Add al files from local folder

.gitignore

@@ -51,6 +51,7 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 cover/
+.json
 
 # Translations
 *.mo
@@ -160,3 +161,17 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+# ---> JupyterNotebooks
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
+

Influxclient.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Oct 17 13:45:38 2022
+
+@author: Daniel Häfliger
+"""
+
+import pandas as pd
+import math
+import requests, json
+from datetime import datetime, date, time
+
+# Convert a date and time to to complex numbers 
+# [year_real, year_imag, day_real, day_imag]
+def Convert_Date(atime):
+    
+    y_r = math.sin(atime.timetuple().tm_yday/365*2*math.pi)
+    y_i = math.cos(atime.timetuple().tm_yday/365*2*math.pi)
+    d_r = math.sin((atime.hour*3600+atime.minute*60+atime.second)/((24*3600))*2*math.pi)
+    d_i = math.cos((atime.hour*3600+atime.minute*60+atime.second)/((24*3600))*2*math.pi)
+    return [y_r, y_i, d_r, d_i] 
+
+
+# Return a list of datas of a specified measurement of an influxDB
+def Get_Value(client, bucket, atime, measurement, datapoint):
+    
+    query_api = client.query_api()
+    query = 'from(bucket:"'+bucket+'")\
+    |> range(start: -'+atime+')\
+    |> filter(fn:(r) => r._field == "'+datapoint+'" )\
+    |> filter(fn:(r) => r._measurement == "'+measurement+'" )'
+    result = query_api.query(org=client.org, query=query)
+    thelist = []
+    for table in result:
+        for record in table.records:
+            thelist.append((record.get_value()))
+    return thelist
+
+
+# Return the time of datas of a specified measurement of an influxDB
+def Get_Time(client, bucket, atime, measurement, datapoint):
+    
+    query_api = client.query_api()
+    query = 'from(bucket:"'+bucket+'")\
+    |> range(start: -'+atime+')\
+    |> filter(fn:(r) => r._field == "'+datapoint+'" )\
+    |> filter(fn:(r) => r._measurement == "'+measurement+'" )'
+    result = query_api.query(org=client.org, query=query)
+    thelist = []
+    for table in result:
+        for record in table.records:
+            thelist.append((record.get_time()))
+            
+            
+    a=[]
+    b=[]
+    c=[]
+    d=[]
+    
+    for i in thelist:
+        x = Convert_Date(i)
+        a.append(x[0])
+        b.append(x[1])
+        c.append(x[2])
+        d.append(x[3])        
+            
+            
+            
+    return [a,b,c,d,thelist]
+
+
+
+
+# Need duration in hours, interval in minutes
+# Retruns array with complex time, timestamp and prediction
+# Works only for max 3 days due api returns only values until then
+def GetPrediction(duration, intervall, datapoint): 
+    
+    xfile = pd.read_xml('config.xml')
+
+    apikey = (xfile.loc[xfile['category'] == 'influx'].weatherapitoken[0])
+    place = (xfile.loc[xfile['category'] == 'influx'].place[0])
+    dats = requests.get("https://api.weatherapi.com/v1/forecast.json?key="+apikey+"&q="+place+"&days=3&aqi=yes&alerts=no")
+    dats=json.loads(dats.content)
+
+    a=[]
+    b=[]
+    c=[]
+    d=[]
+    e=[]
+    f=[]
+    now = datetime.now()
+    daycounter = 0
+    lasthour=now.hour
+    for i in range(0, int(duration*60/intervall)):  
+
+      now = datetime.timestamp(now)+(intervall*60)
+      now = datetime.fromtimestamp(now)
+
+      x = Convert_Date(now)
+      a.append(x[0])
+      b.append(x[1])
+      c.append(x[2])
+      d.append(x[3])        
+      e.append(now)
+      bc = dats["forecast"]["forecastday"][daycounter]["hour"][now.hour][datapoint]
+      
+      f.append(bc)
+      if lasthour>now.hour:
+          daycounter+=1
+      
+      lasthour=now.hour
+      
+    return[a,b,c,d,e,f]

Influxdataframe.py

@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  2 07:46:09 2022
+
+@author: Daniel Häfliger
+"""
+import pandas as pd
+import Influxclient as I
+import influxdb_client
+
+
+# Returns values of an influxDB as they where specified in the config.xml 
+# as a Pandas Dataframe
+def Get_Df():
+
+
+    xfile = pd.read_xml('config.xml')
+    org = (xfile.loc[xfile['category'] == 'influx'].organisation[0])
+    token = (xfile.loc[xfile['category'] == 'influx'].influxtoken[0])
+    url = (xfile.loc[xfile['category'] == 'influx'].url[0])
+
+    client = influxdb_client.InfluxDBClient(
+        url=url,
+        token=token,
+        org=org
+    )
+
+    bucket = (xfile.loc[xfile['category'] == 'influx'].bucket[0])
+    measurement = (xfile.loc[xfile['category'] == 'influx'].measurement[0])
+
+    interval = (xfile.loc[xfile['category'] == 'influx'].interval[0])
+    valuessince = (xfile.loc[xfile['category'] == 'influx'].time[0])
+
+    Input = xfile.loc[xfile['category'] == 'feature'].f[1]
+    #numberofFeature=(xfile.loc[xfile['category']=='feature'].features[1])
+
+    feature = Input.split()
+    pred = xfile.loc[xfile['category'] == 'pred'].p1[2]
+   
+
+    data = {
+
+        "y_r": I.Get_Time(client, bucket, valuessince, measurement, feature[0])[0],
+        "y_i": I.Get_Time(client, bucket, valuessince, measurement, feature[0])[1],
+        "d_r": I.Get_Time(client, bucket, valuessince, measurement, feature[0])[2],
+        "d_i": I.Get_Time(client, bucket, valuessince, measurement, feature[0])[3],
+
+    }
+
+    for i in range(len(feature)):
+        newd = {(feature[i]): I.Get_Value(client, bucket,
+                                          valuessince, measurement, feature[i])}
+        data.update(newd)
+
+    newd = {(pred): I.Get_Value(client, bucket,
+                                      valuessince, measurement, pred)}
+    data.update(newd)
+        
+    return pd.DataFrame(data)
+
+
+
+# Shifts the target variable and adds [shift_number] colums with the shiftet
+# values, then return the new Dataframe
+def ShiftTarget(df, shift_number, targetvariable):
+    dfziel = df[[targetvariable]].copy()
+    df = df.rename(columns={targetvariable:(targetvariable+str(0))})
+    dfziel = dfziel.rename(columns={targetvariable:(targetvariable+str(0))})
+
+    
+    
+    for i in range(shift_number):
+        dfziel = dfziel.rename(columns={(targetvariable+str(i)):targetvariable+str(i+1)})
+        dfziel = dfziel.shift(1)
+        df = pd.concat([df, dfziel], axis=1)
+ 
+    return df    

config.xml

@@ -0,0 +1,30 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<config>
+  
+ <variable category="influx">
+    <organisation>enter influx organisation name</organisation>
+    <url>enter influx url</url>
+    <bucket>enter influx bucket name</bucket>
+    <measurement>enter influx measurement name</measurement>
+    <influxtoken>enter a influx-read token for specific bucket</influxtoken>
+    <weatherapitoken>enter a weatherapitoken for weatherapi.com </weatherapitoken>
+    <place>enter place where weatherapi should predict</place>
+    <interval>5M</interval>
+    <time>10d</time>
+ </variable>
+
+<!-- Enter features here as a array, separated with space -->
+<!-- E. g. <f>feautre1 feautre2 feature3</f> -->
+ <variable category="feature">
+   <f>P_AT_5M_0 </f> 
+ </variable>  
+
+
+<!-- Enter target variable here as a array, separated with space -->
+<!-- Attention! Currently just one target varibale is supported! -->
+ <variable category="pred">
+    <p1>W_AT_5M_0</p1>
+ </variable>
+
+
+</config>

example.py

@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  9 08:51:35 2022
+
+@author: Daniel Häfliger
+
+This file shows an example of how to use the Influxclient and Influxdataframe.
+The example try to fit an prediction of a temperature of a specific measure
+with time and a given prediction of the temperature as features. 
+
+There is no data cleaning ect. in this example, its just to show how the lib
+works...
+
+Be aware that you need to add a influx- and weatherapi-token and aviable datas 
+to the config file. Otherwise the example, as the lib to, wont work.
+
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  2 08:51:19 2022
+
+@author: Daniel Häfliger
+"""
+import pandas as pd
+import Influxdataframe as Idf
+import Influxclient as I
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import r2_score
+from sklearn.neural_network import MLPRegressor
+
+
+# Get a dataframe from the influxDB with the specified values in the config.xml
+df = Idf.Get_Df()
+
+
+# Do a train test split and scale the datas
+train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)
+
+scaler = MinMaxScaler()
+
+train_df = pd.DataFrame(scaler.fit_transform(
+    train_df), columns=train_df.columns, index=train_df.index)
+test_df = pd.DataFrame(scaler.transform(
+    test_df), columns=test_df.columns, index=test_df.index)
+
+X = test_df[["y_r", "y_i", "d_r", "d_i", "P_AT_5M_0"]]
+y = test_df[["W_AT_5M_0"]]
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42)
+
+
+# Train data and print r2
+dt = MLPRegressor(activation='relu')
+dt.fit(X_train, y_train)
+print(r2_score(y_test, dt.predict(X_test)))
+
+
+# Get a df for the current prediciton with values from weather api
+pred = I.GetPrediction(48, 5, "temp_c")
+data = {
+         
+   "y_r": pred[0],
+   "y_i": pred[1],
+   "d_r": pred[2],
+   "d_i": pred[3],
+   "P_AT_5M_0": pred[5],
+   "W_AT_5M_0": pred[5],
+}
+preddf = pd.DataFrame((data))
+
+
+preddf = pd.DataFrame(scaler.transform(
+    preddf), columns=preddf.columns, index=preddf.index)
+
+preddf = preddf[["y_r", "y_i", "d_r", "d_i", "P_AT_5M_0"]]
+
+
+# Predict temprature for the values returned by the api
+ATpred = dt.predict(preddf)
+Prog24 = pd.DataFrame(ATpred)
+
+df_all_cols = pd.concat([preddf, Prog24], axis=1)
+
+Prog24 = scaler.inverse_transform(df_all_cols)
+
+# Rewrite datas to get it into a df
+data = {
+
+    "W_AT_5M_0": Prog24[:, 5].tolist(),
+    "Timestamp": pred[4],
+
+}
+
+prognose = pd.DataFrame(data)
+prognose.plot(x='Timestamp', y='W_AT_5M_0')
+
+

example_timeseries.py

@@ -0,0 +1,108 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  9 09:31:31 2022
+
+@author: Daniel Häfliger
+
+
+This file shows an example of how to use the Influxclient and Influxdataframe.
+The example try to fit an prediction of a temperature of a specific measure
+with time and a given prediction of the temperature as features with time series
+forecasting depending ond them. 
+
+There is no data cleaning ect. in this example, its just to show how the lib
+works...
+
+Be aware that you need to add a influx- and weatherapi-token and aviable datas 
+to the config file. Otherwise the example, as the lib to, wont work.
+
+
+"""
+import pandas as pd
+import Influxdataframe as Idf
+import Influxclient as I
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import r2_score
+from sklearn.neural_network import MLPRegressor
+
+
+target_variable = "W_AT_5M_0"
+interval = 60
+valuesince = 10
+prediction_hours = 24
+
+shift_number=4
+
+df = Idf.Get_Df()
+
+df = df[::12]
+
+pred =I.GetPrediction(prediction_hours, interval, "temp_c")
+    
+
+data = {
+
+    "y_r": pred[0],
+    "y_i": pred[1],
+    "d_r": pred[2],
+    "d_i": pred[3],
+
+    "P_AT_5M_0": pred[5],
+}
+
+
+preddf = pd.DataFrame(data)
+
+df = pd.concat([df, preddf], axis=0).reset_index(drop=True)
+
+
+df = Idf.ShiftTarget(df, shift_number, target_variable)
+
+
+
+df_predict = df.dropna()
+
+train_df, test_df = train_test_split(df_predict, test_size=0.3, random_state=42)
+
+scaler = MinMaxScaler()
+
+train_df = pd.DataFrame(scaler.fit_transform(
+    train_df), columns=train_df.columns, index=train_df.index)
+test_df = pd.DataFrame(scaler.transform(
+    test_df), columns=test_df.columns, index=test_df.index)
+
+y = train_df[[target_variable+str(0)]]
+
+X = train_df
+X.pop(target_variable+str(0))
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42)
+
+dt = MLPRegressor(activation='relu')
+dt.fit(X_train, y_train)
+
+print(r2_score(y_test, dt.predict(X_test)))
+
+
+
+for i in range(int(prediction_hours*60/interval)):
+    preddf = pd.DataFrame(df.iloc[(int(df_predict.shape[0]+shift_number)-1+i)]).transpose()
+    preddf = pd.DataFrame(scaler.transform(
+        preddf), columns=preddf.columns, index=preddf.index)
+    preddf.pop(target_variable+str(0))
+    at = dt.predict(preddf)
+    preddf[target_variable+str(0)]=at
+    newval = pd.DataFrame(scaler.inverse_transform(preddf))
+    df.at[(int(df_predict.shape[0]+shift_number)-1+i), target_variable+str(0)] = newval.at[0, 5]
+    
+    for j in range(shift_number):    
+        df.at[int(df_predict.shape[0]+shift_number)+i, target_variable+str(j+1)] =    df.at[int(df_predict.shape[0]+shift_number)-1+i, target_variable+str(j)] 
+        
+ 
+
+
+sns.lineplot(df.index, df[target_variable+str(0)])
+

xmlconifg.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 13 19:33:47 2022
+
+@author: Daniel Häfliger
+"""
+
+import pandas as pd
+ 
+def GetXmlConfig(xmlfile):
+    
+    return 
+
+df = pd.read_xml('config.xml')
+print(df.loc[df['category']=='influx'].organisation[0])
+
+print(df.loc[df['category']=='influx'].url[0])
+print(df.loc[df['category']=='influx'].bucket[0])
+print(df.loc[df['category']=='influx'].measurement[0])
+print(df.loc[df['category']=='influx'].Influxtoken[0])
+print(df.loc[df['category']=='influx'].Weatherapitoken[0])
+print(df.loc[df['category']=='influx'].place[0])
+print(df.loc[df['category']=='influx'].interval[0])
+print(df.loc[df['category']=='influx'].time[0])
+
+
+
+a=df.loc[df['category']=='feature'].name
+print(df.loc[df['category']=='feature'].name[1])
+print(df.loc[df['category']=='pred'].name[2])
+
+
+
+print(df.loc[df['category']=='pred'].name[2])
+