Hysteresis Processing

Install and import packages¶

Import local data in MagIC format¶

In this demonstration we will be using local data.

The data is from the following publication:

• Swanson-Hysell, N. L., Avery, M. S., Zhang, Y., Hodgin, E. B., Sherwood, R. J., Apen, F. E., et al. (2021). The paleogeography of Laurentia in its early years: New constraints from the Paleoproterozoic East-Central Minnesota Batholith. Tectonics, 40, e2021TC006751. Swanson‐Hysell et al. (2021)

Get hysteresis data using the method code¶

The method codes relevant to hysteresis loops are:

• LP-HYS for regular hysteresis loops

• LP-HYS-O for hysteresis loops as a function of orientation

• LP-HYS-T for hysteresis loops as a function of temperature

We can filter the data to only have data for LP-HYS.

We can see what experimental hysteresis data are available in this dataset:

Process hysteresis data for one experiment¶

We can process the data from one of the experiments using the rmag.process_hyst_loop function. The processing steps behind the function are described in the hysteresis_processing_walk_through.ipynb notebook.

Process hysteresis data for all experiments¶

The decision tree handles the full range of loop behavior within a batch: statistically linear loops terminate with only χ_HF reported, and loops that fail the closure test stop with the slope-independent parameters (M_r, B_rh) and data quality statistics, with the remaining parameters as NaN in the results table. The fit_linear_loop=True and fit_open_loop=True arguments override these exits when full processing is desired.

Export hysteresis loop statistics into a MagIC specimens data table¶

The add_hyst_stats_to_specimens_table function adds the summary hysteresis parameters (hyst_ms_mass, hyst_mr_mass, hyst_bc, hyst_xhf) to the MagIC columns of the specimens table, with the additional quality statistics (Q, Qf, FNL values, sigma, etc.) recorded in the description column.

The updated table can then be written out as a MagIC-format specimens.txt file:

Processing hysteresis data that are not in MagIC format¶

While the workflow above is built around MagIC-formatted data — the recommended practice, since it keeps measurement-level data, metadata, and derived parameters together — the processing functions themselves operate on simple sequences of field and magnetization values. This makes these algorithms accessible to data coming from any stream: an instrument export, a spreadsheet, or a colleague’s text file.

A few conventions to be aware of:

• Field values are expected in tesla. The high-field susceptibility unit conversions assume tesla, and a warning is printed if the field values appear to be in mT or Oe.

• Magnetization can be in any consistent unit. Mass-normalized Am²/kg matches MagIC conventions and makes the resulting M_s and M_r directly comparable to database values.

• Inputs are cleaned automatically by sanitize_hyst_inputs: plain Python lists, numeric strings, and dataframe columns are all accepted; measurement pairs with non-finite values are dropped with a report; and loops measured in either field sweep order (starting from positive or negative saturation) are handled.

Below we process a plain two-column CSV file (the NED18-2c loop exported with field_tesla and moment_Am2_per_kg columns):