In Python, how can we find out the command line arguments that were provided for a script, and process them?
For some more specific examples, see Implementing a "[command] [action] [parameter]" style command-line interfaces? and How do I format positional argument help using Python's optparse?.
import sys
print("\n".join(sys.argv))
sys.argv is a list that contains all the arguments passed to the script on the command line. sys.argv[0] is the script name.
Basically,
import sys
print(sys.argv[1:])
The canonical solution in the standard library is argparse (docs):
Here is an example:
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("-f", "--file", dest="filename",
help="write report to FILE", metavar="FILE")
parser.add_argument("-q", "--quiet",
action="store_false", dest="verbose", default=True,
help="don't print status messages to stdout")
args = parser.parse_args()
argparse supports (among other things):
Multiple options in any order.
Short and long options.
Default values.
Generation of a usage help message.
Just going around evangelizing for argparse which is better for these reasons.. essentially:
(copied from the link)
argparse module can handle positional
and optional arguments, while
optparse can handle only optional
arguments
argparse isn’t dogmatic about
what your command line interface
should look like - options like -file
or /file are supported, as are
required options. Optparse refuses to
support these features, preferring
purity over practicality
argparse produces more
informative usage messages, including
command-line usage determined from
your arguments, and help messages for
both positional and optional
arguments. The optparse module
requires you to write your own usage
string, and has no way to display
help for positional arguments.
argparse supports action that
consume a variable number of
command-line args, while optparse
requires that the exact number of
arguments (e.g. 1, 2, or 3) be known
in advance
argparse supports parsers that
dispatch to sub-commands, while
optparse requires setting
allow_interspersed_args and doing the
parser dispatch manually
And my personal favorite:
argparse allows the type and
action parameters to add_argument()
to be specified with simple
callables, while optparse requires
hacking class attributes like
STORE_ACTIONS or CHECK_METHODS to get
proper argument checking
There is also argparse stdlib module (an "impovement" on stdlib's optparse module). Example from the introduction to argparse:
# script.py
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'integers', metavar='int', type=int, choices=range(10),
nargs='+', help='an integer in the range 0..9')
parser.add_argument(
'--sum', dest='accumulate', action='store_const', const=sum,
default=max, help='sum the integers (default: find the max)')
args = parser.parse_args()
print(args.accumulate(args.integers))
Usage:
$ script.py 1 2 3 4
4
$ script.py --sum 1 2 3 4
10
If you need something fast and not very flexible
main.py:
import sys
first_name = sys.argv[1]
last_name = sys.argv[2]
print("Hello " + first_name + " " + last_name)
Then run python main.py James Smith
to produce the following output:
Hello James Smith
The docopt library is really slick. It builds an argument dict from the usage string for your app.
Eg from the docopt readme:
"""Naval Fate.
Usage:
naval_fate.py ship new <name>...
naval_fate.py ship <name> move <x> <y> [--speed=<kn>]
naval_fate.py ship shoot <x> <y>
naval_fate.py mine (set|remove) <x> <y> [--moored | --drifting]
naval_fate.py (-h | --help)
naval_fate.py --version
Options:
-h --help Show this screen.
--version Show version.
--speed=<kn> Speed in knots [default: 10].
--moored Moored (anchored) mine.
--drifting Drifting mine.
"""
from docopt import docopt
if __name__ == '__main__':
arguments = docopt(__doc__, version='Naval Fate 2.0')
print(arguments)
One way to do it is using sys.argv. This will print the script name as the first argument and all the other parameters that you pass to it.
import sys
for arg in sys.argv:
print arg
#set default args as -h , if no args:
if len(sys.argv) == 1: sys.argv[1:] = ["-h"]
I use optparse myself, but really like the direction Simon Willison is taking with his recently introduced optfunc library. It works by:
"introspecting a function
definition (including its arguments
and their default values) and using
that to construct a command line
argument parser."
So, for example, this function definition:
def geocode(s, api_key='', geocoder='google', list_geocoders=False):
is turned into this optparse help text:
Options:
-h, --help show this help message and exit
-l, --list-geocoders
-a API_KEY, --api-key=API_KEY
-g GEOCODER, --geocoder=GEOCODER
I like getopt from stdlib, eg:
try:
opts, args = getopt.getopt(sys.argv[1:], 'h', ['help'])
except getopt.GetoptError, err:
usage(err)
for opt, arg in opts:
if opt in ('-h', '--help'):
usage()
if len(args) != 1:
usage("specify thing...")
Lately I have been wrapping something similiar to this to make things less verbose (eg; making "-h" implicit).
As you can see optparse "The optparse module is deprecated with and will not be developed further; development will continue with the argparse module."
Pocoo's click is more intuitive, requires less boilerplate, and is at least as powerful as argparse.
The only weakness I've encountered so far is that you can't do much customization to help pages, but that usually isn't a requirement and docopt seems like the clear choice when it is.
import argparse
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('integers', metavar='N', type=int, nargs='+',
help='an integer for the accumulator')
parser.add_argument('--sum', dest='accumulate', action='store_const',
const=sum, default=max,
help='sum the integers (default: find the max)')
args = parser.parse_args()
print(args.accumulate(args.integers))
Assuming the Python code above is saved into a file called prog.py
$ python prog.py -h
Ref-link: https://docs.python.org/3.3/library/argparse.html
You may be interested in a little Python module I wrote to make handling of command line arguments even easier (open source and free to use) - Commando
Yet another option is argh. It builds on argparse, and lets you write things like:
import argh
# declaring:
def echo(text):
"Returns given word as is."
return text
def greet(name, greeting='Hello'):
"Greets the user with given name. The greeting is customizable."
return greeting + ', ' + name
# assembling:
parser = argh.ArghParser()
parser.add_commands([echo, greet])
# dispatching:
if __name__ == '__main__':
parser.dispatch()
It will automatically generate help and so on, and you can use decorators to provide extra guidance on how the arg-parsing should work.
I recommend looking at docopt as a simple alternative to these others.
docopt is a new project that works by parsing your --help usage message rather than requiring you to implement everything yourself. You just have to put your usage message in the POSIX format.
Also with python3 you might find convenient to use Extended Iterable Unpacking to handle optional positional arguments without additional dependencies:
try:
_, arg1, arg2, arg3, *_ = sys.argv + [None] * 2
except ValueError:
print("Not enough arguments", file=sys.stderr) # unhandled exception traceback is meaningful enough also
exit(-1)
The above argv unpack makes arg2 and arg3 "optional" - if they are not specified in argv, they will be None, while if the first is not specified, ValueError will be thouwn:
Traceback (most recent call last):
File "test.py", line 3, in <module>
_, arg1, arg2, arg3, *_ = sys.argv + [None] * 2
ValueError: not enough values to unpack (expected at least 4, got 3)
My solution is entrypoint2. Example:
from entrypoint2 import entrypoint
#entrypoint
def add(file, quiet=True):
''' This function writes report.
:param file: write report to FILE
:param quiet: don't print status messages to stdout
'''
print file,quiet
help text:
usage: report.py [-h] [-q] [--debug] file
This function writes report.
positional arguments:
file write report to FILE
optional arguments:
-h, --help show this help message and exit
-q, --quiet don't print status messages to stdout
--debug set logging level to DEBUG
import sys
# Command line arguments are stored into sys.argv
# print(sys.argv[1:])
# I used the slice [1:] to print all the elements except the first
# This because the first element of sys.argv is the program name
# So the first argument is sys.argv[1], the second is sys.argv[2] ecc
print("File name: " + sys.argv[0])
print("Arguments:")
for i in sys.argv[1:]:
print(i)
Let's name this file command_line.py and let's run it:
C:\Users\simone> python command_line.py arg1 arg2 arg3 ecc
File name: command_line.py
Arguments:
arg1
arg2
arg3
ecc
Now let's write a simple program, sum.py:
import sys
try:
print(sum(map(float, sys.argv[1:])))
except:
print("An error has occurred")
Result:
C:\Users\simone> python sum.py 10 4 6 3
23
This handles simple switches, value switches with optional alternative flags.
import sys
# [IN] argv - array of args
# [IN] switch - switch to seek
# [IN] val - expecting value
# [IN] alt - switch alternative
# returns value or True if val not expected
def parse_cmd(argv,switch,val=None,alt=None):
for idx, x in enumerate(argv):
if x == switch or x == alt:
if val:
if len(argv) > (idx+1):
if not argv[idx+1].startswith('-'):
return argv[idx+1]
else:
return True
//expecting a value for -i
i = parse_cmd(sys.argv[1:],"-i", True, "--input")
//no value needed for -p
p = parse_cmd(sys.argv[1:],"-p")
Several of our biotechnology clients have posed these two questions recently:
How can we execute a Python script as a command?
How can we pass input values to a Python script when it is executed as a command?
I have included a Python script below which I believe answers both questions. Let's assume the following Python script is saved in the file test.py:
#
#----------------------------------------------------------------------
#
# file name: test.py
#
# input values: data - location of data to be processed
# date - date data were delivered for processing
# study - name of the study where data originated
# logs - location where log files should be written
#
# macOS usage:
#
# python3 test.py "/Users/lawrence/data" "20220518" "XYZ123" "/Users/lawrence/logs"
#
# Windows usage:
#
# python test.py "D:\data" "20220518" "XYZ123" "D:\logs"
#
#----------------------------------------------------------------------
#
# import needed modules...
#
import sys
import datetime
def main(argv):
#
# print message that process is starting...
#
print("test process starting at", datetime.datetime.now().strftime("%Y%m%d %H:%M"))
#
# set local values from input values...
#
data = sys.argv[1]
date = sys.argv[2]
study = sys.argv[3]
logs = sys.argv[4]
#
# print input arguments...
#
print("data value is", data)
print("date value is", date)
print("study value is", study)
print("logs value is", logs)
#
# print message that process is ending...
#
print("test process ending at", datetime.datetime.now().strftime("%Y%m%d %H:%M"))
#
# call main() to begin processing...
#
if __name__ == '__main__':
main(sys.argv)
The script can be executed on a macOS computer in a Terminal shell as shown below and the results will be printed to standard output (be sure the current directory includes the test.py file):
$ python3 test.py "/Users/lawrence/data" "20220518" "XYZ123" "/Users/lawrence/logs"
test process starting at 20220518 16:51
data value is /Users/lawrence/data
date value is 20220518
study value is XYZ123
logs value is /Users/lawrence/logs
test process ending at 20220518 16:51
The script can also be executed on a Windows computer in a Command Prompt as shown below and the results will be printed to standard output (be sure the current directory includes the test.py file):
D:\scripts>python test.py "D:\data" "20220518" "XYZ123" "D:\logs"
test process starting at 20220518 17:20
data value is D:\data
date value is 20220518
study value is XYZ123
logs value is D:\logs
test process ending at 20220518 17:20
This script answers both questions posed above and is a good starting point for developing scripts that will be executed as commands with input values.
Reason for the new answer:
Existing answers specify multiple options.
Standard option is to use argparse, a few answers provided examples from the documentation, and one answer suggested the advantage of it. But all fail to explain the answer adequately/clearly to the actual question by OP, at least for newbies.
An example of argparse:
import argparse
def load_config(conf_file):
pass
if __name__ == '__main__':
parser = argparse.ArgumentParser()
//Specifies one argument from the command line
//You can have any number of arguments like this
parser.add_argument("conf_file", help="configuration file for the application")
args = parser.parse_args()
config = load_config(args.conf_file)
Above program expects a config file as an argument. If you provide it, it will execute happily. If not, it will print the following
usage: test.py [-h] conf_file
test.py: error: the following arguments are required: conf_file
You can have the option to specify if the argument is optional.
You can specify the expected type for the argument using type key
parser.add_argument("age", type=int, help="age of the person")
You can specify default value for the arguments by specifying default key
This document will help you to understand it to an extent.
In PyTorch, we can define architectures in multiple ways. Here, I'd like to create a simple LSTM network using the Sequential module.
In Lua's torch I would usually go with:
model = nn.Sequential()
model:add(nn.SplitTable(1,2))
model:add(nn.Sequencer(nn.LSTM(inputSize, hiddenSize)))
model:add(nn.SelectTable(-1)) -- last step of output sequence
model:add(nn.Linear(hiddenSize, classes_n))
However, in PyTorch, I don't find the equivalent of SelectTable to get the last output.
nn.Sequential(
nn.LSTM(inputSize, hiddenSize, 1, batch_first=True),
# what to put here to retrieve last output of LSTM ?,
nn.Linear(hiddenSize, classe_n))
Define a class to extract the last cell output:
# LSTM() returns tuple of (tensor, (recurrent state))
class extract_tensor(nn.Module):
def forward(self,x):
# Output shape (batch, features, hidden)
tensor, _ = x
# Reshape shape (batch, hidden)
return tensor[:, -1, :]
nn.Sequential(
nn.LSTM(inputSize, hiddenSize, 1, batch_first=True),
extract_tensor(),
nn.Linear(hiddenSize, classe_n)
)
According to the LSTM cell documentation the outputs parameter has a shape of (seq_len, batch, hidden_size * num_directions) so you can easily take the last element of the sequence in this way:
rnn = nn.LSTM(10, 20, 2)
input = Variable(torch.randn(5, 3, 10))
h0 = Variable(torch.randn(2, 3, 20))
c0 = Variable(torch.randn(2, 3, 20))
output, hn = rnn(input, (h0, c0))
print(output[-1]) # last element
Tensor manipulation and Neural networks design in PyTorch is incredibly easier than in Torch so you rarely have to use containers. In fact, as stated in the tutorial PyTorch for former Torch users PyTorch is built around Autograd so you don't need anymore to worry about containers. However, if you want to use your old Lua Torch code you can have a look to the Legacy package.
As far as I'm concerned there's nothing like a SplitTable or a SelectTable in PyTorch. That said, you are allowed to concatenate an arbitrary number of modules or blocks within a single architecture, and you can use this property to retrieve the output of a certain layer. Let's make it more clear with a simple example.
Suppose I want to build a simple two-layer MLP and retrieve the output of each layer. I can build a custom class inheriting from nn.Module:
class MyMLP(nn.Module):
def __init__(self, in_channels, out_channels_1, out_channels_2):
# first of all, calling base class constructor
super().__init__()
# now I can build my modular network
self.block1 = nn.Linear(in_channels, out_channels_1)
self.block2 = nn.Linear(out_channels_1, out_channels_2)
# you MUST implement a forward(input) method whenever inheriting from nn.Module
def forward(x):
# first_out will now be your output of the first block
first_out = self.block1(x)
x = self.block2(first_out)
# by returning both x and first_out, you can now access the first layer's output
return x, first_out
In your main file you can now declare the custom architecture and use it:
from myFile import MyMLP
import numpy as np
in_ch = out_ch_1 = out_ch_2 = 64
# some fake input instance
x = np.random.rand(in_ch)
my_mlp = MyMLP(in_ch, out_ch_1, out_ch_2)
# get your outputs
final_out, first_layer_out = my_mlp(x)
Moreover, you could concatenate two MyMLP in a more complex model definition and retrieve the output of each one in a similar way.
I hope this is enough to clarify, but in case you have more questions, please feel free to ask, since I may have omitted something.
I am trying to understand how FiPy works by working an example, in particular I would like to solve the following simple convection equation with periodic boundary:
$$\partial_t u + \partial_x u = 0$$
If initial data is given by $u(x, 0) = F(x)$, then the analytical solution is $u(x, t) = F(x - t)$. I do get a solution, but it is not correct.
What am I missing? Is there a better resource for understanding FiPy than the documentation? It is very sparse...
Here is my attempt
from fipy import *
import numpy as np
# Generate mesh
nx = 20
dx = 2*np.pi/nx
mesh = PeriodicGrid1D(nx=nx, dx=dx)
# Generate solution object with initial discontinuity
phi = CellVariable(name="solution variable", mesh=mesh)
phiAnalytical = CellVariable(name="analytical value", mesh=mesh)
phi.setValue(1.)
phi.setValue(0., where=x > 1.)
# Define the pde
D = [[-1.]]
eq = TransientTerm() == ConvectionTerm(coeff=D)
# Set discretization so analytical solution is exactly one cell translation
dt = 0.01*dx
steps = 2*int(dx/dt)
# Set the analytical value at the end of simulation
phiAnalytical.setValue(np.roll(phi.value, 1))
for step in range(steps):
eq.solve(var=phi, dt=dt)
print(phi.allclose(phiAnalytical, atol=1e-1))
As addressed on the FiPy mailing list, FiPy is not great at handling convection only PDEs (absent diffusion, pure hyperbolic) as it's missing higher order convection schemes. It is better to use CLAWPACK for this class of problem.
FiPy does have one second order scheme that might help with this problem, the VanLeerConvectionTerm, see an example.
If the VanLeerConvectionTerm is used in the above problem, it does do a better job of preserving the shock.
import numpy as np
import fipy
# Generate mesh
nx = 20
dx = 2*np.pi/nx
mesh = fipy.PeriodicGrid1D(nx=nx, dx=dx)
# Generate solution object with initial discontinuity
phi = fipy.CellVariable(name="solution variable", mesh=mesh)
phiAnalytical = fipy.CellVariable(name="analytical value", mesh=mesh)
phi.setValue(1.)
phi.setValue(0., where=mesh.x > 1.)
# Define the pde
D = [[-1.]]
eq = fipy.TransientTerm() == fipy.VanLeerConvectionTerm(coeff=D)
# Set discretization so analytical solution is exactly one cell translation
dt = 0.01*dx
steps = 2*int(dx/dt)
# Set the analytical value at the end of simulation
phiAnalytical.setValue(np.roll(phi.value, 1))
viewer = fipy.Viewer(phi)
for step in range(steps):
eq.solve(var=phi, dt=dt)
viewer.plot()
raw_input('stopped')
print(phi.allclose(phiAnalytical, atol=1e-1))
I'm trying to calculate Adamic-Adar similarity for a network, which have two types of nodes. I'm only interested in calculating similarity between nodes which have outgoing connections. Nodes with incoming connections are a kind of connector and I'm not interested in them.
Data size and characteristic:
> summary(g)
IGRAPH DNW- 3852 24478 --
+ attr: name (v/c), weight (e/n)
Prototype code in Python 2.7:
import glob
import os
import pandas as pd
from igraph import *
os.chdir("data/")
for file in glob.glob("*.graphml"):
print(file)
g = Graph.Read_GraphML(file)
indegree = Graph.degree(g, mode="in")
g['indegree'] = indegree
dev = g.vs.select(indegree == 0)
m = Graph.similarity_inverse_log_weighted(dev.subgraph())
df = pd.melt(m)
df.to_csv(file.split("_only.graphml")[0] + "_similarity.csv", sep=',')
There is something wrong with this code, because dev is of length 1, and m is 0.0, so it doesn't work as expected.
Hint
I have a working code in R, but seems like I'm unable to rewrite it to Python (which I'm doing for the sake of performance, networks are huge). Here it is:
# make sure g is your network
indegree <- degree(g, mode="in")
V(g)$indegree <- indegree
dev <- V(g)[indegree==0]
m <- similarity.invlogweighted(g, dev)
x.m <- melt(m)
colnames(x.m) <- c("dev1", "dev2", "value")
x.m <- x.m[x.m$value > 0, ]
write.csv(x.m, file = sub(".csv",
"_similarity.csv", filename))
You are assigning the in-degrees as a graph attribute, not as a vertex attribute, so you cannot reasonably call g.vs.select() later on. You need this instead:
indegree = g.degree(mode="in")
g.vs["indegree"] = indegree
dev = g.vs.select(indegree=0)
But actually, you could simply write this:
dev = g.vs.select(_indegree=0)
This works because of how the select method works:
Attribute names inferred from keyword arguments are treated specially
if they start with an underscore (_). These are not real attributes
but refer to specific properties of the vertices, e.g., its degree.
The rule is as follows: if an attribute name starts with an underscore,
the rest of the name is interpreted as a method of the Graph object.
This method is called with the vertex sequence as its first argument
(all others left at default values) and vertices are filtered
according to the value returned by the method.
I am trying to run the code below for a level set 1D problem (example in fipy webpage). I am getting this error:
Traceback (most recent call last):
File "C:/Users/sgowda/Documents/pde solver code/level set 1D.py", line 20, in
var.calcDistanceFunction()
File "C:\Users\sgowda\AppData\Local\Continuum\Anaconda\lib\site-packages\fipy\variables\distanceVariable.py", line 335, in calcDistanceFunction
raise Exception, "Neither lsmlib nor skfmm can be found on the $PATH"
Exception: Neither lsmlib nor skfmm can be found on the $PATH
Could you please let me know how to fix this. i tried looking into the distancefucntion() but im not sure what this error means?
from fipy import Grid1D, CellVariable, TransientTerm, DiffusionTerm, Viewer, DistanceVariable
import matplotlib.pyplot as plt
velocity = 1.
dx = 1.
nx = 10
timeStepDuration = 1.
steps = 2
L = nx * dx
interfacePosition = L / 5.
from fipy.tools import serialComm
mesh = Grid1D(dx=dx, nx=nx, communicator=serialComm)
var = DistanceVariable(name='level set variable',
mesh=mesh,
value=-1.,
hasOld=1)
var.setValue(1., where=mesh.cellCenters[0] > interfacePosition)
var.calcDistanceFunction()
advEqn = TransientTerm() + FirstOrderAdvectionTerm(velocity)
viewer = Viewer(vars=var, datamin=-10., datamax=10.)
viewer.plot()
for step in range(steps):
var.updateOld()
advEqn.solve(var, dt=timeStepDuration)
viewer.plot()
plt.show()
FiPy doesn't have a native level set implementation so uses either LSMLIB or Scikit-fmm to provide the level set / fast marching method functionality.
To see whether you have them installed correctly, use either
$ python -c “import pylsmlib; pylsmlib.test()”
or
$ python -c “import skfmm; skfmm.test()”
to test.
The requirement is outlined in the FiPy documentation, see http://www.ctcms.nist.gov/fipy/INSTALLATION.html#level-set-packages
It is probably easier to install Scikit-fmm initially, see https://pythonhosted.org/scikit-fmm/, but
$ pip install scikit-fmm
should work.