Inspecting directional data in a MagIC contribution using PmagPy

Inspecting directional data in a MagIC contribution using PmagPy#

This template notebook enables inspection of the data within a MagIC contribution. We want to get the data from MagIC, import the data into our notebook, and inspect the data by making plots.

Import functions from PmagPy#

To start with, let’s import some functions from PmagPy:

import pmagpy.ipmag as ipmag
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline
%config InlineBackend.figure_format='retina'

Import the data#

We can use ipmag to go download the data from MagIC for us. We can do this in a couple of ways. One way is to use the doi and the function ipmag.download_magic_from_doi(). The other is to use the MagIC contribution ID number with the ipmag.download_magic_from_id() function. Let’s take the id download approach for the study:

Stelten, Thomas, Pivarunas, and Champion (2023). Spatio-temporal clustering of post-caldera eruptions at Yellowstone caldera: implications for volcanic hazards and pre-eruptive magma reservoir configuration. Bulletin of Volcanology 85 (10). doi:10.1007/S00445-023-01665-W.

We can use the MagIC ID of 19640 for this paper which can be replaced with that of another study in the MagIC database.

doi = '10.1007/S00445-023-01665-W'
id = '19640'
directory = './inspecting_MagIC_data/'
result, magic_file_name = ipmag.download_magic_from_id(id, directory = directory)

Running this function will download a file called magic_contribution_19640.txt in the folder that this notebook is in.

In the above code cell, we saved a variable magic_file_name that is the name of the files that was downloaded.

magic_file_name

'./inspecting_MagIC_data/magic_contribution.txt'

Unpacking the tables#

A MagIC contribution is a single .txt file that comprises a number of tables. In the case of this contribution, we have these tables:

  • ages

  • contribution

  • locations

  • measurements

  • samples

  • sites

  • specimens

We want unpack the contribution into these distinct tables.

ipmag.unpack_magic(magic_file_name, dir_path=directory, print_progress=False)

1  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/contribution.txt
4  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/locations.txt
7  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/sites.txt
71  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/samples.txt
178  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/specimens.txt
1247  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/measurements.txt
4  records written to file  /Users/unimos/0000_Github/PmagPy-docs/example_notebooks/template_notebooks/inspecting_MagIC_data/ages.txt

True

Visualizing measurement level data#

The ipmag.zeq_magic() function will plot measurement data from single specimens generating:

  • equal area plot

  • Zijderveld plot

  • demagnetization plot

The n_plots parameter specifies how many specimens data are generated. It will start with the first listed specimen.

ipmag.zeq_magic(save_plots=False, 
                input_dir_path=directory, 
                n_plots=1)

-I- Using online data model
-I- Getting method codes from earthref.org
-I- Importing controlled vocabularies from https://earthref.org

(True, [])
../../_images/3b442989763b65c9c97f8cce4a156b0c847acf12de7b93f27c2b1fd9e7974f9c.png ../../_images/e95d3c10c23818404d1ec10528711083b355ced0aeccef40cfe325121ca1b67c.png ../../_images/10992063e198b297f8c287d862167cfefaa63521b7c68b9551c2521d227b8b90.png

Plot a specific specimen#

We can then have ipmag.zeq_magic plot a specific specimen rather than defaulting to plotting the first one.

specimen_to_plot = 'Y217-9a'

ipmag.zeq_magic(save_plots=False, 
                input_dir_path=directory,
                specimen=specimen_to_plot)

(True, [])
../../_images/292bb2884b8a8ac55dec3586bf5af288c2091f8702b6b31fab01a2ea732465e9.png ../../_images/900b847a52f7093eb25650fdc2d0074099bbef6c7bdde95425184fa651e9ed13.png ../../_images/5751c0812ddf968b7e8dce0209cf370978d0b8441b8cfa481a968a9fae78bede.png 
specimen_to_plot = 'Y213-5a'

ipmag.zeq_magic(save_plots=False, 
                input_dir_path=directory,
                specimen=specimen_to_plot)

(True, [])
../../_images/96087cfa4e271c3364e40c4c3e23ee86976d21931b47ddaacb2032649e4e5bf1.png ../../_images/fc9f600fe67478de1b130eaab2d20419dab90ec1d9628209fc4ccc37a17bf90d.png ../../_images/3f4bfec5cfc090ea6c34fc7da68e6885f684c9374855a7c7e41d2d7143f970b8.png

Visualizing site level data#

Plots can also be saved by setting save_plots to be True

ipmag.eqarea_magic(save_plots=False, 
                   input_dir_path=directory,
                   dir_path=directory,
                   )

7  sites records read in
1  saved in  LO:_Mallard Lake_SI:__SA:__SP:__CO:_g_TY:_eqarea_.svg

(True, ['LO:_Mallard Lake_SI:__SA:__SP:__CO:_g_TY:_eqarea_.svg'])

ipmag.vgpmap_magic(dir_path=directory, flip=True,
                   save_plots=False,
                   lat_0=60)
../../_images/e330dbf675cf1430a0ad7d0929652c076d2b5f42fba0aec583c6bf7d9ca6323a.png 
1  saved in  MC:_19640_TY:_VGP_map.pdf

(True, dict_values(['MC:_19640_TY:_VGP_map.pdf']))

Importing specific MagIC tables#

The functions above ipmag.zeq_magic, ipmag.eqarea_magic, and ipmag.vgpmap_magic are convenience functions that are able to directly read from MagIC files. For some other functions in PmagPy data need to be imported to be Python objects. There is a really nice package for dealing with tabular data in Python called pandas. The code cell below imports this package so that we can use it. We use the typical scientific Python nomenclature of importing it for use to the shorthand pd.

Import the sites table#

We can now use pandas to import the sites table to a pandas dataframe using the function pd.read_csv().

sites = pd.read_csv(directory + 'sites.txt', sep='\t', header=1)
sites
age age_unit analysts citations dir_alpha95 dir_comp_name dir_dec dir_inc dir_k dir_n_samples ... method_codes result_quality samples site software_packages specimens vgp_dm vgp_dp vgp_lat vgp_lon
0 160.2 ka AFP This study 2.4 Y212_chrm 352.1 66.2 396 8 ... LP-DIR-AF:DE-BFL-A:DA-DIR-GEO:LP-DIR-T:DE-BFL:... g Y2102-1:Y2102-2:Y2102-3:Y2102-4:Y2102-5:Y2102-... Y2102 pmagpy-4.2.108: demag_gui.v.3.0 Y2102-1a:Y2102-2a:Y2102-2b:Y2102-3a:Y2102-4a:Y... 3.9 3.2 83.1 199.7
1 159.9 ka AFP This study 7.7 Y211_chrm 339.3 66.2 54 6 ... LP-DIR-T:DE-BFP:DA-DIR-GEO:DE-BFL-A:LP-DIR-AF:... g Y211-1:Y211-2:Y211-3:Y211-4:Y211-5:Y211-7 Y211 pmagpy-4.2.108: demag_gui.v.3.0 Y211-1a:Y211-2a:Y211-3a:Y211-3b:Y211-4a:Y211-5... 12.6 10.3 75.2 183.1
2 160.2 ka AFP This study 4.9 Y213_chrm 332.1 68.1 87 1 ... LP-DIR-AF:DE-BFL:DA-DIR-GEO:LP-DIR-T:DE-BFL-A:... g Y213-10:Y213-1:Y213-2:Y213-3:Y213-4:Y213-5:Y21... Y213 pmagpy-4.2.108: demag_gui.v.3.0 Y213-10a:Y213-1a:Y213-1b:Y213-2a:Y213-3a:Y213-... 8.2 6.9 70.2 189.3
3 161.7 ka AFP This study 4.7 Y214_chrm 337.5 60.9 88 1 ... LP-DIR-AF:DE-BFL:DA-DIR-GEO:DE-BFL-A:LP-DIR-T:... g Y214-11:Y214-1:Y214-2:Y214-3:Y214-4:Y214-5:Y21... Y214 pmagpy-4.2.108: demag_gui.v.3.0 Y214-11a:Y214-1a:Y214-2a:Y214-3a:Y214-4a:Y214-... 7.2 5.5 73.5 157.8
4 160.8 ka AFP This study 3.0 Y215_chrm 333.3 64.1 211 1 ... LP-DIR-AF:DE-BFL:DA-DIR-GEO:DE-BFL-A:LP-DIR-T:... g Y215-10:Y215-1:Y215-2:Y215-3:Y215-4:Y215-5:Y21... Y215 pmagpy-4.2.108: demag_gui.v.3.0 Y215-10a:Y215-1b:Y215-2a:Y215-3b:Y215-4a:Y215-... 4.8 3.8 71.2 172.5
5 160.8 ka AFP This study 1.9 Y216_chrm 326.3 64.7 549 1 ... LP-DIR-T:DE-BFL:DA-DIR-GEO:LP-DIR-AF:DE-BFL-A:... g Y216-10:Y216-11:Y216-1:Y216-2:Y216-3:Y216-4:Y2... Y216 pmagpy-4.2.108: demag_gui.v.3.0 Y216-10a:Y216-11a:Y216-1a:Y216-2a:Y216-3a:Y216... 3.1 2.5 65.4 171.3
6 160.8 ka AFP This study 3.4 Y217_chrm 326.5 64.4 185 1 ... LP-DIR-AF:DE-BFL:DA-DIR-GEO:DE-BFL-A:LP-DIR-T:... g Y217-10:Y217-1:Y217-2:Y217-3:Y217-4:Y217-5:Y21... Y217 pmagpy-4.2.108: demag_gui.v.3.0 Y217-10a:Y217-1a:Y217-2a:Y217-2b:Y217-3a:Y217-... 5.4 4.4 66.6 175.1

7 rows × 32 columns

Plot the sites and calculate the Fisher mean#

We can extract specific columns from the dataframe by using the nomenclature dataframe_name['column_name']. In this case, the dataframe name is sites and the column name might be dir_dec. So sites['dir_dec'] will give us all the declinations.

sites_dec = sites['dir_dec']
sites_inc = sites['dir_inc']

plt.figure(figsize=(6,6))
ipmag.plot_net(1)
ipmag.plot_di(sites_dec, sites_inc, color='black', marker='o', markersize=40)
../../_images/fc4659ad6b75e723f58a47cb0163c0787c5e20ed5a706e8f7e4aeea3dbc0c5e5.png 
mean_direction = ipmag.fisher_mean(sites_dec, sites_inc)
ipmag.print_direction_mean(mean_direction)

Dec: 335.2  Inc: 65.2
Number of directions in mean (n): 7
Angular radius of 95% confidence (a_95): 3.2
Precision parameter (k) estimate: 348.8

plt.figure(figsize=(6,6))
ipmag.plot_net(1)
ipmag.plot_di(sites_dec, sites_inc, color='black', marker='o', markersize=40)
ipmag.plot_di_mean(mean_direction['dec'], mean_direction['inc'], mean_direction['alpha95'],
                   color='red', marker='s', markersize=40)
../../_images/925d62aae2134b9aa870d1e26f0fe530f322683e764fb1a2add7d4176707d7a0.png
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