def get_string_source(self):
import os
import platform
# import pdb; pdb.set_trace()
cmdline = None
if platform.system() == 'Windows':
# TODO use winapi to obtain the command line
return ''
elif platform.system() == 'Linux':
# TODO try to use proc
pass
if cmdline is None and os.name == 'posix':
from subprocess import CalledProcessError, check_output as spawn
try:
cmdline = spawn(
['ps', '-ww', '-p',
str(os.getpid()), '-o', 'command='])
except CalledProcessError:
return ''
else:
# current system doesn't have a way to get the command line
return ''
cmdline = cmdline.decode('utf-8').strip()
cmdline = self.split_cmdline(cmdline)
extra_args = sys.argv[1:]
if len(extra_args) > 0:
if cmdline[-len(extra_args):] != extra_args:
# we can't rely on the output to be correct; fail!
return ''
cmdline = cmdline[1:-len(extra_args)]
skip = 0
for i in range(len(cmdline)):
a = cmdline[i].strip()
if not a.startswith('-c'):
skip += 1
else:
a = a[2:].strip()
if len(a) > 0:
cmdline[i] = a
else:
skip += 1
break
cmdline = cmdline[skip:]
source = ' '.join(cmdline)
return source
def run(command, dir=None, shell=False, stderr=False):
proc = spawn(
shlexSplit(command),
cwd=dir,
shell=shell,
timeout=1,
text=True,
stdout=PIPE,
stderr=PIPE,
)
if stderr:
return proc.stderr.strip()
else:
return proc.stdout.strip()
def run(self):
"""Run this command, i.e. do the actual document generation."""
sphinx = find_executable('sphinx-build')
if sphinx:
sphinx = [sphinx]
else:
try: # try to find version installed with Python tools
# tested with Sphinx 1.1.3
import sphinx as sp
except ImportError:
pass
else:
sphinx = [sys.executable, sp.__file__]
if not sphinx:
self.warn("could not find sphinx-build in PATH")
self.warn("cannot build documentation")
return
doc_dir = os.path.join('share', 'doc', 'roundup', 'html')
temp_dir = os.path.join(self.build_temp, 'doc')
cmd = sphinx + ['-d', temp_dir, 'doc', doc_dir]
spawn(cmd)
define the agent and the behavior tha agent will preform define an application class for the agent to act on the behavior it will recieve define webscraping most likely with scrapy define storage of archived material probably with mongodb define mathematics agent(for complex math required by other fuinctions to call from) define text interface for communication between node and master agents and the user if not included define communication between nodes define MendicantBot(): Master bot, run when program starts and call from botmanager define control system that talks to BotManager call BotManager to spawn a node for a particular task that it oversees # Functions below except maybe botmanger call all from another file define BotManager(): subprocess.spawn(MendicantBotNode) #spawns MendicantBotNodes define agent type from a list of possible agents Popen. define communication between bots and MendicantBot define MendicantBotInstance(): call functions from another file define math(): here all the different types of math to do and they can all go in one function define MendicantArchive(): where data the bot has scraped is stored for recall later
