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()