/usr/lib/python2.7/dist-packages/csb/apps/embd.py is in python-csb 1.2.3+dfsg-1.
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Sharpening of EM maps by non-negative blind deconvolution.
For details see:
Hirsch M, Schoelkopf B and Habeck M (2010)
A New Algorithm for Improving the Resolution of Cryo-EM Density Maps.
"""
import os
import numpy
import csb.apps
from numpy import sum, sqrt
from csb.numeric import convolve, correlate, trim
from csb.bio.io.mrc import DensityMapReader, DensityMapWriter, DensityInfo, DensityMapFormatError
class ExitCodes(csb.apps.ExitCodes):
IO_ERROR = 2
INVALID_DATA = 3
ARGUMENT_ERROR = 4
class AppRunner(csb.apps.AppRunner):
@property
def target(self):
return DeconvolutionApp
def command_line(self):
cmd = csb.apps.ArgHandler(self.program, __doc__)
cmd.add_scalar_option('psf-size', 's', int, 'size of the point spread function', default=15)
cmd.add_scalar_option('output', 'o', str, 'output directory of the sharpened maps', default='.')
cmd.add_scalar_option('iterations', 'i', int, 'number of iterations', default=1000)
cmd.add_scalar_option('output-frequency', 'f', int, 'create a map file each f iterations', default=50)
cmd.add_boolean_option('verbose', 'v', 'verbose mode')
cmd.add_positional_argument('mapfile', str, 'Input Cryo EM file in CCP4 MRC format')
return cmd
class DeconvolutionApp(csb.apps.Application):
def main(self):
if not os.path.isfile(self.args.mapfile):
DeconvolutionApp.exit('Input file not found.', code=ExitCodes.IO_ERROR)
if not os.path.isdir(self.args.output):
DeconvolutionApp.exit('Output directory does not exist.', code=ExitCodes.IO_ERROR)
if self.args.psf_size < 1:
DeconvolutionApp.exit('PSF size must be a positive number.', code=ExitCodes.ARGUMENT_ERROR)
if self.args.iterations < 1:
DeconvolutionApp.exit('Invalid number of iterations.', code=ExitCodes.ARGUMENT_ERROR)
if self.args.output_frequency < 1:
DeconvolutionApp.exit('Output frequency must be a positive number.', code=ExitCodes.ARGUMENT_ERROR)
if self.args.iterations < self.args.output_frequency:
DeconvolutionApp.exit('Output frequency is too low.', code=ExitCodes.ARGUMENT_ERROR)
self.args.output = os.path.abspath(self.args.output)
self.run()
def run(self):
writer = DensityMapWriter()
self.log('Reading input density map...')
try:
input = DensityMapReader(self.args.mapfile).read()
embd = Deconvolution(input.data, self.args.psf_size)
except DensityMapFormatError as e:
msg = 'Error reading input MRC file: {0}'.format(e)
DeconvolutionApp.exit(msg, code=ExitCodes.INVALID_DATA)
self.log('Running {0} iterations...'.format(self.args.iterations))
self.log(' Iteration Loss Correlation Output')
for i in range(1, self.args.iterations + 1):
embd.run_once()
if i % self.args.output_frequency == 0:
output = OutputPathBuilder(self.args, i)
density = DensityInfo(embd.data, None, None, header=input.header)
writer.write_file(output.fullpath, density)
self.log('{0:>9}. {1:15.2f} {2:10.4f} {3}'.format(
i, embd.loss, embd.correlation, output.filename))
self.log('Done: {0}.'.format(output.fullpath))
def log(self, *a, **k):
if self.args.verbose:
super(DeconvolutionApp, self).log(*a, **k)
class OutputPathBuilder(object):
def __init__(self, args, i):
basename = os.path.basename(args.mapfile)
file, extension = os.path.splitext(basename)
self._newfile = '{0}.{1}{2}'.format(file, i, extension)
self._path = os.path.join(args.output, self._newfile)
@property
def fullpath(self):
return self._path
@property
def filename(self):
return os.path.basename(self._newfile)
class Util(object):
@staticmethod
def corr(x, y, center=False):
if center:
x = x - x.mean()
y = y - y.mean()
return sum(x * y) / sqrt(sum(x * x)) / sqrt(sum(x * x))
class Deconvolution(object):
"""
Blind deconvolution for n-dimensional images.
@param data: EM density map data (data field of L{csb.bio.io.mrc.DensityInfo})
@type data: array
@param psf_size: point spread function size
@type psf_size: ints
@param beta_x: hyperparameters of sparseness constraints
@type beta_x: float
@param beta_f: hyperparameters of sparseness constraints
@type beta_f: float
"""
def __init__(self, data, psf_size, beta_x=1e-10, beta_f=1e-10, cache=True):
self._f = []
self._x = []
self._y = numpy.array(data)
self._loss = []
self._corr = []
self._ycache = None
self._cache = bool(cache)
self._beta_x = float(beta_x)
self._beta_f = float(beta_f)
shape_psf = (psf_size, psf_size, psf_size)
self._initialize(shape_psf)
@property
def beta_x(self):
return self._beta_x
@property
def beta_f(self):
return self._beta_f
@property
def loss(self):
"""
Current loss value.
"""
if len(self._loss) > 0:
return float(self._loss[-1])
else:
return None
@property
def correlation(self):
"""
Current correlation value.
"""
if len(self._corr) > 0:
return float(self._corr[-1])
else:
return None
@property
def data(self):
return trim(self._x, self._f.shape)
def _initialize(self, shape_psf):
"""
Initialize with flat image and psf.
"""
self._f = numpy.ones(shape_psf)
self._x = numpy.ones(numpy.array(self._y.shape) + numpy.array(shape_psf) - 1)
self._normalize_psf()
def _normalize_psf(self):
self._f /= self._f.sum()
def _calculate_image(self):
return convolve(self._f, self._x)
def calculate_image(self, cache=False):
if cache and self._ycache is not None:
return self._ycache
else:
y = self._calculate_image()
if self._cache:
self._ycache = y
return y
def _update_map(self):
y = self.calculate_image()
N = correlate(self._f, self._y) - self.beta_x
D = correlate(self._f, y)
self._x *= numpy.clip(N, 1e-300, 1e300) / numpy.clip(D, 1e-300, 1e300)
def _update_psf(self):
y = self.calculate_image()
N = correlate(self._x, self._y) - self.beta_f
D = correlate(self._x, y)
self._f *= numpy.clip(N, 1e-300, 1e300) / numpy.clip(D, 1e-300, 1e300)
self._normalize_psf()
def eval_loss(self, cache=False):
y = self.calculate_image(cache=cache)
return 0.5 * ((self._y - y) ** 2).sum() + \
+ self.beta_f * self._f.sum() + self.beta_x * self._x.sum()
def eval_corr(self, cache=False):
y = self.calculate_image(cache=cache)
return Util.corr(self._y, y)
def run_once(self):
"""
Run a single iteration.
"""
self._loss.append(self.eval_loss(cache=True))
self._corr.append(self.eval_corr(cache=True))
self._update_map()
self._update_psf()
def run(self, iterations):
"""
Run multiple iterations.
@param iterations: number of iterations to run
@type iterations: int
"""
for i in range(iterations):
self.run_once()
def main():
AppRunner().run()
if __name__ == '__main__':
main()
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