Sourcery Starbot ⭐ refactored anebz/boulder

app.py

@@ -52,7 +52,7 @@ def get_current_time():
     current_min = int(dt.split(':')[1])
     distances = [abs(current_min - _min) for _min in minutes]
     closest_min = minutes[distances.index(min(distances))]
-    current_time = dt.replace(':'+str(current_min), ':'+str(closest_min))
+    current_time = dt.replace(f':{current_min}', f':{str(closest_min)}')
     current_time = datetime.datetime.strptime(current_time, "%Y/%m/%d %H:%M")
 
     return current_time
@@ -118,9 +118,8 @@ def st_avg_data(boulderdf, selected_gym):
     #    st_prediction(boulderdf, selected_gym)
     st_avg_data(boulderdf, selected_gym)
 
-    st.markdown(f"""
-    Does your gym show this occupancy data? Make a PR yourself or let us know and we'll add your gym 😎\n
-    Created by [anebz](https://github.com/anebz) and [AnglinaBhambra](https://github.com/AnglinaBhambra).\n
-    Follow us! [![@anebzt](https://img.shields.io/twitter/follow/anebzt?style=social)](https://www.twitter.com/anebzt)
-    [![@_AnglinaB](https://img.shields.io/twitter/follow/_AnglinaB?style=social)](https://www.twitter.com/_AnglinaB)""")
+    st.markdown(
+        """\x1f    Does your gym show this occupancy data? Make a PR yourself or let us know and we'll add your gym 😎\\n\x1f    Created by [anebz](https://github.com/anebz) and [AnglinaBhambra](https://github.com/AnglinaBhambra).\\n\x1f    Follow us! [![@anebzt](https://img.shields.io/twitter/follow/anebzt?style=social)](https://www.twitter.com/anebzt)\x1f    [![@_AnglinaB](https://img.shields.io/twitter/follow/_AnglinaB?style=social)](https://www.twitter.com/_AnglinaB)"""
+    )
+
     streamlit_analytics.stop_tracking(firestore_key_file="firestore-key.json", firestore_collection_name="counts")

capture-data/web_scrape.py

@@ -34,13 +34,13 @@ def get_occupancy_boulderwelt(gym_name:str, url: str) -> tuple():
         if 'queue' in data and int(data['queue']) > 0:
             occupancy += int(data['queue']) / 10
         return occupancy
-    
+
     # admin-ajax.php not working
     page = requests.get(url)
     if page.status_code != 200:
         return 0
     try:
-        occupancy = int(float(re.search(r'style="margin-left:(.*?)%"', page.text).group(1)))
+        occupancy = int(float(re.search(r'style="margin-left:(.*?)%"', page.text)[1]))
     except:
         occupancy = 0
     return occupancy
@@ -68,7 +68,13 @@ def get_occupancy_boulderado(gym_name:str, url: str) -> tuple():
         return 0
     soup = BeautifulSoup(page.content, 'html.parser')
     try:
-        occupancy = int(re.search(r'left: (\d*)%', str(soup.find_all("div", class_="pointer-image")[0]['style'])).group(1))
+        occupancy = int(
+            re.search(
+                r'left: (\d*)%',
+                str(soup.find_all("div", class_="pointer-image")[0]['style']),
+            )[1]
+        )
+
     except:
         occupancy = 0
     return occupancy
@@ -181,8 +187,8 @@ def get_occupancy_einstein(gym_name:str, url: str) -> tuple():
             frame = soup.select("iframe")[0]
             frame_url = urllib.parse.urljoin(url, frame["src"])
             response = session.get(frame_url)
-            frame_soup = BeautifulSoup(response.content, 'html.parser') 
-            occupancy = re.search(r'left: (\d+)%', str(frame_soup)).group(1)
+            frame_soup = BeautifulSoup(response.content, 'html.parser')
+            occupancy = re.search(r'left: (\d+)%', str(frame_soup))[1]
         except:
             occupancy = 0
     return occupancy
@@ -216,12 +222,20 @@ def scrape_websites(current_time: str, gymdatadf: pd.DataFrame) -> pd.DataFrame:
         scrape_data = globals()[gym_data['function']]
         occupancy = scrape_data(gym_name, gym_data['url'])
         print(f"{gym_name}: occupancy={occupancy}, temp={weather_temp}, status={weather_status}")
-        if occupancy == 0 or occupancy == '0/0':
+        if occupancy in [0, '0/0']:
             continue
         webdata.append((current_time, gym_name, occupancy, weather_temp, weather_status))
-
-    webdf = pd.DataFrame(data=webdata, columns=['time', 'gym_name', 'occupancy', 'weather_temp', 'weather_status'])
-    return webdf
+
+    return pd.DataFrame(
+        data=webdata,
+        columns=[
+            'time',
+            'gym_name',
+            'occupancy',
+            'weather_temp',
+            'weather_status',
+        ],
+    )
 
 
 def get_current_time():
@@ -242,9 +256,7 @@ def get_current_time():
     current_min = int(dt.split(':')[1])
     distances = [abs(current_min - _min) for _min in minutes]
     closest_min = minutes[distances.index(min(distances))]
-    current_time = dt.replace(':'+str(current_min), ':'+str(closest_min))
-
-    return current_time
+    return dt.replace(f':{current_min}', f':{str(closest_min)}')
 
 
 def lambda_handler(event, context):

src/fix_missing_values.py

@@ -100,7 +100,7 @@ def fill_nan_values(df: pd.DataFrame) -> pd.DataFrame:
                 for column in ['occupancy', 'waiting', 'weather_temp']:
                     # this might work with the dev version of scipy
                     func = interp1d(x_values, df_gym_day_noNan[column], kind='linear', bounds_error=False,  fill_value='extrapolate')
-                    
+
                     #fill in the interpolated value
                     timestamp_unix = (timestamp-pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
                     values = func([timestamp_unix])
@@ -120,10 +120,7 @@ def fill_nan_values(df: pd.DataFrame) -> pd.DataFrame:
                 df_gym_day_oneNan = df_gym_day_noNan.append(df.loc[index], ignore_index=False)
                 df_gym_day_oneNan = df_gym_day_oneNan.sort_values(by='current_time', ascending=True)
                 #Case 1: it is at the beginning:
-                if df_gym_day_oneNan.iloc[0].isnull().sum() == 1:
-                    shift = -1
-                else:
-                    shift = 1
+                shift = -1 if df_gym_day_oneNan.iloc[0].isnull().sum() == 1 else 1
                 df_gym_day_oneNan_shift = df_gym_day_oneNan.shift(shift)
                 df.loc[index, column] = df_gym_day_oneNan_shift.loc[index, column]
     return df

src/test_fix_missing_values.py

@@ -16,8 +16,7 @@
 class Testget_missing_aquisition_timestamps(unittest.TestCase):
 
     def gaussian(self, n, sigma, shift):
-        gauss = np.exp(-0.5*((n-shift)/sigma)**2)
-        return gauss
+        return np.exp(-0.5*((n-shift)/sigma)**2)
 
     def plot_gaussian(self):
         import matplotlib.pyplot as plt
@@ -37,13 +36,13 @@ def get_testset(self):
         gym1 = 'muenchen-ost'
         gym2 = 'frankfurt'
         gyms_n = [gym1, gym1, gym1, gym2, gym2, gym2]
-        gyms = [[gyms_n[g] for n in day] for g,day in enumerate(days)]
+        gyms = [[gyms_n[g] for _ in day] for g,day in enumerate(days)]
         #occupancies=[[float(random.randint(0,100)) for n in day] for day in days]
         #making a gauss for occupancies
         occupancies = [self.gaussian(np.arange(0,len(day),1),len(day)/5,len(day)/2) for day in days]
         #waitings=[[float(random.randint(0,15)) for n in day] for day in days]
         waitings  =[self.gaussian(np.arange(0,len(day),1),len(day)/5,len(day)/2) for day in days]
-        temps = [[float(random.randint(0,15)) for n in day] for day in days]
+        temps = [[float(random.randint(0,15)) for _ in day] for day in days]
         temps = [self.gaussian(np.arange(0,len(day),1),len(day)/5,len(day)/2)-0.5 for day in days]
         #statuss=[[random.choice(['Clouds', 'Rain', 'Drizzle', 'Clear', 'Thunderstorm', 'Mist']) for n in day] for day in days]
         statuss = [[random.choice(['Clouds', 'Rain', 'Drizzle', 'Clear', 'Thunderstorm', 'Mist'])]*len(day) for day in days]
@@ -110,7 +109,7 @@ def test_testset(self):
     def test_add_missing_timestamps(self):
         df = self.get_testset()
         #remove 8 values at random
-        for n in range(8):
+        for _ in range(8):
             df = df.drop([df.index[random.randint(0, len(df)-1)]])
         df2 = add_missing_timestamps(df,
                                      interval='20min',
@@ -121,7 +120,7 @@ def test_add_missing_timestamps(self):
     def test_fill_nan(self):
         df = self.get_testset()
         #remove 8 values at random
-        for n in range(8):
+        for _ in range(8):
             df = df.drop([df.index[random.randint(0, len(df)-1)]])
         df2 = add_missing_timestamps(df,
                                      interval='20min',
@@ -133,7 +132,7 @@ def test_fill_nan(self):
     def test_drop_additional(self):
         df = self.get_testset()
         #change the time of 8 values at random
-        for n in range(8):
+        for _ in range(8):
             df.current_time[random.randint(0, len(df)-1)]+=pd.to_timedelta(16, unit='min')
         df2 = add_missing_timestamps(df,
                                      interval='20min',
@@ -152,21 +151,25 @@ def test_correct_value_recovered(self):
         df = df_o[:]
         #remove 8 values at random
         dropped = []
-        for n in range(8):
+        for _ in range(8):
             drop = df.index[random.randint(0, len(df)-1)]
             dropped.append(df.loc[drop])
             df = df.drop([drop])
-        
+
         df2 = add_missing_timestamps(df,
                                      interval='20min',
                                      sample_start='07:20',
                                      sample_end='23:40')
         df2 = fill_nan_values(df2)
         #get the values it recovered:
-        recovered=[]
-        for n, drop in enumerate(dropped):
-            recovered.append(df2[(df2.gym_name == drop.gym_name)&
-                                 (df2.current_time == drop.current_time)])
+        recovered = [
+            df2[
+                (df2.gym_name == drop.gym_name)
+                & (df2.current_time == drop.current_time)
+            ]
+            for drop in dropped
+        ]
+
         '''
         #in case of visual inspection
         plt.figure()

src/visualize_data.py

@@ -118,12 +118,15 @@ def given_day(boulderdf: pd.DataFrame, date: str, gym: str) -> pd.DataFrame:
 def plot_data(df: pd.DataFrame):
     # to plot several graphs in the y axis, melt the df
     df = df.melt('time', var_name='name', value_name='occupancy')
-    chart = alt.Chart(df).mark_line(interpolate='basis').encode(
-        x=alt.X('time:N', axis=alt.Axis(grid=True)),
-        y=alt.Y('occupancy:Q', scale=alt.Scale(domain=[0, 100])),
-        color=alt.Color("name:N")
+    return (
+        alt.Chart(df)
+        .mark_line(interpolate='basis')
+        .encode(
+            x=alt.X('time:N', axis=alt.Axis(grid=True)),
+            y=alt.Y('occupancy:Q', scale=alt.Scale(domain=[0, 100])),
+            color=alt.Color("name:N"),
+        )
     )
-    return chart
 
 
 def preprocess_current_data(boulderdf: pd.DataFrame, selected_gym: str, current_time: datetime.date):
@@ -149,6 +152,4 @@ def preprocess_current_data(boulderdf: pd.DataFrame, selected_gym: str, current_
     today_data[onehotlist.index('weather_temp')] = latest_gym_entry['weather_temp']
     today_data[onehotlist.index(latest_gym_entry['weather_status'])] = 1
 
-    # convert to numpy array
-    X_today = [np.asarray(today_data)]
-    return X_today
+    return [np.asarray(today_data)]