Curie temperature¶

Curie temperature.

Find Curie temperature from magnetization vs temperature measurement.

physicslab.experiment.curie_temperature.process(data)[source]¶

Bundle method.

Parameter data must include temperature and magnetization. See Columns for details and column names.

class physicslab.experiment.curie_temperature.Columns[source]¶

Column names.

classmethod mandatory()[source]¶

Get the current values of the mandatory column names.

classmethod output()[source]¶

Get the current values of the process() output column names.

class physicslab.experiment.curie_temperature.Measurement(data)[source]¶

Magnetization vs temperature measurement.

Parameters: data (pandas.DataFrame) – Magnetization and temperature data.
Raises: ValueError – If data is missing a mandatory column

analyze(p0=None)[source]¶

Find Curie temperature.

Parameters: p0 (tuple, optional) – Initial guess of spontaneous magnetization curve parameters. If None, the parameters will be estimated automatically, defaults to None
Returns: Curie temperature
Return type: float

fit(T, M, p0=None, high_temperature_focus=False)[source]¶

Fit spontaneous magnetization curve to the data.

Save the fit into Columns.HIGHTEMPERATUREFIT.

Parameters

T (numpy.ndarray) – Temperature
M (numpy.ndarray) – Magnetization
p0 (tuple, optional) – Initial guess of spontaneous magnetization curve parameters. If None, the parameters will be estimated automatically, defaults to None
high_temperature_focus (bool, optional) – Give high temperature data more weight, defaults to False

Returns

Curie temperature, fit

Return type

tuple(float, numpy.ndarray)

physicslab.experiment.curie_temperature.plot(data_list)[source]¶

Simple plot data and fit for all measurement at once.