Metadata-Version: 1.1
Name: eemont
Version: 0.2.0
Summary: A Python package that extends Google Earth Engine
Home-page: https://github.com/davemlz/eemont
Author: David Montero Loaiza
Author-email: dml.mont@gmail.com
License: MIT
Description: eemont
        ========
        
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        **A Python package that extends** `Google Earth Engine <https://earthengine.google.com/>`_
        
        - GitHub: `https://github.com/davemlz/eemont <https://github.com/davemlz/eemont>`_
        - Documentation: `https://eemont.readthedocs.io/ <https://eemont.readthedocs.io/>`_
        - PyPI: `https://pypi.org/project/eemont/ <https://pypi.org/project/eemont/>`_
        - Conda-Forge: `https://anaconda.org/conda-forge/eemont <https://anaconda.org/conda-forge/eemont>`_
        - Tutorials: `https://github.com/davemlz/eemont/tree/master/tutorials <https://github.com/davemlz/eemont/tree/master/tutorials>`_
        
        **Table of Contents**
        
        - `Overview`_
        - `Google Earth Engine Community: Developer Resources`_
        - `How does it work?`_
        - `Installation`_
        - `Features`_
        - `Supported Platforms`_
        - `License`_
        - `Contributing`_
        
        Overview
        -------------------
        
        `Google Earth Engine <https://earthengine.google.com/>`_ is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a `JavaScript and a Python API <https://developers.google.com/earth-engine/guides>`_ with different methods to process geospatial objects. Google Earth Engine also provides a `HUGE PETABYTE-SCALE CATALOG <https://developers.google.com/earth-engine/datasets/>`_ of raster and vector data that users can process online (e.g. Landsat Missions Image Collections, Sentinel Missions Image Collections, MODIS Products Image Collections, World Database of Protected Areas, etc.). The eemont package extends the `Google Earth Engine Python API <https://developers.google.com/earth-engine/guides/python_install>`_ with pre-processing and processing tools for the most used satellite platforms by adding utility methods for different `Earth Engine Objects <https://developers.google.com/earth-engine/guides/objects_methods_overview>`_ that are friendly with the Python method chaining.
        
        Google Earth Engine Community: Developer Resources
        -----------------------------------------------------
        
        The eemont Python package can be found in the `Earth Engine Community: Developer Resources <https://developers.google.com/earth-engine/tutorials/community/developer-resources>`_ together with other awesome resources such as `geemap <https://geemap.org/>`_ and `rgee <https://github.com/r-spatial/rgee>`_.
        
        How does it work?
        -------------------
        
        The eemont python package extends the following Earth Engine classes:
        
        - `ee.Feature <https://developers.google.com/earth-engine/guides/features>`_
        - `ee.FeatureCollection <https://developers.google.com/earth-engine/guides/feature_collections>`_
        - `ee.Geometry <https://developers.google.com/earth-engine/guides/geometries>`_
        - `ee.Image <https://developers.google.com/earth-engine/guides/image_overview>`_
        - `ee.ImageCollection <https://developers.google.com/earth-engine/guides/ic_creating>`_
        - `ee.List <https://developers.google.com/earth-engine/guides/objects_methods_overview>`_
        - `ee.Number <https://developers.google.com/earth-engine/guides/objects_methods_overview>`_
        
        New utility methods and constructors are added to above-mentioned classes in order to create a more fluid code by being friendly with the Python method chaining. These methods are mandatory for some pre-processing and processing tasks (e.g. clouds masking, shadows masking, image scaling, spectral indices computation, etc.), and they are presented as simple functions that give researchers, students and analysts the chance to analyze data with far fewer lines of code.
        
        Look at this simple example where a `Sentinel-2 Surface Reflectance Image Collection <https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR>`_ is pre-processed and processed in just one step:
        
        .. code-block:: python
        
           import ee, eemont
           
           ee.Authenticate()
           ee.Initialize()
           
           point = ee.Geometry.PointFromQuery('Cali, Colombia',user_agent = 'eemont-example') # Extended constructor
           
           S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
               .filterBounds(point)
               .closest('2020-10-15') # Extended (pre-processing)
               .maskClouds(prob = 70) # Extended (pre-processing)
               .scale() # Extended (pre-processing)
               .index(['NDVI','NDWI','BAIS2'])) # Extended (processing)
        
        And just like that, the collection was pre-processed, processed and ready to be analyzed!
        
        Installation
        ------------
        
        Install the latest eemont version from PyPI by running:
        
        .. code-block::   
              
           pip install eemont
        
        Upgrade eemont by running:
        
        .. code-block::   
              
           pip install -U eemont
        
        Install the development version from GitHub by running:
        
        .. code-block::   
              
           pip install git+https://github.com/davemlz/eemont
           
        Install the latest eemont version from conda-forge by running:
        
        .. code-block::   
              
           conda install -c conda-forge eemont
        
        Features
        --------
        
        Let's see some of the main features of eemont and how simple they are compared to the GEE Python API original methods:
        
        Overloaded Operators
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        The following operators are overloaded: +, -, \*\, /, //, %, \**\ , <<, >>, &, \|\, <, <=, ==, !=, >, >=, -, ~. (and you can avoid the ``ee.Image.expression()`` method!)
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python             
                  
                  ds = 'COPERNICUS/S2_SR'
                  
                  S2 = (ee.ImageCollection(ds)
                    .first())
                  
                  def scaleImage(img):
                      scaling = img.select('B.*')
                      x = scaling.multiply(0.0001)
                      scaling = img.select(['AOT','WVP'])
                      scaling = scaling.multiply(0.001)
                      x = x.addBands(scaling)
                      notScaling = img.select([
                          'SCL',
                          'TCI.*',
                          'MSK.*',
                          'QA.*'
                      ]))
                      return x.addBands(notScaling)
                      
                  S2 = scaleImage(S2)
                  
                  exp = '2.5*(N-R)/(N+(6*R)-(7.5*B)+1)'
                  
                  imgDict = {
                    'N': S2.select('B8'),
                    'R': S2.select('B4'),
                    'B': S2.select('B2')
                  }
           
                  EVI = S2.expression(exp,imgDict)
             - .. code-block:: python                     
           
                  ds = 'COPERNICUS/S2_SR'
                  
                  S2 = (ee.ImageCollection(ds)
                    .first()
                    .scale())
        
                  N = S2.select('B8')
                  R = S2.select('B4')
                  B = S2.select('B2')
        
                  EVI = 2.5*(N-R)/(N+(6*R)-(7.5*B)+1)
        
        Clouds and Shadows Masking
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Masking clouds and shadows can be done using eemont with just one method: ``maskClouds()``!
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python             
                  
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  def maskCloudsShadows(img):
                      c = (1 << 3)
                      s = (1 << 5)
                      qa = 'pixel_qa'
                      qa = img.select(qa)
                      cm = qa.bitwiseAnd(c).eq(0)
                      sm = qa.bitwiseAnd(s).eq(0)
                      mask = cm.And(sm)
                      return img.updateMask(mask)
                      
                  (ee.ImageCollection(ds)
                    .map(maskCloudsShadows))
             - .. code-block:: python                 
           
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  (ee.ImageCollection(ds)
                    .maskClouds())
        
        Image Scaling and Offsetting
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Scaling and offsetting can also be done using eemont with just one method: ``scale()``!
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python                    
           
                  def scaleBands(img):
                      scaling = img.select([
                        'NDVI',
                        'EVI',
                        'sur.*'
                      ])
                      x = scaling.multiply(0.0001)
                      scaling = img.select('.*th')
                      scaling = scaling.multiply(0.01)
                      x = x.addBands(scaling)
                      notScaling = img.select([
                        'DetailedQA',
                        'DayOfYear',
                        'SummaryQA'
                      ])
                      return x.addBands(notScaling)              
                  
                  ds = 'MODIS/006/MOD13Q1'
                  
                  (ee.ImageCollection(ds)
                    .map(scaleBands))
             - .. code-block:: python                    
           
                  ds = 'MODIS/006/MOD13Q1'
                  
                  (ee.ImageCollection(ds)
                    .scale())
        
        Complete Preprocessing
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The complete preprocessing workflow (Masking clouds and shadows, and image scaling and offsetting) can be done using eemont with just one method: ``preprocess()``!
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python          
                  
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  def maskCloudsShadows(img):
                      c = (1 << 3)
                      s = (1 << 5)
                      qa = 'pixel_qa'
                      qa = img.select(qa)
                      cm = qa.bitwiseAnd(c).eq(0)
                      sm = qa.bitwiseAnd(s).eq(0)
                      mask = cm.And(sm)
                      return img.updateMask(mask)
                      
                  def scaleBands(img):
                      scaling = img.select('B[1-7]')
                      x = scaling.multiply(0.0001)
                      scaling = img.select([
                        'B10','B11'
                      ])
                      scaling = scaling.multiply(0.1)
                      x = x.addBands(scaling)
                      notScaling = img.select([
                        'sr_aerosol',
                        'pixel_qa',
                        'radsat_qa'
                      ])
                      return x.addBands(notScaling)
                      
                  (ee.ImageCollection(ds)
                    .map(maskCloudsShadows)
                    .map(scaleBands))
             - .. code-block:: python                
           
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  (ee.ImageCollection(ds)
                    .preprocess())
        
        Spectral Indices
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Do you need to compute several spectral indices? Use the ``index()`` method! A lot of built-in vegetation, burn, water, snow, drought and kernel indices can be computed:
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python                    
           
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  def scaleImage(img):
                      scaling = img.select('B[1-7]')
                      x = scaling.multiply(0.0001)
                      scaling = img.select(['B10','B11'])
                      scaling = scaling.multiply(0.1)
                      x = x.addBands(scaling)
                      notScaling = img.select([
                          'sr_aerosol',
                          'pixel_qa',
                          'radsat_qa'
                      ]))
                      return x.addBands(notScaling)
                  
                  def addIndices(img):
                      x = ['B5','B4']
                      a = img.normalizedDifference(x)
                      a = a.rename('NDVI')
                      x = ['B5','B3']
                      b = img.normalizedDifference(x)
                      b = b.rename('GNDVI')
                      x = ['B3','B6']
                      c = img.normalizedDifference(x)
                      c = b.rename('NDSI')
                      return img.addBands([a,b,c])                    
                  
                  (ee.ImageCollection(ds)
                    .map(scaleImage)
                    .map(addIndices))
                  
             - .. code-block:: python                 
           
                  ds = 'LANDSAT/LC08/C01/T1_SR'
                  
                  (ee.ImageCollection(ds)
                    .scale()
                    .index(['NDVI','GNDVI','NDSI']))
        
        The list of available indices can be retrieved by running:
        
        .. code-block:: python  
           
           eemont.listIndices()
        
        Information about the indices can also be checked:
        
        .. code-block:: python   
               
           indices = eemont.indices() 
           indices.BAIS2.formula
           indices.BAIS2.reference
        
        Closest Image to a Specific Date
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Struggling to get the closest image to a specific date? Here is the solution: the ``closest()`` method!
        
        .. list-table::
           :widths: 50 50
           :header-rows: 1
        
           * - GEE Python API
             - eemont-style     
           * - .. code-block:: python                   
           
                  ds = 'COPERNICUS/S5P/OFFL/L3_NO2'
                  
                  xy = [-76.21, 3.45]
                  poi = ee.Geometry.Point(xy)
                  
                  date = ee.Date('2020-10-15')
                  date = date.millis()
                  
                  def setTimeDelta(img):              
                      prop = 'system:time_start'
                      prop = img.get(prop)
                      prop = ee.Number(prop)              
                      delta = prop.subtract(date)
                      delta = delta.abs()              
                      return img.set(
                        'dateDist',
                        delta)                     
                  
                  (ee.ImageCollection(ds)
                    .filterBounds(poi)
                    .map(setTimeDelta)
                    .sort('dateDist')
                    .first())
                  
             - .. code-block:: python                  
           
                  ds = 'COPERNICUS/S5P/OFFL/L3_NO2'
                  
                  xy = [-76.21, 3.45]
                  poi = ee.Geometry.Point(xy)
                  
                  (ee.ImageCollection(ds)
                    .filterBounds(poi)
                    .closest('2020-10-15'))
               
        Time Series By Regions
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The JavaScript API has a method for time series extraction (included in the ui.Chart module), but this method is missing in the Python API... so, here it is!
        
        PD: Actually, there are two methods that you can use: ``getTimeSeriesByRegion()`` and ``getTimeSeriesByRegions()``!
        
        .. code-block:: python
        
           f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
           f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
           fc = ee.FeatureCollection([f1,f2])
        
           S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
              .filterBounds(fc)
              .filterDate('2020-01-01','2021-01-01')
              .maskClouds()
              .scale()
              .index(['EVI','NDVI']))
        
           # By Region
           ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
                                         geometry = fc,
                                         bands = ['EVI','NDVI'],
                                         scale = 10)
           
           # By Regions
           ts = S2.getTimeSeriesByRegions(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
                                          collection = fc,
                                          bands = ['EVI','NDVI'],
                                          scale = 10)
                                          
        Constructors by Queries
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Don't you have the coordinates of a place? You can construct them by using queries!
        
        .. code-block:: python
        
           usr = 'my-eemont-query-example'
           
           seattle_bbox = ee.Geometry.BBoxFromQuery('Seattle',user_agent = usr)
           cali_coords = ee.Feature.PointFromQuery('Cali, Colombia',user_agent = usr)
           amazonas_river = ee.FeatureCollection.MultiPointFromQuery('Río Amazonas',user_agent = usr)
        
        Supported Platforms
        ------------------------
        
        The Supported Platforms for each method can be found in the eemont documentation.
        
        - Masking clouds and shadows supports Sentinel Missions (Sentinel-2 SR and Sentinel-3), Landsat Missions (SR products) and some MODIS Products. Check all details in User Guide > Masking Clouds and Shadows > Supported Platforms.
        - Image scaling supports Sentinel Missions (Sentinel-2 and Sentinel-3), Landsat Missions and most MODIS Products. Check all details in User Guide > Image Scaling > Supported Platforms.
        - Spectral indices computation supports Sentinel-2 and Landsat Missions. Check all details in User Guide > Spectral Indices > Supported Platforms.
        - Getting the closest image to a specific date and time series supports all image collections with the ``system:time_start`` property.
        
        License
        -------
        
        The project is licensed under the MIT license.
        
        Contributing
        ------------------
        
        Contributions to eemont are welcome! Here you will find how to do it:
        
        - **Bugs:** If you find a bug, please report it by opening an issue. if possible, please attach the error, code, version, and other details. 
        
        - **Fixing Issues:** If you want to contributte by fixing an issue, please   check the eemont issues: contributions are welcome for open issues with labels ``bug`` and ``help wanted``.
        
        - **Enhancement:** New features and modules are welcome! You can check the eemont issues: contributions are welcome for open issues with labels ``enhancement`` and ``help wanted``.
        
        - **Documentation:** You can add examples, notes and references to the eemont documentation by using the NumPy Docstrings of the eemont documentation, or by creating blogs, tutorials or papers.
        
        Contribution Steps
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        First, fork the `eemont <https://github.com/davemlz/eemont>`_ repository and clone it to your local machine. Then, create a development branch::
        
           git checkout -b name-of-dev-branch
           
        eemont is divided according to Earth Engine classes, and you will find a module for each class (e.g. ``imagecollection.py``). Look for the required class as follows:
        
        - ee.Feature: ``feature.py``
        - ee.FeatureCollection: ``featurecollection.py``
        - ee.Geometry: ``geometry.py``
        - ee.Image: ``image.py``
        - ee.ImageCollection: ``imagecollection.py``
        
        The ``common.py`` is used for methods that can be used for more than one Earth Engine class.
        
        When creating new features, please start with the ``self`` argument and add the corresponding decorator (
        ``@extend()`` from the ``extending`` module). Check this example:
        
        .. code-block:: python
        
           from .extending import extend
           
           @extend(ee.image.Image, static = False)
           def my_new_method(self,other):
                '''Returns the addition of and image and a float.
            
                Parameters
                ----------    
                self : ee.Image [this]
                    Image to add.
                other : float
                    Float to add.
        
                Returns
                -------    
                ee.Image
                    Addition of an ee.Image and a float.
        
                Examples
                --------
                >>> import ee, eemont
                >>> ee.Initialize()
                >>> img = ee.Image(0).my_new_method(other = 3.14)
                '''
                return self.add(other)
                
        By using the ``@extend()`` decorator, the ``my_new_method()`` method is added to the ``ee.Image`` class. If you want to add a static method, please set the ``static`` argument to ``False``. Look for the required class as follows:
        
        - ee.Feature: ``ee.feature.Feature``
        - ee.FeatureCollection: ``ee.featurecollection.FeatureCollection``
        - ee.Geometry: ``ee.geometry.Geometry``
        - ee.Image: ``ee.image.Image``
        - ee.ImageCollection: ``ee.imagecollection.ImageCollection``
        - ee.List: ``ee.ee_list.List``
        - ee.Number: ``ee.ee_number.Number``
        
        Remember to use `Black <https://github.com/psf/black>`_!
        
        In order to test additions, you can use ``pytest`` over the ``tests`` folder::
        
           pytest tests
           
        This will autmatically test all modules for the available satellite platforms through eemont. If you have added a new feature, please include it in the tests.
        
        To test across different Python versions, please use ``tox``.
        
        Now it's time to commit your changes and push your development branch::
        
           git add .
           git commit -m "Description of your work"
           git push origin name-of-dev-branch
          
        And finally, submit a pull request.
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
