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

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

bin/exportProcessedData.py

@@ -274,7 +274,6 @@
 
 
 for evFileName in evFileNames:
-
     # open the EV file
     evFile = evApp.OpenFile(os.path.join(evDir, evFileName))
     print("  opened {}".format(evFileName))
@@ -296,7 +295,6 @@
     evFile.Save()
 
     for binSize in binSizes:
-
         # Use this as the reference line for depth grid
         refline = evFile.Lines.FindByName(linename)
         av.Properties.Grid.DepthRangeReferenceLine = refline

echofilter/data/dataset.py

@@ -291,7 +291,6 @@ def num_samples(self):
         return len(self.data_source)
 
     def __iter__(self):
-
         n_sample = len(self.data_source)
         n_dataset = len(self.data_source.cumulative_sizes)
 

echofilter/data/transforms.py

@@ -129,7 +129,6 @@ def __init__(self, output_size, order=1):
         self.order = order
 
     def __call__(self, sample):
-
         order = self.order
         if order is None:
             order = np.random.randint(1)
@@ -224,7 +223,6 @@ def __init__(self, *args, p=0.5, **kwargs):
         self.p = p
 
     def __call__(self, sample):
-
         if random.random() > self.p:
             # Nothing to do
             return sample
@@ -341,7 +339,6 @@ def __init__(self, output_size, p=0.5, sigma=8.0, alpha=0.05, order=1):
         self.alpha = _pair(alpha)
 
     def __call__(self, sample):
-
         if random.random() > self.p:
             # Nothing to do
             return sample
@@ -464,7 +461,6 @@ def __init__(self, center, deviation, robust2stdev=True):
         self.robust2stdev = robust2stdev
 
     def __call__(self, sample):
-
         if not isinstance(self.center, str):
             center = self.center
         elif self.center.lower() == "mean":
@@ -521,7 +517,6 @@ def __init__(self, nan_val=0.0):
         self.nan_val = nan_val
 
     def __call__(self, sample):
-
         # Can't use np.nan_to_num to assign nan to a specific value if
         # numpy version <= 1.17.
         sample["signals"][np.isnan(sample["signals"])] = self.nan_val
@@ -546,7 +541,6 @@ def __init__(self, axis=0, p=0.5):
         self.p = p
 
     def __call__(self, sample):
-
         if random.random() > self.p:
             # Nothing to do
             return sample
@@ -575,7 +569,6 @@ def __init__(self, max_crop_fraction):
         self.max_crop_fraction = max_crop_fraction
 
     def __call__(self, sample):
-
         width = sample["signals"].shape[0]
 
         crop_fraction = random.uniform(0.0, self.max_crop_fraction)
@@ -716,7 +709,6 @@ def __init__(
         self.fraction_close = fraction_close
 
     def __call__(self, sample):
-
         p = random.random()
 
         # Possibility of doing no crop at all

echofilter/ev2csv.py

@@ -266,7 +266,6 @@ def ev2csv(
 
     # Open the EV file
     with echofilter.win.open_ev_file(input, ev_app) as ev_file:
-
         # Find the right variable
         av = ev_file.Variables.FindByName(variable_name).AsVariableAcoustic()
 

echofilter/inference.py

@@ -836,7 +836,6 @@ def run_inference(
             # Make a temporary directory in case we are not caching generated csvs
             # Directory and all its contents are deleted when we leave this context
             with tempfile.TemporaryDirectory() as tmpdirname:
-
                 # Convert ev file to csv, if necessary
                 ev2csv_dir = cache_csv
                 if ev2csv_dir is None:

echofilter/nn/modules/conv.py

@@ -67,7 +67,6 @@ class DepthwiseConv2d(nn.Conv2d):
     def __init__(
         self, in_channels, kernel_size=3, stride=1, padding="same", dilation=1, **kwargs
     ):
-
         if "groups" in kwargs:
             raise ValueError(
                 "Number of groups must equal number of input channels for a depthwise convolution."

echofilter/nn/wrapper.py

@@ -119,7 +119,7 @@ def __init__(
         mapping_extra = {}
         for key in mapping:
             for alias_map in self.aliases:
-                for (alias_a, alias_b) in [alias_map, alias_map[::-1]]:
+                for alias_a, alias_b in [alias_map, alias_map[::-1]]:
                     if "_" + alias_a not in key:
                         continue
                     alt_key = key.replace("_" + alias_a, "_" + alias_b)

echofilter/optim/schedulers.py

@@ -142,7 +142,6 @@ def __init__(
         pct_end=0.75,
         **kwargs,
     ):
-
         # Validate pct_start
         if not isinstance(pct_start, float) or pct_start < 0 or pct_start > 1:
             raise ValueError(

echofilter/optim/torch_backports.py

@@ -94,7 +94,6 @@ class _LRScheduler(object):
     """
 
     def __init__(self, optimizer, last_epoch=-1):
-
         # Attach optimizer
         if not isinstance(optimizer, Optimizer):
             raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
@@ -359,7 +358,6 @@ def __init__(
         final_div_factor=1e4,
         last_epoch=-1,
     ):
-
         # Validate optimizer
         if not isinstance(optimizer, Optimizer):
             raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))

echofilter/train.py

@@ -474,7 +474,6 @@ def train(
     print("Starting training")
     t_start = time.time()
     for epoch in range(start_epoch, n_epoch + 1):
-
         t_epoch_start = time.time()
 
         # Set the seed state at the start of each epoch
@@ -1187,7 +1186,6 @@ def train_epoch(
 
             # Measure and record performance with various metrics
             for chn_cond, meters_k in meters.items():
-
                 chnparts = chn_cond.split("|")
                 chn = chnparts[0].lower()
                 if len(chnparts) < 2:
@@ -1423,7 +1421,6 @@ def validate(
 
             # Measure and record performance with various metrics
             for chn_cond, meters_k in meters.items():
-
                 chnparts = chn_cond.split("|")
                 chn = chnparts[0].lower()
                 if len(chnparts) < 2:

echofilter/ui/checkpoints.py

@@ -363,7 +363,6 @@ def load_checkpoint(
                 checkpoint = torch.load(ckpt_path, **load_args)
 
             except pickle.UnpicklingError:
-
                 msg = "Error: Unable to load checkpoint {}.".format(
                     os.path.abspath(ckpt_path)
                 )