def get_resonance_choices(resonance, correlations, experiment_name, level=95):
"""
Determine which chemical shift ranges at the given confidence
level for an experiment contain the input resonance. If so adds
the matching correlation to a dictionary and scores the resonance
against the probability density functions. The correlation are
sorted by there amino acid in the dictionary.
:param resonance: float or list of float chemical shifts.
:param correlations: list of pluq.base.Correlation
:param experiment_name: one of the key from
inbase.standard_experiments
:param level: int, one of the defined levels normally in
[68, 85, 95]
:return dict[res] = list(Assignment, ...)
"""
pdf_dict = inbase.read_pdf(experiment_name)
levels = list(pdf_dict.attrs['confidence_levels'])
try:
ind = levels.index(level)
except ValueError:
mesg = 'Chose a confidence level from {}'.format(levels)
raise ValueError(mesg)
# Find all the hits
exp = inbase.standard_experiments[experiment_name]
if exp.dims == 1:
new_correlations = []
for corr in correlations:
try:
cs_range = pdf_dict[str(corr) + ',levs'][ind]
except KeyError:
continue
if min(cs_range) <= resonance <= max(cs_range):
new_correlations.append(corr)
correlations = new_correlations
else:
region_dict = fileio.read_region(experiment_name, level)
regions = [region_dict[str(x)] for x in correlations]
# Find all the hits
hits = map(Point(resonance).within, regions)
correlations = list(compress(correlations, hits))
# Score all the hits
assignments = collections.defaultdict(list)
for corr in correlations:
try:
smooth = inbase.get_pdf(corr, pdf_dict)
corr_score = float(smooth.score(resonance))
except ValueError:
corr_score = 0
except KeyError:
corr_score = 0
ss_scores = []
for ss in ['H', 'C', 'E']:
try:
ss_corr = Correlation(corr.aa, corr.atoms, ss)
corr_ss_smooth = inbase.get_pdf(ss_corr, pdf_dict)
ss_scores.append(float(corr_ss_smooth.score(resonance)))
except ValueError:
ss_scores.append(0)
except KeyError:
ss_scores = None
break
assign = Assignment(corr.aa, corr.atoms, corr_score, ss_scores)
assignments[corr.aa].append(assign)
return assignments
if __name__ == "__main__":
corr = Correlation('A', ('CA', 'CB'), 'H')
pacsy = DBMySQL(db='pacsy_local', password='')
pacsy_corr = PacsyCorrelation(corr, pacsy)
data = pacsy_corr.get_cs(piqc=True, model='all', sigma_n=3, like_ss=True)
smooth = estimate_pdf(data,
params={'bandwidth': np.linspace(0.4, 1.5, 15)})
pdf_dict = read_pdf('cc')
other = get_pdf(corr, pdf_dict)
plt.figure(1)
x, y = smooth.grid
z = smooth.pdf
plt.contour(x, y, z, sorted(list(smooth.get_levels(data, 68, 98, 95))))
plt.figure(2)
x, y = other.grid
z = other.pdf
l = other.levels
plt.contour(x, y, z, l)
from pluq.base import Correlation
from pluq.fileio import read_pdf
from pluq.inbase import get_pdf, _region
import matplotlib.pyplot as plt
from descartes import PolygonPatch
corr = Correlation('A', ('CB', 'CA'), ss='X')
pdf_dict = read_pdf('cc')
pdf = get_pdf(corr, pdf_dict)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.imshow(pdf.pdf,
interpolation="none",
aspect='auto',
origin='lower',
extent=(pdf.limits[0][0], pdf.limits[0][1], pdf.limits[1][0],
pdf.limits[1][1]))
shapes = _region(pdf, pdf.levels[1])
for shape in shapes:
patch = PolygonPatch(shape, fc='gray', ec='gray', alpha=0.5, zorder=1)
ax.add_patch(patch)
plt.xlim([0, 100])
plt.ylim([0, 100])
plt.gca().invert_xaxis()