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:
The ability to select and import a specific table from among multiple tables on a webpage,
Intelligent alerts regarding the saved state of exported Python code, and
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.
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.
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 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.
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
Since PyXLL was first released back in 2010 it has grown hugely in popularity and is used by businesses in many different sectors.
The original motivation for PyXLL was to be able to use all the best bits of Excel combined with a modern programming language for scientific computing, in a way that fits naturally and works seamlessly.
Since the beginning, PyXLL development focused on the things that really matter for creating useful real-world spreadsheets; worksheet functions and macro functions. Without these all you can do is just drive Excel by poking numbers in and reading numbers out. At the time the first version of PyXLL was released, that was already possibly using COM, and so providing yet another API to do the same was seen as little value add. On the other hand, being able to write functions and macros in Python opens up possibilities that previously were only available in VBA or writing complicated Excel Addins in C++ or C#.
With the release of PyXLL 3, integrating your Python code into Excel has become more enjoyable than ever. Many things have been simplified to get you up and running faster, and there are some major new features to explore.
If you are new to PyXLL have a look at the Getting Started section of the documentation.
All the features of PyXLL, including these new ones, can be found in the Documentation
NEW FEATURES IN PYXLL V. 3.0
1. Ribbon Customization
Ever wanted to write an add-in that uses the Excel ribbon interface? Previously the only way to do this was to write a COM add-in, which requires a lot of knowledge, skill and perseverance! Now you can do it with PyXLL by defining your ribbon as an XML document and adding it to your PyXLL config. All the callbacks between Excel and your Python code are handled for you.
Enthought is pleased to announce Virtual Core 1.8. Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.
NEW VIRTUAL CORE 1.8 FEATURE: Rotational Alignment on Core CT Sections
Virtual Core 1.8 introduces the ability to perform rotational alignment on core CT sections. Core sections can become misaligned during extraction and data acquisition. The alignment tool allows manual realignment of the individual core sections. Wellbore image logs (like FMI) can be imported and used as a reference when aligning core sections. The Digital Log Interchange Standard (DLIS) is now fully supported, and can be used to import and export data.
Whole-core CT scans are routinely performed on extracted well cores. The data produced from these scans is typically presented as static 2D images of cross sections and video scans. Images are limited to those provided by the vendor, and the raw data, if supplied, is difficult to analyze. However, the CT volume is a rich 3D dataset of compositional and textural information that can be incorporated into core description and analysis workflows.
Enthought’s proprietary Clear Core technology is used to process the raw CT data, which is notoriously difficult to analyze. Raw CT data is stored in 3 foot sections, with each section consisting of many thousands of individual slice images which are approximately .2 mm thick.Continue reading →
Today we officially release Canopy Geoscience 0.10.0, our Python-based analysis environment for geoscience data.
Canopy Geoscience integrates data I/O, visualization, and programming, in an easy-to-use environment. Canopy Geoscience is tightly integrated with Enthought Canopy’s Python distribution, giving you access to hundreds of high-performance scientific libraries to extract information from your data.
The Canopy Geoscience environment allows easy exploration of your data in 2D or 3D. The data is accessible from the embedded Python environment, and can be analyzed, modified, and immediately visualized with simple Python commands.
Feature and capability highlights for Canopy Geoscience version 0.10.0 include:
Read and write common geoscience data formats (LAS, SEG-Y, Eclipse, …)
3D and 2D visualization tools
Well log visualization
Conversion from depth to time domain is integrated in the visualization tools using flexible depth-time models
Integrated IPython shell to programmatically access and analyse the data
Integrated with the Canopy editor for scripting
Extensible with custom-made plugins to fit your personal workflow