"""Module with matplotlib plotting routines. These are not hooked up to
any particular qcdb data structures but can be called with basic
arguments.
"""
from __future__ import absolute_import
from __future__ import print_function
import os
#import matplotlib
#matplotlib.use('Agg')
[docs]def expand_saveas(saveas, def_filename, def_path=os.path.abspath(os.curdir), def_prefix='', relpath=False):
"""Analyzes string *saveas* to see if it contains information on
path to save file, name to save file, both or neither (*saveas*
ends in '/' to indicate directory only) (able to expand '.'). A full
absolute filename is returned, lacking only file extension. Based on
analysis of missing parts of *saveas*, path information from *def_path*
and/or filename information from *def_prefix* + *def_filename* is
inserted. *def_prefix* is intended to be something like ``mplthread_``
to identify the type of figure.
"""
defname = def_prefix + def_filename.replace(' ', '_')
if saveas is None:
pth = def_path
fil = defname
else:
pth, fil = os.path.split(saveas)
pth = pth if pth != '' else def_path
fil = fil if fil != '' else defname
abspathfile = os.path.join(os.path.abspath(pth), fil)
if relpath:
return os.path.relpath(abspathfile, os.getcwd())
else:
return abspathfile
[docs]def segment_color(argcolor, saptcolor):
"""Find appropriate color expression between overall color directive
*argcolor* and particular color availibility *rxncolor*.
"""
import matplotlib
# validate any sapt color
if saptcolor is not None:
if saptcolor < 0.0 or saptcolor > 1.0:
saptcolor = None
if argcolor is None:
# no color argument, so take from rxn
if rxncolor is None:
clr = 'grey'
elif saptcolor is not None:
clr = matplotlib.cm.jet(saptcolor)
else:
clr = rxncolor
elif argcolor == 'sapt':
# sapt color from rxn if available
if saptcolor is not None:
clr = matplotlib.cm.jet(saptcolor)
else:
clr = 'grey'
elif argcolor == 'rgb':
# HB/MX/DD sapt color from rxn if available
if saptcolor is not None:
if saptcolor < 0.333:
clr = 'blue'
elif saptcolor < 0.667:
clr = 'green'
else:
clr = 'red'
else:
clr = 'grey'
else:
# color argument is name of mpl color
clr = argcolor
return clr
[docs]def bars(data, title='', saveas=None, relpath=False, graphicsformat=['pdf'], view=True):
"""Generates a 'gray-bars' diagram between model chemistries with error
statistics in list *data*, which is supplied as part of the dictionary
for each participating bar/modelchem, along with *mc* keys in argument
*data*. The plot is labeled with *title* and each bar with *mc* key and
plotted at a fixed scale to facilitate comparison across projects.
"""
import hashlib
import matplotlib.pyplot as plt
# initialize plot, fix dimensions for consistent Illustrator import
fig, ax = plt.subplots(figsize=(12, 7))
plt.ylim([0, 4.86])
plt.xlim([0, 6])
plt.xticks([])
# label plot and tiers
ax.text(0.4, 4.6, title,
verticalalignment='bottom', horizontalalignment='left',
family='Times New Roman', weight='bold', fontsize=12)
widths = [0.15, 0.02, 0.02, 0.02] # TT, HB, MX, DD
xval = 0.1 # starting posn along x-axis
# plot bar sets
for bar in data:
if bar is not None:
lefts = [xval, xval + 0.025, xval + 0.065, xval + 0.105]
rect = ax.bar(lefts, bar['data'], widths, linewidth=0)
rect[0].set_color('grey')
rect[1].set_color('red')
rect[2].set_color('green')
rect[3].set_color('blue')
ax.text(xval + .08, 4.3, bar['mc'],
verticalalignment='center', horizontalalignment='right', rotation='vertical',
family='Times New Roman', fontsize=8)
xval += 0.20
# save and show
pltuid = title + '_' + hashlib.sha1(title + repr([bar['mc'] for bar in data if bar is not None])).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='bar_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight')
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close()
return files_saved
[docs]def flat(data, color=None, title='', xlimit=4.0, xlines=[0.0, 0.3, 1.0], mae=None, mape=None, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Generates a slat diagram between model chemistries with errors in
single-item list *data*, which is supplied as part of the dictionary
for each participating reaction, along with *dbse* and *rxn* keys in
argument *data*. Limits of plot are *xlimit* from the zero-line. If
*color* is None, slats are black, if 'sapt', colors are taken from
sapt_colors module. Summary statistic *mae* is plotted on the
overbound side and relative statistic *mape* on the underbound side.
Saves a file with name *title* and plots to screen if *view*.
"""
import matplotlib.pyplot as plt
Nweft = 1
positions = range(-1, -1 * Nweft - 1, -1)
# initialize plot
fig, ax = plt.subplots(figsize=(12, 0.33))
plt.xlim([-xlimit, xlimit])
plt.ylim([-1 * Nweft - 1, 0])
plt.yticks([])
plt.xticks([])
# fig.patch.set_visible(False)
# ax.patch.set_visible(False)
ax.axis('off')
for xl in xlines:
plt.axvline(xl, color='grey', linewidth=4)
if xl != 0.0:
plt.axvline(-1 * xl, color='grey', linewidth=4)
# plot reaction errors and threads
for rxn in data:
xvals = rxn['data']
clr = segment_color(color, rxn['color'] if 'color' in rxn else None)
ax.plot(xvals, positions, '|', color=clr, markersize=13.0, mew=4)
# plot trimmings
if mae is not None:
plt.axvline(-1 * mae, color='black', linewidth=12)
if mape is not None: # equivalent to MAE for a 10 kcal/mol interaction energy
ax.plot(0.025 * mape, positions, 'o', color='black', markersize=15.0)
# save and show
pltuid = title # simple (not really unique) filename for LaTeX integration
pltfile = expand_saveas(saveas, pltuid, def_prefix='flat_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight',
frameon=False, pad_inches=0.0)
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close() # give this a try
return files_saved
#def mpl_distslat_multiplot_files(pltfile, dbid, dbname, xmin, xmax, mcdats, labels, titles):
# """Saves a plot with basename *pltfile* with a slat representation
# of the modelchems errors in *mcdat*. Plot is in PNG, PDF, & EPS
# and suitable for download, no mouseover properties. Both labeled
# and labelless (for pub) figures are constructed.
#
# """
# import matplotlib as mpl
# from matplotlib.axes import Subplot
# import sapt_colors
# from matplotlib.figure import Figure
#
# nplots = len(mcdats)
# fht = nplots * 0.8
# fig, axt = plt.subplots(figsize=(12.0, fht))
# plt.subplots_adjust(left=0.01, right=0.99, hspace=0.3)
#
# axt.set_xticks([])
# axt.set_yticks([])
# plt.axis('off')
#
# for item in range(nplots):
# mcdat = mcdats[item]
# label = labels[item]
# title = titles[item]
#
# erdat = np.array(mcdat)
# yvals = np.ones(len(mcdat))
# y = np.array([sapt_colors.sapt_colors[dbname][i] for i in label])
#
# ax = Subplot(fig, nplots, 1, item + 1)
# fig.add_subplot(ax)
# sc = ax.scatter(erdat, yvals, c=y, s=3000, marker="|", cmap=mpl.cm.jet, vmin=0, vmax=1)
#
# ax.set_yticks([])
# ax.set_xticks([])
# ax.set_frame_on(False)
# ax.set_xlim([xmin, xmax])
#
# # Write files with only slats
# plt.savefig('scratch/' + pltfile + '_plain' + '.png', transparent=True, format='PNG')
# plt.savefig('scratch/' + pltfile + '_plain' + '.pdf', transparent=True, format='PDF')
# plt.savefig('scratch/' + pltfile + '_plain' + '.eps', transparent=True, format='EPS')
#
# # Rewrite files with guides and labels
# for item in range(nplots):
# ax_again = fig.add_subplot(nplots, 1, item + 1)
# ax_again.set_title(titles[item], fontsize=8)
# ax_again.text(xmin + 0.3, 1.0, stats(np.array(mcdats[item])), fontsize=7, family='monospace', verticalalignment='center')
# ax_again.plot([0, 0], [0.9, 1.1], color='#cccc00', lw=2)
# ax_again.set_frame_on(False)
# ax_again.set_yticks([])
# ax_again.set_xticks([-12.0, -8.0, -4.0, -2.0, -1.0, 0.0, 1.0, 2.0, 4.0, 8.0, 12.0])
# ax_again.tick_params(axis='both', which='major', labelbottom='off', bottom='off')
# ax_again.set_xticks([-12.0, -8.0, -4.0, -2.0, -1.0, 0.0, 1.0, 2.0, 4.0, 8.0, 12.0])
# ax_again.tick_params(axis='both', which='major', labelbottom='on', bottom='off')
#
# plt.savefig('scratch/' + pltfile + '_trimd' + '.png', transparent=True, format='PNG')
# plt.savefig('scratch/' + pltfile + '_trimd' + '.pdf', transparent=True, format='PDF')
# plt.savefig('scratch/' + pltfile + '_trimd' + '.eps', transparent=True, format='EPS')
[docs]def valerr(data, color=None, title='', xtitle='', view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""
"""
import hashlib
from itertools import cycle
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(4, 6))
ax1 = fig.add_subplot(211)
plt.axhline(0.0, axes=ax1, color='black')
ax1.set_ylabel('Reaction Energy')
plt.title(title)
ax2 = plt.subplot(212, sharex=ax1)
plt.axhline(0.0, axes=ax2, color='#cccc00')
ax2.set_ylabel('Energy Error')
ax2.set_xlabel(xtitle)
xmin = 500.0
xmax = -500.0
vmin = 1.0
vmax = -1.0
emin = 1.0
emax = -1.0
linecycler = cycle(['-', '--', '-.', ':'])
# plot reaction errors and threads
for trace, tracedata in data.iteritems():
vaxis = []
vmcdata = []
verror = []
for rxn in tracedata:
clr = segment_color(color, rxn['color'] if 'color' in rxn else None)
xmin = min(xmin, rxn['axis'])
xmax = max(xmax, rxn['axis'])
ax1.plot(rxn['axis'], rxn['mcdata'], '^', color=clr, markersize=6.0, mew=0, zorder=10)
vmcdata.append(rxn['mcdata'])
vaxis.append(rxn['axis'])
vmin = min(0, vmin, rxn['mcdata'])
vmax = max(0, vmax, rxn['mcdata'])
if rxn['bmdata'] is not None:
ax1.plot(rxn['axis'], rxn['bmdata'], 'o', color='black', markersize=6.0, zorder=1)
vmin = min(0, vmin, rxn['bmdata'])
vmax = max(0, vmax, rxn['bmdata'])
if rxn['error'][0] is not None:
ax2.plot(rxn['axis'], rxn['error'][0], 's', color=clr, mew=0, zorder=8)
emin = min(0, emin, rxn['error'][0])
emax = max(0, emax, rxn['error'][0])
verror.append(rxn['error'][0])
ls = next(linecycler)
ax1.plot(vaxis, vmcdata, ls, color='grey', label=trace, zorder=3)
ax2.plot(vaxis, verror, ls, color='grey', label=trace, zorder=4)
xbuf = max(0.05, abs(0.02 * xmax))
vbuf = max(0.1, abs(0.02 * vmax))
ebuf = max(0.01, abs(0.02 * emax))
plt.xlim([xmin - xbuf, xmax + xbuf])
ax1.set_ylim([vmin - vbuf, vmax + vbuf])
plt.legend(fontsize='x-small', frameon=False)
ax2.set_ylim([emin - ebuf, emax + ebuf])
# save and show
pltuid = title + '_' + hashlib.sha1(title).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='valerr_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight')
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close() # give this a try
return files_saved
[docs]def disthist(data, title='', xtitle='', xmin=None, xmax=None,
me=None, stde=None, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Saves a plot with name *saveas* with a histogram representation
of the reaction errors in *data*. Also plots a gaussian distribution
with mean *me* and standard deviation *stde*. Plot has x-range
*xmin* to *xmax*, x-axis label *xtitle* and overall title *title*.
"""
import hashlib
import numpy as np
import matplotlib.pyplot as plt
def gaussianpdf(u, v, x):
"""*u* is mean, *v* is variance, *x* is value, returns probability"""
return 1.0 / np.sqrt(2.0 * np.pi * v) * np.exp(-pow(x - u, 2) / 2.0 / v)
me = me if me is not None else np.mean(data)
stde = stde if stde is not None else np.std(data, ddof=1)
evenerr = max(abs(me - 4.0 * stde), abs(me + 4.0 * stde))
xmin = xmin if xmin is not None else -1 * evenerr
xmax = xmax if xmax is not None else evenerr
dx = (xmax - xmin) / 40.
nx = int(round((xmax - xmin) / dx)) + 1
pdfx = []
pdfy = []
for i in xrange(nx):
ix = xmin + i * dx
pdfx.append(ix)
pdfy.append(gaussianpdf(me, pow(stde, 2), ix))
fig, ax1 = plt.subplots(figsize=(16, 6))
plt.axvline(0.0, color='#cccc00')
ax1.set_xlim(xmin, xmax)
ax1.hist(data, bins=30, range=(xmin, xmax), color='#2d4065', alpha=0.7)
ax1.set_xlabel(xtitle)
ax1.set_ylabel('Count')
ax2 = ax1.twinx()
ax2.fill(pdfx, pdfy, color='k', alpha=0.2)
ax2.set_ylabel('Probability Density')
plt.title(title)
# save and show
pltuid = title + '_' + hashlib.sha1(title + str(me) + str(stde) + str(xmin) + str(xmax)).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='disthist_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight')
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close()
return files_saved
#def thread(data, labels, color=None, title='', xlimit=4.0, mae=None, mape=None):
# """Generates a tiered slat diagram between model chemistries with
# errors (or simply values) in list *data*, which is supplied as part of the
# dictionary for each participating reaction, along with *dbse* and *rxn* keys
# in argument *data*. The plot is labeled with *title* and each tier with
# an element of *labels* and plotted at *xlimit* from the zero-line. If
# *color* is None, slats are black, if 'sapt', colors are taken from *color*
# key in *data* [0, 1]. Summary statistics *mae* are plotted on the
# overbound side and relative statistics *mape* on the underbound side.
#
# """
# from random import random
# import matplotlib.pyplot as plt
#
# # initialize tiers/wefts
# Nweft = len(labels)
# lenS = 0.2
# gapT = 0.04
# positions = range(-1, -1 * Nweft - 1, -1)
# posnS = []
# for weft in range(Nweft):
# posnS.extend([positions[weft] + lenS, positions[weft] - lenS, None])
# posnT = []
# for weft in range(Nweft - 1):
# posnT.extend([positions[weft] - lenS - gapT, positions[weft + 1] + lenS + gapT, None])
#
# # initialize plot
# fht = Nweft * 0.8
# fig, ax = plt.subplots(figsize=(12, fht))
# plt.subplots_adjust(left=0.01, right=0.99, hspace=0.3)
# plt.xlim([-xlimit, xlimit])
# plt.ylim([-1 * Nweft - 1, 0])
# plt.yticks([])
#
# # label plot and tiers
# ax.text(-0.9 * xlimit, -0.25, title,
# verticalalignment='bottom', horizontalalignment='left',
# family='Times New Roman', weight='bold', fontsize=12)
# for weft in labels:
# ax.text(-0.9 * xlimit, -(1.2 + labels.index(weft)), weft,
# verticalalignment='bottom', horizontalalignment='left',
# family='Times New Roman', weight='bold', fontsize=18)
#
# # plot reaction errors and threads
# for rxn in data:
#
# # preparation
# xvals = rxn['data']
# clr = segment_color(color, rxn['color'] if 'color' in rxn else None)
# slat = []
# for weft in range(Nweft):
# slat.extend([xvals[weft], xvals[weft], None])
# thread = []
# for weft in range(Nweft - 1):
# thread.extend([xvals[weft], xvals[weft + 1], None])
#
# # plotting
# ax.plot(slat, posnS, color=clr, linewidth=1.0, solid_capstyle='round')
# ax.plot(thread, posnT, color=clr, linewidth=0.5, solid_capstyle='round',
# alpha=0.3)
#
# # labeling
# try:
# toplblposn = next(item for item in xvals if item is not None)
# botlblposn = next(item for item in reversed(xvals) if item is not None)
# except StopIteration:
# pass
# else:
# ax.text(toplblposn, -0.75 + 0.6 * random(), rxn['sys'],
# verticalalignment='bottom', horizontalalignment='center',
# family='Times New Roman', fontsize=8)
# ax.text(botlblposn, -1 * Nweft - 0.75 + 0.6 * random(), rxn['sys'],
# verticalalignment='bottom', horizontalalignment='center',
# family='Times New Roman', fontsize=8)
#
# # plot trimmings
# if mae is not None:
# ax.plot([-x for x in mae], positions, 's', color='black')
# if mape is not None: # equivalent to MAE for a 10 kcal/mol IE
# ax.plot([0.025 * x for x in mape], positions, 'o', color='black')
#
# plt.axvline(0, color='black')
# plt.show()
[docs]def threads(data, labels, color=None, title='', xlimit=4.0, mae=None, mape=None,
mousetext=None, mouselink=None, mouseimag=None, mousetitle=None, mousediv=None,
labeled=True, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Generates a tiered slat diagram between model chemistries with
errors (or simply values) in list *data*, which is supplied as part of the
dictionary for each participating reaction, along with *dbse* and *rxn* keys
in argument *data*. The plot is labeled with *title* and each tier with
an element of *labels* and plotted at *xlimit* from the zero-line. If
*color* is None, slats are black, if 'sapt', colors are taken from *color*
key in *data* [0, 1]. Summary statistics *mae* are plotted on the
overbound side and relative statistics *mape* on the underbound side.
HTML code for mouseover if mousetext or mouselink or mouseimag specified
based on recipe of Andrew Dalke from
http://www.dalkescientific.com/writings/diary/archive/2005/04/24/interactive_html.html
"""
import random
import hashlib
import matplotlib.pyplot as plt
import numpy as np # only needed for missing data with mouseiness
# initialize tiers/wefts
Nweft = len(labels)
lenS = 0.2
gapT = 0.04
positions = range(-1, -1 * Nweft - 1, -1)
posnS = []
for weft in range(Nweft):
posnS.extend([positions[weft] + lenS, positions[weft] - lenS, None])
posnT = []
for weft in range(Nweft - 1):
posnT.extend([positions[weft] - lenS - gapT, positions[weft + 1] + lenS + gapT, None])
posnM = []
# initialize plot
fht = Nweft * 0.8
#fig, ax = plt.subplots(figsize=(12, fht))
fig, ax = plt.subplots(figsize=(11, fht))
plt.subplots_adjust(left=0.01, right=0.99, hspace=0.3)
plt.xlim([-xlimit, xlimit])
plt.ylim([-1 * Nweft - 1, 0])
plt.yticks([])
ax.set_frame_on(False)
if labeled:
ax.set_xticks([-0.5 * xlimit, -0.25 * xlimit, 0.0, 0.25 * xlimit, 0.5 * xlimit])
else:
ax.set_xticks([])
for tick in ax.xaxis.get_major_ticks():
tick.tick1line.set_markersize(0)
tick.tick2line.set_markersize(0)
# label plot and tiers
if labeled:
ax.text(-0.9 * xlimit, -0.25, title,
verticalalignment='bottom', horizontalalignment='left',
family='Times New Roman', weight='bold', fontsize=12)
for weft in labels:
ax.text(-0.9 * xlimit, -(1.2 + labels.index(weft)), weft,
verticalalignment='bottom', horizontalalignment='left',
family='Times New Roman', weight='bold', fontsize=18)
# plot reaction errors and threads
for rxn in data:
# preparation
xvals = rxn['data']
clr = segment_color(color, rxn['color'] if 'color' in rxn else None)
slat = []
for weft in range(Nweft):
slat.extend([xvals[weft], xvals[weft], None])
thread = []
for weft in range(Nweft - 1):
thread.extend([xvals[weft], xvals[weft + 1], None])
# plotting
if Nweft == 1:
ax.plot(slat, posnS, '|', color=clr, markersize=20.0, mew=1.5, solid_capstyle='round')
else:
ax.plot(slat, posnS, color=clr, linewidth=1.0, solid_capstyle='round')
ax.plot(thread, posnT, color=clr, linewidth=0.5, solid_capstyle='round', alpha=0.3)
# converting into screen coordinates for image map
npxvals = [np.nan if val is None else val for val in xvals]
xyscreen = ax.transData.transform(zip(npxvals, positions))
xscreen, yscreen = zip(*xyscreen)
posnM.extend(zip([rxn['db']] * Nweft, [rxn['sys']] * Nweft,
npxvals, [rxn['show']] * Nweft, xscreen, yscreen))
# labeling
if not(mousetext or mouselink or mouseimag):
if labeled and len(data) < 200:
try:
toplblposn = next(item for item in xvals if item is not None)
botlblposn = next(item for item in reversed(xvals) if item is not None)
except StopIteration:
pass
else:
ax.text(toplblposn, -0.75 + 0.6 * random.random(), rxn['sys'],
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', fontsize=8)
ax.text(botlblposn, -1 * Nweft - 0.75 + 0.6 * random.random(), rxn['sys'],
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', fontsize=8)
# plot trimmings
if mae is not None:
ax.plot([-x for x in mae], positions, 's', color='black')
if labeled:
if mape is not None: # equivalent to MAE for a 10 kcal/mol IE
ax.plot([0.025 * x for x in mape], positions, 'o', color='black')
plt.axvline(0, color='#cccc00')
# save and show
pltuid = title + '_' + ('lbld' if labeled else 'bare') + '_' + hashlib.sha1(title + repr(labels) + repr(xlimit)).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='thread_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight')
files_saved[ext.lower()] = savefile
if view:
plt.show()
if not (mousetext or mouselink or mouseimag):
plt.close()
return files_saved, None
else:
dpi = 80
img_width = fig.get_figwidth() * dpi
img_height = fig.get_figheight() * dpi
htmlcode = """<SCRIPT>\n"""
htmlcode += """function mouseshow(db, rxn, val, show) {\n"""
if mousetext or mouselink:
htmlcode += """ var cid = document.getElementById("cid");\n"""
if mousetext:
htmlcode += """ cid.innerHTML = %s;\n""" % (mousetext)
if mouselink:
htmlcode += """ cid.href = %s;\n""" % (mouselink)
if mouseimag:
htmlcode += """ var cmpd_img = document.getElementById("cmpd_img");\n"""
htmlcode += """ cmpd_img.src = %s;\n""" % (mouseimag)
htmlcode += """}\n"""
htmlcode += """</SCRIPT>\n"""
if mousediv:
htmlcode += """%s\n""" % (mousediv[0])
if mousetitle:
htmlcode += """%s <BR>""" % (mousetitle)
htmlcode += """<h4>Mouseover</h4><a id="cid"></a><br>\n"""
if mouseimag:
htmlcode += """<div class="text-center">"""
htmlcode += """<IMG ID="cmpd_img" WIDTH="%d" HEIGHT="%d">\n""" % (200, 160)
htmlcode += """</div>"""
if mousediv:
htmlcode += """%s\n""" % (mousediv[1])
#htmlcode += """<IMG SRC="%s" ismap usemap="#points" WIDTH="%d" HEIGHT="%d">\n""" % \
# (pltfile + '.png', img_width, img_height)
htmlcode += """<IMG SRC="%s" ismap usemap="#points" WIDTH="%d">\n""" % \
(pltfile + '.png', img_width)
htmlcode += """<MAP name="points">\n"""
# generating html image map code
# points sorted to avoid overlapping map areas that can overwhelm html for SSI
# y=0 on top for html and on bottom for mpl, so flip the numbers
posnM.sort(key=lambda tup: tup[2])
posnM.sort(key=lambda tup: tup[3])
last = (0, 0)
for dbse, rxn, val, show, x, y in posnM:
if val is None or val is np.nan:
continue
now = (int(x), int(y))
if now == last:
htmlcode += """<!-- map overlap! %s-%s %+.2f skipped -->\n""" % (dbse, rxn, val)
else:
htmlcode += """<AREA shape="rect" coords="%d,%d,%d,%d" onmouseover="javascript:mouseshow('%s', '%s', '%+.2f', '%s');">\n""" % \
(x - 2, img_height - y - 20,
x + 2, img_height - y + 20,
dbse, rxn, val, show)
last = now
htmlcode += """</MAP>\n"""
plt.close()
return files_saved, htmlcode
[docs]def ternary(sapt, title='', labeled=True, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Takes array of arrays *sapt* in form [elst, indc, disp] and builds formatted
two-triangle ternary diagrams. Either fully-readable or dotsonly depending
on *labeled*. Saves in formats *graphicsformat*.
"""
import hashlib
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.path import Path
import matplotlib.patches as patches
# initialize plot
fig, ax = plt.subplots(figsize=(6, 3.6))
plt.xlim([-0.75, 1.25])
plt.ylim([-0.18, 1.02])
plt.xticks([])
plt.yticks([])
ax.set_aspect('equal')
if labeled:
# form and color ternary triangles
codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY]
pathPos = Path([(0., 0.), (1., 0.), (0.5, 0.866), (0., 0.)], codes)
pathNeg = Path([(0., 0.), (-0.5, 0.866), (0.5, 0.866), (0., 0.)], codes)
ax.add_patch(patches.PathPatch(pathPos, facecolor='white', lw=2))
ax.add_patch(patches.PathPatch(pathNeg, facecolor='#fff5ee', lw=2))
# form and color HB/MX/DD dividing lines
ax.plot([0.667, 0.5], [0., 0.866], color='#eeb4b4', lw=0.5)
ax.plot([-0.333, 0.5], [0.577, 0.866], color='#eeb4b4', lw=0.5)
ax.plot([0.333, 0.5], [0., 0.866], color='#7ec0ee', lw=0.5)
ax.plot([-0.167, 0.5], [0.289, 0.866], color='#7ec0ee', lw=0.5)
# label corners
ax.text(1.0, -0.15, u'Elst (\u2212)',
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', weight='bold', fontsize=18)
ax.text(0.5, 0.9, u'Ind (\u2212)',
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', weight='bold', fontsize=18)
ax.text(0.0, -0.15, u'Disp (\u2212)',
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', weight='bold', fontsize=18)
ax.text(-0.5, 0.9, u'Elst (+)',
verticalalignment='bottom', horizontalalignment='center',
family='Times New Roman', weight='bold', fontsize=18)
xvals = []
yvals = []
cvals = []
for sys in sapt:
[elst, indc, disp] = sys
# calc ternary posn and color
Ftop = abs(indc) / (abs(elst) + abs(indc) + abs(disp))
Fright = abs(elst) / (abs(elst) + abs(indc) + abs(disp))
xdot = 0.5 * Ftop + Fright
ydot = 0.866 * Ftop
cdot = 0.5 + (xdot - 0.5) / (1. - Ftop)
if elst > 0.:
xdot = 0.5 * (Ftop - Fright)
ydot = 0.866 * (Ftop + Fright)
#print elst, indc, disp, '', xdot, ydot, cdot
xvals.append(xdot)
yvals.append(ydot)
cvals.append(cdot)
sc = ax.scatter(xvals, yvals, c=cvals, s=15, marker="o", \
cmap=mpl.cm.jet, edgecolor='none', vmin=0, vmax=1, zorder=10)
# remove figure outline
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
# save and show
pltuid = title + '_' + ('lbld' if labeled else 'bare') + '_' + hashlib.sha1(title + repr(sapt)).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='tern_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight',
frameon=False, dpi=450, edgecolor='none', pad_inches=0.0)
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close()
return files_saved
#def thread_mouseover_web(pltfile, dbid, dbname, xmin, xmax, mcdats, labels, titles):
# """Saves a plot with name *pltfile* with a slat representation of
# the modelchems errors in *mcdat*. Mouseover shows geometry and error
# from *labels* based on recipe of Andrew Dalke from
# http://www.dalkescientific.com/writings/diary/archive/2005/04/24/interactive_html.html
#
# """
# from matplotlib.backends.backend_agg import FigureCanvasAgg
# import matplotlib
# import sapt_colors
#
# cmpd_width = 200
# cmpd_height = 160
#
# nplots = len(mcdats)
# fht = nplots * 0.8
# fht = nplots * 0.8 * 1.4
# fig = matplotlib.figure.Figure(figsize=(12.0, fht))
# fig.subplots_adjust(left=0.01, right=0.99, hspace=0.3, top=0.8, bottom=0.2)
# img_width = fig.get_figwidth() * 80
# img_height = fig.get_figheight() * 80
#
# htmlcode = """
#<SCRIPT>
#function mouseandshow(name, id, db, dbname) {
# var cid = document.getElementById("cid");
# cid.innerHTML = name;
# cid.href = "fragmentviewer.py?name=" + id + "&dataset=" + db;
# var cmpd_img = document.getElementById("cmpd_img");
# cmpd_img.src = dbname + "/dimers/" + id + ".png";
#}
#</SCRIPT>
#
#Distribution of Fragment Errors in Interaction Energy (kcal/mol)<BR>
#Mouseover:<BR><a id="cid"></a><br>
#<IMG SRC="scratch/%s" ismap usemap="#points" WIDTH="%d" HEIGHT="%d">
#<IMG ID="cmpd_img" WIDTH="%d" HEIGHT="%d">
#<MAP name="points">
#""" % (pltfile, img_width, img_height, cmpd_width, cmpd_height)
#
# for item in range(nplots):
# print '<br><br><br><br><br><br>'
# mcdat = mcdats[item]
# label = labels[item]
# tttle = titles[item]
#
# erdat = np.array(mcdat)
# # No masked_array because interferes with html map
# #erdat = np.ma.masked_array(mcdat, mask=mask)
# yvals = np.ones(len(mcdat))
# y = np.array([sapt_colors.sapt_colors[dbname][i] for i in label])
#
# ax = fig.add_subplot(nplots, 1, item + 1)
# sc = ax.scatter(erdat, yvals, c=y, s=3000, marker="|", cmap=matplotlib.cm.jet, vmin=0, vmax=1)
# ax.set_title(tttle, fontsize=8)
# ax.set_yticks([])
# lp = ax.plot([0, 0], [0.9, 1.1], color='#cccc00', lw=2)
# ax.set_ylim([0.95, 1.05])
# ax.text(xmin + 0.3, 1.0, stats(erdat), fontsize=7, family='monospace', verticalalignment='center')
# if item + 1 == nplots:
# ax.set_xticks([-12.0, -8.0, -4.0, -2.0, -1.0, 0.0, 1.0, 2.0, 4.0, 8.0, 12.0])
# for tick in ax.xaxis.get_major_ticks():
# tick.tick1line.set_markersize(0)
# tick.tick2line.set_markersize(0)
# else:
# ax.set_xticks([])
# ax.set_frame_on(False)
# ax.set_xlim([xmin, xmax])
#
# # Convert the data set points into screen space coordinates
# #xyscreencoords = ax.transData.transform(zip(erdat, yvals))
# xyscreencoords = ax.transData.transform(zip(erdat, yvals))
# xcoords, ycoords = zip(*xyscreencoords)
#
# # HTML image coordinates have y=0 on the top. Matplotlib
# # has y=0 on the bottom. We'll need to flip the numbers
# for cid, x, y, er in zip(label, xcoords, ycoords, erdat):
# htmlcode += """<AREA shape="rect" coords="%d,%d,%d,%d" onmouseover="javascript:mouseandshow('%s %+.2f', '%s', %s, '%s');">\n""" % \
# (x - 2, img_height - y - 20, x + 2, img_height - y + 20, cid, er, cid, dbid, dbname)
#
# htmlcode += "</MAP>\n"
# canvas = FigureCanvasAgg(fig)
# canvas.print_figure('scratch/' + title, dpi=80, transparent=True)
#
# #plt.savefig('mplflat_' + title + '.pdf', bbox_inches='tight', transparent=True, format='PDF')
# #plt.savefig(os.environ['HOME'] + os.sep + 'mplflat_' + title + '.pdf', bbox_inches='tight', transparent=T rue, format='PDF')
#
# return htmlcode
[docs]def composition_tile(db, aa1, aa2):
"""Takes dictionary *db* of label, error pairs and amino acids *aa1*
and *aa2* and returns a square array of all errors for that amino
acid pair, buffered by zeros.
"""
import re
import numpy as np
bfdbpattern = re.compile("\d\d\d([A-Z][A-Z][A-Z])-\d\d\d([A-Z][A-Z][A-Z])-\d")
tiles = []
for key, val in db.items():
bfdbname = bfdbpattern.match(key)
if (bfdbname.group(1) == aa1 and bfdbname.group(2) == aa2) or \
(bfdbname.group(2) == aa1 and bfdbname.group(1) == aa2):
tiles.append(val)
if not tiles:
# fill in background when no data. only sensible for neutral center colormaps
tiles = [0]
dim = int(np.ceil(np.sqrt(len(tiles))))
pad = dim * dim - len(tiles)
tiles += [0] * pad
return np.reshape(np.array(tiles), (dim, dim))
[docs]def iowa(mcdat, mclbl, title='', xtitle='', xlimit=2.0, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Saves a plot with (extensionless) name *pltfile* with an Iowa
representation of the modelchems errors in *mcdat* for BBI/SSI-style
*labels*.
"""
import numpy as np
import hashlib
import matplotlib
import matplotlib.pyplot as plt
aa = ['ARG', 'HIE', 'LYS', 'ASP', 'GLU', 'SER', 'THR', 'ASN', 'GLN', 'CYS', 'MET', 'GLY', 'ALA', 'VAL', 'ILE', 'LEU', 'PRO', 'PHE', 'TYR', 'TRP']
#aa = ['ILE', 'LEU', 'ASP', 'GLU', 'PHE']
err = dict(zip(mclbl, mcdat))
# handle for frame, overall axis
fig, axt = plt.subplots(figsize=(6, 6))
#axt.set_xticks([]) # for quick nolabel, whiteback
#axt.set_yticks([]) # for quick nolabel, whiteback
axt.set_xticks(np.arange(len(aa)) + 0.3, minor=False)
axt.set_yticks(np.arange(len(aa)) + 0.3, minor=False)
axt.invert_yaxis()
axt.xaxis.tick_top() # comment for quick nolabel, whiteback
axt.set_xticklabels(aa, minor=False, rotation=60, size='small') # comment for quick nolabel, whiteback
axt.set_yticklabels(aa, minor=False, size='small') # comment for quick nolabel, whiteback
axt.xaxis.set_tick_params(width=0, length=0)
axt.yaxis.set_tick_params(width=0, length=0)
#axt.set_title('%s' % (title), fontsize=16, verticalalignment='bottom')
#axt.text(10.0, -1.5, title, horizontalalignment='center', fontsize=16)
# nill spacing between 20x20 heatmaps
plt.subplots_adjust(hspace=0.001, wspace=0.001)
index = 1
for aa1 in aa:
for aa2 in aa:
cb = composition_tile(err, aa1, aa2)
ax = matplotlib.axes.Subplot(fig, len(aa), len(aa), index)
fig.add_subplot(ax)
heatmap = ax.pcolor(cb, vmin=-xlimit, vmax=xlimit, cmap=plt.cm.PRGn)
ax.set_xticks([])
ax.set_yticks([])
index += 1
#plt.title(title)
axt.axvline(x=4.8, linewidth=5, color='k')
axt.axvline(x=8.75, linewidth=5, color='k')
axt.axvline(x=11.6, linewidth=5, color='k')
axt.axhline(y=4.8, linewidth=5, color='k')
axt.axhline(y=8.75, linewidth=5, color='k')
axt.axhline(y=11.6, linewidth=5, color='k')
axt.set_zorder(100)
# save and show
pltuid = title + '_' + hashlib.sha1(title + str(xlimit)).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='iowa_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight')
#plt.savefig(savefile, transparent=False, format=ext, bbox_inches='tight') # for quick nolabel, whiteback
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close()
return files_saved
[docs]def liliowa(mcdat, title='', xlimit=2.0, view=True,
saveas=None, relpath=False, graphicsformat=['pdf']):
"""Saves a plot with a heatmap representation of *mcdat*.
"""
import numpy as np
import hashlib
import matplotlib
import matplotlib.pyplot as plt
# handle for frame, overall axis
fig, axt = plt.subplots(figsize=(1, 1))
axt.set_xticks([])
axt.set_yticks([])
axt.invert_yaxis()
axt.xaxis.set_tick_params(width=0, length=0)
axt.yaxis.set_tick_params(width=0, length=0)
axt.set_aspect('equal')
# remove figure outline
axt.spines['top'].set_visible(False)
axt.spines['right'].set_visible(False)
axt.spines['bottom'].set_visible(False)
axt.spines['left'].set_visible(False)
tiles = mcdat
dim = int(np.ceil(np.sqrt(len(tiles))))
pad = dim * dim - len(tiles)
tiles += [0] * pad
cb = np.reshape(np.array(tiles), (dim, dim))
heatmap = axt.pcolor(cb, vmin=-xlimit, vmax=xlimit, cmap=plt.cm.PRGn)
# save and show
pltuid = title + '_' + hashlib.sha1(title + str(xlimit)).hexdigest()
pltfile = expand_saveas(saveas, pltuid, def_prefix='liliowa_', relpath=relpath)
files_saved = {}
for ext in graphicsformat:
savefile = pltfile + '.' + ext.lower()
plt.savefig(savefile, transparent=True, format=ext, bbox_inches='tight',
frameon=False, pad_inches=0.0)
files_saved[ext.lower()] = savefile
if view:
plt.show()
plt.close()
return files_saved
if __name__ == "__main__":
merge_dats = [
{'show':'a', 'db':'HSG', 'sys':'1', 'data':[0.3508, 0.1234, 0.0364, 0.0731, 0.0388]},
{'show':'b', 'db':'HSG', 'sys':'3', 'data':[0.2036, -0.0736, -0.1650, -0.1380, -0.1806]},
#{'show':'', 'db':'S22', 'sys':'14', 'data':[np.nan, -3.2144, np.nan, np.nan, np.nan]},
{'show':'c', 'db':'S22', 'sys':'14', 'data':[None, -3.2144, None, None, None]},
{'show':'d', 'db':'S22', 'sys':'15', 'data':[-1.5090, -2.5263, -2.9452, -2.8633, -3.1059]},
{'show':'e', 'db':'S22', 'sys':'22', 'data':[0.3046, -0.2632, -0.5070, -0.4925, -0.6359]}]
threads(merge_dats, labels=['d', 't', 'dt', 'q', 'tq'], color='sapt',
title='MP2-CPa[]z', mae=[0.25, 0.5, 0.5, 0.3, 1.0], mape=[20.1, 25, 15, 5.5, 3.6])
more_dats = [
{'mc':'MP2-CP-adz', 'data':[1.0, 0.8, 1.4, 1.6]},
{'mc':'MP2-CP-adtz', 'data':[0.6, 0.2, 0.4, 0.6]},
None,
{'mc':'MP2-CP-adzagain', 'data':[1.0, 0.8, 1.4, 1.6]}]
bars(more_dats, title='asdf')
single_dats = [
{'dbse':'HSG', 'sys':'1', 'data':[0.3508]},
{'dbse':'HSG', 'sys':'3', 'data':[0.2036]},
{'dbse':'S22', 'sys':'14', 'data':[None]},
{'dbse':'S22', 'sys':'15', 'data':[-1.5090]},
{'dbse':'S22', 'sys':'22', 'data':[0.3046]}]
#flat(single_dats, color='sapt', title='fg_MP2_adz', mae=0.25, mape=20.1)
flat([{'sys': '1', 'color': 0.6933450559423702, 'data': [0.45730000000000004]}, {'sys': '2', 'color': 0.7627027688599753, 'data': [0.6231999999999998]}, {'sys': '3', 'color': 0.7579958735528617, 'data': [2.7624999999999993]}, {'sys': '4', 'color': 0.7560883254421639, 'data': [2.108600000000001]}, {'sys': '5', 'color': 0.7515161912065955, 'data': [2.2304999999999993]}, {'sys': '6', 'color': 0.7235223893438876, 'data': [1.3782000000000014]}, {'sys': '7', 'color': 0.7120099024225569, 'data': [1.9519000000000002]}, {'sys': '8', 'color': 0.13721565059144678, 'data': [0.13670000000000004]}, {'sys': '9', 'color': 0.3087395095814767, 'data': [0.2966]}, {'sys': '10', 'color': 0.25493207637105103, 'data': [-0.020199999999999996]}, {'sys': '11', 'color': 0.24093814608979347, 'data': [-1.5949999999999998]}, {'sys': '12', 'color': 0.3304746631959777, 'data': [-1.7422000000000004]}, {'sys': '13', 'color': 0.4156050644764822, 'data': [0.0011999999999989797]}, {'sys': '14', 'color': 0.2667207259626991, 'data': [-2.6083999999999996]}, {'sys': '15', 'color': 0.3767053567641695, 'data': [-1.5090000000000003]}, {'sys': '16', 'color': 0.5572641509433963, 'data': [0.10749999999999993]}, {'sys': '17', 'color': 0.4788598239641578, 'data': [0.29669999999999996]}, {'sys': '18', 'color': 0.3799031371351281, 'data': [0.10209999999999964]}, {'sys': '19', 'color': 0.5053227185999078, 'data': [0.16610000000000014]}, {'sys': '20', 'color': 0.2967660584483015, 'data': [-0.37739999999999974]}, {'sys': '21', 'color': 0.38836460733750316, 'data': [-0.4712000000000005]}, {'sys': '22', 'color': 0.5585849893078809, 'data': [0.30460000000000065]}, {'sys': 'BzBz_PD36-1.8', 'color': 0.1383351040559965, 'data': [-1.1921]}, {'sys': 'BzBz_PD34-2.0', 'color': 0.23086034843049832, 'data': [-1.367]}, {'sys': 'BzBz_T-5.2', 'color': 0.254318060864096, 'data': [-0.32230000000000025]}, {'sys': 'BzBz_T-5.1', 'color': 0.26598486566733337, 'data': [-0.3428]}, {'sys': 'BzBz_T-5.0', 'color': 0.28011258347610224, 'data': [-0.36060000000000025]}, {'sys': 'PyPy_S2-3.9', 'color': 0.14520332101084785, 'data': [-0.9853000000000001]}, {'sys': 'PyPy_S2-3.8', 'color': 0.1690757103699542, 'data': [-1.0932]}, {'sys': 'PyPy_S2-3.5', 'color': 0.25615734567417053, 'data': [-1.4617]}, {'sys': 'PyPy_S2-3.7', 'color': 0.19566550224566906, 'data': [-1.2103999999999995]}, {'sys': 'PyPy_S2-3.6', 'color': 0.22476748600170826, 'data': [-1.3333]}, {'sys': 'BzBz_PD32-2.0', 'color': 0.31605681987208084, 'data': [-1.6637]}, {'sys': 'BzBz_T-4.8', 'color': 0.31533827331543723, 'data': [-0.38759999999999994]}, {'sys': 'BzBz_T-4.9', 'color': 0.2966146678069063, 'data': [-0.3759999999999999]}, {'sys': 'BzH2S-3.6', 'color': 0.38284814928043304, 'data': [-0.1886000000000001]}, {'sys': 'BzBz_PD32-1.7', 'color': 0.3128835191478639, 'data': [-1.8703999999999998]}, {'sys': 'BzMe-3.8', 'color': 0.24117892478245323, 'data': [-0.034399999999999986]}, {'sys': 'BzMe-3.9', 'color': 0.22230903086047088, 'data': [-0.046499999999999986]}, {'sys': 'BzH2S-3.7', 'color': 0.36724255203373696, 'data': [-0.21039999999999992]}, {'sys': 'BzMe-3.6', 'color': 0.284901522674611, 'data': [0.007099999999999884]}, {'sys': 'BzMe-3.7', 'color': 0.2621086166558813, 'data': [-0.01770000000000005]}, {'sys': 'BzBz_PD32-1.9', 'color': 0.314711251903219, 'data': [-1.7353999999999998]}, {'sys': 'BzBz_PD32-1.8', 'color': 0.3136181753200793, 'data': [-1.8039999999999998]}, {'sys': 'BzH2S-3.8', 'color': 0.3542001591399945, 'data': [-0.22230000000000016]}, {'sys': 'BzBz_PD36-1.9', 'color': 0.14128552184232473, 'data': [-1.1517]}, {'sys': 'BzBz_S-3.7', 'color': 0.08862098445220466, 'data': [-1.3414]}, {'sys': 'BzH2S-4.0', 'color': 0.33637540012259076, 'data': [-0.2265999999999999]}, {'sys': 'BzBz_PD36-1.5', 'color': 0.13203548045236127, 'data': [-1.3035]}, {'sys': 'BzBz_S-3.8', 'color': 0.0335358832178858, 'data': [-1.2022]}, {'sys': 'BzBz_S-3.9', 'color': 0.021704594689389095, 'data': [-1.0747]}, {'sys': 'PyPy_T3-5.1', 'color': 0.3207725129126432, 'data': [-0.2958000000000003]}, {'sys': 'PyPy_T3-5.0', 'color': 0.3254925304351165, 'data': [-0.30710000000000015]}, {'sys': 'BzBz_PD36-1.7', 'color': 0.13577087141986593, 'data': [-1.2333000000000003]}, {'sys': 'PyPy_T3-4.8', 'color': 0.3443704059902452, 'data': [-0.32010000000000005]}, {'sys': 'PyPy_T3-4.9', 'color': 0.3333442013628509, 'data': [-0.3158999999999996]}, {'sys': 'PyPy_T3-4.7', 'color': 0.35854000505665756, 'data': [-0.31530000000000014]}, {'sys': 'BzBz_PD36-1.6', 'color': 0.13364651314909243, 'data': [-1.2705000000000002]}, {'sys': 'BzMe-4.0', 'color': 0.20560117919562013, 'data': [-0.05389999999999984]}, {'sys': 'MeMe-3.6', 'color': 0.16934865900383142, 'data': [0.18420000000000003]}, {'sys': 'MeMe-3.7', 'color': 0.1422332591197123, 'data': [0.14680000000000004]}, {'sys': 'MeMe-3.4', 'color': 0.23032794290360467, 'data': [0.29279999999999995]}, {'sys': 'MeMe-3.5', 'color': 0.19879551978386897, 'data': [0.23260000000000003]}, {'sys': 'MeMe-3.8', 'color': 0.11744404936205816, 'data': [0.11680000000000001]}, {'sys': 'BzBz_PD34-1.7', 'color': 0.22537382457222138, 'data': [-1.5286999999999997]}, {'sys': 'BzBz_PD34-1.6', 'color': 0.22434088042760192, 'data': [-1.5754000000000001]}, {'sys': 'BzBz_PD32-2.2', 'color': 0.3189891685300601, 'data': [-1.5093999999999999]}, {'sys': 'BzBz_S-4.1', 'color': 0.10884135031532088, 'data': [-0.8547000000000002]}, {'sys': 'BzBz_S-4.0', 'color': 0.06911476296747143, 'data': [-0.9590000000000001]}, {'sys': 'BzBz_PD34-1.8', 'color': 0.22685419834431494, 'data': [-1.476]}, {'sys': 'BzBz_PD34-1.9', 'color': 0.2287079261672095, 'data': [-1.4223999999999997]}, {'sys': 'BzH2S-3.9', 'color': 0.3439077006047999, 'data': [-0.22739999999999982]}, {'sys': 'FaNNFaNN-4.1', 'color': 0.7512716174974567, 'data': [1.7188999999999997]}, {'sys': 'FaNNFaNN-4.0', 'color': 0.7531388297328865, 'data': [1.9555000000000007]}, {'sys': 'FaNNFaNN-4.3', 'color': 0.7478064149182957, 'data': [1.2514000000000003]}, {'sys': 'FaNNFaNN-4.2', 'color': 0.7493794908838113, 'data': [1.4758000000000013]}, {'sys': 'FaOOFaON-4.0', 'color': 0.7589275618320565, 'data': [2.0586]}, {'sys': 'FaOOFaON-3.7', 'color': 0.7619465815742713, 'data': [3.3492999999999995]}, {'sys': 'FaOOFaON-3.9', 'color': 0.7593958895631474, 'data': [2.4471000000000007]}, {'sys': 'FaOOFaON-3.8', 'color': 0.7605108059280967, 'data': [2.8793999999999986]}, {'sys': 'FaONFaON-4.1', 'color': 0.7577459277014137, 'data': [1.8697999999999997]}, {'sys': 'FaOOFaON-3.6', 'color': 0.7633298028299997, 'data': [3.847599999999998]}, {'sys': 'FaNNFaNN-3.9', 'color': 0.7548200901251662, 'data': [2.2089]}, {'sys': 'FaONFaON-3.8', 'color': 0.7582294603551467, 'data': [2.967699999999999]}, {'sys': 'FaONFaON-3.9', 'color': 0.7575285282217349, 'data': [2.578900000000001]}, {'sys': 'FaONFaON-4.2', 'color': 0.7594549221042256, 'data': [1.5579999999999998]}, {'sys': 'FaOOFaNN-3.6', 'color': 0.7661655616885379, 'data': [3.701599999999999]}, {'sys': 'FaOOFaNN-3.7', 'color': 0.7671068376007428, 'data': [3.156500000000001]}, {'sys': 'FaOOFaNN-3.8', 'color': 0.766947626251711, 'data': [2.720700000000001]}, {'sys': 'FaONFaNN-3.9', 'color': 0.7569836601896789, 'data': [2.4281000000000006]}, {'sys': 'FaONFaNN-3.8', 'color': 0.758024548462959, 'data': [2.7561999999999998]}, {'sys': 'FaOOFaOO-3.6', 'color': 0.7623422640217077, 'data': [3.851800000000001]}, {'sys': 'FaOOFaOO-3.7', 'color': 0.7597430792159379, 'data': [3.2754999999999974]}, {'sys': 'FaOOFaOO-3.4', 'color': 0.7672554950739594, 'data': [5.193299999999999]}, {'sys': 'FaOOFaOO-3.5', 'color': 0.764908813123865, 'data': [4.491900000000001]}, {'sys': 'FaONFaNN-4.2', 'color': 0.7549212942233738, 'data': [1.534699999999999]}, {'sys': 'FaONFaNN-4.0', 'color': 0.7559404310956357, 'data': [2.1133000000000024]}, {'sys': 'FaONFaNN-4.1', 'color': 0.7551574698775625, 'data': [1.813900000000002]}, {'sys': 'FaONFaON-4.0', 'color': 0.7572064604483282, 'data': [2.2113999999999994]}, {'sys': 'FaOOFaOO-3.8', 'color': 0.7573810956831686, 'data': [2.7634000000000007]}, {'sys': '1', 'color': 0.2784121805328983, 'data': [0.3508]}, {'sys': '2', 'color': 0.22013842798900166, 'data': [-0.034600000000000186]}, {'sys': '3', 'color': 0.12832496088281312, 'data': [0.20360000000000023]}, {'sys': '4', 'color': 0.6993695033529733, 'data': [1.9092000000000002]}, {'sys': '5', 'color': 0.7371192790053749, 'data': [1.656600000000001]}, {'sys': '6', 'color': 0.5367033190796172, 'data': [0.27970000000000006]}, {'sys': '7', 'color': 0.3014220615964802, 'data': [0.32289999999999974]}, {'sys': '8', 'color': 0.01605867807629261, 'data': [0.12199999999999994]}, {'sys': '9', 'color': 0.6106300539083558, 'data': [0.3075999999999999]}, {'sys': '10', 'color': 0.6146680031333968, 'data': [0.6436000000000002]}, {'sys': '11', 'color': 0.6139747851721759, 'data': [0.4551999999999996]}, {'sys': '12', 'color': 0.32122739401126593, 'data': [0.44260000000000005]}, {'sys': '13', 'color': 0.24678148099136055, 'data': [-0.11789999999999967]}, {'sys': '14', 'color': 0.23700950710597016, 'data': [0.42689999999999995]}, {'sys': '15', 'color': 0.23103396678138563, 'data': [0.3266]}, {'sys': '16', 'color': 0.1922070769654413, 'data': [0.0696000000000001]}, {'sys': '17', 'color': 0.19082151944747366, 'data': [0.11159999999999992]}, {'sys': '18', 'color': 0.2886200282444196, 'data': [0.4114]}, {'sys': '19', 'color': 0.23560171133945224, 'data': [-0.1392]}, {'sys': '20', 'color': 0.3268270751294533, 'data': [0.5593]}, {'sys': '21', 'color': 0.7324460869158442, 'data': [0.6806000000000001]}],
color='sapt', title='MP2-CP-adz', mae=1.21356003247, mape=24.6665886087, xlimit=4.0)
lin_dats = [-0.5, -0.4, -0.3, 0, .5, .8, 5]
lin_labs = ['008ILE-012LEU-1', '012LEU-085ASP-1', '004GLU-063LEU-2',
'011ILE-014PHE-1', '027GLU-031LEU-1', '038PHE-041ILE-1', '199LEU-202GLU-1']
iowa(lin_dats, lin_labs, title='ttl', xlimit=0.5)
figs = [0.22, 0.41, 0.14, 0.08, 0.47,
0, 0.38, 0.22, 0.10, 0.20,
0, 0, 0.13, 0.07, 0.25,
0, 0, 0, 0.06, 0.22,
0, 0, 0, 0, 0.69]
liliowa(figs, saveas='SSI-default-MP2-CP-aqz', xlimit=1.0)
disthist(lin_dats)
valerrdata = [{'color': 0.14255710779686612, 'db': 'NBC1', 'sys': 'BzBz_S-3.6', 'error': [0.027999999999999803], 'mcdata': -1.231, 'bmdata': -1.259, 'axis': 3.6}, {'color': 0.08862098445220466, 'db': 'NBC1', 'sys': 'BzBz_S-3.7', 'error': [0.02300000000000013], 'mcdata': -1.535, 'bmdata': -1.558, 'axis': 3.7}, {'color': 0.246634626511043, 'db': 'NBC1', 'sys': 'BzBz_S-3.4', 'error': [0.04200000000000001], 'mcdata': 0.189, 'bmdata': 0.147, 'axis': 3.4}, {'color': 0.19526236766857613, 'db': 'NBC1', 'sys': 'BzBz_S-3.5', 'error': [0.03500000000000003], 'mcdata': -0.689, 'bmdata': -0.724, 'axis': 3.5}, {'color': 0.3443039102164425, 'db': 'NBC1', 'sys': 'BzBz_S-3.2', 'error': [0.05999999999999961], 'mcdata': 3.522, 'bmdata': 3.462, 'axis': 3.2}, {'color': 0.29638827303466814, 'db': 'NBC1', 'sys': 'BzBz_S-3.3', 'error': [0.050999999999999934], 'mcdata': 1.535, 'bmdata': 1.484, 'axis': 3.3}, {'color': 0.42859228971962615, 'db': 'NBC1', 'sys': 'BzBz_S-6.0', 'error': [0.0020000000000000018], 'mcdata': -0.099, 'bmdata': -0.101, 'axis': 6.0}, {'color': 0.30970751839224836, 'db': 'NBC1', 'sys': 'BzBz_S-5.0', 'error': [0.0040000000000000036], 'mcdata': -0.542, 'bmdata': -0.546, 'axis': 5.0}, {'color': 0.3750832778147902, 'db': 'NBC1', 'sys': 'BzBz_S-5.5', 'error': [0.0030000000000000027], 'mcdata': -0.248, 'bmdata': -0.251, 'axis': 5.5}, {'color': 0.0335358832178858, 'db': 'NBC1', 'sys': 'BzBz_S-3.8', 'error': [0.019000000000000128], 'mcdata': -1.674, 'bmdata': -1.693, 'axis': 3.8}, {'color': 0.021704594689389095, 'db': 'NBC1', 'sys': 'BzBz_S-3.9', 'error': [0.016000000000000014], 'mcdata': -1.701, 'bmdata': -1.717, 'axis': 3.9}, {'color': 0.22096255119953187, 'db': 'NBC1', 'sys': 'BzBz_S-4.5', 'error': [0.008000000000000007], 'mcdata': -1.058, 'bmdata': -1.066, 'axis': 4.5}, {'color': 0.10884135031532088, 'db': 'NBC1', 'sys': 'BzBz_S-4.1', 'error': [0.01200000000000001], 'mcdata': -1.565, 'bmdata': -1.577, 'axis': 4.1}, {'color': 0.06911476296747143, 'db': 'NBC1', 'sys': 'BzBz_S-4.0', 'error': [0.014000000000000012], 'mcdata': -1.655, 'bmdata': -1.669, 'axis': 4.0}, {'color': 0.14275218373289067, 'db': 'NBC1', 'sys': 'BzBz_S-4.2', 'error': [0.01100000000000012], 'mcdata': -1.448, 'bmdata': -1.459, 'axis': 4.2}, {'color': 0.4740372133275638, 'db': 'NBC1', 'sys': 'BzBz_S-6.5', 'error': [0.0010000000000000009], 'mcdata': -0.028, 'bmdata': -0.029, 'axis': 6.5}, {'color': 0.6672504378283713, 'db': 'NBC1', 'sys': 'BzBz_S-10.0', 'error': [0.0], 'mcdata': 0.018, 'bmdata': 0.018, 'axis': 10.0}]
valerr({'cat': valerrdata},
color='sapt', xtitle='Rang', title='aggh', graphicsformat=['png'])