refactor(utils.py): flake8

utils.py

@@ -1,3 +1,10 @@
+from datetime import datetime, timedelta, time
+from pandas.plotting import register_matplotlib_converters
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+import matplotlib
+import matplotlib.pyplot as plt
+import pandas as pd
 from IPython.display import display, HTML
 import numpy as np
 import sklearn.metrics as metrics
@@ -78,7 +85,7 @@ def encode_one_hot(y):
     3 -> 0,0,0,1,0
     4 -> 0,0,0,0,1
     '''
-    return (y.reshape(-1,1) == np.arange(NUM_CLASSES)).astype(int)
+    return (y.reshape(-1, 1) == np.arange(NUM_CLASSES)).astype(int)
 
 
 def train_hmm(Y_pred, y_true):
@@ -87,12 +94,12 @@ def train_hmm(Y_pred, y_true):
     if Y_pred.ndim == 1 or Y_pred.shape[1] == 1:
         Y_pred = encode_one_hot(Y_pred)
 
-    prior = np.mean(y_true.reshape(-1,1) == np.arange(NUM_CLASSES), axis=0)
+    prior = np.mean(y_true.reshape(-1, 1) == np.arange(NUM_CLASSES), axis=0)
     emission = np.vstack(
-        [np.mean(Y_pred[y_true==i], axis=0) for i in range(NUM_CLASSES)]
+        [np.mean(Y_pred[y_true == i], axis=0) for i in range(NUM_CLASSES)]
     )
     transition = np.vstack(
-        [np.mean(y_true[1:][(y_true==i)[:-1]].reshape(-1,1) == np.arange(NUM_CLASSES), axis=0)
+        [np.mean(y_true[1:][(y_true == i)[:-1]].reshape(-1, 1) == np.arange(NUM_CLASSES), axis=0)
             for i in range(NUM_CLASSES)]
     )
     return prior, emission, transition
@@ -107,19 +114,19 @@ def log(x):
 
     num_obs = len(y_pred)
     probs = np.zeros((num_obs, NUM_CLASSES))
-    probs[0,:] = log(prior) + log(emission[:, y_pred[0]])
+    probs[0, :] = log(prior) + log(emission[:, y_pred[0]])
     for j in range(1, num_obs):
         for i in range(NUM_CLASSES):
-            probs[j,i] = np.max(
-                log(emission[i, y_pred[j]]) + \
-                log(transition[:, i]) + \
-                probs[j-1,:])  # probs already in log scale
+            probs[j, i] = np.max(
+                log(emission[i, y_pred[j]]) +
+                log(transition[:, i]) +
+                probs[j - 1, :])  # probs already in log scale
     viterbi_path = np.zeros_like(y_pred)
-    viterbi_path[-1] = np.argmax(probs[-1,:])
-    for j in reversed(range(num_obs-1)):
+    viterbi_path[-1] = np.argmax(probs[-1, :])
+    for j in reversed(range(num_obs - 1)):
         viterbi_path[j] = np.argmax(
-            log(transition[:, viterbi_path[j+1]]) + \
-            probs[j,:])  # probs already in log scale
+            log(transition[:, viterbi_path[j + 1]]) +
+            probs[j, :])  # probs already in log scale
 
     return viterbi_path
 
@@ -132,11 +139,11 @@ def compute_scores(y_true, y_pred):
     balanced_acuracy = metrics.balanced_accuracy_score(y_true, y_pred)
     kappa = metrics.cohen_kappa_score(y_true, y_pred)
     return {
-        'confusion':confusion,
-        'per_class_recall':per_class_recall,
+        'confusion': confusion,
+        'per_class_recall': per_class_recall,
         'accuracy': accuracy,
         'balanced_accuracy': balanced_acuracy,
-        'kappa':kappa,
+        'kappa': kappa,
     }
 
 
@@ -158,17 +165,12 @@ def print_scores(scores):
 # ----------------------------------------
 #  Function to plot activity timeseries
 # ----------------------------------------
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib
-import matplotlib.lines as mlines
-import matplotlib.patches as mpatches
-from pandas.plotting import register_matplotlib_converters
 register_matplotlib_converters()
-from datetime import datetime, timedelta, time
+
+
 def plot_activity(x, y, t):
     ''' Plot activity timeseries '''
-    BACKGROUND_COLOR = '#d3d3d3' # lightgray
+    BACKGROUND_COLOR = '#d3d3d3'  # lightgray
 
     def split_by_timegap(group, seconds=30):
         subgroupIDs = (group.index.to_series().diff() > timedelta(seconds=seconds)).cumsum()
@@ -177,9 +179,9 @@ def split_by_timegap(group, seconds=30):
 
     convert_date = np.vectorize(
         lambda day, x: matplotlib.dates.date2num(datetime.combine(day, x)))
-    timeseries = pd.DataFrame(data={'x':x, 'y':y, 't':t})
+    timeseries = pd.DataFrame(data={'x': x, 'y': y, 't': t})
     timeseries.set_index('t', inplace=True)
-    timeseries['x'] = timeseries['x'].rolling(window=12, min_periods=1).mean()  #! inplace?
+    timeseries['x'] = timeseries['x'].rolling(window=12, min_periods=1).mean()  # ! inplace?
     ylim_min, ylim_max = np.min(x), np.max(x)
     groups = timeseries.groupby(timeseries.index.date)
     fig, axs = plt.subplots(nrows=len(groups) + 1)
@@ -192,16 +194,16 @@ def split_by_timegap(group, seconds=30):
 
         ax.get_xaxis().grid(True, which='major', color='grey', alpha=0.5)
         ax.get_xaxis().grid(True, which='minor', color='grey', alpha=0.25)
-        ax.set_xlim((datetime.combine(day,time(0, 0, 0, 0)),
-            datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0))))
-        ax.set_xticks(pd.date_range(start=datetime.combine(day,time(0, 0, 0, 0)),
-            end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)),
-            freq='4H'))
-        ax.set_xticks(pd.date_range(start=datetime.combine(day,time(0, 0, 0, 0)),
-            end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)),
-            freq='1H'), minor=True)
+        ax.set_xlim((datetime.combine(day, time(0, 0, 0, 0)),
+                     datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0))))
+        ax.set_xticks(pd.date_range(start=datetime.combine(day, time(0, 0, 0, 0)),
+                                    end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)),
+                                    freq='4H'))
+        ax.set_xticks(pd.date_range(start=datetime.combine(day, time(0, 0, 0, 0)),
+                                    end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)),
+                                    freq='1H'), minor=True)
         ax.set_ylim((ylim_min, ylim_max))
-        ax.get_yaxis().set_ticks([]) # hide y-axis lables
+        ax.get_yaxis().set_ticks([])  # hide y-axis lables
         ax.spines['top'].set_color(BACKGROUND_COLOR)
         ax.spines['right'].set_visible(False)
         ax.spines['left'].set_visible(False)
@@ -225,16 +227,16 @@ def split_by_timegap(group, seconds=30):
     for color, label in zip(COLORS, CLASSES):
         legend_patches.append(mpatches.Patch(facecolor=color, label=label, alpha=0.5))
     axs[-1].legend(handles=legend_patches, bbox_to_anchor=(0., 0., 1., 1.),
-        loc='center', ncol=min(3,len(legend_patches)), mode="best",
-        borderaxespad=0, framealpha=0.6, frameon=True, fancybox=True)
+                   loc='center', ncol=min(3, len(legend_patches)), mode="best",
+                   borderaxespad=0, framealpha=0.6, frameon=True, fancybox=True)
     axs[-1].spines['left'].set_visible(False)
     axs[-1].spines['right'].set_visible(False)
     axs[-1].spines['top'].set_visible(False)
     axs[-1].spines['bottom'].set_visible(False)
 
     # format x-axis to show hours
     fig.autofmt_xdate()
-    hours = [(str(hr) + 'am') if hr<=12 else (str(hr-12) + 'pm') for hr in range(0,24,4)]
+    hours = [(str(hr) + 'am') if hr <= 12 else (str(hr - 12) + 'pm') for hr in range(0, 24, 4)]
     axs[0].set_xticklabels(hours)
     axs[0].tick_params(labelbottom=False, labeltop=True, labelleft=False)