Closes #3354 import scipy.stats functions to arkouda.scipy.stats

Bears-R-Us · Jun 25, 2024 · c7209d4 · c7209d4
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Showing 16 changed files with 383 additions and 30 deletions.
diff --git a/PROTO_tests/tests/dataframe_test.py b/PROTO_tests/tests/dataframe_test.py
@@ -10,7 +10,7 @@
 
 import arkouda as ak
 from arkouda import io_util
-from arkouda.scipy import chisquare as akchisquare
+from arkouda.scipy.stats import chisquare as akchisquare
 
 
 def alternating_1_0(n):

diff --git a/PROTO_tests/tests/groupby_test.py b/PROTO_tests/tests/groupby_test.py
@@ -4,7 +4,7 @@
 
 import arkouda as ak
 from arkouda.groupbyclass import GroupByReductionType
-from arkouda.scipy import chisquare as akchisquare
+from arkouda.scipy.stats import chisquare as akchisquare
 
 
 def to_tuple_dict(labels, values):

diff --git a/PROTO_tests/tests/random_test.py b/PROTO_tests/tests/random_test.py
@@ -6,7 +6,7 @@
 from scipy import stats as sp_stats
 
 import arkouda as ak
-from arkouda.scipy import chisquare as akchisquare
+from arkouda.scipy.stats import chisquare as akchisquare
 
 
 class TestRandom:

diff --git a/PROTO_tests/tests/scipy/scipy_test.py → PROTO_tests/tests/scipy/scipy_stats_test.py b/PROTO_tests/tests/scipy/scipy_test.py → PROTO_tests/tests/scipy/scipy_stats_test.py
@@ -4,8 +4,8 @@
 from scipy.stats import power_divergence as scipy_power_divergence
 
 import arkouda as ak
-from arkouda.scipy import chisquare as ak_chisquare
-from arkouda.scipy import power_divergence as ak_power_divergence
+from arkouda.scipy.stats import chisquare as ak_chisquare
+from arkouda.scipy.stats import power_divergence as ak_power_divergence
 
 DDOF = [0, 1, 2, 3, 4, 5]
 PAIRS = [

diff --git a/arkouda/__init__.py b/arkouda/__init__.py
@@ -40,6 +40,4 @@
     is_registered,
     broadcast_dims,
 )
-from arkouda.scipy.special import *
-from arkouda.scipy import *
-from arkouda.random import *
+from arkouda.random import *
diff --git a/arkouda/scipy/__init__.py b/arkouda/scipy/__init__.py
@@ -1,3 +1 @@
-from ._stats_py import Power_divergenceResult, chisquare, power_divergence
-
-__all__ = ["power_divergence", "chisquare", "Power_divergenceResult"]
+__all__ = ["special", "stats"]
diff --git a/arkouda/scipy/stats/__init__.py b/arkouda/scipy/stats/__init__.py
@@ -1,4 +1,291 @@
 # mypy: ignore-errors
-from scipy.stats import chi2  # type: ignore[import-untyped]
+from scipy.stats import (  # noqa
+    ConstantInputWarning,
+    DegenerateDataWarning,
+    FitError,
+    NearConstantInputWarning,
+    alpha,
+    anglit,
+    arcsine,
+    argus,
+    bernoulli,
+    beta,
+    betabinom,
+    biasedurn,
+    binom,
+    binomtest,
+    boltzmann,
+    bradford,
+    burr,
+    burr12,
+    cauchy,
+    chi,
+    chi2,
+    contingency,
+    cosine,
+    crystalball,
+    dgamma,
+    distributions,
+    dlaplace,
+    dweibull,
+    erlang,
+    expon,
+    exponnorm,
+    exponpow,
+    exponweib,
+    f,
+    fatiguelife,
+    fisk,
+    foldcauchy,
+    foldnorm,
+    gamma,
+    gausshyper,
+    genexpon,
+    genextreme,
+    gengamma,
+    genhalflogistic,
+    genhyperbolic,
+    geninvgauss,
+    genlogistic,
+    gennorm,
+    genpareto,
+    geom,
+    gibrat,
+    gompertz,
+    gumbel_l,
+    gumbel_r,
+    halfcauchy,
+    halfgennorm,
+    halflogistic,
+    halfnorm,
+    hypergeom,
+    hypsecant,
+    invgamma,
+    invgauss,
+    invweibull,
+    johnsonsb,
+    johnsonsu,
+    kappa3,
+    kappa4,
+    kde,
+    ksone,
+    kstwo,
+    kstwobign,
+    laplace,
+    laplace_asymmetric,
+    levy,
+    levy_l,
+    levy_stable,
+    loggamma,
+    logistic,
+    loglaplace,
+    lognorm,
+    logser,
+    loguniform,
+    lomax,
+    maxwell,
+    mielke,
+    morestats,
+    moyal,
+    mstats_basic,
+    mstats_extras,
+    mvn,
+    nakagami,
+    nbinom,
+    ncf,
+    nchypergeom_fisher,
+    nchypergeom_wallenius,
+    nct,
+    ncx2,
+    nhypergeom,
+    norm,
+    norminvgauss,
+    pareto,
+    pearson3,
+    planck,
+    poisson,
+    poisson_means_test,
+    powerlaw,
+    powerlognorm,
+    powernorm,
+    randint,
+    rayleigh,
+    rdist,
+    recipinvgauss,
+    reciprocal,
+    rice,
+    sampling,
+    semicircular,
+    skellam,
+    skewcauchy,
+    skewnorm,
+    stats,
+    studentized_range,
+    t,
+    trapezoid,
+    trapz,
+    triang,
+    truncexpon,
+    truncnorm,
+    truncpareto,
+    truncweibull_min,
+    tukeylambda,
+    uniform,
+    vonmises,
+    vonmises_line,
+    wald,
+    weibull_max,
+    weibull_min,
+    wrapcauchy,
+    yulesimon,
+    zipf,
+    zipfian,
+)
 
-__all__ = ["chi2"]
+from ._stats_py import Power_divergenceResult, chisquare, power_divergence  # noqa
+
+scipy_imports = [
+    "ConstantInputWarning",
+    "DegenerateDataWarning",
+    "FitError",
+    "NearConstantInputWarning",
+    "alpha",
+    "anglit",
+    "arcsine",
+    "argus",
+    "bernoulli",
+    "beta",
+    "betabinom",
+    "biasedurn",
+    "binom",
+    "binomtest",
+    "boltzmann",
+    "bradford",
+    "burr",
+    "burr12",
+    "cauchy",
+    "chi",
+    "chi2",
+    "contingency",
+    "cosine",
+    "crystalball",
+    "dgamma",
+    "distributions",
+    "dlaplace",
+    "dweibull",
+    "erlang",
+    "expon",
+    "exponnorm",
+    "exponpow",
+    "exponweib",
+    "f",
+    "fatiguelife",
+    "fisk",
+    "foldcauchy",
+    "foldnorm",
+    "gamma",
+    "gausshyper",
+    "genexpon",
+    "genextreme",
+    "gengamma",
+    "genhalflogistic",
+    "genhyperbolic",
+    "geninvgauss",
+    "genlogistic",
+    "gennorm",
+    "genpareto",
+    "geom",
+    "gibrat",
+    "gompertz",
+    "gumbel_l",
+    "gumbel_r",
+    "halfcauchy",
+    "halfgennorm",
+    "halflogistic",
+    "halfnorm",
+    "hypergeom",
+    "hypsecant",
+    "invgamma",
+    "invgauss",
+    "invweibull",
+    "johnsonsb",
+    "johnsonsu",
+    "kappa3",
+    "kappa4",
+    "kde",
+    "ksone",
+    "kstwo",
+    "kstwobign",
+    "laplace",
+    "laplace_asymmetric",
+    "levy",
+    "levy_l",
+    "levy_stable",
+    "loggamma",
+    "logistic",
+    "loglaplace",
+    "lognorm",
+    "logser",
+    "loguniform",
+    "lomax",
+    "maxwell",
+    "mielke",
+    "morestats",
+    "moyal",
+    "mstats_basic",
+    "mstats_extras",
+    "mvn",
+    "nakagami",
+    "nbinom",
+    "ncf",
+    "nchypergeom_fisher",
+    "nchypergeom_wallenius",
+    "nct",
+    "ncx2",
+    "nhypergeom",
+    "norm",
+    "norminvgauss",
+    "pareto",
+    "pearson3",
+    "planck",
+    "poisson",
+    "poisson_means_test",
+    "powerlaw",
+    "powerlognorm",
+    "powernorm",
+    "randint",
+    "rayleigh",
+    "rdist",
+    "recipinvgauss",
+    "reciprocal",
+    "rice",
+    "sampling",
+    "semicircular",
+    "skellam",
+    "skewcauchy",
+    "skewnorm",
+    "stats",
+    "studentized_range",
+    "t",
+    "test",
+    "trapezoid",
+    "trapz",
+    "triang",
+    "truncexpon",
+    "truncnorm",
+    "truncpareto",
+    "truncweibull_min",
+    "tukeylambda",
+    "uniform",
+    "vonmises",
+    "vonmises_line",
+    "wald",
+    "weibull_max",
+    "weibull_min",
+    "wrapcauchy",
+    "yulesimon",
+    "zipf",
+    "zipfian",
+]
+
+__all__ = scipy_imports + ["Power_divergenceResult", "power_divergence", "chisquare"]
diff --git a/arkouda/scipy/_stats_py.py → arkouda/scipy/stats/_stats_py.py b/arkouda/scipy/_stats_py.py → arkouda/scipy/stats/_stats_py.py
@@ -2,13 +2,13 @@
 
 import numpy as np
 from numpy import asarray
-from scipy.stats import chi2  # type: ignore
+from scipy.stats import chi2  # type:ignore
 
 import arkouda as ak
-from arkouda.scipy.special import xlogy
 from arkouda.dtypes import float64 as akfloat64
+from arkouda.scipy.special import xlogy
 
-__all__ = ["power_divergence", "chisquare", "Power_divergenceResult"]
+__all__ = ["Power_divergenceResult", "power_divergence", "chisquare"]
 
 
 class Power_divergenceResult(namedtuple("Power_divergenceResult", ("statistic", "pvalue"))):
@@ -74,7 +74,7 @@ def power_divergence(f_obs, f_exp=None, ddof=0, lambda_=None):
 
     >>> import arkouda as ak
     >>> ak.connect()
-    >>> from arkouda.stats import power_divergence
+    >>> from arkouda.scipy.stats import power_divergence
     >>> x = ak.array([10, 20, 30, 10])
     >>> y = ak.array([10, 30, 20, 10])
     >>> power_divergence(x, y, lambda_="pearson")