Hi Folks, Larch 0.9.43 is now available, with installers for Windows, MacOS, and Linux at https://millenia.cars.aps.anl.gov/xraylarch/installation.html If you already have installed Larch, you should be able to update to the latest version with ~> conda update -c gsecars xraylarch from a terminal on Linux or Mac OSX. On Windows, you may have to specify the full path with something like: C:\Users\<YourName>\AppData\Local\Continuum\xraylarch\Scripts\conda.exe -c gsecars xraylarch If you have any trouble upgrading, you can simply remove the xraylarch installation folder and reinstall. If you would like to install Larch into a different Python environment, please read https://xraypy.github.io/xraylarch/installation.html Version 0.9.43 has several improvements to the XAS Viewer GUI application, including: - better handling of read/write cycles of multiple Athena project files. - better default normalization and better control over normalization, especially for XANES data. - improved PCA analysis (in part from the recent discussion here with Joselaine Cáceres Gonzalez), so that it now reports IND values to help determine the number of components and reports eigenvalues from simple matrix inversion (not SVD) so that they more closely match those in the XAFS literature. - addition of Partial Least Squares and LASSO regression analysis for selection and prediction of external quantities (valence, for example) based on training sets of XANES data with known values for these quantities. This machine-learning approach is based on work of M Dyar, et al in a series of papers over the past several years. I encourage and request anyone interested, and especially MacOS users, to try out the XAS Viewer app and let us know what needs improvement. For people interested in using Larch from Python, Version 0.9.43 includes a complete refactoring of the code to make Larch work better as a "normal" Python library. Specifically, the previous code organization with most of the real analysis code in "plugins" has been replaced with all code now placed within the main larch module, still organized by topic. This improves packaging -- `pip install xraylarch` can now work. This code reorganization also means that the python programmer can use more normal imports to get at the Larch library so that one can simply do >>> from larch.xafs import pre_edge, autobk, xftf We also fixed a serious and deep flaw that selected a matplotlib plotting library too early, making larch difficult to use with Jupyter, Spyder, or other Qt-based GUIs. This is now fixed, and import statements like the one above can be seamlessly used in Jupyter notebooks. I should note that the documentation and examples are definitely lagging behind the code especially regarding the most recent developments, but this will be worked on. If you having any questions, trouble, or suggestions on any part of Larch, please let us know. --Matt Newville
Hi Matt,
I just want everyone to know that for Windows, I tried to update larch to
0.9.43 from my existing Anaconda environment, but wasn't able to because of
the Python version (3.6.8). I just recreated a new environment with Python
3.7.3 and I was able to update to the current version. And thanks for
fixing up the import statements! Can't wait to use larch on jupyter
notebook :).
Garret
On Wed, May 1, 2019 at 9:44 AM Matt Newville
Hi Folks,
Larch 0.9.43 is now available, with installers for Windows, MacOS, and Linux at https://millenia.cars.aps.anl.gov/xraylarch/installation.html
If you already have installed Larch, you should be able to update to the latest version with ~> conda update -c gsecars xraylarch
from a terminal on Linux or Mac OSX. On Windows, you may have to specify the full path with something like: C:\Users\<YourName>\AppData\Local\Continuum\xraylarch\Scripts\conda.exe -c gsecars xraylarch
If you have any trouble upgrading, you can simply remove the xraylarch installation folder and reinstall. If you would like to install Larch into a different Python environment, please read https://xraypy.github.io/xraylarch/installation.html
Version 0.9.43 has several improvements to the XAS Viewer GUI application, including: - better handling of read/write cycles of multiple Athena project files. - better default normalization and better control over normalization, especially for XANES data. - improved PCA analysis (in part from the recent discussion here with Joselaine Cáceres Gonzalez), so that it now reports IND values to help determine the number of components and reports eigenvalues from simple matrix inversion (not SVD) so that they more closely match those in the XAFS literature. - addition of Partial Least Squares and LASSO regression analysis for selection and prediction of external quantities (valence, for example) based on training sets of XANES data with known values for these quantities. This machine-learning approach is based on work of M Dyar, et al in a series of papers over the past several years.
I encourage and request anyone interested, and especially MacOS users, to try out the XAS Viewer app and let us know what needs improvement.
For people interested in using Larch from Python, Version 0.9.43 includes a complete refactoring of the code to make Larch work better as a "normal" Python library. Specifically, the previous code organization with most of the real analysis code in "plugins" has been replaced with all code now placed within the main larch module, still organized by topic. This improves packaging -- `pip install xraylarch` can now work. This code reorganization also means that the python programmer can use more normal imports to get at the Larch library so that one can simply do
>>> from larch.xafs import pre_edge, autobk, xftf
We also fixed a serious and deep flaw that selected a matplotlib plotting library too early, making larch difficult to use with Jupyter, Spyder, or other Qt-based GUIs. This is now fixed, and import statements like the one above can be seamlessly used in Jupyter notebooks.
I should note that the documentation and examples are definitely lagging behind the code especially regarding the most recent developments, but this will be worked on. If you having any questions, trouble, or suggestions on any part of Larch, please let us know.
--Matt Newville
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-- Garret Bland PhD Student Carnegie Mellon University Civil and Environmental Engineering Porter Hall 115 Pittsburgh, PA, 15213
Hi Garret,
On Thu, May 2, 2019 at 8:33 AM Garret Bland
Hi Matt, I just want everyone to know that for Windows, I tried to update larch to 0.9.43 from my existing Anaconda environment, but wasn't able to because of the Python version (3.6.8).
Ah, OK. I saw that "update" did not work from an older version using Python 2.7 too. I guess it will not update python versions, though I am not sure of that.... Hopefully the ability to do a conda update will be useful until we switch to Python 3.8, which is probably at least a year away. We did notice yesterday that - at least on Windows - the Warning that things may not work correctly if there is a space in the installation path is a correct warning. Some of the things that we saw not work correctly include making the desktop icons or launching the applications from the auto-generated '.exe' files. I think this is not specific to Larch, and not something I'm going to be able to fix or work around easily. For people who have a space in their username, I believe you will need to install to another folder.
I just recreated a new environment with Python 3.7.3 and I was able to update to the current version. And thanks for fixing up the import statements! Can't wait to use larch on jupyter notebook :).
Great, I'd be interested to see what you use these for. That is, it might be nice to have some example notebooks. --Matt Newville
Dear Matt,
conda update —all
does not help. I think re-install anaconda is the only option. I testet this on a Mac (10.12.6).
best regards
Stefan
Am 02.05.2019 um 19:08 schrieb Matt Newville
Hi Stefan, On Thu, May 2, 2019 at 12:20 PM Mangold, Stefan (IPS) < stefan.mangold@kit.edu> wrote:
Dear Matt,
conda update —all does not help. I think re-install anaconda is the only option. I testet this on a Mac (10.12.6).
OK, thanks! It looks like `conda update --all` cannot update Python versions. I'll update the install instructions to say that may work in some situations, but will *not* work to upgrade from 0.9.42 (or earlier) to 0.9.43. --Matt
participants (3)
-
Garret Bland
-
Mangold, Stefan (IPS)
-
Matt Newville