def lookup(self, geoJsonGeometry, mode=None): cacheStr = ",ts-"+self.layer_id + geoJsonGeometry; cacheKey = sha1(cacheStr.encode()).hexdigest(); pixelsStruct = None; if(self.cache.enable == '1'): cacheResult = self.cache.get(cacheKey) if cacheResult is not None: pixelsStruct = ast.literal_eval(cacheResult) else: dates = self.splitDate() eeResult = [] # Todas as threads usam a mesma queue q = Queue.Queue(); for i in dates: x=Thread(name='Thread',target=self.runjob, args=[i, geoJsonGeometry, q]) x.start() # Pega os resultados bloqueando ate as threads teminarem for i in dates: eeResult.extend(q.get()); groupedValues = self.groupValuesByCoord(eeResult); groupedValues = self.dateJulianToGregorian(groupedValues); groupedValues = self.removeDuplicate(groupedValues); pixelsStruct = self.createPixelStructure(groupedValues); if(self.cache.enable == '1'): self.cache.set(cacheKey, pixelsStruct); dates = pixelsStruct["dates"]; pixelsOriginal = pixelsStruct["series"][0]["pixels"]; result = None if mode == 'series' or mode is None: if not self.ignore_filter: pixelsStruct["series"].extend(self.calcFilters(pixelsOriginal, mode)); result = self.calculateMean(pixelsStruct["series"]); elif mode == 'trend': result = self.calculateMean(pixelsStruct["series"]); resTrend = self.calcTrend(result); if len(resTrend['values']) == len(result['values']): utils.joinArray(result['values'], resTrend['values']); result['series'].append(resTrend['series']) for i, dtRow in enumerate(result["values"]): dtRow.insert(0, dates[i]) return result;
def lookup(self, geoJsonGeometry, mode=None):
result = []
filepath = os.path.join(self.imageDbPath, self.file)
mean = self.zonalStatistics(geoJsonGeometry, filepath)
if "month" == self.temporal_resolution_type:
date = self.localDateByMonth()
else:
date = self.LocalDateByDay()
geoJsonGeometry = ast.literal_eval(geoJsonGeometry)
for i, j in zip(mean, date):
count = []
count.append(j)
count.append(float(i))
result.append(count)
series = [{"position": 1, "type": "original", "id": "original", "label": "Valores originais"}]
for filter in loader.getFilters(self.layer_id):
if filter.id == "Bfast" and mode == "series":
continue
elif filter.id != "Bfast" and mode == "trend":
continue
filters = loader.getFilters(self.layer_id)
bfastIndex = utils.findIndexByAttribute(filters, "id", filter.id)
filteredValues = filters[bfastIndex].run(mean, None, None)
filteredValues = filteredValues if type(filteredValues) == list else filteredValues.tolist()
if len(filteredValues) == len(mean):
series.append({"id": filter.id, "label": filter.label, "position": len(result[0]), "type": "filter"})
utils.joinArray(result, filteredValues)
if mode == "trend":
filters = loader.getFilters(self.layer_id)
bfastIndex = utils.findIndexByAttribute(filters, "id", "Bfast")
filteredValues = filters[bfastIndex].run(mean, None, None)
filteredValues = filteredValues if type(filteredValues) == list else filteredValues.tolist()
if len(filteredValues) == len(mean):
series.append({"id": "Bfast", "label": "BFAST", "position": len(result[0]), "type": "trend"}) # 2
utils.joinArray(result, filteredValues)
return {"series": series, "values": result}
def run(layerId, longitude, latitude):
datasourceInstance = loader.getDatasource(layerId)
timeserieData = datasourceInstance.lockup(longitude, latitude)
values = utils.oneArray(timeserieData)
position = 1
series = [{'id': 'original', 'label': 'Valores originais', 'position': 1}];
for filter in loader.getFilters(layerId):
filteredValues = filter.run(values, longitude, latitude);
if len(filteredValues) == len(values):
position += 1
series.append({ 'id': filter.id, 'label': filter.label, 'position': position })
utils.joinArray(timeserieData, filteredValues)
return {
'series': series,
'values': timeserieData
}
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 lookup(self, geoJsonGeometry, mode=None):
result = []
filepath = os.path.join(self.imageDbPath, self.file)
mean = self.zonalStatistics(geoJsonGeometry, filepath)
if ('month' == self.temporal_resolution_type):
date = self.localDateByMonth()
else:
date = self.LocalDateByDay()
geoJsonGeometry = ast.literal_eval(geoJsonGeometry)
for i, j in zip(mean, date):
count = []
count.append(j)
count.append(float(i))
result.append(count)
series = [{
'position': 1,
'type': 'original',
'id': 'original',
'label': 'Valores originais'
}]
for filter in loader.getFilters(self.layer_id):
if filter.id == 'Bfast' and mode == 'series':
continue
elif filter.id != 'Bfast' and mode == 'trend':
continue
filters = loader.getFilters(self.layer_id)
bfastIndex = utils.findIndexByAttribute(filters, 'id', filter.id)
filteredValues = filters[bfastIndex].run(mean, None, None)
filteredValues = filteredValues if type(
filteredValues) == list else filteredValues.tolist()
if len(filteredValues) == len(mean):
series.append({
'id': filter.id,
'label': filter.label,
'position': len(result[0]),
'type': 'filter'
})
utils.joinArray(result, filteredValues)
if mode == 'trend':
filters = loader.getFilters(self.layer_id)
bfastIndex = utils.findIndexByAttribute(filters, 'id', 'Bfast')
filteredValues = filters[bfastIndex].run(mean, None, None)
filteredValues = filteredValues if type(
filteredValues) == list else filteredValues.tolist()
if len(filteredValues) == len(mean):
series.append({
'id': "Bfast",
'label': 'BFAST',
'position': len(result[0]), #2
'type': 'trend'
})
utils.joinArray(result, filteredValues)
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}
