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predict.py
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predict.py
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import joblib
import pandas as pd
import requests
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import ibm_db_dbi
def model_load():
try:
model = joblib.load('hack_model.jb')
sc = joblib.load('y_sc.jb')
sc1 = joblib.load('y_ind.jb')
return model, sc , sc1
except Exception as ex:
print(ex)
#Error Happen in Model Load
return 101
def get_data_hourly():
try:
r = requests.get("http://dataservice.accuweather.com/forecasts/v1/hourly/1hour/2132755?apikey=fMl4FAfWssd2XZaVJEhA1su8Rh9qI7Ix&language=en-us&details=true&metric=true")
if r.status_code != 200:
r = requests.get("http://dataservice.accuweather.com/forecasts/v1/hourly/1hour/2132755?apikey=MdceFU8V9cxQfSebdGS7RAiGHnyyqg3A&language=en-us&details=true&metric=true")
if r.status_code != 200:
raise Exception('Error happend in Api Request 50 calls completed')
t = r.json()
date = []
wind = []
direc = []
for i in t:
date.append(i['DateTime'][:-6])
wind.append(i["Wind"]['Speed']['Value'])
direc.append(i["Wind"]['Direction']['Degrees'])
df_ne = pd.DataFrame({'wind_sp':wind,'direc':direc},index =date)
return df_ne
except Exception as ex:
print(ex.args)
#Error Happen in Api Loading
return 102
def get_data_api():
try:
r = requests.get("http://dataservice.accuweather.com/forecasts/v1/hourly/12hour/2132755?apikey=MdceFU8V9cxQfSebdGS7RAiGHnyyqg3A&language=en-us&details=true&metric=true")
if r.status_code != 200:
r = requests.get("http://dataservice.accuweather.com/forecasts/v1/hourly/12hour/2132755?apikey=fMl4FAfWssd2XZaVJEhA1su8Rh9qI7Ix&language=en-us&details=true&metric=true")
if r.status_code != 200:
raise Exception('Error happend in Api Request 50 calls completed')
t = r.json()
date = []
wind = []
direc = []
for i in t:
date.append(i['DateTime'][:-6])
wind.append(i["Wind"]['Speed']['Value'])
direc.append(i["Wind"]['Direction']['Degrees'])
df_ne = pd.DataFrame({'wind_sp':wind,'direc':direc},index =date)
return df_ne
except Exception as ex:
print(ex.args)
#Error Happen in Api Loading
return 102
def predict():
try:
model, sc , sc1 = model_load()
df_ne = get_data_api()
df_ne.iloc[:, 0:2] = sc1.transform(df_ne.iloc[:, 0:2])
y_api = model.predict(n_periods=12 , exogenous=df_ne.iloc[:,0:2],return_conf_int=True,alpha=0.05)
df_ne['y_hat'] = sc.inverse_transform(y_api[0].reshape(-1,1))
df_ne['y_lower'] = sc.inverse_transform(y_api[1][:,0:1])
df_ne['y_upper'] = sc.inverse_transform(y_api[1][:,1:2])
df_ne[['wind_sp','direc']] = sc1.inverse_transform(df_ne[['wind_sp','direc']])
return df_ne
except Exception as ex:
print(ex)
print(ex.args)
#Error happen in prediction
return 103
def predict_one(df_ne):
try:
model, sc , sc1 = model_load()
df_ne.iloc[:, 0:2] = sc1.transform(df_ne.iloc[:, 0:2])
y_api = model.predict(n_periods=1 , exogenous=df_ne.iloc[:,0:2],return_conf_int=True,alpha=0.05)
df_ne['y_hat'] = sc.inverse_transform(y_api[0].reshape(-1,1))
df_ne['y_lower'] = sc.inverse_transform(y_api[1][:,0:1])
df_ne['y_upper'] = sc.inverse_transform(y_api[1][:,1:2])
df_ne[['wind_sp','direc']] = sc1.inverse_transform(df_ne[['wind_sp','direc']])
return df_ne
except Exception as ex:
print(ex)
print(ex.args)
#Error happen in prediction
return 105
def make_data(wind_sp ,direc,date):
df = df_ne = pd.DataFrame({'wind_sp':wind_sp,'direc':direc},index=date)
return df
def plot_graph(df_ne):
mig = make_subplots(rows=3, cols=1,
specs=[[{"type": "scatter", "type": "scatter", "type": "scatter"}],
[{"type": "scatter"}],
[{"type": "scatter"}]] , subplot_titles=('Power Prediction Plot','Wind Speed Plot','Direction Plot'))
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['y_hat'],
line=dict(color='red', width=4, dash='dot'),name ='Mean Predicted Power'), 1, 1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['y_lower'],
line=dict(color='royalblue', width=2, dash='dash'),name='Lower Range Power'), 1,1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['y_upper'], fill='tonexty',
line=dict(color='royalblue', width=4, dash='dash'),name='Upper Range Power'), 1, 1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['wind_sp'],name='Wind Speed'), row=2, col=1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['direc'],name='Direction'), row=3, col=1)
mig.update_yaxes(title_text="Power (KWh)", row=1, col=1)
mig.update_yaxes(title_text="Wind speed (m/s)", row=2, col=1)
mig.update_yaxes(title_text="Direction (in degree)", row=3, col=1)
mig.update_xaxes(title_text="Datetime", row=1, col=1)
mig.update_xaxes(title_text="Datetime", row=2, col=1)
mig.update_xaxes(title_text="Datetime", row=3, col=1)
mig.update_layout(height = 900)
graph1 = mig.to_json()
return graph1
def history_data():
graph1 = None
try:
str_conn = "DATABASE=BLUDB;HOSTNAME=dashdb-txn-sbox-yp-lon02-07.services.eu-gb.bluemix.net;PORT=50000;PROTOCOL=TCPIP;UID=bfr48077;PWD=3j4h-zwx77mzftr4"
conn = ibm_db_dbi.connect(str_conn, " ", " ")
df = pd.read_sql(sql='select *from dump_data', con=conn, parse_dates=True)
df_ne = df.groupby(['REC']).mean()
df_ne = df_ne.resample('H')
df_ne = df_ne.interpolate(method='linear')
df2_h = df_ne.resample('24H').sum()
mig = make_subplots(rows=2, cols=1,
specs=[[{"type": "scatter", "type": "scatter", "type": "scatter"}],
[{"type": "bar"}]],
subplot_titles=('Power Prediction Plot', 'Per Day Power Produced Plot'))
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['Y_HAT'],
line=dict(color='red', width=4, dash='dot'), name='Mean Predicted Power'), 1, 1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['Y_LOWER'],
line=dict(color='royalblue', width=2, dash='dash'), name='Lower Range Power'), 1, 1)
mig.add_trace(go.Scatter(x=df_ne.index, y=df_ne['Y_UPPER'], fill='tonexty',
line=dict(color='royalblue', width=4, dash='dash'), name='Upper Range Power'), 1, 1)
mig.add_trace(go.Bar(x=df2_h.index, y=df2_h['Y_HAT'], text=df2_h['Y_HAT'], name='Per Day Power Produced'),
row=2, col=1)
mig.update_yaxes(title_text="Power (KWh)", row=1, col=1)
mig.update_yaxes(title_text="Power (KWh)", row=2, col=1)
mig.update_xaxes(row=1, col=1, rangeslider_visible=True)
mig.update_xaxes(row=2, col=1)
mig.update_traces(texttemplate='%{text:.2s}', textposition='outside', row=2, col=1)
mig.update_layout(height=925)
graph1 = mig.to_json()
except Exception as ex:
print(ex.args)
finally:
conn.close()
return graph1