12880607338

Committed 15 Jan 2025 08:24PM UTC coverage: 54.553% (-1.4%) from 55.908%

Build # 12880607338

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push

github

Committed by

feihoo87

Commit Message

v2.0.1

Run Details

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653 of 1197 relevant lines covered (54.55%)

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0.0

/waveforms/utils.py

from itertools import repeat
from typing import Optional, Sequence

import numpy as np


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:
        phaseList = np.zeros_like(fList)
    if weight.ndim == 1:
        weightList = repeat(weight)
    else:
        weightList = weight
    for f, phase, weight in zip(fList, phaseList, weightList):
        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

NEW 1	from itertools import repeat	×
NEW 2	from typing import Optional, Sequence	×
3
NEW 4	import numpy as np	×
5
6
NEW 7	def getFTMatrix(fList: Sequence[float],	×
8	numOfPoints: int,
9	phaseList: Optional[Sequence[float]] = None,
10	weight: Optional[np.ndarray] = None,
11	sampleRate: float = 1e9) -> np.ndarray:
12	"""
13	get a matrix for Fourier transform
14
15	Args:
16	fList (Sequence[float]): list of frequencies
17	numOfPoints (int): size of signal frame
18	phaseList (Optional[Sequence[float]], optional): list of phase. Defaults to None.
19	weight (Optional[np.ndarray], optional): weight or list of weight. Defaults to None.
20	sampleRate (float, optional): sample rate of signal. Defaults to 1e9.
21
22	Returns:
23	numpy.ndarray: exp matrix
24
25	>>> shots, numOfPoints, sampleRate = 100, 1000, 1e9
26	>>> f1, f2 = -12.7e6, 32.8e6
27	>>> signal = np.random.randn(shots, numOfPoints)
28	>>> e = getFTMatrix([f1, f2], numOfPoints, sampleRate=sampleRate)
29	>>> ret = signal @ e
30	>>> ret.shape
31	(100, 2)
32	>>> t = np.arange(numOfPoints) / sampleRate
33	>>> signal = 0.8 * np.sin(2 * np.pi * f1 * t) + 0.2 * np.cos(2 * np.pi * f2 * t)
34	>>> signal @ e
35	array([-0.00766509-0.79518987j, 0.19531432+0.00207068j])
36	>>> spec = 2 * np.fft.fft(signal) / numOfPoints
37	>>> freq = np.fft.fftfreq(numOfPoints)
38	>>> e = getFTMatrix(freq, numOfPoints, sampleRate=1)
39	>>> np.allclose(spec, signal @ e)
40	True
41	"""
NEW 42	e = []	×
NEW 43	t = np.linspace(0, numOfPoints / sampleRate, numOfPoints, endpoint=False)	×
NEW 44	if weight is None or len(weight) == 0:	×
NEW 45	weight = np.full(numOfPoints, 2 / numOfPoints)	×
NEW 46	if phaseList is None or len(phaseList) == 0:	×
NEW 47	phaseList = np.zeros_like(fList)	×
NEW 48	if weight.ndim == 1:	×
NEW 49	weightList = repeat(weight)	×
50	else:
NEW 51	weightList = weight	×
NEW 52	for f, phase, weight in zip(fList, phaseList, weightList):	×
NEW 53	e.append(weight * np.exp(-1j * (2 * np.pi * f * t + phase)))	×
NEW 54	return np.asarray(e).T	×
55
56
NEW 57	def shift(signal: np.ndarray, delay: float, dt: float) -> np.ndarray:	×
58	"""
59	delay a signal
60
61	Args:
62	signal (np.ndarray): input signal
63	delay (float): delayed time
64	dt (float): time step of signal samples
65
66	Returns:
67	np.ndarray: delayed signal
68	"""
NEW 69	points = int(delay // dt)	×
NEW 70	delta = delay / dt - points	×
71
NEW 72	if delta > 0:	×
NEW 73	ker = np.array([0, 1 - delta, delta])	×
NEW 74	signal = np.convolve(signal, ker, mode='same')	×
75
NEW 76	if points == 0:	×
NEW 77	return signal	×
78
NEW 79	ret = np.zeros_like(signal)	×
NEW 80	if points < 0:	×
NEW 81	ret[:points] = signal[-points:]	×
82	else:
NEW 83	ret[points:] = signal[:-points]	×
NEW 84	return ret	×

feihoo87 / waveforms / 12880607338

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