Package csb :: Package apps :: Module embd |
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# Source Code for Module csb.apps.embd

      1  """
      2  Sharpening of EM maps by non-negative blind deconvolution.
      3  For details see:
      4
      5  Hirsch M, Schoelkopf B and Habeck M (2010)
      6  A New Algorithm for Improving the Resolution of Cryo-EM Density Maps.
      7  """
      8
      9  import os
     10  import numpy
     11  import csb.apps
     12
     13  from numpy import sum, sqrt
     14
     15  from csb.numeric import convolve, correlate, trim
     16  from csb.bio.io.mrc import DensityMapReader, DensityMapWriter, DensityInfo, DensityMapFormatError
     17
     18
     19 -class ExitCodes(csb.apps.ExitCodes):
     20
     21      IO_ERROR = 2
     22      INVALID_DATA = 3
     23      ARGUMENT_ERROR = 4
     24
     25
     26 -class AppRunner(csb.apps.AppRunner):
     27
     28      @property
     29 -    def target(self):
     30          return DeconvolutionApp
     31
     32 -    def command_line(self):
     33
     34
     35          cmd = csb.apps.ArgHandler(self.program, __doc__)
     36
     37          cmd.add_scalar_option('psf-size', 's', int, 'size of the point spread function', default=15)
     38          cmd.add_scalar_option('output', 'o', str, 'output directory of the sharpened maps', default='.')
     39          cmd.add_scalar_option('iterations', 'i', int, 'number of iterations', default=1000)
     40          cmd.add_scalar_option('output-frequency', 'f', int, 'create a map file each f iterations', default=50)
     41          cmd.add_boolean_option('verbose', 'v', 'verbose mode')
     42
     43          cmd.add_positional_argument('mapfile', str, 'Input Cryo EM file in CCP4 MRC format')
     44
     45          return cmd
     46
     47
     48 -class DeconvolutionApp(csb.apps.Application):
     49
     50 -    def main(self):
     51
     52          if not os.path.isfile(self.args.mapfile):
     53              DeconvolutionApp.exit('Input file not found.', code=ExitCodes.IO_ERROR)
     54
     55          if not os.path.isdir(self.args.output):
     56              DeconvolutionApp.exit('Output directory does not exist.', code=ExitCodes.IO_ERROR)
     57
     58          if self.args.psf_size < 1:
     59              DeconvolutionApp.exit('PSF size must be a positive number.', code=ExitCodes.ARGUMENT_ERROR)
     60
     61          if self.args.iterations < 1:
     62              DeconvolutionApp.exit('Invalid number of iterations.', code=ExitCodes.ARGUMENT_ERROR)
     63
     64          if self.args.output_frequency < 1:
     65              DeconvolutionApp.exit('Output frequency must be a positive number.', code=ExitCodes.ARGUMENT_ERROR)
     66
     67          if self.args.iterations < self.args.output_frequency:
     68              DeconvolutionApp.exit('Output frequency is too low.', code=ExitCodes.ARGUMENT_ERROR)
     69
     70          self.args.output = os.path.abspath(self.args.output)
     71
     72          self.run()
     73
     74 -    def run(self):
     75
     76          writer = DensityMapWriter()
     77
     78          self.log('Reading input density map...')
     79          try:
     80              input = DensityMapReader(self.args.mapfile).read()
     81              embd = Deconvolution(input.data, self.args.psf_size)
     82
     83          except DensityMapFormatError as e:
     84              msg = 'Error reading input MRC file: {0}'.format(e)
     85              DeconvolutionApp.exit(msg, code=ExitCodes.INVALID_DATA)
     86
     87          self.log('Running {0} iterations...'.format(self.args.iterations))
     88          self.log(' Iteration             Loss Correlation  Output')
     89
     90          for i in range(1, self.args.iterations + 1):
     91              embd.run_once()
     92
     93              if i % self.args.output_frequency == 0:
     94                  output = OutputPathBuilder(self.args, i)
     95
     96                  density = DensityInfo(embd.data, None, None, header=input.header)
     97                  writer.write_file(output.fullpath, density)
     98
     99                  self.log('{0:>9}.  {1:15.2f}  {2:10.4f}  {3}'.format(
    100                                      i, embd.loss, embd.correlation, output.filename))
    101
    102          self.log('Done: {0}.'.format(output.fullpath))
    103
    104 -    def log(self, *a, **k):
    105
    106          if self.args.verbose:
    107              super(DeconvolutionApp, self).log(*a, **k)
    108
    109
    110 -class OutputPathBuilder(object):
    111
    112 -    def __init__(self, args, i):
    113
    114          basename = os.path.basename(args.mapfile)
    115          file, extension = os.path.splitext(basename)
    116
    117          self._newfile = '{0}.{1}{2}'.format(file, i, extension)
    118          self._path = os.path.join(args.output, self._newfile)
    119
    120      @property
    121 -    def fullpath(self):
    122          return self._path
    123
    124      @property
    125 -    def filename(self):
    126          return os.path.basename(self._newfile)
    127
    128 -class Util(object):
    129
    130      @staticmethod
    131 -    def corr(x, y, center=False):
    132
    133          if center:
    134              x = x - x.mean()
    135              y = y - y.mean()
    136
    137          return sum(x * y) / sqrt(sum(x * x)) / sqrt(sum(x * x))
    138
    139 -class Deconvolution(object):
    140      """
    141      Blind deconvolution for n-dimensional images.
    142
    143      @param data: EM density map data (data field of L{csb.bio.io.mrc.DensityInfo})
    144      @type data: array
    145      @param psf_size: point spread function size
    146      @type psf_size: ints
    147      @param beta_x: hyperparameters of sparseness constraints
    148      @type beta_x: float
    149      @param beta_f: hyperparameters of sparseness constraints
    150      @type beta_f: float
    151      """
    152
    153 -    def __init__(self, data, psf_size, beta_x=1e-10, beta_f=1e-10, cache=True):
    154
    155          self._f = []
    156          self._x = []
    157          self._y = numpy.array(data)
    158          self._loss = []
    159          self._corr = []
    160
    161          self._ycache = None
    162          self._cache = bool(cache)
    163
    164          self._beta_x = float(beta_x)
    165          self._beta_f = float(beta_f)
    166
    167          shape_psf = (psf_size, psf_size, psf_size)
    168          self._initialize(shape_psf)
    169
    170      @property
    171 -    def beta_x(self):
    172          return self._beta_x
    173
    174      @property
    175 -    def beta_f(self):
    176          return self._beta_f
    177
    178      @property
    179 -    def loss(self):
    180          """
    181          Current loss value.
    182          """
    183          if len(self._loss) > 0:
    184              return float(self._loss[-1])
    185          else:
    186              return None
    187
    188      @property
    189 -    def correlation(self):
    190          """
    191          Current correlation value.
    192          """
    193          if len(self._corr) > 0:
    194              return float(self._corr[-1])
    195          else:
    196              return None
    197
    198      @property
    199 -    def data(self):
    200          return trim(self._x, self._f.shape)
    201
    202 -    def _initialize(self, shape_psf):
    203          """
    204          Initialize with flat image and psf.
    205          """
    206          self._f = numpy.ones(shape_psf)
    207          self._x = numpy.ones(numpy.array(self._y.shape) + numpy.array(shape_psf) - 1)
    208
    209          self._normalize_psf()
    210
    211 -    def _normalize_psf(self):
    212          self._f /= self._f.sum()
    213
    214 -    def _calculate_image(self):
    215          return convolve(self._f, self._x)
    216
    217 -    def calculate_image(self, cache=False):
    218
    219          if cache and self._ycache is not None:
    220              return self._ycache
    221          else:
    222              y = self._calculate_image()
    223              if self._cache:
    224                  self._ycache = y
    225              return y
    226
    227 -    def _update_map(self):
    228
    229          y = self.calculate_image()
    230
    231          N = correlate(self._f, self._y) - self.beta_x
    232          D = correlate(self._f, y)
    233
    234          self._x *= numpy.clip(N, 1e-300, 1e300) / numpy.clip(D, 1e-300, 1e300)
    235
    236 -    def _update_psf(self):
    237
    238          y = self.calculate_image()
    239
    240          N = correlate(self._x, self._y) - self.beta_f
    241          D = correlate(self._x, y)
    242
    243          self._f *= numpy.clip(N, 1e-300, 1e300) / numpy.clip(D, 1e-300, 1e300)
    244          self._normalize_psf()
    245
    246 -    def eval_loss(self, cache=False):
    247
    248          y = self.calculate_image(cache=cache)
    249
    250          return 0.5 * ((self._y - y) ** 2).sum() + \
    251                  + self.beta_f * self._f.sum() + self.beta_x * self._x.sum()
    252
    253 -    def eval_corr(self, cache=False):
    254
    255          y = self.calculate_image(cache=cache)
    256          return Util.corr(self._y, y)
    257
    258 -    def run_once(self):
    259          """
    260          Run a single iteration.
    261          """
    262
    263          self._loss.append(self.eval_loss(cache=True))
    264          self._corr.append(self.eval_corr(cache=True))
    265
    266          self._update_map()
    267          self._update_psf()
    268
    269 -    def run(self, iterations):
    270          """
    271          Run multiple iterations.
    272
    273          @param iterations: number of iterations to run
    274          @type iterations: int
    275          """
    276          for i in range(iterations):
    277              self.run_once()
    278
    279
    280
    281  if __name__ == '__main__':
    282
    283      AppRunner().run()
    284