configured flake8 for linting

.flake8

@@ -0,0 +1,4 @@
+[flake8]
+ignore = E722, E226, W504
+max-line-length = 120
+exclude = tests/*, .git. __pycache__, env, examples

.travis.yml

@@ -6,6 +6,6 @@ before-install:
 install:
   - pip install -r requirements.txt
 script:
-  - pytest --cov=./
+  - pytest --cov=./ --flake8
 after-success:
   - bash <(curl -s https://codecov.io/bash)

code/shot_stats.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class shot_stats:
     '''
     Takes in a DataFrame of shot data generated by target_reader.py and
@@ -116,7 +117,7 @@ def cont_to_hist(self, series, bin_min=0, bin_max=None, num_bins=11):
             list (int): histogram of series as a list with indices as bins
             list (float): heatmap color values corresponding to histogram
         '''
-        if bin_max == None:
+        if bin_max is None:
             bin_max = self.trg_size
         bins = np.linspace(bin_min, bin_max, num_bins, endpoint=False)[1:]
         bin_vals = np.searchsorted(bins, series)
@@ -188,4 +189,4 @@ def get_reco_list(self, x_vals, y_vals, actual_score, op_score):
             reco_list.extend([hori_reco, vert_reco])
         else:
             reco_list.extend([vert_reco, hori_reco])
-        return reco_list
+        return reco_list

code/target_reader.py

@@ -67,7 +67,7 @@ class variable 'df' and image steps in 'stage_images.'
         self.df = None
 
         # Loads image from file if it exists
-        if type(file_obj) == type(None):
+        if file_obj is None:
             image = cv2.imread(filename, 1)
 
         # Loads image from werkzeug file object
@@ -79,7 +79,7 @@ class variable 'df' and image steps in 'stage_images.'
                 image = None
 
         # Convert image to proper color channels
-        if type(image) == type(None):
+        if image is None:
             return 'Could not read image file'
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
         self.orig_image = image
@@ -136,7 +136,7 @@ def remove_skew(self):
         # Finds the longest contour that can be approximated by four coordinates
         # Returns error status if no such contour exists
         _, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-        
+
         paper = None
         max_perim = 0
         for c in contours:
@@ -146,7 +146,7 @@ def remove_skew(self):
                 if len(approx) == 4:
                     paper = approx
                     max_perim = perim
-        if type(paper) == type(None):
+        if paper is None:
             return True
 
         # Reorders corner points to Top Left, Top Right, Bottom Right,
@@ -173,7 +173,7 @@ def remove_skew(self):
             [new_w, 0],
             [new_w, new_h],
             [0, new_h]
-        ], dtype = 'float32')
+        ], dtype='float32')
         M = cv2.getPerspectiveTransform(bounds, new_bounds)
         img = cv2.warpPerspective(img, M, (new_w, new_h))
 
@@ -319,12 +319,12 @@ def balance_contrast(self):
         layers = 0
         for mask in circle_masks:
             # Creates histogram and extracts most frequent value bin
-            hist = cv2.calcHist([gray], [0], mask, [32], [0,256])
+            hist = cv2.calcHist([gray], [0], mask, [32], [0, 256])
             mode_idx = np.argmax(hist)
             limit = hist[mode_idx] * pct_max
 
             # Calculates upper and lower value bounds
-            limit_idx = np.argwhere(hist < limit)[:,0]
+            limit_idx = np.argwhere(hist < limit)[:, 0]
             try:
                 low_val = max(limit_idx[limit_idx < mode_idx]) * 8
             except:
@@ -411,7 +411,7 @@ def find_shots(self):
         img = cv2.drawKeypoints(self.image,
                                 self.keypoints,
                                 np.array([]),
-                                (0,255,0),
+                                (0, 255, 0),
                                 cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
 
         # Saves image to class variables and returns status
@@ -441,12 +441,12 @@ def get_shot_data(self):
             arrow_x.append(k.pt[0])
             arrow_y.append(k.pt[1])
             arrow_radii.append(k.size / 2)
-        df = pd.DataFrame({'x':arrow_x, 'y':arrow_y, 'radius':arrow_radii})
+        df = pd.DataFrame({'x': arrow_x, 'y': arrow_y, 'radius': arrow_radii})
 
         # Calculates how many shots created a hole based on the mean radius of
         # the smallest holes
         num_smallest = np.ceil(len(arrow_radii) * pct_smallest).astype(int)
-        single_size = (np.mean(np.sort(arrow_radii)[:num_smallest]) *
+        single_size = (np.mean(np.sort(arrow_radii)[: num_smallest]) *
                        overlap_penalty)
         df['count'] = np.clip(df['radius'] // single_size, 0, max_overlapped)
         df['count'] = df['count'].replace(0, 1).astype(int)
@@ -466,8 +466,7 @@ def get_shot_data(self):
             clus_df['radius'] /= 2
             clus_df['vec_x'] = center - clus_df['x']
             clus_df['vec_y'] = center - clus_df['y']
-            clus_df['mag'] = np.sqrt(np.square(clus_df['vec_x']) +
-                                        np.square(clus_df['vec_y']))
+            clus_df['mag'] = np.sqrt(np.square(clus_df['vec_x']) + np.square(clus_df['vec_y']))
             clus_df['vec_x'] = (clus_df['vec_x'] / clus_df['mag'] *
                                 clus_df['radius'])
             clus_df['vec_y'] = (clus_df['vec_y'] / clus_df['mag'] *
@@ -517,7 +516,7 @@ def read_image(self, filename=None, file_obj=None):
         self.df = None
 
         # Loads image from file if it exists
-        if type(file_obj) == type(None):
+        if file_obj is None:
             image = cv2.imread(filename, 1)
 
         # Loads image from werkzeug file object
@@ -529,7 +528,7 @@ def read_image(self, filename=None, file_obj=None):
                 image = None
 
         # Convert image to proper color channels
-        if type(image) == type(None):
+        if image is None:
             return 'Could not read image file'
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
         self.orig_image = image

requirements.txt

@@ -1,31 +1,36 @@
 attrs==20.2.0
 certifi==2020.6.20
 chardet==3.0.4
+Click==7.0
 codecov==2.1.10
 coverage==5.3
+flake8==3.8.4
+Flask==1.1.1
+gunicorn==19.9.0
 idna==2.10
 importlib-metadata==2.0.0
 iniconfig==1.1.1
+itsdangerous==1.1.0
+Jinja2==2.10.1
+MarkupSafe==1.1.1
+mccabe==0.6.1
+numpy==1.16.4
+opencv-python==3.4.2.17
 packaging==20.4
+pandas==0.24.2
+pkg-resources==0.0.0
 pluggy==0.13.1
 py==1.9.0
+pycodestyle==2.6.0
+pyflakes==2.2.0
 pyparsing==2.4.7
 pytest==6.1.1
 pytest-cov==2.10.1
+python-dateutil==2.8.0
+pytz==2019.1
 requests==2.24.0
 six==1.15.0
 toml==0.10.1
 urllib3==1.25.11
-zipp==3.4.0
-Click==7.0
-Flask==1.1.1
-gunicorn==19.9.0
-itsdangerous==1.1.0
-Jinja2==2.10.1
-MarkupSafe==1.1.1
-numpy==1.16.4
-opencv-python==3.4.2.17
-pandas==0.24.2
-python-dateutil==2.8.0
-pytz==2019.1
 Werkzeug==0.15.5
+zipp==3.4.0