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
git log -1
commit 741b0088af5b40588493c23c46d6bab5d0adeb33
Author: Arnaud Legrand <arnaud.legrand@imag.fr>
Date: Tue Sep 4 12:45:43 2018 +0200
Fix a few typos and provide information on jupyter-git plugins.
git status -u
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 <file>..." to update what will be committed) (use "git checkout -- <file>..." to discard changes in working directory) modified: resources.org Untracked files: (use "git add <file>..." 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.,
git add resources.org;
git commit -m "Completing the section on getting Git information"
git push
[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 ssh://app-learninglab.inria.fr:9418/learning-lab/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.
This topic is discussed on StackOverflow.
import platform print(platform.uname())
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='')
This topic is discussed on StackOverflow. 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:
pip3 freeze
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):
%%sh pip freeze
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:
pip show pandas echo " " pip show statsmodels
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
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, here is a simple
function that lists loaded package (that have a __version__ attribute,
which is unfortunately not completely standard).
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()
**** 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
The easiest way to go is as follows:
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. I doubt it allows to track correctly FORTRAN or C dynamic libraries that are wrapped by Python though.
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.
pip show statsmodels
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:
pip install --upgrade statsmodels
Then the new version should then be:
pip show statsmodels
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.,:
pip install statsmodels==0.6.1
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")).
sessionInfo() devtools::session_info()
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. Those of you willing to have a clean R dependency management should thus have a look at Packrat.
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.
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 |
This section is mostly a cut and paste from the recent post by Ian Pylvainen on this topic. It comprises a very clear explanation of how to proceed.
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.
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. Once you have the URL, you can install it using a command similar to the example below:
packageurl <- "https://cran-archive.r-project.org/bin/windows/contrib/2.13/BBmisc_1.0-58.zip" install.packages(packageurl, repos=NULL, type="binary")
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:
require(devtools) install_version("ggplot2", version = "0.9.1", repos = "http://cran.us.r-project.org")
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.
Once you have the URL, you can install it using a command similar to the example below:
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):
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
There are a few potential issues that may arise with installing older versions of packages: