def daytime_work_comsumption(session, epmdataobject, starttime, endtime):
'''calculates the energy consumption in business time'''
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=300)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data = epmdataobject.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
# EPM return ndarray, we need to transform in pandas dataframe
df = pd.DataFrame({
'Value': data['Value'].tolist(),
'Timestamp': data['Timestamp'].tolist()
})
# set Timestamp as index in dataframe
df.set_index('Timestamp', inplace=True)
# filter work hours
df = df.between_time('08:00', '18:00')
# filter business days
df = df.asfreq(BDay())
total = df['Value'].sum()
print(total)
return epr.ScopeResult(True)
def predict_variable(epmdataobject, starttime, endtime, interval_seconds):
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=interval_seconds)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data = epmdataobject.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
#preprocessing and clening data
#creating array with dataobject values and index
x = data['Value']
y = np.array(np.arange(len(x)))
x = x[~sp.isnan(y)]
y = y[~sp.isnan(y)]
import matplotlib.pyplot as plt
plt.scatter(x, y)
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w * 7 * 24 for w in range(10)],
['week %i' % w for w in range(10)])
plt.autoscale(tight=True)
plt.grid()
plt.show()
def weekly_report(session, dataobject_temperature, dataobject_windspeed,
write_dataobject_temperature, write_dataobject_windspeed):
'''Doc '''
end_date = session.timeEvent
ini_date = end_date - datetime.timedelta(weeks=1)
query_period = epm.QueryPeriod(ini_date, end_date)
process_interval = datetime.timedelta(minutes=10)
aggregate_details = epm.AggregateDetails(process_interval,
epm.AggregateType.Interpolative)
temperature = dataobject_temperature.historyReadAggregate(
aggregate_details, query_period)
windspeed = dataobject_windspeed.historyReadAggregate(
aggregate_details, query_period)
pd_temperature = pd.DataFrame({
'Value':
temperature['Value'].tolist(),
'Timestamp':
temperature['Timestamp'].tolist()
})
pd_windspeed = pd.DataFrame({
'Value': windspeed['Value'].tolist(),
'Timestamp': windspeed['Timestamp'].tolist()
})
def mondays_describe(session, epmdataobject, starttime, endtime):
'''Shows the statistical describe of every Monday in period'''
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=300)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data = epmdataobject.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
# EPM return ndarray, we need to transform in pandas dataframe
df = pd.DataFrame({
'Value': data['Value'].tolist(),
'Timestamp': data['Timestamp'].tolist()
})
#creating a new column with the weekday name
df['day_of_week'] = df['Timestamp'].dt.weekday_name
#fitering by Monday
filtered_data = df[df.day_of_week == 'Monday']
print(filtered_data.describe())
def get_history_interpolative(epmdataobject, starttime, endtime,
interval_seconds):
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=interval_seconds)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data = epmdataobject.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
print(data)
return epr.ScopeResult(True)
def predict_variable(session, epmdataobject1, epmdataobject2, starttime, endtime, write_dataobject):
url = 'http://servicos.cptec.inpe.br/XML/estacao/SBGR/condicoesAtuais.xml'
from xml.etree import ElementTree
response = requests.get(url)
tree = ElementTree.fromstring(response.content)
wind = tree.find('vento_int').text
wind = float(wind)
print('Previsão de vento para a próxima hora: {}'.format(wind))
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=600)
aggregationdetails = epm.AggregateDetails(processInterval, epm.AggregateType.Interpolative)
data1 = epmdataobject1.historyReadAggregate(aggregationdetails, queryperiod)
data2 = epmdataobject2.historyReadAggregate(aggregationdetails, queryperiod)
except Exception:
print('Erro ao consultar dataobjects')
df1 = pd.DataFrame(
{'Value': data1['Value'].tolist()})
df2 = pd.DataFrame(
{'Value': data2['Value'].tolist()})
y = df1.iloc[:, 0:1].values
X = df2.iloc[:, 0:1].values
# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X, y)
# Predict
y_predicted = regr_1.predict(wind)
print('Previsão de potência baseado no modelo gerado em árvore de decisão: {}'.format(float(y_predicted[0])))
date = datetime.datetime.now()
value = float(y_predicted[0])
quality = 0 # zero is Good in OPC UA
write_dataobject.write(value, date, quality)
def weekly_report(session, temperature, windspeed, pathname):
"""
Write weekly statistics in EPM Server
"""
starttime = session.timeEvent
endtime = starttime - datetime.timedelta(weeks=1)
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(minutes=15)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
temperature_data = temperature.historyReadAggregate(
aggregationdetails, queryperiod)
windspeed_data = windspeed.historyReadAggregate(
aggregationdetails, queryperiod)
except:
raise Exception('get interpolative data error')
def filter_lower_limit(session, epmdataobject, limit, starttime, endtime):
'''filter by a limit'''
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=300)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data = epmdataobject.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
# EPM return ndarray, we need to transform in pandas dataframe
df = pd.DataFrame({
'Value': data['Value'].tolist(),
'Timestamp': data['Timestamp'].tolist()
})
df = df.reset_index()
df = df.loc[df['A'] > limit]
print(df)
logger.error("can't found {} in the epm Server".format(bvname))
exit(1)
try:
dataobject2 = connection.getDataObjects('SP01_WindSpeedAvg')
except Exception:
logger.error("can't found {} in the epm Server".format(bvname))
exit(1)
starttime = datetime.datetime(2014, 3, 1, 1, 00, 00)
endtime = datetime.datetime(2014, 3, 30, 1, 00, 00)
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=600)
aggregationdetails = epm.AggregateDetails(processInterval,
epm.AggregateType.Interpolative)
data1 = dataobject1['SP01_PowerAvg'].historyReadAggregate(
aggregationdetails, queryperiod)
data2 = dataobject2['SP01_WindSpeedAvg'].historyReadAggregate(
aggregationdetails, queryperiod)
df1 = pd.DataFrame({'Value': data1['Value'].tolist()})
df2 = pd.DataFrame({'Value': data2['Value'].tolist()})
y = df1.iloc[:, 0:1].values
X = df2.iloc[:, 0:1].values
#y.reshape(1,-1)
def predict_variable(session, epmdataobject1, epmdataobject2, starttime,
endtime, connection, pathname):
r = requests.get(
'http://api.openweathermap.org/data/2.5/weather?q=London&APPID={1c8911a86601fec454d7f103939e5191}'
)
print(r.json())
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=600)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data1 = epmdataobject1.historyReadAggregate(aggregationdetails,
queryperiod)
data2 = epmdataobject2.historyReadAggregate(aggregationdetails,
queryperiod)
except Exception:
print('Erro ao consultar dataobjects')
df1 = pd.DataFrame({'Value': data1['Value'].tolist()})
df2 = pd.DataFrame({'Value': data2['Value'].tolist()})
y = df1.iloc[:, 0:1].values
X = df2.iloc[:, 0:1].values
X_test = np.array([0, 3, 6, 9, 12, 15, 18])
X_test = X_test.reshape(-1, 1)
# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=2)
regr_2 = DecisionTreeRegressor(max_depth=5)
regr_1.fit(X, y)
regr_2.fit(X, y)
# Predict
y_1 = regr_1.predict(X_test)
print(y_1)
print(X_test)
y_2 = regr_2.predict(X_test)
print(y_2)
print(X_test)
# Plot the results
plt.figure()
plt.scatter(X, y, color='red', label="data")
plt.plot(X_test,
regr_1.predict(X_test),
color="blue",
label="max_depth=2",
linewidth=2)
plt.plot(X_test,
regr_2.predict(X_test),
color="yellow",
label="max_depth=5",
linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Decision Tree Regression")
plt.legend()
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
epmConn = getFirstItemFromODict(session.connections)
epResourceManager = epmConn.getPortalResourcesManager()
imgFolder = epResourceManager.getResource(pathname)
resource = imgFolder.upload('predict_Decision_tree_regression.png',
buf,
'Scatterplot gerado pelo Processor',
mimetypes.types_map['.png'],
overrideFile=True)
def scatter_plot(session, connection, epmdataobject_1, epmdataobject_2,
starttime, endtime, pathname):
"""
**ScatterPlot**
Este método gera faz a consulta interpolada de dois Dataobjects e gera um arquivo de imagem, contendo
gráfico do tipo Scatter, salvo nos resources do Portal.
:param session: objeto *session* do EPM Processor
:parm connection: connection para os Resources
:param epmdataobject_1: dataobject.
:param epmdataobject_2: dataobject.
:param starttime: datetime de início da consulta.
:param endtime: datetime de fim da consulta.
:param pathname: Diretório dentro de Resources do Portal
:type filename: nome do arquivo a ser salvo.
.. note::
Esta função serve apenas como validação de caso de uso do sistema.
.. warning::
Não é necessário informar **Process Interval**.
No modo de *TEST* o resultado é apenas impresso na tela.
Não é feita distinção entre execuções de : *TEST*, *PRODUCTION* e *SIMULATION*, ou seja
o resultado sempre será escrita em em arquivo em caso de sucesso na execução.
"""
try:
queryperiod = epm.QueryPeriod(starttime, endtime)
processInterval = datetime.timedelta(seconds=300)
aggregationdetails = epm.AggregateDetails(
processInterval, epm.AggregateType.Interpolative)
data1 = epmdataobject_1.historyReadAggregate(aggregationdetails,
queryperiod)
data2 = epmdataobject_2.historyReadAggregate(aggregationdetails,
queryperiod)
except:
raise Exception('Error in read aggregation')
#gera o chart e salva em buffer
plt.scatter(data1['Value'], data2['Value'])
plt.title('Scatter Plot')
bufBoxplot = io.BytesIO()
plt.savefig(bufBoxplot, format='png')
bufBoxplot.seek(0)
print(session.connections)
epmConn = getFirstItemFromODict(session.connections)
epResourceManager = epmConn.getPortalResourcesManager()
imgFolder = epResourceManager.getResource(pathname)
resource = imgFolder.upload('scatter.png',
bufBoxplot,
'Scatterplot gerado pelo Processor',
mimetypes.types_map['.png'],
overrideFile=True)
