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for name, field in self.representation.fields.items():
fields[name] = self._get_field_doc(field)
return fields"
4947,"def _get_field_doc(self, field):
"""""" Return documentation for a field in the representation. """"""
fieldspec = dict()
fieldspec['type'] = field.__class__.__name__
fieldspec['required'] = field.required
fieldspec['validators'] = [{validator.__class__.__name__: validator.__dict__} for validator in field.validators]
return fieldspec"
4948,"def _get_url_doc(self):
"""""" Return a list of URLs that map to this resource. """"""
resolver = get_resolver(None)
possibilities = resolver.reverse_dict.getlist(self)
urls = [possibility[0] for possibility in possibilities]
return urls"
4949,"def _get_method_doc(self):
"""""" Return method documentations. """"""
ret = {}
for method_name in self.methods:
method = getattr(self, method_name, None)
if method:
ret[method_name] = method.__doc__
return ret"
4950,"def clean(df,error_rate = 0):
"""""" Superficially cleans data, i.e. changing simple things about formatting.
Parameters:
df - DataFrame
DataFrame to clean
error_rate - float {0 <= error_rate <= 1}, default 0
Maximum amount of errors/inconsistencies caused explicitly by cleaning, expressed
as a percentage of total dataframe rows (0 = 0%, .5 = 50%, etc.)
Ex: na values from coercing a column of data to numeric
""""""
df = df.copy()
# Change colnames
basics.clean_colnames(df)
# Eventually use a more advanced function to clean colnames
print('Changed colnames to {}'.format(df.columns))
# Remove extra whitespace
obj_col_list = df.select_dtypes(include = 'object').columns
for col_name in obj_col_list:
df[col_name] = basics.col_strip(df,col_name)
print(""Stripped extra whitespace from '{}'"".format(col_name))
# Coerce columns if possible
for col_name in obj_col_list:
new_dtype = coerce_col(df,col_name,error_rate)
if new_dtype is not None:
print(""Coerced '{}' to datatype '{}'"".format(col_name, new_dtype))
# Scrub columns
obj_col_list = df.select_dtypes(include = 'object').columns
for col_name in obj_col_list:
scrubf, scrubb = smart_scrub(df,col_name,1-error_rate)
if scrubf is not None or scrubb is not None:
print(""Scrubbed '{}' from the front and '{}' from the back of column '{}'"" \
.format(scrubf,scrubb,col_name))
# Coerice columns if possible
for col_name in obj_col_list:
new_dtype = coerce_col(df,col_name,error_rate)
if new_dtype is not None:
print(""Coerced '{}' to datatype '{}'"".format(col_name, new_dtype))
return df"
4951,"def create_process(self, command, shell=True, stdout=None, stderr=None,
env=None):
""""""
Execute a process using subprocess.Popen, setting the backend's DISPLAY
""""""
env = env if env is not None else dict(os.environ)
env['DISPLAY'] = self.display
return subprocess.Popen(command, shell=shell,
stdout=stdout, stderr=stderr,
env=env)"
4952,"def pause(self, instance_id, keep_provisioned=True):
""""""shuts down the instance without destroying it.
The AbstractCloudProvider class uses 'stop' to refer to destroying
a VM, so use 'pause' to mean powering it down while leaving it
allocated.
:param str instance_id: instance identifier
:return: None
""""""
try:
if self._paused:
log.debug(""node %s is already paused"", instance_id)
return
self._paused = True
post_shutdown_action = 'Stopped' if keep_provisioned else \
'StoppedDeallocated'
result = self._subscription._sms.shutdown_role(
service_name=self._cloud_service._name,
deployment_name=self._cloud_service._name,
role_name=self._qualified_name,
post_shutdown_action=post_shutdown_action)