Update functions.py

functions.py

@@ -3,111 +3,16 @@ import requests
 import os
 import joblib
 import pandas as pd
-
 import json
 
 
-def decode_features(df, feature_view):
-    """Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions"""
-    df_res = df.copy()
-
-    import inspect
-
-
-    td_transformation_functions = feature_view._batch_scoring_server._transformation_functions
-
-    res = {}
-    for feature_name in td_transformation_functions:
-        if feature_name in df_res.columns:
-            td_transformation_function = td_transformation_functions[feature_name]
-            sig, foobar_locals = inspect.signature(td_transformation_function.transformation_fn), locals()
-            param_dict = dict([(param.name, param.default) for param in sig.parameters.values() if param.default != inspect._empty])
-            if td_transformation_function.name == "min_max_scaler":
-                df_res[feature_name] = df_res[feature_name].map(
-                    lambda x: x * (param_dict["max_value"] - param_dict["min_value"]) + param_dict["min_value"])
-
-            elif td_transformation_function.name == "standard_scaler":
-                df_res[feature_name] = df_res[feature_name].map(
-                    lambda x: x * param_dict['std_dev'] + param_dict["mean"])
-            elif td_transformation_function.name == "label_encoder":
-                dictionary = param_dict['value_to_index']
-                dictionary_ = {v: k for k, v in dictionary.items()}
-                df_res[feature_name] = df_res[feature_name].map(
-                    lambda x: dictionary_[x])
-    return df_res
-
 
 
-def get_air_json(AIR_QUALITY_API_KEY):
-    return requests.get(f'https://api.waqi.info/feed/Helsinki/?token={AIR_QUALITY_API_KEY}').json()['data']
+def get_weather_csv():
+    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki?unitGroup=metric&include=days&key=FYYH5HKD9558HBXD2D6KWXDGH&contentType=csv').csv()
 
 def get_weather_json_quick(date):
-    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{date}?unitGroup=metric&include=days&key=J7TT2WGMUNNHD8JBEDXAJJXB2&contentType=json').json()
-
-
-def get_weather_data(json):
-
-    data = json['days'][0]
-    print("data parsed sccessfully")
-
-    return data
-
-
-def get_weather_df(data):
-    col_names = [
-        'name',
-        'datetime',
-        'tempmax',
-        'tempmin',
-        'temp',
-        'feelslikemax',
-        'feelslikemin',
-        'feelslike',
-        'dew',
-        'humidity',
-        'precip',
-        'precipprob',
-        'precipcover',
-        'snow',
-        'snowdepth',
-        'windgust',
-        'windspeed',
-        'winddir',
-        'sealevelpressure',
-        'cloudcover',
-        'visibility',
-        'solarradiation',
-        'solarenergy',
-        'uvindex',
-        'conditions'
-    ]
-
-    
-
-    new_data = pd.DataFrame(
-        data,
-        columns=col_names
-    )
-    new_data.datetime = new_data.datetime.apply(timestamp_2_time1)
-    #new_data.rename(columes={'pressure':'sealevelpressure'})
-    return new_data
-
-def get_air_quality_data1():
-    
-    AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY')
-    json = get_air_json(AIR_QUALITY_API_KEY)
-    
-    print(json)
-   # iaqi = json['iaqi']
-   # forecast = json['forecast']['daily']
-    return [
-        json['date'],                 # AQI
-        json['pm25'],
-        json['pm10'], 
-        json['o3'], 
-        json['no2'],
-
-    ]
+    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/today?include=fcst%2Cobs%2Chistfcst%2Cstats%2Cdays&key=J7TT2WGMUNNHD8JBEDXAJJXB2&contentType=json').json()
 
 def get_air_quality_data():
     AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY')
@@ -135,98 +40,44 @@ def get_air_quality_data():
         forecast['uvi'][0]['avg']
     ]
 
-def get_air_quality_df1(data):
-    col_names = [
-        'aqi',
-        'date',
-        'pm25',
-        'pm10',
-        'o3',
-        'no2',
-        
-    ]
-
-    new_data = pd.DataFrame(
-        data,
-        columns=col_names
-    )
-    new_data.date = new_data.date.apply(timestamp_2_time)
-
-    return new_data
-
-def get_air_quality_df(data):
-    col_names = [
-        'aqi',
-        'date',
-        'iaqi_h',
-        'iaqi_p',
-        'iaqi_pm10',
-        'iaqi_t',
-        'o3_avg',
-        'o3_max',
-        'o3_min',
-        'pm10_avg',
-        'pm10_max',
-        'pm10_min',
-        'pm25_avg',
-        'pm25_max',
-        'pm25_min',
-        'uvi_avg',
-        'uvi_max',
-        'uvi_min',
-    ]
-
-    new_data = pd.DataFrame(
-        data,
-        columns=col_names
-    )
-    new_data.date = new_data.date.apply(timestamp_2_time1)
-
-    return new_data
-
 
-def get_weather_json(date, WEATHER_API_KEY):
-    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json()
-
-
-def get_weather_data(date):
-    WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
-    json = get_weather_json(date, WEATHER_API_KEY)
+def get_weather_data(json):
+    #WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
+    
+    #csv = get_weather_csv()
     data = json['days'][0]
-
-    return [
-        json['address'].capitalize(),
-        data['datetime'],
-        data['tempmax'],
-        data['tempmin'],
-        data['temp'],
-        data['feelslikemax'],
-        data['feelslikemin'],
-        data['feelslike'],
-        data['dew'],
-        data['humidity'],
-        data['precip'],
-        data['precipprob'],
-        data['precipcover'],
-        data['snow'],
-        data['snowdepth'],
-        data['windgust'],
-        data['windspeed'],
-        data['winddir'],
-        data['pressure'],
-        data['cloudcover'],
-        data['visibility'],
-        data['solarradiation'],
-        data['solarenergy'],
-        data['uvindex'],
-        data['conditions']
-    ]
+    print("data parsed sccessfully")
+    #return [
+    #    #json['address'].capitalize(),
+    #    data['datetime'],
+    #    data['feelslikemax'],
+    #    data['feelslikemin'],
+    #    data['feelslike'],
+    #    data['dew'],
+    #    data['humidity'],
+    #    data['precip'],
+    #    data['precipprob'],
+    #    data['precipcover'],
+    #    data['snow'],
+    #    data['snowdepth'],
+    #    data['windgust'],
+    #    data['windspeed'],
+    #    data['winddir'],
+    #    data['pressure'],
+    #    data['cloudcover'],
+    #    data['visibility'],
+    #    data['solarradiation'],
+    #    data['solarenergy'],
+    #    data['uvindex'],
+    #    data['conditions']
+    #]
+    return data
 
 
 def get_weather_df(data):
     col_names = [
-        'city',
-        'date',
+        'name',
+        'datetime',
         'tempmax',
         'tempmin',
         'temp',
@@ -243,7 +94,7 @@ def get_weather_df(data):
         'windgust',
         'windspeed',
         'winddir',
-        'pressure',
+        'sealevelpressure',
         'cloudcover',
         'visibility',
         'solarradiation',
@@ -252,12 +103,14 @@ def get_weather_df(data):
         'conditions'
     ]
 
+    
+
     new_data = pd.DataFrame(
         data,
         columns=col_names
     )
-    new_data.date = new_data.date.apply(timestamp_2_time1)
-
+    new_data.datetime = new_data.datetime.apply(timestamp_2_time1)
+    #new_data.rename(columes={'pressure':'sealevelpressure'})
     return new_data
 
 def timestamp_2_time1(x):
@@ -268,4 +121,4 @@ def timestamp_2_time1(x):
 def timestamp_2_time(x):
     dt_obj = datetime.strptime(str(x), '%m/%d/%Y')
     dt_obj = dt_obj.timestamp() * 1000
-    return int(dt_obj)
+    return int(dt_obj)