diff --git a/src/scgenerator/noise.py b/src/scgenerator/noise.py
index f5bfa56..0b19191 100644
--- a/src/scgenerator/noise.py
+++ b/src/scgenerator/noise.py
@@ -80,7 +80,7 @@ class NoiseMeasurement:
         dB: bool = True,
         wavelength: float | None = None,
         power: float | None = None,
-        crop: int = 1,
+        crop: int | float = 1,
     ) -> tuple[np.ndarray, np.ndarray]:
         """
         Transforms the PSD in a way that makes it easy to plot
@@ -117,6 +117,9 @@ class NoiseMeasurement:
         if dB:
             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:]
 
     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:
         return signal[None]
     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)
     off = int(n_init / (num_segments + 1))
     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:
     return units.m_rads(wavelength) * units.hbar * 2 / power