def project_array(self, data):
"""Project an array *data* along the given Projector object.
"""
if self.mode == "nearest":
if not 'valid_index' in self._cache:
self._cache['valid_index'] = self._file_cache['valid_index']
self._cache['valid_output_index'] = \
self._file_cache['valid_output_index']
self._cache['index_array'] = self._file_cache['index_array']
valid_index, valid_output_index, index_array = \
(self._cache['valid_index'],
self._cache['valid_output_index'],
self._cache['index_array'])
res = kd_tree.get_sample_from_neighbour_info('nn',
self.out_area.shape,
data,
valid_index,
valid_output_index,
index_array,
fill_value=None)
elif self.mode == "quick":
if not 'row_idx' in self._cache:
self._cache['row_idx'] = self._file_cache['row_idx']
self._cache['col_idx'] = self._file_cache['col_idx']
row_idx, col_idx = self._cache['row_idx'], self._cache['col_idx']
img = image.ImageContainer(data, self.in_area, fill_value=None)
res = np.ma.array(img.get_array_from_linesample(row_idx, col_idx),
dtype=data.dtype)
return res
def _project_array_quick(self, data):
"""Project array *data* using quick interpolation"""
if 'row_idx' not in self._cache:
self._cache['row_idx'] = self._file_cache['row_idx']
self._cache['col_idx'] = self._file_cache['col_idx']
row_idx, col_idx = self._cache['row_idx'], self._cache['col_idx']
img = image.ImageContainer(data, self.in_area, fill_value=None)
res = np.ma.array(img.get_array_from_linesample(row_idx, col_idx),
dtype=data.dtype)
return res
def test_nearest_neighbour_multi_preproc(self):
data1 = numpy.fromfunction(lambda y, x: y * x * 10 ** -6, (3712, 3712))
data2 = numpy.fromfunction(
lambda y, x: y * x * 10 ** -6, (3712, 3712)) * 2
data = numpy.dstack((data1, data2))
msg_con = image.ImageContainer(data, self.msg_area)
#area_con = msg_con.resample_area_nearest_neighbour(self.area_def, 50000)
row_indices, col_indices = \
utils.generate_nearest_neighbour_linesample_arrays(self.msg_area,
self.area_def,
50000)
res = msg_con.get_array_from_linesample(row_indices, col_indices)
cross_sum1 = res[:, :, 0].sum()
expected1 = 399936.783062
self.assertAlmostEqual(cross_sum1, expected1,
msg='ImageContainer resampling nearest neighbour multi preproc failed')
cross_sum2 = res[:, :, 1].sum()
expected2 = 399936.783062 * 2
self.assertAlmostEqual(cross_sum2, expected2,
msg='ImageContainer resampling nearest neighbour multi preproc failed')
def pyresample_method(self, ref_grid_lim, grid_lim, data):
"""
Method which uses the library "pyresample" which is must faster
"""
# FIXME: Understand how Proj_Dict influences the result
X_MAX, Y_MAX = ref_grid_lim
x_max, y_max = grid_lim
N, M = data.shape
proj_dict = {
'a': '6371228.0',
'units': 'm',
'lon_0': '0',
'proj': 'laea',
'lat_0': '-90'
}
ref_area = geometry.AreaDefinition(
area_id='REF',
name='reference',
proj_id='A',
proj_dict=proj_dict,
x_size=N,
y_size=M,
area_extent=[-X_MAX, -Y_MAX, X_MAX, Y_MAX])
init_area = geometry.AreaDefinition(
area_id='INIT',
name='initial',
proj_id='A',
proj_dict=proj_dict,
x_size=N,
y_size=M,
area_extent=[-x_max, -y_max, x_max, y_max])
base = image.ImageContainer(data, init_area)
row_indices, col_indices = utils.generate_quick_linesample_arrays(
init_area, ref_area)
result = base.get_array_from_linesample(row_indices, col_indices)
return result
def readE_P():
x_size = 251
y_size = 251
description = 'Arctic EASE grid'
proj_id = 'ease_nh'
from pyresample import geometry, utils, image
area_id = 'ease_nh'
area_extent = (-7326849.0625,-7326849.0625,7326849.0625,7326849.0625)
proj_dict = {'a': '6371228.0', 'units': 'm', 'lon_0': '0', \
'proj': 'laea', 'lat_0': '90'}
area_def = geometry.AreaDefinition(area_id, description, proj_id, \
proj_dict, x_size, y_size, area_extent)
e_p=np.zeros((20,241,480),float)
nc=Dataset('evap_precip201411.nc','r')
t=nc.variables['time'][:][20*4+2:30*4+2]
t0=nc.variables['time'][:][0:1]
e=nc.variables['e'][21*4+2:31*4+2,::-1,:]
p=nc.variables['tp'][21*4+2:31*4+2,::-1,:]
for i in range(1,e.shape[0],2):
e[i,:,:]=e[i,:,:]-e[i-1,:,:]
p[i,:,:]=p[i,:,:]-p[i-1,:,:]
print datetime.datetime(1900,1,1)+datetime.timedelta(hours=int(t[0]))
print datetime.datetime(1900,1,1)+datetime.timedelta(hours=int(t0[0]))
print datetime.datetime(1900,1,1)+datetime.timedelta(hours=int(t[-1]))
e_p=(e+p)
lon,lat=np.meshgrid(0+np.arange(480)*0.75,-90+np.arange(241)*0.75)
lon[lon>180]-=360.
grid_def = geometry.GridDefinition(lons=lon, lats=lat)
row_indices, \
col_indices = \
utils.generate_nearest_neighbour_linesample_arrays(grid_def, area_def, 200000)
msg_con = image.ImageContainer(e_p.sum(axis=0), grid_def)
e_p_grid = msg_con.get_array_from_linesample(row_indices, col_indices)
return e_p_grid
area_id = 'ease_nh'
area_extent = (-7326849.0625, -7326849.0625, 7326849.0625, 7326849.0625)
proj_dict = {'a': '6371228.0', 'units': 'm', 'lon_0': '0', \
'proj': 'laea', 'lat_0': '90'}
area_def = geometry.AreaDefinition(area_id, description, proj_id, \
proj_dict, x_size, y_size, area_extent)
import numpy as np
lon, lat = np.meshgrid(-180 + 0.75 / 2 + np.arange(480) * 0.75,
-90 + np.arange(241) * 0.75)
grid_def = geometry.GridDefinition(lons=lon, lats=lat)
row_indices, \
col_indices = \
utils.generate_nearest_neighbour_linesample_arrays(grid_def, area_def, 200000)
msg_con = image.ImageContainer(e_p.sum(axis=0), grid_def)
e_p_grid = msg_con.get_array_from_linesample(row_indices, col_indices)
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'font.size': 13})
m = Basemap(projection='npstere', boundinglat=20, lon_0=0, resolution='l')
plt.suptitle('Moisture Transport from Lagrangian Analysis', fontsize=14)
plt.subplot(111)
m.drawcoastlines()
m.drawparallels(np.arange(-80., 81., 20.), labels=[True, False, False, False])
m.drawmeridians(np.arange(-180., 181., 20.), labels=[False, False, True, True])
xx, yy = area_def.get_lonlats()
x2, y2 = m(xx, yy)
