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add multivariate_normal function

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wyq 2023-02-22 16:07:15 +08:00
parent f0f5e028e1
commit 1848dab363
17 changed files with 652 additions and 456 deletions
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142
meteoinfo-chart/meteoinfo-chart.iml
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@ -13,72 +13,20 @@
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52
meteoinfo-data/meteoinfo-data.iml
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114
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@ -134,6 +53,7 @@
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</module>

14
meteoinfo-lab/milconfig.xml
View File

@ -1,7 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<MeteoInfo File="milconfig.xml" Type="configurefile">
<Path OpenPath="D:\Working\MIScript\Jython\mis\traj">
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\particles"/>
<Path OpenPath="D:\Working\MIScript\Jython\mis\common_math\random">
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\surf"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\text"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl"/>
@ -10,25 +9,24 @@
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\hist"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\others"/>
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<RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math\stats"/>
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<RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\violinplot"/>
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<File>
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meteoinfo-lab/pylib/mipylib/numeric/linalg/linalg.py
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@ -1,21 +1,22 @@
# coding=utf-8
#-----------------------------------------------------
# -----------------------------------------------------
# Author: Yaqiang Wang
# Date: 2017-1-12
# Purpose: MeteoInfoLab linear algebra module
# Note: Jython
#-----------------------------------------------------
# -----------------------------------------------------
from org.meteoinfo.math.linalg import LinalgUtil
#from org.meteoinfo.math.linalg import LinalgUtilJava as LinalgUtil
# from org.meteoinfo.math.linalg import LinalgUtilJava as LinalgUtil
from org.meteoinfo.math.stats import StatsUtil
from .. import core as np
__all__ = [
'solve','cholesky','det','lu','qr', 'svd','eig','inv','lstsq','slogdet','solve_triangular',
'solve', 'cholesky', 'det', 'lu', 'qr', 'svd', 'eig', 'inv', 'lstsq', 'slogdet', 'solve_triangular',
'norm'
]
]
class LinAlgError(Exception):
"""
@ -27,12 +28,14 @@ class LinAlgError(Exception):
"""
pass
def _assert_2d(*arrays):
for a in arrays:
if a.ndim != 2:
raise LinAlgError('%d-dimensional array given. Array must be '
'two-dimensional' % a.ndim)
def solve(a, b):
"""
Solve a linear matrix equation, or system of linear scalar equations.
@ -57,6 +60,7 @@ def solve(a, b):
r = np.NDArray(x)
return r
def solve_triangular(a, b, lower=False):
"""
Solve the equation `a x = b` for `x`, assuming a is a triangular matrix.
@ -78,7 +82,8 @@ def solve_triangular(a, b, lower=False):
"""
x = LinalgUtil.solve(a.asarray(), b.asarray())
return np.NDArray(x)
def cholesky(a, lower=True):
"""
Cholesky decomposition.
@ -105,7 +110,8 @@ def cholesky(a, lower=True):
"""
r = LinalgUtil.cholesky(a.asarray(), lower)
return np.NDArray(r)
def lu(a):
"""
Compute pivoted LU decomposition of a matrix.
@ -144,7 +150,8 @@ def lu(a):
l = np.NDArray(r[1])
u = np.NDArray(r[2])
return p, l, u
def qr(a):
"""
Compute QR decomposition of a matrix.
@ -171,6 +178,7 @@ def qr(a):
r = np.NDArray(r[1])
return q, r
def svd(a, full_matrices=True):
"""
Singular Value Decomposition.
@ -201,7 +209,7 @@ def svd(a, full_matrices=True):
Of shape ``(N,N)``.
"""
r = LinalgUtil.svd(a.asarray())
#r = LinalgUtil.svd_EJML(a.asarray())
# r = LinalgUtil.svd_EJML(a.asarray())
U = np.NDArray(r[0])
s = np.NDArray(r[1])
Vh = np.NDArray(r[2])
@ -210,11 +218,12 @@ def svd(a, full_matrices=True):
if m != n:
k = min(m, n)
if k == m:
Vh = Vh[:k,:].copy()
Vh = Vh[:k, :].copy()
else:
U = U[:,:k].copy()
U = U[:, :k].copy()
return U, s, Vh
def eig(a):
"""
Compute the eigenvalues and right eigenvectors of a square array.
@ -240,11 +249,12 @@ def eig(a):
eigenvalue ``w[i]``.
"""
r = LinalgUtil.eigen(a.asarray())
#r = LinalgUtil.eigen_EJML(a.asarray())
# r = LinalgUtil.eigen_EJML(a.asarray())
w = np.NDArray(r[0])
v = np.NDArray(r[1])
return w, v
def inv(a):
"""
Compute the (multiplicative) inverse of a matrix.
@ -253,9 +263,11 @@ def inv(a):
:returns: Inverse matrix.
"""
a = np.asarray(a)
r = LinalgUtil.inv(a.asarray())
return np.NDArray(r)
def lstsq(a, b):
"""
Compute least-squares solution to equation Ax = b.
@ -278,7 +290,8 @@ def lstsq(a, b):
"""
r = StatsUtil.multipleLineRegress_OLS(b.asarray(), a.asarray(), True)
return np.NDArray(r[0]), np.NDArray(r[1])
def det(a):
"""
Compute the determinant of an array.
@ -293,10 +306,11 @@ def det(a):
det : (...) array_like
Determinant of `a`.
"""
#r = LinalgUtil.determinantOfMatrix(a.asarray())
# r = LinalgUtil.determinantOfMatrix(a.asarray())
r = LinalgUtil.det(a.asarray())
return r
def slogdet(a):
"""
Compute the sign and (natural) logarithm of the determinant of an array.
@ -308,6 +322,7 @@ def slogdet(a):
r = LinalgUtil.sLogDet(a.asarray())
return r[0], r[1]
def norm(x, ord=None, axis=None, keepdims=False):
"""
Matrix or vector norm.
@ -359,7 +374,7 @@ def norm(x, ord=None, axis=None, keepdims=False):
sqnorm = x.dot(x)
ret = np.sqrt(sqnorm)
if keepdims:
ret = ret.reshape(ndim*[1])
ret = ret.reshape(ndim * [1])
return ret
# Normalize the `axis` argument to a tuple.
@ -435,4 +450,4 @@ def norm(x, ord=None, axis=None, keepdims=False):
# ret = ret.reshape(ret_shape)
# return ret
# else:
# raise ValueError("Improper number of dimensions to norm.")
# raise ValueError("Improper number of dimensions to norm.")

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meteoinfo-lab/pylib/mipylib/numeric/random/_random.py
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@ -1,10 +1,10 @@
# coding=utf-8
#-----------------------------------------------------
# -----------------------------------------------------
# Author: Yaqiang Wang
# Date: 2016-7-10
# Purpose: MeteoInfoLab random module
# Note: Jython
#-----------------------------------------------------
# -----------------------------------------------------
from org.meteoinfo.math.random import RandomUtil
from org.meteoinfo.math.distribution import DistributionUtil
@ -14,13 +14,14 @@ from org.apache.commons.statistics.distribution import NormalDistribution, BetaD
LogNormalDistribution, ParetoDistribution, TDistribution, TriangularDistribution, \
UniformContinuousDistribution, WeibullDistribution
from ..core import NDArray
from .. import core as np
__all__ = [
'chisquare','exponential','f','gamma','gumbel','laplace','logistic',
'lognormal','normal','rand','randn','randint','random','pareto','poisson','seed','standard_t',
'triangular','uniform','weibull'
]
'chisquare', 'exponential', 'f', 'gamma', 'gumbel', 'laplace', 'logistic',
'lognormal', 'multivariate_normal', 'normal', 'rand', 'randn', 'randint', 'random', 'pareto',
'poisson', 'seed', 'standard_t', 'triangular', 'uniform', 'weibull'
]
def seed(seed=None):
"""
@ -34,6 +35,7 @@ def seed(seed=None):
RandomUtil.useSeed = True
RandomUtil.seed = seed
def random(size=None):
"""
Random values in a given shape.
@ -49,7 +51,8 @@ def random(size=None):
if size is None:
return RandomUtil.rand()
else:
return NDArray(RandomUtil.rand(size))
return np.NDArray(RandomUtil.rand(size))
def rand(*args):
"""
@ -66,10 +69,11 @@ def rand(*args):
if len(args) == 0:
return RandomUtil.rand()
elif len(args) == 1:
return NDArray(RandomUtil.rand(args[0]))
return np.NDArray(RandomUtil.rand(args[0]))
else:
return NDArray(RandomUtil.rand(args))
return np.NDArray(RandomUtil.rand(args))
def randn(*args):
"""
Return a sample (or samples) from the “standard normal” distribution.
@ -85,10 +89,11 @@ def randn(*args):
if len(args) == 0:
return RandomUtil.randn()
elif len(args) == 1:
return NDArray(RandomUtil.randn(args[0]))
return np.NDArray(RandomUtil.randn(args[0]))
else:
return NDArray(RandomUtil.randn(args))
return np.NDArray(RandomUtil.randn(args))
def randint(low, high=None, size=None):
"""
Return random integers from low (inclusive) to high (exclusive).
@ -114,11 +119,12 @@ def randint(low, high=None, size=None):
r = RandomUtil.randint(high)
r += low
else:
r = NDArray(RandomUtil.randint(high, size))
r = np.NDArray(RandomUtil.randint(high, size))
if low != 0:
r += low
return r
def poisson(lam=1.0, size=None):
"""
Draw samples from a Poisson distribution.
@ -132,9 +138,10 @@ def poisson(lam=1.0, size=None):
if size is None:
r = RandomUtil.poisson(lam)
else:
r = NDArray(RandomUtil.poisson(lam, size))
r = np.NDArray(RandomUtil.poisson(lam, size))
return r
def normal(loc=0.0, scale=1.0, size=None):
"""
Draw random samples from a normal (Gaussian) distribution.
@ -148,8 +155,9 @@ def normal(loc=0.0, scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def chisquare(df, size=None):
"""
Draw samples from a chi-square distribution.
@ -163,8 +171,9 @@ def chisquare(df, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def exponential(scale=1.0, size=None):
"""
Draw samples from a exponential distribution.
@ -178,8 +187,9 @@ def exponential(scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def f(dfnum, dfden, size=None):
"""
Draw random samples from a F distribution.
@ -194,8 +204,9 @@ def f(dfnum, dfden, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def gamma(shape, scale=1.0, size=None):
"""
Draw random samples from a Gamma distribution.
@ -210,8 +221,9 @@ def gamma(shape, scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def gumbel(loc=0.0, scale=1.0, size=None):
"""
Draw random samples from a Gumbel distribution.
@ -226,8 +238,9 @@ def gumbel(loc=0.0, scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def laplace(loc=0.0, scale=1.0, size=None):
"""
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
@ -242,8 +255,9 @@ def laplace(loc=0.0, scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def logistic(loc=0.0, scale=1.0, size=None):
"""
Draw samples from the Logistic distribution.
@ -258,8 +272,9 @@ def logistic(loc=0.0, scale=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def lognormal(mean=0.0, sigma=1.0, size=None):
"""
Draw samples from the log-normal distribution.
@ -274,8 +289,9 @@ def lognormal(mean=0.0, sigma=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def pareto(a, size=None):
"""
Draw samples from a Pareto II or Lomax distribution with specified shape.
@ -289,8 +305,9 @@ def pareto(a, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def standard_t(df, size=None):
"""
Draw samples from a standard Student’s t distribution with df degrees of freedom.
@ -304,8 +321,9 @@ def standard_t(df, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def triangular(left, mode, right, size=None):
"""
Draw samples from the triangular distribution over the interval [left, right].
@ -322,8 +340,9 @@ def triangular(left, mode, right, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def uniform(low=0.0, high=1.0, size=None):
"""
Draw samples from the uniform distribution.
@ -340,8 +359,9 @@ def uniform(low=0.0, high=1.0, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def weibull(a, size=None):
"""
Draw samples from a Weibull distribution.
@ -355,4 +375,23 @@ def weibull(a, size=None):
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return NDArray(r)
return np.NDArray(r)
def multivariate_normal(mean, cov, size=None):
"""
Draw samples from a multiple variate normal distribution.
:param mean: (*array*) Mean values.
:param cov: (*array*) Covariant matrix.
:param size: (*int*) Output shape. If size is None (default), a single value is returned.
:return: (*ndarray or scalar*) Drawn samples from the multiple variate normal distribution.
"""
mean = np.asarray(mean)
cov = np.asarray(cov)
dist = DistributionUtil.mvNormDist(mean.asarray(), cov.asarray())
if size is None:
size = 1
r = DistributionUtil.rvs(dist, size)
return np.NDArray(r)

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@ -1,10 +1,10 @@
# coding=utf-8
#-----------------------------------------------------
# -----------------------------------------------------
# Author: Yaqiang Wang
# Date: 2018-3-20
# Purpose: MeteoInfoLab stats.distributions module
# Note: Jython
#-----------------------------------------------------
# -----------------------------------------------------
from org.apache.commons.statistics.distribution import NormalDistribution, BetaDistribution, CauchyDistribution, \
ChiSquaredDistribution, ExponentialDistribution, FDistribution, GammaDistribution, GumbelDistribution, \
@ -14,9 +14,10 @@ from org.apache.commons.statistics.distribution import NormalDistribution, BetaD
from _distn_infrastructure import rv_continuous
__all__ = [
'norm','beta','cauchy','chi2','expon','f','gamma','gumbel','laplace','levy','logistic','lognorm',
'nakagami','pareto','t','triang','uniform','weibull'
]
'norm', 'beta', 'cauchy', 'chi2', 'expon', 'f', 'gamma', 'gumbel', 'laplace', 'levy', 'logistic', 'lognorm',
'nakagami', 'pareto', 't', 'triang', 'uniform', 'weibull'
]
class norm_gen(rv_continuous):
"""
@ -31,17 +32,19 @@ class norm_gen(rv_continuous):
"""
self.name = "norm"
self._dist = NormalDistribution.of(mean, std)
def _create_distribution(self, *args):
"""
Create a normal distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = NormalDistribution.of(loc, scale)
return dist
norm = norm_gen()
class beta_gen(rv_continuous):
"""
A beta continuous random variable.
@ -55,17 +58,19 @@ class beta_gen(rv_continuous):
"""
self.name = "beta"
self._dist = BetaDistribution.of(alpha, beta)
def _create_distribution(self, *args):
"""
Create a normal distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = BetaDistribution.of(loc, scale)
return dist
beta = beta_gen()
class cauchy_gen(rv_continuous):
"""
A cauchy continuous random variable.
@ -79,17 +84,19 @@ class cauchy_gen(rv_continuous):
"""
self.name = "cauchy"
self._dist = CauchyDistribution.of(median, scale)
def _create_distribution(self, *args):
"""
Create a cauchy distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = CauchyDistribution.of(loc, scale)
return dist
cauchy = cauchy_gen()
class chi2_gen(rv_continuous):
"""
A chi squared continuous random variable.
@ -102,17 +109,19 @@ class chi2_gen(rv_continuous):
"""
self.name = "chi2"
self._dist = ChiSquaredDistribution.of(dof)
def _create_distribution(self, *args):
"""
Create a chi squared distribution object.
"""
dof = self._parse_args(*args)[0]
dof = self._parse_args(*args)[0]
dist = ChiSquaredDistribution.of(dof)
return dist
chi2 = chi2_gen()
class expon_gen(rv_continuous):
"""
A exponential continuous random variable.
@ -125,17 +134,19 @@ class expon_gen(rv_continuous):
"""
self.name = "expon"
self._dist = ExponentialDistribution.of(mean)
def _create_distribution(self, *args):
"""
Create a exponential distribution object.
"""
mean = self._parse_args(*args)[0]
mean = self._parse_args(*args)[0]
dist = ExponentialDistribution.of(mean)
return dist
expon = expon_gen()
class f_gen(rv_continuous):
"""
A F continuous random variable.
@ -149,17 +160,19 @@ class f_gen(rv_continuous):
"""
self.name = "f"
self._dist = FDistribution.of(ndof, ddof)
def _create_distribution(self, *args):
"""
Create a F distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = FDistribution.of(loc, scale)
return dist
f = f_gen()
class gamma_gen(rv_continuous):
"""
A gamma continuous random variable.
@ -173,17 +186,19 @@ class gamma_gen(rv_continuous):
"""
self.name = "gamma"
self._dist = GammaDistribution.of(shape, scale)
def _create_distribution(self, *args):
"""
Create a gamma distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = GammaDistribution.of(loc, scale)
return dist
gamma = gamma_gen()
class gumbel_gen(rv_continuous):
"""
A gumbel continuous random variable.
@ -202,12 +217,14 @@ class gumbel_gen(rv_continuous):
"""
Create a gumbel distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = GumbelDistribution.of(loc, scale)
return dist
gumbel = gumbel_gen()
class laplace_gen(rv_continuous):
"""
A Laplace continuous random variable.
@ -221,17 +238,19 @@ class laplace_gen(rv_continuous):
"""
self.name = "laplace"
self._dist = LaplaceDistribution.of(loc, scale)
def _create_distribution(self, *args):
"""
Create a Laplace distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = LaplaceDistribution.of(loc, scale)
return dist
laplace = laplace_gen()
class levy_gen(rv_continuous):
"""
A Levy continuous random variable.
@ -245,17 +264,19 @@ class levy_gen(rv_continuous):
"""
self.name = "levy"
self._dist = LevyDistribution.of(loc, scale)
def _create_distribution(self, *args):
"""
Create a Levy distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = LevyDistribution.of(loc, scale)
return dist
levy = levy_gen()
class logistic_gen(rv_continuous):
"""
A logistic continuous random variable.
@ -269,17 +290,19 @@ class logistic_gen(rv_continuous):
"""
self.name = "logistic"
self._dist = LogisticDistribution.of(loc, scale)
def _create_distribution(self, *args):
"""
Create a logistic distribution object.
"""
loc, scale = self._parse_args(*args)
loc, scale = self._parse_args(*args)
dist = LogisticDistribution.of(loc, scale)
return dist
logistic = logistic_gen()
class lognorm_gen(rv_continuous):
"""
A Log-normal continuous random variable.
@ -293,17 +316,19 @@ class lognorm_gen(rv_continuous):
"""
self.name = "lognorm"
self._dist = LevyDistribution.of(scale, shape)
def _create_distribution(self, *args):
"""
Create a Log-normal distribution object.
"""
scale, shape = self._parse_args(*args)
scale, shape = self._parse_args(*args)
dist = LogNormalDistribution.of(scale, shape)
return dist
lognorm = lognorm_gen()
class nakagami_gen(rv_continuous):
"""
A Nakagami continuous random variable.
@ -317,7 +342,7 @@ class nakagami_gen(rv_continuous):
"""
self.name = "nakagami"
self._dist = NakagamiDistribution.of(loc, scale)
def _create_distribution(self, *args):
"""
Create a Nakagami distribution object.
@ -326,8 +351,10 @@ class nakagami_gen(rv_continuous):
dist = NakagamiDistribution.of(shape, scale)
return dist
nakagami = nakagami_gen()
class pareto_gen(rv_continuous):
"""
A Pareto continuous random variable.
@ -341,17 +368,19 @@ class pareto_gen(rv_continuous):
"""
self.name = "pareto"
self._dist = ParetoDistribution.of(scale, shape)
def _create_distribution(self, *args):
"""
Create a Pareto distribution object.
"""
scale, shape = self._parse_args(*args)
scale, shape = self._parse_args(*args)
dist = ParetoDistribution.of(scale, shape)
return dist
pareto = pareto_gen()
class t_gen(rv_continuous):
"""
A Student's t continuous random variable.
@ -364,17 +393,19 @@ class t_gen(rv_continuous):
"""
self.name = "t"
self._dist = TDistribution.of(dof)
def _create_distribution(self, *args):
"""
Create a Student's t-distribution object.
"""
dof = self._parse_args(*args)[0]
dof = self._parse_args(*args)[0]
dist = TDistribution.of(dof)
return dist
t = t_gen()
class triang_gen(rv_continuous):
"""
A Triangular continuous random variable.
@ -389,7 +420,7 @@ class triang_gen(rv_continuous):
"""
self.name = "triang"
self._dist = TriangularDistribution.of(a, c, b)
def _create_distribution(self, *args):
"""
Create a Triangular distribution object.
@ -406,8 +437,10 @@ class triang_gen(rv_continuous):
dist = TriangularDistribution.of(a, c, b)
return dist
triang = triang_gen()
class uniform_gen(rv_continuous):
"""
A Uniform continuous random variable.
@ -421,17 +454,19 @@ class uniform_gen(rv_continuous):
"""
self.name = "uniform"
self._dist = UniformContinuousDistribution.of(a, b)
def _create_distribution(self, *args):
"""
Create a Uniform distribution object.
"""
scale, shape = self._parse_args(*args)
scale, shape = self._parse_args(*args)
dist = UniformContinuousDistribution.of(scale, shape)
return dist
uniform = uniform_gen()
class weibull_gen(rv_continuous):
"""
A Weibull continuous random variable.
@ -445,7 +480,7 @@ class weibull_gen(rv_continuous):
"""
self.name = "weibull"
self._dist = WeibullDistribution.of(shape, scale)
def _create_distribution(self, *args):
"""
Create a Weibull distribution object.
@ -454,4 +489,5 @@ class weibull_gen(rv_continuous):
dist = WeibullDistribution.of(shape, scale)
return dist
weibull = weibull_gen()
weibull = weibull_gen()

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@ -603,25 +603,96 @@ class Axes(object):
r_axis = Axis(ax.getAxis(Location.RIGHT))
ax.setAxis(r_axis, Location.RIGHT)
def axis(self, limits):
def set_axis_on(self):
"""
Sets the min and max of the x and y axes, with ``[xmin, xmax, ymin, ymax]`` .
:param limits: (*list*) Min and max of the x and y axes.
Set all axis visible.
"""
if len(limits) == 4:
xmin = limits[0]
xmax = limits[1]
ymin = limits[2]
ymax = limits[3]
extent = Extent(xmin, xmax, ymin, ymax)
self._axes.setDrawExtent(extent)
# self._axes.setExtent(extent.clone())
self._axes.setFixDrawExtent(True)
return True
self.get_axis(Location.BOTTOM).setVisible(True)
self.get_axis(Location.LEFT).setVisible(True)
self.get_axis(Location.TOP).setVisible(True)
self.get_axis(Location.RIGHT).setVisible(True)
def set_axis_off(self):
"""
Set all axis not visible.
"""
self.get_axis(Location.BOTTOM).setVisible(False)
self.get_axis(Location.LEFT).setVisible(False)
self.get_axis(Location.TOP).setVisible(False)
self.get_axis(Location.RIGHT).setVisible(False)
def axis(self, arg=None, **kwargs):
"""
Convenience method to get or set some axis properties.
Call signatures::
xmin, xmax, ymin, ymax = axis()
xmin, xmax, ymin, ymax = axis([xmin, xmax, ymin, ymax])
xmin, xmax, ymin, ymax = axis(option)
xmin, xmax, ymin, ymax = axis(**kwargs)
Parameters
----------
xmin, xmax, ymin, ymax : float, optional
The axis limits to be set. This can also be achieved using ::
ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))
option : bool or str
If a bool, turns axis lines and labels on or off. If a string,
possible values are:
======== ==========================================================
Value Description
======== ==========================================================
'on' Turn on axis lines and labels. Same as ``True``.
'off' Turn off axis lines and labels. Same as ``False``.
'equal' Set equal scaling (i.e., make circles circular) by
changing axis limits. This is the same as
``ax.set_aspect('equal', adjustable='datalim')``.
Explicit data limits may not be respected in this case.
'scaled' Set equal scaling (i.e., make circles circular) by
changing dimensions of the plot box. This is the same as
``ax.set_aspect('equal', adjustable='box', anchor='C')``.
Additionally, further autoscaling will be disabled.
'tight' Set limits just large enough to show all data, then
disable further autoscaling.
'auto' Automatic scaling (fill plot box with data).
'image' 'scaled' with axis limits equal to data limits.
'square' Square plot; similar to 'scaled', but initially forcing
``xmax-xmin == ymax-ymin``.
======== ==========================================================
Returns
-------
xmin, xmax, ymin, ymax : float
The axis limits.
"""
if isinstance(arg, (str, bool)):
if arg is True:
arg = 'on'
if arg is False:
arg = 'off'
arg = arg.lower()
if arg == 'on':
self.set_axis_on()
elif arg == 'off':
self.set_axis_off()
elif arg in ['auto', 'equal']:
self.set_aspect(arg)
else:
raise ValueError("Unrecognized string {} to axis; "
"try 'on' or 'off'".format(arg))
else:
print('The limits parameter must be a list with 4 elements: xmin, xmax, ymin, ymax!')
return None
if arg is not None:
try:
xmin, xmax, ymin, ymax = arg
except (TypeError, ValueError) as err:
raise TypeError('the first argument to axis() must be an '
'iterable of the form '
'[xmin, xmax, ymin, ymax]')
self.set_xlim(xmin, xmax)
self.set_ylim(ymin, ymax)
return self.get_xlim() + self.get_ylim()
def get_xlim(self):
"""

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meteoinfo-lab/pylib/mipylib/plotlib/miplot$py.class
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8
meteoinfo-lab/pylib/mipylib/plotlib/miplot.py
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@ -1423,10 +1423,10 @@ def text3(x, y, z, s, zdir=None, **kwargs):
@_copy_docstring_and_deprecators(Axes.axis)
def axis(limits):
r = g_axes.axis(limits)
if r is not None:
draw_if_interactive()
def axis(arg=None, **kwargs):
r = g_axes.axis(arg, **kwargs)
draw_if_interactive()
return r
@_copy_docstring_and_deprecators(MapAxes.axis)

170
meteoinfo-map/meteoinfo-map.iml
View File

@ -12,101 +12,22 @@
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@ -122,6 +43,7 @@
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@ -137,22 +59,100 @@
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37
meteoinfo-math/src/main/java/org/meteoinfo/math/distribution/DistributionUtil.java
View File

@ -72,6 +72,43 @@ public class DistributionUtil {
Array r = Array.factory(DataType.DOUBLE, new int[]{n, dim}, s);
return r;
}
/**
* Random variates of given type.
* @param dis Distribution.
* @param n Size.
* @return Result array.
*/
public static Array rvs(MultivariateNormalDistribution dis, int n) {
MultivariateNormalDistribution.Sampler sampler = dis.createSampler(RandomSource.MT.create());
int dim = dis.getDimension();
double[][] samples = new double[n][dim];
for (int i = 0; i < n; i++) {
samples[i] = sampler.sample();
}
double[] s = Arrays.stream(samples).flatMapToDouble(x -> Arrays.stream(x)).toArray();
Array r = Array.factory(DataType.DOUBLE, new int[]{n, dim}, s);
return r;
}
/**
* Random variates of given type.
* @param dis Distribution.
* @param n Size.
* @return Result array.
*/
public static Array rvs(MultivariateNormalDistribution dis, Array size) {
MultivariateNormalDistribution.Sampler sampler = dis.createSampler(RandomSource.MT.create());
int dim = dis.getDimension();
int n = (int) size.getSize();
double[][] samples = new double[n][dim];
for (int i = 0; i < n; i++) {
samples[i] = sampler.sample();
}
double[] s = Arrays.stream(samples).flatMapToDouble(x -> Arrays.stream(x)).toArray();
Array r = Array.factory(DataType.DOUBLE, new int[]{n, dim}, s);
return r;
}
/**
* Probability density function at x