def create_climate_covariates(self, climate_dir, start_year, end_year,
key_min, key_max, key_pcp, out_dir):
abstemp = 273.15
year_list = cc.getYearList(start_year, end_year)
climate_covariate = cc.ClimaticCovariates(year_list, climate_dir)
ref_raster = climate_covariate.ref_raster
out_raster = Raster()
crops_id = []
crops = []
with self.conn as cur:
rows = cur.execute("select * from crop").fetchall()
if rows:
print('Start to generate climate covariates...')
for row in rows:
crops_id.append(row['id'])
crops.append(row['crop'])
for crop_id, crop in zip(crops_id, crops):
print(crop)
with self.conn as cur:
covariates = cur.execute(
"select * from covariate_threshold where crop_id=?",
(crop_id, )).fetchall()
if covariates:
for covariate in covariates:
with self.conn as cur:
covariate_name = cur.execute(
"select covariate from Covariate where id=?",
(covariate['covariate_id'],
)).fetchone()['covariate']
print('Generate {} at {} ...'.format(
covariate_name, dt.datetime.now()))
if covariate['t_below'] is not None:
try:
t_below = float(covariate['t_below']) + abstemp
except ValueError:
t_below = None
else:
t_below = None
if covariate['t_above'] is not None:
try:
t_above = float(covariate['t_above']) + abstemp
except ValueError:
t_above = None
else:
t_above = None
if covariate['pcp_above'] is not None:
try:
pcp_above = float(covariate['pcp_above'])
except ValueError:
pcp_above = None
else:
pcp_above = None
if covariate['pcp_below'] is not None:
try:
pcp_below = float(covariate['pcp_below'])
except ValueError:
pcp_below = None
else:
pcp_below = None
out_sub_dir = join(out_dir, crop)
if not os.path.exists(out_sub_dir):
os.makedirs(out_sub_dir, exist_ok=True)
covariate_array = cc.generate(
covariate['covariate_id'], year_list, climate_dir,
covariate['start_date'], covariate['end_date'],
key_min, key_max, key_pcp, t_below, t_above,
pcp_above, pcp_below, out_sub_dir, crop_id,
covariate['covariate_id'], ref_raster)
try:
if covariate_array is not None:
out_raster_file = join(
out_sub_dir, '{}_{}_{}_{}.tif'.format(
crop_id, covariate['covariate_id'],
start_year, end_year))
out_raster.array2Raster(
covariate_array, ref_raster,
out_raster_file)
except:
pass
def mapping(self, crop_id, covariate_rasters, conn, covariate_root_dir,
suit_root_dir):
raster = Raster()
self.no_data = raster.getNoDataValue(covariate_rasters[-1])
ref_rst = covariate_rasters[-1]
covariate_id_list = []
suit_array_stack = []
filepath = strip_end(ref_rst, ref_rst.split('\\')[-1])
out_dir = join(
suit_root_dir,
strip_start(filepath, covariate_root_dir)[1:]
) # the [1:] is for removing the first and the last '\' of string
if not os.path.exists(out_dir):
os.makedirs(out_dir, exist_ok=True)
for rst in covariate_rasters:
filename = rst.split('\\')[-1].split('.')[0]
if len(filename.split('_')) == 1:
covariate_id = filename
covariate_id_list.append(covariate_id)
out_raster = join(
out_dir,
'suitability_{}_{}.tif'.format(crop_id, covariate_id))
else:
covariate_id = filename.split('_')[1]
covariate_id_list.append(covariate_id)
time_span = '{}_{}'.format(
filename.split('_')[-2],
filename.split('_')[-1])
out_raster = join(
out_dir,
'suitability_{}_{}_{}.tif'.format(crop_id, covariate_id,
time_span))
with conn as cur:
rows = cur.execute(
"select * from suitability_rule where crop_id=? and covariate_id=? order by suitability_level",
(
crop_id,
covariate_id,
)).fetchall()
is_continual = cur.execute(
"select * from Covariate where id=?",
(covariate_id, )).fetchone()['iscontinual']
# If the query returns none then move to the next covariate
if rows:
covariate_array = raster.getRasterArray(
rst) # array of the covariate
if is_continual == 1:
suit_array = self.__reclassify_contianual__(
covariate_array, rows)
else:
suit_array = self.__reclassify_catorgorical__(
covariate_array, rows)
suit_array[np.where(
covariate_array == self.no_data)] = self.no_data
raster.array2Raster(suit_array, ref_rst, out_raster)
suit_array_stack.append(suit_array)
else:
print(
'Warning! Suitability ruleset for {}, {} not found! Please check the database.'
.format(crop_id, covariate_id))
if len(suit_array_stack) > 0:
crop_suit_array = ExtractMaxValueOfStack(suit_array_stack)
ref_array = homogenize_nodata_area(suit_array_stack, self.no_data)
crop_suit_array[np.where(ref_array == self.no_data)] = self.no_data
crop_suit_raster = join(out_dir,
'{}_suitability.tif'.format(crop_id))
raster.array2Raster(crop_suit_array, ref_rst, crop_suit_raster)
print('create dominant worst covariate raster at {}...'.format(
dt.datetime.now()))
worst_dominant_array, worst_count_array, worst_dominant_legend_list = ExtractMaxValueIndexBinaryOfStack(
suit_array_stack, covariate_id_list, 4)
worst_dominant_array[np.where(
ref_array == self.no_data)] = self.no_data
worst_dominant_raster_file = join(
out_dir, '{}_worst_dominant.tif'.format(crop_id))
raster.array2Raster(worst_dominant_array, ref_rst,
worst_dominant_raster_file)
worst_count_array[np.where(
ref_array == self.no_data)] = self.no_data
worst_count_raster_file = join(
out_dir, '{}_worst_count.tif'.format(crop_id))
raster.array2Raster(worst_count_array, ref_rst,
worst_count_raster_file)
worst_dominant_legend_csv = join(
out_dir, '{}_worst_dominant_legend.csv'.format(crop_id))
csvw = CSVOperation.CSVWriting()
headers = ['raster value', 'number of restriction', 'covariates']
csvw.WriteLines(worst_dominant_legend_csv, headers,
worst_dominant_legend_list)
else:
print('Warning! No suitability map for {} was created!'.format(
crop_id))
