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bpo-37798: Minor code formatting and comment clean-ups #15526

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Aug 26, 2019
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bpo-37798: Minor code formatting and comment clean-ups #15526

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35 changes: 22 additions & 13 deletions Modules/_statisticsmodule.c
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
/* statistics accelerator C extensor: _statistics module. */
/* statistics accelerator C extension: _statistics module. */

#include "Python.h"
#include "structmember.h"
Expand All @@ -10,11 +10,13 @@ module _statistics
[clinic start generated code]*/
/*[clinic end generated code: output=da39a3ee5e6b4b0d input=864a6f59b76123b2]*/


static PyMethodDef speedups_methods[] = {
_STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
{NULL, NULL, 0, NULL}
};
/*
* There is no closed-form solution to the inverse CDF for the normal
* distribution, so we use a rational approximation instead:
* Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
* Normal Distribution". Applied Statistics. Blackwell Publishing. 37
* (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
*/

/*[clinic input]
_statistics._normal_dist_inv_cdf -> double
Expand All @@ -34,7 +36,7 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
// Algorithm AS 241: The Percentage Points of the Normal Distribution
if(fabs(q) <= 0.425) {
r = 0.180625 - q * q;
// Hash sum AB: 55.88319 28806 14901 4439
// Hash sum-55.8831928806149014439
num = (((((((2.5090809287301226727e+3 * r +
3.3430575583588128105e+4) * r +
6.7265770927008700853e+4) * r +
Expand All @@ -54,11 +56,11 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
x = num / den;
return mu + (x * sigma);
}
r = q <= 0.0? p : 1.0-p;
r = (q <= 0.0) ? p : (1.0 - p);
r = sqrt(-log(r));
if (r <= 5.0) {
r = r - 1.6;
// Hash sum CD: 49.33206 50330 16102 89036
// Hash sum-49.33206503301610289036
num = (((((((7.74545014278341407640e-4 * r +
2.27238449892691845833e-2) * r +
2.41780725177450611770e-1) * r +
Expand All @@ -77,7 +79,7 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
1.0);
} else {
r -= 5.0;
// Hash sum EF: 47.52583 31754 92896 71629
// Hash sum-47.52583317549289671629
num = (((((((2.01033439929228813265e-7 * r +
2.71155556874348757815e-5) * r +
1.24266094738807843860e-3) * r +
Expand All @@ -96,23 +98,30 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
1.0);
}
x = num / den;
if (q < 0.0) x = -x;
if (q < 0.0) {
x = -x;
}
return mu + (x * sigma);
}


static PyMethodDef statistics_methods[] = {
_STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
{NULL, NULL, 0, NULL}
};

static struct PyModuleDef statisticsmodule = {
PyModuleDef_HEAD_INIT,
"_statistics",
_statistics__normal_dist_inv_cdf__doc__,
-1,
speedups_methods,
statistics_methods,
NULL,
NULL,
NULL,
NULL
};


PyMODINIT_FUNC
PyInit__statistics(void)
{
Expand Down