###########################################################
# Write metadata in each band (date + band name)
# ------------------------------------------------------
S2.set_description_metadata(raster, dates)
###########################################################
# Generate a raster with NDVI index
# ---------------------------------------------
# show expression and condition of NDVI index
print(S2.get_index_expression('NDVI'))
# generate raster
S2.generate_raster(input_raster=raster,
output_raster='/tmp/S2.tif',
expression=S2.get_index_expression('NDVI'),
dtype=np.float32)
######################################"
# Plot image
rM = RasterMath(raster)
X = rM.get_random_block()
NDVI = S2.generate_index(X, S2.get_index_expression('NDVI'), dtype=np.float32)
from matplotlib import pyplot as plt
from datetime import datetime
dateToDatetime = [datetime.strptime(str(date), '%Y%m%d') for date in dates]
plt.plot_date(dateToDatetime, NDVI[:10, :].T, '-o')
plt.ylabel('Leaf Chlorophyll Content')
# ------------------------------------------- from museotoolbox.processing import RasterMath from museotoolbox import datasets import numpy as np ############################################################################## # Load HistoricalMap dataset # ------------------------------------------- raster, vector = datasets.load_historical_data() ############################################################################## # Initialize rasterMath with raster # ------------------------------------ rM = RasterMath(raster) print(rM.get_random_block()) ########################## # Let's suppose you want compute the difference between blue and green band. # I suggest you to define type in numpy array to save space while creating the raster! X = rM.get_random_block() sub = lambda X: np.array((X[:, 0] - X[:, 1])).astype(np.int16) rM.add_function(sub, out_image='/tmp/sub_lambda.tif') ########################################################### # Use a python function to use arguments # ----------------------------------------