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int: Slope between self and other.
""""""
X1, Y1, X2, Y2 = self.X, self.Y, other.X, other.Y
Y3 = Y1 - Y2
X3 = X1 - X2
return (Y3 * self.inverse(X3)) % self.P"
675,"def to_jacobian(self):
""""""
Converts this point to a Jacobian representation.
Returns:
JacobianPoint: The Jacobian representation.
""""""
if not self:
return JacobianPoint(X=0, Y=0, Z=0)
return JacobianPoint(X=self.X, Y=self.Y, Z=1)"
676,"def import_model(self, name, path=""floyd.db.models""):
""""""imports a model of name from path, returning from local model
cache if it has been previously loaded otherwise importing""""""
if name in self._model_cache:
return self._model_cache[name]
try:
model = getattr(__import__(path, None, None, [name]), name)
self._model_cache[name] = model
except ImportError:
return False
return model"
677,"def parse_md(self):
""""""Takes a post path and returns a dictionary of variables""""""
post_content = _MARKDOWN.convert(self.raw_src)
if hasattr(_MARKDOWN, 'Meta'):
# 'Meta' in _MARKDOWN and _MARKDOWN.Meta:
for key in _MARKDOWN.Meta:
print ""\t meta: %s: %s (%s)"" % (key, _MARKDOWN.Meta[key][0], type(_MARKDOWN.Meta[key][0]))
if key == 'pubdate':
setattr(self, key, datetime.datetime.fromtimestamp(float(_MARKDOWN.Meta[key][0])))
else:
setattr(self, key, _MARKDOWN.Meta[key][0])
self.content = post_content
self.stub = self.__key__
# set required fields
# @TODO required in schema rather than here
if not hasattr(self, 'pubdate'):
print '\t Notice: setting default pubdate'
setattr(self, 'pubdate', datetime.datetime.now())"
678,"def filter(self, **kwargs):
# @TODO refactor with models as dicts
""""""filter results of dataset eg.
Query('Posts').filter(post_type='post')
""""""
f_field = kwargs.keys()[0]
f_value = kwargs[f_field]
_newset = []
for m in self._dataset:
if hasattr(m, f_field):
if getattr(m, f_field) == f_value:
_newset.append(m)
self._dataset = _newset
return self"
679,"def sort_by(self, sb):
""""""Sort results""""""
self._dataset = self._dataset.sort(key=lambda x: x.pubdate, reverse=True)
return self"
680,"def execute_train_task_with_dependencies(self, task_cls, **kwargs):
""""""
Run the training, as well as any dependencies of the training
task_cls - class of a task
""""""
log.info(""Task {0}"".format(get_task_name(task_cls)))
#Instantiate the task
task_inst = task_cls()
#Grab arguments from the task instance and set them
for arg in task_inst.args:
if arg not in kwargs:
kwargs[arg] = task_inst.args[arg]
#Check for dependencies defined by the task
if hasattr(task_inst, ""dependencies""):
deps = task_inst.dependencies
dep_results = []
#Run the dependencies through recursion (in case of dependencies of dependencies, etc)
for dep in deps:
log.info(""Dependency {0}"".format(get_task_name(dep)))
dep_results.append(self.execute_train_task_with_dependencies(dep.cls, **dep.args))
trained_dependencies = []
#Add executed dependency to trained_dependencies list on the task
for i in xrange(0,len(deps)):
dep = deps[i]
dep_result = dep_results[i]
name = dep.name
namespace = dep.namespace
category = dep.category
trained_dependencies.append(TrainedDependency(category=category, namespace=namespace, name = name, inst = dep))