fixed HUGE bug with shot noise

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scgenerator"
-version = "0.3.26"
+version = "0.4.0"
 description = "Simulate nonlinear pulse propagation in optical fibers"
 readme = "README.md"
 authors = [{ name = "Benoit Sierro", email = "benoit.sierro@iap.unibe.ch" }]

src/scgenerator/physics/pulse.py

@@ -559,10 +559,11 @@ def finalize_pulse(
     dt: float,
     input_transmission: float,
 ) -> np.ndarray:
+    pre_field_0 *= np.sqrt(input_transmission)
     if quantum_noise:
         pre_field_0 = pre_field_0 + shot_noise(w, dt)
 
-    return np.sqrt(input_transmission) * pre_field_0
+    return pre_field_0
 
 
 def A_to_C(A: np.ndarray, effective_area_arr: np.ndarray) -> np.ndarray:
@@ -614,29 +615,7 @@ def compress_pulse(spectra: np.ndarray):
     else:
         return fftshift(ifft(flatten_phase(spectra)), axes=1)
 
-def shot_noise(w: np.ndarray, dt: float):
-    """
 
-    Parameters
-    ----------
-    w : array, shape (n,)
-        angular frequencies
-    dt : float
-        resolution of time grid
-
-    Returns
-    -------
-    np.ndarray, shape (n,)
-        noise field to be added on top of initial field in time domain
-    """
-    dw = w[1] - w[0]
-
-    rand_phase = np.random.rand(len(w)) * 2 * pi
-    shot_noise_phase = np.exp(-1j * rand_phase)
-
-    shot_noise_amplitude = np.sqrt(0.5 * units.hbar * np.abs(w) / dw)
-
-    return ifft(shot_noise_amplitude * shot_noise_phase / dt * np.sqrt(2 * pi))
 def ideal_compressed_pulse(spectra: np.ndarray):
     """
     returns the ideal compressed pulse assuming flat phase

src/scgenerator/spectra.py

@@ -389,7 +389,7 @@ def propagation_series(
         convenient object to work with multiple propagations **built on the same grid**
     """
     if isinstance(files, str) or not isinstance(files, Sequence):
-        files = sorted(Path(files).glob("*.zip"), key=logical_sort_key)
+        files = sorted(Path(files).expanduser().glob("*.zip"), key=logical_sort_key)
     if len(files) == 0:
         raise ValueError("You must provide at least one file to build a propagation series")