def test_argwhere_str():
x = np.array(list("Hello world"))
d = da.from_array(x, chunks=(4, ))
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def test_argwhere_str():
x = np.array(list("Hello world"))
d = da.from_array(x, chunks=(4,))
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def test_argwhere_obj():
x = np.random.randint(10, size=(15, 16)).astype(object)
d = da.from_array(x, chunks=(4, 5))
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def test_argwhere_obj():
x = np.random.randint(10, size=(15, 16)).astype(object)
d = da.from_array(x, chunks=(4, 5))
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def test_argwhere():
for shape, chunks in [(0, ()), ((0, 0), (0, 0)), ((15, 16), (4, 5))]:
x = np.random.randint(10, size=shape)
d = da.from_array(x, chunks=chunks)
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def test_argwhere():
for shape, chunks in [(0, ()), ((0, 0), (0, 0)), ((15, 16), (4, 5))]:
x = np.random.randint(10, size=shape)
d = da.from_array(x, chunks=chunks)
x_nz = np.argwhere(x)
d_nz = da.argwhere(d)
assert_eq(d_nz, x_nz)
def select_valid_oof(y, oof):
if isinstance(oof, da.Array):
oof = DaskToolBox.make_chunk_size_known(oof)
if len(oof.shape) == 1:
nan_rows = da.isnan(oof[:])
elif len(oof.shape) == 2:
nan_rows = da.isnan(oof[:, 0])
elif len(oof.shape) == 3:
nan_rows = da.isnan(oof[:, 0, 0])
else:
raise ValueError(f'Unsupported shape:{oof.shape}')
if nan_rows.sum().compute() == 0:
return y, oof
idx = da.argwhere(~nan_rows)
idx = DaskToolBox.make_chunk_size_known(idx).ravel()
idx = DaskToolBox.make_chunk_size_known(idx)
if isinstance(y, da.Array):
return y[idx], oof[idx]
else:
return DaskToolBox.select_1d(y, idx), oof[idx]
else:
return ToolBox.select_valid_oof(y, oof)
z_pz, z_pz_bins = da.histogramdd(
df[['position_z', 'momentum_z']].to_dask_array(),
bins=[80, 80],
range=[[z_min, z_max], [-8.0e-23, 8.0e-23]],
weights=df["weighting"].to_dask_array())
print(z_pz.compute())
# example4: save all data data to parquet files
delayed_save = delayed(df.to_parquet("electrons.parquet"))
delayed_save.compute()
# Meshes
if found_dask:
E = s.iterations[400].meshes["E"]
E_x = E["x"]
E_y = E["y"]
E_z = E["z"]
darr_x = E_x.to_dask_array()
darr_y = E_y.to_dask_array()
darr_z = E_z.to_dask_array()
# example compute intensity
Intensity = darr_x * darr_x + darr_y * darr_y + darr_z * darr_z
Intensity_max = Intensity.max().compute()
idx_max = da.argwhere(Intensity == Intensity_max).compute()[0]
pos_max = E.grid_unit_SI * 1.0e6 * (idx_max * E.grid_spacing +
E.grid_global_offset)
print("maximum intensity I={} at index={} z={}mu".format(
Intensity_max, idx_max, pos_max[2]))