def algorithms_sarima():
text = desc_list['08sarima']
form = FormSARIMA()
if form.validate_on_submit():
filename = secrets.token_hex(8) + '.csv'
form.dados.data.save(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = files.url(filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# Retrieving results from the form
p = form.p.data
q = form.q.data
d = form.d.data
ptest = form.percent_test.data
P = form.P.data
Q = form.Q.data
D = form.D.data
s = form.sazon.data
predict_range = form.predict_range.data
# Get data from file
separator = form.sep.data
serie, rd, quality_param = deal_inputs(file_path,
filename,
form,
isseries=True)
# Generate plot
elementos, img_name_diag, img_name_mse, img_name_forecast, df_output = fit_sarima(
serie, p, d, q, P, D, Q, s, ptest, predict_range)
image_file_diag = url_for('static', filename='images/' + img_name_diag)
image_file_mse = url_for('static', filename='images/' + img_name_mse)
image_file_forecast = url_for('static',
filename='images/' + img_name_forecast)
### Create report
# Report title
title = dict_lista['08sarima']['title']
# Model parameters
model_param = [['p', str(p)], ['d', str(d)], ['q', str(q)],
['P', str(P)], ['D', str(D)], ['Q', str(Q)],
['s', str(s)], ['Dados para Teste (%)',
str(ptest)],
['Dados Preditos', str(predict_range)]]
readdata_param = readdata_table(form)
summary_param = summary_maincol(serie)
file_title = 'sarima'
address_pdf = create_report_arima(
title, model_param, readdata_param, quality_param, summary_param,
[img_name_diag, img_name_mse, img_name_forecast], file_title,
elementos)
# Generate array of values
# Create csv for downloading
address_csv = '%s_%s.csv' % (file_title, secrets.token_hex(6))
mydir = os.path.join(dir, 'static', 'reports', address_csv)
df_output.to_csv(mydir,
sep=separator,
index=True,
encoding='utf-8-sig',
header=['Dado_predito'])
rd = list(df_output.index)
pred = list(df_output)
return render_template('algorithms_sarima_output.html',
title='SARIMA',
text=text,
form=form,
file_url=file_url,
p=p,
q=q,
d=d,
P=P,
D=D,
Q=Q,
s=s,
listap=list(range(p)),
listaq=list(range(q)),
listaP=list(range(P)),
listaQ=list(range(Q)),
ptest=ptest,
range=predict_range,
image_diag=image_file_diag,
image_mse=image_file_mse,
image_forecast=image_file_forecast,
address_pdf=address_pdf,
address_csv=address_csv,
result_data=elementos,
rd=rd,
pred=pred)
else:
file_url = None
return render_template('algorithms_sarima.html',
title='SARIMA',
text=text,
form=form,
file_url=file_url)
def algorithms_diff():
text = desc_list['07diff']
form = FormDiff()
if form.validate_on_submit() and request.method == 'POST':
filename = secrets.token_hex(8) + '.csv'
form.dados.data.save(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = files.url(filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# Retrieving results from the form
order = form.diff.data
# Get data from file
separator = form.sep.data
serie, rd, quality_param = deal_inputs(file_path,
filename,
form,
isseries=True)
# Generate plot and array of values
img_name, diff_data, df_output = diff(serie, order, rd)
image_file = url_for('static', filename='images/' + img_name)
### Create report
# Report title
title = dict_lista['07diff']['title']
# Model parameters
model_param = [['Ordem', str(order)]]
readdata_param = readdata_table(form)
summary_param = summary_maincol(serie)
file_title = 'diff'
address_pdf = create_report(title, model_param, readdata_param,
quality_param, summary_param, [img_name],
file_title)
# Create csv for downloading
address_csv = '%s_%s.csv' % (file_title, secrets.token_hex(6))
mydir = os.path.join(dir, 'static', 'reports', address_csv)
df_output.to_csv(mydir,
sep=separator,
index=False,
encoding='utf-8-sig')
return render_template('algorithms_diff_output.html',
title='Diferença',
text=text,
form=form,
file_url=file_url,
order=order,
image=image_file,
data_diff=diff_data,
raw_data=rd,
address_pdf=address_pdf,
address_csv=address_csv)
else:
file_url = None
return render_template('algorithms_diff.html',
title='Diferença',
text=text,
form=form,
file_url=file_url)
def algorithms_decomposition():
text = desc_list['03decomposition']
form = FormDecomposition()
if form.validate_on_submit():
filename = secrets.token_hex(8) + '.csv'
form.dados.data.save(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = files.url(filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# Retrieving results from the form
model = form.model.data
frequencia = form.sazon.data
ts = form.reference.data
if ts == 'center':
two_sided = True
elif ts == 'right':
two_sided = False
# Get data from file
separator = form.sep.data
serie, rd, quality_param = deal_inputs(file_path,
filename,
form,
isseries=True)
# Generate plot
img_name = decomposition_plot(serie, model, frequencia, two_sided)
image_file = url_for('static', filename='images/' + img_name)
### Create report
# Report title
title = dict_lista['03decomposition']['title']
# Model parameters
model_param = [['Modelo', str(model)],
['Referência da Média Móvel',
str(ts)], ['Periodicidade',
str(frequencia)]]
readdata_param = readdata_table(form)
summary_param = summary_maincol(serie)
file_title = 'decomposition'
address_pdf = create_report(title, model_param, readdata_param,
quality_param, summary_param, [img_name],
file_title)
# Generate array of values
trend, seasonal, residual, df_output = data_decomposition(
serie, model, frequencia, two_sided, rd)
# Create csv for downloading
address_csv = '%s_%s.csv' % (file_title, secrets.token_hex(6))
mydir = os.path.join(dir, 'static', 'reports', address_csv)
df_output.to_csv(mydir,
sep=separator,
index=False,
encoding='utf-8-sig')
return render_template('algorithms_decomposition_output.html',
title='Decomposição de Séries',
text=text,
form=form,
file_url=file_url,
model=model,
image=image_file,
trend=trend,
seasonal=seasonal,
residual=residual,
rd=rd,
two_sided=ts,
frequencia=frequencia,
address_pdf=address_pdf,
address_csv=address_csv)
else:
file_url = None
return render_template('algorithms_decomposition.html',
title='Decomposição de Séries',
text=text,
form=form,
file_url=file_url)
def algorithms_periodogram():
text = desc_list['05periodogram']
form = FormPeriodogram()
if form.validate_on_submit() and request.method == 'POST':
filename = secrets.token_hex(8) + '.csv'
form.dados.data.save(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = files.url(filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# Retrieving results from the form
# Get data from file
separator = form.sep.data
serie, rd, quality_param = deal_inputs(file_path,
filename,
form,
isseries=True)
# Generate plot
img_name = periodogram_plot(serie)
image_file = url_for('static', filename='images/' + img_name)
### Create report
# Report title
title = dict_lista['05periodogram']['title']
# Model parameters
model_param = [['Não há parâmetros no método', '-']]
readdata_param = readdata_table(form)
summary_param = summary_maincol(serie)
file_title = 'periodogram'
address_pdf = create_report(title, model_param, readdata_param,
quality_param, summary_param, [img_name],
file_title)
# Generate array of values
periodogram_data, freq, df_output, index, freq_max, T = data_periodogram(
serie)
# Create csv for downloading
address_csv = '%s_%s.csv' % (file_title, secrets.token_hex(6))
mydir = os.path.join(dir, 'static', 'reports', address_csv)
df_output.to_csv(mydir, sep=separator, index=False)
return render_template('algorithms_periodogram_output.html',
title='Periodograma',
text=text,
form=form,
file_url=file_url,
image=image_file,
data_periodogram=periodogram_data,
address_pdf=address_pdf,
address_csv=address_csv,
periodo=T,
index=index,
freq_max=freq_max)
else:
file_url = None
return render_template('algorithms_periodogram.html',
title='Periodograma',
text=text,
form=form,
file_url=file_url)
def algorithms_pacf():
text = desc_list['02pacf']
form = FormPACF()
if form.validate_on_submit():
filename = secrets.token_hex(8) + '.csv'
form.dados.data.save(
os.path.join(app.config['UPLOAD_FOLDER'], filename))
file_url = files.url(filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# Retrieving results from the form
lags = form.lags.data
# Get data from file
separator = form.sep.data
serie, rd, quality_param = deal_inputs(file_path,
filename,
form,
isseries=True)
# Generate plot
img_name = pacf_plot(serie, lags)
image_file = url_for('static', filename='images/' + img_name)
### Create report
# Report title
title = dict_lista['02pacf']['title']
# Model parameters
model_param = [['Lags', str(lags)]]
readdata_param = readdata_table(form)
summary_param = summary_maincol(serie)
file_title = 'pacf'
address_pdf = create_report(title, model_param, readdata_param,
quality_param, summary_param, [img_name],
file_title)
# Generate array of values
pacf_data, df_output = data_pacf(serie, lags, rd)
# Create csv for downloading
address_csv = '%s_%s.csv' % (file_title, secrets.token_hex(6))
mydir = os.path.join(dir, 'static', 'reports', address_csv)
df_output.to_csv(mydir,
sep=separator,
index=False,
encoding='utf-8-sig')
return render_template('algorithms_pacf_output.html',
title='Função de Autocorrelação Parcial',
text=text,
form=form,
file_url=file_url,
lag=lags,
image=image_file,
data_acf=pacf_data,
address_pdf=address_pdf,
address_csv=address_csv)
else:
file_url = None
return render_template('algorithms_pacf.html',
title='Função de Autocorrelação Parcial',
text=text,
form=form,
file_url=file_url)
