def fetchPcaData(self, key, cases):
""" @rtype: PlotData """
if key.startswith("PCA:"):
pca_name = key
else:
pca_name ="PCA:%s" % key
pca_data_fetcher = PcaDataFetcher(self.ert())
pca_plot_data = PlotData(pca_name)
if DataTypeKeysModel().isCustomPcaKey(key):
obs_keys = DataTypeKeysModel().getCustomPcaKeyObsKeys(key)
else:
obs_keys = pca_data_fetcher.getObsKeys(key)
for case in cases:
pca_data = pca_data_fetcher.fetchData(obs_keys, case)
if pca_data["x"] is not None:
if not pca_plot_data.hasObservationData():
pca_observation_plot_data = ObservationPlotData(pca_name)
pca_observation_plot_data.setObservationData(pca_data["x"], pca_data["obs_y"], [0.0 for x in pca_data["x"]], False)
pca_observation_plot_data.updateBoundaries(pca_data["min_x"], pca_data["max_x"], pca_data["min_y"], pca_data["max_y"])
pca_plot_data.setObservationData(pca_observation_plot_data)
pca_ensemble_plot_data = EnsemblePlotData(key, case)
pca_ensemble_plot_data.setEnsembleData(pca_data["x"], pca_data["y"], [], [])
pca_ensemble_plot_data.updateBoundaries(pca_data["min_x"], pca_data["max_x"], pca_data["min_y"], pca_data["max_y"])
pca_plot_data.addEnsembleData(pca_ensemble_plot_data)
return pca_plot_data
def __init__(self):
self.__keys = None
self.__observation_keys = None
self.__block_observation_keys = None
self.__summary_keys = None
self.__gen_kw_keys = None
self.__gen_data_keys = None
self.__custom_pca_keys = {"PCA:All": PcaDataFetcher(self.ert()).getAllObsKeys()}
super(DataTypeKeysModel, self).__init__()
def fetchPcaData(self, key, cases):
""" @rtype: PlotData """
if key.startswith("PCA:"):
pca_name = key
else:
pca_name = "PCA:%s" % key
pca_data_fetcher = PcaDataFetcher(self.ert())
pca_plot_data = PlotData(pca_name)
if DataTypeKeysModel().isCustomPcaKey(key):
obs_keys = DataTypeKeysModel().getCustomPcaKeyObsKeys(key)
else:
obs_keys = pca_data_fetcher.getObsKeys(key)
for case in cases:
pca_data = pca_data_fetcher.fetchData(obs_keys, case)
if pca_data["x"] is not None:
if not pca_plot_data.hasObservationData():
pca_observation_plot_data = ObservationPlotData(pca_name)
pca_observation_plot_data.setObservationData(
pca_data["x"], pca_data["obs_y"],
[0.0 for x in pca_data["x"]], False)
pca_observation_plot_data.updateBoundaries(
pca_data["min_x"], pca_data["max_x"],
pca_data["min_y"], pca_data["max_y"])
pca_plot_data.setObservationData(pca_observation_plot_data)
pca_ensemble_plot_data = EnsemblePlotData(key, case)
pca_ensemble_plot_data.setEnsembleData(pca_data["x"],
pca_data["y"], [], [])
pca_ensemble_plot_data.updateBoundaries(
pca_data["min_x"], pca_data["max_x"], pca_data["min_y"],
pca_data["max_y"])
pca_plot_data.addEnsembleData(pca_ensemble_plot_data)
return pca_plot_data
def isPcaDataKey(self, key):
pca_data_fetcher = PcaDataFetcher(self.ert())
return pca_data_fetcher.supportsKey(key) or DataTypeKeysModel().isCustomPcaKey(key)
def isPcaDataKey(self, key):
pca_data_fetcher = PcaDataFetcher(self.ert())
return pca_data_fetcher.supportsKey(
key) or DataTypeKeysModel().isCustomPcaKey(key)
