def _hdf_extract(self,filename,key=None):
if key is None:
key = self.__class__.__name__
if self._legacy_mode(filename,key):
key_split = key.split("/")
parent_key = os.path.join(*key_split[:-1])
hdf_obj = cd.hdfHandler(filename,mode='r',key=parent_key)
msg = "Legacy mode used for coorddata extraction"
if 'CoordDF' in hdf_obj.keys():
self.coord_centered = cd.coordstruct.from_hdf(filename,key=parent_key+"/CoordDF")
self._domain_handler = DomainHandler(1)
self.coord_staggered = self._create_staggered_legacy(self.coord_centered)
elif 'coordDF' in hdf_obj.keys():
self.coord_centered = cd.coordstruct.from_hdf(filename,key=parent_key+"/coordDF")
self._domain_handler = DomainHandler(1)
self.coord_staggered = self._create_staggered_legacy(self.coord_centered)
else:
msg = ("Either the wrong part of the HDF structure"
" has been accessed or legacy mode not handled."
" For information the following keys were "
f"available {list(hdf_obj.keys())}.")
raise ValueError(msg)
warnings.warn(msg)
else:
hdf_obj = cd.hdfHandler(filename,mode='r',key=key)
hdf_obj.check_type_id(self.__class__)
iCase = 1 if hdf_obj.attrs['cart_mode'] else 2
self._domain_handler = DomainHandler(iCase)
self.coord_centered = cd.coordstruct.from_hdf(filename,key=key+"/coord_centered")
self.coord_staggered = cd.coordstruct.from_hdf(filename,key=key+"/coord_staggered")
def _extract_moving_wall(self, file_name, key=None):
if key is None:
key = 'CHAPSim_meta'
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
moving_wall = hdf_obj["moving_wall"][:]
return moving_wall
def save_hdf(self, file_name, write_mode, key=None):
if key is None:
key = 'CHAPSim_meta'
super().save_hdf(file_name, write_mode, key)
hdf_obj = cd.hdfHandler(file_name, 'a', key=key)
hdf_obj.create_dataset("moving_wall", data=self.__moving_wall)
def save_hdf(self, file_name, write_mode, key):
if key is None:
key = self.__class__.__name__
super().save_hdf(file_name, write_mode, key=key)
hdf_obj = cd.hdfHandler(file_name, 'a', key)
hdf_obj.attrs["plane"] = self._plane.encode('utf-8')
def _hdf_extract(self, file_name, key=None):
if key is None:
key = self.__class__.__name__
super()._hdf_extract(file_name, key)
hdf_obj = cd.hdfHandler(file_name, 'r', key=None)
self._plane = hdf_obj.attrs['plane'].decode('utf-8')
def _hdf_extract(self,file_name,key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name,'r',key=key)
hdf_obj.check_type_id(self.__class__)
self._POD_coeffs = hdf_obj["POD_coeffs"][:]
def save_hdf(self, file_name, write_mode, key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name, write_mode, key=key)
hdf_obj.set_type_id(self.__class__)
self._meta_data.save_hdf(file_name, 'a', key + '/meta_data')
self.fluctDF.to_hdf(file_name, key=key + '/fluctDF', mode='a')
def _hdf_extract(self,file_name,key=None):
if key is None:
key = 'CHAPSim_Inst'
hdf_obj = cd.hdfHandler(file_name,'r',key=key)
hdf_obj.check_type_id(self.__class__)
self._meta_data = self._module._meta_class.from_hdf(file_name,key+'/meta_data')
self.InstDF = cd.flowstruct3D.from_hdf(file_name,coorddata=self._coorddata,key=key+'/InstDF')#pd.read_hdf(file_name,base_name+'/InstDF').data(shape)
def to_hdf(self,filename,mode,key=None):
if key is None:
key = self.__class__.__name__
self.coord_centered.to_hdf(filename,key=key+"/coord_centered",mode=mode)
self.coord_staggered.to_hdf(filename,key=key+"/coord_staggered",mode=mode)
hdf_obj = cd.hdfHandler(filename,mode='r',key=key)
cart_mode = False if self._domain_handler.is_cylind else True
hdf_obj.attrs['cart_mode'] = cart_mode
def save_hdf(self, file_name, write_mode, key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name, write_mode, key=key)
hdf_obj.set_type_id(self.__class__)
hdf_obj.create_dataset("eig_values", data=self._eig_values)
self.POD_modesDF.to_hdf(file_name, key=key + "/POD_modesDF", mode='a')
self.avg_data.save_hdf(file_name, 'a', key + "/avg_data")
def save_hdf(self,file_name,write_mode,key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name,write_mode,key=key)
hdf_obj.set_type_id(self.__class__)
hdf_obj.create_dataset("POD_coeffs",data=np.array(self.POD_coeffs))
POD_class = self._POD.__class__.__name__
self._POD.save_hdf(file_name,'a',key+f"/{POD_class}")
def _hdf_extract(self,file_name,key=None):
if key is None:
key = 'CHAPSim_meta'
hdf_obj = cd.hdfHandler(file_name,mode='r',key=key)
hdf_obj.check_type_id(self.__class__)
self.metaDF = cd.metastruct.from_hdf(file_name,key=key+'/metaDF')#pd.read_hdf(file_name,key=base_name+'/metaDF')
self._coorddata = coorddata.from_hdf(file_name,key=key+"/coorddata")
def save_hdf(self,file_name,write_mode,key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name,write_mode,key=key)
hdf_obj.set_type_id(self.__class__)
# group.attrs["path_to_folder"] = self.path_to_folder.encode('utf-8')
# group.attrs["abs_path"] = int(self._abs_path)
# group.create_dataset("NCL",data=self.NCL)
self.metaDF.to_hdf(file_name,key=key+'/metaDF',mode='a')
self._coorddata.to_hdf(file_name,key=key+'/coorddata',mode='a')
def _hdf_extract(self, file_name, key=None):
if key is None:
key = self.__class__.__name__ #'CHAPSim_Quad_Anal'
try:
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
except KeyError:
msg = f"Using legacy default key for class {key}"
warnings.warn(msg)
key = 'CHAPSim_Quad_Anal'
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
hdf_obj.check_type_id(self.__class__)
self._meta_data = self._module.CHAPSim_meta.from_hdf(
file_name, key + '/meta_data')
self._avg_data = self._module._avg_io_class.from_hdf(
file_name, key + '/avg_data')
self.QuadAnalDF = cd.datastruct.from_hdf(
file_name, key=key + '/QuadAnalDF'
) #pd.read_hdf(file_name,key=base_name+'/autocorrDF').data([shape_x,shape_z])
def _hdf_extract(self, file_name, key=None):
if key is None:
key = "POD"
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
hdf_obj.check_type_id(self.__class__)
self._eig_values = hdf_obj["/eig_values"][:]
self.POD_modesDF = cd.datastruct.from_hdf(file_name,
key=key + '/POD_modesDF')
self.avg_data = self._module._avg_class.from_hdf(file_name,
key=key + "/avg_data")
self._meta_data = self.avg_data._meta_data
def save_hdf(self, file_name, write_mode, key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name, write_mode, key=key)
hdf_obj.set_type_id(self.__class__)
# hdf_obj.attrs['shape_x'] = np.array(self.shape_x)
# hdf_obj.attrs['shape_z'] = np.array(self.shape_z)
hdf_obj.attrs['comp'] = np.array([np.string_(x) for x in self.comp])
self._meta_data.save_hdf(file_name, 'a', key + '/meta_data')
self._avg_data.save_hdf(file_name, 'a', key + '/avg_data')
self.autocorrDF.to_hdf(file_name, key=key + '/autocorrDF',
mode='a') #,format='fixed',data_columns=True)
def _legacy_mode(self,filename, key):
key_split = key.split("/")
parent_key = os.path.join(*key_split[:-1])
end_key = key_split[-1]
hdf_obj = cd.hdfHandler(filename,mode='r',key=parent_key)
lower_keys = ["coord_centered","coord_staggered"]
if not hdf_obj.check_key(end_key):
return True
lower_keys_present = all([lkey in hdf_obj[end_key].keys() for lkey in lower_keys])
if not lower_keys_present:
return True
return False
def _hdf_extract(self, file_name, key=None):
if key is None:
key = 'CHAPSim_autocov_tg'
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
hdf_obj.check_type_id(self.__class__)
# self.shape_x = tuple(hdf_obj.attrs["shape_x"][:])
# self.shape_z = tuple(hdf_obj.attrs["shape_z"][:])
self.comp = tuple(np.char.decode(hdf_obj.attrs["comp"][:]))
self.autocorrDF = cd.datastruct.from_hdf(
file_name, key=key + '/autocorrDF'
) #pd.read_hdf(file_name,key=base_name+'/autocorrDF').data([shape_x,shape_z])
self._meta_data = self._module._meta_class.from_hdf(
file_name, key + '/meta_data')
self._avg_data = self._module._avg_tg_base_class.from_hdf(
file_name, key + '/avg_data')
def save_hdf(self, file_name, mode='a', key=None):
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name, mode, key=key)
hdf_obj.set_type_id(self.__class__)
hdf_obj.attrs["y_mode"] = self._y_mode.encode('utf-8')
hdf_obj.create_dataset("x_loc_norm", data=self._x_loc_norm)
self.pdf_arrayDF.to_hdf(file_name, key=key + '/pdf_arrayDF',
mode='a') #,format='fixed',data_columns=True)
self.u_arrayDF.to_hdf(file_name, key=key + '/u_arrayDF',
mode='a') #,format='fixed',data_columns=True)
self.v_arrayDF.to_hdf(file_name, key=key + '/v_arrayDF',
mode='a') #,format='fixed',data_columns=True)
self._meta_data.save_hdf(file_name, 'a', key + "/meta_data")
self.avg_data.save_hdf(file_name, 'a', key + "/avg_data")
def _hdf_extract(self, file_name, key=None):
if key is None:
key = 'CHAPSim_joint_PDF_io'
hdf_obj = cd.hdfHandler(file_name, 'r', key=key)
hdf_obj.check_type_id(self.__class__)
self._y_mode = hdf_obj.attrs['y_mode'].decode('utf-8')
self._x_loc_norm = hdf_obj['x_loc_norm'][:]
self._meta_data = self._module._meta_class.from_hdf(
file_name, key + '/meta_data')
self.avg_data = self._module.CHAPSim_AVG_io.from_hdf(
file_name, key + '/avg_data')
self.pdf_arrayDF = cd.datastruct.from_hdf(file_name,
key=key + '/pdf_arrayDF')
self.u_arrayDF = cd.datastruct.from_hdf(file_name,
key=key + '/u_arrayDF')
self.v_arrayDF = cd.datastruct.from_hdf(file_name,
key=key + '/v_arrayDF')
def save_hdf(self,file_name,write_mode,key=None):
"""
Saves the instance of the class to hdf5 file
Parameters
----------
file_name : str
File path to existing hdf5 file
write_mode : str
The write mode for example append "a" or "w" see documentation for
h5py.File
key : str, optional
path-like, hdf5 key to access the data within the file,
by default None (class name)
"""
if key is None:
key = self.__class__.__name__
hdf_obj = cd.hdfHandler(file_name,write_mode,key=key)
hdf_obj.set_type_id(self.__class__)
self._meta_data.save_hdf(file_name,'a',key=key+'/meta_data')
self.InstDF.to_hdf(file_name,key=key+'/InstDF',mode='a')
