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LLDB Python ReadMemory returns NoneType

I have a script in Python where I check value of x0 register which is a pointer to some memory address and read memory from there. But in my Python script, ReadMemory return None and because of that bytearray function call throws an error. In LLDB console memory read <x0 value> works. Code is below:
import random
import lldb
debugger = lldb.SBDebugger.Create()
target = debugger.GetTargetAtIndex(0)
def modify_memory(debugger, command, result, internal_dict):
thread = debugger.GetThread()
# if called from console itself, debugger argument type is true but if it comes from breakpoint
# debugger type == SBFrame. SBFrame has GetThread() method; so debugger above is actually frame
db = lldb.SBDebugger.Create()
if thread:
#db.HandleCommand("print \"a\"")
frame = thread.GetSelectedFrame()
# Read the value of register x0
x0 = frame.FindRegister("x0")
x0_value = x0.GetValue()
x0_value = int(x0_value,16)
print(x0_value)
# Read memory at the address stored in x0
memory = process.ReadMemory(x0_value+16+4, 256, lldb.SBError())
print(memory)
if memory != None:
print("finally something!")
# Modify a random byte in the memory
random_byte = random.randint(2, 255)
memory = bytearray(memory)
memory[random_byte] = random.randint(0, 255)
# Write the modified memory back to the original location
process.WriteMemory(x0_value, memory, lldb.SBError())
process.Continue()
else:
db.HandleCommand("print \"thread NOT found\"")
process.Continue()
I add modify_memory as a command to breakpoints.
I tried to run command from interactive console but did not manage to re-create thread, process etc variables. Also, I add function modify_memory as command but then variable "debugger" come as SBDebugger (which is actually true :) )but if I add this to a breakpoint, "debugger" variable becomes SBFrame which has the method GetThread

You want to use the newer command definition form:
def command_function(debugger, command, exe_ctx, result, internal_dict):
If you define your function that way, lldb will pass an SBExecutionContext in the exe_ctx parameter that contains the frame/thread/process/target you should act on in the command.
The point is that at any given stop, lldb first queries all the threads, and for each of them that have stopped for a reason, runs the relevant callbacks, then decides whether to stop or not, then computes the selected thread. So at the time breakpoint callbacks are being run, the selected thread is still the one from the last time the debugee stopped.
The original form was really an oversight in the design. We kept the old form around for compatibility reasons, but at this point it's unlikely there are any lldb's around that don't include the more useful form. So that's really the one you want to use.

Segmentation fault when initializing array

I am getting a segmentation fault when initializing an array.
I have a callback function from when an RFID tag gets read
IDS = []
def readTag(e):
epc = str(e.epc, 'utf-8')
if not epc in IDS:
now = datetime.datetime.now().strftime('%m/%d/%Y %H:%M:%S')
IDS.append([epc, now, "name.instrument"])
and a main function from which it's called
def main():
for x in vals:
IDS.append([vals[0], vals[1], vals[2]])
for x in IDS:
print(x[0])
r = mercury.Reader("tmr:///dev/ttyUSB0", baudrate=9600)
r.set_region("NA")
r.start_reading(readTag, on_time=1500)
input("press any key to stop reading: ")
r.stop_reading()
The error occurs because of the line IDS.append([epc, now, "name.instrument"]). I know because when I replace it with a print call instead the program will run just fine. I've tried using different types for the array objects (integers), creating an array of the same objects outside of the append function, etc. For some reason just creating an array inside the "readTag" function causes the segmentation fault like row = [1,2,3]
Does anyone know what causes this error and how I can fix it? Also just to be a little more specific, the readTag function will work fine for the first two (only ever two) calls, but then it crashes and the Reader object that has the start_reading() function is from the mercury-api

This looks like a scoping issue to me; the mercury library doesn't have permission to access your list's memory address, so when it invokes your callback function readTag(e) a segfault occurs. I don't think that the behavior that you want is supported by that library

To extend Michael's answer, this appears to be an issue with scoping and the API you're using. In general pure-Python doesn't seg-fault. Or at least, it shouldn't seg-fault unless there's a bug in the interpreter, or some extension that you're using. That's not to say pure-Python won't break, it's just that a genuine seg-fault indicates the problem is probably the result of something messy outside of your code.
I'm assuming you're using this Python API.
In that case, the README.md mentions that the Reader.start_reader() method you're using is "asynchronous". Meaning it invokes a new thread or process and returns immediately and then the background thread continues to call your callback each time something is scanned.
I don't really know enough about the nitty gritty of CPython to say exactly what going on, but you've declared IDS = [] as a global variable and it seems like the background thread is running the callback with a different context to the main program. So when it attempts to access IDS it's reading memory it doesn't own, hence the seg-fault.
Because of how restrictive the callback is and the apparent lack of a buffer, this might be an oversight on the behalf of the developer. If you really need asynchronous reads it's worth sending them an issue report.
Otherwise, considering you're just waiting for input you probably don't need the asynchronous reads, and you could use the synchronous Reader.read() method inside your own busy loop instead with something like:
try:
while True:
readTags(r.read(timeout=10))
except KeyboardInterrupt: ## break loop on SIGINT (Ctrl-C)
pass
Note that r.read() returns a list of tags rather than just one, so you'd need to modify your callback slightly, and if you're writing more than just a quick script you probably want to use threads to interrupt the loop properly as SIGINT is pretty hacky.

RAM is not freed after a Python function is invoked

I'm using an in-house Python library for scientific computing. I need to consecutively copy an object, modify it, and then delete it. The object is huge which causes my machine to run out of memory after a few cycles.
The first problem is that I use python's del to delete the object, which apparently only dereferences the object, rather than freeing up RAM.
The second problem is that even when I encapsulate the whole process in a function, after the function is invoked, the RAM is still not freed up. Here's a code snippet to better explain the issue.
ws = op.core.Workspace()
net = op.network.Cubic(shape=[100,100,100], spacing=1e-6)
proj = net.project
def f():
for i in range(5):
clone = ws.copy_project(proj)
result = do_something_with(clone)
del clone
f()
gc.collect()
>>> ws
{'sim_01': [<openpnm.network.Cubic object at 0x7fed1c417780>],
'sim_02': [<openpnm.network.Cubic object at 0x7fed1c417888>],
'sim_03': [<openpnm.network.Cubic object at 0x7fed1c417938>],
'sim_04': [<openpnm.network.Cubic object at 0x7fed1c417990>],
'sim_05': [<openpnm.network.Cubic object at 0x7fed1c4179e8>],
'sim_06': [<openpnm.network.Cubic object at 0x7fed1c417a40>]}
My question is how do I completely delete a Python object?
Thanks!
PS. In the code snippet, each time ws.copy_project is called, a copy of proj is stored in ws dictionary.

There are some really smart python people on here. They may be able to tell you better ways to keep your memory clear, but I have used leaky libraries before, and found one (so-far) foolproof way to guarantee that your memory gets cleared after use: execute the memory hog in another process.
To do this, you'd need to arrange for an easy way to make your long calculation be executable separately. I have done this by adding special flags to my existing python script that tells it just to run that function; you may find it easier to put that function in a separate .py file, e.g.:
do_something_with.py
import sys
def do_something_with(i)
# Your example is still too vague. Clearly, something differentiates
# each do_something_with, otherwise you're just taking the
# same inputs 5 times over.
# Whatever the difference is, pass it in as an argument to the function
ws = op.core.Workspace()
net = op.network.Cubic(shape=[100,100,100], spacing=1e-6)
proj = net.project
# You may not even need to clone anymore?
clone = ws.copy_project(proj)
result = do_something_with(clone)
# Whatever arg(s) you need to get to the function, just pass it in on the command line
if __name__ == "__main__":
sys.exit(do_something_with(sys.args[1:]))
You can do this using any of the python tools that handle subprocesses. In python 3.5+, the recommended way to do this is subprocess.run. You could change your bigger function to something like this:
import subprocess
invoke_do_something(i):
completed_args = subprocess.run(["python", "do_something_with.py", str(i)], check=False)
return completed_args.returncode
results = map(invoke_do_something, range(5))
You'll obviously need to tailor this to fit your own situation, but by running in a subprocess, you're guaranteed to not have to worry about the memory getting cleaned up. As an added bonus, you could potentially use multiprocess.Pool.map to use multiple processors at one time. (I deliberately coded this to use map to make such a transition simple. You could still use your for loop if you prefer, and then you don't need the invoke... function.) Multiprocessing could speed up your processing, but since you're already worried about memory, is almost certainly a bad idea - with multiple processes of the big memory hog, your system itself will likely quickly run out of memory and kill your process.
Your example is fairly vague, so I've written this at a high level. I can answer some questions if you need.

Python Mock Process for Unit Testing

Background:
I am currently writing a process monitoring tool (Windows and Linux) in Python and implementing unit test coverage. The process monitor hooks into the Windows API function EnumProcesses on Windows and monitors the /proc directory on Linux to find current processes. The process names and process IDs are then written to a log which is accessible to the unit tests.
Question:
When I unit test the monitoring behavior I need a process to start and terminate. I would love if there would be a (cross-platform?) way to start and terminate a fake system process that I could uniquely name (and track its creation in a unit test).
Initial ideas:
I could use subprocess.Popen() to open any system process but this runs into some issues. The unit tests could falsely pass if the process I'm using to test is run by the system as well. Also, the unit tests are run from the command line and any Linux process I can think of suspends the terminal (nano, etc.).
I could start a process and track it by its process ID but I'm not exactly sure how to do this without suspending the terminal.
These are just thoughts and observations from initial testing and I would love it if someone could prove me wrong on either of these points.
I am using Python 2.6.6.
Edit:
Get all Linux process IDs:
try:
processDirectories = os.listdir(self.PROCESS_DIRECTORY)
except IOError:
return []
return [pid for pid in processDirectories if pid.isdigit()]
Get all Windows process IDs:
import ctypes, ctypes.wintypes
Psapi = ctypes.WinDLL('Psapi.dll')
EnumProcesses = self.Psapi.EnumProcesses
EnumProcesses.restype = ctypes.wintypes.BOOL
count = 50
while True:
# Build arguments to EnumProcesses
processIds = (ctypes.wintypes.DWORD*count)()
size = ctypes.sizeof(processIds)
bytes_returned = ctypes.wintypes.DWORD()
# Call enum processes to find all processes
if self.EnumProcesses(ctypes.byref(processIds), size, ctypes.byref(bytes_returned)):
if bytes_returned.value &lt size:
return processIds
else:
# We weren't able to get all the processes so double our size and try again
count *= 2
else:
print "EnumProcesses failed"
sys.exit()
Windows code is from here

edit: this answer is getting long :), but some of my original answer still applies, so I leave it in :)
Your code is not so different from my original answer. Some of my ideas still apply.
When you are writing Unit Test, you want to only test your logic. When you use code that interacts with the operating system, you usually want to mock that part out. The reason being that you don't have much control over the output of those libraries, as you found out. So it's easier to mock those calls.
In this case, there are two libraries that are interacting with the sytem: os.listdir and EnumProcesses. Since you didn't write them, we can easily fake them to return what we need. Which in this case is a list.
But wait, in your comment you mentioned:
"The issue I'm having with it however is that it really doesn't test
that my code is seeing new processes on the system but rather that the
code is correctly monitoring new items in a list."
The thing is, we don't need to test the code that actually monitors the processes on the system, because it's a third party code. What we need to test is that your code logic handles the returned processes. Because that's the code you wrote. The reason why we are testing over a list, is because that's what your logic is doing. os.listir and EniumProcesses return a list of pids (numeric strings and integers, respectively) and your code acts on that list.
I'm assuming your code is inside a Class (you are using self in your code). I'm also assuming that they are isolated inside their own methods (you are using return). So this will be sort of what I suggested originally, except with actual code :) Idk if they are in the same class or different classes, but it doesn't really matter.
Linux method
Now, testing your Linux process function is not that difficult. You can patch os.listdir to return a list of pids.
def getLinuxProcess(self):
try:
processDirectories = os.listdir(self.PROCESS_DIRECTORY)
except IOError:
return []
return [pid for pid in processDirectories if pid.isdigit()]
Now for the test.
import unittest
from fudge import patched_context
import os
import LinuxProcessClass # class that contains getLinuxProcess method
def test_LinuxProcess(self):
"""Test the logic of our getLinuxProcess.
We patch os.listdir and return our own list, because os.listdir
returns a list. We do this so that we can control the output
(we test *our* logic, not a built-in library's functionality).
"""
# Test we can parse our pdis
fakeProcessIds = ['1', '2', '3']
with patched_context(os, 'listdir', lamba x: fakeProcessIds):
myClass = LinuxProcessClass()
....
result = myClass.getLinuxProcess()
expected = [1, 2, 3]
self.assertEqual(result, expected)
# Test we can handle IOERROR
with patched_context(os, 'listdir', lamba x: raise IOError):
myClass = LinuxProcessClass()
....
result = myClass.getLinuxProcess()
expected = []
self.assertEqual(result, expected)
# Test we only get pids
fakeProcessIds = ['1', '2', '3', 'do', 'not', 'parse']
.....
Windows method
Testing your Window's method is a little trickier. What I would do is the following:
def prepareWindowsObjects(self):
"""Create and set up objects needed to get the windows process"
...
Psapi = ctypes.WinDLL('Psapi.dll')
EnumProcesses = self.Psapi.EnumProcesses
EnumProcesses.restype = ctypes.wintypes.BOOL
self.EnumProcessses = EnumProcess
...
def getWindowsProcess(self):
count = 50
while True:
.... # Build arguments to EnumProcesses and call enun process
if self.EnumProcesses(ctypes.byref(processIds),...
..
else:
return []
I separated the code into two methods to make it easier to read (I believe you are already doing this). Here is the tricky part, EnumProcesses is using pointers and they are not easy to play with. Another thing is, that I don't know how to work with pointers in Python, so I couldn't tell you of an easy way to mock that out =P
What I can tell you is to simply not test it. Your logic there is very minimal. Besides increasing the size of count, everything else in that function is creating the space EnumProcesses pointers will use. Maybe you can add a limit to the count size but other than that, this method is short and sweet. It returns the windows processes and nothing more. Just what I was asking for in my original comment :)
So leave that method alone. Don't test it. Make sure though, that anything that uses getWindowsProcess and getLinuxProcess get's mocked out as per my original suggestion.
Hopefully this makes more sense :) If it doesn't let me know and maybe we can have a chat session or do a video call or something.
original answer
I'm not exactly sure how to do what you are asking, but whenever I need to test code that depends on some outside force (external libraries, popen or in this case processes) I mock out those parts.
Now, I don't know how your code is structured, but maybe you can do something like this:
def getWindowsProcesses(self, ...):
'''Call Windows API function EnumProcesses and
return the list of processes
'''
# ... call EnumProcesses ...
return listOfProcesses
def getLinuxProcesses(self, ...):
'''Look in /proc dir and return list of processes'''
# ... look in /proc ...
return listOfProcessses
These two methods only do one thing, get the list of processes. For Windows, it might just be a call to that API and for Linux just reading the /proc dir. That's all, nothing more. The logic for handling the processes will go somewhere else. This makes these methods extremely easy to mock out since their implementations are just API calls that return a list.
Your code can then easy call them:
def getProcesses(...):
'''Get the processes running.'''
isLinux = # ... logic for determining OS ...
if isLinux:
processes = getLinuxProcesses(...)
else:
processes = getWindowsProcesses(...)
# ... do something with processes, write to log file, etc ...
In your test, you can then use a mocking library such as Fudge. You mock out these two methods to return what you expect them to return.
This way you'll be testing your logic since you can control what the result will be.
from fudge import patched_context
...
def test_getProcesses(self, ...):
monitor = MonitorTool(..)
# Patch the method that gets the processes. Whenever it gets called, return
# our predetermined list.
originalProcesses = [....pids...]
with patched_context(monitor, "getLinuxProcesses", lamba x: originalProcesses):
monitor.getProcesses()
# ... assert logic is right ...
# Let's "add" some new processes and test that our logic realizes new
# processes were added.
newProcesses = [...]
updatedProcesses = originalProcessses + (newProcesses)
with patched_context(monitor, "getLinuxProcesses", lamba x: updatedProcesses):
monitor.getProcesses()
# ... assert logic caught new processes ...
# Let's "kill" our new processes and test that our logic can handle it
with patched_context(monitor, "getLinuxProcesses", lamba x: originalProcesses):
monitor.getProcesses()
# ... assert logic caught processes were 'killed' ...
Keep in mind that if you test your code this way, you won't get 100% code coverage (since your mocked methods won't be run), but this is fine. You're testing your code and not third party's, which is what matters.
Hopefully this might be able to help you. I know it doesn't answer your question, but maybe you can use this to figure out the best way to test your code.

Your original idea of using subprocess is a good one. Just create your own executable and name it something that identifies it as a testing thing. Maybe make it do something like sleep for a while.
Alternately, you could actually use the multiprocessing module. I've not used python in windows much, but you should be able to get process identifying data out of the Process object you create:
p = multiprocessing.Process(target=time.sleep, args=(30,))
p.start()
pid = p.getpid()

How do I restructure this code so Pickle 'Buffer Region' errors do not occur

I was wondering if anyone had any good solutions to the pickling error I am having at the moment. I am trying to set my code up to open several different processes in parallel, each with a fitting process to be display on a matplotlib canvas in real time. Within my main application, I have a button which activates this function:
def process_data(self):
process_list = []
for tab in self.tab_list:
process_list.append(mp.Process(target=process_and_fit, args=(tab,)))
process_list[-1].start()
process_list[-1].join()
return
As you may notice, a 'tab' (PyQt4.QtGui.QTabWidget object) is passed to the function process_and_fit, which I have noticed is not able to be pickled readily (link here) .
However, I am not certain how to change the code to get rid of the frame being passed since it needs to be called in the process_and_fit function indirectly. By indirectly I mean something like this: (psuedo code again)
def process_and_fit(tab): # this just sets up and starts the fitting process
result = lmfit.Minimizer(residual, parameters, fcn_args=(tab,))
result.prepare_fit()
result.leastsq()
def residual(params, tab):
residual_array = Y - model
tab.refreshFigure()
return residual_array
class tab(QtGui.QTabWidget):
def __init__(self, parent, spectra):
# stuff to initialize the tab widget and hold all of the matplotlib lines and canvases
# This just refreshes the GUI stuff everytime that the parameters are fit in the least squares method
def refreshFigure(self):
self.line.set_data(self.spectra.X, self.spectra.model)
self.plot.draw_artist(self.line)
self.plot.figure.canvas.blit(self.plot.bbox)
Does anyone know how to get around this pickling error since the tab associated with a process should have only one set of data associated with it? I looked at Steven Bethard's approach but I really didn't understand where to put the code or how to utilize it. (I am a chemical engineer, not a computer scientist so there's a lot that I don't understand)
Any help is greatly appreciated.
EDIT: I added the links in that I forgot about, as requested.

The main issue is that you can't make UI changes from a separate process from the main UI thread (the one that all of your Qt calls are in). You need to use a mp.Pipe or mp.Queue to communicate back to the main process.
def process_data(self):
for tab in self.tab_list:
consumer, producer = mp.Pipe()
process_list.append(mp.Process(target=process_and_fit, args=(producer,)))
process_list[-1].start()
while (true):
message = consumer.recv() # blocks
if message == 'done':
break
# tab.spectra.X, tab.spectra.model = message
tab.refreshFigure()
process_list[-1].join()
return
def process_and_fit(pipe_conn):
...
pipe_conn.send('done')
def residual(params, pipe_conn):
residual_array = Y - model
pipe_conn.send('refresh') # or replace 'refresh' with (X, model)
return residual_array
One more thing to note: blocking for the consumer.recv() will probably hang the GUI thread. There are plenty of resources to mitigate this, the question "subprocess Popen blocking PyQt GUI" will help, since you should probably switch to QThreads. (Qthread: PySide, PyQt)
The advantage of using QThreads instead of Python threads is that with QThreads, since you're already in Qt's main event loop, you can have asynchronous (non-blocking) callbacks to update the UI.