Quickstart#

Installation#

Install MagentroPy with pip:

pip install magentropy

Or, with conda:

conda install -c conda-forge magentropy

Logging#

The logger can be accessed as follows:

import logging
logger = logging.getLogger('magentropy')

The MagentroData class#

MagentroData serves as a representation of magnetoentropic data and provides methods for reading, processing, and plotting the data. Because MagentroPy depends on other packages such as numpy, scipy, and matplotlib, importing can take several seconds.

from magentropy import MagentroData

Reading data#

The easiest and most common way to read in data is with a Quantum Design .dat output file, such as that produced by an MPMS3 SQUID Magnetometer. The default settings are such that no additional arguments need to be given to the constructor when this is the case. See Reading Data for additional information.

magdata = MagentroData('magdata.dat')

"[Data]" tag detected, assuming QD .dat file.
The sample mass was determined from the QD .dat file: 0.1

The sample mass of 0.1 is parsed from the .dat file. The default mass units are “mg”. We can easily view a summary of the object when using a notebook:

magdata

Sample mass: 0.1 mg

Raw data:

	T	H	M	M_err	M_per_mass	M_per_mass_err	dM_dT	Delta_SM
unit	K	Oe	emu	emu	emu/g	emu/g	cal/K/Oe/g	cal/K/g
0	1.000000	20.000001	0.002023	0.00005	20.232376	0.5	NaN	NaN
1	2.000000	20.000000	0.001977	0.00005	19.770351	0.5	NaN	NaN
2	3.000001	19.999998	0.001969	0.00005	19.691176	0.5	NaN	NaN
3	4.000000	19.999999	0.001970	0.00005	19.703463	0.5	NaN	NaN
4	4.999999	20.000001	0.001886	0.00005	18.861315	0.5	NaN	NaN
...	...	...	...	...	...	...	...	...
495	95.999998	100.000002	0.001403	0.00005	14.030179	0.5	NaN	NaN
496	97.000000	99.999999	0.001446	0.00005	14.458498	0.5	NaN	NaN
497	98.000001	100.000001	0.001324	0.00005	13.244919	0.5	NaN	NaN
498	98.999999	99.999999	0.001184	0.00005	11.835327	0.5	NaN	NaN
499	100.000000	100.000000	0.001172	0.00005	11.722362	0.5	NaN	NaN

500 rows × 8 columns

Converted data:

	T	H	M	M_err	M_per_mass	M_per_mass_err	dM_dT	Delta_SM
unit	K	T	A·m²	A·m²	A·m²/kg	A·m²/kg	J/K/T/kg	J/K/kg
0	1.000000	0.002	0.000002	5.000000e-08	20.232376	0.5	NaN	NaN
1	2.000000	0.002	0.000002	5.000000e-08	19.770351	0.5	NaN	NaN
2	3.000001	0.002	0.000002	5.000000e-08	19.691176	0.5	NaN	NaN
3	4.000000	0.002	0.000002	5.000000e-08	19.703463	0.5	NaN	NaN
4	4.999999	0.002	0.000002	5.000000e-08	18.861315	0.5	NaN	NaN
...	...	...	...	...	...	...	...	...
495	95.999998	0.010	0.000001	5.000000e-08	14.030179	0.5	NaN	NaN
496	97.000000	0.010	0.000001	5.000000e-08	14.458498	0.5	NaN	NaN
497	98.000001	0.010	0.000001	5.000000e-08	13.244919	0.5	NaN	NaN
498	98.999999	0.010	0.000001	5.000000e-08	11.835327	0.5	NaN	NaN
499	100.000000	0.010	0.000001	5.000000e-08	11.722362	0.5	NaN	NaN

500 rows × 8 columns

Processed data:

	T	H	M	M_err	M_per_mass	M_per_mass_err	dM_dT	Delta_SM
unit	K	T	A·m²	A·m²	A·m²/kg	A·m²/kg	J/K/T/kg	J/K/kg

Presets:
{
npoints: 1000,
temp_range: [-inf inf],
fields: [],
decimals: 5,
max_diff: inf,
min_sweep_len: 10,
d_order: 2,
lmbds: [nan],
lmbd_guess: 0.0001,
weight_err: True,
match_err: False,
min_kwargs: {'method': 'Nelder-Mead', 'bounds': ((-inf, inf),), 'options': {'maxfev': 50, 'xatol': 0.01, 'fatol': 1e-06}},
add_zeros: False
}

Above, we see the sample mass, raw data, converted data (SI units), processed data (currently empty), and presets, which are the default data processing settings. These are available individually as the attributes sample_mass, raw_df, converted_df, processed_df, and presets. For example:

magdata.sample_mass

0.1

Raw units can also be obtained using the get_raw_data_units() method:

magdata.get_raw_data_units()

{'T': 'K', 'H': 'Oe', 'M': 'emu', 'sample_mass': 'mg'}

Additional information about reading data and changing units can be found in Reading Data and Units and Conversions, respectively.

Processing data#

The various settings in presets indicate the default arguments for the process_data() method. These are explored further in Processing Data. Here, we smooth the magnetic moment, differentiate with respect to temperature, and integrate with respect to the magnetic field to fill the missing columns in each data attribute.

magdata.process_data()

The data contains the following 5 magnetic field strengths and observations per field:
20.0     100
40.0     100
60.0     100
80.0     100
100.0    100
Name: T, dtype: int64

Processing data using the following settings:
{
	npoints: 1000,
	temp_range: [-inf  inf],
	fields: [],
	decimals: 5,
	max_diff: inf,
	min_sweep_len: 10,
	d_order: 2,
	lmbds: [nan],
	lmbd_guess: 0.0001,
	weight_err: True,
	match_err: False,
	min_kwargs: {'method': 'Nelder-Mead', 'bounds': ((-inf, inf),), 'options': {'maxfev': 50, 'xatol': 0.01, 'fatol': 1e-06}},
	add_zeros: False
}

scipy.optimize.minimize: Optimization terminated successfully.
Processed M(T) at field: 20.0
scipy.optimize.minimize: Optimization terminated successfully.
Processed M(T) at field: 40.0
scipy.optimize.minimize: Optimization terminated successfully.
Processed M(T) at field: 60.0
scipy.optimize.minimize: Optimization terminated successfully.
Processed M(T) at field: 80.0
scipy.optimize.minimize: Optimization terminated successfully.
Processed M(T) at field: 100.0
Calculated raw derivative and entropy.

last_presets set to:
{
	npoints: 1000,
	temp_range: [  0.99999934 100.00000083],
	fields: [ 20.  40.  60.  80. 100.],
	decimals: 5,
	max_diff: inf,
	min_sweep_len: 10,
	d_order: 2,
	lmbds: [0.00091728 0.00054639 0.00072862 0.00091728 0.00095775],
	lmbd_guess: 0.0001,
	weight_err: True,
	match_err: False,
	min_kwargs: {'method': 'Nelder-Mead', 'bounds': ((-inf, inf),), 'options': {'maxfev': 50, 'xatol': 0.01, 'fatol': 1e-06}},
	add_zeros: False
}

Finished.

The field groups and regularization (smoothing) parameters are determined automatically by default. We can see that there are five different magnetic field strengths with 100 temperature points each. The processed_df attribute now contains the results:

magdata.processed_df

	T	H	M	M_err	M_per_mass	M_per_mass_err	dM_dT	Delta_SM
0	0.999999	0.002	0.000002	NaN	19.847003	NaN	-0.098265	-0.000098
1	1.099098	0.002	0.000002	NaN	19.837267	NaN	-0.098240	-0.000098
2	1.198198	0.002	0.000002	NaN	19.827533	NaN	-0.098202	-0.000098
3	1.297297	0.002	0.000002	NaN	19.817803	NaN	-0.098138	-0.000098
4	1.396396	0.002	0.000002	NaN	19.808082	NaN	-0.098050	-0.000098
...	...	...	...	...	...	...	...	...
4995	99.603604	0.010	0.000001	NaN	11.673323	NaN	-0.829550	-0.004619
4996	99.702704	0.010	0.000001	NaN	11.591120	NaN	-0.829466	-0.004620
4997	99.801803	0.010	0.000001	NaN	11.508924	NaN	-0.829407	-0.004620
4998	99.900902	0.010	0.000001	NaN	11.426733	NaN	-0.829371	-0.004620
4999	100.000001	0.010	0.000001	NaN	11.344545	NaN	-0.829347	-0.004620

5000 rows × 8 columns

Similarly, one could check that the derivative and entropy have been calculated for the raw data.

Notice that the error columns in the processed data are empty. An experimental bootstrap() method is implemented to estimate the error in the smoothed moment. See Bootstrap Estimates.

Plotting Data#

import matplotlib.pyplot as plt

Line plots and heat maps can be easily created with plot_lines() and plot_map().

fig, ax = plt.subplots(figsize=(6, 4))
magdata.plot_lines(data_prop='M_per_mass', data_version='compare', ax=ax);

fig, ax = plt.subplots(1, 2, figsize=(9, 3.75), sharey=True)

magdata.plot_map(data_prop='M_per_mass', data_version='converted', ax=ax[0], colorbar=False)

magdata.plot_map(
    data_prop='M_per_mass', data_version='processed', ax=ax[1], colorbar=True,
    colorbar_kwargs={'ax': ax, 'fraction': 0.10, 'pad': 0.05}
)

ax[1].set_ylabel('');

The second figure demonstrates the natural use of matplotlib with the plotting methods. Many additional options are available; see Plotting Data for more information.

Writing output#

Because data is represented as DataFrames, one can use methods such as DataFrame.to_csv() to write output to files. See Reading Data for reading in data from delimited files and Plotting Data for plotting previously-processed data.