Python numpy.isfinite方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def test_load_variable_mask_and_scale(load_variable_data_loader):
    def convert_all_to_missing_val(ds, **kwargs):
        ds['condensation_rain'] = 0. * ds['condensation_rain'] + 1.0e20
        ds['condensation_rain'].attrs['_FillValue'] = 1.0e20
        return ds

    load_variable_data_loader.preprocess_func = convert_all_to_missing_val

    data = load_variable_data_loader.load_variable(
        condensation_rain, DatetimeNoLeap(5, 1, 1),
        DatetimeNoLeap(5, 12, 31),
        intvl_in='monthly')

    num_non_missing = np.isfinite(data).sum().item()
    expected_num_non_missing = 0
    assert num_non_missing == expected_num_non_missing 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _convert_observ(self, observ):
    """Convert the observation to 32 bits.

    Args:
      observ: Numpy observation.

    Raises:
      ValueError: Observation contains infinite values.

    Returns:
      Numpy observation with 32-bit data type.
    """
    if not np.isfinite(observ).all():
      raise ValueError('Infinite observation encountered.')
    if observ.dtype == np.float64:
      return observ.astype(np.float32)
    if observ.dtype == np.int64:
      return observ.astype(np.int32)
    return observ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
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
			]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _initialize(self):
        super()._initialize()

        # Clip initial guess to bounds (SLSQP may fail with bounds-infeasible initial point)
        x = asfarray(self.x0.X.flatten())
        xl, xu = self.problem.bounds()
        have_bound = np.isfinite(xl)
        x[have_bound] = np.clip(x[have_bound], xl[have_bound], np.inf)
        have_bound = np.isfinite(xu)
        x[have_bound] = np.clip(x[have_bound], -np.inf, xu[have_bound])
        self.D["X"] = x

        self.pop = Population()
        self._eval_obj()
        self._eval_grad()
        self._update()
        self._call()

        self.major = True 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _check_rep(self):

        #name
        assert isinstance(self._name, str)
        assert len(self._name) >= 1

        #bounds
        assert np.isfinite(self.ub)
        assert np.isfinite(self.lb)
        assert self.ub >= self.lb

        # value
        assert self._vtype in self._VALID_TYPES
        assert np.isnan(self.c0) or (self.c0 >= 0.0 and np.isfinite(self.c0))

        return True 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _check_maxexp(np_type, maxexp):
    """ True if fp type `np_type` seems to have `maxexp` maximum exponent

    We're testing "maxexp" as returned by numpy. This value is set to one
    greater than the maximum power of 2 that `np_type` can represent.

    Assumes base 2 representation.  Very crude check

    Parameters
    ----------
    np_type : numpy type specifier
        Any specifier for a numpy dtype
    maxexp : int
        Maximum exponent to test against

    Returns
    -------
    tf : bool
        True if `maxexp` is the correct maximum exponent, False otherwise.
    """
    dt = np.dtype(np_type)
    np_type = dt.type
    two = np_type(2).reshape((1,)) # to avoid upcasting
    return (np.isfinite(two ** (maxexp - 1)) and
            not np.isfinite(two ** maxexp)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def beta_divergence(x_indices, x_vals, beta, A, B, C):
    """Computes the total beta-divergence between the current model and
    a sparse X
    """
    rank = len(x_indices[0])
    b_vals = np.zeros(x_vals.shape, dtype=np.float32)
    parafac_sparse(x_indices, b_vals, A, B, C)
    a, b = x_vals, b_vals
    idx = np.isfinite(a)
    idx &= np.isfinite(b)
    idx &= a > 0
    idx &= b > 0
    a = a[idx]
    b = b[idx]
    if beta == 0:
        return a / b - np.log(a / b) - 1
    if beta == 1:
        return a * (np.log(a) - np.log(b)) + b - a
    return (1. / beta / (beta - 1.) * (a ** beta + (beta - 1.)
            * b ** beta - beta * a * b ** (beta - 1))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _sanitize_dists(self, dists):
        """Replace invalid distances."""
        
        dists = np.copy(dists)
        
        # Note there is an issue in scipy.optimize.linear_sum_assignment where
        # it runs forever if an entire row/column is infinite or nan. We therefore
        # make a copy of the distance matrix and compute a safe value that indicates
        # 'cannot assign'. Also note + 1 is necessary in below inv-dist computation
        # to make invdist bigger than max dist in case max dist is zero.
        
        valid_dists = dists[np.isfinite(dists)]
        INVDIST = 2 * valid_dists.max() + 1 if valid_dists.shape[0] > 0 else 1.
        dists[~np.isfinite(dists)] = INVDIST  

        return dists, INVDIST 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def _reward(self):
        reward = self.reward_strategy.get_reward(current_step=self.current_step,
                                                 current_price=self._current_price,
                                                 observations=self.observations,
                                                 account_history=self.account_history,
                                                 net_worths=self.net_worths)

        reward = float(reward) if np.isfinite(float(reward)) else 0

        self.rewards.append(reward)

        if self.stationarize_rewards:
            rewards = difference(self.rewards, inplace=False)
        else:
            rewards = self.rewards

        if self.normalize_rewards:
            mean_normalize(rewards, inplace=True)

        rewards = np.array(rewards).flatten()

        return float(rewards[-1]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def test_generic(self):
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = nan_to_num(np.array((-1., 0, 1))/0.)
        assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
        assert_(vals[1] == 0)
        assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))
        assert_equal(type(vals), np.ndarray)

        # perform the same test but in-place
        with np.errstate(divide='ignore', invalid='ignore'):
            vals = np.array((-1., 0, 1))/0.
        result = nan_to_num(vals, copy=False)

        assert_(result is vals)
        assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0]))
        assert_(vals[1] == 0)
        assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2]))
        assert_equal(type(vals), np.ndarray) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def ts(self, data, lon_cyclic=True, lon_str=LON_STR, lat_str=LAT_STR,
           land_mask_str=LAND_MASK_STR, sfc_area_str=SFC_AREA_STR):
        """Create yearly time-series of region-averaged data.

        Parameters
        ----------
        data : xarray.DataArray
            The array to create the regional timeseries of
        lon_cyclic : { None, True, False }, optional (default True)
            Whether or not the longitudes of ``data`` span the whole globe,
            meaning that they should be wrapped around as necessary to cover
            the Region's full width.
        lat_str, lon_str, land_mask_str, sfc_area_str : str, optional
            The name of the latitude, longitude, land mask, and surface area
            coordinates, respectively, in ``data``.  Defaults are the
            corresponding values in ``aospy.internal_names``.

        Returns
        -------
        xarray.DataArray
            The timeseries of values averaged within the region and within each
            year, one value per year.

        """
        data_masked = self.mask_var(data, lon_cyclic=lon_cyclic,
                                    lon_str=lon_str, lat_str=lat_str)
        sfc_area = data[sfc_area_str]
        sfc_area_masked = self.mask_var(sfc_area, lon_cyclic=lon_cyclic,
                                        lon_str=lon_str, lat_str=lat_str)
        land_mask = _get_land_mask(data, self.do_land_mask,
                                   land_mask_str=land_mask_str)
        weights = sfc_area_masked * land_mask
        # Mask weights where data values are initially invalid in addition
        # to applying the region mask.
        weights = weights.where(np.isfinite(data))
        weights_reg_sum = weights.sum(lon_str).sum(lat_str)
        data_reg_sum = (data_masked * sfc_area_masked *
                        land_mask).sum(lat_str).sum(lon_str)
        return data_reg_sum / weights_reg_sum 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def yearly_average(arr, dt):
    """Average a sub-yearly time-series over each year.

    Resulting timeseries comprises one value for each year in which the
    original array had valid data.  Accounts for (i.e. ignores) masked values
    in original data when computing the annual averages.

    Parameters
    ----------
    arr : xarray.DataArray
        The array to be averaged
    dt : xarray.DataArray
        Array of the duration of each timestep

    Returns
    -------
    xarray.DataArray
        Has the same shape and mask as the original ``arr``, except for the
        time dimension, which is truncated to one value for each year that
        ``arr`` spanned

    """
    assert_matching_time_coord(arr, dt)
    yr_str = TIME_STR + '.year'
    # Retain original data's mask.
    dt = dt.where(np.isfinite(arr))
    return ((arr*dt).groupby(yr_str, restore_coord_dims=True).sum(TIME_STR) /
            dt.groupby(yr_str, restore_coord_dims=True).sum(TIME_STR)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def __init__(self, map_fname=None):
        """
        Args:
            map_fname (Optional[:obj:`str`]): Filename at which the map is stored.
                Defaults to ``None``, meaning that the default filename is used.
        """
        if map_fname is None:
            map_fname = os.path.join(data_dir(), 'marshall', 'marshall.h5')

        with h5py.File(map_fname, 'r') as f:
            self._l = f['l'][:]
            self._b = f['b'][:]
            self._A = f['A'][:]
            self._sigma_A = f['sigma_A'][:]
            self._dist = f['dist'][:]
            self._sigma_dist = f['sigma_dist'][:]

        # self._l.shape = (self._l.size,)
        # self._b.shape = (self._b.size,)
        # self._A.shape = (self._A.shape[0], self._A.shape[1]*self._A.shape[2])

        # Shape of the (l,b)-grid
        self._shape = self._l.shape

        # Number of distance bins in each sightline
        self._n_dists = np.sum(np.isfinite(self._dist), axis=2)

        # idx = ~np.isfinite(self._dist)
        # if np.any(idx):
        #     self._dist[idx] = np.inf

        self._l_bounds = (-100., 100.) # min,max Galactic longitude, in deg
        self._b_bounds = (-10., 10.)    # min,max Galactic latitude, in deg
        self._inv_pix_scale = 4.        # 1 / (pixel scale, in deg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def segment_intersection_2D(p12arg, p34arg, atol=1e-8):
    '''
    segment_intersection((a, b), (c, d)) yields the intersection point between the line segments
    that pass from point a to point b and from point c to point d. If there is no intersection
    point, then (numpy.nan, numpy.nan) is returned.
    '''
    (p1,p2) = p12arg
    (p3,p4) = p34arg
    pi = np.asarray(line_intersection_2D(p12arg, p34arg, atol=atol))
    p1 = np.asarray(p1)
    p2 = np.asarray(p2)
    p3 = np.asarray(p3)
    p4 = np.asarray(p4)
    u12 = p2 - p1
    u34 = p4 - p3
    cfn = lambda px,iis: (px if iis is None or len(px.shape) == 1 or px.shape[1] == len(iis) else
                          px[:,iis])
    dfn = lambda a,b:     a[0]*b[0] + a[1]*b[1]
    sfn = lambda a,b:     ((a-b)                 if len(a.shape) == len(b.shape) else
                           (np.transpose([a])-b) if len(a.shape) <  len(b.shape) else
                           (a - np.transpose([b])))
    fn  = lambda px,iis:  (1 - ((dfn(cfn(u12,iis), sfn(         px, cfn(p1,iis))) > 0) *
                                (dfn(cfn(u34,iis), sfn(         px, cfn(p3,iis))) > 0) *
                                (dfn(cfn(u12,iis), sfn(cfn(p2,iis),          px)) > 0) *
                                (dfn(cfn(u34,iis), sfn(cfn(p4,iis),          px)) > 0)))
    if len(pi.shape) == 1:
        if not np.isfinite(pi[0]): return (np.nan, np.nan)
        bad = fn(pi, None)
        return (np.nan, np.nan) if bad else pi
    else:
        nonpar = np.where(np.isfinite(pi[0]))[0]
        bad = fn(cfn(pi, nonpar), nonpar)
        (xi,yi) = pi
        bad = nonpar[np.where(bad)[0]]
        xi[bad] = np.nan
        yi[bad] = np.nan
        return (xi,yi) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isfinite [as 別名]
def lines_touch_2D(ab, cd, atol=1e-8):
    '''
    lines_touch_2D((a,b), (c,d)) is equivalent to lines_colinear((a,b), (c,d)) |
    numpy.isfinite(line_intersection_2D((a,b), (c,d))[0])
    '''
    return (lines_colinear(ab, cd, atol=atol) |
            np.isfinite(line_intersection_2D(ab, cd, atol=atol)[0]))