[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

draugr/drawers/mpl_drawers/discrete_scroll_plot.py

@@ -25,7 +25,8 @@
 class DiscreteScrollPlot(MplDrawer):
     """
     Waterfall plot
-    only supports a single trajectory at a time, do not supply parallel trajectories to draw method, will get truncated to num actions, effectively dropping actions for other envs than the first."""
+    only supports a single trajectory at a time, do not supply parallel trajectories to draw method, will get truncated to num actions, effectively dropping actions for other envs than the first.
+    """
 
     @passes_kws_to(MplDrawer.__init__)
     def __init__(
@@ -43,7 +44,6 @@ def __init__(
         figure_size: Tuple[int, int] = None,
         **kwargs
     ):
-
         super().__init__(render=render, figure_size=figure_size, **kwargs)
         if not render:
             return

draugr/drawers/mpl_drawers/spectral/fast_fourier_transform_spectrogram.py

@@ -26,7 +26,8 @@
 class FastFourierTransformSpectrogramPlot(MplDrawer):
     """
     TODO: CENTER Align fft maybe, to mimick librosa stft
-    Short Time Fourier Transform (STFT), with step size of 1 and window lenght of n_fft, and no window function ( TODO: Hanning Smoothing)"""
+    Short Time Fourier Transform (STFT), with step size of 1 and window lenght of n_fft, and no window function ( TODO: Hanning Smoothing)
+    """
 
     def __init__(
         self,

draugr/drawers/opencv_drawers/opencv_image_stream.py

@@ -28,7 +28,6 @@ class OpencvImageStream(Drawer):
     def __init__(
         self, title: str = "NoName", render: bool = True, **kwargs: MutableMapping
     ):
-
         super().__init__(**kwargs)
         if not render:
             return

draugr/matlab_utilities/conversion.py

@@ -12,10 +12,8 @@
 from typing import Any
 
 try:
-
     import matlab
 except ImportError:
-
     raise ImportError("Matlab engine not installed")
 import numpy
 from scipy import sparse
@@ -181,7 +179,6 @@ def ndarray_to_matlab(numpy_array):
         order = "C"
 
     if numpy.iscomplexobj(numpy_array):  # complex case
-
         matlab_array = matlab.double(
             initializer=None, size=(1, num_elements), is_complex=True
         )  # create empty matlab.mlarray

draugr/metrics/metric_collection.py

@@ -52,10 +52,10 @@ def append(self, *args: Sequence, **kwargs: MutableMapping):
         :type args:
         :param kwargs:
         :type kwargs:"""
-        for (arg, (k, v)) in zip(args, self._metrics.items()):
+        for arg, (k, v) in zip(args, self._metrics.items()):
             self._metrics[k].append(arg)
 
-        for (k, v) in kwargs:
+        for k, v in kwargs:
             self._metrics[k].append(v)
 
     def remove_metric(self, name):

draugr/multiprocessing_utilities/pooled_queue_processor.py

@@ -55,7 +55,8 @@ class PooledQueueProcessor(object):
     This is a workaround of Pythons extremely slow interprocess communication pipes.
     The ideal solution would be to use a multiprocessing.queue, but it apparently communication is band
     limited.
-    This solution has processes complete tasks (batches) and a thread add the results to a queue.queue."""
+    This solution has processes complete tasks (batches) and a thread add the results to a queue.queue.
+    """
 
     def __init__(
         self,
@@ -68,7 +69,6 @@ def __init__(
         fill_at_construction=True,
         blocking=True,
     ):
-
         if kwargs is None:
             kwargs = {}
 

draugr/opencv_utilities/color_space/edge.py

@@ -43,7 +43,6 @@ def to_edge(
 
     ksize = kwargs.get("ksize", max(next_odd(max(*(img.shape)) // 100), 5))
     if method == ToEdgeMethodEnum.morph_gradient:
-
         return cv2.morphologyEx(
             img,
             MorphTypeEnum.gradient.value,

draugr/opencv_utilities/color_space/noise.py

@@ -45,7 +45,6 @@ def noise_filter(
             sigmaSpace=kwargs.get("sigmaSpace", 17),
         )
     elif method == NoiseFilterMethodEnum.gaussian_blur:
-
         return cv2.GaussianBlur(
             img,
             ksize=(ksize, ksize),

draugr/opencv_utilities/drawing/masks/gauss_circles.py

@@ -39,6 +39,7 @@ def ellipse_bbox(h, k, a, b, theta):
 
 # ----------------------------------------------------------------------------
 
+
 # Rotated elliptical gradient - slow, Python-only approach
 def make_gradient_v1(width, height, h, k, a, b, theta):
     """
@@ -76,6 +77,7 @@ def make_gradient_v1(width, height, h, k, a, b, theta):
 
 # ----------------------------------------------------------------------------
 
+
 # Rotated elliptical gradient - faster, vectorized numpy approach
 def make_gradient_v2(width, height, h, k, a, b, theta):
     """
@@ -111,7 +113,6 @@ def make_gradient_v2(width, height, h, k, a, b, theta):
 
 # ============================================================================
 if __name__ == "__main__":
-
     from pathlib import Path
     from warg import ensure_existence
     from draugr.opencv_utilities import show_image

draugr/opencv_utilities/windows/color_picker.py

@@ -56,7 +56,6 @@ def interactive_hsv_color_picker(
         img = cv2.imread(str(p))
         output = img
         while 1:
-
             h_min = cv2.getTrackbarPos(hmin_label, window_label)
             h_max = cv2.getTrackbarPos(hmax_label, window_label)
 

draugr/os_utilities/mac_utilities/dark_mode.py

@@ -12,11 +12,9 @@
 import ctypes.util
 
 try:  # macOS Big Sur+ use "a built-in dynamic linker cache of all system-provided libraries"
-
     appkit = ctypes.cdll.LoadLibrary("AppKit.framework/AppKit")
     objc = ctypes.cdll.LoadLibrary("libobjc.dylib")
 except OSError:  # revert to full path for older OS versions and hardened programs
-
     appkit = ctypes.cdll.LoadLibrary(ctypes.util.find_library("AppKit"))
     objc = ctypes.cdll.LoadLibrary(ctypes.util.find_library("objc"))
 

draugr/os_utilities/windows_utilities/task_scheduler/api.py

@@ -207,7 +207,6 @@ def set_task_activity(
 
 
 if __name__ == "__main__":
-
     print(f"{socket.gethostname()}\{getpass.getuser()}")
 
     if True:

draugr/pandas_utilities/misc_utilities.py

@@ -62,7 +62,6 @@ def duplicate_columns(frame: pandas.DataFrame) -> List[str]:
     duplicates = []
 
     for t, v in groups.items():
-
         cs = frame[v].columns
         vs = frame[v]
         lcs = len(cs)

draugr/python_utilities/function_wrappers.py

@@ -162,7 +162,6 @@ def str_to_bool(s: str, preds: Tuple[str, ...] = ("true", "1")) -> bool:
 str2bool = str_to_bool
 
 if __name__ == "__main__":
-
     c = namedtuple("C", ("a", "b"))
 
     @wrap_args(c)

draugr/stopping/stopping_key.py

@@ -45,7 +45,6 @@ def add_early_stopping_key_combination(
     verbose: bool = False,
     combinations: Iterable = default_combinations,
 ):  # -> keyboard.Listener:
-
     """
 
     :param combinations:

draugr/tensorboard_utilities/experimental/confusion_matrix.py

@@ -168,7 +168,8 @@ def asda():
 
         def plot_to_image(figure):
             """Converts the matplotlib plot specified by 'figure' to a PNG image and
-            returns it. The supplied figure is closed and inaccessible after this call."""
+            returns it. The supplied figure is closed and inaccessible after this call.
+            """
             # Save the plot to a PNG in memory.
             buf = io.BytesIO()
             pyplot.savefig(buf, format="png")

draugr/tensorboard_utilities/exporting/file_export.py

@@ -23,7 +23,6 @@ def __init__(self, *args: Sequence, **kwargs: MutableMapping):
             raise ValueError("No file provided")
 
     def export(self):
-
         self.file.write("")
         super().export()
 

draugr/torch_utilities/architectures/mlp_variants/concatination.py

@@ -54,7 +54,6 @@ def __init__(
         fusion_hidden_multiplier: int = 10,
         **kwargs: MutableMapping
     ):
-
         forward_shape, *res = input_shape
         self._residual_shape = res
 

draugr/torch_utilities/evaluation/cross_validation.py

@@ -22,7 +22,8 @@ def cross_validation_generator(
     *datasets: Dataset, n_splits: int = 10
 ) -> Tuple[Subset, Subset]:
     """
-    Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. This situation is called overfitting. To avoid it, it is common practice when performing a (supervised) machine learning experiment to hold out part of the available data as a test set"""
+    Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. This situation is called overfitting. To avoid it, it is common practice when performing a (supervised) machine learning experiment to hold out part of the available data as a test set
+    """
 
     cum = ConcatDataset(datasets)
     for train_index, val_index in KFold(n_splits=n_splits).split(cum):

draugr/torch_utilities/exporting/latex_tables.py

@@ -110,5 +110,4 @@ def iasjduh() -> None:
 
 
 if __name__ == "__main__":
-
     adasdassijd()

draugr/torch_utilities/operations/torch_transforms/batch_transforms.py

@@ -41,7 +41,8 @@ class BatchNormalize:
     std (sequence): Sequence of standard deviations for each channel.
     inplace(bool,optional): Bool to make this operation in-place.
     dtype (torch.dtype,optional): The data type of tensors to which the transform will be applied.
-    device (torch.device,optional): The device of tensors to which the transform will be applied."""
+    device (torch.device,optional): The device of tensors to which the transform will be applied.
+    """
 
     def __init__(self, mean, std, inplace=False, dtype=torch.float, device="cpu"):
         self.mean = torch.as_tensor(mean, dtype=dtype, device=device)[
@@ -96,7 +97,8 @@ class BatchRandomCrop:
     padding (int, optional): Optional padding on each border of the image.
         Default is None, i.e no padding.
     dtype (torch.dtype,optional): The data type of tensors to which the transform will be applied.
-    device (torch.device,optional): The device of tensors to which the transform will be applied."""
+    device (torch.device,optional): The device of tensors to which the transform will be applied.
+    """
 
     def __init__(self, size, padding=None, dtype=torch.float, device="cpu"):
         self.size = size

draugr/torch_utilities/optimisation/parameters/complexity.py

@@ -590,6 +590,7 @@ def remove_flops_counter_hook_function(module) -> None:
 
 # --- Masked flops counting
 
+
 # Also being run in the initialization
 def add_flops_mask_variable_or_reset(module) -> None:
     """

draugr/torch_utilities/writers/tensorboard/tensorboard_pytorch_writer.py

@@ -564,7 +564,6 @@ def image(
                             **kwargs,
                         )
                 else:
-
                     raise NotImplementedError(
                         f"{multi_channel_method} is not implemented"
                     )
@@ -607,7 +606,6 @@ def _open(self):
 PTW = TensorBoardPytorchWriter
 
 if __name__ == "__main__":
-
     with TensorBoardPytorchWriter(PROJECT_APP_PATH.user_log / "test") as writer:
         writer.scalar("What", 4)
 

draugr/visualisation/matplotlib_utilities/quirks.py

@@ -114,7 +114,6 @@ def scatter_auto_mark(
             if isinstance(marker, mmarkers.MarkerStyle):
                 marker_obj = marker
             else:
-
                 marker_obj = mmarkers.MarkerStyle(marker, fillstyle=fillstyle)
             path = marker_obj.get_path().transformed(marker_obj.get_transform())
             paths.append(path)

draugr/writers/mixins/embed_writer_mixin.py

@@ -14,7 +14,8 @@
 
 class EmbedWriterMixin(ABC):
     """
-    Writer mixin that provides an interface for 'writing' embeds/projections(2d,3d) for interactive visualisation"""
+    Writer mixin that provides an interface for 'writing' embeds/projections(2d,3d) for interactive visualisation
+    """
 
     @abstractmethod
     def embed(

draugr/writers/terminal/console_writer.py

@@ -26,7 +26,6 @@ def _scalar(self, tag: str, value: float, step: int):
 
 
 if __name__ == "__main__":
-
     with ConsoleWriter() as w:
         for i in range(10):
             w.scalar("lol", i)

samples/drawers/video_stream/opencv_drawer.py

@@ -26,5 +26,4 @@ def asdasf() -> None:
 
 
 if __name__ == "__main__":
-
     asdasf()

samples/drawers/video_stream/pygame_drawer.py

@@ -28,7 +28,6 @@ def surface_to_numpy(surface) -> numpy.ndarray:
 
 
 if __name__ == "__main__":
-
     # Screen settings
     SCREEN = [640, 360]
 
@@ -43,7 +42,6 @@ def surface_to_numpy(surface) -> numpy.ndarray:
     cam.start()
 
     while 1:  # Ze loop
-
         time.sleep(1 / 120)  # 60 frames per second
 
         image = cam.get_image()  # Get current webcam image

samples/drawers/video_stream/qt_drawer.py

@@ -70,5 +70,4 @@ def open(self):
 
 
 if __name__ == "__main__":
-
     aushduah()

samples/drawers/video_stream/wx_drawer.py

@@ -96,5 +96,4 @@ def __init__(self):
 
 
 if __name__ == "__main__":
-
     uahsduiasdj()

samples/opencv_samples/tkinter_app/tk_hough_circles.py

@@ -122,13 +122,11 @@ def load_config(directory, file):
     """
     global config
     try:
-
         xml_file = f"{directory}/{os.path.splitext(file)[0]}.xml"
         if os.path.exists(xml_file):
             root = ET.parse(xml_file).getroot()
             settings = root.find("config")
             if settings:
-
                 for setting in settings:
                     config[setting.tag] = int(setting.text)
             else:

samples/os_utilities/systemd_samples/scripts/busy_script.py

@@ -29,7 +29,6 @@ def asd() -> None:
 
 
 if __name__ == "__main__":
-
     if False:
         from pathlib import Path
 

samples/progress_bar/progress_bar_sample.py

@@ -8,7 +8,6 @@
            """
 
 if __name__ == "__main__":
-
     from draugr.visualisation.progress import progress_bar
 
     for a in progress_bar(range(100)):

samples/torch_samples/hyperparameter_tuning_with_hparams.py

@@ -114,6 +114,7 @@
 #
 # The model will be quite simple: two dense layers with a dropout layer between them. The training code will look familiar, although the hyperparameters are no longer hardcoded. Instead, the hyperparameters are provided in an `hparams` dictionary and used throughout the training function:
 
+
 # %%
 def train_test_model(hparams):
     """description"""
@@ -141,6 +142,7 @@ def train_test_model(hparams):
 # %% [markdown]
 # For each run, log an hparams summary with the hyperparameters and final accuracy:
 
+
 # %%
 def run(run_dir, hparams):
     """description"""

tests/integration_tests/catch_keyboard_interrupt_drawer_close.py

@@ -8,7 +8,6 @@
            """
 
 if __name__ == "__main__":
-
     import numpy
     import pytest