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classify_no_smoothing.py
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#!/usr/bin/env python
#
# Written 17.08.01 by Alex DeGrave
#
import numpy
import matplotlib
import os
import matplotlib.pyplot as pyplot
import matplotlib.gridspec as gridspec
from matplotlib.patches import Rectangle
def movingcircularaverage(orig_interval, window_size):
window= numpy.ones(int(window_size))/float(window_size)
interval = numpy.copy(orig_interval)
interval *= (2.0*numpy.pi)/360.0
sin_interval = numpy.sin(interval)
cos_interval = numpy.cos(interval)
sin_conv = numpy.convolve(sin_interval, window, 'same')
cos_conv = numpy.convolve(cos_interval, window, 'same')
return 360/(numpy.pi*2)*numpy.arctan2(sin_conv, cos_conv)
def multiple_and(tupl):
check = numpy.logical_and(tupl[0], tupl[0])
for data in tupl[1:]:
check = numpy.logical_and(check, data)
return check
def multiple_or(tupl):
check = numpy.logical_or(tupl[0], tupl[0])
for data in tupl[1:]:
check = numpy.logical_or(check, data)
return check
class StateClassifier(object):
def __init__(self, **kwargs):
pass
def _load_data_from_txt(self, datadirpath):
phipath = '{:s}/phi.dat'.format(datadirpath)
psipath = '{:s}/psi.dat'.format(datadirpath)
chipath = '{:s}/chi.dat'.format(datadirpath)
# Load training data into numpy arrays
phi = numpy.loadtxt(phipath, skiprows=1)[:,1:] # skip the timestamps
psi = numpy.loadtxt(psipath, skiprows=1)[:,1:]
chi = numpy.loadtxt(chipath, skiprows=1)[:,1:]
# mod chi2 for Phe residues by 180
chi[:, 11][chi[:, 11] < 0] = chi[:, 11][chi[:, 11] < 0] + 180
chi[:, 18][chi[:, 18] < 0] = chi[:, 18][chi[:, 18] < 0] + 180
chi[:, 30][chi[:, 30] < 0] = chi[:, 30][chi[:, 30] < 0] + 180
if numpy.any(chi[:,11]<0):
print(chi[:,11].max())
raise ValueError
if numpy.any(chi[:,18]<0):
print(chi[:,18].max())
raise ValueError
if numpy.any(chi[:,30]<0):
print(chi[:,30].max())
raise ValueError
data = numpy.concatenate((phi, psi, chi), axis=1)
return data
def load_data(self, datadirpath):
npypath = "{:s}/all_data".format(datadirpath)
if not os.path.exists(npypath+'.npy'):
data = self._load_data_from_txt(datadirpath)
numpy.save(npypath+'.npy', data)
else:
data = numpy.load(npypath+'.npy')
#for i in xrange(data.shape[1]):
# data[:,i] = movingcircularaverage(data[:,i],100)
return data
def fits_statedef(self, array, datalabel, bounds, *other_bounds):
if datalabel == 'psi10':
idx = 43
if datalabel == 'phi11':
idx = 9
if datalabel == 'phi31':
idx = 29
if datalabel == 'chi10_1':
idx = 85
if datalabel == 'psi6':
idx = 39
if datalabel == 'psi11':
idx = 44
if datalabel == 'chi6_1':
idx = 78
if datalabel == 'phi9':
idx = 7
if datalabel == 'psi8':
idx = 41
if datalabel == 'phi10':
idx = 8
if datalabel == 'phi12':
idx = 10
if datalabel == 'psi28':
idx = 61
if datalabel == 'psi29':
idx = 62
if datalabel == 'psi30':
idx = 63
if datalabel == 'psi31':
idx = 64
if datalabel == 'psi32':
idx = 65
if other_bounds:
all_bounds = [bounds] + list(other_bounds)
return multiple_or([numpy.logical_and(array[:,idx]>lb,
array[:,idx]<ub)
for (lb, ub) in all_bounds])
else:
(lb, ub) = bounds
return numpy.logical_and(array[:,idx]>lb,array[:,idx]<ub)
def vector_classify(self, data):
psi10 = data[:,43]
phi11 = data[:,9]
phi31 = data[:,29]
chi10_1 = data[:,85]
psi6 = data[:,39]
psi11 = data[:,44]
chi6_1 = data[:,78]
phi9 = data[:,7]
psi8 = data[:,41]
phi10 = data[:,8]
phi12 = data[:,10]
psi28 = data[:,61]
psi29 = data[:,62]
psi30 = data[:,63]
psi31 = data[:,64]
in_state0 = multiple_and([self.fits_statedef(data, 'psi10', (-20,10)),
self.fits_statedef(data, 'phi11', (60,90)),
self.fits_statedef(data, 'chi10_1', (-75,-55)),
self.fits_statedef(data, 'psi6', (-50,-40)),
self.fits_statedef(data, 'psi11', (0,20)),
self.fits_statedef(data, 'chi6_1', (-180,-175), (-80,-60)),
self.fits_statedef(data, 'phi9', (-85,-60)),
self.fits_statedef(data, 'psi8', (-60,-30)),
self.fits_statedef(data, 'phi10', (-110,-80)),
self.fits_statedef(data, 'phi12', (-165,-145)),
self.fits_statedef(data, 'psi28', (-55,-25)),
self.fits_statedef(data, 'psi29', (-55,-25)),
self.fits_statedef(data, 'psi30', (-55,-25)),
self.fits_statedef(data, 'psi31', (-55,-25))])
in_state1 = multiple_and([self.fits_statedef(data, 'psi10', (-10, 15)),
self.fits_statedef(data, 'phi11', (60,90)),
self.fits_statedef(data, 'chi10_1', (-60,-40)),
self.fits_statedef(data, 'psi6', (-40,-20)),
self.fits_statedef(data, 'psi11', (10,30)),
self.fits_statedef(data, 'chi6_1', (175, 180), (-180,-170), (-95,-75)),
self.fits_statedef(data, 'phi9', (-85,-60)),
self.fits_statedef(data, 'psi8', (-30,-10)),
self.fits_statedef(data, 'phi10', (-140,-110)),
self.fits_statedef(data, 'phi12', (-165,-135)),
self.fits_statedef(data, 'psi28', (-55,-25)),
self.fits_statedef(data, 'psi29', (-55,-25)),
self.fits_statedef(data, 'psi30', (-55,-25)),
self.fits_statedef(data, 'psi31', (-55,-25))])
in_state3 = multiple_and([self.fits_statedef(data, 'psi10', (140, 180)),
self.fits_statedef(data, 'phi11', (-90,-70)),
self.fits_statedef(data, 'chi10_1', (-75,-55)),
self.fits_statedef(data, 'psi6', (-50,-20)),
self.fits_statedef(data, 'psi11', (-40,40)),
self.fits_statedef(data, 'chi6_1', (-180, -155), (-95,-75)),
self.fits_statedef(data, 'phi9', (-135,-75)),
self.fits_statedef(data, 'psi8', (-35,0)),
self.fits_statedef(data, 'phi10', (-150,-80)),
self.fits_statedef(data, 'phi12', (-180,-140), (-80, -55)),
self.fits_statedef(data, 'psi28', (-55,-25)),
self.fits_statedef(data, 'psi29', (-55,-25)),
self.fits_statedef(data, 'psi30', (-55,-25)),
self.fits_statedef(data, 'psi31', (-55,-25))])
in_state2 = multiple_and([self.fits_statedef(data, 'psi10', (130,155)),
self.fits_statedef(data, 'phi11', (-90,-65)),
self.fits_statedef(data, 'chi10_1', (170,180), (-180,-165)),
self.fits_statedef(data, 'psi6', (-55,-35)),
self.fits_statedef(data, 'psi11', (-40,70)),
self.fits_statedef(data, 'chi6_1', (165,180), (-180,-170)),
self.fits_statedef(data, 'phi9', (-135,-75)),
self.fits_statedef(data, 'psi8', (-35,0)),
self.fits_statedef(data, 'phi10', (-90,-55)),
self.fits_statedef(data, 'phi12', (-160,-140), (-80,-55)),
self.fits_statedef(data, 'psi28', (-55,-25)),
self.fits_statedef(data, 'psi29', (-55,-25)),
self.fits_statedef(data, 'psi30', (-55,-25)),
self.fits_statedef(data, 'psi31', (-55,-25))])
# alpha helix 3 is unfolded.
in_state4 = multiple_or([self.fits_statedef(data, 'psi28', (100, 180)),
self.fits_statedef(data, 'psi29', (100, 180)),
self.fits_statedef(data, 'psi30', (100, 180)),
self.fits_statedef(data, 'psi31', (100, 180))])
# if it is in state 4, it is not in another state!
in_state0[in_state4] == False
in_state1[in_state4] == False
in_state2[in_state4] == False
in_state3[in_state4] == False
if multiple_and([in_state0, in_state1, in_state3, in_state4]).sum() != 0:
raise ValueError
output = numpy.ones(data.shape[0])*-1
output[in_state0] = 0
output[in_state1] = 1
output[in_state2] = 2
output[in_state3] = 3
output[in_state4] = 4
return output
def classify(self, datadirpath):
data = self.load_data(datadirpath)
anchorpoints = self.vector_classify(data)
labels = numpy.ones(data.shape[0])*-1
for i, val in enumerate(anchorpoints):
if val > -1:
labels[i] = val
else:
if i == 0:
labels[i] = -1
else:
labels[i] = labels[i-1]
return labels
def main():
sc = StateClassifier()
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.xray.1/')
#numpy.savetxt('no_smooth/ff14sb.xray.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.xray.2/')
#numpy.savetxt('no_smooth/ff14sb.xray.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.xray.3/')
#numpy.savetxt('no_smooth/ff14sb.xray.3', classifications)
#
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.nmr.1/')
#numpy.savetxt('no_smooth/ff14sb.nmr.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.nmr.2/')
#numpy.savetxt('no_smooth/ff14sb.nmr.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.nmr.3/')
#numpy.savetxt('no_smooth/ff14sb.nmr.3', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KLP21D.1/')
#numpy.savetxt('no_smooth/ff14sb.KLP21D.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KLP21D.2/')
#numpy.savetxt('no_smooth/ff14sb.KLP21D.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KLP21D.3/')
#numpy.savetxt('no_smooth/ff14sb.KLP21D.3', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.1/')
#numpy.savetxt('no_smooth/ff14sb.KP21B.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.2/')
#numpy.savetxt('no_smooth/ff14sb.KP21B.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.3/')
#numpy.savetxt('no_smooth/ff14sb.KP21B.3', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.xray.1/')
#numpy.savetxt('no_smooth/ff03w.xray.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.xray.2/')
#numpy.savetxt('no_smooth/ff03w.xray.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.xray.3/')
#numpy.savetxt('no_smooth/ff03w.xray.3', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.nmr.1/')
#numpy.savetxt('no_smooth/ff03w.nmr.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.nmr.2/')
#numpy.savetxt('no_smooth/ff03w.nmr.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.nmr.3/')
#numpy.savetxt('no_smooth/ff03w.nmr.3', classifications)
#
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KLP21D.1/')
#numpy.savetxt('no_smooth/ff03w.KLP21D.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KLP21D.2/')
#numpy.savetxt('no_smooth/ff03w.KLP21D.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KLP21D.3/')
#numpy.savetxt('no_smooth/ff03w.KLP21D.3', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KP21B.1/')
#numpy.savetxt('no_smooth/ff03w.KP21B.1', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KP21B.2/')
#numpy.savetxt('no_smooth/ff03w.KP21B.2', classifications)
#classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff03w.KP21B.3/')
#numpy.savetxt('no_smooth/ff03w.KP21B.3', classifications)
classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.ttet.1/')
numpy.savetxt('no_smooth/ff14sb.KP21B.ttet.1', classifications)
classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.ttet.2/')
numpy.savetxt('no_smooth/ff14sb.KP21B.ttet.2', classifications)
classifications = sc.classify('/mnt/NAS2/VILLIN/brute_force_analysis/ff14sb.KP21B.ttet.3/')
numpy.savetxt('no_smooth/ff14sb.KP21B.ttet.3', classifications)
if __name__ == "__main__":
main()