def extract_box(a, region=None):
""" extract a subregion
a : DimArray instance
region: lon_ll, lat_ll, lon_ur, lat_ur
or Region instance
"""
# interactive drawing of a region
if region is None:
region = regmod.drawbox()
# check the arguments: initialize a BoxRegion if not yet a region
regobj = regmod.check(region)
i, j = regobj.box_idx(a.lon, a.lat)
return a.take({'lat': i, 'lon': j}, indexing='position')
def extract_box(a, region=None):
""" extract a subregion
a : DimArray instance
region: lon_ll, lat_ll, lon_ur, lat_ur
or Region instance
"""
# interactive drawing of a region
if region is None:
region = regmod.drawbox()
# check the arguments: initialize a BoxRegion if not yet a region
regobj = regmod.check(region)
i, j = regobj.box_idx(a.lon, a.lat)
return a.take({'lat':i, 'lon':j}, indexing='position')
def regional_mean(a, region=None):
""" Average along lon/lat
input:
- a: DimArray instance including lat, lon axis
- region, optional:
[lon, lat]: Point
[lon_ll, lat_ll, lon_ur, lat_ur]: BoxRegion
or Region instance
output:
- GeoArray instance, regional average
Notes:
------
Will adjust longitude axis over [0, 360] or [-180, 180] as necessary
Examples:
---------
>>> lon = [0, 50., 100.]
>>> lat = [0, 50.]
>>> a = GeoArray([[1, 2, 4],[4, 5, 6]], lon=lon, lat=lat)
>>> rm = regional_mean(a)
>>> rm
3.3767428905946684
Which just weights data with cos(lat)
>>> lon2, lat2 = np.meshgrid(lon, lat)
>>> w = np.cos(np.radians(lat2))
>>> rm2 = np.sum(a.values*w)/np.sum(w)
>>> round(rm2, 4)
3.3767
GeoArray detects the weights automatically with name "lat"
>>> rm3 = a.mean()
>>> round(rm3,4)
3.3767
>>> a.mean(weights=None)
3.6666666666666665
Also works for multi-dimensional data shaped in any kind of order
>>> a = a.newaxis('z',[0,1,2])
>>> a = a.transpose(('lon','z','lat'))
>>> regional_mean(a)
geoarray: 3 non-null elements (0 null)
dimensions: 'z'
0 / z (3): 0 to 2
array([ 3.37674289, 3.37674289, 3.37674289])
"""
regobj = regmod.check(region)
dims = ('lat', 'lon')
a = GeoArray(a) # make it a GeoArray to check lon, lat
if not set(dims).issubset(a.dims):
raise ValueError("does not have lon, lat axes: {}".format(a))
# rearrange dimensions with dims first
flatdims = [ax.name for ax in a.axes if ax.name not in dims]
newdims = dims + tuple(flatdims)
if a.dims != newdims:
a = a.transpose(newdims)
# If 2-D return a scalar
if a.dims == ('lat', 'lon'):
return regobj.mean(a.lon, a.lat, a.values)
# flatten all except lat, lon, and make it the first axis
agrp = a.group(flatdims, insert=0)
grpaxis = agrp.axes[0] # grouped axis
# iterate over the first, grouped axis
results = []
for i in range(grpaxis.size):
res = regobj.mean(a.lon, a.lat, agrp.values[i])
results.append(res)
# flat object
grp_ave = a._constructor(results, [grpaxis])
average = grp_ave.ungroup() # now ungroup
return average
def regional_mean(a, region=None):
""" Average along lon/lat
input:
- a: DimArray instance including lat, lon axis
- region, optional:
[lon, lat]: Point
[lon_ll, lat_ll, lon_ur, lat_ur]: BoxRegion
or Region instance
output:
- GeoArray instance, regional average
Notes:
------
Will adjust longitude axis over [0, 360] or [-180, 180] as necessary
Examples:
---------
>>> lon = [0, 50., 100.]
>>> lat = [0, 50.]
>>> a = GeoArray([[1, 2, 4],[4, 5, 6]], lon=lon, lat=lat)
>>> rm = regional_mean(a)
>>> rm
3.3767428905946684
Which just weights data with cos(lat)
>>> lon2, lat2 = np.meshgrid(lon, lat)
>>> w = np.cos(np.radians(lat2))
>>> rm2 = np.sum(a.values*w)/np.sum(w)
>>> round(rm2, 4)
3.3767
GeoArray detects the weights automatically with name "lat"
>>> rm3 = a.mean()
>>> round(rm3,4)
3.3767
>>> a.mean(weights=None)
3.6666666666666665
Also works for multi-dimensional data shaped in any kind of order
>>> a = a.newaxis('z',[0,1,2])
>>> a = a.transpose(('lon','z','lat'))
>>> regional_mean(a)
geoarray: 3 non-null elements (0 null)
dimensions: 'z'
0 / z (3): 0 to 2
array([ 3.37674289, 3.37674289, 3.37674289])
"""
regobj = regmod.check(region)
dims = ('lat','lon')
a = GeoArray(a) # make it a GeoArray to check lon, lat
if not set(dims).issubset(a.dims):
raise ValueError("does not have lon, lat axes: {}".format(a))
# rearrange dimensions with dims first
flatdims = [ax.name for ax in a.axes if ax.name not in dims]
newdims = dims + tuple(flatdims)
if a.dims != newdims:
a = a.transpose(newdims)
# If 2-D return a scalar
if a.dims == ('lat', 'lon'):
return regobj.mean(a.lon,a.lat, a.values)
# flatten all except lat, lon, and make it the first axis
agrp = a.group(flatdims, insert=0)
grpaxis = agrp.axes[0] # grouped axis
# iterate over the first, grouped axis
results = []
for i in range(grpaxis.size):
res = regobj.mean(a.lon,a.lat, agrp.values[i])
results.append(res)
# flat object
grp_ave = a._constructor(results, [grpaxis])
average = grp_ave.ungroup() # now ungroup
return average
