def add_plot(self,data_x,data_y=None,label='',mode='lines+markers',row=1,col=1):
"""
Definition to add data to the plot.
Parameters
----------
data_x : ndarray
X axis data to be plotted.
data_y : ndarray
Y axis data to be plotted.
label : str
Label of the plot.
mode : str
Mode for the plot, it can be either lines+markers, lines or markers.
"""
if type(data_y) == type(None):
data_y = np.arange(0,data_x.shape[0])
if np.__name__ == 'cupy':
data_x = np.asnumpy(data_x)
data_y = np.asnumpy(data_y)
self.fig.add_trace(
go.Scatter(
x=data_x,
y=data_y,
mode=mode,
name=label,
),
row=row,
col=col
)
def modulation_transfer_function(line_x, line_y, px_size):
"""
Definition to compute modulation transfer function. This definition is based on the work by Peter Burns. For more consult Burns, Peter D. "Slanted-edge MTF for digital camera and scanner analysis." Is and Ts Pics Conference. SOCIETY FOR IMAGING SCIENCE & TECHNOLOGY, 2000.
Parameters
----------
line_x : ndarray
Slice from an image along X axis. See odak.measurements.roi().
line_y : ndarray
Slice from an image along Y axis. See odak.measurements.roi().
px_size : ndarray
Physical angular sizes that each pixels corresponds to on the image plane both for X and Y axes.
Returns
----------
mtf : ndarray
Calculated modulation transfer function along X and Y axes.
frq : ndarray
Frequencies of the calculated MTF.
"""
# 1st derivative of both values: "Line Spread Function",
der_x = line_spread_function(line_x)
der_y = line_spread_function(line_y)
# Fourier transform of the first derivative: "Modulation Transfer Function",
mtf_x = fourier_transform_1d(der_x)
mtf_y = fourier_transform_1d(der_y)
# Arrange the corresponding frequencies,
n_x = len(der_x) # length of the signal,
k_x = np.arange(n_x)
T_x = n_x * px_size[0]
frq_x = k_x / T_x
frq_x = frq_x[np.arange(0, int(n_x / 2))]
n_y = len(der_y) # length of the signal,
k_y = np.arange(n_y)
T_y = n_y * px_size[1]
frq_y = k_y / T_y
frq_y = frq_y[np.arange(0, int(n_y / 2))]
return [mtf_x, mtf_y], [frq_x, frq_y]
def fourier_transform_1d(line):
"""
Definition to take the 1D fourier transform. This is used only for modulation transfer function calculations.
Parameters
----------
line : ndarray
1D array.
Returns
----------
result : ndarray
Positive side of the fourier transform of the given line.
"""
result = np.fft.fft(line) #/len(der_x)
result /= np.amax(result)
result = result[np.arange(0, int(line.shape[0] / 2))]
return result
def sphere_sample_uniform(no=[10, 10],
radius=1.,
center=[0., 0., 0.],
k=[1, 2]):
"""
Definition to generate an uniform sample set on the surface of a sphere using polar coordinates.
Parameters
----------
no : list
Number of samples.
radius : float
Radius of a sphere.
center : list
Center of a sphere.
k : list
Multipliers for gathering samples. If you set k=[1,2] it will draw samples from a perfect sphere.
Returns
----------
samples : ndarray
Samples generated.
"""
samples = np.zeros((no[0], no[1], 3))
row = np.arange(0, no[0])
psi, teta = np.mgrid[0:no[0], 0:no[1]]
for psi_id in range(0, no[0]):
psi[psi_id] = np.roll(row, psi_id, axis=0)
teta[psi_id] = np.roll(row, -psi_id, axis=0)
psi = k[0] * np.pi / no[0] * psi
teta = k[1] * np.pi / no[1] * teta
samples[:, :, 0] = center[0] + radius * np.sin(psi) * np.cos(teta)
samples[:, :, 1] = center[1] + radius * np.sin(psi) * np.sin(teta)
samples[:, :, 2] = center[2] + radius * np.cos(psi)
samples = samples.reshape((no[0] * no[1], 3))
return samples