PmagPy: Software package for paleomagnetic data ...

GEOCHEMISTRY, GEOPHYSICS, GEOSYSTEMS, VOL. ???, XXXX, DOI:10.1002/,

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PmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database 1

L. Tauxe, , R. Shaar, 5

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L. Jonestrask, N.L. Swanson-Hysell, R. Minnett, 6

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A.A.P. Koppers, C.G. Constable, N. Jarboe, K. Gaastra, and L. 3

Fairchild

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Scripps Institution of Oceanography,

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TAUXE ET AL.: PMAGPY: A BRIDGE TO MAGIC

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-

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vides an archive with a flexible data model for paleomagnetic and rock mag-

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netic data. The PmagPy software package is a cross-platform and open-source

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set of tools written in Python for the analysis of paleomagnetic data that

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serves as one interface to MagIC, accommodating various levels of user ex-

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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

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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-

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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

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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

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data table, making research results fully reproducible. The PmagPy design

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and its development using GitHub accommodates extensions to its capabil-

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ities through development of new tools by the user community. Here we de-

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scribe the PmagPy software package and illustrate the power of data discov-

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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

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ability to draw on archives of previously collected data, to merge them with new results,

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to assess progress, and to re-purpose published data for new problems. An early focus on

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whether and how continents drifted over geological time led the community devoted to the

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study of paleomagnetism to begin archiving paleomagnetic poles in what became known

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as “Pole Lists” (e.g., Irving [1956]). This tradition of data archiving matured significantly

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as more advanced paleomagnetic protocols were developed [McElhinny, 1973] and vari-

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ous subgroups specialized in documenting behavior of the geodynamo through reversals

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[Jacobs, 2005], paleosecular variation and paleointensity studies [Merrill et al., 1996], and

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magnetostratigraphy developed into a powerful tool for assisting geochronological stud-

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ies [Opdyke and Channell , 1996]. The International Association for Geomagnetism and

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Aeronomy (IAGA) encouraged the development of multiple databases which included a

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variety of individual paleomagnetic data compilations (paleomagnetic poles, paleointen-

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sity, paleosecular variation from lavas and sediments, etc). These compilations were pro-

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R Access files with Access forms that supported several search features vided as Microsoft

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[McElhinny and Lock , 1996]. They archived key results of paleomagnetic studies focusing

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on derived data products like paleomagnetic poles rather than the archiving of under-

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lying measurement or intermediate data and the workflow on which the interpretations

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were based. No formal archive existed for magnetic measurements and results outside of

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internal laboratory databases, although some high-level information about methodology

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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

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extraterrestrial samples [Rochette et al., 2009], it became imperative to provide a forum

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for compiling and archiving these data too.

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Taking advantage of faster computers with larger storage capability, the Magnetics In-

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formation Consortium (MagIC) began designing a database (https://earthref.org/

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MagIC/) with the aim of creating an architecture for storing the vast majority of data

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types used in paleomagnetic and rock magnetic investigations. The rationales for such an

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enterprise are: 1) to have a permanent archive of the data undergirding rock and paleo-

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magnetic publications; 2) to allow users to develop new research directions using data or

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products otherwise scattered across existing databases; 3) to make the data more discov-

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erable, increasing awareness of existing data; 4) to allow re-interpretation of archived data

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using different methods or selection criteria; 5) to track the details of data acquisition and

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interpretation with appropriate metadata; 6) to present the data in a homogeneous SI

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unit-based form to the end user, and 7) to satisfy publisher and funding agency require-

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ments for archiving data and provide open-access to the community at large.

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Getting data from the laboratory into the MagIC database, and visualizing and re-

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analyzing data downloaded from the database, makes special demands on data process-

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ing. While there are several recently published programming packages that deal with

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single types of data (demagnetization experiments (e.g., Lurcock and Wilson [2012]),

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paleointensity experiments (e.g., Leonhardt et al. [2004]), and FORC diagrams (e.g.,

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Harrison and Feinberg [2008])), there is a need for a unified set of open source, cross-

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platform software that deals with the variety of data types in a consistent way. Such

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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

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facilitate the generation of the files ready for uploading in the MagIC Search Interface

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(https://earthref.org/MagIC/search/) once they have been published. While alter-

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native routes into the MagIC database are certainly possible and actively encouraged,

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the lack of a user-friendly means of preparing data files for the MagIC database, inspired

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us to extend the software package, PmagPy (https://github.com/PmagPy), which is

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based on the open source Python programming language to address these needs. Details

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about installing and using PmagPy are available in the actively maintained PmagPy

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cookbook at: https://earthref.org/PmagPy/.

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The present article serves as a short introduction to the graphical user interfaces (GUIs)

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MagIC GUI and Pmag GUI as well as the more complete PmagPy command line

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level functions and those that can be used within Jupyter notebooks. Section 2 introduces

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key concepts of the MagIC database. Section 3 summarizes steps for installing PmagPy.

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Section 4 describes how to construct a file suitable for uploading into the MagIC database,

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without access to the underlying measurement data. Section 5 gives a brief tutorial on

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how to download datasets from the MagIC database and how to convert, interpret, and

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upload new data into the MagIC database after publication. We illustrate the power of re-

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interpreting published data to come to new and perhaps surprising conclusions. Section 6

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provides an overview of how PmagPy programs work on the command line, providing

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powerful tools for performing rock and paleomagnetic research beyond what is available

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in the GUIs. Section 7 gives a brief introduction to the use of Jupyter notebooks, in

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particular the use of PmagPy functions to implement statistical tests and conduct more

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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

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project leading to the upload of data into the MagIC database, and ultimately, download-

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ing of those data for re-use. Measurements are made in the laboratory on a variety of

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instruments and in many different formats. The PmagPy package can be used to convert

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laboratory formatted files into the MagIC standard format which can be analyzed using

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the many tools in the PmagPy software package to produce plots and data tables. The

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data can then be uploaded into the MagIC database where they are available to the entire

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community. PmagPy is also installed on EarthRef’s web servers and is used to automat-

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ically create various plots for contributions residing in the MagIC database. These plots

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help in visually browsing and interpreting the data online before downloading for further

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analysis or visualization with the PmagPy package.

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While PmagPy itself can be operated independently of the MagIC database, many

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components are designed to work on data compatible with the MagIC data model so

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some understanding of the structure of that data model is helpful. The data model is

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continually being extended and modified in response to community input, and a complete

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description of the current MagIC data model is available here: https://earthref.org/

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MagIC/data-model/.

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The MagIC data model is based on the hierarchical data and work flow typically used in

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paleomagnetic and rock magnetic studies. For clarification and consistency we briefly re-

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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-

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neous with respect to the magnetic property being measured, typically at a single instant

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in time (e.g. a cooling unit such as a lava flow or dike, or a particular bed in a sedimen-

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tary sequence). ‘Samples’ are either un-oriented or separately oriented pieces taken from

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geological or archaeological units, while ‘specimens’ are the objects that are measured in

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the laboratory (often smaller sub-samples of collected samples). An example for a typical

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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

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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

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cut into one or more 1 inch specimens (Figure 2c) which were then measured using, for

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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.

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Because the MagIC database is intended to archive all paleomagnetic and rock magnetic

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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

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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

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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

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is often the case with ages or compilations of existing datasets. A MagIC contribution in

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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).

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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

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material. 4. The specimens table stores interpretations of measurements on a specimen (which

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may be the entire sample if it was not subdivided).

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5. The measurements table stores measurements on a specimen.

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6. The criteria table contains a list of the selection criteria used for calculating inter-

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pretations in this study. 7. The ages table stores radiometrically and stratigraphically constrained ages.

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Because of the many different sampling, specimen preparation, and measurement meth-

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ods used in rock and paleomagnetic studies, MagIC must have a way of keeping track

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of these in a consistent, searchable way. At each step of the data reduction process,

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PmagPy tags records with “method codes” (e.g. GM-ARAR or FS-C-PISTON) which

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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

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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

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designate the category (GM or FS in the above examples stand for geochronology and

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field sampling respectively). Then there is a second part, and possibly also a third part, to

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describe methods with lesser or greater detail. In this case, the code GM-ARAR refers to

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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-

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ter search outcomes by avoiding spelling errors or differences in abbreviation conven-

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tions. A complete list of controlled vocabularies can be found at https://earthref.

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org/vocabularies/.

3. Getting started with PmagPy 166

Although data can be entered directly into the MagIC tables using a spreadsheet pro-

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gram like Excel, it is easier to generate the necessary tables as a by-product of data pro-

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cessing without having to know the details of the metadata and method codes. PmagPy

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strives to make the development of these tables with the metadata codes a seamless part

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of the data analysis workflow. PmagPy programs and functions are written in Python,

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an open source, platform independent scripting language.

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The complete documentation of PmagPy is available in the PmagPy cookbook at:

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https://earthref.org/PmagPy/. The cookbook explains the installation and use of the

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PmagPy programs, and documents the use of PmagPy with examples and links to the

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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

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open online incarnation at: https://earthref.org/MagIC/books/Tauxe/Essentials/.

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Several fully functional GUIs in the PmagPy package that work without the need to

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install special Python distributions can be downloaded as a compiled executable (‘stan-

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dalone’) programs. These can be used with no knowledge of Python and minimal famil-

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iarity with the MagIC database. Installation of the Standalone GUIs is described here:

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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

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paleomagnetic data and are available through the command line calls or from within a

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Jupyter notebook (Section 7). PmagPy comprises nearly 200 individual programs that

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range from simple scripts for calculating the expected local magnetic field direction at a

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particular time and location to more complex tools allowing visualization and analysis of

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many types of rock and paleomagnetic data. To get started using PmagPy, download

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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

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full installation of PmagPy is done using the ‘pip’ Python package management system

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from the command line. So the first step after installing (or re-installing) Python, is

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to find the command line (https://earthref.org/PmagPy#command_line). Then, type

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these commands on the command line interface.

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pip install --upgrade pip

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pip install --upgrade pmagpy

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pip install --upgrade pmagpy-cli

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Once these packages are installed, typing the name of the program at the command

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line will launch it. Here there is a slight difference between the MacOS/Unix style

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and the PC/MS-DOS style whereby PC users omit the terminal .py in all programs.

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To verify that all is well, type eqarea.py -h (omitting the .py for PC users) on the

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command line to generate a help message. A “command not found” message, means

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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

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https://github.com/PmagPy/PmagPy/issues or contact the authors for help.

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Many functions relevant to the analysis and visualization of demagnetization data and

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paleointensity experiments have been tied together in Pmag GUI (formerly Quick-

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MagIC), meant to be a user-friendly application for converting measurement data from

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laboratory formats into the MagIC data format, analyzing demagnetization data through

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principal component analysis [Kirschvink , 1980], analyzing Thellier-type paleointensity

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experiment data [Tauxe and Yamazaki , 2007] and exporting data and interpretations into

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MagIC data files ready for contribution to the MagIC database. Another program called

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MagIC GUI takes data entered within multiple MagIC tables and converts them into

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the single file for uploading to the database. MagIC GUI is intended for contributions of

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data and associated metadata that are being compiled without measurement level data.

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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

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the MagIC database. It is designed for creating upload files for data without underlying

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measurement data; if there are measurement data available, we recommend the use of

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Pmag GUI instead. MagIC GUI allows a user to add data at the location, site, sample,

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and specimen levels. It is also possible to add result level data and ages at any level. It

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uses a spreadsheet-like grid interface for entering and editing data. Useful features include

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pop-up menus with controlled vocabularies, multi-cell pasting from external spreadsheets,

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and built-in validations for MagIC database requirements. The PmagPy

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(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-

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grams and is designed for interpreting the most common paleomagnetic experimental data

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and for generating figures and data tables for use in publications. After the program is

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launched, the user interface appears (Figure 3) which supports the following tasks:

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1. Converting magnetometer files to the MagIC format, where the supported experi-

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mental procedures include: AF/Thermal demagnetizations, thermal/microwave paleoin-

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tensity experiments, remanence (ARM/TRM) anisotropy, and cooling rate correction ex-

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periments.

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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.

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4. Preparing a MagIC compatible file to be uploaded to the database.

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5. Extracting MagIC formatted files from a standard MagIC text file downloaded from

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the MagIC database. 5.1. Unpacking data downloaded from MagIC

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One way to use Pmag GUI is to unpack a data set downloaded from the MagIC

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database.

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[2009], simply substitute the digital object identifier (DOI) in a suitable browser window:

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https://earthref.org/MagIC/doi/10.1029/2008GC002072/, and the MagIC search en-

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gine will find the data for the paper associated with that DOI. To download the data,

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To locate data from a particular reference, for example Lawrence et al.

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simply click on the file icon below the column labeled ‘Data’. This will save a file to the

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downloads folder. After launching Pmag GUI either as a standalone GUI program or

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the full (expert) distribution by typing pmag gui.py on the command line (pmag gui

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for PC users using the pip install method), change directories by clicking on the “change

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dir” button and selecting a directory dedicated to a given dataset (the ‘Project Directory’,

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a directory with no spaces in its path). The downloaded file can be unpacked by clicking

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on the “unpack txt file downloaded from MagIC” in the Pmag GUI front panel (Figure

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3).

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Pmag GUI uses two additional GUIs to analyze the data. Demag GUI (Figure

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4a) is a tool for analyzing demagnetization data. It allows users to calculate best-fit

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directions of specimens, upper-level means (sample, site, location, study), and VGPs.

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Thellier GUI [Shaar and Tauxe, 2013] is a tool for analyzing paleointensity data (Figure

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4b). It allows users to calculate specimen paleointensities, anisotropy corrections, cooling

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rate corrections, and non-linear acquisition of thermal remenance. Interpretations done

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in each GUI are stored as MagIC tables and are fully documented with the necessary

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method codes describing the analyses used to create each data record. For example, a

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best-fit line calculated in Demag GUI is tagged with the directional estimation method

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code DE-BFL and a Thellier laboratory protocol based paleointensity estimate is tagged

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LP-PI-TRM. Details on the use of the GUIs are documented in the cookbook at https:

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//earthref.org/PmagPy#DemagGUI and https://earthref.org/PmagPy#ThellierGUI,

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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-

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interpret published data in the light of improved interpretation schemes. As an example

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of the power of re-interpreting such data, we take closer look at the data sets compiled by

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Paterson et al. [2014] with the addition of new data published since then from historical

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lava flows in Hawaii [Cromwell et al., 2015]. These data were produced by a variety of

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researchers using either the ‘Zero field-In field’ (ZI) technique of Coe [1967] or the ‘In

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field-zero field, zero-field in field’ (IZZI) technique of Tauxe and Staudigel [2004]. Further

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experimental details are in Paterson et al. [2014], the original references (Table 1) and the

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measurements table itself (available for download from the MagIC database during review

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at: https://earthref.org/MagIC/private:10988/Yar3wxVKfkFT/ (note that the URL

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will change to https://earthref.org/MagIC/10988/ upon publication) in which each

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individual measurement is tagged with method codes describing what sort of step it was.

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Each measurement, and interpretation result in any of the tables, also can be labeled with

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the DOI of the original publication (under the citations column) or labeled as a new result

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in the contribution’s publication (with a citations value of ‘This study’).

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As suggested by Paterson et al. [2014], data sets from specimens that cooled in a

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historical or laboratory field can be used to explore the effectiveness of either the material,

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the measurement protocol or the selection criteria. Here we re-consider the effect of data

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selection criteria on paleointensity estimation. The references, our site names of collections

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of specimens cooled in known fields (Bexp ), the type of material, the location and year

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(if applicable) are listed in Table 1. Note that in some cases the site names used here

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are different from the original publication in light of the MagIC use of the term ‘site’

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as a single cooling unit. Expected field intensities were calculated using the PmagPy

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program, igrf.py

(https://earthref.org/PmagPy#igrf.py) using IGRF12 (https:

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//www.ngdc.noaa.gov/IAGA/vmod/igrf.html) for dates after 1900 and the pfm9k model

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of Nilsson et al. [2014] prior to that (see use of command line programs in Section 6).

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Paterson et al. [2014] published the ‘Standard Paleointensity Definitions’, or SPD,

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available at http://www.paleomag.net/SPD/, which documents the calculations of 40

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statistics that have been used in calculation and selection of paleointensity data by the

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paleointensity community. Any given study uses a small subset of these, but Thellier

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GUI supports all the criteria defined in the SPD.

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In addition to the SPD, Paterson et al. [2014] tested a number of frequently used sets

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of criteria including the ThellierTool A (TTA) set of Leonhardt et al. [2004] (Table 2).

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Since then, Cromwell et al. [2015] recommended the use of another set, here referred to

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as CCRIT (Table 2), which they tested on historical lava flows from Hawaii and claimed

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superior accuracy and precision of the calculated field intensity estimates.

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In this section, we illustrate the value of archiving measurement level data and the way

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in which Thellier GUI can be used to compare the outcome of different sets of selection

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criteria. Because not all the criteria can be displayed at once on the GUI interface, the

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desired criteria can be selected under the ‘Preferences’ menu. Cutoff values can then

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be entered by choosing the ‘Change acceptance criteria’ option under the ‘Acceptance

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criteria’ sub-menu of the ‘Analysis’ menu. These are saved in the criteria file which

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can be included in the information uploaded into the MagIC database along with the

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measurement level data. This file documents the decision making process used in a given

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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

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either in the GUI or in the data tables.

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The results calculated by Thellier GUI for each cooling unit (‘site’) using the two

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sets of criteria are listed in Table 3. Note that several studies included in the analysis by

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Paterson et al. [2014] resulted in no acceptable interpretations and these are not included

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here (Biggin et al. [2007]; Kr´ asa et al. [2003]; Paterson et al. [2010a]). We list only those

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sites with at least three “acceptable” interpretations that agreed within either 6 µT or

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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 [Pérez 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

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applications to timing and extent of eruptive activity, 9◦ -10◦n east pacific rise, Geochem.,

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Geophys., Geosyst., in press.

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Coe, R. S. (1967), The determination of paleo-intensities of the earth’s magnetic field with

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emphasis on mechanisms which could cause non-ideal behavior in thellier’s method, J.

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Geomag. Geoelectr., 19, 157–178.

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Cromwell, G., L. Tauxe, H. Staudigel, and H. Ron (2015), Paleointensity estimates from

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historic and modern hawaiian lava flows using basaltic volcanic glass as a primary source

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to detect multidomain remanence during thellier-thellier experiments, PHys. Chem.

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Muxworthy, A. R., D. Heslop, and D. Paterson, G.and Michalk (2011), A preisach

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isothermal measurements: 2) experimental testing, J. Geophys. Res., 116, B04,103,

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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)







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)


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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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.


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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