Metadata-Version: 2.1
Name: cartoee
Version: 0.0.4
Summary: Publication quality maps using Earth Engine and Cartopy!
Home-page: http://github.com/kmarkert/cartoee
Author: Kel Markert
Author-email: kel.markert@gmail.com
License: GNU GPL v3
Description: # cartoee
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        Publication quality maps using [Earth Engine](https://earthengine.google.com/) and [Cartopy](https://scitools.org.uk/cartopy/docs/latest/)!
        ![alt-text](./docs/_static/intro_fig.png)
        
        ### Installation
        `cartoee` is available to install via `pip`. To install the package, you can use pip  install for your Python environment:
        
        ```
        pip install cartoee
        ```
        
        Or, you can install the package manually from source code using the following commands:
        
        ```
        git clone https://github.com/kmarkert/cartoee.git
        cd cartoee
        python setup.py install
        ```
        
        Please see the [documentation](https://cartoee.readthedocs.io/en/latest/introduction.html#dependencies) for instructions on installing dependencies.
        
        
        ### Working with cartoee
        `cartoee` aims to do only one thing well: getting processing results from Earth Engine into a publication quality mapping interface. `cartoee` simply gets results from Earth Engine and plots it with the correct geographic projections leaving `ee` and `cartopy` to do more of the processing and visualization.
        
        #### A simple case
        
        Here is what a simple workflow looks like to visualize SRTM data on a map:
        
        ```
        import cartoee as cee
        import ee
        
        ee.Initialize()
        
        # get an earth engine image
        srtm = ee.Image("CGIAR/SRTM90_V4")
        
        # plot the result using cartoee
        ax = cee.getMap(srtm,region=[-180,-90,180,90],visParams={'min':0,'max':3000})
        
        ax.coastlines()
        plt.show()
        ```
        ![alt-text](./docs/_static/srtm_fig.png)
        
        Now that we have our EE image as a cartopy/matplotlib object, we can start styling our plot for the publication using the `cartopy` API.
        
        ```
        import cartopy.crs as ccrs
        from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
        
        # set gridlines and spacing
        xticks = [-180,-120,-60,0,60,120,180]
        yticks = [-90,-60,-30,0,30,60,90]
        ax.gridlines(xlocs=xticks, ylocs=yticks,linestyle='--')
        
        # set custom formatting for the tick labels
        ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
        ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
        
        # set tick labels
        ax.set_xticks([-180,-120,-60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
        ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
        ```
        ![alt-text](./docs/_static/srtm_fig2.png)
        
        #### Doing more...
        Now that we have a grasp on a simple example, we can use Earth Engine to to some processing and make a pretty map.
        
        ```
        # function to add NDVI band to imagery
        def calc_ndvi(img):
            ndvi = img.normalizedDifference(['Nadir_Reflectance_Band2','Nadir_Reflectance_Band1'])
            return img.addBands(ndvi.rename('ndvi'))
        
        # MODIS Nadir BRDF-Adjusted Reflectance with NDVI band
        modis = ee.ImageCollection('MODIS/006/MCD43A4')\
                .filterDate('2010-01-01','2016-01-01')\
                .map(calc_ndvi)
        
        # get the cartopy map with EE results
        ax = cee.getMap(modis.mean(),cmap='YlGn'
            visParams={'min':-0.5,'max':0.85,'bands':'ndvi',},
            region=[-180,-90,180,90])
        
        ax.coastlines()
        
        cb = cee.addColorbar(ax,loc='right',cmap='YlGn',visParams={'min':0,'max':1,'bands':'ndvi'})
        ```
        ![alt-text](./docs/_static/global_ndvi.png)
        
        You can see from the example that we calculated NDVI on MODIS imagery from 2010-2015 and created a global map with the mean value per pixel.
        
        What if we want to make multiple maps with some different projections? We can do that by creating our figure and supplying the axes to plot on.
        
        
        ```
        # get land mass feature collection
        land = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')
        
        # get seasonal averages and clip to land features
        djf = modis.filter(ee.Filter.calendarRange(12,3,'month')).mean().clip(land)
        mam = modis.filter(ee.Filter.calendarRange(3,6,'month')).mean().clip(land)
        jja = modis.filter(ee.Filter.calendarRange(6,9,'month')).mean().clip(land)
        son = modis.filter(ee.Filter.calendarRange(9,12,'month')).mean().clip(land)
        
        fig,ax = plt.subplots(ncols=2,nrows=2,subplot_kw={'projection': ccrs.Orthographic(-80,35)})
        
        imgs = np.array([[djf,mam],[jja,son]])
        titles = np.array([['DJF','MAM'],['JJA','SON']])
        
        for i in range(len(imgs)):
            for j in range(len(imgs[i])):
                ax[i,j] = cee.addLayer(imgs[i,j],ax=ax[i,j],
                                       region=bbox,dims=500,
                                       visParams=ndviVis,cmap='YlGn'
                                      )
                ax[i,j].coastlines()
                ax[i,j].gridlines(linestyle='--')
                ax[i,j].set_title(titles[i,j])
        
        cax = fig.add_axes([0.9, 0.2, 0.02, 0.6])
        cb = cee.addColorbar(ax[i,j],cax=cax,cmap='YlGn',visParams=ndviVis)
        ```
        ![alt-text](./docs/_static/seasonal_ndvi.png)
        
        To see more examples, go to the documentation at https://cartoee.readthedocs.io!
        
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