scripts - Command line tools¶
Warning
The Gammapy command line interface (CLI) described here is experimental and only supports a small sub-set of the functionality available via the Gammapy Python package.
Introduction¶
Currently, Gammapy is first and foremost a Python package. This means that to use it you have to write a Python script or Jupyter notebook, where you import the functions and classes needed for a given analysis, and then call them, passing parameters to configure the analysis.
We have also have a analysis - High level interface that provides high level Python functions for the most common needs present in the analysis process.
That said, for some very commonly used and easy to configure analysis tasks we have implemented a command line interface (CLI). It is automatically installed together with the Gammapy python package.
Execute¶
To execute the Gammapy CLI, type the command gammapy at your terminal shell
(not in Python):
$ gammapy --help
or equivalently, just type this:
$ gammapy
Either way, the command should print some help text to the console and then exit:
Usage: gammapy [OPTIONS] COMMAND [ARGS]...
Gammapy command line interface (CLI).
Gammapy is a Python package for gamma-ray astronomy.
Use ``--help`` to see available sub-commands, as well as the available
arguments and options for each sub-command.
For further information, see https://gammapy.org/ and
https://docs.gammapy.org/
Examples
--------
$ gammapy --help
$ gammapy --version
$ gammapy info --help
$ gammapy info
Options:
--log-level [debug|info|warning|error]
Logging verbosity level.
--ignore-warnings Ignore warnings?
--version Print version and exit.
-h, --help Show this message and exit.
Commands:
analysis Automation of configuration driven data reduction process.
check Run checks for Gammapy
download Download datasets and notebooks
info Display information about Gammapy
jupyter Perform actions on notebooks
All CLI functionality for Gammapy is implemented as sub-commands of the main
gammapy command. If a command has sub-commands, they are listed in the help
output. E.g. the help output from gammapy above shows that there is a
sub-command called gammapy analysis. Actually, gammapy analysis itself isn’t a
command that does something, but another command group that is used to group
sub-commands.
So now you know how the Gammapy CLI is structured and how to discover all available sub-commands, arguments and options.
Running config driven data reduction¶
Here’s the main usage of the Gammapy CLI for data processing: use the gammapy analysis
command to first create a default configuration file with default values and then
perform a simple automated data reduction process (i.e. fetching observations from
a datastore and producing the reduced datasets.)
$ gammapy analysis --help
Usage: gammapy analysis [OPTIONS] COMMAND [ARGS]...
Automation of configuration driven data reduction process.
Examples
--------
$ gammapy analysis config
$ gammapy analysis run
$ gammapy analysis config --overwrite
$ gammapy analysis config --filename myconfig.yaml
$ gammapy analysis run --filename myconfig.yaml
Options:
-h, --help Show this message and exit.
Commands:
config Writes default configuration file.
run Performs automated data reduction process.
$ gammapy analysis config
INFO:gammapy.scripts.analysis:Configuration file produced: config.yaml
You can manually edit this produced configuration file and the run the data reduction process:
$ gammapy analysis run
INFO:gammapy.analysis.config:Setting logging config: {'level': 'INFO', 'filename': None, 'filemode': None, 'format': None, 'datefmt': None}
INFO:gammapy.analysis.core:Fetching observations.
INFO:gammapy.analysis.core:Number of selected observations: 4
INFO:gammapy.analysis.core:Reducing spectrum datasets.
INFO:gammapy.analysis.core:Processing observation 23592
INFO:gammapy.analysis.core:Processing observation 23523
INFO:gammapy.analysis.core:Processing observation 23526
INFO:gammapy.analysis.core:Processing observation 23559
Datasets stored in datasets folder.
Write your own CLI¶
This section explains how to write your own command line interface (CLI).
We will focus on the command line aspect, and use a very simple example where we
just call gammapy.stats.CashCountsStatistics.sqrt_ts.
From the interactive Python or IPython prompt or from a Jupyter notebook you just import the functionality you need and call it, like this:
>>> from gammapy.stats import CashCountsStatistic
>>> CashCountsStatistic(n_on=10, mu_bkg=4.2).sqrt_ts
2.397918129147546
If you imagine that the actual computation involves many lines of code (and not just a one-line function call), and that you need to do this computation frequently, you will probably write a Python script that looks something like this:
# Compute significance for a Poisson count observation
from gammapy.stats import CashCountsStatistic
n_observed = 10
mu_background = 4.2
s = CashCountsStatistic(n_observed, mu_background).sqrt_ts
print(s)
2.397918129147546
We have introduced variables that hold the parameters for the analysis and put
them before the computation. Let’s say this script is in a file called
significance.py, then to use it you put the parameters you like and then
execute it via:
$ python significance.py
If you want, you can also put the line #!/usr/bin/env python at the top of
the script, make it executable via chmod +x significance.py and then you’ll
be able to execute it via ./significance.py if you prefer to execute it like
this. This works on Linux and Mac OS, but not on Windows. It is also possible to
omit the .py extension from the filename, i.e. to simply call the file
significance. Either way has some advantages and disadvantages, it’s a
matter of taste. Omitting the .py is nice because users calling the tool
usually don’t care that it’s a Python script, and it’s shorter. But omitting the
.py also means that some advanced users that open up the file in an editor
have a harder time (because the editor might not recognise it as a Python file
and syntax highlight appropriately), or more importantly that importing
functions of classes from that script from other Python files or Jupyter
notebooks is not easily possible, leading some people to rename it or copy &
paste from it. We’re explaining these details, because if you work with
colleagues and share scripts, you’ll encounter the #!/usr/bin/env python and
scripts with and without .py and will need to know how to work with them.
Writing and using such scripts is perfectly fine and a common way to run science
analyses. However, if you use it very frequently it might become annoying to
have to open up and edit the significance.py file every time to use it. In
that case, you can change your script into a command line interface that allows
you to set analysis parameters without having to edit the file, like this:
$ python significance.py --help
Usage: significance.py [OPTIONS] N_OBSERVED MU_BACKGROUND
Compute significance for a Poisson count observation.
The significance is the tail probability to observe N_OBSERVED counts or
more, given a known background level MU_BACKGROUND.
Options:
--value [sqrt_ts|p_value] Square root TS or p_value
--help Show this message and exit.
$ python significance.py 10 4.2
2.39791813
$ python significance.py 10 4.2 --value p_value
0.01648855015875024
In Python, there are several ways to do command line argument parsing and to
create command line interfaces. Of course you’re free to do whatever you like,
but if you’re not sure what to use to build your own CLIs, we suggest you give
click a try. Here is how you’d rewrite your significance.py as a click
CLI:
"""Example how to write a command line tool with Click"""
import click
from gammapy.stats import CashCountsStatistic
# You can call the callback function for the click command anything you like.
# `cli` is just a commonly used generic term for "command line interface".
@click.command()
@click.argument("n_observed", type=float)
@click.argument("mu_background", type=float)
@click.option(
"--value",
type=click.Choice(["sqrt_ts", "p_value"]),
default="sqrt_ts",
help="Significance or p_value",
)
def cli(n_observed, mu_background, value):
"""Compute significance for a Poisson count observation.
The significance is the tail probability to observe N_OBSERVED counts
or more, given a known background level MU_BACKGROUND."""
stat = CashCountsStatistic(n_observed, mu_background)
if value == "sqrt_ts":
s = stat.sqrt_ts
else:
s = stat.p_value
print(s)
if __name__ == "__main__":
cli()
We use click in Gammapy itself. We also use click frequently for our own
projects if we choose to add a CLI (no matter if Gammapy is used or not). Putting
the CLI in a file called make.py makes it easy to go back to a project after
a while and to remember or quickly figure out again how it works (as opposed to
just having a bunch of Python scripts or Jupyter notebooks where it’s harder to
remember where to edit parameters and which ones to run in which order). One example
is the gamma-cat make.py.
If you find that you don’t like click, another popular alternative to create
CLIs is argparse from the Python standard library. To learn argparse, either
read the official documentation, or the PYMOTW argparse tutorial. For basic
use cases argparse is similar to click, the main difference being that
click uses decorators (@command, @argument, @option) attached to
a callback function to execute, whereas argparse uses classes and method
calls to create a parser object, and then you have to call parse_args
yourself and also pass the args to the code or function to execute yourself.
So for basic use cases, but also for more advanced use cases where you define a
CLI with sub-commands, argparse can be used just as well, it’s just a little
harder to learn and use than click (of course that’s a matter of opinion).
Another advantage of choosing Click is that once you’ve learned it, you’ll be
able to quickly read and understand, or even contribute to the Gammapy CLI.
Troubleshooting¶
Command not found¶
Usually tools that install Gammapy (e.g. setuptools via python setup.py
install or pip or package managers like conda) will put the
gammapy command line tool in a directory that is on your PATH, and if
you type gammapy the command is found and executed.
However, due to the large number of supported systems (Linux, Mac OS, Windows)
and different ways to install Python packages like Gammapy (e.g. system install,
user install, virtual environments, conda environments) and environments to
launch command line tools like gammapy (e.g. bash, csh, Windows command
prompt, Jupyter, …) it is not unheard of that users have trouble running
gammapy after installing it.
This usually looks like this:
$ gammapy
-bash: gammapy: command not found
If you just installed Gammapy, search the install log for the message
“Installing gammapy script” to see where gammapy was installed, and check
that this location is on your PATH:
echo $PATH
If you don’t manage to figure out where the gammapy command line tool is
installed, you can try calling it like this instead:
$ python -m gammapy
This also has the advantage that it avoids issues where users have multiple
versions of Python and Gammapy installed and accidentally launch one they don’t
want because it comes first on their PATH. For the same reason these days
the recommended way to use e.g. pip is via python -m pip.
If this still doesn’t work, check if you are using the right Python and have Gammapy installed:
$ which python
$ python -c 'import gammapy'
To see more information about your shell environment, these commands might be helpful:
$ python -m site
$ python -m gammapy info
$ echo $PATH
$ conda info -a # if you're using conda
If you’re still stuck or have any question, feel free to ask for help with installation issues on the Gammapy mailing list of Slack any time!
Reference¶
Here is auto-generated documentation for all available sub-commands, arguments
and options of the gammapy command line interface (CLI).
gammapy¶
Gammapy command line interface (CLI).
Gammapy is a Python package for gamma-ray astronomy.
Use --help to see available sub-commands, as well as the available
arguments and options for each sub-command.
For further information, see https://gammapy.org/ and https://docs.gammapy.org/
gammapy [OPTIONS] COMMAND [ARGS]...
Options
-
--log-level<log_level>¶ Logging verbosity level.
- Options
debug | info | warning | error
-
--ignore-warnings¶ Ignore warnings?
-
--version¶ Print version and exit.
analysis¶
Automation of configuration driven data reduction process.
gammapy analysis [OPTIONS] COMMAND [ARGS]...
download¶
Download notebooks and datasets.
notebooks will download the notebook files into a gammapy-notebooksdatasets will download the datasets used in the documentation into agammapy-datasets folder created at the current working directory.gammapy download [OPTIONS] COMMAND [ARGS]...
info¶
Display information about Gammapy
gammapy info [OPTIONS]
Options
-
--system,--no-system¶ Show system info
-
--version,--no-version¶ Show version
-
--dependencies,--no-dependencies¶ Show dependencies
-
--envvar,--no-envvar¶ Show environment variables
jupyter¶
Perform a series of actions on Jupyter notebooks.
The chosen action is applied by default for every Jupyter notebook present in the current working directory.
gammapy jupyter [OPTIONS] COMMAND [ARGS]...
Options
-
--src<src>¶ Local folder or Jupyter notebook filename.
-
--r¶ Apply to notebooks found recursively in folder.
run¶
Execute Jupyter notebooks.
gammapy jupyter run [OPTIONS]
Options
-
--tutor¶ Tutorials environment?
- Default
False
-
--kernel<kernel>¶ Kernel name
- Default
python3