sns plot barchart and scatter

UnetBasedMethod.py

@@ -45,9 +45,10 @@ def step_1(net, args, device, list, position):
 
 
 def step_2(list, position):
-    for dir in list:
+    for num, dir in enumerate(list):
         df = pd.DataFrame(columns = ('ug', 'iter', 'id', 'size', 'area_mean', 'back_mean'))
-        for name in tqdm(filter(lambda x: x!='.DS_Store', os.listdir('data/imgs/'+dir)),position = position):
+        names = [x for x in filter(lambda x: x != '.DS_Store', os.listdir('data/imgs/' + dir))]
+        for name in tqdm(names, desc = f'Period{num+1}/{len(list)}', position = position):
             match_group = re.match('.*\s([dD]2[oO]|[lL][bB]|.*ug).*\s(.+)\.tif', name)
             img = cv.imread('data/imgs/' + dir + '/' + name, 0)
             mask = cv.imread('data/masks/' + dir + '/' + name, 0)
@@ -57,12 +58,13 @@ def step_2(list, position):
                 'ug') else str.lower(match_group.group(1))
             tmp_df['iter'] = str.lower(match_group.group(2))
             df = df.append(tmp_df, ignore_index = True, sort = True)
-        df['pure'] = df['area_mean'] - df['back_mean']
+        df['Intensity (a. u.)'] = df['area_mean'] - df['back_mean']
         df.sort_values('ug',inplace = True)
-        baseline = df[df['ug']=='d2o']['pure'].mean()*0.62
+        baseline = df[df['ug']=='d2o']['Intensity (a. u.)'].mean()*0.62
         sns.set_style("darkgrid")
-        sns.catplot(x = 'ug', y = 'pure', kind = 'bar', data = df)
-        sns.lineplot(x = 'ug', y=baseline,data = df)
+        sns.catplot(x = 'ug', y = 'Intensity (a. u.)', kind = 'bar', palette = 'vlag', data = df)
+        sns.swarmplot(x = "ug", y = "Intensity (a. u.)", data = df, size = 2, color = ".3", linewidth = 0)
+        plt.axhline(y=baseline)
         plt.savefig('data/output/'+dir+'.png')
 
 
@@ -108,11 +110,10 @@ if __name__ == '__main__':
 
     elif args.step == 2:
         dir = [x for x in filter(lambda x: x != '.DS_Store', os.listdir('data/imgs/'))]
-        sep_dir = divide_list(dir,args.process)
-        step_2(dir,0)
-
-        # pool = Pool(args.process)
-        # for i, list in enumerate(sep_dir):
-        #     pool.apply_async(step_2, args = (list, i))
-        # pool.close()
-        # pool.join()
+        sep_dir = divide_list(dir, args.process)
+
+        pool = Pool(args.process)
+        for i, list in enumerate(sep_dir):
+            pool.apply_async(step_2, args = (list, i))
+        pool.close()
+        pool.join()

resCalc.py

@@ -20,10 +20,10 @@ def save_img(img_list, dir, name):
         plt.title(title)
         plt.imshow(img, 'gray')
     try:
-        os.makedirs('data/output/' + dir + '/graph')
+        os.makedirs('data/output/' + dir)
     except:
         logging.info('Existing dir: data/output/' + dir)
-    plt.savefig('data/output/' + dir + '/graph/' + name[:-4] + '.png')
+    plt.savefig('data/output/' + dir + '/' + name[:-4] + '.png')
     plt.close()
 
 
@@ -42,6 +42,7 @@ def get_subarea_info(img, mask):
             pos = [(group[0][k], group[1][k]) for k in range(len(group[0]))]
 
             area_points = np.array([mask[x, y] if (x, y) in pos else 0 for x in range(200) for y in range(200)],dtype = np.uint8).reshape([200,200])
+            #cv.imwrite('mask.png',area_points)
             kernel = np.ones((15, 15), np.uint8)
             bg_area_mask = cv.erode(area_points, kernel)
             surround_bg_mask = cv.bitwise_xor(bg_area_mask, 255 - area_points)