def equal_vectors(data,region,spacing):
"""
Take an FVCOM data dictionary, a region dictionary and a spacing in meters.
Returns: The element idx that best approximates the given spacing in the region.
"""
centerele=np.argsort((data['uvnodell'][:,1]-(region['region'][3]+region['region'][2])/2)**2+(data['uvnodell'][:,0]-(region['region'][1]+region['region'][0])/2)**2)
xhalf=0.75*np.fabs(region['region'][1]-region['region'][0])*112200
yhalf=0.75*np.fabs(region['region'][3]-region['region'][2])*112200
#xmultiplier=np.floor(np.fabs(xhalf*2)/spacing)
#ymultiplier=np.floor(np.fabs(yhalf*2)/spacing)
XI=np.arange((data['uvnode'][centerele[1],0]-xhalf),(data['uvnode'][centerele[1],0]+xhalf),spacing)
YI=np.arange((data['uvnode'][centerele[1],1]-yhalf),(data['uvnode'][centerele[1],1]+yhalf),spacing)
xv,yv=np.meshgrid(XI,YI)
xytrigrid = mplt.Triangulation(data['x'], data['y'],data['nv'])
host=xytrigrid.get_trifinder().__call__(xv.reshape(-1,1),yv.reshape(-1,1))
idx=dt.get_elements(data,region)
common=np.in1d(host,idx)
return np.unique(host[common].flatten())
def smallestregion(data,host):
minarea=10000000000
for regionname in regions():
region=regions(regionname)
eidx=dt.get_elements(data,region)
if ((np.sum(np.in1d(host,eidx))==len(host)) and (regionarea(region)<minarea)):
minarea=regionarea(region)
bestregion=regionname
return bestregion
def smallestregion(data, host):
minarea = 10000000000
for regionname in regions():
region = regions(regionname)
eidx = dt.get_elements(data, region)
if ((np.sum(np.in1d(host, eidx)) == len(host))
and (regionarea(region) < minarea)):
minarea = regionarea(region)
bestregion = regionname
return bestregion