def _get_tile_data_(_filelist, _outfile, _band_order, _tile_specs, _composite_type):
_vrtfile = '/vsimem/' + Handler(_outfile).basename.split('.tif')[0] + '_tmp_layerstack.vrt'
_outfile = '/vsimem/' + Handler(_outfile).basename.split('.tif')[0] + '_tmp_layerstack.tif'
_mraster = MultiRaster(_filelist)
_layerstack_vrt = _mraster.layerstack(return_vrt=True, outfile=_outfile)
_lras = Raster('_tmp_layerstack')
_lras.datasource = _layerstack_vrt
_lras.initialize()
_lras.make_tile_grid(*_tile_specs)
for _ii in range(_lras.ntiles):
yield _filelist, _outfile, _band_order, _lras.tile_grid[_ii], _composite_type
def _tile_process_(args):
_filelist, _outfile, _band_order, _tile_dict, _composite_type = args
_tile_coords = _tile_dict['block_coords']
_xmin, _ymin = _tile_dict['first_pixel']
_x, _y, _cols, _rows = _tile_coords
_outfile = '/vsimem/' + Handler(_outfile).basename.split('.tif')[0] + \
'_{}.tif'.format('_'.join([str(j) for j in _tile_coords]))
_mraster = MultiRaster(_filelist)
_layerstack_vrt = _mraster.layerstack(return_vrt=True, outfile=_outfile)
_lras = Raster('_tmp_layerstack')
_lras.datasource = _layerstack_vrt
_lras.initialize()
_tile_arr = _lras.get_tile(_band_order, _tile_coords).copy()
if _composite_type == 'mean':
_temp_arr = np.apply_along_axis(lambda x: np.mean(x[x != _lras.nodatavalue])
if (x[x != _lras.nodatavalue]).shape[0] > 0
else _lras.nodatavalue, 0, _tile_arr)
elif _composite_type == 'median':
_temp_arr = np.apply_along_axis(lambda x: np.median(x[x != _lras.nodatavalue])
if (x[x != _lras.nodatavalue]).shape[0] > 0
else _lras.nodatavalue, 0, _tile_arr)
elif _composite_type == 'max':
_temp_arr = np.apply_along_axis(lambda x: np.max(x[x != _lras.nodatavalue])
if (x[x != _lras.nodatavalue]).shape[0] > 0
else _lras.nodatavalue, 0, _tile_arr)
elif _composite_type == 'min':
_temp_arr = np.apply_along_axis(lambda x: np.min(x[x != _lras.nodatavalue])
if (x[x != _lras.nodatavalue]).shape[0] > 0
else _lras.nodatavalue, 0, _tile_arr)
elif 'pctl' in _composite_type:
pctl = int(_composite_type.split('_')[1])
_temp_arr = np.apply_along_axis(lambda x: np.percentile(x[x != _lras.nodatavalue], pctl)
if (x[x != _lras.nodatavalue]).shape[0] > 0
else _lras.nodatavalue, 0, _tile_arr)
else:
_temp_arr = None
_lras = None
_mraster = None
Handler(_outfile).file_delete()
_tile_arr = None
return (_y-_ymin), ((_y-_ymin) + _rows), (_x-_xmin), ((_x-_xmin) + _cols), _temp_arr
])
arr_copy = out_arr
print(ker_size)
print(arr_copy)
plt.plot(range(len(arr)), arr_copy)
plt.show()
arr_copy = arr_copy.astype(dtype)
if type(arr) in (list, tuple, dict, set):
return arr_copy.tolist()
else:
return arr_copy
if __name__ == '__main__':
arr = [12, 4, 7, 13, 9, 8, 1, 13, 7, 2, 1, 36, 33]
arr2 = moving_average(arr, n=7)
print(arr)
print(arr2)
file1 = "D:/temp/above2017_0629_lvis2b_tif/LVIS2_ABoVE2017_0629_R1803_061571_umd_l2b.tif"
outfile1 = "D:/temp/above2017_0629_lvis2b_tif/LVIS2_ABoVE2017_0629_R1803_061571_umd_l2b_clip2.tif"
cutfile = "D:/temp/LVIS2_ABoVE2017_0629_R1803_061571_umd_l2b.shp"
ras = Raster(file1)
ras.clip(cutline_file=cutfile, outfile=outfile1, apply_mask=False)
picklefiles = ("RFalbedo_deciduous_fraction_treecover_50000_cutoff_5_deg1_20200501T185635_spring.pickle",
"RFalbedo_deciduous_fraction_treecover_50000_cutoff_5_deg1_20200501T185635_summer.pickle",
"RFalbedo_deciduous_fraction_treecover_50000_cutoff_5_deg1_20200501T185635_fall.pickle")
picklefiles = [pickle_dir + picklefile for picklefile in picklefiles]
band_name = 'spr_albedo'
outfile = outdir + Handler(Handler(infile).basename).add_to_filename('_output3')
Handler(outfile).file_remove_check()
# raster contains three bands: 1) decid 2) tree cover 3) land extent mask.
# all the bands are in integer format
raster = Raster(infile)
raster.initialize()
raster.bnames = ['decid', 'treecover']
raster.get_stats(True)
Opt.cprint(raster.shape)
regressor = RFRegressor.load_from_pickle(picklefiles[0])
Opt.cprint(regressor)
Opt.cprint(regressor.adjustment)
out_raster = RFRegressor.regress_raster(regressor,
raster,
clip=clip_)
Opt.cprint(rf_regressor1)
Opt.cprint(rf_regressor2)
bandnames1_ = rf_regressor1.features
Opt.cprint('Bands 1 : ')
Opt.cprint(bandnames1_)
bandnames2_ = rf_regressor2.features
Opt.cprint('Bands 2 : ')
Opt.cprint(bandnames2_)
bandnames_all = bandnames1_ + bandnames2_
# get raster metadata
ras = Raster(infile)
ras.initialize()
tile_size = min([ras.shape[1], ras.shape[2]])
Opt.cprint(ras.shape)
Opt.cprint(ras)
bandnames = ras.bnames
Opt.cprint('Raster bands: "' + '", "'.join(bandnames) + '"')
band_order = Sublist(bandnames).sublistfinder(bandnames_all)
Opt.cprint('Band order: ' + ', '.join([str(b) for b in band_order]))
# re-initialize raster
ras.initialize(get_array=True, band_order=band_order)
(num_list[:, 1] >= startdate) & (num_list[:, 1] <= enddate))[0]
filelist = list(all_files[i] for i in file_loc_on_list.tolist())
for file_ in filelist:
Opt.cprint(file_)
Opt.cprint(outfile)
mraster = MultiRaster(filelist=filelist)
Opt.cprint(mraster)
ls_vrt = mraster.layerstack(return_vrt=True, outfile=outfile)
lras = Raster('tmp_layerstack')
lras.datasource = ls_vrt
lras.initialize()
xmin, xmax, ymin, ymax = lras.get_pixel_bounds(image_bounds, 'crs')
lras.transform = (image_bounds[0],
lras.transform[1],
lras.transform[2],
image_bounds[3],
lras.transform[4],
lras.transform[5])
lras.shape = [1, (ymax-ymin), (xmax-xmin)]
lras.make_tile_grid(*tile_specs)
# first slice west of 0 deg lon
first_slice = np.vstack([arr[channel_indx, (arr.shape[1] - row_indx - 1), cut_loc:]
for row_indx in range(arr.shape[1])])
# second slice east of 0 deg lon
second_slice = np.vstack([arr[channel_indx, (arr.shape[1] - row_indx - 1), :cut_loc]
for row_indx in range(arr.shape[1])])
resliced_arr_list.append(np.hstack([first_slice, second_slice]))
# stack all months
arr = np.stack(resliced_arr_list, 0)
# name output file
outfile = Handler(file1).dirname + Handler().sep + 'ALBEDO_CAM5_{}_KERNEL.tif'.format(var_names[variable])
# define raster object
ras = Raster(outfile,
array=arr,
bnames=months,
dtype=GDAL_FIELD_DEF['double'],
shape=arr.shape,
transform=transform,
crs_string=spref.ExportToWkt())
# define no data value
ras.nodatavalue = data._FillValue
# write raster object
ras.write_to_file()