import numpy as np
from ase.io.jsonio import read_json, write_json


class STM:
    def __init__(self, atoms, symmetries=None, use_density=False):
        """Scanning tunneling microscope.

        atoms: Atoms object or filename
            Atoms to scan or name of file to read LDOS from.
        symmetries: list of int
            List of integers 0, 1, and/or 2 indicating which surface
            symmetries have been used to reduce the number of k-points
            for the DFT calculation.  The three integers correspond to
            the following three symmetry operations::

                 [-1  0]   [ 1  0]   [ 0  1]
                 [ 0  1]   [ 0 -1]   [ 1  0]

        use_density: bool
            Use the electron density instead of the LDOS.
        """

        self.use_density = use_density

        if isinstance(atoms, str):
            with open(atoms, 'r') as fd:
                self.ldos, self.bias, self.cell = read_json(fd,
                                                            always_array=False)
            self.atoms = None
        else:
            self.atoms = atoms
            self.cell = atoms.cell
            self.bias = None
            self.ldos = None
            assert not self.cell[2, :2].any() and not self.cell[:2, 2].any()

        self.symmetries = symmetries or []

    def calculate_ldos(self, bias):
        """Calculate local density of states for given bias."""
        if self.ldos is not None and bias == self.bias:
            return

        self.bias = bias

        calc = self.atoms.calc

        if self.use_density:
            self.ldos = calc.get_pseudo_density()
            return

        if bias < 0:
            emin = bias
            emax = 0.0
        else:
            emin = 0
            emax = bias

        nbands = calc.get_number_of_bands()
        weights = calc.get_k_point_weights()
        nkpts = len(weights)
        nspins = calc.get_number_of_spins()
        eigs = np.array([[calc.get_eigenvalues(k, s)
                          for k in range(nkpts)]
                         for s in range(nspins)])
        eigs -= calc.get_fermi_level()
        ldos = np.zeros(calc.get_pseudo_wave_function(0, 0, 0).shape)

        for s in range(nspins):
            for k in range(nkpts):
                for n in range(nbands):
                    e = eigs[s, k, n]
                    if emin < e < emax:
                        psi = calc.get_pseudo_wave_function(n, k, s)
                        ldos += weights[k] * (psi * np.conj(psi)).real

        if 0 in self.symmetries:
            # (x,y) -> (-x,y)
            ldos[1:] += ldos[:0:-1].copy()
            ldos[1:] *= 0.5

        if 1 in self.symmetries:
            # (x,y) -> (x,-y)
            ldos[:, 1:] += ldos[:, :0:-1].copy()
            ldos[:, 1:] *= 0.5

        if 2 in self.symmetries:
            # (x,y) -> (y,x)
            ldos += ldos.transpose((1, 0, 2)).copy()
            ldos *= 0.5

        self.ldos = ldos

    def write(self, filename):
        """Write local density of states to JSON file."""
        write_json(filename, (self.ldos, self.bias, self.cell))

    def get_averaged_current(self, bias, z):
        """Calculate avarage current at height z (in Angstrom).

        Use this to get an idea of what current to use when scanning."""

        self.calculate_ldos(bias)
        nz = self.ldos.shape[2]

        # Find grid point:
        n = z / self.cell[2, 2] * nz
        dn = n - np.floor(n)
        n = int(n) % nz

        # Average and do linear interpolation:
        return ((1 - dn) * self.ldos[:, :, n].mean() +
                dn * self.ldos[:, :, (n + 1) % nz].mean())

    def scan(self, bias, current, z0=None, repeat=(1, 1)):
        """Constant current 2-d scan.

        Returns three 2-d arrays (x, y, z) containing x-coordinates,
        y-coordinates and heights.  These three arrays can be passed to
        matplotlibs contourf() function like this:

        >>> import matplotlib.pyplot as plt
        >>> plt.contourf(x, y, z)
        >>> plt.show()

        """

        self.calculate_ldos(bias)

        L = self.cell[2, 2]
        nz = self.ldos.shape[2]
        h = L / nz

        ldos = self.ldos.reshape((-1, nz))

        heights = np.empty(ldos.shape[0])
        for i, a in enumerate(ldos):
            heights[i] = find_height(a, current, h, z0)

        s0 = heights.shape = self.ldos.shape[:2]
        heights = np.tile(heights, repeat)
        s = heights.shape

        ij = np.indices(s, dtype=float).reshape((2, -1)).T
        x, y = np.dot(ij / s0, self.cell[:2, :2]).T.reshape((2,) + s)

        return x, y, heights

    def scan2(self, bias, z, repeat=(1, 1)):
        """Constant height 2-d scan.

        Returns three 2-d arrays (x, y, I) containing x-coordinates,
        y-coordinates and currents.  These three arrays can be passed to
        matplotlibs contourf() function like this:

        >>> import matplotlib.pyplot as plt
        >>> plt.contourf(x, y, I)
        >>> plt.show()

        """

        self.calculate_ldos(bias)

        nz = self.ldos.shape[2]
        ldos = self.ldos.reshape((-1, nz))

        I = np.empty(ldos.shape[0])

        zp = z / self.cell[2, 2] * nz
        zp = int(zp) % nz

        for i, a in enumerate(ldos):
            I[i] = self.find_current(a, zp)

        s0 = I.shape = self.ldos.shape[:2]
        I = np.tile(I, repeat)
        s = I.shape

        ij = np.indices(s, dtype=float).reshape((2, -1)).T
        x, y = np.dot(ij / s0, self.cell[:2, :2]).T.reshape((2,) + s)

        # Returing scan with axes in Angstrom.
        return x, y, I

    def linescan(self, bias, current, p1, p2, npoints=50, z0=None):
        """Constant current line scan.

        Example::

            stm = STM(...)
            z = ...  # tip position
            c = stm.get_averaged_current(-1.0, z)
            stm.linescan(-1.0, c, (1.2, 0.0), (1.2, 3.0))
        """

        heights = self.scan(bias, current, z0)[2]

        p1 = np.asarray(p1, float)
        p2 = np.asarray(p2, float)
        d = p2 - p1
        s = np.dot(d, d)**0.5

        cell = self.cell[:2, :2]
        shape = np.array(heights.shape, float)
        M = np.linalg.inv(cell)
        line = np.empty(npoints)
        for i in range(npoints):
            p = p1 + i * d / (npoints - 1)
            q = np.dot(p, M) * shape
            line[i] = interpolate(q, heights)
        return np.linspace(0, s, npoints), line

    def pointcurrent(self, bias, x, y, z):
        """Current for a single x, y, z position for a given bias."""

        self.calculate_ldos(bias)

        nx = self.ldos.shape[0]
        ny = self.ldos.shape[1]
        nz = self.ldos.shape[2]

        # Find grid point:
        xp = x / np.linalg.norm(self.cell[0]) * nx
        dx = xp - np.floor(xp)
        xp = int(xp) % nx

        yp = y / np.linalg.norm(self.cell[1]) * ny
        dy = yp - np.floor(yp)
        yp = int(yp) % ny

        zp = z / np.linalg.norm(self.cell[2]) * nz
        dz = zp - np.floor(zp)
        zp = int(zp) % nz

        # 3D interpolation of the LDOS at point (x,y,z) at given bias.
        xyzldos = (((1 - dx) + (1 - dy) + (1 - dz)) * self.ldos[xp, yp, zp] +
                   dx * self.ldos[(xp + 1) % nx, yp, zp] +
                   dy * self.ldos[xp, (yp + 1) % ny, zp] +
                   dz * self.ldos[xp, yp, (zp + 1) % nz])

        return dos2current(bias, xyzldos)

    def sts(self, x, y, z, bias0, bias1, biasstep):
        """Returns the dI/dV curve for position x, y at height z (in Angstrom),
        for bias from bias0 to bias1 with step biasstep."""

        biases = np.arange(bias0, bias1+biasstep, biasstep)
        I = np.zeros(biases.shape)

        for b in np.arange(len(biases)):
            print(b, biases[b])
            I[b] = self.pointcurrent(biases[b], x, y, z)

        dIdV = np.gradient(I, biasstep)

        return biases, I, dIdV

    def find_current(self, ldos, z):
        """ Finds current for given LDOS at height z."""
        nz = self.ldos.shape[2]

        zp = z / self.cell[2, 2] * nz
        dz = zp - np.floor(zp)
        zp = int(zp) % nz

        ldosz = (1 - dz) * ldos[zp] + dz * ldos[(zp + 1) % nz]

        return dos2current(self.bias, ldosz)


def dos2current(bias, dos):
    # Borrowed from gpaw/analyse/simple_stm.py:
    # The connection between density n and current I
    # n [e/Angstrom^3] = 0.0002 sqrt(I [nA])
    # as given in Hofer et al., RevModPhys 75 (2003) 1287
    return 5000. * dos**2 * (1 if bias > 0 else -1)


def interpolate(q, heights):
    qi = q.astype(int)
    f = q - qi
    g = 1 - f
    qi %= heights.shape
    n0, m0 = qi
    n1, m1 = (qi + 1) % heights.shape
    z = (g[0] * g[1] * heights[n0, m0] +
         f[0] * g[1] * heights[n1, m0] +
         g[0] * f[1] * heights[n0, m1] +
         f[0] * f[1] * heights[n1, m1])
    return z


def find_height(ldos, current, h, z0=None):
    if z0 is None:
        n = len(ldos) - 2
    else:
        n = int(z0 / h)
    while n >= 0:
        if ldos[n] > current:
            break
        n -= 1
    else:
        return 0.0

    c2, c1 = ldos[n:n + 2]
    return (n + 1 - (current - c1) / (c2 - c1)) * h


def delta(biases, bias, width):
    """Return a delta-function centered at 'bias'"""
    x = -((biases - bias) / width)**2
    return np.exp(x) / (np.sqrt(np.pi) * width)
