def _buildMODFile(cls, modfile): outputFilename = modfile.getBuiltFilenameFull(ensureBuilt=False) if not Exists(outputFilename): LogMgr.info("Does not exist: building: %s" % outputFilename) modTxtFilename = WriteToFile(modfile.modtxt, suffix=".mod") ModFileCompiler.CheckModFileUnits(modTxtFilename) modDynFilename = BuildModFile(modTxtFilename, modfile=modfile) Move(modDynFilename, outputFilename) else: LogMgr.info("Already Built") return outputFilename
def build_modfile(cls, modfile): output_filename = modfile.get_built_filename_full(ensure_built=False) if not os.path.exists(output_filename): LogMgr.info("Does not exist: building: %s" % output_filename) mod_txt_filename = FileIO.write_to_file(modfile.modtxt, suffix=".mod") ModFileCompiler.check_modfile_units(mod_txt_filename) mod_dyn_filename = _build_mod_file(mod_txt_filename, modfile=modfile) shutil.move(mod_dyn_filename, output_filename) else: LogMgr.info("Already Built") return output_filename
def build_modfile(cls, modfile): output_filename = modfile.get_built_filename_full(ensure_built=False) if not os.path.exists(output_filename): LogMgr.info('Does not exist: building: %s' % output_filename) mod_txt_filename = FileIO.write_to_file(modfile.modtxt, suffix='.mod') ModFileCompiler.check_modfile_units(mod_txt_filename) mod_dyn_filename = _build_mod_file(mod_txt_filename, modfile=modfile) shutil.move(mod_dyn_filename, output_filename) else: LogMgr.info('Already Built') return output_filename
def _pca(X): # Refactored out 'map' in August 2012 # x_mean = array(map(sum, X.T)) / len(X) x_mean = array([sum(col) for col in X.T]) / len(X) x_ = X - x_mean x_t = numpy.dot(x_.T, x_) / len(X) (lam, vec) = linalg.eig(x_t) ans = zip(lam, vec.T) print ans try: ans.sort(reverse=True, key=lambda t: t[0]) except Exception, e: print e assert False, 'What is the exception raised?!' LogMgr.warning('Unable to sort eigenvectors')
def _pca(X): # Refactored out 'map' in August 2012 # x_mean = array(map(sum, X.T)) / len(X) x_mean = array([sum(col) for col in X.T])/len(X) x_ = X - x_mean x_t = numpy.dot(x_.T, x_) / len(X) (lam, vec) = linalg.eig(x_t) ans = zip(lam, vec.T) print ans try: ans.sort(reverse=True, key=lambda t: t[0]) except Exception, e: print e assert False, 'What is the exception raised?!' LogMgr.warning('Unable to sort eigenvectors')
def main(): bundleFilename = sys.argv[1] print "Loading Bundle from ", bundleFilename bundle = SimMetaDataBundle.loadFromFile(bundleFilename) # Load the random number seed if bundle.random_seed is not None: mfrandom.MFRandom.seed(bundle.random_seed) # = morphforge.core.mfrandom.MFRandom._seed result = bundle.getSimulation().Run(doSpawn=False) result.setSimulationTime(tStart, time.time()) LogMgr.info("Simulation Ran OK. Post Processing:") bundle.doPostProcessingActions() LogMgr.info("Bundle Completed OK")
def ensure_built(self): LogMgr.info("Ensuring Modfile is built") from morphforge.simulation.neuron.biophysics.modfilecompiler import ModFileCompiler ModFileCompiler().build_modfile(self, self.strict_modlunit)
def ensureBuilt(self): LogMgr.info("Ensuring Modfile is built") from modfilecompiler import ModFileCompiler ModFileCompiler()._buildMODFile(self)
def ensure_built(self): LogMgr.info('Ensuring Modfile is built') from modfilecompiler import ModFileCompiler ModFileCompiler().build_modfile(self)