import warnings
import os

import nibabel as nb
import numpy as np

pylab_available = True
try:
    import pylab
except:
    pylab_available = False

from ...utils.misc import package_check

have_nipy = True
try:
    package_check('nipy')
except Exception, e:
    have_nipy = False
    warnings.warn('nipy not installed')
else:
    import nipy.modalities.fmri.design_matrix as dm
    import nipy.labs.glm as GLM

if have_nipy:
    try:
        BlockParadigm = dm.BlockParadigm
    except AttributeError:
        from nipy.modalities.fmri.experimental_paradigm import BlockParadigm

from ..base import (BaseInterface, TraitedSpec, traits, File, OutputMultiPath,
                    BaseInterfaceInputSpec, isdefined)

class FitGLMInputSpec(BaseInterfaceInputSpec):
    session_info = traits.List(minlen=1, maxlen=1, exists=True, desc='Session specific information generated by ``modelgen.SpecifyModel``, FitGLM\
    does not  support multiple runs uless they are concatenated (see SpecifyModel options)')
    hrf_model = traits.Enum('Canonical', 'Canonical With Derivative', 'FIR',
desc="that specifies the hemodynamic reponse function it can be 'Canonical', 'Canonical With Derivative' or 'FIR'",
usedefault=True)
    drift_model = traits.Enum("Cosine", "Polynomial", "Blank",
desc = "string that specifies the desired drift model, to be chosen among 'Polynomial', 'Cosine', 'Blank'",
usedefault=True)
    TR = traits.Float(mandatory=True)
    model = traits.Enum("ar1", "spherical", desc="autoregressive mode is available only for the kalman method", usedefault=True)
    method = traits.Enum("kalman", "ols", desc="method to fit the model, ols or kalma; kalman is more time consuming but it supports autoregressive model", nusedefault=True)
    mask = traits.File(exists=True, desc="restrict the fitting only to the region defined by this mask")
    normalize_design_matrix = traits.Bool(False, desc="normalize (zscore) the regressors before fitting", usedefault=True)
    save_residuals = traits.Bool(False, usedefault=True)
    plot_design_matrix = traits.Bool(False, usedefault=True)

class FitGLMOutputSpec(TraitedSpec):
    beta = File(exists=True)
    nvbeta = traits.Any()
    s2 = File(exists=True)
    dof = traits.Any()
    constants = traits.Any()
    axis = traits.Any()
    reg_names = traits.List()
    residuals = traits.File()
    a = File(exists=True)

class FitGLM(BaseInterface):
    '''
    Fit GLM model based on the specified design. Supports only single or concatenated runs.
    '''
    input_spec = FitGLMInputSpec
    output_spec = FitGLMOutputSpec

    def _run_interface(self, runtime):

        session_info = self.inputs.session_info

        functional_runs = self.inputs.session_info[0]['scans']
        if isinstance(functional_runs, str):
            functional_runs = [functional_runs]
        nii = nb.load(functional_runs[0])
        data = nii.get_data()


        if isdefined(self.inputs.mask):
            mask = nb.load(self.inputs.mask).get_data() > 0
        else:
            mask = np.ones(nii.shape[:3]) == 1

        timeseries = data.copy()[mask,:]
        del data

        for functional_run in functional_runs[1:]:
            nii = nb.load(functional_run)
            data = nii.get_data()
            npdata = data.copy()
            del data
            timeseries = np.concatenate((timeseries,npdata[mask,:]), axis=1)
            del npdata

        nscans = timeseries.shape[1]

        if 'hpf' in session_info[0].keys():
            hpf = session_info[0]['hpf']
            drift_model=self.inputs.drift_model
        else:
            hpf=0
            drift_model = "Blank"

        reg_names = []
        for reg in session_info[0]['regress']:
            reg_names.append(reg['name'])

        reg_vals = np.zeros((nscans,len(reg_names)))
        for i in range(len(reg_names)):
            reg_vals[:,i] = np.array(session_info[0]['regress'][i]['val']).reshape(1,-1)


        frametimes= np.linspace(0, (nscans-1)*self.inputs.TR, nscans)

        conditions = []
        onsets = []
        duration = []

        for i,cond in enumerate(session_info[0]['cond']):
            onsets += cond['onset']
            conditions += [cond['name']]*len(cond['onset'])
            if len(cond['duration']) == 1:
                duration += cond['duration']*len(cond['onset'])
            else:
                duration += cond['duration']


        if conditions:
            paradigm =  BlockParadigm(con_id=conditions, onset=onsets, duration=duration)
        else:
            paradigm = None
        design_matrix, self._reg_names = dm.dmtx_light(frametimes, paradigm, drift_model=drift_model, hfcut=hpf,
               hrf_model=self.inputs.hrf_model,
               add_regs=reg_vals,
               add_reg_names=reg_names
               )
        if self.inputs.normalize_design_matrix:
            for i in range(len(self._reg_names)-1):
                design_matrix[:,i] = (design_matrix[:,i]-design_matrix[:,i].mean())/design_matrix[:,i].std()

        if self.inputs.plot_design_matrix:
            if pylab_available:
                pylab.pcolor(design_matrix)
                pylab.savefig("design_matrix.pdf")
                pylab.close()
                pylab.clf()
            else:
                Exception('Pylab not available for saving design matrix image')

        glm = GLM.glm()
        glm.fit(timeseries.T, design_matrix, method=self.inputs.method, model=self.inputs.model)


        self._beta_file = os.path.abspath("beta.nii")
        beta = np.zeros(mask.shape + (glm.beta.shape[0],))
        beta[mask,:] = glm.beta.T
        nb.save(nb.Nifti1Image(beta, nii.get_affine()), self._beta_file)

        self._s2_file = os.path.abspath("s2.nii")
        s2 = np.zeros(mask.shape)
        s2[mask] = glm.s2
        nb.save(nb.Nifti1Image(s2, nii.get_affine()), self._s2_file)

        if self.inputs.save_residuals:
            explained = np.dot(design_matrix,glm.beta)
            residuals = np.zeros(mask.shape + (nscans,))
            residuals[mask,:] = timeseries - explained.T
            self._residuals_file = os.path.abspath("residuals.nii")
            nb.save(nb.Nifti1Image(residuals, nii.get_affine()), self._residuals_file)

        self._nvbeta = glm.nvbeta
        self._dof = glm.dof
        self._constants = glm._constants
        self._axis = glm._axis
        if self.inputs.model == "ar1":
            self._a_file = os.path.abspath("a.nii")
            a = np.zeros(mask.shape)
            a[mask] = glm.a.squeeze()
            nb.save(nb.Nifti1Image(a, nii.get_affine()), self._a_file)
        self._model = glm.model
        self._method = glm.method

        return runtime

    def _list_outputs(self):
        outputs = self._outputs().get()
        outputs["beta"] = self._beta_file
        outputs["nvbeta"] = self._nvbeta
        outputs["s2"] = self._s2_file
        outputs["dof"] = self._dof
        outputs["constants"] = self._constants
        outputs["axis"] = self._axis
        outputs["reg_names"] = self._reg_names
        if self.inputs.model == "ar1":
            outputs["a"] = self._a_file
        if self.inputs.save_residuals:
            outputs["residuals"] = self._residuals_file
        return outputs

class EstimateContrastInputSpec(BaseInterfaceInputSpec):
    contrasts = traits.List(
        traits.Either(traits.Tuple(traits.Str,
                                   traits.Enum('T'),
                                   traits.List(traits.Str),
                                   traits.List(traits.Float)),
                      traits.Tuple(traits.Str,
                                   traits.Enum('T'),
                                   traits.List(traits.Str),
                                   traits.List(traits.Float),
                                   traits.List(traits.Float)),
                      traits.Tuple(traits.Str,
                                   traits.Enum('F'),
                                   traits.List(traits.Either(traits.Tuple(traits.Str,
                                                                          traits.Enum('T'),
                                                                          traits.List(traits.Str),
                                                                          traits.List(traits.Float)),
                                                             traits.Tuple(traits.Str,
                                                                          traits.Enum('T'),
                                                                          traits.List(traits.Str),
                                                                          traits.List(traits.Float),
                                                                          traits.List(traits.Float)))))),
        desc="""List of contrasts with each contrast being a list of the form:
            [('name', 'stat', [condition list], [weight list], [session list])]. if
            session list is None or not provided, all sessions are used. For F
            contrasts, the condition list should contain previously defined
            T-contrasts.""", mandatory=True)
    beta = File(exists=True, desc="beta coefficients of the fitted model",mandatory=True)
    nvbeta = traits.Any(mandatory=True)
    s2 = File(exists=True, desc="squared variance of the residuals",mandatory=True)
    dof = traits.Any(desc="degrees of freedom", mandatory=True)
    constants = traits.Any(mandatory=True)
    axis = traits.Any(mandatory=True)
    reg_names = traits.List(mandatory=True)
    mask = traits.File(exists=True)

class EstimateContrastOutputSpec(TraitedSpec):
    stat_maps = OutputMultiPath(File(exists=True))
    z_maps = OutputMultiPath(File(exists=True))
    p_maps = OutputMultiPath(File(exists=True))

class EstimateContrast(BaseInterface):
    '''
    Estimate contrast of a fitted model.
    '''
    input_spec = EstimateContrastInputSpec
    output_spec = EstimateContrastOutputSpec

    def _run_interface(self, runtime):

        beta_nii = nb.load(self.inputs.beta)
        if isdefined(self.inputs.mask):
            mask = nb.load(self.inputs.mask).get_data() > 0
        else:
            mask = np.ones(beta_nii.shape[:3]) == 1


        glm = GLM.glm()
        nii = nb.load(self.inputs.beta)
        glm.beta = beta_nii.get_data().copy()[mask,:].T
        glm.nvbeta = self.inputs.nvbeta
        glm.s2 = nb.load(self.inputs.s2).get_data().copy()[mask]
        glm.dof = self.inputs.dof
        glm._axis = self.inputs.axis
        glm._constants = self.inputs.constants

        reg_names = self.inputs.reg_names

        self._stat_maps = []
        self._p_maps = []
        self._z_maps = []
        for contrast_def in self.inputs.contrasts:
            name = contrast_def[0]
            _ = contrast_def[1]
            contrast = np.zeros(len(reg_names))

            for i, reg_name in enumerate(reg_names):
                if reg_name in contrast_def[2]:
                    idx = contrast_def[2].index(reg_name)
                    contrast[i] = contrast_def[3][idx]

            est_contrast = glm.contrast(contrast)

            stat_map = np.zeros(mask.shape)
            stat_map[mask] = est_contrast.stat().T
            stat_map_file = os.path.abspath(name + "_stat_map.nii")
            nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
            self._stat_maps.append(stat_map_file)

            p_map = np.zeros(mask.shape)
            p_map[mask] = est_contrast.pvalue().T
            p_map_file = os.path.abspath(name + "_p_map.nii")
            nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
            self._p_maps.append(p_map_file)

            z_map = np.zeros(mask.shape)
            z_map[mask] = est_contrast.zscore().T
            z_map_file = os.path.abspath(name + "_z_map.nii")
            nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
            self._z_maps.append(z_map_file)

        return runtime

    def _list_outputs(self):
        outputs = self._outputs().get()
        outputs["stat_maps"] = self._stat_maps
        outputs["p_maps"] = self._p_maps
        outputs["z_maps"] = self._z_maps
        return outputs

