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question:# Garbage Collector Management and Inspection in Python Implement the following functions using Python's `gc` module: 1. **`is_automatic_gc_enabled() -> bool`** - **Description**: This function should return `True` if automatic garbage collection is enabled, otherwise `False`. 2. **`force_full_garbage_collection() -> int`** - **Description**: This function should manually trigger a garbage collection cycle and return the number of unreachable objects found. 3. **`track_object_references(obj) -> tuple`** - **Description**: This function should return a tuple containing two lists: - The first list should include objects that directly refer to the given object `obj`. - The second list should include objects that are directly referred to by the given object `obj`. 4. **`set_garbage_collection_thresholds(threshold0: int, threshold1: int, threshold2: int) -> None`** - **Description**: This function should set the garbage collection thresholds to the provided values. Setting `threshold0` to zero should disable garbage collection. 5. **`check_and_freeze_objects() -> int`** - **Description**: This function should freeze all objects currently tracked by the garbage collector and return the number of objects put in the permanent generation. 6. **`unfreeze_all_objects() -> None`** - **Description**: This function should unfreeze all objects in the permanent generation, putting them back into the oldest generation. # Constraints and Requirements - You can assume that all inputs to functions are valid and the functions will be called in a manner that respects their logical dependencies (e.g., objects won't be frozen before the garbage collection thresholds are set). - The functions should handle any edge cases related to the garbage collector's operations as specified in the documentation. # Example Usage ```python import gc print(is_automatic_gc_enabled()) # Output: True or False unreachable_objects = force_full_garbage_collection() print(f"Unreachable objects found: {unreachable_objects}") obj = {} set_garbage_collection_thresholds(700, 10, 10) print(track_object_references(obj)) # Output: ([], []) freeze_count = check_and_freeze_objects() print(f"Number of frozen objects: {freeze_count}") unfreeze_all_objects() ``` These functions should demonstrate students' understanding and ability to employ the `gc` module to control and inspect the garbage collection process in Python.
answer:import gc def is_automatic_gc_enabled() -> bool: Returns True if automatic garbage collection is enabled, otherwise False. return gc.isenabled() def force_full_garbage_collection() -> int: Manually triggers a garbage collection cycle and returns the number of unreachable objects found. unreachable_objects = gc.collect() return unreachable_objects def track_object_references(obj) -> tuple: Returns a tuple containing two lists: - The first list includes objects that directly refer to the given object `obj`. - The second list includes objects that are directly referred to by the given object `obj`. return (gc.get_referrers(obj), gc.get_referents(obj)) def set_garbage_collection_thresholds(threshold0: int, threshold1: int, threshold2: int) -> None: Sets the garbage collection thresholds to the provided values. Setting threshold0 to zero disables garbage collection. gc.set_threshold(threshold0, threshold1, threshold2) def check_and_freeze_objects() -> int: Freezes all objects currently tracked by the garbage collector and returns the number of objects put in the permanent generation. return gc.freeze() def unfreeze_all_objects() -> None: Unfreezes all objects in the permanent generation, putting them back into the oldest generation. gc.unfreeze()
question:# Python Coding Assessment Question Objective Your task is to write a Python function to limit the maximum CPU time a process can use and then monitor and print the resource usage statistics before and after performing a CPU-bound task. Function Specifications 1. **Function Name**: `limit_and_monitor` 2. **Parameters**: - `cpu_time_limit` (int): The maximum CPU time (in seconds) the process is allowed to use. 3. **Output**: - The function does not return anything but must print the resource usage statistics before and after the CPU-bound task. Requirements 1. Use the `resource` module to set the CPU time limit for the current process. 2. Retrieve and print the resource usage statistics before starting the CPU-bound task using the `resource.getrusage()` function with `resource.RUSAGE_SELF`. 3. Perform a CPU-bound task, such as a loop performing mathematical operations. 4. Retrieve and print the resource usage statistics again after completing the CPU-bound task. 5. Ensure appropriate exception handling for any potential errors that might be raised due to invalid operations or limits. Constraints - The CPU-bound task should consist of at least `10**6` iterations of a mathematical operation. - Handle any exceptions raised by `setrlimit()` and `getrusage()` and print an appropriate error message. Example Usage ```python def limit_and_monitor(cpu_time_limit): import resource import time try: # Set CPU time limit for the current process resource.setrlimit(resource.RLIMIT_CPU, (cpu_time_limit, cpu_time_limit)) # Get and print resource usage before the task usage_before = resource.getrusage(resource.RUSAGE_SELF) print("Resource usage before:", usage_before) # Perform a CPU-bound task for _ in range(10**6): _ = 1 + 1 # Get and print resource usage after the task usage_after = resource.getrusage(resource.RUSAGE_SELF) print("Resource usage after:", usage_after) except (resource.error, ValueError) as e: print("Error occurred:", e) # Example call to the function limit_and_monitor(2) ``` Note: The exact structure and formatting of the output are left to the student's discretion, but it must include all relevant resource usage statistics.
answer:def limit_and_monitor(cpu_time_limit): import resource import time try: # Set CPU time limit for the current process resource.setrlimit(resource.RLIMIT_CPU, (cpu_time_limit, cpu_time_limit)) # Get and print resource usage before the task usage_before = resource.getrusage(resource.RUSAGE_SELF) print("Resource usage before:", usage_before) # Perform a CPU-bound task for _ in range(10**6): _ = 1 + 1 # Get and print resource usage after the task usage_after = resource.getrusage(resource.RUSAGE_SELF) print("Resource usage after:", usage_after) except (resource.error, ValueError) as e: print("Error occurred:", e)
question:# Seaborn `plotting_context` Customization Problem Statement Seaborn's `plotting_context` function allows you to customize the context parameters for your plots, affecting their overall dimensions and readability. You are tasked with creating a utility function that generates multiple line plots with different plotting contexts. Additionally, this function should allow the user to specify custom parameters. Function Signature ```python def generate_custom_plots(data, contexts, custom_params=None): pass ``` Input - `data`: A dictionary where keys are labels and values are lists of numerical values. Example: `{'Series1': [1, 2, 3], 'Series2': [4, 5, 6]}` - `contexts`: A list of strings representing different plotting contexts. Example: `['paper', 'notebook', 'talk', 'poster']`. - `custom_params` (optional): A dictionary of custom context parameters to override the default ones. Output The function should generate line plots based on the provided data and contexts. Each plot should be displayed with the corresponding context applied. If `custom_params` are provided, they should be applied as well. Constraints 1. You should use the `sns.plotting_context` function to set the context. 2. Each context in the `contexts` list should result in a separate plot. 3. If `custom_params` are provided, they should be consistently applied to all plots. Example ```python data = { 'Series1': [1, 2, 3, 2, 1], 'Series2': [2, 3, 4, 3, 2] } contexts = ['paper', 'notebook', 'talk', 'poster'] custom_params = {'font.size': 12, 'axes.labelsize': 10} generate_custom_plots(data, contexts, custom_params) ``` The function should produce four line plots, one for each context specified in `contexts`, with the custom parameters being applied. Notes - Make sure to import the necessary libraries (`seaborn` and `matplotlib.pyplot`) at the beginning of your code. - The function does not need to return any values; it should simply produce the plots.
answer:import seaborn as sns import matplotlib.pyplot as plt def generate_custom_plots(data, contexts, custom_params=None): Generates line plots for the provided data using different plotting contexts and custom parameters. Parameters: data (dict): A dictionary where keys are labels (strings) and values are lists of numerical values. contexts (list): A list of strings representing different plotting contexts. custom_params (dict, optional): A dictionary of custom context parameters to override the default ones. for context in contexts: sns.set_context(context, rc=custom_params) plt.figure(figsize=(10, 6)) for label, values in data.items(): plt.plot(values, label=label) plt.title(f'Plotting Context: {context}') plt.legend() plt.show()
question:# Custom Serialization Using `copyreg` Module Objective You are required to create a complex class and define a custom pickling function for it using the `copyreg` module. The custom class should have nested data structures, and the pickling function should ensure that all nested structures are correctly serialized and deserialized. Class Requirements 1. Define a class `ComplexObject` that contains the following attributes: - An integer attribute `id`. - A string attribute `name`. - A list of integers attribute `values`. - A dictionary attribute `metadata` with string keys and mixed type values (e.g. integers, strings, lists). 2. Implement the `__init__` method to initialize these attributes. 3. Implement a method `__eq__` to compare two `ComplexObject` instances for equality. Custom Pickling Function 1. Write a function `pickle_complex_object` that takes an instance of `ComplexObject` as an argument and returns a tuple for pickling. The tuple should contain: - The `ComplexObject` class itself. - A tuple of the object's attributes: `id`, `name`, `values`, and `metadata`. 2. Register this pickling function using `copyreg.pickle`. Main Function 1. Create an instance of `ComplexObject` and serialize it using the `pickle.dumps` method. 2. Deserialize the object using `pickle.loads` and verify that the deserialized object is equal to the original object using the `__eq__` method. Input/Output There is no direct user input/output for this task. The correctness of the implementation will be determined by the equality check between the original and deserialized objects. Constraints - Ensure that the class can handle large lists and dictionaries efficiently. - The `metadata` dictionary should support nested lists and dictionaries. Example ```python # Example class initialization obj = ComplexObject( id=1, name="TestObject", values=[10, 20, 30], metadata={"key1": 100, "key2": "value", "key3": [1, 2, 3]} ) # Pickling and unpickling example import pickle # Serialize the object serialized_obj = pickle.dumps(obj) # Deserialize the object deserialized_obj = pickle.loads(serialized_obj) # Verify equality assert obj == deserialized_obj, "Deserialized object does not match original." ``` Implement the class and the pickling function as described. Serialize and deserialize an instance of the class to ensure the custom pickling function works correctly.
answer:import copyreg import pickle class ComplexObject: def __init__(self, id, name, values, metadata): self.id = id self.name = name self.values = values self.metadata = metadata def __eq__(self, other): if not isinstance(other, ComplexObject): return False return (self.id == other.id and self.name == other.name and self.values == other.values and self.metadata == other.metadata) def pickle_complex_object(obj): return (ComplexObject, (obj.id, obj.name, obj.values, obj.metadata)) # Register the pickling function copyreg.pickle(ComplexObject, pickle_complex_object) # Function to test serialization and deserialization def serialize_deserialize(obj): serialized = pickle.dumps(obj) deserialized = pickle.loads(serialized) return deserialized