#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar  4 16:24:50 2020

@author: carl
"""

#import os.path
from pkg_resources import resource_filename
import numpy as np
from scipy.interpolate import PchipInterpolator
#from scipy.io import loadmat
from pymatreader import read_mat


def load_reference(wn, what=None, matfilename=None):
    """
    Loads and normalizes a spectrum from a Matlab file, interpolating at the given points.
        The reference is assumed to cover the entire range of wavenumbers.
    Parameters:
        wn: array of wavenumbers at which to get the spectrum
        what: A string defining what type of reference to get, corresponding to a file in the
        'reference' directory
        matfilename: the name of an arbitrary Matlab file to load data from; the data must be
        in a matrix called AB, with wavenumbers in the first column.
        Returns: spectrum at the points given by wn
    """
    if (what is None) == (matfilename is None):
        raise ValueError("Either 'what' or 'matfilename' must be specified")
    if what is not None:
        matfilename = resource_filename('octavvs.reference_spectra', what + ".mat")
#        matfilename = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__),
#                                       'reference', what + '.mat'))
    ref = read_mat(matfilename)['AB']
    # Handle the case of high-to-low since the interpolator requires low-to-high
    d = 1 if ref[0,0] < ref[-1,0] else -1
    ref = PchipInterpolator(ref[::d,0], ref[::d,1])(wn)
    return ref #/ ref.max()

def nonnegative(y, fracspectra=0, fracvalues=0):
    """
    Make a matrix of spectral data nonnegative by shifting all the spectra up by the same computed
    amount, followed by setting negative values to 0. The shift is chosen such that at most
    fracspectra of the spectra get more than fracvalues of their intensities set to zero.
    Parameters:
    y: array of intensities for (pixel, wavenumber)
    fracspectra: unheeded fraction of the spectra
    fracvalues: maximal fraction of points to clip at 0
    Returns: shifted spectra in the same format as y
    """
    s = int(fracspectra * y.shape[0])
    v = int(fracvalues * y.shape[1])
    if s == 0 or v == 0:
        return y - np.min(y.min(), 0)
    if s >= y.shape[0] or v >= y.shape[1]:
        return np.maximum(y, 0)
    yp = np.partition(y, v, axis=1)[:,v]
    a = np.partition(yp, s)[s]
    return np.maximum(y - a if a < 0 else y, 0)

def pca_nipals(x, ncomp, tol=1e-5, max_iter=1000, copy=True, explainedvariance=None):
    """
    NIPALS algorithm for PCA, based on the code in statmodels.multivariate
    but with optimizations as in Bassan's Matlab implementation to
    sacrifice some accuracy for speed.
    x: ndarray of data, will be altered
    ncomp: number of PCA components to return
    tol: tolerance
    copy: If false, destroy the input matrix x
    explainedvariance: If >0, stop after this fraction of the total variance is explained
    returns: PCA loadings as rows
    """
    if copy:
        x = x.copy()
    if explainedvariance is not None and explainedvariance > 0:
        varlim = (x * x).sum() * (1. - explainedvariance)
    else:
        varlim = 0
    npts, nvar = x.shape
    vecs = np.empty((ncomp, nvar))
    for i in range(ncomp):
        factor = np.ones(npts)
        for j in range(max_iter):
            vec = x.T @ factor #/ (factor @ factor)
            vec = vec / np.sqrt(vec @ vec)
            f_old = factor
            factor = x @ vec #/ (vec @ vec)
            f_old = factor - f_old
            if tol > np.sqrt(f_old @ f_old) / np.sqrt(factor @ factor):
                break
        vecs[i, :] = vec
        if i < ncomp - 1:
            x -= factor[:,None] @ vec[None,:]
            if varlim > 0 and (x * x).sum() < varlim:
                return vecs[:i+1, :]
    return vecs

def find_wn_ranges(wn, ranges):
    """
    Find indexes corresponding to the beginning and end of a list of ranges of wavenumbers. The
    wavenumbers have to be sorted in either direction.
    Parameters:
    wn: array of wavenumbers
    ranges: numpy array of shape (n, 2) with desired wavenumber ranges in order [low,high]
    Returns: numpy array of shape (n, 2) with indexes of the wavenumbers delimiting those ranges
    """
    if isinstance(ranges, list):
        ranges = np.array(ranges)
    if(wn[0] < wn[-1]):
        return np.stack((np.searchsorted(wn, ranges[:,0]),
                         np.searchsorted(wn, ranges[:,1], 'right')), 1)
    return len(wn) - np.stack((np.searchsorted(wn[::-1], ranges[:,1], 'right'),
                              np.searchsorted(wn[::-1], ranges[:,0])), 1)


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar  4 16:24:50 2020

@author: carl
"""

#import os.path
from pkg_resources import resource_filename
import numpy as np
from scipy.interpolate import PchipInterpolator
#from scipy.io import loadmat
from pymatreader import read_mat


def load_reference(wn, what=None, matfilename=None):
    """
    Loads and normalizes a spectrum from a Matlab file, interpolating at the given points.
        The reference is assumed to cover the entire range of wavenumbers.
    Parameters:
        wn: array of wavenumbers at which to get the spectrum
        what: A string defining what type of reference to get, corresponding to a file in the
        'reference' directory
        matfilename: the name of an arbitrary Matlab file to load data from; the data must be
        in a matrix called AB, with wavenumbers in the first column.
        Returns: spectrum at the points given by wn
    """
    if (what is None) == (matfilename is None):
        raise ValueError("Either 'what' or 'matfilename' must be specified")
    if what is not None:
        matfilename = resource_filename('octavvs.reference_spectra', what + ".mat")
#        matfilename = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__),
#                                       'reference', what + '.mat'))
    ref = read_mat(matfilename)['AB']
    # Handle the case of high-to-low since the interpolator requires low-to-high
    d = 1 if ref[0,0] < ref[-1,0] else -1
    ref = PchipInterpolator(ref[::d,0], ref[::d,1])(wn)
    return ref #/ ref.max()

def nonnegative(y, fracspectra=0, fracvalues=0):
    """
    Make a matrix of spectral data nonnegative by shifting all the spectra up by the same computed
    amount, followed by setting negative values to 0. The shift is chosen such that at most
    fracspectra of the spectra get more than fracvalues of their intensities set to zero.
    Parameters:
    y: array of intensities for (pixel, wavenumber)
    fracspectra: unheeded fraction of the spectra
    fracvalues: maximal fraction of points to clip at 0
    Returns: shifted spectra in the same format as y
    """
    s = int(fracspectra * y.shape[0])
    v = int(fracvalues * y.shape[1])
    if s == 0 or v == 0:
        return y - np.min(y.min(), 0)
    if s >= y.shape[0] or v >= y.shape[1]:
        return np.maximum(y, 0)
    yp = np.partition(y, v, axis=1)[:,v]
    a = np.partition(yp, s)[s]
    return np.maximum(y - a if a < 0 else y, 0)

def pca_nipals(x, ncomp, tol=1e-5, max_iter=1000, copy=True, explainedvariance=None):
    """
    NIPALS algorithm for PCA, based on the code in statmodels.multivariate
    but with optimizations as in Bassan's Matlab implementation to
    sacrifice some accuracy for speed.
    x: ndarray of data, will be altered
    ncomp: number of PCA components to return
    tol: tolerance
    copy: If false, destroy the input matrix x
    explainedvariance: If >0, stop after this fraction of the total variance is explained
    returns: PCA loadings as rows
    """
    if copy:
        x = x.copy()
    if explainedvariance is not None and explainedvariance > 0:
        varlim = (x * x).sum() * (1. - explainedvariance)
    else:
        varlim = 0
    npts, nvar = x.shape
    vecs = np.empty((ncomp, nvar))
    for i in range(ncomp):
        factor = np.ones(npts)
        for j in range(max_iter):
            vec = x.T @ factor #/ (factor @ factor)
            vec = vec / np.sqrt(vec @ vec)
            f_old = factor
            factor = x @ vec #/ (vec @ vec)
            f_old = factor - f_old
            if tol > np.sqrt(f_old @ f_old) / np.sqrt(factor @ factor):
                break
        vecs[i, :] = vec
        if i < ncomp - 1:
            x -= factor[:,None] @ vec[None,:]
            if varlim > 0 and (x * x).sum() < varlim:
                return vecs[:i+1, :]
    return vecs

def find_wn_ranges(wn, ranges):
    """
    Find indexes corresponding to the beginning and end of a list of ranges of wavenumbers. The
    wavenumbers have to be sorted in either direction.
    Parameters:
    wn: array of wavenumbers
    ranges: numpy array of shape (n, 2) with desired wavenumber ranges in order [low,high]
    Returns: numpy array of shape (n, 2) with indexes of the wavenumbers delimiting those ranges
    """
    if isinstance(ranges, list):
        ranges = np.array(ranges)
    if(wn[0] < wn[-1]):
        return np.stack((np.searchsorted(wn, ranges[:,0]),
                         np.searchsorted(wn, ranges[:,1], 'right')), 1)
    return len(wn) - np.stack((np.searchsorted(wn[::-1], ranges[:,1], 'right'),
                              np.searchsorted(wn[::-1], ranges[:,0])), 1)


#!/usr/bin/env python3 # -*- coding: utf-8 -*- Created on Wed Mar  4 16:24:50 2020  @author: carl #import os.path #from scipy.io import loadmat Loads and normalizes a spectrum from a Matlab file, interpolating at the given points.         The reference is assumed to cover the entire range of wavenumbers.     Parameters:         wn: array of wavenumbers at which to get the spectrum         what: A string defining what type of reference to get, corresponding to a file in the         'reference' directory         matfilename: the name of an arbitrary Matlab file to load data from; the data must be         in a matrix called AB, with wavenumbers in the first column.         Returns: spectrum at the points given by wn #        matfilename = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__), #                                       'reference', what + '.mat')) # Handle the case of high-to-low since the interpolator requires low-to-high #/ ref.max() Make a matrix of spectral data nonnegative by shifting all the spectra up by the same computed     amount, followed by setting negative values to 0. The shift is chosen such that at most     fracspectra of the spectra get more than fracvalues of their intensities set to zero.     Parameters:     y: array of intensities for (pixel, wavenumber)     fracspectra: unheeded fraction of the spectra     fracvalues: maximal fraction of points to clip at 0     Returns: shifted spectra in the same format as y NIPALS algorithm for PCA, based on the code in statmodels.multivariate     but with optimizations as in Bassan's Matlab implementation to     sacrifice some accuracy for speed.     x: ndarray of data, will be altered     ncomp: number of PCA components to return     tol: tolerance     copy: If false, destroy the input matrix x     explainedvariance: If >0, stop after this fraction of the total variance is explained     returns: PCA loadings as rows #/ (factor @ factor) #/ (vec @ vec) Find indexes corresponding to the beginning and end of a list of ranges of wavenumbers. The     wavenumbers have to be sorted in either direction.     Parameters:     wn: array of wavenumbers     ranges: numpy array of shape (n, 2) with desired wavenumber ranges in order [low,high]     Returns: numpy array of shape (n, 2) with indexes of the wavenumbers delimiting those ranges