README and minor code improvement

.gitignore

@@ -1,2 +1,3 @@
 dev
-env
+env
+*.pyc

README.md

@@ -17,25 +17,27 @@ Inputs to geeet models can be given as [numpy arrays](https://numpy.org/doc/stab
 
 ## Introduction
 
-*geeet* is a Python package providing a common set of building blocks for estimating evapotranspiration (ET) from remote sensing observations. It also features complete ET models such as [PT-JPL](https://doi.org/10.1016/j.rse.2007.06.025) and [TSEB](https://doi.org/10.1016/0168-1923(95)02265-Y). All modules in *geeet* are designed to work with the input data provided in two formats: (1) as [numpy ndarrays](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) and (2) as [Google Earth Engine](https://earthengine.google.com/) (GEE) [images](https://developers.google.com/earth-engine/apidocs/ee-image). GEE is a cloud-based platform dedicated to Earth observation research that offers a [multi-petabyte catalogue](https://developers.google.com/earth-engine/datasets/) of geospatial data. Importantly, GEE offers cloud computing capabilities, which means that a user can interact with this geospatial data directly without having to download or process any data on premises. Access to these cloud-based services requires [signing up for a GEE account](https://earthengine.google.com/signup/). The *geeet* Python package was created to offer ET modeling tools that work for any user, whether they have a GEE account or not. For this reason, numpy is the only requirement for *geeet*. 
+*geeet* is a Python package providing a common set of building blocks for estimating evapotranspiration (ET) from remote sensing observations. It also features complete ET models such as [PT-JPL](https://doi.org/10.1016/j.rse.2007.06.025) and [TSEB](https://doi.org/10.1016/0168-1923(95)02265-Y). All modules in *geeet* are designed to work with the input data provided in two formats: (1) as [numpy ndarrays](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) and (2) as [Google Earth Engine](https://earthengine.google.com/) (GEE) [images](https://developers.google.com/earth-engine/apidocs/ee-image). GEE is a cloud-based platform dedicated to Earth observation research that offers a [multi-petabyte catalogue](https://developers.google.com/earth-engine/datasets/) of geospatial data. Importantly, GEE offers cloud computing capabilities, which means that a user can interact with this geospatial data directly without having to download or process any data on premises. Access to these cloud-based services requires [signing up for a GEE account](https://earthengine.google.com/signup/). The *geeet* Python package was created to offer ET modeling tools that work for any user, whether they have a GEE account or not. For this reason, numpy is the only requirement for *geeet*.
+
+> Integration of geeet with xarray and especially lazy evaluation using dask is currently under development, with the goal of estimating ET using cloud-based assets (e.g., Cloud Optimized Geotiffs through a Spatio Temporal Asset Catalog (STAC)). 
 
 ## Installation
 
 *geeet* is available on the [Python Package Index (pip)](https://pypi.org/project/geeet/). To install *geeet* using pip, run this command:
 
-```
+```bash
 pip install geeet
 ```
 
 If you want to install the latest development version hosted on github, run this command:
 
-```
+```bash
 pip install git+https://github.com/kaust-halo/geeet
 ```
 
 *geeet* is alsao available on [conda-forge](https://anaconda.org/conda-forge/geeet). To install *geeet* using conda, run this command:
 
-```
+```bash
 conda install -c conda-forge geeet
 ```
 

geeet/MOST.py

@@ -120,11 +120,11 @@ def PsiM_unstable(z_g):
         y = -z_g
         y = np.minimum(y, b**-3)  # max y is ~14.56 (i.e. z lowest value is ~-14.56 --> PsiM max is ~1.8)
         x = (y/a)**(1/3)
-        psim = np.log(a+y)\
-               - 3*b*(y**(1/3))\
-               + (b/2)*(a**(1/3))*np.log((1+x)**2 / (1-x+x**2))\
-               + (3**(1/2))*b*(a**(1/3))*np.arctan((2*x-1)/(3**(1/2)))\
-               + Psi_0
+        psim = (np.log(a+y)
+               - 3*b*(y**(1/3))
+               + (b/2)*(a**(1/3))*np.log((1+x)**2 / (1-x+x**2))
+               + (3**(1/2))*b*(a**(1/3))*np.arctan((2*x-1)/(3**(1/2)))
+               + Psi_0)
     return psim
 
 

geeet/ptjpl.py

@@ -23,11 +23,7 @@
 geeet.ptjpl.cite_all() - all references used for this module
 """
 
-import sys
-import numpy as np
 from geeet.common import is_img
-from geeet.meteo import compute_met_params, relative_humidity
-from geeet.vegetation import compute_ftheta
 try: 
     import ee
 except Exception:
@@ -117,11 +113,11 @@ def ptjpl_arid(img=None, # ee.Image with inputs as bands (takes precedence over
         -     Rn: the net radiation.
     '''
     import numpy as np
-    from geeet.vegetation import compute_Rns, compute_lai, compute_fwet, \
-        compute_fg, compute_ft_arid, compute_fapar, compute_fm, \
-        compute_fsm
+    from geeet.vegetation import (compute_Rns, compute_lai, compute_fwet, 
+        compute_fg, compute_ft_arid, compute_fapar, compute_fm, 
+        compute_fsm, compute_ftheta)
     from geeet.solar import compute_g, compute_Rn
-    from geeet.meteo import relative_humidity
+    from geeet.meteo import relative_humidity, compute_met_params
 
     if is_img(img):
         band_names = img.bandNames()

geeet/tseb.py

@@ -301,10 +301,10 @@ def tseb_series(img=None,    # ee.Image with inputs as bands (takes precedence o
         H = Hc.add(Hs)
 
         # Prepare an initial image containing all the variables that need to be updated iteratively:
-        initialImg = Tc.addBands(Ts).addBands(Tac).addBands(Hc).addBands(Hs).addBands(LEc).addBands(LEs)\
-        .addBands(ra).addBands(rs).addBands(rx).addBands(ustar).addBands(Image(AlphaPT)).addBands(Image(0))
-        initialImg = initialImg.rename('Tc','Ts','Tac','Hc','Hs','LEc','LEs','Ra','Rs','Rx',\
-            'Ustar','alphaPT','iteration')
+        initialImg = (ee.Image([Tc, Ts, Tac, Hc, Hs, LEc, LEs,
+                ra, rs, rx, ustar, Image(AlphaPT), Image(0)])
+        .rename(['Tc','Ts','Tac','Hc','Hs','LEc','LEs',
+                'Ra','Rs','Rx', 'Ustar', 'alphaPT', 'iteration']))            
         iterStart = ee.List([initialImg]) # Initial list only contains the initialImg 
 
         # Prepare a dummy image collection for the iterative procedure:
@@ -442,13 +442,11 @@ def tseb_series_iteration(img, list):
             # containing all the updated values. However, note that we will
             # not necessarilly use the updated values everywhere - 
             # only where LEs<0 in the previous iteration.  
-            updatedImg = Tcu.addBands(Tsu).addBands(Tacu)\
-                .addBands(Hcu).addBands(Hsu).addBands(LEcu).addBands(LEsu)\
-                .addBands(rau).addBands(rsu).addBands(rxu)\
-                .addBands(ustaru).addBands(AlphaPTu).addBands(iterationu)
-            updatedImg = updatedImg.rename('Tc','Ts','Tac','Hc','Hs',\
-                'LEc','LEs','Ra','Rs','Rx','Ustar','alphaPT','iteration')
-
+            updatedImg = (ee.Image([Tcu, Tsu, Tacu, Hcu, Hsu, LEcu, LEsu,
+                rau, rsu, rxu, ustaru, AlphaPTu, iterationu])
+            .rename(['Tc','Ts','Tac','Hc','Hs','LEc','LEs',
+                'Ra','Rs','Rx', 'Ustar', 'alphaPT', 'iteration']))            
+
             # Finally, we select the values we will keep
             # (we only update pixels where LEs was previously negative)
             # These values will be the "old" Img in the next iteration
@@ -470,9 +468,12 @@ def tseb_series_iteration(img, list):
         resultImage = resultImage.addBands(LE).addBands(H)
 
         # Also return other fluxes:
-        resultImage = resultImage.addBands(G.rename('G')).addBands(Rn.rename('Rn'))\
-            .addBands(Rns.rename('Rns')).addBands(Rnc.rename('Rnc'))
-
+        resultImage = resultImage.addBands(ee.Image([
+            G.rename('G'),
+            Rn.rename('Rn'),
+            Rns.rename('Rns'),
+            Rnc.rename('Rnc')
+        ])) 
         return img.addBands(resultImage)
     else:
         # Retrieve parameters from hybrid functions: