I am running on Window 7 and I would like to install the Scikit-image package on my Canopy 64-bit. I have looked up the Available-packeges in the package-Manager of Canopy and the scikit-image is included but the only option I have is to click on subscribe. If I click on that the ENTHOUGHT web page pops up offering me to download Canopy-express (the only that is free) but (I guess) I already have that. Is there a way to download only the package that I need?
Thanks in advance
I had the same problem when installing Scikit-image. Canopy is installed but not really helping.
Actually there are installation guides on the page http://scikit-image.org/docs/dev/install.html.
The Build Requirements are:
Python >= 2.6
Numpy >= 1.6
Cython >= 0.19.2
Six >=1.3
And the Runtime requirements are:
SciPy
Matplotlib
NetworkX
Pillow (or PIL)
I had them all installed before I installed scikit-image.
There are maybe other small accessory packages required when you installing above major ones. But there isn't a problem.
Another thing I paid attention to is to ensure the installed scikit-image versions were uninstalled before taking above ones installed 1 by 1. And you may need to exit Python IDLE.
Last, if you are a Windows user, I would recommend downloads from PyPi and http://www.lfd.uci.edu/~gohlke/pythonlibs/.
Good luck.
As you observe, scikit-image is provided pre-built only to subscribers (paid or academic), i.e. not in Canopy Express. If you have Visual Studio 2008 on your system, you can build it yourself, following these general guidelines:
https://support.enthought.com/entries/23389761-Installing-packages-into-Canopy-Python-from-the-command-line
Also, please note the following quirks regarding this package's naming:
https://support.enthought.com/entries/22447950-Scikit-learn-and-Scikit-image-naming-conventions
Related
i am attempting to install plotly 4.5.1 using the anaconda environment - conda install -c plotly plotly.
However i can see from the terminal that Anaconda attempts to downgrade Spyder back to v 3.3.6. See attached picture:
I would like to carry on using Spyder v4.0.1 but also need plotly. What is the best way to deal with this? I guess different anaconda environments, but then would that mean working in Spyder 3.3.6 to do my plotting?
PS: I also plan to install plotly-dash for dash board development. I suspect that this may also run into the same issue.
Thanks for the help in advance.
(Spyder maintainer here) We're trying to solve this problem with the Anaconda people right now. Hopefully it'll be fixed in the next days. In the meantime, please see this issue for possible solutions.
I am having difficulty installing various Julia packages on my Windows 7 laptop. When trying to add certain packages I receive the following error:
(v1.3) pkg> add MbedTLS
Updating registry at `C:\Users\uname\.julia\registries\General`
Updating git-repo `https://github.com/JuliaRegistries/General.git`
Resolving package versions...
ERROR: Unable to automatically install 'MbedTLS' from 'C:\Users\uname\.julia\packages\MbedTLS_jll\wUtL4\Artifacts.toml'
Several packages install happily, and I think I've narrowed it down to those that are supplied via BinaryBuilder.jl, such as MbedTLS, Arpack, OpenSpecFun. If I try to install any packages that have any such packages as a dependency I get the same error message when it hits one of these (initially encountered when I was trying to install Genie.
I am using the latest Julia (1.3.1), although I encountered the same issue previously in 1.2 - I managed to fix things eventually in that case, and tried a similar approach (manually downloading and placing in packages folder) but have not been able to fix things in this instance (although I confess that my notes were a little lacking so can't be certain I'm doing the correct thing). The various packages seem to exist in ...\.julia\packages\ (although not in .julia\compiled), but julia complains whenever I try to add them to some environment.
I don't think I understand julia's package system well enough to see quite what is going on here. I have seen other people with similar issues but not found anything yet which has worked - any help would be much appreciated!
This usually is due to an issue with your powershell installation, which is what we use to download these binaries in Julia 1.3 and 1.4. In particular, most of the internet (including GitHub, where most of our binaries are hosted) disabled SSL v3, TLS 1.0, and TLS 1.1 in 2018. Windows 7 is old enough that it doesn't speak TLS 1.2+ natively; instead you must install two packages:
This TLS easy_fix
Windows Management Framework 3.0 or later, to get Powershell v3+
This is necessary on Windows 7, but not on Windows 10. For more instructions, you can read the Julia platform specific instructions: https://julialang.org/downloads/platform/
I have a new Ryzen CPU and ran into this issue. Eg. default anaconda channel uses Intel MKL and this cripples performance on Ryzen systems. If a numpy version using openblas is used, then it's much faster. The above example is in ubuntu but I need to achieve this in windows as well.
To be more specific I actually managed to install numpy with openblas but as soon I try to install anything on top like scikit-learn it will "downgrade" to mkl again.
What I'm looking for is install instructions for a "SciPy stack" python environment on windows using openblas?
EDIT:
This issue seems to be extremely annoying. While there is since not very long a nomkl package also for windows it doesn't seem to take as it always installs mkl version regardless. Even if I install from pip, conda will just overwrite it, with an mkl version again next time you install something, in my case another lib which requires conda.
EDIT2:
As far as I can tell for now the only "solution" is to install anything SciPy related from pypi (pip): numpy, SciPy, pandas, scikit-learn possibly more. eg. only really a solution if you really need anaconda for a specific package, which I do.
EDIT3:
So the MKL_DEBUG_CPU_TYPE=5 trick indeed works. Performance with mkl is restored and a bit better than with openblas.
I did a very basic test (see the link above) with a fixed seed and the result is the same for mkl and openblas.
I want to implement speech recognition in my windows10 using python2.7. Is it possible to install natlink for python2.7? Because i found that it is only available for python 2.5. If anybody knows how to get natlink and dragonfly for python 2.7 then please guide me. Thanks in advance!!
It is possible, I'm doing it on Win7. I was able to successfully set it up multiple times, when following the following directions exactly:
http://qh.antenna.nl/unimacro/installation/installation.html
And that often included downloading and using the exactly supplied versions they zipped up for use. I don't know if it will work for Win10.
Also, if you can get it working with python 2.5, there's nothing stopping you from having multiple versions on your system.
I installed Dragon Naturally Speaking 13 Premium + Natlink-4.1 (from sourceforge) + Dragonfly with python2.7 on my Windows 8 machine. I also had to install WxPython AFAIR. I had to do a few tests, but this combination works fine. I was able to run some basic tests.
I would like to try scidb as a replacement for hdf5. I would like to test it on my Debian laptop (no clusters) to give it a try.
Is this possible? Might be that Debian (as opposed to Ubuntu) is not supported?
I had no luck with the installation instructions. The deployment script tells that my OS is not supported. The scidb userguide says about some pre-built packages (for Ubuntu, at least). But there is no hint on how to obtain them.
SciDB is limited to RedHat / CentOS, and to Ubuntu as of the 14.9 release. Folk who want to run it on other distros generally compile from code.
Information about how to obtain the sources (as well as current documentation and community discussion) can be found on the forums here ... http://www.scidb.org/forum/. You'll need to register as a forum user.
Specifically, have a look at http://www.scidb.org/forum/viewtopic.php?f=16&t=364. There's a list of releases and links to code bundles there.
I installed SciDB several times using several ways (building from sources and installing from packages, installing the cluster version and the dev version).
Installation from packages
First, if you choose to install from packages (the easiest and fastest way), SciDB is very very sensitive about your Linux version. For example, for the last version of SciDB (14.8), if you choose to install on a Ubuntu, it has to be a Ubuntu 12.04 (and not a 14.04, a common mistake) 64 bits (meaning you have to install the AMD64 version even if you have an Intel processor). It won't work if you have a different version.
If you have an Ubuntu 12.04 AMD64, Paradigm4 provides a deployment script and a documentation with very simple steps:
https://github.com/Paradigm4/deployment
Installation from sources
It's not so difficult but it can be painful and time consuming. I did it because we had to compile a custom plugin for SciDB. You have two types of installation: dev install (in SciDB user directory) and cluster install (in /opt/ directory).
You have to be registered on their forum to have the link to the source code. They provide a specific documentation to build from source.
Good luck.
Several months ago I have dealt with porting SciDB 14.12 to an unsupported Linux - Fedora 19. If your OS is not supported, it will neither be supported if you try to install from the sources. You have to start from the sources, but then you have to adapt the deployment and installation scripts. The sources can be downloaded from SciDB forum.
Namely, add a new platform to deployment/common/os_detect.sh. Then, there are multiple platform specific deployment scripts, such as deployment/common/prepare_toolchain.sh, deployment/common/prepare_coordinator.sh and deployment/common/prepare_chroot.sh. You need to make sure those prepare the environment as they would on the supported OS'. I used Red Hat 6 and CentOS 6 as a reference, as those are both more similar to Fedora. Since your OS is Debian, you can first try falling back to Ubuntu deployment (in os_detect.sh).
Another problem you may encounter are the 3rd party tools, specially Boost. In my case, I had to build it manually from sources.
Sometimes when porting and debugging it is not convenient to run the scripts with deploy.sh, but it's better to run the deployment scripts directly on the target machine (e.g. coordinator).
Probably the best way to install and to start with SciDB is to download a standard image. With this image you only have to import the virtual machine with a software to virtualize. Moreover there are some characteristics of this virtual machine that are great to develop your first applications.
The main advantage, is that you have an API to SciDB queries and another to R. Then you can explore all options and to test SciDB.
This is the version that I downloaded few months ago: http://www.paradigm4.com/forum/viewtopic.php?f=14&t=1329&sid=606f614e401900cfa750375ba56de656
Nevertheless, there is a problem, the community is too poor. There are little people developing with SciDB.