I am trying to go from Python2 to Python3. So the first step is to program forward compatible. Thus I use the from __future__ and the from builtins imports. However this breaks slots.
While
class _Test(object):
__slots__ = ('a', )
test = _Test()
test.b = 1
raises an AttributeError as expected
from builtins import object
class _Test(object):
__slots__ = ('a', )
test = _Test()
test.b = 1
just runs. Now test.__dict__ exists. So how can I use slots in Python3?
Related
I'm working on a documentation (personal) for nested matplotlib (MPL) library, which differs from MPL own provided, by interested submodule packages. I'm writing Python script which I hope will automate document generation from future MPL releases.
I selected interested submodules/packages and want to list their main classes from which I'll generate list and process it with pydoc
Problem is that I can't find a way to instruct Python to load submodule from string. Here is example of what I tried:
import matplotlib.text as text
x = dir(text)
.
i = __import__('matplotlib.text')
y = dir(i)
.
j = __import__('matplotlib')
z = dir(j)
And here is 3 way comparison of above lists through pprint:
I don't understand what's loaded in y object - it's base matplotlib plus something else, but it lack information that I wanted and that is main classes from matplotlib.text package. It's top blue coloured part on screenshot (x list)
Please don't suggest Sphinx as different approach.
The __import__ function can be a bit hard to understand.
If you change
i = __import__('matplotlib.text')
to
i = __import__('matplotlib.text', fromlist=[''])
then i will refer to matplotlib.text.
In Python 3.1 or later, you can use importlib:
import importlib
i = importlib.import_module("matplotlib.text")
Some notes
If you're trying to import something from a sub-folder e.g. ./feature/email.py, the code will look like importlib.import_module("feature.email")
Before Python 3.3 you could not import anything if there was no __init__.py in the folder with file you were trying to import (see caveats before deciding if you want to keep the file for backward compatibility e.g. with pytest).
importlib.import_module is what you are looking for. It returns the imported module.
import importlib
# equiv. of your `import matplotlib.text as text`
text = importlib.import_module('matplotlib.text')
You can thereafter access anything in the module as text.myclass, text.myfunction, etc.
spent some time trying to import modules from a list, and this is the thread that got me most of the way there - but I didnt grasp the use of ___import____ -
so here's how to import a module from a string, and get the same behavior as just import. And try/except the error case, too. :)
pipmodules = ['pycurl', 'ansible', 'bad_module_no_beer']
for module in pipmodules:
try:
# because we want to import using a variable, do it this way
module_obj = __import__(module)
# create a global object containging our module
globals()[module] = module_obj
except ImportError:
sys.stderr.write("ERROR: missing python module: " + module + "\n")
sys.exit(1)
and yes, for python 2.7> you have other options - but for 2.6<, this works.
Apart from using the importlib one can also use exec method to import a module from a string variable.
Here I am showing an example of importing the combinations method from itertools package using the exec method:
MODULES = [
['itertools','combinations'],
]
for ITEM in MODULES:
import_str = "from {0} import {1}".format(ITEM[0],', '.join(str(i) for i in ITEM[1:]))
exec(import_str)
ar = list(combinations([1, 2, 3, 4], 2))
for elements in ar:
print(elements)
Output:
(1, 2)
(1, 3)
(1, 4)
(2, 3)
(2, 4)
(3, 4)
Module auto-install & import from list
Below script works fine with both submodules and pseudo submodules.
# PyPI imports
import pkg_resources, subprocess, sys
modules = {'lxml.etree', 'pandas', 'screeninfo'}
required = {m.split('.')[0] for m in modules}
installed = {pkg.key for pkg in pkg_resources.working_set}
missing = required - installed
if missing:
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--upgrade', 'pip'])
subprocess.check_call([sys.executable, '-m', 'pip', 'install', *missing])
for module in set.union(required, modules):
globals()[module] = __import__(module)
Tests:
print(pandas.__version__)
print(lxml.etree.LXML_VERSION)
I developed these 3 useful functions:
def loadModule(moduleName):
module = None
try:
import sys
del sys.modules[moduleName]
except BaseException as err:
pass
try:
import importlib
module = importlib.import_module(moduleName)
except BaseException as err:
serr = str(err)
print("Error to load the module '" + moduleName + "': " + serr)
return module
def reloadModule(moduleName):
module = loadModule(moduleName)
moduleName, modulePath = str(module).replace("' from '", "||").replace("<module '", '').replace("'>", '').split("||")
if (modulePath.endswith(".pyc")):
import os
os.remove(modulePath)
module = loadModule(moduleName)
return module
def getInstance(moduleName, param1, param2, param3):
module = reloadModule(moduleName)
instance = eval("module." + moduleName + "(param1, param2, param3)")
return instance
And everytime I want to reload a new instance I just have to call getInstance() like this:
myInstance = getInstance("MyModule", myParam1, myParam2, myParam3)
Finally I can call all the functions inside the new Instance:
myInstance.aFunction()
The only specificity here is to customize the params list (param1, param2, param3) of your instance.
You can also use exec built-in function that execute any string as a Python code.
In [1]: module = 'pandas'
...: function = 'DataFrame'
...: alias = 'DF'
In [2]: exec(f"from {module} import {function} as {alias}")
In [3]: DF
Out[3]: pandas.core.frame.DataFrame
For me this was the most readable way to solve my problem.
I am using multiprocessing.Pool to speed up computation, as I call one function multiple times, and then collate the result. Here is a snippet of my code:
import multiprocessing
from functools import partial
def Foo(id:int,constant_arg1:str, constant_arg2:str):
custom_class_obj = CustomClass(constant_arg1, constant_arg2)
custom_class_obj.run() # this changes some attributes of the custom_class_obj
if(something):
return None
else:
return [custom_class_obj]
def parallel_run(iters:int, a:str, b:str):
pool = multiprocessing.Pool(processes=k)
## create the partial function obj before passing it to pool
partial_func = partial(Foo, constant_arg1=a, constant_arg2=b)
## create the variable id list
iter_list = list(range(iters))
all_runs = pool.map(partial_func, iter_list)
return all_runs
This throws the following error in the multiprocessing module:
multiprocessing.pool.MaybeEncodingError: Error sending result: '[[<CustomClass object at 0x1693c7070>], [<CustomClass object at 0x1693b88e0>], ....]'
Reason: 'TypeError("cannot pickle 'module' object")'
How can I resolve this?
I was able to replicate the error message with a minimal example of an un-picklable class. The error basically states the instance of your class can't be pickled because it contains a reference to a module, and modules are not picklable. You need to comb through CustomClass to make sure instances don't hold things like open file handles, module references, etc.. If you need to have those things, you should use __getstate__ and __setstate__ to customize the pickle and unpickle process.
distilled example of your error:
from multiprocessing import Pool
from functools import partial
class klass:
def __init__(self, a):
self.value = a
import os
self.module = os #this fails: can't pickle a module and send it back to main process
def foo(a, b, c):
return klass(a+b+c)
if __name__ == "__main__":
with Pool() as p:
a = 1
b = 2
bar = partial(foo, a, b)
res = p.map(bar, range(10))
print([r.value for r in res])
I have split a large class implementation into different packages [1], and have used an import inside a method body to avoid a compilation cycle, as follows:
# model.py
class MyInt:
def __init__(self, value: int):
self.value = value
def is_prime(self) -> bool:
from methods import is_prime
return is_prime(self)
# methods.py
from model import MyInt
def is_prime(x: MyInt) -> bool:
# TODO: actually implement this
return x.value == 2 or x.value % 2 == 1
However, pytype is not happy about this, failing to find the pyi file when reaching the import cycle:
File "/home/bkim/Projects/mwe/model.py", line 6, in is_prime: Couldn't import pyi for 'methods' [pyi-error]
Can't find pyi for 'model', referenced from 'methods'
How can I avoid this and still get type-checking?
[1] I've done this with just one tiny, utility method, actually. No need to yell about splitting a class across multiple packages.
This solution uses typing.TYPE_CHECKING, to have one behavior during type checking and another during runtime:
import typing
class MyInt:
def is_prime(self) -> bool:
if typing.TYPE_CHECKING:
return False
from methods import is_prime
return is_prime(self)
Curiously, using from typing import TYPE_CHECKING doesn't work, which may be a bug?
I am trying to update a variable of a class by calling a function of the class from a different function which is being run on multi-process.
To achieve the desired result, process (p1) needs to update the variable "transaction" and which should get then modified by process (p2)
I tried the below code and I know i should use Multiprocess.value or manager to achieve the desired result and I am not sure of how to do it as my variable to be updated is in another class
Below is the code:
from multiprocessing import Process
from helper import Helper
camsource = ['a','b']
Pros = []
def sub(i):
HC.trail_func(i)
def main():
for i in camsource:
print ("Camera Thread {} Started!".format(i))
p = Process(target=sub, args=(i))
Pros.append(p)
p.start()
# block until all the threads finish (i.e. block until all function_x calls finish)
for t in Pros:
t.join()
if __name__ == "__main__":
HC = Helper()
main()
Here is the helper code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
class Helper():
def __init__(self):
self.transactions = []
def trail_func(self,preview):
if preview == 'a':
self.transactions.append({"Apple":1})
else:
if self.transactions[0]['Apple'] == 1:
self.transactions[0]['Apple'] = self.transactions[0]['Apple'] + 1
print (self.transactions)
Desired Output:
p1:
transactions = {"Apple":1}
p2:
transactions = {"Apple":2}
I've recently released this module that can help you with your code, as all data frames (data models that can hold any type of data), have locks on them, in order to solve concurrency issues. Anyway, take a look at the README file and the examples.
I've made an example here too, if you'd like to check.
I'm writing a program that ought to work with both python2 and python3.
For this, I would like to have a function to write to stderr that works with both python versions.
Ideal I think would be something like:
def writeStdErr(message):
if sys.version_info >= (3, 0):
print(message, end = "", file = sys.stderr)
else:
sys.stderr.write(message)
the problem with this that python2 is, that print isn't a function, so I get
print(message, end = "", file = sys.stderr)
^
SyntaxError: invalid syntax
I could get rid of this by just adding eval:
def writeStdErr(message):
if sys.version_info >= (3, 0):
eval('print(message, end = "", file = sys.stderr)')
else:
sys.stderr.write(message)
however, I dislike this solution; I think it's general a bad idea to use eval.
Does anyone knows something better/has a better solution?
EDIT:
For anyone having the same problem in future, the following things seem works:
def writeStdErr(message):
sys.stderr.write(message)
or
from __future__ import print_function
import sys
def writeStdErr(message):
print(message, file=sys.stderr)
Thanks to all answers
If you are using Python2.7, you can import the new behaviour:
from __future__ import print_function
That should be the first line of code (but could go after a shebang).
Another alternative compatible with earlier versions is to create it in an external module.
if sys.version_info >= (3, 0):
from print_sderr3 import writeStdErr
else:
from print_stderr2 import writeStdErr
where you have implemented each one accordingly.
That is to answer your question, BUT, you can just use sys.stderr.write for both. The only difference is that in Python 3 it seems to return the number of characters written. If you do it on interactive mode:
>>> sys.stderr.write('aaaa\n')
aaaa
5
You get an extra 5, but that is just the return value.
>>> a = sys.stderr.write('aaaa\n')
aaaa
>>> a
5