77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
""" hdf5 file based storage; this modules adds the possibility to dump dict as
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hdf5 File """
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import numpy as np
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import os
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import h5py
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import collections
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def dictToH5Group(d,group):
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for key,value in d.items():
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if not isinstance(value,(dict,collections.OrderedDict)):
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# hack for special s...
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# h5py can't handle numpy unicode arrays
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if isinstance(value,np.ndarray) and value.dtype.char == "U":
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value = np.asarray([vv.encode('ascii') for vv in value])
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# h5py can't save None
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if value is None: value="NONE_PYTHON_OBJECT"
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group[key] = value
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else:
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group.create_group(key)
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dictToH5Group(value,group[key])
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def dictToH5(h5,d):
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h5 = h5py.File(h5,mode="w")
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# group = h5.create_group("/")
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dictToH5Group(d,h5["/"])
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h5.close()
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def h5dataToDict(h5):
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if isinstance(h5,h5py.Dataset):
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temp = h5[...]
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# hack for special s...
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# unwrap 0d arrays
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if isinstance(temp,np.ndarray) and temp.ndim == 0:
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temp=temp.item()
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# h5py can't handle None
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if temp == "NONE_PYTHON_OBJECT": temp=None
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return temp
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else:
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ret = dict()
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for k,v in h5.items(): ret[k] = h5dataToDict(v)
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return ret
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def h5ToDict(h5):
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with h5py.File(h5,"r") as h:
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ret = h5dataToDict( h["/"] )
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return ret
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def npzToDict(npzFile):
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with np.load(npzFile) as npz: d = dict(npz)
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# unwrap 0d arrays
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for key,value in d.items():
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if isinstance(value,np.ndarray) and value.ndim == 0: d[key]=value.item()
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return d
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def dictToNpz(npzFile,d): np.savez(npzFile,**d)
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def read(fname):
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extension = os.path.splitext(fname)[1]
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if extension == ".npz":
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return npzToDict(fname)
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elif extension == ".h5":
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return h5ToDict(fname)
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else:
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raise ValueError("Extension must be h5 or npz, it was %s"%extension)
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def save(fname,d):
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extension = os.path.splitext(fname)[1]
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if extension == ".npz":
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return dictToNpz(fname,d)
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elif extension == ".h5":
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return dictToH5(fname,d)
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else:
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raise ValueError("Extension must be h5 or npz")
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