Python numpy.isposinf函数代码示例

def assert_almost_equal_inf(x, y, decimal=6, msg=None):
    x = np.atleast_1d(x)
    y = np.atleast_1d(y)
    assert_equal(np.isposinf(x), np.isposinf(y))
    assert_equal(np.isneginf(x), np.isneginf(y))
    assert_equal(np.isnan(x), np.isnan(y))
    assert_almost_equal(x[np.isfinite(x)], y[np.isfinite(y)])

    def test_init_owa_inf(self):
        r"""Test of initialization and __init__ -- OWA.

        Method: An affordance to allow you to set OWA = +Infinity from a JSON
        specs-file is offered by OpticalSystem: if OWA is supplied as 0, it is
        set to +Infinity.  We instantiate OpticalSystem objects and verify that
        this is done.
        """
        for specs in [specs_default, specs_simple, specs_multi]:
            # the input dict is modified in-place -- so copy it
            our_specs = deepcopy(specs)
            our_specs['OWA'] = 0
            for syst in our_specs['starlightSuppressionSystems']:
                syst['OWA'] = 0
            optsys = self.fixture(**deepcopy(our_specs))
            self.assertTrue(np.isposinf(optsys.OWA.value))
            for syst in optsys.starlightSuppressionSystems:
                self.assertTrue(np.isposinf(syst['OWA'].value))
        # repeat, but allow the special value to propagate up
        for specs in [specs_default, specs_simple, specs_multi]:
            # the input dict is modified in-place -- so copy it
            our_specs = deepcopy(specs)
            for syst in our_specs['starlightSuppressionSystems']:
                syst['OWA'] = 0
            optsys = self.fixture(**deepcopy(our_specs))
            self.assertTrue(np.isposinf(optsys.OWA.value))

def non_matches(arr, val):
    '''
    Given a ndarray and an arbitrary 
    value, including np.nan, np.inf, etc.,
    return an ndarray that contains 
    only elements that are *not* equal 
    to val.  
    
    :param arr: n-dimensional numpy array
    :type arr: numpy.ndarray
    :param val: value, including special values numpy.nan, numpy.inf, numpy.neginf, etc.
    :type val: ANY.
    '''
    
    # Special value?
    if np.isfinite(val):
        # No, just normal value:
        return arr[arr != val]
    # Is special value, such as numpy.nan.
    # Create ndarray with True/False entries
    # that reflect which entries are not equal
    # to val:
    elif np.isnan(val):
        cond = np.logical_not(np.isnan(arr))
    elif np.isinf(val):
        cond = np.logical_not(np.isinf(arr))
    elif np.isneginf(val):
        cond = np.logical_not(np.isneginf(arr))
    elif np.isposinf(val):
        cond = np.logical_not(np.isposinf(arr))
        
    # Use the True/False ndarray as a mask
    # over arr:
    return arr[cond]

    def __add__(self, other):

        assert isinstance(other, ShapeFunction), "Can only add other shape function"

        assert self.name == other.name, "Cannot add shapes of different features"

        new_splits = self.splits.copy()
        new_vals = self.values.copy()

        for split, val in zip(other.splits, other.values):
            idx = np.searchsorted(new_splits, split, side='right')
            new_val = val
            if split in new_splits:
                idx_2 = np.argwhere(new_splits == split)
                new_vals[idx_2] = new_vals[idx_2] + new_val
            elif idx == len(new_splits) and (~np.isposinf(split)):
                new_splits = np.append(new_splits, split)
                new_vals = np.append(new_vals, new_val)
            elif np.isposinf(split):
                new_vals[-1] = new_vals[-1] + new_val
            else:
                new_splits = np.insert(new_splits, idx, split)
                new_vals = np.insert(new_vals, idx, new_val)

        return ShapeFunction(new_splits, new_vals, self.name)

def imagesDiffer(imageArr1, imageArr2, skipMaskArr=None, rtol=1.0e-05, atol=1e-08):
    """Compare the pixels of two image arrays; return True if close, False otherwise
    
    Inputs:
    - image1: first image to compare
    - image2: second image to compare
    - skipMaskArr: pixels to ignore; nonzero values are skipped
    - rtol: relative tolerance (see below)
    - atol: absolute tolerance (see below)
    
    rtol and atol are positive, typically very small numbers.
    The relative difference (rtol * abs(b)) and the absolute difference "atol" are added together
    to compare against the absolute difference between "a" and "b".
    
    Return a string describing the error if the images differ significantly, an empty string otherwise
    """
    retStrs = []
    if skipMaskArr != None:
        maskedArr1 = numpy.ma.array(imageArr1, copy=False, mask = skipMaskArr)
        maskedArr2 = numpy.ma.array(imageArr2, copy=False, mask = skipMaskArr)
        filledArr1 = maskedArr1.filled(0.0)
        filledArr2 = maskedArr2.filled(0.0)
    else:
        filledArr1 = imageArr1
        filledArr2 = imageArr2

    nan1 = numpy.isnan(filledArr1)
    nan2 = numpy.isnan(filledArr2)
    if numpy.any(nan1 != nan2):
        retStrs.append("NaNs differ")

    posinf1 = numpy.isposinf(filledArr1)
    posinf2 = numpy.isposinf(filledArr2)
    if numpy.any(posinf1 != posinf2):
        retStrs.append("+infs differ")

    neginf1 = numpy.isneginf(filledArr1)
    neginf2 = numpy.isneginf(filledArr2)
    if numpy.any(neginf1 != neginf2):
        retStrs.append("-infs differ")

    # compare values that should be comparable (are neither infinite, nan nor masked)
    valSkipMaskArr = nan1 | nan2 | posinf1 | posinf2 | neginf1 | neginf2
    if skipMaskArr != None:
        valSkipMaskArr |= skipMaskArr
    valMaskedArr1 = numpy.ma.array(imageArr1, copy=False, mask = valSkipMaskArr)
    valMaskedArr2 = numpy.ma.array(imageArr2, copy=False, mask = valSkipMaskArr)
    valFilledArr1 = valMaskedArr1.filled(0.0)
    valFilledArr2 = valMaskedArr2.filled(0.0)
    
    if not numpy.allclose(valFilledArr1, valFilledArr2, rtol=rtol, atol=atol):
        errArr = numpy.abs(valFilledArr1 - valFilledArr2)
        maxErr = errArr.max()
        maxPosInd = numpy.where(errArr==maxErr)
        maxPosTuple = (maxPosInd[1][0], maxPosInd[0][0])
        errStr = "maxDiff=%s at position %s; value=%s vs. %s" % \
            (maxErr, maxPosTuple, valFilledArr1[maxPosInd][0], valFilledArr2[maxPosInd][0])
        retStrs.insert(0, errStr)
    return "; ".join(retStrs)

def _generate_colorbar_ticks_label(
    data_transform=False, colorbarlabel=None, trans_base_list=None, forcelabel=None, plotlev=None, plotlab=None
):
    """
    Return (colorbar_ticks,colorbar_labels)
    """
    # data_transform==True and levels!=None
    if data_transform == True:
        if colorbarlabel != None:
            colorbarlabel = pb.iteflat(colorbarlabel)
            transformed_colorbarlabel_ticks, x, y, trans_base_list = mathex.plot_array_transg(
                colorbarlabel, trans_base_list, copy=True
            )

        # Note if/else blocks are organized in 1st tire by check if the two
        # ends are -inf/inf and 2nd tire by check if colorbarlabel is None
        if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):
            if colorbarlabel != None:
                ftuple = (transformed_colorbarlabel_ticks, colorbarlabel)
            else:
                ftuple = (plotlev, plotlab[1:-1])
        elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):
            raise ValueError("It's strange to set only side as infitive")
        else:
            if colorbarlabel != None:
                ftuple = (transformed_colorbarlabel_ticks, colorbarlabel)
            else:
                ftuple = (plotlev, plotlab)

    # data_transform==False
    else:
        if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):
            # if colorbarlabel is forced, then ticks and ticklabels will be forced.
            if colorbarlabel != None:
                ftuple = (colorbarlabel, colorbarlabel)
            # This by default will be done, it's maintained here only for clarity.
            else:
                ftuple = (plotlab[1:-1], plotlab[1:-1])
        elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):
            raise ValueError("It's strange to set only side as infitive")
        else:
            if colorbarlabel != None:
                ftuple = (colorbarlabel, colorbarlabel)
            else:
                ftuple = (plotlab, plotlab)

    ftuple = list(ftuple)
    if forcelabel != None:
        if len(forcelabel) != len(ftuple[1]):
            raise ValueError(
                """the length of the forcelabel and the
                length of labeled ticks is not equal!"""
            )
        else:
            ftuple[1] = forcelabel

    return ftuple

def _transform_data(pdata, levels, data_transform):
    """
    Return [pdata,plotlev,plotlab,extend,trans_base_list];
    if data_transform == False, trans_base_list = None.

    Notes:
    ------
    pdata: data used for contourf plotting.
    plotlev: the levels used in contourf plotting.
    extend: the value for parameter extand in contourf.
    trans_base_list: cf. mathex.plot_array_transg
    """
    if levels == None:
        ftuple = (pdata, None, None, "neither")
        if data_transform == True:
            raise Warning("Strange levels is None but data_transform is True")
    else:
        if data_transform == True:
            # make the data transform before plotting.
            pdata_trans, plotlev, plotlab, trans_base_list = mathex.plot_array_transg(pdata, levels, copy=True)
            if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):
                ftuple = (pdata_trans, plotlev[1:-1], plotlab, "both")
            elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):
                raise ValueError(
                    """only one extreme set as infinitive, please
                    set both as infinitive if arrow colorbar is wanted."""
                )
            else:
                ftuple = (pdata_trans, plotlev, plotlab, "neither")
        # data_transform==False
        else:
            plotlev = pb.iteflat(levels)
            plotlab = pb.iteflat(levels)
            if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):
                # here the levels would be like [np.NINF,1,2,3,np.PINF]
                # in following contourf, all values <1 and all values>3 will be
                # automatically plotted in the color of two arrows.
                # easy to see in this example:
                # a=np.tile(np.arange(10),10).reshape(10,10);
                # fig,ax=g.Create_1Axes();
                # cs=ax.contourf(a,levels=np.arange(2,7),extend='both');
                # plt.colorbar(cs)
                ftuple = (pdata, plotlev[1:-1], plotlab, "both")
            elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):
                raise ValueError(
                    """only one extreme set as infinitive, please
                    set both as infinitive if arrow colorbar is wanted."""
                )
            else:
                ftuple = (pdata, plotlev, plotlab, "neither")
    datalist = list(ftuple)

    if data_transform == True:
        datalist.append(trans_base_list)
    else:
        datalist.append(None)
    return datalist

def _diagnose(self):

    # Update log.
    self.logger.debug("diagnose: data: shape: " + str(self.data.shape))
    self.logger.debug("diagnose: data: dtype: " + str(self.data.dtype))
    self.logger.debug("diagnose: data: size: %.2fMB", self.data.nbytes * 9.53674e-7)
    self.logger.debug("diagnose: data: nans: " + str(np.sum(np.isnan(self.data))))
    self.logger.debug("diagnose: data: -inf: " + str(np.sum(np.isneginf(self.data))))
    self.logger.debug("diagnose: data: +inf: " + str(np.sum(np.isposinf(self.data))))
    self.logger.debug("diagnose: data: positives: " + str(np.sum(self.data > 0)))
    self.logger.debug("diagnose: data: negatives: " + str(np.sum(self.data < 0)))
    self.logger.debug("diagnose: data: mean: " + str(np.mean(self.data)))
    self.logger.debug("diagnose: data: min: " + str(np.min(self.data)))
    self.logger.debug("diagnose: data: max: " + str(np.max(self.data)))

    self.logger.debug("diagnose: data_white: shape: " + str(self.data_white.shape))
    self.logger.debug("diagnose: data_white: dtype: " + str(self.data_white.dtype))
    self.logger.debug("diagnose: data_white: size: %.2fMB", self.data_white.nbytes * 9.53674e-7)
    self.logger.debug("diagnose: data_white: nans: " + str(np.sum(np.isnan(self.data_white))))
    self.logger.debug("diagnose: data_white: -inf: " + str(np.sum(np.isneginf(self.data_white))))
    self.logger.debug("diagnose: data_white: +inf: " + str(np.sum(np.isposinf(self.data_white))))
    self.logger.debug("diagnose: data_white: positives: " + str(np.sum(self.data_white > 0)))
    self.logger.debug("diagnose: data_white: negatives: " + str(np.sum(self.data_white < 0)))
    self.logger.debug("diagnose: data_white: mean: " + str(np.mean(self.data_white)))
    self.logger.debug("diagnose: data_white: min: " + str(np.min(self.data_white)))
    self.logger.debug("diagnose: data_white: max: " + str(np.max(self.data_white)))

    self.logger.debug("diagnose: data_dark: shape: " + str(self.data_dark.shape))
    self.logger.debug("diagnose: data_dark: dtype: " + str(self.data_dark.dtype))
    self.logger.debug("diagnose: data_dark: size: %.2fMB", self.data_dark.nbytes * 9.53674e-7)
    self.logger.debug("diagnose: data_dark: nans: " + str(np.sum(np.isnan(self.data_dark))))
    self.logger.debug("diagnose: data_dark: -inf: " + str(np.sum(np.isneginf(self.data_dark))))
    self.logger.debug("diagnose: data_dark: +inf: " + str(np.sum(np.isposinf(self.data_dark))))
    self.logger.debug("diagnose: data_dark: positives: " + str(np.sum(self.data_dark > 0)))
    self.logger.debug("diagnose: data_dark: negatives: " + str(np.sum(self.data_dark < 0)))
    self.logger.debug("diagnose: data_dark: mean: " + str(np.mean(self.data_dark)))
    self.logger.debug("diagnose: data_dark: min: " + str(np.min(self.data_dark)))
    self.logger.debug("diagnose: data_dark: max: " + str(np.max(self.data_dark)))

    self.logger.debug("diagnose: theta: shape: " + str(self.theta.shape))
    self.logger.debug("diagnose: theta: dtype: " + str(self.theta.dtype))
    self.logger.debug("diagnose: theta: size: %.2fMB", self.theta.nbytes * 9.53674e-7)
    self.logger.debug("diagnose: theta: nans: " + str(np.sum(np.isnan(self.theta))))
    self.logger.debug("diagnose: theta: -inf: " + str(np.sum(np.isneginf(self.theta))))
    self.logger.debug("diagnose: theta: +inf: " + str(np.sum(np.isposinf(self.theta))))
    self.logger.debug("diagnose: theta: positives: " + str(np.sum(self.theta > 0)))
    self.logger.debug("diagnose: theta: negatives: " + str(np.sum(self.theta < 0)))
    self.logger.debug("diagnose: theta: mean: " + str(np.mean(self.theta)))
    self.logger.debug("diagnose: theta: min: " + str(np.min(self.theta)))
    self.logger.debug("diagnose: theta: max: " + str(np.max(self.theta)))

    self.logger.info("diagnose [ok]")

    def test_neginf(self):
        arr =np.empty(100)
        arr[:] = -np.inf
        for np_func, acml_func in self.vector_funcs:
            np_out = np_func(arr)
            acml_out = acml_func(arr)

            equal_nan = np.isnan(np_out) == np.isnan(acml_out)
            equal_posinf = np.isposinf(np_out) == np.isposinf(acml_out)
            equal_neginf = np.isneginf(np_out) == np.isneginf(acml_out)
            self.assertTrue( np.alltrue(equal_nan), msg="NaN-test failed for %s" % acml_func)
            self.assertTrue( np.alltrue(equal_posinf), msg="posinf-test failed for %s" % acml_func)
            self.assertTrue( np.alltrue(equal_neginf), msg="neginf-test failed for %s" % acml_func)

def set_logp_to_neg_inf(X, logp, bounds):
    """Set `logp` to negative infinity when `X` is outside the allowed bounds.

    # Arguments
        X: tensorflow.Tensor
            The variable to apply the bounds to
        logp: tensorflow.Tensor
            The log probability corrosponding to `X`
        bounds: list of `Region` objects
            The regions corrosponding to allowed regions of `X`

    # Returns
        logp: tensorflow.Tensor
            The newly bounded log probability
    """
    conditions = []
    for l, u in bounds:
        lower_is_neg_inf = not isinstance(l, tf.Tensor) and np.isneginf(l)
        upper_is_pos_inf = not isinstance(u, tf.Tensor) and np.isposinf(u)

        if not lower_is_neg_inf and upper_is_pos_inf:
            conditions.append(tf.greater(X, l))
        elif lower_is_neg_inf and not upper_is_pos_inf:
            conditions.append(tf.less(X, u))
        elif not (lower_is_neg_inf or upper_is_pos_inf):
            conditions.append(tf.logical_and(tf.greater(X, l), tf.less(X, u)))

    if len(conditions) > 0:
        is_inside_bounds = conditions[0]
        for condition in conditions[1:]:
            is_inside_bounds = tf.logical_or(is_inside_bounds, condition)

        logp = tf.select(is_inside_bounds, logp, tf.fill(tf.shape(X), config.dtype(-np.inf)))

    return logp

def traverse_data(obj, is_numpy=is_numpy, use_numpy=True):
    """ Recursively traverse an object until a flat list is found.

    If NumPy is available, the flat list is converted to a numpy array
    and passed to transform_array() to handle ``nan``, ``inf``, and
    ``-inf``.

    Otherwise, iterate through all items, converting non-JSON items

    Args:
        obj (list) : a list of values or lists
        is_numpy (bool, optional): Whether NumPy is availanble
            (default: True if NumPy is importable)
        use_numpy (bool, optional) toggle NumPy as a dependency for testing
            This argument is only useful for testing (default: True)
    """
    is_numpy = is_numpy and use_numpy
    if is_numpy and all(isinstance(el, np.ndarray) for el in obj):
        return [transform_array(el) for el in obj]
    obj_copy = []
    for item in obj:
        if isinstance(item, (list, tuple)):
            obj_copy.append(traverse_data(item))
        elif isinstance(item, float):
            if np.isnan(item):
                item = 'NaN'
            elif np.isposinf(item):
                item = 'Infinity'
            elif np.isneginf(item):
                item = '-Infinity'
            obj_copy.append(item)
        else:
            obj_copy.append(item)
    return obj_copy

def check_kurt_expect(distfn, arg, m, v, k, msg):
    if np.isfinite(k):
        m4e = distfn.expect(lambda x: np.power(x-m, 4), arg)
        npt.assert_allclose(m4e, (k + 3.) * np.power(v, 2), atol=1e-5, rtol=1e-5,
                err_msg=msg + ' - kurtosis')
    elif not np.isposinf(k):
        npt.assert_(np.isnan(k))

def merciless_print(i, node, fn):
    """Debugging theano. Prints inputs and outputs at every point.
    In case NaN, Inf or -Inf is detected, fires up the pdb debugger."""
    print ''
    print '-------------------------------------------------------'
    print 'Node %s' % str(i)
    theano.printing.debugprint(node)
    print 'Inputs : %s' % [input for input in fn.inputs]
    print 'Outputs: %s' % [output for output in fn.outputs]
    print 'Node:'
    for output in fn.outputs:
        try:
            if numpy.isnan(output[0]).any():
                print '*** NaN detected ***'
                theano.printing.debugprint(node)
                print 'Inputs : %s' % [input[0] for input in fn.inputs]
                print 'Outputs: %s' % [output[0] for output in fn.outputs]
                pdb.set_trace()
                raise ValueError('Found NaN in computation!')
            if numpy.isposinf(output[0]).any() or numpy.isneginf(output[0]).any():
                print '*** Inf detected ***'
                theano.printing.debugprint(node)
                print 'Inputs : %s' % [input[0] for input in fn.inputs]
                print 'Outputs: %s' % [output[0] for output in fn.outputs]
                pdb.set_trace()
                raise ValueError('Found Inf in computation!')
        except TypeError:
            logging.debug('Couldn\'t check node for NaN/Inf: {0}'.format(node))

def encode_fill_value(v, dtype):
    # early out
    if v is None:
        return v
    if dtype.kind == 'f':
        if np.isnan(v):
            return 'NaN'
        elif np.isposinf(v):
            return 'Infinity'
        elif np.isneginf(v):
            return '-Infinity'
        else:
            return float(v)
    elif dtype.kind in 'ui':
        return int(v)
    elif dtype.kind == 'b':
        return bool(v)
    elif dtype.kind in 'SV':
        v = base64.standard_b64encode(v)
        if not PY2:  # pragma: py2 no cover
            v = str(v, 'ascii')
        return v
    elif dtype.kind == 'U':
        return v
    elif dtype.kind in 'mM':
        return int(v.view('u8'))
    else:
        return v

    def _update_parameters(self):
        """
        Update parameters of the acquisition required to evaluate the function. In particular:
            * Sample representer points repr_points
            * Compute their log values repr_points_log
            * Compute belief locations logP
        """
        self.repr_points, self.repr_points_log = self.sampler.get_samples(self.num_repr_points, self.proposal_function, self.burn_in_steps)

        if np.any(np.isnan(self.repr_points_log)) or np.any(np.isposinf(self.repr_points_log)):
            raise RuntimeError("Sampler generated representer points with invalid log values: {}".format(self.repr_points_log))

        # Removing representer points that have 0 probability of being the minimum (corresponding to log probability being minus infinity)
        idx_to_remove = np.where(np.isneginf(self.repr_points_log))[0]
        if len(idx_to_remove) > 0:
            idx = list(set(range(self.num_repr_points)) - set(idx_to_remove))
            self.repr_points = self.repr_points[idx, :]
            self.repr_points_log = self.repr_points_log[idx]

        # We predict with the noise as we need to make sure that var is indeed positive definite.
        mu, _ = self.model.predict(self.repr_points)
        # we need a vector
        mu = np.ndarray.flatten(mu)
        var = self.model.predict_covariance(self.repr_points)
        
        self.logP, self.dlogPdMu, self.dlogPdSigma, self.dlogPdMudMu = epmgp.joint_min(mu, var, with_derivatives=True)
        # add a second dimension to the array
        self.logP = np.reshape(self.logP, (self.logP.shape[0], 1))