Fix all flake8 issues

.flake8

@@ -0,0 +1,4 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+#max-complexity = 10

pyproject.toml

@@ -14,7 +14,8 @@ license = {file = "COPYING"}
 authors = [
     {name = "Andrej Rode", email = "rode@kit.edu"},
     {name = "Shrinivas Chimmalgi", email="shrinivas.chimmalgi@kit.edu"},
-    {name = "Benedikt Geiger", email="benedikt.geiger@kit.edu"}
+    {name = "Benedikt Geiger", email="benedikt.geiger@kit.edu"},
+    {name = "Laurent Schmalen"}
 ]
 
 dependencies = [
@@ -43,7 +44,6 @@ dev = [
     "pytest",
     "black",
     "flake8",
-    "pyproject-flake8",
     "pylint",
     "python-lsp-server[all]",
     "pyls-flake8",
@@ -55,11 +55,6 @@ remotelog = [
     "wandb",
 ]
 
-[tool.flake8]
-max-line-length = 88
-extend-ignore = "E203,"
-max-complexity = 10
-
 [tool.pytest.ini_options]
 addopts = [
     "--import-mode=importlib",
@@ -103,8 +98,9 @@ legacy_tox_ini = """
 [tox]
 description = list of environments against which tox runs the tests
 envlist =
-    reformat
-    check
+    flake8
+    black
+    pydocstyle
     py3-fast
     py37
     py38
@@ -121,7 +117,7 @@ python =
     3.7: py37
     3.8: py38
     3.9: py39
-    3.10: reformat, check, py310
+    3.10: flake8, black, pydocstyle, py310
 
 [testenv]
 # install pytest in the virtualenv where commands will be executed
@@ -139,20 +135,25 @@ usedevelop = False
 commands =
     pytest {env:PYTEST_MARKERS:} {env:PYTEST_EXTRA_ARGS:} {posargs:-vv}
 
-[testenv:check]
+[testenv:flake8]
 description = this environments checks for flake8 and black
 deps =
     black==22.3.0
-    pyproject-flake8
     flake8
     build
 skip_install = true
 commands =
-    pflake8 src tests
-    black --check --diff src tests
-    python -m build
+    flake8 src tests
+
+[testenv:pydocstyle]
+description = this environments checks formatting with pydocstyle
+deps =
+    pydocstyle
+skip_install = true
+commands =
+    pydocstyle --count src/
 
-[testenv:reformat]
+[testenv:black]
 description = reformats the code using black
 deps =
     black==22.3.0

src/mokka/channels/torch.py

@@ -570,7 +570,7 @@ def nonlinear_operator(u):
 
         return (h_dz, h_dz_2, nonlinear_operator)
 
-    def forward(self, u):
+    def forward(self, u):  # noqa: C901
         """
         :param u0: input signal (array)
         :returns: array signal at the end of the fiber
@@ -636,7 +636,8 @@ def forward(self, u):
                     z = z + 2 * dz
                     if iter >= self.maxiter:
                         logger.info(
-                            "WARNING: Failed to converge to goal local error of %s in %s iterations",
+                            "WARNING: Failed to converge to goal local error \
+                            of %s in %s iterations",
                             self.delta_G,
                             self.maxiter,
                         )
@@ -663,9 +664,10 @@ class SSFMPropagationDualPol(torch.nn.Module):
     This class is adapted from Code written by Dominik Rimpf published under the MIT
     license [https://gitlab.com/domrim/bachelorarbeit-code]
 
-    This class solves the coupled nonlinear Schrodinger equation for pulse propagation in an
-    optical fiber using the split-step Fourier method.
-    The implementation and name are based on https://photonics.umd.edu/software/ssprop/vector-version/
+    This class solves the coupled nonlinear Schrodinger equation for pulse propagation
+    in an optical fiber using the split-step Fourier method.
+    The implementation and name are based on
+    https://photonics.umd.edu/software/ssprop/vector-version/
 
     :param dt: time step between samples (sample time)
     :param dz: propagation stepsize (delta z) of SSFM (segment length)
@@ -679,8 +681,11 @@ class SSFMPropagationDualPol(torch.nn.Module):
                      length
     :param delta_G: Goal local error (optional) (default 1e-3)
     :param maxiter: Number of step-size iterations (optional) (default 4)
-    :param psp: Principal eigenstate of the fiber specified as a 2-vector containing the angles Psi and Chi (optional) (default [0,0])
-    :param solution_method: String that specifies which method to use when performing the split-step calculations. Supported methods elliptical and circular (optional) (default elliptical)
+    :param psp: Principal eigenstate of the fiber specified as a 2-vector containing
+                the angles Psi and Chi (optional) (default [0,0])
+    :param solution_method: String that specifies which method to use when performing
+                            the split-step calculations. Supported methods elliptical
+                            and circular (optional) (default elliptical)
 
     """
 
@@ -939,7 +944,8 @@ def forward(self, ux, uy):
 
                     if iter >= self.maxiter:
                         logger.info(
-                            "WARNING: Failed to converge to goal local error of %s in %s iterations",
+                            "WARNING: Failed to converge to goal local error \
+                             of %s in %s iterations",
                             self.delta_G,
                             self.maxiter,
                         )

src/mokka/inft/torch.py

@@ -48,7 +48,6 @@ def BMI(m, N, b_ij, L_ij, p=None):
     return BMI
 
 
-
 def MI(M, PX, N, symbol_idx, Q_YX):
     """
     Calculate the mutual information.

src/mokka/mapping/torch.py

@@ -474,7 +474,8 @@ class ClassicalDemapper(torch.nn.Module):
     """
     Classical Bitwise Soft Demapper with Gaussian Noise assumption.
     It takes the noise_sigma and constellation sorted from [0,2**m -1] and constructs
-    an more or less efficient demapper which outputs LLRS for all m bits for each received symbol
+    an more or less efficient demapper which outputs LLRS for all m bits for
+    each received symbol
     """
 
     def __init__(self, noise_sigma, constellation, optimize=False):
@@ -550,8 +551,9 @@ def symbolwise(self, y, *args):
 class GaussianDemapper(torch.nn.Module):
     """
     Classical Bitwise Soft Demapper with Gaussian Noise assumption.
-    Learns bias and covariance matrix of noise for constellation sorted from [0,2**m -1] and constructs
-    an more or less efficient demapper which outputs LLRS for all m bits for each received symbol
+    Learns bias and covariance matrix of noise for constellation sorted from [0,2**m -1]
+    and constructs a more or less efficient demapper which outputs LLRS for all m bits
+    for each received symbol
     """
 
     def __init__(self, constellation):
@@ -637,7 +639,6 @@ def warm_start(self, sym_idx, y, learning_fraction=0.3, single_circular=False):
                 ]
                 rx_sym = y[idx].squeeze()
                 if y.dtype == torch.complex64:
-                    dim = 2
                     rx_sym = torch.stack(
                         (torch.real(rx_sym), torch.imag(rx_sym))
                     ).squeeze()
@@ -750,9 +751,10 @@ def forward(self, y, *args):
 
 class SeparatedSimpleDemapper(torch.nn.Module):
     """
-    Simplified Demapper which shall approximates output LLRS of bits separated in real and imag
+    Simplified Demapper which shall approximates output LLRS of bits separated in real
+    and imaginary parts
 
-    Do simple function approximation with in -> Linear -> ReLU -> Linear -> out operation
+    Do simple function approximation with in -> Linear -> ReLU -> Linear -> out
     This should be easy to replicate in hardware with LUT
     """
 
@@ -888,7 +890,8 @@ def p_symbols(self, *args):
 
 class MBPCSSampler(torch.nn.Module):
     """
-    This class is supposed to use NNs to find a lambda [0,1] for each given parameter (if given parameters) and then return p_symbols for further simulation
+    This class is supposed to use NNs to find a lambda [0,1] for each given parameter
+    (if given parameters) and then return p_symbols for further simulation
     """
 
     def __init__(

src/mokka/normalization/__init__.py

@@ -1,3 +1,3 @@
-from . import torch
+from . import torch  # noqa
 
-from .torch import energy, centered_energy
+from .torch import energy, centered_energy  # noqa

src/mokka/pulseshaping/__init__.py

@@ -1 +1 @@
-from . import torch
+from . import torch  # noqa

src/mokka/pulseshaping/torch.py

@@ -10,8 +10,8 @@
 @attr.define
 class PulseShaping(torch.nn.Module):
     """
-    PulseShaping filter class which provides several classical shapes, a filter function and a
-    matched filter function.
+    PulseShaping filter class which provides several classical shapes, a filter function
+    and a matched filter function.
     """
 
     _impulse_response: torch.tensor
@@ -80,15 +80,15 @@ def get_rc_ir(cls, syms, r, n_up, learnable=False):
         with torch.no_grad():
             # initialize output length and sample time
             T_symbol = 1.0
-            T_sample = (
-                1.0 / n_up
-            )  # length of one sample is the symbol-duration divided by the oversampling factor (=1/sampling rate)
-            T_ir = (
-                2 * syms * T_symbol
-            )  # Duration of the impulse response is positive and negative normed symbols added multplied by Symbol Duration
-            ir = torch.zeros(
-                int(T_ir / T_sample) + 1
-            )  # samples of impulse response is definied by duration of the ir divided by the sample time plus one for the 0th sample
+            T_sample = 1.0 / n_up
+            # length of one sample is the symbol-duration divided
+            # by the oversampling factor (=1/sampling rate)
+            T_ir = 2 * syms * T_symbol
+            # Duration of the impulse response is positive and negative
+            # normed symbols added multplied by Symbol Duration
+            ir = torch.zeros(int(T_ir / T_sample) + 1)
+            # samples of impulse response is definied by duration
+            # of the ir divided by the sample time plus one for the 0th sample
 
             # time indices and sampled time
             k_steps = torch.arange(
@@ -121,8 +121,8 @@ def get_rc_ir(cls, syms, r, n_up, learnable=False):
     def get_rrc_ir(cls, syms, r, n_up, learnable=False):
         """Determines normed coefficients of an RRC filter
 
-        This function is adapted from Sourcecode written by Dominik Rimpf and was published under the
-        MIT license [https://gitlab.com/domrim/bachelorarbeit-code]
+        This function is adapted from Sourcecode written by Dominik Rimpf and was
+        published under the MIT license [https://gitlab.com/domrim/bachelorarbeit-code]
 
         Formula out of: K.-D. Kammeyer, Nachrichtenübertragung
         At poles, l'Hospital was used
@@ -138,12 +138,12 @@ def get_rrc_ir(cls, syms, r, n_up, learnable=False):
             # initialize output length and sample time
             T_symbol = 1.0
             T_sample = 1 / n_up
-            T_ir = (
-                2 * syms * T_symbol
-            )  # Duration of the impulse response is positive and negative normed symbols added multplied by Symbol Duration
-            ir = torch.zeros(
-                int(T_ir / T_sample) + 1
-            )  # samples of impulse response is definied by duration of the ir divided by the sample time plus one for the 0th sample
+            T_ir = 2 * syms * T_symbol
+            # Duration of the impulse response is positive and negative
+            # normed symbols added multplied by Symbol Duration
+            ir = torch.zeros(int(T_ir / T_sample) + 1)
+            # samples of impulse response is definied by duration of
+            # the ir divided by the sample time plus one for the 0th sample
 
             # time indices and sampled time
             k_steps = torch.arange(

src/mokka/utils/generators/__init__.py

@@ -1,6 +1,6 @@
-from . import numpy
+from . import numpy  # noqa
 
 try:
-    from . import torch
+    from . import torch  # noqa
 except ImportError:
     pass

src/mokka/utils/generators/torch.py

@@ -1 +1 @@
-import torch
+import torch  # noqa

tests/mokka/test_channels.py

@@ -54,9 +54,9 @@ def test_residualphasenoise():
 
 
 def test_opticalnoise():
-    wavelength_bw = 0.1 # nm
-    optical_snr = 25 # dB
-    channel = channels.OpticalNoise(dt, wavelength, wavelength_bw , optical_snr)
+    wavelength_bw = 0.1  # nm
+    optical_snr = 25  # dB
+    channel = channels.OpticalNoise(dt, wavelength, wavelength_bw, optical_snr)
     x = torch.zeros(num_samples, dtype=torch.complex64)
     y = channel(x)
     assert len(y) == num_samples
@@ -103,9 +103,8 @@ def test_SSFM_singlepol():
     assert len(y) == num_samples
 
 
-
 def test_SSFM_dualpol():
-    amp = channels.EDFAAmpDualPol(
+    amp = channels.EDFAAmpDualPol(  # noqa
         span_length,
         amp_gain,
         alphaa_db,

tests/mokka/test_e2e.py

@@ -1 +1 @@
-import mokka.e2e.torch as e2e
+import mokka.e2e.torch as e2e  # noqa

tests/mokka/test_equalizers.py

@@ -1 +1 @@
-import mokka.equalizers.torch as equalizers
+import mokka.equalizers.torch as equalizers  # noqa

tests/mokka/test_pulseshaping.py

@@ -1 +1 @@
-import mokka.pulseshaping.torch as pulseshape
+import mokka.pulseshaping.torch as pulseshape  # noqa

tests/mokka/test_synchronizers.py

@@ -1 +1 @@
-import mokka.synchronizers.phase.torch as phase
+import mokka.synchronizers.phase.torch as phase  # noqa