def test_isclose():
x = np.array([0, np.nan, 1, 1.5])
y = np.array([1e-9, np.nan, 1, 2])
a = da.from_array(x, chunks=(2,))
b = da.from_array(y, chunks=(2,))
assert_eq(da.isclose(a, b, equal_nan=True),
np.isclose(x, y, equal_nan=True))
def test_isclose():
x = np.array([0, np.nan, 1, 1.5])
y = np.array([1e-9, np.nan, 1, 2])
a = from_array(x, chunks=(2,))
b = from_array(y, chunks=(2,))
assert eq(da.isclose(a, b, equal_nan=True),
np.isclose(x, y, equal_nan=True))
def test_isclose():
x = np.array([0, np.nan, 1, 1.5])
y = np.array([1e-9, np.nan, 1, 2])
a = from_array(x, blockshape=(2, ))
b = from_array(y, blockshape=(2, ))
assert eq(da.isclose(a, b, equal_nan=True), np.isclose(x,
y,
equal_nan=True))
def var_equal(*args):
for iarg in args[1:]:
if iarg is args[0]:
continue
if iarg.shape != args[0].shape:
return False
if iarg.dtype.kind == 'f':
if da.any(~da.isclose(iarg._values, args[0]._values)):
return False
else:
if da.any(iarg._values != args[0]._values):
return False
return True
# this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of Intel Corporation nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import dask, time import dask.array as da x = da.random.random((100000, 2000), chunks=(10000, 2000)) t0 = time.time() q, r = da.linalg.qr(x) test = da.all(da.isclose(x, q.dot(r))) test.compute() # compute(get=dask.threaded.get) by default print(time.time() - t0)
case = cases[case_number]
U = U_list[case_number]
a = a_list[case_number]
p0 = p0_list[case_number]
x0 = x0_list[case_number]
## Set paths
filename = f"{pressure_dir}/exp_{case:02.0f}"
figurename = f"{pressure_dir}/fig_exp_{case:02.0f}"
## Compute pressure
print(f"\nComputing pressure field for {case=} ({U=}, {a=}, {p0=}, {x0=})")
p = pressure(xx, yy, tt, t0, U, a, p0, x0).astype(np.float32)
## Remove zero-columns and zero-rows
ix = da.where(~da.all(da.isclose(p, 0), axis=(0, 1)))[0]
iy = da.where(~da.all(da.isclose(p, 0), axis=(0, 2)))[0]
_x = x[ix]
_y = y[iy]
p = p[:, :, ix][:, iy, :]
## Write field
data = fp.convert_to_xarray(t, _x, _y, p.compute())
print(f"Process pressure field for {case=}")
del _x, _y, p
print(f"Writing pressure field for {case=}")
fp.write_pressure(data, filename)
## Write forcing file
print(f"Overwriting forcing file for {case=}")
with open(f"{current_dir}/pressure/forcing_exp_{case:02.0f}.ext",
def _bench(self, get):
q, r = da.linalg.qr(self.x)
test = da.all(da.isclose(self.x, q.dot(r)))
test.compute(get=get)
def _transform_array(image: da.Array,
scale: Tuple[float, ...],
offset: Tuple[float, ...],
shape: Tuple[int, ...],
chunks: Optional[Tuple[int, ...]],
spline_order: int,
recover_nan: bool) -> da.Array:
"""
Apply affine transformation to ND-image.
:param image: ND-image with shape (..., size_y, size_x)
:param scale: Scaling factors (1, ..., 1, sy, sx)
:param offset: Offset values (0, ..., 0, oy, ox)
:param shape: (..., size_y, size_x)
:param chunks: (..., chunk_size_y, chunk_size_x)
:param spline_order: 0 ... 5
:param recover_nan: True/False
:return: Transformed ND-image.
"""
assert_true(len(scale) == image.ndim, 'invalid scale')
assert_true(len(offset) == image.ndim, 'invalid offset')
assert_true(len(shape) == image.ndim, 'invalid shape')
assert_true(chunks is None or len(chunks) == image.ndim,
'invalid chunks')
if _is_no_op(image, scale, offset, shape):
return image
# As of scipy 0.18, matrix = scale is no longer supported.
# Therefore we use the diagonal matrix form here,
# where scale is the diagonal.
matrix = np.diag(scale)
at_kwargs = dict(
offset=offset,
order=spline_order,
output_shape=shape,
output_chunks=chunks,
mode='constant',
)
if recover_nan and spline_order > 0:
# We can "recover" values that are neighbours to NaN values
# that would otherwise become NaN too.
mask = da.isnan(image)
# First check if there are NaN values ar all
if da.any(mask):
# Yes, then
# 1. replace NaN by zero
filled_im = da.where(mask, 0.0, image)
# 2. transform the zeo-filled image
scaled_im = ndinterp.affine_transform(filled_im,
matrix,
**at_kwargs,
cval=0.0)
# 3. transform the inverted mask
scaled_norm = ndinterp.affine_transform(1.0 - mask,
matrix,
**at_kwargs,
cval=0.0)
# 4. put back NaN where there was zero,
# otherwise decode using scaled mask
return da.where(da.isclose(scaled_norm, 0.0),
np.nan, scaled_im / scaled_norm)
# No dealing with NaN required
return ndinterp.affine_transform(image, matrix, **at_kwargs, cval=np.nan)
def qr(x):
t0 = time.time()
q, r = da.linalg.qr(x)
test = da.all(da.isclose(x, q.dot(r)))
test.compute()
print(time.time() - t0)