16159025029

Committed 09 Jul 2025 02:41AM UTC coverage: 53.366% (-0.03%) from 53.393%

Build # 16159025029

Build Type

push

github

Committed by

feihoo87

Commit Message

Update version to 2.1.1 following recent enhancements and maintain backward compatibility.

Run Details

1 of 1 new or added line in 1 file covered. (100.0%)

207 existing lines in 2 files now uncovered.

1316 of 2466 relevant lines covered (53.37%)

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0.0

/waveforms/utils.py

from itertools import repeat
from types import MappingProxyType
from typing import Optional, Sequence, cast

import numpy as np
import scipy.sparse as sp


def freeze(x):
    """Freeze a mutable object.
    """
    if isinstance(x, (int, float, complex, str, bytes, type(None))):
        pass
    elif isinstance(x, (list, tuple)):
        return tuple([freeze(y) for y in x])
    elif isinstance(x, dict):
        return MappingProxyType({k: freeze(v) for k, v in x.items()})
    elif isinstance(x, set):
        return frozenset([freeze(y) for y in x])
    elif isinstance(x, (np.ndarray, np.matrix)):
        x.flags.writeable = False
    elif isinstance(x, sp.spmatrix):
        cast(np.ndarray, getattr(x, 'data')).flags.writeable = False
        if getattr(x, 'format') in {'csr', 'csc', 'bsr'}:
            cast(np.ndarray, getattr(x, 'indices')).flags.writeable = False
            cast(np.ndarray, getattr(x, 'indptr')).flags.writeable = False
        elif getattr(x, 'format') == 'coo':
            cast(np.ndarray, getattr(x, 'row')).flags.writeable = False
            cast(np.ndarray, getattr(x, 'col')).flags.writeable = False
    elif isinstance(x, bytearray):
        x = bytes(x)
    return x


def getFTMatrix(fList: Sequence[float],
                numOfPoints: int,
                phaseList: Optional[Sequence[float]] = None,
                weight: Optional[np.ndarray] = None,
                sampleRate: float = 1e9) -> np.ndarray:
    """
    get a matrix for Fourier transform

    Args:
        fList (Sequence[float]): list of frequencies
        numOfPoints (int): size of signal frame
        phaseList (Optional[Sequence[float]], optional): list of phase. Defaults to None.
        weight (Optional[np.ndarray], optional): weight or list of weight. Defaults to None.
        sampleRate (float, optional): sample rate of signal. Defaults to 1e9.

    Returns:
        numpy.ndarray: exp matrix
    
    >>> shots, numOfPoints, sampleRate = 100, 1000, 1e9
    >>> f1, f2 = -12.7e6, 32.8e6
    >>> signal = np.random.randn(shots, numOfPoints)
    >>> e = getFTMatrix([f1, f2], numOfPoints, sampleRate=sampleRate)
    >>> ret = signal @ e
    >>> ret.shape
    (100, 2)
    >>> t = np.arange(numOfPoints) / sampleRate
    >>> signal = 0.8 * np.sin(2 * np.pi * f1 * t) + 0.2 * np.cos(2 * np.pi * f2 * t)
    >>> signal @ e
    array([-0.00766509-0.79518987j,  0.19531432+0.00207068j])
    >>> spec = 2 * np.fft.fft(signal) / numOfPoints
    >>> freq = np.fft.fftfreq(numOfPoints)
    >>> e = getFTMatrix(freq, numOfPoints, sampleRate=1)
    >>> np.allclose(spec, signal @ e)
    True
    """
    e = []
    t = np.linspace(0, numOfPoints / sampleRate, numOfPoints, endpoint=False)
    if weight is None or len(weight) == 0:
        weight = np.full(numOfPoints, 2 / numOfPoints)
    if phaseList is None or len(phaseList) == 0:
        phase_list = np.zeros_like(fList)
    else:
        phase_list = phaseList
    if weight.ndim == 1:
        weight_list = repeat(weight)
    else:
        weight_list = weight
    for f, phase, weight in zip(fList, phase_list, weight_list):
        e.append(weight * np.exp(-1j * (2 * np.pi * f * t + phase)))
    return np.asarray(e).T


def shift(signal: np.ndarray, delay: float, dt: float) -> np.ndarray:
    """
    delay a signal

    Args:
        signal (np.ndarray): input signal
        delay (float): delayed time
        dt (float): time step of signal samples

    Returns:
        np.ndarray: delayed signal
    """
    points = int(delay // dt)
    delta = delay / dt - points

    if delta > 0:
        ker = np.array([0, 1 - delta, delta])
        signal = np.convolve(signal, ker, mode='same')

    if points == 0:
        return signal

    ret = np.zeros_like(signal)
    if points < 0:
        ret[:points] = signal[-points:]
    else:
        ret[points:] = signal[:-points]
    return ret

1	from itertools import repeat	×
2	from types import MappingProxyType	×
3	from typing import Optional, Sequence, cast	×
4
5	import numpy as np	×
6	import scipy.sparse as sp	×
7
8
9	def freeze(x):	×
10	"""Freeze a mutable object.
11	"""
12	if isinstance(x, (int, float, complex, str, bytes, type(None))):	×
13	pass	×
14	elif isinstance(x, (list, tuple)):	×
15	return tuple([freeze(y) for y in x])	×
16	elif isinstance(x, dict):	×
17	return MappingProxyType({k: freeze(v) for k, v in x.items()})	×
18	elif isinstance(x, set):	×
19	return frozenset([freeze(y) for y in x])	×
20	elif isinstance(x, (np.ndarray, np.matrix)):	×
21	x.flags.writeable = False	×
22	elif isinstance(x, sp.spmatrix):	×
23	cast(np.ndarray, getattr(x, 'data')).flags.writeable = False	×
24	if getattr(x, 'format') in {'csr', 'csc', 'bsr'}:	×
25	cast(np.ndarray, getattr(x, 'indices')).flags.writeable = False	×
26	cast(np.ndarray, getattr(x, 'indptr')).flags.writeable = False	×
27	elif getattr(x, 'format') == 'coo':	×
28	cast(np.ndarray, getattr(x, 'row')).flags.writeable = False	×
29	cast(np.ndarray, getattr(x, 'col')).flags.writeable = False	×
30	elif isinstance(x, bytearray):	×
31	x = bytes(x)	×
32	return x	×
33
34
35	def getFTMatrix(fList: Sequence[float],	×
36	numOfPoints: int,
37	phaseList: Optional[Sequence[float]] = None,
38	weight: Optional[np.ndarray] = None,
39	sampleRate: float = 1e9) -> np.ndarray:
40	"""
41	get a matrix for Fourier transform
42
43	Args:
44	fList (Sequence[float]): list of frequencies
45	numOfPoints (int): size of signal frame
46	phaseList (Optional[Sequence[float]], optional): list of phase. Defaults to None.
47	weight (Optional[np.ndarray], optional): weight or list of weight. Defaults to None.
48	sampleRate (float, optional): sample rate of signal. Defaults to 1e9.
49
50	Returns:
51	numpy.ndarray: exp matrix
52
53	>>> shots, numOfPoints, sampleRate = 100, 1000, 1e9
54	>>> f1, f2 = -12.7e6, 32.8e6
55	>>> signal = np.random.randn(shots, numOfPoints)
56	>>> e = getFTMatrix([f1, f2], numOfPoints, sampleRate=sampleRate)
57	>>> ret = signal @ e
58	>>> ret.shape
59	(100, 2)
60	>>> t = np.arange(numOfPoints) / sampleRate
61	>>> signal = 0.8 * np.sin(2 * np.pi * f1 * t) + 0.2 * np.cos(2 * np.pi * f2 * t)
62	>>> signal @ e
63	array([-0.00766509-0.79518987j, 0.19531432+0.00207068j])
64	>>> spec = 2 * np.fft.fft(signal) / numOfPoints
65	>>> freq = np.fft.fftfreq(numOfPoints)
66	>>> e = getFTMatrix(freq, numOfPoints, sampleRate=1)
67	>>> np.allclose(spec, signal @ e)
68	True
69	"""
70	e = []	×
71	t = np.linspace(0, numOfPoints / sampleRate, numOfPoints, endpoint=False)	×
72	if weight is None or len(weight) == 0:	×
73	weight = np.full(numOfPoints, 2 / numOfPoints)	×
74	if phaseList is None or len(phaseList) == 0:	×
75	phase_list = np.zeros_like(fList)	×
76	else:
77	phase_list = phaseList	×
UNCOV 78	if weight.ndim == 1:	×
79	weight_list = repeat(weight)	×
80	else:
81	weight_list = weight	×
82	for f, phase, weight in zip(fList, phase_list, weight_list):	×
UNCOV 83	e.append(weight * np.exp(-1j * (2 * np.pi * f * t + phase)))	×
UNCOV 84	return np.asarray(e).T	×
85
86
UNCOV 87	def shift(signal: np.ndarray, delay: float, dt: float) -> np.ndarray:	×
88	"""
89	delay a signal
90
91	Args:
92	signal (np.ndarray): input signal
93	delay (float): delayed time
94	dt (float): time step of signal samples
95
96	Returns:
97	np.ndarray: delayed signal
98	"""
UNCOV 99	points = int(delay // dt)	×
100	delta = delay / dt - points	×
101
102	if delta > 0:	×
UNCOV 103	ker = np.array([0, 1 - delta, delta])	×
104	signal = np.convolve(signal, ker, mode='same')	×
105
UNCOV 106	if points == 0:	×
107	return signal	×
108
109	ret = np.zeros_like(signal)	×
UNCOV 110	if points < 0:	×
111	ret[:points] = signal[-points:]	×
112	else:
UNCOV 113	ret[points:] = signal[:-points]	×
UNCOV 114	return ret	×

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