def plot(self,
gname=None,
sname=None,
mtype=None,
series=None,
stype=None,
sval=None,
sval_range=None,
mean=False,
groupmean=False,
invert=False,
id=None,
**plt_props):
samples = self.get_sample(
sname=sname,
mtype=mtype,
series=series,
stype=stype,
sval=sval,
sval_range=sval_range,
mean=mean,
invert=invert,
)
mlists = [[
m for m in s.get_measurement(mtype=mtype,
series=series,
stype=stype,
sval=sval,
sval_range=sval_range,
mean=mean,
invert=invert,
id=id) if m._visuals
] for s in samples]
columns = max(len(ml) for ml in mlists)
if any(m for m in mlists):
fig = RockPy3.Figure(title=self.name, columns=columns)
for ml in mlists:
for m in ml:
vidx = deepcopy(fig._n_visuals)
m.add_visuals(fig, **plt_props)
v = fig.visuals[vidx][2]
v.title = ' '.join((m.sobj.name, v.title))
if m.has_series():
stuples = '\n'.join('{}'.format(s) for s in m.series)
with RockPy3.ignored(IndexError):
fig.visuals[vidx][2].add_feature('generic_text',
transform='ax',
s=stuples,
x=0.05,
y=0.9)
fig.show()
def plot(self,
mtype=None,
series=None,
stype=None,
sval=None,
sval_range=None,
mean=False,
invert=False,
id=None,
result=None,
**plt_props):
mlist = self.get_measurement(mtype=mtype,
series=series,
stype=stype,
sval=sval,
sval_range=sval_range,
mean=mean,
invert=invert,
id=id,
result=result)
# max_columns = max(len(m._visuals) for m in mlist)
max_columns = sum(len(m._visuals)
for m in mlist) if len(mlist) < 4 else max(
len(m._visuals) for m in mlist)
fig = RockPy3.Figure(title=self.name, columns=max_columns)
for m in mlist:
# get index of the first visual of this measurement
vidx = deepcopy(fig._n_visuals)
# only add the measurement if it has _visuals defined (e.g. no mass measurements)
if m._visuals:
m.add_visuals(fig, **plt_props)
if m.has_series():
stuples = '\n'.join('{}'.format(s) for s in m.series)
with RockPy3.ignored(IndexError):
fig.visuals[vidx][2].add_feature('generic_text',
transform='ax',
s=stuples,
x=0.05,
y=0.9)
fig.show()
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)
def set_colorscheme(scheme):
RockPy3.colorscheme = RockPy3.core.utils.colorscheme(scheme)
return RockPy3.core.utils.colorscheme(scheme)
if __name__ == '__main__':
# pass
# S = RockPy3.Study
# s = S.add_sample(name='S1')
# s.add_simulation(mtype='hysteresis')
#
# fig = RockPy3.Figure()
# v = fig.add_visual(visual='hysteresis', data=S)
# print(v.data)
# for f in v.features:
# print(v.features[f]['data'])
#
# fig.show()
# print(RockPy3.implemented_measurements.keys())
# print(RockPy3.implemented_visuals.keys())
RockPy3.set_fontsize(fontsize=14)
RockPy3.core.utils.setLatex(True)
fig = RockPy3.Figure()
v = fig.add_visual('hysteresis')
v.enumerate()
fig.show()
import inspect
class Thermocurve(Visual):
def init_visual(self):
self.standard_features = ['zero_lines', 'thermocurve_data']
self.xlabel = 'Temperatures'
self.ylabel = 'Moment $Am^2$'
@plot(mtypes=['thermocurve'], overwrite_mobj_plt_props={'marker':''})
def feature_thermocurve_data(self, mobj, plt_props=None):
RockPy3.Packages.Mag.Features.thermocurve.thermocurve_data(self.ax, mobj, **plt_props)\
@plot(mtypes=['thermocurve'], overwrite_mobj_plt_props={'marker':''})
def feature_thermocurve_data_colored(self, mobj, plt_props=None):
RockPy3.Packages.Mag.Features.thermocurve.thermocurve_data_colored(self.ax, mobj, **plt_props)
@plot(mtypes=['thermocurve'], overwrite_mobj_plt_props={'marker': ''})
def feature_thermocurve_derivative(self, mobj, twinx=True, plt_props=None):
RockPy3.Packages.Mag.Features.thermocurve.thermocurve_derivative(self.ax, mobj, **plt_props)
if __name__ == '__main__':
vftb_rmp = '/Users/Mike/Dropbox/experimental_data/pyrrhotite/VFTB/msm17591final.rmp'
s2 = RockPy3.Study.add_sample(name='MSM17591')
m = s2.add_measurement(mtype='rmp', ftype='vftb', fpath=vftb_rmp)
f = RockPy3.Figure(data=s2, figsize=(6, 3))
v = f.add_visual('thermocurve')
v.add_feature('thermocurve_derivative', twinx=True)
v.title = s2.name
f.show(xlim=(20, 350))
def QuickFig(data, visuals, **kwargs):
f = RockPy3.Figure(data=data)
visuals = RockPy3._to_tuple(visuals)
for v in visuals:
f.add_visual(visual=v)
f.show(**kwargs)
plt_props['alpha'] = 0.7
RockPy3.Packages.Mag.Features.day.sd_sp_10nm(ax=self.ax, **plt_props)
@plot(single=True)
def feature_errorbars(self, plt_props=None):
# after Dunlup2002a
plt_props['color'] = 'k'
plt_props['alpha'] = 0.7
RockPy3.Packages.Mag.Features.day.errorbars(ax=self.ax,
mobj=plt_props.pop('mobj'),
**plt_props)
if __name__ == '__main__':
Study = RockPy3.Study
s = Study.add_sample(name='S1')
hys = s.add_measurement(
mtype='hysteresis',
fpath='/Users/Mike/GitHub/RockPy_presentation/hys.001',
ftype='vsm')
coe = s.add_measurement(
mtype='backfield',
fpath='/Users/Mike/GitHub/RockPy_presentation/coe.001',
ftype='vsm')
fig = RockPy3.Figure(data=Study)
v = fig.add_visual(visual='day', color='r', markersize=10, xlims=[1, 6])
# print(v.xlims)
v.add_feature(feature='sd_md_mixline_1', marker='')
fig.show()
# # 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()
