packaging sagittal_average.py

.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
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archive/brain_average.csv

@@ -0,0 +1 @@
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0

archive/brain_sample.csv

@@ -0,0 +1,20 @@
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1

archive/old_test_sagittal_brain.py

@@ -0,0 +1,13 @@
+import numpy as np
+import subprocess
+
+ls = [[j+1 for i in range(20)] for j in range(20)]
+input = np.array(ls, dtype=int)
+expected = np.array([float(i+1) for i in range(20)])
+
+np.savetxt('brain_sample.csv', input, fmt='%i', delimiter=',')
+
+subprocess.run(["python", "sagittal_brain.py"])
+output = np.loadtxt('brain_average.csv', dtype=float,  delimiter=',')
+
+np.testing.assert_array_equal(expected, output)

archive/test_call_command.py

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+import subprocess
+
+subprocess.run(["python", "sagittal_brain.py"])

pyproject.toml

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+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "sagittal_average"
+version = "0.1.0"
+dependencies = ["numpy",]
+
+[project.scripts]
+sagittal_average_run = "sagittal_average.command:process"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/sagittal_average",]

src/sagittal_average/command.py

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+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+from sagittal_average.sagittal_brain import run_averages
+
+
+def process():
+    parser = ArgumentParser(description="Calculates the average for each sagittal-horizontal plane.",
+                            formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument('file_input', nargs='?', default="brain_sample.csv",
+                        help="Input CSV file with the results from scikit-brain binning algorithm.")
+    parser.add_argument('--file_output', '-o', default="brain_average.csv",
+                        help="Name of the output CSV file.")
+    arguments = parser.parse_args()
+
+    run_averages(arguments.file_input, arguments.file_output)

src/sagittal_average/sagittal_brain.py

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+import numpy as np
+
+
+def run_averages(file_input='brain_sample.csv', file_output='brain_average.csv'):
+    """
+    Calculates the average through the coronal planes
+    The input file should has as many columns as coronal planes
+    The rows are intersections of the sagittal/horizontal planes
+
+    The result is the average for each sagittal/horizontal plane (rows)
+    """
+    # Open the file to analyse
+    planes = np.loadtxt(file_input, dtype=int,  delimiter=',')
+
+    # Calculates the averages through the sagittal/horizontal planes
+    # and makes it as a row vector
+    averages = planes.mean(axis=1)[np.newaxis, :]
+
+    # write it out on my file
+    np.savetxt(file_output, averages, fmt='%.1f', delimiter=',')
+
+

tests/test_sagittal_brain.py

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+import numpy as np
+import numpy.testing as nt
+from sagittal_average.sagittal_brain import run_averages
+
+def test_averages():
+    data_input = np.zeros((20, 20))
+    data_input[-1, :] = 1
+    np.savetxt("brain_sample.csv", data_input, fmt='%d', delimiter=',')
+    # The expeted result is all zeros, except the last one, it should be 1
+    expected = np.zeros(20)
+    expected[-1] = 1
+    run_averages(file_input="brain_sample.csv",
+             file_output="brain_average.csv")
+    result = np.loadtxt("brain_average.csv",  delimiter=',')
+    nt.assert_array_almost_equal(result, expected)
+
+print(__file__)