#Path to output csv
out = open(
"C:/Users/hgleason/Dropbox/Git_Snow_MODIS/Data/MODIS/Derived/SD_Random_Sampls.csv",
"w")
#For each annual snow metric GeoTiff
for tile in files:
# open dataset
ds = gdal.Open(tile)
#Get the Geotransform
geoTrans = ds.GetGeoTransform()
#Convert the GeoTiff to Numpy Array
img_cube = grast.bands_to_np_cube(ds, 1)
#Get the current year from image path string
year = str(tile)[90:94]
print(year, str(tile))
#For each uniqe EE sample
for samp in range(len(samples)):
#Get sample details
uid = samples[samp, 2]
lat = samples[samp, 0]
lon = samples[samp, 1]
def process_tile(tile_pth, tile_dir, tile_name, strt_idx, frac, ndsi_strt,
ndsi_end, min_obs, EPSG):
#Update user
print("Starting: Process " + tile_pth + " @ " + str(time.time()))
#Open the GeoTiff with gdal
data = gdal.Open(tile_pth)
#Get the geo-transformation
GeoTran = data.GetGeoTransform()
#Get origin of raster
RastOrigin = (GeoTran[0], GeoTran[3])
#Get pixel dimensions
PixWidth = GeoTran[1]
PixHeight = GeoTran[5]
#Get name of fist tile without extension
tile_name = tile_name[:-4]
#Establish output path from directory and current tile
RastOut = tile_dir + "/" + tile_name + "_out.tif"
#Commit the GeoTiff to memory as 3D NumPy array
time_cube = grast.bands_to_np_cube(tile_pth, strt_idx)
#Get data dimensions
rows = len(time_cube[0])
cols = len(time_cube[0][0])
#Initialize output NumPy array with input dimensions and 4 bands
snow_stack = np.arange(rows * cols * 4).reshape(4, rows, cols)
#Progress keeper
qrt = rows * 0.1
thsh = qrt
#For each row
for row in range(rows):
#Update user on progress
if row >= thsh:
print("Finished " + str(round((float(row) / float(rows)) * 100)) +
"% of " + tile_pth)
thsh = thsh + qrt
#For each col
for col in range(cols):
#Get the NDSI time series from the 'time_cube' @ row and col
ndsi_vect = time_cube[:, row, col]
#Check that all data is not missing (-9) or all (0)
if np.sum(ndsi_vect > 0) > 0:
#Get snow duration metrics
metrics = ndsi_lowess_interp(ndsi_vect, frac, ndsi_strt,
ndsi_end, min_obs)
#Update each array with snow metric results
#Snow Start Band
snow_stack[0, row, col] = metrics[0]
#Snow End Band
snow_stack[1, row, col] = metrics[1]
#Snow Dur Band
snow_stack[2, row, col] = metrics[2]
#Snow Obs Band
snow_stack[3, row, col] = metrics[3]
else:
#Populate arrays with missing data value
snow_stack[0, row, col] = -9
snow_stack[1, row, col] = -9
snow_stack[2, row, col] = -9
snow_stack[3, row, col] = -9
#Convert the 3D NumPy annual stack array to mulit-band GeoTiff
grast.array2gtiff(RastOut, RastOrigin, PixWidth, PixHeight, snow_stack,
EPSG)
#Update user
print("Finished: Process " + tile_pth + " @ " + str(time.time()))