Is there a way to group multiple commands, each with their own different parameters under a single function.
At first glance, a MultiCommand or Group might seem like a natural way of doing what I'd like, i.e. have a single main command act as the Group (and pass the invoke_without_command=True flag) then nest auxiliary Commands beneath it as subcommands. However this doesn't quite have the behavior that I'd want, since I'd like all the options from all commands to be able to be specified without explicitly invoking a subcommand. Additionally, using a Group would also not display the help text of the subcommands without invoking the subcommand on the command line as well.
I guess what I'd ideally like to have is a way to group multiple commands together without the nesting inherent to Click's Group API.
Sorry if this question might be somewhat general. Any help, ideas or tips that can point me in the right direction would be much appreciated.
Here's an outline of what I'd like (file name: cli_test.py):
import click
#click.command()
#click.option('--db-file', type=click.File(mode='r'))
def db_reader(db_file):
click.echo(db_file)
#click.command()
#click.option('--xval', type=float)
#click.option('--yval', type=float)
def get_vals(xval, yval):
return xval, yval
#click.command()
#click.option('--absolutize/--no-absolutize')
def flagger(absolutize):
click.echo(absolutize)
#click.command()
def cli_runner():
db = db_reader.main(standalone_mode=False)
vals = flagger.main(standalone_mode=False)
flag = flagger.main(standalone_mode=False)
if __name__ == '__main__':
cli_runner()
Essentially, I'd like a single command that can be run on the CLI (cli_runner in the above example), which would be able to take the parameters of all Click commands called within it, and then dispatch the processing of them to the appropriate Command instance. However as it stands right now, if I were to invoke on the CLI: $ python cli_test.py --xval 4 I'd get the error Error: no such option: --xval. I have also tried playing around with the pass_context and then ctx.invoke() approach, but the same problem exists.
I suppose I could pass parameters from all contained commands into cli_runner, but that would defeat the purpose of what I want to do, which is to ultimately have 3-4 modular "subcommands", that can then be grouped together in different combinations into larger interfaces that serve slightly different use cases.
My robot keyword looks like this:
${HW_list} Get_hw_list ${file}
Run process python python_test.py
Inside my python_test.py
from robot.libraries.BuiltIn import BuiltIn
List_of_modules = BuiltIn().get_variable_value("${HW_list}")
Im having an error saying,
robot.libraries.BuiltIn.RobotNotRunningError: Cannot access execution
context
I've tried searching for similar issues but I can't find where I am wrong. I also have RF==3.1.2 since in 1 post, I think there was an issue that was fixed on this version.
Run process will run your module in a separate interpreter. That's why it cannot find the execution context.
Instead, do the following:
Make a custom keyword from your module code:
from robot.libraries.BuiltIn import BuiltIn
def my_custom_keyword():
List_of_modules = BuiltIn().get_variable_value("${HW_list}")
Import the module as a Library in your robot code:
*** Settings ***
Library python_test.py
Use the keyword in your test, instead of Run process:
${HW_list} Get_hw_list ${file}
My Custom Keyword
Since you are running python_test.py as a separate process, you can't directly use robot variables or keywords in the separate process.
If you don't want to pass the value as arguments, you're going to have to use some other method. For example, you could set an environment variable and have your script pick the data up from the environment. This can only be used to pass strings.
Another option would be for your robot script to write the data to a file or database, and have your script read that file or database to get the value.
I'm running a python script from inside a different software (it provides a python interface to manipulate its data structures).
I'm optimizing my code for speed and would like to see what impact on performance my asserts have.
I'm unable to use python -O. What other options do I have, to programatically disable all asserts in python code? The variable __debug__ (which is cleared by -O flag) cannot be assigned to :(
The docs say,
The value for the built-in variable [__debug__] is determined when the
interpreter starts.
So, if you can not control how the python interpreter is started, then it looks like you can not disable assert.
Here then are some other options:
The safest way is to manually remove all the assert statements.
If all your assert statements occur on lines by themselves, then
perhaps you could remove them with
sed -i 's/assert /pass #assert /g' script.py
Note that this will mangle your code if other code comes after the assert. For example, the sed command above would comment-out the return in a line like this:
assert x; return True
which would change the logic of your program.
If you have code like this, it would probably be best to manually remove the asserts.
There might be a way to remove them programmatically by parsing your
script with the tokenize module, but writing such a program to
remove asserts may take more time than it would take to manually
remove the asserts, especially if this is a one-time job.
If the other piece of software accepts .pyc files, then there is a
dirty trick which seems to work on my machine, though note a Python
core developer warns against this (See Éric Araujo's comment on 2011-09-17). Suppose your script is called script.py.
Make a temporary script called, say, temp.py:
import script
Run python -O temp.py. This creates script.pyo.
Move script.py and script.pyc (if it exists) out of your PYTHONPATH
or whatever directory the other software is reading to find your
script.
Rename script.pyo --> script.pyc.
Now when the other software tries to import your script, it will
only find the pyc file, which has the asserts removed.
For example, if script.py looks like this:
assert False
print('Got here')
then running python temp.py will now print Got here instead of raising an AssertionError.
You may be able to do this with an environment variable, as described in this other answer. Setting PYTHONOPTIMIZE=1 is equivalent to starting Python with the -O option. As an example, this works in Blender 2.78, which embeds Python 3.5:
blender --python-expr 'assert False; print("foo")'
PYTHONOPTIMIZE=1 blender --python-expr 'assert False; print("foo")'
The first command prints a traceback, while the second just prints "foo".
As #unutbu describes, there is no official way of doing this. However, a simple strategy is to define a flag like _test somewhere (for example, as keyword argument to a function, or as a global variable in a module), then include this in your assert statements as follows:
def f(x, _test=True):
assert not _test or x > 0
...
Then you can disable asserts in that function if needed.
f(x, _test=False)
I am working on a python Command-Line-Interface program, and I find it boring when doing testings, for example, here is the help information of the program:
usage: pyconv [-h] [-f ENCODING] [-t ENCODING] [-o file_path] file_path
Convert text file from one encoding to another.
positional arguments:
file_path
optional arguments:
-h, --help show this help message and exit
-f ENCODING, --from ENCODING
Encoding of source file
-t ENCODING, --to ENCODING
Encoding you want
-o file_path, --output file_path
Output file path
When I made changes on the program and want to test something, I must open a terminal,
type the command(with options and arguments), type enter, and see if any error occurs
while running. If error really occurs, I must go back to the editor and check the code
from top to end, guessing where the bug positions, make small changes, write print lines,
return to the terminal, run command again...
Recursively.
So my question is, what is the best way to do testing with CLI program, can it be as easy
as unit testing with normal python scripts?
I think it's perfectly fine to test functionally on a whole-program level. It's still possible to test one aspect/option per test. This way you can be sure that the program really works as a whole. Writing unit-tests usually means that you get to execute your tests quicker and that failures are usually easier to interpret/understand. But unit-tests are typically more tied to the program structure, requiring more refactoring effort when you internally change things.
Anyway, using py.test, here is a little example for testing a latin1 to utf8 conversion for pyconv::
# content of test_pyconv.py
import pytest
# we reuse a bit of pytest's own testing machinery, this should eventually come
# from a separatedly installable pytest-cli plugin.
pytest_plugins = ["pytester"]
#pytest.fixture
def run(testdir):
def do_run(*args):
args = ["pyconv"] + list(args)
return testdir._run(*args)
return do_run
def test_pyconv_latin1_to_utf8(tmpdir, run):
input = tmpdir.join("example.txt")
content = unicode("\xc3\xa4\xc3\xb6", "latin1")
with input.open("wb") as f:
f.write(content.encode("latin1"))
output = tmpdir.join("example.txt.utf8")
result = run("-flatin1", "-tutf8", input, "-o", output)
assert result.ret == 0
with output.open("rb") as f:
newcontent = f.read()
assert content.encode("utf8") == newcontent
After installing pytest ("pip install pytest") you can run it like this::
$ py.test test_pyconv.py
=========================== test session starts ============================
platform linux2 -- Python 2.7.3 -- pytest-2.4.5dev1
collected 1 items
test_pyconv.py .
========================= 1 passed in 0.40 seconds =========================
The example reuses some internal machinery of pytest's own testing by leveraging pytest's fixture mechanism, see http://pytest.org/latest/fixture.html. If you forget about the details for a moment, you can just work from the fact that "run" and "tmpdir" are provided for helping you to prepare and run tests. If you want to play, you can try to insert a failing assert-statement or simply "assert 0" and then look at the traceback or issue "py.test --pdb" to enter a python prompt.
Start from the user interface with functional tests and work down towards unit tests. It can feel difficult, especially when you use the argparse module or the click package, which take control of the application entry point.
The cli-test-helpers Python package has examples and helper functions (context managers) for a holistic approach on writing tests for your CLI. It's a simple idea, and one that works perfectly with TDD:
Start with functional tests (to ensure your user interface definition) and
Work towards unit tests (to ensure your implementation contracts)
Functional tests
NOTE: I assume you develop code that is deployed with a setup.py file or is run as a module (-m).
Is the entrypoint script installed? (tests the configuration in your setup.py)
Can this package be run as a Python module? (i.e. without having to be installed)
Is command XYZ available? etc. Cover your entire CLI usage here!
Those tests are simplistic: They run the shell command you would enter in the terminal, e.g.
def test_entrypoint():
exit_status = os.system('foobar --help')
assert exit_status == 0
Note the trick to use a non-destructive operation (e.g. --help or --version) as we can't mock anything with this approach.
Towards unit tests
To test single aspects inside the application you will need to mimic things like command line arguments and maybe environment variables. You will also need to catch the exiting of your script to avoid the tests to fail for SystemExit exceptions.
Example with ArgvContext to mimic command line arguments:
#patch('foobar.command.baz')
def test_cli_command(mock_command):
"""Is the correct code called when invoked via the CLI?"""
with ArgvContext('foobar', 'baz'), pytest.raises(SystemExit):
foobar.cli.main()
assert mock_command.called
Note that we mock the function that we want our CLI framework (click in this example) to call, and that we catch SystemExit that the framework naturally raises. The context managers are provided by cli-test-helpers and pytest.
Unit tests
The rest is business as usual. With the above two strategies we've overcome the control a CLI framework may have taken away from us. The rest is usual unit testing. TDD-style hopefully.
Disclosure: I am the author of the cli-test-helpers Python package.
So my question is, what is the best way to do testing with CLI program, can it be as easy as unit testing with normal python scripts?
The only difference is that when you run Python module as a script, its __name__ attribute is set to '__main__'. So generally, if you intend to run your script from command line it should have following form:
import sys
# function and class definitions, etc.
# ...
def foo(arg):
pass
def main():
"""Entry point to the script"""
# Do parsing of command line arguments and other stuff here. And then
# make calls to whatever functions and classes that are defined in your
# module. For example:
foo(sys.argv[1])
if __name__ == '__main__':
main()
Now there is no difference, how you would use it: as a script or as a module. So inside your unit-testing code you can just import foo function, call it and make any assertions you want.
Maybe too little too late,
but you can always use
import os.system
result = os.system(<'Insert your command with options here'>
assert(0 == result)
In that way, you can run your program as if it was from command line, and evaluate the exit code.
(Update after I studied pytest)
You can also use capsys.
(from running pytest --fixtures)
capsys
Enable text capturing of writes to sys.stdout and sys.stderr.
The captured output is made available via ``capsys.readouterr()`` method
calls, which return a ``(out, err)`` namedtuple.
``out`` and ``err`` will be ``text`` objects.
This isn't for Python specifically, but what I do to test command-line scripts is to run them with various predetermined inputs and options and store the correct output in a file. Then, to test them when I make changes, I simply run the new script and pipe the output into diff correct_output -. If the files are the same, it outputs nothing. If they're different, it shows you where. This will only work if you are on Linux or OS X; on Windows, you will have to get MSYS.
Example:
python mycliprogram --someoption "some input" | diff correct_output -
To make it even easier, you can add all these test runs to your 'make test' Makefile target, which I assume you already have. ;)
If you are running many of these at once, you could make it a little more obvious where each one ends by adding a fail tag:
python mycliprogram --someoption "some input" | diff correct_output - || tput setaf 1 && echo "FAILED"
The short answer is yes, you can use unit tests, and should. If your code is well structured, it should be quite easy to test each component separately, and if you need to to can always mock sys.argv to simulate running it with different arguments.
pytest-console-scripts is a Pytest plugin for testing python scripts installed via console_scripts entry point of setup.py.
For Python 3.5+, you can use the simpler subprocess.run to call your CLI command from your test.
Using pytest:
import subprocess
def test_command__works_properly():
try:
result = subprocess.run(['command', '--argument', 'value'], check=True, capture_output=True, text=True)
except subprocess.CalledProcessError as error:
print(error.stdout)
print(error.stderr)
raise error
The output can be accessed via result.stdout, result.stderr, and result.returncode if needed.
The check parameter causes an exception to be raised if an error occurs. Note Python 3.7+ is required for the capture_output and text parameters, which simplify capturing and reading stdout/stderr.
Given that you are explicitly asking about testing for a command line application, I believe that you are aware of unit-testing tools in python and that you are actually looking for a tool to automate end-to-end tests of a command line tool. There are a couple of tools out there that are specifically designed for that. If you are looking for something that's pip-installable, I would recommend cram. It integrates well with the rest of the python environment (e.g. through a pytest extension) and it's quite easy to use:
Simply write the commands you want to run prepended with $ and the expected output prepended with . For example, the following would be a valid cram test:
$ echo Hello
Hello
By having four spaces in front of expected output and two in front of the test, you can actually use these tests to also write documentation. More on that on the website.
You can use standard unittest module:
# python -m unittest <test module>
or use nose as a testing framework. Just write classic unittest files in separate directory and run:
# nosetests <test modules directory>
Writing unittests is easy. Just follow online manual for unittesting
I would not test the program as a whole this is not a good test strategy and may not actually catch the actual spot of the error. The CLI interface is just front end to an API. You test the API via your unit tests and then when you make a change to a specific part you have a test case to exercise that change.
So, restructure your application so that you test the API and not the application it self. But, you can have a functional test that actually does run the full application and checks that the output is correct.
In short, yes testing the code is the same as testing any other code, but you must test the individual parts rather than their combination as a whole to ensure that your changes do not break everything.
I'm developing a management script that does a fairly large amount of work via a plethora of command-line options. The first few iterations of the script have used optparse to collect user input and then just run down the page, testing the value of each option in the appropriate order, and doing the action if necessary. This has resulted in a jungle of code that's really hard to read and maintain.
I'm looking for something better.
My hope is to have a system where I can write functions in more or less normal python fashion, and then when the script is run, have options (and help text) generated from my functions, parsed, and executed in the appropriate order. Additionally, I'd REALLY like to be able to build django-style sub-command interfaces, where myscript.py install works completely separately from myscript.py remove (separate options, help, etc.)
I've found simon willison's optfunc and it does a lot of this, but seems to just miss the mark — I want to write each OPTION as a function, rather than try to compress the whole option set into a huge string of options.
I imagine an architecture involving a set of classes for major functions, and each defined method of the class corresponding to a particular option in the command line. This structure provides the advantage of having each option reside near the functional code it modifies, easing maintenance. The thing I don't know quite how to deal with is the ordering of the commands, since the ordering of class methods is not deterministic.
Before I go reinventing the wheel: Are there any other existing bits of code that behave similarly? Other things that would be easy to modify? Asking the question has clarified my own thinking on what would be nice, but feedback on why this is a terrible idea, or how it should work would be welcome.
Don't waste time on "introspection".
Each "Command" or "Option" is an object with two sets of method functions or attributes.
Provide setup information to optparse.
Actually do the work.
Here's the superclass for all commands
class Command( object ):
name= "name"
def setup_opts( self, parser ):
"""Add any options to the parser that this command needs."""
pass
def execute( self, context, options, args ):
"""Execute the command in some application context with some options and args."""
raise NotImplemented
You create sublcasses for Install and Remove and every other command you need.
Your overall application looks something like this.
commands = [
Install(),
Remove(),
]
def main():
parser= optparse.OptionParser()
for c in commands:
c.setup_opts( parser )
options, args = parser.parse()
command= None
for c in commands:
if c.name.startswith(args[0].lower()):
command= c
break
if command:
status= command.execute( context, options, args[1:] )
else:
logger.error( "Command %r is unknown", args[0] )
status= 2
sys.exit( status )
The WSGI library werkzeug provides Management Script Utilities which may do what you want, or at least give you a hint how to do the introspection yourself.
from werkzeug import script
# actions go here
def action_test():
"sample with no args"
pass
def action_foo(name=2, value="test"):
"do some foo"
pass
if __name__ == '__main__':
script.run()
Which will generate the following help message:
$ python /tmp/test.py --help
usage: test.py <action> [<options>]
test.py --help
actions:
foo:
do some foo
--name integer 2
--value string test
test:
sample with no args
An action is a function in the same module starting with "action_" which takes a number of arguments where every argument has a default. The type of the default value specifies the type of the argument.
Arguments can then be passed by position or using --name=value from the shell.