--- title: Tracking environment information date: Tue Feb 19 19:19:03 2019 --- # Table of Contents TOC - [Getting information about your Git repository](#getting-information-about-your-git-repository) - [Getting information about Python(3) libraries](#getting-information-about-python3-libraries) - [Getting information about your system](#getting-information-about-your-system) - [Getting the list of installed packages and their version](#getting-the-list-of-installed-packages-and-their-version) - [How to list imported modules?](#how-to-list-imported-modules) - [Saving and restoring an environment with pip](#saving-and-restoring-an-environment-with-pip) - [Installing a new package or a specific version](#installing-a-new-package-or-a-specific-version) - [Getting information about R libraries](#getting-information-about-r-libraries) - [Getting the list imported modules and their version](#getting-the-list-imported-modules-and-their-version) - [Getting the list of installed packages and their version](#getting-the-list-of-installed-packages-and-their-version-1) - [Installing a new package or a specific version](#installing-a-new-package-or-a-specific-version-1) # Getting information about your Git repository When taking notes, it may be difficult to remember which version of the code or of a file was used. This is what version control is useful for. Here are a few useful commands that we typically insert at the top of our notebooks in shell cells ``` shell git log -1 ``` ``` example commit 741b0088af5b40588493c23c46d6bab5d0adeb33 Author: Arnaud Legrand Date: Tue Sep 4 12:45:43 2018 +0200 Fix a few typos and provide information on jupyter-git plugins. ``` ``` shell git status -u ``` ``` example On branch master Your branch is ahead of 'origin/master' by 4 commits. (use "git push" to publish your local commits) Changes not staged for commit: (use "git add ..." to update what will be committed) (use "git checkout -- ..." to discard changes in working directory) modified: resources.org Untracked files: (use "git add ..." to include in what will be committed) ../../module2/ressources/replicable_article/IEEEtran.bst ../../module2/ressources/replicable_article/IEEEtran.cls ../../module2/ressources/replicable_article/article.bbl ../../module2/ressources/replicable_article/article.tex ../../module2/ressources/replicable_article/data.csv ../../module2/ressources/replicable_article/figure.pdf ../../module2/ressources/replicable_article/logo.png .#resources.org no changes added to commit (use "git add" and/or "git commit -a") ``` *Note: the -u indicates that git should also display the contents of new directories it did not previously know about.* Then, we often include commands at the end of our notebook indicating how to commit the results (adding the new files, committing with a clear message and pushing). E.g., ``` shell git add resources.org; git commit -m "Completing the section on getting Git information" git push ``` ``` example [master 514fe2c1 ] Completing the section on getting Git information 1 file changed, 61 insertions(+) Counting objects: 25, done. Delta compression using up to 4 threads. Compressing objects: 100% (20/20), done. Writing objects: 100% (25/25), 7.31 KiB | 499.00 KiB/s, done. Total 25 (delta 11), reused 0 (delta 0) To gitlab.inria.fr:learninglab/mooc-rr/mooc-rr-ressources.git 6359f8c..1f8a567 master -> master ``` Obviously, in this case you need to save the notebook before running this cell, hence the output of this final command (with the new git hash) will not be stored in the cell. This is not really a problem and is the price to pay for running git from within the notebook itself. # Getting information about Python(3) libraries ## Getting information about your system This topic is discussed on [StackOverflow](https://stackoverflow.com/questions/3103178/how-to-get-the-system-info-with-python). ``` python import platform print(platform.uname()) ``` ``` example uname_result(system='Linux', node='icarus', release='4.15.0-2-amd64', version='#1 SMP Debian 4.15.11-1 (2018-03-20)', machine='x86_64', processor='') ``` ## Getting the list of installed packages and their version This topic is discussed on [StackOverflow](https://stackoverflow.com/questions/20180543/how-to-check-version-of-python-modules). When using `pip` (the Python package installer) within a shell command, it is easy to query the version of all installed packages (note that on your system, you may have to use either `pip` or `pip3` depending on how it is named and which versions of Python are available on your machine Here is for example how I get this information on my machine: ``` shell pip3 freeze ``` ``` example asn1crypto==0.24.0 attrs==17.4.0 bcrypt==3.1.4 beautifulsoup4==4.6.0 bleach==2.1.3 ... pandas==0.22.0 pandocfilters==1.4.2 paramiko==2.4.0 patsy==0.5.0 pexpect==4.2.1 ... traitlets==4.3.2 tzlocal==1.5.1 urllib3==1.22 wcwidth==0.1.7 webencodings==0.5 ``` In a Jupyter notebook, this can easily be done by using the `%%sh` magic. Here is for example what you could do and get on the Jupyter notebooks we deployed for the MOOC (note that here, you should simply use the `pip` command): ``` python %%sh pip freeze ``` ``` example alembic==0.9.9 asn1crypto==0.24.0 attrs==18.1.0 Automat==0.0.0 ... numpy==1.13.3 olefile==0.45.1 packaging==17.1 pamela==0.3.0 pandas==0.22.0 ... webencodings==0.5 widgetsnbextension==3.2.1 xlrd==1.1.0 zope.interface==4.5.0 ``` In the rest of this document, I will assume the correct command is `pip` and I will not systematically insert the `%%sh` magic. Once you know which packages are installed, you can easily get additional information about a given package and in particular check whether it was installed "locally" through pip or whether it is installed system-wide. Again, in a shell command: ``` shell pip show pandas echo " " pip show statsmodels ``` ``` example Name: pandas Version: 0.22.0 Summary: Powerful data structures for data analysis, time series,and statistics Home-page: http://pandas.pydata.org Author: None Author-email: None License: BSD Location: /usr/lib/python3/dist-packages Requires: Name: statsmodels Version: 0.9.0 Summary: Statistical computations and models for Python Home-page: http://www.statsmodels.org/ Author: None Author-email: None License: BSD License Location: /home/alegrand/.local/lib/python3.6/site-packages Requires: patsy, pandas ``` ## How to list imported modules? Without resorting to pip (that will list all available packages), you may want to know which modules are loaded in a Python session as well as their version. Inspired by [StackOverflow](https://stackoverflow.com/questions/4858100/how-to-list-imported-modules), here is a simple function that lists loaded package (that have a `__version__` attribute, which is unfortunately not completely standard). ``` python def print_imported_modules(): import sys for name, val in sorted(sys.modules.items()): if(hasattr(val, '__version__')): print(val.__name__, val.__version__) else: print(val.__name__, "(unknown version)") print("**** Package list in the beginning ****"); print_imported_modules() print("**** Package list after loading pandas ****"); import pandas print_imported_modules() ``` ``` example **** Package list in the beginning **** **** Package list after loading pandas **** _csv 1.0 _ctypes 1.1.0 decimal 1.70 argparse 1.1 csv 1.0 ctypes 1.1.0 cycler 0.10.0 dateutil 2.7.3 decimal 1.70 distutils 3.6.5rc1 ipaddress 1.0 json 2.0.9 logging 0.5.1.2 matplotlib 2.1.1 numpy 1.14.5 numpy.core 1.14.5 numpy.core.multiarray 3.1 numpy.core.umath b'0.4.0' numpy.lib 1.14.5 numpy.linalg._umath_linalg b'0.1.5' pandas 0.22.0 _libjson 1.33 platform 1.0.8 pyparsing 2.2.0 pytz 2018.5 re 2.2.1 six 1.11.0 urllib.request 3.6 zlib 1.0 ``` ## Saving and restoring an environment with pip The easiest way to go is as follows: ``` shell pip3 freeze > requirements.txt # to obtain the list of packages with their version pip3 install -r requirements.txt # to install the previous list of packages, possibly on an other machine ``` If you want to have several installed Python environments, you may want to use [Pipenv](https://docs.pipenv.org/). I doubt it allows to track correctly FORTRAN or C dynamic libraries that are wrapped by Python though. ## Installing a new package or a specific version The Jupyter environment we deployed on our servers for the MOOC is based on the version 4.5.4 of Miniconda and Python 3.6. In this environment you should simply use the `pip` command (remember on your machine, you may have to use `pip3`). If I query the current version of `statsmodels` in a shell command, here is what I will get. ``` shell pip show statsmodels ``` ``` example Name: statsmodels Version: 0.8.0 Summary: Statistical computations and models for Python Home-page: http://www.statsmodels.org/ Author: Skipper Seabold, Josef Perktold Author-email: pystatsmodels@googlegroups.com License: BSD License Location: /opt/conda/lib/python3.6/site-packages Requires: scipy, patsy, pandas ``` I can then easily upgrade `statsmodels`: ``` shell pip install --upgrade statsmodels ``` Then the new version should then be: ``` shell pip show statsmodels ``` ``` example Name: statsmodels Version: 0.9.0 Summary: Statistical computations and models for Python Home-page: http://www.statsmodels.org/ Author: Skipper Seabold, Josef Perktold Author-email: pystatsmodels@googlegroups.com License: BSD License Location: /opt/conda/lib/python3.6/site-packages Requires: scipy, patsy, pandas ``` It is even possible to install a specific (possibly much older) version, e.g.,: ``` shell pip install statsmodels==0.6.1 ``` # Getting information about R libraries ## Getting the list imported modules and their version The best way seems to be to rely on the `devtools` package (if this package is not installed, you should install it first by running in `R` the command `install.packages("devtools")`). ``` r sessionInfo() devtools::session_info() ``` ``` example R version 3.5.1 (2018-07-02) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux buster/sid Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0 locale: [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8 [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8 [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached): [1] compiler_3.5.1 Session info ------------------------------------------------------------------ setting value version R version 3.5.1 (2018-07-02) system x86_64, linux-gnu ui X11 language (EN) collate fr_FR.UTF-8 tz Europe/Paris date 2018-08-01 Packages ---------------------------------------------------------------------- package * version date source base * 3.5.1 2018-07-02 local compiler 3.5.1 2018-07-02 local datasets * 3.5.1 2018-07-02 local devtools 1.13.6 2018-06-27 CRAN (R 3.5.1) digest 0.6.15 2018-01-28 CRAN (R 3.5.0) graphics * 3.5.1 2018-07-02 local grDevices * 3.5.1 2018-07-02 local memoise 1.1.0 2017-04-21 CRAN (R 3.5.1) methods * 3.5.1 2018-07-02 local stats * 3.5.1 2018-07-02 local utils * 3.5.1 2018-07-02 local withr 2.1.2 2018-03-15 CRAN (R 3.5.0) ``` Some actually advocate that [writing a reproducible research compendium is best done by writing an R package](https://github.com/ropensci/rrrpkg). Those of you willing to have a clean R dependency management should thus have a look at [Packrat](https://rstudio.github.io/packrat/). ## Getting the list of installed packages and their version Finally, it is good to know that there is a built-in R command (`installed.packages`) allowing to retrieve and list the details of all packages installed. ``` r head(installed.packages()) ``` | Package | LibPath | Version | Priority | Depends | Imports | LinkingTo | Suggests | Enhances | License | LicenseisFOSS | Licenserestrictsuse | OStype | MD5sum | NeedsCompilation | Built | | | ------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------- | ----------- | ---------------------------------------------------------- | -------------------------------------------------------------- | --------- | ------------- | ------------------------------------- | ------------------------------------ | ------------------------ | ------------------------------ | ----------------- | ------ | ---------------- | ----- | ----- | | BH | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.66.0-1 | nil | nil | nil | nil | nil | nil | BSL-1.0 | nil | nil | nil | nil | no | 3.5.1 | | | Formula | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.2-3 | nil | R (\>= 2.0.0), stats | nil | nil | nil | nil | GPL-2 | GPL-3 | nil | nil | nil | nil | no | 3.5.1 | | Hmisc | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 4.1-1 | nil | lattice, survival (\>= 2.40-1), Formula, ggplot2 (\>= 2.2) | methods, latticeExtra, cluster, rpart, nnet, acepack, foreign, | | | | | | | | | | | | | gtable, grid, gridExtra, data.table, htmlTable (\>= 1.11.0), | | | | | | | | | | | | | | | | | | viridis, htmltools, base64enc | nil | chron, rms, mice, tables, knitr, ff, ffbase, plotly (\>= | | | | | | | | | | | | | | | | 4.5.6) | nil | GPL (\>= 2) | nil | nil | nil | nil | yes | 3.5.1 | | | | | | | | | | Matrix | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.2-14 | recommended | R (\>= 3.2.0) | methods, graphics, grid, stats, utils, lattice | nil | expm, MASS | MatrixModels, graph, SparseM, sfsmisc | GPL (\>= 2) | file LICENCE | nil | nil | nil | nil | yes | 3.5.1 | | StanHeaders | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 2.17.2 | nil | nil | nil | nil | RcppEigen, BH | nil | BSD3clause + file LICENSE | nil | nil | nil | nil | yes | 3.5.1 | | | acepack | /home/alegrand/R/x8664-pc-linux-gnu-library/3.5 | 1.4.1 | nil | nil | nil | nil | testthat | nil | MIT + file LICENSE | nil | nil | nil | nil | yes | 3.5.1 | | ## Installing a new package or a specific version This section is mostly a cut and paste from the [recent post by Ian Pylvainen](https://support.rstudio.com/hc/en-us/articles/219949047-Installing-older-versions-of-packages) on this topic. It comprises a very clear explanation of how to proceed. ### Installing a pre-compiled version If you're on a Debian or a Ubuntu system, it may be difficult to access a specific version without breaking your system. So unless you are moving to the latest version available in your Linux distribution, **we strongly recommend you to build from source**. In this case, you'll need to make sure you have the necessary toolchain to build packages from source (e.g., gcc, FORTRAN, etc.). On Windows, this may require you to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/). If you're on Windows or OS X and looking for a package for an **older version of R** (R 2.1 or below), you can check the [CRAN binary archive](https://cran-archive.r-project.org/bin/). Once you have the URL, you can install it using a command similar to the example below: ``` r packageurl <- "https://cran-archive.r-project.org/bin/windows/contrib/2.13/BBmisc_1.0-58.zip" install.packages(packageurl, repos=NULL, type="binary") ``` ### Using devtools The simplest method to install the version you need is to use the `install_version()` function of the `devtools` package (obviously, you need to install `devtools` first, which can be done by running in `R` the command `install.packages("devtools")`). For instance: ``` r require(devtools) install_version("ggplot2", version = "0.9.1", repos = "http://cran.us.r-project.org") ``` ### Installing from source code Alternatively, you may want to install an older package from source If devtools fails or if you do not want to depend on it, you can install it from source via `install.packages()` directed using the right URL. This URL can be obtained by browsing the [CRAN Package Archive](https://cran.r-project.org/src/contrib/Archive). Once you have the URL, you can install it using a command similar to the example below: ``` r packageurl <- "http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz" install.packages(packageurl, repos=NULL, type="source") ``` If you know the URL, you can also install from source via the command line outside of R. For instance (in bash): ``` shell wget http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz R CMD INSTALL ggplot2_0.9.1.tar.gz ``` ### Potential issues There are a few potential issues that may arise with installing older versions of packages: - You may be losing functionality or bug fixes that are only present in the newer versions of the packages. - The older package version needed may not be compatible with the version of R you have installed. In this case, you will either need to downgrade R to a compatible version or update your R code to work with a newer version of the package.