"""
Directional Variogram
"""
import numpy as np
from scipy.spatial.distance import pdist
from .Variogram import Variogram
from skgstat import plotting
from .MetricSpace import MetricSpace, MetricSpacePair
[docs]class DirectionalVariogram(Variogram):
"""DirectionalVariogram Class
Calculates a variogram of the separating distances in the given
coordinates and relates them to one of the semi-variance measures of the
given dependent values.
The direcitonal version of a Variogram will only form paris of points
that share a specified spatial relationship.
"""
[docs] def __init__(self,
coordinates=None,
values=None,
estimator='matheron',
model='spherical',
dist_func='euclidean',
bin_func='even',
normalize=False,
fit_method='trf',
fit_sigma=None,
directional_model='triangle',
azimuth=0,
tolerance=45.0,
bandwidth='q33',
use_nugget=False,
maxlag=None,
n_lags=10,
verbose=False,
**kwargs
):
r"""Variogram Class
Directional Variogram. The calculation is not performant and not
tested yet.
Parameters
----------
coordinates : numpy.ndarray
Array of shape (m, n). Will be used as m observation points of
n-dimensions. This variogram can be calculated on 1 - n
dimensional coordinates. In case a 1-dimensional array is passed,
a second array of same length containing only zeros will be
stacked to the passed one.
values : numpy.ndarray
Array of values observed at the given coordinates. The length of
the values array has to match the m dimension of the coordinates
array. Will be used to calculate the dependent variable of the
variogram.
estimator : str, callable
String identifying the semi-variance estimator to be used.
Defaults to the Matheron estimator. Possible values are:
* matheron [Matheron, default]
* cressie [Cressie-Hawkins]
* dowd [Dowd-Estimator]
* genton [Genton]
* minmax [MinMax Scaler]
* entropy [Shannon Entropy]
If a callable is passed, it has to accept an array of absoulte
differences, aligned to the 1D distance matrix (flattened upper
triangle) and return a scalar, that converges towards small
values for similarity (high covariance).
model : str
String identifying the theoretical variogram function to be used
to describe the experimental variogram. Can be one of:
* spherical [Spherical, default]
* exponential [Exponential]
* gaussian [Gaussian]
* cubic [Cubic]
* stable [Stable model]
* matern [Matérn model]
* nugget [nugget effect variogram]
dist_func : str
String identifying the distance function. Defaults to
'euclidean'. Can be any metric accepted by
scipy.spatial.distance.pdist. Additional parameters are not (yet)
passed through to pdist. These are accepted by pdist for some of
the metrics. In these cases the default values are used.
bin_func : str
.. versionchanged:: 0.3.8
added 'fd', 'sturges', 'scott', 'sqrt', 'doane'
String identifying the binning function used to find lag class
edges. All methods calculate bin edges on the interval [0, maxlag[.
Possible values are:
* `'even'` (default) finds `n_lags` same width bins
* `'uniform'` forms `n_lags` bins of same data count
* `'fd'` applies Freedman-Diaconis estimator to find `n_lags`
* `'sturges'` applies Sturge's rule to find `n_lags`.
* `'scott'` applies Scott's rule to find `n_lags`
* `'doane'` applies Doane's extension to Sturge's rule to find `n_lags`
* `'sqrt'` uses the square-root of :func:`distance <skgstat.Variogram.distance>`. as `n_lags`.
More details are given in the documentation for :func:`set_bin_func <skgstat.Variogram.set_bin_func>`.
normalize : bool
Defaults to False. If True, the independent and dependent
variable will be normalized to the range [0,1].
fit_method : str
String identifying the method to be used for fitting the
theoretical variogram function to the experimental. More info is
given in the Variogram.fit docs. Can be one of:
* 'lm': Levenberg-Marquardt algorithm for unconstrained
problems. This is the faster algorithm, yet is the fitting of
a variogram not unconstrianed.
* 'trf': Trust Region Reflective function for non-linear
constrained problems. The class will set the boundaries
itself. This is the default function.
fit_sigma : numpy.ndarray, str
Defaults to None. The sigma is used as measure of uncertainty
during variogram fit. If fit_sigma is an array, it has to hold
n_lags elements, giving the uncertainty for all lags classes. If
fit_sigma is None (default), it will give no weight to any lag.
Higher values indicate higher uncertainty and will lower the
influcence of the corresponding lag class for the fit.
If fit_sigma is a string, a pre-defined function of separating
distance will be used to fill the array. Can be one of:
* 'linear': Linear loss with distance. Small bins will have
higher impact.
* 'exp': The weights decrease by a e-function of distance
* 'sqrt': The weights decrease by the squareroot of distance
* 'sq': The weights decrease by the squared distance.
More info is given in the Variogram.fit_sigma documentation.
directional_model : string, function
The model used for selecting all points fulfilling the
directional constraint of the Variogram. A predefined
model can be selected by passing the model name as string.
Optionally a callable accepting the difference vectors
between points in polar form as angles and distances and
returning a mask array can be passed. In this case, the
azimuth, tolerance and bandwidth has to be incorporated by
hand into the model.
* 'compass': includes points in the direction of the
azimuth at given tolerance. The bandwidth parameter will be
ignored.
* 'triangle': constructs a triangle with an angle of
tolerance at the point of interest and union an rectangle
parallel to azimuth, once the hypotenuse length reaches
bandwidth.
* 'circle': constructs a half circle touching the point of
interest, dislocating the center at the distance of
bandwidth in the direction of azimuth. The half circle is
union with an rectangle parallel to azimuth.
Visual representations, usage hints and implementation specifics
are given in the documentation.
azimuth : float
The azimuth of the directional dependence for this Variogram,
given as an angle in **degree**. The East of the coordinate
plane is set to be at 0° and is counted clockwise to 180° and
counter-clockwise to -180°. Only Points lying in the azimuth of a
specific point will be used for forming point pairs.
tolerance : float
The tolerance is given as an angle in **degree**- Points being
dislocated from the exact azimuth by half the tolerance will be
accepted as well. It's half the tolerance as the point may be
dislocated in the positive and negative direction from the azimuth.
bandwidth : float
Maximum tolerance acceptable in **coordinate units**, which is
usually meter. Points at higher distances may be far dislocated
from the azimuth in terms of coordinate distance, as the
tolerance is defined as an angle. THe bandwidth defines a maximum
width for the search window. It will be perpendicular to and
bisected by the azimuth.
use_nugget : bool
Defaults to False. If True, a nugget effet will be added to all
Variogram.models as a third (or fourth) fitting parameter. A
nugget is essentially the y-axis interception of the theoretical
variogram function.
maxlag : float, str
Can specify the maximum lag distance directly by giving a value
larger than 1. The binning function will not find any lag class
with an edge larger than maxlag. If 0 < maxlag < 1, then maxlag
is relative and maxlag * max(Variogram.distance) will be used.
In case maxlag is a string it has to be one of 'median', 'mean'.
Then the median or mean of all Variogram.distance will be used.
Note maxlag=0.5 will use half the maximum separating distance,
this is not the same as 'median', which is the median of all
separating distances
n_lags : int
Specify the number of lag classes to be defined by the binning
function.
verbose : bool
Set the Verbosity of the class. Not Implemented yet.
Keyword Arguments
-----------------
entropy_bins : int, str
.. versionadded:: 0.3.7
If the `estimator <skgstat.Variogram.estimator>` is set to
`'entropy'` this argument sets the number of bins, that should be
used for histogram calculation.
percentile : int
.. versionadded:: 0.3.7
If the `estimator <skgstat.Variogram.estimator>` is set to
`'entropy'` this argument sets the percentile to be used.
"""
# Before we do anything else, make kwargs available
self._kwargs = self._validate_kwargs(**kwargs)
# FIXME: Call __init__ of baseclass?
# No, because the sequence at which the arguments get initialized
# does matter. There is way too much transitive dependence, thus
# it was easiest to copy the init over.
self._direction_mask_cache = None
if not isinstance(coordinates, MetricSpace):
coordinates = np.asarray(coordinates)
coordinates = MetricSpace(coordinates.copy(), dist_func)
# FIXME: Currently _direction_mask / _angles / _euclidean_dist don't get correctly calculated for sparse dspaces
# coordinates = MetricSpace(coordinates.copy(), dist_func, maxlag if maxlag and not isinstance(maxlag, str) and maxlag >= 1 else None)
else:
assert self.dist_func == coordinates.dist_metric, "Distance metric of variogram differs from distance metric of coordinates"
assert coordinates.max_dist is None
# Set coordinates
self._X = coordinates
# pairwise difference
self._diff = None
# set verbosity
self.verbose = verbose
# set values
self._values = None
# calc_diff = False here, because it will be calculated by fit() later
self.set_values(values=values, calc_diff=False)
# distance matrix
self._dist = None
# set distance calculation function
self._dist_func_name = None
self.set_dist_function(func=dist_func)
# Angles and euclidean distances used for direction mask calculation
self._angles = None
self._euclidean_dist = None
# lags and max lag
self.n_lags = n_lags
self._maxlag = None
self.maxlag = maxlag
# estimator can be function or a string
self._estimator = None
self.set_estimator(estimator_name=estimator)
# model can be function or a string
self._model = None
self.set_model(model_name=model)
# azimuth direction
self._azimuth = None
self.azimuth = azimuth
# azimuth tolerance
self._tolerance = None
self.tolerance = tolerance
# tolerance bandwidth
self._bandwidth = None
self.bandwidth = bandwidth
# set the directional model
self._directional_model = None
self.set_directional_model(model_name=directional_model)
# the binning settings
self._bin_func = None
self._groups = None
self._bins = None
self.set_bin_func(bin_func=bin_func)
# specify if the lag should be given absolute or relative to the maxlag
self._normalized = normalize
# set the fitting method and sigma array
self.fit_method = fit_method
self._fit_sigma = None
self.fit_sigma = fit_sigma
# set if nugget effect shall be used
self.use_nugget = use_nugget
# set attributes to be filled during calculation
self.cov = None
self.cof = None
# settings, not reachable by init (not yet)
self._cache_experimental = False
# do the preprocessing and fitting upon initialization
# Note that fit() calls preprocessing
self.fit(force=True)
# finally check if any of the uncertainty propagation kwargs are set
self._experimental_conf_interval = None
self._model_conf_interval = None
if 'obs_sigma' in self._kwargs:
self._propagate_obs_sigma()
[docs] def preprocessing(self, force=False):
self._calc_direction_mask_data(force)
self._calc_diff(force=force)
self._calc_groups(force=force)
[docs] def _calc_direction_mask_data(self, force=False):
r"""
Calculate directional mask data.
For this, the angle between the vector between the two
points, and east (see comment about self.azimuth) is calculated.
The result is stored in self._angles and contains the angle of each
point pair vector to the x-axis in radians.
Parameters
----------
force : bool
If True, a new calculation of all angles is forced, even if they
are already in the cache.
Notes
-----
The masked data is in radias, while azimuth is given in degrees.
For the Vector between a point pair A,B :math:`\overrightarrow{AB}=u` and the
x-axis, represented by vector :math:`\overrightarrow{e} = [1,0]`, the angle
:math:`\Theta` is calculated like:
.. math::
cos(\Theta) = \frac{u \circ e}{|e| \cdot |[1,0]|}
See Also
--------
`azimuth <skgstat.DirectionalVariogram.azimuth>`_
"""
# FIXME: This should be optimized for the sparse case (range << bbox(coordinates)),
# i.e. use the MetricSpace in self._X
# check if already calculated
if self._angles is not None and not force:
return
# if self.coordinates is of just one dimension, concat zeros.
if self.coordinates.ndim == 1:
_x = np.vstack(zip(self.coordinates, np.zeros(len(self.coordinates))))
elif self.coordinates.ndim == 2:
_x = self.coordinates
else:
raise NotImplementedError('N-dimensional coordinates cannot be handled')
# for angles, we need Euklidean distance,
# no matter which distance function is used
# if self._dist_func_name == "euclidean":
# self._euclidean_dist = scipy.spatial.distance.squareform(self.distance_matrix)
# else:
self._euclidean_dist = pdist(_x, "euclidean")
# Calculate the angles
# (a - b).[1,0] = ||a - b|| * ||[1,0]|| * cos(v)
# cos(v) = (a - b).[1,0] / ||a - b||
# cos(v) = (a.[1,0] - b.[1,0]) / ||a - b||
scalar = pdist(np.array([np.dot(_x, [1, 0])]).T, np.subtract)
pos_angles = np.arccos(scalar / self._euclidean_dist)
# cos(v) for [2,1] and [2, -1] is the same,
# but v is not (v vs -v), fix that.
ydiff = pdist(np.array([np.dot(_x, [0, 1])]).T, np.subtract)
# store the angle or negative angle, depending on the
# amount of the x coordinate
self._angles = np.where(ydiff >= 0, pos_angles, -pos_angles)
@property
def azimuth(self):
"""Direction azimuth
Main direction for te selection of points in the formation of point
pairs. East of the coordinate plane is defined to be 0° and then the
azimuth is set clockwise up to 180°and count-clockwise to -180°.
Parameters
----------
angle : float
New azimuth angle in **degree**.
Raises
------
ValueError : in case angle < -180° or angle > 180
"""
return self._azimuth
@azimuth.setter
def azimuth(self, angle):
if angle < -180 or angle > 180:
raise ValueError('The azimuth is an angle in degree and has to '
'meet -180 <= angle <= 180')
else:
self._azimuth = angle
# reset groups and mask cache on azimuth change
self._direction_mask_cache = None
self._groups = None
@property
def tolerance(self):
"""Azimuth tolerance
Tolerance angle of how far a point can be off the azimuth for being
still counted as directional. A tolerance angle will be applied to
the azimuth angle symmetrically.
Parameters
----------
angle : float
New tolerance angle in **degree**. Has to meet 0 <= angle <= 360.
Raises
------
ValueError : in case angle < 0 or angle > 360
"""
return self._tolerance
@tolerance.setter
def tolerance(self, angle):
if angle < 0 or angle > 360:
raise ValueError('The tolerance is an angle in degree and has to '
'meet 0 <= angle <= 360')
else:
self._tolerance = angle
# reset groups and mask on tolerance change
self._direction_mask_cache = None
self._groups = None
@property
def bandwidth(self):
"""Tolerance bandwidth
New bandwidth parameter. As the tolerance from azimuth is given as an
angle, point pairs at high distances can be far off the azimuth in
coordinate distance. The bandwidth limits this distance and has the
unnit of the coordinate system.
Parameters
----------
width : float
Positive coordinate distance.
Raises
------
ValueError : in case width is negative
"""
if self._bandwidth is None:
return 0
else:
return self._bandwidth
@bandwidth.setter
def bandwidth(self, width):
# check if quantiles is given
if isinstance(width, str):
# TODO document and handle more exceptions
q = int(width[1:])
self._bandwidth = np.percentile(self.distance, q)
elif width < 0:
raise ValueError('The bandwidth cannot be negative.')
elif width > np.max(self.distance):
print('The bandwidth is larger than the maximum separating '
'distance. Thus it will have no effect.')
else:
self._bandwidth = width
# reset groups and direction mask cache on bandwidth change
self._direction_mask_cache = None
self._groups = None
[docs] def set_directional_model(self, model_name):
"""Set new directional model
The model used for selecting all points fulfilling the
directional constraint of the Variogram. A predefined model
can be selected by passing the model name as string.
Optionally a callable accepting the difference vectors between
points in polar form as angles and distances and returning a
mask array can be passed. In this case, the azimuth, tolerance
and bandwidth has to be incorporated by hand into the model.
The predefined options are:
* 'compass': includes points in the direction of the azimuth at given
tolerance. The bandwidth parameter will be ignored.
* 'triangle': constructs a triangle with an angle of tolerance at the
point of interest and union an rectangle parallel to azimuth,
once the hypotenuse length reaches bandwidth.
* 'circle': constructs a half circle touching the point of interest,
dislocating the center at the distance of bandwidth in the
direction of azimuth. The half circle is union with an rectangle
parallel to azimuth.
Visual representations, usage hints and implementation specifics
are given in the documentation.
Parameters
----------
model_name : string, callable
The name of the predefined model (string) or a function
that accepts angle and distance arrays and returns a mask
array.
"""
# handle predefined models
if isinstance(model_name, str):
if model_name.lower() == 'compass':
self._directional_model = self._compass
elif model_name.lower() == 'triangle':
self._directional_model = self._triangle
elif model_name.lower() == 'circle':
self._directional_model = self._circle
else:
raise ValueError('%s is not a valid model.' % model_name)
# handle callable
elif callable(model_name):
self._directional_model = model_name
else:
raise ValueError('The directional model has to be identified by a '
'model name, or it has to be the search area '
'itself')
# reset the groups as the directional model changed
self._groups = None
@property
def bins(self):
if self._bins is None:
# get the distances
d = self.distance.copy()
d[np.where(~self._direction_mask())] = np.nan
self._bins, n = self.bin_func(d, self._n_lags, self.maxlag)
# if the binning function returned an N, the n_lags need
# to be adjusted directly (not through the setter)
if n is not None:
self._n_lags = n
return self._bins.copy()
def _calc_groups(self, force=False):
super(DirectionalVariogram, self)._calc_groups(force=force)
# set to outside maxlag group
self._groups[np.where(~self._direction_mask())] = -1
# @jit
[docs] def _direction_mask(self, force=False):
"""Directional Mask
Array aligned to self.distance masking all point pairs which shall be
ignored for binning and grouping. The one dimensional array contains
all row-wise point pair combinations from the upper or lower triangle
of the distance matrix in case either of both is directional.
Returns
-------
mask : numpy.array
Array aligned to self.distance giving for each point pair
combination a boolean value whether the point are directional or
not.
"""
if force or self._direction_mask_cache is None:
self._direction_mask_cache = self._directional_model(self._angles, self._euclidean_dist)
return self._direction_mask_cache
[docs] def pair_field(self, ax=None, cmap="gist_rainbow", points='all', add_points=True, alpha=0.3, **kwargs): # pragma: no cover
"""
Plot a pair field.
Plot a network graph for all point pairs that fulfill the direction
filter and lie within each others search area.
Parameters
----------
ax : matplotlib.Subplot
A matplotlib Axes object to plot the pair field onto.
If ``None``, a new new matplotlib figure will be created.
cmap : string
Any color-map name that is supported by matplotlib
points : 'all', int, list
If not ``'all'``, only the given coordinate (int) or
list of coordinates (list) will be plotted. Recommended, if
the input data is quite large.
add_points : bool
If True (default) The coordinates will be added as black points.
alpha : float
Alpha value for the colors to make overlapping vertices
visualize better. Defaults to ``0.3``.
"""
# get the backend
used_backend = plotting.backend()
if used_backend == 'matplotlib':
return plotting.matplotlib_pair_field(self, ax=ax, cmap=cmap, points=points, add_points=add_points, alpha=alpha, **kwargs)
elif used_backend == 'plotly':
return plotting.plotly_pair_field(self, fig=ax, points=points, add_points=add_points, alpha=alpha, **kwargs)
[docs] def _triangle(self, angles, dists):
r"""Triangular Search Area
Construct a triangular bounded search area for building directional
dependent point pairs. The Search Area will be located onto the
current point of interest and the local x-axis is rotated onto the
azimuth angle.
Parameters
----------
angles, dists : numpy.array
Vectors between point pairs in polar form (angle relative
to east in radians, length in coordinate space units)
Returns
-------
mask : numpy.array(bool)
Point pair mask, indexed as the results of
scipy.spatial.distance.pdist are.
Notes
-----
.. code-block:: text
C
/|\
a / | \ a
/__|h_\
A c B
The point of interest is C and c is the bandwidth. The angle at C
(gamma) is the tolerance. From this, a and then h can be calculated.
When rotated into the local coordinate system, the two points needed
to build the search area A,B are A := (h, 1/2 c) and B:= (h, -1/2 c)
a can be calculated like:
.. math::
a = \frac{c}{2 * sin\left(\frac{\gamma}{2}\right)}
See Also
--------
DirectionalVariogram._compass
DirectionalVariogram._circle
"""
absdiff = np.abs(angles + np.radians(self.azimuth))
absdiff = np.where(absdiff > np.pi, absdiff - np.pi, absdiff)
absdiff = np.where(absdiff > np.pi / 2, np.pi - absdiff, absdiff)
in_tol = absdiff <= np.radians(self.tolerance / 2)
in_band = self.bandwidth / 2 >= np.abs(dists * np.sin(np.abs(angles + np.radians(self.azimuth))))
return in_tol & in_band
def _circle(self, angles, dists):
r"""Circular Search Area
Construct a half-circled bounded search area for building directional
dependent point pairs. The Search Area will be located onto the
current point of interest and the local x-axis is rotated onto the
azimuth angle.
The radius of the half-circle is set to half the bandwidth.
Parameters
----------
angles, dists : numpy.array
Vectors between point pairs in polar form (angle relative
to east in radians, length in coordinate space units)
Returns
-------
mask : numpy.array(bool)
Point pair mask, indexed as the results of
scipy.spatial.distance.pdist are.
Raises
------
ValueError : In case the DirectionalVariogram.bandwidth is None or 0.
See Also
--------
DirectionalVariogram._triangle
DirectionalVariogram._compass
"""
raise NotImplementedError
[docs] def _compass(self, angles, dists):
r"""Compass direction direction mask
Construct a search area for building directional dependent point
pairs. The compass search area will **not** be bounded by the
bandwidth. It will include all point pairs at the azimuth direction
with a given tolerance. The Search Area will be located onto the
current point of interest and the local x-axis is rotated onto the
azimuth angle.
Parameters
----------
angles, dists : numpy.array
Vectors between point pairs in polar form (angle relative
to east in radians, length in coordinate space units)
Returns
-------
mask : numpy.array(bool)
Point pair mask, indexed as the results of
scipy.spatial.distance.pdist are.
See Also
--------
DirectionalVariogram._triangle
DirectionalVariogram._circle
"""
absdiff = np.abs(angles + np.radians(self.azimuth))
absdiff = np.where(absdiff > np.pi, absdiff - np.pi, absdiff)
absdiff = np.where(absdiff > np.pi / 2, np.pi - absdiff, absdiff)
return absdiff <= np.radians(self.tolerance / 2)