re-black'ed

app_streamlit.py

@@ -1,4 +1,4 @@
-''' The streamlit app to visualise and query the sensor data '''
+""" The streamlit app to visualise and query the sensor data """
 
 import os
 from datetime import datetime as dt
@@ -9,7 +9,7 @@
 
 
 def load_daily_data():
-    ''' A wrap-up function to load the daily data '''
+    """A wrap-up function to load the daily data"""
     req = """
              SELECT *
              FROM read_parquet('./data/dat_sensors.parquet/*/*/*.parquet', hive_partitioning = true);
@@ -25,7 +25,7 @@ def load_daily_data():
 
 
 def load_hourly_data():
-    ''' A wrap-up function to load the hourly data '''
+    """A wrap-up function to load the hourly data"""
     req = """
              SELECT *
              FROM read_parquet('./data/dat_sensors_hours.parquet/*/*/*.parquet', hive_partitioning = true);
@@ -41,15 +41,15 @@ def load_hourly_data():
 
 
 def filter_day(df: pd.DataFrame, cb: []) -> pd.DataFrame:
-    ''' Filter the input dataset for days checked in cb '''
+    """Filter the input dataset for days checked in cb"""
     for i in range(7):
         if not cb[i]:
             df = df.drop(df[df.weekday == i].index)
     return df
 
 
 def filter_dates(df: pd.DataFrame, dates: dt, resol: str) -> pd.DataFrame:
-    ''' Filter the input dataset for the days selected by slider'''
+    """Filter the input dataset for the days selected by slider"""
     date_end = dt(dates[1].year, dates[1].month, dates[1].day, 23)
     if resol == "Hourly":
         df["date"] = df[["date", "hour"]].apply(str_as_date_hour, axis=1)
@@ -63,23 +63,23 @@ def filter_dates(df: pd.DataFrame, dates: dt, resol: str) -> pd.DataFrame:
 
 
 def filter_hours(df: pd.DataFrame, dates: dt) -> pd.DataFrame:
-    ''' Filter the input dataset for the hours selected by slider'''
+    """Filter the input dataset for the hours selected by slider"""
     df = df.query(f"hour >= {dates[0]}").query(f"hour <= {dates[1]}")
     return df
 
 
 def str_as_date_day(s: str) -> dt:
-    ''' Take a string and return a datetime '''
+    """Take a string and return a datetime"""
     return dt.strptime(s, "%Y-%m-%d")
 
 
 def str_as_date_hour(s: []) -> dt:
-    ''' Take a list of strings and return a datetime '''
+    """Take a list of strings and return a datetime"""
     return dt.strptime(s.iloc[0] + " " + str(int(s.iloc[1])), "%Y-%m-%d %H")
 
 
 def create_slider_dates(df: pd.DataFrame) -> []:
-    ''' Create a streamlit slider and return the min/max values '''
+    """Create a streamlit slider and return the min/max values"""
     values = st.slider(
         "Select a range of dates",
         str_as_date_day(df.date.min()),
@@ -90,7 +90,7 @@ def create_slider_dates(df: pd.DataFrame) -> []:
 
 
 def create_slider_openinghours() -> []:
-    ''' Create a streamlit slider and return the min/max values '''
+    """Create a streamlit slider and return the min/max values"""
     values = st.slider(
         "Select a range of hours",
         0,
@@ -105,11 +105,10 @@ def create_slider_openinghours() -> []:
 
 if "dat_sensors_hours.parquet" not in os.listdir("./data"):
     os.system("cp ./minidata/dat.csv ./data")
-    with open("process_data.py", encoding='utf-8') as f:
+    with open("process_data.py", encoding="utf-8") as f:
         exec(f.read())
 
 
-
 with st.sidebar:
     df_shops, df_sensors = load_daily_data()
 
@@ -129,16 +128,16 @@ def create_slider_openinghours() -> []:
         ).df()
 
         sensor = st.selectbox(
-            "Which sensor would you like to access? "+\
-            "If none are selected, will return the sum of all sensors.",
+            "Which sensor would you like to access? "
+            + "If none are selected, will return the sum of all sensors.",
             list_sensors,
             index=None,
             placeholder="Select a sensor...",
         )
     else:
         sensor = st.selectbox(
-            "Which sensor would you like to access? "+\
-            "If none are selected, will return the sum of all sensors.",
+            "Which sensor would you like to access? "
+            + "If none are selected, will return the sum of all sensors.",
             "",
             index=None,
             placeholder="Select a shop first...",

download_data_from_client.py

@@ -85,11 +85,7 @@ def hourly_it(start: datetime, finish: datetime) -> None:
 
                 if np.random.rand() < 0.01:
                     dict1.update(
-                        {
-                            list(dict1)[
-                                np.random.randint(0, len(dict1) - 1)
-                            ]: "NULL"
-                        }
+                        {list(dict1)[np.random.randint(0, len(dict1) - 1)]: "NULL"}
                     )
 
                 sensor_data.append(dict1)

process_data.py

@@ -11,15 +11,14 @@
     os.chdir(__file__.split("process_data.py", maxsplit=1)[0])
 
 
-
 os.system("rm -Rf ./data/dat_sensors_hours.parquet")
 os.system("rm -Rf ./data/dat_sensors.parquet")
 os.system("rm -Rf ./data/dat_shops.parquet")
 os.system("rm -Rf ./data/dat_shops_hours.parquet")
 
 
 def get_status(x: str) -> str:
-    ''' A function to interpret the type of value returned by the API '''
+    """A function to interpret the type of value returned by the API"""
     if x == -1:
         return "Measurement failed"
     if x == -2:

src_data_client/__init__.py

@@ -1,6 +1,5 @@
 """ Creation of the data necessary for the API. """
 
-
 from datetime import datetime
 
 try:
@@ -10,7 +9,7 @@
 
 
 def create_app() -> dict:
-    """ Create the data for the API """
+    """Create the data for the API"""
 
     store_name = ["Madrid", "Paris", "Berlin", "Roma", "London"]
     store_n_sensors = [1, 20, 6, 4, 8]