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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) |
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