def test():
# define measurement data file
vftb_file = os.path.join(RockPy.test_data_path, 'vftb', 'MUCVFTB_test.coe')
# create a sample
sample = Sample(name='vftb_test_sample')
# add measurement
M = sample.add_measurement(mtype='backfield',
mfile=vftb_file,
machine='vftb',
suffix='test 1 none')
sg = RockPy.SampleGroup(sample_list=sample)
study = RockPy.Study(samplegroups=sg)
# get bcr
M.calculate_bcr(
) # prints the linear interpolation of the value (internal calculation)
print 'bcr', M.bcr # returns the calculated value
print M.s300 # returns the S300 value
M.calc_all() # calculates all possible results using standard parameters
print M.results # returns the calculated value
# rudimentary plot
M.plt_backfield()
sample.calc_all()
print sample.results
def test():
vftb_file = join(RockPy.test_data_path, 'MUCVFTB_test.hys')
# vsm_file = join(RockPy.test_data_path, 'vsm', 'MUCVSM_test.hys')
vsm_file = join(RockPy.test_data_path, 'vsm', 'LTPY_527,1a_HYS_VSM#XX[mg]___#TEMP_300_K#STD000.000')
# sample = Sample(name='vftb_test_sample')
sample2 = Sample(name='vsm_test_sample')
# M = sample.add_measurement(mtype='hysteresis', mfile=vftb_file, machine='vftb')
# print M.result_ms()
# M.plt_hys()
M = sample2.add_measurement(mtype='hysteresis', mfile=vsm_file, machine='vsm')
M.calc_all()
print M.results
M.plt_hys()
def test():
vftb_file = join(RockPy.test_data_path, 'MUCVFTB_test.hys')
# vsm_file = join(RockPy.test_data_path, 'vsm', 'MUCVSM_test.hys')
vsm_file = join(RockPy.test_data_path, 'vsm',
'LTPY_527,1a_HYS_VSM#XX[mg]___#TEMP_300_K#STD000.000')
# sample = Sample(name='vftb_test_sample')
sample2 = Sample(name='vsm_test_sample')
# M = sample.add_measurement(mtype='hysteresis', mfile=vftb_file, machine='vftb')
# print M.result_ms()
# M.plt_hys()
M = sample2.add_measurement(mtype='hysteresis',
mfile=vsm_file,
machine='vsm')
M.calc_all()
print M.results
M.plt_hys()
def test():
# define measurement data file
vftb_file = os.path.join(RockPy.test_data_path, 'vftb', 'MUCVFTB_test.coe')
# create a sample
sample = Sample(name='vftb_test_sample')
# add measurement
M = sample.add_measurement(mtype='backfield', mfile=vftb_file, machine='vftb', suffix='test 1 none')
sg = RockPy.SampleGroup(sample_list=sample)
study = RockPy.Study(samplegroups=sg)
# get bcr
M.calculate_bcr() # prints the linear interpolation of the value (internal calculation)
print 'bcr', M.bcr # returns the calculated value
print M.s300 # returns the S300 value
M.calc_all() # calculates all possible results using standard parameters
print M.results # returns the calculated value
# rudimentary plot
M.plt_backfield()
sample.calc_all()
print sample.results
def test():
"""
# define measurement data file
ani_file1 = 'test_data/anisotropy/S1_isotropic_sushi_1D.ani'
ani_file2 = 'test_data/anisotropy/S2_sushi.ani'
# create a sample
sample1 = Sample(name='S1')
sample2 = Sample(name='S2')
sample3 = Sample(name='S3')
"""
def do_box_plot(axes, values, label, ylim):
axes.boxplot(values, notch=1, whis=999999) # whis high -> whiskers mark maximum and minimum data
if ylim is not None:
axes.set_ylim(ylim)
pyplot.setp(axes.get_xticklabels(), visible=False)
axes.set_xlabel(label)
axes.set_xlim((0.9, 1.1))
# create samples
evals=(1.0, 1.0, 1.0)
measerr=0.01
method='proj' # 'proj' or 'full'
samples = []
for i in range(100):
s = Sample(name=str(i))
m = s.add_simulation(mtype='anisotropy', color=(random(), random(), random()), evals=evals,
mdirs=[[225.0, 0.0], [45.0, 0.0], [135.0, 0.0], [315.0, 0.0], [90.0, 45.0], [270.0, -45.0],
[90.0, -45.0], [270.0, 45.0], [0.0, -45.0], [180.0, 45.0], [0.0, 45.0], [180.0, -45.0]],
measerr=measerr)
samples.append(s)
sg = RockPy.SampleGroup(sample_list=samples)
study = RockPy.Study(samplegroups=sg)
#filename = 'out_sushi__ev%.2f_%.2f_%.2f_err%.3f.pdf' % (evals[0], evals[1], evals[2], measerr)
filename = 'out_sushi135_0_D_var_ev%.2f_%.2f_%.2f_err%.3f_%s.pdf' % (evals[0], evals[1], evals[2], measerr, method)
print "writing to %s" % filename
pdf_pages = PdfPages(filename)
# values for all offsets
Ds, Tmedians, Pmedians, Lmedians, Fmedians, E12medians, E13medians, E23medians, sdpermedians, QFmedians = [], [], [], [], [], [], [], [], [], []
eval1_err_per_medians, eval2_err_per_medians, eval3_err_per_medians = [], [], []
# make numbers formatting float
samples[0].measurements[0]._data['data'].showfmt['floatfmt'] = '.1f'
samples[0].measurements[0]._data['data'].showfmt['show_rowlabels'] = False
for d in range(10):
if d != 0:
for s in samples:
#modify reference directions
#add to inclination
#m._data['data']['I'] = m._data['data']['I'].v + 2
#add to declination
refI = s.measurements[0]._data['data']['I'].v
refD = s.measurements[0]._data['data']['D'].v
#print refD
#change all declinations
#refD += 1
#refI[8] += 1
#refI[9] -= 1
#change declination of position 3
refD[2] += 1
s.measurements[0]._data['data']['D'] = refD
s.measurements[0]._data['data']['I'] = refI
aniplt = RockPy.Visualize.anisotropy.Anisotropy(study, plt_primary="samples", plt_secondary=None, method=method)
# get figure
fig1 = aniplt.figs[aniplt.name][0]
fig1.text(0.02, 0.02, str(samples[0].measurements[0]._data['data']['D', 'I']))
#print str( samples[0].measurements[0]._data['data']['D', 'I'].v)
L, F, T, P, E12, E13, E23, sdper, QF, eval1_err_per, eval2_err_per, eval3_err_per = [], [], [], [], [], [], [], [], [], [], [], []
#samples[0].measurements[0].calculate_tensor()
#print samples[0].measurements[0].results
for s in samples:
s.measurements[0].calculate_tensor(method='proj')
T.extend(s.measurements[0].results['T'].v)
L.extend(s.measurements[0].results['L'].v)
F.extend(s.measurements[0].results['F'].v)
P.extend(s.measurements[0].results['P'].v)
E12.extend(s.measurements[0].results['E12'].v)
E13.extend(s.measurements[0].results['E13'].v)
E23.extend(s.measurements[0].results['E23'].v)
eval1_err_per.extend(s.measurements[0].results['eval1_err'].v/s.measurements[0].results['eval1'].v*100)
eval2_err_per.extend(s.measurements[0].results['eval2_err'].v/s.measurements[0].results['eval2'].v*100)
eval3_err_per.extend(s.measurements[0].results['eval3_err'].v/s.measurements[0].results['eval3'].v*100)
sdper.extend(s.measurements[0].results['stddev'].v / s.measurements[0].results['M'].v * 100)
QF.extend(s.measurements[0].results['QF'].v)
fig2, axarr = pyplot.subplots(1, 11)
do_box_plot(axarr[0], P, 'P', (.99, 1.25))
do_box_plot(axarr[1], L, 'L', (.99, 1.25))
do_box_plot(axarr[2], F, 'F', (.99, 1.25))
do_box_plot(axarr[3], T, 'T', (-1.0, 1.0))
do_box_plot(axarr[4], eval1_err_per, 'eval1_err_per', (0,8))
do_box_plot(axarr[5], eval2_err_per, 'eval2_err_per', (0,8))
do_box_plot(axarr[6], eval3_err_per, 'eval3_err_per', (0,8))
do_box_plot(axarr[7], E12, 'E12', (0, 90))
do_box_plot(axarr[8], E13, 'E13', (0, 90))
do_box_plot(axarr[9], E23, 'E23', (0, 90))
do_box_plot(axarr[10], sdper, 'stddev %', None)
#do_box_plot(axarr[8], QF, 'QF', None)
pyplot.tight_layout()
fig1.suptitle("meas_err: %.3f, evals: %.2f,%.2f,%.2f, P: %.2f, I offset: %d, method: %s" %
(measerr, evals[0], evals[1], evals[2], max( evals[2], evals[0]) / min( evals[2], evals[0]), d, method))
pdf_pages.savefig(fig1)
pdf_pages.savefig(fig2)
Pmedians.append(np.median(P))
Tmedians.append(np.median(T))
Lmedians.append(np.median(L))
Fmedians.append(np.median(F))
E12medians.append(np.median(E12))
E13medians.append(np.median(E13))
E23medians.append(np.median(E23))
sdpermedians.append(np.median(sdper))
QFmedians.append(np.median(QF))
eval1_err_per_medians.append(np.median(eval1_err_per))
eval2_err_per_medians.append(np.median(eval2_err_per))
eval3_err_per_medians.append(np.median(eval3_err_per))
print d
Ds.append(d)
fig3, (axu, axd) = pyplot.subplots(2, 1)
l = []
l.append(axu.plot(Ds, Pmedians, color='blue', marker='+', label='P'))
l.append(axu.plot(Ds, Lmedians, color='springgreen', marker='+', label='L'))
l.append(axu.plot(Ds, Fmedians, color='forestgreen', marker='+', label='F'))
axu2 = axu.twinx()
l.append(axu2.plot(Ds, Tmedians, color='peru', marker='+', label='T'))
axu.set_ylim((0.99, 1.25))
axu2.set_ylim((-1.05, 1.05))
l.append(axd.plot(Ds, E12medians, 'g+-', label=r'$\varepsilon_{12}$'))
l.append(axd.plot(Ds, E13medians, 'gx-', label=r'$\varepsilon_{13}$'))
l.append(axd.plot(Ds, E23medians, 'g*-', label=r'$\varepsilon_{23}$'))
axd2 = axd.twinx()
l.append(axd2.plot(Ds, sdpermedians, 'bx-', label=r'$\sigma (\%)$'))
l.append(axd2.plot(Ds, eval1_err_per_medians, 'r+-', label=r'$\Delta \lambda_1 (\%)$'))
l.append(axd2.plot(Ds, eval2_err_per_medians, 'rx-', label=r'$\Delta \lambda_2 (\%)$'))
l.append(axd2.plot(Ds, eval3_err_per_medians, 'r*-', label=r'$\Delta \lambda_3 (\%)$'))
axd.set_ylim((0.0, 90.0))
axd.set_ylabel('degree')
axd2.set_ylim((0.0, 2.5))
axd2.set_ylabel('%')
# make legends
axd.legend(loc='center left', bbox_to_anchor=(1.1, 0.7), fancybox=True, shadow=True, ncol=1)
axd2.legend(loc='center left', bbox_to_anchor=(1.1, 0.1), fancybox=True, shadow=True, ncol=1)
axu.legend(loc='center left', bbox_to_anchor=(1.1, 0.7), fancybox=True, shadow=True, ncol=1)
axu2.legend(loc='center left', bbox_to_anchor=(1.1, 0.1), fancybox=True, shadow=True)
# Shrink all x axes by 20%
for ax in (axu, axu2, axd, axd2):
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
pdf_pages.savefig(fig3)
pdf_pages.close()
__author__ = 'wack'
'''
Tutorial to demonstrate functions and plots of directional (x,y,z) data
'''
import RockPy
from RockPy.Structure.sample import Sample
from RockPy.VisualizeV3 import Figure
from os.path import join
af_file = join(RockPy.test_data_path, 'MUCSUSH_af_test.af')
s = Sample("WURM")
afd = s.add_measurement(machine='sushibar', mtype='afdemag', mfile=af_file)
# do plotting stuff
fig = Figure()
h1 = fig.add_visual(visual='stereo', name='mystereo', plt_input=s)
#h1.remove_feature(features=['stereodir_lines'])
fig.show()
def test():
basenames = ('DA6', 'DA7', 'HA2', 'HA3', 'HA6', 'HA8', 'HA9', 'TA1', 'TA3',
'TA4', 'TA6', 'TA7')
bnames = [bn + 'B' for bn in basenames]
cnames = [bn + 'C' for bn in basenames]
cfB = 'test_data/milad/groupB.txt'
cfC = 'test_data/milad/groupC.txt'
# create sample
bsamples = [Sample(name=n) for n in bnames]
csamples = [Sample(name=n) for n in cnames]
for bsample in bsamples:
bsample.add_measurement(mfile=cfB,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='x',
suffix='x')
bsample.add_measurement(mfile=cfB,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='y',
suffix='y')
bsample.add_measurement(mfile=cfB,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='z',
suffix='z')
for csample in csamples:
csample.add_measurement(mfile=cfC,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='x',
suffix='x')
csample.add_measurement(mfile=cfC,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='y',
suffix='y')
csample.add_measurement(mfile=cfC,
mtype='afdemag',
machine='cryomag',
mag_method='IRM',
demag_type='z',
suffix='z')
#print m.data
demag.AfDemag(bsamples, norm='max')
#demag.AfDemag(csamples, norm='max')
# save plots AF demag plots for all samples
if False:
for s in csamples:
demag.AfDemag(s, plot='save')
for s in bsamples:
demag.AfDemag(s, plot='save')
__author__ = 'wack'
'''
Tutorial to demonstrate functions and plots of directional (x,y,z) data
'''
import RockPy
from RockPy.Structure.sample import Sample
from RockPy.VisualizeV3 import Figure
from os.path import join
af_file = join(RockPy.test_data_path, 'MUCSUSH_af_test.af')
s = Sample("WURM")
afd = s.add_measurement(machine='sushibar', mtype='afdemag', mfile=af_file)
# do plotting stuff
fig = Figure()
h1 = fig.add_visual(visual='stereo', name='mystereo', plt_input=s)
#h1.remove_feature(features=['stereodir_lines'])
fig.show()
def test():
"""
# define measurement data file
ani_file1 = 'test_data/anisotropy/S1_isotropic_sushi_1D.ani'
ani_file2 = 'test_data/anisotropy/S2_sushi.ani'
# create a sample
sample1 = Sample(name='S1')
sample2 = Sample(name='S2')
sample3 = Sample(name='S3')
"""
def do_box_plot(axes, values, label, ylim):
axes.boxplot(
values, notch=1,
whis=999999) # whis high -> whiskers mark maximum and minimum data
if ylim is not None:
axes.set_ylim(ylim)
pyplot.setp(axes.get_xticklabels(), visible=False)
axes.set_xlabel(label)
axes.set_xlim((0.9, 1.1))
# create samples
evals = (1.0, 1.0, 1.0)
measerr = 0.01
method = 'proj' # 'proj' or 'full'
samples = []
for i in range(100):
s = Sample(name=str(i))
m = s.add_simulation(mtype='anisotropy',
color=(random(), random(), random()),
evals=evals,
mdirs=[[225.0, 0.0], [45.0, 0.0], [135.0, 0.0],
[315.0, 0.0], [90.0, 45.0], [270.0, -45.0],
[90.0, -45.0], [270.0, 45.0], [0.0, -45.0],
[180.0, 45.0], [0.0, 45.0], [180.0,
-45.0]],
measerr=measerr)
samples.append(s)
sg = RockPy.SampleGroup(sample_list=samples)
study = RockPy.Study(samplegroups=sg)
#filename = 'out_sushi__ev%.2f_%.2f_%.2f_err%.3f.pdf' % (evals[0], evals[1], evals[2], measerr)
filename = 'out_sushi135_0_D_var_ev%.2f_%.2f_%.2f_err%.3f_%s.pdf' % (
evals[0], evals[1], evals[2], measerr, method)
print "writing to %s" % filename
pdf_pages = PdfPages(filename)
# values for all offsets
Ds, Tmedians, Pmedians, Lmedians, Fmedians, E12medians, E13medians, E23medians, sdpermedians, QFmedians = [], [], [], [], [], [], [], [], [], []
eval1_err_per_medians, eval2_err_per_medians, eval3_err_per_medians = [], [], []
# make numbers formatting float
samples[0].measurements[0]._data['data'].showfmt['floatfmt'] = '.1f'
samples[0].measurements[0]._data['data'].showfmt['show_rowlabels'] = False
for d in range(10):
if d != 0:
for s in samples:
#modify reference directions
#add to inclination
#m._data['data']['I'] = m._data['data']['I'].v + 2
#add to declination
refI = s.measurements[0]._data['data']['I'].v
refD = s.measurements[0]._data['data']['D'].v
#print refD
#change all declinations
#refD += 1
#refI[8] += 1
#refI[9] -= 1
#change declination of position 3
refD[2] += 1
s.measurements[0]._data['data']['D'] = refD
s.measurements[0]._data['data']['I'] = refI
aniplt = RockPy.Visualize.anisotropy.Anisotropy(study,
plt_primary="samples",
plt_secondary=None,
method=method)
# get figure
fig1 = aniplt.figs[aniplt.name][0]
fig1.text(0.02, 0.02,
str(samples[0].measurements[0]._data['data']['D', 'I']))
#print str( samples[0].measurements[0]._data['data']['D', 'I'].v)
L, F, T, P, E12, E13, E23, sdper, QF, eval1_err_per, eval2_err_per, eval3_err_per = [], [], [], [], [], [], [], [], [], [], [], []
#samples[0].measurements[0].calculate_tensor()
#print samples[0].measurements[0].results
for s in samples:
s.measurements[0].calculate_tensor(method='proj')
T.extend(s.measurements[0].results['T'].v)
L.extend(s.measurements[0].results['L'].v)
F.extend(s.measurements[0].results['F'].v)
P.extend(s.measurements[0].results['P'].v)
E12.extend(s.measurements[0].results['E12'].v)
E13.extend(s.measurements[0].results['E13'].v)
E23.extend(s.measurements[0].results['E23'].v)
eval1_err_per.extend(s.measurements[0].results['eval1_err'].v /
s.measurements[0].results['eval1'].v * 100)
eval2_err_per.extend(s.measurements[0].results['eval2_err'].v /
s.measurements[0].results['eval2'].v * 100)
eval3_err_per.extend(s.measurements[0].results['eval3_err'].v /
s.measurements[0].results['eval3'].v * 100)
sdper.extend(s.measurements[0].results['stddev'].v /
s.measurements[0].results['M'].v * 100)
QF.extend(s.measurements[0].results['QF'].v)
fig2, axarr = pyplot.subplots(1, 11)
do_box_plot(axarr[0], P, 'P', (.99, 1.25))
do_box_plot(axarr[1], L, 'L', (.99, 1.25))
do_box_plot(axarr[2], F, 'F', (.99, 1.25))
do_box_plot(axarr[3], T, 'T', (-1.0, 1.0))
do_box_plot(axarr[4], eval1_err_per, 'eval1_err_per', (0, 8))
do_box_plot(axarr[5], eval2_err_per, 'eval2_err_per', (0, 8))
do_box_plot(axarr[6], eval3_err_per, 'eval3_err_per', (0, 8))
do_box_plot(axarr[7], E12, 'E12', (0, 90))
do_box_plot(axarr[8], E13, 'E13', (0, 90))
do_box_plot(axarr[9], E23, 'E23', (0, 90))
do_box_plot(axarr[10], sdper, 'stddev %', None)
#do_box_plot(axarr[8], QF, 'QF', None)
pyplot.tight_layout()
fig1.suptitle(
"meas_err: %.3f, evals: %.2f,%.2f,%.2f, P: %.2f, I offset: %d, method: %s"
% (measerr, evals[0], evals[1], evals[2],
max(evals[2], evals[0]) / min(evals[2], evals[0]), d, method))
pdf_pages.savefig(fig1)
pdf_pages.savefig(fig2)
Pmedians.append(np.median(P))
Tmedians.append(np.median(T))
Lmedians.append(np.median(L))
Fmedians.append(np.median(F))
E12medians.append(np.median(E12))
E13medians.append(np.median(E13))
E23medians.append(np.median(E23))
sdpermedians.append(np.median(sdper))
QFmedians.append(np.median(QF))
eval1_err_per_medians.append(np.median(eval1_err_per))
eval2_err_per_medians.append(np.median(eval2_err_per))
eval3_err_per_medians.append(np.median(eval3_err_per))
print d
Ds.append(d)
fig3, (axu, axd) = pyplot.subplots(2, 1)
l = []
l.append(axu.plot(Ds, Pmedians, color='blue', marker='+', label='P'))
l.append(axu.plot(Ds, Lmedians, color='springgreen', marker='+',
label='L'))
l.append(axu.plot(Ds, Fmedians, color='forestgreen', marker='+',
label='F'))
axu2 = axu.twinx()
l.append(axu2.plot(Ds, Tmedians, color='peru', marker='+', label='T'))
axu.set_ylim((0.99, 1.25))
axu2.set_ylim((-1.05, 1.05))
l.append(axd.plot(Ds, E12medians, 'g+-', label=r'$\varepsilon_{12}$'))
l.append(axd.plot(Ds, E13medians, 'gx-', label=r'$\varepsilon_{13}$'))
l.append(axd.plot(Ds, E23medians, 'g*-', label=r'$\varepsilon_{23}$'))
axd2 = axd.twinx()
l.append(axd2.plot(Ds, sdpermedians, 'bx-', label=r'$\sigma (\%)$'))
l.append(
axd2.plot(Ds,
eval1_err_per_medians,
'r+-',
label=r'$\Delta \lambda_1 (\%)$'))
l.append(
axd2.plot(Ds,
eval2_err_per_medians,
'rx-',
label=r'$\Delta \lambda_2 (\%)$'))
l.append(
axd2.plot(Ds,
eval3_err_per_medians,
'r*-',
label=r'$\Delta \lambda_3 (\%)$'))
axd.set_ylim((0.0, 90.0))
axd.set_ylabel('degree')
axd2.set_ylim((0.0, 2.5))
axd2.set_ylabel('%')
# make legends
axd.legend(loc='center left',
bbox_to_anchor=(1.1, 0.7),
fancybox=True,
shadow=True,
ncol=1)
axd2.legend(loc='center left',
bbox_to_anchor=(1.1, 0.1),
fancybox=True,
shadow=True,
ncol=1)
axu.legend(loc='center left',
bbox_to_anchor=(1.1, 0.7),
fancybox=True,
shadow=True,
ncol=1)
axu2.legend(loc='center left',
bbox_to_anchor=(1.1, 0.1),
fancybox=True,
shadow=True)
# Shrink all x axes by 20%
for ax in (axu, axu2, axd, axd2):
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
pdf_pages.savefig(fig3)
pdf_pages.close()
