""" npy/npz/hdf5 file based storage; this modules adds the possibility to dump and load objects in files and a more convenient was of accessing the data via the .attributedict thanks to the DataStorage class """ import numpy as np import os import h5py import collections import logging log = logging.getLogger(__name__) def unwrapArray(a,recursive=True,readH5pyDataset=True): """ This function takes an object (like a dictionary) and recursively unwraps it solving issues like: * the fact that many objects are packaged as 0d array This funciton has also some specific hack for handling h5py limits: * handle the None python object * numpy unicode ... """ # is h5py dataset convert to array if isinstance(a,h5py.Dataset) and readH5pyDataset: a = a[...] if isinstance(a,h5py.Dataset) and a.shape == (): a = a[...] if isinstance(a,h5py.Group) and "IS_LIST_OF_ARRAYS" in a.attrs: items = list(a.keys()) items.sort() a = np.asarray( [a[item][...] for item in items] ) if isinstance(a,np.ndarray) and a.ndim == 0 : a = a.item() if isinstance(a,np.ndarray) and a.dtype.char == "S": a = a.astype(str) if recursive: if "items" in dir(a): # dict, h5py groups, npz file a = dict(a); # convert to dict, otherwise can't asssign values for key,value in a.items(): a[key] = unwrapArray(value) elif isinstance(a,(list,tuple)): a = [unwrapArray(element) for element in a] else: pass if isinstance(a,dict): a = DataStorage(a) # restore None that cannot be saved in h5py if isinstance(a,str) and a == "NONE_PYTHON_OBJECT": a = None # h5py can't save numpy unicode if isinstance(a,np.ndarray) and a.dtype.char == "S": a = a.astype(str) return a def dictToH5Group(d,group): """ helper function that transform (recursive) a dictionary into an hdf group by creating subgroups """ for key,value in d.items(): if isinstance(value,dict): group.create_group(key) dictToH5Group(value,group[key]) else: # h5py can't handle numpy unicode arrays if isinstance(value,np.ndarray) and value.dtype.char == "U": value = np.asarray([vv.encode('ascii') for vv in value]) group[key] = value # check if it is list of array elif isinstance(value,np.ndarray) and value.ndim == 1 and isinstance(value[0],np.ndarray): group.create_group(key) group[key].attrs["IS_LIST_OF_ARRAYS"] = True for index,array in enumerate(value): group["%s/index%05d"%(key,index)] = array # h5py can't save None elif value is None: group[key] = "NONE_PYTHON_OBJECT" else: try: group[key] = value except TypeError: log.error("Can't save %s"%(key)) def dictToH5(h5,d): """ Save a dictionary into an hdf5 file TODO: add capability of saving list of array h5py is not capable of handling dictionaries natively""" h5 = h5py.File(h5,mode="w") # group = h5.create_group("/") dictToH5Group(d,h5["/"]) h5.close() def h5ToDict(h5,readH5pyDataset=True): """ Read a hdf5 file into a dictionary """ with h5py.File(h5,"r") as h: ret = unwrapArray(h,recursive=True,readH5pyDataset=readH5pyDataset) return ret def npzToDict(npzFile): with np.load(npzFile) as npz: d = dict(npz) d = unwrapArray(d,recursive=True) return d def npyToDict(npyFile): d = unwrapArray( np.load(npyFile).item() ,recursive=True) return d def dictToNpz(npzFile,d): np.savez(npzFile,**d) def dictToNpy(npyFile,d): np.save(npyFile,d) def objToDict(o,recursive=True): """ convert a DictWrap to a dictionary (useful for saving); it should work for other objects too TODO: this function does not catch a list of DataStorage instances like objToDict( ( DataStorage(), DataStorage() ) ) is not converted !! """ if "items" not in dir(o): return o d = dict() for k,v in o.items(): try: d[k] = objToDict( v ) except Exception as e: log.info("In objToDict, could not convert key %s to dict, error was"%\ (k,e)) d[k] = v return d def read(fname): extension = os.path.splitext(fname)[1] log.info("Reading storage file %s"%fname) if extension == ".npz": return DataStorage(npzToDict(fname)) elif extension == ".npy": return DataStorage(npyToDict(fname)) elif extension == ".h5": return DataStorage(h5ToDict(fname)) else: raise ValueError("Extension must be h5, npy or npz, it was %s"%extension) def save(fname,d): # make sure the object is dict (recursively) this allows reading it # without the DataStorage module d = objToDict(d,recursive=True) d['filename'] = fname extension = os.path.splitext(fname)[1] log.info("Saving storage file %s"%fname) try: if extension == ".npz": return dictToNpz(fname,d) elif extension == ".h5": return dictToH5(fname,d) elif extension == ".npy": return dictToNpy(fname,d) else: raise ValueError("Extension must be h5, npy or npz, it was %s"%extension) except Exception as e: log.exception("Could not save %s"%fname) class DataStorage(dict): """ Storage for dict like object. recursive : bool recursively convert dict-like objects to DataStorage It can save data to file (format npy,npz or h5) To initialize it: data = DataStorage( a=(1,2,3),b="add",filename='store.npz' ) # recursively by default # data.a will be a DataStorage instance data = DataStorage( a=dict( b = 1)) ); # data.a will be a dictionary data = DataStorage( a=dict( b = 1),recursive=False ) # reads from file if it exists data = DataStorage( 'mysaveddata.npz' ) ; DOES NOT READ FROM FILE (even if it exists)!! data = DataStorage( filename = 'mysaveddata.npz' ); create empty storage (with default filename) data = DataStorage() """ def __init__(self,*args,filename='data_storage.npz',recursive=True,**kwargs): # self.filename = kwargs.pop('filename',"data_storage.npz") self.filename = filename self._recursive = recursive # interpret kwargs as dict if there are if len(kwargs) != 0: fileOrDict = dict(kwargs) elif len(kwargs)==0 and len(args)>0: fileOrDict = args[0] else: fileOrDict = dict() d = dict(); # data dictionary if isinstance(fileOrDict,dict): d = fileOrDict elif isinstance(fileOrDict,str): if os.path.isfile(fileOrDict): d = read(fileOrDict) else: self.filename=fileOrDict d = dict() else: raise ValueError("Invalid DataStorage definition") if recursive: for k in d.keys(): if not isinstance(d[k],DataStorage) and isinstance(d[k],dict): d[k] = DataStorage(d[k]) # allow accessing with .data, .delays, etc. for k,v in d.items(): setattr(self,k,v) # allow accessing as proper dict self.update( **dict(d) ) def __setitem__(self, key, value): #print("__setitem__") setattr(self,key,value) super().__setitem__(key, value) def __setattr__(self, key, value): """ allows to add fields with data.test=4 """ # check if attr exists is essential (or it fails when defining an instance) if hasattr(self,"_recursive") and self._recursive and \ isinstance(value,(dict,collections.OrderedDict)): value = DataStorage(value) super().__setitem__(key, value) super().__setattr__(key,value) def __delitem__(self, key): delattr(self,key) super().__delitem__(key) def __str__(self): keys = list(self.keys()) keys.sort() return "DataStorage obj containing: %s" % ",".join(keys) def __repr__(self): keys = list(self.keys()) keys.sort() if len(keys) == 0: return "Empty DataStorage" nchars = max(map(len,keys)) fmt = "%%%ds %%s" % (nchars) s = ["DataStorage obj containing (sorted): ",] for k in keys: if k[0] == "_": continue obj = self[k] if isinstance(obj,np.ndarray): value_str = "array, size %s, type %s"% ("x".join(map(str,obj.shape)),obj.dtype) elif isinstance(obj,DataStorage): value_str = str(obj)[:50] elif isinstance(obj,(str,DataStorage)): value_str = obj[:50] elif isinstance(obj,(list,tuple)) and all( [isinstance(v,np.ndarray) for v in obj]): value_str = "list of arrays, shapes " + ",".join([str(v.shape) for v in obj[:5]]) + " ..." elif self[k] is None: value_str = "None" else: value_str = str(self[k]) if len(str(obj))>50: value_str += " ..." s.append( fmt % (k,value_str) ) return "\n".join(s) def keys(self): keys = list(super().keys()) keys = [k for k in keys if k != 'filename' ] keys = [k for k in keys if k[0] != '_' ] return keys def save(self,fname=None): if fname is None: fname = self.filename assert fname is not None save(fname,self)