# 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, array
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, zeros_like
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)
x_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 = np.diff(np.concatenate(([0], same_real, [0])))
run_starts = np.where(diffs > 0)[0]
run_stops = np.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],
[0.627847, 0.166575, 0.386276],
[0.633998, 0.168992, 0.383704],
[0.640135, 0.171438, 0.381065],
[0.646260, 0.173914, 0.378359],
[0.652369, 0.176421, 0.375586],
[0.658463, 0.178962, 0.372748],
[0.664540, 0.181539, 0.369846],
[0.670599, 0.184153, 0.366879],
[0.676638, 0.186807, 0.363849],
[0.682656, 0.189501, 0.360757],
[0.688653, 0.192239, 0.357603],
[0.694627, 0.195021, 0.354388],
[0.700576, 0.197851, 0.351113],
[0.706500, 0.200728, 0.347777],
[0.712396, 0.203656, 0.344383],
[0.718264, 0.206636, 0.340931],
[0.724103, 0.209670, 0.337424],
[0.729909, 0.212759, 0.333861],
[0.735683, 0.215906, 0.330245],
[0.741423, 0.219112, 0.326576],
[0.747127, 0.222378, 0.322856],
[0.752794, 0.225706, 0.319085],
[0.758422, 0.229097, 0.315266],
[0.764010, 0.232554, 0.311399],
[0.769556, 0.236077, 0.307485],
[0.775059, 0.239667, 0.303526],
[0.780517, 0.243327, 0.299523],
[0.785929, 0.247056, 0.295477],
[0.791293, 0.250856, 0.291390],
[0.796607, 0.254728, 0.287264],
[0.801871, 0.258674, 0.283099],
[0.807082, 0.262692, 0.278898],
[0.812239, 0.266786, 0.274661],
[0.817341, 0.270954, 0.270390],
[0.822386, 0.275197, 0.266085],
[0.827372, 0.279517, 0.261750],
[0.832299, 0.283913, 0.257383],
[0.837165, 0.288385, 0.252988],
[0.841969, 0.292933, 0.248564],
[0.846709, 0.297559, 0.244113],
[0.851384, 0.302260, 0.239636],
[0.855992, 0.307038, 0.235133],
[0.860533, 0.311892, 0.230606],
[0.865006, 0.316822, 0.226055],
[0.869409, 0.321827, 0.221482],
[0.873741, 0.326906, 0.216886],
[0.878001, 0.332060, 0.212268],
[0.882188, 0.337287, 0.207628],
[0.886302, 0.342586, 0.202968],
[0.890341, 0.347957, 0.198286],
[0.894305, 0.353399, 0.193584],
[0.898192, 0.358911, 0.188860],
[0.902003, 0.364492, 0.184116],
[0.905735, 0.370140, 0.179350],
[0.909390, 0.375856, 0.174563],
[0.912966, 0.381636, 0.169755],
[0.916462, 0.387481, 0.164924],
[0.919879, 0.393389, 0.160070],
[0.923215, 0.399359, 0.155193],
[0.926470, 0.405389, 0.150292],
[0.929644, 0.411479, 0.145367],
[0.932737, 0.417627, 0.140417],
[0.935747, 0.423831, 0.135440],
[0.938675, 0.430091, 0.130438],
[0.941521, 0.436405, 0.125409],
[0.944285, 0.442772, 0.120354],
[0.946965, 0.449191, 0.115272],
[0.949562, 0.455660, 0.110164],
[0.952075, 0.462178, 0.105031],
[0.954506, 0.468744, 0.099874],
[0.956852, 0.475356, 0.094695],
[0.959114, 0.482014, 0.089499],
[0.961293, 0.488716, 0.084289],
[0.963387, 0.495462, 0.079073],
[0.965397, 0.502249, 0.073859],
[0.967322, 0.509078, 0.068659],
[0.969163, 0.515946, 0.063488],
[0.970919, 0.522853, 0.058367],
[0.972590, 0.529798, 0.053324],
[0.974176, 0.536780, 0.048392],
[0.975677, 0.543798, 0.043618],
[0.977092, 0.550850, 0.039050],
[0.978422, 0.557937, 0.034931],
[0.979666, 0.565057, 0.031409],
[0.980824, 0.572209, 0.028508],
[0.981895, 0.579392, 0.026250],
[0.982881, 0.586606, 0.024661],
[0.983779, 0.593849, 0.023770],
[0.984591, 0.601122, 0.023606],
[0.985315, 0.608422, 0.024202],
[0.985952, 0.615750, 0.025592],
[0.986502, 0.623105, 0.027814],
[0.986964, 0.630485, 0.030908],
[0.987337, 0.637890, 0.034916],
[0.987622, 0.645320, 0.039886],
[0.987819, 0.652773, 0.045581],
[0.987926, 0.660250, 0.051750],
[0.987945, 0.667748, 0.058329],
[0.987874, 0.675267, 0.065257],
[0.987714, 0.682807, 0.072489],
[0.987464, 0.690366, 0.079990],
[0.987124, 0.697944, 0.087731],
[0.986694, 0.705540, 0.095694],
[0.986175, 0.713153, 0.103863],
[0.985566, 0.720782, 0.112229],
[0.984865, 0.728427, 0.120785],
[0.984075, 0.736087, 0.129527],
[0.983196, 0.743758, 0.138453],
[0.982228, 0.751442, 0.147565],
[0.981173, 0.759135, 0.156863],
[0.980032, 0.766837, 0.166353],
[0.978806, 0.774545, 0.176037],
[0.977497, 0.782258, 0.185923],
[0.976108, 0.789974, 0.196018],
[0.974638, 0.797692, 0.206332],
[0.973088, 0.805409, 0.216877],
[0.971468, 0.813122, 0.227658],
[0.969783, 0.820825, 0.238686],
[0.968041, 0.828515, 0.249972],
[0.966243, 0.836191, 0.261534],
[0.964394, 0.843848, 0.273391],
[0.962517, 0.851476, 0.285546],
[0.960626, 0.859069, 0.298010],
[0.958720, 0.866624, 0.310820],
[0.956834, 0.874129, 0.323974],
[0.954997, 0.881569, 0.337475],
[0.953215, 0.888942, 0.351369],
[0.951546, 0.896226, 0.365627],
[0.950018, 0.903409, 0.380271],
[0.948683, 0.910473, 0.395289],
[0.947594, 0.917399, 0.410665],
[0.946809, 0.924168, 0.426373],
[0.946392, 0.930761, 0.442367],
[0.946403, 0.937159, 0.458592],
[0.946903, 0.943348, 0.474970],
[0.947937, 0.949318, 0.491426],
[0.949545, 0.955063, 0.507860],
[0.951740, 0.960587, 0.524203],
[0.954529, 0.965896, 0.540361],
[0.957896, 0.971003, 0.556275],
[0.961812, 0.975924, 0.571925],
[0.966249, 0.980678, 0.587206],
[0.971162, 0.985282, 0.602154],
[0.976511, 0.989753, 0.616760],
[0.982257, 0.994109, 0.631017],
[0.988362, 0.998364, 0.644924]]
_plasma_data = [[0.050383, 0.029803, 0.527975],
[0.063536, 0.028426, 0.533124],
[0.075353, 0.027206, 0.538007],
[0.086222, 0.026125, 0.542658],
[0.096379, 0.025165, 0.547103],
[0.105980, 0.024309, 0.551368],
[0.115124, 0.023556, 0.555468],
[0.123903, 0.022878, 0.559423],
[0.132381, 0.022258, 0.563250],
[0.140603, 0.021687, 0.566959],
[0.148607, 0.021154, 0.570562],
[0.156421, 0.020651, 0.574065],
[0.164070, 0.020171, 0.577478],
[0.171574, 0.019706, 0.580806],
[0.178950, 0.019252, 0.584054],
[0.186213, 0.018803, 0.587228],
[0.193374, 0.018354, 0.590330],
[0.200445, 0.017902, 0.593364],
[0.207435, 0.017442, 0.596333],
[0.214350, 0.016973, 0.599239],
[0.221197, 0.016497, 0.602083],
[0.227983, 0.016007, 0.604867],
[0.234715, 0.015502, 0.607592],
[0.241396, 0.014979, 0.610259],
[0.248032, 0.014439, 0.612868],
[0.254627, 0.013882, 0.615419],
[0.261183, 0.013308, 0.617911],
[0.267703, 0.012716, 0.620346],
[0.274191, 0.012109, 0.622722],
[0.280648, 0.011488, 0.625038],
[0.287076, 0.010855, 0.627295],
[0.293478, 0.010213, 0.629490],
[0.299855, 0.009561, 0.631624],
[0.306210, 0.008902, 0.633694],
[0.312543, 0.008239, 0.635700],
[0.318856, 0.007576, 0.637640],
[0.325150, 0.006915, 0.639512],
[0.331426, 0.006261, 0.641316],
[0.337683, 0.005618, 0.643049],
[0.343925, 0.004991, 0.644710],
[0.350150, 0.004382, 0.646298],
[0.356359, 0.003798, 0.647810],
[0.362553, 0.003243, 0.649245],
[0.368733, 0.002724, 0.650601],
[0.374897, 0.002245, 0.651876],
[0.381047, 0.001814, 0.653068],
[0.387183, 0.001434, 0.654177],
[0.393304, 0.001114, 0.655199],
[0.399411, 0.000859, 0.656133],
[0.405503, 0.000678, 0.656977],
[0.411580, 0.000577, 0.657730],
[0.417642, 0.000564, 0.658390],
[0.423689, 0.000646, 0.658956],
[0.429719, 0.000831, 0.659425],
[0.435734, 0.001127, 0.659797],
[0.441732, 0.001540, 0.660069],
[0.447714, 0.002080, 0.660240],
[0.453677, 0.002755, 0.660310],
[0.459623, 0.003574, 0.660277],
[0.465550, 0.004545, 0.660139],
[0.471457, 0.005678, 0.659897],
[0.477344, 0.006980, 0.659549],
[0.483210, 0.008460, 0.659095],
[0.489055, 0.010127, 0.658534],
[0.494877, 0.011990, 0.657865],
[0.500678, 0.014055, 0.657088],
[0.506454, 0.016333, 0.656202],
[0.512206, 0.018833, 0.655209],
[0.517933, 0.021563, 0.654109],
[0.523633, 0.024532, 0.652901],
[0.529306, 0.027747, 0.651586],
[0.534952, 0.031217, 0.650165],
[0.540570, 0.034950, 0.648640],
[0.546157, 0.038954, 0.647010],
[0.551715, 0.043136, 0.645277],
[0.557243, 0.047331, 0.643443],
[0.562738, 0.051545, 0.641509],
[0.568201, 0.055778, 0.639477],
[0.573632, 0.060028, 0.637349],
[0.579029, 0.064296, 0.635126],
[0.584391, 0.068579, 0.632812],
[0.589719, 0.072878, 0.630408],
[0.595011, 0.077190, 0.627917],
[0.600266, 0.081516, 0.625342],
[0.605485, 0.085854, 0.622686],
[0.610667, 0.090204, 0.619951],
[0.615812, 0.094564, 0.617140],
[0.620919, 0.098934, 0.614257],
[0.625987, 0.103312, 0.611305],
[0.631017, 0.107699, 0.608287],
[0.636008, 0.112092, 0.605205],
[0.640959, 0.116492, 0.602065],
[0.645872, 0.120898, 0.598867],
[0.650746, 0.125309, 0.595617],
[0.655580, 0.129725, 0.592317],
[0.660374, 0.134144, 0.588971],
[0.665129, 0.138566, 0.585582],
[0.669845, 0.142992, 0.582154],
[0.674522, 0.147419, 0.578688],
[0.679160, 0.151848, 0.575189],
[0.683758, 0.156278, 0.571660],
[0.688318, 0.160709, 0.568103],
[0.692840, 0.165141, 0.564522],
[0.697324, 0.169573, 0.560919],
[0.701769, 0.174005, 0.557296],
[0.706178, 0.178437, 0.553657],
[0.710549, 0.182868, 0.550004],
[0.714883, 0.187299, 0.546338],
[0.719181, 0.191729, 0.542663],
[0.723444, 0.196158, 0.538981],
[0.727670, 0.200586, 0.535293],
[0.731862, 0.205013, 0.531601],
[0.736019, 0.209439, 0.527908],
[0.740143, 0.213864, 0.524216],
[0.744232, 0.218288, 0.520524],
[0.748289, 0.222711, 0.516834],
[0.752312, 0.227133, 0.513149],
[0.756304, 0.231555, 0.509468],
[0.760264, 0.235976, 0.505794],
[0.764193, 0.240396, 0.502126],
[0.768090, 0.244817, 0.498465],
[0.771958, 0.249237, 0.494813],
[0.775796, 0.253658, 0.491171],
[0.779604, 0.258078, 0.487539],
[0.783383, 0.262500, 0.483918],
[0.787133, 0.266922, 0.480307],
[0.790855, 0.271345, 0.476706],
[0.794549, 0.275770, 0.473117],
[0.798216, 0.280197, 0.469538],
[0.801855, 0.284626, 0.465971],
[0.805467, 0.289057, 0.462415],
[0.809052, 0.293491, 0.458870],
[0.812612, 0.297928, 0.455338],
[0.816144, 0.302368, 0.451816],
[0.819651, 0.306812, 0.448306],
[0.823132, 0.311261, 0.444806],
[0.826588, 0.315714, 0.441316],
[0.830018, 0.320172, 0.437836],
[0.833422, 0.324635, 0.434366],
[0.836801, 0.329105, 0.430905],
[0.840155, 0.333580, 0.427455],
[0.843484, 0.338062, 0.424013],
[0.846788, 0.342551, 0.420579],
[0.850066, 0.347048, 0.417153],
[0.853319, 0.351553, 0.413734],
[0.856547, 0.356066, 0.410322],
[0.859750, 0.360588, 0.406917],
[0.862927, 0.365119, 0.403519],
[0.866078, 0.369660, 0.400126],
[0.869203, 0.374212, 0.396738],
[0.872303, 0.378774, 0.393355],
[0.875376, 0.383347, 0.389976],
[0.878423, 0.387932, 0.386600],
[0.881443, 0.392529, 0.383229],
[0.884436, 0.397139, 0.379860],
[0.887402, 0.401762, 0.376494],
[0.890340, 0.406398, 0.373130],
[0.893250, 0.411048, 0.369768],
[0.896131, 0.415712, 0.366407],
[0.898984, 0.420392, 0.363047],
[0.901807, 0.425087, 0.359688],
[0.904601, 0.429797, 0.356329],
[0.907365, 0.434524, 0.352970],
[0.910098, 0.439268, 0.349610],
[0.912800, 0.444029, 0.346251],
[0.915471, 0.448807, 0.342890],
[0.918109, 0.453603, 0.339529],
[0.920714, 0.458417, 0.336166],
[0.923287, 0.463251, 0.332801],
[0.925825, 0.468103, 0.329435],
[0.928329, 0.472975, 0.326067],
[0.930798, 0.477867, 0.322697],
[0.933232, 0.482780, 0.319325],
[0.935630, 0.487712, 0.315952],
[0.937990, 0.492667, 0.312575],
[0.940313, 0.497642, 0.309197],
[0.942598, 0.502639, 0.305816],
[0.944844, 0.507658, 0.302433],
[0.947051, 0.512699, 0.299049],
[0.949217, 0.517763, 0.295662],
[0.951344, 0.522850, 0.292275],
[0.953428, 0.527960, 0.288883],
[0.955470, 0.533093, 0.285490],
[0.957469, 0.538250, 0.282096],
[0.959424, 0.543431, 0.278701],
[0.961336, 0.548636, 0.275305],
[0.963203, 0.553865, 0.271909],
[0.965024, 0.559118, 0.268513],
[0.966798, 0.564396, 0.265118],
[0.968526, 0.569700, 0.261721],
[0.970205, 0.575028, 0.258325],
[0.971835, 0.580382, 0.254931],
[0.973416, 0.585761, 0.251540],
[0.974947, 0.591165, 0.248151],
[0.976428, 0.596595, 0.244767],
[0.977856, 0.602051, 0.241387],
[0.979233, 0.607532, 0.238013],
[0.980556, 0.613039, 0.234646],
[0.981826, 0.618572, 0.231287],
[0.983041, 0.624131, 0.227937],
[0.984199, 0.629718, 0.224595],
[0.985301, 0.635330, 0.221265],
[0.986345, 0.640969, 0.217948],
[0.987332, 0.646633, 0.214648],
[0.988260, 0.652325, 0.211364],
[0.989128, 0.658043, 0.208100],
[0.989935, 0.663787, 0.204859],
[0.990681, 0.669558, 0.201642],
[0.991365, 0.675355, 0.198453],
[0.991985, 0.681179, 0.195295],
[0.992541, 0.687030, 0.192170],
[0.993032, 0.692907, 0.189084],
[0.993456, 0.698810, 0.186041],
[0.993814, 0.704741, 0.183043],
[0.994103, 0.710698, 0.180097],
[0.994324, 0.716681, 0.177208],
[0.994474, 0.722691, 0.174381],
[0.994553, 0.728728, 0.171622],
[0.994561, 0.734791, 0.168938],
[0.994495, 0.740880, 0.166335],
[0.994355, 0.746995, 0.163821],
[0.994141, 0.753137, 0.161404],
[0.993851, 0.759304, 0.159092],
[0.993482, 0.765499, 0.156891],
[0.993033, 0.771720, 0.154808],
[0.992505, 0.777967, 0.152855],
[0.991897, 0.784239, 0.151042],
[0.991209, 0.790537, 0.149377],
[0.990439, 0.796859, 0.147870],
[0.989587, 0.803205, 0.146529],
[0.988648, 0.809579, 0.145357],
[0.987621, 0.815978, 0.144363],
[0.986509, 0.822401, 0.143557],
[0.985314, 0.828846, 0.142945],
[0.984031, 0.835315, 0.142528],
[0.982653, 0.841812, 0.142303],
[0.981190, 0.848329, 0.142279],
[0.979644, 0.854866, 0.142453],
[0.977995, 0.861432, 0.142808],
[0.976265, 0.868016, 0.143351],
[0.974443, 0.874622, 0.144061],
[0.972530, 0.881250, 0.144923],
[0.970533, 0.887896, 0.145919],
[0.968443, 0.894564, 0.147014],
[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']
