In [2]:

    

%pylab notebook
from __future__ import print_function
import numpy as np
import pandas as pd
import rasterio
from charistools.modelEnv import ModelEnv
from netCDF4 import Dataset


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

from charistools.readers import ModisTileCube


    

In [4]:

    

configFile = "/vagrant/source/charistools/test/modis_tiles_config.ini"


    

In [5]:

    

myEnv = ModelEnv(tileConfigFile=configFile, topDir='/projects/CHARIS/charistools_test_data')


    

In [6]:

    

modscag_filename = myEnv.forcing_filename(type='modscag_gf', tileID="h23v05", year=2001,verbose=True)
modscag_filename


    

    
Out[6]:





'/projects/CHARIS/charistools_test_data/snow_cover/MODSCAG_GF/MODSCAG_GF_Snow.v0.5.h23v05_2001.h5'

In [7]:

    

modscag_cube = ModisTileCube(filename=modscag_filename, varname='fsca')


    

In [8]:

    

scag = modscag_cube.read(1)
scag.shape

np.amin(scag), np.amax(scag)


    

    
Out[8]:





(0.0, 1.0)

In [9]:

    

mod10a1_filename = myEnv.forcing_filename(type='mod10a1_gf', tileID="h23v05", year=2001,verbose=True)
mod10a1_filename


    

    
Out[9]:





'/projects/CHARIS/charistools_test_data/snow_cover/mod10a1_snow_gf/MOD10A1_GF_Snow.v0.5.h23v05_2001.h5'

In [10]:

    

mod10_cube = ModisTileCube(filename=mod10a1_filename, varname='fsca')


    

In [11]:

    

mod10 = mod10_cube.read(1)
print(mod10.shape)
np.amin(mod10), np.amax(mod10)


    

    




(2400, 2400)






    
Out[11]:





(0, 100)

In [12]:

    

f = Dataset(mod10a1_filename, 'r', 'HDF5')
d = f.groups['500m'].variables['fsca']


    

In [13]:

    

d


    

    
Out[13]:





<type 'netCDF4._netCDF4.Variable'>
uint8 fsca(phony_dim_0, phony_dim_1, phony_dim_1)
path = /500m
unlimited dimensions: 
current shape = (365, 2400, 2400)
filling off

In [14]:

    

forig = Dataset(modscag_filename, 'r', 'HDF5')
dorig = forig.groups['500m'].variables['fsca']
dorig


    

    
Out[14]:





<type 'netCDF4._netCDF4.Variable'>
uint8 fsca(phony_dim_0, phony_dim_1, phony_dim_1)
    packing_convention: netCDF
    packing_convention_description: unpacked = scale_factor*packed + add_offset
    scale_factor: 0.01
    add_offset: 0.0
path = /500m
unlimited dimensions: 
current shape = (365, 2400, 2400)
filling off

In [ ]: