Python numpy.equal() Examples

def test_error_message(self):
        with pytest.raises(AssertionError) as exc_info:
            self._assert_func(np.array([1, 2]), np.array([[1, 2]]))
        msg = str(exc_info.value)
        msg2 = msg.replace("shapes (2L,), (1L, 2L)", "shapes (2,), (1, 2)")
        msg_reference = textwrap.dedent("""\

        Arrays are not equal

        (shapes (2,), (1, 2) mismatch)
         x: array([1, 2])
         y: array([[1, 2]])""")

        try:
            assert_equal(msg, msg_reference)
        except AssertionError:
            assert_equal(msg2, msg_reference) 

def getSpotOpts(self, recs, scale=1.0):
        if recs.ndim == 0:
            rec = recs
            symbol = rec['symbol']
            if symbol is None:
                symbol = self.opts['symbol']
            size = rec['size']
            if size < 0:
                size = self.opts['size']
            pen = rec['pen']
            if pen is None:
                pen = self.opts['pen']
            brush = rec['brush']
            if brush is None:
                brush = self.opts['brush']
            return (symbol, size*scale, fn.mkPen(pen), fn.mkBrush(brush))
        else:
            recs = recs.copy()
            recs['symbol'][np.equal(recs['symbol'], None)] = self.opts['symbol']
            recs['size'][np.equal(recs['size'], -1)] = self.opts['size']
            recs['size'] *= scale
            recs['pen'][np.equal(recs['pen'], None)] = fn.mkPen(self.opts['pen'])
            recs['brush'][np.equal(recs['brush'], None)] = fn.mkBrush(self.opts['brush'])
            return recs 

def updateSpots(self, dataSet=None):
        if dataSet is None:
            dataSet = self.data

        invalidate = False
        if self.opts['pxMode']:
            mask = np.equal(dataSet['sourceRect'], None)
            if np.any(mask):
                invalidate = True
                opts = self.getSpotOpts(dataSet[mask])
                sourceRect = self.fragmentAtlas.getSymbolCoords(opts)
                dataSet['sourceRect'][mask] = sourceRect

            self.fragmentAtlas.getAtlas() # generate atlas so source widths are available.

            dataSet['width'] = np.array(list(imap(QtCore.QRectF.width, dataSet['sourceRect'])))/2
            dataSet['targetRect'] = None
            self._maxSpotPxWidth = self.fragmentAtlas.max_width
        else:
            self._maxSpotWidth = 0
            self._maxSpotPxWidth = 0
            self.measureSpotSizes(dataSet)

        if invalidate:
            self.invalidate() 

def render(self):
    """Derive an `Observation` from this `BaseObservationRenderer`'s "canvas".

    Reminders: the values in the returned `Observation` should be accessed in
    a *read-only* manner exclusively; furthermore, if any
    `BaseObservationRenderer` method is called after `render()`, the contents
    of the `Observation` returned in that `render()` call are *undefined*
    (i.e. not guaranteed to be anything---they could be blank, random garbage,
    whatever).

    Returns:
      An `Observation` whose data members are derived from the information
      presented to this `BaseObservationRenderer` since the last call to its
      `clear()` method.
    """
    for character, layer in six.iteritems(self._layers):
      np.equal(self._board, ord(character), out=layer)
    return Observation(board=self._board, layers=self._layers) 

def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
    """
    Returns true if all components of a and b are equal to given tolerances.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.  The relative error rtol should
    be positive and << 1.0 The absolute error atol comes into play for
    those elements of b that are very small or zero; it says how small a
    must be also.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
    return d.ravel() 

def zdivide(a, b, null=0):
    '''
    zdivide(a, b) returns the quotient a / b as a numpy array object. Unlike numpy's divide function
      or a/b syntax, zdivide will thread over the earliest dimension possible; thus if a.shape is
      (4,2) and b.shape is 4, zdivide(a,b) is a equivalent to [ai*zinv(bi) for (ai,bi) in zip(a,b)].

    The optional argument null (default: 0) may be given to specify that zeros in the arary b should
    instead be replaced with the given value in the result. Note that if this value is not equal to
    0, then any sparse array passed as argument b must be reified.

    The zdivide function never raises an error due to divide-by-zero; if you desire this behavior,
    use the divide function instead.

    Note that zdivide(a,b, null=z) is not quite equivalent to a*zinv(b, null=z) unless z is 0; if z
    is not zero, then the same elements that are zet to z in zinv(b, null=z) are set to z in the
    result of zdivide(a,b, null=z) rather than the equivalent element of a times z.
    '''
    (a,b) = unbroadcast(a,b)
    return czdivide(a,b, null=null) 

def test_spatial_to_raster(fp, env):
    if env < fp._significant_min:
        pytest.skip()
    rng = np.random.RandomState(42)
    eps = np.abs(fp.coords).max() * 10 ** -buzz.env.significant

    xy = np.dstack(fp.meshgrid_spatial)
    rxy = np.dstack(fp.meshgrid_raster)
    res = np.equal(
        rxy, fp.spatial_to_raster(xy),
    )
    assert np.all(res)
    res = np.equal(
        rxy, fp.spatial_to_raster(xy + (rng.rand(*xy.shape) * 2 - 1) * eps * LESS_ERROR),
    )
    assert np.all(res)
    res = np.equal(
        rxy, fp.spatial_to_raster(xy + (rng.rand(*xy.shape) * 2 - 1) * eps * MORE_ERROR),
    )
    assert not np.all(res) 

def almost(a, b, decimal=6, fill_value=True):
    """
    Returns True if a and b are equal up to decimal places.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
    return d.ravel() 

def less(x1, x2):
    """
    Return (x1 < x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, greater
    """
    return compare_chararrays(x1, x2, '<', True) 

def test_equal():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Equal", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "equal_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.equal(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

def greater(x1, x2):
    """
    Return (x1 > x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, less
    """
    return compare_chararrays(x1, x2, '>', True) 

def less_equal(x1, x2):
    """
    Return (x1 <= x2) element-wise.

    Unlike `numpy.less_equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, greater, less
    """
    return compare_chararrays(x1, x2, '<=', True) 

def greater_equal(x1, x2):
    """
    Return (x1 >= x2) element-wise.

    Unlike `numpy.greater_equal`, this comparison is performed by
    first stripping whitespace characters from the end of the string.
    This behavior is provided for backward-compatibility with
    numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '>=', True) 

def equal(x1, x2):
    """
    Return (x1 == x2) element-wise.

    Unlike `numpy.equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    not_equal, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '==', True) 

def test_subclass_that_overrides_eq(self):
        # While we cannot guarantee testing functions will always work for
        # subclasses, the tests should ideally rely only on subclasses having
        # comparison operators, not on them being able to store booleans
        # (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
        class MyArray(np.ndarray):
            def __eq__(self, other):
                return bool(np.equal(self, other).all())

            def __ne__(self, other):
                return not self == other

        a = np.array([1., 2.]).view(MyArray)
        b = np.array([2., 3.]).view(MyArray)
        assert_(type(a == a), bool)
        assert_(a == a)
        assert_(a != b)
        self._test_equal(a, a)
        self._test_not_equal(a, b)
        self._test_not_equal(b, a) 

def test_ignore_object_identity_in_equal(self):
        # Check error raised when comparing identical objects whose comparison
        # is not a simple boolean, e.g., arrays that are compared elementwise.
        a = np.array([np.array([1, 2, 3]), None], dtype=object)
        assert_raises(ValueError, np.equal, a, a)

        # Check error raised when comparing identical non-comparable objects.
        class FunkyType(object):
            def __eq__(self, other):
                raise TypeError("I won't compare")

        a = np.array([FunkyType()])
        assert_raises(TypeError, np.equal, a, a)

        # Check identity doesn't override comparison mismatch.
        a = np.array([np.nan], dtype=object)
        assert_equal(np.equal(a, a), [False]) 

def feedforward(self, X):
        output = super().feedforward(X)

        for t in range(1, self.time + 1):
            time_gate = np.equal(t % self.tick_array, 0.)
            Z = np.concatenate((self.inputs[t - 1], output), axis=-1)
            gated_W = self.weights * time_gate[None, :]
            gated_b = self.biases * time_gate
            output = self.activation.forward(Z.dot(gated_W) + gated_b)

            self.Zs.append(Z)
            self.gates.append([time_gate, gated_W])
            self.cache.append(output)

        if self.return_seq:
            self.output = np.stack(self.cache, axis=1)
        else:
            self.output = self.cache[-1]

        return self.output 

def almost(a, b, decimal=6, fill_value=True):
    """
    Returns True if a and b are equal up to decimal places.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
    return d.ravel() 

def VOCap(rec,prec):

    mpre = np.zeros([1,2+len(prec)])
    mpre[0,1:len(prec)+1] = prec
    mrec = np.zeros([1,2+len(rec)])
    mrec[0,1:len(rec)+1] = rec
    mrec[0,len(rec)+1] = 1.0

    for i in range(mpre.size-2,-1,-1):
        mpre[0,i] = max(mpre[0,i],mpre[0,i+1])

    i = np.argwhere( ~np.equal( mrec[0,1:], mrec[0,:mrec.shape[1]-1]) )+1
    i = i.flatten()

    # compute area under the curve
    ap = np.sum( np.multiply( np.subtract( mrec[0,i], mrec[0,i-1]), mpre[0,i] ) )

    return ap 

def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
    """
    Returns true if all components of a and b are equal to given tolerances.

    If fill_value is True, masked values considered equal. Otherwise,
    masked values are considered unequal.  The relative error rtol should
    be positive and << 1.0 The absolute error atol comes into play for
    those elements of b that are very small or zero; it says how small a
    must be also.

    """
    m = mask_or(getmask(a), getmask(b))
    d1 = filled(a)
    d2 = filled(b)
    if d1.dtype.char == "O" or d2.dtype.char == "O":
        return np.equal(d1, d2).ravel()
    x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
    y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
    d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
    return d.ravel() 

def test_array_rank1_eq(self):
        """Test two equal array of rank 1 are found equal."""
        a = np.array([1, 2])
        b = np.array([1, 2])

        self._test_equal(a, b) 

def test_array_diffshape(self):
        """Test two arrays with different shapes are found not equal."""
        a = np.array([1, 2])
        b = np.array([[1, 2], [1, 2]])

        self._test_not_equal(a, b) 

def test_basic_ufuncs(self):
        # Test various functions such as sin, cos.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
        assert_equal(np.cos(x), cos(xm))
        assert_equal(np.cosh(x), cosh(xm))
        assert_equal(np.sin(x), sin(xm))
        assert_equal(np.sinh(x), sinh(xm))
        assert_equal(np.tan(x), tan(xm))
        assert_equal(np.tanh(x), tanh(xm))
        assert_equal(np.sqrt(abs(x)), sqrt(xm))
        assert_equal(np.log(abs(x)), log(xm))
        assert_equal(np.log10(abs(x)), log10(xm))
        assert_equal(np.exp(x), exp(xm))
        assert_equal(np.arcsin(z), arcsin(zm))
        assert_equal(np.arccos(z), arccos(zm))
        assert_equal(np.arctan(z), arctan(zm))
        assert_equal(np.arctan2(x, y), arctan2(xm, ym))
        assert_equal(np.absolute(x), absolute(xm))
        assert_equal(np.angle(x + 1j*y), angle(xm + 1j*ym))
        assert_equal(np.angle(x + 1j*y, deg=True), angle(xm + 1j*ym, deg=True))
        assert_equal(np.equal(x, y), equal(xm, ym))
        assert_equal(np.not_equal(x, y), not_equal(xm, ym))
        assert_equal(np.less(x, y), less(xm, ym))
        assert_equal(np.greater(x, y), greater(xm, ym))
        assert_equal(np.less_equal(x, y), less_equal(xm, ym))
        assert_equal(np.greater_equal(x, y), greater_equal(xm, ym))
        assert_equal(np.conjugate(x), conjugate(xm)) 

def test_masked_where_condition(self):
        # Tests masking functions.
        x = array([1., 2., 3., 4., 5.])
        x[2] = masked
        assert_equal(masked_where(greater(x, 2), x), masked_greater(x, 2))
        assert_equal(masked_where(greater_equal(x, 2), x),
                     masked_greater_equal(x, 2))
        assert_equal(masked_where(less(x, 2), x), masked_less(x, 2))
        assert_equal(masked_where(less_equal(x, 2), x),
                     masked_less_equal(x, 2))
        assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
        assert_equal(masked_where(equal(x, 2), x), masked_equal(x, 2))
        assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
        assert_equal(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
                     [99, 99, 3, 4, 5]) 

def test_string_arrays(self):
        """Test two arrays with different shapes are found not equal."""
        a = np.array(['floupi', 'floupa'])
        b = np.array(['floupi', 'floupa'])

        self._test_equal(a, b)

        c = np.array(['floupipi', 'floupa'])

        self._test_not_equal(c, b) 

def test_array_rank2_eq(self):
        """Test two equal array of rank 2 are found equal."""
        a = np.array([[1, 2], [3, 4]])
        b = np.array([[1, 2], [3, 4]])

        self._test_equal(a, b) 

def assert_array_approx_equal(x, y, decimal=6, err_msg='', verbose=True):
    """
    Checks the equality of two masked arrays, up to given number odecimals.

    The equality is checked elementwise.

    """
    def compare(x, y):
        "Returns the result of the loose comparison between x and y)."
        return approx(x, y, rtol=10. ** -decimal)
    assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
                         header='Arrays are not almost equal') 

def test_build_err_msg_defaults(self):
        x = np.array([1.00001, 2.00002, 3.00003])
        y = np.array([1.00002, 2.00003, 3.00004])
        err_msg = 'There is a mismatch'

        a = build_err_msg([x, y], err_msg)
        b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
             '1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '
             '2.00003, 3.00004])')
        assert_equal(a, b) 

def test_build_err_msg_custom_names(self):
        x = np.array([1.00001, 2.00002, 3.00003])
        y = np.array([1.00002, 2.00003, 3.00004])
        err_msg = 'There is a mismatch'

        a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))
        b = ('\nItems are not equal: There is a mismatch\n FOO: array(['
             '1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '
             '3.00004])')
        assert_equal(a, b) 

def test_testUfuncRegression(self):
        # Tests new ufuncs on MaskedArrays.
        for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                  'sin', 'cos', 'tan',
                  'arcsin', 'arccos', 'arctan',
                  'sinh', 'cosh', 'tanh',
                  'arcsinh',
                  'arccosh',
                  'arctanh',
                  'absolute', 'fabs', 'negative',
                  'floor', 'ceil',
                  'logical_not',
                  'add', 'subtract', 'multiply',
                  'divide', 'true_divide', 'floor_divide',
                  'remainder', 'fmod', 'hypot', 'arctan2',
                  'equal', 'not_equal', 'less_equal', 'greater_equal',
                  'less', 'greater',
                  'logical_and', 'logical_or', 'logical_xor',
                  ]:
            try:
                uf = getattr(umath, f)
            except AttributeError:
                uf = getattr(fromnumeric, f)
            mf = getattr(numpy.ma.core, f)
            args = self.d[:uf.nin]
            ur = uf(*args)
            mr = mf(*args)
            assert_equal(ur.filled(0), mr.filled(0), f)
            assert_mask_equal(ur.mask, mr.mask, err_msg=f)