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)
