def run(self,
timeserieData,
longitude=None,
latitude=None,
h=None,
freq=None,
startdate=None,
enddate=None):
freq = str(freq) if freq else self.frequency
if (h is None):
if self.time_change is not None and self.units is not None:
h = bfast_utils.calculateMinimalSegmentSize(
len(timeserieData), self.time_change, self.units,
float(freq))
h = str(h)
elif (h > 0.5):
raise ValueError("minimum segment size error")
startdate = str(startdate) if startdate else self.start_date
enddate = str(enddate) if enddate else utils.isNow(self.end_date)
# Must be in the following order: h, season, start_date, end_date, timeseriesData, freq
bfast_result = check_output([
"integration/py/time-series/lib/bfast.r",
str(h), self.season, startdate, enddate,
str(timeserieData), freq
])
bfast_res_list = bfast_result.encode('utf-8').split(' ')
bfast_res_float = map(float, bfast_res_list)
return bfast_res_float
def run(self, timeserieData, longitude = None, latitude = None, h = None, freq = None, startdate = None, enddate = None):
freq = str(freq) if freq else self.frequency
if(h is None):
if self.time_change is not None and self.units is not None:
h = bfast_utils.calculateMinimalSegmentSize(len(timeserieData), self.time_change, self.units, float(freq))
h = str(h)
elif(h>0.5):
raise ValueError("minimum segment size error")
startdate = str(startdate) if startdate else self.start_date
enddate = str(enddate) if enddate else utils.isNow(self.end_date)
# Must be in the following order: h, season, start_date, end_date, timeseriesData, freq
bfast_result = check_output(["integration/py/time-series/lib/bfast.r", str(h), self.season,
startdate, enddate, str(timeserieData), freq])
bfast_res_list = bfast_result.encode('utf-8').split(' ')
bfast_res_float = map(float, bfast_res_list)
return bfast_res_float
def trend(layerId, startYear, endYear, interpolation, groupData, timeChange, timeChangeUnits, geoJsonGeometry):
#Obtem a série
datasourceInstance = loader.getDatasource(layerId)
timeserieData = datasourceInstance.lookup(geoJsonGeometry, mode="series")
#Obtem as datas
datesStr = utils.oneArray(timeserieData["values"], 0)
#Obtem os valores entre os anos startYear e endYear
clippedValues = bfast_utils.clipValuesByYear(startYear, endYear, timeserieData["values"], datesStr)
values = clippedValues['values']
dates = clippedValues['dates']
startDate = dates[0]
endDate = dates[-1]
#Encontra o indice do filtro(se houver) e do bfast
filters = loader.getFilters(layerId)
bfastIndex = utils.findIndexByAttribute(filters, 'id', 'Bfast')
# O filtro já foi calculado na datasource
interpolation = interpolation if interpolation != "original" else 'STRING_NOT_FOUND';
interpolationIndex, interpolationPos = next(((i, item['position']) for i, item in enumerate(timeserieData["series"]) if item['id'] == interpolation), (-1, -1))
#Inicializa preparação do objeto de resposta
series = [{'id':'Bfast', 'label':'BFAST', 'position':1, 'type':'trend'}]
#Se há filtro selecionado, executa e adiciona ao objeto de resposta
if interpolationPos != -1:
values = utils.oneArray(timeserieData["values"], interpolationPos)
series.append({'id':timeserieData["series"][interpolationIndex]["id"], 'label': timeserieData["series"][interpolationIndex]["label"], 'position': 2, 'type': 'filter'})
else:
series.append({'id':'original', 'label':'Valores Originais', 'position':2, 'type':'original'})
#Agrupa os valores
groupedData = bfast_utils.groupData(dates, values, groupData)
values = groupedData['values']
frequency = groupedData['frequency']
dates = groupedData['dates']
#Calcula o valor do parâmetro h(minimal segment size) para o bfast
minimalSegmentSize = bfast_utils.calculateMinimalSegmentSize(len(values), timeChange, timeChangeUnits, frequency)
#Executa o BFAST
result = []
if bfastIndex != -1:
result = filters[bfastIndex].run(values, None, None, minimalSegmentSize, frequency, startDate, endDate)
else:
raise IndexError("Bfast filter could not be found.")
#Acrescenta ao resultado as datas, os valores da tendência e os valores originais (agrupados ou não)
datesList = [[i] for i in dates]
utils.joinArray(datesList, result)
result = datesList
utils.joinArray(result, values)
return {
'series': series,
'values': result
}
def trend(layerId, startYear, endYear, interpolation, groupData, timeChange,
timeChangeUnits, geoJsonGeometry):
# Obtem a série
datasourceInstance = loader.getDatasource(layerId)
timeserieData = datasourceInstance.lookup(geoJsonGeometry, mode="series")
# Obtem as datas
datesStr = utils.oneArray(timeserieData["values"], 0)
# Obtem os valores entre os anos startYear e endYear
clippedValues = bfast_utils.clipValuesByYear(startYear, endYear,
timeserieData["values"],
datesStr)
values = clippedValues['values']
dates = clippedValues['dates']
startDate = dates[0]
endDate = dates[-1]
# Encontra o indice do filtro(se houver) e do bfast
filters = loader.getFilters(layerId)
bfastIndex = utils.findIndexByAttribute(filters, 'id', 'Bfast')
# O filtro já foi calculado na datasource
interpolation = interpolation if interpolation != "original" else 'STRING_NOT_FOUND'
interpolationIndex, interpolationPos = next(
((i, item['position'])
for i, item in enumerate(timeserieData["series"])
if item['id'] == interpolation), (-1, -1))
# Inicializa preparação do objeto de resposta
series = [{
'id': 'Bfast',
'label': 'BFAST',
'position': 1,
'type': 'trend'
}]
# Se há filtro selecionado, executa e adiciona ao objeto de resposta
if interpolationPos != -1:
values = utils.oneArray(timeserieData["values"], interpolationPos)
series.append({
'id':
timeserieData["series"][interpolationIndex]["id"],
'label':
timeserieData["series"][interpolationIndex]["label"],
'position':
2,
'type':
'filter'
})
else:
series.append({
'id': 'original',
'label': 'Valores Originais',
'position': 2,
'type': 'original'
})
# Agrupa os valores
groupedData = bfast_utils.groupData(dates, values, groupData)
values = groupedData['values']
frequency = groupedData['frequency']
dates = groupedData['dates']
# Calcula o valor do parâmetro h(minimal segment size) para o bfast
minimalSegmentSize = bfast_utils.calculateMinimalSegmentSize(
len(values), timeChange, timeChangeUnits, frequency)
# Executa o BFAST
result = []
if bfastIndex != -1:
result = filters[bfastIndex].run(values, None, None,
minimalSegmentSize, frequency,
startDate, endDate)
else:
raise IndexError("Bfast filter could not be found.")
# Acrescenta ao resultado as datas, os valores da tendência e os valores originais (agrupados ou não)
datesList = [[i] for i in dates]
utils.joinArray(datesList, result)
result = datesList
utils.joinArray(result, values)
return {'series': series, 'values': result}
