0 and args.count > count:\n print(\"Not enough html pages in the Web Site. Minimum %s > %s found in the Web Site.\" % (args.count, count))\n sys.exit(4)\n\n # Done for now\n print(\"Web site successfully generated!\")\n\n # It is much easier to do all the below, if we chdir()\n os.chdir(sourcepath)\n\n # Copy to result branch\n print(\"Copying web site to branch:\", args.outputbranch)\n\n try:\n subprocess.run((GIT, 'rev-parse', '--verify', \"origin/%s\" % args.outputbranch),\n check=True)\n print(\"- Doing fresh checkout of branch %s\" % args.outputbranch)\n subprocess.run((GIT, 'checkout', args.outputbranch, '-f'), check=True)\n subprocess.run((GIT, 'pull'), check=True)\n except:\n print(\"- Branch %s does not exist (yet), creating it...\" % args.outputbranch)\n # If .asf.yaml exists, which it should, make a copy of it in memory for later\n asfyml = os.path.join(sourcepath, '.asf.yaml')\n myyaml = None\n if os.path.exists(asfyml):\n myyaml = open(asfyml).read()\n subprocess.run((GIT, 'checkout', '--orphan', args.outputbranch), check=True)\n subprocess.run((GIT, 'rm', '-rf', '.'), check=True)\n # Add .asf.yaml back in if we found it.\n if myyaml:\n open(asfyml, \"w\").write(myyaml)\n subprocess.run((GIT, 'add', '.asf.yaml'), check=True)\n\n print(\"- Adding new content to branch\")\n # RM output dir if it already exists\n outputdir = os.path.join(sourcepath, 'output')\n if os.path.isdir(outputdir):\n print(\"Removing existing output dir %s\" % outputdir)\n shutil.rmtree(outputdir)\n shutil.move(buildpath, outputdir)\n subprocess.run((GIT, 'add', 'output/'), check=True)\n\n # Check if there are any changes.\n cp = subprocess.run((GIT, 'diff', '--cached', '--quiet'))\n if cp.returncode == 0:\n # There were no differences reported.\n print('Nothing new to commit. Ignoring this build.')\n else:\n print(\"- Committing to %s\" % args.source)\n subprocess.run((GIT, 'commit', '-m', 'Automatic Site Publish by Buildbot'), check=True)\n\n # If we're not in production, then avoid pushing changes.\n if IS_PRODUCTION:\n print('- Pushing changes, for publishing')\n subprocess.run((GIT, 'push', args.source, args.outputbranch), check=True)\n\n print('Success. Done.')\n # for dev/test provide viewing instructions\n if not IS_PRODUCTION:\n if args.listen:\n try:\n subprocess.run(('pelican','-l'), check=True)\n except KeyboardInterrupt:\n pass\n else:\n print(f'To test output:\\ncd {sourcepath}; pelican -l')","def setup_binutils():\n # Set up binutils\n global binutils_subdir\n binutils_subdir = \"binutils\"\n if binutils_version:\n binutils_subdir = \"binutils-%s\" % binutils_version\n if not os.path.exists(binutils_subdir):\n docmd(\"wget http://ftpmirror.gnu.org/binutils/\"\n \"%s.tar.bz2\" % binutils_subdir)\n docmd(\"tar jxf %s.tar.bz2\" % binutils_subdir)\n else:\n if not os.path.exists(binutils_subdir):\n doscmd(\"git clone --depth 1 %s binutils\" % binutils_git)\n # Configure binutils\n if not os.path.exists(binutils_build_dir):\n docmd(\"mkdir %s\" % binutils_build_dir)\n dochdir(binutils_build_dir)\n doscmd(\"../%s/configure --prefix=%s --target=%s \"\n \"%s \" % (binutils_subdir,\n cross_prefix,\n flag_target_arch,\n flag_use_multilib))\n doscmd(\"make %s \" % flag_parfactor)\n doscmd(\"make %s install\" % flag_parfactor)\n dochdir(\"..\")","def build():\n local('pelican -o {} -s pelicanconf.py'.format(env.deploy_path))","def build(config_file, install=False):\n return exec_fn(lambda: _build(config_file, install))","def run_distutils():\n from distutils.core import setup, Extension\n\n source_dir = os.getcwd()\n build_dir = Params.cmake_binary_dir\n \n gsched_module = Extension('_gsched',\n sources=[build_dir+'/gsched_wrap.c', \n build_dir+'/gsched.c'],\n include_dirs = [source_dir+'/include'],\n libraries = ['gsched'],\n extra_compile_args = ['-fPIC'],\n library_dirs = [build_dir, source_dir], \n \n )\n\n\n# sdf_seq_module = Extension('_sdf_seq',\n# sources=[source_dir+'/libgsched/sdf_seq_wrap.c',\n# source_dir+'/libgsched/sdf_seq.c'],\n# include_dirs = [source_dir+'/include'], \n #)\n \n \n setup(name='pygsched',\n version='1.0',\n description=\"Group Scheduling Python API bindings.\",\n url='http://www.ittc.ku.edu/kusp',\n package_dir={'': '.'},\n include_dirs = [source_dir+'/include'],\n ext_modules = [gsched_module],\n # sdf_seq_module],\n py_modules = ['gschedapi'],\n scripts = ['tools/gschedctrl',\n 'tools/gschedexec',\n 'tools/gschedpprint',\n 'tools/gschedsnapshot',\n 'tools/gschedsh',\n ],\n packages=['pygsched','pygsched.gsconsole']\n )\n pass","def build(which):\n return subprocess.Popen([p['paths']['w2l'],'obj',os.path.abspath(which)]).wait()","def main():\n\n parser = argparse.ArgumentParser(description='Merging rooms and students')\n parser.add_argument('student_dir', type=str, help='Students file path.')\n parser.add_argument('room_dir', type=str, help='Rooms file path')\n parser.add_argument('config_dir', type=str, help='Config file path')\n parser.add_argument('format', choices=['json', 'xml'], help='The output format.')\n args = parser.parse_args()\n make(args.student_dir, args.room_dir, args.config_dir, args.format)","def do_collect(self, scriptname, targetdir, excludes=[], distlib='lib', zip_lib=False, verbose=False):\n self.site_packages = os.path.normcase(distutils.sysconfig.get_python_lib(standard_lib=False))\n self.standard_lib = os.path.normcase(distutils.sysconfig.get_python_lib(standard_lib=True))\n self.sys_prefix = os.path.normcase(sys.prefix)\n \n self.verbose = verbose\n self.log(\"\\nLibCollect v%s running in verbose mode\\n\" % version)\n \n # Initial preparation to create the lib directory\n #\n if os.path.exists(targetdir): \n self.log(\"Directory '%s' exists. Removing it.\" % targetdir)\n shutil.rmtree(targetdir)\n \n libdir = os.path.join(targetdir, distlib)\n self.log(\"Creating path '%s'\" % libdir)\n mkpath(libdir)\n \n # Find the modules we need to collect\n # \n modules = self.find_modules(scriptname, excludes, verbose)\n \n self.log(\"Collecting modules into '%s'\" % libdir)\n # Collect the modules in the lib directory\n #\n for modname, modtype, modfile in modules:\n modname_components = modname.split('.')\n \n if modtype == 'm':\n if len(modname_components) > 1:\n new_path = os.path.join(libdir, *modname_components[0:-1])\n else:\n new_path = libdir\n elif modtype == 'P':\n new_path = os.path.join(libdir, *modname_components)\n else:\n assert False\n \n mkpath(new_path)\n shutil.copy(modfile, new_path)\n \n os.chdir(targetdir)\n \n if zip_lib:\n self.log(\"Zipping directory '%s' into '%s'\" % (libdir, libdir + '.zip'))\n make_zipfile(distlib, distlib)\n self.log(\"Removing directory '%s'\" % libdir)\n shutil.rmtree(distlib)\n path_add = \"os.path.join('\" + distlib + \".zip', '\" + distlib + \"')\"\n else:\n path_add = \"'\" + distlib + \"'\"\n \n # Create the loader script\n #\n self.log(\"Writing loader script: %s\" % scriptname)\n loader = open(os.path.basename(scriptname), 'w')\n loader_name = os.path.splitext(scriptname)[0]\n loader.write(\"import os, sys, runpy\\n\")\n loader.write(\"sys.path.insert(0, %s)\\n\" % path_add)\n loader.write(\"runpy.run_module('%s', run_name=\\\"__main__\\\", alter_sys=True)\\n\" % loader_name)\n loader.close()","def main():\n\n args = parse_arguments()\n show_parameters(args)\n\n bundleDefinitions = get_bundle_definitions(args.bfile)\n show_bundle_definitions(bundleDefinitions)\n\n check_definition_integrity(bundleDefinitions)\n\n bundlesForConfig = determine_bundles_for_config(args.config, bundleDefinitions)\n show_bundles_for_config(args.config, bundlesForConfig)\n\n output_result(bundlesForConfig, args.ofile)\n\n sys.exit(0)","def build(setup_kwargs):\n setup_kwargs.update({\n 'ext_modules': [\n cpp_extension.CppExtension(\n 'torch_persistent_homology.persistent_homology_cpu',\n ['torch_persistent_homology/perisistent_homology_cpu.cpp'],\n extra_link_args=[\n '-Wl,-rpath,' + library_path\n for library_path in torch_library_paths]\n )\n ],\n 'cmdclass': {\n 'build_ext': cpp_extension.BuildExtension\n }\n })","def main(argv):\n target = argv[1] if len(argv) >= 2 else 'all'\n if target not in ('all', 'flatbuffers', 'webp', 'clean'):\n sys.stderr.write('No rule to build target %s.\\n' % target)\n\n if target in ('all', 'flatbuffers'):\n try:\n generate_flatbuffer_binaries()\n except BuildError as error:\n handle_build_error(error)\n return 1\n if target in ('all', 'webp'):\n try:\n generate_webp_textures()\n except BuildError as error:\n handle_build_error(error)\n return 1\n if target == 'clean':\n try:\n clean()\n except OSError as error:\n sys.stderr.write('Error cleaning: %s' % str(error))\n return 1\n\n return 0","def build(ctx, arch, type, build_num, verbose, goal, extra_args_str):\n # Run the supplied build script if there is one, otherwise assume cmake\n # Parse file to find requirements then check that they exist, then build\n project_config = ctx.obj.project_config()\n component = ComponentConfiguration(project_config['components'][0])\n spec = component.get_spec(goal, arch, type)\n build_args = {}\n extra_args = parse_key_value_pairs(extra_args_str)\n build_args.update(spec.build_vars())\n build_args.update(extra_args)\n add_version_args(ctx.obj.repo_root, build_num, build_args)\n if spec.build_script() is None:\n cmake_build(ctx.obj.repo_root, spec.build_arch(), spec.build_type(), spec.build_goal(), verbose, build_args)\n else:\n script_build(ctx.obj.repo_root, spec, build_args, verbose)\n try:\n ctx.obj.build_server().publish_artifacts(spec.build_artifacts())\n except click.ClickException:\n pass","def gather() -> None:\n # pylint: disable=too-many-locals\n\n # First off, clear out any existing output.\n existing_dirs = [\n os.path.join('src/external', d) for d in os.listdir('src/external')\n if d.startswith('python-') and d != 'python-notes.txt'\n ]\n existing_dirs += [\n os.path.join('assets/src', d) for d in os.listdir('assets/src')\n if d.startswith('pylib-')\n ]\n for existing_dir in existing_dirs:\n efrotools.run('rm -rf \"' + existing_dir + '\"')\n\n for buildtype in ['debug', 'release']:\n debug = buildtype == 'debug'\n bsuffix = '_debug' if buildtype == 'debug' else ''\n bsuffix2 = '-debug' if buildtype == 'debug' else ''\n\n libname = 'python' + PYTHON_VERSION_MAJOR + ('dm' if debug else 'm')\n\n bases = {\n 'mac':\n f'build/python_apple_mac{bsuffix}/build/macOS',\n 'ios':\n f'build/python_apple_ios{bsuffix}/build/iOS',\n 'tvos':\n f'build/python_apple_tvos{bsuffix}/build/tvOS',\n 'android_arm':\n f'build/python_android_arm{bsuffix}/build/sysroot',\n 'android_arm64':\n f'build/python_android_arm64{bsuffix}/build/sysroot',\n 'android_x86':\n f'build/python_android_x86{bsuffix}/build/sysroot',\n 'android_x86_64':\n f'build/python_android_x86_64{bsuffix}/build/sysroot'\n }\n\n # Note: only need pylib for the first in each group.\n builds: List[Dict[str, Any]] = [{\n 'name':\n 'macos',\n 'group':\n 'apple',\n 'headers':\n bases['mac'] + '/Support/Python/Headers',\n 'libs': [\n bases['mac'] + '/Support/Python/libPython.a',\n bases['mac'] + '/Support/OpenSSL/libOpenSSL.a',\n bases['mac'] + '/Support/XZ/libxz.a'\n ],\n 'pylib':\n (bases['mac'] + '/python/lib/python' + PYTHON_VERSION_MAJOR),\n }, {\n 'name':\n 'ios',\n 'group':\n 'apple',\n 'headers':\n bases['ios'] + '/Support/Python/Headers',\n 'libs': [\n bases['ios'] + '/Support/Python/libPython.a',\n bases['ios'] + '/Support/OpenSSL/libOpenSSL.a',\n bases['ios'] + '/Support/XZ/libxz.a'\n ],\n }, {\n 'name':\n 'tvos',\n 'group':\n 'apple',\n 'headers':\n bases['tvos'] + '/Support/Python/Headers',\n 'libs': [\n bases['tvos'] + '/Support/Python/libPython.a',\n bases['tvos'] + '/Support/OpenSSL/libOpenSSL.a',\n bases['tvos'] + '/Support/XZ/libxz.a'\n ],\n }, {\n 'name':\n 'android_arm',\n 'group':\n 'android',\n 'headers':\n bases['android_arm'] + f'/usr/include/{libname}',\n 'libs': [\n bases['android_arm'] + f'/usr/lib/lib{libname}.a',\n bases['android_arm'] + '/usr/lib/libssl.a',\n bases['android_arm'] + '/usr/lib/libcrypto.a',\n bases['android_arm'] + '/usr/lib/liblzma.a',\n bases['android_arm'] + '/usr/lib/libsqlite3.a'\n ],\n 'libinst':\n 'android_armeabi-v7a',\n 'pylib': (bases['android_arm'] + '/usr/lib/python' +\n PYTHON_VERSION_MAJOR),\n }, {\n 'name': 'android_arm64',\n 'group': 'android',\n 'headers': bases['android_arm64'] + f'/usr/include/{libname}',\n 'libs': [\n bases['android_arm64'] + f'/usr/lib/lib{libname}.a',\n bases['android_arm64'] + '/usr/lib/libssl.a',\n bases['android_arm64'] + '/usr/lib/libcrypto.a',\n bases['android_arm64'] + '/usr/lib/liblzma.a',\n bases['android_arm64'] + '/usr/lib/libsqlite3.a'\n ],\n 'libinst': 'android_arm64-v8a',\n }, {\n 'name': 'android_x86',\n 'group': 'android',\n 'headers': bases['android_x86'] + f'/usr/include/{libname}',\n 'libs': [\n bases['android_x86'] + f'/usr/lib/lib{libname}.a',\n bases['android_x86'] + '/usr/lib/libssl.a',\n bases['android_x86'] + '/usr/lib/libcrypto.a',\n bases['android_x86'] + '/usr/lib/liblzma.a',\n bases['android_x86'] + '/usr/lib/libsqlite3.a'\n ],\n 'libinst': 'android_x86',\n }, {\n 'name': 'android_x86_64',\n 'group': 'android',\n 'headers': bases['android_x86_64'] + f'/usr/include/{libname}',\n 'libs': [\n bases['android_x86_64'] + f'/usr/lib/lib{libname}.a',\n bases['android_x86_64'] + '/usr/lib/libssl.a',\n bases['android_x86_64'] + '/usr/lib/libcrypto.a',\n bases['android_x86_64'] + '/usr/lib/liblzma.a',\n bases['android_x86_64'] + '/usr/lib/libsqlite3.a'\n ],\n 'libinst': 'android_x86_64',\n }]\n\n for build in builds:\n\n grp = build['group']\n builddir = f'src/external/python-{grp}{bsuffix2}'\n header_dst = os.path.join(builddir, 'include')\n lib_dst = os.path.join(builddir, 'lib')\n assets_src_dst = f'assets/src/pylib-{grp}'\n\n # Do some setup only once per group.\n if not os.path.exists(builddir):\n efrotools.run('mkdir -p \"' + builddir + '\"')\n efrotools.run('mkdir -p \"' + lib_dst + '\"')\n\n # Only pull modules into game assets on release pass.\n if not debug:\n # Copy system modules into the src assets\n # dir for this group.\n efrotools.run('mkdir -p \"' + assets_src_dst + '\"')\n efrotools.run(\n 'rsync --recursive --include \"*.py\"'\n ' --exclude __pycache__ --include \"*/\" --exclude \"*\" \"'\n + build['pylib'] + '/\" \"' + assets_src_dst + '\"')\n\n # Prune a bunch of modules we don't need to cut\n # down on size.\n prune = [\n 'config-*', 'idlelib', 'lib-dynload', 'lib2to3',\n 'multiprocessing', 'pydoc_data', 'site-packages',\n 'ensurepip', 'tkinter', 'wsgiref', 'distutils',\n 'turtle.py', 'turtledemo', 'test', 'sqlite3/test',\n 'unittest', 'dbm', 'venv', 'ctypes/test', 'imaplib.py',\n '_sysconfigdata_*'\n ]\n efrotools.run('cd \"' + assets_src_dst + '\" && rm -rf ' +\n ' '.join(prune))\n\n # Some minor filtering to system scripts:\n # on iOS/tvOS, addusersitepackages() leads to a crash\n # due to _sysconfigdata_dm_ios_darwin module not existing,\n # so let's skip that.\n fname = f'{assets_src_dst}/site.py'\n txt = efrotools.readfile(fname)\n txt = efrotools.replace_one(\n txt,\n ' known_paths = addusersitepackages(known_paths)',\n ' # efro tweak: this craps out on ios/tvos.\\n'\n ' # (and we don\\'t use it anyway)\\n'\n ' # known_paths = addusersitepackages(known_paths)')\n efrotools.writefile(fname, txt)\n\n # Copy in a base set of headers (everything in a group should\n # be using the same headers)\n efrotools.run(f'cp -r \"{build[\"headers\"]}\" \"{header_dst}\"')\n\n # Clear whatever pyconfigs came across; we'll build our own\n # universal one below.\n efrotools.run('rm ' + header_dst + '/pyconfig*')\n\n # Write a master pyconfig header that reroutes to each\n # platform's actual header.\n with open(header_dst + '/pyconfig.h', 'w') as hfile:\n hfile.write(\n '#if BA_OSTYPE_MACOS\\n'\n '#include \"pyconfig-macos.h\"\\n\\n'\n '#elif BA_OSTYPE_IOS\\n'\n '#include \"pyconfig-ios.h\"\\n\\n'\n '#elif BA_OSTYPE_TVOS\\n'\n '#include \"pyconfig-tvos.h\"\\n\\n'\n '#elif BA_OSTYPE_ANDROID and defined(__arm__)\\n'\n '#include \"pyconfig-android_arm.h\"\\n\\n'\n '#elif BA_OSTYPE_ANDROID and defined(__aarch64__)\\n'\n '#include \"pyconfig-android_arm64.h\"\\n\\n'\n '#elif BA_OSTYPE_ANDROID and defined(__i386__)\\n'\n '#include \"pyconfig-android_x86.h\"\\n\\n'\n '#elif BA_OSTYPE_ANDROID and defined(__x86_64__)\\n'\n '#include \"pyconfig-android_x86_64.h\"\\n\\n'\n '#else\\n'\n '#error unknown platform\\n\\n'\n '#endif\\n')\n\n # Now copy each build's config headers in with unique names.\n cfgs = [\n f for f in os.listdir(build['headers'])\n if f.startswith('pyconfig')\n ]\n\n # Copy config headers to their filtered names.\n for cfg in cfgs:\n out = cfg.replace('pyconfig', 'pyconfig-' + build['name'])\n if cfg == 'pyconfig.h':\n\n # For platform's root pyconfig.h we need to filter\n # contents too (those headers can themselves include\n # others; ios for instance points to a arm64 and a\n # x86_64 variant).\n contents = efrotools.readfile(build['headers'] + '/' + cfg)\n contents = contents.replace('pyconfig',\n 'pyconfig-' + build['name'])\n efrotools.writefile(header_dst + '/' + out, contents)\n else:\n # other configs we just rename\n efrotools.run('cp \"' + build['headers'] + '/' + cfg +\n '\" \"' + header_dst + '/' + out + '\"')\n\n # Copy in libs. If the lib gave a specific install name,\n # use that; otherwise use name.\n targetdir = lib_dst + '/' + build.get('libinst', build['name'])\n efrotools.run('rm -rf \"' + targetdir + '\"')\n efrotools.run('mkdir -p \"' + targetdir + '\"')\n for lib in build['libs']:\n efrotools.run('cp \"' + lib + '\" \"' + targetdir + '\"')\n\n print('Great success!')","def main():\n setup(**setup_params)","def lib(names, sources=[], requirements=[], default_build=[], usage_requirements=[]):\n assert is_iterable_typed(names, basestring)\n assert is_iterable_typed(sources, basestring)\n assert is_iterable_typed(requirements, basestring)\n assert is_iterable_typed(default_build, basestring)\n assert is_iterable_typed(usage_requirements, basestring)\n if len(names) > 1:\n if any(r.startswith('<name>') for r in requirements):\n get_manager().errors()(\"When several names are given to the 'lib' rule\\n\" +\n \"it is not allowed to specify the <name> feature.\")\n\n if sources:\n get_manager().errors()(\"When several names are given to the 'lib' rule\\n\" +\n \"it is not allowed to specify sources.\")\n\n project = get_manager().projects().current()\n result = []\n\n for name in names:\n r = requirements[:]\n\n # Support \" lib a ; \" and \" lib a b c ; \" syntax.\n if not sources and not any(r.startswith(\"<name>\") for r in requirements) \\\n and not any(r.startswith(\"<file\") for r in requirements):\n r.append(\"<name>\" + name)\n\n result.append(targets.create_typed_metatarget(name, \"LIB\", sources,\n r,\n default_build,\n usage_requirements))\n return result","def build_orc8r():\n subprocess.check_call('./build.py -a', shell=True, cwd=orc8_docker_path)","def main():\n parser = argparse.ArgumentParser(description=\"Wrapper for the GROMACS make_ndx module.\",\n formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\n parser.add_argument('-c', '--config', required=False, help=\"This file can be a YAML file, JSON file or JSON string\")\n\n # Specific args of each building block\n required_args = parser.add_argument_group('required arguments')\n required_args.add_argument('--input_structure_path', required=True)\n required_args.add_argument('--output_ndx_path', required=True)\n parser.add_argument('--input_ndx_path', required=False)\n\n args = parser.parse_args()\n config = args.config if args.config else None\n properties = settings.ConfReader(config=config).get_prop_dic()\n\n # Specific call of each building block\n make_ndx(input_structure_path=args.input_structure_path,\n output_ndx_path=args.output_ndx_path,\n input_ndx_path=args.input_ndx_path,\n properties=properties)","def install():\n build()\n sh(\"%s setup.py develop\" % PYTHON)","def h(options, buildout, version, opts):\n cwd = os.getcwd()\n md = options['compile-directory']\n c = os.path.join(md, 'configure.py')\n os.chdir(md)\n p = buildout['p'][version]\n opts = ' '.join(opts.split())\n cmd = [p, c, opts]\n print \"Running: %s\" % ' '.join(cmd)\n ret = os.system(' '.join(cmd))\n if ret > 0: raise Exception,('Cannot confiure')\n os.chdir(cwd)","def setUpModule():\n args = [\"bowtie-build\",REF_FILES[\"yeast_fasta\"],_ebwt]\n with open(os.devnull,\"a\") as null:\n try:\n subprocess.check_call(args,stdout=null,stderr=subprocess.STDOUT)\n except CalledProcessError as e:\n print(\"Could not build bowtie index for crossmap test.\")\n raise e","def make(args: Namespace) -> None:\n\n # Unless specifically asked to ignore Langkit_Support, make sure it is\n # built and available to build Libpythonlang and Liblktlang.\n if not args.no_langkit_support:\n build_langkit_support(args)\n add_to_path(os.environ, \"GPR_PROJECT_PATH\", str(SUPPORT_ROOT))\n\n # We need to clean the build space of the langkit libraries we depend\n # upon before we run langkit again. Else, if we installed a newer version\n # of GNAT since those libraries were built, what will happen is that:\n #\n # 1. Langkit will try loading them.\n # 2. This will cause an uncaught exception trying to load some dynamic\n # library from the compiler, preventing langkit to run.\n # 3. Langkit cannot be used to recompile libpythonlang and liblktlang to\n # newer versions.\n if not args.lib:\n shutil.rmtree(PYTHON_LIB_ROOT / 'build', ignore_errors=True)\n shutil.rmtree(LKT_LIB_ROOT / 'build', ignore_errors=True)\n\n lib_types = \",\".join(l.name for l in args.library_types)\n base_argv = [\n sys.executable, \"./manage.py\",\n \"make\", \"-P\",\n \"-Dgnu-full\",\n f\"--library-types={lib_types}\",\n f\"--build-mode={args.build_mode}\",\n f\"-j{args.jobs}\",\n ]\n\n # Forward gargs to each manage.py script\n for gargs in args.gargs or []:\n base_argv.append(f\"--gargs={gargs}\")\n\n libs = selected_libs(args)\n m1: Optional[subprocess.Popen] = None\n m2: Optional[subprocess.Popen] = None\n if \"python\" in libs:\n m1 = subprocess.Popen(\n base_argv + [\"--disable-warning\", \"undocumented-nodes\"],\n cwd=PYTHON_LIB_ROOT\n )\n if \"lkt\" in libs:\n m2 = subprocess.Popen(base_argv, cwd=LKT_LIB_ROOT)\n\n if m1:\n m1.wait()\n assert m1.returncode == 0\n if m2:\n m2.wait()\n assert m2.returncode == 0\n\n # Unless disabled, run mypy to type check Langkit itself. We need to do\n # this after building liblktlang and libythonlang as Langkit depend on\n # them.\n if not args.no_mypy:\n run_mypy(args)","def main():\n parse = argparse.ArgumentParser(\n prog=\"Doctopus\",\n usage=\"\\ndoctopus project [-h] [-t {ziyan, chitu} ] [-v]\")\n parse.add_argument('project', help='project name', nargs='?')\n parse.add_argument('-t',\n '--target',\n choices=['ziyan', 'chitu'],\n default='ziyan',\n help='selelct the target, default ziyan')\n parse.add_argument('-v',\n '--version',\n action='version',\n default=None,\n version='%(prog)s {}'.format(version_))\n project = parse.parse_args().project\n target = parse.parse_args().target\n version = parse.parse_args().version\n\n if not project:\n parse.print_help()\n\n elif version:\n print(version)\n\n elif target == 'ziyan':\n make_ziyan(project)\n\n elif target == 'chitu':\n make_chitu(project)","def main(source):\n pass","def _install():\n download_file='http://www.ipol.im/pub/art/2015/136/inpaint_8.tgz'\n tools.download_and_extract(download_file) \n this_file_path=os.path.dirname(__file__)\n subprocess.call(' mkdir build; cd build; cmake ..; make', shell=True,cwd=exec_folder)","def build(parser):\n parser.add_argument(\n '-i', '--identity-file',\n help=(\n 'A SSH private key file which may be used to pull down '\n 'repositories when building.'\n ),\n )\n parser.add_argument(\n '-e', '--env',\n action='append',\n default=[],\n help=(\n 'Add environ variables to the build. These may be accessed in '\n 'the build scripts. Each variable should be of the format '\n 'KEY=VALUE. This may be used to pass in credentials required '\n 'to access private repositories. May be specified more than once.'\n ),\n )\n parser.add_argument(\n '-b', '--build-dir',\n default=os.getcwd(),\n help=(\n 'This folder should be accessible from the docker instance.'\n ),\n )\n parser.add_argument(\n '--archive',\n help=(\n 'Archive the build files into a local tarball.'\n ),\n )\n parser.add_argument(\n '--archive-only',\n action='store_true',\n default=False,\n help=(\n 'Skip tagging and building the runner image.'\n ),\n )\n parser.add_argument(\n '-t', '--tag',\n help=(\n 'Tag to apply to the built image. '\n 'This will default to the current date/time.'\n ),\n )\n parser.add_argument(\n '--no-cache',\n dest='use_cache',\n action='store_false',\n default=True,\n help=(\n 'Do not mount a cache volume when compiling the app.'\n ),\n )\n parser.add_argument(\n '--cache',\n metavar='CONTAINER:PATH',\n help=(\n 'An optional volume or location for the cache. The format is '\n '\"<volume_id>:<path>\" where the \"volume_id\" must be the '\n 'name or hash of an existing volume. The \"path\" is an absolute '\n 'path to the cache folder/volume within the build container.'\n '\\n\\n'\n 'By default a container will be created by mangling the name of '\n 'the app by appending \"__buildcache\" (e.g. \"myapp__buildcache\").'\n '\\n\\n'\n 'This option is ignored if --no-cache is specified.'\n '\\n\\n'\n 'The \"volume_id\" may be an absolute path on the host filesystem.'\n '\\n\\n'\n 'The \"path\" may be dropped, in which case it will default to '\n '/tmp/cache inside the build container.'\n '\\n\\n'\n 'Examples:'\n '\\n\\n'\n ' # custom volume with default path\\n'\n ' --cache my_cache'\n '\\n\\n'\n ' # custom path inside of volume\\n'\n ' --cache my_cache:/tmp/cache'\n '\\n\\n'\n ' # host filesystem\\n'\n ' --cache /tmp/cache'\n ),\n )\n parser.add_argument(\n '--rebuild-cache',\n action='store_true',\n default=False,\n help=(\n 'Delete any cached artifacts prior to building.'\n ),\n )\n parser.add_argument(\n '--skip-cleanup',\n action='store_true',\n default=False,\n help=(\n 'Skip removal of images and containers.'\n ),\n )\n parser.add_argument(\n 'app',\n help=(\n 'Path to an application folder with a meta.yml file'\n ),\n )","def genLibData(self):\n import mush\n tsMain = string.Template(mush.libGenMain)\n tsIfAltId = string.Template(mush.libGenIfAltId)\n #--Data Records\n for id in ('lib_action','lib_actionCount'):\n glob = self.getRecord('GLOB',id,Glob)\n (glob.type, glob.value) = ('s',0)\n glob.setChanged()\n setAllCode = 'begin lib_setAllGS\\n'\n setNoneCode = 'begin lib_setNoneGS\\n'\n for libId in self.libList:\n (srcId,altId) = self.libMap[libId]\n srcBook = self.srcBooks.get(srcId)[0]\n if not srcBook:\n print '%s: Missing source: %s' % (libId,srcId)\n continue\n #--Global\n glob = self.getRecord('GLOB',libId+'G',Glob)\n (glob.type, glob.value) = ('s',0)\n glob.setChanged()\n #--Script\n scriptId = libId+'LS'\n script = self.getRecord('SCPT',scriptId,Scpt)\n scriptCode = tsMain.substitute(\n libId=libId, srcId=srcId, ifAltId=(\n (altId and tsIfAltId.substitute(libId=libId,altId=altId)) or ''))\n script.setCode(scriptCode)\n script.setChanged()\n #--Book\n srcBook.load(unpack=True)\n book = self.getRecord('BOOK',libId,Book)\n book.model = srcBook.model\n book.title = srcBook.title\n book.icon = srcBook.icon\n book.text = srcBook.text\n book.script = scriptId\n book.setChanged()\n #--Set Scripts\n setAllCode += 'set %sG to 1\\n' % (libId,)\n setNoneCode += 'set %sG to 0\\n' % (libId,)\n #--Set scripts\n for id,code in (('lib_setAllGS',setAllCode),('lib_setNoneGS',setNoneCode)):\n code += ';--Done\\nstopScript %s\\nend\\n' % (id,)\n script = self.getRecord('SCPT',id,Scpt)\n script.setCode(code)\n script.setChanged()","def build_pyinstaller_arguments(\n python_executable_path, starcraft2_path, python_sc2_path, run_file_path, output_folder_path, ecryption_key_path\n) -> list:\n arguments = []\n\n arguments.append(python_executable_path)\n\n # Disable asserts, does not seem to work anymore?\n # arguments.append(\"-OO\")\n\n arguments.append(\"-m\")\n arguments.append(\"PyInstaller\")\n\n arguments.append(\"--onefile\")\n\n sc2_dlls = [\"icuin52.dll\", \"icuuc52.dll\", \"icudt52.dll\"]\n for sc2_dll in sc2_dlls:\n dll_path = os.path.join(starcraft2_path, sc2_dll)\n arguments.append(\"--add-binary\")\n arguments.append(f\"{dll_path};.\")\n\n arguments.append(\"--add-data\")\n arguments.append(f\"{python_sc2_path};sc2\")\n\n # Add scipy dlls\n print(f\"Scipy folder: {scipy_dlls_path.absolute()}\")\n for dll_path in scipy_dlls_path.iterdir():\n arguments.append(\"--add-binary\")\n arguments.append(f\"{dll_path};.\")\n\n # Add paths that may be encrypted during the packing process\n arguments.append(\"--paths\")\n arguments.append(f\"{bot_folder_path}\")\n\n arguments.append(\"--clean\")\n arguments.append(\"--noconfirm\")\n\n # Encryption using the --key=mykey pyinstaller parameter. Checks if pycrypto is installed\n try:\n import pycrypt\n\n encryption_key = \"\"\n if os.path.isfile(ecryption_key_path):\n with open(ecryption_key_path) as f:\n # Remove possibly unwanted newline, tab or space characters, as they also might be invalid characters\n encryption_key = f.read().strip(\" \\n\\t\")\n if encryption_key != \"\":\n arguments.append(\"--key\")\n arguments.append(encryption_key)\n except ImportError:\n print(\"Pycrypto not detected. Skipping encryption\")\n\n arguments.append(\"--distpath\")\n arguments.append(f\"{output_folder_path}\")\n\n arguments.append(f\"{run_file_path}\")\n\n assert all(isinstance(i, str) for i in arguments)\n\n print(\"Pyinstaller Arguments: {}\".format(arguments))\n print(\"Exact command line call: {}\".format(\" \".join(arguments)))\n return arguments","def build(args):\n\n logging.info(\"Parsing configuration...\")\n try:\n config = {\"site\": configurator.get_config(os.path.join(args.src, args.configfile))}\n except Exception as exc:\n sys.exit(\"Error during configuration: \" + str(exc))\n\n if (args.autobaseurl):\n config[\"site\"][\"baseurl\"] = os.path.abspath(args.dest)\n\n logging.info(\"Loading and pre-processing content...\")\n if (os.path.isdir(os.path.join(args.src, paths.POSTS_PATH))):\n try:\n config[\"posts\"] = loader.get_from_folder(os.path.join(args.src, paths.POSTS_PATH), config)\n except ValueError as exc:\n sys.exit(\"Error loading posts: \" + str(exc))\n else:\n config[\"posts\"] = {}\n\n if (os.path.isdir(os.path.join(args.src, paths.PAGES_PATH))):\n try:\n config[\"pages\"] = loader.get_from_folder(os.path.join(args.src, paths.PAGES_PATH), config)\n except ValueError as exc:\n sys.exit(\"Error loading pages: \" + str(exc))\n else:\n config[\"pages\"] = {}\n\n logging.debug(\"Configuring Jinja2 environment...\")\n jinjaEnv = configurator.configure_jinja(config[\"site\"][\"theme\"], args.src)\n\n logging.debug(\"Initializing builder...\")\n Builder(jinjaEnv, config, args.src, args.dest, args.noclean).build()","def setup_script(self, *args, **kwargs):\n pass","def buildFunc(runType):\n buildEnv = runType.replace('build-', '')\n logger.info(\n 'Building package for {}, will be in dist/'.format(buildEnv)\n )\n runProcess(buildEnv, [\n 'build',\n '--requirements',\n 'requirements.txt',\n '--config-file',\n 'run_config.yaml'\n ])","def buildDocumentation():\n helptext = 'usage: build_doc.py <output format> <type of documentation>' \\\n '\\n - html: for html output' \\\n '\\n - pdf: for pdf output' \\\n '\\n\\n - all: complete documentation' \\\n '\\n - dev: only developer documentation' \\\n '\\n - user: only user documentation'\n if len(sys.argv) != 3:\n print helptext\n sys.exit(1)\n\n if sys.argv[1] not in ['pdf', 'html']:\n print helptext\n sys.exit(1)\n if sys.argv[2] not in ['all', 'dev', 'user']:\n print helptext\n sys.exit(1)\n\n copyfile('docs/index_%s.rst.template' % sys.argv[2], 'index.rst') # copy main file into root directory\n os.system('sphinx-build -b %s -c docs -D master_doc=index . docs/output/%s/%s' % (sys.argv[1], sys.argv[1], sys.argv[2]))\n os.remove('index.rst') # delete config file from root directory","def add_library(command):\n from src.praxxis.library import list_library\n\n namespace = app.main(command)\n assert namespace.command == 'al' or namespace.command == \"addlibrary\"\n assert namespace.path == \"test\"","def build_run_bowtie(bowtie_fp, input_contigs_fasta, output_index_fp, pe1_fastq, pe2_fastq, u_fastq, output_sam_fp):\n\n logging.debug('preparing bowtie build and align function calls')\n\n # bowtie build executable should be in the same directory as bowtie2 commands\n bowtie_build_fp = bowtie_fp + '-build'\n\n # build index files\n buildout, builderr = run_bowtie_build(bowtie_build_fp, input_contigs_fasta, output_index_fp)\n\n # once index files are built, run aligner using said index files\n stdout, stderr = run_bowtie(bowtie_fp, output_index_fp, pe1_fastq, pe2_fastq, u_fastq, output_sam_fp)\n\n return buildout, builderr, stdout, stderr","def add_lib(ctx, sources, library, folder, roles, lib_desc, lib_synopsis, datatype, no_file_check, replace, no_biomaj_env):\n\n if not sources:\n print(\"Nothing to do\")\n return\n\n if not library:\n if 'dbname' in os.environ:\n library = os.environ['dbname']\n else:\n raise Exception('No library defined. Use the --library option.')\n\n sources = check_input(sources, check_existence=(not no_file_check), use_biomaj_env=(not no_biomaj_env))\n\n r_roles = []\n if roles:\n print(\"Checking roles\")\n roles = roles.split(',')\n r_roles = get_roles(ctx.gi, roles)\n\n print(\"Adding to data library '\" + str(library) + \"'\")\n\n if not folder:\n folder = '/'\n dest = os.path.normpath(folder)\n dest = dest.split(os.sep)\n dest = [x for x in dest if x] # Remove empty string when sep at the begin or end, or multiple sep\n\n found_lib = get_library(ctx.gi, library, lib_desc, lib_synopsis)\n\n print(\"Preparing folders in library '\" + library + \"'\")\n\n dest_folder = create_tree(ctx.gi, found_lib, dest)\n\n check_existing(ctx.gi, found_lib, dest, sources, replace)\n\n print(\"Adding \" + str(len(sources)) + \" file(s) to the library '\" + library + \"'\")\n add_files(ctx.gi, found_lib, dest_folder, sources, r_roles, datatype)\n\n print(\"Done!\")","def setup_lib(CLIB):\n # {{ SETUP_LIB }}","def scriptGen(self,tmpd='/tmp/jose',libRev='last',submode='qsub',\n redirect=1,PBSoptions=''):\n jobname=self.name\n outdir=self.outd\n qsubdir=scratchdir+'/qsub/'+todayDate() #subdirectory to deposit the script\n if not os.path.exists(qsubdir): pastry('/bin/mkdir -p '+qsubdir)\n script=qsubdir+'/'+jobname+'.sh' #full script file name\n\n if len(jobname) > 15:\n sys.stderr.write('Error: job name '+jobname+' cannot exceed 15 characters')\n return ''\n if not os.path.exists(outdir): os.system('/bin/mkdir -p '+outdir)\n buf=''\n ulimit=int(float(mem_limit)*1024) #maximum resident memory size (Kb) to prevent swapping\n wd=tmpd+'/${PBS_JOBID}'\n #wd=tmpd+'/'+ re.compile('\\W').sub('',self.name) +'_$$' #working directory\n logname=jobname+'.log'\n local_log=wd+'/'+logname\n remote_log=outdir+'/'+logname\n buf= '#!/bin/bash\\n\\n'\n buf+= PBSoptions+'\\n\\n'\n buf+= '#bash function to update library\\n'\n buf+= self.updateNodeLib(libRev)+'\\n\\n'\n buf+= '#bash function to import temporary libs\\n'\n buf+= self.shared_temporal_libraries()+'\\n\\n'\n buf+= '#bash function to clean exit\\n'\n buf+= self.cleanup_exit(submode=submode)+'\\n\\n'\n buf+= 'echo \"'+script+'\"\\n' #write script name withing script body\n buf+= 'hostname\\n' #node where job will be run\n buf+= 'echo $PBS_JOBID\\n'\n buf+= 'ulimit -m '+`ulimit`+' #maximum memory\\n'\n buf+= 'source ~/.bash_profile >/dev/null #environment variables\\n'\n buf+= 'wd='+wd+' #working directory\\n'\n buf+= '/bin/mkdir -p $wd\\n'\n buf+= 'export LOCAL_LOG=\"'+local_log+'\"\\n'\n buf+= '/bin/touch $LOCAL_LOG\\n'\n if submode=='sub' and redirect:\n buf+='exec &> $LOCAL_LOG #redirect STODOUT, STDERR to LOCAL_LOG\\n' \n buf+= 'export REMOTE_LOG=\"'+remote_log+'\"\\n'\n\n but+= '#clean up old log file\\n'\n buf+= 'if [ -f $REMOTE_LOG ]; then\\n' \n buf+= ' /bin/rm -f $REMOTE_LOG\\n'\n buf+= 'fi\\n\\n'\n\n buf+= 'trap \"cleanup_exit 1\" TERM #in case of killing job\\n\\n'\n\n buf+= '#update node code library && import libraries\\n'\n buf+= 'if !('\n buf+= 'updateNodeLib && ' \n buf+= 'shared_temporal_libraries _PREPARE_'\n buf+= ');then\\n'\n buf+= ' cleanup_exit 1\\n'\n buf+= 'fi\\n\\n'\n \n buf+= '/bin/cp '+' '.join(self.inpl)+' $wd #bring input files\\n' \n buf+= 'cd $wd\\n\\n'\n buf+= '#Test command success\\n'\n buf+= 'exs=0 #variable holding script exit status\\n'\n buf+= 'if !('\n buf+= self.exe\n buf+= ');then\\n'\n buf+= ' exs=1\\n'\n buf+= 'fi\\n\\n'\n buf+= '#move even partial results (exs=1)\\n'\n buf+= '/bin/mv '+' '.join(self.outl)+' '+outdir+'\\n'\n buf+= 'cleanup_exit $exs'\n\n open(script,'w').write(buf)\n pastry('chmod u+x '+script)\n\n return script","def main(binary_name, code_directory, verbose, clase):\n print(\"Start of binaries generation\")\n #Directory to iterate\n directory = '../../results/'+code_directory + '/' + clase + '/application_signature/'\n #Directory to store the binaries to generate\n bin_directory = './bin/'\n #Task to performed on the new script\n make_clean = 'make clean\\n'\n for dirs in os.listdir(directory):\n print('Generating binary for path', dirs)\n if os.path.exists(directory+dirs+'/bin/'+dirs):\n os.remove(directory+dirs+'/bin/'+dirs)\n #Creation of the script\n with open(directory+dirs+'/make_bin.sh', 'w') as bin_file:\n bin_file.write('#! /bin/bash\\n')\n bin_file.write(make_clean+'\\n')\n bin_file.write('make '+code_directory+' CLASS='+clase+'\\n')\n bin_file.write('mv '+bin_directory+binary_name+' '+bin_directory+binary_name+'_'+dirs+'\\n')\n bin_file.write(make_clean)\n bin_file.close()\n try:\n #Changing privileges so script can be executed automatically\n os.chmod(directory+dirs+'/make_bin.sh', 0o777)\n #Move to directory where script is to be executed\n cwd = os.getcwd()\n #Change cwd to execute script generating the binary\n os.chdir(directory+dirs)\n if verbose:\n subprocess.check_call('./make_bin.sh')\n else:\n subprocess.check_call('./make_bin.sh', stdout=subprocess.PIPE, shell=False)\n \n os.chdir(cwd)\n except FileNotFoundError as e:\n logger.error(e)\n raise\n print('End of binaries generation')","def build( c ):\n\n print(\"Building Please Respond...\")\n c.run( \"pyinstaller -y please_respond.py\" )","def _cached_create_libspec(\n self,\n libname: str,\n is_builtin: bool,\n target_file: Optional[str],\n args: Optional[str],\n *,\n _internal_force_text=False, # Should only be set from within this function.\n ) -> Optional[str]:\n from robotframework_ls.impl import robot_constants\n\n if not is_builtin:\n if not target_file:\n is_builtin = libname in robot_constants.STDLIBS\n\n import time\n from robocorp_ls_core.subprocess_wrapper import subprocess\n from robocorp_ls_core.robotframework_log import get_log_level\n\n acquire_mutex = _timed_acquire_mutex_for_spec_filename\n\n if _internal_force_text:\n # In this case this is a recursive call and we already have the lock.\n acquire_mutex = NULL\n\n log_exception = log.exception\n if is_builtin and libname == \"Dialogs\" and get_log_level() < 1:\n # Dialogs may have dependencies that are not available, so, don't show\n # it unless verbose mode is enabled.\n log_exception = log.debug\n\n if not libname.replace(\".\", \"\").replace(\"/\", \"\").replace(\"\\\\\", \"\").strip():\n return f\"Unable to generate libspec for: {libname}\"\n\n additional_path = None\n additional_path_exists = False\n\n log_time = True\n cwd = None\n\n if target_file is not None:\n additional_path = os.path.dirname(target_file)\n if os.path.splitext(os.path.basename(target_file))[0] == \"__init__\":\n additional_path = os.path.dirname(additional_path)\n\n additional_path_exists = os.path.exists(additional_path)\n if additional_path and additional_path_exists:\n cwd = additional_path\n if libname.endswith((\"/\", \"\\\\\")):\n libname = libname[:-1]\n libname = os.path.basename(libname)\n if libname.lower().endswith((\".py\", \".class\", \".java\")):\n libname = os.path.splitext(libname)[0]\n\n curtime = time.time()\n\n try:\n try:\n call = [sys.executable]\n major_version = self.get_robot_major_version()\n if major_version < 4:\n call.extend(\"-m robot.libdoc --format XML\".split())\n else:\n call.extend(\n \"-m robot.libdoc --format XML --specdocformat RAW\".split()\n )\n\n if additional_path and additional_path_exists:\n call.extend([\"-P\", os.path.normpath(additional_path)])\n\n if _internal_force_text:\n call.append(\"--docformat\")\n call.append(\"text\")\n\n # Note: always set as a whole, so, iterate in generator is thread-safe.\n for entry in self._additional_pythonpath_folder_to_folder_info:\n if os.path.exists(entry):\n call.extend([\"-P\", os.path.normpath(entry)])\n\n if not args:\n call.append(libname)\n else:\n call.append(\"::\".join([libname, args]))\n\n libspec_filename = self._compute_libspec_filename(\n libname, is_builtin, target_file, args\n )\n\n log.debug(f\"Obtaining mutex to generate libspec: {libspec_filename}.\")\n with acquire_mutex(libspec_filename): # Could fail.\n log.debug(\n f\"Obtained mutex to generate libspec: {libspec_filename}.\"\n )\n call.append(libspec_filename)\n\n mtime: float = -1\n try:\n mtime = os.path.getmtime(libspec_filename)\n except:\n pass\n\n log.debug(\n \"Generating libspec for: %s.\\nCwd:%s\\nCommand line:\\n%s\",\n libname,\n cwd,\n \" \".join(call),\n )\n try:\n try:\n # Note: stdout is always subprocess.PIPE in this call.\n # Note: the env is always inherited (the process which has\n # the LibspecManager must be the target env already).\n self._subprocess_check_output(\n call,\n stderr=subprocess.STDOUT,\n stdin=subprocess.PIPE,\n cwd=cwd,\n )\n except OSError as e:\n log.exception(\"Error calling: %s\", call)\n # We may have something as: Ignore OSError: [WinError 6] The handle is invalid,\n # give the result based on whether the file changed on disk.\n try:\n if mtime != os.path.getmtime(libspec_filename):\n _dump_spec_filename_additional_info(\n self,\n libspec_filename,\n is_builtin=is_builtin,\n obtain_mutex=False,\n )\n return None\n except:\n pass\n\n log.debug(\"Not retrying after OSError failure.\")\n return str(e)\n\n except subprocess.CalledProcessError as e:\n if not _internal_force_text:\n if (\n b\"reST format requires 'docutils' module to be installed\"\n in e.output\n ):\n return self._cached_create_libspec(\n libname,\n is_builtin,\n target_file,\n args,\n _internal_force_text=True,\n )\n\n log_exception(\n \"Error creating libspec: %s.\\nReturn code: %s\\nOutput:\\n%s\",\n libname,\n e.returncode,\n e.output,\n )\n bytes_output = e.output\n output = bytes_output.decode(\"utf-8\", \"replace\")\n\n # Remove things we don't want to show.\n for s in (\"Try --help\", \"--help\", \"Traceback\"):\n index = output.find(s)\n if index >= 0:\n output = output[:index].strip()\n\n if output:\n return output\n return f\"Error creating libspec: {output}\"\n\n _dump_spec_filename_additional_info(\n self,\n libspec_filename,\n is_builtin=is_builtin,\n obtain_mutex=False,\n )\n return None\n except Exception as e:\n log_exception(\"Error creating libspec: %s\", libname)\n return str(e)\n finally:\n if log_time:\n delta = time.time() - curtime\n log.debug(\"Took: %.2fs to generate info for: %s\" % (delta, libname))","def parseArguments():\n parser = argparse.ArgumentParser(\n description='Generate a template embedded ARM project.',\n epilog=\"armwiz {}\".format(__date__))\n parser.add_argument('-p','--projectname',\n help='Specify project name via -p <projectname>',\n metavar=\"<projectname>\",\n default='armwizProject',\n required=False)\n parser.add_argument('-t','--targetname',\n help='Specify target microcontroller or processor via -t <targetname>',\n metavar=\"<targetname>\",\n default=[],\n action=\"append\",\n required=False)\n parser.add_argument('-e','--examplename',\n help='Specify which examples to copy via -e <examplename>',\n metavar=\"<examplename>\",\n default=['blinky'],\n action=\"append\",\n required=False)\n parser.add_argument('-l','--libraryname',\n help='Specify a library to include via -l <libraryname>',\n metavar=\"<libraryname>\",\n default=[],\n action=\"append\",\n required=False)\n parser.add_argument('-L',\n help='List all libraries available to -l <libraryname> and exit.',\n action=\"store_true\",\n required=False)\n parser.add_argument('-T',\n help='List all targets available to -t <targetname> and exit',\n action=\"store_true\",\n required=False)\n parser.add_argument('-E',\n help='List all examples available to -e <examplename> and exit',\n action=\"store_true\",\n required=False)\n parser.add_argument('-c','--configfile',\n help='Specify a configuration file via -t <config file name>',\n default='armwiz.config',\n metavar=\"<config file name>\",\n required=False)\n parser.add_argument('-o','--output',\n help='Target location -o <target location>',\n default='./',\n metavar=\"<target location>\",\n required=False)\n parser.add_argument('-n','--no_header',\n help='Print the wizard',\n default=False,\n action=\"store_true\",\n required=False)\n parser.add_argument('-g','--git',\n help='Initialize project as a git repository. Default is True. Ignored if git is not present.',\n default=True,\n action=\"store_true\",\n required=False)\n parser.add_argument('-v','--verbose',\n help='Print extra information to the terminal',\n action=\"store_true\",\n required=False)\n parser.add_argument('-q','--quite',\n help='Suppress terminal message as best we can',\n action=\"store_true\",\n required=False)\n parser.add_argument('--version',\n version='%(prog)s {version}'.format(version=__version__),\n action='version')\n return parser","def test_apiBuilderScriptMain(self):\n script = BuildAPIDocsScript()\n calls = []\n script.buildAPIDocs = lambda a, b: calls.append((a, b))\n script.main([\"hello\", \"there\"])\n self.assertEqual(calls, [(FilePath(\"hello\"), FilePath(\"there\"))])","def test_apiBuilderScriptMain(self):\n script = BuildAPIDocsScript()\n calls = []\n script.buildAPIDocs = lambda a, b: calls.append((a, b))\n script.main([\"hello\", \"there\"])\n self.assertEquals(calls, [(FilePath(\"hello\"), FilePath(\"there\"))])"],"string":"[\n \"def build(working_directory=None, args=None):\\n from .buildme import main\\n if args is None:\\n args = []\\n return main(working_directory, args)\",\n \"def build(self):\\n env = ConfigureEnvironment(self.deps_cpp_info, self.settings)\\n\\n set_path_command = \\\"\\\"\\n # Download nasm as build tool. This should go to source()\\n if self.options.SSE == True:\\n if self.settings.os == \\\"Linux\\\":\\n # TODO: We should build nasm from source then.\\n self.options.SSE = False # Or is removing here better? I'm not familiar with python..\\n else:\\n nasm_version = \\\"2.12.02\\\"\\n nasm_os_url_id = \\\"\\\" #nasm url identifier\\n if self.settings.os == \\\"Windows\\\":\\n if self.settings.arch == \\\"x86\\\":\\n nasm_os_url_id = \\\"win32\\\"\\n else:\\n nasm_os_url_id = \\\"win64\\\" \\n elif self.settings.os == \\\"Macos\\\":\\n nasm_os_url_id = \\\"macosx\\\"\\n nasm_folder_name = \\\"nasm-%s-%s\\\" % (nasm_version, nasm_os_url_id)\\n nasm_zip_name = \\\"%s.zip\\\" % nasm_folder_name\\n download(\\\"http://www.nasm.us/pub/nasm/releasebuilds/%s/%s/%s\\\" % (nasm_version, nasm_os_url_id, nasm_zip_name), nasm_zip_name)\\n self.output.warn(\\\"Downloading nasm: http://www.nasm.us/pub/nasm/releasebuilds/%s/%s/%s\\\" % (nasm_version, nasm_os_url_id, nasm_zip_name))\\n unzip(nasm_zip_name)\\n os.unlink(nasm_zip_name)\\n nasm_path = os.path.join(os.getcwd(), nasm_folder_name)\\n\\n #env.environ[\\\"PATH\\\"] += os.pathsep + nasm_path #its probably as easy as this, but i cant append to the path self.run operates in.\\n if self.settings.os == \\\"Windows\\\":\\n set_path_command = \\\"set \\\\\\\"PATH=%s\\\\\\\" &&\\\" % os.environ[\\\"PATH\\\"]\\n else:\\n set_path_command = \\\"PATH=\\\\\\\"%s\\\\\\\" &&\\\" % os.environ[\\\"PATH\\\"]\\n\\n if self.settings.os == \\\"Linux\\\" or self.settings.os == \\\"Macos\\\":\\n if self.options.fPIC:\\n env_line = env.command_line.replace('CFLAGS=\\\"', 'CFLAGS=\\\"-fPIC ')\\n else:\\n env_line = env.command_line\\n self.run(\\\"cd %s && autoreconf -fiv\\\" % self.ZIP_FOLDER_NAME)\\n config_options = \\\"\\\"\\n if self.settings.arch == \\\"x86\\\":\\n if self.settings.os == \\\"Linux\\\":\\n config_options = \\\"--host i686-pc-linux-gnu CFLAGS='-O3 -m32' LDFLAGS=-m32\\\"\\n else:\\n config_options = \\\"--host i686-apple-darwin CFLAGS='-O3 -m32' LDFLAGS=-m32\\\"\\n\\n if self.settings.os == \\\"Macos\\\":\\n old_str = '-install_name \\\\$rpath/\\\\$soname'\\n new_str = '-install_name \\\\$soname'\\n replace_in_file(\\\"./%s/configure\\\" % self.ZIP_FOLDER_NAME, old_str, new_str)\\n\\n self.run(\\\"cd %s && %s ./configure %s\\\" % (self.ZIP_FOLDER_NAME, env_line, config_options))\\n self.run(\\\"cd %s && %s make\\\" % (self.ZIP_FOLDER_NAME, env_line))\\n else: # We should (for simplicity) always use cmake shouldnt we?\\n conan_magic_lines = '''project(libjpeg-turbo)\\n cmake_minimum_required(VERSION 3.0)\\n include(../conanbuildinfo.cmake)\\n CONAN_BASIC_SETUP()\\n '''\\n replace_in_file(\\\"%s/CMakeLists.txt\\\" % self.ZIP_FOLDER_NAME, \\\"cmake_minimum_required(VERSION 2.8.8)\\\", conan_magic_lines)\\n replace_in_file(\\\"%s/CMakeLists.txt\\\" % self.ZIP_FOLDER_NAME, \\\"project(libjpeg-turbo C)\\\", \\\"\\\")\\n \\n cmake = CMake(self.settings)\\n builddir = os.path.join(self.ZIP_FOLDER_NAME, \\\"_build\\\")\\n\\n if os.path.exists(builddir):\\n shutil.rmtree(builddir) # We need to remove this folder first for windows\\n os.makedirs(builddir)\\n\\n cmake_options = []\\n if self.options.shared == True:\\n cmake_options += [\\\"-DENABLE_STATIC=0\\\"]\\n else:\\n cmake_options = [\\\"-DENABLE_SHARED=0\\\"]\\n cmake_options += [\\\"-DWITH_SIMD=%s\\\" % \\\"1\\\" if self.options.SSE else \\\"0\\\"]\\n\\n # why this comment: \\\"Don't change runtime, conan will take care of\\\"? conan_basic_setup() runs before this cmake option replaces MT with MD again\\n cmake_options += [\\\"-DWITH_CRT_DLL=%s\\\" % \\\"1\\\" if self.settings.compiler.runtime == \\\"MD\\\" or self.settings.compiler.runtime == \\\"MDd\\\" else \\\"0\\\"]\\n\\n self.run('%s cd %s && cmake .. %s %s' % (set_path_command, builddir, cmake.command_line, \\\" \\\".join(cmake_options)))\\n self.run(\\\"%s cd %s && cmake --build . %s\\\" % (set_path_command, builddir, cmake.build_config))\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--corpus_dir', required=True)\\n parser.add_argument('--feature_module', required=True)\\n\\n args = parser.parse_args()\\n corpus_dir = args.corpus_dir\\n feature_module = args.feature_module\\n\\n return build_file(corpus_dir, feature_module)\",\n \"def build(config):\",\n \"def main(cmd_line_args):\\n if cmd_line_args.clean:\\n clean_wrapper(cmd_line_args)\\n if cmd_line_args.nobuild == False:\\n build_wrapper(cmd_line_args)\",\n \"def build():\\n\\timport subprocess\\n\\tfrom os import listdir, getcwd\\n\\tfrom os.path import isfile, join\\n\\tonlyfiles = [f for f in listdir(getcwd()) if isfile(join(getcwd(), f))]\\n\\n\\tif not 'requirements.txt' in onlyfiles:\\n\\t\\traise SystemExit('File including depencencies not found. You will have to install them manually.')\\n\\n\\tsubprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])\\n\\n\\tprint('All dependencies installed successfully.\\\\nYou can run Simplex now!')\",\n \"def executable(output=None, sources=None, rule=None, slibs=[], libs='', **kwargs):\\n objs = compile(\\n sources = sources,\\n bldprefix = output.name + '_',\\n rule = rule,\\n **kwargs)\\n objs += [str(x) for x in slibs]\\n w.build(str(output), 'executable',\\n inputs = objs,\\n variables = {'libs': libs})\\n return str(output) # convenience\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog='Build', description='Python script for building apps for Pyinstaller')\\n # Flag arguments\\n parser.add_argument('--version', action='version', version='%(prog)s 1.0.0')\\n parser.add_argument('--clean', '-c', action='store_true', default=False, help='Clean build before re-building.')\\n parser.add_argument('--portable', '-p', action='store_true', default=False, help='Build with portable python (windows)')\\n parser.add_argument('name', default=None, help='Name of app')\\n inputs = parser.parse_args()\\n if _PLATFORM == \\\"osx\\\":\\n args = Args(\\\"Rummage.py\\\", inputs.name, True, inputs.clean, \\\".app\\\", abspath(\\\"_icons/rummage.icns\\\"))\\n elif _PLATFORM == \\\"windows\\\":\\n args = Args(\\\"Rummage.py\\\", inputs.name, True, inputs.clean, \\\".exe\\\", abspath(\\\"_icons\\\\\\\\rummage.ico\\\"), inputs.portable)\\n else:\\n args = Args(\\n \\\"Rummage.py\\\", inputs.name, True, inputs.clean, \\\"\\\",\\n imports=[\\n \\\"gobject\\\", \\\"glib\\\", \\\"glib._glib\\\", \\\"glib.option\\\", \\\"object.constants\\\",\\n \\\"gobject._gobject\\\", \\\"gobject.propertyhelper\\\", \\\"gtk\\\", \\\"gtk._gtk\\\"\\n ]\\n )\\n\\n # Parse options\\n build_params = BuildParams()\\n err = parse_options(args, build_params)\\n\\n # Build executable\\n if not err:\\n err = build(build_params)\\n\\n return err\",\n \"def main():\\n argp = argparse.ArgumentParser(prog='-mshlibs', description=('Print the '\\n 'complete list of shared libraries used by the specified binary '\\n 'file(s), (optionally including all child dependencies)'))\\n argp.add_argument('file', nargs='+', help='file(s) to report on')\\n argp.add_argument('-a', '--all', action=\\\"store_true\\\", help=(\\n \\\"recursively resolve all sub-dependencies\\\"))\\n args = argp.parse_args()\\n\\n if args.all:\\n deps = reduce(lambda a, b: a|b,\\n [all_libraries_used(f) for f in args.file])\\n else:\\n deps = reduce(lambda a, b: set(a)|set(b),\\n [libraries_used(f) for f in args.file])\\n\\n for path in sorted(deps):\\n print path\",\n \"def build(parameters):\\n\\n\\n print(\\\"In Build module\\\")\",\n \"def main():\\n for key in UNSAFE_FLAGS:\\n if key in os.environ:\\n del os.environ[key]\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--fcompiler', choices=['pgi', 'intel', 'gfortran'],\\n help='Fortran compiler to use')\\n parser.add_argument('--usecuda', help='whether to also compile CUDA implementation',\\n action='store_true')\\n parser.add_argument('--debug', help='use debug configuration when compiling fortran code',\\n action='store_true')\\n\\n cmd_args = parser.parse_args()\\n args = ['python', 'setup.py', 'build_ext', '--fcompiler=' + FCOMPILER_MAP[cmd_args.fcompiler]]\\n if cmd_args.debug:\\n args += ['--debug']\\n if cmd_args.usecuda:\\n args += ['--usecuda']\\n args += ['build', 'bdist_wheel']\\n\\n proc = subprocess.Popen(['rm', '-rf', 'dist', 'build'])\\n proc.wait()\\n\\n proc = subprocess.Popen(args)\\n proc.wait()\\n if proc.returncode != 0:\\n print('ERROR! build process failed.')\\n exit(1)\\n\\n wheels = glob.glob('./dist/*.whl')\\n latest_wheel = max(wheels, key=os.path.getctime)\\n\\n proc = subprocess.Popen(['pip', 'install', latest_wheel, '--upgrade'])\\n proc.wait()\",\n \"def build():\",\n \"def _copy_bins():\\n # STEP 1: If we're performing a build from a copied source tree,\\n # copy the generated python files into the package\\n\\n _clean_bins()\\n\\n py_z3_build_dir = os.path.join(BUILD_DIR, 'python', 'z3')\\n root_z3_dir = os.path.join(ROOT_DIR, 'z3')\\n shutil.copy(os.path.join(py_z3_build_dir, 'z3core.py'), root_z3_dir)\\n shutil.copy(os.path.join(py_z3_build_dir, 'z3consts.py'), root_z3_dir)\\n\\n # STEP 2: Copy the shared library, the executable and the headers\\n\\n os.mkdir(LIBS_DIR)\\n os.mkdir(BINS_DIR)\\n os.mkdir(HEADERS_DIR)\\n shutil.copy(os.path.join(BUILD_DIR, LIBRARY_FILE), LIBS_DIR)\\n shutil.copy(os.path.join(BUILD_DIR, EXECUTABLE_FILE), BINS_DIR)\\n path1 = glob.glob(os.path.join(BUILD_DIR, \\\"msvcp*\\\"))\\n path2 = glob.glob(os.path.join(BUILD_DIR, \\\"vcomp*\\\"))\\n path3 = glob.glob(os.path.join(BUILD_DIR, \\\"vcrun*\\\"))\\n for filepath in path1 + path2 + path3:\\n shutil.copy(filepath, LIBS_DIR)\\n\\n for header_dir in HEADER_DIRS:\\n for fname in os.listdir(header_dir):\\n if not fname.endswith('.h'):\\n continue\\n shutil.copy(os.path.join(header_dir, fname), os.path.join(HEADERS_DIR, fname))\\n\\n # This hack lets z3 installed libs link on M1 macs; it is a hack, not a proper fix\\n # @TODO: Linked issue: https://github.com/Z3Prover/z3/issues/5926\\n major_minor = '.'.join(_z3_version().split('.')[:2])\\n link_name = None\\n if BUILD_PLATFORM in ('win32', 'cygwin', 'win'):\\n pass # TODO: When windows VMs work on M1, fill this in\\n elif BUILD_PLATFORM in ('darwin', 'osx'):\\n split = LIBRARY_FILE.split('.')\\n link_name = split[0] + '.' + major_minor + '.' + split[1]\\n else:\\n link_name = LIBRARY_FILE + '.' + major_minor\\n if link_name:\\n os.symlink(LIBRARY_FILE, os.path.join(LIBS_DIR, link_name), True)\",\n \"def main():\\n \\n usage = \\\"\\\"\\\"usage: %prog [target]\\nTargets :\\n\\tall\\t\\tCompile all projects in csunplugged directory\\n\\tindex\\t\\tCreate the index page\\n\\tdirectory\\tCompile single project in csunplugged directory\\n \\\"\\\"\\\"\\n parser = optparse.OptionParser(usage)\\n \\n (options, args) = parser.parse_args()\\n if not (len(args) == 1) :\\n parser.error('incorrect number of arguments')\\n \\n if args[0]=='all' :\\n build_all()\\n \\n elif args[0]=='index' :\\n make_index_page()\\n \\n else :\\n jsFile = args[0] + '.js'\\n jsFilePath = os.path.join(csunpluggedPath, args[0], jsFile)\\n if not os.path.exists(jsFilePath) :\\n print 'Can not found ' + jsFilePath+'\\\\n' \\n else :\\n \\tbuild(args[0])\",\n \"def build():\\n local('wintersmith build')\",\n \"def build():\\n local('python' + python_version + ' setup.py bdist_egg')\",\n \"def main():\\n\\n # Parse the command line.\\n parser = ArgumentParser(\\n \\\"Generate Python extension modules for C/C++ libraries.\\\",\\n fromfile_prefix_chars='@')\\n\\n parser.add_argument('specification',\\n help=\\\"the name of the specification file [default stdin]\\\",\\n metavar=\\\"FILE\\\", nargs='?')\\n\\n parser.add_argument('-a', dest='api_extract',\\n help=\\\"the name of the QScintilla API file [default not generated]\\\",\\n metavar=\\\"FILE\\\")\\n\\n parser.add_argument('--abi-version', dest='abi_version',\\n help=\\\"the ABI version\\\", metavar=\\\"VERSION\\\")\\n\\n parser.add_argument('-B', dest='backstops', action='append',\\n help=\\\"add <TAG> to the list of timeline backstops\\\",\\n metavar=\\\"TAG\\\")\\n\\n parser.add_argument('-c', dest='sources_dir',\\n help=\\\"the name of the code directory [default not generated]\\\",\\n metavar=\\\"DIR\\\")\\n\\n parser.add_argument('-D', dest='py_debug', action='store_true',\\n default=False,\\n help=\\\"generate code for a debug build of Python\\\")\\n\\n parser.add_argument('-e', dest='exceptions', action='store_true',\\n default=False,\\n help=\\\"enable support for exceptions [default disabled]\\\")\\n\\n parser.add_argument('-f', dest='warnings_are_errors', action='store_true',\\n default=False,\\n help=\\\"warnings are handled as errors\\\")\\n\\n parser.add_argument('-g', dest='release_gil', action='store_true',\\n default=False,\\n help=\\\"always release and reacquire the GIL [default only when \\\"\\n \\\"specified]\\\")\\n\\n parser.add_argument('-I', dest='include_dirs', action='append',\\n help=\\\"add <DIR> to the list of directories to search when \\\"\\n \\\"importing or including .sip files\\\",\\n metavar=\\\"DIR\\\")\\n\\n parser.add_argument('-j', dest='parts', type=int, default=0,\\n help=\\\"split the generated code into <FILES> files [default 1 per \\\"\\n \\\"class]\\\",\\n metavar=\\\"FILES\\\")\\n\\n parser.add_argument('-m', dest='xml_extract', help=SUPPRESS)\\n\\n parser.add_argument('-n', dest='sip_module',\\n help=\\\"the fully qualified name of the sip module\\\",\\n metavar=\\\"NAME\\\")\\n\\n parser.add_argument('-o', dest='docstrings', action='store_true',\\n default=False,\\n help=\\\"enable the automatic generation of docstrings [default \\\"\\n \\\"disabled]\\\")\\n\\n parser.add_argument('-P', dest='protected_is_public', action='store_true',\\n default=False,\\n help=\\\"enable the protected/public hack [default disabled]\\\")\\n\\n parser.add_argument('-r', dest='tracing', action='store_true',\\n default=False,\\n help=\\\"generate code with tracing enabled [default disabled]\\\")\\n\\n parser.add_argument('-s', dest='source_suffix',\\n help=\\\"the suffix to use for C or C++ source files [default \\\\\\\".c\\\\\\\" \\\"\\n \\\"or \\\\\\\".cpp\\\\\\\"]\\\",\\n metavar=\\\"SUFFIX\\\")\\n\\n parser.add_argument('-t', dest='tags', action='append',\\n help=\\\"add <TAG> to the list of versions/platforms to generate \\\"\\n \\\"code for\\\",\\n metavar=\\\"TAG\\\")\\n\\n parser.add_argument('-w', dest='warnings', action='store_true',\\n default=False, help=\\\"enable warning messages [default disabled]\\\")\\n\\n parser.add_argument('-x', dest='disabled_features', action='append',\\n help=\\\"add <FEATURE> to the list of disabled features\\\",\\n metavar=\\\"FEATURE\\\")\\n\\n parser.add_argument('-X', dest='extracts', action='append',\\n help=\\\"add <ID:FILE> to the list of extracts to generate\\\",\\n metavar=\\\"ID:FILE\\\")\\n\\n parser.add_argument('-y', dest='pyi_extract',\\n help=\\\"the name of the .pyi stub file [default not generated]\\\",\\n metavar=\\\"FILE\\\")\\n\\n args = parser.parse_args()\\n\\n # Configure the handling of warnings.\\n if args.warnings:\\n if args.warnings_are_errors:\\n simplefilter('error', FutureWarning)\\n simplefilter('error', UserWarning)\\n else:\\n # Note that we don't suppress FutureWarnings.\\n simplefilter('ignore', UserWarning)\\n\\n try:\\n sip5(args.specification, sip_module=args.sip_module,\\n abi_version=args.abi_version, sources_dir=args.sources_dir,\\n include_dirs=args.include_dirs, tags=args.tags,\\n backstops=args.backstops,\\n disabled_features=args.disabled_features,\\n exceptions=args.exceptions, parts=args.parts,\\n source_suffix=args.source_suffix, docstrings=args.docstrings,\\n protected_is_public=args.protected_is_public,\\n py_debug=args.py_debug, release_gil=args.release_gil,\\n tracing=args.tracing, extracts=args.extracts,\\n pyi_extract=args.pyi_extract, api_extract=args.api_extract,\\n xml_extract=args.xml_extract)\\n except Exception as e:\\n handle_exception(e)\\n\\n return 0\",\n \"def build_plugin(ctx, name, sources=None, includes=None, defines=None, libraries=None, features=None):\\n log_domain = name\\n plugin_name = name.lower()\\n includes = includes or []\\n defines = defines or []\\n libraries = libraries or []\\n features = features or []\\n\\n is_win32 = target_is_win32(ctx)\\n\\n if not sources:\\n sources = []\\n for directory in includes:\\n bld = ctx.srcnode\\n files = bld.ant_glob('%s/*.c' % directory, src=True, dir=False)\\n sources.extend(files)\\n\\n includes.append(ctx.out_dir)\\n defines.append('G_LOG_DOMAIN=\\\"%s\\\"' % log_domain)\\n libraries.append('GTK')\\n libraries.append('GEANY')\\n features.append('c')\\n features.append('cshlib')\\n task = ctx.new_task_gen(\\n features = features,\\n source = sources,\\n includes = includes,\\n defines = defines,\\n target = plugin_name,\\n use = libraries,\\n install_path = '${G_PREFIX}/${LIBDIR}' if is_win32 else '${LIBDIR}/geany/')\\n\\n install_docs(ctx, plugin_name, 'AUTHORS ChangeLog COPYING NEWS README THANKS TODO'.split())\\n return task\",\n \"def build_all():\\n client = TestClient()\\n client.save({\\\"conanfile.py\\\": GenConanfile().with_setting(\\\"build_type\\\")})\\n client.run(\\\"export . --name=foo --version=1.0 --user=user --channel=testing\\\")\\n client.save({\\\"conanfile.py\\\": GenConanfile().with_require(\\\"foo/1.0@user/testing\\\")\\n .with_setting(\\\"build_type\\\")})\\n client.run(\\\"export . --name=bar --version=1.0 --user=user --channel=testing\\\")\\n client.save({\\\"conanfile.py\\\": GenConanfile().with_require(\\\"foo/1.0@user/testing\\\")\\n .with_require(\\\"bar/1.0@user/testing\\\")\\n .with_setting(\\\"build_type\\\")})\\n client.run(\\\"export . --name=foobar --version=1.0 --user=user --channel=testing\\\")\\n client.run(\\\"install --requires=foobar/1.0@user/testing --build='*'\\\")\\n return client\",\n \"def build_library(self, release=False):\\n\\n verbose = False\\n\\n # Save our working directory\\n cwd = os.getcwd()\\n\\n # Set our current directory to our project path\\n os.chdir(self.project_path)\\n\\n result = []\\n\\n # If our library bin folder does not exit, create it\\n if not os.path.isdir(\\\"./lib/bin\\\"):\\n os.mkdir(\\\"./lib/bin\\\")\\n\\n # Arguments for our compiler\\n cwArg = cwFlags + \\\" \\\" + self.project_includes + \\\" -I%s\\\\\\\\lib\\\\\\\\\\\" % self.project_path\\n\\n # pass down our build mode\\n cwArg += \\\" -DRELEASE\\\" if release else \\\" -DDEBUG\\\"\\n\\n with open(self.project_path + \\\"\\\\\\\\lib\\\\\\\\build.json\\\", \\\"r\\\") as makefile:\\n lib_make = json.load(makefile)\\n os.chdir(self.project_path + \\\"\\\\\\\\..\\\\\\\\tool\\\\\\\\\\\")\\n for file in lib_make[\\\"sources\\\"]:\\n # TODO: bin subfolder not auto created!\\n compiler_string = \\\"./mwcceppc.exe %s -c -o %s/lib/bin/%s.o %s/lib/%s\\\" % (\\n cwArg, self.project_path, file, self.project_path, file)\\n if self.verbose:\\n print(compiler_string)\\n if subprocess.call(compiler_string):\\n raise RuntimeError(\\\"[FATAL] Failed to compile %s!\\\", file)\\n else:\\n result.append(\\\"%s/lib/bin/%s.o\\\" % (self.project_path, file))\\n\\n # Restore our working directory\\n os.chdir(cwd)\\n\\n self.library_objects = result + lib_make[\\\"objects\\\"]\\n\\n return self.library_objects\",\n \"def build(_):\",\n \"def run(self):\\n self._make_lib_file_symbolic_links()\\n self._copy_each_include_files_to_include_dir()\\n self._make_dep_lib_file_sym_links_and_copy_include_files()\\n self.setup_py.add_patchs_to_build_without_pkg_config(\\n self.rpm.lib_dir, self.rpm.include_dir\\n )\\n self.setup_py.apply_and_save()\\n self._build_and_install()\",\n \"def build(self, gyp_file, target=None, **kw):\\n raise NotImplementedError\",\n \"def main():\\n if len(sys.argv) != 2:\\n print('USAGE:\\\\tpython3 inoti_make.py <CONFIG_FILE>')\\n exit(1)\\n config = parse(sys.argv[1])\\n listen_folders(config)\",\n \"def main():\\n licensify(_parse_args())\",\n \"def main ( ) :\\r\\n\\r\\n # checks the command line\\r\\n\\r\\n libraryPath = checkArguments()\\r\\n\\r\\n # checks whether the library path is defined in a file pythonLibrary.txt\\r\\n\\r\\n if libraryPath is None : libraryPath = readLink()\\r\\n\\r\\n # checks for current directory\\r\\n\\r\\n if libraryPath is None : libraryPath = findDirectory( os.getcwd() )\\r\\n\\r\\n # checks whether the current execution prefix ( the directory where we are running python ) contains libraryPython\\r\\n\\r\\n if libraryPath is None : libraryPath = findDirectory( sys.exec_prefix )\\r\\n\\r\\n # not found\\r\\n\\r\\n if libraryPath is None :\\r\\n\\r\\n raw_input( sys.argv[ 0 ] + \\\" - fatal error : library not found. Press any key\\\" )\\r\\n\\r\\n sys.exit( 1 )\\r\\n\\r\\n # adds final / if required\\r\\n\\r\\n libraryPath = libraryPath.rstrip( \\\"\\\\\\\\/\\\" ) + os.sep\\r\\n\\r\\n # writes the link file (contains library path, speeds up next execution)\\r\\n\\r\\n writeLink( libraryPath )\\r\\n\\r\\n # appends library and root to search path\\r\\n\\r\\n if not libraryPath in sys.path : sys.path = [ libraryPath ] + sys.path\\r\\n\\r\\n # appends library and the local copy of python to the system search path so that local python modules have priority\\r\\n\\r\\n if os.path.exists( libraryPath + os.sep + sys.platform ) : localPath = libraryPath + os.sep + sys.platform + os.sep\\r\\n\\r\\n elif os.path.exists( libraryPath + os.sep + \\\"python\\\" ) : localPath = libraryPath + os.sep + \\\"python\\\" + os.sep\\r\\n\\r\\n else : localPath = None\\r\\n\\r\\n if not localPath is None : localPython( localPath )\\r\\n\\r\\n # writes the python library directly in sys, for next modules\\r\\n\\r\\n sys.libraryPath = libraryPath\\r\\n\\r\\n # writes the root of python library in sys, for next modules\\r\\n\\r\\n rootPath, dummy = os.path.split( libraryPath.rstrip( \\\"\\\\\\\\/\\\" ) )\\r\\n\\r\\n rootPath = rootPath + os.sep\\r\\n\\r\\n sys.rootPath = rootPath\\r\\n\\r\\n if not rootPath in sys.path : sys.path = [ rootPath ] + sys.path\\r\\n\\r\\n # sets the environment variables (session) for GUIs\\r\\n\\r\\n if not localPath is None : setEnvironment( localPath )\",\n \"def build(ctx: typer.Context):\\n from .tasks import build, main\\n\\n sys.argv = sys.argv[:1] + (ctx.args or [\\\"list\\\"])\\n main(vars(build))\",\n \"def setup(conf, products, build=False):\\n import distutils.sysconfig\\n paths = {\\n \\\"CPPPATH\\\": distutils.sysconfig.get_python_inc().split(),\\n \\\"LIBPATH\\\": [],\\n }\\n libs = []\\n dir = distutils.sysconfig.get_config_var(\\\"LIBPL\\\")\\n if not dir in paths[\\\"LIBPATH\\\"]:\\n paths[\\\"LIBPATH\\\"] += [dir]\\n pylibrary = distutils.sysconfig.get_config_var(\\\"LIBRARY\\\")\\n mat = re.search(\\\"(python.*)\\\\.(a|so|dylib)$\\\", pylibrary)\\n if mat:\\n libs.append(mat.group(1)) \\n for w in (\\\" \\\".join([distutils.sysconfig.get_config_var(\\\"MODLIBS\\\"),\\n distutils.sysconfig.get_config_var(\\\"SHLIBS\\\")])).split():\\n mat = re.search(r\\\"^-([Ll])(.*)\\\", w)\\n if mat:\\n lL = mat.group(1)\\n arg = mat.group(2)\\n if lL == \\\"l\\\":\\n if not arg in libs:\\n libs.append(arg)\\n else:\\n if os.path.isdir(arg) and not arg in paths[\\\"LIBPATH\\\"]:\\n paths[\\\"LIBPATH\\\"].append(arg)\\n conf.env.PrependUnique(**paths)\\n for lib in libs:\\n if lib not in conf.env.libs[\\\"python\\\"]:\\n conf.env.libs[\\\"python\\\"].append(lib)\\n return {\\\"paths\\\": paths, \\\"libs\\\": {\\\"python\\\": libs}}\",\n \"def build_program(source_files, image_type='bare-metal', opt_level='-O3', cflags=None):\\n assert isinstance(source_files, list)\\n\\n if not os.path.exists(OBJ_DIR):\\n os.makedirs(OBJ_DIR)\\n\\n compiler_args = [COMPILER_DIR + 'clang',\\n '-o', ELF_FILE,\\n '-w',\\n opt_level]\\n\\n if cflags:\\n compiler_args += cflags\\n\\n if image_type == 'raw':\\n compiler_args += ['-Wl,--script,../one-segment.ld,--oformat,binary']\\n elif image_type == 'user':\\n compiler_args += ['-Wl,--image-base=0x1000']\\n\\n compiler_args += source_files\\n\\n if any(name.endswith(('.c', '.cpp')) for name in source_files):\\n compiler_args += ['-I' + LIB_DIR + 'libc/include',\\n '-I' + LIB_DIR + 'libos',\\n LIB_DIR + 'libc/libc.a',\\n LIB_DIR + 'compiler-rt/compiler-rt.a']\\n if image_type == 'user':\\n compiler_args += [LIB_DIR + 'libos/crt0-kern.o',\\n LIB_DIR + 'libos/libos-kern.a']\\n else:\\n compiler_args += [LIB_DIR + 'libos/crt0-bare.o',\\n LIB_DIR + 'libos/libos-bare.a']\\n\\n try:\\n subprocess.check_output(compiler_args, stderr=subprocess.STDOUT)\\n if image_type == 'raw':\\n dump_hex(input_file=ELF_FILE, output_file=HEX_FILE)\\n return HEX_FILE\\n\\n if image_type == 'bare-metal':\\n subprocess.check_output([COMPILER_DIR + 'elf2hex', '-o', HEX_FILE, ELF_FILE],\\n stderr=subprocess.STDOUT)\\n return HEX_FILE\\n\\n return ELF_FILE\\n except subprocess.CalledProcessError as exc:\\n raise TestException('Compilation failed:\\\\n' + exc.output.decode())\",\n \"def build():\\n click.secho('Creating package ...')\\n pkg = utils.create_package()\\n click.secho('Package created: {}'.format(pkg), fg='green')\\n click.secho('Creating wheel...')\\n wheel_path = utils.create_wheel()\\n click.secho('Wheel created in {}'.format(wheel_path), fg='green')\",\n \"def main():\\n # Packaging tools expects either README.txt, README, or README.rst.\\n # Convert the README markdown file to ReStructured text.\\n try:\\n run(\\\"pandoc README.md -f markdown -t rst -o README.rst\\\".split(\\\" \\\"), check=True)\\n except CalledProcessError:\\n print(\\\"pandoc does not appear to be installed. Get it from http://pandoc.org/\\\")\\n exit(1)\\n\\n copy_metadata()\\n\\n for item in (\\\"sdist\\\", \\\"bdist_wheel\\\"):\\n run([\\\"python\\\", \\\"setup.py\\\", item], check=True)\",\n \"def task_build(argv):\\n pytaskmaster.generator(\\\"setup.py.in\\\", \\\"setup.py\\\", config)\\n pytaskmaster.generator(\\\"pytaskmaster/version.py.in\\\", \\\"pytaskmaster/version.py\\\", config)\\n shell(\\\"python setup.py bdist_wheel\\\")\\n if \\\"--sign\\\" in argv:\\n for file in os.listdir(\\\"dist\\\"):\\n asc_file = \\\"dist/\\\" + file + \\\".asc\\\"\\n if file.endswith(\\\".whl\\\") and not os.path.isfile(asc_file):\\n shell(\\\"gpg --detach-sign -a dist/{}\\\".format(file))\",\n \"def build(ctx):\\n generate_build_files(ctx)\\n compile_build_files(ctx)\\n _package_plugins(ctx)\",\n \"def main():\\n setuptools.setup(**setup_params)\",\n \"def compile_package(self):\\n build_package = [\\n self.mock,\\n '--root=%s' % self.root,\\n '--arch=%s' % self.arch,\\n '--shell',\\n '/build_package.sh',\\n \\n ]\\n output, errors = self._run_command(build_package)\",\n \"def run():\\n build_no_documentation()\\n build_sphinx_build()\\n #build_sphinx_pdf()\\n build_graphviz_files()\",\n \"def main(args):\",\n \"def main(args):\",\n \"def partial_build(src, lang, bsp_c3, opt_level, march, reporter):\\n if lang == 'c3':\\n srcs = [\\n relpath('..', 'librt', 'io.c3'),\\n bsp_c3,\\n io.StringIO(src)]\\n o2 = c3c(\\n srcs, [], march, opt_level=opt_level,\\n reporter=reporter, debug=True)\\n objs = [o2]\\n elif lang == 'bf':\\n o3 = bfcompile(src, march, reporter=reporter)\\n o2 = c3c(\\n [bsp_c3], [], march, reporter=reporter)\\n objs = [o2, o3]\\n elif lang == 'c':\\n o2 = c3c(\\n [bsp_c3], [], march, reporter=reporter)\\n coptions = COptions()\\n include_path1 = relpath('..', 'librt', 'libc')\\n coptions.add_include_path(include_path1)\\n with open(relpath('..', 'librt', 'libc', 'lib.c'), 'r') as f:\\n o3 = cc(\\n f, march, coptions=coptions, debug=True,\\n reporter=reporter)\\n o4 = cc(\\n io.StringIO(src), march, coptions=coptions, debug=True,\\n reporter=reporter)\\n objs = [o2, o3, o4]\\n else:\\n raise NotImplementedError('language not implemented')\\n obj = link(\\n objs, partial_link=True,\\n use_runtime=True, reporter=reporter, debug=True)\\n return obj\",\n \"def build_command(self, args):\\n self._validate_common(args)\\n self._set_manifests(args)\\n\\n manifest = self._manager._recipes\\n\\n records = self._get_matching_records(args, manifest)\\n\\n if not len(records):\\n sys.exit(\\\"No matching items found.\\\")\\n\\n for record in records:\\n result = self._manager.build(record['_type'], **record)\\n print('The requested asset has been built: {}'.format(result['_path']))\\n\\n if len(records) > 1:\\n print('All files have been successfully built. Thank you.')\",\n \"def build_cli():\\n parser = argparse.ArgumentParser(prog='main.py',\\n usage='python3 main.py {-u URL | -c} ',\\n description='YouTube video and Audio downloader.',\\n epilog='Enjoy the program :)'\\n )\\n group_one = parser.add_mutually_exclusive_group()\\n group_one.add_argument('-u',\\n '--url',\\n action='store',\\n help='URL of the video from YouTube'\\n )\\n group_one.add_argument('-c',\\n '--clipboard',\\n action='store_true',\\n help='copy URL from the clipboard'\\n )\\n group_two = parser.add_mutually_exclusive_group()\\n group_two.add_argument('-a',\\n '--audio-only',\\n action='store_true',\\n help='download audio only in mp3 format'\\n )\\n group_two.add_argument('-r',\\n '--resolution',\\n action='store',\\n choices=['360p','480p', '720p'],\\n help='resolution (quality) of the video.'\\n )\\n parser.version = '2.0'\\n parser.add_argument('-v',\\n '--version',\\n action='version'\\n )\\n return parser.parse_args()\",\n \"def build_step(self):\\n run_cmd('./compile.sh', log_all=True, simple=True, log_ok=True)\",\n \"def build_linux(self, **kargs):\\n self.linux_files = [\\\"%s/%s/wombat/vmlinux\\\" % (self.builddir, self.name)]\\n LIB_DEPENDS = [self.libs[\\\"mutex\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"iguana\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"l4\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"timer\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"l4e\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"c\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"circular_buffer\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"ll\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"range_fl\\\"][1]]\\n LIB_DEPENDS += [self.libs[\\\"naming\\\"][1]]\\n\\n if \\\"pxa\\\" in self.machine.drivers:\\n LIB_DEPENDS += [self.libs[\\\"pxa\\\"][1]]\\n \\n l4linux = self.Command(self.linux_files, LIB_DEPENDS, buildlinux)\\n l4linux = Flatten([l4linux])[0]\\n Precious(self.linux_files)\\n\\n\\twombat_cflags = \\\"-DENDIAN_%s \\\" % self.machine.endian.upper()\\n\\n\\tif machine.pidreloc == True:\\n wombat_cflags += \\\" -DARM_PID_RELOC \\\"\\n\\n if restrict_vm == True:\\n wombat_cflags += \\\" -DCONFIG_RESTRICTED_VM=1 \\\"\\n\\n\\tif (hasattr(machine, \\\"c_flags\\\")):\\n\\t wombat_cflags += ' '.join(machine.c_flags)\\n\\n # This is horrible :(\\n mutex_include = os.getcwd() + os.sep + self.libs[\\\"mutex\\\"][0][0][1:]\\n ig_include = os.getcwd() + os.sep + self.libs[\\\"iguana\\\"][0][0][1:]\\n ig_idl4_include = self.libs[\\\"iguana\\\"][0][-1]\\n l4_include = os.getcwd() + os.sep + self.libs[\\\"l4\\\"][0][0][1:]\\n timer_include = os.getcwd() + os.sep + self.libs[\\\"timer\\\"][0][0][1:]\\n cb_include = os.getcwd() + os.sep + self.libs[\\\"circular_buffer\\\"][0][0][1:]\\n idl4_include = os.getcwd() + os.sep + self.libs[\\\"idl4\\\"][0][1:] + os.sep\\n naming_include = os.getcwd() + os.sep + self.libs[\\\"naming\\\"][0][0][1:] + os.sep\\n \\n mutex_lib = os.getcwd() + os.sep + self.libs[\\\"mutex\\\"][2][1:] + os.sep\\n ig_lib = os.getcwd() + os.sep + self.libs[\\\"iguana\\\"][2][1:] + os.sep\\n l4_lib = os.getcwd() + os.sep + self.libs[\\\"l4\\\"][2][1:] + os.sep\\n timer_lib = os.getcwd() + os.sep + self.libs[\\\"timer\\\"][2][1:] + os.sep\\n l4e_lib = os.getcwd() + os.sep + self.libs[\\\"l4e\\\"][2][1:] + os.sep\\n c_lib = os.getcwd() + os.sep + self.libs[\\\"c\\\"][2][1:] + os.sep\\n cb_lib = os.getcwd() + os.sep + self.libs[\\\"circular_buffer\\\"][2][1:] + os.sep\\n ll_lib = os.getcwd() + os.sep + self.libs[\\\"ll\\\"][2][1:] + os.sep\\n rfl_lib = os.getcwd() + os.sep + self.libs[\\\"range_fl\\\"][2][1:] + os.sep\\n naming_lib = os.getcwd() + os.sep + self.libs[\\\"naming\\\"][2][1:] + os.sep\\n\\n LIB_ARGS = \\\"\\\"\\n LIB_ARGS += \\\" LIBL4_INCLUDE=%s\\\" % l4_include\\n LIB_ARGS += \\\" LIBTIMER_INCLUDE=%s\\\" % timer_include\\n LIB_ARGS += \\\" LIBCB_INCLUDE=%s\\\" % cb_include\\n LIB_ARGS += \\\" IGUANA_INCLUDE=%s\\\" % ig_include\\n LIB_ARGS += \\\" IGUANA_IDL_INCLUDE=%s\\\" % ig_idl4_include\\n LIB_ARGS += \\\" IDL4_INCLUDE=%s\\\" % idl4_include\\n LIB_ARGS += \\\" NAMING_INCLUDE=%s\\\" % naming_include\\n LIB_ARGS += \\\" MUTEX_INCLUDE=%s\\\" % mutex_include\\n if \\\"pxa\\\" in self.machine.drivers:\\n pxa_include = os.getcwd() + os.sep + self.libs[\\\"pxa\\\"][0][0][1:] + os.sep\\n LIB_ARGS += \\\" LIBPXA_INCLUDE=%s\\\" % pxa_include\\n\\n LIB_ARGS += \\\" LIBCDIR=%s\\\" % c_lib\\n LIB_ARGS += \\\" LIBIGUANADIR=%s\\\" % ig_lib\\n LIB_ARGS += \\\" LIBL4DIR=%s\\\" % l4_lib\\n LIB_ARGS += \\\" LIBTIMERDIR=%s\\\" % timer_lib\\n LIB_ARGS += \\\" LIBL4EDIR=%s\\\" % l4e_lib\\n LIB_ARGS += \\\" LIBCBDIR=%s\\\" % cb_lib\\n LIB_ARGS += \\\" LIBLLDIR=%s\\\" % ll_lib\\n LIB_ARGS += \\\" LIBRANGE_FLDIR=%s\\\" % rfl_lib\\n LIB_ARGS += \\\" LIBNAMINGDIR=%s\\\" % naming_lib\\n LIB_ARGS += \\\" LIBMUTEXDIR=%s\\\" % mutex_lib\\n if \\\"pxa\\\" in self.machine.drivers:\\n pxa_lib = os.getcwd() + os.sep + self.libs[\\\"pxa\\\"][2][1:] + os.sep\\n LIB_ARGS += \\\" LIBPXADIR=%s\\\" % pxa_lib\\n\\n l4linux.linux_build_cmd = \\\"make -C wombat O=%s/%s/wombat WOMBAT_CFLAGS=\\\\'%s\\\\' V=0 %s \\\" \\\\\\n \\\"CROSS_COMPILE=%s \\\" % \\\\\\n (self.builddir, self.name, wombat_cflags, LIB_ARGS, self.toolchain)\\n\\n if cleaning and os.path.exists(\\\"%s/%s/wombat\\\" % (self.builddir, self.name)):\\n shutil.rmtree(\\\"%s/%s/wombat\\\" % (self.builddir, self.name))\\n\\n # As for pistachio we don't track the L4Linux dependencies so the\\n # use needs to explicitly specify scons build_linux= to get L4Linux\\n # rebuilt\\n add_arg(\\\"build_linux\\\", \\\"Set this option if you want to rebuild Wombat on this build\\\", 0)\\n if build_linux != 0:\\n AlwaysBuild(l4linux)\\n\\n\\tenv['EXPECT_TEST_DATA'] = [(\\\"Iguana init starting\\\", None),\\n (\\\"Loading linux\\\", None),\\n (\\\"Memory: \\\\d+k/\\\\d+k available\\\", None),\\n (\\\"Please press Enter to activate this console.\\\", None)]\\n\\n return l4linux\",\n \"def init():\\n\\n @click.option('--release-message', '-m', help='Release message',\\n default='')\\n @click.option('--release-tag', '-t', help='Release tag', default='')\\n @click.option('--source', '-s', help='Treadmill source directory path',\\n required=True)\\n @click.command(name='build-binary')\\n def build_binary(release_message, release_tag, source):\\n \\\"\\\"\\\"Build treadmill binary and RPM\\n \\\"\\\"\\\"\\n subprocess.call([\\n 'bash',\\n source + '/build_binary.sh',\\n release_message,\\n release_tag,\\n ])\\n\\n return build_binary\",\n \"def build_binary(release_message, release_tag, source):\\n subprocess.call([\\n 'bash',\\n source + '/build_binary.sh',\\n release_message,\\n release_tag,\\n ])\",\n \"def main(args=None):\",\n \"def main(args=None):\",\n \"def build(root):\",\n \"def main():\\n setup_params = dict(static_setup_params, **get_dynamic_setup_params())\\n ignore_warning_regex = (\\n r\\\"Unknown distribution option: '(project_urls|python_requires)'\\\"\\n )\\n warnings.filterwarnings(\\n 'ignore',\\n message=ignore_warning_regex,\\n category=UserWarning,\\n module='distutils.dist',\\n )\\n setup(**setup_params)\\n warnings.resetwarnings()\",\n \"def main(text):\\n arguments = _parse_arguments(text)\\n\\n source = os.environ[\\\"REZ_BUILD_SOURCE_PATH\\\"]\\n destination = os.environ[\\\"REZ_BUILD_INSTALL_PATH\\\"]\\n\\n filer.clean(destination)\\n\\n try:\\n filer.build(\\n source,\\n destination,\\n hdas=arguments.hdas,\\n items=arguments.items,\\n eggs=arguments.eggs,\\n symlink=arguments.symlink,\\n symlink_folders=arguments.symlink_folders,\\n symlink_files=arguments.symlink_files,\\n )\\n except exceptions.NonRootItemFound as error:\\n print(error, file=sys.stderr)\\n print(\\\"Please check spelling and try again.\\\")\",\n \"def test_buildTarballsScript(self):\\n builds = []\\n def myBuilder(checkout, destination):\\n builds.append((checkout, destination))\\n tarballBuilder = BuildTarballsScript()\\n tarballBuilder.buildAllTarballs = myBuilder\\n\\n tarballBuilder.main([\\\"checkoutDir\\\", \\\"destinationDir\\\"])\\n self.assertEquals(\\n builds,\\n [(FilePath(\\\"checkoutDir\\\"), FilePath(\\\"destinationDir\\\"))])\",\n \"def Main(parsed_args):\\n logging.basicConfig(\\n format='[[%(filename)s %(funcName)s]] - %(message)s',\\n level=(logging.ERROR if os.environ.get('CI') else logging.INFO))\\n EnsureNodeJsIsInstalled()\\n CheckPrereqs()\\n InstallNodeDependencies()\\n SetupOutDir(out_dir='dist')\\n GenValidatorProtoascii(out_dir='dist')\\n GenValidatorPb2Py(out_dir='dist')\\n GenValidatorProtoGeneratedJs(out_dir='dist')\\n GenValidatorGeneratedJs(out_dir='dist')\\n CompileValidatorMinified(out_dir='dist')\\n RunSmokeTest(out_dir='dist')\\n RunIndexTest()\\n CompileValidatorTestMinified(out_dir='dist')\\n CompileHtmlparserTestMinified(out_dir='dist')\\n CompileParseCssTestMinified(out_dir='dist')\\n CompileParseUrlTestMinified(out_dir='dist')\\n CompileAmp4AdsParseCssTestMinified(out_dir='dist')\\n CompileKeyframesParseCssTestMinified(out_dir='dist')\\n CompileParseSrcsetTestMinified(out_dir='dist')\\n GenerateTestRunner(out_dir='dist')\\n RunTests(update_tests=parsed_args.update_tests, out_dir='dist')\",\n \"def build(filename, verbose):\\n set_verbosity(verbose)\\n hokusai.build(filename)\",\n \"def script_build(repo_root, spec, build_args, verbose):\\n env = os.environ\\n env.update(build_args)\\n for s in spec.build_script():\\n if verbose:\\n click.echo(str(s))\\n ret = subprocess.call(str(s), shell=True, cwd=repo_root, env=env)\\n if ret:\\n raise click.ClickException(\\\"{} exited with code {}\\\".format(str(s), ret))\",\n \"def KengeLibrary(self, name, buildname=None, source = None, public_headers = None, **kargs):\\n library_args = {}\\n\\n library_args[\\\"CPPPATH\\\"] = []\\n\\n if buildname is None:\\n buildname = name\\n\\n if source is None:\\n # User didn't provide any source files\\n # explicitly, so we work out it form them\\n # based on some hueristics.\\n glob_list = []\\n dirs = [\\\"include/interfaces/\\\", \\\"src/\\\", \\\"src/arch-%s/\\\" % env.arch]\\n\\t if self.test_lib == name:\\n\\t\\tdirs.append(\\\"test/\\\")\\n if self[\\\"BUILD_TESTS\\\"]:\\n dirs += [\\\"test/\\\"]\\n for src_ext in env.src_exts:\\n for dir_ in dirs:\\n glob_list.append(dir_ + \\\"*.\\\" + src_ext)\\n else:\\n glob_list = source\\n\\n libs = []\\n \\n if \\\"LIBS\\\" in kargs:\\n if self[\\\"BUILD_TESTS\\\"]:\\n kargs[\\\"LIBS\\\"].append(\\\"check\\\")\\n for lib in kargs[\\\"LIBS\\\"]:\\n libs.append(lib)\\n if lib not in self.libs.keys():\\n raise SCons.Errors.UserError, \\\"Library [%s] was looking for library [%s] but it doesn't exist \\\" \\\\\\n \\\"in environment [%s]\\\\n This environment has: %s\\\" % (name, lib, self.name, self.libs.keys())\\n\\n del kargs[\\\"LIBS\\\"]\\n\\n # He we expand the glob to a list of files\\n source_list = Flatten([src_glob(glob) for glob in glob_list])\\n\\n idl_files = [fn for fn in source_list if fn.endswith(\\\".idl4\\\")]\\n reg_files = [fn for fn in source_list if fn.endswith(\\\".reg\\\")]\\n\\n # Now we go through everything in the kargs:\\n for arg in kargs:\\n if arg.startswith(\\\"EXTRA_\\\"):\\n argname = arg[6:]\\n library_args[argname] = self[argname] + kargs[arg]\\n else:\\n library_args[arg] = kargs[arg]\\n\\n # Generally this is the only public headers\\n if public_headers is None:\\n public_headers = [\\\"#libs/%s/include\\\" % name, \\\"#libs/%s/test\\\" % name]\\n\\n if len(idl_files) or len(reg_files):\\n # Unless we have generated files\\n public_headers.append(Dir(\\\".\\\").abspath + \\\"/include\\\")\\n\\n # Now if we are for real compiling stuff...\\n cpp_path = copy.copy(self[\\\"CPPPATH\\\"])\\n\\tif self.test_lib:\\n\\t\\tcpp_path.append(\\\"#libs/check/include\\\")\\n \\n # Make sure we include any of the libraries header file's\\n for each in public_headers:\\n cpp_path.append(each)\\n\\n # This ensure that any generated header files\\n # Maybe move this somewhere else later though\\n cpp_path.append(Dir('.').abspath + \\\"/src\\\") # Broken\\n cpp_path.append(Dir('.').abspath + \\\"/include\\\")\\n\\n # Find any .idl4 files that should be generated\\n for file in idl_files:\\n gen_file = self.IDL4(file)\\n\\n # Generate any .reg files\\n for file in reg_files:\\n self.Reg(file)\\n \\n library_args[\\\"CPPPATH\\\"] += cpp_path + self.end_cpp_path # End cpp_path is a hack hack hack!\\n \\n # Now we just call the normal StaticLibrary with our simple defaults\\n lib = self.StaticLibrary(buildname, source_list, **library_args)\\n\\n assert(len(lib) == 1)\\n lib = lib[0]\\n\\n if self[\\\"FLINT_RUN\\\"]:\\n for each in lib.children():\\n if str(each).endswith(\\\".o\\\"):\\n if str(each.children()[0]).endswith(\\\".c\\\") or \\\\\\n str(each.children()[0]).endswith(\\\".cc\\\"):\\n self.AddPreAction(each, \\\"$FLINTCOM\\\")\\n\\n # And construct our definition of the library\\n # This should suck muhc much less... how about a class?\\n lib = (public_headers, lib, \\\"#\\\" + os.path.dirname(lib.path), None, libs)\\n return lib\",\n \"def build():\\n os.makedirs(DIST_DIR, exist_ok=True)\\n\\n if \\\"WHEEL\\\" in os.environ:\\n whl = build_wheel()\\n else:\\n click.echo(\\\"Not building wheels.\\\")\\n\\n if \\\"WHEEL\\\" in os.environ and \\\"DOCKER\\\" in os.environ:\\n # Docker image requires wheels\\n build_docker_image(whl)\\n else:\\n click.echo(\\\"Not building Docker image.\\\")\\n\\n if \\\"PYINSTALLER\\\" in os.environ:\\n build_pyinstaller()\\n else:\\n click.echo(\\\"Not building PyInstaller packages.\\\")\",\n \"def AddLibrary(self, lib, buildname=None, **kargs):\\n \\n if buildname is None:\\n buildname = lib\\n lib_builddir = \\\"%s/%s/libs/%s\\\" % (self.builddir, self.name, buildname)\\n\\n # We export \\\"env\\\" and \\\"args\\\" to the library build file\\n env = self\\n Export(\\\"env\\\")\\n args = kargs\\n Export(\\\"args\\\")\\n args[\\\"buildname\\\"] = buildname\\n # We call the libraries SConstruct file to build it.\\n #if self.headers_only:\\n # self.cpp_path += SConscript(\\\"libs/%s/SConstruct\\\" % lib, build_dir=lib_builddir, duplicate=0)\\n #else:\\n self.libs[buildname] = SConscript(\\\"libs/%s\\\" % lib, build_dir=lib_builddir, duplicate=0)\\n #self[\\\"_EXTRA_CPPPATH\\\"] += \\n self[\\\"CPPPATH\\\"] += self.libs[buildname][0]\\n return self.libs[buildname]\",\n \"def KengeProgram(self, name, source = None, **kargs):\\n program_args = {} \\n # we only want unique libraries, since re can't handle huge strings\\n libs = sets.Set()\\n libpath = []\\n cpp_path = [] #copy.copy(self.cpp_path)\\n\\n # First we work out all the required libraries\\n for lib in kargs.get(\\\"LIBS\\\", []):\\n try:\\n\\t\\tlibs.union_update(self.get_libs(lib))\\n except LibraryNotFound, badlib:\\n raise SCons.Errors.UserError, \\\"Program %s was looking for library %s but it doesn't exist \\\" \\\\\\n \\\"in this environment\\\\n This environment has: %s\\\" % (name, badlib, self.libs.keys())\\n\\tlibs = list(libs)\\n \\n del kargs[\\\"LIBS\\\"]\\n\\n # Now we go through to get the library path for all the\\n # libraries\\n for libname in libs:\\n if not self.libs[libname][2] is None:\\n libpath += [self.libs[libname][2]]\\n\\n # This ensure that any generated header files\\n # Maybe move this somewhere else later though\\n\\n cpp_path.append(Dir('.').abspath + \\\"/src\\\")\\n\\n # Now we go through everything in the kargs:\\n for arg in kargs:\\n if arg.startswith(\\\"EXTRA_\\\"):\\n argname = arg[6:]\\n program_args[argname] = self.get(argname, []) + kargs[arg]\\n else:\\n program_args[arg] = kargs[arg]\\n\\n if source is None:\\n # User didn't provide any source files\\n # explicitly, so we work out it form them\\n # based on some hueristics.\\n glob_list = []\\n dirs = [\\\"src/\\\", \\\"src/arch-%s/\\\" % env.arch]\\n for src_ext in env.src_exts:\\n for dir_ in dirs:\\n glob_list.append(dir_ + \\\"*.\\\" + src_ext)\\n else:\\n glob_list = source\\n\\n # He we expand the glob to a list of files\\n source_list = Flatten([src_glob(glob) for glob in glob_list])\\n\\n # Now automatically handle any templates\\n for file_name in source_list:\\n if file_name.endswith(\\\".template\\\"):\\n template_env = kargs.get(\\\"TEMPLATE_ENV\\\", self[\\\"TEMPLATE_ENV\\\"])\\n template = self.Template(file_name, TEMPLATE_ENV=template_env)\\n env.Depends(template, Value(template_env))\\n source_list.append(str(template[0]))\\n\\n program_args[\\\"LIBS\\\"] = libs\\n program_args[\\\"LIBPATH\\\"] = libpath\\n\\n object_list = []\\n for source in source_list:\\n for ext in self.obj_exts:\\n if str(source).endswith(\\\".%s\\\" % ext):\\n object_list.append(source)\\n continue\\n\\n if kargs.has_key(\\\"EXTRAOBJECTS\\\"):\\n object_list += kargs[\\\"EXTRAOBJECTS\\\"]\\n\\n # Prepend the crt\\n if \\\"c\\\" in self.libs.dict.keys():\\n object_list = self.libs[\\\"c\\\"][3]+ object_list\\n\\n prog = self.Program(name, object_list, **program_args)\\n\\n # SCons changed to program returning a list of object. But it makes\\n # much more sense to return a single item\\n assert(len(prog) == 1)\\n prog = prog[0]\\n\\n if \\\"LINKSCRIPTS\\\" in program_args:\\n for linkscript in program_args[\\\"LINKSCRIPTS\\\"]:\\n self.Depends(prog, linkscript)\\n\\n if self[\\\"FLINT_RUN\\\"]:\\n for each in prog.children():\\n if str(each).endswith(\\\".o\\\"):\\n if str(each.children()[0]).endswith(\\\".c\\\") or \\\\\\n str(each.children()[0]).endswith(\\\".cc\\\"):\\n self.AddPreAction(each, \\\"$FLINTCOM\\\")\\n\\n return prog\",\n \"def start_build(args):\\n\\n path = os.path.join(SCRATCH_DIR, args.project)\\n \\n # Set up virtual environment\\n print(\\\"Setting up virtual python environment in %s\\\" % path)\\n venv.create(path, clear=True, symlinks=True, with_pip=False)\\n\\n # Pull in repository data\\n sourcepath = os.path.join(path, 'source')\\n print(\\\"Cloning from git repository %s (branch: %s)\\\" % (args.source, args.sourcebranch))\\n subprocess.run((GIT, 'clone', '--branch', args.sourcebranch, '--depth=1', '--no-single-branch', args.source, sourcepath),\\n check=True)\\n\\n # Activate venv and install pips if needed. For dev/test, we will\\n # assume that all requirements are available at the system level,\\n # rather than needing to install them into the venv.\\n ### note: this makes it difficult to test requirements.txt, but it\\n ### will do for now. Debugging requirements.txt failures on the\\n ### production buildbot is not difficult to correct.\\n if IS_PRODUCTION and os.path.exists(os.path.join(sourcepath, 'requirements.txt')):\\n print(\\\"Installing pips\\\")\\n subprocess.run(('/bin/bash', '-c',\\n 'source bin/activate; pip3 install -r source/requirements.txt'),\\n cwd=path, check=True)\\n else:\\n print(\\\"On dev/test requirements.txt is not processed, skipping pip\\\")\\n\\n # Where are our tools?\\n if IS_PRODUCTION:\\n tool_dir = PELICANFILES\\n else:\\n tool_dir = THIS_DIR\\n print(\\\"TOOLS:\\\", tool_dir)\\n\\n pelconf_yaml = os.path.join(sourcepath, AUTO_SETTINGS_YAML)\\n if os.path.exists(pelconf_yaml):\\n settings_path = os.path.join(path, AUTO_SETTINGS)\\n if IS_PRODUCTION:\\n builtin_plugins = PLUGINS\\n else:\\n builtin_plugins = os.path.join(tool_dir, os.pardir, 'plugins')\\n generate_settings(pelconf_yaml, settings_path, [ builtin_plugins ], sourcepath)\\n else:\\n # The default name, but we'll pass it explicitly.\\n settings_path = os.path.join(sourcepath, 'pelicanconf.py')\\n\\n # Set currently supported plugins\\n ### this needs to be removed, as it is too indeterminate.\\n with open(settings_path, 'a') as f:\\n f.write(\\\"\\\"\\\"\\ntry:\\n PLUGINS += ['toc']\\nexcept:\\n PLUGINS = ['toc', 'gfm']\\n\\\"\\\"\\\")\\n\\n # Call pelican\\n buildpath = os.path.join(path, 'build/output')\\n os.makedirs(buildpath, exist_ok = True)\\n buildcmd = ('/bin/bash', '-c',\\n 'source bin/activate; cd source && '\\n ### note: adding --debug can be handy\\n f'(pelican content --settings {settings_path} -o {buildpath})',\\n )\\n print(\\\"Building web site with:\\\", buildcmd)\\n env = os.environ.copy()\\n env['LIBCMARKDIR'] = LIBCMARKDIR\\n subprocess.run(buildcmd, cwd=path, check=True, env=env)\\n\\n count = len(glob.glob(f'{buildpath}/**/*.html', recursive=True))\\n print(f\\\"{count} html files.\\\")\\n if args.count > 0 and args.count > count:\\n print(\\\"Not enough html pages in the Web Site. Minimum %s > %s found in the Web Site.\\\" % (args.count, count))\\n sys.exit(4)\\n\\n # Done for now\\n print(\\\"Web site successfully generated!\\\")\\n\\n # It is much easier to do all the below, if we chdir()\\n os.chdir(sourcepath)\\n\\n # Copy to result branch\\n print(\\\"Copying web site to branch:\\\", args.outputbranch)\\n\\n try:\\n subprocess.run((GIT, 'rev-parse', '--verify', \\\"origin/%s\\\" % args.outputbranch),\\n check=True)\\n print(\\\"- Doing fresh checkout of branch %s\\\" % args.outputbranch)\\n subprocess.run((GIT, 'checkout', args.outputbranch, '-f'), check=True)\\n subprocess.run((GIT, 'pull'), check=True)\\n except:\\n print(\\\"- Branch %s does not exist (yet), creating it...\\\" % args.outputbranch)\\n # If .asf.yaml exists, which it should, make a copy of it in memory for later\\n asfyml = os.path.join(sourcepath, '.asf.yaml')\\n myyaml = None\\n if os.path.exists(asfyml):\\n myyaml = open(asfyml).read()\\n subprocess.run((GIT, 'checkout', '--orphan', args.outputbranch), check=True)\\n subprocess.run((GIT, 'rm', '-rf', '.'), check=True)\\n # Add .asf.yaml back in if we found it.\\n if myyaml:\\n open(asfyml, \\\"w\\\").write(myyaml)\\n subprocess.run((GIT, 'add', '.asf.yaml'), check=True)\\n\\n print(\\\"- Adding new content to branch\\\")\\n # RM output dir if it already exists\\n outputdir = os.path.join(sourcepath, 'output')\\n if os.path.isdir(outputdir):\\n print(\\\"Removing existing output dir %s\\\" % outputdir)\\n shutil.rmtree(outputdir)\\n shutil.move(buildpath, outputdir)\\n subprocess.run((GIT, 'add', 'output/'), check=True)\\n\\n # Check if there are any changes.\\n cp = subprocess.run((GIT, 'diff', '--cached', '--quiet'))\\n if cp.returncode == 0:\\n # There were no differences reported.\\n print('Nothing new to commit. Ignoring this build.')\\n else:\\n print(\\\"- Committing to %s\\\" % args.source)\\n subprocess.run((GIT, 'commit', '-m', 'Automatic Site Publish by Buildbot'), check=True)\\n\\n # If we're not in production, then avoid pushing changes.\\n if IS_PRODUCTION:\\n print('- Pushing changes, for publishing')\\n subprocess.run((GIT, 'push', args.source, args.outputbranch), check=True)\\n\\n print('Success. Done.')\\n # for dev/test provide viewing instructions\\n if not IS_PRODUCTION:\\n if args.listen:\\n try:\\n subprocess.run(('pelican','-l'), check=True)\\n except KeyboardInterrupt:\\n pass\\n else:\\n print(f'To test output:\\\\ncd {sourcepath}; pelican -l')\",\n \"def setup_binutils():\\n # Set up binutils\\n global binutils_subdir\\n binutils_subdir = \\\"binutils\\\"\\n if binutils_version:\\n binutils_subdir = \\\"binutils-%s\\\" % binutils_version\\n if not os.path.exists(binutils_subdir):\\n docmd(\\\"wget http://ftpmirror.gnu.org/binutils/\\\"\\n \\\"%s.tar.bz2\\\" % binutils_subdir)\\n docmd(\\\"tar jxf %s.tar.bz2\\\" % binutils_subdir)\\n else:\\n if not os.path.exists(binutils_subdir):\\n doscmd(\\\"git clone --depth 1 %s binutils\\\" % binutils_git)\\n # Configure binutils\\n if not os.path.exists(binutils_build_dir):\\n docmd(\\\"mkdir %s\\\" % binutils_build_dir)\\n dochdir(binutils_build_dir)\\n doscmd(\\\"../%s/configure --prefix=%s --target=%s \\\"\\n \\\"%s \\\" % (binutils_subdir,\\n cross_prefix,\\n flag_target_arch,\\n flag_use_multilib))\\n doscmd(\\\"make %s \\\" % flag_parfactor)\\n doscmd(\\\"make %s install\\\" % flag_parfactor)\\n dochdir(\\\"..\\\")\",\n \"def build():\\n local('pelican -o {} -s pelicanconf.py'.format(env.deploy_path))\",\n \"def build(config_file, install=False):\\n return exec_fn(lambda: _build(config_file, install))\",\n \"def run_distutils():\\n from distutils.core import setup, Extension\\n\\n source_dir = os.getcwd()\\n build_dir = Params.cmake_binary_dir\\n \\n gsched_module = Extension('_gsched',\\n sources=[build_dir+'/gsched_wrap.c', \\n build_dir+'/gsched.c'],\\n include_dirs = [source_dir+'/include'],\\n libraries = ['gsched'],\\n extra_compile_args = ['-fPIC'],\\n library_dirs = [build_dir, source_dir], \\n \\n )\\n\\n\\n# sdf_seq_module = Extension('_sdf_seq',\\n# sources=[source_dir+'/libgsched/sdf_seq_wrap.c',\\n# source_dir+'/libgsched/sdf_seq.c'],\\n# include_dirs = [source_dir+'/include'], \\n #)\\n \\n \\n setup(name='pygsched',\\n version='1.0',\\n description=\\\"Group Scheduling Python API bindings.\\\",\\n url='http://www.ittc.ku.edu/kusp',\\n package_dir={'': '.'},\\n include_dirs = [source_dir+'/include'],\\n ext_modules = [gsched_module],\\n # sdf_seq_module],\\n py_modules = ['gschedapi'],\\n scripts = ['tools/gschedctrl',\\n 'tools/gschedexec',\\n 'tools/gschedpprint',\\n 'tools/gschedsnapshot',\\n 'tools/gschedsh',\\n ],\\n packages=['pygsched','pygsched.gsconsole']\\n )\\n pass\",\n \"def build(which):\\n return subprocess.Popen([p['paths']['w2l'],'obj',os.path.abspath(which)]).wait()\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='Merging rooms and students')\\n parser.add_argument('student_dir', type=str, help='Students file path.')\\n parser.add_argument('room_dir', type=str, help='Rooms file path')\\n parser.add_argument('config_dir', type=str, help='Config file path')\\n parser.add_argument('format', choices=['json', 'xml'], help='The output format.')\\n args = parser.parse_args()\\n make(args.student_dir, args.room_dir, args.config_dir, args.format)\",\n \"def do_collect(self, scriptname, targetdir, excludes=[], distlib='lib', zip_lib=False, verbose=False):\\n self.site_packages = os.path.normcase(distutils.sysconfig.get_python_lib(standard_lib=False))\\n self.standard_lib = os.path.normcase(distutils.sysconfig.get_python_lib(standard_lib=True))\\n self.sys_prefix = os.path.normcase(sys.prefix)\\n \\n self.verbose = verbose\\n self.log(\\\"\\\\nLibCollect v%s running in verbose mode\\\\n\\\" % version)\\n \\n # Initial preparation to create the lib directory\\n #\\n if os.path.exists(targetdir): \\n self.log(\\\"Directory '%s' exists. Removing it.\\\" % targetdir)\\n shutil.rmtree(targetdir)\\n \\n libdir = os.path.join(targetdir, distlib)\\n self.log(\\\"Creating path '%s'\\\" % libdir)\\n mkpath(libdir)\\n \\n # Find the modules we need to collect\\n # \\n modules = self.find_modules(scriptname, excludes, verbose)\\n \\n self.log(\\\"Collecting modules into '%s'\\\" % libdir)\\n # Collect the modules in the lib directory\\n #\\n for modname, modtype, modfile in modules:\\n modname_components = modname.split('.')\\n \\n if modtype == 'm':\\n if len(modname_components) > 1:\\n new_path = os.path.join(libdir, *modname_components[0:-1])\\n else:\\n new_path = libdir\\n elif modtype == 'P':\\n new_path = os.path.join(libdir, *modname_components)\\n else:\\n assert False\\n \\n mkpath(new_path)\\n shutil.copy(modfile, new_path)\\n \\n os.chdir(targetdir)\\n \\n if zip_lib:\\n self.log(\\\"Zipping directory '%s' into '%s'\\\" % (libdir, libdir + '.zip'))\\n make_zipfile(distlib, distlib)\\n self.log(\\\"Removing directory '%s'\\\" % libdir)\\n shutil.rmtree(distlib)\\n path_add = \\\"os.path.join('\\\" + distlib + \\\".zip', '\\\" + distlib + \\\"')\\\"\\n else:\\n path_add = \\\"'\\\" + distlib + \\\"'\\\"\\n \\n # Create the loader script\\n #\\n self.log(\\\"Writing loader script: %s\\\" % scriptname)\\n loader = open(os.path.basename(scriptname), 'w')\\n loader_name = os.path.splitext(scriptname)[0]\\n loader.write(\\\"import os, sys, runpy\\\\n\\\")\\n loader.write(\\\"sys.path.insert(0, %s)\\\\n\\\" % path_add)\\n loader.write(\\\"runpy.run_module('%s', run_name=\\\\\\\"__main__\\\\\\\", alter_sys=True)\\\\n\\\" % loader_name)\\n loader.close()\",\n \"def main():\\n\\n args = parse_arguments()\\n show_parameters(args)\\n\\n bundleDefinitions = get_bundle_definitions(args.bfile)\\n show_bundle_definitions(bundleDefinitions)\\n\\n check_definition_integrity(bundleDefinitions)\\n\\n bundlesForConfig = determine_bundles_for_config(args.config, bundleDefinitions)\\n show_bundles_for_config(args.config, bundlesForConfig)\\n\\n output_result(bundlesForConfig, args.ofile)\\n\\n sys.exit(0)\",\n \"def build(setup_kwargs):\\n setup_kwargs.update({\\n 'ext_modules': [\\n cpp_extension.CppExtension(\\n 'torch_persistent_homology.persistent_homology_cpu',\\n ['torch_persistent_homology/perisistent_homology_cpu.cpp'],\\n extra_link_args=[\\n '-Wl,-rpath,' + library_path\\n for library_path in torch_library_paths]\\n )\\n ],\\n 'cmdclass': {\\n 'build_ext': cpp_extension.BuildExtension\\n }\\n })\",\n \"def main(argv):\\n target = argv[1] if len(argv) >= 2 else 'all'\\n if target not in ('all', 'flatbuffers', 'webp', 'clean'):\\n sys.stderr.write('No rule to build target %s.\\\\n' % target)\\n\\n if target in ('all', 'flatbuffers'):\\n try:\\n generate_flatbuffer_binaries()\\n except BuildError as error:\\n handle_build_error(error)\\n return 1\\n if target in ('all', 'webp'):\\n try:\\n generate_webp_textures()\\n except BuildError as error:\\n handle_build_error(error)\\n return 1\\n if target == 'clean':\\n try:\\n clean()\\n except OSError as error:\\n sys.stderr.write('Error cleaning: %s' % str(error))\\n return 1\\n\\n return 0\",\n \"def build(ctx, arch, type, build_num, verbose, goal, extra_args_str):\\n # Run the supplied build script if there is one, otherwise assume cmake\\n # Parse file to find requirements then check that they exist, then build\\n project_config = ctx.obj.project_config()\\n component = ComponentConfiguration(project_config['components'][0])\\n spec = component.get_spec(goal, arch, type)\\n build_args = {}\\n extra_args = parse_key_value_pairs(extra_args_str)\\n build_args.update(spec.build_vars())\\n build_args.update(extra_args)\\n add_version_args(ctx.obj.repo_root, build_num, build_args)\\n if spec.build_script() is None:\\n cmake_build(ctx.obj.repo_root, spec.build_arch(), spec.build_type(), spec.build_goal(), verbose, build_args)\\n else:\\n script_build(ctx.obj.repo_root, spec, build_args, verbose)\\n try:\\n ctx.obj.build_server().publish_artifacts(spec.build_artifacts())\\n except click.ClickException:\\n pass\",\n \"def gather() -> None:\\n # pylint: disable=too-many-locals\\n\\n # First off, clear out any existing output.\\n existing_dirs = [\\n os.path.join('src/external', d) for d in os.listdir('src/external')\\n if d.startswith('python-') and d != 'python-notes.txt'\\n ]\\n existing_dirs += [\\n os.path.join('assets/src', d) for d in os.listdir('assets/src')\\n if d.startswith('pylib-')\\n ]\\n for existing_dir in existing_dirs:\\n efrotools.run('rm -rf \\\"' + existing_dir + '\\\"')\\n\\n for buildtype in ['debug', 'release']:\\n debug = buildtype == 'debug'\\n bsuffix = '_debug' if buildtype == 'debug' else ''\\n bsuffix2 = '-debug' if buildtype == 'debug' else ''\\n\\n libname = 'python' + PYTHON_VERSION_MAJOR + ('dm' if debug else 'm')\\n\\n bases = {\\n 'mac':\\n f'build/python_apple_mac{bsuffix}/build/macOS',\\n 'ios':\\n f'build/python_apple_ios{bsuffix}/build/iOS',\\n 'tvos':\\n f'build/python_apple_tvos{bsuffix}/build/tvOS',\\n 'android_arm':\\n f'build/python_android_arm{bsuffix}/build/sysroot',\\n 'android_arm64':\\n f'build/python_android_arm64{bsuffix}/build/sysroot',\\n 'android_x86':\\n f'build/python_android_x86{bsuffix}/build/sysroot',\\n 'android_x86_64':\\n f'build/python_android_x86_64{bsuffix}/build/sysroot'\\n }\\n\\n # Note: only need pylib for the first in each group.\\n builds: List[Dict[str, Any]] = [{\\n 'name':\\n 'macos',\\n 'group':\\n 'apple',\\n 'headers':\\n bases['mac'] + '/Support/Python/Headers',\\n 'libs': [\\n bases['mac'] + '/Support/Python/libPython.a',\\n bases['mac'] + '/Support/OpenSSL/libOpenSSL.a',\\n bases['mac'] + '/Support/XZ/libxz.a'\\n ],\\n 'pylib':\\n (bases['mac'] + '/python/lib/python' + PYTHON_VERSION_MAJOR),\\n }, {\\n 'name':\\n 'ios',\\n 'group':\\n 'apple',\\n 'headers':\\n bases['ios'] + '/Support/Python/Headers',\\n 'libs': [\\n bases['ios'] + '/Support/Python/libPython.a',\\n bases['ios'] + '/Support/OpenSSL/libOpenSSL.a',\\n bases['ios'] + '/Support/XZ/libxz.a'\\n ],\\n }, {\\n 'name':\\n 'tvos',\\n 'group':\\n 'apple',\\n 'headers':\\n bases['tvos'] + '/Support/Python/Headers',\\n 'libs': [\\n bases['tvos'] + '/Support/Python/libPython.a',\\n bases['tvos'] + '/Support/OpenSSL/libOpenSSL.a',\\n bases['tvos'] + '/Support/XZ/libxz.a'\\n ],\\n }, {\\n 'name':\\n 'android_arm',\\n 'group':\\n 'android',\\n 'headers':\\n bases['android_arm'] + f'/usr/include/{libname}',\\n 'libs': [\\n bases['android_arm'] + f'/usr/lib/lib{libname}.a',\\n bases['android_arm'] + '/usr/lib/libssl.a',\\n bases['android_arm'] + '/usr/lib/libcrypto.a',\\n bases['android_arm'] + '/usr/lib/liblzma.a',\\n bases['android_arm'] + '/usr/lib/libsqlite3.a'\\n ],\\n 'libinst':\\n 'android_armeabi-v7a',\\n 'pylib': (bases['android_arm'] + '/usr/lib/python' +\\n PYTHON_VERSION_MAJOR),\\n }, {\\n 'name': 'android_arm64',\\n 'group': 'android',\\n 'headers': bases['android_arm64'] + f'/usr/include/{libname}',\\n 'libs': [\\n bases['android_arm64'] + f'/usr/lib/lib{libname}.a',\\n bases['android_arm64'] + '/usr/lib/libssl.a',\\n bases['android_arm64'] + '/usr/lib/libcrypto.a',\\n bases['android_arm64'] + '/usr/lib/liblzma.a',\\n bases['android_arm64'] + '/usr/lib/libsqlite3.a'\\n ],\\n 'libinst': 'android_arm64-v8a',\\n }, {\\n 'name': 'android_x86',\\n 'group': 'android',\\n 'headers': bases['android_x86'] + f'/usr/include/{libname}',\\n 'libs': [\\n bases['android_x86'] + f'/usr/lib/lib{libname}.a',\\n bases['android_x86'] + '/usr/lib/libssl.a',\\n bases['android_x86'] + '/usr/lib/libcrypto.a',\\n bases['android_x86'] + '/usr/lib/liblzma.a',\\n bases['android_x86'] + '/usr/lib/libsqlite3.a'\\n ],\\n 'libinst': 'android_x86',\\n }, {\\n 'name': 'android_x86_64',\\n 'group': 'android',\\n 'headers': bases['android_x86_64'] + f'/usr/include/{libname}',\\n 'libs': [\\n bases['android_x86_64'] + f'/usr/lib/lib{libname}.a',\\n bases['android_x86_64'] + '/usr/lib/libssl.a',\\n bases['android_x86_64'] + '/usr/lib/libcrypto.a',\\n bases['android_x86_64'] + '/usr/lib/liblzma.a',\\n bases['android_x86_64'] + '/usr/lib/libsqlite3.a'\\n ],\\n 'libinst': 'android_x86_64',\\n }]\\n\\n for build in builds:\\n\\n grp = build['group']\\n builddir = f'src/external/python-{grp}{bsuffix2}'\\n header_dst = os.path.join(builddir, 'include')\\n lib_dst = os.path.join(builddir, 'lib')\\n assets_src_dst = f'assets/src/pylib-{grp}'\\n\\n # Do some setup only once per group.\\n if not os.path.exists(builddir):\\n efrotools.run('mkdir -p \\\"' + builddir + '\\\"')\\n efrotools.run('mkdir -p \\\"' + lib_dst + '\\\"')\\n\\n # Only pull modules into game assets on release pass.\\n if not debug:\\n # Copy system modules into the src assets\\n # dir for this group.\\n efrotools.run('mkdir -p \\\"' + assets_src_dst + '\\\"')\\n efrotools.run(\\n 'rsync --recursive --include \\\"*.py\\\"'\\n ' --exclude __pycache__ --include \\\"*/\\\" --exclude \\\"*\\\" \\\"'\\n + build['pylib'] + '/\\\" \\\"' + assets_src_dst + '\\\"')\\n\\n # Prune a bunch of modules we don't need to cut\\n # down on size.\\n prune = [\\n 'config-*', 'idlelib', 'lib-dynload', 'lib2to3',\\n 'multiprocessing', 'pydoc_data', 'site-packages',\\n 'ensurepip', 'tkinter', 'wsgiref', 'distutils',\\n 'turtle.py', 'turtledemo', 'test', 'sqlite3/test',\\n 'unittest', 'dbm', 'venv', 'ctypes/test', 'imaplib.py',\\n '_sysconfigdata_*'\\n ]\\n efrotools.run('cd \\\"' + assets_src_dst + '\\\" && rm -rf ' +\\n ' '.join(prune))\\n\\n # Some minor filtering to system scripts:\\n # on iOS/tvOS, addusersitepackages() leads to a crash\\n # due to _sysconfigdata_dm_ios_darwin module not existing,\\n # so let's skip that.\\n fname = f'{assets_src_dst}/site.py'\\n txt = efrotools.readfile(fname)\\n txt = efrotools.replace_one(\\n txt,\\n ' known_paths = addusersitepackages(known_paths)',\\n ' # efro tweak: this craps out on ios/tvos.\\\\n'\\n ' # (and we don\\\\'t use it anyway)\\\\n'\\n ' # known_paths = addusersitepackages(known_paths)')\\n efrotools.writefile(fname, txt)\\n\\n # Copy in a base set of headers (everything in a group should\\n # be using the same headers)\\n efrotools.run(f'cp -r \\\"{build[\\\"headers\\\"]}\\\" \\\"{header_dst}\\\"')\\n\\n # Clear whatever pyconfigs came across; we'll build our own\\n # universal one below.\\n efrotools.run('rm ' + header_dst + '/pyconfig*')\\n\\n # Write a master pyconfig header that reroutes to each\\n # platform's actual header.\\n with open(header_dst + '/pyconfig.h', 'w') as hfile:\\n hfile.write(\\n '#if BA_OSTYPE_MACOS\\\\n'\\n '#include \\\"pyconfig-macos.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_IOS\\\\n'\\n '#include \\\"pyconfig-ios.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_TVOS\\\\n'\\n '#include \\\"pyconfig-tvos.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_ANDROID and defined(__arm__)\\\\n'\\n '#include \\\"pyconfig-android_arm.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_ANDROID and defined(__aarch64__)\\\\n'\\n '#include \\\"pyconfig-android_arm64.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_ANDROID and defined(__i386__)\\\\n'\\n '#include \\\"pyconfig-android_x86.h\\\"\\\\n\\\\n'\\n '#elif BA_OSTYPE_ANDROID and defined(__x86_64__)\\\\n'\\n '#include \\\"pyconfig-android_x86_64.h\\\"\\\\n\\\\n'\\n '#else\\\\n'\\n '#error unknown platform\\\\n\\\\n'\\n '#endif\\\\n')\\n\\n # Now copy each build's config headers in with unique names.\\n cfgs = [\\n f for f in os.listdir(build['headers'])\\n if f.startswith('pyconfig')\\n ]\\n\\n # Copy config headers to their filtered names.\\n for cfg in cfgs:\\n out = cfg.replace('pyconfig', 'pyconfig-' + build['name'])\\n if cfg == 'pyconfig.h':\\n\\n # For platform's root pyconfig.h we need to filter\\n # contents too (those headers can themselves include\\n # others; ios for instance points to a arm64 and a\\n # x86_64 variant).\\n contents = efrotools.readfile(build['headers'] + '/' + cfg)\\n contents = contents.replace('pyconfig',\\n 'pyconfig-' + build['name'])\\n efrotools.writefile(header_dst + '/' + out, contents)\\n else:\\n # other configs we just rename\\n efrotools.run('cp \\\"' + build['headers'] + '/' + cfg +\\n '\\\" \\\"' + header_dst + '/' + out + '\\\"')\\n\\n # Copy in libs. If the lib gave a specific install name,\\n # use that; otherwise use name.\\n targetdir = lib_dst + '/' + build.get('libinst', build['name'])\\n efrotools.run('rm -rf \\\"' + targetdir + '\\\"')\\n efrotools.run('mkdir -p \\\"' + targetdir + '\\\"')\\n for lib in build['libs']:\\n efrotools.run('cp \\\"' + lib + '\\\" \\\"' + targetdir + '\\\"')\\n\\n print('Great success!')\",\n \"def main():\\n setup(**setup_params)\",\n \"def lib(names, sources=[], requirements=[], default_build=[], usage_requirements=[]):\\n assert is_iterable_typed(names, basestring)\\n assert is_iterable_typed(sources, basestring)\\n assert is_iterable_typed(requirements, basestring)\\n assert is_iterable_typed(default_build, basestring)\\n assert is_iterable_typed(usage_requirements, basestring)\\n if len(names) > 1:\\n if any(r.startswith('<name>') for r in requirements):\\n get_manager().errors()(\\\"When several names are given to the 'lib' rule\\\\n\\\" +\\n \\\"it is not allowed to specify the <name> feature.\\\")\\n\\n if sources:\\n get_manager().errors()(\\\"When several names are given to the 'lib' rule\\\\n\\\" +\\n \\\"it is not allowed to specify sources.\\\")\\n\\n project = get_manager().projects().current()\\n result = []\\n\\n for name in names:\\n r = requirements[:]\\n\\n # Support \\\" lib a ; \\\" and \\\" lib a b c ; \\\" syntax.\\n if not sources and not any(r.startswith(\\\"<name>\\\") for r in requirements) \\\\\\n and not any(r.startswith(\\\"<file\\\") for r in requirements):\\n r.append(\\\"<name>\\\" + name)\\n\\n result.append(targets.create_typed_metatarget(name, \\\"LIB\\\", sources,\\n r,\\n default_build,\\n usage_requirements))\\n return result\",\n \"def build_orc8r():\\n subprocess.check_call('./build.py -a', shell=True, cwd=orc8_docker_path)\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Wrapper for the GROMACS make_ndx module.\\\",\\n formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, width=99999))\\n parser.add_argument('-c', '--config', required=False, help=\\\"This file can be a YAML file, JSON file or JSON string\\\")\\n\\n # Specific args of each building block\\n required_args = parser.add_argument_group('required arguments')\\n required_args.add_argument('--input_structure_path', required=True)\\n required_args.add_argument('--output_ndx_path', required=True)\\n parser.add_argument('--input_ndx_path', required=False)\\n\\n args = parser.parse_args()\\n config = args.config if args.config else None\\n properties = settings.ConfReader(config=config).get_prop_dic()\\n\\n # Specific call of each building block\\n make_ndx(input_structure_path=args.input_structure_path,\\n output_ndx_path=args.output_ndx_path,\\n input_ndx_path=args.input_ndx_path,\\n properties=properties)\",\n \"def install():\\n build()\\n sh(\\\"%s setup.py develop\\\" % PYTHON)\",\n \"def h(options, buildout, version, opts):\\n cwd = os.getcwd()\\n md = options['compile-directory']\\n c = os.path.join(md, 'configure.py')\\n os.chdir(md)\\n p = buildout['p'][version]\\n opts = ' '.join(opts.split())\\n cmd = [p, c, opts]\\n print \\\"Running: %s\\\" % ' '.join(cmd)\\n ret = os.system(' '.join(cmd))\\n if ret > 0: raise Exception,('Cannot confiure')\\n os.chdir(cwd)\",\n \"def setUpModule():\\n args = [\\\"bowtie-build\\\",REF_FILES[\\\"yeast_fasta\\\"],_ebwt]\\n with open(os.devnull,\\\"a\\\") as null:\\n try:\\n subprocess.check_call(args,stdout=null,stderr=subprocess.STDOUT)\\n except CalledProcessError as e:\\n print(\\\"Could not build bowtie index for crossmap test.\\\")\\n raise e\",\n \"def make(args: Namespace) -> None:\\n\\n # Unless specifically asked to ignore Langkit_Support, make sure it is\\n # built and available to build Libpythonlang and Liblktlang.\\n if not args.no_langkit_support:\\n build_langkit_support(args)\\n add_to_path(os.environ, \\\"GPR_PROJECT_PATH\\\", str(SUPPORT_ROOT))\\n\\n # We need to clean the build space of the langkit libraries we depend\\n # upon before we run langkit again. Else, if we installed a newer version\\n # of GNAT since those libraries were built, what will happen is that:\\n #\\n # 1. Langkit will try loading them.\\n # 2. This will cause an uncaught exception trying to load some dynamic\\n # library from the compiler, preventing langkit to run.\\n # 3. Langkit cannot be used to recompile libpythonlang and liblktlang to\\n # newer versions.\\n if not args.lib:\\n shutil.rmtree(PYTHON_LIB_ROOT / 'build', ignore_errors=True)\\n shutil.rmtree(LKT_LIB_ROOT / 'build', ignore_errors=True)\\n\\n lib_types = \\\",\\\".join(l.name for l in args.library_types)\\n base_argv = [\\n sys.executable, \\\"./manage.py\\\",\\n \\\"make\\\", \\\"-P\\\",\\n \\\"-Dgnu-full\\\",\\n f\\\"--library-types={lib_types}\\\",\\n f\\\"--build-mode={args.build_mode}\\\",\\n f\\\"-j{args.jobs}\\\",\\n ]\\n\\n # Forward gargs to each manage.py script\\n for gargs in args.gargs or []:\\n base_argv.append(f\\\"--gargs={gargs}\\\")\\n\\n libs = selected_libs(args)\\n m1: Optional[subprocess.Popen] = None\\n m2: Optional[subprocess.Popen] = None\\n if \\\"python\\\" in libs:\\n m1 = subprocess.Popen(\\n base_argv + [\\\"--disable-warning\\\", \\\"undocumented-nodes\\\"],\\n cwd=PYTHON_LIB_ROOT\\n )\\n if \\\"lkt\\\" in libs:\\n m2 = subprocess.Popen(base_argv, cwd=LKT_LIB_ROOT)\\n\\n if m1:\\n m1.wait()\\n assert m1.returncode == 0\\n if m2:\\n m2.wait()\\n assert m2.returncode == 0\\n\\n # Unless disabled, run mypy to type check Langkit itself. We need to do\\n # this after building liblktlang and libythonlang as Langkit depend on\\n # them.\\n if not args.no_mypy:\\n run_mypy(args)\",\n \"def main():\\n parse = argparse.ArgumentParser(\\n prog=\\\"Doctopus\\\",\\n usage=\\\"\\\\ndoctopus project [-h] [-t {ziyan, chitu} ] [-v]\\\")\\n parse.add_argument('project', help='project name', nargs='?')\\n parse.add_argument('-t',\\n '--target',\\n choices=['ziyan', 'chitu'],\\n default='ziyan',\\n help='selelct the target, default ziyan')\\n parse.add_argument('-v',\\n '--version',\\n action='version',\\n default=None,\\n version='%(prog)s {}'.format(version_))\\n project = parse.parse_args().project\\n target = parse.parse_args().target\\n version = parse.parse_args().version\\n\\n if not project:\\n parse.print_help()\\n\\n elif version:\\n print(version)\\n\\n elif target == 'ziyan':\\n make_ziyan(project)\\n\\n elif target == 'chitu':\\n make_chitu(project)\",\n \"def main(source):\\n pass\",\n \"def _install():\\n download_file='http://www.ipol.im/pub/art/2015/136/inpaint_8.tgz'\\n tools.download_and_extract(download_file) \\n this_file_path=os.path.dirname(__file__)\\n subprocess.call(' mkdir build; cd build; cmake ..; make', shell=True,cwd=exec_folder)\",\n \"def build(parser):\\n parser.add_argument(\\n '-i', '--identity-file',\\n help=(\\n 'A SSH private key file which may be used to pull down '\\n 'repositories when building.'\\n ),\\n )\\n parser.add_argument(\\n '-e', '--env',\\n action='append',\\n default=[],\\n help=(\\n 'Add environ variables to the build. These may be accessed in '\\n 'the build scripts. Each variable should be of the format '\\n 'KEY=VALUE. This may be used to pass in credentials required '\\n 'to access private repositories. May be specified more than once.'\\n ),\\n )\\n parser.add_argument(\\n '-b', '--build-dir',\\n default=os.getcwd(),\\n help=(\\n 'This folder should be accessible from the docker instance.'\\n ),\\n )\\n parser.add_argument(\\n '--archive',\\n help=(\\n 'Archive the build files into a local tarball.'\\n ),\\n )\\n parser.add_argument(\\n '--archive-only',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip tagging and building the runner image.'\\n ),\\n )\\n parser.add_argument(\\n '-t', '--tag',\\n help=(\\n 'Tag to apply to the built image. '\\n 'This will default to the current date/time.'\\n ),\\n )\\n parser.add_argument(\\n '--no-cache',\\n dest='use_cache',\\n action='store_false',\\n default=True,\\n help=(\\n 'Do not mount a cache volume when compiling the app.'\\n ),\\n )\\n parser.add_argument(\\n '--cache',\\n metavar='CONTAINER:PATH',\\n help=(\\n 'An optional volume or location for the cache. The format is '\\n '\\\"<volume_id>:<path>\\\" where the \\\"volume_id\\\" must be the '\\n 'name or hash of an existing volume. The \\\"path\\\" is an absolute '\\n 'path to the cache folder/volume within the build container.'\\n '\\\\n\\\\n'\\n 'By default a container will be created by mangling the name of '\\n 'the app by appending \\\"__buildcache\\\" (e.g. \\\"myapp__buildcache\\\").'\\n '\\\\n\\\\n'\\n 'This option is ignored if --no-cache is specified.'\\n '\\\\n\\\\n'\\n 'The \\\"volume_id\\\" may be an absolute path on the host filesystem.'\\n '\\\\n\\\\n'\\n 'The \\\"path\\\" may be dropped, in which case it will default to '\\n '/tmp/cache inside the build container.'\\n '\\\\n\\\\n'\\n 'Examples:'\\n '\\\\n\\\\n'\\n ' # custom volume with default path\\\\n'\\n ' --cache my_cache'\\n '\\\\n\\\\n'\\n ' # custom path inside of volume\\\\n'\\n ' --cache my_cache:/tmp/cache'\\n '\\\\n\\\\n'\\n ' # host filesystem\\\\n'\\n ' --cache /tmp/cache'\\n ),\\n )\\n parser.add_argument(\\n '--rebuild-cache',\\n action='store_true',\\n default=False,\\n help=(\\n 'Delete any cached artifacts prior to building.'\\n ),\\n )\\n parser.add_argument(\\n '--skip-cleanup',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip removal of images and containers.'\\n ),\\n )\\n parser.add_argument(\\n 'app',\\n help=(\\n 'Path to an application folder with a meta.yml file'\\n ),\\n )\",\n \"def genLibData(self):\\n import mush\\n tsMain = string.Template(mush.libGenMain)\\n tsIfAltId = string.Template(mush.libGenIfAltId)\\n #--Data Records\\n for id in ('lib_action','lib_actionCount'):\\n glob = self.getRecord('GLOB',id,Glob)\\n (glob.type, glob.value) = ('s',0)\\n glob.setChanged()\\n setAllCode = 'begin lib_setAllGS\\\\n'\\n setNoneCode = 'begin lib_setNoneGS\\\\n'\\n for libId in self.libList:\\n (srcId,altId) = self.libMap[libId]\\n srcBook = self.srcBooks.get(srcId)[0]\\n if not srcBook:\\n print '%s: Missing source: %s' % (libId,srcId)\\n continue\\n #--Global\\n glob = self.getRecord('GLOB',libId+'G',Glob)\\n (glob.type, glob.value) = ('s',0)\\n glob.setChanged()\\n #--Script\\n scriptId = libId+'LS'\\n script = self.getRecord('SCPT',scriptId,Scpt)\\n scriptCode = tsMain.substitute(\\n libId=libId, srcId=srcId, ifAltId=(\\n (altId and tsIfAltId.substitute(libId=libId,altId=altId)) or ''))\\n script.setCode(scriptCode)\\n script.setChanged()\\n #--Book\\n srcBook.load(unpack=True)\\n book = self.getRecord('BOOK',libId,Book)\\n book.model = srcBook.model\\n book.title = srcBook.title\\n book.icon = srcBook.icon\\n book.text = srcBook.text\\n book.script = scriptId\\n book.setChanged()\\n #--Set Scripts\\n setAllCode += 'set %sG to 1\\\\n' % (libId,)\\n setNoneCode += 'set %sG to 0\\\\n' % (libId,)\\n #--Set scripts\\n for id,code in (('lib_setAllGS',setAllCode),('lib_setNoneGS',setNoneCode)):\\n code += ';--Done\\\\nstopScript %s\\\\nend\\\\n' % (id,)\\n script = self.getRecord('SCPT',id,Scpt)\\n script.setCode(code)\\n script.setChanged()\",\n \"def build_pyinstaller_arguments(\\n python_executable_path, starcraft2_path, python_sc2_path, run_file_path, output_folder_path, ecryption_key_path\\n) -> list:\\n arguments = []\\n\\n arguments.append(python_executable_path)\\n\\n # Disable asserts, does not seem to work anymore?\\n # arguments.append(\\\"-OO\\\")\\n\\n arguments.append(\\\"-m\\\")\\n arguments.append(\\\"PyInstaller\\\")\\n\\n arguments.append(\\\"--onefile\\\")\\n\\n sc2_dlls = [\\\"icuin52.dll\\\", \\\"icuuc52.dll\\\", \\\"icudt52.dll\\\"]\\n for sc2_dll in sc2_dlls:\\n dll_path = os.path.join(starcraft2_path, sc2_dll)\\n arguments.append(\\\"--add-binary\\\")\\n arguments.append(f\\\"{dll_path};.\\\")\\n\\n arguments.append(\\\"--add-data\\\")\\n arguments.append(f\\\"{python_sc2_path};sc2\\\")\\n\\n # Add scipy dlls\\n print(f\\\"Scipy folder: {scipy_dlls_path.absolute()}\\\")\\n for dll_path in scipy_dlls_path.iterdir():\\n arguments.append(\\\"--add-binary\\\")\\n arguments.append(f\\\"{dll_path};.\\\")\\n\\n # Add paths that may be encrypted during the packing process\\n arguments.append(\\\"--paths\\\")\\n arguments.append(f\\\"{bot_folder_path}\\\")\\n\\n arguments.append(\\\"--clean\\\")\\n arguments.append(\\\"--noconfirm\\\")\\n\\n # Encryption using the --key=mykey pyinstaller parameter. Checks if pycrypto is installed\\n try:\\n import pycrypt\\n\\n encryption_key = \\\"\\\"\\n if os.path.isfile(ecryption_key_path):\\n with open(ecryption_key_path) as f:\\n # Remove possibly unwanted newline, tab or space characters, as they also might be invalid characters\\n encryption_key = f.read().strip(\\\" \\\\n\\\\t\\\")\\n if encryption_key != \\\"\\\":\\n arguments.append(\\\"--key\\\")\\n arguments.append(encryption_key)\\n except ImportError:\\n print(\\\"Pycrypto not detected. Skipping encryption\\\")\\n\\n arguments.append(\\\"--distpath\\\")\\n arguments.append(f\\\"{output_folder_path}\\\")\\n\\n arguments.append(f\\\"{run_file_path}\\\")\\n\\n assert all(isinstance(i, str) for i in arguments)\\n\\n print(\\\"Pyinstaller Arguments: {}\\\".format(arguments))\\n print(\\\"Exact command line call: {}\\\".format(\\\" \\\".join(arguments)))\\n return arguments\",\n \"def build(args):\\n\\n logging.info(\\\"Parsing configuration...\\\")\\n try:\\n config = {\\\"site\\\": configurator.get_config(os.path.join(args.src, args.configfile))}\\n except Exception as exc:\\n sys.exit(\\\"Error during configuration: \\\" + str(exc))\\n\\n if (args.autobaseurl):\\n config[\\\"site\\\"][\\\"baseurl\\\"] = os.path.abspath(args.dest)\\n\\n logging.info(\\\"Loading and pre-processing content...\\\")\\n if (os.path.isdir(os.path.join(args.src, paths.POSTS_PATH))):\\n try:\\n config[\\\"posts\\\"] = loader.get_from_folder(os.path.join(args.src, paths.POSTS_PATH), config)\\n except ValueError as exc:\\n sys.exit(\\\"Error loading posts: \\\" + str(exc))\\n else:\\n config[\\\"posts\\\"] = {}\\n\\n if (os.path.isdir(os.path.join(args.src, paths.PAGES_PATH))):\\n try:\\n config[\\\"pages\\\"] = loader.get_from_folder(os.path.join(args.src, paths.PAGES_PATH), config)\\n except ValueError as exc:\\n sys.exit(\\\"Error loading pages: \\\" + str(exc))\\n else:\\n config[\\\"pages\\\"] = {}\\n\\n logging.debug(\\\"Configuring Jinja2 environment...\\\")\\n jinjaEnv = configurator.configure_jinja(config[\\\"site\\\"][\\\"theme\\\"], args.src)\\n\\n logging.debug(\\\"Initializing builder...\\\")\\n Builder(jinjaEnv, config, args.src, args.dest, args.noclean).build()\",\n \"def setup_script(self, *args, **kwargs):\\n pass\",\n \"def buildFunc(runType):\\n buildEnv = runType.replace('build-', '')\\n logger.info(\\n 'Building package for {}, will be in dist/'.format(buildEnv)\\n )\\n runProcess(buildEnv, [\\n 'build',\\n '--requirements',\\n 'requirements.txt',\\n '--config-file',\\n 'run_config.yaml'\\n ])\",\n \"def buildDocumentation():\\n helptext = 'usage: build_doc.py <output format> <type of documentation>' \\\\\\n '\\\\n - html: for html output' \\\\\\n '\\\\n - pdf: for pdf output' \\\\\\n '\\\\n\\\\n - all: complete documentation' \\\\\\n '\\\\n - dev: only developer documentation' \\\\\\n '\\\\n - user: only user documentation'\\n if len(sys.argv) != 3:\\n print helptext\\n sys.exit(1)\\n\\n if sys.argv[1] not in ['pdf', 'html']:\\n print helptext\\n sys.exit(1)\\n if sys.argv[2] not in ['all', 'dev', 'user']:\\n print helptext\\n sys.exit(1)\\n\\n copyfile('docs/index_%s.rst.template' % sys.argv[2], 'index.rst') # copy main file into root directory\\n os.system('sphinx-build -b %s -c docs -D master_doc=index . docs/output/%s/%s' % (sys.argv[1], sys.argv[1], sys.argv[2]))\\n os.remove('index.rst') # delete config file from root directory\",\n \"def add_library(command):\\n from src.praxxis.library import list_library\\n\\n namespace = app.main(command)\\n assert namespace.command == 'al' or namespace.command == \\\"addlibrary\\\"\\n assert namespace.path == \\\"test\\\"\",\n \"def build_run_bowtie(bowtie_fp, input_contigs_fasta, output_index_fp, pe1_fastq, pe2_fastq, u_fastq, output_sam_fp):\\n\\n logging.debug('preparing bowtie build and align function calls')\\n\\n # bowtie build executable should be in the same directory as bowtie2 commands\\n bowtie_build_fp = bowtie_fp + '-build'\\n\\n # build index files\\n buildout, builderr = run_bowtie_build(bowtie_build_fp, input_contigs_fasta, output_index_fp)\\n\\n # once index files are built, run aligner using said index files\\n stdout, stderr = run_bowtie(bowtie_fp, output_index_fp, pe1_fastq, pe2_fastq, u_fastq, output_sam_fp)\\n\\n return buildout, builderr, stdout, stderr\",\n \"def add_lib(ctx, sources, library, folder, roles, lib_desc, lib_synopsis, datatype, no_file_check, replace, no_biomaj_env):\\n\\n if not sources:\\n print(\\\"Nothing to do\\\")\\n return\\n\\n if not library:\\n if 'dbname' in os.environ:\\n library = os.environ['dbname']\\n else:\\n raise Exception('No library defined. Use the --library option.')\\n\\n sources = check_input(sources, check_existence=(not no_file_check), use_biomaj_env=(not no_biomaj_env))\\n\\n r_roles = []\\n if roles:\\n print(\\\"Checking roles\\\")\\n roles = roles.split(',')\\n r_roles = get_roles(ctx.gi, roles)\\n\\n print(\\\"Adding to data library '\\\" + str(library) + \\\"'\\\")\\n\\n if not folder:\\n folder = '/'\\n dest = os.path.normpath(folder)\\n dest = dest.split(os.sep)\\n dest = [x for x in dest if x] # Remove empty string when sep at the begin or end, or multiple sep\\n\\n found_lib = get_library(ctx.gi, library, lib_desc, lib_synopsis)\\n\\n print(\\\"Preparing folders in library '\\\" + library + \\\"'\\\")\\n\\n dest_folder = create_tree(ctx.gi, found_lib, dest)\\n\\n check_existing(ctx.gi, found_lib, dest, sources, replace)\\n\\n print(\\\"Adding \\\" + str(len(sources)) + \\\" file(s) to the library '\\\" + library + \\\"'\\\")\\n add_files(ctx.gi, found_lib, dest_folder, sources, r_roles, datatype)\\n\\n print(\\\"Done!\\\")\",\n \"def setup_lib(CLIB):\\n # {{ SETUP_LIB }}\",\n \"def scriptGen(self,tmpd='/tmp/jose',libRev='last',submode='qsub',\\n redirect=1,PBSoptions=''):\\n jobname=self.name\\n outdir=self.outd\\n qsubdir=scratchdir+'/qsub/'+todayDate() #subdirectory to deposit the script\\n if not os.path.exists(qsubdir): pastry('/bin/mkdir -p '+qsubdir)\\n script=qsubdir+'/'+jobname+'.sh' #full script file name\\n\\n if len(jobname) > 15:\\n sys.stderr.write('Error: job name '+jobname+' cannot exceed 15 characters')\\n return ''\\n if not os.path.exists(outdir): os.system('/bin/mkdir -p '+outdir)\\n buf=''\\n ulimit=int(float(mem_limit)*1024) #maximum resident memory size (Kb) to prevent swapping\\n wd=tmpd+'/${PBS_JOBID}'\\n #wd=tmpd+'/'+ re.compile('\\\\W').sub('',self.name) +'_$$' #working directory\\n logname=jobname+'.log'\\n local_log=wd+'/'+logname\\n remote_log=outdir+'/'+logname\\n buf= '#!/bin/bash\\\\n\\\\n'\\n buf+= PBSoptions+'\\\\n\\\\n'\\n buf+= '#bash function to update library\\\\n'\\n buf+= self.updateNodeLib(libRev)+'\\\\n\\\\n'\\n buf+= '#bash function to import temporary libs\\\\n'\\n buf+= self.shared_temporal_libraries()+'\\\\n\\\\n'\\n buf+= '#bash function to clean exit\\\\n'\\n buf+= self.cleanup_exit(submode=submode)+'\\\\n\\\\n'\\n buf+= 'echo \\\"'+script+'\\\"\\\\n' #write script name withing script body\\n buf+= 'hostname\\\\n' #node where job will be run\\n buf+= 'echo $PBS_JOBID\\\\n'\\n buf+= 'ulimit -m '+`ulimit`+' #maximum memory\\\\n'\\n buf+= 'source ~/.bash_profile >/dev/null #environment variables\\\\n'\\n buf+= 'wd='+wd+' #working directory\\\\n'\\n buf+= '/bin/mkdir -p $wd\\\\n'\\n buf+= 'export LOCAL_LOG=\\\"'+local_log+'\\\"\\\\n'\\n buf+= '/bin/touch $LOCAL_LOG\\\\n'\\n if submode=='sub' and redirect:\\n buf+='exec &> $LOCAL_LOG #redirect STODOUT, STDERR to LOCAL_LOG\\\\n' \\n buf+= 'export REMOTE_LOG=\\\"'+remote_log+'\\\"\\\\n'\\n\\n but+= '#clean up old log file\\\\n'\\n buf+= 'if [ -f $REMOTE_LOG ]; then\\\\n' \\n buf+= ' /bin/rm -f $REMOTE_LOG\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n\\n buf+= 'trap \\\"cleanup_exit 1\\\" TERM #in case of killing job\\\\n\\\\n'\\n\\n buf+= '#update node code library && import libraries\\\\n'\\n buf+= 'if !('\\n buf+= 'updateNodeLib && ' \\n buf+= 'shared_temporal_libraries _PREPARE_'\\n buf+= ');then\\\\n'\\n buf+= ' cleanup_exit 1\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n \\n buf+= '/bin/cp '+' '.join(self.inpl)+' $wd #bring input files\\\\n' \\n buf+= 'cd $wd\\\\n\\\\n'\\n buf+= '#Test command success\\\\n'\\n buf+= 'exs=0 #variable holding script exit status\\\\n'\\n buf+= 'if !('\\n buf+= self.exe\\n buf+= ');then\\\\n'\\n buf+= ' exs=1\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n buf+= '#move even partial results (exs=1)\\\\n'\\n buf+= '/bin/mv '+' '.join(self.outl)+' '+outdir+'\\\\n'\\n buf+= 'cleanup_exit $exs'\\n\\n open(script,'w').write(buf)\\n pastry('chmod u+x '+script)\\n\\n return script\",\n \"def main(binary_name, code_directory, verbose, clase):\\n print(\\\"Start of binaries generation\\\")\\n #Directory to iterate\\n directory = '../../results/'+code_directory + '/' + clase + '/application_signature/'\\n #Directory to store the binaries to generate\\n bin_directory = './bin/'\\n #Task to performed on the new script\\n make_clean = 'make clean\\\\n'\\n for dirs in os.listdir(directory):\\n print('Generating binary for path', dirs)\\n if os.path.exists(directory+dirs+'/bin/'+dirs):\\n os.remove(directory+dirs+'/bin/'+dirs)\\n #Creation of the script\\n with open(directory+dirs+'/make_bin.sh', 'w') as bin_file:\\n bin_file.write('#! /bin/bash\\\\n')\\n bin_file.write(make_clean+'\\\\n')\\n bin_file.write('make '+code_directory+' CLASS='+clase+'\\\\n')\\n bin_file.write('mv '+bin_directory+binary_name+' '+bin_directory+binary_name+'_'+dirs+'\\\\n')\\n bin_file.write(make_clean)\\n bin_file.close()\\n try:\\n #Changing privileges so script can be executed automatically\\n os.chmod(directory+dirs+'/make_bin.sh', 0o777)\\n #Move to directory where script is to be executed\\n cwd = os.getcwd()\\n #Change cwd to execute script generating the binary\\n os.chdir(directory+dirs)\\n if verbose:\\n subprocess.check_call('./make_bin.sh')\\n else:\\n subprocess.check_call('./make_bin.sh', stdout=subprocess.PIPE, shell=False)\\n \\n os.chdir(cwd)\\n except FileNotFoundError as e:\\n logger.error(e)\\n raise\\n print('End of binaries generation')\",\n \"def build( c ):\\n\\n print(\\\"Building Please Respond...\\\")\\n c.run( \\\"pyinstaller -y please_respond.py\\\" )\",\n \"def _cached_create_libspec(\\n self,\\n libname: str,\\n is_builtin: bool,\\n target_file: Optional[str],\\n args: Optional[str],\\n *,\\n _internal_force_text=False, # Should only be set from within this function.\\n ) -> Optional[str]:\\n from robotframework_ls.impl import robot_constants\\n\\n if not is_builtin:\\n if not target_file:\\n is_builtin = libname in robot_constants.STDLIBS\\n\\n import time\\n from robocorp_ls_core.subprocess_wrapper import subprocess\\n from robocorp_ls_core.robotframework_log import get_log_level\\n\\n acquire_mutex = _timed_acquire_mutex_for_spec_filename\\n\\n if _internal_force_text:\\n # In this case this is a recursive call and we already have the lock.\\n acquire_mutex = NULL\\n\\n log_exception = log.exception\\n if is_builtin and libname == \\\"Dialogs\\\" and get_log_level() < 1:\\n # Dialogs may have dependencies that are not available, so, don't show\\n # it unless verbose mode is enabled.\\n log_exception = log.debug\\n\\n if not libname.replace(\\\".\\\", \\\"\\\").replace(\\\"/\\\", \\\"\\\").replace(\\\"\\\\\\\\\\\", \\\"\\\").strip():\\n return f\\\"Unable to generate libspec for: {libname}\\\"\\n\\n additional_path = None\\n additional_path_exists = False\\n\\n log_time = True\\n cwd = None\\n\\n if target_file is not None:\\n additional_path = os.path.dirname(target_file)\\n if os.path.splitext(os.path.basename(target_file))[0] == \\\"__init__\\\":\\n additional_path = os.path.dirname(additional_path)\\n\\n additional_path_exists = os.path.exists(additional_path)\\n if additional_path and additional_path_exists:\\n cwd = additional_path\\n if libname.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n libname = libname[:-1]\\n libname = os.path.basename(libname)\\n if libname.lower().endswith((\\\".py\\\", \\\".class\\\", \\\".java\\\")):\\n libname = os.path.splitext(libname)[0]\\n\\n curtime = time.time()\\n\\n try:\\n try:\\n call = [sys.executable]\\n major_version = self.get_robot_major_version()\\n if major_version < 4:\\n call.extend(\\\"-m robot.libdoc --format XML\\\".split())\\n else:\\n call.extend(\\n \\\"-m robot.libdoc --format XML --specdocformat RAW\\\".split()\\n )\\n\\n if additional_path and additional_path_exists:\\n call.extend([\\\"-P\\\", os.path.normpath(additional_path)])\\n\\n if _internal_force_text:\\n call.append(\\\"--docformat\\\")\\n call.append(\\\"text\\\")\\n\\n # Note: always set as a whole, so, iterate in generator is thread-safe.\\n for entry in self._additional_pythonpath_folder_to_folder_info:\\n if os.path.exists(entry):\\n call.extend([\\\"-P\\\", os.path.normpath(entry)])\\n\\n if not args:\\n call.append(libname)\\n else:\\n call.append(\\\"::\\\".join([libname, args]))\\n\\n libspec_filename = self._compute_libspec_filename(\\n libname, is_builtin, target_file, args\\n )\\n\\n log.debug(f\\\"Obtaining mutex to generate libspec: {libspec_filename}.\\\")\\n with acquire_mutex(libspec_filename): # Could fail.\\n log.debug(\\n f\\\"Obtained mutex to generate libspec: {libspec_filename}.\\\"\\n )\\n call.append(libspec_filename)\\n\\n mtime: float = -1\\n try:\\n mtime = os.path.getmtime(libspec_filename)\\n except:\\n pass\\n\\n log.debug(\\n \\\"Generating libspec for: %s.\\\\nCwd:%s\\\\nCommand line:\\\\n%s\\\",\\n libname,\\n cwd,\\n \\\" \\\".join(call),\\n )\\n try:\\n try:\\n # Note: stdout is always subprocess.PIPE in this call.\\n # Note: the env is always inherited (the process which has\\n # the LibspecManager must be the target env already).\\n self._subprocess_check_output(\\n call,\\n stderr=subprocess.STDOUT,\\n stdin=subprocess.PIPE,\\n cwd=cwd,\\n )\\n except OSError as e:\\n log.exception(\\\"Error calling: %s\\\", call)\\n # We may have something as: Ignore OSError: [WinError 6] The handle is invalid,\\n # give the result based on whether the file changed on disk.\\n try:\\n if mtime != os.path.getmtime(libspec_filename):\\n _dump_spec_filename_additional_info(\\n self,\\n libspec_filename,\\n is_builtin=is_builtin,\\n obtain_mutex=False,\\n )\\n return None\\n except:\\n pass\\n\\n log.debug(\\\"Not retrying after OSError failure.\\\")\\n return str(e)\\n\\n except subprocess.CalledProcessError as e:\\n if not _internal_force_text:\\n if (\\n b\\\"reST format requires 'docutils' module to be installed\\\"\\n in e.output\\n ):\\n return self._cached_create_libspec(\\n libname,\\n is_builtin,\\n target_file,\\n args,\\n _internal_force_text=True,\\n )\\n\\n log_exception(\\n \\\"Error creating libspec: %s.\\\\nReturn code: %s\\\\nOutput:\\\\n%s\\\",\\n libname,\\n e.returncode,\\n e.output,\\n )\\n bytes_output = e.output\\n output = bytes_output.decode(\\\"utf-8\\\", \\\"replace\\\")\\n\\n # Remove things we don't want to show.\\n for s in (\\\"Try --help\\\", \\\"--help\\\", \\\"Traceback\\\"):\\n index = output.find(s)\\n if index >= 0:\\n output = output[:index].strip()\\n\\n if output:\\n return output\\n return f\\\"Error creating libspec: {output}\\\"\\n\\n _dump_spec_filename_additional_info(\\n self,\\n libspec_filename,\\n is_builtin=is_builtin,\\n obtain_mutex=False,\\n )\\n return None\\n except Exception as e:\\n log_exception(\\\"Error creating libspec: %s\\\", libname)\\n return str(e)\\n finally:\\n if log_time:\\n delta = time.time() - curtime\\n log.debug(\\\"Took: %.2fs to generate info for: %s\\\" % (delta, libname))\",\n \"def parseArguments():\\n parser = argparse.ArgumentParser(\\n description='Generate a template embedded ARM project.',\\n epilog=\\\"armwiz {}\\\".format(__date__))\\n parser.add_argument('-p','--projectname',\\n help='Specify project name via -p <projectname>',\\n metavar=\\\"<projectname>\\\",\\n default='armwizProject',\\n required=False)\\n parser.add_argument('-t','--targetname',\\n help='Specify target microcontroller or processor via -t <targetname>',\\n metavar=\\\"<targetname>\\\",\\n default=[],\\n action=\\\"append\\\",\\n required=False)\\n parser.add_argument('-e','--examplename',\\n help='Specify which examples to copy via -e <examplename>',\\n metavar=\\\"<examplename>\\\",\\n default=['blinky'],\\n action=\\\"append\\\",\\n required=False)\\n parser.add_argument('-l','--libraryname',\\n help='Specify a library to include via -l <libraryname>',\\n metavar=\\\"<libraryname>\\\",\\n default=[],\\n action=\\\"append\\\",\\n required=False)\\n parser.add_argument('-L',\\n help='List all libraries available to -l <libraryname> and exit.',\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-T',\\n help='List all targets available to -t <targetname> and exit',\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-E',\\n help='List all examples available to -e <examplename> and exit',\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-c','--configfile',\\n help='Specify a configuration file via -t <config file name>',\\n default='armwiz.config',\\n metavar=\\\"<config file name>\\\",\\n required=False)\\n parser.add_argument('-o','--output',\\n help='Target location -o <target location>',\\n default='./',\\n metavar=\\\"<target location>\\\",\\n required=False)\\n parser.add_argument('-n','--no_header',\\n help='Print the wizard',\\n default=False,\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-g','--git',\\n help='Initialize project as a git repository. Default is True. Ignored if git is not present.',\\n default=True,\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-v','--verbose',\\n help='Print extra information to the terminal',\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('-q','--quite',\\n help='Suppress terminal message as best we can',\\n action=\\\"store_true\\\",\\n required=False)\\n parser.add_argument('--version',\\n version='%(prog)s {version}'.format(version=__version__),\\n action='version')\\n return parser\",\n \"def test_apiBuilderScriptMain(self):\\n script = BuildAPIDocsScript()\\n calls = []\\n script.buildAPIDocs = lambda a, b: calls.append((a, b))\\n script.main([\\\"hello\\\", \\\"there\\\"])\\n self.assertEqual(calls, [(FilePath(\\\"hello\\\"), FilePath(\\\"there\\\"))])\",\n \"def test_apiBuilderScriptMain(self):\\n script = BuildAPIDocsScript()\\n calls = []\\n script.buildAPIDocs = lambda a, b: calls.append((a, b))\\n script.main([\\\"hello\\\", \\\"there\\\"])\\n self.assertEquals(calls, [(FilePath(\\\"hello\\\"), FilePath(\\\"there\\\"))])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60995007","0.60779","0.60349417","0.59920174","0.59571064","0.5946177","0.5943936","0.5908569","0.59018505","0.5898167","0.5886315","0.58850145","0.58688945","0.58599895","0.5858286","0.5827634","0.58227205","0.5804258","0.5778985","0.5758587","0.5740682","0.57365704","0.5735037","0.5730064","0.5728736","0.57253426","0.5707295","0.57015246","0.5680005","0.56736","0.56684685","0.5664685","0.5659922","0.56328446","0.56097287","0.5602525","0.55971134","0.55971134","0.55883014","0.5581987","0.5581457","0.55727434","0.5564546","0.55558413","0.5548917","0.5530715","0.5530715","0.55299324","0.55183583","0.5517319","0.55136","0.55113083","0.55060995","0.5501794","0.54991186","0.5497449","0.54931426","0.5480792","0.5468746","0.54656386","0.54637355","0.5463609","0.54517","0.54461306","0.5443354","0.54283476","0.5420141","0.54192424","0.5413257","0.54111075","0.5409459","0.53828007","0.53781474","0.53585625","0.5356026","0.5342983","0.53399175","0.53388166","0.5338323","0.53369033","0.53160655","0.53154814","0.53051597","0.5302709","0.53020626","0.53004307","0.52986205","0.52962637","0.5294439","0.52941346","0.52915233","0.5284398","0.52812135","0.5280734","0.5275392","0.52742827","0.52729815","0.52713495","0.5271099","0.5269919"],"string":"[\n \"0.60995007\",\n \"0.60779\",\n \"0.60349417\",\n \"0.59920174\",\n \"0.59571064\",\n \"0.5946177\",\n \"0.5943936\",\n \"0.5908569\",\n \"0.59018505\",\n \"0.5898167\",\n \"0.5886315\",\n \"0.58850145\",\n \"0.58688945\",\n \"0.58599895\",\n \"0.5858286\",\n \"0.5827634\",\n \"0.58227205\",\n \"0.5804258\",\n \"0.5778985\",\n \"0.5758587\",\n \"0.5740682\",\n \"0.57365704\",\n \"0.5735037\",\n \"0.5730064\",\n \"0.5728736\",\n \"0.57253426\",\n \"0.5707295\",\n \"0.57015246\",\n \"0.5680005\",\n \"0.56736\",\n \"0.56684685\",\n \"0.5664685\",\n \"0.5659922\",\n \"0.56328446\",\n \"0.56097287\",\n \"0.5602525\",\n \"0.55971134\",\n \"0.55971134\",\n \"0.55883014\",\n \"0.5581987\",\n \"0.5581457\",\n \"0.55727434\",\n \"0.5564546\",\n \"0.55558413\",\n \"0.5548917\",\n \"0.5530715\",\n \"0.5530715\",\n \"0.55299324\",\n \"0.55183583\",\n \"0.5517319\",\n \"0.55136\",\n \"0.55113083\",\n \"0.55060995\",\n \"0.5501794\",\n \"0.54991186\",\n \"0.5497449\",\n \"0.54931426\",\n \"0.5480792\",\n \"0.5468746\",\n \"0.54656386\",\n \"0.54637355\",\n \"0.5463609\",\n \"0.54517\",\n \"0.54461306\",\n \"0.5443354\",\n \"0.54283476\",\n \"0.5420141\",\n \"0.54192424\",\n \"0.5413257\",\n \"0.54111075\",\n \"0.5409459\",\n \"0.53828007\",\n \"0.53781474\",\n \"0.53585625\",\n \"0.5356026\",\n \"0.5342983\",\n \"0.53399175\",\n \"0.53388166\",\n \"0.5338323\",\n \"0.53369033\",\n \"0.53160655\",\n \"0.53154814\",\n \"0.53051597\",\n \"0.5302709\",\n \"0.53020626\",\n \"0.53004307\",\n \"0.52986205\",\n \"0.52962637\",\n \"0.5294439\",\n \"0.52941346\",\n \"0.52915233\",\n \"0.5284398\",\n \"0.52812135\",\n \"0.5280734\",\n \"0.5275392\",\n \"0.52742827\",\n \"0.52729815\",\n \"0.52713495\",\n \"0.5271099\",\n \"0.5269919\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58530253"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":88492,"cells":{"query":{"kind":"string","value":"Get a single action label for the current state, ideally whatever the planner would have outputted if just called on `cstate` in isolation."},"document":{"kind":"string","value":"def single_action_label(self, cstate):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def action2label(action):\n if action[0] == 'shift':\n label = action[0]\n elif action[0] == 'reduce':\n label = '-'.join(list(action))\n else:\n raise ValueError(\"Unrecognized parsing action: {}\".format(action))\n return label","def state_name(self):\n return self.state.name if self.state else None","def get_human_state(self):\n return ReferralState(self.state).label","def state_name(self):\n return TASK_STATE.get(self.state, 'UNKNOWN')","def state_name(self):\n return task_states.to_str(self.state)","def state(self, state):\n def what(s, switch):\n if switch:\n return s.label()\n else:\n return s\n switch = is_FSMState(state)\n\n try:\n return self._states_dict_[what(state, switch)]\n except AttributeError:\n for s in self.iter_states():\n if what(s, not switch) == state:\n return s\n except KeyError:\n pass\n raise LookupError(\"No state with label %s found.\" % (what(state, switch),))","def get_label(self):\n return self.job[self.label_key]","def getLabel(self):\n if self.__state & self.stClean:\n return self.__label\n else:\n raise SmiError('%s object not fully initialized' % self.__class__.__name__)","def _get_label(self):\n return self.label","def _get_label ( self ):\n if self._label is not None:\n return self._label\n return self.name","def label(self):\n return self.__label","def label(self):\n return self.__label","def label(self) -> str:\n return self._underlying.label","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"label\")","def label(self):\r\n return self._name","def label(self):\r\n return self._label","def get_lookup_state(self, state):\n return \"\".join(map(str, state))","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def get_label(self):\n return self.label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def label(self):\n return self._label","def get_label ( self ):\n return self.label","def label(self):\n return self._label_","def label(self) -> str:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"label\"))\r\n return self._name","def label(self) -> str:\n return self[\"label\"]","def get_label(self):\n return self._label","def label(self):\n return self.label_","def get_action(self, state):\r\n\r\n action = self.minimax(state)\r\n print(self.exp, self.pru, self.recCall)\r\n\r\n return action","def stateString(self):\n return self._mdp.stateString(self._cur_state);","def _get_button_label(self,index):\r\n self.l_debug(\"_get_button_label\",\"index=%d\" % (index))\r\n # TODO: Make sure it's a valid index?\r\n return self.parent.harmony_config['info']['functions'][index]['label']","def label(self):\n # type: () -> str\n labels = self.__class__.__labels__\n return force_str(labels.get(self.value, self.name))","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\n return self._label","def label(self) -> str:\r\n\r\n return self.__label","def GetLabel(self):\r\n\r\n return self.label","def GetLabel(self):\r\n\r\n return self.label","def get_state_action_value(self, state, action):\n raise NotImplementedError","def native_value(self) -> str:\n if isinstance(self._state, Enum):\n return self._state.name.lower()\n return self._state.lower()","def getLabel( self, cCtrlName ):\n return self.getControlModelProperty( cCtrlName, \"Label\" )","def action_name(self) -> str:\n return self._values.get(\"action_name\")","def action_name(self) -> str:\n return self._values.get(\"action_name\")","def get_next_state(self, state, action):\n pass","def state(self):\n\t\tif self._state in JOB_PS:\n\t\t\treturn JOB_PS[self._state]\n\t\telse:\n\t\t\treturn str(self._state)","def state_message(self) -> str:\n return pulumi.get(self, \"state_message\")","def GetLabel(self):\n \n return self.label_str","def get_label(urs):\n return assign_term(urs)[1]","def __str__(self):\n return str(self.get_action_display())","def get_current_label(self):\n if self.gt_helper_open:\n return self.gt_helper.get_current_label()\n else:\n return -1","def __str__ ( self ):\n return self.get_label()","def __str__(self):\n return _action_args_dict[self.action].name","def state(self) -> str:","def act_to_str(act: int):\n if act == 0:\n return \"L\"\n elif act == 1:\n return \"D\"\n elif act == 2:\n return \"R\"\n elif act == 3:\n return \"U\"\n else:\n raise ValueError(\"Invalid action value\")","def actionString(self,action):\n return str(self._mdp.A[action])","def GetLabel(self) -> str:\n return self._label","def GetString(self, index):\n \n return self.choices[index].GetLabel()","def state_message(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"state_message\")","def GetBtnLabel(self):\r\n\r\n if self.GetWindowStyleFlag() & CP_GTK_EXPANDER:\r\n return self.GetLabel()\r\n \r\n return self.GetLabel() + (self.IsCollapsed() and [\" >>\"] or [\" <<\"])[0]","def getAction(self, state):\n if 'actionIndex' not in dir(self): self.actionIndex = 0\n i = self.actionIndex\n self.actionIndex += 1\n if i < len(self.actions):\n return self.actions[i]\n else:\n return Directions.STOP","def getAction(self, state):\n if 'actionIndex' not in dir(self): self.actionIndex = 0\n i = self.actionIndex\n self.actionIndex += 1\n if i < len(self.actions):\n return self.actions[i]\n else:\n return Directions.STOP","def getAction(self, state):\n if 'actionIndex' not in dir(self): self.actionIndex = 0\n i = self.actionIndex\n self.actionIndex += 1\n if i < len(self.actions):\n return self.actions[i]\n else:\n return Directions.STOP","def get_label(self):\n\n return self._label","def GetLabel(self):\r\n\r\n return self._label","def get_action_state(self):\n pass","def get_label_name(self):\n command_type = self.get_type()\n if command_type == LABEL_COMMAND_TYPE:\n return self.command[1:-1] # ignores the () at the beginning and the end\n if command_type != EMPTY_COMMAND_TYPE: # increments the line number if it is not a blank line or a label\n self.__line_number += 1","def target_label(self):\n return self._target_label","def get_action_name(action=u''):\r\n result=u''\r\n if len(action):\r\n action = action.split('-')[0]\r\n if action == CONTRACTOR_ADDED:\r\n result=u'Добавление контрагента'\r\n elif action == CONTRACTOR_REMOVED:\r\n result=u'Удаление контрагента'\r\n elif action == CONFIRM_NEW_EMPLOYEE:\r\n result=u'Подтверждение сотрудника'\r\n elif action == RECOMMENDATION_ASKED:\r\n result=u'Запрос рекомендации'\r\n else:\r\n result=u'Не определено'\r\n return result","def Label(self) -> str:","def __str__(self) -> str:\r\n\t\treturn \"{state}\".format(state=self.NextState.__func__.__qualname__)","def to_label(self):\n return self.label","def get_final_label(addr, context, move_id):\n #if addr == 0x6a7:\n # print(\"FFF\", hex(context), move_id)\n assert trace.cpu.trace_done\n assert memorymanager.is_valid_binary_addr(addr)\n assert memorymanager.is_valid_binary_addr(context)\n assert move_id is None or movemanager.is_valid_move_id(move_id)\n name, move_id = label_maker(addr, context, move_id)\n if is_simple_name(name):\n labelmanager.labels[addr].add_explicit_name(name, move_id)\n\n return name","def requested_state(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"requested_state\")","def get_state(self):\n return ONEUP_STATES[self.state][0]","def _get_label(self):\n if self.model.name == '':\n return \"KPI\"\n return \"KPI: {} ({})\".format(self.model.name, self.model.objective)","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"label\")","def label(self):\r\n return self._text","def _GetAction(self, action, text):\n # TODO: replace \"x in y\" checks by startswith if possible.\n if 'airportdProcessDLILEvent' in action:\n network_interface = text.split()[0]\n return 'Interface {0:s} turn up.'.format(network_interface)\n\n if 'doAutoJoin' in action:\n match = self._CONNECTED_RE.match(text)\n if match:\n ssid = match.group(1)[1:-1]\n else:\n ssid = 'Unknown'\n return 'Wi-Fi connected to SSID: {0:s}'.format(ssid)\n\n if 'processSystemPSKAssoc' in action:\n wifi_parameters = self._WIFI_PARAMETERS_RE.search(text)\n if wifi_parameters:\n ssid = wifi_parameters.group(1)\n bssid = wifi_parameters.group(2)\n security = wifi_parameters.group(3)\n if not ssid:\n ssid = 'Unknown'\n if not bssid:\n bssid = 'Unknown'\n if not security:\n security = 'Unknown'\n\n return (\n 'New wifi configured. BSSID: {0:s}, SSID: {1:s}, '\n 'Security: {2:s}.').format(bssid, ssid, security)\n\n return text","def get_state_icon(self):\n connecting = (self.CONNECTING,\n self.WAIT,\n self.AUTH,\n self.GET_CONFIG,\n self.ASSIGN_IP,\n self.ADD_ROUTES)\n connected = (self.CONNECTED,)\n disconnected = (self.DISCONNECTED,\n self.UNRECOVERABLE)\n\n # this can be made smarter,\n # but it's like it'll change,\n # so +readability.\n\n if self.current in connecting:\n return \"connecting\"\n if self.current in connected:\n return \"connected\"\n if self.current in disconnected:\n return \"disconnected\"","def get_label(cls):\r\n return cls._type_name(cls.label)","def get_label(self, ):\n return self.attrs.get(self.AttributeNames.LABEL, None)","def state(self) -> str:\n return pulumi.get(self, \"state\")"],"string":"[\n \"def action2label(action):\\n if action[0] == 'shift':\\n label = action[0]\\n elif action[0] == 'reduce':\\n label = '-'.join(list(action))\\n else:\\n raise ValueError(\\\"Unrecognized parsing action: {}\\\".format(action))\\n return label\",\n \"def state_name(self):\\n return self.state.name if self.state else None\",\n \"def get_human_state(self):\\n return ReferralState(self.state).label\",\n \"def state_name(self):\\n return TASK_STATE.get(self.state, 'UNKNOWN')\",\n \"def state_name(self):\\n return task_states.to_str(self.state)\",\n \"def state(self, state):\\n def what(s, switch):\\n if switch:\\n return s.label()\\n else:\\n return s\\n switch = is_FSMState(state)\\n\\n try:\\n return self._states_dict_[what(state, switch)]\\n except AttributeError:\\n for s in self.iter_states():\\n if what(s, not switch) == state:\\n return s\\n except KeyError:\\n pass\\n raise LookupError(\\\"No state with label %s found.\\\" % (what(state, switch),))\",\n \"def get_label(self):\\n return self.job[self.label_key]\",\n \"def getLabel(self):\\n if self.__state & self.stClean:\\n return self.__label\\n else:\\n raise SmiError('%s object not fully initialized' % self.__class__.__name__)\",\n \"def _get_label(self):\\n return self.label\",\n \"def _get_label ( self ):\\n if self._label is not None:\\n return self._label\\n return self.name\",\n \"def label(self):\\n return self.__label\",\n \"def label(self):\\n return self.__label\",\n \"def label(self) -> str:\\n return self._underlying.label\",\n \"def label(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self):\\r\\n return self._name\",\n \"def label(self):\\r\\n return self._label\",\n \"def get_lookup_state(self, state):\\n return \\\"\\\".join(map(str, state))\",\n \"def get_label(self):\\n return self.label\",\n \"def get_label(self):\\n return self.label\",\n \"def get_label(self):\\n return self.label\",\n \"def get_label(self):\\n return self.label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def label(self):\\n return self._label\",\n \"def get_label ( self ):\\n return self.label\",\n \"def label(self):\\n return self._label_\",\n \"def label(self) -> str:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"label\\\"))\\r\\n return self._name\",\n \"def label(self) -> str:\\n return self[\\\"label\\\"]\",\n \"def get_label(self):\\n return self._label\",\n \"def label(self):\\n return self.label_\",\n \"def get_action(self, state):\\r\\n\\r\\n action = self.minimax(state)\\r\\n print(self.exp, self.pru, self.recCall)\\r\\n\\r\\n return action\",\n \"def stateString(self):\\n return self._mdp.stateString(self._cur_state);\",\n \"def _get_button_label(self,index):\\r\\n self.l_debug(\\\"_get_button_label\\\",\\\"index=%d\\\" % (index))\\r\\n # TODO: Make sure it's a valid index?\\r\\n return self.parent.harmony_config['info']['functions'][index]['label']\",\n \"def label(self):\\n # type: () -> str\\n labels = self.__class__.__labels__\\n return force_str(labels.get(self.value, self.name))\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\n return self._label\",\n \"def label(self) -> str:\\r\\n\\r\\n return self.__label\",\n \"def GetLabel(self):\\r\\n\\r\\n return self.label\",\n \"def GetLabel(self):\\r\\n\\r\\n return self.label\",\n \"def get_state_action_value(self, state, action):\\n raise NotImplementedError\",\n \"def native_value(self) -> str:\\n if isinstance(self._state, Enum):\\n return self._state.name.lower()\\n return self._state.lower()\",\n \"def getLabel( self, cCtrlName ):\\n return self.getControlModelProperty( cCtrlName, \\\"Label\\\" )\",\n \"def action_name(self) -> str:\\n return self._values.get(\\\"action_name\\\")\",\n \"def action_name(self) -> str:\\n return self._values.get(\\\"action_name\\\")\",\n \"def get_next_state(self, state, action):\\n pass\",\n \"def state(self):\\n\\t\\tif self._state in JOB_PS:\\n\\t\\t\\treturn JOB_PS[self._state]\\n\\t\\telse:\\n\\t\\t\\treturn str(self._state)\",\n \"def state_message(self) -> str:\\n return pulumi.get(self, \\\"state_message\\\")\",\n \"def GetLabel(self):\\n \\n return self.label_str\",\n \"def get_label(urs):\\n return assign_term(urs)[1]\",\n \"def __str__(self):\\n return str(self.get_action_display())\",\n \"def get_current_label(self):\\n if self.gt_helper_open:\\n return self.gt_helper.get_current_label()\\n else:\\n return -1\",\n \"def __str__ ( self ):\\n return self.get_label()\",\n \"def __str__(self):\\n return _action_args_dict[self.action].name\",\n \"def state(self) -> str:\",\n \"def act_to_str(act: int):\\n if act == 0:\\n return \\\"L\\\"\\n elif act == 1:\\n return \\\"D\\\"\\n elif act == 2:\\n return \\\"R\\\"\\n elif act == 3:\\n return \\\"U\\\"\\n else:\\n raise ValueError(\\\"Invalid action value\\\")\",\n \"def actionString(self,action):\\n return str(self._mdp.A[action])\",\n \"def GetLabel(self) -> str:\\n return self._label\",\n \"def GetString(self, index):\\n \\n return self.choices[index].GetLabel()\",\n \"def state_message(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"state_message\\\")\",\n \"def GetBtnLabel(self):\\r\\n\\r\\n if self.GetWindowStyleFlag() & CP_GTK_EXPANDER:\\r\\n return self.GetLabel()\\r\\n \\r\\n return self.GetLabel() + (self.IsCollapsed() and [\\\" >>\\\"] or [\\\" <<\\\"])[0]\",\n \"def getAction(self, state):\\n if 'actionIndex' not in dir(self): self.actionIndex = 0\\n i = self.actionIndex\\n self.actionIndex += 1\\n if i < len(self.actions):\\n return self.actions[i]\\n else:\\n return Directions.STOP\",\n \"def getAction(self, state):\\n if 'actionIndex' not in dir(self): self.actionIndex = 0\\n i = self.actionIndex\\n self.actionIndex += 1\\n if i < len(self.actions):\\n return self.actions[i]\\n else:\\n return Directions.STOP\",\n \"def getAction(self, state):\\n if 'actionIndex' not in dir(self): self.actionIndex = 0\\n i = self.actionIndex\\n self.actionIndex += 1\\n if i < len(self.actions):\\n return self.actions[i]\\n else:\\n return Directions.STOP\",\n \"def get_label(self):\\n\\n return self._label\",\n \"def GetLabel(self):\\r\\n\\r\\n return self._label\",\n \"def get_action_state(self):\\n pass\",\n \"def get_label_name(self):\\n command_type = self.get_type()\\n if command_type == LABEL_COMMAND_TYPE:\\n return self.command[1:-1] # ignores the () at the beginning and the end\\n if command_type != EMPTY_COMMAND_TYPE: # increments the line number if it is not a blank line or a label\\n self.__line_number += 1\",\n \"def target_label(self):\\n return self._target_label\",\n \"def get_action_name(action=u''):\\r\\n result=u''\\r\\n if len(action):\\r\\n action = action.split('-')[0]\\r\\n if action == CONTRACTOR_ADDED:\\r\\n result=u'Добавление контрагента'\\r\\n elif action == CONTRACTOR_REMOVED:\\r\\n result=u'Удаление контрагента'\\r\\n elif action == CONFIRM_NEW_EMPLOYEE:\\r\\n result=u'Подтверждение сотрудника'\\r\\n elif action == RECOMMENDATION_ASKED:\\r\\n result=u'Запрос рекомендации'\\r\\n else:\\r\\n result=u'Не определено'\\r\\n return result\",\n \"def Label(self) -> str:\",\n \"def __str__(self) -> str:\\r\\n\\t\\treturn \\\"{state}\\\".format(state=self.NextState.__func__.__qualname__)\",\n \"def to_label(self):\\n return self.label\",\n \"def get_final_label(addr, context, move_id):\\n #if addr == 0x6a7:\\n # print(\\\"FFF\\\", hex(context), move_id)\\n assert trace.cpu.trace_done\\n assert memorymanager.is_valid_binary_addr(addr)\\n assert memorymanager.is_valid_binary_addr(context)\\n assert move_id is None or movemanager.is_valid_move_id(move_id)\\n name, move_id = label_maker(addr, context, move_id)\\n if is_simple_name(name):\\n labelmanager.labels[addr].add_explicit_name(name, move_id)\\n\\n return name\",\n \"def requested_state(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"requested_state\\\")\",\n \"def get_state(self):\\n return ONEUP_STATES[self.state][0]\",\n \"def _get_label(self):\\n if self.model.name == '':\\n return \\\"KPI\\\"\\n return \\\"KPI: {} ({})\\\".format(self.model.name, self.model.objective)\",\n \"def label(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"label\\\")\",\n \"def label(self):\\r\\n return self._text\",\n \"def _GetAction(self, action, text):\\n # TODO: replace \\\"x in y\\\" checks by startswith if possible.\\n if 'airportdProcessDLILEvent' in action:\\n network_interface = text.split()[0]\\n return 'Interface {0:s} turn up.'.format(network_interface)\\n\\n if 'doAutoJoin' in action:\\n match = self._CONNECTED_RE.match(text)\\n if match:\\n ssid = match.group(1)[1:-1]\\n else:\\n ssid = 'Unknown'\\n return 'Wi-Fi connected to SSID: {0:s}'.format(ssid)\\n\\n if 'processSystemPSKAssoc' in action:\\n wifi_parameters = self._WIFI_PARAMETERS_RE.search(text)\\n if wifi_parameters:\\n ssid = wifi_parameters.group(1)\\n bssid = wifi_parameters.group(2)\\n security = wifi_parameters.group(3)\\n if not ssid:\\n ssid = 'Unknown'\\n if not bssid:\\n bssid = 'Unknown'\\n if not security:\\n security = 'Unknown'\\n\\n return (\\n 'New wifi configured. BSSID: {0:s}, SSID: {1:s}, '\\n 'Security: {2:s}.').format(bssid, ssid, security)\\n\\n return text\",\n \"def get_state_icon(self):\\n connecting = (self.CONNECTING,\\n self.WAIT,\\n self.AUTH,\\n self.GET_CONFIG,\\n self.ASSIGN_IP,\\n self.ADD_ROUTES)\\n connected = (self.CONNECTED,)\\n disconnected = (self.DISCONNECTED,\\n self.UNRECOVERABLE)\\n\\n # this can be made smarter,\\n # but it's like it'll change,\\n # so +readability.\\n\\n if self.current in connecting:\\n return \\\"connecting\\\"\\n if self.current in connected:\\n return \\\"connected\\\"\\n if self.current in disconnected:\\n return \\\"disconnected\\\"\",\n \"def get_label(cls):\\r\\n return cls._type_name(cls.label)\",\n \"def get_label(self, ):\\n return self.attrs.get(self.AttributeNames.LABEL, None)\",\n \"def state(self) -> str:\\n return pulumi.get(self, \\\"state\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68593997","0.6409792","0.63704884","0.6344228","0.6292176","0.616831","0.61657256","0.6163822","0.6136787","0.6057938","0.605119","0.605119","0.6047956","0.6044922","0.6044922","0.6044922","0.6044414","0.60374177","0.6018807","0.60048354","0.60048354","0.60048354","0.60048354","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59994596","0.59901565","0.5987406","0.5971108","0.59599876","0.59284025","0.59173065","0.5906859","0.59038925","0.5900521","0.5873073","0.587134","0.587134","0.587134","0.587134","0.587134","0.587134","0.587134","0.58540285","0.58475584","0.58475584","0.5840794","0.58367324","0.5834081","0.58157456","0.58157456","0.58063596","0.5800152","0.5792925","0.5791675","0.57809365","0.57808435","0.57789093","0.576283","0.5752866","0.5749942","0.5739012","0.57373965","0.5736011","0.5732597","0.5721589","0.57149947","0.5713081","0.5713081","0.5713081","0.5712349","0.57116723","0.57073724","0.569104","0.5682694","0.5681983","0.5679005","0.5665851","0.5665256","0.56652254","0.566115","0.56535554","0.5640759","0.5629196","0.5629196","0.5629196","0.5629196","0.56254435","0.5621007","0.5608524","0.56036973","0.5593357","0.55907923"],"string":"[\n \"0.68593997\",\n \"0.6409792\",\n \"0.63704884\",\n \"0.6344228\",\n \"0.6292176\",\n \"0.616831\",\n \"0.61657256\",\n \"0.6163822\",\n \"0.6136787\",\n \"0.6057938\",\n \"0.605119\",\n \"0.605119\",\n \"0.6047956\",\n \"0.6044922\",\n \"0.6044922\",\n \"0.6044922\",\n \"0.6044414\",\n \"0.60374177\",\n \"0.6018807\",\n \"0.60048354\",\n \"0.60048354\",\n \"0.60048354\",\n \"0.60048354\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59994596\",\n \"0.59901565\",\n \"0.5987406\",\n \"0.5971108\",\n \"0.59599876\",\n \"0.59284025\",\n \"0.59173065\",\n \"0.5906859\",\n \"0.59038925\",\n \"0.5900521\",\n \"0.5873073\",\n \"0.587134\",\n \"0.587134\",\n \"0.587134\",\n \"0.587134\",\n \"0.587134\",\n \"0.587134\",\n \"0.587134\",\n \"0.58540285\",\n \"0.58475584\",\n \"0.58475584\",\n \"0.5840794\",\n \"0.58367324\",\n \"0.5834081\",\n \"0.58157456\",\n \"0.58157456\",\n \"0.58063596\",\n \"0.5800152\",\n \"0.5792925\",\n \"0.5791675\",\n \"0.57809365\",\n \"0.57808435\",\n \"0.57789093\",\n \"0.576283\",\n \"0.5752866\",\n \"0.5749942\",\n \"0.5739012\",\n \"0.57373965\",\n \"0.5736011\",\n \"0.5732597\",\n \"0.5721589\",\n \"0.57149947\",\n \"0.5713081\",\n \"0.5713081\",\n \"0.5713081\",\n \"0.5712349\",\n \"0.57116723\",\n \"0.57073724\",\n \"0.569104\",\n \"0.5682694\",\n \"0.5681983\",\n \"0.5679005\",\n \"0.5665851\",\n \"0.5665256\",\n \"0.56652254\",\n \"0.566115\",\n \"0.56535554\",\n \"0.5640759\",\n \"0.5629196\",\n \"0.5629196\",\n \"0.5629196\",\n \"0.5629196\",\n \"0.56254435\",\n \"0.5621007\",\n \"0.5608524\",\n \"0.56036973\",\n \"0.5593357\",\n \"0.55907923\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79932404"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":88493,"cells":{"query":{"kind":"string","value":"Produce an iterable of states visited by the teacher policy when initialised in the given state."},"document":{"kind":"string","value":"def extract_policy_envelope(self, cstate):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def iter_states(self):\n return iter(self._states_)","def state_list(self) -> Sequence[TState]:\n pass","def __iter__(self):\n return self.states()","def get_list_of_states(self):\n return self.states","def get_states(self):\n return product(*[phi.automaton().states for phi in self])","def get_reward_states(self):\n state1 = State(7, 7)\n return [state1]","def iter_initial_states(self):\n from six.moves import filter\n return filter(lambda s:s.is_initial, self.iter_states())","def all_states(self) -> Tuple[State, ...]:\n return self.influence_graph.all_states()","def get_all_states(self):\n return tuple(self._transition_probs.keys())","def transitions(self, state):\n if len(set(state)) < len(state):\n yield self.STOP_STATE\n return\n for hidx in xrange(self.num_players):\n for lidx in xrange(hidx):\n (lower, higher) = (state[lidx], state[hidx])\n yield self.makestate(((2*lower) if (i == lidx) else ((higher - lower) if (i == hidx) else s)) for (i, s) in enumerate(state))","def getAllStates(self):\n return list(itertools.product(self.getAllWorldStates(), self.getAllTheta()))","def initial_states(self):\n return list(self.iter_initial_states())","def getstate(self):\n return [elem.getstate() for elem in self]","def variables(self):\n for state in self.states:\n yield self.assert_state(state)\n yield self.deassert_state(state)","def get_states(self):\n raise NotImplementedError()","def states(self):\n knownstates = set(self.keys())\n for possiblestates in self.values():\n for i in possiblestates:\n knownstates.add(i)\n return list(knownstates)","def get_states():\n # Getting all hidden state through time\n all_hidden_states = tf.scan(GRU, processed_input, \n initializer=initial_hidden, name='states')\n return all_hidden_states","def get_all_states(self):\n return self._states","def state_(state):\n return tuple( [ tuple( row ) for row in state ] )","def get_initial_states(self):\n raise NotImplementedError()","def create_iterables(self):\n iterables = [[0, 1], [0, 1], [0, 1],\n [0, 1], [0, 1], [0, 1],\n [0, 1], [0, 1], [0, 1],\n [0, 1], [0, 1]]\n self.states = []\n for t in itertools.product(*iterables):\n self.states.append(t)","def state_transitions(self, state):\n return self.states(\"ANY PreviousStates.identifier = '%s'\" % _obj_id(state))","def initialize_state(self):\n accepted = False\n while not accepted:\n self.state = self.net.sample(self.evidence)\n accepted = self.net.log_probability(self.state) != utils.LOG_PROB_0","def getSuccessors(self, state):\n\t\tutil.raiseNotDefined()","def state(cls):\n yield","def _iter_transitions_all_(self):\n for state in self.iter_states():\n for t in state.transitions:\n yield t","def all_states(self):\n return self._states","def States(self) -> List[Callable]:\r\n\t\treturn self.__STATES__","def __init__(self, init_state):\n\n self.PUZZLE_TYPE = len(init_state) - 1\n self.initial_state = init_state\n self.current_state = init_state\n self.goal_state = [i for i in range(0, self.PUZZLE_TYPE + 1)]\n self.explored_states = []","def get_next_transitions(\n self, state: State\n ) -> Collection[Tuple[Character, float, State]]:\n _check_is_legal_state(state, self.nb_states)\n return {\n (character, probability, successor)\n for character, (successor, probability) in self.transition_dict[\n state\n ].items()\n }","def getSuccessors(self, state):\n\n successors = []\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\n print('search2Agents state',state)\n state1,goals = state\n x,y = state1\n dx, dy = Actions.directionToVector(action)\n nextx, nexty = int(x + dx), int(y + dy)\n if not self.walls[nextx][nexty]:\n nextState = (nextx, nexty)\n cost = self.costFn(nextState)\n successors.append( ( nextState, action, cost) )\n\n\n # Bookkeeping for display purposes\n self._expanded += 1 # DO NOT CHANGE\n if state1 not in self._visited:\n self._visited[state1] = True\n self._visitedlist.append(state1)\n\n return successors","def iter_transitions(self, from_state=None):\n if from_state is None:\n return self._iter_transitions_all_()\n else:\n return iter(self.state(from_state).transitions)","def get_states(self):\n states = []\n for chords in self.training_data:\n chunks = [chords[x:x+self.order] for x in range(0,\n len(chords), self.order)]\n for chunk in chunks:\n chunk_string = \" \".join(chunk)\n if chunk_string not in states:\n states.append(chunk_string)\n return sorted(states)","def dfs(state):\n\n #if the current state is a goal state, then return it in a list\n if state.is_goal():\n return [state]\n else:\n # else, recurse on the possible next states\n result = []\n \n for s in state.next_states():\n # append all of the s\n result += dfs(s)\n \n return result","def getstate(self):\r\n return SparseGP.getstate(self) + [self.init]","def actions(self, state):\n num_cities = len(state) - 1 #minus 1 because start and end in same city\n actions = []\n for i in range(6):\n num1 = randrange(1, num_cities)\n num2 = randrange(1, num_cities)\n action = [num1, num2]\n actions.append(action)\n return actions","def _getStates(self):\n feature_states = []\n # for i, sim in enumerate(self.sims):\n # state = sim.getState()\n\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\n # if self.features:\n # feature = self.features[long_id] # Get feature for\n # feature_states.append((feature, state))\n # else:\n # feature_states.append((None, state))\n for i in range(self.batch_size):\n while not self.qout[i].empty():\n self.qout[i].get()\n while not self.qtraj[i].empty():\n self.qtraj[i].get()\n\n self.qin[i].put(('state',None))\n \n for i in range(self.batch_size):\n state = self.qout[i].get()\n # print(state)\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n\n return feature_states","def _getStates(self):\n feature_states = []\n # for i, sim in enumerate(self.sims):\n # state = sim.getState()\n\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\n # if self.features:\n # feature = self.features[long_id] # Get feature for\n # feature_states.append((feature, state))\n # else:\n # feature_states.append((None, state))\n for i in range(self.batch_size):\n while not self.qout[i].empty():\n self.qout[i].get()\n while not self.qtraj[i].empty():\n self.qtraj[i].get()\n\n self.qin[i].put(('state',None))\n \n for i in range(self.batch_size):\n state = self.qout[i].get()\n # print(state)\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n\n return feature_states","def fromState(state):","def available_state_for_gene(self, gene: Gene, state: State) -> Tuple[State, ...]:\n result: List[State] = []\n active_multiplex: Tuple[Multiplex] = gene.active_multiplex(state)\n transition: Transition = self.find_transition(gene, active_multiplex)\n current_state: int = state[gene]\n done = set()\n for target_state in transition.states:\n target_state: int = self._state_after_transition(current_state, target_state)\n if target_state not in done:\n done.add(target_state)\n new_state: State = state.copy()\n new_state[gene] = target_state\n result.append(new_state)\n return tuple(result)","def get_states_from_graph(graph):\n return [\n (y, x, td)\n for y in range(graph.size)\n for x in range(graph.size)\n for td in range(2)\n if graph.E[(0, y, x, td)].state\n ]","def getSuccessors(self, state):\n\n successors = []\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\n x,y = state\n dx, dy = Actions.directionToVector(action)\n nextx, nexty = int(x + dx), int(y + dy)\n if not self.walls[nextx][nexty]:\n nextState = (nextx, nexty)\n cost = self.costFn(nextState)\n successors.append( ( nextState, action, cost) )\n\n # Bookkeeping for display purposes\n self._expanded += 1\n if state not in self._visited:\n self._visited[state] = True\n self._visitedlist.append(state)\n\n return successors","def get_initial_states(self):\n return product(*[phi.automaton().states.initial for phi in self])","def getSuccessors(self, state):\n\n successors = []\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\n x, y = state\n dx, dy = Actions.directionToVector(action)\n nextx, nexty = int(x + dx), int(y + dy)\n if not self.walls[nextx][nexty]:\n nextState = (nextx, nexty)\n cost = self.costFn(nextState)\n successors.append((nextState, action, cost))\n\n # Bookkeeping for display purposes\n self._expanded += 1 # DO NOT CHANGE\n if state not in self._visited:\n self._visited[state] = True\n self._visitedlist.append(state)\n\n return successors","def getSuccessors(self, state):\n\n successors = []\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\n x,y = state\n dx, dy = Actions.directionToVector(action)\n nextx, nexty = int(x + dx), int(y + dy)\n if not self.walls[nextx][nexty]:\n nextState = (nextx, nexty)\n cost = self.costFn(nextState)\n successors.append( ( nextState, action, cost) )\n\n # Bookkeeping for display purposes\n self._expanded += 1 # DO NOT CHANGE\n if state not in self._visited:\n self._visited[state] = True\n self._visitedlist.append(state)\n\n return successors","def get_active_pass_exchange_sites(state):\n return FederalSiteResource().list(state, 1).order_by('name')","def get_possible_actions(self, state):\n return tuple(self._transition_probs.get(state, {}).keys())","def transitions(self, from_state=None):\n return list(self.iter_transitions(from_state))","def successorStates(self, state):\r\n\r\n successors = []\r\n\r\n for action in Directions.CARDINAL:\r\n x, y = state\r\n dx, dy = Actions.directionToVector(action)\r\n nextx, nexty = int(x + dx), int(y + dy)\r\n\r\n if (not self.walls[nextx][nexty]):\r\n nextState = (nextx, nexty)\r\n cost = self.costFn(nextState)\r\n\r\n successors.append((nextState, action, cost))\r\n\r\n # Bookkeeping for display purposes (the highlight in the GUI).\r\n self._numExpanded += 1\r\n if (state not in self._visitedLocations):\r\n self._visitedLocations.add(state)\r\n self._visitHistory.append(state)\r\n\r\n return successors","def S(self):\n return self._states","def complete_list_of_states():\n # funny way of getting all the states that are defined in ConcertClientState.msg\n return concert_msgs.ConductorGraph.__slots__","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]","def get_sink_states(self):\n state1 = State(4, 2)\n return [state1]","def final_states(self):\n return list(self.iter_final_states())","def initial_states(self):\n return self._initial_states","def states(self) -> Set[State]:\n return set(range(self.nb_states))","def initialize_state(self, state):\n print 'state initialized'\n return state","def priorProb(self, state):\n actions = []\n for i in range(0, 10):\n actions.append(((i, i+1), random.uniform(0, 1))) \n \n return actions","def get_state(self, evidence):\n state = dict((k, self.detectors[k](evidence)) for k in self.statespace)\n for dim, value in state.iteritems():\n assert value in self.statespace[dim]\n return state","def get_state(self):\n return list(_modeller.mod_state_optimizer_state_get(self._modpt))","def states_list(self, states):\n self.log('List of states: [{}]'.format(\n ' | '.join([(lambda x: x[1:])(s) for s in\n states.keys()])))\n return","def state(self):\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}","def states(self):\n num_states = 2**self.num_vars\n return (int2tuple(s, self.num_vars) for s in range(num_states))","def solution_path(self) -> list[State]:","def _cells_state_info(cells):\n\n return list(itertools.chain(*[c.state_info for c in cells]))","def initial_state():\n\treturn [[EMPTY, EMPTY, EMPTY],\n\t\t\t[EMPTY, EMPTY, EMPTY],\n\t\t\t[EMPTY, EMPTY, EMPTY]]","def _load_state_dict(self, state: dict):\n for o, dct in zip(self.optimizers, state.get('optimizers', [])):\n o.load_state_dict(dct)\n for s, dct in zip(self.schedulers, state.get('schedulers', [])):\n s.load_state_dict(dct)","def _load_state_dict(self, state: dict):\n for o, dct in zip(self.optimizers, state.get('optimizers', [])):\n o.load_state_dict(dct)\n for s, dct in zip(self.schedulers, state.get('schedulers', [])):\n s.load_state_dict(dct)","def initializeStates(n):\n states = []\n for i in range(n):\n states.append(0)\n return states","def all_instigator_state(\n self, repository_origin_id: str = None, instigator_type: InstigatorType = None\n ) -> Iterable[InstigatorState]:","def initial_state():\r\n return [[EMPTY, EMPTY, EMPTY],\r\n [EMPTY, EMPTY, EMPTY],\r\n [EMPTY, EMPTY, EMPTY]]","def initial_state():\r\n return [[EMPTY, EMPTY, EMPTY],\r\n [EMPTY, EMPTY, EMPTY],\r\n [EMPTY, EMPTY, EMPTY]]","def get_active_states(self):\n raise NotImplementedError()","def getStates(self):\n feature_states = []\n for i, sim in enumerate(self.sims):\n state = sim.getState()\n\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n return feature_states","def getStates(self):\n feature_states = []\n for i, sim in enumerate(self.sims):\n state = sim.getState()\n\n long_id = self._make_id(state.scanId, state.location.viewpointId)\n if self.features:\n feature = self.features[long_id] # Get feature for\n feature_states.append((feature, state))\n else:\n feature_states.append((None, state))\n return feature_states"],"string":"[\n \"def iter_states(self):\\n return iter(self._states_)\",\n \"def state_list(self) -> Sequence[TState]:\\n pass\",\n \"def __iter__(self):\\n return self.states()\",\n \"def get_list_of_states(self):\\n return self.states\",\n \"def get_states(self):\\n return product(*[phi.automaton().states for phi in self])\",\n \"def get_reward_states(self):\\n state1 = State(7, 7)\\n return [state1]\",\n \"def iter_initial_states(self):\\n from six.moves import filter\\n return filter(lambda s:s.is_initial, self.iter_states())\",\n \"def all_states(self) -> Tuple[State, ...]:\\n return self.influence_graph.all_states()\",\n \"def get_all_states(self):\\n return tuple(self._transition_probs.keys())\",\n \"def transitions(self, state):\\n if len(set(state)) < len(state):\\n yield self.STOP_STATE\\n return\\n for hidx in xrange(self.num_players):\\n for lidx in xrange(hidx):\\n (lower, higher) = (state[lidx], state[hidx])\\n yield self.makestate(((2*lower) if (i == lidx) else ((higher - lower) if (i == hidx) else s)) for (i, s) in enumerate(state))\",\n \"def getAllStates(self):\\n return list(itertools.product(self.getAllWorldStates(), self.getAllTheta()))\",\n \"def initial_states(self):\\n return list(self.iter_initial_states())\",\n \"def getstate(self):\\n return [elem.getstate() for elem in self]\",\n \"def variables(self):\\n for state in self.states:\\n yield self.assert_state(state)\\n yield self.deassert_state(state)\",\n \"def get_states(self):\\n raise NotImplementedError()\",\n \"def states(self):\\n knownstates = set(self.keys())\\n for possiblestates in self.values():\\n for i in possiblestates:\\n knownstates.add(i)\\n return list(knownstates)\",\n \"def get_states():\\n # Getting all hidden state through time\\n all_hidden_states = tf.scan(GRU, processed_input, \\n initializer=initial_hidden, name='states')\\n return all_hidden_states\",\n \"def get_all_states(self):\\n return self._states\",\n \"def state_(state):\\n return tuple( [ tuple( row ) for row in state ] )\",\n \"def get_initial_states(self):\\n raise NotImplementedError()\",\n \"def create_iterables(self):\\n iterables = [[0, 1], [0, 1], [0, 1],\\n [0, 1], [0, 1], [0, 1],\\n [0, 1], [0, 1], [0, 1],\\n [0, 1], [0, 1]]\\n self.states = []\\n for t in itertools.product(*iterables):\\n self.states.append(t)\",\n \"def state_transitions(self, state):\\n return self.states(\\\"ANY PreviousStates.identifier = '%s'\\\" % _obj_id(state))\",\n \"def initialize_state(self):\\n accepted = False\\n while not accepted:\\n self.state = self.net.sample(self.evidence)\\n accepted = self.net.log_probability(self.state) != utils.LOG_PROB_0\",\n \"def getSuccessors(self, state):\\n\\t\\tutil.raiseNotDefined()\",\n \"def state(cls):\\n yield\",\n \"def _iter_transitions_all_(self):\\n for state in self.iter_states():\\n for t in state.transitions:\\n yield t\",\n \"def all_states(self):\\n return self._states\",\n \"def States(self) -> List[Callable]:\\r\\n\\t\\treturn self.__STATES__\",\n \"def __init__(self, init_state):\\n\\n self.PUZZLE_TYPE = len(init_state) - 1\\n self.initial_state = init_state\\n self.current_state = init_state\\n self.goal_state = [i for i in range(0, self.PUZZLE_TYPE + 1)]\\n self.explored_states = []\",\n \"def get_next_transitions(\\n self, state: State\\n ) -> Collection[Tuple[Character, float, State]]:\\n _check_is_legal_state(state, self.nb_states)\\n return {\\n (character, probability, successor)\\n for character, (successor, probability) in self.transition_dict[\\n state\\n ].items()\\n }\",\n \"def getSuccessors(self, state):\\n\\n successors = []\\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\\n print('search2Agents state',state)\\n state1,goals = state\\n x,y = state1\\n dx, dy = Actions.directionToVector(action)\\n nextx, nexty = int(x + dx), int(y + dy)\\n if not self.walls[nextx][nexty]:\\n nextState = (nextx, nexty)\\n cost = self.costFn(nextState)\\n successors.append( ( nextState, action, cost) )\\n\\n\\n # Bookkeeping for display purposes\\n self._expanded += 1 # DO NOT CHANGE\\n if state1 not in self._visited:\\n self._visited[state1] = True\\n self._visitedlist.append(state1)\\n\\n return successors\",\n \"def iter_transitions(self, from_state=None):\\n if from_state is None:\\n return self._iter_transitions_all_()\\n else:\\n return iter(self.state(from_state).transitions)\",\n \"def get_states(self):\\n states = []\\n for chords in self.training_data:\\n chunks = [chords[x:x+self.order] for x in range(0,\\n len(chords), self.order)]\\n for chunk in chunks:\\n chunk_string = \\\" \\\".join(chunk)\\n if chunk_string not in states:\\n states.append(chunk_string)\\n return sorted(states)\",\n \"def dfs(state):\\n\\n #if the current state is a goal state, then return it in a list\\n if state.is_goal():\\n return [state]\\n else:\\n # else, recurse on the possible next states\\n result = []\\n \\n for s in state.next_states():\\n # append all of the s\\n result += dfs(s)\\n \\n return result\",\n \"def getstate(self):\\r\\n return SparseGP.getstate(self) + [self.init]\",\n \"def actions(self, state):\\n num_cities = len(state) - 1 #minus 1 because start and end in same city\\n actions = []\\n for i in range(6):\\n num1 = randrange(1, num_cities)\\n num2 = randrange(1, num_cities)\\n action = [num1, num2]\\n actions.append(action)\\n return actions\",\n \"def _getStates(self):\\n feature_states = []\\n # for i, sim in enumerate(self.sims):\\n # state = sim.getState()\\n\\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\\n # if self.features:\\n # feature = self.features[long_id] # Get feature for\\n # feature_states.append((feature, state))\\n # else:\\n # feature_states.append((None, state))\\n for i in range(self.batch_size):\\n while not self.qout[i].empty():\\n self.qout[i].get()\\n while not self.qtraj[i].empty():\\n self.qtraj[i].get()\\n\\n self.qin[i].put(('state',None))\\n \\n for i in range(self.batch_size):\\n state = self.qout[i].get()\\n # print(state)\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n\\n return feature_states\",\n \"def _getStates(self):\\n feature_states = []\\n # for i, sim in enumerate(self.sims):\\n # state = sim.getState()\\n\\n # long_id = self._make_id(state.scanId, state.location.viewpointId)\\n # if self.features:\\n # feature = self.features[long_id] # Get feature for\\n # feature_states.append((feature, state))\\n # else:\\n # feature_states.append((None, state))\\n for i in range(self.batch_size):\\n while not self.qout[i].empty():\\n self.qout[i].get()\\n while not self.qtraj[i].empty():\\n self.qtraj[i].get()\\n\\n self.qin[i].put(('state',None))\\n \\n for i in range(self.batch_size):\\n state = self.qout[i].get()\\n # print(state)\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n\\n return feature_states\",\n \"def fromState(state):\",\n \"def available_state_for_gene(self, gene: Gene, state: State) -> Tuple[State, ...]:\\n result: List[State] = []\\n active_multiplex: Tuple[Multiplex] = gene.active_multiplex(state)\\n transition: Transition = self.find_transition(gene, active_multiplex)\\n current_state: int = state[gene]\\n done = set()\\n for target_state in transition.states:\\n target_state: int = self._state_after_transition(current_state, target_state)\\n if target_state not in done:\\n done.add(target_state)\\n new_state: State = state.copy()\\n new_state[gene] = target_state\\n result.append(new_state)\\n return tuple(result)\",\n \"def get_states_from_graph(graph):\\n return [\\n (y, x, td)\\n for y in range(graph.size)\\n for x in range(graph.size)\\n for td in range(2)\\n if graph.E[(0, y, x, td)].state\\n ]\",\n \"def getSuccessors(self, state):\\n\\n successors = []\\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\\n x,y = state\\n dx, dy = Actions.directionToVector(action)\\n nextx, nexty = int(x + dx), int(y + dy)\\n if not self.walls[nextx][nexty]:\\n nextState = (nextx, nexty)\\n cost = self.costFn(nextState)\\n successors.append( ( nextState, action, cost) )\\n\\n # Bookkeeping for display purposes\\n self._expanded += 1\\n if state not in self._visited:\\n self._visited[state] = True\\n self._visitedlist.append(state)\\n\\n return successors\",\n \"def get_initial_states(self):\\n return product(*[phi.automaton().states.initial for phi in self])\",\n \"def getSuccessors(self, state):\\n\\n successors = []\\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\\n x, y = state\\n dx, dy = Actions.directionToVector(action)\\n nextx, nexty = int(x + dx), int(y + dy)\\n if not self.walls[nextx][nexty]:\\n nextState = (nextx, nexty)\\n cost = self.costFn(nextState)\\n successors.append((nextState, action, cost))\\n\\n # Bookkeeping for display purposes\\n self._expanded += 1 # DO NOT CHANGE\\n if state not in self._visited:\\n self._visited[state] = True\\n self._visitedlist.append(state)\\n\\n return successors\",\n \"def getSuccessors(self, state):\\n\\n successors = []\\n for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:\\n x,y = state\\n dx, dy = Actions.directionToVector(action)\\n nextx, nexty = int(x + dx), int(y + dy)\\n if not self.walls[nextx][nexty]:\\n nextState = (nextx, nexty)\\n cost = self.costFn(nextState)\\n successors.append( ( nextState, action, cost) )\\n\\n # Bookkeeping for display purposes\\n self._expanded += 1 # DO NOT CHANGE\\n if state not in self._visited:\\n self._visited[state] = True\\n self._visitedlist.append(state)\\n\\n return successors\",\n \"def get_active_pass_exchange_sites(state):\\n return FederalSiteResource().list(state, 1).order_by('name')\",\n \"def get_possible_actions(self, state):\\n return tuple(self._transition_probs.get(state, {}).keys())\",\n \"def transitions(self, from_state=None):\\n return list(self.iter_transitions(from_state))\",\n \"def successorStates(self, state):\\r\\n\\r\\n successors = []\\r\\n\\r\\n for action in Directions.CARDINAL:\\r\\n x, y = state\\r\\n dx, dy = Actions.directionToVector(action)\\r\\n nextx, nexty = int(x + dx), int(y + dy)\\r\\n\\r\\n if (not self.walls[nextx][nexty]):\\r\\n nextState = (nextx, nexty)\\r\\n cost = self.costFn(nextState)\\r\\n\\r\\n successors.append((nextState, action, cost))\\r\\n\\r\\n # Bookkeeping for display purposes (the highlight in the GUI).\\r\\n self._numExpanded += 1\\r\\n if (state not in self._visitedLocations):\\r\\n self._visitedLocations.add(state)\\r\\n self._visitHistory.append(state)\\r\\n\\r\\n return successors\",\n \"def S(self):\\n return self._states\",\n \"def complete_list_of_states():\\n # funny way of getting all the states that are defined in ConcertClientState.msg\\n return concert_msgs.ConductorGraph.__slots__\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\n return [[EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY],\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def get_sink_states(self):\\n state1 = State(4, 2)\\n return [state1]\",\n \"def final_states(self):\\n return list(self.iter_final_states())\",\n \"def initial_states(self):\\n return self._initial_states\",\n \"def states(self) -> Set[State]:\\n return set(range(self.nb_states))\",\n \"def initialize_state(self, state):\\n print 'state initialized'\\n return state\",\n \"def priorProb(self, state):\\n actions = []\\n for i in range(0, 10):\\n actions.append(((i, i+1), random.uniform(0, 1))) \\n \\n return actions\",\n \"def get_state(self, evidence):\\n state = dict((k, self.detectors[k](evidence)) for k in self.statespace)\\n for dim, value in state.iteritems():\\n assert value in self.statespace[dim]\\n return state\",\n \"def get_state(self):\\n return list(_modeller.mod_state_optimizer_state_get(self._modpt))\",\n \"def states_list(self, states):\\n self.log('List of states: [{}]'.format(\\n ' | '.join([(lambda x: x[1:])(s) for s in\\n states.keys()])))\\n return\",\n \"def state(self):\\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}\",\n \"def states(self):\\n num_states = 2**self.num_vars\\n return (int2tuple(s, self.num_vars) for s in range(num_states))\",\n \"def solution_path(self) -> list[State]:\",\n \"def _cells_state_info(cells):\\n\\n return list(itertools.chain(*[c.state_info for c in cells]))\",\n \"def initial_state():\\n\\treturn [[EMPTY, EMPTY, EMPTY],\\n\\t\\t\\t[EMPTY, EMPTY, EMPTY],\\n\\t\\t\\t[EMPTY, EMPTY, EMPTY]]\",\n \"def _load_state_dict(self, state: dict):\\n for o, dct in zip(self.optimizers, state.get('optimizers', [])):\\n o.load_state_dict(dct)\\n for s, dct in zip(self.schedulers, state.get('schedulers', [])):\\n s.load_state_dict(dct)\",\n \"def _load_state_dict(self, state: dict):\\n for o, dct in zip(self.optimizers, state.get('optimizers', [])):\\n o.load_state_dict(dct)\\n for s, dct in zip(self.schedulers, state.get('schedulers', [])):\\n s.load_state_dict(dct)\",\n \"def initializeStates(n):\\n states = []\\n for i in range(n):\\n states.append(0)\\n return states\",\n \"def all_instigator_state(\\n self, repository_origin_id: str = None, instigator_type: InstigatorType = None\\n ) -> Iterable[InstigatorState]:\",\n \"def initial_state():\\r\\n return [[EMPTY, EMPTY, EMPTY],\\r\\n [EMPTY, EMPTY, EMPTY],\\r\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def initial_state():\\r\\n return [[EMPTY, EMPTY, EMPTY],\\r\\n [EMPTY, EMPTY, EMPTY],\\r\\n [EMPTY, EMPTY, EMPTY]]\",\n \"def get_active_states(self):\\n raise NotImplementedError()\",\n \"def getStates(self):\\n feature_states = []\\n for i, sim in enumerate(self.sims):\\n state = sim.getState()\\n\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n return feature_states\",\n \"def getStates(self):\\n feature_states = []\\n for i, sim in enumerate(self.sims):\\n state = sim.getState()\\n\\n long_id = self._make_id(state.scanId, state.location.viewpointId)\\n if self.features:\\n feature = self.features[long_id] # Get feature for\\n feature_states.append((feature, state))\\n else:\\n feature_states.append((None, state))\\n return feature_states\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6537469","0.6507559","0.6333943","0.62946624","0.6279952","0.6270858","0.62265533","0.6213435","0.6131452","0.61095124","0.6086804","0.6057857","0.60427266","0.6012612","0.59918135","0.5902357","0.5893258","0.58824396","0.58483016","0.58471715","0.5837353","0.5825449","0.5817295","0.58134985","0.5805164","0.58021235","0.5775789","0.5772414","0.5756382","0.57531595","0.5720632","0.5709709","0.56753117","0.5673748","0.5664711","0.56566405","0.5654745","0.5654745","0.564899","0.56365806","0.5633623","0.56264627","0.5622026","0.5616655","0.56140244","0.5594119","0.5577483","0.5551031","0.55456126","0.55297947","0.5522299","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55209535","0.55180675","0.550059","0.54889596","0.5486262","0.54840356","0.54785705","0.5466577","0.54651135","0.5462776","0.545884","0.54575366","0.545698","0.545419","0.54515284","0.5441482","0.5441482","0.5420543","0.5417694","0.5408716","0.5408716","0.54000294","0.5393635","0.5393635"],"string":"[\n \"0.6537469\",\n \"0.6507559\",\n \"0.6333943\",\n \"0.62946624\",\n \"0.6279952\",\n \"0.6270858\",\n \"0.62265533\",\n \"0.6213435\",\n \"0.6131452\",\n \"0.61095124\",\n \"0.6086804\",\n \"0.6057857\",\n \"0.60427266\",\n \"0.6012612\",\n \"0.59918135\",\n \"0.5902357\",\n \"0.5893258\",\n \"0.58824396\",\n \"0.58483016\",\n \"0.58471715\",\n \"0.5837353\",\n \"0.5825449\",\n \"0.5817295\",\n \"0.58134985\",\n \"0.5805164\",\n \"0.58021235\",\n \"0.5775789\",\n \"0.5772414\",\n \"0.5756382\",\n \"0.57531595\",\n \"0.5720632\",\n \"0.5709709\",\n \"0.56753117\",\n \"0.5673748\",\n \"0.5664711\",\n \"0.56566405\",\n \"0.5654745\",\n \"0.5654745\",\n \"0.564899\",\n \"0.56365806\",\n \"0.5633623\",\n \"0.56264627\",\n \"0.5622026\",\n \"0.5616655\",\n \"0.56140244\",\n \"0.5594119\",\n \"0.5577483\",\n \"0.5551031\",\n \"0.55456126\",\n \"0.55297947\",\n \"0.5522299\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55209535\",\n \"0.55180675\",\n \"0.550059\",\n \"0.54889596\",\n \"0.5486262\",\n \"0.54840356\",\n \"0.54785705\",\n \"0.5466577\",\n \"0.54651135\",\n \"0.5462776\",\n \"0.545884\",\n \"0.54575366\",\n \"0.545698\",\n \"0.545419\",\n \"0.54515284\",\n \"0.5441482\",\n \"0.5441482\",\n \"0.5420543\",\n \"0.5417694\",\n \"0.5408716\",\n \"0.5408716\",\n \"0.54000294\",\n \"0.5393635\",\n \"0.5393635\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":88494,"cells":{"query":{"kind":"string","value":"f\"\"\"Perform a single rollout under the expert policy & return the visited states. On probabilistic problems, this differs from extract_policy_envelope in"},"document":{"kind":"string","value":"def expert_policy_rollout(self, cstate, *, len_bound=DEFAULT_MAX_LEN):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def rollout(env, our_policy, expert_policy, num_rollouts, max_steps):\n returns = []\n observations = []\n actions = []\n for i in range(num_rollouts):\n # print('iter', i)\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n our_action = our_policy(obs[None,:])\n expert_action = expert_policy(obs[None,:])\n observations.append(obs)\n # Keep the expert action but execute our own action\n actions.append(expert_action)\n obs, r, done, _ = env.step(our_action)\n\n totalr += r\n steps += 1\n # if steps % 100 == 0: print(\"%i/%i\"%(steps, max_steps))\n if steps >= max_steps:\n break\n returns.append(totalr)\n return returns, observations, actions","def rollout_algorithm(self):\n states, actions = [], []\n done = False\n state = self.env._reset()\n while not done:\n supervisor_label = self.supervisor.eval_policy(self.env)\n action_packed = [supervisor_label, 1]\n next_state, _, done, _ = self.env._step(action_packed)\n\n states.append(state)\n actions.append(supervisor_label)\n state = next_state\n\n return states, actions","def _run_policy(env, policy, num_rollouts):\n start_states, final_states, goal_states, actions, paths = [], [], [], [], []\n\n for i in range(num_rollouts):\n path = rollout(\n env,\n policy,\n max_path_length=100,\n animated=False,\n )\n obs = path[\"observations\"]\n acts = path[\"actions\"]\n\n goal_idx = np.argmax(obs[0, 2:])\n start_x, start_y = obs[0, 0], obs[0, 1]\n acts_x, acts_y = acts[:, 0], acts[:, 1]\n final_x, final_y = obs[len(obs) - 1, 0], obs[len(obs) - 1, 1]\n goal = env.goals[goal_idx]\n goal_x, goal_y = goal[0], goal[1]\n\n start_states.append(np.array([start_x, start_y]))\n final_states.append(np.array([final_x, final_y]))\n goal_states.append(np.array([goal_x, goal_y]))\n actions.append(np.array([acts_x, acts_y]))\n paths.append(path)\n\n return dict(\n start_states=np.array(start_states),\n final_states=np.array(final_states),\n goal_states=np.array(goal_states),\n actions=np.array(actions),\n paths=paths,\n env=env\n )","def rollout_policy(self, timeSteps, episodeNumber):\n total_reward = 0\n curr_state = self.agent.reset()\n episode_states = []\n episode_actions = []\n episode_rewards = []\n episode_next_states = []\n episode_return_from_states = []\n\n for time in range(timeSteps):\n action = self.choose_action(curr_state)\n\n next_state, reward, done = self.agent.single_word_step(action)\n # Update the total reward\n total_reward += reward\n if done or time >= timeSteps:\n break\n # Updating the memory\n self.agent.chars\n print(\"rollout_policy Implement step : \"+ str(time))\n print(\"reward is : {}\".format(reward))\n print(\"action is : {}\".format(action))\n curr_state_l = curr_state.tolist()\n next_state_l = next_state.tolist()\n if curr_state_l not in episode_states:\n episode_states.append(curr_state_l)\n episode_actions.append(action)\n episode_rewards.append(reward)\n episode_next_states.append(next_state_l)\n episode_return_from_states.append(reward)\n for i in range(len(episode_return_from_states) - 1):\n episode_return_from_states[i] += reward\n else:\n # Iterate through the replay memory and update the final return for all states\n for i in range(len(episode_return_from_states)):\n episode_return_from_states[i] += reward\n curr_state = next_state\n\n self.update_memory(episode_states, episode_actions, episode_rewards, episode_next_states,\n episode_return_from_states)\n return episode_states, episode_actions, episode_rewards, episode_next_states, episode_return_from_states, total_reward","def rollout_policy(node):\n # NOTE: Use Simple Agent for now\n agents_obs = node.game_env.get_observations()\n agents_obs[node.agent_id] = utility.combine_agent_obs_and_memory(node.agent_memory, agents_obs[node.agent_id])\n actions = node.game_env.act(agents_obs)\n return actions","def rollout_policy_fn(board):\n # rollout randomly\n moves, true_moves = board.get_avaiable_moves()\n action_probs = np.random.rand(len(moves))\n return zip(true_moves, action_probs)","def rollout(pi, env):\n rollout = defaultdict(list)\n t = 0\n ob = (env.reset())\n features = pi.get_initial_features()\n for _ in range(2000):\n rets = pi.compute(ob, features)\n ac, value, features = rets[0], rets[1], rets[2:]\n rollout[\"obs\"].append(ob)\n rollout[\"vs\"].append(value)\n rollout[\"actions\"].append(ac)\n ob, rew, done, _ = env.step(ac)\n rollout[\"rs\"].append(rew)\n rollout[\"features\"].append(features)\n t += 1\n if done:\n break\n print(\"Cur policy: \", len(rollout[\"obs\"]))\n rollout[\"r\"] = 0\n rollout[\"terminal\"] = True\n return rollout","def rollout_policy_fn(board):\n # rollout randomly\n action_probs = np.random.rand(len(board.availables))\n return zip(board.availables, action_probs)","def rollout(env, policy, max_pathlength, animate=False, obfilter=None):\n init_ob = env.reset()\n # prev_ob = np.float32(np.zeros(init_ob.shape))\n zero_ob = np.array([0.0 for _ in range(6)])\n init_ob = np.concatenate((zero_ob, init_ob[6:]), axis=0)\n print(\"Intial Observation: \", init_ob)\n\n if not obfilter == None:\n init_ob = obfilter(init_ob)\n terminated = False\n\n terminal_rewards = []\n\n obs = []\n acs = []\n ac_dists = []\n logps = []\n rewards = []\n last_ac = np.array([])\n for _ in range(max_pathlength):\n if animate:\n env.render()\n # state = np.concatenate([init_ob[:6], init_ob[6:]], -1)\n state = np.array(init_ob)\n obs.append(state)\n ac, ac_dist, logp = policy.act(state)\n # print(type(ac))\n assert(not np.array_equal(ac, last_ac))\n last_ac = ac\n acs.append(ac)\n ac_dists.append(ac_dist)\n logps.append(logp)\n scaled_ac = env.action_space.low + (ac + 1) * (env.action_space.high - env.action_space.low)\n scaled_ac = np.clip(scaled_ac, env.action_space.low, env.action_space.high)\n ob, rew, done, info = env.step(scaled_ac)\n if obfilter: ob = obfilter(ob)\n rewards.append(rew)\n terminal_rewards.append(rew)\n\n # goal_config, last_goal_config, slave_config, last_slave_config = env.get_all_configs()\n info = info[0]\n force_terminate = info[1]\n assert(len(info)==4, \"Info length not 4\")\n goal_config = info[0]\n last_goal_config= info[1]\n slave_config = info[2]\n last_slave_config = info[3]\n for g in range(100):\n new_goal_config = goal_config \n new_goal_config[3] = incAngle(new_goal_config[3])\n new_goal_config[4] = incAngle(new_goal_config[4])\n percent_error, new_rew = Utils.calculate_reward(new_goal_config, slave_config, last_slave_config)\n # new_ob = slave_config + new_goal_config\n new_ob = np.concatenate((zero_ob, new_goal_config), axis=0)\n if obfilter: new_ob = obfilter(new_ob)\n # new_state = np.concatenate([new_ob[:6], new_ob[6:]], -1)\n new_state = np.array(new_ob)\n \n obs.append(new_state)\n rewards.append(new_rew)\n acs.append(ac)\n ac_dists.append(ac_dist)\n logps.append(logp)\n\n if done:\n terminated = True\n break\n # elif force_terminate:\n # # Bias the data\n # percent_error, new_rew = Utils.calculate_reward(goal_config, goal_config, last_slave_config)\n # new_ob = goal_config + goal_config\n # if obfilter: new_ob = obfilter(new_ob)\n # # new_state = np.concatenate([new_ob[:6], new_ob[6:]], -1)\n # new_state = np.array(new_ob)\n\n # ac = Bfield.getCurrents(goal_config[:3], goal_config[3:])\n\n # scaled_ac = env.action_space.low + (ac + 1) * (env.action_space.high - env.action_space.low)\n\n # scaled_ac = np.clip(scaled_ac, env.action_space.low, env.action_space.high)\n # ob, rew, done, info = env.step(scaled_ac)\n \n # obs.append(new_state)\n # rewards.append(new_rew)\n # acs.append(ac)\n # ac_dists.append(ac_dist)\n # logps.append(logp)\n\n\n prev_ob = np.copy(ob)\n\n return {\"observation\" : np.array(obs), \"terminated\" : terminated,\n \"reward\" : np.array(rewards), \"action\" : np.array(acs),\n \"action_dist\": np.array(ac_dists), \"logp\" : np.array(logps)}, terminal_rewards","def eval_policy(self):\n states, actions, rewards, supervisor_labels, surrogate_losses = [], [], [], [], []\n done = False\n state = self.env._reset()\n while not done:\n supervisor_label = self.supervisor.eval_policy(self.env)\n action = self.learner.eval_policy(state)\n action_packed = [action, 1]\n next_state, reward, done, info_dict = self.env._step(action_packed)\n\n states.append(state)\n actions.append(action)\n rewards.append(reward)\n supervisor_labels.append(supervisor_label)\n surrogate_losses.append(action != supervisor_label)\n state = next_state\n\n self.rewards.append(sum(rewards))\n self.surrogate_losses.append(sum(surrogate_losses))\n\n return states, actions, self.rewards, supervisor_labels, self.surrogate_losses","def get_pwa_trace(self):\n # # each step, but the last, corresponds to a transition\n # for step in steps[:-1]:\n # part_id = self.sal2pwa_map[step.assignments['cell']]\n # sub_model = self.sal2pwa_map[step.tid]\n\n # # Assumption of trace building is that each submodel only\n # # has 1 unique next location. If this violated, we need to\n # # add cell ids/part ids to resolve the ambiguity.\n # assert(len(sub_model.pnexts) == 1)\n\n # assert(sub_model.p.ID == part_id)\n # # this is still untested, so in case assert is off...\n # assert(sub_model.p.ID == part_id)\n # #err.warn('gone case')\n\n # #pwa_trace.extend((part_id, sub_model))\n # pwa_trace.append(sub_model)\n if self.trace is None:\n return None\n steps = self.trace\n transitions = [step.tid for step in steps[:-1]]\n return self.pwa2sal.trace(transitions)","def rollout_policy_fn(board):\n # rollout randomly\n # action_probs = np.random.rand(len(board.availables))\n action_probs = np.random.rand(len(board.availables))\n return zip(board.availables, action_probs)","def planner_trace(planner: Planner,\n prob_meta: ProblemMeta,\n mdpsim_problem: 'mdpsim.Problem',\n obs: FPGObservation) -> List[StateQVs]:\n pairs = [] # type: List[StateQVs]\n props_true = obs.props_true\n start_state_string = format_state_for_ssipp(props_true)\n ssipp_problem = planner.problem\n start_ssipp_state \\\n = ssipp_problem.get_intermediate_state(start_state_string)\n # not sure how expensive this is, but IIRC not very\n pol_dict, overflow = planner.extract_policy(start_ssipp_state)\n if overflow:\n print(\"WARNING: extract_policy exceeded max. returned state count!\")\n prop_re = re.compile(r'(\\([^ \\)]+(?: [^ \\)]+)*\\))')\n for ssipp_state, _ in pol_dict.items():\n state_str = ssipp_problem.string_repr(ssipp_state)\n true_bound_props = []\n # converting props to BoundProps and back like this is probably not\n # necessary now, but I'm keeping it anyway because it serves as nice\n # Python-side error checking.\n for prop_s in prop_re.findall(state_str):\n bound_prop = prob_meta.bound_prop_by_name(prop_s)\n true_bound_props.append(bound_prop)\n mdpsim_state_str = ', '.join(' '.join((p.pred_name, ) + p.arguments)\n for p in true_bound_props)\n mdpsim_state = mdpsim_problem.intermediate_atom_state(mdpsim_state_str)\n new_props_true = mdpsim_problem.prop_truth_mask(mdpsim_state)\n new_acts_enabled = mdpsim_problem.act_applicable_mask(mdpsim_state)\n new_obs = FPGObservation(\n props_true=new_props_true, enabled_actions=new_acts_enabled)\n\n # get q-values for enabled actions\n act_strs = [ba.unique_ident for ba in prob_meta.bound_acts_ordered]\n q_values = planner.q_values(mdpsim_state_str, act_strs)\n assert len(act_strs) == len(q_values)\n qv_tuple = tuple(zip(prob_meta.bound_acts_ordered, q_values))\n\n pairs.append((new_obs, qv_tuple))\n return pairs","def rollout(self):\n self.agent.reset()\n for _ in range(self._n_paths_per_trial):\n self.start_rollout()\n while not self.step_rollout():\n pass\n self._agent_infos['batch_idx'] = np.full(len(self._rewards),\n self._worker_number)\n return self.collect_rollout()","def rollout(\n env,\n agent,\n max_path_length=np.inf,\n accum_context=True,\n train=True,\n animated=False,\n\n use_MPC=False,\n mpc_solver=None,\n gp_human=None,\n test=False,\n pred=None,\n):\n\n observations = []\n next_observations = []\n\n unnormalized_observations = []\n unnormalized_next_observations = []\n\n actions = []\n rewards = []\n terminals = []\n agent_infos = []\n env_infos = []\n o, normalized_o = env.reset()\n next_o = None\n path_length = 0\n pred_horizon = env.Nt\n N_adapt = env.N_adapt\n\n # Custom information\n collisions_human = []\n collisions_table = []\n alerts_human = []\n alerts_table = []\n reaches = []\n navi_parts = []\n col_parts = []\n\n if animated:\n env.render(pred=pred)\n\n # Data for obstacle GP\n prediction = []\n max_data_size = env.Nt\n min_data_size = env.Nt // 4\n solver_opts = {}\n solver_opts['expand'] = False\n human = []\n human_next = []\n dynamic = []\n use_GP, no_GP = 0, 0\n mpc_count, pearl_count = 0, 0\n\n # Collision metric\n alert_radii = 2.\n \n # For periodic use of ther interval\n collision_history = 0\n goal_history = 0\n\n # To use only human data for GP\n # obs = env.restaurant.table_list\n\n while path_length < max_path_length:\n cond1 = env.goal_event\n '''\n if cond1:\n # cond1 = env.goal_event and (goal_history % env.goal_interval == env.goal_interval - 1)\n cond1 = env.goal_event and (np.random.rand() < env.eps)\n goal_history += 1\n '''\n # cond2 = (path_length % env.interval == env.interval - 1)\n cond2 = False\n cond3 = env.collision_event\n '''\n if cond3:\n # cond3 = env.collision_event and (collision_history % env.col_interval == env.col_interval - 1)\n cond3 = env.collision_event and (np.random.rand() < env.eps) \n collision_history += 1\n '''\n turn_on_mpc = (cond1 or cond2 or cond3) and use_MPC\n turn_on_mpc = turn_on_mpc and (np.random.rand() < env.eps)\n turn_on_mpc = turn_on_mpc or test\n # turn_on_mpc = turn_on_mpc and env.task_idx < 180\n # build prediction matrix and initialize\n pred_cov = 0* np.ones((env.Nt, env.num_people, 2))\n pred = np.zeros((env.Nt, env.num_people, 2))\n for t in range(pred.shape[0]):\n pred[t, :, :] = o[3:3+2*env.num_people].reshape(-1, pred.shape[2])\n\n # Build distance from table for safety metric\n table_dist, alert_table = np.zeros(env.num_tables), np.zeros(env.num_tables)\n for i, table in enumerate(env.restaurant.table_list):\n table_dist[i] = np.linalg.norm(table.center - o[:2])\n if table_dist[i] < alert_radii:\n alert_table[i] = 1\n # Adaptive set of table to use (closest ones)\n adapt_table_idxs = np.argsort(table_dist)[:N_adapt] # [0, 1, 5, 4, 2]\n adapt_table_list = [env.restaurant.table_list[i] for i in adapt_table_idxs]\n\n # Build distance from human for safety metric\n human_dist, alert_human = np.zeros(env.num_people), np.zeros(env.num_people)\n for i, human in enumerate(env.restaurant.human_list):\n human_dist[i] = np.linalg.norm(human.center - o[:2])\n if human_dist[i] < alert_radii:\n alert_human[i] = 1\n # Adaptive set of dynamic obstacle to use (closest ones)\n adapt_human_idxs = np.argsort(human_dist)[:N_adapt] # [0, 1, 5, 4, 2]\n\n # use GP-MPC if it is turned on\n\n if env.landed:\n # If the robot lands on the goal, then do not give a control anymore.\n # normalized zero control : lower bound of linear velocity = 0\n a = np.array([0., 0.]) if turn_on_mpc else np.array([-1., 0.])\n agent_info = {}\n Xout = None\n\n else:\n if turn_on_mpc:\n # Use GP to build constraints\n gp_effective = np.zeros(env.num_people)\n if len(observations) > min_data_size:\n '''\n pred, pred_cov, gp_effective = fit_GP_adaptive(unnormalized_observations,\n unnormalized_next_observations,\n pred,\n pred_cov,\n max_data_size=max_data_size,\n min_data_size=min_data_size,\n num_people=env.num_people,\n pred_horizon=env.Nt,\n N_adapt=N_adapt\n )\n '''\n pred, pred_cov, gp_effective = _load_GP_predictions(unnormalized_observations,\n unnormalized_next_observations,\n pred,\n pred_cov,\n env,\n agent,\n max_data_size=10,\n range_adapt=alert_radii,\n N_adapt=N_adapt\n )\n \n # if (rand_var <= env.eps and env.event) or (col and use_MPC and test):\n # Caution! You must pass only the list of static obstacles as an argument to GP-MPC solver\n # TODO2: Pass GP-generated K-step obstacle prediction to MPC as constraint\n # print([o.center for o in table_list_new])\n Xout, a_MPC, flag = mpc_solver.plan_pol(\n o[:3],\n env._goal, # Goal position given to MPC\n *env.cost_weight,\n env.restaurant.table_radii,\n env.restaurant.human_radii,\n env.restaurant.table_list,\n # adapt_table_list,\n pred[:, adapt_human_idxs, :],\n pred_cov[:, adapt_human_idxs, :],\n env._counter,\n gp_effective[adapt_human_idxs]\n )\n \n #a_MPC = np.array([0, 0])\n agent_info = {}\n if flag == 1 or flag == 0:\n a = a_MPC\n mpc_count += 1\n\n else:\n #backup controller (PEARL)\n # a, agent_info = agent.get_action(o)\n #backup controller (0)\n # a, agent_info = agent.get_action(normalized_o)\n a = a_MPC\n Xout = None\n pearl_count += 1\n\n else:\n Xout = None\n a, agent_info = agent.get_action(normalized_o)\n\n prediction.append(pred)\n \n # episode roll-out\n next_o, r, d, a, _, info_custom, env_info, normalized_next_o = env.step(a, train, Xout=Xout, use_MPC=turn_on_mpc)\n \n # append custom info\n collision_human, collision_table, reach, navi_part, col_part = info_custom\n collisions_human.append(collision_human)\n collisions_table.append(collision_table)\n reaches.append(reach)\n navi_parts.append(navi_part)\n col_parts.append(col_part)\n alerts_human.append(np.sum(alert_table))\n alerts_table.append(np.sum(alert_table))\n # update the agent's current context\n human_next.append(next_o[3:3+2*env.num_people].reshape(-1, 2))\n if accum_context:\n agent.update_context([normalized_o, a, r, normalized_next_o, d, env_info])\n # observations.append(o)\n\n # for GP-MPC\n unnormalized_observations.append(o)\n unnormalized_next_observations.append(next_o)\n # next_observations.append(next_o)\n\n # for PEARL\n observations.append(normalized_o)\n next_observations.append(normalized_next_o)\n rewards.append(r)\n terminals.append(d)\n actions.append(a)\n agent_infos.append(agent_info)\n path_length += 1\n o = next_o\n normalized_o = normalized_next_o\n if animated:\n if use_MPC:\n env.render(pred=prediction)\n else:\n env.render(pred=None)\n\n env_infos.append(env_info)\n if d:\n break\n\n \"\"\" Summarize results \"\"\"\n actions = np.array(actions)\n if len(actions.shape) == 1:\n actions = np.expand_dims(actions, 1)\n observations = np.array(observations)\n if len(observations.shape) == 1:\n observations = np.expand_dims(observations, 1)\n normalized_next_o = np.array([normalized_next_o])\n next_observations = np.vstack(\n (\n observations[1:, :],\n np.expand_dims(normalized_next_o, 0)\n )\n )\n return dict(\n observations=observations,\n actions=actions,\n rewards=np.array(rewards).reshape(-1, 1),\n next_observations=next_observations,\n terminals=np.array(terminals).reshape(-1, 1),\n agent_infos=agent_infos,\n env_infos=env_infos,\n collisions_human=collisions_human,\n collisions_table=collisions_table,\n reaches=reaches,\n navi_parts=np.array(navi_parts).reshape(-1, 1),\n col_parts=np.array(col_parts).reshape(-1, 1),\n gp_human=(use_GP, no_GP),\n pred=pred,\n alerts_human=alerts_human,\n alerts_table=alerts_table,\n mpc_count=[mpc_count],\n pearl_count=[pearl_count],\n )","def rollout(self, best_goals, horizon=50, save_path=None):\n temp_copy = copy.deepcopy(self.env.world_state)\n # only require historical world_state 1 timestep ago\n temp_copy['historical_world_state'] = {}\n self.env.world_state['historical_world_state'] = temp_copy\n \n reward_mapping = {}\n action_mapping = {}\n for agent in best_goals:\n reward_mapping[agent] = best_goals[agent][1]['rewards']\n action_mapping[agent] = (\n best_goals[agent][0],\n best_goals[agent][1]['steps'][0]\n )\n if type(best_goals[agent][1]['steps'][0]) == int:\n agent.last_action = str(best_goals[agent][1]['steps'][0])\n else:\n agent.last_action = best_goals[agent][1]['steps'][0][0]\n for agent in action_mapping:\n self.env.world_state['historical_actions'][agent.id] = [action_mapping[agent][1]]\n\n print(action_mapping)\n print('@rollout - Starting step function')\n print([agent.last_action for agent in self.env.world_state['agents']])\n self.env.step(action_mapping, reward_mapping)\n\n # print(f'Historical World State')\n # print(self.env.world_state['historical_world_state'])\n\n explicit_chop_rewards = self.env.world_state['explicit_rewards']['chop']\n explicit_cook_rewards = self.env.world_state['explicit_rewards']['cook']\n explicit_serve_rewards = self.env.world_state['explicit_rewards']['serve']\n print(f'Current EXPLICIT chop rewards: {explicit_chop_rewards}')\n print(f'Current EXPLICIT cook rewards: {explicit_cook_rewards}')\n print(f'Current EXPLICIT serve rewards: {explicit_serve_rewards}')\n\n if not self.is_simulation:\n self.info_df = self.update_experiment_results(self.info_df)\n pg.image.save(self.screen, self.images_folder+f'/episode_{self.env.episode}.png')","def opt_pol_envelope(self, obs: FPGObservation) -> List[StateQVs]:\n return planner_trace(self.planner, self.problem_meta,\n self.p.mdpsim_problem, obs)","def forward_rl_solve(\n env, r_inferred, r_task, r_true, inferred_weight, latent_space, iterations\n):\n env_task = LatentSpaceRewardWrapper(env, latent_space, None, r_task, r_true, 0)\n ppo_task = PPO2(MlpPolicy, DummyVecEnv([lambda: env_task]), verbose=1)\n ppo_task.learn(total_timesteps=iterations, log_interval=10)\n\n env_inferred = LatentSpaceRewardWrapper(\n env, latent_space, r_inferred, None, r_true, 1\n )\n ppo_inferred = PPO2(MlpPolicy, DummyVecEnv([lambda: env_inferred]), verbose=1)\n ppo_inferred.learn(total_timesteps=iterations, log_interval=10)\n\n env = LatentSpaceRewardWrapper(\n env, latent_space, r_inferred, r_task, r_true, inferred_weight\n )\n ppo = PPO2(MlpPolicy, DummyVecEnv([lambda: env]), verbose=1)\n ppo.learn(total_timesteps=iterations, log_interval=10)\n\n N = 10\n print()\n print(\"Random policy:\")\n ret_rand = print_rollout_latent(env, None, N, random=True)\n print(\"Avg. Return\", ret_rand)\n print()\n print(\"Policy trained only on task:\")\n ret_task = print_rollout_latent(env, ppo_task, N)\n print(\"Avg. Return\", ret_task)\n print()\n print(\"Policy trained only on inferred:\")\n ret_inferred = print_rollout_latent(env, ppo_inferred, N)\n print(\"Avg. Return\", ret_inferred)\n print()\n print(\"Policy trained on task + inferred:\")\n ret = print_rollout_latent(env, ppo, N)\n print(\"Avg. Return\", ret)\n print()\n print(\"Random policy return\", ret_rand)\n print(\"Task-only policy return\", ret_task)\n print(\"Inferred-only policy return\", ret_inferred)\n print(\"Task+Inferred policy return\", ret)\n return ret","def logits_expert_is_high(\n self,\n state: th.Tensor,\n action: th.Tensor,\n next_state: th.Tensor,\n done: th.Tensor,\n log_policy_act_prob: Optional[th.Tensor] = None,\n ) -> th.Tensor:\n del log_policy_act_prob\n logits = self._reward_net(state, action, next_state, done)\n assert logits.shape == state.shape[:1]\n return logits","def rollout(leaf, depth):\n if depth <= 0:\n return 0\n\n total_reward = 0\n prev_state = leaf\n\n for i in range(depth):\n cur_state = prev_state.copy()\n agent_actions = rollout_policy(cur_state)\n agents_obs, _, done, _ = cur_state.game_env.step(agent_actions)\n\n # After making a move, update the memory kept on this node\n cur_state.agent_memory = utility.update_agent_memory(cur_state.agent_memory,\n agents_obs[cur_state.agent_id])\n\n reward = decide_reward(prev_state, cur_state)\n total_reward += reward\n\n prev_state = cur_state\n\n if done:\n break\n\n return total_reward","def _policy_improvement(self) -> Tuple[np.ndarray, np.ndarray]:\n # Start with a (random) policy\n policy = np.zeros([self.state_dim, self.action_dim])\n V = np.zeros([self.state_dim])\n #random init the policy\n for s in range(self.state_dim):\n policy[s,0] = 0.0\n policy[s,1] = 0.0\n policy[s,2] = 1.0\n\n V = self._policy_eval(policy)\n\n policy_stable = False\n dr = 0.9\n\n while (policy_stable != True):\n policy_stable = True\n for s in self.mdp._state_dict:\n old_action = (policy[self.mdp._state_dict[s]]).tolist()\n action_dict = {}\n for a in self.mdp._action_dict:\n temp = 0.0\n for next_s in self.mdp._state_dict:\n p = self.mdp.P[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\n r = self.mdp.R[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\n Vs = V[self.mdp._state_dict[next_s]]\n temp = temp + p * (r + dr * Vs)\n action_dict[self.mdp._action_dict[a]]= temp \n max_act = max(action_dict.values())\n V[self.mdp._state_dict[s]] = max_act\n res = [t for t,v in action_dict.items() if v == max_act][0]\n for opt in range(self.action_dim):\n if opt == res:\n policy[self.mdp._state_dict[s],opt] = 1.0\n else:\n policy[self.mdp._state_dict[s],opt] = 0.0\n if (old_action - policy[self.mdp._state_dict[s]]).any() == True:\n \n policy_stable = False\n if policy_stable == False:\n V = self._policy_eval(policy)\n \n return policy, V","def extract_policy_envelope(self, cstate):\n pass","def collect_rollouts(\n self,\n env: VecEnv,\n callback: BaseCallback,\n train_freq: TrainFreq,\n replay_buffer: ReplayBuffer,\n action_noise: Optional[ActionNoise] = None,\n learning_starts: int = 0,\n log_interval: Optional[int] = None,\n ) -> RolloutReturn:\n episode_rewards, total_timesteps = [], []\n num_collected_steps, num_collected_episodes = 0, 0\n\n assert isinstance(env, VecEnv), \"You must pass a VecEnv\"\n assert env.num_envs == 1, \"OffPolicyAlgorithm only support single environment\"\n assert train_freq.frequency > 0, \"Should at least collect one step or episode.\"\n\n if self.use_sde:\n self.actor.reset_noise()\n\n callback.on_rollout_start()\n continue_training = True\n\n while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):\n done = False\n episode_reward, episode_timesteps = 0.0, 0\n\n while not done:\n\n if self.use_sde and self.sde_sample_freq > 0 and num_collected_steps % self.sde_sample_freq == 0:\n # Sample a new noise matrix\n self.actor.reset_noise()\n\n # AF\n action = self.plan[0]\n self.episode_forecast.append(self.forecasts[0])\n buffer_action = action # sb needs this\n # AF END\n\n # Rescale and perform action\n new_obs, reward, done, infos = env.step([action])\n\n self.num_timesteps += 1\n episode_timesteps += 1\n num_collected_steps += 1\n\n # Give access to local variables\n callback.update_locals(locals())\n # Only stop training if return value is False, not when it is None.\n if callback.on_step() is False:\n return RolloutReturn(0.0, num_collected_steps, num_collected_episodes, continue_training=False)\n\n episode_reward += reward\n\n # Retrieve reward and episode length if using Monitor wrapper\n self._update_info_buffer(infos, done)\n\n # Store data in replay buffer (normalized action and unnormalized observation)\n self._store_transition(replay_buffer, buffer_action, new_obs, reward, done, infos)\n\n self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)\n\n # For DQN, check if the target network should be updated\n # and update the exploration schedule\n # For SAC/TD3, the update is done as the same time as the gradient update\n # see https://github.com/hill-a/stable-baselines/issues/900\n self._on_step()\n\n # AF\n self.plan, self.forecasts = self._replan(self._last_obs[0], self.plan, self.forecasts) # self._last_obs was updated in _store_transition\n\n if not should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):\n break\n\n if done:\n num_collected_episodes += 1\n self._episode_num += 1\n episode_rewards.append(episode_reward)\n total_timesteps.append(episode_timesteps)\n\n if action_noise is not None:\n action_noise.reset()\n\n # Log training infos\n if log_interval is not None and self._episode_num % log_interval == 0:\n self._dump_logs()\n\n self._af_on_done()\n\n mean_reward = np.mean(episode_rewards) if num_collected_episodes > 0 else 0.0\n\n callback.on_rollout_end()\n\n return RolloutReturn(mean_reward, num_collected_steps, num_collected_episodes, continue_training)","def update(self):\n with torch.no_grad():\n self.preprocess_rollout()\n \n # DEEP-RL TUTORIALS: КОСТЫЛЬ\n #self.advantages = self.returns[:-1] - self.values[:-1]\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\n \n # going through rollout several (config.epochs) times:\n for epoch in range(self.config.epochs):\n # TODO: drop last = False? What if there is 1 sample?\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\n \n for indices in sampler:\n # retrieving new batch as part of rollout\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\n \n # calculating current value, action_log_prob, entropy\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \n self.entropy_b = dist.entropy()#.sum(dim=-1)\n \n # performing step\n self.gradient_ascent_step()","def policy(agent):","def getPolicy(self, state):\n \"*** YOUR CODE HERE ***\"\n possibleActions = self.mdp.getPossibleActions(state)\n if len(possibleActions) == 0: return None\n results = []\n for action in possibleActions:\n total = 0\n for (nextState, prob) in self.mdp.getTransitionStatesAndProbs(state,action):\n total += (prob * self.values[nextState])\n results.append(total)\n maxIndex = max(enumerate(results), key=lambda x: x[1])[0]\n #print(\"here\")\n return possibleActions[maxIndex]","def _env_runner(env, policy, num_local_steps, horizon, obs_filter):\n last_observation = obs_filter(env.reset())\n try:\n horizon = horizon if horizon else env.spec.tags.get(\n \"wrapper_config.TimeLimit.max_episode_steps\")\n except Exception:\n print(\"Warning, no horizon specified, assuming infinite\")\n if not horizon:\n horizon = 999999\n if hasattr(policy, \"get_initial_features\"):\n last_features = policy.get_initial_features()\n else:\n last_features = []\n features = last_features\n length = 0\n rewards = 0\n rollout_number = 0\n\n while True:\n terminal_end = False\n rollout = PartialRollout(extra_fields=policy.other_output)\n\n for _ in range(num_local_steps):\n action, pi_info = policy.compute(last_observation, *last_features)\n if policy.is_recurrent:\n features = pi_info[\"features\"]\n del pi_info[\"features\"]\n observation, reward, terminal, info = env.step(action)\n observation = obs_filter(observation)\n\n length += 1\n rewards += reward\n if length >= horizon:\n terminal = True\n\n # Concatenate multiagent actions\n if isinstance(action, list):\n action = np.concatenate(action, axis=0).flatten()\n\n # Collect the experience.\n rollout.add(observations=last_observation,\n actions=action,\n rewards=reward,\n terminal=terminal,\n features=last_features,\n **pi_info)\n\n last_observation = observation\n last_features = features\n\n if terminal:\n terminal_end = True\n yield CompletedRollout(length, rewards)\n\n if (length >= horizon or\n not env.metadata.get(\"semantics.autoreset\")):\n last_observation = obs_filter(env.reset())\n if hasattr(policy, \"get_initial_features\"):\n last_features = policy.get_initial_features()\n else:\n last_features = []\n rollout_number += 1\n length = 0\n rewards = 0\n break\n\n if not terminal_end:\n rollout.last_r = policy.value(last_observation, *last_features)\n\n # Once we have enough experience, yield it, and have the ThreadRunner\n # place it on a queue.\n yield rollout","def gen_policy_rollout(self, observations, dynam_model):\n # initialize statespace array in pytorch tensor -> need that good good gradient!\n # raise NotImplementedError(\"Need to implement the rollouts with tensors, or maybe not for gradients\")\n\n # for sampling the state progression\n norm_dist = torch.distributions.Normal(0, 1)\n\n # for storing the costs and gradients\n costs = torch.Tensor()\n baselines = torch.Tensor()\n log_probabilities = torch.Tensor()\n states = torch.Tensor()\n ''' \n # for all of these values, think a row as an updating time series for each particle\n costs = torch.zeros((self.P, self.T))\n baselines = torch.zeros((self.P, self.T))\n probabilities = torch.zeros((self.P, self.T))\n\n # This is what we would do for the states, but it's more efficient to concatenate them\n states = torch.Tensor((self.P,self.T, self.n_in))\n '''\n\n # Change in state vs raw state key\n pred_key = dynam_model.change_state_list\n # print(pred_key)\n\n # TODO: Generate initial states for each particle based on the distribution of data it was trained on\n bound_up = torch.Tensor(np.max(observations, 0))\n bound_low = torch.Tensor(np.min(observations, 0))\n\n obs_dist = torch.distributions.uniform.Uniform(bound_low, bound_up)\n\n # iterate through each particle for the states and probabilites for gradient\n for p in range(self.P):\n\n # Choose the dynamics model from the ensemble\n num_ens = dynam_model.E\n if num_ens == 0:\n model = dynam_model\n else:\n model_idx = random.randint(0, num_ens - 1)\n model = dynam_model.networks[model_idx]\n\n num_obs = np.shape(observations)[1]\n # x0 = torch.Tensor(observations[random.randint(0,num_obs),:])\n x0 = obs_dist.sample()\n\n # TODO: Normalize the states before passing into the NN\n\n state_mat = x0.view((1, 1, -1))\n # print(state_mat)\n # state_mat = x0.unsqueeze(0).unsqueeze(0) # takes a (n_in) vector to a (1,1,n_in) Tensor\n # torch.cat((), axis = 1) to cat the times\n # torch.cat((), axis = 0) to cat the particle\n # is there a way to do this without unsqueeze? Seems like the most efficient way\n log_prob_vect = torch.Tensor([1]) # states the column with 1 for concat'ing\n\n for t in range(self.T):\n # generate action from the policy\n action = self.forward(state_mat[0, t, :])\n # print(action)\n # quit()\n\n # forward pass current state to generate distribution values from dynamics model\n means, var = model.distribution(state_mat[0, t, :], action)\n # print(var)\n # sample the next state from the means and variances of the state transition probabilities\n vals = var * norm_dist.sample((1, self.n_in)) + means\n # need to account for the fact that some states are change in and some are raw here\n\n # batch mode prob calc\n log_probs = -.5 * torch.abs(vals - means) / var\n # log_probs = -.5*torch.abs(vals - means)/(var**2)\n\n # for s in range(self.n_in):\n # # sample predicted new state for each element\n # val = var[s]*np.random.normal()+means[0] # sample from the scaled gaussian with y = sigma*x + mu\n\n # # calculate probability of this state for each sub state\n # p = -.5*(val-means[0])/var[0]\n # states = torch.cat((states, state), 0)\n # probabilities = torch.cat((probabilities, p), 0)\n\n # reduce the probabilities vector to get a single probability of the state transition\n log_prob = torch.sum((log_probs), 1)\n log_prob_vect = torch.cat((log_prob_vect, log_prob))\n\n state = torch.Tensor(vals).view((1, 1, -1))\n\n state_mat = torch.cat((state_mat, state),\n 1) # appends the currnt state to the current particle, without overwriting the otherone\n\n # print(state_mat)\n # calculates costs\n # idea ~ calculate the cost of each each element and then do an cumulative sum for the costs\n # use torch.cumsum\n c_list = []\n for state in state_mat.squeeze():\n c_row = self.cost_fnc(state)\n c_list.append(c_row)\n c_list = torch.stack(c_list)\n\n # note we calc the cum sum on the flipped tensor, then flip costs back\n cost_cum = torch.cumsum(torch.flip(c_list, [0]), 0)\n # Assembles the arrays for the current particle\n\n # costs were given above\n costs = torch.cat((costs, torch.flip(cost_cum, [0]).view(1, -1)), 0)\n\n # update the states array for each particle\n states = torch.cat((states, state_mat), 0)\n\n # concatenates the vector of prob at each time to the 2d array\n log_probabilities = torch.cat(\n (log_probabilities, log_prob_vect.view((1, -1))), 0)\n\n # calculates baselines as the leave one out mean for each particle at each time\n costs_summed = torch.sum(costs, 0)\n costs_summed_exp = costs_summed.expand_as(costs)\n costs_leave_one_out = costs_summed_exp - costs\n baselines = costs_leave_one_out / (self.P - 1)\n\n # freezes gradients on costs and baselines\n # these two lines of code actually do nothing, but are for clarification\n # costs.requires_grad_(requires_grad=False)\n # baselines.requires_grad_(requires_grad=False)\n # . detach() is another way to ensure this\n \"\"\"\n RuntimeError: you can only change requires_grad flags of leaf variables. If you want to use a \n computed variable in a subgraph that doesn't require differentiation use var_no_grad = var.detach().\n \"\"\"\n costs_d = costs.detach()\n baselines_d = baselines.detach()\n # costs_d = costs.requires_grad_(False)\n # baselines_d = baselines.requires_grad_(False)\n # print(baselines)\n # print(probabilities)\n # print(costs)\n # print(log_probabilities)\n return states, log_probabilities, costs_d, baselines_d","def eval_policy(env, policy, obs_key, goal_key, num_rollouts, limit, pos=None, goal=None, filename=None):\n from tqdm import trange\n from ml_logger import logger\n\n logger.summary_cache.clear()\n if filename:\n frames = []\n\n for _ in trange(num_rollouts, desc=\"evaluate\"):\n\n obs, done = env.reset(), False\n if pos is not None or goal is not None:\n obs = env.unwrapped.reset(pos, goal)\n # d2goal = l2(obs['x'], obs['goal'])\n i, R = 0, 0\n while not done:\n if filename:\n _ = np.concatenate([obs[obs_key], obs[goal_key]], axis=-1)[0]\n frames.append(_) # , obs['goal_img']\n action = policy(obs[obs_key], obs[goal_key])\n obs, reward, done, info = env.step(action)\n R += reward\n if info['success'] or limit and i >= limit:\n logger.store_metrics( # d2goal=d2goal,\n success=info['success'])\n break\n i += 1\n\n if filename:\n logger.log_video(np.array(frames), key=filename)\n\n logger.print(f'has finished evaluation.')","def generate_rollouts(self, ex_init=None, record=False, random=False, log_hit_time=False):\n if not self.active:\n return\n self.reset_all_rollouts(ex_init, record=record)\n\n # compute observations\n o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations\n ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals\n o[:] = self.initial_o\n ag[:] = self.initial_ag\n\n qpos = np.empty((self.rollout_batch_size, self.dims['qpos']), np.float32)\n qvel = np.empty((self.rollout_batch_size, self.dims['qvel']), np.float32)\n\n qpos[:] = self.initial_qpos\n qvel[:] = self.initial_qvel\n\n num_envs = self.venv.num_envs\n\n random_action = self.policy._random_action(num_envs)\n\n reached_goal = [False] * num_envs\n hit_time = [None] * num_envs\n\n if random:\n self.exploration = 'random'\n else:\n self.exploration = 'eps_greedy' # 'go'\n\n # generate episodes\n obs, achieved_goals, acts, goals, successes = [], [], [], [], []\n dones = []\n info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]\n Qs, qposes, qvels, hit_times = [], [], [], []\n\n for t in range(self.T):\n policy_output = self.policy.get_actions(\n o, ag, self.g,\n compute_Q=self.compute_Q,\n noise_eps=self.noise_eps if not self.exploit else 0.,\n random_eps=self.random_eps if not self.exploit else 0.,\n use_target_net=self.use_target_net,\n exploration=self.exploration,\n go=np.logical_not(reached_goal),\n random_action=random_action,\n )\n\n if self.compute_Q:\n u, Q = policy_output\n Qs.append(Q)\n else:\n u = policy_output\n\n if u.ndim == 1:\n # The non-batched case should still have a reasonable shape.\n u = u.reshape(1, -1)\n\n # compute new states and observations\n obs_dict_new, _, done, info = self.venv.step(u)\n o_new = obs_dict_new['observation']\n ag_new = obs_dict_new['achieved_goal']\n\n qpos_new = obs_dict_new['qpos']\n qvel_new = obs_dict_new['qvel']\n\n success = np.array([i.get('is_success', 0.0) for i in info])\n\n for e_idx, (suc, ht) in enumerate(zip(success, hit_time)):\n if suc and hit_time[e_idx] is None:\n hit_time[e_idx] = t\n\n reached_goal = [hit is not None for hit in hit_time]\n\n if any(done):\n # here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done\n # trick with using vecenvs is not to add the obs from the environments that are \"done\", because those are already observations\n # after a reset\n break\n\n for i, info_dict in enumerate(info):\n for idx, key in enumerate(self.info_keys):\n info_values[idx][t, i] = info[i][key]\n\n if np.isnan(o_new).any():\n self.logger.warn('NaN caught during rollout generation. Trying again...')\n self.reset_all_rollouts()\n return self.generate_rollouts()\n\n dones.append(done)\n obs.append(o.copy())\n qposes.append(qpos.copy())\n qvels.append(qvel.copy())\n achieved_goals.append(ag.copy())\n successes.append(success.copy())\n acts.append(u.copy())\n goals.append(self.g.copy())\n o[...] = o_new\n ag[...] = ag_new\n qpos[...] = qpos_new\n qvel[...] = qvel_new\n obs.append(o.copy())\n achieved_goals.append(ag.copy())\n qposes.append(qpos.copy())\n qvels.append(qvel.copy())\n\n episode = dict(o=obs,\n u=acts,\n g=goals,\n ag=achieved_goals,\n qpos=qposes,\n qvel=qvels,\n # t=Ts\n )\n\n if self.compute_Q:\n episode[\"Qs\"] = Qs\n\n for key, value in zip(self.info_keys, info_values):\n episode['info_{}'.format(key)] = value\n\n # stats\n if self.exploration != 'random':\n if self.exploration in ['go_explore', 'go']:\n successful = np.asarray([1 if hit is not None else 0 for hit in hit_time])\n elif self.exploration in ['eps_greedy']:\n successful = np.array(successes)[-1, :]\n assert successful.shape == (self.rollout_batch_size,)\n success_rate = np.mean(successful)\n self.success_history.append(success_rate)\n\n hit_times = np.asarray([hit if hit is not None else 0 for hit in hit_time])\n if log_hit_time:\n hit_time_mean = np.mean(hit_times)\n hit_time_std = np.std(hit_times)\n self.hit_time_mean_history.append(hit_time_mean)\n self.hit_time_std_history.append(hit_time_std)\n\n if self.compute_Q:\n self.Q_history.append(np.mean(Qs))\n self.n_episodes += self.rollout_batch_size\n\n return convert_episode_to_batch_major(episode)","def rollout_fn(u_vec: np.ndarray):\n obs_vec, rew_vec, done_vec, _ = sim_env.rollout(u_vec.copy()) #state_vec\n return -1.0*rew_vec #we assume environment returns rewards","def getPolicy(self, state):\n \"*** YOUR CODE HERE ***\"\n # OUR CODE HERE\n possibleActions = self.mdp.getPossibleActions(state)\n #checking for terminal state (no possible actions)\n if len(possibleActions) is 0: \n return None\n \n #attempt at using the Counter\n eValsActions = util.Counter()\n for action in possibleActions:\n for transitionState, probability in self.mdp.getTransitionStatesAndProbs(state, action):\n eValsActions[action] += probability * (self.mdp.getReward( state, action, transitionState) + self.discount * self.values[transitionState])\n \n return eValsActions.argMax()\n \n #fail attempt using lists :(\n \"\"\"\n #list to hold the expected value of the actions\n eValsActions = []\n #iterate through all actions and their transtion states\n for action in possibleActions:\n for transitionState, probability in self.mdp.getTransitionStatesAndProbs(state, action):\n #expected value of reward with discount * the value of the transitions\n eValsActions[action] += probability * (self.mdp.getReward( state, action, transitionState) + self.discount * self.values[transitionState])\n \n #now iterate through and find the action with the best value\n #(that will be the best action)\n maxVal = -float(\"inf\")\n bestAction = None\n for action in possibleActions:\n if eValsActions[action] > maxVal:\n maxVal = eValsAction[action]\n bestAction = action\n \"\"\"\n return action\n # END OUR CODE","def getPolicy(self, state):\n \"\"\"Description:\n Find all of q-values of current state, and choose the action \n with the hight q-value as optimal policy\n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n legalActions = self.getLegalActions(state)\n action = None\n policy = util.Counter() # use counter to store action and its q-value\n \n if len(legalActions) == 0:\n return action\n \n for a in legalActions:\n policy[a] = self.getQValue(state, a)\n action = policy.argMax()\n return action\n\n \"\"\" END CODE \"\"\"","def step(self, observation, last_state):\n # We are omitting the details of network inference here.\n # ...\n feature_screen = observation[3]['feature_screen']\n feature_minimap = observation[3]['feature_minimap']\n feature_units = observation[3]['feature_units']\n feature_player = observation[3]['player']\n available_actions = observation[3]['available_actions']\n score_by_category = observation[3]['score_by_category']\n game_loop = observation[3]['game_loop']\n\n unit_type = feature_screen.unit_type\n empty_space = np.where(unit_type == 0)\n empty_space = np.vstack((empty_space[0], empty_space[1])).T\n random_point = random.choice(empty_space)\n #target = [random_point[0], random_point[1]]\n #action = [actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target])]\n policy_logits = None\n new_state = None\n\n spatial_encoder_output = self.spatial_encoder(np.reshape(feature_screen, [1,128,128,27]))\n\n agent_statistics = get_agent_statistics(score_by_category)\n\n home_race = 'Terran'\n away_race = 'Terran'\n race = get_race_onehot(home_race, away_race)\n\n time = get_gameloop_obs(game_loop)\n\n upgrade_value = get_upgrade_obs(feature_units)\n if upgrade_value != -1:\n self.home_upgrade_array[np.where(upgrade_value[0] == 1)] = 1\n self.away_upgrade_array[np.where(upgrade_value[1] == 1)] = 1\n\n embedded_scalar = np.concatenate((agent_statistics, race, time, self.home_upgrade_array, self.away_upgrade_array), axis=0)\n scalar_encoder_output = self.scalar_encoder(np.reshape(embedded_scalar, [1,307]))\n embedded_feature_units = get_entity_obs(feature_units)\n entity_encoder_output = self.entity_encoder(np.reshape(embedded_feature_units, [1,512,464]))\n encoder_input = np.concatenate((spatial_encoder_output, scalar_encoder_output, entity_encoder_output), axis=1)\n\n core_input = np.reshape(encoder_input, [16, 8, 131])\n whole_seq_output, final_memory_state, final_carry_state = self.core(core_input)\n print(whole_seq_output.shape)\n print(final_memory_state.shape)\n print(final_carry_state.shape)\n\n action = [actions.FUNCTIONS.no_op()]\n\n return action, policy_logits, new_state","def _evaluate_rollout(self, state, limit=1000):\n player = state.turn\n for i in range(limit):\n end, winner = state.if_win()\n if end: break\n action_probs = rollout_policy_fn(state)\n max_action = max(action_probs, key=itemgetter(1))[0]\n state.do_move(max_action)\n else:\n # If no break from the loop, issue a warning.\n print(\"WARNING: rollout reached move limit\")\n return -1\n return 1 if winner == player else -1","def train_off_policy(self, sess):\n \n # sync the local nets from the global\n sess.run([self.sync_local_actor_op, self.sync_local_critic_op])\n\n # sample trajectory from the replay memory\n traj = self.memory.get_trajectory(self.FLAGS.k_steps)\n k = len(traj)\n\n # empty list to store targets\n q_ret_list = []\n q_opc_list = []\n states = []\n actions = []\n mu_dist = []\n val_s = []\n \n # if last episode is not terminal state, use value function to get an estimate\n Q_ret = 0.0\n if not traj[-1].done :\n Q_ret = sess.run([self.val_xi], feed_dict= { \n self.x_i :np.reshape(traj[-1].next_state, (1,-1))} )[0][0,0]\n \n Q_opc = Q_ret \n \n # reverse loop\n for transition in traj[::-1]:\n Q_ret = transition.reward + self.FLAGS.gamma * Q_ret\n Q_opc = transition.reward + self.FLAGS.gamma * Q_opc\n \n #\n x_t = np.reshape(transition.state, (1,-1))\n a_t = np.reshape(transition.action, (1,-1))\n u_t = np.reshape(transition.distribution, (1,-1))\n \n # add to minibatch\n q_ret_list.append(Q_ret)\n q_opc_list.append(Q_opc)\n states.append(x_t)\n actions.append(a_t)\n mu_dist.append(u_t)\n \n \n # get estimates from existing function approximators\n v_t, c_t, p_t, q_t = sess.run([self.val_xi, self.c_i, self.p_i, self.adv_xi_ai], \n feed_dict = {self.x_i : x_t, \n self.a_i: a_t, \n self.u_i: u_t} )\n \n # add the target V_pi\n val_s.append((min(p_t[0,0],1.0) * (Q_ret - q_t[0,0])) + v_t[0,0] )\n \n # update again\n Q_ret = c_t[0,0] * (Q_ret - q_t[0,0]) + v_t[0,0] \n Q_opc = (Q_opc - q_t[0,0]) + v_t[0,0]\n \n \n # create mini-batch here\n opt_feed_dict = {\n self.x_i : np.asarray(states).reshape(-1, self.INPUT_DIM),\n self.a_i : np.asarray(actions).reshape(-1, self.ACTION_DIM),\n self.q_opc : np.asarray(q_opc_list).reshape(-1, 1), \n self.q_ret : np.asarray(q_ret_list).reshape(-1, 1), \n self.u_i : np.asarray(mu_dist).reshape(-1,2*self.ACTION_DIM),\n self.v_target: np.asarray(val_s).reshape(-1,1)\n }\n \n # Train the global estimators using local gradients\n _,_, global_step, critic_summaries, actor_summaries = sess.run([self.actor_train_op, \n self.critic_train_op, \n tf.contrib.framework.get_global_step(),\n self.critic_summary_op, \n self.actor_summary_op ], \n feed_dict=opt_feed_dict)\n \n \n # Write summaries\n if self.summary_writer is not None:\n self.summary_writer.add_summary(critic_summaries, global_step)\n self.summary_writer.add_summary(actor_summaries, global_step)\n self.summary_writer.flush()\n \n \n # update the average policy network\n _ = sess.run([self.soft_update_average_actor_op])\n \n # that's it ","def run_rollout(self, state):\n rewards = np.array([0.0] * state.num_players)\n sim_state = state.clone() # create the simulated state so that the current state is left unchanged\n for _ in range(self.depth):\n if sim_state.is_terminal(): # act and track rewards as long as possible\n break\n action = self.rollout_policy.select_action(sim_state)\n rewards += sim_state.take_action(action)\n\n return rewards","def _forward_exploration(self, batch: NestedDict) -> Mapping[str, Any]:\n output = {}\n\n # TODO (Artur): Remove this once Policy supports RNN\n if self.encoder.config.shared:\n batch[STATE_IN] = None\n else:\n batch[STATE_IN] = {\n ACTOR: None,\n CRITIC: None,\n }\n batch[SampleBatch.SEQ_LENS] = None\n\n # Shared encoder\n encoder_outs = self.encoder(batch)\n # TODO (Artur): Un-uncomment once Policy supports RNN\n # output[STATE_OUT] = encoder_outs[STATE_OUT]\n\n # Value head\n vf_out = self.vf(encoder_outs[ENCODER_OUT][CRITIC])\n output[SampleBatch.VF_PREDS] = vf_out.squeeze(-1)\n\n # Policy head\n action_logits = self.pi(encoder_outs[ENCODER_OUT][ACTOR])\n output[SampleBatch.ACTION_DIST_INPUTS] = action_logits\n output[SampleBatch.ACTION_DIST] = self.action_dist_cls.from_logits(\n logits=action_logits\n )\n return output","def ac_process_rollout(rollout, gamma, lambda_=1.0, clip=0.0):\n # collecting transitions\n batch_si = np.asarray(rollout.states + [rollout.end_state])\n batch_a = np.asarray(rollout.actions)\n\n # collecting target for value network\n # V_t <-> r_t + gamma*r_{t+1} + ... + gamma^n*r_{t+n} + gamma^{n+1}*V_{n+1}\n rewards_plus_v1 = np.asarray(rollout.rewards + [rollout.r]) # bootstrapping\n\n if clip != 0.0:\n rewards_plus_v1 = np.clip( rewards_plus_v1, -clip, clip )\n\n rewards_plus_v2 = np.asarray(rollout.bonuses + [0])\n rewards_plus_v = rewards_plus_v1 + rewards_plus_v2\n \n batch_r = discount( rewards_plus_v, gamma )[:-1] # value network target\n\n # collecting target for policy network\n rewards1 = np.asarray(rollout.rewards)\n if clip != 0.0:\n rewards1 = np.clip( rewards1, -clip, clip )\n\n rewards2 = np.asarray(rollout.bonuses) \n rewards = rewards1 + rewards2\n \n vpred_t = np.asarray( rollout.values + [ rollout.r ] ) \n # \"Generalized Advantage Estimation\": https://arxiv.org/abs/1506.02438\n # Eq (10): delta_t = Rt + gamma*V_{t+1} - V_t\n # Eq (16): batch_adv_t = delta_t + gamma*delta_{t+1} + gamma^2*delta_{t+2} + ...\n delta_t = rewards + gamma * ( vpred_t[1:] - vpred_t[:-1] ).squeeze()\n batch_adv = discount( delta_t, gamma * lambda_ )\n\n features = rollout.features\n\n return Batch( batch_si, batch_a, batch_adv, batch_r, rollout.terminal, features )","def logits_expert_is_high(\n self,\n state: th.Tensor,\n action: th.Tensor,\n next_state: th.Tensor,\n done: th.Tensor,\n log_policy_act_prob: Optional[th.Tensor] = None,\n ) -> th.Tensor:\n if log_policy_act_prob is None:\n raise TypeError(\n \"Non-None `log_policy_act_prob` is required for this method.\",\n )\n reward_output_train = self._reward_net(state, action, next_state, done)\n return reward_output_train - log_policy_act_prob","def perform_rollouts(self,\n session,\n num_parallel_rollouts,\n policy,\n task_modifier,\n sample_vars=None):\n batch_env = self._get_batch_env(task_modifier, num_parallel_rollouts)\n\n state = batch_env.reset().reshape((num_parallel_rollouts, -1))\n states = [[state[idx]] for idx in range(num_parallel_rollouts)]\n actions = [[] for _ in range(num_parallel_rollouts)]\n rewards = [[] for _ in range(num_parallel_rollouts)]\n sample_op, state_var = policy.sample_op()\n\n completed_states = []\n completed_actions = []\n completed_rewards = []\n if sample_vars is None:\n sample_vars = {}\n\n step = 0\n while step < self.max_rollout_len:\n step += 1\n sample_vars[state_var] = state\n action = session.run(sample_op, sample_vars)\n action = np.nan_to_num(action, copy=False)\n new_state, reward, new_tasks_done, _ = batch_env.step(action)\n for idx in range(num_parallel_rollouts):\n states[idx].append(new_state[idx:idx + 1].ravel())\n actions[idx].append(action[idx:idx + 1, :])\n rewards[idx].append(reward[idx])\n if new_tasks_done[idx]:\n completed_states.append(states[idx])\n completed_actions.append(actions[idx])\n completed_rewards.append(rewards[idx])\n states[idx] = [batch_env[idx].reset()]\n new_state[idx] = states[idx][0]\n actions[idx] = []\n rewards[idx] = []\n\n state = new_state.reshape((num_parallel_rollouts, -1))\n\n num_completed_rollouts = len(completed_states)\n rollouts = []\n for idx in range(num_completed_rollouts):\n l = len(completed_states[idx])\n rollouts.append({\n 'timesteps':\n np.hstack((np.arange(l).reshape((-1, 1)), l * np.ones((l, 1)))),\n 'states':\n np.vstack(completed_states[idx]),\n 'actions':\n np.vstack(completed_actions[idx]),\n 'rewards':\n np.array(completed_rewards[idx]).reshape((-1, 1))\n })\n tf.logging.info(\n 'avg rollout length: %f',\n np.mean([rollout['actions'].shape[0] for rollout in rollouts]))\n return rollouts","def trace_learning_objective_mechanism_projections(mech):\n for input_state in mech.inputStates.values():\n for projection in input_state.receivesFromProjections:\n sender = projection.sender.owner\n # If projection is not from another ObjectiveMechanism, ignore\n if not isinstance(sender, (ObjectiveMechanism)):\n continue\n if isinstance(sender, ObjectiveMechanism) and sender.learning_role is TARGET:\n return sender\n if sender.inputStates:\n target_mech = trace_learning_objective_mechanism_projections(sender)\n if target_mech:\n return target_mech\n else:\n continue\n else:\n continue","def step(self, state):\n mcts_action = self.mcts_search(state)\n policy = [(action, (1.0 if action == mcts_action else 0.0))\n for action in state.legal_actions(self.player_id())]\n\n return policy, mcts_action","def evaulate_policy(w, env, number_of_episodes=2):\n\n results = []\n # for e in range(number_of_episodes):\n s_old = env.reset()\n t = 0\n done = False\n while not done:\n # Choose action\n action = None\n if np.inner(w, s_old) > 0:\n action = 1\n else:\n action = 0\n # Take action\n s_new, r, done, _ = env.step(action)\n # env.render()\n\n # Update\n s_old = s_new\n\n t += 1\n if t>200:\n break\n # results.append(t)\n # print(t)\n return t","def get_target_policy(self, state):\n values = self.get_all_state_action_values(state)\n return self.target_policy(values)","def extract_belief(ontology, state, threshold=0.3):\n\n \"\"\"\n need to check if the value at argmax is bigger than a threshold\n \"\"\"\n\n request_idx = np.argmax([item[1] for item in state[\"request\"].items()])\n frequency_idx = np.argmax([item[1] for item in state[\"frequency\"].items()])\n illness_type_idx = np.argmax([item[1] for item in state[\"type\"].items()])\n symptom_idx = np.argmax([item[1] for item in state[\"symptom\"].items()])\n escalation_idx = np.argmax([item[1]\n for item in state[\"escalation\"].items()])\n duration_idx = np.argmax([item[1] for item in state[\"duration\"].items()])\n confirmation_idx = np.argmax([item[1]\n for item in state[\"confirmation\"].items()])\n\n # then it is neccessary to map the indices back to words\n\n request = is_plausible(state[\"request\"].items(\n ), ontology, \"request\", request_idx, threshold)\n frequency = is_plausible(\n state[\"frequency\"].items(), ontology, \"frequency\", frequency_idx, threshold)\n illness_type = is_plausible(\n state[\"type\"].items(), ontology, \"type\", illness_type_idx, threshold)\n symptom = is_plausible(state[\"symptom\"].items(\n ), ontology, \"symptom\", symptom_idx, threshold)\n\n escalation = is_plausible(state[\"escalation\"].items(\n ), ontology, \"escalation\", escalation_idx, threshold)\n\n duration = is_plausible(state[\"duration\"].items(\n ), ontology, \"duration\", duration_idx, threshold)\n confirmation = is_plausible(state[\"confirmation\"].items(\n ), ontology, \"confirmation\", confirmation_idx, threshold)\n\n if request is not None:\n request = \"request \"+request\n if frequency is not None:\n frequency = \"frequency \"+frequency\n if illness_type is not None:\n illness_type = \"type \"+illness_type\n if symptom is not None:\n symptom = \"symptom \"+symptom\n if escalation is not None:\n escalation = \"escalation \"+escalation\n if duration is not None:\n duration = \"duration \"+duration\n if confirmation is not None:\n confirmation = \"confirmation \"+confirmation\n values = [request, frequency, illness_type,\n symptom, escalation, duration, confirmation]\n values = [val for val in values if val is not None]\n return sorted(values)","def update_policy(self):\n # this is update_policy \n # sample batch of 32 from the memory\n batch_of_samples = self.replay_memory.sample(batch_size=32)\n current_state_samples = batch_of_samples['current_state_samples']\n next_state_samples = batch_of_samples['next_state_samples']\n #print type(current_state_samples[0])\n #print current_state_samples\n\n # fetch stuff we need from samples 32*84*84*4\n current_state_images = np.zeros([1, 84, 84, 4])\n #print current_state_samples\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\n\n next_state_images = np.zeros([1, 84, 84, 4])\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\n\n # preprocess\n current_state_images = self.preprocessor.process_batch(current_state_images)\n next_state_images = self.preprocessor.process_batch(next_state_images)\n # print \"current_state_images {} max {} \".format(current_state_images.shape, np.max(current_state_images))\n #print current_state_images.shape\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\n\n # targets\n y_targets_all = q_current #1*num_actions\n #print y_targets_all.shape # [1,6]\n idx = 0 \n last_sample = current_state_samples[-1]\n if last_sample.is_terminal:\n y_targets_all[idx, last_sample.action] = last_sample.reward\n else:\n if self.mode == 'vanilla':\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\n if self.mode == 'double': \n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \n\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\n\n with tf.name_scope('summaries'):\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\n\n if not (self.iter_ctr % self.log_loss_every_nth):\n self.dump_train_loss(loss)\n\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\n # # copy weights\n # print \"Iter {} Updating target Q network\".format(self.iter_ctr)\n # self.target_q_network.set_weights(self.q_network.get_weights())\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\n # for i in range(len(self.target_q_network.trainable_weights))]","def main(args):\n flags = parse_options(args)\n\n data = {\n 'name': [],\n 'step': [],\n 'distance': [],\n }\n\n for dir_name, name in zip(flags.dir_name, flags.name):\n\n # get the hyperparameters\n env_name, policy, hp, seed = get_hyperparameters_from_dir(dir_name)\n\n print(hp.keys())\n del hp['algorithm']\n del hp['date/time']\n\n # create the algorithm object. We will be using the eval environment in\n # this object to perform the rollout.\n alg = OffPolicyRLAlgorithm(\n policy=policy, env=env_name, eval_env=env_name, **hp)\n\n # setup the seed value\n random.seed(seed)\n np.random.seed(seed)\n tf.compat.v1.set_random_seed(seed)\n\n filenames = os.listdir(os.path.join(dir_name, \"checkpoints\"))\n metafiles = [f[:-5] for f in filenames if f[-5:] == \".meta\"]\n metanum = list(sorted([int(f.split(\"-\")[-1]) for f in metafiles]))[:-1]\n\n # get the checkpoint number\n ckpt_num = max(metanum)\n\n # location to the checkpoint\n ckpt = os.path.join(dir_name, \"checkpoints/itr-{}\".format(ckpt_num))\n\n # restore the previous checkpoint\n alg.saver = tf.compat.v1.train.Saver(alg.trainable_vars)\n alg.load(ckpt)\n\n # some variables that will be needed when replaying the rollout\n policy = alg.policy_tf\n\n batches = []\n for b in range(flags.num_batches):\n worker_obs0 = policy.replay_buffer.sample(with_additional=False)[5]\n batches.append(worker_obs0)\n\n for ckpt_num_one, ckpt_num_two in zip(metanum[1:], metanum[:-1]):\n\n # get the checkpoint number\n ckpt_num = ckpt_num_one\n\n # location to the checkpoint\n ckpt = os.path.join(dir_name, \"checkpoints/itr-{}\".format(ckpt_num))\n\n # restore the previous checkpoint\n alg.load(ckpt)\n\n # some variables that will be needed when replaying the rollout\n policy = alg.policy_tf\n\n mean_one = []\n for b in batches:\n a = policy.policy[-1].get_action(b, None, False, False)\n mean_one.append(a)\n\n # get the checkpoint number\n ckpt_num = ckpt_num_two\n\n # location to the checkpoint\n ckpt = os.path.join(dir_name, \"checkpoints/itr-{}\".format(ckpt_num))\n\n # restore the previous checkpoint\n alg.load(ckpt)\n\n # some variables that will be needed when replaying the rollout\n policy = alg.policy_tf\n\n mean_two = []\n for b in batches:\n a = policy.policy[-1].get_action(b, None, False, False)\n mean_two.append(a)\n\n # compute a distance metric between the policies\n mean_one = np.concatenate(mean_one, axis=0)\n mean_two = np.concatenate(mean_two, axis=0)\n kl = np.sum((mean_one - mean_two) ** 2, axis=1).mean()\n print(\"{},{},{},{}\".format(name, ckpt_num_one, ckpt_num_two, kl))\n\n data['name'].append(name)\n data['step'].append(ckpt_num_one)\n data['distance'].append(kl)\n\n df = pd.DataFrame(data, columns=['name', 'step', 'distance'])\n plt.title(\"Manager MDP Non-Stationarity\")\n ax = sns.lineplot(x='step', y='distance', hue='name', data=df)\n plt.savefig('ns.png')","def _evaluate_policy(self, state, legal_actions, step_rewards=None, action=None):\n raise NotImplementedError","def vec_rollout(\n env,\n n_envs, #added this to specify # environments\n agent,\n max_path_length=np.inf,\n render=False,\n render_kwargs=None,\n):\n if render_kwargs is None:\n render_kwargs = {}\n observations = [[] for _ in range(n_envs)]\n actions = [[] for _ in range(n_envs)]\n rewards = [[] for _ in range(n_envs)]\n terminals = [[] for _ in range(n_envs)]\n agent_infos = [[] for _ in range(n_envs)]\n env_infos = [[] for _ in range(n_envs)]\n o = env.reset()\n agent.reset()\n next_o = None\n path_length = 0\n if render:\n env.render(**render_kwargs)\n\n \n #keep track of each env. \n env_idx = list(range(n_envs))\n last = {}\n while path_length < max_path_length:\n # print(path_length)\n tmp_action = []\n action = []\n agent_info = []\n #Have to step through all no matter what. Only keep if in env_idx\n for i in range(n_envs):\n a, ai = agent.get_action(o[i])\n action.append(a)\n agent_info.append(ai)\n next_o, r, d, env_info = env.step(action)\n for i in env_idx:\n observations[i].append(o[i])\n actions[i].append(action[i])\n rewards[i].append(r[i])\n terminals[i].append(d[i])\n env_infos[i].append(env_info[i])\n agent_infos[i].append(agent_info[i])\n\n path_length += 1\n n = len(env_idx)\n #check if any are finished. If so, ignore them. \n tmp = []\n for i in range(n):\n if not d[env_idx[i]]:\n tmp.append(env_idx[i])\n else:\n last[i] = next_o[i]\n env_idx = tmp\n if len(env_idx) == 0:\n break\n\n o = next_o\n if render:\n env.render(**render_kwargs)\n for k, v in last.items():\n next_o[k] = v \n # observations = list(np.concatenate(observations))\n # actions = list(np.concatenate(actions))\n # rewards = list(np.concatenate(rewards))\n # terminals = list(np.concatenate(terminals))\n # env_info = list(np.concatenate(env_info))\n # agent_info = list(np.concatenate(agent_info))\n\n # actions = np.array(actions)\n # if len(actions.shape) == 1:\n # actions = np.expand_dims(actions, 1)\n # observations = np.array(observations)\n # if len(observations.shape) == 1:\n # observations = np.expand_dims(observations, 1)\n # next_o = np.array([next_o])\n #need to return each run of observations for each env on its own\n ans = []\n for i in range(n_envs):\n observations[i] = np.array(observations[i])\n if len(observations[i].shape) == 1: \n observations[i] = np.expand_dims(observations[i], 1)\n next_o[i] = np.array([next_o[i]])\n actions[i] = np.array(actions[i])\n if len(actions[i].shape) == 1:\n actions[i] = np.expand_dims(actions[i], 1)\n #last transition should be same. \n next_observations = np.vstack(\n (\n observations[i][1:, :],\n np.expand_dims(next_o[i], 0)\n )\n )\n item = dict(\n observations=observations[i],\n actions=actions[i],\n rewards=np.array(rewards[i]).reshape(-1, 1),\n next_observations=next_observations,\n terminals=np.array(terminals[i]).reshape(-1, 1),\n agent_infos=agent_infos[i],\n env_infos=env_infos[i],\n )\n ans.append(item)\n return ans","def calc_stay_prob(rollouts):\n states = rollouts.states\n actions = rollouts.actions\n rewards = rollouts.rewards\n\n num_test_episodes = states.shape[0]\n num_trials = states.shape[1]\n count_trial_stayed = 0.01 + np.zeros((2, 2, num_test_episodes)) # [common/uncommon, reward/unrewarded]\n count_trial_all = 0.01 + np.zeros((2, 2, num_test_episodes))\n for epi in range(num_test_episodes):\n for t in range(0, num_trials-2, 2):\n uncommon_transition = int(actions[epi, t] != states[epi, t+1]-1)\n count_trial_all[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += 1\n count_trial_stayed[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += \\\n int(actions[epi, t+2] == actions[epi, t])\n return np.divide(count_trial_stayed, count_trial_all), count_trial_stayed, count_trial_all","def current_policy_step(self, sess, add_to_mem = True):\n \n episode_reward = 0.0\n episode_len = 0 #num of action\n \n #for each episode reset first\n state = self.env.reset()\n \n for t in range(self.FLAGS.max_episode_len):\n \n # take action according to current policy\n \n action, policy_stats = sess.run([self.a_i_, self.policy_xi_stats], \n feed_dict={self.x_i : np.array([state])})\n \n action = np.reshape(action, (self.ACTION_DIM,))\n \n next_state, reward, done, info = self.env.step(action) # next state, reward, terminal\n \n # insert this in memory with a uniform distribution over actions\n if add_to_mem: # can also remove this and still work/optimization\n self.memory.add(Transition(state=state, action=action, \n reward=reward, done=done, \n distribution=policy_stats, next_state=next_state))\n \n # accumulate rewards\n episode_reward += reward\n episode_len += 1 \n \n local_t = next(self.local_counter)\n global_t = next(self.global_counter)\n \n # update the state \n state = next_state \n \n if done:\n # print(\"Episode finished after {} timesteps\".format(t+1))\n break\n \n return episode_reward, episode_len, local_t, global_t","def evaluate_policy(policy, expert_states, expert_actions, episodes, batch_size):\r\n distance_list = []\r\n for _ in range(episodes):\r\n tl = expert_states.shape[0] # training inputs length\r\n mod = tl % batch_size # number of batches resulting from batch size\r\n N = int(np.floor(tl/batch_size)) # number for splitting training data\r\n split_details = [batch_size] * N # N elements of batch_size [batch_size, batch_size, ...]\r\n split_details.append(mod)\r\n batched_states = tf.split(expert_states, split_details)\r\n batched_actions = tf.split(expert_actions, split_details)\r\n\r\n for i in range(len(batched_states)):\r\n predicted_actions = policy.get_action(batched_states[i])\r\n distances = -1 * distance_from_expert(5*predicted_actions, batched_actions[i])\r\n distance_list.extend(list(distances.numpy()))\r\n\r\n return distance_list","def original_solver(state, agent, verbose=3):\n tasks = state.goals[agent]\n if verbose>0: print('** pyhop, verbose={}: **\\n state = {}\\n tasks = {}'.format(verbose, state.__name__, tasks))\n result = seek_plan(state,tasks,[],0,verbose)\n if verbose>0: print('** result =',result,'\\n')\n return result","def rollout(system_env, n_action_features,\n epoch=0, min_epoch_steps=1000,\n agent=None, episodic_update=False):\n print('Epoch', epoch)\n\n # epoch trace data\n epoch_step = 0\n trace = []\n\n while epoch_step < min_epoch_steps:\n observation = system_env.reset()\n\n rewards = []\n done = 0\n episode_step = 0\n while not done:\n # restart = 1 if first episode step, otherwise 0\n restart = int(not(episode_step))\n if episode_step % 10 == 0:\n print(f'step: {episode_step}')\n if agent is None:\n action = system_env.action_space.sample()\n else:\n action = agent.act(observation, restart)\n new_observation, reward, done, _ = system_env.step(action)\n rewards.append(reward)\n\n trace_step = np.hstack(\n (observation, action, reward, restart, epoch))\n trace.append(trace_step)\n\n # update observation\n episode_step += 1\n observation = new_observation\n\n # save last observation before a reset\n n_nans = n_action_features + 1 # NaNs for actions and reward\n last_obs = np.hstack(\n (observation, np.repeat(np.nan, n_nans), 0, epoch))\n trace.append(last_obs)\n\n epoch_step += episode_step\n\n print('Number of episode steps:', episode_step)\n print('Mean reward:', np.mean(rewards))\n print('Return:', np.sum(rewards))\n print('Number of epoch steps:', epoch_step)\n\n if episodic_update:\n # one epoch is exactly one episode\n break\n\n return trace","def _evaluate_rollout(self, state, limit=1000):\n player = state.get_current_player()\n for i in range(limit):\n end, winner = state.game_end()\n if end:\n break\n action_probs = rollout_policy_fn(state)\n max_action = max(action_probs, key=itemgetter(1))[0]\n state.do_move(max_action)\n else:\n # If no break from the loop, issue a warning.\n print(\"WARNING: rollout reached move limit\")\n if winner == -1: # tie\n return 0\n else:\n return 1 if winner == player else -1","def _evaluate_rollout(self, state, limit=1000):\n player = state.get_current_player()\n for i in range(limit):\n end, winner = state.game_end()\n if end:\n break\n action_probs = rollout_policy_fn(state)\n max_action = max(action_probs, key=itemgetter(1))[0]\n state.do_move(max_action)\n else:\n # If no break from the loop, issue a warning.\n print(\"WARNING: rollout reached move limit\")\n if winner == -1: # tie\n return 0\n else:\n return 1 if winner == player else -1","def policy_eval():\r\n \r\n action_prob = [0.125, 0.625, 0.125, 0.125]# actions with probabilities\r\n data = grid_world()\r\n state_axis = np.zeros((9, 9))#initialize states\r\n threshold = .1\r\n prior_state = np.ones((9, 9))\r\n \r\n while np.abs(state_axis - prior_state).max() > threshold:\r\n for x, y in product(range(9), repeat=2):\r\n prior_state = state_axis.copy()\r\n if data.array[x, y] == 'X':\r\n continue\r\n updated_values = [data.next_direction(np.array([x, y]), next_move)\r\n for next_move in data.directions]#Updating states with directions\r\n Sum_Expectation = np.dot(action_prob,\r\n [points_val + 0.9 * state_axis[position[0], position[1]]\r\n for position, points_val in updated_values])\r\n state_axis[x, y] = Sum_Expectation\r\n print(\"\\nExercise 3.1 Shows Value functions for the policy\\n\")\r\n print(state_axis)\r\n build_grid(state_axis, \"Shows Value functions for the policy\")","def get_traj(agent, env, episode_max_length):\n # MARK: changed\n\n env.reset()\n obs = []\n acts = []\n rews = []\n entropy = []\n info = []\n machines = []\n ob = env.observe()\n\n for _ in range(episode_max_length):\n\n act_prob = agent.get_action(ob)\n machine_prob = agent.get_machine(ob)\n # csprob_n = np.cumsum(act_prob)\n # a = (csprob_n > np.random.rand()).argmax()\n # print(act_prob)\n ob = ob.reshape(ob.shape[0]*ob.shape[1])\n obs.append(ob) # store the ob at current decision making step\n acts.append(act_prob)\n machines.append(machine_prob)\n ob, rew, done, info = env.step(act_prob,machine_prob, repeat=True)\n\n rews.append(rew)\n\n entropy.append(get_entropy(act_prob))\n if done: break\n return {'reward': np.array(rews),\n 'ob': np.array(obs),\n 'action': np.array(acts),\n 'entropy': entropy,\n 'info': info,\n 'machine':np.array(machines)\n }","def step(self):\n\n \"\"\" First updates the variables values of the current time form the environment \"\"\"\n self.update_crispval(self.env.context)\n\n \"\"\"\n here the decision making of the agent\n to determine which activity to suggest to the patient\n i apply the creative controller to the current context\n \"\"\"\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\n self.fuzzysets_values, self.variables_universe)\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\n\n \"\"\" i obtain a number of ouput crisp values.\n i determine which one achieves the max expected output w.r.t. the a-rules \"\"\"\n best_a = None\n best_a_val = -1000\n best_a_exphapp = 5\n if self.verbose > Constants.VERBOSE_BASIC:\n print(\"rules activations\")\n for a in rules_activations:\n if rules_activations[a] > 0:\n print(str(a) + \"\\n\\t\\t\\t-> \" + str(rules_activations[a]))\n for item in c_out.items(): # for each pair <activity, crisp output>\n if self.verbose > Constants.VERBOSE_BASIC:\n print(item)\n if not item[\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\n inputs = dict(curr_input) # I create a copy fo the dict\n inputs[item[0]] = item[1]\n assessor_id = self.actions_to_ti[item[0]]\n self.assessors[assessor_id].feed_inputs(inputs)\n is_ac_exception = False\n assout = []\n try:\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\n assout = [a_out[ao] for ao in a_out]\n except:\n is_ac_exception = True\n traceback.print_exc()\n # todo the following assumes that every assessor controller has same eval var\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n if len(assout) == 0:\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\n\n avg_credit_rules_that_suggested_action = 1.0\n nr_rules_that_suggested_action = 0\n for r in rules_activations:\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\n self.rules_credits[str(r)]\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\n if nr_rules_that_suggested_action > 0:\n avg_credit_rules_that_suggested_action = (\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\n repetition_cost = 1.0\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\n if (a_val > best_a_val) and (\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\n best_a = item\n best_a_val = a_val\n best_a_exphapp = mean(assout)\n\n \"\"\"I suggest the activity with best expected outcome and store the information to populate the interactions \n memory \"\"\"\n self.proposeActivity(best_a)\n if not best_a is None:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"proposing activity\" + str(best_a) + \" which has expected feedback: \" + str(\n best_a_exphapp) + \", which weighted is \" + str(best_a_val))\n self.curr_iter_suggestions.append(best_a[0])\n self.last_suggestion = best_a\n else:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"the activity proposed is \" + str(\n best_a) + \" so I don't suggest anything. I will ask a question instead\")\n self.last_suggestion = []\n self.expected_feedback = best_a_exphapp\n self.last_context = self.env.context.copy()\n self.last_rules_activations = rules_activations","def _prepare_policy_input(self, state, legal_actions, step_rewards=None):\n check_for_nans(\"Raw state\", state)\n state_ = state.view(-1)\n\n if step_rewards is None or not step_rewards:\n step_rewards = [None for _ in legal_actions]\n batch_states = []\n\n assert legal_actions\n assert step_rewards\n assert len(legal_actions) == len(step_rewards)\n\n for action, log_likelihood in zip(legal_actions, step_rewards):\n action_ = self.action_factor * torch.tensor([action]).to(self.device, self.dtype)\n\n i, j = self.env.unwrap_action(action)\n pi = state[i, :]\n pj = state[j, :]\n check_for_nans(\"Individual momenta\", pi, pj)\n\n if self.log_likelihood_feature:\n if log_likelihood is None:\n log_likelihood = self._parse_action(action, from_which_env=\"real\")\n if not np.isfinite(log_likelihood):\n log_likelihood = 0.0\n log_likelihood = np.clip(log_likelihood, self.reward_range[0], self.reward_range[1])\n log_likelihood_ = self.log_likelihood_factor * torch.tensor([log_likelihood]).to(\n self.device, self.dtype\n )\n check_for_nans(\"Log likelihood as policy input\", log_likelihood_)\n\n combined_state = torch.cat((action_, pi, pj, log_likelihood_, state_), dim=0)\n check_for_nans(\"Individual policy input entry\", combined_state)\n else:\n combined_state = torch.cat((action_, pi, pj, state_), dim=0)\n check_for_nans(\"Individual policy input entry\", combined_state)\n\n batch_states.append(combined_state.unsqueeze(0))\n\n batch_states = torch.cat(batch_states, dim=0)\n check_for_nans(\"Concatenated policy input\", batch_states)\n return batch_states","def unroll(self, trajectory):\n # We omit the details of network inference here.\n return policy_logits, baselines","def learn(self, state_expert, action_expert, action_expert_ori, s_max, s_min, a_max, a_min, new_dir_path):\n # \n # Information Writing\n #\n file_path = new_dir_path + \"/Readme.txt\"\n f_read = open(file_path, \"a\")\n\n f_read.write(\"paths_per_collect:\\n\" + str(self.config.paths_per_collect) + \"\\n\")\n f_read.write(\"max_step_limit:\\n\" + str(self.config.max_step_limit) + \"\\n\")\n f_read.write(\"min_step_limit:\\n\" + str(self.config.min_step_limit) + \"\\n\")\n f_read.write(\"pre_step:\\n\" + str(self.config.pre_step) + \"\\n\")\n f_read.write(\"n_iter:\\n\" + str(self.config.n_iter) + \"\\n\")\n f_read.write(\"gamma:\\n\" + str(self.config.gamma) + \"\\n\")\n f_read.write(\"lam:\\n\" + str(self.config.lam) + \"\\n\")\n f_read.write(\"max_kl:\\n\" + str(self.config.max_kl) + \"\\n\")\n f_read.write(\"cg_damping:\\n\" + str(self.config.cg_damping) + \"\\n\")\n f_read.write(\"lr_discriminator:\\n\" + str(self.config.lr_discriminator) + \"\\n\")\n f_read.write(\"d_iter:\\n\" + str(self.config.d_iter) + \"\\n\")\n f_read.write(\"clamp_lower:\\n\" + str(self.config.clamp_lower) + \"\\n\")\n f_read.write(\"clamp_upper:\\n\" + str(self.config.clamp_upper) + \"\\n\")\n f_read.write(\"lr_baseline:\\n\" + str(self.config.lr_baseline) + \"\\n\")\n f_read.write(\"b_iter:\\n\" + str(self.config.b_iter) + \"\\n\")\n f_read.write(\"lr_posterior:\\n\" + str(self.config.lr_posterior) + \"\\n\")\n f_read.write(\"lr_discriminator:\\n\" + str(self.config.lr_discriminator) + \"\\n\")\n f_read.write(\"g_iter:\\n\" + str(self.config.g_iter) + \"\\n\")\n f_read.write(\"p_iter:\\n\" + str(self.config.p_iter) + \"\\n\")\n f_read.write(\"buffer_size:\\n\" + str(self.config.buffer_size) + \"\\n\")\n f_read.write(\"sample_size:\\n\" + str(self.config.sample_size) + \"\\n\")\n f_read.write(\"batch_size:\\n\" + str(self.config.batch_size) + \"\\n\")\n f_read.write(\"trajectory_length:\\n\" + str(self.config.trajectory_length) + \"\\n\")\n f_read.write(\"inner_loop:\\n\" + str(self.config.inner_loop) + \"\\n\")\n f_read.write(\"seed:\\n\" + str(self.config.seed) + \"\\n\")\n f_read.write(\"schedule:\\n\" + str(self.config.schedule) + \"\\n\")\n f_read.write(\"beta_for_entropy:\\n\" + str(self.config.beta) + \"\\n\")\n f_read.write(\"w:\\n\" + str(self.config.w) + \"\\n\")\n f_read.write(\"step:\\n\" + str(self.config.step) + \"\\n\")\n\n f_read.close()\n\n np.random.seed(self.seed)\n config = self.config\n start_time = time.time()\n numeptotal = 0\n start_epoch = 0\n progress = 0 # Counter for iterations\n Omax = self.config.sample_size\n\n state_d = state_expert\n actions_d = action_expert\n action_d_no_norm = action_expert_ori\n\n #\n # Learning rate\n #\n losses = []\n generator_loss = []\n generator_D = []\n generator_P = []\n posterior_loss = []\n baseline_loss = []\n\n #\n # Goal Agent\n #\n count_goalagent = 0\n count_goalagent_stack = []\n count_goalagent_new_stack = []\n\n # Iterate over num of iterations\n for i in range(start_epoch, start_epoch + config.n_iter):\n\n progress += 1 # Increment current iteration counter\n\n #\n # Load encode expert data\n #\n demo_dir = \"Expert/\"\n encodes_d = np.load(demo_dir + \"encode_mujoco.npy\")\n\n encode_labels = [(0, 1) for num_expert in range(30)]\n encode_labels = list(np.array(encode_labels).flatten())\n\n #\n # Create Generator's trajectory\n #\n count_goalagent = 0\n Omax_sample = 60\n\n rollouts, count_goalagent = rollout_contin(\n self,\n self.state_dim,\n self.encode_dim,\n self.action_dim,\n s_max,\n s_min,\n a_max,\n a_min,\n config.max_step_limit,\n config.min_step_limit,\n Omax_sample,\n count_goalagent,\n i,\n )\n latent_labels = [r for r in range(self.encode_dim)]\n sub_labels = []\n for r in range(Omax_sample // self.encode_dim):\n sub_labels.extend(latent_labels)\n\n count_goalagent_stack.append(count_goalagent)\n\n #\n # Pool trajecoty\n #\n index_for_labels = 0\n\n for path in rollouts:\n path[\"labels\"] = [sub_labels[index_for_labels]]\n index_for_labels += 1\n\n for path in rollouts:\n self.buffer.add(path)\n\n paths = self.buffer.get_sample(config.sample_size)\n\n #\n # Rearrange paths\n #\n logstds_n = np.concatenate([path[\"logstds\"] for path in paths])\n state_n = np.concatenate([path[\"state\"] for path in paths])\n encodes_n = np.concatenate([path[\"encodes\"] for path in paths])\n actions_n = np.concatenate([path[\"act\"] for path in paths])\n labels_n = np.concatenate([path[\"labels\"] for path in paths])\n policies_n = np.concatenate([path[\"policies\"] for path in paths])\n\n #\n # Pre-run Discriminator\n #\n numdetotal = state_d.shape[0]\n numnototal = state_n.shape[0]\n batch_size = config.batch_size\n start_d = 0\n start_n = 0\n d_iter = self.config.d_iter\n\n loss_sum = 0\n\n idx = np.arange(numnototal)\n np.random.shuffle(idx)\n train_val_ratio = 0.7\n\n #\n # Inner loop Discriminator & Generator\n #\n if i == start_epoch:\n new_labels = []\n new_labels = encode_labels\n if not os.path.exists(new_dir_path + \"/new_labels\"):\n os.mkdir(new_dir_path + \"/new_labels\")\n np.save(new_dir_path + \"/new_labels/iter_%d_labels.npy\" % (i), np.array(new_labels))\n\n for iiter in range(self.config.inner_loop):\n if iiter % 10 == 0:\n print(\"Inner: \", iiter)\n if iiter % 50 == 0:\n print(new_dir_path)\n\n count_goalagent_new = 0\n\n #\n # Create trajectory while over minimal batch_size\n #\n new_labels = encode_labels\n counter_batchsize = 0\n rollouts_new, count_goalagent_new = rollout_contin(\n self,\n self.state_dim,\n self.encode_dim,\n self.action_dim,\n s_max,\n s_min,\n a_max,\n a_min,\n config.max_step_limit,\n config.min_step_limit,\n Omax,\n count_goalagent_new,\n i,\n encode_list=new_labels\n )\n counter_batchsize = len(np.concatenate([path[\"state\"] for path in rollouts_new]))\n print(\"GOAL: \", count_goalagent_new)\n\n if iiter == self.config.inner_loop - 1:\n count_goalagent_new_stack.append(count_goalagent_new)\n\n index_for_labels = 0\n for path in rollouts_new:\n path[\"labels\"] = [new_labels[index_for_labels]]\n index_for_labels += 1\n\n #\n # Pool trajectory (for buffer_new)\n #\n for path in rollouts_new:\n self.buffer_new.add(path)\n\n paths_new = self.buffer_new.get_sample(config.sample_size)\n\n #\n # Create Trajectory\n #\n # TODO: pyTorch\n for path in paths_new:\n path[\"mus\"] = self.sess.run(\n self.action_dist_mu,\n {self.state: path[\"state\"],\n self.encodes: path[\"encodes\"]}\n )\n\n #\n # Rearrange paths_new\n #\n mus_new_n = np.concatenate([path[\"mus\"] for path in paths_new])\n logstds_new_n = np.concatenate([path[\"logstds\"] for path in paths_new])\n state_new_n = np.concatenate([path[\"state\"] for path in paths_new])\n encodes_new_n = np.concatenate([path[\"encodes\"] for path in paths_new])\n actions_new_n = np.concatenate([path[\"act\"] for path in paths_new])\n actions_output_new_n = np.concatenate([path[\"actions\"] for path in paths_new])\n labels_new_n = np.concatenate([path[\"labels\"] for path in paths_new])\n policies_new_n = np.concatenate([path[\"policies\"] for path in paths_new])\n\n #\n # Preparation for update Networks\n #\n numdetotal = state_d.shape[0]\n numnototal = state_new_n.shape[0]\n start_d = 0\n start_n = 0\n\n #\n # Get policy from expert data\n #\n policy_d = []\n for t in range(len(state_d)):\n policy_d.append(self.get_policy_encode([state_d[t]], action_d_no_norm[t], [encodes_d[t]]))\n\n policy_d = np.array(policy_d)\n\n batch_size_agent = len(state_new_n)\n batch_size_expert = len(state_d)\n\n noise_expert = np.random.normal(0, 0.05, (batch_size_expert, 2))\n noise_agent = np.random.normal(0, 0.05, (batch_size_agent, 2))\n # noise_expert = np.zeros((batch_size_expert, 2), dtype=np.float16)\n # noise_agent = np.zeros((batch_size_agent, 2), dtype=np.float16)\n\n #\n # Update Discriminator (using Keras)\n #\n # TODO: pyTorch\n d_loss_real = self.discriminator.train_on_batch(\n [state_d[start_d:start_d + batch_size_expert], actions_d[start_d:start_d + batch_size_expert],\n noise_expert[:batch_size_expert], encodes_d[start_d:start_d + batch_size_expert],\n policy_d[start_d:start_d + batch_size_expert]],\n np.ones(batch_size_expert))\n d_loss_fake = self.discriminator.train_on_batch(\n [state_new_n[start_n:start_n + batch_size_agent], actions_new_n[start_n:start_n + batch_size_agent],\n noise_agent[:batch_size_agent], encodes_new_n[start_n:start_n + batch_size_agent],\n policies_new_n[start_n:start_n + batch_size_agent]],\n np.zeros(batch_size_agent))\n loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n\n for l in self.discriminator.layers:\n weights = l.get_weights()\n l.set_weights(weights)\n\n if iiter == self.config.inner_loop - 1:\n losses.append(loss)\n\n if iiter == self.config.inner_loop - 1:\n path_idx = 0\n start_w = 0\n\n for path in paths_new:\n file_path_world = new_dir_path + \"/Generator_trajectories_resample\"\n if not os.path.exists(file_path_world):\n os.mkdir(file_path_world)\n file_path_world = new_dir_path + \\\n \"/Generator_trajectories_resample/iter_%d_world_%d\" % (i, path_idx)\n\n np.save(file_path_world, path[\"state\"])\n np.save(file_path_world + \"_actions.npy\", actions_new_n[start_w:start_w + path[\"length\"]])\n np.save(file_path_world + \"_policies.npy\", policies_new_n[start_w:start_w + path[\"length\"]])\n np.save(file_path_world + \"_labels.npy\", labels_new_n[path_idx])\n\n path_idx += 1\n\n #\n # Update Generator \n #\n path_i = 0\n for path in paths_new:\n\n path[\"baselines\"] = self.baseline.predict(path)\n\n output_d = self.discriminator.predict(\n [path[\"state\"], path[\"actions\"], noise_agent, path[\"encodes\"], path[\"policies\"]])\n\n anti = 1.0 - copy.copy(output_d)\n\n for an in range(path[\"length\"]):\n if anti[an] == 0.0:\n anti[an] = 1e-10\n\n #\n # Without scheduling\n #\n beta = self.config.beta\n #\n # With scheduling\n #\n if i % self.config.step == 0:\n if self.config.schedule == 1:\n beta = self.config.beta - self.config.w * i\n\n if i % 50 == 0 and path_i == 0:\n file_path2 = new_dir_path + \"/bata.txt\"\n f_read2 = open(file_path2, \"a\")\n f_read2.write(str(i) + \" : \" + str(beta) + \"\\n\")\n f_read2.close()\n\n path_i += 1\n\n # output_d_for_reward = np.log(output_d + 1e-10) - np.log(anti)\n output_d_for_reward = ((np.log(output_d + 1e-10) - np.log(anti)).flatten()\n + beta * np.log(np.max(path[\"policies\"], axis=1) + 1e-10).flatten())\n\n '''\n if path[\"flag\"] == 1:\n output_d_for_reward = output_d_for_reward + np.ones(path[\"length\"]) * 0.1 * np.exp(3*abs((float(config.max_step_limit)-float(path[\"length\"]))/float(config.max_step_limit)))\n '''\n\n path[\"rewards\"] = output_d_for_reward.flatten()\n\n path[\"generator_d\"] = output_d_for_reward.flatten()\n\n path_baselines = np.append(path[\"baselines\"], 0 if\n path[\"flag\"] == 1 else\n path[\"baselines\"][-1])\n\n deltas = path[\"rewards\"] + config.gamma * path_baselines[1:] - \\\n path_baselines[:-1]\n\n path[\"advants\"] = discount(deltas, config.gamma * config.lam)\n path[\"returns\"] = discount(path[\"rewards\"], config.gamma)\n\n path[\"baseline\"] = copy.copy(path_baselines)\n\n if iiter == self.config.inner_loop - 1:\n path_idx = 0\n for path in paths_new:\n file_path = new_dir_path + \"/returns(resample)\"\n if not os.path.exists(file_path):\n os.mkdir(file_path)\n file_path = new_dir_path + \"/returns(resample)/iter_%d_path_%d.txt\" % (i, path_idx)\n f = open(file_path, \"w\")\n\n f.write(\"Baseline:\\n\" + np.array_str(path[\"baseline\"]) + \"\\n\")\n f.write(\"Returns:\\n\" + np.array_str(path[\"returns\"]) + \"\\n\")\n f.write(\"Advants:\\n\" + np.array_str(path[\"advants\"]) + \"\\n\")\n f.write(\"Mus:\\n\" + np.array_str(path[\"mus\"]) + \"\\n\")\n f.write(\"Actions:\\n\" + np.array_str(path[\"actions\"]) + \"\\n\")\n f.write(\"States:\\n\" + np.array_str(path[\"state\"]) + \"\\n\")\n f.write(\"Logstds:\\n\" + np.array_str(path[\"logstds\"]) + \"\\n\")\n\n path_idx += 1\n\n f.close()\n\n #\n # Standardize the advantage function to have mean=0 and std=1\n #\n advants_new_n = np.concatenate([path[\"advants\"] for path in paths_new])\n advants_new_n /= (advants_new_n.std() + 1e-8)\n\n if iiter == self.config.inner_loop - 1:\n #\n # Calcurate Generator's reward(sum)\n #\n episoderewards = np.array([path[\"rewards\"].sum() for path in paths_new])\n generator_d = np.array([path[\"generator_d\"].sum() for path in paths_new])\n\n #\n # Calcurate Generator's reward(average each epochs)\n #\n generator_loss.append(episoderewards.mean())\n generator_D.append(generator_d.mean())\n\n #\n # Computing baseline function for next iter.\n #\n b_loss = self.baseline.fit(paths_new, batch_size_agent)\n if iiter == self.config.inner_loop - 1:\n baseline_loss.append(b_loss)\n\n feed = {self.state: state_new_n,\n self.encodes: encodes_new_n,\n self.actions: actions_output_new_n,\n self.advants: advants_new_n,\n self.action_dist_logstd: logstds_new_n,\n self.oldaction_dist_mu: mus_new_n,\n self.oldaction_dist_logstd: logstds_new_n}\n\n thprev = self.gf()\n\n def fisher_vector_product(p):\n feed[self.flat_tangent] = p\n return self.sess.run(self.fvp, feed) + p * config.cg_damping\n\n g = self.sess.run(self.pg, feed_dict=feed)\n stepdir = conjugate_gradient(fisher_vector_product, -g)\n shs = .5 * stepdir.dot(fisher_vector_product(stepdir))\n if shs <= 0:\n break\n\n lm = np.sqrt(shs / config.max_kl)\n fullstep = stepdir / lm\n neggdotstepdir = -g.dot(stepdir)\n\n def loss(th):\n self.sff(th)\n return self.sess.run(self.losses[0], feed_dict=feed)\n\n theta = linesearch(loss, thprev, fullstep, neggdotstepdir / lm)\n self.sff(theta)\n\n surrafter, kloldnew, entropy = self.sess.run(\n self.losses, feed_dict=feed\n )\n # TODO end\n\n #\n # Display learn results\n #\n stats = {}\n numeptotal += len(episoderewards)\n stats[\"Total number of episodes\"] = numeptotal\n stats[\"Average sum of rewards per episode\"] = episoderewards.mean()\n stats[\"Entropy\"] = entropy\n stats[\"Time elapsed\"] = \"%.2f mins\" % ((time.time() - start_time) / 60.0)\n stats[\"KL between old and new distribution\"] = kloldnew\n stats[\"Surrogate loss\"] = surrafter\n print(\"\\n********** Iteration {} **********\".format(i))\n for k, v in stats.items():\n print(k + \": \" + \" \" * (40 - len(k)) + str(v))\n if entropy != entropy:\n exit(-1)\n\n #\n # parameter saving\n #\n param_dir = new_dir_path + \"/model_parametors/\"\n if not os.path.exists(new_dir_path + \"/model_parametors\"):\n os.mkdir(new_dir_path + \"/model_parametors\")\n\n print(\"Now we save model\")\n self.generator.save_weights(\n param_dir + \"generator_model_%d.h5\" % i, overwrite=True)\n with open(param_dir + \"generator_model_%d.json\" % i, \"w\") as outfile:\n json.dump(self.generator.to_json(), outfile)\n\n self.discriminator.save_weights(\n param_dir + \"discriminator_model_%d.h5\" % i, overwrite=True)\n with open(param_dir + \"discriminator_model_%d.json\" % i, \"w\") as outfile:\n json.dump(self.discriminator.to_json(), outfile)\n\n self.baseline.model.save_weights(\n param_dir + \"baseline_model_%d.h5\" % i, overwrite=True)\n with open(param_dir + \"baseline_model_%d.json\" % i, \"w\") as outfile:\n json.dump(self.baseline.model.to_json(), outfile)\n\n outfile.close()\n\n #\n # Save for loss data\n #\n file_results = new_dir_path + \"/results\"\n if not os.path.exists(file_results):\n os.mkdir(file_results)\n file_results = new_dir_path + \"/results/\"\n\n #\n # Learning rate of Discriminator & Generator\n #\n losses_A = np.array(losses)\n generator_loss_A = np.array(generator_loss)\n np.save(file_results + \"reward_Generator.npy\", generator_loss_A)\n np.save(file_results + \"loss_Discriminator.npy\", losses_A)\n\n #\n # Learning rate of Baseline\n #\n baseline_loss_A = np.array(baseline_loss)\n np.save(file_results + \"baseline_loss.npy\", baseline_loss_A)\n\n #\n # Generator's reward\n #\n generator_D_A = np.array(generator_D)\n np.save(file_results + \"generator_D.npy\", generator_D_A)\n\n #\n # Number of goal agent\n #\n goal_agent_A = np.array(count_goalagent_stack)\n np.save(file_results + \"number_of_goal_agent.npy\", goal_agent_A)\n\n #\n # Making directory\n #\n file_values = new_dir_path + \"/value_function\"\n if not os.path.exists(file_values):\n os.mkdir(file_values)\n\n for enc_dir in range(self.encode_dim):\n file_values_enc = new_dir_path + \"/value_function/encode_%d\" % (enc_dir)\n if not os.path.exists(file_values_enc):\n os.mkdir(file_values_enc)\n\n #\n # Plot learning rate\n #\n file_results = new_dir_path + \"/results\"\n if not os.path.exists(file_results):\n os.mkdir(file_results)\n file_results = new_dir_path + \"/results/\"\n\n #\n # Learning rate of Discriminator & Generator\n #\n losses = np.array(losses)\n generator_loss = np.array(generator_loss)\n t = np.arange(progress)\n fig = plt.figure(figsize=(15, 15))\n ax = fig.add_subplot(2, 1, 1)\n ax2 = fig.add_subplot(2, 1, 2)\n ax.plot(t, losses)\n ax.set_title(\"Discriminator_loss\")\n ax2.plot(t, generator_loss)\n ax2.set_title(\"Generator_reward\")\n plt.savefig(new_dir_path + '/loss.png')\n np.save(file_results + \"reward_Generator.npy\", generator_loss)\n np.save(file_results + \"loss_Discriminator.npy\", losses)\n\n #\n # Learning rate of Baseline\n #\n baseline_loss = np.array(baseline_loss)\n t = np.arange(progress)\n fig = plt.figure(figsize=(15, 15))\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(t, baseline_loss)\n ax.set_title(\"baseline_loss\")\n plt.savefig(new_dir_path + '/baseline_loss.png')\n np.save(file_results + \"baseline_loss.npy\", baseline_loss)\n\n #\n # Number of goal agent\n #\n goal_agent = np.array(count_goalagent_stack)\n t = np.arange(progress)\n fig = plt.figure(figsize=(15, 15))\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(t, goal_agent)\n ax.set_title(\"Goal_agent\")\n plt.savefig(new_dir_path + '/goal_agent.png')\n\n return progress","def sample_draft(\n policy_A,\n policy_B,\n learning_agent,\n winrate_predictor\n):\n state_size = policy_A.state_size\n action_size = policy_A.action_size\n team_indices = get_team_indices(learning_agent)\n policy_A.eval()\n policy_B.eval()\n winrate_predictor.eval()\n\n starting_state = -1 * torch.ones((1, state_size), requires_grad=True).float()\n draft = torch.empty(state_size)\n states = []\n actions = []\n penalties = []\n round = 0\n while not is_done(starting_state):\n # action_odds have size (action_size,)\n if round in team_indices:\n action_odds = policy_A(starting_state)\n else:\n action_odds = policy_B(starting_state)\n\n sampler = Categorical(action_odds)\n action = sampler.sample() + 1\n\n penalty = torch.tensor(\n [get_penalty(starting_state, action, winrate_predictor)]\n )\n\n if round in team_indices:\n penalties.append(penalty)\n states.append(starting_state)\n actions.append(action)\n draft[round] = action\n round += 1\n starting_state = transition(starting_state, action)\n result = get_result(starting_state, winrate_predictor)\n if result == learning_agent:\n reward = 1\n else:\n reward = 0\n\n states = torch.cat(states, dim=0).float()\n actions = torch.cat(actions, dim=0).long()\n penalties = torch.cat(penalties, dim=0).long()\n return draft, states, actions, penalties, reward","def rollout(agent, env):\n # run until episode ends\n episode_reward = 0\n done = False\n obs = env.reset()\n while not done:\n action = agent.compute_action(obs)\n obs, reward, done, info = env.step(action)\n episode_reward += reward\n \n return episode_reward","def inference_chain(self, model, target, verbose=False, fp_penalty=1):\n\n refindex = self.columns[target]\n prior = self.column_priors[refindex]\n refdata = self.data[refindex]\n \n row_priors = self.row_priors\n rows = self.rows\n\n InfChainClass = InferenceChain if verbose else LeanInferenceChain\n result = InfChainClass(prior, reference=target, model=model.name)\n\n for feature, model_val in model.data.items(): \n # determine prior, reference, background values\n ifeature = rows[feature]\n ref_val, bg = refdata[ifeature], row_priors[ifeature]\n \n # avoid calculation if ref_val or model_val are bg\n if (model_val == bg or ref_val == bg) and not verbose:\n continue \n \n # create an evidence items with an initial tpr, fpr\n tpr, fpr = model_val, bg\n datum = (tpr, fpr)\n alpha = 0 \n if verbose:\n datum = InferenceDatum(tpr, fpr)\n \n if model_val > bg and ref_val > bg:\n # True positive\n tpr = model_val\n fpr = bg\n alpha = (ref_val - bg) / (1-bg) \n \n elif model_val > bg and ref_val < bg:\n # False positive \n iancestor = self._positive_ancestor(refindex, ifeature) \n if iancestor is None:\n ancestor_val, ancestor_bg = 1, 1\n else: \n ancestor_bg = row_priors[iancestor]\n ancestor_val = refdata[iancestor]\n if verbose:\n parent = \"NA\"\n if iancestor is not None:\n parent = self.row_names[iancestor]\n datum.set_annotation(\"ancestor_feature\", parent)\n datum.set_annotation(\"ancestor_bg\", ancestor_bg)\n datum.set_annotation(\"ancestor_value\", ancestor_val) \n # adjust probabilities with a combination\n # first adjustment can only bring score up\n beta = tanh(fp_penalty * log10(ancestor_bg/bg)) \n ancestor_estimate = estimate_update(ancestor_bg, bg, model_val)\n tpr0 = ancestor_estimate*(1-beta) + ancestor_bg*beta\n fpr0 = ancestor_bg \n # second part of FP calculation, reduce tpr to penalize far-away FPs \n tpr = tpr0 * (1 - model_val)\n fpr = fpr0 * (1 - bg)\n if ancestor_val > bg:\n alpha = (ancestor_val - bg) / (1-bg) \n \n elif model_val < bg and ref_val > bg:\n # False Negative\n tpr = (1 - bg)\n fpr = (1 - model_val)\n alpha = (ref_val - bg) / (1 - bg) \n \n elif model_val < bg and ref_val < bg:\n # True Negative\n tpr = (1 - model_val)\n fpr = (1 - bg)\n alpha = (bg-ref_val) / bg\n \n else:\n # do nothing (this is when model_val==bg or ref_val==bg)\n if verbose: \n fpr = tpr = bg \n else:\n datum = None \n \n if datum is not None:\n # interpolate based on strength of reference value\n # alpha ~ 0 means weak reference, no adjustment\n # alpha ~ 1 means strong reference, full tpr/fpr adjustment\n tpr = alpha*tpr + (1-alpha)*fpr \n \n if verbose:\n datum.tpr = tpr\n datum.fpr = fpr \n datum.set_annotation(\"feature\", feature)\n datum.set_annotation(\"reference\", ref_val)\n datum.set_annotation(\"background\", bg)\n datum.set_annotation(\"model\", model_val)\n action = comparison_code(model_val, ref_val, bg)\n datum.set_annotation(\"result\", str(action))\n else:\n datum = (tpr, fpr)\n result.add(datum)\n \n return result","def evaluate_rollouts(paths):\n\n\timport ipdb; ipdb.set_trace()\n\ttotal_returns = [path['rewards'].sum() for path in paths]\n\tepisode_lengths = [len(p['rewards']) for p in paths]\n\n\tdiagnostics = OrderedDict((\n\t\t('return-average', np.mean(total_returns)),\n\t\t('return-min', np.min(total_returns)),\n\t\t('return-max', np.max(total_returns)),\n\t\t('return-std', np.std(total_returns)),\n\t\t('episode-length-avg', np.mean(episode_lengths)),\n\t\t('episode-length-min', np.min(episode_lengths)),\n\t\t('episode-length-max', np.max(episode_lengths)),\n\t\t('episode-length-std', np.std(episode_lengths)),\n\t))\n\n\treturn diagnostics","def compute_surr_objective(A_hat, old_policy_model, policy_model, j, state_part_mean, state_part_std, config=PPO_config):\n \n def clip(c, epsilon):\n if c < 1-epsilon:\n return 1- epsilon\n elif c > 1+epsilon:\n return 1+ epsilon\n else:\n return c\n \n clipping_param = np.amax([0.01, (1-j/config['J'])*config['clipping']])\n print(\"Clipping parameters: {}\".format(clipping_param))\n #K = PPO_config['K']\n #assert(K==len(A_hat))\n policy_model.to(device)\n old_policy_model.to(device)\n surr_objective = 0\n kl_est_list = []\n for k in range(len(A_hat)):\n for i in range(len(A_hat[k])):\n # CHECK: if the action is an integer corresponding to the state\n curr_state = torch.tensor(A_hat[k][i][0]).float()\n try:\n curr_state_norm_input = torch.cat((curr_state[:,0].unsqueeze(1), (curr_state[:,1:]-state_part_mean)/state_part_std))\n except:\n curr_state_norm_input = torch.cat((curr_state[0].unsqueeze(0), (curr_state[1:]-state_part_mean)/state_part_std))\n\n curr_state_norm_input[np.isnan(curr_state_norm_input)] = 0\n curr_state_norm_input.to(device)\n curr_action = A_hat[k][i][1]\n\n r_theta_ksi = policy_model(curr_state_norm_input.to(device))[curr_action] / old_policy_model(curr_state_norm_input.to(device))[curr_action] \n r_theta_ksi_num = r_theta_ksi.cpu().detach().numpy()\n single_est_from_r = r_theta_ksi_num-1-np.log(r_theta_ksi_num)\n kl_est_list.append(single_est_from_r)\n A_hat_ksi = A_hat[k][i][2] # Estimate the A_hat\n surr_objective += min(r_theta_ksi * A_hat_ksi, clip(r_theta_ksi, clipping_param) * A_hat_ksi)\n surr_objective /= len(A_hat)\n kl_est = np.mean(np.asarray(kl_est_list))\n\n return surr_objective, kl_est","def _policy_eval(self, policy: np.ndarray) -> np.ndarray:\n V = np.zeros(self.state_dim)\n diff = 1.0\n dr = 0.9\n while (diff >= self.theta):\n diff = 0.0\n for s in self.mdp._state_dict:\n old = V[self.mdp._state_dict[s]]\n temp = 0.0\n for opt in range(self.action_dim):\n if policy[self.mdp._state_dict[s],opt] == 1.0: \n for next_s in self.mdp._state_dict:\n p = self.mdp.P[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\n r = self.mdp.R[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\n Vs = V[self.mdp._state_dict[next_s]]\n temp = temp + p * (r + dr * Vs)\n V[self.mdp._state_dict[s]] = temp\n diff = max(diff,abs(old - V[self.mdp._state_dict[s]]))\n return V","def getSuccessors(self, state):\n\t\tutil.raiseNotDefined()","def termination_heuristics(self, state):\n\n point_state = state[0][0]\n target_mask = get_target_mask(point_state)\n point_state = point_state[:3, target_mask].T\n depth_heuristics = self.graspnet_closure(point_state) \n if (depth_heuristics):\n print('object inside gripper? start retracting...')\n return depth_heuristics","def run_policy(env, policy, scaler, logger, episodes, task_reward_weight, imitation_reward_weight):\n total_steps = 0\n trajectories = []\n for e in range(episodes):\n observes, phases, actions, rewards, unscaled_obs, task_r, imitation_r, imitation_r_logs = run_episode(\n env, policy, scaler, task_reward_weight, imitation_reward_weight)\n total_steps += observes.shape[0]\n trajectory = {'observes': observes,\n 'phases': phases,\n 'actions': actions,\n 'rewards': rewards,\n 'unscaled_obs': unscaled_obs,\n 'task_r': task_r,\n 'imitation_r': imitation_r,\n 'imitation_r_logs': imitation_r_logs}\n trajectories.append(trajectory)\n unscaled = np.concatenate([t['unscaled_obs'] for t in trajectories])\n # update running statistics for scaling observations\n scaler.update(unscaled)\n logger.log({'_MeanReward': np.mean([t['rewards'].sum() for t in trajectories]),\n 'Steps': total_steps})\n\n return trajectories","def policy_improvement(self):\n\n # Set max value to terminal states in order to force actions to them.\n self.V[self.get_index(*self.environment.terminal_state)] = np.max(self.V) + 1\n\n policy_stable = True # Used to check if the Policy has changed or not in this step\n\n for state in range(len(self.V)):\n old_actions_probs = np.copy(self.policy.state_actions_probs[state])\n\n if self.environment.is_wall(*self.get_position(state)):\n self.policy.state_actions_probs[state] = [0, 0, 0, 0]\n else:\n self.policy.state_actions_probs[state] = self.get_greedy_actions(state)\n\n if not np.array_equal(old_actions_probs, self.policy.state_actions_probs[state]):\n policy_stable = False\n\n return self.policy, policy_stable","def train(self, obs, acs, rews_list, next_obs, terminals):\n\n # step 1: calculate q values of each (s_t, a_t) point, \n # using rewards from that full rollout of length T: (r_0, ..., r_t, ..., r_{T-1})\n q_values = self.calculate_q_vals(rews_list)\n\n # step 2: calculate advantages that correspond to each (s_t, a_t) point\n advantage_values = self.estimate_advantage(obs, q_values)\n\n # step 3:\n # TODO: pass the calculated values above into the actor/policy's update, \n # which will perform the actual PG update step\n\n # TODO: define the loss that should be optimized when training a policy with policy gradient\n # HINT1: Recall that the expression that we want to MAXIMIZE\n # is the expectation over collected trajectories of:\n # sum_{t=0}^{T-1} [grad [log pi(a_t|s_t) * (Q_t - b_t)]]\n # HINT2: see define_log_prob (above)\n # to get log pi(a_t|s_t)\n # HINT3: look for a placeholder above that will be populated with advantage values \n # to get [Q_t - b_t]\n # HINT4: don't forget that we need to MINIMIZE this self.loss\n # but the equation above is something that should be maximized\n\n # define the log probability of seen actions/observations under the current policy\n with tf.GradientTape() as tape:\n log_action_probas = self.actor.get_log_prob(obs, acs)\n advantage_values_no_grad = tf.stop_gradient(advantage_values)\n loss = -tf.reduce_mean(advantage_values_no_grad * log_action_probas)\n\n actor_vars = self.actor.trainable_variables\n grads = tape.gradient(loss, actor_vars)\n self.policy_optimizer.apply_gradients(zip(grads, actor_vars))\n\n if self.nn_baseline:\n targets_n = (q_values - np.mean(q_values)) / (np.std(q_values) + 1e-8)\n dataset = tf.data.Dataset.from_tensor_slices(\n (tf.cast(obs, tf.float32), tf.cast(targets_n, tf.float32)))\n dataset = dataset.batch(batch_size=targets_n.shape[0]).repeat()\n # 20 baseline gradient updates with the current data batch.\n self.baseline_model.fit(dataset, epochs=1, steps_per_epoch=20)\n\n return loss.numpy().item()","def rollout(problem, state):\n problem.setState(state)\n while not problem.isTerminal():\n action = random.choice(problem.legalMoves())\n problem.move(action)\n\n return problem.finalScore()","def retrieve_rollout(self, rollout):\n with self.mutex:\n world, scenario = str(rollout.world), str(rollout.scenario)\n # If scenario is done -> discard rollout\n if self.scenario_counter[(world, scenario)] >= self.scenario_samples:\n counter_samples = np.sum(self.scenario_counter.values())\n if counter_samples >= self.batch_size and len(self.buffer) < self.batch_size:\n self.scenario_counter[(world, scenario)\n ] -= (self.batch_size - self.buffer)\n # If scenario is not done -> store rollout data\n else:\n trajectory = zip(rollout.observations, rollout.actions,\n rollout.values, rollout.returns, rollout.advantages)\n for obs, action, value, return_, advantage in trajectory:\n shaped_obs = self.shape_obs(obs)\n self.buffer.append(shaped_obs, action.action, action.logp,\n value, return_, advantage)\n self.scenario_counter[(world, scenario)] += 1\n if self.scenario_counter[(world, scenario)] >= self.scenario_samples:\n self.scenario_idx = (\n self.scenario_idx + 1) % len(self.scenarios)\n break\n self.buffer.episode_rewards.append(np.sum(rollout.rewards))\n self.buffer.episode_lengths.append(rollout.episode_length)","def main():\n print(get_advice())","def learn(self):\n batch = self.agent.replay_buffer.sample(self.batch_size)\n states = torch.tensor([x.state for x in batch], dtype=torch.float32).to(self.agent.device) # shape == (batch_size, 3, 6, 7)\n actions = [x.action for x in batch]\n rewards = torch.tensor([x.reward for x in batch], dtype=torch.float32).to(self.agent.device)\n next_states = torch.tensor([x.next_state for x in batch], dtype=torch.float32).to(self.agent.device)\n dones = [x.done for x in batch]\n\n self.optimizer.zero_grad()\n\n\n q_vals = self.agent.policy_net(states)[range(len(actions)), actions] # Q vals for actions taken\n q_next_vals = self.agent.target_net(next_states).detach() # we don't care about grad wrt target net\n q_next_vals[dones] = 0.0 # terminal states have no future expected value\n q_targets = rewards + self.gamma * torch.max(q_next_vals, dim=1)[0]\n\n # all_q_vals = self.agent.policy_net(states)\n # print()\n # print('actions')\n # print(actions)\n # print()\n # print('original all q vals')\n # print(self.agent.policy_net(states)) \n # print(self.agent.policy_net(states).shape)\n # print()\n # print('QVALS:', q_vals)\n # print(q_vals.shape)\n # print('\\n\\n')\n # print('QTARGETS:', q_targets)\n # print(q_targets.shape)\n\n # breakpoint()\n\n loss = self.loss_fn(q_targets, q_vals).to(self.agent.device)\n loss.backward()\n \n # for layer in self.agent.policy_net.named_parameters():\n \n # # print(f'layer: {layer[0]}')\n # # print(f'grad:', layer[1].grad)\n\n # # print('loss', loss)\n # # print('q_vals grad:', q_vals.grad)\n # # print('states:', )\n\n self.optimizer.step()\n\n self.agent.learning_iters += 1\n if self.agent.learning_iters % self.target_update_freq == 0:\n self.agent.update_target_net()\n # logger.info('Updated target net')","def _rollout_an_episode(self):\n self._steps = 0\n me_id = self._learning_agent_id # short name\n oppo_id = self._oppo_agent_id # short name\n logger.log('episode begins with the task: {}'.format(str(self.task)))\n\n # passing me and oppo hyperparams to the arena interface\n assert self.task.hyperparam is not None\n logger.log('pulling oppo hyperparam of model key {}'.format(\n self.task.model_key2))\n oppo_hyperparam = self._model_pool_apis.pull_attr(attr='hyperparam',\n key=self.task.model_key2)\n logger.log('Done pulling oppo hyperparam')\n oppo_inter_kwargs = ({} if oppo_hyperparam is None\n else oppo_hyperparam.__dict__)\n inter_kwargs = ([self.task.hyperparam.__dict__]\n + [oppo_inter_kwargs] * (self.n_agents - 1))\n\n # agent, env reset\n obs = self.env.reset(inter_kwargs=inter_kwargs)\n for agt, ob in zip(self.agents, obs):\n agt.reset(ob)\n self._update_agents_model(self.task) # for agent Neural Net parameters\n\n me_reward_sum = 0.0\n self.time_beg = time.time()\n self._update_hyperparam(self.task)\n self._changed_task = False\n while True:\n self._steps += 1\n # predictions for each agent\n predictions = self._parallel.run((self._agent_pred, ob, i)\n for i, ob in enumerate(obs))\n me_prediction = predictions[me_id]\n me_action, extra_vars = me_prediction[me_id], me_prediction[oppo_id:]\n # predicted actions for each agent\n actions = [me_action] + [other_action\n for other_action in predictions[oppo_id:]]\n # book-keep obs in previous step\n last_obs = obs\n\n # agent-env interaction\n obs, reward, done, info = self.env.step(actions)\n\n me_rwd_scalar = self._reward_shape(reward[me_id])\n me_reward_sum += me_rwd_scalar\n\n if self._enable_push:\n # put the interested data (obs, rwd, act, ... for each agent) into the\n # _data_queue, which is watched in another Thread (the _push_data_to_learner()\n # method) that the data are dequeued and sent to remote Learner\n if self._data_queue.full():\n logger.log(\"Actor's queue is full.\", level=logger.WARN)\n rwd_to_push = (me_rwd_scalar if self.rwd_shape\n else np.asarray(reward[me_id], np.float32))\n if self.use_oppo_obs:\n if isinstance(extra_vars, tuple):\n extra_vars += (self.agents[self._oppo_agent_id]._last_state,)\n else:\n extra_vars.append(self.agents[self._oppo_agent_id]._last_state)\n data_tuple = (last_obs, tuple(actions), rwd_to_push, info, done, extra_vars)\n self._data_queue.put(data_tuple)\n logger.log('successfully put one tuple.', level=logger.DEBUG)\n\n if self._steps % self._log_interval_steps == 0:\n logger.log('_rollout_an_episode,', 'steps: {},'.format(self._steps),\n 'data qsize: {}'.format(self._data_queue.qsize()))\n\n if done:\n # an episode ends\n if self._replay_dir:\n self._save_replay()\n self.log_kvs(me_reward_sum, info)\n if self._changed_task:\n return None, info\n else:\n return self.log_outcome(info), info\n\n if self._update_model_freq and self._steps % self._update_model_freq == 0:\n # time to update the model for each agent\n if (self._enable_push and\n self._model_pool_apis.pull_attr(\n 'freezetime', self.task.model_key1) is not None):\n # Current task (learning period) finishes, start a new task or continue\n self._finish_task(self.task, None) # notify early abort\n last_task = self.task\n self.task = self._request_task() # try to continue\n if not is_inherit(last_task.model_key1, self.task.model_key1):\n self.log_kvs(me_reward_sum, info)\n return None, info\n if last_task.model_key2 != self.task.model_key2:\n self._changed_task = True\n self._update_agents_model(self.task)","def get_clarifications_winogrande(ex, nlp, comet_model):\n personx, persony = ex['option1'], ex['option2']\n\n # Only extract relations for people\n if personx[0] != personx[0].upper() or persony[0] != persony[0].upper():\n return []\n\n input_event = ex[\"sentence\"]\n outputs = {category: comet_model.predict(input_event, category, num_beams=5) for category in comet_model.categories}\n\n curr_events = []\n for category, prefix in CATEGORY_TO_PREFIX.items():\n for out_event in outputs[category]:\n if out_event != \"none\" and out_event != \"\":\n if not out_event.lower().startswith(\"person\") and not out_event.lower().startswith(\"other\"):\n out_event = \" \".join((prefix, out_event))\n\n out_event = re.sub(\"personx\", personx, out_event, flags=re.I)\n out_event = re.sub(\"person x\", personx, out_event, flags=re.I)\n out_event = re.sub(\"persony\", persony, out_event, flags=re.I)\n out_event = re.sub(\"person y\", persony, out_event, flags=re.I)\n\n question = CATEGORY_TO_QUESTION[category].replace(\"PersonX\", personx)\n curr_events.append((question, out_event))\n\n return curr_events","def frozen_lake_mdp_helper(grid_size=4, p=0.8, is_slippery=True):\n n_action = 4\n\n random_map = generate_random_map(size=grid_size, p=p)\n env = gym.make(\"FrozenLake-v0\", desc=random_map, is_slippery=is_slippery)\n env.reset()\n env.render()\n open_ai_p = env.P\n # print(env.P)\n\n transition_p = np.zeros((n_action, grid_size**2, grid_size**2))\n reward = np.zeros((n_action, grid_size**2, grid_size**2))\n\n for state, state_dict in open_ai_p.items():\n for action, prob_tuple_list in state_dict.items():\n for prob_tuple in prob_tuple_list:\n probability, next_state, r, done = prob_tuple\n\n transition_p[action][state][next_state] += probability\n reward[action][state][next_state] = r*100 - 1\n # # print(r)\n # if probability != 0:\n # print(\"Found\", state, action, probability, next_state, r, done)\n # print(transition_p)\n # # print(reward)\n\n return transition_p, reward, random_map","def define_state_transition_SettlementStatusProcessingAdvice():\n\n state_chart_name = 'FSwiftSettStatusProcessAdviceIn'\n old_state_chart_name = ''\n state_chart = {\n 'Ready': {'Identified': 'Paired',\n 'NotIdentified': 'Unpaired'},\n\n 'Unpaired': {'Identified': 'Paired'},\n\n 'Paired': {'Acknowledge': 'Acknowledged',\n 'NoMatch': 'NotMatched',\n 'Match': 'Matched',\n 'Pending': 'PendingSettlement',\n 'Failing': 'FailingSettlement',\n 'Reject': 'Rejected',\n 'Cancel': 'Cancelled',},\n\n 'Acknowledged': {'NoMatch': 'NotMatched',\n 'Match': 'Matched',\n 'Pending': 'PendingSettlement',\n 'Failing': 'FailingSettlement',\n 'Cancel': 'Cancelled',\n 'Reject': 'Rejected',\n 'AmndCancRequest':'AmendCancelRequested',\n 'AmndCancPending':'AmendCancelPending',\n 'Done':'Processed'},\n\n 'Matched': {'Pending': 'PendingSettlement',\n 'Cancel': 'Cancelled',\n 'Reject': 'Rejected',\n 'Failing': 'FailingSettlement',\n 'AmndCancRequest':'AmendCancelRequested',\n 'AmndCancPending':'AmendCancelPending',\n 'Done':'Processed'\n },\n\n 'FailingSettlement':{'AmndCancRequest':'AmendCancelRequested',\n 'AmndCancPending':'AmendCancelPending',\n 'Reject': 'Rejected',\n 'Cancel': 'Cancelled',\n 'Done':'Processed'},\n\n 'NotMatched': {'Match': 'Matched',\n 'Pending': 'PendingSettlement',\n 'Failing': 'FailingSettlement',\n 'Cancel': 'Cancelled',\n 'Reject': 'Rejected',\n 'AmndCancRequest':'AmendCancelRequested',\n 'AmndCancPending':'AmendCancelPending',\n 'Done':'Processed'},\n\n 'AmendCancelRequested': {'AmndCancComplete':'AmendCancelCompleted',\n 'Done':'Processed'},\n\n 'AmendCancelPending': {'AmndCancComplete':'AmendCancelCompleted',\n 'Done':'Processed'},\n\n 'PendingSettlement': {'Failing': 'FailingSettlement',\n 'Cancel': 'Cancelled',\n 'AmndCancRequest':'AmendCancelRequested',\n 'AmndCancPending':'AmendCancelPending',\n 'Reject': 'Rejected',\n 'Done':'Processed'},\n 'Cancelled' : { 'Done':'Processed'},\n 'AmendCancelCompleted' : { 'Done':'Processed'},\n\n 'Rejected': {'Cancel': 'Cancelled',\n 'Done': 'Processed'}\n\n }\n\n co_string = 'Paired,73,-105;Cancelled,676,329;Not Match,696,-223;Match,1271,173;AmendCancelPending,728,189;Matched,347,-133;Unpaired,-178,-351;Acknowledge,435,-110;Reject,229,538;AmendCancelCompleted,862,90;Rejected,177,338;NotMatched,432,-283;Acknowledged,549,-429;Processed,1072,-131;Pending Settlement,1193,495;PendingSettlement,304,22;FailingSettlement,255,179;Ready,-237,-98;AmendCancelRequested,737,0;'\n #state_charts_list.append({state_chart_name: [state_chart] + [co_string]})\n\n return state_chart_name, state_chart, old_state_chart_name, co_string","def rollout(self, us):\n xs = [self.initialState]\n for m, d, u in zip(self.runningModels, self.runningDatas, us):\n xs.append(m.calc(d, xs[-1], u)[0].copy())\n return xs","def Oozeplorer_Percept(reference):\n self = reference\n self.our_frontier = list()\n self.unsure_moves = list() # Popped moves we didn't want.\n self.ignore_moves = list()\n def knowledge_based_program(percept):\n \"\"\" The agent program\n \"\"\"\n if not self.alive or self.winner:\n return self.location\n # Telling the Dict\n tell_kb(percept, self.location, self.oz_kb)\n local_frontier = _valid_neighbors(self.location, self.some_number)\n self.our_frontier.extend(local_frontier)\n n_count = 0\n for _x in range(len(self.our_frontier)):\n # Update the random moves.\n move = self.our_frontier[n_count]\n if move in self.unsure_moves:\n self.unsure_moves.remove(move)\n if self.explored.has_key(move) and self.explored[move]:\n self.our_frontier.pop(0)\n continue\n # Check for Validity.\n valid_move = ask_kb(percept, move, self)\n if valid_move:\n move = self.our_frontier.pop(n_count)\n self.explored[move] = True\n return move\n else:\n _nmove = self.our_frontier.pop(n_count)\n if not self.explored.has_key(_nmove):\n self.unsure_moves.append(_nmove)\n continue\n move = self.unsure_moves.pop(0)\n self.explored[move] = True\n return move\n return knowledge_based_program","def get_penalty(state, action, winrate_predictor):\n if violate_rule(state, action):\n return -1 \n return 0","def ppo_update(policy_model, policy_optimizer, baseline_model, baseline_optimizer, baseline_criterion,\n ppo_epoch, minibatch_size, obs_n_tensor, log_prob_n_old_tensor, action_n_tensor, rewards_n, mask_n,\n gamma, lam, update_policy=True, update_baseline=True):\n\n ## get number of data\n n_data = obs_n_tensor.shape[0]\n ## get baseline estimations\n baseline_n = baseline_model(obs_n_tensor).detach() ## between -1 and 1\n ## get q values, used for scaling baseline values\n q_n = get_reward_to_go(rewards_n, mask_n, gamma)\n q_n = Tensor(q_n).reshape(-1, 1)\n q_n_mean = q_n.mean()\n q_n_std = q_n.std() + 1e-2\n ## get scaled baseline values\n baseline_n_scaled = baseline_n * q_n_std + q_n_mean\n\n ## get advantage and scaled baseline targets\n ## the adv returned is normalized, baseline_target_scaled is not normalized\n adv_n, baseline_target_n_scaled = get_gae_advantage(rewards_n, baseline_n_scaled, mask_n, gamma, lam)\n adv_n = adv_n.detach()\n\n ## get baseline targets\n baseline_target_n = ((baseline_target_n_scaled - q_n_mean) / q_n_std).detach() ## target now is normalized\n\n ## NOTE MAKE SURE YOU DETACH THINGS OTHERWISE YOU WILL HAVE VERY STRANGE ERRORS\n\n ## for each training iteration, do some epoches\n for i_epoch in range(ppo_epoch):\n ## ppo: shuffle data\n shuffle_indexes = torch.randperm(n_data)\n obs_n_tensor = obs_n_tensor[shuffle_indexes]\n adv_n = adv_n[shuffle_indexes]\n log_prob_n_old_tensor = log_prob_n_old_tensor[shuffle_indexes]\n action_n_tensor = action_n_tensor[shuffle_indexes]\n baseline_target_n = baseline_target_n[shuffle_indexes]\n\n ## after shuffle data, we do the minibatch ppo update\n n_minibatch = int(n_data / minibatch_size)\n for i_minibatch in range(n_minibatch):\n ## get minibatch\n istart = i_minibatch * minibatch_size\n iend = (i_minibatch + 1) * minibatch_size\n obs_m = obs_n_tensor[istart:iend]\n adv_m = adv_n[istart:iend]\n log_prob_old_m = log_prob_n_old_tensor[istart:iend]\n action_m = action_n_tensor[istart:iend]\n baseline_target_m = baseline_target_n[istart:iend]\n\n ## update baseline\n if update_baseline:\n baseline_m = baseline_model(obs_m)\n baseline_optimizer.zero_grad()\n baseline_loss = baseline_criterion(baseline_m, baseline_target_m)\n baseline_loss.backward()\n baseline_optimizer.step()\n\n ## update policy\n ## we need the new policy's log probs so that we can calculate importance sampling term\n if update_policy:\n mu, log_sigma = policy_model(obs_m)\n normal_dist = Normal(mu, log_sigma.exp())\n log_prob_new_m = normal_dist.log_prob(action_m)\n log_prob_new_m = torch.sum(log_prob_new_m, dim=1).reshape(-1, 1)\n ## now we get the importance sampling weight term\n is_term_m = (log_prob_new_m - log_prob_old_m).exp()\n\n ## compute the clipped surrogate\n epsilon = 0.2\n first_term = is_term_m * adv_m\n clipped_term = is_term_m.clamp(1 - epsilon, 1 + epsilon) * adv_m\n obj_term = torch.min(first_term, clipped_term)\n obj_sum = obj_term.sum()\n policy_loss = -obj_sum / n_data\n policy_optimizer.zero_grad()\n policy_loss.backward()\n policy_optimizer.step()","def env_runner(env, policy, num_local_steps, summary_writer):\n last_state = env.reset()\n last_features = policy.get_initial_features()\n length = 0\n rewards = np.zeros(5)\n \n while True:\n terminal_end = False\n rollout = PartialRollout()\n\n for local_step in range(num_local_steps):\n features = policy.features(last_state, *last_features)\n state, reward, terminal, info = env.step(None)\n\n if len(np.shape(terminal)) > 0:\n reward = np.sum(reward, axis=0) / len(terminal)\n state = state[-1]\n terminal = terminal[-1]\n # total_reward = np.zeros_like(reward[0])\n\n # for i, t in enumerate(terminal[:-1]):\n # if t:\n # total_reward += reward[i]\n # else:\n # total_reward += policy.value(state[i], *features)\n\n # if terminal[-1]:\n # total_reward += reward[-1]\n # total_reward /= len(terminal)\n # else:\n # total_reward /= len(terminal[:-1])\n # likelihood *= 1. / len(terminal)\n\n # reward = total_reward\n # state = state[-1]\n # terminal = terminal[-1]\n\n # collect the experience\n # note that the deepcopies seem to be necessary\n rollout.add(\n copy.deepcopy(last_state), \n copy.deepcopy(reward), \n info['weight'], \n terminal, \n copy.deepcopy(last_features))\n \n length += 1\n rewards += reward\n\n last_state = state\n last_features = features\n\n if info:\n summary = tf.Summary()\n for k, v in info.items():\n summary.value.add(tag=k, simple_value=float(v))\n summary_writer.add_summary(summary, policy.global_step.eval())\n summary_writer.flush()\n\n timestep_limit = env.spec.tags.get(\n 'wrapper_config.TimeLimit.max_episode_steps')\n if terminal or length >= timestep_limit:\n terminal_end = True\n if length >= timestep_limit or not env.metadata.get('semantics.autoreset'):\n last_state = env.reset()\n last_features = policy.get_initial_features()\n print(\"Episode finished. Sum of rewards: {}. Length: {}\".format(rewards, length))\n length = 0\n rewards = 0\n break\n\n if not terminal_end:\n rollout.r = policy.value(last_state, *last_features)\n\n # once we have enough experience, yield it\n yield rollout","def rollout(env, acts):\n total_rew = 0\n env.reset()\n steps = 0\n for act in acts:\n _obs, rew, done, _info = env.step(act)\n steps += 1\n total_rew += rew\n if done:\n break\n\n return steps, total_rew","def extractPolicy(self, V):\n\n policy = np.zeros(self.nStates)\n for i in range(self.nStates):\n A = self.helper(i, V)\n best_action = np.argmax(A)\n policy[i] = best_action\n\n return policy","def get_state_action_representation_from_DSL(demos):\n input_demos = demos\n if type(demos) is list:\n input_demos = {'pos' : demos, 'neg' : []}\n X_csr, y = run_all_programs_on_demonstrations(NUM_PROGRAMS, input_demos)\n X = X_csr.todense()\n return np.array(X), X_csr, y","def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n\n # Start with a random (all 0) value function\n V = np.zeros(env.nS)\n \n while True: #any(Vdiff > theta):\n \n delta_V = 0\n\n for i in range(env.nS):\n \n # need to calculate the value of taking each of the available actions\n\n action_val = np.zeros(env.nA)\n\n for a in range(env.nA):\n \n # get transition tuple for this state and action\n tup = env.P[i][a][0]\n \n # calculate the value of this action/state? \n # value = reward + gamma * (prob * V[next_state])\n # error here I think, probability missing\n action_val[a] = tup[0] * (tup[2] + discount_factor * V[tup[1]])\n \n \n Vold = V[i]\n Vnew = np.dot(policy[i],action_val)\n delta_V = max(delta_V,np.abs(Vnew - Vold))\n # get state value by multiplying probability of taking action (policy) by action value\n V[i] = Vnew\n \n #print(action_val)\n #print(policy[i])\n #print(V[i])\n #print(delta_V)\n\n # function only works if I use this delta rule to terminate\n if delta_V < theta:\n break\n return np.array(V)","def result(self, state, action):\n state_after_act = [[0 for i in range(self.col)] for j in range(self.row)]\n for k in action:\n x = k[1][0]\n y = k[1][1]\n if k[0] == \"vaccinate\":\n state_after_act[x][y] = ('I', 1)\n else:\n state_after_act[x][y] = ('Q', 1)\n\n for i in range(self.row):\n for j in range(self.col):\n if state_after_act[i][j] == 0:\n if state[i][j][0] == 'U' or state[i][j][0] == 'I':\n state_after_act[i][j] = state[i][j]\n\n elif state[i][j][0] == 'S':\n if state[i][j][1] == 3:\n state_after_act[i][j] = ('H', 1)\n else:\n if state[i][j][1] == 1:\n state_after_act[i][j] = ('S', 2)\n elif state[i][j][1] == 2:\n state_after_act[i][j] = ('S', 3)\n\n elif state[i][j][0] == 'Q':\n if state[i][j][1] == 2:\n state_after_act[i][j] = ('H', 1)\n else:\n state_after_act[i][j] = ('Q', 2)\n\n elif state[i][j][0] == 'H':\n state_after_act[i][j] = self.healthy(i, j, state,state_after_act)\n state_after_act[i] = tuple(state_after_act[i])\n return tuple(state_after_act)","def process_proposes(self):\n\n # Check which I'm still interested in, could be learned\n accepted = defaultdict(bool)\n for proposal in self.interaction_proposals:\n proposal: Tuple[Guest, str]\n error_prob = .05\n if random.random() < error_prob:\n accepted[proposal] = True\n else:\n if self.knowledge[(proposal[0].role, proposal[1])] >= 0:\n accepted[proposal] = True\n\n self.interaction_proposals = filter(lambda x: accepted[x], self.interaction_proposals)\n # self.interaction_proposals = filter(lambda x: False, self.interaction_proposals)\n\n # self.interaction_proposals = list(self.interaction_proposals)\n\n # Check which the other is still interested in, don't touch\n self.interaction_proposals = filter(lambda x: (self, x[1]) in x[0].interaction_proposals,\n self.interaction_proposals)\n # TODO maybe split this up?\n self.interaction_proposals = list(self.interaction_proposals)\n\n if len(self.interaction_proposals) > 0:\n # Choose a random interaction from the approved ones\n if len(self.interaction_proposals) > 1:\n print(self.unique_id, self.interaction_proposals)\n\n other: Guest\n interaction: str\n other, interaction = random.choice(self.interaction_proposals)\n # print(interaction)\n\n getattr(self.model, interaction)(self, other)\n other.interaction_proposals = []","def transition(rule, sampler, machine, parameters, state, key, σ):\n get_conn_flattened = rule.operator._get_conn_flattened_closure()\n n_conn_from_sections = rule.operator._n_conn_from_sections\n\n @njit4jax(\n (\n jax.core.ShapedArray(σ.shape, σ.dtype),\n jax.core.ShapedArray((σ.shape[0],), σ.dtype),\n )\n )\n def _transition(args):\n # unpack arguments\n v_proposed, log_prob_corr, v, rand_vec = args\n\n log_prob_corr.fill(0)\n sections = np.empty(v.shape[0], dtype=np.int32)\n vp, _ = get_conn_flattened(v, sections)\n\n _choose(vp, sections, rand_vec, v_proposed, log_prob_corr)\n\n # TODO: n_conn(v_proposed, sections) implemented below, but\n # might be slower than fast implementations like ising\n get_conn_flattened(v_proposed, sections)\n n_conn_from_sections(sections)\n\n log_prob_corr -= np.log(sections)\n\n # ideally we would pass the key to python/numba in _choose, initialise a\n # np.random.default_rng(key) and use it to generate random uniform integers.\n # However, numba dose not support np states, and reseeding it's MT1998 implementation\n # would be slow so we generate floats in the [0,1] range in jax and pass those\n # to python\n rand_vec = jax.random.uniform(key, shape=(σ.shape[0],))\n\n σp, log_prob_correction = _transition(σ, rand_vec)\n\n return σp, log_prob_correction","def pack_experience(states, actions, rewards, next_states, dones):\n\n# pdb.set_trace()\n \n return (states.flatten(),\n actions.flatten(),\n rewards,\n next_states.flatten(),\n dones)","def fullWorkflow():\n ptsWf = ProteinTargetSequenceExecutionWorkflow()\n ok = ptsWf.cacheTaxonomy()\n ok = ptsWf.updateUniProtTaxonomy() and ok\n ok = ptsWf.fetchProteinEntityData() and ok\n ok = ptsWf.fetchChemicalReferenceMappingData() and ok\n ok = ptsWf.fetchLigandNeighborMappingData() and ok\n ok = ptsWf.exportFasta() and ok\n ok = ptsWf.createSearchDatabases() and ok\n ok = ptsWf.searchDatabases() and ok\n ok = ptsWf.buildFeatures() and ok\n ok = ptsWf.buildActivityData() and ok\n ok = ptsWf.buildCofactorData() and ok\n ptsWf.resourceCheck()\n return ok","def getPolicy(self, state):\n return self.policy[state]","def slp(self):\n return self.elevcomp","def best_policy(self, U):\n pi = np.zeros((self.nstates),np.int)\n for s in range(self.nstates):\n pi[s] = self.best_action(s,U)\n return pi","def take_one_step(env, policy, state):\n action = sample_action(policy, state)\n new_state, reward, done, _ = env.step(action)\n if (done == True and reward > 0):\n reward = 10.0\n if new_state in hole_states:\n reward = -10.0\n elif not done:\n reward = -1.0\n # print(\"state {0}, new_state {1}, action {2}, reward {3}, done {4}\".format(state, new_state, action, reward, done))\n return action, reward, new_state, done"],"string":"[\n \"def rollout(env, our_policy, expert_policy, num_rollouts, max_steps):\\n returns = []\\n observations = []\\n actions = []\\n for i in range(num_rollouts):\\n # print('iter', i)\\n obs = env.reset()\\n done = False\\n totalr = 0.\\n steps = 0\\n while not done:\\n our_action = our_policy(obs[None,:])\\n expert_action = expert_policy(obs[None,:])\\n observations.append(obs)\\n # Keep the expert action but execute our own action\\n actions.append(expert_action)\\n obs, r, done, _ = env.step(our_action)\\n\\n totalr += r\\n steps += 1\\n # if steps % 100 == 0: print(\\\"%i/%i\\\"%(steps, max_steps))\\n if steps >= max_steps:\\n break\\n returns.append(totalr)\\n return returns, observations, actions\",\n \"def rollout_algorithm(self):\\n states, actions = [], []\\n done = False\\n state = self.env._reset()\\n while not done:\\n supervisor_label = self.supervisor.eval_policy(self.env)\\n action_packed = [supervisor_label, 1]\\n next_state, _, done, _ = self.env._step(action_packed)\\n\\n states.append(state)\\n actions.append(supervisor_label)\\n state = next_state\\n\\n return states, actions\",\n \"def _run_policy(env, policy, num_rollouts):\\n start_states, final_states, goal_states, actions, paths = [], [], [], [], []\\n\\n for i in range(num_rollouts):\\n path = rollout(\\n env,\\n policy,\\n max_path_length=100,\\n animated=False,\\n )\\n obs = path[\\\"observations\\\"]\\n acts = path[\\\"actions\\\"]\\n\\n goal_idx = np.argmax(obs[0, 2:])\\n start_x, start_y = obs[0, 0], obs[0, 1]\\n acts_x, acts_y = acts[:, 0], acts[:, 1]\\n final_x, final_y = obs[len(obs) - 1, 0], obs[len(obs) - 1, 1]\\n goal = env.goals[goal_idx]\\n goal_x, goal_y = goal[0], goal[1]\\n\\n start_states.append(np.array([start_x, start_y]))\\n final_states.append(np.array([final_x, final_y]))\\n goal_states.append(np.array([goal_x, goal_y]))\\n actions.append(np.array([acts_x, acts_y]))\\n paths.append(path)\\n\\n return dict(\\n start_states=np.array(start_states),\\n final_states=np.array(final_states),\\n goal_states=np.array(goal_states),\\n actions=np.array(actions),\\n paths=paths,\\n env=env\\n )\",\n \"def rollout_policy(self, timeSteps, episodeNumber):\\n total_reward = 0\\n curr_state = self.agent.reset()\\n episode_states = []\\n episode_actions = []\\n episode_rewards = []\\n episode_next_states = []\\n episode_return_from_states = []\\n\\n for time in range(timeSteps):\\n action = self.choose_action(curr_state)\\n\\n next_state, reward, done = self.agent.single_word_step(action)\\n # Update the total reward\\n total_reward += reward\\n if done or time >= timeSteps:\\n break\\n # Updating the memory\\n self.agent.chars\\n print(\\\"rollout_policy Implement step : \\\"+ str(time))\\n print(\\\"reward is : {}\\\".format(reward))\\n print(\\\"action is : {}\\\".format(action))\\n curr_state_l = curr_state.tolist()\\n next_state_l = next_state.tolist()\\n if curr_state_l not in episode_states:\\n episode_states.append(curr_state_l)\\n episode_actions.append(action)\\n episode_rewards.append(reward)\\n episode_next_states.append(next_state_l)\\n episode_return_from_states.append(reward)\\n for i in range(len(episode_return_from_states) - 1):\\n episode_return_from_states[i] += reward\\n else:\\n # Iterate through the replay memory and update the final return for all states\\n for i in range(len(episode_return_from_states)):\\n episode_return_from_states[i] += reward\\n curr_state = next_state\\n\\n self.update_memory(episode_states, episode_actions, episode_rewards, episode_next_states,\\n episode_return_from_states)\\n return episode_states, episode_actions, episode_rewards, episode_next_states, episode_return_from_states, total_reward\",\n \"def rollout_policy(node):\\n # NOTE: Use Simple Agent for now\\n agents_obs = node.game_env.get_observations()\\n agents_obs[node.agent_id] = utility.combine_agent_obs_and_memory(node.agent_memory, agents_obs[node.agent_id])\\n actions = node.game_env.act(agents_obs)\\n return actions\",\n \"def rollout_policy_fn(board):\\n # rollout randomly\\n moves, true_moves = board.get_avaiable_moves()\\n action_probs = np.random.rand(len(moves))\\n return zip(true_moves, action_probs)\",\n \"def rollout(pi, env):\\n rollout = defaultdict(list)\\n t = 0\\n ob = (env.reset())\\n features = pi.get_initial_features()\\n for _ in range(2000):\\n rets = pi.compute(ob, features)\\n ac, value, features = rets[0], rets[1], rets[2:]\\n rollout[\\\"obs\\\"].append(ob)\\n rollout[\\\"vs\\\"].append(value)\\n rollout[\\\"actions\\\"].append(ac)\\n ob, rew, done, _ = env.step(ac)\\n rollout[\\\"rs\\\"].append(rew)\\n rollout[\\\"features\\\"].append(features)\\n t += 1\\n if done:\\n break\\n print(\\\"Cur policy: \\\", len(rollout[\\\"obs\\\"]))\\n rollout[\\\"r\\\"] = 0\\n rollout[\\\"terminal\\\"] = True\\n return rollout\",\n \"def rollout_policy_fn(board):\\n # rollout randomly\\n action_probs = np.random.rand(len(board.availables))\\n return zip(board.availables, action_probs)\",\n \"def rollout(env, policy, max_pathlength, animate=False, obfilter=None):\\n init_ob = env.reset()\\n # prev_ob = np.float32(np.zeros(init_ob.shape))\\n zero_ob = np.array([0.0 for _ in range(6)])\\n init_ob = np.concatenate((zero_ob, init_ob[6:]), axis=0)\\n print(\\\"Intial Observation: \\\", init_ob)\\n\\n if not obfilter == None:\\n init_ob = obfilter(init_ob)\\n terminated = False\\n\\n terminal_rewards = []\\n\\n obs = []\\n acs = []\\n ac_dists = []\\n logps = []\\n rewards = []\\n last_ac = np.array([])\\n for _ in range(max_pathlength):\\n if animate:\\n env.render()\\n # state = np.concatenate([init_ob[:6], init_ob[6:]], -1)\\n state = np.array(init_ob)\\n obs.append(state)\\n ac, ac_dist, logp = policy.act(state)\\n # print(type(ac))\\n assert(not np.array_equal(ac, last_ac))\\n last_ac = ac\\n acs.append(ac)\\n ac_dists.append(ac_dist)\\n logps.append(logp)\\n scaled_ac = env.action_space.low + (ac + 1) * (env.action_space.high - env.action_space.low)\\n scaled_ac = np.clip(scaled_ac, env.action_space.low, env.action_space.high)\\n ob, rew, done, info = env.step(scaled_ac)\\n if obfilter: ob = obfilter(ob)\\n rewards.append(rew)\\n terminal_rewards.append(rew)\\n\\n # goal_config, last_goal_config, slave_config, last_slave_config = env.get_all_configs()\\n info = info[0]\\n force_terminate = info[1]\\n assert(len(info)==4, \\\"Info length not 4\\\")\\n goal_config = info[0]\\n last_goal_config= info[1]\\n slave_config = info[2]\\n last_slave_config = info[3]\\n for g in range(100):\\n new_goal_config = goal_config \\n new_goal_config[3] = incAngle(new_goal_config[3])\\n new_goal_config[4] = incAngle(new_goal_config[4])\\n percent_error, new_rew = Utils.calculate_reward(new_goal_config, slave_config, last_slave_config)\\n # new_ob = slave_config + new_goal_config\\n new_ob = np.concatenate((zero_ob, new_goal_config), axis=0)\\n if obfilter: new_ob = obfilter(new_ob)\\n # new_state = np.concatenate([new_ob[:6], new_ob[6:]], -1)\\n new_state = np.array(new_ob)\\n \\n obs.append(new_state)\\n rewards.append(new_rew)\\n acs.append(ac)\\n ac_dists.append(ac_dist)\\n logps.append(logp)\\n\\n if done:\\n terminated = True\\n break\\n # elif force_terminate:\\n # # Bias the data\\n # percent_error, new_rew = Utils.calculate_reward(goal_config, goal_config, last_slave_config)\\n # new_ob = goal_config + goal_config\\n # if obfilter: new_ob = obfilter(new_ob)\\n # # new_state = np.concatenate([new_ob[:6], new_ob[6:]], -1)\\n # new_state = np.array(new_ob)\\n\\n # ac = Bfield.getCurrents(goal_config[:3], goal_config[3:])\\n\\n # scaled_ac = env.action_space.low + (ac + 1) * (env.action_space.high - env.action_space.low)\\n\\n # scaled_ac = np.clip(scaled_ac, env.action_space.low, env.action_space.high)\\n # ob, rew, done, info = env.step(scaled_ac)\\n \\n # obs.append(new_state)\\n # rewards.append(new_rew)\\n # acs.append(ac)\\n # ac_dists.append(ac_dist)\\n # logps.append(logp)\\n\\n\\n prev_ob = np.copy(ob)\\n\\n return {\\\"observation\\\" : np.array(obs), \\\"terminated\\\" : terminated,\\n \\\"reward\\\" : np.array(rewards), \\\"action\\\" : np.array(acs),\\n \\\"action_dist\\\": np.array(ac_dists), \\\"logp\\\" : np.array(logps)}, terminal_rewards\",\n \"def eval_policy(self):\\n states, actions, rewards, supervisor_labels, surrogate_losses = [], [], [], [], []\\n done = False\\n state = self.env._reset()\\n while not done:\\n supervisor_label = self.supervisor.eval_policy(self.env)\\n action = self.learner.eval_policy(state)\\n action_packed = [action, 1]\\n next_state, reward, done, info_dict = self.env._step(action_packed)\\n\\n states.append(state)\\n actions.append(action)\\n rewards.append(reward)\\n supervisor_labels.append(supervisor_label)\\n surrogate_losses.append(action != supervisor_label)\\n state = next_state\\n\\n self.rewards.append(sum(rewards))\\n self.surrogate_losses.append(sum(surrogate_losses))\\n\\n return states, actions, self.rewards, supervisor_labels, self.surrogate_losses\",\n \"def get_pwa_trace(self):\\n # # each step, but the last, corresponds to a transition\\n # for step in steps[:-1]:\\n # part_id = self.sal2pwa_map[step.assignments['cell']]\\n # sub_model = self.sal2pwa_map[step.tid]\\n\\n # # Assumption of trace building is that each submodel only\\n # # has 1 unique next location. If this violated, we need to\\n # # add cell ids/part ids to resolve the ambiguity.\\n # assert(len(sub_model.pnexts) == 1)\\n\\n # assert(sub_model.p.ID == part_id)\\n # # this is still untested, so in case assert is off...\\n # assert(sub_model.p.ID == part_id)\\n # #err.warn('gone case')\\n\\n # #pwa_trace.extend((part_id, sub_model))\\n # pwa_trace.append(sub_model)\\n if self.trace is None:\\n return None\\n steps = self.trace\\n transitions = [step.tid for step in steps[:-1]]\\n return self.pwa2sal.trace(transitions)\",\n \"def rollout_policy_fn(board):\\n # rollout randomly\\n # action_probs = np.random.rand(len(board.availables))\\n action_probs = np.random.rand(len(board.availables))\\n return zip(board.availables, action_probs)\",\n \"def planner_trace(planner: Planner,\\n prob_meta: ProblemMeta,\\n mdpsim_problem: 'mdpsim.Problem',\\n obs: FPGObservation) -> List[StateQVs]:\\n pairs = [] # type: List[StateQVs]\\n props_true = obs.props_true\\n start_state_string = format_state_for_ssipp(props_true)\\n ssipp_problem = planner.problem\\n start_ssipp_state \\\\\\n = ssipp_problem.get_intermediate_state(start_state_string)\\n # not sure how expensive this is, but IIRC not very\\n pol_dict, overflow = planner.extract_policy(start_ssipp_state)\\n if overflow:\\n print(\\\"WARNING: extract_policy exceeded max. returned state count!\\\")\\n prop_re = re.compile(r'(\\\\([^ \\\\)]+(?: [^ \\\\)]+)*\\\\))')\\n for ssipp_state, _ in pol_dict.items():\\n state_str = ssipp_problem.string_repr(ssipp_state)\\n true_bound_props = []\\n # converting props to BoundProps and back like this is probably not\\n # necessary now, but I'm keeping it anyway because it serves as nice\\n # Python-side error checking.\\n for prop_s in prop_re.findall(state_str):\\n bound_prop = prob_meta.bound_prop_by_name(prop_s)\\n true_bound_props.append(bound_prop)\\n mdpsim_state_str = ', '.join(' '.join((p.pred_name, ) + p.arguments)\\n for p in true_bound_props)\\n mdpsim_state = mdpsim_problem.intermediate_atom_state(mdpsim_state_str)\\n new_props_true = mdpsim_problem.prop_truth_mask(mdpsim_state)\\n new_acts_enabled = mdpsim_problem.act_applicable_mask(mdpsim_state)\\n new_obs = FPGObservation(\\n props_true=new_props_true, enabled_actions=new_acts_enabled)\\n\\n # get q-values for enabled actions\\n act_strs = [ba.unique_ident for ba in prob_meta.bound_acts_ordered]\\n q_values = planner.q_values(mdpsim_state_str, act_strs)\\n assert len(act_strs) == len(q_values)\\n qv_tuple = tuple(zip(prob_meta.bound_acts_ordered, q_values))\\n\\n pairs.append((new_obs, qv_tuple))\\n return pairs\",\n \"def rollout(self):\\n self.agent.reset()\\n for _ in range(self._n_paths_per_trial):\\n self.start_rollout()\\n while not self.step_rollout():\\n pass\\n self._agent_infos['batch_idx'] = np.full(len(self._rewards),\\n self._worker_number)\\n return self.collect_rollout()\",\n \"def rollout(\\n env,\\n agent,\\n max_path_length=np.inf,\\n accum_context=True,\\n train=True,\\n animated=False,\\n\\n use_MPC=False,\\n mpc_solver=None,\\n gp_human=None,\\n test=False,\\n pred=None,\\n):\\n\\n observations = []\\n next_observations = []\\n\\n unnormalized_observations = []\\n unnormalized_next_observations = []\\n\\n actions = []\\n rewards = []\\n terminals = []\\n agent_infos = []\\n env_infos = []\\n o, normalized_o = env.reset()\\n next_o = None\\n path_length = 0\\n pred_horizon = env.Nt\\n N_adapt = env.N_adapt\\n\\n # Custom information\\n collisions_human = []\\n collisions_table = []\\n alerts_human = []\\n alerts_table = []\\n reaches = []\\n navi_parts = []\\n col_parts = []\\n\\n if animated:\\n env.render(pred=pred)\\n\\n # Data for obstacle GP\\n prediction = []\\n max_data_size = env.Nt\\n min_data_size = env.Nt // 4\\n solver_opts = {}\\n solver_opts['expand'] = False\\n human = []\\n human_next = []\\n dynamic = []\\n use_GP, no_GP = 0, 0\\n mpc_count, pearl_count = 0, 0\\n\\n # Collision metric\\n alert_radii = 2.\\n \\n # For periodic use of ther interval\\n collision_history = 0\\n goal_history = 0\\n\\n # To use only human data for GP\\n # obs = env.restaurant.table_list\\n\\n while path_length < max_path_length:\\n cond1 = env.goal_event\\n '''\\n if cond1:\\n # cond1 = env.goal_event and (goal_history % env.goal_interval == env.goal_interval - 1)\\n cond1 = env.goal_event and (np.random.rand() < env.eps)\\n goal_history += 1\\n '''\\n # cond2 = (path_length % env.interval == env.interval - 1)\\n cond2 = False\\n cond3 = env.collision_event\\n '''\\n if cond3:\\n # cond3 = env.collision_event and (collision_history % env.col_interval == env.col_interval - 1)\\n cond3 = env.collision_event and (np.random.rand() < env.eps) \\n collision_history += 1\\n '''\\n turn_on_mpc = (cond1 or cond2 or cond3) and use_MPC\\n turn_on_mpc = turn_on_mpc and (np.random.rand() < env.eps)\\n turn_on_mpc = turn_on_mpc or test\\n # turn_on_mpc = turn_on_mpc and env.task_idx < 180\\n # build prediction matrix and initialize\\n pred_cov = 0* np.ones((env.Nt, env.num_people, 2))\\n pred = np.zeros((env.Nt, env.num_people, 2))\\n for t in range(pred.shape[0]):\\n pred[t, :, :] = o[3:3+2*env.num_people].reshape(-1, pred.shape[2])\\n\\n # Build distance from table for safety metric\\n table_dist, alert_table = np.zeros(env.num_tables), np.zeros(env.num_tables)\\n for i, table in enumerate(env.restaurant.table_list):\\n table_dist[i] = np.linalg.norm(table.center - o[:2])\\n if table_dist[i] < alert_radii:\\n alert_table[i] = 1\\n # Adaptive set of table to use (closest ones)\\n adapt_table_idxs = np.argsort(table_dist)[:N_adapt] # [0, 1, 5, 4, 2]\\n adapt_table_list = [env.restaurant.table_list[i] for i in adapt_table_idxs]\\n\\n # Build distance from human for safety metric\\n human_dist, alert_human = np.zeros(env.num_people), np.zeros(env.num_people)\\n for i, human in enumerate(env.restaurant.human_list):\\n human_dist[i] = np.linalg.norm(human.center - o[:2])\\n if human_dist[i] < alert_radii:\\n alert_human[i] = 1\\n # Adaptive set of dynamic obstacle to use (closest ones)\\n adapt_human_idxs = np.argsort(human_dist)[:N_adapt] # [0, 1, 5, 4, 2]\\n\\n # use GP-MPC if it is turned on\\n\\n if env.landed:\\n # If the robot lands on the goal, then do not give a control anymore.\\n # normalized zero control : lower bound of linear velocity = 0\\n a = np.array([0., 0.]) if turn_on_mpc else np.array([-1., 0.])\\n agent_info = {}\\n Xout = None\\n\\n else:\\n if turn_on_mpc:\\n # Use GP to build constraints\\n gp_effective = np.zeros(env.num_people)\\n if len(observations) > min_data_size:\\n '''\\n pred, pred_cov, gp_effective = fit_GP_adaptive(unnormalized_observations,\\n unnormalized_next_observations,\\n pred,\\n pred_cov,\\n max_data_size=max_data_size,\\n min_data_size=min_data_size,\\n num_people=env.num_people,\\n pred_horizon=env.Nt,\\n N_adapt=N_adapt\\n )\\n '''\\n pred, pred_cov, gp_effective = _load_GP_predictions(unnormalized_observations,\\n unnormalized_next_observations,\\n pred,\\n pred_cov,\\n env,\\n agent,\\n max_data_size=10,\\n range_adapt=alert_radii,\\n N_adapt=N_adapt\\n )\\n \\n # if (rand_var <= env.eps and env.event) or (col and use_MPC and test):\\n # Caution! You must pass only the list of static obstacles as an argument to GP-MPC solver\\n # TODO2: Pass GP-generated K-step obstacle prediction to MPC as constraint\\n # print([o.center for o in table_list_new])\\n Xout, a_MPC, flag = mpc_solver.plan_pol(\\n o[:3],\\n env._goal, # Goal position given to MPC\\n *env.cost_weight,\\n env.restaurant.table_radii,\\n env.restaurant.human_radii,\\n env.restaurant.table_list,\\n # adapt_table_list,\\n pred[:, adapt_human_idxs, :],\\n pred_cov[:, adapt_human_idxs, :],\\n env._counter,\\n gp_effective[adapt_human_idxs]\\n )\\n \\n #a_MPC = np.array([0, 0])\\n agent_info = {}\\n if flag == 1 or flag == 0:\\n a = a_MPC\\n mpc_count += 1\\n\\n else:\\n #backup controller (PEARL)\\n # a, agent_info = agent.get_action(o)\\n #backup controller (0)\\n # a, agent_info = agent.get_action(normalized_o)\\n a = a_MPC\\n Xout = None\\n pearl_count += 1\\n\\n else:\\n Xout = None\\n a, agent_info = agent.get_action(normalized_o)\\n\\n prediction.append(pred)\\n \\n # episode roll-out\\n next_o, r, d, a, _, info_custom, env_info, normalized_next_o = env.step(a, train, Xout=Xout, use_MPC=turn_on_mpc)\\n \\n # append custom info\\n collision_human, collision_table, reach, navi_part, col_part = info_custom\\n collisions_human.append(collision_human)\\n collisions_table.append(collision_table)\\n reaches.append(reach)\\n navi_parts.append(navi_part)\\n col_parts.append(col_part)\\n alerts_human.append(np.sum(alert_table))\\n alerts_table.append(np.sum(alert_table))\\n # update the agent's current context\\n human_next.append(next_o[3:3+2*env.num_people].reshape(-1, 2))\\n if accum_context:\\n agent.update_context([normalized_o, a, r, normalized_next_o, d, env_info])\\n # observations.append(o)\\n\\n # for GP-MPC\\n unnormalized_observations.append(o)\\n unnormalized_next_observations.append(next_o)\\n # next_observations.append(next_o)\\n\\n # for PEARL\\n observations.append(normalized_o)\\n next_observations.append(normalized_next_o)\\n rewards.append(r)\\n terminals.append(d)\\n actions.append(a)\\n agent_infos.append(agent_info)\\n path_length += 1\\n o = next_o\\n normalized_o = normalized_next_o\\n if animated:\\n if use_MPC:\\n env.render(pred=prediction)\\n else:\\n env.render(pred=None)\\n\\n env_infos.append(env_info)\\n if d:\\n break\\n\\n \\\"\\\"\\\" Summarize results \\\"\\\"\\\"\\n actions = np.array(actions)\\n if len(actions.shape) == 1:\\n actions = np.expand_dims(actions, 1)\\n observations = np.array(observations)\\n if len(observations.shape) == 1:\\n observations = np.expand_dims(observations, 1)\\n normalized_next_o = np.array([normalized_next_o])\\n next_observations = np.vstack(\\n (\\n observations[1:, :],\\n np.expand_dims(normalized_next_o, 0)\\n )\\n )\\n return dict(\\n observations=observations,\\n actions=actions,\\n rewards=np.array(rewards).reshape(-1, 1),\\n next_observations=next_observations,\\n terminals=np.array(terminals).reshape(-1, 1),\\n agent_infos=agent_infos,\\n env_infos=env_infos,\\n collisions_human=collisions_human,\\n collisions_table=collisions_table,\\n reaches=reaches,\\n navi_parts=np.array(navi_parts).reshape(-1, 1),\\n col_parts=np.array(col_parts).reshape(-1, 1),\\n gp_human=(use_GP, no_GP),\\n pred=pred,\\n alerts_human=alerts_human,\\n alerts_table=alerts_table,\\n mpc_count=[mpc_count],\\n pearl_count=[pearl_count],\\n )\",\n \"def rollout(self, best_goals, horizon=50, save_path=None):\\n temp_copy = copy.deepcopy(self.env.world_state)\\n # only require historical world_state 1 timestep ago\\n temp_copy['historical_world_state'] = {}\\n self.env.world_state['historical_world_state'] = temp_copy\\n \\n reward_mapping = {}\\n action_mapping = {}\\n for agent in best_goals:\\n reward_mapping[agent] = best_goals[agent][1]['rewards']\\n action_mapping[agent] = (\\n best_goals[agent][0],\\n best_goals[agent][1]['steps'][0]\\n )\\n if type(best_goals[agent][1]['steps'][0]) == int:\\n agent.last_action = str(best_goals[agent][1]['steps'][0])\\n else:\\n agent.last_action = best_goals[agent][1]['steps'][0][0]\\n for agent in action_mapping:\\n self.env.world_state['historical_actions'][agent.id] = [action_mapping[agent][1]]\\n\\n print(action_mapping)\\n print('@rollout - Starting step function')\\n print([agent.last_action for agent in self.env.world_state['agents']])\\n self.env.step(action_mapping, reward_mapping)\\n\\n # print(f'Historical World State')\\n # print(self.env.world_state['historical_world_state'])\\n\\n explicit_chop_rewards = self.env.world_state['explicit_rewards']['chop']\\n explicit_cook_rewards = self.env.world_state['explicit_rewards']['cook']\\n explicit_serve_rewards = self.env.world_state['explicit_rewards']['serve']\\n print(f'Current EXPLICIT chop rewards: {explicit_chop_rewards}')\\n print(f'Current EXPLICIT cook rewards: {explicit_cook_rewards}')\\n print(f'Current EXPLICIT serve rewards: {explicit_serve_rewards}')\\n\\n if not self.is_simulation:\\n self.info_df = self.update_experiment_results(self.info_df)\\n pg.image.save(self.screen, self.images_folder+f'/episode_{self.env.episode}.png')\",\n \"def opt_pol_envelope(self, obs: FPGObservation) -> List[StateQVs]:\\n return planner_trace(self.planner, self.problem_meta,\\n self.p.mdpsim_problem, obs)\",\n \"def forward_rl_solve(\\n env, r_inferred, r_task, r_true, inferred_weight, latent_space, iterations\\n):\\n env_task = LatentSpaceRewardWrapper(env, latent_space, None, r_task, r_true, 0)\\n ppo_task = PPO2(MlpPolicy, DummyVecEnv([lambda: env_task]), verbose=1)\\n ppo_task.learn(total_timesteps=iterations, log_interval=10)\\n\\n env_inferred = LatentSpaceRewardWrapper(\\n env, latent_space, r_inferred, None, r_true, 1\\n )\\n ppo_inferred = PPO2(MlpPolicy, DummyVecEnv([lambda: env_inferred]), verbose=1)\\n ppo_inferred.learn(total_timesteps=iterations, log_interval=10)\\n\\n env = LatentSpaceRewardWrapper(\\n env, latent_space, r_inferred, r_task, r_true, inferred_weight\\n )\\n ppo = PPO2(MlpPolicy, DummyVecEnv([lambda: env]), verbose=1)\\n ppo.learn(total_timesteps=iterations, log_interval=10)\\n\\n N = 10\\n print()\\n print(\\\"Random policy:\\\")\\n ret_rand = print_rollout_latent(env, None, N, random=True)\\n print(\\\"Avg. Return\\\", ret_rand)\\n print()\\n print(\\\"Policy trained only on task:\\\")\\n ret_task = print_rollout_latent(env, ppo_task, N)\\n print(\\\"Avg. Return\\\", ret_task)\\n print()\\n print(\\\"Policy trained only on inferred:\\\")\\n ret_inferred = print_rollout_latent(env, ppo_inferred, N)\\n print(\\\"Avg. Return\\\", ret_inferred)\\n print()\\n print(\\\"Policy trained on task + inferred:\\\")\\n ret = print_rollout_latent(env, ppo, N)\\n print(\\\"Avg. Return\\\", ret)\\n print()\\n print(\\\"Random policy return\\\", ret_rand)\\n print(\\\"Task-only policy return\\\", ret_task)\\n print(\\\"Inferred-only policy return\\\", ret_inferred)\\n print(\\\"Task+Inferred policy return\\\", ret)\\n return ret\",\n \"def logits_expert_is_high(\\n self,\\n state: th.Tensor,\\n action: th.Tensor,\\n next_state: th.Tensor,\\n done: th.Tensor,\\n log_policy_act_prob: Optional[th.Tensor] = None,\\n ) -> th.Tensor:\\n del log_policy_act_prob\\n logits = self._reward_net(state, action, next_state, done)\\n assert logits.shape == state.shape[:1]\\n return logits\",\n \"def rollout(leaf, depth):\\n if depth <= 0:\\n return 0\\n\\n total_reward = 0\\n prev_state = leaf\\n\\n for i in range(depth):\\n cur_state = prev_state.copy()\\n agent_actions = rollout_policy(cur_state)\\n agents_obs, _, done, _ = cur_state.game_env.step(agent_actions)\\n\\n # After making a move, update the memory kept on this node\\n cur_state.agent_memory = utility.update_agent_memory(cur_state.agent_memory,\\n agents_obs[cur_state.agent_id])\\n\\n reward = decide_reward(prev_state, cur_state)\\n total_reward += reward\\n\\n prev_state = cur_state\\n\\n if done:\\n break\\n\\n return total_reward\",\n \"def _policy_improvement(self) -> Tuple[np.ndarray, np.ndarray]:\\n # Start with a (random) policy\\n policy = np.zeros([self.state_dim, self.action_dim])\\n V = np.zeros([self.state_dim])\\n #random init the policy\\n for s in range(self.state_dim):\\n policy[s,0] = 0.0\\n policy[s,1] = 0.0\\n policy[s,2] = 1.0\\n\\n V = self._policy_eval(policy)\\n\\n policy_stable = False\\n dr = 0.9\\n\\n while (policy_stable != True):\\n policy_stable = True\\n for s in self.mdp._state_dict:\\n old_action = (policy[self.mdp._state_dict[s]]).tolist()\\n action_dict = {}\\n for a in self.mdp._action_dict:\\n temp = 0.0\\n for next_s in self.mdp._state_dict:\\n p = self.mdp.P[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\\n r = self.mdp.R[self.mdp._state_dict[s],self.mdp._action_dict[a],self.mdp._state_dict[next_s]]\\n Vs = V[self.mdp._state_dict[next_s]]\\n temp = temp + p * (r + dr * Vs)\\n action_dict[self.mdp._action_dict[a]]= temp \\n max_act = max(action_dict.values())\\n V[self.mdp._state_dict[s]] = max_act\\n res = [t for t,v in action_dict.items() if v == max_act][0]\\n for opt in range(self.action_dim):\\n if opt == res:\\n policy[self.mdp._state_dict[s],opt] = 1.0\\n else:\\n policy[self.mdp._state_dict[s],opt] = 0.0\\n if (old_action - policy[self.mdp._state_dict[s]]).any() == True:\\n \\n policy_stable = False\\n if policy_stable == False:\\n V = self._policy_eval(policy)\\n \\n return policy, V\",\n \"def extract_policy_envelope(self, cstate):\\n pass\",\n \"def collect_rollouts(\\n self,\\n env: VecEnv,\\n callback: BaseCallback,\\n train_freq: TrainFreq,\\n replay_buffer: ReplayBuffer,\\n action_noise: Optional[ActionNoise] = None,\\n learning_starts: int = 0,\\n log_interval: Optional[int] = None,\\n ) -> RolloutReturn:\\n episode_rewards, total_timesteps = [], []\\n num_collected_steps, num_collected_episodes = 0, 0\\n\\n assert isinstance(env, VecEnv), \\\"You must pass a VecEnv\\\"\\n assert env.num_envs == 1, \\\"OffPolicyAlgorithm only support single environment\\\"\\n assert train_freq.frequency > 0, \\\"Should at least collect one step or episode.\\\"\\n\\n if self.use_sde:\\n self.actor.reset_noise()\\n\\n callback.on_rollout_start()\\n continue_training = True\\n\\n while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):\\n done = False\\n episode_reward, episode_timesteps = 0.0, 0\\n\\n while not done:\\n\\n if self.use_sde and self.sde_sample_freq > 0 and num_collected_steps % self.sde_sample_freq == 0:\\n # Sample a new noise matrix\\n self.actor.reset_noise()\\n\\n # AF\\n action = self.plan[0]\\n self.episode_forecast.append(self.forecasts[0])\\n buffer_action = action # sb needs this\\n # AF END\\n\\n # Rescale and perform action\\n new_obs, reward, done, infos = env.step([action])\\n\\n self.num_timesteps += 1\\n episode_timesteps += 1\\n num_collected_steps += 1\\n\\n # Give access to local variables\\n callback.update_locals(locals())\\n # Only stop training if return value is False, not when it is None.\\n if callback.on_step() is False:\\n return RolloutReturn(0.0, num_collected_steps, num_collected_episodes, continue_training=False)\\n\\n episode_reward += reward\\n\\n # Retrieve reward and episode length if using Monitor wrapper\\n self._update_info_buffer(infos, done)\\n\\n # Store data in replay buffer (normalized action and unnormalized observation)\\n self._store_transition(replay_buffer, buffer_action, new_obs, reward, done, infos)\\n\\n self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)\\n\\n # For DQN, check if the target network should be updated\\n # and update the exploration schedule\\n # For SAC/TD3, the update is done as the same time as the gradient update\\n # see https://github.com/hill-a/stable-baselines/issues/900\\n self._on_step()\\n\\n # AF\\n self.plan, self.forecasts = self._replan(self._last_obs[0], self.plan, self.forecasts) # self._last_obs was updated in _store_transition\\n\\n if not should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):\\n break\\n\\n if done:\\n num_collected_episodes += 1\\n self._episode_num += 1\\n episode_rewards.append(episode_reward)\\n total_timesteps.append(episode_timesteps)\\n\\n if action_noise is not None:\\n action_noise.reset()\\n\\n # Log training infos\\n if log_interval is not None and self._episode_num % log_interval == 0:\\n self._dump_logs()\\n\\n self._af_on_done()\\n\\n mean_reward = np.mean(episode_rewards) if num_collected_episodes > 0 else 0.0\\n\\n callback.on_rollout_end()\\n\\n return RolloutReturn(mean_reward, num_collected_steps, num_collected_episodes, continue_training)\",\n \"def update(self):\\n with torch.no_grad():\\n self.preprocess_rollout()\\n \\n # DEEP-RL TUTORIALS: КОСТЫЛЬ\\n #self.advantages = self.returns[:-1] - self.values[:-1]\\n #self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-5)\\n \\n # going through rollout several (config.epochs) times:\\n for epoch in range(self.config.epochs):\\n # TODO: drop last = False? What if there is 1 sample?\\n sampler = BatchSampler(SubsetRandomSampler(range(self.env.num_envs * self.config.rollout)), self.config.batch_size, drop_last=False)\\n \\n for indices in sampler:\\n # retrieving new batch as part of rollout\\n self.returns_b = self.returns.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_values_b = self.values.view(-1, *self.config.value_repr_shape)[indices]\\n self.old_action_log_probs_b = self.action_log_probs.view(-1)[indices]\\n #self.advantages_b = self.advantages.view(-1)[indices] # КОСТЫЛЬ\\n \\n # calculating current value, action_log_prob, entropy\\n dist, self.values_b = self.policy(self.observations.view(-1, *self.config.observation_shape)[indices])\\n self.values_b = self.values_b.squeeze() # IMPORTANT ([32] - [32, 1] problem)\\n self.action_log_probs_b = dist.log_prob(self.actions.view(-1, *self.config.actions_shape)[indices])#.sum(dim=-1) \\n self.entropy_b = dist.entropy()#.sum(dim=-1)\\n \\n # performing step\\n self.gradient_ascent_step()\",\n \"def policy(agent):\",\n \"def getPolicy(self, state):\\n \\\"*** YOUR CODE HERE ***\\\"\\n possibleActions = self.mdp.getPossibleActions(state)\\n if len(possibleActions) == 0: return None\\n results = []\\n for action in possibleActions:\\n total = 0\\n for (nextState, prob) in self.mdp.getTransitionStatesAndProbs(state,action):\\n total += (prob * self.values[nextState])\\n results.append(total)\\n maxIndex = max(enumerate(results), key=lambda x: x[1])[0]\\n #print(\\\"here\\\")\\n return possibleActions[maxIndex]\",\n \"def _env_runner(env, policy, num_local_steps, horizon, obs_filter):\\n last_observation = obs_filter(env.reset())\\n try:\\n horizon = horizon if horizon else env.spec.tags.get(\\n \\\"wrapper_config.TimeLimit.max_episode_steps\\\")\\n except Exception:\\n print(\\\"Warning, no horizon specified, assuming infinite\\\")\\n if not horizon:\\n horizon = 999999\\n if hasattr(policy, \\\"get_initial_features\\\"):\\n last_features = policy.get_initial_features()\\n else:\\n last_features = []\\n features = last_features\\n length = 0\\n rewards = 0\\n rollout_number = 0\\n\\n while True:\\n terminal_end = False\\n rollout = PartialRollout(extra_fields=policy.other_output)\\n\\n for _ in range(num_local_steps):\\n action, pi_info = policy.compute(last_observation, *last_features)\\n if policy.is_recurrent:\\n features = pi_info[\\\"features\\\"]\\n del pi_info[\\\"features\\\"]\\n observation, reward, terminal, info = env.step(action)\\n observation = obs_filter(observation)\\n\\n length += 1\\n rewards += reward\\n if length >= horizon:\\n terminal = True\\n\\n # Concatenate multiagent actions\\n if isinstance(action, list):\\n action = np.concatenate(action, axis=0).flatten()\\n\\n # Collect the experience.\\n rollout.add(observations=last_observation,\\n actions=action,\\n rewards=reward,\\n terminal=terminal,\\n features=last_features,\\n **pi_info)\\n\\n last_observation = observation\\n last_features = features\\n\\n if terminal:\\n terminal_end = True\\n yield CompletedRollout(length, rewards)\\n\\n if (length >= horizon or\\n not env.metadata.get(\\\"semantics.autoreset\\\")):\\n last_observation = obs_filter(env.reset())\\n if hasattr(policy, \\\"get_initial_features\\\"):\\n last_features = policy.get_initial_features()\\n else:\\n last_features = []\\n rollout_number += 1\\n length = 0\\n rewards = 0\\n break\\n\\n if not terminal_end:\\n rollout.last_r = policy.value(last_observation, *last_features)\\n\\n # Once we have enough experience, yield it, and have the ThreadRunner\\n # place it on a queue.\\n yield rollout\",\n \"def gen_policy_rollout(self, observations, dynam_model):\\n # initialize statespace array in pytorch tensor -> need that good good gradient!\\n # raise NotImplementedError(\\\"Need to implement the rollouts with tensors, or maybe not for gradients\\\")\\n\\n # for sampling the state progression\\n norm_dist = torch.distributions.Normal(0, 1)\\n\\n # for storing the costs and gradients\\n costs = torch.Tensor()\\n baselines = torch.Tensor()\\n log_probabilities = torch.Tensor()\\n states = torch.Tensor()\\n ''' \\n # for all of these values, think a row as an updating time series for each particle\\n costs = torch.zeros((self.P, self.T))\\n baselines = torch.zeros((self.P, self.T))\\n probabilities = torch.zeros((self.P, self.T))\\n\\n # This is what we would do for the states, but it's more efficient to concatenate them\\n states = torch.Tensor((self.P,self.T, self.n_in))\\n '''\\n\\n # Change in state vs raw state key\\n pred_key = dynam_model.change_state_list\\n # print(pred_key)\\n\\n # TODO: Generate initial states for each particle based on the distribution of data it was trained on\\n bound_up = torch.Tensor(np.max(observations, 0))\\n bound_low = torch.Tensor(np.min(observations, 0))\\n\\n obs_dist = torch.distributions.uniform.Uniform(bound_low, bound_up)\\n\\n # iterate through each particle for the states and probabilites for gradient\\n for p in range(self.P):\\n\\n # Choose the dynamics model from the ensemble\\n num_ens = dynam_model.E\\n if num_ens == 0:\\n model = dynam_model\\n else:\\n model_idx = random.randint(0, num_ens - 1)\\n model = dynam_model.networks[model_idx]\\n\\n num_obs = np.shape(observations)[1]\\n # x0 = torch.Tensor(observations[random.randint(0,num_obs),:])\\n x0 = obs_dist.sample()\\n\\n # TODO: Normalize the states before passing into the NN\\n\\n state_mat = x0.view((1, 1, -1))\\n # print(state_mat)\\n # state_mat = x0.unsqueeze(0).unsqueeze(0) # takes a (n_in) vector to a (1,1,n_in) Tensor\\n # torch.cat((), axis = 1) to cat the times\\n # torch.cat((), axis = 0) to cat the particle\\n # is there a way to do this without unsqueeze? Seems like the most efficient way\\n log_prob_vect = torch.Tensor([1]) # states the column with 1 for concat'ing\\n\\n for t in range(self.T):\\n # generate action from the policy\\n action = self.forward(state_mat[0, t, :])\\n # print(action)\\n # quit()\\n\\n # forward pass current state to generate distribution values from dynamics model\\n means, var = model.distribution(state_mat[0, t, :], action)\\n # print(var)\\n # sample the next state from the means and variances of the state transition probabilities\\n vals = var * norm_dist.sample((1, self.n_in)) + means\\n # need to account for the fact that some states are change in and some are raw here\\n\\n # batch mode prob calc\\n log_probs = -.5 * torch.abs(vals - means) / var\\n # log_probs = -.5*torch.abs(vals - means)/(var**2)\\n\\n # for s in range(self.n_in):\\n # # sample predicted new state for each element\\n # val = var[s]*np.random.normal()+means[0] # sample from the scaled gaussian with y = sigma*x + mu\\n\\n # # calculate probability of this state for each sub state\\n # p = -.5*(val-means[0])/var[0]\\n # states = torch.cat((states, state), 0)\\n # probabilities = torch.cat((probabilities, p), 0)\\n\\n # reduce the probabilities vector to get a single probability of the state transition\\n log_prob = torch.sum((log_probs), 1)\\n log_prob_vect = torch.cat((log_prob_vect, log_prob))\\n\\n state = torch.Tensor(vals).view((1, 1, -1))\\n\\n state_mat = torch.cat((state_mat, state),\\n 1) # appends the currnt state to the current particle, without overwriting the otherone\\n\\n # print(state_mat)\\n # calculates costs\\n # idea ~ calculate the cost of each each element and then do an cumulative sum for the costs\\n # use torch.cumsum\\n c_list = []\\n for state in state_mat.squeeze():\\n c_row = self.cost_fnc(state)\\n c_list.append(c_row)\\n c_list = torch.stack(c_list)\\n\\n # note we calc the cum sum on the flipped tensor, then flip costs back\\n cost_cum = torch.cumsum(torch.flip(c_list, [0]), 0)\\n # Assembles the arrays for the current particle\\n\\n # costs were given above\\n costs = torch.cat((costs, torch.flip(cost_cum, [0]).view(1, -1)), 0)\\n\\n # update the states array for each particle\\n states = torch.cat((states, state_mat), 0)\\n\\n # concatenates the vector of prob at each time to the 2d array\\n log_probabilities = torch.cat(\\n (log_probabilities, log_prob_vect.view((1, -1))), 0)\\n\\n # calculates baselines as the leave one out mean for each particle at each time\\n costs_summed = torch.sum(costs, 0)\\n costs_summed_exp = costs_summed.expand_as(costs)\\n costs_leave_one_out = costs_summed_exp - costs\\n baselines = costs_leave_one_out / (self.P - 1)\\n\\n # freezes gradients on costs and baselines\\n # these two lines of code actually do nothing, but are for clarification\\n # costs.requires_grad_(requires_grad=False)\\n # baselines.requires_grad_(requires_grad=False)\\n # . detach() is another way to ensure this\\n \\\"\\\"\\\"\\n RuntimeError: you can only change requires_grad flags of leaf variables. If you want to use a \\n computed variable in a subgraph that doesn't require differentiation use var_no_grad = var.detach().\\n \\\"\\\"\\\"\\n costs_d = costs.detach()\\n baselines_d = baselines.detach()\\n # costs_d = costs.requires_grad_(False)\\n # baselines_d = baselines.requires_grad_(False)\\n # print(baselines)\\n # print(probabilities)\\n # print(costs)\\n # print(log_probabilities)\\n return states, log_probabilities, costs_d, baselines_d\",\n \"def eval_policy(env, policy, obs_key, goal_key, num_rollouts, limit, pos=None, goal=None, filename=None):\\n from tqdm import trange\\n from ml_logger import logger\\n\\n logger.summary_cache.clear()\\n if filename:\\n frames = []\\n\\n for _ in trange(num_rollouts, desc=\\\"evaluate\\\"):\\n\\n obs, done = env.reset(), False\\n if pos is not None or goal is not None:\\n obs = env.unwrapped.reset(pos, goal)\\n # d2goal = l2(obs['x'], obs['goal'])\\n i, R = 0, 0\\n while not done:\\n if filename:\\n _ = np.concatenate([obs[obs_key], obs[goal_key]], axis=-1)[0]\\n frames.append(_) # , obs['goal_img']\\n action = policy(obs[obs_key], obs[goal_key])\\n obs, reward, done, info = env.step(action)\\n R += reward\\n if info['success'] or limit and i >= limit:\\n logger.store_metrics( # d2goal=d2goal,\\n success=info['success'])\\n break\\n i += 1\\n\\n if filename:\\n logger.log_video(np.array(frames), key=filename)\\n\\n logger.print(f'has finished evaluation.')\",\n \"def generate_rollouts(self, ex_init=None, record=False, random=False, log_hit_time=False):\\n if not self.active:\\n return\\n self.reset_all_rollouts(ex_init, record=record)\\n\\n # compute observations\\n o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations\\n ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals\\n o[:] = self.initial_o\\n ag[:] = self.initial_ag\\n\\n qpos = np.empty((self.rollout_batch_size, self.dims['qpos']), np.float32)\\n qvel = np.empty((self.rollout_batch_size, self.dims['qvel']), np.float32)\\n\\n qpos[:] = self.initial_qpos\\n qvel[:] = self.initial_qvel\\n\\n num_envs = self.venv.num_envs\\n\\n random_action = self.policy._random_action(num_envs)\\n\\n reached_goal = [False] * num_envs\\n hit_time = [None] * num_envs\\n\\n if random:\\n self.exploration = 'random'\\n else:\\n self.exploration = 'eps_greedy' # 'go'\\n\\n # generate episodes\\n obs, achieved_goals, acts, goals, successes = [], [], [], [], []\\n dones = []\\n info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]\\n Qs, qposes, qvels, hit_times = [], [], [], []\\n\\n for t in range(self.T):\\n policy_output = self.policy.get_actions(\\n o, ag, self.g,\\n compute_Q=self.compute_Q,\\n noise_eps=self.noise_eps if not self.exploit else 0.,\\n random_eps=self.random_eps if not self.exploit else 0.,\\n use_target_net=self.use_target_net,\\n exploration=self.exploration,\\n go=np.logical_not(reached_goal),\\n random_action=random_action,\\n )\\n\\n if self.compute_Q:\\n u, Q = policy_output\\n Qs.append(Q)\\n else:\\n u = policy_output\\n\\n if u.ndim == 1:\\n # The non-batched case should still have a reasonable shape.\\n u = u.reshape(1, -1)\\n\\n # compute new states and observations\\n obs_dict_new, _, done, info = self.venv.step(u)\\n o_new = obs_dict_new['observation']\\n ag_new = obs_dict_new['achieved_goal']\\n\\n qpos_new = obs_dict_new['qpos']\\n qvel_new = obs_dict_new['qvel']\\n\\n success = np.array([i.get('is_success', 0.0) for i in info])\\n\\n for e_idx, (suc, ht) in enumerate(zip(success, hit_time)):\\n if suc and hit_time[e_idx] is None:\\n hit_time[e_idx] = t\\n\\n reached_goal = [hit is not None for hit in hit_time]\\n\\n if any(done):\\n # here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done\\n # trick with using vecenvs is not to add the obs from the environments that are \\\"done\\\", because those are already observations\\n # after a reset\\n break\\n\\n for i, info_dict in enumerate(info):\\n for idx, key in enumerate(self.info_keys):\\n info_values[idx][t, i] = info[i][key]\\n\\n if np.isnan(o_new).any():\\n self.logger.warn('NaN caught during rollout generation. Trying again...')\\n self.reset_all_rollouts()\\n return self.generate_rollouts()\\n\\n dones.append(done)\\n obs.append(o.copy())\\n qposes.append(qpos.copy())\\n qvels.append(qvel.copy())\\n achieved_goals.append(ag.copy())\\n successes.append(success.copy())\\n acts.append(u.copy())\\n goals.append(self.g.copy())\\n o[...] = o_new\\n ag[...] = ag_new\\n qpos[...] = qpos_new\\n qvel[...] = qvel_new\\n obs.append(o.copy())\\n achieved_goals.append(ag.copy())\\n qposes.append(qpos.copy())\\n qvels.append(qvel.copy())\\n\\n episode = dict(o=obs,\\n u=acts,\\n g=goals,\\n ag=achieved_goals,\\n qpos=qposes,\\n qvel=qvels,\\n # t=Ts\\n )\\n\\n if self.compute_Q:\\n episode[\\\"Qs\\\"] = Qs\\n\\n for key, value in zip(self.info_keys, info_values):\\n episode['info_{}'.format(key)] = value\\n\\n # stats\\n if self.exploration != 'random':\\n if self.exploration in ['go_explore', 'go']:\\n successful = np.asarray([1 if hit is not None else 0 for hit in hit_time])\\n elif self.exploration in ['eps_greedy']:\\n successful = np.array(successes)[-1, :]\\n assert successful.shape == (self.rollout_batch_size,)\\n success_rate = np.mean(successful)\\n self.success_history.append(success_rate)\\n\\n hit_times = np.asarray([hit if hit is not None else 0 for hit in hit_time])\\n if log_hit_time:\\n hit_time_mean = np.mean(hit_times)\\n hit_time_std = np.std(hit_times)\\n self.hit_time_mean_history.append(hit_time_mean)\\n self.hit_time_std_history.append(hit_time_std)\\n\\n if self.compute_Q:\\n self.Q_history.append(np.mean(Qs))\\n self.n_episodes += self.rollout_batch_size\\n\\n return convert_episode_to_batch_major(episode)\",\n \"def rollout_fn(u_vec: np.ndarray):\\n obs_vec, rew_vec, done_vec, _ = sim_env.rollout(u_vec.copy()) #state_vec\\n return -1.0*rew_vec #we assume environment returns rewards\",\n \"def getPolicy(self, state):\\n \\\"*** YOUR CODE HERE ***\\\"\\n # OUR CODE HERE\\n possibleActions = self.mdp.getPossibleActions(state)\\n #checking for terminal state (no possible actions)\\n if len(possibleActions) is 0: \\n return None\\n \\n #attempt at using the Counter\\n eValsActions = util.Counter()\\n for action in possibleActions:\\n for transitionState, probability in self.mdp.getTransitionStatesAndProbs(state, action):\\n eValsActions[action] += probability * (self.mdp.getReward( state, action, transitionState) + self.discount * self.values[transitionState])\\n \\n return eValsActions.argMax()\\n \\n #fail attempt using lists :(\\n \\\"\\\"\\\"\\n #list to hold the expected value of the actions\\n eValsActions = []\\n #iterate through all actions and their transtion states\\n for action in possibleActions:\\n for transitionState, probability in self.mdp.getTransitionStatesAndProbs(state, action):\\n #expected value of reward with discount * the value of the transitions\\n eValsActions[action] += probability * (self.mdp.getReward( state, action, transitionState) + self.discount * self.values[transitionState])\\n \\n #now iterate through and find the action with the best value\\n #(that will be the best action)\\n maxVal = -float(\\\"inf\\\")\\n bestAction = None\\n for action in possibleActions:\\n if eValsActions[action] > maxVal:\\n maxVal = eValsAction[action]\\n bestAction = action\\n \\\"\\\"\\\"\\n return action\\n # END OUR CODE\",\n \"def getPolicy(self, state):\\n \\\"\\\"\\\"Description:\\n Find all of q-values of current state, and choose the action \\n with the hight q-value as optimal policy\\n \\\"\\\"\\\"\\n \\\"\\\"\\\" YOUR CODE HERE \\\"\\\"\\\"\\n legalActions = self.getLegalActions(state)\\n action = None\\n policy = util.Counter() # use counter to store action and its q-value\\n \\n if len(legalActions) == 0:\\n return action\\n \\n for a in legalActions:\\n policy[a] = self.getQValue(state, a)\\n action = policy.argMax()\\n return action\\n\\n \\\"\\\"\\\" END CODE \\\"\\\"\\\"\",\n \"def step(self, observation, last_state):\\n # We are omitting the details of network inference here.\\n # ...\\n feature_screen = observation[3]['feature_screen']\\n feature_minimap = observation[3]['feature_minimap']\\n feature_units = observation[3]['feature_units']\\n feature_player = observation[3]['player']\\n available_actions = observation[3]['available_actions']\\n score_by_category = observation[3]['score_by_category']\\n game_loop = observation[3]['game_loop']\\n\\n unit_type = feature_screen.unit_type\\n empty_space = np.where(unit_type == 0)\\n empty_space = np.vstack((empty_space[0], empty_space[1])).T\\n random_point = random.choice(empty_space)\\n #target = [random_point[0], random_point[1]]\\n #action = [actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target])]\\n policy_logits = None\\n new_state = None\\n\\n spatial_encoder_output = self.spatial_encoder(np.reshape(feature_screen, [1,128,128,27]))\\n\\n agent_statistics = get_agent_statistics(score_by_category)\\n\\n home_race = 'Terran'\\n away_race = 'Terran'\\n race = get_race_onehot(home_race, away_race)\\n\\n time = get_gameloop_obs(game_loop)\\n\\n upgrade_value = get_upgrade_obs(feature_units)\\n if upgrade_value != -1:\\n self.home_upgrade_array[np.where(upgrade_value[0] == 1)] = 1\\n self.away_upgrade_array[np.where(upgrade_value[1] == 1)] = 1\\n\\n embedded_scalar = np.concatenate((agent_statistics, race, time, self.home_upgrade_array, self.away_upgrade_array), axis=0)\\n scalar_encoder_output = self.scalar_encoder(np.reshape(embedded_scalar, [1,307]))\\n embedded_feature_units = get_entity_obs(feature_units)\\n entity_encoder_output = self.entity_encoder(np.reshape(embedded_feature_units, [1,512,464]))\\n encoder_input = np.concatenate((spatial_encoder_output, scalar_encoder_output, entity_encoder_output), axis=1)\\n\\n core_input = np.reshape(encoder_input, [16, 8, 131])\\n whole_seq_output, final_memory_state, final_carry_state = self.core(core_input)\\n print(whole_seq_output.shape)\\n print(final_memory_state.shape)\\n print(final_carry_state.shape)\\n\\n action = [actions.FUNCTIONS.no_op()]\\n\\n return action, policy_logits, new_state\",\n \"def _evaluate_rollout(self, state, limit=1000):\\n player = state.turn\\n for i in range(limit):\\n end, winner = state.if_win()\\n if end: break\\n action_probs = rollout_policy_fn(state)\\n max_action = max(action_probs, key=itemgetter(1))[0]\\n state.do_move(max_action)\\n else:\\n # If no break from the loop, issue a warning.\\n print(\\\"WARNING: rollout reached move limit\\\")\\n return -1\\n return 1 if winner == player else -1\",\n \"def train_off_policy(self, sess):\\n \\n # sync the local nets from the global\\n sess.run([self.sync_local_actor_op, self.sync_local_critic_op])\\n\\n # sample trajectory from the replay memory\\n traj = self.memory.get_trajectory(self.FLAGS.k_steps)\\n k = len(traj)\\n\\n # empty list to store targets\\n q_ret_list = []\\n q_opc_list = []\\n states = []\\n actions = []\\n mu_dist = []\\n val_s = []\\n \\n # if last episode is not terminal state, use value function to get an estimate\\n Q_ret = 0.0\\n if not traj[-1].done :\\n Q_ret = sess.run([self.val_xi], feed_dict= { \\n self.x_i :np.reshape(traj[-1].next_state, (1,-1))} )[0][0,0]\\n \\n Q_opc = Q_ret \\n \\n # reverse loop\\n for transition in traj[::-1]:\\n Q_ret = transition.reward + self.FLAGS.gamma * Q_ret\\n Q_opc = transition.reward + self.FLAGS.gamma * Q_opc\\n \\n #\\n x_t = np.reshape(transition.state, (1,-1))\\n a_t = np.reshape(transition.action, (1,-1))\\n u_t = np.reshape(transition.distribution, (1,-1))\\n \\n # add to minibatch\\n q_ret_list.append(Q_ret)\\n q_opc_list.append(Q_opc)\\n states.append(x_t)\\n actions.append(a_t)\\n mu_dist.append(u_t)\\n \\n \\n # get estimates from existing function approximators\\n v_t, c_t, p_t, q_t = sess.run([self.val_xi, self.c_i, self.p_i, self.adv_xi_ai], \\n feed_dict = {self.x_i : x_t, \\n self.a_i: a_t, \\n self.u_i: u_t} )\\n \\n # add the target V_pi\\n val_s.append((min(p_t[0,0],1.0) * (Q_ret - q_t[0,0])) + v_t[0,0] )\\n \\n # update again\\n Q_ret = c_t[0,0] * (Q_ret - q_t[0,0]) + v_t[0,0] \\n Q_opc = (Q_opc - q_t[0,0]) + v_t[0,0]\\n \\n \\n # create mini-batch here\\n opt_feed_dict = {\\n self.x_i : np.asarray(states).reshape(-1, self.INPUT_DIM),\\n self.a_i : np.asarray(actions).reshape(-1, self.ACTION_DIM),\\n self.q_opc : np.asarray(q_opc_list).reshape(-1, 1), \\n self.q_ret : np.asarray(q_ret_list).reshape(-1, 1), \\n self.u_i : np.asarray(mu_dist).reshape(-1,2*self.ACTION_DIM),\\n self.v_target: np.asarray(val_s).reshape(-1,1)\\n }\\n \\n # Train the global estimators using local gradients\\n _,_, global_step, critic_summaries, actor_summaries = sess.run([self.actor_train_op, \\n self.critic_train_op, \\n tf.contrib.framework.get_global_step(),\\n self.critic_summary_op, \\n self.actor_summary_op ], \\n feed_dict=opt_feed_dict)\\n \\n \\n # Write summaries\\n if self.summary_writer is not None:\\n self.summary_writer.add_summary(critic_summaries, global_step)\\n self.summary_writer.add_summary(actor_summaries, global_step)\\n self.summary_writer.flush()\\n \\n \\n # update the average policy network\\n _ = sess.run([self.soft_update_average_actor_op])\\n \\n # that's it \",\n \"def run_rollout(self, state):\\n rewards = np.array([0.0] * state.num_players)\\n sim_state = state.clone() # create the simulated state so that the current state is left unchanged\\n for _ in range(self.depth):\\n if sim_state.is_terminal(): # act and track rewards as long as possible\\n break\\n action = self.rollout_policy.select_action(sim_state)\\n rewards += sim_state.take_action(action)\\n\\n return rewards\",\n \"def _forward_exploration(self, batch: NestedDict) -> Mapping[str, Any]:\\n output = {}\\n\\n # TODO (Artur): Remove this once Policy supports RNN\\n if self.encoder.config.shared:\\n batch[STATE_IN] = None\\n else:\\n batch[STATE_IN] = {\\n ACTOR: None,\\n CRITIC: None,\\n }\\n batch[SampleBatch.SEQ_LENS] = None\\n\\n # Shared encoder\\n encoder_outs = self.encoder(batch)\\n # TODO (Artur): Un-uncomment once Policy supports RNN\\n # output[STATE_OUT] = encoder_outs[STATE_OUT]\\n\\n # Value head\\n vf_out = self.vf(encoder_outs[ENCODER_OUT][CRITIC])\\n output[SampleBatch.VF_PREDS] = vf_out.squeeze(-1)\\n\\n # Policy head\\n action_logits = self.pi(encoder_outs[ENCODER_OUT][ACTOR])\\n output[SampleBatch.ACTION_DIST_INPUTS] = action_logits\\n output[SampleBatch.ACTION_DIST] = self.action_dist_cls.from_logits(\\n logits=action_logits\\n )\\n return output\",\n \"def ac_process_rollout(rollout, gamma, lambda_=1.0, clip=0.0):\\n # collecting transitions\\n batch_si = np.asarray(rollout.states + [rollout.end_state])\\n batch_a = np.asarray(rollout.actions)\\n\\n # collecting target for value network\\n # V_t <-> r_t + gamma*r_{t+1} + ... + gamma^n*r_{t+n} + gamma^{n+1}*V_{n+1}\\n rewards_plus_v1 = np.asarray(rollout.rewards + [rollout.r]) # bootstrapping\\n\\n if clip != 0.0:\\n rewards_plus_v1 = np.clip( rewards_plus_v1, -clip, clip )\\n\\n rewards_plus_v2 = np.asarray(rollout.bonuses + [0])\\n rewards_plus_v = rewards_plus_v1 + rewards_plus_v2\\n \\n batch_r = discount( rewards_plus_v, gamma )[:-1] # value network target\\n\\n # collecting target for policy network\\n rewards1 = np.asarray(rollout.rewards)\\n if clip != 0.0:\\n rewards1 = np.clip( rewards1, -clip, clip )\\n\\n rewards2 = np.asarray(rollout.bonuses) \\n rewards = rewards1 + rewards2\\n \\n vpred_t = np.asarray( rollout.values + [ rollout.r ] ) \\n # \\\"Generalized Advantage Estimation\\\": https://arxiv.org/abs/1506.02438\\n # Eq (10): delta_t = Rt + gamma*V_{t+1} - V_t\\n # Eq (16): batch_adv_t = delta_t + gamma*delta_{t+1} + gamma^2*delta_{t+2} + ...\\n delta_t = rewards + gamma * ( vpred_t[1:] - vpred_t[:-1] ).squeeze()\\n batch_adv = discount( delta_t, gamma * lambda_ )\\n\\n features = rollout.features\\n\\n return Batch( batch_si, batch_a, batch_adv, batch_r, rollout.terminal, features )\",\n \"def logits_expert_is_high(\\n self,\\n state: th.Tensor,\\n action: th.Tensor,\\n next_state: th.Tensor,\\n done: th.Tensor,\\n log_policy_act_prob: Optional[th.Tensor] = None,\\n ) -> th.Tensor:\\n if log_policy_act_prob is None:\\n raise TypeError(\\n \\\"Non-None `log_policy_act_prob` is required for this method.\\\",\\n )\\n reward_output_train = self._reward_net(state, action, next_state, done)\\n return reward_output_train - log_policy_act_prob\",\n \"def perform_rollouts(self,\\n session,\\n num_parallel_rollouts,\\n policy,\\n task_modifier,\\n sample_vars=None):\\n batch_env = self._get_batch_env(task_modifier, num_parallel_rollouts)\\n\\n state = batch_env.reset().reshape((num_parallel_rollouts, -1))\\n states = [[state[idx]] for idx in range(num_parallel_rollouts)]\\n actions = [[] for _ in range(num_parallel_rollouts)]\\n rewards = [[] for _ in range(num_parallel_rollouts)]\\n sample_op, state_var = policy.sample_op()\\n\\n completed_states = []\\n completed_actions = []\\n completed_rewards = []\\n if sample_vars is None:\\n sample_vars = {}\\n\\n step = 0\\n while step < self.max_rollout_len:\\n step += 1\\n sample_vars[state_var] = state\\n action = session.run(sample_op, sample_vars)\\n action = np.nan_to_num(action, copy=False)\\n new_state, reward, new_tasks_done, _ = batch_env.step(action)\\n for idx in range(num_parallel_rollouts):\\n states[idx].append(new_state[idx:idx + 1].ravel())\\n actions[idx].append(action[idx:idx + 1, :])\\n rewards[idx].append(reward[idx])\\n if new_tasks_done[idx]:\\n completed_states.append(states[idx])\\n completed_actions.append(actions[idx])\\n completed_rewards.append(rewards[idx])\\n states[idx] = [batch_env[idx].reset()]\\n new_state[idx] = states[idx][0]\\n actions[idx] = []\\n rewards[idx] = []\\n\\n state = new_state.reshape((num_parallel_rollouts, -1))\\n\\n num_completed_rollouts = len(completed_states)\\n rollouts = []\\n for idx in range(num_completed_rollouts):\\n l = len(completed_states[idx])\\n rollouts.append({\\n 'timesteps':\\n np.hstack((np.arange(l).reshape((-1, 1)), l * np.ones((l, 1)))),\\n 'states':\\n np.vstack(completed_states[idx]),\\n 'actions':\\n np.vstack(completed_actions[idx]),\\n 'rewards':\\n np.array(completed_rewards[idx]).reshape((-1, 1))\\n })\\n tf.logging.info(\\n 'avg rollout length: %f',\\n np.mean([rollout['actions'].shape[0] for rollout in rollouts]))\\n return rollouts\",\n \"def trace_learning_objective_mechanism_projections(mech):\\n for input_state in mech.inputStates.values():\\n for projection in input_state.receivesFromProjections:\\n sender = projection.sender.owner\\n # If projection is not from another ObjectiveMechanism, ignore\\n if not isinstance(sender, (ObjectiveMechanism)):\\n continue\\n if isinstance(sender, ObjectiveMechanism) and sender.learning_role is TARGET:\\n return sender\\n if sender.inputStates:\\n target_mech = trace_learning_objective_mechanism_projections(sender)\\n if target_mech:\\n return target_mech\\n else:\\n continue\\n else:\\n continue\",\n \"def step(self, state):\\n mcts_action = self.mcts_search(state)\\n policy = [(action, (1.0 if action == mcts_action else 0.0))\\n for action in state.legal_actions(self.player_id())]\\n\\n return policy, mcts_action\",\n \"def evaulate_policy(w, env, number_of_episodes=2):\\n\\n results = []\\n # for e in range(number_of_episodes):\\n s_old = env.reset()\\n t = 0\\n done = False\\n while not done:\\n # Choose action\\n action = None\\n if np.inner(w, s_old) > 0:\\n action = 1\\n else:\\n action = 0\\n # Take action\\n s_new, r, done, _ = env.step(action)\\n # env.render()\\n\\n # Update\\n s_old = s_new\\n\\n t += 1\\n if t>200:\\n break\\n # results.append(t)\\n # print(t)\\n return t\",\n \"def get_target_policy(self, state):\\n values = self.get_all_state_action_values(state)\\n return self.target_policy(values)\",\n \"def extract_belief(ontology, state, threshold=0.3):\\n\\n \\\"\\\"\\\"\\n need to check if the value at argmax is bigger than a threshold\\n \\\"\\\"\\\"\\n\\n request_idx = np.argmax([item[1] for item in state[\\\"request\\\"].items()])\\n frequency_idx = np.argmax([item[1] for item in state[\\\"frequency\\\"].items()])\\n illness_type_idx = np.argmax([item[1] for item in state[\\\"type\\\"].items()])\\n symptom_idx = np.argmax([item[1] for item in state[\\\"symptom\\\"].items()])\\n escalation_idx = np.argmax([item[1]\\n for item in state[\\\"escalation\\\"].items()])\\n duration_idx = np.argmax([item[1] for item in state[\\\"duration\\\"].items()])\\n confirmation_idx = np.argmax([item[1]\\n for item in state[\\\"confirmation\\\"].items()])\\n\\n # then it is neccessary to map the indices back to words\\n\\n request = is_plausible(state[\\\"request\\\"].items(\\n ), ontology, \\\"request\\\", request_idx, threshold)\\n frequency = is_plausible(\\n state[\\\"frequency\\\"].items(), ontology, \\\"frequency\\\", frequency_idx, threshold)\\n illness_type = is_plausible(\\n state[\\\"type\\\"].items(), ontology, \\\"type\\\", illness_type_idx, threshold)\\n symptom = is_plausible(state[\\\"symptom\\\"].items(\\n ), ontology, \\\"symptom\\\", symptom_idx, threshold)\\n\\n escalation = is_plausible(state[\\\"escalation\\\"].items(\\n ), ontology, \\\"escalation\\\", escalation_idx, threshold)\\n\\n duration = is_plausible(state[\\\"duration\\\"].items(\\n ), ontology, \\\"duration\\\", duration_idx, threshold)\\n confirmation = is_plausible(state[\\\"confirmation\\\"].items(\\n ), ontology, \\\"confirmation\\\", confirmation_idx, threshold)\\n\\n if request is not None:\\n request = \\\"request \\\"+request\\n if frequency is not None:\\n frequency = \\\"frequency \\\"+frequency\\n if illness_type is not None:\\n illness_type = \\\"type \\\"+illness_type\\n if symptom is not None:\\n symptom = \\\"symptom \\\"+symptom\\n if escalation is not None:\\n escalation = \\\"escalation \\\"+escalation\\n if duration is not None:\\n duration = \\\"duration \\\"+duration\\n if confirmation is not None:\\n confirmation = \\\"confirmation \\\"+confirmation\\n values = [request, frequency, illness_type,\\n symptom, escalation, duration, confirmation]\\n values = [val for val in values if val is not None]\\n return sorted(values)\",\n \"def update_policy(self):\\n # this is update_policy \\n # sample batch of 32 from the memory\\n batch_of_samples = self.replay_memory.sample(batch_size=32)\\n current_state_samples = batch_of_samples['current_state_samples']\\n next_state_samples = batch_of_samples['next_state_samples']\\n #print type(current_state_samples[0])\\n #print current_state_samples\\n\\n # fetch stuff we need from samples 32*84*84*4\\n current_state_images = np.zeros([1, 84, 84, 4])\\n #print current_state_samples\\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\\n\\n next_state_images = np.zeros([1, 84, 84, 4])\\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\\n\\n # preprocess\\n current_state_images = self.preprocessor.process_batch(current_state_images)\\n next_state_images = self.preprocessor.process_batch(next_state_images)\\n # print \\\"current_state_images {} max {} \\\".format(current_state_images.shape, np.max(current_state_images))\\n #print current_state_images.shape\\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\\n\\n # targets\\n y_targets_all = q_current #1*num_actions\\n #print y_targets_all.shape # [1,6]\\n idx = 0 \\n last_sample = current_state_samples[-1]\\n if last_sample.is_terminal:\\n y_targets_all[idx, last_sample.action] = last_sample.reward\\n else:\\n if self.mode == 'vanilla':\\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\\n if self.mode == 'double': \\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \\n\\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\\n\\n with tf.name_scope('summaries'):\\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\\n\\n if not (self.iter_ctr % self.log_loss_every_nth):\\n self.dump_train_loss(loss)\\n\\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\\n # # copy weights\\n # print \\\"Iter {} Updating target Q network\\\".format(self.iter_ctr)\\n # self.target_q_network.set_weights(self.q_network.get_weights())\\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\\\\n # for i in range(len(self.target_q_network.trainable_weights))]\",\n \"def main(args):\\n flags = parse_options(args)\\n\\n data = {\\n 'name': [],\\n 'step': [],\\n 'distance': [],\\n }\\n\\n for dir_name, name in zip(flags.dir_name, flags.name):\\n\\n # get the hyperparameters\\n env_name, policy, hp, seed = get_hyperparameters_from_dir(dir_name)\\n\\n print(hp.keys())\\n del hp['algorithm']\\n del hp['date/time']\\n\\n # create the algorithm object. We will be using the eval environment in\\n # this object to perform the rollout.\\n alg = OffPolicyRLAlgorithm(\\n policy=policy, env=env_name, eval_env=env_name, **hp)\\n\\n # setup the seed value\\n random.seed(seed)\\n np.random.seed(seed)\\n tf.compat.v1.set_random_seed(seed)\\n\\n filenames = os.listdir(os.path.join(dir_name, \\\"checkpoints\\\"))\\n metafiles = [f[:-5] for f in filenames if f[-5:] == \\\".meta\\\"]\\n metanum = list(sorted([int(f.split(\\\"-\\\")[-1]) for f in metafiles]))[:-1]\\n\\n # get the checkpoint number\\n ckpt_num = max(metanum)\\n\\n # location to the checkpoint\\n ckpt = os.path.join(dir_name, \\\"checkpoints/itr-{}\\\".format(ckpt_num))\\n\\n # restore the previous checkpoint\\n alg.saver = tf.compat.v1.train.Saver(alg.trainable_vars)\\n alg.load(ckpt)\\n\\n # some variables that will be needed when replaying the rollout\\n policy = alg.policy_tf\\n\\n batches = []\\n for b in range(flags.num_batches):\\n worker_obs0 = policy.replay_buffer.sample(with_additional=False)[5]\\n batches.append(worker_obs0)\\n\\n for ckpt_num_one, ckpt_num_two in zip(metanum[1:], metanum[:-1]):\\n\\n # get the checkpoint number\\n ckpt_num = ckpt_num_one\\n\\n # location to the checkpoint\\n ckpt = os.path.join(dir_name, \\\"checkpoints/itr-{}\\\".format(ckpt_num))\\n\\n # restore the previous checkpoint\\n alg.load(ckpt)\\n\\n # some variables that will be needed when replaying the rollout\\n policy = alg.policy_tf\\n\\n mean_one = []\\n for b in batches:\\n a = policy.policy[-1].get_action(b, None, False, False)\\n mean_one.append(a)\\n\\n # get the checkpoint number\\n ckpt_num = ckpt_num_two\\n\\n # location to the checkpoint\\n ckpt = os.path.join(dir_name, \\\"checkpoints/itr-{}\\\".format(ckpt_num))\\n\\n # restore the previous checkpoint\\n alg.load(ckpt)\\n\\n # some variables that will be needed when replaying the rollout\\n policy = alg.policy_tf\\n\\n mean_two = []\\n for b in batches:\\n a = policy.policy[-1].get_action(b, None, False, False)\\n mean_two.append(a)\\n\\n # compute a distance metric between the policies\\n mean_one = np.concatenate(mean_one, axis=0)\\n mean_two = np.concatenate(mean_two, axis=0)\\n kl = np.sum((mean_one - mean_two) ** 2, axis=1).mean()\\n print(\\\"{},{},{},{}\\\".format(name, ckpt_num_one, ckpt_num_two, kl))\\n\\n data['name'].append(name)\\n data['step'].append(ckpt_num_one)\\n data['distance'].append(kl)\\n\\n df = pd.DataFrame(data, columns=['name', 'step', 'distance'])\\n plt.title(\\\"Manager MDP Non-Stationarity\\\")\\n ax = sns.lineplot(x='step', y='distance', hue='name', data=df)\\n plt.savefig('ns.png')\",\n \"def _evaluate_policy(self, state, legal_actions, step_rewards=None, action=None):\\n raise NotImplementedError\",\n \"def vec_rollout(\\n env,\\n n_envs, #added this to specify # environments\\n agent,\\n max_path_length=np.inf,\\n render=False,\\n render_kwargs=None,\\n):\\n if render_kwargs is None:\\n render_kwargs = {}\\n observations = [[] for _ in range(n_envs)]\\n actions = [[] for _ in range(n_envs)]\\n rewards = [[] for _ in range(n_envs)]\\n terminals = [[] for _ in range(n_envs)]\\n agent_infos = [[] for _ in range(n_envs)]\\n env_infos = [[] for _ in range(n_envs)]\\n o = env.reset()\\n agent.reset()\\n next_o = None\\n path_length = 0\\n if render:\\n env.render(**render_kwargs)\\n\\n \\n #keep track of each env. \\n env_idx = list(range(n_envs))\\n last = {}\\n while path_length < max_path_length:\\n # print(path_length)\\n tmp_action = []\\n action = []\\n agent_info = []\\n #Have to step through all no matter what. Only keep if in env_idx\\n for i in range(n_envs):\\n a, ai = agent.get_action(o[i])\\n action.append(a)\\n agent_info.append(ai)\\n next_o, r, d, env_info = env.step(action)\\n for i in env_idx:\\n observations[i].append(o[i])\\n actions[i].append(action[i])\\n rewards[i].append(r[i])\\n terminals[i].append(d[i])\\n env_infos[i].append(env_info[i])\\n agent_infos[i].append(agent_info[i])\\n\\n path_length += 1\\n n = len(env_idx)\\n #check if any are finished. If so, ignore them. \\n tmp = []\\n for i in range(n):\\n if not d[env_idx[i]]:\\n tmp.append(env_idx[i])\\n else:\\n last[i] = next_o[i]\\n env_idx = tmp\\n if len(env_idx) == 0:\\n break\\n\\n o = next_o\\n if render:\\n env.render(**render_kwargs)\\n for k, v in last.items():\\n next_o[k] = v \\n # observations = list(np.concatenate(observations))\\n # actions = list(np.concatenate(actions))\\n # rewards = list(np.concatenate(rewards))\\n # terminals = list(np.concatenate(terminals))\\n # env_info = list(np.concatenate(env_info))\\n # agent_info = list(np.concatenate(agent_info))\\n\\n # actions = np.array(actions)\\n # if len(actions.shape) == 1:\\n # actions = np.expand_dims(actions, 1)\\n # observations = np.array(observations)\\n # if len(observations.shape) == 1:\\n # observations = np.expand_dims(observations, 1)\\n # next_o = np.array([next_o])\\n #need to return each run of observations for each env on its own\\n ans = []\\n for i in range(n_envs):\\n observations[i] = np.array(observations[i])\\n if len(observations[i].shape) == 1: \\n observations[i] = np.expand_dims(observations[i], 1)\\n next_o[i] = np.array([next_o[i]])\\n actions[i] = np.array(actions[i])\\n if len(actions[i].shape) == 1:\\n actions[i] = np.expand_dims(actions[i], 1)\\n #last transition should be same. \\n next_observations = np.vstack(\\n (\\n observations[i][1:, :],\\n np.expand_dims(next_o[i], 0)\\n )\\n )\\n item = dict(\\n observations=observations[i],\\n actions=actions[i],\\n rewards=np.array(rewards[i]).reshape(-1, 1),\\n next_observations=next_observations,\\n terminals=np.array(terminals[i]).reshape(-1, 1),\\n agent_infos=agent_infos[i],\\n env_infos=env_infos[i],\\n )\\n ans.append(item)\\n return ans\",\n \"def calc_stay_prob(rollouts):\\n states = rollouts.states\\n actions = rollouts.actions\\n rewards = rollouts.rewards\\n\\n num_test_episodes = states.shape[0]\\n num_trials = states.shape[1]\\n count_trial_stayed = 0.01 + np.zeros((2, 2, num_test_episodes)) # [common/uncommon, reward/unrewarded]\\n count_trial_all = 0.01 + np.zeros((2, 2, num_test_episodes))\\n for epi in range(num_test_episodes):\\n for t in range(0, num_trials-2, 2):\\n uncommon_transition = int(actions[epi, t] != states[epi, t+1]-1)\\n count_trial_all[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += 1\\n count_trial_stayed[uncommon_transition, (0 if rewards[epi, t+1] else 1), epi] += \\\\\\n int(actions[epi, t+2] == actions[epi, t])\\n return np.divide(count_trial_stayed, count_trial_all), count_trial_stayed, count_trial_all\",\n \"def current_policy_step(self, sess, add_to_mem = True):\\n \\n episode_reward = 0.0\\n episode_len = 0 #num of action\\n \\n #for each episode reset first\\n state = self.env.reset()\\n \\n for t in range(self.FLAGS.max_episode_len):\\n \\n # take action according to current policy\\n \\n action, policy_stats = sess.run([self.a_i_, self.policy_xi_stats], \\n feed_dict={self.x_i : np.array([state])})\\n \\n action = np.reshape(action, (self.ACTION_DIM,))\\n \\n next_state, reward, done, info = self.env.step(action) # next state, reward, terminal\\n \\n # insert this in memory with a uniform distribution over actions\\n if add_to_mem: # can also remove this and still work/optimization\\n self.memory.add(Transition(state=state, action=action, \\n reward=reward, done=done, \\n distribution=policy_stats, next_state=next_state))\\n \\n # accumulate rewards\\n episode_reward += reward\\n episode_len += 1 \\n \\n local_t = next(self.local_counter)\\n global_t = next(self.global_counter)\\n \\n # update the state \\n state = next_state \\n \\n if done:\\n # print(\\\"Episode finished after {} timesteps\\\".format(t+1))\\n break\\n \\n return episode_reward, episode_len, local_t, global_t\",\n \"def evaluate_policy(policy, expert_states, expert_actions, episodes, batch_size):\\r\\n distance_list = []\\r\\n for _ in range(episodes):\\r\\n tl = expert_states.shape[0] # training inputs length\\r\\n mod = tl % batch_size # number of batches resulting from batch size\\r\\n N = int(np.floor(tl/batch_size)) # number for splitting training data\\r\\n split_details = [batch_size] * N # N elements of batch_size [batch_size, batch_size, ...]\\r\\n split_details.append(mod)\\r\\n batched_states = tf.split(expert_states, split_details)\\r\\n batched_actions = tf.split(expert_actions, split_details)\\r\\n\\r\\n for i in range(len(batched_states)):\\r\\n predicted_actions = policy.get_action(batched_states[i])\\r\\n distances = -1 * distance_from_expert(5*predicted_actions, batched_actions[i])\\r\\n distance_list.extend(list(distances.numpy()))\\r\\n\\r\\n return distance_list\",\n \"def original_solver(state, agent, verbose=3):\\n tasks = state.goals[agent]\\n if verbose>0: print('** pyhop, verbose={}: **\\\\n state = {}\\\\n tasks = {}'.format(verbose, state.__name__, tasks))\\n result = seek_plan(state,tasks,[],0,verbose)\\n if verbose>0: print('** result =',result,'\\\\n')\\n return result\",\n \"def rollout(system_env, n_action_features,\\n epoch=0, min_epoch_steps=1000,\\n agent=None, episodic_update=False):\\n print('Epoch', epoch)\\n\\n # epoch trace data\\n epoch_step = 0\\n trace = []\\n\\n while epoch_step < min_epoch_steps:\\n observation = system_env.reset()\\n\\n rewards = []\\n done = 0\\n episode_step = 0\\n while not done:\\n # restart = 1 if first episode step, otherwise 0\\n restart = int(not(episode_step))\\n if episode_step % 10 == 0:\\n print(f'step: {episode_step}')\\n if agent is None:\\n action = system_env.action_space.sample()\\n else:\\n action = agent.act(observation, restart)\\n new_observation, reward, done, _ = system_env.step(action)\\n rewards.append(reward)\\n\\n trace_step = np.hstack(\\n (observation, action, reward, restart, epoch))\\n trace.append(trace_step)\\n\\n # update observation\\n episode_step += 1\\n observation = new_observation\\n\\n # save last observation before a reset\\n n_nans = n_action_features + 1 # NaNs for actions and reward\\n last_obs = np.hstack(\\n (observation, np.repeat(np.nan, n_nans), 0, epoch))\\n trace.append(last_obs)\\n\\n epoch_step += episode_step\\n\\n print('Number of episode steps:', episode_step)\\n print('Mean reward:', np.mean(rewards))\\n print('Return:', np.sum(rewards))\\n print('Number of epoch steps:', epoch_step)\\n\\n if episodic_update:\\n # one epoch is exactly one episode\\n break\\n\\n return trace\",\n \"def _evaluate_rollout(self, state, limit=1000):\\n player = state.get_current_player()\\n for i in range(limit):\\n end, winner = state.game_end()\\n if end:\\n break\\n action_probs = rollout_policy_fn(state)\\n max_action = max(action_probs, key=itemgetter(1))[0]\\n state.do_move(max_action)\\n else:\\n # If no break from the loop, issue a warning.\\n print(\\\"WARNING: rollout reached move limit\\\")\\n if winner == -1: # tie\\n return 0\\n else:\\n return 1 if winner == player else -1\",\n \"def _evaluate_rollout(self, state, limit=1000):\\n player = state.get_current_player()\\n for i in range(limit):\\n end, winner = state.game_end()\\n if end:\\n break\\n action_probs = rollout_policy_fn(state)\\n max_action = max(action_probs, key=itemgetter(1))[0]\\n state.do_move(max_action)\\n else:\\n # If no break from the loop, issue a warning.\\n print(\\\"WARNING: rollout reached move limit\\\")\\n if winner == -1: # tie\\n return 0\\n else:\\n return 1 if winner == player else -1\",\n \"def policy_eval():\\r\\n \\r\\n action_prob = [0.125, 0.625, 0.125, 0.125]# actions with probabilities\\r\\n data = grid_world()\\r\\n state_axis = np.zeros((9, 9))#initialize states\\r\\n threshold = .1\\r\\n prior_state = np.ones((9, 9))\\r\\n \\r\\n while np.abs(state_axis - prior_state).max() > threshold:\\r\\n for x, y in product(range(9), repeat=2):\\r\\n prior_state = state_axis.copy()\\r\\n if data.array[x, y] == 'X':\\r\\n continue\\r\\n updated_values = [data.next_direction(np.array([x, y]), next_move)\\r\\n for next_move in data.directions]#Updating states with directions\\r\\n Sum_Expectation = np.dot(action_prob,\\r\\n [points_val + 0.9 * state_axis[position[0], position[1]]\\r\\n for position, points_val in updated_values])\\r\\n state_axis[x, y] = Sum_Expectation\\r\\n print(\\\"\\\\nExercise 3.1 Shows Value functions for the policy\\\\n\\\")\\r\\n print(state_axis)\\r\\n build_grid(state_axis, \\\"Shows Value functions for the policy\\\")\",\n \"def get_traj(agent, env, episode_max_length):\\n # MARK: changed\\n\\n env.reset()\\n obs = []\\n acts = []\\n rews = []\\n entropy = []\\n info = []\\n machines = []\\n ob = env.observe()\\n\\n for _ in range(episode_max_length):\\n\\n act_prob = agent.get_action(ob)\\n machine_prob = agent.get_machine(ob)\\n # csprob_n = np.cumsum(act_prob)\\n # a = (csprob_n > np.random.rand()).argmax()\\n # print(act_prob)\\n ob = ob.reshape(ob.shape[0]*ob.shape[1])\\n obs.append(ob) # store the ob at current decision making step\\n acts.append(act_prob)\\n machines.append(machine_prob)\\n ob, rew, done, info = env.step(act_prob,machine_prob, repeat=True)\\n\\n rews.append(rew)\\n\\n entropy.append(get_entropy(act_prob))\\n if done: break\\n return {'reward': np.array(rews),\\n 'ob': np.array(obs),\\n 'action': np.array(acts),\\n 'entropy': entropy,\\n 'info': info,\\n 'machine':np.array(machines)\\n }\",\n \"def step(self):\\n\\n \\\"\\\"\\\" First updates the variables values of the current time form the environment \\\"\\\"\\\"\\n self.update_crispval(self.env.context)\\n\\n \\\"\\\"\\\"\\n here the decision making of the agent\\n to determine which activity to suggest to the patient\\n i apply the creative controller to the current context\\n \\\"\\\"\\\"\\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\\n self.fuzzysets_values, self.variables_universe)\\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\\n\\n \\\"\\\"\\\" i obtain a number of ouput crisp values.\\n i determine which one achieves the max expected output w.r.t. the a-rules \\\"\\\"\\\"\\n best_a = None\\n best_a_val = -1000\\n best_a_exphapp = 5\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(\\\"rules activations\\\")\\n for a in rules_activations:\\n if rules_activations[a] > 0:\\n print(str(a) + \\\"\\\\n\\\\t\\\\t\\\\t-> \\\" + str(rules_activations[a]))\\n for item in c_out.items(): # for each pair <activity, crisp output>\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(item)\\n if not item[\\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\\n inputs = dict(curr_input) # I create a copy fo the dict\\n inputs[item[0]] = item[1]\\n assessor_id = self.actions_to_ti[item[0]]\\n self.assessors[assessor_id].feed_inputs(inputs)\\n is_ac_exception = False\\n assout = []\\n try:\\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\\n assout = [a_out[ao] for ao in a_out]\\n except:\\n is_ac_exception = True\\n traceback.print_exc()\\n # todo the following assumes that every assessor controller has same eval var\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n if len(assout) == 0:\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\\n\\n avg_credit_rules_that_suggested_action = 1.0\\n nr_rules_that_suggested_action = 0\\n for r in rules_activations:\\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\\\\n self.rules_credits[str(r)]\\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\\n if nr_rules_that_suggested_action > 0:\\n avg_credit_rules_that_suggested_action = (\\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\\n repetition_cost = 1.0\\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\\n if (a_val > best_a_val) and (\\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\\n best_a = item\\n best_a_val = a_val\\n best_a_exphapp = mean(assout)\\n\\n \\\"\\\"\\\"I suggest the activity with best expected outcome and store the information to populate the interactions \\n memory \\\"\\\"\\\"\\n self.proposeActivity(best_a)\\n if not best_a is None:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"proposing activity\\\" + str(best_a) + \\\" which has expected feedback: \\\" + str(\\n best_a_exphapp) + \\\", which weighted is \\\" + str(best_a_val))\\n self.curr_iter_suggestions.append(best_a[0])\\n self.last_suggestion = best_a\\n else:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"the activity proposed is \\\" + str(\\n best_a) + \\\" so I don't suggest anything. I will ask a question instead\\\")\\n self.last_suggestion = []\\n self.expected_feedback = best_a_exphapp\\n self.last_context = self.env.context.copy()\\n self.last_rules_activations = rules_activations\",\n \"def _prepare_policy_input(self, state, legal_actions, step_rewards=None):\\n check_for_nans(\\\"Raw state\\\", state)\\n state_ = state.view(-1)\\n\\n if step_rewards is None or not step_rewards:\\n step_rewards = [None for _ in legal_actions]\\n batch_states = []\\n\\n assert legal_actions\\n assert step_rewards\\n assert len(legal_actions) == len(step_rewards)\\n\\n for action, log_likelihood in zip(legal_actions, step_rewards):\\n action_ = self.action_factor * torch.tensor([action]).to(self.device, self.dtype)\\n\\n i, j = self.env.unwrap_action(action)\\n pi = state[i, :]\\n pj = state[j, :]\\n check_for_nans(\\\"Individual momenta\\\", pi, pj)\\n\\n if self.log_likelihood_feature:\\n if log_likelihood is None:\\n log_likelihood = self._parse_action(action, from_which_env=\\\"real\\\")\\n if not np.isfinite(log_likelihood):\\n log_likelihood = 0.0\\n log_likelihood = np.clip(log_likelihood, self.reward_range[0], self.reward_range[1])\\n log_likelihood_ = self.log_likelihood_factor * torch.tensor([log_likelihood]).to(\\n self.device, self.dtype\\n )\\n check_for_nans(\\\"Log likelihood as policy input\\\", log_likelihood_)\\n\\n combined_state = torch.cat((action_, pi, pj, log_likelihood_, state_), dim=0)\\n check_for_nans(\\\"Individual policy input entry\\\", combined_state)\\n else:\\n combined_state = torch.cat((action_, pi, pj, state_), dim=0)\\n check_for_nans(\\\"Individual policy input entry\\\", combined_state)\\n\\n batch_states.append(combined_state.unsqueeze(0))\\n\\n batch_states = torch.cat(batch_states, dim=0)\\n check_for_nans(\\\"Concatenated policy input\\\", batch_states)\\n return batch_states\",\n \"def unroll(self, trajectory):\\n # We omit the details of network inference here.\\n return policy_logits, baselines\",\n \"def learn(self, state_expert, action_expert, action_expert_ori, s_max, s_min, a_max, a_min, new_dir_path):\\n # \\n # Information Writing\\n #\\n file_path = new_dir_path + \\\"/Readme.txt\\\"\\n f_read = open(file_path, \\\"a\\\")\\n\\n f_read.write(\\\"paths_per_collect:\\\\n\\\" + str(self.config.paths_per_collect) + \\\"\\\\n\\\")\\n f_read.write(\\\"max_step_limit:\\\\n\\\" + str(self.config.max_step_limit) + \\\"\\\\n\\\")\\n f_read.write(\\\"min_step_limit:\\\\n\\\" + str(self.config.min_step_limit) + \\\"\\\\n\\\")\\n f_read.write(\\\"pre_step:\\\\n\\\" + str(self.config.pre_step) + \\\"\\\\n\\\")\\n f_read.write(\\\"n_iter:\\\\n\\\" + str(self.config.n_iter) + \\\"\\\\n\\\")\\n f_read.write(\\\"gamma:\\\\n\\\" + str(self.config.gamma) + \\\"\\\\n\\\")\\n f_read.write(\\\"lam:\\\\n\\\" + str(self.config.lam) + \\\"\\\\n\\\")\\n f_read.write(\\\"max_kl:\\\\n\\\" + str(self.config.max_kl) + \\\"\\\\n\\\")\\n f_read.write(\\\"cg_damping:\\\\n\\\" + str(self.config.cg_damping) + \\\"\\\\n\\\")\\n f_read.write(\\\"lr_discriminator:\\\\n\\\" + str(self.config.lr_discriminator) + \\\"\\\\n\\\")\\n f_read.write(\\\"d_iter:\\\\n\\\" + str(self.config.d_iter) + \\\"\\\\n\\\")\\n f_read.write(\\\"clamp_lower:\\\\n\\\" + str(self.config.clamp_lower) + \\\"\\\\n\\\")\\n f_read.write(\\\"clamp_upper:\\\\n\\\" + str(self.config.clamp_upper) + \\\"\\\\n\\\")\\n f_read.write(\\\"lr_baseline:\\\\n\\\" + str(self.config.lr_baseline) + \\\"\\\\n\\\")\\n f_read.write(\\\"b_iter:\\\\n\\\" + str(self.config.b_iter) + \\\"\\\\n\\\")\\n f_read.write(\\\"lr_posterior:\\\\n\\\" + str(self.config.lr_posterior) + \\\"\\\\n\\\")\\n f_read.write(\\\"lr_discriminator:\\\\n\\\" + str(self.config.lr_discriminator) + \\\"\\\\n\\\")\\n f_read.write(\\\"g_iter:\\\\n\\\" + str(self.config.g_iter) + \\\"\\\\n\\\")\\n f_read.write(\\\"p_iter:\\\\n\\\" + str(self.config.p_iter) + \\\"\\\\n\\\")\\n f_read.write(\\\"buffer_size:\\\\n\\\" + str(self.config.buffer_size) + \\\"\\\\n\\\")\\n f_read.write(\\\"sample_size:\\\\n\\\" + str(self.config.sample_size) + \\\"\\\\n\\\")\\n f_read.write(\\\"batch_size:\\\\n\\\" + str(self.config.batch_size) + \\\"\\\\n\\\")\\n f_read.write(\\\"trajectory_length:\\\\n\\\" + str(self.config.trajectory_length) + \\\"\\\\n\\\")\\n f_read.write(\\\"inner_loop:\\\\n\\\" + str(self.config.inner_loop) + \\\"\\\\n\\\")\\n f_read.write(\\\"seed:\\\\n\\\" + str(self.config.seed) + \\\"\\\\n\\\")\\n f_read.write(\\\"schedule:\\\\n\\\" + str(self.config.schedule) + \\\"\\\\n\\\")\\n f_read.write(\\\"beta_for_entropy:\\\\n\\\" + str(self.config.beta) + \\\"\\\\n\\\")\\n f_read.write(\\\"w:\\\\n\\\" + str(self.config.w) + \\\"\\\\n\\\")\\n f_read.write(\\\"step:\\\\n\\\" + str(self.config.step) + \\\"\\\\n\\\")\\n\\n f_read.close()\\n\\n np.random.seed(self.seed)\\n config = self.config\\n start_time = time.time()\\n numeptotal = 0\\n start_epoch = 0\\n progress = 0 # Counter for iterations\\n Omax = self.config.sample_size\\n\\n state_d = state_expert\\n actions_d = action_expert\\n action_d_no_norm = action_expert_ori\\n\\n #\\n # Learning rate\\n #\\n losses = []\\n generator_loss = []\\n generator_D = []\\n generator_P = []\\n posterior_loss = []\\n baseline_loss = []\\n\\n #\\n # Goal Agent\\n #\\n count_goalagent = 0\\n count_goalagent_stack = []\\n count_goalagent_new_stack = []\\n\\n # Iterate over num of iterations\\n for i in range(start_epoch, start_epoch + config.n_iter):\\n\\n progress += 1 # Increment current iteration counter\\n\\n #\\n # Load encode expert data\\n #\\n demo_dir = \\\"Expert/\\\"\\n encodes_d = np.load(demo_dir + \\\"encode_mujoco.npy\\\")\\n\\n encode_labels = [(0, 1) for num_expert in range(30)]\\n encode_labels = list(np.array(encode_labels).flatten())\\n\\n #\\n # Create Generator's trajectory\\n #\\n count_goalagent = 0\\n Omax_sample = 60\\n\\n rollouts, count_goalagent = rollout_contin(\\n self,\\n self.state_dim,\\n self.encode_dim,\\n self.action_dim,\\n s_max,\\n s_min,\\n a_max,\\n a_min,\\n config.max_step_limit,\\n config.min_step_limit,\\n Omax_sample,\\n count_goalagent,\\n i,\\n )\\n latent_labels = [r for r in range(self.encode_dim)]\\n sub_labels = []\\n for r in range(Omax_sample // self.encode_dim):\\n sub_labels.extend(latent_labels)\\n\\n count_goalagent_stack.append(count_goalagent)\\n\\n #\\n # Pool trajecoty\\n #\\n index_for_labels = 0\\n\\n for path in rollouts:\\n path[\\\"labels\\\"] = [sub_labels[index_for_labels]]\\n index_for_labels += 1\\n\\n for path in rollouts:\\n self.buffer.add(path)\\n\\n paths = self.buffer.get_sample(config.sample_size)\\n\\n #\\n # Rearrange paths\\n #\\n logstds_n = np.concatenate([path[\\\"logstds\\\"] for path in paths])\\n state_n = np.concatenate([path[\\\"state\\\"] for path in paths])\\n encodes_n = np.concatenate([path[\\\"encodes\\\"] for path in paths])\\n actions_n = np.concatenate([path[\\\"act\\\"] for path in paths])\\n labels_n = np.concatenate([path[\\\"labels\\\"] for path in paths])\\n policies_n = np.concatenate([path[\\\"policies\\\"] for path in paths])\\n\\n #\\n # Pre-run Discriminator\\n #\\n numdetotal = state_d.shape[0]\\n numnototal = state_n.shape[0]\\n batch_size = config.batch_size\\n start_d = 0\\n start_n = 0\\n d_iter = self.config.d_iter\\n\\n loss_sum = 0\\n\\n idx = np.arange(numnototal)\\n np.random.shuffle(idx)\\n train_val_ratio = 0.7\\n\\n #\\n # Inner loop Discriminator & Generator\\n #\\n if i == start_epoch:\\n new_labels = []\\n new_labels = encode_labels\\n if not os.path.exists(new_dir_path + \\\"/new_labels\\\"):\\n os.mkdir(new_dir_path + \\\"/new_labels\\\")\\n np.save(new_dir_path + \\\"/new_labels/iter_%d_labels.npy\\\" % (i), np.array(new_labels))\\n\\n for iiter in range(self.config.inner_loop):\\n if iiter % 10 == 0:\\n print(\\\"Inner: \\\", iiter)\\n if iiter % 50 == 0:\\n print(new_dir_path)\\n\\n count_goalagent_new = 0\\n\\n #\\n # Create trajectory while over minimal batch_size\\n #\\n new_labels = encode_labels\\n counter_batchsize = 0\\n rollouts_new, count_goalagent_new = rollout_contin(\\n self,\\n self.state_dim,\\n self.encode_dim,\\n self.action_dim,\\n s_max,\\n s_min,\\n a_max,\\n a_min,\\n config.max_step_limit,\\n config.min_step_limit,\\n Omax,\\n count_goalagent_new,\\n i,\\n encode_list=new_labels\\n )\\n counter_batchsize = len(np.concatenate([path[\\\"state\\\"] for path in rollouts_new]))\\n print(\\\"GOAL: \\\", count_goalagent_new)\\n\\n if iiter == self.config.inner_loop - 1:\\n count_goalagent_new_stack.append(count_goalagent_new)\\n\\n index_for_labels = 0\\n for path in rollouts_new:\\n path[\\\"labels\\\"] = [new_labels[index_for_labels]]\\n index_for_labels += 1\\n\\n #\\n # Pool trajectory (for buffer_new)\\n #\\n for path in rollouts_new:\\n self.buffer_new.add(path)\\n\\n paths_new = self.buffer_new.get_sample(config.sample_size)\\n\\n #\\n # Create Trajectory\\n #\\n # TODO: pyTorch\\n for path in paths_new:\\n path[\\\"mus\\\"] = self.sess.run(\\n self.action_dist_mu,\\n {self.state: path[\\\"state\\\"],\\n self.encodes: path[\\\"encodes\\\"]}\\n )\\n\\n #\\n # Rearrange paths_new\\n #\\n mus_new_n = np.concatenate([path[\\\"mus\\\"] for path in paths_new])\\n logstds_new_n = np.concatenate([path[\\\"logstds\\\"] for path in paths_new])\\n state_new_n = np.concatenate([path[\\\"state\\\"] for path in paths_new])\\n encodes_new_n = np.concatenate([path[\\\"encodes\\\"] for path in paths_new])\\n actions_new_n = np.concatenate([path[\\\"act\\\"] for path in paths_new])\\n actions_output_new_n = np.concatenate([path[\\\"actions\\\"] for path in paths_new])\\n labels_new_n = np.concatenate([path[\\\"labels\\\"] for path in paths_new])\\n policies_new_n = np.concatenate([path[\\\"policies\\\"] for path in paths_new])\\n\\n #\\n # Preparation for update Networks\\n #\\n numdetotal = state_d.shape[0]\\n numnototal = state_new_n.shape[0]\\n start_d = 0\\n start_n = 0\\n\\n #\\n # Get policy from expert data\\n #\\n policy_d = []\\n for t in range(len(state_d)):\\n policy_d.append(self.get_policy_encode([state_d[t]], action_d_no_norm[t], [encodes_d[t]]))\\n\\n policy_d = np.array(policy_d)\\n\\n batch_size_agent = len(state_new_n)\\n batch_size_expert = len(state_d)\\n\\n noise_expert = np.random.normal(0, 0.05, (batch_size_expert, 2))\\n noise_agent = np.random.normal(0, 0.05, (batch_size_agent, 2))\\n # noise_expert = np.zeros((batch_size_expert, 2), dtype=np.float16)\\n # noise_agent = np.zeros((batch_size_agent, 2), dtype=np.float16)\\n\\n #\\n # Update Discriminator (using Keras)\\n #\\n # TODO: pyTorch\\n d_loss_real = self.discriminator.train_on_batch(\\n [state_d[start_d:start_d + batch_size_expert], actions_d[start_d:start_d + batch_size_expert],\\n noise_expert[:batch_size_expert], encodes_d[start_d:start_d + batch_size_expert],\\n policy_d[start_d:start_d + batch_size_expert]],\\n np.ones(batch_size_expert))\\n d_loss_fake = self.discriminator.train_on_batch(\\n [state_new_n[start_n:start_n + batch_size_agent], actions_new_n[start_n:start_n + batch_size_agent],\\n noise_agent[:batch_size_agent], encodes_new_n[start_n:start_n + batch_size_agent],\\n policies_new_n[start_n:start_n + batch_size_agent]],\\n np.zeros(batch_size_agent))\\n loss = 0.5 * np.add(d_loss_real, d_loss_fake)\\n\\n for l in self.discriminator.layers:\\n weights = l.get_weights()\\n l.set_weights(weights)\\n\\n if iiter == self.config.inner_loop - 1:\\n losses.append(loss)\\n\\n if iiter == self.config.inner_loop - 1:\\n path_idx = 0\\n start_w = 0\\n\\n for path in paths_new:\\n file_path_world = new_dir_path + \\\"/Generator_trajectories_resample\\\"\\n if not os.path.exists(file_path_world):\\n os.mkdir(file_path_world)\\n file_path_world = new_dir_path + \\\\\\n \\\"/Generator_trajectories_resample/iter_%d_world_%d\\\" % (i, path_idx)\\n\\n np.save(file_path_world, path[\\\"state\\\"])\\n np.save(file_path_world + \\\"_actions.npy\\\", actions_new_n[start_w:start_w + path[\\\"length\\\"]])\\n np.save(file_path_world + \\\"_policies.npy\\\", policies_new_n[start_w:start_w + path[\\\"length\\\"]])\\n np.save(file_path_world + \\\"_labels.npy\\\", labels_new_n[path_idx])\\n\\n path_idx += 1\\n\\n #\\n # Update Generator \\n #\\n path_i = 0\\n for path in paths_new:\\n\\n path[\\\"baselines\\\"] = self.baseline.predict(path)\\n\\n output_d = self.discriminator.predict(\\n [path[\\\"state\\\"], path[\\\"actions\\\"], noise_agent, path[\\\"encodes\\\"], path[\\\"policies\\\"]])\\n\\n anti = 1.0 - copy.copy(output_d)\\n\\n for an in range(path[\\\"length\\\"]):\\n if anti[an] == 0.0:\\n anti[an] = 1e-10\\n\\n #\\n # Without scheduling\\n #\\n beta = self.config.beta\\n #\\n # With scheduling\\n #\\n if i % self.config.step == 0:\\n if self.config.schedule == 1:\\n beta = self.config.beta - self.config.w * i\\n\\n if i % 50 == 0 and path_i == 0:\\n file_path2 = new_dir_path + \\\"/bata.txt\\\"\\n f_read2 = open(file_path2, \\\"a\\\")\\n f_read2.write(str(i) + \\\" : \\\" + str(beta) + \\\"\\\\n\\\")\\n f_read2.close()\\n\\n path_i += 1\\n\\n # output_d_for_reward = np.log(output_d + 1e-10) - np.log(anti)\\n output_d_for_reward = ((np.log(output_d + 1e-10) - np.log(anti)).flatten()\\n + beta * np.log(np.max(path[\\\"policies\\\"], axis=1) + 1e-10).flatten())\\n\\n '''\\n if path[\\\"flag\\\"] == 1:\\n output_d_for_reward = output_d_for_reward + np.ones(path[\\\"length\\\"]) * 0.1 * np.exp(3*abs((float(config.max_step_limit)-float(path[\\\"length\\\"]))/float(config.max_step_limit)))\\n '''\\n\\n path[\\\"rewards\\\"] = output_d_for_reward.flatten()\\n\\n path[\\\"generator_d\\\"] = output_d_for_reward.flatten()\\n\\n path_baselines = np.append(path[\\\"baselines\\\"], 0 if\\n path[\\\"flag\\\"] == 1 else\\n path[\\\"baselines\\\"][-1])\\n\\n deltas = path[\\\"rewards\\\"] + config.gamma * path_baselines[1:] - \\\\\\n path_baselines[:-1]\\n\\n path[\\\"advants\\\"] = discount(deltas, config.gamma * config.lam)\\n path[\\\"returns\\\"] = discount(path[\\\"rewards\\\"], config.gamma)\\n\\n path[\\\"baseline\\\"] = copy.copy(path_baselines)\\n\\n if iiter == self.config.inner_loop - 1:\\n path_idx = 0\\n for path in paths_new:\\n file_path = new_dir_path + \\\"/returns(resample)\\\"\\n if not os.path.exists(file_path):\\n os.mkdir(file_path)\\n file_path = new_dir_path + \\\"/returns(resample)/iter_%d_path_%d.txt\\\" % (i, path_idx)\\n f = open(file_path, \\\"w\\\")\\n\\n f.write(\\\"Baseline:\\\\n\\\" + np.array_str(path[\\\"baseline\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"Returns:\\\\n\\\" + np.array_str(path[\\\"returns\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"Advants:\\\\n\\\" + np.array_str(path[\\\"advants\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"Mus:\\\\n\\\" + np.array_str(path[\\\"mus\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"Actions:\\\\n\\\" + np.array_str(path[\\\"actions\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"States:\\\\n\\\" + np.array_str(path[\\\"state\\\"]) + \\\"\\\\n\\\")\\n f.write(\\\"Logstds:\\\\n\\\" + np.array_str(path[\\\"logstds\\\"]) + \\\"\\\\n\\\")\\n\\n path_idx += 1\\n\\n f.close()\\n\\n #\\n # Standardize the advantage function to have mean=0 and std=1\\n #\\n advants_new_n = np.concatenate([path[\\\"advants\\\"] for path in paths_new])\\n advants_new_n /= (advants_new_n.std() + 1e-8)\\n\\n if iiter == self.config.inner_loop - 1:\\n #\\n # Calcurate Generator's reward(sum)\\n #\\n episoderewards = np.array([path[\\\"rewards\\\"].sum() for path in paths_new])\\n generator_d = np.array([path[\\\"generator_d\\\"].sum() for path in paths_new])\\n\\n #\\n # Calcurate Generator's reward(average each epochs)\\n #\\n generator_loss.append(episoderewards.mean())\\n generator_D.append(generator_d.mean())\\n\\n #\\n # Computing baseline function for next iter.\\n #\\n b_loss = self.baseline.fit(paths_new, batch_size_agent)\\n if iiter == self.config.inner_loop - 1:\\n baseline_loss.append(b_loss)\\n\\n feed = {self.state: state_new_n,\\n self.encodes: encodes_new_n,\\n self.actions: actions_output_new_n,\\n self.advants: advants_new_n,\\n self.action_dist_logstd: logstds_new_n,\\n self.oldaction_dist_mu: mus_new_n,\\n self.oldaction_dist_logstd: logstds_new_n}\\n\\n thprev = self.gf()\\n\\n def fisher_vector_product(p):\\n feed[self.flat_tangent] = p\\n return self.sess.run(self.fvp, feed) + p * config.cg_damping\\n\\n g = self.sess.run(self.pg, feed_dict=feed)\\n stepdir = conjugate_gradient(fisher_vector_product, -g)\\n shs = .5 * stepdir.dot(fisher_vector_product(stepdir))\\n if shs <= 0:\\n break\\n\\n lm = np.sqrt(shs / config.max_kl)\\n fullstep = stepdir / lm\\n neggdotstepdir = -g.dot(stepdir)\\n\\n def loss(th):\\n self.sff(th)\\n return self.sess.run(self.losses[0], feed_dict=feed)\\n\\n theta = linesearch(loss, thprev, fullstep, neggdotstepdir / lm)\\n self.sff(theta)\\n\\n surrafter, kloldnew, entropy = self.sess.run(\\n self.losses, feed_dict=feed\\n )\\n # TODO end\\n\\n #\\n # Display learn results\\n #\\n stats = {}\\n numeptotal += len(episoderewards)\\n stats[\\\"Total number of episodes\\\"] = numeptotal\\n stats[\\\"Average sum of rewards per episode\\\"] = episoderewards.mean()\\n stats[\\\"Entropy\\\"] = entropy\\n stats[\\\"Time elapsed\\\"] = \\\"%.2f mins\\\" % ((time.time() - start_time) / 60.0)\\n stats[\\\"KL between old and new distribution\\\"] = kloldnew\\n stats[\\\"Surrogate loss\\\"] = surrafter\\n print(\\\"\\\\n********** Iteration {} **********\\\".format(i))\\n for k, v in stats.items():\\n print(k + \\\": \\\" + \\\" \\\" * (40 - len(k)) + str(v))\\n if entropy != entropy:\\n exit(-1)\\n\\n #\\n # parameter saving\\n #\\n param_dir = new_dir_path + \\\"/model_parametors/\\\"\\n if not os.path.exists(new_dir_path + \\\"/model_parametors\\\"):\\n os.mkdir(new_dir_path + \\\"/model_parametors\\\")\\n\\n print(\\\"Now we save model\\\")\\n self.generator.save_weights(\\n param_dir + \\\"generator_model_%d.h5\\\" % i, overwrite=True)\\n with open(param_dir + \\\"generator_model_%d.json\\\" % i, \\\"w\\\") as outfile:\\n json.dump(self.generator.to_json(), outfile)\\n\\n self.discriminator.save_weights(\\n param_dir + \\\"discriminator_model_%d.h5\\\" % i, overwrite=True)\\n with open(param_dir + \\\"discriminator_model_%d.json\\\" % i, \\\"w\\\") as outfile:\\n json.dump(self.discriminator.to_json(), outfile)\\n\\n self.baseline.model.save_weights(\\n param_dir + \\\"baseline_model_%d.h5\\\" % i, overwrite=True)\\n with open(param_dir + \\\"baseline_model_%d.json\\\" % i, \\\"w\\\") as outfile:\\n json.dump(self.baseline.model.to_json(), outfile)\\n\\n outfile.close()\\n\\n #\\n # Save for loss data\\n #\\n file_results = new_dir_path + \\\"/results\\\"\\n if not os.path.exists(file_results):\\n os.mkdir(file_results)\\n file_results = new_dir_path + \\\"/results/\\\"\\n\\n #\\n # Learning rate of Discriminator & Generator\\n #\\n losses_A = np.array(losses)\\n generator_loss_A = np.array(generator_loss)\\n np.save(file_results + \\\"reward_Generator.npy\\\", generator_loss_A)\\n np.save(file_results + \\\"loss_Discriminator.npy\\\", losses_A)\\n\\n #\\n # Learning rate of Baseline\\n #\\n baseline_loss_A = np.array(baseline_loss)\\n np.save(file_results + \\\"baseline_loss.npy\\\", baseline_loss_A)\\n\\n #\\n # Generator's reward\\n #\\n generator_D_A = np.array(generator_D)\\n np.save(file_results + \\\"generator_D.npy\\\", generator_D_A)\\n\\n #\\n # Number of goal agent\\n #\\n goal_agent_A = np.array(count_goalagent_stack)\\n np.save(file_results + \\\"number_of_goal_agent.npy\\\", goal_agent_A)\\n\\n #\\n # Making directory\\n #\\n file_values = new_dir_path + \\\"/value_function\\\"\\n if not os.path.exists(file_values):\\n os.mkdir(file_values)\\n\\n for enc_dir in range(self.encode_dim):\\n file_values_enc = new_dir_path + \\\"/value_function/encode_%d\\\" % (enc_dir)\\n if not os.path.exists(file_values_enc):\\n os.mkdir(file_values_enc)\\n\\n #\\n # Plot learning rate\\n #\\n file_results = new_dir_path + \\\"/results\\\"\\n if not os.path.exists(file_results):\\n os.mkdir(file_results)\\n file_results = new_dir_path + \\\"/results/\\\"\\n\\n #\\n # Learning rate of Discriminator & Generator\\n #\\n losses = np.array(losses)\\n generator_loss = np.array(generator_loss)\\n t = np.arange(progress)\\n fig = plt.figure(figsize=(15, 15))\\n ax = fig.add_subplot(2, 1, 1)\\n ax2 = fig.add_subplot(2, 1, 2)\\n ax.plot(t, losses)\\n ax.set_title(\\\"Discriminator_loss\\\")\\n ax2.plot(t, generator_loss)\\n ax2.set_title(\\\"Generator_reward\\\")\\n plt.savefig(new_dir_path + '/loss.png')\\n np.save(file_results + \\\"reward_Generator.npy\\\", generator_loss)\\n np.save(file_results + \\\"loss_Discriminator.npy\\\", losses)\\n\\n #\\n # Learning rate of Baseline\\n #\\n baseline_loss = np.array(baseline_loss)\\n t = np.arange(progress)\\n fig = plt.figure(figsize=(15, 15))\\n ax = fig.add_subplot(1, 1, 1)\\n ax.plot(t, baseline_loss)\\n ax.set_title(\\\"baseline_loss\\\")\\n plt.savefig(new_dir_path + '/baseline_loss.png')\\n np.save(file_results + \\\"baseline_loss.npy\\\", baseline_loss)\\n\\n #\\n # Number of goal agent\\n #\\n goal_agent = np.array(count_goalagent_stack)\\n t = np.arange(progress)\\n fig = plt.figure(figsize=(15, 15))\\n ax = fig.add_subplot(1, 1, 1)\\n ax.plot(t, goal_agent)\\n ax.set_title(\\\"Goal_agent\\\")\\n plt.savefig(new_dir_path + '/goal_agent.png')\\n\\n return progress\",\n \"def sample_draft(\\n policy_A,\\n policy_B,\\n learning_agent,\\n winrate_predictor\\n):\\n state_size = policy_A.state_size\\n action_size = policy_A.action_size\\n team_indices = get_team_indices(learning_agent)\\n policy_A.eval()\\n policy_B.eval()\\n winrate_predictor.eval()\\n\\n starting_state = -1 * torch.ones((1, state_size), requires_grad=True).float()\\n draft = torch.empty(state_size)\\n states = []\\n actions = []\\n penalties = []\\n round = 0\\n while not is_done(starting_state):\\n # action_odds have size (action_size,)\\n if round in team_indices:\\n action_odds = policy_A(starting_state)\\n else:\\n action_odds = policy_B(starting_state)\\n\\n sampler = Categorical(action_odds)\\n action = sampler.sample() + 1\\n\\n penalty = torch.tensor(\\n [get_penalty(starting_state, action, winrate_predictor)]\\n )\\n\\n if round in team_indices:\\n penalties.append(penalty)\\n states.append(starting_state)\\n actions.append(action)\\n draft[round] = action\\n round += 1\\n starting_state = transition(starting_state, action)\\n result = get_result(starting_state, winrate_predictor)\\n if result == learning_agent:\\n reward = 1\\n else:\\n reward = 0\\n\\n states = torch.cat(states, dim=0).float()\\n actions = torch.cat(actions, dim=0).long()\\n penalties = torch.cat(penalties, dim=0).long()\\n return draft, states, actions, penalties, reward\",\n \"def rollout(agent, env):\\n # run until episode ends\\n episode_reward = 0\\n done = False\\n obs = env.reset()\\n while not done:\\n action = agent.compute_action(obs)\\n obs, reward, done, info = env.step(action)\\n episode_reward += reward\\n \\n return episode_reward\",\n \"def inference_chain(self, model, target, verbose=False, fp_penalty=1):\\n\\n refindex = self.columns[target]\\n prior = self.column_priors[refindex]\\n refdata = self.data[refindex]\\n \\n row_priors = self.row_priors\\n rows = self.rows\\n\\n InfChainClass = InferenceChain if verbose else LeanInferenceChain\\n result = InfChainClass(prior, reference=target, model=model.name)\\n\\n for feature, model_val in model.data.items(): \\n # determine prior, reference, background values\\n ifeature = rows[feature]\\n ref_val, bg = refdata[ifeature], row_priors[ifeature]\\n \\n # avoid calculation if ref_val or model_val are bg\\n if (model_val == bg or ref_val == bg) and not verbose:\\n continue \\n \\n # create an evidence items with an initial tpr, fpr\\n tpr, fpr = model_val, bg\\n datum = (tpr, fpr)\\n alpha = 0 \\n if verbose:\\n datum = InferenceDatum(tpr, fpr)\\n \\n if model_val > bg and ref_val > bg:\\n # True positive\\n tpr = model_val\\n fpr = bg\\n alpha = (ref_val - bg) / (1-bg) \\n \\n elif model_val > bg and ref_val < bg:\\n # False positive \\n iancestor = self._positive_ancestor(refindex, ifeature) \\n if iancestor is None:\\n ancestor_val, ancestor_bg = 1, 1\\n else: \\n ancestor_bg = row_priors[iancestor]\\n ancestor_val = refdata[iancestor]\\n if verbose:\\n parent = \\\"NA\\\"\\n if iancestor is not None:\\n parent = self.row_names[iancestor]\\n datum.set_annotation(\\\"ancestor_feature\\\", parent)\\n datum.set_annotation(\\\"ancestor_bg\\\", ancestor_bg)\\n datum.set_annotation(\\\"ancestor_value\\\", ancestor_val) \\n # adjust probabilities with a combination\\n # first adjustment can only bring score up\\n beta = tanh(fp_penalty * log10(ancestor_bg/bg)) \\n ancestor_estimate = estimate_update(ancestor_bg, bg, model_val)\\n tpr0 = ancestor_estimate*(1-beta) + ancestor_bg*beta\\n fpr0 = ancestor_bg \\n # second part of FP calculation, reduce tpr to penalize far-away FPs \\n tpr = tpr0 * (1 - model_val)\\n fpr = fpr0 * (1 - bg)\\n if ancestor_val > bg:\\n alpha = (ancestor_val - bg) / (1-bg) \\n \\n elif model_val < bg and ref_val > bg:\\n # False Negative\\n tpr = (1 - bg)\\n fpr = (1 - model_val)\\n alpha = (ref_val - bg) / (1 - bg) \\n \\n elif model_val < bg and ref_val < bg:\\n # True Negative\\n tpr = (1 - model_val)\\n fpr = (1 - bg)\\n alpha = (bg-ref_val) / bg\\n \\n else:\\n # do nothing (this is when model_val==bg or ref_val==bg)\\n if verbose: \\n fpr = tpr = bg \\n else:\\n datum = None \\n \\n if datum is not None:\\n # interpolate based on strength of reference value\\n # alpha ~ 0 means weak reference, no adjustment\\n # alpha ~ 1 means strong reference, full tpr/fpr adjustment\\n tpr = alpha*tpr + (1-alpha)*fpr \\n \\n if verbose:\\n datum.tpr = tpr\\n datum.fpr = fpr \\n datum.set_annotation(\\\"feature\\\", feature)\\n datum.set_annotation(\\\"reference\\\", ref_val)\\n datum.set_annotation(\\\"background\\\", bg)\\n datum.set_annotation(\\\"model\\\", model_val)\\n action = comparison_code(model_val, ref_val, bg)\\n datum.set_annotation(\\\"result\\\", str(action))\\n else:\\n datum = (tpr, fpr)\\n result.add(datum)\\n \\n return result\",\n \"def evaluate_rollouts(paths):\\n\\n\\timport ipdb; ipdb.set_trace()\\n\\ttotal_returns = [path['rewards'].sum() for path in paths]\\n\\tepisode_lengths = [len(p['rewards']) for p in paths]\\n\\n\\tdiagnostics = OrderedDict((\\n\\t\\t('return-average', np.mean(total_returns)),\\n\\t\\t('return-min', np.min(total_returns)),\\n\\t\\t('return-max', np.max(total_returns)),\\n\\t\\t('return-std', np.std(total_returns)),\\n\\t\\t('episode-length-avg', np.mean(episode_lengths)),\\n\\t\\t('episode-length-min', np.min(episode_lengths)),\\n\\t\\t('episode-length-max', np.max(episode_lengths)),\\n\\t\\t('episode-length-std', np.std(episode_lengths)),\\n\\t))\\n\\n\\treturn diagnostics\",\n \"def compute_surr_objective(A_hat, old_policy_model, policy_model, j, state_part_mean, state_part_std, config=PPO_config):\\n \\n def clip(c, epsilon):\\n if c < 1-epsilon:\\n return 1- epsilon\\n elif c > 1+epsilon:\\n return 1+ epsilon\\n else:\\n return c\\n \\n clipping_param = np.amax([0.01, (1-j/config['J'])*config['clipping']])\\n print(\\\"Clipping parameters: {}\\\".format(clipping_param))\\n #K = PPO_config['K']\\n #assert(K==len(A_hat))\\n policy_model.to(device)\\n old_policy_model.to(device)\\n surr_objective = 0\\n kl_est_list = []\\n for k in range(len(A_hat)):\\n for i in range(len(A_hat[k])):\\n # CHECK: if the action is an integer corresponding to the state\\n curr_state = torch.tensor(A_hat[k][i][0]).float()\\n try:\\n curr_state_norm_input = torch.cat((curr_state[:,0].unsqueeze(1), (curr_state[:,1:]-state_part_mean)/state_part_std))\\n except:\\n curr_state_norm_input = torch.cat((curr_state[0].unsqueeze(0), (curr_state[1:]-state_part_mean)/state_part_std))\\n\\n curr_state_norm_input[np.isnan(curr_state_norm_input)] = 0\\n curr_state_norm_input.to(device)\\n curr_action = A_hat[k][i][1]\\n\\n r_theta_ksi = policy_model(curr_state_norm_input.to(device))[curr_action] / old_policy_model(curr_state_norm_input.to(device))[curr_action] \\n r_theta_ksi_num = r_theta_ksi.cpu().detach().numpy()\\n single_est_from_r = r_theta_ksi_num-1-np.log(r_theta_ksi_num)\\n kl_est_list.append(single_est_from_r)\\n A_hat_ksi = A_hat[k][i][2] # Estimate the A_hat\\n surr_objective += min(r_theta_ksi * A_hat_ksi, clip(r_theta_ksi, clipping_param) * A_hat_ksi)\\n surr_objective /= len(A_hat)\\n kl_est = np.mean(np.asarray(kl_est_list))\\n\\n return surr_objective, kl_est\",\n \"def _policy_eval(self, policy: np.ndarray) -> np.ndarray:\\n V = np.zeros(self.state_dim)\\n diff = 1.0\\n dr = 0.9\\n while (diff >= self.theta):\\n diff = 0.0\\n for s in self.mdp._state_dict:\\n old = V[self.mdp._state_dict[s]]\\n temp = 0.0\\n for opt in range(self.action_dim):\\n if policy[self.mdp._state_dict[s],opt] == 1.0: \\n for next_s in self.mdp._state_dict:\\n p = self.mdp.P[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\\n r = self.mdp.R[self.mdp._state_dict[s],opt,self.mdp._state_dict[next_s]]\\n Vs = V[self.mdp._state_dict[next_s]]\\n temp = temp + p * (r + dr * Vs)\\n V[self.mdp._state_dict[s]] = temp\\n diff = max(diff,abs(old - V[self.mdp._state_dict[s]]))\\n return V\",\n \"def getSuccessors(self, state):\\n\\t\\tutil.raiseNotDefined()\",\n \"def termination_heuristics(self, state):\\n\\n point_state = state[0][0]\\n target_mask = get_target_mask(point_state)\\n point_state = point_state[:3, target_mask].T\\n depth_heuristics = self.graspnet_closure(point_state) \\n if (depth_heuristics):\\n print('object inside gripper? start retracting...')\\n return depth_heuristics\",\n \"def run_policy(env, policy, scaler, logger, episodes, task_reward_weight, imitation_reward_weight):\\n total_steps = 0\\n trajectories = []\\n for e in range(episodes):\\n observes, phases, actions, rewards, unscaled_obs, task_r, imitation_r, imitation_r_logs = run_episode(\\n env, policy, scaler, task_reward_weight, imitation_reward_weight)\\n total_steps += observes.shape[0]\\n trajectory = {'observes': observes,\\n 'phases': phases,\\n 'actions': actions,\\n 'rewards': rewards,\\n 'unscaled_obs': unscaled_obs,\\n 'task_r': task_r,\\n 'imitation_r': imitation_r,\\n 'imitation_r_logs': imitation_r_logs}\\n trajectories.append(trajectory)\\n unscaled = np.concatenate([t['unscaled_obs'] for t in trajectories])\\n # update running statistics for scaling observations\\n scaler.update(unscaled)\\n logger.log({'_MeanReward': np.mean([t['rewards'].sum() for t in trajectories]),\\n 'Steps': total_steps})\\n\\n return trajectories\",\n \"def policy_improvement(self):\\n\\n # Set max value to terminal states in order to force actions to them.\\n self.V[self.get_index(*self.environment.terminal_state)] = np.max(self.V) + 1\\n\\n policy_stable = True # Used to check if the Policy has changed or not in this step\\n\\n for state in range(len(self.V)):\\n old_actions_probs = np.copy(self.policy.state_actions_probs[state])\\n\\n if self.environment.is_wall(*self.get_position(state)):\\n self.policy.state_actions_probs[state] = [0, 0, 0, 0]\\n else:\\n self.policy.state_actions_probs[state] = self.get_greedy_actions(state)\\n\\n if not np.array_equal(old_actions_probs, self.policy.state_actions_probs[state]):\\n policy_stable = False\\n\\n return self.policy, policy_stable\",\n \"def train(self, obs, acs, rews_list, next_obs, terminals):\\n\\n # step 1: calculate q values of each (s_t, a_t) point, \\n # using rewards from that full rollout of length T: (r_0, ..., r_t, ..., r_{T-1})\\n q_values = self.calculate_q_vals(rews_list)\\n\\n # step 2: calculate advantages that correspond to each (s_t, a_t) point\\n advantage_values = self.estimate_advantage(obs, q_values)\\n\\n # step 3:\\n # TODO: pass the calculated values above into the actor/policy's update, \\n # which will perform the actual PG update step\\n\\n # TODO: define the loss that should be optimized when training a policy with policy gradient\\n # HINT1: Recall that the expression that we want to MAXIMIZE\\n # is the expectation over collected trajectories of:\\n # sum_{t=0}^{T-1} [grad [log pi(a_t|s_t) * (Q_t - b_t)]]\\n # HINT2: see define_log_prob (above)\\n # to get log pi(a_t|s_t)\\n # HINT3: look for a placeholder above that will be populated with advantage values \\n # to get [Q_t - b_t]\\n # HINT4: don't forget that we need to MINIMIZE this self.loss\\n # but the equation above is something that should be maximized\\n\\n # define the log probability of seen actions/observations under the current policy\\n with tf.GradientTape() as tape:\\n log_action_probas = self.actor.get_log_prob(obs, acs)\\n advantage_values_no_grad = tf.stop_gradient(advantage_values)\\n loss = -tf.reduce_mean(advantage_values_no_grad * log_action_probas)\\n\\n actor_vars = self.actor.trainable_variables\\n grads = tape.gradient(loss, actor_vars)\\n self.policy_optimizer.apply_gradients(zip(grads, actor_vars))\\n\\n if self.nn_baseline:\\n targets_n = (q_values - np.mean(q_values)) / (np.std(q_values) + 1e-8)\\n dataset = tf.data.Dataset.from_tensor_slices(\\n (tf.cast(obs, tf.float32), tf.cast(targets_n, tf.float32)))\\n dataset = dataset.batch(batch_size=targets_n.shape[0]).repeat()\\n # 20 baseline gradient updates with the current data batch.\\n self.baseline_model.fit(dataset, epochs=1, steps_per_epoch=20)\\n\\n return loss.numpy().item()\",\n \"def rollout(problem, state):\\n problem.setState(state)\\n while not problem.isTerminal():\\n action = random.choice(problem.legalMoves())\\n problem.move(action)\\n\\n return problem.finalScore()\",\n \"def retrieve_rollout(self, rollout):\\n with self.mutex:\\n world, scenario = str(rollout.world), str(rollout.scenario)\\n # If scenario is done -> discard rollout\\n if self.scenario_counter[(world, scenario)] >= self.scenario_samples:\\n counter_samples = np.sum(self.scenario_counter.values())\\n if counter_samples >= self.batch_size and len(self.buffer) < self.batch_size:\\n self.scenario_counter[(world, scenario)\\n ] -= (self.batch_size - self.buffer)\\n # If scenario is not done -> store rollout data\\n else:\\n trajectory = zip(rollout.observations, rollout.actions,\\n rollout.values, rollout.returns, rollout.advantages)\\n for obs, action, value, return_, advantage in trajectory:\\n shaped_obs = self.shape_obs(obs)\\n self.buffer.append(shaped_obs, action.action, action.logp,\\n value, return_, advantage)\\n self.scenario_counter[(world, scenario)] += 1\\n if self.scenario_counter[(world, scenario)] >= self.scenario_samples:\\n self.scenario_idx = (\\n self.scenario_idx + 1) % len(self.scenarios)\\n break\\n self.buffer.episode_rewards.append(np.sum(rollout.rewards))\\n self.buffer.episode_lengths.append(rollout.episode_length)\",\n \"def main():\\n print(get_advice())\",\n \"def learn(self):\\n batch = self.agent.replay_buffer.sample(self.batch_size)\\n states = torch.tensor([x.state for x in batch], dtype=torch.float32).to(self.agent.device) # shape == (batch_size, 3, 6, 7)\\n actions = [x.action for x in batch]\\n rewards = torch.tensor([x.reward for x in batch], dtype=torch.float32).to(self.agent.device)\\n next_states = torch.tensor([x.next_state for x in batch], dtype=torch.float32).to(self.agent.device)\\n dones = [x.done for x in batch]\\n\\n self.optimizer.zero_grad()\\n\\n\\n q_vals = self.agent.policy_net(states)[range(len(actions)), actions] # Q vals for actions taken\\n q_next_vals = self.agent.target_net(next_states).detach() # we don't care about grad wrt target net\\n q_next_vals[dones] = 0.0 # terminal states have no future expected value\\n q_targets = rewards + self.gamma * torch.max(q_next_vals, dim=1)[0]\\n\\n # all_q_vals = self.agent.policy_net(states)\\n # print()\\n # print('actions')\\n # print(actions)\\n # print()\\n # print('original all q vals')\\n # print(self.agent.policy_net(states)) \\n # print(self.agent.policy_net(states).shape)\\n # print()\\n # print('QVALS:', q_vals)\\n # print(q_vals.shape)\\n # print('\\\\n\\\\n')\\n # print('QTARGETS:', q_targets)\\n # print(q_targets.shape)\\n\\n # breakpoint()\\n\\n loss = self.loss_fn(q_targets, q_vals).to(self.agent.device)\\n loss.backward()\\n \\n # for layer in self.agent.policy_net.named_parameters():\\n \\n # # print(f'layer: {layer[0]}')\\n # # print(f'grad:', layer[1].grad)\\n\\n # # print('loss', loss)\\n # # print('q_vals grad:', q_vals.grad)\\n # # print('states:', )\\n\\n self.optimizer.step()\\n\\n self.agent.learning_iters += 1\\n if self.agent.learning_iters % self.target_update_freq == 0:\\n self.agent.update_target_net()\\n # logger.info('Updated target net')\",\n \"def _rollout_an_episode(self):\\n self._steps = 0\\n me_id = self._learning_agent_id # short name\\n oppo_id = self._oppo_agent_id # short name\\n logger.log('episode begins with the task: {}'.format(str(self.task)))\\n\\n # passing me and oppo hyperparams to the arena interface\\n assert self.task.hyperparam is not None\\n logger.log('pulling oppo hyperparam of model key {}'.format(\\n self.task.model_key2))\\n oppo_hyperparam = self._model_pool_apis.pull_attr(attr='hyperparam',\\n key=self.task.model_key2)\\n logger.log('Done pulling oppo hyperparam')\\n oppo_inter_kwargs = ({} if oppo_hyperparam is None\\n else oppo_hyperparam.__dict__)\\n inter_kwargs = ([self.task.hyperparam.__dict__]\\n + [oppo_inter_kwargs] * (self.n_agents - 1))\\n\\n # agent, env reset\\n obs = self.env.reset(inter_kwargs=inter_kwargs)\\n for agt, ob in zip(self.agents, obs):\\n agt.reset(ob)\\n self._update_agents_model(self.task) # for agent Neural Net parameters\\n\\n me_reward_sum = 0.0\\n self.time_beg = time.time()\\n self._update_hyperparam(self.task)\\n self._changed_task = False\\n while True:\\n self._steps += 1\\n # predictions for each agent\\n predictions = self._parallel.run((self._agent_pred, ob, i)\\n for i, ob in enumerate(obs))\\n me_prediction = predictions[me_id]\\n me_action, extra_vars = me_prediction[me_id], me_prediction[oppo_id:]\\n # predicted actions for each agent\\n actions = [me_action] + [other_action\\n for other_action in predictions[oppo_id:]]\\n # book-keep obs in previous step\\n last_obs = obs\\n\\n # agent-env interaction\\n obs, reward, done, info = self.env.step(actions)\\n\\n me_rwd_scalar = self._reward_shape(reward[me_id])\\n me_reward_sum += me_rwd_scalar\\n\\n if self._enable_push:\\n # put the interested data (obs, rwd, act, ... for each agent) into the\\n # _data_queue, which is watched in another Thread (the _push_data_to_learner()\\n # method) that the data are dequeued and sent to remote Learner\\n if self._data_queue.full():\\n logger.log(\\\"Actor's queue is full.\\\", level=logger.WARN)\\n rwd_to_push = (me_rwd_scalar if self.rwd_shape\\n else np.asarray(reward[me_id], np.float32))\\n if self.use_oppo_obs:\\n if isinstance(extra_vars, tuple):\\n extra_vars += (self.agents[self._oppo_agent_id]._last_state,)\\n else:\\n extra_vars.append(self.agents[self._oppo_agent_id]._last_state)\\n data_tuple = (last_obs, tuple(actions), rwd_to_push, info, done, extra_vars)\\n self._data_queue.put(data_tuple)\\n logger.log('successfully put one tuple.', level=logger.DEBUG)\\n\\n if self._steps % self._log_interval_steps == 0:\\n logger.log('_rollout_an_episode,', 'steps: {},'.format(self._steps),\\n 'data qsize: {}'.format(self._data_queue.qsize()))\\n\\n if done:\\n # an episode ends\\n if self._replay_dir:\\n self._save_replay()\\n self.log_kvs(me_reward_sum, info)\\n if self._changed_task:\\n return None, info\\n else:\\n return self.log_outcome(info), info\\n\\n if self._update_model_freq and self._steps % self._update_model_freq == 0:\\n # time to update the model for each agent\\n if (self._enable_push and\\n self._model_pool_apis.pull_attr(\\n 'freezetime', self.task.model_key1) is not None):\\n # Current task (learning period) finishes, start a new task or continue\\n self._finish_task(self.task, None) # notify early abort\\n last_task = self.task\\n self.task = self._request_task() # try to continue\\n if not is_inherit(last_task.model_key1, self.task.model_key1):\\n self.log_kvs(me_reward_sum, info)\\n return None, info\\n if last_task.model_key2 != self.task.model_key2:\\n self._changed_task = True\\n self._update_agents_model(self.task)\",\n \"def get_clarifications_winogrande(ex, nlp, comet_model):\\n personx, persony = ex['option1'], ex['option2']\\n\\n # Only extract relations for people\\n if personx[0] != personx[0].upper() or persony[0] != persony[0].upper():\\n return []\\n\\n input_event = ex[\\\"sentence\\\"]\\n outputs = {category: comet_model.predict(input_event, category, num_beams=5) for category in comet_model.categories}\\n\\n curr_events = []\\n for category, prefix in CATEGORY_TO_PREFIX.items():\\n for out_event in outputs[category]:\\n if out_event != \\\"none\\\" and out_event != \\\"\\\":\\n if not out_event.lower().startswith(\\\"person\\\") and not out_event.lower().startswith(\\\"other\\\"):\\n out_event = \\\" \\\".join((prefix, out_event))\\n\\n out_event = re.sub(\\\"personx\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"person x\\\", personx, out_event, flags=re.I)\\n out_event = re.sub(\\\"persony\\\", persony, out_event, flags=re.I)\\n out_event = re.sub(\\\"person y\\\", persony, out_event, flags=re.I)\\n\\n question = CATEGORY_TO_QUESTION[category].replace(\\\"PersonX\\\", personx)\\n curr_events.append((question, out_event))\\n\\n return curr_events\",\n \"def frozen_lake_mdp_helper(grid_size=4, p=0.8, is_slippery=True):\\n n_action = 4\\n\\n random_map = generate_random_map(size=grid_size, p=p)\\n env = gym.make(\\\"FrozenLake-v0\\\", desc=random_map, is_slippery=is_slippery)\\n env.reset()\\n env.render()\\n open_ai_p = env.P\\n # print(env.P)\\n\\n transition_p = np.zeros((n_action, grid_size**2, grid_size**2))\\n reward = np.zeros((n_action, grid_size**2, grid_size**2))\\n\\n for state, state_dict in open_ai_p.items():\\n for action, prob_tuple_list in state_dict.items():\\n for prob_tuple in prob_tuple_list:\\n probability, next_state, r, done = prob_tuple\\n\\n transition_p[action][state][next_state] += probability\\n reward[action][state][next_state] = r*100 - 1\\n # # print(r)\\n # if probability != 0:\\n # print(\\\"Found\\\", state, action, probability, next_state, r, done)\\n # print(transition_p)\\n # # print(reward)\\n\\n return transition_p, reward, random_map\",\n \"def define_state_transition_SettlementStatusProcessingAdvice():\\n\\n state_chart_name = 'FSwiftSettStatusProcessAdviceIn'\\n old_state_chart_name = ''\\n state_chart = {\\n 'Ready': {'Identified': 'Paired',\\n 'NotIdentified': 'Unpaired'},\\n\\n 'Unpaired': {'Identified': 'Paired'},\\n\\n 'Paired': {'Acknowledge': 'Acknowledged',\\n 'NoMatch': 'NotMatched',\\n 'Match': 'Matched',\\n 'Pending': 'PendingSettlement',\\n 'Failing': 'FailingSettlement',\\n 'Reject': 'Rejected',\\n 'Cancel': 'Cancelled',},\\n\\n 'Acknowledged': {'NoMatch': 'NotMatched',\\n 'Match': 'Matched',\\n 'Pending': 'PendingSettlement',\\n 'Failing': 'FailingSettlement',\\n 'Cancel': 'Cancelled',\\n 'Reject': 'Rejected',\\n 'AmndCancRequest':'AmendCancelRequested',\\n 'AmndCancPending':'AmendCancelPending',\\n 'Done':'Processed'},\\n\\n 'Matched': {'Pending': 'PendingSettlement',\\n 'Cancel': 'Cancelled',\\n 'Reject': 'Rejected',\\n 'Failing': 'FailingSettlement',\\n 'AmndCancRequest':'AmendCancelRequested',\\n 'AmndCancPending':'AmendCancelPending',\\n 'Done':'Processed'\\n },\\n\\n 'FailingSettlement':{'AmndCancRequest':'AmendCancelRequested',\\n 'AmndCancPending':'AmendCancelPending',\\n 'Reject': 'Rejected',\\n 'Cancel': 'Cancelled',\\n 'Done':'Processed'},\\n\\n 'NotMatched': {'Match': 'Matched',\\n 'Pending': 'PendingSettlement',\\n 'Failing': 'FailingSettlement',\\n 'Cancel': 'Cancelled',\\n 'Reject': 'Rejected',\\n 'AmndCancRequest':'AmendCancelRequested',\\n 'AmndCancPending':'AmendCancelPending',\\n 'Done':'Processed'},\\n\\n 'AmendCancelRequested': {'AmndCancComplete':'AmendCancelCompleted',\\n 'Done':'Processed'},\\n\\n 'AmendCancelPending': {'AmndCancComplete':'AmendCancelCompleted',\\n 'Done':'Processed'},\\n\\n 'PendingSettlement': {'Failing': 'FailingSettlement',\\n 'Cancel': 'Cancelled',\\n 'AmndCancRequest':'AmendCancelRequested',\\n 'AmndCancPending':'AmendCancelPending',\\n 'Reject': 'Rejected',\\n 'Done':'Processed'},\\n 'Cancelled' : { 'Done':'Processed'},\\n 'AmendCancelCompleted' : { 'Done':'Processed'},\\n\\n 'Rejected': {'Cancel': 'Cancelled',\\n 'Done': 'Processed'}\\n\\n }\\n\\n co_string = 'Paired,73,-105;Cancelled,676,329;Not Match,696,-223;Match,1271,173;AmendCancelPending,728,189;Matched,347,-133;Unpaired,-178,-351;Acknowledge,435,-110;Reject,229,538;AmendCancelCompleted,862,90;Rejected,177,338;NotMatched,432,-283;Acknowledged,549,-429;Processed,1072,-131;Pending Settlement,1193,495;PendingSettlement,304,22;FailingSettlement,255,179;Ready,-237,-98;AmendCancelRequested,737,0;'\\n #state_charts_list.append({state_chart_name: [state_chart] + [co_string]})\\n\\n return state_chart_name, state_chart, old_state_chart_name, co_string\",\n \"def rollout(self, us):\\n xs = [self.initialState]\\n for m, d, u in zip(self.runningModels, self.runningDatas, us):\\n xs.append(m.calc(d, xs[-1], u)[0].copy())\\n return xs\",\n \"def Oozeplorer_Percept(reference):\\n self = reference\\n self.our_frontier = list()\\n self.unsure_moves = list() # Popped moves we didn't want.\\n self.ignore_moves = list()\\n def knowledge_based_program(percept):\\n \\\"\\\"\\\" The agent program\\n \\\"\\\"\\\"\\n if not self.alive or self.winner:\\n return self.location\\n # Telling the Dict\\n tell_kb(percept, self.location, self.oz_kb)\\n local_frontier = _valid_neighbors(self.location, self.some_number)\\n self.our_frontier.extend(local_frontier)\\n n_count = 0\\n for _x in range(len(self.our_frontier)):\\n # Update the random moves.\\n move = self.our_frontier[n_count]\\n if move in self.unsure_moves:\\n self.unsure_moves.remove(move)\\n if self.explored.has_key(move) and self.explored[move]:\\n self.our_frontier.pop(0)\\n continue\\n # Check for Validity.\\n valid_move = ask_kb(percept, move, self)\\n if valid_move:\\n move = self.our_frontier.pop(n_count)\\n self.explored[move] = True\\n return move\\n else:\\n _nmove = self.our_frontier.pop(n_count)\\n if not self.explored.has_key(_nmove):\\n self.unsure_moves.append(_nmove)\\n continue\\n move = self.unsure_moves.pop(0)\\n self.explored[move] = True\\n return move\\n return knowledge_based_program\",\n \"def get_penalty(state, action, winrate_predictor):\\n if violate_rule(state, action):\\n return -1 \\n return 0\",\n \"def ppo_update(policy_model, policy_optimizer, baseline_model, baseline_optimizer, baseline_criterion,\\n ppo_epoch, minibatch_size, obs_n_tensor, log_prob_n_old_tensor, action_n_tensor, rewards_n, mask_n,\\n gamma, lam, update_policy=True, update_baseline=True):\\n\\n ## get number of data\\n n_data = obs_n_tensor.shape[0]\\n ## get baseline estimations\\n baseline_n = baseline_model(obs_n_tensor).detach() ## between -1 and 1\\n ## get q values, used for scaling baseline values\\n q_n = get_reward_to_go(rewards_n, mask_n, gamma)\\n q_n = Tensor(q_n).reshape(-1, 1)\\n q_n_mean = q_n.mean()\\n q_n_std = q_n.std() + 1e-2\\n ## get scaled baseline values\\n baseline_n_scaled = baseline_n * q_n_std + q_n_mean\\n\\n ## get advantage and scaled baseline targets\\n ## the adv returned is normalized, baseline_target_scaled is not normalized\\n adv_n, baseline_target_n_scaled = get_gae_advantage(rewards_n, baseline_n_scaled, mask_n, gamma, lam)\\n adv_n = adv_n.detach()\\n\\n ## get baseline targets\\n baseline_target_n = ((baseline_target_n_scaled - q_n_mean) / q_n_std).detach() ## target now is normalized\\n\\n ## NOTE MAKE SURE YOU DETACH THINGS OTHERWISE YOU WILL HAVE VERY STRANGE ERRORS\\n\\n ## for each training iteration, do some epoches\\n for i_epoch in range(ppo_epoch):\\n ## ppo: shuffle data\\n shuffle_indexes = torch.randperm(n_data)\\n obs_n_tensor = obs_n_tensor[shuffle_indexes]\\n adv_n = adv_n[shuffle_indexes]\\n log_prob_n_old_tensor = log_prob_n_old_tensor[shuffle_indexes]\\n action_n_tensor = action_n_tensor[shuffle_indexes]\\n baseline_target_n = baseline_target_n[shuffle_indexes]\\n\\n ## after shuffle data, we do the minibatch ppo update\\n n_minibatch = int(n_data / minibatch_size)\\n for i_minibatch in range(n_minibatch):\\n ## get minibatch\\n istart = i_minibatch * minibatch_size\\n iend = (i_minibatch + 1) * minibatch_size\\n obs_m = obs_n_tensor[istart:iend]\\n adv_m = adv_n[istart:iend]\\n log_prob_old_m = log_prob_n_old_tensor[istart:iend]\\n action_m = action_n_tensor[istart:iend]\\n baseline_target_m = baseline_target_n[istart:iend]\\n\\n ## update baseline\\n if update_baseline:\\n baseline_m = baseline_model(obs_m)\\n baseline_optimizer.zero_grad()\\n baseline_loss = baseline_criterion(baseline_m, baseline_target_m)\\n baseline_loss.backward()\\n baseline_optimizer.step()\\n\\n ## update policy\\n ## we need the new policy's log probs so that we can calculate importance sampling term\\n if update_policy:\\n mu, log_sigma = policy_model(obs_m)\\n normal_dist = Normal(mu, log_sigma.exp())\\n log_prob_new_m = normal_dist.log_prob(action_m)\\n log_prob_new_m = torch.sum(log_prob_new_m, dim=1).reshape(-1, 1)\\n ## now we get the importance sampling weight term\\n is_term_m = (log_prob_new_m - log_prob_old_m).exp()\\n\\n ## compute the clipped surrogate\\n epsilon = 0.2\\n first_term = is_term_m * adv_m\\n clipped_term = is_term_m.clamp(1 - epsilon, 1 + epsilon) * adv_m\\n obj_term = torch.min(first_term, clipped_term)\\n obj_sum = obj_term.sum()\\n policy_loss = -obj_sum / n_data\\n policy_optimizer.zero_grad()\\n policy_loss.backward()\\n policy_optimizer.step()\",\n \"def env_runner(env, policy, num_local_steps, summary_writer):\\n last_state = env.reset()\\n last_features = policy.get_initial_features()\\n length = 0\\n rewards = np.zeros(5)\\n \\n while True:\\n terminal_end = False\\n rollout = PartialRollout()\\n\\n for local_step in range(num_local_steps):\\n features = policy.features(last_state, *last_features)\\n state, reward, terminal, info = env.step(None)\\n\\n if len(np.shape(terminal)) > 0:\\n reward = np.sum(reward, axis=0) / len(terminal)\\n state = state[-1]\\n terminal = terminal[-1]\\n # total_reward = np.zeros_like(reward[0])\\n\\n # for i, t in enumerate(terminal[:-1]):\\n # if t:\\n # total_reward += reward[i]\\n # else:\\n # total_reward += policy.value(state[i], *features)\\n\\n # if terminal[-1]:\\n # total_reward += reward[-1]\\n # total_reward /= len(terminal)\\n # else:\\n # total_reward /= len(terminal[:-1])\\n # likelihood *= 1. / len(terminal)\\n\\n # reward = total_reward\\n # state = state[-1]\\n # terminal = terminal[-1]\\n\\n # collect the experience\\n # note that the deepcopies seem to be necessary\\n rollout.add(\\n copy.deepcopy(last_state), \\n copy.deepcopy(reward), \\n info['weight'], \\n terminal, \\n copy.deepcopy(last_features))\\n \\n length += 1\\n rewards += reward\\n\\n last_state = state\\n last_features = features\\n\\n if info:\\n summary = tf.Summary()\\n for k, v in info.items():\\n summary.value.add(tag=k, simple_value=float(v))\\n summary_writer.add_summary(summary, policy.global_step.eval())\\n summary_writer.flush()\\n\\n timestep_limit = env.spec.tags.get(\\n 'wrapper_config.TimeLimit.max_episode_steps')\\n if terminal or length >= timestep_limit:\\n terminal_end = True\\n if length >= timestep_limit or not env.metadata.get('semantics.autoreset'):\\n last_state = env.reset()\\n last_features = policy.get_initial_features()\\n print(\\\"Episode finished. Sum of rewards: {}. Length: {}\\\".format(rewards, length))\\n length = 0\\n rewards = 0\\n break\\n\\n if not terminal_end:\\n rollout.r = policy.value(last_state, *last_features)\\n\\n # once we have enough experience, yield it\\n yield rollout\",\n \"def rollout(env, acts):\\n total_rew = 0\\n env.reset()\\n steps = 0\\n for act in acts:\\n _obs, rew, done, _info = env.step(act)\\n steps += 1\\n total_rew += rew\\n if done:\\n break\\n\\n return steps, total_rew\",\n \"def extractPolicy(self, V):\\n\\n policy = np.zeros(self.nStates)\\n for i in range(self.nStates):\\n A = self.helper(i, V)\\n best_action = np.argmax(A)\\n policy[i] = best_action\\n\\n return policy\",\n \"def get_state_action_representation_from_DSL(demos):\\n input_demos = demos\\n if type(demos) is list:\\n input_demos = {'pos' : demos, 'neg' : []}\\n X_csr, y = run_all_programs_on_demonstrations(NUM_PROGRAMS, input_demos)\\n X = X_csr.todense()\\n return np.array(X), X_csr, y\",\n \"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\\n\\n # Start with a random (all 0) value function\\n V = np.zeros(env.nS)\\n \\n while True: #any(Vdiff > theta):\\n \\n delta_V = 0\\n\\n for i in range(env.nS):\\n \\n # need to calculate the value of taking each of the available actions\\n\\n action_val = np.zeros(env.nA)\\n\\n for a in range(env.nA):\\n \\n # get transition tuple for this state and action\\n tup = env.P[i][a][0]\\n \\n # calculate the value of this action/state? \\n # value = reward + gamma * (prob * V[next_state])\\n # error here I think, probability missing\\n action_val[a] = tup[0] * (tup[2] + discount_factor * V[tup[1]])\\n \\n \\n Vold = V[i]\\n Vnew = np.dot(policy[i],action_val)\\n delta_V = max(delta_V,np.abs(Vnew - Vold))\\n # get state value by multiplying probability of taking action (policy) by action value\\n V[i] = Vnew\\n \\n #print(action_val)\\n #print(policy[i])\\n #print(V[i])\\n #print(delta_V)\\n\\n # function only works if I use this delta rule to terminate\\n if delta_V < theta:\\n break\\n return np.array(V)\",\n \"def result(self, state, action):\\n state_after_act = [[0 for i in range(self.col)] for j in range(self.row)]\\n for k in action:\\n x = k[1][0]\\n y = k[1][1]\\n if k[0] == \\\"vaccinate\\\":\\n state_after_act[x][y] = ('I', 1)\\n else:\\n state_after_act[x][y] = ('Q', 1)\\n\\n for i in range(self.row):\\n for j in range(self.col):\\n if state_after_act[i][j] == 0:\\n if state[i][j][0] == 'U' or state[i][j][0] == 'I':\\n state_after_act[i][j] = state[i][j]\\n\\n elif state[i][j][0] == 'S':\\n if state[i][j][1] == 3:\\n state_after_act[i][j] = ('H', 1)\\n else:\\n if state[i][j][1] == 1:\\n state_after_act[i][j] = ('S', 2)\\n elif state[i][j][1] == 2:\\n state_after_act[i][j] = ('S', 3)\\n\\n elif state[i][j][0] == 'Q':\\n if state[i][j][1] == 2:\\n state_after_act[i][j] = ('H', 1)\\n else:\\n state_after_act[i][j] = ('Q', 2)\\n\\n elif state[i][j][0] == 'H':\\n state_after_act[i][j] = self.healthy(i, j, state,state_after_act)\\n state_after_act[i] = tuple(state_after_act[i])\\n return tuple(state_after_act)\",\n \"def process_proposes(self):\\n\\n # Check which I'm still interested in, could be learned\\n accepted = defaultdict(bool)\\n for proposal in self.interaction_proposals:\\n proposal: Tuple[Guest, str]\\n error_prob = .05\\n if random.random() < error_prob:\\n accepted[proposal] = True\\n else:\\n if self.knowledge[(proposal[0].role, proposal[1])] >= 0:\\n accepted[proposal] = True\\n\\n self.interaction_proposals = filter(lambda x: accepted[x], self.interaction_proposals)\\n # self.interaction_proposals = filter(lambda x: False, self.interaction_proposals)\\n\\n # self.interaction_proposals = list(self.interaction_proposals)\\n\\n # Check which the other is still interested in, don't touch\\n self.interaction_proposals = filter(lambda x: (self, x[1]) in x[0].interaction_proposals,\\n self.interaction_proposals)\\n # TODO maybe split this up?\\n self.interaction_proposals = list(self.interaction_proposals)\\n\\n if len(self.interaction_proposals) > 0:\\n # Choose a random interaction from the approved ones\\n if len(self.interaction_proposals) > 1:\\n print(self.unique_id, self.interaction_proposals)\\n\\n other: Guest\\n interaction: str\\n other, interaction = random.choice(self.interaction_proposals)\\n # print(interaction)\\n\\n getattr(self.model, interaction)(self, other)\\n other.interaction_proposals = []\",\n \"def transition(rule, sampler, machine, parameters, state, key, σ):\\n get_conn_flattened = rule.operator._get_conn_flattened_closure()\\n n_conn_from_sections = rule.operator._n_conn_from_sections\\n\\n @njit4jax(\\n (\\n jax.core.ShapedArray(σ.shape, σ.dtype),\\n jax.core.ShapedArray((σ.shape[0],), σ.dtype),\\n )\\n )\\n def _transition(args):\\n # unpack arguments\\n v_proposed, log_prob_corr, v, rand_vec = args\\n\\n log_prob_corr.fill(0)\\n sections = np.empty(v.shape[0], dtype=np.int32)\\n vp, _ = get_conn_flattened(v, sections)\\n\\n _choose(vp, sections, rand_vec, v_proposed, log_prob_corr)\\n\\n # TODO: n_conn(v_proposed, sections) implemented below, but\\n # might be slower than fast implementations like ising\\n get_conn_flattened(v_proposed, sections)\\n n_conn_from_sections(sections)\\n\\n log_prob_corr -= np.log(sections)\\n\\n # ideally we would pass the key to python/numba in _choose, initialise a\\n # np.random.default_rng(key) and use it to generate random uniform integers.\\n # However, numba dose not support np states, and reseeding it's MT1998 implementation\\n # would be slow so we generate floats in the [0,1] range in jax and pass those\\n # to python\\n rand_vec = jax.random.uniform(key, shape=(σ.shape[0],))\\n\\n σp, log_prob_correction = _transition(σ, rand_vec)\\n\\n return σp, log_prob_correction\",\n \"def pack_experience(states, actions, rewards, next_states, dones):\\n\\n# pdb.set_trace()\\n \\n return (states.flatten(),\\n actions.flatten(),\\n rewards,\\n next_states.flatten(),\\n dones)\",\n \"def fullWorkflow():\\n ptsWf = ProteinTargetSequenceExecutionWorkflow()\\n ok = ptsWf.cacheTaxonomy()\\n ok = ptsWf.updateUniProtTaxonomy() and ok\\n ok = ptsWf.fetchProteinEntityData() and ok\\n ok = ptsWf.fetchChemicalReferenceMappingData() and ok\\n ok = ptsWf.fetchLigandNeighborMappingData() and ok\\n ok = ptsWf.exportFasta() and ok\\n ok = ptsWf.createSearchDatabases() and ok\\n ok = ptsWf.searchDatabases() and ok\\n ok = ptsWf.buildFeatures() and ok\\n ok = ptsWf.buildActivityData() and ok\\n ok = ptsWf.buildCofactorData() and ok\\n ptsWf.resourceCheck()\\n return ok\",\n \"def getPolicy(self, state):\\n return self.policy[state]\",\n \"def slp(self):\\n return self.elevcomp\",\n \"def best_policy(self, U):\\n pi = np.zeros((self.nstates),np.int)\\n for s in range(self.nstates):\\n pi[s] = self.best_action(s,U)\\n return pi\",\n \"def take_one_step(env, policy, state):\\n action = sample_action(policy, state)\\n new_state, reward, done, _ = env.step(action)\\n if (done == True and reward > 0):\\n reward = 10.0\\n if new_state in hole_states:\\n reward = -10.0\\n elif not done:\\n reward = -1.0\\n # print(\\\"state {0}, new_state {1}, action {2}, reward {3}, done {4}\\\".format(state, new_state, action, reward, done))\\n return action, reward, new_state, done\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6982677","0.64091736","0.61041176","0.5740429","0.5698572","0.56384194","0.5564403","0.53721863","0.53550786","0.53181446","0.52759427","0.52260995","0.513249","0.51045525","0.50875306","0.5063328","0.5041458","0.5035391","0.499523","0.49948314","0.49926388","0.49735102","0.48996446","0.48870698","0.488291","0.4878001","0.485031","0.48310184","0.48282644","0.4821674","0.4815286","0.48094627","0.4801965","0.47790247","0.47789603","0.47781193","0.47757316","0.47756132","0.47718012","0.47272786","0.4727124","0.47187275","0.47047904","0.47043037","0.47042912","0.46995825","0.46984643","0.46764776","0.46732783","0.46709067","0.46674412","0.46423945","0.463722","0.46265963","0.46197546","0.46182698","0.46182698","0.4614188","0.46025252","0.46014798","0.45947328","0.459279","0.45911363","0.45843533","0.45775267","0.4567023","0.4563234","0.4561893","0.4556178","0.45496076","0.45468786","0.45449305","0.4523725","0.45222747","0.45190316","0.45129633","0.45123658","0.4511279","0.4509641","0.45090705","0.45043713","0.45037958","0.45022354","0.44970608","0.4496326","0.44939566","0.44934264","0.44910848","0.44759285","0.44737306","0.4471638","0.44579497","0.4456403","0.44543198","0.44494048","0.44444942","0.44311002","0.4430763","0.4426904","0.44176888"],"string":"[\n \"0.6982677\",\n \"0.64091736\",\n \"0.61041176\",\n \"0.5740429\",\n \"0.5698572\",\n \"0.56384194\",\n \"0.5564403\",\n \"0.53721863\",\n \"0.53550786\",\n \"0.53181446\",\n \"0.52759427\",\n \"0.52260995\",\n \"0.513249\",\n \"0.51045525\",\n \"0.50875306\",\n \"0.5063328\",\n \"0.5041458\",\n \"0.5035391\",\n \"0.499523\",\n \"0.49948314\",\n \"0.49926388\",\n \"0.49735102\",\n \"0.48996446\",\n \"0.48870698\",\n \"0.488291\",\n \"0.4878001\",\n \"0.485031\",\n \"0.48310184\",\n \"0.48282644\",\n \"0.4821674\",\n \"0.4815286\",\n \"0.48094627\",\n \"0.4801965\",\n \"0.47790247\",\n \"0.47789603\",\n \"0.47781193\",\n \"0.47757316\",\n \"0.47756132\",\n \"0.47718012\",\n \"0.47272786\",\n \"0.4727124\",\n \"0.47187275\",\n \"0.47047904\",\n \"0.47043037\",\n \"0.47042912\",\n \"0.46995825\",\n \"0.46984643\",\n \"0.46764776\",\n \"0.46732783\",\n \"0.46709067\",\n \"0.46674412\",\n \"0.46423945\",\n \"0.463722\",\n \"0.46265963\",\n \"0.46197546\",\n \"0.46182698\",\n \"0.46182698\",\n \"0.4614188\",\n \"0.46025252\",\n \"0.46014798\",\n \"0.45947328\",\n \"0.459279\",\n \"0.45911363\",\n \"0.45843533\",\n \"0.45775267\",\n \"0.4567023\",\n \"0.4563234\",\n \"0.4561893\",\n \"0.4556178\",\n \"0.45496076\",\n \"0.45468786\",\n \"0.45449305\",\n \"0.4523725\",\n \"0.45222747\",\n \"0.45190316\",\n \"0.45129633\",\n \"0.45123658\",\n \"0.4511279\",\n \"0.4509641\",\n \"0.45090705\",\n \"0.45043713\",\n \"0.45037958\",\n \"0.45022354\",\n \"0.44970608\",\n \"0.4496326\",\n \"0.44939566\",\n \"0.44934264\",\n \"0.44910848\",\n \"0.44759285\",\n \"0.44737306\",\n \"0.4471638\",\n \"0.44579497\",\n \"0.4456403\",\n \"0.44543198\",\n \"0.44494048\",\n \"0.44444942\",\n \"0.44311002\",\n \"0.4430763\",\n \"0.4426904\",\n \"0.44176888\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56915593"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":88495,"cells":{"query":{"kind":"string","value":"Cutoff value (float) for maximum length of a path to reach goal; no valid path returned by the planner has higher cost than this."},"document":{"kind":"string","value":"def dead_end_value(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getCutoffDistance(self):\n return self.cutoffDistance","def cutoff_distance(self):\n return self._cutoff_distance","def GetEpsilonBeer(Abs, conc, pathLength):\n return Abs / (conc * pathLength)","def pathcost_cooler(k=5, lam=0.0003, limit=3000):\n return lambda t: (k * exp(-lam * t) if t < limit else 0)","def getCutoffSpeed(self):\n cmd_string = '?3'\n data = self.sendRcv(cmd_string)\n self.state['cutoff_speed'] = int(data)\n return self.state['cutoff_speed']","def LimitTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_Tool_LimitTolerance(self, *args)","def limit_path_query(self):\n return self._limit_path_query","def limit(self) -> pulumi.Input[float]:\n return pulumi.get(self, \"limit\")","def GetAbsBeer(epsilon, conc, pathLength):\n return epsilon * conc * pathLength","def MaxTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_Tool_MaxTolerance(self, *args)","def _cost_route_fine(self):\n return self.fine","def h_score(self):\n if self.estimated_moves_to_goal is None:\n self.estimated_moves_to_goal = \\\n max(nx.single_source_shortest_path_length(self.graph, self.head_node).items(), key=lambda x: x[1])[1]\n return self.estimated_moves_to_goal","def mostLikelyPath(self, start, length=100):\n path = [start]\n for step in range(length):\n bestValue = -100000000\n bestAction = None\n for action in range(self.A):\n pathValue = self.valueLookAhead(path[-1][0], [1], action)\n # print((self.stateToCoord[nextState[0]], pathValue))\n if pathValue > bestValue:\n bestValue = pathValue\n bestAction = action\n possibleNextStates = self.trans(path[-1][0], path[-1][1])[bestAction]\n path.append(randomSample(possibleNextStates))\n return path","def _find_cutoff(self):\n cutoff = 1\n while ((self.linear_rstar_unnorm(cutoff) -\n self.turing_rstar_unnorm(cutoff))**2\n > self.approx_turing_variance(cutoff)):\n cutoff += 1\n return cutoff","def score_cutoff_(self):\n return self.predictor.score_cutoff_fun(self.scores_,\n self.score_coefficient)","def __find_max_distance(self):\n return utils.find_max_distance(self.__game)","def getBestPath(self):\n if self._bestPathVertex.getNextWaypoint() is None:\n numWaypointsCompleted = len(self._waypoints)\n quality = 2\n if self._vertexQueue.isEmpty():\n quality += 1\n else:\n numWaypointsCompleted = self._bestPathVertex.getNextWaypoint().getIndex()\n quality = 1\n if self._vertexQueue.isEmpty():\n quality -= 1\n \n return outputPath.generatePath(self._bestPathVertex, self._params.waypointAcceptanceRadii, quality, numWaypointsCompleted)","def get_tolerance(self):\n return self.tolerance","def upper_bound(self) -> float:\n ...","def GetConcBeer(Abs, epsilon, pathLength):\n return Abs / (epsilon * pathLength)","def get_lift_limit(self) -> float:\n\n return self.send(self.cmd.GET_LIFT_LIMIT)","def _standardize_cutoff(cutoff):\n cutoff = np.asarray(cutoff)\n cutoff[0] = max(0., cutoff[0])\n cutoff[1] = min(1., cutoff[1])\n cutoff[0] = np.min([cutoff[0], 0.09])\n cutoff[1] = np.max([cutoff[1], 0.91])\n return cutoff","def lmax(self):\n cond = (self.transmit / self.transmit.max()) > 1./100\n return max(self.wavelength[cond])","def setGoalLength(self, length):\n assert isinstance(length, int)\n self.goal_length = length","def cutoff(self, state, depth):\n abstract","def maximum_path(value, mask, max_neg_val=-np.inf):\n value = value * mask\n\n device = value.device\n dtype = value.dtype\n value = value.cpu().detach().numpy()\n mask = mask.cpu().detach().numpy().astype(np.bool)\n\n b, t_x, t_y = value.shape\n direction = np.zeros(value.shape, dtype=np.int64)\n v = np.zeros((b, t_x), dtype=np.float32)\n x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)\n for j in range(t_y):\n v0 = np.pad(v, [[0, 0], [1, 0]], mode=\"constant\", constant_values=max_neg_val)[:, :-1]\n v1 = v\n max_mask = (v1 >= v0)\n v_max = np.where(max_mask, v1, v0)\n direction[:, :, j] = max_mask\n\n index_mask = (x_range <= j)\n v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)\n direction = np.where(mask, direction, 1)\n\n path = np.zeros(value.shape, dtype=np.float32)\n index = mask[:, :, 0].sum(1).astype(np.int64) - 1\n index_range = np.arange(b)\n for j in reversed(range(t_y)):\n path[index_range, index, j] = 1\n index = index + direction[index_range, index, j] - 1\n path = path * mask.astype(np.float32)\n path = torch.from_numpy(path).to(device=device, dtype=dtype)\n return path","def ApproxConc280(Abs, pathLength = 10):\n pass","def get_lmax_limit(self):\n\n if self.pixel == \"HEALPIX\":\n l_max_limit = 3 * self.nside - 1\n elif self.pixel == \"CAR\":\n cdelt = self.data.wcs.wcs.cdelt[1]\n l_max_limit = 360 / cdelt / 4\n return l_max_limit","def backoff_limit(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"backoff_limit\")","async def max_distance(self, *args):\n return await self._rpc.max_distance(*args)","def max_distance(self, right: GeoSpatialValue) -> ir.FloatingValue:\n return ops.GeoMaxDistance(self, right).to_expr()","def Tolerance(self):\n\t\treturn self._get_attribute('tolerance')","def _get_lsp_config_frr_hop_limit(self):\n return self.__lsp_config_frr_hop_limit","def max_path_cost(self, path, attr): \n return max([self.G[path[i]][path[i+1]][attr] for i in range(len(path)-1)])","def path_length(self, u, v):\n mrca = self.mrca(u, v)\n if mrca == -1:\n return math.inf\n return self.depth(u) + self.depth(v) - 2 * self.depth(mrca)","def _maximum(self) -> float:\n if self._type == \"power\":\n return 5.0\n elif self._type == \"setpoint\":\n return self._product.get_data_config_json()[\"_value_setpoint_max\"]\n elif self._type == \"fan1\":\n fan = 1\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n (((fan - 1) * 2) + 1)\n ]\n elif self._type == \"fan2\":\n fan = 2\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n (((fan - 1) * 2) + 1)\n ]\n elif self._type == \"fan3\":\n fan = 3\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n (((fan - 1) * 2) + 1)\n ]","def cutoff_str(g):\n try:\n value = float(g)\n except Exception as e:\n raise argparse.ArgumentTypeError('%s' % e)\n\n # FIXME this is serious hard-coded kludge for now until we can make this more robust\n if value != 200.0:\n raise argparse.ArgumentTypeError('rate, r, in sa/sec must be 200.0 for now')\n\n return value","def steer_max(self):\n return self._steer_max","def SetMaxTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_Tool_SetMaxTolerance(self, *args)","def get_maximum_fitness(self) -> float:\n anticipated_change_cls = [cl for cl in self\n if cl.does_anticipate_change()]\n\n if len(anticipated_change_cls) > 0:\n best_cl = max(anticipated_change_cls, key=lambda cl: cl.fitness)\n return best_cl.fitness\n\n return 0.0","def cut(self, max_lenght):\n self.V_estimates = self.V_estimates[:max_lenght]\n super().cut(max_lenght)","def tolerance(self):\n return self.params['tolerance']","def OptimalWarpingPath( self, colStart=None ):\n rows = len(self.D)\n cols = len(self.D[0])\n n = rows-1\n m = cols-1\n if colStart:\n m=colStart\n path = [(n,m)]\n while n > 0 or m > 0:\n if n == 0 :\n path.insert(0,(0,m-1))\n m -= 1\n elif m == 0 :\n path.insert(0,(n-1,0))\n n -= 1\n else:\n minStep = min( self.D[n-1][m-1], self.D[n-1][m], self.D[n][m-1] )\n if self.D[n-1][m-1] == minStep:\n path.insert(0,(n-1,m-1))\n n -= 1\n m -= 1\n elif self.D[n-1][m] == minStep:\n path.insert(0,(n-1,m))\n n -= 1\n else: # self.D[n][m-1] == min:\n path.insert(0,(n,m-1))\n m -= 1\n return path, self.CostOfPath( path, self.D )","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"max_target_capacity\")","def get_path_length(self) :\n return self.path_length","def _get_max_dist_from_tail(self, snake, board, food_evaluation):\n\n if snake.health_points == self.MAX_HEALTH:\n return 1000\n elif food_evaluation == CANT_FIND:\n return 2\n else:\n return 2 + (self.MAX_HEALTH - snake.health_points) / (\n self.MAX_HEALTH) * (board.width + board.height)","def get_cutoff_dim(self):\n return self.circuit._trunc","def f_max(cls):\n return cls.params[\"f_max\"]","def velocity_limit(self, value):\n self._write(MX_VELOCITY_LIMIT, value)","def longest_path(game, player, cur_length=0, max_length=10):\n moves = game.get_legal_moves(player)\n\n # Break recursion if we reach a dead end or we're at the limit.\n if not moves or cur_length >= max_length:\n return cur_length\n # Otherwise search recursively.\n else:\n best_depth = 0\n for move in moves:\n game_copy = try_move(game, move, player)\n move_depth = longest_path(game_copy, player, cur_length + 1)\n if move_depth > best_depth:\n best_depth = move_depth\n # As soon as we're at the depth limit, there's no reason to\n # search more moves.\n if best_depth == max_length:\n return best_depth\n return best_depth","def __len__(self):\r\n return int(np.ceil(len(self.pathways) / float(self.batch_size)))","def lower_bound(self) -> float:\n ...","def max_findings(self) -> float:\n return pulumi.get(self, \"max_findings\")","def potential_max(self):\n\n return self._args.max","def speed_max(self):\n return self._speed_max","def clamped(self, magnitude):\n return self.unit * magnitude","def speed_constriction(self, velocity, u_bound, l_bound) -> float:\n\n delta_i = (u_bound - l_bound) / 2.\n # user defined max speed\n velocity = min(velocity, delta_i)\n velocity = max(velocity, -delta_i)\n\n return velocity","def _get_length_max(self):\n # data_list = list(range(len(self.files_refined)))\n data_list = range(self.idx_max_length, len(self.files_refined))\n progress = tqdm(data_list)\n for pdb_id in progress:\n features_filt, geo_filt = self._get_features_geo_filtered(pdb_id)\n length = features_filt.shape[1]\n if (length > self.max_length):\n self.max_length = length\n progress.set_postfix({'pdb': self.files_refined[pdb_id],\n 'length': length,\n 'max_langth': self.max_length})\n save_checkpoint_feature(self.path_checkpoint_features, pdb_id, self.max_length, id)\n return self.max_length","def max_value(self) -> Union[int, float]:\n return self.right_boundary['value']","def _get_lsp_frr_path_cost(self):\n return self.__lsp_frr_path_cost","def __max_value(self, game, depth):\n self.__check_time()\n val = float('-inf')\n\n # check if out of moves or at depth limit\n if self.__is_terminal(game, depth):\n val = self.score(game, self)\n else:\n for move in game.get_legal_moves():\n # depth decremented by 1 on each call\n val = max(val, self.__min_value(game.forecast_move(move), depth - 1))\n\n return val","def best_step(self):\r\n return self._best_value_step","def clip_param (self) -> float:\n return self._clip_param","def Qmax(self):\n return (4 * np.pi * np.sin(self.two_theta_max()*radians/2)\n / self.wavelength)","def _maximum(self) -> float:\n return self._config[CONF_MAX]","def max_charge(self):\n return self.strength","def setMaxEditDistance(self, v):\n return self._set(maxEditDistance=v)","def limit_bandwidth_unit(self):\n return self._limit_bandwidth_unit","def local_optimum(self, tour: Tour):\n better = tour\n cnt = 0\n while better is not None:\n # if cnt % 20 == 0:\n print(\"---------------------------------------------\\n The \", cnt, \"time.\")\n print(\"Improved tour:\", list(better.iter_vertices()))\n print(\"Improved cost:\", self.tour_cost(better))\n cnt += 1\n tour = better\n better = self.improve(tour)\n return tour","def acceptability(self, g):\n if g <= self.bounds.cutoff:\n return 1.0\n return 0.0","def get_reached(g, source, hops, timeout):\n\n paths = nx.single_source_shortest_path(g, source, hops)\n total = len(paths)\n reached = total\n\n for v in paths.itervalues():\n for i in range(1, len(v)):\n lat = g[v[i-1]][v[i]][\"latency\"]\n if lat > timeout:\n reached -= 1\n break\n\n return float(reached)/float(total)","def get_ideal_high_pass_filter(self, shape, cutoff):\n\n #Hint: May be one can use the low pass filter function to get a high pass mask\n return 1 - self.get_ideal_low_pass_filter(shape, cutoff)","def curvature_max(self):\n return 1.0 / self.radius_min","def tolerance(self) -> float:\n return self._tolerance","def getMaximumDistances(self):\n pass","def _get_lip_best(self) -> float:\n pass","def path(l_s, l_f, leg_list):\n # candidates =\n return min(heuristic_path([l_s], l_f, leg_list, []), key = len)","def max_gain(self):\n return np.max(self.fr)","def getPathLength(self, path):\r\n path_nodes = path\r\n # print(path_nodes)\r\n total_length = 0 # km\r\n for i in range(len(path_nodes)-1):\r\n next_edge = None\r\n for edge in self.graph.getAdj(path_nodes[i]):\r\n if edge.getExtremityNode() == path_nodes[i+1]:\r\n next_edge = edge\r\n if next_edge is None: # it means the path is invalid\r\n return None\r\n total_length += next_edge.getWeight()\r\n # print(next_edge.getTravelType(), end=\" \")\r\n return total_length","def joint_limits_upper_constraint(q,ee_pos):\n return self.max_angles - q","def cutoff_rmsd(self):\n return self._cutoff_rmsd","def acceptability(self, g):\n if g >= self.bounds.cutoff:\n return 1.0\n return 0.0","def get_plan_limit(self, source):\n commitment = getattr(self.get_subscription(), 'commitment', {})\n return self.get_plan().get_price_data(source, commitment)[1]","def adaptive_limit(self) -> int:\n return pulumi.get(self, \"adaptive_limit\")","def limitValue(self, value, lowerLimit, upperLimit):\n if value > upperLimit:\n return upperLimit\n elif value < lowerLimit:\n return lowerLimit\n else:\n return value","def cutoff_test(self, state, depth):\n return self.depth_limit > 0 and depth > self.depth_limit","def speed_limit(comp):\n return max(min(comp, SPEED_LIMIT), -1 * SPEED_LIMIT)","def get_lift(self):\n return 0.0","def maximumDistance(self):\n from ete2 import Tree\n t = Tree(name='LUCA_root')\n empty_forest = {'sp':t,'gns':t,'fam':t,'ord':t,'cls':t,'phy':t,'kng':t}\n return self.distanceToTree(empty_forest,update_inner_attributes=False)","def RepulsionForceFromObstacle(q,q_oi):\r\n d_i = CalculateDistance(q,q_oi) # Distance to the obstacle\r\n if (d_i < d_0): # Point within obstacle influence\r\n F_oi = k_o * (1/d_i - 1/d_0) /d_i**2 \r\n if (F_oi > F_rep_MaxValue): # F_rep won't exceed set constant\r\n return F_rep_MaxValue\r\n else: # Point without obstacle influence\r\n F_oi = 0\r\n return F_oi","def SetMaxTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ShapeDivide_SetMaxTolerance(self, *args)","def abs_min_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_min_cool_setpoint_limit\", 1600)","def calcPath(self):\n return None","def max_value(self) -> float:\n return DEFAULT_MAX_VALUE"],"string":"[\n \"def getCutoffDistance(self):\\n return self.cutoffDistance\",\n \"def cutoff_distance(self):\\n return self._cutoff_distance\",\n \"def GetEpsilonBeer(Abs, conc, pathLength):\\n return Abs / (conc * pathLength)\",\n \"def pathcost_cooler(k=5, lam=0.0003, limit=3000):\\n return lambda t: (k * exp(-lam * t) if t < limit else 0)\",\n \"def getCutoffSpeed(self):\\n cmd_string = '?3'\\n data = self.sendRcv(cmd_string)\\n self.state['cutoff_speed'] = int(data)\\n return self.state['cutoff_speed']\",\n \"def LimitTolerance(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_Tool_LimitTolerance(self, *args)\",\n \"def limit_path_query(self):\\n return self._limit_path_query\",\n \"def limit(self) -> pulumi.Input[float]:\\n return pulumi.get(self, \\\"limit\\\")\",\n \"def GetAbsBeer(epsilon, conc, pathLength):\\n return epsilon * conc * pathLength\",\n \"def MaxTolerance(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_Tool_MaxTolerance(self, *args)\",\n \"def _cost_route_fine(self):\\n return self.fine\",\n \"def h_score(self):\\n if self.estimated_moves_to_goal is None:\\n self.estimated_moves_to_goal = \\\\\\n max(nx.single_source_shortest_path_length(self.graph, self.head_node).items(), key=lambda x: x[1])[1]\\n return self.estimated_moves_to_goal\",\n \"def mostLikelyPath(self, start, length=100):\\n path = [start]\\n for step in range(length):\\n bestValue = -100000000\\n bestAction = None\\n for action in range(self.A):\\n pathValue = self.valueLookAhead(path[-1][0], [1], action)\\n # print((self.stateToCoord[nextState[0]], pathValue))\\n if pathValue > bestValue:\\n bestValue = pathValue\\n bestAction = action\\n possibleNextStates = self.trans(path[-1][0], path[-1][1])[bestAction]\\n path.append(randomSample(possibleNextStates))\\n return path\",\n \"def _find_cutoff(self):\\n cutoff = 1\\n while ((self.linear_rstar_unnorm(cutoff) -\\n self.turing_rstar_unnorm(cutoff))**2\\n > self.approx_turing_variance(cutoff)):\\n cutoff += 1\\n return cutoff\",\n \"def score_cutoff_(self):\\n return self.predictor.score_cutoff_fun(self.scores_,\\n self.score_coefficient)\",\n \"def __find_max_distance(self):\\n return utils.find_max_distance(self.__game)\",\n \"def getBestPath(self):\\n if self._bestPathVertex.getNextWaypoint() is None:\\n numWaypointsCompleted = len(self._waypoints)\\n quality = 2\\n if self._vertexQueue.isEmpty():\\n quality += 1\\n else:\\n numWaypointsCompleted = self._bestPathVertex.getNextWaypoint().getIndex()\\n quality = 1\\n if self._vertexQueue.isEmpty():\\n quality -= 1\\n \\n return outputPath.generatePath(self._bestPathVertex, self._params.waypointAcceptanceRadii, quality, numWaypointsCompleted)\",\n \"def get_tolerance(self):\\n return self.tolerance\",\n \"def upper_bound(self) -> float:\\n ...\",\n \"def GetConcBeer(Abs, epsilon, pathLength):\\n return Abs / (epsilon * pathLength)\",\n \"def get_lift_limit(self) -> float:\\n\\n return self.send(self.cmd.GET_LIFT_LIMIT)\",\n \"def _standardize_cutoff(cutoff):\\n cutoff = np.asarray(cutoff)\\n cutoff[0] = max(0., cutoff[0])\\n cutoff[1] = min(1., cutoff[1])\\n cutoff[0] = np.min([cutoff[0], 0.09])\\n cutoff[1] = np.max([cutoff[1], 0.91])\\n return cutoff\",\n \"def lmax(self):\\n cond = (self.transmit / self.transmit.max()) > 1./100\\n return max(self.wavelength[cond])\",\n \"def setGoalLength(self, length):\\n assert isinstance(length, int)\\n self.goal_length = length\",\n \"def cutoff(self, state, depth):\\n abstract\",\n \"def maximum_path(value, mask, max_neg_val=-np.inf):\\n value = value * mask\\n\\n device = value.device\\n dtype = value.dtype\\n value = value.cpu().detach().numpy()\\n mask = mask.cpu().detach().numpy().astype(np.bool)\\n\\n b, t_x, t_y = value.shape\\n direction = np.zeros(value.shape, dtype=np.int64)\\n v = np.zeros((b, t_x), dtype=np.float32)\\n x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)\\n for j in range(t_y):\\n v0 = np.pad(v, [[0, 0], [1, 0]], mode=\\\"constant\\\", constant_values=max_neg_val)[:, :-1]\\n v1 = v\\n max_mask = (v1 >= v0)\\n v_max = np.where(max_mask, v1, v0)\\n direction[:, :, j] = max_mask\\n\\n index_mask = (x_range <= j)\\n v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)\\n direction = np.where(mask, direction, 1)\\n\\n path = np.zeros(value.shape, dtype=np.float32)\\n index = mask[:, :, 0].sum(1).astype(np.int64) - 1\\n index_range = np.arange(b)\\n for j in reversed(range(t_y)):\\n path[index_range, index, j] = 1\\n index = index + direction[index_range, index, j] - 1\\n path = path * mask.astype(np.float32)\\n path = torch.from_numpy(path).to(device=device, dtype=dtype)\\n return path\",\n \"def ApproxConc280(Abs, pathLength = 10):\\n pass\",\n \"def get_lmax_limit(self):\\n\\n if self.pixel == \\\"HEALPIX\\\":\\n l_max_limit = 3 * self.nside - 1\\n elif self.pixel == \\\"CAR\\\":\\n cdelt = self.data.wcs.wcs.cdelt[1]\\n l_max_limit = 360 / cdelt / 4\\n return l_max_limit\",\n \"def backoff_limit(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"backoff_limit\\\")\",\n \"async def max_distance(self, *args):\\n return await self._rpc.max_distance(*args)\",\n \"def max_distance(self, right: GeoSpatialValue) -> ir.FloatingValue:\\n return ops.GeoMaxDistance(self, right).to_expr()\",\n \"def Tolerance(self):\\n\\t\\treturn self._get_attribute('tolerance')\",\n \"def _get_lsp_config_frr_hop_limit(self):\\n return self.__lsp_config_frr_hop_limit\",\n \"def max_path_cost(self, path, attr): \\n return max([self.G[path[i]][path[i+1]][attr] for i in range(len(path)-1)])\",\n \"def path_length(self, u, v):\\n mrca = self.mrca(u, v)\\n if mrca == -1:\\n return math.inf\\n return self.depth(u) + self.depth(v) - 2 * self.depth(mrca)\",\n \"def _maximum(self) -> float:\\n if self._type == \\\"power\\\":\\n return 5.0\\n elif self._type == \\\"setpoint\\\":\\n return self._product.get_data_config_json()[\\\"_value_setpoint_max\\\"]\\n elif self._type == \\\"fan1\\\":\\n fan = 1\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n (((fan - 1) * 2) + 1)\\n ]\\n elif self._type == \\\"fan2\\\":\\n fan = 2\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n (((fan - 1) * 2) + 1)\\n ]\\n elif self._type == \\\"fan3\\\":\\n fan = 3\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n (((fan - 1) * 2) + 1)\\n ]\",\n \"def cutoff_str(g):\\n try:\\n value = float(g)\\n except Exception as e:\\n raise argparse.ArgumentTypeError('%s' % e)\\n\\n # FIXME this is serious hard-coded kludge for now until we can make this more robust\\n if value != 200.0:\\n raise argparse.ArgumentTypeError('rate, r, in sa/sec must be 200.0 for now')\\n\\n return value\",\n \"def steer_max(self):\\n return self._steer_max\",\n \"def SetMaxTolerance(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_Tool_SetMaxTolerance(self, *args)\",\n \"def get_maximum_fitness(self) -> float:\\n anticipated_change_cls = [cl for cl in self\\n if cl.does_anticipate_change()]\\n\\n if len(anticipated_change_cls) > 0:\\n best_cl = max(anticipated_change_cls, key=lambda cl: cl.fitness)\\n return best_cl.fitness\\n\\n return 0.0\",\n \"def cut(self, max_lenght):\\n self.V_estimates = self.V_estimates[:max_lenght]\\n super().cut(max_lenght)\",\n \"def tolerance(self):\\n return self.params['tolerance']\",\n \"def OptimalWarpingPath( self, colStart=None ):\\n rows = len(self.D)\\n cols = len(self.D[0])\\n n = rows-1\\n m = cols-1\\n if colStart:\\n m=colStart\\n path = [(n,m)]\\n while n > 0 or m > 0:\\n if n == 0 :\\n path.insert(0,(0,m-1))\\n m -= 1\\n elif m == 0 :\\n path.insert(0,(n-1,0))\\n n -= 1\\n else:\\n minStep = min( self.D[n-1][m-1], self.D[n-1][m], self.D[n][m-1] )\\n if self.D[n-1][m-1] == minStep:\\n path.insert(0,(n-1,m-1))\\n n -= 1\\n m -= 1\\n elif self.D[n-1][m] == minStep:\\n path.insert(0,(n-1,m))\\n n -= 1\\n else: # self.D[n][m-1] == min:\\n path.insert(0,(n,m-1))\\n m -= 1\\n return path, self.CostOfPath( path, self.D )\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def max_target_capacity(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"max_target_capacity\\\")\",\n \"def get_path_length(self) :\\n return self.path_length\",\n \"def _get_max_dist_from_tail(self, snake, board, food_evaluation):\\n\\n if snake.health_points == self.MAX_HEALTH:\\n return 1000\\n elif food_evaluation == CANT_FIND:\\n return 2\\n else:\\n return 2 + (self.MAX_HEALTH - snake.health_points) / (\\n self.MAX_HEALTH) * (board.width + board.height)\",\n \"def get_cutoff_dim(self):\\n return self.circuit._trunc\",\n \"def f_max(cls):\\n return cls.params[\\\"f_max\\\"]\",\n \"def velocity_limit(self, value):\\n self._write(MX_VELOCITY_LIMIT, value)\",\n \"def longest_path(game, player, cur_length=0, max_length=10):\\n moves = game.get_legal_moves(player)\\n\\n # Break recursion if we reach a dead end or we're at the limit.\\n if not moves or cur_length >= max_length:\\n return cur_length\\n # Otherwise search recursively.\\n else:\\n best_depth = 0\\n for move in moves:\\n game_copy = try_move(game, move, player)\\n move_depth = longest_path(game_copy, player, cur_length + 1)\\n if move_depth > best_depth:\\n best_depth = move_depth\\n # As soon as we're at the depth limit, there's no reason to\\n # search more moves.\\n if best_depth == max_length:\\n return best_depth\\n return best_depth\",\n \"def __len__(self):\\r\\n return int(np.ceil(len(self.pathways) / float(self.batch_size)))\",\n \"def lower_bound(self) -> float:\\n ...\",\n \"def max_findings(self) -> float:\\n return pulumi.get(self, \\\"max_findings\\\")\",\n \"def potential_max(self):\\n\\n return self._args.max\",\n \"def speed_max(self):\\n return self._speed_max\",\n \"def clamped(self, magnitude):\\n return self.unit * magnitude\",\n \"def speed_constriction(self, velocity, u_bound, l_bound) -> float:\\n\\n delta_i = (u_bound - l_bound) / 2.\\n # user defined max speed\\n velocity = min(velocity, delta_i)\\n velocity = max(velocity, -delta_i)\\n\\n return velocity\",\n \"def _get_length_max(self):\\n # data_list = list(range(len(self.files_refined)))\\n data_list = range(self.idx_max_length, len(self.files_refined))\\n progress = tqdm(data_list)\\n for pdb_id in progress:\\n features_filt, geo_filt = self._get_features_geo_filtered(pdb_id)\\n length = features_filt.shape[1]\\n if (length > self.max_length):\\n self.max_length = length\\n progress.set_postfix({'pdb': self.files_refined[pdb_id],\\n 'length': length,\\n 'max_langth': self.max_length})\\n save_checkpoint_feature(self.path_checkpoint_features, pdb_id, self.max_length, id)\\n return self.max_length\",\n \"def max_value(self) -> Union[int, float]:\\n return self.right_boundary['value']\",\n \"def _get_lsp_frr_path_cost(self):\\n return self.__lsp_frr_path_cost\",\n \"def __max_value(self, game, depth):\\n self.__check_time()\\n val = float('-inf')\\n\\n # check if out of moves or at depth limit\\n if self.__is_terminal(game, depth):\\n val = self.score(game, self)\\n else:\\n for move in game.get_legal_moves():\\n # depth decremented by 1 on each call\\n val = max(val, self.__min_value(game.forecast_move(move), depth - 1))\\n\\n return val\",\n \"def best_step(self):\\r\\n return self._best_value_step\",\n \"def clip_param (self) -> float:\\n return self._clip_param\",\n \"def Qmax(self):\\n return (4 * np.pi * np.sin(self.two_theta_max()*radians/2)\\n / self.wavelength)\",\n \"def _maximum(self) -> float:\\n return self._config[CONF_MAX]\",\n \"def max_charge(self):\\n return self.strength\",\n \"def setMaxEditDistance(self, v):\\n return self._set(maxEditDistance=v)\",\n \"def limit_bandwidth_unit(self):\\n return self._limit_bandwidth_unit\",\n \"def local_optimum(self, tour: Tour):\\n better = tour\\n cnt = 0\\n while better is not None:\\n # if cnt % 20 == 0:\\n print(\\\"---------------------------------------------\\\\n The \\\", cnt, \\\"time.\\\")\\n print(\\\"Improved tour:\\\", list(better.iter_vertices()))\\n print(\\\"Improved cost:\\\", self.tour_cost(better))\\n cnt += 1\\n tour = better\\n better = self.improve(tour)\\n return tour\",\n \"def acceptability(self, g):\\n if g <= self.bounds.cutoff:\\n return 1.0\\n return 0.0\",\n \"def get_reached(g, source, hops, timeout):\\n\\n paths = nx.single_source_shortest_path(g, source, hops)\\n total = len(paths)\\n reached = total\\n\\n for v in paths.itervalues():\\n for i in range(1, len(v)):\\n lat = g[v[i-1]][v[i]][\\\"latency\\\"]\\n if lat > timeout:\\n reached -= 1\\n break\\n\\n return float(reached)/float(total)\",\n \"def get_ideal_high_pass_filter(self, shape, cutoff):\\n\\n #Hint: May be one can use the low pass filter function to get a high pass mask\\n return 1 - self.get_ideal_low_pass_filter(shape, cutoff)\",\n \"def curvature_max(self):\\n return 1.0 / self.radius_min\",\n \"def tolerance(self) -> float:\\n return self._tolerance\",\n \"def getMaximumDistances(self):\\n pass\",\n \"def _get_lip_best(self) -> float:\\n pass\",\n \"def path(l_s, l_f, leg_list):\\n # candidates =\\n return min(heuristic_path([l_s], l_f, leg_list, []), key = len)\",\n \"def max_gain(self):\\n return np.max(self.fr)\",\n \"def getPathLength(self, path):\\r\\n path_nodes = path\\r\\n # print(path_nodes)\\r\\n total_length = 0 # km\\r\\n for i in range(len(path_nodes)-1):\\r\\n next_edge = None\\r\\n for edge in self.graph.getAdj(path_nodes[i]):\\r\\n if edge.getExtremityNode() == path_nodes[i+1]:\\r\\n next_edge = edge\\r\\n if next_edge is None: # it means the path is invalid\\r\\n return None\\r\\n total_length += next_edge.getWeight()\\r\\n # print(next_edge.getTravelType(), end=\\\" \\\")\\r\\n return total_length\",\n \"def joint_limits_upper_constraint(q,ee_pos):\\n return self.max_angles - q\",\n \"def cutoff_rmsd(self):\\n return self._cutoff_rmsd\",\n \"def acceptability(self, g):\\n if g >= self.bounds.cutoff:\\n return 1.0\\n return 0.0\",\n \"def get_plan_limit(self, source):\\n commitment = getattr(self.get_subscription(), 'commitment', {})\\n return self.get_plan().get_price_data(source, commitment)[1]\",\n \"def adaptive_limit(self) -> int:\\n return pulumi.get(self, \\\"adaptive_limit\\\")\",\n \"def limitValue(self, value, lowerLimit, upperLimit):\\n if value > upperLimit:\\n return upperLimit\\n elif value < lowerLimit:\\n return lowerLimit\\n else:\\n return value\",\n \"def cutoff_test(self, state, depth):\\n return self.depth_limit > 0 and depth > self.depth_limit\",\n \"def speed_limit(comp):\\n return max(min(comp, SPEED_LIMIT), -1 * SPEED_LIMIT)\",\n \"def get_lift(self):\\n return 0.0\",\n \"def maximumDistance(self):\\n from ete2 import Tree\\n t = Tree(name='LUCA_root')\\n empty_forest = {'sp':t,'gns':t,'fam':t,'ord':t,'cls':t,'phy':t,'kng':t}\\n return self.distanceToTree(empty_forest,update_inner_attributes=False)\",\n \"def RepulsionForceFromObstacle(q,q_oi):\\r\\n d_i = CalculateDistance(q,q_oi) # Distance to the obstacle\\r\\n if (d_i < d_0): # Point within obstacle influence\\r\\n F_oi = k_o * (1/d_i - 1/d_0) /d_i**2 \\r\\n if (F_oi > F_rep_MaxValue): # F_rep won't exceed set constant\\r\\n return F_rep_MaxValue\\r\\n else: # Point without obstacle influence\\r\\n F_oi = 0\\r\\n return F_oi\",\n \"def SetMaxTolerance(self, *args):\\n return _ShapeUpgrade.ShapeUpgrade_ShapeDivide_SetMaxTolerance(self, *args)\",\n \"def abs_min_cool_setpoint_limit(self) -> int:\\n return self.cluster.get(\\\"abs_min_cool_setpoint_limit\\\", 1600)\",\n \"def calcPath(self):\\n return None\",\n \"def max_value(self) -> float:\\n return DEFAULT_MAX_VALUE\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66599065","0.6464602","0.59441173","0.5824008","0.5823617","0.5738601","0.5698907","0.563141","0.562286","0.55233145","0.5477712","0.5420577","0.53618807","0.53434753","0.53083557","0.5302423","0.52958834","0.5275068","0.5263953","0.5245157","0.5243079","0.5240081","0.5226085","0.5210576","0.52019644","0.5182738","0.5163379","0.515776","0.5154863","0.51521444","0.514365","0.5136932","0.513446","0.5101988","0.50997585","0.50966597","0.50731254","0.5060479","0.5059473","0.50457555","0.5044792","0.5044159","0.50311655","0.50299066","0.50299066","0.50299066","0.50299066","0.50299066","0.50299066","0.50299066","0.50299066","0.502733","0.5025344","0.50175595","0.5015735","0.50155324","0.5012281","0.5005549","0.49921697","0.49910977","0.4988974","0.49850515","0.49807575","0.49746656","0.4969463","0.49633485","0.49617514","0.49610257","0.4960562","0.49571118","0.4951581","0.4946258","0.4944936","0.4933886","0.49333283","0.49288446","0.492693","0.49252912","0.49229604","0.49226302","0.49163905","0.49163687","0.49128503","0.49120393","0.49093968","0.49064434","0.4894845","0.4894048","0.48930895","0.48871386","0.48817712","0.4879749","0.4877995","0.48769286","0.48760763","0.48757347","0.48745987","0.48733473","0.4867772","0.4867633","0.4864778"],"string":"[\n \"0.66599065\",\n \"0.6464602\",\n \"0.59441173\",\n \"0.5824008\",\n \"0.5823617\",\n \"0.5738601\",\n \"0.5698907\",\n \"0.563141\",\n \"0.562286\",\n \"0.55233145\",\n \"0.5477712\",\n \"0.5420577\",\n \"0.53618807\",\n \"0.53434753\",\n \"0.53083557\",\n \"0.5302423\",\n \"0.52958834\",\n \"0.5275068\",\n \"0.5263953\",\n \"0.5245157\",\n \"0.5243079\",\n \"0.5240081\",\n \"0.5226085\",\n \"0.5210576\",\n \"0.52019644\",\n \"0.5182738\",\n \"0.5163379\",\n \"0.515776\",\n \"0.5154863\",\n \"0.51521444\",\n \"0.514365\",\n \"0.5136932\",\n \"0.513446\",\n \"0.5101988\",\n \"0.50997585\",\n \"0.50966597\",\n \"0.50731254\",\n \"0.5060479\",\n \"0.5059473\",\n \"0.50457555\",\n \"0.5044792\",\n \"0.5044159\",\n \"0.50311655\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.50299066\",\n \"0.502733\",\n \"0.5025344\",\n \"0.50175595\",\n \"0.5015735\",\n \"0.50155324\",\n \"0.5012281\",\n \"0.5005549\",\n \"0.49921697\",\n \"0.49910977\",\n \"0.4988974\",\n \"0.49850515\",\n \"0.49807575\",\n \"0.49746656\",\n \"0.4969463\",\n \"0.49633485\",\n \"0.49617514\",\n \"0.49610257\",\n \"0.4960562\",\n \"0.49571118\",\n \"0.4951581\",\n \"0.4946258\",\n \"0.4944936\",\n \"0.4933886\",\n \"0.49333283\",\n \"0.49288446\",\n \"0.492693\",\n \"0.49252912\",\n \"0.49229604\",\n \"0.49226302\",\n \"0.49163905\",\n \"0.49163687\",\n \"0.49128503\",\n \"0.49120393\",\n \"0.49093968\",\n \"0.49064434\",\n \"0.4894845\",\n \"0.4894048\",\n \"0.48930895\",\n \"0.48871386\",\n \"0.48817712\",\n \"0.4879749\",\n \"0.4877995\",\n \"0.48769286\",\n \"0.48760763\",\n \"0.48757347\",\n \"0.48745987\",\n \"0.48733473\",\n \"0.4867772\",\n \"0.4867633\",\n \"0.4864778\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":88496,"cells":{"query":{"kind":"string","value":"Wraps a method of this class in a SSiPP timeout, so that planning can't take too long. Maintains a blacklist of states that caused timeouts before, and prevents relevant methods from being called again on those states by simply raising another timeout."},"document":{"kind":"string","value":"def _blacklist_decorator(inner_func):\n\n @wraps(inner_func)\n def wrapper_func(self, cstate, *args, **kwargs):\n assert isinstance(cstate, CanonicalState), \\\n \"func %s was expecting a cstate but got '%r'\" \\\n % (inner_func, cstate)\n if cstate._ident_tup() in self._state_blacklist:\n raise TeacherTimeout(\n 'SSiPP timed out on this before; skipping')\n try:\n self._set_deadline()\n return inner_func(self, cstate, *args, **kwargs)\n except self._ssipp.DeadlineReachedException as ex:\n self._state_blacklist.add(cstate._ident_tup())\n raise TeacherTimeout('SSiPP timeout: %s' % ex)\n finally:\n self._clear_deadline()\n\n return wrapper_func"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def timeout(time):\n\n def wrapper(f):\n @wraps(f)\n def wrapped_f(self, event, context):\n return f(self, event, context)\n\n wrapped_f.timeout = time\n return wrapped_f\n\n return wrapper","def timeout(time_limit):\n\n class TimeoutException(Exception):\n \"\"\" Subclass Exception to catch timer expiration during search \"\"\"\n pass\n\n def handler(*args, **kwargs):\n \"\"\" Generic handler to raise an exception when a timer expires \"\"\"\n raise TimeoutException(\"Test aborted due to timeout. Test was \" +\n \"expected to finish in less than {} second(s).\".format(time_limit))\n\n def wrapUnitTest(testcase):\n\n @wraps(testcase)\n def testWrapper(self, *args, **kwargs):\n\n signal.signal(signal.SIGALRM, handler)\n signal.alarm(time_limit)\n\n try:\n return testcase(self, *args, **kwargs)\n finally:\n signal.alarm(0)\n\n return testWrapper\n\n return wrapUnitTest","def functionThatShouldNotTimeout():\n return None","def _timeout(signum, frame):\n # Raise TimeoutException with system default timeout message\n raise TimeoutException()","def pytest_timeout_cancel_timer(item):","def functionThatWillTimeOut():\n time.sleep(5)","def stopped_check(self, timeout=None):","def __call__(self, func):\n @general_helpers.wraps(func)\n def func_with_timeout(*args, **kwargs):\n \"\"\"Wrapped function that adds timeout.\"\"\"\n kwargs['timeout'] = self._timeout\n return func(*args, **kwargs)\n return func_with_timeout","def set_timeout(timeout):\n def decor(f):\n @functools.wraps(f)\n def inner(self, *args, **kwargs):\n self.useFixture(fixtures.Timeout(timeout, gentle=True))\n return f(self, *args, **kwargs)\n return inner\n return decor","def time_limit():\n\n def decorator(fn):\n @wraps(fn)\n def wrapper(*args, **kwargs):\n timer.wait_if_needed()\n return fn(*args, **kwargs)\n\n return wrapper\n\n return decorator","def timeout(seconds, error_message=\"Time out.\"):\n import signal\n def decorator(func):\n\n def __timiout_handler(signum, frame):\n raise TimeoutError(error_message)\n\n @functools.wraps(func)\n def wrapper(*arg, **kw):\n signal.signal(signal.SIGALRM, __timiout_handler)\n signal.alarm(seconds)\n ret = \"\"\n try:\n ret = func(*arg, **kw)\n except TimeoutError,e:\n print \"TimeoutError: \", e\n print \"{name} ran more than {seconds}s.\".format(name=func.__name__, seconds=seconds)\n except Exception,e:\n print \"Error: \",e\n finally:\n signal.alarm(0)\n return ret\n return wrapper\n return decorator","def assert_timeout(self) -> None:","def timer_object_timeout(seconds=5):\n def raise_timeout_exception():\n raise TimeoutReachedException(seconds=seconds)\n\n return Timer(seconds, raise_timeout_exception)","def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):\n def decorator(func):\n def _handle_timeout(signum, frame):\n raise TimeoutError(error_message)\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n try:\n signal.signal(signal.SIGALRM, _handle_timeout)\n signal_disabled = False\n except ValueError:\n # Possibly running in debug mode. Timeouts will be ignored\n signal_disabled = True\n pass\n\n if not signal_disabled:\n signal.alarm(seconds)\n try:\n result = func(*args, **kwargs)\n finally:\n if not signal_disabled:\n signal.alarm(0)\n return result\n\n return wrapper\n return decorator","def _handle_timeout(self, frame=None, **_):\n\n raise TimeOut.TimeOutError(self, frame)","def set_timeout(self, timeout):\n self.timeout = timeout","def _banner_timeout(self):\n self.sm.on_state_event(self.events.BANNERTIMEOUT)","def reset_timeout (self, new_timeout):\n self.timer.cancel()\n self.timer = Timeout(new_timeout, TestIsTakingTooLong(new_timeout))","def doTimeout(self):\n\n log.info('Executed {} calls until timeout'.format(self.calls_to_timeout))\n self.calls_to_timeout = 0\n\n self.resetSession()\n time.sleep(self.timeout)","def reject_waiting_call(self) -> None:","def timeout_decorator(func):\n\n def _new_func(result, old_func, old_func_args, old_func_kwargs):\n try:\n result.append(old_func(*old_func_args, **old_func_kwargs))\n except Exception:\n result[0] = False\n result.append(traceback.format_exc())\n\n def wrapper(*args, **kwargs):\n result = [True]\n new_kwargs = {\n \"result\": result,\n \"old_func\": func,\n \"old_func_args\": args,\n \"old_func_kwargs\": kwargs,\n }\n thd = KThread(target=_new_func, args=(), kwargs=new_kwargs)\n thd.start()\n thd.join(seconds)\n if thd.is_alive():\n thd.kill()\n thd.join()\n raise TimeoutException(err_msg.format(seconds))\n else:\n return result\n\n return functools.wraps(func)(wrapper)","async def timeout(self, failed: bool = False) -> None:\n raise NotImplementedError()","def __init__(self, timeout_time):\n self.timeout_time = timeout_time","def test_timeout_loop(self):\n\n self.assertIsNone(self.state.becomeCandidateTimeout)\n # This should do nothing\n self.state.cancelBecomeCandidateTimeout()\n self.assertIsNone(self.state.becomeCandidateTimeout)\n\n results = self.state.begin()\n\n self.state.resetElectionTimeout()\n self.assertTrue(self.state.electionTimeout > 0.150)\n self.assertTrue(self.state.electionTimeout < 0.350)\n self.assertTrue(isinstance(self.state.becomeCandidateTimeout,\n base.DelayedCall))","def timeout_after(s):\n def outer(fn):\n def inner(*args, **kwargs):\n x = fn\n timer = threading.Timer(s, _timeout, args=[fn.__module__])\n timer.start()\n try:\n result = fn(*args, **kwargs)\n finally:\n timer.cancel()\n return result\n return inner\n return outer","def set_timeout(self, timeout):\n pass","def _idle(self):\n # self._purge_timedout()\n # ...","def check_for_timeouts(self) -> None:\n\n if self.id != self._leader_id:\n return\n\n for instance in self._undecided_instances:\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\n #print('\\033[93m' + \"Instance {0} round {1} exceeded timeout!\".format(instance, self._round_id[instance]) + \"\\033[0m\")\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n self._enable_phase1_optimization = False\n self.prepare_phase_parallel(instance)\n # Handle a single time out per loop to improve responsiveness to input messages\n break\n\n for instance in self._decided_values.keys():\n if not self._acked_decided_values[instance]:\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n decide_message: Decide = Decide(sender=self,\n receiver_role=Role.LEARNER,\n payload=DecidePayload((self._decided_values[instance], instance))\n )\n self.send(decide_message)\n\n # Handle a single time out per loop to improve responsiveness to input messages\n break","def update_lock_timeout(self, timeout):\n\n if not 0 < timeout <= BespokeGlobals.MAX_CHECKOUT_TIME:\n raise FatalError(\"Timeout is out of range!\")\n elif not self._in_use:\n raise CoreError(\"This SystemUnderTest is not currently checked-out!\")\n\n self._lock_expiration = datetime.now() + timedelta(seconds=timeout)","def pytest_timeout_set_timer(item, settings):","def time_limit(seconds):\n def signal_handler(signum, frame):\n raise TimeoutException(\"TIMEOUT\")\n signal.signal(signal.SIGALRM, signal_handler)\n signal.alarm(seconds)\n try:\n yield\n finally:\n signal.alarm(0)","def func_with_timeout(*args, **kwargs):\n kwargs['timeout'] = self._timeout\n return func(*args, **kwargs)","def timeout(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n action = Process(target=func, args=args, kwargs=kwargs)\n action.start()\n action.join(timeout=5)\n if action.is_alive():\n # terminate function\n action.terminate()\n # clean up\n action.join()\n raise (TimeoutError)\n # if process is not 0, is not successful\n if action.exitcode != 0:\n # raise Attribute Error, which is the most probable\n raise (AttributeError)\n return (wrapper)","def testTimeout(self):\n\n class TimeoutTestCase(cros_test_lib.TestCase):\n \"\"\"Test case that raises a TimeoutError because it takes too long.\"\"\"\n\n TEST_CASE_TIMEOUT = 1\n\n def testSleeping(self):\n \"\"\"Sleep for 2 minutes. This should raise a TimeoutError.\"\"\"\n time.sleep(2 * 60)\n raise AssertionError('Test case should have timed out.')\n\n # Run the test case, verifying it raises a TimeoutError.\n test = TimeoutTestCase(methodName='testSleeping')\n self.assertRaises(timeout_util.TimeoutError, test.testSleeping)","def timeout(seconds=60, error_message=os.strerror(errno.ETIME)):\n def decorator(func):\n def _handle_timeout(signum, frame):\n raise TimeoutError(\n '{0} ran for more than {1} seconds'.format(\n func.__name__, seconds))\n def wrapper(*args, **kwargs):\n signal.signal(signal.SIGALRM, _handle_timeout)\n signal.alarm(seconds)\n try:\n result = func(*args, **kwargs)\n finally:\n signal.alarm(0)\n return result\n return wraps(func)(wrapper)\n\n return decorator","def __call__(self, func):\n timeouts = _exponential_timeout_generator(\n self._initial, self._maximum, self._multiplier, self._deadline)\n\n @general_helpers.wraps(func)\n def func_with_timeout(*args, **kwargs):\n \"\"\"Wrapped function that adds timeout.\"\"\"\n kwargs['timeout'] = next(timeouts)\n return func(*args, **kwargs)\n\n return func_with_timeout","def set_retry_timeout(self, retry_timeout):","def timeout_function(seconds=5):\n\n def signal_handler(signum, frame):\n raise TimeoutError(\"Timed out!\")\n\n signal.signal(signal.SIGALRM, signal_handler)\n signal.alarm(seconds)\n\n try:\n yield\n finally:\n signal.alarm(0)","def timeout(time_limit):\n\n def wrapUnitTest(testcase):\n\n @wraps(testcase)\n def testWrapper(self):\n\n queue = Queue()\n\n try:\n p = Thread(target=handler, args=(self, testcase, queue))\n p.daemon = True\n p.start()\n err, res = queue.get(timeout=time_limit)\n p.join()\n if err:\n raise err[0](err[1]).with_traceback(err[2])\n return res\n except QueueEmptyError:\n raise TimeoutError(\"Test aborted due to timeout. Test was \" +\n \"expected to finish in less than {} second(s).\".format(time_limit))\n\n return testWrapper\n\n return wrapUnitTest","def have_a_break(func):\n\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n if 'sleep' in kwargs:\n sleep = kwargs['sleep']\n del kwargs['sleep']\n else:\n sleep = self.sleep\n\n result = func(self, *args, **kwargs)\n gevent.sleep(sleep)\n return result\n\n return wrapper","def raise_timeout_error(api_url, headers, timeout, proxies):\n raise requests.exceptions.Timeout","def raise_timeout_error(api_url, headers, timeout, proxies):\n raise requests.exceptions.Timeout","def timed(limit):\n def decorate(func):\n def newfunc(*arg, **kw):\n start = time.time()\n func(*arg, **kw)\n end = time.time()\n if end - start > limit:\n raise TimeExpired(\"Time limit (%s) exceeded\" % limit)\n newfunc = make_decorator(func)(newfunc)\n return newfunc\n return decorate","def setTimeOut(self, timeout=6.0):\n self.timeout = timeout","def __simulate_sleep_but_awaken_if_stopped(self, timeout):\n deadline = self.__fake_clock.time() + timeout\n\n def deadline_exceeded_or_not_running(current_time):\n return current_time >= deadline or not self.is_running()\n\n self.__fake_clock.simulate_waiting(exit_when=deadline_exceeded_or_not_running)\n\n return not self.is_running()","def settimeout(self,timeout=10):\r\n # Update\r\n self.timeout = timeout","def pytest_timeout_cancel_timer(item):\n tle.lib.cancel()\n return True","def set_timeout(self, timeout_secs):\n self._timeout_secs = timeout_secs","def __init__(self, timeout=120):\n self.m_timeout = timeout","def assert_timeout(self) -> None:\n if self._cancelled:\n raise asyncio.TimeoutError from None","def later(cls, timeout, f, **kwargs):\n def x(*args, **kwargs):\n cls.sleep(timeout)\n return f(*args, **kwargs)\n return cls.new(x, **kwargs)","def timeout(self):\n self._status_update(\"Pyloton: Timeout\")\n time.sleep(3)","def cancelled_to_request_timed_out_error(value, timeout):\n if isinstance(value, failure.Failure):\n value.trap(CancelledError)\n raise RequestTimedOutError()\n return value","def test_timeout_pending(self):\n deadline = Deadline(MS)\n timeout = deadline.timeout()\n self.assertGreater(timeout, 0)\n self.assertLess(timeout, MS)","def func_with_timeout(*args, **kwargs):\n kwargs['timeout'] = next(timeouts)\n return func(*args, **kwargs)","def timeout_syscall(seconds):\n def timeout_handler(signum, frame):\n raise InterruptedError\n\n original_handler = signal.signal(signal.SIGALRM, timeout_handler)\n signal.alarm(seconds)\n try:\n yield\n finally:\n signal.alarm(0)\n signal.signal(signal.SIGALRM, original_handler)","def timeout_handler(interval, recurring = None):\n def decorator(func):\n \"\"\"The decorator\"\"\"\n func._pyxmpp_timeout = interval\n func._pyxmpp_recurring = recurring\n return func\n return decorator","def test_polling_plugin_timeout(self):\n pass","def timeout_change(self, timedelta):\n pass # pylint: disable=unnecessary-pass\n # For backward compatibility only.","def remove_timeouts(self):\n\n Fl.remove_timeout(self.play_seq)\n Fl.remove_timeout(self.gameover)\n Fl.remove_timeout(self.activate_buts)\n for b in self.gamebuttons:\n Fl.remove_timeout(b.auto_click)","def raise_timeout_error_get_votes(api_url, headers, params, timeout, proxies):\n raise requests.exceptions.Timeout","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def power_off(timeout: int = 0) -> None:","async def test_timeout_is_respected():\n parent_cancelled = child_cancelled = False\n\n async def sleepy():\n nonlocal child_cancelled\n try:\n await asyncio.sleep(1000 * 1000)\n except asyncio.CancelledError:\n child_cancelled = True\n\n async with async_timeout.timeout(0.01):\n try:\n async with Nursery() as nursery:\n nursery.start_soon(sleepy())\n await asyncio.sleep(1000 * 1000)\n except asyncio.CancelledError:\n parent_cancelled = True\n\n assert parent_cancelled and child_cancelled","def __init__( self, timeout = 60.0 ):\n\n self.timeout = timeout\n self.alive = None","def timeout(\n seconds: int, err_msg: str = \"Timeout after {} seconds.\"\n) -> Callable[[Callable], Callable]:\n\n def timeout_decorator(func):\n \"\"\"The real decorator used for setup, teardown and testcase methods.\"\"\"\n\n def _new_func(result, old_func, old_func_args, old_func_kwargs):\n try:\n result.append(old_func(*old_func_args, **old_func_kwargs))\n except Exception:\n result[0] = False\n result.append(traceback.format_exc())\n\n def wrapper(*args, **kwargs):\n result = [True]\n new_kwargs = {\n \"result\": result,\n \"old_func\": func,\n \"old_func_args\": args,\n \"old_func_kwargs\": kwargs,\n }\n thd = KThread(target=_new_func, args=(), kwargs=new_kwargs)\n thd.start()\n thd.join(seconds)\n if thd.is_alive():\n thd.kill()\n thd.join()\n raise TimeoutException(err_msg.format(seconds))\n else:\n return result\n\n return functools.wraps(func)(wrapper)\n\n return timeout_decorator","def on_timeout(self):\n pass","def timeout(order):\n return ResultProxy(TaskResult(TaskTimedout('A task has timedout'), order))","def raise_timeout_error_put_votes(api_url, headers, json, timeout, proxies):\n raise requests.exceptions.Timeout","def test_pool_timeout_hw(self):\n self.test_pool_timeout()","def polling(callback=None, timeout=30, sleep=0.01):\n def wrapper(f):\n @wraps(f)\n def wrapped(*args, **kwargs):\n t_start = time.time()\n\n while time.time() <= t_start + timeout:\n try:\n return f(*args, **kwargs)\n except WebDriverException:\n time.sleep(sleep)\n continue\n else:\n raise\n\n return wrapped\n\n if callback is not None:\n return wrapper(callback)\n\n return wrapper","def _wakeup(self, wakeup_timeout=10, response_timeout=3):\n pass","def test_lock_timeout():\n lock_unlock_timeout(0)","def __init__(self, timeout=129600):\n self.timeout = timeout","def until_not(self, method, timeout=None, interval=None, ignored_exceptions=None, message='',\n *args, **kwargs):\n\n self._until(method, True, timeout, interval, ignored_exceptions, message,\n *args, **kwargs)\n return False","def timedcache(method, timeout=300):\n argsmap = {}\n\n @functools.wraps(method)\n def wrapper(*args, **kwargs):\n key = (repr(args), repr(kwargs))\n if key not in argsmap:\n value = method(*args, **kwargs)\n argsmap[key] = (value, time.time())\n elif time.time() - argsmap[key][1] >= timeout:\n value = method(*args, **kwargs)\n argsmap[key] = (value, time.time())\n else:\n value = argsmap[key][0]\n return value\n\n return wrapper","def timeout(self, timeout):\n assert timeout is None or timeout > 0\n self._timeout = timeout","def _check_timeouts(self):\n\n expired_tokens = []\n for token in self._capability_timeouts:\n interval = datetime.utcnow() - self._capability_timeouts[token]\n if interval.total_seconds() >= 10:\n expired_tokens.append(token)\n\n for token in expired_tokens:\n cap_withdraw = mplane.model.Withdrawal(capability=self._capabilities[token])\n self.handle_message(cap_withdraw, self.identity_for(token))","def _timeout_cbk(proc):\n proc.kill()\n raise RuntimeError(\"Timeout popped.\")","def __timeout(self, seconds, func, *args):\n t = threading.Timer(seconds, func, *args)\n self._timer = t\n t.start()","def handler(*args, **kwargs):\n raise TimeoutException(\"Test aborted due to timeout. Test was \" +\n \"expected to finish in less than {} second(s).\".format(time_limit))","def _banner_timeout(self):\n self.hide_banner()\n self._request_redraw()\n self.sm.on_state_event(self.events.TIMEOUT)","def raise_timeout(self, *args, **kwargs):\n\n self.log.error(\"Task timeout encountered.\")\n raise TimeoutError","def StepTimeout(self):\n return recipe_api.StepTimeout","def test_timeout_classic(self):\n with pytest.warns(W25, match='timed out'):\n with conf.set_temp('timeout', 1e-6):\n result = conesearch.conesearch(\n SCS_CENTER, SCS_RADIUS, cache=False,\n verbose=self.verbose, catalog_db=self.url)\n assert result is None","def test_set_timeout_wrong_args(self):\n context = Context(SSLv23_METHOD)\n with pytest.raises(TypeError):\n context.set_timeout(None)","def timeout():\n\t\ttimeout_ip = request.forms.get('ip')\n\t\tprint(\"timeout : {}\".format(timeout_ip))\n\t\tremove_vessel(timeout_ip)\n\t\treturn False","def TimeOutRaiser(signum, frame):\n if( Z3running ):\n raise Exception(\"z3 solver is too slow\")","def set_unavailable(self, time_out=-1):\n if self._available:\n _LOGGER.warning(\n \"ConnectionPool.set_unavailable pool: %s time_out: %s\",\n self,\n time_out,\n )\n self._available = False\n self._timeout_stamp = datetime.now()\n self._timeout = time_out","def test_swact_attempt_timeout(self):\n\n # mock the get_host queries\n # all remaining queries, the host returns 'Controller-Standby'\n self.sysinv_client.get_host.side_effect = itertools.chain(\n itertools.repeat(self.CONTROLLER_STANDBY))\n\n # mock the API call as successful on the subcloud\n self.sysinv_client.swact_host.return_value = self.CONTROLLER_SWACTING\n\n # invoke the strategy state operation on the orch thread\n self.worker.perform_state_action(self.strategy_step)\n\n # verify the swact command was actually attempted\n self.sysinv_client.swact_host.assert_called()\n\n # verify the query was invoked: 1 + max_attempts times\n self.assertEqual(swact_host.DEFAULT_MAX_QUERIES + 2,\n self.sysinv_client.get_host.call_count)\n\n # verify that state failed due to subcloud never finishing the swact\n self.assert_step_updated(self.strategy_step.subcloud_id,\n consts.STRATEGY_STATE_FAILED)","def timeout(self):\n self.timeout_scan_flag=True\n self.timer.stop()\n self.status_sig.emit([\"Update_Status\",\"Timeout during acquisition\",'log'])\n self.status_sig.emit([\"Timeout\"])","def set_timeout(self, timeout):\r\n return self._arm.set_timeout(timeout)","def before_call(self, func: Callable[..., T], *args: Any, **kwargs: Any) -> T:\n timeout = timedelta(seconds=self._breaker.reset_timeout)\n opened_at = self._breaker._state_storage.opened_at\n if opened_at and datetime.utcnow() < opened_at + timeout:\n error_msg = \"Timeout not elapsed yet, circuit breaker still open\"\n raise CircuitBreakerError(error_msg)\n else:\n self._breaker.half_open()\n return self._breaker.call(func, *args, **kwargs)","def timeout(seconds, force_kill=True):\n def wrapper(function):\n def inner(*args, **kwargs):\n now = time.time()\n proc = RunnableProcessing(function, *args, **kwargs)\n proc.start()\n proc.join(seconds)\n if proc.is_alive():\n if force_kill:\n proc.terminate()\n runtime = time.time() - now\n raise TimeoutException('timed out after {0} seconds'.format(runtime))\n assert proc.done()\n success, result = proc.result()\n if success:\n return result\n else:\n raise result\n return inner\n return wrapper","def test_get_timeouts_disabled(self):\n\n self.set_options(timeouts=False, timeout_default=2)\n task = self.create_task(self.context())\n\n self.assertIsNone(task._timeout_for_targets([targetA, targetB]))","def timeout(max_timeout):\n def timeout_decorator(item):\n \"\"\"Wrap the original function.\"\"\"\n @functools.wraps(item)\n def func_wrapper(*args, **kwargs):\n \"\"\"Closure for function.\"\"\"\n pool = multiprocessing.pool.ThreadPool(processes=1)\n async_result = pool.apply_async(item, args, kwargs)\n # raises a TimeoutError if execution exceeds max_timeout\n return async_result.get(max_timeout)\n return func_wrapper\n return timeout_decorator","def spiced_singleton_task(timeout):\n\n def task_exc(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n spices = kwargs.pop('add_spices', 'null')\n lock_id = \"{0}-spiced-singleton-task-{1}-{2}\".format(_PREFIX, func.__name__, spices)\n _execute_function(func, lock_id, timeout, *args, **kwargs)\n\n return wrapper\n\n return task_exc","def makeEvalStop(limit, timer, value=None):\n\n def score(game, player):\n if timer.time_left() < 0:\n raise TimeoutError(\"Timer expired during search. You must \" +\n \"return an answer before the timer reaches 0.\")\n if limit == game.counts[0]:\n timer.time_limit = 0\n return 0\n\n return score","def test(self):\n return test_throttle_method()","def _make_time_pass(self, seconds, timeout, time_mock):\n time_mock.return_value = TIMEOUT_EPOCH\n timeout.start_connect()\n time_mock.return_value = TIMEOUT_EPOCH + seconds\n return timeout"],"string":"[\n \"def timeout(time):\\n\\n def wrapper(f):\\n @wraps(f)\\n def wrapped_f(self, event, context):\\n return f(self, event, context)\\n\\n wrapped_f.timeout = time\\n return wrapped_f\\n\\n return wrapper\",\n \"def timeout(time_limit):\\n\\n class TimeoutException(Exception):\\n \\\"\\\"\\\" Subclass Exception to catch timer expiration during search \\\"\\\"\\\"\\n pass\\n\\n def handler(*args, **kwargs):\\n \\\"\\\"\\\" Generic handler to raise an exception when a timer expires \\\"\\\"\\\"\\n raise TimeoutException(\\\"Test aborted due to timeout. Test was \\\" +\\n \\\"expected to finish in less than {} second(s).\\\".format(time_limit))\\n\\n def wrapUnitTest(testcase):\\n\\n @wraps(testcase)\\n def testWrapper(self, *args, **kwargs):\\n\\n signal.signal(signal.SIGALRM, handler)\\n signal.alarm(time_limit)\\n\\n try:\\n return testcase(self, *args, **kwargs)\\n finally:\\n signal.alarm(0)\\n\\n return testWrapper\\n\\n return wrapUnitTest\",\n \"def functionThatShouldNotTimeout():\\n return None\",\n \"def _timeout(signum, frame):\\n # Raise TimeoutException with system default timeout message\\n raise TimeoutException()\",\n \"def pytest_timeout_cancel_timer(item):\",\n \"def functionThatWillTimeOut():\\n time.sleep(5)\",\n \"def stopped_check(self, timeout=None):\",\n \"def __call__(self, func):\\n @general_helpers.wraps(func)\\n def func_with_timeout(*args, **kwargs):\\n \\\"\\\"\\\"Wrapped function that adds timeout.\\\"\\\"\\\"\\n kwargs['timeout'] = self._timeout\\n return func(*args, **kwargs)\\n return func_with_timeout\",\n \"def set_timeout(timeout):\\n def decor(f):\\n @functools.wraps(f)\\n def inner(self, *args, **kwargs):\\n self.useFixture(fixtures.Timeout(timeout, gentle=True))\\n return f(self, *args, **kwargs)\\n return inner\\n return decor\",\n \"def time_limit():\\n\\n def decorator(fn):\\n @wraps(fn)\\n def wrapper(*args, **kwargs):\\n timer.wait_if_needed()\\n return fn(*args, **kwargs)\\n\\n return wrapper\\n\\n return decorator\",\n \"def timeout(seconds, error_message=\\\"Time out.\\\"):\\n import signal\\n def decorator(func):\\n\\n def __timiout_handler(signum, frame):\\n raise TimeoutError(error_message)\\n\\n @functools.wraps(func)\\n def wrapper(*arg, **kw):\\n signal.signal(signal.SIGALRM, __timiout_handler)\\n signal.alarm(seconds)\\n ret = \\\"\\\"\\n try:\\n ret = func(*arg, **kw)\\n except TimeoutError,e:\\n print \\\"TimeoutError: \\\", e\\n print \\\"{name} ran more than {seconds}s.\\\".format(name=func.__name__, seconds=seconds)\\n except Exception,e:\\n print \\\"Error: \\\",e\\n finally:\\n signal.alarm(0)\\n return ret\\n return wrapper\\n return decorator\",\n \"def assert_timeout(self) -> None:\",\n \"def timer_object_timeout(seconds=5):\\n def raise_timeout_exception():\\n raise TimeoutReachedException(seconds=seconds)\\n\\n return Timer(seconds, raise_timeout_exception)\",\n \"def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):\\n def decorator(func):\\n def _handle_timeout(signum, frame):\\n raise TimeoutError(error_message)\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n try:\\n signal.signal(signal.SIGALRM, _handle_timeout)\\n signal_disabled = False\\n except ValueError:\\n # Possibly running in debug mode. Timeouts will be ignored\\n signal_disabled = True\\n pass\\n\\n if not signal_disabled:\\n signal.alarm(seconds)\\n try:\\n result = func(*args, **kwargs)\\n finally:\\n if not signal_disabled:\\n signal.alarm(0)\\n return result\\n\\n return wrapper\\n return decorator\",\n \"def _handle_timeout(self, frame=None, **_):\\n\\n raise TimeOut.TimeOutError(self, frame)\",\n \"def set_timeout(self, timeout):\\n self.timeout = timeout\",\n \"def _banner_timeout(self):\\n self.sm.on_state_event(self.events.BANNERTIMEOUT)\",\n \"def reset_timeout (self, new_timeout):\\n self.timer.cancel()\\n self.timer = Timeout(new_timeout, TestIsTakingTooLong(new_timeout))\",\n \"def doTimeout(self):\\n\\n log.info('Executed {} calls until timeout'.format(self.calls_to_timeout))\\n self.calls_to_timeout = 0\\n\\n self.resetSession()\\n time.sleep(self.timeout)\",\n \"def reject_waiting_call(self) -> None:\",\n \"def timeout_decorator(func):\\n\\n def _new_func(result, old_func, old_func_args, old_func_kwargs):\\n try:\\n result.append(old_func(*old_func_args, **old_func_kwargs))\\n except Exception:\\n result[0] = False\\n result.append(traceback.format_exc())\\n\\n def wrapper(*args, **kwargs):\\n result = [True]\\n new_kwargs = {\\n \\\"result\\\": result,\\n \\\"old_func\\\": func,\\n \\\"old_func_args\\\": args,\\n \\\"old_func_kwargs\\\": kwargs,\\n }\\n thd = KThread(target=_new_func, args=(), kwargs=new_kwargs)\\n thd.start()\\n thd.join(seconds)\\n if thd.is_alive():\\n thd.kill()\\n thd.join()\\n raise TimeoutException(err_msg.format(seconds))\\n else:\\n return result\\n\\n return functools.wraps(func)(wrapper)\",\n \"async def timeout(self, failed: bool = False) -> None:\\n raise NotImplementedError()\",\n \"def __init__(self, timeout_time):\\n self.timeout_time = timeout_time\",\n \"def test_timeout_loop(self):\\n\\n self.assertIsNone(self.state.becomeCandidateTimeout)\\n # This should do nothing\\n self.state.cancelBecomeCandidateTimeout()\\n self.assertIsNone(self.state.becomeCandidateTimeout)\\n\\n results = self.state.begin()\\n\\n self.state.resetElectionTimeout()\\n self.assertTrue(self.state.electionTimeout > 0.150)\\n self.assertTrue(self.state.electionTimeout < 0.350)\\n self.assertTrue(isinstance(self.state.becomeCandidateTimeout,\\n base.DelayedCall))\",\n \"def timeout_after(s):\\n def outer(fn):\\n def inner(*args, **kwargs):\\n x = fn\\n timer = threading.Timer(s, _timeout, args=[fn.__module__])\\n timer.start()\\n try:\\n result = fn(*args, **kwargs)\\n finally:\\n timer.cancel()\\n return result\\n return inner\\n return outer\",\n \"def set_timeout(self, timeout):\\n pass\",\n \"def _idle(self):\\n # self._purge_timedout()\\n # ...\",\n \"def check_for_timeouts(self) -> None:\\n\\n if self.id != self._leader_id:\\n return\\n\\n for instance in self._undecided_instances:\\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\\n #print('\\\\033[93m' + \\\"Instance {0} round {1} exceeded timeout!\\\".format(instance, self._round_id[instance]) + \\\"\\\\033[0m\\\")\\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n self._enable_phase1_optimization = False\\n self.prepare_phase_parallel(instance)\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\\n\\n for instance in self._decided_values.keys():\\n if not self._acked_decided_values[instance]:\\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n decide_message: Decide = Decide(sender=self,\\n receiver_role=Role.LEARNER,\\n payload=DecidePayload((self._decided_values[instance], instance))\\n )\\n self.send(decide_message)\\n\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\",\n \"def update_lock_timeout(self, timeout):\\n\\n if not 0 < timeout <= BespokeGlobals.MAX_CHECKOUT_TIME:\\n raise FatalError(\\\"Timeout is out of range!\\\")\\n elif not self._in_use:\\n raise CoreError(\\\"This SystemUnderTest is not currently checked-out!\\\")\\n\\n self._lock_expiration = datetime.now() + timedelta(seconds=timeout)\",\n \"def pytest_timeout_set_timer(item, settings):\",\n \"def time_limit(seconds):\\n def signal_handler(signum, frame):\\n raise TimeoutException(\\\"TIMEOUT\\\")\\n signal.signal(signal.SIGALRM, signal_handler)\\n signal.alarm(seconds)\\n try:\\n yield\\n finally:\\n signal.alarm(0)\",\n \"def func_with_timeout(*args, **kwargs):\\n kwargs['timeout'] = self._timeout\\n return func(*args, **kwargs)\",\n \"def timeout(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n action = Process(target=func, args=args, kwargs=kwargs)\\n action.start()\\n action.join(timeout=5)\\n if action.is_alive():\\n # terminate function\\n action.terminate()\\n # clean up\\n action.join()\\n raise (TimeoutError)\\n # if process is not 0, is not successful\\n if action.exitcode != 0:\\n # raise Attribute Error, which is the most probable\\n raise (AttributeError)\\n return (wrapper)\",\n \"def testTimeout(self):\\n\\n class TimeoutTestCase(cros_test_lib.TestCase):\\n \\\"\\\"\\\"Test case that raises a TimeoutError because it takes too long.\\\"\\\"\\\"\\n\\n TEST_CASE_TIMEOUT = 1\\n\\n def testSleeping(self):\\n \\\"\\\"\\\"Sleep for 2 minutes. This should raise a TimeoutError.\\\"\\\"\\\"\\n time.sleep(2 * 60)\\n raise AssertionError('Test case should have timed out.')\\n\\n # Run the test case, verifying it raises a TimeoutError.\\n test = TimeoutTestCase(methodName='testSleeping')\\n self.assertRaises(timeout_util.TimeoutError, test.testSleeping)\",\n \"def timeout(seconds=60, error_message=os.strerror(errno.ETIME)):\\n def decorator(func):\\n def _handle_timeout(signum, frame):\\n raise TimeoutError(\\n '{0} ran for more than {1} seconds'.format(\\n func.__name__, seconds))\\n def wrapper(*args, **kwargs):\\n signal.signal(signal.SIGALRM, _handle_timeout)\\n signal.alarm(seconds)\\n try:\\n result = func(*args, **kwargs)\\n finally:\\n signal.alarm(0)\\n return result\\n return wraps(func)(wrapper)\\n\\n return decorator\",\n \"def __call__(self, func):\\n timeouts = _exponential_timeout_generator(\\n self._initial, self._maximum, self._multiplier, self._deadline)\\n\\n @general_helpers.wraps(func)\\n def func_with_timeout(*args, **kwargs):\\n \\\"\\\"\\\"Wrapped function that adds timeout.\\\"\\\"\\\"\\n kwargs['timeout'] = next(timeouts)\\n return func(*args, **kwargs)\\n\\n return func_with_timeout\",\n \"def set_retry_timeout(self, retry_timeout):\",\n \"def timeout_function(seconds=5):\\n\\n def signal_handler(signum, frame):\\n raise TimeoutError(\\\"Timed out!\\\")\\n\\n signal.signal(signal.SIGALRM, signal_handler)\\n signal.alarm(seconds)\\n\\n try:\\n yield\\n finally:\\n signal.alarm(0)\",\n \"def timeout(time_limit):\\n\\n def wrapUnitTest(testcase):\\n\\n @wraps(testcase)\\n def testWrapper(self):\\n\\n queue = Queue()\\n\\n try:\\n p = Thread(target=handler, args=(self, testcase, queue))\\n p.daemon = True\\n p.start()\\n err, res = queue.get(timeout=time_limit)\\n p.join()\\n if err:\\n raise err[0](err[1]).with_traceback(err[2])\\n return res\\n except QueueEmptyError:\\n raise TimeoutError(\\\"Test aborted due to timeout. Test was \\\" +\\n \\\"expected to finish in less than {} second(s).\\\".format(time_limit))\\n\\n return testWrapper\\n\\n return wrapUnitTest\",\n \"def have_a_break(func):\\n\\n @wraps(func)\\n def wrapper(self, *args, **kwargs):\\n if 'sleep' in kwargs:\\n sleep = kwargs['sleep']\\n del kwargs['sleep']\\n else:\\n sleep = self.sleep\\n\\n result = func(self, *args, **kwargs)\\n gevent.sleep(sleep)\\n return result\\n\\n return wrapper\",\n \"def raise_timeout_error(api_url, headers, timeout, proxies):\\n raise requests.exceptions.Timeout\",\n \"def raise_timeout_error(api_url, headers, timeout, proxies):\\n raise requests.exceptions.Timeout\",\n \"def timed(limit):\\n def decorate(func):\\n def newfunc(*arg, **kw):\\n start = time.time()\\n func(*arg, **kw)\\n end = time.time()\\n if end - start > limit:\\n raise TimeExpired(\\\"Time limit (%s) exceeded\\\" % limit)\\n newfunc = make_decorator(func)(newfunc)\\n return newfunc\\n return decorate\",\n \"def setTimeOut(self, timeout=6.0):\\n self.timeout = timeout\",\n \"def __simulate_sleep_but_awaken_if_stopped(self, timeout):\\n deadline = self.__fake_clock.time() + timeout\\n\\n def deadline_exceeded_or_not_running(current_time):\\n return current_time >= deadline or not self.is_running()\\n\\n self.__fake_clock.simulate_waiting(exit_when=deadline_exceeded_or_not_running)\\n\\n return not self.is_running()\",\n \"def settimeout(self,timeout=10):\\r\\n # Update\\r\\n self.timeout = timeout\",\n \"def pytest_timeout_cancel_timer(item):\\n tle.lib.cancel()\\n return True\",\n \"def set_timeout(self, timeout_secs):\\n self._timeout_secs = timeout_secs\",\n \"def __init__(self, timeout=120):\\n self.m_timeout = timeout\",\n \"def assert_timeout(self) -> None:\\n if self._cancelled:\\n raise asyncio.TimeoutError from None\",\n \"def later(cls, timeout, f, **kwargs):\\n def x(*args, **kwargs):\\n cls.sleep(timeout)\\n return f(*args, **kwargs)\\n return cls.new(x, **kwargs)\",\n \"def timeout(self):\\n self._status_update(\\\"Pyloton: Timeout\\\")\\n time.sleep(3)\",\n \"def cancelled_to_request_timed_out_error(value, timeout):\\n if isinstance(value, failure.Failure):\\n value.trap(CancelledError)\\n raise RequestTimedOutError()\\n return value\",\n \"def test_timeout_pending(self):\\n deadline = Deadline(MS)\\n timeout = deadline.timeout()\\n self.assertGreater(timeout, 0)\\n self.assertLess(timeout, MS)\",\n \"def func_with_timeout(*args, **kwargs):\\n kwargs['timeout'] = next(timeouts)\\n return func(*args, **kwargs)\",\n \"def timeout_syscall(seconds):\\n def timeout_handler(signum, frame):\\n raise InterruptedError\\n\\n original_handler = signal.signal(signal.SIGALRM, timeout_handler)\\n signal.alarm(seconds)\\n try:\\n yield\\n finally:\\n signal.alarm(0)\\n signal.signal(signal.SIGALRM, original_handler)\",\n \"def timeout_handler(interval, recurring = None):\\n def decorator(func):\\n \\\"\\\"\\\"The decorator\\\"\\\"\\\"\\n func._pyxmpp_timeout = interval\\n func._pyxmpp_recurring = recurring\\n return func\\n return decorator\",\n \"def test_polling_plugin_timeout(self):\\n pass\",\n \"def timeout_change(self, timedelta):\\n pass # pylint: disable=unnecessary-pass\\n # For backward compatibility only.\",\n \"def remove_timeouts(self):\\n\\n Fl.remove_timeout(self.play_seq)\\n Fl.remove_timeout(self.gameover)\\n Fl.remove_timeout(self.activate_buts)\\n for b in self.gamebuttons:\\n Fl.remove_timeout(b.auto_click)\",\n \"def raise_timeout_error_get_votes(api_url, headers, params, timeout, proxies):\\n raise requests.exceptions.Timeout\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def power_off(timeout: int = 0) -> None:\",\n \"async def test_timeout_is_respected():\\n parent_cancelled = child_cancelled = False\\n\\n async def sleepy():\\n nonlocal child_cancelled\\n try:\\n await asyncio.sleep(1000 * 1000)\\n except asyncio.CancelledError:\\n child_cancelled = True\\n\\n async with async_timeout.timeout(0.01):\\n try:\\n async with Nursery() as nursery:\\n nursery.start_soon(sleepy())\\n await asyncio.sleep(1000 * 1000)\\n except asyncio.CancelledError:\\n parent_cancelled = True\\n\\n assert parent_cancelled and child_cancelled\",\n \"def __init__( self, timeout = 60.0 ):\\n\\n self.timeout = timeout\\n self.alive = None\",\n \"def timeout(\\n seconds: int, err_msg: str = \\\"Timeout after {} seconds.\\\"\\n) -> Callable[[Callable], Callable]:\\n\\n def timeout_decorator(func):\\n \\\"\\\"\\\"The real decorator used for setup, teardown and testcase methods.\\\"\\\"\\\"\\n\\n def _new_func(result, old_func, old_func_args, old_func_kwargs):\\n try:\\n result.append(old_func(*old_func_args, **old_func_kwargs))\\n except Exception:\\n result[0] = False\\n result.append(traceback.format_exc())\\n\\n def wrapper(*args, **kwargs):\\n result = [True]\\n new_kwargs = {\\n \\\"result\\\": result,\\n \\\"old_func\\\": func,\\n \\\"old_func_args\\\": args,\\n \\\"old_func_kwargs\\\": kwargs,\\n }\\n thd = KThread(target=_new_func, args=(), kwargs=new_kwargs)\\n thd.start()\\n thd.join(seconds)\\n if thd.is_alive():\\n thd.kill()\\n thd.join()\\n raise TimeoutException(err_msg.format(seconds))\\n else:\\n return result\\n\\n return functools.wraps(func)(wrapper)\\n\\n return timeout_decorator\",\n \"def on_timeout(self):\\n pass\",\n \"def timeout(order):\\n return ResultProxy(TaskResult(TaskTimedout('A task has timedout'), order))\",\n \"def raise_timeout_error_put_votes(api_url, headers, json, timeout, proxies):\\n raise requests.exceptions.Timeout\",\n \"def test_pool_timeout_hw(self):\\n self.test_pool_timeout()\",\n \"def polling(callback=None, timeout=30, sleep=0.01):\\n def wrapper(f):\\n @wraps(f)\\n def wrapped(*args, **kwargs):\\n t_start = time.time()\\n\\n while time.time() <= t_start + timeout:\\n try:\\n return f(*args, **kwargs)\\n except WebDriverException:\\n time.sleep(sleep)\\n continue\\n else:\\n raise\\n\\n return wrapped\\n\\n if callback is not None:\\n return wrapper(callback)\\n\\n return wrapper\",\n \"def _wakeup(self, wakeup_timeout=10, response_timeout=3):\\n pass\",\n \"def test_lock_timeout():\\n lock_unlock_timeout(0)\",\n \"def __init__(self, timeout=129600):\\n self.timeout = timeout\",\n \"def until_not(self, method, timeout=None, interval=None, ignored_exceptions=None, message='',\\n *args, **kwargs):\\n\\n self._until(method, True, timeout, interval, ignored_exceptions, message,\\n *args, **kwargs)\\n return False\",\n \"def timedcache(method, timeout=300):\\n argsmap = {}\\n\\n @functools.wraps(method)\\n def wrapper(*args, **kwargs):\\n key = (repr(args), repr(kwargs))\\n if key not in argsmap:\\n value = method(*args, **kwargs)\\n argsmap[key] = (value, time.time())\\n elif time.time() - argsmap[key][1] >= timeout:\\n value = method(*args, **kwargs)\\n argsmap[key] = (value, time.time())\\n else:\\n value = argsmap[key][0]\\n return value\\n\\n return wrapper\",\n \"def timeout(self, timeout):\\n assert timeout is None or timeout > 0\\n self._timeout = timeout\",\n \"def _check_timeouts(self):\\n\\n expired_tokens = []\\n for token in self._capability_timeouts:\\n interval = datetime.utcnow() - self._capability_timeouts[token]\\n if interval.total_seconds() >= 10:\\n expired_tokens.append(token)\\n\\n for token in expired_tokens:\\n cap_withdraw = mplane.model.Withdrawal(capability=self._capabilities[token])\\n self.handle_message(cap_withdraw, self.identity_for(token))\",\n \"def _timeout_cbk(proc):\\n proc.kill()\\n raise RuntimeError(\\\"Timeout popped.\\\")\",\n \"def __timeout(self, seconds, func, *args):\\n t = threading.Timer(seconds, func, *args)\\n self._timer = t\\n t.start()\",\n \"def handler(*args, **kwargs):\\n raise TimeoutException(\\\"Test aborted due to timeout. Test was \\\" +\\n \\\"expected to finish in less than {} second(s).\\\".format(time_limit))\",\n \"def _banner_timeout(self):\\n self.hide_banner()\\n self._request_redraw()\\n self.sm.on_state_event(self.events.TIMEOUT)\",\n \"def raise_timeout(self, *args, **kwargs):\\n\\n self.log.error(\\\"Task timeout encountered.\\\")\\n raise TimeoutError\",\n \"def StepTimeout(self):\\n return recipe_api.StepTimeout\",\n \"def test_timeout_classic(self):\\n with pytest.warns(W25, match='timed out'):\\n with conf.set_temp('timeout', 1e-6):\\n result = conesearch.conesearch(\\n SCS_CENTER, SCS_RADIUS, cache=False,\\n verbose=self.verbose, catalog_db=self.url)\\n assert result is None\",\n \"def test_set_timeout_wrong_args(self):\\n context = Context(SSLv23_METHOD)\\n with pytest.raises(TypeError):\\n context.set_timeout(None)\",\n \"def timeout():\\n\\t\\ttimeout_ip = request.forms.get('ip')\\n\\t\\tprint(\\\"timeout : {}\\\".format(timeout_ip))\\n\\t\\tremove_vessel(timeout_ip)\\n\\t\\treturn False\",\n \"def TimeOutRaiser(signum, frame):\\n if( Z3running ):\\n raise Exception(\\\"z3 solver is too slow\\\")\",\n \"def set_unavailable(self, time_out=-1):\\n if self._available:\\n _LOGGER.warning(\\n \\\"ConnectionPool.set_unavailable pool: %s time_out: %s\\\",\\n self,\\n time_out,\\n )\\n self._available = False\\n self._timeout_stamp = datetime.now()\\n self._timeout = time_out\",\n \"def test_swact_attempt_timeout(self):\\n\\n # mock the get_host queries\\n # all remaining queries, the host returns 'Controller-Standby'\\n self.sysinv_client.get_host.side_effect = itertools.chain(\\n itertools.repeat(self.CONTROLLER_STANDBY))\\n\\n # mock the API call as successful on the subcloud\\n self.sysinv_client.swact_host.return_value = self.CONTROLLER_SWACTING\\n\\n # invoke the strategy state operation on the orch thread\\n self.worker.perform_state_action(self.strategy_step)\\n\\n # verify the swact command was actually attempted\\n self.sysinv_client.swact_host.assert_called()\\n\\n # verify the query was invoked: 1 + max_attempts times\\n self.assertEqual(swact_host.DEFAULT_MAX_QUERIES + 2,\\n self.sysinv_client.get_host.call_count)\\n\\n # verify that state failed due to subcloud never finishing the swact\\n self.assert_step_updated(self.strategy_step.subcloud_id,\\n consts.STRATEGY_STATE_FAILED)\",\n \"def timeout(self):\\n self.timeout_scan_flag=True\\n self.timer.stop()\\n self.status_sig.emit([\\\"Update_Status\\\",\\\"Timeout during acquisition\\\",'log'])\\n self.status_sig.emit([\\\"Timeout\\\"])\",\n \"def set_timeout(self, timeout):\\r\\n return self._arm.set_timeout(timeout)\",\n \"def before_call(self, func: Callable[..., T], *args: Any, **kwargs: Any) -> T:\\n timeout = timedelta(seconds=self._breaker.reset_timeout)\\n opened_at = self._breaker._state_storage.opened_at\\n if opened_at and datetime.utcnow() < opened_at + timeout:\\n error_msg = \\\"Timeout not elapsed yet, circuit breaker still open\\\"\\n raise CircuitBreakerError(error_msg)\\n else:\\n self._breaker.half_open()\\n return self._breaker.call(func, *args, **kwargs)\",\n \"def timeout(seconds, force_kill=True):\\n def wrapper(function):\\n def inner(*args, **kwargs):\\n now = time.time()\\n proc = RunnableProcessing(function, *args, **kwargs)\\n proc.start()\\n proc.join(seconds)\\n if proc.is_alive():\\n if force_kill:\\n proc.terminate()\\n runtime = time.time() - now\\n raise TimeoutException('timed out after {0} seconds'.format(runtime))\\n assert proc.done()\\n success, result = proc.result()\\n if success:\\n return result\\n else:\\n raise result\\n return inner\\n return wrapper\",\n \"def test_get_timeouts_disabled(self):\\n\\n self.set_options(timeouts=False, timeout_default=2)\\n task = self.create_task(self.context())\\n\\n self.assertIsNone(task._timeout_for_targets([targetA, targetB]))\",\n \"def timeout(max_timeout):\\n def timeout_decorator(item):\\n \\\"\\\"\\\"Wrap the original function.\\\"\\\"\\\"\\n @functools.wraps(item)\\n def func_wrapper(*args, **kwargs):\\n \\\"\\\"\\\"Closure for function.\\\"\\\"\\\"\\n pool = multiprocessing.pool.ThreadPool(processes=1)\\n async_result = pool.apply_async(item, args, kwargs)\\n # raises a TimeoutError if execution exceeds max_timeout\\n return async_result.get(max_timeout)\\n return func_wrapper\\n return timeout_decorator\",\n \"def spiced_singleton_task(timeout):\\n\\n def task_exc(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n spices = kwargs.pop('add_spices', 'null')\\n lock_id = \\\"{0}-spiced-singleton-task-{1}-{2}\\\".format(_PREFIX, func.__name__, spices)\\n _execute_function(func, lock_id, timeout, *args, **kwargs)\\n\\n return wrapper\\n\\n return task_exc\",\n \"def makeEvalStop(limit, timer, value=None):\\n\\n def score(game, player):\\n if timer.time_left() < 0:\\n raise TimeoutError(\\\"Timer expired during search. You must \\\" +\\n \\\"return an answer before the timer reaches 0.\\\")\\n if limit == game.counts[0]:\\n timer.time_limit = 0\\n return 0\\n\\n return score\",\n \"def test(self):\\n return test_throttle_method()\",\n \"def _make_time_pass(self, seconds, timeout, time_mock):\\n time_mock.return_value = TIMEOUT_EPOCH\\n timeout.start_connect()\\n time_mock.return_value = TIMEOUT_EPOCH + seconds\\n return timeout\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62377894","0.61471254","0.6033199","0.5992332","0.59493905","0.57512546","0.5742238","0.5699485","0.5643137","0.5635711","0.56170464","0.5599529","0.5585652","0.5580245","0.55567193","0.5546809","0.55230653","0.55142486","0.55070794","0.5488454","0.5446006","0.5421574","0.5409299","0.5408354","0.53852516","0.53675145","0.53587586","0.5357154","0.5354543","0.53285706","0.5327015","0.53077406","0.5304749","0.52877235","0.52832395","0.52814996","0.5278834","0.5275666","0.5275653","0.5262236","0.52618843","0.52618843","0.5245116","0.5233239","0.52304137","0.52239406","0.5223065","0.5221598","0.5216829","0.5211237","0.51996106","0.51836824","0.5173922","0.5162906","0.5162769","0.5155507","0.5143216","0.5129675","0.51240695","0.51228285","0.5121445","0.51109624","0.51070243","0.5104021","0.5101893","0.5094397","0.50898284","0.5086706","0.50725794","0.5063621","0.506207","0.5058095","0.5057375","0.50531083","0.50508255","0.5049022","0.5045612","0.5024699","0.5009203","0.50023097","0.50009626","0.4999328","0.49940255","0.49940112","0.49846038","0.4980372","0.4980326","0.498001","0.49714178","0.4967779","0.49625885","0.4950341","0.49452227","0.4943411","0.49327433","0.49323943","0.49303234","0.49284774","0.49235818","0.4923514"],"string":"[\n \"0.62377894\",\n \"0.61471254\",\n \"0.6033199\",\n \"0.5992332\",\n \"0.59493905\",\n \"0.57512546\",\n \"0.5742238\",\n \"0.5699485\",\n \"0.5643137\",\n \"0.5635711\",\n \"0.56170464\",\n \"0.5599529\",\n \"0.5585652\",\n \"0.5580245\",\n \"0.55567193\",\n \"0.5546809\",\n \"0.55230653\",\n \"0.55142486\",\n \"0.55070794\",\n \"0.5488454\",\n \"0.5446006\",\n \"0.5421574\",\n \"0.5409299\",\n \"0.5408354\",\n \"0.53852516\",\n \"0.53675145\",\n \"0.53587586\",\n \"0.5357154\",\n \"0.5354543\",\n \"0.53285706\",\n \"0.5327015\",\n \"0.53077406\",\n \"0.5304749\",\n \"0.52877235\",\n \"0.52832395\",\n \"0.52814996\",\n \"0.5278834\",\n \"0.5275666\",\n \"0.5275653\",\n \"0.5262236\",\n \"0.52618843\",\n \"0.52618843\",\n \"0.5245116\",\n \"0.5233239\",\n \"0.52304137\",\n \"0.52239406\",\n \"0.5223065\",\n \"0.5221598\",\n \"0.5216829\",\n \"0.5211237\",\n \"0.51996106\",\n \"0.51836824\",\n \"0.5173922\",\n \"0.5162906\",\n \"0.5162769\",\n \"0.5155507\",\n \"0.5143216\",\n \"0.5129675\",\n \"0.51240695\",\n \"0.51228285\",\n \"0.5121445\",\n \"0.51109624\",\n \"0.51070243\",\n \"0.5104021\",\n \"0.5101893\",\n \"0.5094397\",\n \"0.50898284\",\n \"0.5086706\",\n \"0.50725794\",\n \"0.5063621\",\n \"0.506207\",\n \"0.5058095\",\n \"0.5057375\",\n \"0.50531083\",\n \"0.50508255\",\n \"0.5049022\",\n \"0.5045612\",\n \"0.5024699\",\n \"0.5009203\",\n \"0.50023097\",\n \"0.50009626\",\n \"0.4999328\",\n \"0.49940255\",\n \"0.49940112\",\n \"0.49846038\",\n \"0.4980372\",\n \"0.4980326\",\n \"0.498001\",\n \"0.49714178\",\n \"0.4967779\",\n \"0.49625885\",\n \"0.4950341\",\n \"0.49452227\",\n \"0.4943411\",\n \"0.49327433\",\n \"0.49323943\",\n \"0.49303234\",\n \"0.49284774\",\n \"0.49235818\",\n \"0.4923514\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6632625"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":88497,"cells":{"query":{"kind":"string","value":"Turn a unique action identifier string (i.e SSiPPformatted action string) into an index into the action list for this problem."},"document":{"kind":"string","value":"def _act_id(self, act_str):\n\n # FIXME: instead of doing this shit I should just fix the things in\n # state_reprs.py so that they can take action IDs (numeric ones) OR\n # action names OR BoundActions! Have a family of resolve_action_to_*\n # methods that let you convert from any one representation to any\n # other. Right now it seems like I have the same problem with actions\n # that I used to have with states (too many representations, have to be\n # careful which one you use).\n return self._plan_exts.problem_meta.act_unique_id_to_index(act_str)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_action_id(self, action_text: str) -> int:\n\n actions_tuple = tuple(action_text.split('+'))\n return self.action_tuples2ids[actions_tuple] # todo unhandled exception when not found","def str_to_action(str):\n raise NotImplementedError","def get_action_id(self, action_text: str) -> int:\n return self.templates.actions.index(action_text) # todo unhandled exception when not found","def get_action_id(output):\n return output['Action queued with id']","def _interpret_action(self, action_idx: int, team: int):\n if action_idx < 4:\n return action_idx\n\n noops = self._allow_noops[team]\n diags = self._allow_diagonals[team]\n assert noops or diags\n\n if noops and not diags:\n assert action_idx == 4\n return NOOP\n\n if not noops and diags:\n assert action_idx < 8\n return action_idx\n\n if noops and diags:\n assert action_idx < 9\n return action_idx","def actions(self, state):\n\n\t\t# Returns the possible numbers we can use.\n\t\treturn [(str)(x + 1) for x in range (0,self.size)]","def actions(self, state):\n myActionList= (1,2);\n return myActionList","def actionIndexTuple2Int(actionIdxTuple, numActionList):\n rowIdx, colIdx = actionIdxTuple\n assert (\n rowIdx < numActionList[0]\n ), \"The size of evader's action set is {:d} but get index {:d}\".format(\n numActionList[0], rowIdx\n )\n assert (\n colIdx < numActionList[1]\n ), \"The size of pursuer's action set is {:d} but get index {:d}\".format(\n numActionList[1], colIdx\n )\n\n actionIdx = numActionList[1] * rowIdx + colIdx\n return actionIdx","def convert_action(action):\n new = np.zeros(18)\n for act in ['camera', 'forward', 'jump', 'left', 'right']:\n tmp = \"\"\n if act == 'camera':\n new[0] = action[act][0]\n new[1] = action[act][1]\n else:\n tmp += str(action[act])\n new[int(tmp, 2)] = 1\n return new","def _decode_action(self, action_id):\n raise NotImplementedError","def findAction(self, actionId): #$NON-NLS-1$\r","def action2pos(self, action):\n new_pos = [0, 0]\n ###### Shu's modificaiton\n # 4 actions\n # if action == 0:\n # new_pos[0] = self.curr_pos[0]\n # new_pos[1] = self.curr_pos[1]-1\n # elif action == 1:\n # new_pos[0] = self.curr_pos[0]+1\n # new_pos[1] = self.curr_pos[1]\n # elif action == 2:\n # new_pos[0] = self.curr_pos[0]-1\n # new_pos[1] = self.curr_pos[1]\n # elif action == 3:\n # new_pos[0] = self.curr_pos[0]\n # new_pos[1] = self.curr_pos[1]+1\n\n # 8 action\n if action in [5, 0, 4]:\n new_pos[0] = self.curr_pos[0]-1\n elif action in [7, 1, 6]:\n new_pos[0] = self.curr_pos[0]+1\n else:\n new_pos[0] = self.curr_pos[0]\n\n if action in [5, 3, 7]:\n new_pos[1] = self.curr_pos[1]-1\n elif action in [4, 2, 6]:\n new_pos[1] = self.curr_pos[1]+1\n else:\n new_pos[1] = self.curr_pos[1]\n\n return tuple(new_pos)","def label2action(label):\n items = label.split('-')\n if len(items) == 1:\n action = (items[0], None, None)\n elif len(items) == 3:\n action = tuple(items)\n else:\n raise ValueError(\"Unrecognized label: {}\".format(label))\n return action","def create_action_id(*args):\r\n return u'-'.join([unicode(arg) for arg in args])","def action_id2arr(ids):\n return actions[ids]","def _fill_action_info(action):\n def _is_ascii(s):\n return all(ord(c) < 128 for c in s)\n\n if not _is_ascii(action.obj_desc_str):\n tf.logging.info('Found an unconvertable unicode %s', action.obj_desc_str)\n return\n\n if not (isinstance(action.verb_str, str) and isinstance(\n action.obj_desc_str, str) and isinstance(action.input_content_str, str)):\n return\n action.regularize_strs()\n input_str_pos_padding = [\n config.LABEL_DEFAULT_VALUE_INT, config.LABEL_DEFAULT_VALUE_INT\n ]\n\n input_prep_word = _get_input_prep_word()\n swipe_prep_word = _get_swipe_prep_word()\n\n if action.action_rule == common.ActionRules.NO_VERB_RULE:\n action.instruction_str = action.obj_desc_str\n action.verb_str_pos = [0, 0]\n action.obj_str_pos = [0, _count_chars(action.obj_desc_str)]\n action.input_str_pos = input_str_pos_padding\n return\n\n if action.action_type in [common.ActionTypes.CLICK]:\n action.instruction_str = '%s %s' % (action.verb_str, action.obj_desc_str)\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\n action.obj_str_pos = [\n _count_chars(action.verb_str) + 1,\n _count_chars(action.instruction_str)\n ]\n action.input_str_pos = input_str_pos_padding\n\n elif action.action_type in [common.ActionTypes.INPUT]:\n # There is no space between 4th and 5th string because the 2nd string,\n # article word, is optional.\n action.instruction_str = '%s %s %s %s' % (\n action.verb_str, action.input_content_str, input_prep_word,\n action.obj_desc_str)\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\n action.input_str_pos = [\n _count_chars(action.verb_str) + 1,\n _count_chars('%s %s' % (action.verb_str, action.input_content_str))\n ]\n action.obj_str_pos = [\n _count_chars(\n '%s %s %s' %\n (action.verb_str, action.input_content_str, input_prep_word)) + 1,\n _count_chars(action.instruction_str)\n ]\n # All the rests are swipe actions\n else:\n action.instruction_str = '%s %s %s' % (action.verb_str, swipe_prep_word,\n action.obj_desc_str)\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\n action.input_str_pos = input_str_pos_padding\n action.obj_str_pos = [\n _count_chars('%s %s' % (action.verb_str, swipe_prep_word)) + 1,\n _count_chars(action.instruction_str)\n ]","def _decode_action(self, action_id):\n legal_actions = self.game.get_legal_actions()\n if self.actions[action_id] not in legal_actions:\n if \"check\" in legal_actions:\n return \"check\"\n else:\n return \"fold\"\n return self.actions[action_id]","def parseAction(self, action):\n action = self.AGENT_TYPES[action]\n\n\n full_action = {}\n full_action[\"action\"] = action\n if action == \"eli-kw\":\n keywords = self.dataset.getSuggestedKeywords()\n full_action[\"keywords\"] = keywords[:self.N]\n elif action == \"info\" or action == \"info-all\":\n full_action[\"function\"] = self.current_function\n\n elif action == \"sugg\" or action == \"sugg-info-all\":\n top_hit = self.dataset.getTopHits(1)\n if not top_hit:\n full_action[\"action\"] = \"eli-query\"\n else:\n functions = self.dataset.getTopHits(1, self.result_index)\n if functions:\n full_action[\"function\"] = functions[0]\n else:\n full_action[\"function\"] = \"\"\n\n self.result_index += 1\n\n elif action == \"sugg-all\":\n full_action[\"list\"] = self.dataset.getTopHits(self.K, self.result_index)\n\n elif action == \"change-page\":\n self.result_index += self.K\n full_action[\"list\"] = self.dataset.getTopHits(self.K, self.result_index)\n return full_action","def actionIndexInt2Tuple(actionIdx, numActionList):\n numJoinAction = int(numActionList[0] * numActionList[1])\n assert (actionIdx < numJoinAction), (\n \"The size of joint action set is \"\n \"{:d} but get index {:d}\".format(numJoinAction, actionIdx)\n )\n rowIdx = actionIdx // numActionList[1]\n colIdx = actionIdx % numActionList[1]\n return (rowIdx, colIdx)","def convert_symbol_to_raw_actions(self, symbol, rules):\n assert not isinstance(symbol, list)\n assert isinstance(symbol, str) or isinstance(symbol, int)\n symbol = [symbol]\n finished = False\n while not finished:\n new_symbol = []\n for symbol_val in symbol:\n if symbol_val in rules.keys():\n new_symbol.append(rules[symbol_val][0])\n new_symbol.append(rules[symbol_val][1])\n else:\n new_symbol.append(symbol_val)\n if new_symbol == symbol: finished = True\n else: symbol = new_symbol\n new_symbol = tuple(new_symbol)\n return new_symbol","def create_action(verb_dct):\n action = {'What is the action?': [],\n 'Who is the action directed to?': [],\n 'Who is making the action': [],\n 'How is the action made': []\n }\n final_index_verb = verb_dct['indexes'][1]\n init_index_verb = verb_dct['indexes'][0]\n\n return action, init_index_verb, final_index_verb","def action2label(action):\n if action[0] == 'shift':\n label = action[0]\n elif action[0] == 'reduce':\n label = '-'.join(list(action))\n else:\n raise ValueError(\"Unrecognized parsing action: {}\".format(action))\n return label","def actionString(self,action):\n return str(self._mdp.A[action])","def get_action_list(program_name: str) -> str:\n short_name = int(program_name[8:])\n es = Elasticsearch([{'host': 'localhost', 'port': 9200}])\n res = es.search(\n index='actions-index', \n params= {'size': 1}, \n body={\"query\": {\"match\": {'name' : short_name}}})\n for hit in res['hits']['hits']:\n return hit['_source']['actions']\n return \"\"","def convert_to_low_level_action(self, i_state, action):\n pass","def _createAction(self, item, actionString):\n action = {\n \"action\": actionString,\n \"item_id\": item.item_id\n }\n\n pocketLogger.debug(\"Action\" + str(action))\n\n self.actions.append(action)","def action_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"action_id\")","def as_action_str(string: str) -> str:","def action_arr2id(arr):\n ids = []\n for a in arr:\n id = np.where(np.all(actions==a, axis=1))\n ids.append(id[0][0])\n return np.array(ids)","def ia_action(data_map, data_ia, player):\n raw_commands = ia_reflexion(data_ia, data_map, player)\n\n # Rewrite the command into a single string.\n string_commands = ''\n for key in raw_commands:\n string_commands += ('0' + str(raw_commands[key][0][0]))[-2:] + '-' + ('0' + str(raw_commands[key][0][1]))[-2:] + raw_commands[key][1] + ('0' + str(raw_commands[key][2][0]))[-2:] + '-' + ('0' + str(raw_commands[key][2][1]))[-2:] + ' '\n return string_commands","def process_index(index, intensity, interaction_symbol):\n return tuple(index.split(interaction_symbol))","def get_action(command):\n return command.split(\" \")[0]","def createAction(self, act, imList):\n if type(imList[0]) == type(()):\n # process list of (row, col) values\n self.actions[act] = [ self.imhash[row, col] for row, col in imList]\n else:\n # process list of ints\n self.actions[act] = [ self.imhash[i] for i in imList ]","def registerAction(self, actionId, action): #$NON-NLS-1$\r","def split_action_id (id):\n assert isinstance(id, basestring)\n split = id.split ('.', 1)\n toolset = split [0]\n name = ''\n if len (split) > 1:\n name = split [1]\n return (toolset, name)","def asIndex(i):\n return u\"\".join([unichr(0x2050 + ord(c)) for c in str(i)])","def asIndex(i):\n return u\"\".join([unichr(0x2050 + ord(c)) for c in str(i)])","def action_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"action_id\")","def action(self, gstate, actions):\n self.log.debug(\"Picking among actions %s\" % actions)\n return actions[0]","def parse_command_to_actions(moving_command):\n regex = re.compile(r'[A-Z][0-9]*')\n return re.findall(regex, moving_command)","def applyAction(state, action):\r\n if action == 'N':\r\n return (state[0] - 1, state[1])\r\n\r\n if action == 'E':\r\n return (state[0], state[1] + 1)\r\n\r\n if action == 'W':\r\n return (state[0], state[1] - 1)\r\n\r\n if action == 'S':\r\n return (state[0] + 1, state[1])","def actions(self, state):\n \n #les actions sont définies comme étant les nombres possibles dans \n #la case i,j\n theActions = []\n for i in range(size):\n for j in range(size):\n line = i\n col = j\n if(state[i][j] == 0):\n possibleNumbers = [1,2,3,4,5,6,7,8,9]\n config = state\n for a in range(size):\n x = config[line][a]\n if(x in possibleNumbers):\n possibleNumbers.remove(x)\n \n for b in range(size):\n x = config[b][col]\n if(x in possibleNumbers):\n possibleNumbers.remove(x)\n \n #identifie quelle boite on veut vérifier\n hBox = col - col % 3\n vBox = line - line % 3\n \n for c in range(3):\n for d in range(3):\n x = config[c+vBox][d+hBox]\n if(x in possibleNumbers):\n possibleNumbers.remove(x)\n for k in possibleNumbers:\n theActions.append((i,j,k))\n return theActions","def actions(self):\n return {0, 1, 2, 3, 4, 5, 11, 12}","def get_action_from_id(self, id_a):\n return self.id_to_action_dict[id_a]","def action_to_gym(self, action: Dict[str, Any]):\n j1 = action['j1']\n j2 = action['j2']\n j3 = action['j3']\n j4 = action['j4']\n j5 = action['j5']\n j6 = action['j6']\n\n # Half Cheetah environment expects an array of actions\n return [j1, j2, j3, j4, j5, j6]","def get_action_url(checklist, index):\r\n return checklist['items'][index]['action_url']","def findActions(problem, state):\r\n size = len(problem) - 1\r\n legalActions = []\r\n if state[0] > 0 and problem[state[0] - 1][state[1]] != 'w':\r\n legalActions.append('N')\r\n if state[0] < size and problem[state[0] + 1][state[1]] != 'w':\r\n legalActions.append('S')\r\n if state[1] > 0 and problem[state[0]][state[1] - 1] != 'w':\r\n legalActions.append('W')\r\n if state[1] < size and problem[state[0]][state[1] + 1] != 'w':\r\n legalActions.append('E')\r\n return legalActions","def action(self, id_):\n return np.eye(self.action_space)[id_]","def search_actions(strings):\n\tpattern = '|'.join(strings)\n\n\tb = actions[actions.action.str.contains(pattern)] \\\n\t\t.loc[:, ['session', 'bill_id']] \\\n\t\t.drop_duplicates()\n\n\treturn b","def _get_action(verb):\n aux_verbs = \"\"\n for child in verb.children:\n if child.dep_ == \"aux\" or child.dep_ == \"neg\":\n aux_verbs += str(child)\n return SpacyEventExtractor._remove_extra_whitespaces(str(aux_verbs) + ' ' + str(verb))","def create_action_set(number_of_actions=4, add_no_change=True):\n # TO DO:\n # - check if the number of actions is equal.\n split_num = number_of_actions/2\n action_list = []\n for x in range(int(split_num)):\n action_list.append(\"lb\"+\"_\"+str(x))\n action_list.append(\"ub\"+\"_\"+str(x))\n\n if add_no_change == True:\n action_list.append(\"no_change\")\n return action_list","def code(action_sequence):\r\n # refuse any invalid action :\r\n if set(action_sequence) - set(CODE_MAP): # some action was not in the known ones.\r\n return '0' # per spec (test_unknown_action)\r\n\r\n mapdict = dict(zip(CODE_MAP, (1, 2, 4, 8)))\r\n da_code = [mapdict[action] for action in action_sequence]\r\n if sorted(da_code) != da_code: # list is not sorted : assume reversed\r\n da_code.append(16)\r\n return format(sum(da_code), 'b') # sum to integer, binstring, and return","def action_to_id(a):\n if a == [-1.0, 0.0, 0.0]: return LEFT # LEFT: 1\n elif a == [1.0, 0.0, 0.0]: return RIGHT # RIGHT: 2\n elif a == [0.0, 1.0, 0.0]: return ACCELERATE # ACCELERATE: 3\n elif a == [0.0, 0.0, 0.2]: return BRAKE # BRAKE: 4\n else: \n return STRAIGHT # STRAIGHT = 0","def choose_action(data_map, connection, data_ia):\n player = 'player' + str((data_map['main_turn'] % 2) + 1)\n enemy = 'player' + str(2 - (data_map['main_turn'] % 2))\n if data_map['remote']:\n player = 'player' + str(data_map['ia_id'])\n enemy = 'player' + str(data_map['enemy_id'])\n\n # Tells whether IA or player's turn.\n if data_map['main_turn'] % 2 == data_map['ia_id'] or data_map[str(player + 'info')][1] == 'IA':\n game_instruction = ia_action(data_map, data_ia, player)\n if data_map['remote']:\n notify_remote_orders(connection, game_instruction)\n else:\n if data_map['remote']:\n game_instruction = get_remote_orders(connection)\n else:\n game_instruction = raw_input('Enter your commands in format xx_xx -a-> xx_xx or xx_xx -m-> xx_xx')\n\n # Split commands string by string.\n list_action = game_instruction.split()\n\n # grouper instruction par instructions\n list_action2 = []\n for instruction in range(0, len(list_action), 3):\n list_action2.append((list_action[instruction], list_action[instruction + 1], list_action[instruction + 2]))\n\n # Call attack_unit or move_unit in function of instruction.\n attack_counter = 0\n for i in range(len(list_action2)):\n if '-a->' in list_action2[i]:\n data_map, attacked, data_ia = attack_unit(data_map, (int(list_action2[i][0][:2]), int(list_action2[i][0][3:])),\n (int(list_action2[i][2][:2]), int(list_action2[i][2][3:])), player, enemy, data_ia)\n attack_counter += attacked\n elif '-m->' in list_action2[i]:\n data_map, data_ia = move_unit(data_map, (int(list_action2[i][0][:2]), int(list_action2[i][0][3:])),\n (int(list_action2[i][2][:2]), int(list_action2[i][2][3:])), player, enemy, data_ia)\n\n # Save if a player have attacked.\n if attack_counter:\n data_map['attack_turn'] = 0\n else:\n data_map['attack_turn'] += 1\n data_map['main_turn'] += 1\n\n return data_map","def str_to_tile_index(s, index_of_a = 0xe6, index_of_zero = 0xdb, special_cases = None):\n\n\tres = []\n\tfor c in s:\n\t\tindex = None\n\t\tif ord(c) >= ord('a') and ord(c) <= ord('z'):\n\t\t\tindex = ord(c) - ord('a') + index_of_a\n\t\telif ord(c) >= ord('A') and ord(c) <= ord('Z'):\n\t\t\tindex = ord(c) - ord('A') + index_of_a\n\t\telif ord(c) >= ord('0') and ord(c) <= ord('9'):\n\t\t\tindex = ord(c) - ord('0') + index_of_zero\n\t\telif callable(special_cases):\n\t\t\tindex = special_cases(c)\n\t\telif special_cases is not None:\n\t\t\tindex = special_cases.get(c)\n\n\t\tif index is not None:\n\t\t\tres.append(index)\n\n\treturn res","def algebraic_to_index(move: str) -> tuple[int, int]:\r\n return (RANK_TO_INDEX[move[1]], FILE_TO_INDEX[move[0]])","def find_possible_actions(board_state):\n empty_slot_indices = [ind+1 for ind,\n val in enumerate(board_state) if val.isnumeric()]\n\n return empty_slot_indices","def _generate_actions(self) -> list:\n pass","def strip_action_str(string: str) -> str:","def get_action(self, player, action_index):\n if self.game_type == \"hex\":\n size = self.game_config[\"hex\"][\"board_size\"]\n action_coord = index_to_coordinate(action_index, size)\n action = HexAction(player, action_coord)\n else:\n raise ValueError(\"Action Index to Action object is not supported for this game.\")\n return action","def key_to_action(key):\n if key == \"shutdown\":\n return \"STOP\"\n if key == \"delete\":\n return \"DESTROY\"\n if key in ARBITRARY_ACTIONS:\n return None\n else:\n return key.upper()","def T(self, state, action):\n _tr = self.transitions[state,action]\n return izip(_tr.indices, _tr.data)","def split_action(action):\n camera = action['camera']\n discrete = []\n for i in range(len(camera)):\n s = str(action['forward'][i]) + str(action['jump'][i]) + str(action['left'][i]) + str(action['right'][i])\n onehot = np.zeros(16)\n onehot[int(s, 2)] = 1\n discrete.append(onehot)\n return camera, np.array(discrete)","def convert_action_to_string(self, action_number):\n return str(action_number)","def action_to_str(action):\n raise NotImplementedError","def actionlist(username):\n username = username.strip()\n print('Hi, %s' % username)\n filename = '%s.actionlist' % username\n action_list = []\n if os.path.exists(filename):\n with open(filename, 'r') as file_actionlist:\n action_list = [line.strip() for line in file_actionlist]\n else:\n # add clockwise/counterCW gestures ROUNDLEN * SLIDE_SAMPLEN,\n # start position randomly generated\n nonslides = [\n '%s_%s' % (username, item)\n for item in GESTURES_DICT\n #if item not in ('click', 'clockwise', 'countercw')\n for i in range(SAMPLEN)]\n \"\"\"\n clockwises = slides('clockwise', username)\n countercws = slides('countercw', username)\n singleclicks = clicks(username)\n if 'click' in GESTURES_DICT:\n nonslides.extend(singleclicks)\n if 'clockwise' in GESTURES_DICT:\n action_list.extend(clockwises)\n if 'countercw' in GESTURES_DICT:\n action_list.extend(countercws) \n \"\"\" \n random.shuffle(nonslides)\n random_insert_seq(action_list, nonslides)\n action_list = ['%s_%d'% (item, ind)\n for ind, item in enumerate(action_list)]\n return action_list","def _convert_id_to_token(self, index):\n if index in self.fairseq_ids_to_tokens:\n return self.fairseq_ids_to_tokens[index]\n return self.sp_model.IdToPiece(index - self.fairseq_offset)","def format_actions(action_list):\n return {idx: [action[0], float(action[1:])]\n for idx, action in enumerate(action_list)}","def choose_action(self, valid_list):\n \n action_str = input(\"Choose action: \").lower()\n print()\n \n if action_str in valid_list:\n return action_str\n \n else:\n print(\"Invalid action!\")\n return False","def preprocess_slot(self, sequence, word2idx):\n story = []\n for value in sequence:\n v = [word2idx[word] if word in word2idx else UNK_token for word in value.split()] + \\\n [EOS_token]\n story.append(v)\n return story","def get_actions(acts, act_space, action):\n action_spaces = []\n action_spaces_name = []\n idx = 0\n for act in act_space.spaces:\n if act_space[act].__class__.__name__ == 'Discrete':\n action_spaces_name.append(act)\n action_spaces.append(act_space[act].n)\n action[act] = int(acts[idx])\n idx += 1\n elif act_space[act].__class__.__name__ == 'Enum':\n action_spaces_name.append(act)\n action_spaces.append(len(act_space[act].values))\n action[act] = act_space[act].values[int(acts[idx])]\n idx += 1\n elif act == 'camera':\n action_spaces_name.append('pitch')\n action_spaces_name.append('yaw')\n action_spaces.append(36)\n action_spaces.append(36)\n action[act] = [acts[idx] * 10 - 180, acts[idx + 1] * 10 - 180]\n idx += 2\n\n return action","def get_action_list(self, basic_action=False):\n assert len(self.name), 'No character name {} found.'.format(self.name)\n lf2_char = Template(player_id=self.idx) if basic_action \\\n else globals()[self.name](player_id=self.idx)\n action_s = lf2_char.action_space()\n if basic_action:\n action_s.remove('run')\n return action_s","def get_action(player):\n print_action(player)\n chosen_action_index = int(input('Please indicate your selection from the following list by inputting the number: '))\n return player.available_actions[chosen_action_index]","def index_id(i):\n return f\"(i={i})\"","def _tokens_to_index(self,tokens):\n wids = []\n for tk in tokens:\n if tk in self.wtoi.keys():\n wids.append(self.wtoi[tk])\n else:\n wids.append(1) # <UNK>\n for _ in range(self.sentence_max_length - len(wids)):\n wids.append(0)\n if len(wids) > self.sentence_max_length:\n wids = wids[:self.sentence_max_length]\n return wids","def obtain_action(self, timestep):\r\n\t\t# Loops constantly until a valid input is obtained.\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\t# Tries to obtain a valid input manually and convert it to an\r\n\t\t\t\t# integer.\r\n\t\t\t\taction = int(input('Please provide an input action index between 0 and (number of actions - 1): %i: ' % (self.num_actions-1)))\r\n\r\n\t\t\texcept ValueError:\r\n\t\t\t\tprint('Invalid input detected, try again.')\r\n\t\t\t\tcontinue\r\n\r\n\t\t\t# Checks if the input is within the acceptable range of action\r\n\t\t\t# index values.\r\n\t\t\tif 0 <= action < self.num_actions:\r\n\t\t\t\tbreak\r\n\t\t\telse:\r\n\t\t\t\tprint('Action should be an index between 0 and (number of actions - 1): %i' % (self.num_actions-1))\r\n\r\n\t\treturn action","def _convert_id_to_token(self, index, return_unicode=True):\n token = self.sp_model.IdToPiece(index)\n return token","def generate_action_grammar(self, actions):\n assert isinstance(actions, list), actions\n assert not isinstance(actions[0], list), \"Should be 1 long list of actions - {}\".format(actions[0])\n assert len(actions) > 0, \"Need to provide a list of at least 1 action\"\n assert isinstance(actions[0], int), \"The actions should be integers\"\n new_actions, all_rules, rule_usage, rules_episode_appearance_count = self.discover_all_rules_and_new_actions_representation(actions)\n action_usage = self.extract_action_usage_from_rule_usage(rule_usage, all_rules)\n rules_episode_appearance_count = self.extract_action_usage_from_rule_usage(rules_episode_appearance_count,\n all_rules)\n return new_actions, all_rules, action_usage, rules_episode_appearance_count","def valid_actions(self) -> List[str]:\n return list(self.action_map().keys())","def find_indices_srnn(data, action):\n\n SEED = 1234567890\n rng = np.random.RandomState(SEED)\n\n subject = 5\n subaction1 = 1\n subaction2 = 2\n\n T1 = data[(subject, action, subaction1)].shape[0]\n T2 = data[(subject, action, subaction2)].shape[0]\n prefix, suffix = 50, 100\n\n idx = []\n idx.append(rng.randint(16,T1-prefix-suffix))\n idx.append(rng.randint(16,T2-prefix-suffix))\n idx.append(rng.randint(16,T1-prefix-suffix))\n idx.append(rng.randint(16,T2-prefix-suffix))\n idx.append(rng.randint(16,T1-prefix-suffix))\n idx.append(rng.randint(16,T2-prefix-suffix))\n idx.append(rng.randint(16,T1-prefix-suffix))\n idx.append(rng.randint(16,T2-prefix-suffix))\n\n return idx","def rule_id(self, action):\n ext_id = \"{0}_{1}_acl_{2}\".format(\n self.model.short_name.replace('.', '_'),\n self.group.name.replace('.', '_'),\n action,\n )\n return ext_id[-64:]","def parse_position(action,grid_size):\n if action == None or len(action) == 0:\n return None\n #'-Upper Case Character--number-'\n if action[0] in ALPHA[:grid_size] and action[1:].isdigit():\n if 0 <= int(action[1:]) <= grid_size:\n return (ALPHA.index(action[0]),int(action[1:])-1)\n return None","def askForAction(self, role, index, currPos, possActions):\r\n print str(role) + \" -> \" + str(index)\r\n print \"Current position : \" + str(currPos)\r\n print \"Possible actions : \" + str(possActions)\r\n # request = raw_input(\"Destination, TicketType = \").split(',')\r\n request = raw_input(\"Destination, TicketType = \")\r\n while \",\" not in request:\r\n print \"You missed the comma!\"\r\n request = raw_input(\"Destination, TicketType = \")\r\n fields = request.split(\",\")\r\n dest, ticket = int(fields[0]), fields[1].strip().upper()\r\n return dest, ticket","def get_id_from_comments(raw_action):\n act_id_re = re.compile(r\"/\\*[ ]*id:(?P<id>[\\w-]+)\")\n act_id_m = act_id_re.search(raw_action)\n if act_id_m:\n return act_id_m.group(1)\n else:\n logger.error(\"Cannot find id in the following data: %s\" % raw_action)\n exit(-1)","def generate_actions(self):\n \n # For all state nodes\n action = 0\n \n for l in range(self.u0_n):\n for m in range(self.u1_n):\n \n u = np.array([ self.ud[0][l] , self.ud[1][m] ])\n \n # State and grid index based on node #\n self.actions_input[action,:] = u\n self.actions_index[action,:] = np.array([l,m])\n \n # Increment node number\n action = action + 1","def _seq2Indices(self, sequence, vocab, start, end, unknown):\n if start:\n sequence.insert(0, start)\n if end:\n sequence.append(end)\n return [vocab[token] if token in vocab else vocab[unknown] for token in sequence]","def ACTION_MAP(activity_action):\n import amo\n\n return {\n amo.LOG.APPROVE_VERSION.id: APPROVAL,\n amo.LOG.APPROVE_VERSION_WAITING.id: APPROVE_VERSION_WAITING,\n amo.LOG.APP_DISABLED.id: DISABLED,\n amo.LOG.ESCALATE_MANUAL.id: ESCALATION,\n amo.LOG.ESCALATE_VERSION.id: ESCALATION,\n amo.LOG.ESCALATION_VIP_APP.id: ESCALATION,\n amo.LOG.ESCALATED_HIGH_ABUSE.id: ESCALATION_HIGH_ABUSE,\n amo.LOG.ESCALATED_HIGH_REFUNDS.id: ESCALATION_HIGH_REFUNDS,\n amo.LOG.ESCALATION_CLEARED.id: ESCALATION_CLEARED,\n amo.LOG.REQUEST_INFORMATION.id: MORE_INFO_REQUIRED,\n amo.LOG.REJECT_VERSION.id: REJECTION,\n amo.LOG.REREVIEW_CLEARED.id: REREVIEW_CLEARED,\n amo.LOG.WEBAPP_RESUBMIT.id: RESUBMISSION,\n amo.LOG.COMMENT_VERSION.id: REVIEWER_COMMENT,\n }.get(activity_action, NO_ACTION)","def action_to_id(a):\n if all(a == [-1.0, 0.0, 0.0]): return LEFT # LEFT: 1\n elif all(a == [1.0, 0.0, 0.0]): return RIGHT # RIGHT: 2\n elif all(a == [0.0, 1.0, 0.0]): return ACCELERATE # ACCELERATE: 3\n elif all(a == [0.0, 0.0, 0.2]): return BRAKE # BRAKE: 4\n else: \n return STRAIGHT # STRAIGHT = 0","def _GetAction(self, action, text):\n # TODO: replace \"x in y\" checks by startswith if possible.\n if 'airportdProcessDLILEvent' in action:\n network_interface = text.split()[0]\n return 'Interface {0:s} turn up.'.format(network_interface)\n\n if 'doAutoJoin' in action:\n match = self._CONNECTED_RE.match(text)\n if match:\n ssid = match.group(1)[1:-1]\n else:\n ssid = 'Unknown'\n return 'Wi-Fi connected to SSID: {0:s}'.format(ssid)\n\n if 'processSystemPSKAssoc' in action:\n wifi_parameters = self._WIFI_PARAMETERS_RE.search(text)\n if wifi_parameters:\n ssid = wifi_parameters.group(1)\n bssid = wifi_parameters.group(2)\n security = wifi_parameters.group(3)\n if not ssid:\n ssid = 'Unknown'\n if not bssid:\n bssid = 'Unknown'\n if not security:\n security = 'Unknown'\n\n return (\n 'New wifi configured. BSSID: {0:s}, SSID: {1:s}, '\n 'Security: {2:s}.').format(bssid, ssid, security)\n\n return text","def remove_unused_char(action_list):\r\n clean_action_list = []\r\n for action in action_list:\r\n effect_element = action['action']\r\n clearnedstr = (effect_element[effect_element.index(\"effect\")\r\n + len(\"effect\"):])\r\n clean_action_list.append(clearnedstr[:-1])\r\n return clean_action_list","def get_neighbor_action(self, action):\n naction = []\n for i in range(self.n_agent):\n naction.append(action[self.neighbor_mask[i] == 1])\n return naction","def extract_action_usage_from_rule_usage(self, rule_usage, all_rules):\n action_usage = {}\n for key in rule_usage.keys():\n action_usage[self.convert_symbol_to_raw_actions(key, all_rules)] = rule_usage[key]\n return action_usage","def splitaction_ramp(data_frame, \n action_header = 'Action Name', \n action_name = 'viscometry ramp'):\n\n print('\\n Splitting data by action name: ', action_name)\n \n # Gets all the actions within the data frame\n iaction = [x for x in data_frame[action_header].unique() if action_name in x.lower()]\n\n print(iaction)\n data_frame.set_index(action_header, inplace = True)\n \n # Find the location of the desired action, and save to a data frame\n # If the action name is not found, it prints an error message\n try:\n select_data = data_frame.loc[iaction]\n select_data.reset_index(inplace = True)\n except IndexError: \n print('ERROR: Action name not found') \n select_data = None \n return select_data","def convert_to_action4(choices):\n (food_card_index, locp) = choices\n logp = convert_locp(locp, cp_to_gp)\n logb = convert_locp(locp, cp_to_gb)\n lobt = convert_locp(locp, cp_to_bt)\n lort = convert_locp(locp, cp_to_rt)\n return [food_card_index, logp, logb, lobt, lort]","def __action_to_token(self, action, next_states, val_tok_mov):\r\n if (int)(action) == 4: # move_token action returns first valid token to move\r\n for token_id, next_state in enumerate(next_states):\r\n if next_state is not False:\r\n return token_id\r\n\r\n valid_tokens = np.argwhere(val_tok_mov[:,int(action)] == True).squeeze()\r\n if valid_tokens.size > 1:\r\n return np.random.choice(valid_tokens)\r\n else:\r\n return valid_tokens","def extract_action_list_from_path(path_list):\n action_trans = {-81: 'movenorth 1', 81: 'movesouth 1', -1: 'movewest 1', 1: 'moveeast 1'}\n alist = []\n for i in range(len(path_list) - 1):\n curr_block, next_block = path_list[i:(i + 2)]\n alist.append(action_trans[next_block - curr_block])\n\n return alist","def parse_action(element: Element) -> ActionType:\n # edit is a special type of action for strings\n if \"edit\" in element.attributes:\n if element.attributes[\"edit\"] == \"append\":\n return edit_append\n raise error_at(element)(f'invalid edit=\"{element.attributes[\"edit\"]}\"')\n # default action is replace\n action = element.attributes.get(\"action\", \"replace\")\n if action == \"replace\":\n return replace\n if action == \"append\":\n return append\n if action == \"delete\":\n return delete\n if action == \"merge\":\n return merge\n raise error_at(element)('invalid action=\"{:s}\".'.format(action))","def action(self, action):\n action = (action + 1) / 2 # [-1, 1] => [0, 1]\n action *= (self.action_space.high - self.action_space.low)\n action += self.action_space.low\n return action","def get_joss_strategy_actions(\n opponent_moves: list, indices_to_flip: list\n) -> list:\n out = []\n for index, action in enumerate(opponent_moves):\n previous_action = opponent_moves[index - 1]\n if index == 0:\n out.append((C, action))\n elif index in indices_to_flip:\n out.append((previous_action.flip(), action))\n else:\n out.append((previous_action, action))\n return out","def transfer_actions(action, act_space):\n #print(action)\n action_spaces = []\n res = []\n for act in act_space.spaces:\n if act_space[act].__class__.__name__ == 'Discrete':\n action_spaces.append(act_space[act].n)\n res.append(action[act])\n elif act_space[act].__class__.__name__ == 'Enum':\n action_spaces.append(len(act_space[act].values))\n res.append(action[act])\n elif act == 'camera':\n res.append(camera_transform(action[act][0]))\n res.append(camera_transform(action[act][1]))\n action_spaces.append(36)\n action_spaces.append(36)\n\n return res"],"string":"[\n \"def get_action_id(self, action_text: str) -> int:\\n\\n actions_tuple = tuple(action_text.split('+'))\\n return self.action_tuples2ids[actions_tuple] # todo unhandled exception when not found\",\n \"def str_to_action(str):\\n raise NotImplementedError\",\n \"def get_action_id(self, action_text: str) -> int:\\n return self.templates.actions.index(action_text) # todo unhandled exception when not found\",\n \"def get_action_id(output):\\n return output['Action queued with id']\",\n \"def _interpret_action(self, action_idx: int, team: int):\\n if action_idx < 4:\\n return action_idx\\n\\n noops = self._allow_noops[team]\\n diags = self._allow_diagonals[team]\\n assert noops or diags\\n\\n if noops and not diags:\\n assert action_idx == 4\\n return NOOP\\n\\n if not noops and diags:\\n assert action_idx < 8\\n return action_idx\\n\\n if noops and diags:\\n assert action_idx < 9\\n return action_idx\",\n \"def actions(self, state):\\n\\n\\t\\t# Returns the possible numbers we can use.\\n\\t\\treturn [(str)(x + 1) for x in range (0,self.size)]\",\n \"def actions(self, state):\\n myActionList= (1,2);\\n return myActionList\",\n \"def actionIndexTuple2Int(actionIdxTuple, numActionList):\\n rowIdx, colIdx = actionIdxTuple\\n assert (\\n rowIdx < numActionList[0]\\n ), \\\"The size of evader's action set is {:d} but get index {:d}\\\".format(\\n numActionList[0], rowIdx\\n )\\n assert (\\n colIdx < numActionList[1]\\n ), \\\"The size of pursuer's action set is {:d} but get index {:d}\\\".format(\\n numActionList[1], colIdx\\n )\\n\\n actionIdx = numActionList[1] * rowIdx + colIdx\\n return actionIdx\",\n \"def convert_action(action):\\n new = np.zeros(18)\\n for act in ['camera', 'forward', 'jump', 'left', 'right']:\\n tmp = \\\"\\\"\\n if act == 'camera':\\n new[0] = action[act][0]\\n new[1] = action[act][1]\\n else:\\n tmp += str(action[act])\\n new[int(tmp, 2)] = 1\\n return new\",\n \"def _decode_action(self, action_id):\\n raise NotImplementedError\",\n \"def findAction(self, actionId): #$NON-NLS-1$\\r\",\n \"def action2pos(self, action):\\n new_pos = [0, 0]\\n ###### Shu's modificaiton\\n # 4 actions\\n # if action == 0:\\n # new_pos[0] = self.curr_pos[0]\\n # new_pos[1] = self.curr_pos[1]-1\\n # elif action == 1:\\n # new_pos[0] = self.curr_pos[0]+1\\n # new_pos[1] = self.curr_pos[1]\\n # elif action == 2:\\n # new_pos[0] = self.curr_pos[0]-1\\n # new_pos[1] = self.curr_pos[1]\\n # elif action == 3:\\n # new_pos[0] = self.curr_pos[0]\\n # new_pos[1] = self.curr_pos[1]+1\\n\\n # 8 action\\n if action in [5, 0, 4]:\\n new_pos[0] = self.curr_pos[0]-1\\n elif action in [7, 1, 6]:\\n new_pos[0] = self.curr_pos[0]+1\\n else:\\n new_pos[0] = self.curr_pos[0]\\n\\n if action in [5, 3, 7]:\\n new_pos[1] = self.curr_pos[1]-1\\n elif action in [4, 2, 6]:\\n new_pos[1] = self.curr_pos[1]+1\\n else:\\n new_pos[1] = self.curr_pos[1]\\n\\n return tuple(new_pos)\",\n \"def label2action(label):\\n items = label.split('-')\\n if len(items) == 1:\\n action = (items[0], None, None)\\n elif len(items) == 3:\\n action = tuple(items)\\n else:\\n raise ValueError(\\\"Unrecognized label: {}\\\".format(label))\\n return action\",\n \"def create_action_id(*args):\\r\\n return u'-'.join([unicode(arg) for arg in args])\",\n \"def action_id2arr(ids):\\n return actions[ids]\",\n \"def _fill_action_info(action):\\n def _is_ascii(s):\\n return all(ord(c) < 128 for c in s)\\n\\n if not _is_ascii(action.obj_desc_str):\\n tf.logging.info('Found an unconvertable unicode %s', action.obj_desc_str)\\n return\\n\\n if not (isinstance(action.verb_str, str) and isinstance(\\n action.obj_desc_str, str) and isinstance(action.input_content_str, str)):\\n return\\n action.regularize_strs()\\n input_str_pos_padding = [\\n config.LABEL_DEFAULT_VALUE_INT, config.LABEL_DEFAULT_VALUE_INT\\n ]\\n\\n input_prep_word = _get_input_prep_word()\\n swipe_prep_word = _get_swipe_prep_word()\\n\\n if action.action_rule == common.ActionRules.NO_VERB_RULE:\\n action.instruction_str = action.obj_desc_str\\n action.verb_str_pos = [0, 0]\\n action.obj_str_pos = [0, _count_chars(action.obj_desc_str)]\\n action.input_str_pos = input_str_pos_padding\\n return\\n\\n if action.action_type in [common.ActionTypes.CLICK]:\\n action.instruction_str = '%s %s' % (action.verb_str, action.obj_desc_str)\\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\\n action.obj_str_pos = [\\n _count_chars(action.verb_str) + 1,\\n _count_chars(action.instruction_str)\\n ]\\n action.input_str_pos = input_str_pos_padding\\n\\n elif action.action_type in [common.ActionTypes.INPUT]:\\n # There is no space between 4th and 5th string because the 2nd string,\\n # article word, is optional.\\n action.instruction_str = '%s %s %s %s' % (\\n action.verb_str, action.input_content_str, input_prep_word,\\n action.obj_desc_str)\\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\\n action.input_str_pos = [\\n _count_chars(action.verb_str) + 1,\\n _count_chars('%s %s' % (action.verb_str, action.input_content_str))\\n ]\\n action.obj_str_pos = [\\n _count_chars(\\n '%s %s %s' %\\n (action.verb_str, action.input_content_str, input_prep_word)) + 1,\\n _count_chars(action.instruction_str)\\n ]\\n # All the rests are swipe actions\\n else:\\n action.instruction_str = '%s %s %s' % (action.verb_str, swipe_prep_word,\\n action.obj_desc_str)\\n action.verb_str_pos = [0, _count_chars(action.verb_str)]\\n action.input_str_pos = input_str_pos_padding\\n action.obj_str_pos = [\\n _count_chars('%s %s' % (action.verb_str, swipe_prep_word)) + 1,\\n _count_chars(action.instruction_str)\\n ]\",\n \"def _decode_action(self, action_id):\\n legal_actions = self.game.get_legal_actions()\\n if self.actions[action_id] not in legal_actions:\\n if \\\"check\\\" in legal_actions:\\n return \\\"check\\\"\\n else:\\n return \\\"fold\\\"\\n return self.actions[action_id]\",\n \"def parseAction(self, action):\\n action = self.AGENT_TYPES[action]\\n\\n\\n full_action = {}\\n full_action[\\\"action\\\"] = action\\n if action == \\\"eli-kw\\\":\\n keywords = self.dataset.getSuggestedKeywords()\\n full_action[\\\"keywords\\\"] = keywords[:self.N]\\n elif action == \\\"info\\\" or action == \\\"info-all\\\":\\n full_action[\\\"function\\\"] = self.current_function\\n\\n elif action == \\\"sugg\\\" or action == \\\"sugg-info-all\\\":\\n top_hit = self.dataset.getTopHits(1)\\n if not top_hit:\\n full_action[\\\"action\\\"] = \\\"eli-query\\\"\\n else:\\n functions = self.dataset.getTopHits(1, self.result_index)\\n if functions:\\n full_action[\\\"function\\\"] = functions[0]\\n else:\\n full_action[\\\"function\\\"] = \\\"\\\"\\n\\n self.result_index += 1\\n\\n elif action == \\\"sugg-all\\\":\\n full_action[\\\"list\\\"] = self.dataset.getTopHits(self.K, self.result_index)\\n\\n elif action == \\\"change-page\\\":\\n self.result_index += self.K\\n full_action[\\\"list\\\"] = self.dataset.getTopHits(self.K, self.result_index)\\n return full_action\",\n \"def actionIndexInt2Tuple(actionIdx, numActionList):\\n numJoinAction = int(numActionList[0] * numActionList[1])\\n assert (actionIdx < numJoinAction), (\\n \\\"The size of joint action set is \\\"\\n \\\"{:d} but get index {:d}\\\".format(numJoinAction, actionIdx)\\n )\\n rowIdx = actionIdx // numActionList[1]\\n colIdx = actionIdx % numActionList[1]\\n return (rowIdx, colIdx)\",\n \"def convert_symbol_to_raw_actions(self, symbol, rules):\\n assert not isinstance(symbol, list)\\n assert isinstance(symbol, str) or isinstance(symbol, int)\\n symbol = [symbol]\\n finished = False\\n while not finished:\\n new_symbol = []\\n for symbol_val in symbol:\\n if symbol_val in rules.keys():\\n new_symbol.append(rules[symbol_val][0])\\n new_symbol.append(rules[symbol_val][1])\\n else:\\n new_symbol.append(symbol_val)\\n if new_symbol == symbol: finished = True\\n else: symbol = new_symbol\\n new_symbol = tuple(new_symbol)\\n return new_symbol\",\n \"def create_action(verb_dct):\\n action = {'What is the action?': [],\\n 'Who is the action directed to?': [],\\n 'Who is making the action': [],\\n 'How is the action made': []\\n }\\n final_index_verb = verb_dct['indexes'][1]\\n init_index_verb = verb_dct['indexes'][0]\\n\\n return action, init_index_verb, final_index_verb\",\n \"def action2label(action):\\n if action[0] == 'shift':\\n label = action[0]\\n elif action[0] == 'reduce':\\n label = '-'.join(list(action))\\n else:\\n raise ValueError(\\\"Unrecognized parsing action: {}\\\".format(action))\\n return label\",\n \"def actionString(self,action):\\n return str(self._mdp.A[action])\",\n \"def get_action_list(program_name: str) -> str:\\n short_name = int(program_name[8:])\\n es = Elasticsearch([{'host': 'localhost', 'port': 9200}])\\n res = es.search(\\n index='actions-index', \\n params= {'size': 1}, \\n body={\\\"query\\\": {\\\"match\\\": {'name' : short_name}}})\\n for hit in res['hits']['hits']:\\n return hit['_source']['actions']\\n return \\\"\\\"\",\n \"def convert_to_low_level_action(self, i_state, action):\\n pass\",\n \"def _createAction(self, item, actionString):\\n action = {\\n \\\"action\\\": actionString,\\n \\\"item_id\\\": item.item_id\\n }\\n\\n pocketLogger.debug(\\\"Action\\\" + str(action))\\n\\n self.actions.append(action)\",\n \"def action_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"action_id\\\")\",\n \"def as_action_str(string: str) -> str:\",\n \"def action_arr2id(arr):\\n ids = []\\n for a in arr:\\n id = np.where(np.all(actions==a, axis=1))\\n ids.append(id[0][0])\\n return np.array(ids)\",\n \"def ia_action(data_map, data_ia, player):\\n raw_commands = ia_reflexion(data_ia, data_map, player)\\n\\n # Rewrite the command into a single string.\\n string_commands = ''\\n for key in raw_commands:\\n string_commands += ('0' + str(raw_commands[key][0][0]))[-2:] + '-' + ('0' + str(raw_commands[key][0][1]))[-2:] + raw_commands[key][1] + ('0' + str(raw_commands[key][2][0]))[-2:] + '-' + ('0' + str(raw_commands[key][2][1]))[-2:] + ' '\\n return string_commands\",\n \"def process_index(index, intensity, interaction_symbol):\\n return tuple(index.split(interaction_symbol))\",\n \"def get_action(command):\\n return command.split(\\\" \\\")[0]\",\n \"def createAction(self, act, imList):\\n if type(imList[0]) == type(()):\\n # process list of (row, col) values\\n self.actions[act] = [ self.imhash[row, col] for row, col in imList]\\n else:\\n # process list of ints\\n self.actions[act] = [ self.imhash[i] for i in imList ]\",\n \"def registerAction(self, actionId, action): #$NON-NLS-1$\\r\",\n \"def split_action_id (id):\\n assert isinstance(id, basestring)\\n split = id.split ('.', 1)\\n toolset = split [0]\\n name = ''\\n if len (split) > 1:\\n name = split [1]\\n return (toolset, name)\",\n \"def asIndex(i):\\n return u\\\"\\\".join([unichr(0x2050 + ord(c)) for c in str(i)])\",\n \"def asIndex(i):\\n return u\\\"\\\".join([unichr(0x2050 + ord(c)) for c in str(i)])\",\n \"def action_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"action_id\\\")\",\n \"def action(self, gstate, actions):\\n self.log.debug(\\\"Picking among actions %s\\\" % actions)\\n return actions[0]\",\n \"def parse_command_to_actions(moving_command):\\n regex = re.compile(r'[A-Z][0-9]*')\\n return re.findall(regex, moving_command)\",\n \"def applyAction(state, action):\\r\\n if action == 'N':\\r\\n return (state[0] - 1, state[1])\\r\\n\\r\\n if action == 'E':\\r\\n return (state[0], state[1] + 1)\\r\\n\\r\\n if action == 'W':\\r\\n return (state[0], state[1] - 1)\\r\\n\\r\\n if action == 'S':\\r\\n return (state[0] + 1, state[1])\",\n \"def actions(self, state):\\n \\n #les actions sont définies comme étant les nombres possibles dans \\n #la case i,j\\n theActions = []\\n for i in range(size):\\n for j in range(size):\\n line = i\\n col = j\\n if(state[i][j] == 0):\\n possibleNumbers = [1,2,3,4,5,6,7,8,9]\\n config = state\\n for a in range(size):\\n x = config[line][a]\\n if(x in possibleNumbers):\\n possibleNumbers.remove(x)\\n \\n for b in range(size):\\n x = config[b][col]\\n if(x in possibleNumbers):\\n possibleNumbers.remove(x)\\n \\n #identifie quelle boite on veut vérifier\\n hBox = col - col % 3\\n vBox = line - line % 3\\n \\n for c in range(3):\\n for d in range(3):\\n x = config[c+vBox][d+hBox]\\n if(x in possibleNumbers):\\n possibleNumbers.remove(x)\\n for k in possibleNumbers:\\n theActions.append((i,j,k))\\n return theActions\",\n \"def actions(self):\\n return {0, 1, 2, 3, 4, 5, 11, 12}\",\n \"def get_action_from_id(self, id_a):\\n return self.id_to_action_dict[id_a]\",\n \"def action_to_gym(self, action: Dict[str, Any]):\\n j1 = action['j1']\\n j2 = action['j2']\\n j3 = action['j3']\\n j4 = action['j4']\\n j5 = action['j5']\\n j6 = action['j6']\\n\\n # Half Cheetah environment expects an array of actions\\n return [j1, j2, j3, j4, j5, j6]\",\n \"def get_action_url(checklist, index):\\r\\n return checklist['items'][index]['action_url']\",\n \"def findActions(problem, state):\\r\\n size = len(problem) - 1\\r\\n legalActions = []\\r\\n if state[0] > 0 and problem[state[0] - 1][state[1]] != 'w':\\r\\n legalActions.append('N')\\r\\n if state[0] < size and problem[state[0] + 1][state[1]] != 'w':\\r\\n legalActions.append('S')\\r\\n if state[1] > 0 and problem[state[0]][state[1] - 1] != 'w':\\r\\n legalActions.append('W')\\r\\n if state[1] < size and problem[state[0]][state[1] + 1] != 'w':\\r\\n legalActions.append('E')\\r\\n return legalActions\",\n \"def action(self, id_):\\n return np.eye(self.action_space)[id_]\",\n \"def search_actions(strings):\\n\\tpattern = '|'.join(strings)\\n\\n\\tb = actions[actions.action.str.contains(pattern)] \\\\\\n\\t\\t.loc[:, ['session', 'bill_id']] \\\\\\n\\t\\t.drop_duplicates()\\n\\n\\treturn b\",\n \"def _get_action(verb):\\n aux_verbs = \\\"\\\"\\n for child in verb.children:\\n if child.dep_ == \\\"aux\\\" or child.dep_ == \\\"neg\\\":\\n aux_verbs += str(child)\\n return SpacyEventExtractor._remove_extra_whitespaces(str(aux_verbs) + ' ' + str(verb))\",\n \"def create_action_set(number_of_actions=4, add_no_change=True):\\n # TO DO:\\n # - check if the number of actions is equal.\\n split_num = number_of_actions/2\\n action_list = []\\n for x in range(int(split_num)):\\n action_list.append(\\\"lb\\\"+\\\"_\\\"+str(x))\\n action_list.append(\\\"ub\\\"+\\\"_\\\"+str(x))\\n\\n if add_no_change == True:\\n action_list.append(\\\"no_change\\\")\\n return action_list\",\n \"def code(action_sequence):\\r\\n # refuse any invalid action :\\r\\n if set(action_sequence) - set(CODE_MAP): # some action was not in the known ones.\\r\\n return '0' # per spec (test_unknown_action)\\r\\n\\r\\n mapdict = dict(zip(CODE_MAP, (1, 2, 4, 8)))\\r\\n da_code = [mapdict[action] for action in action_sequence]\\r\\n if sorted(da_code) != da_code: # list is not sorted : assume reversed\\r\\n da_code.append(16)\\r\\n return format(sum(da_code), 'b') # sum to integer, binstring, and return\",\n \"def action_to_id(a):\\n if a == [-1.0, 0.0, 0.0]: return LEFT # LEFT: 1\\n elif a == [1.0, 0.0, 0.0]: return RIGHT # RIGHT: 2\\n elif a == [0.0, 1.0, 0.0]: return ACCELERATE # ACCELERATE: 3\\n elif a == [0.0, 0.0, 0.2]: return BRAKE # BRAKE: 4\\n else: \\n return STRAIGHT # STRAIGHT = 0\",\n \"def choose_action(data_map, connection, data_ia):\\n player = 'player' + str((data_map['main_turn'] % 2) + 1)\\n enemy = 'player' + str(2 - (data_map['main_turn'] % 2))\\n if data_map['remote']:\\n player = 'player' + str(data_map['ia_id'])\\n enemy = 'player' + str(data_map['enemy_id'])\\n\\n # Tells whether IA or player's turn.\\n if data_map['main_turn'] % 2 == data_map['ia_id'] or data_map[str(player + 'info')][1] == 'IA':\\n game_instruction = ia_action(data_map, data_ia, player)\\n if data_map['remote']:\\n notify_remote_orders(connection, game_instruction)\\n else:\\n if data_map['remote']:\\n game_instruction = get_remote_orders(connection)\\n else:\\n game_instruction = raw_input('Enter your commands in format xx_xx -a-> xx_xx or xx_xx -m-> xx_xx')\\n\\n # Split commands string by string.\\n list_action = game_instruction.split()\\n\\n # grouper instruction par instructions\\n list_action2 = []\\n for instruction in range(0, len(list_action), 3):\\n list_action2.append((list_action[instruction], list_action[instruction + 1], list_action[instruction + 2]))\\n\\n # Call attack_unit or move_unit in function of instruction.\\n attack_counter = 0\\n for i in range(len(list_action2)):\\n if '-a->' in list_action2[i]:\\n data_map, attacked, data_ia = attack_unit(data_map, (int(list_action2[i][0][:2]), int(list_action2[i][0][3:])),\\n (int(list_action2[i][2][:2]), int(list_action2[i][2][3:])), player, enemy, data_ia)\\n attack_counter += attacked\\n elif '-m->' in list_action2[i]:\\n data_map, data_ia = move_unit(data_map, (int(list_action2[i][0][:2]), int(list_action2[i][0][3:])),\\n (int(list_action2[i][2][:2]), int(list_action2[i][2][3:])), player, enemy, data_ia)\\n\\n # Save if a player have attacked.\\n if attack_counter:\\n data_map['attack_turn'] = 0\\n else:\\n data_map['attack_turn'] += 1\\n data_map['main_turn'] += 1\\n\\n return data_map\",\n \"def str_to_tile_index(s, index_of_a = 0xe6, index_of_zero = 0xdb, special_cases = None):\\n\\n\\tres = []\\n\\tfor c in s:\\n\\t\\tindex = None\\n\\t\\tif ord(c) >= ord('a') and ord(c) <= ord('z'):\\n\\t\\t\\tindex = ord(c) - ord('a') + index_of_a\\n\\t\\telif ord(c) >= ord('A') and ord(c) <= ord('Z'):\\n\\t\\t\\tindex = ord(c) - ord('A') + index_of_a\\n\\t\\telif ord(c) >= ord('0') and ord(c) <= ord('9'):\\n\\t\\t\\tindex = ord(c) - ord('0') + index_of_zero\\n\\t\\telif callable(special_cases):\\n\\t\\t\\tindex = special_cases(c)\\n\\t\\telif special_cases is not None:\\n\\t\\t\\tindex = special_cases.get(c)\\n\\n\\t\\tif index is not None:\\n\\t\\t\\tres.append(index)\\n\\n\\treturn res\",\n \"def algebraic_to_index(move: str) -> tuple[int, int]:\\r\\n return (RANK_TO_INDEX[move[1]], FILE_TO_INDEX[move[0]])\",\n \"def find_possible_actions(board_state):\\n empty_slot_indices = [ind+1 for ind,\\n val in enumerate(board_state) if val.isnumeric()]\\n\\n return empty_slot_indices\",\n \"def _generate_actions(self) -> list:\\n pass\",\n \"def strip_action_str(string: str) -> str:\",\n \"def get_action(self, player, action_index):\\n if self.game_type == \\\"hex\\\":\\n size = self.game_config[\\\"hex\\\"][\\\"board_size\\\"]\\n action_coord = index_to_coordinate(action_index, size)\\n action = HexAction(player, action_coord)\\n else:\\n raise ValueError(\\\"Action Index to Action object is not supported for this game.\\\")\\n return action\",\n \"def key_to_action(key):\\n if key == \\\"shutdown\\\":\\n return \\\"STOP\\\"\\n if key == \\\"delete\\\":\\n return \\\"DESTROY\\\"\\n if key in ARBITRARY_ACTIONS:\\n return None\\n else:\\n return key.upper()\",\n \"def T(self, state, action):\\n _tr = self.transitions[state,action]\\n return izip(_tr.indices, _tr.data)\",\n \"def split_action(action):\\n camera = action['camera']\\n discrete = []\\n for i in range(len(camera)):\\n s = str(action['forward'][i]) + str(action['jump'][i]) + str(action['left'][i]) + str(action['right'][i])\\n onehot = np.zeros(16)\\n onehot[int(s, 2)] = 1\\n discrete.append(onehot)\\n return camera, np.array(discrete)\",\n \"def convert_action_to_string(self, action_number):\\n return str(action_number)\",\n \"def action_to_str(action):\\n raise NotImplementedError\",\n \"def actionlist(username):\\n username = username.strip()\\n print('Hi, %s' % username)\\n filename = '%s.actionlist' % username\\n action_list = []\\n if os.path.exists(filename):\\n with open(filename, 'r') as file_actionlist:\\n action_list = [line.strip() for line in file_actionlist]\\n else:\\n # add clockwise/counterCW gestures ROUNDLEN * SLIDE_SAMPLEN,\\n # start position randomly generated\\n nonslides = [\\n '%s_%s' % (username, item)\\n for item in GESTURES_DICT\\n #if item not in ('click', 'clockwise', 'countercw')\\n for i in range(SAMPLEN)]\\n \\\"\\\"\\\"\\n clockwises = slides('clockwise', username)\\n countercws = slides('countercw', username)\\n singleclicks = clicks(username)\\n if 'click' in GESTURES_DICT:\\n nonslides.extend(singleclicks)\\n if 'clockwise' in GESTURES_DICT:\\n action_list.extend(clockwises)\\n if 'countercw' in GESTURES_DICT:\\n action_list.extend(countercws) \\n \\\"\\\"\\\" \\n random.shuffle(nonslides)\\n random_insert_seq(action_list, nonslides)\\n action_list = ['%s_%d'% (item, ind)\\n for ind, item in enumerate(action_list)]\\n return action_list\",\n \"def _convert_id_to_token(self, index):\\n if index in self.fairseq_ids_to_tokens:\\n return self.fairseq_ids_to_tokens[index]\\n return self.sp_model.IdToPiece(index - self.fairseq_offset)\",\n \"def format_actions(action_list):\\n return {idx: [action[0], float(action[1:])]\\n for idx, action in enumerate(action_list)}\",\n \"def choose_action(self, valid_list):\\n \\n action_str = input(\\\"Choose action: \\\").lower()\\n print()\\n \\n if action_str in valid_list:\\n return action_str\\n \\n else:\\n print(\\\"Invalid action!\\\")\\n return False\",\n \"def preprocess_slot(self, sequence, word2idx):\\n story = []\\n for value in sequence:\\n v = [word2idx[word] if word in word2idx else UNK_token for word in value.split()] + \\\\\\n [EOS_token]\\n story.append(v)\\n return story\",\n \"def get_actions(acts, act_space, action):\\n action_spaces = []\\n action_spaces_name = []\\n idx = 0\\n for act in act_space.spaces:\\n if act_space[act].__class__.__name__ == 'Discrete':\\n action_spaces_name.append(act)\\n action_spaces.append(act_space[act].n)\\n action[act] = int(acts[idx])\\n idx += 1\\n elif act_space[act].__class__.__name__ == 'Enum':\\n action_spaces_name.append(act)\\n action_spaces.append(len(act_space[act].values))\\n action[act] = act_space[act].values[int(acts[idx])]\\n idx += 1\\n elif act == 'camera':\\n action_spaces_name.append('pitch')\\n action_spaces_name.append('yaw')\\n action_spaces.append(36)\\n action_spaces.append(36)\\n action[act] = [acts[idx] * 10 - 180, acts[idx + 1] * 10 - 180]\\n idx += 2\\n\\n return action\",\n \"def get_action_list(self, basic_action=False):\\n assert len(self.name), 'No character name {} found.'.format(self.name)\\n lf2_char = Template(player_id=self.idx) if basic_action \\\\\\n else globals()[self.name](player_id=self.idx)\\n action_s = lf2_char.action_space()\\n if basic_action:\\n action_s.remove('run')\\n return action_s\",\n \"def get_action(player):\\n print_action(player)\\n chosen_action_index = int(input('Please indicate your selection from the following list by inputting the number: '))\\n return player.available_actions[chosen_action_index]\",\n \"def index_id(i):\\n return f\\\"(i={i})\\\"\",\n \"def _tokens_to_index(self,tokens):\\n wids = []\\n for tk in tokens:\\n if tk in self.wtoi.keys():\\n wids.append(self.wtoi[tk])\\n else:\\n wids.append(1) # <UNK>\\n for _ in range(self.sentence_max_length - len(wids)):\\n wids.append(0)\\n if len(wids) > self.sentence_max_length:\\n wids = wids[:self.sentence_max_length]\\n return wids\",\n \"def obtain_action(self, timestep):\\r\\n\\t\\t# Loops constantly until a valid input is obtained.\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t# Tries to obtain a valid input manually and convert it to an\\r\\n\\t\\t\\t\\t# integer.\\r\\n\\t\\t\\t\\taction = int(input('Please provide an input action index between 0 and (number of actions - 1): %i: ' % (self.num_actions-1)))\\r\\n\\r\\n\\t\\t\\texcept ValueError:\\r\\n\\t\\t\\t\\tprint('Invalid input detected, try again.')\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\t\\t# Checks if the input is within the acceptable range of action\\r\\n\\t\\t\\t# index values.\\r\\n\\t\\t\\tif 0 <= action < self.num_actions:\\r\\n\\t\\t\\t\\tbreak\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tprint('Action should be an index between 0 and (number of actions - 1): %i' % (self.num_actions-1))\\r\\n\\r\\n\\t\\treturn action\",\n \"def _convert_id_to_token(self, index, return_unicode=True):\\n token = self.sp_model.IdToPiece(index)\\n return token\",\n \"def generate_action_grammar(self, actions):\\n assert isinstance(actions, list), actions\\n assert not isinstance(actions[0], list), \\\"Should be 1 long list of actions - {}\\\".format(actions[0])\\n assert len(actions) > 0, \\\"Need to provide a list of at least 1 action\\\"\\n assert isinstance(actions[0], int), \\\"The actions should be integers\\\"\\n new_actions, all_rules, rule_usage, rules_episode_appearance_count = self.discover_all_rules_and_new_actions_representation(actions)\\n action_usage = self.extract_action_usage_from_rule_usage(rule_usage, all_rules)\\n rules_episode_appearance_count = self.extract_action_usage_from_rule_usage(rules_episode_appearance_count,\\n all_rules)\\n return new_actions, all_rules, action_usage, rules_episode_appearance_count\",\n \"def valid_actions(self) -> List[str]:\\n return list(self.action_map().keys())\",\n \"def find_indices_srnn(data, action):\\n\\n SEED = 1234567890\\n rng = np.random.RandomState(SEED)\\n\\n subject = 5\\n subaction1 = 1\\n subaction2 = 2\\n\\n T1 = data[(subject, action, subaction1)].shape[0]\\n T2 = data[(subject, action, subaction2)].shape[0]\\n prefix, suffix = 50, 100\\n\\n idx = []\\n idx.append(rng.randint(16,T1-prefix-suffix))\\n idx.append(rng.randint(16,T2-prefix-suffix))\\n idx.append(rng.randint(16,T1-prefix-suffix))\\n idx.append(rng.randint(16,T2-prefix-suffix))\\n idx.append(rng.randint(16,T1-prefix-suffix))\\n idx.append(rng.randint(16,T2-prefix-suffix))\\n idx.append(rng.randint(16,T1-prefix-suffix))\\n idx.append(rng.randint(16,T2-prefix-suffix))\\n\\n return idx\",\n \"def rule_id(self, action):\\n ext_id = \\\"{0}_{1}_acl_{2}\\\".format(\\n self.model.short_name.replace('.', '_'),\\n self.group.name.replace('.', '_'),\\n action,\\n )\\n return ext_id[-64:]\",\n \"def parse_position(action,grid_size):\\n if action == None or len(action) == 0:\\n return None\\n #'-Upper Case Character--number-'\\n if action[0] in ALPHA[:grid_size] and action[1:].isdigit():\\n if 0 <= int(action[1:]) <= grid_size:\\n return (ALPHA.index(action[0]),int(action[1:])-1)\\n return None\",\n \"def askForAction(self, role, index, currPos, possActions):\\r\\n print str(role) + \\\" -> \\\" + str(index)\\r\\n print \\\"Current position : \\\" + str(currPos)\\r\\n print \\\"Possible actions : \\\" + str(possActions)\\r\\n # request = raw_input(\\\"Destination, TicketType = \\\").split(',')\\r\\n request = raw_input(\\\"Destination, TicketType = \\\")\\r\\n while \\\",\\\" not in request:\\r\\n print \\\"You missed the comma!\\\"\\r\\n request = raw_input(\\\"Destination, TicketType = \\\")\\r\\n fields = request.split(\\\",\\\")\\r\\n dest, ticket = int(fields[0]), fields[1].strip().upper()\\r\\n return dest, ticket\",\n \"def get_id_from_comments(raw_action):\\n act_id_re = re.compile(r\\\"/\\\\*[ ]*id:(?P<id>[\\\\w-]+)\\\")\\n act_id_m = act_id_re.search(raw_action)\\n if act_id_m:\\n return act_id_m.group(1)\\n else:\\n logger.error(\\\"Cannot find id in the following data: %s\\\" % raw_action)\\n exit(-1)\",\n \"def generate_actions(self):\\n \\n # For all state nodes\\n action = 0\\n \\n for l in range(self.u0_n):\\n for m in range(self.u1_n):\\n \\n u = np.array([ self.ud[0][l] , self.ud[1][m] ])\\n \\n # State and grid index based on node #\\n self.actions_input[action,:] = u\\n self.actions_index[action,:] = np.array([l,m])\\n \\n # Increment node number\\n action = action + 1\",\n \"def _seq2Indices(self, sequence, vocab, start, end, unknown):\\n if start:\\n sequence.insert(0, start)\\n if end:\\n sequence.append(end)\\n return [vocab[token] if token in vocab else vocab[unknown] for token in sequence]\",\n \"def ACTION_MAP(activity_action):\\n import amo\\n\\n return {\\n amo.LOG.APPROVE_VERSION.id: APPROVAL,\\n amo.LOG.APPROVE_VERSION_WAITING.id: APPROVE_VERSION_WAITING,\\n amo.LOG.APP_DISABLED.id: DISABLED,\\n amo.LOG.ESCALATE_MANUAL.id: ESCALATION,\\n amo.LOG.ESCALATE_VERSION.id: ESCALATION,\\n amo.LOG.ESCALATION_VIP_APP.id: ESCALATION,\\n amo.LOG.ESCALATED_HIGH_ABUSE.id: ESCALATION_HIGH_ABUSE,\\n amo.LOG.ESCALATED_HIGH_REFUNDS.id: ESCALATION_HIGH_REFUNDS,\\n amo.LOG.ESCALATION_CLEARED.id: ESCALATION_CLEARED,\\n amo.LOG.REQUEST_INFORMATION.id: MORE_INFO_REQUIRED,\\n amo.LOG.REJECT_VERSION.id: REJECTION,\\n amo.LOG.REREVIEW_CLEARED.id: REREVIEW_CLEARED,\\n amo.LOG.WEBAPP_RESUBMIT.id: RESUBMISSION,\\n amo.LOG.COMMENT_VERSION.id: REVIEWER_COMMENT,\\n }.get(activity_action, NO_ACTION)\",\n \"def action_to_id(a):\\n if all(a == [-1.0, 0.0, 0.0]): return LEFT # LEFT: 1\\n elif all(a == [1.0, 0.0, 0.0]): return RIGHT # RIGHT: 2\\n elif all(a == [0.0, 1.0, 0.0]): return ACCELERATE # ACCELERATE: 3\\n elif all(a == [0.0, 0.0, 0.2]): return BRAKE # BRAKE: 4\\n else: \\n return STRAIGHT # STRAIGHT = 0\",\n \"def _GetAction(self, action, text):\\n # TODO: replace \\\"x in y\\\" checks by startswith if possible.\\n if 'airportdProcessDLILEvent' in action:\\n network_interface = text.split()[0]\\n return 'Interface {0:s} turn up.'.format(network_interface)\\n\\n if 'doAutoJoin' in action:\\n match = self._CONNECTED_RE.match(text)\\n if match:\\n ssid = match.group(1)[1:-1]\\n else:\\n ssid = 'Unknown'\\n return 'Wi-Fi connected to SSID: {0:s}'.format(ssid)\\n\\n if 'processSystemPSKAssoc' in action:\\n wifi_parameters = self._WIFI_PARAMETERS_RE.search(text)\\n if wifi_parameters:\\n ssid = wifi_parameters.group(1)\\n bssid = wifi_parameters.group(2)\\n security = wifi_parameters.group(3)\\n if not ssid:\\n ssid = 'Unknown'\\n if not bssid:\\n bssid = 'Unknown'\\n if not security:\\n security = 'Unknown'\\n\\n return (\\n 'New wifi configured. BSSID: {0:s}, SSID: {1:s}, '\\n 'Security: {2:s}.').format(bssid, ssid, security)\\n\\n return text\",\n \"def remove_unused_char(action_list):\\r\\n clean_action_list = []\\r\\n for action in action_list:\\r\\n effect_element = action['action']\\r\\n clearnedstr = (effect_element[effect_element.index(\\\"effect\\\")\\r\\n + len(\\\"effect\\\"):])\\r\\n clean_action_list.append(clearnedstr[:-1])\\r\\n return clean_action_list\",\n \"def get_neighbor_action(self, action):\\n naction = []\\n for i in range(self.n_agent):\\n naction.append(action[self.neighbor_mask[i] == 1])\\n return naction\",\n \"def extract_action_usage_from_rule_usage(self, rule_usage, all_rules):\\n action_usage = {}\\n for key in rule_usage.keys():\\n action_usage[self.convert_symbol_to_raw_actions(key, all_rules)] = rule_usage[key]\\n return action_usage\",\n \"def splitaction_ramp(data_frame, \\n action_header = 'Action Name', \\n action_name = 'viscometry ramp'):\\n\\n print('\\\\n Splitting data by action name: ', action_name)\\n \\n # Gets all the actions within the data frame\\n iaction = [x for x in data_frame[action_header].unique() if action_name in x.lower()]\\n\\n print(iaction)\\n data_frame.set_index(action_header, inplace = True)\\n \\n # Find the location of the desired action, and save to a data frame\\n # If the action name is not found, it prints an error message\\n try:\\n select_data = data_frame.loc[iaction]\\n select_data.reset_index(inplace = True)\\n except IndexError: \\n print('ERROR: Action name not found') \\n select_data = None \\n return select_data\",\n \"def convert_to_action4(choices):\\n (food_card_index, locp) = choices\\n logp = convert_locp(locp, cp_to_gp)\\n logb = convert_locp(locp, cp_to_gb)\\n lobt = convert_locp(locp, cp_to_bt)\\n lort = convert_locp(locp, cp_to_rt)\\n return [food_card_index, logp, logb, lobt, lort]\",\n \"def __action_to_token(self, action, next_states, val_tok_mov):\\r\\n if (int)(action) == 4: # move_token action returns first valid token to move\\r\\n for token_id, next_state in enumerate(next_states):\\r\\n if next_state is not False:\\r\\n return token_id\\r\\n\\r\\n valid_tokens = np.argwhere(val_tok_mov[:,int(action)] == True).squeeze()\\r\\n if valid_tokens.size > 1:\\r\\n return np.random.choice(valid_tokens)\\r\\n else:\\r\\n return valid_tokens\",\n \"def extract_action_list_from_path(path_list):\\n action_trans = {-81: 'movenorth 1', 81: 'movesouth 1', -1: 'movewest 1', 1: 'moveeast 1'}\\n alist = []\\n for i in range(len(path_list) - 1):\\n curr_block, next_block = path_list[i:(i + 2)]\\n alist.append(action_trans[next_block - curr_block])\\n\\n return alist\",\n \"def parse_action(element: Element) -> ActionType:\\n # edit is a special type of action for strings\\n if \\\"edit\\\" in element.attributes:\\n if element.attributes[\\\"edit\\\"] == \\\"append\\\":\\n return edit_append\\n raise error_at(element)(f'invalid edit=\\\"{element.attributes[\\\"edit\\\"]}\\\"')\\n # default action is replace\\n action = element.attributes.get(\\\"action\\\", \\\"replace\\\")\\n if action == \\\"replace\\\":\\n return replace\\n if action == \\\"append\\\":\\n return append\\n if action == \\\"delete\\\":\\n return delete\\n if action == \\\"merge\\\":\\n return merge\\n raise error_at(element)('invalid action=\\\"{:s}\\\".'.format(action))\",\n \"def action(self, action):\\n action = (action + 1) / 2 # [-1, 1] => [0, 1]\\n action *= (self.action_space.high - self.action_space.low)\\n action += self.action_space.low\\n return action\",\n \"def get_joss_strategy_actions(\\n opponent_moves: list, indices_to_flip: list\\n) -> list:\\n out = []\\n for index, action in enumerate(opponent_moves):\\n previous_action = opponent_moves[index - 1]\\n if index == 0:\\n out.append((C, action))\\n elif index in indices_to_flip:\\n out.append((previous_action.flip(), action))\\n else:\\n out.append((previous_action, action))\\n return out\",\n \"def transfer_actions(action, act_space):\\n #print(action)\\n action_spaces = []\\n res = []\\n for act in act_space.spaces:\\n if act_space[act].__class__.__name__ == 'Discrete':\\n action_spaces.append(act_space[act].n)\\n res.append(action[act])\\n elif act_space[act].__class__.__name__ == 'Enum':\\n action_spaces.append(len(act_space[act].values))\\n res.append(action[act])\\n elif act == 'camera':\\n res.append(camera_transform(action[act][0]))\\n res.append(camera_transform(action[act][1]))\\n action_spaces.append(36)\\n action_spaces.append(36)\\n\\n return res\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6287347","0.62635666","0.6073907","0.604243","0.5834542","0.5821824","0.5821046","0.58161885","0.5769511","0.57691544","0.56986296","0.5698094","0.5687889","0.5677664","0.5606154","0.55739564","0.5573762","0.55202705","0.5516037","0.55082506","0.54843324","0.5474047","0.5468542","0.546532","0.5430485","0.54028267","0.5382371","0.5348416","0.53481525","0.53126734","0.53116435","0.5302074","0.5295357","0.52596515","0.5238934","0.52098817","0.52098817","0.52043283","0.52020603","0.5195271","0.5192767","0.5188595","0.517664","0.51614934","0.5158632","0.513023","0.5126302","0.5100174","0.5098999","0.5097548","0.509161","0.5084838","0.5078775","0.5070685","0.50569284","0.505505","0.50422573","0.50412256","0.50305384","0.50292534","0.5028943","0.5020505","0.50196916","0.5018653","0.501544","0.5009288","0.50079393","0.5007695","0.49891496","0.49866137","0.49788195","0.4977119","0.49701932","0.49629378","0.49575895","0.4953124","0.49507174","0.49371752","0.4933876","0.49318427","0.4925778","0.49226648","0.492047","0.49149606","0.49041015","0.49020618","0.4895648","0.48907506","0.4887763","0.48855084","0.48837757","0.4879072","0.4878887","0.48770618","0.487513","0.48672664","0.48565975","0.48541608","0.48529536","0.48527575"],"string":"[\n \"0.6287347\",\n \"0.62635666\",\n \"0.6073907\",\n \"0.604243\",\n \"0.5834542\",\n \"0.5821824\",\n \"0.5821046\",\n \"0.58161885\",\n \"0.5769511\",\n \"0.57691544\",\n \"0.56986296\",\n \"0.5698094\",\n \"0.5687889\",\n \"0.5677664\",\n \"0.5606154\",\n \"0.55739564\",\n \"0.5573762\",\n \"0.55202705\",\n \"0.5516037\",\n \"0.55082506\",\n \"0.54843324\",\n \"0.5474047\",\n \"0.5468542\",\n \"0.546532\",\n \"0.5430485\",\n \"0.54028267\",\n \"0.5382371\",\n \"0.5348416\",\n \"0.53481525\",\n \"0.53126734\",\n \"0.53116435\",\n \"0.5302074\",\n \"0.5295357\",\n \"0.52596515\",\n \"0.5238934\",\n \"0.52098817\",\n \"0.52098817\",\n \"0.52043283\",\n \"0.52020603\",\n \"0.5195271\",\n \"0.5192767\",\n \"0.5188595\",\n \"0.517664\",\n \"0.51614934\",\n \"0.5158632\",\n \"0.513023\",\n \"0.5126302\",\n \"0.5100174\",\n \"0.5098999\",\n \"0.5097548\",\n \"0.509161\",\n \"0.5084838\",\n \"0.5078775\",\n \"0.5070685\",\n \"0.50569284\",\n \"0.505505\",\n \"0.50422573\",\n \"0.50412256\",\n \"0.50305384\",\n \"0.50292534\",\n \"0.5028943\",\n \"0.5020505\",\n \"0.50196916\",\n \"0.5018653\",\n \"0.501544\",\n \"0.5009288\",\n \"0.50079393\",\n \"0.5007695\",\n \"0.49891496\",\n \"0.49866137\",\n \"0.49788195\",\n \"0.4977119\",\n \"0.49701932\",\n \"0.49629378\",\n \"0.49575895\",\n \"0.4953124\",\n \"0.49507174\",\n \"0.49371752\",\n \"0.4933876\",\n \"0.49318427\",\n \"0.4925778\",\n \"0.49226648\",\n \"0.492047\",\n \"0.49149606\",\n \"0.49041015\",\n \"0.49020618\",\n \"0.4895648\",\n \"0.48907506\",\n \"0.4887763\",\n \"0.48855084\",\n \"0.48837757\",\n \"0.4879072\",\n \"0.4878887\",\n \"0.48770618\",\n \"0.487513\",\n \"0.48672664\",\n \"0.48565975\",\n \"0.48541608\",\n \"0.48529536\",\n \"0.48527575\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66609216"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":88498,"cells":{"query":{"kind":"string","value":"Dynamically call the function according 'oper'"},"document":{"kind":"string","value":"def process_data(self, oper='SCAN'):\n func_name = oper.lower()\n return getattr(self, func_name)()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _execute(self, op, time):\n raise NotImplementedError","def do_run(calc, a=int, b=int):\n of = calc.oper_func\n calc.log.msg(10, 'Calculating', operation=of, a=a, b=b)\n res = getattr(operator, of, calc.not_found)(a, b)\n calc.log.msg(20, 'Returning', result=res)\n return res","def __call__(fun_name):","def math(oper):\n a=int(request.args.get('a'))\n b=int(request.args.get('b'))\n result = math_oper[oper](a,b)\n return str(result)","def get_func(op):\n if op == \"-e\":\n return func\n elif op == \"-d\":\n return unfunc","def run(self, in_op):\n raise NotImplementedError","def _support_op(*args):\n def inner(func):\n for one_arg in args:\n _op_mapping_[one_arg] = func\n return func\n\n return inner","def _op_easy(self, op, reg_list, param_list=None): # pylint: disable-msg=invalid-name\n\n has_op = hasattr(self.circuit, op)\n\n if has_op:\n if param_list:\n # DEBUG\n # print(\"********** op {} param_list {} reg_list {}\".format(op, param_list, reg_list)) # pylint: disable-msg=line-too-long\n # END-DEBUG\n getattr(self.circuit, op)(*param_list, *reg_list)\n else:\n getattr(self.circuit, op)(*reg_list)\n\n return has_op","def fun(op, v1, v2):\n if op == '+':\n return v1+v2\n elif op == '-':\n return v1-v2\n elif op == '*':\n return v1*v2\n elif op == '/':\n return v1","def do_oprn(self, *args, operator=None, **kwargs):\n\t\tself.operator = operator\n\n\t\tif not self.operator:\n\t\t\treturn f'No operator provided'\n\n\t\tif self.operator == '+':\n\t\t\treturn self.sum(*args, **kwargs)\n\t\telif self.operator == '-':\n\t\t\treturn self.subtract(*args, **kwargs)\n\t\telif self.operator == '*':\n\t\t\treturn self.multiple(*args, **kwargs)\n\t\telif self.operator == '/':\n\t\t\treturn self.division(*args, **kwargs)\n\t\telse:\n\t\t\treturn f'Currently Operator ({operator}) is not Applicable'","def operate(\n self, op: OperatorType, *other: Any, **kwargs: Any\n ) -> Operators:\n raise NotImplementedError(str(op))","def _process_operator(self, expr, operator, func, *args, **kwargs):\n for elt in self.model.xml_element_children(expr):\n self._process_operator(elt, operator, func, *args, **kwargs)\n if isinstance(expr, mathml_apply) and expr.operator().localName == operator:\n func(expr, *args, **kwargs)","def __call__(self, tf_node, input_ops):\n op_name = tf_node.op\n\n # if op not handled, gets -1\n ng_op = getattr(self, op_name, None)\n\n if ng_op:\n return ng_op(tf_node, input_ops)\n else:\n # ignored op set to None\n print(tf_node.name, \"ignored.\")\n return None","def calc(a,b,operation):\n\n if operation == 'product':\n return a * b\n else:\n return a + b","def doOperation(op, list, var, sortkey):\r\n \r\n \r\n func = itemgetter(sortkey)\r\n \r\n if op == 'max':\r\n return max(list, key=func)\r\n elif op == 'min':\r\n return min(list, key=func)\r\n elif op == 'sort asc':\r\n return sorted(list, key=func)\r\n elif op == 'sort desc':\r\n return sorted(list, key=func, reverse=True)\r\n else:\r\n print 'Error: Bad input operation in doOpertion'\r\n return list","def perform_operation(operator, num_1, num_2):\n\n if operator == \"*\":\n return num_1 * num_2\n if operator == \"+\":\n return num_1 + num_2\n if operator == \"-\":\n return num_1 - num_2\n if operator == \"/\":\n return num_1 / num_2","def operation_func():\n\n operation_input = input(\"\\nPlease type in the math operation you would like to complete:\\n+ for addition\\n- for subtraction\\n* for multiplication\\n/ for division : \")\n operation_list = [\"+\", \"-\", \"*\", \"/\"]\n if not operation_input in operation_list: \n print('\\nYou have not typed a valid operator!!! \\nPlease enter the math operation again.')\n operation_func()\n\n return calculate_func(operation_input)","def parse_logic(self, logic):\n if 'xxx' in logic.conf:\n # self.function(logic['name'])\n pass","def runOperation(operation, num1, num2):\n if operation == 1 or operation == '+':\n print(add(num1, num2))\n elif operation == 2 or operation == '-':\n print(sub(num1, num2))\n elif operation == 3 or operation == '*':\n print(mul(num1, num2))\n elif operation == 4 or operation == '/':\n print(div(num1, num2))\n else:\n print(\"I don't understand\")","def exec_function(self, args):\n raise NotImplementedError()","def execute(actual, forecast, op_exec):\n # pass\n\n return_value = None\n if op_exec == \"MSE\":\n return_value = viz.MSE(actual, forecast)\n elif op_exec == \"RMSE\":\n return_value = viz.RMSE(actual, forecast)\n elif op_exec == \"MAPE\":\n return_value = viz.MAPE(actual, forecast)\n elif op_exec == \"sMAPE\":\n return_value = viz.sMAPE(actual, forecast)\n return return_value","def execute_operation(self, frac1, operator, frac2):\n try:\n ops = {\"+\": (lambda x, y: Frac(x) + Frac(y)), \"-\": (lambda x, y: Frac(x) - Frac(y)), \"*\":\n (lambda x, y: Frac(x) * Frac(y)), \"/\": (lambda x, y: Frac(x) / Frac(y))}\n if operator in ops:\n return str(ops[operator](frac1, frac2))\n else:\n print(self.operator_not_Valid)\n return False\n except Exception as e:\n print(self.operator_not_Valid, e)\n return False","def execute(input_file, op_exec):\n # pass\n # input_file = viz.read_matrix(input_file)\n input_file = viz.csv_to_ts(input_file)\n input_file = prep.impute_missing_data(input_file)\n input_file = prep.impute_outliers(input_file)\n\n return_value = None\n if op_exec == \"plot\":\n return_value = viz.plot(input_file)\n elif op_exec == \"histogram\":\n return_value = viz.histogram(input_file)\n elif op_exec == \"summary\":\n return_value = viz.summary(input_file)\n elif op_exec == \"box_plot\":\n return_value = viz.box_plot(input_file)\n elif op_exec == \"shapiro_wilk\":\n return_value = viz.shapiro_wilk(input_file)\n elif op_exec == \"d_agostino\":\n return_value = viz.d_agostino(input_file)\n elif op_exec == \"anderson_darling\":\n return_value = viz.anderson_darling(input_file)\n elif op_exec == \"qq_plot\":\n return_value = viz.qq_plot(input_file)\n return return_value","def exec_builtin(self, cmd):\r\n func = Builtin.builtins.get(cmd[0])\r\n if func is None:\r\n return False\r\n func(self, cmd)\r\n return True","def operation(code, val1, val2):\n if code == 1:\n return val1 + val2\n elif code == 2:\n return val1 * val2\n elif code == 99:\n return -1","def list_operations():","def get_func(self, class_name, arg, stored_objects):\n find_func = re.match('([a-z]+)', arg) # returns a matching object\n func_name = find_func.group()\n args = re.findall('\"([^\"]+)\",?', arg) # return a list of arguments\n\n if len(args) == 0:\n if func_name == \"all\":\n self.all(class_name, stored_objects)\n elif func_name == \"count\":\n self.count(class_name, stored_objects)\n else:\n print(\"** instance id missing **\")\n\n elif len(args) == 1:\n if self.check_instance(class_name, args[0], stored_objects):\n if func_name == \"show\":\n self.show(class_name, args[0], stored_objects)\n elif func_name == \"destroy\":\n self.destroy(class_name, args[0], stored_objects)\n elif func_name == \"update\":\n print(\"** attribute name missing **\")\n\n elif len(args) == 2 and func_name == \"update\":\n print(\"** value missing **\")\n\n elif len(args) == 3 and func_name == \"update\":\n if self.check_instance(class_name, args[0], stored_objects):\n self.update(class_name, args, stored_objects)","def eval(self, *args, **kwargs):\n raise NotImplementedError","def call(self, data):\n\t\treturn self.fnc(data)","def get_op(ring_size: int, op_str: str) -> Callable[..., Any]:\n ops = RING_SIZE_TO_OP.get(ring_size, None)\n\n if ops is None:\n raise ValueError(f\"Do not have operations for ring size {ring_size}\")\n\n op = ops.get(op_str, None)\n if op is None:\n raise ValueError(\n f\"Operator {op_str} does not exist for ring size {ring_size}\"\n )\n\n return op","def cmd_calculation():","def eval(*args, **kwargs):\n\n pass","def _call_unprocessed(\n self,\n x: np.ndarray,\n sensi_orders: Tuple[int, ...],\n mode: str\n ) -> Dict:\n if mode == MODE_FUN:\n result = self._call_mode_fun(x, sensi_orders)\n elif mode == MODE_RES:\n result = self._call_mode_res(x, sensi_orders)\n else:\n raise ValueError(\"This mode is not supported.\")\n return result","def __compile_operator(self, op, caller):\r\n if op == \"+\":\r\n self.__vmwriter.write_arithmetic(\"add\")\r\n elif op == \"-\" and caller == \"expression\":\r\n self.__vmwriter.write_arithmetic(\"sub\")\r\n elif op == \"*\":\r\n self.__vmwriter.write_call(\"Math.multiply\", 2)\r\n elif op == \"/\":\r\n self.__vmwriter.write_call(\"Math.divide\", 2)\r\n elif op == \"&\":\r\n self.__vmwriter.write_arithmetic(\"and\")\r\n elif op == \"|\":\r\n self.__vmwriter.write_arithmetic(\"or\")\r\n elif op == \"<\":\r\n self.__vmwriter.write_arithmetic(\"lt\")\r\n elif op == \">\":\r\n self.__vmwriter.write_arithmetic(\"gt\")\r\n elif op == \"=\":\r\n self.__vmwriter.write_arithmetic(\"eq\")\r\n elif op == \"-\":\r\n self.__vmwriter.write_arithmetic(\"neg\")\r\n elif op == \"~\":\r\n self.__vmwriter.write_arithmetic(\"not\")","def _invoke_cmd(self, cmd):\n if cmd in self.COMMANDS:\n self.COMMANDS[cmd]()\n else:\n print(ERROR_UNKNOWN_COMMAND.format(cmd=cmd))","def exec(self,**kwargs):\r\n pass","def _calc2call(func):\n def _converter(inp, *x):\n if func.n_inputs == 1:\n retvals = func.evaluate(x[0], *inp)\n else:\n retvals = func.evaluate(x[0], x[1], *inp)\n return retvals\n return _converter","def execute(self, i: dict) -> None:\n # execute instruction for instruction processor\n if i[\"recipient\"] == \"proc\":\n if self.suffixes_condition[i[\"suf\"]]():\n if i[\"type\"] == \"proc_alu\":\n # TODO: cmpi_ fix\n i[\"func\"](self.regs[i[\"dest\"]], self.regs[i[\"op1\"]], self.regs[i[\"dest\"]], self.regs[\"flags\"])\n elif i[\"type\"] == \"proc_mem_flags\" or i[\"func\"] == movi_:\n i[\"func\"](self.regs[i[\"dest\"]], self.regs[i[\"op1\"]], self.regs[\"flags\"])\n elif i[\"type\"] == \"proc_mem_num\":\n i[\"func\"](self.regs[i[\"dest\"]], i[\"op1\"], self.regs[\"flags\"])\n elif i[\"type\"] == \"proc_mem_suffix\":\n i[\"func\"](self.regs[i[\"dest\"]], self.regs[i[\"op1\"]], self.__RAM, self.regs[\"flags\"])\n elif i[\"type\"] == \"jumps\":\n i[\"func\"](self.regs[\"ip\"], self.regs[i[\"dest\"]], self.regs[\"flags\"])\n else:\n for core in self.__cores:\n core.execute(i)","def _execute_op(self, op):\n if op.op_type == Operation.Type.GET:\n if op.key in self._store:\n return Result.OK, self._store[op.key]\n else:\n return Result.NOT_FOUND, \"\"\n elif op.op_type == Operation.Type.PUT:\n self._store[op.key] = op.value\n return Result.OK, \"\"\n elif op.op_type == Operation.Type.DEL:\n self._store.pop(op.key, None)\n return Result.OK, \"\"\n else:\n raise ValueError(\"Invalid operation type\")","def _execute_op(self, op):\n if op.op_type == Operation.Type.GET:\n if op.key in self._store:\n return Result.OK, self._store[op.key]\n else:\n return Result.NOT_FOUND, \"\"\n elif op.op_type == Operation.Type.PUT:\n self._store[op.key] = op.value\n return Result.OK, \"\"\n elif op.op_type == Operation.Type.DEL:\n self._store.pop(op.key, None)\n return Result.OK, \"\"\n else:\n raise ValueError(\"Invalid operation type\")","def test(self):\n self.eval()","def getCallable():","def __call__(self, node, operations, last_operation):\n if last_operation == NO_OPERATION:\n return 0\n return 1","def _control_predefined(operation, num_ctrl_qubits):\n if operation.name == 'x' and num_ctrl_qubits in [1, 2]:\n if num_ctrl_qubits == 1:\n import qiskit.extensions.standard.cx\n cgate = qiskit.extensions.standard.cx.CnotGate()\n else:\n import qiskit.extensions.standard.ccx\n cgate = qiskit.extensions.standard.ccx.ToffoliGate()\n elif operation.name == 'y':\n import qiskit.extensions.standard.cy\n cgate = qiskit.extensions.standard.cy.CyGate()\n elif operation.name == 'z':\n import qiskit.extensions.standard.cz\n cgate = qiskit.extensions.standard.cz.CzGate()\n elif operation.name == 'h':\n import qiskit.extensions.standard.ch\n cgate = qiskit.extensions.standard.ch.CHGate()\n elif operation.name in {'rx', 'ry', 'rz'}:\n if operation.name == 'rx':\n import qiskit.extensions.standard.crx\n cgate = qiskit.extensions.standard.crx.CrxGate(*operation.params)\n elif operation.name == 'ry':\n import qiskit.extensions.standard.cry\n cgate = qiskit.extensions.standard.cry.CryGate(*operation.params)\n else: # operation.name == 'rz'\n import qiskit.extensions.standard.crz\n cgate = qiskit.extensions.standard.crz.CrzGate(*operation.params)\n if num_ctrl_qubits == 1:\n return cgate\n else:\n # only predefined for one control qubit\n return cgate.control(num_ctrl_qubits - 1)\n elif operation.name == 'swap':\n import qiskit.extensions.standard.cswap\n cgate = qiskit.extensions.standard.cswap.FredkinGate()\n elif operation.name == 'u1':\n import qiskit.extensions.standard.cu1\n cgate = qiskit.extensions.standard.cu1.Cu1Gate(*operation.params)\n elif operation.name == 'u3':\n import qiskit.extensions.standard.cu3\n cgate = qiskit.extensions.standard.cu3.Cu3Gate(*operation.params)\n elif operation.name == 'cx':\n import qiskit.extensions.standard.ccx\n cgate = qiskit.extensions.standard.ccx.ToffoliGate()\n else:\n raise QiskitError('No standard controlled gate for \"{}\"'.format(\n operation.name))\n return cgate","def execute(arr, i, alg, pointer):\r\n\r\n # Get the current command:\r\n order = alg[pointer]\r\n\r\n ####################### Order Selector #######################\r\n\r\n if order == '>': # > : Move forward:\r\n return move_forward(arr, i, alg, pointer)\r\n\r\n elif order == '<': # \"<\" : Move backward:\r\n return move_backward(arr, i, alg, pointer)\r\n\r\n elif order == '+': # \"+\" : Increase value in the current cell by 1:\r\n return increase_value(arr, i, alg, pointer)\r\n\r\n elif order == '-': # \"-\" : Decrease value in the current cell by 1:\r\n return decrease_value(arr, i, alg, pointer)\r\n\r\n elif order == '.': # \".\" : Convert ASCII code from current cell into symbol and print:\r\n return print_value(arr, i, alg, pointer)\r\n\r\n elif order == ',': # \",\" : Get input form user and save it in the current cell:\r\n return save_value(arr, i, alg, pointer)\r\n\r\n elif order == '[': # \"[\" : Start of cycle:\r\n return start_cycle(arr, i, alg, pointer)\r\n\r\n\r\n elif order == ']': # \"]\" : End of cycle:\r\n return end_cycle(arr, i, alg, pointer)","def eval_ops(opcodes):\n output = []\n for op in opcodes:\n if op in [\"+\", \"*\"]:\n b = output.pop(-1)\n a = output.pop(-1)\n value = ops[op](a, b)\n output.append(value)\n else:\n output.append(op)\n\n assert len(output) == 1\n return output[0]","def generic_execute(self, query, suffix, **arguments) -> Any:\n query_name = self.reverse_queries[query]\n self.queries.append((query_name, arguments))\n return getattr(self, f\"{query_name}_{suffix}\")(**arguments)","def dynamic_comparison(v1, op, v2):\n assert op in ['gt', 'lt']\n\n operator_map = {'gt': operator.gt,\n 'lt': operator.lt}\n\n return operator_map[op](v1, v2)","def eval(self, i, node, fn):\r\n if self.pre_func is not None:\r\n self.pre_func(i, node, fn)\r\n fn()\r\n if self.post_func is not None:\r\n self.post_func(i, node, fn)","def replace_operators_by_calls(topconstruct, opname, call, call_id_construct):\n # find all computations\n for computation in query([is_computation], TreeItem(topconstruct)):\n replace_op_by_call(computation.construct, opname, call, call_id_construct)","def _call(self, args):\n a = args.split(' ', 1)\n if a:\n getattr(self, a[0])(*a[1:])","def run_operator(scope_node, node, name, op, code, f_globals):\n operators = __get_operators()\n if op not in operators:\n raise TypeError(\"failed to load operator '%s'\" % op)\n scope_key = scope_node.scope_key\n pair = operators[op](code, scope_key, f_globals)\n if isinstance(name, tuple):\n # The template inst binding with a single name will take this\n # path by using a length-1 name tuple. See bug #78.\n bind_extended_member(node, name, pair, scope_key)\n else:\n item = getattr(node.klass, name, None)\n if isinstance(item, Alias):\n bind_aliased_member(node, name, item, pair, scope_key)\n else:\n # This is the path for a standard binding on a child def.\n # It does not need the closure scope key. See bug #78.\n bind_member(node, name, pair)","def _binaryop(self, other, op: str):\n raise NotImplementedError","def testOperation(self):\n gen = self.gen\n prof = self.profile\n\n # Try the direct evaluation\n gen.operation()\n self.assertTrue(array_equal(prof.x, prof.ycalc))\n\n # Try evaluation through __call__\n gen(prof.x)\n self.assertTrue(array_equal(prof.x, prof.ycalc))\n return","def _call_op_sugar(self, op_idx, *args):\n if not all(isinstance(a, six.integer_types) for a in args):\n raise TypeError('All args passed to call_op must be integers '\n '(LoomResult ids.) Did you forget to call constant?')\n result = self._weaver.CallOp(op_idx, args)\n if not result:\n raise AssertionError('Weaver op call failed: %s' %\n self._weaver.error_string())\n if len(result) == 1:\n return result[0]\n return result","def apply_gate_operation(self, operation:[str, cirq.Gate], qubit_expr, params:Optional[Dict]=None):\n gate_ops = self.get_gate_operation(operation, qubit_expr, params)\n strategy = self.insert_strategy\n self.append(gate_ops, strategy=strategy)","def __set_operation_function(self):\n if self.operation_function is not None:\n return self.operation_function\n else:\n self.operation_function = symm_eval","def _arithmetize2(self, left: Any, right: Any, op: str) -> Any:\n op_func = getattr(operator, op)\n left, right = _recycle_left_right(left, right)\n return op_func(left, right)","def func():","def _execute(self, _):\r\n pass","def __exec_function_by_code(self,command,*args):\n\t\tself.__printer(\"Preparing to execute function: {0}\".format(command),level=LL_DEBUG)\n\t\t\n\t\tif command is None:\n\t\t\treturn\n\t\t\t\n\t\tcmd_exec = Commands()\n\t\tif command not in cmd_exec.command_list:\n\t\t\treturn\n\t\t\n\t\tif args:\n\t\t\tvalid_params = cmd_exec.validate_args(command,*args)\n\t\telse:\n\t\t\tvalid_params = None\n\t\t\t\t\n\t\t# Check if we have an event for this..\n\t\tif self.event_mode_command:\n\t\t\n\t\t\tfor emc in self.event_mode_command:\n\t\t\t\tif command in emc['command']:\n\t\t\t\t\t\n\t\t\t\t\t# TODO, check if a required mode is specified\n\t\t\t\t\t#if any(x in new_active_modes for x in emc['modes']):\n\t\t\t\t\t\n\t\t\t\t\t# HIT!\n\t\t\t\t\tprint \"DEBUG EVENT-MODE HIT!\"\n\t\t\t\t\tprint emc\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t \"name\": \"mode_track\"\n\t\t\t\t\t, \"type\": \"mode_change\"\n\t\t\t\t\t, \"modes\": [ \"track\" ]\n\t\t\t\t\t, \"device\": \"rgb_1\"\n\t\t\t\t\t, \"pattern\": \"on\"\n\t\t\t\t\t, \"rgb\": \"#ff0000\"\n\t\t\t\t\t\"\"\"\n\t\t\t\t\trgb_dev = self.get_device_config(emc['device'])\n\t\t\t\t\tpin_r = rgb_dev['r']\n\t\t\t\t\tpin_g = rgb_dev['g']\n\t\t\t\t\tpin_b = rgb_dev['b']\n\t\t\t\t\t\n\t\t\t\t\t# ignore pattern for now..\n\t\t\t\t\t#turn on rgb_1, using ff0000\n\t\t\t\t\tself.gpio.pwm_rgb(pin_r,pin_g,pin_b,emc['rgb'])\n\t\t\t\t\t\n\t\tif callable(self.callback_function):\n\t\t\tprint \"-> Calling callback\"\t#blocking ??? yeah, probably..\n\t\t\tself.callback_function(command,valid_params)\n\t\t\t\n\t\t# return to original led color","def test_custom_order_exec(self):\n self.call_idx = 0\n class Mock(object):\n def __init__(self, order):\n self.order = order\n self.execd = -1\n\n def evolve(self1, t, dt):\n self1.execd = self.call_idx\n self.call_idx += 1\n\n G1 = Mock(3)\n G2 = Mock(-1)\n G3 = Mock(2)\n sim = simulation.Simulation(G1, G2, G3)\n sim.run(sim.dt)\n\n self.assertEqual(G1.execd, 2)\n self.assertEqual(G2.execd, 0)\n self.assertEqual(G3.execd, 1)","def execute(train_ts, train_inputs, test_inputs, op_exec):\n if op_exec == \"mlp\":\n model = mod.mlp_model()\n model.fit(train_inputs, train_ts)\n forecast = model.forecast(test_inputs)\n return forecast\n elif op_exec == \"rf\":\n model = mod.rf_model()\n model.fit(train_inputs, train_ts)\n forecast = model.forecast(test_inputs)\n return forecast","def run(self):\n self._validate_inputs()\n if self._destination_out_exists:\n print('Destination{} already skipping run'.format(self._inputs[-1]))\n try:\n if self.operator_func:\n self.operator_func(*self._inputs)\n else:\n raise RuntimeError('self.operator_func is not defined !!')\n except Exception as ex:\n print(ex)\n raise RuntimeError('self.operator_func failed !!')","def _generate_cmp_invocation(self, op, lineno, offset):\n to_insert = []\n start_offset = offset\n const_atheris = self._get_const(sys.modules[TARGET_MODULE])\n name_cmp = self._get_name(COMPARE_FUNCTION)\n const_op = self._get_const(op)\n const_pc = self._get_pc()\n const_False = self._get_const(False)\n\n to_insert.append(\n Instruction(lineno, offset, dis.opmap[\"LOAD_CONST\"], const_atheris))\n offset += to_insert[-1].get_size()\n to_insert.append(\n Instruction(lineno, offset, dis.opmap[\"LOAD_ATTR\"], name_cmp))\n offset += to_insert[-1].get_size()\n to_insert.append(Instruction(lineno, offset, dis.opmap[\"ROT_THREE\"]))\n offset += to_insert[-1].get_size()\n to_insert.append(\n Instruction(lineno, offset, dis.opmap[\"LOAD_CONST\"], const_op))\n offset += to_insert[-1].get_size()\n to_insert.append(\n Instruction(lineno, offset, dis.opmap[\"LOAD_CONST\"], const_pc))\n offset += to_insert[-1].get_size()\n to_insert.append(\n Instruction(lineno, offset, dis.opmap[\"LOAD_CONST\"], const_False))\n offset += to_insert[-1].get_size()\n to_insert.append(Instruction(lineno, offset, dis.opmap[\"CALL_FUNCTION\"], 5))\n offset += to_insert[-1].get_size()\n\n return offset - start_offset, to_insert","def call_lua_op(op_name, *args):\n\n # convert the python objects to lua\n args_table = util.pack_args(args)\n try:\n func = executor.eval_lua(q_consts.lua_op_fn_str)\n result = func(op_name, args_table)\n except Exception as e:\n # TODO: Handle operator level failures properly\n print(str(e))\n result = None\n if result:\n # wrap the lua objects to python\n result = util.wrap_output(op_name, result)\n return result","def callFunction(cmdname, far_args, far_kwargs, fn):\n ret = None\n if fn not in registered_functions:\n print(\"%s is not a registered function!\" % fn)\n return None\n try:\n funct = registered_functions[fn]\n ret = funct(*far_args,**far_kwargs)\n except:\n print(\"Error ocurred executing %s\" % fn)\n print(traceback.format_exc())\n \n return ret","def __call__(self, *args, **kw):\r\n debug.write(\"[SourceRPG] Executing the function %s within the CommandsDatabase\" % self.function, 4)\r\n vars(CommandsDatabase)[self.function](self.instance, *args, **kw)","def op_to_function(self, op):\n name = op.__class__.__name__.lower()\n name = operator_table.get(name, name)\n return to_attribute(self.operator, name)","def __call__(self):\n # apply(self.func, self.args)\n self.func(*self.args)","def _pull_argops(op_dict):\n import inspect\n out = []\n keys = op_dict.keys()\n keys.sort() # Not necessary, but makes scanning the printout easier\n for k in keys:\n # Create a dictionary that will be used to fill the 'code' template\n d = {}\n d[\"enum_name\"] = enum_name = op_dict[k][3:] # <NAME>\n d[\"funct_name\"] = \"%s\" % enum_name.lower() # <name>\n class_name = \"%s4args\" % enum_name\n klass = getattr(_type, class_name, None)\n if klass is None:\n # This operation takes no arguments\n d[\"funct_args\"] = d[\"create_args\"] = d[\"set_args\"] = \"\"\n else:\n if type(klass) is dict:\n arg_list = \"enum_value\"\n d[\"create_args\"] = \"args = enum_value\"\n else:\n arg_list = \", \".join(inspect.getargspec(klass.__init__)[0][1:])\n d[\"create_args\"] = \"args = _type.%s(%s)\" % (class_name, arg_list)\n d[\"funct_args\"] = arg_list\n if enum_name.startswith(\"CB_\"):\n d[\"set_args\"] = \"opcb%s=args\" % enum_name.lower()[3:]\n else:\n d[\"set_args\"] = \"op%s=args\" % enum_name.lower()\n if enum_name.startswith(\"CB_\"):\n d[\"argop\"] = \"nfs_cb_argop4\"\n else:\n d[\"argop\"] = \"nfs_argop4\"\n out.append(d)\n return out","def _command_operation(self, operation):\r\n if self.current_number != '0' and\\\r\n (self.current_operation is not None) and\\\r\n self.storage_number != '0':\r\n # check if user already selected a operation\r\n # if it is, do the equal function once and display\r\n # the resolve\r\n self._command_equal()\r\n self.current_operation = operation\r\n else:\r\n # else storage the operation\r\n self.storage_number = self.current_number\r\n self.current_operation = operation\r\n self.current_number = '0'","def __or__(self, func):\n return self.bind(func)","def call(self, func):\n\t\targs = tuple(self.__dict__.values())\n\t\ttry:\n\t\t\treturn eval(\"func\" + str(args))\n\t\texcept Exception, e:\n\t\t\traise ValueError(\"Given Function is not valid for calling: %s\" % e)","def execute(self, expr, mode = \"exec\"):\r\n return self.sync_request(\"handle_execute\", expr, mode)","def execute_binary_operator(cls, val, x, y):\n\n\t\tif val == 0:\n\t\t\treturn operator.add(x,y)\n\t\telif val == 1:\n\t\t\treturn operator.sub(x,y)\n\t\telif val == 2:\n\t\t\treturn operator.mul(x,y)\n\t\telif val == 3:\n\t\t\treturn operator.div(x,y)\n\t\telif val == 4:\n\t\t\treturn operator.lt(x,y)\n\t\telif val == 5:\n\t\t\treturn operator.gt(x,y)\n\t\telif val == 6:\n\t\t\treturn operator.le(x,y)\n\t\telif val == 7:\n\t\t\treturn operator.ge(x,y)\n\t\telif val == 8:\n\t\t\treturn operator.eq(x,y)\n\t\telif val == 9:\n\t\t\treturn operator.ne(x,y)\n\t\telif val == 12:\n\t\t\treturn operator.mod(x,y)","def dispatch(ch):\n\n if(ch == \"1\"):\n b1()\n elif(ch == \"2\"):\n b2()\n elif(ch == \"3\"):\n b3()\n else:\n print(\"Error: Invalid Option\")","def c_action(option,opt_str,value,parser):\n #print \"Processing %s '%s'\" % (opt_str,value)\n exec value in __main__.__dict__\n global something_executed\n something_executed=True","def _call_impl(self, t):\n return self.interp(t)","def _execute_conditional(self, cond, series, ds):\n\n ret = None\n func = cond['function']\n if func not in ['std', 'mean', 'value']:\n self.debug('invalid function. \"{}\"'.format(func))\n return\n\n attr = cond['attribute']\n action = cond.get('action', 'cancel')\n atypes = cond.get('analysis_types', None)\n bin_hours = cond.get('bin_hours', 6)\n\n tolerance_seconds = 60 * 60 * bin_hours\n dd = ds > tolerance_seconds\n bounds = where(dd)[0]\n itemidx = bounds[-1] if bounds else 0\n\n if atypes:\n series = [si for si in series if si['analysis_type'] in atypes]\n\n series_v = [si[attr] for si in series[itemidx:] if attr in si]\n\n if func == 'value':\n if series_v[-1] != series_v[-2]:\n ret = action\n else:\n minx = cond.get('min_n', 10)\n if len(series) <= minx:\n return\n\n x = array(series_v)\n if func == 'std':\n x = x.std()\n elif func == 'mean':\n x = x.mean()\n\n comp = cond['comparison']\n if eval(comp, {'x': x}):\n ret = action\n\n return ret","def execute():","def parse_operation(self, data, ip):\n json_decoded = json.loads(data)\n op = json_decoded['OPERATION']\n if op in self._callbacks:\n self.logger.info(\"Got Operation: \" + op)\n self._callbacks[op](json_decoded, ip)\n else:\n self.logger.error(\"Unknown operation\")","def _operators_conductor(operator_name, _bool=None):\n func = getattr(Series, operator_name)\n if _bool is None:\n # return bool series.\n _pre, _post = bool, bool\n else:\n # return ints.\n _pre, _post = int, int\n\n @wraps(func)\n def operator_method(self, other=None):\n if other is None:\n # for unary such as pos, neg, invert\n def not_(df: dF):\n return func(df.pipe(self.copy().pop())).apply(_post)\n\n return not_\n\n # if not isinstance(other, Condition):\n # raise TypeError(\"only conditions can add, got %r\" % type(other))\n\n def comb(df: dF) -> Series:\n return func(df.pipe(self).apply(_pre), df.pipe(other).apply(_pre)).apply(_post)\n\n return comb\n\n return operator_method","def RunOperator(op_def):\n RunOperatorCC(_stringify_proto(op_def))","def __call__():","def __call__():","def __call__():","def __call__():","def __call__():","def eval(self):\n pass","def eval(self):\n pass","def eval(self):\n pass","def fetch_operators_function(self, operator):\n operators_function = self.operators_dict[operator]['function']\n return operators_function","def operation(self):\n pass","def RunOperators(ops_def):\n RunOperatorsCC([_stringify_proto(op_def) for op_def in ops_def])","def _compose_polars_ops(self, op: data_algebra.data_ops_types.OperatorPlatform, *, data_map: Dict[str, Any]):\n assert isinstance(op, data_algebra.data_ops_types.OperatorPlatform)\n assert isinstance(data_map, Dict)\n res = self._method_dispatch_table[op.node_name](op=op, data_map=data_map)\n return res","def calculator(): \n\n #asks for user's input \n user_input = raw_input(\"Type in the math expression and two numbers like + 1 2 : \")\n #splits the user's input into a list\n math_op = user_input.split(\" \")\n\n #pulls the appropriate function based on the user's input\n if math_op[0] == '+':\n print add(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '-':\n print subtract(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '*':\n print multiply(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == '/':\n print divide(int(math_op[1]), int(math_op[2]))\n \n elif math_op[0] == \"square\":\n print square(int(math_op[1]))\n\n elif math_op[0] == 'cube':\n print cube(int(math_op[1]))\n\n elif math_op[0] == 'pow':\n print power(int(math_op[1]), int(math_op[2]))\n\n elif math_op[0] == 'mod':\n print mod(int(math_op[1]), int(math_op[2]))\n\n else:\n print \"That is not a valid input. Please try any of the following operator: + - * / square cube pow mod.\"","def __call__(self, approach):\n return self.op(self.get(approach), self.value)","def _arithmetize1(self, operand: Any, op: str) -> Any:\n op_func = getattr(operator, op)\n # Data length might be changed after evaluation\n # operand = recycle_value(operand, self.data.shape[0])\n return op_func(operand)","def _Call(self, t):\n # check calls but let attributes check in their own dispatcher\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\n if isinstance(t.func, ast.Name):\n if (t.func.id not in funcs):\n self.RaiseWarning(t, \"Function call is not a defined FLAME GPU device function or a supported python built in.\")\n # dispatch even if warning raised\n self.dispatch(t.func)\n elif isinstance(t.func, ast.Lambda):\n self.dispatch(t.func) # not supported\n else:\n # special handler for dispatching member function calls\n # This would otherwise be an attribute\n self.dispatchMemberFunction(t.func, t) \n self.write(\"(\")\n self._CallArguments(t)\n self.write(\")\")"],"string":"[\n \"def _execute(self, op, time):\\n raise NotImplementedError\",\n \"def do_run(calc, a=int, b=int):\\n of = calc.oper_func\\n calc.log.msg(10, 'Calculating', operation=of, a=a, b=b)\\n res = getattr(operator, of, calc.not_found)(a, b)\\n calc.log.msg(20, 'Returning', result=res)\\n return res\",\n \"def __call__(fun_name):\",\n \"def math(oper):\\n a=int(request.args.get('a'))\\n b=int(request.args.get('b'))\\n result = math_oper[oper](a,b)\\n return str(result)\",\n \"def get_func(op):\\n if op == \\\"-e\\\":\\n return func\\n elif op == \\\"-d\\\":\\n return unfunc\",\n \"def run(self, in_op):\\n raise NotImplementedError\",\n \"def _support_op(*args):\\n def inner(func):\\n for one_arg in args:\\n _op_mapping_[one_arg] = func\\n return func\\n\\n return inner\",\n \"def _op_easy(self, op, reg_list, param_list=None): # pylint: disable-msg=invalid-name\\n\\n has_op = hasattr(self.circuit, op)\\n\\n if has_op:\\n if param_list:\\n # DEBUG\\n # print(\\\"********** op {} param_list {} reg_list {}\\\".format(op, param_list, reg_list)) # pylint: disable-msg=line-too-long\\n # END-DEBUG\\n getattr(self.circuit, op)(*param_list, *reg_list)\\n else:\\n getattr(self.circuit, op)(*reg_list)\\n\\n return has_op\",\n \"def fun(op, v1, v2):\\n if op == '+':\\n return v1+v2\\n elif op == '-':\\n return v1-v2\\n elif op == '*':\\n return v1*v2\\n elif op == '/':\\n return v1\",\n \"def do_oprn(self, *args, operator=None, **kwargs):\\n\\t\\tself.operator = operator\\n\\n\\t\\tif not self.operator:\\n\\t\\t\\treturn f'No operator provided'\\n\\n\\t\\tif self.operator == '+':\\n\\t\\t\\treturn self.sum(*args, **kwargs)\\n\\t\\telif self.operator == '-':\\n\\t\\t\\treturn self.subtract(*args, **kwargs)\\n\\t\\telif self.operator == '*':\\n\\t\\t\\treturn self.multiple(*args, **kwargs)\\n\\t\\telif self.operator == '/':\\n\\t\\t\\treturn self.division(*args, **kwargs)\\n\\t\\telse:\\n\\t\\t\\treturn f'Currently Operator ({operator}) is not Applicable'\",\n \"def operate(\\n self, op: OperatorType, *other: Any, **kwargs: Any\\n ) -> Operators:\\n raise NotImplementedError(str(op))\",\n \"def _process_operator(self, expr, operator, func, *args, **kwargs):\\n for elt in self.model.xml_element_children(expr):\\n self._process_operator(elt, operator, func, *args, **kwargs)\\n if isinstance(expr, mathml_apply) and expr.operator().localName == operator:\\n func(expr, *args, **kwargs)\",\n \"def __call__(self, tf_node, input_ops):\\n op_name = tf_node.op\\n\\n # if op not handled, gets -1\\n ng_op = getattr(self, op_name, None)\\n\\n if ng_op:\\n return ng_op(tf_node, input_ops)\\n else:\\n # ignored op set to None\\n print(tf_node.name, \\\"ignored.\\\")\\n return None\",\n \"def calc(a,b,operation):\\n\\n if operation == 'product':\\n return a * b\\n else:\\n return a + b\",\n \"def doOperation(op, list, var, sortkey):\\r\\n \\r\\n \\r\\n func = itemgetter(sortkey)\\r\\n \\r\\n if op == 'max':\\r\\n return max(list, key=func)\\r\\n elif op == 'min':\\r\\n return min(list, key=func)\\r\\n elif op == 'sort asc':\\r\\n return sorted(list, key=func)\\r\\n elif op == 'sort desc':\\r\\n return sorted(list, key=func, reverse=True)\\r\\n else:\\r\\n print 'Error: Bad input operation in doOpertion'\\r\\n return list\",\n \"def perform_operation(operator, num_1, num_2):\\n\\n if operator == \\\"*\\\":\\n return num_1 * num_2\\n if operator == \\\"+\\\":\\n return num_1 + num_2\\n if operator == \\\"-\\\":\\n return num_1 - num_2\\n if operator == \\\"/\\\":\\n return num_1 / num_2\",\n \"def operation_func():\\n\\n operation_input = input(\\\"\\\\nPlease type in the math operation you would like to complete:\\\\n+ for addition\\\\n- for subtraction\\\\n* for multiplication\\\\n/ for division : \\\")\\n operation_list = [\\\"+\\\", \\\"-\\\", \\\"*\\\", \\\"/\\\"]\\n if not operation_input in operation_list: \\n print('\\\\nYou have not typed a valid operator!!! \\\\nPlease enter the math operation again.')\\n operation_func()\\n\\n return calculate_func(operation_input)\",\n \"def parse_logic(self, logic):\\n if 'xxx' in logic.conf:\\n # self.function(logic['name'])\\n pass\",\n \"def runOperation(operation, num1, num2):\\n if operation == 1 or operation == '+':\\n print(add(num1, num2))\\n elif operation == 2 or operation == '-':\\n print(sub(num1, num2))\\n elif operation == 3 or operation == '*':\\n print(mul(num1, num2))\\n elif operation == 4 or operation == '/':\\n print(div(num1, num2))\\n else:\\n print(\\\"I don't understand\\\")\",\n \"def exec_function(self, args):\\n raise NotImplementedError()\",\n \"def execute(actual, forecast, op_exec):\\n # pass\\n\\n return_value = None\\n if op_exec == \\\"MSE\\\":\\n return_value = viz.MSE(actual, forecast)\\n elif op_exec == \\\"RMSE\\\":\\n return_value = viz.RMSE(actual, forecast)\\n elif op_exec == \\\"MAPE\\\":\\n return_value = viz.MAPE(actual, forecast)\\n elif op_exec == \\\"sMAPE\\\":\\n return_value = viz.sMAPE(actual, forecast)\\n return return_value\",\n \"def execute_operation(self, frac1, operator, frac2):\\n try:\\n ops = {\\\"+\\\": (lambda x, y: Frac(x) + Frac(y)), \\\"-\\\": (lambda x, y: Frac(x) - Frac(y)), \\\"*\\\":\\n (lambda x, y: Frac(x) * Frac(y)), \\\"/\\\": (lambda x, y: Frac(x) / Frac(y))}\\n if operator in ops:\\n return str(ops[operator](frac1, frac2))\\n else:\\n print(self.operator_not_Valid)\\n return False\\n except Exception as e:\\n print(self.operator_not_Valid, e)\\n return False\",\n \"def execute(input_file, op_exec):\\n # pass\\n # input_file = viz.read_matrix(input_file)\\n input_file = viz.csv_to_ts(input_file)\\n input_file = prep.impute_missing_data(input_file)\\n input_file = prep.impute_outliers(input_file)\\n\\n return_value = None\\n if op_exec == \\\"plot\\\":\\n return_value = viz.plot(input_file)\\n elif op_exec == \\\"histogram\\\":\\n return_value = viz.histogram(input_file)\\n elif op_exec == \\\"summary\\\":\\n return_value = viz.summary(input_file)\\n elif op_exec == \\\"box_plot\\\":\\n return_value = viz.box_plot(input_file)\\n elif op_exec == \\\"shapiro_wilk\\\":\\n return_value = viz.shapiro_wilk(input_file)\\n elif op_exec == \\\"d_agostino\\\":\\n return_value = viz.d_agostino(input_file)\\n elif op_exec == \\\"anderson_darling\\\":\\n return_value = viz.anderson_darling(input_file)\\n elif op_exec == \\\"qq_plot\\\":\\n return_value = viz.qq_plot(input_file)\\n return return_value\",\n \"def exec_builtin(self, cmd):\\r\\n func = Builtin.builtins.get(cmd[0])\\r\\n if func is None:\\r\\n return False\\r\\n func(self, cmd)\\r\\n return True\",\n \"def operation(code, val1, val2):\\n if code == 1:\\n return val1 + val2\\n elif code == 2:\\n return val1 * val2\\n elif code == 99:\\n return -1\",\n \"def list_operations():\",\n \"def get_func(self, class_name, arg, stored_objects):\\n find_func = re.match('([a-z]+)', arg) # returns a matching object\\n func_name = find_func.group()\\n args = re.findall('\\\"([^\\\"]+)\\\",?', arg) # return a list of arguments\\n\\n if len(args) == 0:\\n if func_name == \\\"all\\\":\\n self.all(class_name, stored_objects)\\n elif func_name == \\\"count\\\":\\n self.count(class_name, stored_objects)\\n else:\\n print(\\\"** instance id missing **\\\")\\n\\n elif len(args) == 1:\\n if self.check_instance(class_name, args[0], stored_objects):\\n if func_name == \\\"show\\\":\\n self.show(class_name, args[0], stored_objects)\\n elif func_name == \\\"destroy\\\":\\n self.destroy(class_name, args[0], stored_objects)\\n elif func_name == \\\"update\\\":\\n print(\\\"** attribute name missing **\\\")\\n\\n elif len(args) == 2 and func_name == \\\"update\\\":\\n print(\\\"** value missing **\\\")\\n\\n elif len(args) == 3 and func_name == \\\"update\\\":\\n if self.check_instance(class_name, args[0], stored_objects):\\n self.update(class_name, args, stored_objects)\",\n \"def eval(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def call(self, data):\\n\\t\\treturn self.fnc(data)\",\n \"def get_op(ring_size: int, op_str: str) -> Callable[..., Any]:\\n ops = RING_SIZE_TO_OP.get(ring_size, None)\\n\\n if ops is None:\\n raise ValueError(f\\\"Do not have operations for ring size {ring_size}\\\")\\n\\n op = ops.get(op_str, None)\\n if op is None:\\n raise ValueError(\\n f\\\"Operator {op_str} does not exist for ring size {ring_size}\\\"\\n )\\n\\n return op\",\n \"def cmd_calculation():\",\n \"def eval(*args, **kwargs):\\n\\n pass\",\n \"def _call_unprocessed(\\n self,\\n x: np.ndarray,\\n sensi_orders: Tuple[int, ...],\\n mode: str\\n ) -> Dict:\\n if mode == MODE_FUN:\\n result = self._call_mode_fun(x, sensi_orders)\\n elif mode == MODE_RES:\\n result = self._call_mode_res(x, sensi_orders)\\n else:\\n raise ValueError(\\\"This mode is not supported.\\\")\\n return result\",\n \"def __compile_operator(self, op, caller):\\r\\n if op == \\\"+\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"add\\\")\\r\\n elif op == \\\"-\\\" and caller == \\\"expression\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"sub\\\")\\r\\n elif op == \\\"*\\\":\\r\\n self.__vmwriter.write_call(\\\"Math.multiply\\\", 2)\\r\\n elif op == \\\"/\\\":\\r\\n self.__vmwriter.write_call(\\\"Math.divide\\\", 2)\\r\\n elif op == \\\"&\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"and\\\")\\r\\n elif op == \\\"|\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"or\\\")\\r\\n elif op == \\\"<\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"lt\\\")\\r\\n elif op == \\\">\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"gt\\\")\\r\\n elif op == \\\"=\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"eq\\\")\\r\\n elif op == \\\"-\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"neg\\\")\\r\\n elif op == \\\"~\\\":\\r\\n self.__vmwriter.write_arithmetic(\\\"not\\\")\",\n \"def _invoke_cmd(self, cmd):\\n if cmd in self.COMMANDS:\\n self.COMMANDS[cmd]()\\n else:\\n print(ERROR_UNKNOWN_COMMAND.format(cmd=cmd))\",\n \"def exec(self,**kwargs):\\r\\n pass\",\n \"def _calc2call(func):\\n def _converter(inp, *x):\\n if func.n_inputs == 1:\\n retvals = func.evaluate(x[0], *inp)\\n else:\\n retvals = func.evaluate(x[0], x[1], *inp)\\n return retvals\\n return _converter\",\n \"def execute(self, i: dict) -> None:\\n # execute instruction for instruction processor\\n if i[\\\"recipient\\\"] == \\\"proc\\\":\\n if self.suffixes_condition[i[\\\"suf\\\"]]():\\n if i[\\\"type\\\"] == \\\"proc_alu\\\":\\n # TODO: cmpi_ fix\\n i[\\\"func\\\"](self.regs[i[\\\"dest\\\"]], self.regs[i[\\\"op1\\\"]], self.regs[i[\\\"dest\\\"]], self.regs[\\\"flags\\\"])\\n elif i[\\\"type\\\"] == \\\"proc_mem_flags\\\" or i[\\\"func\\\"] == movi_:\\n i[\\\"func\\\"](self.regs[i[\\\"dest\\\"]], self.regs[i[\\\"op1\\\"]], self.regs[\\\"flags\\\"])\\n elif i[\\\"type\\\"] == \\\"proc_mem_num\\\":\\n i[\\\"func\\\"](self.regs[i[\\\"dest\\\"]], i[\\\"op1\\\"], self.regs[\\\"flags\\\"])\\n elif i[\\\"type\\\"] == \\\"proc_mem_suffix\\\":\\n i[\\\"func\\\"](self.regs[i[\\\"dest\\\"]], self.regs[i[\\\"op1\\\"]], self.__RAM, self.regs[\\\"flags\\\"])\\n elif i[\\\"type\\\"] == \\\"jumps\\\":\\n i[\\\"func\\\"](self.regs[\\\"ip\\\"], self.regs[i[\\\"dest\\\"]], self.regs[\\\"flags\\\"])\\n else:\\n for core in self.__cores:\\n core.execute(i)\",\n \"def _execute_op(self, op):\\n if op.op_type == Operation.Type.GET:\\n if op.key in self._store:\\n return Result.OK, self._store[op.key]\\n else:\\n return Result.NOT_FOUND, \\\"\\\"\\n elif op.op_type == Operation.Type.PUT:\\n self._store[op.key] = op.value\\n return Result.OK, \\\"\\\"\\n elif op.op_type == Operation.Type.DEL:\\n self._store.pop(op.key, None)\\n return Result.OK, \\\"\\\"\\n else:\\n raise ValueError(\\\"Invalid operation type\\\")\",\n \"def _execute_op(self, op):\\n if op.op_type == Operation.Type.GET:\\n if op.key in self._store:\\n return Result.OK, self._store[op.key]\\n else:\\n return Result.NOT_FOUND, \\\"\\\"\\n elif op.op_type == Operation.Type.PUT:\\n self._store[op.key] = op.value\\n return Result.OK, \\\"\\\"\\n elif op.op_type == Operation.Type.DEL:\\n self._store.pop(op.key, None)\\n return Result.OK, \\\"\\\"\\n else:\\n raise ValueError(\\\"Invalid operation type\\\")\",\n \"def test(self):\\n self.eval()\",\n \"def getCallable():\",\n \"def __call__(self, node, operations, last_operation):\\n if last_operation == NO_OPERATION:\\n return 0\\n return 1\",\n \"def _control_predefined(operation, num_ctrl_qubits):\\n if operation.name == 'x' and num_ctrl_qubits in [1, 2]:\\n if num_ctrl_qubits == 1:\\n import qiskit.extensions.standard.cx\\n cgate = qiskit.extensions.standard.cx.CnotGate()\\n else:\\n import qiskit.extensions.standard.ccx\\n cgate = qiskit.extensions.standard.ccx.ToffoliGate()\\n elif operation.name == 'y':\\n import qiskit.extensions.standard.cy\\n cgate = qiskit.extensions.standard.cy.CyGate()\\n elif operation.name == 'z':\\n import qiskit.extensions.standard.cz\\n cgate = qiskit.extensions.standard.cz.CzGate()\\n elif operation.name == 'h':\\n import qiskit.extensions.standard.ch\\n cgate = qiskit.extensions.standard.ch.CHGate()\\n elif operation.name in {'rx', 'ry', 'rz'}:\\n if operation.name == 'rx':\\n import qiskit.extensions.standard.crx\\n cgate = qiskit.extensions.standard.crx.CrxGate(*operation.params)\\n elif operation.name == 'ry':\\n import qiskit.extensions.standard.cry\\n cgate = qiskit.extensions.standard.cry.CryGate(*operation.params)\\n else: # operation.name == 'rz'\\n import qiskit.extensions.standard.crz\\n cgate = qiskit.extensions.standard.crz.CrzGate(*operation.params)\\n if num_ctrl_qubits == 1:\\n return cgate\\n else:\\n # only predefined for one control qubit\\n return cgate.control(num_ctrl_qubits - 1)\\n elif operation.name == 'swap':\\n import qiskit.extensions.standard.cswap\\n cgate = qiskit.extensions.standard.cswap.FredkinGate()\\n elif operation.name == 'u1':\\n import qiskit.extensions.standard.cu1\\n cgate = qiskit.extensions.standard.cu1.Cu1Gate(*operation.params)\\n elif operation.name == 'u3':\\n import qiskit.extensions.standard.cu3\\n cgate = qiskit.extensions.standard.cu3.Cu3Gate(*operation.params)\\n elif operation.name == 'cx':\\n import qiskit.extensions.standard.ccx\\n cgate = qiskit.extensions.standard.ccx.ToffoliGate()\\n else:\\n raise QiskitError('No standard controlled gate for \\\"{}\\\"'.format(\\n operation.name))\\n return cgate\",\n \"def execute(arr, i, alg, pointer):\\r\\n\\r\\n # Get the current command:\\r\\n order = alg[pointer]\\r\\n\\r\\n ####################### Order Selector #######################\\r\\n\\r\\n if order == '>': # > : Move forward:\\r\\n return move_forward(arr, i, alg, pointer)\\r\\n\\r\\n elif order == '<': # \\\"<\\\" : Move backward:\\r\\n return move_backward(arr, i, alg, pointer)\\r\\n\\r\\n elif order == '+': # \\\"+\\\" : Increase value in the current cell by 1:\\r\\n return increase_value(arr, i, alg, pointer)\\r\\n\\r\\n elif order == '-': # \\\"-\\\" : Decrease value in the current cell by 1:\\r\\n return decrease_value(arr, i, alg, pointer)\\r\\n\\r\\n elif order == '.': # \\\".\\\" : Convert ASCII code from current cell into symbol and print:\\r\\n return print_value(arr, i, alg, pointer)\\r\\n\\r\\n elif order == ',': # \\\",\\\" : Get input form user and save it in the current cell:\\r\\n return save_value(arr, i, alg, pointer)\\r\\n\\r\\n elif order == '[': # \\\"[\\\" : Start of cycle:\\r\\n return start_cycle(arr, i, alg, pointer)\\r\\n\\r\\n\\r\\n elif order == ']': # \\\"]\\\" : End of cycle:\\r\\n return end_cycle(arr, i, alg, pointer)\",\n \"def eval_ops(opcodes):\\n output = []\\n for op in opcodes:\\n if op in [\\\"+\\\", \\\"*\\\"]:\\n b = output.pop(-1)\\n a = output.pop(-1)\\n value = ops[op](a, b)\\n output.append(value)\\n else:\\n output.append(op)\\n\\n assert len(output) == 1\\n return output[0]\",\n \"def generic_execute(self, query, suffix, **arguments) -> Any:\\n query_name = self.reverse_queries[query]\\n self.queries.append((query_name, arguments))\\n return getattr(self, f\\\"{query_name}_{suffix}\\\")(**arguments)\",\n \"def dynamic_comparison(v1, op, v2):\\n assert op in ['gt', 'lt']\\n\\n operator_map = {'gt': operator.gt,\\n 'lt': operator.lt}\\n\\n return operator_map[op](v1, v2)\",\n \"def eval(self, i, node, fn):\\r\\n if self.pre_func is not None:\\r\\n self.pre_func(i, node, fn)\\r\\n fn()\\r\\n if self.post_func is not None:\\r\\n self.post_func(i, node, fn)\",\n \"def replace_operators_by_calls(topconstruct, opname, call, call_id_construct):\\n # find all computations\\n for computation in query([is_computation], TreeItem(topconstruct)):\\n replace_op_by_call(computation.construct, opname, call, call_id_construct)\",\n \"def _call(self, args):\\n a = args.split(' ', 1)\\n if a:\\n getattr(self, a[0])(*a[1:])\",\n \"def run_operator(scope_node, node, name, op, code, f_globals):\\n operators = __get_operators()\\n if op not in operators:\\n raise TypeError(\\\"failed to load operator '%s'\\\" % op)\\n scope_key = scope_node.scope_key\\n pair = operators[op](code, scope_key, f_globals)\\n if isinstance(name, tuple):\\n # The template inst binding with a single name will take this\\n # path by using a length-1 name tuple. See bug #78.\\n bind_extended_member(node, name, pair, scope_key)\\n else:\\n item = getattr(node.klass, name, None)\\n if isinstance(item, Alias):\\n bind_aliased_member(node, name, item, pair, scope_key)\\n else:\\n # This is the path for a standard binding on a child def.\\n # It does not need the closure scope key. See bug #78.\\n bind_member(node, name, pair)\",\n \"def _binaryop(self, other, op: str):\\n raise NotImplementedError\",\n \"def testOperation(self):\\n gen = self.gen\\n prof = self.profile\\n\\n # Try the direct evaluation\\n gen.operation()\\n self.assertTrue(array_equal(prof.x, prof.ycalc))\\n\\n # Try evaluation through __call__\\n gen(prof.x)\\n self.assertTrue(array_equal(prof.x, prof.ycalc))\\n return\",\n \"def _call_op_sugar(self, op_idx, *args):\\n if not all(isinstance(a, six.integer_types) for a in args):\\n raise TypeError('All args passed to call_op must be integers '\\n '(LoomResult ids.) Did you forget to call constant?')\\n result = self._weaver.CallOp(op_idx, args)\\n if not result:\\n raise AssertionError('Weaver op call failed: %s' %\\n self._weaver.error_string())\\n if len(result) == 1:\\n return result[0]\\n return result\",\n \"def apply_gate_operation(self, operation:[str, cirq.Gate], qubit_expr, params:Optional[Dict]=None):\\n gate_ops = self.get_gate_operation(operation, qubit_expr, params)\\n strategy = self.insert_strategy\\n self.append(gate_ops, strategy=strategy)\",\n \"def __set_operation_function(self):\\n if self.operation_function is not None:\\n return self.operation_function\\n else:\\n self.operation_function = symm_eval\",\n \"def _arithmetize2(self, left: Any, right: Any, op: str) -> Any:\\n op_func = getattr(operator, op)\\n left, right = _recycle_left_right(left, right)\\n return op_func(left, right)\",\n \"def func():\",\n \"def _execute(self, _):\\r\\n pass\",\n \"def __exec_function_by_code(self,command,*args):\\n\\t\\tself.__printer(\\\"Preparing to execute function: {0}\\\".format(command),level=LL_DEBUG)\\n\\t\\t\\n\\t\\tif command is None:\\n\\t\\t\\treturn\\n\\t\\t\\t\\n\\t\\tcmd_exec = Commands()\\n\\t\\tif command not in cmd_exec.command_list:\\n\\t\\t\\treturn\\n\\t\\t\\n\\t\\tif args:\\n\\t\\t\\tvalid_params = cmd_exec.validate_args(command,*args)\\n\\t\\telse:\\n\\t\\t\\tvalid_params = None\\n\\t\\t\\t\\t\\n\\t\\t# Check if we have an event for this..\\n\\t\\tif self.event_mode_command:\\n\\t\\t\\n\\t\\t\\tfor emc in self.event_mode_command:\\n\\t\\t\\t\\tif command in emc['command']:\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# TODO, check if a required mode is specified\\n\\t\\t\\t\\t\\t#if any(x in new_active_modes for x in emc['modes']):\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# HIT!\\n\\t\\t\\t\\t\\tprint \\\"DEBUG EVENT-MODE HIT!\\\"\\n\\t\\t\\t\\t\\tprint emc\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t \\\"name\\\": \\\"mode_track\\\"\\n\\t\\t\\t\\t\\t, \\\"type\\\": \\\"mode_change\\\"\\n\\t\\t\\t\\t\\t, \\\"modes\\\": [ \\\"track\\\" ]\\n\\t\\t\\t\\t\\t, \\\"device\\\": \\\"rgb_1\\\"\\n\\t\\t\\t\\t\\t, \\\"pattern\\\": \\\"on\\\"\\n\\t\\t\\t\\t\\t, \\\"rgb\\\": \\\"#ff0000\\\"\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\trgb_dev = self.get_device_config(emc['device'])\\n\\t\\t\\t\\t\\tpin_r = rgb_dev['r']\\n\\t\\t\\t\\t\\tpin_g = rgb_dev['g']\\n\\t\\t\\t\\t\\tpin_b = rgb_dev['b']\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# ignore pattern for now..\\n\\t\\t\\t\\t\\t#turn on rgb_1, using ff0000\\n\\t\\t\\t\\t\\tself.gpio.pwm_rgb(pin_r,pin_g,pin_b,emc['rgb'])\\n\\t\\t\\t\\t\\t\\n\\t\\tif callable(self.callback_function):\\n\\t\\t\\tprint \\\"-> Calling callback\\\"\\t#blocking ??? yeah, probably..\\n\\t\\t\\tself.callback_function(command,valid_params)\\n\\t\\t\\t\\n\\t\\t# return to original led color\",\n \"def test_custom_order_exec(self):\\n self.call_idx = 0\\n class Mock(object):\\n def __init__(self, order):\\n self.order = order\\n self.execd = -1\\n\\n def evolve(self1, t, dt):\\n self1.execd = self.call_idx\\n self.call_idx += 1\\n\\n G1 = Mock(3)\\n G2 = Mock(-1)\\n G3 = Mock(2)\\n sim = simulation.Simulation(G1, G2, G3)\\n sim.run(sim.dt)\\n\\n self.assertEqual(G1.execd, 2)\\n self.assertEqual(G2.execd, 0)\\n self.assertEqual(G3.execd, 1)\",\n \"def execute(train_ts, train_inputs, test_inputs, op_exec):\\n if op_exec == \\\"mlp\\\":\\n model = mod.mlp_model()\\n model.fit(train_inputs, train_ts)\\n forecast = model.forecast(test_inputs)\\n return forecast\\n elif op_exec == \\\"rf\\\":\\n model = mod.rf_model()\\n model.fit(train_inputs, train_ts)\\n forecast = model.forecast(test_inputs)\\n return forecast\",\n \"def run(self):\\n self._validate_inputs()\\n if self._destination_out_exists:\\n print('Destination{} already skipping run'.format(self._inputs[-1]))\\n try:\\n if self.operator_func:\\n self.operator_func(*self._inputs)\\n else:\\n raise RuntimeError('self.operator_func is not defined !!')\\n except Exception as ex:\\n print(ex)\\n raise RuntimeError('self.operator_func failed !!')\",\n \"def _generate_cmp_invocation(self, op, lineno, offset):\\n to_insert = []\\n start_offset = offset\\n const_atheris = self._get_const(sys.modules[TARGET_MODULE])\\n name_cmp = self._get_name(COMPARE_FUNCTION)\\n const_op = self._get_const(op)\\n const_pc = self._get_pc()\\n const_False = self._get_const(False)\\n\\n to_insert.append(\\n Instruction(lineno, offset, dis.opmap[\\\"LOAD_CONST\\\"], const_atheris))\\n offset += to_insert[-1].get_size()\\n to_insert.append(\\n Instruction(lineno, offset, dis.opmap[\\\"LOAD_ATTR\\\"], name_cmp))\\n offset += to_insert[-1].get_size()\\n to_insert.append(Instruction(lineno, offset, dis.opmap[\\\"ROT_THREE\\\"]))\\n offset += to_insert[-1].get_size()\\n to_insert.append(\\n Instruction(lineno, offset, dis.opmap[\\\"LOAD_CONST\\\"], const_op))\\n offset += to_insert[-1].get_size()\\n to_insert.append(\\n Instruction(lineno, offset, dis.opmap[\\\"LOAD_CONST\\\"], const_pc))\\n offset += to_insert[-1].get_size()\\n to_insert.append(\\n Instruction(lineno, offset, dis.opmap[\\\"LOAD_CONST\\\"], const_False))\\n offset += to_insert[-1].get_size()\\n to_insert.append(Instruction(lineno, offset, dis.opmap[\\\"CALL_FUNCTION\\\"], 5))\\n offset += to_insert[-1].get_size()\\n\\n return offset - start_offset, to_insert\",\n \"def call_lua_op(op_name, *args):\\n\\n # convert the python objects to lua\\n args_table = util.pack_args(args)\\n try:\\n func = executor.eval_lua(q_consts.lua_op_fn_str)\\n result = func(op_name, args_table)\\n except Exception as e:\\n # TODO: Handle operator level failures properly\\n print(str(e))\\n result = None\\n if result:\\n # wrap the lua objects to python\\n result = util.wrap_output(op_name, result)\\n return result\",\n \"def callFunction(cmdname, far_args, far_kwargs, fn):\\n ret = None\\n if fn not in registered_functions:\\n print(\\\"%s is not a registered function!\\\" % fn)\\n return None\\n try:\\n funct = registered_functions[fn]\\n ret = funct(*far_args,**far_kwargs)\\n except:\\n print(\\\"Error ocurred executing %s\\\" % fn)\\n print(traceback.format_exc())\\n \\n return ret\",\n \"def __call__(self, *args, **kw):\\r\\n debug.write(\\\"[SourceRPG] Executing the function %s within the CommandsDatabase\\\" % self.function, 4)\\r\\n vars(CommandsDatabase)[self.function](self.instance, *args, **kw)\",\n \"def op_to_function(self, op):\\n name = op.__class__.__name__.lower()\\n name = operator_table.get(name, name)\\n return to_attribute(self.operator, name)\",\n \"def __call__(self):\\n # apply(self.func, self.args)\\n self.func(*self.args)\",\n \"def _pull_argops(op_dict):\\n import inspect\\n out = []\\n keys = op_dict.keys()\\n keys.sort() # Not necessary, but makes scanning the printout easier\\n for k in keys:\\n # Create a dictionary that will be used to fill the 'code' template\\n d = {}\\n d[\\\"enum_name\\\"] = enum_name = op_dict[k][3:] # <NAME>\\n d[\\\"funct_name\\\"] = \\\"%s\\\" % enum_name.lower() # <name>\\n class_name = \\\"%s4args\\\" % enum_name\\n klass = getattr(_type, class_name, None)\\n if klass is None:\\n # This operation takes no arguments\\n d[\\\"funct_args\\\"] = d[\\\"create_args\\\"] = d[\\\"set_args\\\"] = \\\"\\\"\\n else:\\n if type(klass) is dict:\\n arg_list = \\\"enum_value\\\"\\n d[\\\"create_args\\\"] = \\\"args = enum_value\\\"\\n else:\\n arg_list = \\\", \\\".join(inspect.getargspec(klass.__init__)[0][1:])\\n d[\\\"create_args\\\"] = \\\"args = _type.%s(%s)\\\" % (class_name, arg_list)\\n d[\\\"funct_args\\\"] = arg_list\\n if enum_name.startswith(\\\"CB_\\\"):\\n d[\\\"set_args\\\"] = \\\"opcb%s=args\\\" % enum_name.lower()[3:]\\n else:\\n d[\\\"set_args\\\"] = \\\"op%s=args\\\" % enum_name.lower()\\n if enum_name.startswith(\\\"CB_\\\"):\\n d[\\\"argop\\\"] = \\\"nfs_cb_argop4\\\"\\n else:\\n d[\\\"argop\\\"] = \\\"nfs_argop4\\\"\\n out.append(d)\\n return out\",\n \"def _command_operation(self, operation):\\r\\n if self.current_number != '0' and\\\\\\r\\n (self.current_operation is not None) and\\\\\\r\\n self.storage_number != '0':\\r\\n # check if user already selected a operation\\r\\n # if it is, do the equal function once and display\\r\\n # the resolve\\r\\n self._command_equal()\\r\\n self.current_operation = operation\\r\\n else:\\r\\n # else storage the operation\\r\\n self.storage_number = self.current_number\\r\\n self.current_operation = operation\\r\\n self.current_number = '0'\",\n \"def __or__(self, func):\\n return self.bind(func)\",\n \"def call(self, func):\\n\\t\\targs = tuple(self.__dict__.values())\\n\\t\\ttry:\\n\\t\\t\\treturn eval(\\\"func\\\" + str(args))\\n\\t\\texcept Exception, e:\\n\\t\\t\\traise ValueError(\\\"Given Function is not valid for calling: %s\\\" % e)\",\n \"def execute(self, expr, mode = \\\"exec\\\"):\\r\\n return self.sync_request(\\\"handle_execute\\\", expr, mode)\",\n \"def execute_binary_operator(cls, val, x, y):\\n\\n\\t\\tif val == 0:\\n\\t\\t\\treturn operator.add(x,y)\\n\\t\\telif val == 1:\\n\\t\\t\\treturn operator.sub(x,y)\\n\\t\\telif val == 2:\\n\\t\\t\\treturn operator.mul(x,y)\\n\\t\\telif val == 3:\\n\\t\\t\\treturn operator.div(x,y)\\n\\t\\telif val == 4:\\n\\t\\t\\treturn operator.lt(x,y)\\n\\t\\telif val == 5:\\n\\t\\t\\treturn operator.gt(x,y)\\n\\t\\telif val == 6:\\n\\t\\t\\treturn operator.le(x,y)\\n\\t\\telif val == 7:\\n\\t\\t\\treturn operator.ge(x,y)\\n\\t\\telif val == 8:\\n\\t\\t\\treturn operator.eq(x,y)\\n\\t\\telif val == 9:\\n\\t\\t\\treturn operator.ne(x,y)\\n\\t\\telif val == 12:\\n\\t\\t\\treturn operator.mod(x,y)\",\n \"def dispatch(ch):\\n\\n if(ch == \\\"1\\\"):\\n b1()\\n elif(ch == \\\"2\\\"):\\n b2()\\n elif(ch == \\\"3\\\"):\\n b3()\\n else:\\n print(\\\"Error: Invalid Option\\\")\",\n \"def c_action(option,opt_str,value,parser):\\n #print \\\"Processing %s '%s'\\\" % (opt_str,value)\\n exec value in __main__.__dict__\\n global something_executed\\n something_executed=True\",\n \"def _call_impl(self, t):\\n return self.interp(t)\",\n \"def _execute_conditional(self, cond, series, ds):\\n\\n ret = None\\n func = cond['function']\\n if func not in ['std', 'mean', 'value']:\\n self.debug('invalid function. \\\"{}\\\"'.format(func))\\n return\\n\\n attr = cond['attribute']\\n action = cond.get('action', 'cancel')\\n atypes = cond.get('analysis_types', None)\\n bin_hours = cond.get('bin_hours', 6)\\n\\n tolerance_seconds = 60 * 60 * bin_hours\\n dd = ds > tolerance_seconds\\n bounds = where(dd)[0]\\n itemidx = bounds[-1] if bounds else 0\\n\\n if atypes:\\n series = [si for si in series if si['analysis_type'] in atypes]\\n\\n series_v = [si[attr] for si in series[itemidx:] if attr in si]\\n\\n if func == 'value':\\n if series_v[-1] != series_v[-2]:\\n ret = action\\n else:\\n minx = cond.get('min_n', 10)\\n if len(series) <= minx:\\n return\\n\\n x = array(series_v)\\n if func == 'std':\\n x = x.std()\\n elif func == 'mean':\\n x = x.mean()\\n\\n comp = cond['comparison']\\n if eval(comp, {'x': x}):\\n ret = action\\n\\n return ret\",\n \"def execute():\",\n \"def parse_operation(self, data, ip):\\n json_decoded = json.loads(data)\\n op = json_decoded['OPERATION']\\n if op in self._callbacks:\\n self.logger.info(\\\"Got Operation: \\\" + op)\\n self._callbacks[op](json_decoded, ip)\\n else:\\n self.logger.error(\\\"Unknown operation\\\")\",\n \"def _operators_conductor(operator_name, _bool=None):\\n func = getattr(Series, operator_name)\\n if _bool is None:\\n # return bool series.\\n _pre, _post = bool, bool\\n else:\\n # return ints.\\n _pre, _post = int, int\\n\\n @wraps(func)\\n def operator_method(self, other=None):\\n if other is None:\\n # for unary such as pos, neg, invert\\n def not_(df: dF):\\n return func(df.pipe(self.copy().pop())).apply(_post)\\n\\n return not_\\n\\n # if not isinstance(other, Condition):\\n # raise TypeError(\\\"only conditions can add, got %r\\\" % type(other))\\n\\n def comb(df: dF) -> Series:\\n return func(df.pipe(self).apply(_pre), df.pipe(other).apply(_pre)).apply(_post)\\n\\n return comb\\n\\n return operator_method\",\n \"def RunOperator(op_def):\\n RunOperatorCC(_stringify_proto(op_def))\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def eval(self):\\n pass\",\n \"def eval(self):\\n pass\",\n \"def eval(self):\\n pass\",\n \"def fetch_operators_function(self, operator):\\n operators_function = self.operators_dict[operator]['function']\\n return operators_function\",\n \"def operation(self):\\n pass\",\n \"def RunOperators(ops_def):\\n RunOperatorsCC([_stringify_proto(op_def) for op_def in ops_def])\",\n \"def _compose_polars_ops(self, op: data_algebra.data_ops_types.OperatorPlatform, *, data_map: Dict[str, Any]):\\n assert isinstance(op, data_algebra.data_ops_types.OperatorPlatform)\\n assert isinstance(data_map, Dict)\\n res = self._method_dispatch_table[op.node_name](op=op, data_map=data_map)\\n return res\",\n \"def calculator(): \\n\\n #asks for user's input \\n user_input = raw_input(\\\"Type in the math expression and two numbers like + 1 2 : \\\")\\n #splits the user's input into a list\\n math_op = user_input.split(\\\" \\\")\\n\\n #pulls the appropriate function based on the user's input\\n if math_op[0] == '+':\\n print add(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '-':\\n print subtract(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '*':\\n print multiply(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == '/':\\n print divide(int(math_op[1]), int(math_op[2]))\\n \\n elif math_op[0] == \\\"square\\\":\\n print square(int(math_op[1]))\\n\\n elif math_op[0] == 'cube':\\n print cube(int(math_op[1]))\\n\\n elif math_op[0] == 'pow':\\n print power(int(math_op[1]), int(math_op[2]))\\n\\n elif math_op[0] == 'mod':\\n print mod(int(math_op[1]), int(math_op[2]))\\n\\n else:\\n print \\\"That is not a valid input. Please try any of the following operator: + - * / square cube pow mod.\\\"\",\n \"def __call__(self, approach):\\n return self.op(self.get(approach), self.value)\",\n \"def _arithmetize1(self, operand: Any, op: str) -> Any:\\n op_func = getattr(operator, op)\\n # Data length might be changed after evaluation\\n # operand = recycle_value(operand, self.data.shape[0])\\n return op_func(operand)\",\n \"def _Call(self, t):\\n # check calls but let attributes check in their own dispatcher\\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\\n if isinstance(t.func, ast.Name):\\n if (t.func.id not in funcs):\\n self.RaiseWarning(t, \\\"Function call is not a defined FLAME GPU device function or a supported python built in.\\\")\\n # dispatch even if warning raised\\n self.dispatch(t.func)\\n elif isinstance(t.func, ast.Lambda):\\n self.dispatch(t.func) # not supported\\n else:\\n # special handler for dispatching member function calls\\n # This would otherwise be an attribute\\n self.dispatchMemberFunction(t.func, t) \\n self.write(\\\"(\\\")\\n self._CallArguments(t)\\n self.write(\\\")\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61901075","0.61292493","0.61219674","0.60508186","0.60292137","0.594123","0.5882175","0.5842769","0.58308256","0.5827575","0.58041626","0.57888895","0.5762354","0.5761433","0.5750888","0.5736811","0.5736043","0.5727928","0.5691805","0.56814253","0.5672321","0.563258","0.5588115","0.5579232","0.5552363","0.55489546","0.5542409","0.55394113","0.55269784","0.55023354","0.548214","0.5480933","0.5461215","0.5451293","0.54395354","0.5439092","0.5430297","0.542194","0.5419295","0.5419295","0.5412184","0.54074687","0.5402837","0.5395511","0.5392523","0.5392499","0.5380092","0.53565484","0.53521883","0.53481007","0.53472173","0.5346033","0.5341535","0.5340515","0.5336136","0.5335085","0.53255945","0.53246343","0.5315092","0.53072214","0.53048074","0.5302639","0.5301786","0.53002715","0.52988684","0.52974385","0.52958983","0.52922803","0.52901924","0.52886766","0.52808565","0.52774787","0.52744186","0.52676374","0.5257704","0.5255076","0.52539426","0.52513355","0.52486145","0.52485144","0.52369606","0.5236198","0.5234139","0.5227989","0.5226515","0.5226515","0.5226515","0.5226515","0.5226515","0.5219049","0.5219049","0.5219049","0.521699","0.5214839","0.5207859","0.5201377","0.5199826","0.5196101","0.51888806","0.5182559"],"string":"[\n \"0.61901075\",\n \"0.61292493\",\n \"0.61219674\",\n \"0.60508186\",\n \"0.60292137\",\n \"0.594123\",\n \"0.5882175\",\n \"0.5842769\",\n \"0.58308256\",\n \"0.5827575\",\n \"0.58041626\",\n \"0.57888895\",\n \"0.5762354\",\n \"0.5761433\",\n \"0.5750888\",\n \"0.5736811\",\n \"0.5736043\",\n \"0.5727928\",\n \"0.5691805\",\n \"0.56814253\",\n \"0.5672321\",\n \"0.563258\",\n \"0.5588115\",\n \"0.5579232\",\n \"0.5552363\",\n \"0.55489546\",\n \"0.5542409\",\n \"0.55394113\",\n \"0.55269784\",\n \"0.55023354\",\n \"0.548214\",\n \"0.5480933\",\n \"0.5461215\",\n \"0.5451293\",\n \"0.54395354\",\n \"0.5439092\",\n \"0.5430297\",\n \"0.542194\",\n \"0.5419295\",\n \"0.5419295\",\n \"0.5412184\",\n \"0.54074687\",\n \"0.5402837\",\n \"0.5395511\",\n \"0.5392523\",\n \"0.5392499\",\n \"0.5380092\",\n \"0.53565484\",\n \"0.53521883\",\n \"0.53481007\",\n \"0.53472173\",\n \"0.5346033\",\n \"0.5341535\",\n \"0.5340515\",\n \"0.5336136\",\n \"0.5335085\",\n \"0.53255945\",\n \"0.53246343\",\n \"0.5315092\",\n \"0.53072214\",\n \"0.53048074\",\n \"0.5302639\",\n \"0.5301786\",\n \"0.53002715\",\n \"0.52988684\",\n \"0.52974385\",\n \"0.52958983\",\n \"0.52922803\",\n \"0.52901924\",\n \"0.52886766\",\n \"0.52808565\",\n \"0.52774787\",\n \"0.52744186\",\n \"0.52676374\",\n \"0.5257704\",\n \"0.5255076\",\n \"0.52539426\",\n \"0.52513355\",\n \"0.52486145\",\n \"0.52485144\",\n \"0.52369606\",\n \"0.5236198\",\n \"0.5234139\",\n \"0.5227989\",\n \"0.5226515\",\n \"0.5226515\",\n \"0.5226515\",\n \"0.5226515\",\n \"0.5226515\",\n \"0.5219049\",\n \"0.5219049\",\n \"0.5219049\",\n \"0.521699\",\n \"0.5214839\",\n \"0.5207859\",\n \"0.5201377\",\n \"0.5199826\",\n \"0.5196101\",\n \"0.51888806\",\n \"0.5182559\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64394444"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":88499,"cells":{"query":{"kind":"string","value":"Implemnets the scan method in BasePlugin class. In this mac module, mac addesses were retrieved by snmpwalk python lib."},"document":{"kind":"string","value":"def scan(self):\n walk_result = utils.snmp_walk(self.host, self.credential,\n \"BRIDGE-MIB::dot1dTpFdbPort\")\n if not walk_result:\n return None\n\n mac_list = []\n for result in walk_result:\n if not result or result['value'] == str(0):\n continue\n temp = {}\n mac_numbers = result['iid'].split('.')\n temp['mac'] = self._get_mac_address(mac_numbers)\n temp['port'] = self._get_port(result['value'])\n temp['vlan'] = self._get_vlan_id(temp['port'])\n mac_list.append(temp)\n\n return mac_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def Scan(self, plugin):\n raise 'Method not implemented'","def scan(self):\n return","def on_scanner_start(self, scanner):","def _scan(self): # pragma: no cover\n raise NotImplementedError()","def get_scan(self):\n pass","def scan(self):\n for angle in range(self.MIDPOINT-400, self.MIDPOINT+401, 100):\n self.servo(angle)\n self.scan_data[angle] = self.read_distance()\n #sort the scan data for easier analysis\n self.scan_data = OrderedDict(sorted(self.scan_data.items()))","def scan_devices(self):\n self._update_info()\n return [client[\"mac\"] for client in self.last_results]","def scan_devices(self):\n self._update_info()\n\n return [client['mac'] for client in self.last_results]","def scan(self):\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 35):\n self.servo(angle)\n self.scan_data[angle] = self.read_distance()\n #sort the scan data for easier analysis\n self.scan_data = OrderedDict(sorted(self.scan_data.items()))","def setup_scan(self):\n scan = self.scan\n # First setup the laser\n laser_params = scan['laser']['params']\n laser = self.devices[scan['laser']['name']]\n if 'wavelength_sweeps' not in laser_params:\n laser_params['wavelength_sweeps'] = 1 # This to avoid conflicts in downstream code.\n elif laser_params['wavelength_sweeps'] != 1:\n laser_params['wavelength_sweeps'] = 1 # This to avoid conflicts in downstream code.\n \n laser_params['wavelength'] = laser_params['start_wavelength']\n\n num_points = (laser_params['stop_wavelength'] - laser_params['start_wavelength']) / laser_params['interval_trigger']\n num_points = (int(round(num_points.m_as('')))+1) * laser_params['wavelength_sweeps']\n\n # Some NI DAQs misbehave with odd number of data points.\n if num_points % 2 != 0:\n laser_params['stop_wavelength'] += laser_params['interval_trigger']\n num_points += 1\n\n scan['laser']['params'] = laser_params\n laser.apply_values(scan['laser']['params'])\n\n # Clear the array to start afresh\n for d in self.daqs:\n self.daqs[d]['monitor'] = []\n \n # Lets see what happens with the devices to monitor\n devices_to_monitor = scan['detectors']\n \n for dev in devices_to_monitor:\n dev = self.devices[dev] #added by Karindra 11/22/2017\n self.daqs[dev.properties['connection']['device']]['monitor'].append(dev)\n \n dev_to_scan = scan['axis']['device']['name']\n output_to_scan = scan['axis']['device']['property']\n if dev_to_scan != 'time':\n dev_range = scan['axis']['device']['range']\n start = Q_(dev_range[0])\n stop = Q_(dev_range[1])\n step = Q_(dev_range[2])\n num_points_dev = int(((stop-start)/step).m_as(''))+1\n else:\n dev_range = scan['axis']['device']['range']\n start = 1\n stop = dev_range[1]\n num_points_dev = stop\n # This is temporal accuracy for the DAQ.\n print('Number of points device: {}'.format(num_points_dev))\n print('Number of points scan: {}'.format(num_points))\n accuracy = laser.params['interval_trigger'] / laser.params['wavelength_speed']\n\n conditions = {\n 'accuracy': accuracy,\n 'points': num_points*num_points_dev,\n }\n scan['total_points'] = conditions['points']\n # Then setup the DAQs\n for d in self.daqs:\n daq = self.daqs[d] # Get the DAQ from the dictionary of daqs.\n daq_driver = self.devices[d] # Gets the link to the DAQ\n \n if len(daq['monitor']) > 0:\n print('DAQ: %s' % d) \n devs_to_monitor = daq['monitor'] # daqs dictionary groups the channels by daq to which they are plugged\n print('Devs to monitor:')\n print(devs_to_monitor)\n conditions['points'] *= len(daq['monitor'])\n conditions['devices'] = devs_to_monitor\n if 'trigger' not in scan['daq']:\n raise Exception('A trigger has to be specified in the daq section of the scan')\n conditions['trigger'] = scan['daq']['trigger']\n print('Trigger: {}'.format(conditions['trigger']))\n if conditions['trigger'] == 'external':\n conditions['trigger_source'] = scan['daq']['trigger_source']\n print('Trigger source: {}'.format(conditions['trigger_source']))\n elif conditions['trigger'] == 'internal':\n if self.monitor['daq']['trigger_source'] is not None:\n raise Warning('Specifying trigger even if set to Internal')\n else:\n raise Exception('Trigger not recognized. Check the config. Only external or internal can be used.')\n\n if 'start_trigger' in scan['daq']:\n if scan['daq']['start_trigger'] is not None:\n conditions['start_source'] = scan['daq']['start_source']\n conditions['start_mode'] = 'digital'\n else:\n conditions['start_mode'] = 'software'\n\n conditions['sampling'] = 'continuous'\n daq['monitor_task'] = daq_driver.driver.analog_input_setup(conditions)\n self.daqs[d] = daq # Store it back to the class variable\n if not daq_driver.driver.is_task_complete(daq['monitor_task']):\n daq_driver.stop_task(daq['monitor_task'])\n #self.read_continuous_scans() # Empties the memory of the DAQ before starting a new measurement\n daq_driver.driver.trigger_analog(None)\n self.read_continuous_scans()\n\n approx_time_to_scan = (laser.params['stop_wavelength'] - laser.params['start_wavelength']) / laser.params['wavelength_speed']\n\n scan['approx_time_to_scan'] = approx_time_to_scan\n self.scan = scan","def probe(self):","def scan(this):\n\t\treturn this._SCAN","def __init__(self, scan=None):\n super().__init__(scan=scan)","def help_scan(self):\n print(SCAN)","def script_scan(self):\n self._scanned = True\n return self._scanner.scan(self._ips, self._ports, arguments='-Pn -sn -sC')","def scan_devices(self):\n _LOGGER.debug(\"Scan_devices invoked.\")\n if self._update_info() == False:\n # self.hass.data[DOMAIN]['devices'] = None\n self.hass.data[DOMAIN]['scanning'] = False\n _LOGGER.warning(\"Can't update device list\")\n return []\n else:\n clients = [client.mac for client in self.last_results]\n self.hass.data[DOMAIN]['devices'] = clients\n self.hass.data[DOMAIN]['scanning'] = True\n return clients","def post_add_scan_CB(self) -> None:\n pass","def scan_callback(self, scan_msg):\n self.scan = scan_msg","def scan(self, meter, precise=False, interactive=True, add_failed=False):\n options = set()\n for i, macronization in enumerate(self.macronizations):\n meter_patterns = meter.get_matching_scansions(macronization, precise)\n for pattern in meter_patterns:\n scansion = macronization.apply_mask(pattern)\n options.add(scansion)\n # TODO consider a very rare but theoretically possible case, when to scansions are\n # the same, but words are macronized diffrently\n\n manual_options = self.__get_manual_options(meter, precise)\n if len(options) > 1 and len(manual_options) != 1:\n options = self.__resolve(options, interactive)\n return self.__finish_scansion(options, manual_options, add_failed)","def detect_os_service_scan(self):\n self._scanned = True\n return self._scanner.scan(self._ips, self._ports, arguments='-A')","def _update(call: ServiceCall) -> None:\n hdmi_network.scan()","def scan(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast/arp_request\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\n target_list=[]\n for element in answered_list:\n target_dict = {\"ip\":element[1].psrc, \"mac\":element[1].hwsrc}\n target_list.append(target_dict)\n return target_list","def test_get_scan(self):\n pass","def search_mac_in_arp(args):\n arp_table = get_arp_table()\n \n mac = convert_mac(args.mac, 2)\n columns = ['IP', 'AGE(min)', 'MAC', 'INTERFACE', 'VENDOR']\n result = []\n for row in arp_table.split('\\n'):\n if mac in row:\n lists = []\n _,ip,age,r_mac,_,interface = row.strip().split()\n r_mac = convert_mac(mac, 1)\n vendor = convert_mac(r_mac, 3)\n vendor = find_in_database(vendor)\n r_mac = blue + r_mac + endblue\n lists = [ip, age, r_mac, interface, vendor]\n result.append(lists)\n else:\n pass\n print(tabulate(result, headers=columns))","def scan(backend, timeout=10):\n result = []\n for (mac, name) in backend.scan_for_devices(timeout):\n print(mac + \" \" + name)\n return result","def on_scanner_finish(self, scanner):","def scan(controller, path):","def full_scan(self):\n self._scanned = True\n return self._scanner.scan(self._ips, self._ports, arguments='-sV -sS -T4')","def scanOptions(self, options):","def host_discover(self):\n self._scanned = True\n return self._scanner.scan(self._ips, arguments='-sP')","def run_scan(self, ):\n options = {'t': self.tcp_scan,\n 'u': self.udp_scan,\n 'a': self.ack_scan,\n 's': self.stealth_connection,\n 'f': self.fin_scan,\n }\n try:\n options[self.scan_type]()\n print \"Scan Took:\", timeit.default_timer() - self.start\n print \"Total Open ports:\", self.open_ports\n except Exception, ex:\n print '%s is not vaild scan_type, bye bye' % self.scan_type\n exit(1)","def start_scan(self):\n try:\n out = self.get_output(\"scan on\")\n except BluetoothctlError, e:\n print(e)\n return None","def start_scan(self):\n try:\n self.send(\"scan on\")\n except Exception as e:\n logger.error(e)","async def scan(\n loop: asyncio.AbstractEventLoop,\n timeout: int = 5,\n identifier: str = None,\n protocol: Protocol = None,\n hosts: List[str] = None,\n) -> List[conf.AppleTV]:\n\n def _should_include(atv):\n if not atv.ready:\n return False\n\n if identifier and identifier not in atv.all_identifiers:\n return False\n\n if protocol and atv.get_service(protocol) is None:\n return False\n\n return True\n\n scanner: BaseScanner\n if hosts:\n scanner = UnicastMdnsScanner([IPv4Address(host) for host in hosts], loop)\n else:\n scanner = ZeroconfScanner(loop)\n\n devices = (await scanner.discover(timeout)).values()\n return [device for device in devices if _should_include(device)]","def process_scan(self, msg):\n print(msg)\n self.last_scan = msg","def load_scan_energies(self):\n\n raise NotImplementedError('Could not find a successfully load Orca scan:Parser is not implemented')","def start_scan(self, timeout_sec):\n raise NotImplementedError","def udp_scan(self):\n self._scanned = True\n return self._scanner.scan(self._ips, self._ports, arguments='-sU')","def _collectCallback(self):\n log.debug(\"Scanning for processes from %s [%s]\",\n self._devId, self._manageIp)\n\n self.state = ZenProcessTask.STATE_SCANNING_PROCS\n\ttables = [NAMETABLE, PATHTABLE, ARGSTABLE]\n\tif AS400PLUG in self._device.zCollectorPlugins: tables = [AS400NAME]\n try:\n tableResult = yield self._getTables(tables)\n summary = 'Process table up for device %s' % self._devId\n self._clearSnmpError(\"%s - timeout cleared\" % summary, 'table_scan_timeout')\n if self.snmpConnInfo.zSnmpVer == 'v3':\n self._clearSnmpError(\"%s - v3 error cleared\" % summary, 'table_scan_v3_error')\n processes = self._parseProcessNames(tableResult)\n self._clearSnmpError(summary, 'resource_mib')\n self._deviceStats.update(self._device)\n processStatuses = self._determineProcessStatus(processes)\n self._sendProcessEvents(processStatuses)\n self._clearSnmpError(summary)\n yield self._fetchPerf()\n log.debug(\"Device %s [%s] scanned successfully\",\n self._devId, self._manageIp)\n except HostResourceMIBExecption as e:\n summary = 'Device %s does not publish HOST-RESOURCES-MIB' %\\\n self._devId\n resolution = \"Verify with snmpwalk %s %s\" %\\\n (self._devId, NAMETABLE )\n log.warn(summary)\n self._sendSnmpError(summary, \"resource_mib\", resolution=resolution)\n\n except error.TimeoutError as e:\n log.debug('Timeout fetching tables on device %s' % self._devId)\n self._sendSnmpError('%s; Timeout on device' % PROC_SCAN_ERROR % self._devId, 'table_scan_timeout')\n except Snmpv3Error as e:\n msg = \"Cannot connect to SNMP agent on {0._devId}: {1.value}\".format(self, str(e))\n log.debug(msg)\n self._sendSnmpError('%s; %s' % (PROC_SCAN_ERROR % self._devId, msg), 'table_scan_v3_error')\n except Exception as e:\n log.exception('Unexpected Error on device %s' % self._devId)\n msg = '%s; error: %s' % (PROC_SCAN_ERROR % self._devId, e)\n self._sendSnmpError(msg)","def _find_devices_mac(self):\n self.keyboards.append(Keyboard(self))\n self.mice.append(MightyMouse(self))\n self.mice.append(Mouse(self))","def scan(self):\n inst_dict = {}\n if self.status: self.status.Warning(\"Scan function not implemented yet!\")\n return inst_dict\n #raise AssertionError(\"%s not implemented\" % sys._getframe().f_code.co_name)","def discover(self):\n pass","def Scan(self, argin):\n handler = self.get_command_object(\"Scan\")\n handler(argin)","def getDevices():\n \n scannedDevices = list()\n \n proc = subprocess.Popen('bluetoothctl scan on', shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=8192, universal_newlines=True)\n \n time.sleep(10)\n \n proc.stdin.write('scan off')\n \n try:\n stdout, stderr = proc.communicate()\n except subprocess.TimeoutExpired:\n proc.kill()\n stdout, stderr = proc.communicate()\n\n ansiEscapePattern = re.compile(r'\\x1B[@-_][0-?]*[ -/]*[@-~]')\n stdout = ansiEscapePattern.sub('', stdout)\n \n #deviceNamePattern = re.compile('^\\[NEW\\] Device [A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2} ')\n \n for line in stdout.split('\\n'):\n if '[NEW] Device' in line:\n device = list()\n device.append(line[13:31])\n device.append(line[31:])\n scannedDevices.append(device)\n \n return scannedDevices","async def request_scan(self) -> None:\n await self.dbus.Device.Wireless.call_request_scan({})","def do(self, argin):\n # overwrites the do hook\n\n device = self.target\n\n scan = json.loads(argin)\n\n device._scan_ID = int(scan[\"scan_id\"])\n\n data = tango.DeviceData()\n data.insert(tango.DevString, str(device._scan_ID))\n device._group_vcc.command_inout(\"Scan\", data)\n\n device._group_fsp_corr_subarray.command_inout(\"Scan\", data)\n device._group_fsp_pss_subarray.command_inout(\"Scan\", data)\n device._group_fsp_pst_subarray.command_inout(\"Scan\", data)\n\n # return message\n message = \"Scan command successful\"\n self.logger.info(message)\n return (ResultCode.STARTED, message)","def scan(self):\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 50):\n self.servo(angle)\n self.scan_data[angle] = self.read_distance()","def launch ():\n #core.addListenerByName(\"UpEvent\", _go_up)\n core.registerNew(MAC_Filter)","def fast_scan(self):\n self._scanned = True\n return self._scanner.scan(self._ips, self._ports)","def test_start_scan(self):\n pass","def scan_devices(self):\n self._update_info()\n\n return self.last_results","def do_scan(self):\n scan = self.scan\n laser = self.devices[scan['laser']['name']]\n dev_to_scan = scan['axis']['device']['name']\n output = scan['axis']['device']['property']\n approx_time_to_scan = (laser.params['stop_wavelength']-laser.params['start_wavelength'])/laser.params['wavelength_speed']\n # Scan the laser and the values of the given device\n if output != 'time':\n dev_range = scan['axis']['device']['range']\n start = Q_(dev_range[0])\n units = start.u\n stop = Q_(dev_range[1])\n step = Q_(dev_range[2])\n \n num_points_dev = ((stop-start)/step).to('')\n else:\n dev_range = scan['axis']['device']['range']\n start = 1\n stop = dev_range[1]\n num_points_dev = stop\n\n num_points_dev += 1 # So the last bit of information is true.\n\n for value in np.linspace(start, stop, num_points_dev, endpoint=True):\n if output != 'time':\n self.set_value_to_device(dev_to_scan, {output: value * units})\n dev = self.devices[dev_to_scan]\n time.sleep(0.1)\n while not dev.driver.finished_moving:\n time.sleep(0.2)\n\n self.do_line_scan()\n \n return True","def _handle_scan(self, data):\n self.lidar_data = data.ranges","def do(self):\n device = self.target\n\n # if aborted from SCANNING, needs to set VCC and PSS subarray \n # to READY state otherwise when \n if device.scanID != 0:\n self.logger.info(\"scanning\")\n device._group_vcc.command_inout(\"EndScan\")\n device._group_fsp_corr_subarray.command_inout(\"EndScan\")\n device._group_fsp_pss_subarray.command_inout(\"EndScan\")\n device._group_fsp_pst_subarray.command_inout(\"EndScan\")\n \n (result_code,message)=super().do()\n \n return (result_code,message)","def scan_item(barcode):\n return scanner.scan(barcode)","def getNodeScanMethods(self,node):\n data = self.connect('get','nodes/%s/scan' % (node),None)\n return data","def __init__(self) -> None:\n self._found_devices = {} # type: Dict[IPv4Address, conf.BaseService]","def __init__(self):\n self._discovered_devices = {}\n self._discovered_ip = None","def intGet(): \n macchanger, ip, iwconfig = pathGet()\n interfaces = []\n a = str(subprocess.check_output(\"{} link show\".format(ip), shell=True))\n ints = a.split(': ')\n for i in range(len(ints)):\n if len(ints[i].split()) == 1:\n if ints[i] not in [\"1\", \"lo\", \"b'1\"]:\n interface = {'name':str(ints[i])}\n interfaces.append(interface)\n # Get interface properties\n for interface in interfaces:\n name = interface['name']\n macs = subprocess.check_output(\"{} -s {}\".format(macchanger, name), shell=True).decode(\"utf-8\")\n interface['cMac'] = macs.split()[2]\n interface['cVend'] = macs.split(\"(\")[1].split(\")\")[0]\n interface['pMac'] = macs.split(\"\\n\")[1].split()[2]\n interface['pVend'] = macs.split(\"\\n\")[1].split(\"(\")[1].split(\")\")[0]\n try:\n mon = subprocess.check_output(\"{} {} 2> /dev/null\".format(iwconfig, name), shell=True).split()\n mon1 = mon[3].decode(\"utf-8\").split(':')[1]\n if mon1 == 'off/any':\n mon1 = mon[4].decode(\"utf-8\").split(':')[1]\n interface['mon'] = mon1\n except:\n interface['mon'] = 'Wired'\n return(interfaces)","def _ParseScanResult(self, output):\n # Split access points into a list. Since we split on a string encountered\n # at the very beginning of the output, the first element is blank (thus\n # we skip the first element). Remaining elements are in groups of three,\n # in groups of: (BSSID, associated, other).\n bssid_ap_list = self._ACCESS_POINT_RE.split(output)[1:]\n bssid_ap_tuples = [bssid_ap_list[x:x+3]\n for x in range(0, len(bssid_ap_list), 3)]\n\n # Parse each AP.\n aps = []\n for bssid, associated, ap_data in bssid_ap_tuples:\n active = bool(associated)\n aps.append(self._ParseScanAccessPoint(bssid, active, ap_data))\n\n # Return AP list.\n return aps","async def discover(cls, timeout=5.0, *, return_adv=False, **kwargs):\n async with cls(**kwargs) as scanner:\n await asyncio.sleep(timeout)\n\n if return_adv:\n return scanner.discovered_devices_and_advertisement_data\n\n return scanner.discovered_devices","def do(self):\n super().do()\n\n device = self.target\n device._scan_id = 0\n\n device._sdp_addresses = {\n \"outputHost\": [],\n \"outputMac\": [],\n \"outputPort\": [],\n }\n device._sdp_links_active = [\n False,\n ]\n device._sdp_output_data_rate = 0.0\n\n device._config_id = \"\"\n\n # JSON string, deliberately left in Tango layer\n device._last_scan_configuration = \"\"\n\n # _list_of_devices_completed_task: for each task/command reports\n # the list of the devices that successfully completed the task.\n # Implemented as a defualt dictionary:\n # keys: the command name in lower case (configurescan, assignresources, etc.)\n # values: the list of devices' FQDN\n device._list_of_devices_completed_task = defaultdict(list)\n\n # _cmd_progress: command execution's progress percentage\n # implemented as a default dictionary:\n # keys: the command name in lower case(configurescan,..)\n # values: the progress percentage (default 0)\n device._cmd_progress = defaultdict(int)\n\n # _cmd_maximun_duration: command execution's expected maximum duration (sec.)\n # implemented as a default dictionary:\n # keys: the command name in lower case(configurescan, assignresources,..)\n # values: the expected maximum duration in sec.\n device._cmd_maximum_duration = defaultdict(float)\n\n # _cmd_measure_duration: command execution's measured duration (sec.)\n # implemented as a default dictionary:\n # keys: the command name in lower case(configurescan, assignresources,..)\n # values: the measured execution time (sec.)\n device._cmd_measured_duration = defaultdict(float)\n\n # _timeout_expired: boolean flag to signal timeout during command execution.\n # To check and reset before a command execution.\n # keys: the command name in lower case(configurescan, assignresources,..)\n # values: True/False\n device._timeout_expired = defaultdict(bool)\n # configure the flags to push event from the device server\n device.set_change_event(\"configureScanTimeoutExpiredFlag\", True, True)\n device.set_archive_event(\"configureScanTimeoutExpiredFlag\", True, True)\n device.set_change_event(\"assignResourcesTimeoutExpiredFlag\", True, True)\n device.set_archive_event(\"assignResourcesTimeoutExpiredFlag\", True, True)\n device.set_change_event(\"releaseResourcesTimeoutExpiredFlag\", True, True)\n device.set_archive_event(\"releaseResourcesTimeoutExpiredFlag\", True, True)\n\n message = \"CspSubElementSubarray Init command completed OK\"\n device.logger.info(message)\n return (ResultCode.OK, message)","def plugh():","def UpdateScan(self, msg):\n self.Lidar = msg","def __init__(self):\n self._discovered_adv: SwitchBotAdvertisement | None = None\n self._discovered_advs: dict[str, SwitchBotAdvertisement] = {}","def scan_bluetooth(self):\n nearby_devices = bluetooth.discover_devices(lookup_names=True)\n print(\"Found {} devices at {}\".format(len(nearby_devices), datetime.now()))\n timestamp = datetime.now().strftime('%m/%d/%Y %H:%M:%S')\n self.capture = self.MonitorCapture(timestamp=timestamp, structure=nearby_devices, ip_addr=self.ip_addr,\n location=self.location)\n for name, addr in nearby_devices:\n print(\" %s - %s\" % (addr, name))\n\n self.capture = json.dumps(self.capture.__dict__)","def laser_cb(self, msg):\n #rospy.loginfo(\"Received new scan\")\n self.laser = msg","def __scan_progressive__(self, hosts, ports, arguments, callback, sudo):\n try:\n scan_data = self._nm.scan(hosts, ports, arguments, sudo)\n except PortScannerError:\n scan_data = None\n\n if callback is not None:\n callback(hosts, scan_data)","def scan():\n debug(\"CBA4.scan()\")\n num = MpOrLibUsb.get_device_count()\n devices = []\n i = 0\n while i < num:\n cba = CBA4(interface=MpOrLibUsb(i))\n i += 1\n sn = cba.get_serial_number()\n if sn:\n devices.append(sn)\n cba.close()\n #end loop\n return devices\n #end scan()","def _scan_table(self, uri):\n cmd = 'kiji scan {kiji_uri}/{uri} --max-versions=10'.format(\n kiji_uri=self.kiji_uri,\n uri=uri)\n self._run_kiji_job(cmd)","def requestScan(self) -> None:\n self._protocol.write_line(CMD_LASER)","def run_app():\n global interface\n global filter_results\n\n description = 'Simple Wifi scanner for 2.4 GHz range'\n epilog = 'The author of this code take no responsibility for your use or misuse'\n parser = argparse.ArgumentParser(prog='ScanWifi.py', description=description, epilog=epilog)\n parser.add_argument(\"interface\", help='Your interface in monitor mode')\n parser.add_argument('-c', '--channel', help='Channel number for 2.4 GHz range (min 1/max 14)', default=1, type=int)\n parser.add_argument('--all', help='Scan on all channels for 2.4 GHz range', default=False, action='store_true')\n parser.add_argument('--filter', help='Filter results only for STA (Probe Req)', default=False, action='store_true')\n args = parser.parse_args()\n\n if len(args.interface) < 1:\n print('You did not provide any interface?')\n exit(1)\n else:\n interface = args.interface\n\n if not args.all and (args.channel < 1 or args.channel > 14):\n print('You will scan on channel {}?'.format(args.channel))\n exit(1)\n\n if not args.all and args.channel in range(1, 14):\n set_specific_channel(args.channel)\n\n if args.all:\n channel_changer = Thread(target=change_channel)\n channel_changer.daemon = True\n channel_changer.start()\n\n if args.filter:\n print(\"-\" * 85)\n sniff(prn=evaluate_sniffing_packet_sta, iface=interface)\n else:\n print(\"-\" * 85)\n print(\"{:<24} {:<35} {:<5} {:<7} {}\".format(\"BSSID\", \"SSID\", \"dbm\", \"CH\", \"ENC\"))\n print(\"-\" * 85)\n sniff(prn=evaluate_sniffing_packet_ap, iface=interface)","def scan(self, mask):","def connected_devices_arp(self, run_test):\n\n\n if not run_test:\n return\n\n res = {}\n\n ts = int(time.time())\n\n route_cmd = \"ip r | grep /24 | awk '{print $1;}'\"\n subnet = Popen(route_cmd, shell=True,\n stdout=PIPE).stdout.read().decode('utf-8')\n\n nmap_cmd = f'nmap -sn {subnet}'\n Popen(nmap_cmd, shell=True, stdout=PIPE)\n\n arp_cmd = (\"/usr/sbin/arp -i eth0 -n | grep : |\"\n \"grep -v '_gateway' | tr -s ' ' | \"\n \"cut -f3 -d' ' | sort | uniq\")\n\n arp_res = Popen(arp_cmd, shell=True,\n stdout=PIPE).stdout.read().decode('utf-8')\n res['arp'] = arp_res\n\n devices = set(arp_res.strip().split(\"\\n\"))\n active_devices = [[dev, ts, 1] for dev in devices]\n\n for device in active_devices:\n if self.dev_db.contains(where('mac_addr') == device[0]):\n self.dev_db.update(increment(\"n\"),\n where('mac_addr') == device[0])\n self.dev_db.update(tdb_set('last_seen', device[1]),\n where('mac_addr') == device[0])\n else:\n self.dev_db.insert({'mac_addr': device[0],\n 'last_seen': device[1],\n 'n': device[2]})\n\n print(self.dev_db.all())\n ndev_past_day = len(self.dev_db.search(\n where('last_seen') > (ts - 86400)))\n ndev_past_week = len(self.dev_db.search(\n where('last_seen') > (ts - 86400*7)))\n\n print(ndev_past_day)\n self.results[\"devices_active\"] = len(active_devices)\n self.results[\"devices_total\"] = self.dev_db.count(where('n') >= 1)\n self.results[\"devices_1day\"] = ndev_past_day\n self.results[\"devices_1week\"] = ndev_past_week\n\n if not self.quiet:\n print('\\n --- Number of Devices ---')\n print(f'Number of active devices: '\n f'{self.results[\"devices_active\"]}')\n print(f'Number of total devices: '\n f'{self.results[\"devices_total\"]}')\n print(f'Number of devices in last 1 day:'\n f' {self.results[\"devices_1day\"]}')\n print(f'Number of devices in last week:'\n f' {self.results[\"devices_1week\"]}')\n return res","def connection(self):\n if (self.symbol.type == self.scanner.NAME):\n device_name = self.names.get_name_string(self.symbol.id)\n first_device_id = self.names.query(device_name)\n self.old_symbol = self.symbol # for undeclared device names\n self.symbol = self.scanner.get_symbol()\n\n if (self.symbol.type == self.scanner.PERIOD):\n self.symbol = self.scanner.get_symbol()\n\n if(self.symbol.type == self.scanner.OUT_PIN):\n pin_name = self.names.get_name_string(self.symbol.id)\n first_port_id = self.names.query(pin_name)\n self.symbol = self.scanner.get_symbol()\n else:\n # Error: Output pin has to be 'Q' or 'QBAR'\n # Stopping symbols: ';', '}' , '=', 'MONITOR' or 'END'\n # KEYWORD\n self.error(self.OUTPUT_PIN,\n [self.scanner.KEYWORD,\n self.scanner.SEMICOLON, self.scanner.EQUALS,\n self.scanner.RIGHT_CURLY],\n [self.scanner.MONITOR_ID,\n self.scanner.END_ID])\n else:\n # Device with only a single output port\n first_port_id = None\n\n if (self.symbol.type == self.scanner.EQUALS):\n self.symbol = self.scanner.get_symbol()\n\n if (self.symbol.type == self.scanner.NAME):\n device_name = self.names.get_name_string(self.symbol.id)\n second_device_id = self.names.query(device_name)\n self.symbol = self.scanner.get_symbol()\n\n if (self.symbol.type == self.scanner.PERIOD):\n self.symbol = self.scanner.get_symbol()\n\n if(self.symbol.type == self.scanner.IN_PIN):\n pin_name = self.names.get_name_string(\n self.symbol.id)\n second_port_id = self.names.query(pin_name)\n self.symbol = self.scanner.get_symbol()\n\n if(self.symbol.type == self.scanner.SEMICOLON):\n self.symbol = self.scanner.get_symbol()\n else:\n # Error: Connection has to be terminated by ';'\n # Stopping symbols: NAME, ';', '}' , 'MONITOR'\n # or 'END' KEYWORD\n self.error(\n self.NO_CONNECT_SEMICOLON, [\n self.scanner.KEYWORD,\n self.scanner.SEMICOLON,\n self.scanner.NAME,\n self.scanner.RIGHT_CURLY], [\n self.scanner.MONITOR_ID,\n self.scanner.END_ID])\n else:\n # Error: Valid input pin required\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END'\n # KEYWORD\n self.error(\n self.INPUT_PIN, [\n self.scanner.KEYWORD,\n self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY], [\n self.scanner.MONITOR_ID,\n self.scanner.END_ID])\n else:\n # Error: Period required to specify input pin\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END'\n # KEYWORD\n self.error(\n self.PERIOD_INPUT_PIN, [\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY],\n [self.scanner.MONITOR_ID, self.scanner.END_ID])\n else:\n # Error: Name string of input device required\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\n self.error(\n self.NAME_INPUT, [\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY],\n [self.scanner.MONITOR_ID, self.scanner.END_ID])\n else:\n # Error: '=' Assignment operator requried\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\n self.error(\n self.ASSIGNMENT, [\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\n self.scanner.END_ID])\n else:\n # Error: Valid string name required\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\n self.error(\n self.NAME_STRING, [\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\n self.scanner.END_ID])\n\n # Check for Connection Semantic errors\n if self.error_count == 0:\n # Only check for semantic errors if no errors so far\n err = self.network.make_connection(\n first_device_id, first_port_id,\n second_device_id, second_port_id)\n if err != self.network.NO_ERROR:\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\n self.error(\n err, [\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\n self.scanner.END_ID])","def probe(self):\n return","def test_get_scans(self):\n pass","def scan(self, attribute=None):\n\n raise NotImplementedError","def arp_monitor_callback(self, pkt: Ether):\n if pkt[Ether].src in self.users:\n self.logger.info(\"%s の人が来ました！！！\", pkt[Ether].src)\n\n if ARP not in pkt:\n return\n\n # all packet information will be logged\n self.logger.debug(\"MAC Address:\\t%s\", pkt[ARP].hwsrc) # ARPSourceMACField: mac address\n self.logger.debug(\"IP Address:\\t%s\", pkt[ARP].psrc) # SourceIPField: source ip address\n\n if pkt[ARP].op not in (1, 2):\n # pkt[ARP].op == 1: who-has\n # pkt[ARP].op == 2: is-at\n return\n\n for button in self.buttons:\n if pkt[ARP].hwsrc == button[\"address\"]:\n current_time = int(datetime.now().strftime(\"%s\"))\n button[\"next_time\"] = button.get(\"next_time\", 0)\n if button[\"next_time\"] > current_time:\n continue\n button[\"next_time\"] = current_time + button[\"interval\"]\n\n self.logger.info(\"Dash Button %s was pushed\", button[\"name\"])\n requests.post(button[\"url\"], data=json.dumps(button[\"data\"]))","def scan(self) -> List[int]:","def scan(self) -> List[int]:","def _AllAccessPoints(self, interface, frequency):\n command = ['iw', 'dev', interface, 'scan']\n if frequency is not None:\n command += ['freq', str(frequency)]\n try:\n # First, bring the device up. If it is already up, this will succeed\n # anyways.\n self.BringsUpInterface(interface, 0)\n\n output = self._device.CheckOutput(command, log=True)\n return self._ParseScanResult(output)\n except device_types.CalledProcessError:\n return []","def __init__(self, main):\n InjectionScannerBase.__init__(self, main)","def discover(self):\n self.ola_thread.run_discovery(self.universe.get(), self._upon_discover)\n if self.auto_disc.get():\n self.ola_thread.add_event(5000, self.discover)\n else: \n print \"auto_disc is off\"","async def async_find_available_devices(hass: HomeAssistant, username: str, password: str):\n\n result = []\n\n devices = await DeviceScanner.async_find_devices(hass)\n\n _LOGGER.debug(\"Found %d AwoX devices\" % (len(devices)))\n\n for mac, dev in devices.items():\n _LOGGER.debug(\"Device %s [%s]\" % (dev['name'], dev['mac']))\n try:\n mylight = DeviceScanner._connect(dev['mac'], username, password)\n if mylight.session_key:\n result.append({\n 'mac': dev['mac'],\n 'name': mylight.getModelNumber()\n })\n mylight.disconnect()\n except:\n _LOGGER.debug('Failed to connect [%s]' % dev['mac'])","def do_scan(self, arg):\n results = self._table.scan()\n for item in results[\"Items\"]:\n self._print(_pretty(item))","async def websocket_usb_scan(\n hass: HomeAssistant,\n connection: ActiveConnection,\n msg: dict[str, Any],\n) -> None:\n usb_discovery: USBDiscovery = hass.data[DOMAIN]\n if not usb_discovery.observer_active:\n await usb_discovery.async_request_scan()\n connection.send_result(msg[\"id\"])","def Scan(self):\n if self.debug_mode:\n print 'scanning start'\n self.SendStartScanSignal()\n time.sleep(.2)\n scan = self.wifi.Scan(str(self.hidden_essid))\n self.LastScan = scan\n if self.debug_mode:\n print 'scanning done'\n print 'found ' + str(len(scan)) + ' networks:'\n for i, network in enumerate(scan):\n self.ReadWirelessNetworkProfile(i)\n self.SendEndScanSignal()","def is_scanning(self):\n raise NotImplementedError","def scanExtract(self, plot=False):\n # check if their are any scans:\n\n if (self.raw['OPDC'].data.field('STATE')==20).any() or \\\n (self.raw['OPDC'].data.field('STATE')==21).any():\n self.scan_opdc='OPDC'\n self.scan_DL = self.DLtrack\n else:\n self.scan_opdc='' # nothing in OPDC\n\n if (self.raw['DOPDC'].data.field('STATE')==20).any() or \\\n (self.raw['DOPDC'].data.field('STATE')==21).any():\n self.scan_opdc='DOPDC'\n self.scan_DL = self.DDLtrack\n\n if self.scan_opdc=='':\n if self.verbose:\n print ' info: no scans'\n self.scanning=False\n return\n\n self.scanning=True\n print ' ~ setting .scan_opdc to '+self.scan_opdc\n\n # determine which is the FSU which is scanning:\n if self.scan_opdc=='OPDC':\n self.scan_fsu = self.getKeyword('DEL FT SENSOR')\n if self.scan_fsu=='FSUA':\n self.ftk_fsu = 'FSUB'\n else:\n self.ftk_fsu = 'FSUA'\n elif self.getKeyword('DEL FT SENSOR')=='FSUA':\n self.scan_fsu = 'FSUB'\n self.ftk_fsu = 'FSUA'\n else :\n self.scan_fsu = 'FSUA'\n self.ftk_fsu = 'FSUB'\n\n print ' ~ setting .scan_fsu to '+self.scan_fsu\n\n # exctract scans\n previous_state = 0\n scan_start= []\n scan_end = []\n cond = self.raw[self.scan_opdc].data.field('STATE')\n state=[]\n\n if cond[0]==20 or cond[0]==21: # already scanning\n scan_start.append(0)\n for i in np.arange(1,cond.size-1):\n if cond[i]!=cond[i-1]: # change of state\n if cond[i-1]==20 or cond[i-1]==21: # end of previous scan\n scan_end.append(i-1)\n if cond[i]==20 or cond[i]==21: # start of new scan\n scan_start.append(i)\n if cond[-1]==20 or cond[-1]==21: # still scanning\n scan_end.append(cond.size-1)\n\n print ' ~ setting .scan_start (time stamps)'\n self.scan_start= self.raw[self.scan_opdc].data.field('TIME')[scan_start]\n print ' ~ setting .scan_end (time stamps)'\n self.scan_end= self.raw[self.scan_opdc].data.field('TIME')[scan_end]\n print ' ~ setting .scan_nscans to ', len(self.scan_start)\n self.scan_nscans = len(self.scan_start)\n if plot:\n if isinstance(plot, int):\n plt.figure(plot)\n plt.clf()\n plt.title(self.filename)\n plt.plot(self.raw[self.scan_opdc].data.field('TIME'), cond,\\\n color='green')\n plt.plot(self.scan_start, 0*self.scan_start+20.45,'b+')\n plt.plot(self.scan_end, 0*self.scan_start+20.55,'r+')\n scan_start = scan_end = cond = []\n return self.scan_nscans","def scan(self, param):\n\t\tself.left(355)","def ScanSignal(self, service, antenna, scan_count):\n raise NotImplementedError","def __init__(self, encap, unidir_mode, mtu, rx_bandwidth, tx_bandwidth, snmp_index, islayer2, display_name, mac_address, description, ip_redirect, ip_unreachable, ip_proxy_arp, ip_unicast_reverse_path, vrf, speed = None, duplex = None, duplex_conf = None, fwdClassID = None, flow_control = None, auto_neg = None):\n self.encap = encap\n self.unidir_mode = unidir_mode\n self.mtu = mtu\n self.rx_bandwidth = rx_bandwidth\n self.tx_bandwidth = tx_bandwidth\n self.snmp_index = snmp_index\n self.islayer2 = islayer2\n self.display_name = display_name\n self.mac_address = mac_address\n self.description = description\n self.iphelper = []\n self.ip_redirect = bool(ip_redirect)\n self.ip_unreachable = bool(ip_unreachable)\n self.ip_proxy_arp = bool(ip_proxy_arp)\n self.ip_unicast_reverse_path = bool(ip_unicast_reverse_path)\n self.vrf = vrf\n self.speed = speed\n self.duplex = duplex\n self.duplex_conf = duplex_conf\n self.fwd_class_id = fwdClassID\n self.flow_control = flow_control\n self.auto_neg = auto_neg","def scan(self) -> list[int]:","def _run(self):\n\n self.sendStatus(C.MSG_PROBE, 'Detecting Management Interface')\n\n data = self.getData()\n host = data.p.host\n for params in data.p.interfacesList:\n port = params['port']\n interfaceHref = params['interfaceHref']\n self.sendStatus(C.MSG_PROBE, 'Checking %s:%s' % (host, port))\n if self._queryService(host, port):\n self._sendResponse(data, interfaceHref, port)\n self.sendStatus(C.OK, 'Found management interface on %s:%s'\n % (host, port))\n return\n\n self._sendResponse(data)\n self.sendStatus(C.OK_1, 'No management interface discovered')","def scan(self):\n if self.isconnected is False:\n return False\n nets = self._wlan.scan()\n nets_list = {}\n for net in nets:\n nets_list['SSID'] = str(net[0], 'utf8')\n # nets_list['bssid'] = str(net[1])\n nets_list['CHANNEL'] = str(net[2])\n nets_list['RSSI'] = str(net[3]) + ' dBm'\n nets_list['SECURITY'] = self._get_secure(net[4])\n print(nets_list)","async def _async_start_scan(self):\n _LOGGER.debug(\"Start scanning\")\n await self.async_setup()\n if not self._track_interval:\n self._track_interval = async_track_time_interval(\n self._hass, self.async_scan, DISCOVERY_INTERVAL\n )\n self.async_scan()","def scan(message):\n analyze = lexicon()\n return analyze.scan(message=message)","def MacSecurityCustomScan(window,referenceid):\n try:\n allobjects = getAllObjects(window)\n atomacclick(allobjects[52])\n ldtp.wait(2)\n Runwindow = getChildwindows(referenceid)\n buttons = getAppButtons(Runwindow)\n atomacclick(buttons[0])\n except Exception as er:\n print \"Not able to click on MacSecurityCustomScan\"\n return False","def connect_ap(self):\n\n # scan passively now that the AP has started\n self.host.scan_passive(200)\n \n # connect to AP\n self.host.sta_connect(self.ap1.macaddr)\n\n # expect a management frame for the AP\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\n \n # sanity checks\n assert(mpdu.typesubtype == wifi.fctl_probereq)\n assert(wifi.find_ie(mpdu.typesubtype, mpdu.bd, wifi.ie_ssid) == \"louis\")\n \n # send a probe response (optional if the join timeout is long enough)\n \n # expect an authentication request\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\n \n # sanity checks\n assert(mpdu.typesubtype == wifi.fctl_authentication)\n \n # send an authenticate response\n self.ap1.tx_msdu(da=self.host.macaddr, body=wifi.authenticate(), \n fctl=wifi.fctl_authentication)\n\n # expect an association request\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\n # sanity checks\n assert(mpdu.typesubtype == wifi.fctl_associationreq)\n \n # send an association response\n self.ap1.tx_msdu(da=self.host.macaddr, body=wifi.associationresponse(), \n fctl=wifi.fctl_associationresp)\n \n # expect a STA connection confirmation with a correct status\n assert(self.host.sta_connect_confirm() == True)"],"string":"[\n \"def Scan(self, plugin):\\n raise 'Method not implemented'\",\n \"def scan(self):\\n return\",\n \"def on_scanner_start(self, scanner):\",\n \"def _scan(self): # pragma: no cover\\n raise NotImplementedError()\",\n \"def get_scan(self):\\n pass\",\n \"def scan(self):\\n for angle in range(self.MIDPOINT-400, self.MIDPOINT+401, 100):\\n self.servo(angle)\\n self.scan_data[angle] = self.read_distance()\\n #sort the scan data for easier analysis\\n self.scan_data = OrderedDict(sorted(self.scan_data.items()))\",\n \"def scan_devices(self):\\n self._update_info()\\n return [client[\\\"mac\\\"] for client in self.last_results]\",\n \"def scan_devices(self):\\n self._update_info()\\n\\n return [client['mac'] for client in self.last_results]\",\n \"def scan(self):\\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 35):\\n self.servo(angle)\\n self.scan_data[angle] = self.read_distance()\\n #sort the scan data for easier analysis\\n self.scan_data = OrderedDict(sorted(self.scan_data.items()))\",\n \"def setup_scan(self):\\n scan = self.scan\\n # First setup the laser\\n laser_params = scan['laser']['params']\\n laser = self.devices[scan['laser']['name']]\\n if 'wavelength_sweeps' not in laser_params:\\n laser_params['wavelength_sweeps'] = 1 # This to avoid conflicts in downstream code.\\n elif laser_params['wavelength_sweeps'] != 1:\\n laser_params['wavelength_sweeps'] = 1 # This to avoid conflicts in downstream code.\\n \\n laser_params['wavelength'] = laser_params['start_wavelength']\\n\\n num_points = (laser_params['stop_wavelength'] - laser_params['start_wavelength']) / laser_params['interval_trigger']\\n num_points = (int(round(num_points.m_as('')))+1) * laser_params['wavelength_sweeps']\\n\\n # Some NI DAQs misbehave with odd number of data points.\\n if num_points % 2 != 0:\\n laser_params['stop_wavelength'] += laser_params['interval_trigger']\\n num_points += 1\\n\\n scan['laser']['params'] = laser_params\\n laser.apply_values(scan['laser']['params'])\\n\\n # Clear the array to start afresh\\n for d in self.daqs:\\n self.daqs[d]['monitor'] = []\\n \\n # Lets see what happens with the devices to monitor\\n devices_to_monitor = scan['detectors']\\n \\n for dev in devices_to_monitor:\\n dev = self.devices[dev] #added by Karindra 11/22/2017\\n self.daqs[dev.properties['connection']['device']]['monitor'].append(dev)\\n \\n dev_to_scan = scan['axis']['device']['name']\\n output_to_scan = scan['axis']['device']['property']\\n if dev_to_scan != 'time':\\n dev_range = scan['axis']['device']['range']\\n start = Q_(dev_range[0])\\n stop = Q_(dev_range[1])\\n step = Q_(dev_range[2])\\n num_points_dev = int(((stop-start)/step).m_as(''))+1\\n else:\\n dev_range = scan['axis']['device']['range']\\n start = 1\\n stop = dev_range[1]\\n num_points_dev = stop\\n # This is temporal accuracy for the DAQ.\\n print('Number of points device: {}'.format(num_points_dev))\\n print('Number of points scan: {}'.format(num_points))\\n accuracy = laser.params['interval_trigger'] / laser.params['wavelength_speed']\\n\\n conditions = {\\n 'accuracy': accuracy,\\n 'points': num_points*num_points_dev,\\n }\\n scan['total_points'] = conditions['points']\\n # Then setup the DAQs\\n for d in self.daqs:\\n daq = self.daqs[d] # Get the DAQ from the dictionary of daqs.\\n daq_driver = self.devices[d] # Gets the link to the DAQ\\n \\n if len(daq['monitor']) > 0:\\n print('DAQ: %s' % d) \\n devs_to_monitor = daq['monitor'] # daqs dictionary groups the channels by daq to which they are plugged\\n print('Devs to monitor:')\\n print(devs_to_monitor)\\n conditions['points'] *= len(daq['monitor'])\\n conditions['devices'] = devs_to_monitor\\n if 'trigger' not in scan['daq']:\\n raise Exception('A trigger has to be specified in the daq section of the scan')\\n conditions['trigger'] = scan['daq']['trigger']\\n print('Trigger: {}'.format(conditions['trigger']))\\n if conditions['trigger'] == 'external':\\n conditions['trigger_source'] = scan['daq']['trigger_source']\\n print('Trigger source: {}'.format(conditions['trigger_source']))\\n elif conditions['trigger'] == 'internal':\\n if self.monitor['daq']['trigger_source'] is not None:\\n raise Warning('Specifying trigger even if set to Internal')\\n else:\\n raise Exception('Trigger not recognized. Check the config. Only external or internal can be used.')\\n\\n if 'start_trigger' in scan['daq']:\\n if scan['daq']['start_trigger'] is not None:\\n conditions['start_source'] = scan['daq']['start_source']\\n conditions['start_mode'] = 'digital'\\n else:\\n conditions['start_mode'] = 'software'\\n\\n conditions['sampling'] = 'continuous'\\n daq['monitor_task'] = daq_driver.driver.analog_input_setup(conditions)\\n self.daqs[d] = daq # Store it back to the class variable\\n if not daq_driver.driver.is_task_complete(daq['monitor_task']):\\n daq_driver.stop_task(daq['monitor_task'])\\n #self.read_continuous_scans() # Empties the memory of the DAQ before starting a new measurement\\n daq_driver.driver.trigger_analog(None)\\n self.read_continuous_scans()\\n\\n approx_time_to_scan = (laser.params['stop_wavelength'] - laser.params['start_wavelength']) / laser.params['wavelength_speed']\\n\\n scan['approx_time_to_scan'] = approx_time_to_scan\\n self.scan = scan\",\n \"def probe(self):\",\n \"def scan(this):\\n\\t\\treturn this._SCAN\",\n \"def __init__(self, scan=None):\\n super().__init__(scan=scan)\",\n \"def help_scan(self):\\n print(SCAN)\",\n \"def script_scan(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, self._ports, arguments='-Pn -sn -sC')\",\n \"def scan_devices(self):\\n _LOGGER.debug(\\\"Scan_devices invoked.\\\")\\n if self._update_info() == False:\\n # self.hass.data[DOMAIN]['devices'] = None\\n self.hass.data[DOMAIN]['scanning'] = False\\n _LOGGER.warning(\\\"Can't update device list\\\")\\n return []\\n else:\\n clients = [client.mac for client in self.last_results]\\n self.hass.data[DOMAIN]['devices'] = clients\\n self.hass.data[DOMAIN]['scanning'] = True\\n return clients\",\n \"def post_add_scan_CB(self) -> None:\\n pass\",\n \"def scan_callback(self, scan_msg):\\n self.scan = scan_msg\",\n \"def scan(self, meter, precise=False, interactive=True, add_failed=False):\\n options = set()\\n for i, macronization in enumerate(self.macronizations):\\n meter_patterns = meter.get_matching_scansions(macronization, precise)\\n for pattern in meter_patterns:\\n scansion = macronization.apply_mask(pattern)\\n options.add(scansion)\\n # TODO consider a very rare but theoretically possible case, when to scansions are\\n # the same, but words are macronized diffrently\\n\\n manual_options = self.__get_manual_options(meter, precise)\\n if len(options) > 1 and len(manual_options) != 1:\\n options = self.__resolve(options, interactive)\\n return self.__finish_scansion(options, manual_options, add_failed)\",\n \"def detect_os_service_scan(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, self._ports, arguments='-A')\",\n \"def _update(call: ServiceCall) -> None:\\n hdmi_network.scan()\",\n \"def scan(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast/arp_request\\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\\n target_list=[]\\n for element in answered_list:\\n target_dict = {\\\"ip\\\":element[1].psrc, \\\"mac\\\":element[1].hwsrc}\\n target_list.append(target_dict)\\n return target_list\",\n \"def test_get_scan(self):\\n pass\",\n \"def search_mac_in_arp(args):\\n arp_table = get_arp_table()\\n \\n mac = convert_mac(args.mac, 2)\\n columns = ['IP', 'AGE(min)', 'MAC', 'INTERFACE', 'VENDOR']\\n result = []\\n for row in arp_table.split('\\\\n'):\\n if mac in row:\\n lists = []\\n _,ip,age,r_mac,_,interface = row.strip().split()\\n r_mac = convert_mac(mac, 1)\\n vendor = convert_mac(r_mac, 3)\\n vendor = find_in_database(vendor)\\n r_mac = blue + r_mac + endblue\\n lists = [ip, age, r_mac, interface, vendor]\\n result.append(lists)\\n else:\\n pass\\n print(tabulate(result, headers=columns))\",\n \"def scan(backend, timeout=10):\\n result = []\\n for (mac, name) in backend.scan_for_devices(timeout):\\n print(mac + \\\" \\\" + name)\\n return result\",\n \"def on_scanner_finish(self, scanner):\",\n \"def scan(controller, path):\",\n \"def full_scan(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, self._ports, arguments='-sV -sS -T4')\",\n \"def scanOptions(self, options):\",\n \"def host_discover(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, arguments='-sP')\",\n \"def run_scan(self, ):\\n options = {'t': self.tcp_scan,\\n 'u': self.udp_scan,\\n 'a': self.ack_scan,\\n 's': self.stealth_connection,\\n 'f': self.fin_scan,\\n }\\n try:\\n options[self.scan_type]()\\n print \\\"Scan Took:\\\", timeit.default_timer() - self.start\\n print \\\"Total Open ports:\\\", self.open_ports\\n except Exception, ex:\\n print '%s is not vaild scan_type, bye bye' % self.scan_type\\n exit(1)\",\n \"def start_scan(self):\\n try:\\n out = self.get_output(\\\"scan on\\\")\\n except BluetoothctlError, e:\\n print(e)\\n return None\",\n \"def start_scan(self):\\n try:\\n self.send(\\\"scan on\\\")\\n except Exception as e:\\n logger.error(e)\",\n \"async def scan(\\n loop: asyncio.AbstractEventLoop,\\n timeout: int = 5,\\n identifier: str = None,\\n protocol: Protocol = None,\\n hosts: List[str] = None,\\n) -> List[conf.AppleTV]:\\n\\n def _should_include(atv):\\n if not atv.ready:\\n return False\\n\\n if identifier and identifier not in atv.all_identifiers:\\n return False\\n\\n if protocol and atv.get_service(protocol) is None:\\n return False\\n\\n return True\\n\\n scanner: BaseScanner\\n if hosts:\\n scanner = UnicastMdnsScanner([IPv4Address(host) for host in hosts], loop)\\n else:\\n scanner = ZeroconfScanner(loop)\\n\\n devices = (await scanner.discover(timeout)).values()\\n return [device for device in devices if _should_include(device)]\",\n \"def process_scan(self, msg):\\n print(msg)\\n self.last_scan = msg\",\n \"def load_scan_energies(self):\\n\\n raise NotImplementedError('Could not find a successfully load Orca scan:Parser is not implemented')\",\n \"def start_scan(self, timeout_sec):\\n raise NotImplementedError\",\n \"def udp_scan(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, self._ports, arguments='-sU')\",\n \"def _collectCallback(self):\\n log.debug(\\\"Scanning for processes from %s [%s]\\\",\\n self._devId, self._manageIp)\\n\\n self.state = ZenProcessTask.STATE_SCANNING_PROCS\\n\\ttables = [NAMETABLE, PATHTABLE, ARGSTABLE]\\n\\tif AS400PLUG in self._device.zCollectorPlugins: tables = [AS400NAME]\\n try:\\n tableResult = yield self._getTables(tables)\\n summary = 'Process table up for device %s' % self._devId\\n self._clearSnmpError(\\\"%s - timeout cleared\\\" % summary, 'table_scan_timeout')\\n if self.snmpConnInfo.zSnmpVer == 'v3':\\n self._clearSnmpError(\\\"%s - v3 error cleared\\\" % summary, 'table_scan_v3_error')\\n processes = self._parseProcessNames(tableResult)\\n self._clearSnmpError(summary, 'resource_mib')\\n self._deviceStats.update(self._device)\\n processStatuses = self._determineProcessStatus(processes)\\n self._sendProcessEvents(processStatuses)\\n self._clearSnmpError(summary)\\n yield self._fetchPerf()\\n log.debug(\\\"Device %s [%s] scanned successfully\\\",\\n self._devId, self._manageIp)\\n except HostResourceMIBExecption as e:\\n summary = 'Device %s does not publish HOST-RESOURCES-MIB' %\\\\\\n self._devId\\n resolution = \\\"Verify with snmpwalk %s %s\\\" %\\\\\\n (self._devId, NAMETABLE )\\n log.warn(summary)\\n self._sendSnmpError(summary, \\\"resource_mib\\\", resolution=resolution)\\n\\n except error.TimeoutError as e:\\n log.debug('Timeout fetching tables on device %s' % self._devId)\\n self._sendSnmpError('%s; Timeout on device' % PROC_SCAN_ERROR % self._devId, 'table_scan_timeout')\\n except Snmpv3Error as e:\\n msg = \\\"Cannot connect to SNMP agent on {0._devId}: {1.value}\\\".format(self, str(e))\\n log.debug(msg)\\n self._sendSnmpError('%s; %s' % (PROC_SCAN_ERROR % self._devId, msg), 'table_scan_v3_error')\\n except Exception as e:\\n log.exception('Unexpected Error on device %s' % self._devId)\\n msg = '%s; error: %s' % (PROC_SCAN_ERROR % self._devId, e)\\n self._sendSnmpError(msg)\",\n \"def _find_devices_mac(self):\\n self.keyboards.append(Keyboard(self))\\n self.mice.append(MightyMouse(self))\\n self.mice.append(Mouse(self))\",\n \"def scan(self):\\n inst_dict = {}\\n if self.status: self.status.Warning(\\\"Scan function not implemented yet!\\\")\\n return inst_dict\\n #raise AssertionError(\\\"%s not implemented\\\" % sys._getframe().f_code.co_name)\",\n \"def discover(self):\\n pass\",\n \"def Scan(self, argin):\\n handler = self.get_command_object(\\\"Scan\\\")\\n handler(argin)\",\n \"def getDevices():\\n \\n scannedDevices = list()\\n \\n proc = subprocess.Popen('bluetoothctl scan on', shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=8192, universal_newlines=True)\\n \\n time.sleep(10)\\n \\n proc.stdin.write('scan off')\\n \\n try:\\n stdout, stderr = proc.communicate()\\n except subprocess.TimeoutExpired:\\n proc.kill()\\n stdout, stderr = proc.communicate()\\n\\n ansiEscapePattern = re.compile(r'\\\\x1B[@-_][0-?]*[ -/]*[@-~]')\\n stdout = ansiEscapePattern.sub('', stdout)\\n \\n #deviceNamePattern = re.compile('^\\\\[NEW\\\\] Device [A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2}:[A-F0-9]{2} ')\\n \\n for line in stdout.split('\\\\n'):\\n if '[NEW] Device' in line:\\n device = list()\\n device.append(line[13:31])\\n device.append(line[31:])\\n scannedDevices.append(device)\\n \\n return scannedDevices\",\n \"async def request_scan(self) -> None:\\n await self.dbus.Device.Wireless.call_request_scan({})\",\n \"def do(self, argin):\\n # overwrites the do hook\\n\\n device = self.target\\n\\n scan = json.loads(argin)\\n\\n device._scan_ID = int(scan[\\\"scan_id\\\"])\\n\\n data = tango.DeviceData()\\n data.insert(tango.DevString, str(device._scan_ID))\\n device._group_vcc.command_inout(\\\"Scan\\\", data)\\n\\n device._group_fsp_corr_subarray.command_inout(\\\"Scan\\\", data)\\n device._group_fsp_pss_subarray.command_inout(\\\"Scan\\\", data)\\n device._group_fsp_pst_subarray.command_inout(\\\"Scan\\\", data)\\n\\n # return message\\n message = \\\"Scan command successful\\\"\\n self.logger.info(message)\\n return (ResultCode.STARTED, message)\",\n \"def scan(self):\\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 50):\\n self.servo(angle)\\n self.scan_data[angle] = self.read_distance()\",\n \"def launch ():\\n #core.addListenerByName(\\\"UpEvent\\\", _go_up)\\n core.registerNew(MAC_Filter)\",\n \"def fast_scan(self):\\n self._scanned = True\\n return self._scanner.scan(self._ips, self._ports)\",\n \"def test_start_scan(self):\\n pass\",\n \"def scan_devices(self):\\n self._update_info()\\n\\n return self.last_results\",\n \"def do_scan(self):\\n scan = self.scan\\n laser = self.devices[scan['laser']['name']]\\n dev_to_scan = scan['axis']['device']['name']\\n output = scan['axis']['device']['property']\\n approx_time_to_scan = (laser.params['stop_wavelength']-laser.params['start_wavelength'])/laser.params['wavelength_speed']\\n # Scan the laser and the values of the given device\\n if output != 'time':\\n dev_range = scan['axis']['device']['range']\\n start = Q_(dev_range[0])\\n units = start.u\\n stop = Q_(dev_range[1])\\n step = Q_(dev_range[2])\\n \\n num_points_dev = ((stop-start)/step).to('')\\n else:\\n dev_range = scan['axis']['device']['range']\\n start = 1\\n stop = dev_range[1]\\n num_points_dev = stop\\n\\n num_points_dev += 1 # So the last bit of information is true.\\n\\n for value in np.linspace(start, stop, num_points_dev, endpoint=True):\\n if output != 'time':\\n self.set_value_to_device(dev_to_scan, {output: value * units})\\n dev = self.devices[dev_to_scan]\\n time.sleep(0.1)\\n while not dev.driver.finished_moving:\\n time.sleep(0.2)\\n\\n self.do_line_scan()\\n \\n return True\",\n \"def _handle_scan(self, data):\\n self.lidar_data = data.ranges\",\n \"def do(self):\\n device = self.target\\n\\n # if aborted from SCANNING, needs to set VCC and PSS subarray \\n # to READY state otherwise when \\n if device.scanID != 0:\\n self.logger.info(\\\"scanning\\\")\\n device._group_vcc.command_inout(\\\"EndScan\\\")\\n device._group_fsp_corr_subarray.command_inout(\\\"EndScan\\\")\\n device._group_fsp_pss_subarray.command_inout(\\\"EndScan\\\")\\n device._group_fsp_pst_subarray.command_inout(\\\"EndScan\\\")\\n \\n (result_code,message)=super().do()\\n \\n return (result_code,message)\",\n \"def scan_item(barcode):\\n return scanner.scan(barcode)\",\n \"def getNodeScanMethods(self,node):\\n data = self.connect('get','nodes/%s/scan' % (node),None)\\n return data\",\n \"def __init__(self) -> None:\\n self._found_devices = {} # type: Dict[IPv4Address, conf.BaseService]\",\n \"def __init__(self):\\n self._discovered_devices = {}\\n self._discovered_ip = None\",\n \"def intGet(): \\n macchanger, ip, iwconfig = pathGet()\\n interfaces = []\\n a = str(subprocess.check_output(\\\"{} link show\\\".format(ip), shell=True))\\n ints = a.split(': ')\\n for i in range(len(ints)):\\n if len(ints[i].split()) == 1:\\n if ints[i] not in [\\\"1\\\", \\\"lo\\\", \\\"b'1\\\"]:\\n interface = {'name':str(ints[i])}\\n interfaces.append(interface)\\n # Get interface properties\\n for interface in interfaces:\\n name = interface['name']\\n macs = subprocess.check_output(\\\"{} -s {}\\\".format(macchanger, name), shell=True).decode(\\\"utf-8\\\")\\n interface['cMac'] = macs.split()[2]\\n interface['cVend'] = macs.split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n interface['pMac'] = macs.split(\\\"\\\\n\\\")[1].split()[2]\\n interface['pVend'] = macs.split(\\\"\\\\n\\\")[1].split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n try:\\n mon = subprocess.check_output(\\\"{} {} 2> /dev/null\\\".format(iwconfig, name), shell=True).split()\\n mon1 = mon[3].decode(\\\"utf-8\\\").split(':')[1]\\n if mon1 == 'off/any':\\n mon1 = mon[4].decode(\\\"utf-8\\\").split(':')[1]\\n interface['mon'] = mon1\\n except:\\n interface['mon'] = 'Wired'\\n return(interfaces)\",\n \"def _ParseScanResult(self, output):\\n # Split access points into a list. Since we split on a string encountered\\n # at the very beginning of the output, the first element is blank (thus\\n # we skip the first element). Remaining elements are in groups of three,\\n # in groups of: (BSSID, associated, other).\\n bssid_ap_list = self._ACCESS_POINT_RE.split(output)[1:]\\n bssid_ap_tuples = [bssid_ap_list[x:x+3]\\n for x in range(0, len(bssid_ap_list), 3)]\\n\\n # Parse each AP.\\n aps = []\\n for bssid, associated, ap_data in bssid_ap_tuples:\\n active = bool(associated)\\n aps.append(self._ParseScanAccessPoint(bssid, active, ap_data))\\n\\n # Return AP list.\\n return aps\",\n \"async def discover(cls, timeout=5.0, *, return_adv=False, **kwargs):\\n async with cls(**kwargs) as scanner:\\n await asyncio.sleep(timeout)\\n\\n if return_adv:\\n return scanner.discovered_devices_and_advertisement_data\\n\\n return scanner.discovered_devices\",\n \"def do(self):\\n super().do()\\n\\n device = self.target\\n device._scan_id = 0\\n\\n device._sdp_addresses = {\\n \\\"outputHost\\\": [],\\n \\\"outputMac\\\": [],\\n \\\"outputPort\\\": [],\\n }\\n device._sdp_links_active = [\\n False,\\n ]\\n device._sdp_output_data_rate = 0.0\\n\\n device._config_id = \\\"\\\"\\n\\n # JSON string, deliberately left in Tango layer\\n device._last_scan_configuration = \\\"\\\"\\n\\n # _list_of_devices_completed_task: for each task/command reports\\n # the list of the devices that successfully completed the task.\\n # Implemented as a defualt dictionary:\\n # keys: the command name in lower case (configurescan, assignresources, etc.)\\n # values: the list of devices' FQDN\\n device._list_of_devices_completed_task = defaultdict(list)\\n\\n # _cmd_progress: command execution's progress percentage\\n # implemented as a default dictionary:\\n # keys: the command name in lower case(configurescan,..)\\n # values: the progress percentage (default 0)\\n device._cmd_progress = defaultdict(int)\\n\\n # _cmd_maximun_duration: command execution's expected maximum duration (sec.)\\n # implemented as a default dictionary:\\n # keys: the command name in lower case(configurescan, assignresources,..)\\n # values: the expected maximum duration in sec.\\n device._cmd_maximum_duration = defaultdict(float)\\n\\n # _cmd_measure_duration: command execution's measured duration (sec.)\\n # implemented as a default dictionary:\\n # keys: the command name in lower case(configurescan, assignresources,..)\\n # values: the measured execution time (sec.)\\n device._cmd_measured_duration = defaultdict(float)\\n\\n # _timeout_expired: boolean flag to signal timeout during command execution.\\n # To check and reset before a command execution.\\n # keys: the command name in lower case(configurescan, assignresources,..)\\n # values: True/False\\n device._timeout_expired = defaultdict(bool)\\n # configure the flags to push event from the device server\\n device.set_change_event(\\\"configureScanTimeoutExpiredFlag\\\", True, True)\\n device.set_archive_event(\\\"configureScanTimeoutExpiredFlag\\\", True, True)\\n device.set_change_event(\\\"assignResourcesTimeoutExpiredFlag\\\", True, True)\\n device.set_archive_event(\\\"assignResourcesTimeoutExpiredFlag\\\", True, True)\\n device.set_change_event(\\\"releaseResourcesTimeoutExpiredFlag\\\", True, True)\\n device.set_archive_event(\\\"releaseResourcesTimeoutExpiredFlag\\\", True, True)\\n\\n message = \\\"CspSubElementSubarray Init command completed OK\\\"\\n device.logger.info(message)\\n return (ResultCode.OK, message)\",\n \"def plugh():\",\n \"def UpdateScan(self, msg):\\n self.Lidar = msg\",\n \"def __init__(self):\\n self._discovered_adv: SwitchBotAdvertisement | None = None\\n self._discovered_advs: dict[str, SwitchBotAdvertisement] = {}\",\n \"def scan_bluetooth(self):\\n nearby_devices = bluetooth.discover_devices(lookup_names=True)\\n print(\\\"Found {} devices at {}\\\".format(len(nearby_devices), datetime.now()))\\n timestamp = datetime.now().strftime('%m/%d/%Y %H:%M:%S')\\n self.capture = self.MonitorCapture(timestamp=timestamp, structure=nearby_devices, ip_addr=self.ip_addr,\\n location=self.location)\\n for name, addr in nearby_devices:\\n print(\\\" %s - %s\\\" % (addr, name))\\n\\n self.capture = json.dumps(self.capture.__dict__)\",\n \"def laser_cb(self, msg):\\n #rospy.loginfo(\\\"Received new scan\\\")\\n self.laser = msg\",\n \"def __scan_progressive__(self, hosts, ports, arguments, callback, sudo):\\n try:\\n scan_data = self._nm.scan(hosts, ports, arguments, sudo)\\n except PortScannerError:\\n scan_data = None\\n\\n if callback is not None:\\n callback(hosts, scan_data)\",\n \"def scan():\\n debug(\\\"CBA4.scan()\\\")\\n num = MpOrLibUsb.get_device_count()\\n devices = []\\n i = 0\\n while i < num:\\n cba = CBA4(interface=MpOrLibUsb(i))\\n i += 1\\n sn = cba.get_serial_number()\\n if sn:\\n devices.append(sn)\\n cba.close()\\n #end loop\\n return devices\\n #end scan()\",\n \"def _scan_table(self, uri):\\n cmd = 'kiji scan {kiji_uri}/{uri} --max-versions=10'.format(\\n kiji_uri=self.kiji_uri,\\n uri=uri)\\n self._run_kiji_job(cmd)\",\n \"def requestScan(self) -> None:\\n self._protocol.write_line(CMD_LASER)\",\n \"def run_app():\\n global interface\\n global filter_results\\n\\n description = 'Simple Wifi scanner for 2.4 GHz range'\\n epilog = 'The author of this code take no responsibility for your use or misuse'\\n parser = argparse.ArgumentParser(prog='ScanWifi.py', description=description, epilog=epilog)\\n parser.add_argument(\\\"interface\\\", help='Your interface in monitor mode')\\n parser.add_argument('-c', '--channel', help='Channel number for 2.4 GHz range (min 1/max 14)', default=1, type=int)\\n parser.add_argument('--all', help='Scan on all channels for 2.4 GHz range', default=False, action='store_true')\\n parser.add_argument('--filter', help='Filter results only for STA (Probe Req)', default=False, action='store_true')\\n args = parser.parse_args()\\n\\n if len(args.interface) < 1:\\n print('You did not provide any interface?')\\n exit(1)\\n else:\\n interface = args.interface\\n\\n if not args.all and (args.channel < 1 or args.channel > 14):\\n print('You will scan on channel {}?'.format(args.channel))\\n exit(1)\\n\\n if not args.all and args.channel in range(1, 14):\\n set_specific_channel(args.channel)\\n\\n if args.all:\\n channel_changer = Thread(target=change_channel)\\n channel_changer.daemon = True\\n channel_changer.start()\\n\\n if args.filter:\\n print(\\\"-\\\" * 85)\\n sniff(prn=evaluate_sniffing_packet_sta, iface=interface)\\n else:\\n print(\\\"-\\\" * 85)\\n print(\\\"{:<24} {:<35} {:<5} {:<7} {}\\\".format(\\\"BSSID\\\", \\\"SSID\\\", \\\"dbm\\\", \\\"CH\\\", \\\"ENC\\\"))\\n print(\\\"-\\\" * 85)\\n sniff(prn=evaluate_sniffing_packet_ap, iface=interface)\",\n \"def scan(self, mask):\",\n \"def connected_devices_arp(self, run_test):\\n\\n\\n if not run_test:\\n return\\n\\n res = {}\\n\\n ts = int(time.time())\\n\\n route_cmd = \\\"ip r | grep /24 | awk '{print $1;}'\\\"\\n subnet = Popen(route_cmd, shell=True,\\n stdout=PIPE).stdout.read().decode('utf-8')\\n\\n nmap_cmd = f'nmap -sn {subnet}'\\n Popen(nmap_cmd, shell=True, stdout=PIPE)\\n\\n arp_cmd = (\\\"/usr/sbin/arp -i eth0 -n | grep : |\\\"\\n \\\"grep -v '_gateway' | tr -s ' ' | \\\"\\n \\\"cut -f3 -d' ' | sort | uniq\\\")\\n\\n arp_res = Popen(arp_cmd, shell=True,\\n stdout=PIPE).stdout.read().decode('utf-8')\\n res['arp'] = arp_res\\n\\n devices = set(arp_res.strip().split(\\\"\\\\n\\\"))\\n active_devices = [[dev, ts, 1] for dev in devices]\\n\\n for device in active_devices:\\n if self.dev_db.contains(where('mac_addr') == device[0]):\\n self.dev_db.update(increment(\\\"n\\\"),\\n where('mac_addr') == device[0])\\n self.dev_db.update(tdb_set('last_seen', device[1]),\\n where('mac_addr') == device[0])\\n else:\\n self.dev_db.insert({'mac_addr': device[0],\\n 'last_seen': device[1],\\n 'n': device[2]})\\n\\n print(self.dev_db.all())\\n ndev_past_day = len(self.dev_db.search(\\n where('last_seen') > (ts - 86400)))\\n ndev_past_week = len(self.dev_db.search(\\n where('last_seen') > (ts - 86400*7)))\\n\\n print(ndev_past_day)\\n self.results[\\\"devices_active\\\"] = len(active_devices)\\n self.results[\\\"devices_total\\\"] = self.dev_db.count(where('n') >= 1)\\n self.results[\\\"devices_1day\\\"] = ndev_past_day\\n self.results[\\\"devices_1week\\\"] = ndev_past_week\\n\\n if not self.quiet:\\n print('\\\\n --- Number of Devices ---')\\n print(f'Number of active devices: '\\n f'{self.results[\\\"devices_active\\\"]}')\\n print(f'Number of total devices: '\\n f'{self.results[\\\"devices_total\\\"]}')\\n print(f'Number of devices in last 1 day:'\\n f' {self.results[\\\"devices_1day\\\"]}')\\n print(f'Number of devices in last week:'\\n f' {self.results[\\\"devices_1week\\\"]}')\\n return res\",\n \"def connection(self):\\n if (self.symbol.type == self.scanner.NAME):\\n device_name = self.names.get_name_string(self.symbol.id)\\n first_device_id = self.names.query(device_name)\\n self.old_symbol = self.symbol # for undeclared device names\\n self.symbol = self.scanner.get_symbol()\\n\\n if (self.symbol.type == self.scanner.PERIOD):\\n self.symbol = self.scanner.get_symbol()\\n\\n if(self.symbol.type == self.scanner.OUT_PIN):\\n pin_name = self.names.get_name_string(self.symbol.id)\\n first_port_id = self.names.query(pin_name)\\n self.symbol = self.scanner.get_symbol()\\n else:\\n # Error: Output pin has to be 'Q' or 'QBAR'\\n # Stopping symbols: ';', '}' , '=', 'MONITOR' or 'END'\\n # KEYWORD\\n self.error(self.OUTPUT_PIN,\\n [self.scanner.KEYWORD,\\n self.scanner.SEMICOLON, self.scanner.EQUALS,\\n self.scanner.RIGHT_CURLY],\\n [self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\\n else:\\n # Device with only a single output port\\n first_port_id = None\\n\\n if (self.symbol.type == self.scanner.EQUALS):\\n self.symbol = self.scanner.get_symbol()\\n\\n if (self.symbol.type == self.scanner.NAME):\\n device_name = self.names.get_name_string(self.symbol.id)\\n second_device_id = self.names.query(device_name)\\n self.symbol = self.scanner.get_symbol()\\n\\n if (self.symbol.type == self.scanner.PERIOD):\\n self.symbol = self.scanner.get_symbol()\\n\\n if(self.symbol.type == self.scanner.IN_PIN):\\n pin_name = self.names.get_name_string(\\n self.symbol.id)\\n second_port_id = self.names.query(pin_name)\\n self.symbol = self.scanner.get_symbol()\\n\\n if(self.symbol.type == self.scanner.SEMICOLON):\\n self.symbol = self.scanner.get_symbol()\\n else:\\n # Error: Connection has to be terminated by ';'\\n # Stopping symbols: NAME, ';', '}' , 'MONITOR'\\n # or 'END' KEYWORD\\n self.error(\\n self.NO_CONNECT_SEMICOLON, [\\n self.scanner.KEYWORD,\\n self.scanner.SEMICOLON,\\n self.scanner.NAME,\\n self.scanner.RIGHT_CURLY], [\\n self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\\n else:\\n # Error: Valid input pin required\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END'\\n # KEYWORD\\n self.error(\\n self.INPUT_PIN, [\\n self.scanner.KEYWORD,\\n self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY], [\\n self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\\n else:\\n # Error: Period required to specify input pin\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END'\\n # KEYWORD\\n self.error(\\n self.PERIOD_INPUT_PIN, [\\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY],\\n [self.scanner.MONITOR_ID, self.scanner.END_ID])\\n else:\\n # Error: Name string of input device required\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\\n self.error(\\n self.NAME_INPUT, [\\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY],\\n [self.scanner.MONITOR_ID, self.scanner.END_ID])\\n else:\\n # Error: '=' Assignment operator requried\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\\n self.error(\\n self.ASSIGNMENT, [\\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\\n else:\\n # Error: Valid string name required\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\\n self.error(\\n self.NAME_STRING, [\\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\\n\\n # Check for Connection Semantic errors\\n if self.error_count == 0:\\n # Only check for semantic errors if no errors so far\\n err = self.network.make_connection(\\n first_device_id, first_port_id,\\n second_device_id, second_port_id)\\n if err != self.network.NO_ERROR:\\n # Stopping symbols: ';' , '}', 'MONITOR' or 'END' KEYWORD\\n self.error(\\n err, [\\n self.scanner.KEYWORD, self.scanner.SEMICOLON,\\n self.scanner.RIGHT_CURLY], [self.scanner.MONITOR_ID,\\n self.scanner.END_ID])\",\n \"def probe(self):\\n return\",\n \"def test_get_scans(self):\\n pass\",\n \"def scan(self, attribute=None):\\n\\n raise NotImplementedError\",\n \"def arp_monitor_callback(self, pkt: Ether):\\n if pkt[Ether].src in self.users:\\n self.logger.info(\\\"%s の人が来ました！！！\\\", pkt[Ether].src)\\n\\n if ARP not in pkt:\\n return\\n\\n # all packet information will be logged\\n self.logger.debug(\\\"MAC Address:\\\\t%s\\\", pkt[ARP].hwsrc) # ARPSourceMACField: mac address\\n self.logger.debug(\\\"IP Address:\\\\t%s\\\", pkt[ARP].psrc) # SourceIPField: source ip address\\n\\n if pkt[ARP].op not in (1, 2):\\n # pkt[ARP].op == 1: who-has\\n # pkt[ARP].op == 2: is-at\\n return\\n\\n for button in self.buttons:\\n if pkt[ARP].hwsrc == button[\\\"address\\\"]:\\n current_time = int(datetime.now().strftime(\\\"%s\\\"))\\n button[\\\"next_time\\\"] = button.get(\\\"next_time\\\", 0)\\n if button[\\\"next_time\\\"] > current_time:\\n continue\\n button[\\\"next_time\\\"] = current_time + button[\\\"interval\\\"]\\n\\n self.logger.info(\\\"Dash Button %s was pushed\\\", button[\\\"name\\\"])\\n requests.post(button[\\\"url\\\"], data=json.dumps(button[\\\"data\\\"]))\",\n \"def scan(self) -> List[int]:\",\n \"def scan(self) -> List[int]:\",\n \"def _AllAccessPoints(self, interface, frequency):\\n command = ['iw', 'dev', interface, 'scan']\\n if frequency is not None:\\n command += ['freq', str(frequency)]\\n try:\\n # First, bring the device up. If it is already up, this will succeed\\n # anyways.\\n self.BringsUpInterface(interface, 0)\\n\\n output = self._device.CheckOutput(command, log=True)\\n return self._ParseScanResult(output)\\n except device_types.CalledProcessError:\\n return []\",\n \"def __init__(self, main):\\n InjectionScannerBase.__init__(self, main)\",\n \"def discover(self):\\n self.ola_thread.run_discovery(self.universe.get(), self._upon_discover)\\n if self.auto_disc.get():\\n self.ola_thread.add_event(5000, self.discover)\\n else: \\n print \\\"auto_disc is off\\\"\",\n \"async def async_find_available_devices(hass: HomeAssistant, username: str, password: str):\\n\\n result = []\\n\\n devices = await DeviceScanner.async_find_devices(hass)\\n\\n _LOGGER.debug(\\\"Found %d AwoX devices\\\" % (len(devices)))\\n\\n for mac, dev in devices.items():\\n _LOGGER.debug(\\\"Device %s [%s]\\\" % (dev['name'], dev['mac']))\\n try:\\n mylight = DeviceScanner._connect(dev['mac'], username, password)\\n if mylight.session_key:\\n result.append({\\n 'mac': dev['mac'],\\n 'name': mylight.getModelNumber()\\n })\\n mylight.disconnect()\\n except:\\n _LOGGER.debug('Failed to connect [%s]' % dev['mac'])\",\n \"def do_scan(self, arg):\\n results = self._table.scan()\\n for item in results[\\\"Items\\\"]:\\n self._print(_pretty(item))\",\n \"async def websocket_usb_scan(\\n hass: HomeAssistant,\\n connection: ActiveConnection,\\n msg: dict[str, Any],\\n) -> None:\\n usb_discovery: USBDiscovery = hass.data[DOMAIN]\\n if not usb_discovery.observer_active:\\n await usb_discovery.async_request_scan()\\n connection.send_result(msg[\\\"id\\\"])\",\n \"def Scan(self):\\n if self.debug_mode:\\n print 'scanning start'\\n self.SendStartScanSignal()\\n time.sleep(.2)\\n scan = self.wifi.Scan(str(self.hidden_essid))\\n self.LastScan = scan\\n if self.debug_mode:\\n print 'scanning done'\\n print 'found ' + str(len(scan)) + ' networks:'\\n for i, network in enumerate(scan):\\n self.ReadWirelessNetworkProfile(i)\\n self.SendEndScanSignal()\",\n \"def is_scanning(self):\\n raise NotImplementedError\",\n \"def scanExtract(self, plot=False):\\n # check if their are any scans:\\n\\n if (self.raw['OPDC'].data.field('STATE')==20).any() or \\\\\\n (self.raw['OPDC'].data.field('STATE')==21).any():\\n self.scan_opdc='OPDC'\\n self.scan_DL = self.DLtrack\\n else:\\n self.scan_opdc='' # nothing in OPDC\\n\\n if (self.raw['DOPDC'].data.field('STATE')==20).any() or \\\\\\n (self.raw['DOPDC'].data.field('STATE')==21).any():\\n self.scan_opdc='DOPDC'\\n self.scan_DL = self.DDLtrack\\n\\n if self.scan_opdc=='':\\n if self.verbose:\\n print ' info: no scans'\\n self.scanning=False\\n return\\n\\n self.scanning=True\\n print ' ~ setting .scan_opdc to '+self.scan_opdc\\n\\n # determine which is the FSU which is scanning:\\n if self.scan_opdc=='OPDC':\\n self.scan_fsu = self.getKeyword('DEL FT SENSOR')\\n if self.scan_fsu=='FSUA':\\n self.ftk_fsu = 'FSUB'\\n else:\\n self.ftk_fsu = 'FSUA'\\n elif self.getKeyword('DEL FT SENSOR')=='FSUA':\\n self.scan_fsu = 'FSUB'\\n self.ftk_fsu = 'FSUA'\\n else :\\n self.scan_fsu = 'FSUA'\\n self.ftk_fsu = 'FSUB'\\n\\n print ' ~ setting .scan_fsu to '+self.scan_fsu\\n\\n # exctract scans\\n previous_state = 0\\n scan_start= []\\n scan_end = []\\n cond = self.raw[self.scan_opdc].data.field('STATE')\\n state=[]\\n\\n if cond[0]==20 or cond[0]==21: # already scanning\\n scan_start.append(0)\\n for i in np.arange(1,cond.size-1):\\n if cond[i]!=cond[i-1]: # change of state\\n if cond[i-1]==20 or cond[i-1]==21: # end of previous scan\\n scan_end.append(i-1)\\n if cond[i]==20 or cond[i]==21: # start of new scan\\n scan_start.append(i)\\n if cond[-1]==20 or cond[-1]==21: # still scanning\\n scan_end.append(cond.size-1)\\n\\n print ' ~ setting .scan_start (time stamps)'\\n self.scan_start= self.raw[self.scan_opdc].data.field('TIME')[scan_start]\\n print ' ~ setting .scan_end (time stamps)'\\n self.scan_end= self.raw[self.scan_opdc].data.field('TIME')[scan_end]\\n print ' ~ setting .scan_nscans to ', len(self.scan_start)\\n self.scan_nscans = len(self.scan_start)\\n if plot:\\n if isinstance(plot, int):\\n plt.figure(plot)\\n plt.clf()\\n plt.title(self.filename)\\n plt.plot(self.raw[self.scan_opdc].data.field('TIME'), cond,\\\\\\n color='green')\\n plt.plot(self.scan_start, 0*self.scan_start+20.45,'b+')\\n plt.plot(self.scan_end, 0*self.scan_start+20.55,'r+')\\n scan_start = scan_end = cond = []\\n return self.scan_nscans\",\n \"def scan(self, param):\\n\\t\\tself.left(355)\",\n \"def ScanSignal(self, service, antenna, scan_count):\\n raise NotImplementedError\",\n \"def __init__(self, encap, unidir_mode, mtu, rx_bandwidth, tx_bandwidth, snmp_index, islayer2, display_name, mac_address, description, ip_redirect, ip_unreachable, ip_proxy_arp, ip_unicast_reverse_path, vrf, speed = None, duplex = None, duplex_conf = None, fwdClassID = None, flow_control = None, auto_neg = None):\\n self.encap = encap\\n self.unidir_mode = unidir_mode\\n self.mtu = mtu\\n self.rx_bandwidth = rx_bandwidth\\n self.tx_bandwidth = tx_bandwidth\\n self.snmp_index = snmp_index\\n self.islayer2 = islayer2\\n self.display_name = display_name\\n self.mac_address = mac_address\\n self.description = description\\n self.iphelper = []\\n self.ip_redirect = bool(ip_redirect)\\n self.ip_unreachable = bool(ip_unreachable)\\n self.ip_proxy_arp = bool(ip_proxy_arp)\\n self.ip_unicast_reverse_path = bool(ip_unicast_reverse_path)\\n self.vrf = vrf\\n self.speed = speed\\n self.duplex = duplex\\n self.duplex_conf = duplex_conf\\n self.fwd_class_id = fwdClassID\\n self.flow_control = flow_control\\n self.auto_neg = auto_neg\",\n \"def scan(self) -> list[int]:\",\n \"def _run(self):\\n\\n self.sendStatus(C.MSG_PROBE, 'Detecting Management Interface')\\n\\n data = self.getData()\\n host = data.p.host\\n for params in data.p.interfacesList:\\n port = params['port']\\n interfaceHref = params['interfaceHref']\\n self.sendStatus(C.MSG_PROBE, 'Checking %s:%s' % (host, port))\\n if self._queryService(host, port):\\n self._sendResponse(data, interfaceHref, port)\\n self.sendStatus(C.OK, 'Found management interface on %s:%s'\\n % (host, port))\\n return\\n\\n self._sendResponse(data)\\n self.sendStatus(C.OK_1, 'No management interface discovered')\",\n \"def scan(self):\\n if self.isconnected is False:\\n return False\\n nets = self._wlan.scan()\\n nets_list = {}\\n for net in nets:\\n nets_list['SSID'] = str(net[0], 'utf8')\\n # nets_list['bssid'] = str(net[1])\\n nets_list['CHANNEL'] = str(net[2])\\n nets_list['RSSI'] = str(net[3]) + ' dBm'\\n nets_list['SECURITY'] = self._get_secure(net[4])\\n print(nets_list)\",\n \"async def _async_start_scan(self):\\n _LOGGER.debug(\\\"Start scanning\\\")\\n await self.async_setup()\\n if not self._track_interval:\\n self._track_interval = async_track_time_interval(\\n self._hass, self.async_scan, DISCOVERY_INTERVAL\\n )\\n self.async_scan()\",\n \"def scan(message):\\n analyze = lexicon()\\n return analyze.scan(message=message)\",\n \"def MacSecurityCustomScan(window,referenceid):\\n try:\\n allobjects = getAllObjects(window)\\n atomacclick(allobjects[52])\\n ldtp.wait(2)\\n Runwindow = getChildwindows(referenceid)\\n buttons = getAppButtons(Runwindow)\\n atomacclick(buttons[0])\\n except Exception as er:\\n print \\\"Not able to click on MacSecurityCustomScan\\\"\\n return False\",\n \"def connect_ap(self):\\n\\n # scan passively now that the AP has started\\n self.host.scan_passive(200)\\n \\n # connect to AP\\n self.host.sta_connect(self.ap1.macaddr)\\n\\n # expect a management frame for the AP\\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\\n \\n # sanity checks\\n assert(mpdu.typesubtype == wifi.fctl_probereq)\\n assert(wifi.find_ie(mpdu.typesubtype, mpdu.bd, wifi.ie_ssid) == \\\"louis\\\")\\n \\n # send a probe response (optional if the join timeout is long enough)\\n \\n # expect an authentication request\\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\\n \\n # sanity checks\\n assert(mpdu.typesubtype == wifi.fctl_authentication)\\n \\n # send an authenticate response\\n self.ap1.tx_msdu(da=self.host.macaddr, body=wifi.authenticate(), \\n fctl=wifi.fctl_authentication)\\n\\n # expect an association request\\n mpdu = self.ap1.rx_mpdu(wifi.AIR_MGMT)\\n # sanity checks\\n assert(mpdu.typesubtype == wifi.fctl_associationreq)\\n \\n # send an association response\\n self.ap1.tx_msdu(da=self.host.macaddr, body=wifi.associationresponse(), \\n fctl=wifi.fctl_associationresp)\\n \\n # expect a STA connection confirmation with a correct status\\n assert(self.host.sta_connect_confirm() == True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.664008","0.5926287","0.5923922","0.59137505","0.5759931","0.55758125","0.5553623","0.55237395","0.54862815","0.54580957","0.5450833","0.5449223","0.5423793","0.54232156","0.5345217","0.53041476","0.5296159","0.52909553","0.5288499","0.5279574","0.5227543","0.52208644","0.5206643","0.52026826","0.5183174","0.5172757","0.5159475","0.51534396","0.5145913","0.5128077","0.5127419","0.5111137","0.5101898","0.5084671","0.50801873","0.50769264","0.5069192","0.50686693","0.50677574","0.5067267","0.5064569","0.5052325","0.503576","0.5032216","0.50231135","0.5002509","0.5000147","0.49984655","0.4982182","0.49543372","0.49178842","0.49177334","0.48760477","0.48745775","0.4874249","0.48682973","0.48509583","0.4840244","0.48229972","0.4810311","0.48096848","0.47938845","0.47900802","0.47718558","0.4752964","0.47514212","0.47441605","0.4739319","0.47335348","0.47271135","0.47206795","0.47205907","0.47184163","0.47092488","0.4703534","0.47026813","0.47018123","0.46937138","0.46870688","0.46853215","0.46853215","0.46843517","0.4676492","0.46687636","0.46679407","0.46635643","0.4646259","0.4643786","0.46399263","0.4619191","0.46156707","0.46143818","0.46128944","0.46119744","0.46006986","0.4597378","0.45948115","0.45918775","0.459115","0.45815817"],"string":"[\n \"0.664008\",\n \"0.5926287\",\n \"0.5923922\",\n \"0.59137505\",\n \"0.5759931\",\n \"0.55758125\",\n \"0.5553623\",\n \"0.55237395\",\n \"0.54862815\",\n \"0.54580957\",\n \"0.5450833\",\n \"0.5449223\",\n \"0.5423793\",\n \"0.54232156\",\n \"0.5345217\",\n \"0.53041476\",\n \"0.5296159\",\n \"0.52909553\",\n \"0.5288499\",\n \"0.5279574\",\n \"0.5227543\",\n \"0.52208644\",\n \"0.5206643\",\n \"0.52026826\",\n \"0.5183174\",\n \"0.5172757\",\n \"0.5159475\",\n \"0.51534396\",\n \"0.5145913\",\n \"0.5128077\",\n \"0.5127419\",\n \"0.5111137\",\n \"0.5101898\",\n \"0.5084671\",\n \"0.50801873\",\n \"0.50769264\",\n \"0.5069192\",\n \"0.50686693\",\n \"0.50677574\",\n \"0.5067267\",\n \"0.5064569\",\n \"0.5052325\",\n \"0.503576\",\n \"0.5032216\",\n \"0.50231135\",\n \"0.5002509\",\n \"0.5000147\",\n \"0.49984655\",\n \"0.4982182\",\n \"0.49543372\",\n \"0.49178842\",\n \"0.49177334\",\n \"0.48760477\",\n \"0.48745775\",\n \"0.4874249\",\n \"0.48682973\",\n \"0.48509583\",\n \"0.4840244\",\n \"0.48229972\",\n \"0.4810311\",\n \"0.48096848\",\n \"0.47938845\",\n \"0.47900802\",\n \"0.47718558\",\n \"0.4752964\",\n \"0.47514212\",\n \"0.47441605\",\n \"0.4739319\",\n \"0.47335348\",\n \"0.47271135\",\n \"0.47206795\",\n \"0.47205907\",\n \"0.47184163\",\n \"0.47092488\",\n \"0.4703534\",\n \"0.47026813\",\n \"0.47018123\",\n \"0.46937138\",\n \"0.46870688\",\n \"0.46853215\",\n \"0.46853215\",\n \"0.46843517\",\n \"0.4676492\",\n \"0.46687636\",\n \"0.46679407\",\n \"0.46635643\",\n \"0.4646259\",\n \"0.4643786\",\n \"0.46399263\",\n \"0.4619191\",\n \"0.46156707\",\n \"0.46143818\",\n \"0.46128944\",\n \"0.46119744\",\n \"0.46006986\",\n \"0.4597378\",\n \"0.45948115\",\n \"0.45918775\",\n \"0.459115\",\n \"0.45815817\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5530806"},"document_rank":{"kind":"string","value":"7"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":884,"numItemsPerPage":100,"numTotalItems":95772,"offset":88400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTgxNjc1OSwianRpIjoiYTRjZThkNzctN2NlYS00ZmZiLWFjYWItZWEzNjNiZTU2ODgxIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU4MjAzNTksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.bKtid1poSrWNctgUJ_SB69rtGctBnwOA8g1P3U1REgBC2ID0X-Pf4HJWBm2p1-dJlc4cwpb_9MEkoqbZmwdLCQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
in this view the teacher can view other or their own posts information and its content with comments | def teacher_forum_read(request, post_id):
# Deleting admin-typed user session
# Deleting programmer-typed-user session
# Get the current users
current_basic_user = get_current_user(request, User, ObjectDoesNotExist)
current_basic_user_profile = get_current_user_profile(
request,
Us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post(self):\n subject = self.request.get('subject')\n content = self.request.get('post_content')\n post_id = self.request.get('post_id')\n post = Posts.get_by_id(int(post_id))\n user = self.get_active_user()\n user_id = int(user.key().id())\n\n if post and user ... | [
"0.70892215",
"0.7027051",
"0.6993676",
"0.6992631",
"0.69625515",
"0.67901164",
"0.6782363",
"0.6731911",
"0.6589651",
"0.6572402",
"0.6564665",
"0.6538776",
"0.65338194",
"0.63951313",
"0.6388007",
"0.6384502",
"0.635212",
"0.6340285",
"0.63027096",
"0.62704444",
"0.6267225... | 0.62222695 | 27 |
in this view the teacher can update their own posts | def teacher_forum_update(request, post_id):
# Deleting admin-typed user session
# Deleting programmer-typed-user session
# Get the current users
current_basic_user = get_current_user(request, User, ObjectDoesNotExist)
current_basic_user_profile = get_current_user_profile(
request,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post_update():\n\n\n user_id = session['user_id']\n post = request.form.get('post')\n\n Update.add_update(user_id, post)\n\n return \"Updated Post\"",
"def post(self):\n subject = self.request.get('subject')\n content = self.request.get('post_content')\n post_id = self.reques... | [
"0.719283",
"0.71187025",
"0.6917328",
"0.69051486",
"0.6806218",
"0.6707744",
"0.65740657",
"0.6540123",
"0.64509684",
"0.6392218",
"0.63798857",
"0.63631964",
"0.636004",
"0.6351142",
"0.6350981",
"0.63290304",
"0.63229775",
"0.6306676",
"0.6287355",
"0.6259736",
"0.6248741... | 0.7201709 | 0 |
Called after every training iter to evaluate the results. | def after_train_iter(self, trainer: CLIPTrainer):
unwrapped_model = getattr(trainer.model, 'module', trainer.model)
logit_scale = unwrapped_model.clip_model.logit_scale
logit_scale.data = torch.clamp(logit_scale.data, 0, 4.6052) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _evaluate_during_fit(self, test_loader, epoch):",
"def evaluate(self):\n self.training = False",
"def on_train_end(self):",
"def after_train_iter(self, runner):\n if not self.enabled or (runner.iter % self.interval != 0):\n return\n\n # EMA\n self._ema_model()",
"... | [
"0.7540861",
"0.74407065",
"0.7307486",
"0.71198803",
"0.7046394",
"0.7030449",
"0.6988127",
"0.6908733",
"0.68926156",
"0.68738407",
"0.685959",
"0.68251514",
"0.68121606",
"0.67596513",
"0.6704245",
"0.6695779",
"0.66937315",
"0.66708964",
"0.6656686",
"0.6656203",
"0.66471... | 0.0 | -1 |
This function returns the expected return of a policy. | def compute_expected_return_(domain, N, policy, p_init, s_init):
pos = p_init
speed = s_init
expected_return = 0
for i in range(N):
if domain.terminalState(pos, speed):
break
action = policy((pos, speed))
if isinstance(action,str):
action = doma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def policy(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"policy\")",
"def policy(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"policy\")",
"def policy(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"policy\")",
"def get_policy(self):\n return self.ag... | [
"0.69697285",
"0.69697285",
"0.69697285",
"0.6905277",
"0.68301946",
"0.68301946",
"0.6812905",
"0.6812905",
"0.6812905",
"0.6795406",
"0.66836554",
"0.6634797",
"0.66274107",
"0.66274107",
"0.65744597",
"0.6567626",
"0.6544161",
"0.65080476",
"0.6495071",
"0.64834815",
"0.64... | 0.6447522 | 22 |
Verify that an empty Project instance can be created | def test_empty(self):
p = Project()
assert isinstance(p, "Project")
assert len(p.samples) == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_empty_project_create(self):\n\n responses.add(\n responses.POST,\n self.host + \"/manager\",\n json={'message': \"Project name cannot be empty.\", 'status':\"error\"},\n status=200\n )\n\n with self.assertRaises(CreateError):\n se... | [
"0.7565686",
"0.7472565",
"0.7472565",
"0.7472565",
"0.7403371",
"0.718804",
"0.71662986",
"0.7149225",
"0.71124834",
"0.7100387",
"0.7081178",
"0.7073791",
"0.7022151",
"0.70001334",
"0.6987323",
"0.69563067",
"0.69153684",
"0.688426",
"0.68389845",
"0.6823242",
"0.68211466"... | 0.7878026 | 0 |
A Capsule Network for Text Similarity. | def __init__(self, logger, p, h,
save_folder, folder_name,
word_embedding_weights,
filters=[3,4,5,6,7], n_filter_out=30,
capsule_num=5, capsule_dim=6, routings=3,
lr=None, dr_rate=0.2,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CapsNet(input_shape, n_class, routings):\n x = layers.Input(shape=input_shape)\n\n # Layer 1: Just a conventional Conv2D layer\n conv1 = layers.Conv2D(filters=256, kernel_size=7, strides=1, padding='valid', kernel_initializer='he_normal', activation='relu', name='conv1')(x)\n # conv1 = layers.Batch... | [
"0.60780746",
"0.5440451",
"0.5418689",
"0.54040563",
"0.5176601",
"0.5173884",
"0.5169176",
"0.5153165",
"0.5043501",
"0.50323147",
"0.49938765",
"0.4992409",
"0.49833193",
"0.49833193",
"0.4961386",
"0.4961299",
"0.4948286",
"0.4947138",
"0.49295563",
"0.4928063",
"0.490538... | 0.0 | -1 |
Get rss data from url, print single entry to show latest news, else give index number | def get_rss(limit):
rss_data = feedparser.parse(URL)
if limit == 1:
title = rss_data.entries[0].title
link = rss_data.entries[0].link
rss_print(title, link)
else:
for i in range(0, limit):
title = rss_data.entries[i].title
link = rss_data.entries[i].li... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_news(url):\r\n \r\n # parse RSS feed into list of dictionaries\r\n feed = feedparser.parse(url)\r\n\r\n # no RSS feed articles for url\r\n if len(feed['entries']) == 0:\r\n return []\r\n \r\n # get first ten articles from the RSS feed\r\n news = []\r\n i = 0\r\n while T... | [
"0.75352377",
"0.6732173",
"0.66765153",
"0.665058",
"0.65504193",
"0.6381726",
"0.6367431",
"0.63618726",
"0.63228035",
"0.6274171",
"0.6259605",
"0.6249335",
"0.6235714",
"0.623111",
"0.62301534",
"0.6225683",
"0.6224893",
"0.6210909",
"0.6184807",
"0.6138288",
"0.61182076"... | 0.69751287 | 1 |
item cf base contribution score by user | def base_contribute_score():
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score(self):",
"def scoring(self):\n pass",
"def get_contribution_score(self):\n self._extract()\n for key in BitBucketEnum.USER_CONTRIBUTION_MAPPING:\n if key.high >= self.total_no_of_repos >= key.low:\n return BitBucketEnum.USER_CONTRIBUTION_MAPPING[key]\n ... | [
"0.620806",
"0.61869097",
"0.6163936",
"0.61014056",
"0.60496336",
"0.5992737",
"0.5977021",
"0.59710246",
"0.5925449",
"0.59224397",
"0.5915847",
"0.5837098",
"0.58159584",
"0.5804917",
"0.5716641",
"0.5706325",
"0.5704022",
"0.569648",
"0.5693049",
"0.5656052",
"0.5626995",... | 0.66897124 | 0 |
item cf update sim contribution score by user | def upgrad_one_contribute_score(user_total_click_num):
return 1/math.log(1 + user_total_click_num) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_score():\n pass",
"async def changescore(self, ctx, num: int, *, user: discord.Member):\n self.data[ctx.guild.id]['score'][user.id] += num\n return await ctx.send(f\"{user}'s score has been changed to {self.data[ctx.guild.id]['score'][user.id]}.\")",
"def update_score(self):\n ... | [
"0.6815297",
"0.63673705",
"0.6107736",
"0.5988937",
"0.578196",
"0.57493675",
"0.57471263",
"0.57374215",
"0.57188326",
"0.5709215",
"0.56854475",
"0.5675903",
"0.5665249",
"0.5660525",
"0.5657919",
"0.5650883",
"0.5641297",
"0.5637313",
"0.56192154",
"0.56125504",
"0.560394... | 0.51820666 | 99 |
item cf update two sim contribution score by user | def upgrad_two_contribute_score(click_time_one, click_time_two):
delata_time = abs(click_time_two - click_time_one) # 基于秒，在秒的维度，score差别不大
total_sec = 24 * 60 * 60
delata_time = delata_time / total_sec
return 1 / (delata_time + 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_score():\n pass",
"def test_update_score_multiple(self):\r\n self.update_score_multiple()\r\n score = self.openendedmodule.latest_score()\r\n self.assertEquals(score, 1)",
"def update_user_user_sim(self, user_A_info, user_B_info, item_popularity):\n user_A_id, user... | [
"0.663773",
"0.61361444",
"0.60892826",
"0.60406524",
"0.59592223",
"0.59180367",
"0.58450896",
"0.57660127",
"0.5701052",
"0.5667632",
"0.56443375",
"0.5642383",
"0.56333405",
"0.5611411",
"0.5609061",
"0.5608028",
"0.56075853",
"0.5588425",
"0.558617",
"0.5579193",
"0.55763... | 0.0 | -1 |
main flow of itemcf | def main_flow():
user_click, user_click_time = reader.get_user_click("../data/rating.txt") # dict: key:user_id value:[item0,item1...]
item_info = reader.get_item_info("../data/movies.txt")
sim_info = cal_sim_info(user_click, user_click_time)
debug_itemsim(item_info, sim_info)
recom_result = cal_re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def items():",
"def action(self,item):\r\n pass",
"def item(self, item_name):\n\tself.log.info('Not implemented yet... Sorry!')\n\tpass",
"def __init__(self, factions, items):\n self.factions = factions\n self.items = items",
"def items(self):",
"def items(self, course):\r\n p... | [
"0.6443082",
"0.6387463",
"0.63777965",
"0.60275745",
"0.6019542",
"0.5994673",
"0.59896845",
"0.59896845",
"0.58588225",
"0.57363003",
"0.5667128",
"0.5661275",
"0.5661275",
"0.5661275",
"0.56331366",
"0.56323844",
"0.5631494",
"0.5607624",
"0.5602413",
"0.5579382",
"0.55758... | 0.5543466 | 23 |
function that normalizes an unactivated output of a neural network using batch normalization | def batch_norm(Z, gamma, beta, epsilon):
m = np.mean(Z, axis=0)
s = np.std(Z, axis=0)
Z_norm = (Z - m) / np.sqrt(s**2 + epsilon)
Z_ = gamma*Z_norm+beta
return Z_ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def batch_normalization(input_var=None):\n\n # Hyperparameters\n hp = Hyperparameters()\n hp('batch_size', 30)\n hp('n_epochs', 1000)\n hp('learning_rate', 0.01)\n hp('l1_reg', 0.00)\n hp('l2_reg', 0.0001)\n hp('patience', 5000)\n\n # Create connected layers\n # Input layer\n l_in ... | [
"0.7328072",
"0.7253965",
"0.71714145",
"0.7171106",
"0.7072247",
"0.68661773",
"0.68411905",
"0.6806717",
"0.67806345",
"0.6777476",
"0.67409617",
"0.6729022",
"0.67117846",
"0.6690852",
"0.66811794",
"0.6668156",
"0.6655927",
"0.6641299",
"0.66372794",
"0.66166836",
"0.6616... | 0.0 | -1 |
Print a text passed as argument | def _verbose(self,text):
if self.verbose:
print(text) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Print(self, text):\n pass",
"def _print(self, text):\n\t\tif self.verbose:\n\t\t\tprint text",
"def print_text(self, text1, text2):",
"def print_to_string(*args, text):\n print(*args)\n text.append(' '.join(args)+'\\n')",
"def output_text(text):\n print(text)",
"def emu_print(text):\n... | [
"0.8138202",
"0.78461736",
"0.7601609",
"0.75549704",
"0.7503951",
"0.74257314",
"0.7383068",
"0.73288554",
"0.72891396",
"0.7249413",
"0.7162841",
"0.7094371",
"0.7049727",
"0.6959261",
"0.6944948",
"0.6926056",
"0.69175637",
"0.6871908",
"0.68524003",
"0.6846737",
"0.684653... | 0.61918527 | 81 |
Check if p < alpha | def _result(self, p, alpha):
if p < alpha:
self._verbose('reject H0')
return False
else:
self._verbose('Fail to reject H0')
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pvalue_test(self, alpha=0.01):\n CL = int((1-alpha)*100) # confidence level\n \n if self.p_value < alpha:\n print(\"Null hypothesis rejected at {:d}%CL => distributions are different\".format(CL))\n else:\n print(\"Null hypothesis NOT rejected => distr... | [
"0.6903913",
"0.65936184",
"0.64266586",
"0.6404423",
"0.632386",
"0.617827",
"0.6169871",
"0.6154222",
"0.6103611",
"0.60738355",
"0.6071435",
"0.60705245",
"0.6026349",
"0.6003107",
"0.59858894",
"0.5979116",
"0.5966189",
"0.5961026",
"0.59589374",
"0.59494054",
"0.5945314"... | 0.7522837 | 0 |
Check if samples follow a normal distribution, according to the Shapiro test. In case of two samples, check if both the samples follow a normal distribution. | def check_normality(self,alpha = 0.05):
stat1, p = shapiro(self.x)
if self.y is not None:
stat2, p2 = shapiro(self.y)
if p < alpha:
if self.y is not None:
if p2 < alpha:
self._verbose('x and y do not look Gaussian... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_normality(df, features_to_analyse, p_value_threshold=0.05):\n \n is_normal_threshold = 1 - p_value_threshold\n\n normality_results = pd.DataFrame(data=None, index=['stat','pval'], columns=features_to_analyse)\n for f, feature in enumerate(features_to_analyse):\n try:\n stat,... | [
"0.6643706",
"0.6607907",
"0.65663075",
"0.656181",
"0.64410543",
"0.64410543",
"0.64410543",
"0.6429082",
"0.6350439",
"0.6350439",
"0.6350439",
"0.6209372",
"0.60482424",
"0.6045995",
"0.60372585",
"0.58879375",
"0.5857885",
"0.5827552",
"0.5822744",
"0.57681113",
"0.572109... | 0.7163608 | 0 |
Check if samples are correlated. If samples follow a normal distribution, the Pearson Correlation Coefficient is used, otherwise the Spearman Rank Correlation is used. This is a simple test, which does not return the statistics. Correlation is calculated only on the basis of pvalue. | def check_correlation(self, alpha = 0.05):
if self.y is None:
return None
if len(self.x) != len(self.y):
return None
if self.is_normal:
stat, p = pearsonr(self.x, self.y)
else:
stat, p = spearmanr(self.x, self.y)
return s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_correlation_test_perfect_correlation(self):\r\n # These results were verified with R.\r\n obs = correlation_test([1, 2, 3, 4], [1, 2, 3, 4])\r\n self.assertFloatEqual(obs[:2],\r\n (0.99999999999999978, 2.2204460492503131e-16))\r\n self.assertEqual(l... | [
"0.74774",
"0.7184711",
"0.7119329",
"0.70897657",
"0.6877289",
"0.6834545",
"0.67533475",
"0.67221105",
"0.66276866",
"0.6546544",
"0.65236306",
"0.6508728",
"0.6496093",
"0.6476523",
"0.6470434",
"0.6450455",
"0.64336354",
"0.6405079",
"0.6379323",
"0.6374457",
"0.63576996"... | 0.66762716 | 8 |
Check if the sample has been generated in a random way. | def check_randomness(self, alpha = 0.05, cutoff='mean'):
stat, p = runstest_1samp(self.x, cutoff=cutoff)
return self._result(p,alpha) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def confused(self, rand):\n return rand > 0",
"def should_sample(self):\n sampling_probability = 1.0 / self._trans_sample_rate\n log.debug(\"should_sample (sampling prob: %f)\", sampling_probability)\n\n return random() <= sampling_probability",
"def _sample_using_random(\n s... | [
"0.790469",
"0.7621618",
"0.72218984",
"0.72212857",
"0.71029085",
"0.71029085",
"0.71029085",
"0.71029085",
"0.70999885",
"0.7068966",
"0.7065843",
"0.7065843",
"0.683941",
"0.6784703",
"0.6736935",
"0.672948",
"0.6573652",
"0.6567017",
"0.647614",
"0.646925",
"0.646925",
... | 0.64064646 | 29 |
Compare the sample mean to a theoretical value, or compare samples means. If samples follow a normal distribution, the ttest is used if the number of samples is less than n. The ztest, otherwise. If the samples are not normal, the Wilcoxon test is used. | def compare_means(self, value = None, alpha = 0.05, n = 50):
p = None
if self.y is None:
# one sample test
if self.is_normal:
if len(self.x) <= n:
stat, p = ttest_1samp(self.x, value)
else:
stat, p = ztest(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ztest_mean(self, value=0, alternative=\"two-sided\"):\n tstat = (self.mean - value) / self.std_mean\n # TODO: use outsourced\n if alternative == \"two-sided\":\n pvalue = stats.norm.sf(np.abs(tstat)) * 2\n elif alternative == \"larger\":\n pvalue = stats.norm.s... | [
"0.7094629",
"0.6431837",
"0.6431837",
"0.6431837",
"0.63993895",
"0.6372074",
"0.6372074",
"0.6372074",
"0.6092988",
"0.59103686",
"0.5790642",
"0.57710034",
"0.57710034",
"0.57670575",
"0.57560855",
"0.5721325",
"0.56856143",
"0.56267905",
"0.56255215",
"0.56172353",
"0.557... | 0.7326671 | 0 |
Compare the sample distribution to a given cdf (cumulative distribution function), if one sample is provided. The KolmogorovSmirnov Test is used. Compare the samples distribution, if two samples are provided. In this case, the Chi Square test is used. | def compare_distributions(self, alpha = 0.05, cdf = None, args=(), freq = False):
if freq:
if self.y is not None:
stat, p = chisquare(self.x, f_exp = self.y)
else:
return None
else:
if cdf is not None:
stat, p = kstest(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def f_test(chi1,df1,chi2,df2,red_chi = True):\n\n# if chi1/df1 > chi2/df2:\n#\tprob = 2. * f.cdf(chi1/df1, chi2/df2, df1, df2)\n# else:\n#\tprob = 2. * f.cdf(chi2/df2, chi1/df1, df2, df1)\n if red_chi:\n\tfval = (chi1/df1) / (chi2/df2)\n else:\n\tfval = chi1 / chi2\n prob = 2. * f.cdf((chi1/df1) / (... | [
"0.7114142",
"0.62854636",
"0.61177313",
"0.61136746",
"0.5970565",
"0.5863553",
"0.585731",
"0.5784674",
"0.5686073",
"0.55763865",
"0.55630934",
"0.5556157",
"0.5546654",
"0.55157024",
"0.55142057",
"0.5466505",
"0.54631937",
"0.5452893",
"0.54494053",
"0.54422474",
"0.5438... | 0.7335901 | 0 |
Default / route, in case someone decides to navigate to the tracker root. | def index():
return 'There is nothing here.' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def home_page():\n return redirect(url_for(_DEFAULT_ROUTE, _external=True))",
"def root_redirect():\r\n return redirect(url_for(\"display_top\"))",
"def setDefaultRoute( self, intf ):\n self.cmd( 'ip route flush root 0/0' )\n return self.cmd( 'route add default ' + intf )",
"def homepage(... | [
"0.6797867",
"0.62675285",
"0.60095525",
"0.5990974",
"0.58593035",
"0.58282346",
"0.5826653",
"0.57567704",
"0.57441",
"0.5743467",
"0.5726533",
"0.5726533",
"0.5726533",
"0.57250667",
"0.5719507",
"0.5640453",
"0.56262094",
"0.55911887",
"0.5589991",
"0.5589991",
"0.5560961... | 0.0 | -1 |
Handle announces. This is the main, most important pieces, of Telescope. | def announce(user):
time_now = datetime.datetime.now()
q = request.query
update_torrent_in_db = False
# do we track this torrent?
torrent = STORAGE.lookup_torrent(q['info_hash'])
if not torrent:
# we don't
util.fail(REASON_UNREGISTERED_TORRENT)
# we do!
# is leeching o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onBounce(self):\n pass",
"def arp_announce(self):\n pass",
"def announceGenerate(self):",
"async def announce(self, ctx, *, msg):\n if self._announce_msg is not None:\n await self.bot.say(\"Already announcing, wait until complete to\"\n \" iss... | [
"0.59229904",
"0.5829532",
"0.5780667",
"0.5646563",
"0.5597837",
"0.54603344",
"0.5343028",
"0.53424466",
"0.53347164",
"0.5284047",
"0.5257975",
"0.52425146",
"0.5188517",
"0.5169333",
"0.5155788",
"0.5147466",
"0.51322675",
"0.5091058",
"0.505806",
"0.5054131",
"0.5022248"... | 0.0 | -1 |
This function selects num peers from the torrent. If select_seeders is True, then we only select seeders otherwise, we only select leechers. By default we use random.sample, but feel free to implement your own selection system. | def select_people(torrent, select_seeders=True, num=50):
selector = torrent.seeders if select_seeders else torrent.leechers
resp = random.sample(selector, min(num, len(selector)))
return resp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accept_seeders(self, accept_seeds):\n if accept_seeds:\n call = \"d.accepting_seeders.enable\"\n else:\n call = \"d.accepting_seeders.disable\"\n\n m = rtorrent9.rpc.Multicall(self)\n self.multicall_add(m, call)\n\n return m.call()[-1]",
"def select_cl... | [
"0.6207165",
"0.598683",
"0.55595607",
"0.55328155",
"0.54304093",
"0.5372144",
"0.535326",
"0.5268179",
"0.5265857",
"0.52191955",
"0.5192883",
"0.5186847",
"0.5138152",
"0.50706697",
"0.50696266",
"0.5061419",
"0.505665",
"0.50276333",
"0.5012703",
"0.5010171",
"0.5006359",... | 0.8250936 | 0 |
This function performs the compact announce peerlist building | def format_compact(peer, torrent, num):
rawpeers = select_people(torrent, peer.left > 0, num)
peers = []
peers_v6 = []
for rpeer in rawpeers:
try:
rpeer = util.conjure_peer(torrent.peers[rpeer])
except KeyError:
print "Error trying to find %s in %s!" % (rpeer, tor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _announceContainers(self):\n distance = float(self.config.container_manager.announce_distance)\n targetpeers = self.config.owner.getByDistance(self.session, distance)\n\n containers = self.announcequeue\n self.announcequeue = []\n msg = 'announcing %d %ss to %d peers'\n ... | [
"0.59987557",
"0.5810986",
"0.5473048",
"0.537243",
"0.5337154",
"0.53327066",
"0.52817154",
"0.5222992",
"0.5212361",
"0.5167724",
"0.514673",
"0.5068437",
"0.506451",
"0.5046118",
"0.4968012",
"0.48542842",
"0.48511732",
"0.4842829",
"0.48255283",
"0.48201734",
"0.47549656"... | 0.49166185 | 15 |
This function performs the 'standard' announce peerlist building | def format_normal(peer, torrent, num, no_peer_id=False):
rawpeers = select_people(torrent, peer.left > 0, num)
peers = []
for rpeer in rawpeers:
rpeer = util.conjure_peer(torrent.peers[rpeer])
peers.append("d2:ip%d:%s4:port%d:%s" % (len(rpeer.ip), rpeer.ip, len(rpeer.port), rpeer.port))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def announceGenerate(self):",
"def _announceContainers(self):\n distance = float(self.config.container_manager.announce_distance)\n targetpeers = self.config.owner.getByDistance(self.session, distance)\n\n containers = self.announcequeue\n self.announcequeue = []\n msg = 'annou... | [
"0.6372707",
"0.63178474",
"0.6009953",
"0.59099126",
"0.5766325",
"0.5695178",
"0.566982",
"0.5651715",
"0.5625587",
"0.5520921",
"0.5513589",
"0.5486276",
"0.5473989",
"0.54553825",
"0.5452486",
"0.5325521",
"0.53041273",
"0.52763605",
"0.52356195",
"0.52241623",
"0.5223752... | 0.0 | -1 |
Create some tasks by default to show off the site | def create_dummy_content(user_id):
task = TodoItem(
user=user_id,
task=u'Find a shrubbery',
tags=[u'quest', u'ni', u'knight'],
due_date=datetime.utcnow() + timedelta(days=60),
)
DBSession.add(task)
task = TodoItem(
user=user_id,
task=u'Search for the holy ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tasks():",
"def create_task():",
"def generate_tasks(self, task):",
"def create_tasks(self):\n self.create_passport_task()\n\n self.create_visa_task()\n\n self.create_vaccines_task()\n self.create_malaria_task()\n\n self.create_weather_task()\n self.create_flight... | [
"0.7419465",
"0.7254781",
"0.7205577",
"0.71818626",
"0.6948934",
"0.6753616",
"0.65834916",
"0.65572876",
"0.64935565",
"0.64540464",
"0.64521545",
"0.64098644",
"0.6391006",
"0.63651496",
"0.6327748",
"0.63197774",
"0.63186055",
"0.6314866",
"0.63021183",
"0.6296633",
"0.62... | 0.0 | -1 |
The FNN enc and FNN dec of the Denoiser. | def __init__(self, input_dim):
super(ps_FNNDenoiser, self).__init__()
self._input_dim = input_dim
self.fnn_enc = Linear(self._input_dim, int(self._input_dim / 2))
self.fnn_dec = Linear(int(self._input_dim / 2), self._input_dim)
self.initialize_module() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _encode(self):\n with tf.variable_scope('encoding'):\n self.sep_p_encodes, _ = bilstm_layer(self.p_emb, self.p_length, self.hidden_size)\n tf.get_variable_scope().reuse_variables()\n self.sep_q_encodes, _ = bilstm_layer(self.q_emb, self.q_length, self.hidden_size)\n ... | [
"0.55594534",
"0.5484632",
"0.5475248",
"0.5433192",
"0.53741807",
"0.5338545",
"0.53328925",
"0.5324473",
"0.5303475",
"0.52772224",
"0.5273364",
"0.5268513",
"0.5253051",
"0.5249744",
"0.52083373",
"0.5200783",
"0.51895785",
"0.5175783",
"0.51673234",
"0.5162198",
"0.513767... | 0.64204884 | 0 |
Creates a new user with a unique email address. | def create_user():
try:
payload = _validatePayload(request)
timestamp = int(time.time() * 1000)
user = {
'name': payload.get('name'),
'email': payload.get('email'),
'password': _encodePassword(payload.get('password')),
'createdAt': timestamp,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_user(email, password):\n try:\n User(email=email, password=password)\n except IntegrityError:\n print('Error: Duplicate email address')",
"def create_user(email='user@example.com', password='testpass123'):\n return get_user_model().objects.create_user(email=email... | [
"0.82201904",
"0.8129927",
"0.8125364",
"0.8110234",
"0.8090691",
"0.8072226",
"0.80139476",
"0.80017096",
"0.7963306",
"0.7933557",
"0.7929344",
"0.79290503",
"0.79205734",
"0.7909613",
"0.79067945",
"0.79038435",
"0.78828025",
"0.78814787",
"0.7878793",
"0.7878176",
"0.7877... | 0.7804401 | 42 |
Check that all expected fields are in payload and have valid values. | def _validatePayload(request):
return {
'name': _validateField(request, 'name'),
'email': _validateField(request, 'email'),
'password': _validateField(request, 'password'),
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_fields(self, content: JsonDict) -> None:\n self.assertIn(\"id\", content)\n self.assertIn(\"received_ts\", content)\n self.assertIn(\"room_id\", content)\n self.assertIn(\"event_id\", content)\n self.assertIn(\"user_id\", content)\n self.assertIn(\"sender\", con... | [
"0.70033276",
"0.6977844",
"0.6937503",
"0.6816091",
"0.66672194",
"0.66445965",
"0.64546615",
"0.63872355",
"0.63409096",
"0.6291471",
"0.6289986",
"0.62696654",
"0.62582856",
"0.6215967",
"0.6204885",
"0.6201266",
"0.61564136",
"0.61518383",
"0.6137182",
"0.6115781",
"0.611... | 0.65139276 | 6 |
Check that all field is a string with one or more characters. | def _validateField(request, field):
val = request.json.get(field, '')
if type(val) is not str or len(val) < 1:
raise Exception('Invalid {}'.format(field))
elif field == 'password':
return _validatePassword(val)
else:
return val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_empty_string(self):\n self.assertTrue(all_unique_chars(\"\"))",
"def test_empty_string(self):\n self.assertTrue(all_unique_chars_no_set(\"\"))",
"def check_input(input_string):\n if len(input_string) > 50: # check if length of name is less than 50 ir not\n return False\n el... | [
"0.6745913",
"0.6696813",
"0.6637041",
"0.66326654",
"0.6543104",
"0.6416302",
"0.63635653",
"0.63391274",
"0.632886",
"0.6321404",
"0.62952805",
"0.6273974",
"0.6257625",
"0.623946",
"0.6231969",
"0.6192615",
"0.61773145",
"0.61684555",
"0.6159218",
"0.6149323",
"0.61417013"... | 0.0 | -1 |
Checks that password is longer than 8 characters and that it contains upper and lowercase letters and at least one number. | def _validatePassword(password):
uppercaseChars = re.search('[A-Z]', password)
lowercaseChars = re.search('[a-z]', password)
if len(password) < 8:
raise Exception("Password must be at lest 8 letters")
elif re.search('[0-9]', password) is None:
raise Exception("Password must contain atl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_password(password):\n return isinstance(password, str) and len(password) >= 8 and \\\n re.search(r'[A-Z]', password) and re.search(r'[0-9]', password)",
"def validate_password(self, password_field):\n if len(password_field.data) < 8:\n raise ValidationError('Y... | [
"0.8766334",
"0.81289023",
"0.81200564",
"0.80847394",
"0.8010414",
"0.7993104",
"0.78297937",
"0.7824315",
"0.78155774",
"0.7803249",
"0.7767423",
"0.77415377",
"0.7721763",
"0.76823556",
"0.7681701",
"0.7652257",
"0.76191694",
"0.7600995",
"0.75715125",
"0.7533356",
"0.7520... | 0.84100276 | 1 |
This route is here to make testing user creatation easier. Since, it's a demo project we assume every user is an admin. | def list():
try:
data = table.scan()
users = data.get('Items', None)
if users is None:
return jsonify({'error': 'Error fetching users'}), 400
resp = {
'count': len(users),
'users': users
}
return jsonify(resp)
except BaseExcept... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_09_admin_users_as_admin(self):\r\n self.register()\r\n res = self.app.get('/admin/users', follow_redirects=True)\r\n assert \"Manage Admin Users\" in res.data, res.data",
"def test_admin_create_user(self):\n resp = self.admin_register()\n reply = self.admin_login()\n ... | [
"0.76988465",
"0.7372535",
"0.73059255",
"0.73059255",
"0.73059255",
"0.7280059",
"0.7163433",
"0.71625036",
"0.7158194",
"0.71431285",
"0.71431285",
"0.7040476",
"0.70340353",
"0.7007233",
"0.69826454",
"0.69826454",
"0.69826454",
"0.69826454",
"0.6981487",
"0.69673336",
"0.... | 0.0 | -1 |
Chooses a random word from this list of ridiculous English words and returns it | def get_computer_play():
return random.choice(['Ailurophile', 'Assemblage', 'Becoming', 'Beleaguer',
'Brood', 'Bucolic', 'Bungalow', 'Chatoyant', 'Comely',
'Conflate', 'Cynosure', 'Dalliance', 'Demesne', 'Demure',
'Denouement', 'De... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def return_word():\n wordlist = load_words()\n word = random.choice(wordlist)\n return word",
"def random_word(wordlist):\n return random.choice(wordlist)",
"def choose_word(word_list):\n word = random.choice(word_list)\n word = word.lower()\n return word",
"def randomWord(wordList):\n ... | [
"0.8440277",
"0.8287809",
"0.8253476",
"0.81828856",
"0.81828856",
"0.81828856",
"0.8174142",
"0.8137592",
"0.8044216",
"0.7982263",
"0.7900026",
"0.7900026",
"0.7900026",
"0.7837851",
"0.7800308",
"0.77765477",
"0.7773012",
"0.7644745",
"0.7625416",
"0.76155937",
"0.75968814... | 0.0 | -1 |
Runs the word game, user vs computer, using your functions. Will not work until you have them implemented correctly! | def play_game():
cutoff = 30 # CHANGE THIS IF YOU WANT A LONGER GAME!
user_total = 0
comp_total = 0
print "First to", cutoff, "points wins!"
print
while user_total < cutoff and comp_total < cutoff:
# get the user and computer words, convert to lower case
user_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n ans = random_word()\n run_game(ans, N_TURNS)",
"def main():\n secret_word = get_word()\n play_game(secret_word)",
"def main():\r\n # Initialize words from specific file\r\n words_list = hangman_helper.load_words()\r\n # Run single game with given word list to choose from\r\n ... | [
"0.7434662",
"0.73721844",
"0.70454",
"0.6908657",
"0.68199605",
"0.6661747",
"0.6661747",
"0.6566236",
"0.65410364",
"0.6530803",
"0.65254414",
"0.6475175",
"0.63247544",
"0.6285411",
"0.62551254",
"0.6210292",
"0.6180333",
"0.61633396",
"0.6153147",
"0.61445504",
"0.6130061... | 0.6605025 | 7 |
Make sure that bzr is available when running in CI. | def test_ensure_bzr_available():
assert is_bzr_installed() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_deploy():\n from fabdeploy.django import test as django_test\n django_test()\n git.add_commit_pull()\n git.push()",
"def _get_bzr_version():\n try:\n value, output, _ = run_shell_command('bzr --version',\n shell=True,\n ... | [
"0.6085656",
"0.5957647",
"0.57713366",
"0.57140285",
"0.56965965",
"0.564991",
"0.5644301",
"0.55707353",
"0.5557706",
"0.5546851",
"0.55418056",
"0.5531211",
"0.5515171",
"0.5443203",
"0.54164296",
"0.53966993",
"0.5394848",
"0.5389168",
"0.5381132",
"0.5380146",
"0.5363527... | 0.7557124 | 0 |
Gets valid user credentials from storage. If nothing has been stored, or if the stored credentials are invalid, the OAuth2 flow is completed to obtain the new credentials. | def get_credentials(commandline_flags=None):
home_dir = os.path.expanduser('~')
credential_dir = os.path.join(home_dir, '.credentials')
if not os.path.exists(credential_dir):
os.makedirs(credential_dir)
credential_path = os.path.join(credential_dir,
'drive-pyth... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_user_credentials(self, storage):\n # Set up a Flow object to be used if we need to authenticate.\n flow = client.flow_from_clientsecrets(\n self.client_secrets,\n scope=self.api_scopes,\n message=tools.message_if_missing(self.client_secrets))\n\n # Re... | [
"0.7571535",
"0.7355913",
"0.71540344",
"0.7120326",
"0.70709026",
"0.7026004",
"0.6991126",
"0.69621813",
"0.6947445",
"0.69403243",
"0.69352114",
"0.69063586",
"0.68998873",
"0.6899442",
"0.6857314",
"0.6828924",
"0.679187",
"0.6784848",
"0.677493",
"0.677493",
"0.677493",
... | 0.6167278 | 90 |
Evaluate a policy given an environment and a full description of the environment's dynamics. | def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):
# Start with a random (all 0) value function
V = np.zeros(env.nS)
while True: #any(Vdiff > theta):
delta_V = 0
for i in range(env.nS):
# need to calculate the value of taking each of ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def policy_eval(env, policy, V, discount_factor):\n policy_value = np.zeros(env.nS)\n for state, action in enumerate(policy):\n for probablity, next_state, reward, info in env.P[state][action]:\n policy_value[state] += probablity * (reward + (discount_factor * V[next_state]))\n\n return ... | [
"0.68249816",
"0.64548814",
"0.642272",
"0.6244586",
"0.6161337",
"0.6145352",
"0.60711825",
"0.6059267",
"0.6047221",
"0.5992679",
"0.5959183",
"0.5959183",
"0.5846315",
"0.5827876",
"0.5783816",
"0.5746275",
"0.57402617",
"0.5729169",
"0.5669494",
"0.5669214",
"0.56520754",... | 0.60442084 | 9 |
If memory uses threads. | def is_async(self) -> bool:
return self.__is_async | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def manage_threads(_) -> int:\n return 1 << 33",
"def manage_threads(_) -> int:\n return 1 << 33",
"def parallel_safe(self):\n return True",
"def use_public_threads(_) -> int:\n return 1 << 34",
"def use_public_threads(_) -> int:\n return 1 << 34",
"def getThreads():\r\... | [
"0.6849884",
"0.6849884",
"0.6510201",
"0.64524364",
"0.64524364",
"0.639639",
"0.63069963",
"0.63069963",
"0.6204849",
"0.6157713",
"0.6146597",
"0.5962862",
"0.5948838",
"0.5911457",
"0.59005356",
"0.5876403",
"0.5805998",
"0.5766067",
"0.5755047",
"0.5743835",
"0.5706686",... | 0.0 | -1 |
Fixture responsible for searching a folder with the same name of tests module and, if available, moving all contents to a temporary directory so tests can use them freely. | def datadir(tmpdir, request):
filename = request.module.__file__
test_dir, _ = os.path.splitext(filename)
if os.path.isdir(test_dir):
dir_util.copy_tree(test_dir, str(tmpdir))
return tmpdir | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_from_temp_directory(self):",
"def fixture_project_dir(tmpdir_factory) -> Path:\n my_tmpdir = Path(tmpdir_factory.mktemp(\"data\"))\n yield my_tmpdir\n shutil.rmtree(str(my_tmpdir))",
"def setUp(self):\n tempDir.safe_mkdir(parents=True)\n os.chdir(tempDir.as_posix())",
"def testing_fil... | [
"0.70360804",
"0.69717216",
"0.67078876",
"0.6637878",
"0.66373396",
"0.6632224",
"0.6616858",
"0.66160154",
"0.6607836",
"0.65883696",
"0.65883696",
"0.6542226",
"0.6530276",
"0.6501414",
"0.64873177",
"0.6470115",
"0.6458611",
"0.64384073",
"0.6433042",
"0.6402625",
"0.6392... | 0.0 | -1 |
Calculates the accuracy of the NaiveBayes algorithm | def calculateResults(predictions, answers):
t = 0
f = 0
for i in range(len(answers)):
if predictions[i] == answers[i]:
t += 1
else:
f += 1
print("The Percent of Correct Predictions is {t}%".format(t=round((t * 100 / len(answers)), 1)))
print("The Pe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def naiveBayes(self):\n acc = 0\n #for each example in the test-set\n for d in self.dev:\n pred_good = self.prob_True\n pred_bad = self.prob_False\n #calc the probability for yes and no\n for index in range(len(d[0])):\n pred_good *= s... | [
"0.74899286",
"0.7438116",
"0.72386056",
"0.7164012",
"0.71495265",
"0.71005416",
"0.7094217",
"0.7050752",
"0.70441353",
"0.7004448",
"0.69885844",
"0.69650227",
"0.6953509",
"0.6929957",
"0.69283235",
"0.6926801",
"0.6924115",
"0.69222695",
"0.6908321",
"0.68867135",
"0.687... | 0.0 | -1 |
Removes the api version from the href. | def remove_trailing_version_from_href(href):
parsed_url = urlparse.urlsplit(href)
url_parts = parsed_url.path.rsplit('/', 1)
# NOTE: this should match vX.X or vX
expression = re.compile(r'^v([0-9]+|[0-9]+\.[0-9]+)(/.*|$)')
if not expression.match(url_parts.pop()):
LOG.debug('href %s does no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_link():",
"def _strip_version(endpoint):\n if endpoint.endswith('/'):\n endpoint = endpoint[:-1]\n url_bits = endpoint.split('/')\n if re.match(r'v\\d+\\.?\\d*', url_bits[-1]):\n endpoint = '/'.join(url_bits[:-1])\n return endpoint",
"def remove_study_version_dbgap_link(app... | [
"0.6193792",
"0.60921365",
"0.6078247",
"0.5901628",
"0.58109987",
"0.5665509",
"0.5512722",
"0.5504835",
"0.54142016",
"0.54117763",
"0.54097027",
"0.529149",
"0.5282961",
"0.5278921",
"0.5244824",
"0.5241325",
"0.5213375",
"0.52127796",
"0.5143578",
"0.51431906",
"0.5127807... | 0.6932704 | 0 |
Convenience method for joining parts of a URL Any leading and trailing '/' characters are removed, and the parts joined together with '/' as a separator. If last element of 'parts' is an empty string, the returned URL will have a trailing slash. | def url_join(*parts):
parts = parts or [""]
clean_parts = [part.strip("/") for part in parts if part]
if not parts[-1]:
# Empty last element should add a trailing slash
clean_parts.append("")
return "/".join(clean_parts) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _url_join(self, *parts):\n return \"/\".join(map(lambda fragment: fragment.rstrip('/'), parts))",
"def urljoin(*parts):\n def _gen(parts):\n prev = None\n for part in parts:\n if not part:\n continue\n if not prev:\n prev = part\n ... | [
"0.84102356",
"0.82989657",
"0.81839633",
"0.8064334",
"0.79943556",
"0.7882533",
"0.786418",
"0.7630931",
"0.7371415",
"0.6973159",
"0.6902921",
"0.68932605",
"0.68614244",
"0.68232083",
"0.68219864",
"0.67865187",
"0.6781647",
"0.677915",
"0.6769976",
"0.66617376",
"0.66453... | 0.8668859 | 0 |
Get project id from request url if present or empty string otherwise | def _get_project_id(self, request):
project_id = request.environ["masakari.context"].project_id
if project_id in request.url:
return project_id
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_project_id():\n return os.environ.get('project')",
"def __get_project_id(self):\n request = urllib2.Request(self.host_api+\"projects?owner=\"+urllib2.quote(self.owner)+\"&display_name=\"+urllib2.quote(self.project_name))\n # request = urllib2.Request(self.host_api+\"projects?owner=\"+sel... | [
"0.7154755",
"0.70934504",
"0.6984654",
"0.6971364",
"0.6971364",
"0.6971364",
"0.6971364",
"0.6971364",
"0.6708459",
"0.66086614",
"0.6594982",
"0.6558914",
"0.6558914",
"0.6518638",
"0.64804995",
"0.64804995",
"0.64804995",
"0.64804995",
"0.6430157",
"0.63776463",
"0.635488... | 0.8752246 | 0 |
Return href string with proper limit and marker params. | def _get_next_link(self, request, identifier, collection_name):
params = request.params.copy()
params["marker"] = identifier
prefix = self._update_masakari_link_prefix(request.application_url)
url = url_join(prefix,
self._get_project_id(request),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _insert_links(self, limit, offset):\n if limit is None:\n limit = 0\n if offset is None:\n offset = 0\n\n import ckan.plugins.toolkit as toolkit\n import urllib\n import urllib2\n import urlparse\n '''Adds link to the next/prev part (same l... | [
"0.6050559",
"0.58291566",
"0.58065987",
"0.5738649",
"0.57187855",
"0.5685541",
"0.5682918",
"0.56696624",
"0.5668175",
"0.56174916",
"0.5611827",
"0.556549",
"0.55634326",
"0.55570424",
"0.5539954",
"0.5521814",
"0.55146676",
"0.54406375",
"0.5396114",
"0.5370539",
"0.53625... | 0.6111639 | 0 |
Return an href string pointing to this object. | def _get_href_link(self, request, identifier, collection_name):
prefix = self._update_masakari_link_prefix(request.application_url)
return url_join(prefix,
self._get_project_id(request),
collection_name,
str(identifier)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def href(self) -> str:\n result = self.get_href()\n if result is None:\n raise ValueError(f\"{self} does not have an HREF set.\")\n return result",
"def href(self):\n return self._href",
"def href(self):\n return self._href",
"def get_anchor_tag(self):\n r... | [
"0.85301834",
"0.8274691",
"0.8274691",
"0.74851286",
"0.74171",
"0.7406751",
"0.7405322",
"0.73963124",
"0.7284488",
"0.7258416",
"0.7235905",
"0.7235905",
"0.7235905",
"0.71688205",
"0.71688205",
"0.7152213",
"0.7152213",
"0.7152213",
"0.7152213",
"0.7152213",
"0.7152213",
... | 0.65351176 | 55 |
Create a URL that refers to a specific resource. | def _get_bookmark_link(self, request, identifier, collection_name):
base_url = remove_trailing_version_from_href(request.application_url)
base_url = self._update_masakari_link_prefix(base_url)
return url_join(base_url,
self._get_project_id(request),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def anchor_to_resource(resource, post_create_func=None, title=None):\n href = resource.get('href')\n resource = {\n \"description\": title or resource.text_content().encode('utf8'),\n \"name\": href.split('/')[-1],\n \"url\": href,\n \"format\": href[href.rfind(\".\")+1:].upper()... | [
"0.6941272",
"0.68629503",
"0.67490333",
"0.67356056",
"0.67045265",
"0.6702004",
"0.6671467",
"0.66025174",
"0.657523",
"0.6491968",
"0.64915454",
"0.6396327",
"0.63947356",
"0.6392973",
"0.6361397",
"0.6360611",
"0.63332766",
"0.6327195",
"0.6323129",
"0.62874043",
"0.62748... | 0.0 | -1 |
Extract integer param from request or fail. | def _get_int_param(request, param):
try:
int_param = utils.validate_integer(request.GET[param], param,
min_value=0)
except exception.InvalidInput as e:
raise webob.exc.HTTPBadRequest(explanation=e.format_message())
return int_param | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_positive_int_param(request, query_params, param_name):\n param = query_params.get(param_name)\n if not param:\n return None\n try:\n param = int(param)\n if param <= 0:\n raise ValueError()\n return param\n except ValueError:\n request.respond('query parameter \"%s\" must be inte... | [
"0.737176",
"0.6762896",
"0.65760314",
"0.654616",
"0.64901876",
"0.64549387",
"0.64112216",
"0.6378887",
"0.62955576",
"0.6280391",
"0.6223324",
"0.6150399",
"0.61250275",
"0.6049242",
"0.60073894",
"0.60064703",
"0.597818",
"0.5947589",
"0.594178",
"0.5940132",
"0.5923045",... | 0.7985158 | 0 |
Extract marker id from request or fail. | def _get_marker_param(request):
return request.GET['marker'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_marker(self, req):\n try:\n marker = int(req.str_params.get('marker', None))\n except ValueError:\n raise exc.HTTPBadRequest(\"marker param must be an integer\")\n return marker",
"def _get_marker(self, req):\n marker = req.params.get('marker', None)\n\n... | [
"0.7499812",
"0.7286604",
"0.6193086",
"0.6187371",
"0.6064134",
"0.5921961",
"0.5867724",
"0.5837601",
"0.5775515",
"0.5747198",
"0.56819403",
"0.55907786",
"0.55435884",
"0.5493284",
"0.53950775",
"0.53246915",
"0.53200275",
"0.52863353",
"0.5239298",
"0.5203371",
"0.519169... | 0.61523575 | 4 |
Return marker, limit tuple from request. | def get_pagination_params(request):
params = {}
if 'limit' in request.GET:
params['limit'] = _get_int_param(request, 'limit')
if 'page_size' in request.GET:
params['page_size'] = _get_int_param(request, 'page_size')
if 'marker' in request.GET:
params['marker'] = _get_marker_param... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_limit_and_marker(request, max_limit=CONF.osapi_max_limit):\n params = get_pagination_params(request)\n limit = params.get('limit', max_limit)\n limit = min(max_limit, limit)\n marker = params.get('marker')\n\n return limit, marker",
"def _offset_request(self, limit: int, offset: int) -> Di... | [
"0.8131",
"0.65567756",
"0.6452999",
"0.6240873",
"0.5774155",
"0.5759827",
"0.56829715",
"0.5569735",
"0.5445113",
"0.5394796",
"0.53077626",
"0.5298797",
"0.5257216",
"0.5257216",
"0.52530754",
"0.5225444",
"0.5222232",
"0.52028453",
"0.519762",
"0.51738304",
"0.51058054",
... | 0.58075047 | 4 |
get limited parameter from request. | def get_limit_and_marker(request, max_limit=CONF.osapi_max_limit):
params = get_pagination_params(request)
limit = params.get('limit', max_limit)
limit = min(max_limit, limit)
marker = params.get('marker')
return limit, marker | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_limit(self, req):\n try:\n limit = int(req.str_params.get('limit', MAX_ITEM_LIMIT))\n except ValueError:\n raise exc.HTTPBadRequest(\"limit param must be an integer\")\n\n if limit < 0:\n raise exc.HTTPBadRequest(\"limit param must be positive\")\n\n ... | [
"0.71339375",
"0.6901796",
"0.66054535",
"0.65772295",
"0.6466222",
"0.63059694",
"0.6286302",
"0.62795705",
"0.62795705",
"0.62707806",
"0.61873186",
"0.6120285",
"0.60292095",
"0.6021805",
"0.6021805",
"0.59807783",
"0.59795976",
"0.5977163",
"0.58563024",
"0.57849246",
"0.... | 0.66025305 | 3 |
Retrieves sort keys/directions parameters. Processes the parameters to create a list of sort keys and sort directions that correspond to the 'sort_key' and 'sort_dir' parameter values. These sorting parameters can be specified multiple times in order to generate the list of sort keys and directions. The input parameter... | def get_sort_params(input_params, default_key='created_at',
default_dir='desc'):
params = input_params.copy()
sort_keys = []
sort_dirs = []
while 'sort_key' in params:
sort_keys.append(params.pop('sort_key').strip())
while 'sort_dir' in params:
sort_dirs.append(p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_sort_params(sort_keys, sort_dirs, default_keys=None,\n default_dir='asc'):\n if default_keys is None:\n default_keys = ['created_at']\n\n # Determine direction to use for when adding default keys\n if sort_dirs and len(sort_dirs):\n default_dir_value = sort... | [
"0.72372377",
"0.61290574",
"0.58718145",
"0.5770037",
"0.5766348",
"0.5757473",
"0.57474947",
"0.5730805",
"0.5716173",
"0.566162",
"0.566162",
"0.55787706",
"0.55084854",
"0.54334277",
"0.5427028",
"0.5402491",
"0.53888994",
"0.5301868",
"0.5285601",
"0.52648246",
"0.524790... | 0.75024617 | 0 |
Calculates angular separation in degrees between two objects. ra1, dec1, ra2, dec2 are the coordinates of the two targets in decimal | def ang_sep(ra1,ra2,dec1,dec2):
#print('target_coords: ',ra1,dec1,ra2,dec2)
dra = abs(ra1-ra2)
if dra > 180:
dra = 360 - dra
#convert relevant angles to radians
dec1_rad = np.radians(dec1)
dec2_rad = np.radians(dec2)
ra1_rad = np.radians(ra1)
ra2_rad = np.radians(ra2)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def angular_separation(r1: np.ndarray, r2: np.ndarray) -> float:\n # First compute the rotation that maps r1 to r2.\n dr = r2 @ r1.transpose()\n # Then extract the angle.\n _, angle = transforms3d.axangles.mat2axangle(dr)\n # Normalise the angle.\n if angle > np.pi:\n angle = 2 * np.pi - a... | [
"0.75287974",
"0.7102566",
"0.7089916",
"0.70077497",
"0.64021134",
"0.6401823",
"0.63344175",
"0.6267705",
"0.62399036",
"0.6158805",
"0.5949738",
"0.5878258",
"0.5862246",
"0.5824026",
"0.5824026",
"0.5782027",
"0.5755602",
"0.5734374",
"0.5721164",
"0.56958395",
"0.5695083... | 0.7627773 | 0 |
Generate and import Neo4j nodes and edges tables. | def main(load: bool, load_only: bool, force: bool, with_sudo: bool):
paths = []
na = NodeAssembler()
for processor_cls in processor_resolver:
if not processor_cls.importable:
continue
click.secho(f"Checking {processor_cls.name}", fg="green", bold=True)
if not load_only:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_all_tables(self):\n pass",
"def writeImports2File(self, file, indent = \" \"):\r\n # import each entity and its associated graphical file\r\n for obj in self.listNodes.keys():\r\n file.write(indent+\"from \"+obj+\" import \"+obj+\"\\n\")\r\n if not obj[0:4] == \"ASG... | [
"0.58826673",
"0.5827632",
"0.5805161",
"0.5751973",
"0.5716268",
"0.5685903",
"0.5562104",
"0.54953635",
"0.5493187",
"0.5487213",
"0.547548",
"0.54685795",
"0.5467668",
"0.54589504",
"0.54585034",
"0.54548496",
"0.54250515",
"0.5400322",
"0.5389987",
"0.5389987",
"0.538599"... | 0.5555874 | 7 |
>>> data = ' CHANG CHUB SEM CHOG RIN PO CHE' >>> normalizeVerseBreak(data) 'CHANG CHUB SEM CHOG RIN PO CHE' >>> data = 'CHANG CHUB SEM CHOG RIN PO CHE /' >>> normalizeVerseBreak(data) 'CHANG CHUB SEM CHOG RIN PO CHE / ' >>> data = 'CHANG CHUB SEM CHOG RIN PO CHE/' >>> normalizeVerseBreak(data) 'CHANG CHUB SEM CHOG RIN ... | def normalizeVerseBreak(string):
string = string.strip()
string = re.sub('\s+/\s?', '/', string)
string = string.replace('/', ' / ')
return string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_data(data):\n return ' '.join(data.upper().split())",
"def whitespace_sorter(sentence):\r\n sentence_copy = str(sentence)\r\n sentence_copy = sentence_copy.strip() #Remove leading and trailing whitespace (/s)\r\n sentence_copy = re.sub(\" +\", \" \", sentence_copy) #Coerces all multiple... | [
"0.5627176",
"0.5514943",
"0.5392916",
"0.5288041",
"0.5284384",
"0.52544844",
"0.5122039",
"0.5120344",
"0.5118473",
"0.5098055",
"0.50781465",
"0.5062507",
"0.50251794",
"0.5006783",
"0.49919733",
"0.49425045",
"0.4937896",
"0.49310207",
"0.49207133",
"0.48972574",
"0.48935... | 0.7600039 | 0 |
string that repersents the boundary condition imposed to the energy space wavefunctions | def case(self) -> str:
return self._case | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _boundary_value(self) -> str:\n ...",
"def __str__(self):\n\t\tself.__normalize()\n\t\tmyStr ='# min = %lf\\n'%(float(self._min))\n\t\tmyStr+='# max = %lf\\n'%(float(self._max))\n\t\tmyStr+='# dx = %lf\\n'%(float(self._dx))\n\t\tmyStr+='# av = %lf (sErr = %lf)\\n'%(self.av,self.sErr)\n\t\tmyStr+... | [
"0.6199592",
"0.5976905",
"0.5922983",
"0.5851359",
"0.5840542",
"0.57978845",
"0.565",
"0.5632981",
"0.56277174",
"0.56101704",
"0.55923355",
"0.5581757",
"0.5577677",
"0.54971206",
"0.54722154",
"0.5458786",
"0.5429907",
"0.54255545",
"0.5425042",
"0.54207623",
"0.5415468",... | 0.0 | -1 |
object of type 'New_Style_Boundary' containing all the funcionality that specifically depends on the choice of the boundary condition' | def boundary_lib(self) -> New_Style_Boundary:
return self._boundary_lib | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def boundary(self): # -> BaseGeometry:\n ...",
"def boundary_conditions(self):\n pass",
"def boundary_conditions(particle_outer_radius, boundary_temp):\n\n boundary_condition = [particle_outer_radius, boundary_temp]\n\n return boundary_condition",
"def apply_boundary_conditions(self):\n ... | [
"0.65627015",
"0.6453875",
"0.62970746",
"0.6121181",
"0.60591966",
"0.60413367",
"0.5958082",
"0.5926671",
"0.59197557",
"0.5884173",
"0.58623636",
"0.581189",
"0.58097094",
"0.5777472",
"0.57357526",
"0.5722252",
"0.5700501",
"0.5657504",
"0.56291044",
"0.5623114",
"0.56054... | 0.6487195 | 1 |
parameter to specify the Robin boundary conditions under the | def gamma(self) -> float:
return self._gamma | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def boundary_conditions(self):\n pass",
"def boundary_conditions(particle_outer_radius, boundary_temp):\n\n boundary_condition = [particle_outer_radius, boundary_temp]\n\n return boundary_condition",
"def boundary_conditions(self):\n ce = 2 * self.dy * self.g * self.mu * self.m_u / self.kb\... | [
"0.7321438",
"0.62348247",
"0.60754645",
"0.5894727",
"0.58483815",
"0.5836908",
"0.5830072",
"0.5813218",
"0.58012766",
"0.571765",
"0.565312",
"0.5634793",
"0.56021154",
"0.55712533",
"0.5564351",
"0.5494645",
"0.5490693",
"0.5476504",
"0.54438883",
"0.542073",
"0.54016256"... | 0.0 | -1 |
the length of the interval (box) | def L(self) -> float:
return self._L | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def length(self):\n return pyvista.Box(self.bounds).length",
"def intervalLen(self):\n return self.end-self.start+1",
"def box_size(self) -> np.ndarray:\n return self.upper - self.lower + 1",
"def box_size(self) -> np.ndarray:\n return self.upper - self.lower + 1",
"def len_squa... | [
"0.76909757",
"0.7267467",
"0.6992213",
"0.6992213",
"0.6758656",
"0.6718922",
"0.66236997",
"0.65887195",
"0.6577734",
"0.65670806",
"0.6564583",
"0.6464234",
"0.64607036",
"0.642546",
"0.64243233",
"0.6397626",
"0.6390193",
"0.6366058",
"0.6351269",
"0.62648505",
"0.6231517... | 0.0 | -1 |
the number of energy states the 'particle in a box'state is projected onto | def num_energy_states(self) -> int:
return self._num_energy_states | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __len__(self):\n return self.nparticles",
"def n_pos(self):\n running_total = 0\n for i in range(self.prob.num):\n if self.alphas[i] > 1e-5 > self.prob.C - self.deltas[i] and self.prob.Y[i] == 1:\n running_total += 1\n return running_total if running_tota... | [
"0.6625179",
"0.6520586",
"0.6520586",
"0.64324784",
"0.6431588",
"0.6414873",
"0.63809717",
"0.63734233",
"0.6323586",
"0.6288493",
"0.62755984",
"0.62718546",
"0.6233775",
"0.6207782",
"0.6170476",
"0.6145455",
"0.6141519",
"0.61399394",
"0.6132359",
"0.61210644",
"0.610804... | 0.6643349 | 0 |
unorderd list containing the quantum numbers 'l' of the energy states onto which the 'particle in a box'state is projected | def energy_states(self) -> List[int]:
return self._energy_states | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def xyz(self: Qs) -> List:\n\n new_states = []\n\n for ket in self.qs:\n new_states.append([ket.x, ket.y, ket.z])\n\n return new_states",
"def get_boxes(self):\r\n\r\n boxes = [(\" \", self.worldbox.tl, self.worldbox.br)]\r\n# boxes = []\r\n boxes += [(\".\", ... | [
"0.60017014",
"0.59401447",
"0.5881057",
"0.57378083",
"0.57150584",
"0.55337393",
"0.5454964",
"0.54214126",
"0.5416198",
"0.5387321",
"0.5382153",
"0.5329017",
"0.5294709",
"0.5290597",
"0.52467066",
"0.5244865",
"0.52356905",
"0.5235567",
"0.52125275",
"0.520428",
"0.52018... | 0.0 | -1 |
list of the same order as 'energy_states' containing the k_l values that correspond to the quantum numbers 'l' found in 'energy_states' | def k_kappa_l(self) -> List[complex]:
return self._k_kappa_l_array | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def energy_states(self) -> List[int]:\n return self._energy_states",
"def ising_energies(states,L):\r\n J=np.zeros((L,L),)\r\n for i in range(L):\r\n J[i,(i+1)%L]-=1.0\r\n # compute energies\r\n E = np.einsum('...i,ij,...j->...',states,J,states)\r\n return E, J",
"def ising_energie... | [
"0.61947995",
"0.59555596",
"0.5916479",
"0.58130866",
"0.5792258",
"0.5747695",
"0.56861466",
"0.5649747",
"0.56434804",
"0.561509",
"0.555946",
"0.5533187",
"0.55266356",
"0.55232155",
"0.5479074",
"0.54776806",
"0.54776806",
"0.5466385",
"0.5432786",
"0.5414882",
"0.538297... | 0.57441205 | 6 |
see documentation of 'l_to_kl_mapper' | def l_kl_map(self) -> l_to_kl_mapper:
return self._l_kl_map | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def KL2kL(NL, KL, BL):\n # cycle through BL, finding matching inds in NL and thus KL\n # for row in BL, get KL value in row BL[i,0] and col where(NL[BL[i,0],:]==BL[i,1])[0]\n if (BL < 0).any():\n aBL = np.abs(BL)\n kL = np.array([KL[aBL[i, 0], np.where(NL[aBL[i, 0], :] == aBL[i, 1])[0]][0] f... | [
"0.56921595",
"0.5569048",
"0.5549203",
"0.54740924",
"0.54145914",
"0.5406118",
"0.53970444",
"0.53854877",
"0.53566736",
"0.5277632",
"0.52508193",
"0.52366",
"0.5219296",
"0.5178262",
"0.5145818",
"0.51157826",
"0.5106366",
"0.5102655",
"0.5078339",
"0.5077449",
"0.5038405... | 0.8680803 | 0 |
Get input for the application. Use Config to determine the source of the data. | def get_input(self):
if self._config.use_stdin():
return stdin.read()
else:
try:
with open(self._config.values['input'], 'r') as f:
return f.read()
except:
raise InputFileException('Error opening and reading the inpu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def input(self):\n return self[\"input\"]",
"def input(self):\n return self[\"input\"]",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
"def _get_input(self):\n return self.__input",
... | [
"0.70721096",
"0.70721096",
"0.7016102",
"0.7016102",
"0.7016102",
"0.7016102",
"0.7016102",
"0.7016102",
"0.7016102",
"0.6866804",
"0.682956",
"0.6824514",
"0.6662567",
"0.6662567",
"0.6662567",
"0.65906376",
"0.65392363",
"0.6490492",
"0.6468117",
"0.6466041",
"0.64284444",... | 0.6876856 | 9 |
Get the content of the formatting file | def get_format_table(self):
try:
with open(self._config.values['format'], 'r') as f:
return f.read()
except:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_content(self):\n return self.file.read()",
"def get_file_content(self):\n return \"\\n\".join(self._vim.current.buffer)",
"def get_file_content(self):\n after = self.file.revisions.filter(\n revision_number__gt=self.revision_number)\n content = self.file.content\... | [
"0.6722635",
"0.6655623",
"0.66384435",
"0.64865637",
"0.64729464",
"0.64545316",
"0.6389236",
"0.63692665",
"0.6315058",
"0.6314024",
"0.6297957",
"0.62597746",
"0.62527674",
"0.62383825",
"0.6225904",
"0.62182045",
"0.61744064",
"0.61735946",
"0.61523366",
"0.6151303",
"0.6... | 0.62714297 | 11 |
Print the output to the console or to a file | def present_output(self, output: str):
if self._config.use_stdout():
print(output, flush=True, end='')
else:
try:
# print("OUTPUT FILE IS THERE!!!!", self._config.values['output'])
with open(self._config.values['output'], mode='w', newline='') as ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output(*args):\n print(*args, end='', file=file)",
"def print_out():\n pass",
"def printOutput(self):\n pass",
"def print_out(self, *lst):\n self.print2file(self.stdout, True, True, *lst)",
"def main(args):\n # Results: print to console and also write to output file... | [
"0.76134956",
"0.76095504",
"0.73696345",
"0.7185569",
"0.7096256",
"0.70948833",
"0.70309347",
"0.701113",
"0.6972133",
"0.69573224",
"0.6937068",
"0.68955594",
"0.68706226",
"0.6834418",
"0.68031895",
"0.66229516",
"0.6622299",
"0.6573759",
"0.65713024",
"0.656254",
"0.6561... | 0.65202826 | 22 |
2 T 5 N 1 2 3 4 5 N numeros 3 3 5 6 | def leer_entrada_1():
A, B = map(int,str.split(entrada.readline()))
test = ''
recicledpairsfound = 0
for i in range(A,B+1):
test = str(i)
if (len(test) > 1):
swapped = 0
for j in range(1,len(test)):
swapped = int(test[-j:] + test[:len(test... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mots_Nlettre(L:list, n)->list:\n lst= []\n mot = 0\n for i in range(len(L)):\n mot = L[i] \n cpt = 0\n for e in mot:\n cpt += 1\n if cpt == n:\n lst.append(mot)\n return lst",
"def numerize():\n pass",
"def nits(self):",
"def I (self, ... | [
"0.6185058",
"0.61487234",
"0.6011286",
"0.5962796",
"0.5959609",
"0.595229",
"0.58828706",
"0.5801601",
"0.58003134",
"0.57770985",
"0.57568866",
"0.5727559",
"0.56404084",
"0.56246775",
"0.55655366",
"0.55538875",
"0.55318946",
"0.5530798",
"0.5525625",
"0.5506544",
"0.5500... | 0.0 | -1 |
Input 4 1 9 10 40 100 500 1111 2222 Output | def leer_entrada_2():
var1 = int(entrada.readline())
var2 = int(entrada.readline())
lista = []
for i in xrange(1,var2):
lista = lista + [map(int,str.split(entrada.readline()))]
return correr_tarea(var1,var2,lista) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processed(N:int)->tuple:\n l1= str(N)\n a,b = '',''\n for i in range(len(l1)):\n if l1[i] == '4':\n a+='2'\n b+='2'\n else:\n a+=str(l1[i])\n b+='0'\n return int(a), int(b)",
"def main():\n next_val_string = '1'\n\n for counter in ra... | [
"0.6020323",
"0.60092735",
"0.591648",
"0.5801599",
"0.5785172",
"0.5785172",
"0.5785",
"0.56829536",
"0.56707966",
"0.5650203",
"0.5626738",
"0.5598183",
"0.5597939",
"0.55951333",
"0.55717784",
"0.5565832",
"0.5565102",
"0.5556577",
"0.55332583",
"0.55315197",
"0.55264056",... | 0.0 | -1 |
Pad a singlenested list or a sequence of nd array (torch.tensor or np.ndarray) into a (n+1)d array, only allow the first dim has variable lengths. | def pad_sequences_1d(sequences, dtype=torch.long, device=torch.device("cpu"), fixed_length=None):
if isinstance(sequences[0], list):
if "torch" in str(dtype):
sequences = [torch.tensor(s, dtype=dtype, device=device) for s in sequences]
else:
sequences = [np.asarray(s, dtype=d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad_list(xs: torch.Tensor, pad_value: int):\n n_batch = len(xs)\n max_len = max(x.size(0) for x in xs)\n pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)\n\n for i in range(n_batch):\n pad[i, : xs[i].size(0)] = xs[i]\n\n return pad",
"def list_to_padded_array(x_list... | [
"0.7142818",
"0.6955065",
"0.69292176",
"0.69206595",
"0.67178845",
"0.6659169",
"0.66363806",
"0.66253",
"0.66230506",
"0.6605494",
"0.65888196",
"0.6520145",
"0.65112054",
"0.6511188",
"0.65110904",
"0.6510516",
"0.64411896",
"0.63961047",
"0.6319004",
"0.6310096",
"0.63074... | 0.638518 | 18 |
Pad a doublenested list or a sequence of nd torch tensor into a (n+1)d tensor, only allow the first two dims has variable lengths | def pad_sequences_2d(sequences, dtype=torch.long):
bsz = len(sequences)
para_lengths = [len(seq) for seq in sequences]
max_para_len = max(para_lengths)
sen_lengths = [[len(word_seq) for word_seq in seq] for seq in sequences]
max_sen_len = max([max(e) for e in sen_lengths])
if isinstance(sequenc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad_lists(lists, pad_int, pad_len=None, dtype=torch.float, device=device):\n\n if pad_len is None:\n pad_len = max([len(lst) for lst in lists])\n new_list = []\n for lst in lists:\n if len(lst) < pad_len:\n new_list.append(lst + [pad_int] * (pad_len - len(lst)))\n else:... | [
"0.7546675",
"0.7244427",
"0.709511",
"0.70657486",
"0.696946",
"0.6854676",
"0.6830674",
"0.67936957",
"0.6771958",
"0.67284626",
"0.6721991",
"0.6693022",
"0.667271",
"0.6560058",
"0.65521586",
"0.64398044",
"0.6357635",
"0.6346128",
"0.6315901",
"0.62978333",
"0.6295265",
... | 0.57962435 | 82 |
Find a list of (k1, k2) where k1 < k2 with the maximum values of st_prob[k1] ed_prob[k2] | def find_max_triples(st_prob, ed_prob, top_n=5, prob_thd=None, tensor_type="torch"):
if tensor_type == "torch":
st_prob, ed_prob = st_prob.data.numpy(), ed_prob.data.numpy()
product = np.einsum("bm,bn->bmn", st_prob, ed_prob)
# (N, L, L) the lower part becomes zeros, start_idx < ed_idx
upper_pro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_greatest_probability(self):\n greatest = -1\n for i in range(self.dim):\n for j in range(self.dim):\n if self.kb[i][j] > greatest:\n greatest = self.kb[i][j]\n\n return greatest",
"def find_max_triples(p1, p2, topN=5, prob_thd=None):\n ... | [
"0.6394993",
"0.6110282",
"0.6072714",
"0.60599595",
"0.6017126",
"0.6015208",
"0.5910312",
"0.58973885",
"0.58359766",
"0.582623",
"0.58196145",
"0.5813802",
"0.5803053",
"0.58028036",
"0.57886326",
"0.57541484",
"0.5689953",
"0.56894594",
"0.5671669",
"0.5668233",
"0.566437... | 0.64712745 | 0 |
Find a list of (k1, k2) where k1 < k2 with the maximum values of p1[k1] p2[k2] | def find_max_triples_from_upper_triangle_product(upper_product, top_n=5, prob_thd=None):
batched_sorted_triple = []
for idx, e in enumerate(upper_product):
sorted_triple = top_n_array_2d(e, top_n=top_n)
if prob_thd is not None:
sorted_triple = sorted_triple[sorted_triple[2] >= prob_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findHighestPkPair(x, pkp): #{\n vrbMsg(5, 'findHighestPkPair() x = [...], pkp = ' + str(pkp))\n mi = [0,0]\n mv = x[pkp[0]]\n for i in range(1, len(pkp)): #{\n nv = x[pkp[i]]\n if(nv > mv): #{\n mi[0] = i\n mv = nv\n #}\n #}\n mv = None\n for i in range(0, len(pkp)): #{\n if(i != m... | [
"0.6645103",
"0.6619337",
"0.64487",
"0.6393022",
"0.63164264",
"0.61364424",
"0.60545003",
"0.6047857",
"0.60373175",
"0.59977555",
"0.59454757",
"0.5940216",
"0.5936271",
"0.59169334",
"0.59063673",
"0.5887921",
"0.58286756",
"0.5818385",
"0.57923603",
"0.57911456",
"0.5770... | 0.0 | -1 |
Get topN indices and values of a 2d array, return a tuple of indices and their values, ranked by the value | def top_n_array_2d(array_2d, top_n):
row_indices, column_indices = np.unravel_index(np.argsort(array_2d, axis=None), array_2d.shape)
row_indices = row_indices[::-1][:top_n]
column_indices = column_indices[::-1][:top_n]
sorted_values = array_2d[row_indices, column_indices]
return np.stack([row_indice... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topN_array_2d(array_2d, topN=None):\n row_indices, column_indices = np.unravel_index(np.argsort(array_2d, axis=None), array_2d.shape)\n row_indices = row_indices[::-1][:topN]\n column_indices = column_indices[::-1][:topN]\n sorted_values = array_2d[row_indices, column_indices]\n sorted_triples =... | [
"0.74073654",
"0.7327787",
"0.71032065",
"0.6694238",
"0.6656095",
"0.6633193",
"0.6503937",
"0.6362402",
"0.63310814",
"0.62197495",
"0.6209443",
"0.6191257",
"0.6189325",
"0.61888635",
"0.6164573",
"0.61391664",
"0.6070247",
"0.60639524",
"0.598961",
"0.5986245",
"0.5982238... | 0.7583843 | 0 |
Create application for the tests. | def app():
_app = create_app("flaskapp.settings.testing")
_app.logger.setLevel(logging.CRITICAL)
ctx = _app.test_request_context()
ctx.push()
yield _app
ctx.pop() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def application():\n yield create_test_application()",
"def create_and_run():\n\n app = App()\n app.run()",
"def create_app(self):\n\n app = create_app()\n app.config.from_object('project.config.TestingConfig')\n return app",
"def app():\n print('creating app with test vars')... | [
"0.78650266",
"0.78183943",
"0.779372",
"0.7706425",
"0.7696785",
"0.76925087",
"0.76771784",
"0.76373273",
"0.7630568",
"0.76160675",
"0.7544439",
"0.7523237",
"0.75079054",
"0.7506175",
"0.74288905",
"0.74103117",
"0.73966044",
"0.737695",
"0.73750865",
"0.7362577",
"0.7358... | 0.0 | -1 |
return signal quality in dbm. wlanSignalQuality is percentage value that represents the signal quality of the network. WLAN_SIGNAL_QUALITY is of type ULONG. This member contains a value between 0 and 100. A value of 0 implies an actual RSSI signal strength of 100 dbm. A value of 100 implies an actual RSSI signal streng... | def getSignalQualityInDBM(self):
return (float(self.wlanSignalQuality) / 2.0) - 100.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_signal_percent(self):\n # RSSI or this signal value is measured in decibels from 0 (zero) to\n # -120 (minus 120). The closer the value to 0 (zero), the stronger the\n # signal will be.\n if self.rssi < -80:\n return 20\n elif self.rssi < -70:\n retu... | [
"0.6074094",
"0.60217094",
"0.5889179",
"0.57901305",
"0.57396984",
"0.5680173",
"0.56357545",
"0.5527894",
"0.5477314",
"0.54561406",
"0.5389357",
"0.5375582",
"0.5350388",
"0.5338676",
"0.52917403",
"0.5262764",
"0.5253507",
"0.52252936",
"0.52248573",
"0.5217617",
"0.52172... | 0.83052623 | 0 |
return the reason code string | def getWlanReasonCodeString(reasonCode):
rcStr = ''
try:
buf = create_unicode_buffer(256)
bufSize = DWORD(256)
ret = WlanReasonCodeToString( reasonCode, bufSize, buf, None)
if ret != ERROR_SUCCESS:
raise WinError(ret)
rcStr = buf.value
except Exception,err... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getReason():",
"def reason(self) -> str:\n return pulumi.get(self, \"reason\")",
"def reason(self) -> str:\n return self._reason",
"def _get_exc_reason(cls, exc: Exception) -> str:\n reason = str(exc)\n for reason_re in cls.REASON_RES:\n if reason_re.search(reason):... | [
"0.7898791",
"0.7654689",
"0.7463807",
"0.74114925",
"0.7312814",
"0.70631933",
"0.700501",
"0.6937393",
"0.69278306",
"0.69059575",
"0.67653686",
"0.67569005",
"0.66947216",
"0.6684652",
"0.6585945",
"0.64782953",
"0.6468771",
"0.6448249",
"0.6415473",
"0.6304019",
"0.621460... | 0.74249285 | 3 |
return an interface using miniDesc name | def getInterface(self, miniDesc):
for iface in self.ifaces:
if iface.miniDesc() == miniDesc:
return iface
else:
raise Exception('Interface with miniDesc "%s" not found' % miniDesc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_interface(name=''):\n global interfaces\n return interfaces[name]",
"def _get_interface_name(self):\n return self.__interface_name",
"def _extract_interface(interface_name):\n return re.match(r'e(th?)?((?P<fex>\\d+)/)?(?P<mod>\\d+)/(?P<int>\\d+)', interface_name).groupdict()",
"def interf... | [
"0.681402",
"0.66090214",
"0.6328007",
"0.6283059",
"0.6162838",
"0.6135661",
"0.6117199",
"0.5963388",
"0.5935286",
"0.5923882",
"0.5910261",
"0.5909605",
"0.5899179",
"0.58971596",
"0.5891223",
"0.58635175",
"0.58635175",
"0.58635175",
"0.58635175",
"0.58635175",
"0.5863517... | 0.78897786 | 0 |
return network with profile name | def getProfile(self, profile):
for network in self.networks:
if network.getProfileName() == profile:
return network
else:
raise Exception('Network with profile name "%s" not found' % profile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_network_profile(arn=None):\n pass",
"def network_profile(self) -> Optional[pulumi.Input['NetworkProfileArgs']]:\n return pulumi.get(self, \"network_profile\")",
"def network_profile(self) -> Optional[pulumi.Input['NetworkProfileArgs']]:\n return pulumi.get(self, \"network_profile\")",
... | [
"0.8034974",
"0.7666455",
"0.7666455",
"0.7470822",
"0.7312471",
"0.7185045",
"0.678201",
"0.6757629",
"0.6633127",
"0.65804386",
"0.65450716",
"0.6517732",
"0.64937603",
"0.64295596",
"0.6365375",
"0.6311133",
"0.6305287",
"0.62889874",
"0.628199",
"0.6279119",
"0.6276328",
... | 0.82880425 | 0 |
return network using ssid | def getSSID(self, ssid):
for network in self.networks:
if network.getSSID() == ssid:
return network
else:
raise Exception('Network with SSID "%s" not found' % profile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getnetwork(ipaddr):\n return '192.168.1.0/24'",
"def get_network(self):\n return self.get_ip_network()[-1]",
"def joinwifi():\n station = network.WLAN(network.STA_IF) # initiate a station mode\n\n if not station.isconnected():\n print('connecting to network:', ssid())\n ... | [
"0.7319217",
"0.66508794",
"0.651576",
"0.6394354",
"0.6324551",
"0.6259074",
"0.6236336",
"0.6175038",
"0.6138658",
"0.61321485",
"0.60615045",
"0.6059806",
"0.6049224",
"0.6032489",
"0.60294557",
"0.60238683",
"0.5988079",
"0.5976195",
"0.59709895",
"0.5966414",
"0.59601575... | 0.75938874 | 0 |
open handle to Wlan | def openHandle(self):
NegotiatedVersion = DWORD()
self._handle = HANDLE()
log.debug('WlanOpenHandle()')
ret = WlanOpenHandle(1, None, byref(NegotiatedVersion), byref(self._handle))
if ret != ERROR_SUCCESS:
raise WinError(ret)
if self._useCallback:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open(self, handle):\n raise NotImplementedError",
"def open(self) -> None:",
"def open(self) -> None:",
"def open(self) -> None:",
"def open(self):\n pn_link_open(self._impl)",
"def openSensel():\n handle = None\n (error, device_list) = sensel.getDeviceList()\n if device_list.n... | [
"0.63797355",
"0.6268434",
"0.6268434",
"0.6268434",
"0.62281483",
"0.62180805",
"0.60923684",
"0.6082648",
"0.6039862",
"0.6021626",
"0.6021626",
"0.59682363",
"0.59587044",
"0.5914516",
"0.5914516",
"0.5914516",
"0.5914516",
"0.5914516",
"0.5914516",
"0.5914516",
"0.5894515... | 0.6322617 | 1 |
update the description string | def updateDesc(self, wlanIfData=None):
if wlanIfData:
self._lstIfaces = [str(iface) for iface in wlanIfData.ifaces]
else:
with self.enumInterfaces() as wlanIfData:
self._lstIfaces = [str(iface) for iface in wlanIfData.ifaces] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description(self, new_description):\r\n self.set({\"description\": new_description})",
"def description(self, newDescription=None):\n pass",
"def request_description_update():\n global should_update_description\n should_update_description = True",
"def update_description_debounced(sel... | [
"0.7761274",
"0.7686069",
"0.7643725",
"0.74393356",
"0.7404592",
"0.74001515",
"0.73334724",
"0.7328683",
"0.7264786",
"0.72593653",
"0.7226315",
"0.7226315",
"0.7226315",
"0.7226315",
"0.71891356",
"0.7162867",
"0.71464103",
"0.714478",
"0.71401286",
"0.71049523",
"0.710237... | 0.0 | -1 |
connect a wlan interface using a profile | def wlanConnect(self, iface, profile):
log.debug('WlanInterface wlanConnect() - iface:%s profile:"%s"' % (iface.miniDesc(), profile))
wcp = WLAN_CONNECTION_PARAMETERS()
wcp.wlanConnectMode = wlan_connection_mode_profile
wcp.strProfile = profile
wcp.pDot11Ssid = None # byref(ssid)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_session(profile):\n os.system('ssh ' + profile)",
"def connect():\n \n print(\"*****Starting connection*****\")\n \n ssid = id_key.network_id #hidden ssid\n key = id_key.network_key #hidden key\n \n station = network.WLAN(network.STA_IF)\n \n if station.isconnected() == True:\n print(\... | [
"0.68901867",
"0.67133456",
"0.6624861",
"0.66222256",
"0.6520829",
"0.64085853",
"0.638917",
"0.6374321",
"0.6367763",
"0.6331932",
"0.6144454",
"0.6085704",
"0.5949704",
"0.59170735",
"0.5864868",
"0.5848089",
"0.5840637",
"0.5806825",
"0.5786279",
"0.5785304",
"0.5770876",... | 0.78789246 | 0 |
disconnect a wlan interface | def wlanDisconnect(self, iface):
log.debug('WlanInterface wlanDisconnect() - iface:%s' % (iface.miniDesc()))
ret = WlanDisconnect( self._handle,
byref(iface.InterfaceGuid),
None)
if ret != ERROR_SUCCESS:
raise WinError(ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _DisconnectAP(self):\n disconnect_command = 'iw dev {interface} disconnect'.format(\n interface=self.interface)\n # This call may fail if we are not connected to any network.\n self._device.Call(disconnect_command)",
"def disconnect_port(self, iface):\n self.iface_config(iface, adminMo... | [
"0.72159076",
"0.70738363",
"0.67676616",
"0.6624292",
"0.66076785",
"0.66066116",
"0.6585626",
"0.65664726",
"0.6516199",
"0.65145594",
"0.64772666",
"0.6396341",
"0.63787943",
"0.63662654",
"0.6359681",
"0.63501954",
"0.6323497",
"0.6318395",
"0.62968045",
"0.626833",
"0.62... | 0.7975275 | 0 |
return profile XML for a defined profile | def wlanGetProfile(self, iface, profile, saveToFile=None):
log.debug('WlanInterface wlanGetProfile() - profile:"%s" saveToFile:%s' % (profile,saveToFile))
sProfile = c_wchar_p(profile)
sProfileXML = c_wchar_p() # create_unicode_buffer(1024)
flags = DWORD(0)
grantedAccess = DWOR... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_wlan_profile_xml(ssid, auth_method, encrypt_method, key_type = \"\", key_material = \"\", key_index = \"\", use_onex = False):\n\n # Try to generate a temporary file for storing the XML profile\n fd, path = tempfile.mkstemp(\".xml\")\n\n # Fill the content\n os.write(fd, '<?xml version=\"1.0\... | [
"0.6350907",
"0.63488406",
"0.6266231",
"0.6177022",
"0.61312056",
"0.6096144",
"0.6061613",
"0.605447",
"0.60457754",
"0.6005076",
"0.5981148",
"0.5963808",
"0.59127927",
"0.58966184",
"0.5859962",
"0.5842467",
"0.58242536",
"0.5814976",
"0.57743406",
"0.5754428",
"0.5705603... | 0.6570712 | 0 |
return profile XML for a defined profile | def wlanSetProfile(self, iface, profileXML, overwrite=True):
log.debug('WlanInterface wlanSetProfile()')
flags = DWORD(0)
sProfileXML = c_wchar_p(profileXML)
dwReasonCode = DWORD()
bOverwrite = BOOL(overwrite)
ret = WlanSetProfile( self._handle,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wlanGetProfile(self, iface, profile, saveToFile=None):\n log.debug('WlanInterface wlanGetProfile() - profile:\"%s\" saveToFile:%s' % (profile,saveToFile))\n sProfile = c_wchar_p(profile)\n sProfileXML = c_wchar_p() # create_unicode_buffer(1024)\n flags = DWORD(0)\n grantedA... | [
"0.6570087",
"0.6350916",
"0.63491035",
"0.6265601",
"0.61771524",
"0.61315066",
"0.6096711",
"0.6060701",
"0.60537815",
"0.604565",
"0.6003376",
"0.598159",
"0.59635496",
"0.59146196",
"0.58956265",
"0.5859714",
"0.5842899",
"0.5824503",
"0.58148515",
"0.57747084",
"0.575358... | 0.0 | -1 |
Create a new profile from an existing profile | def wlanCopyProfile(self, iface, profile, newProfile, ssid=None, pass_phrase=None, saveOrigProfile=None, saveNewProfile=None):
sXML = self.wlanGetProfile(iface, profile, saveToFile=saveOrigProfile)
#print sXML
reProf = re.compile('<{0}>.*</{0}>'.format('name'))
reProf2 = re.compile('<{0}... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createProfile(self):\n if self.profile:\n return\n from soc.modules.gsoc.models.profile import GSoCProfile\n user = self.createUser()\n properties = {'link_id': user.link_id, 'student_info': None, 'user': user,\n 'parent': user, 'scope': self.program, 'status': 'active'}\n ... | [
"0.8025856",
"0.74423146",
"0.73864764",
"0.73674834",
"0.73625",
"0.73201007",
"0.7273355",
"0.7258695",
"0.72443515",
"0.72335404",
"0.7229877",
"0.7214494",
"0.7181721",
"0.715497",
"0.7084597",
"0.7083544",
"0.70644885",
"0.706198",
"0.7054918",
"0.70378166",
"0.7000497",... | 0.0 | -1 |
Requests that the native 802.11 Wireless LAN driver scan for available wireless networks. | def wlanScan(self, iface):
log.debug('WlanInterface wlanScan()')
ret = WlanScan( self._handle,
byref(iface.InterfaceGuid),
None,None,None)
if ret != ERROR_SUCCESS:
raise WinError(ret) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scan(self):\n if self.isconnected is False:\n return False\n nets = self._wlan.scan()\n nets_list = {}\n for net in nets:\n nets_list['SSID'] = str(net[0], 'utf8')\n # nets_list['bssid'] = str(net[1])\n nets_list['CHANNEL'] = str(net[2])\n... | [
"0.6717677",
"0.66940945",
"0.63552207",
"0.63373786",
"0.62656766",
"0.61971956",
"0.61675155",
"0.61275834",
"0.6100425",
"0.60858244",
"0.60420954",
"0.5974406",
"0.58970606",
"0.5790645",
"0.5784938",
"0.5771123",
"0.57069147",
"0.5658612",
"0.5653181",
"0.56500864",
"0.5... | 0.50168884 | 66 |
list all interfaces available | def _ifList(self):
bNetworks = False
for cmd in self.lstCmd[1:]:
if cmd == 'networks' or cmd == 'n':
bNetworks = True
print 'enum interfaces ...'
with self.wlan.enumInterfaces() as wlanIfData:
# find each available network for each interface
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list():\n\n\treturn netifaces.interfaces()",
"def interfaces(self):\n if self._interfaces is None:\n self._interfaces = list(x[\"interface\"] for x in self._interfaces_detailed_list())\n\n return self._interfaces",
"def _get_interfaces(self):\n return self.__interfaces",
"def ... | [
"0.8556273",
"0.7459789",
"0.7315116",
"0.7315116",
"0.7315116",
"0.72669274",
"0.72245455",
"0.721408",
"0.7142727",
"0.71341616",
"0.7067674",
"0.7016781",
"0.69752765",
"0.6932081",
"0.6854325",
"0.67948496",
"0.6784868",
"0.6717955",
"0.6681753",
"0.6681753",
"0.6619683",... | 0.7075943 | 10 |
Return an unstructured grid with the connectivity given by matplotlib Code borrowed from Chris Richardson | def mesh2triang(mesh: df.Mesh) -> tri.Triangulation:
xy = mesh.coordinates()
return tri.Triangulation(xy[:, 0], xy[:, 1], mesh.cells()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildGrid(self, plot=False):\r\n\r\n print(\"Constructing grid\")\r\n # print(\"Grid dims\", self.ne, self.nn, self.nz)\r\n # print(\"Num points\", 2*(self.ne+1)*(self.nn+1)*3, len(self.coords))\r\n\r\n # number of edges\r\n self.ndx = self.ne + 1\r\n self.ndy = self.n... | [
"0.6508896",
"0.63165975",
"0.6273557",
"0.62416875",
"0.61100554",
"0.6023052",
"0.5950282",
"0.58708537",
"0.587006",
"0.5862989",
"0.5830344",
"0.58211243",
"0.5815232",
"0.57970977",
"0.5794793",
"0.57859427",
"0.5774907",
"0.57310355",
"0.57252765",
"0.5724067",
"0.57131... | 0.0 | -1 |
Return a pseudocolor plot of an unstructured triangular grid. | def mplot_cellfunction(cell_function: df.MeshFunction) -> Tuple[plt.Figure, Any]:
fig, ax = plt.subplots(1)
tri = mesh2triang(cell_function.mesh())
ax.tripcolor(tri, facecolors=cell_function.array())
return fig, ax | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mpl_triangulation(self):\n tris=[] # [ (n1,n2,n3), ...]\n\n for c in self.valid_cell_iter():\n nodes=np.array(self.cell_to_nodes(c))\n\n # this only works for convex cells\n for i in range(1,len(nodes)-1):\n tris.append( nodes[ [0,i,i+1] ] )\n\n ... | [
"0.55358636",
"0.54399073",
"0.54330146",
"0.5422613",
"0.53930223",
"0.5346241",
"0.5303776",
"0.52686137",
"0.52419925",
"0.5238832",
"0.5218028",
"0.5201694",
"0.5186084",
"0.51579493",
"0.51142186",
"0.5112351",
"0.5108906",
"0.50909567",
"0.5072048",
"0.5052789",
"0.5038... | 0.48508424 | 34 |
Plot the meh as an unstructured grid. | def mplot_mesh(meshtriang: df.Mesh) -> Tuple[plt.Figure, Any]:
fig, ax = plt.subplots(1)
ax.triplot(meshtriang, 'ko-', lw=1)
return fig, ax | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotgrid(data,d=10,shape=(30,30)):\n ion()\n gray()\n clf()\n for i in range(min(d*d,len(data))):\n subplot(d,d,i+1)\n row = data[i]\n if shape is not None: row = row.reshape(shape)\n imshow(row)\n ginput(1,timeout=0.1)",
"def plot(self):\n\t\tself.plotOfHeatingCurr... | [
"0.6244365",
"0.62126404",
"0.62066466",
"0.61587167",
"0.60979223",
"0.6090224",
"0.59458405",
"0.59377295",
"0.5907159",
"0.58908015",
"0.5879286",
"0.5874144",
"0.5863606",
"0.5861239",
"0.5772227",
"0.5736954",
"0.5704334",
"0.5672166",
"0.5670842",
"0.5664432",
"0.564503... | 0.52730376 | 94 |
Plot a function. The kind of plot depends on the function. | def mplot_function(
function: df.Function,
vmin=None,
vmax=None,
shading="gouraud",
colourbar=False,
colourbar_label=None
) -> Tuple[plt.Figure, Any]:
mesh = function.function_space().mesh()
if mesh.geometry().dim() != 2:
raise AttributeError("Mesh must be 2D")
fig, ax = plt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot():\n pass",
"def plot(\n self,\n function: Callable[[float], float],\n x_range: Sequence[float] | None = None,\n use_vectorized: bool = False,\n **kwargs,\n ):\n\n t_range = np.array(self.x_range, dtype=float)\n if x_range is not None:\n ... | [
"0.7440588",
"0.7227534",
"0.70364696",
"0.67569745",
"0.6756228",
"0.6744774",
"0.6693308",
"0.66897386",
"0.6668732",
"0.6635225",
"0.6518727",
"0.6354156",
"0.6338658",
"0.63316536",
"0.63066083",
"0.6276415",
"0.62636936",
"0.6238976",
"0.62312776",
"0.62132955",
"0.61974... | 0.6611125 | 10 |
Parser used for script command with all its necessary parameters needed to be run on the cluster | def parse_options():
global parser
parser.add_option("-r", "--regions", dest="input_brain_regions",
help="Input file for brain region data",
action="store", type='string')
parser.add_option("-g", "--gray", dest="input_gray_levels",
help="Inp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse():\n intro = \"\"\"\\\n Use this script to bootstrap, join nodes within a Galera Cluster\n ----------------------------------------------------------------\n Avoid joining more than one node at once!\n \"\"\"\n parser = argparse.ArgumentParser(\n formatter_c... | [
"0.6959925",
"0.6677796",
"0.66506535",
"0.6597472",
"0.64935666",
"0.63764864",
"0.6372577",
"0.6371842",
"0.6340508",
"0.6328619",
"0.63115484",
"0.63084835",
"0.6289498",
"0.6243776",
"0.6233198",
"0.62203646",
"0.62061137",
"0.62059563",
"0.62047905",
"0.6189151",
"0.6176... | 0.0 | -1 |
builds a library given required scripts to includes and other arguments | def build(request):
def get(name):
return request.GET.get(name)
def get_arr(name):
val = get(name)
if val:
return val.split(",")
else:
return []
require = get_arr("require")
exclude = get_arr("exclude")
excludeLibs = get_arr("excludeLibs")
requireLibs = get_arr("requireLibs")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(working_directory=None, args=None):\n from .buildme import main\n if args is None:\n args = []\n return main(working_directory, args)",
"def build(self):\n env = ConfigureEnvironment(self.deps_cpp_info, self.settings)\n\n set_path_command = \"\"\n # Download nasm as... | [
"0.60995007",
"0.60779",
"0.60349417",
"0.59920174",
"0.59571064",
"0.5946177",
"0.5943936",
"0.5908569",
"0.59018505",
"0.5898167",
"0.5886315",
"0.58850145",
"0.58688945",
"0.58599895",
"0.5858286",
"0.5827634",
"0.58227205",
"0.5804258",
"0.5778985",
"0.5758587",
"0.574068... | 0.58530253 | 15 |
Get a single action label for the current state, ideally whatever the planner would have outputted if just called on `cstate` in isolation. | def single_action_label(self, cstate):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def action2label(action):\n if action[0] == 'shift':\n label = action[0]\n elif action[0] == 'reduce':\n label = '-'.join(list(action))\n else:\n raise ValueError(\"Unrecognized parsing action: {}\".format(action))\n return label",
"def state_name(self):\n return self.stat... | [
"0.68593997",
"0.6409792",
"0.63704884",
"0.6344228",
"0.6292176",
"0.616831",
"0.61657256",
"0.6163822",
"0.6136787",
"0.6057938",
"0.605119",
"0.605119",
"0.6047956",
"0.6044922",
"0.6044922",
"0.6044922",
"0.6044414",
"0.60374177",
"0.6018807",
"0.60048354",
"0.60048354",
... | 0.79932404 | 0 |
Produce an iterable of states visited by the teacher policy when initialised in the given state. | def extract_policy_envelope(self, cstate):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_states(self):\n return iter(self._states_)",
"def state_list(self) -> Sequence[TState]:\n pass",
"def __iter__(self):\n return self.states()",
"def get_list_of_states(self):\n return self.states",
"def get_states(self):\n return product(*[phi.automaton().states f... | [
"0.6537469",
"0.6507559",
"0.6333943",
"0.62946624",
"0.6279952",
"0.6270858",
"0.62265533",
"0.6213435",
"0.6131452",
"0.61095124",
"0.6086804",
"0.6057857",
"0.60427266",
"0.6012612",
"0.59918135",
"0.5902357",
"0.5893258",
"0.58824396",
"0.58483016",
"0.58471715",
"0.58373... | 0.0 | -1 |
f"""Perform a single rollout under the expert policy & return the visited states. On probabilistic problems, this differs from extract_policy_envelope in | def expert_policy_rollout(self, cstate, *, len_bound=DEFAULT_MAX_LEN):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rollout(env, our_policy, expert_policy, num_rollouts, max_steps):\n returns = []\n observations = []\n actions = []\n for i in range(num_rollouts):\n # print('iter', i)\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n ... | [
"0.6982677",
"0.64091736",
"0.61041176",
"0.5740429",
"0.5698572",
"0.56384194",
"0.5564403",
"0.53721863",
"0.53550786",
"0.53181446",
"0.52759427",
"0.52260995",
"0.513249",
"0.51045525",
"0.50875306",
"0.5063328",
"0.5041458",
"0.5035391",
"0.499523",
"0.49948314",
"0.4992... | 0.56915593 | 5 |
Cutoff value (float) for maximum length of a path to reach goal; no valid path returned by the planner has higher cost than this. | def dead_end_value(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCutoffDistance(self):\n return self.cutoffDistance",
"def cutoff_distance(self):\n return self._cutoff_distance",
"def GetEpsilonBeer(Abs, conc, pathLength):\n return Abs / (conc * pathLength)",
"def pathcost_cooler(k=5, lam=0.0003, limit=3000):\n return lambda t: (k * exp(-lam * t... | [
"0.66599065",
"0.6464602",
"0.59441173",
"0.5824008",
"0.5823617",
"0.5738601",
"0.5698907",
"0.563141",
"0.562286",
"0.55233145",
"0.5477712",
"0.5420577",
"0.53618807",
"0.53434753",
"0.53083557",
"0.5302423",
"0.52958834",
"0.5275068",
"0.5263953",
"0.5245157",
"0.5243079"... | 0.0 | -1 |
Wraps a method of this class in a SSiPP timeout, so that planning can't take too long. Maintains a blacklist of states that caused timeouts before, and prevents relevant methods from being called again on those states by simply raising another timeout. | def _blacklist_decorator(inner_func):
@wraps(inner_func)
def wrapper_func(self, cstate, *args, **kwargs):
assert isinstance(cstate, CanonicalState), \
"func %s was expecting a cstate but got '%r'" \
% (inner_func, cstate)
if cstate._ident_tup() in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def timeout(time):\n\n def wrapper(f):\n @wraps(f)\n def wrapped_f(self, event, context):\n return f(self, event, context)\n\n wrapped_f.timeout = time\n return wrapped_f\n\n return wrapper",
"def timeout(time_limit):\n\n class TimeoutException(Exception):\n ... | [
"0.62377894",
"0.61471254",
"0.6033199",
"0.5992332",
"0.59493905",
"0.57512546",
"0.5742238",
"0.5699485",
"0.5643137",
"0.5635711",
"0.56170464",
"0.5599529",
"0.5585652",
"0.5580245",
"0.55567193",
"0.5546809",
"0.55230653",
"0.55142486",
"0.55070794",
"0.5488454",
"0.5446... | 0.6632625 | 0 |
Turn a unique action identifier string (i.e SSiPPformatted action string) into an index into the action list for this problem. | def _act_id(self, act_str):
# FIXME: instead of doing this shit I should just fix the things in
# state_reprs.py so that they can take action IDs (numeric ones) OR
# action names OR BoundActions! Have a family of resolve_action_to_*
# methods that let you convert from any one representa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_action_id(self, action_text: str) -> int:\n\n actions_tuple = tuple(action_text.split('+'))\n return self.action_tuples2ids[actions_tuple] # todo unhandled exception when not found",
"def str_to_action(str):\n raise NotImplementedError",
"def get_action_id(self, action_text: str) ... | [
"0.6287347",
"0.62635666",
"0.6073907",
"0.604243",
"0.5834542",
"0.5821824",
"0.5821046",
"0.58161885",
"0.5769511",
"0.57691544",
"0.56986296",
"0.5698094",
"0.5687889",
"0.5677664",
"0.5606154",
"0.55739564",
"0.5573762",
"0.55202705",
"0.5516037",
"0.55082506",
"0.5484332... | 0.66609216 | 0 |
Dynamically call the function according 'oper' | def process_data(self, oper='SCAN'):
func_name = oper.lower()
return getattr(self, func_name)() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _execute(self, op, time):\n raise NotImplementedError",
"def do_run(calc, a=int, b=int):\n of = calc.oper_func\n calc.log.msg(10, 'Calculating', operation=of, a=a, b=b)\n res = getattr(operator, of, calc.not_found)(a, b)\n calc.log.msg(20, 'Returning', result=res)\n ... | [
"0.61901075",
"0.61292493",
"0.61219674",
"0.60508186",
"0.60292137",
"0.594123",
"0.5882175",
"0.5842769",
"0.58308256",
"0.5827575",
"0.58041626",
"0.57888895",
"0.5762354",
"0.5761433",
"0.5750888",
"0.5736811",
"0.5736043",
"0.5727928",
"0.5691805",
"0.56814253",
"0.56723... | 0.64394444 | 0 |
Implemnets the scan method in BasePlugin class. In this mac module, mac addesses were retrieved by snmpwalk python lib. | def scan(self):
walk_result = utils.snmp_walk(self.host, self.credential,
"BRIDGE-MIB::dot1dTpFdbPort")
if not walk_result:
return None
mac_list = []
for result in walk_result:
if not result or result['value'] == str(0):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Scan(self, plugin):\n raise 'Method not implemented'",
"def scan(self):\n return",
"def on_scanner_start(self, scanner):",
"def _scan(self): # pragma: no cover\n raise NotImplementedError()",
"def get_scan(self):\n pass",
"def scan(self):\n for angle in range(self.... | [
"0.664008",
"0.5926287",
"0.5923922",
"0.59137505",
"0.5759931",
"0.55758125",
"0.5553623",
"0.55237395",
"0.54862815",
"0.54580957",
"0.5450833",
"0.5449223",
"0.5423793",
"0.54232156",
"0.5345217",
"0.53041476",
"0.5296159",
"0.52909553",
"0.5288499",
"0.5279574",
"0.522754... | 0.5530806 | 7 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.