def test_write_read_roundtrip(self):
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
try:
doc = document.clone()
except:
document.clone()
raise
url = os.path.join(
self._tmp_dir, 'test{}v{}{}'.format(i, version, ext))
nineml.write(url, doc, format=format, version=version,
indent=2)
if self.print_serialized and format in self.printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True)
self.assertTrue(doc.equals(reread_doc),
doc.find_mismatch(reread_doc))
def test_write_read_roundtrip(self):
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
try:
doc = document.clone()
except:
document.clone()
raise
url = os.path.join(self._tmp_dir,
'test{}v{}{}'.format(i, version, ext))
nineml.write(url,
doc,
format=format,
version=version,
indent=2)
if self.print_serialized and format in self.printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True)
self.assertTrue(doc.equals(reread_doc),
doc.find_mismatch(reread_doc))
def function():
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
doc = document.clone()
url = os.path.join(_tmp_dir,
'test{}v{}{}'.format(i, version, ext))
nineml.write(url,
doc,
format=format,
version=version,
indent=2)
if print_serialized and format in printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True) # @UnusedVariable
shutil.rmtree(_tmp_dir)
def test_url_resolution(self):
tmp_dir = tempfile.mkdtemp()
os.chdir(tmp_dir)
write(self.tmp_path, dynA, dynB)
reread_dynA = read('{}#dynA'.format(self.tmp_path))
self.assertEqual(dynA, reread_dynA)
# Read again using document cache via Dynamics Properties
dynBProps = DynamicsProperties(
name='dynBProps',
definition='{}#dynB'.format(os.path.join(tmp_dir, self.tmp_path)),
properties={'P1': 1, 'P2': 2, 'P3': 3})
self.assertEqual(dynB, dynBProps.component_class)
def test_url_resolution(self):
tmp_dir = tempfile.mkdtemp()
os.chdir(tmp_dir)
write(self.tmp_path, dynA, dynB)
reread_dynA = read('{}#dynA'.format(self.tmp_path))
self.assertEqual(dynA, reread_dynA)
# Read again using document cache via Dynamics Properties
dynBProps = DynamicsProperties(
name='dynBProps',
definition='{}#dynB'.format(os.path.join(tmp_dir, self.tmp_path)),
properties={
'P1': 1,
'P2': 2,
'P3': 3
})
self.assertEqual(dynB, dynBProps.component_class)
def function():
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
doc = document.clone()
url = os.path.join(
_tmp_dir, 'test{}v{}{}'.format(i, version, ext))
nineml.write(url, doc, format=format, version=version,
indent=2)
if print_serialized and format in printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True) # @UnusedVariable
shutil.rmtree(_tmp_dir)
"""
"""
import nineml.abstraction as al
from nineml.units import per_time, time, ms, per_ms
model = al.Dynamics(
name="Poisson",
regimes=[
al.Regime(
name="default",
transitions=al.On("t > t_next",
do=["t_next = t + one_ms * random.exponential(thousand_per_millisecond / rate)",
al.OutputEvent('spikeOutput')]))
],
event_ports=[al.EventSendPort('spikeOutput')],
state_variables=[al.StateVariable('t_next', dimension=time)],
parameters=[al.Parameter('rate', dimension=per_time)],
constants=[al.Constant('thousand_per_millisecond', value=1000.0, units=per_ms),
al.Constant('one_ms', value=1.0, units=ms)]
)
if __name__ == "__main__":
import nineml
filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml")
nineml.write(model, filename)
import nineml.abstraction as al
from nineml.units import current, A, s
model = al.Dynamics(
name="StaticConnection",
regimes=[
al.Regime(
name="default",
time_derivatives=[
"dfixed_weight/dt = zero"],
)
],
state_variables=[
al.StateVariable('fixed_weight', dimension=current), # TGC 4/15 what is the point of this state variable, where is it read? @IgnorePep8
],
analog_ports=[al.AnalogSendPort("fixed_weight", dimension=current)],
constants=[al.Constant('zero', 0.0, A / s)],
)
if __name__ == "__main__":
import nineml
filename = __file__[0].upper() + __file__[1:].replace(".py", ".xml")
nineml.write(model, filename)
def write(self, fname, **kwargs):
"""
Writes the top-level NineML object to file in XML.
"""
nineml.write(fname, self, **kwargs)
def write(self, fname, **kwargs):
"""
Writes the top-level NineML object to file in XML.
"""
nineml.write(fname, self, **kwargs)