""" 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__)  # __name__ is "foo.bar" here

def unwrapArray(a,recursive=True,readH5pyDataset=True):
  """ This function takes an object (like a dictionary) and recursivively
      unwraps it solving many issues like the fact that many objects are 
      packaged as 0d array
      This funciton has also some specific hack for handling h5py limit to 
      handle for example the None object or the 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,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):
      for index in range(len(a)): a[index] = unwrapArray(a[i])
    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])
      # h5py can't save None
      if value is None: value="NONE_PYTHON_OBJECT"
      try:
        group[key] = value
      except TypeError:
        log.error("Can't save %s"%(key))

def dictToH5(h5,d):
  """ Save a dictionary into an hdf5 file
      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 dictToNpz(npzFile,d): np.savez(npzFile,**d)

def read(fname):
  extension = os.path.splitext(fname)[1]
  log.info("Reading storage file %s"%fname)
  if extension == ".npz": 
    return npzToDict(fname)
  elif extension == ".h5":
    return h5ToDict(fname)
  else:
    raise ValueError("Extension must be h5 or npz, it was %s"%extension) 

def save(fname,d):
  extension = os.path.splitext(fname)[1]
  log.info("Saving storage file %s"%fname)
  if extension == ".npz": 
    return dictToNpz(fname,d)
  elif extension == ".h5":
    return dictToH5(fname,d)
  else:
    raise ValueError("Extension must be h5 or npz") 

class DataStorage(dict):
  """ Storage for 1d integrated info """
  def __init__(self,fileOrDict,default_name='pyfai_1d',default_ext='npz'):
    if isinstance(fileOrDict,dict):
      self.filename = None
      d = fileOrDict
    else:
      assert isinstance(fileOrDict,str)
      if os.path.isdir(fileOrDict):
        fileOrDict = fileOrDict + "/" + default_name + "." + default_ext
      self.filename = fileOrDict
      d = read(fileOrDict)

    # 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):
    setattr(self,key,value)
    super().__setitem__(key, value)

  def __delitem__(self, key):
    delattr(self,key)
    super().__delitem__(key)

  def save(self,fname=None):
    if fname is None: fname = self.filename
    assert fname is not None
    save(fname,dict(self))