"""Functions for manipulating Higher Order Modes."""
import logging
import numbers
import numpy as np
import scipy.special
from ..env import warn
from ..exceptions import ContextualValueError
LOGGER = logging.getLogger(__name__)
[docs]def make_modes(select=None, maxtem=None):
"""Construct a 2D :class:`numpy.ndarray` of HOM indices.
Parameters
----------
select : sequence, str, optional; default: None
Identifier for the mode indices to generate. This can be:
- An iterable of mode indices, where each element in the iterable
must unpack to two integer convertible values.
- A string identifying the type of modes to include, must be
one of "even", "odd", "tangential" (or "x") or "sagittal" (or "y").
maxtem : int, optional; default: None
Optional maximum mode order, applicable only for when `select` is
a string. This is ignored if `select` is not a string.
Returns
-------
modes : :class:`numpy.ndarray`
An array of mode indices.
Raises
------
ValueError
If either of the arguments `select`, `maxtem` are invalid or non-unique.
See Also
--------
insert_modes : Add modes to an existing mode indices array at the correct positions.
Examples
--------
Modes up to a maximum order of 2:
>>> make_modes(maxtem=2)
array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [2, 0]], dtype=int32)
Even modes up to order 4:
>>> make_modes("even", maxtem=4)
array([[0, 0], [0, 2], [0, 4], [2, 0], [2, 2], [4, 0]], dtype=int32)
Sagittal modes up to order 3:
>>> make_modes("y", maxtem=3)
array([[0, 0], [0, 1], [0, 2], [0, 3]], dtype=int32)
Modes from a list of strings:
>>> make_modes(["00", "11", "22"])
array([[0, 0], [1, 1], [2, 2]], dtype=int32)
"""
if select is None and maxtem is None:
raise ContextualValueError(
{
"select": ContextualValueError.empty,
"maxtem": ContextualValueError.empty,
},
"cannot both be empty",
)
if select is None:
_check_maxtem(maxtem)
limit = 1 + int(maxtem)
N = int(limit * (1 + limit) / 2)
modes = np.zeros(N, dtype=(np.intc, 2))
count = 0
for i in range(0, limit):
for j in range(0, i + 1):
modes[count] = (i - j, j)
count += 1
elif isinstance(select, str):
switch = {
"even": _make_even_modes,
"odd": _make_odd_modes,
"tangential": _make_tangential_modes,
"sagittal": _make_sagittal_modes,
"x": _make_tangential_modes,
"y": _make_sagittal_modes,
}
if select.casefold() not in switch:
msg = f"""
Mode arument (= {select}) not recognised as a valid identifier. It must be:
- "even" for generating even modes up to the given maxtem,
- "odd" for generating odd modes up to the given maxtem,
- "tangential" or "x" for generating tangential modes up to maxtem,
- or "sagittal" or "y" for generating sagittal modes up to maxtem.
"""
raise ValueError(msg.strip())
modes = switch[select.casefold()](maxtem)
else:
if maxtem is not None:
warn(
"Ignoring maxtem argument given to make_modes as an iterable has "
"already been provided."
)
modes = np.zeros(len(select), dtype=(np.intc, 2))
for i, mode in enumerate(select):
try:
mode = list(mode)
except TypeError:
raise ValueError("Expected mode list to be a two-dimensional list")
if len(mode) != 2:
msg = (
f"Expected element {mode} of mode list to be an iterable of "
f"length 2 but instead got an iterable of size {len(mode)}."
)
raise ValueError(msg)
try:
n, m = mode
n = int(n)
m = int(m)
if n < 0 or m < 0:
raise ValueError()
modes[i] = (n, m)
except (TypeError, ValueError):
msg = (
f"Expected n (= {n}) and m (= {m}) of element {mode} "
f"of mode list to be convertible to non-negative integers."
)
raise TypeError(msg)
(_, counts) = np.unique(modes, axis=0, return_counts=True)
if np.any(counts > 1):
raise ValueError(f"Mode array has non-unique values: {modes}")
return modes
def _make_even_modes(maxtem):
all_modes = make_modes(maxtem=maxtem)
return np.array([(n, m) for n, m in all_modes if not n % 2 and not m % 2])
def _make_odd_modes(maxtem):
all_modes = make_modes(maxtem=maxtem)
return np.array(
[(n, m) for n, m in all_modes if (n % 2 or not n) and (m % 2 or not m)]
)
def _make_tangential_modes(maxtem):
_check_maxtem(maxtem)
N = 1 + maxtem
modes = np.zeros(N, dtype=(np.intc, 2))
for n in range(N):
modes[n] = (n, 0)
return modes
def _make_sagittal_modes(maxtem):
_check_maxtem(maxtem)
N = 1 + maxtem
modes = np.zeros(N, dtype=(np.intc, 2))
for m in range(N):
modes[m] = (0, m)
return modes
[docs]def insert_modes(modes, new_modes):
"""Inserts the mode indices in `new_modes` into the `modes` array at the correct
(sorted) position(s).
Parameters
----------
modes : :class:`numpy.ndarray`
An array of HOM indices.
new_modes : sequence, str
A single mode index pair or an iterable of mode indices. Each
element must unpack to two integer convertible values.
Returns
-------
out : :class:`numpy.ndarray`
A sorted array of HOM indices consisting of the original contents
of `modes` with the mode indices from `new_modes` included.
Raises
------
ValueError
If `new_modes` is not a mode index pair or iterable of mode indices.
See Also
--------
make_modes
Examples
--------
Make an array of even modes and insert new modes into this:
>>> modes = make_modes("even", 2)
>>> modes
array([[0, 0], [0, 2], [2, 0]], dtype=int32)
>>> insert_modes(modes, ["11", "32"])
array([[0, 0], [0, 2], [1, 1], [2, 0], [3, 2]], dtype=int32)
"""
if not hasattr(new_modes, "__getitem__"):
raise ValueError(
"Argument 'new_modes' must be a single mode index pair "
"or an iterable of mode index pairs."
)
if not hasattr(new_modes[0], "__getitem__") or isinstance(new_modes, str):
new_modes = [new_modes]
new = np.array([(int(n), int(m)) for n, m in new_modes], dtype=np.intc)
return np.unique(np.vstack((modes, new)), axis=0)
[docs]def remove_modes(modes, remove):
if not hasattr(remove, "__getitem__"):
raise ValueError(
"Argument remove must be a single mode index pair "
"or an iterable of mode index pairs."
)
if not hasattr(remove[0], "__getitem__") or isinstance(remove, str):
remove = [remove]
for n, m in remove:
ni = int(n)
mi = int(m)
index = np.where(np.bitwise_and(modes[:, 0] == ni, modes[:, 1] == mi))
modes = np.delete(modes, index, axis=0)
return modes
[docs]def surface_diopt_to_roc(roc, d):
"""Convert a dioptre shift, at a surface, to a radius of curvature.
Parameters
----------
roc : float
The initial radius of curvature of the surface.
d : float, array-like
A value or array of values representing the dioptre shift.
Returns
-------
out : float, array-like
The new values of the radius of curvature.
"""
return 2 / (d + 2 / roc)
[docs]def lens_diopt_to_f(f, d):
"""Convert a dioptre shift, at a lens, to a focal length.
Parameters
----------
f : float
The initial focal length of the lens.
d : float, array-like
A value or array of values representing the dioptre shift.
Returns
-------
out : float, array-like
The new value(s) of the focal length.
"""
return 1 / (d + 1 / f)
def _check_maxtem(maxtem):
if (
not isinstance(maxtem, numbers.Number)
or maxtem < 0
or (
hasattr(maxtem, "is_integer")
and not maxtem.is_integer()
and not isinstance(maxtem, numbers.Integral)
)
):
raise ValueError("Argument maxtem must be a non-negative integer.")
[docs]def jacobi_real_x(n, a, b, x):
r"""Jacobi Polynomial P_n^{a,b}(x) for real x.
n / n+a \ / n+b \ / x-1 \^(s) / x+1 \^(n-s)
P_n^{a,b}(x)= Sum | | | | | --- | | --- |
s=0 \ n-s / \ s / \ 2 / \ 2 /
Parameters
----------
n, a, b : int
Polynomial coefficients
x : float
Polynomial argument
Notes
-----
Implementation of Jacobi fucntion using binominal coefficients.
This can handle values of alpha, beta < -1 which the special.eval_jacobi
function does not.
"""
P = 0.0
for s in np.arange(0, n + 1):
P += (
scipy.special.binom(n + a, n - s)
* scipy.special.binom(n + b, s)
* ((x - 1.0) / 2) ** (s)
* ((x + 1.0) / 2) ** (n - s)
)
return P
[docs]def HG_to_LG(n, m):
"""Returns the coefficients and mode indices of the Laguerre-Gaussian modes required
to create a particular Hermote-Gaussian mode.
Parameters
----------
n, m: integer
Indices of the HG mode to re-create.
Returns
-------
coeffcients: array_like
Complex coefficients for each order=n+m LG mode required to
re-create HG_n,m.
ps, ls: array_like
LG mode indices corresponding to coefficients.
"""
factorial = np.math.factorial
# Mode order
N = n + m
# Create empty vectors for LG coefficients/ indices
coefficients = np.linspace(0, 0, N + 1, dtype=np.complex128)
ps = np.linspace(0, 0, N + 1)
ls = np.linspace(0, 0, N + 1)
# Calculate coefficients
for j in np.arange(0, N + 1):
# Indices for coefficients
l = 2 * j - N
p = int((N - np.abs(l)) / 2)
ps[j] = p
ls[j] = l
signl = np.sign(l)
if l == 0:
signl = 1.0
# Coefficient
c = (signl * 1j) ** m * np.sqrt(
factorial(N - m)
* factorial(m)
/ (2**N * factorial(np.abs(l) + p) * factorial(p))
)
c = (
c
* (-1.0) ** p
* (-2.0) ** m
* jacobi_real_x(m, np.abs(l) + p - m, p - m, 0.0)
)
coefficients[j] = c
return tuple(zip(coefficients, np.array(tuple(zip(ps, ls)))))
[docs]def make_modes_LG(maxtem):
"""Returns an array of LG modes ordered in increasing polynomial order, 2p+|l|.
Parameters
----------
maxtem : int
Maximum LG order to include, maxtem > 0.
Returns
-------
pl : array_like
array of (p,l) indicies
"""
modes = []
orders = []
for p in np.arange(maxtem + 1):
for l in np.arange(-maxtem, maxtem + 1):
order = 2 * p + abs(l)
if order <= maxtem:
modes.append((p, l))
orders.append(order)
return np.array(modes)[np.argsort(orders)]
[docs]def HG_to_LG_matrix(hgs, lgs):
"""Returns a matrix that will convert a Hermite-Gaussian mode vector into Laguerre-
Gaussian modes. The HG and LG modes provided to this function must contain all the
required modes. A 2nd order HG mode will require 2nd order LG modes.
Parameters
----------
hgs : array_like
Array of (n,m) indicies
lgs : array_like
Array of (p,l) indicies
Returns
-------
K : matrix
Matrix to multiply with a HG mode vector to get the equivalent LG modes.
"""
hg_idx = {(n, m): i for i, (n, m) in enumerate(hgs)}
lg_idx = {(p, l): i for i, (p, l) in enumerate(lgs)}
K = np.zeros((hgs.shape[0], lgs.shape[0]), dtype=complex)
for nm in hgs:
fpl = HG_to_LG(*nm)
h = hg_idx[nm[0], nm[1]]
for factor, pl in fpl:
l = lg_idx[pl[0], pl[1]]
K[l, h] = factor
return K