Commit 5e4f50a2 authored by Unknown's avatar Unknown
Browse files

Process tutorial update

parent 7b90afdd
......@@ -48,6 +48,7 @@ import matplotlib.pyplot as plt
# Finally import pycroscopy for certain scientific analysis:
import pycroscopy as px
field_names = ['Amplitude [V]', 'Frequency [Hz]', 'Quality Factor', 'Phase [rad]']
sho32 = np.dtype({'names': field_names,
'formats': [np.float32 for name in field_names]})
......@@ -114,131 +115,9 @@ class ShoGuess(px.Process):
# this should stop the computation.
@staticmethod
def sho_function(parms, w_vec):
"""
Generates the SHO response over the given frequency band
Parameters
-----------
parms : list or tuple
SHO parameters=(Amplitude, frequency ,Quality factor, phase)
w_vec : 1D numpy array
Vector of frequency values
"""
return parms[0] * exp(1j * parms[3]) * parms[1] ** 2 / (
w_vec ** 2 - 1j * w_vec * parms[1] / parms[2] - parms[1] ** 2)
@staticmethod
def _unit_function(resp_vec, w_vec=None, num_points=5):
"""
Generates good initial guesses for fitting
Parameters
------------
resp_vec : 1D complex numpy array or list
BE response vector as a function of frequency
w_vec : 1D numpy array or list, Optional
Vector of BE frequencies
num_points : (Optional) unsigned int
Quality factor of the SHO peak
Returns
---------
retval : tuple
SHO fit parameters arranged as amplitude, frequency, quality factor, phase
"""
if w_vec is None:
# Some default value
w_vec = np.linspace(300E+3, 350E+3, resp_vec.size)
ii = np.argsort(abs(resp_vec))[::-1]
a_mat = np.array([])
e_vec = np.array([])
for c1 in range(num_points):
for c2 in range(c1 + 1, num_points):
w1 = w_vec[ii[c1]]
w2 = w_vec[ii[c2]]
X1 = real(resp_vec[ii[c1]])
X2 = real(resp_vec[ii[c2]])
Y1 = imag(resp_vec[ii[c1]])
Y2 = imag(resp_vec[ii[c2]])
denom = (w1 * (X1 ** 2 - X1 * X2 + Y1 * (Y1 - Y2)) + w2 * (-X1 * X2 + X2 ** 2 - Y1 * Y2 + Y2 ** 2))
if denom > 0:
a = ((w1 ** 2 - w2 ** 2) * (w1 * X2 * (X1 ** 2 + Y1 ** 2) - w2 * X1 * (X2 ** 2 + Y2 ** 2))) / denom
b = ((w1 ** 2 - w2 ** 2) * (w1 * Y2 * (X1 ** 2 + Y1 ** 2) - w2 * Y1 * (X2 ** 2 + Y2 ** 2))) / denom
c = ((w1 ** 2 - w2 ** 2) * (X2 * Y1 - X1 * Y2)) / denom
d = (w1 ** 3 * (X1 ** 2 + Y1 ** 2) -
w1 ** 2 * w2 * (X1 * X2 + Y1 * Y2) -
w1 * w2 ** 2 * (X1 * X2 + Y1 * Y2) +
w2 ** 3 * (X2 ** 2 + Y2 ** 2)) / denom
if d > 0:
a_mat = append(a_mat, [a, b, c, d])
A_fit = abs(a + 1j * b) / d
w0_fit = sqrt(d)
Q_fit = -sqrt(d) / c
phi_fit = arctan2(-b, -a)
H_fit = A_fit * w0_fit ** 2 * exp(1j * phi_fit) / (
w_vec ** 2 - 1j * w_vec * w0_fit / Q_fit - w0_fit ** 2)
e_vec = append(e_vec,
sum((real(H_fit) - real(resp_vec)) ** 2) +
sum((imag(H_fit) - imag(resp_vec)) ** 2))
if a_mat.size > 0:
a_mat = a_mat.reshape(-1, 4)
weight_vec = (1 / e_vec) ** 4
w_sum = sum(weight_vec)
a_w = sum(weight_vec * a_mat[:, 0]) / w_sum
b_w = sum(weight_vec * a_mat[:, 1]) / w_sum
c_w = sum(weight_vec * a_mat[:, 2]) / w_sum
d_w = sum(weight_vec * a_mat[:, 3]) / w_sum
A_fit = abs(a_w + 1j * b_w) / d_w
w0_fit = sqrt(d_w)
Q_fit = -sqrt(d_w) / c_w
phi_fit = np.arctan2(-b_w, -a_w)
H_fit = A_fit * w0_fit ** 2 * exp(1j * phi_fit) / (w_vec ** 2 - 1j * w_vec * w0_fit / Q_fit - w0_fit ** 2)
if np.std(abs(resp_vec)) / np.std(abs(resp_vec - H_fit)) < 1.2 or w0_fit < np.min(w_vec) or w0_fit > np.max(
w_vec):
p0 = ShoGuess.sho_fast_guess(w_vec, resp_vec)
else:
p0 = np.array([A_fit, w0_fit, Q_fit, phi_fit])
else:
p0 = ShoGuess.sho_fast_guess(resp_vec, w_vec)
return p0
def _unit_function():
return px.be_sho.SHOestimateGuess
@staticmethod
def sho_fast_guess(resp_vec, w_vec, qual_factor=200):
"""
Default SHO guess from the maximum value of the response
Parameters
------------
resp_vec : 1D complex numpy array or list
BE response vector as a function of frequency
w_vec : 1D numpy array or list
Vector of BE frequencies
qual_factor : float
Quality factor of the SHO peak
Returns
-------
retval : 1D numpy array
SHO fit parameters arranged as [amplitude, frequency, quality factor, phase]
"""
amp_vec = abs(resp_vec)
i_max = int(len(resp_vec) / 2)
return np.array([np.mean(amp_vec) / qual_factor, w_vec[i_max], qual_factor, np.angle(resp_vec[i_max])])
#########################################################################
# Load the dataset
......@@ -254,9 +133,9 @@ h5_path = 'temp.h5'
url = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'
if os.path.exists(h5_path):
os.remove(h5_path)
_ = wget.download(url, h5_path)
_ = wget.download(url, h5_path, bar=None)
#########################################################################
########################################################################
# Open the file in read-only mode
h5_file = h5py.File(h5_path, mode='r+')
......@@ -273,7 +152,7 @@ h5_main = h5_meas_grp['Channel_000/Raw_Data']
h5_spec_vals = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Values')[-1]
freq_vec = np.squeeze(h5_spec_vals.value) * 1E-3
fitter = ShoGuess(h5_main, cores=4)
fitter = ShoGuess(h5_main, cores=1)
h5_results_grp = fitter.compute()
h5_guess = h5_results_grp['Guess']
......@@ -284,7 +163,7 @@ norm_guess_parms = h5_guess[pix_ind]
# Converting from compound to real:
norm_guess_parms = px.io_utils.compound_to_scalar(norm_guess_parms)
print('Functional fit returned:', norm_guess_parms)
norm_resp = fitter.sho_function(norm_guess_parms, freq_vec)
norm_resp = px.be_sho.SHOfunc(norm_guess_parms, freq_vec)
fig, axes = plt.subplots(nrows=2, sharex=True, figsize=(5, 10))
for axis, func, title in zip(axes.flat, [np.abs, np.angle], ['Amplitude (a.u.)', 'Phase (rad)']):
......@@ -297,7 +176,7 @@ axes[1].set_xlabel('Frequency (kHz)', fontsize=14)
axes[0].set_ylim([0, np.max(np.abs(resp_vec)) * 1.1])
axes[1].set_ylim([-np.pi, np.pi])
#########################################################################
########################################################################
# **Delete the temporarily downloaded file**
h5_file.close()
......
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