale, level in itertools.product(scales, levels): modified_format_dict.update({'scale': scale, 'level': level}) target_path = self.target_path(modified_format_dict) target_dir = os.path.dirname(target_path) if not os.path.isdir(target_dir): os.makedirs(target_dir) for member_path in self.zip_file_contents(modified_format_dict): ext = os.path.splitext(member_path)[1] target = os.path.splitext(target_path)[0] + ext member = zfh.getinfo(member_path) with open(target, 'wb') as fh: fh.write(zfh.open(member).read()) zfh.close() def acquire_resource(self, target_path, format_dict): """ Downloads the zip file and extracts the files listed in :meth:`zip_file_contents` to the target path. .. note: Because some of the GSHSS data is available with the cartopy repository, scales of "l" or "c" will not be downloaded if they exist in the ``cartopy.config['repo_data_dir']`` directory. """ repo_fname_pattern = os.path.join(config['repo_data_dir'], 'shapefiles', 'gshhs', '{scale}', 'GSHHS_{scale}_L?.shp') repo_fname_pattern = repo_fname_pattern.format(**format_dict) repo_fnames = glob.glob(repo_fname_pattern) if repo_fnames: assert len(repo_fnames) == 1, '>1 repo files found for GSHHS' return repo_fnames[0] self.acquire_all_resources(format_dict) if not os.path.exists(target_path): raise RuntimeError('Failed to download and extract GSHHS ' 'shapefile to {!r}.'.format(target_path)) return target_path @staticmethod def default_downloader(): """ Returns a GSHHSShpDownloader instance that expects (and if necessary downloads and installs) shapefiles in the data directory of the cartopy installation. Typically, a user will not need to call this staticmethod. To find the path template of the GSHHSShpDownloader: >>> gshhs_dnldr = GSHHSShpDownloader.default_downloader() >>> print(gshhs_dnldr.target_path_template) {config[data_dir]}/shapefiles/gshhs/{scale}/\ GSHHS_{scale}_L{level}.shp """ default_spec = ('shapefiles', 'gshhs', '{scale}', 'GSHHS_{scale}_L{level}.shp') gshhs_path_template = os.path.join('{config[data_dir]}', *default_spec) pre_path_tmplt = os.path.join('{config[pre_existing_data_dir]}', *default_spec) return GSHHSShpDownloader(target_path_template=gshhs_path_template, pre_downloaded_path_template=pre_path_tmplt) # Add a GSHHS shapefile downloader to the config dictionary's # 'downloaders' section. _gshhs_key = ('shapefiles', 'gshhs') config['downloaders'].setdefault(_gshhs_key, GSHHSShpDownloader.default_downloader()) Cartopy-0.14.2/lib/cartopy/io/srtm.npz0000644001374000021030000000513212700747662017337 0ustar itpeavd00000000000000PK*ZG꽝, mask.npyݽ$5`;)6[.p"ġ=2)xan\v]U./S7;Ӷ?xnO޽qݿ{w/||=<=o>>׷<|/^Q'=`!ht>OIKLhC=9[: #ɼdv> dzM!LeI7t$2lpLt%JgZ]^iu-~ФYGBIsqC-je,.znh4BsWD˘˰jlFN'ZfbrgAse;}-)ʘht$TZE{r-ɡ<Շ.*LZVtZ訚.nh! :B*ch (Rήl .Eq: MB&4hEZP5x>|>t<:jUi} O.V~ʸ9Nػ֒ZADƅjZ6SqtKESS$4"55[FD3Yja@۾r c;OG:WF_sG/m-&\ n+tbEFvFV>O2:|r\A;VBV_TB+d d,M*)ڟ7AW܁yu6|m3=(=0JN*!є7B#+fZhh/$%&,]|Vޛ|+QG]λS DfhF=Γkyuzh|d'EqE'dV@qg:qeQFKk6Wݘ?d933WpHp~TuqRtga L2A_r' "gx9Z$B' Yyrl=.%i3l<{S&^ѫ5AOyWY;>3E{Y7w)M"J9nEL^jۙvp^*&"տ (U_n_9GM8;a>zeJ̐p՘e (h\D̝UnT+A^BR׸jmc/D:> {6bܞ@`? %o9™]CV bɫ1~\ /U;p N}\oQ,7EGd[Oqj~d|qkS~.PwP؂1A]jhlU3Vw`ka: f'|{I(ЂFwvSiGm{(ԍS}5,E/^Օ|XkxUQ! 6}e Fv8߬7WMAh*MdI wkZ+ iΓ竸_"˖G;[٨ٿк倷 \ٮ Q߉ۻ_wFI_Q-(RER-^B-Q{,FdCٱ(mKh"2.C.Dm4jh:ڃ9CDmӃmA6molSd:__mDoMmxcѝlNіц/mG}CkEڌ] 7 < {2MDKϑ LM2 0>?;|4~)SmRmOAΊaq4qD [L܍ D]w8sn-}L"qWZsB#~żҢ%r sHu &gʽYFhÙb.JDkh0Rw5Q&Nzc.mt8ChMͳ% +D_W6yq\v]ߝa3ofpԍ}Ћ9U< =UM?T{hfL5gjK ~pqCZ+iF$$?ŊŘfj%lvJ6֊zd N!tO 1 =GtVtt#E9K#[k/n+oq4MCk:?[[k`C/v3UylY10ꢣ`T0|E-o}D [a}@z=|a4ѫ7 =yLk"MPK*ZG꽝, mask.npyPK6 Cartopy-0.14.2/lib/cartopy/io/srtm.py0000644001374000021030000004407612700747662017172 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained digital elevation models on a near-global scale from 56S to 60N, to generate the most complete high-resolution digital topographic database of Earth prior to the release of the ASTER GDEM in 2009. - Wikipedia (August 2012) """ from __future__ import (absolute_import, division, print_function) import os import warnings import numpy as np import six from cartopy import config import cartopy.crs as ccrs from cartopy.io import fh_getter, Downloader, RasterSource, LocatedImage class _SRTMSource(RasterSource): """ A source of SRTM data, which implements Cartopy's :ref:`RasterSource interface `. """ def __init__(self, resolution, downloader, max_nx, max_ny): """ Parameters ========== resolution : int The resolution of SRTM to download, in arc-seconds. Data is available at resolutions of 3 and 1 arc-seconds. downloader : :class:`cartopy.io.Downloader` instance or None The downloader to use for the SRTM dataset. If None, the downloader will be taken using :class:`cartopy.io.Downloader.from_config` with ('SRTM', 'SRTM{resolution}') as the target. max_nx : int The maximum number of x tiles to be combined when producing a wider composite for this RasterSource. max_ny : int The maximum number of y tiles to be combined when producing a taller composite for this RasterSource. """ if resolution == 3: self._shape = (1201, 1201) elif resolution == 1: self._shape = (3601, 3601) else: raise ValueError( 'Resolution is an unexpected value ({}).'.format(resolution)) self._resolution = resolution #: The CRS of the underlying SRTM data. self.crs = ccrs.PlateCarree() #: The Cartopy Downloader which can handle SRTM data. Normally, this #: will be a :class:`SRTMDownloader` instance. self.downloader = downloader if self.downloader is None: self.downloader = Downloader.from_config( ('SRTM', 'SRTM' + str(resolution))) #: A tuple of (max_x_tiles, max_y_tiles). self._max_tiles = (max_nx, max_ny) def validate_projection(self, projection): return projection == self.crs def fetch_raster(self, projection, extent, target_resolution): """ Fetch SRTM elevation for the given projection and approximate extent. """ if not self.validate_projection(projection): raise ValueError( 'Unsupported projection for the SRTM{} source.'.format( self._resolution)) min_x, max_x, min_y, max_y = extent min_x, min_y = np.floor([min_x, min_y]) nx = int(np.ceil(max_x) - min_x) ny = int(np.ceil(max_y) - min_y) skip = False if nx > self._max_tiles[0]: warnings.warn( 'Required SRTM{} tile count ({}) exceeds maximum ({}). ' 'Increase max_nx limit.'.format(self._resolution, nx, self._max_tiles[0])) skip = True if ny > self._max_tiles[1]: warnings.warn( 'Required SRTM{} tile count ({}) exceeds maximum ({}). ' 'Increase max_ny limit.'.format(self._resolution, ny, self._max_tiles[1])) skip = True if skip: return [] else: img, _, extent = self.combined(min_x, min_y, nx, ny) return [LocatedImage(np.flipud(img), extent)] def srtm_fname(self, lon, lat): """ Return the filename for the given lon/lat SRTM tile (downloading if necessary), or None if no such tile exists (i.e. the tile would be entirely over water, or out of latitude range). """ if int(lon) != lon or int(lat) != lat: raise ValueError('Integer longitude/latitude values required.') x = '%s%03d' % ('E' if lon >= 0 else 'W', abs(int(lon))) y = '%s%02d' % ('N' if lat >= 0 else 'S', abs(int(lat))) srtm_downloader = Downloader.from_config( ('SRTM', 'SRTM' + str(self._resolution))) params = {'config': config, 'resolution': self._resolution, 'x': x, 'y': y} # If the URL doesn't exist then we are over sea/north/south of the # limits of the SRTM data and we return None. if srtm_downloader.url(params) is None: return None else: return self.downloader.path(params) def combined(self, lon_min, lat_min, nx, ny): """ Return an image and its extent for the group of nx by ny tiles starting at the given bottom left location. """ bottom_left_ll = (lon_min, lat_min) shape = np.array(self._shape) img = np.zeros(shape * (ny, nx)) for i, j in np.ndindex(nx, ny): x_img_slice = slice(i * shape[1], (i + 1) * shape[1]) y_img_slice = slice(j * shape[0], (j + 1) * shape[0]) try: tile_img, _, _ = self.single_tile(bottom_left_ll[0] + i, bottom_left_ll[1] + j) except ValueError: img[y_img_slice, x_img_slice] = 0 else: img[y_img_slice, x_img_slice] = tile_img extent = (bottom_left_ll[0], bottom_left_ll[0] + nx, bottom_left_ll[1], bottom_left_ll[1] + ny) return img, self.crs, extent def single_tile(self, lon, lat): fname = self.srtm_fname(lon, lat) if fname is None: raise ValueError('No srtm tile found for those coordinates.') return read_SRTM(fname) class SRTM3Source(_SRTMSource): """ A source of SRTM3 data, which implements Cartopy's :ref:`RasterSource interface `. """ def __init__(self, downloader=None, max_nx=3, max_ny=3): """ Parameters ========== downloader : :class:`cartopy.io.Downloader` instance or None The downloader to use for the SRTM3 dataset. If None, the downloader will be taken using :class:`cartopy.io.Downloader.from_config` with ('SRTM', 'SRTM3') as the target. max_nx : int The maximum number of x tiles to be combined when producing a wider composite for this RasterSource. max_ny : int The maximum number of y tiles to be combined when producing a taller composite for this RasterSource. """ super(SRTM3Source, self).__init__(resolution=3, downloader=downloader, max_nx=max_nx, max_ny=max_ny) class SRTM1Source(_SRTMSource): """ A source of SRTM1 data, which implements Cartopy's :ref:`RasterSource interface `. """ def __init__(self, downloader=None, max_nx=3, max_ny=3): """ Parameters ========== downloader : :class:`cartopy.io.Downloader` instance or None The downloader to use for the SRTM1 dataset. If None, the downloader will be taken using :class:`cartopy.io.Downloader.from_config` with ('SRTM', 'SRTM1') as the target. max_nx : int The maximum number of x tiles to be combined when producing a wider composite for this RasterSource. max_ny : int The maximum number of y tiles to be combined when producing a taller composite for this RasterSource. """ super(SRTM1Source, self).__init__(resolution=1, downloader=downloader, max_nx=max_nx, max_ny=max_ny) def srtm(lon, lat): """ Return (elevation, crs, extent) for the given longitude latitude. Elevation is in meters. """ warnings.warn("This method has been deprecated. " "See the \"What's new\" section for v0.12.") return SRTM3Source().single_tile(lon, lat) def add_shading(elevation, azimuth, altitude): """Adds shading to SRTM elevation data, using azimuth and altitude of the sun. :type elevation: numpy.ndarray :param elevation: SRTM elevation data (in meters) :type azimuth: float :param azimuth: azimuth of the Sun (in degrees) :type altitude: float :param altitude: altitude of the Sun (in degrees) :rtype: numpy.ndarray :return: shaded SRTM relief map. """ azimuth = np.deg2rad(azimuth) altitude = np.deg2rad(altitude) x, y = np.gradient(elevation) slope = np.pi/2. - np.arctan(np.sqrt(x*x + y*y)) # -x here because of pixel orders in the SRTM tile aspect = np.arctan2(-x, y) shaded = np.sin(altitude) * np.sin(slope)\ + np.cos(altitude) * np.cos(slope)\ * np.cos((azimuth - np.pi/2.) - aspect) return shaded def fill_gaps(elevation, max_distance=10): """Fills gaps in SRTM elevation data for which the distance from missing pixel to nearest existing one is smaller than `max_distance`. This function requires osgeo/gdal to work. :type elevation: numpy.ndarray :param elevation: SRTM elevation data (in meters) :type max_distance: int :param max_distance: maximal distance (in pixels) between a missing point and the nearest valid one. :rtype: numpy.ndarray :return: SRTM elevation data with filled gaps.. """ warnings.warn("The fill_gaps function has been deprecated. " "See the \"What's new\" section for v0.14.") # Lazily import osgeo - it is only an optional dependency for cartopy. from osgeo import gdal from osgeo import gdal_array src_ds = gdal_array.OpenArray(elevation) srcband = src_ds.GetRasterBand(1) dstband = srcband maskband = srcband smoothing_iterations = 0 options = [] gdal.FillNodata(dstband, maskband, max_distance, smoothing_iterations, options, callback=None) elevation = dstband.ReadAsArray() return elevation def srtm_composite(lon_min, lat_min, nx, ny): warnings.warn("This method has been deprecated. " "See the \"What's new\" section for v0.12.") return SRTM3Source().combined(lon_min, lat_min, nx, ny) def read_SRTM(fh): """ Read the array of (y, x) elevation data from the given named file-handle. Parameters ========== fh : file-handle like A named file-like as passed through to :func:`cartopy.io.fh_getter`. The filename is used to determine the extent of the resulting array. Returns ======= elevation : numpy array The elevation values from the SRTM file. Data is flipped vertically such that the higher the y-index, the further north the data. Data shape is automatically determined by the size of data read from file, and is either (1201, 1201) for 3 arc-second data or (3601, 3601) for 1 arc-second data. crs : :class:`cartopy.crs.CRS` The coordinate reference system of the extents. extents : 4-tuple (x0, x1, y0, y1) The boundaries of the returned elevation array. """ fh, fname = fh_getter(fh, needs_filename=True) if fname.endswith('.zip'): from zipfile import ZipFile zfh = ZipFile(fh, 'rb') fh = zfh.open(os.path.basename(fname[:-4]), 'r') elev = np.fromfile(fh, dtype=np.dtype('>i2')) if elev.size == 12967201: elev.shape = (3601, 3601) elif elev.size == 1442401: elev.shape = (1201, 1201) else: raise ValueError( 'Shape of SRTM data ({}) is unexpected.'.format(elev.size)) fname = os.path.basename(fname) y_dir, y, x_dir, x = fname[0], int(fname[1:3]), fname[3], int(fname[4:7]) if y_dir == 'S': y *= -1 if x_dir == 'W': x *= -1 return elev[::-1, ...], ccrs.PlateCarree(), (x, x + 1, y, y + 1) read_SRTM3 = read_SRTM read_SRTM1 = read_SRTM def SRTM3_retrieve(lon, lat): """ Return the path of a .hgt file for the given SRTM location. If no such .hgt file exists (because it is over the ocean) None will be returned. """ warnings.warn("This method has been deprecated. " "See the \"What's new\" section for v0.12.") return SRTM3Source().srtm_fname(lon, lat) class SRTMDownloader(Downloader): """ Provides a SRTM download mechanism. """ FORMAT_KEYS = ('config', 'resolution', 'x', 'y') _SRTM_BASE_URL = ('http://e4ftl01.cr.usgs.gov/SRTM/SRTMGL{resolution}.003/' '2000.02.11/') _SRTM_LOOKUP_CACHE = os.path.join(os.path.dirname(__file__), 'srtm.npz') _SRTM_LOOKUP_MASK = np.load(_SRTM_LOOKUP_CACHE)['mask'] """ The SRTM lookup mask determines whether keys such as 'N43E043' are available to download. """ def __init__(self, target_path_template, pre_downloaded_path_template='', ): # adds some SRTM defaults to the __init__ of a Downloader # namely, the URL is determined on the fly using the # ``SRTMDownloader._SRTM_LOOKUP_MASK`` array Downloader.__init__(self, None, target_path_template, pre_downloaded_path_template) def url(self, format_dict): # override the url method, looking up the url from the # ``SRTMDownloader._SRTM_LOOKUP_MASK`` array lat = int(format_dict['y'][1:]) # Change to co-latitude. if format_dict['y'][0] == 'N': colat = 90 - lat else: colat = 90 + lat lon = int(format_dict['x'][1:4]) # Ensure positive. if format_dict['x'][0] == 'W': lon = 360 - lon if SRTMDownloader._SRTM_LOOKUP_MASK[lon, colat]: return (SRTMDownloader._SRTM_BASE_URL + u'{y}{x}.SRTMGL{resolution}.hgt.zip').format(**format_dict) else: return None def acquire_resource(self, target_path, format_dict): from zipfile import ZipFile target_dir = os.path.dirname(target_path) if not os.path.isdir(target_dir): os.makedirs(target_dir) url = self.url(format_dict) srtm_online = self._urlopen(url) zfh = ZipFile(six.BytesIO(srtm_online.read()), 'r') zip_member_path = u'{y}{x}.hgt'.format(**format_dict) member = zfh.getinfo(zip_member_path) with open(target_path, 'wb') as fh: fh.write(zfh.open(member).read()) srtm_online.close() zfh.close() return target_path @staticmethod def _create_srtm_mask(resolution, filename=None): """ Returns a NumPy mask of available lat/lon. This is slow as it must query the SRTM server to identify the continent from which the tile comes. Hence a NumPy file with this content exists in ``SRTMDownloader._SRTM_LOOKUP_CACHE``. The NumPy file was created with:: import cartopy.io.srtm as srtm import numpy as np np.savez_compressed(srtm.SRTMDownloader._SRTM_LOOKUP_CACHE, mask=srtm.SRTMDownloader._create_srtm_mask(3)) """ # lazy imports. In most situations, these are not # dependencies of cartopy. from bs4 import BeautifulSoup if filename is None: from six.moves.urllib.request import urlopen url = SRTMDownloader._SRTM_BASE_URL.format(resolution=resolution) with urlopen(url) as f: html = f.read() else: with open(filename) as f: html = f.read() mask = np.zeros((360, 181), dtype=np.bool) soup = BeautifulSoup(html) for link in soup('a'): name = str(link.text).strip() if name[0] in 'NS' and name.endswith('.hgt.zip'): lat = int(name[1:3]) # Change to co-latitude. if name[0] == 'N': colat = 90 - lat else: colat = 90 + lat lon = int(name[4:7]) # Ensure positive. if name[3] == 'W': lon = 360 - lon mask[lon, colat] = True return mask @classmethod def default_downloader(cls): """ Returns a typical downloader for this class. In general, this static method is used to create the default configuration in cartopy.config """ default_spec = ('SRTM', 'SRTMGL{resolution}', '{y}{x}.hgt') target_path_template = os.path.join('{config[data_dir]}', *default_spec) pre_path_template = os.path.join('{config[pre_existing_data_dir]}', *default_spec) return cls(target_path_template=target_path_template, pre_downloaded_path_template=pre_path_template) # add a generic SRTM downloader to the config 'downloaders' section. config['downloaders'].setdefault(('SRTM', 'SRTM3'), SRTMDownloader.default_downloader()) config['downloaders'].setdefault(('SRTM', 'SRTM1'), SRTMDownloader.default_downloader()) Cartopy-0.14.2/lib/cartopy/mpl/0000755001374000021030000000000012706121712015765 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/mpl/__init__.py0000644001374000021030000000143712700747662020117 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) Cartopy-0.14.2/lib/cartopy/mpl/clip_path.py0000644001374000021030000001231412700747662020317 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import matplotlib.path as mpath import numpy as np def clip_path_python(subject, clip, point_inside_clip_path): """ Clip the subject path with the given clip path using the Sutherland-Hodgman polygon clipping algorithm. Args: * subject - The subject path to be clipped. Must be a simple, single polygon path with straight line segments only. * clip - The clip path to use. Must be a simple, single polygon path with straight line segments only. * point_inside_clip_path - a point which can be found inside the clip path polygon. """ inside_pt = point_inside_clip_path output_verts = subject.vertices for i in xrange(clip.vertices.shape[0] - 1): clip_edge = clip.vertices[i:i + 2, :] input_verts = output_verts output_verts = [] inside = np.cross(clip_edge[1, :] - clip_edge[0, :], inside_pt - clip_edge[0, :]) try: s = input_verts[-1] except IndexError: break for e in input_verts: e_clip_cross = np.cross(clip_edge[1, :] - clip_edge[0, :], e - clip_edge[0, :]) s_clip_cross = np.cross(clip_edge[1, :] - clip_edge[0, :], s - clip_edge[0, :]) if np.sign(e_clip_cross) == np.sign(inside): if np.sign(s_clip_cross) != np.sign(inside): p = intersection_point(clip_edge[0, :], clip_edge[1, :], e, s) output_verts.append(p) output_verts.append(e) elif np.sign(s_clip_cross) == np.sign(inside): p = intersection_point(clip_edge[0, :], clip_edge[1, :], e, s) output_verts.append(p) s = e if output_verts == []: path = mpath.Path([[0, 0]], codes=[mpath.Path.MOVETO]) else: # If the subject polygon was closed, then the return should be too. if np.all(subject.vertices[0, :] == subject.vertices[-1, :]): output_verts.append(output_verts[0]) path = mpath.Path(np.array(output_verts)) return path def intersection_point(p0, p1, p2, p3): """ Return the intersection point of the two infinite lines that pass through point p0->p1 and p2->p3 respectively. """ x_1, y_1 = p0 x_2, y_2 = p1 x_3, y_3 = p2 x_4, y_4 = p3 div = (x_1 - x_2) * (y_3 - y_4) - (y_1 - y_2) * (x_3 - x_4) if div == 0: raise ValueError('Lines are parallel and cannot ' 'intersect at any one point.') x = ((x_1 * y_2 - y_1 * x_2) * (x_3 - x_4) - (x_1 - x_2) * (x_3 * y_4 - y_3 * x_4)) / div y = ((x_1 * y_2 - y_1 * x_2) * (y_3 - y_4) - (y_1 - y_2) * (x_3 * y_4 - y_3 * x_4)) / div return x, y # Provide a clip_path function which clips the given path to the given Bbox. # There is inbuilt mpl functionality with v1.2.1 and beyond, but we provide # a shim here for older mpl versions. if hasattr(mpath.Path, 'clip_to_bbox'): def clip_path(subject, clip_bbox): """ Clip the given path to the given bounding box. """ return subject.clip_to_bbox(clip_bbox) else: def clip_path(subject, clip_bbox): """ Clip the given path to the given bounding box. """ # A shim on clip_path_python to support Bbox path clipping. bbox_patch = bbox_to_path(clip_bbox) bbox_center = ((clip_bbox.x0 + clip_bbox.x1) / 2, (clip_bbox.y0 + clip_bbox.y1) / 2) return clip_path_python(subject, bbox_patch, bbox_center) def lines_intersect(p0, p1, p2, p3): """ Return whether the two lines defined by p0->p1 and p2->p3 intersect. """ x_1, y_1 = p0 x_2, y_2 = p1 x_3, y_3 = p2 x_4, y_4 = p3 return (x_1 - x_2) * (y_3 - y_4) - (y_1 - y_2) * (x_3 - x_4) != 0 cp1 = np.cross(p1 - p0, p2 - p0) cp2 = np.cross(p1 - p0, p3 - p0) return np.sign(cp1) == np.sign(cp2) and cp1 != 0 def bbox_to_path(bbox): """ Turn the given :class:`matplotlib.transforms.Bbox` instance into a :class:`matplotlib.path.Path` instance. """ verts = np.array([[bbox.x0, bbox.y0], [bbox.x1, bbox.y0], [bbox.x1, bbox.y1], [bbox.x0, bbox.y1], [bbox.x0, bbox.y0]]) return mpath.Path(verts) Cartopy-0.14.2/lib/cartopy/mpl/feature_artist.py0000644001374000021030000001444312700747662021402 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module defines the :class:`FeatureArtist` class, for drawing :class:`Feature` instances with matplotlib. """ from __future__ import (absolute_import, division, print_function) import warnings import weakref import matplotlib.artist import matplotlib.collections import cartopy.mpl.patch as cpatch class _GeomKey(object): """ Provide id() based equality and hashing for geometries. Instances of this class must be treated as immutable for the caching to operate correctly. A workaround for Shapely polygons no longer being hashable as of 1.5.13. """ def __init__(self, geom): self._id = id(geom) def __eq__(self, other): return self._id == other._id def __hash__(self): return hash(self._id) class FeatureArtist(matplotlib.artist.Artist): """ A subclass of :class:`~matplotlib.artist.Artist` capable of drawing a :class:`cartopy.feature.Feature`. """ _geom_key_to_geometry_cache = weakref.WeakValueDictionary() """ A mapping from _GeomKey to geometry to assist with the caching of transformed matplotlib paths. """ _geom_key_to_path_cache = weakref.WeakKeyDictionary() """ A nested mapping from geometry (converted to a _GeomKey) and target projection to the resulting transformed matplotlib paths:: {geom: {target_projection: list_of_paths}} This provides a significant boost when producing multiple maps of the same projection. """ def __init__(self, feature, **kwargs): """ Args: * feature: an instance of :class:`cartopy.feature.Feature` to draw. * kwargs: keyword arguments to be used when drawing the feature. These will override those shared with the feature. """ super(FeatureArtist, self).__init__() if kwargs is None: kwargs = {} self._kwargs = dict(kwargs) # Set default zorder so that features are drawn before # lines e.g. contours but after images. # Note that the zorder of Patch, PatchCollection and PathCollection # are all 1 by default. Assuming equal zorder drawing takes place in # the following order: collections, patches, lines (default zorder=2), # text (default zorder=3), then other artists e.g. FeatureArtist. if self._kwargs.get('zorder') is not None: self.set_zorder(self._kwargs['zorder']) elif feature.kwargs.get('zorder') is not None: self.set_zorder(feature.kwargs['zorder']) else: # The class attribute matplotlib.collections.PathCollection.zorder # was removed after mpl v1.2.0, so the hard-coded value of 1 is # used instead. self.set_zorder(1) self._feature = feature @matplotlib.artist.allow_rasterization def draw(self, renderer, *args, **kwargs): """ Draws the geometries of the feature that intersect with the extent of the :class:`cartopy.mpl.GeoAxes` instance to which this object has been added. """ if not self.get_visible(): return ax = self.axes feature_crs = self._feature.crs # Get geometries that we need to draw. extent = None try: extent = ax.get_extent(feature_crs) except ValueError: warnings.warn('Unable to determine extent. Defaulting to global.') geoms = self._feature.intersecting_geometries(extent) # Project (if necessary) and convert geometries to matplotlib paths. paths = [] key = ax.projection for geom in geoms: # As Shapely geometries cannot be relied upon to be # hashable, we have to use a WeakValueDictionary to manage # their weak references. The key can then be a simple, # "disposable", hashable geom-key object that just uses the # id() of a geometry to determine equality and hash value. # The only persistent, strong reference to the geom-key is # in the WeakValueDictionary, so when the geometry is # garbage collected so is the geom-key. # The geom-key is also used to access the WeakKeyDictionary # cache of transformed geometries. So when the geom-key is # garbage collected so are the transformed geometries. geom_key = _GeomKey(geom) FeatureArtist._geom_key_to_geometry_cache.setdefault( geom_key, geom) mapping = FeatureArtist._geom_key_to_path_cache.setdefault( geom_key, {}) geom_paths = mapping.get(key) if geom_paths is None: if ax.projection != feature_crs: projected_geom = ax.projection.project_geometry( geom, feature_crs) else: projected_geom = geom geom_paths = cpatch.geos_to_path(projected_geom) mapping[key] = geom_paths paths.extend(geom_paths) # Build path collection and draw it. transform = ax.projection._as_mpl_transform(ax) # Combine all the keyword args in priority order final_kwargs = dict(self._feature.kwargs) final_kwargs.update(self._kwargs) final_kwargs.update(kwargs) c = matplotlib.collections.PathCollection(paths, transform=transform, **final_kwargs) c.set_clip_path(ax.patch) c.set_figure(ax.figure) return c.draw(renderer) Cartopy-0.14.2/lib/cartopy/mpl/geoaxes.py0000644001374000021030000020644012705644356020015 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module defines the :class:`GeoAxes` class, for use with matplotlib. When a matplotlib figure contains a GeoAxes the plotting commands can transform plot results from source coordinates to the GeoAxes' target projection. """ from __future__ import (absolute_import, division, print_function) import collections import contextlib import warnings import weakref import matplotlib as mpl import matplotlib.artist import matplotlib.axes from matplotlib.image import imread import matplotlib.transforms as mtransforms import matplotlib.patches as mpatches import matplotlib.path as mpath import matplotlib.ticker as mticker import numpy as np import numpy.ma as ma import shapely.geometry as sgeom from cartopy import config import cartopy.crs as ccrs import cartopy.feature import cartopy.img_transform from cartopy.mpl.clip_path import clip_path import cartopy.mpl.feature_artist as feature_artist import cartopy.mpl.patch as cpatch from cartopy.mpl.slippy_image_artist import SlippyImageArtist from cartopy.vector_transform import vector_scalar_to_grid assert matplotlib.__version__ >= '1.3', ('Cartopy is only supported with ' 'matplotlib 1.3 or greater.') _PATH_TRANSFORM_CACHE = weakref.WeakKeyDictionary() """ A nested mapping from path, source CRS, and target projection to the resulting transformed paths:: {path: {(source_crs, target_projection): list_of_paths}} Provides a significant performance boost for contours which, at matplotlib 1.2.0 called transform_path_non_affine twice unnecessarily. """ # XXX call this InterCRSTransform class InterProjectionTransform(mtransforms.Transform): """ Transforms coordinates from the source_projection to the ``target_projection``. """ input_dims = 2 output_dims = 2 is_separable = False has_inverse = True def __init__(self, source_projection, target_projection): """ Create the transform object from the given projections. Args: * source_projection - A :class:`~cartopy.crs.CRS`. * target_projection - A :class:`~cartopy.crs.CRS`. """ # assert target_projection is cartopy.crs.Projection # assert source_projection is cartopy.crs.CRS self.source_projection = source_projection self.target_projection = target_projection mtransforms.Transform.__init__(self) def __repr__(self): return ('< {!s} {!s} -> {!s} >'.format(self.__class__.__name__, self.source_projection, self.target_projection)) def transform_non_affine(self, xy): """ Transforms from source to target coordinates. Args: * xy - An (n,2) array of points in source coordinates. Returns: * An (n,2) array of transformed points in target coordinates. """ prj = self.target_projection if isinstance(xy, np.ndarray): return prj.transform_points(self.source_projection, xy[:, 0], xy[:, 1])[:, 0:2] else: x, y = xy x, y = prj.transform_point(x, y, self.source_projection) return x, y def transform_path_non_affine(self, src_path): """ Transforms from source to target coordinates. Caches results, so subsequent calls with the same *src_path* argument (and the same source and target projections) are faster. Args: * src_path - A matplotlib :class:`~matplotlib.path.Path` object with vertices in source coordinates. Returns * A matplotlib :class:`~matplotlib.path.Path` with vertices in target coordinates. """ mapping = _PATH_TRANSFORM_CACHE.get(src_path) if mapping is not None: key = (self.source_projection, self.target_projection) result = mapping.get(key) if result is not None: return result # Allow the vertices to be quickly transformed, if # quick_vertices_transform allows it. new_vertices = self.target_projection.quick_vertices_transform( src_path.vertices, self.source_projection) if new_vertices is not None: if new_vertices is src_path.vertices: return src_path else: return mpath.Path(new_vertices, src_path.codes) if src_path.vertices.shape == (1, 2): return mpath.Path(self.transform(src_path.vertices)) transformed_geoms = [] # Check whether this transform has the "force_path_ccw" attribute set. # This is a cartopy extension to the Transform API to allow finer # control of Path orientation handling (Path ordering is not important # in matplotlib, but is in Cartopy). geoms = cpatch.path_to_geos(src_path, getattr(self, 'force_path_ccw', False)) for geom in geoms: proj_geom = self.target_projection.project_geometry( geom, self.source_projection) transformed_geoms.append(proj_geom) if not transformed_geoms: result = mpath.Path(np.empty([0, 2])) else: paths = cpatch.geos_to_path(transformed_geoms) if not paths: return mpath.Path(np.empty([0, 2])) points, codes = list(zip(*[cpatch.path_segments(path, curves=False, simplify=False) for path in paths])) result = mpath.Path(np.concatenate(points, 0), np.concatenate(codes)) # store the result in the cache for future performance boosts key = (self.source_projection, self.target_projection) if mapping is None: _PATH_TRANSFORM_CACHE[src_path] = {key: result} else: mapping[key] = result return result def inverted(self): """ Return a matplotlib :class:`~matplotlib.transforms.Transform` from target to source coordinates. """ return InterProjectionTransform(self.target_projection, self.source_projection) class GeoAxes(matplotlib.axes.Axes): """ A subclass of :class:`matplotlib.axes.Axes` which represents a map :class:`~cartopy.crs.Projection`. This class replaces the matplotlib :class:`~matplotlib.axes.Axes` class when created with the *projection* keyword. For example:: # Set up a standard map for latlon data. geo_axes = pyplot.axes(projection=cartopy.crs.PlateCarree()) # Set up an OSGB map. geo_axes = pyplot.subplot(2, 2, 1, projection=cartopy.crs.OSGB()) When a source projection is provided to one of it's plotting methods, using the *transform* keyword, the standard matplotlib plot result is transformed from source coordinates to the target projection. For example:: # Plot latlon data on an OSGB map. pyplot.axes(projection=cartopy.crs.OSGB()) pyplot.contourf(x, y, data, transform=cartopy.crs.PlateCarree()) """ def __init__(self, *args, **kwargs): """ Create a GeoAxes object using standard matplotlib :class:`~matplotlib.axes.Axes` args and kwargs. Kwargs: * map_projection - The target :class:`~cartopy.crs.Projection` of this Axes object. All other args and keywords are passed through to :class:`matplotlib.axes.Axes`. """ self.projection = kwargs.pop('map_projection') """The :class:`cartopy.crs.Projection` of this GeoAxes.""" self.outline_patch = None """The patch that provides the line bordering the projection.""" self.background_patch = None """The patch that provides the filled background of the projection.""" super(GeoAxes, self).__init__(*args, **kwargs) self._gridliners = [] self.img_factories = [] self._done_img_factory = False def add_image(self, factory, *args, **kwargs): """ Adds an image "factory" to the Axes. Any image "factory" added, will be asked to retrieve an image with associated metadata for a given bounding box at draw time. The advantage of this approach is that the limits of the map do not need to be known when adding the image factory, but can be deferred until everything which can effect the limits has been added. Currently an image "factory" is just an object with a ``image_for_domain`` method. Examples of image factories are :class:`cartopy.io.img_nest.NestedImageCollection` and :class:`cartopy.io.image_tiles.GoogleTiles`. """ if hasattr(factory, 'image_for_domain'): # XXX TODO: Needs deprecating. self.img_factories.append([factory, args, kwargs]) else: # Args and kwargs not allowed. assert not bool(args) and not bool(kwargs) image = factory try: super(GeoAxes, self).add_image(image) except AttributeError: # If add_image method doesn't exist (only available from # v1.4 onwards) we implement it ourselves. self._set_artist_props(image) self.images.append(image) image._remove_method = lambda h: self.images.remove(h) return image @contextlib.contextmanager def hold_limits(self, hold=True): """ Keep track of the original view and data limits for the life of this context manager, optionally reverting any changes back to the original values after the manager exits. Parameters ---------- hold : bool (default True) Whether to revert the data and view limits after the context manager exits. """ data_lim = self.dataLim.frozen().get_points() view_lim = self.viewLim.frozen().get_points() other = (self.ignore_existing_data_limits, self._autoscaleXon, self._autoscaleYon) try: yield finally: if hold: self.dataLim.set_points(data_lim) self.viewLim.set_points(view_lim) (self.ignore_existing_data_limits, self._autoscaleXon, self._autoscaleYon) = other @matplotlib.artist.allow_rasterization def draw(self, renderer=None, inframe=False): """ Extends the standard behaviour of :func:`matplotlib.axes.Axes.draw`. Draws grid lines and image factory results before invoking standard matplotlib drawing. A global range is used if no limits have yet been set. """ # If data has been added (i.e. autoscale hasn't been turned off) # then we should autoscale the view. if self.get_autoscale_on() and self.ignore_existing_data_limits: self.autoscale_view() if self.outline_patch.reclip or self.background_patch.reclip: clipped_path = clip_path(self.outline_patch.orig_path, self.viewLim) self.outline_patch._path = clipped_path self.background_patch._path = clipped_path for gl in self._gridliners: gl._draw_gridliner(background_patch=self.background_patch) self._gridliners = [] # XXX This interface needs a tidy up: # image drawing on pan/zoom; # caching the resulting image; # buffering the result by 10%...; if not self._done_img_factory: for factory, args, kwargs in self.img_factories: img, extent, origin = factory.image_for_domain( self._get_extent_geom(factory.crs), args[0]) self.imshow(img, extent=extent, origin=origin, transform=factory.crs, *args[1:], **kwargs) self._done_img_factory = True return matplotlib.axes.Axes.draw(self, renderer=renderer, inframe=inframe) def __str__(self): return '< GeoAxes: %s >' % self.projection def cla(self): """Clears the current axes and adds boundary lines.""" result = matplotlib.axes.Axes.cla(self) self.xaxis.set_visible(False) self.yaxis.set_visible(False) # Enable tight autoscaling. self._tight = True self.set_aspect('equal') with self.hold_limits(): self._boundary() # XXX consider a margin - but only when the map is not global... # self._xmargin = 0.15 # self._ymargin = 0.15 self.dataLim.intervalx = self.projection.x_limits self.dataLim.intervaly = self.projection.y_limits return result def format_coord(self, x, y): """Return a string formatted for the matplotlib GUI status bar.""" lon, lat = ccrs.Geodetic().transform_point(x, y, self.projection) ns = 'N' if lat >= 0.0 else 'S' ew = 'E' if lon >= 0.0 else 'W' return u'%.4g, %.4g (%f\u00b0%s, %f\u00b0%s)' % (x, y, abs(lat), ns, abs(lon), ew) def coastlines(self, resolution='110m', color='black', **kwargs): """ Adds coastal **outlines** to the current axes from the Natural Earth "coastline" shapefile collection. Kwargs: * resolution - a named resolution to use from the Natural Earth dataset. Currently can be one of "110m", "50m", and "10m". .. note:: Currently no clipping is done on the coastlines before adding them to the axes. This means, if very high resolution coastlines are being used, performance is likely to be severely effected. This should be resolved transparently by v0.5. """ kwargs['edgecolor'] = color kwargs['facecolor'] = 'none' feature = cartopy.feature.NaturalEarthFeature('physical', 'coastline', resolution, **kwargs) return self.add_feature(feature) def tissot(self, rad_km=5e5, lons=None, lats=None, n_samples=80, **kwargs): """ Adds Tissot's indicatrices to the axes. Kwargs: * rad_km - The radius in km of the the circles to be drawn. * lons - A numpy.ndarray, list or tuple of longitude values that locate the centre of each circle. Specifying more than one dimension allows individual points to be drawn whereas a 1D array produces a grid of points. * lats - A numpy.ndarray, list or tuple of latitude values that that locate the centre of each circle. See lons. * n_samples - Integer number of points sampled around the circumference of each circle. **kwargs are passed through to `class:ShapelyFeature`. """ from cartopy import geodesic geod = geodesic.Geodesic() geoms = [] if lons is None: lons = np.linspace(-180, 180, 6, endpoint=False) else: lons = np.asarray(lons) if lats is None: lats = np.linspace(-80, 80, 6) else: lats = np.asarray(lats) if lons.ndim == 1 or lats.ndim == 1: lons, lats = np.meshgrid(lons, lats) lons, lats = lons.flatten(), lats.flatten() if lons.shape != lats.shape: raise ValueError('lons and lats must have the same shape.') for i in range(len(lons)): circle = geod.circle(lons[i], lats[i], rad_km, n_samples=n_samples) geoms.append(sgeom.Polygon(circle)) feature = cartopy.feature.ShapelyFeature(geoms, ccrs.Geodetic(), **kwargs) return self.add_feature(feature) def natural_earth_shp(self, name='land', resolution='110m', category='physical', **kwargs): """ Adds the geometries from the specified Natural Earth shapefile to the Axes as a :class:`~matplotlib.collections.PathCollection`. ``**kwargs`` are passed through to the :class:`~matplotlib.collections.PathCollection` constructor. Returns the created :class:`~matplotlib.collections.PathCollection`. .. note:: Currently no clipping is done on the geometries before adding them to the axes. This means, if very high resolution geometries are being used, performance is likely to be severely effected. This should be resolved transparently by v0.5. """ warnings.warn('This method has been deprecated.' ' Please use `add_feature` instead.') kwargs.setdefault('edgecolor', 'face') kwargs.setdefault('facecolor', cartopy.feature.COLORS['land']) feature = cartopy.feature.NaturalEarthFeature(category, name, resolution, **kwargs) return self.add_feature(feature) def add_feature(self, feature, **kwargs): """ Adds the given :class:`~cartopy.feature.Feature` instance to the axes. Args: * feature: An instance of :class:`~cartopy.feature.Feature`. Kwargs: Keyword arguments to be used when drawing the feature. This allows standard matplotlib control over aspects such as 'facecolor', 'alpha', etc. Returns: * A :class:`cartopy.mpl.feature_artist.FeatureArtist` instance responsible for drawing the feature. """ # Instantiate an artist to draw the feature and add it to the axes. artist = feature_artist.FeatureArtist(feature, **kwargs) return self.add_artist(artist) def add_geometries(self, geoms, crs, **kwargs): """ Add the given shapely geometries (in the given crs) to the axes. Args: * geoms: A collection of shapely geometries. * crs: The cartopy CRS in which the provided geometries are defined. Kwargs: Keyword arguments to be used when drawing this feature. Returns: A :class:`cartopy.mpl.feature_artist.FeatureArtist` instance responsible for drawing the geometries. """ feature = cartopy.feature.ShapelyFeature(geoms, crs, **kwargs) return self.add_feature(feature) def get_extent(self, crs=None): """ Get the extent (x0, x1, y0, y1) of the map in the given coordinate system. If no crs is given, the returned extents' coordinate system will be the CRS of this Axes. """ p = self._get_extent_geom(crs) r = p.bounds x1, y1, x2, y2 = r return x1, x2, y1, y2 def _get_extent_geom(self, crs=None): # Perform the calculations for get_extent(), which just repackages it. with self.hold_limits(): if self.get_autoscale_on(): self.autoscale_view() [x1, y1], [x2, y2] = self.viewLim.get_points() domain_in_src_proj = sgeom.Polygon([[x1, y1], [x2, y1], [x2, y2], [x1, y2], [x1, y1]]) # Determine target projection based on requested CRS. if crs is None: proj = self.projection elif isinstance(crs, ccrs.Projection): proj = crs else: # Attempt to select suitable projection for # non-projection CRS. if isinstance(crs, ccrs.RotatedGeodetic): proj = ccrs.RotatedPole(crs.proj4_params['lon_0'] - 180, crs.proj4_params['o_lat_p']) warnings.warn('Approximating coordinate system {!r} with a ' 'RotatedPole projection.'.format(crs)) elif hasattr(crs, 'is_geodetic') and crs.is_geodetic(): proj = ccrs.PlateCarree(crs.globe) warnings.warn('Approximating coordinate system {!r} with the ' 'PlateCarree projection.'.format(crs)) else: raise ValueError('Cannot determine extent in' ' coordinate system {!r}'.format(crs)) # Calculate intersection with boundary and project if necesary. boundary_poly = sgeom.Polygon(self.projection.boundary) if proj != self.projection: # Erode boundary by threshold to avoid transform issues. # This is a workaround for numerical issues at the boundary. eroded_boundary = boundary_poly.buffer(-self.projection.threshold) geom_in_src_proj = eroded_boundary.intersection( domain_in_src_proj) geom_in_crs = proj.project_geometry(geom_in_src_proj, self.projection) else: geom_in_crs = boundary_poly.intersection(domain_in_src_proj) return geom_in_crs def set_extent(self, extents, crs=None): """ Set the extent (x0, x1, y0, y1) of the map in the given coordinate system. If no crs is given, the extents' coordinate system will be assumed to be the Geodetic version of this axes' projection. """ # TODO: Implement the same semantics as plt.xlim and # plt.ylim - allowing users to set None for a minimum and/or # maximum value x1, x2, y1, y2 = extents domain_in_crs = sgeom.polygon.LineString([[x1, y1], [x2, y1], [x2, y2], [x1, y2], [x1, y1]]) projected = None # Sometimes numerical issues cause the projected vertices of the # requested extents to appear outside the projection domain. # This results in an empty geometry, which has an empty `bounds` # tuple, which causes an unpack error. # This workaround avoids using the projection when the requested # extents are obviously the same as the projection domain. try_workaround = ((crs is None and isinstance(self.projection, ccrs.PlateCarree)) or crs == self.projection) if try_workaround: boundary = self.projection.boundary if boundary.equals(domain_in_crs): projected = boundary if projected is None: projected = self.projection.project_geometry(domain_in_crs, crs) try: # This might fail with an unhelpful error message ('need more # than 0 values to unpack') if the specified extents fall outside # the projection extents, so try and give a better error message. x1, y1, x2, y2 = projected.bounds except ValueError: msg = ('Failed to determine the required bounds in projection ' 'coordinates. Check that the values provided are within ' 'the valid range (x_limits=[{xlim[0]}, {xlim[1]}], ' 'y_limits=[{ylim[0]}, {ylim[1]}]).') raise ValueError(msg.format(xlim=self.projection.x_limits, ylim=self.projection.y_limits)) self.set_xlim([x1, x2]) self.set_ylim([y1, y2]) def set_global(self): """ Set the extent of the Axes to the limits of the projection. .. note:: In some cases where the projection has a limited sensible range the ``set_global`` method does not actually make the whole globe visible. Instead, the most appropriate extents will be used (e.g. Ordnance Survey UK will set the extents to be around the British Isles. """ self.set_xlim(self.projection.x_limits) self.set_ylim(self.projection.y_limits) def set_xticks(self, ticks, minor=False, crs=None): """ Set the x ticks. Args: * ticks - list of floats denoting the desired position of x ticks. Kwargs: * minor - boolean flag indicating whether the ticks should be minor ticks i.e. small and unlabelled (default is False). * crs - An instance of :class:`~cartopy.crs.CRS` indicating the coordinate system of the provided tick values. If no coordinate system is specified then the values are assumed to be in the coordinate system of the projection. Only transformations from one rectangular coordinate system to another rectangular coordinate system are supported. .. note:: This interface is subject to change whilst functionality is added to support other map projections. """ # Project ticks if crs differs from axes' projection if crs is not None and crs != self.projection: if not isinstance(crs, (ccrs._RectangularProjection, ccrs.Mercator)) or \ not isinstance(self.projection, (ccrs._RectangularProjection, ccrs.Mercator)): raise RuntimeError('Cannot handle non-rectangular coordinate ' 'systems.') proj_xyz = self.projection.transform_points(crs, np.asarray(ticks), np.zeros(len(ticks))) xticks = proj_xyz[..., 0] else: xticks = ticks # Switch on drawing of x axis self.xaxis.set_visible(True) return super(GeoAxes, self).set_xticks(xticks, minor) def set_yticks(self, ticks, minor=False, crs=None): """ Set the y ticks. Args: * ticks - list of floats denoting the desired position of y ticks. Kwargs: * minor - boolean flag indicating whether the ticks should be minor ticks i.e. small and unlabelled (default is False). * crs - An instance of :class:`~cartopy.crs.CRS` indicating the coordinate system of the provided tick values. If no coordinate system is specified then the values are assumed to be in the coordinate system of the projection. Only transformations from one rectangular coordinate system to another rectangular coordinate system are supported. .. note:: This interface is subject to change whilst functionality is added to support other map projections. """ # Project ticks if crs differs from axes' projection if crs is not None and crs != self.projection: if not isinstance(crs, (ccrs._RectangularProjection, ccrs.Mercator)) or \ not isinstance(self.projection, (ccrs._RectangularProjection, ccrs.Mercator)): raise RuntimeError('Cannot handle non-rectangular coordinate ' 'systems.') proj_xyz = self.projection.transform_points(crs, np.zeros(len(ticks)), np.asarray(ticks)) yticks = proj_xyz[..., 1] else: yticks = ticks # Switch on drawing of y axis self.yaxis.set_visible(True) return super(GeoAxes, self).set_yticks(yticks, minor) def stock_img(self, name='ne_shaded'): """ Add a standard image to the map. Currently, the only (and default) option is a downsampled version of the Natural Earth shaded relief raster. """ if name == 'ne_shaded': import os source_proj = ccrs.PlateCarree() fname = os.path.join(config["repo_data_dir"], 'raster', 'natural_earth', '50-natural-earth-1-downsampled.png') return self.imshow(imread(fname), origin='upper', transform=source_proj, extent=[-180, 180, -90, 90]) else: raise ValueError('Unknown stock image %r.' % name) def add_raster(self, raster_source, **slippy_image_kwargs): """ Add the given raster source to the GeoAxes. Parameters ---------- raster_source : :class:`cartopy.io.RasterSource` like instance ``raster_source`` may be any object which implements the RasterSource interface, including instances of objects such as :class:`~cartopy.io.ogc_clients.WMSRasterSource` and :class:`~cartopy.io.ogc_clients.WMTSRasterSource`. Note that image retrievals are done at draw time, not at creation time. """ # Allow a fail-fast error if the raster source cannot provide # images in the current projection. raster_source.validate_projection(self.projection) img = SlippyImageArtist(self, raster_source, **slippy_image_kwargs) with self.hold_limits(): self.add_image(img) return img def _regrid_shape_aspect(self, regrid_shape, target_extent): """ Helper for setting regridding shape which is used in several plotting methods. """ if not isinstance(regrid_shape, collections.Sequence): target_size = int(regrid_shape) x_range, y_range = np.diff(target_extent)[::2] desired_aspect = x_range / y_range if x_range >= y_range: regrid_shape = (target_size * desired_aspect, target_size) else: regrid_shape = (target_size, target_size / desired_aspect) return regrid_shape def imshow(self, img, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.imshow'. Parameters ---------- transform : :class:`~cartopy.crs.Projection` or matplotlib transform The coordinate system in which the given image is rectangular. regrid_shape : int or pair of ints The shape of the desired image if it needs to be transformed. If a single integer is given then that will be used as the minimum length dimension, while the other dimension will be scaled up according to the target extent's aspect ratio. The default is for the minimum dimension of a transformed image to have length 750, so for an image being transformed into a global PlateCarree projection the resulting transformed image would have a shape of ``(750, 1500)``. extent : tuple The corner coordinates of the image in the form ``(left, right, bottom, top)``. The coordinates should be in the coordinate system passed to the transform keyword. origin : {'lower', 'upper'} The origin of the vertical pixels. See :func:`matplotlib.pyplot.imshow` for further details. Default is ``'lower'``. """ transform = kwargs.pop('transform', None) if 'update_datalim' in kwargs: raise ValueError('The update_datalim keyword has been removed in ' 'imshow. To hold the data and view limits see ' 'GeoAxes.hold_limits.') kwargs.setdefault('origin', 'lower') same_projection = (isinstance(transform, ccrs.Projection) and self.projection == transform) if transform is None or transform == self.transData or same_projection: if isinstance(transform, ccrs.Projection): transform = transform._as_mpl_transform(self) result = matplotlib.axes.Axes.imshow(self, img, *args, **kwargs) else: extent = kwargs.pop('extent', None) img = np.asanyarray(img) if kwargs['origin'] == 'upper': # It is implicitly assumed by the regridding operation that the # origin of the image is 'lower', so simply adjust for that # here. img = img[::-1] kwargs['origin'] = 'lower' if not isinstance(transform, ccrs.Projection): raise ValueError('Expected a projection subclass. Cannot ' 'handle a %s in imshow.' % type(transform)) target_extent = self.get_extent(self.projection) regrid_shape = kwargs.pop('regrid_shape', 750) regrid_shape = self._regrid_shape_aspect(regrid_shape, target_extent) warp_array = cartopy.img_transform.warp_array img, extent = warp_array(img, source_proj=transform, source_extent=extent, target_proj=self.projection, target_res=regrid_shape, target_extent=target_extent, mask_extrapolated=True, ) # As a workaround to a matplotlib limitation, turn any images # which are RGB with a mask into RGBA images with an alpha # channel. if (isinstance(img, np.ma.MaskedArray) and img.shape[2:3] == (3, ) and img.mask is not False): old_img = img img = np.zeros(img.shape[:2] + (4, ), dtype=img.dtype) img[:, :, 0:3] = old_img # Put an alpha channel in if the image was masked. img[:, :, 3] = ~ np.any(old_img.mask, axis=2) if img.dtype.kind == 'u': img[:, :, 3] *= 255 result = matplotlib.axes.Axes.imshow(self, img, *args, extent=extent, **kwargs) # clip the image. This does not work as the patch moves with mouse # movement, but the clip path doesn't # This could definitely be fixed in matplotlib # if result.get_clip_path() in [None, self.patch]: # # image does not already have clipping set, clip to axes patch # result.set_clip_path(self.outline_patch) return result def gridlines(self, crs=None, draw_labels=False, xlocs=None, ylocs=None, **kwargs): """ Automatically adds gridlines to the axes, in the given coordinate system, at draw time. Kwargs: * crs The :class:`cartopy._crs.CRS` defining the coordinate system in which gridlines are drawn. Default is :class:`cartopy.crs.PlateCarree`. * draw_labels Label gridlines like axis ticks, around the edge. * xlocs An iterable of gridline locations or a :class:`matplotlib.ticker.Locator` instance which will be used to determine the locations of the gridlines in the x-coordinate of the given CRS. Defaults to None, which implies automatic locating of the gridlines. * ylocs An iterable of gridline locations or a :class:`matplotlib.ticker.Locator` instance which will be used to determine the locations of the gridlines in the y-coordinate of the given CRS. Defaults to None, which implies automatic locating of the gridlines. Returns: A :class:`cartopy.mpl.gridliner.Gridliner` instance. All other keywords control line properties. These are passed through to :class:`matplotlib.collections.Collection`. """ if crs is None: crs = ccrs.PlateCarree() from cartopy.mpl.gridliner import Gridliner if xlocs is not None and not isinstance(xlocs, mticker.Locator): xlocs = mticker.FixedLocator(xlocs) if ylocs is not None and not isinstance(ylocs, mticker.Locator): ylocs = mticker.FixedLocator(ylocs) gl = Gridliner( self, crs=crs, draw_labels=draw_labels, xlocator=xlocs, ylocator=ylocs, collection_kwargs=kwargs) self._gridliners.append(gl) return gl def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'): # generate some axes spines, as some Axes super class machinery # requires them. Just make them invisible spines = matplotlib.axes.Axes._gen_axes_spines(self, locations=locations, offset=offset, units=units) for spine in spines.values(): spine.set_visible(False) return spines def _boundary(self): """ Adds the map's boundary to this GeoAxes, attaching the appropriate artists to :data:`.outline_patch` and :data:`.background_patch`. .. note:: The boundary is not the ``axes.patch``. ``axes.patch`` is made invisible by this method - its only remaining purpose is to provide a rectilinear clip patch for all Axes artists. """ # Hide the old "background" patch used by matplotlib - it is not # used by cartopy's GeoAxes. self.patch.set_facecolor((1, 1, 1, 0)) self.patch.set_edgecolor((0.5, 0.5, 0.5)) self.patch.set_visible(False) self.background_patch = None self.outline_patch = None path, = cpatch.geos_to_path(self.projection.boundary) # Get the outline path in terms of self.transData proj_to_data = self.projection._as_mpl_transform(self) - self.transData trans_path = proj_to_data.transform_path(path) # Set the boundary - we can make use of the rectangular clipping. self.set_boundary(trans_path, use_as_clip_path=False) # Attach callback events for when the xlim or ylim are changed. This # is what triggers the patches to be re-clipped at draw time. self.callbacks.connect('xlim_changed', _trigger_patch_reclip) self.callbacks.connect('ylim_changed', _trigger_patch_reclip) def set_boundary(self, path, transform=None, use_as_clip_path=True): """ Given a path, update the :data:`.outline_patch` and :data:`.background_patch` to take its shape. Parameters ---------- path : :class:`matplotlib.path.Path` The path of the desired boundary. transform : None or :class:`matplotlib.transforms.Transform` The coordinate system of the given path. Currently this must be convertible to data coordinates, and therefore cannot extend beyond the limits of the axes' projection. use_as_clip_path : bool Whether axes.patch should be updated. Updating axes.patch means that any artists subsequently created will inherit clipping from this path, rather than the standard unit square in axes coordinates. """ if transform is None: transform = self.transData if isinstance(transform, cartopy.crs.CRS): transform = transform._as_mpl_transform(self) if self.background_patch is None: background = matplotlib.patches.PathPatch(path, edgecolor='none', facecolor='white', zorder=-1, clip_on=False, transform=transform) else: background = matplotlib.patches.PathPatch(path, zorder=-1, clip_on=False) background.update_from(self.background_patch) self.background_patch.remove() background.set_transform(transform) if self.outline_patch is None: outline = matplotlib.patches.PathPatch(path, edgecolor='black', facecolor='none', zorder=2.5, clip_on=False, transform=transform) else: outline = matplotlib.patches.PathPatch(path, zorder=2.5, clip_on=False) outline.update_from(self.outline_patch) self.outline_patch.remove() outline.set_transform(transform) # Attach the original path to the patches. This will be used each time # a new clipped path is calculated. outline.orig_path = path background.orig_path = path # Attach a "reclip" attribute, which determines if the patch's path is # reclipped before drawing. A callback is used to change the "reclip" # state. outline.reclip = True background.reclip = True # Add the patches to the axes, and also make them available as # attributes. self.background_patch = background self.outline_patch = outline if use_as_clip_path: self.patch = background with self.hold_limits(): self.add_patch(outline) self.add_patch(background) def contour(self, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.contour'. Extra kwargs: transform - a :class:`~cartopy.crs.Projection`. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical contouring is not supported - ' ' consider using PlateCarree/RotatedPole.') if isinstance(t, ccrs.Projection): kwargs['transform'] = t._as_mpl_transform(self) else: kwargs['transform'] = t result = matplotlib.axes.Axes.contour(self, *args, **kwargs) self.autoscale_view() return result def contourf(self, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.contourf'. Extra kwargs: transform - a :class:`~cartopy.crs.Projection`. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical contouring is not supported - ' ' consider using PlateCarree/RotatedPole.') if isinstance(t, ccrs.Projection): kwargs['transform'] = t = t._as_mpl_transform(self) else: kwargs['transform'] = t # Set flag to indicate correcting orientation of paths if not ccw if isinstance(t, mtransforms.Transform): for sub_trans, _ in t._iter_break_from_left_to_right(): if isinstance(sub_trans, InterProjectionTransform): if not hasattr(sub_trans, 'force_path_ccw'): sub_trans.force_path_ccw = True result = matplotlib.axes.Axes.contourf(self, *args, **kwargs) # We need to compute the dataLim correctly for contours. if matplotlib.__version__ >= '1.4': extent = mtransforms.Bbox.union([col.get_datalim(self.transData) for col in result.collections]) self.dataLim.update_from_data_xy(extent.get_points()) self.autoscale_view() return result def scatter(self, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.scatter'. Extra kwargs: transform - a :class:`~cartopy.crs.Projection`. """ t = kwargs.get('transform', None) # Keep this bit - even at mpl v1.2 if t is None: t = self.projection if hasattr(t, '_as_mpl_transform'): kwargs['transform'] = t._as_mpl_transform(self) # exclude Geodetic as a vaild source CS if (isinstance(kwargs.get('transform', None), InterProjectionTransform) and kwargs['transform'].source_projection.is_geodetic()): raise ValueError('Cartopy cannot currently do spherical ' 'contouring. The source CRS cannot be a ' 'geodetic, consider using the cyllindrical form ' '(PlateCarree or RotatedPole).') result = matplotlib.axes.Axes.scatter(self, *args, **kwargs) self.autoscale_view() return result def pcolormesh(self, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolormesh'. Extra kwargs: transform - a :class:`~cartopy.crs.Projection`. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical pcolormesh is not supported - ' ' consider using PlateCarree/RotatedPole.') kwargs.setdefault('transform', t) result = self._pcolormesh_patched(*args, **kwargs) self.autoscale_view() return result def _pcolormesh_patched(self, *args, **kwargs): """ A temporary, modified duplicate of :func:`~matplotlib.pyplot.pcolormesh'. This function contains a workaround for a matplotlib issue and will be removed once the issue has been resolved. https://github.com/matplotlib/matplotlib/pull/1314 """ import warnings import numpy as np import numpy.ma as ma import matplotlib as mpl import matplotlib.cbook as cbook import matplotlib.colors as mcolors import matplotlib.cm as cm from matplotlib import docstring import matplotlib.transforms as transforms import matplotlib.artist as artist from matplotlib.artist import allow_rasterization import matplotlib.backend_bases as backend_bases import matplotlib.path as mpath import matplotlib.mlab as mlab import matplotlib.collections as mcoll if not self._hold: self.cla() alpha = kwargs.pop('alpha', None) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) shading = kwargs.pop('shading', 'flat').lower() antialiased = kwargs.pop('antialiased', False) kwargs.setdefault('edgecolors', 'None') allmatch = (shading == 'gouraud') X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) Ny, Nx = X.shape # convert to one dimensional arrays C = C.ravel() X = X.ravel() Y = Y.ravel() coords = np.zeros(((Nx * Ny), 2), dtype=float) coords[:, 0] = X coords[:, 1] = Y collection = mcoll.QuadMesh( Nx - 1, Ny - 1, coords, antialiased=antialiased, shading=shading, **kwargs) collection.set_alpha(alpha) collection.set_array(C) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) collection.set_cmap(cmap) collection.set_norm(norm) collection.set_clim(vmin, vmax) collection.autoscale_None() self.grid(False) # Transform from native to data coordinates? t = collection._transform if (not isinstance(t, mtransforms.Transform) and hasattr(t, '_as_mpl_transform')): t = t._as_mpl_transform(self.axes) if t and any(t.contains_branch_seperately(self.transData)): trans_to_data = t - self.transData pts = np.vstack([X, Y]).T.astype(np.float) transformed_pts = trans_to_data.transform(pts) X = transformed_pts[..., 0] Y = transformed_pts[..., 1] ######################## # PATCH # XXX Non-standard matplotlib thing. no_inf = (X != np.inf) & (Y != np.inf) X = X[no_inf] Y = Y[no_inf] # END OF PATCH ############## minx = np.amin(X) maxx = np.amax(X) miny = np.amin(Y) maxy = np.amax(Y) corners = (minx, miny), (maxx, maxy) ######################## # PATCH # XXX Non-standard matplotlib thing. collection._corners = mtransforms.Bbox(corners) collection.get_datalim = lambda transData: collection._corners # END OF PATCH ############## self.update_datalim(corners) self.add_collection(collection) self.autoscale_view() ######################## # PATCH # XXX Non-standard matplotlib thing. # Handle a possible wrap around for rectangular projections. t = kwargs.get('transform', None) if isinstance(t, ccrs.CRS): wrap_proj_types = (ccrs._RectangularProjection, ccrs._WarpedRectangularProjection, ccrs.InterruptedGoodeHomolosine, ccrs.Mercator) if isinstance(t, wrap_proj_types) and \ isinstance(self.projection, wrap_proj_types): C = C.reshape((Ny - 1, Nx - 1)) transformed_pts = transformed_pts.reshape((Ny, Nx, 2)) # compute the vertical line angles of the pcolor in # transformed coordinates with np.errstate(invalid='ignore'): horizontal_vert_angles = np.arctan2( np.diff(transformed_pts[..., 0], axis=1), np.diff(transformed_pts[..., 1], axis=1) ) # if the change in angle is greater than 90 degrees (absolute), # then mark it for masking later on. dx_horizontal = np.diff(horizontal_vert_angles) to_mask = ((np.abs(dx_horizontal) > np.pi / 2) | np.isnan(dx_horizontal)) if np.any(to_mask): if collection.get_cmap()._rgba_bad[3] != 0.0: warnings.warn("The colormap's 'bad' has been set, but " "in order to wrap pcolormesh across the " "map it must be fully transparent.") # at this point C has a shape of (Ny-1, Nx-1), to_mask has # a shape of (Ny, Nx-2) and pts has a shape of (Ny*Nx, 2) mask = np.zeros(C.shape, dtype=np.bool) # mask out the neighbouring cells if there was a cell # found with an angle change of more than pi/2 . NB. # Masking too much only has a detrimental impact on # performance. to_mask_y_shift = to_mask[:-1, :] mask[:, :-1][to_mask_y_shift] = True mask[:, 1:][to_mask_y_shift] = True to_mask_x_shift = to_mask[1:, :] mask[:, :-1][to_mask_x_shift] = True mask[:, 1:][to_mask_x_shift] = True C_mask = getattr(C, 'mask', None) if C_mask is not None: dmask = mask | C_mask else: dmask = mask # create the masked array to be used with this pcolormesh pcolormesh_data = np.ma.array(C, mask=mask) collection.set_array(pcolormesh_data.ravel()) # now that the pcolormesh has masked the bad values, # create a pcolor with just those values that were masked pcolor_data = pcolormesh_data.copy() # invert the mask pcolor_data.mask = ~pcolor_data.mask # remember to re-apply the original data mask to the array if C_mask is not None: pcolor_data.mask = pcolor_data.mask | C_mask pts = pts.reshape((Ny, Nx, 2)) if np.any(~pcolor_data.mask): # plot with slightly lower zorder to avoid odd issue # where the main plot is obscured zorder = collection.zorder - .1 kwargs.pop('zorder', None) kwargs.setdefault('snap', False) pcolor_col = self.pcolor(pts[..., 0], pts[..., 1], pcolor_data, zorder=zorder, **kwargs) pcolor_col.set_cmap(cmap) pcolor_col.set_norm(norm) pcolor_col.set_clim(vmin, vmax) # scale the data according to the *original* data pcolor_col.norm.autoscale_None(C) # put the pcolor_col on the pcolormesh collection so # that if really necessary, users can do things post # this method collection._wrapped_collection_fix = pcolor_col # Clip the QuadMesh to the projection boundary, which is required # to keep the shading inside the projection bounds. collection.set_clip_path(self.outline_patch) # END OF PATCH ############## return collection def pcolor(self, *args, **kwargs): """ Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolor'. Extra kwargs: transform - a :class:`~cartopy.crs.Projection`. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical pcolor is not supported - ' ' consider using PlateCarree/RotatedPole.') kwargs.setdefault('transform', t) result = matplotlib.axes.Axes.pcolor(self, *args, **kwargs) # Update the datalim for this pcolor. limits = result.get_datalim(self.axes.transData) self.axes.update_datalim(limits) self.autoscale_view() return result def quiver(self, x, y, u, v, *args, **kwargs): """ Plot a field of arrows. Extra Kwargs: * transform: :class:`cartopy.crs.Projection` or matplotlib transform The coordinate system in which the vectors are defined. * regrid_shape: int or 2-tuple of ints If given, specifies that the points where the arrows are located will be interpolated onto a regular grid in projection space. If a single integer is given then that will be used as the minimum grid length dimension, while the other dimension will be scaled up according to the target extent's aspect ratio. If a pair of ints are given they determine the grid length in the x and y directions respectively. * target_extent: 4-tuple If given, specifies the extent in the target CRS that the regular grid defined by *regrid_shape* will have. Defaults to the current extent of the map projection. See :func:`matplotlib.pyplot.quiver` for details on arguments and other keyword arguments. .. note:: The vector components must be defined as grid eastward and grid northward. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical quiver is not supported - ' ' consider using PlateCarree/RotatedPole.') if isinstance(t, ccrs.Projection): kwargs['transform'] = t._as_mpl_transform(self) else: kwargs['transform'] = t regrid_shape = kwargs.pop('regrid_shape', None) target_extent = kwargs.pop('target_extent', self.get_extent(self.projection)) if regrid_shape is not None: # If regridding is required then we'll be handling transforms # manually and plotting in native coordinates. regrid_shape = self._regrid_shape_aspect(regrid_shape, target_extent) if args: # Interpolate color array as well as vector components. x, y, u, v, c = vector_scalar_to_grid( t, self.projection, regrid_shape, x, y, u, v, args[0], target_extent=target_extent) args = (c,) + args[1:] else: x, y, u, v = vector_scalar_to_grid( t, self.projection, regrid_shape, x, y, u, v, target_extent=target_extent) kwargs.pop('transform', None) elif t != self.projection: # Transform the vectors if the projection is not the same as the # data transform. if (x.ndim == 1 and y.ndim == 1) and (x.shape != u.shape): x, y = np.meshgrid(x, y) u, v = self.projection.transform_vectors(t, x, y, u, v) return matplotlib.axes.Axes.quiver(self, x, y, u, v, *args, **kwargs) def barbs(self, x, y, u, v, *args, **kwargs): """ Plot a 2-D field of barbs. Extra Kwargs: * transform: :class:`cartopy.crs.Projection` or matplotlib transform The coordinate system in which the vectors are defined. * regrid_shape: int or 2-tuple of ints If given, specifies that the points where the arrows are located will be interpolated onto a regular grid in projection space. If a single integer is given then that will be used as the minimum grid length dimension, while the other dimension will be scaled up according to the target extent's aspect ratio. If a pair of ints are given they determine the grid length in the x and y directions respectively. * target_extent: 4-tuple If given, specifies the extent in the target CRS that the regular grid defined by *regrid_shape* will have. Defaults to the current extent of the map projection. See :func:`matplotlib.pyplot.barbs` for details on arguments and keyword arguments. .. note:: The vector components must be defined as grid eastward and grid northward. """ t = kwargs.get('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical barbs are not supported - ' ' consider using PlateCarree/RotatedPole.') if isinstance(t, ccrs.Projection): kwargs['transform'] = t._as_mpl_transform(self) else: kwargs['transform'] = t regrid_shape = kwargs.pop('regrid_shape', None) target_extent = kwargs.pop('target_extent', self.get_extent(self.projection)) if regrid_shape is not None: # If regridding is required then we'll be handling transforms # manually and plotting in native coordinates. regrid_shape = self._regrid_shape_aspect(regrid_shape, target_extent) if args: # Interpolate color array as well as vector components. x, y, u, v, c = vector_scalar_to_grid( t, self.projection, regrid_shape, x, y, u, v, args[0], target_extent=target_extent) args = (c,) + args[1:] else: x, y, u, v = vector_scalar_to_grid( t, self.projection, regrid_shape, x, y, u, v, target_extent=target_extent) kwargs.pop('transform', None) elif t != self.projection: # Transform the vectors if the projection is not the same as the # data transform. if x.ndim == 1 and y.ndim == 1: x, y = np.meshgrid(x, y) u, v = self.projection.transform_vectors(t, x, y, u, v) return matplotlib.axes.Axes.barbs(self, x, y, u, v, *args, **kwargs) def streamplot(self, x, y, u, v, **kwargs): """ Draws streamlines of a vector flow. Extra Kwargs: * transform: :class:`cartopy.crs.Projection` or matplotlib transform The coordinate system in which the vector field is defined. See :func:`matplotlib.pyplot.streamplot` for details on arguments and keyword arguments. .. note:: The vector components must be defined as grid eastward and grid northward. """ t = kwargs.pop('transform', None) if t is None: t = self.projection if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection): raise ValueError('invalid transform:' ' Spherical streamplot is not supported - ' ' consider using PlateCarree/RotatedPole.') # Regridding is required for streamplot, it must have an evenly spaced # grid to work correctly. Choose our destination grid based on the # density keyword. The grid need not be bigger than the grid used by # the streamplot integrator. density = kwargs.get('density', 1) if np.isscalar(density): regrid_shape = [int(30 * density)] * 2 else: regrid_shape = [int(25 * d) for d in density] # The color and linewidth keyword arguments can be arrays so they will # need to be gridded also. c = kwargs.get('color', None) l = kwargs.get('linewidth', None) scalars = [] color_array = isinstance(c, np.ndarray) linewidth_array = isinstance(l, np.ndarray) if color_array: scalars.append(c) if linewidth_array: scalars.append(l) # Do the regridding including any scalar fields. target_extent = self.get_extent(self.projection) gridded = vector_scalar_to_grid(t, self.projection, regrid_shape, x, y, u, v, *scalars, target_extent=target_extent) x, y, u, v = gridded[:4] # If scalar fields were regridded then replace the appropriate keyword # arguments with the gridded arrays. scalars = list(gridded[4:]) if linewidth_array: kwargs['linewidth'] = scalars.pop() if color_array: kwargs['color'] = ma.masked_invalid(scalars.pop()) with warnings.catch_warnings(): # The workaround for nan values in streamplot colors gives rise to # a warning which is not at all important so it is hidden from the # user to avoid confusion. message = 'Warning: converting a masked element to nan.' warnings.filterwarnings('ignore', message=message, category=UserWarning) sp = matplotlib.axes.Axes.streamplot(self, x, y, u, v, **kwargs) return sp def add_wmts(self, wmts, layer_name, **kwargs): """ Add the specified WMTS layer to the axes. This function requires owslib and PIL to work. Args: * wmts - The URL of the WMTS, or an owslib.wmts.WebMapTileService instance. * layer_name - The name of the layer to use. All other keywords are passed through to the construction of the image artist. See :meth:`~matplotlib.axes.Axes.imshow()` for more details. """ from cartopy.io.ogc_clients import WMTSRasterSource wmts = WMTSRasterSource(wmts, layer_name) return self.add_raster(wmts, **kwargs) def add_wms(self, wms, layers, wms_kwargs=None, **kwargs): """ Add the specified WMS layer to the axes. This function requires owslib and PIL to work. Parameters ---------- wms : string or :class:`owslib.wms.WebMapService` instance The web map service URL or owslib WMS instance to use. layers : string or iterable of string The name of the layer(s) to use. wms_kwargs : dict or None Passed through to the :class:`~cartopy.io.ogc_clients.WMSRasterSource` constructor's ``getmap_extra_kwargs`` for defining getmap time keyword arguments. All other keywords are passed through to the construction of the image artist. See :meth:`~matplotlib.axes.Axes.imshow()` for more details. """ from cartopy.io.ogc_clients import WMSRasterSource wms = WMSRasterSource(wms, layers, getmap_extra_kwargs=wms_kwargs) return self.add_raster(wms, **kwargs) # Define the GeoAxesSubplot class, so that a type(ax) will emanate from # cartopy.mpl.geoaxes, not matplotlib.axes. class GeoAxesSubplot(matplotlib.axes.SubplotBase, GeoAxes): _axes_class = GeoAxes try: matplotlib.axes._subplots._subplot_classes[GeoAxes] = GeoAxesSubplot except AttributeError: matplotlib.axes._subplot_classes[GeoAxes] = GeoAxesSubplot def _trigger_patch_reclip(event): """ Defines an event callback for a GeoAxes which forces the outline and background patches to be re-clipped next time they are drawn. """ axes = event.axes # trigger the outline and background patches to be re-clipped axes.outline_patch.reclip = True axes.background_patch.reclip = True Cartopy-0.14.2/lib/cartopy/mpl/gridliner.py0000644001374000021030000004246212700747662020342 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import matplotlib import matplotlib.collections as mcollections import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtrans import numpy as np import six import cartopy from cartopy.crs import Projection, _RectangularProjection degree_locator = mticker.MaxNLocator(nbins=9, steps=[1, 2, 3, 6, 15, 18]) _DEGREE_SYMBOL = u'\u00B0' def _fix_lons(lons): """ Fix the given longitudes into the range ``[-180, 180]``. """ lons = np.array(lons, copy=False, ndmin=1) fixed_lons = ((lons + 180) % 360) - 180 # Make the positive 180s positive again. fixed_lons[(fixed_lons == -180) & (lons > 0)] *= -1 return fixed_lons def _lon_heimisphere(longitude): """Return the hemisphere (E, W or '' for 0) for the given longitude.""" longitude = _fix_lons(longitude) if longitude > 0: hemisphere = 'E' elif longitude < 0: hemisphere = 'W' else: hemisphere = '' return hemisphere def _lat_heimisphere(latitude): """Return the hemisphere (N, S or '' for 0) for the given latitude.""" if latitude > 0: hemisphere = 'N' elif latitude < 0: hemisphere = 'S' else: hemisphere = '' return hemisphere def _east_west_formatted(longitude, num_format='g'): fmt_string = u'{longitude:{num_format}}{degree}{hemisphere}' return fmt_string.format(longitude=abs(longitude), num_format=num_format, hemisphere=_lon_heimisphere(longitude), degree=_DEGREE_SYMBOL) def _north_south_formatted(latitude, num_format='g'): fmt_string = u'{latitude:{num_format}}{degree}{hemisphere}' return fmt_string.format(latitude=abs(latitude), num_format=num_format, hemisphere=_lat_heimisphere(latitude), degree=_DEGREE_SYMBOL) #: A formatter which turns longitude values into nice longitudes such as 110W LONGITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos: _east_west_formatted(v)) #: A formatter which turns longitude values into nice longitudes such as 45S LATITUDE_FORMATTER = mticker.FuncFormatter(lambda v, pos: _north_south_formatted(v)) class Gridliner(object): # NOTE: In future, one of these objects will be add-able to a GeoAxes (and # maybe even a plain old mpl axes) and it will call the "_draw_gridliner" # method on draw. This will enable automatic gridline resolution # determination on zoom/pan. def __init__(self, axes, crs, draw_labels=False, xlocator=None, ylocator=None, collection_kwargs=None): """ Object used by :meth:`cartopy.mpl.geoaxes.GeoAxes.gridlines` to add gridlines and tick labels to a map. Args: * axes The :class:`cartopy.mpl.geoaxes.GeoAxes` object to be drawn on. * crs The :class:`cartopy.crs.CRS` defining the coordinate system that the gridlines are drawn in. * draw_labels Toggle whether to draw labels. For finer control, attributes of :class:`Gridliner` may be modified individually. * xlocator A :class:`matplotlib.ticker.Locator` instance which will be used to determine the locations of the gridlines in the x-coordinate of the given CRS. Defaults to None, which implies automatic locating of the gridlines. * ylocator A :class:`matplotlib.ticker.Locator` instance which will be used to determine the locations of the gridlines in the y-coordinate of the given CRS. Defaults to None, which implies automatic locating of the gridlines. * collection_kwargs Dictionary controlling line properties, passed to :class:`matplotlib.collections.Collection`. """ self.axes = axes #: The :class:`~matplotlib.ticker.Locator` to use for the x #: gridlines and labels. self.xlocator = xlocator or degree_locator #: The :class:`~matplotlib.ticker.Locator` to use for the y #: gridlines and labels. self.ylocator = ylocator or degree_locator #: The :class:`~matplotlib.ticker.Formatter` to use for the x labels. self.xformatter = mticker.ScalarFormatter() self.xformatter.create_dummy_axis() #: The :class:`~matplotlib.ticker.Formatter` to use for the y labels. self.yformatter = mticker.ScalarFormatter() self.yformatter.create_dummy_axis() #: Whether to draw labels on the top of the map. self.xlabels_top = draw_labels #: Whether to draw labels on the bottom of the map. self.xlabels_bottom = draw_labels #: Whether to draw labels on the left hand side of the map. self.ylabels_left = draw_labels #: Whether to draw labels on the right hand side of the map. self.ylabels_right = draw_labels #: Whether to draw the x gridlines. self.xlines = True #: Whether to draw the y gridlines. self.ylines = True #: A dictionary passed through to ``ax.text`` on x label creation #: for styling of the text labels. self.xlabel_style = {} #: A dictionary passed through to ``ax.text`` on y label creation #: for styling of the text labels. self.ylabel_style = {} self.crs = crs # if the user specifies tick labels at this point, check if they can # be drawn. The same check will take place at draw time in case # public attributes are changed after instantiation. if draw_labels: self._assert_can_draw_ticks() #: The number of interpolation points which are used to draw the #: gridlines. self.n_steps = 30 #: A dictionary passed through to #: ``matplotlib.collections.LineCollection`` on grid line creation. self.collection_kwargs = collection_kwargs #: The x gridlines which were created at draw time. self.xline_artists = [] #: The y gridlines which were created at draw time. self.yline_artists = [] #: The x labels which were created at draw time. self.xlabel_artists = [] #: The y labels which were created at draw time. self.ylabel_artists = [] def _crs_transform(self): """ Get the drawing transform for our gridlines. .. note:: this depends on the transform of our 'axes', so it may change dynamically. """ transform = self.crs if not isinstance(transform, mtrans.Transform): transform = transform._as_mpl_transform(self.axes) return transform def _add_gridline_label(self, value, axis, upper_end): """ Create a Text artist on our axes for a gridline label. Args: * value Coordinate value of this gridline. The text contains this value, and is positioned centred at that point. * axis which axis the label is on: 'x' or 'y'. * upper_end If True, place at the maximum of the "other" coordinate (Axes coordinate == 1.0). Else 'lower' end (Axes coord = 0.0). """ transform = self._crs_transform() shift_dist_points = 5 # A margin from the map edge. if upper_end is False: shift_dist_points = -shift_dist_points if axis == 'x': x = value y = 1.0 if upper_end else 0.0 h_align = 'center' v_align = 'bottom' if upper_end else 'top' tr_x = transform tr_y = self.axes.transAxes + \ mtrans.ScaledTranslation( 0.0, shift_dist_points * (1.0 / 72), self.axes.figure.dpi_scale_trans) str_value = self.xformatter(value) user_label_style = self.xlabel_style elif axis == 'y': y = value x = 1.0 if upper_end else 0.0 v_align = 'center' h_align = 'left' if upper_end else 'right' tr_y = transform tr_x = self.axes.transAxes + \ mtrans.ScaledTranslation( shift_dist_points * (1.0 / 72), 0.0, self.axes.figure.dpi_scale_trans) str_value = self.yformatter(value) user_label_style = self.ylabel_style else: raise ValueError( "Unknown axis, {!r}, must be either 'x' or 'y'".format(axis)) # Make a 'blended' transform for label text positioning. # One coord is geographic, and the other a plain Axes # coordinate with an appropriate offset. label_transform = mtrans.blended_transform_factory( x_transform=tr_x, y_transform=tr_y) label_style = {'verticalalignment': v_align, 'horizontalalignment': h_align, } label_style.update(user_label_style) # Create and add a Text artist with these properties text_artist = mtext.Text(x, y, str_value, clip_on=False, transform=label_transform, **label_style) if axis == 'x': self.xlabel_artists.append(text_artist) elif axis == 'y': self.ylabel_artists.append(text_artist) self.axes.add_artist(text_artist) def _draw_gridliner(self, nx=None, ny=None, background_patch=None): """Create Artists for all visible elements and add to our Axes.""" x_lim, y_lim = self._axes_domain(nx=nx, ny=ny, background_patch=background_patch) transform = self._crs_transform() rc_params = matplotlib.rcParams n_steps = self.n_steps x_ticks = self.xlocator.tick_values(x_lim[0], x_lim[1]) y_ticks = self.ylocator.tick_values(y_lim[0], y_lim[1]) # XXX this bit is cartopy specific. (for circular longitudes) # Purpose: omit plotting the last x line, as it may overlap the first. x_gridline_points = x_ticks[:] crs = self.crs if (isinstance(crs, Projection) and isinstance(crs, _RectangularProjection) and abs(np.diff(x_lim)) == abs(np.diff(crs.x_limits))): x_gridline_points = x_gridline_points[:-1] collection_kwargs = self.collection_kwargs if collection_kwargs is None: collection_kwargs = {} collection_kwargs = collection_kwargs.copy() collection_kwargs['transform'] = transform # XXX doesn't gracefully handle lw vs linewidth aliases... collection_kwargs.setdefault('color', rc_params['grid.color']) collection_kwargs.setdefault('linestyle', rc_params['grid.linestyle']) collection_kwargs.setdefault('linewidth', rc_params['grid.linewidth']) if self.xlines: lines = [] for x in x_gridline_points: l = list(zip(np.zeros(n_steps) + x, np.linspace(min(y_ticks), max(y_ticks), n_steps))) lines.append(l) x_lc = mcollections.LineCollection(lines, **collection_kwargs) self.xline_artists.append(x_lc) self.axes.add_collection(x_lc, autolim=False) if self.ylines: lines = [] for y in y_ticks: l = list(zip(np.linspace(min(x_ticks), max(x_ticks), n_steps), np.zeros(n_steps) + y)) lines.append(l) y_lc = mcollections.LineCollection(lines, **collection_kwargs) self.yline_artists.append(y_lc) self.axes.add_collection(y_lc, autolim=False) ################# # Label drawing # ################# # Trim outside-area points from the label coords. # Tickers may round *up* the desired range to something tidy, not # all of which is necessarily visible. We must be stricter with # our texts, as they are drawn *without clipping*. x_label_points = [x for x in x_ticks if x_lim[0] <= x <= x_lim[1]] y_label_points = [y for y in y_ticks if y_lim[0] <= y <= y_lim[1]] if self.xlabels_bottom or self.xlabels_top: self._assert_can_draw_ticks() self.xformatter.set_locs(x_label_points) for x in x_label_points: if self.xlabels_bottom: self._add_gridline_label(x, axis='x', upper_end=False) if self.xlabels_top: self._add_gridline_label(x, axis='x', upper_end=True) if self.ylabels_left or self.ylabels_right: self._assert_can_draw_ticks() self.yformatter.set_locs(y_label_points) for y in y_label_points: if self.ylabels_left: self._add_gridline_label(y, axis='y', upper_end=False) if self.ylabels_right: self._add_gridline_label(y, axis='y', upper_end=True) def _assert_can_draw_ticks(self): """ Check to see if ticks can be drawn. Either returns True or raises an exception. """ # Check labelling is supported, currently a limited set of options. if not isinstance(self.crs, cartopy.crs.PlateCarree): raise TypeError('Cannot label {crs.__class__.__name__} gridlines.' ' Only PlateCarree gridlines are currently ' 'supported.'.format(crs=self.crs)) if not isinstance(self.axes.projection, (cartopy.crs.PlateCarree, cartopy.crs.Mercator)): raise TypeError('Cannot label gridlines on a ' '{prj.__class__.__name__} plot. Only PlateCarree' ' and Mercator plots are currently ' 'supported.'.format(prj=self.axes.projection)) return True def _axes_domain(self, nx=None, ny=None, background_patch=None): """Returns x_range, y_range""" DEBUG = False transform = self._crs_transform() ax_transform = self.axes.transAxes desired_trans = ax_transform - transform nx = nx or 30 ny = ny or 30 x = np.linspace(1e-9, 1 - 1e-9, nx) y = np.linspace(1e-9, 1 - 1e-9, ny) x, y = np.meshgrid(x, y) coords = np.concatenate([x.flatten()[:, None], y.flatten()[:, None]], 1) in_data = desired_trans.transform(coords) ax_to_bkg_patch = self.axes.transAxes - \ background_patch.get_transform() ok = np.zeros(in_data.shape[:-1], dtype=np.bool) # XXX Vectorise contains_point for i, val in enumerate(in_data): # convert the coordinates of the data to the background # patches coordinates background_coord = ax_to_bkg_patch.transform(coords[i:i + 1, :]) bkg_patch_contains = background_patch.get_path().contains_point if bkg_patch_contains(background_coord[0, :]): color = 'r' ok[i] = True else: color = 'b' if DEBUG: import matplotlib.pyplot as plt plt.plot(coords[i, 0], coords[i, 1], 'o' + color, clip_on=False, transform=ax_transform) # plt.text(coords[i, 0], coords[i, 1], str(val), clip_on=False, # transform=ax_transform, rotation=23, # horizontalalignment='right') inside = in_data[ok, :] # If there were no data points in the axes we just use the x and y # range of the projection. if inside.size == 0: x_range = self.crs.x_limits y_range = self.crs.y_limits else: x_range = np.nanmin(inside[:, 0]), np.nanmax(inside[:, 0]) y_range = np.nanmin(inside[:, 1]), np.nanmax(inside[:, 1]) # XXX Cartopy specific thing. Perhaps make this bit a specialisation # in a subclass... crs = self.crs if isinstance(crs, Projection): x_range = np.clip(x_range, *crs.x_limits) y_range = np.clip(y_range, *crs.y_limits) # if the limit is >90% of the full x limit, then just use the full # x limit (this makes circular handling better) prct = np.abs(np.diff(x_range) / np.diff(crs.x_limits)) if prct > 0.9: x_range = crs.x_limits return x_range, y_range Cartopy-0.14.2/lib/cartopy/mpl/patch.py0000644001374000021030000002153712700747662017462 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Provides shapely geometry <-> matplotlib path support. See also `Shapely Geometric Objects `_ and `Matplotlib Path API `_. .. see_also_shapely: http://toblerity.org/shapely/manual.html#geometric-objects """ from __future__ import (absolute_import, division, print_function) import numpy as np import matplotlib.path from matplotlib.path import Path import shapely.geometry as sgeom def geos_to_path(shape): """ Creates a list of :class:`matplotlib.path.Path` objects that describe a shape. Args: * shape A list, tuple or single instance of any of the following types: :class:`shapely.geometry.point.Point`, :class:`shapely.geometry.linestring.LineString`, :class:`shapely.geometry.polygon.Polygon`, :class:`shapely.geometry.multipoint.MultiPoint`, :class:`shapely.geometry.multipolygon.MultiPolygon`, :class:`shapely.geometry.multilinestring.MultiLineString`, :class:`shapely.geometry.collection.GeometryCollection`, or any type with a _as_mpl_path() method. Returns: A list of :class:`matplotlib.path.Path` objects. """ if isinstance(shape, (list, tuple)): paths = [] for shp in shape: paths.extend(geos_to_path(shp)) return paths if isinstance(shape, (sgeom.LineString, sgeom.Point)): return [Path(np.vstack(shape.xy).T)] elif isinstance(shape, sgeom.Polygon): def poly_codes(poly): codes = np.ones(len(poly.xy[0])) * Path.LINETO codes[0] = Path.MOVETO return codes if shape.is_empty: return [] vertices = np.concatenate([np.array(shape.exterior.xy)] + [np.array(ring.xy) for ring in shape.interiors], 1).T codes = np.concatenate([poly_codes(shape.exterior)] + [poly_codes(ring) for ring in shape.interiors]) return [Path(vertices, codes)] elif isinstance(shape, (sgeom.MultiPolygon, sgeom.GeometryCollection, sgeom.MultiLineString, sgeom.MultiPoint)): paths = [] for geom in shape.geoms: paths.extend(geos_to_path(geom)) return paths elif hasattr(shape, '_as_mpl_path'): vertices, codes = shape._as_mpl_path() return [Path(vertices, codes)] else: raise ValueError('Unsupported shape type {}.'.format(type(shape))) def path_segments(path, transform=None, remove_nans=False, clip=None, quantize=False, simplify=False, curves=False, stroke_width=1.0, snap=False): """ Creates an array of vertices and a corresponding array of codes from a :class:`matplotlib.path.Path`. Args: * path A :class:`matplotlib.path.Path` instance. Kwargs: See :func:`matplotlib.path.iter_segments` for details of the keyword arguments. Returns: A (vertices, codes) tuple, where vertices is a numpy array of coordinates, and codes is a numpy array of matplotlib path codes. See :class:`matplotlib.path.Path` for information on the types of codes and their meanings. """ # XXX assigned to avoid a ValueError inside the mpl C code... a = transform, remove_nans, clip, quantize, simplify, curves # Series of cleanups and conversions to the path e.g. it # can convert curved segments to line segments. vertices, codes = matplotlib.path.cleanup_path(path, transform, remove_nans, clip, snap, stroke_width, simplify, curves) # Remove the final vertex (with code 0) return vertices[:-1, :], codes[:-1] # Matplotlib v1.3+ deprecates the use of matplotlib.path.cleanup_path. Instead # there is a method on a Path instance to simplify this. if hasattr(matplotlib.path.Path, 'cleaned'): _path_segments_doc = path_segments.__doc__ def path_segments(path, **kwargs): pth = path.cleaned(**kwargs) return pth.vertices[:-1, :], pth.codes[:-1] path_segments.__doc__ = _path_segments_doc def path_to_geos(path, force_ccw=False): """ Creates a list of Shapely geometric objects from a :class:`matplotlib.path.Path`. Args: * path A :class:`matplotlib.path.Path` instance. Kwargs: * force_ccw Boolean flag determining whether the path can be inverted to enforce ccw. Returns: A list of :class:`shapely.geometry.polygon.Polygon`, :class:`shapely.geometry.linestring.LineString` and/or :class:`shapely.geometry.multilinestring.MultiLineString` instances. """ # Convert path into numpy array of vertices (and associated codes) path_verts, path_codes = path_segments(path, curves=False) # Split into subarrays such that each subarray consists of connected # line segments based on the start of each one being marked by a # matplotlib MOVETO code. verts_split_inds = np.where(path_codes == Path.MOVETO)[0] verts_split = np.split(path_verts, verts_split_inds) codes_split = np.split(path_codes, verts_split_inds) # Iterate through the vertices generating a list of # (external_geom, [internal_polygons]) tuples. other_result_geoms = [] collection = [] for path_verts, path_codes in zip(verts_split, codes_split): if len(path_verts) == 0: continue # XXX A path can be given which does not end with close poly, in that # situation, we have to guess? verts_same_as_first = np.all(path_verts[0, :] == path_verts[1:, :], axis=1) if all(verts_same_as_first): geom = sgeom.Point(path_verts[0, :]) elif (path_verts.shape[0] > 2 and (path_codes[-1] == Path.CLOSEPOLY or verts_same_as_first[-1])): if path_codes[-1] == Path.CLOSEPOLY: geom = sgeom.Polygon(path_verts[:-1, :]) else: geom = sgeom.Polygon(path_verts) else: geom = sgeom.LineString(path_verts) # If geom is a Polygon and is contained within the last geom in # collection, add it to its list of internal polygons, otherwise # simply append it as a new external geom. if geom.is_empty: pass elif (len(collection) > 0 and isinstance(collection[-1][0], sgeom.Polygon) and isinstance(geom, sgeom.Polygon) and collection[-1][0].contains(geom.exterior)): collection[-1][1].append(geom.exterior) elif isinstance(geom, sgeom.Point): other_result_geoms.append(geom) else: collection.append((geom, [])) # Convert each (external_geom, [internal_polygons]) pair into a # a shapely Polygon that encapsulates the internal polygons, if the # external geom is a LineString leave it alone. geom_collection = [] for external_geom, internal_polys in collection: if internal_polys: # XXX worry about islands within lakes geom = sgeom.Polygon(external_geom.exterior, internal_polys) else: geom = external_geom # Correctly orientate the polygon (ccw) if isinstance(geom, sgeom.Polygon): if force_ccw and not geom.exterior.is_ccw: geom = sgeom.polygon.orient(geom) geom_collection.append(geom) # If the geom_collection only contains LineStrings combine them # into a single MultiLinestring. if geom_collection and all(isinstance(geom, sgeom.LineString) for geom in geom_collection): geom_collection = [sgeom.MultiLineString(geom_collection)] # Remove any zero area Polygons def not_zero_poly(geom): return ((isinstance(geom, sgeom.Polygon) and not geom._is_empty and geom.area != 0) or not isinstance(geom, sgeom.Polygon)) result = list(filter(not_zero_poly, geom_collection)) return result + other_result_geoms Cartopy-0.14.2/lib/cartopy/mpl/slippy_image_artist.py0000644001374000021030000000425012700747662022424 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Defines the SlippyImageArtist class, which interfaces with :class:`cartopy.io.RasterSource` instances at draw time, for interactive dragging and zooming of raster data. """ from __future__ import (absolute_import, division, print_function) from matplotlib.image import AxesImage import matplotlib.artist class SlippyImageArtist(AxesImage): """ A subclass of :class:`~matplotlib.image.AxesImage` which provides an interface for getting a raster from the given object with interactive slippy map type functionality. Kwargs are passed to the AxesImage constructor. """ def __init__(self, ax, raster_source, **kwargs): self.raster_source = raster_source super(SlippyImageArtist, self).__init__(ax, **kwargs) self.set_clip_path(ax.outline_patch) @matplotlib.artist.allow_rasterization def draw(self, renderer, *args, **kwargs): if not self.get_visible(): return ax = self.axes window_extent = ax.get_window_extent() [x1, y1], [x2, y2] = ax.viewLim.get_points() located_images = self.raster_source.fetch_raster( ax.projection, extent=[x1, x2, y1, y2], target_resolution=(window_extent.width, window_extent.height)) for img, extent in located_images: self.set_array(img) with ax.hold_limits(): self.set_extent(extent) super(SlippyImageArtist, self).draw(renderer, *args, **kwargs) Cartopy-0.14.2/lib/cartopy/mpl/ticker.py0000644001374000021030000002437512700747662017647 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """This module contains tools for handling tick marks in cartopy.""" from __future__ import (absolute_import, division, print_function) from matplotlib.ticker import Formatter import cartopy.crs as ccrs from cartopy.mpl.geoaxes import GeoAxes class _PlateCarreeFormatter(Formatter): """ Base class for formatting ticks on geographical axes using a rectangular projection (e.g. Plate Carree, Mercator). """ _target_projection = ccrs.PlateCarree() def __init__(self, degree_symbol=u'\u00B0', number_format='g', transform_precision=1e-8): """ Base class for simpler implementation of specialised formatters for latitude and longitude axes. """ self._degree_symbol = degree_symbol self._number_format = number_format self._transform_precision = transform_precision def __call__(self, value, pos=None): if not isinstance(self.axis.axes, GeoAxes): raise TypeError("This formatter can only be " "used with cartopy axes.") # We want to produce labels for values in the familiar Plate Carree # projection, so convert the tick values from their own projection # before formatting them. source = self.axis.axes.projection if not isinstance(source, (ccrs._RectangularProjection, ccrs.Mercator)): raise TypeError("This formatter cannot be used with " "non-rectangular projections.") projected_value = self._apply_transform(value, self._target_projection, source) # Round the transformed value using a given precision for display # purposes. Transforms can introduce minor rounding errors that make # the tick values look bad, these need to be accounted for. f = 1. / self._transform_precision projected_value = round(f * projected_value) / f # Return the formatted values, the formatter has both the re-projected # tick value and the original axis value available to it. return self._format_value(projected_value, value) def _format_value(self, value, original_value): hemisphere = self._hemisphere(value, original_value) fmt_string = u'{value:{number_format}}{degree}{hemisphere}' return fmt_string.format(value=abs(value), number_format=self._number_format, degree=self._degree_symbol, hemisphere=hemisphere) def _apply_transform(self, value, target_proj, source_crs): """ Given a single value, a target projection and a source CRS, transforms the value from the source CRS to the target projection, returning a single value. """ raise NotImplementedError("A subclass must implement this method.") def _hemisphere(self, value, value_source_crs): """ Given both a tick value in the Plate Carree projection and the same value in the source CRS returns a string indicating the hemisphere that the value is in. Must be over-ridden by the derived class. """ raise NotImplementedError("A subclass must implement this method.") class LatitudeFormatter(_PlateCarreeFormatter): """Tick formatter for latitude axes.""" def __init__(self, degree_symbol=u'\u00B0', number_format='g', transform_precision=1e-8): """ Tick formatter for a latitude axis. The axis must be part of an axes defined on a rectangular projection (e.g. Plate Carree, Mercator). .. note:: A formatter can only be used for one axis. A new formatter must be created for every axis that needs formatted labels. Kwargs: * degree_symbol (string): The character(s) used to represent the degree symbol in the tick labels. Defaults to u'\u00B0' which is the unicode degree symbol. Can be an empty string if no degree symbol is desired. * number_format (string): Format string to represent the tick values. Defaults to 'g'. * transform_precision (float): Sets the precision (in degrees) to which transformed tick values are rounded. The default is 1e-7, and should be suitable for most use cases. To control the appearance of tick labels use the *number_format* keyword. Examples: Label latitudes from -90 to 90 on a Plate Carree projection:: ax = plt.axes(projection=PlateCarree()) ax.set_global() ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree()) lat_formatter = LatitudeFormatter() ax.yaxis.set_major_formatter(lat_formatter) Label latitudes from -80 to 80 on a Mercator projection, this time omitting the degree symbol:: ax = plt.axes(projection=Mercator()) ax.set_global() ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree()) lat_formatter = LatitudeFormatter(degree_symbol='') ax.yaxis.set_major_formatter(lat_formatter) """ super(LatitudeFormatter, self).__init__( degree_symbol=degree_symbol, number_format=number_format, transform_precision=transform_precision) def _apply_transform(self, value, target_proj, source_crs): return target_proj.transform_point(0, value, source_crs)[1] def _hemisphere(self, value, value_source_crs): if value > 0: hemisphere = 'N' elif value < 0: hemisphere = 'S' else: hemisphere = '' return hemisphere class LongitudeFormatter(_PlateCarreeFormatter): """Tick formatter for a longitude axis.""" def __init__(self, zero_direction_label=False, dateline_direction_label=False, degree_symbol=u'\u00B0', number_format='g', transform_precision=1e-8): """ Create a formatter for longitude values. The axis must be part of an axes defined on a rectangular projection (e.g. Plate Carree, Mercator). .. note:: A formatter can only be used for one axis. A new formatter must be created for every axis that needs formatted labels. Kwargs: * zero_direction_label (False | True): If *True* a direction label (E or W) will be drawn next to longitude labels with the value 0. If *False* then these labels will not be drawn. Defaults to *False* (no direction labels). * dateline_direction_label (False | True): If *True* a direction label (E or W) will be drawn next to longitude labels with the value 180. If *False* then these labels will not be drawn. Defaults to *False* (no direction labels). * degree_symbol (string): The symbol used to represent degrees. Defaults to u'\u00B0' which is the unicode degree symbol. * number_format (string): Format string to represent the longitude values. Defaults to 'g'. * transform_precision (float): Sets the precision (in degrees) to which transformed tick values are rounded. The default is 1e-7, and should be suitable for most use cases. To control the appearance of tick labels use the *number_format* keyword. Examples: Label longitudes from -180 to 180 on a Plate Carree projection with a central longitude of 0:: ax = plt.axes(projection=PlateCarree()) ax.set_global() ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) lon_formatter = LongitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) Label longitudes from 0 to 360 on a Plate Carree projection with a central longitude of 180:: ax = plt.axes(projection=PlateCarree(central_longitude=180)) ax.set_global() ax.set_xticks([0, 60, 120, 180, 240, 300, 360], crs=ccrs.PlateCarree()) ont_formatter = LongitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) """ super(LongitudeFormatter, self).__init__( degree_symbol=degree_symbol, number_format=number_format, transform_precision=transform_precision) self._zero_direction_labels = zero_direction_label self._dateline_direction_labels = dateline_direction_label def _apply_transform(self, value, target_proj, source_crs): return target_proj.transform_point(value, 0, source_crs)[0] def _hemisphere(self, value, value_source_crs): # Perform basic hemisphere detection. if value < 0: hemisphere = 'W' elif value > 0: hemisphere = 'E' else: hemisphere = '' # Correct for user preferences: if value == 0 and self._zero_direction_labels: # Use the original tick value to determine the hemisphere. if value_source_crs < 0: hemisphere = 'E' else: hemisphere = 'W' if value in (-180, 180) and not self._dateline_direction_labels: hemisphere = '' return hemisphere Cartopy-0.14.2/lib/cartopy/sphinxext/0000755001374000021030000000000012706121712017227 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/sphinxext/__init__.py0000644001374000021030000000143712700747662021361 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) Cartopy-0.14.2/lib/cartopy/sphinxext/gallery.py0000644001374000021030000002400212700747662021252 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import os.path import sys from cartopy.sphinxext.summarise_package import walk_module import cartopy.tests def out_of_date(original_fname, target_fname): """ Checks to see if the ``target_fname`` exists, and if so, whether the modification timestamp suggests that ``original_fname`` is newer than ``target_fname``. """ return (not os.path.exists(target_fname) or os.path.getmtime(original_fname) > os.path.getmtime(target_fname)) def same_contents(fname, contents_str): """ Checks to see if the given fname contains the contents given by ``contents_str``. The result could be used to determine if the contents need to be written to the given fname. """ if os.path.exists(fname): with open(fname, 'r') as fh: return fh.read() == contents_str return False def parent_module(module): """ Returns the direct module ascendent of the given module. For example, giving a module ``a.b.c`` would return the ``a.b`` module. If the module is a top level package, None will be returned. .. note:: Requires the __name__ attribute on the given module to be correctly defined and the parent module to be importable. >>> import numpy.ma.core >>> from cartopy.sphinxext.gallery import parent_module >>> parent_module(numpy.ma.core) # doctest: +ELLIPSIS """ result = None name = module.__name__ by_dot = name.split('.') if len(by_dot) > 1: parent_name = '.'.join(by_dot[:-1]) result = get_module(parent_name) return result def get_module(mod_name): """ Return the module instance of the given name, by importing it into the system. """ __import__(mod_name) return sys.modules[mod_name] def safe_mod_name_and_fname(module_name, ancestor_module_name): """ Returns a safe module name (for linking too) and safe filename (suffixed with ".rst") for the given module name, relative to the given ancestor module. >>> from cartopy.sphinxext.gallery import safe_mod_name_and_fname >>> safe_mod_name_and_fname('numpy.ma.core', 'numpy') ('ma-core', 'ma/core.rst') """ mod = get_module(module_name) ancestor_package = get_module(ancestor_module_name) safe_fname = os.path.relpath(os.path.splitext(mod.__file__)[0], os.path.dirname(ancestor_package.__file__)) safe_name = safe_fname.replace(os.path.sep, '-') safe_fname = safe_fname + '.rst' return safe_name, safe_fname def examples_code(examples_mod_name, source_directory, output_directory='examples'): """ Generates the rst code for the given examples module. examples_mod_name - the name of the parent (importable) module which should be recursively walked when looking for examples source_directory - the path to the top level source directory containing the rst content of the documentation output_directory - the directory for the output to be put in. Should be relative to the source_directory """ for mod_name, root_dir, fname, _ in walk_module(examples_mod_name): if fname.startswith('__init__'): continue rst, rst_fname = individual_example_rst(mod_name, examples_mod_name, output_directory) rst_fname = os.path.join(source_directory, output_directory, rst_fname) py_fname = os.path.join(source_directory, output_directory, os.path.splitext(rst_fname)[0] + '.py') if not os.path.isdir(os.path.dirname(py_fname)): os.makedirs(os.path.dirname(py_fname)) if out_of_date(os.path.join(root_dir, fname), py_fname): with open(os.path.join(root_dir, fname), 'r') as in_fh: with open(py_fname, 'w') as out_fh: for line in in_fh: # Crudely remove the __tags__ line. if line.startswith('__tags__ = '): continue out_fh.write(line) if not same_contents(rst_fname, rst): with open(rst_fname, 'w') as fh: fh.write(rst) def gallery_code(examples_mod_name): """ Returns rst code suitable for generating a html gallery using sphinx. examples_mod_name - the name of the importable (sub)module which contains the examples """ # Store a dictionary mapping tag_name to (mod_name, mod_instance, tags) examples_by_tag = {} for mod_name, _, _, _ in walk_module(examples_mod_name): if mod_name != examples_mod_name: __import__(mod_name) mod = sys.modules[mod_name] tags = getattr(mod, '__tags__', ['Miscellanea']) for tag in tags: examples_by_tag.setdefault(tag, []).append((mod_name, mod)) result = ['Gallery', '=======', 'Tags:\n', '.. container:: inline-paragraphs\n' ] examples_by_tag = sorted(iter(examples_by_tag.items()), key=lambda pair: (pair[0] == 'Miscellanea', pair[0])) for tag, _ in examples_by_tag: result.append('\t:ref:`gallery-tag-{}`\n'.format(tag)) for tag, mod_list in examples_by_tag: result.extend(['.. _gallery-tag-{}:\n'.format(tag), '{}'.format(tag), '-' * len(tag) + '\n', '.. container:: gallery_images\n']) for (mod_name, mod) in mod_list: safe_name, _ = safe_mod_name_and_fname(mod_name, examples_mod_name) # XXX The path is currently determined out of process by # the plot directive. It would be nice to figure out the # naming scheme to handle multiple plots in a single example. img_path = 'examples/{}_00_00.png'.format( mod_name.split('.')[-1]) thumb_path = 'examples/{}_00_00.thumb.png'.format( mod_name.split('.')[-1]) entry = ["|image_{}|_\n".format(safe_name), ".. |image_{}| image:: {}".format(safe_name, thumb_path), # XXX Hard-codes the html - rst cannot do nested inline # elements (very well). ".. _image_{}: examples/{}.html".format( safe_name, safe_name)] result.extend(['\n\n\t' + line for line in entry]) return '\n'.join(result) def individual_example_rst(example_mod_name, examples_mod_name, output_directory): """ Generates the rst code for the given example and suggests a sensible rst filename. example_mod_name - the name of the importable submodule which contains the individual example which is to be documented examples_mod_name - the name of the importable (sub)module which contains the examples output_directory - is a path to the examples output directory, relative to the source directory. """ mod = get_module(example_mod_name) safe_name, safe_fname = safe_mod_name_and_fname(example_mod_name, examples_mod_name) fname_base = os.path.splitext(safe_fname)[0] + '.py' example_code_fname = os.path.join(output_directory, fname_base) result = ['.. _examples-{}:\n'.format(safe_name)] if mod.__doc__: result.append(mod.__doc__ + '\n') else: result.extend(['{} example'.format(safe_name), '-' * (len(safe_name) + 8) + '\n']) result.extend(['.. plot:: {}\n'.format(example_code_fname), '.. literalinclude:: {}\n'.format(fname_base)]) return '\n'.join(result), safe_fname def gen_gallery(app): """Produces the gallery rst file.""" example_package_name = app.config.examples_package_name fname = app.config.gallery_name if example_package_name is None: raise ValueError('A config value for gallery_package_name should be ' 'defined.') outdir = app.builder.srcdir fname = os.path.join(outdir, fname) gallery_rst = gallery_code(example_package_name) if not same_contents(fname, gallery_rst): with open(fname, 'w') as fh: fh.write(gallery_rst) def gen_examples(app): """Produces the examples directory.""" example_package_name = app.config.examples_package_name source_dir = app.builder.srcdir examples_code(example_package_name, source_dir, 'examples') @cartopy.tests.not_a_nose_fixture def setup(app): app.connect('builder-inited', gen_gallery) app.connect('builder-inited', gen_examples) # Allow users to define a config value to determine the name of the # gallery rst file (with file extension included) app.add_config_value('gallery_name', 'gallery.rst', 'env') # Allow users to define a config value to determine the name of the # importable examples (sub)package app.add_config_value('examples_package_name', None, 'env') Cartopy-0.14.2/lib/cartopy/sphinxext/summarise_package.py0000644001374000021030000001654712700747662023312 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import inspect import itertools import os import sys import warnings import six import cartopy.tests def walk_module(mod_name, exclude_folders=None): """ Recursively walks the given module name. Returns: A generator of:: (fully_qualified_import_name, root_directory_of_subpackage, fname_in_root_directory, sub_folders_in_root_directory) """ __import__(mod_name) mod = sys.modules[mod_name] mod_dir = os.path.dirname(mod.__file__) exclude_folders = exclude_folders or [] for root, folders, files in os.walk(mod_dir): for folder in exclude_folders: try: folders.remove(folder) except ValueError: pass # only allow python packages if '__init__.py' not in files: del folders[:] continue # Sort by filename and folder name. files.sort() folders.sort() def is_py_src(fname): root, ext = os.path.splitext(fname) return ext in ('.py', '.so') files = filter(is_py_src, files) for fname in files: sub_mod_name = mod_name relpath = os.path.relpath(root, mod_dir) if relpath == '.': relpath = '' for sub_mod in [f for f in relpath.split(os.path.sep) if f]: sub_mod_name += '.' + sub_mod if fname != '__init__.py': sub_mod_name += '.' + os.path.splitext(fname)[0] yield sub_mod_name, root, fname, folders def objects_to_document(module_name): """ Creates a generator of (obj_name, obj) that the given module of the given name should document. The module name may be any importable, including submodules (e.g. ``cartopy.io``) """ try: __import__(module_name) except ImportError: warnings.warn('Failed to document {}'.format(module_name)) return [] module = sys.modules[module_name] elems = dir(module) if '__all__' in elems: document_these = [(obj, getattr(module, obj)) for obj in module.__all__] else: document_these = [(obj, getattr(module, obj)) for obj in elems if not inspect.ismodule(getattr(module, obj)) and not obj.startswith('_')] def is_from_this_module(x): return getattr(x[1], '__module__', '') == module_name document_these = filter(is_from_this_module, document_these) document_these = sorted(document_these, key=lambda x: (str(type(x[1])), not x[0].isupper(), x[0])) # allow a developer to add other things to the documentation if hasattr(module, '__document_these__'): extra_objects_to_document = tuple((obj, getattr(module, obj)) for obj in module.__document_these__) document_these = extra_objects_to_document + tuple(document_these) return document_these def main(package_name, exclude_folders=None): """ Return a string containing the rst that documents the given package name. """ result = '' mod_walk = walk_module(package_name, exclude_folders=exclude_folders) for mod, _, fname, folders in mod_walk: for folder in folders: if folder.startswith('_'): folders.remove(folder) if fname.startswith('_') and fname != '__init__.py': continue result += '\n' result += mod + '\n' result += '*' * len(mod) + '\n' result += '\n' result += '.. currentmodule:: {}\n'.format(mod) + '\n' mod_objects = objects_to_document(mod) if mod_objects: result += '.. csv-table::\n' + '\n' table_elements = itertools.cycle(('\n\t', ) + (', ', ) * 3) for table_elem, (obj_name, _) in zip(table_elements, mod_objects): result += '{}:py:obj:`{}`'.format(table_elem, obj_name) result += '\n' return result def gen_summary_rst(app): """ Creates the rst file to summarise the desired packages. """ package_names = app.config.summarise_package_names exclude_dirs = app.config.summarise_package_exclude_directories fnames = app.config.summarise_package_fnames if isinstance(package_names, six.string_types): package_names = [package_names] if package_names is None: raise ValueError('Please define a config value containing a list ' 'of packages to summarise.') if exclude_dirs is None: exclude_dirs = [None] * len(package_names) else: exception = ValueError('Please provide a list of exclude directory ' 'lists (one list for each package to ' 'summarise).') if len(exclude_dirs) != len(package_names): raise exception for exclude_dirs_individual in exclude_dirs: if isinstance(exclude_dirs_individual, six.string_types): raise exception if fnames is None: fnames = ['outline_of_{}.rst'.format(package_name) for package_name in package_names] else: if isinstance(fnames, six.string_types) or \ len(fnames) != len(package_names): raise TypeError('Please provide a list of filenames for each of ' 'the packages which are to be summarised.') outdir = app.builder.srcdir for package_name, out_fname, exclude_folders in zip(package_names, fnames, exclude_dirs): out_fpath = os.path.join(outdir, out_fname) content = main(package_name, exclude_folders=exclude_folders) with open(out_fpath, 'w') as fh: fh.write(content) @cartopy.tests.not_a_nose_fixture def setup(app): """ Defined the Sphinx application interface for the summary generation. """ app.connect('builder-inited', gen_summary_rst) # Allow users to define a config value to determine the names to summarise app.add_config_value('summarise_package_names', None, 'env') # Allow users to define a config value to determine the folders to exclude app.add_config_value('summarise_package_exclude_directories', None, 'env') # Allow users to define a config value to determine name of the output file app.add_config_value('summarise_package_fnames', None, 'env') Cartopy-0.14.2/lib/cartopy/tests/0000755001374000021030000000000012706121712016337 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/crs/0000755001374000021030000000000012706121712017126 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/crs/__init__.py0000644001374000021030000000152312700747662021254 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for specific Cartopy CRS subclasses. """ from __future__ import (absolute_import, division, print_function) Cartopy-0.14.2/lib/cartopy/tests/crs/test_albers_equal_area.py0000644001374000021030000001354612700747662024213 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for the Albers Equal Area coordinate system. """ from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestAlbersEqualArea(unittest.TestCase): def test_default(self): aea = ccrs.AlbersEqualArea() expected = ('+ellps=WGS84 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=20.0 +lat_2=50.0 +no_defs') assert_equal(aea.proj4_init, expected) assert_almost_equal(np.array(aea.x_limits), [-17702759.799178038, 17702759.799178038], decimal=0) assert_almost_equal(np.array(aea.y_limits), [-4782937.05107294, 15922623.93176938], decimal=4) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500, ellipse=None) aea = ccrs.AlbersEqualArea(globe=globe) expected = ('+a=1000 +b=500 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=20.0 +lat_2=50.0 +no_defs') assert_equal(aea.proj4_init, expected) assert_almost_equal(np.array(aea.x_limits), [-2323.47073363411, 2323.47073363411], decimal=-2) assert_almost_equal(np.array(aea.y_limits), [-572.556243423972, 2402.36176984391], decimal=10) def test_eastings(self): aea_offset = ccrs.AlbersEqualArea(false_easting=1234, false_northing=-4321) expected = ('+ellps=WGS84 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=1234 +y_0=-4321 +lat_1=20.0 +lat_2=50.0 +no_defs') assert_equal(aea_offset.proj4_init, expected) def test_standard_parallels(self): aea = ccrs.AlbersEqualArea(standard_parallels=(13, 37)) expected = ('+ellps=WGS84 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=13 +lat_2=37 +no_defs') assert_equal(aea.proj4_init, expected) aea = ccrs.AlbersEqualArea(standard_parallels=(13, )) expected = ('+ellps=WGS84 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=13 +no_defs') assert_equal(aea.proj4_init, expected) aea = ccrs.AlbersEqualArea(standard_parallels=13) expected = ('+ellps=WGS84 +proj=aea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=13 +no_defs') assert_equal(aea.proj4_init, expected) def test_sphere_transform(self): # USGS Professional Paper 1395, pg 291 globe = ccrs.Globe(semimajor_axis=1.0, semiminor_axis=1.0, ellipse=None) lat_1 = 29 + 30 / 60 lat_2 = 45 + 30 / 60 aea = ccrs.AlbersEqualArea(central_latitude=23.0, central_longitude=-96.0, standard_parallels=(lat_1, lat_2), globe=globe) geodetic = aea.as_geodetic() expected = ('+a=1.0 +b=1.0 +proj=aea +lon_0=-96.0 +lat_0=23.0 ' '+x_0=0.0 +y_0=0.0 +lat_1=29.5 +lat_2=45.5 +no_defs') assert_equal(aea.proj4_init, expected) assert_almost_equal(np.array(aea.x_limits), [-2.6525072042232, 2.6525072042232], decimal=3) assert_almost_equal(np.array(aea.y_limits), [-1.09628087472359, 2.39834724057551], decimal=10) result = aea.transform_point(-75.0, 35.0, geodetic) assert_almost_equal(result, [0.2952720, 0.2416774]) def test_ellipsoid_transform(self): # USGS Professional Paper 1395, pp 292 -- 293 globe = ccrs.Globe(semimajor_axis=6378206.4, flattening=1 - np.sqrt(1 - 0.00676866), ellipse=None) lat_1 = 29 + 30 / 60 lat_2 = 45 + 30 / 60 aea = ccrs.AlbersEqualArea(central_latitude=23.0, central_longitude=-96.0, standard_parallels=(lat_1, lat_2), globe=globe) geodetic = aea.as_geodetic() expected = ('+a=6378206.4 +f=0.003390076308689371 +proj=aea ' '+lon_0=-96.0 +lat_0=23.0 +x_0=0.0 +y_0=0.0 ' '+lat_1=29.5 +lat_2=45.5 +no_defs') assert_equal(aea.proj4_init, expected) assert_almost_equal(np.array(aea.x_limits), [-16900972.674607, 16900972.674607], decimal=-3) assert_almost_equal(np.array(aea.y_limits), [-6971893.11311231, 15298166.8919989], decimal=1) result = aea.transform_point(-75.0, 35.0, geodetic) assert_almost_equal(result, [1885472.7, 1535925.0], decimal=1) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_azimuthal_equidistant.py0000644001374000021030000002740212700747662025170 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal, assert_array_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestAzimuthalEquidistant(unittest.TestCase): def test_default(self): aeqd = ccrs.AzimuthalEquidistant() expected = ('+ellps=WGS84 +proj=aeqd +lon_0=0.0 ' '+lat_0=0.0 +x_0=0.0 +y_0=0.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-20037508.34278924, 20037508.34278924], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), [-20037508.34278924, 20037508.34278924], decimal=6) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500, ellipse=None) aeqd = ccrs.AzimuthalEquidistant(globe=globe) expected = ('+a=1000 +b=500 +proj=aeqd +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-3141.59265359, 3141.59265359], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), [-1570.796326795, 1570.796326795], decimal=6) def test_eastings(self): aeqd_offset = ccrs.AzimuthalEquidistant(false_easting=1234, false_northing=-4321) expected = ('+ellps=WGS84 +proj=aeqd +lon_0=0.0 +lat_0=0.0 ' '+x_0=1234 +y_0=-4321 +no_defs') assert_equal(aeqd_offset.proj4_init, expected) assert_almost_equal(np.array(aeqd_offset.x_limits), [-20036274.34278924, 20038742.34278924], decimal=6) assert_almost_equal(np.array(aeqd_offset.y_limits), [-20041829.34278924, 20033187.34278924], decimal=6) def test_grid(self): # USGS Professional Paper 1395, pp 196--197, Table 30 globe = ccrs.Globe(ellipse=None, semimajor_axis=1.0, semiminor_axis=1.0) aeqd = ccrs.AzimuthalEquidistant(central_latitude=0.0, central_longitude=0.0, globe=globe) geodetic = aeqd.as_geodetic() expected = ('+a=1.0 +b=1.0 +proj=aeqd +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-3.14159265, 3.14159265], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), [-3.14159265, 3.14159265], decimal=6) lats, lons = np.mgrid[0:100:10, 0:100:10] result = aeqd.transform_points(geodetic, lons.ravel(), lats.ravel()) expected_x = np.array([ [0.00000, 0.17453, 0.34907, 0.52360, 0.69813, 0.87266, 1.04720, 1.22173, 1.39626, 1.57080], [0.00000, 0.17275, 0.34546, 0.51807, 0.69054, 0.86278, 1.03472, 1.20620, 1.37704, 1.54693], [0.00000, 0.16736, 0.33454, 0.50137, 0.66762, 0.83301, 0.99719, 1.15965, 1.31964, 1.47607], [0.00000, 0.15822, 0.31607, 0.47314, 0.62896, 0.78296, 0.93436, 1.08215, 1.22487, 1.36035], [0.00000, 0.14511, 0.28959, 0.43276, 0.57386, 0.71195, 0.84583, 0.97392, 1.09409, 1.20330], [0.00000, 0.12765, 0.25441, 0.37931, 0.50127, 0.61904, 0.73106, 0.83535, 0.92935, 1.00969], [0.00000, 0.10534, 0.20955, 0.31145, 0.40976, 0.50301, 0.58948, 0.66711, 0.73343, 0.78540], [0.00000, 0.07741, 0.15362, 0.22740, 0.29744, 0.36234, 0.42056, 0.47039, 0.50997, 0.53724], [0.00000, 0.04281, 0.08469, 0.12469, 0.16188, 0.19529, 0.22399, 0.24706, 0.26358, 0.27277], [0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000], ]).ravel() assert_almost_equal(result[:, 0], expected_x, decimal=5) expected_y = np.array([ [0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000], [0.17453, 0.17541, 0.17810, 0.18270, 0.18943, 0.19859, 0.21067, 0.22634, 0.24656, 0.27277], [0.34907, 0.35079, 0.35601, 0.36497, 0.37803, 0.39579, 0.41910, 0.44916, 0.48772, 0.53724], [0.52360, 0.52606, 0.53355, 0.54634, 0.56493, 0.59010, 0.62291, 0.66488, 0.71809, 0.78540], [0.69813, 0.70119, 0.71046, 0.72626, 0.74912, 0.77984, 0.81953, 0.86967, 0.93221, 1.00969], [0.87266, 0.87609, 0.88647, 0.90408, 0.92938, 0.96306, 1.00602, 1.05942, 1.12464, 1.20330], [1.04720, 1.05068, 1.06119, 1.07891, 1.10415, 1.13733, 1.17896, 1.22963, 1.28993, 1.36035], [1.22173, 1.22481, 1.23407, 1.24956, 1.27137, 1.29957, 1.33423, 1.37533, 1.42273, 1.47607], [1.39626, 1.39829, 1.40434, 1.41435, 1.42823, 1.44581, 1.46686, 1.49104, 1.51792, 1.54693], [1.57080, 1.57080, 1.57080, 1.57080, 1.57080, 1.57080, 1.57080, 1.57080, 1.57080, 1.57080], ]).ravel() assert_almost_equal(result[:, 1], expected_y, decimal=5) def test_sphere_transform(self): # USGS Professional Paper 1395, pg 337 globe = ccrs.Globe(ellipse=None, semimajor_axis=3.0, semiminor_axis=3.0) aeqd = ccrs.AzimuthalEquidistant(central_latitude=40.0, central_longitude=-100.0, globe=globe) geodetic = aeqd.as_geodetic() expected = ('+a=3.0 +b=3.0 +proj=aeqd +lon_0=-100.0 +lat_0=40.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-9.42477796, 9.42477796], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), [-9.42477796, 9.42477796], decimal=6) result = aeqd.transform_point(100.0, -20.0, geodetic) assert_array_almost_equal(result, [-5.8311398, 5.5444634]) def test_ellipsoid_polar_transform(self): # USGS Professional Paper 1395, pp 338--339 globe = ccrs.Globe(ellipse=None, semimajor_axis=6378388.0, flattening=1 - np.sqrt(1 - 0.00672267)) aeqd = ccrs.AzimuthalEquidistant(central_latitude=90.0, central_longitude=-100.0, globe=globe) geodetic = aeqd.as_geodetic() expected = ('+a=6378388.0 +f=0.003367003355798981 +proj=aeqd ' '+lon_0=-100.0 +lat_0=90.0 +x_0=0.0 +y_0=0.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-20038296.88254529, 20038296.88254529], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), # TODO: This is wrong. Globe.semiminor_axis does # not account for flattening. # [-19970827.86969727, 19970827.86969727] [-20038296.88254529, 20038296.88254529], decimal=6) result = aeqd.transform_point(5.0, 80.0, geodetic) assert_array_almost_equal(result, [1078828.3, 289071.2], decimal=1) def test_ellipsoid_guam_transform(self): # USGS Professional Paper 1395, pp 339--340 globe = ccrs.Globe(ellipse=None, semimajor_axis=6378206.4, flattening=1 - np.sqrt(1 - 0.00676866)) lat_0 = 13 + (28 + 20.87887 / 60) / 60 lon_0 = 144 + (44 + 55.50254 / 60) / 60 aeqd = ccrs.AzimuthalEquidistant(central_latitude=lat_0, central_longitude=lon_0, false_easting=50000.0, false_northing=50000.0, globe=globe) geodetic = aeqd.as_geodetic() expected = ('+a=6378206.4 +f=0.003390076308689371 +proj=aeqd ' '+lon_0=144.7487507055556 +lat_0=13.47246635277778 ' '+x_0=50000.0 +y_0=50000.0 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-19987726.36931940, 20087726.36931940], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), # TODO: This is wrong. Globe.semiminor_axis does # not account for flattening. # [-19919796.94787477, 20019796.94787477] [-19987726.36931940, 20087726.36931940], decimal=6) pt_lat = 13 + (20 + 20.53846 / 60) / 60 pt_lon = 144 + (38 + 7.19265 / 60) / 60 result = aeqd.transform_point(pt_lon, pt_lat, geodetic) # The paper uses an approximation, so we cannot match it exactly, # hence, decimal=1, not 2. assert_array_almost_equal(result, [37712.48, 35242.00], decimal=1) def test_ellipsoid_micronesia_transform(self): # USGS Professional Paper 1395, pp 340--341 globe = ccrs.Globe(ellipse=None, semimajor_axis=6378206.4, flattening=1 - np.sqrt(1 - 0.00676866)) lat_0 = 15 + (11 + 5.6830 / 60) / 60 lon_0 = 145 + (44 + 29.9720 / 60) / 60 aeqd = ccrs.AzimuthalEquidistant(central_latitude=lat_0, central_longitude=lon_0, false_easting=28657.52, false_northing=67199.99, globe=globe) geodetic = aeqd.as_geodetic() expected = ('+a=6378206.4 +f=0.003390076308689371 +proj=aeqd ' '+lon_0=145.7416588888889 +lat_0=15.18491194444444 ' '+x_0=28657.52 +y_0=67199.99000000001 +no_defs') assert_equal(aeqd.proj4_init, expected) assert_almost_equal(np.array(aeqd.x_limits), [-20009068.84931940, 20066383.88931940], decimal=6) assert_almost_equal(np.array(aeqd.y_limits), # TODO: This is wrong. Globe.semiminor_axis does # not account for flattening. # [-19902596.95787477, 20036996.93787477] [-19970526.37931940, 20104926.35931940], decimal=6) pt_lat = 15 + (14 + 47.4930 / 60) / 60 pt_lon = 145 + (47 + 34.9080 / 60) / 60 result = aeqd.transform_point(pt_lon, pt_lat, geodetic) assert_array_almost_equal(result, [34176.20, 74017.88], decimal=2) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_geostationary.py0000644001374000021030000000563612700747662023455 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for the Geostationary projection. """ from __future__ import (absolute_import, division, print_function) import unittest from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestGeostationary(unittest.TestCase): def check_proj4_params(self, crs, expected): pro4_params = sorted(crs.proj4_init.split(' +')) assert_equal(expected, pro4_params) def test_default(self): geos = ccrs.Geostationary() expected = ['+ellps=WGS84', 'h=35785831', 'lat_0=0', 'lon_0=0.0', 'no_defs', 'proj=geos', 'units=m', 'x_0=0', 'y_0=0'] self.check_proj4_params(geos, expected) assert_almost_equal(geos.boundary.bounds, (-5372584.78443894, -5372584.78443894, 5372584.78443894, 5372584.78443894), decimal=4) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=10000, semiminor_axis=5000, ellipse=None) geos = ccrs.Geostationary(satellite_height=50000, globe=globe) expected = ['+a=10000', 'b=5000', 'h=50000', 'lat_0=0', 'lon_0=0.0', 'no_defs', 'proj=geos', 'units=m', 'x_0=0', 'y_0=0'] self.check_proj4_params(geos, expected) assert_almost_equal(geos.boundary.bounds, (-8257.4338, -4532.9943, 8257.4338, 4532.9943), decimal=4) def test_eastings(self): geos = ccrs.Geostationary(false_easting=5000000, false_northing=-125000,) expected = ['+ellps=WGS84', 'h=35785831', 'lat_0=0', 'lon_0=0.0', 'no_defs', 'proj=geos', 'units=m', 'x_0=5000000', 'y_0=-125000'] self.check_proj4_params(geos, expected) assert_almost_equal(geos.boundary.bounds, (-372584.78443894, -5497584.78443894, 10372584.78443894, 5247584.78443894), decimal=4) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_lambert_azimuthal_equal_area.py0000644001374000021030000000547112700747662026445 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestLambertAzimuthalEqualArea(unittest.TestCase): def test_default(self): crs = ccrs.LambertAzimuthalEqualArea() expected = ('+ellps=WGS84 +proj=laea +lon_0=0.0 ' '+lat_0=0.0 +x_0=0.0 +y_0=0.0 +no_defs') assert_equal(crs.proj4_init, expected) assert_almost_equal(np.array(crs.x_limits), [-12755636.1863, 12755636.1863], decimal=4) assert_almost_equal(np.array(crs.y_limits), [-12727770.598700099, 12727770.598700099], decimal=4) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500, ellipse=None) crs = ccrs.LambertAzimuthalEqualArea(globe=globe) expected = ('+a=1000 +b=500 +proj=laea +lon_0=0.0 +lat_0=0.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(crs.proj4_init, expected) assert_almost_equal(np.array(crs.x_limits), [-1999.9, 1999.9], decimal=1) assert_almost_equal(np.array(crs.y_limits), [-1380.17298647, 1380.17298647], decimal=4) def test_offset(self): crs = ccrs.LambertAzimuthalEqualArea() crs_offset = ccrs.LambertAzimuthalEqualArea(false_easting=1234, false_northing=-4321) expected = ('+ellps=WGS84 +proj=laea +lon_0=0.0 +lat_0=0.0 ' '+x_0=1234 +y_0=-4321 +no_defs') assert_equal(crs_offset.proj4_init, expected) assert_equal(tuple(np.array(crs.x_limits) + 1234), crs_offset.x_limits) assert_equal(tuple(np.array(crs.y_limits) - 4321), crs_offset.y_limits) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_lambert_conformal.py0000644001374000021030000001031412700747662024240 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) from numpy.testing import assert_array_almost_equal from nose.tools import assert_equal, assert_not_equal, assert_true import unittest import cartopy.crs as ccrs def test_defaults(): crs = ccrs.LambertConformal() assert_equal(crs.proj4_init, ('+ellps=WGS84 +proj=lcc +lon_0=-96.0 ' '+lat_0=39.0 +x_0=0.0 +y_0=0.0 +lat_1=33 ' '+lat_2=45 +no_defs')) def test_default_with_cutoff(): crs = ccrs.LambertConformal(cutoff=-80) crs2 = ccrs.LambertConformal(cutoff=-80) default = ccrs.LambertConformal() assert_equal(crs.proj4_init, ('+ellps=WGS84 +proj=lcc +lon_0=-96.0 ' '+lat_0=39.0 +x_0=0.0 +y_0=0.0 +lat_1=33 ' '+lat_2=45 +no_defs')) # Check the behaviour of !=, == and (not ==) for the different cutoffs. assert_equal(crs, crs2) assert_true(crs != default) assert_not_equal(crs, default) assert_not_equal(hash(crs), hash(default)) assert_equal(hash(crs), hash(crs2)) assert_array_almost_equal(crs.y_limits, (-49788019.81831982, 30793476.08487709)) def test_specific_lambert(): # This projection comes from EPSG Projection 3034 - ETRS89 / ETRS-LCC. crs = ccrs.LambertConformal(central_longitude=10, standard_parallels=(35, 65), central_latitude=52, false_easting=4000000, false_northing=2800000, globe=ccrs.Globe(ellipse='GRS80')) assert_equal(crs.proj4_init, ('+ellps=GRS80 +proj=lcc +lon_0=10 ' '+lat_0=52 +x_0=4000000 +y_0=2800000 ' '+lat_1=35 +lat_2=65 +no_defs')) class Test_LambertConformal_standard_parallels(unittest.TestCase): def test_single_value(self): crs = ccrs.LambertConformal(standard_parallels=[1.]) assert_equal(crs.proj4_init, ('+ellps=WGS84 +proj=lcc +lon_0=-96.0 ' '+lat_0=39.0 +x_0=0.0 +y_0=0.0 ' '+lat_1=1.0 +no_defs')) def test_no_parallel(self): with self.assertRaisesRegexp(ValueError, '1 or 2 standard parallels'): ccrs.LambertConformal(standard_parallels=[]) def test_too_many_parallel(self): with self.assertRaisesRegexp(ValueError, '1 or 2 standard parallels'): ccrs.LambertConformal(standard_parallels=[1, 2, 3]) def test_single_spole(self): s_pole_crs = ccrs.LambertConformal(standard_parallels=[-1.]) assert_array_almost_equal(s_pole_crs.x_limits, (-19840440, 19840440.), decimal=0) assert_array_almost_equal(s_pole_crs.y_limits, (-370239953, -8191953), decimal=0) def test_single_npole(self): n_pole_crs = ccrs.LambertConformal(standard_parallels=[1.]) assert_array_almost_equal(n_pole_crs.x_limits, (-20222156, 20222156), decimal=0) assert_array_almost_equal(n_pole_crs.y_limits, (-8164817, 360848720), decimal=0) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_mercator.py0000644001374000021030000000521212700747662022367 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest from numpy.testing import assert_almost_equal from nose.tools import assert_equal, assert_true, assert_not_equal import cartopy.crs as ccrs def test_default(): crs = ccrs.Mercator() assert_equal(crs.proj4_init, ('+ellps=WGS84 +proj=merc +lon_0=0.0 +k=1 ' '+units=m +no_defs')) assert_almost_equal(crs.boundary.bounds, [-20037508, -15496571, 20037508, 18764656], decimal=0) def test_eccentric_globe(): globe = ccrs.Globe(semimajor_axis=10000, semiminor_axis=5000, ellipse=None) crs = ccrs.Mercator(globe=globe, min_latitude=-40, max_latitude=40) assert_equal(crs.proj4_init, ('+a=10000 +b=5000 +proj=merc +lon_0=0.0 ' '+k=1 +units=m +no_defs')) assert_almost_equal(crs.boundary.bounds, [-31415.93, -2190.5, 31415.93, 2190.5], decimal=2) assert_almost_equal(crs.x_limits, [-31415.93, 31415.93], decimal=2) assert_almost_equal(crs.y_limits, [-2190.5, 2190.5], decimal=2) def test_equality(): default = ccrs.Mercator() crs = ccrs.Mercator(min_latitude=0) crs2 = ccrs.Mercator(min_latitude=0) # Check the == and != operators. assert_equal(crs, crs2) assert_not_equal(crs, default) assert_true(crs != default) assert_not_equal(hash(crs), hash(default)) assert_equal(hash(crs), hash(crs2)) def test_central_longitude(): cl = 10.0 crs = ccrs.Mercator(central_longitude=cl) proj4_str = ('+ellps=WGS84 +proj=merc +lon_0={} +k=1 ' '+units=m +no_defs'.format(cl)) assert_equal(crs.proj4_init, proj4_str) assert_almost_equal(crs.boundary.bounds, [-20037508, -15496570, 20037508, 18764656], decimal=0) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_robinson.py0000644001374000021030000000751112705644356022411 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . ''' Tests for Robinson projection. For now, mostly tests the workaround for a specific problem. Problem report in : https://github.com/SciTools/cartopy/issues/23 Fix covered in : https://github.com/SciTools/cartopy/pull/277 ''' from __future__ import (absolute_import, division, print_function) import sys import unittest from nose.tools import assert_true, assert_false, assert_equal import numpy as np from numpy.testing import assert_array_almost_equal as assert_arr_almost_eq import cartopy.crs as ccrs _NAN = float('nan') _CRS_PC = ccrs.PlateCarree() _CRS_ROB = ccrs.Robinson() # Increase tolerance if using older proj.4 releases _TOL = -1 if ccrs.PROJ4_VERSION < (4, 9) else 7 def test_transform_point(): # this way has always worked result = _CRS_ROB.transform_point(35.0, 70.0, _CRS_PC) assert_arr_almost_eq(result, (2376187.27182751, 7275317.81573085), _TOL) # this always did something, but result has altered result = _CRS_ROB.transform_point(_NAN, 70.0, _CRS_PC) assert_true(np.all(np.isnan(result))) # this used to crash + is now fixed result = _CRS_ROB.transform_point(35.0, _NAN, _CRS_PC) assert_true(np.all(np.isnan(result))) def test_transform_points(): # these always worked result = _CRS_ROB.transform_points(_CRS_PC, np.array([35.0]), np.array([70.0])) assert_arr_almost_eq(result, [[2376187.27182751, 7275317.81573085, 0]], _TOL) result = _CRS_ROB.transform_points(_CRS_PC, np.array([35.0]), np.array([70.0]), np.array([0.0])) assert_arr_almost_eq(result, [[2376187.27182751, 7275317.81573085, 0]], _TOL) # this always did something, but result has altered result = _CRS_ROB.transform_points(_CRS_PC, np.array([_NAN]), np.array([70.0])) assert_true(np.all(np.isnan(result))) # this used to crash + is now fixed result = _CRS_ROB.transform_points(_CRS_PC, np.array([35.0]), np.array([_NAN])) assert_true(np.all(np.isnan(result))) # multipoint case x = np.array([10.0, 21.0, 0.0, 77.7, _NAN, 0.0]) y = np.array([10.0, _NAN, 10.0, 77.7, 55.5, 0.0]) z = np.array([10.0, 0.0, 0.0, _NAN, 55.5, 0.0]) expect_result = np.array( [[9.40422591e+05, 1.06952091e+06, 1.00000000e+01], [11.1, 11.2, 11.3], [0.0, 1069520.91213902, 0.0], [22.1, 22.2, 22.3], [33.1, 33.2, 33.3], [0.0, 0.0, 0.0]]) result = _CRS_ROB.transform_points(_CRS_PC, x, y, z) assert_equal(result.shape, (6, 3)) assert_true(np.all(np.isnan(result[[1, 3, 4], :]))) result[[1, 3, 4], :] = expect_result[[1, 3, 4], :] assert_false(np.any(np.isnan(result))) assert_true(np.allclose(result, expect_result)) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_rotated_geodetic.py0000644001374000021030000000312512700747662024061 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for the Transverse Mercator projection, including OSGB and OSNI. """ from __future__ import (absolute_import, division, print_function) import unittest from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestRotatedPole(unittest.TestCase): def check_proj4_params(self, crs, expected): pro4_params = sorted(crs.proj4_init.split(' +')) assert_equal(expected, pro4_params) def test_default(self): geos = ccrs.RotatedPole(60, 50, 80) expected = ['+ellps=WGS84', 'lon_0=240', 'no_defs', 'o_lat_p=50', 'o_lon_p=80', 'o_proj=latlon', 'proj=ob_tran', 'to_meter=0.0174532925199433'] self.check_proj4_params(geos, expected) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_rotated_pole.py0000644001374000021030000000312712700747662023237 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for the Rotated Geodetic coordinate system. """ from __future__ import (absolute_import, division, print_function) import unittest from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestRotatedGeodetic(unittest.TestCase): def check_proj4_params(self, crs, expected): pro4_params = sorted(crs.proj4_init.split(' +')) assert_equal(expected, pro4_params) def test_default(self): geos = ccrs.RotatedGeodetic(30, 15, 27) expected = ['+datum=WGS84', 'ellps=WGS84', 'lon_0=210', 'no_defs', 'o_lat_p=15', 'o_lon_p=27', 'o_proj=latlon', 'proj=ob_tran', 'to_meter=0.0174532925199433'] self.check_proj4_params(geos, expected) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_sinusoidal.py0000644001374000021030000000673212700747662022735 0ustar itpeavd00000000000000# (C) British Crown Copyright 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestSinusoidal(unittest.TestCase): def test_default(self): crs = ccrs.Sinusoidal() expected = ('+ellps=WGS84 +proj=sinu +lon_0=0.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(crs.proj4_init, expected) assert_almost_equal(np.array(crs.x_limits), [-20037508.3428, 20037508.3428], decimal=4) assert_almost_equal(np.array(crs.y_limits), [-10001965.7293, 10001965.7293], decimal=4) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500, ellipse=None) crs = ccrs.Sinusoidal(globe=globe) expected = ('+a=1000 +b=500 +proj=sinu +lon_0=0.0 +x_0=0.0 ' '+y_0=0.0 +no_defs') assert_equal(crs.proj4_init, expected) assert_almost_equal(np.array(crs.x_limits), [-3141.59, 3141.59], decimal=2) assert_almost_equal(np.array(crs.y_limits), [-1216.60, 1216.60], decimal=2) def test_offset(self): crs = ccrs.Sinusoidal() crs_offset = ccrs.Sinusoidal(false_easting=1234, false_northing=-4321) expected = ('+ellps=WGS84 +proj=sinu +lon_0=0.0 +x_0=1234 ' '+y_0=-4321 +no_defs') assert_equal(crs_offset.proj4_init, expected) assert_equal(tuple(np.array(crs.x_limits) + 1234), crs_offset.x_limits) assert_equal(tuple(np.array(crs.y_limits) - 4321), crs_offset.y_limits) def test_MODIS(self): # Testpoints verified with MODLAND Tile Calculator # http://landweb.nascom.nasa.gov/cgi-bin/developer/tilemap.cgi # Settings: Sinusoidal, Global map coordinates, Forward mapping crs = ccrs.Sinusoidal.MODIS lons = np.array([-180, -50, 40, 180]) lats = np.array([-89.999, 30, 20, 89.999]) expected_x = np.array([-349.33, -4814886.99, 4179566.79, 349.33]) expected_y = np.array([-10007443.48, 3335851.56, 2223901.04, 10007443.48]) assert_almost_equal(crs.transform_points(crs.as_geodetic(), lons, lats), np.c_[expected_x, expected_y, [0, 0, 0, 0]], decimal=2) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_stereographic.py0000644001374000021030000000633012700747662023414 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal from nose.tools import assert_equal import cartopy.crs as ccrs class TestStereographic(unittest.TestCase): def test_default(self): stereo = ccrs.Stereographic() expected = ('+ellps=WGS84 +proj=stere +lat_0=0.0 ' '+lon_0=0.0 +x_0=0.0 +y_0=0.0 +no_defs') assert_equal(stereo.proj4_init, expected) assert_almost_equal(np.array(stereo.x_limits), [-5e7, 5e7], decimal=4) assert_almost_equal(np.array(stereo.y_limits), [-5e7, 5e7], decimal=4) def test_eccentric_globe(self): globe = ccrs.Globe(semimajor_axis=1000, semiminor_axis=500, ellipse=None) stereo = ccrs.Stereographic(globe=globe) expected = ('+a=1000 +b=500 +proj=stere +lat_0=0.0 +lon_0=0.0 ' '+x_0=0.0 +y_0=0.0 +no_defs') assert_equal(stereo.proj4_init, expected) # The limits in this test are sensible values, but are by no means # a "correct" answer - they mean that plotting the crs results in a # reasonable map. assert_almost_equal(np.array(stereo.x_limits), [-7839.27971444, 7839.27971444], decimal=4) assert_almost_equal(np.array(stereo.y_limits), [-3932.82587779, 3932.82587779], decimal=4) def test_true_scale(self): # The "true_scale_latitude" parameter to Stereographic appears # meaningless. This test just ensures that the correct proj4 # string is being created. (#339) stereo = ccrs.Stereographic(true_scale_latitude=10) expected = ('+ellps=WGS84 +proj=stere +lat_0=0.0 +lon_0=0.0 ' '+x_0=0.0 +y_0=0.0 +lat_ts=10 +no_defs') assert_equal(stereo.proj4_init, expected) def test_eastings(self): stereo = ccrs.Stereographic() stereo_offset = ccrs.Stereographic(false_easting=1234, false_northing=-4321) expected = ('+ellps=WGS84 +proj=stere +lat_0=0.0 +lon_0=0.0 ' '+x_0=1234 +y_0=-4321 +no_defs') assert_equal(stereo_offset.proj4_init, expected) assert_equal(tuple(np.array(stereo.x_limits) + 1234), stereo_offset.x_limits) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/crs/test_transverse_mercator.py0000644001374000021030000001113012700747662024637 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Tests for the Transverse Mercator projection, including OSGB and OSNI. """ from __future__ import (absolute_import, division, print_function) import unittest import numpy as np import numpy.testing import cartopy.crs as ccrs class TestTransverseMercator(unittest.TestCase): def setUp(self): self.point_a = (-3.474083, 50.727301) self.point_b = (0.5, 50.5) self.src_crs = ccrs.PlateCarree() def test_default(self): proj = ccrs.TransverseMercator() res = proj.transform_point(*self.point_a, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (-245269.53180633, 5627508.74354959)) res = proj.transform_point(*self.point_b, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (35474.63566645, 5596583.41949901)) def test_osgb_vals(self): proj = ccrs.TransverseMercator(central_longitude=-2, central_latitude=49, scale_factor=0.9996012717, false_easting=400000, false_northing=-100000, globe=ccrs.Globe(datum='OSGB36', ellipse='airy')) res = proj.transform_point(*self.point_a, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (295971.28667707, 93064.27666368)) res = proj.transform_point(*self.point_b, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (577274.98380140, 69740.49227181)) def test_nan(self): proj = ccrs.TransverseMercator() res = proj.transform_point(0.0, float('nan'), src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) res = proj.transform_point(float('nan'), 0.0, src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) class TestOSGB(unittest.TestCase): def setUp(self): self.point_a = (-3.474083, 50.727301) self.point_b = (0.5, 50.5) self.src_crs = ccrs.PlateCarree() self.nan = float('nan') def test_default(self): proj = ccrs.OSGB() res = proj.transform_point(*self.point_a, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (295971.28667707, 93064.27666368)) res = proj.transform_point(*self.point_b, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (577274.98380140, 69740.49227181)) def test_nan(self): proj = ccrs.OSGB() res = proj.transform_point(0.0, float('nan'), src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) res = proj.transform_point(float('nan'), 0.0, src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) class TestOSNI(unittest.TestCase): def setUp(self): self.point_a = (-6.826286, 54.725116) self.src_crs = ccrs.PlateCarree() self.nan = float('nan') def test_default(self): proj = ccrs.OSNI() res = proj.transform_point(*self.point_a, src_crs=self.src_crs) np.testing.assert_array_almost_equal(res, (275614.87105610, 386984.15347340)) def test_nan(self): proj = ccrs.OSNI() res = proj.transform_point(0.0, float('nan'), src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) res = proj.transform_point(float('nan'), 0.0, src_crs=self.src_crs) self.assertTrue(np.all(np.isnan(res))) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/io/0000755001374000021030000000000012706121712016746 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/io/__init__.py0000644001374000021030000000143712700747662021100 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) Cartopy-0.14.2/lib/cartopy/tests/io/test_downloaders.py0000644001374000021030000001753112700747662022723 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import contextlib import os import shutil import tempfile import warnings from nose.tools import assert_equal, assert_raises import cartopy import cartopy.io as cio from cartopy.io.shapereader import NEShpDownloader from cartopy.tests.mpl.test_caching import CallCounter def test_Downloader_data(): di = cio.Downloader('https://testing.com/{category}/{name}.zip', os.path.join('{data_dir}', '{category}', 'shape.shp'), '/project/foobar/{category}/sample.shp') replacement_dict = {'category': 'example', 'name': 'test', 'data_dir': os.path.join('/wibble', 'foo', 'bar')} assert_equal(di.url(replacement_dict), 'https://testing.com/example/test.zip') assert_equal(di.target_path(replacement_dict), os.path.join('/wibble', 'foo', 'bar', 'example', 'shape.shp') ) assert_equal(di.pre_downloaded_path(replacement_dict), '/project/foobar/example/sample.shp' ) @contextlib.contextmanager def config_replace(replacement_dict): """ Provides a context manager to replace the ``cartopy.config['downloaders']`` dict with the given dictionary. Great for testing purposes! """ downloads_orig = cartopy.config['downloaders'] cartopy.config['downloaders'] = replacement_dict yield cartopy.config['downloaders'] = downloads_orig @contextlib.contextmanager def download_to_temp(): """ Context manager which defaults the "data_dir" to a temporary directory which is automatically cleaned up on exit. """ old_downloads_dict = cartopy.config['downloaders'].copy() old_dir = cartopy.config['data_dir'] tmp_dir = tempfile.mkdtemp(suffix='_cartopy_data') cartopy.config['data_dir'] = tmp_dir try: yield tmp_dir cartopy.config['downloaders'] = old_downloads_dict cartopy.config['data_dir'] = old_dir finally: shutil.rmtree(tmp_dir) def test_from_config(): generic_url = 'https://example.com/generic_ne/{name}.zip' land_downloader = cio.Downloader(generic_url, '', '') generic_ne_downloader = cio.Downloader(generic_url, '', '') ocean_spec = ('shapefile', 'natural_earth', '110m', 'physical', 'ocean') land_spec = ('shapefile', 'natural_earth', '110m', 'physical', 'land') generic_spec = ('shapefile', 'natural_earth') target_config = {land_spec: land_downloader, generic_spec: generic_ne_downloader, } with config_replace(target_config): # ocean spec is not explicitly in the config, but a subset of it is, # so check that an appropriate downloader is returned r = cio.Downloader.from_config(ocean_spec) # check the resulting download item produces a sensible url. assert_equal(r.url({'name': 'ocean'}), 'https://example.com/generic_ne/ocean.zip') downloaders = cio.config['downloaders'] r = cio.Downloader.from_config(land_spec) assert r is land_downloader def test_downloading_simple_ascii(): # downloads a file from the Google APIs. (very high uptime and file will # always be there - if this goes down, most of the internet would break!) # to test the downloading mechanisms. file_url = 'https://ajax.googleapis.com/ajax/libs/jquery/1.8.2/{name}.js' format_dict = {'name': 'jquery'} with download_to_temp() as tmp_dir: target_template = os.path.join(tmp_dir, '{name}.txt') tmp_fname = target_template.format(**format_dict) dnld_item = cio.Downloader(file_url, target_template) assert_equal(dnld_item.target_path(format_dict), tmp_fname) with warnings.catch_warnings(record=True) as w: assert_equal(dnld_item.path(format_dict), tmp_fname) assert len(w) == 1, ('Expected a single download warning to be ' 'raised. Got {}.'.format(len(w))) assert issubclass(w[0].category, cio.DownloadWarning) with open(tmp_fname, 'r') as fh: _ = fh.readline() assert_equal(" * jQuery JavaScript Library v1.8.2\n", fh.readline()) # check that calling path again doesn't try re-downloading with CallCounter(dnld_item, 'acquire_resource') as counter: assert_equal(dnld_item.path(format_dict), tmp_fname) assert counter.count == 0, 'Item was re-downloaded.' def test_natural_earth_downloader(): # downloads a file to a temporary location, and uses that temporary # location, then: # * Checks that the file is only downloaded once even when calling # multiple times # * Checks that shapefiles have all the necessary files when downloaded # * Checks that providing a path in a download item gets used rather # than triggering another download tmp_dir = tempfile.mkdtemp() shp_path_template = os.path.join(tmp_dir, '{category}_{resolution}_{name}.shp') # picking a small-ish file to speed up download times, the file itself # isn't important - it is the download mechanism that is. format_dict = {'category': 'physical', 'name': 'rivers_lake_centerlines', 'resolution': '110m'} try: dnld_item = NEShpDownloader(target_path_template=shp_path_template) # check that the file gets downloaded the first time path is called with CallCounter(dnld_item, 'acquire_resource') as counter: shp_path = dnld_item.path(format_dict) assert counter.count == 1, 'Item not downloaded.' assert_equal(shp_path_template.format(**format_dict), shp_path) # check that calling path again doesn't try re-downloading with CallCounter(dnld_item, 'acquire_resource') as counter: assert_equal(dnld_item.path(format_dict), shp_path) assert counter.count == 0, 'Item was re-downloaded.' # check that we have the shp and the shx exts = ['.shp', '.shx'] for ext in exts: stem = os.path.splitext(shp_path)[0] msg = "Shapefile's {0} file doesn't exist in {1}{0}".format(ext, stem) assert os.path.exists(stem + ext), msg # check that providing a pre downloaded path actually works pre_dnld = NEShpDownloader(target_path_template='/not/a/real/file.txt', pre_downloaded_path_template=shp_path ) # check that the pre_dnld downloader doesn't re-download, but instead # uses the path of the previously downloaded item with CallCounter(pre_dnld, 'acquire_resource') as counter: assert_equal(pre_dnld.path(format_dict), shp_path) assert counter.count == 0, 'Aquire resource called more than once.' finally: shutil.rmtree(tmp_dir) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/io/test_ogc_clients.py0000644001374000021030000002143212700747662022666 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import cartopy.io.ogc_clients as ogc from cartopy.io.ogc_clients import _OWSLIB_AVAILABLE try: from owslib.wfs import WebFeatureService from owslib.wms import WebMapService from owslib.wmts import ContentMetadata, WebMapTileService except ImportError: WebMapService = None ContentMetadata = None WebMapTileService = None import unittest import cartopy.crs as ccrs try: from unittest import mock except ImportError: import mock import numpy as np RESOLUTION = (30, 30) @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') class test_WMSRasterSource(unittest.TestCase): URI = 'http://vmap0.tiles.osgeo.org/wms/vmap0' layer = 'basic' layers = ['basic', 'ocean'] projection = ccrs.PlateCarree() def test_string_service(self): source = ogc.WMSRasterSource(self.URI, self.layer) self.assertIsInstance(source.service, WebMapService) self.assertIsInstance(source.layers, list) self.assertEqual(source.layers, [self.layer]) def test_wms_service_instance(self): service = WebMapService(self.URI) source = ogc.WMSRasterSource(service, self.layer) self.assertIs(source.service, service) def test_multiple_layers(self): source = ogc.WMSRasterSource(self.URI, self.layers) self.assertEqual(source.layers, self.layers) def test_no_layers(self): msg = 'One or more layers must be defined.' with self.assertRaisesRegexp(ValueError, msg): ogc.WMSRasterSource(self.URI, []) def test_extra_kwargs_empty(self): source = ogc.WMSRasterSource(self.URI, self.layer, getmap_extra_kwargs={}) self.assertEqual(source.getmap_extra_kwargs, {}) def test_extra_kwargs_None(self): source = ogc.WMSRasterSource(self.URI, self.layer, getmap_extra_kwargs=None) self.assertEqual(source.getmap_extra_kwargs, {'transparent': True}) def test_extra_kwargs_non_empty(self): kwargs = {'another': 'kwarg'} source = ogc.WMSRasterSource(self.URI, self.layer, getmap_extra_kwargs=kwargs) self.assertEqual(source.getmap_extra_kwargs, kwargs) def test_supported_projection(self): source = ogc.WMSRasterSource(self.URI, self.layer) source.validate_projection(self.projection) def test_unsupported_projection(self): source = ogc.WMSRasterSource(self.URI, self.layer) # Patch dict of known Proj->SRS mappings so that it does # not include any of the available SRSs from the WMS. with mock.patch.dict('cartopy.io.ogc_clients._CRS_TO_OGC_SRS', {ccrs.OSGB(): 'EPSG:27700'}, clear=True): msg = 'not available' with self.assertRaisesRegexp(ValueError, msg): source.validate_projection(ccrs.Miller()) def test_fetch_img(self): source = ogc.WMSRasterSource(self.URI, self.layer) extent = [-10, 10, 40, 60] located_image, = source.fetch_raster(self.projection, extent, RESOLUTION) img = np.array(located_image.image) self.assertEqual(img.shape, RESOLUTION + (4,)) # No transparency in this image. self.assertEqual(img[:, :, 3].min(), 255) self.assertEqual(extent, located_image.extent) def test_fetch_img_different_projection(self): source = ogc.WMSRasterSource(self.URI, self.layer) extent = [-570000, 5100000, 870000, 3500000] located_image, = source.fetch_raster(ccrs.Orthographic(), extent, RESOLUTION) img = np.array(located_image.image) self.assertEqual(img.shape, RESOLUTION + (4,)) def test_multi_image_result(self): source = ogc.WMSRasterSource(self.URI, self.layer) crs = ccrs.PlateCarree(central_longitude=180) extent = [-15, 25, 45, 85] located_images = source.fetch_raster(crs, extent, RESOLUTION) self.assertEqual(len(located_images), 2) def test_float_resolution(self): # The resolution (in pixels) should be cast to ints. source = ogc.WMSRasterSource(self.URI, self.layer) extent = [-570000, 5100000, 870000, 3500000] located_image, = source.fetch_raster(self.projection, extent, [19.5, 39.1]) img = np.array(located_image.image) self.assertEqual(img.shape, (40, 20, 4)) @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') class test_WMTSRasterSource(unittest.TestCase): URI = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' layer_name = 'VIIRS_CityLights_2012' projection = ccrs.PlateCarree() def test_string_service(self): source = ogc.WMTSRasterSource(self.URI, self.layer_name) self.assertIsInstance(source.wmts, WebMapTileService) self.assertIsInstance(source.layer, ContentMetadata) self.assertEqual(source.layer.name, self.layer_name) def test_wmts_service_instance(self): service = WebMapTileService(self.URI) source = ogc.WMTSRasterSource(service, self.layer_name) self.assertIs(source.wmts, service) def test_supported_projection(self): source = ogc.WMTSRasterSource(self.URI, self.layer_name) source.validate_projection(self.projection) def test_unsupported_projection(self): source = ogc.WMTSRasterSource(self.URI, self.layer_name) msg = 'Unable to find tile matrix for projection.' with self.assertRaisesRegexp(ValueError, msg): source.validate_projection(ccrs.Miller()) def test_fetch_img(self): source = ogc.WMTSRasterSource(self.URI, self.layer_name) extent = [-10, 10, 40, 60] located_image, = source.fetch_raster(self.projection, extent, RESOLUTION) img = np.array(located_image.image) self.assertEqual(img.shape, (512, 512, 4)) # No transparency in this image. self.assertEqual(img[:, :, 3].min(), 255) self.assertEqual((-180.0, 107.99999999999994, -197.99999999999994, 90.0), located_image.extent) @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') class test_WFSGeometrySource(unittest.TestCase): URI = 'https://nsidc.org/cgi-bin/atlas_south?service=WFS' typename = 'land_excluding_antarctica' native_projection = ccrs.Stereographic(central_latitude=-90, true_scale_latitude=-71) def test_string_service(self): service = WebFeatureService(self.URI) source = ogc.WFSGeometrySource(self.URI, self.typename) self.assertIsInstance(source.service, type(service)) self.assertEqual(source.features, [self.typename]) def test_wfs_service_instance(self): service = WebFeatureService(self.URI) source = ogc.WFSGeometrySource(service, self.typename) self.assertIs(source.service, service) self.assertEqual(source.features, [self.typename]) def test_default_projection(self): source = ogc.WFSGeometrySource(self.URI, self.typename) self.assertEqual(source.default_projection(), self.native_projection) def test_unsupported_projection(self): source = ogc.WFSGeometrySource(self.URI, self.typename) with self.assertRaisesRegexp(ValueError, 'Geometries are only available ' 'in projection'): source.fetch_geometries(ccrs.PlateCarree(), [-180, 180, -90, 90]) def test_fetch_geometries(self): source = ogc.WFSGeometrySource(self.URI, self.typename) # Extent covering New Zealand. extent = (-99012, 1523166, -6740315, -4589165) geoms = source.fetch_geometries(self.native_projection, extent) self.assertEqual(len(geoms), 23) if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/io/test_srtm.py0000644001374000021030000001260612700747662021365 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import warnings import numpy as np from numpy.testing import assert_array_equal import cartopy.crs as ccrs import cartopy.io.srtm from cartopy.tests.io.test_downloaders import download_to_temp def _test_srtm_retrieve(Source, read_SRTM, max_, min_, pt): # test that the download mechanism for SRTM works with download_to_temp() as tmp_dir: with warnings.catch_warnings(record=True) as w: r = Source().srtm_fname(-4, 50) assert len(w) == 1 assert issubclass(w[0].category, cartopy.io.DownloadWarning) assert r.startswith(tmp_dir), 'File not downloaded to tmp dir' img, _, _ = read_SRTM(r) # check that the data is fairly sensible msg = 'SRTM data has changed. Arbitrary value testing failed. Got {}.' assert img.max() == max_, msg.format(img.max()) assert img.min() == min_, msg.format(img.min()) assert img[-10, 12] == pt, msg.format(img[-10, 12]) def test_srtm3_retrieve(): _test_srtm_retrieve(cartopy.io.srtm.SRTM3Source, cartopy.io.srtm.read_SRTM3, 602, -34, 78) def test_srtm1_retrieve(): _test_srtm_retrieve(cartopy.io.srtm.SRTM1Source, cartopy.io.srtm.read_SRTM1, 602, -37, 50) def _test_srtm_out_of_range(Source, shape): # Somewhere over the pacific the elevation should be 0. img, _, _ = Source().combined(120, 2, 2, 2) assert_array_equal(img, np.zeros(np.array(shape) * 2)) def test_srtm3_out_of_range(): _test_srtm_out_of_range(cartopy.io.srtm.SRTM3Source, (1201, 1201)) def test_srtm1_out_of_range(): _test_srtm_out_of_range(cartopy.io.srtm.SRTM1Source, (3601, 3601)) class TestSRTMSource__single_tile(unittest.TestCase): def _out_of_range(self, source): msg = 'No srtm tile found for those coordinates.' with self.assertRaisesRegexp(ValueError, msg): source.single_tile(-25, 50) def test_out_of_range3(self): self._out_of_range(cartopy.io.srtm.SRTM3Source()) def test_out_of_range1(self): self._out_of_range(cartopy.io.srtm.SRTM1Source()) def _in_range(self, source, shape): img, crs, extent = source.single_tile(-1, 50) self.assertIsInstance(img, np.ndarray) self.assertEqual(img.shape, shape) self.assertEqual(img.dtype, np.dtype('>i2')) self.assertEqual(crs, ccrs.PlateCarree()) self.assertEqual(extent, (-1, 0, 50, 51)) def test_in_range3(self): self._in_range(cartopy.io.srtm.SRTM3Source(), (1201, 1201)) def test_in_range1(self): self._in_range(cartopy.io.srtm.SRTM1Source(), (3601, 3601)) def _zeros(self, source): _, _, extent = source.single_tile(0, 50) self.assertEqual(extent, (0, 1, 50, 51)) def test_zeros3(self): self._zeros(cartopy.io.srtm.SRTM3Source()) def test_zeros1(self): self._zeros(cartopy.io.srtm.SRTM1Source()) class TestSRTMSource__combined(unittest.TestCase): def _trivial(self, source): e_img, e_crs, e_extent = source.single_tile(-3, 50) r_img, r_crs, r_extent = source.combined(-3, 50, 1, 1) assert_array_equal(e_img, r_img) self.assertEqual(e_crs, r_crs) self.assertEqual(e_extent, r_extent) def test_trivial3(self): self._trivial(cartopy.io.srtm.SRTM3Source()) def test_trivial1(self): self._trivial(cartopy.io.srtm.SRTM1Source()) def _2by2(self, source): e_img, _, e_extent = source.combined(-1, 50, 2, 1) self.assertEqual(e_extent, (-1, 1, 50, 51)) imgs = [source.single_tile(-1, 50)[0], source.single_tile(0, 50)[0]] assert_array_equal(np.hstack(imgs), e_img) def test_2by2_3(self): self._2by2(cartopy.io.srtm.SRTM3Source()) def test_2by2_1(self): self._2by2(cartopy.io.srtm.SRTM1Source()) class TestSRTM3Source_fetch_raster(unittest.TestCase): def _as_combined(self, source): e_img, e_crs, e_extent = source.combined(-1, 50, 2, 1) imgs = source.fetch_raster(ccrs.PlateCarree(), (-0.9, 0.1, 50.1, 50.999), None) self.assertEqual(len(imgs), 1) r_img, r_extent = imgs[0] self.assertEqual(e_extent, r_extent) assert_array_equal(e_img[::-1, :], r_img) def test_as_combined3(self): self._as_combined(cartopy.io.srtm.SRTM3Source()) def test_as_combined1(self): self._as_combined(cartopy.io.srtm.SRTM1Source()) if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/0000755001374000021030000000000012706121712017127 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/mpl/baseline_images/0000755001374000021030000000000012706121712022236 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/mpl/baseline_images/mpl/0000755001374000021030000000000012706121712023026 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/mpl/baseline_images/mpl/test_crs/0000755001374000021030000000000012706121712024654 5ustar itpeavd00000000000000Cartopy-0.14.2/lib/cartopy/tests/mpl/baseline_images/mpl/test_crs/lambert_conformal_south.png0000644001374000021030000027711112520135744032307 0ustar itpeavd00000000000000PNG  IHDR XvpsBIT|d pHYsaa?i IDATxwXg5tk؂%QDEc"E#5F%+vc (؃ ~?oj\Wtv fsDD@DDDDDJ """"ƒHDD(DGG#..iiiHOOmc`bboz(QlmmaaaJ}!(h4DDDѣGxybo{U^_ _ c6K)׫m}*VҥK1, DD "">`nnڵkuԁ=QdI_QBDdjKƫ W;y=Td,jB"ENOD?FA@@Ο????\t wޅ@R?I/#j5ݻTXXDC&MТE hjՂvODO, DD 11.]/"..W6mzN:]6,,,L9͛x"n޼ Z kkk4k ͛7G-иqcXZZ*Hq, DD:#mí[h`ccMhF&KLLĕ+WYn]BQbE Qܿ>>>8}4Ο?'Oo.[h5k2BN ((H[Hp=@Ѽys|pttDʕ KD, DD %%gΜ||| CCCTV ]vEѼys!Idd숯/._ ZիSNԩZn sss kBBBÇ̙3HMME ЩS'8::m۶V:*qqq8qℶ>~fffϵO>ψ@`!"? 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See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import base64 import os import glob import shutil import warnings import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.patches as mpatches import matplotlib.testing.compare as mcompare import matplotlib.tests as mtests import matplotlib._pylab_helpers as pyplot_helpers class ImageTesting(object): """ Provides a convenient class for running visual matplotlib tests. In general, this class should be used as a decorator to a test function which generates one (or more) figures. :: @ImageTesting(['simple_test']) def test_simple(): import matplotlib.pyplot as plt plt.plot(range(10)) To find out where the result and expected images reside one can create a empty ImageTesting class instance and get the paths from the :meth:`expected_path` and :meth:`result_path` methods:: >>> import os >>> import cartopy.tests.mpl >>> img_testing = cartopy.tests.mpl.ImageTesting([]) >>> exp_fname = img_testing.expected_path('', '') >>> result_fname = img_testing.result_path('', '') >>> img_test_mod_dir = os.path.dirname(cartopy.__file__) >>> print 'Result:', os.path.relpath(result_fname, img_test_mod_dir) Result: tests/mpl/output//result-.png >>> print 'Expected:', os.path.relpath(exp_fname, img_test_mod_dir) Expected: tests/mpl/baseline_images/mpl//.png .. note:: Subclasses of the ImageTesting class may decide to change the location of the expected and result images. However, the same technique for finding the locations of the images should hold true. """ #: The path where the standard ``baseline_images`` exist. root_image_results = os.path.dirname(__file__) #: The path where the images generated by the tests should go. image_output_directory = os.path.join(root_image_results, 'output') if not os.access(image_output_directory, os.W_OK): if not os.access(os.getcwd(), os.W_OK): raise IOError('Write access to a local disk is required to run ' 'image tests. Run the tests from a current working ' 'directory you have write access to to avoid this ' 'issue.') else: image_output_directory = os.path.join(os.getcwd(), 'cartopy_test_output') def __init__(self, img_names, tolerance=(0.1 if mpl.__version__ < '1.4' else 0.5)): # With matplotlib v1.3 the tolerance keyword is an RMS of the pixel # differences, as computed by matplotlib.testing.compare.calculate_rms self.img_names = img_names self.tolerance = tolerance def expected_path(self, test_name, img_name, ext='.png'): """ Return the full path (minus extension) of where the expected image should be found, given the name of the image being tested and the name of the test being run. """ expected_fname = os.path.join(self.root_image_results, 'baseline_images', 'mpl', test_name, img_name) return expected_fname + ext def result_path(self, test_name, img_name, ext='.png'): """ Return the full path (minus extension) of where the result image should be given the name of the image being tested and the name of the test being run. """ result_fname = os.path.join(self.image_output_directory, test_name, 'result-' + img_name) return result_fname + ext def run_figure_comparisons(self, figures, test_name): """ Run the figure comparisons against the ``image_names``. The number of figures passed must be equal to the number of image names in ``self.image_names``. .. note:: The figures are not closed by this method. If using the decorator version of ImageTesting, they will be closed for you. """ n_figures_msg = ('Expected %s figures (based on the number of ' 'image result filenames), but there are %s figures ' 'available. The most likely reason for this is that ' 'this test is producing too many figures, ' '(alternatively if not using ImageCompare as a ' 'decorator, it is possible that a test run prior to ' 'this one has not closed its figures).' '' % (len(self.img_names), len(figures)) ) assert len(figures) == len(self.img_names), n_figures_msg for img_name, figure in zip(self.img_names, figures): expected_path = self.expected_path(test_name, img_name, '.png') result_path = self.result_path(test_name, img_name, '.png') if not os.path.isdir(os.path.dirname(expected_path)): os.makedirs(os.path.dirname(expected_path)) if not os.path.isdir(os.path.dirname(result_path)): os.makedirs(os.path.dirname(result_path)) self.save_figure(figure, result_path) self.do_compare(result_path, expected_path, self.tolerance) def save_figure(self, figure, result_fname): """ The actual call which saves the figure. Returns nothing. May be overridden to do figure based pre-processing (such as removing text objects etc.) """ figure.savefig(result_fname) def do_compare(self, result_fname, expected_fname, tol): """ Runs the comparison of the result file with the expected file. If an RMS difference greater than ``tol`` is found an assertion error is raised with an appropriate message with the paths to the files concerned. """ if not os.path.exists(expected_fname): warnings.warn('Created image in %s' % expected_fname) shutil.copy2(result_fname, expected_fname) err = mcompare.compare_images(expected_fname, result_fname, tol=tol, in_decorator=True) if err: msg = ('Images were different (RMS: %s).\n%s %s %s\nConsider ' 'running idiff to inspect these differences.' '' % (err['rms'], err['actual'], err['expected'], err['diff'])) assert False, msg def __call__(self, test_func): """Called when the decorator is applied to a function.""" test_name = test_func.__name__ mod_name = test_func.__module__ if mod_name == '__main__': import sys fname = sys.modules[mod_name].__file__ mod_name = os.path.basename(os.path.splitext(fname)[0]) mod_name = mod_name.rsplit('.', 1)[-1] def wrapped(*args, **kwargs): orig_backend = plt.get_backend() plt.switch_backend('agg') mtests.setup() if pyplot_helpers.Gcf.figs: warnings.warn('Figures existed before running the %s %s test.' ' All figures should be closed after they run. ' 'They will be closed automatically now.' % (mod_name, test_name)) pyplot_helpers.Gcf.destroy_all() r = test_func(*args, **kwargs) fig_managers = pyplot_helpers.Gcf._activeQue figures = [manager.canvas.figure for manager in fig_managers] try: self.run_figure_comparisons(figures, test_name=mod_name) finally: for figure in figures: pyplot_helpers.Gcf.destroy_fig(figure) plt.switch_backend(orig_backend) return r # nose needs the function's name to be in the form "test_*" to # pick it up wrapped.__name__ = test_name return wrapped def failed_images_iter(): """ Return a generator of [expected, actual, diff] filenames for all failed image tests since the test output directory was created. """ baseline_img_dir = os.path.join(ImageTesting.root_image_results, 'baseline_images', 'mpl') diff_dir = os.path.join(ImageTesting.image_output_directory) baselines = sorted(glob.glob(os.path.join(baseline_img_dir, '*', '*.png'))) for expected_fname in baselines: # Get the relative path of the expected image 2 folders up. expected_rel = os.path.relpath( expected_fname, os.path.dirname(os.path.dirname(expected_fname))) result_fname = os.path.join( diff_dir, os.path.dirname(expected_rel), 'result-' + os.path.basename(expected_rel)) diff_fname = result_fname[:-4] + '-failed-diff.png' if os.path.exists(diff_fname): yield expected_fname, result_fname, diff_fname def failed_images_html(): """ Generates HTML which shows the image failures side-by-side when viewed in a web browser. """ data_uri_template = '{alt}' def image_as_base64(fname): with open(fname, "rb") as fh: return base64.b64encode(fh.read()).decode("ascii") html = ['', '', ''] for expected, actual, diff in failed_images_iter(): expected_html = data_uri_template.format( alt='expected', img=image_as_base64(expected)) actual_html = data_uri_template.format( alt='actual', img=image_as_base64(actual)) diff_html = data_uri_template.format( alt='diff', img=image_as_base64(diff)) html.extend([expected, '
', expected_html, actual_html, diff_html, '
']) html.extend(['', '']) return '\n'.join(html) def show(projection, geometry): orig_backend = matplotlib.get_backend() plt.switch_backend('tkagg') if geometry.type == 'MultiPolygon' and 1: multi_polygon = geometry for polygon in multi_polygon: import cartopy.mpl.patch as patch paths = patch.geos_to_path(polygon) for pth in paths: patch = mpatches.PathPatch(pth, edgecolor='none', lw=0, alpha=0.2) plt.gca().add_patch(patch) line_string = polygon.exterior plt.plot(*zip(*line_string.coords), marker='+', linestyle='-') elif geometry.type == 'MultiPolygon': multi_polygon = geometry for polygon in multi_polygon: line_string = polygon.exterior plt.plot(*zip(*line_string.coords), marker='+', linestyle='-') elif geometry.type == 'MultiLineString': multi_line_string = geometry for line_string in multi_line_string: plt.plot(*zip(*line_string.coords), marker='+', linestyle='-') elif geometry.type == 'LinearRing': plt.plot(*zip(*geometry.coords), marker='+', linestyle='-') if 1: # Whole map domain plt.autoscale() elif 0: # The left-hand triangle plt.xlim(-1.65e7, -1.2e7) plt.ylim(0.3e7, 0.65e7) elif 0: # The tip of the left-hand triangle plt.xlim(-1.65e7, -1.55e7) plt.ylim(0.3e7, 0.4e7) elif 1: # The very tip of the left-hand triangle plt.xlim(-1.632e7, -1.622e7) plt.ylim(0.327e7, 0.337e7) elif 1: # The tip of the right-hand triangle plt.xlim(1.55e7, 1.65e7) plt.ylim(0.3e7, 0.4e7) plt.plot(*zip(*projection.boundary.coords), marker='o', scalex=False, scaley=False, zorder=-1) plt.show() plt.switch_backend(orig_backend) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_axes.py0000644001374000021030000000564412700747662021525 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest from matplotlib.testing.decorators import cleanup import matplotlib.path as mpath import matplotlib.pyplot as plt from nose.tools import assert_equal import numpy as np import cartopy.crs as ccrs from cartopy.mpl.geoaxes import InterProjectionTransform from .test_caching import CallCounter class TestNoSpherical(unittest.TestCase): def setUp(self): self.ax = plt.axes(projection=ccrs.PlateCarree()) self.data = np.arange(12).reshape((3, 4)) def tearDown(self): plt.clf() plt.close() def test_contour(self): with self.assertRaises(ValueError): self.ax.contour(self.data, transform=ccrs.Geodetic()) def test_contourf(self): with self.assertRaises(ValueError): self.ax.contourf(self.data, transform=ccrs.Geodetic()) def test_pcolor(self): with self.assertRaises(ValueError): self.ax.pcolor(self.data, transform=ccrs.Geodetic()) def test_pcolormesh(self): with self.assertRaises(ValueError): self.ax.pcolormesh(self.data, transform=ccrs.Geodetic()) def test_transform_PlateCarree_shortcut(): src = ccrs.PlateCarree(central_longitude=0) target = ccrs.PlateCarree(central_longitude=180) # of the 3 paths, 2 of them cannot be short-cutted. pth1 = mpath.Path([[0.5, 0], [10, 10]]) pth2 = mpath.Path([[0.5, 91], [10, 10]]) pth3 = mpath.Path([[-0.5, 0], [10, 10]]) trans = InterProjectionTransform(src, target) counter = CallCounter(target, 'project_geometry') with counter: trans.transform_path(pth1) # pth1 should allow a short-cut. assert_equal(counter.count, 0) with counter: trans.transform_path(pth2) assert_equal(counter.count, 1) with counter: trans.transform_path(pth3) assert_equal(counter.count, 2) @cleanup def test_geoaxes_subplot(): ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree()) assert_equal(str(ax.__class__), "") if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_caching.py0000644001374000021030000002121212700747662022146 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import gc import six import unittest try: from owslib.wmts import WebMapTileService except ImportError as e: WebMapTileService = None import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.collections import PatchCollection from matplotlib.path import Path import cartopy.crs as ccrs from cartopy.mpl.feature_artist import FeatureArtist from cartopy.io.ogc_clients import WMTSRasterSource, _OWSLIB_AVAILABLE import cartopy.io.shapereader import cartopy.mpl.geoaxes as cgeoaxes import cartopy.mpl.patch from cartopy.examples.waves import sample_data class CallCounter(object): """ Exposes a context manager which can count the number of calls to a specific function. (useful for cache checking!) Internally, the target function is replaced with a new one created by this context manager which then increments ``self.count`` every time it is called. Example usage:: show_counter = CallCounter(plt, 'show') with show_counter: plt.show() plt.show() plt.show() print show_counter.count # <--- outputs 3 """ def __init__(self, parent, function_name): self.count = 0 self.parent = parent self.function_name = function_name self.orig_fn = getattr(parent, function_name) def __enter__(self): def replacement_fn(*args, **kwargs): self.count += 1 return self.orig_fn(*args, **kwargs) setattr(self.parent, self.function_name, replacement_fn) return self def __exit__(self, exc_type, exc_val, exc_tb): setattr(self.parent, self.function_name, self.orig_fn) def test_coastline_loading_cache(): # a5caae040ee11e72a62a53100fe5edc355304419 added coastline caching. # This test ensures it is working. # Create coastlines to ensure they are cached. ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree()) ax1.coastlines() plt.draw() # Create another instance of the coastlines and count # the number of times shapereader.Reader is created. counter = CallCounter(cartopy.io.shapereader.Reader, '__init__') with counter: ax2 = plt.subplot(2, 1, 1, projection=ccrs.Robinson()) ax2.coastlines() plt.draw() assert counter.count == 0, ('The shapereader Reader class was created {} ' 'times, indicating that the caching is not ' 'working.'.format(counter.count)) plt.close() def test_shapefile_transform_cache(): # a5caae040ee11e72a62a53100fe5edc355304419 added shapefile mpl # geometry caching based on geometry object id. This test ensures # it is working. coastline_path = cartopy.io.shapereader.natural_earth(resolution="50m", category='physical', name='coastline') geoms = cartopy.io.shapereader.Reader(coastline_path).geometries() # Use the first 10 of them. geoms = tuple(geoms)[:10] n_geom = len(geoms) ax = plt.axes(projection=ccrs.Robinson()) # Empty the cache. FeatureArtist._geom_key_to_geometry_cache.clear() FeatureArtist._geom_key_to_path_cache.clear() assert len(FeatureArtist._geom_key_to_geometry_cache) == 0 assert len(FeatureArtist._geom_key_to_path_cache) == 0 counter = CallCounter(ax.projection, 'project_geometry') with counter: ax.add_geometries(geoms, ccrs.PlateCarree()) ax.add_geometries(geoms, ccrs.PlateCarree()) ax.add_geometries(geoms[:], ccrs.PlateCarree()) ax.figure.canvas.draw() # Without caching the count would have been # n_calls * n_geom, but should now be just n_geom. assert counter.count == n_geom, ('The given geometry was transformed too ' 'many times (expected: %s; got %s) - the' ' caching is not working.' ''.format(n_geom, n_geom, counter.count)) # Check the cache has an entry for each geometry. assert len(FeatureArtist._geom_key_to_geometry_cache) == n_geom assert len(FeatureArtist._geom_key_to_path_cache) == n_geom # Check that the cache is empty again once we've dropped all references # to the source paths. plt.clf() del geoms gc.collect() assert len(FeatureArtist._geom_key_to_geometry_cache) == 0 assert len(FeatureArtist._geom_key_to_path_cache) == 0 plt.close() def test_contourf_transform_path_counting(): ax = plt.axes(projection=ccrs.Robinson()) ax.figure.canvas.draw() # Capture the size of the cache before our test. gc.collect() initial_cache_size = len(cgeoaxes._PATH_TRANSFORM_CACHE) path_to_geos_counter = CallCounter(cartopy.mpl.patch, 'path_to_geos') with path_to_geos_counter: x, y, z = sample_data((30, 60)) cs = plt.contourf(x, y, z, 5, transform=ccrs.PlateCarree()) n_geom = sum([len(c.get_paths()) for c in cs.collections]) del cs if not six.PY3: del c ax.figure.canvas.draw() # Before the performance enhancement, the count would have been 2 * n_geom, # but should now be just n_geom. msg = ('The given geometry was transformed too many times (expected: %s; ' 'got %s) - the caching is not working.' '' % (n_geom, path_to_geos_counter.count)) assert path_to_geos_counter.count == n_geom, msg # Check the cache has an entry for each geometry. assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size + n_geom # Check that the cache is empty again once we've dropped all references # to the source paths. plt.clf() gc.collect() assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size plt.close() @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') def test_wmts_tile_caching(): image_cache = WMTSRasterSource._shared_image_cache image_cache.clear() assert len(image_cache) == 0 url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' wmts = WebMapTileService(url) layer_name = 'MODIS_Terra_CorrectedReflectance_TrueColor' source = WMTSRasterSource(wmts, layer_name) gettile_counter = CallCounter(wmts, 'gettile') crs = ccrs.PlateCarree() extent = (-180, 180, -90, 90) resolution = (20, 10) with gettile_counter: source.fetch_raster(crs, extent, resolution) n_tiles = 2 assert gettile_counter.count == n_tiles, ('Too many tile requests - ' 'expected {}, got {}.'.format( n_tiles, gettile_counter.count) ) gc.collect() assert len(image_cache) == 1 assert len(image_cache[wmts]) == 1 tiles_key = (layer_name, '0') assert len(image_cache[wmts][tiles_key]) == n_tiles # Second time around we shouldn't request any more tiles so the # call count will stay the same. with gettile_counter: source.fetch_raster(crs, extent, resolution) assert gettile_counter.count == n_tiles, ('Too many tile requests - ' 'expected {}, got {}.'.format( n_tiles, gettile_counter.count) ) gc.collect() assert len(image_cache) == 1 assert len(image_cache[wmts]) == 1 tiles_key = (layer_name, '0') assert len(image_cache[wmts][tiles_key]) == n_tiles # Once there are no live references the weak-ref cache should clear. del source, wmts, gettile_counter gc.collect() assert len(image_cache) == 0 if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_crs.py0000644001374000021030000000321112700747662021340 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.tests.mpl import ImageTesting @ImageTesting(['lambert_conformal_south']) def test_lambert_south(): # Reference image: http://www.icsm.gov.au/mapping/map_projections.html crs = ccrs.LambertConformal(central_longitude=140, cutoff=65, secant_latitudes=(-30, -60)) ax = plt.axes(projection=crs) ax.coastlines() ax.gridlines() @ImageTesting(['mercator_squashed']) def test_mercator_squashed(): globe = ccrs.Globe(semimajor_axis=10000, semiminor_axis=9000, ellipse=None) crs = ccrs.Mercator(globe=globe, min_latitude=-40, max_latitude=40) ax = plt.axes(projection=crs) ax.coastlines() ax.gridlines() if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_examples.py0000644001374000021030000000314512700747662022375 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import numpy as np import matplotlib.pyplot as plt from cartopy.tests.mpl import ImageTesting class ExampleImageTesting(ImageTesting): """Subclasses ImageTesting to nullify the plt.show commands.""" def __call__(self, test_func): fn = ImageTesting.__call__(self, test_func) def new_fn(*args, **kwargs): try: show = plt.show plt.show = lambda *args, **kwargs: None r = fn(*args, **kwargs) finally: plt.show = show return r new_fn.__name__ = fn.__name__ return new_fn @ExampleImageTesting(['global_map']) def test_global_map(): import cartopy.examples.global_map as c c.main() if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_features.py0000644001374000021030000000531512700747662022376 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature from cartopy.io.ogc_clients import _OWSLIB_AVAILABLE from cartopy.tests.mpl import ImageTesting @ImageTesting(['natural_earth']) def test_natural_earth(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.add_feature(cfeature.LAND) ax.add_feature(cfeature.OCEAN) ax.coastlines() ax.add_feature(cfeature.BORDERS, linestyle=':') ax.add_feature(cfeature.LAKES, alpha=0.5) ax.add_feature(cfeature.RIVERS) ax.set_xlim((-20, 60)) ax.set_ylim((-40, 40)) @ImageTesting(['natural_earth_custom']) def test_natural_earth_custom(): ax = plt.axes(projection=ccrs.PlateCarree()) feature = cfeature.NaturalEarthFeature('physical', 'coastline', '50m', edgecolor='black', facecolor='none') ax.add_feature(feature) ax.set_xlim((-26, -12)) ax.set_ylim((58, 72)) @ImageTesting(['gshhs_coastlines']) def test_gshhs(): ax = plt.axes(projection=ccrs.Mollweide()) ax.set_extent([138, 142, 32, 42], ccrs.Geodetic()) ax.stock_img() # Draw coastlines. ax.add_feature(cfeature.GSHHSFeature('coarse', edgecolor='red')) # Draw higher resolution lakes (and test overriding of kwargs) ax.add_feature(cfeature.GSHHSFeature('low', levels=[2], facecolor='green'), facecolor='blue') @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') @ImageTesting(['wfs']) def test_wfs(): ax = plt.axes(projection=ccrs.OSGB()) url = 'https://nsidc.org/cgi-bin/atlas_south?service=WFS' typename = 'land_excluding_antarctica' feature = cfeature.WFSFeature(url, typename, edgecolor='red') ax.add_feature(feature) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_gridliner.py0000644001374000021030000001406612705644356022543 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import matplotlib as mpl from matplotlib.backends.backend_agg import FigureCanvasAgg import matplotlib.pyplot as plt import matplotlib.ticker as mticker try: from unittest import mock except ImportError: import mock from nose.tools import assert_raises import numpy as np import cartopy.crs as ccrs from cartopy.mpl.geoaxes import GeoAxes from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER from cartopy.tests.mpl import ImageTesting @ImageTesting(['gridliner1']) def test_gridliner(): ny, nx = 2, 4 plt.figure(figsize=(10, 10)) ax = plt.subplot(nx, ny, 1, projection=ccrs.PlateCarree()) ax.set_global() ax.coastlines() ax.gridlines() ax = plt.subplot(nx, ny, 2, projection=ccrs.OSGB()) ax.set_global() ax.coastlines() ax.gridlines() ax = plt.subplot(nx, ny, 3, projection=ccrs.OSGB()) ax.set_global() ax.coastlines() ax.gridlines(ccrs.PlateCarree(), color='blue', linestyle='-') ax.gridlines(ccrs.OSGB()) ax = plt.subplot(nx, ny, 4, projection=ccrs.PlateCarree()) ax.set_global() ax.coastlines() ax.gridlines(ccrs.NorthPolarStereo(), alpha=0.5, linewidth=1.5, linestyle='-') ax = plt.subplot(nx, ny, 5, projection=ccrs.PlateCarree()) ax.set_global() ax.coastlines() osgb = ccrs.OSGB() ax.set_extent(tuple(osgb.x_limits) + tuple(osgb.y_limits), crs=osgb) ax.gridlines(osgb) ax = plt.subplot(nx, ny, 6, projection=ccrs.NorthPolarStereo()) ax.set_global() ax.coastlines() ax.gridlines(alpha=0.5, linewidth=1.5, linestyle='-') ax = plt.subplot(nx, ny, 7, projection=ccrs.NorthPolarStereo()) ax.set_global() ax.coastlines() osgb = ccrs.OSGB() ax.set_extent(tuple(osgb.x_limits) + tuple(osgb.y_limits), crs=osgb) ax.gridlines(osgb) ax = plt.subplot(nx, ny, 8, projection=ccrs.Robinson(central_longitude=135)) ax.set_global() ax.coastlines() ax.gridlines(ccrs.PlateCarree(), alpha=0.5, linewidth=1.5, linestyle='-') delta = 1.5e-2 plt.subplots_adjust(left=0 + delta, right=1 - delta, top=1 - delta, bottom=0 + delta) def test_gridliner_specified_lines(): xs = [0, 60, 120, 180, 240, 360] ys = [-90, -60, -30, 0, 30, 60, 90] ax = mock.Mock(_gridliners=[], spec=GeoAxes) gl = GeoAxes.gridlines(ax, xlocs=xs, ylocs=ys) assert isinstance(gl.xlocator, mticker.FixedLocator) assert isinstance(gl.ylocator, mticker.FixedLocator) assert gl.xlocator.tick_values(None, None).tolist() == xs assert gl.ylocator.tick_values(None, None).tolist() == ys # The tolerance on this test is particularly high because of the high number # of text objects. A new testing strategy is needed for this kind of test. @ImageTesting(['gridliner_labels' if mpl.__version__ >= '1.5' else 'gridliner_labels_pre_mpl_1.5']) def test_grid_labels(): plt.figure(figsize=(8, 10)) crs_pc = ccrs.PlateCarree() crs_merc = ccrs.Mercator() crs_osgb = ccrs.OSGB() ax = plt.subplot(3, 2, 1, projection=crs_pc) ax.coastlines() ax.gridlines(draw_labels=True) # Check that adding labels to Mercator gridlines gives an error. # (Currently can only label PlateCarree gridlines.) ax = plt.subplot(3, 2, 2, projection=ccrs.PlateCarree(central_longitude=180)) ax.coastlines() with assert_raises(TypeError): ax.gridlines(crs=crs_merc, draw_labels=True) ax.set_title('Known bug') gl = ax.gridlines(crs=crs_pc, draw_labels=True) gl.xlabels_top = False gl.ylabels_left = False gl.xlines = False ax = plt.subplot(3, 2, 3, projection=crs_merc) ax.coastlines() ax.gridlines(draw_labels=True) # Check that labelling the gridlines on an OSGB plot gives an error. # (Currently can only draw these on PlateCarree or Mercator plots.) ax = plt.subplot(3, 2, 4, projection=crs_osgb) ax.coastlines() with assert_raises(TypeError): ax.gridlines(draw_labels=True) ax = plt.subplot(3, 2, 4, projection=crs_pc) ax.coastlines() gl = ax.gridlines( crs=crs_pc, linewidth=2, color='gray', alpha=0.5, linestyle='--') gl.xlabels_bottom = True gl.ylabels_right = True gl.xlines = False gl.xlocator = mticker.FixedLocator([-180, -45, 45, 180]) gl.xformatter = LONGITUDE_FORMATTER gl.yformatter = LATITUDE_FORMATTER gl.xlabel_style = {'size': 15, 'color': 'gray'} gl.xlabel_style = {'color': 'red'} # trigger a draw at this point and check the appropriate artists are # populated on the gridliner instance FigureCanvasAgg(plt.gcf()).draw() assert len(gl.xlabel_artists) == 4 assert len(gl.ylabel_artists) == 5 assert len(gl.ylabel_artists) == 5 assert len(gl.xline_artists) == 0 ax = plt.subplot(3, 2, 5, projection=crs_pc) ax.set_extent([-20, 10.0, 45.0, 70.0]) ax.coastlines() ax.gridlines(draw_labels=True) ax = plt.subplot(3, 2, 6, projection=crs_merc) ax.set_extent([-20, 10.0, 45.0, 70.0], crs=crs_pc) ax.coastlines() ax.gridlines(draw_labels=True) # Increase margins between plots to stop them bumping into one another. plt.subplots_adjust(wspace=0.25, hspace=0.25) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_images.py0000644001374000021030000001453012700747662022024 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import os import types import numpy as np import matplotlib.pyplot as plt from PIL import Image import shapely.geometry as sgeom from cartopy import config import cartopy.crs as ccrs import cartopy.io.img_tiles as cimgt from cartopy.tests.mpl import ImageTesting import cartopy.tests.test_img_nest as ctest_nest import cartopy.tests.test_img_tiles as ctest_tiles NATURAL_EARTH_IMG = os.path.join(config["repo_data_dir"], 'raster', 'natural_earth', '50-natural-earth-1-downsampled.png') REGIONAL_IMG = os.path.join(config['repo_data_dir'], 'raster', 'sample', 'Miriam.A2012270.2050.2km.jpg') # We have an exceptionally large tolerance for the web_tiles test. # The basemap changes on a regular basis (for seasons) and we really only # care that it is putting images onto the map which are roughly correct. @ImageTesting(['web_tiles'], tolerance=2) def test_web_tiles(): extent = [-15, 0.1, 50, 60] target_domain = sgeom.Polygon([[extent[0], extent[1]], [extent[2], extent[1]], [extent[2], extent[3]], [extent[0], extent[3]], [extent[0], extent[1]]]) map_prj = cimgt.GoogleTiles().crs ax = plt.subplot(3, 2, 1, projection=map_prj) gt = cimgt.GoogleTiles() gt._image_url = types.MethodType(ctest_tiles.GOOGLE_IMAGE_URL_REPLACEMENT, gt) img, extent, origin = gt.image_for_domain(target_domain, 1) ax.imshow(np.array(img), extent=extent, transform=gt.crs, interpolation='bilinear', origin=origin) ax.coastlines(color='white') ax = plt.subplot(3, 2, 2, projection=map_prj) qt = cimgt.QuadtreeTiles() img, extent, origin = qt.image_for_domain(target_domain, 1) ax.imshow(np.array(img), extent=extent, transform=qt.crs, interpolation='bilinear', origin=origin) ax.coastlines(color='white') ax = plt.subplot(3, 2, 3, projection=map_prj) mq_osm = cimgt.MapQuestOSM() img, extent, origin = mq_osm.image_for_domain(target_domain, 1) ax.imshow(np.array(img), extent=extent, transform=mq_osm.crs, interpolation='bilinear', origin=origin) ax.coastlines() ax = plt.subplot(3, 2, 4, projection=map_prj) mq_oa = cimgt.MapQuestOpenAerial() img, extent, origin = mq_oa.image_for_domain(target_domain, 1) ax.imshow(np.array(img), extent=extent, transform=mq_oa.crs, interpolation='bilinear', origin=origin) ax.coastlines() ax = plt.subplot(3, 2, 5, projection=map_prj) osm = cimgt.OSM() img, extent, origin = osm.image_for_domain(target_domain, 1) ax.imshow(np.array(img), extent=extent, transform=osm.crs, interpolation='bilinear', origin=origin) ax.coastlines() @ImageTesting(['image_nest'], tolerance=1.5) def test_image_nest(): nest_z0_z1 = ctest_nest.gen_nest() ax = plt.axes(projection=ccrs.Mercator()) shper_globe = ccrs.Globe(semimajor_axis=np.rad2deg(1)) spher_merc = ccrs.Mercator(globe=shper_globe) ax.set_extent([-45, 45, -45, 90], spher_merc) ax.coastlines() ax.add_image(nest_z0_z1, 'aerial z1 test') @ImageTesting(['image_merge']) def test_image_merge(): # tests the basic image merging functionality tiles = [] for i in range(1, 4): for j in range(0, 3): tiles.append((i, j, 2)) gt = cimgt.GoogleTiles() gt._image_url = types.MethodType(ctest_tiles.GOOGLE_IMAGE_URL_REPLACEMENT, gt) images_to_merge = [] for tile in tiles: img, extent, origin = gt.get_image(tile) img = np.array(img) x = np.linspace(extent[0], extent[1], img.shape[1], endpoint=False) y = np.linspace(extent[2], extent[3], img.shape[0], endpoint=False) images_to_merge.append([img, x, y, origin]) img, extent, origin = cimgt._merge_tiles(images_to_merge) ax = plt.axes(projection=gt.crs) ax.set_global() ax.coastlines() plt.imshow(img, origin=origin, extent=extent, alpha=0.5) @ImageTesting(['imshow_natural_earth_ortho']) def test_imshow(): source_proj = ccrs.PlateCarree() img = plt.imread(NATURAL_EARTH_IMG) # Convert the image to a byte array, rather than float, which is the # form that JPG images would be loaded with imread. img = (img * 255).astype('uint8') ax = plt.axes(projection=ccrs.Orthographic()) ax.imshow(img, origin='upper', transform=source_proj, extent=[-180, 180, -90, 90]) @ImageTesting(['imshow_regional_projected']) def test_imshow_projected(): source_proj = ccrs.PlateCarree() img_extent = (-120.67660000000001, -106.32104523100001, 13.2301484511245, 30.766899999999502) img = plt.imread(REGIONAL_IMG) ax = plt.axes(projection=ccrs.LambertConformal()) ax.set_extent(img_extent, crs=source_proj) ax.coastlines(resolution='50m') ax.imshow(img, extent=img_extent, origin='upper', transform=source_proj) @ImageTesting(['imshow_natural_earth_ortho'], tolerance=0.7) def test_stock_img(): ax = plt.axes(projection=ccrs.Orthographic()) ax.stock_img() @ImageTesting(['imshow_natural_earth_ortho']) def test_pil_Image(): img = Image.open(NATURAL_EARTH_IMG) source_proj = ccrs.PlateCarree() ax = plt.axes(projection=ccrs.Orthographic()) ax.imshow(img, origin='upper', transform=source_proj, extent=[-180, 180, -90, 90]) if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_img_transform.py0000644001374000021030000001242512700747662023427 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import operator import os import unittest import matplotlib import matplotlib.pyplot as plt import numpy as np from cartopy import config from cartopy.tests.mpl import ImageTesting import cartopy.crs as ccrs import cartopy.img_transform as im_trans from functools import reduce class TestRegrid(unittest.TestCase): def test_array_dims(self): # Source data source_nx = 100 source_ny = 100 source_x = np.linspace(-180.0, 180.0, source_nx).astype(np.float64) source_y = np.linspace(-90, 90.0, source_ny).astype(np.float64) source_x, source_y = np.meshgrid(source_x, source_y) data = np.arange(source_nx * source_ny, dtype=np.int32).reshape(source_ny, source_nx) source_cs = ccrs.Geodetic() # Target grid target_nx = 23 target_ny = 45 target_proj = ccrs.PlateCarree() target_x, target_y, extent = im_trans.mesh_projection(target_proj, target_nx, target_ny) # Perform regrid new_array = im_trans.regrid(data, source_x, source_y, source_cs, target_proj, target_x, target_y) # Check dimensions of return array self.assertEqual(new_array.shape, target_x.shape) self.assertEqual(new_array.shape, target_y.shape) self.assertEqual(new_array.shape, (target_ny, target_nx)) def test_different_dims(self): # Source data source_nx = 100 source_ny = 100 source_x = np.linspace(-180.0, 180.0, source_nx).astype(np.float64) source_y = np.linspace(-90, 90.0, source_ny).astype(np.float64) source_x, source_y = np.meshgrid(source_x, source_y) data = np.arange(source_nx * source_ny, dtype=np.int32).reshape(source_ny, source_nx) source_cs = ccrs.Geodetic() # Target grids (different shapes) target_x_shape = (23, 45) target_y_shape = (23, 44) target_x = np.arange(reduce(operator.mul, target_x_shape), dtype=np.float64).reshape(target_x_shape) target_y = np.arange(reduce(operator.mul, target_y_shape), dtype=np.float64).reshape(target_y_shape) target_proj = ccrs.PlateCarree() # Attempt regrid with self.assertRaises(ValueError): im_trans.regrid(data, source_x, source_y, source_cs, target_proj, target_x, target_y) @ImageTesting(['regrid_image'], tolerance=0.4) def test_regrid_image(): # Source data fname = os.path.join(config["repo_data_dir"], 'raster', 'natural_earth', '50-natural-earth-1-downsampled.png') nx = 720 ny = 360 source_proj = ccrs.PlateCarree() source_x, source_y, _ = im_trans.mesh_projection(source_proj, nx, ny) data = plt.imread(fname) # Flip vertically to match source_x/source_y orientation data = data[::-1] # Target grid target_nx = 300 target_ny = 300 target_proj = ccrs.InterruptedGoodeHomolosine() target_x, target_y, target_extent = im_trans.mesh_projection(target_proj, target_nx, target_ny) # Perform regrid new_array = im_trans.regrid(data, source_x, source_y, source_proj, target_proj, target_x, target_y) # Plot fig = plt.figure(figsize=(10, 10)) gs = matplotlib.gridspec.GridSpec(nrows=4, ncols=1, hspace=1.5, wspace=0.5) # Set up axes and title ax = plt.subplot(gs[0], frameon=False, projection=target_proj) plt.imshow(new_array, origin='lower', extent=target_extent) ax.coastlines() # Plot each color slice (tests masking) cmaps = {'red': 'Reds', 'green': 'Greens', 'blue': 'Blues'} for i, color in enumerate(['red', 'green', 'blue']): ax = plt.subplot(gs[i + 1], frameon=False, projection=target_proj) plt.imshow(new_array[:, :, i], extent=target_extent, origin='lower', cmap=cmaps[color]) ax.coastlines() # Tighten up layout gs.tight_layout(plt.gcf()) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_mpl_integration.py0000644001374000021030000004451512705644356023761 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import math import warnings from nose.tools import assert_equal try: from nose.tools import assert_regex except ImportError: from nose.tools import assert_regexp_matches as assert_regex import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import six import cartopy.crs as ccrs from cartopy.tests.mpl import ImageTesting _ROB_TOL = 0.5 if ccrs.PROJ4_VERSION < (4, 9) else 0.1 @ImageTesting(['global_contour_wrap']) def test_global_contour_wrap_new_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.contour(x, y, data, transform=ccrs.PlateCarree()) @ImageTesting(['global_contour_wrap']) def test_global_contour_wrap_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.contour(x, y, data) @ImageTesting(['global_contourf_wrap']) def test_global_contourf_wrap_new_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.contourf(x, y, data, transform=ccrs.PlateCarree()) @ImageTesting(['global_contourf_wrap']) def test_global_contourf_wrap_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.contourf(x, y, data) global_pcolor_wrap = ('global_pcolor_wrap' if mpl.__version__ >= '1.5' else 'global_pcolor_wrap_pre_mpl_1.5') @ImageTesting([global_pcolor_wrap]) def test_global_pcolor_wrap_new_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.pcolor(x, y, data, transform=ccrs.PlateCarree()) @ImageTesting([global_pcolor_wrap]) def test_global_pcolor_wrap_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.pcolor(x, y, data) @ImageTesting(['global_scatter_wrap']) def test_global_scatter_wrap_new_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) # By default the coastline feature will be drawn after patches. # By setting zorder we can ensure our scatter points are drawn # after the coastlines. ax.coastlines(zorder=0) x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.scatter(x, y, c=data, transform=ccrs.PlateCarree()) @ImageTesting(['global_scatter_wrap']) def test_global_scatter_wrap_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines(zorder=0) x, y = np.meshgrid(np.linspace(0, 360), np.linspace(-90, 90)) data = np.sin(np.sqrt(x ** 2 + y ** 2)) plt.scatter(x, y, c=data) @ImageTesting(['global_map'], tolerance=16 if ccrs.PROJ4_VERSION < (4, 9) else 0.1) def test_global_map(): ax = plt.axes(projection=ccrs.Robinson()) # ax.coastlines() # ax.gridlines(5) plt.plot(-0.08, 51.53, 'o', transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], color='red', transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], color='blue', transform=ccrs.Geodetic()) @ImageTesting(['simple_global']) def test_simple_global(): plt.axes(projection=ccrs.PlateCarree()) plt.gca().coastlines() # produces a global map, despite not having needed to set the limits @ImageTesting(['multiple_projections1']) def test_multiple_projections(): projections = [ccrs.PlateCarree(), ccrs.Robinson(), ccrs.RotatedPole(pole_latitude=45, pole_longitude=180), ccrs.OSGB(), ccrs.TransverseMercator(), ccrs.Mercator( globe=ccrs.Globe(semimajor_axis=math.degrees(1)), min_latitude=-85., max_latitude=85.), ccrs.LambertCylindrical(), ccrs.Miller(), ccrs.Gnomonic(), ccrs.Stereographic(), ccrs.NorthPolarStereo(), ccrs.SouthPolarStereo(), ccrs.Orthographic(), ccrs.Mollweide(), ccrs.InterruptedGoodeHomolosine(), ] fig = plt.figure(figsize=(10, 10)) for i, prj in enumerate(projections, 1): ax = fig.add_subplot(5, 5, i, projection=prj) ax.set_global() ax.coastlines() plt.plot(-0.08, 51.53, 'o', transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], color='red', transform=ccrs.PlateCarree()) plt.plot([-0.08, 132], [51.53, 43.17], color='blue', transform=ccrs.Geodetic()) def test_cursor_values(): ax = plt.axes(projection=ccrs.NorthPolarStereo()) x, y = np.array([-969100.]), np.array([-4457000.]) r = ax.format_coord(x, y) assert_equal(r.encode('ascii', 'ignore'), six.b('-9.691e+05, -4.457e+06 (50.716617N, 12.267069W)')) ax = plt.axes(projection=ccrs.PlateCarree()) x, y = np.array([-181.5]), np.array([50.]) r = ax.format_coord(x, y) assert_equal(r.encode('ascii', 'ignore'), six.b('-181.5, 50 (50.000000N, 178.500000E)')) ax = plt.axes(projection=ccrs.Robinson()) x, y = np.array([16060595.2]), np.array([2363093.4]) r = ax.format_coord(x, y) assert_regex(r.encode('ascii', 'ignore'), six.b('1.606e\\+07, 2.363e\\+06 ' '\\(22.09[0-9]{4}N, 173.70[0-9]{4}E\\)')) plt.close() @ImageTesting(['natural_earth_interface'], tolerance=_ROB_TOL) def test_axes_natural_earth_interface(): rob = ccrs.Robinson() ax = plt.axes(projection=rob) with warnings.catch_warnings(record=True) as all_warnings: warnings.simplefilter('always') ax.natural_earth_shp('rivers_lake_centerlines', edgecolor='black', facecolor='none') ax.natural_earth_shp('lakes', facecolor='blue') assert_equal(len(all_warnings), 2) for warning in all_warnings: msg = str(warning.message) assert 'deprecated' in msg assert 'add_feature' in msg @ImageTesting(['pcolormesh_global_wrap1']) def test_pcolormesh_global_with_wrap1(): # make up some realistic data with bounds (such as data from the UM) nx, ny = 36, 18 xbnds = np.linspace(0, 360, nx, endpoint=True) ybnds = np.linspace(-90, 90, ny, endpoint=True) x, y = np.meshgrid(xbnds, ybnds) data = np.exp(np.sin(np.deg2rad(x)) + np.cos(np.deg2rad(y))) data = data[:-1, :-1] ax = plt.subplot(211, projection=ccrs.PlateCarree()) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible ax = plt.subplot(212, projection=ccrs.PlateCarree(180)) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible @ImageTesting(['pcolormesh_global_wrap2']) def test_pcolormesh_global_with_wrap2(): # make up some realistic data with bounds (such as data from the UM) nx, ny = 36, 18 xbnds, xstep = np.linspace(0, 360, nx - 1, retstep=True, endpoint=True) ybnds, ystep = np.linspace(-90, 90, nx - 1, retstep=True, endpoint=True) xbnds -= xstep / 2 ybnds -= ystep / 2 xbnds = np.append(xbnds, xbnds[-1] + xstep) ybnds = np.append(ybnds, ybnds[-1] + ystep) x, y = np.meshgrid(xbnds, ybnds) data = np.exp(np.sin(np.deg2rad(x)) + np.cos(np.deg2rad(y))) data = data[:-1, :-1] ax = plt.subplot(211, projection=ccrs.PlateCarree()) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible ax = plt.subplot(212, projection=ccrs.PlateCarree(180)) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible @ImageTesting(['pcolormesh_global_wrap3'], tolerance=_ROB_TOL) def test_pcolormesh_global_with_wrap3(): nx, ny = 33, 17 xbnds = np.linspace(-1.875, 358.125, nx, endpoint=True) ybnds = np.linspace(91.25, -91.25, ny, endpoint=True) xbnds, ybnds = np.meshgrid(xbnds, ybnds) data = np.exp(np.sin(np.deg2rad(xbnds)) + np.cos(np.deg2rad(ybnds))) # this step is not necessary, but makes the plot even harder to do (i.e. # it really puts cartopy through its paces) ybnds = np.append(ybnds, ybnds[:, 1:2], axis=1) xbnds = np.append(xbnds, xbnds[:, 1:2] + 360, axis=1) data = np.ma.concatenate([data, data[:, 0:1]], axis=1) data = data[:-1, :-1] data = np.ma.masked_greater(data, 2.6) ax = plt.subplot(311, projection=ccrs.PlateCarree(-45)) c = plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) assert c._wrapped_collection_fix is not None, \ 'No pcolormesh wrapping was done when it should have been.' ax.coastlines() ax.set_global() # make sure everything is visible ax = plt.subplot(312, projection=ccrs.PlateCarree(-1.87499952)) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible ax = plt.subplot(313, projection=ccrs.Robinson(-2)) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.set_global() # make sure everything is visible @ImageTesting(['pcolormesh_limited_area_wrap']) def test_pcolormesh_limited_area_wrap(): # make up some realistic data with bounds (such as data from the UM's North # Atlantic Europe model) nx, ny = 22, 36 xbnds = np.linspace(311.91998291, 391.11999512, nx, endpoint=True) ybnds = np.linspace(-23.59000015, 24.81000137, ny, endpoint=True) x, y = np.meshgrid(xbnds, ybnds) data = ((np.sin(np.deg2rad(x))) / 10. + np.exp(np.cos(np.deg2rad(y)))) data = data[:-1, :-1] rp = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) plt.figure(figsize=(10, 6)) ax = plt.subplot(221, projection=ccrs.PlateCarree()) plt.pcolormesh(xbnds, ybnds, data, transform=rp, cmap='Set1') ax.coastlines() ax = plt.subplot(222, projection=ccrs.PlateCarree(180)) plt.pcolormesh(xbnds, ybnds, data, transform=rp, cmap='Set1') ax.coastlines() ax.set_global() # draw the same plot, only more zoomed in, and using the 2d versions # of the coordinates (just to test that 1d and 2d are both suitably # being fixed) ax = plt.subplot(223, projection=ccrs.PlateCarree(180)) plt.pcolormesh(x, y, data, transform=rp, cmap='Set1') ax.coastlines() ax.set_extent([-70, 0, 0, 80]) ax = plt.subplot(224, projection=rp) plt.pcolormesh(xbnds, ybnds, data, transform=rp, cmap='Set1') ax.coastlines() @ImageTesting(['pcolormesh_goode_wrap']) def test_pcolormesh_goode_wrap(): # global data on an Interrupted Goode Homolosine projection # shouldn't spill outside projection boundary x = np.linspace(0, 360, 73) y = np.linspace(-87.5, 87.5, 36) X, Y = np.meshgrid(*[np.deg2rad(c) for c in (x, y)]) Z = np.cos(Y) + 0.375 * np.sin(2. * X) Z = Z[:-1, :-1] ax = plt.axes(projection=ccrs.InterruptedGoodeHomolosine()) ax.coastlines() ax.pcolormesh(x, y, Z, transform=ccrs.PlateCarree()) @ImageTesting(['pcolormesh_mercator_wrap']) def test_pcolormesh_mercator_wrap(): x = np.linspace(0, 360, 73) y = np.linspace(-87.5, 87.5, 36) X, Y = np.meshgrid(*[np.deg2rad(c) for c in (x, y)]) Z = np.cos(Y) + 0.375 * np.sin(2. * X) Z = Z[:-1, :-1] ax = plt.axes(projection=ccrs.Mercator()) ax.coastlines() ax.pcolormesh(x, y, Z, transform=ccrs.PlateCarree()) @ImageTesting(['quiver_plate_carree']) def test_quiver_plate_carree(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] plt.figure(figsize=(6, 6)) # plot on native projection ax = plt.subplot(211, projection=ccrs.PlateCarree()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.quiver(x, y, u, v, mag) # plot on a different projection ax = plt.subplot(212, projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.quiver(x, y, u, v, mag, transform=ccrs.PlateCarree()) @ImageTesting(['quiver_rotated_pole']) def test_quiver_rotated_pole(): nx, ny = 22, 36 x = np.linspace(311.91998291, 391.11999512, nx, endpoint=True) y = np.linspace(-23.59000015, 24.81000137, ny, endpoint=True) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = -2. * np.cos(2. * np.deg2rad(y2d)) * np.sin(np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 rp = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) plot_extent = [x[0], x[-1], y[0], y[-1]] # plot on native projection plt.figure(figsize=(6, 6)) ax = plt.subplot(211, projection=rp) ax.set_extent(plot_extent, crs=rp) ax.coastlines() ax.quiver(x, y, u, v, mag) # plot on different projection ax = plt.subplot(212, projection=ccrs.PlateCarree()) ax.set_extent(plot_extent, crs=rp) ax.coastlines() ax.quiver(x, y, u, v, mag, transform=rp) @ImageTesting(['quiver_regrid']) def test_quiver_regrid(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] plt.figure(figsize=(6, 3)) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.quiver(x, y, u, v, mag, transform=ccrs.PlateCarree(), regrid_shape=30) @ImageTesting(['quiver_regrid_with_extent']) def test_quiver_regrid_with_extent(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] target_extent = [-3e6, 2e6, -6e6, -2.5e6] plt.figure(figsize=(6, 3)) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.quiver(x, y, u, v, mag, transform=ccrs.PlateCarree(), regrid_shape=10, target_extent=target_extent) @ImageTesting(['barbs_plate_carree']) def test_barbs(): x = np.arange(-60, 45, 5) y = np.arange(30, 75, 5) x2d, y2d = np.meshgrid(x, y) u = 40 * np.cos(np.deg2rad(y2d)) v = 40 * np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] plt.figure(figsize=(6, 6)) # plot on native projection ax = plt.subplot(211, projection=ccrs.PlateCarree()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.barbs(x, y, u, v, length=4, linewidth=.25) # plot on a different projection ax = plt.subplot(212, projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.barbs(x, y, u, v, transform=ccrs.PlateCarree(), length=4, linewidth=.25) @ImageTesting(['barbs_regrid']) def test_barbs_regrid(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = 40 * np.cos(np.deg2rad(y2d)) v = 40 * np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] plt.figure(figsize=(6, 3)) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.barbs(x, y, u, v, mag, transform=ccrs.PlateCarree(), length=4, linewidth=.4, regrid_shape=20) @ImageTesting(['barbs_regrid_with_extent']) def test_barbs_regrid_with_extent(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = 40 * np.cos(np.deg2rad(y2d)) v = 40 * np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] target_extent = [-3e6, 2e6, -6e6, -2.5e6] plt.figure(figsize=(6, 3)) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.barbs(x, y, u, v, mag, transform=ccrs.PlateCarree(), length=4, linewidth=.25, regrid_shape=10, target_extent=target_extent) @ImageTesting(['streamplot' if mpl.__version__ >= '1.5' else 'streamplot_pre_mpl_1.5']) def test_streamplot(): x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = np.cos(2. * np.deg2rad(x2d)) mag = (u**2 + v**2)**.5 plot_extent = [-60, 40, 30, 70] plt.figure(figsize=(6, 3)) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(plot_extent, crs=ccrs.PlateCarree()) ax.coastlines() ax.streamplot(x, y, u, v, transform=ccrs.PlateCarree(), density=(1.5, 2), color=mag, linewidth=2*mag) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_patch.py0000644001374000021030000000406012700747662021653 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import six import unittest from matplotlib.path import Path import shapely.geometry as sgeom import cartopy.mpl.patch as cpatch class Test_path_to_geos(unittest.TestCase): def test_empty_polyon(self): p = Path([[0, 0], [0, 0], [0, 0], [0, 0], [1, 2], [1, 2], [1, 2], [1, 2]], codes=[1, 2, 2, 79, 1, 2, 2, 79]) geoms = cpatch.path_to_geos(p) self.assertEqual(list(type(geom) for geom in geoms), [sgeom.Point, sgeom.Point]) self.assertEqual(len(geoms), 2) def test_polygon_with_interior_and_singularity(self): # A geometry with two interiors, one a single point. p = Path([[0, -90], [200, -40], [200, 40], [0, 40], [0, -90], [126, 26], [126, 26], [126, 26], [126, 26], [126, 26], [114, 5], [103, 8], [126, 12], [126, 0], [114, 5]], codes=[1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2]) geoms = cpatch.path_to_geos(p) self.assertEqual(list(type(geom) for geom in geoms), [sgeom.Polygon, sgeom.Point]) self.assertEqual(len(geoms[0].interiors), 1) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_pseudo_color.py0000644001374000021030000000513412705644356023255 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import io import matplotlib.pyplot as plt try: from unittest import mock except ImportError: import mock from nose.tools import assert_equal import numpy as np import cartopy.crs as ccrs def test_pcolormesh_fully_masked(): data = np.ma.masked_all((30, 40)) # Check that a fully masked data array doesn't trigger a pcolor call. with mock.patch('cartopy.mpl.geoaxes.GeoAxes.pcolor') as pcolor: ax = plt.axes(projection=ccrs.PlateCarree()) ax.pcolormesh(np.linspace(-90, 90, 40), np.linspace(0, 360, 30), data) assert_equal(pcolor.call_count, 0, ("pcolor shouldn't have been " "called, but was.")) plt.close() def test_pcolormesh_partially_masked(): data = np.ma.masked_all((30, 40)) data[0:100] = 10 # Check that a partially masked data array does trigger a pcolor call. with mock.patch('cartopy.mpl.geoaxes.GeoAxes.pcolor') as pcolor: ax = plt.axes(projection=ccrs.PlateCarree()) ax.pcolormesh(np.linspace(-90, 90, 40), np.linspace(0, 360, 30), data) assert_equal(pcolor.call_count, 1, ("pcolor should have been " "called exactly once.")) plt.close() def test_savefig_tight(): nx, ny = 36, 18 xbnds = np.linspace(0, 360, nx, endpoint=True) ybnds = np.linspace(-90, 90, ny, endpoint=True) x, y = np.meshgrid(xbnds, ybnds) data = np.exp(np.sin(np.deg2rad(x)) + np.cos(np.deg2rad(y))) data = data[:-1, :-1] ax = plt.subplot(211, projection=ccrs.Robinson()) plt.pcolormesh(xbnds, ybnds, data, transform=ccrs.PlateCarree()) buf = io.BytesIO() plt.savefig(buf, format='png', bbox_inches='tight') plt.close() if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_quiver.py0000644001374000021030000000606312700747662022074 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest try: from unittest import mock except ImportError: import mock from nose.tools import raises, assert_equal import numpy as np import matplotlib.pyplot as plt from matplotlib.testing.decorators import cleanup import cartopy.crs as ccrs # Note, other tests for quiver exist in test_mpl_integration. class TestQuiverShapes(unittest.TestCase): def setUp(self): self.x = np.linspace(-60, 42.5, 10) self.y = np.linspace(30, 72.5, 7) self.x2d, self.y2d = np.meshgrid(self.x, self.y) self.u = np.cos(np.deg2rad(self.y2d)) self.v = np.cos(2. * np.deg2rad(self.x2d)) self.rp = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) self.pc = ccrs.PlateCarree() self.ax = plt.axes(projection=self.pc) @cleanup def test_quiver_transform_xyuv_1d(self): with mock.patch('matplotlib.axes.Axes.quiver') as patch: self.ax.quiver(self.x2d.ravel(), self.y2d.ravel(), self.u.ravel(), self.v.ravel(), transform=self.rp) args, kwargs = patch.call_args assert_equal(len(args), 5) assert_equal(sorted(kwargs.keys()), [u'transform']) shapes = [arg.shape for arg in args[1:]] # Assert that all the shapes have been broadcast. assert_equal(shapes, [(70, )] * 4) @cleanup def test_quiver_transform_xy_1d_uv_2d(self): with mock.patch('matplotlib.axes.Axes.quiver') as patch: self.ax.quiver(self.x, self.y, self.u, self.v, transform=self.rp) args, kwargs = patch.call_args assert_equal(len(args), 5) assert_equal(sorted(kwargs.keys()), [u'transform']) shapes = [arg.shape for arg in args[1:]] # Assert that all the shapes have been broadcast. assert_equal(shapes, [(7, 10)] * 4) @raises(ValueError) def test_quiver_transform_xy_2d_uv_1d(self): self.ax.quiver(self.x2d, self.y2d, self.u.ravel(), self.v.ravel(), transform=self.rp) @raises(ValueError) def test_quiver_transform_inconsistent_shape(self): self.ax.quiver(self.x, self.y, self.u.ravel(), self.v.ravel(), transform=self.rp) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_set_extent.py0000644001374000021030000001452512700747662022745 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import tempfile from matplotlib.testing.decorators import cleanup import matplotlib.pyplot as plt import numpy as np from numpy.testing import assert_array_almost_equal, assert_array_equal import cartopy.crs as ccrs @cleanup def test_extents(): # tests that one can set the extents of a map in a variety of coordinate # systems, for a variety of projection uk = [-12.5, 4, 49, 60] uk_crs = ccrs.Geodetic() ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent(uk, crs=uk_crs) # enable to see what is going on (and to make sure it is a plot of the uk) # ax.coastlines() assert_array_almost_equal(ax.viewLim.get_points(), np.array([[-12.5, 49.], [4., 60.]])) ax = plt.axes(projection=ccrs.NorthPolarStereo()) ax.set_extent(uk, crs=uk_crs) # enable to see what is going on (and to make sure it is a plot of the uk) # ax.coastlines() assert_array_almost_equal(ax.viewLim.get_points(), np.array([[-1034046.22566261, -4765889.76601514], [333263.47741164, -3345219.0594531]]) ) # given that we know the PolarStereo coordinates of the UK, try using # those in a PlateCarree plot ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent([-1034046, 333263, -4765889, -3345219], crs=ccrs.NorthPolarStereo()) # enable to see what is going on (and to make sure it is a plot of the uk) # ax.coastlines() assert_array_almost_equal(ax.viewLim.get_points(), np.array([[-17.17698577, 48.21879707], [5.68924381, 60.54218893]]) ) @cleanup def test_domain_extents(): # Setting the extent to global or the domain limits. ax = plt.axes(projection=ccrs.PlateCarree()) ax.set_extent((-180, 180, -90, 90)) assert_array_equal(ax.viewLim.get_points(), [[-180, -90], [180, 90]]) ax.set_extent((-180, 180, -90, 90), ccrs.PlateCarree()) assert_array_equal(ax.viewLim.get_points(), [[-180, -90], [180, 90]]) ax = plt.axes(projection=ccrs.PlateCarree(90)) ax.set_extent((-180, 180, -90, 90)) assert_array_equal(ax.viewLim.get_points(), [[-180, -90], [180, 90]]) ax.set_extent((-180, 180, -90, 90), ccrs.PlateCarree(90)) assert_array_equal(ax.viewLim.get_points(), [[-180, -90], [180, 90]]) ax = plt.axes(projection=ccrs.OSGB()) ax.set_extent((0, 7e5, 0, 13e5), ccrs.OSGB()) assert_array_equal(ax.viewLim.get_points(), [[0, 0], [7e5, 13e5]]) def test_update_lim(): # check that the standard data lim setting works ax = plt.axes(projection=ccrs.PlateCarree()) ax.update_datalim([(-10, -10), (-5, -5)]) assert_array_almost_equal(ax.dataLim.get_points(), np.array([[-10., -10.], [-5., -5.]])) plt.close() def test_limits_contour(): xs, ys = np.meshgrid(np.linspace(250, 350, 15), np.linspace(-45, 45, 20)) data = np.sin((xs * ys) * 1.e7) resulting_extent = np.array([[250 - 180, -45.], [-10. + 180, 45.]]) ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() plt.contourf(xs, ys, data, transform=ccrs.PlateCarree(180)) assert_array_almost_equal(ax.dataLim, resulting_extent) plt.close() ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() plt.contour(xs, ys, data, transform=ccrs.PlateCarree(180)) assert_array_almost_equal(ax.dataLim, resulting_extent) plt.close() def test_limits_pcolor(): xs, ys = np.meshgrid(np.linspace(250, 350, 15), np.linspace(-45, 45, 20)) data = (np.sin((xs * ys) * 1.e7))[:-1, :-1] resulting_extent = np.array([[250 - 180, -45.], [-10. + 180, 45.]]) ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() plt.pcolor(xs, ys, data, transform=ccrs.PlateCarree(180)) assert_array_almost_equal(ax.dataLim, resulting_extent) plt.close() ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() plt.pcolormesh(xs, ys, data, transform=ccrs.PlateCarree(180)) assert_array_almost_equal(ax.dataLim, resulting_extent) plt.close() def test_view_lim_autoscaling(): x = np.linspace(0.12910209, 0.42141822) y = np.linspace(0.03739792, 0.33029076) x, y = np.meshgrid(x, y) ax = plt.axes(projection=ccrs.RotatedPole(37.5, 357.5)) plt.scatter(x, y, x * y, transform=ccrs.PlateCarree()) expected = np.array([[86.12433701, 52.51570463], [86.69696603, 52.86372057]]) assert_array_almost_equal(ax.viewLim.frozen().get_points(), expected, decimal=2) plt.draw() assert_array_almost_equal(ax.viewLim.frozen().get_points(), expected, decimal=2) ax.autoscale_view(tight=False) expected_non_tight = np.array([[86, 52.45], [86.8, 52.9]]) assert_array_almost_equal(ax.viewLim.frozen().get_points(), expected_non_tight, decimal=1) plt.close() def test_view_lim_default_global(): ax = plt.axes(projection=ccrs.PlateCarree()) # The view lim should be the default unit bbox until it is drawn. assert_array_almost_equal(ax.viewLim.frozen().get_points(), [[0, 0], [1, 1]]) with tempfile.TemporaryFile() as tmp: plt.savefig(tmp) expected = np.array([[-180, -90], [180, 90]]) assert_array_almost_equal(ax.viewLim.frozen().get_points(), expected) plt.close() if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_shapely_to_mpl.py0000644001374000021030000001070112705644356023573 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.collections import PatchCollection from matplotlib.path import Path import shapely.geometry as sgeom import cartopy.crs as ccrs import cartopy.mpl.patch as cpatch from cartopy.tests.mpl import ImageTesting @ImageTesting(['poly_interiors' if mpl.__version__ >= '1.5' else 'poly_interiors_pre_mpl_1.5']) def test_polygon_interiors(): ax = plt.subplot(211, projection=ccrs.PlateCarree()) ax.coastlines() ax.set_global() pth = Path([[0, -45], [60, -45], [60, 45], [0, 45], [0, 45], [10, -20], [10, 20], [40, 20], [40, -20], [10, 20]], [1, 2, 2, 2, 79, 1, 2, 2, 2, 79]) patches_native = [] patches = [] for geos in cpatch.path_to_geos(pth): for pth in cpatch.geos_to_path(geos): patches.append(mpatches.PathPatch(pth)) # buffer by 10 degrees (leaves a small hole in the middle) geos_buffered = geos.buffer(10) for pth in cpatch.geos_to_path(geos_buffered): patches_native.append(mpatches.PathPatch(pth)) # Set high zorder to ensure the polygons are drawn on top of coastlines. collection = PatchCollection(patches_native, facecolor='red', alpha=0.4, transform=ax.projection, zorder=10) ax.add_collection(collection) collection = PatchCollection(patches, facecolor='yellow', alpha=0.4, transform=ccrs.Geodetic(), zorder=10) ax.add_collection(collection) # test multiple interior polygons ax = plt.subplot(212, projection=ccrs.PlateCarree(), xlim=[-5, 15], ylim=[-5, 15]) ax.coastlines() exterior = np.array(sgeom.box(0, 0, 12, 12).exterior.coords) interiors = [np.array(sgeom.box(1, 1, 2, 2, ccw=False).exterior.coords), np.array(sgeom.box(1, 8, 2, 9, ccw=False).exterior.coords)] poly = sgeom.Polygon(exterior, interiors) patches = [] for pth in cpatch.geos_to_path(poly): patches.append(mpatches.PathPatch(pth)) collection = PatchCollection(patches, facecolor='yellow', alpha=0.4, transform=ccrs.Geodetic(), zorder=10) ax.add_collection(collection) @ImageTesting(['contour_with_interiors']) def test_contour_interiors(): # produces a polygon with multiple holes: nx, ny = 10, 10 numlev = 2 lons, lats = np.meshgrid(np.linspace(-50, 50, nx), np.linspace(-45, 45, ny)) data = np.sin(np.sqrt(lons ** 2 + lats ** 2)) ax = plt.subplot(221, projection=ccrs.PlateCarree()) ax.set_global() plt.contourf(lons, lats, data, numlev, transform=ccrs.PlateCarree()) ax.coastlines() plt.subplot(222, projection=ccrs.Robinson()) ax = plt.gca() ax.set_global() plt.contourf(lons, lats, data, numlev, transform=ccrs.PlateCarree()) ax.coastlines() # produces singular polygons (zero area polygons) numlev = 2 x, y = np.meshgrid(np.arange(-5.5, 5.5, 0.25), np.arange(-5.5, 5.5, 0.25)) dim = x.shape[0] data = np.sin(np.sqrt(x ** 2 + y ** 2)) lats = np.arange(dim) + 30 lons = np.arange(dim) - 20 ax = plt.subplot(223, projection=ccrs.PlateCarree()) ax.set_global() plt.contourf(lons, lats, data, numlev, transform=ccrs.PlateCarree()) ax.coastlines() plt.subplot(224, projection=ccrs.Robinson()) ax = plt.gca() ax.set_global() plt.contourf(lons, lats, data, numlev, transform=ccrs.PlateCarree()) ax.coastlines() if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_ticker.py0000644001374000021030000002113412700747662022036 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) try: from unittest.mock import Mock except ImportError: from mock import Mock from nose.tools import assert_equal try: from nose.tools import assert_raises_regex except ImportError: from nose.tools import assert_raises_regexp as assert_raises_regex from matplotlib.axes import Axes import cartopy.crs as ccrs from cartopy.mpl.geoaxes import GeoAxes from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter def test_LatitudeFormatter_bad_axes(): formatter = LatitudeFormatter() formatter.axis = Mock(axes=Mock(Axes, projection=ccrs.PlateCarree())) message = 'This formatter can only be used with cartopy axes.' with assert_raises_regex(TypeError, message): formatter(0) def test_LatitudeFormatter_bad_projection(): formatter = LatitudeFormatter() formatter.axis = Mock(axes=Mock(GeoAxes, projection=ccrs.Orthographic())) message = 'This formatter cannot be used with non-rectangular projections.' with assert_raises_regex(TypeError, message): formatter(0) def test_LongitudeFormatter_bad_axes(): formatter = LongitudeFormatter() formatter.axis = Mock(axes=Mock(Axes, projection=ccrs.PlateCarree())) message = 'This formatter can only be used with cartopy axes.' with assert_raises_regex(TypeError, message): formatter(0) def test_LongitudeFormatter_bad_projection(): formatter = LongitudeFormatter() formatter.axis = Mock(axes=Mock(GeoAxes, projection=ccrs.Orthographic())) message = 'This formatter cannot be used with non-rectangular projections.' with assert_raises_regex(TypeError, message): formatter(0) def test_LatitudeFormatter(): formatter = LatitudeFormatter() p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-90, -60, -30, 0, 30, 60, 90] result = [formatter(tick) for tick in test_ticks] expected = [u'90\u00B0S', u'60\u00B0S', u'30\u00B0S', u'0\u00B0', u'30\u00B0N', u'60\u00B0N', u'90\u00B0N'] assert_equal(result, expected) def test_LatitudeFormatter_degree_symbol(): formatter = LatitudeFormatter(degree_symbol='') p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-90, -60, -30, 0, 30, 60, 90] result = [formatter(tick) for tick in test_ticks] expected = [u'90S', u'60S', u'30S', u'0', u'30N', u'60N', u'90N'] assert_equal(result, expected) def test_LatitudeFormatter_number_format(): formatter = LatitudeFormatter(number_format='.2f') p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-90, -60, -30, 0, 30, 60, 90] result = [formatter(tick) for tick in test_ticks] expected = [u'90.00\u00B0S', u'60.00\u00B0S', u'30.00\u00B0S', u'0.00\u00B0', u'30.00\u00B0N', u'60.00\u00B0N', u'90.00\u00B0N'] assert_equal(result, expected) def test_LatitudeFormatter_mercator(): formatter = LatitudeFormatter() p = ccrs.Mercator() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-15496570.739707904, -8362698.548496634, -3482189.085407435, 0.0, 3482189.085407435, 8362698.548496634, 15496570.739707898] result = [formatter(tick) for tick in test_ticks] expected = [u'80\u00B0S', u'60\u00B0S', u'30\u00B0S', u'0\u00B0', u'30\u00B0N', u'60\u00B0N', u'80\u00B0N'] assert_equal(result, expected) def test_LatitudeFormatter_small_numbers(): formatter = LatitudeFormatter(number_format='.7f') p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [40.1275150, 40.1275152, 40.1275154] result = [formatter(tick) for tick in test_ticks] expected = [u'40.1275150\u00B0N', u'40.1275152\u00B0N', u'40.1275154\u00B0N'] assert_equal(result, expected) def test_LongitudeFormatter_central_longitude_0(): formatter = LongitudeFormatter(dateline_direction_label=True) p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-180, -120, -60, 0, 60, 120, 180] result = [formatter(tick) for tick in test_ticks] expected = [u'180\u00B0W', u'120\u00B0W', u'60\u00B0W', u'0\u00B0', u'60\u00B0E', u'120\u00B0E', u'180\u00B0E'] assert_equal(result, expected) def test_LongitudeFormatter_central_longitude_180(): formatter = LongitudeFormatter(zero_direction_label=True) p = ccrs.PlateCarree(central_longitude=180) formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-180, -120, -60, 0, 60, 120, 180] result = [formatter(tick) for tick in test_ticks] expected = [u'0\u00B0E', u'60\u00B0E', u'120\u00B0E', u'180\u00B0', u'120\u00B0W', u'60\u00B0W', u'0\u00B0W'] assert_equal(result, expected) def test_LongitudeFormatter_central_longitude_120(): formatter = LongitudeFormatter() p = ccrs.PlateCarree(central_longitude=120) formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-180, -120, -60, 0, 60, 120, 180] result = [formatter(tick) for tick in test_ticks] expected = [u'60\u00B0W', u'0\u00B0', u'60\u00B0E', u'120\u00B0E', u'180\u00B0', u'120\u00B0W', u'60\u00B0W'] assert_equal(result, expected) def test_LongitudeFormatter_degree_symbol(): formatter = LongitudeFormatter(degree_symbol='', dateline_direction_label=True) p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-180, -120, -60, 0, 60, 120, 180] result = [formatter(tick) for tick in test_ticks] expected = [u'180W', u'120W', u'60W', u'0', u'60E', u'120E', u'180E'] assert_equal(result, expected) def test_LongitudeFormatter_number_format(): formatter = LongitudeFormatter(number_format='.2f', dateline_direction_label=True) p = ccrs.PlateCarree() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-180, -120, -60, 0, 60, 120, 180] result = [formatter(tick) for tick in test_ticks] expected = [u'180.00\u00B0W', u'120.00\u00B0W', u'60.00\u00B0W', u'0.00\u00B0', u'60.00\u00B0E', u'120.00\u00B0E', u'180.00\u00B0E'] assert_equal(result, expected) def test_LongitudeFormatter_mercator(): formatter = LongitudeFormatter(dateline_direction_label=True) p = ccrs.Mercator() formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-20037508.342783064, -13358338.895188706, -6679169.447594353, 0.0, 6679169.447594353, 13358338.895188706, 20037508.342783064] result = [formatter(tick) for tick in test_ticks] expected = [u'180\u00B0W', u'120\u00B0W', u'60\u00B0W', u'0\u00B0', u'60\u00B0E', u'120\u00B0E', u'180\u00B0E'] assert_equal(result, expected) def test_LongitudeFormatter_small_numbers_0(): formatter = LongitudeFormatter(number_format='.7f') p = ccrs.PlateCarree(central_longitude=0) formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-17.1142343, -17.1142340, -17.1142337] result = [formatter(tick) for tick in test_ticks] expected = [u'17.1142343\u00B0W', u'17.1142340\u00B0W', u'17.1142337\u00B0W'] assert_equal(result, expected) def test_LongitudeFormatter_small_numbers_180(): formatter = LongitudeFormatter(zero_direction_label=True, number_format='.7f') p = ccrs.PlateCarree(central_longitude=180) formatter.axis = Mock(axes=Mock(GeoAxes, projection=p)) test_ticks = [-17.1142343, -17.1142340, -17.1142337] result = [formatter(tick) for tick in test_ticks] expected = [u'162.8857657\u00B0E', u'162.8857660\u00B0E', u'162.8857663\u00B0E'] assert_equal(result, expected) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_ticks.py0000644001374000021030000001203712700747662021674 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import math import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.ticker import nose.tools import cartopy.crs as ccrs from cartopy.tests.mpl import ImageTesting def _format_lat(val, i): if val > 0: return '%.0fN' % val elif val < 0: return '%.0fS' % abs(val) else: return '0' def _format_lon(val, i): # Apply periodic boundary conditions, with an almost equal test on 180 lon. while val > 180: val -= 360 while val < -180: val += 360 if abs(abs(val) - 180.) <= 1e-06 or val == 0: return '%.0f' % abs(val) elif val > 0: return '%.0fE' % val elif val < 0: return '%.0fW' % abs(val) test_fn_suffix = '' if mpl.__version__ >= '1.5' else '_pre_mpl_1.5' @ImageTesting(['xticks_no_transform' + test_fn_suffix], tolerance=0.5) def test_set_xticks_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines('110m') ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lon)) ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lat)) ax.set_xticks([-180, -90, 0, 90, 180]) ax.set_xticks([-135, -45, 45, 135], minor=True) @ImageTesting(['xticks_cylindrical' + test_fn_suffix], tolerance=0.5) def test_set_xticks_cylindrical(): ax = plt.axes(projection=ccrs.Mercator( min_latitude=-85., max_latitude=85., globe=ccrs.Globe(semimajor_axis=math.degrees(1)))) ax.coastlines('110m') ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lon)) ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lat)) ax.set_xticks([-180, -90, 0, 90, 180], crs=ccrs.PlateCarree()) ax.set_xticks([-135, -45, 45, 135], minor=True, crs=ccrs.PlateCarree()) def test_set_xticks_non_cylindrical(): ax = plt.axes(projection=ccrs.Orthographic()) with nose.tools.assert_raises(RuntimeError): ax.set_xticks([-180, -90, 0, 90, 180], crs=ccrs.Geodetic()) with nose.tools.assert_raises(RuntimeError): ax.set_xticks([-135, -45, 45, 135], minor=True, crs=ccrs.Geodetic()) plt.close() @ImageTesting(['yticks_no_transform' + test_fn_suffix], tolerance=0.5) def test_set_yticks_no_transform(): ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines('110m') ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lon)) ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lat)) ax.set_yticks([-60, -30, 0, 30, 60]) ax.set_yticks([-75, -45, 15, 45, 75], minor=True) @ImageTesting(['yticks_cylindrical' + test_fn_suffix], tolerance=0.5) def test_set_yticks_cylindrical(): ax = plt.axes(projection=ccrs.Mercator( min_latitude=-85., max_latitude=85., globe=ccrs.Globe(semimajor_axis=math.degrees(1)))) ax.coastlines('110m') ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lon)) ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(_format_lat)) ax.set_yticks([-60, -30, 0, 30, 60], crs=ccrs.PlateCarree()) ax.set_yticks([-75, -45, 15, 45, 75], minor=True, crs=ccrs.PlateCarree()) def test_set_yticks_non_cylindrical(): ax = plt.axes(projection=ccrs.Orthographic()) with nose.tools.assert_raises(RuntimeError): ax.set_yticks([-60, -30, 0, 30, 60], crs=ccrs.Geodetic()) with nose.tools.assert_raises(RuntimeError): ax.set_yticks([-75, -45, 15, 45, 75], minor=True, crs=ccrs.Geodetic()) plt.close() @ImageTesting(['xyticks' + test_fn_suffix], tolerance=0.5) def test_set_xyticks(): fig = plt.figure(figsize=(10, 10)) projections = (ccrs.PlateCarree(), ccrs.Mercator(globe=ccrs.Globe( semimajor_axis=math.degrees(1))), ccrs.TransverseMercator()) x = -3.275024 y = 50.753998 for i, prj in enumerate(projections, 1): ax = fig.add_subplot(3, 1, i, projection=prj) ax.set_extent([-12.5, 4, 49, 60], ccrs.Geodetic()) ax.coastlines('110m') p, q = prj.transform_point(x, y, ccrs.Geodetic()) ax.set_xticks([p]) ax.set_yticks([q]) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/mpl/test_web_services.py0000644001374000021030000000316312700747662023237 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import matplotlib.pyplot as plt from cartopy.tests.mpl import ImageTesting import cartopy.crs as ccrs from cartopy.io.ogc_clients import _OWSLIB_AVAILABLE @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') @ImageTesting(['wmts']) def test_wmts(): ax = plt.axes(projection=ccrs.PlateCarree()) url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' # Use a layer which doesn't change over time. ax.add_wmts(url, 'MODIS_Water_Mask') @unittest.skipIf(not _OWSLIB_AVAILABLE, 'OWSLib is unavailable.') @ImageTesting(['wms']) def test_wms(): ax = plt.axes(projection=ccrs.Orthographic()) url = 'http://vmap0.tiles.osgeo.org/wms/vmap0' layer = 'basic' ax.add_wms(url, layer) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/__init__.py0000644001374000021030000000255012705644356020467 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import contextlib import functools import re import tempfile import shutil import types @contextlib.contextmanager def temp_dir(suffix=None): if suffix is None: suffix = '' dname = tempfile.mkdtemp(suffix=suffix) try: yield dname finally: shutil.rmtree(dname) def not_a_nose_fixture(function): """ Provides a decorator to mark a function as not a nose fixture. """ @functools.wraps(function) def setup(app): if isinstance(app, types.ModuleType): return return function(app) return setup Cartopy-0.14.2/lib/cartopy/tests/test_coastline.py0000644001374000021030000000321112700747662021742 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import cartopy import cartopy.io.shapereader as shp COASTLINE_PATH = shp.natural_earth() class TestCoastline(unittest.TestCase): def test_robust(self): # Make sure all the coastlines can be projected without raising any # exceptions. projection = cartopy.crs.TransverseMercator(central_longitude=-90) reader = shp.Reader(COASTLINE_PATH) all_geometries = list(reader.geometries()) geometries = [] geometries += all_geometries # geometries += all_geometries[48:52] # Aus & Taz # geometries += all_geometries[72:73] # GB # for geometry in geometries: for i, geometry in enumerate(geometries[93:]): for line_string in geometry: multi_line_string = projection.project_geometry(line_string) if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_coding_standards.py0000644001374000021030000002317512700747662023302 0ustar itpeavd00000000000000# (C) British Crown Copyright 2012 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) from datetime import datetime from fnmatch import fnmatch from itertools import chain import os import re import subprocess import unittest import pep8 import cartopy LICENSE_TEMPLATE = """ # (C) British Crown Copyright {YEARS}, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see .""".strip() LICENSE_RE_PATTERN = re.escape(LICENSE_TEMPLATE).replace('\{YEARS\}', '(.*?)') # Add shebang possibility or C comment starter to the LICENSE_RE_PATTERN LICENSE_RE_PATTERN = r'((\#\!.*|\/\*)\n)?' + LICENSE_RE_PATTERN LICENSE_RE = re.compile(LICENSE_RE_PATTERN, re.MULTILINE) # Guess cartopy repo directory of cartopy - realpath is used to mitigate # against Python finding the cartopy package via a symlink. CARTOPY_DIR = os.path.realpath(os.path.dirname(cartopy.__file__)) REPO_DIR = os.getenv('CARTOPY_GIT_DIR', os.path.dirname(os.path.dirname(CARTOPY_DIR))) class TestLicenseHeaders(unittest.TestCase): @staticmethod def years_of_license_in_file(fh): """ Using :data:`LICENSE_RE` look for the years defined in the license header of the given file handle. If the license cannot be found in the given fh, None will be returned, else a tuple of (start_year, end_year) will be returned. """ license_matches = LICENSE_RE.match(fh.read()) if not license_matches: # no license found in file. return None years = license_matches.groups()[-1] if len(years) == 4: start_year = end_year = int(years) elif len(years) == 11: start_year, end_year = int(years[:4]), int(years[7:]) else: fname = getattr(fh, 'name', 'unknown filename') raise ValueError("Unexpected year(s) string in {}'s copyright " "notice: {!r}".format(fname, years)) return (start_year, end_year) @staticmethod def last_change_by_fname(): """ Return a dictionary of all the files under git which maps to the datetime of their last modification in the git history. .. note:: This function raises a ValueError if the repo root does not have a ".git" folder. If git is not installed on the system, or cannot be found by subprocess, an IOError may also be raised. """ # Check the ".git" folder exists at the repo dir. if not os.path.isdir(os.path.join(REPO_DIR, '.git')): raise ValueError('{} is not a git repository.'.format(REPO_DIR)) # Call "git whatchanged" to get the details of all the files and when # they were last changed. output = subprocess.check_output(['git', 'ls-tree', '-r', '--name-only', 'HEAD'], cwd=REPO_DIR) output = output.decode().split('\n') res = {} for fname in output: dt = subprocess.check_output(['git', 'log', '-1', '--pretty=%ct', '--', fname], cwd=REPO_DIR) dt = datetime.fromtimestamp(int(dt)) res[fname] = dt return res def test_license_headers(self): exclude_patterns = ('build/*', 'dist/*', 'docs/build/*', 'docs/source/examples/*.py', 'docs/source/sphinxext/*.py', 'lib/cartopy/examples/*.py') try: last_change_by_fname = self.last_change_by_fname() except ValueError as e: # Caught the case where this is not a git repo. return self.skipTest('cartopy installation did not look like a ' 'git repo: ' + str(e)) failed = False for fname, last_change in sorted(last_change_by_fname.items()): full_fname = os.path.join(REPO_DIR, fname) root, ext = os.path.splitext(full_fname) if ext in ('.py', '.pyx', '.c', '.cpp', '.h') and \ os.path.isfile(full_fname) and \ not any(fnmatch(fname, pat) for pat in exclude_patterns): with open(full_fname) as fh: years = TestLicenseHeaders.years_of_license_in_file(fh) if years is None: print('The file {} has no valid header license and ' 'has not been excluded from the license header ' 'test.'.format(fname)) failed = True elif last_change.year > years[1]: print('The file header at {} is out of date. The last' ' commit was in {}, but the copyright states it' ' was {}.'.format(fname, last_change.year, years[1])) failed = True if failed: raise ValueError('There were license header failures. See stdout.') class TestCodeFormat(unittest.TestCase): def test_pep8_conformance(self): # Tests the cartopy codebase against the "pep8" tool. # # Users can add their own excluded files (should files exist in the # local directory which is not in the repository) by adding a # ".pep8_test_exclude.txt" file in the same directory as this test. # The file should be a line separated list of filenames/directories # as can be passed to the "pep8" tool's exclude list. pep8style = pep8.StyleGuide(quiet=False) pep8style.options.exclude.extend(['trace.py', '_crs.py', '*/cartopy/geodesic/_geodesic.py']) # Ignore E402 module level import not at top of file pep8style.options.ignore += ('E402', ) # allow users to add their own exclude list extra_exclude_file = os.path.join(os.path.dirname(__file__), '.pep8_test_exclude.txt') if os.path.exists(extra_exclude_file): with open(extra_exclude_file, 'r') as fh: extra_exclude = [line.strip() for line in fh if line.strip()] pep8style.options.exclude.extend(extra_exclude) result = pep8style.check_files([CARTOPY_DIR]) self.assertEqual(result.total_errors, 0, "Found code syntax " "errors (and warnings).") class TestFutureImports(unittest.TestCase): excluded = ( '*/cartopy/examples/*.py', '*/docs/source/examples/*.py', '*/cartopy/_crs.py', # A file created by setuptools for so loading. '*/cartopy/trace.py', # Ditto. '*/cartopy/geodesic/_geodesic.py', ) future_imports_pattern = re.compile( r"^from __future__ import \(absolute_import,\s*division,\s*" r"print_function(,\s*unicode_literals)?\)$", flags=re.MULTILINE) def test_future_imports(self): # Tests that every single Python file includes the appropriate # __future__ import to enforce consistent behaviour. check_paths = [CARTOPY_DIR] failed = False for dirpath, _, files in chain.from_iterable(os.walk(path) for path in check_paths): for fname in files: full_fname = os.path.join(dirpath, fname) if not full_fname.endswith('.py'): continue if not os.path.isfile(full_fname): continue if any(fnmatch(full_fname, pat) for pat in self.excluded): continue with open(full_fname, "r") as fh: content = fh.read() if re.search(self.future_imports_pattern, content) is None: print('The file {} has no valid __future__ imports ' 'and has not been excluded from the imports ' 'test.'.format(full_fname)) failed = True if failed: raise ValueError('There were __future__ import check failures. ' 'See stdout.') if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_crs.py0000644001374000021030000002342612700747662020562 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) from io import BytesIO import pickle import unittest import numpy as np from numpy.testing import assert_array_almost_equal as assert_arr_almost_eq from nose.tools import assert_equal try: import pyepsg except ImportError: pyepsg = None import shapely.geometry as sgeom import cartopy.crs as ccrs class TestCRS(unittest.TestCase): def test_hash(self): stereo = ccrs.Stereographic(90) north = ccrs.NorthPolarStereo() self.assertEqual(stereo, north) self.assertFalse(stereo != north) self.assertEqual(hash(stereo), hash(north)) self.assertEqual(ccrs.Geodetic(), ccrs.Geodetic()) def test_osni(self): osni = ccrs.OSNI() ll = ccrs.Geodetic() # results obtained by nearby.org.uk. lat, lon = np.array([54.5622169298669, -5.54159863617957], dtype=np.double) east, north = np.array([359000, 371000], dtype=np.double) assert_arr_almost_eq(osni.transform_point(lon, lat, ll), np.array([east, north]), -1) assert_arr_almost_eq(ll.transform_point(east, north, osni), np.array([lon, lat]), 3) def _check_osgb(self, osgb): ll = ccrs.Geodetic() # results obtained by streetmap.co.uk. lat, lon = np.array([50.462023, -3.478831], dtype=np.double) east, north = np.array([295131, 63511], dtype=np.double) # note the handling of precision here... assert_arr_almost_eq(np.array(osgb.transform_point(lon, lat, ll)), np.array([east, north]), 1) assert_arr_almost_eq(ll.transform_point(east, north, osgb), [lon, lat], 2) r_lon, r_lat = ll.transform_point(east, north, osgb) r_inverted = np.array(osgb.transform_point(r_lon, r_lat, ll)) assert_arr_almost_eq(r_inverted, [east, north], 3) r_east, r_north = osgb.transform_point(lon, lat, ll) r_inverted = np.array(ll.transform_point(r_east, r_north, osgb)) assert_arr_almost_eq(r_inverted, [lon, lat]) def test_osgb(self): self._check_osgb(ccrs.OSGB()) @unittest.skipIf(pyepsg is None, 'requires pyepsg') def test_epsg(self): uk = ccrs.epsg(27700) self.assertEqual(uk.epsg_code, 27700) self.assertEqual(uk.x_limits, (-84667.135022467002, 676354.14167904831)) self.assertEqual(uk.y_limits, (-2957.1831134549138, 1242951.4397385262 )) self.assertEqual(uk.threshold, 7610.2127670151531) self._check_osgb(uk) def test_europp(self): europp = ccrs.EuroPP() proj4_init = europp.proj4_init # Transverse Mercator, UTM zone 32, self.assertTrue('+proj=utm' in proj4_init) self.assertTrue('+zone=32' in proj4_init) # International 1924 ellipsoid. self.assertTrue('+ellps=intl' in proj4_init) def test_transform_points_nD(self): rlons = np.array([[350., 352., 354.], [350., 352., 354.]]) rlats = np.array([[-5., -0., 1.], [-4., -1., 0.]]) src_proj = ccrs.RotatedGeodetic(pole_longitude=178.0, pole_latitude=38.0) target_proj = ccrs.Geodetic() res = target_proj.transform_points(x=rlons, y=rlats, src_crs=src_proj) unrotated_lon = res[..., 0] unrotated_lat = res[..., 1] # Solutions derived by proj4 direct. solx = np.array([[-16.42176094, -14.85892262, -11.90627520], [-16.71055023, -14.58434624, -11.68799988]]) soly = np.array([[46.00724251, 51.29188893, 52.59101488], [46.98728486, 50.30706042, 51.60004528]]) assert_arr_almost_eq(unrotated_lon, solx) assert_arr_almost_eq(unrotated_lat, soly) def test_transform_points_1D(self): rlons = np.array([350., 352., 354., 356.]) rlats = np.array([-5., -0., 5., 10.]) src_proj = ccrs.RotatedGeodetic(pole_longitude=178.0, pole_latitude=38.0) target_proj = ccrs.Geodetic() res = target_proj.transform_points(x=rlons, y=rlats, src_crs=src_proj) unrotated_lon = res[..., 0] unrotated_lat = res[..., 1] # Solutions derived by proj4 direct. solx = np.array([-16.42176094, -14.85892262, -12.88946157, -10.35078336]) soly = np.array([46.00724251, 51.29188893, 56.55031485, 61.77015703]) assert_arr_almost_eq(unrotated_lon, solx) assert_arr_almost_eq(unrotated_lat, soly) def test_transform_points_xyz(self): # Test geodetic transforms when using z value rx = np.array([2574.32516e3]) ry = np.array([837.562e3]) rz = np.array([5761.325e3]) src_proj = ccrs.Geocentric() target_proj = ccrs.Geodetic() res = target_proj.transform_points(x=rx, y=ry, z=rz, src_crs=src_proj) glat = res[..., 0] glon = res[..., 1] galt = res[..., 2] # Solution generated by pyproj solx = np.array([18.0224043189]) soly = np.array([64.9796515089]) solz = np.array([5048.03893734]) assert_arr_almost_eq(glat, solx) assert_arr_almost_eq(glon, soly) assert_arr_almost_eq(galt, solz) def test_globe(self): # Ensure the globe affects output. rugby_globe = ccrs.Globe(semimajor_axis=9000000, semiminor_axis=1000000) footy_globe = ccrs.Globe(semimajor_axis=1000000, semiminor_axis=1000000) rugby_moll = ccrs.Mollweide(globe=rugby_globe) footy_moll = ccrs.Mollweide(globe=footy_globe) rugby_pt = rugby_moll.transform_point(10, 10, ccrs.Geodetic()) footy_pt = footy_moll.transform_point(10, 10, ccrs.Geodetic()) assert_arr_almost_eq(rugby_pt, (1400915, 1741319), decimal=0) assert_arr_almost_eq(footy_pt, (155657, 193479), decimal=0) def test_project_point(self): point = sgeom.Point([0, 45]) multi_point = sgeom.MultiPoint([point, sgeom.Point([180, 45])]) pc = ccrs.PlateCarree() pc_rotated = ccrs.PlateCarree(central_longitude=180) result = pc_rotated.project_geometry(point, pc) assert_arr_almost_eq(result.xy, [[-180.], [45.]]) result = pc_rotated.project_geometry(multi_point, pc) self.assertIsInstance(result, sgeom.MultiPoint) self.assertEqual(len(result), 2) assert_arr_almost_eq(result[0].xy, [[-180.], [45.]]) assert_arr_almost_eq(result[1].xy, [[0], [45.]]) def test_utm(self): utm30n = ccrs.UTM(30) ll = ccrs.Geodetic() lat, lon = np.array([51.5, -3.0], dtype=np.double) east, north = np.array([500000, 5705429.2], dtype=np.double) assert_arr_almost_eq(utm30n.transform_point(lon, lat, ll), [east, north], decimal=1) assert_arr_almost_eq(ll.transform_point(east, north, utm30n), [lon, lat], decimal=1) utm38s = ccrs.UTM(38, southern_hemisphere=True) lat, lon = np.array([-18.92, 47.5], dtype=np.double) east, north = np.array([763316.7, 7906160.8], dtype=np.double) assert_arr_almost_eq(utm38s.transform_point(lon, lat, ll), [east, north], decimal=1) assert_arr_almost_eq(ll.transform_point(east, north, utm38s), [lon, lat], decimal=1) def test_pickle(): # check that we can pickle a simple CRS fh = BytesIO() pickle.dump(ccrs.PlateCarree(), fh) fh.seek(0) pc = pickle.load(fh) assert pc == ccrs.PlateCarree() def test_PlateCarree_shortcut(): central_lons = [[0, 0], [0, 180], [0, 10], [10, 0], [-180, 180], [ 180, -180]] target = [([[-180, -180], [-180, 180]], 0), ([[-180, 0], [0, 180]], 180), ([[-180, -170], [-170, 180]], 10), ([[-180, 170], [170, 180]], -10), ([[-180, 180], [180, 180]], 360), ([[-180, -180], [-180, 180]], -360), ] assert len(target) == len(central_lons) for expected, (s_lon0, t_lon0) in zip(target, central_lons): expected_bboxes, expected_offset = expected src = ccrs.PlateCarree(central_longitude=s_lon0) target = ccrs.PlateCarree(central_longitude=t_lon0) bbox, offset = src._bbox_and_offset(target) assert_equal(offset, expected_offset) assert_equal(bbox, expected_bboxes) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/test_crs_transform_vectors.py0000644001374000021030000001741012700747662024416 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import warnings import numpy as np from numpy.testing import assert_array_almost_equal import cartopy.crs as ccrs class TestTransformVectors(unittest.TestCase): def test_transform(self): # Test some simple vectors to make sure they are transformed # correctly. rlons = np.array([-90., 0, 90., 180.]) rlats = np.array([0., 0., 0., 0.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) # transform grid eastward vectors ut, vt = target_proj.transform_vectors(src_proj, rlons, rlats, np.ones([4]), np.zeros([4])) assert_array_almost_equal(ut, np.array([0, 1, 0, -1]), decimal=2) assert_array_almost_equal(vt, np.array([-1, 0, 1, 0]), decimal=2) # transform grid northward vectors ut, vt = target_proj.transform_vectors(src_proj, rlons, rlats, np.zeros([4]), np.ones([4])) assert_array_almost_equal(ut, np.array([1, 0, -1, 0]), decimal=2) assert_array_almost_equal(vt, np.array([0, 1, 0, -1]), decimal=2) # transform grid north-eastward vectors ut, vt = target_proj.transform_vectors(src_proj, rlons, rlats, np.ones([4]), np.ones([4])) assert_array_almost_equal(ut, np.array([1, 1, -1, -1]), decimal=2) assert_array_almost_equal(vt, np.array([-1, 1, 1, -1]), decimal=2) def test_transform_and_inverse(self): # Check a full circle transform back to the native projection. x = np.arange(-60, 42.5, 2.5) y = np.arange(30, 72.5, 2.5) x2d, y2d = np.meshgrid(x, y) u = np.cos(np.deg2rad(y2d)) v = np.cos(2. * np.deg2rad(x2d)) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) proj_xyz = target_proj.transform_points(src_proj, x2d, y2d) xt, yt = proj_xyz[..., 0], proj_xyz[..., 1] ut, vt = target_proj.transform_vectors(src_proj, x2d, y2d, u, v) utt, vtt = src_proj.transform_vectors(target_proj, xt, yt, ut, vt) assert_array_almost_equal(u, utt, decimal=4) assert_array_almost_equal(v, vtt, decimal=4) def test_invalid_input_domain(self): # If an input coordinate is outside the input projection domain # we should be able to handle it correctly. rlon = np.array([270.]) rlat = np.array([0.]) u = np.array([1.]) v = np.array([0.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) ut, vt = target_proj.transform_vectors(src_proj, rlon, rlat, u, v) assert_array_almost_equal(ut, np.array([0]), decimal=2) assert_array_almost_equal(vt, np.array([-1]), decimal=2) def test_invalid_x_domain(self): # If the point we need to calculate the vector angle falls outside the # source projection x-domain it should be handled correctly as long as # it is not a corner point. rlon = np.array([180.]) rlat = np.array([0.]) u = np.array([1.]) v = np.array([0.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) ut, vt = target_proj.transform_vectors(src_proj, rlon, rlat, u, v) assert_array_almost_equal(ut, np.array([-1]), decimal=2) assert_array_almost_equal(vt, np.array([0.]), decimal=2) def test_invalid_y_domain(self): # If the point we need to calculate the vector angle falls outside the # source projection y-domain it should be handled correctly as long as # it is not a corner point. rlon = np.array([0.]) rlat = np.array([90.]) u = np.array([0.]) v = np.array([1.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) ut, vt = target_proj.transform_vectors(src_proj, rlon, rlat, u, v) assert_array_almost_equal(ut, np.array([0.]), decimal=2) assert_array_almost_equal(vt, np.array([1.]), decimal=2) def test_invalid_xy_domain_corner(self): # If the point we need to calculate the vector angle falls outside the # source projection x and y-domain it should be handled correctly. rlon = np.array([180.]) rlat = np.array([90.]) u = np.array([1.]) v = np.array([1.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) ut, vt = target_proj.transform_vectors(src_proj, rlon, rlat, u, v) assert_array_almost_equal(ut, np.array([0.]), decimal=2) assert_array_almost_equal(vt, np.array([-2**.5]), decimal=2) def test_invalid_x_domain_corner(self): # If the point we need to calculate the vector angle falls outside the # source projection x-domain and is a corner point, it may be handled # incorrectly and a warning should be raised. rlon = np.array([180.]) rlat = np.array([90.]) u = np.array([1.]) v = np.array([-1.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) with warnings.catch_warnings(): warnings.simplefilter('error') with self.assertRaises(UserWarning): ut, vt = target_proj.transform_vectors( src_proj, rlon, rlat, u, v) def test_invalid_y_domain_corner(self): # If the point we need to calculate the vector angle falls outside the # source projection y-domain and is a corner point, it may be handled # incorrectly and a warning should be raised. rlon = np.array([180.]) rlat = np.array([90.]) u = np.array([-1.]) v = np.array([1.]) src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(central_latitude=90, central_longitude=0) with warnings.catch_warnings(): warnings.simplefilter('error') with self.assertRaises(UserWarning): ut, vt = target_proj.transform_vectors( src_proj, rlon, rlat, u, v) Cartopy-0.14.2/lib/cartopy/tests/test_geodesic.py0000644001374000021030000001432412700747662021552 0ustar itpeavd00000000000000# (C) British Crown Copyright 2015 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np from numpy.testing import assert_almost_equal, assert_array_almost_equal from nose.tools import assert_equal from cartopy import geodesic class TestGeodesic(unittest.TestCase): def setUp(self): """ Data sampled from the GeographicLib Test Data for Geodesics at: http://geographiclib.sourceforge.net/html/geodesic.html#testgeod """ self.geod = geodesic.Geodesic() # Fill a 10 by 7 numpy array with starting lons, lats, azimuths; ending # lons, lats and azimuths and distances to travel. data = np.array([(0.0000000000, 36.5300423550, 176.1258751622, 5.7623446947, -48.1642707791, 175.3343083163, 9398502.0434687007), (0.0000000000, 20.8766024619, 6.9012827094, 163.9792202999, 64.2764863397, 165.0440144913, 10462971.2273696996), (0.0000000000, 59.7405712203, 80.9569174535, 80.1969954660, 30.9857449391, 144.4488137288, 6549489.1863671001), (0.0000000000, 38.6508883588, 18.3455177945, 23.5931524958, 66.3457305181, 37.7145989984, 3425212.4767990001), (0.0000000000, 23.2214345509, 165.5720618611, 148.3625110902, -68.8453788967, 39.2692310682, 14506511.2971898001), (0.0000000000, 31.2989275984, 155.7723493796, 93.8764112107, -69.2776346668, 98.5250397385, 13370814.5013951007), (0.0000000000, 49.6823298563, 1.0175398481, 5.3554086646, 83.8681965431, 6.1667605618, 3815028.2543704999), (0.0000000000, 32.7651878215, 98.6494285944, 70.3527194957, 2.4777491770, 123.5999412794, 8030520.7178932996), (0.0000000000, 46.3648067071, 94.9148631993, 56.5676529172, 25.2581951337, 130.4405565458, 5485075.9286326999), (0.0000000000, 33.7321188396, 147.9041907517, 33.1346935645, -26.3211288531, 150.4502346224, 7512675.5414637001)], dtype=[('start_lon', np.float64), ('start_lat', np.float64), ('start_azi', np.float64), ('end_lon', np.float64), ('end_lat', np.float64), ('end_azi', np.float64), ('dist', np.float64)]) self.data = data.view(np.recarray) self.start_pts = np.array([self.data.start_lon, self.data.start_lat]).T self.end_pts = np.array([self.data.end_lon, self.data.end_lat]).T self.dir_soln = np.array([self.data.end_lon, self.data.end_lat, self.data.end_azi]).T self.inv_soln = np.array([self.data.dist, self.data.start_azi, self.data.end_azi]).T def test_dir(self): geod_dir = self.geod.direct(self.start_pts, self.data.start_azi, self.data.dist) assert_array_almost_equal(geod_dir, self.dir_soln, decimal=5) def test_dir_broadcast(self): repeat_dists = np.repeat(np.array([self.data.dist[0]]), 10, axis=0) repeat_start_pts = np.repeat(np.array([self.start_pts[0]]), 10, axis=0) repeat_results = np.repeat(np.array([self.dir_soln[0]]), 10, axis=0) geod_dir1 = self.geod.direct(self.start_pts[0], self.data.start_azi[0], repeat_dists) geod_dir2 = self.geod.direct(repeat_start_pts, self.data.start_azi[0], self.data.dist[0]) assert_array_almost_equal(geod_dir1, repeat_results, decimal=5) assert_array_almost_equal(geod_dir2, repeat_results, decimal=5) def test_inverse(self): geod_inv = self.geod.inverse(self.start_pts, self.end_pts) assert_array_almost_equal(geod_inv, self.inv_soln, decimal=5) def test_inverse_broadcast(self): repeat_start_pts = np.repeat(np.array([self.start_pts[0]]), 10, axis=0) repeat_end_pts = np.repeat(np.array([self.end_pts[0]]), 10, axis=0) repeat_results = np.repeat(np.array([self.inv_soln[0]]), 10, axis=0) geod_inv1 = self.geod.inverse(self.start_pts[0], repeat_end_pts) geod_inv2 = self.geod.inverse(repeat_start_pts, self.end_pts[0]) assert_array_almost_equal(geod_inv1, repeat_results, decimal=5) assert_array_almost_equal(geod_inv2, repeat_results, decimal=5) def test_circle(self): geod_circle = self.geod.circle(40, 50, 500000, n_samples=3) assert_almost_equal(geod_circle, np.array([[40., 54.49349757], [34.23766162, 47.60355349], [45.76233838, 47.60355349]]), decimal=5) def test_str(self): expected = '' assert_equal(expected, str(self.geod)) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/test_img_nest.py0000644001374000021030000003660612700747662021604 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import io import os import shutil import warnings from nose.tools import assert_equal, assert_in, assert_true import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal from PIL import Image import shapely.geometry as sgeom from six.moves import cPickle as pickle from cartopy import config import cartopy.crs as ccrs import cartopy.io.img_tiles as cimgt import cartopy.io.img_nest as cimg_nest import cartopy.tests as tests #: An integer version which should be increased if the test data needs #: to change in some way. _TEST_DATA_VERSION = 1 _TEST_DATA_DIR = os.path.join(config["data_dir"], 'wmts', 'aerial') #: A global to determine whether the test data has already been made available #: in this session. _TEST_DATA_AVAILABLE = False def test_world_files(): func = cimg_nest.Img.world_files fname = 'one' expected = ['one.w', 'one.W', 'ONE.w', 'ONE.W'] assert_equal(func(fname), expected) fname = 'one.png' expected = ['one.pngw', 'one.pgw', 'one.PNGW', 'one.PGW', 'ONE.pngw', 'ONE.pgw', 'ONE.PNGW', 'ONE.PGW'] assert_equal(func(fname), expected) fname = '/one.png' expected = ['/one.pngw', '/one.pgw', '/one.PNGW', '/one.PGW', '/ONE.pngw', '/ONE.pgw', '/ONE.PNGW', '/ONE.PGW'] assert_equal(func(fname), expected) fname = '/one/two.png' expected = ['/one/two.pngw', '/one/two.pgw', '/one/two.PNGW', '/one/two.PGW', '/one/TWO.pngw', '/one/TWO.pgw', '/one/TWO.PNGW', '/one/TWO.PGW'] assert_equal(func(fname), expected) fname = '/one/two/THREE.png' expected = ['/one/two/THREE.pngw', '/one/two/THREE.pgw', '/one/two/THREE.PNGW', '/one/two/THREE.PGW', '/one/two/three.pngw', '/one/two/three.pgw', '/one/two/three.PNGW', '/one/two/three.PGW'] assert_equal(func(fname), expected) def _save_world(fname, args): _world = ('{x_pix_size}\n' '{y_rotation}\n' '{x_rotation}\n' '{y_pix_size}\n' '{x_center}\n' '{y_center}\n') with open(fname, 'w') as fh: fh.write(_world.format(**args)) def test_intersect(): with tests.temp_dir() as base_dir: # Zoom level zero. # File 1: Parent space of all images. z_0_dir = os.path.join(base_dir, 'z_0') os.mkdir(z_0_dir) world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=1, y_center=1) im = Image.new('RGB', (50, 50)) fname = os.path.join(z_0_dir, 'p0.tfw') _save_world(fname, world) fname = os.path.join(z_0_dir, 'p0.tif') im.save(fname) # Zoom level one. # File 1: complete containment within p0. z_1_dir = os.path.join(base_dir, 'z_1') os.mkdir(z_1_dir) world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=21, y_center=21) im = Image.new('RGB', (30, 30)) fname = os.path.join(z_1_dir, 'p1.tfw') _save_world(fname, world) fname = os.path.join(z_1_dir, 'p1.tif') im.save(fname) # Zoom level two. # File 1: intersect right edge with p1 left edge. z_2_dir = os.path.join(base_dir, 'z_2') os.mkdir(z_2_dir) world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=6, y_center=21) im = Image.new('RGB', (5, 5)) fname = os.path.join(z_2_dir, 'p2-1.tfw') _save_world(fname, world) fname = os.path.join(z_2_dir, 'p2-1.tif') im.save(fname) # File 2: intersect upper right corner with p1 # lower left corner. world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=6, y_center=6) im = Image.new('RGB', (5, 5)) fname = os.path.join(z_2_dir, 'p2-2.tfw') _save_world(fname, world) fname = os.path.join(z_2_dir, 'p2-2.tif') im.save(fname) # File 3: complete containment within p1. world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=41, y_center=41) im = Image.new('RGB', (5, 5)) fname = os.path.join(z_2_dir, 'p2-3.tfw') _save_world(fname, world) fname = os.path.join(z_2_dir, 'p2-3.tif') im.save(fname) # File 4: overlap with p1 right edge. world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=76, y_center=61) im = Image.new('RGB', (5, 5)) fname = os.path.join(z_2_dir, 'p2-4.tfw') _save_world(fname, world) fname = os.path.join(z_2_dir, 'p2-4.tif') im.save(fname) # File 5: overlap with p1 bottom right corner. world = dict(x_pix_size=2, y_rotation=0, x_rotation=0, y_pix_size=2, x_center=76, y_center=76) im = Image.new('RGB', (5, 5)) fname = os.path.join(z_2_dir, 'p2-5.tfw') _save_world(fname, world) fname = os.path.join(z_2_dir, 'p2-5.tif') im.save(fname) # Provided in reverse order in order to test the area sorting. items = [('dummy-z-2', z_2_dir), ('dummy-z-1', z_1_dir), ('dummy-z-0', z_0_dir)] nic = cimg_nest.NestedImageCollection.from_configuration('dummy', None, items) names = [collection.name for collection in nic._collections] zoom_levels = ['dummy-z-0', 'dummy-z-1', 'dummy-z-2'] assert_true(names, zoom_levels) # Check all images are loaded. for zoom, expected_image_count in zip(zoom_levels, [1, 1, 5]): images = nic._collections_by_name[zoom].images assert_equal(len(images), expected_image_count) # Check the image ancestry. zoom_levels = ['dummy-z-0', 'dummy-z-1'] assert_equal(sorted(k[0] for k in nic._ancestry.keys()), zoom_levels) expected = [('dummy-z-0', ['p1.tif']), ('dummy-z-1', ['p2-3.tif', 'p2-4.tif', 'p2-5.tif'])] for zoom, image_names in expected: key = [k for k in nic._ancestry.keys() if k[0] == zoom][0] ancestry = nic._ancestry[key] fnames = sorted([os.path.basename(item[1].filename) for item in ancestry]) assert_equal(image_names, fnames) # Check image retrieval for specific domain. items = [(sgeom.box(20, 20, 80, 80), 3), (sgeom.box(20, 20, 75, 75), 1), (sgeom.box(40, 40, 85, 85), 3)] for domain, expected in items: result = [image for image in nic.find_images(domain, 'dummy-z-2')] assert_equal(len(result), expected) def _tile_from_img(img): """ Turns an img into the appropriate x, y, z tile based on its filename. Imgs have a filename attribute which is something like "lib/cartopy/data/wmts/aerial/z_0/x_0_y0.png" """ _, z = os.path.basename(os.path.dirname(img.filename)).split('_') xy, _ = os.path.splitext(os.path.basename(img.filename)) _, x, _, y = xy.split('_') return int(x), int(y), int(z) class RoundedImg(cimg_nest.Img): @staticmethod def world_file_extent(*args, **kwargs): """ Takes account for the fact that the image tiles are stored with imprecise tfw files. """ extent, pix_size = cimg_nest.Img.world_file_extent(*args, **kwargs) # round the extent extent = tuple(round(v, 4) for v in extent) pix_size = tuple(round(v, 4) for v in pix_size) return extent, pix_size def test_nest(): crs = cimgt.GoogleTiles().crs z0 = cimg_nest.ImageCollection('aerial z0 test', crs) z0.scan_dir_for_imgs(os.path.join(_TEST_DATA_DIR, 'z_0'), glob_pattern='*.png', img_class=RoundedImg) z1 = cimg_nest.ImageCollection('aerial z1 test', crs) z1.scan_dir_for_imgs(os.path.join(_TEST_DATA_DIR, 'z_1'), glob_pattern='*.png', img_class=RoundedImg) z2 = cimg_nest.ImageCollection('aerial z2 test', crs) z2.scan_dir_for_imgs(os.path.join(_TEST_DATA_DIR, 'z_2'), glob_pattern='*.png', img_class=RoundedImg) # make sure all the images from z1 are contained by the z0 image. The # only reason this might occur is if the tfw files are handling # floating point values badly for img in z1.images: if not z0.images[0].bbox().contains(img.bbox()): raise IOError('The test images aren\'t all "contained" by the ' 'z0 images, the nest cannot possibly work.\n ' 'img {!s} not contained by {!s}\nExtents: {!s}; ' '{!s}'.format(img, z0.images[0], img.extent, z0.images[0].extent)) nest_z0_z1 = cimg_nest.NestedImageCollection('aerial test', crs, [z0, z1]) nest = cimg_nest.NestedImageCollection('aerial test', crs, [z0, z1, z2]) z0_key = ('aerial z0 test', z0.images[0]) assert_true(z0_key in nest_z0_z1._ancestry.keys()) assert_equal(len(nest_z0_z1._ancestry), 1) # check that it has figured out that all the z1 images are children of # the only z0 image for img in z1.images: key = ('aerial z0 test', z0.images[0]) assert_in(('aerial z1 test', img), nest_z0_z1._ancestry[key]) x1_y0_z1, = [img for img in z1.images if img.filename.endswith('z_1/x_1_y_0.png')] assert_equal((1, 0, 1), _tile_from_img(x1_y0_z1)) assert_equal([(2, 0, 2), (2, 1, 2), (3, 0, 2), (3, 1, 2)], sorted([_tile_from_img(img) for z, img in nest.subtiles(('aerial z1 test', x1_y0_z1))])) nest_from_config = gen_nest() # check that the the images in the nest from configuration are the # same as those created by hand. for name in nest_z0_z1._collections_by_name.keys(): for img in nest_z0_z1._collections_by_name[name].images: collection = nest_from_config._collections_by_name[name] assert_in(img, collection.images) assert_equal(nest_z0_z1._ancestry, nest_from_config._ancestry) # check that a nest can be pickled and unpickled easily. s = io.BytesIO() pickle.dump(nest_z0_z1, s) s.seek(0) nest_z0_z1_from_pickle = pickle.load(s) assert_equal(nest_z0_z1._ancestry, nest_z0_z1_from_pickle._ancestry) def test_img_pickle_round_trip(): """Check that __getstate__ for Img instances is working correctly.""" img = cimg_nest.Img('imaginary file', (0, 1, 2, 3), 'lower', (1, 2)) img_from_pickle = pickle.loads(pickle.dumps(img)) assert_equal(img, img_from_pickle) assert_equal(hasattr(img_from_pickle, '_bbox'), True) def requires_wmts_data(function): """ A decorator which ensures that the WMTS data is available for use in testing. """ aerial = cimgt.MapQuestOpenAerial() # get web tiles upto 3 zoom levels deep tiles = [(0, 0, 0)] for tile in aerial.subtiles((0, 0, 0)): tiles.append(tile) for tile in tiles[1:]: for sub_tile in aerial.subtiles(tile): tiles.append(sub_tile) fname_template = os.path.join(_TEST_DATA_DIR, 'z_{}', 'x_{}_y_{}.png') if not os.path.isdir(_TEST_DATA_DIR): os.makedirs(_TEST_DATA_DIR) data_version_fname = os.path.join(_TEST_DATA_DIR, 'version.txt') test_data_version = None try: with open(data_version_fname, 'r') as fh: test_data_version = int(fh.read().strip()) except IOError: pass finally: if test_data_version != _TEST_DATA_VERSION: warnings.warn('WMTS test data is out of date, regenerating at ' '{}.'.format(_TEST_DATA_DIR)) shutil.rmtree(_TEST_DATA_DIR) os.makedirs(_TEST_DATA_DIR) with open(data_version_fname, 'w') as fh: fh.write(str(_TEST_DATA_VERSION)) # Download the tiles. for tile in tiles: x, y, z = tile fname = fname_template.format(z, x, y) if not os.path.exists(fname): if not os.path.isdir(os.path.dirname(fname)): os.makedirs(os.path.dirname(fname)) img, extent, _ = aerial.get_image(tile) nx, ny = 256, 256 x_rng = extent[1] - extent[0] y_rng = extent[3] - extent[2] pix_size_x = x_rng / nx pix_size_y = y_rng / ny upper_left_center = (extent[0] + pix_size_x / 2, extent[2] + pix_size_y / 2) pgw_fname = fname[:-4] + '.pgw' pgw_keys = {'x_pix_size': np.float64(pix_size_x), 'y_rotation': 0, 'x_rotation': 0, 'y_pix_size': np.float64(pix_size_y), 'x_center': np.float64(upper_left_center[0]), 'y_center': np.float64(upper_left_center[1]), } _save_world(pgw_fname, pgw_keys) img.save(fname) global _TEST_DATA_AVAILABLE _TEST_DATA_AVAILABLE = True return function @requires_wmts_data def test_find_images(): z2_dir = os.path.join(_TEST_DATA_DIR, 'z_2') img_fname = os.path.join(z2_dir, 'x_2_y_0.png') world_file_fname = os.path.join(z2_dir, 'x_2_y_0.pgw') img = RoundedImg.from_world_file(img_fname, world_file_fname) assert_equal(img.filename, img_fname) assert_array_almost_equal(img.extent, (0., 10018754.17139462, 10018754.17139462, 20037508.342789244), decimal=4) assert_equal(img.origin, 'lower') assert_array_equal(img, np.array(Image.open(img.filename))) assert_equal(img.pixel_size, (39135.7585, 39135.7585)) @requires_wmts_data def gen_nest(): from_config = cimg_nest.NestedImageCollection.from_configuration files = [['aerial z0 test', os.path.join(_TEST_DATA_DIR, 'z_0')], ['aerial z1 test', os.path.join(_TEST_DATA_DIR, 'z_1')], ] crs = cimgt.GoogleTiles().crs nest_z0_z1 = from_config('aerial test', crs, files, glob_pattern='*.png', img_class=RoundedImg) return nest_z0_z1 if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/test_img_tiles.py0000644001374000021030000001742212700747662021746 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import types from nose.tools import assert_equal, assert_raises import numpy as np from numpy.testing import assert_array_almost_equal as assert_arr_almost import shapely.geometry as sgeom import cartopy.crs as ccrs import cartopy.io.img_tiles as cimgt #: Maps Google tile coordinates to native mercator coordinates as defined #: by https://goo.gl/pgJi. KNOWN_EXTENTS = {(0, 0, 0): (-20037508.342789244, 20037508.342789244, -20037508.342789244, 20037508.342789244), (2, 0, 2): (0., 10018754.17139462, 10018754.17139462, 20037508.342789244), (0, 2, 2): (-20037508.342789244, -10018754.171394622, -10018754.171394622, 0), (2, 2, 2): (0, 10018754.17139462, -10018754.171394622, 0), (8, 9, 4): (0, 2504688.542848654, -5009377.085697312, -2504688.542848654), } def GOOGLE_IMAGE_URL_REPLACEMENT(self, tile): url = ('https://chart.googleapis.com/chart?chst=d_text_outline&' 'chs=256x256&chf=bg,s,00000055&chld=FFFFFF|16|h|000000|b||||' 'Google:%20%20(' + str(tile[0]) + ',' + str(tile[1]) + ')' '|Zoom%20' + str(tile[2]) + '||||||______________________' '______') return url def test_google_tile_styles(): """ Tests that setting the Google Maps tile style works as expected. This is essentially just assures information is properly propagated through the class structure. """ reference_url = ("https://mts0.google.com/vt/lyrs={style}@177000000&hl=en" "&src=api&x=1&y=2&z=3&s=G") tile = ["1", "2", "3"] # Default is street. gt = cimgt.GoogleTiles() url = gt._image_url(tile) assert_equal(reference_url.format(style="m"), url) # Street gt = cimgt.GoogleTiles(style="street") url = gt._image_url(tile) assert_equal(reference_url.format(style="m"), url) # Satellite gt = cimgt.GoogleTiles(style="satellite") url = gt._image_url(tile) assert_equal(reference_url.format(style="s"), url) # Terrain gt = cimgt.GoogleTiles(style="terrain") url = gt._image_url(tile) assert_equal(reference_url.format(style="t"), url) # Streets only gt = cimgt.GoogleTiles(style="only_streets") url = gt._image_url(tile) assert_equal(reference_url.format(style="h"), url) # Exception is raised if unknown style is passed. with assert_raises(ValueError): cimgt.GoogleTiles(style="random_style") def test_google_wts(): gt = cimgt.GoogleTiles() ll_target_domain = sgeom.box(-15, 50, 0, 60) multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree()) target_domain = multi_poly.geoms[0] with assert_raises(AssertionError): list(gt.find_images(target_domain, -1)) assert_equal(tuple(gt.find_images(target_domain, 0)), ((0, 0, 0),)) assert_equal(tuple(gt.find_images(target_domain, 2)), ((1, 1, 2), (2, 1, 2))) assert_equal(list(gt.subtiles((0, 0, 0))), [(0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)]) assert_equal(list(gt.subtiles((1, 0, 1))), [(2, 0, 2), (2, 1, 2), (3, 0, 2), (3, 1, 2)]) with assert_raises(AssertionError): gt.tileextent((0, 1, 0)) assert_arr_almost(gt.tileextent((0, 0, 0)), KNOWN_EXTENTS[(0, 0, 0)]) assert_arr_almost(gt.tileextent((2, 0, 2)), KNOWN_EXTENTS[(2, 0, 2)]) assert_arr_almost(gt.tileextent((0, 2, 2)), KNOWN_EXTENTS[(0, 2, 2)]) assert_arr_almost(gt.tileextent((2, 2, 2)), KNOWN_EXTENTS[(2, 2, 2)]) assert_arr_almost(gt.tileextent((8, 9, 4)), KNOWN_EXTENTS[(8, 9, 4)]) def test_tile_bbox_y0_at_south_pole(): tms = cimgt.MapQuestOpenAerial() # Check the y0_at_north_pole keywords returns the appropriate bounds. assert_arr_almost(tms.tile_bbox(8, 6, 4, y0_at_north_pole=False), np.array(KNOWN_EXTENTS[(8, 9, 4)]).reshape([2, 2])) def test_tile_find_images(): gt = cimgt.GoogleTiles() # Test the find_images method on a GoogleTiles instance. ll_target_domain = sgeom.box(-10, 50, 10, 60) multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree()) target_domain = multi_poly.geoms[0] assert_equal([(7, 4, 4), (7, 5, 4), (8, 4, 4), (8, 5, 4)], list(gt.find_images(target_domain, 4))) def test_image_for_domain(): gt = cimgt.GoogleTiles() gt._image_url = types.MethodType(GOOGLE_IMAGE_URL_REPLACEMENT, gt) ll_target_domain = sgeom.box(-10, 50, 10, 60) multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree()) target_domain = multi_poly.geoms[0] _, extent, _ = gt.image_for_domain(target_domain, 6) ll_extent = ccrs.Geodetic().transform_points(gt.crs, np.array(extent[:2]), np.array(extent[2:])) assert_arr_almost(ll_extent[:, :2], [[-11.25, 48.92249926], [11.25, 61.60639637]]) def test_quadtree_wts(): qt = cimgt.QuadtreeTiles() ll_target_domain = sgeom.box(-15, 50, 0, 60) multi_poly = qt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree()) target_domain = multi_poly.geoms[0] with assert_raises(ValueError): list(qt.find_images(target_domain, 0)) assert_equal(qt.tms_to_quadkey((1, 1, 1)), '1') assert_equal(qt.quadkey_to_tms('1'), (1, 1, 1)) assert_equal(qt.tms_to_quadkey((8, 9, 4)), '1220') assert_equal(qt.quadkey_to_tms('1220'), (8, 9, 4)) assert_equal(tuple(qt.find_images(target_domain, 1)), ('0', '1')) assert_equal(tuple(qt.find_images(target_domain, 2)), ('03', '12')) assert_equal(list(qt.subtiles('0')), ['00', '01', '02', '03']) assert_equal(list(qt.subtiles('11')), ['110', '111', '112', '113']) with assert_raises(ValueError): qt.tileextent('4') assert_arr_almost(qt.tileextent(''), KNOWN_EXTENTS[(0, 0, 0)]) assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)), KNOWN_EXTENTS[(2, 0, 2)]) assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((0, 2, 2), google=True)), KNOWN_EXTENTS[(0, 2, 2)]) assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)), KNOWN_EXTENTS[(2, 0, 2)]) assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 2, 2), google=True)), KNOWN_EXTENTS[(2, 2, 2)]) assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((8, 9, 4), google=True)), KNOWN_EXTENTS[(8, 9, 4)]) def test_mapbox_tiles(): token = 'foo' map_id = 'bar' tile = [0, 1, 2] exp_url = 'https://api.tiles.mapbox.com/v4/bar/2/0/1.png?access_token=foo' mapbox_sample = cimgt.MapboxTiles(token, map_id) url_str = mapbox_sample._image_url(tile) assert_equal(url_str, exp_url) if __name__ == '__main__': import nose nose.runmodule(argv=['-s', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/test_img_transform.py0000644001374000021030000000704012700747662022634 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import numpy as np from numpy.testing import assert_array_equal import cartopy.img_transform as img_trans import cartopy.crs as ccrs def test_griding_data_std_range(): # Data which exists inside the standard projection bounds i.e. # [-180, 180]. target_prj = ccrs.PlateCarree() # create 3 data points lats = np.array([65, 10, -45]) lons = np.array([-90, 0, 90]) data = np.array([1, 2, 3]) data_trans = ccrs.Geodetic() target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4) image = img_trans.regrid(data, lons, lats, data_trans, target_prj, target_x, target_y, mask_extrapolated=True) # The expected image. n.b. on a map the data is reversed in the y axis. expected = np.array([[3, 3, 3, 3, 3, 3, 3, 3], [3, 1, 2, 2, 2, 3, 3, 3], [1, 1, 1, 2, 2, 2, 3, 1], [1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64) expected_mask = np.array( [[True, True, True, True, True, True, True, True], [True, False, False, False, False, False, False, True], [True, False, False, False, False, False, False, True], [True, True, True, True, True, True, True, True]]) assert_array_equal([-180, 180, -90, 90], extent) assert_array_equal(expected, image) assert_array_equal(expected_mask, image.mask) def test_griding_data_outside_projection(): # Data which exists outside the standard projection e.g. [0, 360] rather # than [-180, 180]. target_prj = ccrs.PlateCarree() # create 3 data points lats = np.array([65, 10, -45]) lons = np.array([120, 180, 240]) data = np.array([1, 2, 3]) data_trans = ccrs.Geodetic() target_x, target_y, extent = img_trans.mesh_projection(target_prj, 8, 4) image = img_trans.regrid(data, lons, lats, data_trans, target_prj, target_x, target_y, mask_extrapolated=True) # The expected image. n.b. on a map the data is reversed in the y axis. expected = np.array( [[3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 1, 2, 2], [2, 2, 3, 1, 1, 1, 1, 2], [1, 1, 1, 1, 1, 1, 1, 1]], dtype=np.float64) expected_mask = np.array( [[True, True, True, True, True, True, True, True], [False, False, True, True, True, True, False, False], [False, False, True, True, True, True, False, False], [True, True, True, True, True, True, True, True]]) assert_array_equal([-180, 180, -90, 90], extent) assert_array_equal(expected, image) assert_array_equal(expected_mask, image.mask) if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/tests/test_line_string.py0000644001374000021030000002616312700747662022311 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import itertools import time import unittest import numpy as np import shapely.geometry as sgeom import cartopy.crs as ccrs class TestLineString(unittest.TestCase): def test_out_of_bounds(self): # Check that a line that is completely out of the map boundary produces # a valid LineString projection = ccrs.TransverseMercator(central_longitude=0) # For both start & end, define a point that results in well-defined # projection coordinates and one that results in NaN. start_points = [(86, 0), (130, 0)] end_points = [(88, 0), (120, 0)] # Try all four combinations of valid/NaN vs valid/NaN. for start, end in itertools.product(start_points, end_points): line_string = sgeom.LineString([start, end]) multi_line_string = projection.project_geometry(line_string) if start[0] == 130 and end[0] == 120: expected = 0 else: expected = 1 self.assertEqual(len(multi_line_string), expected, 'Unexpected line when working from {} ' 'to {}'.format(start, end)) def test_simple_fragment_count(self): projection = ccrs.PlateCarree() tests = [ ([(150, 0), (-150, 0)], 2), ([(10, 0), (90, 0), (180, 0), (-90, 0), (-10, 0)], 2), ([(-10, 0), (10, 0)], 1), ([(-45, 0), (45, 30)], 1), ] for coords, pieces in tests: line_string = sgeom.LineString(coords) multi_line_string = projection.project_geometry(line_string) # from cartopy.tests.mpl import show # show(projection, multi_line_string) self.assertEqual(len(multi_line_string), pieces) def test_split(self): projection = ccrs.Robinson(170.5) line_string = sgeom.LineString([(-10, 30), (10, 60)]) multi_line_string = projection.project_geometry(line_string) # from cartopy.tests.mpl import show # show(projection, multi_line_string) self.assertEqual(len(multi_line_string), 2) def test_out_of_domain_efficiency(self): # Check we're efficiently dealing with lines that project # outside the map domain. # Because the south pole projects to an *enormous* circle # (radius ~ 1e23) this will take a *long* time to project if the # within-domain exactness criteria are used. line_string = sgeom.LineString([(0, -90), (2, -90)]) tgt_proj = ccrs.NorthPolarStereo() src_proj = ccrs.PlateCarree() cutoff_time = time.time() + 1 tgt_proj.project_geometry(line_string, src_proj) self.assertLess(time.time(), cutoff_time, 'Projection took too long') class FakeProjection(ccrs.PlateCarree): def __init__(self, left_offset=0, right_offset=0): self.left_offset = left_offset self.right_offset = right_offset self._half_width = 180 self._half_height = 90 ccrs.PlateCarree.__init__(self) @property def boundary(self): # XXX Should this be a LinearRing? w, h = self._half_width, self._half_height return sgeom.LineString([(-w + self.left_offset, -h), (-w + self.left_offset, h), (w - self.right_offset, h), (w - self.right_offset, -h), (-w + self.left_offset, -h)]) class TestBisect(unittest.TestCase): # A bunch of tests to check the bisection algorithm is robust for a # variety of simple and/or pathological cases. def test_repeated_point(self): projection = FakeProjection() line_string = sgeom.LineString([(10, 0), (10, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_interior_repeated_point(self): projection = FakeProjection() line_string = sgeom.LineString([(0, 0), (10, 0), (10, 0), (20, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 4) def test_circular_repeated_point(self): projection = FakeProjection() line_string = sgeom.LineString([(0, 0), (360, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_short(self): projection = FakeProjection() line_string = sgeom.LineString([(0, 0), (1e-12, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_empty(self): projection = FakeProjection(right_offset=10) line_string = sgeom.LineString([(175, 0), (175, 10)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 0) def test_simple_run_in(self): projection = FakeProjection(right_offset=10) line_string = sgeom.LineString([(160, 0), (175, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_simple_wrap(self): projection = FakeProjection() line_string = sgeom.LineString([(160, 0), (-160, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 2) self.assertEqual(len(multi_line_string[0].coords), 2) self.assertEqual(len(multi_line_string[1].coords), 2) def test_simple_run_out(self): projection = FakeProjection(left_offset=10) line_string = sgeom.LineString([(-175, 0), (-160, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_point_on_boundary(self): projection = FakeProjection() line_string = sgeom.LineString([(180, 0), (-160, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) # Add a small offset to the left-hand boundary to make things # even more pathological. projection = FakeProjection(left_offset=5) line_string = sgeom.LineString([(180, 0), (-160, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_nan_start(self): projection = ccrs.TransverseMercator(central_longitude=-90) line_string = sgeom.LineString([(10, 50), (-10, 30)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) for line_string in multi_line_string: for coord in line_string.coords: self.assertFalse(any(np.isnan(coord)), 'Unexpected NaN in projected coords.') def test_nan_end(self): projection = ccrs.TransverseMercator(central_longitude=-90) line_string = sgeom.LineString([(-10, 30), (10, 50)]) multi_line_string = projection.project_geometry(line_string) # from cartopy.tests.mpl import show # show(projection, multi_line_string) self.assertEqual(len(multi_line_string), 1) for line_string in multi_line_string: for coord in line_string.coords: self.assertFalse(any(np.isnan(coord)), 'Unexpected NaN in projected coords.') class TestMisc(unittest.TestCase): def test_misc(self): projection = ccrs.TransverseMercator(central_longitude=-90) line_string = sgeom.LineString([(10, 50), (-10, 30)]) multi_line_string = projection.project_geometry(line_string) # from cartopy.tests.mpl import show # show(projection, multi_line_string) for line_string in multi_line_string: for coord in line_string.coords: self.assertFalse(any(np.isnan(coord)), 'Unexpected NaN in projected coords.') def test_something(self): projection = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) line_string = sgeom.LineString([(0, 0), (1e-14, 0)]) multi_line_string = projection.project_geometry(line_string) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string[0].coords), 2) def test_global_boundary(self): linear_ring = sgeom.LineString([(-180, -180), (-180, 180), (180, 180), (180, -180)]) pc = ccrs.PlateCarree() merc = ccrs.Mercator() multi_line_string = pc.project_geometry(linear_ring, merc) assert len(multi_line_string) > 0 # check the identity transform multi_line_string = merc.project_geometry(linear_ring, merc) assert len(multi_line_string) > 0 class TestSymmetry(unittest.TestCase): @unittest.expectedFailure def test_curve(self): # Obtain a simple, curved path. projection = ccrs.PlateCarree() coords = [(-0.08, 51.53), (132.00, 43.17)] # London to Vladivostock line_string = sgeom.LineString(coords) multi_line_string = projection.project_geometry(line_string) # Compute the reverse path. line_string = sgeom.LineString(coords[::-1]) multi_line_string2 = projection.project_geometry(line_string) # Make sure that they generated the same points. # (Although obviously they will be in the opposite order!) self.assertEqual(len(multi_line_string), 1) self.assertEqual(len(multi_line_string2), 1) coords = multi_line_string[0].coords coords2 = multi_line_string2[0].coords np.testing.assert_allclose(coords, coords2[::-1], err_msg='Asymmetric curve generation') if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_linear_ring.py0000644001374000021030000001736412700747662022270 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import shapely.geometry as sgeom import numpy as np import cartopy.crs as ccrs class TestBoundary(unittest.TestCase): def test_cuts(self): # Check that fragments do not start or end with one of the # original ... ? linear_ring = sgeom.LinearRing([(-10, 30), (10, 60), (10, 50)]) projection = ccrs.Robinson(170.5) rings, multi_line_string = projection.project_geometry(linear_ring) # The original ring should have been split into multiple pieces. self.assertGreater(len(multi_line_string), 1) self.assertFalse(rings) def assert_intersection_with_boundary(segment_coords): # Double the length of the segment. start = segment_coords[0] end = segment_coords[1] end = [end[i] + 2 * (end[i] - start[i]) for i in (0, 1)] extended_segment = sgeom.LineString([start, end]) # And see if it crosses the boundary. intersection = extended_segment.intersection(projection.boundary) self.assertFalse(intersection.is_empty, 'Bad topology near boundary') # Each line resulting from the split should start and end with a # segment that crosses the boundary when extended to double length. # (This is important when considering polygon rings which need to be # attached to the boundary.) for line_string in multi_line_string: coords = list(line_string.coords) self.assertGreaterEqual(len(coords), 2) assert_intersection_with_boundary(coords[1::-1]) assert_intersection_with_boundary(coords[-2:]) def test_out_of_bounds(self): # Check that a ring that is completely out of the map boundary # produces an empty result. # XXX Check efficiency? projection = ccrs.TransverseMercator(central_longitude=0) rings = [ # All valid ([(86, 1), (86, -1), (88, -1), (88, 1)], -1), # One NaN ([(86, 1), (86, -1), (130, -1), (88, 1)], 1), # A NaN segment ([(86, 1), (86, -1), (130, -1), (130, 1)], 1), # All NaN ([(120, 1), (120, -1), (130, -1), (130, 1)], 0), ] # Try all four combinations of valid/NaN vs valid/NaN. for coords, expected_n_lines in rings: linear_ring = sgeom.LinearRing(coords) rings, mlinestr = projection.project_geometry(linear_ring) if expected_n_lines == -1: self.assertTrue(rings) self.assertFalse(mlinestr) else: self.assertEqual(len(mlinestr), expected_n_lines) if expected_n_lines == 0: self.assertTrue(mlinestr.is_empty) class TestMisc(unittest.TestCase): def test_small(self): # What happens when a small (i.e. < threshold) feature crosses the # boundary? projection = ccrs.Mercator() linear_ring = sgeom.LinearRing([ (-179.9173693847652942, -16.5017831356493616), (-180.0000000000000000, -16.0671326636424396), (-179.7933201090486079, -16.0208822567412312), ]) rings, multi_line_string = projection.project_geometry(linear_ring) # There should be one, and only one, returned ring. self.assertIsInstance(multi_line_string, sgeom.MultiLineString) self.assertEqual(len(multi_line_string), 0) self.assertEqual(len(rings), 1) # from cartopy.tests.mpl import show # show(projection, multi_line_string) def test_three_points(self): # The following LinearRing when projected from PlateCarree() to # PlateCarree(180.0) results in three points all in close proximity. # If an attempt is made to form a LinearRing from the three points # by combining the first and last an exception will be raised. # Check that this object can be projected without error. coords = [(0.0, -45.0), (0.0, -44.99974961593933), (0.000727869825138, -45.0), (0.0, -45.000105851567454), (0.0, -45.0)] linear_ring = sgeom.LinearRing(coords) src_proj = ccrs.PlateCarree() target_proj = ccrs.PlateCarree(180.0) try: _ = target_proj.project_geometry(linear_ring, src_proj) except ValueError: self.fail("Failed to project LinearRing.") def test_stitch(self): # The following LinearRing wanders in/out of the map domain # but importantly the "vertical" lines at 0'E and 360'E are both # chopped by the map boundary. This results in their ends being # *very* close to each other and confusion over which occurs # first when navigating around the boundary. # Check that these ends are stitched together to avoid the # boundary ordering ambiguity. # NB. This kind of polygon often occurs with MPL's contouring. coords = [(0.0, -70.70499926182919), (0.0, -71.25), (0.0, -72.5), (0.0, -73.49076371657017), (360.0, -73.49076371657017), (360.0, -72.5), (360.0, -71.25), (360.0, -70.70499926182919), (350, -73), (10, -73)] src_proj = ccrs.PlateCarree() target_proj = ccrs.Stereographic(80) linear_ring = sgeom.LinearRing(coords) rings, mlinestr = target_proj.project_geometry(linear_ring, src_proj) self.assertEqual(len(mlinestr), 1) self.assertEqual(len(rings), 0) # Check the stitch works in either direction. linear_ring = sgeom.LinearRing(coords[::-1]) rings, mlinestr = target_proj.project_geometry(linear_ring, src_proj) self.assertEqual(len(mlinestr), 1) self.assertEqual(len(rings), 0) def test_at_boundary(self): # Check that a polygon is split and recombined correctly # as a result of being on the boundary, determined by tolerance. exterior = np.array( [[177.5, -79.912], [178.333, -79.946], [181.666, -83.494], [180.833, -83.570], [180., -83.620], [178.438, -83.333], [178.333, -83.312], [177.956, -83.888], [180., -84.086], [180.833, -84.318], [183., -86.], [183., -78.], [177.5, -79.912]]) tring = sgeom.LinearRing(exterior) tcrs = ccrs.PlateCarree() scrs = ccrs.PlateCarree() rings, mlinestr = tcrs._project_linear_ring(tring, scrs) # Number of linearstrings self.assertEqual(len(mlinestr), 4) self.assertFalse(rings) # Test area of smallest Polygon that contains all the points in the # geometry. self.assertAlmostEqual(mlinestr.convex_hull.area, 2347.75623076) if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_polygon.py0000644001374000021030000004175312700747662021465 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import unittest import numpy as np import shapely.geometry as sgeom import shapely.wkt import cartopy.crs as ccrs class TestBoundary(unittest.TestCase): def test_no_polygon_boundary_reversal(self): # Check that polygons preserve their clockwise or counter-clockwise # ordering when they are attached to the boundary. # Failure to do so will result in invalid polygons (their boundaries # cross-over). polygon = sgeom.Polygon([(-10, 30), (10, 60), (10, 50)]) projection = ccrs.Robinson(170.5) multi_polygon = projection.project_geometry(polygon) for polygon in multi_polygon: self.assertTrue(polygon.is_valid) def test_polygon_boundary_attachment(self): # Check the polygon is attached to the boundary even when no # intermediate point for one of the crossing segments would normally # exist. polygon = sgeom.Polygon([(-10, 30), (10, 60), (10, 50)]) projection = ccrs.Robinson(170.6) # This will raise an exception if the polygon/boundary intersection # fails. multi_polygon = projection.project_geometry(polygon) def test_out_of_bounds(self): # Check that a polygon that is completely out of the map boundary # doesn't produce an empty result. projection = ccrs.TransverseMercator(central_longitude=0) polys = [ # All valid ([(86, -1), (86, 1), (88, 1), (88, -1)], 1), # One out of backwards projection range ([(86, -1), (86, 1), (130, 1), (88, -1)], 1), # An out of backwards projection range segment ([(86, -1), (86, 1), (130, 1), (130, -1)], 1), # All out of backwards projection range ([(120, -1), (120, 1), (130, 1), (130, -1)], 0), ] # Try all four combinations of valid/NaN vs valid/NaN. for coords, expected_polys in polys: polygon = sgeom.Polygon(coords) multi_polygon = projection.project_geometry(polygon) self.assertEqual(len(multi_polygon), expected_polys) class TestMisc(unittest.TestCase): def test_misc(self): projection = ccrs.TransverseMercator(central_longitude=-90) polygon = sgeom.Polygon([(-10, 30), (10, 60), (10, 50)]) multi_polygon = projection.project_geometry(polygon) def test_small(self): projection = ccrs.Mercator() polygon = sgeom.Polygon([ (-179.7933201090486079, -16.0208822567412312), (-180.0000000000000000, -16.0671326636424396), (-179.9173693847652942, -16.5017831356493616), ]) multi_polygon = projection.project_geometry(polygon) self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].exterior.coords), 4) def test_former_infloop_case(self): # test a polygon which used to get stuck in an infinite loop # see https://github.com/SciTools/cartopy/issues/60 coords = [(260.625, 68.90383337092122), (360.0, 79.8556091996901), (360.0, 77.76848175458498), (0.0, 88.79068047337279), (210.0, 90.0), (135.0, 88.79068047337279), (260.625, 68.90383337092122)] geom = sgeom.Polygon(coords) target_projection = ccrs.PlateCarree() source_crs = ccrs.Geodetic() multi_polygon = target_projection.project_geometry(geom, source_crs) # check the result is non-empty self.assertFalse(multi_polygon.is_empty) def test_project_previous_infinite_loop(self): mstring1 = shapely.wkt.loads( 'MULTILINESTRING (' '(-179.9999990464349651 -80.2000000000000171, ' '-179.5000000001111005 -80.2000000000000171, ' '-179.5000000001111005 -79.9000000000000199, ' '-179.9999995232739138 -79.9499999523163041, ' '-179.8000000001110550 -80.0000000000000000, ' '-179.8000000001110550 -80.0999999999999943, ' '-179.9999999047436177 -80.0999999999999943), ' '(179.9999995231628702 -79.9499999523163041, ' '179.5000000000000000 -79.9000000000000199, ' '179.5000000000000000 -80.0000000000000000, ' '179.9999995231628702 -80.0499999523162842, ' '179.5000000000000000 -80.0999999999999943, ' '179.5000000000000000 -80.2000000000000171, ' '179.9999990463256836 -80.2000000000000171))') mstring2 = shapely.wkt.loads( 'MULTILINESTRING (' '(179.9999996185302678 -79.9999999904632659, ' '179.5999999999999943 -79.9899999999999949, ' '179.5999999999999943 -79.9399999999999977, ' '179.9999996185302678 -79.9599999809265114), ' '(-179.9999999047436177 -79.9600000000000080, ' '-179.9000000001110777 -79.9600000000000080, ' '-179.9000000001110777 -80.0000000000000000, ' '-179.9999999047436177 -80.0000000000000000))') multi_line_strings = [mstring1, mstring2] src = ccrs.PlateCarree() src._attach_lines_to_boundary(multi_line_strings, True) def test_3pt_poly(self): projection = ccrs.OSGB() polygon = sgeom.Polygon([(-1000, -1000), (-1000, 200000), (200000, -1000)]) multi_polygon = projection.project_geometry(polygon, ccrs.OSGB()) self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].exterior.coords), 4) def test_self_intersecting_1(self): # Geometry comes from a matplotlib contourf (see #537) wkt = ('POLYGON ((366.22000122 -9.71489298, ' '366.73212393 -9.679999349999999, ' '366.77412634 -8.767753000000001, ' '366.17762962 -9.679999349999999, ' '366.22000122 -9.71489298), ' '(366.22000122 -9.692636309999999, ' '366.32998657 -9.603356099999999, ' '366.74765799 -9.019999500000001, ' '366.5094086 -9.63175386, ' '366.22000122 -9.692636309999999))') geom = shapely.wkt.loads(wkt) source, target = ccrs.RotatedPole(198.0, 39.25), ccrs.EuroPP() projected = target.project_geometry(geom, source) # Before handling self intersecting interiors, the area would be # approximately 13262233761329. area = projected.area self.assertTrue(2.2e9 < area < 2.3e9, msg='Got area {}, expecting ~2.2e9'.format(area)) def test_self_intersecting_2(self): # Geometry comes from a matplotlib contourf (see #509) wkt = ('POLYGON ((343 20, 345 23, 342 25, 343 22, ' '340 25, 341 25, 340 25, 343 20), (343 21, ' '343 22, 344 23, 343 21))') geom = shapely.wkt.loads(wkt) source = target = ccrs.RotatedPole(193.0, 41.0) projected = target.project_geometry(geom, source) # Before handling self intersecting interiors, the area would be # approximately 64808. self.assertTrue(7.9 < projected.area < 8.1) def test_tiny_point_between_boundary_points(self): # Geometry comes from #259. target = ccrs.Orthographic(0, -75) source = ccrs.PlateCarree() wkt = 'POLYGON ((132 -40, 133 -6, 125.3 1, 115 -6, 132 -40))' geom = shapely.wkt.loads(wkt) target = ccrs.Orthographic(central_latitude=90., central_longitude=0) source = ccrs.PlateCarree() projected = target.project_geometry(geom, source) area = projected.area # Before fixing, this geometry used to fill the whole disk. Approx # 1.2e14. self.assertTrue(81330 < area < 81340, msg='Got area {}, expecting ~81336'.format(area)) class TestQuality(unittest.TestCase): def setUp(self): projection = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) polygon = sgeom.Polygon([ (177.5, -57.38460319), (180.0, -57.445077), (175.0, -57.19913331), ]) self.multi_polygon = projection.project_geometry(polygon) # from cartopy.tests.mpl import show # show(projection, self.multi_polygon) def test_split(self): # Start simple ... there should be two projected polygons. self.assertEqual(len(self.multi_polygon), 2) def test_repeats(self): # Make sure we don't have repeated points at the boundary, because # they mess up the linear extrapolation to the boundary. # Make sure there aren't any repeated points. xy = np.array(self.multi_polygon[0].exterior.coords) same = (xy[1:] == xy[:-1]).all(axis=1) self.assertFalse(any(same), 'Repeated points in projected geometry.') def test_symmetry(self): # Make sure the number of points added on the way towards the # boundary is similar to the number of points added on the way away # from the boundary. # Identify all the contiguous sets of non-boundary points. xy = np.array(self.multi_polygon[0].exterior.coords) boundary = np.logical_or(xy[:, 1] == 90, xy[:, 1] == -90) regions = (boundary[1:] != boundary[:-1]).cumsum() regions = np.insert(regions, 0, 0) # For each region, check if the number of increasing steps is roughly # equal to the number of decreasing steps. for i in range(boundary[0], regions.max(), 2): indices = np.where(regions == i) x = xy[indices, 0] delta = np.diff(x) num_incr = np.count_nonzero(delta > 0) num_decr = np.count_nonzero(delta < 0) self.assertLess(abs(num_incr - num_decr), 3, 'Too much asymmetry.') class PolygonTests(unittest.TestCase): def _assert_bounds(self, bounds, x1, y1, x2, y2, delta=1): self.assertAlmostEqual(bounds[0], x1, delta=delta) self.assertAlmostEqual(bounds[1], y1, delta=delta) self.assertAlmostEqual(bounds[2], x2, delta=delta) self.assertAlmostEqual(bounds[3], y2, delta=delta) class TestWrap(PolygonTests): # Test that Plate Carree projection "does the right thing"(tm) with # source data tha extends outside the [-180, 180] range. def test_plate_carree_no_wrap(self): proj = ccrs.PlateCarree() poly = sgeom.box(0, 0, 10, 10) multi_polygon = proj.project_geometry(poly, proj) # Check the structure self.assertEqual(len(multi_polygon), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, 0, 0, 10, 10) def test_plate_carree_partial_wrap(self): proj = ccrs.PlateCarree() poly = sgeom.box(170, 0, 190, 10) multi_polygon = proj.project_geometry(poly, proj) # Check the structure self.assertEqual(len(multi_polygon), 2) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, 170, 0, 180, 10) polygon = multi_polygon[1] self._assert_bounds(polygon.bounds, -180, 0, -170, 10) def test_plate_carree_wrap(self): proj = ccrs.PlateCarree() poly = sgeom.box(200, 0, 220, 10) multi_polygon = proj.project_geometry(poly, proj) # Check the structure self.assertEqual(len(multi_polygon), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, -160, 0, -140, 10) def ring(minx, miny, maxx, maxy, ccw): box = sgeom.box(minx, miny, maxx, maxy, ccw) return np.array(box.exterior.coords) class TestHoles(PolygonTests): def test_simple(self): proj = ccrs.PlateCarree() poly = sgeom.Polygon(ring(-40, -40, 40, 40, True), [ring(-20, -20, 20, 20, False)]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].interiors), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, -40, -47, 40, 47) self._assert_bounds(polygon.interiors[0].bounds, -20, -21, 20, 21) def test_wrapped_poly_simple_hole(self): proj = ccrs.PlateCarree(-150) poly = sgeom.Polygon(ring(-40, -40, 40, 40, True), [ring(-20, -20, 20, 20, False)]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 2) self.assertEqual(len(multi_polygon[0].interiors), 1) self.assertEqual(len(multi_polygon[1].interiors), 0) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, 110, -47, 180, 47) self._assert_bounds(polygon.interiors[0].bounds, 130, -21, 170, 21) polygon = multi_polygon[1] self._assert_bounds(polygon.bounds, -180, -43, -170, 43) def test_wrapped_poly_wrapped_hole(self): proj = ccrs.PlateCarree(-180) poly = sgeom.Polygon(ring(-40, -40, 40, 40, True), [ring(-20, -20, 20, 20, False)]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 2) self.assertEqual(len(multi_polygon[0].interiors), 0) self.assertEqual(len(multi_polygon[1].interiors), 0) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, 140, -47, 180, 47) polygon = multi_polygon[1] self._assert_bounds(polygon.bounds, -180, -47, -140, 47) def test_inverted_poly_simple_hole(self): proj = ccrs.NorthPolarStereo() poly = sgeom.Polygon([(0, 0), (-90, 0), (-180, 0), (-270, 0)], [[(0, -30), (90, -30), (180, -30), (270, -30)]]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].interiors), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, -2.4e7, -2.4e7, 2.4e7, 2.4e7, 1e6) self._assert_bounds(polygon.interiors[0].bounds, - 1.2e7, -1.2e7, 1.2e7, 1.2e7, 1e6) def test_inverted_poly_clipped_hole(self): proj = ccrs.NorthPolarStereo() poly = sgeom.Polygon([(0, 0), (-90, 0), (-180, 0), (-270, 0)], [[(-135, -60), (-45, -60), (45, -60), (135, -60)]]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].interiors), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, -5.0e7, -5.0e7, 5.0e7, 5.0e7, 1e6) self._assert_bounds(polygon.interiors[0].bounds, - 1.2e7, -1.2e7, 1.2e7, 1.2e7, 1e6) self.assertAlmostEqual(polygon.area, 7.30e15, delta=1e13) def test_inverted_poly_removed_hole(self): proj = ccrs.NorthPolarStereo(globe=ccrs.Globe(ellipse='WGS84')) poly = sgeom.Polygon([(0, 0), (-90, 0), (-180, 0), (-270, 0)], [[(-135, -75), (-45, -75), (45, -75), (135, -75)]]) multi_polygon = proj.project_geometry(poly) # Check the structure self.assertEqual(len(multi_polygon), 1) self.assertEqual(len(multi_polygon[0].interiors), 1) # Check the rough shape polygon = multi_polygon[0] self._assert_bounds(polygon.bounds, -5.0e7, -5.0e7, 5.0e7, 5.0e7, 1e6) self._assert_bounds(polygon.interiors[0].bounds, - 1.2e7, -1.2e7, 1.2e7, 1.2e7, 1e6) self.assertAlmostEqual(polygon.area, 7.34e15, delta=1e13) def test_multiple_interiors(self): exterior = ring(0, 0, 12, 12, True) interiors = [ring(1, 1, 2, 2, False), ring(1, 8, 2, 9, False)] poly = sgeom.Polygon(exterior, interiors) target = ccrs.PlateCarree() source = ccrs.Geodetic() assert len(list(target.project_geometry(poly, source))) == 1 if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_shapereader.py0000644001374000021030000001166612700747662022261 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import os.path import unittest import numpy as np from numpy.testing import assert_array_almost_equal import six import cartopy.io.shapereader as shp LAKES_PATH = shp.natural_earth(resolution='110m', category='physical', name='lakes') RIVERS_PATH = shp.natural_earth(resolution='110m', category='physical', name='rivers_lake_centerlines') class TestLakes(unittest.TestCase): def setUp(self): self.reader = shp.Reader(LAKES_PATH) def _assert_geometry(self, geometry): self.assertEqual(geometry.type, 'MultiPolygon') self.assertEqual(len(geometry), 1) polygon = geometry[0] expected = np.array([(-84.85548682324658, 11.147898667846633), (-85.29013729525353, 11.176165676310276), (-85.79132117383625, 11.509737046754324), (-85.8851655748783, 11.900100816287136), (-85.5653401354239, 11.940330918826362), (-85.03684526237491, 11.5216484643976), (-84.85548682324658, 11.147898667846633), (-84.85548682324658, 11.147898667846633)]) assert_array_almost_equal(expected, polygon.exterior.coords) self.assertEqual(len(polygon.interiors), 0) def test_geometry(self): geometries = list(self.reader.geometries()) self.assertEqual(len(geometries), len(self.reader)) # Choose a nice small lake lake = geometries[14] self._assert_geometry(lake) def test_record(self): records = list(self.reader.records()) self.assertEqual(len(records), len(self.reader)) # Choose a nice small lake lake_record = records[14] self.assertEqual(lake_record.attributes.get('name'), 'Lago de\rNicaragua') self.assertEqual(sorted(lake_record.attributes.keys()), sorted(['admin', 'featurecla', 'scalerank', 'name_alt', 'name'])) lake = lake_record.geometry self._assert_geometry(lake) def test_bounds(self): # tests that a file which has a record with a bbox can # use the bbox without first creating the geometry record = next(self.reader.records()) self.assertEqual(record._geometry, False, ('The geometry was loaded ' 'before it was needed.')) self.assertEqual(len(record._bounds), 4) self.assertEqual(record._bounds, record.bounds) self.assertEqual(record._geometry, False, ('The geometry was loaded ' 'in order to create the ' 'bounds.')) class TestRivers(unittest.TestCase): def setUp(self): self.reader = shp.Reader(RIVERS_PATH) def _assert_geometry(self, geometry): self.assertEqual(geometry.type, 'MultiLineString') self.assertEqual(len(geometry), 1) linestring = geometry[0] coords = linestring.coords self.assertAlmostEqual(coords[0][0], -113.823382738076) self.assertAlmostEqual(coords[0][1], 58.7102151556671) self.assertAlmostEqual(coords[1][0], -113.71351864302348) self.assertAlmostEqual(coords[1][1], 58.669261583075794) def test_geometry(self): geometries = list(self.reader.geometries()) self.assertEqual(len(geometries), len(self.reader)) # Choose a nice small river river = geometries[6] self._assert_geometry(river) def test_record(self): records = list(self.reader.records()) self.assertEqual(len(records), len(self.reader)) # Choose a nice small lake river_record = records[6] self.assertEqual( river_record.attributes, {'featurecla': 'River', 'scalerank': 2, 'name': 'Peace', 'name_alt': six.b(' ' * 254)}) river = river_record.geometry self._assert_geometry(river) if __name__ == '__main__': unittest.main() Cartopy-0.14.2/lib/cartopy/tests/test_util.py0000644001374000021030000000573012700747662020746 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) from nose.tools import raises import numpy as np import numpy.ma as ma from numpy.testing import assert_array_equal from cartopy.util import add_cyclic_point class Test_add_cyclic_point(object): @classmethod def setup_class(cls): cls.lons = np.arange(0, 360, 60) cls.data2d = np.ones([3, 6]) * np.arange(6) cls.data4d = np.ones([4, 6, 2, 3]) * \ np.arange(6)[..., np.newaxis, np.newaxis] def test_data_only(self): c_data = add_cyclic_point(self.data2d) r_data = np.concatenate((self.data2d, self.data2d[:, :1]), axis=1) assert_array_equal(c_data, r_data) def test_data_and_coord(self): c_data, c_lons = add_cyclic_point(self.data2d, coord=self.lons) r_data = np.concatenate((self.data2d, self.data2d[:, :1]), axis=1) r_lons = np.concatenate((self.lons, np.array([360.]))) assert_array_equal(c_data, r_data) assert_array_equal(c_lons, r_lons) def test_data_only_with_axis(self): c_data = add_cyclic_point(self.data4d, axis=1) r_data = np.concatenate((self.data4d, self.data4d[:, :1]), axis=1) assert_array_equal(c_data, r_data) def test_data_and_coord_with_axis(self): c_data, c_lons = add_cyclic_point(self.data4d, coord=self.lons, axis=1) r_data = np.concatenate((self.data4d, self.data4d[:, :1]), axis=1) r_lons = np.concatenate((self.lons, np.array([360.]))) assert_array_equal(c_data, r_data) assert_array_equal(c_lons, r_lons) def test_masked_data(self): new_data = ma.masked_less(self.data2d, 3) c_data = add_cyclic_point(new_data) r_data = ma.concatenate((self.data2d, self.data2d[:, :1]), axis=1) assert_array_equal(c_data, r_data) @raises(ValueError) def test_invalid_coord_dimensionality(self): lons2d = np.repeat(self.lons[np.newaxis], 3, axis=0) c_data, c_lons = add_cyclic_point(self.data2d, coord=lons2d) @raises(ValueError) def test_invalid_coord_size(self): c_data, c_lons = add_cyclic_point(self.data2d, coord=self.lons[:-1]) @raises(ValueError) def test_invalid_axis(self): c_data = add_cyclic_point(self.data2d, axis=-3) Cartopy-0.14.2/lib/cartopy/tests/test_vector_transform.py0000644001374000021030000002256012700747662023366 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) import numpy as np from numpy.testing import assert_array_equal, assert_array_almost_equal import cartopy.vector_transform as vec_trans import cartopy.crs as ccrs def _sample_plate_carree_coordinates(): x = np.array([-10, 0, 10, -9, 0, 9]) y = np.array([10, 10, 10, 5, 5, 5]) return x, y def _sample_plate_carree_scalar_field(): return np.array([2, 4, 2, 1.2, 3, 1.2]) def _sample_plate_carree_vector_field(): u = np.array([2, 4, 2, 1.2, 3, 1.2]) v = np.array([5.5, 4, 5.5, 1.2, .3, 1.2]) return u, v class Test_interpolate_to_grid(object): @classmethod def setup_class(cls): cls.x, cls.y = _sample_plate_carree_coordinates() cls.s = _sample_plate_carree_scalar_field() def test_data_extent(self): # Interpolation to a grid with extents of the input data. expected_x_grid = np.array([[-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.]]) expected_y_grid = np.array([[5., 5., 5., 5., 5.], [7.5, 7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10., 10]]) expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) x_grid, y_grid, s_grid = vec_trans._interpolate_to_grid( 5, 3, self.x, self.y, self.s) assert_array_equal(x_grid, expected_x_grid) assert_array_equal(y_grid, expected_y_grid) assert_array_almost_equal(s_grid, expected_s_grid) def test_explicit_extent(self): # Interpolation to a grid with explicit extents. expected_x_grid = np.array([[-5., 0., 5., 10.], [-5., 0., 5., 10.]]) expected_y_grid = np.array([[7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10]]) expected_s_grid = np.array([[2.5, 3.5, 2.5, np.nan], [3., 4., 3., 2.]]) extent = (-5, 10, 7.5, 10) x_grid, y_grid, s_grid = vec_trans._interpolate_to_grid( 4, 2, self.x, self.y, self.s, target_extent=extent) assert_array_equal(x_grid, expected_x_grid) assert_array_equal(y_grid, expected_y_grid) assert_array_almost_equal(s_grid, expected_s_grid) def test_multiple_fields(self): # Interpolation of multiple fields in one go. expected_x_grid = np.array([[-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.]]) expected_y_grid = np.array([[5., 5., 5., 5., 5.], [7.5, 7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10., 10]]) expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) x_grid, y_grid, s_grid1, s_grid2, s_grid3 = \ vec_trans._interpolate_to_grid(5, 3, self.x, self.y, self.s, self.s, self.s) assert_array_equal(x_grid, expected_x_grid) assert_array_equal(y_grid, expected_y_grid) assert_array_almost_equal(s_grid1, expected_s_grid) assert_array_almost_equal(s_grid2, expected_s_grid) assert_array_almost_equal(s_grid3, expected_s_grid) class Test_vector_scalar_to_grid(object): @classmethod def setup_class(cls): cls.x, cls.y = _sample_plate_carree_coordinates() cls.u, cls.v = _sample_plate_carree_vector_field() cls.s = _sample_plate_carree_scalar_field() def test_no_transform(self): # Transform and regrid vector (with no projection transform). expected_x_grid = np.array([[-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.]]) expected_y_grid = np.array([[5., 5., 5., 5., 5.], [7.5, 7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10., 10]]) expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan], [np.nan, 2.675, 2.15, 2.675, np.nan], [5.5, 4.75, 4., 4.75, 5.5]]) src_crs = target_crs = ccrs.PlateCarree() x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid( src_crs, target_crs, (5, 3), self.x, self.y, self.u, self.v) assert_array_equal(x_grid, expected_x_grid) assert_array_equal(y_grid, expected_y_grid) assert_array_almost_equal(u_grid, expected_u_grid) assert_array_almost_equal(v_grid, expected_v_grid) def test_with_transform(self): # Transform and regrid vector. target_crs = ccrs.PlateCarree() src_crs = ccrs.NorthPolarStereo() input_coords = [src_crs.transform_point(xp, yp, target_crs) for xp, yp in zip(self.x, self.y)] x_nps = np.array([ic[0] for ic in input_coords]) y_nps = np.array([ic[1] for ic in input_coords]) u_nps, v_nps = src_crs.transform_vectors(target_crs, self.x, self.y, self.u, self.v) expected_x_grid = np.array([[-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.]]) expected_y_grid = np.array([[5., 5., 5., 5., 5.], [7.5, 7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10., 10]]) expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan], [np.nan, 2.675, 2.15, 2.675, np.nan], [5.5, 4.75, 4., 4.75, 5.5]]) x_grid, y_grid, u_grid, v_grid = vec_trans.vector_scalar_to_grid( src_crs, target_crs, (5, 3), x_nps, y_nps, u_nps, v_nps) assert_array_almost_equal(x_grid, expected_x_grid) assert_array_almost_equal(y_grid, expected_y_grid) # Vector transforms are somewhat approximate, so we are more lenient # with the returned values since we have transformed twice. assert_array_almost_equal(u_grid, expected_u_grid, decimal=4) assert_array_almost_equal(v_grid, expected_v_grid, decimal=4) def test_with_scalar_field(self): # Transform and regrid vector (with no projection transform) with an # additional scalar field. expected_x_grid = np.array([[-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.], [-10., -5., 0., 5., 10.]]) expected_y_grid = np.array([[5., 5., 5., 5., 5.], [7.5, 7.5, 7.5, 7.5, 7.5], [10., 10., 10., 10., 10]]) expected_u_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) expected_v_grid = np.array([[np.nan, .8, .3, .8, np.nan], [np.nan, 2.675, 2.15, 2.675, np.nan], [5.5, 4.75, 4., 4.75, 5.5]]) expected_s_grid = np.array([[np.nan, 2., 3., 2., np.nan], [np.nan, 2.5, 3.5, 2.5, np.nan], [2., 3., 4., 3., 2.]]) src_crs = target_crs = ccrs.PlateCarree() x_grid, y_grid, u_grid, v_grid, s_grid = \ vec_trans.vector_scalar_to_grid(src_crs, target_crs, (5, 3), self.x, self.y, self.u, self.v, self.s) assert_array_equal(x_grid, expected_x_grid) assert_array_equal(y_grid, expected_y_grid) assert_array_almost_equal(u_grid, expected_u_grid) assert_array_almost_equal(v_grid, expected_v_grid) assert_array_almost_equal(s_grid, expected_s_grid) if __name__ == '__main__': import nose nose.runmodule(argv=['-sv', '--with-doctest'], exit=False) Cartopy-0.14.2/lib/cartopy/__init__.py0000644001374000021030000001041012705644356017317 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import (absolute_import, division, print_function) __version__ = '0.14.2' __document_these__ = ['config'] # Enable shapely performance enhancements import shapely.speedups if shapely.speedups.available: shapely.speedups.enable() # Configuration import os.path # for the writable data directory (i.e. the one where new data goes), follow # the XDG guidelines found at # https://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html _writable_dir = os.path.join(os.path.expanduser('~'), '.local', 'share') _data_dir = os.path.join(os.environ.get("XDG_DATA_HOME", _writable_dir), 'cartopy') config = {'pre_existing_data_dir': '', 'data_dir': _data_dir, 'repo_data_dir': os.path.join(os.path.dirname(__file__), 'data'), 'downloaders': {}, } """ The config dictionary stores global configuration values for cartopy. In the first instance, the config is defined in ``cartopy/__init__.py``. It is possible to provide site wide customisations by including a ``siteconfig.py`` file along with the cartopy source code. ``siteconfig.py`` should contain a function called ``update_config`` which takes the config dictionary instance as its first and only argument (from where it is possible to update the dictionary howsoever desired). For users without write permission to the cartopy source directory, a package called ``cartopy_userconfig`` should be made importable (consider putting it in ``site.getusersitepackages()``) and should expose a function called ``update_config`` which takes the config dictionary as its first and only argument. Keys in the config dictionary: * ``pre_existing_data_dir`` - the absolute path to a directory where standard data (such as that from NaturalEarth) can be found. If it is not found in this location the ``data_dir`` config item will be used. * ``data_dir`` - the absolute path to a directory where standard data (such as that from NaturalEarth) can be found. If it is not found and the item is downloadable cartopy will download the appropriate file(s) to a subdirectory of this directory, therefore ``data_dir`` should be writable by the user. * ``repo_data_dir`` - the absolute path to the directory where the data delivered with the cartopy repository is stored. Typically this will only be set by OS packagers and system administrators for site wide deployments. * ``downloaders`` - a dictionary mapping standard "specifications" to the appropriate :class:`~cartopy.io.Downloader`. For further documentation and an example see :func:`cartopy.io.Downloader.from_config`. """ # n.b. docstring changes should be propagated to docs/source/cartopy.rst del _data_dir del _writable_dir # Try importing a siteconfig file which exposes an update_config function, # otherwise, fail gracefully. try: from cartopy.siteconfig import update_config as _update_config _update_config(config) except ImportError: pass # Try importing a cartopy_userconfig file which exposes an update_config # function, otherwise, fail gracefully. try: from cartopy_userconfig import update_config as _update_config _update_config(config) except ImportError: pass # Commonly used sub-modules. Imported here to provide end-user # convenience. import cartopy.crs import cartopy.feature Cartopy-0.14.2/lib/cartopy/_crs.pxd0000644001374000021030000000173012700747662016655 0ustar itpeavd00000000000000# (C) British Crown Copyright 2010 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . cdef extern from "proj_api.h": ctypedef void *projPJ cdef class CRS: """ Defines a Coordinate Reference System using proj.4. """ cdef projPJ proj4 cdef readonly proj4_init cdef proj4_params cpdef is_geodetic(self) Cartopy-0.14.2/lib/cartopy/_crs.pyx0000644001374000021030000004553412705644356016715 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module defines the core CRS class which can interface with Proj.4. The CRS class is the base-class for all projections defined in :mod:`cartopy.crs`. """ from collections import OrderedDict import re import warnings import numpy as np import six cimport numpy as np from cython.operator cimport dereference as deref cdef extern from "proj_api.h": ctypedef void *projPJ projPJ pj_init_plus(char *) void pj_free(projPJ) int pj_transform(projPJ, projPJ, long, int, double *, double *, double *) int pj_is_latlong(projPJ) char *pj_strerrno(int) int *pj_get_errno_ref() char *pj_get_release() double DEG_TO_RAD double RAD_TO_DEG cdef double NAN = float('nan') PROJ4_RELEASE = pj_get_release() if six.PY3: PROJ4_RELEASE = PROJ4_RELEASE.decode() _match = re.search(r"\d+\.\d+.\d+", PROJ4_RELEASE) if _match is not None: PROJ4_VERSION = tuple(int(v) for v in _match.group().split('.')) else: PROJ4_VERSION = () class Proj4Error(Exception): """ Raised when there has been an exception calling proj.4. Adds a ``status`` attribute to the exception which has the proj.4 error reference. """ def __init__(self): cdef int status status = deref(pj_get_errno_ref()) msg = 'Error from proj.4: {}'.format(pj_strerrno(status)) self.status = status Exception.__init__(self, msg) class Globe(object): """ Defines an ellipsoid and, optionally, how to relate it to the real world. """ def __init__(self, datum=None, ellipse='WGS84', semimajor_axis=None, semiminor_axis=None, flattening=None, inverse_flattening=None, towgs84=None, nadgrids=None): """ Keywords: * datum - Proj4 "datum" definiton. Default to no datum. * ellipse - Proj4 "ellps" definiton. Default to 'WGS84'. * semimajor_axis - Semimajor axis of the spheroid / ellipsoid. * semiminor_axis - Semiminor axis of the ellipsoid. * flattening - Flattening of the ellipsoid. * inverse_flattening - Inverse flattening of the ellipsoid. * towgs84 - Passed through to the Proj4 definition. * nadgrids - Passed through to the Proj4 definition. """ self.datum = datum self.ellipse = ellipse self.semimajor_axis = semimajor_axis self.semiminor_axis = semiminor_axis self.flattening = flattening self.inverse_flattening = inverse_flattening self.towgs84 = towgs84 self.nadgrids = nadgrids def to_proj4_params(self): """ Create an OrderedDict of key value pairs which represents this globe in terms of proj4 params. """ proj4_params = (['datum', self.datum], ['ellps', self.ellipse], ['a', self.semimajor_axis], ['b', self.semiminor_axis], ['f', self.flattening], ['rf', self.inverse_flattening], ['towgs84', self.towgs84], ['nadgrids', self.nadgrids]) return OrderedDict((k, v) for k, v in proj4_params if v is not None) cdef class CRS: """ Defines a Coordinate Reference System using proj.4. """ def __init__(self, proj4_params, globe=None): """ Parameters ---------- proj4_params : iterable of key-value pairs The proj4 parameters required to define the desired CRS. The parameters should not describe the desired elliptic model, instead create an appropriate Globe instance. The ``proj4_params`` parameters will override any parameters that the Globe defines. globe : :class:`~cartopy.crs.Globe` instance, optional If omitted, the default Globe instance will be created. See :class:`~cartopy.crs.Globe` for details. """ self.globe = globe or Globe() self.proj4_params = self.globe.to_proj4_params() self.proj4_params.update(proj4_params) init_items = [] for k, v in self.proj4_params.items(): if v is not None: if isinstance(v, float): init_items.append('+{}={:.16}'.format(k, v)) elif isinstance(v, np.float32): init_items.append('+{}={:.8}'.format(k, v)) else: init_items.append('+{}={}'.format(k, v)) else: init_items.append('+{}'.format(k)) self.proj4_init = ' '.join(init_items) + ' +no_defs' proj4_init_bytes = six.b(self.proj4_init) self.proj4 = pj_init_plus(proj4_init_bytes) if not self.proj4: raise Proj4Error() # Cython uses this method instead of the normal rich comparisons. def __richcmp__(self, other, op): # We're only interested in: # == -> 2 # != -> 3 result = NotImplemented if isinstance(other, CRS): if op == 2: result = self.proj4_init == other.proj4_init elif op == 3: result = self.proj4_init != other.proj4_init return result def __hash__(self): """Hashes the CRS based on its proj4_init string.""" return hash(self.proj4_init) def __reduce__(self): """ Implements the __reduce__ API so that unpickling produces a stateless instance of this class (e.g. an empty tuple). The state will then be added via __getstate__ and __setstate__. """ return self.__class__, tuple() def __getstate__(self): """Returns the full state of this instance for reconstruction in ``__setstate__``.""" return {'proj4_params': self.proj4_params} def __setstate__(self, state): """ Takes the dictionary created by ``__getstate__`` and passes it through to the class's __init__ method. """ self.__init__(self, **state) # TODO #def __str__ #def _geod(self): # to return the pyproj.Geod def _as_mpl_transform(self, axes=None): """ Casts this CRS instance into a :class:`matplotlib.axes.Axes` using the matplotlib ``_as_mpl_transform`` interface. """ # lazy import mpl.geoaxes (and therefore matplotlib) as mpl # is only an optional dependency import cartopy.mpl.geoaxes as geoaxes if not isinstance(axes, geoaxes.GeoAxes): raise ValueError('Axes should be an instance of GeoAxes, got %s' % type(axes)) return geoaxes.InterProjectionTransform(self, axes.projection) + axes.transData property proj4_params: def __get__(self): return dict(self.proj4_params) def as_geocentric(self): """ Returns a new Geocentric CRS with the same ellipse/datum as this CRS. """ return Geocentric(self.globe) def as_geodetic(self): """ Returns a new Geodetic CRS with the same ellipse/datum as this CRS. """ return Geodetic(self.globe) cpdef is_geodetic(self): return bool(pj_is_latlong(self.proj4)) def transform_point(self, double x, double y, CRS src_crs not None, trap=True): """ transform_point(x, y, src_crs) Transform the given float64 coordinate pair, in the given source coordinate system (``src_crs``), to this coordinate system. Args: * x - the x coordinate, in ``src_crs`` coordinates, to transform * y - the y coordinate, in ``src_crs`` coordinates, to transform * src_crs - instance of :class:`CRS` that represents the coordinate system of ``x`` and ``y``. * trap - Whether proj.4 errors for "latitude or longitude exceeded limits" and "tolerance condition error" should be trapped. Returns: (x, y) - in this coordinate system """ cdef: double cx, cy int status cx = x cy = y if src_crs.is_geodetic(): cx *= DEG_TO_RAD cy *= DEG_TO_RAD status = pj_transform(src_crs.proj4, self.proj4, 1, 1, &cx, &cy, NULL); if trap and status == -14 or status == -20: # -14 => "latitude or longitude exceeded limits" # -20 => "tolerance condition error" cx = cy = NAN elif trap and status != 0: raise Proj4Error() if self.is_geodetic(): cx *= RAD_TO_DEG cy *= RAD_TO_DEG return (cx, cy) def transform_points(self, CRS src_crs not None, np.ndarray x not None, np.ndarray y not None, np.ndarray z=None): """ transform_points(src_crs, x, y[, z]) Transform the given coordinates, in the given source coordinate system (``src_crs``), to this coordinate system. Args: * src_crs - instance of :class:`CRS` that represents the coordinate system of ``x``, ``y`` and ``z``. * x - the x coordinates (array), in ``src_crs`` coordinates, to transform. May be 1 or 2 dimensional. * y - the y coordinates (array), in ``src_crs`` coordinates, to transform * z - (optional) the z coordinates (array), in ``src_crs`` coordinates, to transform. Returns: Array of shape ``x.shape + (3, )`` in this coordinate system. """ cdef np.ndarray[np.double_t, ndim=2] result result_shape = tuple(x.shape[i] for i in range(x.ndim)) + (3, ) if z is None: if x.ndim > 2 or y.ndim > 2: raise ValueError('x and y arrays must be 1 or 2 dimensional') elif x.ndim != 1 or y.ndim != 1: x, y = x.flatten(), y.flatten() if x.shape[0] != y.shape[0]: raise ValueError('x and y arrays must have the same length') else: if x.ndim > 2 or y.ndim > 2 or z.ndim > 2: raise ValueError('x, y and z arrays must be 1 or 2 ' 'dimensional') elif x.ndim != 1 or y.ndim != 1 or z.ndim != 1: x, y, z = x.flatten(), y.flatten(), z.flatten() if not x.shape[0] == y.shape[0] == z.shape[0]: raise ValueError('x, y, and z arrays must have the same ' 'length') npts = x.shape[0] result = np.empty([npts, 3], dtype=np.double) if src_crs.is_geodetic(): result[:, 0] = np.deg2rad(x) result[:, 1] = np.deg2rad(y) else: result[:, 0] = x result[:, 1] = y # if a z has been given, put it in the result array which will be # transformed in-place if z is None: result[:, 2] = 0 else: result[:, 2] = z # call proj.4. The result array is modified in place. status = pj_transform(src_crs.proj4, self.proj4, npts, 3, &result[0, 0], &result[0, 1], &result[0, 2]) if self.is_geodetic(): result[:, :2] = np.rad2deg(result[:, :2]) #if status: # raise Proj4Error() if len(result_shape) > 2: return result.reshape(result_shape) return result def transform_vectors(self, src_crs, x, y, u, v): """ transform_vectors(src_crs, x, y, u, v) Transform the given vector components, with coordinates in the given source coordinate system (``src_crs``), to this coordinate system. The vector components must be given relative to the source coordinate system (grid eastward and grid northward). Args: * src_crs: The :class:`CRS` that represents the coordinate system the vectors are defined in. * x, y: The x and y coordinates, in the source CRS coordinates, where the vector components are located. May be 1 or 2 dimensional, but must have matching shapes. * u, v: The grid eastward and grid northward components of the vector field respectively. Their shape must match the shape of the x and y coordinates. Returns: * ut, vt: The transformed vector components. .. note:: The algorithm used to transform vectors is an approximation rather than an exact transform, but the accuracy should be good enough for visualization purposes. """ if not (x.shape == y.shape == u.shape == v.shape): raise ValueError('x, y, u and v arrays must be the same shape') if x.ndim not in (1, 2): raise ValueError('x, y, u and v must be 1 or 2 dimensional') # Transform the coordinates to the target projection. proj_xyz = self.transform_points(src_crs, x, y) target_x, target_y = proj_xyz[..., 0], proj_xyz[..., 1] # Rotate the input vectors to the projection. # # 1: Find the magnitude and direction of the input vectors. vector_magnitudes = (u**2 + v**2)**0.5 vector_angles = np.arctan2(v, u) # 2: Find a point in the direction of the original vector that is # a small distance away from the base point of the vector (near # the poles the point may have to be in the opposite direction # to be valid). factor = 360000. delta = (src_crs.x_limits[1] - src_crs.x_limits[0]) / factor x_perturbations = delta * np.cos(vector_angles) y_perturbations = delta * np.sin(vector_angles) # 3: Handle points that are invalid. These come from picking a new # point that is outside the domain of the CRS. The first step is # to apply the native transform to the input coordinates to make # sure they are in the appropriate range. Then detect all the # coordinates where the perturbation takes the point out of the # valid x-domain and fix them. After that do the same for points # that are outside the valid y-domain, which may reintroduce some # points outside of the valid x-domain proj_xyz = src_crs.transform_points(src_crs, x, y) source_x, source_y = proj_xyz[..., 0], proj_xyz[..., 1] # Detect all the coordinates where the perturbation takes the point # outside of the valid x-domain, and reverse the direction of the # perturbation to fix this. eps = 1e-9 invalid_x = np.logical_or( source_x + x_perturbations < src_crs.x_limits[0]-eps, source_x + x_perturbations > src_crs.x_limits[1]+eps) if invalid_x.any(): x_perturbations[invalid_x] *= -1 y_perturbations[invalid_x] *= -1 # Do the same for coordinates where the perturbation takes the point # outside of the valid y-domain. This may reintroduce some points # that will be outside the x-domain when the perturbation is # applied. invalid_y = np.logical_or( source_y + y_perturbations < src_crs.y_limits[0]-eps, source_y + y_perturbations > src_crs.y_limits[1]+eps) if invalid_y.any(): x_perturbations[invalid_y] *= -1 y_perturbations[invalid_y] *= -1 # Keep track of the points where the perturbation direction was # reversed. reversed_vectors = np.logical_xor(invalid_x, invalid_y) # See if there were any points where we cannot reverse the direction # of the perturbation to get the perturbed point within the valid # domain of the projection, and issue a warning if there are. problem_points = np.logical_or( source_x + x_perturbations < src_crs.x_limits[0]-eps, source_x + x_perturbations > src_crs.x_limits[1]+eps) if problem_points.any(): warnings.warn('Some vectors at source domain corners ' 'may not have been transformed correctly') # 4: Transform this set of points to the projection coordinates and # find the angle between the base point and the perturbed point # in the projection coordinates (reversing the direction at any # points where the original was reversed in step 3). proj_xyz = self.transform_points(src_crs, source_x + x_perturbations, source_y + y_perturbations) target_x_perturbed = proj_xyz[..., 0] target_y_perturbed = proj_xyz[..., 1] projected_angles = np.arctan2(target_y_perturbed - target_y, target_x_perturbed - target_x) if reversed_vectors.any(): projected_angles[reversed_vectors] += np.pi # 5: Form the projected vector components, preserving the magnitude # of the original vectors. projected_u = vector_magnitudes * np.cos(projected_angles) projected_v = vector_magnitudes * np.sin(projected_angles) return projected_u, projected_v class Geodetic(CRS): """ Defines a latitude/longitude coordinate system with spherical topology, geographical distance and coordinates are measured in degrees. """ def __init__(self, globe=None): """ Kwargs: * globe - A :class:`cartopy.crs.Globe`. Defaults to a "WGS84" datum. """ proj4_params = [('proj', 'lonlat')] globe = globe or Globe(datum='WGS84') super(Geodetic, self).__init__(proj4_params, globe) # XXX Implement fwd such as Basemap's Geod. Would be used in the tissot example. # Could come from http://geographiclib.sourceforge.net class Geocentric(CRS): """ Defines a Geocentric coordinate system, where x, y, z are Cartesian coordinates from the center of the Earth. """ def __init__(self, globe=None): """ Kwargs: * globe - A :class:`cartopy.crs.Globe`. Defaults to a "WGS84" datum. """ proj4_params = [('proj', 'geocent')] globe = globe or Globe(datum='WGS84') super(Geocentric, self).__init__(proj4_params, globe) Cartopy-0.14.2/lib/cartopy/_epsg.py0000644001374000021030000000667212700747662016673 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ Provides support for converting EPSG codes to Projection instances. """ from __future__ import (absolute_import, division, print_function) import cartopy.crs as ccrs import numpy as np import pyepsg import shapely.geometry as sgeom _GLOBE_PARAMS = {'datum': 'datum', 'ellps': 'ellipse', 'a': 'semimajor_axis', 'b': 'semiminor_axis', 'f': 'flattening', 'rf': 'inverse_flattening', 'towgs84': 'towgs84', 'nadgrids': 'nadgrids'} class _EPSGProjection(ccrs.Projection): def __init__(self, code): projection = pyepsg.get(code) if not isinstance(projection, pyepsg.ProjectedCRS): raise ValueError('EPSG code does not define a projection') self.epsg_code = code proj4_str = projection.as_proj4().strip() terms = [term.strip('+').split('=') for term in proj4_str.split(' ')] globe_terms = filter(lambda term: term[0] in _GLOBE_PARAMS, terms) globe = ccrs.Globe(**{_GLOBE_PARAMS[name]: value for name, value in globe_terms}) other_terms = [] for term in terms: if term[0] not in _GLOBE_PARAMS: if len(term) == 1: other_terms.append([term[0], None]) else: other_terms.append(term) super(_EPSGProjection, self).__init__(other_terms, globe) # Convert lat/lon bounds to projected bounds. # GML defines gmd:EX_GeographicBoundingBox as: # Geographic area of the entire dataset referenced to WGS 84 # NB. We can't use a polygon transform at this stage because # that relies on the existence of the map boundary... the very # thing we're trying to work out! ;-) x0, x1, y0, y1 = projection.domain_of_validity() geodetic = ccrs.Geodetic() lons = np.array([x0, x0, x1, x1]) lats = np.array([y0, y1, y1, y0]) points = self.transform_points(geodetic, lons, lats) x = points[:, 0] y = points[:, 1] self.bounds = (x.min(), x.max(), y.min(), y.max()) def __repr__(self): return '_EPSGProjection({})'.format(self.epsg_code) @property def boundary(self): x0, x1, y0, y1 = self.bounds return sgeom.LineString([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)]) @property def x_limits(self): x0, x1, y0, y1 = self.bounds return (x0, x1) @property def y_limits(self): x0, x1, y0, y1 = self.bounds return (y0, y1) @property def threshold(self): x0, x1, y0, y1 = self.bounds return min(x1 - x0, y1 - y0) / 100. Cartopy-0.14.2/lib/cartopy/_trace.cpp0000644001374000021030000004630112700747662017156 0ustar itpeavd00000000000000/* # (C) British Crown Copyright 2010 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . */ #include #include #include #include #include "_trace.h" #ifdef _MSC_VER #include #define isnan _isnan #define isfinite _finite #endif Interpolator::~Interpolator(){} void Interpolator::set_line(const Point &start, const Point &end) { m_start = start; m_end = end; } CartesianInterpolator::CartesianInterpolator(projPJ src_proj, projPJ dest_proj) { m_src_proj = src_proj; m_dest_proj = dest_proj; } Point CartesianInterpolator::interpolate(double t) { Point xy; xy.x = m_start.x + (m_end.x - m_start.x) * t; xy.y = m_start.y + (m_end.y - m_start.y) * t; return project(xy); } Point CartesianInterpolator::project(const Point &src_xy) { Point dest_xy; projLP xy; xy.u = src_xy.x; xy.v = src_xy.y; int status = pj_transform(m_src_proj, m_dest_proj, 1, 1, &xy.u, &xy.v, NULL); if (status == -14 || status == -20) { // -14 => "latitude or longitude exceeded limits" // -20 => "tolerance condition error" xy.u = xy.v = HUGE_VAL; } else if (status != 0) { // TODO: Raise a Python exception instead std::cerr << "*******************" << std::endl; std::cerr << status << std::endl; std::cerr << pj_strerrno(status) << std::endl; exit(2); } dest_xy.x = xy.u; dest_xy.y = xy.v; return dest_xy; } SphericalInterpolator::SphericalInterpolator(projPJ src_proj, projPJ dest_proj) { m_src_proj = src_proj; m_dest_proj = dest_proj; } void SphericalInterpolator::set_line(const Point &start, const Point &end) { m_start = start; m_end = end; if (start.x != end.x || start.y != end.y) { double lon, lat; double t, x, y; Vec3 end3, axis3; // Convert lon/lat to unit vectors lon = start.x * DEG_TO_RAD; lat = start.y * DEG_TO_RAD; t = cos(lat); m_start3.x = t * sin(lon); m_start3.y = sin(lat); m_start3.z = t * cos(lon); lon = end.x * DEG_TO_RAD; lat = end.y * DEG_TO_RAD; t = cos(lat); end3.x = t * sin(lon); end3.y = sin(lat); end3.z = t * cos(lon); // Determine the rotation axis for the great circle. // axis = ||start x end|| axis3.x = m_start3.y * end3.z - m_start3.z * end3.y; axis3.y = m_start3.z * end3.x - m_start3.x * end3.z; axis3.z = m_start3.x * end3.y - m_start3.y * end3.x; t = sqrt(axis3.x * axis3.x + axis3.y * axis3.y + axis3.z * axis3.z); axis3.x /= t; axis3.y /= t; axis3.z /= t; // Figure out the remaining basis vector. // perp = axis x start m_perp3.x = axis3.y * m_start3.z - axis3.z * m_start3.y; m_perp3.y = axis3.z * m_start3.x - axis3.x * m_start3.z; m_perp3.z = axis3.x * m_start3.y - axis3.y * m_start3.x; // Derive the rotation angle around the rotation axis. x = m_start3.x * end3.x + m_start3.y * end3.y + m_start3.z * end3.z; y = m_perp3.x * end3.x + m_perp3.y * end3.y + m_perp3.z * end3.z; m_angle = atan2(y, x); } else { m_angle = 0.0; } } Point SphericalInterpolator::interpolate(double t) { Point lonlat; if (m_angle == 0.0) { lonlat = m_start; } else { double angle; double c, s; double x, y, z; double lon, lat; angle = t * m_angle; c = cos(angle); s = sin(angle); x = m_start3.x * c + m_perp3.x * s; y = m_start3.y * c + m_perp3.y * s; z = m_start3.z * c + m_perp3.z * s; lat = asin(y); if(isnan(lat)) { lat = y > 0.0 ? 90.0 : -90.0; } else { lat = lat * RAD_TO_DEG; } lon = atan2(x, z) * RAD_TO_DEG; lonlat.x = lon; lonlat.y = lat; } return project(lonlat); } Point SphericalInterpolator::project(const Point &lonlat) { Point xy; projLP dest; //std::cerr << "lon/lat: " << lonlat.x << ", " << lonlat.y; dest.u = lonlat.x * DEG_TO_RAD; dest.v = lonlat.y * DEG_TO_RAD; //std::cerr << " => " << dest.u << ", " << dest.v; int status = pj_transform(m_src_proj, m_dest_proj, 1, 1, &dest.u, &dest.v, NULL); if (status == -14 || status == -20) { // -14 => "latitude or longitude exceeded limits" // -20 => "tolerance condition error" dest.u = dest.v = HUGE_VAL; } else if (status != 0) { // TODO: Raise a Python exception instead std::cerr << "*******************" << std::endl; std::cerr << status << std::endl; std::cerr << pj_strerrno(status) << std::endl; exit(2); } //std::cerr << " -> " << dest.u << ", " << dest.v; xy.x = dest.u; xy.y = dest.v; //std::cerr << "xy: " << xy.x << ", " << xy.y << std::endl; return xy; } typedef std::list Line; class LineAccumulator { public: LineAccumulator(); void new_line(); void add_point(const Point &point); void add_point_if_empty(const Point &point); GEOSGeometry *as_geom(GEOSContextHandle_t handle); std::list::size_type size() const { return m_lines.size(); } private: std::list m_lines; }; LineAccumulator::LineAccumulator() { new_line(); } void LineAccumulator::new_line() { //std::cerr << "NEW LINE" << std::endl; Line line; m_lines.push_back(line); } void LineAccumulator::add_point(const Point &point) { //std::cerr << "ADD POINT: " << point.x << ", " << point.y << std::endl; m_lines.back().push_back(point); } void LineAccumulator::add_point_if_empty(const Point &point) { //std::cerr << "ADD POINT IF EMPTY " << m_lines.back().size() << std::endl; if (m_lines.back().empty()) { add_point(point); } //std::cerr << " FROM EMPTY " << std::endl; } bool degenerate_line(const Line &value) { return value.size() < 2; } bool close(double a, double b) { return fabs(a - b) <= (1e-8 + 1e-5 * fabs(b)); } GEOSGeometry *LineAccumulator::as_geom(GEOSContextHandle_t handle) { m_lines.remove_if(degenerate_line); if(m_lines.size() > 1) { //std::cerr << "Checking first & last" << std::endl; Point first, last; first = m_lines.front().front(); last = m_lines.back().back(); //std::cerr << "first: " << first.x << ", " << first.y << std::endl; //std::cerr << "last: " << last.x << ", " << last.y << std::endl; if(close(first.x, last.x) && close(first.y, last.y)) { m_lines.front().pop_front(); m_lines.back().splice(m_lines.back().end(), m_lines.front()); m_lines.pop_front(); } } std::vector geoms; std::list::const_iterator ilines; for(ilines = m_lines.begin(); ilines != m_lines.end(); ++ilines) { std::list::const_iterator ipoints; int i; GEOSCoordSequence *coords = GEOSCoordSeq_create_r(handle, (*ilines).size(), 2); for(ipoints = (*ilines).begin(), i = 0; ipoints != (*ilines).end(); ++ipoints, ++i) { GEOSCoordSeq_setX_r(handle, coords, i, ipoints->x); GEOSCoordSeq_setY_r(handle, coords, i, ipoints->y); } geoms.push_back(GEOSGeom_createLineString_r(handle, coords)); } GEOSGeometry *geom; if(geoms.empty()) { geom = GEOSGeom_createEmptyCollection_r(handle, GEOS_MULTILINESTRING); } else { geom = GEOSGeom_createCollection_r(handle, GEOS_MULTILINESTRING, &geoms[0], geoms.size()); } return geom; } enum State { POINT_IN=1, POINT_OUT, POINT_NAN }; State get_state(const Point &point, const GEOSPreparedGeometry *gp_domain, GEOSContextHandle_t handle) { State state; if (isfinite(point.x) && isfinite(point.y)) { // TODO: Avoid create-destroy GEOSCoordSequence *coords = GEOSCoordSeq_create_r(handle, 1, 2); GEOSCoordSeq_setX_r(handle, coords, 0, point.x); GEOSCoordSeq_setY_r(handle, coords, 0, point.y); GEOSGeometry *g_point = GEOSGeom_createPoint_r(handle, coords); state = GEOSPreparedCovers_r(handle, gp_domain, g_point) ? POINT_IN : POINT_OUT; GEOSGeom_destroy_r(handle, g_point); } else { state = POINT_NAN; } return state; } /* * Return whether the given line segment is suitable as an * approximation of the projection of the source line. * * t_start: Interpolation parameter for the start point. * p_start: Projected start point. * t_end: Interpolation parameter for the end point. * p_start: Projected end point. * interpolator: Interpolator for current source line. * threshold: Lateral tolerance in target projection coordinates. * handle: Thread-local context handle for GEOS. * gp_domain: Prepared polygon of target map domain. * inside: Whether the start point is within the map domain. */ bool straightAndDomain(double t_start, const Point &p_start, double t_end, const Point &p_end, Interpolator *interpolator, double threshold, GEOSContextHandle_t handle, const GEOSPreparedGeometry *gp_domain, bool inside) { // Straight and in-domain (de9im[7] == 'F') bool valid; // This could be optimised out of the loop. if (!(isfinite(p_start.x) && isfinite(p_start.y))) { valid = false; } else if (!(isfinite(p_end.x) && isfinite(p_end.y))) { valid = false; } else { // TODO: Re-use geometries, instead of create-destroy! // Create a LineString for the current end-point. GEOSCoordSequence *coords = GEOSCoordSeq_create_r(handle, 2, 2); GEOSCoordSeq_setX_r(handle, coords, 0, p_start.x); GEOSCoordSeq_setY_r(handle, coords, 0, p_start.y); GEOSCoordSeq_setX_r(handle, coords, 1, p_end.x); GEOSCoordSeq_setY_r(handle, coords, 1, p_end.y); GEOSGeometry *g_segment = GEOSGeom_createLineString_r(handle, coords); // Find the projected mid-point double t_mid = (t_start + t_end) * 0.5; Point p_mid = interpolator->interpolate(t_mid); // Make it into a GEOS geometry coords = GEOSCoordSeq_create_r(handle, 1, 2); GEOSCoordSeq_setX_r(handle, coords, 0, p_mid.x); GEOSCoordSeq_setY_r(handle, coords, 0, p_mid.y); GEOSGeometry *g_mid = GEOSGeom_createPoint_r(handle, coords); double along = GEOSProjectNormalized_r(handle, g_segment, g_mid); if(isnan(along)) { valid = true; } else { valid = 0.0 < along && along < 1.0; if (valid) { double separation; GEOSDistance_r(handle, g_segment, g_mid, &separation); if (inside) { // Scale the lateral threshold by the distance from // the nearest end. I.e. Near the ends the lateral // threshold is much smaller; it only has its full // value in the middle. valid = separation <= threshold * 2.0 * (0.5 - fabs(0.5 - along)); } else { // Check if the mid-point makes less than ~11 degree // angle with the straight line. // sin(11') => 0.2 // To save the square-root we just use the square of // the lengths, hence: // 0.2 ^ 2 => 0.04 double hypot_dx = p_mid.x - p_start.x; double hypot_dy = p_mid.y - p_start.y; double hypot = hypot_dx * hypot_dx + hypot_dy * hypot_dy; valid = ((separation * separation) / hypot) < 0.04; } } } if (valid) { if(inside) valid = GEOSPreparedCovers_r(handle, gp_domain, g_segment); else valid = GEOSPreparedDisjoint_r(handle, gp_domain, g_segment); } GEOSGeom_destroy_r(handle, g_segment); GEOSGeom_destroy_r(handle, g_mid); } return valid; } void bisect(double t_start, const Point &p_start, const Point &p_end, GEOSContextHandle_t handle, const GEOSPreparedGeometry *gp_domain, State &state, Interpolator *interpolator, double threshold, double &t_min, Point &p_min, double &t_max, Point &p_max) { double t_current; Point p_current; // Initialise our bisection range to the start and end points. t_min = t_start; p_min = p_start; t_max = 1.0; p_max = p_end; // Start the search at the end. t_current = t_max; p_current = p_max; // TODO: See if we can convert the 't' threshold into one based on the // projected coordinates - e.g. the resulting line length. // while (fabs(t_max - t_min) > 1.0e-6) { #ifdef DEBUG std::cerr << "t: " << t_current << std::endl; #endif bool valid; if (state == POINT_IN) { // Straight and entirely-inside-domain valid = straightAndDomain(t_start, p_start, t_current, p_current, interpolator, threshold, handle, gp_domain, true); } else if(state == POINT_OUT) { // Straight and entirely-outside-domain valid = straightAndDomain(t_start, p_start, t_current, p_current, interpolator, threshold, handle, gp_domain, false); } else { valid = (!isfinite(p_current.x)) || (!isfinite(p_current.y)); } #ifdef DEBUG std::cerr << " => valid: " << valid << std::endl; #endif if (valid) { t_min = t_current; p_min = p_current; } else { t_max = t_current; p_max = p_current; } t_current = (t_min + t_max) * 0.5; p_current = interpolator->interpolate(t_current); } } void _project_segment(GEOSContextHandle_t handle, const GEOSCoordSequence *src_coords, unsigned int src_idx_from, unsigned int src_idx_to, Interpolator *interpolator, const GEOSPreparedGeometry *gp_domain, double threshold, LineAccumulator &lines) { Point p_current, p_min, p_max, p_end; double t_current, t_min, t_max; State state; GEOSCoordSeq_getX_r(handle, src_coords, src_idx_from, &p_current.x); GEOSCoordSeq_getY_r(handle, src_coords, src_idx_from, &p_current.y); GEOSCoordSeq_getX_r(handle, src_coords, src_idx_to, &p_end.x); GEOSCoordSeq_getY_r(handle, src_coords, src_idx_to, &p_end.y); #ifdef DEBUG std::cerr << "Setting line:" << std::endl; std::cerr << " " << p_current.x << ", " << p_current.y << std::endl; std::cerr << " " << p_end.x << ", " << p_end.y << std::endl; #endif interpolator->set_line(p_current, p_end); p_current = interpolator->project(p_current); p_end = interpolator->project(p_end); #ifdef DEBUG std::cerr << "Projected as:" << std::endl; std::cerr << " " << p_current.x << ", " << p_current.y << std::endl; std::cerr << " " << p_end.x << ", " << p_end.y << std::endl; #endif t_current = 0.0; state = get_state(p_current, gp_domain, handle); while(t_current < 1.0 && lines.size() < 500) { //std::cerr << "Bisecting" << std::endl; #ifdef DEBUG std::cerr << "Working from: " << t_current << " ("; if (state == POINT_IN) std::cerr << "IN"; else if (state == POINT_OUT) std::cerr << "OUT"; else std::cerr << "NAN"; std::cerr << ")" << std::endl; std::cerr << " " << p_current.x << ", " << p_current.y << std::endl; std::cerr << " " << p_end.x << ", " << p_end.y << std::endl; #endif bisect(t_current, p_current, p_end, handle, gp_domain, state, interpolator, threshold, t_min, p_min, t_max, p_max); #ifdef DEBUG std::cerr << " => " << t_min << " to " << t_max << std::endl; std::cerr << " => (" << p_min.x << ", " << p_min.y << ") to (" << p_max.x << ", " << p_max.y << ")" << std::endl; #endif if (state == POINT_IN) { lines.add_point_if_empty(p_current); if (t_min != t_current) { lines.add_point(p_min); t_current = t_min; p_current = p_min; } else { t_current = t_max; p_current = p_max; state = get_state(p_current, gp_domain, handle); if(state == POINT_IN) lines.new_line(); } } else if(state == POINT_OUT) { if (t_min != t_current) { t_current = t_min; p_current = p_min; } else { t_current = t_max; p_current = p_max; state = get_state(p_current, gp_domain, handle); if(state == POINT_IN) lines.new_line(); } } else { t_current = t_max; p_current = p_max; state = get_state(p_current, gp_domain, handle); if(state == POINT_IN) lines.new_line(); } } } GEOSGeometry *_project_line_string(GEOSContextHandle_t handle, GEOSGeometry *g_line_string, Interpolator *interpolator, GEOSGeometry *g_domain, double threshold) { const GEOSCoordSequence *src_coords = GEOSGeom_getCoordSeq_r(handle, g_line_string); unsigned int src_size, src_idx; const GEOSPreparedGeometry *gp_domain = GEOSPrepare_r(handle, g_domain); GEOSCoordSeq_getSize_r(handle, src_coords, &src_size); // check exceptions LineAccumulator lines; for(src_idx = 1; src_idx < src_size; src_idx++) { _project_segment(handle, src_coords, src_idx - 1, src_idx, interpolator, gp_domain, threshold, lines); } GEOSPreparedGeom_destroy_r(handle, gp_domain); return lines.as_geom(handle); } Cartopy-0.14.2/lib/cartopy/_trace.h0000644001374000021030000000422212700747662016617 0ustar itpeavd00000000000000/* # (C) British Crown Copyright 2010 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . */ #ifndef _TRACE_H #define _TRACE_H #include #include #include typedef struct { double x; double y; } Point; typedef struct { double x; double y; double z; } Vec3; class Interpolator { public: virtual ~Interpolator(); virtual void set_line(const Point &start, const Point &end); virtual Point interpolate(double t)=0; virtual Point project(const Point &point)=0; protected: Point m_start, m_end; }; class CartesianInterpolator : public Interpolator { public: CartesianInterpolator(projPJ src_proj, projPJ dest_proj); Point interpolate(double t); Point project(const Point &point); private: projPJ m_src_proj, m_dest_proj; }; class SphericalInterpolator : public Interpolator { public: // XXX Move the constructor and members up to the superclass? SphericalInterpolator(projPJ src_proj, projPJ dest_proj); void set_line(const Point &start, const Point &end); Point interpolate(double t); Point project(const Point &point); private: projPJ m_src_proj, m_dest_proj; Vec3 m_start3, m_perp3; double m_angle; }; GEOSGeometry *_project_line_string(GEOSContextHandle_t handle, GEOSGeometry *g_line_string, Interpolator *interpolator, GEOSGeometry *g_domain, double threshold); #endif // _TRACE_H Cartopy-0.14.2/lib/cartopy/crs.py0000644001374000021030000020576012705644356016365 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ The crs module defines Coordinate Reference Systems and the transformations between them. """ from __future__ import (absolute_import, division, print_function) from abc import ABCMeta, abstractproperty import math import warnings import numpy as np import shapely.geometry as sgeom from shapely.prepared import prep import six from cartopy._crs import CRS, Geocentric, Geodetic, Globe, PROJ4_VERSION import cartopy.trace __document_these__ = ['CRS', 'Geocentric', 'Geodetic', 'Globe'] WGS84_SEMIMAJOR_AXIS = 6378137.0 WGS84_SEMIMINOR_AXIS = 6356752.3142 class RotatedGeodetic(CRS): """ Defines a rotated latitude/longitude coordinate system with spherical topology and geographical distance. Coordinates are measured in degrees. """ def __init__(self, pole_longitude, pole_latitude, central_rotated_longitude=0.0, globe=None): """ Create a RotatedGeodetic CRS. The class uses proj4 to perform an ob_tran operation, using the pole_longitude to set a lon_0 then performing two rotations based on pole_latitude and central_rotated_longitude. This is equivalent to setting the new pole to a location defined by the pole_latitude and pole_longitude values in the GeogCRS defined by globe, then rotating this new CRS about it's pole using the central_rotated_longitude value. Args: * pole_longitude - Pole longitude position, in unrotated degrees. * pole_latitude - Pole latitude position, in unrotated degrees. * central_rotated_longitude - Longitude rotation about the new pole, in degrees. Kwargs: * globe - An optional :class:`cartopy.crs.Globe`. Defaults to a "WGS84" datum. """ proj4_params = [('proj', 'ob_tran'), ('o_proj', 'latlon'), ('o_lon_p', central_rotated_longitude), ('o_lat_p', pole_latitude), ('lon_0', 180 + pole_longitude), ('to_meter', math.radians(1))] globe = globe or Globe(datum='WGS84') super(RotatedGeodetic, self).__init__(proj4_params, globe=globe) class Projection(six.with_metaclass(ABCMeta, CRS)): """ Defines a projected coordinate system with flat topology and Euclidean distance. """ _method_map = { 'Point': '_project_point', 'LineString': '_project_line_string', 'LinearRing': '_project_linear_ring', 'Polygon': '_project_polygon', 'MultiPoint': '_project_multipoint', 'MultiLineString': '_project_multiline', 'MultiPolygon': '_project_multipolygon', } @abstractproperty def boundary(self): pass @abstractproperty def threshold(self): pass @abstractproperty def x_limits(self): pass @abstractproperty def y_limits(self): pass @property def cw_boundary(self): try: boundary = self._cw_boundary except AttributeError: boundary = sgeom.LineString(self.boundary) self._cw_boundary = boundary return boundary @property def ccw_boundary(self): try: boundary = self._ccw_boundary except AttributeError: boundary = sgeom.LineString(self.boundary.coords[::-1]) self._ccw_boundary = boundary return boundary @property def domain(self): try: domain = self._domain except AttributeError: domain = self._domain = sgeom.Polygon(self.boundary) return domain def _as_mpl_axes(self): import cartopy.mpl.geoaxes as geoaxes return geoaxes.GeoAxes, {'map_projection': self} def project_geometry(self, geometry, src_crs=None): """ Projects the given geometry into this projection. :param geometry: The geometry to (re-)project. :param src_crs: The source CRS, or geodetic CRS if None. :rtype: Shapely geometry. If src_crs is None, the source CRS is assumed to be a geodetic version of the target CRS. """ if src_crs is None: src_crs = self.as_geodetic() elif not isinstance(src_crs, CRS): raise TypeError('Source CRS must be an instance of CRS' ' or one of its subclasses, or None.') geom_type = geometry.geom_type method_name = self._method_map.get(geom_type) if not method_name: raise ValueError('Unsupported geometry ' 'type {!r}'.format(geom_type)) return getattr(self, method_name)(geometry, src_crs) def _project_point(self, point, src_crs): return sgeom.Point(*self.transform_point(point.x, point.y, src_crs)) def _project_line_string(self, geometry, src_crs): return cartopy.trace.project_linear(geometry, src_crs, self) def _project_linear_ring(self, linear_ring, src_crs): """ Projects the given LinearRing from the src_crs into this CRS and returns a list of LinearRings and a single MultiLineString. """ debug = False # 1) Resolve the initial lines into projected segments # 1abc # def23ghi # jkl41 multi_line_string = cartopy.trace.project_linear(linear_ring, src_crs, self) # Threshold for whether a point is close enough to be the same # point as another. threshold = max(np.abs(self.x_limits + self.y_limits)) * 1e-5 # 2) Simplify the segments where appropriate. if len(multi_line_string) > 1: # Stitch together segments which are close to continuous. # This is important when: # 1) The first source point projects into the map and the # ring has been cut by the boundary. # Continuing the example from above this gives: # def23ghi # jkl41abc # 2) The cut ends of segments are too close to reliably # place into an order along the boundary. line_strings = list(multi_line_string) any_modified = False i = 0 if debug: first_coord = np.array([ls.coords[0] for ls in line_strings]) last_coord = np.array([ls.coords[-1] for ls in line_strings]) print('Distance matrix:') np.set_printoptions(precision=2) x = first_coord[:, np.newaxis, :] y = last_coord[np.newaxis, :, :] print(np.abs(x - y).max(axis=-1)) while i < len(line_strings): modified = False j = 0 while j < len(line_strings): if i != j and np.allclose(line_strings[i].coords[0], line_strings[j].coords[-1], atol=threshold): if debug: print('Joining together {} and {}.'.format(i, j)) last_coords = list(line_strings[j].coords) first_coords = list(line_strings[i].coords)[1:] combo = sgeom.LineString(last_coords + first_coords) if j < i: i, j = j, i del line_strings[j], line_strings[i] line_strings.append(combo) modified = True any_modified = True break else: j += 1 if not modified: i += 1 if any_modified: multi_line_string = sgeom.MultiLineString(line_strings) # 3) Check for rings that have been created by the projection stage. rings = [] line_strings = [] for line in multi_line_string: if len(line.coords) > 3 and np.allclose(line.coords[0], line.coords[-1], atol=threshold): result_geometry = sgeom.LinearRing(line.coords[:-1]) rings.append(result_geometry) else: line_strings.append(line) # If we found any rings, then we should re-create the multi-line str. if rings: multi_line_string = sgeom.MultiLineString(line_strings) return rings, multi_line_string def _project_multipoint(self, geometry, src_crs): geoms = [] for geom in geometry.geoms: geoms.append(self._project_point(geom, src_crs)) if geoms: return sgeom.MultiPoint(geoms) else: return sgeom.MultiPoint() def _project_multiline(self, geometry, src_crs): geoms = [] for geom in geometry.geoms: r = self._project_line_string(geom, src_crs) if r: geoms.extend(r.geoms) if geoms: return sgeom.MultiLineString(geoms) else: return [] def _project_multipolygon(self, geometry, src_crs): geoms = [] for geom in geometry.geoms: r = self._project_polygon(geom, src_crs) if r: geoms.extend(r.geoms) if geoms: result = sgeom.MultiPolygon(geoms) else: result = sgeom.MultiPolygon() return result def _project_polygon(self, polygon, src_crs): """ Returns the projected polygon(s) derived from the given polygon. """ # Determine orientation of polygon. # TODO: Consider checking the internal rings have the opposite # orientation to the external rings? if src_crs.is_geodetic(): is_ccw = True else: is_ccw = polygon.exterior.is_ccw # Project the polygon exterior/interior rings. # Each source ring will result in either a ring, or one or more # lines. rings = [] multi_lines = [] for src_ring in [polygon.exterior] + list(polygon.interiors): p_rings, p_mline = self._project_linear_ring(src_ring, src_crs) if p_rings: rings.extend(p_rings) if len(p_mline) > 0: multi_lines.append(p_mline) # Convert any lines to rings by attaching them to the boundary. if multi_lines: rings.extend(self._attach_lines_to_boundary(multi_lines, is_ccw)) # Resolve all the inside vs. outside rings, and convert to the # final MultiPolygon. return self._rings_to_multi_polygon(rings, is_ccw) def _attach_lines_to_boundary(self, multi_line_strings, is_ccw): """ Returns a list of LinearRings by attaching the ends of the given lines to the boundary, paying attention to the traversal directions of the lines and boundary. """ debug = False debug_plot_edges = False # Accumulate all the boundary and segment end points, along with # their distance along the boundary. edge_things = [] # Get the boundary as a LineString of the correct orientation # so we can compute distances along it. if is_ccw: boundary = self.ccw_boundary else: boundary = self.cw_boundary def boundary_distance(xy): return boundary.project(sgeom.Point(*xy)) # Squash all the LineStrings into a single list. line_strings = [] for multi_line_string in multi_line_strings: line_strings.extend(multi_line_string) # Record the positions of all the segment ends for i, line_string in enumerate(line_strings): first_dist = boundary_distance(line_string.coords[0]) thing = _BoundaryPoint(first_dist, False, (i, 'first', line_string.coords[0])) edge_things.append(thing) last_dist = boundary_distance(line_string.coords[-1]) thing = _BoundaryPoint(last_dist, False, (i, 'last', line_string.coords[-1])) edge_things.append(thing) # Record the positions of all the boundary vertices for xy in boundary.coords[:-1]: point = sgeom.Point(*xy) dist = boundary.project(point) thing = _BoundaryPoint(dist, True, point) edge_things.append(thing) if debug_plot_edges: import matplotlib.pyplot as plt current_fig = plt.gcf() fig = plt.figure() # Reset the current figure so we don't upset anything. plt.figure(current_fig.number) ax = fig.add_subplot(1, 1, 1) # Order everything as if walking around the boundary. # NB. We make line end-points take precedence over boundary points # to ensure that end-points are still found and followed when they # coincide. edge_things.sort(key=lambda thing: (thing.distance, thing.kind)) remaining_ls = dict(enumerate(line_strings)) prev_thing = None for edge_thing in edge_things[:]: if (prev_thing is not None and not edge_thing.kind and not prev_thing.kind and edge_thing.data[0] == prev_thing.data[0]): j = edge_thing.data[0] # Insert a edge boundary point in between this geometry. mid_dist = (edge_thing.distance + prev_thing.distance) * 0.5 mid_point = boundary.interpolate(mid_dist) new_thing = _BoundaryPoint(mid_dist, True, mid_point) if debug: print('Artificially insert boundary: {}'.format(new_thing)) ind = edge_things.index(edge_thing) edge_things.insert(ind, new_thing) prev_thing = None else: prev_thing = edge_thing if debug: print() print('Edge things') for thing in edge_things: print(' ', thing) if debug_plot_edges: for thing in edge_things: if isinstance(thing.data, sgeom.Point): ax.plot(*thing.data.xy, marker='o') else: ax.plot(*thing.data[2], marker='o') ls = line_strings[thing.data[0]] coords = np.array(ls.coords) ax.plot(coords[:, 0], coords[:, 1]) ax.text(coords[0, 0], coords[0, 1], thing.data[0]) ax.text(coords[-1, 0], coords[-1, 1], '{}.'.format(thing.data[0])) processed_ls = [] while remaining_ls: # Rename line_string to current_ls i, current_ls = remaining_ls.popitem() if debug: import sys sys.stdout.write('+') sys.stdout.flush() print() print('Processing: %s, %s' % (i, current_ls)) # We only want to consider boundary-points, the starts-and-ends of # all other line-strings, or the start-point of the current # line-string. def filter_fn(t): return (t.kind or t.data[0] != i or t.data[1] != 'last') edge_things = list(filter(filter_fn, edge_things)) added_linestring = set() while True: # Find out how far around this linestring's last # point is on the boundary. We will use this to find # the next point on the boundary. d_last = boundary_distance(current_ls.coords[-1]) if debug: print(' d_last: {!r}'.format(d_last)) next_thing = _find_first_gt(edge_things, d_last) # Remove this boundary point from the edge. edge_things.remove(next_thing) if debug: print(' next_thing:', next_thing) if next_thing.kind: # We've just got a boundary point, add it, and keep going. if debug: print(' adding boundary point') boundary_point = next_thing.data combined_coords = (list(current_ls.coords) + [(boundary_point.x, boundary_point.y)]) current_ls = sgeom.LineString(combined_coords) elif next_thing.data[0] == i and next_thing.data[1] == 'first': # We've gone all the way around and are now back at the # first boundary thing. if debug: print(' close loop') processed_ls.append(current_ls) if debug_plot_edges: coords = np.array(current_ls.coords) ax.plot(coords[:, 0], coords[:, 1], color='black', linestyle='--') break else: if debug: print(' adding line') j = next_thing.data[0] line_to_append = line_strings[j] if j in remaining_ls: remaining_ls.pop(j) coords_to_append = list(line_to_append.coords) if next_thing.data[1] == 'last': coords_to_append = coords_to_append[::-1] # Build up the linestring. current_ls = sgeom.LineString((list(current_ls.coords) + coords_to_append)) # Catch getting stuck in an infinite loop by checking that # linestring only added once. if j not in added_linestring: added_linestring.add(j) else: if debug_plot_edges: plt.show() raise RuntimeError('Unidentified problem with ' 'geometry, linestring being ' 're-added. Please raise an issue.') # filter out any non-valid linear rings processed_ls = [linear_ring for linear_ring in processed_ls if len(linear_ring.coords) > 2] linear_rings = [sgeom.LinearRing(line) for line in processed_ls] if debug: print(' DONE') return linear_rings def _rings_to_multi_polygon(self, rings, is_ccw): exterior_rings = [] interior_rings = [] for ring in rings: if ring.is_ccw != is_ccw: interior_rings.append(ring) else: exterior_rings.append(ring) polygon_bits = [] # Turn all the exterior rings into polygon definitions, # "slurping up" any interior rings they contain. for exterior_ring in exterior_rings: polygon = sgeom.Polygon(exterior_ring) prep_polygon = prep(polygon) holes = [] for interior_ring in interior_rings[:]: if prep_polygon.contains(interior_ring): holes.append(interior_ring) interior_rings.remove(interior_ring) elif polygon.crosses(interior_ring): # Likely that we have an invalid geometry such as # that from #509 or #537. holes.append(interior_ring) interior_rings.remove(interior_ring) polygon_bits.append((exterior_ring.coords, [ring.coords for ring in holes])) # Any left over "interior" rings need "inverting" with respect # to the boundary. if interior_rings: boundary_poly = self.domain x3, y3, x4, y4 = boundary_poly.bounds bx = (x4 - x3) * 0.1 by = (y4 - y3) * 0.1 x3 -= bx y3 -= by x4 += bx y4 += by for ring in interior_rings: polygon = sgeom.Polygon(ring) if polygon.is_valid: x1, y1, x2, y2 = polygon.bounds bx = (x2 - x1) * 0.1 by = (y2 - y1) * 0.1 x1 -= bx y1 -= by x2 += bx y2 += by box = sgeom.box(min(x1, x3), min(y1, y3), max(x2, x4), max(y2, y4)) # Invert the polygon polygon = box.difference(polygon) # Intersect the inverted polygon with the boundary polygon = boundary_poly.intersection(polygon) if not polygon.is_empty: polygon_bits.append(polygon) if polygon_bits: multi_poly = sgeom.MultiPolygon(polygon_bits) else: multi_poly = sgeom.MultiPolygon() return multi_poly def quick_vertices_transform(self, vertices, src_crs): """ Where possible, return a vertices array transformed to this CRS from the given vertices array of shape ``(n, 2)`` and the source CRS. .. important:: This method may return None to indicate that the vertices cannot be transformed quickly, and a more complex geometry transformation is required (see :meth:`cartopy.crs.Projection.project_geometry`). """ return_value = None if self == src_crs: x = vertices[:, 0] y = vertices[:, 1] x_limits = self.x_limits y_limits = self.y_limits if (x.min() >= x_limits[0] and x.max() <= x_limits[1] and y.min() >= y_limits[0] and y.max() <= y_limits[1]): return_value = vertices return return_value class _RectangularProjection(Projection): """ The abstract superclass of projections with a rectangular domain which is symmetric about the origin. """ def __init__(self, proj4_params, half_width, half_height, globe=None): self._half_width = half_width self._half_height = half_height super(_RectangularProjection, self).__init__(proj4_params, globe=globe) @property def boundary(self): # XXX Should this be a LinearRing? w, h = self._half_width, self._half_height return sgeom.LineString([(-w, -h), (-w, h), (w, h), (w, -h), (-w, -h)]) @property def x_limits(self): return (-self._half_width, self._half_width) @property def y_limits(self): return (-self._half_height, self._half_height) class _CylindricalProjection(_RectangularProjection): """ The abstract class which denotes cylindrical projections where we want to allow x values to wrap around. """ def _ellipse_boundary(semimajor=2, semiminor=1, easting=0, northing=0, n=201): """ Defines a projection boundary using an ellipse. This type of boundary is used by several projections. """ t = np.linspace(0, 2 * np.pi, n) coords = np.vstack([semimajor * np.cos(t), semiminor * np.sin(t)]) coords += ([easting], [northing]) return coords[:, ::-1] class PlateCarree(_CylindricalProjection): def __init__(self, central_longitude=0.0, globe=None): proj4_params = [('proj', 'eqc'), ('lon_0', central_longitude)] if globe is None: globe = Globe(semimajor_axis=math.degrees(1)) a_rad = math.radians(globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) x_max = a_rad * 180 y_max = a_rad * 90 # Set the threshold around 0.5 if the x max is 180. self._threshold = x_max / 360. super(PlateCarree, self).__init__(proj4_params, x_max, y_max, globe=globe) @property def threshold(self): return self._threshold def _bbox_and_offset(self, other_plate_carree): """ Returns a pair of (xmin, xmax) pairs and an offset which can be used for identification of whether data in ``other_plate_carree`` needs to be transformed to wrap appropriately. >>> import cartopy.crs as ccrs >>> src = ccrs.PlateCarree(central_longitude=10) >>> bboxes, offset = ccrs.PlateCarree()._bbox_and_offset(src) >>> print(bboxes) [[-180.0, -170.0], [-170.0, 180.0]] >>> print(offset) 10.0 The returned values are longitudes in ``other_plate_carree``'s coordinate system. .. important:: The two CRSs must be identical in every way, other than their central longitudes. No checking of this is done. """ self_lon_0 = self.proj4_params['lon_0'] other_lon_0 = other_plate_carree.proj4_params['lon_0'] lon_0_offset = other_lon_0 - self_lon_0 lon_lower_bound_0 = self.x_limits[0] lon_lower_bound_1 = (other_plate_carree.x_limits[0] + lon_0_offset) if lon_lower_bound_1 < self.x_limits[0]: lon_lower_bound_1 += np.diff(self.x_limits)[0] lon_lower_bound_0, lon_lower_bound_1 = sorted( [lon_lower_bound_0, lon_lower_bound_1]) bbox = [[lon_lower_bound_0, lon_lower_bound_1], [lon_lower_bound_1, lon_lower_bound_0]] bbox[1][1] += np.diff(self.x_limits)[0] return bbox, lon_0_offset def quick_vertices_transform(self, vertices, src_crs): return_value = super(PlateCarree, self).quick_vertices_transform(vertices, src_crs) # Optimise the PlateCarree -> PlateCarree case where no # wrapping or interpolation needs to take place. if return_value is None and isinstance(src_crs, PlateCarree): self_params = self.proj4_params.copy() src_params = src_crs.proj4_params.copy() self_params.pop('lon_0'), src_params.pop('lon_0') xs, ys = vertices[:, 0], vertices[:, 1] potential = (self_params == src_params and self.y_limits[0] <= ys.min() and self.y_limits[1] >= ys.max()) if potential: mod = np.diff(src_crs.x_limits)[0] bboxes, proj_offset = self._bbox_and_offset(src_crs) x_lim = xs.min(), xs.max() y_lim = ys.min(), ys.max() for poly in bboxes: # Arbitrarily choose the number of moduli to look # above and below the -180->180 range. If data is beyond # this range, we're not going to transform it quickly. for i in [-1, 0, 1, 2]: offset = mod * i - proj_offset if ((poly[0] + offset) <= x_lim[0] and (poly[1] + offset) >= x_lim[1]): return_value = vertices + [[-offset, 0]] break if return_value is not None: break return return_value class TransverseMercator(Projection): """ A Transverse Mercator projection. """ def __init__(self, central_longitude=0.0, central_latitude=0.0, false_easting=0.0, false_northing=0.0, scale_factor=1.0, globe=None): """ Kwargs: * central_longitude - The true longitude of the central meridian in degrees. Defaults to 0. * central_latitude - The true latitude of the planar origin in degrees. Defaults to 0. * false_easting - X offset from the planar origin in metres. Defaults to 0. * false_northing - Y offset from the planar origin in metres. Defaults to 0. * scale_factor - Scale factor at the central meridian. Defaults to 1. * globe - An instance of :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ proj4_params = [('proj', 'tmerc'), ('lon_0', central_longitude), ('lat_0', central_latitude), ('k', scale_factor), ('x_0', false_easting), ('y_0', false_northing), ('units', 'm')] super(TransverseMercator, self).__init__(proj4_params, globe=globe) @property def threshold(self): return 1e4 @property def boundary(self): x0, x1 = self.x_limits y0, y1 = self.y_limits return sgeom.LineString([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)]) @property def x_limits(self): return (-2e7, 2e7) @property def y_limits(self): return (-1e7, 1e7) class OSGB(TransverseMercator): def __init__(self): super(OSGB, self).__init__(central_longitude=-2, central_latitude=49, scale_factor=0.9996012717, false_easting=400000, false_northing=-100000, globe=Globe(datum='OSGB36', ellipse='airy')) @property def boundary(self): w = self.x_limits[1] - self.x_limits[0] h = self.y_limits[1] - self.y_limits[0] return sgeom.LineString([(0, 0), (0, h), (w, h), (w, 0), (0, 0)]) @property def x_limits(self): return (0, 7e5) @property def y_limits(self): return (0, 13e5) class OSNI(TransverseMercator): def __init__(self): globe = Globe(semimajor_axis=6377340.189, semiminor_axis=6356034.447938534) super(OSNI, self).__init__(central_longitude=-8, central_latitude=53.5, scale_factor=1.000035, false_easting=200000, false_northing=250000, globe=globe) @property def boundary(self): w = self.x_limits[1] - self.x_limits[0] h = self.y_limits[1] - self.y_limits[0] return sgeom.LineString([(0, 0), (0, h), (w, h), (w, 0), (0, 0)]) @property def x_limits(self): return (18814.9667, 386062.3293) @property def y_limits(self): return (11764.8481, 464720.9559) class UTM(Projection): """ Universal Transverse Mercator projection. """ def __init__(self, zone, southern_hemisphere=False, globe=None): """ Kwargs: * zone - the numeric zone of the UTM required. * globe - An instance of :class:`cartopy.crs.Globe`. If omitted, a default globe is created. * southern_hemisphere - set to True if the zone is in the southern hemisphere, defaults to False. """ proj4_params = [('proj', 'utm'), ('units', 'm'), ('zone', zone)] if southern_hemisphere: proj4_params.append(('south', None)) super(UTM, self).__init__(proj4_params, globe=globe) @property def boundary(self): x0, x1 = self.x_limits y0, y1 = self.y_limits return sgeom.LineString([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)]) @property def threshold(self): return 1e2 @property def x_limits(self): easting = 5e5 # allow 50% overflow return (0 - easting/2, 2 * easting + easting/2) @property def y_limits(self): northing = 1e7 # allow 50% overflow return (0 - northing, 2 * northing + northing/2) class EuroPP(UTM): """ UTM Zone 32 projection for EuroPP domain. Ellipsoid is International 1924, Datum is ED50. """ def __init__(self): globe = Globe(ellipse='intl') super(EuroPP, self).__init__(32, globe=globe) @property def x_limits(self): return (-1.4e6, 2e6) @property def y_limits(self): return (4e6, 7.9e6) class Mercator(Projection): """ A Mercator projection. """ def __init__(self, central_longitude=0.0, min_latitude=-80.0, max_latitude=84.0, globe=None): """ Kwargs: * central_longitude - the central longitude. Defaults to 0. * min_latitude - the maximum southerly extent of the projection. Defaults to -80 degrees. * max_latitude - the maximum northerly extent of the projection. Defaults to 84 degrees. * globe - A :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ proj4_params = [('proj', 'merc'), ('lon_0', central_longitude), ('k', 1), ('units', 'm')] super(Mercator, self).__init__(proj4_params, globe=globe) # Calculate limits. limits = self.transform_points(Geodetic(), np.array([-180, 180]) + central_longitude, np.array([min_latitude, max_latitude])) self._xlimits = tuple(limits[..., 0]) self._ylimits = tuple(limits[..., 1]) self._threshold = np.diff(self.x_limits)[0] / 720 def __eq__(self, other): res = super(Mercator, self).__eq__(other) if hasattr(other, "_ylimits") and hasattr(other, "_xlimits"): res = res and self._ylimits == other._ylimits and \ self._xlimits == other._xlimits return res def __ne__(self, other): return not self == other def __hash__(self): return hash((self.proj4_init, self._xlimits, self._ylimits)) @property def threshold(self): return self._threshold @property def boundary(self): x0, x1 = self.x_limits y0, y1 = self.y_limits return sgeom.LineString([(x0, y0), (x0, y1), (x1, y1), (x1, y0), (x0, y0)]) @property def x_limits(self): return self._xlimits @property def y_limits(self): return self._ylimits # Define a specific instance of a Mercator projection, the Google mercator. Mercator.GOOGLE = Mercator(min_latitude=-85.0511287798066, max_latitude=85.0511287798066, globe=Globe(ellipse=None, semimajor_axis=WGS84_SEMIMAJOR_AXIS, semiminor_axis=WGS84_SEMIMAJOR_AXIS, nadgrids='@null')) # Deprecated form GOOGLE_MERCATOR = Mercator.GOOGLE class LambertCylindrical(_RectangularProjection): def __init__(self, central_longitude=0.0): proj4_params = [('proj', 'cea'), ('lon_0', central_longitude)] globe = Globe(semimajor_axis=math.degrees(1)) super(LambertCylindrical, self).__init__(proj4_params, 180, math.degrees(1), globe=globe) @property def threshold(self): return 0.5 class LambertConformal(Projection): """ A Lambert Conformal conic projection. """ def __init__(self, central_longitude=-96.0, central_latitude=39.0, false_easting=0.0, false_northing=0.0, secant_latitudes=None, standard_parallels=None, globe=None, cutoff=-30): """ Kwargs: * central_longitude - The central longitude. Defaults to -96. * central_latitude - The central latitude. Defaults to 39. * false_easting - X offset from planar origin in metres. Defaults to 0. * false_northing - Y offset from planar origin in metres. Defaults to 0. * standard_parallels - Standard parallel latitude(s). Defaults to (33, 45). * globe - A :class:`cartopy.crs.Globe`. If omitted, a default globe is created. * cutoff - Latitude of map cutoff. The map extends to infinity opposite the central pole so we must cut off the map drawing before then. A value of 0 will draw half the globe. Defaults to -30. """ proj4_params = [('proj', 'lcc'), ('lon_0', central_longitude), ('lat_0', central_latitude), ('x_0', false_easting), ('y_0', false_northing)] if secant_latitudes and standard_parallels: raise TypeError('standard_parallels replaces secant_latitudes.') elif secant_latitudes is not None: warnings.warn('secant_latitudes has been deprecated in v0.12. ' 'The standard_parallels keyword can be used as a ' 'direct replacement.') standard_parallels = secant_latitudes elif standard_parallels is None: # The default. Put this as a keyword arg default once # secant_latitudes is removed completely. standard_parallels = (33, 45) n_parallels = len(standard_parallels) if not 1 <= n_parallels <= 2: raise ValueError('1 or 2 standard parallels must be specified. ' 'Got {} ({})'.format(n_parallels, standard_parallels)) proj4_params.append(('lat_1', standard_parallels[0])) if n_parallels == 2: proj4_params.append(('lat_2', standard_parallels[1])) super(LambertConformal, self).__init__(proj4_params, globe=globe) # Compute whether this projection is at the "north pole" or the # "south pole" (after the central lon/lat have been taken into # account). if n_parallels == 1: plat = 90 if standard_parallels[0] > 0 else -90 else: # Which pole are the parallels closest to? That is the direction # that the cone converges. if abs(standard_parallels[0]) > abs(standard_parallels[1]): poliest_sec = standard_parallels[0] else: poliest_sec = standard_parallels[1] plat = 90 if poliest_sec > 0 else -90 self.cutoff = cutoff n = 91 lons = [0] lats = [plat] lons.extend(np.linspace(central_longitude - 180 + 0.001, central_longitude + 180 - 0.001, n)) lats.extend(np.array([cutoff] * n)) lons.append(0) lats.append(plat) points = self.transform_points(PlateCarree(), np.array(lons), np.array(lats)) if plat == 90: # Ensure clockwise points = points[::-1, :] self._boundary = sgeom.LineString(points) bounds = self._boundary.bounds self._x_limits = bounds[0], bounds[2] self._y_limits = bounds[1], bounds[3] def __eq__(self, other): res = super(LambertConformal, self).__eq__(other) if hasattr(other, "cutoff"): res = res and self.cutoff == other.cutoff return res def __ne__(self, other): return not self == other def __hash__(self): return hash((self.proj4_init, self.cutoff)) @property def boundary(self): return self._boundary @property def threshold(self): return 1e5 @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class LambertAzimuthalEqualArea(Projection): """ A Lambert Azimuthal Equal-Area projection. """ def __init__(self, central_longitude=0.0, central_latitude=0.0, false_easting=0.0, false_northing=0.0, globe=None): """ Kwargs: * central_longitude - The central longitude. Defaults to 0. * central_latitude - The central latitude. Defaults to 0. * false_easting - X offset from planar origin in metres. Defaults to 0. * false_northing - Y offset from planar origin in metres. Defaults to 0. * globe - A :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ proj4_params = [('proj', 'laea'), ('lon_0', central_longitude), ('lat_0', central_latitude), ('x_0', false_easting), ('y_0', false_northing)] super(LambertAzimuthalEqualArea, self).__init__(proj4_params, globe=globe) a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) b = np.float(self.globe.semiminor_axis or WGS84_SEMIMINOR_AXIS) lon, lat = central_longitude + 180, - central_latitude + 0.01 x, max_y = self.transform_point(lon, lat, PlateCarree()) coords = _ellipse_boundary(a * 1.9999, max_y - false_northing, false_easting, false_northing, 61) self._boundary = sgeom.polygon.LinearRing(coords.T) self._x_limits = self._boundary.bounds[::2] self._y_limits = self._boundary.bounds[1::2] self._threshold = np.diff(self._x_limits)[0] * 1e-3 @property def boundary(self): return self._boundary @property def threshold(self): return self._threshold @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class Miller(_RectangularProjection): def __init__(self, central_longitude=0.0): proj4_params = [('proj', 'mill'), ('lon_0', central_longitude)] globe = Globe(semimajor_axis=math.degrees(1)) # XXX How can we derive the vertical limit of 131.98? super(Miller, self).__init__(proj4_params, 180, 131.98, globe=globe) @property def threshold(self): return 0.5 class RotatedPole(_CylindricalProjection): """ Defines a rotated latitude/longitude projected coordinate system with cylindrical topology and projected distance. Coordinates are measured in projection metres. """ def __init__(self, pole_longitude=0.0, pole_latitude=90.0, central_rotated_longitude=0.0, globe=None): """ Create a RotatedPole CRS. The class uses proj4 to perform an ob_tran operation, using the pole_longitude to set a lon_0 then performing two rotations based on pole_latitude and central_rotated_longitude. This is equivalent to setting the new pole to a location defined by the pole_latitude and pole_longitude values in the GeogCRS defined by globe, then rotating this new CRS about it's pole using the central_rotated_longitude value. Args: * pole_longitude - Pole longitude position, in unrotated degrees. * pole_latitude - Pole latitude position, in unrotated degrees. * central_rotated_longitude - Longitude rotation about the new pole, in degrees. Kwargs: * globe - An optional :class:`cartopy.crs.Globe`. Defaults to a "WGS84" datum. """ proj4_params = [('proj', 'ob_tran'), ('o_proj', 'latlon'), ('o_lon_p', central_rotated_longitude), ('o_lat_p', pole_latitude), ('lon_0', 180 + pole_longitude), ('to_meter', math.radians(1))] super(RotatedPole, self).__init__(proj4_params, 180, 90, globe=globe) @property def threshold(self): return 0.5 class Gnomonic(Projection): def __init__(self, central_latitude=0.0, globe=None): proj4_params = [('proj', 'gnom'), ('lat_0', central_latitude)] super(Gnomonic, self).__init__(proj4_params, globe=globe) self._max = 5e7 @property def boundary(self): return sgeom.Point(0, 0).buffer(self._max).exterior @property def threshold(self): return 1e5 @property def x_limits(self): return (-self._max, self._max) @property def y_limits(self): return (-self._max, self._max) class Stereographic(Projection): def __init__(self, central_latitude=0.0, central_longitude=0.0, false_easting=0.0, false_northing=0.0, true_scale_latitude=None, globe=None): proj4_params = [('proj', 'stere'), ('lat_0', central_latitude), ('lon_0', central_longitude), ('x_0', false_easting), ('y_0', false_northing)] if true_scale_latitude: proj4_params.append(('lat_ts', true_scale_latitude)) super(Stereographic, self).__init__(proj4_params, globe=globe) # TODO: Let the globe return the semimajor axis always. a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) b = np.float(self.globe.semiminor_axis or WGS84_SEMIMINOR_AXIS) # Note: The magic number has been picked to maintain consistent # behaviour with a wgs84 globe. There is no guarantee that the scaling # should even be linear. x_axis_offset = 5e7 / WGS84_SEMIMAJOR_AXIS y_axis_offset = 5e7 / WGS84_SEMIMINOR_AXIS self._x_limits = (-a * x_axis_offset + false_easting, a * x_axis_offset + false_easting) self._y_limits = (-b * y_axis_offset + false_northing, b * y_axis_offset + false_northing) if self._x_limits[1] == self._y_limits[1]: point = sgeom.Point(false_easting, false_northing) self._boundary = point.buffer(self._x_limits[1]).exterior else: coords = _ellipse_boundary(self._x_limits[1], self._y_limits[1], false_easting, false_northing, 91) self._boundary = sgeom.LinearRing(coords.T) self._threshold = np.diff(self._x_limits)[0] * 1e-3 @property def boundary(self): return self._boundary @property def threshold(self): return self._threshold @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class NorthPolarStereo(Stereographic): def __init__(self, central_longitude=0.0, globe=None): super(NorthPolarStereo, self).__init__( central_latitude=90, central_longitude=central_longitude, globe=globe) class SouthPolarStereo(Stereographic): def __init__(self, central_longitude=0.0, globe=None): super(SouthPolarStereo, self).__init__( central_latitude=-90, central_longitude=central_longitude, globe=globe) class Orthographic(Projection): def __init__(self, central_longitude=0.0, central_latitude=0.0, globe=None): proj4_params = [('proj', 'ortho'), ('lon_0', central_longitude), ('lat_0', central_latitude)] super(Orthographic, self).__init__(proj4_params, globe=globe) # TODO: Let the globe return the semimajor axis always. a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) b = np.float(self.globe.semiminor_axis or a) if b != a: warnings.warn('The proj4 "ortho" projection does not appear to ' 'handle elliptical globes.') # To stabilise the projection of geometries, we reduce the boundary by # a tiny fraction at the cost of the extreme edges. coords = _ellipse_boundary(a * 0.99999, b * 0.99999, n=61) self._boundary = sgeom.polygon.LinearRing(coords.T) self._xlim = self._boundary.bounds[::2] self._ylim = self._boundary.bounds[1::2] self._threshold = np.diff(self._xlim)[0] * 0.02 @property def boundary(self): return self._boundary @property def threshold(self): return self._threshold @property def x_limits(self): return self._xlim @property def y_limits(self): return self._ylim class _WarpedRectangularProjection(Projection): def __init__(self, proj4_params, central_longitude, globe=None): super(_WarpedRectangularProjection, self).__init__(proj4_params, globe=globe) # Obtain boundary points points = [] n = 91 geodetic_crs = self.as_geodetic() for lat in np.linspace(-90, 90, n): points.append( self.transform_point(180 + central_longitude, lat, geodetic_crs) ) for lat in np.linspace(90, -90, n): points.append( self.transform_point(-180 + central_longitude, lat, geodetic_crs) ) points.append( self.transform_point(180 + central_longitude, -90, geodetic_crs)) self._boundary = sgeom.LineString(points[::-1]) x = [p[0] for p in points] y = [p[1] for p in points] self._x_limits = min(x), max(x) self._y_limits = min(y), max(y) @property def boundary(self): return self._boundary @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class Mollweide(_WarpedRectangularProjection): def __init__(self, central_longitude=0, globe=None): proj4_params = [('proj', 'moll'), ('lon_0', central_longitude)] super(Mollweide, self).__init__(proj4_params, central_longitude, globe=globe) @property def threshold(self): return 1e5 class Robinson(_WarpedRectangularProjection): def __init__(self, central_longitude=0, globe=None): # Warn when using Robinson with proj4 4.8 due to discontinuity at # 40 deg N introduced by incomplete fix to issue #113 (see # https://trac.osgeo.org/proj/ticket/113). import re if PROJ4_VERSION != (): if (4, 8) <= PROJ4_VERSION < (4, 9): warnings.warn('The Robinson projection in the v4.8.x series ' 'of Proj.4 contains a discontinuity at ' '40 deg latitude. Use this projection with ' 'caution.') else: warnings.warn('Cannot determine Proj.4 version. The Robinson ' 'projection may be unreliable and should be used ' 'with caution.') proj4_params = [('proj', 'robin'), ('lon_0', central_longitude)] super(Robinson, self).__init__(proj4_params, central_longitude, globe=globe) @property def threshold(self): return 1e4 def transform_point(self, x, y, src_crs): """ Capture and handle any input NaNs, else invoke parent function, :meth:`_WarpedRectangularProjection.transform_point`. Needed because input NaNs can trigger a fatal error in the underlying implementation of the Robinson projection. .. note:: Although the original can in fact translate (nan, lat) into (nan, y-value), this patched version doesn't support that. """ if np.isnan(x) or np.isnan(y): result = (np.nan, np.nan) else: result = super(Robinson, self).transform_point(x, y, src_crs) return result def transform_points(self, src_crs, x, y, z=None): """ Capture and handle NaNs in input points -- else as parent function, :meth:`_WarpedRectangularProjection.transform_points`. Needed because input NaNs can trigger a fatal error in the underlying implementation of the Robinson projection. .. note:: Although the original can in fact translate (nan, lat) into (nan, y-value), this patched version doesn't support that. Instead, we invalidate any of the points that contain a NaN. """ input_point_nans = np.isnan(x) | np.isnan(y) if z is not None: input_point_nans |= np.isnan(z) handle_nans = np.any(input_point_nans) if handle_nans: # Remove NaN points from input data to avoid the error. x[input_point_nans] = 0.0 y[input_point_nans] = 0.0 if z is not None: z[input_point_nans] = 0.0 result = super(Robinson, self).transform_points(src_crs, x, y, z) if handle_nans: # Result always has shape (N, 3). # Blank out each (whole) point where we had a NaN in the input. result[input_point_nans] = np.nan return result class InterruptedGoodeHomolosine(Projection): def __init__(self, central_longitude=0, globe=None): proj4_params = [('proj', 'igh'), ('lon_0', central_longitude)] super(InterruptedGoodeHomolosine, self).__init__(proj4_params, globe=globe) # Obtain boundary points points = [] n = 31 geodetic_crs = self.as_geodetic() # Right boundary for lat in np.linspace(-90, 90, n): points.append(self.transform_point(180 + central_longitude, lat, geodetic_crs)) # Top boundary interrupted_lons = (-40.0,) delta = 0.001 for lon in interrupted_lons: for lat in np.linspace(90, 0, n): points.append(self.transform_point(lon + delta + central_longitude, lat, geodetic_crs)) for lat in np.linspace(0, 90, n): points.append(self.transform_point(lon - delta + central_longitude, lat, geodetic_crs)) # Left boundary for lat in np.linspace(90, -90, n): points.append(self.transform_point(-180 + central_longitude, lat, geodetic_crs)) # Bottom boundary interrupted_lons = (-100.0, -20.0, 80.0) delta = 0.001 for lon in interrupted_lons: for lat in np.linspace(-90, 0, n): points.append(self.transform_point(lon - delta + central_longitude, lat, geodetic_crs)) for lat in np.linspace(0, -90, n): points.append(self.transform_point(lon + delta + central_longitude, lat, geodetic_crs)) # Close loop points.append(self.transform_point(180 + central_longitude, -90, geodetic_crs)) self._boundary = sgeom.LineString(points[::-1]) x = [p[0] for p in points] y = [p[1] for p in points] self._x_limits = min(x), max(x) self._y_limits = min(y), max(y) @property def boundary(self): return self._boundary @property def threshold(self): return 2e4 @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class Geostationary(Projection): def __init__(self, central_longitude=0.0, satellite_height=35785831, false_easting=0, false_northing=0, globe=None): proj4_params = [('proj', 'geos'), ('lon_0', central_longitude), ('lat_0', 0), ('h', satellite_height), ('x_0', false_easting), ('y_0', false_northing), ('units', 'm')] super(Geostationary, self).__init__(proj4_params, globe=globe) # TODO: Let the globe return the semimajor axis always. a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) b = np.float(self.globe.semiminor_axis or a) h = np.float(satellite_height) max_x = h * math.atan(a / (a + h)) max_y = h * math.atan(b / (b + h)) coords = _ellipse_boundary(max_x, max_y, false_easting, false_northing, 61) self._boundary = sgeom.LinearRing(coords.T) self._xlim = self._boundary.bounds[::2] self._ylim = self._boundary.bounds[1::2] self._threshold = np.diff(self._xlim)[0] * 0.02 @property def boundary(self): return self._boundary @property def threshold(self): return self._threshold @property def x_limits(self): return self._xlim @property def y_limits(self): return self._ylim class AlbersEqualArea(Projection): """ An Albers Equal Area projection This projection is conic and equal-area, and is commonly used for maps of the conterminous United States. """ def __init__(self, central_longitude=0.0, central_latitude=0.0, false_easting=0.0, false_northing=0.0, standard_parallels=(20.0, 50.0), globe=None): """ Kwargs: * central_longitude - The central longitude. Defaults to 0. * central_latitude - The central latitude. Defaults to 0. * false_easting - X offset from planar origin in metres. Defaults to 0. * false_northing - Y offset from planar origin in metres. Defaults to 0. * standard_parallels - The one or two latitudes of correct scale. Defaults to (20, 50). * globe - A :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ proj4_params = [('proj', 'aea'), ('lon_0', central_longitude), ('lat_0', central_latitude), ('x_0', false_easting), ('y_0', false_northing)] if standard_parallels is not None: try: proj4_params.append(('lat_1', standard_parallels[0])) try: proj4_params.append(('lat_2', standard_parallels[1])) except IndexError: pass except TypeError: proj4_params.append(('lat_1', standard_parallels)) super(AlbersEqualArea, self).__init__(proj4_params, globe=globe) # bounds n = 103 lons = np.empty(2 * n + 1) lats = np.empty(2 * n + 1) tmp = np.linspace(central_longitude - 180, central_longitude + 180, n) lons[:n] = tmp lats[:n] = 90 lons[n:-1] = tmp[::-1] lats[n:-1] = -90 lons[-1] = lons[0] lats[-1] = lats[0] points = self.transform_points(self.as_geodetic(), lons, lats) self._boundary = sgeom.LineString(points) bounds = self._boundary.bounds self._x_limits = bounds[0], bounds[2] self._y_limits = bounds[1], bounds[3] @property def boundary(self): return self._boundary @property def threshold(self): return 1e5 @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class AzimuthalEquidistant(Projection): """ An Azimuthal Equidistant projection This projection provides accurate angles about and distances through the central position. Other angles, distances, or areas may be distorted. """ def __init__(self, central_longitude=0.0, central_latitude=0.0, false_easting=0.0, false_northing=0.0, globe=None): """ Kwargs: * central_longitude - The true longitude of the central meridian in degrees. Defaults to 0. * central_latitude - The true latitude of the planar origin in degrees. Defaults to 0. * false_easting - X offset from the planar origin in metres. Defaults to 0. * false_northing - Y offset from the planar origin in metres. Defaults to 0. * globe - An instance of :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ # Warn when using Azimuthal Equidistant with proj4 < 4.9.2 due to # incorrect transformation past 90 deg distance (see # https://github.com/OSGeo/proj.4/issues/246). if PROJ4_VERSION != (): if PROJ4_VERSION < (4, 9, 2): warnings.warn('The Azimuthal Equidistant projection in Proj.4 ' 'older than 4.9.2 incorrectly transforms points ' 'farther than 90 deg from the origin. Use this ' 'projection with caution.') else: warnings.warn('Cannot determine Proj.4 version. The Azimuthal ' 'Equidistant projection may be unreliable and ' 'should be used with caution.') proj4_params = [('proj', 'aeqd'), ('lon_0', central_longitude), ('lat_0', central_latitude), ('x_0', false_easting), ('y_0', false_northing)] super(AzimuthalEquidistant, self).__init__(proj4_params, globe=globe) # TODO: Let the globe return the semimajor axis always. a = np.float(self.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS) b = np.float(self.globe.semiminor_axis or a) coords = _ellipse_boundary(a * np.pi, b * np.pi, false_easting, false_northing, 61) self._boundary = sgeom.LinearRing(coords.T) bounds = self._boundary.bounds self._x_limits = bounds[0], bounds[2] self._y_limits = bounds[1], bounds[3] @property def boundary(self): return self._boundary @property def threshold(self): return 1e5 @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits class Sinusoidal(Projection): """ A Sinusoidal projection. This projection is equal-area. """ def __init__(self, central_longitude=0.0, false_easting=0.0, false_northing=0.0, globe=None): """ Kwargs: * central_longitude - The central longitude. Defaults to 0. * false_easting - X offset from planar origin in metres. Defaults to 0. * false_northing - Y offset from planar origin in metres. Defaults to 0. * globe - A :class:`cartopy.crs.Globe`. If omitted, a default globe is created. """ proj4_params = [('proj', 'sinu'), ('lon_0', central_longitude), ('x_0', false_easting), ('y_0', false_northing)] super(Sinusoidal, self).__init__(proj4_params, globe=globe) # Obtain boundary points points = [] n = 91 geodetic_crs = self.as_geodetic() for lat in np.linspace(-90, 90, n): points.append( self.transform_point(180 + central_longitude, lat, geodetic_crs) ) for lat in np.linspace(90, -90, n): points.append( self.transform_point(-180 + central_longitude, lat, geodetic_crs) ) points.append( self.transform_point(180 + central_longitude, -90, geodetic_crs)) self._boundary = sgeom.LineString(points[::-1]) minx, miny, maxx, maxy = self._boundary.bounds self._x_limits = minx, maxx self._y_limits = miny, maxy self._threshold = max(np.abs(self.x_limits + self.y_limits)) * 1e-5 @property def boundary(self): return self._boundary @property def threshold(self): return self._threshold @property def x_limits(self): return self._x_limits @property def y_limits(self): return self._y_limits # MODIS data products use a Sinusoidal projection of a spherical Earth # http://modis-land.gsfc.nasa.gov/GCTP.html Sinusoidal.MODIS = Sinusoidal(globe=Globe(ellipse=None, semimajor_axis=6371007.181, semiminor_axis=6371007.181)) class _BoundaryPoint(object): def __init__(self, distance, kind, data): """ A representation for a geometric object which is connected to the boundary. Parameters ========== distance - float The distance along the boundary that this object can be found. kind - bool Whether this object represents a point from the pre-computed boundary. data - point or namedtuple The actual data that this boundary object represents. """ self.distance = distance self.kind = kind self.data = data def __repr__(self): return '_BoundaryPoint(%r, %r, %s)' % (self.distance, self.kind, self.data) def _find_first_gt(a, x): for v in a: if v.distance > x: return v # We've gone all the way around, so pick the first point again. return a[0] def epsg(code): """ Return the projection which corresponds to the given EPSG code. The EPSG code must correspond to a "projected coordinate system", so EPSG codes such as 4326 (WGS-84) which define a "geodetic coordinate system" will not work. .. note:: The conversion is performed by querying https://epsg.io/ so a live internet connection is required. """ import cartopy._epsg return cartopy._epsg._EPSGProjection(code) Cartopy-0.14.2/lib/cartopy/feature.py0000644001374000021030000002762012700747662017225 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module defines :class:`Feature` instances, for use with ax.add_feature(). """ from __future__ import (absolute_import, division, print_function) from abc import ABCMeta, abstractmethod import os.path import numpy as np import shapely.geometry as sgeom import six import cartopy.io.shapereader as shapereader import cartopy.crs COLORS = {'land': np.array((240, 240, 220)) / 256., 'land_alt1': np.array((220, 220, 220)) / 256., 'water': np.array((152, 183, 226)) / 256.} """ A dictionary of colors useful for drawing Features. The named keys in this dictionary represent the "type" of feature being plotted. """ _NATURAL_EARTH_GEOM_CACHE = {} """ Caches a mapping between (name, category, scale) and a tuple of the resulting geometries. Provides a significant performance benefit (when combined with object id caching in GeoAxes.add_geometries) when producing multiple maps of the same projection. """ class Feature(six.with_metaclass(ABCMeta)): """ Represents a collection of points, lines and polygons with convenience methods for common drawing and filtering operations. Args: * crs - the coordinate reference system of this Feature Kwargs: Keyword arguments to be used when drawing this feature. .. seealso:: To add features to the current matplotlib axes, see :func:`GeoAxes `. """ def __init__(self, crs, **kwargs): self._crs = crs self._kwargs = dict(kwargs) @property def crs(self): """The cartopy CRS for the geometries in this feature.""" return self._crs @property def kwargs(self): """ The read-only dictionary of keyword arguments that are used when creating the matplotlib artists for this feature. """ return dict(self._kwargs) @abstractmethod def geometries(self): """ Returns an iterator of (shapely) geometries for this feature. """ pass def intersecting_geometries(self, extent): """ Returns an iterator of shapely geometries that intersect with the given extent. The extent is assumed to be in the CRS of the feature. If extent is None, the method returns all geometries for this dataset. """ if extent is not None: extent_geom = sgeom.box(extent[0], extent[2], extent[1], extent[3]) return (geom for geom in self.geometries() if extent_geom.intersects(geom)) else: return self.geometries() class ShapelyFeature(Feature): """ A class capable of drawing a collection of shapely geometries. """ def __init__(self, geometries, crs, **kwargs): """ Args: * geometries: A collection of shapely geometries. * crs: The cartopy CRS in which the provided geometries are defined. Kwargs: Keyword arguments to be used when drawing this feature. """ super(ShapelyFeature, self).__init__(crs, **kwargs) self._geoms = tuple(geometries) def geometries(self): return iter(self._geoms) class NaturalEarthFeature(Feature): """ A simple interface to Natural Earth shapefiles. See http://www.naturalearthdata.com/ """ def __init__(self, category, name, scale, **kwargs): """ Args: * category: The category of the dataset, i.e. either 'cultural' or 'physical'. * name: The name of the dataset, e.g. 'admin_0_boundary_lines_land'. * scale: The dataset scale, i.e. one of '10m', '50m', or '110m'. Corresponding to 1:10,000,000, 1:50,000,000, and 1:110,000,000 respectively. Kwargs: Keyword arguments to be used when drawing this feature. """ super(NaturalEarthFeature, self).__init__(cartopy.crs.PlateCarree(), **kwargs) self.category = category self.name = name self.scale = scale def geometries(self): key = (self.name, self.category, self.scale) if key not in _NATURAL_EARTH_GEOM_CACHE: path = shapereader.natural_earth(resolution=self.scale, category=self.category, name=self.name) geometries = tuple(shapereader.Reader(path).geometries()) _NATURAL_EARTH_GEOM_CACHE[key] = geometries else: geometries = _NATURAL_EARTH_GEOM_CACHE[key] return iter(geometries) class GSHHSFeature(Feature): """ An interface to the GSHHS dataset. See https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html Args: * scale: The dataset scale. One of 'auto', 'coarse', 'low', 'intermediate', 'high, or 'full' (default is 'auto'). * levels: A list of integers 1-4 corresponding to the desired GSHHS feature levels to draw (default is [1] which corresponds to coastlines). Kwargs: Keyword arguments to be used when drawing the feature. Defaults are edgecolor='black' and facecolor='none'. """ _geometries_cache = {} """ A mapping from scale and level to GSHHS shapely geometry:: {(scale, level): geom} This provides a perfomance boost when plotting in interactive mode or instantiating multiple GSHHS artists, by reducing repeated file IO. """ def __init__(self, scale='auto', levels=None, **kwargs): super(GSHHSFeature, self).__init__(cartopy.crs.PlateCarree(), **kwargs) if scale not in ('auto', 'a', 'coarse', 'c', 'low', 'l', 'intermediate', 'i', 'high', 'h', 'full', 'f'): raise ValueError("Unknown GSHHS scale '{}'.".format(scale)) self._scale = scale if levels is None: levels = [1] self._levels = set(levels) unknown_levels = self._levels.difference([1, 2, 3, 4]) if unknown_levels: raise ValueError("Unknown GSHHS levels " "'{}'.".format(unknown_levels)) # Default kwargs self._kwargs.setdefault('edgecolor', 'black') self._kwargs.setdefault('facecolor', 'none') def _scale_from_extent(self, extent): """ Returns the appropriate scale (e.g. 'i') for the given extent expressed in PlateCarree CRS. """ # Default to coarse scale scale = 'c' if extent is not None: # Upper limit on extent in degrees. scale_limits = (('c', 20.0), ('l', 10.0), ('i', 2.0), ('h', 0.5), ('f', 0.1)) width = abs(extent[1] - extent[0]) height = abs(extent[3] - extent[2]) min_extent = min(width, height) if min_extent != 0: for scale, limit in scale_limits: if min_extent > limit: break return scale def geometries(self): return self.intersecting_geometries(extent=None) def intersecting_geometries(self, extent): if self._scale == 'auto': scale = self._scale_from_extent(extent) else: scale = self._scale[0] if extent is not None: extent_geom = sgeom.box(extent[0], extent[2], extent[1], extent[3]) for level in self._levels: geoms = GSHHSFeature._geometries_cache.get((scale, level)) if geoms is None: # Load GSHHS geometries from appropriate shape file. # TODO selective load based on bbox of each geom in file. path = shapereader.gshhs(scale, level) geoms = tuple(shapereader.Reader(path).geometries()) GSHHSFeature._geometries_cache[(scale, level)] = geoms for geom in geoms: if extent is None or extent_geom.intersects(geom): yield geom class WFSFeature(Feature): """ A class capable of drawing a collection of geometries obtained from an OGC Web Feature Service (WFS). This feature requires additional dependencies. If installed via pip, try ``pip install cartopy[ows]``. """ def __init__(self, wfs, features, **kwargs): """ Args: * wfs: string or :class:`owslib.wfs.WebFeatureService` instance The WebFeatureService instance, or URL of a WFS service, from which to retrieve the geometries. * features: string or list of strings The typename(s) of features available from the web service that will be retrieved. Somewhat analogous to layers in WMS/WMTS. Kwargs: Keyword arguments to be used when drawing this feature. """ try: from cartopy.io.ogc_clients import WFSGeometrySource except ImportError as e: six.raise_from(ImportError( 'WFSFeature requires additional dependencies. If installed ' 'via pip, try `pip install cartopy[ows]`.\n'), e) self.source = WFSGeometrySource(wfs, features) crs = self.source.default_projection() super(WFSFeature, self).__init__(crs, **kwargs) # Default kwargs self._kwargs.setdefault('edgecolor', 'black') self._kwargs.setdefault('facecolor', 'none') def geometries(self): min_x, min_y, max_x, max_y = self.crs.boundary.bounds geoms = self.source.fetch_geometries(self.crs, extent=(min_x, max_x, min_y, max_y)) return iter(geoms) def intersecting_geometries(self, extent): geoms = self.source.fetch_geometries(self.crs, extent) return iter(geoms) BORDERS = NaturalEarthFeature('cultural', 'admin_0_boundary_lines_land', '110m', edgecolor='black', facecolor='none') """Small scale (1:110m) country boundaries.""" COASTLINE = NaturalEarthFeature('physical', 'coastline', '110m', edgecolor='black', facecolor='none') """Small scale (1:110m) coastline, including major islands.""" LAKES = NaturalEarthFeature('physical', 'lakes', '110m', edgecolor='face', facecolor=COLORS['water']) """Small scale (1:110m) natural and artificial lakes.""" LAND = NaturalEarthFeature('physical', 'land', '110m', edgecolor='face', facecolor=COLORS['land']) """Small scale (1:110m) land polygons, including major islands.""" OCEAN = NaturalEarthFeature('physical', 'ocean', '110m', edgecolor='face', facecolor=COLORS['water']) """Small scale (1:110m) ocean polygons.""" RIVERS = NaturalEarthFeature('physical', 'rivers_lake_centerlines', '110m', edgecolor=COLORS['water'], facecolor='none') """Small scale (1:110m) single-line drainages, including lake centerlines.""" Cartopy-0.14.2/lib/cartopy/img_transform.py0000644001374000021030000003432612700747662020442 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module contains generic functionality to support Cartopy image transformations. """ from __future__ import (absolute_import, division, print_function) import numpy as np import scipy.spatial import cartopy.crs as ccrs def mesh_projection(projection, nx, ny, x_extents=[None, None], y_extents=[None, None]): """ Returns sample points in the given projection which span the entire projection range evenly. The range of the x-direction and y-direction sample points will be within the bounds of the projection or specified extents. Args: * projection: A :class:`~cartopy.crs.Projection` instance. * nx: The number of sample points in the projection x-direction. * ny: The number of sample points in the projection y-direction. Kwargs: * x_extents: The (lower, upper) x-direction extent of the projection. Defaults to the :attribute:`~cartopy.crs.Projection.x_limits`. * y_extents: The (lower, upper) y-direction extent of the projection. Defaults to the :attribute:`~cartopy.crs.Projection.y_limits`. Returns: A tuple of three items. The x-direction sample points :class:`numpy.ndarray` of shape (nx, ny), y-direction sample points :class:`numpy.ndarray` of shape (nx, ny), and the extent of the projection range as ``(x-lower, x-upper, y-lower, y-upper)``. """ # Establish the x-direction and y-direction extents. x_lower = x_extents[0] or projection.x_limits[0] x_upper = x_extents[1] or projection.x_limits[1] y_lower = y_extents[0] or projection.y_limits[0] y_upper = y_extents[1] or projection.y_limits[1] # Calculate evenly spaced sample points spanning the # extent - excluding endpoint. x, xstep = np.linspace(x_lower, x_upper, nx, retstep=True, endpoint=False) y, ystep = np.linspace(y_lower, y_upper, ny, retstep=True, endpoint=False) # Offset the sample points to be within the extent range. x += 0.5 * xstep y += 0.5 * ystep # Generate the x-direction and y-direction meshgrids. x, y = np.meshgrid(x, y) return x, y, [x_lower, x_upper, y_lower, y_upper] def warp_img(fname, target_proj, source_proj=None, target_res=(400, 200)): """ Regrid the image file from the source projection to the target projection. Args: * fname: Image filename to be loaded and warped. * target_proj: The target :class:`~cartopy.crs.Projection` instance for the image. Kwargs: * source_proj: The source :class:`~cartopy.crs.Projection` instance of the image. Defaults to a :class:`~cartopy.crs.PlateCarree` projection. * target_res: The (nx, ny) resolution of the target projection. Where nx defaults to 400 sample points, and ny defaults to 200 sample points. """ if source_proj is None: source_proj = ccrs.PlateCarree() raise NotImplementedError('Not yet implemented.') def warp_array(array, target_proj, source_proj=None, target_res=(400, 200), source_extent=None, target_extent=None, mask_extrapolated=False): """ Regrid the data array from the source projection to the target projection. Also see, :function:`~cartopy.img_transform.regrid`. Args: * array: The :class:`numpy.ndarray` of data to be regridded to the target projection. * target_proj: The target :class:`~cartopy.crs.Projection` instance for the data. Kwargs: * source_proj: The source :class:`~cartopy.crs.Projection' instance of the data. Defaults to a :class:`~cartopy.crs.PlateCarree` projection. * target_res: The (nx, ny) resolution of the target projection. Where nx defaults to 400 sample points, and ny defaults to 200 sample points. * source_extent: The (x-lower, x-upper, y-lower, y-upper) extent in native source projection coordinates. * target_extent: The (x-lower, x-upper, y-lower, y-upper) extent in native target projection coordinates. Kwargs: * mask_extrapolated: Assume that the source coordinate is rectilinear and so mask the resulting target grid values which lie outside the source grid domain. Returns: A tuple of the regridded :class:`numpy.ndarray` in the target projection and the (x-lower, x-upper, y-lower, y-upper) target projection extent. """ # source_extent is in source coordinates. if source_extent is None: source_extent = [None] * 4 # target_extent is in target coordinates. if target_extent is None: target_extent = [None] * 4 source_x_extents = source_extent[:2] source_y_extents = source_extent[2:] target_x_extents = target_extent[:2] target_y_extents = target_extent[2:] if source_proj is None: source_proj = ccrs.PlateCarree() ny, nx = array.shape[:2] source_native_xy = mesh_projection(source_proj, nx, ny, x_extents=source_x_extents, y_extents=source_y_extents) # XXX Take into account the extents of the original to determine # target_extents? target_native_x, target_native_y, extent = mesh_projection( target_proj, target_res[0], target_res[1], x_extents=target_x_extents, y_extents=target_y_extents) array = regrid(array, source_native_xy[0], source_native_xy[1], source_proj, target_proj, target_native_x, target_native_y, mask_extrapolated) return array, extent def _determine_bounds(x_coords, y_coords, source_cs): # Returns bounds corresponding to one or two rectangles depending on # transformation between ranges. bounds = dict(x=[]) half_px = abs(np.diff(x_coords[:2])).max() / 2. if (((hasattr(source_cs, 'is_geodetic') and source_cs.is_geodetic()) or isinstance(source_cs, ccrs.PlateCarree)) and x_coords.max() > 180): if x_coords.min() < 180: bounds['x'].append([x_coords.min() - half_px, 180]) bounds['x'].append([-180, x_coords.max() - 360 + half_px]) else: bounds['x'].append([x_coords.min() - 180 - half_px, x_coords.max() - 180 + half_px]) else: bounds['x'].append([x_coords.min() - half_px, x_coords.max() + half_px]) bounds['y'] = [y_coords.min(), y_coords.max()] return bounds def regrid(array, source_x_coords, source_y_coords, source_cs, target_proj, target_x_points, target_y_points, mask_extrapolated=False): """ Regrid the data array from the source projection to the target projection. Args: * array: The :class:`numpy.ndarray` of data to be regridded to the target projection. * source_x_coords: A 2-dimensional source projection :class:`numpy.ndarray` of x-direction sample points. * source_y_coords: A 2-dimensional source projection :class:`numpy.ndarray` of y-direction sample points. * source_cs: The source :class:`~cartopy.crs.Projection` instance. * target_cs: The target :class:`~cartopy.crs.Projection` instance. * target_x_points: A 2-dimensional target projection :class:`numpy.ndarray` of x-direction sample points. * target_y_points: A 2-dimensional target projection :class:`numpy.ndarray` of y-direction sample points. Kwargs: * mask_extrapolated: Assume that the source coordinate is rectilinear and so mask the resulting target grid values which lie outside the source grid domain. Returns: The data array regridded in the target projection. """ # n.b. source_cs is actually a projection (the coord system of the # source coordinates), but not necessarily the native projection of # the source array (i.e. you can provide a warped image with lat lon # coordinates). # XXX NB. target_x and target_y must currently be rectangular (i.e. # be a 2d np array) geo_cent = source_cs.as_geocentric() xyz = geo_cent.transform_points(source_cs, source_x_coords.flatten(), source_y_coords.flatten()) target_xyz = geo_cent.transform_points(target_proj, target_x_points.flatten(), target_y_points.flatten()) kdtree = scipy.spatial.cKDTree(xyz) distances, indices = kdtree.query(target_xyz, k=1) mask = np.isinf(distances) desired_ny, desired_nx = target_x_points.shape if array.ndim == 1: if np.any(mask): array_1d = np.ma.array(array[indices], mask=mask) else: array_1d = array[indices] new_array = array_1d.reshape(desired_ny, desired_nx) elif array.ndim == 2: # Handle missing neighbours using a masked array if np.any(mask): indices = np.where(np.logical_not(mask), indices, 0) array_1d = np.ma.array(array.reshape(-1)[indices], mask=mask) else: array_1d = array.reshape(-1)[indices] new_array = array_1d.reshape(desired_ny, desired_nx) elif array.ndim == 3: # Handle missing neighbours using a masked array if np.any(mask): indices = np.where(np.logical_not(mask), indices, 0) array_2d = array.reshape(-1, array.shape[-1])[indices] mask, array_2d = np.broadcast_arrays( mask.reshape(-1, 1), array_2d) array_2d = np.ma.array(array_2d, mask=mask) else: array_2d = array.reshape(-1, array.shape[-1])[indices] new_array = array_2d.reshape(desired_ny, desired_nx, array.shape[-1]) else: raise ValueError( 'Expected array.ndim to be 1, 2 or 3, got {}'.format(array.ndim)) # Do double transform to clip points that do not map back and forth # to the same point to within a fixed fractional offset. # XXX THIS ONLY NEEDS TO BE DONE FOR (PSEUDO-)CYLINDRICAL PROJECTIONS # (OR ANY OTHERS WHICH HAVE THE CONCEPT OF WRAPPING) source_desired_xyz = source_cs.transform_points(target_proj, target_x_points.flatten(), target_y_points.flatten()) back_to_target_xyz = target_proj.transform_points(source_cs, source_desired_xyz[:, 0], source_desired_xyz[:, 1]) back_to_target_x = back_to_target_xyz[:, 0].reshape(desired_ny, desired_nx) back_to_target_y = back_to_target_xyz[:, 1].reshape(desired_ny, desired_nx) FRACTIONAL_OFFSET_THRESHOLD = 0.1 # data has moved by 10% of the map x_extent = np.abs(target_proj.x_limits[1] - target_proj.x_limits[0]) y_extent = np.abs(target_proj.y_limits[1] - target_proj.y_limits[0]) non_self_inverse_points = (np.abs(target_x_points - back_to_target_x) / x_extent) > FRACTIONAL_OFFSET_THRESHOLD if np.any(non_self_inverse_points): if np.ma.isMaskedArray(new_array): new_array[non_self_inverse_points] = np.ma.masked else: new_array = np.ma.array(new_array, mask=False) if new_array.ndim == 3: for i in range(new_array.shape[2]): new_array[non_self_inverse_points, i] = np.ma.masked else: new_array[non_self_inverse_points] = np.ma.masked non_self_inverse_points = (np.abs(target_y_points - back_to_target_y) / y_extent) > FRACTIONAL_OFFSET_THRESHOLD if np.any(non_self_inverse_points): if np.ma.isMaskedArray(new_array): new_array[non_self_inverse_points] = np.ma.masked else: new_array = np.ma.array(new_array, mask=non_self_inverse_points) # Transform the target points to the source projection and mask any points # that fall outside the original source domain. if mask_extrapolated: target_in_source_xyz = source_cs.transform_points( target_proj, target_x_points, target_y_points) target_in_source_x = target_in_source_xyz[..., 0] target_in_source_y = target_in_source_xyz[..., 1] bounds = _determine_bounds(source_x_coords, source_y_coords, source_cs) outside_source_domain = ((target_in_source_y >= bounds['y'][1]) | (target_in_source_y <= bounds['y'][0])) tmp_inside = np.zeros_like(outside_source_domain) for bound_x in bounds['x']: tmp_inside = tmp_inside | ((target_in_source_x <= bound_x[1]) & (target_in_source_x >= bound_x[0])) outside_source_domain = outside_source_domain | ~tmp_inside if np.ma.isMaskedArray(new_array): if np.any(outside_source_domain): new_array[outside_source_domain] = np.ma.masked else: new_array = np.ma.array(new_array, mask=False) if new_array.ndim == 3: for i in range(new_array.shape[2]): new_array[outside_source_domain, i] = np.ma.masked else: new_array[outside_source_domain] = np.ma.masked return new_array Cartopy-0.14.2/lib/cartopy/trace.pyx0000644001374000021030000001035412700747662017054 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module pulls together _trace.cpp, proj.4, GEOS and _crs.pyx to implement a function to project a LinearRing/LineString. In general, this should never be called manually, instead leaving the processing to be done by the :class:`cartopy.crs.Projection` subclasses. """ from libc.stdint cimport uintptr_t as ptr cdef extern from "geos_c.h": ctypedef void *GEOSContextHandle_t ctypedef struct GEOSGeometry: pass from cartopy._crs cimport CRS import shapely.geometry as sgeom from shapely.geos import lgeos cdef extern from "proj_api.h": ctypedef void *projPJ cdef extern from "_trace.h": cdef cppclass Interpolator: pass cdef cppclass SphericalInterpolator: SphericalInterpolator(projPJ src_proj, projPJ dest_proj) cdef cppclass CartesianInterpolator: CartesianInterpolator(projPJ src_proj, projPJ dest_proj) # XXX Rename? It handles LinearRings too. GEOSGeometry *_project_line_string(GEOSContextHandle_t handle, GEOSGeometry *g_line_string, Interpolator *interpolator, GEOSGeometry *g_domain, double threshold) cdef GEOSContextHandle_t get_geos_context_handle(): cdef ptr handle = lgeos.geos_handle return handle cdef GEOSGeometry *geos_from_shapely(shapely_geom) except *: """Get the GEOS pointer from the given shapely geometry.""" cdef ptr geos_geom = shapely_geom._geom return geos_geom cdef shapely_from_geos(GEOSGeometry *geom): """Turn the given GEOS geometry pointer into a shapely geometry.""" # This is the "correct" way to do it... # return geom_factory(geom) # ... but it's quite slow, so we do it by hand. multi_line_string = sgeom.base.BaseGeometry() multi_line_string.__class__ = sgeom.MultiLineString multi_line_string.__geom__ = geom multi_line_string.__parent__ = None multi_line_string._ndim = 2 return multi_line_string def project_linear(geometry not None, CRS src_crs not None, dest_projection not None): """ Returns the MultiLineString which results from projecting the given geometry from the source projection into the destination projection. Args: * line_string: A shapely LineString or LinearRing to be projected. * src_crs: The cartopy.crs.CRS defining the coordinate system of the line to be projected. * dest_projection: The cartopy.crs.Projection defining the projection for the resulting projected line. """ cdef: double threshold = dest_projection.threshold GEOSContextHandle_t handle = get_geos_context_handle() GEOSGeometry *g_linear = geos_from_shapely(geometry) Interpolator *interpolator GEOSGeometry *g_domain GEOSGeometry *g_multi_line_string g_domain = geos_from_shapely(dest_projection.domain) if src_crs.is_geodetic(): interpolator = new SphericalInterpolator( src_crs.proj4, (dest_projection).proj4) else: interpolator = new CartesianInterpolator( src_crs.proj4, (dest_projection).proj4) g_multi_line_string = _project_line_string(handle, g_linear, interpolator, g_domain, threshold) del interpolator multi_line_string = shapely_from_geos(g_multi_line_string) return multi_line_string Cartopy-0.14.2/lib/cartopy/util.py0000644001374000021030000000726412700747662016551 0ustar itpeavd00000000000000# (C) British Crown Copyright 2014 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module contains utilities that are useful in conjunction with cartopy. """ from __future__ import (absolute_import, division, print_function) import numpy as np import numpy.ma as ma def add_cyclic_point(data, coord=None, axis=-1): """ Add a cyclic point to an array and optionally a corresponding coordinate. Args: * data: An n-dimensional array of data to add a cyclic point to. Kwargs: * coord: A 1-dimensional array which specifies the coordinate values for the dimension the cyclic point is to be added to. The coordinate values must be regularly spaced. * axis: Specifies the axis of the data array to add the cyclic point to. Defaults to the right-most axis. Returns: * cyclic_data: The data array with a cyclic point added. * cyclic_coord: The coordinate with a cyclic point, only returned if the coord keyword was supplied. Examples: Adding a cyclic point to a data array, where the cyclic dimension is the right-most dimension >>> import numpy as np >>> data = np.ones([5, 6]) * np.arange(6) >>> cyclic_data = add_cyclic_point(data) >>> print(cyclic_data) [[ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.]] Adding a cyclic point to a data array and an associated coordinate >>> lons = np.arange(0, 360, 60) >>> cyclic_data, cyclic_lons = add_cyclic_point(data, coord=lons) >>> print(cyclic_data) [[ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.] [ 0. 1. 2. 3. 4. 5. 0.]] >>> print(cyclic_lons) [ 0 60 120 180 240 300 360] """ if coord is not None: if coord.ndim != 1: raise ValueError('The coordinate must be 1-dimensional.') if len(coord) != data.shape[axis]: raise ValueError('The length of the coordinate does not match ' 'the size of the corresponding dimension of ' 'the data array: len(coord) = {}, ' 'data.shape[{}] = {}.'.format( len(coord), axis, data.shape[axis])) delta_coord = np.diff(coord) if not np.allclose(delta_coord, delta_coord[0]): raise ValueError('The coordinate must be equally spaced.') new_coord = ma.concatenate((coord, coord[-1:] + delta_coord[0])) slicer = [slice(None)] * data.ndim try: slicer[axis] = slice(0, 1) except IndexError: raise ValueError('The specified axis does not correspond to an ' 'array dimension.') new_data = ma.concatenate((data, data[slicer]), axis=axis) if coord is None: return_value = new_data else: return_value = new_data, new_coord return return_value Cartopy-0.14.2/lib/cartopy/vector_transform.py0000644001374000021030000001150012700747662021155 0ustar itpeavd00000000000000# (C) British Crown Copyright 2013 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . """ This module contains generic functionality to support Cartopy vector transforms. """ from __future__ import (absolute_import, division, print_function) import numpy as np from scipy.interpolate import griddata def _interpolate_to_grid(nx, ny, x, y, *scalars, **kwargs): """ Interpolates two vector components and zero or more scalar fields, which can be irregular, to a regular grid. Kwargs: * target_extent: The extent in the target CRS that the grid should occupy, in the form ``(x-lower, x-upper, y-lower, y-upper)``. Defaults to cover the full extent of the vector field. """ target_extent = kwargs.get('target_extent', None) if target_extent is None: target_extent = (x.min(), x.max(), y.min(), y.max()) points = np.array([x.ravel(), y.ravel()]).T x0, x1, y0, y1 = target_extent x_grid, y_grid = np.meshgrid(np.linspace(x0, x1, nx), np.linspace(y0, y1, ny)) s_grid_tuple = tuple() for s in scalars: s_grid_tuple += (griddata(points, s.ravel(), (x_grid, y_grid), method='linear'),) return (x_grid, y_grid) + s_grid_tuple def vector_scalar_to_grid(src_crs, target_proj, regrid_shape, x, y, u, v, *scalars, **kwargs): """ Transform and interpolate a vector field to a regular grid in the target projection. Args: * src_crs: The :class:`~cartopy.crs.CRS` that represents the coordinate system the vectors are defined in. * target_proj: The :class:`~cartopy.crs.Projection` that represents the projection the vectors are to be transformed to. * regrid_shape: The regular grid dimensions. If a single integer then the grid will have that number of points in the x and y directions. A 2-tuple of integers specify the size of the regular grid in the x and y directions respectively. * x, y: The x and y coordinates, in the source CRS coordinates, where the vector components are located. * u, v: The grid eastward and grid northward components of the vector field respectively. Their shapes must match. * scalars: Zero or more scalar fields to regrid along with the vector components. Each scalar field must have the same shape as the vector components. Kwargs: * target_extent: The extent in the target CRS that the grid should occupy, in the form ``(x-lower, x-upper, y-lower, y-upper)``. Defaults to cover the full extent of the vector field. Returns: * x_grid, y_grid: The x and y coordinates of the regular grid points as 2-dimensional arrays. * u_grid, v_grid: The eastward and northward components of the vector field on the regular grid. * scalars_grid: The scalar fields on the regular grid. The number of returned scalar fields is the same as the number that were passed in. """ if u.shape != v.shape: raise ValueError('u and v must be the same shape') if x.shape != u.shape: x, y = np.meshgrid(x, y) if not (x.shape == y.shape == u.shape): raise ValueError('x and y coordinates are not compatible ' 'with the shape of the vector components') if scalars: for s in scalars: if s.shape != u.shape: raise ValueError('scalar fields must have the same ' 'shape as the vector components') try: nx, ny = regrid_shape except TypeError: nx = ny = regrid_shape if target_proj != src_crs: # Transform the vectors to the target CRS. u, v = target_proj.transform_vectors(src_crs, x, y, u, v) # Convert Coordinates to the target CRS. proj_xyz = target_proj.transform_points(src_crs, x, y) x, y = proj_xyz[..., 0], proj_xyz[..., 1] # Now interpolate to a regular grid in projection space, treating each # component as a scalar field. return _interpolate_to_grid(nx, ny, x, y, u, v, *scalars, **kwargs) Cartopy-0.14.2/requirements/0000755001374000021030000000000012706121712015471 5ustar itpeavd00000000000000Cartopy-0.14.2/requirements/default.txt0000644001374000021030000000010412700747662017665 0ustar itpeavd00000000000000numpy>=1.6 shapely>=1.5.6 pyshp>=1.1.4 six>=1.3.0 setuptools>=0.7.2 Cartopy-0.14.2/requirements/epsg.txt0000644001374000021030000000001612520135745017172 0ustar itpeavd00000000000000pyepsg>=0.2.0 Cartopy-0.14.2/requirements/ows.txt0000644001374000021030000000003412700747470017050 0ustar itpeavd00000000000000OWSLib>=0.8.7 pillow>=1.7.8 Cartopy-0.14.2/requirements/plotting.txt0000644001374000021030000000007112520135745020075 0ustar itpeavd00000000000000matplotlib>=1.3.0 GDAL>=1.10.0 pillow>=1.7.8 scipy>=0.10 Cartopy-0.14.2/requirements/tests.txt0000644001374000021030000000004412520135745017377 0ustar itpeavd00000000000000mock>=1.0.1 nose>=1.2.1 pep8>=1.3.3 Cartopy-0.14.2/CHANGES0000644001374000021030000000006612520135743013746 0ustar itpeavd00000000000000Please see docs/source/whats_new.rst for a changelog. Cartopy-0.14.2/CONTRIBUTING.md0000644001374000021030000000243412700747662015216 0ustar itpeavd00000000000000How to contribute ================= Cartopy is driven by a community of people all passionate about seeing cartopy flourish as a world class mapping package for Python. This page lists the guidelines for contributors which will help ease the process of getting your hard work accepted back into the cartopy repository. Getting started --------------- 1. If you've not already got one, sign up for a [GitHub account](https://github.com/signup/free). 1. Fork the Cartopy repository, create your new fix/feature branch, and start committing code. We broadly follow the [gitwash guidelines](https://matthew-brett.github.io/pydagogue/gitwash/git_development.html). 1. Remember to add appropriate documentation and tests to supplement any new or changed functionality. 1. 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Cartopy-0.14.2/INSTALL0000644001374000021030000001001612700747662014011 0ustar itpeavd00000000000000Installing Cartopy ================== Pre-built binaries ------------------ The easiest route to installing cartopy is through `Conda `_. For all platforms installing cartopy can be done with:: conda install -c scitools cartopy Additional options include: * `Enthought Canopy `_. * `Christoph Gohlke `_ maintains unofficial Windows binaries of cartopy. * `OSGeo Live `_. Building from source -------------------- The latest release of Cartopy is available from https://github.com/SciTools/Cartopy. Once you have satisfied the requirements detailed below, simply run:: python setup.py install For non-standard locations, additional build lib & include paths can be provided as per-usual at build_ext phase:: python setup.py build_ext --inplace -I/path/to/include -L/path/to/lib python setup.py install Requirements ~~~~~~~~~~~~ These external packages are required to install Cartopy or gain access to significant Cartopy functionality. Many of these packages are available in Linux package managers such as aptitude and yum. For example, it may be possible to install Numpy using:: apt-get install python-numpy If you are installing dependencies with a package manager on Linux, you may need to install the development packages (look for a "-dev" suffix) in addition to the core packages. Many of these dependencies are built as part of Cartopy's conda distribution. The recipes for these can be found at https://github.com/SciTools/conda-recipes-scitools. **python** 2.7 or later (https://www.python.org/) Cartopy requires Python 2.7 or later. **Cython** 0.15.1 or later (https://pypi.python.org/pypi/Cython/) **numpy** 1.6 or later (http://www.numpy.org/) Python package for scientific computing including a powerful N-dimensional array object. **GEOS** 3.3.3 or later (https://trac.osgeo.org/geos/) GEOS is an API of spatial predicates and functions for processing geometry written in C++. **shapely** 1.5.6 or later (https://github.com/Toblerity/Shapely) Python package for the manipulation and analysis of planar geometric objects. **pyshp** 1.1.4 or later (https://pypi.python.org/pypi/pyshp) Pure Python read/write support for ESRI Shapefile format. **PROJ.4** 4.9.0 or later (https://trac.osgeo.org/proj/) Cartographic Projections library. **six** 1.3.0 or later (https://pypi.python.org/pypi/six) Python 2 and 3 compatibility. Optional Dependencies ~~~~~~~~~~~~~~~~~~~~~ These are optional packages which you may want to install to enable additional Cartopy functionality. **matplotlib** 1.3.0 or later (http://matplotlib.org/) Python package for 2D plotting. This package is required for any graphical capability. **GDAL** version 1.10.0 (http://www.gdal.org/) GDAL is a translator library for raster and vector geospatial data formats, which has powerful data transformation and processing capabilities. **Pillow** 1.7.8 or later (https://pypi.python.org/pypi/Pillow/2.3.0) Popular fork of PythonImagingLibrary. **pyepsg** 0.2.0 or later (https://github.com/rhattersley/pyepsg) A simple Python interface to https://epsg.io **scipy** 0.10 or later (https://www.scipy.org/) Python package for scientific computing. **OWSLib** 0.8.7 (https://pypi.python.org/pypi/OWSLib) Python package for client programming with Open Geospatial Consortium (OGC) web service. Gives access to cartopy ogc clients. Testing Dependencies ~~~~~~~~~~~~~~~~~~~~ These packages are required for the full Cartopy test suite to run. **mock** 1.0.1 (https://pypi.python.org/pypi/mock/) Python mocking and patching package for testing. Note that this package is only required to support the Cartopy unit tests. **nose** 1.2.1 or later (https://nose.readthedocs.org/en/latest/) Python package for software testing. **pep8** 1.3.3 or later (https://pypi.python.org/pypi/pep8) Python package for software testing. Cartopy-0.14.2/MANIFEST.in0000644001374000021030000000035012700747662014516 0ustar itpeavd00000000000000include *.md include CHANGES include COPYING* include INSTALL include README.rst include requirements/*.txt include lib/cartopy/data/* include lib/cartopy/io/srtm.npz recursive-include lib *.py recursive-include lib *.pyx *.pxd *.h Cartopy-0.14.2/README.md0000644001374000021030000000112712520135743014231 0ustar itpeavd00000000000000![Cartopy logo](http://scitools.org.uk/cartopy/docs/latest/_static/cartopy.png) Cartopy ------- Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: * object oriented projection definitions * point, line, polygon and image transformations between projections * integration to expose advanced mapping in matplotlib with a simple and intuitive interface * powerful vector data handling by integrating shapefile reading with Shapely capabilities Licence ------- Cartopy is licenced under GNU Lesser General Public License (LGPLv3). Cartopy-0.14.2/README.rst0000644001374000021030000000133512520135743014442 0ustar itpeavd00000000000000 ======= Cartopy ======= .. image:: http://scitools.org.uk/cartopy/docs/latest/_static/cartopy.png Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: * object oriented projection definitions * point, line, polygon and image transformations between projections * integration to expose advanced mapping in matplotlib with a simple and intuitive interface * powerful vector data handling by integrating shapefile reading with Shapely capabilities Documentation can be found at http://scitools.org.uk/cartopy/docs/latest/. ---- Code ---- Cartopy is licenced under GNU Lesser General Public License (LGPLv3). Development occurs at https://github.com/SciTools/cartopy. Cartopy-0.14.2/setup.py0000644001374000021030000003552612706121703014473 0ustar itpeavd00000000000000# (C) British Crown Copyright 2011 - 2016, Met Office # # This file is part of cartopy. # # cartopy is free software: you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License as published by the # Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # cartopy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with cartopy. If not, see . from __future__ import print_function """ Distribution definition for Cartopy. """ import setuptools from setuptools import setup, Extension from setuptools import Command from setuptools import convert_path from distutils.spawn import find_executable from distutils.sysconfig import get_config_var import fnmatch import os import subprocess import sys import warnings # Ensure build-time dependencies are available. # See https://stackoverflow.com/a/12061891 setuptools.dist.Distribution( dict( setup_requires=['Cython>=0.15.1', 'numpy>=1.6'])) try: from Cython.Distutils import build_ext except ImportError: raise ImportError('Cython 0.15.1+ is required to install cartopy.') try: import numpy as np except ImportError: raise ImportError('NumPy 1.6+ is required to install cartopy.') PY3 = (sys.version_info[0] == 3) # Please keep in sync with INSTALL file. GEOS_MIN_VERSION = (3, 3, 3) PROJ_MIN_VERSION = (4, 9, 0) HERE = os.path.dirname(__file__) def file_walk_relative(top, remove=''): """ Returns a generator of files from the top of the tree, removing the given prefix from the root/file result. """ top = top.replace('/', os.path.sep) remove = remove.replace('/', os.path.sep) for root, dirs, files in os.walk(top): for file in files: yield os.path.join(root, file).replace(remove, '') def find_package_tree(root_path, root_package): """ Returns the package and all its sub-packages. Automated package discovery - extracted/modified from Distutils Cookbook: https://wiki.python.org/moin/Distutils/Cookbook/AutoPackageDiscovery """ packages = [root_package] # Accept a root_path with Linux path separators. root_path = root_path.replace('/', os.path.sep) root_count = len(root_path.split(os.path.sep)) for (dir_path, dir_names, _) in os.walk(convert_path(root_path)): # Prune dir_names *in-place* to prevent unwanted directory recursion for dir_name in list(dir_names): if not os.path.isfile(os.path.join(dir_path, dir_name, '__init__.py')): dir_names.remove(dir_name) if dir_names: prefix = dir_path.split(os.path.sep)[root_count:] packages.extend(['.'.join([root_package] + prefix + [dir_name]) for dir_name in dir_names]) return packages class MissingHeaderError(Exception): """ Raised when one or more files do not have the required copyright and licence header. """ pass class HeaderCheck(Command): """ Checks that all the necessary files have the copyright and licence header. """ description = "check for copyright/licence headers" user_options = [] exclude_patterns = ('./setup.py', './build/*', './docs/build/*', './dist/*', './lib/cartopy/examples/*.py') def initialize_options(self): pass def finalize_options(self): pass def run(self): check_paths = [] for root, dirs, files in os.walk('.'): for file in files: if file.endswith('.py') or file.endswith('.c'): path = os.path.join(root, file) check_paths.append(path) for pattern in self.exclude_patterns: exclude = lambda path: not fnmatch.fnmatch(path, pattern) check_paths = list(filter(exclude, check_paths)) bad_paths = list(filter(self._header_bad, check_paths)) if bad_paths: raise MissingHeaderError(bad_paths) def _header_bad(self, path): target = '(C) British Crown Copyright 2011 - 2012, Met Office' with open(path, 'rt') as text_file: # Check for the header on the first line. line = text_file.readline().rstrip() bad = target not in line # Check if it was an executable script, with the header # starting on the second line. if bad and line == '#!/usr/bin/env python': line = text_file.readline().rstrip() bad = target not in line return bad # Dependency checks # ================= # GEOS try: geos_version = subprocess.check_output(['geos-config', '--version']) geos_version = tuple(int(v) for v in geos_version.split(b'.')) geos_includes = subprocess.check_output(['geos-config', '--includes']) geos_clibs = subprocess.check_output(['geos-config', '--clibs']) except (OSError, ValueError, subprocess.CalledProcessError): warnings.warn( 'Unable to determine GEOS version. Ensure you have %s or later ' 'installed, or installation may fail.' % ( '.'.join(str(v) for v in GEOS_MIN_VERSION), )) geos_includes = [] geos_library_dirs = [] if sys.platform.startswith('win'): geos_libraries = ['geos'] else: geos_libraries = ['geos_c'] else: if geos_version < GEOS_MIN_VERSION: print('GEOS version %s is installed, but cartopy requires at least ' 'version %s.' % ('.'.join(str(v) for v in geos_version), '.'.join(str(v) for v in GEOS_MIN_VERSION)), file=sys.stderr) exit(1) if PY3: geos_includes = geos_includes.decode() geos_clibs = geos_clibs.decode() geos_includes = geos_includes.split() geos_libraries = [] geos_library_dirs = [] for entry in geos_clibs.split(): if entry.startswith('-L'): geos_library_dirs.append(entry[2:]) elif entry.startswith('-l'): geos_libraries.append(entry[2:]) # Proj4 def find_proj_version_by_program(conda=None): proj = find_executable('proj') if proj is None: print( 'Proj4 %s must be installed.' % ( '.'.join(str(v) for v in PROJ_MIN_VERSION), ), file=sys.stderr) exit(1) if conda is not None and conda not in proj: print( 'Proj4 %s must be installed in Conda environment "%s".' % ( '.'.join(str(v) for v in PROJ_MIN_VERSION), conda), file=sys.stderr) exit(1) try: proj_version = subprocess.check_output([proj], stderr=subprocess.STDOUT) proj_version = proj_version.split()[1].split(b'.') proj_version = tuple(int(v.strip(b',')) for v in proj_version) except (OSError, IndexError, ValueError, subprocess.CalledProcessError): warnings.warn( 'Unable to determine Proj4 version. Ensure you have %s or later ' 'installed, or installation may fail.' % ( '.'.join(str(v) for v in PROJ_MIN_VERSION), )) proj_version = (0, 0, 0) return proj_version conda = os.getenv('CONDA_DEFAULT_ENV') if conda is not None and conda in sys.prefix: # Conda does not provide pkg-config compatibility, but the search paths # should be set up so that nothing extra is required. We'll still check # the version, though. proj_version = find_proj_version_by_program(conda) if proj_version < PROJ_MIN_VERSION: print( 'Proj4 version %s is installed, but cartopy requires at least ' 'version %s.' % ('.'.join(str(v) for v in proj_version), '.'.join(str(v) for v in PROJ_MIN_VERSION)), file=sys.stderr) exit(1) proj_includes = [] proj_libraries = ['proj'] proj_library_dirs = [] else: try: proj_version = subprocess.check_output(['pkg-config', '--modversion', 'proj'], stderr=subprocess.STDOUT) proj_version = tuple(int(v) for v in proj_version.split(b'.')) proj_includes = subprocess.check_output(['pkg-config', '--cflags', 'proj']) proj_clibs = subprocess.check_output(['pkg-config', '--libs', 'proj']) except (OSError, ValueError, subprocess.CalledProcessError): proj_version = find_proj_version_by_program() if proj_version < PROJ_MIN_VERSION: print( 'Proj4 version %s is installed, but cartopy requires at least ' 'version %s.' % ('.'.join(str(v) for v in proj_version), '.'.join(str(v) for v in PROJ_MIN_VERSION)), file=sys.stderr) exit(1) proj_includes = [] proj_libraries = ['proj'] proj_library_dirs = [] else: if proj_version < PROJ_MIN_VERSION: print( 'Proj4 version %s is installed, but cartopy requires at least ' 'version %s.' % ('.'.join(str(v) for v in proj_version), '.'.join(str(v) for v in PROJ_MIN_VERSION)), file=sys.stderr) exit(1) if PY3: proj_includes = proj_includes.decode() proj_clibs = proj_clibs.decode() proj_includes = [proj_include[2:] if proj_include.startswith('-I') else proj_include for proj_include in proj_includes.split()] proj_libraries = [] proj_library_dirs = [] for entry in proj_clibs.split(): if entry.startswith('-L'): proj_library_dirs.append(entry[2:]) elif entry.startswith('-l'): proj_libraries.append(entry[2:]) # Python dependencies extras_require = {} for name in os.listdir(os.path.join(HERE, 'requirements')): with open(os.path.join(HERE, 'requirements', name), 'r') as fh: section, ext = os.path.splitext(name) extras_require[section] = [] for line in fh: if line.startswith('#'): pass elif line.startswith('-'): pass else: extras_require[section].append(line.strip()) install_requires = extras_require.pop('default') tests_require = extras_require.pop('tests', []) # General extension paths if sys.platform.startswith('win'): def get_config_var(name): return '.' include_dir = get_config_var('INCLUDEDIR') library_dir = get_config_var('LIBDIR') if sys.platform.startswith('win'): extra_extension_args = {} else: extra_extension_args = dict( runtime_library_dirs=[get_config_var('LIBDIR')]) # Description # =========== with open(os.path.join(HERE, 'README.rst'), 'r') as fh: description = ''.join(fh.readlines()) # Main setup # ========== setup( name='Cartopy', version='0.14.2', url='http://scitools.org.uk/cartopy/docs/latest/', download_url='https://github.com/SciTools/cartopy', author='UK Met Office', description='A cartographic python library with matplotlib support for visualisation', long_description=description, license = "LGPLv3", keywords = "cartography map transform projection proj.4 geos shapely shapefile", install_requires=install_requires, extras_require=extras_require, tests_require=tests_require, packages=find_package_tree('lib/cartopy', 'cartopy'), package_dir={'': 'lib'}, package_data={'cartopy': list(file_walk_relative('lib/cartopy/tests/mpl/baseline_images/', remove='lib/cartopy/')) +\ list(file_walk_relative('lib/cartopy/data/raster', remove='lib/cartopy/')) +\ list(file_walk_relative('lib/cartopy/data/netcdf', remove='lib/cartopy/')) +\ list(file_walk_relative('lib/cartopy/data/wmts', remove='lib/cartopy/')) +\ list(file_walk_relative('lib/cartopy/data/shapefiles/natural_earth', remove='lib/cartopy/')) +\ list(file_walk_relative('lib/cartopy/data/shapefiles/gshhs', remove='lib/cartopy/')) +\ ['io/srtm.npz'] }, # requires proj4 headers ext_modules=[ Extension( 'cartopy.trace', ['lib/cartopy/trace.pyx', 'lib/cartopy/_trace.cpp'], include_dirs=[include_dir, './lib/cartopy'] + proj_includes + geos_includes, libraries=proj_libraries + geos_libraries, library_dirs=[library_dir] + proj_library_dirs + geos_library_dirs, language='c++', **extra_extension_args ), Extension( 'cartopy._crs', ['lib/cartopy/_crs.pyx'], include_dirs=[include_dir, np.get_include()] + proj_includes, libraries=proj_libraries, library_dirs=[library_dir] + proj_library_dirs, **extra_extension_args ), # Requires proj4 v4.9 Extension('cartopy.geodesic._geodesic', ['lib/cartopy/geodesic/_geodesic.pyx'], include_dirs=[include_dir, np.get_include()] + proj_includes, libraries=proj_libraries, library_dirs=[library_dir] + proj_library_dirs, **extra_extension_args ), ], cmdclass={'build_ext': build_ext}, classifiers=[ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Operating System :: POSIX :: AIX', 'Operating System :: POSIX :: Linux', 'Programming Language :: C++', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: GIS', 'Topic :: Scientific/Engineering :: Visualization', ], ) Cartopy-0.14.2/PKG-INFO0000644001374000021030000000427512706121712014053 0ustar itpeavd00000000000000Metadata-Version: 1.1 Name: Cartopy Version: 0.14.2 Summary: A cartographic python library with matplotlib support for visualisation Home-page: http://scitools.org.uk/cartopy/docs/latest/ Author: UK Met Office Author-email: UNKNOWN License: LGPLv3 Download-URL: https://github.com/SciTools/cartopy Description: ======= Cartopy ======= .. image:: http://scitools.org.uk/cartopy/docs/latest/_static/cartopy.png Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: * object oriented projection definitions * point, line, polygon and image transformations between projections * integration to expose advanced mapping in matplotlib with a simple and intuitive interface * powerful vector data handling by integrating shapefile reading with Shapely capabilities Documentation can be found at http://scitools.org.uk/cartopy/docs/latest/. ---- Code ---- Cartopy is licenced under GNU Lesser General Public License (LGPLv3). Development occurs at https://github.com/SciTools/cartopy. Keywords: cartography map transform projection proj.4 geos shapely shapefile Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+) Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: Microsoft :: Windows Classifier: Operating System :: POSIX Classifier: Operating System :: POSIX :: AIX Classifier: Operating System :: POSIX :: Linux Classifier: Programming Language :: C++ Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: GIS Classifier: Topic :: Scientific/Engineering :: Visualization Cartopy-0.14.2/setup.cfg0000644001374000021030000000007312706121712014567 0ustar itpeavd00000000000000[egg_info] tag_build = tag_date = 0 tag_svn_revision = 0

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