mojindri
diff --git a/‎lib/matplotlib/cbook/__init__.py
Copy file name to clipboardExpand all lines: lib/matplotlib/cbook/__init__.py
-256Lines changed: 0 additions & 256 deletions b/‎lib/matplotlib/cbook/__init__.py
Copy file name to clipboardExpand all lines: lib/matplotlib/cbook/__init__.py
-256Lines changed: 0 additions & 256 deletions
diff --git a/‎lib/matplotlib/tests/test_cbook.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_cbook.py
-61Lines changed: 0 additions & 61 deletions b/‎lib/matplotlib/tests/test_cbook.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_cbook.py
-61Lines changed: 0 additions & 61 deletions
@@ -2538,262 +2538,6 @@ def __exit__(self, exc_type, exc_value, traceback):
                     pass
 
 
-class _FuncInfo(object):
-    """
-    Class used to store a function.
-
-    """
-
-    def __init__(self, function, inverse, bounded_0_1=True, check_params=None):
-        """
-        Parameters
-        ----------
-
-        function : callable
-            A callable implementing the function receiving the variable as
-            first argument and any additional parameters in a list as second
-            argument.
-        inverse : callable
-            A callable implementing the inverse function receiving the variable
-            as first argument and any additional parameters in a list as
-            second argument. It must satisfy 'inverse(function(x, p), p) == x'.
-        bounded_0_1: bool or callable
-            A boolean indicating whether the function is bounded in the [0,1]
-            interval, or a callable taking a list of values for the additional
-            parameters, and returning a boolean indicating whether the function
-            is bounded in the [0,1] interval for that combination of
-            parameters. Default True.
-        check_params: callable or None
-            A callable taking a list of values for the additional parameters
-            and returning a boolean indicating whether that combination of
-            parameters is valid. It is only required if the function has
-            additional parameters and some of them are restricted.
-            Default None.
-
-        """
-
-        self.function = function
-        self.inverse = inverse
-
-        if callable(bounded_0_1):
-            self._bounded_0_1 = bounded_0_1
-        else:
-            self._bounded_0_1 = lambda x: bounded_0_1
-
-        if check_params is None:
-            self._check_params = lambda x: True
-        elif callable(check_params):
-            self._check_params = check_params
-        else:
-            raise ValueError("Invalid 'check_params' argument.")
-
-    def is_bounded_0_1(self, params=None):
-        """
-        Returns a boolean indicating if the function is bounded in the [0,1]
-        interval for a particular set of additional parameters.
-
-        Parameters
-        ----------
-
-        params : list
-            The list of additional parameters. Default None.
-
-        Returns
-        -------
-
-        out : bool
-            True if the function is bounded in the [0,1] interval for
-            parameters 'params'. Otherwise False.
-
-        """
-
-        return self._bounded_0_1(params)
-
-    def check_params(self, params=None):
-        """
-        Returns a boolean indicating if the set of additional parameters is
-        valid.
-
-        Parameters
-        ----------
-
-        params : list
-            The list of additional parameters. Default None.
-
-        Returns
-        -------
-
-        out : bool
-            True if 'params' is a valid set of additional parameters for the
-            function. Otherwise False.
-
-        """
-
-        return self._check_params(params)
-
-
-class _StringFuncParser(object):
-    """
-    A class used to convert predefined strings into
-    _FuncInfo objects, or to directly obtain _FuncInfo
-    properties.
-
-    """
-
-    _funcs = {}
-    _funcs['linear'] = _FuncInfo(lambda x: x,
-                                 lambda x: x,
-                                 True)
-    _funcs['quadratic'] = _FuncInfo(np.square,
-                                    np.sqrt,
-                                    True)
-    _funcs['cubic'] = _FuncInfo(lambda x: x**3,
-                                lambda x: x**(1. / 3),
-                                True)
-    _funcs['sqrt'] = _FuncInfo(np.sqrt,
-                               np.square,
-                               True)
-    _funcs['cbrt'] = _FuncInfo(lambda x: x**(1. / 3),
-                               lambda x: x**3,
-                               True)
-    _funcs['log10'] = _FuncInfo(np.log10,
-                                lambda x: (10**(x)),
-                                False)
-    _funcs['log'] = _FuncInfo(np.log,
-                              np.exp,
-                              False)
-    _funcs['log2'] = _FuncInfo(np.log2,
-                               lambda x: (2**x),
-                               False)
-    _funcs['x**{p}'] = _FuncInfo(lambda x, p: x**p[0],
-                                 lambda x, p: x**(1. / p[0]),
-                                 True)
-    _funcs['root{p}(x)'] = _FuncInfo(lambda x, p: x**(1. / p[0]),
-                                     lambda x, p: x**p,
-                                     True)
-    _funcs['log{p}(x)'] = _FuncInfo(lambda x, p: (np.log(x) /
-                                                  np.log(p[0])),
-                                    lambda x, p: p[0]**(x),
-                                    False,
-                                    lambda p: p[0] > 0)
-    _funcs['log10(x+{p})'] = _FuncInfo(lambda x, p: np.log10(x + p[0]),
-                                       lambda x, p: 10**x - p[0],
-                                       lambda p: p[0] > 0)
-    _funcs['log(x+{p})'] = _FuncInfo(lambda x, p: np.log(x + p[0]),
-                                     lambda x, p: np.exp(x) - p[0],
-                                     lambda p: p[0] > 0)
-    _funcs['log{p}(x+{p})'] = _FuncInfo(lambda x, p: (np.log(x + p[1]) /
-                                                      np.log(p[0])),
-                                        lambda x, p: p[0]**(x) - p[1],
-                                        lambda p: p[1] > 0,
-                                        lambda p: p[0] > 0)
-
-    def __init__(self, str_func):
-        """
-        Parameters
-        ----------
-        str_func : string
-            String to be parsed.
-
-        """
-
-        if not isinstance(str_func, six.string_types):
-            raise ValueError("'%s' must be a string." % str_func)
-        self._str_func = six.text_type(str_func)
-        self._key, self._params = self._get_key_params()
-        self._func = self._parse_func()
-
-    def _parse_func(self):
-        """
-        Parses the parameters to build a new _FuncInfo object,
-        replacing the relevant parameters if necessary in the lambda
-        functions.
-
-        """
-
-        func = self._funcs[self._key]
-
-        if not self._params:
-            func = _FuncInfo(func.function, func.inverse,
-                             func.is_bounded_0_1())
-        else:
-            m = func.function
-            function = (lambda x, m=m: m(x, self._params))
-
-            m = func.inverse
-            inverse = (lambda x, m=m: m(x, self._params))
-
-            is_bounded_0_1 = func.is_bounded_0_1(self._params)
-
-            func = _FuncInfo(function, inverse,
-                             is_bounded_0_1)
-        return func
-
-    @property
-    def func_info(self):
-        """
-        Returns the _FuncInfo object.
-
-        """
-        return self._func
-
-    @property
-    def function(self):
-        """
-        Returns the callable for the direct function.
-
-        """
-        return self._func.function
-
-    @property
-    def inverse(self):
-        """
-        Returns the callable for the inverse function.
-
-        """
-        return self._func.inverse
-
-    @property
-    def is_bounded_0_1(self):
-        """
-        Returns a boolean indicating if the function is bounded
-        in the [0-1 interval].
-
-        """
-        return self._func.is_bounded_0_1()
-
-    def _get_key_params(self):
-        str_func = self._str_func
-        # Checking if it comes with parameters
-        regex = r'\{(.*?)\}'
-        params = re.findall(regex, str_func)
-
-        for i, param in enumerate(params):
-            try:
-                params[i] = float(param)
-            except ValueError:
-                raise ValueError("Parameter %i is '%s', which is "
-                                 "not a number." %
-                                 (i, param))
-
-        str_func = re.sub(regex, '{p}', str_func)
-
-        try:
-            func = self._funcs[str_func]
-        except (ValueError, KeyError):
-            raise ValueError("'%s' is an invalid string. The only strings "
-                             "recognized as functions are %s." %
-                             (str_func, list(self._funcs)))
-
-        # Checking that the parameters are valid
-        if not func.check_params(params):
-            raise ValueError("%s are invalid values for the parameters "
-                             "in %s." %
-                             (params, str_func))
-
-        return str_func, params
-
-
 def _topmost_artist(
         artists,
         _cached_max=functools.partial(max, key=operator.attrgetter("zorder"))):
 
@@ -503,64 +503,3 @@ def test_flatiter():
 
     assert 0 == next(it)
     assert 1 == next(it)
-
-
-class TestFuncParser(object):
-    x_test = np.linspace(0.01, 0.5, 3)
-    validstrings = ['linear', 'quadratic', 'cubic', 'sqrt', 'cbrt',
-                    'log', 'log10', 'log2', 'x**{1.5}', 'root{2.5}(x)',
-                    'log{2}(x)',
-                    'log(x+{0.5})', 'log10(x+{0.1})', 'log{2}(x+{0.1})',
-                    'log{2}(x+{0})']
-    results = [(lambda x: x),
-               np.square,
-               (lambda x: x**3),
-               np.sqrt,
-               (lambda x: x**(1. / 3)),
-               np.log,
-               np.log10,
-               np.log2,
-               (lambda x: x**1.5),
-               (lambda x: x**(1 / 2.5)),
-               (lambda x: np.log2(x)),
-               (lambda x: np.log(x + 0.5)),
-               (lambda x: np.log10(x + 0.1)),
-               (lambda x: np.log2(x + 0.1)),
-               (lambda x: np.log2(x))]
-
-    bounded_list = [True, True, True, True, True,
-                    False, False, False, True, True,
-                    False,
-                    True, True, True,
-                    False]
-
-    @pytest.mark.parametrize("string, func",
-                             zip(validstrings, results),
-                             ids=validstrings)
-    def test_values(self, string, func):
-        func_parser = cbook._StringFuncParser(string)
-        f = func_parser.function
-        assert_array_almost_equal(f(self.x_test), func(self.x_test))
-
-    @pytest.mark.parametrize("string", validstrings, ids=validstrings)
-    def test_inverse(self, string):
-        func_parser = cbook._StringFuncParser(string)
-        f = func_parser.func_info
-        fdir = f.function
-        finv = f.inverse
-        assert_array_almost_equal(finv(fdir(self.x_test)), self.x_test)
-
-    @pytest.mark.parametrize("string", validstrings, ids=validstrings)
-    def test_get_inverse(self, string):
-        func_parser = cbook._StringFuncParser(string)
-        finv1 = func_parser.inverse
-        finv2 = func_parser.func_info.inverse
-        assert_array_almost_equal(finv1(self.x_test), finv2(self.x_test))
-
-    @pytest.mark.parametrize("string, bounded",
-                             zip(validstrings, bounded_list),
-                             ids=validstrings)
-    def test_bounded(self, string, bounded):
-        func_parser = cbook._StringFuncParser(string)
-        b = func_parser.is_bounded_0_1
-        assert_array_equal(b, bounded)