def run(self, mlnFiles, evidenceDB, method, queries, engine="PyMLNs", output_filename="", params="{}", **settings):
'''
runs an MLN inference method with the given parameters
mlnFiles: list of one or more MLN input files
evidenceDB: name of the MLN database file from which to read evidence data
engine: either "PyMLNs"/"internal", "J-MLNs" or one of the keys in the configured Alchemy versions (see configMLN.py)
method: name of the inference method
queries: comma-separated list of queries
output_filename (compulsory only when using Alchemy): name of the file to which to save results
For the internal engine, specify saveResults=True as an additional settings to save the results
params: additional parameters to pass to inference method. For the internal engine, it is a comma-separated
list of assignments to parameters (dictionary-type string), for the others it's just a string of command-line
options to pass on
settings: additional settings that control the inference process, which are usually set by the GUI (see code)
returns a mapping (dictionary) from ground atoms to probability values.
For J-MLNs, results are only returned if settings are saved to a file (settings["saveResults"]=True and output_filename given)
'''
self.settings = dict(self.default_settings)
self.settings.update(settings)
input_files = mlnFiles
db = evidenceDB
query = ",".join(queries)
results_suffix = ".results"
output_base_filename = output_filename
if output_base_filename[-len(results_suffix):] == results_suffix:
output_base_filename = output_base_filename[:-len(results_suffix)]
# determine closed-world preds
cwPreds = []
if "cwPreds" in self.settings:
cwPreds = filter(lambda x: x != "", map(str.strip, self.settings["cwPreds"].split(",")))
haveOutFile = False
results = None
# engine-specific handling
if engine in ("internal", "PyMLNs"):
try:
print "\nStarting %s...\n" % method
print "\nqueries: %s...\n" % queries
# read queries
_queries = []
q = ""
for s in queries:
if q != "": q += ','
q += s
if MLN.balancedParentheses(q):
_queries.append(q)
q = ""
print "\n_queries: %s...\n" % _queries
if q != "": raise Exception("Unbalanced parentheses in queries!")
# create MLN
verbose = True
print input_files
mln = MLN.MLN(input_files, verbose=verbose, defaultInferenceMethod=MLN.InferenceMethods.byName(method))
# set closed-world predicates
for pred in cwPreds:
mln.setClosedWorldPred(pred)
# create ground MRF
mrf = mln.groundMRF(db, verbose=verbose)
# collect inference arguments
args = {"details":True, "verbose":verbose, "shortOutput":True, "debugLevel":1}
args.update(eval("dict(%s)" % params)) # add additional parameters
if args.get("debug", False) and args["debugLevel"] > 1:
print "\nground formulas:"
mrf.printGroundFormulas()
print
if self.settings["numChains"] != "":
args["numChains"] = int(self.settings["numChains"])
if self.settings["maxSteps"] != "":
args["maxSteps"] = int(self.settings["maxSteps"])
outFile = None
if self.settings["saveResults"]:
haveOutFile = True
outFile = file(output_filename, "w")
args["outFile"] = outFile
args["probabilityFittingResultFileName"] = output_base_filename + "_fitted.mln"
# check for print/write requests
if "printGroundAtoms" in args:
if args["printGroundAtoms"]:
mrf.printGroundAtoms()
if "printGroundFormulas" in args:
if args["printGroundFormulas"]:
mrf.printGroundFormulas()
if "writeGraphML" in args:
if args["writeGraphML"]:
graphml_filename = output_base_filename + ".graphml"
print "writing ground MRF as GraphML to %s..." % graphml_filename
mrf.writeGraphML(graphml_filename)
# invoke inference and retrieve results
mrf.infer(_queries, **args)
results = {}
for gndFormula, p in mrf.getResultsDict().iteritems():
results[str(gndFormula)] = p
# close output file and open if requested
if outFile != None:
outFile.close()
except:
cls, e, tb = sys.exc_info()
sys.stderr.write("Error: %s\n" % str(e))
traceback.print_tb(tb)
return results
def start(self):
#try:
# get mln, db, qf and output filename
mln = self.selected_mln.get()
db = self.selected_db.get()
qf = self.selected_qf.get()
mln_text = self.selected_mln.get_text()
db_text = self.selected_db.get_text()
qf_text = self.selected_qf.get_text()
output = self.output_filename.get()
method = self.selected_method.get()
keep_written_db = True
params = self.params.get()
# update settings
self.settings["mln"] = mln
self.settings["mln_rename"] = self.selected_mln.rename_on_edit.get()
self.settings["db"] = db
self.settings["db_rename"] = self.selected_db.rename_on_edit.get()
self.settings["method%d" % int(self.internalMode)] = method
self.settings["params%d" % int(self.internalMode)] = params
self.settings["query"] = self.query.get()
self.settings["engine"] = self.selected_engine.get()
self.settings["qf"] = qf
self.settings["output_filename"] = output
self.settings["openWorld"] = self.open_world.get()
self.settings["cwPreds"] = self.cwPreds.get()
self.settings["convertAlchemy"] = self.convert_to_alchemy.get()
self.settings["maxSteps"] = self.maxSteps.get()
self.settings["numChains"] = self.numChains.get()
self.settings["geometry"] = self.master.winfo_geometry()
self.settings["saveResults"] = self.save_results.get()
# write query
# - to file
write_query_file = False
if write_query_file:
query_file = "%s.query" % db
f = file(query_file, "w")
f.write(self.settings["query"])
f.close()
# - to settings
self.settings["queryByDB"][db] = self.settings["query"]
# write settings
pickle.dump(self.settings, file(configname, "w+"))
# hide main window
self.master.withdraw()
# some information
print "\n--- query ---\n%s" % self.settings["query"]
print "\n--- evidence (%s) ---\n%s" % (db, db_text.strip())
# engine
haveOutFile = False
if self.settings["engine"] == "internal": # internal engine
try:
print "\nStarting %s...\n" % method
# read queries
queries = []
query = ""
for s in map(str.strip, self.settings["query"].split(",")):
if query != "": query += ','
query += s
if MLN.balancedParentheses(query):
queries.append(query)
query = ""
if query != "":
raise Exception("Unbalanced parentheses in queries!")
# create MLN and evidence conjunction
mln = MLN.MLN(
mln,
verbose=True,
defaultInferenceMethod=MLN.InferenceMethods._byName.get(
method))
evidence = MLN.evidence2conjunction(
mln.combineDB(db, verbose=True))
# set closed-world predicates
cwPreds = map(str.strip, self.settings["cwPreds"].split(","))
for pred in cwPreds:
if pred != "": mln.setClosedWorldPred(pred)
# collect inference arguments
args = {
"details": True,
"verbose": True,
"shortOutput": True,
"debugLevel": 1
}
args.update(eval("dict(%s)" %
params)) # add additional parameters
if args.get("debug", False) and args["debugLevel"] > 1:
print "\nground formulas:"
mln.printGroundFormulas()
print
if self.settings["numChains"] != "":
args["numChains"] = int(self.settings["numChains"])
if self.settings["maxSteps"] != "":
args["maxSteps"] = int(self.settings["maxSteps"])
outFile = None
if self.settings["saveResults"]:
haveOutFile = True
outFile = file(output, "w")
args["outFile"] = outFile
# check for print requests
if "printGroundAtoms" in args:
mln.printGroundAtoms()
# invoke inference
results = mln.infer(queries, evidence, **args)
# close output file and open if requested
if outFile != None:
outFile.close()
except:
cls, e, tb = sys.exc_info()
sys.stderr.write("Error: %s\n" % str(e))
traceback.print_tb(tb)
else: # engine is Alchemy
haveOutFile = True
infile = mln
mlnObject = None
# explicitly convert MLN to Alchemy format, i.e. resolve weights that are arithm. expressions (on request) -> create temporary file
if self.settings["convertAlchemy"]:
print "\n--- temporary MLN ---\n"
mlnObject = MLN.MLN(mln)
infile = mln[:mln.rfind(".")] + ".alchemy.mln"
f = file(infile, "w")
mlnObject.write(f)
f.close()
mlnObject.write(sys.stdout)
print "\n---"
# get alchemy version-specific data
alchemy_version = self.alchemy_versions[self.selected_engine.get()]
print alchemy_version
print type(alchemy_version)
if type(alchemy_version) != dict:
alchemy_version = {"path": str(alchemy_version)}
usage = config.default_infer_usage
if "usage" in alchemy_version:
usage = alchemy_version["usage"]
# parse additional parameters for input files
input_files = [infile]
add_params = params.split()
i = 0
while i < len(add_params):
if add_params[i] == "-i":
input_files.append(add_params[i + 1])
del add_params[i]
del add_params[i]
continue
i += 1
# create command to execute
params = ' -i "%s" -e "%s" -r "%s" -q "%s" %s %s' % (
",".join(input_files), db, output, self.settings["query"],
self.alchemy_methods[method], " ".join(add_params))
if self.settings["numChains"] != "":
params += " %s %s" % (usage["numChains"],
self.settings["numChains"])
if self.settings["maxSteps"] != "":
params += " %s %s" % (usage["maxSteps"],
self.settings["maxSteps"])
path = alchemy_version["path"]
path2 = os.path.join(path, "bin")
if os.path.exists(path2):
path = path2
if self.settings["openWorld"] == 1:
print "\nFinding predicate names..."
if mlnObject is None:
mlnObject = MLN.MLN(mln)
params += " %s %s" % (usage["openWorld"], ",".join(
mlnObject.predicates))
command = '%s %s' % (os.path.join(path, "infer"), params)
# remove old output file (if any)
if os.path.exists(output):
os.remove(output)
# execute
print "\nStarting Alchemy..."
print "\ncommand:\n%s\n" % command
t_start = time.time()
os.system(command)
t_taken = time.time() - t_start
# print results file
if True:
print "\n\n--- output ---\n"
os.system("cat %s" % output)
print "\n"
# append information on query and mln to results file
f = file(output, "a")
f.write(
"\n\n/*\n\n--- command ---\n%s\n\n--- evidence ---\n%s\n\n--- mln ---\n%s\ntime taken: %fs\n\n*/"
% (command, db_text.strip(), mln_text.strip(), t_taken))
f.close()
# delete written db
if not (keep_written_db) and wrote_db:
os.unlink(db)
# delete temporary mln
if self.settings["convertAlchemy"] and not config_value(
"keep_alchemy_conversions", True):
os.unlink(infile)
# open results file
if haveOutFile and config.query_edit_outfile_when_done:
editor = config.editor
print 'starting editor: %s %s' % (editor, output)
run = os.spawnl
if "spawnlp" in dir(os):
run = os.spawnlp
run(os.P_NOWAIT, editor, editor, output)
# restore main window
self.master.deiconify()
self.setGeometry()
# reload the files (in case they changed)
self.selected_mln.reloadFile()
self.selected_db.reloadFile()
def start(self):
#try:
# get mln, db, qf and output filename
mln = self.selected_mln.get()
db = self.selected_db.get()
qf = self.selected_qf.get()
mln_text = self.selected_mln.get_text()
db_text = self.selected_db.get_text()
qf_text = self.selected_qf.get_text()
output = self.output_filename.get()
method = self.selected_method.get()
keep_written_db = True
params = self.params.get()
# update settings
self.settings["mln"] = mln
self.settings["mln_rename"] = self.selected_mln.rename_on_edit.get()
self.settings["db"] = db
self.settings["db_rename"] = self.selected_db.rename_on_edit.get()
self.settings["method%d" % int(self.internalMode)] = method
self.settings["params%d" % int(self.internalMode)] = params
self.settings["query"] = self.query.get()
self.settings["engine"] = self.selected_engine.get()
self.settings["qf"] = qf
self.settings["output_filename"] = output
self.settings["openWorld"] = self.open_world.get()
self.settings["cwPreds"] = self.cwPreds.get()
self.settings["convertAlchemy"] = self.convert_to_alchemy.get()
self.settings["maxSteps"] = self.maxSteps.get()
self.settings["numChains"] = self.numChains.get()
self.settings["geometry"] = self.master.winfo_geometry()
self.settings["saveResults"] = self.save_results.get()
# write query
# - to file
write_query_file = False
if write_query_file:
query_file = "%s.query" % db
f = file(query_file, "w")
f.write(self.settings["query"])
f.close()
# - to settings
self.settings["queryByDB"][db] = self.settings["query"]
# write settings
pickle.dump(self.settings, file(configname, "w+"))
# hide main window
self.master.withdraw()
# some information
print "\n--- query ---\n%s" % self.settings["query"]
print "\n--- evidence (%s) ---\n%s" % (db, db_text.strip())
# engine
haveOutFile = False
if self.settings["engine"] == "internal": # internal engine
try:
print "\nStarting %s...\n" % method
# read queries
queries = []
query = ""
for s in map(str.strip, self.settings["query"].split(",")):
if query != "": query += ','
query += s
if MLN.balancedParentheses(query):
queries.append(query)
query = ""
if query != "": raise Exception("Unbalanced parentheses in queries!")
# create MLN and evidence conjunction
mln = MLN.MLN(mln, verbose=True, defaultInferenceMethod=MLN.InferenceMethods._byName.get(method))
evidence = MLN.evidence2conjunction(mln.combineDB(db, verbose=True))
# set closed-world predicates
cwPreds = map(str.strip, self.settings["cwPreds"].split(","))
for pred in cwPreds:
if pred != "": mln.setClosedWorldPred(pred)
# collect inference arguments
args = {"details":True, "verbose":True, "shortOutput":True, "debugLevel":1}
args.update(eval("dict(%s)" % params)) # add additional parameters
if args.get("debug", False) and args["debugLevel"] > 1:
print "\nground formulas:"
mln.printGroundFormulas()
print
if self.settings["numChains"] != "":
args["numChains"] = int(self.settings["numChains"])
if self.settings["maxSteps"] != "":
args["maxSteps"] = int(self.settings["maxSteps"])
outFile = None
if self.settings["saveResults"]:
haveOutFile = True
outFile = file(output, "w")
args["outFile"] = outFile
# check for print requests
if "printGroundAtoms" in args:
mln.printGroundAtoms()
# invoke inference
results = mln.infer(queries, evidence, **args)
# close output file and open if requested
if outFile != None:
outFile.close()
except:
cls, e, tb = sys.exc_info()
sys.stderr.write("Error: %s\n" % str(e))
traceback.print_tb(tb)
else: # engine is Alchemy
haveOutFile = True
infile = mln
mlnObject = None
# explicitly convert MLN to Alchemy format, i.e. resolve weights that are arithm. expressions (on request) -> create temporary file
if self.settings["convertAlchemy"]:
print "\n--- temporary MLN ---\n"
mlnObject = MLN.MLN(mln)
infile = mln[:mln.rfind(".")]+".alchemy.mln"
f = file(infile, "w")
mlnObject.write(f)
f.close()
mlnObject.write(sys.stdout)
print "\n---"
# get alchemy version-specific data
alchemy_version = self.alchemy_versions[self.selected_engine.get()]
print alchemy_version
print type(alchemy_version)
if type(alchemy_version) != dict:
alchemy_version = {"path": str(alchemy_version)}
usage = config.default_infer_usage
if "usage" in alchemy_version:
usage = alchemy_version["usage"]
# parse additional parameters for input files
input_files = [infile]
add_params = params.split()
i = 0
while i < len(add_params):
if add_params[i] == "-i":
input_files.append(add_params[i+1])
del add_params[i]
del add_params[i]
continue
i += 1
# create command to execute
params = ' -i "%s" -e "%s" -r "%s" -q "%s" %s %s' % (",".join(input_files), db, output, self.settings["query"], self.alchemy_methods[method], " ".join(add_params))
if self.settings["numChains"] != "":
params += " %s %s" % (usage["numChains"], self.settings["numChains"])
if self.settings["maxSteps"] != "":
params += " %s %s" % (usage["maxSteps"], self.settings["maxSteps"])
path = alchemy_version["path"]
path2 = os.path.join(path, "bin")
if os.path.exists(path2):
path = path2
if self.settings["openWorld"] == 1:
print "\nFinding predicate names..."
if mlnObject is None:
mlnObject = MLN.MLN(mln)
params += " %s %s" % (usage["openWorld"], ",".join(mlnObject.predicates))
command = '%s %s' % (os.path.join(path, "infer"), params)
# remove old output file (if any)
if os.path.exists(output):
os.remove(output)
# execute
print "\nStarting Alchemy..."
print "\ncommand:\n%s\n" % command
t_start = time.time()
os.system(command)
t_taken = time.time() - t_start
# print results file
if True:
print "\n\n--- output ---\n"
os.system("cat %s" % output)
print "\n"
# append information on query and mln to results file
f = file(output, "a")
f.write("\n\n/*\n\n--- command ---\n%s\n\n--- evidence ---\n%s\n\n--- mln ---\n%s\ntime taken: %fs\n\n*/" % (command, db_text.strip(), mln_text.strip(), t_taken))
f.close()
# delete written db
if not(keep_written_db) and wrote_db:
os.unlink(db)
# delete temporary mln
if self.settings["convertAlchemy"] and not config_value("keep_alchemy_conversions", True):
os.unlink(infile)
# open results file
if haveOutFile and config.query_edit_outfile_when_done:
editor = config.editor
print 'starting editor: %s %s' % (editor, output)
run = os.spawnl
if "spawnlp" in dir(os):
run = os.spawnlp
run(os.P_NOWAIT, editor, editor, output)
# restore main window
self.master.deiconify()
self.setGeometry()
# reload the files (in case they changed)
self.selected_mln.reloadFile()
self.selected_db.reloadFile()
def run(self, mlnFiles, evidenceDB, method, queries, engine="PyMLNs", output_filename=None, params="", **settings):
'''
runs an MLN inference method with the given parameters
mlnFiles: list of one or more MLN input files
evidenceDB: name of the MLN database file from which to read evidence data
engine: either "PyMLNs"/"internal", "J-MLNs" or one of the keys in the configured Alchemy versions (see configMLN.py)
method: name of the inference method
queries: comma-separated list of queries
output_filename (compulsory only when using Alchemy): name of the file to which to save results
For the internal engine, specify saveResults=True as an additional settings to save the results
params: additional parameters to pass to inference method. For the internal engine, it is a comma-separated
list of assignments to parameters (dictionary-type string), for the others it's just a string of command-line
options to pass on
settings: additional settings that control the inference process, which are usually set by the GUI (see code)
returns a mapping (dictionary) from ground atoms to probability values.
For J-MLNs, results are only returned if settings are saved to a file (settings["saveResults"]=True and output_filename given)
'''
self.settings = dict(self.default_settings)
self.settings.update(settings)
input_files = mlnFiles
db = evidenceDB
query = queries
results_suffix = ".results"
output_base_filename = output_filename
if output_base_filename[-len(results_suffix):] == results_suffix:
output_base_filename = output_base_filename[:-len(results_suffix)]
# determine closed-world preds
cwPreds = []
if "cwPreds" in self.settings:
cwPreds = filter(lambda x: x != "", map(str.strip, self.settings["cwPreds"].split(",")))
haveOutFile = False
results = None
# engine-specific handling
if engine in ("internal", "PyMLNs"):
try:
print "\nStarting %s...\n" % method
# read queries
queries = []
q = ""
for s in map(str.strip, query.split(",")):
if q != "": q += ','
q += s
if MLN.balancedParentheses(q):
queries.append(q)
q = ""
if q != "": raise Exception("Unbalanced parentheses in queries!")
# create MLN
verbose = True
mln = MLN.MLN(input_files, verbose=verbose, defaultInferenceMethod=MLN.InferenceMethods.byName(method))
# set closed-world predicates
for pred in cwPreds:
mln.setClosedWorldPred(pred)
# create ground MRF
mrf = mln.groundMRF(db, verbose=verbose)
# collect inference arguments
args = {"details":True, "verbose":verbose, "shortOutput":True, "debugLevel":1}
args.update(eval("dict(%s)" % params)) # add additional parameters
if args.get("debug", False) and args["debugLevel"] > 1:
print "\nground formulas:"
mrf.printGroundFormulas()
print
if self.settings["numChains"] != "":
args["numChains"] = int(self.settings["numChains"])
if self.settings["maxSteps"] != "":
args["maxSteps"] = int(self.settings["maxSteps"])
outFile = None
if self.settings["saveResults"]:
haveOutFile = True
outFile = file(output_filename, "w")
args["outFile"] = outFile
args["probabilityFittingResultFileName"] = output_base_filename + "_fitted.mln"
# check for print/write requests
if "printGroundAtoms" in args:
if args["printGroundAtoms"]:
mrf.printGroundAtoms()
if "printGroundFormulas" in args:
if args["printGroundFormulas"]:
mrf.printGroundFormulas()
if "writeGraphML" in args:
if args["writeGraphML"]:
graphml_filename = output_base_filename + ".graphml"
print "writing ground MRF as GraphML to %s..." % graphml_filename
mrf.writeGraphML(graphml_filename)
# invoke inference and retrieve results
mrf.infer(queries, **args)
results = {}
for gndFormula, p in mrf.getResultsDict().iteritems():
results[str(gndFormula)] = p
# close output file and open if requested
if outFile != None:
outFile.close()
except:
cls, e, tb = sys.exc_info()
sys.stderr.write("Error: %s\n" % str(e))
traceback.print_tb(tb)
elif engine == "J-MLNs": # engine is J-MLNs (ProbCog's Java implementation)
# create command to execute
app = "MLNinfer"
params = [app, "-i", ",".join(input_files), "-e", db, "-q", query, self.jmlns_methods[method]] + shlex.split(params)
if self.settings["saveResults"]:
params += ["-r", output_filename]
if self.settings["maxSteps"] != "":
params += ["-maxSteps", self.settings["maxSteps"]]
if len(cwPreds) > 0:
params += ["-cw", ",".join(cwPreds)]
outFile = None
if self.settings["saveResults"]:
outFile = output_filename
params += ["-r", outFile]
# execute
params = map(str, params)
print "\nStarting J-MLNs..."
print "\ncommand:\n%s\n" % " ".join(params)
t_start = time.time()
call(params)
t_taken = time.time() - t_start
if outFile is not None:
results = dict(readAlchemyResults(outFile))
else: # engine is Alchemy
haveOutFile = True
infile = mlnFiles[0]
mlnObject = None
# explicitly convert MLN to Alchemy format, i.e. resolve weights that are arithm. expressions (on request) -> create temporary file
if self.settings["convertAlchemy"]:
print "\n--- temporary MLN ---\n"
mlnObject = MLN.MLN(input_files)
infile = input_files[0]
infile = infile[:infile.rfind(".")]+".alchemy.mln"
f = file(infile, "w")
mlnObject.write(f)
f.close()
mlnObject.write(sys.stdout)
input_files = [infile]
print "\n---"
# get alchemy version-specific data
alchemy_version = self.alchemy_versions[engine]
if type(alchemy_version) != dict:
alchemy_version = {"path": str(alchemy_version)}
usage = config.default_infer_usage
if "usage" in alchemy_version:
usage = alchemy_version["usage"]
# find alchemy binary
path = alchemy_version["path"]
path2 = os.path.join(path, "bin")
if os.path.exists(path2):
path = path2
alchemyInfer = os.path.join(path, "infer")
if not os.path.exists(alchemyInfer) and not os.path.exists(alchemyInfer+".exe"):
error = "Alchemy's infer/infer.exe binary not found in %s. Please configure Alchemy in python/configMLN.py" % path
tkMessageBox.showwarning("Error", error)
raise Exception(error)
# parse additional parameters for input files
add_params = shlex.split(params)
i = 0
while i < len(add_params):
if add_params[i] == "-i":
input_files.append(add_params[i+1])
del add_params[i]
del add_params[i]
continue
i += 1
# create command to execute
if output_filename is None: raise Exception("For Alchemy, provide an output filename!")
params = [alchemyInfer, "-i", ",".join(input_files), "-e", db, "-q", query, "-r", output_filename, self.alchemy_methods[method]] + add_params
if self.settings["numChains"] != "":
params += [usage["numChains"], self.settings["numChains"]]
if self.settings["maxSteps"] != "":
params += [usage["maxSteps"], self.settings["maxSteps"]]
owPreds = []
if self.settings["openWorld"]:
print "\nFinding predicate names..."
preds = MLN.getPredicateList(infile)
owPreds = filter(lambda x: x not in cwPreds, preds)
params += [usage["openWorld"], ",".join(owPreds)]
if len(cwPreds) > 0:
params += ["-cw", ",".join(cwPreds)]
# remove old output file (if any)
if os.path.exists(output_filename):
os.remove(output_filename)
pass
# execute
params = map(str, params)
print "\nStarting Alchemy..."
command = subprocess.list2cmdline(params)
print "\ncommand:\n%s\n" % " ".join(params)
t_start = time.time()
call(params)
t_taken = time.time() - t_start
# print results file
if True:
print "\n\n--- output ---\n"
results = dict(readAlchemyResults(output_filename))
for atom, prob in results.iteritems():
print "%.4f %s" % (prob, atom)
print "\n"
# append information on query and mln to results file
f = file(output_filename, "a")
dbfile = file(db, "r")
db_text = dbfile.read()
dbfile.close()
infile = file(infile, "r")
mln_text = infile.read()
infile.close()
f.write("\n\n/*\n\n--- command ---\n%s\n\n--- evidence ---\n%s\n\n--- mln ---\n%s\ntime taken: %fs\n\n*/" % (command, db_text.strip(), mln_text.strip(), t_taken))
f.close()
# delete temporary mln
if self.settings["convertAlchemy"] and not config_value("keep_alchemy_conversions", True):
os.unlink(infile)
# open output file in editor
if False and haveOutFile and config.query_edit_outfile_when_done: # this is mostly useless
editor = config.editor
params = [editor, output_filename]
print 'starting editor: %s' % subprocess.list2cmdline(params)
subprocess.Popen(params, shell=False)
return results