# Copyright (C) 2012  Alex Nitz
#
#
# 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; either version 3 of the License, or (at your
# option) any later version.
#
# 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, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.


#
# =============================================================================
#
#                                   Preamble
#
# =============================================================================
#
"""This file contains backported functionality from future versions of libraries.
Mostly done with monkey-patching.
"""

def block_diag(*arrs):
    import numpy as np
    from numpy import atleast_1d, atleast_2d, array
    if arrs == ():
        arrs = ([],)
    arrs = [np.atleast_2d(a) for a in arrs]

    bad_args = [k for k in range(len(arrs)) if arrs[k].ndim > 2]
    if bad_args:
        raise ValueError("arguments in the following positions have dimension "
                            "greater than 2: %s" % bad_args)

    shapes = np.array([a.shape for a in arrs])
    out = np.zeros(np.sum(shapes, axis=0), dtype=arrs[0].dtype)

    r, c = 0, 0
    for i, (rr, cc) in enumerate(shapes):
        out[r:r + rr, c:c + cc] = arrs[i]
        r += rr
        c += cc
    return out

def zpk2sos(z, p, k, pairing='nearest'):
    """Stolen from scipy, please kill me and upgrade scipy...
    """

    import numpy as np
    from numpy import zeros
    from scipy.signal import zpk2tf, lfilter

    valid_pairings = ['nearest', 'keep_odd']
    if pairing not in valid_pairings:
        raise ValueError('pairing must be one of %s, not %s'
                         % (valid_pairings, pairing))
    if len(z) == len(p) == 0:
        return array([[k, 0., 0., 1., 0., 0.]])

    # ensure we have the same number of poles and zeros, and make copies
    p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0))))
    z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0))))
    n_sections = (max(len(p), len(z)) + 1) // 2
    sos = zeros((n_sections, 6))

    if len(p) % 2 == 1 and pairing == 'nearest':
        p = np.concatenate((p, [0.]))
        z = np.concatenate((z, [0.]))
    assert len(p) == len(z)

    # Ensure we have complex conjugate pairs
    # (note that _cplxreal only gives us one element of each complex pair):
    z = np.concatenate(_cplxreal(z))
    p = np.concatenate(_cplxreal(p))

    p_sos = np.zeros((n_sections, 2), np.complex128)
    z_sos = np.zeros_like(p_sos)
    for si in range(n_sections):
        # Select the next "worst" pole
        p1_idx = np.argmin(np.abs(1 - np.abs(p)))
        p1 = p[p1_idx]
        p = np.delete(p, p1_idx)

        # Pair that pole with a zero

        if np.isreal(p1) and np.isreal(p).sum() == 0:
            # Special case to set a first-order section
            z1_idx = _nearest_real_complex_idx(z, p1, 'real')
            z1 = z[z1_idx]
            z = np.delete(z, z1_idx)
            p2 = z2 = 0
        else:
            if not np.isreal(p1) and np.isreal(z).sum() == 1:
                # Special case to ensure we choose a complex zero to pair
                # with so later (setting up a first-order section)
                z1_idx = _nearest_real_complex_idx(z, p1, 'complex')
                assert not np.isreal(z[z1_idx])
            else:
                # Pair the pole with the closest zero (real or complex)
                z1_idx = np.argmin(np.abs(p1 - z))
            z1 = z[z1_idx]
            z = np.delete(z, z1_idx)

            # Now that we have p1 and z1, figure out what p2 and z2 need to be
            if not np.isreal(p1):
                if not np.isreal(z1):  # complex pole, complex zero
                    p2 = p1.conj()
                    z2 = z1.conj()
                else:  # complex pole, real zero
                    p2 = p1.conj()
                    z2_idx = _nearest_real_complex_idx(z, p1, 'real')
                    z2 = z[z2_idx]
                    assert np.isreal(z2)
                    z = np.delete(z, z2_idx)
            else:
                if not np.isreal(z1):  # real pole, complex zero
                    z2 = z1.conj()
                    p2_idx = _nearest_real_complex_idx(p, z1, 'real')
                    p2 = p[p2_idx]
                    assert np.isreal(p2)
                else:  # real pole, real zero
                    # pick the next "worst" pole to use
                    idx = np.where(np.isreal(p))[0]
                    assert len(idx) > 0
                    p2_idx = idx[np.argmin(np.abs(np.abs(p[idx]) - 1))]
                    p2 = p[p2_idx]
                    # find a real zero to match the added pole
                    assert np.isreal(p2)
                    z2_idx = _nearest_real_complex_idx(z, p2, 'real')
                    z2 = z[z2_idx]
                    assert np.isreal(z2)
                    z = np.delete(z, z2_idx)
                p = np.delete(p, p2_idx)
        p_sos[si] = [p1, p2]
        z_sos[si] = [z1, z2]
    assert len(p) == len(z) == 0  # we've consumed all poles and zeros
    del p, z

    # Construct the system, reversing order so the "worst" are last
    p_sos = np.reshape(p_sos[::-1], (n_sections, 2))
    z_sos = np.reshape(z_sos[::-1], (n_sections, 2))
    gains = np.ones(n_sections)
    gains[0] = k
    for si in range(n_sections):
        x = zpk2tf(z_sos[si], p_sos[si], gains[si])
        sos[si] = np.concatenate(x)
    return sos

def sosfilt(sos, x, axis=-1, zi=None):
    """Stolen from scipy, please kill me and upgrade scipy...
    """

    import numpy as np
    from numpy import atleast_1d, atleast_2d, array
    from scipy.signal import lfilter

    x = np.asarray(x)

    sos = atleast_2d(sos)
    if sos.ndim != 2:
        raise ValueError('sos array must be 2D')

    n_sections, m = sos.shape
    if m != 6:
        raise ValueError('sos array must be shape (n_sections, 6)')

    use_zi = zi is not None
    if use_zi:
        zi = np.asarray(zi)
        e_zi_shape = list(x.shape)
        x_zi_shape[axis] = 2
        x_zi_shape = tuple([n_sections] + x_zi_shape)
        if zi.shape != x_zi_shape:
            raise ValueError('Invalid zi shape.  With axis=%r, an input with '
                             'shape %r, and an sos array with %d sections, zi '
                             'must have shape %r.' %
                             (axis, x.shape, n_sections, x_zi_shape))
        zf = zeros_like(zi)

    for section in range(n_sections):
        if use_zi:
            x, zf[section] = lfilter(sos[section, :3], sos[section, 3:],
                                     x, axis, zi=zi[section])
        else:
            x = lfilter(sos[section, :3], sos[section, 3:], x, axis)
    out = (x, zf) if use_zi else x
    return out

def _cplxreal(z, tol=None):

    import numpy as np
    from numpy import atleast_1d, atleast_2d, array

    z = atleast_1d(z)
    if z.size == 0:
        return z, z
    elif z.ndim != 1:
        raise ValueError('_cplxreal only accepts 1D input')

    if tol is None:
        # Get tolerance from dtype of input
        tol = 100 * np.finfo((1.0 * z).dtype).eps

    # Sort by real part, magnitude of imaginary part (speed up further sorting)
    z = z[np.lexsort((abs(z.imag), z.real))]

    # Split reals from conjugate pairs
    real_indices = abs(z.imag) <= tol * abs(z)
    zr = z[real_indices].real

    if len(zr) == len(z):
        # Input is entirely real
        return array([]), zr

    # Split positive and negative halves of conjugates
    z = z[~real_indices]
    zp = z[z.imag > 0]
    zn = z[z.imag < 0]

    if len(zp) != len(zn):
        raise ValueError('Array contains complex value with no matching '
                         'conjugate.')

    # Find runs of (approximately) the same real part
    same_real = np.diff(zp.real) <= tol * abs(zp[:-1])
    diffs = numpy.diff(concatenate(([0], same_real, [0])))
    run_starts = numpy.where(diffs > 0)[0]
    run_stops = numpy.where(diffs < 0)[0]

    # Sort each run by their imaginary parts
    for i in range(len(run_starts)):
        start = run_starts[i]
        stop = run_stops[i] + 1
        for chunk in (zp[start:stop], zn[start:stop]):
            chunk[...] = chunk[np.lexsort([abs(chunk.imag)])]

    # Check that negatives match positives
    if any(abs(zp - zn.conj()) > tol * abs(zn)):
        raise ValueError('Array contains complex value with no matching '
                         'conjugate.')

    # Average out numerical inaccuracy in real vs imag parts of pairs
    zc = (zp + zn.conj()) / 2

    return zc, zr

def _nearest_real_complex_idx(fro, to, which):

    import numpy as np

    """Get the next closest real or complex element based on distance"""
    assert which in ('real', 'complex')
    order = np.argsort(np.abs(fro - to))
    mask = np.isreal(fro[order])
    if which == 'complex':
        mask = ~mask
    return order[np.where(mask)[0][0]]

"""A parser for HTML and XHTML."""

# This file is based on sgmllib.py, but the API is slightly different.

# XXX There should be a way to distinguish between PCDATA (parsed
# character data -- the normal case), RCDATA (replaceable character
# data -- only char and entity references and end tags are special)
# and CDATA (character data -- only end tags are special).


import markupbase
import re

# Regular expressions used for parsing

interesting_normal = re.compile('[&<]')
incomplete = re.compile('&[a-zA-Z#]')

entityref = re.compile('&([a-zA-Z][-.a-zA-Z0-9]*)[^a-zA-Z0-9]')
charref = re.compile('&#(?:[0-9]+|[xX][0-9a-fA-F]+)[^0-9a-fA-F]')

starttagopen = re.compile('<[a-zA-Z]')
piclose = re.compile('>')
commentclose = re.compile(r'--\s*>')

# see http://www.w3.org/TR/html5/tokenization.html#tag-open-state
# and http://www.w3.org/TR/html5/tokenization.html#tag-name-state
# note: if you change tagfind/attrfind remember to update locatestarttagend too
tagfind = re.compile('([a-zA-Z][^\t\n\r\f />\x00]*)(?:\s|/(?!>))*')
# this regex is currently unused, but left for backward compatibility
tagfind_tolerant = re.compile('[a-zA-Z][^\t\n\r\f />\x00]*')

attrfind = re.compile(
    r'((?<=[\'"\s/])[^\s/>][^\s/=>]*)(\s*=+\s*'
    r'(\'[^\']*\'|"[^"]*"|(?![\'"])[^>\s]*))?(?:\s|/(?!>))*')

locatestarttagend = re.compile(r"""
  <[a-zA-Z][^\t\n\r\f />\x00]*       # tag name
  (?:[\s/]*                          # optional whitespace before attribute name
    (?:(?<=['"\s/])[^\s/>][^\s/=>]*  # attribute name
      (?:\s*=+\s*                    # value indicator
        (?:'[^']*'                   # LITA-enclosed value
          |"[^"]*"                   # LIT-enclosed value
          |(?!['"])[^>\s]*           # bare value
         )
       )?(?:\s|/(?!>))*
     )*
   )?
  \s*                                # trailing whitespace
""", re.VERBOSE)
endendtag = re.compile('>')
# the HTML 5 spec, section 8.1.2.2, doesn't allow spaces between
# </ and the tag name, so maybe this should be fixed
endtagfind = re.compile('</\s*([a-zA-Z][-.a-zA-Z0-9:_]*)\s*>')


class HTMLParseError(Exception):
    """Exception raised for all parse errors."""

    def __init__(self, msg, position=(None, None)):
        assert msg
        self.msg = msg
        self.lineno = position[0]
        self.offset = position[1]

    def __str__(self):
        result = self.msg
        if self.lineno is not None:
            result = result + ", at line %d" % self.lineno
        if self.offset is not None:
            result = result + ", column %d" % (self.offset + 1)
        return result


class HTMLParser(markupbase.ParserBase):
    """Find tags and other markup and call handler functions.

    Usage:
        p = HTMLParser()
        p.feed(data)
        ...
        p.close()

    Start tags are handled by calling self.handle_starttag() or
    self.handle_startendtag(); end tags by self.handle_endtag().  The
    data between tags is passed from the parser to the derived class
    by calling self.handle_data() with the data as argument (the data
    may be split up in arbitrary chunks).  Entity references are
    passed by calling self.handle_entityref() with the entity
    reference as the argument.  Numeric character references are
    passed to self.handle_charref() with the string containing the
    reference as the argument.
    """

    CDATA_CONTENT_ELEMENTS = ("script", "style")


    def __init__(self):
        """Initialize and reset this instance."""
        self.reset()

    def reset(self):
        """Reset this instance.  Loses all unprocessed data."""
        self.rawdata = ''
        self.lasttag = '???'
        self.interesting = interesting_normal
        self.cdata_elem = None
        markupbase.ParserBase.reset(self)

    def feed(self, data):
        r"""Feed data to the parser.

        Call this as often as you want, with as little or as much text
        as you want (may include '\n').
        """
        self.rawdata = self.rawdata + data
        self.goahead(0)

    def close(self):
        """Handle any buffered data."""
        self.goahead(1)

    def error(self, message):
        raise HTMLParseError(message, self.getpos())

    __starttag_text = None

    def get_starttag_text(self):
        """Return full source of start tag: '<...>'."""
        return self.__starttag_text

    def set_cdata_mode(self, elem):
        self.cdata_elem = elem.lower()
        self.interesting = re.compile(r'</\s*%s\s*>' % self.cdata_elem, re.I)

    def clear_cdata_mode(self):
        self.interesting = interesting_normal
        self.cdata_elem = None

    # Internal -- handle data as far as reasonable.  May leave state
    # and data to be processed by a subsequent call.  If 'end' is
    # true, force handling all data as if followed by EOF marker.
    def goahead(self, end):
        rawdata = self.rawdata
        i = 0
        n = len(rawdata)
        while i < n:
            match = self.interesting.search(rawdata, i) # < or &
            if match:
                j = match.start()
            else:
                if self.cdata_elem:
                    break
                j = n
            if i < j: self.handle_data(rawdata[i:j])
            i = self.updatepos(i, j)
            if i == n: break
            startswith = rawdata.startswith
            if startswith('<', i):
                if starttagopen.match(rawdata, i): # < + letter
                    k = self.parse_starttag(i)
                elif startswith("</", i):
                    k = self.parse_endtag(i)
                elif startswith("<!--", i):
                    k = self.parse_comment(i)
                elif startswith("<?", i):
                    k = self.parse_pi(i)
                elif startswith("<!", i):
                    k = self.parse_html_declaration(i)
                elif (i + 1) < n:
                    self.handle_data("<")
                    k = i + 1
                else:
                    break
                if k < 0:
                    if not end:
                        break
                    k = rawdata.find('>', i + 1)
                    if k < 0:
                        k = rawdata.find('<', i + 1)
                        if k < 0:
                            k = i + 1
                    else:
                        k += 1
                    self.handle_data(rawdata[i:k])
                i = self.updatepos(i, k)
            elif startswith("&#", i):
                match = charref.match(rawdata, i)
                if match:
                    name = match.group()[2:-1]
                    self.handle_charref(name)
                    k = match.end()
                    if not startswith(';', k-1):
                        k = k - 1
                    i = self.updatepos(i, k)
                    continue
                else:
                    if ";" in rawdata[i:]:  # bail by consuming '&#'
                        self.handle_data(rawdata[i:i+2])
                        i = self.updatepos(i, i+2)
                    break
            elif startswith('&', i):
                match = entityref.match(rawdata, i)
                if match:
                    name = match.group(1)
                    self.handle_entityref(name)
                    k = match.end()
                    if not startswith(';', k-1):
                        k = k - 1
                    i = self.updatepos(i, k)
                    continue
                match = incomplete.match(rawdata, i)
                if match:
                    # match.group() will contain at least 2 chars
                    if end and match.group() == rawdata[i:]:
                        self.error("EOF in middle of entity or char ref")
                    # incomplete
                    break
                elif (i + 1) < n:
                    # not the end of the buffer, and can't be confused
                    # with some other construct
                    self.handle_data("&")
                    i = self.updatepos(i, i + 1)
                else:
                    break
            else:
                assert 0, "interesting.search() lied"
        # end while
        if end and i < n and not self.cdata_elem:
            self.handle_data(rawdata[i:n])
            i = self.updatepos(i, n)
        self.rawdata = rawdata[i:]

    # Internal -- parse html declarations, return length or -1 if not terminated
    # See w3.org/TR/html5/tokenization.html#markup-declaration-open-state
    # See also parse_declaration in _markupbase
    def parse_html_declaration(self, i):
        rawdata = self.rawdata
        if rawdata[i:i+2] != '<!':
            self.error('unexpected call to parse_html_declaration()')
        if rawdata[i:i+4] == '<!--':
            # this case is actually already handled in goahead()
            return self.parse_comment(i)
        elif rawdata[i:i+3] == '<![':
            return self.parse_marked_section(i)
        elif rawdata[i:i+9].lower() == '<!doctype':
            # find the closing >
            gtpos = rawdata.find('>', i+9)
            if gtpos == -1:
                return -1
            self.handle_decl(rawdata[i+2:gtpos])
            return gtpos+1
        else:
            return self.parse_bogus_comment(i)

    # Internal -- parse bogus comment, return length or -1 if not terminated
    # see http://www.w3.org/TR/html5/tokenization.html#bogus-comment-state
    def parse_bogus_comment(self, i, report=1):
        rawdata = self.rawdata
        if rawdata[i:i+2] not in ('<!', '</'):
            self.error('unexpected call to parse_comment()')
        pos = rawdata.find('>', i+2)
        if pos == -1:
            return -1
        if report:
            self.handle_comment(rawdata[i+2:pos])
        return pos + 1

    # Internal -- parse processing instr, return end or -1 if not terminated
    def parse_pi(self, i):
        rawdata = self.rawdata
        assert rawdata[i:i+2] == '<?', 'unexpected call to parse_pi()'
        match = piclose.search(rawdata, i+2) # >
        if not match:
            return -1
        j = match.start()
        self.handle_pi(rawdata[i+2: j])
        j = match.end()
        return j

    # Internal -- handle starttag, return end or -1 if not terminated
    def parse_starttag(self, i):
        self.__starttag_text = None
        endpos = self.check_for_whole_start_tag(i)
        if endpos < 0:
            return endpos
        rawdata = self.rawdata
        self.__starttag_text = rawdata[i:endpos]

        # Now parse the data between i+1 and j into a tag and attrs
        attrs = []
        match = tagfind.match(rawdata, i+1)
        assert match, 'unexpected call to parse_starttag()'
        k = match.end()
        self.lasttag = tag = match.group(1).lower()

        while k < endpos:
            m = attrfind.match(rawdata, k)
            if not m:
                break
            attrname, rest, attrvalue = m.group(1, 2, 3)
            if not rest:
                attrvalue = None
            elif attrvalue[:1] == '\'' == attrvalue[-1:] or \
                 attrvalue[:1] == '"' == attrvalue[-1:]:
                attrvalue = attrvalue[1:-1]
            if attrvalue:
                attrvalue = self.unescape(attrvalue)
            attrs.append((attrname.lower(), attrvalue))
            k = m.end()

        end = rawdata[k:endpos].strip()
        if end not in (">", "/>"):
            lineno, offset = self.getpos()
            if "\n" in self.__starttag_text:
                lineno = lineno + self.__starttag_text.count("\n")
                offset = len(self.__starttag_text) \
                         - self.__starttag_text.rfind("\n")
            else:
                offset = offset + len(self.__starttag_text)
            self.handle_data(rawdata[i:endpos])
            return endpos
        if end.endswith('/>'):
            # XHTML-style empty tag: <span attr="value" />
            self.handle_startendtag(tag, attrs)
        else:
            self.handle_starttag(tag, attrs)
            if tag in self.CDATA_CONTENT_ELEMENTS:
                self.set_cdata_mode(tag)
        return endpos

    # Internal -- check to see if we have a complete starttag; return end
    # or -1 if incomplete.
    def check_for_whole_start_tag(self, i):
        rawdata = self.rawdata
        m = locatestarttagend.match(rawdata, i)
        if m:
            j = m.end()
            next = rawdata[j:j+1]
            if next == ">":
                return j + 1
            if next == "/":
                if rawdata.startswith("/>", j):
                    return j + 2
                if rawdata.startswith("/", j):
                    # buffer boundary
                    return -1
                # else bogus input
                self.updatepos(i, j + 1)
                self.error("malformed empty start tag")
            if next == "":
                # end of input
                return -1
            if next in ("abcdefghijklmnopqrstuvwxyz=/"
                        "ABCDEFGHIJKLMNOPQRSTUVWXYZ"):
                # end of input in or before attribute value, or we have the
                # '/' from a '/>' ending
                return -1
            if j > i:
                return j
            else:
                return i + 1
        raise AssertionError("we should not get here!")

    # Internal -- parse endtag, return end or -1 if incomplete
    def parse_endtag(self, i):
        rawdata = self.rawdata
        assert rawdata[i:i+2] == "</", "unexpected call to parse_endtag"
        match = endendtag.search(rawdata, i+1) # >
        if not match:
            return -1
        gtpos = match.end()
        match = endtagfind.match(rawdata, i) # </ + tag + >
        if not match:
            if self.cdata_elem is not None:
                self.handle_data(rawdata[i:gtpos])
                return gtpos
            # find the name: w3.org/TR/html5/tokenization.html#tag-name-state
            namematch = tagfind.match(rawdata, i+2)
            if not namematch:
                # w3.org/TR/html5/tokenization.html#end-tag-open-state
                if rawdata[i:i+3] == '</>':
                    return i+3
                else:
                    return self.parse_bogus_comment(i)
            tagname = namematch.group(1).lower()
            # consume and ignore other stuff between the name and the >
            # Note: this is not 100% correct, since we might have things like
            # </tag attr=">">, but looking for > after tha name should cover
            # most of the cases and is much simpler
            gtpos = rawdata.find('>', namematch.end())
            self.handle_endtag(tagname)
            return gtpos+1

        elem = match.group(1).lower() # script or style
        if self.cdata_elem is not None:
            if elem != self.cdata_elem:
                self.handle_data(rawdata[i:gtpos])
                return gtpos

        self.handle_endtag(elem)
        self.clear_cdata_mode()
        return gtpos

    # Overridable -- finish processing of start+end tag: <tag.../>
    def handle_startendtag(self, tag, attrs):
        self.handle_starttag(tag, attrs)
        self.handle_endtag(tag)

    # Overridable -- handle start tag
    def handle_starttag(self, tag, attrs):
        pass

    # Overridable -- handle end tag
    def handle_endtag(self, tag):
        pass

    # Overridable -- handle character reference
    def handle_charref(self, name):
        pass

    # Overridable -- handle entity reference
    def handle_entityref(self, name):
        pass

    # Overridable -- handle data
    def handle_data(self, data):
        pass

    # Overridable -- handle comment
    def handle_comment(self, data):
        pass

    # Overridable -- handle declaration
    def handle_decl(self, decl):
        pass

    # Overridable -- handle processing instruction
    def handle_pi(self, data):
        pass

    def unknown_decl(self, data):
        pass

    # Internal -- helper to remove special character quoting
    entitydefs = None
    def unescape(self, s):
        if '&' not in s:
            return s
        def replaceEntities(s):
            s = s.groups()[0]
            try:
                if s[0] == "#":
                    s = s[1:]
                    if s[0] in ['x','X']:
                        c = int(s[1:], 16)
                    else:
                        c = int(s)
                    return unichr(c)
            except ValueError:
                return '&#'+s+';'
            else:
                # Cannot use name2codepoint directly, because HTMLParser supports apos,
                # which is not part of HTML 4
                import htmlentitydefs
                if HTMLParser.entitydefs is None:
                    entitydefs = HTMLParser.entitydefs = {'apos':u"'"}
                    for k, v in htmlentitydefs.name2codepoint.iteritems():
                        entitydefs[k] = unichr(v)
                try:
                    return self.entitydefs[s]
                except KeyError:
                    return '&'+s+';'

        return re.sub(r"&(#?[xX]?(?:[0-9a-fA-F]+|\w{1,8}));", replaceEntities, s)

# What follows are a list of colormaps for matplolib, which were introduced in
# later matplotlib versions. This contains the raw data, so is at the bottom
# of this file.
_magma_data = [[0.001462, 0.000466, 0.013866],
               [0.002258, 0.001295, 0.018331],
               [0.003279, 0.002305, 0.023708],
               [0.004512, 0.003490, 0.029965],
               [0.005950, 0.004843, 0.037130],
               [0.007588, 0.006356, 0.044973],
               [0.009426, 0.008022, 0.052844],
               [0.011465, 0.009828, 0.060750],
               [0.013708, 0.011771, 0.068667],
               [0.016156, 0.013840, 0.076603],
               [0.018815, 0.016026, 0.084584],
               [0.021692, 0.018320, 0.092610],
               [0.024792, 0.020715, 0.100676],
               [0.028123, 0.023201, 0.108787],
               [0.031696, 0.025765, 0.116965],
               [0.035520, 0.028397, 0.125209],
               [0.039608, 0.031090, 0.133515],
               [0.043830, 0.033830, 0.141886],
               [0.048062, 0.036607, 0.150327],
               [0.052320, 0.039407, 0.158841],
               [0.056615, 0.042160, 0.167446],
               [0.060949, 0.044794, 0.176129],
               [0.065330, 0.047318, 0.184892],
               [0.069764, 0.049726, 0.193735],
               [0.074257, 0.052017, 0.202660],
               [0.078815, 0.054184, 0.211667],
               [0.083446, 0.056225, 0.220755],
               [0.088155, 0.058133, 0.229922],
               [0.092949, 0.059904, 0.239164],
               [0.097833, 0.061531, 0.248477],
               [0.102815, 0.063010, 0.257854],
               [0.107899, 0.064335, 0.267289],
               [0.113094, 0.065492, 0.276784],
               [0.118405, 0.066479, 0.286321],
               [0.123833, 0.067295, 0.295879],
               [0.129380, 0.067935, 0.305443],
               [0.135053, 0.068391, 0.315000],
               [0.140858, 0.068654, 0.324538],
               [0.146785, 0.068738, 0.334011],
               [0.152839, 0.068637, 0.343404],
               [0.159018, 0.068354, 0.352688],
               [0.165308, 0.067911, 0.361816],
               [0.171713, 0.067305, 0.370771],
               [0.178212, 0.066576, 0.379497],
               [0.184801, 0.065732, 0.387973],
               [0.191460, 0.064818, 0.396152],
               [0.198177, 0.063862, 0.404009],
               [0.204935, 0.062907, 0.411514],
               [0.211718, 0.061992, 0.418647],
               [0.218512, 0.061158, 0.425392],
               [0.225302, 0.060445, 0.431742],
               [0.232077, 0.059889, 0.437695],
               [0.238826, 0.059517, 0.443256],
               [0.245543, 0.059352, 0.448436],
               [0.252220, 0.059415, 0.453248],
               [0.258857, 0.059706, 0.457710],
               [0.265447, 0.060237, 0.461840],
               [0.271994, 0.060994, 0.465660],
               [0.278493, 0.061978, 0.469190],
               [0.284951, 0.063168, 0.472451],
               [0.291366, 0.064553, 0.475462],
               [0.297740, 0.066117, 0.478243],
               [0.304081, 0.067835, 0.480812],
               [0.310382, 0.069702, 0.483186],
               [0.316654, 0.071690, 0.485380],
               [0.322899, 0.073782, 0.487408],
               [0.329114, 0.075972, 0.489287],
               [0.335308, 0.078236, 0.491024],
               [0.341482, 0.080564, 0.492631],
               [0.347636, 0.082946, 0.494121],
               [0.353773, 0.085373, 0.495501],
               [0.359898, 0.087831, 0.496778],
               [0.366012, 0.090314, 0.497960],
               [0.372116, 0.092816, 0.499053],
               [0.378211, 0.095332, 0.500067],
               [0.384299, 0.097855, 0.501002],
               [0.390384, 0.100379, 0.501864],
               [0.396467, 0.102902, 0.502658],
               [0.402548, 0.105420, 0.503386],
               [0.408629, 0.107930, 0.504052],
               [0.414709, 0.110431, 0.504662],
               [0.420791, 0.112920, 0.505215],
               [0.426877, 0.115395, 0.505714],
               [0.432967, 0.117855, 0.506160],
               [0.439062, 0.120298, 0.506555],
               [0.445163, 0.122724, 0.506901],
               [0.451271, 0.125132, 0.507198],
               [0.457386, 0.127522, 0.507448],
               [0.463508, 0.129893, 0.507652],
               [0.469640, 0.132245, 0.507809],
               [0.475780, 0.134577, 0.507921],
               [0.481929, 0.136891, 0.507989],
               [0.488088, 0.139186, 0.508011],
               [0.494258, 0.141462, 0.507988],
               [0.500438, 0.143719, 0.507920],
               [0.506629, 0.145958, 0.507806],
               [0.512831, 0.148179, 0.507648],
               [0.519045, 0.150383, 0.507443],
               [0.525270, 0.152569, 0.507192],
               [0.531507, 0.154739, 0.506895],
               [0.537755, 0.156894, 0.506551],
               [0.544015, 0.159033, 0.506159],
               [0.550287, 0.161158, 0.505719],
               [0.556571, 0.163269, 0.505230],
               [0.562866, 0.165368, 0.504692],
               [0.569172, 0.167454, 0.504105],
               [0.575490, 0.169530, 0.503466],
               [0.581819, 0.171596, 0.502777],
               [0.588158, 0.173652, 0.502035],
               [0.594508, 0.175701, 0.501241],
               [0.600868, 0.177743, 0.500394],
               [0.607238, 0.179779, 0.499492],
               [0.613617, 0.181811, 0.498536],
               [0.620005, 0.183840, 0.497524],
               [0.626401, 0.185867, 0.496456],
               [0.632805, 0.187893, 0.495332],
               [0.639216, 0.189921, 0.494150],
               [0.645633, 0.191952, 0.492910],
               [0.652056, 0.193986, 0.491611],
               [0.658483, 0.196027, 0.490253],
               [0.664915, 0.198075, 0.488836],
               [0.671349, 0.200133, 0.487358],
               [0.677786, 0.202203, 0.485819],
               [0.684224, 0.204286, 0.484219],
               [0.690661, 0.206384, 0.482558],
               [0.697098, 0.208501, 0.480835],
               [0.703532, 0.210638, 0.479049],
               [0.709962, 0.212797, 0.477201],
               [0.716387, 0.214982, 0.475290],
               [0.722805, 0.217194, 0.473316],
               [0.729216, 0.219437, 0.471279],
               [0.735616, 0.221713, 0.469180],
               [0.742004, 0.224025, 0.467018],
               [0.748378, 0.226377, 0.464794],
               [0.754737, 0.228772, 0.462509],
               [0.761077, 0.231214, 0.460162],
               [0.767398, 0.233705, 0.457755],
               [0.773695, 0.236249, 0.455289],
               [0.779968, 0.238851, 0.452765],
               [0.786212, 0.241514, 0.450184],
               [0.792427, 0.244242, 0.447543],
               [0.798608, 0.247040, 0.444848],
               [0.804752, 0.249911, 0.442102],
               [0.810855, 0.252861, 0.439305],
               [0.816914, 0.255895, 0.436461],
               [0.822926, 0.259016, 0.433573],
               [0.828886, 0.262229, 0.430644],
               [0.834791, 0.265540, 0.427671],
               [0.840636, 0.268953, 0.424666],
               [0.846416, 0.272473, 0.421631],
               [0.852126, 0.276106, 0.418573],
               [0.857763, 0.279857, 0.415496],
               [0.863320, 0.283729, 0.412403],
               [0.868793, 0.287728, 0.409303],
               [0.874176, 0.291859, 0.406205],
               [0.879464, 0.296125, 0.403118],
               [0.884651, 0.300530, 0.400047],
               [0.889731, 0.305079, 0.397002],
               [0.894700, 0.309773, 0.393995],
               [0.899552, 0.314616, 0.391037],
               [0.904281, 0.319610, 0.388137],
               [0.908884, 0.324755, 0.385308],
               [0.913354, 0.330052, 0.382563],
               [0.917689, 0.335500, 0.379915],
               [0.921884, 0.341098, 0.377376],
               [0.925937, 0.346844, 0.374959],
               [0.929845, 0.352734, 0.372677],
               [0.933606, 0.358764, 0.370541],
               [0.937221, 0.364929, 0.368567],
               [0.940687, 0.371224, 0.366762],
               [0.944006, 0.377643, 0.365136],
               [0.947180, 0.384178, 0.363701],
               [0.950210, 0.390820, 0.362468],
               [0.953099, 0.397563, 0.361438],
               [0.955849, 0.404400, 0.360619],
               [0.958464, 0.411324, 0.360014],
               [0.960949, 0.418323, 0.359630],
               [0.963310, 0.425390, 0.359469],
               [0.965549, 0.432519, 0.359529],
               [0.967671, 0.439703, 0.359810],
               [0.969680, 0.446936, 0.360311],
               [0.971582, 0.454210, 0.361030],
               [0.973381, 0.461520, 0.361965],
               [0.975082, 0.468861, 0.363111],
               [0.976690, 0.476226, 0.364466],
               [0.978210, 0.483612, 0.366025],
               [0.979645, 0.491014, 0.367783],
               [0.981000, 0.498428, 0.369734],
               [0.982279, 0.505851, 0.371874],
               [0.983485, 0.513280, 0.374198],
               [0.984622, 0.520713, 0.376698],
               [0.985693, 0.528148, 0.379371],
               [0.986700, 0.535582, 0.382210],
               [0.987646, 0.543015, 0.385210],
               [0.988533, 0.550446, 0.388365],
               [0.989363, 0.557873, 0.391671],
               [0.990138, 0.565296, 0.395122],
               [0.990871, 0.572706, 0.398714],
               [0.991558, 0.580107, 0.402441],
               [0.992196, 0.587502, 0.406299],
               [0.992785, 0.594891, 0.410283],
               [0.993326, 0.602275, 0.414390],
               [0.993834, 0.609644, 0.418613],
               [0.994309, 0.616999, 0.422950],
               [0.994738, 0.624350, 0.427397],
               [0.995122, 0.631696, 0.431951],
               [0.995480, 0.639027, 0.436607],
               [0.995810, 0.646344, 0.441361],
               [0.996096, 0.653659, 0.446213],
               [0.996341, 0.660969, 0.451160],
               [0.996580, 0.668256, 0.456192],
               [0.996775, 0.675541, 0.461314],
               [0.996925, 0.682828, 0.466526],
               [0.997077, 0.690088, 0.471811],
               [0.997186, 0.697349, 0.477182],
               [0.997254, 0.704611, 0.482635],
               [0.997325, 0.711848, 0.488154],
               [0.997351, 0.719089, 0.493755],
               [0.997351, 0.726324, 0.499428],
               [0.997341, 0.733545, 0.505167],
               [0.997285, 0.740772, 0.510983],
               [0.997228, 0.747981, 0.516859],
               [0.997138, 0.755190, 0.522806],
               [0.997019, 0.762398, 0.528821],
               [0.996898, 0.769591, 0.534892],
               [0.996727, 0.776795, 0.541039],
               [0.996571, 0.783977, 0.547233],
               [0.996369, 0.791167, 0.553499],
               [0.996162, 0.798348, 0.559820],
               [0.995932, 0.805527, 0.566202],
               [0.995680, 0.812706, 0.572645],
               [0.995424, 0.819875, 0.579140],
               [0.995131, 0.827052, 0.585701],
               [0.994851, 0.834213, 0.592307],
               [0.994524, 0.841387, 0.598983],
               [0.994222, 0.848540, 0.605696],
               [0.993866, 0.855711, 0.612482],
               [0.993545, 0.862859, 0.619299],
               [0.993170, 0.870024, 0.626189],
               [0.992831, 0.877168, 0.633109],
               [0.992440, 0.884330, 0.640099],
               [0.992089, 0.891470, 0.647116],
               [0.991688, 0.898627, 0.654202],
               [0.991332, 0.905763, 0.661309],
               [0.990930, 0.912915, 0.668481],
               [0.990570, 0.920049, 0.675675],
               [0.990175, 0.927196, 0.682926],
               [0.989815, 0.934329, 0.690198],
               [0.989434, 0.941470, 0.697519],
               [0.989077, 0.948604, 0.704863],
               [0.988717, 0.955742, 0.712242],
               [0.988367, 0.962878, 0.719649],
               [0.988033, 0.970012, 0.727077],
               [0.987691, 0.977154, 0.734536],
               [0.987387, 0.984288, 0.742002],
               [0.987053, 0.991438, 0.749504]]

_inferno_data = [[0.001462, 0.000466, 0.013866],
                 [0.002267, 0.001270, 0.018570],
                 [0.003299, 0.002249, 0.024239],
                 [0.004547, 0.003392, 0.030909],
                 [0.006006, 0.004692, 0.038558],
                 [0.007676, 0.006136, 0.046836],
                 [0.009561, 0.007713, 0.055143],
                 [0.011663, 0.009417, 0.063460],
                 [0.013995, 0.011225, 0.071862],
                 [0.016561, 0.013136, 0.080282],
                 [0.019373, 0.015133, 0.088767],
                 [0.022447, 0.017199, 0.097327],
                 [0.025793, 0.019331, 0.105930],
                 [0.029432, 0.021503, 0.114621],
                 [0.033385, 0.023702, 0.123397],
                 [0.037668, 0.025921, 0.132232],
                 [0.042253, 0.028139, 0.141141],
                 [0.046915, 0.030324, 0.150164],
                 [0.051644, 0.032474, 0.159254],
                 [0.056449, 0.034569, 0.168414],
                 [0.061340, 0.036590, 0.177642],
                 [0.066331, 0.038504, 0.186962],
                 [0.071429, 0.040294, 0.196354],
                 [0.076637, 0.041905, 0.205799],
                 [0.081962, 0.043328, 0.215289],
                 [0.087411, 0.044556, 0.224813],
                 [0.092990, 0.045583, 0.234358],
                 [0.098702, 0.046402, 0.243904],
                 [0.104551, 0.047008, 0.253430],
                 [0.110536, 0.047399, 0.262912],
                 [0.116656, 0.047574, 0.272321],
                 [0.122908, 0.047536, 0.281624],
                 [0.129285, 0.047293, 0.290788],
                 [0.135778, 0.046856, 0.299776],
                 [0.142378, 0.046242, 0.308553],
                 [0.149073, 0.045468, 0.317085],
                 [0.155850, 0.044559, 0.325338],
                 [0.162689, 0.043554, 0.333277],
                 [0.169575, 0.042489, 0.340874],
                 [0.176493, 0.041402, 0.348111],
                 [0.183429, 0.040329, 0.354971],
                 [0.190367, 0.039309, 0.361447],
                 [0.197297, 0.038400, 0.367535],
                 [0.204209, 0.037632, 0.373238],
                 [0.211095, 0.037030, 0.378563],
                 [0.217949, 0.036615, 0.383522],
                 [0.224763, 0.036405, 0.388129],
                 [0.231538, 0.036405, 0.392400],
                 [0.238273, 0.036621, 0.396353],
                 [0.244967, 0.037055, 0.400007],
                 [0.251620, 0.037705, 0.403378],
                 [0.258234, 0.038571, 0.406485],
                 [0.264810, 0.039647, 0.409345],
                 [0.271347, 0.040922, 0.411976],
                 [0.277850, 0.042353, 0.414392],
                 [0.284321, 0.043933, 0.416608],
                 [0.290763, 0.045644, 0.418637],
                 [0.297178, 0.047470, 0.420491],
                 [0.303568, 0.049396, 0.422182],
                 [0.309935, 0.051407, 0.423721],
                 [0.316282, 0.053490, 0.425116],
                 [0.322610, 0.055634, 0.426377],
                 [0.328921, 0.057827, 0.427511],
                 [0.335217, 0.060060, 0.428524],
                 [0.341500, 0.062325, 0.429425],
                 [0.347771, 0.064616, 0.430217],
                 [0.354032, 0.066925, 0.430906],
                 [0.360284, 0.069247, 0.431497],
                 [0.366529, 0.071579, 0.431994],
                 [0.372768, 0.073915, 0.432400],
                 [0.379001, 0.076253, 0.432719],
                 [0.385228, 0.078591, 0.432955],
                 [0.391453, 0.080927, 0.433109],
                 [0.397674, 0.083257, 0.433183],
                 [0.403894, 0.085580, 0.433179],
                 [0.410113, 0.087896, 0.433098],
                 [0.416331, 0.090203, 0.432943],
                 [0.422549, 0.092501, 0.432714],
                 [0.428768, 0.094790, 0.432412],
                 [0.434987, 0.097069, 0.432039],
                 [0.441207, 0.099338, 0.431594],
                 [0.447428, 0.101597, 0.431080],
                 [0.453651, 0.103848, 0.430498],
                 [0.459875, 0.106089, 0.429846],
                 [0.466100, 0.108322, 0.429125],
                 [0.472328, 0.110547, 0.428334],
                 [0.478558, 0.112764, 0.427475],
                 [0.484789, 0.114974, 0.426548],
                 [0.491022, 0.117179, 0.425552],
                 [0.497257, 0.119379, 0.424488],
                 [0.503493, 0.121575, 0.423356],
                 [0.509730, 0.123769, 0.422156],
                 [0.515967, 0.125960, 0.420887],
                 [0.522206, 0.128150, 0.419549],
                 [0.528444, 0.130341, 0.418142],
                 [0.534683, 0.132534, 0.416667],
                 [0.540920, 0.134729, 0.415123],
                 [0.547157, 0.136929, 0.413511],
                 [0.553392, 0.139134, 0.411829],
                 [0.559624, 0.141346, 0.410078],
                 [0.565854, 0.143567, 0.408258],
                 [0.572081, 0.145797, 0.406369],
                 [0.578304, 0.148039, 0.404411],
                 [0.584521, 0.150294, 0.402385],
                 [0.590734, 0.152563, 0.400290],
                 [0.596940, 0.154848, 0.398125],
                 [0.603139, 0.157151, 0.395891],
                 [0.609330, 0.159474, 0.393589],
                 [0.615513, 0.161817, 0.391219],
                 [0.621685, 0.164184, 0.388781],
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                [0.966271, 0.901249, 0.148180],
                [0.964021, 0.907950, 0.149370],
                [0.961681, 0.914672, 0.150520],
                [0.959276, 0.921407, 0.151566],
                [0.956808, 0.928152, 0.152409],
                [0.954287, 0.934908, 0.152921],
                [0.951726, 0.941671, 0.152925],
                [0.949151, 0.948435, 0.152178],
                [0.946602, 0.955190, 0.150328],
                [0.944152, 0.961916, 0.146861],
                [0.941896, 0.968590, 0.140956],
                [0.940015, 0.975158, 0.131326]]

_viridis_data = [[0.267004, 0.004874, 0.329415],
                 [0.268510, 0.009605, 0.335427],
                 [0.269944, 0.014625, 0.341379],
                 [0.271305, 0.019942, 0.347269],
                 [0.272594, 0.025563, 0.353093],
                 [0.273809, 0.031497, 0.358853],
                 [0.274952, 0.037752, 0.364543],
                 [0.276022, 0.044167, 0.370164],
                 [0.277018, 0.050344, 0.375715],
                 [0.277941, 0.056324, 0.381191],
                 [0.278791, 0.062145, 0.386592],
                 [0.279566, 0.067836, 0.391917],
                 [0.280267, 0.073417, 0.397163],
                 [0.280894, 0.078907, 0.402329],
                 [0.281446, 0.084320, 0.407414],
                 [0.281924, 0.089666, 0.412415],
                 [0.282327, 0.094955, 0.417331],
                 [0.282656, 0.100196, 0.422160],
                 [0.282910, 0.105393, 0.426902],
                 [0.283091, 0.110553, 0.431554],
                 [0.283197, 0.115680, 0.436115],
                 [0.283229, 0.120777, 0.440584],
                 [0.283187, 0.125848, 0.444960],
                 [0.283072, 0.130895, 0.449241],
                 [0.282884, 0.135920, 0.453427],
                 [0.282623, 0.140926, 0.457517],
                 [0.282290, 0.145912, 0.461510],
                 [0.281887, 0.150881, 0.465405],
                 [0.281412, 0.155834, 0.469201],
                 [0.280868, 0.160771, 0.472899],
                 [0.280255, 0.165693, 0.476498],
                 [0.279574, 0.170599, 0.479997],
                 [0.278826, 0.175490, 0.483397],
                 [0.278012, 0.180367, 0.486697],
                 [0.277134, 0.185228, 0.489898],
                 [0.276194, 0.190074, 0.493001],
                 [0.275191, 0.194905, 0.496005],
                 [0.274128, 0.199721, 0.498911],
                 [0.273006, 0.204520, 0.501721],
                 [0.271828, 0.209303, 0.504434],
                 [0.270595, 0.214069, 0.507052],
                 [0.269308, 0.218818, 0.509577],
                 [0.267968, 0.223549, 0.512008],
                 [0.266580, 0.228262, 0.514349],
                 [0.265145, 0.232956, 0.516599],
                 [0.263663, 0.237631, 0.518762],
                 [0.262138, 0.242286, 0.520837],
                 [0.260571, 0.246922, 0.522828],
                 [0.258965, 0.251537, 0.524736],
                 [0.257322, 0.256130, 0.526563],
                 [0.255645, 0.260703, 0.528312],
                 [0.253935, 0.265254, 0.529983],
                 [0.252194, 0.269783, 0.531579],
                 [0.250425, 0.274290, 0.533103],
                 [0.248629, 0.278775, 0.534556],
                 [0.246811, 0.283237, 0.535941],
                 [0.244972, 0.287675, 0.537260],
                 [0.243113, 0.292092, 0.538516],
                 [0.241237, 0.296485, 0.539709],
                 [0.239346, 0.300855, 0.540844],
                 [0.237441, 0.305202, 0.541921],
                 [0.235526, 0.309527, 0.542944],
                 [0.233603, 0.313828, 0.543914],
                 [0.231674, 0.318106, 0.544834],
                 [0.229739, 0.322361, 0.545706],
                 [0.227802, 0.326594, 0.546532],
                 [0.225863, 0.330805, 0.547314],
                 [0.223925, 0.334994, 0.548053],
                 [0.221989, 0.339161, 0.548752],
                 [0.220057, 0.343307, 0.549413],
                 [0.218130, 0.347432, 0.550038],
                 [0.216210, 0.351535, 0.550627],
                 [0.214298, 0.355619, 0.551184],
                 [0.212395, 0.359683, 0.551710],
                 [0.210503, 0.363727, 0.552206],
                 [0.208623, 0.367752, 0.552675],
                 [0.206756, 0.371758, 0.553117],
                 [0.204903, 0.375746, 0.553533],
                 [0.203063, 0.379716, 0.553925],
                 [0.201239, 0.383670, 0.554294],
                 [0.199430, 0.387607, 0.554642],
                 [0.197636, 0.391528, 0.554969],
                 [0.195860, 0.395433, 0.555276],
                 [0.194100, 0.399323, 0.555565],
                 [0.192357, 0.403199, 0.555836],
                 [0.190631, 0.407061, 0.556089],
                 [0.188923, 0.410910, 0.556326],
                 [0.187231, 0.414746, 0.556547],
                 [0.185556, 0.418570, 0.556753],
                 [0.183898, 0.422383, 0.556944],
                 [0.182256, 0.426184, 0.557120],
                 [0.180629, 0.429975, 0.557282],
                 [0.179019, 0.433756, 0.557430],
                 [0.177423, 0.437527, 0.557565],
                 [0.175841, 0.441290, 0.557685],
                 [0.174274, 0.445044, 0.557792],
                 [0.172719, 0.448791, 0.557885],
                 [0.171176, 0.452530, 0.557965],
                 [0.169646, 0.456262, 0.558030],
                 [0.168126, 0.459988, 0.558082],
                 [0.166617, 0.463708, 0.558119],
                 [0.165117, 0.467423, 0.558141],
                 [0.163625, 0.471133, 0.558148],
                 [0.162142, 0.474838, 0.558140],
                 [0.160665, 0.478540, 0.558115],
                 [0.159194, 0.482237, 0.558073],
                 [0.157729, 0.485932, 0.558013],
                 [0.156270, 0.489624, 0.557936],
                 [0.154815, 0.493313, 0.557840],
                 [0.153364, 0.497000, 0.557724],
                 [0.151918, 0.500685, 0.557587],
                 [0.150476, 0.504369, 0.557430],
                 [0.149039, 0.508051, 0.557250],
                 [0.147607, 0.511733, 0.557049],
                 [0.146180, 0.515413, 0.556823],
                 [0.144759, 0.519093, 0.556572],
                 [0.143343, 0.522773, 0.556295],
                 [0.141935, 0.526453, 0.555991],
                 [0.140536, 0.530132, 0.555659],
                 [0.139147, 0.533812, 0.555298],
                 [0.137770, 0.537492, 0.554906],
                 [0.136408, 0.541173, 0.554483],
                 [0.135066, 0.544853, 0.554029],
                 [0.133743, 0.548535, 0.553541],
                 [0.132444, 0.552216, 0.553018],
                 [0.131172, 0.555899, 0.552459],
                 [0.129933, 0.559582, 0.551864],
                 [0.128729, 0.563265, 0.551229],
                 [0.127568, 0.566949, 0.550556],
                 [0.126453, 0.570633, 0.549841],
                 [0.125394, 0.574318, 0.549086],
                 [0.124395, 0.578002, 0.548287],
                 [0.123463, 0.581687, 0.547445],
                 [0.122606, 0.585371, 0.546557],
                 [0.121831, 0.589055, 0.545623],
                 [0.121148, 0.592739, 0.544641],
                 [0.120565, 0.596422, 0.543611],
                 [0.120092, 0.600104, 0.542530],
                 [0.119738, 0.603785, 0.541400],
                 [0.119512, 0.607464, 0.540218],
                 [0.119423, 0.611141, 0.538982],
                 [0.119483, 0.614817, 0.537692],
                 [0.119699, 0.618490, 0.536347],
                 [0.120081, 0.622161, 0.534946],
                 [0.120638, 0.625828, 0.533488],
                 [0.121380, 0.629492, 0.531973],
                 [0.122312, 0.633153, 0.530398],
                 [0.123444, 0.636809, 0.528763],
                 [0.124780, 0.640461, 0.527068],
                 [0.126326, 0.644107, 0.525311],
                 [0.128087, 0.647749, 0.523491],
                 [0.130067, 0.651384, 0.521608],
                 [0.132268, 0.655014, 0.519661],
                 [0.134692, 0.658636, 0.517649],
                 [0.137339, 0.662252, 0.515571],
                 [0.140210, 0.665859, 0.513427],
                 [0.143303, 0.669459, 0.511215],
                 [0.146616, 0.673050, 0.508936],
                 [0.150148, 0.676631, 0.506589],
                 [0.153894, 0.680203, 0.504172],
                 [0.157851, 0.683765, 0.501686],
                 [0.162016, 0.687316, 0.499129],
                 [0.166383, 0.690856, 0.496502],
                 [0.170948, 0.694384, 0.493803],
                 [0.175707, 0.697900, 0.491033],
                 [0.180653, 0.701402, 0.488189],
                 [0.185783, 0.704891, 0.485273],
                 [0.191090, 0.708366, 0.482284],
                 [0.196571, 0.711827, 0.479221],
                 [0.202219, 0.715272, 0.476084],
                 [0.208030, 0.718701, 0.472873],
                 [0.214000, 0.722114, 0.469588],
                 [0.220124, 0.725509, 0.466226],
                 [0.226397, 0.728888, 0.462789],
                 [0.232815, 0.732247, 0.459277],
                 [0.239374, 0.735588, 0.455688],
                 [0.246070, 0.738910, 0.452024],
                 [0.252899, 0.742211, 0.448284],
                 [0.259857, 0.745492, 0.444467],
                 [0.266941, 0.748751, 0.440573],
                 [0.274149, 0.751988, 0.436601],
                 [0.281477, 0.755203, 0.432552],
                 [0.288921, 0.758394, 0.428426],
                 [0.296479, 0.761561, 0.424223],
                 [0.304148, 0.764704, 0.419943],
                 [0.311925, 0.767822, 0.415586],
                 [0.319809, 0.770914, 0.411152],
                 [0.327796, 0.773980, 0.406640],
                 [0.335885, 0.777018, 0.402049],
                 [0.344074, 0.780029, 0.397381],
                 [0.352360, 0.783011, 0.392636],
                 [0.360741, 0.785964, 0.387814],
                 [0.369214, 0.788888, 0.382914],
                 [0.377779, 0.791781, 0.377939],
                 [0.386433, 0.794644, 0.372886],
                 [0.395174, 0.797475, 0.367757],
                 [0.404001, 0.800275, 0.362552],
                 [0.412913, 0.803041, 0.357269],
                 [0.421908, 0.805774, 0.351910],
                 [0.430983, 0.808473, 0.346476],
                 [0.440137, 0.811138, 0.340967],
                 [0.449368, 0.813768, 0.335384],
                 [0.458674, 0.816363, 0.329727],
                 [0.468053, 0.818921, 0.323998],
                 [0.477504, 0.821444, 0.318195],
                 [0.487026, 0.823929, 0.312321],
                 [0.496615, 0.826376, 0.306377],
                 [0.506271, 0.828786, 0.300362],
                 [0.515992, 0.831158, 0.294279],
                 [0.525776, 0.833491, 0.288127],
                 [0.535621, 0.835785, 0.281908],
                 [0.545524, 0.838039, 0.275626],
                 [0.555484, 0.840254, 0.269281],
                 [0.565498, 0.842430, 0.262877],
                 [0.575563, 0.844566, 0.256415],
                 [0.585678, 0.846661, 0.249897],
                 [0.595839, 0.848717, 0.243329],
                 [0.606045, 0.850733, 0.236712],
                 [0.616293, 0.852709, 0.230052],
                 [0.626579, 0.854645, 0.223353],
                 [0.636902, 0.856542, 0.216620],
                 [0.647257, 0.858400, 0.209861],
                 [0.657642, 0.860219, 0.203082],
                 [0.668054, 0.861999, 0.196293],
                 [0.678489, 0.863742, 0.189503],
                 [0.688944, 0.865448, 0.182725],
                 [0.699415, 0.867117, 0.175971],
                 [0.709898, 0.868751, 0.169257],
                 [0.720391, 0.870350, 0.162603],
                 [0.730889, 0.871916, 0.156029],
                 [0.741388, 0.873449, 0.149561],
                 [0.751884, 0.874951, 0.143228],
                 [0.762373, 0.876424, 0.137064],
                 [0.772852, 0.877868, 0.131109],
                 [0.783315, 0.879285, 0.125405],
                 [0.793760, 0.880678, 0.120005],
                 [0.804182, 0.882046, 0.114965],
                 [0.814576, 0.883393, 0.110347],
                 [0.824940, 0.884720, 0.106217],
                 [0.835270, 0.886029, 0.102646],
                 [0.845561, 0.887322, 0.099702],
                 [0.855810, 0.888601, 0.097452],
                 [0.866013, 0.889868, 0.095953],
                 [0.876168, 0.891125, 0.095250],
                 [0.886271, 0.892374, 0.095374],
                 [0.896320, 0.893616, 0.096335],
                 [0.906311, 0.894855, 0.098125],
                 [0.916242, 0.896091, 0.100717],
                 [0.926106, 0.897330, 0.104071],
                 [0.935904, 0.898570, 0.108131],
                 [0.945636, 0.899815, 0.112838],
                 [0.955300, 0.901065, 0.118128],
                 [0.964894, 0.902323, 0.123941],
                 [0.974417, 0.903590, 0.130215],
                 [0.983868, 0.904867, 0.136897],
                 [0.993248, 0.906157, 0.143936]]

from matplotlib.colors import ListedColormap

cmaps = {}
for (name, data) in (('magma', _magma_data),
                     ('inferno', _inferno_data),
                     ('plasma', _plasma_data),
                     ('viridis', _viridis_data)):

    cmaps[name] = ListedColormap(data, name=name)

magma = cmaps['magma']
inferno = cmaps['inferno']
plasma = cmaps['plasma']
viridis = cmaps['viridis']
