import numpy as np
import struct
from os import path
from collections import OrderedDict


def archwrite(filename, *args):  # {{{
    """ARCHWRITE - Write data to a field, given the file name, field name, and data.

    Usage:
        archwrite('archive101.arch', 'variable_name', data)
    """

    nargs = len(args)
    if nargs % 2 != 0:
        raise ValueError('Incorrect number of arguments.')
    # open file
    try:
        if not path.isfile(filename):
            fid = open(filename, 'wb')
        else:
            fid = open(filename, 'ab')
    except IOError as e:
        raise IOError("archwrite error: could not open '{}' to write to due to:".format(filename), e)
    nfields = len(args) / 2
    # generate data to write
    for i in range(int(nfields)):
        # write field name
        name = args[2 * i]
        write_field_name(fid, name)
        # write data associated with field name
        data = args[2 * i + 1]
        code = format_archive_code(data)
        if code == 1:
            raise ValueError("archwrite : error writing data, string should not be written as field data")
        elif code == 2:
            write_scalar(fid, data)
        elif code == 3:
            write_vector(fid, data)
        else:
            raise ValueError("archwrite : error writing data, invalid code entered '{}'".format(code))
    fid.close()
    # }}}


def archread(filename, fieldname):  # {{{
    """ARCHREAD - Given an arch file name, and a field name, find and return 
    the data associated with that field name.

    Usage:
        archread('archive101.arch', 'field_var_1')
    """
    try:
        if path.isfile(filename):
            fid = open(filename, 'rb')
        else:
            raise IOError("archread error : file '{}' does not exist".format(filename))
    except IOError as e:
        raise IOError("archread error : could not open file '{}' to read from due to :".format(filename), e)

    archive_results = []
    # read first result
    result = read_field(fid)
    while result:
        if fieldname == result['field_name']:
            # found the data we wanted
            archive_results = result['data']  # we only want the data
            break
    # read next result
        result = read_field(fid)
    # close file
    fid.close()
    return archive_results
    # }}}


def archdisp(filename):  # {{{
    """ARCHDISP - Given an arch filename, display the contents of that file

    Usage:
        archdisp('archive101.arch')
    """
    try:
        if path.isfile(filename):
            fid = open(filename, 'rb')
        else:
            raise IOError("archread error : file '{}' does not exist".format(filename))
    except IOError as e:
        raise IOError("archread error : could not open file '{}' to read from due to ".format(filename), e)
    print('Source file: ')
    print(('\t{0}'.format(filename)))
    print('Variables: ')
    result = read_field(fid)
    while result:
        print(('\t{0}'.format(result['field_name'])))
        print(('\t\tSize:\t\t{0}'.format(result['size'])))
        print(('\t\tDatatype:\t{0}'.format(result['data_type'])))
        # go to next result
        result = read_field(fid)
    # close file
    fid.close()
    # }}}


# Helper functions
def write_field_name(fid, data):  # {{{
    """Routine to write field name (variable name) to an archive file.
    """
    # write the length of the record
    # length to write + string size (len) + format code
    reclen = len(data) + 4 + 4
    fid.write(struct.pack('>i', reclen))
    # write format code
    code = format_archive_code(data)
    if code != 1:
        raise TypeError("archwrite : error writing field name, expected string, but got %s" % type(data))
    fid.write(struct.pack('>i', 1))
    # write string length, and then the string
    fid.write(struct.pack('>i', len(data)))
    fid.write(struct.pack('>{}s'.format(len(data)), data.encode('utf8')))
    # }}}


def write_scalar(fid, data):  # {{{
    """Procedure to write a double to an arch file pointed to by fid
    """
    # write length of record
    # double (8 bytes) + format code (4 bytes)
    reclen = 8 + 4
    fid.write(struct.pack('>i', reclen))

    # write the format code (2 for scalar)
    fid.write(struct.pack('>i', 2))
    # write the double
    fid.write(struct.pack('>d', data))
    # }}}


def write_vector(fid, data):  # {{{
    """Procedure to write a np.array to an arch file
    """
    # Make sure our vector is the correct shape.
    # Reshape it into a row vector if it is not correct.
    if isinstance(data, (bool, int, float)):
        data = np.array([data])
    elif isinstance(data, (list, tuple)):
        data = np.array(data).reshape(-1, )
    if np.ndim(data) == 1:
        if np.size(data):
            data = data.reshape(np.size(data), )
        else:
            data = data.reshape(0, 0)
    # get size of data
    sz = data.shape
    # write length of record
    # format code + row size + col size + (double size * row amt * col amt)
    reclen = 4 + 4 + 4 + 8 * sz[0] * sz[1]
    # make sure we can fit data into file
    if reclen > pow(2, 31):
        raise ValueError("archwrite error : can not write vector to binary file because it is too large")
    fid.write(struct.pack('>i', reclen))
    # write format code
    fid.write(struct.pack('>i', 3))
    # write vector
    fid.write(struct.pack('>i', sz[0]))
    fid.write(struct.pack('>i', sz[1]))
    for i in range(sz[0]):
        for j in range(sz[1]):
            fid.write(struct.pack('>d', float(data[i][j])))
    # }}}


def read_field(fid):  # {{{
    """Procedure to read a field and return a results list with the following 
    attributes:

        result['field_name']    -> the name of the variable that was just read
        result['size']          -> size (dimensions) of the variable just read
        result['data_type']     -> the type of data that was just read
        result['data']          -> the actual data
    """

    try:
        # first, read the string
        # first read the size and continue reading
        struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]  # name length
        check_name = struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]
        if check_name != 1:
            raise ValueError('archread error : a string was not present at the start of the arch file')
        namelen = struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]
        fieldname = struct.unpack('>{}s'.format(namelen), fid.read(namelen))[0]
        # then, read the data
        # first read the size and continue reading
        struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]  # data length
        data_type = struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]

        if data_type == 2:
            # struct.upack scalar
            data = struct.unpack('>d', fid.read(struct.calcsize('>d')))[0]
        elif data_type == 3:
            rows = struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]
            cols = struct.unpack('>i', fid.read(struct.calcsize('>i')))[0]
            raw_data = np.zeros(shape=(rows, cols), dtype=float)
            for i in range(rows):
                raw_data[i, :] = struct.unpack('>{}d'.format(cols), fid.read(cols * struct.calcsize('>d')))
                # The matrix will be struct.upacked in order and will be filled left -> right by column
                # We need to reshape and transpose the matrix so it can be read correctly
            data = raw_data.reshape(raw_data.shape[::-1]).T
        else:
            raise TypeError("Cannot read data type {}".format(data_type))

        # give additional data to user
        if data_type == 2:
            data_size = '1x1'
            data_type_str = 'double'
        elif data_type == 3:
            data_size = '{0}x{1}'.format(rows, cols)
            data_type_str = 'vector/matrix'

        result = OrderedDict()
        result['field_name'] = fieldname.decode('utf8')
        result['size'] = data_size
        result['data_type'] = data_type_str
        result['data'] = data

    except struct.error as e:
        result = None
        print("result is empty due to", e)

    return result
    # }}}


def format_archive_code(var):  # {{{
    """Given a variable, determine it's type and return an integer value:

        1 : string
        2 : double (scalar)
        3 : vector or matrix (of type double)
    """
    if isinstance(var, str):
        code = 1
    elif var.shape[0] == 1 and var.shape[1] == 1:
        code = 2
    elif isinstance(var, (list, tuple, np.ndarray)):
        code = 3
    else:
        raise TypeError('archwrite error: data type {} is not valid.'.format(type(var)))
    return code
    # }}}
