# -*- coding: utf-8 -*-
# Aqua-Duct, a tool facilitating analysis of the flow of solvent molecules in molecular dynamic simulations
# Copyright (C) 2016-2017 Tomasz Magdziarz, Alicja Płuciennik, Michał Stolarczyk <info@aquaduct.pl>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, 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, see <http://www.gnu.org/licenses/>.
import numpy as np
# from matplotlib.colors import colorConverter
import scipy.spatial.distance as distance
from aquaduct.traj.paths import GenericPathTypeCodes as gptc
from aquaduct.traj.paths import PathTypesCodes as ptc
from aquaduct.visual.cmaps import default as default_cmap
from aquaduct.utils.helpers import zip_zip, is_number, lind
_cl2rgba = {'b': (0.0, 0.0, 1.0, 1.0),
'c': (0.0, 0.75, 0.75, 1.0),
'g': (0.0, 0.5, 0.0, 1.0),
'k': (0.0, 0.0, 0.0, 1.0),
'm': (0.75, 0, 0.75, 1.0),
'r': (1.0, 0.0, 0.0, 1.0),
'y': (0.75, 0.75, 0, 1.0),
'w': (1.0, 1.0, 1.0, 1.0),
's': (0.5, 0.5, 0.5, 1.0)}
[docs]def euclidean(A,B):
return distance.cdist(A, B, 'euclidean')
[docs]def cityblock(A,B):
return distance.cdist(A, B, 'cityblock')
# cc = lambda c, alpha=1.0: colorConverter.to_rgb(c)
[docs]def cc_safe(c):
# color converter
if c in 'rgbcmyk':
c = _cl2rgba[c]
c = tuple(c)
assert len(c) in [3, 4], 'Color should be given either as one letter (rgbcmyk) or as rgb or rgba vector.'
for e in c:
assert is_number(e), 'Color vector has to be specified as numbers.'
assert 0 <= e <= 1, 'Color vector elements have to be in range of 0 to 1.'
return c[:3]
[docs]def cc(c):
# color converter faster
if c in 'rgbcmykws':
c = _cl2rgba[c]
return c[:3]
_dcc_is = ptc.path_in_code + gptc.scope_name
_dcc_cc = ptc.path_object_code + gptc.object_name
_dcc_cs = ptc.path_object_code + gptc.scope_name
_dcc_os = ptc.path_out_code + gptc.scope_name
_dcc_ws = ptc.path_walk_code + gptc.scope_name
_dcc_i = ptc.path_in_code
_dcc_c = ptc.path_object_code
_dcc_o = ptc.path_out_code
_dcc_w = ptc.path_walk_code
_default_color_codes = {_dcc_is: 'r',
_dcc_cc: 'g',
_dcc_cs: 'y',
_dcc_os: 'b',
_dcc_ws: 's',
_dcc_i: 'r',
_dcc_c: 'g',
_dcc_o: 'b',
_dcc_w: 's'}
default_color_codes = _default_color_codes
[docs]def color_codes(code, custom_codes=None):
if custom_codes is None:
return default_color_codes[code]
else:
return custom_codes[code]
[docs]def get_cmap(size):
return [e[0][0] for e in zip_zip(default_cmap, N=size)]
[docs]class ColorMapDistMap(object):
grey = (0.5, 0.5, 0.5, 1)
[docs] def __init__(self):
self.cmap = default_cmap
self.cmap = self.__do_cadex()
[docs] def distance(self,E1,E2):
# E1 and E2 are RGBs matrices
D = []
for e1 in E1:
DD = []
for e2 in E2:
DD.append(self.color_distance(e1,e2))
D.append(DD)
return np.array(D)
[docs] @staticmethod
def color_distance(e1,e2):
# e1 and e2 are colors defs of rgb components in this order
# Taken from http://www.compuphase.com/cmetric.htm
# scale them to 0-255 range
def to0255(E):
return [int(e*255) for e in E]
e1 = to0255(e1)
e2 = to0255(e2)
rmean = (e1[0]+e2[0])/2
r = e1[0]-e2[0]
g = e1[1]-e2[1]
b = e1[2]-e2[2]
return np.sqrt((((512 + rmean) * r * r) >> 8) + 4 * g * g + (((767 - rmean) * b * b) >> 8))
def __do_cadex(self):
m = len(self.cmap) # number of objects
k = len(self.cmap)
# indices
mi = [] # model
ti = range(m) # test
# FIRST OBJECT
# get distance to mean object
Xm = np.array(self.cmap).mean(axis=0) # mean object
Xm.shape = (1,Xm.shape[0]) # fix shape
Dm = self.distance(Xm,np.array(self.cmap))
D = self.distance(np.array(self.cmap),np.array(self.cmap))
# min value will be the first object
#mi.append(ti.pop(Dm.argmin()))
mi.append(ti.pop(-1))
if k > 1:
# SECOND OBJECT
Xm = np.array(self.cmap)[mi[-1]]
Xm.shape = (1,Xm.shape[0]) # fix shape
Dm = self.distance(Xm,np.array(self.cmap)[ti,:])
# max value will be the first object
mi.append(ti.pop(Dm.argmax()))
if k > 2:
while (len(mi)<k):
Dm = D[:,ti][mi,:]
mi.append(ti.pop(Dm.min(axis=0).argmax()))
return lind(self.cmap,mi+ti)
[docs] def __call__(self, node):
if 0 < node:
# cycle...
return self.cmap[(node-1) % len(self.cmap)][:3]
#return self.cmap[int(np.round(self.cm_size * f_like(node)))][:3]
# return grey otherwise
return self.grey[:3]
[docs]def f_like(n):
if n == 1:
return 0.0
if n == 2:
return 0.5
n -= 1
order = np.floor(np.log(n) / np.log(2))
parts = 2 ** order
current = n - parts
return 0.5 / parts + 1. / parts * current
