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moved rng to time_series method

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dedebenui
2024-03-14 15:05:05 +01:00
parent 187f90a72d
commit b95a0e4282
1 changed files with 6 additions and 10 deletions
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16
src/scgenerator/noise.py
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@@ -12,11 +12,8 @@ from scgenerator import math, units
class NoiseMeasurement: class NoiseMeasurement:
freq: np.ndarray freq: np.ndarray
psd: np.ndarray psd: np.ndarray
rng: np.random.Generator
def __init__( def __init__(self, freq: np.ndarray, psd: np.ndarray):
self, freq: np.ndarray, psd: np.ndarray, rng: np.random.Generator | int | None = None
):
if freq.ndim > 1: if freq.ndim > 1:
raise TypeError(f"freq must be 1 dim, got {freq.shape}") raise TypeError(f"freq must be 1 dim, got {freq.shape}")
elif psd.shape[-1] != freq.shape[-1]: elif psd.shape[-1] != freq.shape[-1]:
@@ -25,12 +22,8 @@ class NoiseMeasurement:
f"got {psd.shape[-1]} and {freq.shape[-1]}" f"got {psd.shape[-1]} and {freq.shape[-1]}"
) )
if rng is None or isinstance(rng, int):
rng = np.random.default_rng(rng)
self.freq = freq self.freq = freq
self.psd = psd self.psd = psd
self.rng = rng
@classmethod @classmethod
def from_dBc(cls, freq: np.ndarray, psd_dBc: np.ndarray) -> NoiseMeasurement: def from_dBc(cls, freq: np.ndarray, psd_dBc: np.ndarray) -> NoiseMeasurement:
@@ -221,6 +214,7 @@ class NoiseMeasurement:
nseg: int = 1, nseg: int = 1,
dt: float | None = None, dt: float | None = None,
log_mode: bool = False, log_mode: bool = False,
rng: np.random.Generator | int | None = None,
) -> tuple[np.ndarray, np.ndarray]: ) -> tuple[np.ndarray, np.ndarray]:
""" """
computes a pulse train whose psd matches the measurement computes a pulse train whose psd matches the measurement
@@ -238,6 +232,8 @@ class NoiseMeasurement:
sample on a log-log scale rather than on a linear scale, by default False sample on a log-log scale rather than on a linear scale, by default False
""" """
nf = nf or len(self.freq) nf = nf or len(self.freq)
if rng is None or isinstance(rng, int):
rng = np.random.default_rng(rng)
freq, amp = self.sample_spectrum(nf, dt, log_mode, left=0) freq, amp = self.sample_spectrum(nf, dt, log_mode, left=0)
fs = freq[-1] * 2 fs = freq[-1] * 2
@@ -254,11 +250,11 @@ class NoiseMeasurement:
if nseg > 1: if nseg > 1:
amp = np.tile(amp, (nseg, 1)).T amp = np.tile(amp, (nseg, 1)).T
amp[1:right] *= np.exp(2j * np.pi * self.rng.random((phase_n, nseg))) amp[1:right] *= np.exp(2j * np.pi * rng.random((phase_n, nseg)))
time, signal = ss.istft(amp, fs, nperseg=(nf - 1) * 2, noverlap=nf - 1, scaling="psd") time, signal = ss.istft(amp, fs, nperseg=(nf - 1) * 2, noverlap=nf - 1, scaling="psd")
signal *= np.sqrt(2) signal *= np.sqrt(2)
else: else:
amp[1:right] *= np.exp(2j * np.pi * self.rng.random(phase_n)) amp[1:right] *= np.exp(2j * np.pi * rng.random(phase_n))
signal = np.fft.irfft(amp) * np.sqrt((amp.size - 1) * 2 * fs) signal = np.fft.irfft(amp) * np.sqrt((amp.size - 1) * 2 * fs)
time = np.arange(len(signal)) / fs time = np.arange(len(signal)) / fs
return time, signal return time, signal