Practice_02

⚔️ Phase 02\_Practice: The Data Forge Crucible 🛡️

"Simulation escalated. You now have the complete architectural toolkit: data structures, control flows, and modular subroutines. This gauntlet will test your ability to synthesize these components into complex, unbreakable logic."

These challenges require combining everything you have learned: variables, dictionaries, lists, loops, conditionals, and advanced function parameters (*args, **kwargs, global, and recursion). Do not use any external libraries. Pure MicroPython logic only.

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🎯 THE CHALLENGES

Challenge 01: The Corrupted Payload Parser

Objective: Master dictionary manipulation (.get()), list operations, loops, and conditionals.

The Scenario: You are receiving a raw, unreliable stream of data payloads from a network of sensors. Some payloads are missing keys, and some are throwing errors.

raw_payloads = [
    {"id": 1, "temp": 22.5, "status": "OK"},
    {"id": 2, "status": "ERROR", "error_code": 404},
    {"id": 3, "temp": 25.1, "status": "OK"},
    {"id": 4, "temp": -99, "status": "OK"}, # Faulty reading
    {"id": 5, "status": "OK"}               # Missing temp key entirely
]

Your Task: Write a function sanitize_payloads(payload_list) that iterates through the list.

1. If the "status" is not "OK", skip the payload entirely.
2. Use .get() to extract the "temp". If the key doesn't exist, default it to 0.0.
3. If the temperature is valid (between 0.0 and 50.0), append it to a new clean_temps list.
4. Return the sorted clean_temps list in descending order.

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Challenge 02: The Global Multiplexer

Objective: Handle variable scopes (global), arbitrary arguments (*args, **kwargs), and list mapping.

The Scenario: You have a global system calibration multiplier that occasionally updates, and a logging function that must handle an unpredictable number of incoming readings.

Your Task:

1. Define a global variable: CALIBRATION_FACTOR = 1.5
2. Write a function process_telemetry(*readings, **config).
3. Inside the function, multiply every reading in *readings by the CALIBRATION_FACTOR. (Hint: You can use a for loop or map()).
4. Check the **config dictionary. If config.get("strict_mode") is True, filter out any calibrated readings that exceed 100.0.
5. Return the final list of processed readings.

Test your function with: process_telemetry(10, 50, 80, 25, strict_mode=True, location="Sector 7")

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Challenge 03: The Nested Cipher (Recursion)

Objective: Safely implement a recursive function, combine it with type() checking, and manage lists.

The Scenario: A critical data packet arrived heavily fragmented and nested within multiple layers of arrays.

fragmented_data = [1, [2, 3], [[4], 5], 6, [[[7]]]]

Your Task: Write a recursive function flatten_data(nested_list) that breaks this down into a single, flat list: [1, 2, 3, 4, 5, 6, 7].

• Hint: Create an empty list flat = [] inside the function. Loop through each item in nested_list. Use if type(item) is list: to check if the current item is another list. If it is, recursively call flatten_data(item) and .extend() the result into flat. Otherwise, just .append() the item.

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Challenge 04: The Lambda Anomaly

Objective: Write highly condensed, functional code using lambda, filter(), map(), and the ternary operator.

The Scenario: CPU cycles are critical, and you have exactly two lines of code to clean a raw array of voltage spikes.

raw_voltages = [-5.0, 12.5, 105.0, 8.2, 0.0]

Your Task: Without using any def functions or for loops:

1. Create a variable positive_only and assign it the result of using filter() and a lambda to strip out any values less than or equal to 0.0.
2. Create a variable capped_voltages and assign it the result of using map() and a lambda with a ternary operator (x if condition else y) to cap the remaining values at 24.0 (i.e., if a value is greater than 24.0, change it to 24.0).
3. Print capped_voltages as a list.

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