def main(out,maps,ms,keepdtype,ndv,createopts):
# set mapset
set_mapset(ms)
# turn categorical names into dummy names
maps=parse_feature_names(maps)
# make desired output_mask
# slopes above 55 degrees masked and points on top of water or snow masked
make_output_mask('(slope < 55) && (lcc == 2 || lcc == 3)','buffered_basin_border')
# run separately for each map
for m in maps:
dtype = gscript.parse_command('r.info',flags='g',map=m)['datatype']
if dtype == 'CELL':
export_maps([m],out,'Byte',keepdtype,ndv,createopts)
else:
export_maps([m],out,'Float32',keepdtype,ndv,createopts)
# remove group and mask
gscript.run_command('g.remove',
type='group',
name='to_export',
quiet=True,
flags='f')
drop_mask()
def main(mapset,map_name,remove,msk_type):
set_mapset(mapset)
if remove:
drop_mask(msk_type)
else:
set_mask(name,msk_type)
def main(out,mapy,mapx,ms,residname,estimatesname,seed):
mapx = parse_feature_names(mapx)
gscript.run_command('g.mapset',mapset=ms)
# run regression
run_regression(out,mapy,mapx,residname,estimatesname)
# develop mask from residuals map
set_mask(residname)
# calculate sum of squares of residuals and number of observations (will be dividing by too many degrees
# of freedom, but N is so big that the variance of the residuals is a reasonable approximation of the unbiased estimator of
# the variance of the dependent variable.
stats = get_stats(residname)
sigma_hat2 = float(stats['variance'])
# calculate standard error for each parameter estimate in the regression and output to file
se_list = get_param_se(mapx,sigma_hat2)
add_output_lines(out,mapx,se_list)
# drop the mask from this mapset
drop_mask()