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train | ApiGenerator._add_auth | Add Auth configuration to the Swagger file, if necessary | samtranslator/model/api/api_generator.py | def _add_auth(self):
"""
Add Auth configuration to the Swagger file, if necessary
"""
if not self.auth:
return
if self.auth and not self.definition_body:
raise InvalidResourceException(self.logical_id,
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"""
Add Auth configuration to the Swagger file, if necessary
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train | ApiGenerator._add_gateway_responses | Add Gateway Response configuration to the Swagger file, if necessary | samtranslator/model/api/api_generator.py | def _add_gateway_responses(self):
"""
Add Gateway Response configuration to the Swagger file, if necessary
"""
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"""
Add Gateway Response configuration to the Swagger file, if necessary
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train | ApiGenerator._get_permission | Constructs and returns the Lambda Permission resource allowing the Authorizer to invoke the function.
:returns: the permission resource
:rtype: model.lambda_.LambdaPermission | samtranslator/model/api/api_generator.py | def _get_permission(self, authorizer_name, authorizer_lambda_function_arn):
"""Constructs and returns the Lambda Permission resource allowing the Authorizer to invoke the function.
:returns: the permission resource
:rtype: model.lambda_.LambdaPermission
"""
rest_api = ApiGateway... | def _get_permission(self, authorizer_name, authorizer_lambda_function_arn):
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train | ApiGenerator._set_endpoint_configuration | Sets endpoint configuration property of AWS::ApiGateway::RestApi resource
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:param string/dict value: Value to be set | samtranslator/model/api/api_generator.py | def _set_endpoint_configuration(self, rest_api, value):
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Sets endpoint configuration property of AWS::ApiGateway::RestApi resource
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"""
Sets endpoint configuration property of AWS::ApiGateway::RestApi resource
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rest_api.EndpointConfiguration = {"Types": [value]}
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train | retry | The retry function will keep retrying `task_to_try` until either:
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"""
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train | to_cloudformation | Returns the Lambda function, role, and event resources to which this SAM Function corresponds.
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train | _get_resolved_alias_name | Alias names can be supplied as an intrinsic function. This method tries to extract alias name from a reference
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method raises an exception. If alias name is just a plain string, it will return as is
... | samtranslator/model/sam_resources.py | def _get_resolved_alias_name(self, property_name, original_alias_value, intrinsics_resolver):
"""
Alias names can be supplied as an intrinsic function. This method tries to extract alias name from a reference
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train | _construct_lambda_function | Constructs and returns the Lambda function.
:returns: a list containing the Lambda function and execution role resources
:rtype: list | samtranslator/model/sam_resources.py | def _construct_lambda_function(self):
"""Constructs and returns the Lambda function.
:returns: a list containing the Lambda function and execution role resources
:rtype: list
"""
lambda_function = LambdaFunction(self.logical_id, depends_on=self.depends_on,
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"""Constructs and returns the Lambda function.
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lambda_function = LambdaFunction(self.logical_id, depends_on=self.depends_on,
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train | _construct_role | Constructs a Lambda execution role based on this SAM function's Policies property.
:returns: the generated IAM Role
:rtype: model.iam.IAMRole | samtranslator/model/sam_resources.py | def _construct_role(self, managed_policy_map):
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:rtype: model.iam.IAMRole
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execution_role = IAMRole(self.logical_id + 'Role', attributes=self.get_passthrough_... | def _construct_role(self, managed_policy_map):
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train | _validate_dlq | Validates whether the DeadLetterQueue LogicalId is validation
:raise: InvalidResourceException | samtranslator/model/sam_resources.py | def _validate_dlq(self):
"""Validates whether the DeadLetterQueue LogicalId is validation
:raise: InvalidResourceException
"""
# Validate required logical ids
valid_dlq_types = str(list(self.dead_letter_queue_policy_actions.keys()))
if not self.DeadLetterQueue.get('Type')... | def _validate_dlq(self):
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train | _generate_event_resources | Generates and returns the resources associated with this function's events.
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:param iam.IAMRole execution_role: generated Lambda execution role
:param implicit_api: Global Implicit API resource where the implicit APIs... | samtranslator/model/sam_resources.py | def _generate_event_resources(self, lambda_function, execution_role, event_resources, lambda_alias=None):
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:param iam.IAMRole execution_ro... | def _generate_event_resources(self, lambda_function, execution_role, event_resources, lambda_alias=None):
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train | _construct_version | Constructs a Lambda Version resource that will be auto-published when CodeUri of the function changes.
Old versions will not be deleted without a direct reference from the CloudFormation template.
:param model.lambda_.LambdaFunction function: Lambda function object that is being connected to a version
... | samtranslator/model/sam_resources.py | def _construct_version(self, function, intrinsics_resolver):
"""Constructs a Lambda Version resource that will be auto-published when CodeUri of the function changes.
Old versions will not be deleted without a direct reference from the CloudFormation template.
:param model.lambda_.LambdaFunctio... | def _construct_version(self, function, intrinsics_resolver):
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:param model.lambda_.LambdaFunction function: Lambda function object to associate the alias with
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train | SamApi.to_cloudformation | Returns the API Gateway RestApi, Deployment, and Stage to which this SAM Api corresponds.
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train | SamApplication._construct_nested_stack | Constructs a AWS::CloudFormation::Stack resource | samtranslator/model/sam_resources.py | def _construct_nested_stack(self):
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nested_stack = NestedStack(self.logical_id, depends_on=self.depends_on,
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train | SamApplication._get_application_tags | Adds tags to the stack if this resource is using the serverless app repo | samtranslator/model/sam_resources.py | def _get_application_tags(self):
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train | SamLayerVersion._construct_lambda_layer | Constructs and returns the Lambda function.
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train | SamLayerVersion._get_retention_policy_value | Sets the deletion policy on this resource. The default is 'Retain'.
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train | order_flowers | Performs dialog management and fulfillment for ordering flowers.
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Performs dialog management and fulfillment for ordering flowers.
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"""
flower_type = get_slots(intent_request)["Fl... | def order_flowers(intent_request):
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Performs dialog management and fulfillment for ordering flowers.
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train | dispatch | Called when the user specifies an intent for this bot. | examples/apps/lex-order-flowers-python/lambda_function.py | def dispatch(intent_request):
"""
Called when the user specifies an intent for this bot.
"""
logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))
intent_name = intent_request['currentIntent']['name']
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Called when the user specifies an intent for this bot.
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train | PushEventSource._construct_permission | Constructs the Lambda Permission resource allowing the source service to invoke the function this event
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:returns: the permission resource
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train | Schedule.to_cloudformation | Returns the CloudWatch Events Rule and Lambda Permission to which this Schedule event source corresponds.
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:rtype: list | samtranslator/model/eventsources/push.py | def to_cloudformation(self, **kwargs):
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train | CloudWatchEvent._construct_target | Constructs the Target property for the CloudWatch Events Rule.
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target = {
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train | S3.to_cloudformation | Returns the Lambda Permission resource allowing S3 to invoke the function this event source triggers.
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:rtype: list | samtranslator/model/eventsources/push.py | def to_cloudformation(self, **kwargs):
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train | S3._depend_on_lambda_permissions | Make the S3 bucket depends on Lambda Permissions resource because when S3 adds a Notification Configuration,
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train | S3._depend_on_lambda_permissions_using_tag | Since conditional DependsOn is not supported this undocumented way of
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"""
Since conditional DependsOn is not supported this undocumented way of
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train | Api.resources_to_link | If this API Event Source refers to an explicit API resource, resolve the reference and grab
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"""
If this API Event Source refers to an explicit API resource, resolve the reference and grab
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"""
rest_api_id = self.RestApiId
if isinstance(rest_api_id, dict) and "Ref" in rest_api_id:
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train | Api.to_cloudformation | If the Api event source has a RestApi property, then simply return the Lambda Permission resource allowing
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train | Api._add_swagger_integration | Adds the path and method for this Api event source to the Swagger body for the provided RestApi.
:param model.apigateway.ApiGatewayRestApi rest_api: the RestApi to which the path and method should be added. | samtranslator/model/eventsources/push.py | def _add_swagger_integration(self, api, function):
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train | IntrinsicsResolver.resolve_parameter_refs | Resolves references to parameters within the given dictionary recursively. Other intrinsic functions such as
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Resolves references to parameters within the given dictionary recursively. Other intrinsic functions such as
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train | IntrinsicsResolver.resolve_sam_resource_refs | Customers can provide a reference to a "derived" SAM resource such as Alias of a Function or Stage of an API
resource. This method recursively walks the tree, converting all derived references to the real resource name,
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Example:
{"Ref": "MyFunction.Alias"} -> {"Ref... | samtranslator/intrinsics/resolver.py | def resolve_sam_resource_refs(self, input, supported_resource_refs):
"""
Customers can provide a reference to a "derived" SAM resource such as Alias of a Function or Stage of an API
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Customers can provide a reference to a "derived" SAM resource such as Alias of a Function or Stage of an API
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train | IntrinsicsResolver.resolve_sam_resource_id_refs | Some SAM resources have their logical ids mutated from the original id that the customer writes in the
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Example:
{"Ref": "MyLayer"} -> {"Ref": ... | samtranslator/intrinsics/resolver.py | def resolve_sam_resource_id_refs(self, input, supported_resource_id_refs):
"""
Some SAM resources have their logical ids mutated from the original id that the customer writes in the
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train | IntrinsicsResolver._traverse | Driver method that performs the actual traversal of input and calls the appropriate `resolver_method` when
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:param input: Any primitive type (dict, array, string etc) whose value might contain an intrinsic function
:param resolution_data: Data that will help with reso... | samtranslator/intrinsics/resolver.py | def _traverse(self, input, resolution_data, resolver_method):
"""
Driver method that performs the actual traversal of input and calls the appropriate `resolver_method` when
to perform the resolution.
:param input: Any primitive type (dict, array, string etc) whose value might contain a... | def _traverse(self, input, resolution_data, resolver_method):
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train | IntrinsicsResolver._traverse_dict | Traverse a dictionary to resolve intrinsic functions on every value
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train | IntrinsicsResolver._traverse_list | Traverse a list to resolve intrinsic functions on every element
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train | IntrinsicsResolver._try_resolve_parameter_refs | Try to resolve parameter references on the given input object. The object could be of any type.
If the input is not in the format used by intrinsics (ie. dictionary with one key), input is returned
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Try to resolve parameter references on the given input object. The object could be of any type.
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Try to resolve parameter references on the given input object. The object could be of any type.
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train | IntrinsicsResolver._try_resolve_sam_resource_refs | Try to resolve SAM resource references on the given template. If the given object looks like one of the
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Try to resolve SAM resource references on the given template. If the given object looks like one of the
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Try to resolve SAM resource references on the given template. If the given object looks like one of the
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train | IntrinsicsResolver._try_resolve_sam_resource_id_refs | Try to resolve SAM resource id references on the given template. If the given object looks like one of the
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train | CloudWatchLogs.to_cloudformation | Returns the CloudWatch Logs Subscription Filter and Lambda Permission to which this CloudWatch Logs event source
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:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this push event expands
:rtype: list | samtranslator/model/eventsources/cloudwatchlogs.py | def to_cloudformation(self, **kwargs):
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train | PolicyTemplatesProcessor.convert | Converts the given template to IAM-ready policy statement by substituting template parameters with the given
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:param template_name: Name of the template
:param parameter_values: Values for all parameters of the template
:return dict: Dictionary containing policy statement
... | samtranslator/policy_template_processor/processor.py | def convert(self, template_name, parameter_values):
"""
Converts the given template to IAM-ready policy statement by substituting template parameters with the given
values.
:param template_name: Name of the template
:param parameter_values: Values for all parameters of the templ... | def convert(self, template_name, parameter_values):
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Converts the given template to IAM-ready policy statement by substituting template parameters with the given
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train | PolicyTemplatesProcessor._is_valid_templates_dict | Is this a valid policy template dictionary
:param dict policy_templates_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing policy template
:return: True, if it is valid.
:raises ValueError: If the template dictionary doesn't match up ... | samtranslator/policy_template_processor/processor.py | def _is_valid_templates_dict(policy_templates_dict, schema=None):
"""
Is this a valid policy template dictionary
:param dict policy_templates_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing policy template
:return: True, if... | def _is_valid_templates_dict(policy_templates_dict, schema=None):
"""
Is this a valid policy template dictionary
:param dict policy_templates_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing policy template
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train | RenderEngine.render_chart_to_file | Render a chart or page to local html files.
:param chart: A Chart or Page object
:param path: The destination file which the html code write to
:param template_name: The name of template file. | pyecharts/render/engine.py | def render_chart_to_file(self, template_name: str, chart: Any, path: str):
"""
Render a chart or page to local html files.
:param chart: A Chart or Page object
:param path: The destination file which the html code write to
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... | def render_chart_to_file(self, template_name: str, chart: Any, path: str):
"""
Render a chart or page to local html files.
:param chart: A Chart or Page object
:param path: The destination file which the html code write to
:param template_name: The name of template file.
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train | decode_base64 | Decode base64, padding being optional.
:param data: Base64 data as an ASCII byte string
:returns: The decoded byte string. | pyecharts/render/snapshot.py | def decode_base64(data: str) -> bytes:
"""Decode base64, padding being optional.
:param data: Base64 data as an ASCII byte string
:returns: The decoded byte string.
"""
missing_padding = len(data) % 4
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return base64.decodeb... | def decode_base64(data: str) -> bytes:
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train | Tree._set_collapse_interval | 间隔折叠节点，当节点过多时可以解决节点显示过杂间隔。
:param data: 节点数据
:param interval: 指定间隔 | pyecharts/charts/basic_charts/tree.py | def _set_collapse_interval(data, interval):
"""
间隔折叠节点，当节点过多时可以解决节点显示过杂间隔。
:param data: 节点数据
:param interval: 指定间隔
"""
if interval <= 0:
return data
if data and isinstance(data, list):
for d in data:
children = d... | def _set_collapse_interval(data, interval):
"""
间隔折叠节点，当节点过多时可以解决节点显示过杂间隔。
:param data: 节点数据
:param interval: 指定间隔
"""
if interval <= 0:
return data
if data and isinstance(data, list):
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train | parse_pin | Parses a string and returns a pin-num. | ports/nrf/boards/make-pins.py | def parse_pin(name_str):
"""Parses a string and returns a pin-num."""
if len(name_str) < 1:
raise ValueError("Expecting pin name to be at least 4 charcters.")
if name_str[0] != 'P':
raise ValueError("Expecting pin name to start with P")
pin_str = name_str[1:].split('/')[0]
if not pin... | def parse_pin(name_str):
"""Parses a string and returns a pin-num."""
if len(name_str) < 1:
raise ValueError("Expecting pin name to be at least 4 charcters.")
if name_str[0] != 'P':
raise ValueError("Expecting pin name to start with P")
pin_str = name_str[1:].split('/')[0]
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train | AlternateFunction.ptr | Returns the numbered function (i.e. USART6) for this AF. | ports/nrf/boards/make-pins.py | def ptr(self):
"""Returns the numbered function (i.e. USART6) for this AF."""
if self.fn_num is None:
return self.func
return '{:s}{:d}'.format(self.func, self.fn_num) | def ptr(self):
"""Returns the numbered function (i.e. USART6) for this AF."""
if self.fn_num is None:
return self.func
return '{:s}{:d}'.format(self.func, self.fn_num) | [
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train | AlternateFunction.print | Prints the C representation of this AF. | ports/nrf/boards/make-pins.py | def print(self):
"""Prints the C representation of this AF."""
if self.supported:
print(' AF', end='')
else:
print(' //', end='')
fn_num = self.fn_num
if fn_num is None:
fn_num = 0
print('({:2d}, {:8s}, {:2d}, {:10s}, {:8s}), // {:s}... | def print(self):
"""Prints the C representation of this AF."""
if self.supported:
print(' AF', end='')
else:
print(' //', end='')
fn_num = self.fn_num
if fn_num is None:
fn_num = 0
print('({:2d}, {:8s}, {:2d}, {:10s}, {:8s}), // {:s}... | [
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train | run_loop | Start the loop.
:param `leds`: Which LEDs to light up upon switch press.
:type `leds`: sequence of LED objects | examples/switch.py | def run_loop(leds=all_leds):
"""
Start the loop.
:param `leds`: Which LEDs to light up upon switch press.
:type `leds`: sequence of LED objects
"""
print('Loop started.\nPress Ctrl+C to break out of the loop.')
while 1:
try:
if switch():
[led.on() for led... | def run_loop(leds=all_leds):
"""
Start the loop.
:param `leds`: Which LEDs to light up upon switch press.
:type `leds`: sequence of LED objects
"""
print('Loop started.\nPress Ctrl+C to break out of the loop.')
while 1:
try:
if switch():
[led.on() for led... | [
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train | find_c_file | Search vpaths for the c file that matches the provided object_file.
:param str obj_file: object file to find the matching c file for
:param List[str] vpath: List of base paths, similar to gcc vpath
:return: str path to c file or None | py/makemoduledefs.py | def find_c_file(obj_file, vpath):
""" Search vpaths for the c file that matches the provided object_file.
:param str obj_file: object file to find the matching c file for
:param List[str] vpath: List of base paths, similar to gcc vpath
:return: str path to c file or None
"""
c_file = None
r... | def find_c_file(obj_file, vpath):
""" Search vpaths for the c file that matches the provided object_file.
:param str obj_file: object file to find the matching c file for
:param List[str] vpath: List of base paths, similar to gcc vpath
:return: str path to c file or None
"""
c_file = None
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train | find_module_registrations | Find any MP_REGISTER_MODULE definitions in the provided c file.
:param str c_file: path to c file to check
:return: List[(module_name, obj_module, enabled_define)] | py/makemoduledefs.py | def find_module_registrations(c_file):
""" Find any MP_REGISTER_MODULE definitions in the provided c file.
:param str c_file: path to c file to check
:return: List[(module_name, obj_module, enabled_define)]
"""
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""" Find any MP_REGISTER_MODULE definitions in the provided c file.
:param str c_file: path to c file to check
:return: List[(module_name, obj_module, enabled_define)]
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train | generate_module_table_header | Generate header with module table entries for builtin modules.
:param List[(module_name, obj_module, enabled_define)] modules: module defs
:return: None | py/makemoduledefs.py | def generate_module_table_header(modules):
""" Generate header with module table entries for builtin modules.
:param List[(module_name, obj_module, enabled_define)] modules: module defs
:return: None
"""
# Print header file for all external modules.
mod_defs = []
print("// Automatically ge... | def generate_module_table_header(modules):
""" Generate header with module table entries for builtin modules.
:param List[(module_name, obj_module, enabled_define)] modules: module defs
:return: None
"""
# Print header file for all external modules.
mod_defs = []
print("// Automatically ge... | [
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train | readfiles | Reads test files | tools/gen-cpydiff.py | def readfiles():
""" Reads test files """
tests = list(filter(lambda x: x.endswith('.py'), os.listdir(TESTPATH)))
tests.sort()
files = []
for test in tests:
text = open(TESTPATH + test, 'r').read()
try:
class_, desc, cause, workaround, code = [x.rstrip() for x in \
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""" Reads test files """
tests = list(filter(lambda x: x.endswith('.py'), os.listdir(TESTPATH)))
tests.sort()
files = []
for test in tests:
text = open(TESTPATH + test, 'r').read()
try:
class_, desc, cause, workaround, code = [x.rstrip() for x in \
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train | uimports | converts CPython module names into MicroPython equivalents | tools/gen-cpydiff.py | def uimports(code):
""" converts CPython module names into MicroPython equivalents """
for uimport in UIMPORTLIST:
uimport = bytes(uimport, 'utf8')
code = code.replace(uimport, b'u' + uimport)
return code | def uimports(code):
""" converts CPython module names into MicroPython equivalents """
for uimport in UIMPORTLIST:
uimport = bytes(uimport, 'utf8')
code = code.replace(uimport, b'u' + uimport)
return code | [
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train | indent | indents paragraphs of text for rst formatting | tools/gen-cpydiff.py | def indent(block, spaces):
""" indents paragraphs of text for rst formatting """
new_block = ''
for line in block.split('\n'):
new_block += spaces + line + '\n'
return new_block | def indent(block, spaces):
""" indents paragraphs of text for rst formatting """
new_block = ''
for line in block.split('\n'):
new_block += spaces + line + '\n'
return new_block | [
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train | gen_table | creates a table given any set of columns | tools/gen-cpydiff.py | def gen_table(contents):
""" creates a table given any set of columns """
xlengths = []
ylengths = []
for column in contents:
col_len = 0
for entry in column:
lines = entry.split('\n')
for line in lines:
col_len = max(len(line) + 2, col_len)
... | def gen_table(contents):
""" creates a table given any set of columns """
xlengths = []
ylengths = []
for column in contents:
col_len = 0
for entry in column:
lines = entry.split('\n')
for line in lines:
col_len = max(len(line) + 2, col_len)
... | [
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train | gen_rst | creates restructured text documents to display tests | tools/gen-cpydiff.py | def gen_rst(results):
""" creates restructured text documents to display tests """
# make sure the destination directory exists
try:
os.mkdir(DOCPATH)
except OSError as e:
if e.args[0] != errno.EEXIST and e.args[0] != errno.EISDIR:
raise
toctree = []
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... | def gen_rst(results):
""" creates restructured text documents to display tests """
# make sure the destination directory exists
try:
os.mkdir(DOCPATH)
except OSError as e:
if e.args[0] != errno.EEXIST and e.args[0] != errno.EISDIR:
raise
toctree = []
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train | main | Main function | tools/gen-cpydiff.py | def main():
""" Main function """
# set search path so that test scripts find the test modules (and no other ones)
os.environ['PYTHONPATH'] = TESTPATH
os.environ['MICROPYPATH'] = TESTPATH
files = readfiles()
results = run_tests(files)
gen_rst(results) | def main():
""" Main function """
# set search path so that test scripts find the test modules (and no other ones)
os.environ['PYTHONPATH'] = TESTPATH
os.environ['MICROPYPATH'] = TESTPATH
files = readfiles()
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train | init | Initializes the found DFU device so that we can program it. | tools/pydfu.py | def init():
"""Initializes the found DFU device so that we can program it."""
global __dev, __cfg_descr
devices = get_dfu_devices(idVendor=__VID, idProduct=__PID)
if not devices:
raise ValueError('No DFU device found')
if len(devices) > 1:
raise ValueError("Multiple DFU devices found... | def init():
"""Initializes the found DFU device so that we can program it."""
global __dev, __cfg_descr
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if not devices:
raise ValueError('No DFU device found')
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train | mass_erase | Performs a MASS erase (i.e. erases the entire device. | tools/pydfu.py | def mass_erase():
"""Performs a MASS erase (i.e. erases the entire device."""
# Send DNLOAD with first byte=0x41
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
"\x41", __TIMEOUT)
# Execute last command
if get_status() != __DFU_STATE_DFU_DOWNLOAD_BUSY:
ra... | def mass_erase():
"""Performs a MASS erase (i.e. erases the entire device."""
# Send DNLOAD with first byte=0x41
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
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# Execute last command
if get_status() != __DFU_STATE_DFU_DOWNLOAD_BUSY:
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train | page_erase | Erases a single page. | tools/pydfu.py | def page_erase(addr):
"""Erases a single page."""
if __verbose:
print("Erasing page: 0x%x..." % (addr))
# Send DNLOAD with first byte=0x41 and page address
buf = struct.pack("<BI", 0x41, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
# Execute last co... | def page_erase(addr):
"""Erases a single page."""
if __verbose:
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# Send DNLOAD with first byte=0x41 and page address
buf = struct.pack("<BI", 0x41, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
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train | set_address | Sets the address for the next operation. | tools/pydfu.py | def set_address(addr):
"""Sets the address for the next operation."""
# Send DNLOAD with first byte=0x21 and page address
buf = struct.pack("<BI", 0x21, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
# Execute last command
if get_status() != __DFU_STATE_DFU_DO... | def set_address(addr):
"""Sets the address for the next operation."""
# Send DNLOAD with first byte=0x21 and page address
buf = struct.pack("<BI", 0x21, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
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train | write_memory | Writes a buffer into memory. This routine assumes that memory has
already been erased. | tools/pydfu.py | def write_memory(addr, buf, progress=None, progress_addr=0, progress_size=0):
"""Writes a buffer into memory. This routine assumes that memory has
already been erased.
"""
xfer_count = 0
xfer_bytes = 0
xfer_total = len(buf)
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while xfer_bytes < xfer_total:
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"""Writes a buffer into memory. This routine assumes that memory has
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"""
xfer_count = 0
xfer_bytes = 0
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train | write_page | Writes a single page. This routine assumes that memory has already
been erased. | tools/pydfu.py | def write_page(buf, xfer_offset):
"""Writes a single page. This routine assumes that memory has already
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"""
xfer_base = 0x08000000
# Set mem write address
set_address(xfer_base+xfer_offset)
# Send DNLOAD with fw data
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 2, __DFU_INTERF... | def write_page(buf, xfer_offset):
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xfer_base = 0x08000000
# Set mem write address
set_address(xfer_base+xfer_offset)
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train | exit_dfu | Exit DFU mode, and start running the program. | tools/pydfu.py | def exit_dfu():
"""Exit DFU mode, and start running the program."""
# set jump address
set_address(0x08000000)
# Send DNLOAD with 0 length to exit DFU
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
None, __TIMEOUT)
try:
# Execute last command
... | def exit_dfu():
"""Exit DFU mode, and start running the program."""
# set jump address
set_address(0x08000000)
# Send DNLOAD with 0 length to exit DFU
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
None, __TIMEOUT)
try:
# Execute last command
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train | consume | Parses the struct defined by `fmt` from `data`, stores the parsed fields
into a named tuple using `names`. Returns the named tuple, and the data
with the struct stripped off. | tools/pydfu.py | def consume(fmt, data, names):
"""Parses the struct defined by `fmt` from `data`, stores the parsed fields
into a named tuple using `names`. Returns the named tuple, and the data
with the struct stripped off."""
size = struct.calcsize(fmt)
return named(struct.unpack(fmt, data[:size]), names), data[s... | def consume(fmt, data, names):
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size = struct.calcsize(fmt)
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train | read_dfu_file | Reads a DFU file, and parses the individual elements from the file.
Returns an array of elements. Each element is a dictionary with the
following keys:
num - The element index
address - The address that the element data should be written to.
size - The size of the element ddata.
... | tools/pydfu.py | def read_dfu_file(filename):
"""Reads a DFU file, and parses the individual elements from the file.
Returns an array of elements. Each element is a dictionary with the
following keys:
num - The element index
address - The address that the element data should be written to.
size ... | def read_dfu_file(filename):
"""Reads a DFU file, and parses the individual elements from the file.
Returns an array of elements. Each element is a dictionary with the
following keys:
num - The element index
address - The address that the element data should be written to.
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train | get_dfu_devices | Returns a list of USB device which are currently in DFU mode.
Additional filters (like idProduct and idVendor) can be passed in to
refine the search. | tools/pydfu.py | def get_dfu_devices(*args, **kwargs):
"""Returns a list of USB device which are currently in DFU mode.
Additional filters (like idProduct and idVendor) can be passed in to
refine the search.
"""
# convert to list for compatibility with newer pyusb
return list(usb.core.find(*args, find_all=True,
... | def get_dfu_devices(*args, **kwargs):
"""Returns a list of USB device which are currently in DFU mode.
Additional filters (like idProduct and idVendor) can be passed in to
refine the search.
"""
# convert to list for compatibility with newer pyusb
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train | get_memory_layout | Returns an array which identifies the memory layout. Each entry
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addr - Address of this memory segment
last_addr - Last address contained within the memory segment.
size - size of the segment, in bytes
num... | tools/pydfu.py | def get_memory_layout(device):
"""Returns an array which identifies the memory layout. Each entry
of the array will contain a dictionary with the following keys:
addr - Address of this memory segment
last_addr - Last address contained within the memory segment.
size - siz... | def get_memory_layout(device):
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addr - Address of this memory segment
last_addr - Last address contained within the memory segment.
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train | list_dfu_devices | Prints a lits of devices detected in DFU mode. | tools/pydfu.py | def list_dfu_devices(*args, **kwargs):
"""Prints a lits of devices detected in DFU mode."""
devices = get_dfu_devices(*args, **kwargs)
if not devices:
print("No DFU capable devices found")
return
for device in devices:
print("Bus {} Device {:03d}: ID {:04x}:{:04x}"
... | def list_dfu_devices(*args, **kwargs):
"""Prints a lits of devices detected in DFU mode."""
devices = get_dfu_devices(*args, **kwargs)
if not devices:
print("No DFU capable devices found")
return
for device in devices:
print("Bus {} Device {:03d}: ID {:04x}:{:04x}"
... | [
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train | write_elements | Writes the indicated elements into the target memory,
erasing as needed. | tools/pydfu.py | def write_elements(elements, mass_erase_used, progress=None):
"""Writes the indicated elements into the target memory,
erasing as needed.
"""
mem_layout = get_memory_layout(__dev)
for elem in elements:
addr = elem['addr']
size = elem['size']
data = elem['data']
elem_... | def write_elements(elements, mass_erase_used, progress=None):
"""Writes the indicated elements into the target memory,
erasing as needed.
"""
mem_layout = get_memory_layout(__dev)
for elem in elements:
addr = elem['addr']
size = elem['size']
data = elem['data']
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train | cli_progress | Prints a progress report suitable for use on the command line. | tools/pydfu.py | def cli_progress(addr, offset, size):
"""Prints a progress report suitable for use on the command line."""
width = 25
done = offset * width // size
print("\r0x{:08x} {:7d} [{}{}] {:3d}% "
.format(addr, size, '=' * done, ' ' * (width - done),
offset * 100 // size), end="")
... | def cli_progress(addr, offset, size):
"""Prints a progress report suitable for use on the command line."""
width = 25
done = offset * width // size
print("\r0x{:08x} {:7d} [{}{}] {:3d}% "
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train | main | Test program for verifying this files functionality. | tools/pydfu.py | def main():
"""Test program for verifying this files functionality."""
global __verbose
# Parse CMD args
parser = argparse.ArgumentParser(description='DFU Python Util')
#parser.add_argument("path", help="file path")
parser.add_argument(
"-l", "--list",
help="list available DFU de... | def main():
"""Test program for verifying this files functionality."""
global __verbose
# Parse CMD args
parser = argparse.ArgumentParser(description='DFU Python Util')
#parser.add_argument("path", help="file path")
parser.add_argument(
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train | parse_port_pin | Parses a string and returns a (port-num, pin-num) tuple. | ports/stm32/boards/make-pins.py | def parse_port_pin(name_str):
"""Parses a string and returns a (port-num, pin-num) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 charcters.")
if name_str[0] != 'P':
raise ValueError("Expecting pin name to start with P")
if name_str[1] < 'A' or name... | def parse_port_pin(name_str):
"""Parses a string and returns a (port-num, pin-num) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 charcters.")
if name_str[0] != 'P':
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train | AlternateFunction.print | Prints the C representation of this AF. | ports/stm32/boards/make-pins.py | def print(self):
"""Prints the C representation of this AF."""
cond_var = None
if self.supported:
cond_var = conditional_var('{}{}'.format(self.func, self.fn_num))
print_conditional_if(cond_var)
print(' AF', end='')
else:
print(' //', en... | def print(self):
"""Prints the C representation of this AF."""
cond_var = None
if self.supported:
cond_var = conditional_var('{}{}'.format(self.func, self.fn_num))
print_conditional_if(cond_var)
print(' AF', end='')
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train | parse_port_pin | Parses a string and returns a (port, gpio_bit) tuple. | ports/cc3200/boards/make-pins.py | def parse_port_pin(name_str):
"""Parses a string and returns a (port, gpio_bit) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 characters")
if name_str[:2] != 'GP':
raise ValueError("Expecting pin name to start with GP")
if not name_str[2:].isdigit(... | def parse_port_pin(name_str):
"""Parses a string and returns a (port, gpio_bit) tuple."""
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raise ValueError("Expecting pin name to be at least 3 characters")
if name_str[:2] != 'GP':
raise ValueError("Expecting pin name to start with GP")
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train | Backend.run_node | Simple run one operator and return the results.
Args:
outputs_info: a list of tuples, which contains the element type and
shape of each output. First element of the tuple is the dtype, and
the second element is the shape. More use case can be found in
https://gith... | onnx/backend/base.py | def run_node(cls,
node, # type: NodeProto
inputs, # type: Any
device='CPU', # type: Text
outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
**kwargs # type: Dict[Text, Any]
): # ty... | def run_node(cls,
node, # type: NodeProto
inputs, # type: Any
device='CPU', # type: Text
outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
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train | load_external_data_for_tensor | Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data. | onnx/external_data_helper.py | def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None
"""
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
if tensor.HasField("raw_data"): # already loaded
... | def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None
"""
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
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train | load_external_data_for_model | Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data | onnx/external_data_helper.py | def load_external_data_for_model(model, base_dir): # type: (ModelProto, Text) -> None
"""
Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data
"""
for tensor in _get_all_tensors(model):
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"""
Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data
"""
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train | convert_model_to_external_data | call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
@params
model: ModelProto to be converted.
all_tensors_to_one_file: If true, save all tensors to one external file specified by location.
If false, save ... | onnx/external_data_helper.py | def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None):
# type: (ModelProto, bool, Optional[Text]) -> None
"""
call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
@params
model: ModelProto to be... | def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None):
# type: (ModelProto, bool, Optional[Text]) -> None
"""
call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
@params
model: ModelProto to be... | [
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train | convert_model_from_external_data | call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
@params
model: ModelProto to be converted. | onnx/external_data_helper.py | def convert_model_from_external_data(model): # type: (ModelProto) -> None
"""
call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
@params
model: ModelProto to be converted.
"""
for tensor in _get_all_tensors(model):
... | def convert_model_from_external_data(model): # type: (ModelProto) -> None
"""
call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
@params
model: ModelProto to be converted.
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train | save_external_data | Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored | onnx/external_data_helper.py | def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None
"""
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
""... | def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None
"""
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
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train | _get_attribute_tensors | Create an iterator of tensors from node attributes of an ONNX model. | onnx/external_data_helper.py | def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto]
"""Create an iterator of tensors from node attributes of an ONNX model."""
for node in onnx_model_proto.graph.node:
for attribute in node.attribute:
if attribute.HasField("t"):
yield ... | def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto]
"""Create an iterator of tensors from node attributes of an ONNX model."""
for node in onnx_model_proto.graph.node:
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train | remove_external_data_field | Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
field_key: The key of the field to be removed | onnx/external_data_helper.py | def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None
"""
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
field_key: The key of the field to be remove... | def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None
"""
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
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train | write_external_data_tensors | Write external data of all tensors to files on disk.
Note: This function also strips basepath information from all tensors' external_data fields.
@params
model: Model object which is the source of tensors to serialize.
filepath: System path to the directory which should be treated as base path for ext... | onnx/external_data_helper.py | def write_external_data_tensors(model, filepath): # type: (ModelProto, Text) -> ModelProto
"""
Write external data of all tensors to files on disk.
Note: This function also strips basepath information from all tensors' external_data fields.
@params
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Write external data of all tensors to files on disk.
Note: This function also strips basepath information from all tensors' external_data fields.
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train | PkgWriter._import | Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional" | tools/protoc-gen-mypy.py | def _import(self, path, name):
# type: (Text, Text) -> Text
"""Imports a stdlib path and returns a handle to it
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train | PkgWriter._import_message | Import a referenced message and return a handle | tools/protoc-gen-mypy.py | def _import_message(self, type_name):
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train | mypy_type_check.run | Run command. | setup.py | def run(self):
"""Run command."""
onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
returncode = subprocess.call([sys.executable, onnx_script])
sys.exit(returncode) | def run(self):
"""Run command."""
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returncode = subprocess.call([sys.executable, onnx_script])
sys.exit(returncode) | [
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train | make_node | Construct a NodeProto.
Arguments:
op_type (string): The name of the operator to construct
inputs (list of string): list of input names
outputs (list of string): list of output names
name (string, default None): optional unique identifier for NodeProto
doc_string (string, def... | onnx/helper.py | def make_node(
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inputs, # type: Sequence[Text]
outputs, # type: Sequence[Text]
name=None, # type: Optional[Text]
doc_string=None, # type: Optional[Text]
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**kwargs # type: Any
): # type: (...) -> NodeP... | def make_node(
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inputs, # type: Sequence[Text]
outputs, # type: Sequence[Text]
name=None, # type: Optional[Text]
doc_string=None, # type: Optional[Text]
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train | make_operatorsetid | Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id | onnx/helper.py | def make_operatorsetid(
domain, # type: Text
version, # type: int
): # type: (...) -> OperatorSetIdProto
"""Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
"""
operatorsetid =... | def make_operatorsetid(
domain, # type: Text
version, # type: int
): # type: (...) -> OperatorSetIdProto
"""Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
"""
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train | _to_bytes_or_false | An internal graph to convert the input to a bytes or to False.
The criteria for conversion is as follows and should be python 2 and 3
compatible:
- If val is py2 str or py3 bytes: return bytes
- If val is py2 unicode or py3 str: return val.decode('utf-8')
- Otherwise, return False | onnx/helper.py | def _to_bytes_or_false(val): # type: (Union[Text, bytes]) -> Union[bytes, bool]
"""An internal graph to convert the input to a bytes or to False.
The criteria for conversion is as follows and should be python 2 and 3
compatible:
- If val is py2 str or py3 bytes: return bytes
- If val is py2 unicod... | def _to_bytes_or_false(val): # type: (Union[Text, bytes]) -> Union[bytes, bool]
"""An internal graph to convert the input to a bytes or to False.
The criteria for conversion is as follows and should be python 2 and 3
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train | make_attribute | Makes an AttributeProto based on the value type. | onnx/helper.py | def make_attribute(
key, # type: Text
value, # type: Any
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): # type: (...) -> AttributeProto
"""Makes an AttributeProto based on the value type."""
attr = AttributeProto()
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train | make_tensor_value_info | Makes a ValueInfoProto based on the data type and shape. | onnx/helper.py | def make_tensor_value_info(
name, # type: Text
elem_type, # type: int
shape, # type: Optional[Sequence[Union[Text, int]]]
doc_string="", # type: Text
shape_denotation=None, # type: Optional[List[Text]]
): # type: (...) -> ValueInfoProto
"""Makes a ValueInfoProto based o... | def make_tensor_value_info(
name, # type: Text
elem_type, # type: int
shape, # type: Optional[Sequence[Union[Text, int]]]
doc_string="", # type: Text
shape_denotation=None, # type: Optional[List[Text]]
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train | strip_doc_string | Empties `doc_string` field on any nested protobuf messages | onnx/helper.py | def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None
"""
Empties `doc_string` field on any nested protobuf messages
"""
assert isinstance(proto, google.protobuf.message.Message)
for descriptor in proto.DESCRIPTOR.fields:
if descriptor.name == 'doc_string':
... | def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None
"""
Empties `doc_string` field on any nested protobuf messages
"""
assert isinstance(proto, google.protobuf.message.Message)
for descriptor in proto.DESCRIPTOR.fields:
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train | to_array | Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array. | onnx/numpy_helper.py | def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any]
"""Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array.
"""
if tensor.HasField("segment"):
raise ValueError(
"Currently not supporti... | def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any]
"""Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array.
"""
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train | from_array | Converts a numpy array to a tensor def.
Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def. | onnx/numpy_helper.py | def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto
"""Converts a numpy array to a tensor def.
Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def.
"""
tensor = TensorProto()... | def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto
"""Converts a numpy array to a tensor def.
Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def.
"""
tensor = TensorProto()... | [
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train | _serialize | Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes | onnx/__init__.py | def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes
'''
Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes
'''
if isinstance(proto, bytes):
return... | def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes
'''
Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes
'''
if isinstance(proto, bytes):
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train | _deserialize | Parse bytes into a in-memory proto
@params
s is bytes containing serialized proto
proto is a in-memory proto object
@return
The proto instance filled in by s | onnx/__init__.py | def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto
'''
Parse bytes into a in-memory proto
@params
s is bytes containing serialized proto
proto is a in-memory proto object
@return
The proto instance filled in by s
'''
if not isinstance(s, bytes):
raise ValueError... | def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto
'''
Parse bytes into a in-memory proto
@params
s is bytes containing serialized proto
proto is a in-memory proto object
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The proto instance filled in by s
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