chore: flake8

src/polyA_classifier.py

-<<<<<<< HEAD
 """Module for classifying polyA tails as internal or real."""
 
-=======
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
 import torch
 from torch.nn import Linear, ReLU, Sequential, MaxPool1d, Module, BatchNorm1d, Conv1d
 import numpy as np
@@ -10,13 +7,10 @@ from typing import Union
 
 
 class Net(Module):
-<<<<<<< HEAD
-    """Two layer 1D convolutional neural net"""
-=======
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
+    """Two layer 1D convolutional neural net."""
 
     def __init__(self):
-
+        """Returns Net object."""
         super(Net, self).__init__()
 
         self.cnn_layers = Sequential(
@@ -36,14 +30,8 @@ class Net(Module):
             Linear(4 * 50, 10)
         )
 
-<<<<<<< HEAD
     def forward(self, x):
         """Forward pass function."""
-=======
-    # Defining the forward pass
-    def forward(self, x):
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
-
         x = self.cnn_layers(x)
         x = x.view(x.size(0), -1)
         x = self.linear_layers(x)
@@ -51,23 +39,16 @@ class Net(Module):
 
 
 class PolyAClassifier:
-<<<<<<< HEAD
-    """Classifier object using the state-dict of a pretrained pytorch model"""
-=======
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
+    """Classifier object using the state-dict of a pretrained pytorch model."""
 
     enum = {
         'A': 0.0,
         'U': 1 / 3,
-<<<<<<< HEAD
         'T': 1 / 3,
-=======
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
         'G': 2 / 3,
         'C': 1.0
     }
 
-<<<<<<< HEAD
     def __init__(self, model: Module = Net, state_dict_path: str = './models/internal_priming.pth'):
         """Returns a stateless classifier with the model loaded.
 
@@ -75,10 +56,6 @@ class PolyAClassifier:
             model: An object subclassing the pytorch Module
             state_dict_path: A path to a saved state-dict of said object at a trained state.
         """
-=======
-    def __init__(self, model=Net, state_dict_path='./models/internal_priming.pth'):
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
-
         self.model = model()
         self.model.load_state_dict(torch.load(state_dict_path))
 
@@ -94,13 +71,9 @@ class PolyAClassifier:
         Raises:
             TypeError: If sequence is not str or list(str)
             ValueError: If some or all sequences are not of length 200
-<<<<<<< HEAD
             ValueError: If non-allowed letters in string
-=======
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
 
         """
-
         if type(sequence) is list:
 
             sequences = [list(seq) for seq in sequence]
@@ -115,7 +88,6 @@ class PolyAClassifier:
 
         enum_seqs = []
 
-<<<<<<< HEAD
         try:
             for s in sequences:
                 enum_sequence = [self.enum[key.upper()] for key in s]
@@ -136,27 +108,6 @@ class PolyAClassifier:
         if test_shape[1] != 200:
             raise ValueError('Sequences not of length 200')
 
-
-=======
-        for s in sequences:
-            enum_sequence = [self.enum[key] for key in s]
-            enum_seqs.append(enum_sequence)
-
-        # convert to ndarray and reshape for pytorch
-        test = np.array(enum_seqs, dtype=np.float32)
-
-        try:
-            test_shape = test.shape
-            test = test.reshape(test_shape[0], 1, test_shape[1])
-
-            if test_shape[1] != 200:
-                raise ValueError('Sequences not of length 200')
-
-        except IndexError:
-
-            raise ValueError('Not all sequences of length 200')
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df
-
         test = torch.from_numpy(test)
 
         # make prediction
@@ -173,29 +124,4 @@ class PolyAClassifier:
 
         else:
 
-<<<<<<< HEAD
             return predictions.tolist()
-=======
-            return predictions
-
-
-if __name__ == '__main__':
-
-    mod = PolyAClassifier(state_dict_path='../models/internal_priming.pth')
-
-    real_str = 'CGCCGGAAGAACGAAUCUCCCACUGCCCGGGCAUCCAAUGGACUUCAUAGGAAUGGCAGCUGAUAACACCGCCCCCUGUGGCGCGCCAGAGGGCGCGCUUCGUGUAGGCUUCGAUGUCGCGGUAAAAUUCUUGGAUUAAAGAAGGGGCCCUGUGGUAGCAAGUUUUUUAUUCUGUGGGCGCUCUUACGCGUGUAUUGUCU'
-    fake_str = 'GUUUGAGGCGCAUGACGCGUUUCGGGGGCCUUGCGUCGCCCACGCCGGCGUUCUCUUUAAAAGGAGCAACGACACCACGCCCCAUGGACCAUGCCGCAGGGUGAACGUCGUCCCGCAACUGCCGUGCACCCGUCAAAAGGAGGCGUCUUCAAAAAAAAAACAAAAUAAAAACACAUACCGCGGCGCGUAUUAGAGCGGCG'
-    list_test = [real_str, fake_str]
-
-    pred = mod.classify(real_str)
-
-    print(pred)
-
-    pred = mod.classify(fake_str)
-
-    print(pred)
-
-    pred = mod.classify(list_test)
-
-    print(pred)
->>>>>>> fb8e822ed92fba85e584305fcb18bdf45ad601df