Added _util.py + fixed a bug in test_fit.py

wafo/stats/_util.py

@@ -0,0 +1,267 @@
+from __future__ import division, print_function, absolute_import
+
+import functools
+import operator
+import sys
+import warnings
+import numbers
+from collections import namedtuple
+import inspect
+
+import numpy as np
+
+
+def _valarray(shape, value=np.nan, typecode=None):
+    """Return an array of all value.
+    """
+
+    out = np.ones(shape, dtype=bool) * value
+    if typecode is not None:
+        out = out.astype(typecode)
+    if not isinstance(out, np.ndarray):
+        out = np.asarray(out)
+    return out
+
+
+def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
+    """
+    np.where(cond, x, fillvalue) always evaluates x even where cond is False.
+    This one only evaluates f(arr1[cond], arr2[cond], ...).
+    For example,
+    >>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
+    >>> def f(a, b):
+        return a*b
+    >>> _lazywhere(a > 2, (a, b), f, np.nan)
+    array([ nan,  nan,  21.,  32.])
+    Notice it assumes that all `arrays` are of the same shape, or can be
+    broadcasted together.
+    """
+    if fillvalue is None:
+        if f2 is None:
+            raise ValueError("One of (fillvalue, f2) must be given.")
+        else:
+            fillvalue = np.nan
+    else:
+        if f2 is not None:
+            raise ValueError("Only one of (fillvalue, f2) can be given.")
+
+    arrays = np.broadcast_arrays(*arrays)
+    temp = tuple(np.extract(cond, arr) for arr in arrays)
+    out = _valarray(np.shape(arrays[0]), value=fillvalue)
+    np.place(out, cond, f(*temp))
+    if f2 is not None:
+        temp = tuple(np.extract(~cond, arr) for arr in arrays)
+        np.place(out, ~cond, f2(*temp))
+
+    return out
+
+
+def _aligned_zeros(shape, dtype=float, order="C", align=None):
+    """Allocate a new ndarray with aligned memory.
+    Primary use case for this currently is working around a f2py issue
+    in Numpy 1.9.1, where dtype.alignment is such that np.zeros() does
+    not necessarily create arrays aligned up to it.
+    """
+    dtype = np.dtype(dtype)
+    if align is None:
+        align = dtype.alignment
+    if not hasattr(shape, '__len__'):
+        shape = (shape,)
+    size = functools.reduce(operator.mul, shape) * dtype.itemsize
+    buf = np.empty(size + align + 1, np.uint8)
+    offset = buf.__array_interface__['data'][0] % align
+    if offset != 0:
+        offset = align - offset
+    # Note: slices producing 0-size arrays do not necessarily change
+    # data pointer --- so we use and allocate size+1
+    buf = buf[offset:offset+size+1][:-1]
+    data = np.ndarray(shape, dtype, buf, order=order)
+    data.fill(0)
+    return data
+
+
+class DeprecatedImport(object):
+    """
+    Deprecated import, with redirection + warning.
+    Examples
+    --------
+    Suppose you previously had in some module::
+        from foo import spam
+    If this has to be deprecated, do::
+        spam = DeprecatedImport("foo.spam", "baz")
+    to redirect users to use "baz" module instead.
+    """
+
+    def __init__(self, old_module_name, new_module_name):
+        self._old_name = old_module_name
+        self._new_name = new_module_name
+        __import__(self._new_name)
+        self._mod = sys.modules[self._new_name]
+
+    def __dir__(self):
+        return dir(self._mod)
+
+    def __getattr__(self, name):
+        warnings.warn("Module %s is deprecated, use %s instead"
+                      % (self._old_name, self._new_name),
+                      DeprecationWarning)
+        return getattr(self._mod, name)
+
+
+# copy-pasted from scikit-learn utils/validation.py
+def check_random_state(seed):
+    """Turn seed into a np.random.RandomState instance
+    If seed is None (or np.random), return the RandomState singleton used
+    by np.random.
+    If seed is an int, return a new RandomState instance seeded with seed.
+    If seed is already a RandomState instance, return it.
+    Otherwise raise ValueError.
+    """
+    if seed is None or seed is np.random:
+        return np.random.mtrand._rand
+    if isinstance(seed, (numbers.Integral, np.integer)):
+        return np.random.RandomState(seed)
+    if isinstance(seed, np.random.RandomState):
+        return seed
+    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
+                     ' instance' % seed)
+
+
+def _asarray_validated(a, check_finite=True,
+                       sparse_ok=False, objects_ok=False, mask_ok=False,
+                       as_inexact=False):
+    """
+    Helper function for scipy argument validation.
+    Many scipy linear algebra functions do support arbitrary array-like
+    input arguments.  Examples of commonly unsupported inputs include
+    matrices containing inf/nan, sparse matrix representations, and
+    matrices with complicated elements.
+    Parameters
+    ----------
+    a : array_like
+        The array-like input.
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+        Default: True
+    sparse_ok : bool, optional
+        True if scipy sparse matrices are allowed.
+    objects_ok : bool, optional
+        True if arrays with dype('O') are allowed.
+    mask_ok : bool, optional
+        True if masked arrays are allowed.
+    as_inexact : bool, optional
+        True to convert the input array to a np.inexact dtype.
+    Returns
+    -------
+    ret : ndarray
+        The converted validated array.
+    """
+    if not sparse_ok:
+        import scipy.sparse
+        if scipy.sparse.issparse(a):
+            msg = ('Sparse matrices are not supported by this function. '
+                   'Perhaps one of the scipy.sparse.linalg functions '
+                   'would work instead.')
+            raise ValueError(msg)
+    if not mask_ok:
+        if np.ma.isMaskedArray(a):
+            raise ValueError('masked arrays are not supported')
+    toarray = np.asarray_chkfinite if check_finite else np.asarray
+    a = toarray(a)
+    if not objects_ok:
+        if a.dtype is np.dtype('O'):
+            raise ValueError('object arrays are not supported')
+    if as_inexact:
+        if not np.issubdtype(a.dtype, np.inexact):
+            a = toarray(a, dtype=np.float_)
+    return a
+
+
+# Add a replacement for inspect.getargspec() which is deprecated in python 3.5
+# The version below is borrowed from Django,
+# https://github.com/django/django/pull/4846
+
+# Note an inconsistency between inspect.getargspec(func) and
+# inspect.signature(func). If `func` is a bound method, the latter does *not*
+# list `self` as a first argument, while the former *does*.
+# Hence cook up a common ground replacement: `getargspec_no_self` which
+# mimics `inspect.getargspec` but does not list `self`.
+#
+# This way, the caller code does not need to know whether it uses a legacy
+# .getargspec or bright and shiny .signature.
+
+try:
+    # is it python 3.3 or higher?
+    inspect.signature  # @UndefinedVariable
+
+    # Apparently, yes. Wrap inspect.signature
+
+    ArgSpec = namedtuple('ArgSpec', ['args', 'varargs', 'keywords', 'defaults'])
+
+    def getargspec_no_self(func):
+        """inspect.getargspec replacement using inspect.signature.
+        inspect.getargspec is deprecated in python 3. This is a replacement
+        based on the (new in python 3.3) `inspect.signature`.
+        Parameters
+        ----------
+        func : callable
+            A callable to inspect
+        Returns
+        -------
+        argspec : ArgSpec(args, varargs, varkw, defaults)
+            This is similar to the result of inspect.getargspec(func) under
+            python 2.x.
+            NOTE: if the first argument of `func` is self, it is *not*, I repeat
+            *not* included in argspec.args.
+            This is done for consistency between inspect.getargspec() under
+            python 2.x, and inspect.signature() under python 3.x.
+        """
+        sig = inspect.signature(func)
+        args = [
+            p.name for p in sig.parameters.values()
+            if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
+        ]
+        varargs = [
+            p.name for p in sig.parameters.values()
+            if p.kind == inspect.Parameter.VAR_POSITIONAL
+        ]
+        varargs = varargs[0] if varargs else None
+        varkw = [
+            p.name for p in sig.parameters.values()
+            if p.kind == inspect.Parameter.VAR_KEYWORD
+        ]
+        varkw = varkw[0] if varkw else None
+        defaults = [
+            p.default for p in sig.parameters.values()
+            if (p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD and
+               p.default is not p.empty)
+        ] or None
+        return ArgSpec(args, varargs, varkw, defaults)
+
+except AttributeError:
+    # python 2.x
+    def getargspec_no_self(func):
+        """inspect.getargspec replacement for compatibility with python 3.x.
+        inspect.getargspec is deprecated in python 3. This wraps it, and
+        *removes* `self` from the argument list of `func`, if present.
+        This is done for forward compatibility with python 3.
+        Parameters
+        ----------
+        func : callable
+            A callable to inspect
+        Returns
+        -------
+        argspec : ArgSpec(args, varargs, varkw, defaults)
+            This is similar to the result of inspect.getargspec(func) under
+            python 2.x.
+            NOTE: if the first argument of `func` is self, it is *not*, I repeat
+            *not* included in argspec.args.
+            This is done for consistency between inspect.getargspec() under
+            python 2.x, and inspect.signature() under python 3.x.
+        """
+        argspec = inspect.getargspec(func)
+        if argspec.args[0] == 'self':
+            argspec.args.pop(0)
+        return argspec

wafo/stats/estimation.py

@@ -12,7 +12,6 @@ import warnings
 
 from wafo.plotbackend import plotbackend
 from wafo.misc import ecross, findcross, argsreduce
-from wafo.stats._util import check_random_state
 from wafo.stats._constants import _EPS, _XMAX
 from wafo.stats._distn_infrastructure import rv_frozen
 from scipy._lib.six import string_types
@@ -600,7 +599,7 @@ class FitDistribution(rv_frozen):
 #    par_upper - upper (1-alpha)% confidence bound for the parameters
 #
 #        '''
-        self.__doc__ = rv_frozen.__doc__ + extradoc
+        self.__doc__ = str(rv_frozen.__doc__) + extradoc
         self.dist = dist
         numargs = dist.numargs
 

wafo/stats/tests/test_fit.py

@@ -76,7 +76,7 @@ def check_cont_fit(distname,arg):
     truearg = np.hstack([arg,[0.0,1.0]])
     diffthreshold = np.max(np.vstack([truearg*thresh_percent,
                                       np.ones(distfn.numargs+2)*thresh_min]),0)
-
+    opt = options.copy()
     for fit_size in fit_sizes:
         # Note that if a fit succeeds, the other fit_sizes are skipped
         np.random.seed(1234)
@@ -85,7 +85,7 @@ def check_cont_fit(distname,arg):
             rvs = distfn.rvs(size=fit_size, *arg)
             # phat = distfn.fit2(rvs)
 
-            phat = distfn.fit2(rvs, **options)
+            phat = distfn.fit2(rvs, **opt)
 
             est = phat.par
             #est = distfn.fit(rvs)  # start with default values