# **************************************************************************
# *
# * Authors: Szu-Chi Chung (phonchi@stat.sinica.edu.tw)
# * Yunior C. Fonseca Reyna (cfonseca@cnb.csic.es)
# *
# * SABID Laboratory, Institute of Statistical Science, Academia Sinica
# * 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 emtable
import pwem.objects as pwobj
import pwem.protocols as pwprot
import pyworkflow.utils as pwutils
from pyworkflow.protocol.params import *
from .protocol_base import ProtCryosparcBase
from ..convert import (defineArgs, convertCs2Star, createItemMatrix,
setCryosparcAttributes)
from ..utils import (addSymmetryParam, addComputeSectionParams,
calculateNewSamplingRate,
cryosparcValidate, gpusValidate, getSymmetry,
waitForCryosparc, clearIntermediateResults, enqueueJob,
fixVolume, copyFiles)
from ..constants import (NOISE_MODEL_CHOICES, REFINE_MASK_CHOICES,
RELIONCOLUMNS)
[docs]class ProtCryoSparcRefine3D(ProtCryosparcBase, pwprot.ProtRefine3D):
""" Protocol to refine a 3D map using cryosparc.
Rapidly refine a single homogeneous structure to high-resolution and
validate using the gold-standard FSC.
"""
_label = '3D homogeneous refinement(Legacy)'
_fscColumns = 6
_className = "homo_refine"
# --------------------------- DEFINE param functions ----------------------
def _defineFileNames(self):
""" Centralize how files are called within the protocol. """
myDict = {
'input_particles': self._getTmpPath('input_particles.star'),
'out_particles': self._getPath() + '/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',
label="Input particles", important=True,
validators=[Positive],
help='Select the input images from the project.')
form.addParam('referenceVolume', PointerParam, pointerClass='Volume',
important=True,
label="Input volume",
help='Initial reference 3D map, it should have the same '
'dimensions and the same pixel size as your input '
'particles.')
form.addParam('refMask', PointerParam, pointerClass='VolumeMask',
default=None,
label='Mask to be applied to this map(Optional)',
allowsNull=True,
help='A volume mask containing a (soft) mask with '
'the same dimensions as the reference(s), '
'and values between 0 and 1, with 1 being 100% '
'protein and 0 being 100% solvent. The '
'reconstructed reference map will be multiplied '
'by this mask. If no mask is given, a soft '
'spherical mask based on the <radius> of the '
'mask for the experimental images will be '
'applied.')
# --------------[Homogeneous Refinement]---------------------------
form.addSection(label='Refinement')
form.addParam('refine_N', IntParam, default=0,
expertLevel=LEVEL_ADVANCED,
label="Refinement box size (Voxels)",
help='The volume size to use for refinement. If this is '
'0, use the full image size. Otherwise images '
'are automatically downsampled')
addSymmetryParam(form, help="Symmetry String (C, D, I, O, T). E.g. C1, "
"D7, C4, etc")
form.addParam('refine_symmetry_do_align', BooleanParam, default=True,
label="Do symmetry alignment",
help='Align the input structure to the symmetry axes')
form.addParam('refine_do_init_scale_est', BooleanParam, default=True,
label="Re-estimate greyscale level of input reference")
form.addParam('refine_num_final_iterations', IntParam, default=0,
expertLevel=LEVEL_ADVANCED,
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=30,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Initial lowpass resolution (A)",
help='Applied to input structure')
form.addParam('refine_res_gsfsc_split', IntParam, default=20,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="GSFSC split resolution (A)",
help='Resolution beyond which two GS-FSC halves are '
'independent')
form.addParam('refine_SPW', BooleanParam, default=False,
expertLevel=LEVEL_ADVANCED,
label="Use SPW")
form.addParam('refine_clip', BooleanParam, default=False,
expertLevel=LEVEL_ADVANCED,
label="Enforce non-negativity",
help='Clip negative density. Probably should be false')
form.addParam('refine_window', BooleanParam, default=True,
expertLevel=LEVEL_ADVANCED,
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,
expertLevel=LEVEL_ADVANCED,
label="Window structure in real space",
help='Leave this as true')
form.addParam('refine_ignore_dc', BooleanParam, default=True,
expertLevel=LEVEL_ADVANCED,
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=False,
expertLevel=LEVEL_ADVANCED,
label="Use scales from previous iteration during "
"alignment")
form.addParam('refine_scale_ctf_use_current', BooleanParam,
expertLevel=LEVEL_ADVANCED,
default=False,
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,
expertLevel=LEVEL_ADVANCED,
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,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Noise priorw",
help='Weight of the prior for estimating noise (units of '
'# of images)')
form.addParam('refine_noise_initw', IntParam, default=200,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Noise initw",
help='Weight of the initial noise estimate (units of # '
'of images)')
form.addParam('refine_noise_init_sigmascale', IntParam, default=3,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Noise initial sigma-scale",
help='Scale factor initially applied to the base noise '
'estimate')
form.addParam('refine_minisize', IntParam, default=2000,
expertLevel=LEVEL_ADVANCED,
validators=[Positive],
label="Computational minibatch size",
help='Number of images to use in each minibatch - only '
'affects computational performance. 1000 is a good '
'number, but try 4000 if you have lots of RAM')
form.addParam('refine_mask', EnumParam,
choices=['dynamic', 'static', 'null'],
default=0,
expertLevel=LEVEL_ADVANCED,
label="Mask:",
help='Type of masking to use. Either "dynamic", '
'"static", or "null"')
form.addParam('refine_dynamic_mask_thresh_factor', FloatParam, default=0.2,
expertLevel=LEVEL_ADVANCED,
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=6.0,
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=14.0,
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)')
form.addParam('refine_dynamic_mask_start_res', IntParam,
expertLevel=LEVEL_ADVANCED,
default=12,
validators=[Positive],
label="Dynamic mask start resolution (A)",
help='Map resolution at which to start dynamic masking '
'(in A)')
form.addParam('refine_dynamic_mask_use_abs', BooleanParam,
expertLevel=LEVEL_ADVANCED,
default=False,
label="Dynamic mask use absolute value",
help='Include negative regions if they are more negative '
'than the threshold')
# --------------[Compute settings]---------------------------
form.addSection(label="Compute settings")
addComputeSectionParams(form, allowMultipleGPUs=True)
# --------------------------- 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("Refinement started..."), flush=True)
self.doRunRefine()
[docs] def createOutputStep(self):
"""
Create the protocol output. Convert cryosparc file to Relion file
"""
print(pwutils.yellowStr("Creating the output..."), flush=True)
csOutputFolder = os.path.join(self.projectPath, self.projectName.get(),
self.runRefine.get())
idd, itera = self.findLastIteration(self.runRefine.get())
csOutputPattern = "cryosparc_%s_%s_%s" % (self.projectName.get(),
self.runRefine.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 = pwobj.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)
def _validate(self):
validateMsgs = cryosparcValidate()
if not validateMsgs:
validateMsgs = gpusValidate(self.getGpuList())
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('referenceVolume'))
summary.append("Input Mask: %s" %
self.getObjectTag('refMask'))
summary.append("Symmetry: %s" %
getSymmetry(self.symmetryGroup.get(),
self.symmetryOrder.get())
)
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 _getInputVolume(self):
return self.referenceVolume.get()
def _fillDataFromIter(self, imgSet):
outImgsFn = 'particles@' + self._getFileName('out_particles')
imgSet.setAlignmentProj()
imgSet.copyItems(self._getInputParticles(),
updateItemCallback=self._createItemMatrix,
itemDataIterator=emtable.Table.iterRows(fileName=outImgsFn))
def _createItemMatrix(self, particle, row):
createItemMatrix(particle, row, align=pwobj.ALIGN_PROJ)
setCryosparcAttributes(particle, row, RELIONCOLUMNS.rlnRandomSubset.value)
def _defineParamsName(self):
""" Define a list with all protocol parameters names"""
self._paramsName = ['refine_N',
'refine_symmetry',
'refine_symmetry_do_align',
'refine_do_init_scale_est',
'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_noise_init_sigmascale',
'refine_minisize', 'refine_mask',
'refine_dynamic_mask_thresh_factor',
'refine_dynamic_mask_near_ang',
'refine_dynamic_mask_far_ang',
'refine_dynamic_mask_start_res',
'refine_dynamic_mask_use_abs',
'compute_use_ssd']
self.lane = str(self.getAttributeValue('compute_lane'))
[docs] def doRunRefine(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()}
params = {}
for paramName in self._paramsName:
if (paramName != 'refine_symmetry' and
paramName != 'refine_noise_model' and
paramName != 'refine_mask' and
paramName != 'refine_N'):
params[str(paramName)] = str(self.getAttributeValue(paramName))
elif (paramName == 'refine_N' and
int(self.getAttributeValue(paramName)) > 0):
params[str(paramName)] = str(self.getAttributeValue(paramName))
elif paramName == 'refine_symmetry':
symetryValue = getSymmetry(self.symmetryGroup.get(),
self.symmetryOrder.get())
params[str(paramName)] = symetryValue
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
runRefineJob = enqueueJob(self._className, self.projectName.get(),
self.workSpaceName.get(),
str(params).replace('\'', '"'),
str(input_group_connect).replace('\'', '"'),
self.lane, gpusToUse)
self.runRefine = String(runRefineJob.get())
self.currenJob.set(runRefineJob.get())
self._store(self)
waitForCryosparc(self.projectName.get(), self.runRefine.get(),
"An error occurred in the Refinement process. "
"Please, go to cryoSPARC software for more "
"details.")
clearIntermediateResults(self.projectName.get(), self.runRefine.get())