"""
Impulse reponse-related code
"""



import numpy as np
import numpy.linalg as la
import scipy.linalg as L

from scipy import stats

from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools.tools import chain_dot
#from statsmodels.tsa.api import VAR

import statsmodels.tsa.tsatools as tsa
import statsmodels.tsa.vector_ar.plotting as plotting
import statsmodels.tsa.vector_ar.util as util

mat = np.array

class BaseIRAnalysis(object):
    """
    Base class for plotting and computing IRF-related statistics, want to be
    able to handle known and estimated processes
    """

    def __init__(self, model, P=None, periods=10, order=None, svar=False):
        self.model = model
        self.periods = periods
        self.neqs, self.lags, self.T = model.neqs, model.k_ar, model.nobs

        self.order = order

        if P is None:
            sigma = model.sigma_u

            # TODO, may be difficult at the moment
            # if order is not None:
            #     indexer = [model.get_eq_index(name) for name in order]
            #     sigma = sigma[:, indexer][indexer, :]

            #     if sigma.shape != model.sigma_u.shape:
            #         raise ValueError('variable order is wrong length')

            P = la.cholesky(sigma)

        self.P = P

        self.svar = svar

        self.irfs = model.ma_rep(periods)
        if svar:
            self.svar_irfs = model.svar_ma_rep(periods, P=P)
        else:
            self.orth_irfs = model.orth_ma_rep(periods)

        self.cum_effects = self.irfs.cumsum(axis=0)
        if svar:
            self.svar_cum_effects = self.svar_irfs.cumsum(axis=0)
        else:
            self.orth_cum_effects = self.orth_irfs.cumsum(axis=0)

        self.lr_effects = model.long_run_effects()
        if svar:
            self.svar_lr_effects = np.dot(model.long_run_effects(), P)
        else:
            self.orth_lr_effects = np.dot(model.long_run_effects(), P)


        # auxiliary stuff
        self._A = util.comp_matrix(model.coefs)

    def cov(self, *args, **kwargs):
        raise NotImplementedError

    def cum_effect_cov(self, *args, **kwargs):
        raise NotImplementedError

    def plot(self, orth=False, impulse=None, response=None,
             signif=0.05, plot_params=None, subplot_params=None,
             plot_stderr=True, stderr_type='asym', repl=1000,
             seed=None, component=None):
        """
        Plot impulse responses

        Parameters
        ----------
        orth : bool, default False
            Compute orthogonalized impulse responses
        impulse : string or int
            variable providing the impulse
        response : string or int
            variable affected by the impulse
        signif : float (0 < signif < 1)
            Significance level for error bars, defaults to 95% CI
        subplot_params : dict
            To pass to subplot plotting funcions. Example: if fonts are too big,
            pass {'fontsize' : 8} or some number to your taste.
        plot_params : dict

        plot_stderr: bool, default True
            Plot standard impulse response error bands
        stderr_type: string
            'asym': default, computes asymptotic standard errors
            'mc': monte carlo standard errors (use rpl)
        repl: int, default 1000
            Number of replications for Monte Carlo and Sims-Zha standard errors
        seed: int
            np.random.seed for Monte Carlo replications
        component: array or vector of principal component indices
        """
        periods = self.periods
        model = self.model
        svar = self.svar

        if orth and svar:
            raise ValueError("For SVAR system, set orth=False")

        if orth:
            title = 'Impulse responses (orthogonalized)'
            irfs = self.orth_irfs
        elif svar:
            title = 'Impulse responses (structural)'
            irfs = self.svar_irfs
        else:
            title = 'Impulse responses'
            irfs = self.irfs

        if plot_stderr == False:
            stderr = None

        elif stderr_type not in ['asym', 'mc', 'sz1', 'sz2','sz3']:
            raise ValueError("Error type must be either 'asym', 'mc','sz1','sz2', or 'sz3'")
        else:
            if stderr_type == 'asym':
                stderr = self.cov(orth=orth)
            if stderr_type == 'mc':
                stderr = self.errband_mc(orth=orth, svar=svar,
                                         repl=repl, signif=signif,
                                         seed=seed)
            if stderr_type == 'sz1':
                stderr = self.err_band_sz1(orth=orth, svar=svar,
                                           repl=repl, signif=signif,
                                           seed=seed,
                                           component=component)
            if stderr_type == 'sz2':
                stderr = self.err_band_sz2(orth=orth, svar=svar,
                                           repl=repl, signif=signif,
                                           seed=seed,
                                           component=component)
            if stderr_type == 'sz3':
                stderr = self.err_band_sz3(orth=orth, svar=svar,
                                           repl=repl, signif=signif,
                                           seed=seed,
                                           component=component)

        plotting.irf_grid_plot(irfs, stderr, impulse, response,
                               self.model.names, title, signif=signif,
                               subplot_params=subplot_params,
                               plot_params=plot_params, stderr_type=stderr_type)

    def plot_cum_effects(self, orth=False, impulse=None, response=None,
                         signif=0.05, plot_params=None,
                         subplot_params=None, plot_stderr=True,
                         stderr_type='asym', repl=1000, seed=None):
        """
        Plot cumulative impulse response functions

        Parameters
        ----------
        orth : bool, default False
            Compute orthogonalized impulse responses
        impulse : string or int
            variable providing the impulse
        response : string or int
            variable affected by the impulse
        signif : float (0 < signif < 1)
            Significance level for error bars, defaults to 95% CI
        subplot_params : dict
            To pass to subplot plotting funcions. Example: if fonts are too big,
            pass {'fontsize' : 8} or some number to your taste.
        plot_params : dict

        plot_stderr: bool, default True
            Plot standard impulse response error bands
        stderr_type: string
            'asym': default, computes asymptotic standard errors
            'mc': monte carlo standard errors (use rpl)
        repl: int, default 1000
            Number of replications for monte carlo standard errors
        seed: int
            np.random.seed for Monte Carlo replications

        """

        if orth:
            title = 'Cumulative responses responses (orthogonalized)'
            cum_effects = self.orth_cum_effects
            lr_effects = self.orth_lr_effects
        else:
            title = 'Cumulative responses'
            cum_effects = self.cum_effects
            lr_effects = self.lr_effects

        if stderr_type not in ['asym', 'mc']:
            raise TypeError
        else:
            if stderr_type == 'asym':
                stderr = self.cum_effect_cov(orth=orth)
            if stderr_type == 'mc':
                stderr = self.cum_errband_mc(orth=orth, repl=repl,
                                                signif=signif, seed=seed)
        if not plot_stderr:
            stderr = None

        plotting.irf_grid_plot(cum_effects, stderr, impulse, response,
                               self.model.names, title, signif=signif,
                               hlines=lr_effects, subplot_params=subplot_params,
                               plot_params=plot_params, stderr_type=stderr_type)

class IRAnalysis(BaseIRAnalysis):
    """
    Impulse response analysis class. Computes impulse responses, asymptotic
    standard errors, and produces relevant plots

    Parameters
    ----------
    model : VAR instance

    Notes
    -----
    Using Lutkepohl (2005) notation
    """
    def __init__(self, model, P=None, periods=10, order=None, svar=False):
        BaseIRAnalysis.__init__(self, model, P=P, periods=periods,
                                order=order, svar=svar)

        self.cov_a = model._cov_alpha
        self.cov_sig = model._cov_sigma

        # memoize dict for G matrix function
        self._g_memo = {}

    def cov(self, orth=False):
        """
        Compute asymptotic standard errors for impulse response coefficients

        Notes
        -----
        Lutkepohl eq 3.7.5

        Returns
        -------
        """
        if orth:
            return self._orth_cov()

        covs = self._empty_covm(self.periods + 1)
        covs[0] = np.zeros((self.neqs ** 2, self.neqs ** 2))
        for i in range(1, self.periods + 1):
            Gi = self.G[i - 1]
            covs[i] = chain_dot(Gi, self.cov_a, Gi.T)

        return covs

    def errband_mc(self, orth=False, svar=False, repl=1000,
                   signif=0.05, seed=None, burn=100):
        """
        IRF Monte Carlo integrated error bands
        """
        model = self.model
        periods = self.periods
        if svar == True:
            return model.sirf_errband_mc(orth=orth, repl=repl, T=periods,
                                        signif=signif, seed=seed,
                                        burn=burn, cum=False)
        else:
            return model.irf_errband_mc(orth=orth, repl=repl, T=periods,
                                        signif=signif, seed=seed,
                                        burn=burn, cum=False)
    def err_band_sz1(self, orth=False, svar=False, repl=1000,
                     signif=0.05, seed=None, burn=100, component=None):
        """
        IRF Sims-Zha error band method 1. Assumes symmetric error bands around
        mean.

        Parameters
        ----------
        orth : bool, default False
            Compute orthogonalized impulse responses
        repl : int, default 1000
            Number of MC replications
        signif : float (0 < signif < 1)
            Significance level for error bars, defaults to 95% CI
        seed : int, default None
            np.random seed
        burn : int, default 100
            Number of initial simulated obs to discard
        component : neqs x neqs array, default to largest for each
            Index of column of eigenvector/value to use for each error band
            Note: period of impulse (t=0) is not included when computing
            principle component

        References
        ----------
        Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse
        Response". Econometrica 67: 1113-1155.
        """

        model = self.model
        periods = self.periods
        if orth:
            irfs = self.orth_irfs
        elif svar:
            irfs = self.svar_irfs
        else:
            irfs = self.irfs
        neqs = self.neqs
        irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed,
                                   burn=100)
        q = util.norm_signif_level(signif)

        W, eigva, k =self._eigval_decomp_SZ(irf_resim)

        if component != None:
            if np.shape(component) != (neqs,neqs):
                raise ValueError("Component array must be " + str(neqs) + " x " + str(neqs))
            if np.argmax(component) >= neqs*periods:
                raise ValueError("Atleast one of the components does not exist")
            else:
                k = component

        # here take the kth column of W, which we determine by finding the largest eigenvalue of the covaraince matrix
        lower = np.copy(irfs)
        upper = np.copy(irfs)
        for i in range(neqs):
            for j in range(neqs):
                lower[1:,i,j] = irfs[1:,i,j] + W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]])
                upper[1:,i,j] = irfs[1:,i,j] - W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]])


        return lower, upper

    def err_band_sz2(self, orth=False, repl=1000, signif=0.05,
                     seed=None, burn=100, component=None):
        """
        IRF Sims-Zha error band method 2.

        This method Does not assume symmetric error bands around mean.

        Parameters
        ----------
        orth : bool, default False
            Compute orthogonalized impulse responses
        repl : int, default 1000
            Number of MC replications
        signif : float (0 < signif < 1)
            Significance level for error bars, defaults to 95% CI
        seed : int, default None
            np.random seed
        burn : int, default 100
            Number of initial simulated obs to discard
        component : neqs x neqs array, default to largest for each
            Index of column of eigenvector/value to use for each error band
            Note: period of impulse (t=0) is not included when computing
            principle component

        References
        ----------
        Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse
        Response". Econometrica 67: 1113-1155.
        """
        model = self.model
        periods = self.periods
        if orth:
            irfs = self.orth_irfs
        elif svar:
            irfs = self.svar_irfs
        else:
            irfs = self.irfs
        neqs = self.neqs
        irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed,
                                   burn=100)

        W, eigva, k = self._eigval_decomp_SZ(irf_resim)

        if component != None:
            if np.shape(component) != (neqs,neqs):
                raise ValueError("Component array must be " + str(neqs) + " x " + str(neqs))
            if np.argmax(component) >= neqs*periods:
                raise ValueError("Atleast one of the components does not exist")
            else:
                k = component

        gamma = np.zeros((repl, periods+1, neqs, neqs))
        for p in range(repl):
            for i in range(neqs):
                for j in range(neqs):
                    gamma[p,1:,i,j] = W[i,j,k[i,j],:] * irf_resim[p,1:,i,j]

        gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles
        indx = round(signif/2*repl)-1,round((1-signif/2)*repl)-1

        lower = np.copy(irfs)
        upper = np.copy(irfs)
        for i in range(neqs):
            for j in range(neqs):
                lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j]
                upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j]

        return lower, upper

    def err_band_sz3(self, orth=False, repl=1000, signif=0.05,
                     seed=None, burn=100, component=None):
        """
        IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean.

        Parameters
        ----------
        orth : bool, default False
            Compute orthogonalized impulse responses
        repl : int, default 1000
            Number of MC replications
        signif : float (0 < signif < 1)
            Significance level for error bars, defaults to 95% CI
        seed : int, default None
            np.random seed
        burn : int, default 100
            Number of initial simulated obs to discard
        component : vector length neqs, default to largest for each
            Index of column of eigenvector/value to use for each error band
            Note: period of impulse (t=0) is not included when computing
            principle component

        References
        ----------
        Sims, Christopher A., and Tao Zha. 1999. "Error Bands for Impulse
        Response". Econometrica 67: 1113-1155.
        """

        model = self.model
        periods = self.periods
        if orth:
            irfs = self.orth_irfs
        elif svar:
            irfs = self.svar_irfs
        else:
            irfs = self.irfs
        neqs = self.neqs
        irf_resim = model.irf_resim(orth=orth, repl=repl, T=periods, seed=seed,
                                   burn=100)
        stack = np.zeros((neqs, repl, periods*neqs))

        #stack left to right, up and down

        for p in range(repl):
            for i in range(neqs):
                stack[i, p,:] = np.ravel(irf_resim[p,1:,:,i].T)

        stack_cov=np.zeros((neqs, periods*neqs, periods*neqs))
        W = np.zeros((neqs, periods*neqs, periods*neqs))
        eigva = np.zeros((neqs, periods*neqs))
        k = np.zeros((neqs))

        if component != None:
            if np.size(component) != (neqs):
                raise ValueError("Component array must be of length " + str(neqs))
            if np.argmax(component) >= neqs*periods:
                raise ValueError("Atleast one of the components does not exist")
            else:
                k = component

        #compute for eigen decomp for each stack
        for i in range(neqs):
            stack_cov[i] = np.cov(stack[i],rowvar=0)
            W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i])

        gamma = np.zeros((repl, periods+1, neqs, neqs))
        for p in range(repl):
            c=0
            for j in range(neqs):
                for i in range(neqs):
                        gamma[p,1:,i,j] = W[j,k[j],i*periods:(i+1)*periods] * irf_resim[p,1:,i,j]
                        if i == neqs-1:
                            gamma[p,1:,i,j] = W[j,k[j],i*periods:] * irf_resim[p,1:,i,j]

        gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles
        indx = round(signif/2*repl)-1,round((1-signif/2)*repl)-1

        lower = np.copy(irfs)
        upper = np.copy(irfs)
        for i in range(neqs):
            for j in range(neqs):
                lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j]
                upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j]

        return lower, upper

    def _eigval_decomp_SZ(self, irf_resim):
        """
        Returns
        -------
        W: array of eigenvectors
        eigva: list of eigenvalues
        k: matrix indicating column # of largest eigenvalue for each c_i,j

        """
        neqs = self.neqs
        periods = self.periods

        cov_hold = np.zeros((neqs, neqs, periods, periods))
        for i in range(neqs):
            for j in range(neqs):
                cov_hold[i,j,:,:] = np.cov(irf_resim[:,1:,i,j],rowvar=0)

        W = np.zeros((neqs, neqs, periods, periods))
        eigva = np.zeros((neqs, neqs, periods, 1))
        k = np.zeros((neqs, neqs))

        for i in range(neqs):
            for j in range(neqs):
                W[i,j,:,:], eigva[i,j,:,0], k[i,j] = util.eigval_decomp(cov_hold[i,j,:,:])
        return W, eigva, k

    @cache_readonly
    def G(self):
        # Gi matrices as defined on p. 111

        K = self.neqs

        # nlags = self.model.p
        # J = np.hstack((np.eye(K),) + (np.zeros((K, K)),) * (nlags - 1))

        def _make_g(i):
            # p. 111 Lutkepohl
            G = 0.
            for m in range(i):
                # be a bit cute to go faster
                idx = i - 1 - m
                if idx in self._g_memo:
                    apow = self._g_memo[idx]
                else:
                    apow = la.matrix_power(self._A.T, idx)
                    # apow = np.dot(J, apow)
                    apow = apow[:K]
                    self._g_memo[idx] = apow

                # take first K rows
                piece = np.kron(apow, self.irfs[m])
                G = G + piece

            return G

        return [_make_g(i) for i in range(1, self.periods + 1)]

    def _orth_cov(self):
        # Lutkepohl 3.7.8

        Ik = np.eye(self.neqs)
        PIk = np.kron(self.P.T, Ik)
        H = self.H

        covs = self._empty_covm(self.periods + 1)
        for i in range(self.periods + 1):
            if i == 0:
                apiece = 0
            else:
                Ci = np.dot(PIk, self.G[i-1])
                apiece = chain_dot(Ci, self.cov_a, Ci.T)

            Cibar = np.dot(np.kron(Ik, self.irfs[i]), H)
            bpiece = chain_dot(Cibar, self.cov_sig, Cibar.T) / self.T

            # Lutkepohl typo, cov_sig correct
            covs[i] = apiece + bpiece

        return covs

    def cum_effect_cov(self, orth=False):
        """
        Compute asymptotic standard errors for cumulative impulse response
        coefficients

        Parameters
        ----------
        orth : boolean

        Notes
        -----
        eq. 3.7.7 (non-orth), 3.7.10 (orth)

        Returns
        -------

        """
        Ik = np.eye(self.neqs)
        PIk = np.kron(self.P.T, Ik)

        F = 0.
        covs = self._empty_covm(self.periods + 1)
        for i in range(self.periods + 1):
            if i > 0:
                F = F + self.G[i - 1]

            if orth:
                if i == 0:
                    apiece = 0
                else:
                    Bn = np.dot(PIk, F)
                    apiece = chain_dot(Bn, self.cov_a, Bn.T)

                Bnbar = np.dot(np.kron(Ik, self.cum_effects[i]), self.H)
                bpiece = chain_dot(Bnbar, self.cov_sig, Bnbar.T) / self.T

                covs[i] = apiece + bpiece
            else:
                if i == 0:
                    covs[i] = np.zeros((self.neqs**2, self.neqs**2))
                    continue

                covs[i] = chain_dot(F, self.cov_a, F.T)

        return covs

    def cum_errband_mc(self, orth=False, repl=1000,
                          signif=0.05, seed=None, burn=100):
        """
        IRF Monte Carlo integrated error bands of cumulative effect
        """
        model = self.model
        periods = self.periods
        return model.irf_errband_mc(orth=orth, repl=repl,
                                    T=periods, signif=signif, seed=seed, burn=burn, cum=True)

    def lr_effect_cov(self, orth=False):
        """
        Returns
        -------

        """
        lre = self.lr_effects
        Finfty = np.kron(np.tile(lre.T, self.lags), lre)
        Ik = np.eye(self.neqs)

        if orth:
            Binf = np.dot(np.kron(self.P.T, np.eye(self.neqs)), Finfty)
            Binfbar = np.dot(np.kron(Ik, lre), self.H)

            return (chain_dot(Binf, self.cov_a, Binf.T) +
                    chain_dot(Binfbar, self.cov_sig, Binfbar.T))
        else:
            return chain_dot(Finfty, self.cov_a, Finfty.T)

    def stderr(self, orth=False):
        return np.array([tsa.unvec(np.sqrt(np.diag(c)))
                         for c in self.cov(orth=orth)])

    def cum_effect_stderr(self, orth=False):
        return np.array([tsa.unvec(np.sqrt(np.diag(c)))
                         for c in self.cum_effect_cov(orth=orth)])

    def lr_effect_stderr(self, orth=False):
        cov = self.lr_effect_cov(orth=orth)
        return tsa.unvec(np.sqrt(np.diag(cov)))

    def _empty_covm(self, periods):
        return np.zeros((periods, self.neqs ** 2, self.neqs ** 2),
                        dtype=float)

    @cache_readonly
    def H(self):
        k = self.neqs
        Lk = tsa.elimination_matrix(k)
        Kkk = tsa.commutation_matrix(k, k)
        Ik = np.eye(k)

        # B = chain_dot(Lk, np.eye(k**2) + commutation_matrix(k, k),
        #               np.kron(self.P, np.eye(k)), Lk.T)

        # return np.dot(Lk.T, L.inv(B))

        B = chain_dot(Lk,
                      np.dot(np.kron(Ik, self.P), Kkk) + np.kron(self.P, Ik),
                      Lk.T)

        return np.dot(Lk.T, L.inv(B))

    def fevd_table(self):
        pass


