|
"""Functions copypasted from newer versions of numpy.
"""
else: def _assert_warns(warning_class, func, *args, **kw): r""" Fail unless the given callable throws the specified warning.
This definition is copypasted from numpy 1.9.0.dev. The version in earlier numpy returns None.
Parameters ---------- warning_class : class The class defining the warning that `func` is expected to throw. func : callable The callable to test. *args : Arguments Arguments passed to `func`. **kwargs : Kwargs Keyword arguments passed to `func`.
Returns ------- The value returned by `func`.
""" with warnings.catch_warnings(record=True) as l: warnings.simplefilter('always') result = func(*args, **kw) if not len(l) > 0: raise AssertionError("No warning raised when calling %s" % func.__name__) if not l[0].category is warning_class: raise AssertionError("First warning for %s is not a " "%s( is %s)" % (func.__name__, warning_class, l[0])) return result
else: # Definition of `broadcast_to` from numpy 1.10.0.
def _maybe_view_as_subclass(original_array, new_array): if type(original_array) is not type(new_array): # if input was an ndarray subclass and subclasses were OK, # then view the result as that subclass. new_array = new_array.view(type=type(original_array)) # Since we have done something akin to a view from original_array, we # should let the subclass finalize (if it has it implemented, i.e., is # not None). if new_array.__array_finalize__: new_array.__array_finalize__(original_array) return new_array
def _broadcast_to(array, shape, subok, readonly): shape = tuple(shape) if np.iterable(shape) else (shape,) array = np.array(array, copy=False, subok=subok) if not shape and array.shape: raise ValueError('cannot broadcast a non-scalar to a scalar array') if any(size < 0 for size in shape): raise ValueError('all elements of broadcast shape must be non-' 'negative') broadcast = np.nditer( (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'], op_flags=['readonly'], itershape=shape, order='C').itviews[0] result = _maybe_view_as_subclass(array, broadcast) if not readonly and array.flags.writeable: result.flags.writeable = True return result
def broadcast_to(array, shape, subok=False): return _broadcast_to(array, shape, subok=subok, readonly=True)
else: # the return_counts keyword was added in 1.9.0 def unique(ar, return_index=False, return_inverse=False, return_counts=False): """ Find the unique elements of an array.
Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values, the indices of the unique array that reconstruct the input array, and the number of times each unique value comes up in the input array.
Parameters ---------- ar : array_like Input array. This will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of `ar` that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array that can be used to reconstruct `ar`. return_counts : bool, optional If True, also return the number of times each unique value comes up in `ar`.
.. versionadded:: 1.9.0
Returns ------- unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if `return_index` is True. unique_inverse : ndarray, optional The indices to reconstruct the (flattened) original array from the unique array. Only provided if `return_inverse` is True. unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if `return_counts` is True.
.. versionadded:: 1.9.0
Notes ----- Taken over from numpy 1.12.0-dev (c8408bf9c). Omitted examples, see numpy documentation for those.
""" ar = np.asanyarray(ar).flatten()
optional_indices = return_index or return_inverse optional_returns = optional_indices or return_counts
if ar.size == 0: if not optional_returns: ret = ar else: ret = (ar,) if return_index: ret += (np.empty(0, np.bool),) if return_inverse: ret += (np.empty(0, np.bool),) if return_counts: ret += (np.empty(0, np.intp),) return ret
if optional_indices: perm = ar.argsort(kind='mergesort' if return_index else 'quicksort') aux = ar[perm] else: ar.sort() aux = ar flag = np.concatenate(([True], aux[1:] != aux[:-1]))
if not optional_returns: ret = aux[flag] else: ret = (aux[flag],) if return_index: ret += (perm[flag],) if return_inverse: iflag = np.cumsum(flag) - 1 inv_idx = np.empty(ar.shape, dtype=np.intp) inv_idx[perm] = iflag ret += (inv_idx,) if return_counts: idx = np.concatenate(np.nonzero(flag) + ([ar.size],)) ret += (np.diff(idx),) return ret
else: def polyvalfromroots(x, r, tensor=True): r""" Evaluate a polynomial specified by its roots at points x.
This function is copypasted from numpy 1.12.0.dev.
If `r` is of length `N`, this function returns the value
.. math:: p(x) = \prod_{n=1}^{N} (x - r_n)
The parameter `x` is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either `x` or its elements must support multiplication and addition both with themselves and with the elements of `r`.
If `r` is a 1-D array, then `p(x)` will have the same shape as `x`. If `r` is multidimensional, then the shape of the result depends on the value of `tensor`. If `tensor is ``True`` the shape will be r.shape[1:] + x.shape; that is, each polynomial is evaluated at every value of `x`. If `tensor` is ``False``, the shape will be r.shape[1:]; that is, each polynomial is evaluated only for the corresponding broadcast value of `x`. Note that scalars have shape (,).
Parameters ---------- x : array_like, compatible object If `x` is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, `x` or its elements must support addition and multiplication with with themselves and with the elements of `r`. r : array_like Array of roots. If `r` is multidimensional the first index is the root index, while the remaining indices enumerate multiple polynomials. For instance, in the two dimensional case the roots of each polynomial may be thought of as stored in the columns of `r`. tensor : boolean, optional If True, the shape of the roots array is extended with ones on the right, one for each dimension of `x`. Scalars have dimension 0 for this action. The result is that every column of coefficients in `r` is evaluated for every element of `x`. If False, `x` is broadcast over the columns of `r` for the evaluation. This keyword is useful when `r` is multidimensional. The default value is True.
Returns ------- values : ndarray, compatible object The shape of the returned array is described above.
See Also -------- polyroots, polyfromroots, polyval
Examples -------- >>> from numpy.polynomial.polynomial import polyvalfromroots >>> polyvalfromroots(1, [1,2,3]) 0.0 >>> a = np.arange(4).reshape(2,2) >>> a array([[0, 1], [2, 3]]) >>> polyvalfromroots(a, [-1, 0, 1]) array([[ -0., 0.], [ 6., 24.]]) >>> r = np.arange(-2, 2).reshape(2,2) # multidimensional coefficients >>> r # each column of r defines one polynomial array([[-2, -1], [ 0, 1]]) >>> b = [-2, 1] >>> polyvalfromroots(b, r, tensor=True) array([[-0., 3.], [ 3., 0.]]) >>> polyvalfromroots(b, r, tensor=False) array([-0., 0.]) """ r = np.array(r, ndmin=1, copy=0) if r.dtype.char in '?bBhHiIlLqQpP': r = r.astype(np.double) if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray): if tensor: r = r.reshape(r.shape + (1,)*x.ndim) elif x.ndim >= r.ndim: raise ValueError("x.ndim must be < r.ndim when tensor == " "False") return np.prod(x - r, axis=0)
except ImportError: class suppress_warnings(object): """ Context manager and decorator doing much the same as ``warnings.catch_warnings``.
However, it also provides a filter mechanism to work around http://bugs.python.org/issue4180.
This bug causes Python before 3.4 to not reliably show warnings again after they have been ignored once (even within catch_warnings). It means that no "ignore" filter can be used easily, since following tests might need to see the warning. Additionally it allows easier specificity for testing warnings and can be nested.
Parameters ---------- forwarding_rule : str, optional One of "always", "once", "module", or "location". Analogous to the usual warnings module filter mode, it is useful to reduce noise mostly on the outmost level. Unsuppressed and unrecorded warnings will be forwarded based on this rule. Defaults to "always". "location" is equivalent to the warnings "default", match by exact location the warning warning originated from.
Notes ----- Filters added inside the context manager will be discarded again when leaving it. Upon entering all filters defined outside a context will be applied automatically.
When a recording filter is added, matching warnings are stored in the ``log`` attribute as well as in the list returned by ``record``.
If filters are added and the ``module`` keyword is given, the warning registry of this module will additionally be cleared when applying it, entering the context, or exiting it. This could cause warnings to appear a second time after leaving the context if they were configured to be printed once (default) and were already printed before the context was entered.
Nesting this context manager will work as expected when the forwarding rule is "always" (default). Unfiltered and unrecorded warnings will be passed out and be matched by the outer level. On the outmost level they will be printed (or caught by another warnings context). The forwarding rule argument can modify this behaviour.
Like ``catch_warnings`` this context manager is not threadsafe.
Examples -------- >>> with suppress_warnings() as sup: ... sup.filter(DeprecationWarning, "Some text") ... sup.filter(module=np.ma.core) ... log = sup.record(FutureWarning, "Does this occur?") ... command_giving_warnings() ... # The FutureWarning was given once, the filtered warnings were ... # ignored. All other warnings abide outside settings (may be ... # printed/error) ... assert_(len(log) == 1) ... assert_(len(sup.log) == 1) # also stored in log attribute
Or as a decorator:
>>> sup = suppress_warnings() >>> sup.filter(module=np.ma.core) # module must match exact >>> @sup >>> def some_function(): ... # do something which causes a warning in np.ma.core ... pass """ def __init__(self, forwarding_rule="always"): self._entered = False
# Suppressions are either instance or defined inside one with block: self._suppressions = []
if forwarding_rule not in {"always", "module", "once", "location"}: raise ValueError("unsupported forwarding rule.") self._forwarding_rule = forwarding_rule
def _clear_registries(self): if hasattr(warnings, "_filters_mutated"): # clearing the registry should not be necessary on new pythons, # instead the filters should be mutated. warnings._filters_mutated() return # Simply clear the registry, this should normally be harmless, # note that on new pythons it would be invalidated anyway. for module in self._tmp_modules: if hasattr(module, "__warningregistry__"): module.__warningregistry__.clear()
def _filter(self, category=Warning, message="", module=None, record=False): if record: record = [] # The log where to store warnings else: record = None if self._entered: if module is None: warnings.filterwarnings( "always", category=category, message=message) else: module_regex = module.__name__.replace('.', r'\.') + '$' warnings.filterwarnings( "always", category=category, message=message, module=module_regex) self._tmp_modules.add(module) self._clear_registries()
self._tmp_suppressions.append( (category, message, re.compile(message, re.I), module, record)) else: self._suppressions.append( (category, message, re.compile(message, re.I), module, record))
return record
def filter(self, category=Warning, message="", module=None): """ Add a new suppressing filter or apply it if the state is entered.
Parameters ---------- category : class, optional Warning class to filter message : string, optional Regular expression matching the warning message. module : module, optional Module to filter for. Note that the module (and its file) must match exactly and cannot be a submodule. This may make it unreliable for external modules.
Notes ----- When added within a context, filters are only added inside the context and will be forgotten when the context is exited. """ self._filter(category=category, message=message, module=module, record=False)
def record(self, category=Warning, message="", module=None): """ Append a new recording filter or apply it if the state is entered.
All warnings matching will be appended to the ``log`` attribute.
Parameters ---------- category : class, optional Warning class to filter message : string, optional Regular expression matching the warning message. module : module, optional Module to filter for. Note that the module (and its file) must match exactly and cannot be a submodule. This may make it unreliable for external modules.
Returns ------- log : list A list which will be filled with all matched warnings.
Notes ----- When added within a context, filters are only added inside the context and will be forgotten when the context is exited. """ return self._filter(category=category, message=message, module=module, record=True)
def __enter__(self): if self._entered: raise RuntimeError("cannot enter suppress_warnings twice.")
self._orig_show = warnings.showwarning self._filters = warnings.filters warnings.filters = self._filters[:]
self._entered = True self._tmp_suppressions = [] self._tmp_modules = set() self._forwarded = set()
self.log = [] # reset global log (no need to keep same list)
for cat, mess, _, mod, log in self._suppressions: if log is not None: del log[:] # clear the log if mod is None: warnings.filterwarnings( "always", category=cat, message=mess) else: module_regex = mod.__name__.replace('.', r'\.') + '$' warnings.filterwarnings( "always", category=cat, message=mess, module=module_regex) self._tmp_modules.add(mod) warnings.showwarning = self._showwarning self._clear_registries()
return self
def __exit__(self, *exc_info): warnings.showwarning = self._orig_show warnings.filters = self._filters self._clear_registries() self._entered = False del self._orig_show del self._filters
def _showwarning(self, message, category, filename, lineno, *args, **kwargs): use_warnmsg = kwargs.pop("use_warnmsg", None) for cat, _, pattern, mod, rec in ( self._suppressions + self._tmp_suppressions)[::-1]: if (issubclass(category, cat) and pattern.match(message.args[0]) is not None): if mod is None: # Message and category match, either recorded or ignored if rec is not None: msg = WarningMessage(message, category, filename, lineno, **kwargs) self.log.append(msg) rec.append(msg) return # Use startswith, because warnings strips the c or o from # .pyc/.pyo files. elif mod.__file__.startswith(filename): # The message and module (filename) match if rec is not None: msg = WarningMessage(message, category, filename, lineno, **kwargs) self.log.append(msg) rec.append(msg) return
# There is no filter in place, so pass to the outside handler # unless we should only pass it once if self._forwarding_rule == "always": if use_warnmsg is None: self._orig_show(message, category, filename, lineno, *args, **kwargs) else: self._orig_showmsg(use_warnmsg) return
if self._forwarding_rule == "once": signature = (message.args, category) elif self._forwarding_rule == "module": signature = (message.args, category, filename) elif self._forwarding_rule == "location": signature = (message.args, category, filename, lineno)
if signature in self._forwarded: return self._forwarded.add(signature) if use_warnmsg is None: self._orig_show(message, category, filename, lineno, *args, **kwargs) else: self._orig_showmsg(use_warnmsg)
def __call__(self, func): """ Function decorator to apply certain suppressions to a whole function. """ @wraps(func) def new_func(*args, **kwargs): with self: return func(*args, **kwargs)
return new_func |