```# -*- coding: utf-8 -*-

u"""

“Geographic masking is the process of altering the coordinates of point
location data to limit the risk of reidentification upon release of the
data.  In effect, the purpose of geographic masking is to make it much
more difficult to accurately reverse geocode the released data.”

-- from `chapter 6 of Ensuring Confidentiality of Geocoded Health Data:
Assessing Geographic Masking Strategies for Individual-Level Data
<https://www.hindawi.com/journals/amed/2014/567049/#sec6>`_

Some implementations are inspired by `chapter 7 of Ensuring Confidentiality of
Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-
Level Data <https://www.hindawi.com/journals/amed/2014/567049/#sec7>`_.
"""

from geopy.point import Point
import math
import random

# This implementation uses human readable degrees with reduced precision
# random.uniform(0, 360) * math.pi / 180

# We simplfy the implementation by calculating directly in radians
return random.uniform(0, 2) * math.pi

[docs]def limit_precision(point, precisions=(None, None, None)):
"""
Masked points have a limited precision, hence we cut decimal places.

In the given `precisions` tupel positive integers simply cut decimal places
after the comma.  Negative integers define the amount decimals to keep
before the comma, while cutting all decimal places after the comma.  In
both cases standard mathematical rounding is applied to the first digit
which won't be cut, before cutting the other digits.  The first value is
the longitude's precision, the second value is the latitude's precision and
the third value is the altitude's precision.

If we define a coordinate like ...

>>> coordinate = Point(12.3456, 12.3456, 123.456)

... and call this function like ...

>>> limit_precision(coordinate)
Point(12.3456, 12.3456, 123.456)

>>> limit_precision(coordinate, (0, 0, 0))
Point(12.3456, 12.3456, 123.456)

>>> limit_precision(coordinate, (None, None, None))
Point(12.3456, 12.3456, 123.456)

... the given point returns unchanged.

If we assign to the second parameter tuple some positive integers the given
point will return with altered decimal places after the comma and rounding
applied:

>>> limit_precision(coordinate, (1, 1, 1))
Point(12.3, 12.3, 123.5)

>>> limit_precision(coordinate, (2, 2, 2))
Point(12.35, 12.35, 123.46)

>>> limit_precision(coordinate, (3, 3, 3))
Point(12.346, 12.346, 123.456)

Assigning negative integers to the second parameter tuple will return a
point with altered decimal places before the comma and all decimal places
cut after the comma:

>>> limit_precision(coordinate, (-1, -1, -1))
Point(10.0, 10.0, 120.0)

>>> limit_precision(coordinate, (-2, -2, -2))
Point(10.0, 10.0, 100.0)

Mind that decimals with less digits than the given absolute precision, keep
the last remaining digit intact.

>>> limit_precision(coordinate, (-3, -3, -3))
Point(10.0, 10.0, 100.0)

The mathematical rounding can lead to unexpected results, if applied before
the comma:

>>> limit_precision(Point(65.4321, 65.4321, 654.321), (-1, -1, -1))
Point(70.0, 70.0, 650.0)

>>> limit_precision(Point(65.4321, 65.4321, 654.321), (-2, -2, -2))
Point(70.0, 70.0, 700.0)

"""
def _limit(value, precision):
if 0 < precision:
value = round(value, precision)
elif 0 > precision:
calculus = 10 ** (-1 * precision)
if calculus < value:
value = round(value / calculus) * calculus
else:
value = _limit(value, precision + 1)
return value

return Point(
_limit(point[0], precisions[0] or 0),
_limit(point[1], precisions[1] or 0),
_limit(point[2], precisions[2] or 0)
)

"""
Masked points are displaced by a fixed vector, hence we move the point.

If we define a coordinate like ...

>>> coordinate = Point(12.3456, 12.3456, 12.3456)

... and call this function like ...

Point(12.3456, 12.3456, 12.3456)

Point(12.3456, 12.3456, 12.3456)

Point(12.3456, 12.3456, 12.3456)

... the given point returns unchanged.

In all other cases the given point will be moved by the given values:

Point(13.3456, 13.3456, 13.3456)

Mind that geodesic points with latitude, longitude, and altitude are used.
This results in points with limited value range, hence we rotate the points
around the globe:

Point(-67.65440000000001, 112.3456, 112.3456)

Point(-87.6544, -87.6544, -87.6544)

"""

return Point(
point[0] + (vector[0] or 0.0),
point[1] + (vector[1] or 0.0),
point[2] + (vector[2] or 0.0)
)

"""
Masked points are placed on a random location on a circle around the
original location.  Masked points are not placed inside the circle itself.

If we define a coordinate like ...

>>> coordinate = Point(0.0, 0.0, 0.0)

... and call this function without any radius ...

>>> displace_on_a_circle(coordinate)
Point(0.0, 0.0, 0.0)

>>> displace_on_a_circle(coordinate, 0)
Point(0.0, 0.0, 0.0)

>>> displace_on_a_circle(coordinate, None)
Point(0.0, 0.0, 0.0)

... the given point returns unchanged.

With a given radius, a randomly circular displaced point will return.  That
implies the altitude always remains untouched.  The given coodinates are
the circle's center, the given radius is the distance between given and
resulting coodinate:

>>> displace_on_a_circle(coordinate, 1) # doctest: +ELLIPSIS
Point(..., 0.0)

>>> displace_on_a_circle(coordinate, -1) # doctest: +ELLIPSIS
Point(..., 0.0)

>>> random.seed(1)
>>> displace_on_a_circle(coordinate, 1)
Point(0.7474634341555553, 0.6643029539301958, 0.0)

"""

return point

# beware that longitude is x and latitude is y !

"""
Masked points are placed on a random location on a sphere around the
original location.  Masked points are not placed inside the sphere itself.

If we define a coordinate like ...

>>> coordinate = Point(0.0, 0.0, 0.0)

... and call this function without any radius ...

>>> displace_on_a_sphere(coordinate)
Point(0.0, 0.0, 0.0)

>>> displace_on_a_sphere(coordinate, 0)
Point(0.0, 0.0, 0.0)

>>> displace_on_a_sphere(coordinate, None)
Point(0.0, 0.0, 0.0)

... the given point returns unchanged.

With a given radius, a randomly spherical displaced point will return.  The
given coodinates are the sphere's center, the given radius is the distance
between given and resulting coodinate:

>>> displace_on_a_sphere(coordinate, 1) # doctest: +ELLIPSIS
Point(...)

>>> displace_on_a_sphere(coordinate, -1) # doctest: +ELLIPSIS
Point(...)

>>> random.seed(1)
>>> displace_on_a_sphere(coordinate, 1)
Point(-0.6117158867827159, -0.5436582607079324, 0.5746645712253897)

"""

return point

x = math.cos(a1) * math.sin(a2) * radius
y = math.sin(a1) * math.sin(a2) * radius

# beware that longitude is x and latitude is y !

"""
Masked locations are placed anywhere within a circular area around the
original location.  Since every location within the circle is equally
likely, masked locations are more likely to be placed at larger distances
compared to small distances.  A variation on this technique is the use of
displaced using a vector with random direction and random radius.  The
radius is constrained by a maximum value. This effectively results in a
locations are as likely to be at large distances compared to small
distances.  These two techniques therefore only differ slightly in the
probability of how close masked locations are placed to the original
locations.

If we define a coordinate like ...

>>> coordinate = Point(0.0, 0.0, 0.0)

... and call this function without any radius ...

>>> displace_within_a_circle(coordinate)
Point(0.0, 0.0, 0.0)

>>> displace_within_a_circle(coordinate, 0)
Point(0.0, 0.0, 0.0)

>>> displace_within_a_circle(coordinate, None)
Point(0.0, 0.0, 0.0)

... the given point returns unchanged.

With a given radius, a randomly circular displaced point will return.  That
implies the altitude always remains untouched.  The given coodinates are
the circle's center, the given radius is the maximum distance between given
and resulting coodinate:

>>> displace_within_a_circle(coordinate, 1) # doctest: +ELLIPSIS
Point(..., 0.0)

>>> displace_within_a_circle(coordinate, -1) # doctest: +ELLIPSIS
Point(..., 0.0)

>>> random.seed(1)
>>> displace_within_a_circle(coordinate, 1)
Point(-0.10996222555283103, 0.07721437073087664, 0.0)

"""

return point

"""
Masked locations are placed anywhere within a spherical space around the
original location.  Since every location within the sphere is equally
likely, masked locations are more likely to be placed at larger distances
compared to small distances.  A variation on this technique is the use of
displaced using a vector with random direction and random radius.  The
radius is constrained by a maximum value. This effectively results in a
locations are as likely to be at large distances compared to small
distances.  These two techniques therefore only differ slightly in the
probability of how close masked locations are placed to the original
locations.

If we define a coordinate like ...

>>> coordinate = Point(0.0, 0.0, 0.0)

... and call this function with any radius ...

>>> displace_within_a_sphere(coordinate)
Point(0.0, 0.0, 0.0)

>>> displace_within_a_sphere(coordinate, 0)
Point(0.0, 0.0, 0.0)

>>> displace_within_a_sphere(coordinate, None)
Point(0.0, 0.0, 0.0)

... the given point returns unchanged.

With a given radius, a randomly spherical displaced point will return.  The
given coodinates are the sphere's center, the given radius is the maximum
distance between given and resulting coodinate:

>>> displace_within_a_sphere(coordinate, 1) # doctest: +ELLIPSIS
Point(...)

>>> displace_within_a_sphere(coordinate, -1) # doctest: +ELLIPSIS
Point(...)

>>> random.seed(1)
>>> displace_within_a_sphere(coordinate, 1)
Point(0.10955063884671598, -0.07692535867829137, 0.011614508874230087)

"""

return point

[docs]def displace_within_a_circular_donut(point,
"""
This technique is similar to random displacement within a circle, but a
smaller internal circle is utilized within which displacement is not
allowed.  In effect, this sets a minimum and maximum level for the
displacement.  Masked locations are placed anywhere within the allowable
area.  A slightly different approach to donut masking is the use of a
random direction and two random radii: one for maximum and one for minimum
displacement.  These two techniques only differ slightly in the probability
of how close masked locations are placed to the original locations.  Both
approaches enforce a minimum amount of displacement.

With a given radius, a randomly circular displaced point will return.  That
implies the altitude always remains untouched.  The given coodinates are
the circle's center, the given radii are the minimum and maximum distance
between given and resulting coodinate:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> displace_within_a_circular_donut(coordinate)
Point(-0.4641756357658914, 0.32593947097813314, 0.0)

>>> random.seed(1)
>>> displace_within_a_circular_donut(coordinate, 0.5, 1.0)
Point(-0.4641756357658914, 0.32593947097813314, 0.0)

"""

[docs]def displace_within_a_spherical_donut(point,
"""
This technique is similar to random displacement within a sphere, but a
smaller internal sphere is utilized within which displacement is not
allowed.  In effect, this sets a minimum and maximum level for the
displacement.  Masked locations are placed anywhere within the allowable
space.  A slightly different approach to donut masking is the use of a
random direction and two random radii: one for maximum and one for minimum
displacement.  These two techniques only differ slightly in the probability
of how close masked locations are placed to the original locations.  Both
approaches enforce a minimum amount of displacement.

With a given radius, a randomly spherical displaced point will return.  The
given coodinates are the sphere's center, the given radii are the minimum
and maximum distance between given and resulting coodinate:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> displace_within_a_spherical_donut(coordinate)
Point(0.4624382343989833, -0.32471948518229893, 0.049027491156171304)

>>> random.seed(1)
>>> displace_within_a_spherical_donut(coordinate, 0.5, 1.0)
Point(0.4624382343989833, -0.32471948518229893, 0.049027491156171304)

"""

[docs]def circular_gaussian_displacement(point, mu=1.0, sigma=1.0):
"""
The direction of displacement is random, but the distance follows a
Gaussian distribution, where `mu` is the mean and `sigma` is the standard
deviation.  The dispersion of the distribution can be varied based on other
parameters of interest, such as local population density.

With a given radius, a randomly circular displaced point will return.  That
implies the altitude always remains untouched.  The given coodinates are
the circle's center:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> circular_gaussian_displacement(coordinate)
Point(-2.279620117247094, 0.19779177337662887, 0.0)

>>> random.seed(1)
>>> circular_gaussian_displacement(coordinate, 1.0, 1.0)
Point(-2.279620117247094, 0.19779177337662887, 0.0)

"""

[docs]def spherical_gaussian_displacement(point, mu=1.0, sigma=1.0):
"""
The direction of displacement is random, but the distance follows a
Gaussian distribution, where `mu` is the mean and `sigma` is the standard
deviation.  The dispersion of the distribution can be varied based on other
parameters of interest, such as local population density.

With a given radius, a randomly spherical displaced point will return.  The
given coodinates are the sphere's center:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> spherical_gaussian_displacement(coordinate)
Point(-2.278463993019554, 0.19769146198725365, -0.07286551271936394)

>>> random.seed(1)
>>> spherical_gaussian_displacement(coordinate, 1.0, 1.0)
Point(-2.278463993019554, 0.19769146198725365, -0.07286551271936394)

"""

[docs]def circular_bimodal_gaussian_displacement(point,
inner_mu=1.0,
inner_sigma=1.0,
outer_mu=2.0,
outer_sigma=1.0):
"""
This is a variation on the Gaussian masking technique, employing a bimodal
Gaussian distribution for the random distance function.  In effect, this
approximates donut masking, but with a less uniform probability of
placement.

With a given radius, a randomly circular displaced point will return.  That
implies the altitude always remains untouched.  The given coodinates are
the circle's center:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> circular_bimodal_gaussian_displacement(coordinate)
Point(3.1735160345853632, -0.10110949606778825, 0.0)

>>> random.seed(1)
>>> circular_bimodal_gaussian_displacement(coordinate,
...                                        1.0, 1.0, 2.0, 1.0)
Point(3.1735160345853632, -0.10110949606778825, 0.0)

"""

[docs]def spherical_bimodal_gaussian_displacement(point,
inner_mu=1.0,
inner_sigma=1.0,
outer_mu=2.0,
outer_sigma=1.0):
"""
This is a variation on the Gaussian masking technique, employing a bimodal
Gaussian distribution for the random distance function.  In effect, this
approximates donut masking, but with a less uniform probability of
placement.

With a given radius, a randomly spherical displaced point will return.  The
given coodinates are the sphere's center:

>>> coordinate = Point(0.0, 0.0, 0.0)

>>> random.seed(1)
>>> spherical_bimodal_gaussian_displacement(coordinate)
Point(0.09101092182341257, -0.002899644539981792, -3.173820372380246)

>>> random.seed(1)
>>> spherical_bimodal_gaussian_displacement(coordinate,
...                                         1.0, 1.0, 2.0, 1.0)
Point(0.09101092182341257, -0.002899644539981792, -3.173820372380246)

"""