def main(mapset,map_name,remove,msk_type):
set_mapset(mapset)
if remove:
drop_mask(msk_type)
else:
set_mask(name,msk_type)
def make_output_mask(mapcalc_str,border_vec):
gscript.run_command('v.to.rast',input=border_vec,
output='{}_rast'.format(border_vec),
use='val',
value=1,
overwrite=True)
gscript.mapcalc('output_mask = {}_rast && ({})'.format(border_vec,mapcalc_str),
overwrite=True,
verbose=True)
set_mask('output_mask',maskcats=1)
def start(self):
"""Initialize a new word to start playing."""
if not self.suspended:
return
new_word = utilities.get_new_word(self.lexicon)
if new_word == "":
raise ValueError("no word found!")
self.__word = new_word
self.__valid_chars = utilities.decompose(new_word)
self.mask, symbols = utilities.set_mask(new_word)
self.symbols.extend(symbols)
self.suspended = False
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()
