def test_word_count():
"""Test the counting of words.
The example poem is Risk, by Anais Nin.
"""
risk_poem_counts = {
'the': 3,
'risk': 2,
'to': 2,
'and': 1,
'then': 1,
'day': 1,
'came': 1,
'when': 1,
'remain': 1,
'tight': 1,
'in': 1,
'a': 1,
'bud': 1,
'was': 1,
'more': 1,
'painful': 1,
'than': 1,
'it': 1,
'took': 1,
'blossom': 1
}
expected_result = Counter(risk_poem_counts)
with open('test_data/risk.txt', 'r') as reader:
actual_result = countwords.count_words(reader)
assert actual_result == expected_result
def test_regression():
"""Regression test for Dracula."""
with open('data/dracula.txt', 'r') as reader:
word_counts_dict = countwords.count_words(reader)
counts_array = np.array(list(word_counts_dict.values()))
actual_alpha = plotcounts.get_power_law_params(counts_array)
expected_alpha = pytest.approx(1.087, abs=0.001)
assert actual_alpha == expected_alpha
def test_integration():
"""Test the full word count to alpha parameter workflow."""
with open('test_data/random_words.txt', 'r') as reader:
word_counts_dict = countwords.count_words(reader)
counts_array = np.array(list(word_counts_dict.values()))
actual_alpha = plotcounts.get_power_law_params(counts_array)
expected_alpha = pytest.approx(1.0, abs=0.01)
assert actual_alpha == expected_alpha
def test_if_words_are_unique(self):
input = ["python", "apple", "ruby"]
output = {'python': 1, 'apple': 1, 'ruby': 1}
result = count_words(input)
self.assertEqual(output, result)
def test_if_dictionary_is_empty(self):
input = []
output = {}
result = count_words(input)
self.assertEqual(output, result)
assert actual_alpha == expected_alpha
def test_word_count():
"""Test the counting of words.
The example poem is Risk, by Anais Nin.
"""
risk_poem_counts = {'the': 3, 'risk': 2, 'to': 2, 'and': 1,
'then': 1, 'day': 1, 'came': 1, 'when': 1, 'remain': 1,
'tight': 1, 'in': 1, 'a': 1, 'bud': 1, 'was': 1,
'more': 1, 'painful': 1, 'than': 1, 'it': 1, 'took': 1,
'blossom': 1}
expected_result = Counter(risk_poem_counts)
with open('test_data/risk.txt', 'r') as reader:
actual_result = countwords.count_words(reader)
assert actual_result == expected_result
def test_integration():
"""Test the full word count to alpha parameter workflow."""
with open('test_data/random_words.txt', 'r') as reader:
word_counts_dict = countwords.count_words(reader)
counts_array = np.array(list(word_counts_dict.values()))
actual_alpha = plotcounts.get_power_law_params(counts_array)
expected_alpha = pytest.approx(1.0, abs=0.01)
assert actual_alpha == expected_alpha
def test_regression():
"""Regression test for Dracula."""