From ea65ead1887e44f7206fb05b3562dd0f8b5fb864 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Beno=C3=AEt=20Sierro?= <dedebenui@protonmail.com>
Date: Mon, 29 Apr 2024 08:55:23 +0200
Subject: [PATCH] formatting

---
 src/scgenerator/cache.py         |  1 +
 src/scgenerator/noise.py         | 16 ++++++++++++----
 src/scgenerator/physics/fiber.py | 18 +++---------------
 src/scgenerator/spectra.py       | 20 ++++++++------------
 4 files changed, 24 insertions(+), 31 deletions(-)

diff --git a/src/scgenerator/cache.py b/src/scgenerator/cache.py
index 4f0f51a..61ff949 100644
--- a/src/scgenerator/cache.py
+++ b/src/scgenerator/cache.py
@@ -107,6 +107,7 @@ class Cache:
                 return data
 
             wrapped.cached_only = cached_only
+            wrapped.func = func
 
             return wrapped
 
diff --git a/src/scgenerator/noise.py b/src/scgenerator/noise.py
index 071d83a..3cb273f 100644
--- a/src/scgenerator/noise.py
+++ b/src/scgenerator/noise.py
@@ -259,20 +259,28 @@ class NoiseMeasurement:
             time = np.arange(len(signal)) / fs
         return time, signal
 
-    def root_integrated(self) -> np.ndarray:
+    def root_integrated(self, from_right: bool = True) -> np.ndarray:
         """
         returns the sqrt of the integrated spectrum
         The 0th component is the total RIN in the frequency range covered by the measurement
         Caution! this may be 0 frequency
+
+        Parameters
+        ----------
+        from_right : bool, optional
+            start integration at f[-1], by default True.
         """
-        return integrated_noise(self.freq, self.psd)
+        return integrated_noise(self.freq, self.psd, from_right)
 
 
-def integrated_noise(freq: np.ndarray, psd: np.ndarray) -> float:
+def integrated_noise(freq: np.ndarray, psd: np.ndarray, from_right: bool = True) -> np.ndarray:
     """
     given a normalized spectrum, computes the total rms RIN in the provided frequency window
     """
-    return np.sqrt(cumulative_trapezoid(np.abs(psd)[::-1], -freq[::-1], initial=0)[::-1])
+    s = slice(None, None, -1) if from_right else slice(None)
+    return np.sqrt(
+        cumulative_trapezoid(np.abs(psd)[s], (1 - 2 * from_right) * freq[s], initial=0)[s]
+    )
 
 
 def quantum_noise_limit(wavelength: float, power: float) -> float:
diff --git a/src/scgenerator/physics/fiber.py b/src/scgenerator/physics/fiber.py
index 0e774ca..c1d3912 100644
--- a/src/scgenerator/physics/fiber.py
+++ b/src/scgenerator/physics/fiber.py
@@ -58,9 +58,7 @@ def dispersion_parameter_to_beta2(dispersion_parameter: float, wavelength: T) ->
 def dispersion_slope_to_beta3(
     dispersion_parameter: float, dispersion_slope: float, wavelength
 ) -> float:
-    return (wavelength**2 / pi2c) ** 2 * (
-        dispersion_slope + 2 * dispersion_parameter / wavelength
-    )
+    return (wavelength**2 / pi2c) ** 2 * (dispersion_slope + 2 * dispersion_parameter / wavelength)
 
 
 def handle_dispersion_parameter(
@@ -598,18 +596,8 @@ def saitoh_paramters(pcf_pitch_ratio: float) -> tuple[float, float]:
     di2 = np.array([9, 6.58, 10, 0.41])
     di3 = np.array([10, 24.8, 15, 6])
 
-    A = (
-        ai0
-        + ai1 * pcf_pitch_ratio**bi1
-        + ai2 * pcf_pitch_ratio**bi2
-        + ai3 * pcf_pitch_ratio**bi3
-    )
-    B = (
-        ci0
-        + ci1 * pcf_pitch_ratio**di1
-        + ci2 * pcf_pitch_ratio**di2
-        + ci3 * pcf_pitch_ratio**di3
-    )
+    A = ai0 + ai1 * pcf_pitch_ratio**bi1 + ai2 * pcf_pitch_ratio**bi2 + ai3 * pcf_pitch_ratio**bi3
+    B = ci0 + ci1 * pcf_pitch_ratio**di1 + ci2 * pcf_pitch_ratio**di2 + ci3 * pcf_pitch_ratio**di3
     return A, B
 
 
diff --git a/src/scgenerator/spectra.py b/src/scgenerator/spectra.py
index 072d953..27dd5b6 100644
--- a/src/scgenerator/spectra.py
+++ b/src/scgenerator/spectra.py
@@ -179,20 +179,13 @@ class Spectrum(np.ndarray):
 
     @property
     def wl_amp(self):
-        return (
-            np.sqrt(
-                units.to_WL(
-                    math.abs2(self),
-                    self.l,
-                )
-            )
-            * self
-            / np.abs(self)
-        )[..., self.l_order]
+        return (np.sqrt(units.to_WL(math.abs2(self), self.l)) * self / np.abs(self))[
+            ..., self.l_order
+        ]
 
     @property
     def afreq_amp(self):
-        return self[..., self.w_order[::-1]] * self.spectrum_factor
+        return self[..., self.w_order] * self.spectrum_factor
 
     @property
     def time_amp(self):
@@ -242,7 +235,10 @@ class Spectrum(np.ndarray):
         band = peak * np.exp(-(((self.l - pos) / (pulse.fwhm_to_T0_fac["gaussian"] * width)) ** 2))
         if shot_noise:
             sn = np.reshape(
-                [pulse.shot_noise(self.w, *shot_noise) for _ in range(np.prod(self.shape[:-1]))],
+                [
+                    pulse.shot_noise(self.w, *shot_noise)
+                    for _ in range(np.prod(self.shape[:-1] + (1,)))
+                ],
                 self.shape,
             )
             return self * band + np.sqrt(1 - band**2) * sn