def run(self):
lulc_nodata = raster_utils.get_nodata_from_uri(GLOBAL_LANDCOVER_URI)
forest_lulc_codes = [1, 2, 3, 4, 5]
mask_uri = os.path.join(OUTPUT_DIR, "forest_mask.tif")
mask_nodata = 2
def mask_nonforest(lulc):
"""Takes in a numpy array of landcover values and returns 1s
where they match forest codes and 0 otherwise"""
mask = numpy.empty(lulc.shape, dtype=numpy.int8)
mask[:] = 1
for lulc_code in forest_lulc_codes:
mask[lulc == lulc_code] = 0
mask[lulc == lulc_nodata] = mask_nodata
return mask
cell_size = raster_utils.get_cell_size_from_uri(GLOBAL_LANDCOVER_URI)
raster_utils.vectorize_datasets(
[GLOBAL_LANDCOVER_URI,], mask_nonforest, mask_uri, gdal.GDT_Byte,
mask_nodata, cell_size, 'intersection', dataset_to_align_index=0,
dataset_to_bound_index=None, aoi_uri=None,
assert_datasets_projected=True, process_pool=None,
vectorize_op=False, datasets_are_pre_aligned=True)
raster_utils.distance_transform_edt(
mask_uri, FOREST_EDGE_DISTANCE_URI)
def run(self):
nodata = raster_utils.get_nodata_from_uri(UNION_LANDCOVER_URI)
cell_size = raster_utils.get_cell_size_from_uri(UNION_LANDCOVER_URI)
raster_utils.vectorize_datasets(
[UNION_LANDCOVER_URI, UNION_BIOMASS_URI], lambda x, y: x,
GLOBAL_LANDCOVER_URI,
gdal.GDT_Int16, nodata, cell_size, "intersection",
dataset_to_align_index=0, vectorize_op=False)
def _align_raster_with_biomass(input_uri, output_uri):
"""Function to use internally to take an input and align it with the
GLOBAL_BIOMASS_URI raster"""
nodata = raster_utils.get_nodata_from_uri(input_uri)
if nodata is None:
nodata = -9999
cell_size = raster_utils.get_cell_size_from_uri(GLOBAL_BIOMASS_URI)
raster_utils.vectorize_datasets(
[input_uri, GLOBAL_BIOMASS_URI], lambda x, y: x,
output_uri, gdal.GDT_Float32, nodata, cell_size, "dataset",
dataset_to_bound_index=1, vectorize_op=False)
def run(self):
def union_op(*array_list):
"""Given an array stack return an array that has a value defined
in the stack that is not nodata. used for overlapping nodata
stacks."""
output_array = array_list[0]
for array in array_list[1:]:
output_array = numpy.where(
array != nodata, array, output_array)
return output_array
nodata = raster_utils.get_nodata_from_uri(self.dataset_uri_list[0])
cell_size = raster_utils.get_cell_size_from_uri(
self.dataset_uri_list[0])
raster_utils.vectorize_datasets(
list(self.dataset_uri_list), union_op, self.dataset_out_uri,
gdal.GDT_Int16, nodata, cell_size, "union",
dataset_to_align_index=0, vectorize_op=False)
def run(self): biomass_raster_list = [ "C:/Users/rpsharp/Dropbox_stanford/Dropbox/forest_edge_carbon/af_biov2ct1.tif", "C:/Users/rpsharp/Dropbox_stanford/Dropbox/forest_edge_carbon/am_biov2ct1.tif", "C:/Users/rpsharp/Dropbox_stanford/Dropbox/forest_edge_carbon/as_biov2ct1.tif", ] nodata = raster_utils.get_nodata_from_uri(biomass_raster_list[0]) cell_size = raster_utils.get_cell_size_from_uri(biomass_raster_list[0]) def union_op(*biomass_array_list): output_array = biomass_array_list[0] for biomass_array in biomass_array_list[1:]: output_array = numpy.where( biomass_array != nodata, biomass_array, output_array) return output_array raster_utils.create_directories([os.path.dirname(self.output_uri)]) raster_utils.vectorize_datasets( biomass_raster_list, union_op, self.output_uri, gdal.GDT_Int16, nodata, cell_size, 'union', dataset_to_align_index=0, vectorize_op=False)
def average_layers():
base_table_uri = "C:/Users/rich/Desktop/all_grid_results_100km_clean_v2.csv"
base_table_file = open(base_table_uri, 'rU')
table_header = base_table_file.readline()
#need to mask the average layers to the biomass regions
giant_layer_uri = "C:/Users/rich/Desktop/average_layers_projected/giant_layer.tif"
af_uri = "C:/Users/rich/Desktop/af_biov2ct1.tif"
am_uri = "C:/Users/rich/Desktop/am_biov2ct1.tif"
as_uri = "C:/Users/rich/Desktop/as_biov2ct1.tif"
cell_size = raster_utils.get_cell_size_from_uri(am_uri)
#raster_utils.vectorize_datasets(
# [af_uri, am_uri, as_uri], lambda x,y,z: x+y+z, giant_layer_uri, gdal.GDT_Float32,
# -1, cell_size, 'union', vectorize_op=False)
table_uri = base_table_uri
table_file = open(table_uri, 'rU')
table_header = table_file.readline().rstrip()
lookup_table = raster_utils.get_lookup_from_csv(table_uri, 'ID100km')
out_table_uri = "C:/Users/rich/Desktop/all_grid_results_100km_human_elevation.csv"
out_table_file = codecs.open(out_table_uri, 'w', 'utf-8')
average_raster_list = [
("C:/Users/rich/Desktop/average_layers_projected/lighted_area_luminosity.tif", 'Lighted area density'),
("C:/Users/rich/Desktop/average_layers_projected/fi_average.tif", 'Fire densities'),
("C:/Users/rich/Desktop/average_layers_projected/glbctd1t0503m.tif", 'FAO_Cattle'),
("C:/Users/rich/Desktop/average_layers_projected/glbgtd1t0503m.tif", 'FAO_Goat'),
("C:/Users/rich/Desktop/average_layers_projected/glbpgd1t0503m.tif", 'FAO_Pig'),
("C:/Users/rich/Desktop/average_layers_projected/glbshd1t0503m.tif", 'FAO_Sheep'),
("C:/Users/rich/Desktop/average_layers_projected/glds00ag.tif", 'Human population density AG'),
("C:/Users/rich/Desktop/average_layers_projected/glds00g.tif", 'Human population density G'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_11.tif', '"11: Urban, Dense settlement"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_12.tif', '"12: Dense settlements, Dense settlements"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_22.tif', '"22: Irrigated villages, Villages"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_23.tif', '"23: Cropped & pastoral villages, Villages"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_24.tif', '"24: Pastoral villages, Villages"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_25.tif', '"25: Rainfed villages, Villages"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_26.tif', '"26: Rainfed mosaic villages, Villages"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_31.tif', '"31: Residential irrigated cropland, Croplands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_32.tif', '"32: Residential rainfed mosaic, Croplands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_33.tif', '"33: Populated irrigated cropland, Croplands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_34.tif', '"34: Populated rainfed cropland, Croplands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_35.tif', '"35: Remote croplands, Croplands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_41.tif', '"41: Residential rangelands, Rangelands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_42.tif', '"42: Populated rangelands, Rangelands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_43.tif', '"43: Remote rangelands, Rangelands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_51.tif', '"51: Populated forests, Forested"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_52.tif', '"52: Remote forests, Forested"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_61.tif', '"61: Wild forests, Wildlands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_62.tif', '"62: Sparse trees, Wildlands"'),
('C:/Users/rich/Desktop/average_layers_projected/anthrome_63.tif', '"63: Barren, Wildlands"'),
("C:/Users/rich/Desktop/average_layers_projected/5km_global_pantropic_dem.tif", '"Average Elevation"'),
]
clipped_raster_list = []
for average_raster_uri, header in average_raster_list:
print 'clipping ' + average_raster_uri
clipped_raster_uri = os.path.join(os.path.dirname(average_raster_uri), 'temp', os.path.basename(average_raster_uri))
cell_size = raster_utils.get_cell_size_from_uri(average_raster_uri)
raster_utils.vectorize_datasets(
[average_raster_uri, giant_layer_uri], lambda x,y: x, clipped_raster_uri, gdal.GDT_Float32,
-1, cell_size, 'intersection', vectorize_op=False)
clipped_raster_list.append((clipped_raster_uri, header))
dataset_list = [gdal.Open(uri) for uri, label in clipped_raster_list]
band_list = [ds.GetRasterBand(1) for ds in dataset_list]
nodata_list = [band.GetNoDataValue() for band in band_list]
extended_table_headers = ','.join([header for _, header in average_raster_list])
def write_to_file(value):
try:
out_table_file.write(value)
except UnicodeDecodeError as e:
out_table_file.write(value.decode('latin-1'))
write_to_file(table_header + ',' + extended_table_headers + '\n')
#print table_header + ',' + extended_table_headers
for line in table_file:
split_line = line.rstrip().split(',')
grid_id = split_line[2]
#for grid_id in lookup_table:
try:
split_grid_id = grid_id.split('-')
grid_row_index, grid_col_index = map(int, split_grid_id)
except ValueError as e:
month_to_number = {
'Jan': 1,
'Feb': 2,
'Mar': 3,
'Apr': 4,
'May': 5,
'Jun': 6,
'Jul': 7,
'Aug': 8,
'Sep': 9,
'Oct': 10,
'Nov': 11,
'Dec': 12,
}
grid_row_index, grid_col_index = month_to_number[split_grid_id[0]], int(split_grid_id[1])
print 'processing grid id ' + grid_id
ds = dataset_list[0]
base_srs = osr.SpatialReference(ds.GetProjection())
lat_lng_srs = base_srs.CloneGeogCS()
coord_transform = osr.CoordinateTransformation(
base_srs, lat_lng_srs)
gt = ds.GetGeoTransform()
grid_resolution = 100 #100km
row_coord = grid_row_index * grid_resolution * 1000 + GLOBAL_UPPER_LEFT_ROW
col_coord = grid_col_index * grid_resolution * 1000 + GLOBAL_UPPER_LEFT_COL
lng_coord, lat_coord, _ = coord_transform.TransformPoint(
col_coord, row_coord)
write_to_file(','.join(split_line[0:2]) + ',%d-%d,' % (grid_row_index, grid_col_index) + ','.join(split_line[3:11]) +',%f,%f,' % (lat_coord, lng_coord)+','.join(split_line[13:]))
for (_, header), band, ds, nodata in zip(clipped_raster_list, band_list, dataset_list, nodata_list):
gt = ds.GetGeoTransform()
n_rows = ds.RasterYSize
n_cols = ds.RasterXSize
xoff = int(grid_col_index * (grid_resolution * 1000.0) / (gt[1]))
yoff = int(grid_row_index * (grid_resolution * 1000.0) / (-gt[5]))
win_xsize = int((grid_resolution * 1000.0) / (gt[1]))
win_ysize = int((grid_resolution * 1000.0) / (gt[1]))
if xoff + win_xsize > n_cols:
win_xsize = n_cols - xoff
if yoff + win_ysize > n_rows:
win_ysize = n_rows - yoff
block = band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize, win_ysize=win_ysize)
block_average = numpy.average(block[block != nodata])
write_to_file(',%f' % block_average)
write_to_file('\n')
import os
import numpy
import gdal
from invest_natcap import raster_utils
anthrome_uri = "C:/Users/rich/Desktop/average_layers_projected/gl_anthrome.tif"
anthrome_ds = gdal.Open(anthrome_uri)
anthrome_band = anthrome_ds.GetRasterBand(1)
anthrome_array = anthrome_band.ReadAsArray()
unique_anthrome_ids = numpy.unique(anthrome_array)
for anthrome_id in unique_anthrome_ids:
output_uri = os.path.join(os.path.dirname(anthrome_uri), 'anthrome_%d.tif' % anthrome_id)
out_pixel_size = raster_utils.get_cell_size_from_uri(anthrome_uri)
raster_utils.vectorize_datasets(
[anthrome_uri], lambda x: x==anthrome_id, output_uri, gdal.GDT_Byte,
127, out_pixel_size, "union", vectorize_op=False)
print numpy.unique(anthrome_array).shape
def process_ecoregion(prefix):
ecoregion_shapefile_uri = os.path.join(
DATA_DIR, 'ecoregions', 'ecoregions_projected.shp')
ecoregion_lookup = raster_utils.extract_datasource_table_by_key(
ecoregion_shapefile_uri, 'ECO_ID_U')
ecoregion_nodata = -1
ecoregion_lookup[ecoregion_nodata] = {
'ECO_NAME': 'UNKNOWN',
'ECODE_NAME': 'UNKNOWN',
'WWF_MHTNAM': 'UNKNOWN',
}
lulc_raw_uri = os.path.join(DATA_DIR, '%s%s' % (prefix, LULC_BASE))
biomass_raw_uri = os.path.join(DATA_DIR, '%s%s' % (prefix, BIOMASS_BASE))
cell_size = raster_utils.get_cell_size_from_uri(lulc_raw_uri)
lulc_uri = os.path.join(OUTPUT_DIR, "%s_lulc_aligned.tif" % (prefix))
biomass_uri = os.path.join(OUTPUT_DIR, "%s_biomass_aligned.tif" % (prefix))
raster_utils.align_dataset_list(
[lulc_raw_uri, biomass_raw_uri], [lulc_uri, biomass_uri], ['nearest']*2,
cell_size, 'intersection', 0, dataset_to_bound_index=None,
aoi_uri=None, assert_datasets_projected=True, process_pool=None)
#create ecoregion id
ecoregion_dataset_uri = os.path.join(
OUTPUT_DIR, "%s_ecoregion_id.tif" % (prefix))
raster_utils.new_raster_from_base_uri(
lulc_uri, ecoregion_dataset_uri, 'GTiff', ecoregion_nodata, gdal.GDT_Int16)
raster_utils.rasterize_layer_uri(
ecoregion_dataset_uri, ecoregion_shapefile_uri,
option_list=["ATTRIBUTE=ECO_ID_U"])
lulc_nodata = raster_utils.get_nodata_from_uri(lulc_uri)
forest_lulc_codes = [1, 2, 3, 4, 5]
mask_uri = os.path.join(OUTPUT_DIR, "%s_mask.tif" % prefix)
mask_nodata = 2
def mask_nonforest(lulc):
mask = numpy.empty(lulc.shape, dtype=numpy.int8)
mask[:] = 1
for lulc_code in forest_lulc_codes:
mask[lulc == lulc_code] = 0
mask[lulc == lulc_nodata] = mask_nodata
return mask
raster_utils.vectorize_datasets(
[lulc_uri,], mask_nonforest, mask_uri, gdal.GDT_Byte,
mask_nodata, cell_size, 'intersection', dataset_to_align_index=0,
dataset_to_bound_index=None, aoi_uri=None,
assert_datasets_projected=True, process_pool=None, vectorize_op=False,
datasets_are_pre_aligned=True)
forest_edge_distance_uri = os.path.join(OUTPUT_DIR, "%s_forest_edge.tif" % prefix)
raster_utils.distance_transform_edt(mask_uri, forest_edge_distance_uri)
biomass_stats_uri = os.path.join(OUTPUT_DIR, "%s_biomass_stats.csv" % prefix)
_aggregate_results(forest_edge_distance_uri, biomass_uri, ecoregion_dataset_uri, ecoregion_lookup, biomass_stats_uri)
def _aggregate_results(forest_edge_distance_uri, biomass_uri, ecoregion_dataset_uri, ecoregion_lookup, biomass_stats_uri):
cell_size = raster_utils.get_cell_size_from_uri(forest_edge_distance_uri)
forest_edge_nodata = raster_utils.get_nodata_from_uri(forest_edge_distance_uri)
biomass_nodata = raster_utils.get_nodata_from_uri(biomass_uri)
outfile = open(biomass_stats_uri, 'w')
ecoregion_dataset = gdal.Open(ecoregion_dataset_uri)
ecoregion_band = ecoregion_dataset.GetRasterBand(1)
biomass_ds = gdal.Open(biomass_uri, gdal.GA_ReadOnly)
biomass_band = biomass_ds.GetRasterBand(1)
forest_edge_distance_ds = gdal.Open(forest_edge_distance_uri)
forest_edge_distance_band = forest_edge_distance_ds.GetRasterBand(1)
n_rows, n_cols = raster_utils.get_row_col_from_uri(biomass_uri)
base_srs = osr.SpatialReference(biomass_ds.GetProjection())
lat_lng_srs = base_srs.CloneGeogCS()
coord_transform = osr.CoordinateTransformation(
base_srs, lat_lng_srs)
gt = biomass_ds.GetGeoTransform()
grid_resolution_list = [25, 50, 100, 150, 200, 300, 400, 500]
grid_coordinates = dict((resolution, {}) for resolution in grid_resolution_list)
block_col_size, block_row_size = biomass_band.GetBlockSize()
n_global_block_rows = int(math.ceil(float(n_rows) / block_row_size))
n_global_block_cols = int(math.ceil(float(n_cols) / block_col_size))
last_time = time.time()
for global_block_row in xrange(n_global_block_rows):
current_time = time.time()
if current_time - last_time > 5.0:
print "aggregation %.1f%% complete" % (global_block_row / float(n_global_block_rows) * 100)
last_time = current_time
for global_block_col in xrange(n_global_block_cols):
xoff = global_block_col * block_col_size
yoff = global_block_row * block_row_size
win_xsize = min(block_col_size, n_cols - xoff)
win_ysize = min(block_row_size, n_rows - yoff)
biomass_block = biomass_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize, win_ysize=win_ysize)
forest_edge_distance_block = forest_edge_distance_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize, win_ysize=win_ysize)
ecoregion_id_block = ecoregion_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize, win_ysize=win_ysize)
for global_row in xrange(global_block_row*block_row_size, min((global_block_row+1)*block_row_size, n_rows)):
for global_col in xrange(global_block_col*block_col_size, min((global_block_col+1)*block_col_size, n_cols)):
row_coord = gt[3] + global_row * gt[5]
col_coord = gt[0] + global_col * gt[1]
local_row = global_row - global_block_row * block_row_size
local_col = global_col - global_block_col * block_col_size
lng_coord, lat_coord, _ = coord_transform.TransformPoint(
col_coord, row_coord)
#normalize the coordinates so they don't go negative
global_grid_row = row_coord - GLOBAL_UPPER_LEFT_ROW
global_grid_col = col_coord - GLOBAL_UPPER_LEFT_COL
ecoregion_id = ecoregion_id_block[local_row, local_col]
if (forest_edge_distance_block[local_row, local_col] != forest_edge_nodata and
forest_edge_distance_block[local_row, local_col] > 0.0 and
biomass_block[local_row, local_col] != biomass_nodata):
outfile.write("%f;%f;%f;%f;%s;%s;%s" % (
forest_edge_distance_block[local_row, local_col] * cell_size,
biomass_block[local_row, local_col], lat_coord, lng_coord,
ecoregion_lookup[ecoregion_id]['ECO_NAME'],
ecoregion_lookup[ecoregion_id]['ECODE_NAME'],
ecoregion_lookup[ecoregion_id]['WWF_MHTNAM']))
outfile.write(";%f;%f" % (global_grid_row, global_grid_col))
for global_grid_resolution in grid_resolution_list:
#output a grid coordinate in the form 'grid_row-grid_col'
grid_row = int(global_grid_row/(global_grid_resolution*1000))
grid_col = int(global_grid_col/(global_grid_resolution*1000))
grid_id = str(grid_row) + '-' + str(grid_col)
outfile.write(";%s" % grid_id)
if grid_id not in grid_coordinates[global_grid_resolution]:
grid_row_center = grid_row * global_grid_resolution*1000 + GLOBAL_UPPER_LEFT_ROW
grid_col_center = grid_col * global_grid_resolution*1000 + GLOBAL_UPPER_LEFT_COL
grid_lng_coord, grid_lat_coord, _ = coord_transform.TransformPoint(
grid_col_center, grid_row_center)
grid_coordinates[global_grid_resolution][grid_id] = (grid_lat_coord, grid_lng_coord)
print grid_lat_coord, grid_lng_coord
outfile.write('/n')
outfile.close()
for global_grid_resolution in grid_resolution_list:
output_dir, base_filename = os.path.split(biomass_stats_uri)
basename = os.path.basename(base_filename)
grid_output_file = open(os.path.join(output_dir, basename + '_' + str(global_grid_resolution) + '.csv'), 'w')
grid_output_file.write('grid id;lat_coord;lng_coord/n')
open(biomass_stats_uri, 'w')
for grid_id, (lat, lng) in grid_coordinates[global_grid_resolution].iteritems():
grid_output_file.write('%s;%s;%s/n' % (grid_id, lat, lng))
grid_output_file.close()
def run(self):
ecoregion_lookup = raster_utils.extract_datasource_table_by_key(
ECOREGION_SHAPEFILE_URI, 'ECO_ID_U')
ecoregion_nodata = -1
ecoregion_lookup[ecoregion_nodata] = {
'ECO_NAME': 'UNKNOWN',
'ECODE_NAME': 'UNKNOWN',
'WWF_MHTNAM': 'UNKNOWN',
}
cell_size = raster_utils.get_cell_size_from_uri(
FOREST_EDGE_DISTANCE_URI)
forest_edge_nodata = raster_utils.get_nodata_from_uri(
FOREST_EDGE_DISTANCE_URI)
biomass_nodata = raster_utils.get_nodata_from_uri(GLOBAL_BIOMASS_URI)
outfile = open(BIOMASS_STATS_URI, 'w')
ecoregion_dataset = gdal.Open(ECOREGION_DATASET_URI)
ecoregion_band = ecoregion_dataset.GetRasterBand(1)
biomass_ds = gdal.Open(GLOBAL_BIOMASS_URI, gdal.GA_ReadOnly)
biomass_band = biomass_ds.GetRasterBand(1)
forest_edge_distance_ds = gdal.Open(FOREST_EDGE_DISTANCE_URI)
forest_edge_distance_band = forest_edge_distance_ds.GetRasterBand(1)
n_rows, n_cols = raster_utils.get_row_col_from_uri(GLOBAL_BIOMASS_URI)
base_srs = osr.SpatialReference(biomass_ds.GetProjection())
lat_lng_srs = base_srs.CloneGeogCS()
coord_transform = osr.CoordinateTransformation(
base_srs, lat_lng_srs)
geo_trans = biomass_ds.GetGeoTransform()
block_col_size, block_row_size = biomass_band.GetBlockSize()
n_global_block_rows = int(math.ceil(float(n_rows) / block_row_size))
n_global_block_cols = int(math.ceil(float(n_cols) / block_col_size))
last_time = time.time()
for global_block_row in xrange(n_global_block_rows):
current_time = time.time()
if current_time - last_time > 5.0:
print (
"aggregation %.1f%% complete" %
(global_block_row / float(n_global_block_rows) * 100))
last_time = current_time
for global_block_col in xrange(n_global_block_cols):
xoff = global_block_col * block_col_size
yoff = global_block_row * block_row_size
win_xsize = min(block_col_size, n_cols - xoff)
win_ysize = min(block_row_size, n_rows - yoff)
biomass_block = biomass_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize)
forest_edge_distance_block = (
forest_edge_distance_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize))
ecoregion_id_block = ecoregion_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize)
for global_row in xrange(
global_block_row*block_row_size,
min((global_block_row+1)*block_row_size, n_rows)):
for global_col in xrange(
global_block_col*block_col_size,
min((global_block_col+1)*block_col_size, n_cols)):
row_coord = (
geo_trans[3] + global_row * geo_trans[5])
col_coord = (
geo_trans[0] + global_col * geo_trans[1])
local_row = (
global_row - global_block_row * block_row_size)
local_col = (
global_col - global_block_col * block_col_size)
lng_coord, lat_coord, _ = (
coord_transform.TransformPoint(
col_coord, row_coord))
ecoregion_id = ecoregion_id_block[local_row, local_col]
if (forest_edge_distance_block[local_row, local_col] !=
forest_edge_nodata and
forest_edge_distance_block
[local_row, local_col] > 0.0 and
biomass_block
[local_row, local_col] != biomass_nodata):
outfile.write("%f;%f;%f;%f;%s;%s;%s" % (
forest_edge_distance_block
[local_row, local_col] * cell_size,
biomass_block[local_row, local_col],
lat_coord, lng_coord,
ecoregion_lookup[ecoregion_id]['ECO_NAME'],
ecoregion_lookup[ecoregion_id]['ECODE_NAME'],
ecoregion_lookup[ecoregion_id]['WWF_MHTNAM']))
for global_grid_resolution in GRID_RESOLUTION_LIST:
#output a grid coordinate in the form
#'grid_row-grid_col'
grid_row = (
int((geo_trans[3] - row_coord) /
(global_grid_resolution*1000)))
grid_col = (
int((col_coord - geo_trans[0]) /
(global_grid_resolution*1000)))
grid_id = str(grid_row) + '-' + str(grid_col)
outfile.write(";%s" % grid_id)
outfile.write('\n')
outfile.close()
