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Robustness fixes in test suite machinery....
Robustness fixes in test suite machinery. Added a module-level INSTALLED flag, which can be set to false if the test suite is being run in-place (without ipython having been installed at all). This is because how we call and import things must be done differently depending on whether the code is installed or is being run in-place. The only ones that can know this reliably are the entry-point scripts, so those are responsible for setting this flag. Also made the code that validates ipython in subprocesses report errors better, by checking stderr for errors before validating stdout output, as anything on stderr will be likely informative of the real problem.

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clearcmd.py
87 lines | 2.6 KiB | text/x-python | PythonLexer
# -*- coding: utf-8 -*-
""" IPython extension: add %clear magic """
from IPython.core import ipapi
import gc
ip = ipapi.get()
def clear_f(self,arg):
""" Clear various data (e.g. stored history data)
%clear in - clear input history
%clear out - clear output history
%clear shadow_compress - Compresses shadow history (to speed up ipython)
%clear shadow_nuke - permanently erase all entries in shadow history
%clear dhist - clear dir history
%clear array - clear only variables that are NumPy arrays
Examples:
In [1]: clear in
Flushing input history
In [2]: clear shadow_compress
Compressing shadow history
In [3]: clear shadow_nuke
Erased all keys from shadow history
In [4]: clear dhist
Clearing directory history
"""
api = self.getapi()
user_ns = self.user_ns # local lookup, heavily used
for target in arg.split():
if target == 'out':
print "Flushing output cache (%d entries)" % len(user_ns['_oh'])
self.outputcache.flush()
elif target == 'in':
print "Flushing input history"
pc = self.outputcache.prompt_count + 1
for n in range(1, pc):
key = '_i'+`n`
user_ns.pop(key,None)
try:
del user_ns[key]
except: pass
# must be done in-place
self.input_hist[:] = ['\n'] * pc
self.input_hist_raw[:] = ['\n'] * pc
elif target == 'array':
# Support cleaning up numpy arrays
try:
from numpy import ndarray
# This must be done with items and not iteritems because we're
# going to modify the dict in-place.
for x,val in user_ns.items():
if isinstance(val,ndarray):
del user_ns[x]
except AttributeError:
print "Clear array only works if Numpy is available."
elif target == 'shadow_compress':
print "Compressing shadow history"
api.db.hcompress('shadowhist')
elif target == 'shadow_nuke':
print "Erased all keys from shadow history "
for k in ip.db.keys('shadowhist/*'):
del ip.db[k]
elif target == 'dhist':
print "Clearing directory history"
del user_ns['_dh'][:]
gc.collect()
# Activate the extension
ip.define_magic("clear",clear_f)
import ipy_completers
ipy_completers.quick_completer(
'%clear','in out shadow_nuke shadow_compress dhist')