id int64 1 6.07M | name stringlengths 1 295 | code stringlengths 12 426k | language stringclasses 1
value | source_file stringlengths 5 202 | start_line int64 1 158k | end_line int64 1 158k | repo dict |
|---|---|---|---|---|---|---|---|
15,701 | __enter__ | def __enter__(self):
self._old_cwd.append(os.getcwd())
os.chdir(self.path) | python | Lib/contextlib.py | 809 | 811 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,702 | __exit__ | def __exit__(self, *excinfo):
os.chdir(self._old_cwd.pop()) | python | Lib/contextlib.py | 813 | 814 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,703 | _sum | def _sum(data):
"""_sum(data) -> (type, sum, count)
Return a high-precision sum of the given numeric data as a fraction,
together with the type to be converted to and the count of items.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 0.25])
(<class 'float'>, Fraction(19, 2), 5)
Some... | python | Lib/statistics.py | 158 | 209 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,704 | _ss | def _ss(data, c=None):
"""Return the exact mean and sum of square deviations of sequence data.
Calculations are done in a single pass, allowing the input to be an iterator.
If given *c* is used the mean; otherwise, it is calculated from the data.
Use the *c* argument with care, as it can lead to garba... | python | Lib/statistics.py | 212 | 250 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,705 | _isfinite | def _isfinite(x):
try:
return x.is_finite() # Likely a Decimal.
except AttributeError:
return math.isfinite(x) # Coerces to float first. | python | Lib/statistics.py | 253 | 257 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,706 | _coerce | def _coerce(T, S):
"""Coerce types T and S to a common type, or raise TypeError.
Coercion rules are currently an implementation detail. See the CoerceTest
test class in test_statistics for details.
"""
# See http://bugs.python.org/issue24068.
assert T is not bool, "initial type T is bool"
#... | python | Lib/statistics.py | 260 | 288 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,707 | _exact_ratio | def _exact_ratio(x):
"""Return Real number x to exact (numerator, denominator) pair.
>>> _exact_ratio(0.25)
(1, 4)
x is expected to be an int, Fraction, Decimal or float.
"""
# XXX We should revisit whether using fractions to accumulate exact
# ratios is the right way to go.
# The in... | python | Lib/statistics.py | 291 | 334 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,708 | _convert | def _convert(value, T):
"""Convert value to given numeric type T."""
if type(value) is T:
# This covers the cases where T is Fraction, or where value is
# a NAN or INF (Decimal or float).
return value
if issubclass(T, int) and value.denominator != 1:
T = float
try:
... | python | Lib/statistics.py | 337 | 352 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,709 | _fail_neg | def _fail_neg(values, errmsg='negative value'):
"""Iterate over values, failing if any are less than zero."""
for x in values:
if x < 0:
raise StatisticsError(errmsg)
yield x | python | Lib/statistics.py | 355 | 360 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,710 | _rank | def _rank(data, /, *, key=None, reverse=False, ties='average', start=1) -> list[float]:
"""Rank order a dataset. The lowest value has rank 1.
Ties are averaged so that equal values receive the same rank:
>>> data = [31, 56, 31, 25, 75, 18]
>>> _rank(data)
[3.5, 5.0, 3.5, 2.0, 6.0, 1.0]... | python | Lib/statistics.py | 363 | 414 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,711 | _integer_sqrt_of_frac_rto | def _integer_sqrt_of_frac_rto(n: int, m: int) -> int:
"""Square root of n/m, rounded to the nearest integer using round-to-odd."""
# Reference: https://www.lri.fr/~melquion/doc/05-imacs17_1-expose.pdf
a = math.isqrt(n // m)
return a | (a*a*m != n) | python | Lib/statistics.py | 417 | 421 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,712 | _float_sqrt_of_frac | def _float_sqrt_of_frac(n: int, m: int) -> float:
"""Square root of n/m as a float, correctly rounded."""
# See principle and proof sketch at: https://bugs.python.org/msg407078
q = (n.bit_length() - m.bit_length() - _sqrt_bit_width) // 2
if q >= 0:
numerator = _integer_sqrt_of_frac_rto(n, m << 2... | python | Lib/statistics.py | 429 | 439 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,713 | _decimal_sqrt_of_frac | def _decimal_sqrt_of_frac(n: int, m: int) -> Decimal:
"""Square root of n/m as a Decimal, correctly rounded."""
# Premise: For decimal, computing (n/m).sqrt() can be off
# by 1 ulp from the correctly rounded result.
# Method: Check the result, moving up or down a step if needed.
if n <=... | python | Lib/statistics.py | 442 | 467 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,714 | mean | def mean(data):
"""Return the sample arithmetic mean of data.
>>> mean([1, 2, 3, 4, 4])
2.8
>>> from fractions import Fraction as F
>>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375... | python | Lib/statistics.py | 472 | 491 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,715 | fmean | def fmean(data, weights=None):
"""Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float.
If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25])
4.25
"""
if weights is None:
tr... | python | Lib/statistics.py | 494 | 527 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,716 | count | def count(iterable):
nonlocal n
for n, x in enumerate(iterable, start=1):
yield x | python | Lib/statistics.py | 509 | 512 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,717 | geometric_mean | def geometric_mean(data):
"""Convert data to floats and compute the geometric mean.
Raises a StatisticsError if the input dataset is empty
or if it contains a negative value.
Returns zero if the product of inputs is zero.
No special efforts are made to achieve exact results.
(However, this ma... | python | Lib/statistics.py | 530 | 562 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,718 | count_positive | def count_positive(iterable):
nonlocal n, found_zero
for n, x in enumerate(iterable, start=1):
if x > 0.0 or math.isnan(x):
yield x
elif x == 0.0:
found_zero = True
else:
raise StatisticsError('No negative inputs allowed... | python | Lib/statistics.py | 546 | 554 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,719 | harmonic_mean | def harmonic_mean(data, weights=None):
"""Return the harmonic mean of data.
The harmonic mean is the reciprocal of the arithmetic mean of the
reciprocals of the data. It can be used for averaging ratios or
rates, for example speeds.
Suppose a car travels 40 km/hr for 5 km and then speeds-up to
... | python | Lib/statistics.py | 565 | 618 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,720 | median | def median(data):
"""Return the median (middle value) of numeric data.
When the number of data points is odd, return the middle data point.
When the number of data points is even, the median is interpolated by
taking the average of the two middle values:
>>> median([1, 3, 5])
3
>>> median(... | python | Lib/statistics.py | 621 | 642 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,721 | median_low | def median_low(data):
"""Return the low median of numeric data.
When the number of data points is odd, the middle value is returned.
When it is even, the smaller of the two middle values is returned.
>>> median_low([1, 3, 5])
3
>>> median_low([1, 3, 5, 7])
3
"""
data = sorted(data... | python | Lib/statistics.py | 645 | 664 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,722 | median_high | def median_high(data):
"""Return the high median of data.
When the number of data points is odd, the middle value is returned.
When it is even, the larger of the two middle values is returned.
>>> median_high([1, 3, 5])
3
>>> median_high([1, 3, 5, 7])
5
"""
data = sorted(data)
... | python | Lib/statistics.py | 667 | 683 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,723 | median_grouped | def median_grouped(data, interval=1.0):
"""Estimates the median for numeric data binned around the midpoints
of consecutive, fixed-width intervals.
The *data* can be any iterable of numeric data with each value being
exactly the midpoint of a bin. At least one value must be present.
The *interval... | python | Lib/statistics.py | 686 | 755 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,724 | mode | def mode(data):
"""Return the most common data point from discrete or nominal data.
``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools:
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
This also works with nominal (... | python | Lib/statistics.py | 758 | 785 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,725 | multimode | def multimode(data):
"""Return a list of the most frequently occurring values.
Will return more than one result if there are multiple modes
or an empty list if *data* is empty.
>>> multimode('aabbbbbbbbcc')
['b']
>>> multimode('aabbbbccddddeeffffgg')
['b', 'd', 'f']
>>> multimode('')
... | python | Lib/statistics.py | 788 | 805 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,726 | kde | def kde(data, h, kernel='normal', *, cumulative=False):
"""Kernel Density Estimation: Create a continuous probability density
function or cumulative distribution function from discrete samples.
The basic idea is to smooth the data using a kernel function
to help draw inferences about a population from... | python | Lib/statistics.py | 808 | 1,017 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,727 | pdf | def pdf(x):
n = len(data)
return sum(K((x - x_i) / h) for x_i in data) / (n * h) | python | Lib/statistics.py | 978 | 980 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,728 | cdf | def cdf(x):
n = len(data)
return sum(W((x - x_i) / h) for x_i in data) / n | python | Lib/statistics.py | 982 | 984 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,729 | pdf | def pdf(x):
nonlocal n, sample
if len(data) != n:
sample = sorted(data)
n = len(data)
i = bisect_left(sample, x - bandwidth)
j = bisect_right(sample, x + bandwidth)
supported = sample[i : j]
return sum(K((x - x_i) / ... | python | Lib/statistics.py | 991 | 999 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,730 | cdf | def cdf(x):
nonlocal n, sample
if len(data) != n:
sample = sorted(data)
n = len(data)
i = bisect_left(sample, x - bandwidth)
j = bisect_right(sample, x + bandwidth)
supported = sample[i : j]
return sum((W((x - x_i) /... | python | Lib/statistics.py | 1,001 | 1,009 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,731 | quantiles | def quantiles(data, *, n=4, method='exclusive'):
"""Divide *data* into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99... | python | Lib/statistics.py | 1,057 | 1,102 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,732 | variance | def variance(data, xbar=None):
"""Return the sample variance of data.
data should be an iterable of Real-valued numbers, with at least two
values. The optional argument xbar, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function... | python | Lib/statistics.py | 1,111 | 1,152 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,733 | pvariance | def pvariance(data, mu=None):
"""Return the population variance of ``data``.
data should be a sequence or iterable of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Us... | python | Lib/statistics.py | 1,155 | 1,193 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,734 | stdev | def stdev(data, xbar=None):
"""Return the square root of the sample variance.
See ``variance`` for arguments and other details.
>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
1.0810874155219827
"""
T, ss, c, n = _ss(data, xbar)
if n < 2:
raise StatisticsError('stdev requires at leas... | python | Lib/statistics.py | 1,196 | 1,211 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,735 | pstdev | def pstdev(data, mu=None):
"""Return the square root of the population variance.
See ``pvariance`` for arguments and other details.
>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
0.986893273527251
"""
T, ss, c, n = _ss(data, mu)
if n < 1:
raise StatisticsError('pstdev requires at l... | python | Lib/statistics.py | 1,214 | 1,229 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,736 | _mean_stdev | def _mean_stdev(data):
"""In one pass, compute the mean and sample standard deviation as floats."""
T, ss, xbar, n = _ss(data)
if n < 2:
raise StatisticsError('stdev requires at least two data points')
mss = ss / (n - 1)
try:
return float(xbar), _float_sqrt_of_frac(mss.numerator, mss... | python | Lib/statistics.py | 1,232 | 1,242 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,737 | _sqrtprod | def _sqrtprod(x: float, y: float) -> float:
"Return sqrt(x * y) computed with improved accuracy and without overflow/underflow."
h = sqrt(x * y)
if not isfinite(h):
if isinf(h) and not isinf(x) and not isinf(y):
# Finite inputs overflowed, so scale down, and recompute.
scale ... | python | Lib/statistics.py | 1,244 | 1,263 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,738 | covariance | def covariance(x, y, /):
"""Covariance
Return the sample covariance of two inputs *x* and *y*. Covariance
is a measure of the joint variability of two inputs.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> covariance(x, y)
0.75
>>> z = [9, 8, 7, 6, 5, 4, 3,... | python | Lib/statistics.py | 1,273 | 1,298 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,739 | correlation | def correlation(x, y, /, *, method='linear'):
"""Pearson's correlation coefficient
Return the Pearson's correlation coefficient for two inputs. Pearson's
correlation coefficient *r* takes values between -1 and +1. It measures
the strength and direction of a linear relationship.
>>> x = [1, 2, 3, 4... | python | Lib/statistics.py | 1,301 | 1,346 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,740 | linear_regression | def linear_regression(x, y, /, *, proportional=False):
"""Slope and intercept for simple linear regression.
Return the slope and intercept of simple linear regression
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between an independent variable
*... | python | Lib/statistics.py | 1,352 | 1,407 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,741 | _normal_dist_inv_cdf | def _normal_dist_inv_cdf(p, mu, sigma):
# 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 Publishin... | python | Lib/statistics.py | 1,413 | 1,484 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,742 | __init__ | def __init__(self, mu=0.0, sigma=1.0):
"NormalDist where mu is the mean and sigma is the standard deviation."
if sigma < 0.0:
raise StatisticsError('sigma must be non-negative')
self._mu = float(mu)
self._sigma = float(sigma) | python | Lib/statistics.py | 1,504 | 1,509 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,743 | from_samples | def from_samples(cls, data):
"Make a normal distribution instance from sample data."
return cls(*_mean_stdev(data)) | python | Lib/statistics.py | 1,512 | 1,514 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,744 | samples | def samples(self, n, *, seed=None):
"Generate *n* samples for a given mean and standard deviation."
rnd = random.random if seed is None else random.Random(seed).random
inv_cdf = _normal_dist_inv_cdf
mu = self._mu
sigma = self._sigma
return [inv_cdf(rnd(), mu, sigma) for _... | python | Lib/statistics.py | 1,516 | 1,522 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,745 | pdf | def pdf(self, x):
"Probability density function. P(x <= X < x+dx) / dx"
variance = self._sigma * self._sigma
if not variance:
raise StatisticsError('pdf() not defined when sigma is zero')
diff = x - self._mu
return exp(diff * diff / (-2.0 * variance)) / sqrt(tau * va... | python | Lib/statistics.py | 1,524 | 1,530 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,746 | cdf | def cdf(self, x):
"Cumulative distribution function. P(X <= x)"
if not self._sigma:
raise StatisticsError('cdf() not defined when sigma is zero')
return 0.5 * (1.0 + erf((x - self._mu) / (self._sigma * _SQRT2))) | python | Lib/statistics.py | 1,532 | 1,536 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,747 | inv_cdf | def inv_cdf(self, p):
"""Inverse cumulative distribution function. x : P(X <= x) = p
Finds the value of the random variable such that the probability of
the variable being less than or equal to that value equals the given
probability.
This function is also called the percent p... | python | Lib/statistics.py | 1,538 | 1,550 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,748 | quantiles | def quantiles(self, n=4):
"""Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points t... | python | Lib/statistics.py | 1,552 | 1,561 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,749 | overlap | def overlap(self, other):
"""Compute the overlapping coefficient (OVL) between two normal distributions.
Measures the agreement between two normal probability distributions.
Returns a value between 0.0 and 1.0 giving the overlapping area in
the two underlying probability density functio... | python | Lib/statistics.py | 1,563 | 1,595 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,750 | zscore | def zscore(self, x):
"""Compute the Standard Score. (x - mean) / stdev
Describes *x* in terms of the number of standard deviations
above or below the mean of the normal distribution.
"""
# https://www.statisticshowto.com/probability-and-statistics/z-score/
if not self._... | python | Lib/statistics.py | 1,597 | 1,606 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,751 | mean | def mean(self):
"Arithmetic mean of the normal distribution."
return self._mu | python | Lib/statistics.py | 1,609 | 1,611 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,752 | median | def median(self):
"Return the median of the normal distribution"
return self._mu | python | Lib/statistics.py | 1,614 | 1,616 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,753 | mode | def mode(self):
"""Return the mode of the normal distribution
The mode is the value x where which the probability density
function (pdf) takes its maximum value.
"""
return self._mu | python | Lib/statistics.py | 1,619 | 1,625 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,754 | stdev | def stdev(self):
"Standard deviation of the normal distribution."
return self._sigma | python | Lib/statistics.py | 1,628 | 1,630 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,755 | variance | def variance(self):
"Square of the standard deviation."
return self._sigma * self._sigma | python | Lib/statistics.py | 1,633 | 1,635 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,756 | __add__ | def __add__(x1, x2):
"""Add a constant or another NormalDist instance.
If *other* is a constant, translate mu by the constant,
leaving sigma unchanged.
If *other* is a NormalDist, add both the means and the variances.
Mathematically, this works only if the two distributions are... | python | Lib/statistics.py | 1,637 | 1,649 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,757 | __sub__ | def __sub__(x1, x2):
"""Subtract a constant or another NormalDist instance.
If *other* is a constant, translate by the constant mu,
leaving sigma unchanged.
If *other* is a NormalDist, subtract the means and add the variances.
Mathematically, this works only if the two distribu... | python | Lib/statistics.py | 1,651 | 1,663 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,758 | __mul__ | def __mul__(x1, x2):
"""Multiply both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
"""
return NormalDist(x1._mu * x2, x1._sigma * fabs(x2)) | python | Lib/statistics.py | 1,665 | 1,671 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,759 | __truediv__ | def __truediv__(x1, x2):
"""Divide both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
"""
return NormalDist(x1._mu / x2, x1._sigma / fabs(x2)) | python | Lib/statistics.py | 1,673 | 1,679 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,760 | __pos__ | def __pos__(x1):
"Return a copy of the instance."
return NormalDist(x1._mu, x1._sigma) | python | Lib/statistics.py | 1,681 | 1,683 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,761 | __neg__ | def __neg__(x1):
"Negates mu while keeping sigma the same."
return NormalDist(-x1._mu, x1._sigma) | python | Lib/statistics.py | 1,685 | 1,687 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,762 | __rsub__ | def __rsub__(x1, x2):
"Subtract a NormalDist from a constant or another NormalDist."
return -(x1 - x2) | python | Lib/statistics.py | 1,691 | 1,693 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,763 | __eq__ | def __eq__(x1, x2):
"Two NormalDist objects are equal if their mu and sigma are both equal."
if not isinstance(x2, NormalDist):
return NotImplemented
return x1._mu == x2._mu and x1._sigma == x2._sigma | python | Lib/statistics.py | 1,697 | 1,701 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,764 | __hash__ | def __hash__(self):
"NormalDist objects hash equal if their mu and sigma are both equal."
return hash((self._mu, self._sigma)) | python | Lib/statistics.py | 1,703 | 1,705 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,765 | __repr__ | def __repr__(self):
return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})' | python | Lib/statistics.py | 1,707 | 1,708 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,766 | __getstate__ | def __getstate__(self):
return self._mu, self._sigma | python | Lib/statistics.py | 1,710 | 1,711 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,767 | __setstate__ | def __setstate__(self, state):
self._mu, self._sigma = state | python | Lib/statistics.py | 1,713 | 1,714 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,768 | _newton_raphson | def _newton_raphson(f_inv_estimate, f, f_prime, tolerance=1e-12):
def f_inv(y):
"Return x such that f(x) ≈ y within the specified tolerance."
x = f_inv_estimate(y)
while abs(diff := f(x) - y) > tolerance:
x -= diff / f_prime(x)
return x
return f_inv | python | Lib/statistics.py | 1,719 | 1,726 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,769 | f_inv | def f_inv(y):
"Return x such that f(x) ≈ y within the specified tolerance."
x = f_inv_estimate(y)
while abs(diff := f(x) - y) > tolerance:
x -= diff / f_prime(x)
return x | python | Lib/statistics.py | 1,720 | 1,725 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,770 | _quartic_invcdf_estimate | def _quartic_invcdf_estimate(p):
sign, p = (1.0, p) if p <= 1/2 else (-1.0, 1.0 - p)
x = (2.0 * p) ** 0.4258865685331 - 1.0
if p >= 0.004 < 0.499:
x += 0.026818732 * sin(7.101753784 * p + 2.73230839482953)
return x * sign | python | Lib/statistics.py | 1,728 | 1,733 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,771 | _triweight_invcdf_estimate | def _triweight_invcdf_estimate(p):
sign, p = (1.0, p) if p <= 1/2 else (-1.0, 1.0 - p)
x = (2.0 * p) ** 0.3400218741872791 - 1.0
return x * sign | python | Lib/statistics.py | 1,740 | 1,743 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,772 | kde_random | def kde_random(data, h, kernel='normal', *, seed=None):
"""Return a function that makes a random selection from the estimated
probability density function created by kde(data, h, kernel).
Providing a *seed* allows reproducible selections within a single
thread. The seed may be an integer, float, str, ... | python | Lib/statistics.py | 1,766 | 1,807 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,773 | rand | def rand():
return choice(data) + h * kernel_invcdf(random()) | python | Lib/statistics.py | 1,802 | 1,803 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,774 | _f | def _f(): pass | python | Lib/_collections_abc.py | 40 | 40 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,775 | _coro | async def _coro(): pass | python | Lib/_collections_abc.py | 90 | 90 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,776 | _ag | async def _ag(): yield | python | Lib/_collections_abc.py | 96 | 96 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,777 | _check_methods | def _check_methods(C, *methods):
mro = C.__mro__
for method in methods:
for B in mro:
if method in B.__dict__:
if B.__dict__[method] is None:
return NotImplemented
break
else:
return NotImplemented
return True | python | Lib/_collections_abc.py | 104 | 114 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,778 | __hash__ | def __hash__(self):
return 0 | python | Lib/_collections_abc.py | 121 | 122 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,779 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is Hashable:
return _check_methods(C, "__hash__")
return NotImplemented | python | Lib/_collections_abc.py | 125 | 128 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,780 | __await__ | def __await__(self):
yield | python | Lib/_collections_abc.py | 136 | 137 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,781 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is Awaitable:
return _check_methods(C, "__await__")
return NotImplemented | python | Lib/_collections_abc.py | 140 | 143 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,782 | send | def send(self, value):
"""Send a value into the coroutine.
Return next yielded value or raise StopIteration.
"""
raise StopIteration | python | Lib/_collections_abc.py | 153 | 157 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,783 | throw | def throw(self, typ, val=None, tb=None):
"""Raise an exception in the coroutine.
Return next yielded value or raise StopIteration.
"""
if val is None:
if tb is None:
raise typ
val = typ()
if tb is not None:
val = val.with_traceb... | python | Lib/_collections_abc.py | 160 | 170 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,784 | close | def close(self):
"""Raise GeneratorExit inside coroutine.
"""
try:
self.throw(GeneratorExit)
except (GeneratorExit, StopIteration):
pass
else:
raise RuntimeError("coroutine ignored GeneratorExit") | python | Lib/_collections_abc.py | 172 | 180 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,785 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is Coroutine:
return _check_methods(C, '__await__', 'send', 'throw', 'close')
return NotImplemented | python | Lib/_collections_abc.py | 183 | 186 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,786 | __aiter__ | def __aiter__(self):
return AsyncIterator() | python | Lib/_collections_abc.py | 197 | 198 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,787 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is AsyncIterable:
return _check_methods(C, "__aiter__")
return NotImplemented | python | Lib/_collections_abc.py | 201 | 204 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,788 | __anext__ | async def __anext__(self):
"""Return the next item or raise StopAsyncIteration when exhausted."""
raise StopAsyncIteration | python | Lib/_collections_abc.py | 214 | 216 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,789 | __aiter__ | def __aiter__(self):
return self | python | Lib/_collections_abc.py | 218 | 219 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,790 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is AsyncIterator:
return _check_methods(C, "__anext__", "__aiter__")
return NotImplemented | python | Lib/_collections_abc.py | 222 | 225 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,791 | __anext__ | async def __anext__(self):
"""Return the next item from the asynchronous generator.
When exhausted, raise StopAsyncIteration.
"""
return await self.asend(None) | python | Lib/_collections_abc.py | 232 | 236 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,792 | asend | async def asend(self, value):
"""Send a value into the asynchronous generator.
Return next yielded value or raise StopAsyncIteration.
"""
raise StopAsyncIteration | python | Lib/_collections_abc.py | 239 | 243 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,793 | athrow | async def athrow(self, typ, val=None, tb=None):
"""Raise an exception in the asynchronous generator.
Return next yielded value or raise StopAsyncIteration.
"""
if val is None:
if tb is None:
raise typ
val = typ()
if tb is not None:
... | python | Lib/_collections_abc.py | 246 | 256 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,794 | aclose | async def aclose(self):
"""Raise GeneratorExit inside coroutine.
"""
try:
await self.athrow(GeneratorExit)
except (GeneratorExit, StopAsyncIteration):
pass
else:
raise RuntimeError("asynchronous generator ignored GeneratorExit") | python | Lib/_collections_abc.py | 258 | 266 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,795 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is AsyncGenerator:
return _check_methods(C, '__aiter__', '__anext__',
'asend', 'athrow', 'aclose')
return NotImplemented | python | Lib/_collections_abc.py | 269 | 273 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,796 | __iter__ | def __iter__(self):
while False:
yield None | python | Lib/_collections_abc.py | 284 | 286 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,797 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is Iterable:
return _check_methods(C, "__iter__")
return NotImplemented | python | Lib/_collections_abc.py | 289 | 292 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,798 | __next__ | def __next__(self):
'Return the next item from the iterator. When exhausted, raise StopIteration'
raise StopIteration | python | Lib/_collections_abc.py | 302 | 304 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,799 | __iter__ | def __iter__(self):
return self | python | Lib/_collections_abc.py | 306 | 307 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
15,800 | __subclasshook__ | def __subclasshook__(cls, C):
if cls is Iterator:
return _check_methods(C, '__iter__', '__next__')
return NotImplemented | python | Lib/_collections_abc.py | 310 | 313 | {
"name": "PublicHealthInformationTechnology/cpython",
"url": "https://github.com/PublicHealthInformationTechnology/cpython.git",
"license": "NOASSERTION",
"stars": 0,
"forks": 0
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.