code formatting

photontorch/components/component.py

@@ -40,7 +40,7 @@ def __init__(self, name=None):
         """ Component
 
         Args:
-            name (str): the name of the component
+            name (optional, str): the name of the component
         """
         super(Component, self).__init__()
         self.name = name
@@ -185,8 +185,7 @@ def get_S(self):
                 denotes the stacked real and imaginary part.
         """
         S = torch.zeros(
-            (2, self.env.num_wl, self.num_ports, self.num_ports),
-            device=self.device,
+            (2, self.env.num_wl, self.num_ports, self.num_ports), device=self.device,
         )
         self.set_S(S)
         return S

photontorch/components/directionalcouplers.py

@@ -43,7 +43,7 @@ def __init__(self, coupling=0.5, trainable=True, name=None):
         Args:
             coupling (float): power coupling of the directional coupler (between 0 and 1)
             trainable (bool): makes the coupling trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(DirectionalCoupler, self).__init__(name=name)
 
@@ -120,7 +120,7 @@ def __init__(
                 purposes.
             trainable_phase (bool): makes the phase parameter trainable
             trainable_coupling (bool): makes the coupling parameter trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(DirectionalCouplerWithLength, self).__init__(name=name)
         # Handle inputs
@@ -213,7 +213,7 @@ def __init__(
             de2_k0 (float): second derivative of k0 w.r.t. wavelength
             de2_n0 (float): second derivative of n0 w.r.t. wavelength
             wl0 (float): the center wavelength for which the parameters are defined
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
 
         Note:
             The default parameters are based on a directional coupler with

photontorch/components/gratingcouplers.py

@@ -48,7 +48,7 @@ def __init__(
             Tmax (float): maximum transmission at center wavelength
             bandwidth (float): 3dB Bandwidth of the grating coupler
             wl0 (float): Center wavelength of the grating coupler
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(GratingCoupler, self).__init__(name=name)
         self.R = R

photontorch/components/mirrors.py

@@ -37,7 +37,7 @@ def __init__(self, R=0.5, trainable=True, name=None):
         Args:
             R (float): reflectivity of the mirror (between 0 and 1)
             trainable (bool): makes the reflection trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(Mirror, self).__init__(name=name)
 

photontorch/components/mmis.py

@@ -51,7 +51,7 @@ def __init__(self, weights=None, trainable=True, name=None):
                 wageguides and output waveguides. If no weights are specified, the
                 component defaults to a 1x2 splitter.
             trainable (bool): makes the interconnection weights trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
 
         # validate weights

photontorch/components/mzis.py

@@ -63,7 +63,7 @@ def __init__(
             length (float): length of the waveguide in meter.
             loss (float): loss in the waveguide [dB/m]
             trainable (bool): whether phi and theta are trainable
-            name (str): name of this specific MZI
+            name (optional, str): name of this specific MZI
         """
         super(Mzi, self).__init__(name=name)
 

photontorch/components/soas.py

@@ -45,7 +45,7 @@ def __init__(self, amplification=2, trainable=True, name=None):
         Args:
             amplification (float): Amplification of the soa
             trainable (bool): makes the amplification trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(LinearSoa, self).__init__(name=name)
 
@@ -147,7 +147,7 @@ def __init__(
             amplification (float): the maximum amplification of the soa
             startup_time (float): how long it takes before the soa reaches max amplification
             trainable (bool): makes the amplification trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         super(Soa, self).__init__(name=name)
 
@@ -202,19 +202,19 @@ def __init__(
     ):
         """
         Args:
-            L (loat): length of soa
-            W (loat): width of soa
-            H (loat): height of soa
-            N0 (loat): transparency carrier density
-            a (loat): differenctial gain coefficient
-            I (loat): current through soa
-            tc (loat): lifetime of the carriers
+            L (float): length of soa
+            W (float): width of soa
+            H (float): height of soa
+            N0 (float): transparency carrier density
+            a (float): differenctial gain coefficient
+            I (float): current through soa
+            tc (float): lifetime of the carriers
             gamma (float): confinement factor
             alpha (float): linewidth enhancement
             neff (float): effective index used to calculate phase offset
             ng (float): group index used to calculate delay of soa
             wl (float): wavelength of the simulation
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
 
         super(AgrawalSoa, self).__init__(name=name)
@@ -251,11 +251,7 @@ def initialize(self):
         )  # saturation power of the SOA
         self.delay = self.L * self.ng / env.c  # delay introduced by the soa
         self.phase = (
-            self.L
-            * self.neff
-            * 2
-            * np.pi
-            * np.mean(env.wl)  # TODO: make this an array
+            self.L * self.neff * 2 * np.pi * np.mean(env.wl)  # TODO: make this an array
         )  # phase introduced by the soa
 
         super(AgrawalSoa, self).initialize()

photontorch/components/waveguides.py

@@ -53,7 +53,7 @@ def __init__(
                 by the length of the waveguide. Adding this can be useful for training
                 purposes.
             trainable (bool): makes the phase parameter trainable
-            name (str): the name of the component (default: lowercase classname)
+            name (optional, str): the name of the component (default: lowercase classname)
         """
         Connection.__init__(self, name=name)
         # Handle inputs