class Generic(object):
create_logger('RockPy.series')
def __init__(self, stype, value, unit, comment=None):
#self.log = logging.getLogger('RockPy.series.' + type(self).__name__)
#self.log.info('CREATING series << %s >>' % stype)
self.stype = stype.lower()
self.value = float(value)
self.data = RockPyData(column_names=stype, data=value)
self.unit = unit
self.comment = comment
@property
def label(self):
return '%.2f [%s]' %(self.value, self.unit)
def add_value(self, type, value, unit=None):
self.data.append_columns(column_names=type)
self.data[type] = value
def __repr__(self):
return '<RockPy.series> %s, %.2f, [%s]' %(self.stype, self.value, self.unit)
@property
def v(self):
return self.value
@property
def u(self):
return self.unit
class Generic(object):
create_logger('RockPy.series')
def __init__(self, stype, value, unit, comment=None):
#self.log = logging.getLogger('RockPy.series.' + type(self).__name__)
#self.log.info('CREATING series << %s >>' % stype)
self.stype = stype.lower()
self.value = float(value)
self.data = RockPyData(column_names=stype, data=value)
self.unit = unit
self.comment = comment
@property
def label(self):
return '%.2f [%s]' % (self.value, self.unit)
def add_value(self, type, value, unit=None):
self.data.append_columns(column_names=type)
self.data[type] = value
def __repr__(self):
return '<RockPy.series> %s, %.2f, [%s]' % (self.stype, self.value,
self.unit)
@property
def v(self):
return self.value
@property
def u(self):
return self.unit
def format_sushibar(self):
data = RockPyData(column_names=['field', 'x', 'y', 'z'],
data=self.machine_data.out_afdemag()
) # , units=['mT', 'Am2', 'Am2', 'Am2'])
data.define_alias('m', ('x', 'y', 'z'))
self._raw_data['data'] = data.append_columns('mag',
data.magnitude('m'))
def format_jr6(self):
data = self.machine_data.get_data()
data = RockPyData(column_names=['x', 'y', 'z'],
data=data,
units=['A m^2', 'A m^2', 'A m^2'])
data.define_alias('m', ( 'x', 'y', 'z'))
self._raw_data = {'data': data.append_columns('mag', data.magnitude('m'))}
def format_cryomag(self):
data = self.machine_data.float_data
header = self.machine_data.float_header
data = RockPyData(column_names=header, data=data)
data.define_alias('m', ( 'x', 'y', 'z'))
# data = data.append_columns('mag', data.magnitude('m'))
self._raw_data = {'data': data.append_columns('mag', data.magnitude('m'))}
def format_sushibar(self):
data = RockPyData(column_names=['temp', 'x', 'y', 'z', 'sm'],
data=self.machine_data.out_trm(),
# units=['C', 'mT', 'A m^2', 'A m^2', 'A m^2']
)
data.define_alias('m', ( 'x', 'y', 'z'))
# data = data.append_columns('mag', data.magnitude('m'))
self._raw_data = {'data': data.append_columns('mag', data.magnitude('m'))}
def format_cryomag(self):
data = self.machine_data.float_data
header = self.machine_data.float_header
data = RockPyData(column_names=header, data=data)
data.define_alias('m', ('x', 'y', 'z'))
# data = data.append_columns('mag', data.magnitude('m'))
self._raw_data = {
'data': data.append_columns('mag', data.magnitude('m'))
}
def format_jr6(self):
data = self.machine_data.get_data()
data = RockPyData(column_names=['x', 'y', 'z'],
data=data,
units=['A m^2', 'A m^2', 'A m^2'])
data.define_alias('m', ('x', 'y', 'z'))
self._raw_data = {
'data': data.append_columns('mag', data.magnitude('m'))
}
def format_cryomag(self):
data = RockPyData(column_names=self.machine_data.float_header,
data=self.machine_data.get_float_data())
if self.demag_type != 'af3':
idx = [i for i, v in enumerate(self.machine_data.steps) if v == self.demag_type]
data = data.filter_idx(idx)
data.define_alias('m', ('x', 'y', 'z'))
self._raw_data['data'] = data.append_columns('mag', data.magnitude('m'))
self._raw_data['data'].rename_column('step', 'field')
def format_sushibar(self):
data = RockPyData(
column_names=['temp', 'x', 'y', 'z', 'sm'],
data=self.machine_data.out_trm(),
# units=['C', 'mT', 'A m^2', 'A m^2', 'A m^2']
)
data.define_alias('m', ('x', 'y', 'z'))
# data = data.append_columns('mag', data.magnitude('m'))
self._raw_data = {
'data': data.append_columns('mag', data.magnitude('m'))
}
def format_cryomag(self):
data = RockPyData(column_names=self.machine_data.float_header,
data=self.machine_data.get_float_data())
if self.demag_type != 'af3':
idx = [
i for i, v in enumerate(self.machine_data.steps)
if v == self.demag_type
]
data = data.filter_idx(idx)
data.define_alias('m', ('x', 'y', 'z'))
self._raw_data['data'] = data.append_columns('mag',
data.magnitude('m'))
self._raw_data['data'].rename_column('step', 'field')
def format_pmd(self):
data = RockPyData(column_names=['field', 'x', 'y', 'z'], data=self.machine_data.out_afdemag())
data.define_alias('m', ('x', 'y', 'z'))
self._raw_data['data'] = data.append_columns('mag', data.magnitude('m'))
class Parm_Spectra(base.Measurement):
'''
'''
def __init__(self,
sample_obj,
mtype,
mfile,
machine,
mag_method='',
**options):
super(Parm_Spectra, self).__init__(sample_obj, mtype, mfile, machine,
**options)
def format_sushibar(self):
data = self.machine_data.out_parm_spectra()
self.af3 = RockPyData(column_names=data[1],
data=data[0][0],
units=data[2])
self.af3.define_alias('m', ('x', 'y', 'z'))
self.af3 = self.af3.append_columns('mag', self.af3.magnitude('m'))
self.af3 = self.af3.append_columns(
column_names='mean_window',
data=np.array(
[self.af3['upper_window'].v + self.af3['lower_window'].v])[0] /
2)
self.data = RockPyData(column_names=data[1],
data=data[0][1:],
units=data[2])
self.data.define_alias('m', ('x', 'y', 'z'))
self.data = self.data.append_columns('mag', self.data.magnitude('m'))
self.data = self.data.append_columns(
column_names='mean_window',
data=np.array([
self.data['upper_window'].v + self.data['lower_window'].v
])[0] / 2)
self.data.define_alias('variable', 'mean_window')
def _get_cumulative_data(self, subtract_af3=True):
cumulative_data = deepcopy(self.data)
if subtract_af3:
cumulative_data['x'] = cumulative_data['x'].v - self.af3['x'].v
cumulative_data['y'] = cumulative_data['y'].v - self.af3['y'].v
cumulative_data['z'] = cumulative_data['z'].v - self.af3['z'].v
cumulative_data['mag'] = cumulative_data.magnitude('m')
cumulative_data['x'] = [
np.sum(cumulative_data['x'].v[:i])
for i, v in enumerate(cumulative_data['x'].v)
]
cumulative_data['y'] = [
np.sum(cumulative_data['y'].v[:i])
for i, v in enumerate(cumulative_data['y'].v)
]
cumulative_data['z'] = [
np.sum(cumulative_data['z'].v[:i])
for i, v in enumerate(cumulative_data['z'].v)
]
cumulative_data['mag'] = cumulative_data.magnitude('m')
return cumulative_data
def plt_parm_spectra(self,
subtract_af3=True,
rtn=False,
norm=False,
fill=False):
plot_data = deepcopy(self.data)
if subtract_af3:
plot_data['x'] = plot_data['x'].v - self.af3['x'].v
plot_data['y'] = plot_data['y'].v - self.af3['y'].v
plot_data['z'] = plot_data['z'].v - self.af3['z'].v
plot_data['mag'] = plot_data.magnitude('m')
if norm:
norm_factor = max(plot_data['mag'].v)
else:
norm_factor = 1
if fill:
plt.fill_between(plot_data['mean_window'].v,
0,
plot_data['mag'].v / norm_factor,
alpha=0.1,
label='pARM spetra')
else:
plt.plot(plot_data['mean_window'].v,
0,
plot_data['mag'].v / norm_factor,
label='pARM spetra')
if not rtn:
plt.show()
def plt_parm_acquisition(self, subtract_af3=True, rtn=False, norm=False):
plot_data = self._get_cumulative_data(subtract_af3=subtract_af3)
if norm:
norm_factor = max(plot_data['mag'].v)
else:
norm_factor = 1
plt.plot(plot_data['mean_window'].v,
plot_data['mag'].v / norm_factor,
label='pARM acquisition')
if not rtn:
plt.show()
def plt_acq_spec(self, subtract_af3=True, norm=True):
self.plt_parm_spectra(subtract_af3=subtract_af3,
rtn=True,
norm=norm,
fill=True)
self.plt_parm_acquisition(subtract_af3=subtract_af3,
rtn=True,
norm=norm)
plt.xlabel('AF field [mT]')
plt.grid()
plt.show()
class Measurement(object):
"""
HOW TO get at stuff
===================
HowTo:
======
stypeS
++++++
want all series types (e.g. pressure, temperature...):
as list: measurement.stypes
print measurement.stypes
>>> ['pressure']
as dictionary with corresponding series as value: measurement.tdict
print measurement.tdict
>>> {'pressure': <RockPy.Series> pressure, 0.60, [GPa]}
as dictionary with itself as value: measurement._self_tdict
>>> {'pressure': {0.6: <RockPy.Measurements.thellier.Thellier object at 0x10e2ef890>}}
svalUES
+++++++
want all values for any series in a measurement
as list: measurement.svals
print measuremtn.svals
>>> [0.6]
"""
logger = logging.getLogger('RockPy.MEASUREMENT')
# @classmethod
# def _standard_parameter(cls):
# return {i:{} for i in cls.result_methods}
@classmethod
def simulate(cls, **parameter):
"""
pseudo abstract method that should be overridden in subclasses to return a simulated measurement
based on given parameters
"""
return None
@classmethod
def implemented_machines(cls):
# setting implemented machines
# looking for all subclasses of RockPy.Readin.base.Machine
# generating a dictionary of implemented machines : {implemented out_* method : machine_class}
implemented_machines = {
cl.__name__.lower(): cl
for cl in RockPy.Readin.base.Machine.__subclasses__()
}
return implemented_machines
@classmethod
def inheritors(cls):
"""
Method that gets all children and childrens-children ... from a class
Returns
-------
list
"""
subclasses = set()
work = [cls]
while work:
parent = work.pop()
for child in parent.__subclasses__():
if child not in subclasses:
subclasses.add(child)
work.append(child)
return subclasses
@classmethod
def implemented_measurements(self):
"""
method that dynamically creates a dictionary with "obj.name : obj" entries
Returns
-------
dict
"""
return {i.__name__.lower(): i for i in Measurement.inheritors()}
@classmethod
def measurement_formatters(cls):
# measurement formatters are important!
# if they are not inside the measurement class, the measurement has not been implemented for this machine.
# the following machine formatters:
# 1. looks through all implemented measurements
# 2. for each measurement stores the machine and the applicable readin class in a dictonary
measurement_formatters = {
cl.__name__.lower(): {
'_'.join(i.split('_')[1:]).lower():
Measurement.implemented_machines()['_'.join(
i.split('_')[1:]).lower()]
for i in dir(cl) if i.startswith('format_')
}
for cl in Measurement.inheritors()
}
return measurement_formatters
@classmethod
def get_subclass_name(cls):
return cls.__name__
@property
def standard_parameter(self):
"""
property that returns the standard calculation parameter if specified, otherwise returns an empty dictionary
:return:
"""
print self.__class__._standard_parameter
if hasattr(self.__class__, '_standard_parameter'):
return self.__class__._standard_parameter
else:
print 'test'
return {}
def __init__(self,
sample_obj,
mtype,
mfile,
machine,
mdata=None,
color=None,
series=None,
**options):
"""
sample_obj:
mtype:
mfile:
machine:
mdata: when mdata is set, this will be directly used as measurement data without formatting from file
color: color used for plotting if specified
options:
:return:
"""
self.logger = logging.getLogger('RockPy.MEASURMENT.' +
self.get_subclass_name())
self.color = color
self.has_data = True
self._data = {}
self._raw_data = {}
self.is_initial_state = False
self.is_mean = False # flag for mean measurements
if machine is not None:
machine = machine.lower() # for consistency in code
if mtype is not None:
mtype = mtype.lower() # for consistency in code
''' initialize parameters '''
self.machine_data = None # returned data from Readin.machines()
self.suffix = options.get('suffix', '')
''' initial state '''
self.is_machine_data = None # returned data from Readin.machines()
self.initial_state = None
''' series '''
self._series = []
self._series_opt = series
self.__initialize()
if mtype in Measurement.measurement_formatters():
self.logger.debug('MTYPE << %s >> implemented' % mtype)
self.mtype = mtype # set mtype
if mdata is not None: # we have mdata -> ignore mfile and just use that data directly
self.logger.debug(
'mdata passed -> using as measurement data without formatting'
)
self.sample_obj = sample_obj
self._data = mdata
return # done
if machine in Measurement.measurement_formatters(
)[mtype] or machine == 'combined':
self.logger.debug('MACHINE << %s >> implemented' % machine)
self.machine = machine # set machine
self.sample_obj = sample_obj # set sample_obj
if not mfile:
self.logger.debug(
'NO machine or mfile passed -> no raw_data will be generated'
)
return
else:
self.mfile = mfile
self.import_data()
self.has_data = self.machine_data.has_data
if not self.machine_data.has_data:
self.logger.error(
'NO DATA passed: check sample name << %s >>' %
sample_obj.name)
else:
self.logger.error('UNKNOWN MACHINE: << %s >>' % machine)
self.logger.error(
'most likely cause is the \"format_%s\" method is missing in the measurement << %s >>'
% (machine, mtype))
else:
self.logger.error('UNKNOWN\t MTYPE: << %s >>' % mtype)
# dynamic data formatting
# checks if format_'machine_name' exists. If exists it formats self.raw_data according to format_'machine_name'
if machine == 'combined':
pass
elif callable(getattr(self, 'format_' + machine)):
if self.has_data:
self.logger.debug('FORMATTING raw data from << %s >>' %
machine)
getattr(self, 'format_' + machine)()
else:
self.logger.debug('NO raw data transfered << %s >>' % machine)
else:
self.logger.error(
'FORMATTING raw data from << %s >> not possible, probably not implemented, yet.'
% machine)
# add series if provied
# has to come past __initialize()
if self._series_opt:
self._add_series_from_opt()
@property
def m_idx(self):
return self.sample_obj.measurements.index(self)
@property
def fname(self):
"""
Returns only filename from self.file
Returns
-------
str: filename from full path
"""
return os.path.split(self.mfile)[-1]
def __initialize(self):
"""
Initialize function is called inside the __init__ function, it is also called when the object is reconstructed
with pickle.
:return:
"""
# dynamical creation of entries in results data. One column for each results_* method.
# calculation_* methods are not creating columns -> if a result is calculated a result_* method
# has to be written
self.result_methods = [
i[7:] for i in dir(self) if i.startswith('result_')
if not i.endswith('generic') if not i.endswith('result')
] # search for implemented results methods
self.results = RockPyData(
column_names=self.result_methods,
data=[np.nan for i in self.result_methods
]) # dynamic entry creation for all available result methods
# ## warning with calculation of results:
# M.result_slope() -> 1.2
# M.calculate_vds(t_min=300) -> ***
# M.results['slope'] -> 1.2
# M.result_slope(t_min=300) -> 0.9
#
# the results are stored for the calculation parameters that were used to calculate it.
# This means calculating a different result with different parameters can lead to inconsistencies.
# One has to be aware that comparing the two may not be useful
# dynamically generating the calculation and standard parameters for each calculation method.
# This just sets the values to non, the values have to be specified in the class itself
self.calculation_methods = [
i for i in dir(self) if i.startswith('calculate_')
if not i.endswith('generic')
]
self.calculation_parameter = {i: dict() for i in self.result_methods}
self._standard_parameter = {
i[10:]: None
for i in dir(self) if i.startswith('calculate_')
if not i.endswith('generic')
}
if self._series:
for t in self._series:
self._add_sval_to_results(t)
# self._add_sval_to_data(t)
self.is_normalized = False # normalized flag for visuals, so its not normalized twize
self.norm = None # the actual parameters
self._info_dict = self.__create_info_dict()
def __getstate__(self):
'''
returned dict will be pickled
:return:
'''
pickle_me = {
k: v
for k, v in self.__dict__.iteritems() if k in (
'mtype',
'machine',
'mfile',
'has_data',
'machine_data',
'_raw_data',
'_data',
'initial_state',
'is_initial_state',
'sample_obj',
'_series_opt',
'_series',
'suffix',
)
}
return pickle_me
def __setstate__(self, d):
'''
d is unpickled data
d:
:return:
'''
self.__dict__.update(d)
self.__initialize()
def reset__data(self, recalc_mag=False):
pass
def __getattr__(self, attr):
# print attr, self.__dict__.keys()
if attr in self.__getattribute__('_data').keys():
return self._data[attr]
if attr in self.__getattribute__('result_methods'):
return getattr(self, 'result_' + attr)().v[0]
raise AttributeError(attr)
def import_data(self, rtn_raw_data=None, **options):
'''
Importing the data from mfile and machine
rtn_raw_data:
options:
:return:
'''
self.logger.info('IMPORTING << %s , %s >> data' %
(self.machine, self.mtype))
machine = options.get('machine', self.machine)
mtype = options.get('mtype', self.mtype)
mfile = options.get('mfile', self.mfile)
raw_data = self.measurement_formatters()[mtype][machine](
mfile, self.sample_obj.name)
if raw_data is None:
self.logger.error(
'IMPORTING\t did not transfer data - CHECK sample name and data file'
)
return
else:
if rtn_raw_data:
self.logger.info('RETURNING raw_data for << %s , %s >> data' %
(machine, mtype))
return raw_data
else:
self.machine_data = raw_data
def set_initial_state(
self,
mtype,
mfile,
machine, # standard
**options):
"""
creates a new measurement (ISM) as initial state of base measurement (BSM).
It dynamically calls the measurement _init_ function and assigns the created measurement to the
self.initial_state value. It also sets a flag for the ISM to check if a measurement is a MIS.
Parameters
----------
mtype: str
measurement type
mfile: str
measurement data file
machine: str
measurement machine
options:
"""
mtype = mtype.lower()
machnine = machine.lower()
self.logger.info(
'CREATING << %s >> initial state measurement << %s >> data' %
(mtype, self.mtype))
implemented = {i.__name__.lower(): i for i in Measurement.inheritors()}
# can only be created if the measurement is actually implemented
if mtype in implemented:
self.initial_state = implemented[mtype](self.sample_obj, mtype,
mfile, machine)
self.initial_state.is_initial_state = True
return self.initial_state
else:
self.logger.error('UNABLE to find measurement << %s >>' % (mtype))
### INFO DICTIONARY
@property
def info_dict(self):
if not hasattr(self, '_info_dict'):
self._info_dict = self.__create_info_dict()
if not all(i in self._info_dict['series'] for i in self.series):
self._recalc_info_dict()
return self._info_dict
def __create_info_dict(self):
"""
creates all info dictionaries
Returns
-------
dict
Dictionary with a permutation of ,type, stype and sval.
"""
d = ['stype', 'sval']
keys = [
'_'.join(i) for n in range(3)
for i in itertools.permutations(d, n) if not len(i) == 0
]
out = {i: {} for i in keys}
out.update({'series': []})
return out
def _recalc_info_dict(self):
"""
Re-calculates the info_dictionary for the measurement
"""
self._info_dict = self.__create_info_dict()
map(self.add_s2_info_dict, self.series)
def add_s2_info_dict(self, series):
"""
adds a measurement to the info dictionary.
Parameters
----------
series: RockPy.Series
Series to be added to the info_dictionary
"""
if not series in self._info_dict['series']:
self._info_dict['stype'].setdefault(series.stype, []).append(self)
self._info_dict['sval'].setdefault(series.value, []).append(self)
self._info_dict['sval_stype'].setdefault(series.value, {})
self._info_dict['sval_stype'][series.value].setdefault(
series.stype, []).append(self)
self._info_dict['stype_sval'].setdefault(series.stype, {})
self._info_dict['stype_sval'][series.stype].setdefault(
series.value, []).append(self)
self._info_dict['series'].append(series)
@property
def stypes(self):
"""
list of all stypes
"""
out = [t.stype for t in self.series]
return self.__sort_list_set(out)
@property
def svals(self):
"""
list of all stypes
"""
out = [t.value for t in self.series]
return self.__sort_list_set(out)
@property
def stype_dict(self):
"""
dictionary of stype: series}
"""
out = {t.stype: t for t in self.series}
return out
@property
def tdict(self):
"""
dictionary of stype: series}
"""
out = {t.stype: t.value for t in self.series}
return out
@property
def _self_tdict(self):
"""
dictionary of stype: {svalue: self}
"""
out = {i.stype: {i.value: self} for i in self.series}
return out
@property
def data(self):
if self._data == {}:
self._data = deepcopy(self._raw_data)
return self._data
# ## DATA RELATED
### Calculation and parameters
def result_generic(self, recalc=False):
'''
Generic for for result implementation. Every calculation of result should be in the self.results data structure
before calculation.
It should then be tested if a value for it exists, and if not it should be created by calling
_calculate_result_(result_name).
'''
parameter = {}
self.calc_result(parameter, recalc)
return self.results['generic']
def calculate_generic(self, **parameter):
'''
actual calculation of the result
:return:
'''
self.results['generic'] = 0
def calculate_result(self, result, **parameter):
"""
Helper function to dynamically call a result. Used in VisualizeV3
Parameters
----------
result:
parameter:
"""
if not self.has_result(result):
self.logger.warning(
'%s does not have result << %s >>' % self.mtype, result)
return
else:
# todo figuer out why logger wrong when called from VisualizeV3
self.logger = logging.getLogger('RockPy.MEASURMENT.' + self.mtype +
'[%s]' % self.sample_obj.name)
self.logger.info('CALCULATING << %s >>' % result)
out = getattr(self, 'result_' + result)(**parameter)
return out
def calc_generic(self, **parameter):
'''
helper function
actual calculation of the result
:return:
'''
self.results['generic'] = 0
def calc_result(self, parameter=None, recalc=False, force_method=None):
'''
Helper function:
Calls any calculate_* function, but checks first:
1. does this calculation method exist
2. has it been calculated before
NO : calculate the result
YES: are given parameters equal to previous calculation parameters
if YES::
NO : calculate result with new parameters
YES: return previous result
parameter: dict
dictionary with parameters needed for calculation
force_caller: not dynamically retrieved caller name.
:return:
'''
caller = '_'.join(inspect.stack()[1][3].split(
'_')[1:]) # get calling function #todo get rid of inspect
if not parameter: # todo streamline the generation of standard parameters
try:
parameter = self.standard_parameter[caller]
except AttributeError:
parameter = dict(caller={})
except KeyError:
parameter = dict(caller={})
# get the method to be used for calculation. It is either the calling method determined by inspect
# or the method specified with force_method
if force_method is not None:
method = force_method # method for calculation if any: result_CALLER_method
else:
method = caller # if CALLER = METHOD
if callable(getattr(self, 'calculate_' +
method)): # check if calculation function exists
# check for None and replaces it with standard
parameter = self.compare_parameters(method, parameter, recalc)
# if results dont exist or force recalc
if self.results[caller] is None or self.results[
caller] == np.nan or recalc:
# recalc causes a forced racalculation of the result
if recalc:
self.logger.debug('FORCED recalculation of << %s >>' %
(method))
else:
self.logger.debug(
'CANNOT find result << %s >> -> calculating' %
(method))
getattr(self, 'calculate_' +
method)(**parameter) # calling calculation method
else:
self.logger.debug('FOUND previous << %s >> parameters' %
(method))
if self.check_parameters(
caller, parameter
): # are parameters equal to previous parameters
self.logger.debug(
'RESULT parameters different from previous calculation -> recalculating'
)
getattr(self, 'calculate_' + method)(
**parameter) # recalculating if parameters different
else:
self.logger.debug(
'RESULT parameters equal to previous calculation')
else:
self.logger.error(
'CALCULATION of << %s >> not possible, probably not implemented, yet.'
% method)
def calc_all(self, **parameter):
parameter['recalc'] = True
for result_method in self.result_methods:
getattr(self, 'result_' + result_method)(**parameter)
return self.results
def compare_parameters(self, caller, parameter, recalc):
"""
checks if given parameter[key] is None and replaces it with standard parameter or calculation_parameter.
e.g. calculation_generic(A=1, B=2)
calculation_generic() # will calculate with A=1, B=2
calculation_generic(A=3) # will calculate with A=3, B=2
calculation_generic(A=2, recalc=True) # will calculate with A=2 B=standard_parameter['B']
caller: str
name of calling function ('result_generic' should be given as 'generic')
parameter:
Parameters to check
recalc: Boolean
True if forced recalculation, False if not
:return:
"""
if not parameter: parameter = dict()
for key, value in parameter.iteritems():
if value is None:
if self.calculation_parameter[caller] and not recalc:
parameter[key] = self.calculation_parameter[caller][key]
else:
parameter[key] = self.standard_parameter[caller][key]
return parameter
def delete_dtype_var_val(self, dtype, var, val):
"""
deletes step with var = var and val = val
dtype: the step type to be deleted e.g. th
var: the variable e.g. temperature
val: the value of that step e.g. 500
example: measurement.delete_step(step='th', var='temp', val=500) will delete the th step where the temperature is 500
"""
idx = self._get_idx_dtype_var_val(dtype=dtype, var=var, val=val)
self.data[dtype] = self.data[dtype].filter_idx(idx, invert=True)
return self
def check_parameters(self, caller, parameter):
'''
Checks if previous calculation used the same parameters, if yes returns the previous calculation
if no calculates with new parameters
Parameters
----------
caller: str
name of calling function ('result_generic' should be given as 'generic')
parameter:
Returns
-------
bool
returns true is parameters are not the same
'''
if self.calculation_parameter[caller]:
# parameter for new calculation
a = []
for key in self.calculation_parameter[caller]:
if key in parameter:
a.append(parameter[key])
else:
a.append(self.calculation_parameter[caller][key])
# a = [parameter[i] for i in self.calculation_parameter[caller]]
# get parameter values used for calculation
b = [
self.calculation_parameter[caller][i]
for i in self.calculation_parameter[caller]
]
if a != b:
return True
else:
return False
else:
return True
def has_result(self, result):
"""
Checks if the measurement contains a certain result
Parameters
----------
result: str
the result that should be found e.g. result='ms' would give True for 'hys' and 'backfield'
Returns
-------
out: bool
True if it has result, False if not
"""
if result in self.result_methods:
return True
else:
return False
### series RELATED
def has_series(self, stype=None, sval=None):
"""
checks if a measurement actually has a series
:return:
"""
if self._series and not stype:
return True
if self._series and self.get_series(stypes=stype, svals=sval):
return True
else:
return False
@property
def series(self):
if self.has_series():
return self._series
else:
series = RockPy.Series(stype='none', value=np.nan, unit='')
return [series]
def _get_series_from_suffix(self):
"""
takes a given suffix and extracts series data-for quick assessment. For more series control
use add_series method.
suffix must be given in the form of
stype: s_value [s_unit] | next series...
:return:
"""
if self.suffix:
s_type = self.suffix.split(':')[0]
if len(s_type) > 1:
s_value = float(self.suffix.split()[1])
try:
s_unit = self.suffix.split('[')[1].strip(']')
except IndexError:
s_unit = None
return s_type, s_value, s_unit
else:
return None
def _add_series_from_opt(self):
"""
Takes series specified in options and adds them to self.series
:return:
"""
series = self._get_series_from_opt()
for t in series:
self.add_sval(stype=t[0], sval=t[1], unit=t[2])
def _get_series_from_opt(self):
"""
creates a list of series from the series option
e.g. Pressure_1_GPa;Temp_200_C
:return:
"""
if self._series_opt:
series = self._series_opt.replace(' ', '').replace(',', '.').split(
';') # split ; for multiple series
series = [i.split('_')
for i in series] # split , for type, value, unit
for i in series:
try:
i[1] = float(i[1])
except:
raise TypeError('%s can not be converted to float' % i)
else:
series = None
return series
def get_series(self, stypes=None, svals=None):
"""
searches for given stypes and svals in self.series and returns them
Parameters
----------
stypes: list, str
stype or stypes to be looked up
svals: float
sval or svals to be looked up
Returns
"""
out = self.series
if stypes:
stypes = to_list(stypes)
out = [i for i in out if i.stype in stypes]
if svals:
svals = to_list(svals)
out = [i for i in out if i.value in svals]
return out
def add_sval(self,
stype=None,
sval=None,
unit=None,
series_obj=None,
comment=''):
"""
adds a series to measurement.series, then adds is to the data and results datastructure
Parameters
----------
stype: str
series type to be added
sval: float or int
series value to be added
unit: str
unit to be added. can be None #todo change so it uses Pint
comment: str
adds a comment to the series
Returns
-------
RockPy.Series instance
"""
if series_obj:
series = series_obj
else:
series = RockPy.Series(stype=stype,
value=sval,
unit=unit,
comment=comment)
self._series.append(series)
self._add_sval_to_data(series)
self._add_sval_to_results(series)
self.sample_obj.add_series2_mdict(series=series, mobj=self)
return series
def _add_sval_to_data(self, sobj):
"""
Adds stype as a column and adds svals to data. Only if stype != none.
Parameter
---------
sobj: series instance
"""
if sobj.stype != 'none':
for dtype in self._raw_data:
if self._raw_data[dtype]:
data = np.ones(len(
self.data[dtype]['variable'].v)) * sobj.value
if not 'stype ' + sobj.stype in self.data[
dtype].column_names:
self.data[dtype] = self.data[dtype].append_columns(
column_names='stype ' + sobj.stype,
data=data) # , unit=sobj.unit) #todo add units
def _add_sval_to_results(self, sobj):
"""
Adds the stype as a column and the value as value to the results. Only if stype != none.
Parameter
---------
sobj: series instance
"""
if sobj.stype != 'none':
# data = np.ones(len(self.results['variable'].v)) * sobj.value
if not 'stype ' + sobj.stype in self.results.column_names:
self.results = self.results.append_columns(
column_names='stype ' + sobj.stype,
data=[sobj.value]) # , unit=sobj.unit) #todo add units
def __sort_list_set(self, values):
"""
returns a sorted list of non duplicate values
values:
:return:
"""
return sorted(list(set(values)))
def _get_idx_dtype_var_val(self, dtype, var, val, *args):
"""
returns the index of the closest value with the variable(var) and the step(step) to the value(val)
option: inverse:
returns all indices except this one
"""
out = [np.argmin(abs(self.data[dtype][var].v - val))]
return out
"""
Normalize functions
+++++++++++++++++++
"""
def normalize(self,
reference='data',
ref_dtype='mag',
norm_dtypes='all',
vval=None,
norm_method='max',
norm_factor=None,
normalize_variable=False,
dont_normalize=None,
norm_initial_state=True):
"""
normalizes all available data to reference value, using norm_method
Parameter
---------
reference: str
reference state, to which to normalize to e.g. 'NRM'
also possible to normalize to mass
ref_dtype: str
component of the reference, if applicable. standard - 'mag'
norm_dtypes: list
dtype to be normalized, if dtype = 'all' all variables will be normalized
vval: float
variable value, if reference == value then it will search for the point closest to the vval
norm_method: str
how the norm_factor is generated, could be min
normalize_variable: bool
if True, variable is also normalized
default: False
dont_normalize: list
list of dtypes that will not be normalized
default: None
norm_initial_state: bool
if true, initial state values are normalized in the same manner as normal data
default: True
"""
# todo normalize by results
#getting normalization factor
if not norm_factor: # if norm_factor specified
norm_factor = self._get_norm_factor(reference, ref_dtype, vval,
norm_method)
norm_dtypes = _to_tuple(norm_dtypes) # make sure its a list/tuple
for dtype, dtype_data in self.data.iteritems(
): #cycling through all dtypes in data
if dtype_data: #if dtype_data == None
if 'all' in norm_dtypes: # if all, all non stype data will be normalized
norm_dtypes = [
i for i in dtype_data.column_names if not 'stype' in i
]
### DO not normalize:
# variable
if not normalize_variable:
variable = dtype_data.column_names[
dtype_data.column_dict['variable'][0]]
norm_dtypes = [i for i in norm_dtypes if not i == variable]
if dont_normalize:
dont_normalize = _to_tuple(dont_normalize)
norm_dtypes = [
i for i in norm_dtypes if not i in dont_normalize
]
for ntype in norm_dtypes: #else use norm_dtypes specified
try:
dtype_data[ntype] = dtype_data[ntype].v / norm_factor
except KeyError:
self.logger.warning(
'CAN\'T normalize << %s, %s >> to %s' %
(self.sample_obj.name, self.mtype, ntype))
if 'mag' in dtype_data.column_names:
try:
self.data[dtype]['mag'] = self.data[dtype].magnitude(
('x', 'y', 'z'))
except:
self.logger.debug(
'no (x,y,z) data found keeping << mag >>')
if self.initial_state and norm_initial_state:
for dtype, dtype_rpd in self.initial_state.data.iteritems():
self.initial_state.data[dtype] = dtype_rpd / norm_factor
if 'mag' in self.initial_state.data[dtype].column_names:
self.initial_state.data[dtype][
'mag'] = self.initial_state.data[dtype].magnitude(
('x', 'y', 'z'))
return self
def _get_norm_factor(self, reference, rtype, vval, norm_method):
"""
Calculates the normalization factor from the data according to specified input
Parameter
---------
reference: str
the type of data to be referenced. e.g. 'NRM' -> norm_factor will be calculated from self.data['NRM']
if not given, will return 1
rtype:
vval:
norm_method:
Returns
-------
normalization factor: float
"""
norm_factor = 1 # inititalize
if reference:
if reference == 'nrm' and reference not in self.data and 'data' in self.data:
reference = 'data'
if reference in self.data:
norm_factor = self._norm_method(norm_method, vval, rtype,
self.data[reference])
if reference in ['is', 'initial', 'initial_state']:
if self.initial_state:
norm_factor = self._norm_method(
norm_method, vval, rtype,
self.initial_state.data['data'])
if self.is_initial_state:
norm_factor = self._norm_method(norm_method, vval, rtype,
self.data['data'])
if reference == 'mass':
m = self.get_mtype_prior_to(mtype='mass')
norm_factor = m.data['data']['mass'].v[0]
if isinstance(reference, float) or isinstance(reference, int):
norm_factor = float(reference)
else:
self.logger.warning(
'NO reference specified, do not know what to normalize to.')
return norm_factor
def _norm_method(self, norm_method, vval, rtype, data):
methods = {
'max': max,
'min': min,
# 'val': self.get_val_from_data,
}
if not vval:
if not norm_method in methods:
raise NotImplemented('NORMALIZATION METHOD << %s >>' %
norm_method)
return
else:
return methods[norm_method](data[rtype].v)
if vval:
idx = np.argmin(abs(data['variable'].v - vval))
out = data.filter_idx([idx])[rtype].v[0]
return out
def get_mtype_prior_to(self, mtype, include_parameter_m=False):
"""
search for last mtype prior to self
Parameters
----------
mtype: str
the type of measurement that is supposed to be returned
include_parameter_m: bool
if True measurements from the parameter category are also normalized. e.g. mass, volume, length...
Returns
-------
RockPy.Measurement
"""
measurements = self.sample_obj.get_measurements(mtypes=mtype, )
if measurements:
out = [i for i in measurements if i.m_idx <= self.m_idx]
return out[-1]
else:
return None
def _add_stype_to_results(self):
"""
adds a column with stype stype.name to the results for each stype in measurement.series
:return:
"""
if self._series:
for t in self.series:
if t.stype:
if t.stype not in self.results.column_names:
self.results.append_columns(
column_names='stype ' + t.stype,
data=t.value,
# unit = t.unit # todo add units
)
def get_series_labels(self):
out = ''
if self.has_series():
for series in self.series:
if not str(series.value) + ' ' + series.unit in out:
out += str(series.value) + ' ' + series.unit
out += ' '
return out
"""
CORRECTIONS
"""
def correct_dtype(self,
dtype='th',
var='variable',
val='last',
initial_state=True):
"""
corrects the remaining moment from the last th_step
dtype:
var:
val:
initial_state: also corrects the iinitial state if one exists
"""
try:
calc_data = self.data[dtype]
except KeyError:
self.log.error('REFERENCE << %s >> can not be found ' % (dtype))
if val == 'last':
val = calc_data[var].v[-1]
if val == 'first':
val = calc_data[var].v[0]
idx = self._get_idx_dtype_var_val(step=dtype, var=var, val=val)
correction = self.data[dtype].filter_idx(idx) # correction step
for dtype in self.data:
# calculate correction
self._data[dtype][
'm'] = self._data[dtype]['m'].v - correction['m'].v
# recalc mag for safety
self.data[dtype]['mag'] = self.data[dtype].magnitude(
('x', 'y', 'z'))
self.reset__data()
if self.initial_state and initial_state:
for dtype in self.initial_state.data:
self.initial_state.data[dtype]['m'] = self.initial_state.data[
dtype]['m'].v - correction['m'].v
self.initial_state.data[dtype][
'mag'] = self.initial_state.data[dtype].magnitude(
('x', 'y', 'z'))
return self
'''' PLOTTING ''' ''
@property
def plottable(self):
"""
returns a list of all possible Visuals for this measurement
:return:
"""
out = {}
for visual in RockPy.Visualize.base.Generic.inheritors():
if visual._required == [self.mtype]:
out.update({visual.__name__: visual})
return out
def show_plots(self):
for visual in self.plottable:
self.plottable[visual](self, show=True)
def set_get_attr(self, attr, value=None):
"""
checks if attribute exists, if not, creates attribute with value None
attr:
:return:
"""
if not hasattr(self, attr):
setattr(self, attr, value)
return getattr(self, attr)
class TestRockPyData(TestCase):
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 test_column_names(self):
self.assertEqual(self.RPD.column_names, list(self.col_names))
def test_column_count(self):
self.assertEqual(self.RPD.column_count, len(self.col_names))
def test__find_duplicate_variable_rows(self):
# self.assertTrue((self.RPD._find_duplicate_variables()[0] == np.array([0, 1, 2])).all())
self.assertEqual(self.RPD._find_duplicate_variable_rows(), [(0, 1, 2, 3)])
# redefine variabe alias to the first two columns
self.RPD.define_alias('variable', ('F', 'Mx'))
self.assertEqual(self.RPD._find_duplicate_variable_rows(), [(0, 2), (1, 3)])
def test_rename_column(self):
self.RPD.rename_column('Mx', 'M_x')
self.assertEqual(self.RPD.column_names, ['F', 'M_x', 'My', 'Mz'])
def test_append_rows(self):
d1 = [[5, 6, 7, 8], [9, 10, 11, 12]]
self.RPD = self.RPD.append_rows(d1, ('5.Zeile', '6.Zeile'))
self.assertTrue(np.array_equal(self.RPD.v[-2:, :], np.array(d1)))
d2 = [5, 6, 7, 8]
self.RPD = self.RPD.append_rows(d2, '5.Zeile')
self.assertTrue(np.array_equal(self.RPD.v[-1, :], np.array(d2)))
# lets try with other RockPyData object
rpd = copy.deepcopy(self.RPD)
rpd.rename_column('Mx', 'M_x')
self.RPD = self.RPD.append_rows(rpd)
# TODO: add assert
#print self.RPD
def test_delete_rows(self):
self.RPD = self.RPD.delete_rows((0, 2))
self.assertTrue(np.array_equal(self.RPD.v, np.array(self.testdata)[(1, 3), :]))
def test_eliminate_duplicate_variable_rows(self):
# check for one variable column
self.RPD = self.RPD.eliminate_duplicate_variable_rows()
self.assertTrue(np.array_equal(self.RPD.v, np.array([]).reshape(0, 4)))
def test_eliminate_duplicate_variable_rows2(self):
# check for two variable columns
self.RPD.define_alias('variable', ('F', 'Mx'))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='mean')
self.assertTrue(np.array_equal(rpd.v, np.array([[1., 2., 7., 8.], [1., 6., 31., 37.]])))
self.assertTrue(np.array_equal(rpd.e, np.array([[0., 0., 4., 4.], [0., 0., 24., 29.]])))
rpd = self.RPD.eliminate_duplicate_variable_rows(substfunc='last')
self.assertTrue(np.array_equal(rpd.v, np.array([[1., 2., 11., 12.], [1., 6., 55., 66.]])))
def test_mean(self):
self.RPD = self.RPD.mean()
self.assertTrue(np.array_equal(self.RPD.v, np.array([[1., 4., 19., 22.5]])))
np.testing.assert_allclose(self.RPD.e, np.array([[0., 2., 20.976, 25.273]]), atol=0.01)
def test_max(self):
self.RPD = self.RPD.max()
self.assertTrue(np.array_equal(self.RPD.v, np.array([[1., 6., 55., 66.]])))
def test_filter_row_names(self):
self.assertEqual(self.RPD.filter_row_names(('1.Zeile', '3.Zeile')).row_names, ['1.Zeile', '3.Zeile'])
def test_filter_match_row_names(self):
# get all rows ending with '_A'
self.assertEqual(self.RPD.filter_match_row_names('.*_A').row_names, ['2.Zeile_A', '4.Zeile_A'])
def test_append_columns(self):
cb = self.RPD.column_count
d = (8, 7, 6, 5)
self.RPD = self.RPD.append_columns('neue Spalte', d)
self.assertEqual(cb + 1, self.RPD.column_count)
self.assertTrue(np.array_equal(self.RPD['neue Spalte'].v, np.array(d)))
def test_sort(self):
self.assertTrue(np.array_equal(self.RPD.sort('Mx')['Mx'].v, np.array((2, 2, 6, 6))))
#def test_interpolate(self):
# self.RPD.define_alias('variable', 'My')
# iv = (1, 11, 33, 55, 100)
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['My'].v, np.array(iv)))
# self.assertTrue(np.array_equal((self.RPD.interpolate(iv))['Mx'].v[1:-1], np.array([2., 4., 6.])))
def test_magnitude(self):
self.RPD.define_alias('m', ('Mx', 'My', 'Mz'))
self.RPD = self.RPD.append_columns('mag', self.RPD.magnitude('m'))
np.testing.assert_allclose(self.RPD['mag'].v, np.array([5.38516481, 12.20655562, 16.40121947, 86.12200648]), atol=1e-5)
def test_column_names_to_indices(self):
self.assertEqual( self.RPD.column_names_to_indices(('Mx', 'Mz')), [1,3])
def test_interation(self):
# TODO: add proper assertion
for l in self.RPD:
#print l
pass
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 test_data_assignment(self):
print self.RPD
# set only values
self.RPD['Mx'] = [1.1, 1.2, 1.3, 1.4]
print self.RPD
# set values and errors
self.RPD['Mx'] = [[[1.1, 0.11]], [[1.2, 0.12]], [[1.3, 0.13]], [[1.4, 0.14]]]
print self.RPD
class Measurement(object):
"""
HOW TO get at stuff
===================
HowTo:
======
stypeS
++++++
want all series types (e.g. pressure, temperature...):
as list: measurement.stypes
print measurement.stypes
>>> ['pressure']
as dictionary with corresponding series as value: measurement.tdict
print measurement.tdict
>>> {'pressure': <RockPy.Series> pressure, 0.60, [GPa]}
as dictionary with itself as value: measurement._self_tdict
>>> {'pressure': {0.6: <RockPy.Measurements.thellier.Thellier object at 0x10e2ef890>}}
svalUES
+++++++
want all values for any series in a measurement
as list: measurement.svals
print measuremtn.svals
>>> [0.6]
"""
logger = logging.getLogger('RockPy.MEASUREMENT')
# @classmethod
# def _standard_parameter(cls):
# return {i:{} for i in cls.result_methods}
@classmethod
def simulate(cls, **parameter):
"""
pseudo abstract method that should be overridden in subclasses to return a simulated measurement
based on given parameters
"""
return None
@classmethod
def implemented_machines(cls):
# setting implemented machines
# looking for all subclasses of RockPy.Readin.base.Machine
# generating a dictionary of implemented machines : {implemented out_* method : machine_class}
implemented_machines = {cl.__name__.lower(): cl for cl in RockPy.Readin.base.Machine.__subclasses__()}
return implemented_machines
@classmethod
def inheritors(cls):
"""
Method that gets all children and childrens-children ... from a class
Returns
-------
list
"""
subclasses = set()
work = [cls]
while work:
parent = work.pop()
for child in parent.__subclasses__():
if child not in subclasses:
subclasses.add(child)
work.append(child)
return subclasses
@classmethod
def implemented_measurements(self):
"""
method that dynamically creates a dictionary with "obj.name : obj" entries
Returns
-------
dict
"""
return {i.__name__.lower(): i for i in Measurement.inheritors()}
@classmethod
def measurement_formatters(cls):
# measurement formatters are important!
# if they are not inside the measurement class, the measurement has not been implemented for this machine.
# the following machine formatters:
# 1. looks through all implemented measurements
# 2. for each measurement stores the machine and the applicable readin class in a dictonary
measurement_formatters = {cl.__name__.lower():
{'_'.join(i.split('_')[1:]).lower():
Measurement.implemented_machines()['_'.join(i.split('_')[1:]).lower()]
for i in dir(cl) if i.startswith('format_')}
for cl in Measurement.inheritors()}
return measurement_formatters
@classmethod
def get_subclass_name(cls):
return cls.__name__
@property
def standard_parameter(self):
"""
property that returns the standard calculation parameter if specified, otherwise returns an empty dictionary
:return:
"""
print self.__class__._standard_parameter
if hasattr(self.__class__, '_standard_parameter'):
return self.__class__._standard_parameter
else:
print 'test'
return {}
def __init__(self, sample_obj,
mtype, mfile, machine, mdata=None, color=None,
series = None,
**options):
"""
sample_obj:
mtype:
mfile:
machine:
mdata: when mdata is set, this will be directly used as measurement data without formatting from file
color: color used for plotting if specified
options:
:return:
"""
self.logger = logging.getLogger('RockPy.MEASURMENT.' + self.get_subclass_name())
self.color = color
self.has_data = True
self._data = {}
self._raw_data = {}
self.is_initial_state = False
self.is_mean = False # flag for mean measurements
if machine is not None:
machine = machine.lower() # for consistency in code
if mtype is not None:
mtype = mtype.lower() # for consistency in code
''' initialize parameters '''
self.machine_data = None # returned data from Readin.machines()
self.suffix = options.get('suffix', '')
''' initial state '''
self.is_machine_data = None # returned data from Readin.machines()
self.initial_state = None
''' series '''
self._series = []
self._series_opt = series
self.__initialize()
if mtype in Measurement.measurement_formatters():
self.logger.debug('MTYPE << %s >> implemented' % mtype)
self.mtype = mtype # set mtype
if mdata is not None: # we have mdata -> ignore mfile and just use that data directly
self.logger.debug('mdata passed -> using as measurement data without formatting')
self.sample_obj = sample_obj
self._data = mdata
return # done
if machine in Measurement.measurement_formatters()[mtype] or machine == 'combined':
self.logger.debug('MACHINE << %s >> implemented' % machine)
self.machine = machine # set machine
self.sample_obj = sample_obj # set sample_obj
if not mfile:
self.logger.debug('NO machine or mfile passed -> no raw_data will be generated')
return
else:
self.mfile = mfile
self.import_data()
self.has_data = self.machine_data.has_data
if not self.machine_data.has_data:
self.logger.error('NO DATA passed: check sample name << %s >>' % sample_obj.name)
else:
self.logger.error('UNKNOWN MACHINE: << %s >>' % machine)
self.logger.error(
'most likely cause is the \"format_%s\" method is missing in the measurement << %s >>' % (
machine, mtype))
else:
self.logger.error('UNKNOWN\t MTYPE: << %s >>' % mtype)
# dynamic data formatting
# checks if format_'machine_name' exists. If exists it formats self.raw_data according to format_'machine_name'
if machine == 'combined':
pass
elif callable(getattr(self, 'format_' + machine)):
if self.has_data:
self.logger.debug('FORMATTING raw data from << %s >>' % machine)
getattr(self, 'format_' + machine)()
else:
self.logger.debug('NO raw data transfered << %s >>' % machine)
else:
self.logger.error(
'FORMATTING raw data from << %s >> not possible, probably not implemented, yet.' % machine)
# add series if provied
# has to come past __initialize()
if self._series_opt:
self._add_series_from_opt()
@property
def m_idx(self):
return self.sample_obj.measurements.index(self)
@property
def fname(self):
"""
Returns only filename from self.file
Returns
-------
str: filename from full path
"""
return os.path.split(self.mfile)[-1]
def __initialize(self):
"""
Initialize function is called inside the __init__ function, it is also called when the object is reconstructed
with pickle.
:return:
"""
# dynamical creation of entries in results data. One column for each results_* method.
# calculation_* methods are not creating columns -> if a result is calculated a result_* method
# has to be written
self.result_methods = [i[7:] for i in dir(self) if i.startswith('result_') if
not i.endswith('generic') if
not i.endswith('result')] # search for implemented results methods
self.results = RockPyData(
column_names=self.result_methods,
data=[np.nan for i in self.result_methods]) # dynamic entry creation for all available result methods
# ## warning with calculation of results:
# M.result_slope() -> 1.2
# M.calculate_vds(t_min=300) -> ***
# M.results['slope'] -> 1.2
# M.result_slope(t_min=300) -> 0.9
#
# the results are stored for the calculation parameters that were used to calculate it.
# This means calculating a different result with different parameters can lead to inconsistencies.
# One has to be aware that comparing the two may not be useful
# dynamically generating the calculation and standard parameters for each calculation method.
# This just sets the values to non, the values have to be specified in the class itself
self.calculation_methods = [i for i in dir(self) if i.startswith('calculate_') if not i.endswith('generic')]
self.calculation_parameter = {i: dict() for i in self.result_methods}
self._standard_parameter = {i[10:]: None for i in dir(self) if i.startswith('calculate_') if
not i.endswith('generic')}
if self._series:
for t in self._series:
self._add_sval_to_results(t)
# self._add_sval_to_data(t)
self.is_normalized = False # normalized flag for visuals, so its not normalized twize
self.norm = None # the actual parameters
self._info_dict = self.__create_info_dict()
def __getstate__(self):
'''
returned dict will be pickled
:return:
'''
pickle_me = {k: v for k, v in self.__dict__.iteritems() if k in
(
'mtype', 'machine', 'mfile',
'has_data', 'machine_data',
'_raw_data', '_data',
'initial_state', 'is_initial_state',
'sample_obj',
'_series_opt', '_series',
'suffix',
)
}
return pickle_me
def __setstate__(self, d):
'''
d is unpickled data
d:
:return:
'''
self.__dict__.update(d)
self.__initialize()
def reset__data(self, recalc_mag=False):
pass
def __getattr__(self, attr):
# print attr, self.__dict__.keys()
if attr in self.__getattribute__('_data').keys():
return self._data[attr]
if attr in self.__getattribute__('result_methods'):
return getattr(self, 'result_' + attr)().v[0]
raise AttributeError(attr)
def import_data(self, rtn_raw_data=None, **options):
'''
Importing the data from mfile and machine
rtn_raw_data:
options:
:return:
'''
self.logger.info('IMPORTING << %s , %s >> data' % (self.machine, self.mtype))
machine = options.get('machine', self.machine)
mtype = options.get('mtype', self.mtype)
mfile = options.get('mfile', self.mfile)
raw_data = self.measurement_formatters()[mtype][machine](mfile, self.sample_obj.name)
if raw_data is None:
self.logger.error('IMPORTING\t did not transfer data - CHECK sample name and data file')
return
else:
if rtn_raw_data:
self.logger.info('RETURNING raw_data for << %s , %s >> data' % (machine, mtype))
return raw_data
else:
self.machine_data = raw_data
def set_initial_state(self,
mtype, mfile, machine, # standard
**options):
"""
creates a new measurement (ISM) as initial state of base measurement (BSM).
It dynamically calls the measurement _init_ function and assigns the created measurement to the
self.initial_state value. It also sets a flag for the ISM to check if a measurement is a MIS.
Parameters
----------
mtype: str
measurement type
mfile: str
measurement data file
machine: str
measurement machine
options:
"""
mtype = mtype.lower()
machnine = machine.lower()
self.logger.info('CREATING << %s >> initial state measurement << %s >> data' % (mtype, self.mtype))
implemented = {i.__name__.lower(): i for i in Measurement.inheritors()}
# can only be created if the measurement is actually implemented
if mtype in implemented:
self.initial_state = implemented[mtype](self.sample_obj, mtype, mfile, machine)
self.initial_state.is_initial_state = True
return self.initial_state
else:
self.logger.error('UNABLE to find measurement << %s >>' % (mtype))
### INFO DICTIONARY
@property
def info_dict(self):
if not hasattr(self,'_info_dict'):
self._info_dict = self.__create_info_dict()
if not all( i in self._info_dict['series'] for i in self.series):
self._recalc_info_dict()
return self._info_dict
def __create_info_dict(self):
"""
creates all info dictionaries
Returns
-------
dict
Dictionary with a permutation of ,type, stype and sval.
"""
d = ['stype', 'sval']
keys = ['_'.join(i) for n in range(3) for i in itertools.permutations(d, n) if not len(i) == 0]
out = {i: {} for i in keys}
out.update({'series': []})
return out
def _recalc_info_dict(self):
"""
Re-calculates the info_dictionary for the measurement
"""
self._info_dict = self.__create_info_dict()
map(self.add_s2_info_dict, self.series)
def add_s2_info_dict(self, series):
"""
adds a measurement to the info dictionary.
Parameters
----------
series: RockPy.Series
Series to be added to the info_dictionary
"""
if not series in self._info_dict['series']:
self._info_dict['stype'].setdefault(series.stype, []).append(self)
self._info_dict['sval'].setdefault(series.value, []).append(self)
self._info_dict['sval_stype'].setdefault(series.value, {})
self._info_dict['sval_stype'][series.value].setdefault(series.stype, []).append(self)
self._info_dict['stype_sval'].setdefault(series.stype, {})
self._info_dict['stype_sval'][series.stype].setdefault(series.value, []).append(self)
self._info_dict['series'].append(series)
@property
def stypes(self):
"""
list of all stypes
"""
out = [t.stype for t in self.series]
return self.__sort_list_set(out)
@property
def svals(self):
"""
list of all stypes
"""
out = [t.value for t in self.series]
return self.__sort_list_set(out)
@property
def stype_dict(self):
"""
dictionary of stype: series}
"""
out = {t.stype: t for t in self.series}
return out
@property
def tdict(self):
"""
dictionary of stype: series}
"""
out = {t.stype: t.value for t in self.series}
return out
@property
def _self_tdict(self):
"""
dictionary of stype: {svalue: self}
"""
out = {i.stype: {i.value: self} for i in self.series}
return out
@property
def data(self):
if self._data == {}:
self._data = deepcopy(self._raw_data)
return self._data
# ## DATA RELATED
### Calculation and parameters
def result_generic(self, recalc=False):
'''
Generic for for result implementation. Every calculation of result should be in the self.results data structure
before calculation.
It should then be tested if a value for it exists, and if not it should be created by calling
_calculate_result_(result_name).
'''
parameter = {}
self.calc_result(parameter, recalc)
return self.results['generic']
def calculate_generic(self, **parameter):
'''
actual calculation of the result
:return:
'''
self.results['generic'] = 0
def calculate_result(self, result, **parameter):
"""
Helper function to dynamically call a result. Used in VisualizeV3
Parameters
----------
result:
parameter:
"""
if not self.has_result(result):
self.logger.warning('%s does not have result << %s >>' % self.mtype, result)
return
else:
# todo figuer out why logger wrong when called from VisualizeV3
self.logger = logging.getLogger('RockPy.MEASURMENT.' + self.mtype+'[%s]'%self.sample_obj.name)
self.logger.info('CALCULATING << %s >>' % result)
out = getattr(self, 'result_'+result)(**parameter)
return out
def calc_generic(self, **parameter):
'''
helper function
actual calculation of the result
:return:
'''
self.results['generic'] = 0
def calc_result(self, parameter=None, recalc=False, force_method=None):
'''
Helper function:
Calls any calculate_* function, but checks first:
1. does this calculation method exist
2. has it been calculated before
NO : calculate the result
YES: are given parameters equal to previous calculation parameters
if YES::
NO : calculate result with new parameters
YES: return previous result
parameter: dict
dictionary with parameters needed for calculation
force_caller: not dynamically retrieved caller name.
:return:
'''
caller = '_'.join(inspect.stack()[1][3].split('_')[1:]) # get calling function #todo get rid of inspect
if not parameter: # todo streamline the generation of standard parameters
try:
parameter = self.standard_parameter[caller]
except AttributeError:
parameter = dict(caller = {})
except KeyError:
parameter = dict(caller = {})
# get the method to be used for calculation. It is either the calling method determined by inspect
# or the method specified with force_method
if force_method is not None:
method = force_method # method for calculation if any: result_CALLER_method
else:
method = caller # if CALLER = METHOD
if callable(getattr(self, 'calculate_' + method)): # check if calculation function exists
# check for None and replaces it with standard
parameter = self.compare_parameters(method, parameter, recalc)
# if results dont exist or force recalc
if self.results[caller] is None or self.results[caller] == np.nan or recalc:
# recalc causes a forced racalculation of the result
if recalc:
self.logger.debug('FORCED recalculation of << %s >>' % (method))
else:
self.logger.debug('CANNOT find result << %s >> -> calculating' % (method))
getattr(self, 'calculate_' + method)(**parameter) # calling calculation method
else:
self.logger.debug('FOUND previous << %s >> parameters' % (method))
if self.check_parameters(caller, parameter): # are parameters equal to previous parameters
self.logger.debug('RESULT parameters different from previous calculation -> recalculating')
getattr(self, 'calculate_' + method)(**parameter) # recalculating if parameters different
else:
self.logger.debug('RESULT parameters equal to previous calculation')
else:
self.logger.error(
'CALCULATION of << %s >> not possible, probably not implemented, yet.' % method)
def calc_all(self, **parameter):
parameter['recalc'] = True
for result_method in self.result_methods:
getattr(self, 'result_' + result_method)(**parameter)
return self.results
def compare_parameters(self, caller, parameter, recalc):
"""
checks if given parameter[key] is None and replaces it with standard parameter or calculation_parameter.
e.g. calculation_generic(A=1, B=2)
calculation_generic() # will calculate with A=1, B=2
calculation_generic(A=3) # will calculate with A=3, B=2
calculation_generic(A=2, recalc=True) # will calculate with A=2 B=standard_parameter['B']
caller: str
name of calling function ('result_generic' should be given as 'generic')
parameter:
Parameters to check
recalc: Boolean
True if forced recalculation, False if not
:return:
"""
if not parameter: parameter = dict()
for key, value in parameter.iteritems():
if value is None:
if self.calculation_parameter[caller] and not recalc:
parameter[key] = self.calculation_parameter[caller][key]
else:
parameter[key] = self.standard_parameter[caller][key]
return parameter
def delete_dtype_var_val(self, dtype, var, val):
"""
deletes step with var = var and val = val
dtype: the step type to be deleted e.g. th
var: the variable e.g. temperature
val: the value of that step e.g. 500
example: measurement.delete_step(step='th', var='temp', val=500) will delete the th step where the temperature is 500
"""
idx = self._get_idx_dtype_var_val(dtype=dtype, var=var, val=val)
self.data[dtype] = self.data[dtype].filter_idx(idx, invert=True)
return self
def check_parameters(self, caller, parameter):
'''
Checks if previous calculation used the same parameters, if yes returns the previous calculation
if no calculates with new parameters
Parameters
----------
caller: str
name of calling function ('result_generic' should be given as 'generic')
parameter:
Returns
-------
bool
returns true is parameters are not the same
'''
if self.calculation_parameter[caller]:
# parameter for new calculation
a = []
for key in self.calculation_parameter[caller]:
if key in parameter:
a.append(parameter[key])
else:
a.append(self.calculation_parameter[caller][key])
# a = [parameter[i] for i in self.calculation_parameter[caller]]
# get parameter values used for calculation
b = [self.calculation_parameter[caller][i] for i in self.calculation_parameter[caller]]
if a != b:
return True
else:
return False
else:
return True
def has_result(self, result):
"""
Checks if the measurement contains a certain result
Parameters
----------
result: str
the result that should be found e.g. result='ms' would give True for 'hys' and 'backfield'
Returns
-------
out: bool
True if it has result, False if not
"""
if result in self.result_methods:
return True
else:
return False
### series RELATED
def has_series(self, stype=None, sval=None):
"""
checks if a measurement actually has a series
:return:
"""
if self._series and not stype:
return True
if self._series and self.get_series(stypes=stype, svals=sval):
return True
else:
return False
@property
def series(self):
if self.has_series():
return self._series
else:
series = RockPy.Series(stype='none', value=np.nan, unit='')
return [series]
def _get_series_from_suffix(self):
"""
takes a given suffix and extracts series data-for quick assessment. For more series control
use add_series method.
suffix must be given in the form of
stype: s_value [s_unit] | next series...
:return:
"""
if self.suffix:
s_type = self.suffix.split(':')[0]
if len(s_type) > 1:
s_value = float(self.suffix.split()[1])
try:
s_unit = self.suffix.split('[')[1].strip(']')
except IndexError:
s_unit = None
return s_type, s_value, s_unit
else:
return None
def _add_series_from_opt(self):
"""
Takes series specified in options and adds them to self.series
:return:
"""
series = self._get_series_from_opt()
for t in series:
self.add_sval(stype=t[0], sval=t[1], unit=t[2])
def _get_series_from_opt(self):
"""
creates a list of series from the series option
e.g. Pressure_1_GPa;Temp_200_C
:return:
"""
if self._series_opt:
series = self._series_opt.replace(' ', '').replace(',', '.').split(';') # split ; for multiple series
series = [i.split('_') for i in series] # split , for type, value, unit
for i in series:
try:
i[1] = float(i[1])
except:
raise TypeError('%s can not be converted to float' % i)
else:
series = None
return series
def get_series(self, stypes=None, svals=None):
"""
searches for given stypes and svals in self.series and returns them
Parameters
----------
stypes: list, str
stype or stypes to be looked up
svals: float
sval or svals to be looked up
Returns
"""
out = self.series
if stypes:
stypes = to_list(stypes)
out = [i for i in out if i.stype in stypes]
if svals:
svals = to_list(svals)
out = [i for i in out if i.value in svals]
return out
def add_sval(self, stype=None, sval=None, unit=None, series_obj=None, comment=''):
"""
adds a series to measurement.series, then adds is to the data and results datastructure
Parameters
----------
stype: str
series type to be added
sval: float or int
series value to be added
unit: str
unit to be added. can be None #todo change so it uses Pint
comment: str
adds a comment to the series
Returns
-------
RockPy.Series instance
"""
if series_obj:
series = series_obj
else:
series = RockPy.Series(stype=stype, value=sval, unit=unit, comment=comment)
self._series.append(series)
self._add_sval_to_data(series)
self._add_sval_to_results(series)
self.sample_obj.add_series2_mdict(series=series, mobj=self)
return series
def _add_sval_to_data(self, sobj):
"""
Adds stype as a column and adds svals to data. Only if stype != none.
Parameter
---------
sobj: series instance
"""
if sobj.stype != 'none':
for dtype in self._raw_data:
if self._raw_data[dtype]:
data = np.ones(len(self.data[dtype]['variable'].v)) * sobj.value
if not 'stype ' + sobj.stype in self.data[dtype].column_names:
self.data[dtype] = self.data[dtype].append_columns(column_names='stype ' + sobj.stype,
data=data) # , unit=sobj.unit) #todo add units
def _add_sval_to_results(self, sobj):
"""
Adds the stype as a column and the value as value to the results. Only if stype != none.
Parameter
---------
sobj: series instance
"""
if sobj.stype != 'none':
# data = np.ones(len(self.results['variable'].v)) * sobj.value
if not 'stype ' + sobj.stype in self.results.column_names:
self.results = self.results.append_columns(column_names='stype ' + sobj.stype,
data=[sobj.value]) # , unit=sobj.unit) #todo add units
def __sort_list_set(self, values):
"""
returns a sorted list of non duplicate values
values:
:return:
"""
return sorted(list(set(values)))
def _get_idx_dtype_var_val(self, dtype, var, val, *args):
"""
returns the index of the closest value with the variable(var) and the step(step) to the value(val)
option: inverse:
returns all indices except this one
"""
out = [np.argmin(abs(self.data[dtype][var].v - val))]
return out
"""
Normalize functions
+++++++++++++++++++
"""
def normalize(self,
reference='data', ref_dtype='mag', norm_dtypes='all', vval=None,
norm_method='max', norm_factor=None,
normalize_variable=False, dont_normalize=None,
norm_initial_state=True):
"""
normalizes all available data to reference value, using norm_method
Parameter
---------
reference: str
reference state, to which to normalize to e.g. 'NRM'
also possible to normalize to mass
ref_dtype: str
component of the reference, if applicable. standard - 'mag'
norm_dtypes: list
dtype to be normalized, if dtype = 'all' all variables will be normalized
vval: float
variable value, if reference == value then it will search for the point closest to the vval
norm_method: str
how the norm_factor is generated, could be min
normalize_variable: bool
if True, variable is also normalized
default: False
dont_normalize: list
list of dtypes that will not be normalized
default: None
norm_initial_state: bool
if true, initial state values are normalized in the same manner as normal data
default: True
"""
# todo normalize by results
#getting normalization factor
if not norm_factor: # if norm_factor specified
norm_factor = self._get_norm_factor(reference, ref_dtype, vval, norm_method)
norm_dtypes = _to_tuple(norm_dtypes) # make sure its a list/tuple
for dtype, dtype_data in self.data.iteritems(): #cycling through all dtypes in data
if dtype_data: #if dtype_data == None
if 'all' in norm_dtypes: # if all, all non stype data will be normalized
norm_dtypes = [i for i in dtype_data.column_names if not 'stype' in i]
### DO not normalize:
# variable
if not normalize_variable:
variable = dtype_data.column_names[dtype_data.column_dict['variable'][0]]
norm_dtypes = [i for i in norm_dtypes if not i == variable]
if dont_normalize:
dont_normalize = _to_tuple(dont_normalize)
norm_dtypes = [i for i in norm_dtypes if not i in dont_normalize]
for ntype in norm_dtypes: #else use norm_dtypes specified
try:
dtype_data[ntype] = dtype_data[ntype].v / norm_factor
except KeyError:
self.logger.warning('CAN\'T normalize << %s, %s >> to %s' %(self.sample_obj.name, self.mtype, ntype))
if 'mag' in dtype_data.column_names:
try:
self.data[dtype]['mag'] = self.data[dtype].magnitude(('x', 'y', 'z'))
except:
self.logger.debug('no (x,y,z) data found keeping << mag >>')
if self.initial_state and norm_initial_state:
for dtype, dtype_rpd in self.initial_state.data.iteritems():
self.initial_state.data[dtype] = dtype_rpd / norm_factor
if 'mag' in self.initial_state.data[dtype].column_names:
self.initial_state.data[dtype]['mag'] = self.initial_state.data[dtype].magnitude(('x', 'y', 'z'))
return self
def _get_norm_factor(self, reference, rtype, vval, norm_method):
"""
Calculates the normalization factor from the data according to specified input
Parameter
---------
reference: str
the type of data to be referenced. e.g. 'NRM' -> norm_factor will be calculated from self.data['NRM']
if not given, will return 1
rtype:
vval:
norm_method:
Returns
-------
normalization factor: float
"""
norm_factor = 1 # inititalize
if reference:
if reference == 'nrm' and reference not in self.data and 'data' in self.data:
reference = 'data'
if reference in self.data:
norm_factor = self._norm_method(norm_method, vval, rtype, self.data[reference])
if reference in ['is', 'initial', 'initial_state']:
if self.initial_state:
norm_factor = self._norm_method(norm_method, vval, rtype, self.initial_state.data['data'])
if self.is_initial_state:
norm_factor = self._norm_method(norm_method, vval, rtype, self.data['data'])
if reference == 'mass':
m = self.get_mtype_prior_to(mtype='mass')
norm_factor = m.data['data']['mass'].v[0]
if isinstance(reference, float) or isinstance(reference, int):
norm_factor = float(reference)
else:
self.logger.warning('NO reference specified, do not know what to normalize to.')
return norm_factor
def _norm_method(self, norm_method, vval, rtype, data):
methods = {'max': max,
'min': min,
# 'val': self.get_val_from_data,
}
if not vval:
if not norm_method in methods:
raise NotImplemented('NORMALIZATION METHOD << %s >>' % norm_method)
return
else:
return methods[norm_method](data[rtype].v)
if vval:
idx = np.argmin(abs(data['variable'].v - vval))
out = data.filter_idx([idx])[rtype].v[0]
return out
def get_mtype_prior_to(self, mtype, include_parameter_m = False):
"""
search for last mtype prior to self
Parameters
----------
mtype: str
the type of measurement that is supposed to be returned
include_parameter_m: bool
if True measurements from the parameter category are also normalized. e.g. mass, volume, length...
Returns
-------
RockPy.Measurement
"""
measurements = self.sample_obj.get_measurements(mtypes=mtype, )
if measurements:
out = [i for i in measurements if i.m_idx <= self.m_idx]
return out[-1]
else:
return None
def _add_stype_to_results(self):
"""
adds a column with stype stype.name to the results for each stype in measurement.series
:return:
"""
if self._series:
for t in self.series:
if t.stype:
if t.stype not in self.results.column_names:
self.results.append_columns(column_names='stype ' + t.stype,
data=t.value,
# unit = t.unit # todo add units
)
def get_series_labels(self):
out = ''
if self.has_series():
for series in self.series:
if not str(series.value) + ' ' + series.unit in out:
out += str(series.value) + ' ' + series.unit
out += ' '
return out
"""
CORRECTIONS
"""
def correct_dtype(self, dtype='th', var='variable', val='last', initial_state=True):
"""
corrects the remaining moment from the last th_step
dtype:
var:
val:
initial_state: also corrects the iinitial state if one exists
"""
try:
calc_data = self.data[dtype]
except KeyError:
self.log.error('REFERENCE << %s >> can not be found ' % (dtype))
if val == 'last':
val = calc_data[var].v[-1]
if val == 'first':
val = calc_data[var].v[0]
idx = self._get_idx_dtype_var_val(step=dtype, var=var, val=val)
correction = self.data[dtype].filter_idx(idx) # correction step
for dtype in self.data:
# calculate correction
self._data[dtype]['m'] = self._data[dtype]['m'].v - correction['m'].v
# recalc mag for safety
self.data[dtype]['mag'] = self.data[dtype].magnitude(('x', 'y', 'z'))
self.reset__data()
if self.initial_state and initial_state:
for dtype in self.initial_state.data:
self.initial_state.data[dtype]['m'] = self.initial_state.data[dtype]['m'].v - correction['m'].v
self.initial_state.data[dtype]['mag'] = self.initial_state.data[dtype].magnitude(('x', 'y', 'z'))
return self
'''' PLOTTING '''''
@property
def plottable(self):
"""
returns a list of all possible Visuals for this measurement
:return:
"""
out = {}
for visual in RockPy.Visualize.base.Generic.inheritors():
if visual._required == [self.mtype]:
out.update({visual.__name__: visual})
return out
def show_plots(self):
for visual in self.plottable:
self.plottable[visual](self, show=True)
def set_get_attr(self, attr, value=None):
"""
checks if attribute exists, if not, creates attribute with value None
attr:
:return:
"""
if not hasattr(self, attr):
setattr(self, attr, value)
return getattr(self, attr)
class Parm_Spectra(base.Measurement):
'''
'''
def __init__(self, sample_obj,
mtype, mfile, machine,
mag_method='',
**options):
super(Parm_Spectra, self).__init__(sample_obj,
mtype, mfile, machine,
**options)
def format_sushibar(self):
data = self.machine_data.out_parm_spectra()
self.af3 = RockPyData(column_names=data[1],
data=data[0][0],
units=data[2])
self.af3.define_alias('m', ( 'x', 'y', 'z'))
self.af3 = self.af3.append_columns('mag', self.af3.magnitude('m'))
self.af3 = self.af3.append_columns(column_names='mean_window',
data=np.array([self.af3['upper_window'].v + self.af3['lower_window'].v])[
0] / 2)
self.data = RockPyData(column_names=data[1],
data=data[0][1:],
units=data[2])
self.data.define_alias('m', ( 'x', 'y', 'z'))
self.data = self.data.append_columns('mag', self.data.magnitude('m'))
self.data = self.data.append_columns(column_names='mean_window',
data=np.array([self.data['upper_window'].v + self.data['lower_window'].v])[
0] / 2)
self.data.define_alias('variable', 'mean_window')
def _get_cumulative_data(self, subtract_af3=True):
cumulative_data = deepcopy(self.data)
if subtract_af3:
cumulative_data['x'] = cumulative_data['x'].v - self.af3['x'].v
cumulative_data['y'] = cumulative_data['y'].v - self.af3['y'].v
cumulative_data['z'] = cumulative_data['z'].v - self.af3['z'].v
cumulative_data['mag'] = cumulative_data.magnitude('m')
cumulative_data['x'] = [np.sum(cumulative_data['x'].v[:i]) for i,v in enumerate(cumulative_data['x'].v)]
cumulative_data['y'] = [np.sum(cumulative_data['y'].v[:i]) for i,v in enumerate(cumulative_data['y'].v)]
cumulative_data['z'] = [np.sum(cumulative_data['z'].v[:i]) for i,v in enumerate(cumulative_data['z'].v)]
cumulative_data['mag'] = cumulative_data.magnitude('m')
return cumulative_data
def plt_parm_spectra(self, subtract_af3=True, rtn=False, norm=False, fill=False):
plot_data = deepcopy(self.data)
if subtract_af3:
plot_data['x'] = plot_data['x'].v - self.af3['x'].v
plot_data['y'] = plot_data['y'].v - self.af3['y'].v
plot_data['z'] = plot_data['z'].v - self.af3['z'].v
plot_data['mag'] = plot_data.magnitude('m')
if norm:
norm_factor = max(plot_data['mag'].v)
else:
norm_factor = 1
if fill:
plt.fill_between(plot_data['mean_window'].v, 0, plot_data['mag'].v / norm_factor,
alpha=0.1,
label='pARM spetra')
else:
plt.plot(plot_data['mean_window'].v, 0, plot_data['mag'].v / norm_factor,
label='pARM spetra')
if not rtn:
plt.show()
def plt_parm_acquisition(self, subtract_af3=True, rtn=False, norm=False):
plot_data = self._get_cumulative_data(subtract_af3=subtract_af3)
if norm:
norm_factor = max(plot_data['mag'].v)
else:
norm_factor = 1
plt.plot(plot_data['mean_window'].v, plot_data['mag'].v / norm_factor, label='pARM acquisition')
if not rtn:
plt.show()
def plt_acq_spec(self, subtract_af3=True, norm=True):
self.plt_parm_spectra(subtract_af3=subtract_af3, rtn=True, norm=norm, fill=True)
self.plt_parm_acquisition(subtract_af3=subtract_af3, rtn=True, norm=norm)
plt.xlabel('AF field [mT]')
plt.grid()
plt.show()
def test():
# define some data for tutorials.rst
testdata = ((1, 2, 3, 4),
(2, 6, 7, 8),
(9, 10, 11, 12))
testdata2 = ((1, 1),
(2, 2),
(19, 3))
# create a rockpydata object with named columns and filled with testdata
d = RockPyData(column_names=('F', 'Mx', 'My', 'Mz'), row_names=('1.Zeile', '2.Zeile', '3.Zeile'),
units=('T', 'mT', 'fT', 'pT'), data=testdata)
d_json = numpyson.dumps(d)
print d_json
dd = numpyson.loads(d_json)
print repr(dd)
print "dd:", dd
#d = d.eliminate_duplicate_variable_rows(substfunc='last')
#print d._find_unique_variable_rows()
print('d:\n%s' % d)
e = RockPyData(column_names=('F', 'Mx'), row_names=('1.Zeile', '2.Zeile', '3.Zeile'),
units=('T', 'mT', 'fT', 'pT'), data=testdata2)
print('e:\n%s' % e)
print('e+d:\n%s' % (e+d))
print('e-d:\n%s' % (e-d))
print('e/d:\n%s' % (e/d))
print('e*d:\n%s' % (e*d))
print('d/e:\n%s' % (d/e))
print('d*e:\n%s' % (d*e))
print('d+e:\n%s' % (d+e))
print('d-e:\n%s' % (d-e))
print d.units
# define as many aliases as you want
d.define_alias('M', ('Mx', 'My', 'Mz'))
d.define_alias('Mzx', ('Mz', 'Mx'))
# show some data
# aliases 'all', 'variable' and 'dep_var' are predefined
print('all:\n%s' % d['all'])
print('Mzx:\n%s' % d['Mzx'])
# lets alter some data
d['Mx'] = np.array((13, 24, 35))
# show M with modified Mx component
print('M:\n%s' % d['M'])
# show Mx
print('Mx:\n%s' % d['Mx'])
# we can also alter several columns at once
d['M'] = ((2, 3, 4),
(18, 88, 98),
(39, 89, 99))
print('M:\n%s' % d['M'])
# some math fun
# calculate magnitude of vector 'M' and save it as new column 'magM'
d = d.append_columns('magM', d.magnitude('M'))
# calculate values of 'magM' normalized to 100
#d.append_columns('normM', d.normalize('magM', 100))
# we can also add arbitrary data in a new column
d = d.append_columns(("T",), np.array((1, 2, 3)))
# we can also add an empty column
d = d.append_columns(("empty",))
# renaming a column
d.rename_column('T', 'temp')
# show all data again, now including magM and T as the last two columns
print d
# do a plot of F vs magM
# plt.plot(d['F'], d['magM'])
# plt.show()
# fancy filtering of data
tf_array = (d['Mx'].v > 10) & (d['Mx'].v < 20)
print 'filtering:'
filtered_d = d.filter(tf_array)
print filtered_d['Mx']
# arithmetic operations
e = deepcopy(d)
# mutlipy one column with value
e['Mx'].v *= 2
# calculate difference of two rockpydata objects
#c = e - d
#print c
#c = e + d
#print c
#c = e / d
#print c
#c = e * d
#print c
#print repr(c)
# test single line object
l = RockPyData(column_names=('A', 'B', 'C', 'D'), row_names=('1.Zeile',),
units=('T', 'mT', 'fT', 'pT'), data=((1, 2, 3, 4),))
l = l.append_columns('X', (5,))
print l
print l['X']
#print d.mean()
print d
print d + (1, 2, 3, 4, 5, 6)
print d.interpolate(np.arange(2, 10, .5), includesourcedata=True)
#d.define_alias('variable', 'Mx')
#print d.interpolate(np.arange(0, 10, .5))
print "condense:"
print condense([d, d*2, d*3], substfunc='median')
