preparing for use of optional arguments to function

This commit is contained in:
Martin Diehl 2019-09-11 21:20:14 -07:00
parent de313269d9
commit 3db3e9e762
1 changed files with 32 additions and 16 deletions

View File

@ -83,10 +83,20 @@ class DADF5():
self.filename = filename
self.mode = mode
def get_candidates(self,l):
"""
Get groups that contain all requested datasets.
Parameters
----------
l : list of str
Names of datasets that need to be located in the group.
"""
groups = []
if type(l) is not list:
print('mist')
raise TypeError('Candidates should be given as a list')
with h5py.File(self.filename,'r') as f:
for g in self.get_active_groups():
if set(l).issubset(f[g].keys()): groups.append(g)
@ -94,6 +104,9 @@ class DADF5():
def get_active_groups(self):
"""
Get groups that are currently considered for evaluation.
"""
groups = []
for i,x in enumerate(self.active['increments']):
group_inc = 'inc{:05}'.format(self.active['increments'][i]['inc'])
@ -109,8 +122,9 @@ class DADF5():
groups.append(group_output_types)
return groups
def list_data(self):
"""Shows information on all datasets in the file."""
"""Shows information on all active datasets in the file."""
with h5py.File(self.filename,'r') as f:
group_inc = 'inc{:05}'.format(self.active['increments'][0]['inc'])
for c in self.active['constituents']:
@ -216,7 +230,7 @@ class DADF5():
'unit':'Pa',
'Description': 'Cauchy stress calculated from 1st Piola-Kirchhoff stress and deformation gradient'}
self.add_generic_pointwise_vectorized(Cauchy,args,result)
self.add_generic_pointwise_vectorized(Cauchy,args,None,result)
def add_Mises_stress(self,stress='sigma'):
@ -397,7 +411,7 @@ class DADF5():
pool.wait_completion()
def add_generic_pointwise_vectorized(self,func,args,result):
def add_generic_pointwise_vectorized(self,func,args,args2=None,result=None):
"""
General function to add pointwise data.
@ -407,9 +421,16 @@ class DADF5():
groups = self.get_fitting(args)
def job(args):
"""
A job. It has different args!
"""
print('args for job',args)
out = args['out']
datasets_in = args['dat']
func = args['fun']
# try:
# out = func(*datasets_in,*args['fun_args'])
# except:
out = func(*datasets_in)
args['results'].put({'out':out,'group':args['group']})
@ -422,21 +443,16 @@ class DADF5():
with h5py.File(self.filename,'r') as f:
datasets_in = [f[g+'/'+u['label']][()] for u in args]
# figure out dimension of results
testArg = tuple([d[0:1,] for d in datasets_in]) # to call function with first point
out = np.empty([datasets_in[0].shape[0]] + list(func(*testArg).shape[1:])) # shape is Npoints x shape of the results for one point
todo.append({'dat':datasets_in,'fun':func,'out':out,'group':g,'results':results})
if args2 is not None:
todo.append({'dat':datasets_in,'fun':func,'group':g,'results':results,'func_args':args,'out':None})
else:
todo.append({'dat':datasets_in,'fun':func,'group':g,'results':results,'out':None})
# Instantiate a thread pool with worker threads
pool = util.ThreadPool(Nthreads)
missingResults = len(todo)
# Add the jobs in bulk to the thread pool. Alternatively you could use
# `pool.add_task` to add single jobs. The code will block here, which
# makes it possible to cancel the thread pool with an exception when
# the currently running batch of workers is finished
pool.map(job, todo[:Nthreads+1])
i = 0
while missingResults > 0: