How to use the pyinterp.Axis function in pyinterp
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CNES / pangeo-pyinterp / tests / test_binning.py View on Github 
def init(self, dtype):
ds = xr.load_dataset(self.GRID)
x_axis = pyinterp.Axis(np.arange(-180, 180, 5), is_circle=True)
y_axis = pyinterp.Axis(np.arange(-90, 95, 5))
binning = pyinterp.Binning2D(x_axis,
y_axis,
pyinterp.geodetic.System(),
dtype=dtype)
self.assertEqual(x_axis, binning.x)
self.assertEqual(y_axis, binning.y)
self.assertIsInstance(str(binning), str)
lon, lat = np.meshgrid(ds.lon, ds.lat)
binning.push(lon, lat, ds.mss, simple=True)
simple_mean = binning.variable('mean')
self.assertIsInstance(simple_mean, np.ndarray)
binning.clear()
binning.push(lon, lat, ds.mss, simple=False)
linear_mean = binning.variable('mean')def test(self):
grid = pyinterp.backends.xarray.Grid3D(xr.load_dataset(self.GRID).tcw,
increasing_axes=True)
self.assertIsInstance(grid, pyinterp.backends.xarray.Grid3D)
self.assertIsInstance(grid, pyinterp.Grid3D)
other = pickle.loads(pickle.dumps(grid))
self.assertIsInstance(other, pyinterp.backends.xarray.Grid3D)
self.assertIsInstance(grid, pyinterp.Grid3D)
self.assertIsInstance(grid.x, pyinterp.Axis)
self.assertIsInstance(grid.y, pyinterp.Axis)
self.assertIsInstance(grid.z, pyinterp.Axis)
self.assertIsInstance(grid.array, np.ndarray)
lon = np.arange(-180, 180, 1) + 1 / 3.0
lat = np.arange(-90, 90, 1) + 1 / 3.0
time = np.array([datetime.datetime(2002, 7, 2, 15, 0)],
grid.time_unit())
x, y, t = np.meshgrid(lon, lat, time, indexing="ij")
z = grid.trivariate(
collections.OrderedDict(longitude=x.flatten(),
latitude=y.flatten(),
time=t.flatten()))
self.assertIsInstance(z, np.ndarray)
z = grid.bicubic(
collections.OrderedDict(longitude=x.flatten()[1:2],def init(self, dtype):
ds = xr.load_dataset(self.GRID)
x_axis = pyinterp.Axis(np.arange(-180, 180, 5), is_circle=True)
y_axis = pyinterp.Axis(np.arange(-90, 95, 5))
binning = pyinterp.Binning2D(x_axis,
y_axis,
pyinterp.geodetic.System(),
dtype=dtype)
self.assertEqual(x_axis, binning.x)
self.assertEqual(y_axis, binning.y)
self.assertIsInstance(str(binning), str)
lon, lat = np.meshgrid(ds.lon, ds.lat)
binning.push(lon, lat, ds.mss, simple=True)
simple_mean = binning.variable('mean')
self.assertIsInstance(simple_mean, np.ndarray)
binning.clear()
binning.push(lon, lat, ds.mss, simple=False)first_guess='zero',
num_threads=0)
data = np.copy(grid.array)
data[np.isnan(data)] = 0
filled0[np.isnan(filled0)] = 0
filled1[np.isnan(filled1)] = 0
filled2[np.isnan(filled2)] = 0
self.assertEqual((filled0 - filled1).mean(), 0)
self.assertEqual(np.ma.fix_invalid(grid.array - filled1).mean(), 0)
self.assertNotEqual((data - filled1).mean(), 0)
self.assertNotEqual((filled2 - filled1).mean(), 0)
with self.assertRaises(ValueError):
pyinterp.fill.gauss_seidel(grid, '_')
x_axis = pyinterp.Axis(np.linspace(-180, 180, 10), is_circle=True)
y_axis = pyinterp.Axis(np.linspace(-90, 90, 10), is_circle=False)
data = np.random.rand(len(x_axis), len(y_axis))
grid = pyinterp.Grid2D(x_axis, y_axis, data)
_, filled0 = pyinterp.fill.gauss_seidel(grid, num_threads=0)
self.assertIsInstance(filled0, np.ndarray)self.assertNotEqual((z - other).mean(), 0)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True,
boundary="sym")
x_axis = pyinterp.Axis(np.linspace(-180, 179, 360), is_circle=True)
y_axis = pyinterp.Axis(np.linspace(-90, 90, 181), is_circle=False)
z_axis = pyinterp.Axis(np.linspace(0, 10, 10), is_circle=False)
matrix, _ = np.meshgrid(x_axis[:], y_axis[:])
grid = pyinterp.Grid2D(x_axis, y_axis, matrix.T)
self.assertIsInstance(grid, pyinterp.Grid2D)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), fitting_model='_')
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), boundary='_')
grid = pyinterp.Grid2D(x_axis.flip(inplace=False), y_axis, matrix.T)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten())
grid = pyinterp.Grid2D(x_axis, y_axis.flip(), matrix.T)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten())def test_core_class_suffix(self):
lon = pyinterp.Axis(np.arange(0, 360, 1), is_circle=True)
lat = pyinterp.Axis(np.arange(-80, 80, 1), is_circle=False)
for dtype in [
"float64", "float32", "int64", "uint64", "int32", "uint32",
"int16", "uint16", "int8", "uint8"
]:
matrix, _ = np.meshgrid(lon[:], lat[:])
self.assertIsInstance(
pyinterp.Grid2D(lon, lat,
matrix.T.astype(dtype=getattr(np, dtype))),
pyinterp.Grid2D)
with self.assertRaises(ValueError):
pyinterp.Grid2D(lon, lat, matrix.astype(np.complex))def test__core_function_suffix(self):
with self.assertRaises(TypeError):
pyinterp.interface._core_function_suffix(1)
lon = pyinterp.Axis(np.arange(0, 360, 1), is_circle=True)
lat = pyinterp.Axis(np.arange(-80, 80, 1), is_circle=False)
matrix, _ = np.meshgrid(lon[:], lat[:])
self.assertEqual(
pyinterp.interface._core_function_suffix(
pyinterp.core.Grid2DFloat64(lon, lat, matrix.T)), "float64")
self.assertEqual(
pyinterp.interface._core_function_suffix(
pyinterp.core.Grid2DFloat32(lon, lat, matrix.T)), "float32")fitting_model=fitting_model)
self.assertNotEqual((z - other).mean(), 0)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True,
boundary="sym")
x_axis = pyinterp.Axis(np.linspace(-180, 179, 360), is_circle=True)
y_axis = pyinterp.Axis(np.linspace(-90, 90, 181), is_circle=False)
z_axis = pyinterp.Axis(np.linspace(0, 10, 10), is_circle=False)
matrix, _ = np.meshgrid(x_axis[:], y_axis[:])
grid = pyinterp.Grid2D(x_axis, y_axis, matrix.T)
self.assertIsInstance(grid, pyinterp.Grid2D)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), fitting_model='_')
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), boundary='_')
grid = pyinterp.Grid2D(x_axis.flip(inplace=False), y_axis, matrix.T)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten())
grid = pyinterp.Grid2D(x_axis, y_axis.flip(), matrix.T)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten())lat=y.flatten()),
fitting_model=fitting_model)
self.assertNotEqual((z - other).mean(), 0)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True)
with self.assertRaises(ValueError):
grid.bicubic(collections.OrderedDict(lon=x.flatten(),
lat=y.flatten()),
bounds_error=True,
boundary="sym")
x_axis = pyinterp.Axis(np.linspace(-180, 179, 360), is_circle=True)
y_axis = pyinterp.Axis(np.linspace(-90, 90, 181), is_circle=False)
z_axis = pyinterp.Axis(np.linspace(0, 10, 10), is_circle=False)
matrix, _ = np.meshgrid(x_axis[:], y_axis[:])
grid = pyinterp.Grid2D(x_axis, y_axis, matrix.T)
self.assertIsInstance(grid, pyinterp.Grid2D)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), fitting_model='_')
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten(), boundary='_')
grid = pyinterp.Grid2D(x_axis.flip(inplace=False), y_axis, matrix.T)
with self.assertRaises(ValueError):
pyinterp.bicubic(grid, x.flatten(), y.flatten())
grid = pyinterp.Grid2D(x_axis, y_axis.flip(), matrix.T)
with self.assertRaises(ValueError):num_threads=0)
data = np.copy(grid.array)
data[np.isnan(data)] = 0
filled0[np.isnan(filled0)] = 0
filled1[np.isnan(filled1)] = 0
filled2[np.isnan(filled2)] = 0
self.assertEqual((filled0 - filled1).mean(), 0)
self.assertEqual(np.ma.fix_invalid(grid.array - filled1).mean(), 0)
self.assertNotEqual((data - filled1).mean(), 0)
self.assertNotEqual((filled2 - filled1).mean(), 0)
with self.assertRaises(ValueError):
pyinterp.fill.gauss_seidel(grid, '_')
x_axis = pyinterp.Axis(np.linspace(-180, 180, 10), is_circle=True)
y_axis = pyinterp.Axis(np.linspace(-90, 90, 10), is_circle=False)
data = np.random.rand(len(x_axis), len(y_axis))
grid = pyinterp.Grid2D(x_axis, y_axis, data)
_, filled0 = pyinterp.fill.gauss_seidel(grid, num_threads=0)
self.assertIsInstance(filled0, np.ndarray)pyinterp
Interpolation of geo-referenced data for Python.
Package Health Score
72 / 100Popular pyinterp functions
- pyinterp.Axis
- pyinterp.backends.xarray.Grid2D
- pyinterp.bicubic
- pyinterp.Binning2D
- pyinterp.core
- pyinterp.core.Axis
- pyinterp.core.cartesian
- pyinterp.core.geodetic
- pyinterp.core.geodetic.Coordinates
- pyinterp.core.geodetic.Point2D
- pyinterp.core.geodetic.System
- pyinterp.core.trivariate_float64
- pyinterp.fill.gauss_seidel
- pyinterp.fill.loess
- pyinterp.grid.Grid2D
- pyinterp.grid.Grid3D
- pyinterp.Grid2D
- pyinterp.interface._core_function_suffix
- pyinterp.RTree