#!/usr/bin/env python

# Python imports
from __future__ import division

import os
import sys
import zlib
import base64
import xdrlib
import xmlrpclib
import struct
import itertools
import cStringIO
import platform

import matplotlib as mpl
mpl.use('Agg')

import numpy as np

import vespa.analysis.src.block_prep_fidsum as block_prep_fidsum
import vespa.analysis.src.mrs_dataset as mrs_dataset
import vespa.analysis.src.util_import as util_import
import vespa.analysis.src.figure_layouts as figure_layouts
import vespa.common.mrs_data_raw_fidsum as mrs_data_raw_fidsum
import vespa.common.util.export as util_export
import vespa.common.constants as constants

from SimpleXMLRPCServer import SimpleXMLRPCServer
#from test1_encoded import test1

# RAWDATA_SCALE is based on ICE_RAWDATA_SCALE in SpecRoFtFunctor.cpp
RAWDATA_SCALE = 131072.0 * 256.0

# _EXIT_TIMER_DELAY defines a recommended delay between when client calls
#  "quit" and when this server will most likely actually have quitted. This
#  is in case the client is trying to quit and re-start the sever.
_EXIT_TIMER_DELAY = 0.1

test1 = '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'

#------------------------------------------------------------------------------
# Local Handlers - direct methods and instances

def kill_server():
    """
    Called by the ListenRequestHandler when it is time for this server to exit

    """
    global server
    server.quit = True

class ListenRequestHandler(object):
    """
    This class does most of the interesting work. It defines and provides
    the API that's exposed to the world through XMLRPC.
    """
    def __init__(self, kill_server, verbose=False):

        self.verbose = verbose
        # kill_server is a function that this class can use to tell the listener to quit.
        self._kill_server = kill_server

    def div(self, x, y):
        """A minimalist method for testing - div()."""
        if self.verbose: print "test_xmlrpc_server: div() --"
        return x // y

    def add(self, x, y):
        """A minimalist method for testing - add()."""
        if self.verbose: print "test_xmlrpc_server: add() --"
        return x + y

    def bump2(self, x, y):
        '''
        Test for what is returned when more than 1 result returned.
        In C/C++ ICE, I was able to use xmlrpc_decompose_value() as:
           xmlrpc_decompose_value(&m_xmlrpc_env, result2, "(ii)", &res1, &res2);
        to parse a known return value set.

        '''
        if self.verbose: print "test_xmlrpc_server: bump2() --"
        return x+1, y+1

    def is_available(self):
        """
        Currently a no-op.

        To be called when the scanner is initializing the ICE program and
        wants to check if there is an XML-RPD server listening for it.
        """
        if self.verbose: print "test_simple_server: is_available() --"
        pass

    def quit(self):
        """
        Kills the listener. Should only be called from RtView.

        It returns a float that measures the number of seconds that will elapse
        before the app actually exits. RtView should not attempt to start
        a new listener until at least that amount of time has elapsed.

        """
        if self.verbose: print "listener: quit() --"
        #
        # I would like to call sys.exit() directly here, but an XMLRPC call
        # has to return, otherwise xmlrpclib raises an error. So instead I set
        # a flag via the call to _kill_server() and ask the caller to wait for
        # _EXIT_TIMER_DELAY seconds.
        #
        self._kill_server()
        return _EXIT_TIMER_DELAY

    def get_fake_result(self, x):
        """
        The fake data is an encoded string previously saved of an RGB file
        screenshot of a Vespa-Analysis results layout. We wrap it in and
        xmlrpclib friendly format and ship it back.

        """

        if x == 0:
            # these steps decode the original encoded list of ints
            data = base64.b64decode(test1)
            data = zlib.decompress(data)
            p = xdrlib.Unpacker(data)
            data = p.unpack_farray(len(data) // 4, p.unpack_int)     # 4 since we expect INT32 here
        else:
            data = [0,] * (512*512*3)

        # now convert the list of ints to a string, which Binary requires
        data = "".join(map(chr, data))

        val = xmlrpclib.Binary(data)
        return val

    def vespa_process(self, ncol, ncha, nave, os_remove, nleft, nright, os_factor, dwell, freq, delta, seqte, nucstr, seqstr, tapstr, data64):

        if self.verbose: print "currently running: vespa_process() --"

        if platform.system() == 'Windows':
            #presetfile  = "C:/bsoher/code/xmlrpc_server_vespa/preset_svs_se_water_v1.xml"
            #presetfile  = "C:/bsoher/code/xmlrpc_server_vespa/preset_svs_se_braino_v1.xml"
            presetfile   = "C:/bsoher/code/xmlrpc_server_vespa/preset_svs_se_brian_v1.xml"
            out_filename = "C:/bsoher/code/xmlrpc_server_vespa/ice_dataset_out_8.xml"

            print 'seqstr = '+str(seqstr)

            if seqstr == 'svs_seF':
                out_rgb_fname = "C:/bsoher/code/xmlrpc_server_vespa/debug_last_run_seq_svs_seF.bin"
            elif seqstr == 'svs_sead':
                out_rgb_fname = "C:/bsoher/code/xmlrpc_server_vespa/debug_last_run_seq_svs_sead.bin"
            else:
                out_rgb_fname = "C:/bsoher/code/xmlrpc_server_vespa/debug_last_run_seq_svs_xxxx.bin"

        else:
            presetfile   = "/opt/med/lib/soher/preset_svs_se_brian_v1.xml"
            out_filename = "/opt/med/lib/soher/ice_dataset_out_8.xml"

            if seqstr == 'svs_seF':
                out_rgb_fname = "/opt/med/lib/soher/debug_last_run_seq_svs_seF.bin"
            elif seqstr == 'svs_sead':
                out_rgb_fname = "/opt/med/lib/soher/debug_last_run_seq_svs_sead.bin"
            else:
                out_rgb_fname = "/opt/med/lib/soher/debug_last_run_seq_svs_xxxx.bin"

        try:
            count  = 2 * ncol * ncha * nave
            fmt = '<%d%s' % (count, 'f')

            data1 = cStringIO.StringIO(data64.data)
            data2 = data1.read(struct.calcsize(fmt))
            data3 = struct.unpack(fmt, data2)

            data_iter = iter(data3)
            data4 = [complex(r, i) for r, i in itertools.izip(data_iter, data_iter)]
            print 'count, len(data4) = ', count, len(data4)

            data5 = np.array(data4)
            data5.shape = nave, ncha, ncol      # acquisition order

            ncol0 = 1<<((ncol-1).bit_length()-1) # ensure power of 2

        except Exception, e:
            print str(e)

        # Set up header info ----------------------------------------

        d = { }
        d["header"] = "XmlRpc Server Ice Vespa - vespa_process()"
        d["sw"]             = (1.0 / float(dwell))
        d["remove_os"]      = os_remove
        d["readout_os"]     = float(os_factor)
        d["sequence_type"]  = seqstr
        d["frequency"]      = float(freq)
        d["dims"]           = mrs_data_raw_fidsum.DataRawFidsum.DEFAULT_DIMS
        d["dims"][0]        = int(ncol0/d["readout_os"])
        d["dims"][1]        = int(nave)
        d["dims"][2]        = int(ncha)
        d["dims"][3]        = 1
        d["seqte"]          = float(seqte)     # comes in as msec
        print 'seqte = ', seqte
        d["start_point"]    = nleft
        d["nucleus"]        = nucstr
        if nucstr == '1H':
            d["midppm"] = constants.DEFAULT_PROTON_CENTER_PPM
        else:
            d["midppm"] = constants.DEFAULT_XNUCLEI_CENTER_PPM

        dims        = d["dims"]
        dim0        = dims[0]
        ncoils      = dims[2]
        nfids       = dims[1]
        acqdim0     = dims[0] * int(d["readout_os"])
        remove_os   = d["remove_os"]
        start_point = d['start_point']
        end_point   = start_point + acqdim0

        scale       = RAWDATA_SCALE / float(nfids)
        dat         = np.empty([ncoils,nfids,dim0], dtype=np.complex128)

        for i in range(nfids):
            for j in range(ncoils):

                chan = data5[i,j,start_point:end_point].copy() * scale                     # in pulseq acquisition order

                if remove_os:
                    chan = np.fft.fft(chan)
                    chan = np.roll(chan, int(dim0/2))
                    chan = np.fft.ifft(np.roll(chan[:dim0], int(-dim0/2)))
                else:
                    chan = chan[:dim0]
                chan = np.conjugate(chan)

                dat[j,i,:] = chan       # index coils on outside so eventually collapse to 1,1,nfid,dim0

        if d["remove_os"]: d["sw"] = d["sw"] / 2.0

        d["data"] = dat
        d["data_source"] = "XmlRpc Server Ice Vespa - vespa_process()"
        raw = mrs_data_raw_fidsum.DataRawFidsum(d)

        dataset = _import_siemens_ice([raw,], open_dataset=None)
        dataset = dataset[0]

        preset = _import_preset(presetfile)

        # Update dataset with preset ------------------------------------
        dataset.apply_preset(preset, voxel=(0,0,0))

        # Process and fit data ------------------------------------------
        chain_outputs = _process_all_blocks(dataset)

        fig_call = figure_layouts.analysis_brp512
        fig = fig_call( dataset,
                        viffpath='Analysis - Siemens ICE Inline',
                        vespa_version='0.9.x',
                        timestamp='',
                        fontname='Courier New',
                        minplot=0.1,
                        maxplot=4.9,
                        nobase=True,
                        extfig=None,
                        fixphase=True,
                        verbose=False,
                        debug=False)

        buf1 = fig.canvas.tostring_rgb()

        # convert string to byte arry and write to a debug file
        print "Saving degug RGB file to - " + str(out_rgb_fname)
        cbuf = np.fromstring(buf1, dtype=np.uint8)
        cbuf.tofile(out_rgb_fname)

        print 'buf1 = fig.canvas.tostring_rgb()  - len() = ', len(buf1)
        buf2 = xmlrpclib.Binary(buf1)

        dataset.dataset_filename = out_filename

        util_export.export(out_filename, [dataset], None, None, False)

        print 'finished vespa_process()'

        return buf2

def _process_all_blocks(dataset):
    """ for all voxels, run chain in all blocks to update """

    chain_outputs = {}

    try:
        voxel = dataset.all_voxels
        for key in dataset.blocks.keys():
            if key == 'spectral':
                key = 'spectral'
                block = dataset.blocks[key]
                tmp = block.chain.run(voxel, entry='all')
                chain_outputs[key] = tmp

                print 'block._svd_outputs = ', block.get_svd_output([0,0,0])

                if 'fit' in dataset.blocks.keys():
                    key = 'fit'
                    block = dataset.blocks[key]
                    block.chain.run(voxel, entry='initial_only')
                    key = 'spectral'
                    block = dataset.blocks[key]
                    block.set_do_fit(True, voxel[0])
                    tmp = block.chain.run(voxel, entry='all')
                    chain_outputs[key] = tmp
            else:
                block = dataset.blocks[key]
                tmp = block.chain.run(voxel, entry='all')
                chain_outputs[key] = tmp
    except Exception, e:
        print str(e)

    return chain_outputs

def _import_preset(presetfile):

    try:
        msg = ""
        try:
            importer = util_import.DatasetImporter(presetfile)
        except Exception, e:
            msg = str(e)
            print str(e)

#         except IOError:
#             msg = """I can't read the preset file "%s".""" % presetfile
#         except SyntaxError:
#             msg = """The preset file "%s" isn't valid Vespa Interchange File Format.""" % presetfile

        if msg:
            print msg
        else:
            # Time to rock and roll!
            presets = importer.go()
            preset  = presets[0]

            return preset

    except Exception, e:
        print str(e)

def _import_siemens_ice(raws, open_dataset=None):
    """
    Stolen from Analysis main.py module - trimmed for CLI usage

    Assumption here is that we are opening one file in the reader. If there is
    an 'open_dataset' sent in, then the current reader is for an associated
    file. We will associate the current file with the open one at the end of
    the code.

    """
    datasets = [ ]

    # Convert these raw objects into fully-fledged dataset objects.
    if open_dataset:
        zero_fill_multiplier = open_dataset.zero_fill_multiplier
    else:
        zero_fill_multiplier = 0

    # Step 2
    #
    # See if any data types need special classes. We usually only
    # look for raw fidsum classes which trigger a prep fidsum block.
    block_class_specs = []
    for raw in raws:
        d = { }
        if isinstance(raw, mrs_data_raw_fidsum.DataRawFidsum):
            d["prep"] = block_prep_fidsum.BlockPrepFidsum
        block_class_specs.append(d)

    f = lambda raw, block_classes: mrs_dataset.dataset_from_raw(raw, block_classes, zero_fill_multiplier)
    datasets = map(f, raws, block_class_specs)

    if datasets:

        if open_dataset is not None:
            open_dataset.blocks['raw'].set_associated_datasets([datasets[0], ])

        return datasets[0], open_dataset

    else:
        return None, open_dataset

#------------------------------------------------------------------------------
# Create server

class MyXmlRpcServer(SimpleXMLRPCServer):
    quit = False

    def serve_until_quit(self):
        self.quit = False
        while not self.quit:
            print "MyXmlRpcServer: handling next request"
            self.handle_request()
        print "MyXmlRpcServer: self.quit set, exiting..."

address = "127.0.0.1"
port    = 8080

server = MyXmlRpcServer((address, port), logRequests=True, allow_none=True)
server.register_introspection_functions()

rh = ListenRequestHandler(kill_server, verbose=True)

server.register_instance(rh)
server.register_function(kill_server)

print "listener is listening..."
server.serve_until_quit()
print "listener is exiting."

# # Run the server's main loop
# try:
#     print 'Use Control-C to exit'
#     server.serve_forever()
#
# except KeyboardInterrupt:
#     print 'Exiting'

#-------------------------------------
# Here starts another example from
#  https://pymotw.com/2/xmlrpclib/

# from SimpleXMLRPCServer import SimpleXMLRPCServer
# from xmlrpclib import Binary
# import datetime
#
# server = SimpleXMLRPCServer(('localhost', 9000), logRequests=True, allow_none=True)
# server.register_introspection_functions()
# server.register_multicall_functions()
#
# class ExampleService:
#
#     def ping(self):
#         """Simple function to respond when called to demonstrate connectivity."""
#         return True
#
#     def now(self):
#         """Returns the server current date and time."""
#         return datetime.datetime.now()
#
#     def show_type(self, arg):
#         """Illustrates how types are passed in and out of server methods.
#
#         Accepts one argument of any type.
#         Returns a tuple with string representation of the value,
#         the name of the type, and the value itself.
#         """
#         return (str(arg), str(type(arg)), arg)
#
#     def raises_exception(self, msg):
#         "Always raises a RuntimeError with the message passed in"
#         raise RuntimeError(msg)
#
#     def send_back_binary(self, bin):
#         "Accepts single Binary argument, unpacks and repacks it to return it"
#         data = bin.data
#         response = Binary(data)
#         return response
#
# server.register_instance(ExampleService())
#
# try:
#     print 'Use Control-C to exit'
#     server.serve_forever()
# except KeyboardInterrupt:
#     print 'Exiting'

# Here is my research on how to change list of floats to [nave,ncha,npts] for mrs_data_raw
#
# bob = [[[1,1,1,1],[2,2,2,2],[3,3,3,3]],[[4,4,4,4],[5,5,5,5],[6,6,6,6]]]
# bob1 = np.array(bob)
# bob1.shape ===> (2L, 3L, 4L)
# bob ===>
# array([[[1, 1, 1, 1],
#         [2, 2, 2, 2],
#         [3, 3, 3, 3]],
#
#        [[4, 4, 4, 4],
#         [5, 5, 5, 5],
#         [6, 6, 6, 6]]])
# bob1[1,0,:] = [4,4,4,4]
#
# tom = [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6]
# tom1 = np.array(tom)
# tom ===> array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6])
# tom1.shape = 2,3,4
# tom1 ===>
# array([[[1, 1, 1, 1],
#         [2, 2, 2, 2],
#         [3, 3, 3, 3]],
#
#        [[4, 4, 4, 4],
#         [5, 5, 5, 5],
#         [6, 6, 6, 6]]])