# **************************************************************************
# *
# * Authors: Yunior C. Fonseca Reyna (cfonseca@cnb.csic.es)
# *
# *
# * Unidad de Bioinformatica of Centro Nacional de Biotecnologia , CSIC
# *
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 2 of the License, or
# * (at your option) any later version.
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# * GNU General Public License for more details.
# *
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307 USA
# *
# * All comments concerning this program package may be sent to the
# * e-mail address 'scipion@cnb.csic.es'
# *
# **************************************************************************
import os
import emtable
from pwem import ALIGN_PROJ
from pwem.protocols import ProtOperateParticles
import pyworkflow.utils as pwutils
from pyworkflow.object import String
from pyworkflow.protocol.params import (PointerParam, FloatParam,
LEVEL_ADVANCED, IntParam, Positive,
BooleanParam, EnumParam)
from pwem.objects import Volume
from .protocol_base import ProtCryosparcBase
from ..convert import (defineArgs, convertCs2Star, createItemMatrix,
setCryosparcAttributes)
from ..utils import (addComputeSectionParams, calculateNewSamplingRate,
cryosparcValidate, gpusValidate, enqueueJob,
waitForCryosparc, clearIntermediateResults, fixVolume,
copyFiles)
from ..constants import *
[docs]class ProtCryoSparcNaiveLocalRefine(ProtCryosparcBase, ProtOperateParticles):
""" Signal subtraction protocol of cryoSPARC.
Subtract projections of a masked volume from particles.
"""
_label = 'naive local refinement(Legacy)'
_className = "naive_local_refine"
_fscColumns = 6
def _initialize(self):
self._defineFileNames()
def _defineFileNames(self):
""" Centralize how files are called. """
myDict = {
'input_particles': self._getTmpPath('input_particles.star'),
'out_particles': self._getExtraPath('output_particle.star'),
'stream_log': self._getPath() + '/stream.log'
}
self._updateFilenamesDict(myDict)
def _defineParams(self, form):
form.addSection(label='Input')
form.addParam('inputParticles', PointerParam,
pointerClass='SetOfParticles',
pointerCondition='hasAlignmentProj',
label="Input particles", important=True,
help='Select the experimental particles.')
form.addParam('refVolume', PointerParam, pointerClass='Volume',
label="Input map to be projected",
important=True,
help='Provide the input volume that will be used to '
'calculate projections, which will be subtracted '
'from the experimental particles. Make sure this '
'map was calculated by RELION from the same '
'particles as above, and preferably with those '
'orientations, as it is crucial that the absolute '
'greyscale is the same as in the experimental '
'particles.')
form.addParam('refMask', PointerParam, pointerClass='VolumeMask',
label='Mask to be applied to this map',
important=True,
allowsNull=False,
help="Provide a soft mask where the protein density "
"you wish to subtract from the experimental "
"particles is white (1) and the rest of the "
"protein and the solvent is black (0). "
"That is: *the mask should INCLUDE the part of the "
"volume that you wish to SUBTRACT.*")
# -----------[Local Refinement]------------------------
form.addSection(label="Naive local refinement")
form.addParam('local_align_extent_pix', IntParam, default=3,
validators=[Positive],
label='Local shift search extent (pix)',
help='The maximum extent of local shifts that will be '
'searched over, in pixels')
form.addParam('local_align_extent_deg', IntParam, default=10,
label='Local rotation search extent (degrees)',
help='The maximum magnitude of the change in rotations '
'to search over, in degrees')
form.addParam('local_align_max_align', FloatParam, default=0.5,
validators=[Positive],
label='Alignment resolution (degrees)',
help='Smallest search distance between angles, in degrees')
form.addParam('local_align_grid_r', IntParam, default=9,
validators=[Positive],
label='Local shift search grid size',
help='The number of points on the search grid for local '
'shifts')
form.addParam('local_align_grid_t', IntParam, default=9,
validators=[Positive],
label='Local rotation search grid size',
help='The number of points on the search grid for local '
'rotations')
form.addParam('override_final_radwn', BooleanParam,
default=False,
label='Override final radwn')
form.addParam('n_iterations', IntParam, default=1,
validators=[Positive],
label='Override number of iterations')
# -----[Non Uniform Refinement]----------------------------------------
form.addSection(label='Non-uniform refinement')
form.addParam('NU-refine', BooleanParam, default=False,
label='Use Non-Uniform Refinement')
# -----[Refinement]----------------------------------------
form.addSection(label='Refinement')
form.addParam('refine_num_final_iterations', IntParam, default=1,
label="Number of extra final passes",
help='Number of extra passes through the data to do '
'after the GS-FSC resolution has stopped improving')
form.addParam('refine_res_init', IntParam, default=20,
validators=[Positive],
label="Initial lowpass resolution (A)",
help='Applied to input structure')
form.addParam('refine_res_gsfsc_split', IntParam, default=20,
validators=[Positive],
label="GSFSC split resolution (A)",
help='Resolution beyond which two GS-FSC halves are '
'independent')
form.addParam('refine_FSC_inflate_factor', IntParam, default=1,
validators=[Positive],
expertLevel=LEVEL_ADVANCED,
label="FSC Inflate Factor")
form.addParam('refine_clip', BooleanParam, default=True,
label="Enforce non-negativity",
help='Clip negative density. Probably should be false')
form.addParam('refine_window', BooleanParam, default=True,
label="Skip interpolant premult",
help='Softly window the structure in real space with a '
'spherical window. Should be true')
form.addParam('refine_skip_premult', BooleanParam, default=True,
label="Window structure in real space",
help='Leave this as true')
form.addParam('refine_ignore_dc', BooleanParam, default=True,
label="Ignore DC component",
help='Ignore the DC component of images. Should be true')
form.addParam('refine_batchsize_init', IntParam, default=0,
expertLevel=LEVEL_ADVANCED,
label="Initial batchsize",
help='Number of images used in the initial iteration. '
'Set to zero to autotune')
form.addParam('refine_batchsize_epsilon', FloatParam, default=0.001,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Batchsize epsilon",
help='Controls batch size when autotuning batchsizes. '
'Set closer to zero for larger batches')
form.addParam('refine_batchsize_snrfactor', FloatParam, default=40.0,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Batchsize snrfactor",
help='Specifies the desired improvement in SNR from the '
'images when autotuning batchsizes. Directly '
'multiplies the number of images in the batch')
form.addParam('refine_scale_min', BooleanParam, default=False,
expertLevel=LEVEL_ADVANCED,
label="Minimize over per-particle scale")
form.addParam('refine_scale_align_use_prev', BooleanParam,
default=True,
expertLevel=LEVEL_ADVANCED,
label="Use scales from previous iteration during "
"alignment")
form.addParam('refine_scale_ctf_use_current', BooleanParam,
expertLevel=LEVEL_ADVANCED,
default=True,
label="Use scales from current alignment in reconstruction",
help='Use scales from current alignment in reconstruction')
form.addParam('refine_scale_start_iter', IntParam, default=0,
label="Scale min/use start iter",
help='Iteration to start minimizing over per-particle scale')
form.addParam('refine_noise_model', EnumParam,
choices=['symmetric', 'white', 'coloured'],
default=0,
label="Noise model:",
help='Noise model to be used. Valid options are white, '
'coloured or symmetric. Symmetric is the default, '
'meaning coloured with radial symmetry')
form.addParam('refine_noise_priorw', IntParam, default=50,
validators=[Positive],
expertLevel=LEVEL_ADVANCED,
label="Noise priorw",
help='Weight of the prior for estimating noise (units of '
'# of images)')
form.addParam('refine_noise_initw', IntParam, default=200,
validators=[Positive],
expertLevel=LEVEL_ADVANCED,
label="Noise initw",
help='Weight of the initial noise estimate (units of # '
'of images)')
form.addParam('refine_noise_init_sigmascale', IntParam, default=3,
validators=[Positive],
expertLevel=LEVEL_ADVANCED,
label="Noise initial sigma-scale",
help='Scale factor initially applied to the base noise '
'estimate')
form.addParam('refine_mask', EnumParam,
choices=['dynamic', 'static', 'null'],
default=0,
label="Mask:",
help='Type of masking to use. Either "dynamic", '
'"static", or "null"')
form.addParam('refine_dynamic_mask_thresh_factor', FloatParam,
expertLevel=LEVEL_ADVANCED,
default=0.2,
validators=[Positive],
label="Dynamic mask threshold (0-1)",
help='Level set threshold for selecting regions that are '
'included in the dynamic mask. Probably don\'t need '
'to change this')
form.addParam('refine_dynamic_mask_near_ang', FloatParam,
expertLevel=LEVEL_ADVANCED,
default=3.0,
validators=[Positive],
label="Dynamic mask near (A)",
help='Controls extent to which mask is expanded. At the '
'near distance, the mask value is 1.0 (in A)')
form.addParam('refine_dynamic_mask_far_ang', FloatParam,
expertLevel=LEVEL_ADVANCED,
default=6,
validators=[Positive],
label="Dynamic mask far (A)",
help='Controls extent to which mask is expanded. At the '
'far distance the mask value becomes 0.0 (in A)')
# --------------[Compute settings]---------------------------
form.addSection(label="Compute settings")
addComputeSectionParams(form, allowMultipleGPUs=False)
# --------------------------- INSERT steps functions -----------------------
def _insertAllSteps(self):
self._defineFileNames()
self._defineParamsName()
self._initializeCryosparcProject()
self._insertFunctionStep(self.convertInputStep)
self._insertFunctionStep(self.processStep)
self._insertFunctionStep(self.createOutputStep)
# --------------------------- STEPS functions ------------------------------
[docs] def processStep(self):
print(pwutils.yellowStr("Local Refinement started..."), flush=True)
self.doLocalRefine()
[docs] def createOutputStep(self):
"""
Create the protocol output. Convert cryosparc file to Relion file
"""
self._initializeUtilsVariables()
idd, itera = self.findLastIteration(self.runLocalRefinement.get())
csOutputFolder = os.path.join(self.projectPath, self.projectName.get(),
self.runLocalRefinement.get())
csOutputPattern = "cryosparc_%s_%s_%s" % (self.projectName.get(),
self.runLocalRefinement.get(),
itera)
csParticlesName = csOutputPattern + "_particles.cs"
fnVolName = csOutputPattern + "_volume_map.mrc"
half1Name = csOutputPattern + "_volume_map_half_A.mrc"
half2Name = csOutputPattern + "_volume_map_half_B.mrc"
# Copy the CS output volume and half to extra folder
copyFiles(csOutputFolder, self._getExtraPath(), files=[csParticlesName,
fnVolName,
half1Name,
half2Name])
csFile = os.path.join(self._getExtraPath(), csParticlesName)
outputStarFn = self._getFileName('out_particles')
argsList = [csFile, outputStarFn]
parser = defineArgs()
args = parser.parse_args(argsList)
convertCs2Star(args)
fnVol = os.path.join(self._getExtraPath(), fnVolName)
half1 = os.path.join(self._getExtraPath(), half1Name)
half2 = os.path.join(self._getExtraPath(), half2Name)
imgSet = self._getInputParticles()
vol = Volume()
fixVolume([fnVol, half1, half2])
vol.setFileName(fnVol)
vol.setSamplingRate(calculateNewSamplingRate(vol.getDim(),
imgSet.getSamplingRate(),
imgSet.getDim()))
vol.setHalfMaps([half1, half2])
outImgSet = self._createSetOfParticles()
outImgSet.copyInfo(imgSet)
self._fillDataFromIter(outImgSet)
self._defineOutputs(outputVolume=vol)
self._defineSourceRelation(self.inputParticles.get(), vol)
self._defineOutputs(outputParticles=outImgSet)
self._defineTransformRelation(self.inputParticles.get(), outImgSet)
self.createFSC(idd, imgSet, vol)
# --------------------------- INFO functions -------------------------------
def _validate(self):
""" Should be overwritten in subclasses to
return summary message for NORMAL EXECUTION.
"""
validateMsgs = cryosparcValidate()
if not validateMsgs:
validateMsgs = gpusValidate(self.getGpuList(), checkSingleGPU=True)
if not validateMsgs:
self._validateDim(self._getInputParticles(), self.refVolume.get(),
validateMsgs, 'Input particles', 'Input volume')
if not validateMsgs:
particles = self._getInputParticles()
if not particles.hasCTF():
validateMsgs.append(
"The Particles has not associated a "
"CTF model")
return validateMsgs
def _summary(self):
summary = []
if (not hasattr(self, 'outputVolume') or
not hasattr(self, 'outputParticles')):
summary.append("Output objects not ready yet.")
else:
summary.append("Input Particles: %s" %
self.getObjectTag('inputParticles'))
summary.append("Input Volume: %s" %
self.getObjectTag('refVolume'))
summary.append("Input Mask: %s" %
self.getObjectTag('refMask'))
summary.append("------------------------------------------")
summary.append("Output particles %s" %
self.getObjectTag('outputParticles'))
summary.append("Output volume %s" %
self.getObjectTag('outputVolume'))
if self.hasAttribute('mapResolution'):
summary.append("\nMap Resolution: %s" % self.mapResolution.get())
if self.hasAttribute('estBFactor'):
summary.append('\nEstimated Bfactor: %s' % self.estBFactor.get())
return summary
# ---------------Utils Functions-------------------------------------------
def _fillDataFromIter(self, imgSet):
outImgsFn = 'particles@' + self._getFileName('out_particles')
imgSet.setAlignmentProj()
imgSet.copyItems(self._getInputParticles(),
updateItemCallback=self._createItemMatrix,
itemDataIterator=emtable.Table.iterRows(outImgsFn))
def _createItemMatrix(self, particle, row):
createItemMatrix(particle, row, align=ALIGN_PROJ)
setCryosparcAttributes(particle, row,
RELIONCOLUMNS.rlnRandomSubset.value)
def _defineParamsName(self):
""" Define a list with all protocol parameters names"""
self._paramsName = ['local_align_extent_pix', 'local_align_extent_deg',
'local_align_max_align', 'local_align_grid_r',
'local_align_grid_t', 'override_final_radwn',
'n_iterations',
'refine_num_final_iterations',
'refine_res_init',
'refine_res_gsfsc_split',
'refine_clip',
'refine_window', 'refine_skip_premult',
'refine_ignore_dc',
'refine_batchsize_init',
'refine_batchsize_snrfactor',
'refine_batchsize_epsilon',
'refine_scale_min', 'refine_scale_align_use_prev',
'refine_scale_ctf_use_current',
'refine_scale_start_iter',
'refine_noise_model', 'refine_noise_priorw',
'refine_noise_initw',
'refine_mask',
'refine_dynamic_mask_thresh_factor',
'refine_dynamic_mask_near_ang',
'refine_dynamic_mask_far_ang',
'compute_use_ssd']
self.lane = str(self.getAttributeValue('compute_lane'))
[docs] def doLocalRefine(self):
"""
:return:
"""
if self.mask.get() is not None:
input_group_connect = {"particles": self.particles.get(),
"volume": self.volume.get(),
"mask": self.mask.get()}
else:
input_group_connect = {"particles": self.particles.get(),
"volume": self.volume.get()}
input_result_connect = None
if self._getInputVolume().hasHalfMaps():
input_result_connect = {"volume.0.map_half_A": self.importVolumeHalfA.get(),
"volume.0.map_half_B": self.importVolumeHalfB.get()}
params = {}
for paramName in self._paramsName:
if (paramName != 'refine_noise_model' and
paramName != 'refine_mask'):
params[str(paramName)] = str(self.getAttributeValue(paramName))
elif paramName == 'refine_noise_model':
params[str(paramName)] = str(
NOISE_MODEL_CHOICES[self.refine_noise_model.get()])
elif paramName == 'refine_mask':
params[str(paramName)] = str(
REFINE_MASK_CHOICES[self.refine_mask.get()])
# Determinate the GPUs to use (in dependence of
# the cryosparc version)
try:
gpusToUse = self.getGpuList()
except Exception:
gpusToUse = False
runLocalRefinementJob = enqueueJob(self._className, self.projectName.get(),
self.workSpaceName.get(),
str(params).replace('\'', '"'),
str(input_group_connect).replace('\'', '"'),
self.lane, gpusToUse,
result_connect=input_result_connect)
self.runLocalRefinement = String(runLocalRefinementJob.get())
self.currenJob.set(runLocalRefinementJob.get())
self._store(self)
waitForCryosparc(self.projectName.get(), self.runLocalRefinement.get(),
"An error occurred in the local refinement process. "
"Please, go to cryoSPARC software for more "
"details.")
clearIntermediateResults(self.projectName.get(), self.runLocalRefinement.get())