bcb-0011
1.000
Challenge · difficulty 3/5
# BigCodeBench/11
Implement a file **`solution.py`** that completes the function below. Keep the given name and signature; define `task_func` at module level.
Allowed libraries: `numpy`, `itertools`, `random`.
```python
import numpy as np
import itertools
import random
def task_func(T1, max_value=100):
"""
Converts elements in 'T1', a tuple of tuples containing string representations
of integers, to integers and creates a list of random integers. The size of the
list equals the sum of these integers. Returns the 25th, 50th, and 75th percentiles
of this list.
Parameters:
T1 (tuple of tuple of str): A tuple of tuples, each containing string representations of integers.
max_value (int): The upper bound for random number generation, exclusive. Default is 100.
Returns:
tuple: A tuple (p25, p50, p75) representing the 25th, 50th, and 75th percentiles of the list.
Requirements:
- numpy
- itertools
- random
Example:
>>> import random
>>> random.seed(42)
>>> T1 = (('13', '17', '18', '21', '32'), ('07', '11', '13', '14', '28'), ('01', '05', '06', '08', '15', '16'))
>>> percentiles = task_func(T1)
>>> print(percentiles)
(24.0, 48.0, 77.0)
"""
```
<!-- imported from BigCodeBench (BigCodeBench/11) -->
tests/test_bcb_0011.py
# Auto-generated from BigCodeBench BigCodeBench/11. Do not edit by hand.
import pathlib as _pathlib
exec(_pathlib.Path(__file__).with_name("solution.py").read_text(), globals())
import unittest
from unittest.mock import patch
class TestCases(unittest.TestCase):
@patch('random.randint')
def test_case_1(self, mock_randint):
"""Test with diverse values and the default range to ensure percentile calculation."""
mock_randint.return_value = 50 # Mocking random.randint to always return 50
T1 = (('13', '17', '18', '21', '32'), ('07', '11', '13', '14', '28'), ('01', '05', '06', '08', '15', '16'))
p25, p50, p75 = task_func(T1)
self.assertEqual(p25, 50)
self.assertEqual(p50, 50)
self.assertEqual(p75, 50)
@patch('random.randint')
def test_case_2(self, mock_randint):
"""Check consistency when the total number of elements are small but repeated."""
mock_randint.return_value = 30 # Consistent lower value for a different perspective
T1 = (('10',), ('10', '10', '10'))
p25, p50, p75 = task_func(T1)
self.assertEqual(p25, 30)
self.assertEqual(p50, 30)
self.assertEqual(p75, 30)
@patch('random.randint')
def test_case_3(self, mock_randint):
"""Ensure that percentile calculations are consistent for mixed low and medium values."""
mock_randint.return_value = 75 # Higher consistent value
T1 = (('5', '5', '5', '5'), ('10', '15'), ('1', '2', '3', '4', '5'))
p25, p50, p75 = task_func(T1)
self.assertEqual(p25, 75)
self.assertEqual(p50, 75)
self.assertEqual(p75, 75)
@patch('random.randint')
def test_case_4(self, mock_randint):
"""Tests percentile values for a simple large-value case."""
mock_randint.return_value = 10 # Low consistent value to see impact on percentiles
T1 = (('50',), ('25', '25'))
p25, p50, p75 = task_func(T1)
self.assertEqual(p25, 10)
self.assertEqual(p50, 10)
self.assertEqual(p75, 10)
@patch('random.randint')
def test_case_5(self, mock_randint):
"""Test with an extreme case where all random numbers are the same, ensuring no variability."""
mock_randint.return_value = 90 # High consistent value
T1 = (('1', '1', '1', '1', '1', '1', '1', '1', '1', '1'), ('10', '10'))
p25, p50, p75 = task_func(T1)
self.assertEqual(p25, 90)
self.assertEqual(p50, 90)
self.assertEqual(p75, 90)
System prompt
You are an expert programmer. Solve the task exactly as specified. Output your solution as fenced code blocks using the required file name(s) and the exact function/type signatures requested. Prefer correctness; do not include prose outside code unless asked.
Proposed solution
```python
import numpy as np
import itertools
import random
def task_func(T1, max_value=100):
# Convert all string representations of integers to integers
integers = []
for tup in T1:
for s in tup:
integers.append(int(s))
# Calculate the sum of these integers
total_size = sum(integers)
# Generate a list of random integers with the calculated size
random_list = [random.randint(0, max_value - 1) for _ in range(total_size)]
# Calculate the 25th, 50th, and 75th percentiles
p25 = np.percentile(random_list, 25)
p50 = np.percentile(random_list, 50)
p75 = np.percentile(random_list, 75)
return (p25, p50, p75)
```Test output (stdout)
..... [100%] 5 passed in 0.07s