test

MDANSE/Framework/ClassRegistry.py

+import abc
+
+class _Meta(type):
+    '''
+    Metaclass that allows to use the __getitem__ method at a class level for the class that has been built.
+     
+    The class that uses this metaclass must define a class attribute named _registry that will be used
+    by the __getitem__ method.
+    '''
+         
+    def __getitem__(self, item):
+        """
+        Returns a given item stored in the class registry
+        """
+         
+        return self._registry[item]
+    
+class ClassRegistry(abc.ABCMeta):
+    '''
+    Metaclass that registers the subclasses of bases classes.
+
+    The internal registry is defined as a nested dictionary whose keys 
+    are the |type| class attribute of the base classes and values another dictionary 
+    whose keys are the |type| class attribute of the subclasses and values are the corresponding 
+    class instances.
+
+    Hence any base or child class that does not define |type| class attribute will not be resgistered.
+    '''
+    
+    __metaclass__ = _Meta
+
+    __interfaces = []
+
+    _registry = {}
+                
+    def __init__(self, name, bases, namespace):
+        '''
+        Constructor of a class metaclassed by ClassFactory
+        
+        :param name: the name of the class to be built by this metaclass
+        :param bases: the base classes of the class to be built by this metaclass
+        :param namespace: the attributes and methods of the class to be built by this metaclass
+        '''
+        
+        super(ClassRegistry, self).__init__(name, bases, namespace)
+        
+        # Get the typ of the class
+        typ = getattr(self, 'type', None)
+                
+        if typ is None:
+            return
+
+        metaClass = namespace.get("__metaclass__", None)
+                
+        if metaClass is ClassRegistry:
+            ClassRegistry.__interfaces.append(self)
+            ClassRegistry._registry[typ] = {}
+
+        else:
+                                
+            for interface in ClassRegistry.__interfaces:
+                
+                if issubclass(self, interface):
+                    ClassRegistry._registry[interface.type][typ] = self
+                    break
+
+    @classmethod
+    def info(cls, interface):
+        '''
+        Returns informations about the subclasses of a given base class  stored in the registry.
+        
+        :param cls: the ClassRegsitry instance
+        :param interface: the name of base class of whom information about its subclasses is requested
+        '''
+                
+        if not cls._registry.has_key(interface):
+            return "The interface " + interface + " is not registered"
+
+        import inspect
+        import os
+
+        # Dictionnay whose keys are the package names and values and list of (job name, job path) stored in the corresponding package. 
+        packages = {}
+
+        # Loop over the registry items.
+        for k, v in cls._registry[interface].items():
+            
+            # Get the module corresponding to the job class.
+            mod = inspect.getmodule(v)
+
+            # The package hosting the module.
+            modPackage = mod.__package__
+        
+            # The module file.
+            modFilename = mod.__file__
+        
+            # If no package could be found, guess a name using the directory name of the module file.
+            if modPackage is None:
+                modPackage = os.path.split(os.path.dirname(modFilename))[1]
+        
+            # Update the packages dictionary.
+            if packages.has_key(modPackage):
+                packages[modPackage].append([k, v.__name__])
+            else:
+                packages[modPackage] = [[k, v.__name__]]
+    
+        contents = []
+        
+        # Print the contents of the packages dictionary.
+        contents.append("="*130)
+        contents.append("%-50s %-40s %-s" % ("Package", "Name", "Class"))
+        contents.append("="*130)
+        for k, v in sorted(packages.items()):
+            for vv in sorted(v): 
+                contents.append("%-50s %-40s %-s" % (k, vv[0], vv[1]))
+            contents.append('-' * 130)
+        
+        contents = "\n".join(contents)
+    
+        return contents
+

MDANSE/Framework/Configurables/InstrumentResolutions.py

+'''
+MDANSE : Molecular Dynamics Analysis for Neutron Scattering Experiments
+------------------------------------------------------------------------------------------
+Copyright (C)
+2015- Eric C. Pellegrini Institut Laue-Langevin
+BP 156
+6, rue Jules Horowitz
+38042 Grenoble Cedex 9
+France
+pellegrini[at]ill.fr
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library 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
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+ 
+Created on Mar 23, 2015
+
+@author: pellegrini
+'''
+
+import abc
+
+import numpy
+
+from MDANSE import REGISTRY
+from MDANSE.Framework.Configurables.Configurable import Configurable
+from MDANSE.Framework.Configurables.Configurators import ConfiguratorsDict
+from MDANSE.Core.Error import Error
+
+class InstrumentResolutionError(Error):
+    pass
+
+class InstrumentResolution(Configurable):
+
+    __metaclass__ = REGISTRY
+    
+    type = "instrument resolution"
+    
+    def __init__(self):
+        
+        Configurable.__init__(self)
+                        
+        self._frequencyWindow = None
+
+        self._timeWindow = None
+        
+    @abc.abstractmethod
+    def set_kernel(self, frequencies, dt, parameters=None):
+        pass    
+    
+    @property
+    def frequencyWindow(self):
+        
+        if self._frequencyWindow is None:
+            raise InstrumentResolutionError("Undefined frequency window")
+        
+        return self._frequencyWindow
+
+    @property
+    def timeWindow(self):
+        
+        if self._timeWindow is None:
+            raise InstrumentResolutionError("Undefined time window")
+        
+        return self._timeWindow
+
+class IdealInstrumentResolution(InstrumentResolution):
+    """Defines an ideal instrument resolution with a Dirac response 
+    """
+    
+    type = 'ideal'
+    
+    configurators = ConfiguratorsDict()
+
+    __doc__ += configurators.build_doc()
+
+    def set_kernel(self, frequencies, dt):
+                
+        self._frequencyWindow = numpy.zeros(len(frequencies), dtype=numpy.float64)
+        self._frequencyWindow[len(frequencies)/2] = 1.0
+
+        self._timeWindow = numpy.ones(len(frequencies), dtype=numpy.float64)
+
+class GaussianInstrumentResolution(InstrumentResolution):
+    """Defines an instrument resolution with a gaussian response
+    """
+    
+    type = 'gaussian'
+
+    configurators = ConfiguratorsDict()
+    configurators.add_item('mu', 'float', default=0.0)
+    configurators.add_item('sigma', 'float', default=1.0)
+
+    __doc__ += configurators.build_doc()
+                
+    def set_kernel(self, frequencies, dt):
+
+        mu = self._configuration["mu"]["value"]
+        sigma = self._configuration["sigma"]["value"]
+        
+        self._frequencyWindow = (1.0/(sigma*numpy.sqrt(2.0*numpy.pi)))*numpy.exp(-0.5*((frequencies-mu)/sigma)**2)
+        self._timeWindow = numpy.fft.fftshift(numpy.abs(numpy.fft.ifft(self._frequencyWindow))/dt)
+                 
+class LorentzianInstrumentResolution(InstrumentResolution):
+    """
+    Defines an instrument resolution with a lorentzian response
+    """
+    
+    type = 'lorentzian'
+
+    configurators = ConfiguratorsDict()
+    configurators.add_item('mu', 'float', default=0.0)
+    configurators.add_item('sigma', 'float', default=1.0)
+
+    __doc__ += configurators.build_doc()
+
+    def set_kernel(self, frequencies, dt):
+
+        mu = self._configuration["mu"]["value"]
+        sigma = self._configuration["sigma"]["value"]
+                
+        fact = 0.5*sigma
+                          
+        self._frequencyWindow = (1.0/numpy.pi)*(fact/((frequencies-mu)**2 + fact**2))
+                
+        self._timeWindow = numpy.fft.fftshift(numpy.abs(numpy.fft.ifft(self._frequencyWindow))/dt)
+
+class PseudoVoigtInstrumentResolution(InstrumentResolution):
+    """Defines an instrument resolution with a pseudo-voigt response
+    """
+
+    type = 'pseudo-voigt'
+
+    configurators = ConfiguratorsDict()
+    configurators.add_item('eta','float', mini=0.0, maxi=1.0, default=0.5)
+    configurators.add_item('mu_lorentzian','float', default=0.0)
+    configurators.add_item('sigma_lorentzian','float', default=1.0)
+    configurators.add_item('mu_gaussian','float', default=0.0)
+    configurators.add_item('sigma_gaussian','float', default=1.0)
+
+    __doc__ += configurators.build_doc()
+            
+    def set_kernel(self, frequencies, dt):
+
+        eta = self._configuration["eta"]["value"]
+        muL = self._configuration["mu_lorentzian"]["value"]
+        sigmaL = self._configuration["sigma_lorentzian"]["value"]
+        muG = self._configuration["mu_gaussian"]["value"]
+        sigmaG = self._configuration["sigma_gaussian"]["value"]
+
+        gContribution = (1.0/(sigmaG*numpy.sqrt(2.0*numpy.pi)))*numpy.exp(-0.5*((frequencies-muG)/sigmaG)**2)
+
+        fact = 0.5*sigmaL
+                          
+        lContribution = (1.0/numpy.pi)*(fact/((frequencies-muL)**2 + fact**2))
+        
+        self._frequencyWindow = eta*lContribution + (1.0-eta)*gContribution
+                
+        self._timeWindow = numpy.fft.fftshift(numpy.abs(numpy.fft.ifft(self._frequencyWindow))/dt)
+        
+class TriangularInstrumentResolution(InstrumentResolution):
+    """Defines an instrument resolution with a triangular response
+    """
+    
+    type = 'triangular'
+
+    configurators = ConfiguratorsDict()
+    configurators.add_item('mu', 'float', default=0.0)
+    configurators.add_item('sigma', 'float', default=1.0)
+
+    __doc__ += configurators.build_doc()
+    
+    def set_kernel(self, frequencies, dt):
+
+        mu = self._configuration["mu"]["value"]
+        sigma = self._configuration["sigma"]["value"]
+                                
+        val = numpy.abs(frequencies-mu) - sigma
+                        
+        self._frequencyWindow = numpy.where( val >= 0, 0.0, -val/sigma**2)
+                                
+        self._timeWindow = numpy.fft.fftshift(numpy.abs(numpy.fft.ifft(self._frequencyWindow))/dt)
+
+class SquareInstrumentResolution(InstrumentResolution):
+    """Defines an instrument resolution with a square response
+    """
+    
+    type = 'square'
+
+    configurators = ConfiguratorsDict()
+    configurators.add_item('mu', 'float', default=0.0)
+    configurators.add_item('sigma', 'float', default=1.0)
+
+    __doc__ += configurators.build_doc()
+        
+    def set_kernel(self, frequencies, dt):
+                                
+        mu = self._configuration["mu"]["value"]
+        sigma = self._configuration["sigma"]["value"]
+                                
+        self._frequencyWindow = numpy.where((numpy.abs(frequencies-mu)-sigma) > 0,0.0,1.0/(2.0*sigma))
+                        
+        self._timeWindow = numpy.fft.fftshift(numpy.abs(numpy.fft.ifft(self._frequencyWindow))/dt)
+
+if __name__ == "__main__":
+    
+    res = REGISTRY["instrument resolution"]["square"]()
+    res.setup({"mu":2.0,"sigma":1.0})
+    res.set_kernel(numpy.array([1,2,3,4,5,6,7,8]),0.1)

MDANSE/Framework/Configurables/Jobs/Job.py

+import abc
+import glob
+import os
+import random
+import stat
+import string
+import subprocess
+import sys
+
+from MDANSE import LOGGER, PLATFORM, REGISTRY
+from MDANSE.Core.Error import Error
+from MDANSE.Framework.Configurables.Configurable import Configurable
+from MDANSE.Framework.Configurables.Jobs.JobStatus import JobStatus
+from MDANSE.Framework.IO.OutputVariables import OutputData
+
+class JobError(Error):
+    pass
+
+def key_generator(size=4, chars=None, prefix=""):
+    
+    if chars is None:
+        chars = string.ascii_lowercase + string.digits
+    
+    key = ''.join(random.choice(chars) for _ in range(size))
+    if prefix:
+        key = "%s_%s" % (prefix,key)
+    
+    return key
+        
+class Job(Configurable):
+    """
+    This class handles a nMOLDYN job. In nMOLDYN any task modeled by a loop can be
+    considered a nMOLDYN job. 
+    """
+            
+    __metaclass__ = REGISTRY
+    
+    type = "job"
+    
+    section = "job"
+        
+    @staticmethod
+    def set_name():
+        """
+        Sets a name for the job that is not already in use by another running job.
+        """
+        
+        prefix = '%d' % PLATFORM.pid()
+    
+        # The list of the registered jobs.
+        registeredJobs = [os.path.basename(f) for f in glob.glob(os.path.join(PLATFORM.temporary_files_directory(),'*'))]
+        
+        while True:     
+    
+            # Followed by 4 random letters.
+            name = key_generator(4, prefix=prefix)
+            
+            if not name in registeredJobs:
+                break
+                        
+        return name
+        
+    def __init__(self, status=None):
+        """
+        The base class constructor.        
+        """
+
+        Configurable.__init__(self)
+                
+        self._outputData = OutputData()
+
+        self._name = Job.set_name()
+        
+        self._info = ""
+                                        
+        if status is not None:
+            self._status = JobStatus(self)
+        else:
+            self._status = None
+                                
+    def build_documentation(self):
+                
+        doc = [self.__doc__]
+        for conf in self.configurators.values():
+            if conf.__doc__ is None:
+                doc.append("%s : %s\n" % (conf.type,'undocumented'))
+            else:
+                doc.append("%s : %s\n" % (conf.type,conf.__doc__))
+                
+        doc = '\n'.join(doc)
+           
+        return doc
+    
+    @classmethod
+    def build_parallelization_test(cls, testFile, parameters=None):
+        """
+        Produce a file like object for a given job.\n
+        :Parameters:
+            #. parameters (dict): optional. If not None, the parameters with which the job file will be built.
+        """
+         
+        # Try to open the output test file to see whether it is allowed.
+        try:
+            f = open(testFile, 'w')
+        # Case where for whatever reason, the file could not be opened for writing. Return.        
+        except IOError as e:
+            raise JobError(["Error when saving python batch file.",e])
+           
+        # The first line contains the call to the python executable. This is necessary for the file to
+        # be autostartable.
+        f.write('#!%s\n\n' % sys.executable)
+
+        f.write('import os\n')
+        f.write('import unittest\n')
+        f.write('import numpy\n')
+        f.write('from Scientific.IO.NetCDF import NetCDFFile\n')
+        f.write('from Tests.UnitTest import UnitTest\n')
+        f.write('from nMOLDYN import REGISTRY\n\n')
+                
+        f.write('class Test%sParallel(UnitTest):\n\n' % cls.type.upper())
+        f.write('    def setUp(self):\n')
+        f.write('        from nMOLDYN import LOGGER\n')
+        f.write('        LOGGER.stop()\n\n')
+                    
+        f.write('    def test(self):\n')      
+
+        # Writes the line that will initialize the |parameters| dictionary.
+        f.write('        parameters = {}\n')
+        
+        for k, v in sorted(parameters.items()):
+            f.write('        parameters[%r] = %r\n' % (k, v))
+                
+        f.write('\n        job = REGISTRY[%r](%r)\n\n' % ('job',cls.type))
+
+        f.write('        parameters["running_mode"] = ("monoprocessor",1)\n')
+        f.write('        self.assertNotRaises(job.run,parameters,False)\n\n')
+
+        f.write('        f = NetCDFFile(job.configuration["output_files"]["files"][0],"r")\n')
+        f.write('        resMono = {}\n')
+        f.write('        for k,v in f.variables.items():\n')
+        f.write('            resMono[k] = v.getValue()\n')
+        f.write('        f.close()\n\n')
+
+        f.write('        parameters["running_mode"] = ("multiprocessor",2)\n')
+        f.write('        self.assertNotRaises(job.run,parameters,False)\n\n')
+
+        f.write('        f = NetCDFFile(job.configuration["output_files"]["files"][0],"r")\n')
+        f.write('        resMulti = {}\n')
+        f.write('        for k,v in f.variables.items():\n')
+        f.write('            resMulti[k] = v.getValue()\n')
+        f.write('        f.close()\n\n')
+
+        f.write('        for k in resMono.keys():\n')
+        f.write('            self.assertTrue(numpy.allclose(resMono[k],resMulti[k]))\n\n')
+            
+        f.write('def suite():\n')
+        f.write('    loader = unittest.TestLoader()\n')
+        f.write('    s = unittest.TestSuite()\n')
+        f.write('    s.addTest(loader.loadTestsFromTestCase(Test%sParallel))\n' % cls.type.upper())
+        f.write('    return s\n\n')
+        f.write('if __name__ == "__main__":\n')
+        f.write('    unittest.main(verbosity=2)\n')
+        
+        f.close()
+        
+        os.chmod(testFile,stat.S_IRWXU)
+
+    @staticmethod
+    def set_pyro_server():
+    
+        import Pyro.errors
+        import Pyro.naming
+    
+        # Gets a Pyro proxy for the name server.
+        locator = Pyro.naming.NameServerLocator()
+    
+        # Try to get an existing name server.        
+        try:
+            ns = locator.getNS()
+            
+        # Otherwise, start a new one.        
+        except Pyro.errors.NamingError:
+            
+            subprocess.Popen([sys.executable, '-O', '-c', "import Pyro.naming; Pyro.naming.main([])"], stdout = subprocess.PIPE)
+            ns = None
+            while ns is None:
+                try:
+                    ns = locator.getNS()
+                except Pyro.errors.NamingError:
+                    pass
+                            
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def configuration(self):
+        return self._configuration
+
+    @abc.abstractmethod
+    def finalize(self):        
+        pass
+        
+    @abc.abstractmethod
+    def initialize(self):        
+        pass
+
+    @abc.abstractmethod
+    def run_step(self):
+        pass
+            
+    @classmethod
+    def save(cls, jobFile, parameters=None):
+        """
+        Save a job file for a given job.\n
+        :Parameters:
+            #. jobFile (str): The name of the output job file.\n
+            #. parameters (dict): optional. If not None, the parameters with which the job file will be built.
+        """
+                         
+        # Try to open the output job file to see whether it is allowed.
+        try:
+            f = open(jobFile, 'w')
+        
+        # Case where for whatever reason, the file could not be opened for writing. Return.        
+        except IOError as e:
+            raise JobError(["Error when saving python batch file.",e])
+           
+        # The first line contains the call to the python executable. This is necessary for the file to
+        # be autostartable.
+        f.write('#!%s\n\n' % sys.executable)
+        
+        # Writes the input file header.
+        f.write('########################################################\n')
+        f.write('# This is an automatically generated MDANSE run script #\n')
+        f.write('#######################################################\n\n')
+                                    
+        # Write the import.
+        f.write("from MDANSE import REGISTRY\n\n")
+                        
+        f.write('################################################################\n')
+        f.write('# Job parameters                                               #\n')
+        f.write('################################################################\n\n')
+
+        # Writes the line that will initialize the |parameters| dictionary.
+        f.write('parameters = {}\n')
+        
+        if parameters is None:
+            parameters = cls.configurators.get_default_parameters()
+        
+        for k, v in sorted(parameters.items()):
+            f.write('parameters[%r] = %r\n' % (k, v))
+
+        f.write('\n')
+        f.write('################################################################\n')
+