# -*- coding: utf-8 -*-
# dcf
# ---
# A Python library for generating discounted cashflows.
#
# Author: sonntagsgesicht, based on a fork of Deutsche Postbank [pbrisk]
# Version: 0.7, copyright Wednesday, 11 May 2022
# Website: https://github.com/sonntagsgesicht/dcf
# License: Apache License 2.0 (see LICENSE file)
from bisect import bisect_left, bisect_right
from math import exp, log
[docs]class base_interpolation(object):
"""
Basic class to interpolate given data.
"""
def __init__(self, x_list=list(), y_list=list()):
r""" interpolation class
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
"""
self.x_list = list()
self.y_list = list()
self._update(x_list, y_list)
def __call__(self, x):
raise NotImplementedError
def __contains__(self, item):
return item in self.x_list
def _update(self, x_list=list(), y_list=list()):
""" _update interpolation data
:param list(float) x_list: x values
:param list(float) y_list: y values
"""
if not y_list:
for x in x_list:
if x in self.x_list:
i = self.x_list.index(float(x))
self.x_list.pop(i)
self.y_list.pop(i)
else:
x_list = list(map(float, x_list))
y_list = list(map(float, y_list))
data = [(x, y) for x, y in
zip(self.x_list, self.y_list) if x not in x_list]
data.extend(list(zip(x_list, y_list)))
data = sorted(data)
self.x_list = [float(x) for (x, y) in data]
self.y_list = [float(y) for (x, y) in data]
[docs] @classmethod
def from_dict(cls, xy_dict):
r"""create an interpolation instance object from **dict**
:param xy_dict: dictionary with sorted keys serving as **x_list**
and values as **y_list**
:return: interpolation object
"""
return cls(sorted(xy_dict), (xy_dict[k] for k in sorted(xy_dict)))
[docs]class flat(base_interpolation):
def __init__(self, y=0.0):
r""" flat or constant interpolation
:param y: constant return value $\hat{y}$
A |flat()| object is a function $f$
returning a constant value $\hat{y}$.
$$f(x)=\hat{y}\text{ const.}$$
for all $x$.
>>> from dcf.interpolation import flat
>>> c = flat(1.1)
>>> c(0)
1.1
>>> c(2.1)
1.1
"""
super(flat, self).__init__([0.0], [y])
def __call__(self, x):
return self.y_list[0]
[docs]class default_value_interpolation(base_interpolation):
def __init__(self, x_list=list(), y_list=list(), default_value=None):
r""" default value interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
:param default_value: default value $d$
A |default_value_interpolation()| object is a function $f$
returning at $x$ the value $y_i$
with $i$ to be the first matching index such that $x=x_i$
$$f(x)=y_i\text{ for } x=x_i$$
and $d$ if no matching $x_i$ is found.
>>> from dcf.interpolation import default_value_interpolation
>>> c = default_value_interpolation([1,2,3,1], [1,2,3,4], default_value=42)
>>> c(1)
1.0
>>> c(2)
2.0
>>> c(4)
42
""" # noqa 501
self._default = default_value
super().__init__(x_list, y_list)
def __call__(self, x):
if x not in self.x_list:
return self._default
return self.y_list[self.x_list.index(x)]
[docs]class no(default_value_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" no interpolation at all
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |no()| object is a function $f$
returning at $x$ the value $y_i$
with $i$ to be the first matching index such that $x=x_i$
$$f(x)=y_i\text{ for } x=x_i \text{ else None}$$
>>> from dcf.interpolation import no
>>> c = no([1,2,3,1], [1,2,3,4])
>>> c(1)
1.0
>>> c(2)
2.0
>>> c(4)
"""
super().__init__(x_list, y_list)
[docs]class zero(default_value_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" interpolation by filling with zeros between points
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |zero()| object is a function $f$
returning at $x$ the value $y_i$
if $x=x_i$ else zero.
with $i$ to be the first matching index such that
$$f(x)=y_i\text{ for } x=x_i \text{ else } 0$$
>>> from dcf.interpolation import zero
>>> c = zero([1,2,3,1], [1,2,3,4])
>>> c(1)
1.0
>>> c(1.1)
0.0
>>> c(2)
2.0
>>> c(4)
0.0
"""
super().__init__(x_list, y_list, 0.0)
[docs]class left(base_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" left interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |left()| object is a function $f$
returning at $x$ the last given value $y_i$
reading from left to right, i.e.
with $i$ to be the matching index such that
$$f(x)=y_i\text{ for } x_i \leq x < x_{i+1}$$
or $y_1$ if $x<x_1$.
>>> from dcf.interpolation import left
>>> c = left([1,3], [1,2])
>>> c(0)
1.0
>>> c(1)
1.0
>>> c(2)
1.0
>>> c(4)
2.0
"""
super().__init__(x_list, y_list)
def __call__(self, x):
if len(self.y_list) == 0:
raise OverflowError
if x in self.x_list:
i = self.x_list.index(x)
else:
i = bisect_left(self.x_list, float(x), 1,
len(self.x_list)) - 1
return self.y_list[i]
[docs]class constant(left):
def __init__(self, x_list=list(), y_list=list()):
r""" constant interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
Same as |left()|.
"""
super().__init__(x_list, y_list)
[docs]class right(base_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" right interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |right()| object is a function $f$
returning at $x$ the last given value $y_i$
reading from right to left, i.e.
with $i$ to be the matching index such that
$$f(x)=y_i\text{ for } x_i < x \leq x_{i+1}$$
or $y_n$ if $x_n < x$.
>>> from dcf.interpolation import right
>>> c = right([1,3], [1,2])
>>> c(0)
1.0
>>> c(1)
1.0
>>> c(2)
2.0
>>> c(4)
2.0
"""
super().__init__(x_list, y_list)
def __call__(self, x):
if len(self.y_list) == 0:
raise OverflowError
if x in self.x_list:
i = self.x_list.index(x)
else:
i = bisect_right(self.x_list, float(x), 0,
len(self.x_list) - 1)
return self.y_list[i]
[docs]class nearest(base_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" nearest interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |nearest()| object is a function $f$
returning at $x$ the given value $y_i$
of the nearest $x_i$
from both left and right, i.e.
with $i$ to be the matching index such that
$$f(x)=y_i \text{ for } \mid x_i -x \mid = \min_j \mid x_j -x \mid$$
>>> from dcf.interpolation import nearest
>>> c = nearest([1,2,3], [1,2,3])
>>> c(0)
1.0
>>> c(1)
1.0
>>> c(1.5)
1.0
>>> c(1.51)
2.0
>>> c(2)
2.0
>>> c(4)
3.0
"""
super().__init__(x_list, y_list)
def __call__(self, x):
if len(self.y_list) == 0:
raise OverflowError
if len(self.y_list) == 1:
return self.y_list[0]
if x in self.x_list:
i = self.x_list.index(x)
else:
i = bisect_left(self.x_list, float(x), 1, len(self.x_list) - 1)
if (self.x_list[i - 1] - x) / (self.x_list[i - 1] -
self.x_list[i]) <= 0.5:
i -= 1
return self.y_list[i]
[docs]class linear(base_interpolation):
def __init__(self, x_list=list(), y_list=list()):
r""" linear interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |linear()| object is a function $f$
returning at $x$ the linear interpolated value of
$y_i$ and $y_{i+1}$ when $x_i \leq x < x_{i+1}$, i.e.
$$f(x)=(y_{i+1}-y_i) \cdot \frac{x-x_i}{x_{i+1}-x_i}$$
>>> from dcf.interpolation import linear
>>> c = linear([1,2,3], [2,3,4])
>>> c(0)
1.0
>>> c(1)
2.0
>>> c(1.5)
2.5
>>> c(1.51)
2.51
>>> c(2)
3.0
>>> c(4)
5.0
"""
super().__init__(x_list, y_list)
def __call__(self, x):
if len(self.y_list) == 0:
raise OverflowError
if len(self.y_list) == 1:
return self.y_list[0]
i = bisect_left(self.x_list, float(x), 1, len(self.x_list) - 1)
return self.y_list[i - 1] + (self.y_list[i] - self.y_list[i - 1]) * \
(self.x_list[i - 1] - x) / (self.x_list[i - 1] - self.x_list[i])
[docs]class loglinear(linear):
def __init__(self, x_list=list(), y_list=list()):
r""" log-linear interpolation
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |loglinear()| object is a function $f$
returning at $x$ the value $\exp(y)$
of the linear interpolated value $y$ of
$\log(y_i)$ and $\log(y_{i+1})$ when $x_i \leq x < x_{i+1}$, i.e.
$$f(x)=\exp\Big((\log(y_{i+1})-\log(y_i))
\cdot \frac{x-x_i}{x_{i+1}-x_i}\Big)$$
>>> from math import log, exp
>>> from dcf.interpolation import loglinear
>>> c = loglinear([1,2,3], [exp(2),exp(3),exp(4)])
>>> log(c(0))
1.0
>>> log(c(1))
2.0
>>> log(c(1.5))
2.5
>>> log(c(1.51))
2.51
>>> log(c(2))
3.0
>>> log(c(4))
5.0
.. note::
**loglinear** requires strictly positive values $0<y_1 \dots y_n$.
"""
if not all(0. < y for y in y_list):
raise ValueError('log interpolation requires positive values.')
log_y_list = [log(y) for y in y_list]
super(loglinear, self).__init__(x_list, log_y_list)
def __call__(self, x):
log_y = super(loglinear, self).__call__(x)
return exp(log_y)
[docs]class loglinearrate(linear):
def __init__(self, x_list=list(), y_list=list()):
r""" log-linear interpolation by annual rates
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |loglinearrate()| object is a function $f$
returning at $x$ the value $\exp(x \cdot y)$
of the linear interpolated value $y$ of
$\log(\frac{y_i}{x_i})$ and $\log(\frac{y_{i+1}}{x_{i+1}})$
when $x_i \leq x < x_{i+1}$, i.e.
$$f(x)=\exp\Big(x \cdot
(\log(\frac{y_{i+1}}{x_{i+1}})-\log(\frac{y_i}{x_i}))
\cdot \frac{x-x_i}{x_{i+1}-x_i}\Big)$$
>>> from math import log, exp
>>> from dcf.interpolation import loglinear
>>> c = loglinear([1,2,3], [exp(1*2),exp(2*3),exp(2*4)])
>>> log(c(0))
-2.0
>>> log(c(1))
2.0
>>> log(c(1.5))
4.0
>>> log(c(1.51))
4.04
>>> log(c(2))
6.0
>>> log(c(4))
10.0
.. note::
**loglinear** requires strictly positive values $0<y_1 \dots y_n$.
"""
if not all(0. < y for y in y_list):
raise ValueError(
'log interpolation requires positive values. Got %s' % str(
y_list))
z = [x for x in x_list if not x]
self._y_at_zero = y_list[x_list.index(z[0])] if z else None
log_y_list = [log(y) / x for x, y in zip(x_list, y_list) if x]
x_list = [x for x in x_list if x]
super(loglinearrate, self).__init__(x_list, log_y_list)
def __call__(self, x):
if not x:
return self._y_at_zero
log_y = super(loglinearrate, self).__call__(x)
return exp(log_y * x)
[docs]class logconstantrate(constant):
def __init__(self, x_list=list(), y_list=list()):
r""" log-constant interpolation by annual rates
:param x_list: points $x_1 \dots x_n$
:param y_list: values $y_1 \dots y_n$
A |logconstantrate()| object is a function $f$
returning at $x$ the value $\exp(x \cdot y)$
of the constant interpolated value $y$ of
$\log(\frac{y_i}{x_i})$
when $x_i \leq x < x_{i+1}$, i.e.
$$f(x)=\exp\Big(x \cdot \log(\frac{y_i}{x_i})\Big)$$
>>> from math import log, exp
>>> from dcf.interpolation import logconstantrate
>>> c = logconstantrate([1,2,3], [exp(1*2),exp(2*3),exp(2*4)])
>>> log(c(1))
2.0
>>> log(c(1.5))
3.0
>>> log(c(1.51))
3.02
>>> log(c(2))
6.0
>>> log(c(3))
8.0
.. note::
**logconstantrate** requires strictly positive values $0<y_1 \dots y_n$.
""" # noqa 501
if not all(0. < y for y in y_list):
raise ValueError('log interpolation requires positive values.')
z = [x for x in x_list if not x]
self._y_at_zero = y_list[x_list.index(z[0])] if z else None
log_y_list = [-log(y) / x for x, y in zip(x_list, y_list) if x]
x_list = [x for x in x_list if x]
super(logconstantrate, self).__init__(x_list, log_y_list)
def __call__(self, x):
if not x:
return self._y_at_zero
log_y = super(logconstantrate, self).__call__(x)
return exp(-log_y * x)
[docs]class interpolation_scheme(object):
_interpolation = constant, linear, constant
def __init__(self, domain, data, interpolation=None):
r"""class to build piecewise interpolation function
:param list(float) domain: points $x_1 \dots x_n$
:param list(float) data: values $y_1 \dots y_n$
:param function interpolation:
interpolation function on **x_list** (optional)
or triple of (**left**, **mid**, **right**)
interpolation functions with
* **left** for $x < x_1$ (as default **right** is used)
* **mid** for $x_1 \leq x \leq x_n$ (as default |linear| is used)
* **right** for $x > x_n$ (as default |constant| is used)
Curve object to build function
$$f(x) = y$$
using piecewise various interpolation functions.
"""
if not interpolation:
interpolation = self.__class__._interpolation
y_left, y_mid, y_right = self.__class__._interpolation
if isinstance(interpolation, (tuple, list)):
if len(interpolation) == 3:
y_left, y_mid, y_right = interpolation
elif len(interpolation) == 2:
y_mid, y_right = interpolation
y_left = y_right
elif len(interpolation) == 1:
y_mid, = interpolation
else:
raise ValueError
elif issubclass(interpolation, base_interpolation):
y_mid = interpolation
else:
raise AttributeError
if not len(domain) == len(data):
raise AssertionError()
if not len(domain) == len(set(domain)):
raise AssertionError()
#: Interpolation:
self._y_mid = y_mid(domain, data)
self._y_right = y_right(domain, data)
self._y_left = y_left(domain, data)
def __call__(self, x):
y = 0.0
if x < self._y_left.x_list[0]:
# extrapolate to left
y = self._y_left(x)
elif x > self._y_right.x_list[-1]:
# extrapolate to right
y = self._y_right(x)
else:
# interpolate in the middle
y = self._y_mid(x)
return y
def _dyn_scheme(left, mid, right):
name = left.__name__ + '_' + mid.__name__ + '_' + right.__name__
return type(name, (interpolation_scheme,),
{'_interpolation': (left, mid, right)})
constant_linear_constant = \
_dyn_scheme(constant, linear, constant)
linear_scheme = constant_linear_constant
logconstant_loglinear_logconstant = \
_dyn_scheme(constant, loglinear, constant)
log_linear_scheme = logconstant_loglinear_logconstant
logconstantrate_loglinearrate_logconstantrate = \
_dyn_scheme(logconstantrate, loglinearrate, logconstantrate)
log_linear_rate_scheme = logconstantrate_loglinearrate_logconstantrate
zero_linear_constant = \
_dyn_scheme(zero, linear, constant)
zero_linear_scheme = zero_linear_constant