# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""The spm module provides basic functions for interfacing with SPM  tools."""

__docformat__ = 'restructuredtext'

# Standard library imports
import os
from copy import deepcopy

# Third-party imports
import numpy as np
from scipy.io import savemat

# Local imports
from nipype.interfaces.base import (BaseInterface, traits, isdefined,
                                    InputMultiPath, BaseInterfaceInputSpec,
                                    Directory)

from nibabel import load
from nipype.interfaces.matlab import MatlabCommand

import nipype.utils.spm_docs as sd

from ... import logging
logger = logging.getLogger('interface')


def func_is_3d(in_file):
    """Checks if input functional files are 3d."""

    if isinstance(in_file, list):
        return func_is_3d(in_file[0])
    else:
        img = load(in_file)
        shape = img.get_shape()
        if len(shape) == 3 or (len(shape) == 4 and shape[3] == 1):
            return True
        else:
            return False


def get_first_3dfile(in_files):
    if not func_is_3d(in_files):
        return None
    if isinstance(in_files[0], list):
        return in_files[0]
    return in_files


def scans_for_fname(fname):
    """Reads a nifti file and converts it to a numpy array storing
    individual nifti volumes.

    Opens images so will fail if they are not found.

    """
    if isinstance(fname, list):
        scans = np.zeros((len(fname),), dtype=object)
        for sno, f in enumerate(fname):
            scans[sno] = '%s,1' % f
        return scans
    img = load(fname)
    if len(img.get_shape()) == 3:
        return np.array(('%s,1' % fname,), dtype=object)
    else:
        n_scans = img.get_shape()[3]
        scans = np.zeros((n_scans,), dtype=object)
        for sno in range(n_scans):
            scans[sno] = '%s,%d' % (fname, sno + 1)
        return scans


def scans_for_fnames(fnames, keep4d=False, separate_sessions=False):
    """Converts a list of files to a concatenated numpy array for each
    volume.

    keep4d : boolean
        keeps the entries of the numpy array as 4d files instead of
        extracting the individual volumes.
    separate_sessions: boolean
        if 4d nifti files are being used, then separate_sessions
        ensures a cell array per session is created in the structure.

    """
    flist = None
    if not isinstance(fnames[0], list):
        if func_is_3d(fnames[0]):
            fnames = [fnames]
    if separate_sessions or keep4d:
        flist = np.zeros((len(fnames),), dtype=object)
    for i, f in enumerate(fnames):
        if separate_sessions:
            if keep4d:
                if isinstance(f, list):
                    flist[i] = np.array(f, dtype=object)
                else:
                    flist[i] = np.array([f], dtype=object)
            else:
                flist[i] = scans_for_fname(f)
        else:
            if keep4d:
                flist[i] = f
            else:
                scans = scans_for_fname(f)
                if flist is None:
                    flist = scans
                else:
                    flist = np.concatenate((flist, scans))
    return flist


class Info(object):
    """Handles SPM version information
    """
    @staticmethod
    def version(matlab_cmd=None):
        """Returns the path to the SPM directory in the Matlab path
        If path not found, returns None.

        Parameters
        ----------
        matlab_cmd : String specifying default matlab command

            default None, will look for environment variable MATLABCMD
            and use if found, otherwise falls back on MatlabCommand
            default of 'matlab -nodesktop -nosplash'

        Returns
        -------
        spm_path : string representing path to SPM directory

            returns None of path not found
        """
        if matlab_cmd is None:
            try:
                matlab_cmd = os.environ['MATLABCMD']
            except:
                matlab_cmd = 'matlab -nodesktop -nosplash'
        mlab = MatlabCommand(matlab_cmd=matlab_cmd)
        mlab.inputs.script = """
if isempty(which('spm')),
throw(MException('SPMCheck:NotFound','SPM not in matlab path'));
end;
spm_path = spm('dir');
[name, version] = spm('ver');
fprintf(1, 'NIPYPE path:%s|name:%s|release:%s', spm_path, name, version);
exit;
        """
        mlab.inputs.mfile = False
        try:
            out = mlab.run()
        except (IOError, RuntimeError), e:
            # if no Matlab at all -- exception could be raised
            # No Matlab -- no spm
            logger.debug(str(e))
            return None
        else:
            out = sd._strip_header(out.runtime.stdout)
            out_dict = {}
            for part in out.split('|'):
                key, val = part.split(':')
                out_dict[key] = val
            return out_dict


def no_spm():
    """ Checks if SPM is NOT installed
    used with nosetests skipif to skip tests
    that will fail if spm is not installed"""

    if Info.version() is None or 'NIPYPE_NO_MATLAB' in os.environ:
        return True
    else:
        return False


class SPMCommandInputSpec(BaseInterfaceInputSpec):
    matlab_cmd = traits.Str(desc='matlab command to use')
    paths = InputMultiPath(Directory(), desc='Paths to add to matlabpath')
    mfile = traits.Bool(True, desc='Run m-code using m-file',
                        usedefault=True)
    use_mcr = traits.Bool(desc='Run m-code using SPM MCR')


class SPMCommand(BaseInterface):
    """Extends `BaseInterface` class to implement SPM specific interfaces.

    WARNING: Pseudo prototype class, meant to be subclassed
    """
    input_spec = SPMCommandInputSpec

    _jobtype = 'basetype'
    _jobname = 'basename'

    _matlab_cmd = None
    _paths = None
    _use_mcr = None

    def __init__(self, **inputs):
        super(SPMCommand, self).__init__(**inputs)
        self.inputs.on_trait_change(self._matlab_cmd_update, ['matlab_cmd',
                                                              'mfile',
                                                              'paths',
                                                              'use_mcr'])
        self._check_mlab_inputs()
        self._matlab_cmd_update()

    @classmethod
    def set_mlab_paths(cls, matlab_cmd=None, paths=None, use_mcr=None):
        cls._matlab_cmd = matlab_cmd
        cls._paths = paths
        cls._use_mcr = use_mcr

    def _matlab_cmd_update(self):
        # MatlabCommand has to be created here,
        # because matlab_cmb is not a proper input
        # and can be set only during init
        self.mlab = MatlabCommand(matlab_cmd=self.inputs.matlab_cmd,
                                  mfile=self.inputs.mfile,
                                  paths=self.inputs.paths,
                                  uses_mcr=self.inputs.use_mcr)
        self.mlab.inputs.script_file = 'pyscript_%s.m' % \
            self.__class__.__name__.split('.')[-1].lower()

    @property
    def jobtype(self):
        return self._jobtype

    @property
    def jobname(self):
        return self._jobname

    def _check_mlab_inputs(self):
        if not isdefined(self.inputs.matlab_cmd) and self._matlab_cmd:
            self.inputs.matlab_cmd = self._matlab_cmd
        if not isdefined(self.inputs.paths) and self._paths:
            self.inputs.paths = self._paths
        if not isdefined(self.inputs.use_mcr) and self._use_mcr:
            self.inputs.use_mcr = self._use_mcr

    def _run_interface(self, runtime):
        """Executes the SPM function using MATLAB."""
        self.mlab.inputs.script = self._make_matlab_command(
            deepcopy(self._parse_inputs()))
        results = self.mlab.run()
        runtime.returncode = results.runtime.returncode
        if self.mlab.inputs.uses_mcr:
            if 'Skipped' in results.runtime.stdout:
                self.raise_exception(runtime)
        runtime.stdout = results.runtime.stdout
        runtime.stderr = results.runtime.stderr
        runtime.merged = results.runtime.merged
        return runtime

    def _list_outputs(self):
        """Determine the expected outputs based on inputs."""

        raise NotImplementedError

    def _format_arg(self, opt, spec, val):
        """Convert input to appropriate format for SPM."""
        if spec.is_trait_type(traits.Bool):
            return int(val)
        else:
            return val

    def _parse_inputs(self, skip=()):
        spmdict = {}
        metadata = dict(field=lambda t: t is not None)
        for name, spec in self.inputs.traits(**metadata).items():
            if skip and name in skip:
                continue
            value = getattr(self.inputs, name)
            if not isdefined(value):
                continue
            field = spec.field
            if '.' in field:
                fields = field.split('.')
                dictref = spmdict
                for f in fields[:-1]:
                    if f not in dictref.keys():
                        dictref[f] = {}
                    dictref = dictref[f]
                dictref[fields[-1]] = self._format_arg(name, spec, value)
            else:
                spmdict[field] = self._format_arg(name, spec, value)
        return [spmdict]

    def _reformat_dict_for_savemat(self, contents):
        """Encloses a dict representation within hierarchical lists.

        In order to create an appropriate SPM job structure, a Python
        dict storing the job needs to be modified so that each dict
        embedded in dict needs to be enclosed as a list element.

        Examples
        --------
        >>> a = SPMCommand()._reformat_dict_for_savemat(dict(a=1,b=dict(c=2,d=3)))
        >>> print a
        [{'a': 1, 'b': [{'c': 2, 'd': 3}]}]

        """
        newdict = {}
        try:
            for key, value in contents.items():
                if isinstance(value, dict):
                    if value:
                        newdict[key] = self._reformat_dict_for_savemat(value)
                    # if value is None, skip
                else:
                    newdict[key] = value

            return [newdict]
        except TypeError:
            print 'Requires dict input'

    def _generate_job(self, prefix='', contents=None):
        """Recursive function to generate spm job specification as a string

        Parameters
        ----------
        prefix : string
            A string that needs to get
        contents : dict
            A non-tuple Python structure containing spm job
            information gets converted to an appropriate sequence of
            matlab commands.

        """
        jobstring = ''
        if contents is None:
            return jobstring
        if isinstance(contents, list):
            for i, value in enumerate(contents):
                if prefix.endswith(")"):
                    newprefix = "%s,%d)" % (prefix[:-1], i + 1)
                else:
                    newprefix = "%s(%d)" % (prefix, i + 1)
                jobstring += self._generate_job(newprefix, value)
            return jobstring
        if isinstance(contents, dict):
            for key, value in contents.items():
                newprefix = "%s.%s" % (prefix, key)
                jobstring += self._generate_job(newprefix, value)
            return jobstring
        if isinstance(contents, np.ndarray):
            if contents.dtype == np.dtype(object):
                if prefix:
                    jobstring += "%s = {...\n" % (prefix)
                else:
                    jobstring += "{...\n"
                for i, val in enumerate(contents):
                    if isinstance(val, np.ndarray):
                        jobstring += self._generate_job(prefix=None,
                                                        contents=val)
                    elif isinstance(val, str):
                        jobstring += '\'%s\';...\n' % (val)
                    else:
                        jobstring += '%s;...\n' % str(val)
                jobstring += '};\n'
            else:
                for i, val in enumerate(contents):
                    for field in val.dtype.fields:
                        if prefix:
                            newprefix = "%s(%d).%s" % (prefix, i + 1, field)
                        else:
                            newprefix = "(%d).%s" % (i + 1, field)
                        jobstring += self._generate_job(newprefix,
                                                        val[field])
            return jobstring
        if isinstance(contents, str):
            jobstring += "%s = '%s';\n" % (prefix, contents)
            return jobstring
        jobstring += "%s = %s;\n" % (prefix, str(contents))
        return jobstring

    def _make_matlab_command(self, contents, postscript=None):
        """Generates a mfile to build job structure
        Parameters
        ----------

        contents : list
            a list of dicts generated by _parse_inputs
            in each subclass

        cwd : string
            default os.getcwd()

        Returns
        -------
        mscript : string
            contents of a script called by matlab

        """
        cwd = os.getcwd()
        mscript = """
        %% Generated by nipype.interfaces.spm
        if isempty(which('spm')),
             throw(MException('SPMCheck:NotFound','SPM not in matlab path'));
        end
        [name, ver] = spm('ver');
        fprintf('SPM version: %s Release: %s\\n',name, ver);
        fprintf('SPM path: %s\\n',which('spm'));
        spm('Defaults','fMRI');

        if strcmp(spm('ver'),'SPM8'), spm_jobman('initcfg');end\n
        """
        if self.mlab.inputs.mfile:
            if self.jobname in ['st', 'smooth', 'preproc', 'preproc8',
                                'fmri_spec', 'fmri_est', 'factorial_design',
                                'defs']:
                # parentheses
                mscript += self._generate_job('jobs{1}.%s{1}.%s(1)' %
                                              (self.jobtype, self.jobname),
                                              contents[0])
            else:
                #curly brackets
                mscript += self._generate_job('jobs{1}.%s{1}.%s{1}' %
                                              (self.jobtype, self.jobname),
                                              contents[0])
        else:
            jobdef = {'jobs': [{self.jobtype:
                                  [{self.jobname:self.reformat_dict_for_savemat
                                      (contents[0])}]}]}
            savemat(os.path.join(cwd, 'pyjobs_%s.mat' % self.jobname), jobdef)
            mscript += "load pyjobs_%s;\n\n" % self.jobname
        mscript += """
        if strcmp(spm('ver'),'SPM8'),
           jobs=spm_jobman('spm5tospm8',{jobs});
        end
        spm_jobman(\'run_nogui\',jobs);\n
        """
        if postscript is not None:
            mscript += postscript
        return mscript

    @property
    def version(self):
        version_dict = Info.version()
        if version_dict:
            return '.'.join((version_dict['name'].split('SPM')[-1],
                             version_dict['release']))
        return version_dict
