Update channel documentation to include input and output parameters f…

…or AWGN, Binary Erasure, Binary Symmetric, and Discrete Memoryless channels

src/komm/_channels/AWGNChannel.py

@@ -17,18 +17,23 @@ class AWGNChannel:
     $$
     where $P = \mathrm{E}[X^2_n]$ is the average power of the input signal, and $N = \mathrm{E}[Z^2_n]$ is the average power (and variance) of the noise. For more details, see <cite>CT06, Ch. 9</cite>.
 
-    To invoke the channel, call the object giving the input signal as parameter (see example below).
-
     Attributes:
         signal_power (float | str): The input signal power $P$. If equal to the string `'measured'`, then every time the channel is invoked the input signal power will be computed from the input itself (i.e., its squared Euclidean norm).
 
         snr (Optional[float]): The channel signal-to-noise ratio $\snr$ (linear, not decibel). The default value is `np.inf`, which corresponds to a noiseless channel.
 
+    Parameters: Input:
+        in0 (Array1D[float]): The input signal $X_n$.
+
+    Parameters: Output:
+        out0 (Array1D[float]): The output signal $Y_n$.
+
     Examples:
         >>> np.random.seed(1)
         >>> awgn = komm.AWGNChannel(signal_power=5.0, snr=200.0)
         >>> x = [1.0, 3.0, -3.0, -1.0, -1.0, 1.0, 3.0, 1.0, -1.0, 3.0]
-        >>> y = awgn(x); np.around(y, decimals=2)  # doctest: +NORMALIZE_WHITESPACE
+        >>> y = awgn(x)
+        >>> np.around(y, decimals=2)  # doctest: +NORMALIZE_WHITESPACE
         array([ 1.26,  2.9 , -3.08, -1.17, -0.86,  0.64,  3.28,  0.88, -0.95,  2.96])
     """
 

src/komm/_channels/BinaryErasureChannel.py

@@ -17,19 +17,24 @@ class BinaryErasureChannel(DiscreteMemorylessChannel):
     $$
     where the parameter $\epsilon$ is called the *erasure probability* of the channel. For more details, see <cite>CT06, Sec. 7.1.5</cite>.
 
-    To invoke the channel, call the object giving the input signal as parameter (see example below).
-
     Attributes:
         erasure_probability (Optional[float]): The channel erasure probability $\epsilon$. Must satisfy $0 \leq \epsilon \leq 1$. Default value is `0.0`, which corresponds to a noiseless channel.
 
+    Parameters: Input:
+        in0 (Array1D[int]): The input sequence.
+
+    Parameters: Output:
+        out0 (Array1D[int]): The output sequence.
+
     Examples:
         >>> np.random.seed(1)
         >>> bec = komm.BinaryErasureChannel(0.1)
         >>> bec.transition_matrix
         array([[0.9, 0. , 0.1],
                [0. , 0.9, 0.1]])
         >>> x = [1, 1, 1, 0, 0, 0, 1, 0, 1, 0]
-        >>> y = bec(x); y
+        >>> y = bec(x)
+        >>> y
         array([1, 1, 2, 0, 0, 2, 1, 0, 1, 0])
     """
 

src/komm/_channels/BinarySymmetricChannel.py

@@ -18,19 +18,24 @@ class BinarySymmetricChannel(DiscreteMemorylessChannel):
     $$
     where $Z_n$ are iid Bernoulli random variables with $\Pr[Z_n = 1] = p$. For more details, see <cite>CT06, Sec. 7.1.4</cite>.
 
-    To invoke the channel, call the object giving the input signal as parameter (see example below).
-
     Attributes:
         crossover_probability (Optional[float]): The channel crossover probability $p$. Must satisfy $0 \leq p \leq 1$. The default value is `0.0`, which corresponds to a noiseless channel.
 
+    Parameters: Input:
+        in0 (Array1D[int]): The input sequence.
+
+    Parameters: Output:
+        out0 (Array1D[int]): The output sequence.
+
     Examples:
         >>> np.random.seed(1)
         >>> bsc = komm.BinarySymmetricChannel(0.1)
         >>> bsc.transition_matrix
         array([[0.9, 0.1],
                [0.1, 0.9]])
         >>> x = [0, 1, 1, 1, 0, 0, 0, 0, 0, 1]
-        >>> y = bsc(x); y
+        >>> y = bsc(x)
+        >>> y
         array([0, 1, 0, 1, 0, 1, 0, 0, 0, 1])
     """
 

src/komm/_channels/DiscreteMemorylessChannel.py

@@ -9,16 +9,21 @@ class DiscreteMemorylessChannel:
     r"""
     Discrete memoryless channel (DMC). It is defined by an *input alphabet* $\mathcal{X}$, an *output alphabet* $\mathcal{Y}$, and a *transition probability matrix* $p_{Y \mid X}$. Here, for simplicity, the input and output alphabets are always taken as $\mathcal{X} = \\{ 0, 1, \ldots, |\mathcal{X}| - 1 \\}$ and $\mathcal{Y} = \\{ 0, 1, \ldots, |\mathcal{Y}| - 1 \\}$, respectively. The transition probability matrix $p_{Y \mid X}$, of size $|\mathcal{X}|$-by-$|\mathcal{Y}|$, gives the conditional probability of receiving $Y = y$ given that $X = x$ is transmitted. For more details, see <cite>CT06, Ch. 7</cite>.
 
-    To invoke the channel, call the object giving the input signal as parameter (see example below).
-
     Attributes:
         transition_matrix (Array2D[float]): The channel transition probability matrix $p_{Y \mid X}$. The element in row $x \in \mathcal{X}$ and column $y \in \mathcal{Y}$ must be equal to $p_{Y \mid X}(y \mid x)$.
 
+    Parameters: Input:
+        in0 (Array1D[int]): The input sequence.
+
+    Parameters: Output:
+        out0 (Array1D[int]): The output sequence.
+
     Examples:
         >>> np.random.seed(1)
         >>> dmc = komm.DiscreteMemorylessChannel([[0.9, 0.05, 0.05], [0.0, 0.5, 0.5]])
         >>> x = [0, 1, 0, 1, 1, 1, 0, 0, 0, 1]
-        >>> y = dmc(x); y
+        >>> y = dmc(x)
+        >>> y
         array([0, 2, 0, 1, 1, 1, 0, 0, 0, 2])
     """