Scientists Use Enthought’s Virtual Core Software to Study Asteroid Impact

Chicxulub Impact Crater Expedition Recovers Core to Further Discovery on the Impact on Life and the Historical Dinosaur Extinction

From April to May 2016, a team of international scientists drilled into the site of an asteroid impact, known as the Chicxulub Impact Crater, which occurred 66 million years ago. The crater is buried several hundred meters below the surface in the Yucatán region of Mexico. Until that time, dinosaurs and marine reptiles dominated the world, but the series of catastrophic events that followed the impact caused the extinction of all large animals, leading to the rise of mammals and evolution of mankind. This joint expedition, organized by the International Ocean Discovery Program (IODP) and International Continental Scientific Drilling Program (ICDP) recovered a nearly complete set of rock cores from 506 to 1335 meters below the modern day seafloor.  These cores are now being studied in detail by an international team of scientists to understand the effects of the impact on life and as a case study of how impacts affect planets.

CT Scans of Cores Provide Deeper Insight Into Core Description and Analysis

Before being shipped to Germany (where the onshore science party took place from September to October 2016), the cores were sent to Houston, TX for CT scanning and imaging. The scanning was done at Weatherford Labs, who performed a high resolution dual energy scan on the entire core.  Dual energy scanning utilizes x-rays at two different energy levels. This provides the information necessary to calculate the bulk density and effective atomic numbers of the core. Enthought processed the raw CT data, and provided cleaned CT data along with density and effective atomic number images.  The expedition scientists were able to use these images to assist with core description and analysis.

CT Scans of Chicxulub Crater Core Samples

Digital images of the CT scans of the recovered core are displayed side by side with the physical cores for analysis


Information not evident in physical observation (bottom, core photograph) can be observed in CT scans (top)

These images are helping scientists understand the processes that occurred during the impact, how the rock was damaged, and how the properties of the rock were affected.  From analysis of images, well log data and laboratory tests it appears that the impact had a permanent effect on rock properties such as density, and the shattered granite in the core is yielding new insights into the mechanics of large impacts.

Virtual Core Provides Co-Visualization of CT Data with Well Log Data, Borehole Images, and Line Scan Photographs for Detailed Interrogation

Enthought’s Virtual Core software was used by the expedition scientists to integrate the CT data along with well log data, borehole images and line scan photographs.  This gave the scientists access to high resolution 2D and 3D images of the core, and allowed them to quickly interrogate regions in more detail when questions arose. Virtual Core also provides machine learning feature detection intelligence and visualization capabilities for detailed insight into the composition and structure of the core, which has proved to be a valuable tool both during the onshore science party and ongoing studies of the Chicxulub core.


Enthought’s Virtual Core software was used by the expedition scientists to visualize the CT data alongside well log data, borehole images and line scan photographs.

Related Articles

Drilling to Doomsday
Discover Magazine, October 27, 2016

Chicxulub ‘dinosaur crater’ investigation begins in earnest
BBC News, October 11, 2016

How CT scans help Chicxulub Crater scientists
Integrated Ocean Drilling Program (IODP) Chicxulub Impact Crater Expedition Blog, October 3, 2016

Chicxulub ‘dinosaur’ crater drill project declared a success
BBC Science, May 25, 2016

Scientists hit pay dirt in drilling of dinosaur-killing impact crater
Science Magazine, May 3, 2016

Scientists gear up to drill into ‘ground zero’ of the impact that killed the dinosaurs
Science Magazine, March 3, 2016

Texas scientists probe crater they think led to dinosaur doomsday
Austin American-Statesman, June 2, 2016

Mayavi (Python 3D Data Visualization and Plotting Library) adds major new features in recent release

Key updates include: Jupyter notebook integration, movie recording capabilities, time series animation, updated VTK compatibility, and Python 3 support

by Prabhu Ramachandran, core developer of Mayavi and director, Enthought India

The Mayavi development team is pleased to announce Mayavi 4.5.0, which is an important release both for new features and core functionality updates.

Mayavi is a general purpose, cross-platform Python package for interactive 2-D and 3-D scientific data visualization. Mayavi integrates seamlessly with NumPy (fast numeric computation library for Python) and provides a convenient Pythonic wrapper for the powerful VTK (Visualization Toolkit) library. Mayavi provides a standalone UI to help visualize data, and is easy to extend and embed in your own dialogs and UIs. For full information, please see the Mayavi documentation.

Mayavi is part of the Enthought Tool Suite of open source application development packages and is available to install through Enthought Canopy’s Package Manager (you can download Canopy here).

Mayavi 4.5.0 is an important release which adds the following features:

  1. Jupyter notebook support: Adds basic support for displaying Mayavi images or interactive X3D scenes
  2. Support for recording movies and animating time series
  3. Support for the new matplotlib color schemes
  4. Improvements on the experimental Python 3 support from the previous release
  5. Compatibility with VTK-5.x, VTK-6.x, and 7.x. For more details on the full set of changes see here.

Let’s take a look at some of these new features in more detail:

Jupyter Notebook Support

This feature is still basic and experimental, but it is convenient. The feature allows one to embed either a static PNG image of the scene or a richer X3D scene into a Jupyter notebook. To use this feature, one should first initialize the notebook with the following:

from mayavi import mlab

Subsequently, one may simply do:

s = mlab.test_plot3d()

This will embed a 3-D visualization producing something like this:

Mayavi in a Jupyter Notebook

Embedded 3-D visualization in a Jupyter notebook using Mayavi

When the init_notebook method is called it configures the Mayavi objects so they can be rendered on the Jupyter notebook. By default the init_notebook function selects the X3D backend. This will require a network connection and also reasonable off-screen support. This currently will not work on a remote Linux/OS X server unless VTK has been built with off-screen support via OSMesa as discussed here.

For more documentation on the Jupyter support see here.

Animating Time Series

This feature makes it very easy to animate a time series. Let us say one has a set of files that constitute a time series (files of the form some_name[0-9]*.ext). If one were to load any file that is part of this time series like so:

from mayavi import mlab
src ='data_01.vti')

Animating these is now very easy if one simply does the following: = True

This can also be done on the UI. There is also a convenient option to synchronize multiple time series files using the “sync timestep” option on the UI or from Python. The screenshot below highlights the new features in action on the UI:

Time Series Animation in Mayavi

New time series animation feature in the Python Mayavi 3D visualization library.

Recording Movies

One can also create a movie (really a stack of images) while playing a time series or running any animation. On the UI, one can select a Mayavi scene and navigate to the movie tab and select the “record” checkbox. Any animations will then record screenshots of the scene. For example:

from mayavi import mlab
f = mlab.figure()
f.scene.movie_maker.record = True

This will create a set of images, one for each step of the animation. A gif animation of these is shown below:

Recording movies with Mayavi

Recording movies as gif animations using Mayavi

More than 50 pull requests were merged since the last release. We are thankful to Prabhu Ramachandran, Ioannis Tziakos, Kit Choi, Stefano Borini, Gregory R. Lee, Patrick Snape, Ryan Pepper, SiggyF, and daytonb for their contributions towards this release.

Additional Resources on Mayavi:

Geophysical Tutorial: Facies Classification using Machine Learning and Python

Published in the October 2016 edition of The Leading Edge magazine by the Society of Exploration Geophysicists. Read the full article here.

By Brendon Hall, Enthought Geosciences Applications Engineer 
Coordinated by Matt Hall, Agile Geoscience


There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist’s toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

One of the best examples is scikit-learn, a collection of tools for machine learning in Python. What is machine learning? You can think of it as a set of data-analysis methods that includes classification, clustering, and regression. These algorithms can be used to discover features and trends within the data without being explicitly programmed, in essence learning from the data itself.

Well logs and facies classification results from a single well.

Well logs and facies classification results from a single well.

In this tutorial, we will demonstrate how to use a classification algorithm known as a support vector machine to identify lithofacies based on well-log measurements. A support vector machine (or SVM) is a type of supervised-learning algorithm, which needs to be supplied with training data to learn the relationships between the measurements (or features) and the classes to be assigned. In our case, the features will be well-log data from nine gas wells. These wells have already had lithofacies classes assigned based on core descriptions. Once we have trained a classifier, we will use it to assign facies to wells that have not been described.

See the tutorial in The Leading Edge here.


Canopy Data Import Tool: New Updates

In May of 2016 we released the Canopy Data Import Tool, a significant new feature of our Canopy graphical analysis environment software. With the Data Import Tool, users can now quickly and easily import CSVs and other structured text files into Pandas DataFrames through a graphical interface, manipulate the data, and create reusable Python scripts to speed future data wrangling.

Watch a 2-minute demo video to see how the Canopy Data Import Tool works:

With the latest version of the Data Import Tool released this month (v. 1.0.4), we’ve added new capabilities and enhancements, including:

  1. The ability to select and import a specific table from among multiple tables on a webpage,
  2. Intelligent alerts regarding the saved state of exported Python code, and
  3. Unlimited file sizes supported for import.

Download Canopy and start a free 7 day trial of the data import tool Continue reading

Webinar: Introducing the NEW Python Integration Toolkit for LabVIEW

See a recording of the webinar:

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. In August 2016, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environments.

In this webinar, we’ll demonstrate:

  1. How the new Python Integration Toolkit for LabVIEW from Enthought seamlessly brings the power of the Python ecosystem of scientific and engineering tools to LabVIEW
  2. Examples of how you can extend LabVIEW with Python, including using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more

Continue reading

5 Simple Steps to Create a Real-Time Twitter Feed in Excel using Python and PyXLL

PyXLL 3.0 introduced a new, simpler, way of streaming real time data to Excel from Python.

Excel has had support for real time data (RTD) for a long time, but it requires a certain knowledge of COM to get it to work. With the new RTD features in PyXLL 3.0 it is now a lot simpler to get streaming data into Excel without having to write any COM code.

This blog will show how to build a simple real time data feed from Twitter in Python using the tweepy package, and then show how to stream that data into Excel using PyXLL.

(Note: The code from this blog is available on github 

Create a real-time Twitter data feed using Python and PyXLL.

Create a real-time Twitter feed in Excel using Python and PyXLL.

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AAPG 2016 Conference Technical Presentation: Unlocking Whole Core CT Data for Advanced Description and Analysis

Microscale Imaging for Unconventional Plays Track Technical Presentation:

Unlocking Whole Core CT Data for Advanced Description and Analysis

Brendon Hall, Geoscience Applications Engineer, EnthoughtAmerican Association of Petroleum Geophysicists (AAPG)
2016 Annual Convention and Exposition Technical Presentation
Tuesday June 21st at 4:15 PM, Hall B, Room 2, BMO Centre, Calgary

Presented by: Brendon Hall, Geoscience Applications Engineer, Enthought, and Andrew Govert, Geologist, Cimarex Energy


It has become an industry standard for whole-core X-ray computed tomography (CT) scans to be collected over cored intervals. The resulting data is typically presented as static 2D images, video scans, and as 1D density curves.

CT scan of core pre- and post-processing

CT scans of cores before and after processing to remove artifacts and normalize features.

However, the CT volume is a rich data set of compositional and textural information that can be incorporated into core description and analysis workflows. In order to access this information the raw CT data initially has to be processed to remove artifacts such as the aluminum tubing, wax casing and mud filtrate. CT scanning effects such as beam hardening are also accounted for. The resulting data is combined into contiguous volume of CT intensity values which can be directly calibrated to plug bulk density.

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Webinar: Fast Forward Through the “Dirty Work” of Data Analysis: New Python Data Import and Manipulation Tool Makes Short Work of Data Munging Drudgery

Python Import & Manipulation Tool Intro Webinar

Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:

Acquire > Wrangle > Analyze and Model > Share and Refine > Publish

The problem is that often 50 to 80 percent of time is spent wading through the tedium of the first two stepsacquiring and wrangling data – before even getting to the real work of analysis and insight. (See The New York Times, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights)


Enthought Canopy Data Import Tool

Try the Data Import Tool with your own data. Download here.

In this webinar we’ll demonstrate how the new Canopy Data Import Tool can significantly reduce the time you spend on data analysis “dirty work,” by helping you:

  • Load various data file types and URLs containing embedded tables into Pandas DataFrames
  • Perform common data munging tasks that improve raw data
  • Handle complicated and/or messy data
  • Extend the work done with the tool to other data files

Continue reading