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noise update (log_mode)

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This commit is contained in:
Benoît Sierro
2023-10-17 10:46:15 +02:00
parent 967efa9d13
commit 0e1eb502a4
1 changed files with 28 additions and 5 deletions
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29
src/scgenerator/noise.py
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@@ -122,7 +122,9 @@ class NoiseMeasurement:
return self.freq[crop:], psd[crop:] return self.freq[crop:], psd[crop:]
def sample_spectrum(self, nt: int, dt: float | None = None) -> tuple[np.ndarray, np.ndarray]: def sample_spectrum(
self, nt: int, dt: float | None = None, log_mode: bool = False
) -> tuple[np.ndarray, np.ndarray]:
""" """
sample an amplitude spectrum with nt points. The corresponding sample spacing in the time sample an amplitude spectrum with nt points. The corresponding sample spacing in the time
is 1/freq.max(). is 1/freq.max().
@@ -134,6 +136,8 @@ class NoiseMeasurement:
dt : float | None, optional dt : float | None, optional
if given, freq will only be sampled up to 0.5/dt. if that value is higher than the if given, freq will only be sampled up to 0.5/dt. if that value is higher than the
max of freq, an exception is raised. max of freq, an exception is raised.
log_mode : bool, optional
sample on a log-log scale rather than on a linear scale, by default False
""" """
if nt % 2: if nt % 2:
raise ValueError(f"nt must be an even number, got {nt!r}") raise ValueError(f"nt must be an even number, got {nt!r}")
@@ -146,12 +150,29 @@ class NoiseMeasurement:
) )
f = np.linspace(0, fmax, nt // 2 + 1) f = np.linspace(0, fmax, nt // 2 + 1)
if log_mode:
interp = np.zeros_like(f)
ind = self.freq > 0
interp[1:] = np.exp(
np.interp(
np.log(f[1:]),
np.log(self.freq[ind]),
np.log(self.psd[ind]),
left=np.log(self.psd[ind][0]),
right=np.log(self.psd[ind][-1]),
)
)
else:
interp = np.interp(f, self.freq, self.psd, left=0, right=self.psd[-1]) interp = np.interp(f, self.freq, self.psd, left=0, right=self.psd[-1])
interp[0] = 0 interp[0] = 0
return f, interp return f, interp
def time_series( def time_series(
self, nt: int | np.ndarray, phase: np.ndarray | None = None, dt: float | None = None self,
nt: int | np.ndarray,
phase: np.ndarray | None = None,
dt: float | None = None,
log_mode: bool = False,
) -> 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
@@ -165,9 +186,11 @@ class NoiseMeasurement:
if None, A random phase is then applied. if None, A random phase is then applied.
dt : float | None, optional dt : float | None, optional
if given, choose a sample spacing of dt instead of 1/f_max if given, choose a sample spacing of dt instead of 1/f_max
log_mode : bool, optional
sample on a log-log scale rather than on a linear scale, by default False
""" """
freq, spec = self.sample_spectrum(nt, dt) freq, spec = self.sample_spectrum(nt, dt, log_mode)
if phase is None: if phase is None:
phase = 2 * np.pi * self.rng.random(len(freq)) phase = 2 * np.pi * self.rng.random(len(freq))
time, dt = math.irfftfreq(freq, True) time, dt = math.irfftfreq(freq, True)