def test_add_errors(self):
d = RockPyData(column_names=['A', 'B'])
#d['A'].v = 1 # Attribute Error NoneType has no attribute, maybe initialize to np.nan?
#d['B'] = 2
#d['A'].e = 4
#d['B'].e = 5
d = d.append_rows([1, 2])
#print d
d.e = [[4, 5]]
self.assertEqual(5., d['B'].e)
def from_simulation(cls,
sobj,
idx=None,
ms=250.,
bmax=0.5,
E=0.005,
G=0.3,
steps=20,
log_steps=False,
noise=None,
color=None,
marker=None,
linestyle=None):
"""
Simulates a backfield measurement based on a single log-normal gaussian distribution.
E: Median destructive field - represents the mean value of the log-Gaussian distribution, and therefore, the
logarithmic field value of the maximum gradient.
G: G describes the standard deviation or half-width of the distribution.
"""
cls.clslog.info('CREATING simulation measurement with {}'.format(
locals()))
calculation = np.arange(0, 2, 1e-6)
fields = np.round(np.linspace(1e-9, bmax, steps), 4)
dist = lognorm([G], loc=E)
calc_mag = dist.cdf(calculation)
indices = [np.argmin(abs(v - calculation)) for v in fields]
mag = [calc_mag[i] for i in indices]
# mag /= 1/2 * max(mag)
# mag -= 1
# mag *= ms
# mag = G * (np.sqrt(np.pi) / 2) * (sp.special.erf((log_fields - E) / G) - sp.special.erf((-E) / G)) #leonhardt2004
# if noise:
# n = np.random.normal(0, ms * (noise / 100), steps)
# mag = mag + n
mdata = cls.empty_mdata()
mdata['remanence'] = RockPyData(column_names=['field', 'mag'],
data=np.c_[fields - bmax, mag])
return cls(sobj,
fpath=None,
mdata=mdata,
ftype='simulation',
color=color,
marker=marker,
linestyle=linestyle,
idx=idx)
def setUp(self):
# run before each test
self.testdata = ((1, 2, 3, 4), (1, 6, 7, 8), (1, 2, 11, 12), (1, 6, 55,
66))
self.col_names = ('F', 'Mx', 'My', 'Mz')
self.row_names = ('1.Zeile', '2.Zeile_A', '3.Zeile', '4.Zeile_A')
self.units = ('T', 'mT', 'fT', 'pT')
self.RPD = RockPyData(column_names=self.col_names,
row_names=self.row_names,
units=self.units,
data=self.testdata)
def format_vftb(ftype_data, sobj_name=None):
'''
formats the output from vftb to measurement.data
:return:
'''
data = ftype_data.data
header = ftype_data.header
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[0])
mdata['data']['mag'] = mdata['data']['mag'].v * ftype_data.mass
mdata['data'].define_alias('variable', 'field')
return mdata
def format_vsm(ftype_data, sobj_name=None):
"""
formats the vsm output to be compatible with irm acquisition measurements
:return:
"""
data = ftype_data.data
header = ftype_data.header
# check if vsm file actually contains a dcd measurement
if not ftype_data.info_header['include irm?']:
return
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[0])
mdata['data'].rename_column('remanence', 'mag')
return mdata
def format_vftb(ftype_data, sobj_name=None):
data = ftype_data.data
units = ftype_data.units
header = ftype_data.header
mdata = OrderedDict(warming=None, cooling=None)
if len(data) > 2:
RockPy3.logger.warning(
'LENGTH of machine.out_thermocurve =! 2. Assuming data[0] = heating data[1] = cooling'
)
if 3 > len(data) > 1:
for idx, dtype in enumerate(('warming', 'cooling')):
mdata[dtype] = RockPyData(column_names=header,
data=data[idx],
units=units)
else:
RockPy3.logger.error('LENGTH of machine.out_thermocurve < 2.')
return mdata
def format_vsm(ftype_data, sobj_name=None):
'''
Reading routine for thermal demagnetization curves measured with a VSM.
'''
header = ftype_data.header
segments = ftype_data.segment_data
data = ftype_data.data
mdata = OrderedDict()
aux = np.array([j for i in data for j in i]) # combine all data arrays
a = np.array([(i, v)
for i, v in enumerate(np.diff(aux, axis=0)[:, 0])])
sign = np.sign(np.diff(aux, axis=0)[:, 1])
threshold = 3
zero_crossings = [
i + 1 for i in range(len(a[:, 1]) - 1)
if a[i, 1] > 0 > a[i + 1, 1] and a[i, 1] > 0 > a[i + 2, 1]
or a[i, 1] < 0 < a[i + 1, 1] and a[i, 1] < 0 < a[i + 2, 1]
]
zero_crossings = [0] + zero_crossings # start with zero index
zero_crossings += [len(aux)] # append last index
ut = 0 # running number warming
dt = 0 # running number cooling
for i, v in enumerate(zero_crossings):
if v < zero_crossings[-1]: # prevents index Error
if sum(a[v:zero_crossings[i + 1], 1]) < 0: # cooling
name = 'cooling%02i' % (ut)
ut += 1
else:
name = 'warming%02i' % (dt)
dt += 1
data = aux[v:zero_crossings[i + 1] + 1]
rpd = RockPyData(column_names=header, data=data)
rpd.rename_column('temperature', 'temp')
rpd.rename_column('moment', 'mag')
mdata.update({name: rpd})
return mdata
def format_vsm(ftype_data, sobj_name=None):
'''
formats the output from vsm to measurement.data
:return:
'''
idx = 0
data = ftype_data.data
header = ftype_data.header
# check if vsm file actually contains a dcd measurement
if not ftype_data.info_header['include dcd?']:
return
else:
if ftype_data.info_header['include irm?']:
idx += 1
mdata = {}
mdata['data'] = RockPyData(column_names=header, data=data[idx])
mdata['data'].rename_column('remanence', 'mag')
return mdata
def format_mpms(ftype_data, sobj_name=None):
if ftype_data.name == sobj_name:
temp_index = ftype_data.units.index('K')
# see if it a cooling or a warming curve
mean_temp_difference_between_steps = np.mean(
np.diff(ftype_data.data[:, temp_index]))
if mean_temp_difference_between_steps > 0:
dtype = 'warming'
else:
dtype = 'cooling'
mdata = RockPyData(column_names=ftype_data.header,
data=ftype_data.data,
units=ftype_data.units)
mdata.rename_column('Temperature', 'temp')
mdata.rename_column('Long Moment', 'mag')
data = {dtype: mdata}
return data
else:
RockPy3.logger.error(
'SAMPLE name of file does not match specified sample_name. << {} != {} >>'
.format(ftype_data.name, sobj_name))
