def test_TopHat():
test_dataset = cp.CSDM(
dependent_variables=[cp.as_dependent_variable(np.ones(500))],
dimensions=[cp.LinearDimension(500, "1 s")],
)
processor = sp.SignalProcessor()
processor.operations = [
sp.apodization.TopHat(rising_edge="100 s", falling_edge="400 s")
]
rise_and_fall_dataset = processor.apply_operations(test_dataset.copy())
rise_and_fall_should_be = np.zeros(500)
rise_and_fall_should_be[100:400] = 1
assert np.allclose(rise_and_fall_dataset.y[0].components,
rise_and_fall_should_be)
processor.operations = [sp.apodization.TopHat(rising_edge="100 s")]
rise_only_dataset = processor.apply_operations(test_dataset.copy())
rise_only_should_be = np.zeros(500)
rise_only_should_be[100:] = 1
assert np.allclose(rise_only_dataset.y[0].components, rise_only_should_be)
processor.operations = [sp.apodization.TopHat(falling_edge="400 s")]
fall_only_dataset = processor.apply_operations(test_dataset.copy())
fall_only_should_be = np.zeros(500)
fall_only_should_be[:400] = 1
assert np.allclose(fall_only_dataset.y[0].components, fall_only_should_be)
def to_csdm(self):
"""
Return a csdm object containing data.
Returns
-------
data : csdm object
CSDM object containing parameters and data
"""
try:
import csdmpy as cp
# self._oproc = {}
# self._odtype = str(self._data.dtype)
# create a list of dimension objects
dimensions = [
cp.LinearDimension(
count=value["size"],
increment=f'{1 / value["sw"]} s',
reciprocal={
"coordinates_offset": f'{value["car"]} Hz',
"origin_offset": f'{value["obs"]} MHz',
},
label=value["label"],
) for key, value in list(self._udic.items())
if type(key) == int and value["size"] != 1
]
return cp.CSDM(
dimensions=dimensions[::-1],
dependent_variables=[
cp.as_dependent_variable(self._data.copy())
],
)
except:
raise ImportError(
"csdmpy must be installed to use this function. Please install by typing 'pip install csdmpy' in the terminal."
)
])
# %%
# Here the applied offset will be the following function
#
# .. math::
#
# f(x) = 0.00001 \cdot x^2 + 0.2
#
# Next we create a CSDM object with a test dataset which our signal processor will
# operate on. Here, the dataset spans 500 Hz with a delta function centered at
# 100 Hz.
test_data = np.zeros(500)
test_data[350] = 1
csdm_object = cp.CSDM(
dependent_variables=[cp.as_dependent_variable(test_data)],
dimensions=[
cp.LinearDimension(count=500, increment="1 Hz", complex_fft=True)
],
)
# %%
# Now to apply the processor to the CSDM object, use the
# :py:meth:`~mrsimulator.signal_processor.SignalProcessor.apply_operations` method as
# follows
processed_dataset = processor.apply_operations(dataset=csdm_object.copy()).real
# %%
# To see the results of the exponential apodization, we create a simple plot using the
# ``matplotlib`` library.
import matplotlib.pyplot as plt
processor = sp.SignalProcessor(
operations=[
sp.IFFT(),
sp.apodization.TopHat(rising_edge="1 s", falling_edge="9 s"),
sp.FFT(),
]
)
# %%
# Next we create a CSDM object with a test dataset which our signal processor will
# operate on. Here, the dataset is a delta function centered at 0 Hz with a some
# applied Gaussian line broadening.
test_data = np.zeros(500)
test_data[250] = 1
csdm_object = cp.CSDM(
dependent_variables=[cp.as_dependent_variable(test_data)],
dimensions=[cp.LinearDimension(count=500, increment="0.1 Hz", complex_fft=True)],
)
# %%
# To apply the previously defined signal processor, we use the
# :py:meth:`~mrsimulator.signal_processor.SignalProcessor.apply_operations` method as
# as follows
processed_dataset = processor.apply_operations(dataset=csdm_object).real
# %%
# To see the results of the top hat apodization, we create a simple plot using the
# ``matplotlib`` library.
fig, ax = plt.subplots(1, 2, figsize=(8, 3.5), subplot_kw={"projection": "csdm"})
ax[0].plot(csdm_object, color="black", linewidth=1)
ax[0].set_title("Before")
def generate_data():
dv1 = cp.as_dependent_variable(np.random.rand(20))
dv2 = cp.as_dependent_variable(np.random.rand(20))
dv3 = cp.as_dependent_variable(np.random.rand(20))
dim = cp.as_dimension(np.arange(20))
return cp.CSDM(dependent_variables=[dv1, dv2, dv3], dimensions=[dim])