diff --git a/README.md b/README.md index 33a0133..3196104 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,11 @@ Personal solutions to [**Python** track][python_track] exercises from [**Exercis 2. ["**Guido's Gorgeous Lasagna**" solution](python/guidos-gorgeous-lasagna/lasagna.py). 3. ["**Ghost Gobble Arcade Game**" solution](python/ghost-gobble-arcade-game/arcade_game.py). 4. ["**Currency Exchange**" solution](python/currency-exchange/exchange.py). +5. ["**Meltdown Mitigation**" solution](python/meltdown-mitigation/conditionals.py). [python_version_badge]: https://img.shields.io/badge/Python%203.13-3776AB?logo=python&logoColor=FFD43B -[track_completion_badge]: https://img.shields.io/badge/Track%20completion-2.7%25-604fcd?logo=exercism&logoColor=604fcd&labelColor=e9ecef -[exercises_completed_badge]: https://img.shields.io/badge/Exercises%20completed-4%2F146-604fcd?logo=exercism&logoColor=604fcd&labelColor=e9ecef +[track_completion_badge]: https://img.shields.io/badge/Track%20completion-3.4%25-604fcd?logo=exercism&logoColor=604fcd&labelColor=e9ecef +[exercises_completed_badge]: https://img.shields.io/badge/Exercises%20completed-5%2F146-604fcd?logo=exercism&logoColor=604fcd&labelColor=e9ecef [python_track]: https://exercism.org/tracks/python [exercism]: https://exercism.org diff --git a/python/meltdown-mitigation/HINTS.md b/python/meltdown-mitigation/HINTS.md new file mode 100644 index 0000000..bee2779 --- /dev/null +++ b/python/meltdown-mitigation/HINTS.md @@ -0,0 +1,50 @@ +# Hints + +## General + +- The Python Docs on [Control Flow Tools][control flow tools] and the Real Python tutorial on [conditionals][real python conditionals] are great places to start. +- The Python Docs on [Boolean Operations][boolean operations] can be a great refresher on `bools`, as can the Real Python tutorial on [booleans][python booleans]. +- The Python Docs on [Comparisons][comparisons] and [comparisons examples][python comparisons examples] can be a great refresher for comparisons. + +## 1. Check for criticality + +- Comparison operators ([comparisons][comparisons review]) and boolean operations ([concept:python/bools]()) can be combined and used with conditionals. +- Conditional expressions must evaluate to `True` or `False`. +- `else` can be used for a code block that will execute when all conditional tests return `False`. + + ```python + >>> item = 'blue' + >>> item_2 = 'green' + + >>> if len(item) >= 3 and len(item_2) < 5: + print('Both pass the test!') + elif len(item) >= 3 or len(item_2) < 5: + print('One passes the test!') + else: + print('None pass the test!') + ... + One passes the test! + ``` + +## 2. Determine the Power output range + +- Comparison operators can be combined and used with conditionals. +- Any number of `elif` statements can be used as decision "branches". +- Each "branch" can have a separate `return`, although it might be considered "bad form" by linting tools. +- If the linter complains, consider assigning the output of a branch to a common variable, and then `return`ing that variable. + +## 3. Fail Safe Mechanism + +- Comparison operators can be combined and used with conditionals. +- Any number of `elif` statements can be used as decision "branches". +- Each "branch" can have a separate `return`, although it might be considered "bad form" by linting tools. +- If the linter complains, consider assigning the output of a branch to a common variable, and then `return`ing that variable. + + +[boolean operations]: https://docs.python.org/3/library/stdtypes.html#boolean-operations-and-or-not +[comparisons review]: https://www.learnpython.dev/02-introduction-to-python/090-boolean-logic/20-comparisons/ +[comparisons]: https://docs.python.org/3/library/stdtypes.html#comparisons +[control flow tools]: https://docs.python.org/3/tutorial/controlflow.html +[python booleans]: https://realpython.com/python-boolean/ +[python comparisons examples]: https://www.tutorialspoint.com/python/comparison_operators_example.htm +[real python conditionals]: https://realpython.com/python-conditional-statements/ \ No newline at end of file diff --git a/python/meltdown-mitigation/README.md b/python/meltdown-mitigation/README.md new file mode 100644 index 0000000..8e05f05 --- /dev/null +++ b/python/meltdown-mitigation/README.md @@ -0,0 +1,171 @@ +# Meltdown Mitigation + +Welcome to Meltdown Mitigation on Exercism's Python Track. +If you get stuck on the exercise, check out `HINTS.md`, but try and solve it without using those first :) + +## Introduction + +In Python, [`if`][if statement], `elif` (_a contraction of 'else and if'_) and `else` statements are used to [control the flow][control flow tools] of execution and make decisions in a program. +Unlike many other programming languages, Python versions 3.9 and below do not offer a formal case-switch statement, instead using multiple `elif` statements to serve a similar purpose. + +Python 3.10 introduces a variant case-switch statement called `structural pattern matching`, which will be covered separately in another concept. + +Conditional statements use expressions that must resolve to `True` or `False` -- either by returning a `bool` type directly, or by evaluating as ["truthy" or "falsy"][truth value testing]. + +```python +x = 10 +y = 5 + +# The comparison '>' returns the bool 'True', +# so the statement is printed. +if x > y: + print("x is greater than y") +... +>>> x is greater than y +``` + +When paired with `if`, an optional `else` code block will execute when the original `if` condition evaluates to `False`: + +```python +x = 5 +y = 10 + +# The comparison '>' here returns the bool 'False', +# so the 'else' block is executed instead of the 'if' block. +if x > y: + print("x is greater than y") +else: + print("y is greater than x") +... +>>> y is greater than x +``` + +`elif` allows for multiple evaluations/branches. + +```python +x = 5 +y = 10 +z = 20 + +# The 'elif' statement allows for the checking of more conditions. +if x > y: + print("x is greater than y and z") +elif y > z: + print("y is greater than x and z") +else: + print("z is greater than x and y") +... +>>> z is greater than x and y +``` + +[Boolean operations][boolean operations] and [comparisons][comparisons] can be combined with conditionals for more complex testing: + +```python +>>> def classic_fizzbuzz(number): + if number % 3 == 0 and number % 5 == 0: + say = 'FizzBuzz!' + elif number % 5 == 0: + say = 'Buzz!' + elif number % 3 == 0: + say = 'Fizz!' + else: + say = str(number) + + return say + +>>> classic_fizzbuzz(15) +'FizzBuzz!' + +>>> classic_fizzbuzz(13) +'13' +``` + +[boolean operations]: https://docs.python.org/3/library/stdtypes.html#boolean-operations-and-or-not +[comparisons]: https://docs.python.org/3/library/stdtypes.html#comparisons +[control flow tools]: https://docs.python.org/3/tutorial/controlflow.html#more-control-flow-tools +[if statement]: https://docs.python.org/3/reference/compound_stmts.html#the-if-statement +[truth value testing]: https://docs.python.org/3/library/stdtypes.html#truth-value-testing + +## Instructions + +In this exercise, we'll develop a simple control system for a nuclear reactor. + +For a reactor to produce the power it must be in a state of _criticality_. +If the reactor is in a state less than criticality, it can become damaged. +If the reactor state goes beyond criticality, it can overload and result in a meltdown. +We want to mitigate the chances of meltdown and correctly manage reactor state. + +The following three tasks are all related to writing code for maintaining ideal reactor state. + +## 1. Check for criticality + +The first thing a control system has to do is check if the reactor is _balanced in criticality_. +A reactor is said to be balanced in criticality if it satisfies the following conditions: + +- The temperature is less than 800 K. +- The number of neutrons emitted per second is greater than 500. +- The product of temperature and neutrons emitted per second is less than 500000. + +Implement the function `is_criticality_balanced()` that takes `temperature` measured in kelvin and `neutrons_emitted` as parameters, and returns `True` if the criticality conditions are met, `False` if not. + +```python +>>> is_criticality_balanced(750, 600) +True +``` + +## 2. Determine the Power output range + +Once the reactor has started producing power its efficiency needs to be determined. +Efficiency can be grouped into 4 bands: + +1. `green` -> efficiency of 80% or more, +2. `orange` -> efficiency of less than 80% but at least 60%, +3. `red` -> efficiency below 60%, but still 30% or more, +4. `black` -> less than 30% efficient. + +The percentage value can be calculated as `(generated_power/theoretical_max_power)*100` +where `generated_power` = `voltage` * `current`. +Note that the percentage value is usually not an integer number, so make sure to consider the +proper use of the `<` and `<=` comparisons. + +Implement the function `reactor_efficiency(, , )`, with three parameters: `voltage`, +`current`, and `theoretical_max_power`. +This function should return the efficiency band of the reactor : 'green', 'orange', 'red', or 'black'. + +```python +>>> reactor_efficiency(200,50,15000) +'orange' +``` + +## 3. Fail Safe Mechanism + +Your final task involves creating a fail-safe mechanism to avoid overload and meltdown. +This mechanism will determine if the reactor is below, at, or above the ideal criticality threshold. +Criticality can then be increased, decreased, or stopped by inserting (or removing) control rods into the reactor. + +Implement the function called `fail_safe()`, which takes 3 parameters: `temperature` measured in kelvin, +`neutrons_produced_per_second`, and `threshold`, and outputs a status code for the reactor. + +- If `temperature * neutrons_produced_per_second` < 90% of `threshold`, output a status code of 'LOW' + indicating that control rods must be removed to produce power. + +- If the value `temperature * neutrons_produced_per_second` is within 10% of the `threshold` (so either 0-10% less than the threshold, at the threshold, or 0-10% greater than the threshold), the reactor is in _criticality_ and the status code of 'NORMAL' should be output, indicating that the reactor is in optimum condition and control rods are in an ideal position. + +- If `temperature * neutrons_produced_per_second` is not in the above-stated ranges, the reactor is + going into meltdown and a status code of 'DANGER' must be passed to immediately shut down the reactor. + +```python +>>> fail_safe(temperature=1000, neutrons_produced_per_second=30, threshold=5000) +'DANGER' +``` + +## Source + +### Created by + +- @sachsom95 +- @BethanyG + +### Contributed to by + +- @kbuc \ No newline at end of file diff --git a/python/meltdown-mitigation/conditionals.py b/python/meltdown-mitigation/conditionals.py new file mode 100644 index 0000000..868e669 --- /dev/null +++ b/python/meltdown-mitigation/conditionals.py @@ -0,0 +1,87 @@ +"""Functions to prevent a nuclear meltdown.""" + + +def is_criticality_balanced(temperature, neutrons_emitted): + """Verify criticality is balanced. + + Parameters: + temperature (int or float): The temperature value in kelvin. + neutrons_emitted (int or float): The number of neutrons emitted per second. + + Returns: + bool: Is criticality balanced? + + Note: + A reactor is said to be balanced in criticality if it satisfies the following conditions: + - The temperature is less than 800 K. + - The number of neutrons emitted per second is greater than 500. + - The product of temperature and neutrons emitted per second is less than 500000. + + """ + + if temperature >= 800: + return False + if neutrons_emitted <= 500: + return False + if temperature * neutrons_emitted >= 5e5: + return False + return True + + +def reactor_efficiency(voltage, current, theoretical_max_power): + """Assess reactor efficiency zone. + + Parameters: + voltage (int or float): Voltage value. + current (int or float): Current value. + theoretical_max_power (int or float): The power level that corresponds to a 100% efficiency. + + Returns: + str: One of ('green', 'orange', 'red', or 'black'). + + Note: + Efficiency can be grouped into 4 bands: + 1. green -> efficiency of 80% or more, + 2. orange -> efficiency of less than 80% but at least 60%, + 3. red -> efficiency below 60%, but still 30% or more, + 4. black -> less than 30% efficient. + + The percentage value is calculated as + (generated power/ theoretical max power)*100 + where generated power = voltage * current + """ + + generated_power = voltage * current + efficiency = generated_power / theoretical_max_power * 100 + if efficiency >= 80: + return 'green' + if efficiency >= 60: + return 'orange' + if efficiency >= 30: + return 'red' + return 'black' + + +def fail_safe(temperature, neutrons_produced_per_second, threshold): + """Assess and return status code for the reactor. + + Parameters: + temperature (int or float): The value of the temperature in kelvin. + neutrons_produced_per_second (int or float): The neutron flux. + threshold (int or float): The threshold for the category. + + Returns: + str: One of ('LOW', 'NORMAL', 'DANGER'). + + Note: + 1. 'LOW' -> `temperature * neutrons per second` < 90% of `threshold` + 2. 'NORMAL' -> `temperature * neutrons per second` +/- 10% of `threshold` + 3. 'DANGER' -> `temperature * neutrons per second` is not in the above-stated ranges + """ + + status = temperature * neutrons_produced_per_second + if status < threshold * 0.9: + return 'LOW' + if status <= threshold * 1.1: + return 'NORMAL' + return 'DANGER' diff --git a/python/meltdown-mitigation/conditionals_test.py b/python/meltdown-mitigation/conditionals_test.py new file mode 100644 index 0000000..5e48ca3 --- /dev/null +++ b/python/meltdown-mitigation/conditionals_test.py @@ -0,0 +1,82 @@ +import unittest +import pytest +from conditionals import (is_criticality_balanced, + reactor_efficiency, + fail_safe) + + +class MeltdownMitigationTest(unittest.TestCase): + """Test cases for Meltdown mitigation exercise. + """ + + @pytest.mark.task(taskno=1) + def test_is_criticality_balanced(self): + """Testing border cases around typical points. + + T, n == (800, 500), (625, 800), (500, 1000), etc. + + """ + + test_data = ((750, 650, True), (799, 501, True), (500, 600, True), + (1000, 800, False), (800, 500, False), (800, 500.01, False), + (799.99, 500, False), (500.01, 999.99, False), (625, 800, False), + (625.99, 800, False), (625.01, 799.99, False), (799.99, 500.01, True), + (624.99, 799.99, True), (500, 1000, False), (500.01, 1000, False), + (499.99, 1000, True)) + + for variant, data in enumerate(test_data, start=1): + temp, neutrons_emitted, expected = data + with self.subTest(f'variation #{variant}', temp=temp, neutrons_emitted=neutrons_emitted, expected=expected): + + # pylint: disable=assignment-from-no-return + actual_result = is_criticality_balanced(temp, neutrons_emitted) + failure_message = (f'Called is_criticality_balanced({temp}, {neutrons_emitted}). ' + f' The function returned {actual_result}, ' + f'but the test expected {expected} as the return value.') + + self.assertEqual(actual_result, expected, failure_message) + + @pytest.mark.task(taskno=2) + def test_reactor_efficiency(self): + voltage = 10 + theoretical_max_power = 10000 + + # The numbers are chosen so that current == 10 x percentage + test_data = ((1000, 'green'), (999, 'green'), (800, 'green'), + (799, 'orange'), (700, 'orange'), (600, 'orange'), + (599, 'red'), (560, 'red'), (400, 'red'), (300, 'red'), + (299, 'black'), (200, 'black'), (0, 'black')) + + for variant, data in enumerate(test_data, start=1): + current, expected = data + with self.subTest(f'variation #{variant}', voltage=voltage, current=current, + theoretical_max_power=theoretical_max_power, expected=expected): + + # pylint: disable=assignment-from-no-return + actual_result = reactor_efficiency(voltage, current, theoretical_max_power) + failure_message =(f'Called reactor_efficiency({voltage}, {current}, {theoretical_max_power}). ' + f'The function returned {actual_result}, ' + f'but the test expected {expected} as the return value.') + + self.assertEqual(actual_result, expected, failure_message) + + @pytest.mark.task(taskno=3) + def test_fail_safe(self): + temp = 10 + threshold = 10000 + test_data = ((399, 'LOW'), (300, 'LOW'), (1, 'LOW'), + (0, 'LOW'), (901, 'NORMAL'), (1000, 'NORMAL'), + (1099, 'NORMAL'), (899, 'LOW'), (700, 'LOW'), + (400, 'LOW'), (1101, 'DANGER'), (1200, 'DANGER')) + + for variant, (neutrons_per_second, expected) in enumerate(test_data, start=1): + with self.subTest(f'variation #{variant}', temp=temp, neutrons_per_second=neutrons_per_second, + threshold=threshold, expected=expected): + + # pylint: disable=assignment-from-no-return + actual_result = fail_safe(temp, neutrons_per_second, threshold) + failure_message = (f'Called fail_safe({temp}, {neutrons_per_second}, {threshold}). ' + f'The function returned {actual_result}, ' + f'but the test expected {expected} as the return value.') + + self.assertEqual(actual_result, expected, failure_message)