def bars_from_file(in_file, out_dir):
"""
Create a bar graph from data in a file.
"""
figwidth = 2 * 8.5 / 2.54
try:
os.makedirs(out_dir)
except OSError:
pass
bar_lines = open(in_file, 'r').readlines()
header = [file_handler.line_to_words(line[:-1]) for line in bar_lines[0:3]]
bar_list = []
bar_titles = []
for line in bar_lines[3:]:
t_list = file_handler.line_to_words(line[:-1])
bar_list += [[eval(word) for word in t_list[1:]]]
bar_titles += [t_list[0]]
#print mean_lines[1:2]
for i, bars in enumerate(bar_list):
pylab.figure(figsize=(2 * figwidth, .75 * figwidth))
xlist = [eval(num) for num in header[0][1:]]
pylab.bar(xlist, bars)
pylab.title(bar_titles[i])
pylab.savefig(out_dir + '/' + bar_titles[i] + '.png',
orientation='landscape')
def ret_renumbered_structure_file(self, ren_file, name=''):
"""
Renumber the structure with input from <ren_file>.
"""
ren_lines = open(ren_file, 'r').readlines()
ren_list = [[eval(word) for word in file_handler.line_to_words(line)] for line in ren_lines]
return self.ret_renumbered_structure(ren_list, name)
def nma_positions(xind, yind, i_tst_list, symbol='ko', file_name='nma.txt'):
"""
Plot time steps specified in nested list <i_t_list> on the surface of normal coordinates with indices <xind> and <yind>.
"""
# not efficiently written but time is not critical
for i, tstep in i_tst_list:
lines = open('TRAJ' + str(i) + '/RESULTS/' + file_name).readlines()
vec = [
eval(word) for word in file_handler.line_to_words(lines[tstep + 3])
]
pylab.plot([vec[xind + 1]], [vec[yind + 1]], symbol)
def mean_std_from_files(mean_file,
out_dir,
xlist,
std_file=None,
col_list=None):
"""
Makes a number of mean/std plots from a file containing means and a file containing standard deviations and saves them to directory out_dir.
Optionally <std_file> can be left out and just the means are plotted.
If <col_list> is given, a joined plot with the modes contained in <col_list> is given.
"""
figwidth = 2 * 8.5 / 2.54
try:
os.makedirs(out_dir)
except OSError:
pass
if not std_file == None:
with_std = True
else:
with_std = False
mean_lines = open(mean_file, 'r').readlines()
if with_std: std_lines = open(std_file, 'r').readlines()
header = [
file_handler.line_to_words(line[:-1]) for line in mean_lines[0:3]
]
#print mean_lines[1:2]
mean_list = []
if with_std: std_list = []
for line in mean_lines[3:]:
mean_list += [[
eval(word) for word in file_handler.line_to_words(line[:-1])
]]
if with_std:
for line in std_lines[3:]:
std_list += [[
eval(word) for word in file_handler.line_to_words(line[:-1])
]]
mean_array = numpy.array(mean_list, float).transpose()
if with_std: std_array = numpy.array(std_list, float).transpose()
#print mean_array
#print std_array
if col_list == None:
for i in xrange(numpy.size(mean_array, 0)):
pylab.figure(figsize=(figwidth, .75 * figwidth))
pylab.plot(xlist[:len(mean_array[i].tolist())],
mean_array[i].tolist()[:len(xlist)],
color='k',
linewidth=1)
if with_std:
plot_std(mean_array[i].tolist()[:len(xlist)],
std_array[i].tolist()[:len(xlist)],
xlist[:len(mean_array[i].tolist())])
#pylab.title('20ag'+' - '+str(header[1][i])+'/cm - '+str(header[2][i]+'fs'))
pylab.title(
str(header[0][i]) + ' - ' + str(header[1][i]) + '/cm - ' +
str(header[2][i] + 'fs'))
pylab.xlabel("time (fs)")
pylab.savefig(out_dir + '/mode_' + str(i + 1).rjust(3, '0') +
'.png',
orientation='landscape')
else:
linestyles = ['-'] #,'--',':','-.']
colors = ['k', 'b', 'r', 'g']
pylab.figure(figsize=(figwidth, .75 * figwidth)) # - temp
for run_ind, col_ind in enumerate(col_list):
i = col_ind - 1
pylab.plot(xlist[:len(mean_array[i].tolist())],
mean_array[i].tolist()[:len(xlist)],
color=colors[run_ind % len(colors)],
linewidth=1,
label=str(col_ind),
linestyle=linestyles[run_ind % len(linestyles)])
if with_std:
plot_std(mean_array[i].tolist()[:len(xlist)],
std_array[i].tolist()[:len(xlist)],
xlist[:len(mean_array[i].tolist())])
pylab.legend() # - temp
pylab.xlabel("time (fs)")
pylab.ylabel("Displacement (A)")
out_file = 'modes_' + str(col_list[0])
for col_ind in col_list[1:]:
out_file += '_' + str(col_ind)
#out_file += '_new' # + temp
out_file += '.png'
pylab.savefig(os.path.join(out_dir, out_file), orientation='landscape')
def mean_std_from_file(file, out_dir, col_list=None, num_steps=None):
"""
Makes a number of mean/std plots from one file containing means standard deviations and saves them to directory out_dir.
mean_value.3 can be plotted with this routine.
If <col_list> is given, a joined plot with the modes contained in <col_list> is given.
<num_steps> gives the maximum number of plotted steps.
"""
figwidth = 2 * 8.5 / 2.54
try:
os.makedirs(out_dir)
except OSError:
pass
lines = open(file, 'r').readlines()
read_list = []
for line in lines[:num_steps]:
words = file_handler.line_to_words(line)
if not words[1] == '0':
read_list += [[eval(word) for word in words]]
else:
print 'No trajectories at timestep ' + str(words[0])
plot_array = numpy.array(read_list, float).transpose()
if col_list == None:
for i in xrange(numpy.size(plot_array, 0) / 2 - 1):
pylab.figure(figsize=(figwidth, .75 * figwidth))
pylab.plot(plot_array[0].tolist(),
plot_array[2 + 2 * i].tolist(),
color=[0., 0., 0.],
linewidth=1)
plot_std(plot_array[2 + 2 * i].tolist(),
plot_array[3 + 2 * i].tolist(), plot_array[0].tolist())
pylab.title(str(i + 1))
pylab.xlabel("time (fs)")
pylab.savefig(out_dir + '/int_coor_' + str(i + 1).rjust(3, '0') +
'.png',
orientation='landscape')
else:
linestyles = ['-', '--', ':', '-.']
pylab.figure(figsize=(figwidth, .75 * figwidth))
for run_ind, col_ind in enumerate(col_list):
i = col_ind - 1
pylab.plot(plot_array[0].tolist(),
plot_array[2 + 2 * i].tolist(),
color=[0., 0., 0.],
linewidth=1,
label=str(col_ind),
linestyle=linestyles[run_ind % 4])
plot_std(plot_array[2 + 2 * i].tolist(),
plot_array[3 + 2 * i].tolist(), plot_array[0].tolist())
pylab.legend()
pylab.xlabel("time (fs)")
out_file = 'int_coors_' + str(col_list[0])
for col_ind in col_list[1:]:
out_file += '_' + str(col_ind)
pylab.savefig(os.path.join(out_dir, out_file), orientation='landscape')
