May 9, 2016 - analysis and a bridge to the Magnetics Information. 2 ... PMAGPY: A BRIDGE TO MAGIC ..... sample 'F' is marked with a pink tag in Figure 2b).
GEOCHEMISTRY, GEOPHYSICS, GEOSYSTEMS, VOL. ???, XXXX, DOI:10.1002/,
1
2
3
PmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database 1
L. Tauxe, , R. Shaar, 5
1,2
1
3
4
L. Jonestrask, N.L. Swanson-Hysell, R. Minnett, 6
1
3
A.A.P. Koppers, C.G. Constable, N. Jarboe, K. Gaastra, and L. 3
Fairchild
1
Scripps Institution of Oceanography,
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University of California, San Diego, La Jolla, CA 92093-0220 2
The Institute of Earth Sciences, The
Hebrew University of Jerusalem, Jerusalem, 91904, Israel 3
Department of Earth and Planetary
Science, University of California, Berkeley, CA 94720 4
Cogense, Inc., San Diego, CA
92130-2279. 5
College of Earth, Ocean, and
Atmospheric Sciences, Oregon State University, Corvallis, OR 97331-5503 6
Institute of Geophysics and Planetary
Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0225
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Key Points. ◦ PmagPy software provides an interface to interact with Magnetics Information Consortium (MagIC) data ◦ PmagPy has tools to support visualization and interpretation across a range of rock and paleomagnetic data types ◦ Novices as well as experienced programmers can use PmagPy for data analysis and MagIC database preparation 4
Abstract.
The Magnetics Information Consortium (MagIC) database pro-
5
vides an archive with a flexible data model for paleomagnetic and rock mag-
6
netic data. The PmagPy software package is a cross-platform and open-source
7
set of tools written in Python for the analysis of paleomagnetic data that
8
serves as one interface to MagIC, accommodating various levels of user ex-
9
pertise. PmagPy facilitates thorough documentation of sampling, measure-
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ments, datasets, visualization, and interpretation of paleomagnetic and rock
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magnetic experimental data. Although not the only route into the MagIC
12
database, PmagPy makes preparation of newly published datasets for con-
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tribution to MagIC as a byproduct of normal data analysis and allows ma-
14
nipulation as well as reanalysis of datasets downloaded from MagIC with a
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single software package. The graphical user interface (GUI), Pmag GUI en-
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ables use of much of PmagPy’s functionality, but the full capabilities of PmagPy
17
extend well beyond that. Over 400 programs and functions can be called from
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the command line interface mode, or from within the interactive Jupyter note-
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books. Use of PmagPy within a notebook allows for documentation of the
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workflow from the laboratory to the production of each published figure or
21
data table, making research results fully reproducible. The PmagPy design
22
and its development using GitHub accommodates extensions to its capabil-
23
ities through development of new tools by the user community. Here we de-
24
scribe the PmagPy software package and illustrate the power of data discov-
25
ery and re-use through a re-analysis of published paleointensity data which
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illustrates how the effectiveness of selection criteria can be tested.
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1. Introduction 27
New frontiers in paleomagnetic and rock magnetic research increasingly rely on the
28
ability to draw on archives of previously collected data, to merge them with new results,
29
to assess progress, and to re-purpose published data for new problems. An early focus on
30
whether and how continents drifted over geological time led the community devoted to the
31
study of paleomagnetism to begin archiving paleomagnetic poles in what became known
32
as “Pole Lists” (e.g., Irving [1956]). This tradition of data archiving matured significantly
33
as more advanced paleomagnetic protocols were developed [McElhinny, 1973] and vari-
34
ous subgroups specialized in documenting behavior of the geodynamo through reversals
35
[Jacobs, 2005], paleosecular variation and paleointensity studies [Merrill et al., 1996], and
36
magnetostratigraphy developed into a powerful tool for assisting geochronological stud-
37
ies [Opdyke and Channell , 1996]. The International Association for Geomagnetism and
38
Aeronomy (IAGA) encouraged the development of multiple databases which included a
39
variety of individual paleomagnetic data compilations (paleomagnetic poles, paleointen-
40
sity, paleosecular variation from lavas and sediments, etc). These compilations were pro-
41
R Access files with Access forms that supported several search features vided as Microsoft
42
[McElhinny and Lock , 1996]. They archived key results of paleomagnetic studies focusing
43
on derived data products like paleomagnetic poles rather than the archiving of under-
44
lying measurement or intermediate data and the workflow on which the interpretations
45
were based. No formal archive existed for magnetic measurements and results outside of
46
internal laboratory databases, although some high-level information about methodology
47
did accompany paleomagnetic results. Given increasing interest in environmental mag-
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netism [Evans and Heller , 2003], biogeomagnetism [Winklhofer and Petersen, 2007], and
49
extraterrestrial samples [Rochette et al., 2009], it became imperative to provide a forum
50
for compiling and archiving these data too.
51
Taking advantage of faster computers with larger storage capability, the Magnetics In-
52
formation Consortium (MagIC) began designing a database (https://earthref.org/
53
MagIC/) with the aim of creating an architecture for storing the vast majority of data
54
types used in paleomagnetic and rock magnetic investigations. The rationales for such an
55
enterprise are: 1) to have a permanent archive of the data undergirding rock and paleo-
56
magnetic publications; 2) to allow users to develop new research directions using data or
57
products otherwise scattered across existing databases; 3) to make the data more discov-
58
erable, increasing awareness of existing data; 4) to allow re-interpretation of archived data
59
using different methods or selection criteria; 5) to track the details of data acquisition and
60
interpretation with appropriate metadata; 6) to present the data in a homogeneous SI
61
unit-based form to the end user, and 7) to satisfy publisher and funding agency require-
62
ments for archiving data and provide open-access to the community at large.
63
Getting data from the laboratory into the MagIC database, and visualizing and re-
64
analyzing data downloaded from the database, makes special demands on data process-
65
ing. While there are several recently published programming packages that deal with
66
single types of data (demagnetization experiments (e.g., Lurcock and Wilson [2012]),
67
paleointensity experiments (e.g., Leonhardt et al. [2004]), and FORC diagrams (e.g.,
68
Harrison and Feinberg [2008])), there is a need for a unified set of open source, cross-
69
platform software that deals with the variety of data types in a consistent way. Such
70
a toolset should facilitate converting data from the myriad laboratory formats into the
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common standardized and versioned (MagIC) format, provide tools for data analysis and
72
facilitate the generation of the files ready for uploading in the MagIC Search Interface
73
(https://earthref.org/MagIC/search/) once they have been published. While alter-
74
native routes into the MagIC database are certainly possible and actively encouraged,
75
the lack of a user-friendly means of preparing data files for the MagIC database, inspired
76
us to extend the software package, PmagPy (https://github.com/PmagPy), which is
77
based on the open source Python programming language to address these needs. Details
78
about installing and using PmagPy are available in the actively maintained PmagPy
79
cookbook at: https://earthref.org/PmagPy/.
80
The present article serves as a short introduction to the graphical user interfaces (GUIs)
81
MagIC GUI and Pmag GUI as well as the more complete PmagPy command line
82
level functions and those that can be used within Jupyter notebooks. Section 2 introduces
83
key concepts of the MagIC database. Section 3 summarizes steps for installing PmagPy.
84
Section 4 describes how to construct a file suitable for uploading into the MagIC database,
85
without access to the underlying measurement data. Section 5 gives a brief tutorial on
86
how to download datasets from the MagIC database and how to convert, interpret, and
87
upload new data into the MagIC database after publication. We illustrate the power of re-
88
interpreting published data to come to new and perhaps surprising conclusions. Section 6
89
provides an overview of how PmagPy programs work on the command line, providing
90
powerful tools for performing rock and paleomagnetic research beyond what is available
91
in the GUIs. Section 7 gives a brief introduction to the use of Jupyter notebooks, in
92
particular the use of PmagPy functions to implement statistical tests and conduct more
93
involved filtering and wrangling of the data. Finally, Section 8 is a summary and suggests
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ways in which members of the rock and paleomagnetic community can contribute to the
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PmagPy project.
2. MagIC database basics 96
In Figure 1, we illustrate the general workflow of a typical paleo- or rock magnetic
97
project leading to the upload of data into the MagIC database, and ultimately, download-
98
ing of those data for re-use. Measurements are made in the laboratory on a variety of
99
instruments and in many different formats. The PmagPy package can be used to convert
100
laboratory formatted files into the MagIC standard format which can be analyzed using
101
the many tools in the PmagPy software package to produce plots and data tables. The
102
data can then be uploaded into the MagIC database where they are available to the entire
103
community. PmagPy is also installed on EarthRef’s web servers and is used to automat-
104
ically create various plots for contributions residing in the MagIC database. These plots
105
help in visually browsing and interpreting the data online before downloading for further
106
analysis or visualization with the PmagPy package.
107
While PmagPy itself can be operated independently of the MagIC database, many
108
components are designed to work on data compatible with the MagIC data model so
109
some understanding of the structure of that data model is helpful. The data model is
110
continually being extended and modified in response to community input, and a complete
111
description of the current MagIC data model is available here: https://earthref.org/
112
MagIC/data-model/.
113
The MagIC data model is based on the hierarchical data and work flow typically used in
114
paleomagnetic and rock magnetic studies. For clarification and consistency we briefly re-
115
view the definition of the hierarchical elements: A ‘location’ is a geographically bounded
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area with multiple sampling sites or a single stratigraphic section. A ‘site’ is homoge-
117
neous with respect to the magnetic property being measured, typically at a single instant
118
in time (e.g. a cooling unit such as a lava flow or dike, or a particular bed in a sedimen-
119
tary sequence). ‘Samples’ are either un-oriented or separately oriented pieces taken from
120
geological or archaeological units, while ‘specimens’ are the objects that are measured in
121
the laboratory (often smaller sub-samples of collected samples). An example for a typical
122
paleomagnetic study is shown in Figure 2 whereby a number of temporally distinct lava
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flows (‘sites’; shown as red dots in Figure 2a) were drilled from a ‘location’ along the
124
North Shore of Lake Superior. Multiple samples were drilled at each site (the drill hole of
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sample ‘F’ is marked with a pink tag in Figure 2b). In the laboratory, each sample was
126
cut into one or more 1 inch specimens (Figure 2c) which were then measured using, for
127
example, a rock magnetometer. MagIC also accommodates synthetic specimens used in
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rock magnetic studies for which location, site and age data are not necessarily relevant.
129
Because the MagIC database is intended to archive all paleomagnetic and rock magnetic
130
data types, the model is necessarily complex. In its present incarnation, version 3.0,
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there are seven data tables with about 650 columns, but most studies use only a limited
132
number of these. Considerable effort has been made to simplify and consolidate the data
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model recently since previous models have had 31 tables and over 1200 columns. Data
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contributed as rows in these tables can include some or all of the columns defined in
135
the data model and only a few are absolutely required if the table is included in the
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contribution. Each row can also be cited as previously published in another study, which
137
is often the case with ages or compilations of existing datasets. A MagIC contribution in
138
version 3.0 consists of data in some or all of the following tables:
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1. The locations table stores data describing groups of sites (e.g. poles or grand means).
140
2. The sites table stores data describing units at the site level.
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3. The samples table stores data about a sample collected from the site or a synthetic
142
material. 4. The specimens table stores interpretations of measurements on a specimen (which
143
144
may be the entire sample if it was not subdivided).
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5. The measurements table stores measurements on a specimen.
146
6. The criteria table contains a list of the selection criteria used for calculating inter-
147
pretations in this study. 7. The ages table stores radiometrically and stratigraphically constrained ages.
148
149
Because of the many different sampling, specimen preparation, and measurement meth-
150
ods used in rock and paleomagnetic studies, MagIC must have a way of keeping track
151
of these in a consistent, searchable way. At each step of the data reduction process,
152
PmagPy tags records with “method codes” (e.g. GM-ARAR or FS-C-PISTON) which
153
are short codes that describe various methods associated with a particular data record
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(see https://earthref.org/MagIC/methods/ for a complete list). Most of the time, the
155
individual investigator using PmagPy does not need to know which ones to assign, but
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it is helpful to know something about them. Method codes start with a few letters which
157
designate the category (GM or FS in the above examples stand for geochronology and
158
field sampling respectively). Then there is a second part, and possibly also a third part, to
159
describe methods with lesser or greater detail. In this case, the code GM-ARAR refers to
160
161
40
Ar/39 Ar geochronology. In the code FS-C-PISTON, C stands for ‘coring’ and PISTON
refers to the type of coring method.
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In addition to method codes, there are controlled vocabulary lists that allow bet-
163
ter search outcomes by avoiding spelling errors or differences in abbreviation conven-
164
tions. A complete list of controlled vocabularies can be found at https://earthref.
165
org/vocabularies/.
3. Getting started with PmagPy 166
Although data can be entered directly into the MagIC tables using a spreadsheet pro-
167
gram like Excel, it is easier to generate the necessary tables as a by-product of data pro-
168
cessing without having to know the details of the metadata and method codes. PmagPy
169
strives to make the development of these tables with the metadata codes a seamless part
170
of the data analysis workflow. PmagPy programs and functions are written in Python,
171
an open source, platform independent scripting language.
172
The complete documentation of PmagPy is available in the PmagPy cookbook at:
173
https://earthref.org/PmagPy/. The cookbook explains the installation and use of the
174
PmagPy programs, and documents the use of PmagPy with examples and links to the
175
help messages. It also provides links to the source code itself, stored at https://github.
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com/PmagPy and ties the code to explanations in the book by Tauxe et al. [2010] in its latest
177
open online incarnation at: https://earthref.org/MagIC/books/Tauxe/Essentials/.
178
Several fully functional GUIs in the PmagPy package that work without the need to
179
install special Python distributions can be downloaded as a compiled executable (‘stan-
180
dalone’) programs. These can be used with no knowledge of Python and minimal famil-
181
iarity with the MagIC database. Installation of the Standalone GUIs is described here:
182
https://earthref.org/PmagPy#standalone.
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In addition to the standalone versions of several GUIs, there are many programs and
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functions included in PmagPy that provide a wide range of functionality relevant to
185
paleomagnetic data and are available through the command line calls or from within a
186
Jupyter notebook (Section 7). PmagPy comprises nearly 200 individual programs that
187
range from simple scripts for calculating the expected local magnetic field direction at a
188
particular time and location to more complex tools allowing visualization and analysis of
189
many types of rock and paleomagnetic data. To get started using PmagPy, download
190
and install the Python distribution recommended in the Full PmagPy install section of
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the PmagPy cookbook: https://earthref.org/PmagPy#full_install. Currently, the
192
full installation of PmagPy is done using the ‘pip’ Python package management system
193
from the command line. So the first step after installing (or re-installing) Python, is
194
to find the command line (https://earthref.org/PmagPy#command_line). Then, type
195
these commands on the command line interface.
196
pip install --upgrade pip
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pip install --upgrade pmagpy
198
pip install --upgrade pmagpy-cli
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Once these packages are installed, typing the name of the program at the command
201
line will launch it. Here there is a slight difference between the MacOS/Unix style
202
and the PC/MS-DOS style whereby PC users omit the terminal .py in all programs.
203
To verify that all is well, type eqarea.py -h (omitting the .py for PC users) on the
204
command line to generate a help message. A “command not found” message, means
205
that there is a problem with the installation; see the trouble shooting page in the cook-
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book: https://earthref.org/PmagPy#trouble, post an issue in the GitHub repository
207
https://github.com/PmagPy/PmagPy/issues or contact the authors for help.
208
Many functions relevant to the analysis and visualization of demagnetization data and
209
paleointensity experiments have been tied together in Pmag GUI (formerly Quick-
210
MagIC), meant to be a user-friendly application for converting measurement data from
211
laboratory formats into the MagIC data format, analyzing demagnetization data through
212
principal component analysis [Kirschvink , 1980], analyzing Thellier-type paleointensity
213
experiment data [Tauxe and Yamazaki , 2007] and exporting data and interpretations into
214
MagIC data files ready for contribution to the MagIC database. Another program called
215
MagIC GUI takes data entered within multiple MagIC tables and converts them into
216
the single file for uploading to the database. MagIC GUI is intended for contributions of
217
data and associated metadata that are being compiled without measurement level data.
218
Use of these GUIs is described in the following sections.
4. MagIC GUI for compiling data for uploading to MagIC 219
MagIC GUI enables users to compile a contribution of published data for uploading to
220
the MagIC database. It is designed for creating upload files for data without underlying
221
measurement data; if there are measurement data available, we recommend the use of
222
Pmag GUI instead. MagIC GUI allows a user to add data at the location, site, sample,
223
and specimen levels. It is also possible to add result level data and ages at any level. It
224
uses a spreadsheet-like grid interface for entering and editing data. Useful features include
225
pop-up menus with controlled vocabularies, multi-cell pasting from external spreadsheets,
226
and built-in validations for MagIC database requirements. The PmagPy
227
(https://earthref.org/PmagPy/) provides further details on use of the program.
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5. Pmag GUI for data conversion and analysis 228
Pmag GUI provides a user-friendly path to the most frequently used PmagPy pro-
229
grams and is designed for interpreting the most common paleomagnetic experimental data
230
and for generating figures and data tables for use in publications. After the program is
231
launched, the user interface appears (Figure 3) which supports the following tasks:
232
1. Converting magnetometer files to the MagIC format, where the supported experi-
233
mental procedures include: AF/Thermal demagnetizations, thermal/microwave paleoin-
234
tensity experiments, remanence (ARM/TRM) anisotropy, and cooling rate correction ex-
235
periments.
236
237
238
239
2. Interactively adding meta-data defined in the MagIC data model (Section 2) for the studied specimens, samples, sites, locations. 3. Analyzing directional and intensity paleomagnetic data and saving these interpretations to the relevant tables.
240
4. Preparing a MagIC compatible file to be uploaded to the database.
241
5. Extracting MagIC formatted files from a standard MagIC text file downloaded from
242
the MagIC database. 5.1. Unpacking data downloaded from MagIC
243
One way to use Pmag GUI is to unpack a data set downloaded from the MagIC
244
database.
245
[2009], simply substitute the digital object identifier (DOI) in a suitable browser window:
246
https://earthref.org/MagIC/doi/10.1029/2008GC002072/, and the MagIC search en-
247
gine will find the data for the paper associated with that DOI. To download the data,
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simply click on the file icon below the column labeled ‘Data’. This will save a file to the
249
downloads folder. After launching Pmag GUI either as a standalone GUI program or
250
the full (expert) distribution by typing pmag gui.py on the command line (pmag gui
251
for PC users using the pip install method), change directories by clicking on the “change
252
dir” button and selecting a directory dedicated to a given dataset (the ‘Project Directory’,
253
a directory with no spaces in its path). The downloaded file can be unpacked by clicking
254
on the “unpack txt file downloaded from MagIC” in the Pmag GUI front panel (Figure
255
3).
256
Pmag GUI uses two additional GUIs to analyze the data. Demag GUI (Figure
257
4a) is a tool for analyzing demagnetization data. It allows users to calculate best-fit
258
directions of specimens, upper-level means (sample, site, location, study), and VGPs.
259
Thellier GUI [Shaar and Tauxe, 2013] is a tool for analyzing paleointensity data (Figure
260
4b). It allows users to calculate specimen paleointensities, anisotropy corrections, cooling
261
rate corrections, and non-linear acquisition of thermal remenance. Interpretations done
262
in each GUI are stored as MagIC tables and are fully documented with the necessary
263
method codes describing the analyses used to create each data record. For example, a
264
best-fit line calculated in Demag GUI is tagged with the directional estimation method
265
code DE-BFL and a Thellier laboratory protocol based paleointensity estimate is tagged
266
LP-PI-TRM. Details on the use of the GUIs are documented in the cookbook at https:
267
//earthref.org/PmagPy#DemagGUI and https://earthref.org/PmagPy#ThellierGUI,
268
respectively (see also Shaar and Tauxe [2013] for more on Thellier GUI). 5.2. Re-use of published data with MagIC and PmagPy
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One of the benefits of archiving measurement level data in MagIC is the ability to re-
270
interpret published data in the light of improved interpretation schemes. As an example
271
of the power of re-interpreting such data, we take closer look at the data sets compiled by
272
Paterson et al. [2014] with the addition of new data published since then from historical
273
lava flows in Hawaii [Cromwell et al., 2015]. These data were produced by a variety of
274
researchers using either the ‘Zero field-In field’ (ZI) technique of Coe [1967] or the ‘In
275
field-zero field, zero-field in field’ (IZZI) technique of Tauxe and Staudigel [2004]. Further
276
experimental details are in Paterson et al. [2014], the original references (Table 1) and the
277
measurements table itself (available for download from the MagIC database during review
278
at: https://earthref.org/MagIC/private:10988/Yar3wxVKfkFT/ (note that the URL
279
will change to https://earthref.org/MagIC/10988/ upon publication) in which each
280
individual measurement is tagged with method codes describing what sort of step it was.
281
Each measurement, and interpretation result in any of the tables, also can be labeled with
282
the DOI of the original publication (under the citations column) or labeled as a new result
283
in the contribution’s publication (with a citations value of ‘This study’).
284
As suggested by Paterson et al. [2014], data sets from specimens that cooled in a
285
historical or laboratory field can be used to explore the effectiveness of either the material,
286
the measurement protocol or the selection criteria. Here we re-consider the effect of data
287
selection criteria on paleointensity estimation. The references, our site names of collections
288
of specimens cooled in known fields (Bexp ), the type of material, the location and year
289
(if applicable) are listed in Table 1. Note that in some cases the site names used here
290
are different from the original publication in light of the MagIC use of the term ‘site’
291
as a single cooling unit. Expected field intensities were calculated using the PmagPy
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292
program, igrf.py
(https://earthref.org/PmagPy#igrf.py) using IGRF12 (https:
293
//www.ngdc.noaa.gov/IAGA/vmod/igrf.html) for dates after 1900 and the pfm9k model
294
of Nilsson et al. [2014] prior to that (see use of command line programs in Section 6).
295
Paterson et al. [2014] published the ‘Standard Paleointensity Definitions’, or SPD,
296
available at http://www.paleomag.net/SPD/, which documents the calculations of 40
297
statistics that have been used in calculation and selection of paleointensity data by the
298
paleointensity community. Any given study uses a small subset of these, but Thellier
299
GUI supports all the criteria defined in the SPD.
300
In addition to the SPD, Paterson et al. [2014] tested a number of frequently used sets
301
of criteria including the ThellierTool A (TTA) set of Leonhardt et al. [2004] (Table 2).
302
Since then, Cromwell et al. [2015] recommended the use of another set, here referred to
303
as CCRIT (Table 2), which they tested on historical lava flows from Hawaii and claimed
304
superior accuracy and precision of the calculated field intensity estimates.
305
In this section, we illustrate the value of archiving measurement level data and the way
306
in which Thellier GUI can be used to compare the outcome of different sets of selection
307
criteria. Because not all the criteria can be displayed at once on the GUI interface, the
308
desired criteria can be selected under the ‘Preferences’ menu. Cutoff values can then
309
be entered by choosing the ‘Change acceptance criteria’ option under the ‘Acceptance
310
criteria’ sub-menu of the ‘Analysis’ menu. These are saved in the criteria file which
311
can be included in the information uploaded into the MagIC database along with the
312
measurement level data. This file documents the decision making process used in a given
313
publication. Thellier GUI’s ‘Auto Interpreter’ function calculates fits to specimen data
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and displays them. The tables can be saved under the ‘File’ menu and the results viewed
315
either in the GUI or in the data tables.
316
The results calculated by Thellier GUI for each cooling unit (‘site’) using the two
317
sets of criteria are listed in Table 3. Note that several studies included in the analysis by
318
Paterson et al. [2014] resulted in no acceptable interpretations and these are not included
319
here (Biggin et al. [2007]; Kr´ asa et al. [2003]; Paterson et al. [2010a]). We list only those
320
sites with at least three “acceptable” interpretations that agreed within either 6 µT or
321
15%.
322
In Figure 5a, we plot the average fields estimated for each site using the different
323
selection criteria against the expected fields from Table 1. CCRIT yields slightly more
324
accurate results in general with a slope of 0.96 as opposed to 0.93 for the TTA criteria;
325
the results are also significantly more precise with an R2 value of 0.94 as opposed to 0.74
326
for the TTA criteria. But this improved accuracy and precision comes at the price of
327
a decreased ‘success’ rate (Figure 5b,c). While only one site had fewer specimens that
328
passed the criteria using TTA rather than CCRIT, ten had more specimens selected using
329
TTA. Two performed equally well. In our view, strict selection criteria that yield more
330
accurate results are preferable to criteria that have a higher ‘success rate.’
331
The aim of this section is not to advocate for or against any particular set of selection
332
criteria (although we consider the improved performance of CCRIT compared to TTA to
333
be a significant new result). Rather, we wish to illustrate that any set of criteria can be
334
compared against another using the software in the PmagPy distribution and the data
335
archived in the MagIC database. If better methods are developed in the future, their
336
validity could be established using the dataset compiled here and applied to other data
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337
archived in the database. New statistics can be contributed to the SPD on request and
338
these can be added to those supported by PmagPy and applied as desired to the data in
339
the database. The ability to incorporate new statistical methods into PmagPy ensures
340
the forward compatibility of MagIC data that are archived at the measurement level and
341
prevents much effort from being wasted. 5.3. Conversion of laboratory data to the MagIC format
342
The data downloaded in Sections 5.1 and 5.2 have already been published. Many users
343
will want to analyze their own data and upload them to MagIC once they are published,
344
satisfying publisher and funding agency requirements for data archiving, improving trans-
345
parency of data interpretation, and increasing discoverability and citation rates. Upload-
346
ing to MagIC requires converting laboratory measurement files to the MagIC format. To
347
upload the data into MagIC, besides the measurement level data, there are additional
348
metadata such as site locations and ages that make the data and their interpretations
349
more useful and are required by MagIC. Supplying these metadata is a more complicated
350
task that involves a sequence of actions. To simplify this procedure Pmag GUI separates
351
these actions into three stages (Figure 3b):
352
1. Convert magnetometer files to MagIC format: Clicking this button runs a sequence
353
of dialog boxes that allow a user to convert files in a variety of laboratory formats to the
354
MagIC format.
355
2. (optional) Calculate geographic/tilt-corrected directions: Clicking this button opens
356
a program that calculates the orientation of the samples based on inclinometer and mag-
357
netic compass measurements, sun compass data, or differential GPS data. Many lab
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358
formats will have these different coordinate systems already calculated in which case this
359
step is not necessary.
360
3. Fill EarthRef data: Clicking this button runs a sequence of interactive dialog win-
361
dows that allow users to supply additional metadata needed for upload to the MagIC
362
database.
363
After data conversion, the Pmag GUI data analysis programs (Demag GUI and
364
Thellier GUI) can be launched from within Pmag GUI) and used to visualize data
365
and develop interpretations. These programs can also be used to produce figures and
366
tables for publications. 5.4. Uploading to the MagIC database
367
After interpreting the data using Demag GUI or Thellier GUI, the upload file for
368
contribution for the MagIC database can be prepared by clicking the ‘prepare upload
369
txt file’ button. This action creates a file with the location name and date of creation
370
in the Project Directory. To upload a contribution to the database, go to the MagIC
371
search interface at: https://earthref.org/MagIC/search/ and log in. A MagIC profile
372
is necessary for uploading data (but not for downloading). Click on the ‘Create’ button
373
to find or create a new reference. Then click on the ‘upload’ button and ‘drag and drop’
374
the newly created file to the MagIC upload wizard. If the data are connected to a digital
375
object identifier (DOI), it will now be possible to ‘activate’ the contribution, making it
376
publicly available. MagIC allows uploading data by any member of the community, but
377
original authors have primary ownership, if they claim it.
378
The alternative program MagIC GUI allows creation of upload files for data without
379
the underlying measurement data and is particularly useful for archiving data for which
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380
the measurement data are not available, but for which interpretations are published. After
381
the basic data tables are available in MagIC, it is possible that an original author could
382
supply the measurement data at a later time.
6. A deeper look at PmagPy 383
Digging deeper into data analysis and visualization than is allowed with the GUI ap-
384
proach requires some knowledge of the inner workings of the PmagPy software, including
385
how to use the command line scripts. In this section we describe the basic functioning
386
of the programs at the command line level to allow the user a more powerful and flexible
387
approach to using this code for paleomagnetic research.
388
Both the PmagPy command line (pmagpy-cli) and Pmag GUI use functions in the
389
pmag.py and pmagplotlib.py modules for calculations and plotting. For people who do
390
not wish to use command line programs and prefer graphical user interfaces with menus,
391
etc., some of the key programs for interpreting paleomagnetic and rock magnetic data are
392
packaged together in Pmag GUI (Section 5). Nonetheless, some understanding of what
393
is actually happening is helpful, because the GUI is much more limited than the full range
394
of PmagPy programs.
395
The source code and the help messages for all programs in the PmagPy package are
396
available online through the cookbook at https://earthref.org/PmagPy/ as well as
397
examples of their use. The link to the documentation for a particular program (say,
398
igrf.py) has the form: https://earthref.org/PmagPy#igrf.py. Click on the link with
399
the form [program name docs] at each program section for program documentation. There
400
is also a chapter on rudimentary Python programming to help researchers to begin to
401
modify and write new code for contribution to PmagPy as well as for their own use.
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7. Python notebooks with PmagPy 402
Data analysis in Python benefits from an increasingly robust ecosystem of packages
403
for scientific computing and plotting. A tool that is gaining more widespread use for
404
conducting and presenting data analysis is the Jupyter notebook. The resulting work
405
flow and results are well-documented and the notebooks themselves can be provided as
406
supplementary material linked to the published articles (such as the example notebooks
407
provided with this publication). The IPython/Jupyter project began as a way to bring
408
increased interactivity to data analysis in Python [P´erez and Granger , 2007] and has
409
evolved to include the interactive notebook environment that seeks to enable the full
410
trajectory of scientific computing from initial analysis and visualization onwards to col-
411
laboration and publication. The IPython project has expanded to enable a reproducible
412
interactive computing environment for many other programming languages (such as R,
413
MATLAB and Julia) and the language-agnostic parts of its architecture being developed
414
as Project Jupyter (https://jupyter.org). Jupyter notebooks allow for executable code,
415
results, text and graphical output to coexist seamlessly in a single document. With these
416
combined components, they are excellent tools both for conducting data analysis and
417
presenting results.
418
One of the major challenges and opportunities in modern science is the communication
419
of complex and varied data analysis in a way that is open and reproducible. The MagIC
420
database plays a significant role as a repository for data and the PmagPy software enables
421
analysis of these data. However, it is difficult to document the many steps that can go
422
into data analysis and synthesis. Using PmagPy code within a notebook environment
423
can play a powerful role both as a research tool and a research product that bridges this
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424
gap. All the code can be run to replicate the data analysis or to modify and/or extend
425
it. An additional benefit of using notebooks is that they can easily be shared as static
426
html files or exported as pdf files that can be included as the supplementary materials to
427
submitted articles (e.g. Swanson-Hysell et al. [2014] and Swanson-Hysell et al. [2015]).
428
To illustrate some of what is possible in terms of data analysis using PmagPy in
429
a notebook environment, we have created example notebooks that can be downloaded
430
here: https://github.com/PmagPy/2016_Tauxe-et-al_PmagPy_Notebooks or viewed
431
as rendered html here: http://pmagpy.github.io/Example_PmagPy_Notebook.html
432
and here: http://pmagpy.github.io/Additional_PmagPy_Examples.html. The main
433
example PmagPy notebook unpacks data downloaded from the MagIC database, filters
434
them, plots them in various ways and conducts some statistical analyses (an example of
435
which is shown in Figure 6).
436
Scientific Python distributions such as Enthought Canopy, which we recommend using,
437
and Anaconda, another powerful distribution, install IPython and Jupyter by default.
438
Other Python distributions, may require installation of IPython (instructions can cur-
439
rently be found here: https://ipython.org/install.html). To launch a notebook,
440
type jupyter notebook on the command line. This command will open up a local
441
IPython server in your default web browser. Notebooks are constructed as a series of
442
‘cells’ which can be text or code. To view the ‘source’ of a text cell or code cell, just click
443
on it. To render text or execute code, click on the ‘run’ button (sideways triangle on the
444
toolbar) or use the keyboard shortcut. Underlying the PmagPy programs that are acces-
445
sible through the command line and the GUI interfaces described in previous sections are
446
two main function modules: pmag.py and pmagplotlib.py. The functions within these
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447
modules can be imported and called upon within an IPython/Jupyter notebook. More-
448
over, any command line program can be called within the notebook. For greater ease
449
of use, we have created a module called ipmag.py that is in active development. The
450
ipmag.py module contains functions that replicate and extend many of the tools that are
451
found within the PmagPy command line programs for use in the notebook environment.
452
The main example notebook combines data from two different studies for the sake
453
of developing a mean paleomagnetic pole from the upper portion of a sequence of vol-
454
canics in the North American Midcontinent Rift [Halls, 1974; Swanson-Hysell et al.,
455
2014].
456
MagIC database. The digital object identifier (DOI) search option allows for the data
457
files to be readily located as https://earthref.org/MagIC/DOI/10.1139/e74-113/ and
458
https://earthref.org/MagIC/DOI/10.1002/2013GC005180/. Downloading these data
459
files from the database and putting them into folders within a local ‘Project Directory’
460
allows them to be accessed within the Jupyter notebook. Within the notebook, these
461
data are unpacked into their respective MagIC-formatted tab-delimited data files. The
462
data are then loaded into dataframes and filtered using several different criteria (strati-
463
graphic height and polarity). Several functions from the ipmag module are used for
464
making equal area plots and calculating statistics. In addition to combining the data sets
465
to calculate the mean pole, the code in the notebook conducts a bootstrap fold test on
466
the data using the approach of Tauxe and Watson [1994] as well as a common mean test.
467
The data recombinations and calculations done in this notebook are examples of portions
468
of the data analysis workflow which are often difficult to document and reproduce. The
469
examples illustrate a small sliver of the potential for the use of notebooks for data ma-
The two data files used within the notebook can be downloaded from the
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470
nipulation and analysis of paleomagnetic data. Additional functionality available within
471
PmagPy is demonstrated within the additional PmagPy examples notebook as small
472
vignettes of example code. In addition to viewing the examples contained within these
473
notebooks, the reader is encouraged to learn more in the PmagPy cookbook and other
474
online documentation.
8. Summary 475
This paper serves as a reference and starting point for installation and use of the
476
PmagPy software package. We illustrate the power of data re-use on a well-documented
477
paleointensity data set compiled for samples magnetized in known field intensities and
478
demonstrate that the CCRIT selection criteria [Cromwell et al., 2015] has better accuracy
479
and precision when estimating paleointensities in this data set. Full documentation of the
480
PmagPy code and its uses is available on the website: https://earthref.org/PmagPy/
481
and the reader is directed there for up-to-date information. We also provide a brief in-
482
troduction to the use of Jupyter notebooks with PmagPy functions. With fully open
483
source code and documentation, PmagPy allows users to view the code that underlies
484
their data analysis. Any member of the community can contribute to the project through
485
the collaborative tools available through GitHub by downloading (forking) a copy of the
486
code, making modifications and requesting that they be merged with the master branch
487
of PmagPy. Such contributions are welcome and will enrich this public domain project
488
greatly.
489
Acknowledgments. This work was partially funded by NSF grants EAR-1225520,
490
EAR-1347297, EAR-1141840 and EAR-1419894. We are grateful to the many users
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491
of PmagPy over the years for their help identifying bugs and providing constructive
492
feedback on the project. We also thank the many contributors of open source software
493
from which we have borrowed, in particular, Jeffery S. Gee. Data re-analyzed in this
494
paper can be accessed in the MagIC database: https://earthref.org/MagIC/10988/
495
upon publication. The example Jupyter notebooks can be viewed these links: http://
496
pmagpy.github.io/Example_PmagPy_Notebook.html and http://pmagpy.github.io/
497
Additional_PmagPy_Examples.html. We wish to acknowledge two anonymous review-
498
ers and the associate editor, Joshua Feinberg for helpful comments which improved the
499
manuscript.
References 500
501
Biggin, A., M. Perrin, and M. Dekkers (2007), A reliable absolute paleointensity determination obtained from a non-ideal recorder, Earth and Planet. Sci. Lett., 257, 545–563.
502
Bowles, J., J. Gee, D. V. Kent, M. Perfit, A. Soule, and D. Fornari (2006), Paleointensity
503
applications to timing and extent of eruptive activity, 9◦ -10◦n east pacific rise, Geochem.,
504
Geophys., Geosyst., in press.
505
Coe, R. S. (1967), The determination of paleo-intensities of the earth’s magnetic field with
506
emphasis on mechanisms which could cause non-ideal behavior in thellier’s method, J.
507
Geomag. Geoelectr., 19, 157–178.
508
Cromwell, G., L. Tauxe, H. Staudigel, and H. Ron (2015), Paleointensity estimates from
509
historic and modern hawaiian lava flows using basaltic volcanic glass as a primary source
510
material, Phys. Earth Planet. Int., 241, 44–56.
D R A F T
May 9, 2016, 9:44am
D R A F T
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
X - 27
511
Donadini, F., M. Kovacheva, M. Kostadinova, C. Ll., and L. Pesonen (2007), New ar-
512
chaeointensity results from scandinavia and bulgaria: Rock magnetic studies inference
513
and geophysical application, Phys. Earth and Planet. Inter., 165, 229–247.
514
515
516
517
Evans, M., and F. Heller (2003), Environmental Magnetism: Principles and Applications of Enviromagnetics, Academic Press. Halls, H. (1974), A paleomagnetic reversal in the osler volcanic group, northern lake superior, Can. J. Earth Sci., 11, 1200–1207, doi:10.1139/e74-113.
518
Harrison, R., and J. Feinberg (2008), Forcinel: An improved algorithm for calculating first-
519
order reversal curve (forc) distributions using locally-weighted regression smoothing,
520
Geochem. Geophys. Geosyst., 9, Q05016, doi:10.1029/2008GC001987.
521
522
523
524
525
526
Irving, E. (1956), Paleomagnetic and paleoclimatological aspects of polar wandering, Rev. Geofisica Pura e Applicata, Milan, 33, 23–41. Jacobs, J. (2005), Reversals of the Earth’s Magnetic Field, 2nd edition ed., Cambridge University Press. Kirschvink, J. L. (1980), The least-squares line and plane and the analysis of paleomagnetic data, Geophys. Jour. Roy. Astron. Soc., 62, 699–718.
527
Kr´asa, D., C. Heunemann, R. Leonhardt, and N. Petersen (2003), Experimental procedure
528
to detect multidomain remanence during thellier-thellier experiments, PHys. Chem.
529
Earth, 28, 681–687.
530
Lawrence, K. P., L. Tauxe, H. Staudigel, C. Constable, A. Koppers, W. C. McIntosh,
531
and C. L. Johnson (2009), Paleomagnetic field properties near the southern hemisphere
532
tangent cylinder, Geochem. Geophys. Geosyst., 10, Q01005, doi:10.1029/2008GC00207.
D R A F T
May 9, 2016, 9:44am
D R A F T
X - 28
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
533
Leonhardt, R., C. Heunemann, and D. Kra´asa (2004), Analyzing absolute paleointensity
534
determinations: Acceptance criteria and the software thelliertool4.0, Geochem. Geophys.
535
Geosys., 5, Q12016, doi:10.1029/2004GC000807.
536
Lurcock, P., and G. Wilson (2012), Puffinplot:
537
gram for paleomagnetic analysis,
538
doi:10.1029/2012GC004098.
539
540
541
542
543
544
A versatile, user-friendly pro-
Geochem. Geophys. Geosys.,
13,
Q06Z45,
McElhinny, M., and J. Lock (1996), Iaga paleomagnetic databases with access, Surv. of Geophys., 17, 575–591. McElhinny, M. W. (1973), Paleomagnetism and Plate Tectonics, Cambridge University Press, Cambridge. Merrill, R. T., M. W. McElhinny, and P. L. McFadden (1996), The Magnetic Field of the Earth: Paleomagnetism, the Core, and the Deep Mantle, Academic Press.
545
Muxworthy, A. R., D. Heslop, and D. Paterson, G.and Michalk (2011), A preisach
546
method for estimating absolute paleofield intensity under the constraint of using only
547
isothermal measurements: 2) experimental testing, J. Geophys. Res., 116, B04,103,
548
doi:10.1029/2010JB007,844.
549
Nilsson, A., R. Holme, M. Korte, N. Suttie, and M. Hill (2014), Reconstructing holocene
550
geomagnetic field variation: new methods, models and implications, Geophys. J. Int.,
551
198, 229–248, doi:10.1093/gji/ggu120.
552
Opdyke, N. D., and J. E. T. Channell (1996), Magnetic Stratigraphy, Academic Press.
553
Paterson, G., A. Muxworthy, A. Roberts, and C. Mac Niocaill (2010a), Assessment of
554
the usefulness of lithic clasts from pytoclastic deposits for paleointensity determination,
555
Jour. Geophy. Res., 115, doi:10.1029/2009JB006,475.
D R A F T
May 9, 2016, 9:44am
D R A F T
X - 29
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC 556
557
Paterson, G., D. Heslop, and A. Muxworthy (2010b), Deriving confidence in paleointensity estimates, Geochem. Geophys. Geosys., 11, Q07Z18, doi:10.1029/2010GC003,071.
558
Paterson, G., L. Tauxe, A. Biggin, R. Shaar, and L. Jonestrask (2014), On improving
559
the selection of thellier-type paleointensity data, Geochem. Geophys. Geosys., 15, 1–13,
560
doi:10.1002/2013GC005135.
561
562
563
564
565
566
P´erez, F., and B. E. Granger (2007), IPython: a system for interactive scientific computing, Computing in Science and Engineering, 9, doi: 10.1109/MCSE.2007.53, 21–29. Pick, T., and L. Tauxe (1993), Geomagnetic paleointensities during the cretaceous normal superchron measured using submarine basaltic glass, Nature, 366, 238–242. Rochette, P., B. Weiss, and J. Gattacceca (2009), Magnetism of extraterrestrial materials, Elements, 5, 223–228, doi:10.2113/gselements.5.4.223.
567
Shaar, R., and L. Tauxe (2013), Thellier gui: An integrated tool for analyzing paleoin-
568
tensity data from thellier-type experiments, Geochem. Geophys. Geosys., 14, 677–692,
569
doi:doi:10.1002/ggge.20062.
570
Shaar, R., H. Ron, L. Tauxe, R. Kessel, A. Agnon, E. Ben Yosef, and J. Feinberg (2010),
571
Testing the accuracy of absolute intensity estimates of the ancient geomagnetic field
572
using copper slag material, Earth and Planetary Science Letters, 290, 201–213.
573
Shaar, R., H. Ron, L. Tauxe, R. Kessel, and A. Agnon (2011b), Paleomagnetic field
574
intensity derived from non-sd: Testing the thellier izzi technique on md slag and a new
575
bootstrap procedure, Earth and Planetary Science Letters, 310 (213-224).
576
Swanson-Hysell, N., T. Killian, and R. Hanson (2015), A new grand mean palaeomagnetic
577
pole for the 1.11 ga umkondo large igneous province with implications for palaeogeog-
578
raphy and the geomagnetic field, Geophys. J. Int., 203, 2237–2247.
D R A F T
May 9, 2016, 9:44am
D R A F T
X - 30
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
579
Swanson-Hysell, N. L., A. A. Vaughan, M. R. Mustain, and K. E. Asp (2014), Confir-
580
mation of progressive plate motion during the midcontinent rift’s early magmatic stage
581
from the osler volcanic group, ontario, canada, Geochemistry Geophysics Geosystems,
582
15, 2039–2047, doi:10.1002/2013GC005180.
583
584
Tanaka, H., Y. Hashimoto, and N. Morita (2012), Palaeointensity determinations from historical and holocene basalt lavas in iceland, Geophys. J. Int., 189, 833–845.
585
Tauxe, L., and H. Staudigel (2004), Strength of the geomagnetic field in the cretaceous
586
normal superchron: New data from submarine basaltic glass of the troodos ophiolite,
587
Geochem. Geophys. Geosyst., 5 (2), Q02H06, doi:10.1029/2003GC000,635.
588
589
590
591
592
593
Tauxe, L., and G. S. Watson (1994), The fold test: an eigen analysis approach, Earth Planet. Sci. Lett., 122, 331–341. Tauxe, L., and T. Yamazaki (2007), Paleointensities, Treatise on Geophysics, vol. 5, pp. 509–563, doi:10.1016/B978–044452,748–6/00,098–5, Elsevier. Tauxe, L., S. K. Banerjee, R. Butler, and R. van der Voo (2010), Essentials of Paleomagnetism, University of California Press, Berkeley.
594
Winklhofer, M., and N. Petersen (2007), Paleomagnetism and magnetic bacteria, in Mag-
595
netoreception and Magnetosomes in Bacteria, vol. 3, edited by D. Schueler, pp. 255–273,
596
Springer, doi:10.1007/7171 046.
597
Yamamoto, Y., and H. Hoshi (2008), Paleomagnetic and rock magnetic studies of the
598
sakurajima 1914 and 1946 andesitic lavas from japan: A comparison of the ltd-dht shaw
599
and thellier paleointensity methods, Phys. Earth and Planet. Inter., 167, 118–143.
600
Yamamoto, Y., H. Tsunakawa, and H. Shibuya (2003), Palaeointensity study of the hawai-
601
ian 1960 lava: implications for possible causes of erroneously high intensities, Geophys
D R A F T
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X - 31
J Int, 153 (1), 263–276.
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Table 1. Example of re-interpretation of data: list of studies. All data used here are available at: https://earthref.org/
Site
Lat. (◦ )N Lon. (◦ )E Year Bexp (µT)
MagIC/10988/. DOI 9.8
Type
Citation 1991-1992 Eruption Site SBGa
Cromwell et al. (2015)
Cromwell et al. (2015)
Cromwell et al. (2015)
Cromwell et al. (2015)
Cromwell et al. (2015)
Cromwell et al. (2015)
Cromwell et al. (2015)
10.1016/j.pepi.2007.10.002
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
10.1016/j.pepi.2014.12.007
P
BR06
hw201
hw241
hw128
hw126
hw123
hw226
hw108
basalt
brick
basalt
basalt
basalt
basalt
basalt
basalt
basalt
19.3
60.1
19.36
19.52
19.26
19.69
19.073
19.64
19.9
-102.1
24.9
-154.97
-155.81
-155.87
-155.46
-155.714
-155.51
-155.9
1943 44.6
1906 49.7
1990 35.2
1960 36
1950 36.2
1935 36.6
1907 37.7
1843 40.1
1859 39.0
1991 36.2
10.1029/2005GC001141
Donadini et al. (2007) 10.1029/2010JB007844
-104.3
Bowles et al. (2006)
Muxworthy et al. (2011)
1944 43.8
30.0
14.5
60.0
40.8
synthetic
90.0
basalt
RS25
synthetic
60.0
VM
10.1016/j.epsl.2009.12.022
RS26
synthetic
80.0
10.1029/2010JB007844
10.1016/j.epsl.20s09.12.022
RS27
synthetic
1984 52.0
Muxworthy et al. (2011)
Shaar et al. (2010)
10.1016/j.epsl.2009.12.022
RS61
synthetic -16.8
1960 36.0
1990 36.2
Shaar et al. (2010)
10.1016/j.epsl.2011.08.024
RS63
65.7
-155.81
1946 46.4
-104.3
Shaar et al. (2010)
10.1016/j.epsl.2011.08.024
basalt
19.52
-130.6
1914 47.8
9.8
Shaar et al. (2011)
10.1111/j.1365-246X.2012.05412.x KF
basalt
31.6
-130.6
SBG
Shaar et al. (2011)
10.1046/j.1365-246X.2003.01909.x Hawaii 1960 Flow
andesite
31.6
BBQ
Tanaka et al. (2012)
SW
andesite
10.1029/93jb01160
Yamamoto et al. (2003)
10.1016/j.pepi.2008.03.006
TS
Pick and Tauxe (1993)
Yamamoto and Hoshi (2008)
10.1016/j.pepi.2008.03.006 SBG is submarine basaltic glass.
Yamamoto and Hoshi (2008) a
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Table 2. Example of re-interpretation of data: Criteria. n f FRAC β q MAD MADAnc DANG α npT RM 0.78 0.1 - 5 10 - 2 C 4 T 5 0.5 0.1 5 10 5 C: CCRIT, T: TTA. For definitions of statistics see Paterson
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SCAT δCK δpal δTR δt* True 5 5 10 3 et al. [2014].
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Table 3. Example of re-interpretation of data: Results. CCRIT TTA Site B IEF(%) ∆ (µT) σ (µT) σ (%) N N% B IEF(%) ∆ (µT) σ (µT) σ (%) N N% anc anc 1991/1992 Site 36.7 1.4 0.5 3.0 8.2 21 39.6 36.1 -0.3 -0.1 2.6 7.3 25 47.2 39.3 -1.5 -0.6 5.0 2.5 7 54.2 39.1 -2.0 -0.8 2.4 6.0 9 75.0 hw226 hw108 40.9 4.1 1.6 6.4 15.7 10 43.5 39.7 1.0 0.4 4.5 11.2 18 78.3 hw123 47.6 26.3 9.9 28.4 59.6 4 33.3 34.9 -7.4 -2.8 2.6 7.4 5 41.7 35.4 -2.7 -1.0 2.1 6.1 9 69.2 35.5 -2.5 -0.9 1.3 3.7 9 69.2 hw126 34.8 -3.9 -1.4 3.5 10.0 23 88.5 34.3 -5.2 -1.9 2.8 8.2 25 96.2 hw128 hw241 35.9 -0.3 -0.1 3.2 8.9 7 38.9 38.9 8.1 2.9 8.0 20.6 13 72.2 32.7 -7.1 -2.5 1.3 3.9 4 33.3 33.3 -5.4 -1.9 0.1 0.4 6 50.0 hw201 BR06 43.0 -13.5 -6.7 0.7 1.7 3 100.0 42.2 -15.1 -7.5 0.0 0.1 3 100.0 47.5 6.5 2.9 3.7 7.9 21 58.3 51.6 15.7 7.0 7.9 15.3 23 63.9 P 33.3 -24.0 -10.5 2.6 7.8 7 38.9 46.5 6.2 2.7 25.7 55.3 10 55.6 VM BBQ 35.9 -0.8 -0.3 1.8 5.1 7 58.3 35.5 -1.9 -0.7 1.4 4.0 10 83.3 29.3 -2.3 -0.7 3.0 10.2 4 26.7 28.4 -5.3 -1.6 2.3 8.2 5 33.3 RS25 RS26 56.2 -6.3 -3.8 1.5 2.7 4 26.7 0.0 89.7 -0.3 -0.3 13.6 15.2 7 28.0 95.5 6.1 5.5 11.6 12.2 7 28.0 RS27 60.6 1.0 0.6 0.1 0.0 3 60.0 RS61 RS63 78.2 -2.3 -1.8 0.1 0.2 3 60.0 0.0 43.6 -16.2 -8.4 0.5 1.1 3 100.0 42.6 -18.1 -9.4 1.3 3.2 3 100.0 KF 41.5 15.3 5.5 6.3 15.2 11 50.0 42.9 19.2 6.9 4.4 10.2 14 63.6 1960 Flow SW 48.3 4.1 1.9 2.4 5.0 16 84.2 50.4 8.6 4.0 1.5 3.0 18 94.7 49.8 4.2 2.0 6.1 12.2 44 83.0 51.8 8.4 4.0 7.1 13.7 34 64.2 TS CCRIT and TTA are the selection criteria defined in Table 3. Banc : ‘ancient’ (or estimated) magnetic field (in µT). IEF (%): the ‘Intensity Error Fraction’ of Paterson et al. [2010b] calculated as 100(Banc − Best )/Best ). ∆: Banc − Best . σ: standard deviation of the average of all ‘accepted’ specimen interpretations. N: number of accepted specimens. N%: Percentage of accepted specimens of the original total.
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Figure 1.
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Workflow of a typical paleomagnetic or rock magnetic project using PmagPy and
interfacing with the MagIC database.
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a)
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
Site b)
Location
Samples
c)
Specimens
Lake Superior
Figure 2.
Hierarchical structure of a typical study. a) A number of samples are typically
taken from a geological unit representing a single instant in time, known as a ‘site’. Collections of sites from a single region or stratigraphic section are grouped into ‘locations’. b) Pink tag is inserted into sample drill hole from a lava flow (site). c) Samples are sliced into specimens ready for measurement.
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Figure 3. The Pmag GUI user interface for converting measurement data into MagIC format, preparing contributions to the MagIC database, unpacking downloaded datasets. Demag GUI (Figure 5a) can be launched through this portal for the visualization and interpretation of directional data. Thellier GUI (Figure 5b) can be launched through this portal for the visualization and interpretation of Thellier-type paleointensity data.
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TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
a)
b)
Figure 4.
Front panels of frequently used Graphical User Interfaces. a) Demag GUI allows
interpretation of demagnetization data using alternating field or thermal methods. b) Thellier GUI allows interpretation of paleointensity data with Thellier-type experimental protocols.
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Figure 5.
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Results of re-analysis of paleointensity experimental data using different selection
criteria. a) Comparison of estimated intensity values using the criteria of Cromwell et al. [2015] (CCRIT) and the default values of Thellier Tool Leonhardt et al. [2004] (TTA) and the R2 value of the linear regression. CCRIT is apparently more precise and is also more accurate, with a slope closer to unity (see text). b) Comparison of the success rate of experiments (number of specimens selected) for the two different sets of criteria and c) success rate expressed as a percentage of the available specimens.
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Figure 6.
TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC
Screenshot from one of the example Jupyter notebooks that accompanies this
paper. The notebook environment allows for interspersed text, code and code output. Such a notebook can be provided as supplemental materials to publications enabling fully documented and reproducible data analysis. In this code snippet, data are simulated from antipodal Fisher distributions and a bootstrap reversal test is conducted on the data. The test results show the confidence intervals associated with cumulative distributions of the Cartesian coordinates of the bootstrapped means for the normal and reverse antipodes to overlap, indicating that the two means cannot be distinguished at the 95% level of confidence (as would be expected given that they are drawn from antipodal distributions). D R A F T May 9, 2016, 9:44am
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