Example 5 - Reproducing ArchKalmag14k

In this notebook we show how to reproduce the model ArchKalmag14k.r and import it in pymagglobal. We also distribute ArchKalmag14k.r this way. Additional access is provided via a dedicated website.

Set up the data

We first set up the data.

import io
import requests

import numpy as np

# load the rejection list
rejection_response = requests.get(
    'https://nextcloud.gfz-potsdam.de/s/'
    'WLxDTddq663zFLP/download/rejection_list.npz',
)
rejection_response.raise_for_status()

with np.load(io.BytesIO(rejection_response.content), allow_pickle=True) as fh:
    to_reject = fh['to_reject']

from paleokalmag import ChunkedData
from paleokalmag.data_handling import read_data

data_response = requests.get(
    'https://nextcloud.gfz-potsdam.de/s/'
    'r6YxrrABRJjideS/download/archkalmag_data.csv',
)
data_response.raise_for_status()
# Since we load the data from the web, we read it manually
# and pass the dataframe to ChunkedData
data = read_data(
    io.BytesIO(data_response.content),
    rejection_lists=to_reject,
)

cdat = ChunkedData(
    data,
    delta_t=10,
)

Rejected 347 outliers.

/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:160: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:166: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:180: UserWarning: Records with indices [   72    73    74    75    76    77    78    79    80    81   108   109
   357   358  1065  1066  1153  2402  2575  2671  2753  2756  2757  3036
  3215  3217  3218  3219  3220  3221  3222  3223  3224  3225  3503  3772
  3988  4047  4235  4415  4504  4530  4531  4655  5172  5326  5434  5496
  5551  5707  5763  5802  5805  5959  6053  6105  6647  6706  6781  6809
  7149  7580  7585  7593  7692  7702  7735  7776  7806  7826  7881  7979
  8067  8296  8327  8427  8464  8510  8670  8708  8752  9224  9653 10002
 10132 10331 10509 10604 10647 11527 11758] contain declination, but not inclination! The errors need special treatment!
To be able to use the provided data, these records have been dropped from the output.
/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:189: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:197: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


/builds/sec23/korte/paleokalmag/paleokalmag/data_handling.py:198: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.

Set up and run the model

We next set up a model with the estimated hyperparameters. They have been estimated using the procedure illustrated in example 3.

from paleokalmag.corefieldmodel import CoreFieldModel

a14kModel = CoreFieldModel(
    lmax=20,
    R=2800,
    gamma=-38.00,
    alpha_dip=39.00,
    tau_dip=171.34,
    alpha_wodip=118.22,
    tau_wodip=379.59,
    rho=2.25,
)

Next we run the model:

a14kModel.forward(cdat, output_every=5, quiet=False)
a14kModel.save('./ArchKalmag14k_forward.npz')
a14kModel.backward(quiet=False)
a14kModel.save('./ArchKalmag14k_smoothed.npz')

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Use the model with pymagglobal

Finally, we load the model and compare it to other models by using pymagglobal.

from paleokalmag import PaleoKalmag

archkalmag14k = PaleoKalmag(
    './ArchKalmag14k_smoothed.npz',
    name='ArchKalmag14k',
)

from matplotlib import pyplot as plt
from pymagglobal import Model, dipole_series

plt.rcParams['font.size'] = '14'


fig, ax = plt.subplots(1, 1, figsize=(10, 6))

a14ktimes = np.linspace(-12000, 1900, 1001)
dip, a16, a84 = dipole_series(a14ktimes, archkalmag14k)
ax.plot(a14ktimes, dip, color='C0', zorder=2, label=archkalmag14k.name)
ax.fill_between(
    a14ktimes,
    a16,
    a84,
    color='C0',
    alpha=0.4,
    zorder=0,
)

arch10k = Model('ARCH10k.1')
# different times due to different valid interval
a10ktimes = np.linspace(-8000, 1900, 1001)
dip = dipole_series(a10ktimes, arch10k)
ax.plot(a10ktimes, dip, color='C1', zorder=2, label=arch10k.name)

ax.legend();
ax.set_ylabel(r'Dipole moment [$10^{22}$Am$^2$]');
ax.set_xlabel(r'time [yrs.]');