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Add implementation of standard deviation on data #1

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075e95b
Add implementation of standard deviation on data
thomaskileyukaea Jul 27, 2023
179c581
Update models.py
clmould Nov 23, 2023
1908eb3
Merge branch 'main' into feature-std-dev
clmould Nov 24, 2023
23f6700
Update models.py
clmould Nov 24, 2023
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12 changes: 7 additions & 5 deletions inflammation-analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,11 +26,13 @@ def main(args):
for filename in in_files:
inflammation_data = models.load_csv(filename)

view_data = {
"average": models.daily_mean(inflammation_data),
"max": models.daily_max(inflammation_data),
"min": models.daily_min(inflammation_data),
}
view_data = {'average': models.daily_mean(inflammation_data),
'max': models.daily_max(inflammation_data),
'min': models.daily_min(inflammation_data),
**(models.s_dev(inflammation_data))
}



views.visualize(view_data)

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10 changes: 10 additions & 0 deletions inflammation/models.py
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Expand Up @@ -70,6 +70,16 @@ def daily_min(data):
return np.min(data, axis=0)


def s_dev(data):
"""Computes and returns standard deviation for data."""
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@ltaling ltaling Nov 23, 2023

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This function description can be made longer with more detail. For instance, in what format is the data outputted? Unless we look at the code itself, there is no way of knowing it outputs a dictionary object -- a behaviour different from the other functions in the code -- so I would recommend saying this in the docstring!

mean_data = daily_mean(data)
devs = []
for entry in data:
devs.append((entry - mean_data) * (entry - mean_data))

s_dev2 = sum(devs) / len(data)
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@ltaling ltaling Nov 23, 2023

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Missing a square root?

return {'standard deviation': s_dev2}
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@ltaling ltaling Nov 23, 2023

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To allow this function to be more general (i.e., so it can be easily used elsewhere in the code), consider making it return s_dev simply as a list, rather than a dictionary object. Then you can turn it into a dictionary in the inflammation_analysis.py file


def patient_normalise(data):
"""
Normalise patient data from a 2D inflammation data array.
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11 changes: 11 additions & 0 deletions tests/test_models.py
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Expand Up @@ -3,6 +3,7 @@
import os
import numpy as np
import numpy.testing as npt

import pytest
from inflammation.models import daily_mean
from inflammation.models import daily_max
Expand Down Expand Up @@ -67,6 +68,16 @@ def test_load_from_json(tmpdir):
npt.assert_array_equal(result, [[1, 2, 3], [4, 5, 6]])


@pytest.mark.parametrize('data, expected_standard_deviation', [
([0, 0, 0], 0.0),
([1.0, 1.0, 1.0], 0),
([0.0, 2.0], 1.0)
])
def test_daily_standard_deviation(data, expected_standard_deviation):
from inflammation.models import s_dev
result_data = s_dev(data)['standard deviation']
npt.assert_approx_equal(result_data, expected_standard_deviation)

@pytest.mark.parametrize(
"test, expected, expect_raises",
[
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