moved rng to time_series method

src/scgenerator/noise.py

@@ -12,11 +12,8 @@ from scgenerator import math, units
 class NoiseMeasurement:
     freq: np.ndarray
     psd: np.ndarray
-    rng: np.random.Generator
 
-    def __init__(
-        self, freq: np.ndarray, psd: np.ndarray, rng: np.random.Generator | int | None = None
-    ):
+    def __init__(self, freq: np.ndarray, psd: np.ndarray):
         if freq.ndim > 1:
             raise TypeError(f"freq must be 1 dim, got {freq.shape}")
         elif psd.shape[-1] != freq.shape[-1]:
@@ -25,12 +22,8 @@ class NoiseMeasurement:
                 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.psd = psd
-        self.rng = rng
 
     @classmethod
     def from_dBc(cls, freq: np.ndarray, psd_dBc: np.ndarray) -> NoiseMeasurement:
@@ -221,6 +214,7 @@ class NoiseMeasurement:
         nseg: int = 1,
         dt: float | None = None,
         log_mode: bool = False,
+        rng: np.random.Generator | int | None = None,
     ) -> tuple[np.ndarray, np.ndarray]:
         """
         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
         """
         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)
         fs = freq[-1] * 2
@@ -254,11 +250,11 @@ class NoiseMeasurement:
 
         if nseg > 1:
             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")
             signal *= np.sqrt(2)
         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)
             time = np.arange(len(signal)) / fs
         return time, signal