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dhafli01
2022-12-09 11:21:29 +01:00
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114
Influxclient.py
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# -*- 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]

77
Influxdataframe.py
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# -*- 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

30
config.xml
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<?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>

100
example.py
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# -*- 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')

108
example_timeseries.py
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# -*- 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)])

35
xmlconifg.py
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# -*- 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])