def read_all_data(filenames, options):
directory = op.split(__file__)[0]
areas = read_file_info(op.join(directory, options.cross_sections_filename))
lengths = read_file_info(op.join(directory, options.init_lengths_filename))
datas = []
cols = options.columns
for i_file, filename in enumerate(filenames):
data = Data.from_file(filename, sep=options.separator,
icycles=cols.get('cycle', None),
itime=cols.get('time', 2),
idispl=cols.get('displ', 1),
iforce=cols.get('force', 0))
data.set_initial_values(lengths=lengths, areas=areas)
datas.append(data)
return datas
def main():
"""
The following command line options are available:
- def_ss : the final value of strain for small deformations
- def_ls : the interval of strain for large deformations
- legend_fontsize : a font size of the legends
- file_name_out : output file name
- one_cycle : data contain only one cycle
- cycles : data contain only cycles
- cut_first_cycle : the first cycle will be cut from the evaluation
- s : save the pictures with evaluated curves and files with mechanical
properties
- mean_val : count the mean value of moduli of elasticity of all inserted
stress-strain curves
- cut_strain : cut the region of defined strain
- ultim_val : get ultimate values of stress and strain
- sampling : the sampling interval
- sensitivity : the sensitivity of approximation
- conf : use configuration file
- mean_plot : plot the mean stress-strain curve
"""
parser = OptionParser(usage=usage, version="%prog ")
parser.add_option("", "--def-ss", type=float, metavar='float',
action="store", dest="def_ss",
default=None, help=help['def_ss'])
parser.add_option("", "--def-ls", type='str', metavar='float,float',
action="callback", dest="def_ls",
callback=parse_def_ls, help=help['def_ls'])
parser.add_option("-o", "", metavar='string',
action="store", dest="file_name_out",
default=None, help=help['file_name_out'])
parser.add_option("", "--legend-fontsize", type=int, metavar='int',
action="store", dest="legend_fontsize",
default=None, help=help['legend_fontsize'])
parser.add_option("", "--one-cycle",
action="store_true", dest="one_cycle",
default=None, help=help['one_cycle'])
parser.add_option("", "--cycles",
action="store_true", dest="cycles",
default=None, help=help['cycles'])
parser.add_option("", "--cut-first-cycle",
action="store_true", dest="cut_first_cycle",
default=None, help=help['cut_first_cycle'])
parser.add_option("-s", "",
action="store_true", dest="s",
default=None, help=help['s'])
parser.add_option("", "--mean-val",
action="store_true", dest="mean_val",
default=None, help=help['mean_val'])
parser.add_option("", "--cut-strain", type=float, metavar='float',
action="store", dest="cut_strain",
default=None, help=help['cut_strain'])
parser.add_option("", "--ultim-val",
action="store_true", dest="ultim_val",
default=None, help=help['ultim_val'])
parser.add_option("", "--sampling", type=int, metavar='int',
action="store", dest="sampling",
default=None, help=help['sampling'])
parser.add_option("", "--sensitivity", type=float, metavar='float',
action="store", dest="sensitivity",
default=None, help=help['sensitivity'])
parser.add_option("-c", "--conf", metavar='filename',
action="store", dest="conf_filename",
default=None, help=help['conf_filename'])
parser.add_option("", "--mean-plot",
action="store_true", dest="mean_plot",
default=None, help=help['mean_plot'])
cmdl_options, args = parser.parse_args()
expanded_args = []
for arg in args:
expanded_args.extend(glob.glob(arg))
args = expanded_args
file_number = len(args)
if file_number == 0:
parser.print_help()
return
can_override = set()
for key, default in default_options.iteritems():
val = getattr(cmdl_options, key)
if val is None:
setattr(cmdl_options, key, default)
else:
can_override.add(key)
if cmdl_options.conf_filename is not None:
config = Config.from_file(cmdl_options.conf_filename,
defaults=default_options)
else:
conf = {'options' : {'default' : default_options},
'options_default' : default_options}
config = Config.from_conf(conf)
config.override(cmdl_options, can_override)
options = Object(name='options', **(config.options['default']))
filename_out = options.file_name_out
markers, linestyles, color_vector = define_plot_attributes()
fp = fm.FontProperties()
fp.set_size(options.legend_fontsize)
directory = op.split(__file__)[0]
areas = read_file_info(op.join(directory,'cross_sections.txt'))
lengths = read_file_info(op.join(directory,'init_lengths.txt'))
isFinal = False
isLast = False
isPlot = 0
if not options.one_cycle:
p.figure(1)
p.clf()
if not options.cycles:
p.figure(5)
p.clf()
all_fits = {}
list_fits = []
ks = []
h_datas = []
ult_strain=[]
ult_stress=[]
strain_all = []
stress_all = []
strain_size = []
k_fig = 5
for i_file in range( 0, file_number ):
filename = args[i_file]
print 'file:', filename
if filename in config.options:
specific_options = Object(name='options', **(config.options[filename]))
else:
specific_options = options
fits, strain, stress = fit_data(filename, specific_options, lengths, areas,
isPlot = isPlot )
if specific_options.mean_plot:
strain_size.append(strain.shape[0])
strain_all.append(strain)
stress_all.append(stress)
print 'strain_all', strain_all, strain_size
if not specific_options.cycles:
k0, k1, h_fit, h_data = fit_stress_strain_lines(k_fig, filename, strain,
stress, specific_options, i_file,
color_vector, markers,
isPlot = isPlot )
ks.append( (k0, k1) )
h_datas.extend(h_data)
if options.ultim_val:
ultim_strain, ultim_stress = get_ultimate_values(strain, stress)
ult_strain.append(ultim_strain)
ult_stress.append(ultim_stress)
if i_file == 0:
avg_fits = np.zeros_like(fits)
avg_fits += fits
all_fits[filename] = fits
list_fits.append(fits)
avg_fits /= float(file_number)
if specific_options.mean_plot:
index_length = np.min(strain_size)
print index_length
strain_mean = np.mean(strain_all, 0)
stress_mean = np.mean(stress_all, 0)
p.figure(61)
p.plot(strain_mean, stress_mean, marker = 'p', color = 'k')
if options.ultim_val:
ult_strain = np.array(ult_strain, dtype = np.float64)
ult_strain_average = np.sum(ult_strain, 0) / ult_strain.shape[0]
ult_strain_dev = np.std(ult_strain, 0)
print 'ultimate_strain', ult_strain_average, '\pm', ult_strain_dev
ult_stress = np.array(ult_stress, dtype = np.float64)
ult_stress_average = np.sum(ult_stress, 0) / ult_stress.shape[0]
ult_stress_dev = np.std(ult_stress, 0)
print 'ultimate_stress', ult_stress_average, '\pm', ult_stress_dev
list_fits.insert(0, avg_fits)
a_fits = np.array(list_fits, dtype = np.float64).T
print a_fits.shape
if options.s:
print 'output file:', filename_out
fd = open(op.splitext( filename_out)[0] + '_moduli_cycles.txt',
'w' )
for row in a_fits:
fd.write(' '.join([('%.3e' % ii) for ii in row]))
fd.write('\n')
fd.close()
if not options.cycles:
if options.mean_val:
ks = np.array(ks, dtype = np.float64)
ks_average = np.sum(ks, 0) / ks.shape[0]
ks_dev = np.std(ks, 0)
print ks
print ks_average
print ks_dev
if options.s:
np.savetxt(op.splitext( filename_out )[0] + '_moduli.txt', ks)
fig = p.figure(k_fig)
p.xlabel( 'strain' )
p.ylabel( 'stress [MPa]' )
texts = [r'$E_0 = %.2e\ \pm\ %.2e$' % (ks_average[0], ks_dev[0]),
r'$E_1 = %.2e\ \pm\ %.2e$' % (ks_average[1], ks_dev[1])]
leg = make_legend_text(args, ks)
fig.legend(h_datas, leg, loc = (0.6, 0.35), prop=fp)
p.legend(h_fit, texts, loc = 'lower right', prop=fp)
if options.s:
fig_name = op.splitext(filename_out )[0] + '_stress_strain.pdf'
p.savefig(fig_name, dpi = 300)
else:
ks = np.array(ks, dtype = np.float64)
print ks
if options.s:
np.savetxt(op.splitext(filename_out)[0] + '_moduli.txt', ks)
p.figure(k_fig)
leg = make_legend_text(args, ks)
p.xlabel( 'strain' )
p.ylabel( 'stress [MPa]' )
p.legend(h_datas, leg, loc = 'upper right', prop=fp)
if options.s:
fig_name = op.splitext(filename_out)[0] + '_stress_strain.pdf'
p.savefig(fig_name, dpi = 300)
if isLast:
to = avg_fits.shape[0]
else:
to = avg_fits.shape[0] - 1
if not options.one_cycle:
p.figure( 1 )
cycle = np.arange( 1, to + 1 )
if not isFinal:
leg = []
ii=0
for name, fits in all_fits.iteritems():
p.plot(cycle, fits[:to], color=color_vector[ii,:3], marker=markers[ii], markersize = 3, label=op.splitext(name)[0])
ii=ii+1
if options.mean_val:
p.plot(cycle, avg_fits[:to], 'ro')
else:
pass
if options.mean_val:
p.errorbar(cycle, avg_fits[:to],
yerr = np.std(a_fits[:to,1:], 1),
marker = 'o', mfc = 'red', label='mean_value $\pm$ SD')
else:
pass
p.legend(loc = 'upper right' , prop=fp)
p.xlim(0, a_fits.shape[0] + 1)
p.xlabel('cycle number')
p.ylabel('modulus of elasticity [MPa]')
if options.s:
if isFinal:
fig_name = op.splitext(filename_out)[0] + '.pdf'
else:
fig_name = op.splitext(filename_out)[0] + '_all.pdf'
p.savefig(fig_name, dpi = 300)
p.show()
