dedebenui/scgenerator
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

added get_frequency function

Browse Source
This commit is contained in:
Benoît Sierro
2023-09-29 09:44:59 +02:00
parent b386a5e816
commit 3e9d68d3a5
1 changed files with 18 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
src/scgenerator/noise.py
View File

@@ -80,7 +80,7 @@ class NoiseMeasurement:
dB: bool = True, dB: bool = True,
wavelength: float | None = None, wavelength: float | None = None,
power: float | None = None, power: float | None = None,
crop: int = 1, crop: int | float = 1,
) -> tuple[np.ndarray, np.ndarray]: ) -> tuple[np.ndarray, np.ndarray]:
""" """
Transforms the PSD in a way that makes it easy to plot Transforms the PSD in a way that makes it easy to plot
@@ -117,6 +117,9 @@ class NoiseMeasurement:
if dB: if dB:
psd = math.to_dB(psd, ref=1.0) psd = math.to_dB(psd, ref=1.0)
if isinstance(crop, (float, np.floating)):
crop = math.argclosest(self.freq, crop)
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) -> tuple[np.ndarray, np.ndarray]:
@@ -221,11 +224,24 @@ def segments(signal: np.ndarray, num_segments: int) -> np.ndarray:
if num_segments == 1: if num_segments == 1:
return signal[None] return signal[None]
n_init = len(signal) n_init = len(signal)
seg_size = 1 << int(np.log2(n_init / (num_segments + 1))) + 1 seg_size = segement_size(n_init, num_segments)
seg = np.arange(seg_size) seg = np.arange(seg_size)
off = int(n_init / (num_segments + 1)) off = int(n_init / (num_segments + 1))
return np.array([signal[seg + i * off] for i in range(num_segments)]) return np.array([signal[seg + i * off] for i in range(num_segments)])
def segement_size(nt: int, num_segments: int) -> int:
return 1 << int(np.log2(nt / (num_segments + 1))) + 1
def get_frequencies(nt: int, num_segments: int, dt: float) -> np.ndarray:
"""
returns the frequency array that would be associated to a NoiseMeasurement.from_time_series
call, where `nt` is the size of the signal.
"""
return np.fft.rfftfreq(segement_size(nt, num_segments), dt)
def quantum_noise_limit(wavelength: float, power: float) -> float: def quantum_noise_limit(wavelength: float, power: float) -> float:
return units.m_rads(wavelength) * units.hbar * 2 / power return units.m_rads(wavelength) * units.hbar * 2 / power