def box_idx(self, lon, lat):
""" return lat/lon index slices of the smallest containing box
Designed to read from netCDF
input:
lon, lat: 1-D arrays (must be a regular grid)
output:
ii, jj: slice
"""
lon = rectify_longitude(lon, lon0=self.lon0)
lon_llcorner, lat_llcorner, lon_urcorner, lat_urcorner = self.coords
ii = (lat >= lat_llcorner) & (lat <= lat_urcorner)
jj = (lon >= lon_llcorner) & (lon <= lon_urcorner)
# find indices
ii, jj = np.where(ii)[0], np.where(jj)[0]
# make it a slice
ii = slice(ii[0], ii[-1]+1)
jj = slice(jj[0], jj[-1]+1)
return ii, jj
def box_idx(self, lon, lat):
""" return lat/lon index slices of the smallest containing box
Designed to read from netCDF
input:
lon, lat: 1-D arrays (must be a regular grid)
output:
ii, jj: slice
"""
lon = rectify_longitude(lon, lon0=self.lon0)
lon_llcorner, lat_llcorner, lon_urcorner, lat_urcorner = self.coords
ii = (lat >= lat_llcorner) & (lat <= lat_urcorner)
jj = (lon >= lon_llcorner) & (lon <= lon_urcorner)
# find indices
ii, jj = np.where(ii)[0], np.where(jj)[0]
# make it a slice
ii = slice(ii[0], ii[-1] + 1)
jj = slice(jj[0], jj[-1] + 1)
return ii, jj
def extract(self, lon, lat, data):
""" extract data from the region
"""
if hasattr(self, 'lon0'):
lon = rectify_longitude(lon, lon0 = self.lon0)
ii, jj = self.box_idx(lon, lat) # for a rectangular box, box_idx exactly contains the region
return lon[jj], lat[ii], data[ii, jj]
def mean(self, lon, lat, data):
""" return average data over the region (weighted mean)
input:
lon, lat, data
returns:
regional mean of data
"""
if np.ndim(lon) == 2:
raise Exception('must be a regular grid !')
if hasattr(self, 'lon0'):
lon = rectify_longitude(lon, lon0=self.lon0)
lonr, latr, datar = self.extract(lon, lat,
data) # extract regional data
LO, LA = np.meshgrid(lonr, latr)
w = np.cos(np.radians(LA)) # weights
if isinstance(datar, np.ma.MaskedArray):
datar = datar.filled(np.nan)
w[np.isnan(datar)] = np.nan
return np.nansum(datar * w) / np.nansum(w) # weighted mean
def extract(self, lon, lat, data):
""" extract data from the region
"""
if hasattr(self, 'lon0'):
lon = rectify_longitude(lon, lon0=self.lon0)
ii, jj = self.box_idx(
lon,
lat) # for a rectangular box, box_idx exactly contains the region
return lon[jj], lat[ii], data[ii, jj]
def plot(self, *args, **kwargs):
""" plot a region
same arguments as plot, except...
lon0: if provided, adjust the data to have the right alignment...
"""
import matplotlib.pyplot as plt
lons, lats = self.contour()
if 'lon0' in kwargs:
lons = rectify_longitude(lons, lon0 = kwargs.pop('lon0'))
return plt.plot(lons, lats, *args, **kwargs)
def plot(self, *args, **kwargs):
""" plot a region
same arguments as plot, except...
lon0: if provided, adjust the data to have the right alignment...
"""
import matplotlib.pyplot as plt
lons, lats = self.contour()
if 'lon0' in kwargs:
lons = rectify_longitude(lons, lon0=kwargs.pop('lon0'))
return plt.plot(lons, lats, *args, **kwargs)
def contains(self, lon, lat):
""" return True if x, y is in the Polygon
"""
if np.size(lon) > 1:
#return np.array([self.contains(xx, yy) for xx, yy in zip(lon, lat)])
return self.contains_arrays(lon, lat)
from shapely.geometry import Point
poly = self.to_shapely()
lon = rectify_longitude(lon, lon0=self.lon0, sort=False)
res = poly.contains(Point(lon, lat))
assert res is not None
return res
def contains(self, lon, lat):
""" return True if x, y is in the Polygon
"""
if np.size(lon) > 1:
#return np.array([self.contains(xx, yy) for xx, yy in zip(lon, lat)])
return self.contains_arrays(lon, lat)
from shapely.geometry import Point
poly = self.to_shapely()
lon = rectify_longitude(lon, lon0=self.lon0, sort=False)
res = poly.contains(Point(lon, lat))
assert res is not None
return res
def contains_arrays(self, lon, lat):
""" return an array of same size as lon and lat
lon, lat: arrays of longitude and latitude
"""
from matplotlib.nxutils import points_inside_poly as inpoly
lon = np.asarray(lon)
lat = np.asarray(lat)
lon = rectify_longitude(lon, lon0=self.lon0, sort=False)
#if lon.ndim == 1:
# lon, lat = np.meshgrid(lon, lat)
xypoints = np.vstack((lon.flatten(), lat.flatten())).T
xyvertices = np.asarray(self.to_shapely().exterior.xy).T # contours of the polynom
mask = inpoly(xypoints, xyvertices)
return mask.reshape(lon.shape)
def contains_arrays(self, lon, lat):
""" return an array of same size as lon and lat
lon, lat: arrays of longitude and latitude
"""
from matplotlib.nxutils import points_inside_poly as inpoly
lon = np.asarray(lon)
lat = np.asarray(lat)
lon = rectify_longitude(lon, lon0=self.lon0, sort=False)
#if lon.ndim == 1:
# lon, lat = np.meshgrid(lon, lat)
xypoints = np.vstack((lon.flatten(), lat.flatten())).T
xyvertices = np.asarray(
self.to_shapely().exterior.xy).T # contours of the polynom
mask = inpoly(xypoints, xyvertices)
return mask.reshape(lon.shape)
def mean(self, lon, lat, data):
""" return average data over the region (weighted mean)
input:
lon, lat, data
returns:
regional mean of data
"""
if np.ndim(lon) == 2:
raise Exception('must be a regular grid !')
if hasattr(self, 'lon0'):
lon = rectify_longitude(lon, lon0 = self.lon0)
lonr, latr, datar = self.extract(lon, lat, data) # extract regional data
LO, LA = np.meshgrid(lonr, latr)
w = np.cos(np.radians(LA)) # weights
if isinstance(datar, np.ma.MaskedArray):
datar = datar.filled(np.nan)
w[np.isnan(datar)] = np.nan
return np.nansum(datar*w)/np.nansum(w) # weighted mean
