#!/usr/bin/env python3
#
# Calculates the covariance from neutron events.
#
# @author Tobias Weber <tweber@ill.fr>
# @date 30-mar-2019
# @license GNU GPLv3
#
# @descr This tool comes from Takin 2: https://dx.doi.org/10.5281/zenodo.4117437
# @descr For a good explanation of the covariance matrix method, see T. Arens et al., "Mathematik", 2015, ISBN: 978-3-642-44919-2, pp. 795 and 1372.
# @descr Reimplements the functionality of https://github.com/McStasMcXtrace/McCode/blob/master/tools/Legacy-Perl/mcresplot.pl
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#

import os
import tas

try:
	import numpy as np
	import numpy.linalg as la
except ImportError:
	print("Numpy could not be imported!")
	exit(-1)


try:
	np.set_printoptions(
		precision = 4,
		floatmode = "fixed")
except TypeError:
	print("Warning: Numpy print options could not be set.")


options = {
	"verbose" : True,		# console outputs
	"plot_results" : True,		# show plot window
	"plot_neutrons" : True,		# also plot neutron events
	"centre_on_Q" : False,		# centre plots on Q or zero?
	"ellipse_points" : 128,		# number of points to draw ellipses
	"symsize" : 32.,
	"dpi" : 600,
	"use_tex" : False,

	# column indices in ki,kf files
	"ki_start_idx" : 0,		# start index of ki 3-vector
	"kf_start_idx" : 3,		# start index of kf 3-vector
	"wi_idx" : 9,			# start index of ki weight factor
	"wf_idx" : 10,			# start index of kf weight factor

	# column indices in Q,E files
	"Q_start_idx" : 0,
	"E_idx" : 3,
	"w_idx" : 4,

	"filter_eps" : 1e-4,
}


# constants
sig2hwhm = np.sqrt(2. * np.log(2.))
sig2fwhm = 2.*sig2hwhm



#
# normalises events and filters out too low probabilities
#
def filter_events(Q, E, w):
	if w.size == 0:
		raise ValueError("No neutron events available.")

	# normalise intensity/probability
	maxw = np.max(w)
	if np.abs(maxw) < np.finfo(w[0].__class__).eps:
		raise ValueError("Neutron probability factors are zero.")
	w /= maxw

	# filter out too low probabilities
	beloweps = lambda d: np.abs(d) <= options["filter_eps"]
	nonzero_idx = [i for i in range(len(w)) if not beloweps(w[i])]

	Q = Q[nonzero_idx]
	E = E[nonzero_idx]
	w = w[nonzero_idx]

	if w.size == 0:
		raise ValueError("No neutron events left after filtering.")

	return [Q, E, w]



#
# loads a list of neutron events in the [ ki_vec, kf_vec, pos_vec, wi, wf ] format
#
def load_events_kikf(filename):
	dat = np.loadtxt(filename)
	ki = dat[:, options["ki_start_idx"]:options["ki_start_idx"]+3]
	kf = dat[:, options["kf_start_idx"]:options["kf_start_idx"]+3]
	wi = dat[:, options["wi_idx"]]
	wf = dat[:, options["wf_idx"]]

	w = wi * wf
	Q = ki - kf
	E = tas.k2_to_E * (np.multiply(ki, ki).sum(1) - np.multiply(kf, kf).sum(1))

	return filter_events(Q, E, w)



#
# loads a list of neutron events in the [ h, k, l, E, w ] format
#
def load_events_QE(filename):
	dat = np.loadtxt(filename)

	Q = dat[:, options["Q_start_idx"]:options["Q_start_idx"]+3]
	E = dat[:, options["E_idx"]]
	w = dat[:, options["w_idx"]]

	return filter_events(Q, E, w)



#
# calculates the covariance matrix of the (Q, E) 4-vectors
#
def calc_covar(Q, E, w, Qpara, Qperp):
	# make a [Q, E] 4-vector
	Q4 = np.insert(Q, 3, E, axis=1)

	# calculate the mean Q 4-vector
	Qmean = [ np.average(Q4[:,i], weights = w) for i in range(4) ]
	if options["verbose"]:
		print("Mean (Q, E) vector in lab system:\n%s\n" % Qmean)

	# get the weighted covariance matrix
	Qcov = np.cov(Q4, rowvar = False, aweights = np.abs(w), ddof = 0)
	if options["verbose"]:
		print("Covariance matrix in lab system:\n%s\n" % Qcov)

	# the resolution is the inverse of the covariance
	Qres = la.inv(Qcov)
	if options["verbose"]:
		print("Resolution matrix in lab system:\n%s\n" % Qres)


	# create a matrix to transform into the coordinate system with Q along x
	# choose given coordinate system
	if len(Qpara) == 3 and len(Qperp) == 3:
		Qnorm = Qpara / la.norm(Qpara)
		Qside = Qperp / la.norm(Qperp)
		Qup = np.cross(Qnorm, Qside)
	else:
		Qnorm = Qmean[0:3] / la.norm(Qmean[0:3])
		Qup = np.array([0, 1, 0])
		Qside = np.cross(Qup, Qnorm)

	if options["verbose"]:
		print("Qpara = %s\nQperp = %s\nQup = %s\n" % (Qnorm, Qside, Qup))

	# trafo matrix
	T = np.transpose(np.array([
		np.insert(Qnorm, 3, 0),
		np.insert(Qside, 3, 0),
		np.insert(Qup, 3, 0),
		[0, 0, 0, 1] ]))

	if options["verbose"]:
		print("Transformation into (Qpara, Qperp, Qup, E) system:\n%s\n" % T)

	# transform mean Q vector
	Qmean_Q = np.dot(np.transpose(T), Qmean)
	if options["verbose"]:
		print("Mean (Q, E) vector in (Qpara, Qperp, Qup, E) system:\n%s\n" % Qmean_Q)

	# transform the covariance matrix
	Qcov_Q = np.dot(np.transpose(T), np.dot(Qcov, T))
	if options["verbose"]:
		print("Covariance matrix in (Qpara, Qperp, Qup, E) system:\n%s\n" % Qcov_Q)

	# the resolution is the inverse of the covariance
	Qres_Q = la.inv(Qcov_Q)
	if options["verbose"]:
		print("Resolution matrix in (Qpara, Qperp, Qup, E) system:\n%s\n" % Qres_Q)

	#[ evals, evecs ] = la.eig(Qcov_Q)
	#print("Ellipsoid fwhm radii:\n%s\n" % (np.sqrt(np.abs(evals)) * sig2fwhm))

	# transform all neutron events
	Q4_Q = np.array([])
	if options["plot_neutrons"]:
		Q4_Q = np.dot(Q4, T)
		if not options["centre_on_Q"]:
			Q4_Q -= Qmean_Q


	return [Qres_Q, Q4_Q, Qmean_Q]



#
# calculates the characteristics of a given ellipse by principal axis trafo
#
def descr_ellipse(quadric):
	[ evals, evecs ] = la.eig(quadric)
	#print("Evals: %s" % evals)

	fwhms = 1./np.sqrt(np.abs(evals)) * sig2fwhm

	angles = np.array([])
	if len(quadric) == 2:
		angles = np.array([ np.arctan2(evecs[1][0], evecs[0][0]) ])

	return [fwhms, angles/np.pi*180., evecs]



#
# projects along one axis of the quadric
# see equ. 57 and 58 in: G. Eckold and O. Sobolev, NIM A 752, pp. 54-64 (2014), doi: 10.1016/j.nima.2014.03.019
#
def proj_quad(_E, idx):
	E = np.delete(np.delete(_E, idx, axis=0), idx, axis=1)
	if np.abs(_E[idx, idx]) < 1e-8:
		return E

	v = (_E[idx,:] + _E[:,idx]) * 0.5
	vv = np.outer(v, v) / _E[idx, idx]
	vv = np.delete(np.delete(vv, idx, axis=0), idx, axis=1)

	return E - vv



#
# describes the ellipsoid by a principal axis trafo and by 2d cuts
#
def calc_ellipses(Qres_Q):
	# 4d ellipsoid
	[fwhms, angles, rot] = descr_ellipse(Qres_Q)

	axes_to_delete = [ [2, 1], [2, 0], [1,0], [3,2] ]
	slice_first = [ True, True, True, False ]
	results = []

	for ellidx in range(len(axes_to_delete)):
		# sliced 2d ellipse
		Qres = np.delete(np.delete(Qres_Q, axes_to_delete[ellidx][0], axis=0), axes_to_delete[ellidx][0], axis=1)
		Qres = np.delete(np.delete(Qres, axes_to_delete[ellidx][1], axis=0), axes_to_delete[ellidx][1], axis=1)
		[fwhms, angles, rot] = descr_ellipse(Qres)

		# projected 2d ellipse
		if slice_first[ellidx]:
			Qres_proj = np.delete(np.delete(Qres_Q, axes_to_delete[ellidx][0], axis=0), axes_to_delete[ellidx][0], axis=1)
			Qres_proj = proj_quad(Qres_proj, axes_to_delete[ellidx][1])
		else:
			Qres_proj = proj_quad(Qres_Q, axes_to_delete[ellidx][0])
			Qres_proj = np.delete(np.delete(Qres_proj, axes_to_delete[ellidx][1], axis=0), axes_to_delete[ellidx][1], axis=1)
		[fwhms_proj, angles_proj, rot_proj] = descr_ellipse(Qres_proj)

		results.append({ "fwhms" : fwhms, "angles" : angles, "rot" : rot,
			"fwhms_proj" : fwhms_proj, "angles_proj" : angles_proj, "rot_proj" : rot_proj })


	if options["verbose"]:
		print("4d resolution ellipsoid diagonal elements fwhm (coherent-elastic scattering) lengths:\n%s\n" \
			% (1./np.sqrt(np.abs(np.diag(Qres_Q))) * sig2fwhm))

		print("4d resolution ellipsoid principal axes fwhm: %s" % fwhms)

		Qres_proj = proj_quad(proj_quad(proj_quad(Qres_Q, 2), 1), 0)
		print("Incoherent-elastic fwhm: %.4f meV\n" % (1./np.sqrt(np.abs(Qres_proj[0,0])) * sig2fwhm))

		print("Qx,E sliced ellipse fwhm and slope angle: %s, %.4f" % (results[0]["fwhms"], results[0]["angles"][0]))
		print("Qy,E sliced ellipse fwhm and slope angle: %s, %.4f" % (results[1]["fwhms"], results[1]["angles"][0]))
		print("Qz,E sliced ellipse fwhm and slope angle: %s, %.4f" % (results[2]["fwhms"], results[2]["angles"][0]))
		print("Qx,Qy sliced ellipse fwhm and slope angle: %s, %.4f" % (results[3]["fwhms"], results[3]["angles"][0]))
		print()
		print("Qx,E projected ellipse fwhm and slope angle: %s, %.4f" % (results[0]["fwhms_proj"], results[0]["angles_proj"][0]))
		print("Qy,E projected ellipse fwhm and slope angle: %s, %.4f" % (results[1]["fwhms_proj"], results[1]["angles_proj"][0]))
		print("Qz,E projected ellipse fwhm and slope angle: %s, %.4f" % (results[2]["fwhms_proj"], results[2]["angles_proj"][0]))
		print("Qx,Qy projected ellipse fwhm and slope angle: %s, %.4f" % (results[3]["fwhms_proj"], results[3]["angles_proj"][0]))


	return results




#
# shows the 2d ellipses
#
def plot_ellipses(file, Q4, w, Qmean, ellis):
	try:
		import mpl_toolkits.mplot3d as mplot3d
		import matplotlib
		import matplotlib.pyplot as plot
	except ImportError:
		print("Matplotlib could not be imported!")
		exit(-1)

	matplotlib.rc("text", usetex=options["use_tex"])

	thesymsize = options["symsize"] * w
	themarker = "."


	ellfkt = lambda rad, rot, phi, Qmean2d : \
		np.dot(rot, np.array([ rad[0]*np.cos(phi), rad[1]*np.sin(phi) ])) + Qmean2d


	# 2d plots
	fig = plot.figure()

	num_ellis = len(ellis)
	coord_axes = [[0,3], [1,3], [2,3], [0,1]]
	coord_names = ["Qpara (1/A)", "Qperp (1/A)", "Qup (1/A)", "E (meV)"]

	if options["use_tex"]:
		coord_names[0] = "$Q_{\\parallel}$ (\\AA$^{-1}$)"
		coord_names[1] = "$Q_{\\perp}$ (\\AA$^{-1}$)"
		coord_names[2] = "$Q_{up}$ (\\AA$^{-1}$)"


	ellplots = []
	for ellidx in range(num_ellis):
		# centre plots on zero or mean Q vector ?
		QxE = np.array([[0], [0]])

		if options["centre_on_Q"]:
			QxE = np.array([[Qmean[coord_axes[ellidx][0]]], [Qmean[coord_axes[ellidx][0]]]])


		phi = np.linspace(0, 2.*np.pi, options["ellipse_points"])

		ell_QxE = ellfkt(ellis[ellidx]["fwhms"]*0.5, ellis[ellidx]["rot"], phi, QxE)
		ell_QxE_proj = ellfkt(ellis[ellidx]["fwhms_proj"]*0.5, ellis[ellidx]["rot_proj"], phi, QxE)
		ellplots.append({"sliced":ell_QxE, "proj":ell_QxE_proj})


		subplot_QxE = fig.add_subplot(221 + ellidx)
		subplot_QxE.set_xlabel(coord_names[coord_axes[ellidx][0]])
		subplot_QxE.set_ylabel(coord_names[coord_axes[ellidx][1]])
		if len(Q4.shape)==2 and len(Q4)>0 and len(Q4[0])==4:
			subplot_QxE.scatter(Q4[:, coord_axes[ellidx][0]], Q4[:, coord_axes[ellidx][1]], marker=themarker, s=thesymsize)
		subplot_QxE.plot(ell_QxE[0], ell_QxE[1], c="black", linestyle="dashed")
		subplot_QxE.plot(ell_QxE_proj[0], ell_QxE_proj[1], c="black", linestyle="solid")

	plot.tight_layout()


	# 3d plot
	fig3d = plot.figure()
	subplot3d = fig3d.add_subplot(111, projection="3d")

	subplot3d.set_xlabel(coord_names[0])
	subplot3d.set_ylabel(coord_names[1])
	subplot3d.set_zlabel(coord_names[3])

	if len(Q4.shape)==2 and len(Q4)>0 and len(Q4[0])==4:
		subplot3d.scatter(Q4[:,0], Q4[:,1], Q4[:,3], marker=themarker, s=thesymsize)
	# xE
	subplot3d.plot(ellplots[0]["sliced"][0], ellplots[0]["sliced"][1], zs=0., zdir="y", c="black", linestyle="dashed")
	subplot3d.plot(ellplots[0]["proj"][0], ellplots[0]["proj"][1], zs=0., zdir="y", c="black", linestyle="solid")
	# yE
	subplot3d.plot(ellplots[1]["sliced"][0], ellplots[1]["sliced"][1], zs=0., zdir="x", c="black", linestyle="dashed")
	subplot3d.plot(ellplots[1]["proj"][0], ellplots[1]["proj"][1], zs=0., zdir="x", c="black", linestyle="solid")
	# xy
	subplot3d.plot(ellplots[3]["sliced"][0], ellplots[3]["sliced"][1], zs=0., zdir="z", c="black", linestyle="dashed")
	subplot3d.plot(ellplots[3]["proj"][0], ellplots[3]["proj"][1], zs=0., zdir="z", c="black", linestyle="solid")


	if file != "":
		splitext = os.path.splitext(file)
		file3d = splitext[0] + "_3d" + splitext[1]

		if options["verbose"]:
			print("Saving 2d plot to \"%s\"." % file)
			print("Saving 3d plot to \"%s\"." % file3d)
		fig.savefig(file, dpi=options["dpi"])
		fig3d.savefig(file3d, dpi=options["dpi"])

	if options["plot_results"]:
		plot.show()



#
# checks versions of needed packages
#
def check_versions():
	npver = np.version.version.split(".")
	if int(npver[0]) >= 2:
		return
	if int(npver[0]) < 1 or int(npver[1]) < 10:
		print("Numpy version >= 1.10 is required, but installed version is %s." % np.version.version)
		exit(-1)



#
# entry point
#
def run_cov():
	print("This is a covariance matrix calculator using neutron events,"
		"\n\tdoi: 10.5281/zenodo.4117437,"
		"\n\twritten by T. Weber <tweber@ill.fr>, 30 March 2019.\n")
	check_versions()

	try:
		import argparse as arg
	except ImportError:
		print("Argparse could not be imported!")
		exit(-1)

	args = arg.ArgumentParser(description="Calculates the covariance matrix of neutron scattering events.")
	args.add_argument("file", type=str, help="input file")
	args.add_argument("-s", "--save", default="", type=str, nargs="?", help="save plot to file")
	args.add_argument("--ellipse", default=options["ellipse_points"], type=int, nargs="?", help="number of points to draw ellipses")
	args.add_argument("--ki", default=options["ki_start_idx"], type=int, nargs="?", help="index of ki vector's first column in kikf file")
	args.add_argument("--kf", default=options["kf_start_idx"], type=int, nargs="?", help="index of kf vector's first column in kikf file")
	args.add_argument("--wi", default=options["wi_idx"], type=int, nargs="?", help="index of ki weight factor column in kikf file")
	args.add_argument("--wf", default=options["wf_idx"], type=int, nargs="?", help="index of kf weight factor column in kikf file")
	args.add_argument("--w", default=options["w_idx"], type=int, nargs="?", help="index of neutron weight factor column in QE file")
	args.add_argument("--Q", default=options["Q_start_idx"], type=int, nargs="?", help="index of Q vector's first column in QE file")
	args.add_argument("--E", default=options["E_idx"], type=int, nargs="?", help="index of E column in QE file")
	args.add_argument("--QEfile", action="store_true", help="use the QE file type")
	args.add_argument("--centreonQ", action="store_true", help="centre plots on mean Q")
	args.add_argument("--noverbose", action="store_true", help="don't show console logs")
	args.add_argument("--noplot", action="store_true", help="don't show any plot windows")
	args.add_argument("--noneutrons", action="store_true", help="don't show neutron events in plots")
	args.add_argument("--symsize", default=options["symsize"], type=float, nargs="?", help="size of the symbols in plots")
	args.add_argument("--ax", default=None, type=float, nargs="?", help="x component of first orientation vector")
	args.add_argument("--ay", default=None, type=float, nargs="?", help="y component of first orientation vector")
	args.add_argument("--az", default=None, type=float, nargs="?", help="z component of first orientation vector")
	args.add_argument("--bx", default=None, type=float, nargs="?", help="x component of second orientation vector")
	args.add_argument("--by", default=None, type=float, nargs="?", help="y component of second orientation vector")
	args.add_argument("--bz", default=None, type=float, nargs="?", help="z component of second orientation vector")
	args.add_argument("--a", "--as", "-a", default=None, type=float, nargs="?", help="lattice constant a (only needed in case data is in rlu)")
	args.add_argument("--b", "--bs", "-b", default=None, type=float, nargs="?", help="lattice constant b (only needed in case data is in rlu)")
	args.add_argument("--c", "--cs", "-c", default=None, type=float, nargs="?", help="lattice constant c (only needed in case data is in rlu)")
	args.add_argument("--aa", "--alpha", default=90., type=float, nargs="?", help="lattice angle alpha (only needed in case data is in rlu)")
	args.add_argument("--bb", "--beta", default=90., type=float, nargs="?", help="lattice angle beta (only needed in case data is in rlu)")
	args.add_argument("--cc", "--gamma", default=90., type=float, nargs="?", help="lattice angle gamma (only needed in case data is in rlu)")
	args.add_argument("--filtereps", default=options["filter_eps"], type=float, nargs="?", help="epsilon probability below which neutron events are filtered out")
	args.add_argument("--dpi", default=options["dpi"], type=int, nargs="?", help="DPI of output plot file")
	args.add_argument("--tex", action="store_true", help="use tex in plots")
	argv = args.parse_args()

	options["verbose"] = (argv.noverbose==False)
	options["plot_results"] = (argv.noplot==False)
	options["plot_neutrons"] = (argv.noneutrons==False)
	options["centre_on_Q"] = argv.centreonQ
	options["dpi"] = argv.dpi
	options["use_tex"] = argv.tex

	B = []
	if argv.a!=None and argv.b!=None and argv.c!=None and argv.aa!=None and argv.bb!=None and argv.cc!=None:
		lattice = np.array([ argv.a, argv.b, argv.c,  ])
		angles = np.array([ argv.aa, argv.bb, argv.cc ]) / 180.*np.pi

		B = tas.get_B(lattice, angles)

		if options["verbose"]:
			print("Crystal B matrix:\n%s\n" % B)


	infile = argv.file
	outfile = argv.save
	options["ellipse_points"] = argv.ellipse
	options["ki_start_idx"] = argv.ki
	options["kf_start_idx"] = argv.kf
	options["wi_idx"] = argv.wi
	options["wf_idx"] = argv.wf
	options["filter_eps"] = argv.filtereps
	options["symsize"] = argv.symsize
	avec = [ argv.az, argv.ay, argv.az ]
	bvec = [ argv.bx, argv.by, argv.bz ]


	try:
		# input file is in h k l E w format?
		if argv.QEfile:
			[Q, E, w] = load_events_QE(infile)
			# convert rlu to 1/A
			if len(B) != 0:
				Q = np.dot(Q, np.transpose(B))
		# input file is in the kix kiy kiz kfx kfy kfz wi wf format?
		else:
			[Q, E, w] = load_events_kikf(infile)
	except OSError:
		print("Could not load input file %s." % infile)
		exit(-1)
	except NameError:
		print("Error processing input file %s." % infile)
		exit(-1)


	if avec[0]!=None and avec[1]!=None and avec[2]!=None and bvec[0]!=None and bvec[1]!=None and bvec[2]!=None:
		Qpara = np.array(avec)
		Qperp = np.array(bvec)

		# convert rlu to 1/A
		if len(B) != 0:
			Qpara = np.dot(B, Qpara)
			Qperp = np.dot(B, Qperp)
	else:
		Qpara = np.array([])
		Qperp = np.array([])

	[Qres, Q4, Qmean] = calc_covar(Q, E, w, Qpara, Qperp)
	calcedellis = calc_ellipses(Qres)

	if options["plot_results"] or outfile!="":
		plot_ellipses(outfile, Q4, w, Qmean, calcedellis)



#
# main
#
if __name__ == "__main__":
	run_cov()