def move_peak_onto_spectrum(self, peak):
freq = peak.frequency
pos = sputil.alias_onto_spectrum(freq, peak.spectrum)
if pos != freq:
peak.position = pos
peak.alias = pyutil.subtract_tuples(freq, pos)
def move_peak_onto_spectrum(self, peak):
freq = peak.frequency
pos = sputil.alias_onto_spectrum(freq, peak.spectrum)
if pos != freq:
peak.position = pos
peak.alias = pyutil.subtract_tuples(freq, pos)
def place_peaks(self, spectrum):
hc_axes = pyutil.order(('1H', '13C'), spectrum.nuclei)
if spectrum.dimension != 2 or hc_axes == None:
self.progress_report('Spectrum must 2D with H and C axes.')
return
molecule = spectrum.molecule
condition = spectrum.condition
self.stoppable_loop('resonances', 50)
for rH in condition.resonance_list():
self.check_for_stop()
if rH.atom.nucleus == '1H':
catom = atoms.attached_heavy_atom(rH.atom, molecule)
if catom and catom.nucleus == '13C':
rC = condition.find_resonance(catom)
if rC:
assignment = pyutil.permute((rH, rC), hc_axes)
if not spectrum.find_peak(assignment):
freq = pyutil.permute((rH.frequency, rC.frequency), hc_axes)
pos = sputil.alias_onto_spectrum(freq, spectrum)
peak = spectrum.place_peak(pos)
peak.assign(hc_axes[0], rH.group.name, rH.atom.name)
peak.assign(hc_axes[1], rC.group.name, rC.atom.name)
if freq != pos:
peak.alias = pyutil.subtract_tuples(freq, pos)
peak.show_assignment_label()
def estimate_parameter_error(self, y_sd, trials):
trial_params = []
n = self.fit_function.parameter_count()
param_sd = n * [0]
if self.converged:
while len(trial_params) < trials:
new_xy_pairs = []
for x, y in self.xy_pairs:
new_y = y + random.gauss(0, y_sd)
new_xy_pairs.append((x, new_y))
fit = least_squares_fit(new_xy_pairs, self.fit_function,
self.params, self.tolerance)
if fit.converged:
trial_params.append(fit.params)
sum_d2 = n * [0]
for params in trial_params:
diff = pyutil.subtract_tuples(params, self.params)
diff2 = map(lambda d: d*d, diff)
sum_d2 = pyutil.add_tuples(sum_d2, diff2)
param_sd = map(lambda d, m=trials: math.sqrt(d/m), sum_d2)
self.trial_params = trial_params
self.param_sd = param_sd
def write_spectrum_table_entry(self, assignable_spectrum, file):
type = assignable_spectrum.autoassign_type
ref_type = self.root_spectrum
if type == ref_type:
ref_type = 'ROOT'
file_name = type + '.pks'
intra = type in self.intra_peak_spectra
inter = type in self.inter_peak_spectra
phase = assignable_spectrum.autoassign_phase
file.write('%s %s %s %d %d 0 phase: {%s} {\n' %
(type, ref_type, file_name, intra, inter, phase))
tolerances = assignable_spectrum.tolerances
spectrum = assignable_spectrum.spectrum
min_ppm, max_ppm = spectrum.region
min_ppm = pyutil.subtract_tuples(max_ppm, spectrum.sweep_width)
axis_names = self.autoassign_axis_names(spectrum, type)
for a in range(spectrum.dimension):
file.write('{%10s %8.3f %8.3f 0 %8.3f unfolded }\n' %
(axis_names[a], min_ppm[a], max_ppm[a], tolerances[a]))
file.write('}\n')
def estimate_parameter_error(self, y_sd, trials):
trial_params = []
n = self.fit_function.parameter_count()
param_sd = n * [0]
if self.converged:
while len(trial_params) < trials:
new_xy_pairs = []
for x, y in self.xy_pairs:
new_y = y + random.gauss(0, y_sd)
new_xy_pairs.append((x, new_y))
fit = least_squares_fit(new_xy_pairs, self.fit_function,
self.params, self.tolerance)
if fit.converged:
trial_params.append(fit.params)
sum_d2 = n * [0]
for params in trial_params:
diff = pyutil.subtract_tuples(params, self.params)
diff2 = map(lambda d: d * d, diff)
sum_d2 = pyutil.add_tuples(sum_d2, diff2)
param_sd = map(lambda d, m=trials: math.sqrt(d / m), sum_d2)
self.trial_params = trial_params
self.param_sd = param_sd
def fold_spectrum(spectrum, axis, multiples):
if axis >= spectrum.dimension:
return
sweepwidth = spectrum.sweep_width[axis]
delta = spectrum.dimension * [0]
delta[axis] = multiples * sweepwidth
spectrum.scale_offset = pyutil.add_tuples(spectrum.scale_offset, delta)
#
# Add alias to every peak
#
for peak in spectrum.peak_list():
peak.alias = pyutil.subtract_tuples(peak.alias, delta)
peaklets = peak.peaklets()
if peaklets:
for p in peaklets:
p.alias = pyutil.subtract_tuples(p.alias, delta)
return dict(dim_list)
# place assigned peaks
from sputil import split_group_atom, alias_onto_spectrum
from pyutil import subtract_tuples
dim_dict = detect_dimension(peak_list, spec)
if dim_dict == None:
s.show_message('Error',
'Spectrum does not look the correct experiment to use.')
raise SystemExit
for peak in peak_list:
gas, freqs = peak[0], peak[1]
gas2, freqs2 = list(gas), list(freqs)
for a in range(spec.dimension):
b = dim_dict[a]
gas2[b], freqs2[b] = gas[a], freqs[a]
peak = spec.place_peak(freqs2)
# alias if edge of the spectrum
freqs3 = alias_onto_spectrum(freqs2, spec)
if freqs2 != freqs3:
peak.position = freqs3
peak.alias = subtract_tuples(freqs2, freqs3)
# attach assignment labels
for i in range(spec.dimension):
g, a = split_group_atom(gas2[i])
peak.assign(i, g, a)
peak.show_assignment_label()