Python numpy.isfinite() 使用实例

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Example 1

def xyz_array_to_pointcloud2(points, stamp=None, frame_id=None):
    '''
    Create a sensor_msgs.PointCloud2 from an array
    of points.
    '''
    msg = PointCloud2()
    if stamp:
        msg.header.stamp = stamp
    if frame_id:
        msg.header.frame_id = frame_id
    if len(points.shape) == 3:
        msg.height = points.shape[1]
        msg.width = points.shape[0]
    else:
        msg.height = 1
        msg.width = len(points)
    msg.fields = [
        PointField('x', 0, PointField.FLOAT32, 1),
        PointField('y', 4, PointField.FLOAT32, 1),
        PointField('z', 8, PointField.FLOAT32, 1)]
    msg.is_bigendian = False
    msg.point_step = 12
    msg.row_step = 12*points.shape[0]
    msg.is_dense = int(np.isfinite(points).all())
    msg.data = np.asarray(points, np.float32).tostring()

    return msg 

Example 2

def check_stoplimit_prices(price, label):
    """
    Check to make sure the stop/limit prices are reasonable and raise
    a BadOrderParameters exception if not.
    """
    try:
        if not isfinite(price):
            raise BadOrderParameters(
                msg="""Attempted to place an order with a {} price
                of {}.""".format(label, price)
            )
    # This catches arbitrary objects
    except TypeError:
        raise BadOrderParameters(
            msg="""Attempted to place an order with a {} price
            of {}.""".format(label, type(price))
        )

    if price < 0:
        raise BadOrderParameters(
            msg="""Can't place a {} order
             with a negative price.""".format(label)
        ) 

Example 3

def get_xyz_points(cloud_array, remove_nans=True):
    '''
    Pulls out x, y, and z columns from the cloud recordarray, and returns a 3xN matrix.
    '''
    # remove crap points
    if remove_nans:
        mask = np.isfinite(cloud_array['x']) & np.isfinite(cloud_array['y']) & np.isfinite(cloud_array['z'])
        cloud_array = cloud_array[mask]
    
    # pull out x, y, and z values
    points = np.zeros(list(cloud_array.shape) + [3], dtype=np.float)
    points[...,0] = cloud_array['x']
    points[...,1] = cloud_array['y']
    points[...,2] = cloud_array['z']

    return points 

Example 4

def _build_gmm(self, data): 
        """
        Build gmm from data
        """
        st = time.time()

        self.gmm = GMM(n_components=self.K, covariance_type='diag')
        self.gmm.fit(data)

        # Setup codebook for closest center lookup
        self.codebook = self.gmm.means_

        print 'Vocab construction from data %s (%s KB, %s) => GMM %s took %5.3f s' % \
            (data.shape, data.nbytes / 1024, data.dtype, self.gmm.means_.shape, time.time() - st)
        print 'GMM: %s' % ('GOOD' if np.isfinite(self.gmm.means_).all() else 'BAD')

        # Save codebook, and index
        self.index_codebook() 

Example 5

def add(self, pts, ids=None, prune=True): 
        # Add only if valid and non-zero
        if not len(pts): 
            return

        # Retain valid points
        valid = np.isfinite(pts).all(axis=1)
        pts = pts[valid]

        # ID valid points
        max_id = np.max(self.ids) + 1 if len(self.ids) else 0
        tids = np.arange(len(pts), dtype=np.int64) + max_id if ids is None else ids[valid].astype(np.int64)
        
        # Add pts to track
        for tid, pt in zip(tids, pts): 
            self.tracks_[tid].append(self.index_, pt)

        # If features are propagated
        if prune: 
            self.prune()

        # Frame counter
        self.index_ += 1 

Example 6

def initialize(self, length=None):
        """see ``__init__``"""
        if length is None:
            length = len(self.bounds)
        max_i = min((len(self.bounds) - 1, length - 1))
        self._lb = array([self.bounds[min((i, max_i))][0]
                          if self.bounds[min((i, max_i))][0] is not None
                          else -np.Inf
                          for i in range(length)], copy=False)
        self._ub = array([self.bounds[min((i, max_i))][1]
                          if self.bounds[min((i, max_i))][1] is not None
                          else np.Inf
                          for i in range(length)], copy=False)
        lb = self._lb
        ub = self._ub
        # define added values for lower and upper bound
        self._al = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(lb[i])) / 20])
                             if isfinite(lb[i]) else 1 for i in rglen(lb)], copy=False)
        self._au = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(ub[i])) / 20])
                             if isfinite(ub[i]) else 1 for i in rglen(ub)], copy=False) 

Example 7

def __getitem__(self, item: str) -> Any:
        if self._query_values or item in self._values:
            return self._values.get(item)

        hyperparameter = self.configuration_space._hyperparameters[item]
        item_idx = self.configuration_space._hyperparameter_idx[item]

        if not np.isfinite(self._vector[item_idx]):
            raise KeyError()

        value = hyperparameter._transform(self._vector[item_idx])
        # Truncate the representation of the float to be of constant
        # length for a python version
        if isinstance(hyperparameter, FloatHyperparameter):
            value = float(repr(value))
        # TODO make everything faster, then it'll be possible to init all values
        # at the same time and use an OrderedDict instead of only a dict here to
        # support iterating that dict in the same order as the actual order of
        # hyperparameters
        self._values[item] = value
        return self._values[item] 

Example 8

def test_posterior_zeros(self):
        p = np.asarray([.5, 0., 0.]).reshape((1, 3))

        posterior = self.eval(self.posterior, p)
        print 'posterior', posterior
        posterior_grad = self.eval(self.posterior_grad, p)
        print 'posterior grad', posterior_grad

        kl = self.eval(self.posterior_kl, p)
        print kl
        self.assertGreater(kl.sum(), 0)
        self.assertFalse(np.isnan(kl).any())
        self.assertTrue(np.isfinite(kl).all())

        grad = self.eval(self.posterior_kl_grad, p)
        print grad
        self.assertFalse(np.isnan(grad).any())
        self.assertTrue(np.isfinite(grad).all()) 

Example 9

def to_cartesian(r_dev, pos, normal):
    """ Transform radial deviations from an ellipsoidal grid to Cartesian

    Parameters
    ----------
    r_dev : ndarray, shape (N, )
        Array containing the N radial deviations from the ellipse. r < 0 means
        inside the ellipse.
    pos : ndarray, shape (2, N)
        The N (y, x) positions of the ellipse (as given by ``ellipse_grid``)
    normal : ndarray, shape (2, N)
        The N (y, x) unit normals of the ellipse (as given by ``ellipse_grid``)
    """
    coord_new = pos + r_dev * normal
    coord_new = coord_new[:, np.isfinite(coord_new).all(0)]
    return coord_new 

Example 10

def estimate_theta(self, samples):
        '''
        Estimates the theta parameters from the given samples.

        Parameters
        ----------
        samples : array_like
            n-by-2 matrix of samples where n is the number of samples.
        '''
        if self.theta is not None:
            bnds = self.theta_bounds()

            def cost(theta):
                '''
                Calculates the cost of a given `theta` parameter.
                '''
                self.theta = np.asarray(theta)
                vals = self.logpdf(samples)
                # For optimization, filter out inifinity values
                return -np.sum(vals[np.isfinite(vals)])

            result = minimize(cost, self.theta, method='TNC', bounds=bnds)
            self.theta = result.x 

Example 11

def test_complex_nan_comparisons():
    nans = [complex(np.nan, 0), complex(0, np.nan), complex(np.nan, np.nan)]
    fins = [complex(1, 0), complex(-1, 0), complex(0, 1), complex(0, -1),
            complex(1, 1), complex(-1, -1), complex(0, 0)]

    with np.errstate(invalid='ignore'):
        for x in nans + fins:
            x = np.array([x])
            for y in nans + fins:
                y = np.array([y])

                if np.isfinite(x) and np.isfinite(y):
                    continue

                assert_equal(x < y, False, err_msg="%r < %r" % (x, y))
                assert_equal(x > y, False, err_msg="%r > %r" % (x, y))
                assert_equal(x <= y, False, err_msg="%r <= %r" % (x, y))
                assert_equal(x >= y, False, err_msg="%r >= %r" % (x, y))
                assert_equal(x == y, False, err_msg="%r == %r" % (x, y)) 

Example 12

def __ipow__(self, other):
        """
        Raise self to the power other, in place.

        """
        other_data = getdata(other)
        other_mask = getmask(other)
        with np.errstate(divide='ignore', invalid='ignore'):
            self._data.__ipow__(np.where(self._mask, self.dtype.type(1),
                                         other_data))
        invalid = np.logical_not(np.isfinite(self._data))
        if invalid.any():
            if self._mask is not nomask:
                self._mask |= invalid
            else:
                self._mask = invalid
            np.copyto(self._data, self.fill_value, where=invalid)
        new_mask = mask_or(other_mask, invalid)
        self._mask = mask_or(self._mask, new_mask)
        return self 

Example 13

def step(self, action):
        self.forward_dynamics(action)
        comvel = self.get_body_comvel("torso")
        forward_reward = comvel[0]
        lb, ub = self.action_bounds
        scaling = (ub - lb) * 0.5
        ctrl_cost = 0.5 * 1e-2 * np.sum(np.square(action / scaling))
        contact_cost = 0.5 * 1e-3 * np.sum(
            np.square(np.clip(self.model.data.cfrc_ext, -1, 1))),
        survive_reward = 0.05
        reward = forward_reward - ctrl_cost - contact_cost + survive_reward
        state = self._state
        notdone = np.isfinite(state).all() \
            and state[2] >= 0.2 and state[2] <= 1.0
        done = not notdone
        ob = self.get_current_obs()
        return Step(ob, float(reward), done) 

Example 14

def _preprocess(t, v):
    """ Raises and exception if any of the inputs are not valid.
    Otherwise, returns a list of Points, ordered by t.
    """
    # Validate the inputs.
    if len(t) != len(v):
        raise ValueError('`t` and `v` must have the same length.')
    t_arr, v_arr = np.array(t), np.array(v)
    if not np.all(np.isfinite(t)):
        raise ValueError('All values in `t` must be finite.')
    finite_mask = np.isfinite(v_arr)
    if np.sum(finite_mask) < 2:
        raise ValueError('`v` must have at least 2 finite values.')
    t_arr, v_arr = t_arr[finite_mask], v_arr[finite_mask]
    if len(np.unique(t_arr)) != len(t_arr):
        raise ValueError('All `t` values must be unique.')

    # Order both arrays by t-values.
    sort_order = np.argsort(t_arr)
    t_arr, v_arr = t_arr[sort_order], v_arr[sort_order]

    return t_arr, v_arr 

Example 15

def step(self, action):
        self.forward_dynamics(action)
        comvel = self.get_body_comvel("torso")
        forward_reward = comvel[0]
        lb, ub = self.action_bounds
        scaling = (ub - lb) * 0.5
        ctrl_cost = 0.5 * 1e-2 * np.sum(np.square(action / scaling))
        contact_cost = 0.5 * 1e-3 * np.sum(
            np.square(np.clip(self.model.data.cfrc_ext, -1, 1))),
        survive_reward = 0.05
        reward = forward_reward - ctrl_cost - contact_cost + survive_reward
        state = self._state
        notdone = np.isfinite(state).all() \
            and state[2] >= 0.2 and state[2] <= 1.0
        done = not notdone
        ob = self.get_current_obs()
        return Step(ob, float(reward), done) 

Example 16

def initialize(self, length=None):
        """see ``__init__``"""
        if length is None:
            length = len(self.bounds)
        max_i = min((len(self.bounds) - 1, length - 1))
        self._lb = array([self.bounds[min((i, max_i))][0]
                          if self.bounds[min((i, max_i))][0] is not None
                          else -np.Inf
                          for i in range(length)], copy=False)
        self._ub = array([self.bounds[min((i, max_i))][1]
                          if self.bounds[min((i, max_i))][1] is not None
                          else np.Inf
                          for i in range(length)], copy=False)
        lb = self._lb
        ub = self._ub
        # define added values for lower and upper bound
        self._al = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(lb[i])) / 20])
                             if isfinite(lb[i]) else 1 for i in rglen(lb)], copy=False)
        self._au = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(ub[i])) / 20])
                             if isfinite(ub[i]) else 1 for i in rglen(ub)], copy=False) 

Example 17

def _zscore(a):
    """ Calculating z-score of data on the first axis.
        If the numbers in any column are all equal, scipy.stats.zscore
        will return NaN for this column. We shall correct them all to
        be zeros.

    Parameters
    ----------
    a: numpy array

    Returns
    -------
    zscore: numpy array
        The z-scores of input "a", with any columns including non-finite
        numbers replaced by all zeros.
    """
    assert a.ndim > 1, 'a must have more than one dimensions'
    zscore = scipy.stats.zscore(a, axis=0)
    zscore[:, np.logical_not(np.all(np.isfinite(zscore), axis=0))] = 0
    return zscore 

Example 18

def test_funcs(self):
        data = self.data['binary']
        truth = self.truths['binary']
        nlp = self.truths_to_nlp(truth)
        params = self.joker_params['binary']

        p = np.concatenate((nlp, [truth['K'].value], [self.fd.v0.value]))
        mcmc_p = to_mcmc_params(p)
        p2 = from_mcmc_params(mcmc_p)
        assert np.allclose(p, p2.reshape(p.shape)) # test roundtrip

        lp = ln_prior(p, params)
        assert np.isfinite(lp)

        ll = ln_likelihood(p, params, data)
        assert np.isfinite(ll).all()

        # remove jitter from params passed in to mcmc_p
        mcmc_p = list(mcmc_p)
        mcmc_p.pop(5) # log-jitter is 5th index in mcmc packed
        lnpost = ln_posterior(mcmc_p, params, data)

        assert np.isfinite(lnpost)
        assert np.allclose(lnpost, lp+ll.sum()) 

Example 19

def test_sample_prior(self):

        rnd1 = np.random.RandomState(42)
        joker1 = TheJoker(self.joker_params['binary'], random_state=rnd1)

        rnd2 = np.random.RandomState(42)
        joker2 = TheJoker(self.joker_params['triple'], random_state=rnd2)

        samples1 = joker1.sample_prior(8)
        samples2 = joker2.sample_prior(8)

        for key in samples1.keys():
            assert quantity_allclose(samples1[key], samples2[key])

        samples, ln_vals = joker2.sample_prior(8, return_logprobs=True)
        assert np.isfinite(ln_vals).all() 

Example 20

def get_blazar_redshifts(blazar_type):
    table = Table.read(filename, hdu='LAT_Point_Source_Catalog')
    known_redshift_mask = np.isfinite(table['Redshift'])
    known_redshift_table = table[known_redshift_mask]
    if blazar_type == "bll":
        class_1 = known_redshift_table['CLASS'] == "bll    "
        class_2 = known_redshift_table['CLASS'] == "BLL    "
    if blazar_type == "fsrq":
        class_1 = known_redshift_table['CLASS'] == "fsrq   "
        class_2 = known_redshift_table['CLASS'] == "FSRQ   "
    if blazar_type == "bcu":
        class_1 = known_redshift_table['CLASS'] == "bcu    "
        class_2 = known_redshift_table['CLASS'] == "BCU    "
    class_type_mask = np.logical_or.reduce((class_1, class_2))
    sub_table = known_redshift_table[class_type_mask]
    return sub_table["Redshift"] 

Example 21

def get_data(self, element, ranges, style):
        if self.geographic:
            vdim = element.vdims[0] if element.vdims else None
            value = element.level
            if vdim is not None and (value is not None and np.isfinite(value)):
                self._norm_kwargs(element, ranges, style, vdim)
                style['clim'] = style.pop('vmin'), style.pop('vmax')
                style['array'] = np.array([value])
            return ([element.data], element.crs), style, {}
        else:
            SkipRendering('Shape can only be plotted on geographic plot, '
                          'supply a coordinate reference system.')

        
########################################
#  Geographic features and annotations #
######################################## 

Example 22

def get_extents(self, element, ranges):
        """
        Subclasses the get_extents method using the GeoAxes
        set_extent method to project the extents to the
        Elements coordinate reference system.
        """
        extents = super(GeoPlot, self).get_extents(element, ranges)
        if not getattr(element, 'crs', None) or not self.geographic:
            return extents
        elif any(e is None or not np.isfinite(e) for e in extents):
            extents = None
        else:
            try:
                extents = project_extents(extents, element.crs, DEFAULT_PROJ)
            except:
                extents = None
        return (np.NaN,)*4 if not extents else extents 

Example 23

def test_convert_xy(x, y):
    assume(x != 0 and y != 0)
    assume(np.isfinite(x) and np.isfinite(y))
    assume(abs(x) < 1E6 and abs(y) < 1E6)
    assume(abs(x) > 0.01 and abs(y) > 0.01)

    # Test radians
    r, theta = to_polar(x, y)
    x_new, y_new = to_cartesian(r, theta)
    assert np.allclose(x, x_new)
    assert np.allclose(y, y_new)

    # Test degrees
    r, theta = to_polar(x, y, theta_units="degrees")
    x_new, y_new = to_cartesian(r, theta, theta_units="degrees")
    assert np.allclose(x, x_new)
    assert np.allclose(y, y_new) 

Example 24

def _get_viewpoint_estimation_labels(viewpoint_data, clss, num_classes):
    """Bounding-box regression targets are stored in a compact form in the
    roidb.

    This function expands those targets into the 4-of-4*K representation used
    by the network (i.e. only one class has non-zero targets). The loss weights
    are similarly expanded.

    Returns:
        view_target_data (ndarray): N x 3K blob of regression targets
        view_loss_weights (ndarray): N x 3K blob of loss weights
    """
    view_targets = np.zeros((clss.size, 3 * num_classes), dtype=np.float32)
    view_loss_weights = np.zeros(view_targets.shape, dtype=np.float32)
    inds = np.where( (clss > 0) & np.isfinite(viewpoint_data[:,0]) & np.isfinite(viewpoint_data[:,1]) & np.isfinite(viewpoint_data[:,2]) )[0]
    for ind in inds:
        cls = clss[ind]
        start = 3 * cls
        end = start + 3
        view_targets[ind, start:end] = viewpoint_data[ind, :]
        view_loss_weights[ind, start:end] = [1., 1., 1.]

    assert not np.isinf(view_targets).any(), 'viewpoint undefined'
    return view_targets, view_loss_weights 

Example 25

def correlations(A,B,pc_n=100):
	p = (1 - distance.correlation(A.flatten(),B.flatten()))
	spear = spearmanr(A.flatten(),B.flatten())
	dist_genes = np.zeros(A.shape[0])
	for i in range(A.shape[0]):
		dist_genes[i] = 1 - distance.correlation(A[i],B[i])
	pg = (np.average(dist_genes[np.isfinite(dist_genes)]))
	dist_sample = np.zeros(A.shape[1])
	for i in range(A.shape[1]):
		dist_sample[i] = 1 - distance.correlation(A[:,i],B[:,i])
	ps = (np.average(dist_sample[np.isfinite(dist_sample)]))
	pc_dist = []
	if pc_n > 0:
		u0,s0,vt0 = np.linalg.svd(A)
		u,s,vt = np.linalg.svd(B)
		for i in range(pc_n):
			pc_dist.append(abs(1 - distance.cosine(u0[:,i],u[:,i])))
		pc_dist = np.array(pc_dist)
	return p,spear[0],pg,ps,pc_dist 

Example 26

def check_stoplimit_prices(price, label):
    """
    Check to make sure the stop/limit prices are reasonable and raise
    a BadOrderParameters exception if not.
    """
    try:
        if not isfinite(price):
            raise BadOrderParameters(
                msg="Attempted to place an order with a {} price "
                    "of {}.".format(label, price)
            )
    # This catches arbitrary objects
    except TypeError:
        raise BadOrderParameters(
            msg="Attempted to place an order with a {} price "
                "of {}.".format(label, type(price))
        )

    if price < 0:
        raise BadOrderParameters(
            msg="Can't place a {} order with a negative price.".format(label)
        ) 

Example 27

def step(self, action):
        self.forward_dynamics(action)
        comvel = self.get_body_comvel("torso")
        forward_reward = comvel[0]
        lb, ub = self.action_bounds
        scaling = (ub - lb) * 0.5
        ctrl_cost = 0.5 * 1e-2 * np.sum(np.square(action / scaling))
        contact_cost = 0.5 * 1e-3 * np.sum(
            np.square(np.clip(self.model.data.cfrc_ext, -1, 1))),
        survive_reward = 0.05
        reward = forward_reward - ctrl_cost - contact_cost + survive_reward
        state = self._state
        notdone = np.isfinite(state).all() \
            and state[2] >= 0.2 and state[2] <= 1.0
        done = not notdone
        ob = self.get_current_obs()
        return Step(ob, float(reward), done) 

Example 28

def initialize(self, length=None):
        """see ``__init__``"""
        if length is None:
            length = len(self.bounds)
        max_i = min((len(self.bounds) - 1, length - 1))
        self._lb = array([self.bounds[min((i, max_i))][0]
                          if self.bounds[min((i, max_i))][0] is not None
                          else -np.Inf
                          for i in range(length)], copy=False)
        self._ub = array([self.bounds[min((i, max_i))][1]
                          if self.bounds[min((i, max_i))][1] is not None
                          else np.Inf
                          for i in range(length)], copy=False)
        lb = self._lb
        ub = self._ub
        # define added values for lower and upper bound
        self._al = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(lb[i])) / 20])
                             if isfinite(lb[i]) else 1 for i in rglen(lb)], copy=False)
        self._au = array([min([(ub[i] - lb[i]) / 2, (1 + np.abs(ub[i])) / 20])
                             if isfinite(ub[i]) else 1 for i in rglen(ub)], copy=False) 

Example 29

def sanitize_array(array):
    """
    Replace NaN and Inf (there should not be any!)
    :param array:
    :return:
    """
    a = np.ravel(array)
    #maxi = np.nanmax((filter(lambda x: x != float('inf'), a))
    #                 )  # Max except NaN and Inf
    #mini = np.nanmin((filter(lambda x: x != float('-inf'), a))
    #                 )  # Mini except NaN and Inf
    maxi = np.nanmax(a[np.isfinite(a)])
    mini = np.nanmin(a[np.isfinite(a)])
    array[array == float('inf')] = maxi
    array[array == float('-inf')] = mini
    mid = (maxi + mini) / 2
    array[np.isnan(array)] = mid
    return array 

Example 30

def _calculate(self, X, y, categorical, metafeatures, helpers):
        skews = helpers.get_value("Skewnesses")
        std = np.nanstd(skews) if len(skews) > 0 else 0
        return std if np.isfinite(std) else 0


# @metafeatures.define("cancor1")
# def cancor1(X, y):
#    pass

# @metafeatures.define("cancor2")
# def cancor2(X, y):
#    pass

################################################################################
# Information-theoretic metafeatures 

Example 31

def getROIHeight(self):
		
		"""Returns height of ROI.
		
		Returns:
			float: Height of ROI.
		
		"""
	
		if np.isfinite(self.zmax):
			zMax=self.zmax
		else:
			dump,zMax=self.getMeshIdxZExtend()
		
		if np.isfinite(self.zmin):
			zMin=self.zmin
		else:
			zMin,dump=self.getMeshIdxZExtend()
			
		return abs(zMax-zMin) 

Example 32

def test_contextual_optimizers_follow_standard_protocol():
    for name, ContextualOptimizer in ALL_CONTEXTUALOPTIMIZERS:
        opt = ContextualOptimizer()
        n_params = 1
        n_context_dims = 1
        opt.init(n_params, n_context_dims)
        context = opt.get_desired_context()
        if context is None:
            context = np.zeros(n_context_dims)
        opt.set_context(context)
        assert_false(opt.is_behavior_learning_done())
        params = np.empty(n_params)
        opt.get_next_parameters(params)
        assert_true(np.isfinite(params).all())
        opt.set_evaluation_feedback(np.array([0.0]))

        policy = opt.best_policy()
        assert_true(np.isfinite(policy(context)).all())

        assert_pickle(name, opt) 

Example 33

def test_random_behavior():
    beh = RandomBehavior(random_state=0)
    beh.init(4, 5)

    assert_equal(beh.get_n_params(), 0)
    assert_array_equal(beh.get_params(), np.array([]))

    outputs = np.empty(5)
    outputs[:] = np.nan
    beh.get_outputs(outputs)
    assert_true(np.isfinite(outputs).all())

    assert_raises_regexp(
        NotImplementedError, "does not accept any meta parameters",
        beh.set_meta_parameters, ["key"], [0.0])
    beh.reset()

    assert_raises_regexp(
        ValueError, "Length of parameter vector must be 0",
        beh.set_params, np.zeros(2))
    beh.set_params(np.array([])) 

Example 34

def get_bon_thresh(normalized,power): #same
	"""
	Calculate the bonferroni correction threshold.

	Divide the power by the sum of all finite values (all non-nan values).
	
	:param normalized: an array of all normalized p-values. Normalized p-values are -log10(p) where p is the p-value.
	:param power: the threshold power being used (usually 0.05)
	:type normalized: numpy array
	:type power: float

	:returns: The bonferroni correction
	:rtype: float

	"""
	return power/sum(np.isfinite(normalized)) 

Example 35

def get_fdr_thresh(p_values, power):
	"""
	Calculate the false discovery rate threshold.
	
	:param p_values: a list of p-values obtained by executing the regression
	:param power: the thershold power being used (usually 0.05)
	:type p_values: numpy array
	:type power: float

	:returns: the false discovery rate
	:rtype: float
	"""
	sn = np.sort(p_values)
	sn = sn[np.isfinite(sn)]
	sn = sn[::-1]
	for i in range(len(sn)):
		thresh=0.05*i/len(sn)
		if sn[i]<=power:
			break
	return sn[i] 

Example 36

def get_bon_thresh(normalized, power):  # same
    """
    Calculate the bonferroni correction threshold.

    Divide the power by the sum of all finite values (all non-nan values).

    :param normalized: an array of all normalized p-values. Normalized p-values are -log10(p) where p is the p-value.
    :param power: the threshold power being used (usually 0.05)
    :type normalized: numpy array
    :type power: float

    :returns: The bonferroni correction
    :rtype: float

    """
    return power / sum(np.isfinite(normalized)) 

Example 37

def get_fdr_thresh(p_values, power):
    """
    Calculate the false discovery rate threshold.

    :param p_values: a list of p-values obtained by executing the regression
    :param power: the thershold power being used (usually 0.05)
    :type p_values: numpy array
    :type power: float

    :returns: the false discovery rate
    :rtype: float
    """
    sn = np.sort(p_values)
    sn = sn[np.isfinite(sn)]
    sn = sn[::-1]
    for i in range(len(sn)):
        thresh = 0.05 * i / len(sn)
        if sn[i] <= power:
            break
    return sn[i] 

Example 38

def censor_diagnosis(genotype_file,phenotype_file,final_pfile, field ='na',start_time=float('nan'),end_time=float('nan')):
        import pandas as pd
        import numpy as np
        genotypes = pd.read_csv(genotype_file)
        phenotypes = pd.read_csv(phenotype_file)
        mg=pd.merge(phenotypes,genotypes,on='id')
        if np.isnan(start_time) and np.isnan(end_time):
                print("Choose appropriate time period")
        if field=='na':
                if np.isfinite(start_time) and np.isnan(end_time):
                        final = mg[mg['AgeAtICD']>start_time]
                elif np.isnan(start_time) and np.isfinite(end_time):
                        final = mg[mg['AgeAtICD']<end_time]
                else:
                        final = mg[(mg['AgeAtICD']>start_time)&(mg['AgeAtICD']<end_time)]

        else:
                mg['diff']=mg[field]-mg['AgeAtICD']
                if np.isfinite(start_time) and np.isnan(end_time):
                        final = mg[(mg['diff']>start_time)|(np.isnan(mg['diff']))]
                elif np.isnan(start_time) and np.isfinite(end_time):
                        final = mg[(mg['diff']<end_time)|(np.isnan(mg['diff']))]
                else:
                        final = mg[(mg['diff']>start_time)&(mg['diff']<end_time)|(np.isnan(mg['diff']))]
        final[['id','icd9','AgeAtICD']].to_csv(final_pfile) 

Example 39

def get_fdr_thresh(p_values, power):
    """
	Calculate the false discovery rate threshold.

	:param p_values: a list of p-values obtained by executing the regression
	:param power: the thershold power being used (usually 0.05)
	:type p_values: numpy array
	:type power: float

	:returns: the false discovery rate
	:rtype: float
	"""
    sn = np.sort(p_values)
    sn = sn[np.isfinite(sn)]
    sn = sn[::-1]
    for i in range(len(sn)):
        thresh = power * i / len(sn)
        if sn[i] <= thresh:
            break
    return sn[i] 

Example 40

def get_bhy_thresh(p_values, power):
    """
	Calculate the false discovery rate threshold.

	:param p_values: a list of p-values obtained by executing the regression
	:param power: the thershold power being used (usually 0.05)
	:type p_values: numpy array
	:type power: float

	:returns: the false discovery rate
	:rtype: float
	"""
    sn = np.sort(p_values)
    sn = sn[np.isfinite(sn)]
    sn = sn[::-1]
    for i in range(len(sn)):
        thresh = power * i / (8.1*len(sn))
        if sn[i] <= thresh:
            break
    return sn[i] 

Example 41

def get_bon_thresh(normalized, power):  # same
    """
	Calculate the bonferroni correction threshold.

	Divide the power by the sum of all finite values (all non-nan values).

	:param normalized: an array of all normalized p-values. Normalized p-values are -log10(p) where p is the p-value.
	:param power: the threshold power being used (usually 0.05)
	:type normalized: numpy array
	:type power: float

	:returns: The bonferroni correction
	:rtype: float

	"""
    return power / sum(np.isfinite(normalized)) 

Example 42

def get_fdr_thresh(p_values, power):
    """
	Calculate the false discovery rate threshold.

	:param p_values: a list of p-values obtained by executing the regression
	:param power: the thershold power being used (usually 0.05)
	:type p_values: numpy array
	:type power: float

	:returns: the false discovery rate
	:rtype: float
	"""
    sn = np.sort(p_values)
    sn = sn[np.isfinite(sn)]
    sn = sn[::-1]
    for i in range(len(sn)):
        thresh = power * i / len(sn)
        if sn[i] <= thresh:
            break
    return sn[i] 

Example 43

def get_bon_thresh(normalized,power): #same
	"""
	Calculate the bonferroni correction threshold.

	Divide the power by the sum of all finite values (all non-nan values).
	
	:param normalized: an array of all normalized p-values. Normalized p-values are -log10(p) where p is the p-value.
	:param power: the threshold power being used (usually 0.05)
	:type normalized: numpy array
	:type power: float

	:returns: The bonferroni correction
	:rtype: float

	"""
	return power/sum(np.isfinite(normalized)) 

Example 44

def get_fdr_thresh(p_values, power):
	"""
	Calculate the false discovery rate threshold.
	
	:param p_values: a list of p-values obtained by executing the regression
	:param power: the thershold power being used (usually 0.05)
	:type p_values: numpy array
	:type power: float

	:returns: the false discovery rate
	:rtype: float
	"""
	sn = np.sort(p_values)
	sn = sn[np.isfinite(sn)]
	sn = sn[::-1]
	for i in range(len(sn)):
		thresh = power * i / len(sn)
		if sn[i] <= thresh:
			break
	return sn[i] 

Example 45

def _get_viewpoint_estimation_labels(viewpoint_data, clss, num_classes):
    """Bounding-box regression targets are stored in a compact form in the
    roidb.

    This function expands those targets into the 4-of-4*K representation used
    by the network (i.e. only one class has non-zero targets). The loss weights
    are similarly expanded.

    Returns:
        view_target_data (ndarray): N x 3K blob of regression targets
        view_loss_weights (ndarray): N x 3K blob of loss weights
    """
    view_targets = np.zeros((clss.size, 3 * num_classes), dtype=np.float32)
    view_loss_weights = np.zeros(view_targets.shape, dtype=np.float32)
    inds = np.where( (clss > 0) & np.isfinite(viewpoint_data[:,0]) & np.isfinite(viewpoint_data[:,1]) & np.isfinite(viewpoint_data[:,2]) )[0]
    for ind in inds:
        cls = clss[ind]
        start = 3 * cls
        end = start + 3
        view_targets[ind, start:end] = viewpoint_data[ind, :]
        view_loss_weights[ind, start:end] = [1., 1., 1.]

    assert not np.isinf(view_targets).any(), 'viewpoint undefined'
    return view_targets, view_loss_weights 

Example 46

def __call__(self, params, params_args, obj, idxs, alpha, prop_mode):
        params_dict = unflatten_dict(params, params_args)
        f, grad_dict = obj.objective_function(
            params_dict, idxs, alpha=alpha, prop_mode=prop_mode)
        g, _ = flatten_dict(grad_dict)
        g_is_fin = np.isfinite(g)
        if np.all(g_is_fin):
            self.previous_x = params
            return f, g
        else:
            print("Warning: inf or nan in gradient: replacing with zeros")
            return f, np.where(g_is_fin, g, 0.)

# def objective_wrapper(params, params_args, obj, idxs, alpha):
#     params_dict = unflatten_dict(params, params_args)
#     f, grad_dict = obj.objective_function(
#         params_dict, idxs, alpha=alpha)
#     g, _ = flatten_dict(grad_dict)
#     g_is_fin = np.isfinite(g)
#     if np.all(g_is_fin):
#         return f, g
#     else:
#         print("Warning: inf or nan in gradient: replacing with zeros")
#         return f, np.where(g_is_fin, g, 0.) 

Example 47

def calc_state(self):
		self.theta, theta_dot = self.j1.current_position()
		x, vx = self.slider.current_position()
		#assert( np.isfinite(x) )

		if not np.isfinite(x):
			print("x is inf")
			x = 0

		if not np.isfinite(vx):
			print("vx is inf")
			vx = 0

		if not np.isfinite(self.theta):
			print("theta is inf")
			self.theta = 0

		if not np.isfinite(theta_dot):
			print("theta_dot is inf")
			theta_dot = 0

		return np.array([
			x, vx,
			np.cos(self.theta), np.sin(self.theta), theta_dot
			]) 

Example 48

def residual_multigauss(param, dataimage, nonfinite = 0.0, ravelresidual=True, showimages=False, verbose=False):
    """
    Calculating the residual bestween the multigaussian model with the paramters 'param' and the data.

    --- INPUT ---
    param         Parameters of multi-gaussian model to generate. See modelimage_multigauss() header for details
    dataimage     Data image to take residual
    nonfinite     Value to replace non-finite entries in residual with
    ravelresidual To np.ravel() the residual image set this to True. Needed by scipy.optimize.leastsq()
                  optimizer function
    showimages    To show model and residiual images set to True
    verbose       Toggle verbosity

    --- EXAMPLE OF USE ---
    import tdose_model_FoV as tmf
    param      = [18,31,1*0.3,2.1*0.3,1.2*0.3,30*0.3,    110,90,200*0.5,20.1*0.5,15.2*0.5,0*0.5]
    dataimg    = pyfits.open('/Users/kschmidt/work/TDOSE/mock_cube_sourcecat161213_tdose_mock_cube.fits')[0].data[0,:,:]
    residual   = tmf.residual_multigauss(param, dataimg, showimages=True)

    """
    if verbose: ' - Estimating residual (= model - data) between model and data image'
    imgsize      = dataimage.shape
    xgrid, ygrid = tu.gen_gridcomponents(imgsize)
    modelimg     = tmf.modelimage_multigauss((xgrid, ygrid),param,imgsize,showmodelimg=showimages, verbose=verbose)

    residualimg  = modelimg - dataimage

    if showimages:
        plt.imshow(residualimg,interpolation='none', vmin=1e-5, vmax=np.max(residualimg), norm=mpl.colors.LogNorm())
        plt.title('Resdiaul (= model - data) image')
        plt.show()

    if nonfinite is not None:
        residualimg[~np.isfinite(residualimg)] = 0.0

    if ravelresidual:
        residualimg = np.ravel(residualimg)

    return residualimg
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

Example 49

def test_server_logprob_shape(model):
    table = TINY_TABLE
    server = TreeCatServer(model)
    logprobs = server.logprob(table.data)
    N = table.num_rows
    assert logprobs.dtype == np.float32
    assert logprobs.shape == (N, )
    assert np.isfinite(logprobs).all() 

Example 50

def test_ensemble_logprob_shape(ensemble):
    table = TINY_TABLE
    server = EnsembleServer(ensemble)
    logprobs = server.logprob(table.data)
    N = table.num_rows
    assert logprobs.dtype == np.float32
    assert logprobs.shape == (N, )
    assert np.isfinite(logprobs).all() 
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