def grid_unidec(mzgrid, igrid, numit=1000, fwhm=5, mode=0, msig=0, zsig=1):
pmat = make_pmat(mzgrid, fwhm)
b = deepcopy(igrid)
b2 = b
diff = 1
i = 0
while diff > 1e-9 and i < numit:
# Blur
b = blur(b, msig, zsig)
# Deconvolve
if mode == 0:
b = ud.safedivide(b * igrid, conv(b, pmat))
elif mode == 1:
b = ud.safedivide(b * igrid * igrid, conv(b * igrid, pmat))
elif mode == 2:
b *= conv(ud.safedivide(igrid, conv(b, pmat)), pmat)
else:
print("Mode is invalid:", mode)
exit()
# Convergence Test
b /= np.amax(b)
diff = np.sum((b2 - b)**2.)
i += 1
b2 = b
print("Interations:", i)
return b
pass
def dd_core(self, data, kernel):
ckernel = kernel[::-1]
I = deepcopy(data)
i = 0
diff = 1
while i < 50 and diff > 0.0001:
newI = I * cconv2(ud.safedivide(data, cconv2(kernel, I)), ckernel)
diff = np.sum((I - newI) ** 2) / np.sum(I)
I = newI
i += 1
I /= np.amax(I)
return I
def get_resolution(testdata):
"""
Get the median resolution of 1D MS data.
:param testdata: N x 2 data (mz, intensity)
:return: Median resolution (float)
"""
diffs = np.transpose([testdata[1:, 0], np.diff(testdata[:, 0])])
resolutions = ud.safedivide(diffs[:, 0], diffs[:, 1])
# popt, pcov = scipy.optimize.curve_fit(fit_line, diffs[:, 0], resolutions, maxfev=1000000)
# fitLine = fit_line(diffs[:, 0], *popt)
# fitMax = np.max(fitLine)
# fitMin = np.min(fitLine)
# diffs_new = diffs[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]
# resolutions_new = resolutions[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]
# popt2, pcov2 = scipy.optimize.curve_fit(fit_line, diffs_new[:, 0], resolutions_new, maxfev=1000000)
# plt.figure()
# plt.plot(diffs[:,0], resolutions)
# plt.plot(diffs[:, 0], fit_line(diffs[:, 0], *popt2), 'r-')
# plt.show()
# Currently use A * m ^1.5 (0.5?)
# Maybe use a*M^b
# return popt2
return np.median(resolutions)