Metadata-Version: 2.1
Name: global-land-mask
Version: 1.0.0
Summary: Check whether a lat/lon point in on land or on sea
Home-page: https://github.com/toddkarin/global-land-mask
Author: toddkarin
Author-email: pvtools.lbl@gmail.com
License: UNKNOWN
Description: # global-land-mask
        Check whether a lat/lon point is on land for any point on earth.
        
        <img src="https://github.com/toddkarin/global-land-mask/blob/master/example_plot_globe_map_us.png" width="200">
        
        # Install
        
        Install using pip:
        ```bash
        pip install global-land-mask
        ```
        
        Requirements:
        - numpy
        
        # Description
        This python module, global-land-mask, contains scripts for checking whether a lat/lon point is on land or sea. In order to do this, we use the GLOBE dataset, which samples the entire earth at 1 km resolution. We then simply extract all 'invalid' values from this elevation map and save to file.
        
        The global mask is of shape (21600, 43200), equating to about 980 mB when saved without compression. This data can be compressed to 2.5 mb using numpy savez_compressed, making for a very compact package.
        
        The raw elevation data from the GLOBE dataset can be downloaded from 
        https://www.ngdc.noaa.gov/mgg/topo/gltiles.html
        It is not necessary to download this data in order to use the global land mask. However, the user can build a global elevation dataset using the exampel scripts provided.
        
        This package provides globe.is_land(), an alaternative to Basemap.is_land(). For 10,000 data points, globe.is_land is around 6000 times faster than Basemap.is_land.
        
        The function globe.is_land uses a nearest-neighbor lookup on a regular grid.  
        
        
        
        # Simple example
        
        Here is a simple example showing the use of global_land_mask to check if lat/lon points are on land.
        ```python
        from global_land_mask import globe
        import numpy as np
        
        # Check if a point is on land:
        lat = 40
        lon = -120
        is_on_land = globe.is_land(lat, lon)
        
        print('lat={}, lon={} is on land: {}'.format(lat,lon,is_on_land))
        # lat=40, lon=-120 is on land: True
        
        # Check if several points are in the ocean
        lat = 40
        lon = np.linspace(-150,-110,3)
        is_in_ocean = globe.is_ocean(lat, lon)
        print('lat={}, lon={} is in ocean: {}'.format(lat,lon,is_in_ocean))
        # lat=40, lon=[-150. -130. -110.] is in ocean: [ True  True False]
        
        ```
        
        # Speed test
        
        Compare performance of global_land_mask and Basemap.
        ```python
        from global_land_mask import globe
        from mpl_toolkits.basemap import Basemap
        import matplotlib
        matplotlib.use('TkAgg')
        import numpy as np
        import time
        
        
        # Lat/lon points to get
        lat = np.linspace(-20,50,100)
        lon = np.linspace(-130,-70,100)
        
        # Make a grid
        lon_grid, lat_grid = np.meshgrid(lon,lat)
        
        # Get whether the points are on land using globe.is_land
        start_time = time.time()
        globe_land_mask = globe.is_land(lat_grid, lon_grid)
        globe_run_time = time.time()-start_time
        print('Time to run globe.is_land(): {}'.format(globe_run_time))
        
        # Get whether the points are on land using Basemap.is_land
        # bm = Basemap(projection='cyl',resolution='i')
        bm = Basemap(projection='cyl', llcrnrlat=-60, urcrnrlat=90, \
                    llcrnrlon=-180, urcrnrlon=180, resolution='c')
        f = np.vectorize(bm.is_land)
        
        start_time = time.time()
        xpt, ypt = bm( lon_grid, lat_grid)
        basemap_land_mask = f(xpt,ypt)
        basemap_run_time = time.time()-start_time
        print('Time to run Basemap.is_land(): {}'.format(basemap_run_time))
        
        print('Speed up: {}'.format(basemap_run_time/globe_run_time))
        
        # Check agreement (note there is a different treatment for lakes
        fraction_agreed = np.sum(globe_land_mask == basemap_land_mask)/len(globe_land_mask.flatten())
        print('Fraction agreeing: {}'.format(fraction_agreed))
        ```
        
        # Example of map over US
        
        Try running
        
        ```python
        from global_land_mask import globe
        from mpl_toolkits.basemap import Basemap
        import numpy as np
        import matplotlib
        matplotlib.use('TkAgg')
        import matplotlib.pyplot as plt
        
        
        # Lat/lon points to get
        lat = np.linspace(-20,90,1000)
        lon = np.linspace(-130,-60,1002)
        
        # Make a grid
        lon_grid, lat_grid = np.meshgrid(lon,lat)
        
        # Get whether the points are on land.
        z = globe.is_land(lat_grid, lon_grid)
        
        # Set up map
        fig = plt.figure(0, figsize=(5.5, 4.5))
        plt.clf()
        ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
        
        m = Basemap(llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-64, urcrnrlat=49,
                    projection='lcc', lat_1=33, lat_2=45, lon_0=-95,
                    area_thresh=200,
                    resolution='i')
        m.drawstates(linewidth=0.2)
        m.drawcoastlines(linewidth=0.2)
        m.drawcountries(linewidth=0.2)
        
        
        cs = m.contourf(lon_grid, lat_grid, z,
                        levels=[-0.5, 0.5,1.5],
                        cmap="jet",
                        latlon=True)
        plt.show()
        
        plt.savefig('example_plot_globe_map_us.png',
                    bbox_inches='tight',
                    dpi=400)
        
        ```
        
        to create the binary mask for the US, shown in the image below:
        
        ![Map of Land Mask](https://github.com/toddkarin/global-land-mask/blob/master/example_plot_globe_map_us.png "Map of Land Mask")
        
        Note that lakes are included as "on land" and the resolution isn't perfect, but it's good enough for many purposes!
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
