# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2009- Spyder Kernels Contributors
#
# Licensed under the terms of the MIT License
# (see spyder_kernels/__init__.py for details)
# -----------------------------------------------------------------------------

"""
Input/Output Utilities

Note: 'load' functions has to return a dictionary from which a globals()
      namespace may be updated
"""

from __future__ import print_function

# Standard library imports
import sys
import os
import os.path as osp
import tarfile
import tempfile
import shutil
import types
import warnings
import json
import inspect
import dis
import copy
import glob

# Third party imports
# - If pandas fails to import here (for any reason), Spyder
#   will crash at startup (e.g. see Issue 2300)
# - This also prevents Spyder to start IPython kernels
#   (see Issue 2456)
try:
    import pandas as pd
except:
    pd = None            #analysis:ignore

# Local imports
from spyder_kernels.py3compat import getcwd, pickle, PY2, to_text_string


class MatlabStruct(dict):
    """
    Matlab style struct, enhanced.

    Supports dictionary and attribute style access.  Can be pickled,
    and supports code completion in a REPL.

    Examples
    ========
    >>> from spyder.utils.iofuncs import MatlabStruct
    >>> a = MatlabStruct()
    >>> a.b = 'spam'  # a["b"] == 'spam'
    >>> a.c["d"] = 'eggs'  # a.c.d == 'eggs'
    >>> print(a)
    {'c': {'d': 'eggs'}, 'b': 'spam'}

    """
    def __getattr__(self, attr):
        """Access the dictionary keys for unknown attributes."""
        try:
            return self[attr]
        except KeyError:
            msg = "'MatlabStruct' object has no attribute %s" % attr
            raise AttributeError(msg)

    def __getitem__(self, attr):
        """
        Get a dict value; create a MatlabStruct if requesting a submember.

        Do not create a key if the attribute starts with an underscore.
        """
        if attr in self.keys() or attr.startswith('_'):
            return dict.__getitem__(self, attr)
        frame = inspect.currentframe()
        # step into the function that called us
        if frame.f_back.f_back and self._is_allowed(frame.f_back.f_back):
            dict.__setitem__(self, attr, MatlabStruct())
        elif self._is_allowed(frame.f_back):
            dict.__setitem__(self, attr, MatlabStruct())
        return dict.__getitem__(self, attr)

    def _is_allowed(self, frame):
        """Check for allowed op code in the calling frame"""
        allowed = [dis.opmap['STORE_ATTR'], dis.opmap['LOAD_CONST'],
                   dis.opmap.get('STOP_CODE', 0)]
        bytecode = frame.f_code.co_code
        instruction = bytecode[frame.f_lasti + 3]
        instruction = ord(instruction) if PY2 else instruction
        return instruction in allowed

    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    @property
    def __dict__(self):
        """Allow for code completion in a REPL"""
        return self.copy()


def get_matlab_value(val):
    """
    Extract a value from a Matlab file

    From the oct2py project, see
    https://pythonhosted.org/oct2py/conversions.html
    """
    import numpy as np

    # Extract each item of a list.
    if isinstance(val, list):
        return [get_matlab_value(v) for v in val]

    # Ignore leaf objects.
    if not isinstance(val, np.ndarray):
        return val

    # Convert user defined classes.
    if hasattr(val, 'classname'):
        out = dict()
        for name in val.dtype.names:
            out[name] = get_matlab_value(val[name].squeeze().tolist())
        cls = type(val.classname, (object,), out)
        return cls()

    # Extract struct data.
    elif val.dtype.names:
        out = MatlabStruct()
        for name in val.dtype.names:
            out[name] = get_matlab_value(val[name].squeeze().tolist())
        val = out

    # Extract cells.
    elif val.dtype.kind == 'O':
        val = val.squeeze().tolist()
        if not isinstance(val, list):
            val = [val]
        val = get_matlab_value(val)

    # Compress singleton values.
    elif val.size == 1:
        val = val.item()

    # Compress empty values.
    elif val.size == 0:
        if val.dtype.kind in 'US':
            val = ''
        else:
            val = []

    return val


try:
    import numpy as np
    try:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            import scipy.io as spio
    except AttributeError:
        # Python 2.5: warnings.catch_warnings was introduced in Python 2.6
        import scipy.io as spio  # analysis:ignore
    except:
        spio = None

    if spio is None:
        load_matlab = None
        save_matlab = None
    else:
        def load_matlab(filename):
            try:
                out = spio.loadmat(filename, struct_as_record=True)
                data = dict()
                for (key, value) in out.items():
                    data[key] = get_matlab_value(value)
                return data, None
            except Exception as error:
                return None, str(error)

        def save_matlab(data, filename):
            try:
                spio.savemat(filename, data, oned_as='row')
            except Exception as error:
                return str(error)
except:
    load_matlab = None
    save_matlab = None


try:
    import numpy as np  # analysis:ignore

    def load_array(filename):
        try:
            name = osp.splitext(osp.basename(filename))[0]
            data = np.load(filename)
            if hasattr(data, 'keys'):
                return data, None
            else:
                return {name: data}, None
        except Exception as error:
            return None, str(error)

    def __save_array(data, basename, index):
        """Save numpy array"""
        fname = basename + '_%04d.npy' % index
        np.save(fname, data)
        return fname
except:
    load_array = None


try:
    from spyder.pil_patch import Image

    if sys.byteorder == 'little':
        _ENDIAN = '<'
    else:
        _ENDIAN = '>'
    DTYPES = {
              "1": ('|b1', None),
              "L": ('|u1', None),
              "I": ('%si4' % _ENDIAN, None),
              "F": ('%sf4' % _ENDIAN, None),
              "I;16": ('|u2', None),
              "I;16S": ('%si2' % _ENDIAN, None),
              "P": ('|u1', None),
              "RGB": ('|u1', 3),
              "RGBX": ('|u1', 4),
              "RGBA": ('|u1', 4),
              "CMYK": ('|u1', 4),
              "YCbCr": ('|u1', 4),
              }
    def __image_to_array(filename):
        img = Image.open(filename)
        try:
            dtype, extra = DTYPES[img.mode]
        except KeyError:
            raise RuntimeError("%s mode is not supported" % img.mode)
        shape = (img.size[1], img.size[0])
        if extra is not None:
            shape += (extra,)
        return np.array(img.getdata(), dtype=np.dtype(dtype)).reshape(shape)

    def load_image(filename):
        try:
            name = osp.splitext(osp.basename(filename))[0]
            return {name: __image_to_array(filename)}, None
        except Exception as error:
            return None, str(error)
except:
    load_image = None


def load_pickle(filename):
    """Load a pickle file as a dictionary"""
    try:
        if pd:
            return pd.read_pickle(filename), None
        else:
            with open(filename, 'rb') as fid:
                data = pickle.load(fid)
            return data, None
    except Exception as err:
        return None, str(err)


def load_json(filename):
    """Load a json file as a dictionary"""
    try:
        if PY2:
            args = 'rb'
        else:
            args = 'r'
        with open(filename, args) as fid:
            data = json.load(fid)
        return data, None
    except Exception as err:
        return None, str(err)


def save_dictionary(data, filename):
    """Save dictionary in a single file .spydata file"""
    filename = osp.abspath(filename)
    old_cwd = getcwd()
    os.chdir(osp.dirname(filename))
    error_message = None
    skipped_keys = []
    data_copy = {}

    try:
        # Copy dictionary before modifying it to fix #6689
        for obj_name, obj_value in data.items():
            # Skip modules, since they can't be pickled, users virtually never
            # would want them to be and so they don't show up in the skip list.
            # Skip callables, since they are only pickled by reference and thus
            # must already be present in the user's environment anyway.
            if not (callable(obj_value) or isinstance(obj_value,
                                                      types.ModuleType)):
                # If an object cannot be deepcopied, then it cannot be pickled.
                # Ergo, we skip it and list it later.
                try:
                    data_copy[obj_name] = copy.deepcopy(obj_value)
                except Exception:
                    skipped_keys.append(obj_name)
        data = data_copy
        if not data:
            raise RuntimeError('No supported objects to save')

        saved_arrays = {}
        if load_array is not None:
            # Saving numpy arrays with np.save
            arr_fname = osp.splitext(filename)[0]
            for name in list(data.keys()):
                try:
                    if isinstance(data[name],
                                  np.ndarray) and data[name].size > 0:
                        # Save arrays at data root
                        fname = __save_array(data[name], arr_fname,
                                             len(saved_arrays))
                        saved_arrays[(name, None)] = osp.basename(fname)
                        data.pop(name)
                    elif isinstance(data[name], (list, dict)):
                        # Save arrays nested in lists or dictionaries
                        if isinstance(data[name], list):
                            iterator = enumerate(data[name])
                        else:
                            iterator = iter(list(data[name].items()))
                        to_remove = []
                        for index, value in iterator:
                            if isinstance(value,
                                          np.ndarray) and value.size > 0:
                                fname = __save_array(value, arr_fname,
                                                     len(saved_arrays))
                                saved_arrays[(name, index)] = (
                                    osp.basename(fname))
                                to_remove.append(index)
                        for index in sorted(to_remove, reverse=True):
                            data[name].pop(index)
                except (RuntimeError, pickle.PicklingError, TypeError,
                        AttributeError, IndexError):
                    # If an array can't be saved with numpy for some reason,
                    # leave the object intact and try to save it normally.
                    pass
            if saved_arrays:
                data['__saved_arrays__'] = saved_arrays

        pickle_filename = osp.splitext(filename)[0] + '.pickle'
        # Attempt to pickle everything.
        # If pickling fails, iterate through to eliminate problem objs & retry.
        with open(pickle_filename, 'w+b') as fdesc:
            try:
                pickle.dump(data, fdesc, protocol=2)
            except (pickle.PicklingError, AttributeError, TypeError,
                    ImportError, IndexError, RuntimeError):
                data_filtered = {}
                for obj_name, obj_value in data.items():
                    try:
                        pickle.dumps(obj_value, protocol=2)
                    except Exception:
                        skipped_keys.append(obj_name)
                    else:
                        data_filtered[obj_name] = obj_value
                if not data_filtered:
                    raise RuntimeError('No supported objects to save')
                pickle.dump(data_filtered, fdesc, protocol=2)

        # Use PAX (POSIX.1-2001) format instead of default GNU.
        # This improves interoperability and UTF-8/long variable name support.
        with tarfile.open(filename, "w", format=tarfile.PAX_FORMAT) as tar:
            for fname in ([pickle_filename]
                          + [fn for fn in list(saved_arrays.values())]):
                tar.add(osp.basename(fname))
                os.remove(fname)
    except (RuntimeError, pickle.PicklingError, TypeError) as error:
        error_message = to_text_string(error)
    else:
        if skipped_keys:
            skipped_keys.sort()
            error_message = ('Some objects could not be saved: '
                             + ', '.join(skipped_keys))
    finally:
        os.chdir(old_cwd)
    return error_message


def load_dictionary(filename):
    """Load dictionary from .spydata file"""
    filename = osp.abspath(filename)
    old_cwd = getcwd()
    tmp_folder = tempfile.mkdtemp()
    os.chdir(tmp_folder)
    data = None
    error_message = None
    try:
        with tarfile.open(filename, "r") as tar:
            tar.extractall()
        pickle_filename = glob.glob('*.pickle')[0]
        # 'New' format (Spyder >=2.2 for Python 2 and Python 3)
        with open(pickle_filename, 'rb') as fdesc:
            data = pickle.loads(fdesc.read())
        saved_arrays = {}
        if load_array is not None:
            # Loading numpy arrays saved with np.save
            try:
                saved_arrays = data.pop('__saved_arrays__')
                for (name, index), fname in list(saved_arrays.items()):
                    arr = np.load( osp.join(tmp_folder, fname) )
                    if index is None:
                        data[name] = arr
                    elif isinstance(data[name], dict):
                        data[name][index] = arr
                    else:
                        data[name].insert(index, arr)
            except KeyError:
                pass
    # Except AttributeError from e.g. trying to load function no longer present
    except (AttributeError, EOFError, ValueError) as error:
        error_message = to_text_string(error)
    # To ensure working dir gets changed back and temp dir wiped no matter what
    finally:
        os.chdir(old_cwd)
        try:
            shutil.rmtree(tmp_folder)
        except OSError as error:
            error_message = to_text_string(error)
    return data, error_message


class IOFunctions(object):
    def __init__(self):
        self.load_extensions = None
        self.save_extensions = None
        self.load_filters = None
        self.save_filters = None
        self.load_funcs = None
        self.save_funcs = None

    def setup(self):
        iofuncs = self.get_internal_funcs()+self.get_3rd_party_funcs()
        load_extensions = {}
        save_extensions = {}
        load_funcs = {}
        save_funcs = {}
        load_filters = []
        save_filters = []
        load_ext = []
        for ext, name, loadfunc, savefunc in iofuncs:
            filter_str = to_text_string(name + " (*%s)" % ext)
            if loadfunc is not None:
                load_filters.append(filter_str)
                load_extensions[filter_str] = ext
                load_funcs[ext] = loadfunc
                load_ext.append(ext)
            if savefunc is not None:
                save_extensions[filter_str] = ext
                save_filters.append(filter_str)
                save_funcs[ext] = savefunc
        load_filters.insert(0, to_text_string("Supported files"+" (*"+\
                                              " *".join(load_ext)+")"))
        load_filters.append(to_text_string("All files (*.*)"))
        self.load_filters = "\n".join(load_filters)
        self.save_filters = "\n".join(save_filters)
        self.load_funcs = load_funcs
        self.save_funcs = save_funcs
        self.load_extensions = load_extensions
        self.save_extensions = save_extensions

    def get_internal_funcs(self):
        return [
                ('.spydata', "Spyder data files",
                             load_dictionary, save_dictionary),
                ('.npy', "NumPy arrays", load_array, None),
                ('.npz', "NumPy zip arrays", load_array, None),
                ('.mat', "Matlab files", load_matlab, save_matlab),
                ('.csv', "CSV text files", 'import_wizard', None),
                ('.txt', "Text files", 'import_wizard', None),
                ('.jpg', "JPEG images", load_image, None),
                ('.png', "PNG images", load_image, None),
                ('.gif', "GIF images", load_image, None),
                ('.tif', "TIFF images", load_image, None),
                ('.pkl', "Pickle files", load_pickle, None),
                ('.pickle', "Pickle files", load_pickle, None),
                ('.json', "JSON files", load_json, None),
                ]

    def get_3rd_party_funcs(self):
        other_funcs = []
        try:
            from spyder.otherplugins import get_spyderplugins_mods
            for mod in get_spyderplugins_mods(io=True):
                try:
                    other_funcs.append((mod.FORMAT_EXT, mod.FORMAT_NAME,
                                        mod.FORMAT_LOAD, mod.FORMAT_SAVE))
                except AttributeError as error:
                    print("%s: %s" % (mod, str(error)), file=sys.stderr)
        except ImportError:
            pass
        return other_funcs

    def save(self, data, filename):
        ext = osp.splitext(filename)[1].lower()
        if ext in self.save_funcs:
            return self.save_funcs[ext](data, filename)
        else:
            return "<b>Unsupported file type '%s'</b>" % ext

    def load(self, filename):
        ext = osp.splitext(filename)[1].lower()
        if ext in self.load_funcs:
            return self.load_funcs[ext](filename)
        else:
            return None, "<b>Unsupported file type '%s'</b>" % ext

iofunctions = IOFunctions()
iofunctions.setup()


def save_auto(data, filename):
    """Save data into filename, depending on file extension"""
    pass


if __name__ == "__main__":
    import datetime
    testdict = {'d': 1, 'a': np.random.rand(10, 10), 'b': [1, 2]}
    testdate = datetime.date(1945, 5, 8)
    example = {'str': 'kjkj kj k j j kj k jkj',
               'unicode': u'éù',
               'list': [1, 3, [4, 5, 6], 'kjkj', None],
               'tuple': ([1, testdate, testdict], 'kjkj', None),
               'dict': testdict,
               'float': 1.2233,
               'array': np.random.rand(4000, 400),
               'empty_array': np.array([]),
               'date': testdate,
               'datetime': datetime.datetime(1945, 5, 8),
               }
    import time
    t0 = time.time()
    save_dictionary(example, "test.spydata")
    print(" Data saved in %.3f seconds" % (time.time()-t0))  # spyder: test-skip
    t0 = time.time()
    example2, ok = load_dictionary("test.spydata")
    os.remove("test.spydata")

    print("Data loaded in %.3f seconds" % (time.time()-t0))  # spyder: test-skip
