def __add__(self, other):
"""
Adds two studies and returns a new study with all samples and measurements from both studies.
The old studies will get a new samplegroup from the name of the study
Parameters
----------
other: second samplegroup to be added
Returns
-------
RockPyStudy
"""
study = RockPy3.RockPyStudy()
for s in self.samplelist:
study.add_sample(sobj=s, samplegroup=self.name)
for s in other.samplelist:
study.add_sample(sobj=s, samplegroup=other.name)
return study
def test__add_input_to_plot(self):
# only samples without mean
NoMeanStudy = RockPy3.RockPyStudy(name='NoMeanStudy')
# only samples with mean
MeanStudy = RockPy3.RockPyStudy(name='MeanStudy')
# samples with mean and without
MixedStudy = RockPy3.RockPyStudy(name='MixedStudy')
#
# sample without mean
NoMeanSample = RockPy3.Sample()
Measurement_NoMeanSample = NoMeanSample.add_simulation(mtype='hys')
# # sample with mean
MeanSample = RockPy3.Sample()
Measurement_MeanSample1 = MeanSample.add_simulation(mtype='hys')
Measurement_MeanSample2 = MeanSample.add_simulation(mtype='hys')
MeanMeasurement12 = MeanSample.create_mean_measurement(mtype='hys')
#
NoMeanStudy.add_sample(sobj=NoMeanSample)
MeanStudy.add_sample(sobj=MeanSample)
MixedStudy.add_sample(sobj=NoMeanSample)
MixedStudy.add_sample(sobj=MeanSample)
SampleList = [NoMeanSample, MeanSample]
NoMeanList = [Measurement_NoMeanSample, Measurement_MeanSample1]
MeanList = [Measurement_NoMeanSample, Measurement_MeanSample1, MeanMeasurement12]
for i, test in enumerate([NoMeanStudy,
MeanStudy, MixedStudy, #NoMeanSample, MeanSample,
# Measurement_NoMeanSample, Measurement_MeanSample1,
# SampleList, NoMeanList, MeanList
]):
fig = RockPy3.Figure()
v = fig.add_visual('hysteresis')
# print('plot_mean=True, plot_base=True')
mlist = v._add_input_to_plot(test, plot_mean=True, plot_base=True, base_alpha=0.1)
# print('plot_mean=False, plot_base=True')
mlist_NoMean = v._add_input_to_plot(test, plot_mean=False, plot_base=True, base_alpha=0.2)
# print('plot_mean=True, plot_base=False')
mlist_NoBase = v._add_input_to_plot(test, plot_mean=True, plot_base=False, base_alpha=0.3)
# print('plot_mean=False, plot_base=False')
mlist_NoMeanNoBase = v._add_input_to_plot(test, plot_mean=False, plot_base=False, base_alpha=0.4)
# print('plot_mean=False, plot_base=False, plot_other=False')
mlist_Nothing = v._add_input_to_plot(test, plot_mean=False, plot_base=False, plot_other=False)
if i == 0: #NoMeanStudy
self.assertEquals([Measurement_NoMeanSample], mlist)
self.assertEquals([Measurement_NoMeanSample], mlist_NoMean)
self.assertEquals([Measurement_NoMeanSample], mlist_NoBase)
self.assertEquals([Measurement_NoMeanSample], mlist_NoMeanNoBase)
self.assertEquals([], mlist_Nothing)
# alpha_prop = [m._plt_props['alpha'] for m in mlist_NoMean]
# self.assertFalse(all(i == 0.1 for i in alpha_prop))
if i == 1: #MeanStudy
self.assertEquals([MeanMeasurement12, Measurement_MeanSample1, Measurement_MeanSample2], mlist)
self.assertEquals([Measurement_MeanSample1, Measurement_MeanSample2], mlist_NoMean)
self.assertEquals([MeanMeasurement12], mlist_NoBase)
self.assertEquals([], mlist_NoMeanNoBase)
alpha_prop = [m._plt_props['alpha'] for m in mlist_NoMean if 'alpha' in m._plt_props]
self.assertEqual([0.1, 0.1], alpha_prop)
if i == 2: #MixedStudy
self.assertEquals([MeanMeasurement12, Measurement_MeanSample1, Measurement_MeanSample2, Measurement_NoMeanSample], mlist)
self.assertEquals([Measurement_MeanSample1, Measurement_MeanSample2, Measurement_NoMeanSample], mlist_NoMean)
self.assertEquals([MeanMeasurement12, Measurement_NoMeanSample], mlist_NoBase)
self.assertEquals([Measurement_NoMeanSample], mlist_NoMeanNoBase)
alpha_prop = [m._plt_props['alpha'] for m in mlist_NoMean if 'alpha' in m._plt_props]
self.assertEqual([0.1, 0.1], alpha_prop)
# """ vftb sample """
# # s = S.add_sample(name='MSM17591')
# # s.add_measurement(mtype='thermocurve', fpath=vftb_rmp, ftype='vftb')
#
# """ mpms sample """
# # s = S.add_sample(name='pyrr17591_a')
# # s.add_measurement(mtype='thermocurve', fpath=file, ftype='mpms')
#
# """ vsm sample """
# s = S.add_sample('167a', sgroup='LTPY')
# s.add_measurement(fpath='/Users/mike/Dropbox/experimental_data/RMP/LTPY/LTPY_167a_RMP_VSM#[]_[]_[]##STD001.000')
#
#
# fig = RockPy3.Figure()
# v = fig.add_visual(visual='thermocurve', data=s)
# fig.show()
S = RockPy3.RockPyStudy()
s = S.add_sample('test')
m = s.add_measurement(
fpath=
'/Users/mike/Dropbox/experimental_data/RMP/LTPY/LTPY_167a_RMP_VSM#[]_[]_[]##STD001.000'
)
print(deepcopy(m).__dict__.keys())
# fig = RockPy3.Figure()
#
# # 167a
# v = fig.add_visual('thermocurve', data=s)
# v.enumerate()
# v.normalize('cooling00', vval=300)
# fig.show()
log.info("reading study {}".format(name))
study = cls(name=name)
# readin the samples
for sample_node in s.findall(RockPy3.core.sample.Sample.SAMPLE):
study.add_sample(
sobj=RockPy3.core.sample.Sample.from_etree(sample_node))
return study
@classmethod
def load_from_xml(cls, file_name, folder=None):
if not folder:
folder = RockPy3.core.file_operations.default_folder
log.info("reading xml data from {}".format(join(folder, file_name)))
tree = etree.parse(join(folder, file_name))
root = tree.getroot()
return cls.from_etree(root)
if __name__ == '__main__':
# RockPy3.logger.setLevel('DEBUG')
S = RockPy3.RockPyStudy(
folder='/Users/mike/Dropbox/experimental_data/pyrrhotite/hys||c')
S.label_add_series(stype='temp', add_stype=False, add_unit=True)
RockPy3.QuickFig(S, 'hysteresis', sort_labels=False)
# # dictionaries
# self._mdict = self._create_mdict()
# self._mean_mdict = self._create_mdict()
# self._rdict = self._create_mdict()
self.index = MeanSample.snum
self._samplegroups = []
self.study = RockPy3.Study
self.idx = MeanSample.snum
MeanSample.snum += 1
def __repr__(self):
return '<< RockPy.MeanSample.{} >>'.format(self.name)
if __name__ == '__main__':
RockPy3.logger.setLevel('DEBUG')
#S = RockPy3.RockPyStudy(folder='/Users/mike/Dropbox/experimental_data/pyrrhotite/hys||c')
S = RockPy3.RockPyStudy(
folder=
'/Users/mike/Dropbox/experimental_data/FeNiX/FeNi20K/separation test')
f = RockPy3.Figure(data=S)
v = f.add_visual('hysteresis')
v = f.add_visual('hysteresis', features='reversible_data')
v = f.add_visual('hysteresis', features='irreversible_data')
f.show()
